diff --git a/.github/workflows/test-pytest.yaml b/.github/workflows/test-pytest.yaml
index 599c62a6..514c85d5 100644
--- a/.github/workflows/test-pytest.yaml
+++ b/.github/workflows/test-pytest.yaml
@@ -8,7 +8,7 @@ jobs:
       - uses: actions/checkout@v3
       - uses: actions/setup-python@v4
         with:
-          python-version: 3.8
+          python-version: 3.11
           cache: "pip"
           cache-dependency-path: settings.ini
       - name: Run pytest
diff --git a/.gitignore b/.gitignore
index 5f112e28..99237c7e 100644
--- a/.gitignore
+++ b/.gitignore
@@ -5,6 +5,7 @@ _proc/
 .gitattributes
 .last_checked
 .gitconfig
+.cursorignore
 *.bak
 *.log
 *~
diff --git a/CHANGELOG.md b/CHANGELOG.md
index a71ebf15..167859fe 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -2,6 +2,49 @@
 
 <!-- do not remove -->
 
+## v2025.03.27
+
+### New Features
+
+- **Horizontal Plots**: Users can now create horizontal layout plots, providing compact data visualization. This can be achieved by setting `horizontal=True` in the `plot()` method.
+
+- **Forest Plots**: Forest plots provide a simple and intuitive way to visualize many delta-delta (or Deltas’ g), mini-meta, or regular delta effect sizes at once from multiple different dabest objects without presenting the raw data.
+
+- **Gridkey**: Users can now represent their experimental labels in a gridkey format. This can be accessed with the `gridkey` argument in the `plot()` method. 
+
+- **Aesthetic Updates**: We have made several aesthetic improvements to the plots, including:
+   - **Raw, Contrast, and Summary bars**: We added bars highlighting the various groups' differences. These bars can be customized to suit the user’s needs. Raw and contrast bars are provided by default, summary bars can be added by the user.
+  
+   - **Delta-Delta and Mini-Meta Plots**: We have adjusted the spacing of delta-delta and mini-meta plots to reduce whitespace. The new format brings the added effect size closer to the regular effect sizes. In addition, delta-delta plots now have a gap in the zeroline to separate the delta-delta and regular effect sizes.
+
+   - **Delta-delta Effect Sizes for Proportion Plots**: Delta-delta experimental plotting now supports binary data.
+
+   - **Proportion Plots Sample Sizes**: The sample size of each binary option for each group can now be displayed. These can be toggled on or off via the `prop_sample_counts` parameter.
+
+   - **Effect Size Lines for Paired Plots**: Paired plots now display lines linking the effect sizes within a group together in the contrast axes. These can be toggled on or off via the `contrast_paired_lines` parameter.
+
+   - **Baseline Error Curves**: Baseline error dot and curve are now available to represent the baseline/control group in the contrast axes. The dot is shown by default, while the curve can be toggled on/off by the user (via the `show_baseline_ec` parameter).
+
+   - **Delta Text**: Effect size deltas are now displayed as text next to their respective effect size. This can be toggled on or off via the `delta_text` parameter.
+
+   - **Empty Circle Color Palette**: A new swarmplot color palette modification is available for unpaired plots via the `empty_circle` parameter in the `plot()` method. This option modifies the two-group swarmplots to have empty circles for the control group and filled circles for the experimental group.
+
+### Enhancement
+
+- **Python 3.13 Support**: DABEST now supports Python 3.10-3.13.
+
+- **Numba for Speed Improvements**: We have included Numba to speed up the various calculations in DABEST. This should make the calculations faster and more efficient. Importing DABEST may take a little longer than before, and a progress bar will appear during the import process to show the calculations being performed. Once imported, loading and plotting data should now be faster.
+
+- **Terminology Updates**: We have made several updates to the documentation and terminology to improve clarity and consistency. For example:
+    - Plot arguments have been adjusted to bring more clarity and consistency in naming. Arguments relating to the rawdata plot axis will now be typically referred to with ‘raw’ while arguments relating to the contrast axis will be referred to with ‘contrast’. For example, ‘raw_label’ replaces ‘swarm_label’ and ‘bar_label’. The various kwargs relating to each different type of plot (e.g., swarmplot_kwargs) remain unchanged. 
+    - The method to utilise the Deltas’ g effect size is now via the .hedges_g.plot() method rather than creating a whole new Delta_g object as before. The functionality remains the same, it plots hedges_g effect sizes and then the Deltas’ g effect size alongside these (if a delta-delta experiment was loaded correctly).
+
+- **Updated Tutorial Pages**: We have updated the tutorial pages to reflect the new features and changes. The tutorial pages are now more comprehensive and (hopefully!) more intuitive!
+
+- **Results Dataframe for Delta-delta and Mini-meta Plots**: A results dataframe can now be extracted for both the delta-delta and mini-meta effect size data (similar to the results dataframe for the regular effect sizes). These can be found via the `.results` attribute of the `.delta_delta` or `.mini_meta` object.
+
+
+
 ## v2024.03.29
 
 ### New Features
diff --git a/README.md b/README.md
index e8966a6f..02b2ec88 100644
--- a/README.md
+++ b/README.md
@@ -1,9 +1,10 @@
 # DABEST-Python
 
+
 <!-- WARNING: THIS FILE WAS AUTOGENERATED! DO NOT EDIT! -->
 
 [![minimal Python
-version](https://img.shields.io/badge/Python%3E%3D-3.8-6666ff.svg)](https://www.anaconda.com/distribution/)
+version](https://img.shields.io/badge/Python%3E%3D-3.10-6666ff.svg)](https://www.anaconda.com/distribution/)
 [![PyPI
 version](https://badge.fury.io/py/dabest.svg)](https://badge.fury.io/py/dabest)
 [![Downloads](https://img.shields.io/pepy/dt/dabest.svg)](https://pepy.tech/project/dabest)
@@ -13,37 +14,139 @@ citation](https://zenodo.org/badge/DOI/10.1038/s41592-019-0470-3.svg)](https://r
 
 ## Recent Version Update
 
-We are proud to announce **DABEST Version Ondeh (v2024.03.29)**. This
-new version of the DABEST Python library provides several new features
-and includes performance improvements.
-
-1.  **New Paired Proportion Plot**: This feature builds upon the
-    existing proportional analysis capabilities by introducing advanced
-    aesthetics and clearer visualization of changes in proportions
-    between different groups, inspired by the informative nature of
-    Sankey Diagrams. It’s particularly useful for studies that require
-    detailed examination of how proportions shift in paired
-    observations.
-
-2.  **Customizable Swarm Plot**: Enhancements allow for tailored swarm
-    plot aesthetics, notably the adjustment of swarm sides to produce
-    asymmetric swarm plots. This customization enhances data
-    representation, making visual distinctions more pronounced and
-    interpretations clearer.
-
-3.  **Standardized Delta-delta Effect Size**: We added a new metric akin
-    to a Hedges’ g for delta-delta effect size, which allows comparisons
-    between delta-delta effects generated from metrics with different
-    units.
-
-4.  **Miscellaneous Improvements**: This version also encompasses a
-    broad range of miscellaneous enhancements, including bug fixes,
-    Bootstrapping speed improvements, new templates for raising issues,
-    and updated unit tests. These improvements are designed to
-    streamline the user experience, increase the software’s stability,
-    and expand its versatility. By addressing user feedback and
-    identified issues, DABEST continues to refine its functionality and
-    reliability.
+We are proud to announce **DABEST Version Dadar (v2025.03.27)** This new
+version of the DABEST Python library includes several new features and
+performance improvements. It’s a big one!
+
+1.  **Python 3.13 Support**: DABEST now supports Python 3.10—3.13.
+
+2.  **Horizontal Plots**: Users can now create horizontal layout plots,
+    providing compact data visualization. This can be achieved by
+    setting `horizontal=True` in the `.plot()` method. See the
+    [Horizontal Plots
+    tutorial](https://acclab.github.io/DABEST-python/tutorials/08-horizontal_plot.html)
+    for more details.
+
+3.  **Forest Plots**: Forest plots provide a simple and intuitive way to
+    visualize many delta-delta (or delta *g*), mini-meta, or regular
+    delta effect sizes at once from multiple different dabest objects
+    without presenting the raw data. See the [Forest Plots
+    tutorial](https://acclab.github.io/DABEST-python/tutorials/07-forest_plot.html)
+    for more details.
+
+4.  **Gridkey**: Users can now represent experimental labels in a
+    ‘gridkey’ table. This can be accessed with the `gridkey` parameter
+    in the `.plot()` method. See the gridkey section in the [Plot
+    Aesthetics
+    tutorial](https://acclab.github.io/DABEST-python/tutorials/09-plot_aesthetics.html)
+    for more details.
+
+5.  **Other Visualization Improvements**:
+
+    - **Comparing means and effect sizes**: The estimation plots now
+      include three types of customizable visual features to enhance
+      contextualization and comparison of means and effect sizes:
+
+      - **Bars for the mean of the observed values (`raw_bars`)**:
+        Colored rectangles that extend from the zero line to the mean of
+        each group’s raw data. These bars visually highlight the central
+        tendency of the raw data.
+
+      - **Bars for effect size/s (`contrast_bars`)**: Similar to raw
+        bars, these highlight the effect-size difference between two
+        groups (typically test and control) in the contrast axis. They
+        provide a visual representation of the differences between
+        groups.
+
+      - **Summary bands (`reference_band`)**: An optional band or ribbon
+        that can be added to emphasize a specific effect size’s
+        confidence interval that is used as a reference range across the
+        entire contrast axis. Unlike raw and contrast bars, these span
+        horizontally (or vertically if `horizontal=True`) and are not
+        displayed by default.
+
+        Raw and contrast bars are shown by default. Users can customize
+        these bars and add summary bands as needed. For detailed
+        customization instructions, please refer to the [Plot Aesthetics
+        tutorial](https://acclab.github.io/DABEST-python/tutorials/09-plot_aesthetics.html).
+
+    - **Tighter spacing in delta-delta and mini-meta plots**: We have
+      adjusted the spacing of delta-delta and mini-meta plots to reduce
+      whitespace. The new format brings the overall effect size closer
+      to the two-groups effect sizes. In addition, delta-delta plots now
+      have a gap in the zero line to separate the delta-delta from the ∆
+      effect sizes.
+
+    - **Delta-delta effect sizes for proportion plots**: In addition to
+      continuous data, delta-delta plots now support binary data
+      (proportions). This means that 2-way designs for binary outcomes
+      can be analyzed with DABEST.
+
+    - **Proportion plots sample sizes**: The sample size of each binary
+      option for each group can now be displayed. These can be toggled
+      on/off via the `prop_sample_counts` parameter.
+
+    - **Effect size lines for paired plots**: Along with lines
+      connecting paired observed values, the paired plots now also
+      display lines linking the effect sizes within a group in the
+      contrast axes. These lines can be toggled on/off via the
+      `contrast_paired_lines` parameter.
+
+    - **Baseline error curves**: To represent the baseline/control group
+      in the contrast axes, it is now possible to plot the baseline dot
+      and the baseline error curve. The dot is shown by default, while
+      the curve can be toggled on/off via the `show_baseline_ec`
+      parameter. This dot helps make it clear where the baseline comes
+      from i.e. the control minus itself. The baseline error curve can
+      be used to show that the baseline itself is an estimate inferred
+      from the observed values of the control data.
+
+    - **Delta text**: Effect-size deltas (e.g. mean differences) are now
+      displayed as numerals next to their respective effect size. This
+      can be toggled on/off via the `delta_text` parameter.
+
+    - **Empty circle color palette**: A new swarmplot color palette
+      modification is available for unpaired plots via the
+      `empty_circle` parameter in the `.plot()` method. This option
+      modifies the two-group swarmplots to have empty circles for the
+      control group and filled circles for the experimental group.
+
+6.  **Miscellaneous Improvements & Adjustments**
+
+    - **Numba for speed improvements**: We have added
+      [Numba](https://numba.pydata.org/) to speed up the various
+      calculations in DABEST. Precalculations will be performed during
+      import, which will help speed up the subsequent loading and
+      plotting of data.
+
+    - **Terminology/naming updates**: During the refactoring of the
+      code, we have made several updates to the documentation and
+      terminology to improve clarity and consistency. For example:
+
+      - Plot arguments have been adjusted to bring more clarity and
+        consistency in naming. Arguments relating to the rawdata plot
+        axis will now be typically referred to with `raw` while
+        arguments relating to the contrast axis will be referred to with
+        `contrast`. For example, `raw_label` replaces `swarm_label` and
+        `bar_label`. The various kwargs relating to each different type
+        of plot (e.g., `swarmplot_kwargs`) remain unchanged.
+
+      - The method to utilise the Delta *g* effect size is now via the
+        .hedges_g.plot() method rather than creating a whole new Delta_g
+        object as before. The functionality remains the same, it plots
+        hedges_g effect sizes and then the Delta *g* effect size
+        alongside these (if a delta-delta experiment was loaded
+        correctly).
+
+    - **Updated tutorial pages**: We have updated the tutorial pages to
+      reflect the new features and changes. The tutorial pages are now
+      more comprehensive and (hopefully!) more intuitive!
+
+    - **Results dataframe for delta-delta and mini-meta plots**: A
+      results dataframe can now be extracted for both the delta-delta
+      and mini-meta effect size data (similar to the results dataframe
+      for the regular effect sizes). These can be found via the
+      `.results` attribute of the `.delta_delta` or `.mini_meta` object.
 
 ## Contents
 
@@ -91,7 +194,7 @@ allowing everyone access to high-quality estimation plots.
 
 ## Installation
 
-This package is tested on Python 3.8 and onwards. It is highly
+This package is tested on Python 3.11 and onwards. It is highly
 recommended to download the [Anaconda
 distribution](https://www.continuum.io/downloads) of Python in order to
 obtain the dependencies easily.
@@ -160,17 +263,17 @@ page](https://github.com/ACCLAB/DABEST-python/issues/new).
 ## Contributing
 
 All contributions are welcome; please read the [Guidelines for
-contributing](CONTRIBUTING.md) first.
+contributing](../CONTRIBUTING.md) first.
 
-We also have a [Code of Conduct](CODE_OF_CONDUCT.md) to foster an
+We also have a [Code of Conduct](../CODE_OF_CONDUCT.md) to foster an
 inclusive and productive space.
 
 ### A wish list for new features
 
 If you have any specific comments and ideas for new features that you
 would like to share with us, please read the [Guidelines for
-contributing](CONTRIBUTING.md), create a new issue using Feature request
-template or create a new post in [our Google
+contributing](../CONTRIBUTING.md), create a new issue using Feature
+request template or create a new post in [our Google
 Group](https://groups.google.com/g/estimationstats).
 
 ## Acknowledgements
@@ -197,7 +300,7 @@ The test suite ensures that the bootstrapping functions and the plotting
 functions perform as expected.
 
 For detailed information, please refer to the [test
-folder](nbs/tests/README.md)
+folder](../nbs/tests/README.md)
 
 ## DABEST in other languages
 
diff --git a/dabest/__init__.py b/dabest/__init__.py
index 6f7d114e..d973b713 100644
--- a/dabest/__init__.py
+++ b/dabest/__init__.py
@@ -1,6 +1,15 @@
 from ._api import load, prop_dataset
 from ._stats_tools import effsize as effsize
+from ._stats_tools import confint_2group_diff as ci_2g
 from ._effsize_objects import TwoGroupsEffectSize, PermutationTest
 from ._dabest_object import Dabest
+from .forest_plot import forest_plot
 
-__version__ = "2024.03.29"
+
+import os
+if os.environ.get('SKIP_NUMBA_COMPILE') != '1':
+    from ._stats_tools.precompile import precompile_all, _NUMBA_COMPILED
+    if not _NUMBA_COMPILED:
+        precompile_all()
+
+__version__ = "2025.03.27"
\ No newline at end of file
diff --git a/dabest/_api.py b/dabest/_api.py
index 7c8d0eac..0f4ad140 100644
--- a/dabest/_api.py
+++ b/dabest/_api.py
@@ -1,3 +1,5 @@
+"""Loading data and relevant groups"""
+
 # AUTOGENERATED! DO NOT EDIT! File to edit: ../nbs/API/load.ipynb.
 
 # %% auto 0
@@ -20,6 +22,7 @@ def load(
     experiment_label=None,
     x1_level=None,
     mini_meta=False,
+    ps_adjust=False,
 ):
     """
     Loads data in preparation for estimation statistics.
@@ -80,6 +83,9 @@ def load(
         is True; otherwise it can only be a string.
     mini_meta : boolean, default False
         Indicator of weighted delta calculation.
+    ps_adjust : boolean, default False
+        Indicator of whether to adjust calculated p-value according to Phipson & Smyth (2010)
+        # https://doi.org/10.2202/1544-6115.1585
 
     Returns
     -------
@@ -103,6 +109,7 @@ def load(
         experiment_label,
         x1_level,
         mini_meta,
+        ps_adjust,
     )
 
 # %% ../nbs/API/load.ipynb 5
diff --git a/dabest/_bootstrap_tools.py b/dabest/_bootstrap_tools.py
index 0951ffb5..7a3e979c 100644
--- a/dabest/_bootstrap_tools.py
+++ b/dabest/_bootstrap_tools.py
@@ -66,7 +66,9 @@ def __init__(
         reps: int = 5000,  # Number of bootstrap iterations to perform.
     ):
         # Turn to pandas series.
-        x1 = pd.Series(x1).dropna()
+        # x1 = pd.Series(x1).dropna()
+        x1 = x1[~np.isnan(x1)]
+
         diff = False
 
         # Initialise stat_function
@@ -89,7 +91,9 @@ def __init__(
             if x2 is None:
                 raise ValueError("Please specify x2.")
             
-            x2 = pd.Series(x2).dropna()
+            # x2 = pd.Series(x2).dropna()
+            x2 = x1[~np.isnan(x2)]
+
             if len(x1) != len(x2):
                 raise ValueError("x1 and x2 are not the same length.")
 
@@ -134,7 +138,8 @@ def __init__(
 
         elif x2 is not None and paired is None:
             diff = True
-            x2 = pd.Series(x2).dropna()
+            # x2 = pd.Series(x2).dropna()
+            x2 = x2[~np.isnan(x2)]
             # Generate statarrays for both arrays.
             ref_statarray = sns.algorithms.bootstrap(x1, **sns_bootstrap_kwargs)
             exp_statarray = sns.algorithms.bootstrap(x2, **sns_bootstrap_kwargs)
diff --git a/dabest/_dabest_object.py b/dabest/_dabest_object.py
index 3f618a2a..a055cdd1 100644
--- a/dabest/_dabest_object.py
+++ b/dabest/_dabest_object.py
@@ -1,16 +1,20 @@
+"""Main class for estimating statistics and generating plots."""
+
 # AUTOGENERATED! DO NOT EDIT! File to edit: ../nbs/API/dabest_object.ipynb.
 
 # %% auto 0
 __all__ = ['Dabest']
 
-# %% ../nbs/API/dabest_object.ipynb 4
+# %% ../nbs/API/dabest_object.ipynb 5
 # Import standard data science libraries
+import warnings
 from numpy import array, repeat, random, issubdtype, number
+import numpy as np
 import pandas as pd
 from scipy.stats import norm
 from scipy.stats import randint
 
-# %% ../nbs/API/dabest_object.ipynb 6
+# %% ../nbs/API/dabest_object.ipynb 7
 class Dabest(object):
 
     """
@@ -34,6 +38,7 @@ def __init__(
         experiment_label,
         x1_level,
         mini_meta,
+        ps_adjust,
     ):
         """
         Parses and stores pandas DataFrames in preparation for estimation
@@ -50,118 +55,19 @@ def __init__(
         self.__is_paired = paired
         self.__resamples = resamples
         self.__random_seed = random_seed
-        self.__proportional = proportional
-        self.__mini_meta = mini_meta
+        self.__is_proportional = proportional
+        self.__is_mini_meta = mini_meta
+        self.__ps_adjust = ps_adjust
 
         # after this call the attributes self.__experiment_label and self.__x1_level are updated
         self._check_errors(x, y, idx, experiment, experiment_label, x1_level)
-        
-
-        # Check if there is NaN under any of the paired settings
-        if self.__is_paired and self.__output_data.isnull().values.any():
-            import warnings
-            warn1 = f"NaN values detected under paired setting and removed,"
-            warn2 = f" please check your data."
-            warnings.warn(warn1 + warn2)
-            if x is not None and y is not None:
-                rmname = self.__output_data[self.__output_data[y].isnull()][self.__id_col].tolist()
-                self.__output_data = self.__output_data[~self.__output_data[self.__id_col].isin(rmname)]
-            elif x is None and y is None:
-                self.__output_data.dropna(inplace=True)
 
         # create new x & idx and record the second variable if this is a valid 2x2 ANOVA case
-        if idx is None and x is not None and y is not None:
-            # Add a length check for unique values in the first element in list x,
-            # if the length is greater than 2, force delta2 to be False
-            # Should be removed if delta2 for situations other than 2x2 is supported
-            if len(self.__output_data[x[0]].unique()) > 2 and self.__x1_level is None:
-                self.__delta2 = False
-                # stop the loop if delta2 is False
-
-            # add a new column which is a combination of experiment and the first variable
-            new_col_name = experiment + x[0]
-            while new_col_name in self.__output_data.columns:
-                new_col_name += "_"
-
-            self.__output_data[new_col_name] = (
-                self.__output_data[x[0]].astype(str)
-                + " "
-                + self.__output_data[experiment].astype(str)
-            )
-
-            # create idx and record the first and second x variable
-            idx = []
-            for i in list(map(lambda x: str(x), self.__experiment_label)):
-                temp = []
-                for j in list(map(lambda x: str(x), self.__x1_level)):
-                    temp.append(j + " " + i)
-                idx.append(temp)
-
-            self.__idx = idx
-            self.__x1 = x[0]
-            self.__x2 = x[1]
-            x = new_col_name
-        else:
-            self.__idx = idx
-            self.__x1 = None
-            self.__x2 = None
-
-        # Determine the kind of estimation plot we need to produce.
-        if all([isinstance(i, (str, int, float)) for i in idx]):
-            # flatten out idx.
-            all_plot_groups = pd.unique([t for t in idx]).tolist()
-            if len(idx) > len(all_plot_groups):
-                err0 = "`idx` contains duplicated groups. Please remove any duplicates and try again."
-                raise ValueError(err0)
-
-            # We need to re-wrap this idx inside another tuple so as to
-            # easily loop thru each pairwise group later on.
-            self.__idx = (idx,)
-
-        elif all([isinstance(i, (tuple, list)) for i in idx]):
-            all_plot_groups = pd.unique([tt for t in idx for tt in t]).tolist()
-
-            actual_groups_given = sum([len(i) for i in idx])
-
-            if actual_groups_given > len(all_plot_groups):
-                err0 = "Groups are repeated across tuples,"
-                err1 = " or a tuple has repeated groups in it."
-                err2 = " Please remove any duplicates and try again."
-                raise ValueError(err0 + err1 + err2)
-
-        else:  # mix of string and tuple?
-            err = "There seems to be a problem with the idx you " "entered--{}.".format(
-                idx
-            )
-            raise ValueError(err)
-
-        # Check if there is a typo on paired
-        if self.__is_paired and self.__is_paired not in ("baseline", "sequential"):
-            err = "{} assigned for `paired` is not valid.".format(self.__is_paired)
-            raise ValueError(err)
-
-        # Determine the type of data: wide or long.
-        if x is None and y is not None:
-            err = "You have only specified `y`. Please also specify `x`."
-            raise ValueError(err)
-
-        if x is not None and y is None:
-            err = "You have only specified `x`. Please also specify `y`."
-            raise ValueError(err)
+        idx, x, all_plot_groups = self._prep_idx(idx, x, y, experiment)
 
         self.__plot_data = self._get_plot_data(x, y, all_plot_groups)
         self.__all_plot_groups = all_plot_groups
 
-        # Check if `id_col` is valid
-        if self.__is_paired:
-            if id_col is None:
-                err = "`id_col` must be specified if `paired` is assigned with a not NoneType value."
-                raise IndexError(err)
-
-            if id_col not in self.__plot_data.columns:
-                err = "{} is not a column in `data`. ".format(id_col)
-                raise IndexError(err)
-
         self._compute_effectsize_dfs()
 
     def __repr__(self):
@@ -210,7 +116,7 @@ def __repr__(self):
                 )
             )
 
-        if self.__mini_meta:
+        if self.__is_mini_meta:
             comparisons.append("weighted delta (only for mean difference)")
 
         for j, g in enumerate(comparisons):
@@ -222,6 +128,74 @@ def __repr__(self):
 
         return "\n".join(out)
 
+
+    def _prep_idx(self, idx, x, y, experiment):
+        """
+        Function to prepare the idx.
+        """
+        if idx is None and x is not None and y is not None:
+            # Add a length check for unique values in the first element in list x,
+            # if the length is greater than 2, force delta2 to be False
+            # Should be removed if delta2 for situations other than 2x2 is supported
+            if len(self.__output_data[x[0]].unique()) > 2:
+                self.__delta2 = False
+
+            # add a new column which is a combination of experiment and the first variable
+            new_col_name = experiment + x[0]
+            while new_col_name in self.__output_data.columns:
+                new_col_name += "_"
+
+            self.__output_data[new_col_name] = (
+                self.__output_data[x[0]].astype(str)
+                + " "
+                + self.__output_data[experiment].astype(str)
+            )
+
+            # create idx and record the first and second x variable
+            idx = []
+            for i in list(map(lambda x: str(x), self.__experiment_label)):
+                temp = []
+                for j in list(map(lambda x: str(x), self.__x1_level)):
+                    temp.append(j + " " + i)
+                idx.append(temp)
+
+            self.__idx = idx
+            self.__x1 = x[0]
+            self.__x2 = x[1]
+            x = new_col_name
+        else:
+            self.__idx = idx
+            self.__x1 = None
+            self.__x2 = None
+
+        # Determine the kind of estimation plot we need to produce.
+        if all([isinstance(i, (str, int, float)) for i in self.__idx]):
+            # flatten out idx.
+            all_plot_groups = pd.Series([t for t in self.__idx]).unique().tolist()
+            if len(self.__idx) > len(all_plot_groups):
+                err0 = "`idx` contains duplicated groups. Please remove any duplicates and try again."
+                raise ValueError(err0)
+
+            # We need to re-wrap this idx inside another tuple so as to
+            # easily loop thru each pairwise group later on.
+            self.__idx = (idx,)
+
+        elif all([isinstance(i, (tuple, list)) for i in self.__idx]):
+            all_plot_groups = pd.Series([tt for t in self.__idx for tt in t]).unique().tolist()
+            actual_groups_given = sum([len(i) for i in self.__idx])
+
+            if actual_groups_given > len(all_plot_groups):
+                err0 = "Groups are repeated across tuples,"
+                err1 = " or a tuple has repeated groups in it."
+                err2 = " Please remove any duplicates and try again."
+                raise ValueError(err0 + err1 + err2)
+
+        else:  # mix of string and tuple?
+            err = "There seems to be a problem with the idx you " "entered--{}.".format(self.__idx)
+            raise ValueError(err)
+        
+        return idx, x, all_plot_groups
+
     @property
     def mean_diff(self):
         """
@@ -270,12 +244,14 @@ def cliffs_delta(self):
         """
         return self.__cliffs_delta
 
+
     @property
     def delta_g(self):
         """
-        Returns an :py:class:`EffectSizeDataFrame` for deltas' g, its confidence interval, and relevant statistics, for all comparisons as indicated via the `idx` and `paired` argument in `dabest.load()`.
+        Returns an :py:class:`EffectSizeDataFrame` for delta g, its confidence interval, and relevant statistics, for all comparisons as indicated via the `idx` and `paired` argument in `dabest.load()`.
         """
-        return self.__delta_g
+        raise DeprecationWarning("delta_g has been depreciated  - Please use hedges_g (with delta2=True) for delta g experiments")
+
 
     @property
     def input_data(self):
@@ -340,6 +316,14 @@ def delta2(self):
         situation.
         """
         return self.__delta2
+    
+    @property
+    def is_delta_delta(self):
+        """
+        Returns the boolean parameter indicating if this is a delta-delta
+        situation.
+        """
+        return self.__delta2
 
     @property
     def is_paired(self):
@@ -416,18 +400,18 @@ def _plot_data(self):
         return self.__plot_data
 
     @property
-    def proportional(self):
+    def is_proportional(self):
         """
         Returns the proportional parameter class.
         """
-        return self.__proportional
+        return self.__is_proportional
 
     @property
-    def mini_meta(self):
+    def is_mini_meta(self):
         """
         Returns the mini_meta boolean parameter.
         """
-        return self.__mini_meta
+        return self.__is_mini_meta
 
     @property
     def _all_plot_groups(self):
@@ -442,10 +426,17 @@ def _check_errors(self, x, y, idx, experiment, experiment_label, x1_level):
         At the end of this function these two class attributes are updated
                 self.__experiment_label and self.__x1_level
         '''
+
+        # Check if idx is present (if not a 2x2 Anova case)
+        if idx is None:
+            if not self.__delta2:
+                err0 = "Please specify `idx`."
+                raise ValueError(err0)
+
         # Check if it is a valid mini_meta case
-        if self.__mini_meta:
+        if self.__is_mini_meta:
             # Only mini_meta calculation but not proportional and delta-delta function
-            if self.__proportional:
+            if self.__is_proportional:
                 err0 = "`proportional` and `mini_meta` cannot be True at the same time."
                 raise ValueError(err0)
             if self.__delta2:
@@ -477,7 +468,7 @@ def _check_errors(self, x, y, idx, experiment, experiment_label, x1_level):
 
             # Handling str type condition
             if is_str_condition_met:
-                if len(pd.unique(idx).tolist()) != 2:
+                if len(np.unique(idx).tolist()) != 2:
                     err0 = "`mini_meta` is True, but `idx` ({})".format(idx)
                     err1 = "does not contain exactly 2 unique columns."
                     raise ValueError(err0 + err1)
@@ -497,10 +488,6 @@ def _check_errors(self, x, y, idx, experiment, experiment_label, x1_level):
             if x is None:
                 error_msg = "If `delta2` is True. `x` parameter cannot be None. String or list expected"
                 raise ValueError(error_msg)
-                
-            if self.__proportional:
-                err0 = "`proportional` and `delta2` cannot be True at the same time."
-                raise ValueError(err0)
 
             # idx should not be specified
             if idx:
@@ -562,7 +549,6 @@ def _check_errors(self, x, y, idx, experiment, experiment_label, x1_level):
                             i, experiment
                         )
                         raise IndexError(err)
-
             else:
                 x1_level = self.__output_data[x[0]].unique()
 
@@ -572,36 +558,65 @@ def _check_errors(self, x, y, idx, experiment, experiment_label, x1_level):
         self.__experiment_label = experiment_label
         self.__x1_level = x1_level
 
-    def _get_plot_data(self, x, y, all_plot_groups):
-        """
-        Function to prepare some attributes for plotting
-        """
-        # Check if there is NaN under any of the paired settings
-        if self.__is_paired is not None and self.__output_data.isnull().values.any():
-            print("Nan")
-            import warnings
+        if self.__is_paired and self.__output_data.isnull().values.any():
             warn1 = f"NaN values detected under paired setting and removed,"
             warn2 = f" please check your data."
             warnings.warn(warn1 + warn2)
-            rmname = self.__output_data[self.__output_data[y].isnull()][self.__id_col].tolist()
-            self.__output_data = self.__output_data[~self.__output_data[self.__id_col].isin(rmname)]
-                
-        # Identify the type of data that was passed in.
-        if x is not None and y is not None:
-            # Assume we have a long dataset.
-            # check both x and y are column names in data.
-            if x not in self.__output_data.columns:
-                err = "{0} is not a column in `data`. Please check.".format(x)
+            if x is not None and y is not None:
+                rmname = self.__output_data[self.__output_data[y].isnull()][self.__id_col].tolist()
+                self.__output_data = self.__output_data[~self.__output_data[self.__id_col].isin(rmname)]
+            elif x is None and y is None:
+                self.__output_data.dropna(inplace=True)
+
+        # Check if there is a typo on paired
+        if self.__is_paired and self.__is_paired not in ("baseline", "sequential"):
+            err = "'{}' assigned for `paired` is not valid. Please use either 'baseline' or 'sequential'.".format(self.__is_paired)
+            raise ValueError(err)
+        
+        # Check if `id_col` is valid
+        if self.__is_paired:
+            if self.__id_col is None:
+                err = "`id_col` must be specified if `paired` is assigned with a not NoneType value."
                 raise IndexError(err)
-            if y not in self.__output_data.columns:
-                err = "{0} is not a column in `data`. Please check.".format(y)
+
+            if self.__id_col not in self.__output_data.columns:
+                err = "`id_col` was given as '{}'; however, '{}' is not a column in `data`.".format(self.__id_col, self.__id_col)
                 raise IndexError(err)
+            
+        # Check if x and y are supplied (relevant to long format data)
+        if x is None and y is not None:
+            err = "You have only specified `y`. Please also specify `x` (for long format data)."
+            raise ValueError(err)
 
-            # check y is numeric.
+        if x is not None and y is None:
+            err = "You have only specified `x`. Please also specify `y` (for long format data)."
+            raise ValueError(err)
+        
+        if x is not None and y is not None:
+            # Assume we have a long dataset.
+            # check both x and y are column names in data.
+            if not self.__delta2:
+                if x not in self.__output_data.columns:
+                    err = "'{0}' is not a column in `data`. Please check.".format(x)
+                    raise IndexError(err)
+                if y not in self.__output_data.columns:
+                    err = "'{0}' is not a column in `data`. Please check.".format(y)
+                    raise IndexError(err)
+            # Check that the `y` column is numeric.
             if not issubdtype(self.__output_data[y].dtype, number):
-                err = "{0} is a column in `data`, but it is not numeric.".format(y)
+                err = "The `y` column in `data` is not numeric. Please check."
                 raise ValueError(err)
 
+
+    def _get_plot_data(self, x, y, all_plot_groups):
+    # def _get_plot_data(self, x, y):
+        """
+        Function to prepare some attributes for plotting
+        """
+        # all_plot_groups = self.__all_plot_groups
+        # Identify the type of data that was passed in.
+        if x is not None and y is not None:
+            # Assume we have a long dataset.
             # check all the idx can be found in self.__output_data[x]
             for g in all_plot_groups:
                 if g not in self.__output_data[x].unique():
@@ -627,14 +642,7 @@ def _get_plot_data(self, x, y, all_plot_groups):
             self.__x = None
             self.__y = None
             self.__xvar = "group"
-            self.__yvar = "value"
-
-            # Check if there is NaN under any of the paired settings
-            if self.__is_paired is not None and self.__output_data.isnull().values.any():
-                import warnings
-                warn1 = f"NaN values detected under paired setting and removed,"
-                warn2 = f" please check your data."
-                warnings.warn(warn1 + warn2)
+            self.__yvar = "Value"
 
             # First, check we have all columns in the dataset.
             for g in all_plot_groups:
@@ -660,12 +668,12 @@ def _get_plot_data(self, x, y, all_plot_groups):
 
 
         if isinstance(plot_data[self.__xvar].dtype, pd.CategoricalDtype):
-            plot_data[self.__xvar].cat.remove_unused_categories(inplace=True)
+            plot_data[self.__xvar].cat.remove_unused_categories()
             plot_data[self.__xvar].cat.reorder_categories(
-                all_plot_groups, ordered=True, inplace=True
+                all_plot_groups, ordered=True
             )
         else:
-            plot_data.loc[:, self.__xvar] = pd.Categorical(
+            plot_data[self.__xvar] = pd.Categorical(
                 plot_data[self.__xvar], categories=all_plot_groups, ordered=True
             )
 
@@ -683,12 +691,13 @@ def _compute_effectsize_dfs(self):
             is_paired=self.__is_paired,
             random_seed=self.__random_seed,
             resamples=self.__resamples,
-            proportional=self.__proportional,
+            proportional=self.__is_proportional,
             delta2=self.__delta2,
             experiment_label=self.__experiment_label,
             x1_level=self.__x1_level,
             x2=self.__x2,
-            mini_meta=self.__mini_meta,
+            mini_meta=self.__is_mini_meta,
+            ps_adjust=self.__ps_adjust,
         )
 
         self.__mean_diff = EffectSizeDataFrame(
@@ -705,8 +714,6 @@ def _compute_effectsize_dfs(self):
 
         self.__hedges_g = EffectSizeDataFrame(self, "hedges_g", **effectsize_df_kwargs)
 
-        self.__delta_g = EffectSizeDataFrame(self, "delta_g", **effectsize_df_kwargs)
-
         if not self.__is_paired:
             self.__cliffs_delta = EffectSizeDataFrame(
                 self, "cliffs_delta", **effectsize_df_kwargs
diff --git a/dabest/_delta_objects.py b/dabest/_delta_objects.py
index 30c44895..909aba6e 100644
--- a/dabest/_delta_objects.py
+++ b/dabest/_delta_objects.py
@@ -1,3 +1,5 @@
+"""Auxiliary delta classes for estimating statistics and generating plots."""
+
 # AUTOGENERATED! DO NOT EDIT! File to edit: ../nbs/API/delta_objects.ipynb.
 
 # %% auto 0
@@ -15,7 +17,7 @@
 
 # %% ../nbs/API/delta_objects.ipynb 6
 class DeltaDelta(object):
-    """
+    r"""
     A class to compute and store the delta-delta statistics for experiments with a 2-by-2 arrangement where two independent variables, A and B, each have two categorical values, 1 and 2. The data is divided into two pairs of two groups, and a primary delta is first calculated as the mean difference between each of the pairs:
 
 
@@ -31,7 +33,7 @@ class DeltaDelta(object):
 
     $$\Delta_{\Delta} = \Delta_{2} - \Delta_{1}$$
 
-    and a deltas' g value is calculated as the mean difference between the two primary deltas divided by
+    and a delta g value is calculated as the mean difference between the two primary deltas divided by
     the standard deviation of the delta-delta value, which is calculated from a pooled variance of the 4 samples:
 
     $$\Delta_{g} = \frac{\Delta_{\Delta}}{s_{\Delta_{\Delta}}}$$
@@ -61,7 +63,7 @@ def __init__(
         self.__control = self.__dabest_obj.experiment_label[0]
         self.__test = self.__dabest_obj.experiment_label[1]
 
-        # Compute the bootstrap delta-delta or deltas' g and the true dela-delta based on the raw data
+        # Compute the bootstrap delta-delta or delta g and the true dela-delta based on the raw data
         if self.__effect_size == "mean_diff":
             self.__bootstraps_delta_delta = bootstraps_delta_delta[2]
             self.__difference = (
@@ -155,7 +157,7 @@ def __repr__(self, header=True, sigfig=3):
         if self.__effect_size == "mean_diff":
             out1 = "The delta-delta between {control} and {test} ".format(**first_line)
         else:
-            out1 = "The deltas' g between {control} and {test} ".format(**first_line)
+            out1 = "The delta g between {control} and {test} ".format(**first_line)
 
         base_string_fmt = "{:." + str(sigfig) + "}"
         if "." in str(self.__ci):
@@ -207,11 +209,29 @@ def to_dict(self):
         dictionary.
         """
         # Only get public (user-facing) attributes.
-        attrs = [a for a in dir(self) if not a.startswith(("_", "to_dict"))]
+        attrs = [a for a in dir(self) if not a.startswith(("_", "to_dict", "results"))]
         out = {}
         for a in attrs:
             out[a] = getattr(self, a)
         return out
+    
+    def __compute_results(self):
+        # With some inspiration from @jungyangliao
+        delta_delta_results_df = pd.Series(self.to_dict()).to_frame().T
+
+        column_index = ['control', 'test', 'difference', 'ci', 'bca_low', 'bca_high', 'bca_interval_idx', 
+                        'pct_low', 'pct_high', 'pct_interval_idx', 'bootstraps_control', 'bootstraps_test', 
+                        'bootstraps_delta_delta', 'permutations_control', 'permutations_test', 'permutations_delta_delta',
+                        'pvalue_permutation', 'permutation_count', 'bias_correction', 'jackknives'
+                        ]
+        delta_delta_results_df['bootstraps_control'] = [delta_delta_results_df['bootstraps'][0][0]]
+        delta_delta_results_df['bootstraps_test'] = [delta_delta_results_df['bootstraps'][0][1]]
+        delta_delta_results_df['permutations_control'] = [delta_delta_results_df['permutations'][0][0]]
+        delta_delta_results_df['permutations_test'] = [delta_delta_results_df['permutations'][0][1]]
+        delta_delta_results_df = delta_delta_results_df.reindex(columns=column_index)
+
+        self.__results = delta_delta_results_df
+        return self.__results
 
     @property
     def ci(self):
@@ -350,6 +370,17 @@ def permutations_delta_delta(self):
         except AttributeError:
             self.__permutation_test()
             return self.__permutations_delta_delta
+        
+    @property
+    def results(self):
+        """
+        Return the results of the delta-delta analysis.
+        """
+        try:
+            return self.__results
+        except AttributeError:
+            self.__compute_results()
+        return self.__results
 
 # %% ../nbs/API/delta_objects.ipynb 10
 class MiniMetaDelta(object):
@@ -386,13 +417,14 @@ def __init__(self, effectsizedataframe, permutation_count,
         # compute the variances of each control group and each test group
         control_var=[]
         test_var=[]
+        grouped_data = {name: group[yvar].copy() for name, group in dat.groupby(xvar, observed=False)}
         for j, current_tuple in enumerate(idx):
             cname = current_tuple[0]
-            control = dat[dat[xvar] == cname][yvar].copy()
+            control = grouped_data[cname]
             control_var.append(np.var(control, ddof=1))
 
             tname = current_tuple[1]
-            test = dat[dat[xvar] == tname][yvar].copy()
+            test = grouped_data[tname]
             test_var.append(np.var(test, ddof=1))
         self.__control_var = np.array(control_var)
         self.__test_var    = np.array(test_var)
@@ -412,7 +444,7 @@ def __init__(self, effectsizedataframe, permutation_count,
                                                           self.__bootstraps)
 
         # Compute the weighted average mean difference based on the raw data
-        self.__difference = es.weighted_delta(self.__effsizedf["difference"],
+        self.__difference = es.weighted_delta(np.array(self.__effsizedf["difference"]),
                                                    self.__group_var)
 
         sorted_weighted_deltas = npsort(self.__bootstraps_weighted_delta)
@@ -572,11 +604,30 @@ def to_dict(self):
         """
         # Only get public (user-facing) attributes.
         attrs = [a for a in dir(self)
-                 if not a.startswith(("_", "to_dict"))]
+                 if not a.startswith(("_", "to_dict", "results"))]
         out = {}
         for a in attrs:
             out[a] = getattr(self, a)
         return out
+    
+
+    def __compute_results(self):
+        # With some inspiration from @jungyangliao
+        """
+        Returns all attributes of the `dabest.MiniMetaDelta` object as a
+        DataFrame.
+        """
+        mini_meta_delta_results_df = pd.Series(self.to_dict()).to_frame().T
+        column_index = ['control', 'test', 'control_N', 'test_N', 'control_var', 'test_var', 'group_var',
+                        'difference', 'ci', 'bca_low', 'bca_high', 'bca_interval_idx', 
+                        'pct_low', 'pct_high', 'pct_interval_idx', 'bootstraps', 'bootstraps_weighted_delta', 
+                        'permutations', 'permutations_var', 'permutations_weighted_delta', 'pvalue_permutation', 
+                        'permutation_count', 'bias_correction', 'jackknives']
+        mini_meta_delta_results_df = mini_meta_delta_results_df.reindex(columns=column_index)
+        mini_meta_delta_results_df.rename(columns={'bootstraps': 'bootstraps_deltas'}, inplace=True)
+
+        self.__results = mini_meta_delta_results_df
+        return self.__results
 
 
     @property
@@ -798,4 +849,13 @@ def permutations_weighted_delta(self):
             self.__permutation_test()
             return self.__permutations_weighted_delta
 
-
+    @property
+    def results(self):
+        """
+        Return the results of the mini-meta analysis.
+        """
+        try:
+            return self.__results
+        except AttributeError:
+            self.__compute_results()
+        return self.__results
diff --git a/dabest/_effsize_objects.py b/dabest/_effsize_objects.py
index f8bf3846..e29e9c1a 100644
--- a/dabest/_effsize_objects.py
+++ b/dabest/_effsize_objects.py
@@ -1,3 +1,5 @@
+"""The auxiliary classes involved in the computations of bootstrapped effect sizes."""
+
 # AUTOGENERATED! DO NOT EDIT! File to edit: ../nbs/API/effsize_objects.ipynb.
 
 # %% auto 0
@@ -7,6 +9,11 @@
 import pandas as pd
 import lqrt
 from scipy.stats import norm
+import numpy as np
+from scipy.special import binom as binomcoeff  # devMJBL
+from scipy.stats import binom  # devMJBL
+from scipy.integrate import fixed_quad  # devMJBL
+from numpy import arange, mean  # devMJBL
 from numpy import array, isnan, isinf, repeat, random, isin, abs, var
 from numpy import sort as npsort
 from numpy import nan as npnan
@@ -47,6 +54,10 @@ class TwoGroupsEffectSize(object):
             `random_seed` is used to seed the random number generator during
             bootstrap resampling. This ensures that the confidence intervals
             reported are replicable.
+        ps_adjust : boolean, default False.
+            If True, adjust calculated p-value according to Phipson & Smyth (2010)
+            # https://doi.org/10.2202/1544-6115.1585
+            
 
         Returns
         -------
@@ -84,6 +95,7 @@ def __init__(
         resamples=5000,
         permutation_count=5000,
         random_seed=12345,
+        ps_adjust=False,
     ):
         from ._stats_tools import confint_2group_diff as ci2g
         from ._stats_tools import effsize as es
@@ -95,15 +107,15 @@ def __init__(
             "cohens_h": "Cohen's h",
             "hedges_g": "Hedges' g",
             "cliffs_delta": "Cliff's delta",
-            "delta_g": "deltas' g",
         }
-
+  
         self.__is_paired = is_paired
         self.__resamples = resamples
         self.__effect_size = effect_size
         self.__random_seed = random_seed
         self.__ci = ci
-        self.__proportional = proportional
+        self.__is_proportional = proportional
+        self.__ps_adjust = ps_adjust
         self._check_errors(control, test)
 
         # Convert to numpy arrays for speed.
@@ -165,6 +177,8 @@ def __init__(
         self.__pct_interval_idx = (pct_idx_low, pct_idx_high)
         self.__pct_low = sorted_bootstraps[pct_idx_low]
         self.__pct_high = sorted_bootstraps[pct_idx_high]
+        
+        self._get_bootstrap_baseline_ec()
 
         self._perform_statistical_test()
 
@@ -232,7 +246,7 @@ def __repr__(self, show_resample_count=True, define_pval=True, sigfig=3):
             return "{}\n{}\n\n{}\n{}".format(out, pvalue, bs, pval_def)
         elif not show_resample_count and define_pval:
             return "{}\n{}\n\n{}".format(out, pvalue, pval_def)
-        elif show_resample_count and ~define_pval:
+        elif show_resample_count and not define_pval:
             return "{}\n{}\n\n{}".format(out, pvalue, bs)
         else:
             return "{}\n{}".format(out, pvalue)
@@ -251,15 +265,15 @@ def _check_errors(self, control, test):
             err1 = "`paired` is not None; therefore Cliff's delta is not defined."
             raise ValueError(err1)
 
-        if self.__proportional and self.__effect_size not in ["mean_diff", "cohens_h"]:
-            err1 = "`proportional` is True; therefore effect size other than mean_diff and cohens_h is not defined."
+        if self.__is_proportional and self.__effect_size not in ["mean_diff", "cohens_h"]:
+            err1 = "`is_proportional` is True; therefore effect size other than mean_diff and cohens_h is not defined."
             raise ValueError(err1)
 
-        if self.__proportional and (
+        if self.__is_proportional and (
             isin(control, [0, 1]).all() == False or isin(test, [0, 1]).all() == False
         ):
             err1 = (
-                "`proportional` is True; Only accept binary data consisting of 0 and 1."
+                "`is_proportional` is True; Only accept binary data consisting of 0 and 1."
             )
             raise ValueError(err1)
 
@@ -325,9 +339,10 @@ def _perform_statistical_test(self):
             self.__effect_size,
             self.__is_paired,
             self.__permutation_count,
+            ps_adjust = self.__ps_adjust,
         )
 
-        if self.__is_paired and not self.__proportional:
+        if self.__is_paired and not self.__is_proportional:
             # Wilcoxon, a non-parametric version of the paired T-test.
             try:
                 wilcoxon = spstats.wilcoxon(self.__control, self.__test)
@@ -348,22 +363,21 @@ def _perform_statistical_test(self):
                 self.__pvalue_paired_students_t = paired_t.pvalue
                 self.__statistic_paired_students_t = paired_t.statistic
 
-        elif self.__is_paired and self.__proportional:
+        elif self.__is_paired and self.__is_proportional:
             # for binary paired data, use McNemar's test
             # References:
             # https://en.wikipedia.org/wiki/McNemar%27s_test
 
-            df_temp = pd.DataFrame({"control": self.__control, "test": self.__test})
-            x1 = len(df_temp[(df_temp["control"] == 0) & (df_temp["test"] == 0)])
-            x2 = len(df_temp[(df_temp["control"] == 0) & (df_temp["test"] == 1)])
-            x3 = len(df_temp[(df_temp["control"] == 1) & (df_temp["test"] == 0)])
-            x4 = len(df_temp[(df_temp["control"] == 1) & (df_temp["test"] == 1)])
-            table = [[x1, x2], [x3, x4]]
+            x1 = np.sum((self.__control == 0) & (self.__test == 0))
+            x2 = np.sum((self.__control == 0) & (self.__test == 1))
+            x3 = np.sum((self.__control == 1) & (self.__test == 0))
+            x4 = np.sum((self.__control == 1) & (self.__test == 1))
+            table = np.array([[x1, x2], [x3, x4]])
             _mcnemar = mcnemar(table, exact=True, correction=True)
             self.__pvalue_mcnemar = _mcnemar.pvalue
             self.__statistic_mcnemar = _mcnemar.statistic
 
-        elif self.__proportional:
+        elif self.__is_proportional:
             # The Cohen's h calculation is for binary categorical data
             try:
                 self.__proportional_difference = es.cohens_h(
@@ -433,6 +447,92 @@ def to_dict(self):
         for a in attrs:
             out[a] = getattr(self, a)
         return out
+    
+    def _get_bootstrap_baseline_ec(self):
+        from ._stats_tools import confint_2group_diff as ci2g
+        from ._stats_tools import effsize as es
+        
+        # Cannot use self.__is_paired because it's for baseline curve
+        is_paired = None
+        
+        difference = es.two_group_difference(
+            self.__control, self.__control, is_paired, self.__effect_size
+        )
+        self.__bec_difference = difference
+
+        jackknives = ci2g.compute_meandiff_jackknife(
+            self.__control, self.__control, is_paired, self.__effect_size
+        )
+
+        acceleration_value = ci2g._calc_accel(jackknives)
+
+        bootstraps = ci2g.compute_bootstrapped_diff(
+            self.__control,
+            self.__control,
+            is_paired,
+            self.__effect_size,
+            self.__resamples,
+            self.__random_seed,
+        )
+        self.__bootstraps_baseline_ec = bootstraps
+
+        sorted_bootstraps = npsort(self.__bootstraps_baseline_ec)
+        # We don't have to consider infinities in bootstrap_baseline_ec
+
+        bias_correction = ci2g.compute_meandiff_bias_correction(
+            self.__bootstraps_baseline_ec, difference
+        )
+
+        # Compute BCa intervals.
+        bca_idx_low, bca_idx_high = ci2g.compute_interval_limits(
+            bias_correction,
+            acceleration_value,
+            self.__resamples,
+            self.__ci,
+        )
+
+        self.__bec_bca_interval_idx = (bca_idx_low, bca_idx_high)
+
+        if ~isnan(bca_idx_low) and ~isnan(bca_idx_high):
+            self.__bec_bca_low = sorted_bootstraps[bca_idx_low]
+            self.__bec_bca_high = sorted_bootstraps[bca_idx_high]
+
+            err1 = "The $lim_type limit of the interval"
+            err2 = "was in the $loc 10 values."
+            err3 = "The result for baseline curve should be considered unstable."
+            err_temp = Template(" ".join([err1, err2, err3]))
+
+            if bca_idx_low <= 10:
+                warnings.warn(
+                    err_temp.substitute(lim_type="lower", loc="bottom"), stacklevel=1
+                )
+
+            if bca_idx_high >= self.__resamples - 9:
+                warnings.warn(
+                    err_temp.substitute(lim_type="upper", loc="top"), stacklevel=1
+                )
+
+        else:
+            err1 = "The $lim_type limit of the BCa interval of baseline curve cannot be computed."
+            err2 = "It is set to the effect size itself."
+            err3 = "All bootstrap values were likely all the same."
+            err_temp = Template(" ".join([err1, err2, err3]))
+
+            if isnan(bca_idx_low):
+                self.__bec_bca_low = difference
+                warnings.warn(err_temp.substitute(lim_type="lower"), stacklevel=0)
+
+            if isnan(bca_idx_high):
+                self.__bec_bca_high = difference
+                warnings.warn(err_temp.substitute(lim_type="upper"), stacklevel=0)
+
+        # Compute percentile intervals.
+        pct_idx_low = int((self.__alpha / 2) * self.__resamples)
+        pct_idx_high = int((1 - (self.__alpha / 2)) * self.__resamples)
+
+        self.__bec_pct_interval_idx = (pct_idx_low, pct_idx_high)
+        self.__bec_pct_low = sorted_bootstraps[pct_idx_low]
+        self.__bec_pct_high = sorted_bootstraps[pct_idx_high]
 
     @property
     def difference(self):
@@ -453,8 +553,8 @@ def is_paired(self):
         return self.__is_paired
 
     @property
-    def proportional(self):
-        return self.__proportional
+    def is_proportional(self):
+        return self.__is_proportional
 
     @property
     def ci(self):
@@ -669,6 +769,54 @@ def proportional_difference(self):
             return self.__proportional_difference
         except AttributeError:
             return npnan
+      
+    @property
+    def bec_difference(self):
+        return self.__bec_difference   
+        
+    @property
+    def bec_bootstraps(self):
+        """
+        The generated baseline error bootstraps.
+        """
+        return self.__bootstraps_baseline_ec
+
+    @property
+    def bec_bca_interval_idx(self):
+        return self.__bec_bca_interval_idx
+
+    @property
+    def bec_bca_low(self):
+        """
+        The bias-corrected and accelerated confidence interval lower limit for baseline error.
+        """
+        return self.__bec_bca_low
+
+    @property
+    def bec_bca_high(self):
+        """
+        The bias-corrected and accelerated confidence interval upper limit for baseline error.
+        """
+        return self.__bec_bca_high
+
+    @property
+    def bec_pct_interval_idx(self):
+        return self.__bec_pct_interval_idx
+
+    @property
+    def bec_pct_low(self):
+        """
+        The percentile confidence interval lower limit for baseline error.
+        """
+        return self.__bec_pct_low
+
+    @property
+    def bec_pct_high(self):
+        """
+        The percentile confidence interval lower limit for baseline error.
+        """
+        return self.__bec_pct_high
+        
 
 # %% ../nbs/API/effsize_objects.ipynb 10
 class EffectSizeDataFrame(object):
@@ -690,6 +838,7 @@ def __init__(
         delta2=False,
         experiment_label=None,
         mini_meta=False,
+        ps_adjust=False,
     ):
         """
         Parses the data from a Dabest object, enabling plotting and printing
@@ -703,12 +852,13 @@ def __init__(
         self.__resamples = resamples
         self.__permutation_count = permutation_count
         self.__random_seed = random_seed
-        self.__proportional = proportional
+        self.__is_proportional = proportional
         self.__x1_level = x1_level
         self.__experiment_label = experiment_label
         self.__x2 = x2
         self.__delta2 = delta2
-        self.__mini_meta = mini_meta
+        self.__is_mini_meta = mini_meta
+        self.__ps_adjust = ps_adjust
 
     def __pre_calc(self):
         from .misc_tools import print_greeting, get_varname
@@ -723,18 +873,19 @@ def __pre_calc(self):
         out = []
         reprs = []
 
+        grouped_data = {name: group[yvar].copy() for name, group in dat.groupby(xvar, observed=False)}
         if self.__delta2:
             mixed_data = []
             for j, current_tuple in enumerate(idx):
                 if self.__is_paired != "sequential":
                     cname = current_tuple[0]
-                    control = dat[dat[xvar] == cname][yvar].copy()
+                    control = grouped_data[cname]
 
                 for ix, tname in enumerate(current_tuple[1:]):
                     if self.__is_paired == "sequential":
                         cname = current_tuple[ix]
-                        control = dat[dat[xvar] == cname][yvar].copy()
-                    test = dat[dat[xvar] == tname][yvar].copy()
+                        control = grouped_data[cname]
+                    test = grouped_data[tname]
                     mixed_data.append(control)
                     mixed_data.append(test)
             bootstraps_delta_delta = ci2g.compute_delta2_bootstrapped_diff(
@@ -745,29 +896,30 @@ def __pre_calc(self):
                 self.__is_paired,
                 self.__resamples,
                 self.__random_seed,
+                self.__is_proportional,
             )
 
         for j, current_tuple in enumerate(idx):
             if self.__is_paired != "sequential":
                 cname = current_tuple[0]
-                control = dat[dat[xvar] == cname][yvar].copy()
+                control = grouped_data[cname]
 
             for ix, tname in enumerate(current_tuple[1:]):
                 if self.__is_paired == "sequential":
                     cname = current_tuple[ix]
-                    control = dat[dat[xvar] == cname][yvar].copy()
-                test = dat[dat[xvar] == tname][yvar].copy()
-
+                    control = grouped_data[cname]
+                test = grouped_data[tname]
                 result = TwoGroupsEffectSize(
                     control,
                     test,
                     self.__effect_size,
-                    self.__proportional,
+                    self.__is_proportional,
                     self.__is_paired,
                     self.__ci,
                     self.__resamples,
                     self.__permutation_count,
                     self.__random_seed,
+                    self.__ps_adjust
                 )
                 r_dict = result.to_dict()
                 r_dict["control"] = cname
@@ -776,10 +928,10 @@ def __pre_calc(self):
                 r_dict["test_N"] = int(len(test))
                 out.append(r_dict)
                 if j == len(idx) - 1 and ix == len(current_tuple) - 2:
-                    if self.__delta2 and self.__effect_size in ["mean_diff", "delta_g"]:
+                    if self.__delta2 and self.__effect_size in ["mean_diff", "hedges_g"]:
                         resamp_count = False
                         def_pval = False
-                    elif self.__mini_meta and self.__effect_size == "mean_diff":
+                    elif self.__is_mini_meta and self.__effect_size == "mean_diff":
                         resamp_count = False
                         def_pval = False
                     else:
@@ -841,6 +993,14 @@ def __pre_calc(self):
             "pvalue_kruskal",
             "statistic_kruskal",
             "proportional_difference",
+            "bec_difference",
+            "bec_bootstraps",
+            "bec_bca_interval_idx",
+            "bec_bca_low",
+            "bec_bca_high",
+            "bec_pct_interval_idx",
+            "bec_pct_low",
+            "bec_pct_high",
         ]
         self.__results = out_.reindex(columns=columns_in_order)
         self.__results.dropna(axis="columns", how="all", inplace=True)
@@ -852,32 +1012,33 @@ def __pre_calc(self):
             )
 
         # Create and compute the delta-delta statistics
-        if self.__delta2:
+        if self.__delta2 and self.__effect_size not in ["mean_diff", "hedges_g"]:
+            self.__delta_delta = "Delta-delta is not supported for {}.".format(
+                self.__effect_size
+            )
+        elif self.__delta2:
             self.__delta_delta = DeltaDelta(
                 self, self.__permutation_count, bootstraps_delta_delta, self.__ci
             )
             reprs.append(self.__delta_delta.__repr__(header=False))
-        elif self.__delta2 and self.__effect_size not in ["mean_diff", "delta_g"]:
-            self.__delta_delta = "Delta-delta is not supported for {}.".format(
-                self.__effect_size
-            )
+
         else:
             self.__delta_delta = (
                 "`delta2` is False; delta-delta is therefore not calculated."
             )
 
         # Create and compute the weighted average statistics
-        if self.__mini_meta and self.__effect_size == "mean_diff":
-            self.__mini_meta_delta = MiniMetaDelta(
+        if self.__is_mini_meta and self.__effect_size == "mean_diff":
+            self.__mini_meta = MiniMetaDelta(
                 self, self.__permutation_count, self.__ci
             )
-            reprs.append(self.__mini_meta_delta.__repr__(header=False))
-        elif self.__mini_meta and self.__effect_size != "mean_diff":
-            self.__mini_meta_delta = "Weighted delta is not supported for {}.".format(
+            reprs.append(self.__mini_meta.__repr__(header=False))
+        elif self.__is_mini_meta and self.__effect_size != "mean_diff":
+            self.__mini_meta = "Weighted delta is not supported for {}.".format(
                 self.__effect_size
             )
         else:
-            self.__mini_meta_delta = (
+            self.__mini_meta = (
                 "`mini_meta` is False; weighted delta is therefore not calculated."
             )
 
@@ -909,16 +1070,18 @@ def __calc_lqrt(self):
 
         out = []
 
+        grouped_data = {name:group[yvar].copy() for name, group in dat.groupby(xvar)}
+
         for j, current_tuple in enumerate(db_obj.idx):
             if self.__is_paired != "sequential":
                 cname = current_tuple[0]
-                control = dat[dat[xvar] == cname][yvar].copy()
+                control = grouped_data[cname]
 
             for ix, tname in enumerate(current_tuple[1:]):
                 if self.__is_paired == "sequential":
                     cname = current_tuple[ix]
-                    control = dat[dat[xvar] == cname][yvar].copy()
-                test = dat[dat[xvar] == tname][yvar].copy()
+                    control = grouped_data[cname]
+                test = grouped_data[tname]
 
                 if self.__is_paired:
                     # Refactored here in v0.3.0 for performance issues.
@@ -963,53 +1126,53 @@ def plot(
         self,
         color_col=None,
         raw_marker_size=6,
-        es_marker_size=9,
-        swarm_label=None,
+        contrast_marker_size=9, # es_marker_size=9, OLD
+
+        raw_label=None, # swarm_label=None, OLD # bar_label=None, OLD
         contrast_label=None,
         delta2_label=None,
-        swarm_ylim=None,
+
+        raw_ylim=None, # swarm_ylim=None, OLD # bar_ylim=None, OLD
         contrast_ylim=None,
         delta2_ylim=None,
-        swarm_side=None,
+
         custom_palette=None,
-        swarm_desat=0.5,
-        halfviolin_desat=1,
-        halfviolin_alpha=0.8,
+        swarm_side=None,
+        empty_circle=False,  # Not very intuitive name
+
         face_color=None,
-        # bar plot
-        bar_label=None,
-        bar_desat=0.5,
+
+        raw_desat=0.5, # swarm_desat=0.5, OLD # bar_desat=0.5, OLD
+        contrast_desat=1, # halfviolin_desat=1, OLD
+
+        raw_alpha=None, # NEW
+        contrast_alpha=0.8, # halfviolin_alpha=0.8, OLD
+
         bar_width=0.5,
-        bar_ylim=None,
-        # error bar of proportion plot
-        ci=None,
+        # ci=None, # Seems to be unused
         ci_type="bca",
-        err_color=None,
+
         float_contrast=True,
         show_pairs=True,
-        show_delta2=True,
+        show_sample_size=True,
+        show_delta2=True, # Would pref switch to delta_delta instead of delta2
         show_mini_meta=True,
-        group_summaries=None,
-        group_summaries_offset=0.1,
+
+        group_summaries="mean_sd",
+        # err_color=None, # Not intuitive name and doesnt fit with group_summaries argument    
         fig_size=None,
         dpi=100,
         ax=None,
-        contrast_show_es=False,
-        es_sf=2,
-        es_fontsize=10,
-        contrast_show_deltas=True,
-        gridkey_rows=None,
-        gridkey_merge_pairs=False,
-        gridkey_show_Ns=True,
-        gridkey_show_es=True,
+
         swarmplot_kwargs=None,
-        barplot_kwargs=None,
-        violinplot_kwargs=None,
         slopegraph_kwargs=None,
+        barplot_kwargs=None,
         sankey_kwargs=None,
+        contrast_kwargs=None, # violinplot_kwargs=None, OLD
         reflines_kwargs=None,
-        group_summary_kwargs=None,
+        group_summaries_kwargs=None,
         legend_kwargs=None,
+
         title=None,
         fontsize_title=16,
         fontsize_rawxlabel=12,
@@ -1017,6 +1180,42 @@ def plot(
         fontsize_contrastxlabel=12,
         fontsize_contrastylabel=12,
         fontsize_delta2label=12,
+
+        # Raw bars, Contrast bars, delta text, and delta dots
+        raw_bars=True, # swarm_bars=True, OLD          
+        raw_bars_kwargs=None, # swarm_bars_kwargs=None, OLD
+        contrast_bars=True,
+        contrast_bars_kwargs=None,
+        reference_band=None,
+        reference_band_kwargs=None,
+        delta_text=True,
+        delta_text_kwargs=None,
+        delta_dot=True,
+        delta_dot_kwargs=None,
+
+        # Horizontal Plots
+        horizontal=False,
+        horizontal_table_kwargs=None,
+
+        # Gridkey
+        gridkey=None, # gridkey_rows=None, OLD
+        gridkey_merge_pairs=False,
+        gridkey_show_Ns=True,
+        gridkey_show_es=True,
+        gridkey_delimiters=[';', '>', '_'],
+        gridkey_kwargs=None,
+
+        contrast_marker_kwargs=None, # es_marker_kwargs=None, OLD
+        contrast_errorbar_kwargs=None, # es_errorbar_kwargs=None, OLD
+
+        prop_sample_counts=False,
+        prop_sample_counts_kwargs=None,
+
+        contrast_paired_lines=True, # es_paired_lines=True, OLD
+        contrast_paired_lines_kwargs=None, # es_paired_lines_kwargs=None, OLD
+        
+		# Baseline Effect Size Curve
+		show_baseline_ec=False,
     ):
         """
         Creates an estimation plot for the effect size of interest.
@@ -1029,18 +1228,18 @@ def plot(
         raw_marker_size : float, default 6
             The diameter (in points) of the marker dots plotted in the
             swarmplot.
-        es_marker_size : float, default 9
+        contrast_marker_size : float, default 9
             The size (in points) of the effect size points on the difference
             axes.
-        swarm_label, contrast_label, delta2_label : strings, default None
-            Set labels for the y-axis of the swarmplot and the contrast plot,
-            respectively. If `swarm_label` is not specified, it defaults to
-            "value", unless a column name was passed to `y`. If
-            `contrast_label` is not specified, it defaults to the effect size
-            being plotted. If `delta2_label` is not specifed, it defaults to
-            "delta - delta"
-        swarm_ylim, contrast_ylim, delta2_ylim : tuples, default None
-            The desired y-limits of the raw data (swarmplot) axes, the
+        raw_label, contrast_label, delta2_label : strings, default None
+            Set labels for the y-axis of the raw plot and the contrast plot,
+            respectively. If `raw_label` is not specified, it defaults to
+            "Value" for non binary data (and "Proportion of Success" for binary data), 
+            unless a column name was passed to `y`. If `contrast_label` is not specified, 
+            it defaults to the effect size being plotted. If `delta2_label` is not specifed, 
+            it defaults to "delta - delta".
+        raw_ylim, contrast_ylim, delta2_ylim : tuples, default None
+            The desired y-limits of the raw data axes, the
             difference axes and the delta-delta axes respectively, as a tuple.
             These will be autoscaled to sensible values if they are not
             specified. The delta2 axes and contrast axes should have the same
@@ -1064,15 +1263,32 @@ def plot(
             https://seaborn.pydata.org/generated/seaborn.cubehelix_palette.html
             The named colors of matplotlib can be found here:
             https://matplotlib.org/examples/color/named_colors.html
-        swarm_desat : float, default 1
-            Decreases the saturation of the colors in the swarmplot by the
+        swarm_side: string, default None
+            The side on which points are swarmed for swarmplots ("center", "left", or "right").
+        empty_circle: boolean, default False
+            Boolean value determining if empty circles will be used for plotting of
+            swarmplot for control groups. Color of each individual swarm is also now
+            dependent on the comparison group.
+        face_color: string, default None
+            The face color of the plot. Defaults to "white".
+        raw_desat : float, default 1
+            Decreases the saturation of the colors in the rawplot by the
             desired proportion. Uses `seaborn.desaturate()` to acheive this.
-        halfviolin_desat : float, default 0.5
+        contrast_desat : float, default 0.5
             Decreases the saturation of the colors of the half-violin bootstrap
             curves by the desired proportion. Uses `seaborn.desaturate()` to
             acheive this.
-        halfviolin_alpha : float, default 0.8
+        raw_alpha : float, default None
+            The alpha (transparency) level of the raw plot elements. This defaults
+            to 1.0 for all plots except sankey and slopegraphs, whereby it defaults to 0.4
+            and 0.5, respectively.
+        contrast_alpha : float, default 0.8
             The alpha (transparency) level of the half-violin bootstrap curves.
+        bar_width : float, default 0.5
+            The width of the bars in the barplot (binary, non-paired data).
+        ci_type : string, default
+            The confidence interval of the contrast plot to display. Defaults
+            to "bca". Otherwise, the user can choose "pct" for percentile. 
         float_contrast : boolean, default True
             Whether or not to display the halfviolin bootstrapped difference
             distribution alongside the raw data.
@@ -1080,18 +1296,17 @@ def plot(
             If the data is paired, whether or not to show the raw data as a
             swarmplot, or as slopegraph, with a line joining each pair of
             observations.
+        show_sample_size : boolean, default True
+            Whether or not to display the sample size of each group in the axis label.
         show_delta2, show_mini_meta : boolean, default True
             If delta-delta or mini-meta delta is calculated, whether or not to
             show the delta-delta plot or mini-meta plot.
-        group_summaries : ['mean_sd', 'median_quartiles', 'None'], default None.
+        group_summaries : ['mean_sd', 'median_quartiles', 'None'], default "mean_sd".
             Plots the summary statistics for each group. If 'mean_sd', then
             the mean and standard deviation of each group is plotted as a
             notched line beside each group. If 'median_quantiles', then the
             median and 25th and 75th percentiles of each group is plotted
             instead. If 'None', the summaries are not shown.
-        group_summaries_offset : float, default 0.1
-            If group summaries are displayed, they will be offset from the raw
-            data swarmplot groups by this value.
         fig_size : tuple, default None
             The desired dimensions of the figure as a (length, width) tuple.
         dpi : int, default 100
@@ -1099,53 +1314,52 @@ def plot(
         ax : matplotlib.Axes, default None
             Provide an existing Axes for the plots to be created. If no Axes is
             specified, a new matplotlib Figure will be created.
-        gridkey_rows : list, default None
-            Provide a list of row labels for the gridkey. The supplied idx is
-            checked against the row labels to determine whether the corresponding
-            cell should be populated or not.
         swarmplot_kwargs : dict, default None
             Pass any keyword arguments accepted by the seaborn `swarmplot`
             command here, as a dict. If None, the following keywords are
             passed to sns.swarmplot : {'size':`raw_marker_size`}.
-        violinplot_kwargs : dict, default None
-            Pass any keyword arguments accepted by the matplotlib `
-            pyplot.violinplot` command here, as a dict. If None, the following
-            keywords are passed to violinplot : {'widths':0.5, 'vert':True,
-            'showextrema':False, 'showmedians':False}.
         slopegraph_kwargs : dict, default None
             This will change the appearance of the lines used to join each pair
             of observations when `show_pairs=True`. Pass any keyword arguments
             accepted by matplotlib `plot()` function here, as a dict.
             If None, the following keywords are
-            passed to plot() : {'linewidth':1, 'alpha':0.5}.
+            passed to plot() : {'linewidth':1, 'alpha':0.5, 'jitter':0, 'jitter_seed':9876543210}.
+        barplot_kwargs : dict, default None
+            By default, the keyword arguments passed are:
+            {"estimator": np.mean, "errorbar": plot_kwargs["ci"], "err_kws" : {'color':'black'}}
         sankey_kwargs: dict, default None
             Whis will change the appearance of the sankey diagram used to depict
-            paired proportional data when `show_pairs=True` and `proportional=True`.
+            paired proportional data when `show_pairs=True` and `is_proportional=True`.
             Pass any keyword arguments accepted by plot_tools.sankeydiag() function
             here, as a dict. If None, the following keywords are passed to sankey diagram:
             {"width": 0.5, "align": "center", "alpha": 0.4, "bar_width": 0.1, "rightColor": False}
+        contrast_kwargs : dict, default None
+            Pass any keyword arguments accepted by the matplotlib `
+            pyplot.violinplot` command here, as a dict. If None, the following
+            keywords are passed to violinplot : {'widths':0.5, 'vert':True,
+            'showextrema':False, 'showmedians':False}.
         reflines_kwargs : dict, default None
             This will change the appearance of the zero reference lines. Pass
             any keyword arguments accepted by the matplotlib Axes `hlines`
             command here, as a dict. If None, the following keywords are
             passed to Axes.hlines : {'linestyle':'solid', 'linewidth':0.75,
             'zorder':2, 'color' : default y-tick color}.
-        group_summary_kwargs : dict, default None
+        group_summaries_kwargs : dict, default None
             Pass any keyword arguments accepted by the matplotlib.lines.Line2D
             command here, as a dict. This will change the appearance of the
             vertical summary lines for each group, if `group_summaries` is not
             'None'. If None, the following keywords are passed to
-            matplotlib.lines.Line2D : {'lw':2, 'alpha':1, 'zorder':3}.
+            matplotlib.lines.Line2D : {'lw':2, 'alpha':1, 'zorder':3, 
+            'gap_width_percent':1.5, 'offset':0.1, 'color':None}.
         legend_kwargs : dict, default None
             Pass any keyword arguments accepted by the matplotlib Axes
             `legend` command here, as a dict. If None, the following keywords
-            are passed to matplotlib.Axes.legend : {'loc':'upper left',
-            'frameon':False}.
+            are passed to matplotlib.Axes.legend : {'frameon':False}.
         title : string, default None
             Title for the plot. If None, no title will be displayed. Pass any
             keyword arguments accepted by the matplotlib.pyplot.suptitle `t` command here,
             as a string.
-        fontsize_title : float or {'xx-small', 'x-small', 'small', 'medium', 'large', 'x-large', 'xx-large'}, default 'large'
+        fontsize_title : float or {'xx-small', 'x-small', 'small', 'medium', 'large', 'x-large', 'xx-large'}, default 16
             Font size for the plot title. If a float, the fontsize in points. The
             string values denote sizes relative to the default font size. Pass any keyword arguments accepted
             by the matplotlib.pyplot.suptitle `fontsize` command here, as a string.
@@ -1159,7 +1373,89 @@ def plot(
             Font size for the contrast axes ylabel.
         fontsize_delta2label : float, default 12
             Font size for the delta-delta axes ylabel.
-
+            
+        raw_bars : boolean, default True
+            Whether or not to display the raw bars.
+        raw_bars_kwargs : dict, default None
+            Pass relevant keyword arguments to the raw bars. Pass any keyword arguments accepted by 
+            matplotlib.patches.Rectangle here, as a string. If None, the following keywords are passed:
+            {"color": None, "zorder":-3}
+        contrast_bars : boolean, default True
+            Whether or not to display the contrast bars.
+        contrast_bars_kwargs : dict, default None
+            Pass relevant keyword arguments to the contrast bars. Pass any keyword arguments accepted by 
+            matplotlib.patches.Rectangle here, as a string. If None, the following keywords are passed:
+            {"color": None, "zorder":-3}
+        reference_band : list, default None
+            Pass a list of indices of the contrast objects to have reference bands displayed on the plot.
+            For example, [0,1] will show reference bands for the first two contrast objects.
+        reference_band_kwargs: dict, default None
+            If None, the following keywords are passed: {"span_ax": False, "color": None, "alpha": 0.15, "zorder":-3}
+        delta_text : boolean, default True
+            Whether or not to display the text deltas.
+        delta_text_kwargs : dict, default None
+            Pass relevant keyword arguments to the delta text. Pass any keyword arguments accepted by
+            matplotlib.text.Text here, as a string. If None, the following keywords are passed:
+            {"color": None, "alpha": 1, "fontsize": 10, "ha": 'center', "va": 'center', "rotation": 0, 
+            "x_location": 'right', "x_coordinates": None, "y_coordinates": None, "offset": 0}
+            Use "x_coordinates" and "y_coordinates" if you would like to specify the text locations manually. 
+            Use "x_adjust" to adjust the x location of all the texts (positive moves right, negative left).
+        delta_dot : boolean, default True
+            Whether or not to display the delta dots on paired or repeated measure plots.
+        delta_dot_kwargs : dict, default None
+            Pass relevant keyword arguments. If None, the following keywords are passed:
+            {"color": 'k', "marker": "^", "alpha": 0.5, "zorder": 2, "size": 3, "side": "right"}
+        horizontal : boolean, default False
+            Whether to display plots in the horizontal format. Default is False. 
+        horizontal_table_kwargs : dict, default None
+            {'show: True, 'color' : 'yellow', 'alpha' :0.2, 'fontsize' : 12, 'text_color' : 'black', 
+            'text_units' : None, 'control_marker' : '-', 'fontsize_label': 12, 'label': 'Δ'}
+            
+        gridkey : list, default None
+            Provide either a list of grid keys or 'auto' for automatic grid selection.
+        gridkey_merge_pairs : boolean, default False
+            Merges the paired grid key groups together.
+        gridkey_show_Ns : boolean, default True
+            Whether to display the sample size row.
+        gridkey_show_es : boolean, default True
+            Whether to show the effect size row. 
+        gridkey_delimiters : list, default [';', '>', '_']
+            The delimiter used to split gridkey groups if required.
+        gridkey_kwargs : dict, default None
+            Pass relevant keyword arguments to the gridkey. If None, the following keywords are passed:
+            {   'show_es' : True,                   # If True, the gridkey will show the effect size of each comparison.
+                'show_Ns' :True,                    # If True, the gridkey will show the number of observations in eachgroup.
+                'merge_pairs' : False,              # If True, the gridkey will merge the pairs of groups into a single cell. This is useful for when the groups are paired.
+                'delimiters': [';', '>', '_'],      # Delimiters to split the group names.
+                'marker': "\u25CF",                 # Marker for the gridkey dots.
+            }
+
+        contrast_marker_kwargs: dict, default None
+            Pass relevant keyword arguments to the effectsize marker plotting. If none, the following keywords are passed:
+            {'marker': 'o', 'size': plot_kwargs['contrast_marker_size'], 'color': 'black', 'alpha': 1, 'zorder': 1}
+        contrast_errorbar_kwargs: dict, default None
+            Pass relevant keyword arguments to the effectsize errorbar plotting. If none, the following keywords are passed:
+            {'color': 'black', 'lw': 2, 'linestyle': '-', 'alpha': 1,'zorder': 1,}
+
+        prop_sample_counts: bool, default False
+            Show the sample counts for each group in proportional plots
+        prop_sample_counts_kwargs: dict, default None
+            Pass relevant keyword arguments. If None, the following keywords are passed:
+            {'color': 'k', 'zorder': 5, 'ha': 'center', 'va': 'center'},
+
+        contrast_paired_lines: bool, default True
+            Whether or not to add lines to connect the effect size curves in paired plots.
+        contrast_paired_lines_kwargs: dict, default None
+            Pass relevant plot keyword arguments. If None, the following keywords are passed:
+            {"linestyle": "-", "linewidth": 2, "zorder": -2, "color": 'dimgray', "alpha": 1}
+        
+		show_baseline_ec : boolean, default False
+            Whether or not to display the baseline error curve. The baseline error curve
+            represents the distribution of the effect size when comparing the control
+            group to itself, providing a reference for the inherent variability or noise
+            in the data. When True, this curve is plotted alongside the main effect size
+            distribution, allowing for a visual comparison of the observed effect against
+            the baseline variability.
 
         Returns
         -------
@@ -1180,29 +1476,28 @@ def plot(
         if hasattr(self, "results") is False:
             self.__pre_calc()
 
-        if self.__delta2:
-            color_col = self.__x2
-
-        # if self.__proportional:
-        #     raw_marker_size = 0.01
+        if raw_alpha is None:
+            raw_alpha = (0.4 if self.is_proportional and self.is_paired 
+                         else 0.5 if self.is_paired
+                         else 1.0
+            )
 
-        # Modification incurred due to update of Seaborn
-        ci = ("ci", ci) if ci is not None else None
+        if self.__delta2 and not empty_circle:
+            color_col = self.__x2
 
         all_kwargs = locals()
         del all_kwargs["self"]
 
         out = effectsize_df_plotter(self, **all_kwargs)
-
         return out
 
     @property
-    def proportional(self):
+    def is_proportional(self):
         """
         Returns the proportional parameter
         class.
         """
-        return self.__proportional
+        return self.__is_proportional
 
     @property
     def results(self):
@@ -1284,10 +1579,6 @@ def x2(self):
     def experiment_label(self):
         return self.__experiment_label
 
-    @property
-    def delta2(self):
-        return self.__delta2
-
     @property
     def resamples(self):
         """
@@ -1315,13 +1606,6 @@ def dabest_obj(self):
         """
         return self.__dabest_obj
 
-    @property
-    def proportional(self):
-        """
-        Returns the proportional parameter
-        class.
-        """
-        return self.__proportional
 
     @property
     def lqrt(self):
@@ -1337,33 +1621,41 @@ def lqrt(self):
             return self.__lqrt_results
 
     @property
-    def mini_meta(self):
+    def is_mini_meta(self):
         """
         Returns the mini_meta boolean parameter.
         """
-        return self.__mini_meta
+        return self.__is_mini_meta
 
     @property
-    def mini_meta_delta(self):
+    def mini_meta(self):
         """
         Returns the mini_meta results.
         """
         try:
-            return self.__mini_meta_delta
+            return self.__mini_meta
         except AttributeError:
             self.__pre_calc()
-            return self.__mini_meta_delta
+            return self.__mini_meta
 
     @property
     def delta_delta(self):
         """
-        Returns the mini_meta results.
+        Returns the delta_delta results.
         """
         try:
             return self.__delta_delta
         except AttributeError:
             self.__pre_calc()
             return self.__delta_delta
+    
+    @property
+    def delta2(self):
+        return self.__delta2
+    
+    @property
+    def is_delta_delta(self):
+        return self.__delta2
 
 # %% ../nbs/API/effsize_objects.ipynb 29
 class PermutationTest:
@@ -1377,7 +1669,7 @@ class PermutationTest:
         These should be numerical iterables.
     effect_size : string.
         Any one of the following are accepted inputs:
-        'mean_diff', 'median_diff', 'cohens_d', 'hedges_g', 'delta_g" or 'cliffs_delta'
+        'mean_diff', 'median_diff', 'cohens_d', 'hedges_g', or 'cliffs_delta'
     is_paired : string, default None
     permutation_count : int, default 10000
         The number of permutations (reshuffles) to perform.
@@ -1385,6 +1677,10 @@ class PermutationTest:
         `random_seed` is used to seed the random number generator during
         bootstrap resampling. This ensures that the generated permutations
         are replicable.
+    ps_adjust : bool, default False
+        If True, the p-value is adjusted according to Phipson & Smyth (2010).
+        # https://doi.org/10.2202/1544-6115.1585
+
         
     Returns
     -------
@@ -1403,6 +1699,7 @@ def __init__(self, control: array,
                  is_paired:str=None,
                  permutation_count:int=5000, # The number of permutations (reshuffles) to perform.
                  random_seed:int=12345,#`random_seed` is used to seed the random number generator during bootstrap resampling. This ensures that the generated permutations are replicable.
+                 ps_adjust:bool=False,
                  **kwargs):
         from ._stats_tools.effsize import two_group_difference
         from ._stats_tools.confint_2group_diff import calculate_group_var
@@ -1427,6 +1724,7 @@ def __init__(self, control: array,
 
         BAG = array([*control, *test])
         CONTROL_LEN = int(len(control))
+        TEST_LEN = int(len(test)) # devMJBL
         EXTREME_COUNT = 0.
         THRESHOLD = abs(two_group_difference(control, test, 
                                                 is_paired, effect_size))
@@ -1466,13 +1764,43 @@ def __init__(self, control: array,
 
             if abs(es) > THRESHOLD:
                 EXTREME_COUNT += 1.
+                
+        if ps_adjust:
+            # devMJBL
+            # adjust calculated p-value according to Phipson & Smyth (2010)
+            # https://doi.org/10.2202/1544-6115.1585
+            # as per R code in statmod::permp
+            # https://rdrr.io/cran/statmod/src/R/permp.R
+            # (assumes two-sided test)
+
+            if CONTROL_LEN == TEST_LEN:
+                totalPermutations = binomcoeff(CONTROL_LEN + TEST_LEN, TEST_LEN)/2
+            else:
+                totalPermutations = binomcoeff(CONTROL_LEN + TEST_LEN, TEST_LEN)
+
+            if totalPermutations <= 10e3:
+                # use exact calculation
+                p = arange(1, totalPermutations + 1)/totalPermutations
+                x2 = repeat(EXTREME_COUNT, repeats=totalPermutations)
+                Y = binom.cdf(k=x2, n=permutation_count, p=p)
+                self.pvalue = mean(Y)
+            else:
+                # use integral approximation
+                def binomcdf(p, k, n):
+                    return binom.cdf(k, n, p)
 
+                integrationVal, _ = fixed_quad(binomcdf,
+                                            a=0, b=0.5/totalPermutations,
+                                            args=(EXTREME_COUNT, permutation_count),
+                                            n=128)
+
+                self.pvalue = (EXTREME_COUNT + 1)/(permutation_count + 1) - integrationVal
+        else:
+            self.pvalue = EXTREME_COUNT / self.__permutation_count
+            
         self.__permutations = array(self.__permutations)
         self.__permutations_var = array(self.__permutations_var)
 
-        self.pvalue = EXTREME_COUNT / self.__permutation_count
-
-
     def __repr__(self):
         return("{} permutations were taken. The p-value is {}.".format(self.__permutation_count, 
                                                                       self.pvalue))
diff --git a/dabest/_modidx.py b/dabest/_modidx.py
index 14bfa3da..d51151af 100644
--- a/dabest/_modidx.py
+++ b/dabest/_modidx.py
@@ -25,6 +25,8 @@
                                                                                                                         'dabest/_stats_tools/confint_2group_diff.py'),
                                                          'dabest._stats_tools.confint_2group_diff._create_two_group_jackknife_indexes': ( 'API/confint_2group_diff.html#_create_two_group_jackknife_indexes',
                                                                                                                                           'dabest/_stats_tools/confint_2group_diff.py'),
+                                                         'dabest._stats_tools.confint_2group_diff.bootstrap_indices': ( 'API/confint_2group_diff.html#bootstrap_indices',
+                                                                                                                        'dabest/_stats_tools/confint_2group_diff.py'),
                                                          'dabest._stats_tools.confint_2group_diff.calculate_group_var': ( 'API/confint_2group_diff.html#calculate_group_var',
                                                                                                                           'dabest/_stats_tools/confint_2group_diff.py'),
                                                          'dabest._stats_tools.confint_2group_diff.calculate_weighted_delta': ( 'API/confint_2group_diff.html#calculate_weighted_delta',
@@ -42,11 +44,17 @@
                                                          'dabest._stats_tools.confint_2group_diff.create_jackknife_indexes': ( 'API/confint_2group_diff.html#create_jackknife_indexes',
                                                                                                                                'dabest/_stats_tools/confint_2group_diff.py'),
                                                          'dabest._stats_tools.confint_2group_diff.create_repeated_indexes': ( 'API/confint_2group_diff.html#create_repeated_indexes',
-                                                                                                                              'dabest/_stats_tools/confint_2group_diff.py')},
-            'dabest._stats_tools.effsize': { 'dabest._stats_tools.effsize._compute_hedges_correction_factor': ( 'API/effsize.html#_compute_hedges_correction_factor',
+                                                                                                                              'dabest/_stats_tools/confint_2group_diff.py'),
+                                                         'dabest._stats_tools.confint_2group_diff.delta2_bootstrap_loop': ( 'API/confint_2group_diff.html#delta2_bootstrap_loop',
+                                                                                                                            'dabest/_stats_tools/confint_2group_diff.py')},
+            'dabest._stats_tools.effsize': { 'dabest._stats_tools.effsize._cliffs_delta_core': ( 'API/effsize.html#_cliffs_delta_core',
+                                                                                                 'dabest/_stats_tools/effsize.py'),
+                                             'dabest._stats_tools.effsize._compute_hedges_correction_factor': ( 'API/effsize.html#_compute_hedges_correction_factor',
                                                                                                                 'dabest/_stats_tools/effsize.py'),
                                              'dabest._stats_tools.effsize._compute_standardizers': ( 'API/effsize.html#_compute_standardizers',
                                                                                                      'dabest/_stats_tools/effsize.py'),
+                                             'dabest._stats_tools.effsize._mann_whitney_u': ( 'API/effsize.html#_mann_whitney_u',
+                                                                                              'dabest/_stats_tools/effsize.py'),
                                              'dabest._stats_tools.effsize.cliffs_delta': ( 'API/effsize.html#cliffs_delta',
                                                                                            'dabest/_stats_tools/effsize.py'),
                                              'dabest._stats_tools.effsize.cohens_d': ( 'API/effsize.html#cohens_d',
@@ -61,13 +69,43 @@
                                                                                                    'dabest/_stats_tools/effsize.py'),
                                              'dabest._stats_tools.effsize.weighted_delta': ( 'API/effsize.html#weighted_delta',
                                                                                              'dabest/_stats_tools/effsize.py')},
-            'dabest.forest_plot': { 'dabest.forest_plot.extract_plot_data': ( 'API/forest_plot.html#extract_plot_data',
-                                                                              'dabest/forest_plot.py'),
+            'dabest._stats_tools.precompile': { 'dabest._stats_tools.precompile.precompile_all': ( 'API/precompile.html#precompile_all',
+                                                                                                   'dabest/_stats_tools/precompile.py')},
+            'dabest.forest_plot': { 'dabest.forest_plot.check_for_errors': ( 'API/forest_plot.html#check_for_errors',
+                                                                             'dabest/forest_plot.py'),
+                                    'dabest.forest_plot.color_palette': ('API/forest_plot.html#color_palette', 'dabest/forest_plot.py'),
                                     'dabest.forest_plot.forest_plot': ('API/forest_plot.html#forest_plot', 'dabest/forest_plot.py'),
+                                    'dabest.forest_plot.get_kwargs': ('API/forest_plot.html#get_kwargs', 'dabest/forest_plot.py'),
                                     'dabest.forest_plot.load_plot_data': ('API/forest_plot.html#load_plot_data', 'dabest/forest_plot.py')},
-            'dabest.misc_tools': { 'dabest.misc_tools.get_varname': ('API/misc_tools.html#get_varname', 'dabest/misc_tools.py'),
+            'dabest.misc_tools': { 'dabest.misc_tools.add_counts_to_ticks': ( 'API/misc_tools.html#add_counts_to_ticks',
+                                                                              'dabest/misc_tools.py'),
+                                   'dabest.misc_tools.color_picker': ('API/misc_tools.html#color_picker', 'dabest/misc_tools.py'),
+                                   'dabest.misc_tools.draw_zeroline': ('API/misc_tools.html#draw_zeroline', 'dabest/misc_tools.py'),
+                                   'dabest.misc_tools.extract_contrast_plotting_ticks': ( 'API/misc_tools.html#extract_contrast_plotting_ticks',
+                                                                                          'dabest/misc_tools.py'),
+                                   'dabest.misc_tools.extract_group_summaries': ( 'API/misc_tools.html#extract_group_summaries',
+                                                                                  'dabest/misc_tools.py'),
+                                   'dabest.misc_tools.gardner_altman_adjustments': ( 'API/misc_tools.html#gardner_altman_adjustments',
+                                                                                     'dabest/misc_tools.py'),
+                                   'dabest.misc_tools.get_color_palette': ('API/misc_tools.html#get_color_palette', 'dabest/misc_tools.py'),
+                                   'dabest.misc_tools.get_kwargs': ('API/misc_tools.html#get_kwargs', 'dabest/misc_tools.py'),
+                                   'dabest.misc_tools.get_params': ('API/misc_tools.html#get_params', 'dabest/misc_tools.py'),
+                                   'dabest.misc_tools.get_plot_groups': ('API/misc_tools.html#get_plot_groups', 'dabest/misc_tools.py'),
+                                   'dabest.misc_tools.get_unique_categories': ( 'API/misc_tools.html#get_unique_categories',
+                                                                                'dabest/misc_tools.py'),
+                                   'dabest.misc_tools.get_varname': ('API/misc_tools.html#get_varname', 'dabest/misc_tools.py'),
+                                   'dabest.misc_tools.initialize_fig': ('API/misc_tools.html#initialize_fig', 'dabest/misc_tools.py'),
                                    'dabest.misc_tools.merge_two_dicts': ('API/misc_tools.html#merge_two_dicts', 'dabest/misc_tools.py'),
+                                   'dabest.misc_tools.prepare_bars_for_plot': ( 'API/misc_tools.html#prepare_bars_for_plot',
+                                                                                'dabest/misc_tools.py'),
                                    'dabest.misc_tools.print_greeting': ('API/misc_tools.html#print_greeting', 'dabest/misc_tools.py'),
+                                   'dabest.misc_tools.redraw_dependent_spines': ( 'API/misc_tools.html#redraw_dependent_spines',
+                                                                                  'dabest/misc_tools.py'),
+                                   'dabest.misc_tools.redraw_independent_spines': ( 'API/misc_tools.html#redraw_independent_spines',
+                                                                                    'dabest/misc_tools.py'),
+                                   'dabest.misc_tools.set_xaxis_ticks_and_lims': ( 'API/misc_tools.html#set_xaxis_ticks_and_lims',
+                                                                                   'dabest/misc_tools.py'),
+                                   'dabest.misc_tools.show_legend': ('API/misc_tools.html#show_legend', 'dabest/misc_tools.py'),
                                    'dabest.misc_tools.unpack_and_add': ('API/misc_tools.html#unpack_and_add', 'dabest/misc_tools.py')},
             'dabest.plot_tools': { 'dabest.plot_tools.SwarmPlot': ('API/plot_tools.html#swarmplot', 'dabest/plot_tools.py'),
                                    'dabest.plot_tools.SwarmPlot.__init__': ( 'API/plot_tools.html#swarmplot.__init__',
@@ -82,14 +120,31 @@
                                                                                     'dabest/plot_tools.py'),
                                    'dabest.plot_tools.SwarmPlot._swarm': ('API/plot_tools.html#swarmplot._swarm', 'dabest/plot_tools.py'),
                                    'dabest.plot_tools.SwarmPlot.plot': ('API/plot_tools.html#swarmplot.plot', 'dabest/plot_tools.py'),
+                                   'dabest.plot_tools.add_bars_to_plot': ('API/plot_tools.html#add_bars_to_plot', 'dabest/plot_tools.py'),
+                                   'dabest.plot_tools.add_counts_to_prop_plots': ( 'API/plot_tools.html#add_counts_to_prop_plots',
+                                                                                   'dabest/plot_tools.py'),
+                                   'dabest.plot_tools.barplotter': ('API/plot_tools.html#barplotter', 'dabest/plot_tools.py'),
                                    'dabest.plot_tools.check_data_matches_labels': ( 'API/plot_tools.html#check_data_matches_labels',
                                                                                     'dabest/plot_tools.py'),
+                                   'dabest.plot_tools.delta_dots_plotter': ( 'API/plot_tools.html#delta_dots_plotter',
+                                                                             'dabest/plot_tools.py'),
+                                   'dabest.plot_tools.delta_text_plotter': ( 'API/plot_tools.html#delta_text_plotter',
+                                                                             'dabest/plot_tools.py'),
+                                   'dabest.plot_tools.effect_size_curve_plotter': ( 'API/plot_tools.html#effect_size_curve_plotter',
+                                                                                    'dabest/plot_tools.py'),
                                    'dabest.plot_tools.error_bar': ('API/plot_tools.html#error_bar', 'dabest/plot_tools.py'),
                                    'dabest.plot_tools.get_swarm_spans': ('API/plot_tools.html#get_swarm_spans', 'dabest/plot_tools.py'),
+                                   'dabest.plot_tools.gridkey_plotter': ('API/plot_tools.html#gridkey_plotter', 'dabest/plot_tools.py'),
                                    'dabest.plot_tools.halfviolin': ('API/plot_tools.html#halfviolin', 'dabest/plot_tools.py'),
                                    'dabest.plot_tools.normalize_dict': ('API/plot_tools.html#normalize_dict', 'dabest/plot_tools.py'),
+                                   'dabest.plot_tools.plot_minimeta_or_deltadelta_violins': ( 'API/plot_tools.html#plot_minimeta_or_deltadelta_violins',
+                                                                                              'dabest/plot_tools.py'),
                                    'dabest.plot_tools.sankeydiag': ('API/plot_tools.html#sankeydiag', 'dabest/plot_tools.py'),
                                    'dabest.plot_tools.single_sankey': ('API/plot_tools.html#single_sankey', 'dabest/plot_tools.py'),
+                                   'dabest.plot_tools.slopegraph_plotter': ( 'API/plot_tools.html#slopegraph_plotter',
+                                                                             'dabest/plot_tools.py'),
                                    'dabest.plot_tools.swarmplot': ('API/plot_tools.html#swarmplot', 'dabest/plot_tools.py'),
+                                   'dabest.plot_tools.table_for_horizontal_plots': ( 'API/plot_tools.html#table_for_horizontal_plots',
+                                                                                     'dabest/plot_tools.py'),
                                    'dabest.plot_tools.width_determine': ('API/plot_tools.html#width_determine', 'dabest/plot_tools.py')},
             'dabest.plotter': {'dabest.plotter.effectsize_df_plotter': ('API/plotter.html#effectsize_df_plotter', 'dabest/plotter.py')}}}
diff --git a/dabest/_stats_tools/confint_1group.py b/dabest/_stats_tools/confint_1group.py
index a9b0beb1..744a7142 100644
--- a/dabest/_stats_tools/confint_1group.py
+++ b/dabest/_stats_tools/confint_1group.py
@@ -1,3 +1,5 @@
+"""A range of functions to compute bootstraps for a single sample."""
+
 # AUTOGENERATED! DO NOT EDIT! File to edit: ../../nbs/API/confint_1group.ipynb.
 
 # %% auto 0
diff --git a/dabest/_stats_tools/confint_2group_diff.py b/dabest/_stats_tools/confint_2group_diff.py
index 3b07eb96..5063b8d3 100644
--- a/dabest/_stats_tools/confint_2group_diff.py
+++ b/dabest/_stats_tools/confint_2group_diff.py
@@ -1,9 +1,12 @@
+"""A range of functions to compute bootstraps for the mean difference"""
+
 # AUTOGENERATED! DO NOT EDIT! File to edit: ../../nbs/API/confint_2group_diff.ipynb.
 
 # %% auto 0
-__all__ = ['create_jackknife_indexes', 'create_repeated_indexes', 'compute_meandiff_jackknife', 'compute_bootstrapped_diff',
-           'compute_delta2_bootstrapped_diff', 'compute_meandiff_bias_correction', 'compute_interval_limits',
-           'calculate_group_var', 'calculate_weighted_delta']
+__all__ = ['create_jackknife_indexes', 'create_repeated_indexes', 'compute_meandiff_jackknife', 'bootstrap_indices',
+           'compute_bootstrapped_diff', 'delta2_bootstrap_loop', 'compute_delta2_bootstrapped_diff',
+           'compute_meandiff_bias_correction', 'compute_interval_limits', 'calculate_group_var',
+           'calculate_weighted_delta']
 
 # %% ../../nbs/API/confint_2group_diff.ipynb 4
 import numpy as np
@@ -11,11 +14,12 @@
 from numpy import mean as npmean
 from numpy import sum as npsum
 from numpy.random import PCG64, RandomState
-import pandas as pd
+from numba import njit, prange
 from scipy.stats import norm
 from numpy import isnan
 
 # %% ../../nbs/API/confint_2group_diff.ipynb 5
+@njit(cache=True, parallel=True)
 def create_jackknife_indexes(data):
     """
     Given an array-like, creates a jackknife bootstrap.
@@ -32,18 +36,25 @@ def create_jackknife_indexes(data):
     Generator that yields all jackknife bootstrap samples.
     """
 
-    index_range = arange(0, len(data))
-    return (delete(index_range, i) for i in index_range)
+    n = len(data)
+    indexes = np.empty((n, n - 1), dtype=np.int64)
+    for i in prange(n):
+        indexes[i] = np.concatenate((np.arange(i), np.arange(i + 1, n)))
+    return indexes
 
 
+@njit(cache=True, parallel=True)
 def create_repeated_indexes(data):
     """
     Convenience function. Given an array-like with length N,
     returns a generator that yields N indexes [0, 1, ..., N].
     """
 
-    index_range = arange(0, len(data))
-    return (index_range for i in index_range)
+    n = len(data)
+    indexes = np.empty((n, n), dtype=np.int64)  # Pre-allocate the output array
+    for i in prange(n):
+        indexes[i, :] = np.arange(n)  # Fill each row with the full index range
+    return indexes
 
 
 def _create_two_group_jackknife_indexes(x0, x1, is_paired):
@@ -111,6 +122,20 @@ def _calc_accel(jack_dist):
         return numer / denom
 
 
+@njit(cache=True) # parallelization must be turned off for random number generation
+def bootstrap_indices(is_paired, x0_len, x1_len, resamples, random_seed):
+    np.random.seed(random_seed)
+    indices = np.empty((resamples, x0_len if is_paired else x0_len + x1_len), dtype=np.int64)
+    
+    for i in range(resamples):
+        if is_paired:
+            indices[i, :x0_len] = np.random.choice(x0_len, x0_len)
+        else:  
+            indices[i, :x0_len] = np.random.choice(x0_len, x0_len)
+            indices[i, x0_len:x0_len+x1_len] = np.random.choice(x1_len, x1_len)
+    return indices
+
+
 def compute_bootstrapped_diff(
     x0, x1, is_paired, effect_size, resamples=5000, random_seed=12345
 ):
@@ -118,95 +143,106 @@ def compute_bootstrapped_diff(
 
     from . import effsize as __es
 
-    rng = RandomState(PCG64(random_seed))
-
-    out = np.repeat(np.nan, resamples)
-    x0_len = len(x0)
-    x1_len = len(x1)
+    x0_len, x1_len = len(x0), len(x1)
+    indices = bootstrap_indices(is_paired, x0_len, x1_len, resamples, random_seed)
+    out = np.empty(resamples, dtype=np.float64)
 
-    for i in range(int(resamples)):
+    for i in range(resamples):
         if is_paired:
-            if x0_len != x1_len:
-                raise ValueError("The two arrays do not have the same length.")
-            random_idx = rng.choice(x0_len, x0_len, replace=True)
-            x0_sample = x0[random_idx]
-            x1_sample = x1[random_idx]
+            x0_sample = x0[indices[i, :x0_len]]
+            x1_sample = x1[indices[i, :x0_len]]
         else:
-            x0_sample = rng.choice(x0, x0_len, replace=True)
-            x1_sample = rng.choice(x1, x1_len, replace=True)
+            x0_sample = x0[indices[i, :x0_len]]
+            x1_sample = x1[indices[i, x0_len:x0_len+x1_len]]
 
         out[i] = __es.two_group_difference(x0_sample, x1_sample, is_paired, effect_size)
 
     return out
 
 
-def compute_delta2_bootstrapped_diff(
-    x1: np.ndarray,  # Control group 1
-    x2: np.ndarray,  # Test group 1
-    x3: np.ndarray,  # Control group 2
-    x4: np.ndarray,  # Test group 2
-    is_paired: str = None,
-    resamples: int = 5000,  # The number of bootstrap resamples to be taken for the calculation of the confidence interval limits.
-    random_seed: int = 12345,  # `random_seed` is used to seed the random number generator during bootstrap resampling. This ensures that the confidence intervals reported are replicable.
-) -> (
-    tuple
-):  # bootstraped result and empirical result of deltas' g, and the bootstraped result of delta-delta
+@njit(cache=True)
+def delta2_bootstrap_loop(x1, x2, x3, x4, resamples, pooled_sd, rng_seed, is_paired, proportional=False):
     """
-    Bootstraps the effect size deltas' g.
-
+    Compute bootstrapped differences for delta-delta, handling both regular and proportional data
     """
-
-    rng = RandomState(PCG64(random_seed))
-
-    x1, x2, x3, x4 = map(np.asarray, [x1, x2, x3, x4])
-
-    # Calculating pooled sample standard deviation
-    stds = [np.std(x) for x in [x1, x2, x3, x4]]
-    ns = [len(x) for x in [x1, x2, x3, x4]]
-
-    sd_numerator = sum((n - 1) * s**2 for n, s in zip(ns, stds))
-    sd_denominator = sum(n - 1 for n in ns)
-
-    # Avoid division by zero
-    if sd_denominator == 0:
-        raise ValueError("Insufficient data to compute pooled standard deviation.")
-
-    pooled_sample_sd = np.sqrt(sd_numerator / sd_denominator)
-
-    # Ensure pooled_sample_sd is not NaN or zero (to avoid division by zero later)
-    if np.isnan(pooled_sample_sd) or pooled_sample_sd == 0:
-        raise ValueError("Pooled sample standard deviation is NaN or zero.")
-
-    out_delta_g = np.empty(resamples)
+    np.random.seed(rng_seed)
     deltadelta = np.empty(resamples)
+    out_delta_g = np.empty(resamples)
+    
+    n1, n2, n3, n4 = len(x1), len(x2), len(x3), len(x4)
+    if is_paired and (n1 != n2 or n3 != n4):
+        raise ValueError("Each control group must have the same length as its corresponding test group in paired analysis.")
 
     # Bootstrapping
     for i in range(resamples):
         # Paired or unpaired resampling
         if is_paired:
-            if len(x1) != len(x2) or len(x3) != len(x4):
-                raise ValueError("Each control group must have the same length as its corresponding test group in paired analysis.")
-            indices_1 = rng.choice(len(x1), len(x1), replace=True)
-            indices_2 = rng.choice(len(x3), len(x3), replace=True)
-
+            indices_1 = np.random.choice(len(x1), len(x1))
+            indices_2 = np.random.choice(len(x3), len(x3))
             x1_sample, x2_sample = x1[indices_1], x2[indices_1]
             x3_sample, x4_sample = x3[indices_2], x4[indices_2]
         else:
-            x1_sample = rng.choice(x1, len(x1), replace=True)
-            x2_sample = rng.choice(x2, len(x2), replace=True)
-            x3_sample = rng.choice(x3, len(x3), replace=True)
-            x4_sample = rng.choice(x4, len(x4), replace=True)
-
-        # Calculating deltas
+            indices_1 = np.random.randint(0, len(x1), len(x1))
+            indices_2 = np.random.randint(0, len(x2), len(x2))
+            indices_3 = np.random.randint(0, len(x3), len(x3))
+            indices_4 = np.random.randint(0, len(x4), len(x4))
+            x1_sample, x2_sample = x1[indices_1], x2[indices_2]
+            x3_sample, x4_sample = x3[indices_3], x4[indices_4]
+
+        # Calculate deltas
         delta_1 = np.mean(x2_sample) - np.mean(x1_sample)
         delta_2 = np.mean(x4_sample) - np.mean(x3_sample)
         delta_delta = delta_2 - delta_1
-
+        
         deltadelta[i] = delta_delta
-        out_delta_g[i] = delta_delta / pooled_sample_sd
 
-    # Empirical delta_g calculation
-    delta_g = ((np.mean(x4) - np.mean(x3)) - (np.mean(x2) - np.mean(x1))) / pooled_sample_sd
+        out_delta_g[i] = delta_delta if proportional else delta_delta/pooled_sd
+
+    return out_delta_g, deltadelta
+
+
+def compute_delta2_bootstrapped_diff(
+    x1: np.ndarray,  # Control group 1
+    x2: np.ndarray,  # Test group 1
+    x3: np.ndarray,  # Control group 2
+    x4: np.ndarray,  # Test group 2
+    is_paired: str = None,
+    resamples: int = 5000,
+    random_seed: int = 12345,
+    proportional: bool = False
+) -> tuple:
+    """
+    Bootstraps the effect size deltas' g or proportional delta-delta
+    """
+    x1, x2, x3, x4 = map(np.asarray, [x1, x2, x3, x4])
+    
+    if proportional:
+        # For proportional data, pass 1.0 as dummy pooled_sd (won't be used)
+        out_delta_g, deltadelta = delta2_bootstrap_loop(
+            x1, x2, x3, x4, resamples, 1.0, random_seed, is_paired, proportional=True
+        )
+        # For proportional data, delta_g is the empirical delta-delta
+        delta_g = ((np.mean(x4) - np.mean(x3)) - (np.mean(x2) - np.mean(x1)))
+    else:
+        # Calculate pooled sample standard deviation for non-proportional data
+        stds = [np.std(x) for x in [x1, x2, x3, x4]]
+        ns = [len(x) for x in [x1, x2, x3, x4]]
+        
+        sd_numerator = sum((n - 1) * s**2 for n, s in zip(ns, stds))
+        sd_denominator = sum(n - 1 for n in ns)
+        
+        if sd_denominator == 0:
+            raise ValueError("Insufficient data to compute pooled standard deviation.")
+            
+        pooled_sample_sd = np.sqrt(sd_numerator / sd_denominator)
+        
+        if np.isnan(pooled_sample_sd) or pooled_sample_sd == 0:
+            raise ValueError("Pooled sample standard deviation is NaN or zero.")
+            
+        out_delta_g, deltadelta = delta2_bootstrap_loop(
+            x1, x2, x3, x4, resamples, pooled_sample_sd, random_seed, is_paired, proportional=False
+        )
+        delta_g = ((np.mean(x4) - np.mean(x3)) - (np.mean(x2) - np.mean(x1))) / pooled_sample_sd
 
     return out_delta_g, delta_g, deltadelta
 
@@ -240,6 +276,7 @@ def _compute_alpha_from_ci(ci):
     return (100.0 - ci) / 100.0
 
 
+@njit(cache=True)
 def _compute_quantile(z, bias, acceleration):
     numer = bias + z
     denom = 1 - (acceleration * numer)
@@ -275,8 +312,12 @@ def compute_interval_limits(bias, acceleration, n_boots, ci=95):
     return low, high
 
 
+@njit(cache=True)
 def calculate_group_var(control_var, control_N, test_var, test_N):
-    return control_var / control_N + test_var / test_N
+    
+    pooled_var = ((control_N - 1) * control_var + (test_N - 1) * test_var) / (control_N + test_N - 2) 
+    
+    return pooled_var
 
 
 def calculate_weighted_delta(group_var, differences):
@@ -286,6 +327,7 @@ def calculate_weighted_delta(group_var, differences):
 
     weight = 1 / group_var
     denom = np.sum(weight)
-    num = np.sum(weight[i] * differences[i] for i in range(0, len(weight)))
-
+    num = 0.0
+    for i in range(len(weight)):
+        num += weight[i] * differences[i]
     return num / denom
diff --git a/dabest/_stats_tools/effsize.py b/dabest/_stats_tools/effsize.py
index 32f965b1..11f28d4c 100644
--- a/dabest/_stats_tools/effsize.py
+++ b/dabest/_stats_tools/effsize.py
@@ -1,12 +1,16 @@
+"""A range of functions to compute various effect sizes."""
+
 # AUTOGENERATED! DO NOT EDIT! File to edit: ../../nbs/API/effsize.ipynb.
 
 # %% ../../nbs/API/effsize.ipynb 4
 from __future__ import annotations
 import numpy as np
+from numba import njit
 import warnings
 from scipy.special import gamma
 from scipy.stats import mannwhitneyu
 
+
 # %% auto 0
 __all__ = ['two_group_difference', 'func_difference', 'cohens_d', 'cohens_h', 'hedges_g', 'cliffs_delta', 'weighted_delta']
 
@@ -60,6 +64,10 @@ def two_group_difference(control:list|tuple|np.ndarray, #Accepts lists, tuples,
 
     """
 
+    if not isinstance(control, np.ndarray):
+        control = np.array(control)
+    if not isinstance(test, np.ndarray):
+        test    = np.array(test)
 
     if effect_size == "mean_diff":
         return func_difference(control, test, np.mean, is_paired)
@@ -69,7 +77,7 @@ def two_group_difference(control:list|tuple|np.ndarray, #Accepts lists, tuples,
                 "result in a biased estimate and cause problems with " + \
                 "BCa confidence intervals. Consider using a different statistic, such as the mean.\n"
         mes2 = "When plotting, please consider using percetile confidence intervals " + \
-                "by specifying `ci_type='percentile'`. For detailed information, " + \
+                "by specifying `ci_type='pct'`. For detailed information, " + \
                 "refer to https://github.com/ACCLAB/DABEST-python/issues/129 \n"
         warnings.warn(message=mes1+mes2, category=UserWarning)
         return func_difference(control, test, np.median, is_paired)
@@ -80,7 +88,7 @@ def two_group_difference(control:list|tuple|np.ndarray, #Accepts lists, tuples,
     if effect_size == "cohens_h":
         return cohens_h(control, test)
 
-    if effect_size == "hedges_g" or effect_size == "delta_g":
+    if effect_size == "hedges_g":
         return hedges_g(control, test, is_paired)
 
     if effect_size == "cliffs_delta":
@@ -105,9 +113,9 @@ def func_difference(control:list|tuple|np.ndarray, # NaNs are automatically disc
 
     # Convert to numpy arrays for speed.
     # NaNs are automatically dropped.
-    if ~isinstance(control, np.ndarray):
+    if not isinstance(control, np.ndarray):
         control = np.array(control)
-    if ~isinstance(test, np.ndarray):
+    if not isinstance(test, np.ndarray):
         test    = np.array(test)
 
     if is_paired:
@@ -115,19 +123,11 @@ def func_difference(control:list|tuple|np.ndarray, # NaNs are automatically disc
             err = "The two arrays supplied do not have the same length."
             raise ValueError(err)
 
-        control_nan = np.where(np.isnan(control))[0]
-        test_nan    = np.where(np.isnan(test))[0]
+        non_nan_mask = ~np.isnan(control) & ~np.isnan(test)
+        control_non_nan = control[non_nan_mask]
+        test_non_nan = test[non_nan_mask]
 
-        indexes_to_drop = np.unique(np.concatenate([control_nan,
-                                                    test_nan]))
-
-        good_indexes = [i for i in range(0, len(control))
-                        if i not in indexes_to_drop]
-
-        control = control[good_indexes]
-        test    = test[good_indexes]
-
-        return func(test - control)
+        return func(test_non_nan - control_non_nan)
 
     
     control = control[~np.isnan(control)]
@@ -136,6 +136,7 @@ def func_difference(control:list|tuple|np.ndarray, # NaNs are automatically disc
 
 
 # %% ../../nbs/API/effsize.ipynb 7
+@njit(cache=True)
 def cohens_d(control:list|tuple|np.ndarray,
              test:list|tuple|np.ndarray,
              is_paired:str=None # If not None, the paired Cohen's d is returned.
@@ -180,12 +181,6 @@ def cohens_d(control:list|tuple|np.ndarray,
         - https://en.wikipedia.org/wiki/Standard_deviation#Corrected_sample_standard_deviation
     """
 
-    # Convert to numpy arrays for speed.
-    # NaNs are automatically dropped.
-    if ~isinstance(control, np.ndarray):
-        control = np.array(control)
-    if ~isinstance(test, np.ndarray):
-        test    = np.array(test)
     control = control[~np.isnan(control)]
     test    = test[~np.isnan(test)]
 
@@ -216,12 +211,13 @@ def cohens_d(control:list|tuple|np.ndarray,
     return M / divisor
 
 # %% ../../nbs/API/effsize.ipynb 8
+# @njit(cache=True) # It uses np.seterr which is not supported by Numba
 def cohens_h(control:list|tuple|np.ndarray, 
              test:list|tuple|np.ndarray
             )->float:
     '''
     Computes Cohen's h for test v.s. control.
-    See [here](https://en.wikipedia.org/wiki/Cohen%27s_h for reference.)
+    See [here](https://en.wikipedia.org/wiki/Cohen%27s_h) for reference.
     
     `Notes`:
     
@@ -238,10 +234,6 @@ def cohens_h(control:list|tuple|np.ndarray,
     # Convert to numpy arrays for speed.
     # NaNs are automatically dropped.
     # Aligned with cohens_d calculation.
-    if ~isinstance(control, np.ndarray):
-        control = np.array(control)
-    if ~isinstance(test, np.ndarray):
-        test    = np.array(test)
     control = control[~np.isnan(control)]
     test = test[~np.isnan(test)]
 
@@ -270,10 +262,6 @@ def hedges_g(control:list|tuple|np.ndarray,
 
     # Convert to numpy arrays for speed.
     # NaNs are automatically dropped.
-    if ~isinstance(control, np.ndarray):
-        control = np.array(control)
-    if ~isinstance(test, np.ndarray):
-        test    = np.array(test)
     control = control[~np.isnan(control)]
     test    = test[~np.isnan(test)]
 
@@ -284,6 +272,29 @@ def hedges_g(control:list|tuple|np.ndarray,
     return correction_factor * d
 
 # %% ../../nbs/API/effsize.ipynb 10
+@njit(cache=True)
+def _mann_whitney_u(x, y):
+    """Numba-optimized Mann-Whitney U calculation"""
+    n1, n2 = len(x), len(y)
+    combined = np.concatenate((x, y))
+    
+    # Use numpy broadcasting for comparison
+    less_than = (combined.reshape(-1, 1) > combined).sum(axis=1)
+    equal_to = (combined.reshape(-1, 1) == combined).sum(axis=1)
+    
+    # Calculate ranks directly
+    ranks = less_than + (equal_to + 1) / 2
+    
+    R1 = np.sum(ranks[:n1])
+    U1 = R1 - (n1 * (n1 + 1)) / 2
+    return U1
+
+@njit(cache=True)
+def _cliffs_delta_core(control, test):
+    """Numba-optimized Cliff's delta calculation"""
+    U = _mann_whitney_u(test, control)
+    return ((2 * U) / (len(control) * len(test))) - 1
+
 def cliffs_delta(control:list|tuple|np.ndarray, 
                  test:list|tuple|np.ndarray
                 )->float:
@@ -291,28 +302,13 @@ def cliffs_delta(control:list|tuple|np.ndarray,
     Computes Cliff's delta for 2 samples.
     See [here](https://en.wikipedia.org/wiki/Effect_size#Effect_size_for_ordinal_data)
     """
-
-    # Convert to numpy arrays for speed.
-    # NaNs are automatically dropped.
-    if ~isinstance(control, np.ndarray):
-        control = np.array(control)
-    if ~isinstance(test, np.ndarray):
-        test    = np.array(test)
-
     c = control[~np.isnan(control)]
     t = test[~np.isnan(test)]
-
-    control_n = len(c)
-    test_n = len(t)
-
-    # Note the order of the control and test arrays.
-    U, _ = mannwhitneyu(t, c, alternative='two-sided')
-    cliffs_delta = ((2 * U) / (control_n * test_n)) - 1
-
-    return cliffs_delta
+    return _cliffs_delta_core(c, t)
 
 
 # %% ../../nbs/API/effsize.ipynb 11
+@njit(cache=True)
 def _compute_standardizers(control, test):
     """
     Computes the pooled and average standard deviations for two datasets.
@@ -346,9 +342,9 @@ def _compute_standardizers(control, test):
     control_n = len(control)
     test_n = len(test)
 
-    control_var = np.var(control, ddof=1) # use N-1 to compute the variance.
-    test_var = np.var(test, ddof=1)
-
+    # ddof parameter is not supported by numba.
+    control_var = np.var(control)*control_n/(control_n-1) # use N-1 to compute the variance.
+    test_var = np.var(test)*test_n/(test_n-1)
 
     # For unpaired 2-groups standardized mean difference.
     pooled = np.sqrt(((control_n - 1) * control_var + (test_n - 1) * test_var) /
@@ -377,6 +373,7 @@ def _compute_hedges_correction_factor(n1,
     """
 
     df = n1 + n2 - 2
+    # gamma function is not supported by numba.
     numer = gamma(df / 2)
     denom0 = gamma((df - 1) / 2)
     denom = np.sqrt(df / 2) * denom0
@@ -394,6 +391,7 @@ def _compute_hedges_correction_factor(n1,
     return out
 
 # %% ../../nbs/API/effsize.ipynb 13
+@njit(cache=True)
 def weighted_delta(difference, group_var):
     '''
     Compute the weighted deltas where the weight is the inverse of the
diff --git a/dabest/_stats_tools/precompile.py b/dabest/_stats_tools/precompile.py
new file mode 100644
index 00000000..46cc2bc4
--- /dev/null
+++ b/dabest/_stats_tools/precompile.py
@@ -0,0 +1,53 @@
+"""A tool to pre-compile Numba functions for speeding up DABEST bootstrapping"""
+
+# AUTOGENERATED! DO NOT EDIT! File to edit: ../../nbs/API/precompile.ipynb.
+
+# %% auto 0
+__all__ = ['precompile_all']
+
+# %% ../../nbs/API/precompile.ipynb 4
+import numpy as np
+from tqdm import tqdm
+from . import effsize
+from . import confint_2group_diff
+
+# %% ../../nbs/API/precompile.ipynb 5
+_NUMBA_COMPILED = False
+
+def precompile_all():
+    """Pre-compile all numba functions with dummy data"""
+    global _NUMBA_COMPILED
+    
+    if _NUMBA_COMPILED:
+        return
+        
+    print("Pre-compiling numba functions for DABEST...")
+    
+    # Create dummy data
+    dummy_control = np.array([1.0, 2.0, 3.0])
+    dummy_test = np.array([4.0, 5.0, 6.0])
+    
+    funcs = [
+        # effsize.py functions
+        (effsize.cohens_d, (dummy_control, dummy_test)),
+        (effsize._mann_whitney_u, (dummy_control, dummy_test)),
+        (effsize._cliffs_delta_core, (dummy_control, dummy_test)),
+        (effsize._compute_standardizers, (dummy_control, dummy_test)),
+        (effsize.weighted_delta, (np.array([1.0, 2.0]), np.array([0.1, 0.2]))),
+        
+        # confint_2group_diff.py functions
+        (confint_2group_diff.create_jackknife_indexes, (dummy_control,)),
+        (confint_2group_diff.create_repeated_indexes, (dummy_control,)),
+        (confint_2group_diff.bootstrap_indices, (True, 3, 3, 10, 12345)),
+        (confint_2group_diff.delta2_bootstrap_loop, 
+            (dummy_control, dummy_test, dummy_control, dummy_test, 10, 1.0, 12345, False)),
+        (confint_2group_diff._compute_quantile, (0.5, 0.1, 0.1)),
+        (confint_2group_diff.calculate_group_var, (1.0, 3, 1.0, 3))
+    ]
+    
+    for func, args in tqdm(funcs, desc="Compiling numba functions"):
+        func(*args)
+    
+    _NUMBA_COMPILED = True
+    
+    print("Numba compilation complete!")
diff --git a/dabest/forest_plot.py b/dabest/forest_plot.py
index 7d29464f..72d82cc2 100644
--- a/dabest/forest_plot.py
+++ b/dabest/forest_plot.py
@@ -1,18 +1,26 @@
+"""Creating forest plots from contrast objects."""
+
 # AUTOGENERATED! DO NOT EDIT! File to edit: ../nbs/API/forest_plot.ipynb.
 
 # %% auto 0
-__all__ = ['load_plot_data', 'extract_plot_data', 'forest_plot']
+__all__ = ['load_plot_data', 'check_for_errors', 'get_kwargs', 'color_palette', 'forest_plot']
 
 # %% ../nbs/API/forest_plot.ipynb 5
 import matplotlib.pyplot as plt
 # %matplotlib inline
 import seaborn as sns
 from typing import List, Optional, Union
-
+import numpy as np
+import matplotlib.axes as axes
+import matplotlib.patches as mpatches
 
 # %% ../nbs/API/forest_plot.ipynb 6
 def load_plot_data(
-    contrasts: List, effect_size: str = "mean_diff", contrast_type: str = "delta2"
+            data: List, 
+            effect_size: str = "mean_diff", 
+            contrast_type: str = None,
+            ci_type: str = "bca",
+            idx: Optional[List[int]] = None
 ) -> List:
     """
     Loads plot data based on specified effect size and contrast type.
@@ -24,277 +32,738 @@ def load_plot_data(
     effect_size: str
         Type of effect size ('mean_diff', 'median_diff', etc.).
     contrast_type: str
-        Type of contrast ('delta2', 'mini_meta').
+        Type of dabest object to plot ('delta2' or 'mini-meta' or 'delta').
+    ci_type: str
+        Type of confidence interval to plot ('bca' or 'pct')
+    idx: Optional[List[int]], default=None
+        List of indices to select from the contrast objects if delta-delta experiment. 
+        If None, only the delta-delta objects are plotted.
 
     Returns
     -------
     List: Contrast plot data based on specified parameters.
     """
-    effect_attr_map = {
-        "mean_diff": "mean_diff",
-        "median_diff": "median_diff",
-        "cliffs_delta": "cliffs_delta",
-        "cohens_d": "cohens_d",
-        "hedges_g": "hedges_g",
-        "delta_g": "delta_g"
-    }
+    # Effect size and contrast types
+    effect_attr = "hedges_g" if effect_size == 'delta_g' else effect_size
+    contrast_attr = {"delta2": "delta_delta", "mini_meta": "mini_meta"}.get(contrast_type)
+
+    # Testing
+    if idx is not None:
+        bootstraps, differences, bcalows, bcahighs = [], [], [], []
+        for current_idx, index_group in enumerate(idx):
+            current_contrast = data[current_idx]
+            if len(index_group)>0:
+                for index in index_group:
+                    current_plot_data = getattr(current_contrast, effect_attr)
+                    if contrast_type == 'delta2':
+                        if index == 2:
+                            current_plot_data = getattr(current_plot_data, contrast_attr)
+                            bootstrap_name, index_val = "bootstraps_delta_delta", 0
+                        elif index == 0 or index == 1:
+                            bootstrap_name, index_val = "bootstraps", index
+                        else:
+                            raise ValueError("The selected indices must be 0, 1, or 2.")
+                    elif contrast_type == "mini_meta":
+                        num_of_groups = len(getattr(current_contrast, effect_attr).results)
+                        if index == num_of_groups:
+                            current_plot_data = getattr(getattr(current_contrast, effect_attr), contrast_attr)
+                            bootstrap_name, index_val = "bootstraps_weighted_delta", 0
+                        elif index < num_of_groups:
+                            bootstrap_name, index_val = "bootstraps", index
+                        else:
+                            msg1 = "There are only {} groups (starting from zero) in this dabest object. ".format(num_of_groups)
+                            msg2 = "The idx given is {}.".format(index)
+                            raise ValueError(msg1+msg2)        
+                    else: # contrast_type == 'delta'
+                        bootstrap_name, index_val = "bootstraps", index              
+
+                    bootstraps.append(getattr(current_plot_data.results, bootstrap_name)[index_val])
+                    differences.append(current_plot_data.results.difference[index_val])
+                    bcalows.append(current_plot_data.results.get(ci_type+'_low')[index_val])
+                    bcahighs.append(current_plot_data.results.get(ci_type+'_high')[index_val])    
+    else:
+        if contrast_type == 'delta':
+            contrast_plot_data = [getattr(contrast, effect_attr)  for contrast in data]
+            bootstraps_nested = [result.results.bootstraps.to_list() for result in contrast_plot_data]
+            differences_nested = [result.results.difference.to_list() for result in contrast_plot_data]
+            bcalows_nested = [result.results.get(ci_type+'_low').to_list() for result in contrast_plot_data]
+            bcahighs_nested = [result.results.get(ci_type+'_high').to_list() for result in contrast_plot_data]
+            
+            bootstraps = [element for innerList in bootstraps_nested for element in innerList]
+            differences = [element for innerList in differences_nested for element in innerList]
+            bcalows = [element for innerList in bcalows_nested for element in innerList]
+            bcahighs = [element for innerList in bcahighs_nested for element in innerList]
 
-    contrast_attr_map = {"delta2": "delta_delta", "mini_meta": "mini_meta_delta"}
+        else: # contrast_type == 'delta2' or 'mini_meta'
+            contrast_plot_data = [getattr(getattr(contrast, effect_attr), contrast_attr) for contrast in data]
+            attribute_suffix = "weighted_delta" if contrast_type == "mini_meta" else "delta_delta"
 
-    effect_attr = effect_attr_map.get(effect_size)
-    contrast_attr = contrast_attr_map.get(contrast_type)
+            bootstraps = [getattr(result, f"bootstraps_{attribute_suffix}") for result in contrast_plot_data]
+            differences = [result.difference for result in contrast_plot_data]
+            bcalows = [result.results.get(ci_type+'_low')[0] for result in contrast_plot_data]
+            bcahighs = [result.results.get(ci_type+'_high')[0] for result in contrast_plot_data]
 
-    if not effect_attr:
-        raise ValueError(f"Invalid effect_size: {effect_size}") 
-    if not contrast_attr:
-        raise ValueError(f"Invalid contrast_type: {contrast_type}. Available options: [`delta2`, `mini_meta`]")
+    return bootstraps, differences, bcalows, bcahighs
+
+def check_for_errors(**kwargs):
+    data = kwargs.get('data')
+    # Contrasts
+    if not isinstance(data, list) or not data:
+        raise ValueError("The `data` argument must be a non-empty list of dabest objects.")
+    
+    ## Check if all contrasts are delta-delta or all are mini-meta
+    contrast_type = ("delta2" if data[0].delta2 
+                     else "mini_meta" if data[0].is_mini_meta
+                     else "delta"
+                    )
 
-    return [
-        getattr(getattr(contrast, effect_attr), contrast_attr) for contrast in contrasts
-    ]
+    # contrast_type = "delta2" if data[0].delta2 else "mini_meta"
+    for contrast in data:
+        check_contrast_type = ("delta2" if contrast.delta2 
+                               else "mini_meta" if contrast.is_mini_meta
+                               else "delta"
+                              )
+        if check_contrast_type != contrast_type:
+            raise ValueError("Each dabest object supplied must be the same experimental type (mini-meta or delta-delta or neither.)")
 
+    # Idx
+    idx = kwargs.get('idx')
+    effect_size = kwargs.get('effect_size')
+    if idx is not None:
+        if not isinstance(idx, (tuple, list)):
+            raise TypeError("`idx` must be a tuple or list of integers.")
 
-def extract_plot_data(contrast_plot_data, contrast_type):
-    """Extracts bootstrap, difference, and confidence intervals based on contrast labels."""
-    if contrast_type == "mini_meta":
-        attribute_suffix = "weighted_delta"
+        msg1 = "The `idx` argument must have the same length as the number of dabest objects. "
+        msg2 = "E.g., If two dabest objects are supplied, there should be two lists within `idx`. "
+        msg3 = "E.g., `idx` = [[1,2],[0,1]]."
+        _total = 0
+        for _group in idx:
+            if isinstance(_group, int | float):
+                raise ValueError(msg1+msg2+msg3)
+            else:
+                _total += 1
+        if _total != len(data):
+            raise ValueError(msg1+msg2+msg3)
+        
+    if idx is not None:
+        number_of_curves_to_plot = sum([len(i) for i in idx])
     else:
-        attribute_suffix = "delta_delta"
+        if contrast_type == 'delta':
+            number_of_curves_to_plot = sum(len(getattr(i, effect_size).results) for i in data)
+        else:
+            number_of_curves_to_plot = len(data)
 
-    bootstraps = [
-        getattr(result, f"bootstraps_{attribute_suffix}")
-        for result in contrast_plot_data
-    ]
-    
-    differences = [result.difference for result in contrast_plot_data]
-    bcalows = [result.bca_low for result in contrast_plot_data]
-    bcahighs = [result.bca_high for result in contrast_plot_data]
+    # Axes
+    ax = kwargs.get('ax')
+    fig_size = kwargs.get('fig_size')
+    if ax is not None and not isinstance(ax, plt.Axes):
+        raise TypeError("The `ax` must be a `matplotlib.axes.Axes` instance or `None`.")
     
-    return bootstraps, differences, bcalows, bcahighs
+    # Figure size
+    if fig_size is not None and not isinstance(fig_size, (tuple, list)):
+        raise TypeError("`fig_size` must be a tuple or list of two positive integers.")
 
+    # Effect size
+    effect_size_options = ['mean_diff', 'median_diff', 'cohens_d', 'cohens_h', 'cliffs_delta', 'hedges_g', 'delta_g']
+    if not isinstance(effect_size, str) or effect_size not in effect_size_options:
+        raise TypeError("The `effect_size` argument must be a string and please choose from the following effect sizes: 'mean_diff', 'median_diff', 'cohens_d', 'cohens_h', 'cliffs_delta', 'hedges_g', 'delta_g'.")
+    if data[0].is_mini_meta and effect_size != 'mean_diff':
+        raise ValueError("The `effect_size` argument must be `mean_diff` for mini-meta analyses.")
+    if data[0].delta2 and effect_size not in ['mean_diff', 'hedges_g', 'delta_g']:
+        raise ValueError("The `effect_size` argument must be `mean_diff`, `hedges_g`, or `delta_g` for delta-delta analyses.")
+    
+    # CI type
+    ci_type = kwargs.get('ci_type')
+    if ci_type not in ('bca', 'pct'):
+        raise TypeError("`ci_type` must be either 'bca' or 'pct'.")
 
-def forest_plot(
-    contrasts: List,
-    selected_indices: Optional[List] = None,
-    contrast_type: str = "delta2",
-    xticklabels: Optional[List] = None,
-    effect_size: str = "mean_diff",
-    contrast_labels: List[str] = None,
-    ylabel: str = "value",
-    plot_elements_to_extract: Optional[List] = None,
-    title: str = "ΔΔ Forest",
-    custom_palette: Optional[Union[dict, list, str]] = None,
-    fontsize: int = 20,
-    violin_kwargs: Optional[dict] = None,
-    marker_size: int = 20,
-    ci_line_width: float = 2.5,
-    zero_line_width: int = 1,
-    remove_spines: bool = True,
-    ax: Optional[plt.Axes] = None,
-    additional_plotting_kwargs: Optional[dict] = None,
-    rotation_for_xlabels: int = 45,
-    alpha_violin_plot: float = 0.4,
-    horizontal: bool = False  # New argument for horizontal orientation
-)-> plt.Figure:
-    """  
-    Custom function that generates a forest plot from given contrast objects, suitable for a range of data analysis types, including those from packages like DABEST-python.
+    # Horizontal
+    horizontal = kwargs.get('horizontal')
+    if not isinstance(horizontal, bool):
+        raise TypeError("`horizontal` must be a boolean value.")
 
-    Parameters
-    ----------
-    contrasts : List
-        List of contrast objects.
-    selected_indices : Optional[List], default=None
-        Indices of specific contrasts to plot, if not plotting all.
-    analysis_type : str
-         the type of analysis (e.g., 'delta2', 'minimeta').
-    xticklabels : Optional[List], default=None
-        Custom labels for the x-axis ticks.
-    effect_size : str
-        Type of effect size to plot (e.g., 'mean_diff', 'median_diff').
-    contrast_labels : List[str]
-        Labels for each contrast.
-    ylabel : str
-        Label for the y-axis, describing the plotted data or effect size.
-    plot_elements_to_extract : Optional[List], default=None
-        Elements to extract for detailed plot customization.
-    title : str
-        Plot title, summarizing the visualized data.
-    ylim : Tuple[float, float]
-        Limits for the y-axis.
-    custom_palette : Optional[Union[dict, list, str]], default=None
-        Custom color palette for the plot.
-    fontsize : int
-        Font size for text elements in the plot.
-    violin_kwargs : Optional[dict], default=None
-        Additional arguments for violin plot customization.
-    marker_size : int
-        Marker size for plotting mean differences or effect sizes.
-    ci_line_width : float
-        Width of confidence interval lines.
-    zero_line_width : int
-        Width of the line indicating zero effect size.
-    remove_spines : bool, default=False
-        If True, removes top and right plot spines.
-    ax : Optional[plt.Axes], default=None
-        Matplotlib Axes object for the plot; creates new if None.
-        additional_plotting_kwargs : Optional[dict], default=None
-        Further customization arguments for the plot.
-    rotation_for_xlabels : int, default=0
-        Rotation angle for x-axis labels, improving readability.
-    alpha_violin_plot : float, default=1.0
-        Transparency level for violin plots.
+    # Marker size
+    marker_size = kwargs.get('marker_size')
+    if not isinstance(marker_size, (int, float)) or marker_size <= 0:
+        raise TypeError("`marker_size` must be a positive integer or float.")
 
-    Returns
-    -------
-    plt.Figure
-        The matplotlib figure object with the generated forest plot.
-    """
-    from .plot_tools import halfviolin
+    # Custom palette
+    custom_palette = kwargs.get('custom_palette')
+    labels = kwargs.get('labels')
+    if custom_palette is not None and not isinstance(custom_palette, (dict, list, tuple, str, type(None))):
+        raise TypeError("The `custom_palette` must be either a dictionary, list, string, or `None`.")
+    if isinstance(custom_palette, dict) and labels is None:
+        raise ValueError("The `labels` argument must be provided if `custom_palette` is a dictionary.")
+    if isinstance(custom_palette, (list, tuple)) and len(custom_palette) < number_of_curves_to_plot:
+        raise ValueError("The `custom_palette` list/tuple must have the same length as the number of `data` provided.")
 
-    # Validate inputs
-    if contrasts is None:
-        raise ValueError("The `contrasts` parameter cannot be None")
-    
-    if not isinstance(contrasts, list) or not contrasts:
-        raise ValueError("The `contrasts` argument must be a non-empty list.")
+    # Contrast alpha and desat
+    contrast_alpha = kwargs.get('contrast_alpha')
+    contrast_desat = kwargs.get('contrast_desat')
+    if not isinstance(contrast_alpha, float) or not 0 <= contrast_alpha <= 1:
+        raise TypeError("`contrast_alpha` must be a float between 0 and 1.")
     
-    if selected_indices is not None and not isinstance(selected_indices, (list, type(None))):
-        raise TypeError("The `selected_indices` must be a list of integers or `None`.")
+    if not isinstance(contrast_desat, (float, int)) or not 0 <= contrast_desat <= 1:
+        raise TypeError("`contrast_desat` must be a float between 0 and 1 or an int (1).")
     
-    if not isinstance(contrast_type, str):
-        raise TypeError("The `contrast_type` argument must be a string.")
+    # Contrast labels
+    labels_fontsize = kwargs.get('labels_fontsize')
+    labels_rotation = kwargs.get('labels_rotation')
+    if labels is not None and not all(isinstance(label, str) for label in labels):
+        raise TypeError("The `labels` must be a list of strings or `None`.")
     
-    if xticklabels is not None and not all(isinstance(label, str) for label in xticklabels):
-        raise TypeError("The `xticklabels` must be a list of strings or `None`.")
+    if labels is not None and len(labels) != number_of_curves_to_plot:
+        raise ValueError("`labels` must match the number of `data` provided.")
     
-    if not isinstance(effect_size, str):
-        raise TypeError("The `effect_size` argument must be a string.")
+    if not isinstance(labels_fontsize, (int, float)):
+        raise TypeError("`labels_fontsize` must be an integer or float.")
     
-    if contrast_labels is not None and not all(isinstance(label, str) for label in contrast_labels):
-        raise TypeError("The `contrast_labels` must be a list of strings or `None`.")
+    if labels_rotation is not None and (not isinstance(labels_rotation, (int, float)) or not 0 <= labels_rotation <= 360):
+        raise TypeError("`labels_rotation` must be an integer or float between 0 and 360.")   
+
+    # Title
+    title = kwargs.get('title')
+    title_fontsize = kwargs.get('title_fontsize')
+    if title is not None and not isinstance(title, str):
+        raise TypeError("The `title` argument must be a string.")
     
-    if contrast_labels is not None and len(contrast_labels) != len(contrasts):
-        raise ValueError("`contrast_labels` must match the number of `contrasts` if provided.")
+    if not isinstance(title_fontsize, (int, float)):
+        raise TypeError("`title_fontsize` must be an integer or float.")
     
-    if not isinstance(ylabel, str):
+    # Y-label
+    ylabel = kwargs.get('ylabel')
+    ylabel_fontsize = kwargs.get('ylabel_fontsize')
+    if ylabel is not None and not isinstance(ylabel, str):
         raise TypeError("The `ylabel` argument must be a string.")
+
+    if not isinstance(ylabel_fontsize, (int, float)):
+        raise TypeError("`ylabel_fontsize` must be an integer or float.")
     
-    if custom_palette is not None and not isinstance(custom_palette, (dict, list, str, type(None))):
-        raise TypeError("The `custom_palette` must be either a dictionary, list, string, or `None`.")
-    
-    if not isinstance(fontsize, (int, float)):
-        raise TypeError("`fontsize` must be an integer or float.")
-    
-    if not isinstance(marker_size, (int, float)) or marker_size <= 0:
-        raise TypeError("`marker_size` must be a positive integer or float.")
+    # Y-lim
+    ylim = kwargs.get('ylim')
+    if ylim is not None and not isinstance(ylim, (tuple, list)):
+        raise TypeError("`ylim` must be a tuple or list of two floats.")
+    if ylim is not None and len(ylim) != 2:
+        raise ValueError("`ylim` must be a tuple or list of two floats.")
+
+    # Y-ticks
+    yticks = kwargs.get('yticks')
+    if yticks is not None and not isinstance(yticks, (tuple, list)):
+        raise TypeError("`yticks` must be a tuple or list of floats.")
     
-    if not isinstance(ci_line_width, (int, float)) or ci_line_width <= 0:
-        raise TypeError("`ci_line_width` must be a positive integer or float.")
+    # Y-ticklabels
+    yticklabels = kwargs.get('yticklabels')
+    if yticklabels is not None and not isinstance(yticklabels, (tuple, list)):
+        raise TypeError("`yticklabels` must be a tuple or list of strings.")
     
-    if not isinstance(zero_line_width, (int, float)) or zero_line_width <= 0:
-        raise TypeError("`zero_line_width` must be a positive integer or float.")
+    if yticklabels is not None and not all(isinstance(label, str) for label in yticklabels):
+        raise TypeError("`yticklabels` must be a list of strings.")
     
+    # Remove spines
+    remove_spines = kwargs.get('remove_spines')
     if not isinstance(remove_spines, bool):
         raise TypeError("`remove_spines` must be a boolean value.")
     
-    if ax is not None and not isinstance(ax, plt.Axes):
-        raise TypeError("`ax` must be a `matplotlib.axes.Axes` instance or `None`.")
-    
-    if not isinstance(rotation_for_xlabels, (int, float)) or not 0 <= rotation_for_xlabels <= 360:
-        raise TypeError("`rotation_for_xlabels` must be an integer or float between 0 and 360.")
+    # Reference band
+    reference_band = kwargs.get('reference_band')
+    if reference_band is not None:
+        if not isinstance(reference_band, list | tuple):
+            raise TypeError("`reference_band` must be a list/tuple of indices (ints).")
+        if not all(isinstance(i, int) for i in reference_band):
+            raise TypeError("`reference_band` must be a list/tuple of indices (ints).")
+        if any(i >= number_of_curves_to_plot for i in reference_band):
+            raise ValueError("Index {} chosen is out of range for the contrast objects.".format([i for i in reference_band if i >= number_of_curves_to_plot]))
     
-    if not isinstance(alpha_violin_plot, float) or not 0 <= alpha_violin_plot <= 1:
-        raise TypeError("`alpha_violin_plot` must be a float between 0 and 1.")
-    
-    if not isinstance(horizontal, bool):
-        raise TypeError("`horizontal` must be a boolean value.")
+    # Delta text
+    delta_text = kwargs.get('delta_text')
+    if delta_text is not None:
+        if not isinstance(delta_text, bool):
+            raise TypeError("`delta_text` must be a boolean value.")
 
-    # Load plot data
-    contrast_plot_data = load_plot_data(contrasts, effect_size, contrast_type)
+    # Contrast bars
+    contrast_bars = kwargs.get('contrast_bars')
+    if contrast_bars is not None:
+        if not isinstance(contrast_bars, bool):
+            raise TypeError("`contrast_bars` must be a boolean value.")
 
-    # Extract data for plotting
-    bootstraps, differences, bcalows, bcahighs = extract_plot_data(
-        contrast_plot_data, contrast_type
-    )
-    # Adjust figure size based on orientation
-    all_groups_count = len(contrasts)
-    if horizontal:
-        fig_size = (4, 1.5 * all_groups_count)
-    else:
-        fig_size = (1.5 * all_groups_count, 4)
+    return contrast_type    
 
-    if ax is None:
-        fig, ax = plt.subplots(figsize=fig_size)
-    else:
-        fig = ax.figure
+def get_kwargs(
+        violin_kwargs,
+        zeroline_kwargs,
+        horizontal,
+        marker_kwargs,
+        errorbar_kwargs,
+        delta_text_kwargs,
+        contrast_bars_kwargs,
+        reference_band_kwargs,
+        marker_size
+    ):
+    from .misc_tools import merge_two_dicts
 
-    # Adjust violin plot orientation based on the 'horizontal' argument
-    violin_kwargs = violin_kwargs or {
+    # Violin kwargs
+    default_violin_kwargs = {
         "widths": 0.5,
         "showextrema": False,
         "showmedians": False,
+        "orientation": 'horizontal' if horizontal else 'vertical',
+    }
+    if violin_kwargs is None:
+        violin_kwargs = default_violin_kwargs
+    else:
+        violin_kwargs = merge_two_dicts(default_violin_kwargs, violin_kwargs)
+
+    # zeroline kwargs
+    default_zeroline_kwargs = {
+        "linewidth": 1,
+        "color": "black"
     }
-    violin_kwargs["vert"] = not horizontal
-    v = ax.violinplot(bootstraps, **violin_kwargs)
+    if zeroline_kwargs is None:
+        zeroline_kwargs = default_zeroline_kwargs
+    else:
+        zeroline_kwargs = merge_two_dicts(default_zeroline_kwargs, zeroline_kwargs)
 
-    # Adjust the halfviolin function call based on 'horizontal'
-    if horizontal:
-        half = "top"
+    # Effect size marker kwargs
+    default_marker_kwargs = {
+                'marker': 'o',
+                'markersize': marker_size,
+                'color': 'black',
+                'alpha': 1,
+                'zorder': 2,
+    }
+    if marker_kwargs is None:
+        marker_kwargs = default_marker_kwargs
     else:
-        half = "right"  # Assuming "right" is the default or another appropriate value
+        marker_kwargs = merge_two_dicts(default_marker_kwargs, marker_kwargs)
 
-    # Assuming halfviolin has been updated to accept a 'half' parameter
-    halfviolin(v, alpha=alpha_violin_plot, half=half)
-    
-    # Handle the custom color palette
-    if custom_palette:
+    # Effect size error bar kwargs
+    default_errorbar_kwargs = {
+                'color': 'black',
+                'lw': 2.5,
+                'linestyle': '-',
+                'alpha': 1,
+                'zorder': 1,
+    }
+    if errorbar_kwargs is None:
+        errorbar_kwargs = default_errorbar_kwargs
+    else:
+        errorbar_kwargs = merge_two_dicts(default_errorbar_kwargs, errorbar_kwargs)
+
+    # Delta text kwargs
+    default_delta_text_kwargs = {
+                "color": None, 
+                "alpha": 1,
+                "fontsize": 10, 
+                "ha": 'center', 
+                "va": 'center', 
+                "rotation": 0, 
+                "x_coordinates": None, 
+                "y_coordinates": None,
+                "offset": 0
+    }
+    if delta_text_kwargs is None:
+        delta_text_kwargs = default_delta_text_kwargs
+    else:
+        delta_text_kwargs = merge_two_dicts(default_delta_text_kwargs, delta_text_kwargs)
+
+    # Contrast bars kwargs.
+    default_contrast_bars_kwargs = {
+                    "color": None, 
+                    "zorder":-3,
+                    'alpha': 0.15
+    }
+    if contrast_bars_kwargs is None:
+        contrast_bars_kwargs = default_contrast_bars_kwargs
+    else:
+        contrast_bars_kwargs = merge_two_dicts(default_contrast_bars_kwargs, contrast_bars_kwargs)
+
+    # reference band kwargs.
+    default_reference_band_kwargs = {
+                    "span_ax": False,
+                    "color": None, 
+                    "alpha": 0.15,
+                    "zorder":-3
+    }
+    if reference_band_kwargs is None:
+        reference_band_kwargs = default_reference_band_kwargs
+    else:
+        reference_band_kwargs = merge_two_dicts(default_reference_band_kwargs, reference_band_kwargs)
+
+    return (violin_kwargs, zeroline_kwargs, marker_kwargs, errorbar_kwargs, 
+            delta_text_kwargs, contrast_bars_kwargs, reference_band_kwargs)
+
+def color_palette(
+        custom_palette, 
+        labels, 
+        number_of_curves_to_plot,
+        contrast_desat
+    ):
+    if custom_palette is not None:
         if isinstance(custom_palette, dict):
             violin_colors = [
-                custom_palette.get(c, sns.color_palette()[0]) for c in contrasts
+                custom_palette.get(c, sns.color_palette()[0]) for c in labels
             ]
         elif isinstance(custom_palette, list):
-            violin_colors = custom_palette[: len(contrasts)]
+            violin_colors = custom_palette[: number_of_curves_to_plot]
         elif isinstance(custom_palette, str):
             if custom_palette in plt.colormaps():
-                violin_colors = sns.color_palette(custom_palette, len(contrasts))
+                violin_colors = sns.color_palette(custom_palette, number_of_curves_to_plot)
             else:
                 raise ValueError(
                     f"The specified `custom_palette` {custom_palette} is not a recognized Matplotlib palette."
                 )
     else:
-        violin_colors = sns.color_palette()[: len(contrasts)]
+        violin_colors = sns.color_palette(n_colors=number_of_curves_to_plot)
+    violin_colors = [sns.desaturate(color, contrast_desat) for color in violin_colors]
+    return violin_colors
 
-    for patch, color in zip(v["bodies"], violin_colors):
-        patch.set_facecolor(color)
-        patch.set_alpha(alpha_violin_plot)
+def forest_plot(
+    data: list,
+    idx: Optional[list[int]] = None,
+    ax: Optional[plt.Axes] = None,
+    fig_size: tuple[int, int] = None,
+    effect_size: str = "mean_diff",
+    ci_type='bca',
+    horizontal: bool = False, 
+
+    marker_size: int = 10,
+    custom_palette: Optional[Union[dict, list, str]] = None,
+    contrast_alpha: float = 0.8,
+    contrast_desat: float = 1,
+
+    labels: list[str] = None,
+    labels_rotation: int = None,
+    labels_fontsize: int = 10,
+    title: str = None,
+    title_fontsize: int = 16,
+    ylabel: str = None,
+    ylabel_fontsize: int = 12,
+    ylim: Optional[list[float, float]] = None,
+    yticks: Optional[list[float]] = None,
+    yticklabels: Optional[list[str]] = None,
+    remove_spines: bool = True,
+
+    delta_text: bool = True,
+    delta_text_kwargs: dict = None,
+
+    contrast_bars: bool = True,
+    contrast_bars_kwargs: dict = None,
+    reference_band: list|tuple = None,
+    reference_band_kwargs: dict = None,
+
+    violin_kwargs: Optional[dict] = None,
+    zeroline_kwargs: Optional[dict] = None,
+    marker_kwargs: Optional[dict] = None,
+    errorbar_kwargs: Optional[dict] = None,
+)-> plt.Figure:
+    """  
+    Custom function that generates a forest plot from given contrast objects, suitable for a range of data analysis types, including those from packages like DABEST-python.
+
+    Parameters
+    ----------
+    data : List
+        List of contrast objects.
+    idx : Optional[List[int]], default=None
+        List of indices to select from the contrast objects if delta-delta experiment. 
+        If None, only the delta-delta objects are plotted.
+    ax : Optional[plt.Axes], default=None
+        Matplotlib Axes object for the plot; creates new if None.
+        additional_plotting_kwargs : Optional[dict], default=None
+        Further customization arguments for the plot.
+    fig_size : Tuple[int, int], default=None
+        Figure size for the plot.
+    effect_size : str
+        Type of effect size to plot (e.g., 'mean_diff', `hedges_g` or 'delta_g').
+    ci_type : str
+        Type of confidence interval to plot (bca' or 'pct')
+    horizontal : bool, default=False
+        If True, the plot will be horizontal.
+    marker_size : int, default=12
+        Marker size for plotting effect size dots.
+    custom_palette : Optional[Union[dict, list, str]], default=None
+        Custom color palette for the plot.
+    contrast_alpha : float, default=0.8
+        Transparency level for violin plots.
+    contrast_desat : float, default=1
+        Saturation level for violin plots.
+    labels : List[str]
+        Labels for each contrast. If None, defaults to 'Contrast 1', 'Contrast 2', etc.
+    labels_rotation : int, default=45 for vertical, 0 for horizontal
+        Rotation angle for contrast labels.
+    labels_fontsize : int, default=10
+        Font size for contrast labels.
+    title : str
+        Plot title, summarizing the visualized data.
+    title_fontsize : int, default=16
+        Font size for the plot title.
+    ylabel : str
+        Label for the y-axis, describing the plotted data or effect size.
+    ylabel_fontsize : int, default=12
+        Font size for the y-axis label.
+    ylim : Optional[Tuple[float, float]]
+        Limits for the y-axis.
+    yticks : Optional[List[float]]
+        Custom y-ticks for the plot.
+    yticklabels : Optional[List[str]]
+        Custom y-tick labels for the plot.
+    remove_spines : bool, default=True
+        If True, removes plot spines (except the relevant dependent variable spine).
+    delta_text : bool, default=True
+        If True, it adds text next to each curve representing the effect size value.
+    delta_text_kwargs : dict, default=None
+        Additional keyword arguments for the delta_text.
+    contrast_bars : bool, default=True
+        If True, it adds bars from the zeroline to the effect size curve.
+    contrast_bars_kwargs : dict, default=None
+        Additional keyword arguments for the contrast_bars.
+    reference_band: list | tuple, default=None,
+        It adds reference bands to the relevant effect size curves.
+    reference_band_kwargs : dict, default=None,
+        Additional keyword arguments for the reference_band.
+    violin_kwargs : Optional[dict], default=None
+        Additional arguments for violin plot customization.
+    zeroline_kwargs : Optional[dict], default=None
+        Additional arguments for the zero line customization.
+    marker_kwargs : Optional[dict], default=None
+        Additional arguments for the effect size marker customization.
+    errorbar_kwargs : Optional[dict], default=None
+        Additional arguments for the effect size error bar customization.
+
+    Returns
+    -------
+    plt.Figure
+        The matplotlib figure object with the generated forest plot.
+    """
+    from .plot_tools import halfviolin
+
+    # Check for errors in the input arguments
+    all_kwargs = locals()
+    contrast_type = check_for_errors(**all_kwargs)
+
+    # Load plot data and extract info
+    bootstraps, differences, bcalows, bcahighs = load_plot_data(
+                                                        data = data, 
+                                                        effect_size = effect_size, 
+                                                        contrast_type = contrast_type,
+                                                        ci_type = ci_type,
+                                                        idx = idx
+    )
+    # Adjust figure size based on orientation
+    number_of_curves_to_plot = len(bootstraps)
+    if ax is not None:
+        fig = ax.figure
+    else:
+        if fig_size is None:
+            fig_size = (4, 1.3 * number_of_curves_to_plot) if horizontal else (1.3 * number_of_curves_to_plot, 4)
+        fig, ax = plt.subplots(figsize=fig_size)
 
-    # Flipping the axes for plotting based on 'horizontal'
-    for k in range(1, len(contrasts) + 1):
+    # Get Kwargs
+    (violin_kwargs, zeroline_kwargs, marker_kwargs, errorbar_kwargs, 
+     delta_text_kwargs, contrast_bars_kwargs, reference_band_kwargs) = get_kwargs(
+                                                                        violin_kwargs = violin_kwargs,
+                                                                        zeroline_kwargs = zeroline_kwargs,
+                                                                        horizontal = horizontal,
+                                                                        marker_kwargs = marker_kwargs,
+                                                                        errorbar_kwargs = errorbar_kwargs,
+                                                                        delta_text_kwargs = delta_text_kwargs,
+                                                                        contrast_bars_kwargs = contrast_bars_kwargs,
+                                                                        reference_band_kwargs = reference_band_kwargs,
+                                                                        marker_size = marker_size
+    )
+                                            
+    # Plot the violins and make adjustments
+    v = ax.violinplot(
+        bootstraps, 
+        **violin_kwargs
+    )
+    halfviolin(
+            v, 
+            alpha = contrast_alpha, 
+            half = "bottom" if horizontal else "right",
+        )
+    
+    ## Plotting the effect sizes and confidence intervals
+    for k in range(1, number_of_curves_to_plot + 1):
         if horizontal:
-            ax.plot(differences[k - 1], k, "k.", markersize=marker_size)  # Flipped axes
-            ax.plot([bcalows[k - 1], bcahighs[k - 1]], [k, k], "k", linewidth=ci_line_width)  # Flipped axes
+            ax.plot(differences[k - 1], k, **marker_kwargs)  
+            ax.plot([bcalows[k - 1], bcahighs[k - 1]], [k, k], **errorbar_kwargs) 
         else:
-            ax.plot(k, differences[k - 1], "k.", markersize=marker_size)
-            ax.plot([k, k], [bcalows[k - 1], bcahighs[k - 1]], "k", linewidth=ci_line_width)
+            ax.plot(k, differences[k - 1], **marker_kwargs)
+            ax.plot([k, k], [bcalows[k - 1], bcahighs[k - 1]], **errorbar_kwargs)
+    
+    # Aesthetic Adjustments
+    ## Handle the custom color palette
+    violin_colors = color_palette(
+                        custom_palette = custom_palette, 
+                        labels = labels, 
+                        number_of_curves_to_plot = number_of_curves_to_plot,
+                        contrast_desat = contrast_desat
+                    )
+    
+    for patch, color in zip(v["bodies"], violin_colors):
+        patch.set_facecolor(color)
 
-    # Adjusting labels, ticks, and limits based on 'horizontal'
+    ## Add a zero line to the plot
     if horizontal:
-        ax.set_yticks(range(1, len(contrasts) + 1))
-        ax.set_yticklabels(contrast_labels,  rotation=rotation_for_xlabels, fontsize=fontsize)
-        ax.set_xlabel(ylabel, fontsize=fontsize)
+        ax.plot([0, 0], [0, number_of_curves_to_plot+1], **zeroline_kwargs)   
     else:
-        ax.set_xticks(range(1, len(contrasts) + 1))
-        ax.set_xticklabels(contrast_labels, rotation=rotation_for_xlabels, fontsize=fontsize)
-        ax.set_ylabel(ylabel, fontsize=fontsize)
+        ax.plot([0, number_of_curves_to_plot+1], [0, 0], **zeroline_kwargs)
+
+    ## lims
+    ### Indepedent variable
+    if horizontal:
+        ax.set_ylim([0.7, number_of_curves_to_plot + 0.2])
+    else:
+        ax.set_xlim([0.7, number_of_curves_to_plot + 0.5])
+
+    ## Depedent variable
+    if ylim is not None:
+        lim_key = ax.set_xlim if horizontal else ax.set_ylim
+        lim_key(ylim)
+
+    ## Ticks
+    ### Indepedent variable
+    lim_key = ax.set_yticks if horizontal else ax.set_xticks
+    lim_key(range(1, number_of_curves_to_plot + 1))
+
+    if labels_rotation == None:
+        labels_rotation = 0 if horizontal else 45
+    if labels is None:
+        labels = [f"Contrast {i}" for i in range(1, number_of_curves_to_plot + 1)]
+    lim_key = ax.set_yticklabels if horizontal else ax.set_xticklabels
+    lim_key(labels, rotation=labels_rotation, fontsize=labels_fontsize, ha="right")
+
+    ### Depedent variable
+    if yticks is not None:
+        lim_key = ax.set_xticks if horizontal else ax.set_yticks
+        lim_key(yticks)
+
+    if yticklabels is not None:
+        lim_key = ax.set_xticklabels if horizontal else ax.set_yticklabels
+        lim_key(yticklabels)
+
+    ## y-label 
+    if ylabel is None:
+        effect_attr_map = {
+            "mean_diff": "Mean Difference",
+            "median_diff": "Median Difference", 
+            "cohens_d": "Cohen's d",
+            "cohens_h": "Cohen's h",
+            "cliffs_delta": "Cliff's delta",
+            "hedges_g": "Hedges' g",
+            "delta_g": "Delta g"
+        }
+        if contrast_type=='delta2' and idx is None and effect_size == "hedges_g":
+            ylabel = "Delta g"
+        elif contrast_type=='delta2' and idx is not None and (effect_size == "delta_g" or effect_size == "hedges_g"):
+            ylabel = "Hedges' g with Delta g"
+        else:
+            ylabel = effect_attr_map[effect_size]
+    lim_key = ax.set_xlabel if horizontal else ax.set_ylabel
+    lim_key(ylabel, fontsize=ylabel_fontsize)
+
+    ## Setting the title
+    if title is not None:
+        ax.set_title(title, fontsize=title_fontsize)
 
-    # Setting the title and adjusting spines as before
-    ax.set_title(title, fontsize=fontsize)
+    ## Adjust Spines
     if remove_spines:
-        for spine in ax.spines.values():
-            spine.set_visible(False)
+        spines = ["top", "right", "left"] if horizontal else ["top", "right", "bottom"]
+        ax.spines[spines].set_visible(False)
 
-    # Apply additional customizations if provided
-    if additional_plotting_kwargs:
-        ax.set(**additional_plotting_kwargs)
+    # Delta Text
+    if delta_text:
+        if delta_text_kwargs.get('color') is not None:
+            delta_text_colors = [delta_text_kwargs.pop('color')] * number_of_curves_to_plot
+        else:
+            delta_text_colors = violin_colors
+            delta_text_kwargs.pop('color')
+
+        # Collect the X-coordinates for the delta text
+        delta_text_x_coordinates = delta_text_kwargs.pop('x_coordinates')
+        delta_text_x_adjustment = delta_text_kwargs.pop('offset')
+
+        if delta_text_x_coordinates is not None:
+            if not isinstance(delta_text_x_coordinates, (list, tuple)) or not all(isinstance(x, (int, float)) for x in delta_text_x_coordinates):
+                raise TypeError("delta_text_kwargs['x_coordinates'] must be a list of x-coordinates.")
+            if len(delta_text_x_coordinates) != number_of_curves_to_plot:
+                raise ValueError("delta_text_kwargs['x_coordinates'] must have the same length as the number of ticks to plot.")
+        else:
+            delta_text_x_coordinates = (np.arange(1, number_of_curves_to_plot + 1) 
+                                        + (0.5 if not horizontal else -0.4)
+                                        + delta_text_x_adjustment
+                                    )
+
+        # Collect the Y-coordinates for the delta text
+        delta_text_y_coordinates = delta_text_kwargs.pop('y_coordinates')
+
+        if delta_text_y_coordinates is not None:
+            if not isinstance(delta_text_y_coordinates, (list, tuple)) or not all(isinstance(y, (int, float)) for y in delta_text_y_coordinates):
+                raise TypeError("delta_text_kwargs['y_coordinates'] must be a list of y-coordinates.")
+            if len(delta_text_y_coordinates) != number_of_curves_to_plot:
+                raise ValueError("delta_text_kwargs['y_coordinates'] must have the same length as the number of ticks to plot.")
+        else:
+            delta_text_y_coordinates = differences
+
+        if horizontal:
+            delta_text_x_coordinates, delta_text_y_coordinates = delta_text_y_coordinates, delta_text_x_coordinates
+
+        for idx, x, y, delta in zip(np.arange(0, number_of_curves_to_plot, 1), delta_text_x_coordinates, 
+                                    delta_text_y_coordinates, differences):
+            delta_text = np.format_float_positional(delta, precision=2, sign=True, trim="k", min_digits=2)
+            ax.text(x, y, delta_text, color=delta_text_colors[idx], zorder=5, **delta_text_kwargs)
+
+    # Contrast bars
+    if contrast_bars:
+        _bar_color = contrast_bars_kwargs.pop('color')
+        if _bar_color is not None:
+            bar_colors = [_bar_color] * number_of_curves_to_plot
+        else:
+            bar_colors = violin_colors
+        for x, y in zip(np.arange(1, number_of_curves_to_plot + 1), differences):
+            if horizontal:
+                ax.add_patch(mpatches.Rectangle((0, x-0.25), y, 0.25, color=bar_colors[x-1], **contrast_bars_kwargs))
+            else:
+                ax.add_patch(mpatches.Rectangle((x, 0), 0.25, y, color=bar_colors[x-1], **contrast_bars_kwargs))
+
+    # Reference band
+    if reference_band:
+        _bar_color = reference_band_kwargs.pop('color')
+        if _bar_color is not None:
+            bar_colors = [_bar_color] * number_of_curves_to_plot
+        else:
+            bar_colors = violin_colors
+
+        span_ax = reference_band_kwargs.pop("span_ax")
+        summary_xmin, summary_xmax = ax.get_xlim()
+        summary_ymin, summary_ymax = ax.get_ylim()
+
+        for summary_index in reference_band:
+            if span_ax == True:
+                starting_location = summary_ymin if horizontal else summary_xmin
+            else:
+                starting_location = summary_index+1   
+
+            summary_color = bar_colors[summary_index]
+            summary_ci_low, summary_ci_high = bcalows[summary_index], bcahighs[summary_index]
+
+            if horizontal:
+                ax.add_patch(mpatches.Rectangle(
+                    (summary_ci_low, starting_location),
+                    summary_ci_high-summary_ci_low, summary_ymax+1, 
+                    color=summary_color, 
+                    **reference_band_kwargs)
+                    )
+            else:
+                ax.add_patch(mpatches.Rectangle(
+                    (starting_location, summary_ci_low),
+                    summary_xmax+1, summary_ci_high-summary_ci_low, 
+                    color=summary_color, 
+                    **reference_band_kwargs)
+                    )
+
+    ## Invert Y-axis if horizontal 
+    if horizontal:
+        ax.invert_yaxis()
 
     return fig
diff --git a/dabest/misc_tools.py b/dabest/misc_tools.py
index 7c5b2020..1ddad39b 100644
--- a/dabest/misc_tools.py
+++ b/dabest/misc_tools.py
@@ -1,11 +1,23 @@
+"""Convenience functions that don't directly deal with plotting or bootstrap computations are placed here."""
+
 # AUTOGENERATED! DO NOT EDIT! File to edit: ../nbs/API/misc_tools.ipynb.
 
 # %% auto 0
-__all__ = ['merge_two_dicts', 'unpack_and_add', 'print_greeting', 'get_varname']
+__all__ = ['merge_two_dicts', 'unpack_and_add', 'print_greeting', 'get_varname', 'get_unique_categories', 'get_params',
+           'get_kwargs', 'get_color_palette', 'initialize_fig', 'get_plot_groups', 'add_counts_to_ticks',
+           'extract_contrast_plotting_ticks', 'set_xaxis_ticks_and_lims', 'show_legend', 'gardner_altman_adjustments',
+           'draw_zeroline', 'redraw_independent_spines', 'redraw_dependent_spines', 'extract_group_summaries',
+           'color_picker', 'prepare_bars_for_plot']
 
 # %% ../nbs/API/misc_tools.ipynb 4
 import datetime as dt
+import numpy as np
 from numpy import repeat
+import pandas as pd
+import seaborn as sns
+import matplotlib.pyplot as plt
+import matplotlib
+import matplotlib.axes as axes
 
 # %% ../nbs/API/misc_tools.ipynb 5
 def merge_two_dicts(
@@ -68,3 +80,1881 @@ def get_varname(obj):
     if len(matching_vars) > 0:
         return matching_vars[0]
     return ""
+	
+
+def get_unique_categories(names):
+    """
+    Extract unique categories from various input types.
+    """
+    if isinstance(names, list):
+        return names
+    if isinstance(names, np.ndarray):
+        return names  # numpy.unique() returns a sorted array
+    elif isinstance(names, (pd.Categorical, pd.Series)):
+        return names.cat.categories if hasattr(names, 'cat') else names.unique()
+    else:
+        # For dict_keys and other iterables
+        return np.unique(list(names))
+
+def get_params(
+        effectsize_df: object, 
+        plot_kwargs: dict,
+        sankey_kwargs: dict,
+        barplot_kwargs: dict
+    ):
+    """
+    Extracts parameters from the `effectsize_df` and `plot_kwargs` objects for use in the plotter function.
+    
+    Parameters
+    ----------
+    effectsize_df : object
+        A `dabest` EffectSizeDataFrame object.
+    plot_kwargs : dict
+        Kwargs passed to the plot function.
+    sankey kwargs : dict
+        Kwargs relating to the sankey diagram plots
+    barplot_kwargs : dict
+        Kwargs relating to the barplot
+    """
+    plot_data = effectsize_df._plot_data
+    xvar = effectsize_df.xvar
+    yvar = effectsize_df.yvar
+    is_paired = effectsize_df.is_paired
+    delta2 = effectsize_df.delta2
+    is_mini_meta = effectsize_df.is_mini_meta
+    effect_size = effectsize_df.effect_size
+    proportional = effectsize_df.is_proportional
+    results = effectsize_df.results
+    dabest_obj = effectsize_df.dabest_obj
+    all_plot_groups = dabest_obj._all_plot_groups
+    idx = dabest_obj.idx
+    x1_level = dabest_obj.x1_level
+    experiment_label = dabest_obj.experiment_label
+    
+    if effect_size not in ["mean_diff", "hedges_g"] or not delta2:
+        show_delta2 = False
+    else:
+        show_delta2 = plot_kwargs["show_delta2"]
+
+    if effect_size != "mean_diff" or not is_mini_meta:
+        show_mini_meta = False
+    else:
+        show_mini_meta = plot_kwargs["show_mini_meta"]
+
+    if show_delta2 and show_mini_meta: raise ValueError("`show_delta2` and `show_mini_meta` cannot be True at the same time.")
+
+   # Horizontal
+    horizontal = plot_kwargs["horizontal"]
+
+    # Disable Gardner-Altman plotting if any of the idxs comprise of more than
+    # two groups or if it is a delta-delta plot.
+    float_contrast = plot_kwargs["float_contrast"]
+    if len(idx) > 1 or len(idx[0]) > 2:
+        float_contrast = False
+
+    if effect_size in ["cliffs_delta"]:
+        float_contrast = False
+
+    if show_delta2 or show_mini_meta or horizontal:
+        float_contrast = False
+
+    if not is_paired:
+        show_pairs = False
+    else:
+        show_pairs = plot_kwargs["show_pairs"]
+
+    # Group summaries
+    group_summaries = plot_kwargs["group_summaries"]
+    group_summaries = None if barplot_kwargs['errorbar'] is not None else group_summaries
+
+    # Contrast Axes kwargs
+    ci_type = plot_kwargs["ci_type"]
+    if ci_type not in ["bca", "pct"]:
+        raise ValueError("Invalid `ci_type`. Must be either 'bca' or 'pct'.")
+        
+    # Boolean for showing Baseline Curve
+    show_baseline_ec = plot_kwargs["show_baseline_ec"]
+
+    # Sankey details
+    # We need to extract the `sankey` and `flow` from the kwargs
+    # to use for varying different kinds of paired proportional plots
+    # We also don't want to pop the parameter from the kwargs
+    one_sankey = (
+        False if is_paired is not None else None
+    )  # Flag to indicate if only one sankey is plotted.
+    two_col_sankey = (
+        True if proportional and not one_sankey and sankey_kwargs["sankey"] and not sankey_kwargs["flow"] else False
+    )
+
+    # Asymmetric side for swarmplots
+    asymmetric_side = (
+        plot_kwargs["swarm_side"]  # Default asymmetric side is right
+        if plot_kwargs["swarm_side"] is not None
+        else "right" if not horizontal
+        else "left"
+    )  
+    # Whether to show sample sizes with ticklabels
+    show_sample_size = plot_kwargs["show_sample_size"]
+        
+    return (dabest_obj, plot_data, xvar, yvar, is_paired, effect_size, proportional, all_plot_groups, 
+            idx, show_delta2, show_mini_meta, float_contrast, show_pairs, group_summaries, 
+            horizontal, results, ci_type, x1_level, experiment_label, show_baseline_ec, 
+            one_sankey, two_col_sankey, asymmetric_side, show_sample_size)
+
+def get_kwargs(
+        plot_kwargs: dict, 
+        ytick_color
+    ):
+    """
+    Extracts the kwargs from the `plot_kwargs` object for use in the plotter function.
+
+    Parameters
+    ----------
+    plot_kwargs : dict
+        Kwargs passed to the plot function.
+    ytick_color : str or color list
+        Color of the yticks.
+    """
+    from .misc_tools import merge_two_dicts
+
+    # Swarmplot kwargs
+    default_swarmplot_kwargs = {
+        "size": plot_kwargs["raw_marker_size"],
+        "alpha": plot_kwargs["raw_alpha"],
+        "fontsize": plot_kwargs.get("fontsize_rawxlabel"),
+    }
+    if plot_kwargs["swarmplot_kwargs"] is None:
+        swarmplot_kwargs = default_swarmplot_kwargs
+    else:
+        swarmplot_kwargs = merge_two_dicts(
+            default_swarmplot_kwargs, plot_kwargs["swarmplot_kwargs"]
+        )
+
+    # Barplot kwargs
+    default_barplot_kwargs = {
+        "estimator": np.mean, 
+        "errorbar": None,
+        "width": plot_kwargs["bar_width"],
+        "alpha": plot_kwargs["raw_alpha"],
+        "err_kws": {'color': 'black'},
+        "fontsize": plot_kwargs["fontsize_rawxlabel"]
+    }
+    if plot_kwargs["barplot_kwargs"] is None:
+        barplot_kwargs = default_barplot_kwargs
+    else:
+        barplot_kwargs = merge_two_dicts(
+            default_barplot_kwargs, plot_kwargs["barplot_kwargs"]
+        )
+
+    # Sankey Diagram kwargs
+    default_sankey_kwargs = {
+        "width": 0.4,
+        "align": "center",
+        "sankey": True,
+        "flow": True,
+        "alpha": plot_kwargs['raw_alpha'],
+        "rightColor": False,
+        "bar_width": 0.2,
+        "fontsize": plot_kwargs.get("fontsize_rawxlabel")
+    }
+    if plot_kwargs["sankey_kwargs"] is None:
+        sankey_kwargs = default_sankey_kwargs
+    else:
+        sankey_kwargs = merge_two_dicts(
+            default_sankey_kwargs, plot_kwargs["sankey_kwargs"]
+        )
+
+    # Violinplot kwargs.
+    default_contrast_kwargs = {
+        "widths": 0.5,
+        "orientation": 'vertical',
+        "showextrema": False,
+        "showmedians": False,
+        "alpha": plot_kwargs["contrast_alpha"],
+        
+    }
+    if plot_kwargs["contrast_kwargs"] is None:
+        contrast_kwargs = default_contrast_kwargs
+    else:
+        contrast_kwargs = merge_two_dicts(
+            default_contrast_kwargs, plot_kwargs["contrast_kwargs"]
+        )
+
+    # Slopegraph kwargs.
+    default_slopegraph_kwargs = {
+        "linewidth": 1, 
+        "alpha": plot_kwargs["raw_alpha"],
+        'jitter': 0, 
+        'jitter_seed': 9876543210,
+    }
+    if plot_kwargs["slopegraph_kwargs"] is None:
+        slopegraph_kwargs = default_slopegraph_kwargs
+    else:
+        slopegraph_kwargs = merge_two_dicts(
+            default_slopegraph_kwargs, plot_kwargs["slopegraph_kwargs"]
+        )
+
+    # Zero reference-line kwargs.
+    default_reflines_kwargs = {
+        "linestyle": "solid",
+        "linewidth": 0.75,
+        "zorder": 2,
+        "color": ytick_color,
+    }
+    if plot_kwargs["reflines_kwargs"] is None:
+        reflines_kwargs = default_reflines_kwargs
+    else:
+        reflines_kwargs = merge_two_dicts(
+            default_reflines_kwargs, plot_kwargs["reflines_kwargs"]
+        )
+
+    # Legend kwargs.
+    default_legend_kwargs = {
+        "loc": "upper left", 
+        "frameon": False,
+    }
+    if plot_kwargs["legend_kwargs"] is None:
+        legend_kwargs = default_legend_kwargs
+    else:
+        legend_kwargs = merge_two_dicts(
+            default_legend_kwargs, plot_kwargs["legend_kwargs"]
+        )
+
+    # Group summaries kwargs.
+    gs_default = {
+        "mean_sd", 
+        "median_quartiles", 
+        None
+    }
+    if plot_kwargs["group_summaries"] not in gs_default:
+        raise ValueError(
+            "group_summaries must be one of" " these: {}.".format(gs_default)
+        )
+
+    default_group_summaries_kwargs = {
+                        "zorder": 3, 
+                        "lw": 2, 
+                        "alpha": 1,
+                        'gap_width_percent': 1.5,
+                        'offset': 0.1,
+                        'color': None
+    }
+    if plot_kwargs["group_summaries_kwargs"] is None:
+        group_summaries_kwargs = default_group_summaries_kwargs
+    else:
+        group_summaries_kwargs = merge_two_dicts(
+            default_group_summaries_kwargs, plot_kwargs["group_summaries_kwargs"]
+        )
+
+    # Redraw axes kwargs.
+    redraw_axes_kwargs = {
+        "colors": ytick_color,
+        "facecolors": ytick_color,
+        "lw": 1,
+        "zorder": 10,
+        "clip_on": False,
+    }
+    
+    # Delta dots kwargs.
+    default_delta_dot_kwargs = {
+                "color": 'k',
+                "marker": "^", 
+                "alpha": 0.5, 
+                "zorder": 2, 
+                "size": 3, 
+                "side": "right"
+    }
+    if plot_kwargs["delta_dot_kwargs"] is None:
+        delta_dot_kwargs = default_delta_dot_kwargs
+    else:
+        delta_dot_kwargs = merge_two_dicts(default_delta_dot_kwargs, plot_kwargs["delta_dot_kwargs"])
+
+    # Delta text kwargs.
+    default_delta_text_kwargs = {
+                "alpha": 1,
+                "fontsize": 10, 
+                "ha": 'center', 
+                "va": 'center', 
+                "rotation": 0, 
+                "x_coordinates": None, 
+                "y_coordinates": None,
+                "offset": 0
+    }
+    if plot_kwargs["delta_text_kwargs"] is None:
+        delta_text_kwargs = default_delta_text_kwargs
+    else:
+        delta_text_kwargs = merge_two_dicts(default_delta_text_kwargs, plot_kwargs["delta_text_kwargs"])
+
+    # Reference band kwargs.
+    default_reference_band_kwargs = {
+                    "span_ax": False,
+                    "alpha": 0.15,
+                    "zorder":-3
+    }
+    if plot_kwargs["reference_band_kwargs"] is None:
+        reference_band_kwargs = default_reference_band_kwargs
+    else:
+        reference_band_kwargs = merge_two_dicts(default_reference_band_kwargs, plot_kwargs["reference_band_kwargs"])
+
+    # Swarm bars kwargs.
+    default_raw_bars_kwargs = {
+                    "zorder":-3,
+                    "alpha": 0.2
+    }
+    if plot_kwargs["raw_bars_kwargs"] is None:
+        raw_bars_kwargs = default_raw_bars_kwargs
+    else:
+        raw_bars_kwargs = merge_two_dicts(default_raw_bars_kwargs, plot_kwargs["raw_bars_kwargs"])
+
+    # Contrast bars kwargs.
+    default_contrast_bars_kwargs = {
+                    "zorder":-3,
+                    "alpha": 0.2
+    }
+    if plot_kwargs["contrast_bars_kwargs"] is None:
+        contrast_bars_kwargs = default_contrast_bars_kwargs
+    else:
+        contrast_bars_kwargs = merge_two_dicts(default_contrast_bars_kwargs, plot_kwargs["contrast_bars_kwargs"])
+
+    # Table axes for horizontal plot kwargs.
+    default_table_kwargs = {
+                'show': True,
+                'color' : 'yellow',
+                'alpha' : 0.2,
+                'fontsize' : 12,
+                'text_color' : 'black', 
+                'text_units' : '',
+                'control_marker' : '-',
+                'fontsize_label': 12,
+                'label': 'Δ'
+    }
+    if plot_kwargs["horizontal_table_kwargs"] is None:
+        table_kwargs = default_table_kwargs
+    else:
+        table_kwargs = merge_two_dicts(default_table_kwargs, plot_kwargs["horizontal_table_kwargs"])
+
+    # Gridkey kwargs.
+    default_gridkey_kwargs = {
+                'show_es' : plot_kwargs['gridkey_show_es'],           # If True, the gridkey will show the effect size of each comparison.
+                'show_Ns' : plot_kwargs['gridkey_show_Ns'],           # If True, the gridkey will show the number of observations in eachgroup.
+                'merge_pairs' : plot_kwargs['gridkey_merge_pairs'],   # If True, the gridkey will merge the pairs of groups into a single cell. This is useful for when the groups are paired.
+                'delimiters': plot_kwargs['gridkey_delimiters'],      # Delimiters to split the group names.
+                'marker': "\u25CF",                                   # Marker for the gridkey dots.
+    }
+    if plot_kwargs["gridkey_kwargs"] is None:
+        gridkey_kwargs = default_gridkey_kwargs
+    else:
+        gridkey_kwargs = merge_two_dicts(default_gridkey_kwargs, plot_kwargs["gridkey_kwargs"])
+
+    # Effect size marker kwargs
+    default_contrast_marker_kwargs = {
+                'marker': 'o',
+                'markersize': plot_kwargs['contrast_marker_size'],
+                'color': ytick_color,
+                'alpha': 1,
+                'zorder': 2,
+    }
+    if plot_kwargs['contrast_marker_kwargs'] is None:
+        contrast_marker_kwargs = default_contrast_marker_kwargs
+    else:
+        contrast_marker_kwargs = merge_two_dicts(default_contrast_marker_kwargs, plot_kwargs['contrast_marker_kwargs'])
+
+    # Effect size error bar kwargs
+    default_contrast_errorbar_kwargs = {
+                'color': ytick_color,
+                'lw': 2,
+                'linestyle': '-',
+                'alpha': 1,
+                'zorder': 1,
+    }
+    if plot_kwargs['contrast_errorbar_kwargs'] is None:
+        contrast_errorbar_kwargs = default_contrast_errorbar_kwargs
+    else:
+        contrast_errorbar_kwargs = merge_two_dicts(default_contrast_errorbar_kwargs, plot_kwargs['contrast_errorbar_kwargs'])
+
+    # Prop sample counts kwargs
+    default_prop_sample_counts_kwargs = {
+                                        'color': 'k', 
+                                        'zorder': 5, 
+                                        'ha': 'center', 
+                                        'va': 'center'
+    }
+    if plot_kwargs['prop_sample_counts_kwargs'] is None:
+        prop_sample_counts_kwargs = default_prop_sample_counts_kwargs
+    else:
+        prop_sample_counts_kwargs = merge_two_dicts(default_prop_sample_counts_kwargs, plot_kwargs['prop_sample_counts_kwargs'])
+
+
+    # RM Lines kwargs
+    default_contrast_paired_lines_kwargs = {
+        "linestyle": "-",
+        "linewidth": 2,
+        "zorder": -2,
+        "color": 'dimgray',
+        "alpha": 1
+    }
+    if plot_kwargs["contrast_paired_lines_kwargs"] is None:
+        contrast_paired_lines_kwargs = default_contrast_paired_lines_kwargs
+    else:
+        contrast_paired_lines_kwargs = merge_two_dicts(default_contrast_paired_lines_kwargs, plot_kwargs["contrast_paired_lines_kwargs"])
+
+    # Return the kwargs.
+    return (swarmplot_kwargs, barplot_kwargs, sankey_kwargs, contrast_kwargs, slopegraph_kwargs, 
+            reflines_kwargs, legend_kwargs, group_summaries_kwargs, redraw_axes_kwargs, delta_dot_kwargs,
+            delta_text_kwargs, reference_band_kwargs, raw_bars_kwargs, contrast_bars_kwargs, table_kwargs, gridkey_kwargs,
+            contrast_marker_kwargs, contrast_errorbar_kwargs, prop_sample_counts_kwargs, contrast_paired_lines_kwargs)
+
+
+def get_color_palette(
+        plot_kwargs: dict, 
+        plot_data: pd.DataFrame, 
+        xvar: str, 
+        show_pairs: bool, 
+        idx: list, 
+        all_plot_groups: list,
+        delta2: bool,
+        sankey: bool
+    ):
+    """
+    Create the color palette to be used in the plotter function.
+
+    Parameters
+    ----------
+    plot_kwargs : dict
+        Kwargs passed to the plot function.
+    plot_data : object (Dataframe)
+        A dataframe of plot data.
+    xvar : str
+        The name of the x-axis variable.
+    show_pairs : bool
+        A boolean flag to determine if the plot is for paired data.
+    idx : list
+        A list of tuples containing the group names.
+    all_plot_groups : list
+        A list of all the group names.
+    delta2 : bool
+        A boolean flag to determine if the plot will have a delta-delta effect size.
+    sankey : bool
+        A boolean flag to determine if the plot is for a Sankey diagram.
+    """
+    # Create color palette that will be shared across subplots.
+    color_col = plot_kwargs["color_col"]
+    if color_col is None:
+        color_groups = pd.unique(plot_data[xvar])
+        bootstraps_color_by_group = True
+    else:
+        if color_col not in plot_data.columns:
+            raise KeyError("``{}`` is not a column in the data.".format(color_col))
+        color_groups = pd.unique(plot_data[color_col])
+        bootstraps_color_by_group = False
+    if show_pairs:
+        bootstraps_color_by_group = False
+
+    # Handle the color palette.
+    filled = True
+    empty_circle = plot_kwargs["empty_circle"]
+    color_by_subgroups = (
+        True if empty_circle else False
+    )  # boolean flag to determine if colour is being grouped by subgroup or the default
+    if empty_circle:
+        # Handling color_by_subgroups
+        # For now, color_by_subgroups can only be True for multi-2-group and 2-group comparison
+        if isinstance(idx[0], str):
+            if len(idx) > 2:
+                color_by_subgroups = False
+        else:
+            for group_i in idx:
+                if len(group_i) > 2:
+                    color_by_subgroups = False
+
+        # filled is now a list, which determines the which group in idx has their dots filled for the swarmplot
+        filled = []
+        for i in range(len(idx)):
+            filled.append(False)
+            filled.extend([True] * (len(idx[i]) - 1))
+
+    if color_col is not None:
+        if sankey:
+            names = [1, 0]
+        else:
+            names = color_groups if not color_by_subgroups else idx
+    else:
+        if sankey:
+            names = [1, 0]
+        else:
+            names = all_plot_groups if not color_by_subgroups else idx
+
+    n_groups = len(color_groups)
+    custom_pal = plot_kwargs["custom_palette"]
+    raw_desat = plot_kwargs["raw_desat"]
+    contrast_desat = plot_kwargs["contrast_desat"]
+
+    if custom_pal is None:
+        unsat_colors = sns.color_palette(n_colors=n_groups)
+        if empty_circle and not color_by_subgroups:
+            unsat_colors = [sns.color_palette("gray")[3]] + unsat_colors
+    else:
+        if isinstance(custom_pal, dict):
+            if delta2:
+                groups_in_palette = {
+                    k: custom_pal[k] for k in color_groups
+                }
+            elif sankey:
+                groups_in_palette = {
+                    k: custom_pal[k] for k in [1, 0]
+                }
+            elif color_col is None:
+                groups_in_palette = {
+                    k: custom_pal[k] for k in all_plot_groups if k in color_groups
+                }
+            else:
+                raise ValueError("The `custom_palette` dictionary is not supported when `color_col` is not None.")
+
+            names = groups_in_palette.keys()
+            unsat_colors = groups_in_palette.values()
+
+        elif isinstance(custom_pal, list):
+            if sankey:
+                if len(custom_pal) != 2:
+                    raise ValueError("To specify a custom palette for a Sankey diagram, you must provide exactly two colors.")
+                else:
+                    groups_in_palette = {
+                        k: custom_pal[k] for k in [1, 0]
+                    }
+                    names = groups_in_palette.keys()
+                    unsat_colors = groups_in_palette.values()
+            elif len(custom_pal) < n_groups:
+                err1 = "The specified `custom_palette` has fewer colors than the number of groups."
+                err2 = " Please specify a custom palette with at least {} colors.".format(n_groups)
+                raise ValueError(err1 + err2)
+            else:
+                unsat_colors = custom_pal[0:n_groups]
+
+        elif isinstance(custom_pal, str):
+            # check it is in the list of matplotlib palettes.
+            if custom_pal in plt.colormaps():
+                unsat_colors = sns.color_palette(custom_pal, n_groups)
+            else:
+                err1 = "The specified `custom_palette` {}".format(custom_pal)
+                err2 = " is not a matplotlib palette. Please check."
+                raise ValueError(err1 + err2)
+
+    if custom_pal is None and color_col is None:
+        categories = get_unique_categories(names)
+        raw_colors = [sns.desaturate(c, raw_desat) for c in unsat_colors]
+        contrast_colors = [sns.desaturate(c, contrast_desat) for c in unsat_colors]
+        if color_by_subgroups:
+            plot_palette_raw = dict()
+            plot_palette_contrast = dict()
+            for i in range(len(idx)):
+                for names_i in idx[i]:
+                    plot_palette_raw[names_i] = raw_colors[i]
+                    plot_palette_contrast[names_i] = contrast_colors[i]
+        else:
+            plot_palette_raw = dict(zip(categories, raw_colors))
+            plot_palette_contrast = dict(zip(categories, contrast_colors))
+    else:
+        raw_colors = [sns.desaturate(c, raw_desat) for c in unsat_colors]
+        contrast_colors = [sns.desaturate(c, contrast_desat) for c in unsat_colors]
+        if color_by_subgroups:
+            plot_palette_raw = dict()
+            plot_palette_contrast = dict()
+            for i in range(len(idx)):
+                for names_i in idx[i]:
+                    plot_palette_raw[names_i] = raw_colors[i]
+                    plot_palette_contrast[names_i] = contrast_colors[i]
+        else:
+            plot_palette_raw = dict(zip(names, raw_colors))
+            plot_palette_contrast = dict(zip(names, contrast_colors))
+            plot_palette_sankey = dict(zip(names, unsat_colors))
+
+    # For Sankey Diagram plot, each bar will have the same two colors if custom_pal is None
+    # default color palette will be set to "hls"
+    if custom_pal is None:
+        plot_palette_sankey = None
+
+    return (color_col, bootstraps_color_by_group, n_groups, filled, raw_colors,
+            plot_palette_raw, plot_palette_contrast, plot_palette_sankey)
+
+def initialize_fig(
+        plot_kwargs: dict, 
+        dabest_obj: object, 
+        show_delta2: bool, 
+        show_mini_meta: bool, 
+        is_paired: bool, 
+        show_pairs: bool, 
+        proportional: bool,
+        float_contrast: bool,
+        effect_size_type: str, 
+        yvar: str, 
+        horizontal: bool, 
+        show_table: bool,
+        color_col: str,
+    ):
+    """
+    Initialize the figure and axes for the plotter function.
+
+    Parameters
+    ----------
+    plot_kwargs : dict
+        Kwargs passed to the plot function.
+    dabest_obj : object (EffectSizeDataFrame)
+        A `dabest` EffectSizeDataFrame object.
+    show_delta2 : bool
+        A boolean flag to determine if the plot will have a delta-delta effect size.
+    show_mini_meta : bool
+        A boolean flag to determine if the plot will have a mini-meta effect size.
+    is_paired : bool
+        A boolean flag to determine if the plot is for paired data.
+    show_pairs : bool
+        A boolean flag to determine if the plot will show the paired data.
+    proportional : bool
+        A boolean flag to determine if the plot is for proportional data.
+    float_contrast : bool
+        A boolean flag to determine if the plot is for floating contrast data.
+    effect_size_type : str
+        The type of effect size to be plotted.
+    yvar : str
+        The name of the y-axis variable.
+    horizontal : bool
+        A boolean flag to determine if the plot is for horizontal plotting.
+    show_table : dict
+        A boolean flag to determine if the table will be shown in horizontal plot.
+    color_col : str
+        The column name for coloring the data points.
+    """
+    # Params
+    fig_size = plot_kwargs["fig_size"]
+    face_color = plot_kwargs["face_color"]
+    if plot_kwargs["face_color"] is None:
+        face_color = "white"
+
+    # Create Figure and Axes
+    if fig_size is None:
+        all_groups_count = np.sum([len(i) for i in dabest_obj.idx])
+        # Increase the width (vertical layout) or height (horizontal layout) for delta-delta or mini-meta graph
+        if show_delta2 or show_mini_meta:
+            all_groups_count += 1
+   
+        if horizontal:
+            frac = 0.3 if is_paired or show_mini_meta else 0.5
+            fig_size = (7, 1 + (frac * all_groups_count))
+        else:
+            if is_paired and show_pairs and proportional is False:
+                if color_col is not None and float_contrast:
+                    frac = 0.9
+                else:
+                    frac = 0.8
+            else:
+                frac = 1
+            if float_contrast:
+                height_inches = 4
+                each_group_width_inches = 2.5 * frac
+            else:
+                height_inches = 6
+                each_group_width_inches = 1.5 * frac
+
+            width_inches = each_group_width_inches * all_groups_count
+            fig_size = (width_inches, height_inches) 
+
+    init_fig_kwargs = dict(figsize=fig_size, dpi=plot_kwargs["dpi"], tight_layout=True)
+
+    width_ratios_ga = [2.5, 1]
+    h_space_cummings = (0.1 if plot_kwargs["gridkey"] is not None
+                        else 0.3)
+
+    if plot_kwargs["ax"] is not None:
+        # New in v0.2.6.
+        # Use inset axes to create the estimation plot inside a single axes.
+        # Author: Adam L Nekimken. (PR #73)
+        rawdata_axes = plot_kwargs["ax"]
+        ax_position = rawdata_axes.get_position()  # [[x0, y0], [x1, y1]]
+
+        fig = rawdata_axes.get_figure()
+        fig.patch.set_facecolor(face_color)
+
+        if horizontal:
+            plot_width_ratios = [1, 0.7, 0.3]
+            contrast_wspace = 0.05
+            contrast_axes = rawdata_axes.inset_axes(
+                    [1+contrast_wspace, 0, (plot_width_ratios[1]/plot_width_ratios[0]), 1]
+                    )
+            if show_table:
+                table_axes = rawdata_axes.inset_axes(
+                        [1+contrast_wspace+(plot_width_ratios[1]/plot_width_ratios[0]), 0, (plot_width_ratios[2]/plot_width_ratios[0]), 1]
+                        )
+            else:
+                table_axes = None
+
+            rawdata_axes.set_position(
+                    [ax_position.x0,
+                     ax_position.y0,
+                     (ax_position.x1 - ax_position.x0) * (plot_width_ratios[0] / sum(plot_width_ratios)),
+                     (ax_position.y1 - ax_position.y0)]
+                     )
+            rawdata_axes.contrast_axes = contrast_axes
+            rawdata_axes.table_axes = table_axes
+        
+        else:
+            if float_contrast:
+                axins = rawdata_axes.inset_axes(
+                    [1, 0, width_ratios_ga[1] / width_ratios_ga[0], 1]
+                )
+                rawdata_axes.set_position(  # [l, b, w, h]
+                    [
+                        ax_position.x0,
+                        ax_position.y0,
+                        (ax_position.x1 - ax_position.x0)
+                        * (width_ratios_ga[0] / sum(width_ratios_ga)),
+                        (ax_position.y1 - ax_position.y0),
+                    ]
+                )
+
+                contrast_axes = axins
+            else:
+                axins = rawdata_axes.inset_axes([0, -1 - h_space_cummings, 1, 1])
+                plot_height = (ax_position.y1 - ax_position.y0) / (2 + h_space_cummings)
+                rawdata_axes.set_position(
+                    [
+                        ax_position.x0,
+                        ax_position.y0 + (1 + h_space_cummings) * plot_height,
+                        (ax_position.x1 - ax_position.x0),
+                        plot_height,
+                    ]
+                )
+
+            # Set axes
+            contrast_axes = axins
+            rawdata_axes.contrast_axes = axins
+            table_axes = None
+
+    else:
+        # Here, we hardcode some figure parameters.
+        if horizontal:
+            if show_table:
+                fig, axx = plt.subplots(
+                    ncols=3, gridspec_kw={'width_ratios' : [1,0.7,0.3], 'wspace' : 0.05}, **init_fig_kwargs
+                )
+            else:
+                fig, axx = plt.subplots(
+                    ncols=2, gridspec_kw={'width_ratios' : [1,0.7], 'wspace' : 0.05}, **init_fig_kwargs
+                )
+        else:
+            if float_contrast:
+                fig, axx = plt.subplots(
+                    ncols=2,
+                    gridspec_kw={"width_ratios": width_ratios_ga, "wspace": 0},
+                    **init_fig_kwargs
+                )
+            else:
+                fig, axx = plt.subplots(
+                    nrows=2, gridspec_kw={"hspace": h_space_cummings}, **init_fig_kwargs
+                )
+        fig.patch.set_facecolor(face_color)
+
+        # Set axes   
+        rawdata_axes = axx[0]
+        contrast_axes = axx[1]
+        table_axes = axx[2] if horizontal and show_table else None
+
+
+    # Title
+    title, fontsize_title = plot_kwargs["title"], plot_kwargs["fontsize_title"]
+    if title is not None:
+        if plot_kwargs["ax"] is not None:
+            rawdata_axes.set_title(title, fontsize=fontsize_title)
+        else:
+            fig.suptitle(title, fontsize=fontsize_title)
+
+    rawdata_axes.set_frame_on(False)
+    contrast_axes.set_frame_on(False)
+    if horizontal and show_table:
+        table_axes.set_frame_on(False)
+    
+    # Swarmplot ylim (Vertical) or xlim (Horizontal)
+    raw_ylim = plot_kwargs["raw_ylim"]
+    if raw_ylim is not None:
+        if not isinstance(raw_ylim, list) and not isinstance(raw_ylim, tuple) or len(raw_ylim) != 2:
+            raise ValueError("`raw_ylim` must be a tuple/list of the lower and upper bound.")
+        if horizontal:
+            rawdata_axes.set_xlim(raw_ylim)
+        else:
+            rawdata_axes.set_ylim(raw_ylim)
+
+    # Contrastplot ylim (Vertical) or xlim (Horizontal)
+    if horizontal or not float_contrast:
+        contrast_ylim, delta2_ylim = plot_kwargs["contrast_ylim"], plot_kwargs["delta2_ylim"]
+        if contrast_ylim is not None or (delta2_ylim is not None and show_delta2):
+            if contrast_ylim is not None:
+                if delta2_ylim is not None and show_delta2:
+                    if contrast_ylim != delta2_ylim:
+                        raise ValueError("Please check if `contrast_ylim` and `delta2_ylim` are assigned with same values.")
+            else:
+                contrast_ylim = delta2_ylim
+
+            if not isinstance(contrast_ylim, list) and not isinstance(contrast_ylim, tuple) or len(contrast_ylim) != 2:
+                raise ValueError("`contrast_ylim` must be a tuple/list of the lower and upper bound.")
+
+            if effect_size_type == "cliffs_delta":
+                # Ensure the ylims for a cliffs_delta plot never exceed [-1, 1].
+                l = contrast_ylim[0]
+                h = contrast_ylim[1]
+                low = -1 if l < -1 else l
+                high = 1 if h > 1 else h
+                if horizontal:
+                    contrast_axes.set_xlim(low, high)
+                else:
+                    contrast_axes.set_ylim(low, high)
+            else:
+                if horizontal:
+                    contrast_axes.set_xlim(contrast_ylim)
+                else:
+                    contrast_axes.set_ylim(contrast_ylim)
+
+    # Set raw axes y-label.
+    raw_label = plot_kwargs["raw_label"]
+    if raw_label is None:
+        if proportional:
+            raw_label = "Proportion of Success" if effect_size_type != "cohens_h" else "Value"
+        else:
+            raw_label = yvar 
+
+    fontsize_rawylabel = plot_kwargs["fontsize_rawylabel"]
+    if horizontal:
+        rawdata_axes.set_xlabel(raw_label, fontsize=fontsize_rawylabel)
+        rawdata_axes.set_ylabel("")
+    else:
+        rawdata_axes.set_ylabel(raw_label, fontsize=fontsize_rawylabel)
+        rawdata_axes.set_xlabel("")
+
+    # Set contrast axes y-label.
+    contrast_label_dict = {
+        "mean_diff": "Mean Difference",
+        "median_diff": "Median Difference",
+        "cohens_d": "Cohen's d",
+        "hedges_g": "Hedges' g",
+        "cliffs_delta": "Cliff's delta",
+        "cohens_h": "Cohen's h",
+    }
+
+    if proportional and effect_size_type != "cohens_h":
+        default_contrast_label = "Proportion Difference"
+    else:
+        default_contrast_label = contrast_label_dict[effect_size_type]
+
+    if plot_kwargs["contrast_label"] is None:
+        if is_paired:
+            contrast_label = "Paired\n{}".format(default_contrast_label)
+        else:
+            contrast_label = default_contrast_label
+    else:
+        contrast_label = plot_kwargs["contrast_label"]
+
+    fontsize_contrastylabel = plot_kwargs["fontsize_contrastylabel"]
+
+    if horizontal:
+        contrast_axes.set_xlabel(contrast_label, fontsize=fontsize_contrastylabel)
+    else:
+        contrast_axes.set_ylabel(contrast_label, fontsize=fontsize_contrastylabel)
+        if float_contrast:
+            contrast_axes.yaxis.set_label_position("right")
+
+    return fig, rawdata_axes, contrast_axes, table_axes
+
+def get_plot_groups(
+        is_paired: bool, 
+        idx: list, 
+        proportional: bool, 
+        all_plot_groups: list
+    ):
+    """
+    Extract the plot groups from the `idx` object for use in the plotter function.
+
+    Parameters
+    ----------
+    is_paired : bool
+        A boolean flag to determine if the plot is for paired data.
+    idx : list
+        A list of tuples containing the group names.
+    proportional : bool
+        A boolean flag to determine if the plot is for proportional data.
+    all_plot_groups : list
+        A list of all the group names.
+    """
+
+    if is_paired == "baseline":
+        idx_pairs = [
+            (control, test)
+            for i in idx
+            for control, test in zip([i[0]] * (len(i) - 1), i[1:])
+        ]
+        temp_idx = idx if not proportional else idx_pairs
+    else:
+        idx_pairs = [
+            (control, test) for i in idx for control, test in zip(i[:-1], i[1:])
+        ]
+        temp_idx = idx if not proportional else idx_pairs
+
+    # Determine temp_all_plot_groups based on proportional condition
+    plot_groups = [item for i in temp_idx for item in i]
+    temp_all_plot_groups = all_plot_groups if not proportional else plot_groups
+    
+    return temp_idx, temp_all_plot_groups
+
+
+def add_counts_to_ticks(
+        plot_data: pd.DataFrame, 
+        xvar: str, 
+        yvar: str, 
+        rawdata_axes: axes.Axes, 
+        plot_kwargs: dict, 
+        flow: bool, 
+        horizontal: bool
+    ):
+    """
+
+    Add the counts to the raw data axes labels.
+
+    Parameters
+    ----------
+    plot_data : object (Dataframe)
+        A dataframe of plot data.
+    xvar : str
+        The name of the x-axis variable.
+    yvar : str
+        The name of the y-axis variable.
+    rawdata_axes : object (Axes)
+        The raw data axes.
+    plot_kwargs : dict
+        Kwargs passed to the plot function.
+    flow : bool
+        Whether sankey flow is enabled or not.
+    horizontal : bool
+        A boolean flag to determine if the plot is for horizontal plotting.
+    """
+
+    # Add the counts to the rawdata axes xticks.
+    counts = plot_data.groupby(xvar, observed=False).count()[yvar]
+    
+    def lookup_value(text):
+        try:
+            return str(counts.loc[text])
+        except KeyError:
+            try:
+                numeric_key = pd.to_numeric(text, errors='coerce')
+                if pd.notnull(numeric_key):
+                    return str(counts.loc[numeric_key])
+            except (ValueError, KeyError):
+                pass
+        print(f"Key '{text}' not found in counts.")
+        return "N/A"
+    
+    ticks_with_counts = []
+    if horizontal:
+        get_label, get_ticks = rawdata_axes.get_yticklabels, rawdata_axes.get_yticks
+        set_label, set_major_loc_method= rawdata_axes.set_yticklabels, rawdata_axes.yaxis.set_major_locator
+    else:
+        get_label, get_ticks = rawdata_axes.get_xticklabels, rawdata_axes.get_xticks
+        set_label, set_major_loc_method = rawdata_axes.set_xticklabels, rawdata_axes.xaxis.set_major_locator
+    
+    for ticklab in get_label():
+        t = ticklab.get_text()
+
+        if horizontal and not flow:
+            te = t.split('v.s. ')[-1]  # Get the last line of the label
+        else:
+            te = t.split('\n')[-1]  # Get the last line of the label
+
+        value = lookup_value(te)
+        if horizontal:
+            ticks_with_counts.append(f"{t} (N={value})")
+        else:
+            ticks_with_counts.append(f"{t}\n(N={value})")
+
+    set_major_loc_method(plt.FixedLocator(get_ticks()))
+
+    # label = ticks_with_counts if plot_kwargs['show_sample_size'] else get_label()
+    # set_label(label, fontsize=plot_kwargs.get("fontsize_rawxlabel"))
+
+    set_label(ticks_with_counts, fontsize=plot_kwargs.get("fontsize_rawxlabel"))
+
+    # Ensure ticks are at the correct locations
+    set_major_loc_method(plt.FixedLocator(get_ticks()))
+
+def extract_contrast_plotting_ticks(
+        is_paired: bool, 
+        show_pairs: bool, 
+        two_col_sankey: bool, 
+        plot_groups: list, 
+        idx: list, 
+        sankey_control_group: list
+    ):
+    """
+    Extract the contrast plotting ticks from the `idx` object for use in the plotter function.
+
+    Parameters
+    ----------
+    is_paired : bool
+        A boolean flag to determine if the plot is for paired data.
+    show_pairs : bool
+        A boolean flag to determine if the plot will show the paired data.
+    two_col_sankey : bool
+        A boolean flag to determine if the plot will show a two-column sankey diagram.
+    plot_groups : list
+        A list of the plot groups.
+    idx : list
+        A list of tuples containing the group names.
+    sankey_control_group : list
+        A list of the control group names.
+    """
+    # Take note of where the `control` groups are.
+    ticks_to_skip_contrast = None
+    ticks_to_start_twocol_sankey = None
+    if is_paired == "baseline" and show_pairs:
+        if two_col_sankey:
+            ticks_to_skip = []
+            ticks_to_plot = np.arange(0, len(plot_groups) / 2).tolist()
+            ticks_to_start_twocol_sankey = np.cumsum([len(i) - 1 for i in idx]).tolist()
+            ticks_to_start_twocol_sankey.pop()
+            ticks_to_start_twocol_sankey.insert(0, 0)
+        else:
+            ticks_to_skip = np.cumsum([len(t) for t in idx])[:-1].tolist()
+            ticks_to_skip.insert(0, 0)
+            # Then obtain the ticks where we have to plot the effect sizes.
+            ticks_to_plot = [
+                t for t in range(0, len(plot_groups)) if t not in ticks_to_skip
+            ]
+            ticks_to_skip_contrast = np.cumsum([(len(t)) for t in idx])[:-1].tolist()
+            ticks_to_skip_contrast.insert(0, 0)
+    else:
+        if two_col_sankey:
+            ticks_to_skip = [len(sankey_control_group)]
+            # Then obtain the ticks where we have to plot the effect sizes.
+            ticks_to_plot = [
+                t for t in range(0, len(plot_groups)) if t not in ticks_to_skip
+            ]
+            ticks_to_skip = []
+            ticks_to_start_twocol_sankey = np.cumsum([len(i) - 1 for i in idx]).tolist()
+            ticks_to_start_twocol_sankey.pop()
+            ticks_to_start_twocol_sankey.insert(0, 0)
+        else:
+            ticks_to_skip = np.cumsum([len(t) for t in idx])[:-1].tolist()
+            ticks_to_skip.insert(0, 0)
+            # Then obtain the ticks where we have to plot the effect sizes.
+            ticks_to_plot = [
+                t for t in range(0, len(plot_groups)) if t not in ticks_to_skip
+            ]
+    
+    ticks_for_baseline_ec = ticks_to_skip
+    
+    return ticks_to_skip, ticks_to_plot, ticks_for_baseline_ec, ticks_to_skip_contrast, ticks_to_start_twocol_sankey
+
+def set_xaxis_ticks_and_lims(
+        show_delta2: bool, 
+        show_mini_meta: bool, 
+        rawdata_axes: axes.Axes, 
+        contrast_axes: axes.Axes, 
+        show_pairs: bool, 
+        float_contrast: bool,
+        ticks_to_skip: list, 
+        contrast_xtick_labels: list, 
+        plot_kwargs: dict, 
+        proportional: bool, 
+        horizontal: bool
+    ):
+    """
+    Set the x-axis/yaxis ticks and limits for the plotter function.
+
+    Parameters
+    ----------
+    show_delta2 : bool
+        A boolean flag to determine if the plot will have a delta-delta effect size.
+    show_mini_meta : bool
+        A boolean flag to determine if the plot will have a mini-meta effect size.
+    rawdata_axes : object (Axes)
+        The raw data axes.
+    contrast_axes : object (Axes)
+        The contrast axes.
+    show_pairs : bool
+        A boolean flag to determine if the plot will show the paired data.
+    float_contrast : bool
+        A boolean flag to determine if the plot is a GA or Cumming design.
+    ticks_to_skip : list
+        A list of ticks to skip.
+    contrast_xtick_labels : list
+        A list of contrast xtick labels.
+    plot_kwargs : dict
+        Kwargs passed to the plot function.
+    proportional: bool
+        A boolean flag to determine if the plot is a proportional plot.
+    horizontal : bool
+        A boolean flag to determine if the plot is for horizontal plotting.
+    """
+
+    if horizontal:
+        # Ticks
+        if show_delta2 is False and show_mini_meta is False:
+            contrast_axes.set_yticks(rawdata_axes.get_yticks())
+        else:
+            temp = rawdata_axes.get_yticks()
+            temp = np.append(temp, [max(temp) + 0, max(temp) + 1])
+            contrast_axes.set_yticks(temp)       
+
+        # Lims
+        if show_pairs:
+            max_x = contrast_axes.get_ylim()[1]
+            rawdata_axes.set_ylim(-0.375, max_x)
+
+            if proportional:
+                rawdata_axes.set_ylim(-0.375, max_x+0.1)
+
+        if show_delta2 or show_mini_meta:
+            # Increase the ylim of raw data by 2
+            temp = rawdata_axes.get_ylim()
+            if show_pairs:
+                rawdata_axes.set_ylim(temp[0], temp[1] + 0.00)
+            else:
+                rawdata_axes.set_ylim(temp[0], temp[1] + 1)
+            contrast_axes.set_ylim(rawdata_axes.get_ylim())
+        else:
+            contrast_axes.set_ylim(rawdata_axes.get_ylim())
+    # Vertical
+    else:
+        # Ticks
+        if show_delta2 is False and show_mini_meta is False:
+            contrast_axes.set_xticks(rawdata_axes.get_xticks())
+        else:
+            temp = rawdata_axes.get_xticks()
+            temp = np.append(temp, [max(temp) + 1])
+            contrast_axes.set_xticks(temp)
+
+        # Lims
+        if show_pairs:
+            max_x = contrast_axes.get_xlim()[1]
+            rawdata_axes.set_xlim(-0.375, max_x)
+
+        if float_contrast:
+            contrast_axes.set_xlim(0.5, 1.5)
+
+        elif show_delta2:
+            if show_pairs:
+                rawdata_axes.set_xlim(-0.375, 4.75)
+            else:
+                rawdata_axes.set_xlim(-0.5, 4.75)
+            contrast_axes.set_xlim(rawdata_axes.get_xlim())
+
+        elif show_mini_meta:
+            # Increase the xlim of raw data by 2
+            temp = rawdata_axes.get_xlim()
+            if show_pairs:
+                rawdata_axes.set_xlim(temp[0], temp[1] + 0.5)
+            else:
+                rawdata_axes.set_xlim(temp[0], temp[1] + 1)
+            contrast_axes.set_xlim(rawdata_axes.get_xlim())
+        else:
+            contrast_axes.set_xlim(rawdata_axes.get_xlim())
+
+        # Properly label the contrast ticks.
+        for t in ticks_to_skip:
+            contrast_xtick_labels.insert(t, "")
+
+        contrast_axes.set_xticklabels(
+            contrast_xtick_labels, fontsize=plot_kwargs["fontsize_contrastxlabel"]
+        )
+
+
+def show_legend(
+        legend_labels: list, 
+        legend_handles: list, 
+        rawdata_axes: axes.Axes, 
+        contrast_axes: axes.Axes, 
+        table_axes: axes.Axes, 
+        float_contrast: bool, 
+        show_pairs: bool, 
+        horizontal: bool, 
+        legend_kwargs: dict, 
+        table_kwargs: dict
+    ):
+    """
+    Show the legend for the plotter function.
+
+    Parameters
+    ----------
+    legend_labels : list
+        A list of legend labels.
+    legend_handles : list
+        A list of legend handles.
+    rawdata_axes : object (Axes)
+        The raw data axes.
+    contrast_axes : object (Axes)
+        The contrast axes.
+    table_axes : object (Axes)
+        The table axes.
+    float_contrast : bool
+        A boolean flag to determine if the plot is GA or Cumming format.
+    show_pairs : bool
+        A boolean flag to determine if the plot will show the paired data.
+    horizontal : bool
+        A boolean flag to determine if the plot is for horizontal plotting.
+    legend_kwargs : dict
+        Kwargs passed to the legend function.
+    """
+
+    legend_labels_unique = np.unique(legend_labels)
+    unique_idx = np.unique(legend_labels, return_index=True)[1]
+    legend_handles_unique = (
+        pd.Series(legend_handles, dtype="object").loc[unique_idx]
+    ).tolist()
+
+    # Location of the legend
+    if "bbox_to_anchor" not in legend_kwargs.keys():
+        if horizontal:
+            bta = (1,1)
+        else:
+            if float_contrast:
+                bta = (2.00, 1.02) if show_pairs else (1.5, 1.02)
+            else:
+                bta = (1.02, 1.0) if show_pairs else (1.0, 1.0)
+        legend_kwargs.update({'bbox_to_anchor': bta})
+
+    # Pick the ax to plot
+    if horizontal:
+        if table_kwargs['show']:
+            axes_with_legend = table_axes
+        else:
+            axes_with_legend = contrast_axes
+    elif float_contrast:
+        axes_with_legend = contrast_axes
+    else:
+        axes_with_legend = rawdata_axes
+
+    # Plot the legend
+    if len(legend_handles_unique) > 0:
+        leg = axes_with_legend.legend(
+                            legend_handles_unique,
+                            legend_labels_unique,
+                            handlelength=0.5,
+                            **legend_kwargs
+        )
+        if show_pairs:
+            for line in leg.get_lines():
+                line.set_linewidth(3.0)
+    
+def gardner_altman_adjustments(
+        effect_size_type: str, 
+        plot_data: pd.DataFrame, 
+        xvar: str, 
+        yvar: str, 
+        current_control: str, 
+        current_group: str,
+        rawdata_axes: axes.Axes, 
+        contrast_axes: axes.Axes, 
+        results: pd.DataFrame, 
+        current_effsize: float, 
+        is_paired: bool, 
+        one_sankey: bool,
+        reflines_kwargs: dict, 
+        redraw_axes_kwargs: dict
+    ):
+    """
+    Aesthetic adjustments specific to Gardner-Altman plots (float_contrast=True).
+    
+    Parameters
+    ----------
+    effect_size_type : str
+        The type of effect size.
+    plot_data : object (Dataframe)
+        A dataframe of plot data.
+    xvar : str
+        The name of the x-axis variable.
+    yvar : str
+        The name of the y-axis variable.
+    current_control : str
+        The name of the current control group.
+    current_group : str
+        The name of the current test group.
+    rawdata_axes : object (Axes)
+        The raw data axes.
+    contrast_axes : object (Axes)
+        The contrast axes.
+    results : object (DataFrame)
+        A dataframe of the results.
+    current_effsize : float
+        The current effect size.
+    is_paired : bool
+        A boolean flag to determine if the plot is for paired data.
+    one_sankey : bool
+        A boolean flag to determine if the plot is for a single sankey diagram.
+    reflines_kwargs : dict
+        Kwargs passed to the reference lines.
+    redraw_axes_kwargs : dict
+        Kwargs passed to the redraw axes.
+    """
+    from ._stats_tools.effsize import (
+        _compute_standardizers,
+        _compute_hedges_correction_factor,
+    )
+
+    og_xlim_raw, og_ylim_raw = rawdata_axes.get_xlim(), rawdata_axes.get_ylim()
+    
+    # Normalize ylims and despine the floating contrast axes.
+    # Check that the effect size is within the swarm ylims.
+    if effect_size_type in ["mean_diff", "cohens_d", "hedges_g", "cohens_h"]:
+        control_group_summary = (
+            plot_data.groupby(xvar, observed=False)
+            .mean(numeric_only=True)
+            .loc[current_control, yvar]
+        )
+        test_group_summary = (
+            plot_data.groupby(xvar, observed=False).mean(numeric_only=True).loc[current_group, yvar]
+        )
+    elif effect_size_type == "median_diff":
+        control_group_summary = (
+            plot_data.groupby(xvar, observed=False).median(numeric_only=True).loc[current_control, yvar]
+        )
+        test_group_summary = (
+            plot_data.groupby(xvar, observed=False).median(numeric_only=True).loc[current_group, yvar]
+        )
+
+    _, contrast_xlim_max = contrast_axes.get_xlim()
+
+    difference = float(results.difference[0])
+
+    if effect_size_type in ["mean_diff", "median_diff"]:
+        # Align 0 of contrast_axes to reference group mean of rawdata_axes.
+        # If the effect size is positive, shift the contrast axis up.
+        rawdata_ylims = np.array(rawdata_axes.get_ylim())
+        if current_effsize > 0:
+            rightmin, rightmax = rawdata_ylims - current_effsize
+        # If the effect size is negative, shift the contrast axis down.
+        elif current_effsize < 0:
+            rightmin, rightmax = rawdata_ylims + current_effsize
+        else:
+            rightmin, rightmax = rawdata_ylims
+
+        contrast_axes.set_ylim(rightmin, rightmax)
+
+        og_ylim_contrast = rawdata_axes.get_ylim() - np.array(control_group_summary)
+
+        contrast_axes.set_ylim(og_ylim_contrast)
+        contrast_axes.set_xlim(contrast_xlim_max - 1, contrast_xlim_max)
+
+    elif effect_size_type in ["cohens_d", "hedges_g", "cohens_h"]:
+
+        which_std = 1 if is_paired else 0   ############################ Unused line of code
+        temp_control = np.array(plot_data[plot_data[xvar] == current_control][yvar])
+        temp_test = np.array(plot_data[plot_data[xvar] == current_group][yvar])
+
+        stds = _compute_standardizers(temp_control, temp_test)
+        if is_paired:
+            pooled_sd = stds[1]
+        else:
+            pooled_sd = stds[0]
+
+        if effect_size_type == "hedges_g":
+            gby_count = plot_data.groupby(xvar, observed=False).count()
+            len_control = gby_count.loc[current_control, yvar]
+            len_test = gby_count.loc[current_group, yvar]
+
+            hg_correction_factor = _compute_hedges_correction_factor(
+                len_control, len_test
+            )
+
+            ylim_scale_factor = pooled_sd / hg_correction_factor
+
+        elif effect_size_type == "cohens_h":
+            ylim_scale_factor = (
+                np.mean(temp_test) - np.mean(temp_control)
+            ) / difference
+
+        else:
+            ylim_scale_factor = pooled_sd
+
+        scaled_ylim = (
+            (rawdata_axes.get_ylim() - control_group_summary) / ylim_scale_factor
+        ).tolist()
+
+        contrast_axes.set_ylim(scaled_ylim)
+        og_ylim_contrast = scaled_ylim
+
+        contrast_axes.set_xlim(contrast_xlim_max - 1, contrast_xlim_max)
+
+    if one_sankey is None:
+        # Draw summary lines for control and test groups..
+        for jj, axx in enumerate([rawdata_axes, contrast_axes]):
+            # Draw effect size line.
+            if jj == 0:
+                ref = control_group_summary
+                diff = test_group_summary
+                effsize_line_start = 1
+
+            elif jj == 1:
+                ref = 0
+                diff = ref + difference
+                effsize_line_start = contrast_xlim_max - 1.1
+
+            xlimlow, xlimhigh = axx.get_xlim()
+
+            # Draw reference line.
+            axx.hlines(
+                ref,  # y-coordinates
+                0,
+                xlimhigh,  # x-coordinates, start and end.
+                **reflines_kwargs
+            )
+
+            # Draw effect size line.
+            axx.hlines(diff, effsize_line_start, xlimhigh, **reflines_kwargs)
+    else:
+        ref = 0
+        diff = ref + difference
+        effsize_line_start = contrast_xlim_max - 0.9
+        xlimlow, xlimhigh = contrast_axes.get_xlim()
+        # Draw reference line.
+        contrast_axes.hlines(
+            ref,  # y-coordinates
+            effsize_line_start,
+            xlimhigh,  # x-coordinates, start and end.
+            **reflines_kwargs
+        )
+
+        # Draw effect size line.
+        contrast_axes.hlines(diff, effsize_line_start, xlimhigh, **reflines_kwargs)
+    rawdata_axes.set_xlim(og_xlim_raw)  # to align the axis
+    # Despine appropriately.
+    sns.despine(ax=rawdata_axes, bottom=True)
+    sns.despine(ax=contrast_axes, left=True, right=False)
+
+    # Insert break between the rawdata axes and the contrast axes
+    # by re-drawing the x-spine.
+    rawdata_axes.hlines(
+        og_ylim_raw[0],  # yindex
+        rawdata_axes.get_xlim()[0],
+        1.3,  # xmin, xmax
+        **redraw_axes_kwargs
+    )
+    rawdata_axes.set_ylim(og_ylim_raw)
+
+    contrast_axes.hlines(
+        contrast_axes.get_ylim()[0],
+        contrast_xlim_max - 0.8,
+        contrast_xlim_max,
+        **redraw_axes_kwargs
+    )
+
+def draw_zeroline(
+        ax : axes.Axes,
+        horizontal : bool,
+        reflines_kwargs : dict,
+        extra_delta : bool,
+    ):
+    """
+    Draw the independent axis spine lines.
+
+    Parameters
+    ----------
+    ax : object (Axes)
+        The contrast data axes.
+    horizontal : bool
+        A boolean flag to determine if the plot is for horizontal plotting.
+    reflines_kwargs : dict
+        Additional keyword arguments to be passed to the zeroline.
+    extra_delta : bool
+        A boolean flag to determine if the plot includes an extra delta (delta-delta or mini-meta).
+    """
+    # If 0 lies within the ylim of the contrast axes, draw a zero reference line.
+    if extra_delta and not horizontal:
+        contrast_xlim = [-0.5, 3.4]
+        delta2_xlim = [3.6, 4.75]
+        
+        if ax.get_ylim()[0] < ax.get_ylim()[1]:
+            contrast_lim_low, contrast_lim_high = ax.get_ylim()
+        else:
+            contrast_lim_high, contrast_lim_low = ax.get_ylim()
+
+        if contrast_lim_low < 0 < contrast_lim_high:
+            ax.hlines(y=0, xmin=contrast_xlim[0], xmax=contrast_xlim[1], **reflines_kwargs)
+            ax.hlines(y=0, xmin=delta2_xlim[0], xmax=delta2_xlim[1], **reflines_kwargs)
+    else:
+        ax_lim = ax.get_xlim() if horizontal else ax.get_ylim()
+        method = ax.axvline if horizontal else ax.axhline
+
+        if ax_lim[0] < ax_lim[1]:
+            contrast_lim_low, contrast_lim_high = ax_lim
+        else:
+            contrast_lim_high, contrast_lim_low = ax_lim
+
+        if contrast_lim_low < 0 < contrast_lim_high:
+            method(0, **reflines_kwargs)
+
+def redraw_independent_spines(
+        rawdata_axes : axes.Axes,
+        contrast_axes : axes.Axes,
+        horizontal : bool,
+        two_col_sankey : bool,
+        ticks_to_start_twocol_sankey : list,
+        idx : list,
+        is_paired : str,
+        show_pairs : bool,
+        proportional : bool,
+        ticks_to_skip : list,
+        temp_idx : list,
+        ticks_to_skip_contrast : list,
+        redraw_axes_kwargs : dict
+    ):
+    """
+    Draw the independent axis spine lines.
+
+    Parameters
+    ----------
+    rawdata_axes : object (Axes)
+        The raw data axes.
+    contrast_axes : object (Axes)
+        The contrast axes.
+    horizontal : bool
+        A boolean flag to determine if the plot is for horizontal plotting.
+    two_col_sankey : bool
+        A boolean flag to determine if the plot is for two-col sankey.
+    ticks_to_start_twocol_sankey : list
+        A list of ticks to start for sankey plot.
+    idx : list
+        A list of indices.
+    is_paired : bool
+        A boolean flag to determine if the data is paired.
+    show_pairs : bool
+        A boolean flag to determine if pairs should be shown.
+    proportional : bool
+        A boolean flag to determine if the plot is proportional/binary.
+    ticks_to_skip : list,
+        A list of ticks to be skipped in the raw data axes.
+    temp_idx : list,
+        A temporary list of indices to be used for skipping ticks in the raw data axes.
+    ticks_to_skip_contrast : list,
+        A list of ticks to be skipped in the contrast axes.
+    redraw_axes_kwargs : dict
+        Kwargs passed to the redraw axes.
+    """
+    # Extract the ticks
+    if two_col_sankey:
+        rightend_ticks_raw = rightend_ticks_contrast = np.array([len(i) - 2 for i in idx]) + np.array(ticks_to_start_twocol_sankey)
+        starting_ticks_raw = starting_ticks_contrast = ticks_to_start_twocol_sankey
+    else:
+        if is_paired == "baseline" and show_pairs:
+            if proportional and is_paired is not None:
+                rightend_ticks_raw = rightend_ticks_contrast = np.array([len(i) - 1 for i in idx]) + np.array(ticks_to_skip)
+            else:
+                rightend_ticks_raw = np.array([len(i) - 1 for i in temp_idx]) + np.array(ticks_to_skip)
+                temp_length = [(len(i) - 1) * 2 - 1 for i in idx] if proportional else [(len(i) - 1) for i in idx]
+                rightend_ticks_contrast = np.array(temp_length) + np.array(ticks_to_skip_contrast)
+            starting_ticks_raw, starting_ticks_contrast = ticks_to_skip, ticks_to_skip_contrast
+        else:
+            rightend_ticks_raw = rightend_ticks_contrast = np.array([len(i) - 1 for i in idx]) + np.array(ticks_to_skip)
+            starting_ticks_raw = starting_ticks_contrast = ticks_to_skip
+
+    # Plot the spines
+    if horizontal:
+        sns.despine(ax=rawdata_axes, left=True)
+        xlim, ylim = rawdata_axes.get_xlim(), rawdata_axes.get_ylim()
+        redraw_axes_kwargs["x"] = xlim[0]
+        for k, start_tick in enumerate(starting_ticks_raw):
+            end_tick = rightend_ticks_raw[k]
+            rawdata_axes.vlines(
+                    ymin = start_tick, 
+                    ymax = end_tick, 
+                    **redraw_axes_kwargs
+            )
+        rawdata_axes.set_xlim(xlim)
+        rawdata_axes.set_ylim(ylim)
+        del redraw_axes_kwargs["x"] 
+
+        # Remove y ticks and labels from the contrast axes.
+        sns.despine(ax=contrast_axes, left=True)
+        contrast_axes.set_yticks([])
+        contrast_axes.set_yticklabels([])
+    
+    else:
+        for ax, starting_ticks_current, rightend_ticks_current in zip(
+                [rawdata_axes, contrast_axes],
+                [starting_ticks_raw, starting_ticks_contrast],
+                [rightend_ticks_raw, rightend_ticks_contrast],
+            ):
+            sns.despine(ax=ax, bottom=True)
+            xlim, ylim = ax.get_xlim(), ax.get_ylim()
+            redraw_axes_kwargs["y"] = ylim[0]
+            for k, start_tick in enumerate(starting_ticks_current):
+                end_tick = rightend_ticks_current[k]
+                ax.hlines(
+                    xmin=start_tick, 
+                    xmax=end_tick, 
+                    **redraw_axes_kwargs
+                    )
+            ax.set_xlim(xlim)
+            ax.set_ylim(ylim)
+            del redraw_axes_kwargs["y"]
+            
+def redraw_dependent_spines(
+        rawdata_axes: axes.Axes, 
+        contrast_axes: axes.Axes, 
+        redraw_axes_kwargs: dict, 
+        float_contrast: bool, 
+        horizontal: bool,
+        show_delta2: bool, 
+        delta2_axes: axes.Axes
+    ):
+    """
+    Draw the dependent axis spine lines.
+
+    Parameters
+    ----------
+    rawdata_axes : object (Axes)
+        The raw data axes.
+    contrast_axes : object (Axes)
+        The contrast axes.
+    redraw_axes_kwargs : dict
+        Kwargs passed to the redraw axes.
+    float_contrast : bool
+        A boolean flag to determine if the plot is GA or Cum
+    horizontal : bool
+        A boolean flag to determine if the plot is for horizontal plotting.
+    show_delta2 : bool
+        A boolean flag to determine if the plot will have a delta-delta effect size.
+    delta2_axes : object (Axes)
+        The delta2 axes.
+    """
+
+    # Because we turned the axes frame off, we also need to draw back the x-spine for both axes.
+    og_xlim_raw, og_ylim_raw = rawdata_axes.get_xlim(), rawdata_axes.get_ylim()
+    og_xlim_contrast, og_ylim_contrast = contrast_axes.get_xlim(), contrast_axes.get_ylim()
+    if horizontal:
+        for current_ax, current_ylim, current_xlim in zip((rawdata_axes, contrast_axes), (og_ylim_raw, og_ylim_contrast), 
+                                                          (og_xlim_raw, og_xlim_contrast)):
+            current_ax.hlines(
+                current_ylim[0], 
+                current_xlim[0], 
+                current_xlim[1], 
+                **redraw_axes_kwargs
+            )    
+    else:
+        for current_ax, current_ylim, current_xlim in zip((rawdata_axes, contrast_axes), (og_ylim_raw, og_ylim_contrast), 
+                                                          (og_xlim_raw[0], og_xlim_contrast[1] if float_contrast else og_xlim_contrast[0])):
+            current_ax.vlines(
+                current_xlim, 
+                current_ylim[0], 
+                current_ylim[1], 
+                **redraw_axes_kwargs
+            )
+
+        if show_delta2:
+            og_xlim_delta, og_ylim_delta = contrast_axes.get_xlim(), contrast_axes.get_ylim()
+            delta2_axes.set_ylim(og_ylim_delta)
+            
+            delta2_axes.vlines(
+                og_xlim_delta[1], 
+                og_ylim_delta[0], 
+                og_ylim_delta[1], 
+                **redraw_axes_kwargs
+            )
+
+    for current_ax, xlim, ylim in zip([rawdata_axes, contrast_axes], [og_xlim_raw, og_xlim_contrast], [og_ylim_raw, og_ylim_contrast]):
+        current_ax.set_xlim(xlim)
+        current_ax.set_ylim(ylim)
+
+def extract_group_summaries(
+        proportional: bool, 
+        rawdata_axes: axes.Axes, 
+        asymmetric_side: str, 
+        horizontal: bool, 
+        bootstraps_color_by_group: bool, 
+        plot_palette_raw: list, 
+        all_plot_groups: list,
+        n_groups: int, 
+        color_col, 
+        ytick_color, 
+        group_summaries_kwargs: dict
+    ):
+    """
+    Extract the group summaries for the plotter function.
+
+    Parameters
+    ----------
+    proportional : bool
+        A boolean flag to determine if the plot is for proportional data.
+    rawdata_axes : object (Axes)
+        The raw data axes.
+    asymmetric_side : str
+        The side of the asymmetric error bars.
+    horizontal : bool
+        A boolean flag to determine if the plot is for horizontal plotting.
+    bootstraps_color_by_group : bool
+        A boolean flag to determine if the bootstraps are colored by group.
+    plot_palette_raw : list
+        A list of the plot palette colors.
+    all_plot_groups : list
+        A list of all the plot groups.
+    n_groups : int
+        The number of groups.
+    color_col : str
+        The name of the color column.
+    ytick_color : str
+        The color of the y-ticks.
+    group_summaries_kwargs : dict
+        Kwargs passed to the group summaries.
+    """
+    
+    from .plot_tools import get_swarm_spans
+
+    if proportional:
+        group_summaries_method = "proportional_error_bar"
+        group_summaries_offset = 0
+        group_summaries_line_color = "black"
+    else:
+        # Create list to gather xspans.
+        xspans = []
+        line_colors = []
+        for jj, c in enumerate(rawdata_axes.collections):
+            try:
+                if asymmetric_side == "right":
+                    # currently offset is hardcoded with value of -0.2
+                    x_max_span = -0.2
+                else:
+                    if horizontal:
+                        x_max_span = 0.1 # currently offset is hardcoded with value of 0.1
+                    else:
+                        _, x_max, _, _ = get_swarm_spans(c)
+                        x_max_span = x_max - jj
+                xspans.append(x_max_span)
+            except TypeError:
+                # we have got a None, so skip and move on.
+                pass
+
+            if bootstraps_color_by_group:
+                line_colors.append(plot_palette_raw[all_plot_groups[jj]])
+
+            # Break the loop since hue in Seaborn adds collections to axes and it will result in index out of range
+            if jj >= n_groups - 1 and color_col is None:
+                break
+
+        if len(line_colors) != len(all_plot_groups):
+            line_colors = ytick_color
+            
+        # hue in swarmplot would add collections to axes which will result in len(xspans) = len(all_plot_groups) + len(unique groups in hue)
+        if len(xspans) > len(all_plot_groups):
+            xspans = xspans[:len(all_plot_groups)]
+
+        group_summaries_method = "gapped_lines"
+        group_summaries_offset = xspans + np.array(group_summaries_kwargs["offset"])
+        group_summaries_line_color = line_colors
+
+    if group_summaries_kwargs['color'] is not None:
+        group_summaries_line_color = group_summaries_kwargs.pop("color")
+    group_summaries_kwargs.pop("offset")
+
+    return group_summaries_method, group_summaries_offset, group_summaries_line_color
+
+def color_picker(color_type: str,
+                 kwargs: dict, 
+                 elements: list, 
+                 color_col: str, 
+                 show_pairs: bool, 
+                 color_palette: dict) -> list:
+    num_of_elements = len(elements)
+    colors = (
+        [kwargs.pop('color')] * num_of_elements
+        if kwargs.get('color', None) is not None
+        else ['black'] * num_of_elements
+        if color_col is not None or show_pairs 
+        else list(color_palette.values())
+    )
+    if color_type in ['contrast', 'summary', 'delta_text']:
+        if len(colors) == num_of_elements:
+            final_colors = colors
+        else:
+            final_colors = []
+            for tick in elements:
+                final_colors.append(colors[int(tick)])
+    else:
+        final_colors = colors
+    return final_colors
+
+
+def prepare_bars_for_plot(bar_type, bar_kwargs, horizontal, plot_palette_raw, color_col, show_pairs,
+                          plot_data = None, xvar = None, yvar = None,  # Raw data
+                          results = None, ticks_to_plot = None, extra_delta = None, # Contrast data
+                          reference_band = None, summary_axes = None, ci_type = None  # Summary data
+                          ):
+    from .misc_tools import color_picker
+    bar_dict = {}
+    if bar_type in ['raw', 'contrast']:
+        if bar_type == 'raw':
+            if isinstance(plot_data[xvar].dtype, pd.CategoricalDtype):
+                order = pd.unique(plot_data[xvar]).categories
+            else:
+                order = pd.unique(plot_data[xvar])
+            means = plot_data.groupby(xvar, observed=False)[yvar].mean().reindex(index=order).values
+            ticks = list(range(len(order)))
+        elif bar_type == 'contrast':
+            means = results.difference.to_list()
+            ticks = ticks_to_plot.copy()
+            if extra_delta is not None:
+                ticks.append(ticks[-1]+1)  # Add an extra tick
+                means.append(extra_delta)
+
+        num_of_bars = len(means)
+        y_start_values, y_distances = [0]*num_of_bars, means
+        x_start_values, x_distances = [num - (0.5 if horizontal else 0.25) for num in ticks], [0.5,]*num_of_bars
+
+    elif bar_type == 'summary':
+        # Begin checks        
+        if not isinstance(reference_band, list):
+            raise TypeError("reference_band must be a list of indices (ints).")
+        if not all(isinstance(i, int) for i in reference_band):
+            raise TypeError("reference_band must be a list of indices (ints).")
+        if any(i >= len(results) for i in reference_band):
+            raise ValueError("Index {} chosen is out of range for the contrast objects.".format([i for i in reference_band if i >= len(results)]))
+
+        ticks = [ticks_to_plot[tick] for tick in reference_band]
+        summary_xmin, summary_xmax = summary_axes.get_xlim()
+        summary_ymin, summary_ymax = summary_axes.get_ylim()
+        span_ax = bar_kwargs.pop("span_ax")
+
+        x_start_values, y_start_values, x_distances, y_distances = [], [], [], []
+        for summary_index in reference_band:
+            summary_ci_low = results.get(ci_type+'_low')[summary_index]
+            summary_ci_high = results.get(ci_type+'_high')[summary_index]   
+
+            if span_ax == True:
+                starting_location = summary_ymax if horizontal else summary_xmin
+            else:
+                starting_location = ticks_to_plot[summary_index]  
+            x_distance = summary_ymin if horizontal else summary_xmax 
+
+            x_start_values.append(starting_location)
+            y_start_values.append(summary_ci_low)
+            x_distances.append(x_distance + 1)
+            y_distances.append(summary_ci_high - summary_ci_low)
+    else:
+        raise ValueError("Invalid bar_type. Must be 'raw' or 'contrast'.")
+    
+    if horizontal:
+        x_start_values, y_start_values = y_start_values, x_start_values
+        x_distances, y_distances = y_distances, x_distances
+
+    for name, values in zip(['x_start_values', 'x_distances', 'y_start_values', 'y_distance'],
+                            [x_start_values, x_distances, y_start_values, y_distances]
+                        ):
+        bar_dict[name] = values
+
+    # Colors
+    colors = color_picker(
+                color_type = bar_type,
+                kwargs = bar_kwargs, 
+                elements = ticks_to_plot if bar_type=='contrast' else ticks, 
+                color_col = color_col, 
+                show_pairs = show_pairs, 
+                color_palette = plot_palette_raw
+            )
+    if bar_type == 'contrast' and extra_delta is not None:
+        colors.append('black')
+    bar_dict['colors'] = colors
+
+    return bar_dict, bar_kwargs
diff --git a/dabest/plot_tools.py b/dabest/plot_tools.py
index 65fea009..b6872058 100644
--- a/dabest/plot_tools.py
+++ b/dabest/plot_tools.py
@@ -1,3 +1,5 @@
+"""A set of convenience functions used for producing plots in `dabest`."""
+
 # AUTOGENERATED! DO NOT EDIT! File to edit: ../nbs/API/plot_tools.ipynb.
 
 # %% ../nbs/API/plot_tools.ipynb 2
@@ -5,7 +7,9 @@
 
 # %% auto 0
 __all__ = ['halfviolin', 'get_swarm_spans', 'error_bar', 'check_data_matches_labels', 'normalize_dict', 'width_determine',
-           'single_sankey', 'sankeydiag', 'swarmplot', 'SwarmPlot']
+           'single_sankey', 'sankeydiag', 'add_bars_to_plot', 'delta_text_plotter', 'delta_dots_plotter',
+           'slopegraph_plotter', 'plot_minimeta_or_deltadelta_violins', 'effect_size_curve_plotter', 'gridkey_plotter',
+           'barplotter', 'table_for_horizontal_plots', 'add_counts_to_prop_plots', 'swarmplot', 'SwarmPlot']
 
 # %% ../nbs/API/plot_tools.ipynb 4
 import math
@@ -17,6 +21,7 @@
 import matplotlib.pyplot as plt
 import matplotlib.lines as mlines
 import matplotlib.axes as axes
+import matplotlib.patches as mpatches
 from collections import defaultdict
 from typing import List, Tuple, Dict, Iterable, Union
 from pandas.api.types import CategoricalDtype
@@ -75,6 +80,7 @@ def error_bar(
         1,
     ],  # The positions of the error bars for the sankey_error_bar method.
     method: str = "gapped_lines",  # The method to use for drawing the error bars. Options are: 'gapped_lines', 'proportional_error_bar', and 'sankey_error_bar'.
+    horizontal: bool = False,  # If True, the error bars will be horizontal. If False, the error bars will be vertical.
     **kwargs: dict,
 ):
     """
@@ -96,7 +102,11 @@ def error_bar(
 
     if ax is None:
         ax = plt.gca()
-    ax_ylims = ax.get_ylim()
+
+    if horizontal:
+        ax_ylims = ax.get_xlim()
+    else:
+        ax_ylims = ax.get_ylim()
     ax_yspan = np.abs(ax_ylims[1] - ax_ylims[0])
     gap_width = ax_yspan * gap_width_percent / 100
 
@@ -115,15 +125,15 @@ def error_bar(
     else:
         group_order = pd.unique(data[x])
 
-    means = data.groupby(x)[y].mean().reindex(index=group_order)
+    means = data.groupby(x, observed=False)[y].mean().reindex(index=group_order)
 
     if method in ["proportional_error_bar", "sankey_error_bar"]:
         g = lambda x: np.sqrt(
             (np.sum(x) * (len(x) - np.sum(x))) / (len(x) * len(x) * len(x))
         )
-        sd = data.groupby(x)[y].apply(g)
+        sd = data.groupby(x, observed=False)[y].apply(g)
     else:
-        sd = data.groupby(x)[y].std().reindex(index=group_order)
+        sd = data.groupby(x, observed=False)[y].std().reindex(index=group_order)
 
     lower_sd = means - sd
     upper_sd = means + sd
@@ -131,9 +141,9 @@ def error_bar(
     if (lower_sd < ax_ylims[0]).any() or (upper_sd > ax_ylims[1]).any():
         kwargs["clip_on"] = True
 
-    medians = data.groupby(x)[y].median().reindex(index=group_order)
+    medians = data.groupby(x, observed=False)[y].median().reindex(index=group_order)
     quantiles = (
-        data.groupby(x)[y].quantile([0.25, 0.75]).unstack().reindex(index=group_order)
+        data.groupby(x, observed=False)[y].quantile([0.25, 0.75]).unstack().reindex(index=group_order)
     )
     lower_quartiles = quantiles[0.25]
     upper_quartiles = quantiles[0.75]
@@ -173,7 +183,8 @@ def error_bar(
 
     kwargs["zorder"] = kwargs["zorder"]
 
-    for xpos, central_measure in enumerate(central_measures):
+    for xpos, val in enumerate(central_measures.index):
+        central_measure = central_measures[val]
         kwargs["color"] = custom_palette[xpos]
 
         if method == "sankey_error_bar":
@@ -181,30 +192,36 @@ def error_bar(
         else:
             _xpos = xpos + offset[xpos]
 
-        low = lows[xpos]
-        high = highs[xpos]
-        if low == high == central_measure:
-            low_to_mean = mlines.Line2D(
-                [_xpos, _xpos], [low, central_measure], **kwargs
-            )
-            ax.add_line(low_to_mean)
+        # Fix for the non-string x-axis issue #108
+        if central_measures.index.dtype.name == "category":
+            low = lows[xpos]
+            high = highs[xpos]
+        else: 
+            low = lows[val]
+            high = highs[val]
 
-            mean_to_high = mlines.Line2D(
-                [_xpos, _xpos], [central_measure, high], **kwargs
-            )
-            ax.add_line(mean_to_high)
+        if low == high == central_measure:
+            if horizontal:
+                low2mean_x, low2mean_y = [low, central_measure], [_xpos, _xpos]
+                mean2high_x, mean2high_y = [central_measure, high], [_xpos, _xpos]
+            else:
+                low2mean_x, low2mean_y = [_xpos, _xpos], [low, central_measure]
+                mean2high_x, mean2high_y = [_xpos, _xpos], [central_measure, high]
         else:
-            low_to_mean = mlines.Line2D(
-                [_xpos, _xpos], [low, central_measure - gap_width], **kwargs
-            )
-            ax.add_line(low_to_mean)
-
-            mean_to_high = mlines.Line2D(
-                [_xpos, _xpos], [central_measure + gap_width, high], **kwargs
-            )
-            ax.add_line(mean_to_high)
-
-
+            if horizontal:
+                low2mean_x, low2mean_y = [low, central_measure - gap_width], [_xpos, _xpos]
+                mean2high_x, mean2high_y = [central_measure + gap_width, high], [_xpos, _xpos]
+            else:
+                low2mean_x, low2mean_y = [_xpos, _xpos], [low, central_measure - gap_width]
+                mean2high_x, mean2high_y = [_xpos, _xpos], [central_measure + gap_width, high]
+        # Add lines
+        ax.add_line(mlines.Line2D(
+                    low2mean_x, low2mean_y, **kwargs
+                ))
+        ax.add_line(mlines.Line2D(
+                    mean2high_x, mean2high_y, **kwargs
+                ))
+        
 def check_data_matches_labels(
     labels,  # list of input labels
     data,  # Pandas Series of input data
@@ -353,11 +370,14 @@ def single_sankey(
     strip_on: bool = True,  # if True, draw strip for each group comparison
     one_sankey: bool = False,  # if True, only draw one sankey diagram
     right_color: bool = False,  # if True, each strip of the diagram will be colored according to the corresponding left labels
-    align: bool = "center",  # if 'center', the diagram will be centered on each xtick,  if 'edge', the diagram will be aligned with the left edge of each xtick
+    align: str = "center",  # if 'center', the diagram will be centered on each xtick,  if 'edge', the diagram will be aligned with the left edge of each xtick
+    horizontal: bool = False,  # if True, the horizontal format for the sankey diagram will be used
 ):
     """
     Make a single Sankey diagram showing proportion flow from left to right
+
     Original code from: https://github.com/anazalea/pySankey
+    
     Changes are added to normalize each diagram's height to be 1
 
     """
@@ -434,6 +454,7 @@ def single_sankey(
     if align not in ("center", "edge"):
         err = "{} assigned for `align` is not valid.".format(align)
         raise ValueError(err)
+    
     if align == "center":
         try:
             leftpos = xpos - width / 2
@@ -513,16 +534,24 @@ def single_sankey(
 
     # Plot vertical bars for each label
     for left_label in left_labels:
-        ax.fill_between(
-            [leftpos + (-(bar_width) * xMax * 0.5), leftpos + (bar_width * xMax * 0.5)],
-            2 * [leftWidths_norm[left_label]["bottom"]],
-            2 * [leftWidths_norm[left_label]["top"]],
-            color=colorDict[left_label],
-            alpha=0.99,
-        )
+        if horizontal:
+            fill_method = ax.fill_betweenx
+        else:
+            fill_method = ax.fill_between
+        fill_method(
+                [leftpos + (-(bar_width) * xMax * 0.5), leftpos + (bar_width * xMax * 0.5)],
+                2 * [leftWidths_norm[left_label]["bottom"]],
+                2 * [leftWidths_norm[left_label]["top"]],
+                color=colorDict[left_label],
+                alpha=0.99,
+            )
     if (not flow and sankey) or one_sankey:
         for right_label in right_labels:
-            ax.fill_between(
+            if horizontal:
+                fill_method = ax.fill_betweenx
+            else:
+                fill_method = ax.fill_between
+            fill_method(
                 [
                     xMax + leftpos + (-bar_width * xMax * 0.5),
                     leftpos + xMax + (bar_width * xMax * 0.5),
@@ -535,16 +564,29 @@ def single_sankey(
 
     # Plot error bars
     if error_bar_on and strip_on:
-        error_bar(
-            concatenated_df,
-            x="groups",
-            y="values",
-            ax=ax,
-            offset=0,
-            gap_width_percent=2,
-            method="sankey_error_bar",
-            pos=[leftpos, leftpos + xMax],
-        )
+        if horizontal:
+            error_bar(
+                concatenated_df,
+                x="groups",
+                y="values",
+                ax=ax,
+                offset=0,
+                gap_width_percent=2,
+                method="sankey_error_bar",
+                pos=[leftpos, leftpos + xMax],
+                horizontal=True,
+            )
+        else:
+            error_bar(
+                concatenated_df,
+                x="groups",
+                y="values",
+                ax=ax,
+                offset=0,
+                gap_width_percent=2,
+                method="sankey_error_bar",
+                pos=[leftpos, leftpos + xMax],
+            )
 
     # Determine widths of individual strips, all widths are normalized to 1
     ns_l = defaultdict()
@@ -602,7 +644,11 @@ def single_sankey(
                 rightWidths_norm[right_label]["bottom"] += ns_r_norm[left_label][
                     right_label
                 ]
-                ax.fill_between(
+                if horizontal:
+                    fill_method = ax.fill_betweenx
+                else:
+                    fill_method = ax.fill_between
+                fill_method(
                     np.linspace(
                         leftpos + (bar_width * xMax * 0.5),
                         leftpos + xMax - (bar_width * xMax * 0.5),
@@ -615,13 +661,13 @@ def single_sankey(
                     edgecolor="none",
                 )
 
-
 def sankeydiag(
     data: pd.DataFrame,
     xvar: str,  # x column to be plotted.
     yvar: str,  # y column to be plotted.
-    left_idx: str,  # the value in column xvar that is on the left side of each sankey diagram
-    right_idx: str,  # the value in column xvar that is on the right side of each sankey diagram, if len(left_idx) == 1, it will be broadcasted to the same length as right_idx, otherwise it should have the same length as right_idx
+    temp_all_plot_groups: list,
+    idx: list,
+    temp_idx: list,
     left_labels: list = None,  # labels for the left side of the diagram. The diagram will be sorted by these labels.
     right_labels: list = None,  # labels for the right side of the diagram. The diagram will be sorted by these labels.
     palette: str | dict = None,
@@ -633,6 +679,7 @@ def sankeydiag(
     right_color: bool = False,  # if True, each strip of the diagram will be colored according to the corresponding left labels
     align: str = "center",  # the alignment of each sankey diagram, can be 'center' or 'left'
     alpha: float = 0.65,  # the transparency of each strip
+    horizontal: bool = False,  # if True, the horizontal format for the sankey diagram will be used
     **kwargs,
 ):
     """
@@ -642,7 +689,6 @@ def sankeydiag(
     right_idx in the column xvar is on the right side of each sankey diagram
 
     """
-
     if "width" in kwargs:
         width = kwargs["width"]
 
@@ -664,9 +710,38 @@ def sankeydiag(
     if "flow" in kwargs:
         flow = kwargs["flow"]
 
+    fontsize = kwargs.pop("fontsize")
+
     if ax is None:
         ax = plt.gca()
 
+    left_idx = []
+    right_idx = []
+    # Design for Sankey Flow Diagram
+    sankey_idx = (
+    [
+        (control, test)
+        for i in idx
+        for control, test in zip(
+            i[:],
+            (tuple(i[1:]) + (i[0],)) if isinstance(i, tuple) else (list(i[1:]) + [i[0]])
+        )
+    ]
+    if flow
+    else temp_idx   
+    )
+
+    for i in sankey_idx:
+        left_idx.append(i[0])
+        right_idx.append(i[1])
+
+    if len(temp_all_plot_groups) == 2:
+        one_sankey = True
+        left_idx.pop()
+        right_idx.pop()  # Remove the last element from two lists
+
+    # two_col_sankey = True if proportional == True and one_sankey == False and sankey == True and flow == False else False
+
     allLabels = pd.Series(np.sort(data[yvar].unique())[::-1]).unique()
 
     # Check if all the elements in left_idx and right_idx are in xvar column
@@ -736,6 +811,7 @@ def sankeydiag(
                 flow=flow,
                 align=align,
                 alpha=alpha,
+                horizontal=horizontal,
             )
             xpos += 1
         else:
@@ -759,31 +835,1237 @@ def sankeydiag(
                 flow=False,
                 align="edge",
                 alpha=alpha,
+                horizontal=horizontal,
             )
 
     # Now only draw vs xticks for two-column sankey diagram
+
     if not one_sankey or (sankey and not flow):
-        sankey_ticks = (
+        sankey_tick_vals = (
             [f"{left}" for left in broadcasted_left]
             if flow
-            else [
-                f"{left}\n v.s.\n{right}"
+            else [f"{left} v.s. {right}" if horizontal
+                else f"{left}\n v.s.\n{right}"
                 for left, right in zip(broadcasted_left, right_idx)
             ]
         )
-        ax.get_xaxis().set_ticks(np.arange(len(right_idx)))
-        ax.get_xaxis().set_ticklabels(sankey_ticks)
+        sankey_tick_locs = np.arange(len(right_idx))
+    else:
+        sankey_tick_vals, sankey_tick_locs = [broadcasted_left[0], right_idx[0]], [0, 1]
+
+    if horizontal:
+        ax.set_yticks(sankey_tick_locs)
+        ax.set_yticklabels(sankey_tick_vals, fontsize = fontsize)
+    else:
+        ax.set_xticks(sankey_tick_locs)
+        ax.set_xticklabels(sankey_tick_vals, fontsize = fontsize)
+
+    return (left_idx, right_idx)
+
+def add_bars_to_plot(bar_dict: dict, ax: axes.Axes, bar_kwargs: dict):
+    """
+    Add bars to the relevant axes.
+
+    Parameters
+    ----------
+    bar_dict : dict
+        Dictionary of bar values.
+    ax : axes.Axes
+        Matplotlib axis object to plot on.
+    bar_kwargs : dict
+        Keyword arguments for the bars.
+    """
+    og_xlim, og_ylim = ax.get_xlim(), ax.get_ylim()
+
+    x_start_values, x_distances, y_start_values, y_distances, colors = bar_dict.values()
+
+    for start_x, start_y, distance_x, distance_y, current_color in zip(
+                                                                        x_start_values, 
+                                                                        y_start_values, 
+                                                                        x_distances, 
+                                                                        y_distances, 
+                                                                        colors
+                                                                    ):
+        ax.add_patch(mpatches.Rectangle((start_x, start_y), 
+                                        distance_x, distance_y, 
+                                        color=current_color, **bar_kwargs
+                                        )
+                    )
+    ax.set_xlim(og_xlim)
+    ax.set_ylim(og_ylim) 
+
+def delta_text_plotter(
+        results: pd.DataFrame, 
+        ax_to_plot: object, 
+        ticks_to_plot: list, 
+        delta_text_kwargs: dict, 
+        color_col: str, 
+        plot_palette_raw: dict, 
+        show_pairs: bool,
+        float_contrast: bool,
+        extra_delta: float,
+    ):
+    """
+    Add delta text to the contrast plot.
+
+    Parameters
+    ----------
+    results : object (Dataframe)
+        Dataframe of contrast object comparisons.
+    ax_to_plot : axes.Axes
+        Matplotlib axis object to plot on.
+    ticks_to_plot : list
+        List of indices of the contrast objects.
+    delta_text_kwargs : dict
+        Keyword arguments for the delta text.
+    color_col : str
+        Column name of the color column.
+    plot_palette_raw : dict
+        Dictionary of colors used in the plot.
+    show_pairs : bool
+        Whether the data is paired and show pairs.
+    float_contrast : bool
+        Whether the DABEST plot uses Gardner-Altman or Cummings.
+    extra_delta : float or None
+        The extra mini-meta or delta-delta value if applicable.
+    """
+    # Colors
+    from .misc_tools import color_picker
+    delta_text_colors = color_picker(color_type = 'delta_text',
+                                     kwargs = delta_text_kwargs, 
+                                     elements = ticks_to_plot, 
+                                     color_col = color_col, 
+                                     show_pairs = show_pairs, 
+                                     color_palette = plot_palette_raw
+                                    )
+
+    num_of_elements = len(ticks_to_plot) + 1 if extra_delta is not None else len(ticks_to_plot)
+
+    # Collect the means for the delta text
+    delta_values = []
+    for j, tick in enumerate(ticks_to_plot):
+        delta_values.append(results.difference[int(j)])
+    if extra_delta is not None: 
+        delta_values.append(extra_delta)
+        delta_text_colors.append('black')
+
+    # Collect the X-coordinates for the delta text
+    delta_text_x_coordinates = delta_text_kwargs.pop('x_coordinates')
+    delta_text_x_offset = delta_text_kwargs.pop('offset')
+
+    if delta_text_x_coordinates is not None:
+        if not isinstance(delta_text_x_coordinates, (list, tuple)) or not all(isinstance(x, (int, float)) for x in delta_text_x_coordinates):
+            raise TypeError("delta_text_kwargs['x_coordinates'] must be a list of x-coordinates.")
+        if len(delta_text_x_coordinates) != num_of_elements:
+            raise ValueError("delta_text_kwargs['x_coordinates'] must have the same length as the number of ticks to plot.")
+    else:
+        x_adjust = (-0.4 if float_contrast else 0.48) + delta_text_x_offset
+        delta_text_x_coordinates = [x+x_adjust for x in ticks_to_plot]
+        if extra_delta is not None: delta_text_x_coordinates.append(max(ticks_to_plot)+1+x_adjust)
+
+    # Collect the Y-coordinates for the delta text
+    delta_text_y_coordinates = delta_text_kwargs.pop('y_coordinates')
+    if float_contrast: delta_text_kwargs["va"] = 'bottom' if results.difference[0] >= 0 else 'top'
+
+    if delta_text_y_coordinates is not None:
+        if not isinstance(delta_text_y_coordinates, (list, tuple)) or not all(isinstance(y, (int, float)) for y in delta_text_y_coordinates):
+            raise TypeError("delta_text_kwargs['y_coordinates'] must be a list of y-coordinates.")
+        if len(delta_text_y_coordinates) != num_of_elements:
+            raise ValueError("delta_text_kwargs['y_coordinates'] must have the same length as the number of ticks to plot.")
+    else:
+        delta_text_y_coordinates = delta_values
+
+    # Plot the delta text
+    for x, y, text, color in zip(delta_text_x_coordinates, delta_text_y_coordinates, delta_values, delta_text_colors):
+        delta_text = np.format_float_positional(text, precision=2, sign=True, trim="k", min_digits=2)
+        ax_to_plot.text(x, y, delta_text, color=color, zorder=5, **delta_text_kwargs)
+
+def delta_dots_plotter(
+        plot_data: pd.DataFrame, 
+        contrast_axes: axes.Axes, 
+        delta_id_col: str, 
+        idx: list, 
+        xvar: str, 
+        yvar: str, 
+        is_paired: bool, 
+        color_col: str, 
+        float_contrast: bool, 
+        plot_palette_raw: dict, 
+        delta_dot_kwargs: dict, 
+        horizontal: bool
+    ):
+    """
+    Parameters
+    ----------
+    plot_data : object (Dataframe)
+        Dataframe of the plot data.
+    contrast_axes : axes.Axes
+        Matplotlib axis object to plot on.
+    delta_id_col : str
+        Column name of the delta id column.
+    idx : list
+        List of indices of the contrast objects.
+    xvar : str
+        Column name of the x variable.
+    yvar : str
+        Column name of the y variable.
+    is_paired : bool
+        Whether the data is paired.
+    color_col : str
+        Column name of the color column.
+    float_contrast : bool
+        Whether the DABEST plot uses Gardner-Altman or Cummings
+    plot_palette_raw : dict
+        Dictionary of colors used in the plot.
+    delta_dot_kwargs : dict
+        Keyword arguments for the delta dots.
+    horizontal : bool
+        If the rawplot is horizontal.
+    """
+
+    # Checks and initializations
+    # from .plot_tools import swarmplot
+    delta_dot_color = delta_dot_kwargs.pop('color')
+    if color_col is not None:
+        plot_palette_deltapts = plot_palette_raw
+        delta_plot_data = plot_data[[xvar, yvar, delta_id_col, color_col]]
+    else:
+        plot_palette_deltapts = delta_dot_color
+        delta_plot_data = plot_data[[xvar, yvar, delta_id_col]]
+
+    # TODO: to make jitter value more accurate and not just a hardcoded eyeball value
+    jitter = 0.6 if float_contrast else 1 
+
+    # Create dataframe of delta values
+    final_deltas = pd.DataFrame()
+    for i in idx:
+        for j in i:
+            if i.index(j) != 0:
+                temp_df_exp = delta_plot_data[
+                    delta_plot_data[xvar].str.contains(j)
+                ].reset_index(drop=True)
+                if is_paired == "baseline":
+                    temp_df_cont = delta_plot_data[
+                        delta_plot_data[xvar].str.contains(i[0])
+                    ].reset_index(drop=True)
+                elif is_paired == "sequential":
+                    temp_df_cont = delta_plot_data[
+                        delta_plot_data[xvar].str.contains(
+                            i[i.index(j) - 1]
+                        )
+                    ].reset_index(drop=True)
+                delta_df = temp_df_exp.copy()
+                delta_df[yvar] = temp_df_exp[yvar] - temp_df_cont[yvar]
+                final_deltas = pd.concat([final_deltas, delta_df])
+
+    if horizontal:
+        delta_dot_kwargs.update({'side': 'left'})
+    # Plot the delta dots
+    swarmplot(
+        data=final_deltas,
+        x=xvar,
+        y=yvar,
+        ax=contrast_axes,
+        order=None,
+        hue=color_col,
+        palette=plot_palette_deltapts,
+        jitter=jitter,
+        is_drop_gutter=True,
+        gutter_limit=1,
+        horizontal=horizontal,
+        **delta_dot_kwargs
+    )
+    contrast_axes.legend().set_visible(False)
+
+
+def slopegraph_plotter(
+        dabest_obj: object, 
+        plot_data: pd.DataFrame, 
+        xvar: str, 
+        yvar: str, 
+        color_col: str, 
+        plot_palette_raw: dict, 
+        slopegraph_kwargs: dict, 
+        rawdata_axes: axes.Axes, 
+        ytick_color: str, 
+        temp_idx: list, 
+        horizontal: bool,
+        temp_all_plot_groups: list,
+        plot_kwargs: dict
+    ):
+    """
+    Add slopegraph to the rawdata axes.
+
+    Parameters
+    ----------
+    dabest_obj : object
+        DABEST object.
+    plot_data : object (Dataframe)
+        Dataframe of the plot data.
+    xvar : str
+        Column name of the x variable.
+    yvar : str
+        Column name of the y variable.
+    color_col : str
+        Column name of the color column.
+    plot_palette_raw : dict
+        Dictionary of colors used in the plot.
+    slopegraph_kwargs : dict
+        Keyword arguments for the slopegraph.
+    rawdata_axes : axes.Axes
+        Matplotlib axis object to plot on.
+    ytick_color : str
+        Color of the yticks.
+    temp_idx : list
+        List of indices of the contrast objects.
+    horizontal : bool
+        If the plotting will be in horizontal format.
+    temp_all_plot_groups : list
+        List of all plot groups.
+    plot_kwargs : dict
+        Keyword arguments for the plot.
+
+    """
+    # Jitter Kwargs 
+    # With help from GitHub user: devMJBL
+    jitter = slopegraph_kwargs.pop("jitter")
+    if jitter > 1:
+        err0 = "Jitter value is too high. Defaulting to 1."
+        warnings.warn(err0)
+        jitter = 1
+    rng = np.random.default_rng(slopegraph_kwargs.pop("jitter_seed"))
+
+    # Pivot the long (melted) data.
+    if color_col is None:
+        pivot_values = [yvar]
+    else:
+        pivot_values = [yvar, color_col]
+    pivoted_plot_data = pd.pivot(
+        data=plot_data,
+        index=dabest_obj.id_col,
+        columns=xvar,
+        values=pivot_values,
+    )
+
+    x_start = 0
+    for ii, current_tuple in enumerate(temp_idx):
+        current_pair = pivoted_plot_data.loc[
+            :, pd.MultiIndex.from_product([pivot_values, current_tuple])
+        ].dropna()
+
+        # Check for correct pairing
+        if len(current_pair) == 0:
+            raise ValueError('There are no pairs to plot... check original dataframe for correct ID pairing')
+
+        current_pair = pivoted_plot_data.loc[
+            :, pd.MultiIndex.from_product([pivot_values, current_tuple])
+        ]
+        grp_count = len(current_tuple)
+
+        # Iterate through the data for the current tuple.
+        for ID, observation in current_pair.iterrows():
+            x_points = [t + 0.15*jitter*rng.standard_t(df=6, size=None) for t in range(x_start, x_start + grp_count)] # devMJBL
+            y_points = observation[yvar].tolist()
+
+            if color_col is None:
+                slopegraph_kwargs["color"] = ytick_color
+            else:
+                color_key = observation[color_col].iloc[0]
+                if isinstance(color_key, (str, np.int64, np.float64)):
+                    slopegraph_kwargs["color"] = plot_palette_raw[color_key]
+                    slopegraph_kwargs["label"] = color_key
+
+            x_points, y_points = (y_points, x_points) if horizontal else (x_points, y_points)
+            rawdata_axes.plot(x_points, y_points, **slopegraph_kwargs)
+
+        x_start = x_start + grp_count
+
+    # Set the tick labels, because the slopegraph plotting doesn't.
+    if horizontal:
+        rawdata_axes.set_yticks(np.arange(0, len(temp_all_plot_groups)))
+        rawdata_axes.set_yticklabels(temp_all_plot_groups, fontsize = plot_kwargs.get("fontsize_rawxlabel"))
+    else:
+        rawdata_axes.set_xticks(np.arange(0, len(temp_all_plot_groups)))
+        rawdata_axes.set_xticklabels(temp_all_plot_groups, fontsize = plot_kwargs.get("fontsize_rawxlabel"))
+    
+
+def plot_minimeta_or_deltadelta_violins(
+        dabest_obj: object,
+        type: str,
+        ci_type: str, 
+        rawdata_axes: axes.Axes,
+        contrast_axes: axes.Axes, 
+        contrast_kwargs: dict, 
+        contrast_xtick_labels: list, 
+        effect_size: str, 
+        plot_kwargs: dict, 
+        horizontal: bool, 
+        show_pairs: bool,
+        contrast_marker_kwargs: dict, 
+        contrast_errorbar_kwargs: dict,
+    ):
+    """
+    Add mini meta-analysis or delta-delta violin plots to the contrast plot.
+
+    Parameters
+    ----------
+    dabest_obj : object
+        DABEST Effectsize object delta-delta or mini_meta
+    type: str
+        mini_meta or delta_delta
+    ci_type : str
+        Type of confidence interval to plot.
+    rawdata_axes : axes.Axes
+        Matplotlib axis object to plot on.
+    contrast_axes : axes.Axes
+        Matplotlib axis object to plot on.
+    contrast_kwargs : dict
+        Keyword arguments for the violinplot.
+    contrast_xtick_labels : list
+        List of xtick labels for the contrast plot.
+    effect_size : str
+        Type of effect size to plot.
+    plot_kwargs : dict
+        Keyword arguments for the plot.
+    horizontal : bool
+        If the plot is horizontal.
+    show_pairs : bool
+        Whether the data is paired and shown in pairs.
+    contrast_marker_kwargs: dict
+        Keyword arguments for the effectsize marker.
+    contrast_errorbar_kwargs: dict
+        Keyword arguments for the effectsize errorbar.
+    """
+
+    # Plot the curve
+    def extract_curve_data(dabest_object):
+        try:
+            data = dabest_object.bootstraps_weighted_delta
+        except AttributeError:
+            data = dabest_object.bootstraps_delta_delta
+
+        ci_low, ci_high = dabest_object.results.get(ci_type+'_low')[0], dabest_object.results.get(ci_type+'_high')[0]
+        return data, dabest_object.difference, ci_low, ci_high
+
+    data, difference, ci_low, ci_high = extract_curve_data(dabest_obj)
+
+    if contrast_kwargs.get('alpha') is not None:
+        contrast_alpha = contrast_kwargs.pop('alpha')
+
+    if horizontal:  
+        contrast_kwargs.update({'orientation': 'horizontal', 'widths': 1})
+        position = max(rawdata_axes.get_yticks()) + 1
+        half = "bottom"
+        effsize_x, effsize_y = difference, [position]
+        ci_x, ci_y = [ci_low, ci_high], [position, position]
     else:
-        sankey_ticks = [broadcasted_left[0], right_idx[0]]
-        ax.set_xticks([0, 1])
-        ax.set_xticklabels(sankey_ticks)
+        position = max(rawdata_axes.get_xticks()) + 1
+        half = "right"
+        effsize_x, effsize_y = [position], difference
+        ci_x, ci_y = [position, position], [ci_low, ci_high]
+
+    v = contrast_axes.violinplot(
+        data[~np.isinf(data)], positions=[position], **contrast_kwargs
+        )
+
+    halfviolin(v, fill_color="grey", alpha=contrast_alpha, half=half)
+
+    # Plot the effect size.
+    contrast_axes.plot(
+        effsize_x,
+        effsize_y,
+        **contrast_marker_kwargs
+    )
+    # Plot the confidence interval.
+    contrast_axes.plot(
+        ci_x,
+        ci_y,
+        **contrast_errorbar_kwargs
+    )
+
+    # Add labels and ticks
+    if horizontal:
+        current_ylabels = rawdata_axes.get_yticklabels()
+        if type == 'mini_meta':
+            current_ylabels.extend(["Weighted Delta"])
+        elif effect_size == "hedges_g":
+            current_ylabels.extend(["Delta g"])
+        else:
+            current_ylabels.extend(["Delta-Delta"])
+
+        rawdata_axes.set_yticks(np.append(rawdata_axes.get_yticks(), position))
+        rawdata_axes.set_yticklabels(current_ylabels)
+    else:
+        if type == 'mini_meta':
+            if show_pairs:
+                contrast_xtick_labels.extend(["Weighted\n Delta"])
+            else:
+                contrast_xtick_labels.extend(["Weighted Delta"])
+        elif effect_size == "hedges_g":
+            contrast_xtick_labels.extend(["Delta g"])
+        else:
+            contrast_xtick_labels.extend(["Delta-Delta"])
+
+    # Create the delta-delta axes.
+    if type == 'delta_delta' and not horizontal:
+        if plot_kwargs["delta2_label"] is not None:
+            delta2_label = plot_kwargs["delta2_label"]
+        elif effect_size == "mean_diff":
+            delta2_label = "Delta-Delta"
+        else:
+            delta2_label = "Delta g"
+        fontsize_delta2label = plot_kwargs["fontsize_delta2label"]
+        delta2_axes = contrast_axes.twinx()
+        delta2_axes.set_frame_on(False)
+        delta2_axes.set_ylabel(delta2_label, fontsize=fontsize_delta2label)
+        og_xlim_delta, og_ylim_delta = contrast_axes.get_xlim(), contrast_axes.get_ylim()
+        delta2_axes.set_ylim(og_ylim_delta)
+    else:
+        delta2_axes = None
+
+    return delta2_axes, contrast_xtick_labels
+
+
+def effect_size_curve_plotter(
+        ticks_to_plot: list, 
+        ticks_for_baseline_ec: list, 
+        results: pd.DataFrame, 
+        ci_type: str, 
+        contrast_axes: axes.Axes, 
+        contrast_kwargs: dict, 
+        bootstraps_color_by_group: bool, 
+        plot_palette_contrast: dict,
+        horizontal: bool, 
+        contrast_marker_kwargs: dict, 
+        contrast_errorbar_kwargs: dict,
+        idx: list, 
+        is_paired: bool, 
+        contrast_paired_lines: bool, 
+        contrast_paired_lines_kwargs: dict,
+        show_baseline_ec: bool = False
+    ):
+    """
+    Add effect size curves to the contrast plot.
+
+    Parameters
+    ----------
+    ticks_to_plot : list
+        List of indices of the contrast objects.
+    ticks_for_baseline_ec : list
+        List of indices of the baseline effect curve objects.
+    results : object (Dataframe)
+        Dataframe of contrast object comparisons.
+    ci_type : str
+        Type of confidence interval to plot.
+    contrast_axes : axes.Axes
+        Matplotlib axis object to plot on.
+    contrast_kwargs : dict
+        Keyword arguments for the violinplot.
+    bootstraps_color_by_group : bool
+        Whether to color the bootstraps by group.
+    plot_palette_contrast : dict
+        Dictionary of colors used in the contrast plot.
+    horizontal : bool
+        If the plot is horizontal.
+    contrast_marker_kwargs: dict
+        Keyword arguments for the effectsize marker.
+    contrast_errorbar_kwargs: dict
+        Keyword arguments for the effectsize errorbar.
+    idx : list
+        List of indices of the raw groups.
+    is_paired : bool
+        Whether the data is paired.
+    contrast_paired_lines : bool
+        Whether to add lines for repeated measures data.
+    contrast_paired_lines_kwargs : dict
+        Keyword arguments for the repeated measures lines.
+    show_baseline_ec : bool
+        Whether to show the baseline effect curve.
+    """
+
+    def plot_effect_size(tick, group, control, bootstrap, effsize, ci_low, ci_high):
+        # Create the violinplot
+        if horizontal:  
+            contrast_kwargs.update({'orientation': 'horizontal', 'widths': 1})
+            
+        v = contrast_axes.violinplot(
+            bootstrap[~np.isinf(bootstrap)],
+            positions=[tick],
+            **contrast_kwargs
+        )
+        
+        # Color the violin plot
+        fc = plot_palette_contrast[group] if bootstraps_color_by_group else "grey"
+        half = "bottom" if horizontal else "right"
+        halfviolin(v, fill_color=fc, alpha=contrast_alpha, half=half)
+
+        # Plot the confidence interval
+        if horizontal:
+            ci_x, ci_y = [ci_low, ci_high], [tick, tick]
+        else:
+            ci_x, ci_y = [tick, tick], [ci_low, ci_high]
+            
+        contrast_axes.plot(ci_x, ci_y, **contrast_errorbar_kwargs)
+        
+        return "{}\nminus\n{}".format(group, control)
+    
+    if contrast_kwargs.get('alpha') is not None:
+        contrast_alpha = contrast_kwargs.pop('alpha')
+
+    # Plot the curves
+    contrast_xtick_labels = []
+    for j, tick in enumerate(ticks_to_plot):
+        current_group = results.test[int(j)]
+        current_control = results.control[int(j)]
+        current_bootstrap = results.bootstraps[int(j)]
+        current_effsize = results.difference[int(j)]
+        current_ci_low = results.get(ci_type+'_low')[int(j)]
+        current_ci_high = results.get(ci_type+'_high')[int(j)]
+
+        # Plot the effect size marker
+        if horizontal:
+            effsize_x, effsize_y = current_effsize, [tick]
+        else:
+            effsize_x, effsize_y = [tick], current_effsize
+
+        contrast_axes.plot(
+            effsize_x,
+            effsize_y,
+            **contrast_marker_kwargs
+        )
+
+        label = plot_effect_size(tick, current_group, current_control, current_bootstrap,
+                               current_effsize, current_ci_low, current_ci_high)
+        contrast_xtick_labels.append(label)
+
+    # Add baseline effect curve plotting
+    bec_results = results.drop_duplicates(subset='control', keep='first').reset_index(drop=True)
+    for j, tick in enumerate(ticks_for_baseline_ec):
+        bec_group = bec_results.control[j]
+        bec_control = bec_results.control[j]
+        bec_bootstrap = bec_results.bec_bootstraps[j]
+        bec_effsize = bec_results.bec_difference[j]
+        bec_ci_low = bec_results.get('bec_'+ci_type+'_low')[j]
+        bec_ci_high = bec_results.get('bec_'+ci_type+'_high')[j]
+        
+        # Plot the effect size marker regardless of show_baseline_ec
+        if horizontal:
+            effsize_x, effsize_y = bec_effsize, [tick]
+        else:
+            effsize_x, effsize_y = [tick], bec_effsize
+            
+        contrast_axes.plot(effsize_x, effsize_y, **contrast_marker_kwargs)
+        
+        if show_baseline_ec:
+            _ = plot_effect_size(tick, bec_group, bec_control, bec_bootstrap, 
+                               bec_effsize, bec_ci_low, bec_ci_high)
+            # Baseline Curve doesn't need tick text
+
+    # Add lines for repeated measures data
+    if is_paired and contrast_paired_lines:
+        temp_num = 0
+        lines_to_plot_list = []
+
+        for group in idx:
+            new_group = []
+            if len(group) >= 2:
+                new_group.append(temp_num)
+                for i in range(1, len(group)):
+                    new_group.append(temp_num+i)
+            temp_num += len(group)
+            lines_to_plot_list.append(new_group)
+
+        for group in lines_to_plot_list:
+            if len(group) > 0:
+                mean_diffs_for_lines = []
+                for ticks in group:
+                    if ticks in ticks_to_plot:
+                        mean_diffs_for_lines.append(results.loc[ticks_to_plot.index(ticks)]["difference"])
+                    else:
+                        mean_diffs_for_lines.append(int(0))
+
+                x_data = mean_diffs_for_lines if horizontal else group
+                y_data = group if horizontal else mean_diffs_for_lines
+
+                contrast_axes.plot(
+                    x_data, 
+                    y_data,
+                    **contrast_paired_lines_kwargs
+                )
+
+    contrast_kwargs['alpha'] = contrast_alpha
+    return current_group, current_control, current_effsize, contrast_xtick_labels
+
+def gridkey_plotter(
+        is_paired: bool, 
+        idx: list,
+        all_plot_groups: list, 
+        gridkey: list, 
+        rawdata_axes: axes.Axes, 
+        contrast_axes: axes.Axes, 
+        plot_data: pd.DataFrame, 
+        xvar: str, 
+        yvar: str, 
+        results: pd.DataFrame, 
+        show_delta2: bool, 
+        show_mini_meta: bool, 
+        x1_level: list, 
+        experiment_label: list, 
+        float_contrast: bool, 
+        horizontal: bool, 
+        delta_delta: object, 
+        mini_meta: object, 
+        effect_size: str, 
+        gridkey_kwargs: dict,
+    ):
+    """
+    Add gridkey to the contrast plot.
+
+    Parameters
+    ----------
+    is_paired : bool
+        Whether the data is paired.
+    idx : list
+        List of indices of the contrast objects.
+    all_plot_groups : list
+        List of all plot groups.
+    gridkey : list
+        List of gridkey rows.
+    rawdata_axes : axes.Axes
+        Matplotlib axis object for the raw data.
+    contrast_axes : axes.Axes
+        Matplotlib axis object for the contrast data.
+    plot_data : object (Dataframe)
+        Dataframe of the plot data.
+    xvar : str
+        Column name of the x variable.
+    yvar : str
+        Column name of the y variable.
+    results : object (Dataframe)
+        Dataframe of contrast object comparisons.
+    show_delta2 : bool
+        Whether to show the delta-delta.
+    show_mini_meta : bool
+        Whether to show the mini meta-analysis.
+    x1_level : list
+        List of x1 levels.
+    experiment_label : list
+        List of experiment labels.
+    float_contrast : bool
+        Whether the DABEST plot uses Gardner-Altman or Cummings
+    horizontal : bool
+        If the plot is horizontal.
+    delta_delta : object
+        delta-delta object.
+    mini_meta : object
+        Mini meta-analysis object.
+    effect_size : str
+        Type of effect size to plot
+    gridkey_kwargs : dict
+        Keyword arguments for the gridkey.
+    """
+    # Extract relevant kwargs
+    gridkey_show_Ns = gridkey_kwargs["show_Ns"]
+    gridkey_show_es = gridkey_kwargs["show_es"]
+    gridkey_merge_pairs = gridkey_kwargs["merge_pairs"]
+    gridkey_marker = gridkey_kwargs["marker"]
+    gridkey_delimiters = gridkey_kwargs["delimiters"] 
+    labels_fontsize = gridkey_kwargs.get('labels_fontsize')
+    fontsize = gridkey_kwargs.get('fontsize')
+
+    # Auto parser for gridkey - implemented by SangyuXu
+    if gridkey == "auto" or gridkey == True:
+        if experiment_label is not None:
+            gridkey = list(np.concatenate([experiment_label, x1_level]))
+        else:
+            temp_groups = ";".join(all_plot_groups)
+            for delimiter in gridkey_delimiters:
+                temp_groups = temp_groups.replace(delimiter, ";")
+            temp_groups = [i.strip() for i in temp_groups.split(';')]
+            unique_groups = list(set(temp_groups))
+            rank = [sum([temp_groups.index(i) for i in temp_groups if(j in i)]) for j in unique_groups]
+            gridkey = [x for _,x in sorted(zip(rank,unique_groups))]
+    
+    # Raise error if there are more than 2 items in any idx and gridkey_merge_pairs is True and is_paired is not None
+    if gridkey_merge_pairs and is_paired is not None:
+        for i in idx:
+            if len(i) > 2:
+                warnings.warn(
+                    "gridkey_merge_pairs=True only works if all idx in tuples have only two items. gridkey_merge_pairs has automatically been set to False"
+                )
+                gridkey_merge_pairs = False
+                break
+    elif gridkey_merge_pairs and is_paired is None:
+        warnings.warn(
+            "gridkey_merge_pairs=True is only applicable for paired data."
+        )
+        gridkey_merge_pairs = False
+
+    # Checks for gridkey_merge_pairs and is_paired; if both are true, "merges" the gridkey per pair
+    if gridkey_merge_pairs and is_paired is not None:
+        groups_for_gridkey = []
+        for i in idx:
+            groups_for_gridkey.append(i[1])
+    else:
+        groups_for_gridkey = all_plot_groups
+
+    # raise errors if gridkey is not a list, or if the list is empty
+    if isinstance(gridkey, list) is False:
+        raise TypeError("gridkey must be a list (or a string 'auto').")
+    if any(isinstance(i, str) is False for i in gridkey):
+        raise TypeError("gridkey must contain only strings.")
+    if len(gridkey) == 0:
+        warnings.warn("gridkey is an empty list.")
+
+    # raise Warning if an item in gridkey is not contained in any idx
+    for i in gridkey:
+        in_idx = 0
+        for j in groups_for_gridkey:
+            if i in j:
+                in_idx += 1
+        if in_idx == 0:
+            if is_paired is not None:
+                warnings.warn(
+                    i
+                    + " is not in any idx. Please check. Alternatively, merging gridkey pairs may not be suitable for your data; try passing gridkey_merge_pairs=False."
+                )
+            else:
+                warnings.warn(i + " is not in any idx. Please check.")
+
+    # Populate table: checks if idx for each column contains rowlabel name
+    # IF so, marks that element as present w black dot (default "\u25CF"), or space if not present
+    table_cellcols = []
+    for i in gridkey:
+        thisrow = []
+        for q in groups_for_gridkey:
+            if str(i) in q:
+                thisrow.append(gridkey_marker)
+            else:
+                thisrow.append("")
+        table_cellcols.append(thisrow)
+
+    # Adds a row for Ns with the Ns values
+    if gridkey_show_Ns:
+        gridkey.append("Ns")
+        list_of_Ns = []
+        for i in groups_for_gridkey:
+            list_of_Ns.append(str(plot_data.groupby(xvar, observed=False).count()[yvar].loc[i]))
+        table_cellcols.append(list_of_Ns)
+
+    # Adds a row for effectsizes with effectsize values
+    if gridkey_show_es and not horizontal:
+        gridkey.append("\u0394")
+        effsize_list = []
+        results_list = results.test.to_list()
+
+        # get the effect size, append + or -, 2 dec places
+        for i in enumerate(groups_for_gridkey):
+            if i[1] in results_list:
+                curr_esval = results.loc[results["test"] == i[1]]["difference"].iloc[0]
+                curr_esval_str = np.format_float_positional(
+                    curr_esval,
+                    precision=2,
+                    sign=True,
+                    trim="k",
+                    min_digits=2,
+                )
+                effsize_list.append(curr_esval_str)
+            else:
+                effsize_list.append("-")
+
+        table_cellcols.append(effsize_list)
+
+    # Set the axes to plot on
+    if float_contrast or horizontal:
+        ax_to_plot = rawdata_axes
+    else:
+        ax_to_plot = contrast_axes
+
+    # Add delta-delta or mini_meta details to the table
+    if show_mini_meta or show_delta2:
+        if show_delta2:
+            added_group_name = ["Deltas' g"] if effect_size == "hedges_g" else ["Delta-Delta"]
+        else:
+            added_group_name = ["Weighted Delta"]
+        gridkey = added_group_name + gridkey
+        table_cellcols = [[""]*len(table_cellcols[0])] + table_cellcols
+
+        if not horizontal and show_delta2:
+            extra_table_cellcols = [[] for i in range(len(table_cellcols))]
+
+            for group_idx, group_vals in enumerate(extra_table_cellcols):
+                if group_idx == 0:
+                    added_group = [gridkey_marker]
+                elif gridkey_show_es and (group_idx == len(extra_table_cellcols)-1) and not horizontal:
+                    added_delta_effectsize = delta_delta.difference
+                    added_delta_effectsize_str = np.format_float_positional(
+                                                                            added_delta_effectsize,
+                                                                            precision=2,
+                                                                            sign=True,
+                                                                            trim="k",
+                                                                            min_digits=2,
+                                                                        )
+                    added_group = [added_delta_effectsize_str]
+                else:
+                    added_group = ['']
+                for n in added_group:
+                    group_vals.append(n)
+
+        elif horizontal or show_mini_meta:
+            for group_idx, group_vals in enumerate(table_cellcols):
+                if group_idx == 0:
+                    added_group = [gridkey_marker]
+                elif gridkey_show_es and (group_idx == len(table_cellcols)-1) and not horizontal:
+                    added_delta_effectsize = delta_delta.difference if show_delta2 else mini_meta.difference
+                    added_delta_effectsize_str = np.format_float_positional(
+                                                                            added_delta_effectsize,
+                                                                            precision=2,
+                                                                            sign=True,
+                                                                            trim="k",
+                                                                            min_digits=2,
+                                                                        )
+                    added_group = [added_delta_effectsize_str]
+                else:
+                    added_group = ['']
+                for n in added_group:
+                    group_vals.append(n)
+
+    # Create the table object
+    def add_table(celltext, bbox, rowlabels=None):
+        gridkey_to_plot = ax_to_plot.table(
+                    cellText=celltext,
+                    rowLabels=rowlabels,
+                    cellLoc="center",
+                    bbox=bbox,
+        )
+        return gridkey_to_plot
+
+    if horizontal:
+        # Convert the cells format for horizontal table plotting
+        converted_list = []
+        for j in range(0, len(table_cellcols[0])):
+            temp_list = []
+            for i in table_cellcols:
+                temp_list.append(i[j])
+            converted_list.append(temp_list)
+
+        gridkey_to_plot = add_table(celltext = converted_list, bbox = [-len(gridkey) * 0.2, 0, len(gridkey) * 0.2, 1])
+
+        # Add the column labels as text below the table
+        text_locs = np.arange((-len(gridkey)*0.2) +0.1, 0, 0.2)
+        for loc, txt in zip(text_locs, gridkey):
+            ax_to_plot.text(
+                        loc+0.04, 
+                        -0.01, 
+                        txt, 
+                        transform=ax_to_plot.transAxes, 
+                        ha='right',
+                        rotation=45,
+                        fontsize=labels_fontsize if labels_fontsize is not None else 10,
+                        va='top',
+                    )
+    else:
+        # Plot the table for vertical format
+        if show_mini_meta:
+            gridkey_to_plot = add_table(celltext = table_cellcols, rowlabels=gridkey, bbox = [0, -len(gridkey) * 0.1 - 0.05, 1, len(gridkey) * 0.1])
+        elif show_delta2:
+            gridkey_to_plot = add_table(celltext = table_cellcols, rowlabels=gridkey, bbox = [0, -len(gridkey) * 0.1 - 0.05, 0.75, len(gridkey) * 0.1])
+            extra_gridkey  =  add_table(celltext = extra_table_cellcols, bbox = [0.78, -len(gridkey) * 0.1 - 0.05, 0.15, len(gridkey) * 0.1])
+        else:
+            gridkey_to_plot = add_table(celltext = table_cellcols, rowlabels=gridkey, bbox = [0, -len(gridkey) * 0.1 - 0.05, 1, len(gridkey) * 0.1]) 
+
+        # modifies row label cells
+        for cell in gridkey_to_plot._cells:
+            if cell[1] == -1:
+                gridkey_to_plot._cells[cell].visible_edges = "open"
+                gridkey_to_plot._cells[cell].set_text_props(**{"ha": "right"})
+
+    if fontsize is not None:
+        gridkey_to_plot.auto_set_font_size(False)
+        gridkey_to_plot.set_fontsize(fontsize)
+        if show_delta2 and not horizontal:
+            extra_gridkey.auto_set_font_size(False)
+            extra_gridkey.set_fontsize(fontsize)
+
+    if labels_fontsize is not None and not horizontal:
+        gridkey_to_plot.auto_set_font_size(False)
+        for cell in gridkey_to_plot._cells:
+            if cell[1] == -1:
+                gridkey_to_plot._cells[cell].set_text_props(**{"fontsize": labels_fontsize})
+
+    # turns off both x axes
+    if horizontal:
+        rawdata_axes.get_yaxis().set_visible(False)
+        contrast_axes.get_yaxis().set_visible(False)
+    else:
+        rawdata_axes.get_xaxis().set_visible(False)
+        contrast_axes.get_xaxis().set_visible(False)
+
+def barplotter(
+        xvar: str, 
+        yvar: str, 
+        all_plot_groups: list, 
+        rawdata_axes: axes.Axes, 
+        plot_data: pd.DataFrame, 
+        raw_colors: str, 
+        plot_palette_raw: dict, 
+        color_col: str,
+        barplot_kwargs: dict, 
+        horizontal: bool
+    ):
+    """
+    Add bars to the raw data plot.
+
+    Parameters
+    ----------
+    xvar : str
+        Column name of the x variable.
+    yvar : str
+        Column name of the y variable.
+    all_plot_groups : list
+        List of all plot groups.
+    rawdata_axes : object
+        Matplotlib axis object to plot on.
+    plot_data : object (Dataframe)
+        Dataframe of the plot data.
+    raw_colors : str
+        Color of the bar.
+    plot_palette_raw : dict
+        Dictionary of colors used in the bar plot.
+    color_col : str
+        Column name of the color column.
+    barplot_kwargs : dict
+        Keyword arguments for the barplot.
+    horizontal : bool
+        If the plot is horizontal.
+    """
+    bar_width = barplot_kwargs.get('width', 0.5)
+    fontsize = barplot_kwargs.pop('fontsize')
+
+    x_label, y_label = rawdata_axes.get_xlabel(), rawdata_axes.get_ylabel()
+    if horizontal:
+        x_var, y_var, orient = np.ones(len(all_plot_groups)), all_plot_groups, "h"
+    else:
+        x_var, y_var, orient = all_plot_groups, np.ones(len(all_plot_groups)), "v"
+
+    # Create bar1_df with basic columns
+    bar1_df = pd.DataFrame({
+        xvar: x_var, 
+        "proportion": y_var
+    })
+
+    # Handle colors
+    if color_col:
+        # Get first color value for each group
+        color_mapping = plot_data.groupby(xvar, observed=False)[color_col].first()
+        bar1_df[color_col] = [color_mapping.get(group) for group in all_plot_groups]
+        
+        # Map colors, defaulting to bar_color if no match
+        edge_colors = [
+            plot_palette_raw.get(hue_val, raw_colors) 
+            for hue_val in bar1_df[color_col]
+        ]
+    else:
+        edge_colors = raw_colors
+
+    bar1 = sns.barplot(
+        data=bar1_df,
+        x=xvar,
+        y="proportion",
+        ax=rawdata_axes,
+        order=all_plot_groups,
+        linewidth=2,
+        facecolor=(1, 1, 1, 0),
+        edgecolor=edge_colors,
+        zorder=1,
+        orient=orient,
+    )
+
+    bar2 = sns.barplot(
+        data=plot_data,
+        x=yvar if horizontal else xvar,
+        y=xvar if horizontal else yvar,
+        hue=xvar if color_col is None else color_col,
+        ax=rawdata_axes,
+        order=all_plot_groups,
+        palette=plot_palette_raw,
+        dodge=False,
+        zorder=1,
+        orient=orient,
+        **barplot_kwargs
+    )
+
+    # adjust the width of bars
+    if horizontal:
+        for bar in bar1.patches:
+            y = bar.get_y()
+            height = bar.get_height()
+            centre = y + height / 2.0
+            bar.set_y(centre - bar_width / 2.0)
+            bar.set_height(bar_width)
+    else:
+        for bar in bar1.patches:
+            x = bar.get_x()
+            width = bar.get_width()
+            centre = x + width / 2.0
+            bar.set_x(centre - bar_width / 2.0)
+            bar.set_width(bar_width)
+
+    # reset the x and y labels
+    rawdata_axes.set_xlabel(x_label)
+    rawdata_axes.set_ylabel(y_label)
+
+    if horizontal:
+        rawdata_axes.set_yticks(rawdata_axes.get_yticks())
+        rawdata_axes.set_yticklabels(rawdata_axes.get_yticklabels(), fontsize = fontsize)
+    else:
+        rawdata_axes.set_xticks(rawdata_axes.get_xticks())
+        rawdata_axes.set_xticklabels(rawdata_axes.get_xticklabels(), fontsize = fontsize)
+
+def table_for_horizontal_plots(
+        effectsize_df: object, 
+        ax: axes.Axes, 
+        contrast_axes: axes.Axes, 
+        ticks_to_plot: list, 
+        show_mini_meta: bool, 
+        show_delta2: bool, 
+        table_kwargs: dict,
+        ticks_to_skip: list
+    ):
+    """
+    Add table axes for showing the deltas for horizontal plots.
+
+    Parameters
+    ----------
+    effectsize_df : object
+        Effect size DABEST object.
+    ax : object
+        Matplotlib axis object to plot the table axes.
+    contrast_axes : object
+        Matplotlib axis object to plot the contrast axes.
+    ticks_to_plot : list
+        List of indices of the contrast objects.
+    show_mini_meta : bool
+        Whether to show the mini meta-analysis.
+    show_delta2 : bool
+        Whether to show the delta-delta.
+    table_kwargs : dict
+        Keyword arguments for the table.
+    ticks_to_skip:  list
+        List of ticks to skip in the table.
+    """
+
+    table_color = table_kwargs['color']
+    table_alpha = table_kwargs['alpha']
+    table_font_size = table_kwargs['fontsize']
+    table_text_color = table_kwargs['text_color']
+    text_units = table_kwargs['text_units']
+    table_font_size -= 2 if text_units != '' else 0
+    control_marker = table_kwargs['control_marker']  
+    fontsize_label = table_kwargs['fontsize_label']
+    label = table_kwargs['label']
+
+    ### Create a table of deltas
+    cols=['Δ','N']
+    lst = []
+    for n in np.arange(0, len(effectsize_df.results.difference), 1):
+        lst.append([effectsize_df.results.difference[n], 0])
+    if show_mini_meta:
+        lst.append([effectsize_df.mini_meta.difference, 0])
+    elif show_delta2:
+        lst.append([effectsize_df.delta_delta.difference, 0])
+    tab = pd.DataFrame(lst, columns=cols)
+
+    ### Plot the text
+    if show_mini_meta or show_delta2:
+        new_ticks = ticks_to_plot + [max(ticks_to_plot)+1]
+    else:
+        new_ticks = ticks_to_plot.copy()
+    for i,loc in zip(tab.index, new_ticks):
+        ax.text(0.5, loc, "{:+.2f}".format(tab.iloc[i,0])+text_units, ha="center", va="center", color=table_text_color, size=table_font_size)
+
+    # Plot the dashes
+    if control_marker is not None:
+        for loc in ticks_to_skip:
+            ax.text(0.5, loc, control_marker, ha="center", va="center", color=table_text_color, size=table_font_size)
+
+    ### Parameters for table
+    ax.axvspan(0, 1, facecolor=table_color, alpha=table_alpha)  #### Plot the background color
+    ax.set_xticks([0.5])
+    ax.set_xticklabels([])
+    ax.set_ylim(contrast_axes.get_ylim())
+    ax.set_yticks([])
+    ax.set_yticklabels([])
+    ax.tick_params(left=False, bottom=False)
+    ax.set_xlabel(label, fontsize=fontsize_label) # Set the x-axis label - hardcoded for now
+    sns.despine(ax=ax, left=True, bottom=True)
+
+
+def add_counts_to_prop_plots(
+        plot_data: pd.DataFrame, 
+        xvar: str, 
+        yvar: str, 
+        rawdata_axes: axes.Axes, 
+        horizontal: bool, 
+        is_paired: bool, 
+        prop_sample_counts_kwargs: dict
+    ):
+    """
+    Add counts to the proportion plots.
+
+    Parameters
+    ----------
+    plot_data : object (Dataframe)
+        Dataframe of the plot data.
+    xvar : str
+        Column name of the x variable.
+    yvar : str
+        Column name of the y variable.
+    rawdata_axes : axes.Axes
+        Matplotlib axis object to plot on.
+    horizontal : bool
+        If the plot is horizontal.
+    is_paired : bool
+        Whether the data is paired.
+    prop_sample_counts_kwargs : dict
+        Keyword arguments for the sample counts.
+    """
+
+    # Group orders
+    if isinstance(plot_data[xvar].dtype, pd.CategoricalDtype):
+        sample_size_text_order = pd.unique(plot_data[xvar]).categories
+    else:
+        sample_size_text_order = pd.unique(plot_data[xvar])
+
+    # Get the sample size values
+    ones, zeros = plot_data[plot_data[yvar] == 1], plot_data[plot_data[yvar] == 0]
+
+    sample_size_val1 = ones.groupby(xvar, observed=False)[yvar].count().reindex(index=sample_size_text_order)
+    sample_size_val0 = zeros.groupby(xvar, observed=False)[yvar].count().reindex(index=sample_size_text_order)
+
+    if "fontsize" not in prop_sample_counts_kwargs.keys():
+        fontsize = 8 if horizontal else 10
+        fontsize -= 2 if is_paired else 0
+        prop_sample_counts_kwargs.update({'fontsize': fontsize})
+
+    for sample_text_x, sample_text_y0, sample_text_y1 in zip(
+                                                            np.arange(0, len(sample_size_text_order) + 1, 1), 
+                                                            sample_size_val0,
+                                                            sample_size_val1,
+                                                            ):
+        if horizontal:
+            rawdata_axes.text(0.05, sample_text_x, sample_text_y1, **prop_sample_counts_kwargs)
+            rawdata_axes.text(0.95, sample_text_x, sample_text_y0, **prop_sample_counts_kwargs)
+        else:
+            rawdata_axes.text(sample_text_x, 0.05, sample_text_y1, **prop_sample_counts_kwargs)
+            rawdata_axes.text(sample_text_x, 0.95, sample_text_y0, **prop_sample_counts_kwargs)
 
 # %% ../nbs/API/plot_tools.ipynb 6
 def swarmplot(
     data: pd.DataFrame,
     x: str,
     y: str,
-    ax: axes.Subplot,
+    ax: axes.Axes,
     order: List = None,
     hue: str = None,
     palette: Union[Iterable, str] = "black",
@@ -791,8 +2073,10 @@ def swarmplot(
     size: float = 5,
     side: str = "center",
     jitter: float = 1,
+    filled: Union[bool, List, Tuple] = True,
     is_drop_gutter: bool = True,
     gutter_limit: float = 0.5,
+    horizontal: bool = False,
     **kwargs,
 ):
     """
@@ -806,8 +2090,8 @@ def swarmplot(
         The column in the DataFrame to be used as the x-axis.
     y : str
         The column in the DataFrame to be used as the y-axis.
-    ax : axes._subplots.Subplot | axes._axes.Axes
-        Matplotlib AxesSubplot object for which the plot would be drawn on. Default is None.
+    ax : axes.Axes
+        Matplotlib axes.Axes object for which the plot would be drawn on. Default is None.
     order : List
         The order in which x-axis categories should be displayed. Default is None.
     hue : str
@@ -823,20 +2107,27 @@ def swarmplot(
         The side on which points are swarmed ("center", "left", or "right"). Default is "center".
     jitter : int | float
         Determines the distance between points. Default is 1.
+    filled : bool | List | Tuple
+        Determines whether the dots in the swarmplot are filled or not. If set to False,
+        dots are not filled. If provided as a List or Tuple, it should contain boolean values,
+        each corresponding to a swarm group in order, indicating whether the dot should be
+        filled or not.
     is_drop_gutter : bool
         If True, drop points that hit the gutters; otherwise, readjust them.
     gutter_limit : int | float
         The limit for points hitting the gutters.
+    horizontal : bool
+        If True, the swarm plot is drawn horizontally. Default is False.
     **kwargs:
         Additional keyword arguments to be passed to the swarm plot.
 
     Returns
     -------
-    axes._subplots.Subplot | axes._axes.Axes
-        Matplotlib AxesSubplot object for which the swarm plot has been drawn on.
+    axes.Axes
+        Matplotlib axes.Axes object for which the swarm plot has been drawn on.
     """
-    s = SwarmPlot(data, x, y, ax, order, hue, palette, zorder, size, side, jitter)
-    ax = s.plot(is_drop_gutter, gutter_limit, ax, **kwargs)
+    s = SwarmPlot(data, x, y, ax, order, hue, palette, zorder, size, side, jitter, horizontal)
+    ax = s.plot(is_drop_gutter, gutter_limit, ax, filled, horizontal, **kwargs)
     return ax
 
 
@@ -846,7 +2137,7 @@ def __init__(
         data: pd.DataFrame,
         x: str,
         y: str,
-        ax: axes.Subplot,
+        ax: axes.Axes,
         order: List = None,
         hue: str = None,
         palette: Union[Iterable, str] = "black",
@@ -854,6 +2145,7 @@ def __init__(
         size: float = 5,
         side: str = "center",
         jitter: float = 1,
+        horizontal: bool = False,
     ):
         """
         Initialize a SwarmPlot instance.
@@ -866,8 +2158,8 @@ def __init__(
             The column in the DataFrame to be used as the x-axis.
         y : str
             The column in the DataFrame to be used as the y-axis.
-        ax : axes.Subplot
-            Matplotlib AxesSubplot object for which the plot would be drawn on.
+        ax : axes.Axes
+            Matplotlib axes.Axes object for which the plot would be drawn on.
         order : List
             The order in which x-axis categories should be displayed. Default is None.
         hue : str
@@ -883,6 +2175,8 @@ def __init__(
             The side on which points are swarmed ("center", "left", or "right"). Default is "center".
         jitter : int | float
             Determines the distance between points. Default is 1.
+        horizontal : bool
+            If True, the swarm plot is drawn horizontally. Default is False.
 
         Returns
         -------
@@ -925,21 +2219,28 @@ def __init__(
 
         x_min = min(x_vals)
         x_max = max(x_vals)
-        ax.set_xlim(left=x_min - 0.5, right=x_max + 0.5)
-
         y_range = max(y_vals) - min(y_vals)
         y_min = min(y_vals) - 0.05 * y_range
         y_max = max(y_vals) + 0.05 * y_range
 
-        # ylim is set manually to override Axes.autoscale if it hasn't already been scaled at least once
-        if ax.get_autoscaley_on():
-            ax.set_ylim(bottom=y_min, top=y_max)
+        if horizontal:
+            ax.set_ylim(bottom=x_min - 0.5, top=x_max + 0.5)
+            # ylim is set manually to override Axes.autoscale if it hasn't already been scaled at least once
+            if ax.get_autoscalex_on():
+                ax.set_xlim(left=y_min, right=y_max)
+        else:
+            ax.set_xlim(left=x_min - 0.5, right=x_max + 0.5)
+            # ylim is set manually to override Axes.autoscale if it hasn't already been scaled at least once
+            if ax.get_autoscaley_on():
+                ax.set_ylim(bottom=y_min, top=y_max)
 
         figw, figh = ax.get_figure().get_size_inches()
         w = (ax.get_position().xmax - ax.get_position().xmin) * figw
         h = (ax.get_position().ymax - ax.get_position().ymin) * figh
         ax_xspan = ax.get_xlim()[1] - ax.get_xlim()[0]
         ax_yspan = ax.get_ylim()[1] - ax.get_ylim()[0]
+        if horizontal:
+            ax_xspan, ax_yspan = ax_yspan, ax_xspan
 
         # increases jitter distance based on number of swarms that is going to be drawn
         jitter = jitter * (1 + 0.05 * (math.log(ax_xspan)))
@@ -954,7 +2255,7 @@ def __init__(
         self.__dsize = dsize
 
     def _check_errors(
-        self, data: pd.DataFrame, ax: axes.Subplot, size: float, side: str
+        self, data: pd.DataFrame, ax: axes.Axes, size: float, side: str
     ) -> None:
         """
         Check the validity of input parameters. Raises exceptions if detected.
@@ -963,8 +2264,8 @@ def _check_errors(
         ----------
         data : pd.Dataframe
             Input data used for generation of the swarmplot.
-        ax : axes.Subplot
-            Matplotlib AxesSubplot object for which the plot would be drawn on.
+        ax : axes.Axes
+            Matplotlib axes.Axes object for which the plot would be drawn on.
         size : int | float
             scalar value determining size of dots of the swarmplot.
         side: str
@@ -977,9 +2278,9 @@ def _check_errors(
         # Type enforcement
         if not isinstance(data, pd.DataFrame):
             raise ValueError("`data` must be a Pandas Dataframe.")
-        if not isinstance(ax, (axes._subplots.Subplot, axes._axes.Axes)):
+        if not isinstance(ax, axes.Axes):
             raise ValueError(
-                f"`ax` must be a Matplotlib AxesSubplot. The current `ax` is a {type(ax)}"
+                f"`ax` must be a Matplotlib axes.Axes. The current `ax` is a {type(ax)}"
             )
         if not isinstance(size, (int, float)):
             raise ValueError("`size` must be a scalar or float.")
@@ -996,7 +2297,9 @@ def _check_errors(
         if not isinstance(self.__jitter, (int, float)):
             raise ValueError("`jitter` must be a scalar or float.")
         if not isinstance(self.__palette, (str, Iterable)):
-            raise ValueError("`palette` must be either a string indicating a color name or an Iterable.")
+            raise ValueError(
+                "`palette` must be either a string indicating a color name or an Iterable."
+            )
         if self.__hue is not None and not isinstance(self.__hue, str):
             raise ValueError("`hue` must be either a string or None.")
         if self.__order is not None and not isinstance(self.__order, Iterable):
@@ -1026,7 +2329,6 @@ def _check_errors(
             err = "`palette` cannot be an empty string. It must be either a string indicating a color name or an Iterable."
             raise ValueError(err)
         if isinstance(self.__palette, dict):
-            # TODO: to add detection of when dict length is less than size of unique_items
             for group_i, color_i in self.__palette.items():
                 if group_i not in pd.unique(data[color_col]):
                     err = (
@@ -1036,8 +2338,10 @@ def _check_errors(
                     )
                     raise IndexError(err)
                 if isinstance(color_i, str) and color_i.strip() == "":
-                    err = "The color mapping for {0} in `palette` is an empty string. It must contain a color name.".format(group_i)
-                    raise ValueError(err) 
+                    err = "The color mapping for {0} in `palette` is an empty string. It must contain a color name.".format(
+                        group_i
+                    )
+                    raise ValueError(err)
 
         if side.lower() not in ["center", "right", "left"]:
             raise ValueError(
@@ -1136,9 +2440,10 @@ def _swarm(
             raise ValueError("`dsize` must be a scalar or float.")
 
         # Sorting algorithm based off of: https://github.com/mgymrek/pybeeswarm
-        points_data = pd.DataFrame(
-            {"y": [yval * 1.0 / dsize for yval in values], "x": [0] * len(values)}
-        )
+        points_data = pd.DataFrame({
+            "y": [yval * 1.0 / dsize for yval in values],
+            "x": np.zeros(len(values), dtype=float)  # Initialize with float zeros
+        })
         for i in range(1, points_data.shape[0]):
             y_i = points_data["y"].values[i]
             points_placed = points_data[0:i]
@@ -1173,7 +2478,7 @@ def _swarm(
                         bad_x_offsets.append(True)
                     else:
                         bad_x_offsets.append(False)
-                potential_x_offsets[bad_x_offsets] = np.infty
+                potential_x_offsets[bad_x_offsets] = np.inf
                 abs_potential_x_offsets = [abs(_) for _ in potential_x_offsets]
                 valid_x_offset = potential_x_offsets[
                     abs_potential_x_offsets.index(min(abs_potential_x_offsets))
@@ -1239,8 +2544,14 @@ def _adjust_gutter_points(
         return points_data
 
     def plot(
-        self, is_drop_gutter: bool, gutter_limit: float, ax: axes.Subplot, **kwargs
-    ) -> axes.Subplot:
+        self,
+        is_drop_gutter: bool,
+        gutter_limit: float,
+        ax: axes.Subplot,
+        filled: Union[bool, List, Tuple],
+        horizontal: bool,
+        **kwargs,
+    ) -> axes.Axes:
         """
         Generate a swarm plot.
 
@@ -1250,28 +2561,50 @@ def plot(
             If True, drop points that hit the gutters; otherwise, readjust them.
         gutter_limit : int | float
             The limit for points hitting the gutters.
-        ax : axes.Subplot
+        ax : axes.Axes
             The matplotlib figure object to which the swarm plot will be added.
+        filled : bool | List | Tuple
+            Determines whether the dots in the swarmplot are filled or not. If set to False,
+            dots are not filled. If provided as a List or Tuple, it should contain boolean values,
+            each corresponding to a swarm group in order, indicating whether the dot should be
+            filled or not.
         **kwargs:
             Additional keyword arguments to be passed to the scatter plot.
 
         Returns
         -------
-        axes.Subplot:
-            The matplotlib figure containing the swarm plot.
+        axes.Axes:
+            The matplotlib axes containing the swarm plot.
         """
         # Input validation
         if not isinstance(is_drop_gutter, bool):
             raise ValueError("`is_drop_gutter` must be a boolean.")
         if not isinstance(gutter_limit, (int, float)):
             raise ValueError("`gutter_limit` must be a scalar or float.")
+        if not isinstance(filled, (bool, list, tuple)):
+            raise ValueError("`filled` must be a boolean, list or tuple.")
+        
+        fontsize = kwargs.pop('fontsize', 12)
+
+        # More thorough input validation checks
+        if isinstance(filled, (list, tuple)):
+            if len(filled) != len(self.__order):
+                err = (
+                    "There are {0} unique values in `x` column in `data` "
+                    "but `filled` has a length of {1}. If `filled` is a list "
+                    "or a tuple, it must have the same length as the number of "
+                    "unique values/groups in the `x` column of data."
+                ).format(len(self.__order), len(filled))
+                raise ValueError(err)
+            if not all(isinstance(_, bool) for _ in filled):
+                raise ValueError("All values in `filled` must be a boolean.")
 
         # Assumptions are that self.__data_copy is already sorted according to self.__order
         x_position = (
             0  # x-coordinate of center of each individual swarm of the swarm plot
         )
         x_tick_tabels = []
-        for group_i, values_i in self.__data_copy.groupby(self.__x):
+        for group_i, values_i in self.__data_copy.groupby(self.__x, observed=False):
             x_new = []
             values_i_y = values_i[self.__y]
             x_offset = self._swarm(
@@ -1292,8 +2625,8 @@ def plot(
 
             if values_i.empty:
                 ax.scatter(
-                    values_i["x_new"],
-                    values_i[self.__y],
+                    values_i["x_new"] if not horizontal else values_i[self.__y],
+                    values_i[self.__y] if not horizontal else values_i["x_new"],
                     s=self.__size,
                     zorder=self.__zorder,
                     **kwargs,
@@ -1308,10 +2641,11 @@ def plot(
                 cmap = []
                 for cmap_group_i in cmap_values:
                     cmap.append(self.__palette[cmap_group_i])
+
                 cmap = ListedColormap(cmap)
                 ax.scatter(
-                    values_i["x_new"],
-                    values_i[self.__y],
+                    values_i["x_new"] if not horizontal else values_i[self.__y],
+                    values_i[self.__y] if not horizontal else values_i["x_new"],
                     s=self.__size,
                     c=index,
                     cmap=cmap,
@@ -1319,19 +2653,46 @@ def plot(
                     edgecolor="face",
                     **kwargs,
                 )
+
             else:
                 # color swarms based on `x` column
+                if not isinstance(filled, bool):
+                    facecolor = (
+                        "none"
+                        if not filled[x_position - 1]
+                        else self.__palette[group_i]
+                    )
+                else:
+                    facecolor = "none" if not filled else self.__palette[group_i]
+
                 ax.scatter(
-                    values_i["x_new"],
-                    values_i[self.__y],
+                    values_i["x_new"] if not horizontal else values_i[self.__y],
+                    values_i[self.__y] if not horizontal else values_i["x_new"],
                     s=self.__size,
-                    c=self.__palette[group_i],
                     zorder=self.__zorder,
-                    edgecolor="face",
+                    facecolor=facecolor,
+                    edgecolor=self.__palette[group_i],
+                    label=group_i,
                     **kwargs,
                 )
 
-        ax.get_xaxis().set_ticks(np.arange(x_position))
-        ax.get_xaxis().set_ticklabels(x_tick_tabels)
+        # Handling of legends
+        # This is currently a workaround because c and cmap is unable to map the labels when calling scatter()
+        # labels has to be used to designate legend labels and handles in scatter() due to the potential calling of ax.get_legend_handles_labels()
+        if self.__hue is not None:
+            for cmap_group_i in self.__palette:
+                ax.scatter(
+                    [],
+                    [],
+                    color=self.__palette[cmap_group_i],
+                    label=cmap_group_i,
+                )
 
+        if horizontal:
+            ax.get_yaxis().set_ticks(np.arange(x_position))
+            ax.get_yaxis().set_ticklabels(x_tick_tabels, fontsize = fontsize)
+        else:
+            ax.get_xaxis().set_ticks(np.arange(x_position))
+            ax.get_xaxis().set_ticklabels(x_tick_tabels, fontsize = fontsize)
+            
         return ax
diff --git a/dabest/plotter.py b/dabest/plotter.py
index fcd65ee5..02bec9ca 100644
--- a/dabest/plotter.py
+++ b/dabest/plotter.py
@@ -1,3 +1,5 @@
+"""Creating estimation plots."""
+
 # AUTOGENERATED! DO NOT EDIT! File to edit: ../nbs/API/plotter.ipynb.
 
 # %% auto 0
@@ -8,13 +10,15 @@
 import seaborn as sns
 import matplotlib
 import matplotlib.pyplot as plt
+import matplotlib.patches as mpatches
+from matplotlib.lines import Line2D
 import pandas as pd
 import warnings
 import logging
 
 # %% ../nbs/API/plotter.ipynb 5
 # TODO refactor function name
-def effectsize_df_plotter(effectsize_df, **plot_kwargs):
+def effectsize_df_plotter(effectsize_df: object, **plot_kwargs) -> matplotlib.figure.Figure:
     """
     Custom function that creates an estimation plot from an EffectSizeDataFrame.
     Keywords
@@ -25,46 +29,88 @@ def effectsize_df_plotter(effectsize_df, **plot_kwargs):
         A `dabest` EffectSizeDataFrame object.
     plot_kwargs
         color_col=None
-        raw_marker_size=6, es_marker_size=9,
-        swarm_label=None, contrast_label=None, delta2_label=None,
-        swarm_ylim=None, contrast_ylim=None, delta2_ylim=None,
-        custom_palette=None, swarm_desat=0.5, halfviolin_desat=1,
-        halfviolin_alpha=0.8,
-        face_color = None,
-        bar_label=None, bar_desat=0.8, bar_width = 0.5,bar_ylim = None,
-        ci=None, ci_type='bca', err_color=None,
+        raw_marker_size=6, contrast_marker_kwargs=9,
+        raw_label=None, contrast_label=None, delta2_label=None,
+        raw_ylim=None, contrast_ylim=None, delta2_ylim=None,
+        custom_palette=None, 
+        swarm_side=None, 
+        empty_circle=False,
+        face_color=None,
+        raw_desat=0.5, contrast_desat=1,
+        raw_alpha=None, contrast_alpha=0.8,
+        bar_width = 0.5,
+        ci_type='bca',
         float_contrast=True,
         show_pairs=True,
-        show_delta2=True,
-        group_summaries=None,
-        group_summaries_offset=0.1,
+        show_sample_size=True,
+        show_delta2=True, show_mini_meta=True,
+        group_summaries="mean_sd",
         fig_size=None,
         dpi=100,
         ax=None,
-        gridkey_rows=None,
         swarmplot_kwargs=None,
-        violinplot_kwargs=None,
         slopegraph_kwargs=None,
+        barplot_kwargs=None,
         sankey_kwargs=None,
+        contrast_kwargs=None,
         reflines_kwargs=None,
-        group_summary_kwargs=None,
+        group_summaries_kwargs=None,
         legend_kwargs=None,
         title=None, fontsize_title=16,
         fontsize_rawxlabel=12, fontsize_rawylabel=12,
         fontsize_contrastxlabel=12, fontsize_contrastylabel=12,
-        fontsize_delta2label=12
+        fontsize_delta2label=12,
+
+        raw_bars=True, raw_bars_kwargs=None,
+        contrast_bars=True, contrast_bars_kwargs=None,
+        reference_band=None, reference_band_kwargs=None,
+        delta_text=True, delta_text_kwargs=None,
+        delta_dot=True, delta_dot_kwargs=None,
+
+        horizontal=False, horizontal_table_kwargs=None,
+        gridkey=None, 
+        gridkey_merge_pairs=False,
+        gridkey_show_Ns=True,
+        gridkey_show_es=True,
+        gridkey_delimiters=[';', '>', '_'],
+        gridkey_kwargs=None,
+        contrast_marker_kwargs=None, contrast_errorbar_kwargs=None,
+        prop_sample_counts=False, prop_sample_counts_kwargs=None, 
+        contrast_paired_lines=True, contrast_paired_lines
+		show_baseline_ec=False,
+
     """
-    from .misc_tools import merge_two_dicts
+    from .misc_tools import (
+        get_params,
+        get_kwargs,
+        get_color_palette,
+        initialize_fig,
+        get_plot_groups,
+        add_counts_to_ticks,
+        extract_contrast_plotting_ticks,
+        set_xaxis_ticks_and_lims,
+        show_legend,
+        gardner_altman_adjustments,
+        extract_group_summaries,
+        draw_zeroline,
+        redraw_dependent_spines,
+        redraw_independent_spines,
+        prepare_bars_for_plot
+    )
     from .plot_tools import (
-        halfviolin,
-        get_swarm_spans,
         error_bar,
         sankeydiag,
         swarmplot,
-    )
-    from ._stats_tools.effsize import (
-        _compute_standardizers,
-        _compute_hedges_correction_factor,
+        delta_text_plotter,
+        delta_dots_plotter,
+        slopegraph_plotter,
+        plot_minimeta_or_deltadelta_violins,
+        effect_size_curve_plotter,
+        gridkey_plotter,
+        barplotter,
+        table_for_horizontal_plots,
+        add_counts_to_prop_plots,
+        add_bars_to_plot
     )
 
     warnings.filterwarnings(
@@ -82,1513 +128,492 @@ def effectsize_df_plotter(effectsize_df, **plot_kwargs):
         original_rcParams[parameter] = plt.rcParams[parameter]
 
     plt.rcParams["axes.grid"] = False
-
     ytick_color = plt.rcParams["ytick.color"]
-    face_color = plot_kwargs["face_color"]
-
-    if plot_kwargs["face_color"] is None:
-        face_color = "white"
-
-    dabest_obj = effectsize_df.dabest_obj
-    plot_data = effectsize_df._plot_data
-    xvar = effectsize_df.xvar
-    yvar = effectsize_df.yvar
-    is_paired = effectsize_df.is_paired
-    delta2 = effectsize_df.delta2
-    mini_meta = effectsize_df.mini_meta
-    effect_size = effectsize_df.effect_size
-    proportional = effectsize_df.proportional
-
-    all_plot_groups = dabest_obj._all_plot_groups
-    idx = dabest_obj.idx
-
-    if effect_size not in ["mean_diff", "delta_g"] or not delta2:
-        show_delta2 = False
-    else:
-        show_delta2 = plot_kwargs["show_delta2"]
-
-    if effect_size != "mean_diff" or not mini_meta:
-        show_mini_meta = False
-    else:
-        show_mini_meta = plot_kwargs["show_mini_meta"]
-
-    if show_delta2 and show_mini_meta:
-        err0 = "`show_delta2` and `show_mini_meta` cannot be True at the same time."
-        raise ValueError(err0)
-
-    # Disable Gardner-Altman plotting if any of the idxs comprise of more than
-    # two groups or if it is a delta-delta plot.
-    float_contrast = plot_kwargs["float_contrast"]
-    effect_size_type = effectsize_df.effect_size
-    if len(idx) > 1 or len(idx[0]) > 2:
-        float_contrast = False
-
-    if effect_size_type in ["cliffs_delta"]:
-        float_contrast = False
-
-    if show_delta2 or show_mini_meta:
-        float_contrast = False
-
-    if not is_paired:
-        show_pairs = False
-    else:
-        show_pairs = plot_kwargs["show_pairs"]
 
-    # Set default kwargs first, then merge with user-dictated ones.
-    # Swarmplot kwargs
-    default_swarmplot_kwargs = {"size": plot_kwargs["raw_marker_size"]}
-    if plot_kwargs["swarmplot_kwargs"] is None:
-        swarmplot_kwargs = default_swarmplot_kwargs
-    else:
-        swarmplot_kwargs = merge_two_dicts(
-            default_swarmplot_kwargs, plot_kwargs["swarmplot_kwargs"]
-        )
-    asymmetric_side = (
-        "left"  # TODO: allow users to control side for swarms of swarmplot.
+    # Extract parameters and set kwargs
+    (swarmplot_kwargs, barplot_kwargs, sankey_kwargs, contrast_kwargs, 
+     slopegraph_kwargs, reflines_kwargs, legend_kwargs, group_summaries_kwargs, 
+     redraw_axes_kwargs, delta_dot_kwargs, delta_text_kwargs, reference_band_kwargs, 
+     raw_bars_kwargs, contrast_bars_kwargs, table_kwargs, gridkey_kwargs, contrast_marker_kwargs, 
+     contrast_errorbar_kwargs, prop_sample_counts_kwargs, contrast_paired_lines_kwargs) = get_kwargs(
+                                                                                                plot_kwargs = plot_kwargs, 
+                                                                                                ytick_color = ytick_color
     )
 
-    # Barplot kwargs
-    default_barplot_kwargs = {"estimator": np.mean, "errorbar": plot_kwargs["ci"]}
-
-    if plot_kwargs["barplot_kwargs"] is None:
-        barplot_kwargs = default_barplot_kwargs
-    else:
-        barplot_kwargs = merge_two_dicts(
-            default_barplot_kwargs, plot_kwargs["barplot_kwargs"]
-        )
-
-    # Sankey Diagram kwargs
-    default_sankey_kwargs = {
-        "width": 0.4,
-        "align": "center",
-        "sankey": True,
-        "flow": True,
-        "alpha": 0.4,
-        "rightColor": False,
-        "bar_width": 0.2,
-    }
-    if plot_kwargs["sankey_kwargs"] is None:
-        sankey_kwargs = default_sankey_kwargs
-    else:
-        sankey_kwargs = merge_two_dicts(
-            default_sankey_kwargs, plot_kwargs["sankey_kwargs"]
-        )
-    # We also need to extract the `sankey` and `flow` from the kwargs for plotter.py
-    # to use for varying different kinds of paired proportional plots
-    # We also don't want to pop the parameter from the kwargs
-    sankey = sankey_kwargs["sankey"]
-    flow = sankey_kwargs["flow"]
-
-    # Violinplot kwargs.
-    default_violinplot_kwargs = {
-        "widths": 0.5,
-        "vert": True,
-        "showextrema": False,
-        "showmedians": False,
-    }
-    if plot_kwargs["violinplot_kwargs"] is None:
-        violinplot_kwargs = default_violinplot_kwargs
-    else:
-        violinplot_kwargs = merge_two_dicts(
-            default_violinplot_kwargs, plot_kwargs["violinplot_kwargs"]
-        )
-
-    # Slopegraph kwargs.
-    default_slopegraph_kwargs = {"linewidth": 1, "alpha": 0.5}
-    if plot_kwargs["slopegraph_kwargs"] is None:
-        slopegraph_kwargs = default_slopegraph_kwargs
-    else:
-        slopegraph_kwargs = merge_two_dicts(
-            default_slopegraph_kwargs, plot_kwargs["slopegraph_kwargs"]
-        )
-
-    # Zero reference-line kwargs.
-    default_reflines_kwargs = {
-        "linestyle": "solid",
-        "linewidth": 0.75,
-        "zorder": 2,
-        "color": ytick_color,
-    }
-    if plot_kwargs["reflines_kwargs"] is None:
-        reflines_kwargs = default_reflines_kwargs
-    else:
-        reflines_kwargs = merge_two_dicts(
-            default_reflines_kwargs, plot_kwargs["reflines_kwargs"]
-        )
-
-    # Legend kwargs.
-    default_legend_kwargs = {"loc": "upper left", "frameon": False}
-    if plot_kwargs["legend_kwargs"] is None:
-        legend_kwargs = default_legend_kwargs
-    else:
-        legend_kwargs = merge_two_dicts(
-            default_legend_kwargs, plot_kwargs["legend_kwargs"]
-        )
-
-    ################################################### GRIDKEY WIP - extracting arguments
-
-    gridkey_rows = plot_kwargs["gridkey_rows"]
-    gridkey_merge_pairs = plot_kwargs["gridkey_merge_pairs"]
-    gridkey_show_Ns = plot_kwargs["gridkey_show_Ns"]
-    gridkey_show_es = plot_kwargs["gridkey_show_es"]
-
-    if gridkey_rows is None:
-        gridkey_show_Ns = False
-        gridkey_show_es = False
-
-    ################################################### END GRIDKEY WIP - extracting arguments
-
-    # Group summaries kwargs.
-    gs_default = {"mean_sd", "median_quartiles", None}
-    if plot_kwargs["group_summaries"] not in gs_default:
-        raise ValueError(
-            "group_summaries must be one of" " these: {}.".format(gs_default)
-        )
-
-    default_group_summary_kwargs = {"zorder": 3, "lw": 2, "alpha": 1}
-    if plot_kwargs["group_summary_kwargs"] is None:
-        group_summary_kwargs = default_group_summary_kwargs
-    else:
-        group_summary_kwargs = merge_two_dicts(
-            default_group_summary_kwargs, plot_kwargs["group_summary_kwargs"]
-        )
-
-    # Create color palette that will be shared across subplots.
-    color_col = plot_kwargs["color_col"]
-    if color_col is None:
-        color_groups = pd.unique(plot_data[xvar])
-        bootstraps_color_by_group = True
-    else:
-        if color_col not in plot_data.columns:
-            raise KeyError("``{}`` is not a column in the data.".format(color_col))
-        color_groups = pd.unique(plot_data[color_col])
-        bootstraps_color_by_group = False
-    if show_pairs:
-        bootstraps_color_by_group = False
-
-    # Handle the color palette.
-    names = color_groups
-    n_groups = len(color_groups)
-    custom_pal = plot_kwargs["custom_palette"]
-    swarm_desat = plot_kwargs["swarm_desat"]
-    bar_desat = plot_kwargs["bar_desat"]
-    contrast_desat = plot_kwargs["halfviolin_desat"]
-
-    if custom_pal is None:
-        unsat_colors = sns.color_palette(n_colors=n_groups)
-    else:
-        if isinstance(custom_pal, dict):
-            groups_in_palette = {
-                k: v for k, v in custom_pal.items() if k in color_groups
-            }
-
-            names = groups_in_palette.keys()
-            unsat_colors = groups_in_palette.values()
-
-        elif isinstance(custom_pal, list):
-            unsat_colors = custom_pal[0:n_groups]
-
-        elif isinstance(custom_pal, str):
-            # check it is in the list of matplotlib palettes.
-            if custom_pal in plt.colormaps():
-                unsat_colors = sns.color_palette(custom_pal, n_groups)
-            else:
-                err1 = "The specified `custom_palette` {}".format(custom_pal)
-                err2 = " is not a matplotlib palette. Please check."
-                raise ValueError(err1 + err2)
-
-    if custom_pal is None and color_col is None:
-        swarm_colors = [sns.desaturate(c, swarm_desat) for c in unsat_colors]
-        plot_palette_raw = dict(zip(names.categories, swarm_colors))
-
-        bar_color = [sns.desaturate(c, bar_desat) for c in unsat_colors]
-        plot_palette_bar = dict(zip(names.categories, bar_color))
-
-        contrast_colors = [sns.desaturate(c, contrast_desat) for c in unsat_colors]
-        plot_palette_contrast = dict(zip(names.categories, contrast_colors))
-
-        # For Sankey Diagram plot, no need to worry about the color, each bar will have the same two colors
-        # default color palette will be set to "hls"
-        plot_palette_sankey = None
-
-    else:
-        swarm_colors = [sns.desaturate(c, swarm_desat) for c in unsat_colors]
-        plot_palette_raw = dict(zip(names, swarm_colors))
-
-        bar_color = [sns.desaturate(c, bar_desat) for c in unsat_colors]
-        plot_palette_bar = dict(zip(names, bar_color))
-
-        contrast_colors = [sns.desaturate(c, contrast_desat) for c in unsat_colors]
-        plot_palette_contrast = dict(zip(names, contrast_colors))
-
-        plot_palette_sankey = custom_pal
-
-    # Infer the figsize.
-    fig_size = plot_kwargs["fig_size"]
-    if fig_size is None:
-        all_groups_count = np.sum([len(i) for i in dabest_obj.idx])
-        # Increase the width for delta-delta graph
-        if show_delta2 or show_mini_meta:
-            all_groups_count += 2
-        if is_paired and show_pairs and proportional is False:
-            frac = 0.75
-        else:
-            frac = 1
-        if float_contrast:
-            height_inches = 4
-            each_group_width_inches = 2.5 * frac
-        else:
-            height_inches = 6
-            each_group_width_inches = 1.5 * frac
+    (dabest_obj, plot_data, xvar, yvar, is_paired, effect_size, proportional, 
+     all_plot_groups, idx, show_delta2, show_mini_meta, float_contrast, 
+     show_pairs, group_summaries, horizontal, results, ci_type, x1_level, experiment_label, 
+     show_baseline_ec, one_sankey, two_col_sankey, asymmetric_side, show_sample_size) = get_params(
+                                                                                            effectsize_df = effectsize_df, 
+                                                                                            plot_kwargs = plot_kwargs,
+                                                                                            sankey_kwargs = sankey_kwargs,
+                                                                                            barplot_kwargs = barplot_kwargs
+    )
 
-        width_inches = each_group_width_inches * all_groups_count
-        fig_size = (width_inches, height_inches)
+    # Extract Color palette
+    (color_col, bootstraps_color_by_group, n_groups, filled, raw_colors,
+     plot_palette_raw, plot_palette_contrast, plot_palette_sankey) = get_color_palette(
+                                                                                plot_kwargs = plot_kwargs, 
+                                                                                plot_data = plot_data, 
+                                                                                xvar = xvar, 
+                                                                                show_pairs = show_pairs,
+                                                                                idx = idx,
+                                                                                all_plot_groups = all_plot_groups,
+                                                                                delta2 = effectsize_df.delta2,
+                                                                                sankey = True if proportional and show_pairs else False,
+    )
 
     # Initialise the figure.
-    init_fig_kwargs = dict(figsize=fig_size, dpi=plot_kwargs["dpi"], tight_layout=True)
-
-    width_ratios_ga = [2.5, 1]
-
-    ###################### GRIDKEY HSPACE ALTERATION
-
-    # Sets hspace for cummings plots if gridkey is shown.
-    if gridkey_rows is not None:
-        h_space_cummings = 0.1
-    else:
-        h_space_cummings = 0.3
-
-    ###################### END GRIDKEY HSPACE ALTERATION
-
-    if plot_kwargs["ax"] is not None:
-        # New in v0.2.6.
-        # Use inset axes to create the estimation plot inside a single axes.
-        # Author: Adam L Nekimken. (PR #73)
-        rawdata_axes = plot_kwargs["ax"]
-        ax_position = rawdata_axes.get_position()  # [[x0, y0], [x1, y1]]
-
-        fig = rawdata_axes.get_figure()
-        fig.patch.set_facecolor(face_color)
-
-        if float_contrast:
-            axins = rawdata_axes.inset_axes(
-                [1, 0, width_ratios_ga[1] / width_ratios_ga[0], 1]
+    fig, rawdata_axes, contrast_axes, table_axes = initialize_fig(
+                                                            plot_kwargs = plot_kwargs, 
+                                                            dabest_obj = dabest_obj, 
+                                                            show_delta2 = show_delta2, 
+                                                            show_mini_meta = show_mini_meta, 
+                                                            is_paired = is_paired, 
+                                                            show_pairs = show_pairs, 
+                                                            proportional = proportional, 
+                                                            float_contrast = float_contrast,
+                                                            effect_size_type = effect_size,
+                                                            yvar = yvar,
+                                                            horizontal = horizontal,
+                                                            show_table = table_kwargs['show'],
+                                                            color_col = color_col
+    )
+    
+    # Plotting the rawdata.
+    if show_pairs:  ## Paired plots!
+        temp_idx, temp_all_plot_groups = get_plot_groups(
+                                                    is_paired = is_paired, 
+                                                    idx = idx, 
+                                                    proportional = proportional, 
+                                                    all_plot_groups = all_plot_groups
+        )
+        if proportional:  ## Plot the raw data as a set of Sankey Diagrams aligned like barplot.
+            if sankey_kwargs["flow"] == False and len(temp_all_plot_groups) == 2: 
+                sankey_kwargs["flow"], two_col_sankey = True, False
+                warnings.warn("Sankey flow must be true for singular two-group sankey plots")
+            sankey_control_test_groups = sankeydiag(
+                                                    plot_data,
+                                                    xvar = xvar,
+                                                    yvar = yvar,
+                                                    temp_all_plot_groups = temp_all_plot_groups,
+                                                    idx = idx,
+                                                    temp_idx = temp_idx,
+                                                    palette = plot_palette_sankey,
+                                                    ax = rawdata_axes,
+                                                    horizontal = horizontal,
+                                                    **sankey_kwargs
             )
-            rawdata_axes.set_position(  # [l, b, w, h]
-                [
-                    ax_position.x0,
-                    ax_position.y0,
-                    (ax_position.x1 - ax_position.x0)
-                    * (width_ratios_ga[0] / sum(width_ratios_ga)),
-                    (ax_position.y1 - ax_position.y0),
-                ]
+        else: ## Plot the raw data as a slopegraph.
+            slopegraph_plotter(
+                dabest_obj = dabest_obj, 
+                plot_data = plot_data, 
+                xvar = xvar, 
+                yvar = yvar, 
+                color_col = color_col, 
+                plot_palette_raw = plot_palette_raw, 
+                slopegraph_kwargs = slopegraph_kwargs, 
+                rawdata_axes = rawdata_axes, 
+                ytick_color = ytick_color, 
+                temp_idx = temp_idx,
+                horizontal = horizontal,
+                temp_all_plot_groups = temp_all_plot_groups, 
+                plot_kwargs = plot_kwargs,
             )
-
-            contrast_axes = axins
-
-        else:
-            axins = rawdata_axes.inset_axes([0, -1 - h_space_cummings, 1, 1])
-            plot_height = (ax_position.y1 - ax_position.y0) / (2 + h_space_cummings)
-            rawdata_axes.set_position(
-                [
-                    ax_position.x0,
-                    ax_position.y0 + (1 + h_space_cummings) * plot_height,
-                    (ax_position.x1 - ax_position.x0),
-                    plot_height,
-                ]
+            
+            ## Add delta dots to the contrast axes for paired plots.
+            show_delta_dots = plot_kwargs["delta_dot"]
+            unavailable_effect_sizes = ["hedges_g", "delta_g", "cohens_d"]
+            if show_delta_dots and is_paired and not any([es in effect_size for es in unavailable_effect_sizes]):
+                delta_dots_plotter(
+                    plot_data = plot_data, 
+                    contrast_axes = contrast_axes, 
+                    delta_id_col = dabest_obj.id_col, 
+                    idx = idx, 
+                    xvar = xvar, 
+                    yvar = yvar, 
+                    is_paired = is_paired, 
+                    color_col = color_col, 
+                    float_contrast = float_contrast, 
+                    plot_palette_raw = plot_palette_raw, 
+                    delta_dot_kwargs = delta_dot_kwargs,
+                    horizontal = horizontal,
+                    )
+                
+    else:  ## Unpaired plots!
+        if proportional:  # Plot the raw data as a barplot.
+            barplotter(
+                xvar = xvar, 
+                yvar = yvar, 
+                all_plot_groups = all_plot_groups, 
+                rawdata_axes = rawdata_axes, 
+                plot_data = plot_data, 
+                raw_colors = raw_colors, 
+                plot_palette_raw = plot_palette_raw, 
+                color_col = color_col,
+                barplot_kwargs = barplot_kwargs,
+                horizontal = horizontal,
             )
-
-        contrast_axes = axins
-        rawdata_axes.contrast_axes = axins
-
-    else:
-        # Here, we hardcode some figure parameters.
-        if float_contrast:
-            fig, axx = plt.subplots(
-                ncols=2,
-                gridspec_kw={"width_ratios": width_ratios_ga, "wspace": 0},
-                **init_fig_kwargs
+        else:   ## Plot the raw data as a swarmplot.
+            ## swarmplot() plots swarms based on current size of ax
+            ## Therefore, since the ax size for show_mini_meta and show_delta changes later on, there has to be increased jitter
+            rawdata_plot = swarmplot(
+                            data = plot_data,
+                            x = xvar,
+                            y = yvar,
+                            ax = rawdata_axes,
+                            order = all_plot_groups,
+                            hue = color_col,
+                            palette = plot_palette_raw,
+                            zorder = 1,
+                            side = asymmetric_side,
+                            jitter = 1.25 if show_mini_meta else 1.4 if show_delta2 else 1, # TODO: to make jitter value more accurate and not just a hardcoded eyeball value
+                            filled = filled,
+                            is_drop_gutter = True,
+                            gutter_limit = 0.45,
+                            horizontal = horizontal,
+                            **swarmplot_kwargs
             )
-            fig.patch.set_facecolor(face_color)
-
-        else:
-            fig, axx = plt.subplots(
-                nrows=2, gridspec_kw={"hspace": h_space_cummings}, **init_fig_kwargs
-            )
-            fig.patch.set_facecolor(face_color)
-
-        # Title
-        title = plot_kwargs["title"]
-        fontsize_title = plot_kwargs["fontsize_title"]
-        if title is not None:
-            fig.suptitle(title, fontsize=fontsize_title)
-        rawdata_axes = axx[0]
-        contrast_axes = axx[1]
-    rawdata_axes.set_frame_on(False)
-    contrast_axes.set_frame_on(False)
-
-    redraw_axes_kwargs = {
-        "colors": ytick_color,
-        "facecolors": ytick_color,
-        "lw": 1,
-        "zorder": 10,
-        "clip_on": False,
-    }
-
-    swarm_ylim = plot_kwargs["swarm_ylim"]
-
-    if swarm_ylim is not None:
-        rawdata_axes.set_ylim(swarm_ylim)
-
-    one_sankey = (
-        False if is_paired is not None else None
-    )  # Flag to indicate if only one sankey is plotted.
-    two_col_sankey = (
-        True if proportional and not one_sankey and sankey and not flow else False
-    )
-
-    if show_pairs:
-        # Determine temp_idx based on is_paired and proportional conditions
-        if is_paired == "baseline":
-            idx_pairs = [
-                (control, test)
-                for i in idx
-                for control, test in zip([i[0]] * (len(i) - 1), i[1:])
-            ]
-            temp_idx = idx if not proportional else idx_pairs
-        else:
-            idx_pairs = [
-                (control, test) for i in idx for control, test in zip(i[:-1], i[1:])
-            ]
-            temp_idx = idx if not proportional else idx_pairs
-
-        # Determine temp_all_plot_groups based on proportional condition
-        plot_groups = [item for i in temp_idx for item in i]
-        temp_all_plot_groups = all_plot_groups if not proportional else plot_groups
-
-        if not proportional:
-            # Plot the raw data as a slopegraph.
-            # Pivot the long (melted) data.
             if color_col is None:
-                pivot_values = [yvar]
-            else:
-                pivot_values = [yvar, color_col]
-            pivoted_plot_data = pd.pivot(
-                data=plot_data,
-                index=dabest_obj.id_col,
-                columns=xvar,
-                values=pivot_values,
-            )
-            x_start = 0
-            for ii, current_tuple in enumerate(temp_idx):
-                current_pair = pivoted_plot_data.loc[
-                    :, pd.MultiIndex.from_product([pivot_values, current_tuple])
-                ].dropna()
-                grp_count = len(current_tuple)
-                # Iterate through the data for the current tuple.
-                for ID, observation in current_pair.iterrows():
-                    x_points = [t for t in range(x_start, x_start + grp_count)]
-                    y_points = observation[yvar].tolist()
-
-                    if color_col is None:
-                        slopegraph_kwargs["color"] = ytick_color
-                    else:
-                        color_key = observation[color_col][0]
-                        if isinstance(color_key, (str, np.int64, np.float64)):
-                            slopegraph_kwargs["color"] = plot_palette_raw[color_key]
-                            slopegraph_kwargs["label"] = color_key
-
-                    rawdata_axes.plot(x_points, y_points, **slopegraph_kwargs)
-
-                x_start = x_start + grp_count
-
-            ##################### DELTA PTS ON CONTRAST PLOT WIP
-
-            contrast_show_deltas = plot_kwargs["contrast_show_deltas"]
-
-            if is_paired is None:
-                contrast_show_deltas = False
-
-            if contrast_show_deltas:
-                delta_plot_data_temp = plot_data.copy()
-                delta_id_col = dabest_obj.id_col
-                if color_col is not None:
-                    plot_palette_deltapts = plot_palette_raw
-                    delta_plot_data = delta_plot_data_temp[
-                        [xvar, yvar, delta_id_col, color_col]
-                    ]
-                    deltapts_args = {
-                        "marker": "^",
-                        "alpha": 0.5,
-                    }
-
-                else:
-                    plot_palette_deltapts = "k"
-                    delta_plot_data = delta_plot_data_temp[[xvar, yvar, delta_id_col]]
-                    deltapts_args = {"marker": "^", "alpha": 0.5}
-
-                final_deltas = pd.DataFrame()
-                for i in idx:
-                    for j in i:
-                        if i.index(j) != 0:
-                            temp_df_exp = delta_plot_data[
-                                delta_plot_data[xvar].str.contains(j)
-                            ].reset_index(drop=True)
-                            if is_paired == "baseline":
-                                temp_df_cont = delta_plot_data[
-                                    delta_plot_data[xvar].str.contains(i[0])
-                                ].reset_index(drop=True)
-                            elif is_paired == "sequential":
-                                temp_df_cont = delta_plot_data[
-                                    delta_plot_data[xvar].str.contains(
-                                        i[i.index(j) - 1]
-                                    )
-                                ].reset_index(drop=True)
-                            delta_df = temp_df_exp.copy()
-                            delta_df[yvar] = temp_df_exp[yvar] - temp_df_cont[yvar]
-                            final_deltas = pd.concat([final_deltas, delta_df])
-
-                # swarmplot() plots swarms based on current size of ax
-                # Therefore, since the ax size for Gardner-Altman plot changes later on, there has to be decreased jitter
-                # TODO: to make jitter value more accurate and not just a hardcoded eyeball value
-                if float_contrast:
-                    jitter = 0.6
-                else:
-                    jitter = 1
-
-                # Plot the raw data as a swarmplot.
-                deltapts_plot = swarmplot(
-                    data=final_deltas,
-                    x=xvar,
-                    y=yvar,
-                    ax=contrast_axes,
-                    order=None,
-                    hue=color_col,
-                    palette=plot_palette_deltapts,
-                    zorder=2,
-                    size=3,
-                    side="right",
-                    jitter=jitter,
-                    is_drop_gutter=True,
-                    gutter_limit=1,
-                    **deltapts_args
-                )
-                contrast_axes.legend().set_visible(False)
-
-            ##################### DELTA PTS ON CONTRAST PLOT END
-
-            # Set the tick labels, because the slopegraph plotting doesn't.
-            rawdata_axes.set_xticks(np.arange(0, len(temp_all_plot_groups)))
-            rawdata_axes.set_xticklabels(temp_all_plot_groups)
-
-        else:
-            # Plot the raw data as a set of Sankey Diagrams aligned like barplot.
-            group_summaries = plot_kwargs["group_summaries"]
-            if group_summaries is None:
-                group_summaries = "mean_sd"
-            err_color = plot_kwargs["err_color"]
-            if err_color is None:
-                err_color = "black"
-
-            if show_pairs:
-                sankey_control_group = []
-                sankey_test_group = []
-                # Design for Sankey Flow Diagram
-                sankey_idx = (
-                    [
-                        (control, test)
-                        for i in idx
-                        for control, test in zip(i[:], (i[1:] + (i[0],)))
-                    ]
-                    if flow
-                    else temp_idx
-                )
-                for i in sankey_idx:
-                    sankey_control_group.append(i[0])
-                    sankey_test_group.append(i[1])
-
-            if len(temp_all_plot_groups) == 2:
-                one_sankey = True
-                sankey_control_group.pop()
-                sankey_test_group.pop()  # Remove the last element from two lists
-
-            # two_col_sankey = True if proportional == True and one_sankey == False and sankey == True and flow == False else False
-
-            # Replace the paired proportional plot with sankey diagram
-            sankeyplot = sankeydiag(
-                plot_data,
-                xvar=xvar,
-                yvar=yvar,
-                left_idx=sankey_control_group,
-                right_idx=sankey_test_group,
-                palette=plot_palette_sankey,
-                ax=rawdata_axes,
-                one_sankey=one_sankey,
-                **sankey_kwargs
-            )
-
-    else:
-        if not proportional:
-            # Plot the raw data as a swarmplot.
-            asymmetric_side = (
-                plot_kwargs["swarm_side"] if plot_kwargs["swarm_side"] is not None else "right"
-            )  # Default asymmetric side is right
-
-            # swarmplot() plots swarms based on current size of ax
-            # Therefore, since the ax size for mini_meta and show_delta changes later on, there has to be increased jitter
-            # TODO: to make jitter value more accurate and not just a hardcoded eyeball value
-            if show_mini_meta:
-                jitter = 1.25
-            elif show_delta2:
-                jitter = 1.4
-            else:
-                jitter = 1
-
-            if color_col is None: # Determine the use of hue
-                rawdata_plot = swarmplot(
-                    data=plot_data,
-                    x=xvar,
-                    y=yvar,
-                    ax=rawdata_axes,
-                    order=all_plot_groups,
-                    hue=xvar,
-                    palette=plot_palette_raw,
-                    zorder=1,
-                    side=asymmetric_side,
-                    jitter=jitter,
-                    is_drop_gutter=True,
-                    gutter_limit=0.45,
-                    **swarmplot_kwargs
-                )
                 rawdata_plot.legend().set_visible(False)
-            else:
-                rawdata_plot = swarmplot(
-                    data=plot_data,
-                    x=xvar,
-                    y=yvar,
-                    ax=rawdata_axes,
-                    order=all_plot_groups,
-                    hue=color_col,
-                    palette=plot_palette_raw,
-                    zorder=1,
-                    side=asymmetric_side,
-                    jitter=jitter,
-                    is_drop_gutter=True,
-                    gutter_limit=0.45,
-                    **swarmplot_kwargs
-                )
-        else:
-            # Plot the raw data as a barplot.
-            bar1_df = pd.DataFrame(
-                {xvar: all_plot_groups, "proportion": np.ones(len(all_plot_groups))}
-            )
-            bar1 = sns.barplot(
-                data=bar1_df,
-                x=xvar,
-                y="proportion",
-                ax=rawdata_axes,
-                order=all_plot_groups,
-                linewidth=2,
-                facecolor=(1, 1, 1, 0),
-                edgecolor=bar_color,
-                zorder=1,
-            )
-            bar2 = sns.barplot(
-                data=plot_data,
-                x=xvar,
-                y=yvar,
-                ax=rawdata_axes,
-                order=all_plot_groups,
-                palette=plot_palette_bar,
-                zorder=1,
-                **barplot_kwargs
-            )
-            # adjust the width of bars
-            bar_width = plot_kwargs["bar_width"]
-            for bar in bar1.patches:
-                x = bar.get_x()
-                width = bar.get_width()
-                centre = x + width / 2.0
-                bar.set_x(centre - bar_width / 2.0)
-                bar.set_width(bar_width)
-
-        # Plot the gapped line summaries, if this is not a Cumming plot.
-        # Also, we will not plot gapped lines for paired plots. For now.
-        group_summaries = plot_kwargs["group_summaries"]
-        if group_summaries is None:
-            group_summaries = "mean_sd"
-
-        if group_summaries is not None and not proportional:
-            # Create list to gather xspans.
-            xspans = []
-            line_colors = []
-            for jj, c in enumerate(rawdata_axes.collections):
-                try:
-                    if asymmetric_side == "right":
-                        # currently offset is hardcoded with value of -0.2
-                        x_max_span = -0.2
-                    else:
-                        _, x_max, _, _ = get_swarm_spans(c)
-                        x_max_span = x_max - jj
-                    xspans.append(x_max_span)
-                except TypeError:
-                    # we have got a None, so skip and move on.
-                    pass
-
-                if bootstraps_color_by_group:
-                    line_colors.append(plot_palette_raw[all_plot_groups[jj]])
-
-                # Break the loop since hue in Seaborn adds collections to axes and it will result in index out of range
-                if jj >= n_groups - 1 and color_col is None:
-                    break
-
-            if len(line_colors) != len(all_plot_groups):
-                line_colors = ytick_color
-
-            error_bar(
-                plot_data,
-                x=xvar,
-                y=yvar,
-                # Hardcoded offset...
-                offset=xspans + np.array(plot_kwargs["group_summaries_offset"]),
-                line_color=line_colors,
-                gap_width_percent=1.5,
-                type=group_summaries,
-                ax=rawdata_axes,
-                method="gapped_lines",
-                **group_summary_kwargs
+            
+        ## Plot the error bars on unpaired plots.
+        if group_summaries is not None:
+            (group_summaries_method, 
+             group_summaries_offset, group_summaries_line_color) = extract_group_summaries(
+                                                                            proportional = proportional, 
+                                                                            rawdata_axes = rawdata_axes, 
+                                                                            asymmetric_side = asymmetric_side if not proportional else None, 
+                                                                            horizontal = horizontal, 
+                                                                            bootstraps_color_by_group = bootstraps_color_by_group, 
+                                                                            plot_palette_raw = plot_palette_raw, 
+                                                                            all_plot_groups = all_plot_groups,
+                                                                            n_groups = n_groups, 
+                                                                            color_col = color_col, 
+                                                                            ytick_color = ytick_color, 
+                                                                            group_summaries_kwargs = group_summaries_kwargs
             )
-
-        if group_summaries is not None and proportional:
-            err_color = plot_kwargs["err_color"]
-            if err_color is None:
-                err_color = "black"
+            ## Plot the error bar
             error_bar(
                 plot_data,
-                x=xvar,
-                y=yvar,
-                offset=0,
-                line_color=err_color,
-                gap_width_percent=1.5,
-                type=group_summaries,
-                ax=rawdata_axes,
-                method="proportional_error_bar",
-                **group_summary_kwargs
+                x = xvar,
+                y = yvar,
+                offset = group_summaries_offset,
+                line_color = group_summaries_line_color,
+                type = group_summaries,
+                ax = rawdata_axes,
+                method = group_summaries_method,
+                horizontal = horizontal,
+                **group_summaries_kwargs
             )
 
     # Add the counts to the rawdata axes xticks.
-    counts = plot_data.groupby(xvar).count()[yvar]
-    ticks_with_counts = []
-    ticks_loc = rawdata_axes.get_xticks()
-    rawdata_axes.xaxis.set_major_locator(matplotlib.ticker.FixedLocator(ticks_loc))
-    for xticklab in rawdata_axes.xaxis.get_ticklabels():
-        t = xticklab.get_text()
-        if t.rfind("\n") != -1:
-            te = t[t.rfind("\n") + len("\n") :]
-            N = str(counts.loc[te])
-            te = t
-        else:
-            te = t
-            N = str(counts.loc[te])
-
-        ticks_with_counts.append("{}\nN = {}".format(te, N))
-
-    if plot_kwargs["fontsize_rawxlabel"] is not None:
-        fontsize_rawxlabel = plot_kwargs["fontsize_rawxlabel"]
-    rawdata_axes.set_xticklabels(ticks_with_counts, fontsize=fontsize_rawxlabel)
-
-    # Save the handles and labels for the legend.
-    handles, labels = rawdata_axes.get_legend_handles_labels()
-    legend_labels = [l for l in labels]
-    legend_handles = [h for h in handles]
-    if bootstraps_color_by_group is False:
-        rawdata_axes.legend().set_visible(False)
-
-    # Enforce the xtick of rawdata_axes to be 0 and 1 after drawing only one sankey
-    if one_sankey:
-        rawdata_axes.set_xticks([0, 1])
-
-    # Plot effect sizes and bootstraps.
-    # Take note of where the `control` groups are.
-    if is_paired == "baseline" and show_pairs:
-        if two_col_sankey:
-            ticks_to_skip = []
-            ticks_to_plot = np.arange(0, len(temp_all_plot_groups) / 2).tolist()
-            ticks_to_start_twocol_sankey = np.cumsum([len(i) - 1 for i in idx]).tolist()
-            ticks_to_start_twocol_sankey.pop()
-            ticks_to_start_twocol_sankey.insert(0, 0)
-        else:
-            # ticks_to_skip = np.arange(0, len(temp_all_plot_groups), 2).tolist()
-            # ticks_to_plot = np.arange(1, len(temp_all_plot_groups), 2).tolist()
-            ticks_to_skip = np.cumsum([len(t) for t in idx])[:-1].tolist()
-            ticks_to_skip.insert(0, 0)
-            # Then obtain the ticks where we have to plot the effect sizes.
-            ticks_to_plot = [
-                t for t in range(0, len(all_plot_groups)) if t not in ticks_to_skip
-            ]
-            ticks_to_skip_contrast = np.cumsum([(len(t)) for t in idx])[:-1].tolist()
-            ticks_to_skip_contrast.insert(0, 0)
-    else:
-        if two_col_sankey:
-            ticks_to_skip = [len(sankey_control_group)]
-            # Then obtain the ticks where we have to plot the effect sizes.
-            ticks_to_plot = [
-                t for t in range(0, len(temp_idx)) if t not in ticks_to_skip
-            ]
-            ticks_to_skip = []
-            ticks_to_start_twocol_sankey = np.cumsum([len(i) - 1 for i in idx]).tolist()
-            ticks_to_start_twocol_sankey.pop()
-            ticks_to_start_twocol_sankey.insert(0, 0)
-        else:
-            ticks_to_skip = np.cumsum([len(t) for t in idx])[:-1].tolist()
-            ticks_to_skip.insert(0, 0)
-            # Then obtain the ticks where we have to plot the effect sizes.
-            ticks_to_plot = [
-                t for t in range(0, len(all_plot_groups)) if t not in ticks_to_skip
-            ]
-
-    # Plot the bootstraps, then the effect sizes and CIs.
-    es_marker_size = plot_kwargs["es_marker_size"]
-    halfviolin_alpha = plot_kwargs["halfviolin_alpha"]
-
-    ci_type = plot_kwargs["ci_type"]
-
-    results = effectsize_df.results
-    contrast_xtick_labels = []
-
-    for j, tick in enumerate(ticks_to_plot):
-        current_group = results.test[j]
-        current_control = results.control[j]
-        current_bootstrap = results.bootstraps[j]
-        current_effsize = results.difference[j]
-        if ci_type == "bca":
-            current_ci_low = results.bca_low[j]
-            current_ci_high = results.bca_high[j]
-        else:
-            current_ci_low = results.pct_low[j]
-            current_ci_high = results.pct_high[j]
-
-        # Create the violinplot.
-        # New in v0.2.6: drop negative infinities before plotting.
-        v = contrast_axes.violinplot(
-            current_bootstrap[~np.isinf(current_bootstrap)],
-            positions=[tick],
-            **violinplot_kwargs
+    if show_sample_size:
+        add_counts_to_ticks(
+                plot_data = plot_data, 
+                xvar = xvar, 
+                yvar = yvar, 
+                rawdata_axes = rawdata_axes, 
+                plot_kwargs = plot_kwargs,
+                flow = sankey_kwargs["flow"],
+                horizontal = horizontal,
         )
-        # Turn the violinplot into half, and color it the same as the swarmplot.
-        # Do this only if the color column is not specified.
-        # Ideally, the alpha (transparency) fo the violin plot should be
-        # less than one so the effect size and CIs are visible.
-        if bootstraps_color_by_group:
-            fc = plot_palette_contrast[current_group]
-        else:
-            fc = "grey"
-
-        halfviolin(v, fill_color=fc, alpha=halfviolin_alpha)
-
-        # Plot the effect size.
-        contrast_axes.plot(
-            [tick],
-            current_effsize,
-            marker="o",
-            color=ytick_color,
-            markersize=es_marker_size,
-        )
-
-        ################## SHOW ES ON CONTRAST PLOT WIP
-
-        contrast_show_es = plot_kwargs["contrast_show_es"]
-        es_sf = plot_kwargs["es_sf"]
-        es_fontsize = plot_kwargs["es_fontsize"]
-
-        if gridkey_show_es:
-            contrast_show_es = False
-
-        effsize_for_print = current_effsize
 
-        printed_es = np.format_float_positional(
-            effsize_for_print, precision=es_sf, sign=True, trim="k", min_digits=es_sf
+    # Add counts to prop plots (embedded in the plot bars)
+    if proportional and plot_kwargs['prop_sample_counts'] and sankey_kwargs["flow"]:
+        add_counts_to_prop_plots(
+                        plot_data = plot_data, 
+                        xvar = xvar, 
+                        yvar = yvar, 
+                        rawdata_axes = rawdata_axes, 
+                        horizontal = horizontal,
+                        is_paired = is_paired,
+                        prop_sample_counts_kwargs = prop_sample_counts_kwargs,
         )
-        if contrast_show_es:
-            if effsize_for_print < 0:
-                textoffset = 10
-            else:
-                textoffset = 15
-            contrast_axes.annotate(
-                text=printed_es,
-                xy=(tick, effsize_for_print),
-                xytext=(
-                    -textoffset - len(printed_es) * es_fontsize / 2,
-                    -es_fontsize / 2,
-                ),
-                textcoords="offset points",
-                **{"fontsize": es_fontsize}
-            )
-
-        ################## SHOW ES ON CONTRAST PLOT END
 
-        # Plot the confidence interval.
-        contrast_axes.plot(
-            [tick, tick],
-            [current_ci_low, current_ci_high],
-            linestyle="-",
-            color=ytick_color,
-            linewidth=group_summary_kwargs["lw"],
-        )
+    ## Swarm bars
+    raw_bars = plot_kwargs["raw_bars"]
+    if raw_bars and not proportional and not horizontal: #Currently not supporting swarm bars for horizontal plots (looks weird)
+        raw_bars_dict, raw_bars_kwargs = prepare_bars_for_plot(
+                                                    bar_type = 'raw', 
+                                                    bar_kwargs = raw_bars_kwargs, 
+                                                    horizontal = horizontal,
+                                                    plot_palette_raw = plot_palette_raw,
+                                                    color_col = color_col, 
+                                                    show_pairs = show_pairs, 
+                                                    plot_data = plot_data,
+                                                    xvar = xvar, 
+                                                    yvar = yvar,      
+                                        )
+        add_bars_to_plot(bar_dict = raw_bars_dict, 
+                         ax = rawdata_axes, 
+                         bar_kwargs = raw_bars_kwargs
+                        )
+
+    # Plot the contrast axes - effect sizes and bootstraps!
+    plot_groups = (temp_all_plot_groups if (is_paired == "baseline" and show_pairs and two_col_sankey) 
+                   else temp_idx if two_col_sankey 
+                   else all_plot_groups
+    )
 
-        contrast_xtick_labels.append(
-            "{}\nminus\n{}".format(current_group, current_control)
-        )
+    ## Extract ticks for contrast plot
+    (ticks_to_skip, ticks_to_plot, ticks_for_baseline_ec,
+     ticks_to_skip_contrast, ticks_to_start_twocol_sankey) = extract_contrast_plotting_ticks(
+                                                                                    is_paired = is_paired, 
+                                                                                    show_pairs = show_pairs, 
+                                                                                    two_col_sankey = two_col_sankey, 
+                                                                                    plot_groups = plot_groups,
+                                                                                    idx = idx,
+                                                                                    sankey_control_group = sankey_control_test_groups[0] if two_col_sankey else None,
+    )                                                                  
+
+    ## Adjust contrast tick locations to account for different plotting styles in horizontal plots
+    table_axes_ticks_to_plot = ticks_to_plot
+    if (horizontal and proportional and not show_pairs) or (horizontal and plot_kwargs["swarm_side"] == "right"):
+        ticks_to_plot = [x+0.25 for x in ticks_to_plot]
+
+    ## Plot the bootstraps, then the effect sizes and CIs.
+    contrast_paired_lines = False if float_contrast or not sankey_kwargs["flow"] else plot_kwargs["contrast_paired_lines"]
+    (current_group, current_control,
+     current_effsize, contrast_xtick_labels) = effect_size_curve_plotter(
+                                                                ticks_to_plot = ticks_to_plot, 
+                                                                ticks_for_baseline_ec = ticks_for_baseline_ec,
+                                                                results = results, 
+                                                                ci_type = ci_type, 
+                                                                contrast_axes = contrast_axes, 
+                                                                contrast_kwargs = contrast_kwargs, 
+                                                                bootstraps_color_by_group = bootstraps_color_by_group,
+                                                                plot_palette_contrast = plot_palette_contrast,
+                                                                horizontal = horizontal,
+                                                                contrast_marker_kwargs = contrast_marker_kwargs,
+                                                                contrast_errorbar_kwargs = contrast_errorbar_kwargs,
+                                                                idx = idx,
+                                                                is_paired = is_paired,
+                                                                contrast_paired_lines = contrast_paired_lines,
+																contrast_paired_lines_kwargs = contrast_paired_lines_kwargs,
+																show_baseline_ec = show_baseline_ec,
+    )
 
-    # Plot mini-meta violin
+    ## Plot mini-meta or delta-delta violin
+    delta2_axes = None
     if show_mini_meta or show_delta2:
-        if show_mini_meta:
-            mini_meta_delta = effectsize_df.mini_meta_delta
-            data = mini_meta_delta.bootstraps_weighted_delta
-            difference = mini_meta_delta.difference
-            if ci_type == "bca":
-                ci_low = mini_meta_delta.bca_low
-                ci_high = mini_meta_delta.bca_high
-            else:
-                ci_low = mini_meta_delta.pct_low
-                ci_high = mini_meta_delta.pct_high
-        else:
-            delta_delta = effectsize_df.delta_delta
-            data = delta_delta.bootstraps_delta_delta
-            difference = delta_delta.difference
-            if ci_type == "bca":
-                ci_low = delta_delta.bca_low
-                ci_high = delta_delta.bca_high
-            else:
-                ci_low = delta_delta.pct_low
-                ci_high = delta_delta.pct_high
-        # Create the violinplot.
-        # New in v0.2.6: drop negative infinities before plotting.
-        position = max(rawdata_axes.get_xticks()) + 2
-        v = contrast_axes.violinplot(
-            data[~np.isinf(data)], positions=[position], **violinplot_kwargs
+        delta2_axes, contrast_xtick_labels = plot_minimeta_or_deltadelta_violins(
+                                                                dabest_obj = effectsize_df.mini_meta if show_mini_meta else effectsize_df.delta_delta,
+                                                                type = 'mini_meta' if show_mini_meta else 'delta_delta',
+                                                                ci_type = ci_type, 
+                                                                rawdata_axes = rawdata_axes,
+                                                                contrast_axes = contrast_axes, 
+                                                                contrast_kwargs = contrast_kwargs, 
+                                                                contrast_xtick_labels = contrast_xtick_labels, 
+                                                                effect_size = effect_size,
+                                                                plot_kwargs = plot_kwargs, 
+                                                                horizontal = horizontal,
+                                                                show_pairs = show_pairs,
+                                                                contrast_marker_kwargs = contrast_marker_kwargs,
+                                                                contrast_errorbar_kwargs = contrast_errorbar_kwargs,
         )
-
-        fc = "grey"
-
-        halfviolin(v, fill_color=fc, alpha=halfviolin_alpha)
-
-        # Plot the effect size.
-        contrast_axes.plot(
-            [position],
-            difference,
-            marker="o",
-            color=ytick_color,
-            markersize=es_marker_size,
-        )
-        # Plot the confidence interval.
-        contrast_axes.plot(
-            [position, position],
-            [ci_low, ci_high],
-            linestyle="-",
-            color=ytick_color,
-            linewidth=group_summary_kwargs["lw"],
+    ## Contrast bars
+    contrast_bars = plot_kwargs["contrast_bars"]
+    if contrast_bars:
+        contrast_bars_dict, contrast_bars_kwargs = prepare_bars_for_plot(
+                                                    bar_type = 'contrast', 
+                                                    bar_kwargs = contrast_bars_kwargs, 
+                                                    horizontal = horizontal,
+                                                    plot_palette_raw = plot_palette_raw,
+                                                    color_col = color_col, 
+                                                    show_pairs = show_pairs, 
+                                                    results = results, 
+                                                    ticks_to_plot = ticks_to_plot, 
+                                                    extra_delta = (effectsize_df.mini_meta.difference if show_mini_meta 
+                                                                  else effectsize_df.delta_delta.difference if show_delta2
+                                                                  else None)
+                                                )
+        add_bars_to_plot(bar_dict = contrast_bars_dict, 
+                         ax = contrast_axes, 
+                         bar_kwargs = contrast_bars_kwargs
+                        )
+    
+    ## Delta text
+    delta_text = plot_kwargs["delta_text"]
+    if delta_text and not horizontal: 
+        delta_text_plotter(
+                    results = results, 
+                    ax_to_plot = contrast_axes, 
+                    ticks_to_plot = ticks_to_plot, 
+                    delta_text_kwargs = delta_text_kwargs, 
+                    color_col = color_col, 
+                    plot_palette_raw = plot_palette_raw, 
+                    show_pairs = show_pairs,
+                    float_contrast = float_contrast, 
+                    extra_delta = (effectsize_df.mini_meta.difference if show_mini_meta 
+                                                                  else effectsize_df.delta_delta.difference if show_delta2
+                                                                  else None),
         )
-        if show_mini_meta:
-            contrast_xtick_labels.extend(["", "Weighted delta"])
-        elif effect_size == "delta_g":
-            contrast_xtick_labels.extend(["", "deltas' g"])
-        else:
-            contrast_xtick_labels.extend(["", "delta-delta"])
-
-    # Make sure the contrast_axes x-lims match the rawdata_axes xlims,
-    # and add an extra violinplot tick for delta-delta plot.
-    if show_delta2 is False and show_mini_meta is False:
-        contrast_axes.set_xticks(rawdata_axes.get_xticks())
-    else:
-        temp = rawdata_axes.get_xticks()
-        temp = np.append(temp, [max(temp) + 1, max(temp) + 2])
-        contrast_axes.set_xticks(temp)
-
-    if show_pairs:
-        max_x = contrast_axes.get_xlim()[1]
-        rawdata_axes.set_xlim(-0.375, max_x)
-
-    if float_contrast:
-        contrast_axes.set_xlim(0.5, 1.5)
-    elif show_delta2 or show_mini_meta:
-        # Increase the xlim of raw data by 2
-        temp = rawdata_axes.get_xlim()
-        if show_pairs:
-            rawdata_axes.set_xlim(temp[0], temp[1] + 0.25)
-        else:
-            rawdata_axes.set_xlim(temp[0], temp[1] + 2)
-        contrast_axes.set_xlim(rawdata_axes.get_xlim())
-    else:
-        contrast_axes.set_xlim(rawdata_axes.get_xlim())
-
-    # Properly label the contrast ticks.
-    for t in ticks_to_skip:
-        contrast_xtick_labels.insert(t, "")
-
-    if plot_kwargs["fontsize_contrastxlabel"] is not None:
-        fontsize_contrastxlabel = plot_kwargs["fontsize_contrastxlabel"]
 
-    contrast_axes.set_xticklabels(
-        contrast_xtick_labels, fontsize=fontsize_contrastxlabel
+    ## Make sure the contrast_axes x-lims match the rawdata_axes xlims,
+    ## and add an extra violinplot tick for delta-delta plot.
+    ## Name is xaxis but it is actually y-axis for horizontal plots
+    set_xaxis_ticks_and_lims(
+                    show_delta2 = show_delta2, 
+                    show_mini_meta = show_mini_meta, 
+                    rawdata_axes = rawdata_axes, 
+                    contrast_axes = contrast_axes, 
+                    show_pairs = show_pairs, 
+                    float_contrast = float_contrast,
+                    ticks_to_skip = ticks_to_skip, 
+                    contrast_xtick_labels = contrast_xtick_labels, 
+                    plot_kwargs = plot_kwargs,
+                    proportional = proportional,
+                    horizontal = horizontal,
     )
-
-    if bootstraps_color_by_group is False:
-        legend_labels_unique = np.unique(legend_labels)
-        unique_idx = np.unique(legend_labels, return_index=True)[1]
-        legend_handles_unique = (
-            pd.Series(legend_handles, dtype="object").loc[unique_idx]
-        ).tolist()
-
-        if len(legend_handles_unique) > 0:
-            if float_contrast:
-                axes_with_legend = contrast_axes
-                if show_pairs:
-                    bta = (1.75, 1.02)
-                else:
-                    bta = (1.5, 1.02)
-            else:
-                axes_with_legend = rawdata_axes
-                if show_pairs:
-                    bta = (1.02, 1.0)
-                else:
-                    bta = (1.0, 1.0)
-            leg = axes_with_legend.legend(
-                legend_handles_unique,
-                legend_labels_unique,
-                bbox_to_anchor=bta,
-                **legend_kwargs
-            )
-            if show_pairs:
-                for line in leg.get_lines():
-                    line.set_linewidth(3.0)
-
-    og_ylim_raw = rawdata_axes.get_ylim()
-    og_xlim_raw = rawdata_axes.get_xlim()
-
-    if float_contrast:
-        # For Gardner-Altman plots only.
-
-        # Normalize ylims and despine the floating contrast axes.
-        # Check that the effect size is within the swarm ylims.
-        if effect_size_type in ["mean_diff", "cohens_d", "hedges_g", "cohens_h"]:
-            control_group_summary = (
-                plot_data.groupby(xvar)
-                .mean(numeric_only=True)
-                .loc[current_control, yvar]
-            )
-            test_group_summary = (
-                plot_data.groupby(xvar).mean(numeric_only=True).loc[current_group, yvar]
-            )
-        elif effect_size_type == "median_diff":
-            control_group_summary = (
-                plot_data.groupby(xvar).median().loc[current_control, yvar]
-            )
-            test_group_summary = (
-                plot_data.groupby(xvar).median().loc[current_group, yvar]
-            )
-
-        if swarm_ylim is None:
-            swarm_ylim = rawdata_axes.get_ylim()
-
-        _, contrast_xlim_max = contrast_axes.get_xlim()
-
-        difference = float(results.difference[0])
-
-        if effect_size_type in ["mean_diff", "median_diff"]:
-            # Align 0 of contrast_axes to reference group mean of rawdata_axes.
-            # If the effect size is positive, shift the contrast axis up.
-            rawdata_ylims = np.array(rawdata_axes.get_ylim())
-            if current_effsize > 0:
-                rightmin, rightmax = rawdata_ylims - current_effsize
-            # If the effect size is negative, shift the contrast axis down.
-            elif current_effsize < 0:
-                rightmin, rightmax = rawdata_ylims + current_effsize
-            else:
-                rightmin, rightmax = rawdata_ylims
-
-            contrast_axes.set_ylim(rightmin, rightmax)
-
-            og_ylim_contrast = rawdata_axes.get_ylim() - np.array(control_group_summary)
-
-            contrast_axes.set_ylim(og_ylim_contrast)
-            contrast_axes.set_xlim(contrast_xlim_max - 1, contrast_xlim_max)
-
-        elif effect_size_type in ["cohens_d", "hedges_g", "cohens_h"]:
-            if is_paired:
-                which_std = 1
-            else:
-                which_std = 0
-            temp_control = plot_data[plot_data[xvar] == current_control][yvar]
-            temp_test = plot_data[plot_data[xvar] == current_group][yvar]
-
-            stds = _compute_standardizers(temp_control, temp_test)
-            if is_paired:
-                pooled_sd = stds[1]
-            else:
-                pooled_sd = stds[0]
-
-            if effect_size_type == "hedges_g":
-                gby_count = plot_data.groupby(xvar).count()
-                len_control = gby_count.loc[current_control, yvar]
-                len_test = gby_count.loc[current_group, yvar]
-
-                hg_correction_factor = _compute_hedges_correction_factor(
-                    len_control, len_test
-                )
-
-                ylim_scale_factor = pooled_sd / hg_correction_factor
-
-            elif effect_size_type == "cohens_h":
-                ylim_scale_factor = (
-                    np.mean(temp_test) - np.mean(temp_control)
-                ) / difference
-
-            else:
-                ylim_scale_factor = pooled_sd
-
-            scaled_ylim = (
-                (rawdata_axes.get_ylim() - control_group_summary) / ylim_scale_factor
-            ).tolist()
-
-            contrast_axes.set_ylim(scaled_ylim)
-            og_ylim_contrast = scaled_ylim
-
-            contrast_axes.set_xlim(contrast_xlim_max - 1, contrast_xlim_max)
-
-        if one_sankey is None:
-            # Draw summary lines for control and test groups..
-            for jj, axx in enumerate([rawdata_axes, contrast_axes]):
-                # Draw effect size line.
-                if jj == 0:
-                    ref = control_group_summary
-                    diff = test_group_summary
-                    effsize_line_start = 1
-
-                elif jj == 1:
-                    ref = 0
-                    diff = ref + difference
-                    effsize_line_start = contrast_xlim_max - 1.1
-
-                xlimlow, xlimhigh = axx.get_xlim()
-
-                # Draw reference line.
-                axx.hlines(
-                    ref,  # y-coordinates
-                    0,
-                    xlimhigh,  # x-coordinates, start and end.
-                    **reflines_kwargs
-                )
-
-                # Draw effect size line.
-                axx.hlines(diff, effsize_line_start, xlimhigh, **reflines_kwargs)
-        else:
-            ref = 0
-            diff = ref + difference
-            effsize_line_start = contrast_xlim_max - 0.9
-            xlimlow, xlimhigh = contrast_axes.get_xlim()
-            # Draw reference line.
-            contrast_axes.hlines(
-                ref,  # y-coordinates
-                effsize_line_start,
-                xlimhigh,  # x-coordinates, start and end.
-                **reflines_kwargs
-            )
-
-            # Draw effect size line.
-            contrast_axes.hlines(diff, effsize_line_start, xlimhigh, **reflines_kwargs)
-        rawdata_axes.set_xlim(og_xlim_raw)  # to align the axis
-        # Despine appropriately.
-        sns.despine(ax=rawdata_axes, bottom=True)
-        sns.despine(ax=contrast_axes, left=True, right=False)
-
-        # Insert break between the rawdata axes and the contrast axes
-        # by re-drawing the x-spine.
-        rawdata_axes.hlines(
-            og_ylim_raw[0],  # yindex
-            rawdata_axes.get_xlim()[0],
-            1.3,  # xmin, xmax
-            **redraw_axes_kwargs
+    # Plot aesthetic adjustments.
+    if float_contrast: # For Gardner-Altman (float contrast) plots only.
+        gardner_altman_adjustments(
+                                effect_size_type = effect_size, 
+                                plot_data = plot_data, 
+                                xvar = xvar, 
+                                yvar = yvar, 
+                                current_control = current_control, 
+                                current_group = current_group,
+                                rawdata_axes = rawdata_axes, 
+                                contrast_axes = contrast_axes, 
+                                results = results, 
+                                current_effsize = current_effsize, 
+                                is_paired = is_paired, 
+                                one_sankey = one_sankey,
+                                reflines_kwargs = reflines_kwargs, 
+                                redraw_axes_kwargs = redraw_axes_kwargs, 
         )
-        rawdata_axes.set_ylim(og_ylim_raw)
-
-        contrast_axes.hlines(
-            contrast_axes.get_ylim()[0],
-            contrast_xlim_max - 0.8,
-            contrast_xlim_max,
-            **redraw_axes_kwargs
+    else: # For Cumming plots only.
+        ## Add Zero line if lies within the ylim of contrast axes
+        draw_zeroline(
+                ax = contrast_axes,
+                horizontal = horizontal,
+                reflines_kwargs = reflines_kwargs,
+                extra_delta = True if show_delta2 else False,
         )
+        ## Axes independent spine lines
+        is_gridkey = True if plot_kwargs["gridkey"] is not None else False
+        if not is_gridkey:
+            redraw_independent_spines(
+                        rawdata_axes = rawdata_axes,
+                        contrast_axes = contrast_axes,
+                        horizontal = horizontal,
+                        two_col_sankey = two_col_sankey,
+                        ticks_to_start_twocol_sankey = ticks_to_start_twocol_sankey,
+                        idx = idx,
+                        is_paired = is_paired,
+                        show_pairs = show_pairs,
+                        proportional = proportional,
+                        ticks_to_skip = ticks_to_skip,
+                        temp_idx = temp_idx if is_paired == "baseline" and show_pairs else None,
+                        ticks_to_skip_contrast = ticks_to_skip_contrast,
+                        redraw_axes_kwargs = redraw_axes_kwargs
+            )
 
-    else:
-        # For Cumming Plots only.
-
-        # Set custom contrast_ylim, if it was specified.
-        if plot_kwargs["contrast_ylim"] is not None or (
-            plot_kwargs["delta2_ylim"] is not None and show_delta2
-        ):
-            if plot_kwargs["contrast_ylim"] is not None:
-                custom_contrast_ylim = plot_kwargs["contrast_ylim"]
-                if plot_kwargs["delta2_ylim"] is not None and show_delta2:
-                    custom_delta2_ylim = plot_kwargs["delta2_ylim"]
-                    if custom_contrast_ylim != custom_delta2_ylim:
-                        err1 = "Please check if `contrast_ylim` and `delta2_ylim` are assigned"
-                        err2 = "with same values."
-                        raise ValueError(err1 + err2)
-            else:
-                custom_delta2_ylim = plot_kwargs["delta2_ylim"]
-                custom_contrast_ylim = custom_delta2_ylim
-
-            if len(custom_contrast_ylim) != 2:
-                err1 = "Please check `contrast_ylim` consists of "
-                err2 = "exactly two numbers."
-                raise ValueError(err1 + err2)
-
-            if effect_size_type == "cliffs_delta":
-                # Ensure the ylims for a cliffs_delta plot never exceed [-1, 1].
-                l = plot_kwargs["contrast_ylim"][0]
-                h = plot_kwargs["contrast_ylim"][1]
-                low = -1 if l < -1 else l
-                high = 1 if h > 1 else h
-                contrast_axes.set_ylim(low, high)
-            else:
-                contrast_axes.set_ylim(custom_contrast_ylim)
-
-        # If 0 lies within the ylim of the contrast axes,
-        # draw a zero reference line.
-        contrast_axes_ylim = contrast_axes.get_ylim()
-        if contrast_axes_ylim[0] < contrast_axes_ylim[1]:
-            contrast_ylim_low, contrast_ylim_high = contrast_axes_ylim
-        else:
-            contrast_ylim_high, contrast_ylim_low = contrast_axes_ylim
-        if contrast_ylim_low < 0 < contrast_ylim_high:
-            contrast_axes.axhline(y=0, **reflines_kwargs)
-
-        if is_paired == "baseline" and show_pairs:
-            if two_col_sankey:
-                rightend_ticks_raw = np.array([len(i) - 2 for i in idx]) + np.array(
-                    ticks_to_start_twocol_sankey
-                )
-            elif proportional and is_paired is not None:
-                rightend_ticks_raw = np.array([len(i) - 1 for i in idx]) + np.array(
-                    ticks_to_skip
-                )
-            else:
-                rightend_ticks_raw = np.array(
-                    [len(i) - 1 for i in temp_idx]
-                ) + np.array(ticks_to_skip)
-            for ax in [rawdata_axes]:
-                sns.despine(ax=ax, bottom=True)
-
-                ylim = ax.get_ylim()
-                xlim = ax.get_xlim()
-                redraw_axes_kwargs["y"] = ylim[0]
-
-                if two_col_sankey:
-                    for k, start_tick in enumerate(ticks_to_start_twocol_sankey):
-                        end_tick = rightend_ticks_raw[k]
-                        ax.hlines(xmin=start_tick, xmax=end_tick, **redraw_axes_kwargs)
-                else:
-                    for k, start_tick in enumerate(ticks_to_skip):
-                        end_tick = rightend_ticks_raw[k]
-                        ax.hlines(xmin=start_tick, xmax=end_tick, **redraw_axes_kwargs)
-                ax.set_ylim(ylim)
-                del redraw_axes_kwargs["y"]
-
-            if not proportional:
-                temp_length = [(len(i) - 1) for i in idx]
-            else:
-                temp_length = [(len(i) - 1) * 2 - 1 for i in idx]
-            if two_col_sankey:
-                rightend_ticks_contrast = np.array(
-                    [len(i) - 2 for i in idx]
-                ) + np.array(ticks_to_start_twocol_sankey)
-            elif proportional and is_paired is not None:
-                rightend_ticks_contrast = np.array(
-                    [len(i) - 1 for i in idx]
-                ) + np.array(ticks_to_skip)
-            else:
-                rightend_ticks_contrast = np.array(temp_length) + np.array(
-                    ticks_to_skip_contrast
-                )
-            for ax in [contrast_axes]:
-                sns.despine(ax=ax, bottom=True)
-
-                ylim = ax.get_ylim()
-                xlim = ax.get_xlim()
-                redraw_axes_kwargs["y"] = ylim[0]
-
-                if two_col_sankey:
-                    for k, start_tick in enumerate(ticks_to_start_twocol_sankey):
-                        end_tick = rightend_ticks_contrast[k]
-                        ax.hlines(xmin=start_tick, xmax=end_tick, **redraw_axes_kwargs)
-                else:
-                    for k, start_tick in enumerate(ticks_to_skip_contrast):
-                        end_tick = rightend_ticks_contrast[k]
-                        ax.hlines(xmin=start_tick, xmax=end_tick, **redraw_axes_kwargs)
-
-                ax.set_ylim(ylim)
-                del redraw_axes_kwargs["y"]
-        else:
-            # Compute the end of each x-axes line.
-            if two_col_sankey:
-                rightend_ticks = np.array([len(i) - 2 for i in idx]) + np.array(
-                    ticks_to_start_twocol_sankey
-                )
-            else:
-                rightend_ticks = np.array([len(i) - 1 for i in idx]) + np.array(
-                    ticks_to_skip
-                )
-
-            for ax in [rawdata_axes, contrast_axes]:
-                sns.despine(ax=ax, bottom=True)
-
-                ylim = ax.get_ylim()
-                xlim = ax.get_xlim()
-                redraw_axes_kwargs["y"] = ylim[0]
-
-                if two_col_sankey:
-                    for k, start_tick in enumerate(ticks_to_start_twocol_sankey):
-                        end_tick = rightend_ticks[k]
-                        ax.hlines(xmin=start_tick, xmax=end_tick, **redraw_axes_kwargs)
-                else:
-                    for k, start_tick in enumerate(ticks_to_skip):
-                        end_tick = rightend_ticks[k]
-                        ax.hlines(xmin=start_tick, xmax=end_tick, **redraw_axes_kwargs)
-
-                ax.set_ylim(ylim)
-                del redraw_axes_kwargs["y"]
-
-    if show_delta2 or show_mini_meta:
-        ylim = contrast_axes.get_ylim()
-        redraw_axes_kwargs["y"] = ylim[0]
-        x_ticks = contrast_axes.get_xticks()
-        contrast_axes.hlines(xmin=x_ticks[-2], xmax=x_ticks[-1], **redraw_axes_kwargs)
-        del redraw_axes_kwargs["y"]
-
-    # Set raw axes y-label.
-    swarm_label = plot_kwargs["swarm_label"]
-    if swarm_label is None and yvar is None:
-        swarm_label = "value"
-    elif swarm_label is None and yvar is not None:
-        swarm_label = yvar
-
-    bar_label = plot_kwargs["bar_label"]
-    if bar_label is None and effect_size_type != "cohens_h":
-        bar_label = "proportion of success"
-    elif bar_label is None and effect_size_type == "cohens_h":
-        bar_label = "value"
-
-    # Place contrast axes y-label.
-    contrast_label_dict = {
-        "mean_diff": "mean difference",
-        "median_diff": "median difference",
-        "cohens_d": "Cohen's d",
-        "hedges_g": "Hedges' g",
-        "cliffs_delta": "Cliff's delta",
-        "cohens_h": "Cohen's h",
-        "delta_g": "mean difference",
-    }
-
-    if proportional and effect_size_type != "cohens_h":
-        default_contrast_label = "proportion difference"
-    elif effect_size_type == "delta_g":
-        default_contrast_label = "Hedges' g"
-    else:
-        default_contrast_label = contrast_label_dict[effectsize_df.effect_size]
-
-    if plot_kwargs["contrast_label"] is None:
-        if is_paired:
-            contrast_label = "paired\n{}".format(default_contrast_label)
-        else:
-            contrast_label = default_contrast_label
-        contrast_label = contrast_label.capitalize()
-    else:
-        contrast_label = plot_kwargs["contrast_label"]
-
-    if plot_kwargs["fontsize_rawylabel"] is not None:
-        fontsize_rawylabel = plot_kwargs["fontsize_rawylabel"]
-    if plot_kwargs["fontsize_contrastylabel"] is not None:
-        fontsize_contrastylabel = plot_kwargs["fontsize_contrastylabel"]
-    if plot_kwargs["fontsize_delta2label"] is not None:
-        fontsize_delta2label = plot_kwargs["fontsize_delta2label"]
-
-    contrast_axes.set_ylabel(contrast_label, fontsize=fontsize_contrastylabel)
-    if float_contrast:
-        contrast_axes.yaxis.set_label_position("right")
-
-    # Set the rawdata axes labels appropriately
-    if not proportional:
-        rawdata_axes.set_ylabel(swarm_label, fontsize=fontsize_rawylabel)
-    else:
-        rawdata_axes.set_ylabel(bar_label, fontsize=fontsize_rawylabel)
-    rawdata_axes.set_xlabel("")
-
-    # Because we turned the axes frame off, we also need to draw back
-    # the y-spine for both axes.
-    if not float_contrast:
-        rawdata_axes.set_xlim(contrast_axes.get_xlim())
-    og_xlim_raw = rawdata_axes.get_xlim()
-    rawdata_axes.vlines(
-        og_xlim_raw[0], og_ylim_raw[0], og_ylim_raw[1], **redraw_axes_kwargs
-    )
-
-    og_xlim_contrast = contrast_axes.get_xlim()
-
-    if float_contrast:
-        xpos = og_xlim_contrast[1]
-    else:
-        xpos = og_xlim_contrast[0]
-
-    og_ylim_contrast = contrast_axes.get_ylim()
-    contrast_axes.vlines(
-        xpos, og_ylim_contrast[0], og_ylim_contrast[1], **redraw_axes_kwargs
+    # Modify ylims of axes to flip the plot for horizontal format
+    if horizontal:
+        if not proportional or (proportional and show_pairs):
+            raw_ylim, contrast_ylim = rawdata_axes.get_ylim(), contrast_axes.get_ylim()
+            rawdata_axes.set_ylim(raw_ylim[1], raw_ylim[0])
+            contrast_axes.set_ylim(contrast_ylim[1], contrast_ylim[0])
+
+        ## Modify the ylim to reduce whitespace in specific plots.
+        if show_delta2 or show_mini_meta or (proportional and show_pairs):
+            raw_ylim, contrast_ylim = rawdata_axes.get_ylim(), contrast_axes.get_ylim()
+            rawdata_axes.set_ylim(raw_ylim[0]-0.5, raw_ylim[1])
+            contrast_axes.set_ylim(contrast_ylim[0]-0.5, contrast_ylim[1])
+
+    # Add the dependent axes spines back in.
+    redraw_dependent_spines(
+        rawdata_axes = rawdata_axes, 
+        contrast_axes = contrast_axes, 
+        redraw_axes_kwargs = redraw_axes_kwargs, 
+        float_contrast = float_contrast, 
+        horizontal = horizontal,
+        show_delta2 = show_delta2, 
+        delta2_axes = delta2_axes
     )
 
-    if show_delta2:
-        if plot_kwargs["delta2_label"] is not None:
-            delta2_label = plot_kwargs["delta2_label"]
-        elif effect_size == "mean_diff":
-            delta2_label = "delta - delta"
-        else:
-            delta2_label = "deltas' g"
-        delta2_axes = contrast_axes.twinx()
-        delta2_axes.set_frame_on(False)
-        delta2_axes.set_ylabel(delta2_label, fontsize=fontsize_delta2label)
-        og_xlim_delta = contrast_axes.get_xlim()
-        og_ylim_delta = contrast_axes.get_ylim()
-        delta2_axes.set_ylim(og_ylim_delta)
-        delta2_axes.vlines(
-            og_xlim_delta[1], og_ylim_delta[0], og_ylim_delta[1], **redraw_axes_kwargs
+    # Table Axes for horizontal plots
+    if horizontal and table_kwargs['show']:
+        table_for_horizontal_plots(
+                            effectsize_df = effectsize_df,
+                            ax = table_axes,
+                            contrast_axes = contrast_axes,
+                            ticks_to_plot = table_axes_ticks_to_plot, 
+                            show_mini_meta = show_mini_meta,
+                            show_delta2 = show_delta2,
+                            table_kwargs = table_kwargs,
+                            ticks_to_skip = ticks_to_skip
         )
 
-    ################################################### GRIDKEY MAIN CODE WIP
-
-    # if gridkey_rows is None, skip everything here
-    if gridkey_rows is not None:
-        # Raise error if there are more than 2 items in any idx and gridkey_merge_pairs is True and is_paired is not None
-        if gridkey_merge_pairs and is_paired is not None:
-            for i in idx:
-                if len(i) > 2:
-                    warnings.warn(
-                        "gridkey_merge_pairs=True only works if all idx in tuples have only two items. gridkey_merge_pairs has automatically been set to False"
-                    )
-                    gridkey_merge_pairs = False
-                    break
-        elif gridkey_merge_pairs and is_paired is None:
-            warnings.warn(
-                "gridkey_merge_pairs=True is only applicable for paired data."
-            )
-            gridkey_merge_pairs = False
-
-        # Checks for gridkey_merge_pairs and is_paired; if both are true, "merges" the gridkey per pair
-        if gridkey_merge_pairs and is_paired is not None:
-            groups_for_gridkey = []
-            for i in idx:
-                groups_for_gridkey.append(i[1])
-        else:
-            groups_for_gridkey = all_plot_groups
-
-        # raise errors if gridkey_rows is not a list, or if the list is empty
-        if isinstance(gridkey_rows, list) is False:
-            raise TypeError("gridkey_rows must be a list.")
-        elif len(gridkey_rows) == 0:
-            warnings.warn("gridkey_rows is an empty list.")
-
-        # raise Warning if an item in gridkey_rows is not contained in any idx
-        for i in gridkey_rows:
-            in_idx = 0
-            for j in groups_for_gridkey:
-                if i in j:
-                    in_idx += 1
-            if in_idx == 0:
-                if is_paired is not None:
-                    warnings.warn(
-                        i
-                        + " is not in any idx. Please check. Alternatively, merging gridkey pairs may not be suitable for your data; try passing gridkey_merge_pairs=False."
-                    )
-                else:
-                    warnings.warn(i + " is not in any idx. Please check.")
-
-        # Populate table: checks if idx for each column contains rowlabel name
-        # IF so, marks that element as present w black dot, or space if not present
-        table_cellcols = []
-        for i in gridkey_rows:
-            thisrow = []
-            for q in groups_for_gridkey:
-                if str(i) in q:
-                    thisrow.append("\u25CF")
-                else:
-                    thisrow.append("")
-            table_cellcols.append(thisrow)
-
-        # Adds a row for Ns with the Ns values
-        if gridkey_show_Ns:
-            gridkey_rows.append("Ns")
-            list_of_Ns = []
-            for i in groups_for_gridkey:
-                list_of_Ns.append(str(counts.loc[i]))
-            table_cellcols.append(list_of_Ns)
-
-        # Adds a row for effectsizes with effectsize values
-        if gridkey_show_es:
-            gridkey_rows.append("\u0394")
-            effsize_list = []
-            results_list = results.test.to_list()
-
-            # get the effect size, append + or -, 2 dec places
-            for i in enumerate(groups_for_gridkey):
-                if i[1] in results_list:
-                    curr_esval = results.loc[results["test"] == i[1]][
-                        "difference"
-                    ].iloc[0]
-                    curr_esval_str = np.format_float_positional(
-                        curr_esval,
-                        precision=es_sf,
-                        sign=True,
-                        trim="k",
-                        min_digits=es_sf,
-                    )
-                    effsize_list.append(curr_esval_str)
-                else:
-                    effsize_list.append("-")
-
-            table_cellcols.append(effsize_list)
-
-        # If Gardner-Altman plot, plot on raw data and not contrast axes
-        if float_contrast:
-            axes_ploton = rawdata_axes
-        else:
-            axes_ploton = contrast_axes
-
-        # Account for extended x axis in case of show_delta2 or show_mini_meta
-        x_groups_for_width = len(groups_for_gridkey)
-        if show_delta2 or show_mini_meta:
-            x_groups_for_width += 2
-        gridkey_width = len(groups_for_gridkey) / x_groups_for_width
-
-        gridkey = axes_ploton.table(
-            cellText=table_cellcols,
-            rowLabels=gridkey_rows,
-            cellLoc="center",
-            bbox=[
-                0,
-                -len(gridkey_rows) * 0.1 - 0.05,
-                gridkey_width,
-                len(gridkey_rows) * 0.1,
-            ],
-            **{"alpha": 0.5}
+    # Gridkey
+    gridkey = plot_kwargs["gridkey"]
+    if gridkey is not None:
+        gridkey_plotter(
+                is_paired = is_paired, 
+                idx = idx, 
+                all_plot_groups = all_plot_groups, 
+                gridkey = gridkey, 
+                rawdata_axes = rawdata_axes,
+                contrast_axes = contrast_axes,
+                plot_data = plot_data, 
+                xvar = xvar, 
+                yvar = yvar, 
+                results = results, 
+                show_delta2 = show_delta2, 
+                show_mini_meta = show_mini_meta, 
+                x1_level = x1_level,
+                experiment_label = experiment_label,
+                float_contrast = float_contrast,
+                horizontal = horizontal,
+                delta_delta = effectsize_df.delta_delta if show_delta2 else None,
+                mini_meta = effectsize_df.mini_meta if show_mini_meta else None,
+                effect_size = effect_size,
+                gridkey_kwargs = gridkey_kwargs,
+        )
+    
+    #  Reference band
+    reference_band = plot_kwargs["reference_band"]
+    if reference_band is not None and not float_contrast:
+        reference_band_dict, reference_band_kwargs = prepare_bars_for_plot(bar_type = 'summary', 
+                                                                       bar_kwargs = reference_band_kwargs, 
+                                                                       horizontal = horizontal, 
+                                                                       plot_palette_raw = plot_palette_raw, 
+                                                                       color_col = color_col, 
+                                                                       show_pairs = show_pairs,
+                                                                       results = results,    
+                                                                       ticks_to_plot = ticks_to_plot, 
+                                                                       reference_band = reference_band, 
+                                                                       summary_axes = contrast_axes, 
+                                                                       ci_type = ci_type,
+                                                                    )
+        
+        add_bars_to_plot(bar_dict = reference_band_dict,
+                        ax = contrast_axes,
+                        bar_kwargs = reference_band_kwargs
         )
 
-        # modifies row label cells
-        for cell in gridkey._cells:
-            if cell[1] == -1:
-                gridkey._cells[cell].visible_edges = "open"
-                gridkey._cells[cell].set_text_props(**{"ha": "right"})
-
-        # turns off both x axes
-        rawdata_axes.get_xaxis().set_visible(False)
-        contrast_axes.get_xaxis().set_visible(False)
+    # Legend
+    handles, labels = rawdata_axes.get_legend_handles_labels()
+    legend_labels = [l for l in labels]
+    legend_handles = [h for h in handles]
 
-    ####################################################### END GRIDKEY MAIN CODE WIP
+    if bootstraps_color_by_group is False and color_col is not None:
+        rawdata_axes.legend().set_visible(False)
+        show_legend(
+            legend_labels = legend_labels, 
+            legend_handles = legend_handles, 
+            rawdata_axes = rawdata_axes, 
+            contrast_axes = contrast_axes, 
+            table_axes = table_axes,
+            float_contrast = float_contrast, 
+            show_pairs = show_pairs, 
+            horizontal = horizontal,
+            legend_kwargs = legend_kwargs,
+            table_kwargs = table_kwargs
+        )
 
     # Make sure no stray ticks appear!
     rawdata_axes.xaxis.set_ticks_position("bottom")
@@ -1603,4 +628,3 @@ def effectsize_df_plotter(effectsize_df, **plot_kwargs):
 
     # Return the figure.
     return fig
-
diff --git a/nbs/01-getting_started.ipynb b/nbs/01-getting_started.ipynb
index 49e11c2e..299e40f4 100644
--- a/nbs/01-getting_started.ipynb
+++ b/nbs/01-getting_started.ipynb
@@ -46,7 +46,7 @@
    "id": "e4c2e459",
    "metadata": {},
    "source": [
-    "DABEST powers [estimationstats.com](estimationstats.com), allowing everyone access to high-quality estimation plots."
+    "DABEST powers [estimationstats.com](https://www.estimationstats.com/#/), allowing everyone access to high-quality estimation plots."
    ]
   },
   {
@@ -64,15 +64,16 @@
    "source": [
     "\n",
     "\n",
-    "Python 3.10 is strongly recommended. DABEST has also been tested with Python 3.8 and onwards.\n",
+    "Python 3.11 is recommended. DABEST has also been tested with Python 3.10 and onwards.\n",
     "\n",
     "In addition, the following packages are also required (listed with their minimal versions):\n",
     "\n",
-    "* [numpy 1.23.5](https://www.numpy.org)\n",
-    "* [scipy 1.9.3](https://www.scipy.org)\n",
-    "* [matplotlib 3.6.3](https://www.matplotlib.org)\n",
-    "* [pandas 1.5.0](https://pandas.pydata.org)\n",
-    "* [seaborn 0.12.2](https://seaborn.pydata.org)\n",
+    "* [numpy 2.1.3](https://www.numpy.org)\n",
+    "* [scipy 1.15.2](https://www.scipy.org)\n",
+    "* [matplotlib 3.10.0](https://www.matplotlib.org)\n",
+    "* [pandas 2.2.3](https://pandas.pydata.org)\n",
+    "* [seaborn 0.13.2](https://seaborn.pydata.org)\n",
+    "* [numba 0.61.0](https://numba.pydata.org)\n",
     "* [lqrt 0.3.3](https://github.com/alyakin314/lqrt)\n",
     "\n",
     "To obtain these package dependencies easily, it is highly recommended to download the [Anaconda](https://www.continuum.io/downloads) distribution of Python.\n"
diff --git a/nbs/02-about.ipynb b/nbs/02-about.ipynb
index 2dec09aa..ce1b00f6 100644
--- a/nbs/02-about.ipynb
+++ b/nbs/02-about.ipynb
@@ -17,6 +17,8 @@
     "\n",
     "DABEST is written in Python by [Joses W. Ho](https://twitter.com/jacuzzijo), with design and input from [Adam Claridge-Chang](https://twitter.com/adamcchang) and other [lab members](https://www.claridgechang.net/people.html).\n",
     "\n",
+    "Features in v2025.03.27 were added by [Jonathan Anns](https://github.com/JAnns98), [Zinan Lu](https://github.com/Jacobluke-), [Kah Seng Lian](https://github.com/sunroofgod), and [Lucas Wang Zhuoyu](https://github.com/Lucas1213WZY).\n",
+    "\n",
     "Features in v2024.03.29 were added by [Zinan Lu](https://github.com/Jacobluke-), [Kah Seng Lian](https://github.com/sunroofgod), [Ana Rosa Castillo](https://github.com/cyberosa).\n",
     "\n",
     "Features in v2023.02.14 were added by [Yixuan Li](https://github.com/LI-Yixuan), [Zinan Lu](https://github.com/Jacobluke-) and [Rou Zhang](https://github.com/ZHANGROU-99).\n",
@@ -42,6 +44,8 @@
     "\n",
     "- Marin Manuel ([@MarinManuel](https://github.com/MarinManuel)) with [PR #109](https://github.com/ACCLAB/DABEST-python/pull/109): Fixed bug preventing non-string columns from being used.\n",
     "\n",
+    "- Mike Lotinga ([@mlotinga](https://github.com/mlotinga)): Helped with addition of jitter and the adjusted p-value calculation, both of which are included in the *v2025.03.27* release.\n",
+    "\n",
     "\n",
     "\n",
     "## Typography\n",
@@ -83,6 +87,12 @@
     "POSSIBILITY OF SUCH DAMAGE.\n",
     "</div>\n"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e1dcfc63",
+   "metadata": {},
+   "source": []
   }
  ],
  "metadata": {
diff --git a/nbs/API/bootstrap.ipynb b/nbs/API/bootstrap.ipynb
index eb33a083..79de8c0e 100644
--- a/nbs/API/bootstrap.ipynb
+++ b/nbs/API/bootstrap.ipynb
@@ -114,7 +114,9 @@
     "        reps: int = 5000,  # Number of bootstrap iterations to perform.\n",
     "    ):\n",
     "        # Turn to pandas series.\n",
-    "        x1 = pd.Series(x1).dropna()\n",
+    "        # x1 = pd.Series(x1).dropna()\n",
+    "        x1 = x1[~np.isnan(x1)]\n",
+    "\n",
     "        diff = False\n",
     "\n",
     "        # Initialise stat_function\n",
@@ -137,7 +139,9 @@
     "            if x2 is None:\n",
     "                raise ValueError(\"Please specify x2.\")\n",
     "            \n",
-    "            x2 = pd.Series(x2).dropna()\n",
+    "            # x2 = pd.Series(x2).dropna()\n",
+    "            x2 = x1[~np.isnan(x2)]\n",
+    "\n",
     "            if len(x1) != len(x2):\n",
     "                raise ValueError(\"x1 and x2 are not the same length.\")\n",
     "\n",
@@ -182,7 +186,8 @@
     "\n",
     "        elif x2 is not None and paired is None:\n",
     "            diff = True\n",
-    "            x2 = pd.Series(x2).dropna()\n",
+    "            # x2 = pd.Series(x2).dropna()\n",
+    "            x2 = x2[~np.isnan(x2)]\n",
     "            # Generate statarrays for both arrays.\n",
     "            ref_statarray = sns.algorithms.bootstrap(x1, **sns_bootstrap_kwargs)\n",
     "            exp_statarray = sns.algorithms.bootstrap(x2, **sns_bootstrap_kwargs)\n",
diff --git a/nbs/API/confint_2group_diff.ipynb b/nbs/API/confint_2group_diff.ipynb
index dd6477aa..29ca48ae 100644
--- a/nbs/API/confint_2group_diff.ipynb
+++ b/nbs/API/confint_2group_diff.ipynb
@@ -60,7 +60,7 @@
     "from numpy import mean as npmean\n",
     "from numpy import sum as npsum\n",
     "from numpy.random import PCG64, RandomState\n",
-    "import pandas as pd\n",
+    "from numba import njit, prange\n",
     "from scipy.stats import norm\n",
     "from numpy import isnan"
    ]
@@ -73,6 +73,7 @@
    "outputs": [],
    "source": [
     "#| export\n",
+    "@njit(cache=True, parallel=True)\n",
     "def create_jackknife_indexes(data):\n",
     "    \"\"\"\n",
     "    Given an array-like, creates a jackknife bootstrap.\n",
@@ -89,18 +90,25 @@
     "    Generator that yields all jackknife bootstrap samples.\n",
     "    \"\"\"\n",
     "\n",
-    "    index_range = arange(0, len(data))\n",
-    "    return (delete(index_range, i) for i in index_range)\n",
+    "    n = len(data)\n",
+    "    indexes = np.empty((n, n - 1), dtype=np.int64)\n",
+    "    for i in prange(n):\n",
+    "        indexes[i] = np.concatenate((np.arange(i), np.arange(i + 1, n)))\n",
+    "    return indexes\n",
     "\n",
     "\n",
+    "@njit(cache=True, parallel=True)\n",
     "def create_repeated_indexes(data):\n",
     "    \"\"\"\n",
     "    Convenience function. Given an array-like with length N,\n",
     "    returns a generator that yields N indexes [0, 1, ..., N].\n",
     "    \"\"\"\n",
     "\n",
-    "    index_range = arange(0, len(data))\n",
-    "    return (index_range for i in index_range)\n",
+    "    n = len(data)\n",
+    "    indexes = np.empty((n, n), dtype=np.int64)  # Pre-allocate the output array\n",
+    "    for i in prange(n):\n",
+    "        indexes[i, :] = np.arange(n)  # Fill each row with the full index range\n",
+    "    return indexes\n",
     "\n",
     "\n",
     "def _create_two_group_jackknife_indexes(x0, x1, is_paired):\n",
@@ -168,6 +176,20 @@
     "        return numer / denom\n",
     "\n",
     "\n",
+    "@njit(cache=True) # parallelization must be turned off for random number generation\n",
+    "def bootstrap_indices(is_paired, x0_len, x1_len, resamples, random_seed):\n",
+    "    np.random.seed(random_seed)\n",
+    "    indices = np.empty((resamples, x0_len if is_paired else x0_len + x1_len), dtype=np.int64)\n",
+    "    \n",
+    "    for i in range(resamples):\n",
+    "        if is_paired:\n",
+    "            indices[i, :x0_len] = np.random.choice(x0_len, x0_len)\n",
+    "        else:  \n",
+    "            indices[i, :x0_len] = np.random.choice(x0_len, x0_len)\n",
+    "            indices[i, x0_len:x0_len+x1_len] = np.random.choice(x1_len, x1_len)\n",
+    "    return indices\n",
+    "\n",
+    "\n",
     "def compute_bootstrapped_diff(\n",
     "    x0, x1, is_paired, effect_size, resamples=5000, random_seed=12345\n",
     "):\n",
@@ -175,95 +197,106 @@
     "\n",
     "    from . import effsize as __es\n",
     "\n",
-    "    rng = RandomState(PCG64(random_seed))\n",
-    "\n",
-    "    out = np.repeat(np.nan, resamples)\n",
-    "    x0_len = len(x0)\n",
-    "    x1_len = len(x1)\n",
+    "    x0_len, x1_len = len(x0), len(x1)\n",
+    "    indices = bootstrap_indices(is_paired, x0_len, x1_len, resamples, random_seed)\n",
+    "    out = np.empty(resamples, dtype=np.float64)\n",
     "\n",
-    "    for i in range(int(resamples)):\n",
+    "    for i in range(resamples):\n",
     "        if is_paired:\n",
-    "            if x0_len != x1_len:\n",
-    "                raise ValueError(\"The two arrays do not have the same length.\")\n",
-    "            random_idx = rng.choice(x0_len, x0_len, replace=True)\n",
-    "            x0_sample = x0[random_idx]\n",
-    "            x1_sample = x1[random_idx]\n",
+    "            x0_sample = x0[indices[i, :x0_len]]\n",
+    "            x1_sample = x1[indices[i, :x0_len]]\n",
     "        else:\n",
-    "            x0_sample = rng.choice(x0, x0_len, replace=True)\n",
-    "            x1_sample = rng.choice(x1, x1_len, replace=True)\n",
+    "            x0_sample = x0[indices[i, :x0_len]]\n",
+    "            x1_sample = x1[indices[i, x0_len:x0_len+x1_len]]\n",
     "\n",
     "        out[i] = __es.two_group_difference(x0_sample, x1_sample, is_paired, effect_size)\n",
     "\n",
     "    return out\n",
     "\n",
     "\n",
-    "def compute_delta2_bootstrapped_diff(\n",
-    "    x1: np.ndarray,  # Control group 1\n",
-    "    x2: np.ndarray,  # Test group 1\n",
-    "    x3: np.ndarray,  # Control group 2\n",
-    "    x4: np.ndarray,  # Test group 2\n",
-    "    is_paired: str = None,\n",
-    "    resamples: int = 5000,  # The number of bootstrap resamples to be taken for the calculation of the confidence interval limits.\n",
-    "    random_seed: int = 12345,  # `random_seed` is used to seed the random number generator during bootstrap resampling. This ensures that the confidence intervals reported are replicable.\n",
-    ") -> (\n",
-    "    tuple\n",
-    "):  # bootstraped result and empirical result of deltas' g, and the bootstraped result of delta-delta\n",
+    "@njit(cache=True)\n",
+    "def delta2_bootstrap_loop(x1, x2, x3, x4, resamples, pooled_sd, rng_seed, is_paired, proportional=False):\n",
     "    \"\"\"\n",
-    "    Bootstraps the effect size deltas' g.\n",
-    "\n",
+    "    Compute bootstrapped differences for delta-delta, handling both regular and proportional data\n",
     "    \"\"\"\n",
-    "\n",
-    "    rng = RandomState(PCG64(random_seed))\n",
-    "\n",
-    "    x1, x2, x3, x4 = map(np.asarray, [x1, x2, x3, x4])\n",
-    "\n",
-    "    # Calculating pooled sample standard deviation\n",
-    "    stds = [np.std(x) for x in [x1, x2, x3, x4]]\n",
-    "    ns = [len(x) for x in [x1, x2, x3, x4]]\n",
-    "\n",
-    "    sd_numerator = sum((n - 1) * s**2 for n, s in zip(ns, stds))\n",
-    "    sd_denominator = sum(n - 1 for n in ns)\n",
-    "\n",
-    "    # Avoid division by zero\n",
-    "    if sd_denominator == 0:\n",
-    "        raise ValueError(\"Insufficient data to compute pooled standard deviation.\")\n",
-    "\n",
-    "    pooled_sample_sd = np.sqrt(sd_numerator / sd_denominator)\n",
-    "\n",
-    "    # Ensure pooled_sample_sd is not NaN or zero (to avoid division by zero later)\n",
-    "    if np.isnan(pooled_sample_sd) or pooled_sample_sd == 0:\n",
-    "        raise ValueError(\"Pooled sample standard deviation is NaN or zero.\")\n",
-    "\n",
-    "    out_delta_g = np.empty(resamples)\n",
+    "    np.random.seed(rng_seed)\n",
     "    deltadelta = np.empty(resamples)\n",
+    "    out_delta_g = np.empty(resamples)\n",
+    "    \n",
+    "    n1, n2, n3, n4 = len(x1), len(x2), len(x3), len(x4)\n",
+    "    if is_paired and (n1 != n2 or n3 != n4):\n",
+    "        raise ValueError(\"Each control group must have the same length as its corresponding test group in paired analysis.\")\n",
     "\n",
     "    # Bootstrapping\n",
     "    for i in range(resamples):\n",
     "        # Paired or unpaired resampling\n",
     "        if is_paired:\n",
-    "            if len(x1) != len(x2) or len(x3) != len(x4):\n",
-    "                raise ValueError(\"Each control group must have the same length as its corresponding test group in paired analysis.\")\n",
-    "            indices_1 = rng.choice(len(x1), len(x1), replace=True)\n",
-    "            indices_2 = rng.choice(len(x3), len(x3), replace=True)\n",
-    "\n",
+    "            indices_1 = np.random.choice(len(x1), len(x1))\n",
+    "            indices_2 = np.random.choice(len(x3), len(x3))\n",
     "            x1_sample, x2_sample = x1[indices_1], x2[indices_1]\n",
     "            x3_sample, x4_sample = x3[indices_2], x4[indices_2]\n",
     "        else:\n",
-    "            x1_sample = rng.choice(x1, len(x1), replace=True)\n",
-    "            x2_sample = rng.choice(x2, len(x2), replace=True)\n",
-    "            x3_sample = rng.choice(x3, len(x3), replace=True)\n",
-    "            x4_sample = rng.choice(x4, len(x4), replace=True)\n",
-    "\n",
-    "        # Calculating deltas\n",
+    "            indices_1 = np.random.randint(0, len(x1), len(x1))\n",
+    "            indices_2 = np.random.randint(0, len(x2), len(x2))\n",
+    "            indices_3 = np.random.randint(0, len(x3), len(x3))\n",
+    "            indices_4 = np.random.randint(0, len(x4), len(x4))\n",
+    "            x1_sample, x2_sample = x1[indices_1], x2[indices_2]\n",
+    "            x3_sample, x4_sample = x3[indices_3], x4[indices_4]\n",
+    "\n",
+    "        # Calculate deltas\n",
     "        delta_1 = np.mean(x2_sample) - np.mean(x1_sample)\n",
     "        delta_2 = np.mean(x4_sample) - np.mean(x3_sample)\n",
     "        delta_delta = delta_2 - delta_1\n",
-    "\n",
+    "        \n",
     "        deltadelta[i] = delta_delta\n",
-    "        out_delta_g[i] = delta_delta / pooled_sample_sd\n",
     "\n",
-    "    # Empirical delta_g calculation\n",
-    "    delta_g = ((np.mean(x4) - np.mean(x3)) - (np.mean(x2) - np.mean(x1))) / pooled_sample_sd\n",
+    "        out_delta_g[i] = delta_delta if proportional else delta_delta/pooled_sd\n",
+    "\n",
+    "    return out_delta_g, deltadelta\n",
+    "\n",
+    "\n",
+    "def compute_delta2_bootstrapped_diff(\n",
+    "    x1: np.ndarray,  # Control group 1\n",
+    "    x2: np.ndarray,  # Test group 1\n",
+    "    x3: np.ndarray,  # Control group 2\n",
+    "    x4: np.ndarray,  # Test group 2\n",
+    "    is_paired: str = None,\n",
+    "    resamples: int = 5000,\n",
+    "    random_seed: int = 12345,\n",
+    "    proportional: bool = False\n",
+    ") -> tuple:\n",
+    "    \"\"\"\n",
+    "    Bootstraps the effect size deltas' g or proportional delta-delta\n",
+    "    \"\"\"\n",
+    "    x1, x2, x3, x4 = map(np.asarray, [x1, x2, x3, x4])\n",
+    "    \n",
+    "    if proportional:\n",
+    "        # For proportional data, pass 1.0 as dummy pooled_sd (won't be used)\n",
+    "        out_delta_g, deltadelta = delta2_bootstrap_loop(\n",
+    "            x1, x2, x3, x4, resamples, 1.0, random_seed, is_paired, proportional=True\n",
+    "        )\n",
+    "        # For proportional data, delta_g is the empirical delta-delta\n",
+    "        delta_g = ((np.mean(x4) - np.mean(x3)) - (np.mean(x2) - np.mean(x1)))\n",
+    "    else:\n",
+    "        # Calculate pooled sample standard deviation for non-proportional data\n",
+    "        stds = [np.std(x) for x in [x1, x2, x3, x4]]\n",
+    "        ns = [len(x) for x in [x1, x2, x3, x4]]\n",
+    "        \n",
+    "        sd_numerator = sum((n - 1) * s**2 for n, s in zip(ns, stds))\n",
+    "        sd_denominator = sum(n - 1 for n in ns)\n",
+    "        \n",
+    "        if sd_denominator == 0:\n",
+    "            raise ValueError(\"Insufficient data to compute pooled standard deviation.\")\n",
+    "            \n",
+    "        pooled_sample_sd = np.sqrt(sd_numerator / sd_denominator)\n",
+    "        \n",
+    "        if np.isnan(pooled_sample_sd) or pooled_sample_sd == 0:\n",
+    "            raise ValueError(\"Pooled sample standard deviation is NaN or zero.\")\n",
+    "            \n",
+    "        out_delta_g, deltadelta = delta2_bootstrap_loop(\n",
+    "            x1, x2, x3, x4, resamples, pooled_sample_sd, random_seed, is_paired, proportional=False\n",
+    "        )\n",
+    "        delta_g = ((np.mean(x4) - np.mean(x3)) - (np.mean(x2) - np.mean(x1))) / pooled_sample_sd\n",
     "\n",
     "    return out_delta_g, delta_g, deltadelta\n",
     "\n",
@@ -297,6 +330,7 @@
     "    return (100.0 - ci) / 100.0\n",
     "\n",
     "\n",
+    "@njit(cache=True)\n",
     "def _compute_quantile(z, bias, acceleration):\n",
     "    numer = bias + z\n",
     "    denom = 1 - (acceleration * numer)\n",
@@ -332,8 +366,12 @@
     "    return low, high\n",
     "\n",
     "\n",
+    "@njit(cache=True)\n",
     "def calculate_group_var(control_var, control_N, test_var, test_N):\n",
-    "    return control_var / control_N + test_var / test_N\n",
+    "    \n",
+    "    pooled_var = ((control_N - 1) * control_var + (test_N - 1) * test_var) / (control_N + test_N - 2) \n",
+    "    \n",
+    "    return pooled_var\n",
     "\n",
     "\n",
     "def calculate_weighted_delta(group_var, differences):\n",
@@ -343,18 +381,11 @@
     "\n",
     "    weight = 1 / group_var\n",
     "    denom = np.sum(weight)\n",
-    "    num = np.sum(weight[i] * differences[i] for i in range(0, len(weight)))\n",
-    "\n",
+    "    num = 0.0\n",
+    "    for i in range(len(weight)):\n",
+    "        num += weight[i] * differences[i]\n",
     "    return num / denom"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "87e0c164",
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
diff --git a/nbs/API/dabest_object.ipynb b/nbs/API/dabest_object.ipynb
index 776b4fb1..4054d6a6 100644
--- a/nbs/API/dabest_object.ipynb
+++ b/nbs/API/dabest_object.ipynb
@@ -13,6 +13,14 @@
     "- order: 2"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2654032b",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -56,7 +64,9 @@
    "source": [
     "#| export\n",
     "# Import standard data science libraries\n",
+    "import warnings\n",
     "from numpy import array, repeat, random, issubdtype, number\n",
+    "import numpy as np\n",
     "import pandas as pd\n",
     "from scipy.stats import norm\n",
     "from scipy.stats import randint"
@@ -67,7 +77,36 @@
    "execution_count": null,
    "id": "204a64b4",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Pre-compiling numba functions for DABEST...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Compiling numba functions: 100%|██████████| 11/11 [00:04<00:00,  2.20it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Numba compilation complete!\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
    "source": [
     "#| hide\n",
     "import dabest"
@@ -104,6 +143,7 @@
     "        experiment_label,\n",
     "        x1_level,\n",
     "        mini_meta,\n",
+    "        ps_adjust,\n",
     "    ):\n",
     "        \"\"\"\n",
     "        Parses and stores pandas DataFrames in preparation for estimation\n",
@@ -120,118 +160,19 @@
     "        self.__is_paired = paired\n",
     "        self.__resamples = resamples\n",
     "        self.__random_seed = random_seed\n",
-    "        self.__proportional = proportional\n",
-    "        self.__mini_meta = mini_meta\n",
+    "        self.__is_proportional = proportional\n",
+    "        self.__is_mini_meta = mini_meta\n",
+    "        self.__ps_adjust = ps_adjust\n",
     "\n",
     "        # after this call the attributes self.__experiment_label and self.__x1_level are updated\n",
     "        self._check_errors(x, y, idx, experiment, experiment_label, x1_level)\n",
-    "        \n",
-    "\n",
-    "        # Check if there is NaN under any of the paired settings\n",
-    "        if self.__is_paired and self.__output_data.isnull().values.any():\n",
-    "            import warnings\n",
-    "            warn1 = f\"NaN values detected under paired setting and removed,\"\n",
-    "            warn2 = f\" please check your data.\"\n",
-    "            warnings.warn(warn1 + warn2)\n",
-    "            if x is not None and y is not None:\n",
-    "                rmname = self.__output_data[self.__output_data[y].isnull()][self.__id_col].tolist()\n",
-    "                self.__output_data = self.__output_data[~self.__output_data[self.__id_col].isin(rmname)]\n",
-    "            elif x is None and y is None:\n",
-    "                self.__output_data.dropna(inplace=True)\n",
     "\n",
     "        # create new x & idx and record the second variable if this is a valid 2x2 ANOVA case\n",
-    "        if idx is None and x is not None and y is not None:\n",
-    "            # Add a length check for unique values in the first element in list x,\n",
-    "            # if the length is greater than 2, force delta2 to be False\n",
-    "            # Should be removed if delta2 for situations other than 2x2 is supported\n",
-    "            if len(self.__output_data[x[0]].unique()) > 2 and self.__x1_level is None:\n",
-    "                self.__delta2 = False\n",
-    "                # stop the loop if delta2 is False\n",
-    "\n",
-    "            # add a new column which is a combination of experiment and the first variable\n",
-    "            new_col_name = experiment + x[0]\n",
-    "            while new_col_name in self.__output_data.columns:\n",
-    "                new_col_name += \"_\"\n",
-    "\n",
-    "            self.__output_data[new_col_name] = (\n",
-    "                self.__output_data[x[0]].astype(str)\n",
-    "                + \" \"\n",
-    "                + self.__output_data[experiment].astype(str)\n",
-    "            )\n",
-    "\n",
-    "            # create idx and record the first and second x variable\n",
-    "            idx = []\n",
-    "            for i in list(map(lambda x: str(x), self.__experiment_label)):\n",
-    "                temp = []\n",
-    "                for j in list(map(lambda x: str(x), self.__x1_level)):\n",
-    "                    temp.append(j + \" \" + i)\n",
-    "                idx.append(temp)\n",
-    "\n",
-    "            self.__idx = idx\n",
-    "            self.__x1 = x[0]\n",
-    "            self.__x2 = x[1]\n",
-    "            x = new_col_name\n",
-    "        else:\n",
-    "            self.__idx = idx\n",
-    "            self.__x1 = None\n",
-    "            self.__x2 = None\n",
-    "\n",
-    "        # Determine the kind of estimation plot we need to produce.\n",
-    "        if all([isinstance(i, (str, int, float)) for i in idx]):\n",
-    "            # flatten out idx.\n",
-    "            all_plot_groups = pd.unique([t for t in idx]).tolist()\n",
-    "            if len(idx) > len(all_plot_groups):\n",
-    "                err0 = \"`idx` contains duplicated groups. Please remove any duplicates and try again.\"\n",
-    "                raise ValueError(err0)\n",
-    "\n",
-    "            # We need to re-wrap this idx inside another tuple so as to\n",
-    "            # easily loop thru each pairwise group later on.\n",
-    "            self.__idx = (idx,)\n",
-    "\n",
-    "        elif all([isinstance(i, (tuple, list)) for i in idx]):\n",
-    "            all_plot_groups = pd.unique([tt for t in idx for tt in t]).tolist()\n",
-    "\n",
-    "            actual_groups_given = sum([len(i) for i in idx])\n",
-    "\n",
-    "            if actual_groups_given > len(all_plot_groups):\n",
-    "                err0 = \"Groups are repeated across tuples,\"\n",
-    "                err1 = \" or a tuple has repeated groups in it.\"\n",
-    "                err2 = \" Please remove any duplicates and try again.\"\n",
-    "                raise ValueError(err0 + err1 + err2)\n",
-    "\n",
-    "        else:  # mix of string and tuple?\n",
-    "            err = \"There seems to be a problem with the idx you \" \"entered--{}.\".format(\n",
-    "                idx\n",
-    "            )\n",
-    "            raise ValueError(err)\n",
-    "\n",
-    "        # Check if there is a typo on paired\n",
-    "        if self.__is_paired and self.__is_paired not in (\"baseline\", \"sequential\"):\n",
-    "            err = \"{} assigned for `paired` is not valid.\".format(self.__is_paired)\n",
-    "            raise ValueError(err)\n",
-    "\n",
-    "        # Determine the type of data: wide or long.\n",
-    "        if x is None and y is not None:\n",
-    "            err = \"You have only specified `y`. Please also specify `x`.\"\n",
-    "            raise ValueError(err)\n",
-    "\n",
-    "        if x is not None and y is None:\n",
-    "            err = \"You have only specified `x`. Please also specify `y`.\"\n",
-    "            raise ValueError(err)\n",
+    "        idx, x, all_plot_groups = self._prep_idx(idx, x, y, experiment)\n",
     "\n",
     "        self.__plot_data = self._get_plot_data(x, y, all_plot_groups)\n",
     "        self.__all_plot_groups = all_plot_groups\n",
     "\n",
-    "        # Check if `id_col` is valid\n",
-    "        if self.__is_paired:\n",
-    "            if id_col is None:\n",
-    "                err = \"`id_col` must be specified if `paired` is assigned with a not NoneType value.\"\n",
-    "                raise IndexError(err)\n",
-    "\n",
-    "            if id_col not in self.__plot_data.columns:\n",
-    "                err = \"{} is not a column in `data`. \".format(id_col)\n",
-    "                raise IndexError(err)\n",
-    "\n",
     "        self._compute_effectsize_dfs()\n",
     "\n",
     "    def __repr__(self):\n",
@@ -280,7 +221,7 @@
     "                )\n",
     "            )\n",
     "\n",
-    "        if self.__mini_meta:\n",
+    "        if self.__is_mini_meta:\n",
     "            comparisons.append(\"weighted delta (only for mean difference)\")\n",
     "\n",
     "        for j, g in enumerate(comparisons):\n",
@@ -292,6 +233,74 @@
     "\n",
     "        return \"\\n\".join(out)\n",
     "\n",
+    "\n",
+    "    def _prep_idx(self, idx, x, y, experiment):\n",
+    "        \"\"\"\n",
+    "        Function to prepare the idx.\n",
+    "        \"\"\"\n",
+    "        if idx is None and x is not None and y is not None:\n",
+    "            # Add a length check for unique values in the first element in list x,\n",
+    "            # if the length is greater than 2, force delta2 to be False\n",
+    "            # Should be removed if delta2 for situations other than 2x2 is supported\n",
+    "            if len(self.__output_data[x[0]].unique()) > 2:\n",
+    "                self.__delta2 = False\n",
+    "\n",
+    "            # add a new column which is a combination of experiment and the first variable\n",
+    "            new_col_name = experiment + x[0]\n",
+    "            while new_col_name in self.__output_data.columns:\n",
+    "                new_col_name += \"_\"\n",
+    "\n",
+    "            self.__output_data[new_col_name] = (\n",
+    "                self.__output_data[x[0]].astype(str)\n",
+    "                + \" \"\n",
+    "                + self.__output_data[experiment].astype(str)\n",
+    "            )\n",
+    "\n",
+    "            # create idx and record the first and second x variable\n",
+    "            idx = []\n",
+    "            for i in list(map(lambda x: str(x), self.__experiment_label)):\n",
+    "                temp = []\n",
+    "                for j in list(map(lambda x: str(x), self.__x1_level)):\n",
+    "                    temp.append(j + \" \" + i)\n",
+    "                idx.append(temp)\n",
+    "\n",
+    "            self.__idx = idx\n",
+    "            self.__x1 = x[0]\n",
+    "            self.__x2 = x[1]\n",
+    "            x = new_col_name\n",
+    "        else:\n",
+    "            self.__idx = idx\n",
+    "            self.__x1 = None\n",
+    "            self.__x2 = None\n",
+    "\n",
+    "        # Determine the kind of estimation plot we need to produce.\n",
+    "        if all([isinstance(i, (str, int, float)) for i in self.__idx]):\n",
+    "            # flatten out idx.\n",
+    "            all_plot_groups = pd.Series([t for t in self.__idx]).unique().tolist()\n",
+    "            if len(self.__idx) > len(all_plot_groups):\n",
+    "                err0 = \"`idx` contains duplicated groups. Please remove any duplicates and try again.\"\n",
+    "                raise ValueError(err0)\n",
+    "\n",
+    "            # We need to re-wrap this idx inside another tuple so as to\n",
+    "            # easily loop thru each pairwise group later on.\n",
+    "            self.__idx = (idx,)\n",
+    "\n",
+    "        elif all([isinstance(i, (tuple, list)) for i in self.__idx]):\n",
+    "            all_plot_groups = pd.Series([tt for t in self.__idx for tt in t]).unique().tolist()\n",
+    "            actual_groups_given = sum([len(i) for i in self.__idx])\n",
+    "\n",
+    "            if actual_groups_given > len(all_plot_groups):\n",
+    "                err0 = \"Groups are repeated across tuples,\"\n",
+    "                err1 = \" or a tuple has repeated groups in it.\"\n",
+    "                err2 = \" Please remove any duplicates and try again.\"\n",
+    "                raise ValueError(err0 + err1 + err2)\n",
+    "\n",
+    "        else:  # mix of string and tuple?\n",
+    "            err = \"There seems to be a problem with the idx you \" \"entered--{}.\".format(self.__idx)\n",
+    "            raise ValueError(err)\n",
+    "        \n",
+    "        return idx, x, all_plot_groups\n",
+    "\n",
     "    @property\n",
     "    def mean_diff(self):\n",
     "        \"\"\"\n",
@@ -340,12 +349,14 @@
     "        \"\"\"\n",
     "        return self.__cliffs_delta\n",
     "\n",
+    "\n",
     "    @property\n",
     "    def delta_g(self):\n",
     "        \"\"\"\n",
-    "        Returns an :py:class:`EffectSizeDataFrame` for deltas' g, its confidence interval, and relevant statistics, for all comparisons as indicated via the `idx` and `paired` argument in `dabest.load()`.\n",
+    "        Returns an :py:class:`EffectSizeDataFrame` for delta g, its confidence interval, and relevant statistics, for all comparisons as indicated via the `idx` and `paired` argument in `dabest.load()`.\n",
     "        \"\"\"\n",
-    "        return self.__delta_g\n",
+    "        raise DeprecationWarning(\"delta_g has been depreciated  - Please use hedges_g (with delta2=True) for delta g experiments\")\n",
+    "\n",
     "\n",
     "    @property\n",
     "    def input_data(self):\n",
@@ -410,6 +421,14 @@
     "        situation.\n",
     "        \"\"\"\n",
     "        return self.__delta2\n",
+    "    \n",
+    "    @property\n",
+    "    def is_delta_delta(self):\n",
+    "        \"\"\"\n",
+    "        Returns the boolean parameter indicating if this is a delta-delta\n",
+    "        situation.\n",
+    "        \"\"\"\n",
+    "        return self.__delta2\n",
     "\n",
     "    @property\n",
     "    def is_paired(self):\n",
@@ -486,18 +505,18 @@
     "        return self.__plot_data\n",
     "\n",
     "    @property\n",
-    "    def proportional(self):\n",
+    "    def is_proportional(self):\n",
     "        \"\"\"\n",
     "        Returns the proportional parameter class.\n",
     "        \"\"\"\n",
-    "        return self.__proportional\n",
+    "        return self.__is_proportional\n",
     "\n",
     "    @property\n",
-    "    def mini_meta(self):\n",
+    "    def is_mini_meta(self):\n",
     "        \"\"\"\n",
     "        Returns the mini_meta boolean parameter.\n",
     "        \"\"\"\n",
-    "        return self.__mini_meta\n",
+    "        return self.__is_mini_meta\n",
     "\n",
     "    @property\n",
     "    def _all_plot_groups(self):\n",
@@ -512,10 +531,17 @@
     "        At the end of this function these two class attributes are updated\n",
     "                self.__experiment_label and self.__x1_level\n",
     "        '''\n",
+    "\n",
+    "        # Check if idx is present (if not a 2x2 Anova case)\n",
+    "        if idx is None:\n",
+    "            if not self.__delta2:\n",
+    "                err0 = \"Please specify `idx`.\"\n",
+    "                raise ValueError(err0)\n",
+    "\n",
     "        # Check if it is a valid mini_meta case\n",
-    "        if self.__mini_meta:\n",
+    "        if self.__is_mini_meta:\n",
     "            # Only mini_meta calculation but not proportional and delta-delta function\n",
-    "            if self.__proportional:\n",
+    "            if self.__is_proportional:\n",
     "                err0 = \"`proportional` and `mini_meta` cannot be True at the same time.\"\n",
     "                raise ValueError(err0)\n",
     "            if self.__delta2:\n",
@@ -547,7 +573,7 @@
     "\n",
     "            # Handling str type condition\n",
     "            if is_str_condition_met:\n",
-    "                if len(pd.unique(idx).tolist()) != 2:\n",
+    "                if len(np.unique(idx).tolist()) != 2:\n",
     "                    err0 = \"`mini_meta` is True, but `idx` ({})\".format(idx)\n",
     "                    err1 = \"does not contain exactly 2 unique columns.\"\n",
     "                    raise ValueError(err0 + err1)\n",
@@ -567,10 +593,6 @@
     "            if x is None:\n",
     "                error_msg = \"If `delta2` is True. `x` parameter cannot be None. String or list expected\"\n",
     "                raise ValueError(error_msg)\n",
-    "                \n",
-    "            if self.__proportional:\n",
-    "                err0 = \"`proportional` and `delta2` cannot be True at the same time.\"\n",
-    "                raise ValueError(err0)\n",
     "\n",
     "            # idx should not be specified\n",
     "            if idx:\n",
@@ -632,7 +654,6 @@
     "                            i, experiment\n",
     "                        )\n",
     "                        raise IndexError(err)\n",
-    "\n",
     "            else:\n",
     "                x1_level = self.__output_data[x[0]].unique()\n",
     "\n",
@@ -642,36 +663,65 @@
     "        self.__experiment_label = experiment_label\n",
     "        self.__x1_level = x1_level\n",
     "\n",
-    "    def _get_plot_data(self, x, y, all_plot_groups):\n",
-    "        \"\"\"\n",
-    "        Function to prepare some attributes for plotting\n",
-    "        \"\"\"\n",
-    "        # Check if there is NaN under any of the paired settings\n",
-    "        if self.__is_paired is not None and self.__output_data.isnull().values.any():\n",
-    "            print(\"Nan\")\n",
-    "            import warnings\n",
+    "        if self.__is_paired and self.__output_data.isnull().values.any():\n",
     "            warn1 = f\"NaN values detected under paired setting and removed,\"\n",
     "            warn2 = f\" please check your data.\"\n",
     "            warnings.warn(warn1 + warn2)\n",
-    "            rmname = self.__output_data[self.__output_data[y].isnull()][self.__id_col].tolist()\n",
-    "            self.__output_data = self.__output_data[~self.__output_data[self.__id_col].isin(rmname)]\n",
-    "                \n",
-    "        # Identify the type of data that was passed in.\n",
-    "        if x is not None and y is not None:\n",
-    "            # Assume we have a long dataset.\n",
-    "            # check both x and y are column names in data.\n",
-    "            if x not in self.__output_data.columns:\n",
-    "                err = \"{0} is not a column in `data`. Please check.\".format(x)\n",
+    "            if x is not None and y is not None:\n",
+    "                rmname = self.__output_data[self.__output_data[y].isnull()][self.__id_col].tolist()\n",
+    "                self.__output_data = self.__output_data[~self.__output_data[self.__id_col].isin(rmname)]\n",
+    "            elif x is None and y is None:\n",
+    "                self.__output_data.dropna(inplace=True)\n",
+    "\n",
+    "        # Check if there is a typo on paired\n",
+    "        if self.__is_paired and self.__is_paired not in (\"baseline\", \"sequential\"):\n",
+    "            err = \"'{}' assigned for `paired` is not valid. Please use either 'baseline' or 'sequential'.\".format(self.__is_paired)\n",
+    "            raise ValueError(err)\n",
+    "        \n",
+    "        # Check if `id_col` is valid\n",
+    "        if self.__is_paired:\n",
+    "            if self.__id_col is None:\n",
+    "                err = \"`id_col` must be specified if `paired` is assigned with a not NoneType value.\"\n",
     "                raise IndexError(err)\n",
-    "            if y not in self.__output_data.columns:\n",
-    "                err = \"{0} is not a column in `data`. Please check.\".format(y)\n",
+    "\n",
+    "            if self.__id_col not in self.__output_data.columns:\n",
+    "                err = \"`id_col` was given as '{}'; however, '{}' is not a column in `data`.\".format(self.__id_col, self.__id_col)\n",
     "                raise IndexError(err)\n",
+    "            \n",
+    "        # Check if x and y are supplied (relevant to long format data)\n",
+    "        if x is None and y is not None:\n",
+    "            err = \"You have only specified `y`. Please also specify `x` (for long format data).\"\n",
+    "            raise ValueError(err)\n",
     "\n",
-    "            # check y is numeric.\n",
+    "        if x is not None and y is None:\n",
+    "            err = \"You have only specified `x`. Please also specify `y` (for long format data).\"\n",
+    "            raise ValueError(err)\n",
+    "        \n",
+    "        if x is not None and y is not None:\n",
+    "            # Assume we have a long dataset.\n",
+    "            # check both x and y are column names in data.\n",
+    "            if not self.__delta2:\n",
+    "                if x not in self.__output_data.columns:\n",
+    "                    err = \"'{0}' is not a column in `data`. Please check.\".format(x)\n",
+    "                    raise IndexError(err)\n",
+    "                if y not in self.__output_data.columns:\n",
+    "                    err = \"'{0}' is not a column in `data`. Please check.\".format(y)\n",
+    "                    raise IndexError(err)\n",
+    "            # Check that the `y` column is numeric.\n",
     "            if not issubdtype(self.__output_data[y].dtype, number):\n",
-    "                err = \"{0} is a column in `data`, but it is not numeric.\".format(y)\n",
+    "                err = \"The `y` column in `data` is not numeric. Please check.\"\n",
     "                raise ValueError(err)\n",
     "\n",
+    "\n",
+    "    def _get_plot_data(self, x, y, all_plot_groups):\n",
+    "    # def _get_plot_data(self, x, y):\n",
+    "        \"\"\"\n",
+    "        Function to prepare some attributes for plotting\n",
+    "        \"\"\"\n",
+    "        # all_plot_groups = self.__all_plot_groups\n",
+    "        # Identify the type of data that was passed in.\n",
+    "        if x is not None and y is not None:\n",
+    "            # Assume we have a long dataset.\n",
     "            # check all the idx can be found in self.__output_data[x]\n",
     "            for g in all_plot_groups:\n",
     "                if g not in self.__output_data[x].unique():\n",
@@ -697,14 +747,7 @@
     "            self.__x = None\n",
     "            self.__y = None\n",
     "            self.__xvar = \"group\"\n",
-    "            self.__yvar = \"value\"\n",
-    "\n",
-    "            # Check if there is NaN under any of the paired settings\n",
-    "            if self.__is_paired is not None and self.__output_data.isnull().values.any():\n",
-    "                import warnings\n",
-    "                warn1 = f\"NaN values detected under paired setting and removed,\"\n",
-    "                warn2 = f\" please check your data.\"\n",
-    "                warnings.warn(warn1 + warn2)\n",
+    "            self.__yvar = \"Value\"\n",
     "\n",
     "            # First, check we have all columns in the dataset.\n",
     "            for g in all_plot_groups:\n",
@@ -730,12 +773,12 @@
     "\n",
     "\n",
     "        if isinstance(plot_data[self.__xvar].dtype, pd.CategoricalDtype):\n",
-    "            plot_data[self.__xvar].cat.remove_unused_categories(inplace=True)\n",
+    "            plot_data[self.__xvar].cat.remove_unused_categories()\n",
     "            plot_data[self.__xvar].cat.reorder_categories(\n",
-    "                all_plot_groups, ordered=True, inplace=True\n",
+    "                all_plot_groups, ordered=True\n",
     "            )\n",
     "        else:\n",
-    "            plot_data.loc[:, self.__xvar] = pd.Categorical(\n",
+    "            plot_data[self.__xvar] = pd.Categorical(\n",
     "                plot_data[self.__xvar], categories=all_plot_groups, ordered=True\n",
     "            )\n",
     "\n",
@@ -753,12 +796,13 @@
     "            is_paired=self.__is_paired,\n",
     "            random_seed=self.__random_seed,\n",
     "            resamples=self.__resamples,\n",
-    "            proportional=self.__proportional,\n",
+    "            proportional=self.__is_proportional,\n",
     "            delta2=self.__delta2,\n",
     "            experiment_label=self.__experiment_label,\n",
     "            x1_level=self.__x1_level,\n",
     "            x2=self.__x2,\n",
-    "            mini_meta=self.__mini_meta,\n",
+    "            mini_meta=self.__is_mini_meta,\n",
+    "            ps_adjust=self.__ps_adjust,\n",
     "        )\n",
     "\n",
     "        self.__mean_diff = EffectSizeDataFrame(\n",
@@ -775,8 +819,6 @@
     "\n",
     "        self.__hedges_g = EffectSizeDataFrame(self, \"hedges_g\", **effectsize_df_kwargs)\n",
     "\n",
-    "        self.__delta_g = EffectSizeDataFrame(self, \"delta_g\", **effectsize_df_kwargs)\n",
-    "\n",
     "        if not self.__is_paired:\n",
     "            self.__cliffs_delta = EffectSizeDataFrame(\n",
     "                self, \"cliffs_delta\", **effectsize_df_kwargs\n",
@@ -805,13 +847,13 @@
     {
      "data": {
       "text/plain": [
-       "DABEST v2024.03.29\n",
+       "DABEST v2025.03.27\n",
        "==================\n",
        "                  \n",
-       "Good afternoon!\n",
-       "The current time is Tue Mar 19 15:33:25 2024.\n",
+       "Good morning!\n",
+       "The current time is Tue Mar 25 10:08:38 2025.\n",
        "\n",
-       "The unpaired mean difference between control and test is 0.5 [95%CI -0.0412, 1.0].\n",
+       "The unpaired mean difference between control and test is 0.5 [95%CI 0.00172, 1.04].\n",
        "The p-value of the two-sided permutation t-test is 0.0758, calculated for legacy purposes only. \n",
        "\n",
        "5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.\n",
@@ -865,16 +907,26 @@
    "id": "8e9b8635",
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/jonathananns/GitHub/DABEST-python/dabest/_stats_tools/effsize.py:82: UserWarning: Using median as the statistic in bootstrapping may result in a biased estimate and cause problems with BCa confidence intervals. Consider using a different statistic, such as the mean.\n",
+      "When plotting, please consider using percetile confidence intervals by specifying `ci_type='pct'`. For detailed information, refer to https://github.com/ACCLAB/DABEST-python/issues/129 \n",
+      "\n",
+      "  warnings.warn(message=mes1+mes2, category=UserWarning)\n"
+     ]
+    },
     {
      "data": {
       "text/plain": [
-       "DABEST v2024.03.29\n",
+       "DABEST v2025.03.27\n",
        "==================\n",
        "                  \n",
-       "Good afternoon!\n",
-       "The current time is Tue Mar 19 15:33:26 2024.\n",
+       "Good morning!\n",
+       "The current time is Tue Mar 25 10:08:39 2025.\n",
        "\n",
-       "The unpaired median difference between control and test is 0.5 [95%CI -0.0758, 0.991].\n",
+       "The unpaired median difference between control and test is 0.5 [95%CI -0.0401, 1.04].\n",
        "The p-value of the two-sided permutation t-test is 0.103, calculated for legacy purposes only. \n",
        "\n",
        "5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.\n",
@@ -947,13 +999,13 @@
     {
      "data": {
       "text/plain": [
-       "DABEST v2024.03.29\n",
+       "DABEST v2025.03.27\n",
        "==================\n",
        "                  \n",
-       "Good afternoon!\n",
-       "The current time is Tue Mar 19 15:33:27 2024.\n",
+       "Good morning!\n",
+       "The current time is Tue Mar 25 10:08:39 2025.\n",
        "\n",
-       "The unpaired Cohen's d between control and test is 0.471 [95%CI -0.0843, 0.976].\n",
+       "The unpaired Cohen's d between control and test is 0.471 [95%CI -0.0405, 0.973].\n",
        "The p-value of the two-sided permutation t-test is 0.0758, calculated for legacy purposes only. \n",
        "\n",
        "5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.\n",
@@ -1041,13 +1093,13 @@
     {
      "data": {
       "text/plain": [
-       "DABEST v2024.03.29\n",
+       "DABEST v2025.03.27\n",
        "==================\n",
        "                  \n",
-       "Good afternoon!\n",
-       "The current time is Tue Mar 19 15:33:29 2024.\n",
+       "Good morning!\n",
+       "The current time is Tue Mar 25 10:08:41 2025.\n",
        "\n",
-       "The unpaired Cohen's h between control and test is 0.0 [95%CI -0.613, 0.429].\n",
+       "The unpaired Cohen's h between control and test is 0.0 [95%CI -0.563, 0.474].\n",
        "The p-value of the two-sided permutation t-test is 0.799, calculated for legacy purposes only. \n",
        "\n",
        "5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.\n",
@@ -1116,13 +1168,13 @@
     {
      "data": {
       "text/plain": [
-       "DABEST v2024.03.29\n",
+       "DABEST v2025.03.27\n",
        "==================\n",
        "                  \n",
-       "Good afternoon!\n",
-       "The current time is Tue Mar 19 15:33:30 2024.\n",
+       "Good morning!\n",
+       "The current time is Tue Mar 25 10:08:41 2025.\n",
        "\n",
-       "The unpaired Hedges' g between control and test is 0.465 [95%CI -0.0832, 0.963].\n",
+       "The unpaired Hedges' g between control and test is 0.465 [95%CI -0.04, 0.96].\n",
        "The p-value of the two-sided permutation t-test is 0.0758, calculated for legacy purposes only. \n",
        "\n",
        "5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.\n",
@@ -1190,13 +1242,13 @@
     {
      "data": {
       "text/plain": [
-       "DABEST v2024.03.29\n",
+       "DABEST v2025.03.27\n",
        "==================\n",
        "                  \n",
-       "Good afternoon!\n",
-       "The current time is Tue Mar 19 15:33:41 2024.\n",
+       "Good morning!\n",
+       "The current time is Tue Mar 25 10:08:41 2025.\n",
        "\n",
-       "The unpaired Cliff's delta between control and test is 0.28 [95%CI -0.0244, 0.533].\n",
+       "The unpaired Cliff's delta between control and test is 0.28 [95%CI -0.0111, 0.544].\n",
        "The p-value of the two-sided permutation t-test is 0.061, calculated for legacy purposes only. \n",
        "\n",
        "5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.\n",
@@ -1249,7 +1301,7 @@
    "id": "bd341f7c",
    "metadata": {},
    "source": [
-    "#### Example: delta_g"
+    "#### Example: delta_g via hedges_g"
    ]
   },
   {
@@ -1261,19 +1313,19 @@
     {
      "data": {
       "text/plain": [
-       "DABEST v2024.03.29\n",
+       "DABEST v2025.03.27\n",
        "==================\n",
        "                  \n",
-       "Good afternoon!\n",
-       "The current time is Tue Mar 19 15:33:45 2024.\n",
+       "Good morning!\n",
+       "The current time is Tue Mar 25 10:08:42 2025.\n",
        "\n",
-       "The unpaired deltas' g between W Placebo and M Placebo is 1.74 [95%CI 1.1, 2.31].\n",
+       "The unpaired Hedges' g between W Placebo and M Placebo is 1.74 [95%CI 1.09, 2.33].\n",
        "The p-value of the two-sided permutation t-test is 0.0, calculated for legacy purposes only. \n",
        "\n",
-       "The unpaired deltas' g between W Drug and M Drug is 1.33 [95%CI 0.611, 1.96].\n",
+       "The unpaired Hedges' g between W Drug and M Drug is 1.33 [95%CI 0.632, 1.98].\n",
        "The p-value of the two-sided permutation t-test is 0.0, calculated for legacy purposes only. \n",
        "\n",
-       "The deltas' g between Placebo and Drug is -0.651 [95%CI -1.59, 0.165].\n",
+       "The delta g between Placebo and Drug is -0.651 [95%CI -1.53, 0.21].\n",
        "The p-value of the two-sided permutation t-test is 0.0694, calculated for legacy purposes only. \n",
        "\n",
        "5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.\n",
@@ -1281,7 +1333,7 @@
        "assuming the null hypothesis of zero difference is true.\n",
        "For each p-value, 5000 reshuffles of the control and test labels were performed.\n",
        "\n",
-       "To get the results of all valid statistical tests, use `.delta_g.statistical_tests`"
+       "To get the results of all valid statistical tests, use `.hedges_g.statistical_tests`"
       ]
      },
      "execution_count": null,
@@ -1315,7 +1367,7 @@
     "                          'Treatment': treatment,\n",
     "                          'Y'         : y})\n",
     "unpaired_delta2 = dabest.load(data = df_delta2, x = [\"Genotype\", \"Genotype\"], y = \"Y\", delta2 = True, experiment = \"Treatment\")\n",
-    "unpaired_delta2.delta_g"
+    "unpaired_delta2.hedges_g"
    ]
   },
   {
@@ -1324,7 +1376,7 @@
    "id": "8d41dad3",
    "metadata": {},
    "source": [
-    "Deltas' g is an effect size that only applied on experiments with a 2-by-2 arrangement where two independent variables, A and B, each have two categorical values, 1 and 2, which calculates `hedges_g` for delta-delta statistics.\n",
+    "Delta g is an effect size that only applied on experiments with a 2-by-2 arrangement where two independent variables, A and B, each have two categorical values, 1 and 2, which calculates `hedges_g` for delta-delta statistics.\n",
     "\n",
     "\n",
     "   $$\\Delta_{1} = \\overline{X}_{A_{2}, B_{1}} - \\overline{X}_{A_{1}, B_{1}}$$\n",
@@ -1345,11 +1397,17 @@
     "\n",
     "where $s$ is the standard deviation and $n$ is the sample size.\n",
     "\n",
-    "A deltas' g value is then calculated as delta-delta value divided by pooled standard deviation $s_{\\Delta_{\\Delta}}$:\n",
+    "A delta g value is then calculated as delta-delta value divided by pooled standard deviation $s_{\\Delta_{\\Delta}}$:\n",
     "\n",
     "\n",
     "$\\Delta_{g} = \\frac{\\Delta_{\\Delta}}{s_{\\Delta_{\\Delta}}}$"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "33b5fc3c",
+   "metadata": {},
+   "source": []
   }
  ],
  "metadata": {
diff --git a/nbs/API/delta_objects.ipynb b/nbs/API/delta_objects.ipynb
index 358e45ad..bae2ffca 100644
--- a/nbs/API/delta_objects.ipynb
+++ b/nbs/API/delta_objects.ipynb
@@ -77,7 +77,7 @@
    "source": [
     "#| export\n",
     "class DeltaDelta(object):\n",
-    "    \"\"\"\n",
+    "    r\"\"\"\n",
     "    A class to compute and store the delta-delta statistics for experiments with a 2-by-2 arrangement where two independent variables, A and B, each have two categorical values, 1 and 2. The data is divided into two pairs of two groups, and a primary delta is first calculated as the mean difference between each of the pairs:\n",
     "\n",
     "\n",
@@ -93,7 +93,7 @@
     "\n",
     "    $$\\Delta_{\\Delta} = \\Delta_{2} - \\Delta_{1}$$\n",
     "\n",
-    "    and a deltas' g value is calculated as the mean difference between the two primary deltas divided by\n",
+    "    and a delta g value is calculated as the mean difference between the two primary deltas divided by\n",
     "    the standard deviation of the delta-delta value, which is calculated from a pooled variance of the 4 samples:\n",
     "\n",
     "    $$\\Delta_{g} = \\frac{\\Delta_{\\Delta}}{s_{\\Delta_{\\Delta}}}$$\n",
@@ -123,7 +123,7 @@
     "        self.__control = self.__dabest_obj.experiment_label[0]\n",
     "        self.__test = self.__dabest_obj.experiment_label[1]\n",
     "\n",
-    "        # Compute the bootstrap delta-delta or deltas' g and the true dela-delta based on the raw data\n",
+    "        # Compute the bootstrap delta-delta or delta g and the true dela-delta based on the raw data\n",
     "        if self.__effect_size == \"mean_diff\":\n",
     "            self.__bootstraps_delta_delta = bootstraps_delta_delta[2]\n",
     "            self.__difference = (\n",
@@ -217,7 +217,7 @@
     "        if self.__effect_size == \"mean_diff\":\n",
     "            out1 = \"The delta-delta between {control} and {test} \".format(**first_line)\n",
     "        else:\n",
-    "            out1 = \"The deltas' g between {control} and {test} \".format(**first_line)\n",
+    "            out1 = \"The delta g between {control} and {test} \".format(**first_line)\n",
     "\n",
     "        base_string_fmt = \"{:.\" + str(sigfig) + \"}\"\n",
     "        if \".\" in str(self.__ci):\n",
@@ -269,11 +269,29 @@
     "        dictionary.\n",
     "        \"\"\"\n",
     "        # Only get public (user-facing) attributes.\n",
-    "        attrs = [a for a in dir(self) if not a.startswith((\"_\", \"to_dict\"))]\n",
+    "        attrs = [a for a in dir(self) if not a.startswith((\"_\", \"to_dict\", \"results\"))]\n",
     "        out = {}\n",
     "        for a in attrs:\n",
     "            out[a] = getattr(self, a)\n",
     "        return out\n",
+    "    \n",
+    "    def __compute_results(self):\n",
+    "        # With some inspiration from @jungyangliao\n",
+    "        delta_delta_results_df = pd.Series(self.to_dict()).to_frame().T\n",
+    "\n",
+    "        column_index = ['control', 'test', 'difference', 'ci', 'bca_low', 'bca_high', 'bca_interval_idx', \n",
+    "                        'pct_low', 'pct_high', 'pct_interval_idx', 'bootstraps_control', 'bootstraps_test', \n",
+    "                        'bootstraps_delta_delta', 'permutations_control', 'permutations_test', 'permutations_delta_delta',\n",
+    "                        'pvalue_permutation', 'permutation_count', 'bias_correction', 'jackknives'\n",
+    "                        ]\n",
+    "        delta_delta_results_df['bootstraps_control'] = [delta_delta_results_df['bootstraps'][0][0]]\n",
+    "        delta_delta_results_df['bootstraps_test'] = [delta_delta_results_df['bootstraps'][0][1]]\n",
+    "        delta_delta_results_df['permutations_control'] = [delta_delta_results_df['permutations'][0][0]]\n",
+    "        delta_delta_results_df['permutations_test'] = [delta_delta_results_df['permutations'][0][1]]\n",
+    "        delta_delta_results_df = delta_delta_results_df.reindex(columns=column_index)\n",
+    "\n",
+    "        self.__results = delta_delta_results_df\n",
+    "        return self.__results\n",
     "\n",
     "    @property\n",
     "    def ci(self):\n",
@@ -411,7 +429,18 @@
     "            return self.__permutations_delta_delta\n",
     "        except AttributeError:\n",
     "            self.__permutation_test()\n",
-    "            return self.__permutations_delta_delta"
+    "            return self.__permutations_delta_delta\n",
+    "        \n",
+    "    @property\n",
+    "    def results(self):\n",
+    "        \"\"\"\n",
+    "        Return the results of the delta-delta analysis.\n",
+    "        \"\"\"\n",
+    "        try:\n",
+    "            return self.__results\n",
+    "        except AttributeError:\n",
+    "            self.__compute_results()\n",
+    "        return self.__results"
    ]
   },
   {
@@ -528,13 +557,14 @@
     "        # compute the variances of each control group and each test group\n",
     "        control_var=[]\n",
     "        test_var=[]\n",
+    "        grouped_data = {name: group[yvar].copy() for name, group in dat.groupby(xvar, observed=False)}\n",
     "        for j, current_tuple in enumerate(idx):\n",
     "            cname = current_tuple[0]\n",
-    "            control = dat[dat[xvar] == cname][yvar].copy()\n",
+    "            control = grouped_data[cname]\n",
     "            control_var.append(np.var(control, ddof=1))\n",
     "\n",
     "            tname = current_tuple[1]\n",
-    "            test = dat[dat[xvar] == tname][yvar].copy()\n",
+    "            test = grouped_data[tname]\n",
     "            test_var.append(np.var(test, ddof=1))\n",
     "        self.__control_var = np.array(control_var)\n",
     "        self.__test_var    = np.array(test_var)\n",
@@ -554,7 +584,7 @@
     "                                                          self.__bootstraps)\n",
     "\n",
     "        # Compute the weighted average mean difference based on the raw data\n",
-    "        self.__difference = es.weighted_delta(self.__effsizedf[\"difference\"],\n",
+    "        self.__difference = es.weighted_delta(np.array(self.__effsizedf[\"difference\"]),\n",
     "                                                   self.__group_var)\n",
     "\n",
     "        sorted_weighted_deltas = npsort(self.__bootstraps_weighted_delta)\n",
@@ -714,11 +744,30 @@
     "        \"\"\"\n",
     "        # Only get public (user-facing) attributes.\n",
     "        attrs = [a for a in dir(self)\n",
-    "                 if not a.startswith((\"_\", \"to_dict\"))]\n",
+    "                 if not a.startswith((\"_\", \"to_dict\", \"results\"))]\n",
     "        out = {}\n",
     "        for a in attrs:\n",
     "            out[a] = getattr(self, a)\n",
     "        return out\n",
+    "    \n",
+    "\n",
+    "    def __compute_results(self):\n",
+    "        # With some inspiration from @jungyangliao\n",
+    "        \"\"\"\n",
+    "        Returns all attributes of the `dabest.MiniMetaDelta` object as a\n",
+    "        DataFrame.\n",
+    "        \"\"\"\n",
+    "        mini_meta_delta_results_df = pd.Series(self.to_dict()).to_frame().T\n",
+    "        column_index = ['control', 'test', 'control_N', 'test_N', 'control_var', 'test_var', 'group_var',\n",
+    "                        'difference', 'ci', 'bca_low', 'bca_high', 'bca_interval_idx', \n",
+    "                        'pct_low', 'pct_high', 'pct_interval_idx', 'bootstraps', 'bootstraps_weighted_delta', \n",
+    "                        'permutations', 'permutations_var', 'permutations_weighted_delta', 'pvalue_permutation', \n",
+    "                        'permutation_count', 'bias_correction', 'jackknives']\n",
+    "        mini_meta_delta_results_df = mini_meta_delta_results_df.reindex(columns=column_index)\n",
+    "        mini_meta_delta_results_df.rename(columns={'bootstraps': 'bootstraps_deltas'}, inplace=True)\n",
+    "\n",
+    "        self.__results = mini_meta_delta_results_df\n",
+    "        return self.__results\n",
     "\n",
     "\n",
     "    @property\n",
@@ -939,7 +988,17 @@
     "        except AttributeError:\n",
     "            self.__permutation_test()\n",
     "            return self.__permutations_weighted_delta\n",
-    "\n"
+    "\n",
+    "    @property\n",
+    "    def results(self):\n",
+    "        \"\"\"\n",
+    "        Return the results of the mini-meta analysis.\n",
+    "        \"\"\"\n",
+    "        try:\n",
+    "            return self.__results\n",
+    "        except AttributeError:\n",
+    "            self.__compute_results()\n",
+    "        return self.__results"
    ]
   },
   {
@@ -1009,7 +1068,7 @@
     "                   'Control 2' : c2,     'Test 2' : t2,\n",
     "                   'Control 3' : c3,     'Test 3' : t3})\n",
     "my_dabest_object = dabest.load(my_df, idx=((\"Control 1\", \"Test 1\"), (\"Control 2\", \"Test 2\"), (\"Control 3\", \"Test 3\")), mini_meta=True)\n",
-    "my_dabest_object.mean_diff.mini_meta_delta"
+    "my_dabest_object.mean_diff.mini_meta"
    ]
   },
   {
@@ -1018,12 +1077,17 @@
    "source": [
     "As of version 2023.02.14, weighted delta can only be calculated for mean difference, and not for standardized measures such as Cohen's *d*.\n",
     "\n",
-    "Details about the calculated weighted delta are accessed as attributes of the ``mini_meta_delta`` class. See the `minimetadelta` for details on usage.\n",
+    "Details about the calculated weighted delta are accessed as attributes of the ``mini_meta`` class. See the `minimetadelta` for details on usage.\n",
     "\n",
     "Refer to Chapter 10 of the Cochrane handbook for further information on meta-analysis: \n",
     "https://training.cochrane.org/handbook/current/chapter-10\n",
     "\t\t"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
   }
  ],
  "metadata": {
diff --git a/nbs/API/effsize.ipynb b/nbs/API/effsize.ipynb
index b2232515..65259d81 100644
--- a/nbs/API/effsize.ipynb
+++ b/nbs/API/effsize.ipynb
@@ -56,9 +56,10 @@
     "#| export\n",
     "from __future__ import annotations\n",
     "import numpy as np\n",
+    "from numba import njit\n",
     "import warnings\n",
     "from scipy.special import gamma\n",
-    "from scipy.stats import mannwhitneyu"
+    "from scipy.stats import mannwhitneyu\n"
    ]
   },
   {
@@ -118,6 +119,10 @@
     "\n",
     "    \"\"\"\n",
     "\n",
+    "    if not isinstance(control, np.ndarray):\n",
+    "        control = np.array(control)\n",
+    "    if not isinstance(test, np.ndarray):\n",
+    "        test    = np.array(test)\n",
     "\n",
     "    if effect_size == \"mean_diff\":\n",
     "        return func_difference(control, test, np.mean, is_paired)\n",
@@ -127,7 +132,7 @@
     "                \"result in a biased estimate and cause problems with \" + \\\n",
     "                \"BCa confidence intervals. Consider using a different statistic, such as the mean.\\n\"\n",
     "        mes2 = \"When plotting, please consider using percetile confidence intervals \" + \\\n",
-    "                \"by specifying `ci_type='percentile'`. For detailed information, \" + \\\n",
+    "                \"by specifying `ci_type='pct'`. For detailed information, \" + \\\n",
     "                \"refer to https://github.com/ACCLAB/DABEST-python/issues/129 \\n\"\n",
     "        warnings.warn(message=mes1+mes2, category=UserWarning)\n",
     "        return func_difference(control, test, np.median, is_paired)\n",
@@ -138,7 +143,7 @@
     "    if effect_size == \"cohens_h\":\n",
     "        return cohens_h(control, test)\n",
     "\n",
-    "    if effect_size == \"hedges_g\" or effect_size == \"delta_g\":\n",
+    "    if effect_size == \"hedges_g\":\n",
     "        return hedges_g(control, test, is_paired)\n",
     "\n",
     "    if effect_size == \"cliffs_delta\":\n",
@@ -170,9 +175,9 @@
     "\n",
     "    # Convert to numpy arrays for speed.\n",
     "    # NaNs are automatically dropped.\n",
-    "    if ~isinstance(control, np.ndarray):\n",
+    "    if not isinstance(control, np.ndarray):\n",
     "        control = np.array(control)\n",
-    "    if ~isinstance(test, np.ndarray):\n",
+    "    if not isinstance(test, np.ndarray):\n",
     "        test    = np.array(test)\n",
     "\n",
     "    if is_paired:\n",
@@ -180,19 +185,11 @@
     "            err = \"The two arrays supplied do not have the same length.\"\n",
     "            raise ValueError(err)\n",
     "\n",
-    "        control_nan = np.where(np.isnan(control))[0]\n",
-    "        test_nan    = np.where(np.isnan(test))[0]\n",
-    "\n",
-    "        indexes_to_drop = np.unique(np.concatenate([control_nan,\n",
-    "                                                    test_nan]))\n",
-    "\n",
-    "        good_indexes = [i for i in range(0, len(control))\n",
-    "                        if i not in indexes_to_drop]\n",
+    "        non_nan_mask = ~np.isnan(control) & ~np.isnan(test)\n",
+    "        control_non_nan = control[non_nan_mask]\n",
+    "        test_non_nan = test[non_nan_mask]\n",
     "\n",
-    "        control = control[good_indexes]\n",
-    "        test    = test[good_indexes]\n",
-    "\n",
-    "        return func(test - control)\n",
+    "        return func(test_non_nan - control_non_nan)\n",
     "\n",
     "    \n",
     "    control = control[~np.isnan(control)]\n",
@@ -208,6 +205,7 @@
    "outputs": [],
    "source": [
     "#| export\n",
+    "@njit(cache=True)\n",
     "def cohens_d(control:list|tuple|np.ndarray,\n",
     "             test:list|tuple|np.ndarray,\n",
     "             is_paired:str=None # If not None, the paired Cohen's d is returned.\n",
@@ -252,12 +250,6 @@
     "        - https://en.wikipedia.org/wiki/Standard_deviation#Corrected_sample_standard_deviation\n",
     "    \"\"\"\n",
     "\n",
-    "    # Convert to numpy arrays for speed.\n",
-    "    # NaNs are automatically dropped.\n",
-    "    if ~isinstance(control, np.ndarray):\n",
-    "        control = np.array(control)\n",
-    "    if ~isinstance(test, np.ndarray):\n",
-    "        test    = np.array(test)\n",
     "    control = control[~np.isnan(control)]\n",
     "    test    = test[~np.isnan(test)]\n",
     "\n",
@@ -296,12 +288,13 @@
    "outputs": [],
    "source": [
     "#| export\n",
+    "# @njit(cache=True) # It uses np.seterr which is not supported by Numba\n",
     "def cohens_h(control:list|tuple|np.ndarray, \n",
     "             test:list|tuple|np.ndarray\n",
     "            )->float:\n",
     "    '''\n",
     "    Computes Cohen's h for test v.s. control.\n",
-    "    See [here](https://en.wikipedia.org/wiki/Cohen%27s_h for reference.)\n",
+    "    See [here](https://en.wikipedia.org/wiki/Cohen%27s_h) for reference.\n",
     "    \n",
     "    `Notes`:\n",
     "    \n",
@@ -318,10 +311,6 @@
     "    # Convert to numpy arrays for speed.\n",
     "    # NaNs are automatically dropped.\n",
     "    # Aligned with cohens_d calculation.\n",
-    "    if ~isinstance(control, np.ndarray):\n",
-    "        control = np.array(control)\n",
-    "    if ~isinstance(test, np.ndarray):\n",
-    "        test    = np.array(test)\n",
     "    control = control[~np.isnan(control)]\n",
     "    test = test[~np.isnan(test)]\n",
     "\n",
@@ -357,10 +346,6 @@
     "\n",
     "    # Convert to numpy arrays for speed.\n",
     "    # NaNs are automatically dropped.\n",
-    "    if ~isinstance(control, np.ndarray):\n",
-    "        control = np.array(control)\n",
-    "    if ~isinstance(test, np.ndarray):\n",
-    "        test    = np.array(test)\n",
     "    control = control[~np.isnan(control)]\n",
     "    test    = test[~np.isnan(test)]\n",
     "\n",
@@ -379,6 +364,29 @@
    "outputs": [],
    "source": [
     "#| export\n",
+    "@njit(cache=True)\n",
+    "def _mann_whitney_u(x, y):\n",
+    "    \"\"\"Numba-optimized Mann-Whitney U calculation\"\"\"\n",
+    "    n1, n2 = len(x), len(y)\n",
+    "    combined = np.concatenate((x, y))\n",
+    "    \n",
+    "    # Use numpy broadcasting for comparison\n",
+    "    less_than = (combined.reshape(-1, 1) > combined).sum(axis=1)\n",
+    "    equal_to = (combined.reshape(-1, 1) == combined).sum(axis=1)\n",
+    "    \n",
+    "    # Calculate ranks directly\n",
+    "    ranks = less_than + (equal_to + 1) / 2\n",
+    "    \n",
+    "    R1 = np.sum(ranks[:n1])\n",
+    "    U1 = R1 - (n1 * (n1 + 1)) / 2\n",
+    "    return U1\n",
+    "\n",
+    "@njit(cache=True)\n",
+    "def _cliffs_delta_core(control, test):\n",
+    "    \"\"\"Numba-optimized Cliff's delta calculation\"\"\"\n",
+    "    U = _mann_whitney_u(test, control)\n",
+    "    return ((2 * U) / (len(control) * len(test))) - 1\n",
+    "\n",
     "def cliffs_delta(control:list|tuple|np.ndarray, \n",
     "                 test:list|tuple|np.ndarray\n",
     "                )->float:\n",
@@ -386,25 +394,9 @@
     "    Computes Cliff's delta for 2 samples.\n",
     "    See [here](https://en.wikipedia.org/wiki/Effect_size#Effect_size_for_ordinal_data)\n",
     "    \"\"\"\n",
-    "\n",
-    "    # Convert to numpy arrays for speed.\n",
-    "    # NaNs are automatically dropped.\n",
-    "    if ~isinstance(control, np.ndarray):\n",
-    "        control = np.array(control)\n",
-    "    if ~isinstance(test, np.ndarray):\n",
-    "        test    = np.array(test)\n",
-    "\n",
     "    c = control[~np.isnan(control)]\n",
     "    t = test[~np.isnan(test)]\n",
-    "\n",
-    "    control_n = len(c)\n",
-    "    test_n = len(t)\n",
-    "\n",
-    "    # Note the order of the control and test arrays.\n",
-    "    U, _ = mannwhitneyu(t, c, alternative='two-sided')\n",
-    "    cliffs_delta = ((2 * U) / (control_n * test_n)) - 1\n",
-    "\n",
-    "    return cliffs_delta\n"
+    "    return _cliffs_delta_core(c, t)\n"
    ]
   },
   {
@@ -415,6 +407,7 @@
    "outputs": [],
    "source": [
     "#| export\n",
+    "@njit(cache=True)\n",
     "def _compute_standardizers(control, test):\n",
     "    \"\"\"\n",
     "    Computes the pooled and average standard deviations for two datasets.\n",
@@ -448,9 +441,9 @@
     "    control_n = len(control)\n",
     "    test_n = len(test)\n",
     "\n",
-    "    control_var = np.var(control, ddof=1) # use N-1 to compute the variance.\n",
-    "    test_var = np.var(test, ddof=1)\n",
-    "\n",
+    "    # ddof parameter is not supported by numba.\n",
+    "    control_var = np.var(control)*control_n/(control_n-1) # use N-1 to compute the variance.\n",
+    "    test_var = np.var(test)*test_n/(test_n-1)\n",
     "\n",
     "    # For unpaired 2-groups standardized mean difference.\n",
     "    pooled = np.sqrt(((control_n - 1) * control_var + (test_n - 1) * test_var) /\n",
@@ -487,6 +480,7 @@
     "    \"\"\"\n",
     "\n",
     "    df = n1 + n2 - 2\n",
+    "    # gamma function is not supported by numba.\n",
     "    numer = gamma(df / 2)\n",
     "    denom0 = gamma((df - 1) / 2)\n",
     "    denom = np.sqrt(df / 2) * denom0\n",
@@ -512,6 +506,7 @@
    "outputs": [],
    "source": [
     "#| export\n",
+    "@njit(cache=True)\n",
     "def weighted_delta(difference, group_var):\n",
     "    '''\n",
     "    Compute the weighted deltas where the weight is the inverse of the\n",
diff --git a/nbs/API/effsize_objects.ipynb b/nbs/API/effsize_objects.ipynb
index fd8496c1..29827005 100644
--- a/nbs/API/effsize_objects.ipynb
+++ b/nbs/API/effsize_objects.ipynb
@@ -46,7 +46,36 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Pre-compiling numba functions for DABEST...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Compiling numba functions: 100%|██████████| 11/11 [00:02<00:00,  3.82it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Numba compilation complete!\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
    "source": [
     "#| hide\n",
     "import dabest"
@@ -62,6 +91,11 @@
     "import pandas as pd\n",
     "import lqrt\n",
     "from scipy.stats import norm\n",
+    "import numpy as np\n",
+    "from scipy.special import binom as binomcoeff  # devMJBL\n",
+    "from scipy.stats import binom  # devMJBL\n",
+    "from scipy.integrate import fixed_quad  # devMJBL\n",
+    "from numpy import arange, mean  # devMJBL\n",
     "from numpy import array, isnan, isinf, repeat, random, isin, abs, var\n",
     "from numpy import sort as npsort\n",
     "from numpy import nan as npnan\n",
@@ -109,6 +143,10 @@
     "            `random_seed` is used to seed the random number generator during\n",
     "            bootstrap resampling. This ensures that the confidence intervals\n",
     "            reported are replicable.\n",
+    "        ps_adjust : boolean, default False.\n",
+    "            If True, adjust calculated p-value according to Phipson & Smyth (2010)\n",
+    "            # https://doi.org/10.2202/1544-6115.1585\n",
+    "            \n",
     "\n",
     "        Returns\n",
     "        -------\n",
@@ -146,6 +184,7 @@
     "        resamples=5000,\n",
     "        permutation_count=5000,\n",
     "        random_seed=12345,\n",
+    "        ps_adjust=False,\n",
     "    ):\n",
     "        from ._stats_tools import confint_2group_diff as ci2g\n",
     "        from ._stats_tools import effsize as es\n",
@@ -157,15 +196,15 @@
     "            \"cohens_h\": \"Cohen's h\",\n",
     "            \"hedges_g\": \"Hedges' g\",\n",
     "            \"cliffs_delta\": \"Cliff's delta\",\n",
-    "            \"delta_g\": \"deltas' g\",\n",
     "        }\n",
-    "\n",
+    "  \n",
     "        self.__is_paired = is_paired\n",
     "        self.__resamples = resamples\n",
     "        self.__effect_size = effect_size\n",
     "        self.__random_seed = random_seed\n",
     "        self.__ci = ci\n",
-    "        self.__proportional = proportional\n",
+    "        self.__is_proportional = proportional\n",
+    "        self.__ps_adjust = ps_adjust\n",
     "        self._check_errors(control, test)\n",
     "\n",
     "        # Convert to numpy arrays for speed.\n",
@@ -227,6 +266,8 @@
     "        self.__pct_interval_idx = (pct_idx_low, pct_idx_high)\n",
     "        self.__pct_low = sorted_bootstraps[pct_idx_low]\n",
     "        self.__pct_high = sorted_bootstraps[pct_idx_high]\n",
+    "        \n",
+    "        self._get_bootstrap_baseline_ec()\n",
     "\n",
     "        self._perform_statistical_test()\n",
     "\n",
@@ -294,7 +335,7 @@
     "            return \"{}\\n{}\\n\\n{}\\n{}\".format(out, pvalue, bs, pval_def)\n",
     "        elif not show_resample_count and define_pval:\n",
     "            return \"{}\\n{}\\n\\n{}\".format(out, pvalue, pval_def)\n",
-    "        elif show_resample_count and ~define_pval:\n",
+    "        elif show_resample_count and not define_pval:\n",
     "            return \"{}\\n{}\\n\\n{}\".format(out, pvalue, bs)\n",
     "        else:\n",
     "            return \"{}\\n{}\".format(out, pvalue)\n",
@@ -313,15 +354,15 @@
     "            err1 = \"`paired` is not None; therefore Cliff's delta is not defined.\"\n",
     "            raise ValueError(err1)\n",
     "\n",
-    "        if self.__proportional and self.__effect_size not in [\"mean_diff\", \"cohens_h\"]:\n",
-    "            err1 = \"`proportional` is True; therefore effect size other than mean_diff and cohens_h is not defined.\"\n",
+    "        if self.__is_proportional and self.__effect_size not in [\"mean_diff\", \"cohens_h\"]:\n",
+    "            err1 = \"`is_proportional` is True; therefore effect size other than mean_diff and cohens_h is not defined.\"\n",
     "            raise ValueError(err1)\n",
     "\n",
-    "        if self.__proportional and (\n",
+    "        if self.__is_proportional and (\n",
     "            isin(control, [0, 1]).all() == False or isin(test, [0, 1]).all() == False\n",
     "        ):\n",
     "            err1 = (\n",
-    "                \"`proportional` is True; Only accept binary data consisting of 0 and 1.\"\n",
+    "                \"`is_proportional` is True; Only accept binary data consisting of 0 and 1.\"\n",
     "            )\n",
     "            raise ValueError(err1)\n",
     "\n",
@@ -387,9 +428,10 @@
     "            self.__effect_size,\n",
     "            self.__is_paired,\n",
     "            self.__permutation_count,\n",
+    "            ps_adjust = self.__ps_adjust,\n",
     "        )\n",
     "\n",
-    "        if self.__is_paired and not self.__proportional:\n",
+    "        if self.__is_paired and not self.__is_proportional:\n",
     "            # Wilcoxon, a non-parametric version of the paired T-test.\n",
     "            try:\n",
     "                wilcoxon = spstats.wilcoxon(self.__control, self.__test)\n",
@@ -410,22 +452,21 @@
     "                self.__pvalue_paired_students_t = paired_t.pvalue\n",
     "                self.__statistic_paired_students_t = paired_t.statistic\n",
     "\n",
-    "        elif self.__is_paired and self.__proportional:\n",
+    "        elif self.__is_paired and self.__is_proportional:\n",
     "            # for binary paired data, use McNemar's test\n",
     "            # References:\n",
     "            # https://en.wikipedia.org/wiki/McNemar%27s_test\n",
     "\n",
-    "            df_temp = pd.DataFrame({\"control\": self.__control, \"test\": self.__test})\n",
-    "            x1 = len(df_temp[(df_temp[\"control\"] == 0) & (df_temp[\"test\"] == 0)])\n",
-    "            x2 = len(df_temp[(df_temp[\"control\"] == 0) & (df_temp[\"test\"] == 1)])\n",
-    "            x3 = len(df_temp[(df_temp[\"control\"] == 1) & (df_temp[\"test\"] == 0)])\n",
-    "            x4 = len(df_temp[(df_temp[\"control\"] == 1) & (df_temp[\"test\"] == 1)])\n",
-    "            table = [[x1, x2], [x3, x4]]\n",
+    "            x1 = np.sum((self.__control == 0) & (self.__test == 0))\n",
+    "            x2 = np.sum((self.__control == 0) & (self.__test == 1))\n",
+    "            x3 = np.sum((self.__control == 1) & (self.__test == 0))\n",
+    "            x4 = np.sum((self.__control == 1) & (self.__test == 1))\n",
+    "            table = np.array([[x1, x2], [x3, x4]])\n",
     "            _mcnemar = mcnemar(table, exact=True, correction=True)\n",
     "            self.__pvalue_mcnemar = _mcnemar.pvalue\n",
     "            self.__statistic_mcnemar = _mcnemar.statistic\n",
     "\n",
-    "        elif self.__proportional:\n",
+    "        elif self.__is_proportional:\n",
     "            # The Cohen's h calculation is for binary categorical data\n",
     "            try:\n",
     "                self.__proportional_difference = es.cohens_h(\n",
@@ -495,6 +536,92 @@
     "        for a in attrs:\n",
     "            out[a] = getattr(self, a)\n",
     "        return out\n",
+    "    \n",
+    "    def _get_bootstrap_baseline_ec(self):\n",
+    "        from ._stats_tools import confint_2group_diff as ci2g\n",
+    "        from ._stats_tools import effsize as es\n",
+    "        \n",
+    "        # Cannot use self.__is_paired because it's for baseline curve\n",
+    "        is_paired = None\n",
+    "        \n",
+    "        difference = es.two_group_difference(\n",
+    "            self.__control, self.__control, is_paired, self.__effect_size\n",
+    "        )\n",
+    "        self.__bec_difference = difference\n",
+    "\n",
+    "        jackknives = ci2g.compute_meandiff_jackknife(\n",
+    "            self.__control, self.__control, is_paired, self.__effect_size\n",
+    "        )\n",
+    "\n",
+    "        acceleration_value = ci2g._calc_accel(jackknives)\n",
+    "\n",
+    "        bootstraps = ci2g.compute_bootstrapped_diff(\n",
+    "            self.__control,\n",
+    "            self.__control,\n",
+    "            is_paired,\n",
+    "            self.__effect_size,\n",
+    "            self.__resamples,\n",
+    "            self.__random_seed,\n",
+    "        )\n",
+    "        self.__bootstraps_baseline_ec = bootstraps\n",
+    "\n",
+    "        sorted_bootstraps = npsort(self.__bootstraps_baseline_ec)\n",
+    "        # We don't have to consider infinities in bootstrap_baseline_ec\n",
+    "\n",
+    "        bias_correction = ci2g.compute_meandiff_bias_correction(\n",
+    "            self.__bootstraps_baseline_ec, difference\n",
+    "        )\n",
+    "\n",
+    "        # Compute BCa intervals.\n",
+    "        bca_idx_low, bca_idx_high = ci2g.compute_interval_limits(\n",
+    "            bias_correction,\n",
+    "            acceleration_value,\n",
+    "            self.__resamples,\n",
+    "            self.__ci,\n",
+    "        )\n",
+    "\n",
+    "        self.__bec_bca_interval_idx = (bca_idx_low, bca_idx_high)\n",
+    "\n",
+    "        if ~isnan(bca_idx_low) and ~isnan(bca_idx_high):\n",
+    "            self.__bec_bca_low = sorted_bootstraps[bca_idx_low]\n",
+    "            self.__bec_bca_high = sorted_bootstraps[bca_idx_high]\n",
+    "\n",
+    "            err1 = \"The $lim_type limit of the interval\"\n",
+    "            err2 = \"was in the $loc 10 values.\"\n",
+    "            err3 = \"The result for baseline curve should be considered unstable.\"\n",
+    "            err_temp = Template(\" \".join([err1, err2, err3]))\n",
+    "\n",
+    "            if bca_idx_low <= 10:\n",
+    "                warnings.warn(\n",
+    "                    err_temp.substitute(lim_type=\"lower\", loc=\"bottom\"), stacklevel=1\n",
+    "                )\n",
+    "\n",
+    "            if bca_idx_high >= self.__resamples - 9:\n",
+    "                warnings.warn(\n",
+    "                    err_temp.substitute(lim_type=\"upper\", loc=\"top\"), stacklevel=1\n",
+    "                )\n",
+    "\n",
+    "        else:\n",
+    "            err1 = \"The $lim_type limit of the BCa interval of baseline curve cannot be computed.\"\n",
+    "            err2 = \"It is set to the effect size itself.\"\n",
+    "            err3 = \"All bootstrap values were likely all the same.\"\n",
+    "            err_temp = Template(\" \".join([err1, err2, err3]))\n",
+    "\n",
+    "            if isnan(bca_idx_low):\n",
+    "                self.__bec_bca_low = difference\n",
+    "                warnings.warn(err_temp.substitute(lim_type=\"lower\"), stacklevel=0)\n",
+    "\n",
+    "            if isnan(bca_idx_high):\n",
+    "                self.__bec_bca_high = difference\n",
+    "                warnings.warn(err_temp.substitute(lim_type=\"upper\"), stacklevel=0)\n",
+    "\n",
+    "        # Compute percentile intervals.\n",
+    "        pct_idx_low = int((self.__alpha / 2) * self.__resamples)\n",
+    "        pct_idx_high = int((1 - (self.__alpha / 2)) * self.__resamples)\n",
+    "\n",
+    "        self.__bec_pct_interval_idx = (pct_idx_low, pct_idx_high)\n",
+    "        self.__bec_pct_low = sorted_bootstraps[pct_idx_low]\n",
+    "        self.__bec_pct_high = sorted_bootstraps[pct_idx_high]\n",
     "\n",
     "    @property\n",
     "    def difference(self):\n",
@@ -515,8 +642,8 @@
     "        return self.__is_paired\n",
     "\n",
     "    @property\n",
-    "    def proportional(self):\n",
-    "        return self.__proportional\n",
+    "    def is_proportional(self):\n",
+    "        return self.__is_proportional\n",
     "\n",
     "    @property\n",
     "    def ci(self):\n",
@@ -730,7 +857,55 @@
     "        try:\n",
     "            return self.__proportional_difference\n",
     "        except AttributeError:\n",
-    "            return npnan"
+    "            return npnan\n",
+    "      \n",
+    "    @property\n",
+    "    def bec_difference(self):\n",
+    "        return self.__bec_difference   \n",
+    "        \n",
+    "    @property\n",
+    "    def bec_bootstraps(self):\n",
+    "        \"\"\"\n",
+    "        The generated baseline error bootstraps.\n",
+    "        \"\"\"\n",
+    "        return self.__bootstraps_baseline_ec\n",
+    "\n",
+    "    @property\n",
+    "    def bec_bca_interval_idx(self):\n",
+    "        return self.__bec_bca_interval_idx\n",
+    "\n",
+    "    @property\n",
+    "    def bec_bca_low(self):\n",
+    "        \"\"\"\n",
+    "        The bias-corrected and accelerated confidence interval lower limit for baseline error.\n",
+    "        \"\"\"\n",
+    "        return self.__bec_bca_low\n",
+    "\n",
+    "    @property\n",
+    "    def bec_bca_high(self):\n",
+    "        \"\"\"\n",
+    "        The bias-corrected and accelerated confidence interval upper limit for baseline error.\n",
+    "        \"\"\"\n",
+    "        return self.__bec_bca_high\n",
+    "\n",
+    "    @property\n",
+    "    def bec_pct_interval_idx(self):\n",
+    "        return self.__bec_pct_interval_idx\n",
+    "\n",
+    "    @property\n",
+    "    def bec_pct_low(self):\n",
+    "        \"\"\"\n",
+    "        The percentile confidence interval lower limit for baseline error.\n",
+    "        \"\"\"\n",
+    "        return self.__bec_pct_low\n",
+    "\n",
+    "    @property\n",
+    "    def bec_pct_high(self):\n",
+    "        \"\"\"\n",
+    "        The percentile confidence interval lower limit for baseline error.\n",
+    "        \"\"\"\n",
+    "        return self.__bec_pct_high\n",
+    "        "
    ]
   },
   {
@@ -748,17 +923,18 @@
     {
      "data": {
       "text/plain": [
-       "The unpaired mean difference is -0.253 [95%CI -0.78, 0.25].\n",
+       "The unpaired mean difference is -0.253 [95%CI -0.782, 0.241].\n",
        "The p-value of the two-sided permutation t-test is 0.348, calculated for legacy purposes only. \n",
        "\n",
        "5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.\n",
        "Any p-value reported is the probability of observing theeffect size (or greater),\n",
-       "assuming the null hypothesis ofzero difference is true.\n",
+       "assuming the null hypothesis of zero difference is true.\n",
        "For each p-value, 5000 reshuffles of the control and test labels were performed."
       ]
      },
+     "execution_count": null,
      "metadata": {},
-     "output_type": "display_data"
+     "output_type": "execute_result"
     }
    ],
    "source": [
@@ -778,18 +954,28 @@
      "data": {
       "text/plain": [
        "{'alpha': 0.05,\n",
-       " 'bca_high': 0.24951887238295106,\n",
-       " 'bca_interval_idx': (125, 4875),\n",
-       " 'bca_low': -0.7801782111071534,\n",
-       " 'bootstraps': array([-0.3649424 , -0.45018155, -0.56034412, ..., -0.49805581,\n",
-       "        -0.25334475, -0.55206229]),\n",
+       " 'bca_high': 0.2413346581369784,\n",
+       " 'bca_interval_idx': (109, 4858),\n",
+       " 'bca_low': -0.7818088458343655,\n",
+       " 'bec_bca_high': 0.5352403905584314,\n",
+       " 'bec_bca_interval_idx': (130, 4880),\n",
+       " 'bec_bca_low': -0.4982839949134528,\n",
+       " 'bec_bootstraps': array([-0.48953946, -0.18565285, -0.23896785, ..., -0.55130928,\n",
+       "         0.16037238, -0.07364879]),\n",
+       " 'bec_difference': 0.0,\n",
+       " 'bec_pct_high': 0.5280564736117328,\n",
+       " 'bec_pct_interval_idx': (125, 4875),\n",
+       " 'bec_pct_low': -0.5041777340626885,\n",
+       " 'bootstraps': array([-0.23923425, -0.66013733, -0.42672232, ..., -0.33191074,\n",
+       "        -0.16543251, -0.34179536]),\n",
        " 'ci': 95,\n",
        " 'difference': -0.25315417702752846,\n",
        " 'effect_size': 'mean difference',\n",
        " 'is_paired': None,\n",
-       " 'pct_high': 0.24951887238295106,\n",
+       " 'is_proportional': False,\n",
+       " 'pct_high': 0.25135646125431527,\n",
        " 'pct_interval_idx': (125, 4875),\n",
-       " 'pct_low': -0.7801782111071534,\n",
+       " 'pct_low': -0.763588353717278,\n",
        " 'permutation_count': 5000,\n",
        " 'permutations': array([ 0.17221029,  0.03112419, -0.13911387, ..., -0.38007941,\n",
        "         0.30261507, -0.09073054]),\n",
@@ -817,8 +1003,9 @@
        " 'statistic_wilcoxon': nan}"
       ]
      },
+     "execution_count": null,
      "metadata": {},
-     "output_type": "display_data"
+     "output_type": "execute_result"
     }
    ],
    "source": [
@@ -851,6 +1038,7 @@
     "        delta2=False,\n",
     "        experiment_label=None,\n",
     "        mini_meta=False,\n",
+    "        ps_adjust=False,\n",
     "    ):\n",
     "        \"\"\"\n",
     "        Parses the data from a Dabest object, enabling plotting and printing\n",
@@ -864,12 +1052,13 @@
     "        self.__resamples = resamples\n",
     "        self.__permutation_count = permutation_count\n",
     "        self.__random_seed = random_seed\n",
-    "        self.__proportional = proportional\n",
+    "        self.__is_proportional = proportional\n",
     "        self.__x1_level = x1_level\n",
     "        self.__experiment_label = experiment_label\n",
     "        self.__x2 = x2\n",
     "        self.__delta2 = delta2\n",
-    "        self.__mini_meta = mini_meta\n",
+    "        self.__is_mini_meta = mini_meta\n",
+    "        self.__ps_adjust = ps_adjust\n",
     "\n",
     "    def __pre_calc(self):\n",
     "        from .misc_tools import print_greeting, get_varname\n",
@@ -884,18 +1073,19 @@
     "        out = []\n",
     "        reprs = []\n",
     "\n",
+    "        grouped_data = {name: group[yvar].copy() for name, group in dat.groupby(xvar, observed=False)}\n",
     "        if self.__delta2:\n",
     "            mixed_data = []\n",
     "            for j, current_tuple in enumerate(idx):\n",
     "                if self.__is_paired != \"sequential\":\n",
     "                    cname = current_tuple[0]\n",
-    "                    control = dat[dat[xvar] == cname][yvar].copy()\n",
+    "                    control = grouped_data[cname]\n",
     "\n",
     "                for ix, tname in enumerate(current_tuple[1:]):\n",
     "                    if self.__is_paired == \"sequential\":\n",
     "                        cname = current_tuple[ix]\n",
-    "                        control = dat[dat[xvar] == cname][yvar].copy()\n",
-    "                    test = dat[dat[xvar] == tname][yvar].copy()\n",
+    "                        control = grouped_data[cname]\n",
+    "                    test = grouped_data[tname]\n",
     "                    mixed_data.append(control)\n",
     "                    mixed_data.append(test)\n",
     "            bootstraps_delta_delta = ci2g.compute_delta2_bootstrapped_diff(\n",
@@ -906,29 +1096,30 @@
     "                self.__is_paired,\n",
     "                self.__resamples,\n",
     "                self.__random_seed,\n",
+    "                self.__is_proportional,\n",
     "            )\n",
     "\n",
     "        for j, current_tuple in enumerate(idx):\n",
     "            if self.__is_paired != \"sequential\":\n",
     "                cname = current_tuple[0]\n",
-    "                control = dat[dat[xvar] == cname][yvar].copy()\n",
+    "                control = grouped_data[cname]\n",
     "\n",
     "            for ix, tname in enumerate(current_tuple[1:]):\n",
     "                if self.__is_paired == \"sequential\":\n",
     "                    cname = current_tuple[ix]\n",
-    "                    control = dat[dat[xvar] == cname][yvar].copy()\n",
-    "                test = dat[dat[xvar] == tname][yvar].copy()\n",
-    "\n",
+    "                    control = grouped_data[cname]\n",
+    "                test = grouped_data[tname]\n",
     "                result = TwoGroupsEffectSize(\n",
     "                    control,\n",
     "                    test,\n",
     "                    self.__effect_size,\n",
-    "                    self.__proportional,\n",
+    "                    self.__is_proportional,\n",
     "                    self.__is_paired,\n",
     "                    self.__ci,\n",
     "                    self.__resamples,\n",
     "                    self.__permutation_count,\n",
     "                    self.__random_seed,\n",
+    "                    self.__ps_adjust\n",
     "                )\n",
     "                r_dict = result.to_dict()\n",
     "                r_dict[\"control\"] = cname\n",
@@ -937,10 +1128,10 @@
     "                r_dict[\"test_N\"] = int(len(test))\n",
     "                out.append(r_dict)\n",
     "                if j == len(idx) - 1 and ix == len(current_tuple) - 2:\n",
-    "                    if self.__delta2 and self.__effect_size in [\"mean_diff\", \"delta_g\"]:\n",
+    "                    if self.__delta2 and self.__effect_size in [\"mean_diff\", \"hedges_g\"]:\n",
     "                        resamp_count = False\n",
     "                        def_pval = False\n",
-    "                    elif self.__mini_meta and self.__effect_size == \"mean_diff\":\n",
+    "                    elif self.__is_mini_meta and self.__effect_size == \"mean_diff\":\n",
     "                        resamp_count = False\n",
     "                        def_pval = False\n",
     "                    else:\n",
@@ -1002,6 +1193,14 @@
     "            \"pvalue_kruskal\",\n",
     "            \"statistic_kruskal\",\n",
     "            \"proportional_difference\",\n",
+    "            \"bec_difference\",\n",
+    "            \"bec_bootstraps\",\n",
+    "            \"bec_bca_interval_idx\",\n",
+    "            \"bec_bca_low\",\n",
+    "            \"bec_bca_high\",\n",
+    "            \"bec_pct_interval_idx\",\n",
+    "            \"bec_pct_low\",\n",
+    "            \"bec_pct_high\",\n",
     "        ]\n",
     "        self.__results = out_.reindex(columns=columns_in_order)\n",
     "        self.__results.dropna(axis=\"columns\", how=\"all\", inplace=True)\n",
@@ -1013,32 +1212,33 @@
     "            )\n",
     "\n",
     "        # Create and compute the delta-delta statistics\n",
-    "        if self.__delta2:\n",
+    "        if self.__delta2 and self.__effect_size not in [\"mean_diff\", \"hedges_g\"]:\n",
+    "            self.__delta_delta = \"Delta-delta is not supported for {}.\".format(\n",
+    "                self.__effect_size\n",
+    "            )\n",
+    "        elif self.__delta2:\n",
     "            self.__delta_delta = DeltaDelta(\n",
     "                self, self.__permutation_count, bootstraps_delta_delta, self.__ci\n",
     "            )\n",
     "            reprs.append(self.__delta_delta.__repr__(header=False))\n",
-    "        elif self.__delta2 and self.__effect_size not in [\"mean_diff\", \"delta_g\"]:\n",
-    "            self.__delta_delta = \"Delta-delta is not supported for {}.\".format(\n",
-    "                self.__effect_size\n",
-    "            )\n",
+    "\n",
     "        else:\n",
     "            self.__delta_delta = (\n",
     "                \"`delta2` is False; delta-delta is therefore not calculated.\"\n",
     "            )\n",
     "\n",
     "        # Create and compute the weighted average statistics\n",
-    "        if self.__mini_meta and self.__effect_size == \"mean_diff\":\n",
-    "            self.__mini_meta_delta = MiniMetaDelta(\n",
+    "        if self.__is_mini_meta and self.__effect_size == \"mean_diff\":\n",
+    "            self.__mini_meta = MiniMetaDelta(\n",
     "                self, self.__permutation_count, self.__ci\n",
     "            )\n",
-    "            reprs.append(self.__mini_meta_delta.__repr__(header=False))\n",
-    "        elif self.__mini_meta and self.__effect_size != \"mean_diff\":\n",
-    "            self.__mini_meta_delta = \"Weighted delta is not supported for {}.\".format(\n",
+    "            reprs.append(self.__mini_meta.__repr__(header=False))\n",
+    "        elif self.__is_mini_meta and self.__effect_size != \"mean_diff\":\n",
+    "            self.__mini_meta = \"Weighted delta is not supported for {}.\".format(\n",
     "                self.__effect_size\n",
     "            )\n",
     "        else:\n",
-    "            self.__mini_meta_delta = (\n",
+    "            self.__mini_meta = (\n",
     "                \"`mini_meta` is False; weighted delta is therefore not calculated.\"\n",
     "            )\n",
     "\n",
@@ -1070,16 +1270,18 @@
     "\n",
     "        out = []\n",
     "\n",
+    "        grouped_data = {name:group[yvar].copy() for name, group in dat.groupby(xvar)}\n",
+    "\n",
     "        for j, current_tuple in enumerate(db_obj.idx):\n",
     "            if self.__is_paired != \"sequential\":\n",
     "                cname = current_tuple[0]\n",
-    "                control = dat[dat[xvar] == cname][yvar].copy()\n",
+    "                control = grouped_data[cname]\n",
     "\n",
     "            for ix, tname in enumerate(current_tuple[1:]):\n",
     "                if self.__is_paired == \"sequential\":\n",
     "                    cname = current_tuple[ix]\n",
-    "                    control = dat[dat[xvar] == cname][yvar].copy()\n",
-    "                test = dat[dat[xvar] == tname][yvar].copy()\n",
+    "                    control = grouped_data[cname]\n",
+    "                test = grouped_data[tname]\n",
     "\n",
     "                if self.__is_paired:\n",
     "                    # Refactored here in v0.3.0 for performance issues.\n",
@@ -1124,53 +1326,53 @@
     "        self,\n",
     "        color_col=None,\n",
     "        raw_marker_size=6,\n",
-    "        es_marker_size=9,\n",
-    "        swarm_label=None,\n",
+    "        contrast_marker_size=9, # es_marker_size=9, OLD\n",
+    "\n",
+    "        raw_label=None, # swarm_label=None, OLD # bar_label=None, OLD\n",
     "        contrast_label=None,\n",
     "        delta2_label=None,\n",
-    "        swarm_ylim=None,\n",
+    "\n",
+    "        raw_ylim=None, # swarm_ylim=None, OLD # bar_ylim=None, OLD\n",
     "        contrast_ylim=None,\n",
     "        delta2_ylim=None,\n",
-    "        swarm_side=None,\n",
+    "\n",
     "        custom_palette=None,\n",
-    "        swarm_desat=0.5,\n",
-    "        halfviolin_desat=1,\n",
-    "        halfviolin_alpha=0.8,\n",
+    "        swarm_side=None,\n",
+    "        empty_circle=False,  # Not very intuitive name\n",
+    "\n",
     "        face_color=None,\n",
-    "        # bar plot\n",
-    "        bar_label=None,\n",
-    "        bar_desat=0.5,\n",
+    "\n",
+    "        raw_desat=0.5, # swarm_desat=0.5, OLD # bar_desat=0.5, OLD\n",
+    "        contrast_desat=1, # halfviolin_desat=1, OLD\n",
+    "\n",
+    "        raw_alpha=None, # NEW\n",
+    "        contrast_alpha=0.8, # halfviolin_alpha=0.8, OLD\n",
+    "\n",
     "        bar_width=0.5,\n",
-    "        bar_ylim=None,\n",
-    "        # error bar of proportion plot\n",
-    "        ci=None,\n",
+    "        # ci=None, # Seems to be unused\n",
     "        ci_type=\"bca\",\n",
-    "        err_color=None,\n",
+    "\n",
     "        float_contrast=True,\n",
     "        show_pairs=True,\n",
-    "        show_delta2=True,\n",
+    "        show_sample_size=True,\n",
+    "        show_delta2=True, # Would pref switch to delta_delta instead of delta2\n",
     "        show_mini_meta=True,\n",
-    "        group_summaries=None,\n",
-    "        group_summaries_offset=0.1,\n",
+    "\n",
+    "        group_summaries=\"mean_sd\",\n",
+    "        # err_color=None, # Not intuitive name and doesnt fit with group_summaries argument    \n",
     "        fig_size=None,\n",
     "        dpi=100,\n",
     "        ax=None,\n",
-    "        contrast_show_es=False,\n",
-    "        es_sf=2,\n",
-    "        es_fontsize=10,\n",
-    "        contrast_show_deltas=True,\n",
-    "        gridkey_rows=None,\n",
-    "        gridkey_merge_pairs=False,\n",
-    "        gridkey_show_Ns=True,\n",
-    "        gridkey_show_es=True,\n",
+    "\n",
     "        swarmplot_kwargs=None,\n",
-    "        barplot_kwargs=None,\n",
-    "        violinplot_kwargs=None,\n",
     "        slopegraph_kwargs=None,\n",
+    "        barplot_kwargs=None,\n",
     "        sankey_kwargs=None,\n",
+    "        contrast_kwargs=None, # violinplot_kwargs=None, OLD\n",
     "        reflines_kwargs=None,\n",
-    "        group_summary_kwargs=None,\n",
+    "        group_summaries_kwargs=None,\n",
     "        legend_kwargs=None,\n",
+    "\n",
     "        title=None,\n",
     "        fontsize_title=16,\n",
     "        fontsize_rawxlabel=12,\n",
@@ -1178,6 +1380,42 @@
     "        fontsize_contrastxlabel=12,\n",
     "        fontsize_contrastylabel=12,\n",
     "        fontsize_delta2label=12,\n",
+    "\n",
+    "        # Raw bars, Contrast bars, delta text, and delta dots\n",
+    "        raw_bars=True, # swarm_bars=True, OLD          \n",
+    "        raw_bars_kwargs=None, # swarm_bars_kwargs=None, OLD\n",
+    "        contrast_bars=True,\n",
+    "        contrast_bars_kwargs=None,\n",
+    "        reference_band=None,\n",
+    "        reference_band_kwargs=None,\n",
+    "        delta_text=True,\n",
+    "        delta_text_kwargs=None,\n",
+    "        delta_dot=True,\n",
+    "        delta_dot_kwargs=None,\n",
+    "\n",
+    "        # Horizontal Plots\n",
+    "        horizontal=False,\n",
+    "        horizontal_table_kwargs=None,\n",
+    "\n",
+    "        # Gridkey\n",
+    "        gridkey=None, # gridkey_rows=None, OLD\n",
+    "        gridkey_merge_pairs=False,\n",
+    "        gridkey_show_Ns=True,\n",
+    "        gridkey_show_es=True,\n",
+    "        gridkey_delimiters=[';', '>', '_'],\n",
+    "        gridkey_kwargs=None,\n",
+    "\n",
+    "        contrast_marker_kwargs=None, # es_marker_kwargs=None, OLD\n",
+    "        contrast_errorbar_kwargs=None, # es_errorbar_kwargs=None, OLD\n",
+    "\n",
+    "        prop_sample_counts=False,\n",
+    "        prop_sample_counts_kwargs=None,\n",
+    "\n",
+    "        contrast_paired_lines=True, # es_paired_lines=True, OLD\n",
+    "        contrast_paired_lines_kwargs=None, # es_paired_lines_kwargs=None, OLD\n",
+    "        \n",
+    "\t\t# Baseline Effect Size Curve\n",
+    "\t\tshow_baseline_ec=False,\n",
     "    ):\n",
     "        \"\"\"\n",
     "        Creates an estimation plot for the effect size of interest.\n",
@@ -1190,18 +1428,18 @@
     "        raw_marker_size : float, default 6\n",
     "            The diameter (in points) of the marker dots plotted in the\n",
     "            swarmplot.\n",
-    "        es_marker_size : float, default 9\n",
+    "        contrast_marker_size : float, default 9\n",
     "            The size (in points) of the effect size points on the difference\n",
     "            axes.\n",
-    "        swarm_label, contrast_label, delta2_label : strings, default None\n",
-    "            Set labels for the y-axis of the swarmplot and the contrast plot,\n",
-    "            respectively. If `swarm_label` is not specified, it defaults to\n",
-    "            \"value\", unless a column name was passed to `y`. If\n",
-    "            `contrast_label` is not specified, it defaults to the effect size\n",
-    "            being plotted. If `delta2_label` is not specifed, it defaults to\n",
-    "            \"delta - delta\"\n",
-    "        swarm_ylim, contrast_ylim, delta2_ylim : tuples, default None\n",
-    "            The desired y-limits of the raw data (swarmplot) axes, the\n",
+    "        raw_label, contrast_label, delta2_label : strings, default None\n",
+    "            Set labels for the y-axis of the raw plot and the contrast plot,\n",
+    "            respectively. If `raw_label` is not specified, it defaults to\n",
+    "            \"Value\" for non binary data (and \"Proportion of Success\" for binary data), \n",
+    "            unless a column name was passed to `y`. If `contrast_label` is not specified, \n",
+    "            it defaults to the effect size being plotted. If `delta2_label` is not specifed, \n",
+    "            it defaults to \"delta - delta\".\n",
+    "        raw_ylim, contrast_ylim, delta2_ylim : tuples, default None\n",
+    "            The desired y-limits of the raw data axes, the\n",
     "            difference axes and the delta-delta axes respectively, as a tuple.\n",
     "            These will be autoscaled to sensible values if they are not\n",
     "            specified. The delta2 axes and contrast axes should have the same\n",
@@ -1225,15 +1463,32 @@
     "            https://seaborn.pydata.org/generated/seaborn.cubehelix_palette.html\n",
     "            The named colors of matplotlib can be found here:\n",
     "            https://matplotlib.org/examples/color/named_colors.html\n",
-    "        swarm_desat : float, default 1\n",
-    "            Decreases the saturation of the colors in the swarmplot by the\n",
+    "        swarm_side: string, default None\n",
+    "            The side on which points are swarmed for swarmplots (\"center\", \"left\", or \"right\").\n",
+    "        empty_circle: boolean, default False\n",
+    "            Boolean value determining if empty circles will be used for plotting of\n",
+    "            swarmplot for control groups. Color of each individual swarm is also now\n",
+    "            dependent on the comparison group.\n",
+    "        face_color: string, default None\n",
+    "            The face color of the plot. Defaults to \"white\".\n",
+    "        raw_desat : float, default 1\n",
+    "            Decreases the saturation of the colors in the rawplot by the\n",
     "            desired proportion. Uses `seaborn.desaturate()` to acheive this.\n",
-    "        halfviolin_desat : float, default 0.5\n",
+    "        contrast_desat : float, default 0.5\n",
     "            Decreases the saturation of the colors of the half-violin bootstrap\n",
     "            curves by the desired proportion. Uses `seaborn.desaturate()` to\n",
     "            acheive this.\n",
-    "        halfviolin_alpha : float, default 0.8\n",
+    "        raw_alpha : float, default None\n",
+    "            The alpha (transparency) level of the raw plot elements. This defaults\n",
+    "            to 1.0 for all plots except sankey and slopegraphs, whereby it defaults to 0.4\n",
+    "            and 0.5, respectively.\n",
+    "        contrast_alpha : float, default 0.8\n",
     "            The alpha (transparency) level of the half-violin bootstrap curves.\n",
+    "        bar_width : float, default 0.5\n",
+    "            The width of the bars in the barplot (binary, non-paired data).\n",
+    "        ci_type : string, default\n",
+    "            The confidence interval of the contrast plot to display. Defaults\n",
+    "            to \"bca\". Otherwise, the user can choose \"pct\" for percentile. \n",
     "        float_contrast : boolean, default True\n",
     "            Whether or not to display the halfviolin bootstrapped difference\n",
     "            distribution alongside the raw data.\n",
@@ -1241,18 +1496,17 @@
     "            If the data is paired, whether or not to show the raw data as a\n",
     "            swarmplot, or as slopegraph, with a line joining each pair of\n",
     "            observations.\n",
+    "        show_sample_size : boolean, default True\n",
+    "            Whether or not to display the sample size of each group in the axis label.\n",
     "        show_delta2, show_mini_meta : boolean, default True\n",
     "            If delta-delta or mini-meta delta is calculated, whether or not to\n",
     "            show the delta-delta plot or mini-meta plot.\n",
-    "        group_summaries : ['mean_sd', 'median_quartiles', 'None'], default None.\n",
+    "        group_summaries : ['mean_sd', 'median_quartiles', 'None'], default \"mean_sd\".\n",
     "            Plots the summary statistics for each group. If 'mean_sd', then\n",
     "            the mean and standard deviation of each group is plotted as a\n",
     "            notched line beside each group. If 'median_quantiles', then the\n",
     "            median and 25th and 75th percentiles of each group is plotted\n",
     "            instead. If 'None', the summaries are not shown.\n",
-    "        group_summaries_offset : float, default 0.1\n",
-    "            If group summaries are displayed, they will be offset from the raw\n",
-    "            data swarmplot groups by this value.\n",
     "        fig_size : tuple, default None\n",
     "            The desired dimensions of the figure as a (length, width) tuple.\n",
     "        dpi : int, default 100\n",
@@ -1260,53 +1514,52 @@
     "        ax : matplotlib.Axes, default None\n",
     "            Provide an existing Axes for the plots to be created. If no Axes is\n",
     "            specified, a new matplotlib Figure will be created.\n",
-    "        gridkey_rows : list, default None\n",
-    "            Provide a list of row labels for the gridkey. The supplied idx is\n",
-    "            checked against the row labels to determine whether the corresponding\n",
-    "            cell should be populated or not.\n",
     "        swarmplot_kwargs : dict, default None\n",
     "            Pass any keyword arguments accepted by the seaborn `swarmplot`\n",
     "            command here, as a dict. If None, the following keywords are\n",
     "            passed to sns.swarmplot : {'size':`raw_marker_size`}.\n",
-    "        violinplot_kwargs : dict, default None\n",
-    "            Pass any keyword arguments accepted by the matplotlib `\n",
-    "            pyplot.violinplot` command here, as a dict. If None, the following\n",
-    "            keywords are passed to violinplot : {'widths':0.5, 'vert':True,\n",
-    "            'showextrema':False, 'showmedians':False}.\n",
     "        slopegraph_kwargs : dict, default None\n",
     "            This will change the appearance of the lines used to join each pair\n",
     "            of observations when `show_pairs=True`. Pass any keyword arguments\n",
     "            accepted by matplotlib `plot()` function here, as a dict.\n",
     "            If None, the following keywords are\n",
-    "            passed to plot() : {'linewidth':1, 'alpha':0.5}.\n",
+    "            passed to plot() : {'linewidth':1, 'alpha':0.5, 'jitter':0, 'jitter_seed':9876543210}.\n",
+    "        barplot_kwargs : dict, default None\n",
+    "            By default, the keyword arguments passed are:\n",
+    "            {\"estimator\": np.mean, \"errorbar\": plot_kwargs[\"ci\"], \"err_kws\" : {'color':'black'}}\n",
     "        sankey_kwargs: dict, default None\n",
     "            Whis will change the appearance of the sankey diagram used to depict\n",
-    "            paired proportional data when `show_pairs=True` and `proportional=True`.\n",
+    "            paired proportional data when `show_pairs=True` and `is_proportional=True`.\n",
     "            Pass any keyword arguments accepted by plot_tools.sankeydiag() function\n",
     "            here, as a dict. If None, the following keywords are passed to sankey diagram:\n",
     "            {\"width\": 0.5, \"align\": \"center\", \"alpha\": 0.4, \"bar_width\": 0.1, \"rightColor\": False}\n",
+    "        contrast_kwargs : dict, default None\n",
+    "            Pass any keyword arguments accepted by the matplotlib `\n",
+    "            pyplot.violinplot` command here, as a dict. If None, the following\n",
+    "            keywords are passed to violinplot : {'widths':0.5, 'vert':True,\n",
+    "            'showextrema':False, 'showmedians':False}.\n",
     "        reflines_kwargs : dict, default None\n",
     "            This will change the appearance of the zero reference lines. Pass\n",
     "            any keyword arguments accepted by the matplotlib Axes `hlines`\n",
     "            command here, as a dict. If None, the following keywords are\n",
     "            passed to Axes.hlines : {'linestyle':'solid', 'linewidth':0.75,\n",
     "            'zorder':2, 'color' : default y-tick color}.\n",
-    "        group_summary_kwargs : dict, default None\n",
+    "        group_summaries_kwargs : dict, default None\n",
     "            Pass any keyword arguments accepted by the matplotlib.lines.Line2D\n",
     "            command here, as a dict. This will change the appearance of the\n",
     "            vertical summary lines for each group, if `group_summaries` is not\n",
     "            'None'. If None, the following keywords are passed to\n",
-    "            matplotlib.lines.Line2D : {'lw':2, 'alpha':1, 'zorder':3}.\n",
+    "            matplotlib.lines.Line2D : {'lw':2, 'alpha':1, 'zorder':3, \n",
+    "            'gap_width_percent':1.5, 'offset':0.1, 'color':None}.\n",
     "        legend_kwargs : dict, default None\n",
     "            Pass any keyword arguments accepted by the matplotlib Axes\n",
     "            `legend` command here, as a dict. If None, the following keywords\n",
-    "            are passed to matplotlib.Axes.legend : {'loc':'upper left',\n",
-    "            'frameon':False}.\n",
+    "            are passed to matplotlib.Axes.legend : {'frameon':False}.\n",
     "        title : string, default None\n",
     "            Title for the plot. If None, no title will be displayed. Pass any\n",
     "            keyword arguments accepted by the matplotlib.pyplot.suptitle `t` command here,\n",
     "            as a string.\n",
-    "        fontsize_title : float or {'xx-small', 'x-small', 'small', 'medium', 'large', 'x-large', 'xx-large'}, default 'large'\n",
+    "        fontsize_title : float or {'xx-small', 'x-small', 'small', 'medium', 'large', 'x-large', 'xx-large'}, default 16\n",
     "            Font size for the plot title. If a float, the fontsize in points. The\n",
     "            string values denote sizes relative to the default font size. Pass any keyword arguments accepted\n",
     "            by the matplotlib.pyplot.suptitle `fontsize` command here, as a string.\n",
@@ -1320,7 +1573,89 @@
     "            Font size for the contrast axes ylabel.\n",
     "        fontsize_delta2label : float, default 12\n",
     "            Font size for the delta-delta axes ylabel.\n",
-    "\n",
+    "            \n",
+    "        raw_bars : boolean, default True\n",
+    "            Whether or not to display the raw bars.\n",
+    "        raw_bars_kwargs : dict, default None\n",
+    "            Pass relevant keyword arguments to the raw bars. Pass any keyword arguments accepted by \n",
+    "            matplotlib.patches.Rectangle here, as a string. If None, the following keywords are passed:\n",
+    "            {\"color\": None, \"zorder\":-3}\n",
+    "        contrast_bars : boolean, default True\n",
+    "            Whether or not to display the contrast bars.\n",
+    "        contrast_bars_kwargs : dict, default None\n",
+    "            Pass relevant keyword arguments to the contrast bars. Pass any keyword arguments accepted by \n",
+    "            matplotlib.patches.Rectangle here, as a string. If None, the following keywords are passed:\n",
+    "            {\"color\": None, \"zorder\":-3}\n",
+    "        reference_band : list, default None\n",
+    "            Pass a list of indices of the contrast objects to have reference bands displayed on the plot.\n",
+    "            For example, [0,1] will show reference bands for the first two contrast objects.\n",
+    "        reference_band_kwargs: dict, default None\n",
+    "            If None, the following keywords are passed: {\"span_ax\": False, \"color\": None, \"alpha\": 0.15, \"zorder\":-3}\n",
+    "        delta_text : boolean, default True\n",
+    "            Whether or not to display the text deltas.\n",
+    "        delta_text_kwargs : dict, default None\n",
+    "            Pass relevant keyword arguments to the delta text. Pass any keyword arguments accepted by\n",
+    "            matplotlib.text.Text here, as a string. If None, the following keywords are passed:\n",
+    "            {\"color\": None, \"alpha\": 1, \"fontsize\": 10, \"ha\": 'center', \"va\": 'center', \"rotation\": 0, \n",
+    "            \"x_location\": 'right', \"x_coordinates\": None, \"y_coordinates\": None, \"offset\": 0}\n",
+    "            Use \"x_coordinates\" and \"y_coordinates\" if you would like to specify the text locations manually. \n",
+    "            Use \"x_adjust\" to adjust the x location of all the texts (positive moves right, negative left).\n",
+    "        delta_dot : boolean, default True\n",
+    "            Whether or not to display the delta dots on paired or repeated measure plots.\n",
+    "        delta_dot_kwargs : dict, default None\n",
+    "            Pass relevant keyword arguments. If None, the following keywords are passed:\n",
+    "            {\"color\": 'k', \"marker\": \"^\", \"alpha\": 0.5, \"zorder\": 2, \"size\": 3, \"side\": \"right\"}\n",
+    "        horizontal : boolean, default False\n",
+    "            Whether to display plots in the horizontal format. Default is False. \n",
+    "        horizontal_table_kwargs : dict, default None\n",
+    "            {'show: True, 'color' : 'yellow', 'alpha' :0.2, 'fontsize' : 12, 'text_color' : 'black', \n",
+    "            'text_units' : None, 'control_marker' : '-', 'fontsize_label': 12, 'label': 'Δ'}\n",
+    "            \n",
+    "        gridkey : list, default None\n",
+    "            Provide either a list of grid keys or 'auto' for automatic grid selection.\n",
+    "        gridkey_merge_pairs : boolean, default False\n",
+    "            Merges the paired grid key groups together.\n",
+    "        gridkey_show_Ns : boolean, default True\n",
+    "            Whether to display the sample size row.\n",
+    "        gridkey_show_es : boolean, default True\n",
+    "            Whether to show the effect size row. \n",
+    "        gridkey_delimiters : list, default [';', '>', '_']\n",
+    "            The delimiter used to split gridkey groups if required.\n",
+    "        gridkey_kwargs : dict, default None\n",
+    "            Pass relevant keyword arguments to the gridkey. If None, the following keywords are passed:\n",
+    "            {   'show_es' : True,                   # If True, the gridkey will show the effect size of each comparison.\n",
+    "                'show_Ns' :True,                    # If True, the gridkey will show the number of observations in eachgroup.\n",
+    "                'merge_pairs' : False,              # If True, the gridkey will merge the pairs of groups into a single cell. This is useful for when the groups are paired.\n",
+    "                'delimiters': [';', '>', '_'],      # Delimiters to split the group names.\n",
+    "                'marker': \"\\u25CF\",                 # Marker for the gridkey dots.\n",
+    "            }\n",
+    "\n",
+    "        contrast_marker_kwargs: dict, default None\n",
+    "            Pass relevant keyword arguments to the effectsize marker plotting. If none, the following keywords are passed:\n",
+    "            {'marker': 'o', 'size': plot_kwargs['contrast_marker_size'], 'color': 'black', 'alpha': 1, 'zorder': 1}\n",
+    "        contrast_errorbar_kwargs: dict, default None\n",
+    "            Pass relevant keyword arguments to the effectsize errorbar plotting. If none, the following keywords are passed:\n",
+    "            {'color': 'black', 'lw': 2, 'linestyle': '-', 'alpha': 1,'zorder': 1,}\n",
+    "\n",
+    "        prop_sample_counts: bool, default False\n",
+    "            Show the sample counts for each group in proportional plots\n",
+    "        prop_sample_counts_kwargs: dict, default None\n",
+    "            Pass relevant keyword arguments. If None, the following keywords are passed:\n",
+    "            {'color': 'k', 'zorder': 5, 'ha': 'center', 'va': 'center'},\n",
+    "\n",
+    "        contrast_paired_lines: bool, default True\n",
+    "            Whether or not to add lines to connect the effect size curves in paired plots.\n",
+    "        contrast_paired_lines_kwargs: dict, default None\n",
+    "            Pass relevant plot keyword arguments. If None, the following keywords are passed:\n",
+    "            {\"linestyle\": \"-\", \"linewidth\": 2, \"zorder\": -2, \"color\": 'dimgray', \"alpha\": 1}\n",
+    "        \n",
+    "\t\tshow_baseline_ec : boolean, default False\n",
+    "            Whether or not to display the baseline error curve. The baseline error curve\n",
+    "            represents the distribution of the effect size when comparing the control\n",
+    "            group to itself, providing a reference for the inherent variability or noise\n",
+    "            in the data. When True, this curve is plotted alongside the main effect size\n",
+    "            distribution, allowing for a visual comparison of the observed effect against\n",
+    "            the baseline variability.\n",
     "\n",
     "        Returns\n",
     "        -------\n",
@@ -1341,29 +1676,28 @@
     "        if hasattr(self, \"results\") is False:\n",
     "            self.__pre_calc()\n",
     "\n",
-    "        if self.__delta2:\n",
-    "            color_col = self.__x2\n",
-    "\n",
-    "        # if self.__proportional:\n",
-    "        #     raw_marker_size = 0.01\n",
+    "        if raw_alpha is None:\n",
+    "            raw_alpha = (0.4 if self.is_proportional and self.is_paired \n",
+    "                         else 0.5 if self.is_paired\n",
+    "                         else 1.0\n",
+    "            )\n",
     "\n",
-    "        # Modification incurred due to update of Seaborn\n",
-    "        ci = (\"ci\", ci) if ci is not None else None\n",
+    "        if self.__delta2 and not empty_circle:\n",
+    "            color_col = self.__x2\n",
     "\n",
     "        all_kwargs = locals()\n",
     "        del all_kwargs[\"self\"]\n",
     "\n",
     "        out = effectsize_df_plotter(self, **all_kwargs)\n",
-    "\n",
     "        return out\n",
     "\n",
     "    @property\n",
-    "    def proportional(self):\n",
+    "    def is_proportional(self):\n",
     "        \"\"\"\n",
     "        Returns the proportional parameter\n",
     "        class.\n",
     "        \"\"\"\n",
-    "        return self.__proportional\n",
+    "        return self.__is_proportional\n",
     "\n",
     "    @property\n",
     "    def results(self):\n",
@@ -1446,10 +1780,6 @@
     "        return self.__experiment_label\n",
     "\n",
     "    @property\n",
-    "    def delta2(self):\n",
-    "        return self.__delta2\n",
-    "\n",
-    "    @property\n",
     "    def resamples(self):\n",
     "        \"\"\"\n",
     "        The number of resamples (with replacement) during bootstrap resampling.\"\n",
@@ -1476,13 +1806,6 @@
     "        \"\"\"\n",
     "        return self.__dabest_obj\n",
     "\n",
-    "    @property\n",
-    "    def proportional(self):\n",
-    "        \"\"\"\n",
-    "        Returns the proportional parameter\n",
-    "        class.\n",
-    "        \"\"\"\n",
-    "        return self.__proportional\n",
     "\n",
     "    @property\n",
     "    def lqrt(self):\n",
@@ -1498,33 +1821,41 @@
     "            return self.__lqrt_results\n",
     "\n",
     "    @property\n",
-    "    def mini_meta(self):\n",
+    "    def is_mini_meta(self):\n",
     "        \"\"\"\n",
     "        Returns the mini_meta boolean parameter.\n",
     "        \"\"\"\n",
-    "        return self.__mini_meta\n",
+    "        return self.__is_mini_meta\n",
     "\n",
     "    @property\n",
-    "    def mini_meta_delta(self):\n",
+    "    def mini_meta(self):\n",
     "        \"\"\"\n",
     "        Returns the mini_meta results.\n",
     "        \"\"\"\n",
     "        try:\n",
-    "            return self.__mini_meta_delta\n",
+    "            return self.__mini_meta\n",
     "        except AttributeError:\n",
     "            self.__pre_calc()\n",
-    "            return self.__mini_meta_delta\n",
+    "            return self.__mini_meta\n",
     "\n",
     "    @property\n",
     "    def delta_delta(self):\n",
     "        \"\"\"\n",
-    "        Returns the mini_meta results.\n",
+    "        Returns the delta_delta results.\n",
     "        \"\"\"\n",
     "        try:\n",
     "            return self.__delta_delta\n",
     "        except AttributeError:\n",
     "            self.__pre_calc()\n",
-    "            return self.__delta_delta"
+    "            return self.__delta_delta\n",
+    "    \n",
+    "    @property\n",
+    "    def delta2(self):\n",
+    "        return self.__delta2\n",
+    "    \n",
+    "    @property\n",
+    "    def is_delta_delta(self):\n",
+    "        return self.__delta2"
    ]
   },
   {
@@ -1584,7 +1915,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 500x400 with 2 Axes>"
       ]
@@ -1608,7 +1939,18 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "fig2 = my_data.hedges_g.plot();"
    ]
@@ -1624,7 +1966,18 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "fig3 = my_data.mean_diff.plot(float_contrast=True);"
    ]
@@ -1640,7 +1993,18 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 400x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "my_data_paired = dabest.load(df, idx=(\"Control 1\", \"Test 1\"),\n",
     "                       id_col = \"ID\", paired='baseline')\n",
@@ -1658,7 +2022,28 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/jonathananns/GitHub/DABEST-python/dabest/plot_tools.py:2778: UserWarning: 5.0% of the points cannot be placed. You might want to decrease the size of the markers.\n",
+      "  warnings.warn(err)\n",
+      "/Users/jonathananns/GitHub/DABEST-python/dabest/plot_tools.py:2778: UserWarning: 10.0% of the points cannot be placed. You might want to decrease the size of the markers.\n",
+      "  warnings.warn(err)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "my_multi_groups = dabest.load(df, id_col = \"ID\", \n",
     "                             idx=((\"Control 1\", \"Test 1\"),\n",
@@ -1677,7 +2062,26 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/jonathananns/GitHub/DABEST-python/dabest/plot_tools.py:2778: UserWarning: 10.0% of the points cannot be placed. You might want to decrease the size of the markers.\n",
+      "  warnings.warn(err)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "my_shared_control = dabest.load(df, id_col = \"ID\",\n",
     "                                 idx=(\"Control 1\", \"Test 1\",\n",
@@ -1696,7 +2100,18 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 480x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "my_rm_baseline = dabest.load(df, id_col = \"ID\", paired = \"baseline\",\n",
     "                                 idx=(\"Control 1\", \"Test 1\",\n",
@@ -1715,7 +2130,18 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 480x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "my_rm_sequential = dabest.load(df, id_col = \"ID\", paired = \"sequential\",\n",
     "                                 idx=(\"Control 1\", \"Test 1\",\n",
@@ -1748,7 +2174,7 @@
     "        These should be numerical iterables.\n",
     "    effect_size : string.\n",
     "        Any one of the following are accepted inputs:\n",
-    "        'mean_diff', 'median_diff', 'cohens_d', 'hedges_g', 'delta_g\" or 'cliffs_delta'\n",
+    "        'mean_diff', 'median_diff', 'cohens_d', 'hedges_g', or 'cliffs_delta'\n",
     "    is_paired : string, default None\n",
     "    permutation_count : int, default 10000\n",
     "        The number of permutations (reshuffles) to perform.\n",
@@ -1756,6 +2182,10 @@
     "        `random_seed` is used to seed the random number generator during\n",
     "        bootstrap resampling. This ensures that the generated permutations\n",
     "        are replicable.\n",
+    "    ps_adjust : bool, default False\n",
+    "        If True, the p-value is adjusted according to Phipson & Smyth (2010).\n",
+    "        # https://doi.org/10.2202/1544-6115.1585\n",
+    "\n",
     "        \n",
     "    Returns\n",
     "    -------\n",
@@ -1774,6 +2204,7 @@
     "                 is_paired:str=None,\n",
     "                 permutation_count:int=5000, # The number of permutations (reshuffles) to perform.\n",
     "                 random_seed:int=12345,#`random_seed` is used to seed the random number generator during bootstrap resampling. This ensures that the generated permutations are replicable.\n",
+    "                 ps_adjust:bool=False,\n",
     "                 **kwargs):\n",
     "        from ._stats_tools.effsize import two_group_difference\n",
     "        from ._stats_tools.confint_2group_diff import calculate_group_var\n",
@@ -1798,6 +2229,7 @@
     "\n",
     "        BAG = array([*control, *test])\n",
     "        CONTROL_LEN = int(len(control))\n",
+    "        TEST_LEN = int(len(test)) # devMJBL\n",
     "        EXTREME_COUNT = 0.\n",
     "        THRESHOLD = abs(two_group_difference(control, test, \n",
     "                                                is_paired, effect_size))\n",
@@ -1837,13 +2269,43 @@
     "\n",
     "            if abs(es) > THRESHOLD:\n",
     "                EXTREME_COUNT += 1.\n",
+    "                \n",
+    "        if ps_adjust:\n",
+    "            # devMJBL\n",
+    "            # adjust calculated p-value according to Phipson & Smyth (2010)\n",
+    "            # https://doi.org/10.2202/1544-6115.1585\n",
+    "            # as per R code in statmod::permp\n",
+    "            # https://rdrr.io/cran/statmod/src/R/permp.R\n",
+    "            # (assumes two-sided test)\n",
+    "\n",
+    "            if CONTROL_LEN == TEST_LEN:\n",
+    "                totalPermutations = binomcoeff(CONTROL_LEN + TEST_LEN, TEST_LEN)/2\n",
+    "            else:\n",
+    "                totalPermutations = binomcoeff(CONTROL_LEN + TEST_LEN, TEST_LEN)\n",
+    "\n",
+    "            if totalPermutations <= 10e3:\n",
+    "                # use exact calculation\n",
+    "                p = arange(1, totalPermutations + 1)/totalPermutations\n",
+    "                x2 = repeat(EXTREME_COUNT, repeats=totalPermutations)\n",
+    "                Y = binom.cdf(k=x2, n=permutation_count, p=p)\n",
+    "                self.pvalue = mean(Y)\n",
+    "            else:\n",
+    "                # use integral approximation\n",
+    "                def binomcdf(p, k, n):\n",
+    "                    return binom.cdf(k, n, p)\n",
+    "\n",
+    "                integrationVal, _ = fixed_quad(binomcdf,\n",
+    "                                            a=0, b=0.5/totalPermutations,\n",
+    "                                            args=(EXTREME_COUNT, permutation_count),\n",
+    "                                            n=128)\n",
     "\n",
+    "                self.pvalue = (EXTREME_COUNT + 1)/(permutation_count + 1) - integrationVal\n",
+    "        else:\n",
+    "            self.pvalue = EXTREME_COUNT / self.__permutation_count\n",
+    "            \n",
     "        self.__permutations = array(self.__permutations)\n",
     "        self.__permutations_var = array(self.__permutations_var)\n",
     "\n",
-    "        self.pvalue = EXTREME_COUNT / self.__permutation_count\n",
-    "\n",
-    "\n",
     "    def __repr__(self):\n",
     "        return(\"{} permutations were taken. The p-value is {}.\".format(self.__permutation_count, \n",
     "                                                                      self.pvalue))\n",
@@ -1903,7 +2365,18 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "5000 permutations were taken. The p-value is 0.0758."
+      ]
+     },
+     "execution_count": null,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "control = norm.rvs(loc=0, size=30, random_state=12345)\n",
     "test = norm.rvs(loc=0.5, size=30, random_state=12345)\n",
@@ -1921,7 +2394,13 @@
    "source": []
   }
  ],
- "metadata": {},
+ "metadata": {
+  "kernelspec": {
+   "display_name": "python3",
+   "language": "python",
+   "name": "python3"
+  }
+ },
  "nbformat": 4,
  "nbformat_minor": 2
 }
diff --git a/nbs/API/forest_plot.ipynb b/nbs/API/forest_plot.ipynb
index 9725a619..ece45d7c 100644
--- a/nbs/API/forest_plot.ipynb
+++ b/nbs/API/forest_plot.ipynb
@@ -62,7 +62,10 @@
     "import matplotlib.pyplot as plt\n",
     "# %matplotlib inline\n",
     "import seaborn as sns\n",
-    "from typing import List, Optional, Union\n"
+    "from typing import List, Optional, Union\n",
+    "import numpy as np\n",
+    "import matplotlib.axes as axes\n",
+    "import matplotlib.patches as mpatches"
    ]
   },
   {
@@ -73,7 +76,11 @@
    "source": [
     "#| export\n",
     "def load_plot_data(\n",
-    "    contrasts: List, effect_size: str = \"mean_diff\", contrast_type: str = \"delta2\"\n",
+    "            data: List, \n",
+    "            effect_size: str = \"mean_diff\", \n",
+    "            contrast_type: str = None,\n",
+    "            ci_type: str = \"bca\",\n",
+    "            idx: Optional[List[int]] = None\n",
     ") -> List:\n",
     "    \"\"\"\n",
     "    Loads plot data based on specified effect size and contrast type.\n",
@@ -85,278 +92,739 @@
     "    effect_size: str\n",
     "        Type of effect size ('mean_diff', 'median_diff', etc.).\n",
     "    contrast_type: str\n",
-    "        Type of contrast ('delta2', 'mini_meta').\n",
+    "        Type of dabest object to plot ('delta2' or 'mini-meta' or 'delta').\n",
+    "    ci_type: str\n",
+    "        Type of confidence interval to plot ('bca' or 'pct')\n",
+    "    idx: Optional[List[int]], default=None\n",
+    "        List of indices to select from the contrast objects if delta-delta experiment. \n",
+    "        If None, only the delta-delta objects are plotted.\n",
     "\n",
     "    Returns\n",
     "    -------\n",
     "    List: Contrast plot data based on specified parameters.\n",
     "    \"\"\"\n",
-    "    effect_attr_map = {\n",
-    "        \"mean_diff\": \"mean_diff\",\n",
-    "        \"median_diff\": \"median_diff\",\n",
-    "        \"cliffs_delta\": \"cliffs_delta\",\n",
-    "        \"cohens_d\": \"cohens_d\",\n",
-    "        \"hedges_g\": \"hedges_g\",\n",
-    "        \"delta_g\": \"delta_g\"\n",
-    "    }\n",
+    "    # Effect size and contrast types\n",
+    "    effect_attr = \"hedges_g\" if effect_size == 'delta_g' else effect_size\n",
+    "    contrast_attr = {\"delta2\": \"delta_delta\", \"mini_meta\": \"mini_meta\"}.get(contrast_type)\n",
+    "\n",
+    "    # Testing\n",
+    "    if idx is not None:\n",
+    "        bootstraps, differences, bcalows, bcahighs = [], [], [], []\n",
+    "        for current_idx, index_group in enumerate(idx):\n",
+    "            current_contrast = data[current_idx]\n",
+    "            if len(index_group)>0:\n",
+    "                for index in index_group:\n",
+    "                    current_plot_data = getattr(current_contrast, effect_attr)\n",
+    "                    if contrast_type == 'delta2':\n",
+    "                        if index == 2:\n",
+    "                            current_plot_data = getattr(current_plot_data, contrast_attr)\n",
+    "                            bootstrap_name, index_val = \"bootstraps_delta_delta\", 0\n",
+    "                        elif index == 0 or index == 1:\n",
+    "                            bootstrap_name, index_val = \"bootstraps\", index\n",
+    "                        else:\n",
+    "                            raise ValueError(\"The selected indices must be 0, 1, or 2.\")\n",
+    "                    elif contrast_type == \"mini_meta\":\n",
+    "                        num_of_groups = len(getattr(current_contrast, effect_attr).results)\n",
+    "                        if index == num_of_groups:\n",
+    "                            current_plot_data = getattr(getattr(current_contrast, effect_attr), contrast_attr)\n",
+    "                            bootstrap_name, index_val = \"bootstraps_weighted_delta\", 0\n",
+    "                        elif index < num_of_groups:\n",
+    "                            bootstrap_name, index_val = \"bootstraps\", index\n",
+    "                        else:\n",
+    "                            msg1 = \"There are only {} groups (starting from zero) in this dabest object. \".format(num_of_groups)\n",
+    "                            msg2 = \"The idx given is {}.\".format(index)\n",
+    "                            raise ValueError(msg1+msg2)        \n",
+    "                    else: # contrast_type == 'delta'\n",
+    "                        bootstrap_name, index_val = \"bootstraps\", index              \n",
+    "\n",
+    "                    bootstraps.append(getattr(current_plot_data.results, bootstrap_name)[index_val])\n",
+    "                    differences.append(current_plot_data.results.difference[index_val])\n",
+    "                    bcalows.append(current_plot_data.results.get(ci_type+'_low')[index_val])\n",
+    "                    bcahighs.append(current_plot_data.results.get(ci_type+'_high')[index_val])    \n",
+    "    else:\n",
+    "        if contrast_type == 'delta':\n",
+    "            contrast_plot_data = [getattr(contrast, effect_attr)  for contrast in data]\n",
+    "            bootstraps_nested = [result.results.bootstraps.to_list() for result in contrast_plot_data]\n",
+    "            differences_nested = [result.results.difference.to_list() for result in contrast_plot_data]\n",
+    "            bcalows_nested = [result.results.get(ci_type+'_low').to_list() for result in contrast_plot_data]\n",
+    "            bcahighs_nested = [result.results.get(ci_type+'_high').to_list() for result in contrast_plot_data]\n",
+    "            \n",
+    "            bootstraps = [element for innerList in bootstraps_nested for element in innerList]\n",
+    "            differences = [element for innerList in differences_nested for element in innerList]\n",
+    "            bcalows = [element for innerList in bcalows_nested for element in innerList]\n",
+    "            bcahighs = [element for innerList in bcahighs_nested for element in innerList]\n",
+    "\n",
+    "        else: # contrast_type == 'delta2' or 'mini_meta'\n",
+    "            contrast_plot_data = [getattr(getattr(contrast, effect_attr), contrast_attr) for contrast in data]\n",
+    "            attribute_suffix = \"weighted_delta\" if contrast_type == \"mini_meta\" else \"delta_delta\"\n",
     "\n",
-    "    contrast_attr_map = {\"delta2\": \"delta_delta\", \"mini_meta\": \"mini_meta_delta\"}\n",
+    "            bootstraps = [getattr(result, f\"bootstraps_{attribute_suffix}\") for result in contrast_plot_data]\n",
+    "            differences = [result.difference for result in contrast_plot_data]\n",
+    "            bcalows = [result.results.get(ci_type+'_low')[0] for result in contrast_plot_data]\n",
+    "            bcahighs = [result.results.get(ci_type+'_high')[0] for result in contrast_plot_data]\n",
     "\n",
-    "    effect_attr = effect_attr_map.get(effect_size)\n",
-    "    contrast_attr = contrast_attr_map.get(contrast_type)\n",
+    "    return bootstraps, differences, bcalows, bcahighs\n",
     "\n",
-    "    if not effect_attr:\n",
-    "        raise ValueError(f\"Invalid effect_size: {effect_size}\") \n",
-    "    if not contrast_attr:\n",
-    "        raise ValueError(f\"Invalid contrast_type: {contrast_type}. Available options: [`delta2`, `mini_meta`]\")\n",
+    "def check_for_errors(**kwargs):\n",
+    "    data = kwargs.get('data')\n",
+    "    # Contrasts\n",
+    "    if not isinstance(data, list) or not data:\n",
+    "        raise ValueError(\"The `data` argument must be a non-empty list of dabest objects.\")\n",
+    "    \n",
+    "    ## Check if all contrasts are delta-delta or all are mini-meta\n",
+    "    contrast_type = (\"delta2\" if data[0].delta2 \n",
+    "                     else \"mini_meta\" if data[0].is_mini_meta\n",
+    "                     else \"delta\"\n",
+    "                    )\n",
     "\n",
-    "    return [\n",
-    "        getattr(getattr(contrast, effect_attr), contrast_attr) for contrast in contrasts\n",
-    "    ]\n",
+    "    # contrast_type = \"delta2\" if data[0].delta2 else \"mini_meta\"\n",
+    "    for contrast in data:\n",
+    "        check_contrast_type = (\"delta2\" if contrast.delta2 \n",
+    "                               else \"mini_meta\" if contrast.is_mini_meta\n",
+    "                               else \"delta\"\n",
+    "                              )\n",
+    "        if check_contrast_type != contrast_type:\n",
+    "            raise ValueError(\"Each dabest object supplied must be the same experimental type (mini-meta or delta-delta or neither.)\")\n",
     "\n",
+    "    # Idx\n",
+    "    idx = kwargs.get('idx')\n",
+    "    effect_size = kwargs.get('effect_size')\n",
+    "    if idx is not None:\n",
+    "        if not isinstance(idx, (tuple, list)):\n",
+    "            raise TypeError(\"`idx` must be a tuple or list of integers.\")\n",
     "\n",
-    "def extract_plot_data(contrast_plot_data, contrast_type):\n",
-    "    \"\"\"Extracts bootstrap, difference, and confidence intervals based on contrast labels.\"\"\"\n",
-    "    if contrast_type == \"mini_meta\":\n",
-    "        attribute_suffix = \"weighted_delta\"\n",
+    "        msg1 = \"The `idx` argument must have the same length as the number of dabest objects. \"\n",
+    "        msg2 = \"E.g., If two dabest objects are supplied, there should be two lists within `idx`. \"\n",
+    "        msg3 = \"E.g., `idx` = [[1,2],[0,1]].\"\n",
+    "        _total = 0\n",
+    "        for _group in idx:\n",
+    "            if isinstance(_group, int | float):\n",
+    "                raise ValueError(msg1+msg2+msg3)\n",
+    "            else:\n",
+    "                _total += 1\n",
+    "        if _total != len(data):\n",
+    "            raise ValueError(msg1+msg2+msg3)\n",
+    "        \n",
+    "    if idx is not None:\n",
+    "        number_of_curves_to_plot = sum([len(i) for i in idx])\n",
     "    else:\n",
-    "        attribute_suffix = \"delta_delta\"\n",
+    "        if contrast_type == 'delta':\n",
+    "            number_of_curves_to_plot = sum(len(getattr(i, effect_size).results) for i in data)\n",
+    "        else:\n",
+    "            number_of_curves_to_plot = len(data)\n",
     "\n",
-    "    bootstraps = [\n",
-    "        getattr(result, f\"bootstraps_{attribute_suffix}\")\n",
-    "        for result in contrast_plot_data\n",
-    "    ]\n",
-    "    \n",
-    "    differences = [result.difference for result in contrast_plot_data]\n",
-    "    bcalows = [result.bca_low for result in contrast_plot_data]\n",
-    "    bcahighs = [result.bca_high for result in contrast_plot_data]\n",
+    "    # Axes\n",
+    "    ax = kwargs.get('ax')\n",
+    "    fig_size = kwargs.get('fig_size')\n",
+    "    if ax is not None and not isinstance(ax, plt.Axes):\n",
+    "        raise TypeError(\"The `ax` must be a `matplotlib.axes.Axes` instance or `None`.\")\n",
     "    \n",
-    "    return bootstraps, differences, bcalows, bcahighs\n",
+    "    # Figure size\n",
+    "    if fig_size is not None and not isinstance(fig_size, (tuple, list)):\n",
+    "        raise TypeError(\"`fig_size` must be a tuple or list of two positive integers.\")\n",
     "\n",
+    "    # Effect size\n",
+    "    effect_size_options = ['mean_diff', 'median_diff', 'cohens_d', 'cohens_h', 'cliffs_delta', 'hedges_g', 'delta_g']\n",
+    "    if not isinstance(effect_size, str) or effect_size not in effect_size_options:\n",
+    "        raise TypeError(\"The `effect_size` argument must be a string and please choose from the following effect sizes: 'mean_diff', 'median_diff', 'cohens_d', 'cohens_h', 'cliffs_delta', 'hedges_g', 'delta_g'.\")\n",
+    "    if data[0].is_mini_meta and effect_size != 'mean_diff':\n",
+    "        raise ValueError(\"The `effect_size` argument must be `mean_diff` for mini-meta analyses.\")\n",
+    "    if data[0].delta2 and effect_size not in ['mean_diff', 'hedges_g', 'delta_g']:\n",
+    "        raise ValueError(\"The `effect_size` argument must be `mean_diff`, `hedges_g`, or `delta_g` for delta-delta analyses.\")\n",
+    "    \n",
+    "    # CI type\n",
+    "    ci_type = kwargs.get('ci_type')\n",
+    "    if ci_type not in ('bca', 'pct'):\n",
+    "        raise TypeError(\"`ci_type` must be either 'bca' or 'pct'.\")\n",
     "\n",
-    "def forest_plot(\n",
-    "    contrasts: List,\n",
-    "    selected_indices: Optional[List] = None,\n",
-    "    contrast_type: str = \"delta2\",\n",
-    "    xticklabels: Optional[List] = None,\n",
-    "    effect_size: str = \"mean_diff\",\n",
-    "    contrast_labels: List[str] = None,\n",
-    "    ylabel: str = \"value\",\n",
-    "    plot_elements_to_extract: Optional[List] = None,\n",
-    "    title: str = \"ΔΔ Forest\",\n",
-    "    custom_palette: Optional[Union[dict, list, str]] = None,\n",
-    "    fontsize: int = 20,\n",
-    "    violin_kwargs: Optional[dict] = None,\n",
-    "    marker_size: int = 20,\n",
-    "    ci_line_width: float = 2.5,\n",
-    "    zero_line_width: int = 1,\n",
-    "    remove_spines: bool = True,\n",
-    "    ax: Optional[plt.Axes] = None,\n",
-    "    additional_plotting_kwargs: Optional[dict] = None,\n",
-    "    rotation_for_xlabels: int = 45,\n",
-    "    alpha_violin_plot: float = 0.4,\n",
-    "    horizontal: bool = False  # New argument for horizontal orientation\n",
-    ")-> plt.Figure:\n",
-    "    \"\"\"  \n",
-    "    Custom function that generates a forest plot from given contrast objects, suitable for a range of data analysis types, including those from packages like DABEST-python.\n",
+    "    # Horizontal\n",
+    "    horizontal = kwargs.get('horizontal')\n",
+    "    if not isinstance(horizontal, bool):\n",
+    "        raise TypeError(\"`horizontal` must be a boolean value.\")\n",
     "\n",
-    "    Parameters\n",
-    "    ----------\n",
-    "    contrasts : List\n",
-    "        List of contrast objects.\n",
-    "    selected_indices : Optional[List], default=None\n",
-    "        Indices of specific contrasts to plot, if not plotting all.\n",
-    "    analysis_type : str\n",
-    "         the type of analysis (e.g., 'delta2', 'minimeta').\n",
-    "    xticklabels : Optional[List], default=None\n",
-    "        Custom labels for the x-axis ticks.\n",
-    "    effect_size : str\n",
-    "        Type of effect size to plot (e.g., 'mean_diff', 'median_diff').\n",
-    "    contrast_labels : List[str]\n",
-    "        Labels for each contrast.\n",
-    "    ylabel : str\n",
-    "        Label for the y-axis, describing the plotted data or effect size.\n",
-    "    plot_elements_to_extract : Optional[List], default=None\n",
-    "        Elements to extract for detailed plot customization.\n",
-    "    title : str\n",
-    "        Plot title, summarizing the visualized data.\n",
-    "    ylim : Tuple[float, float]\n",
-    "        Limits for the y-axis.\n",
-    "    custom_palette : Optional[Union[dict, list, str]], default=None\n",
-    "        Custom color palette for the plot.\n",
-    "    fontsize : int\n",
-    "        Font size for text elements in the plot.\n",
-    "    violin_kwargs : Optional[dict], default=None\n",
-    "        Additional arguments for violin plot customization.\n",
-    "    marker_size : int\n",
-    "        Marker size for plotting mean differences or effect sizes.\n",
-    "    ci_line_width : float\n",
-    "        Width of confidence interval lines.\n",
-    "    zero_line_width : int\n",
-    "        Width of the line indicating zero effect size.\n",
-    "    remove_spines : bool, default=False\n",
-    "        If True, removes top and right plot spines.\n",
-    "    ax : Optional[plt.Axes], default=None\n",
-    "        Matplotlib Axes object for the plot; creates new if None.\n",
-    "        additional_plotting_kwargs : Optional[dict], default=None\n",
-    "        Further customization arguments for the plot.\n",
-    "    rotation_for_xlabels : int, default=0\n",
-    "        Rotation angle for x-axis labels, improving readability.\n",
-    "    alpha_violin_plot : float, default=1.0\n",
-    "        Transparency level for violin plots.\n",
+    "    # Marker size\n",
+    "    marker_size = kwargs.get('marker_size')\n",
+    "    if not isinstance(marker_size, (int, float)) or marker_size <= 0:\n",
+    "        raise TypeError(\"`marker_size` must be a positive integer or float.\")\n",
     "\n",
-    "    Returns\n",
-    "    -------\n",
-    "    plt.Figure\n",
-    "        The matplotlib figure object with the generated forest plot.\n",
-    "    \"\"\"\n",
-    "    from .plot_tools import halfviolin\n",
+    "    # Custom palette\n",
+    "    custom_palette = kwargs.get('custom_palette')\n",
+    "    labels = kwargs.get('labels')\n",
+    "    if custom_palette is not None and not isinstance(custom_palette, (dict, list, tuple, str, type(None))):\n",
+    "        raise TypeError(\"The `custom_palette` must be either a dictionary, list, string, or `None`.\")\n",
+    "    if isinstance(custom_palette, dict) and labels is None:\n",
+    "        raise ValueError(\"The `labels` argument must be provided if `custom_palette` is a dictionary.\")\n",
+    "    if isinstance(custom_palette, (list, tuple)) and len(custom_palette) < number_of_curves_to_plot:\n",
+    "        raise ValueError(\"The `custom_palette` list/tuple must have the same length as the number of `data` provided.\")\n",
     "\n",
-    "    # Validate inputs\n",
-    "    if contrasts is None:\n",
-    "        raise ValueError(\"The `contrasts` parameter cannot be None\")\n",
+    "    # Contrast alpha and desat\n",
+    "    contrast_alpha = kwargs.get('contrast_alpha')\n",
+    "    contrast_desat = kwargs.get('contrast_desat')\n",
+    "    if not isinstance(contrast_alpha, float) or not 0 <= contrast_alpha <= 1:\n",
+    "        raise TypeError(\"`contrast_alpha` must be a float between 0 and 1.\")\n",
     "    \n",
-    "    if not isinstance(contrasts, list) or not contrasts:\n",
-    "        raise ValueError(\"The `contrasts` argument must be a non-empty list.\")\n",
+    "    if not isinstance(contrast_desat, (float, int)) or not 0 <= contrast_desat <= 1:\n",
+    "        raise TypeError(\"`contrast_desat` must be a float between 0 and 1 or an int (1).\")\n",
     "    \n",
-    "    if selected_indices is not None and not isinstance(selected_indices, (list, type(None))):\n",
-    "        raise TypeError(\"The `selected_indices` must be a list of integers or `None`.\")\n",
+    "    # Contrast labels\n",
+    "    labels_fontsize = kwargs.get('labels_fontsize')\n",
+    "    labels_rotation = kwargs.get('labels_rotation')\n",
+    "    if labels is not None and not all(isinstance(label, str) for label in labels):\n",
+    "        raise TypeError(\"The `labels` must be a list of strings or `None`.\")\n",
     "    \n",
-    "    if not isinstance(contrast_type, str):\n",
-    "        raise TypeError(\"The `contrast_type` argument must be a string.\")\n",
+    "    if labels is not None and len(labels) != number_of_curves_to_plot:\n",
+    "        raise ValueError(\"`labels` must match the number of `data` provided.\")\n",
     "    \n",
-    "    if xticklabels is not None and not all(isinstance(label, str) for label in xticklabels):\n",
-    "        raise TypeError(\"The `xticklabels` must be a list of strings or `None`.\")\n",
+    "    if not isinstance(labels_fontsize, (int, float)):\n",
+    "        raise TypeError(\"`labels_fontsize` must be an integer or float.\")\n",
     "    \n",
-    "    if not isinstance(effect_size, str):\n",
-    "        raise TypeError(\"The `effect_size` argument must be a string.\")\n",
-    "    \n",
-    "    if contrast_labels is not None and not all(isinstance(label, str) for label in contrast_labels):\n",
-    "        raise TypeError(\"The `contrast_labels` must be a list of strings or `None`.\")\n",
+    "    if labels_rotation is not None and (not isinstance(labels_rotation, (int, float)) or not 0 <= labels_rotation <= 360):\n",
+    "        raise TypeError(\"`labels_rotation` must be an integer or float between 0 and 360.\")   \n",
+    "\n",
+    "    # Title\n",
+    "    title = kwargs.get('title')\n",
+    "    title_fontsize = kwargs.get('title_fontsize')\n",
+    "    if title is not None and not isinstance(title, str):\n",
+    "        raise TypeError(\"The `title` argument must be a string.\")\n",
     "    \n",
-    "    if contrast_labels is not None and len(contrast_labels) != len(contrasts):\n",
-    "        raise ValueError(\"`contrast_labels` must match the number of `contrasts` if provided.\")\n",
+    "    if not isinstance(title_fontsize, (int, float)):\n",
+    "        raise TypeError(\"`title_fontsize` must be an integer or float.\")\n",
     "    \n",
-    "    if not isinstance(ylabel, str):\n",
+    "    # Y-label\n",
+    "    ylabel = kwargs.get('ylabel')\n",
+    "    ylabel_fontsize = kwargs.get('ylabel_fontsize')\n",
+    "    if ylabel is not None and not isinstance(ylabel, str):\n",
     "        raise TypeError(\"The `ylabel` argument must be a string.\")\n",
+    "\n",
+    "    if not isinstance(ylabel_fontsize, (int, float)):\n",
+    "        raise TypeError(\"`ylabel_fontsize` must be an integer or float.\")\n",
     "    \n",
-    "    if custom_palette is not None and not isinstance(custom_palette, (dict, list, str, type(None))):\n",
-    "        raise TypeError(\"The `custom_palette` must be either a dictionary, list, string, or `None`.\")\n",
-    "    \n",
-    "    if not isinstance(fontsize, (int, float)):\n",
-    "        raise TypeError(\"`fontsize` must be an integer or float.\")\n",
-    "    \n",
-    "    if not isinstance(marker_size, (int, float)) or marker_size <= 0:\n",
-    "        raise TypeError(\"`marker_size` must be a positive integer or float.\")\n",
+    "    # Y-lim\n",
+    "    ylim = kwargs.get('ylim')\n",
+    "    if ylim is not None and not isinstance(ylim, (tuple, list)):\n",
+    "        raise TypeError(\"`ylim` must be a tuple or list of two floats.\")\n",
+    "    if ylim is not None and len(ylim) != 2:\n",
+    "        raise ValueError(\"`ylim` must be a tuple or list of two floats.\")\n",
+    "\n",
+    "    # Y-ticks\n",
+    "    yticks = kwargs.get('yticks')\n",
+    "    if yticks is not None and not isinstance(yticks, (tuple, list)):\n",
+    "        raise TypeError(\"`yticks` must be a tuple or list of floats.\")\n",
     "    \n",
-    "    if not isinstance(ci_line_width, (int, float)) or ci_line_width <= 0:\n",
-    "        raise TypeError(\"`ci_line_width` must be a positive integer or float.\")\n",
+    "    # Y-ticklabels\n",
+    "    yticklabels = kwargs.get('yticklabels')\n",
+    "    if yticklabels is not None and not isinstance(yticklabels, (tuple, list)):\n",
+    "        raise TypeError(\"`yticklabels` must be a tuple or list of strings.\")\n",
     "    \n",
-    "    if not isinstance(zero_line_width, (int, float)) or zero_line_width <= 0:\n",
-    "        raise TypeError(\"`zero_line_width` must be a positive integer or float.\")\n",
+    "    if yticklabels is not None and not all(isinstance(label, str) for label in yticklabels):\n",
+    "        raise TypeError(\"`yticklabels` must be a list of strings.\")\n",
     "    \n",
+    "    # Remove spines\n",
+    "    remove_spines = kwargs.get('remove_spines')\n",
     "    if not isinstance(remove_spines, bool):\n",
     "        raise TypeError(\"`remove_spines` must be a boolean value.\")\n",
     "    \n",
-    "    if ax is not None and not isinstance(ax, plt.Axes):\n",
-    "        raise TypeError(\"`ax` must be a `matplotlib.axes.Axes` instance or `None`.\")\n",
-    "    \n",
-    "    if not isinstance(rotation_for_xlabels, (int, float)) or not 0 <= rotation_for_xlabels <= 360:\n",
-    "        raise TypeError(\"`rotation_for_xlabels` must be an integer or float between 0 and 360.\")\n",
-    "    \n",
-    "    if not isinstance(alpha_violin_plot, float) or not 0 <= alpha_violin_plot <= 1:\n",
-    "        raise TypeError(\"`alpha_violin_plot` must be a float between 0 and 1.\")\n",
+    "    # Reference band\n",
+    "    reference_band = kwargs.get('reference_band')\n",
+    "    if reference_band is not None:\n",
+    "        if not isinstance(reference_band, list | tuple):\n",
+    "            raise TypeError(\"`reference_band` must be a list/tuple of indices (ints).\")\n",
+    "        if not all(isinstance(i, int) for i in reference_band):\n",
+    "            raise TypeError(\"`reference_band` must be a list/tuple of indices (ints).\")\n",
+    "        if any(i >= number_of_curves_to_plot for i in reference_band):\n",
+    "            raise ValueError(\"Index {} chosen is out of range for the contrast objects.\".format([i for i in reference_band if i >= number_of_curves_to_plot]))\n",
     "    \n",
-    "    if not isinstance(horizontal, bool):\n",
-    "        raise TypeError(\"`horizontal` must be a boolean value.\")\n",
+    "    # Delta text\n",
+    "    delta_text = kwargs.get('delta_text')\n",
+    "    if delta_text is not None:\n",
+    "        if not isinstance(delta_text, bool):\n",
+    "            raise TypeError(\"`delta_text` must be a boolean value.\")\n",
     "\n",
-    "    # Load plot data\n",
-    "    contrast_plot_data = load_plot_data(contrasts, effect_size, contrast_type)\n",
+    "    # Contrast bars\n",
+    "    contrast_bars = kwargs.get('contrast_bars')\n",
+    "    if contrast_bars is not None:\n",
+    "        if not isinstance(contrast_bars, bool):\n",
+    "            raise TypeError(\"`contrast_bars` must be a boolean value.\")\n",
     "\n",
-    "    # Extract data for plotting\n",
-    "    bootstraps, differences, bcalows, bcahighs = extract_plot_data(\n",
-    "        contrast_plot_data, contrast_type\n",
-    "    )\n",
-    "    # Adjust figure size based on orientation\n",
-    "    all_groups_count = len(contrasts)\n",
-    "    if horizontal:\n",
-    "        fig_size = (4, 1.5 * all_groups_count)\n",
-    "    else:\n",
-    "        fig_size = (1.5 * all_groups_count, 4)\n",
+    "    return contrast_type    \n",
     "\n",
-    "    if ax is None:\n",
-    "        fig, ax = plt.subplots(figsize=fig_size)\n",
-    "    else:\n",
-    "        fig = ax.figure\n",
+    "def get_kwargs(\n",
+    "        violin_kwargs,\n",
+    "        zeroline_kwargs,\n",
+    "        horizontal,\n",
+    "        marker_kwargs,\n",
+    "        errorbar_kwargs,\n",
+    "        delta_text_kwargs,\n",
+    "        contrast_bars_kwargs,\n",
+    "        reference_band_kwargs,\n",
+    "        marker_size\n",
+    "    ):\n",
+    "    from .misc_tools import merge_two_dicts\n",
     "\n",
-    "    # Adjust violin plot orientation based on the 'horizontal' argument\n",
-    "    violin_kwargs = violin_kwargs or {\n",
+    "    # Violin kwargs\n",
+    "    default_violin_kwargs = {\n",
     "        \"widths\": 0.5,\n",
     "        \"showextrema\": False,\n",
     "        \"showmedians\": False,\n",
+    "        \"orientation\": 'horizontal' if horizontal else 'vertical',\n",
     "    }\n",
-    "    violin_kwargs[\"vert\"] = not horizontal\n",
-    "    v = ax.violinplot(bootstraps, **violin_kwargs)\n",
+    "    if violin_kwargs is None:\n",
+    "        violin_kwargs = default_violin_kwargs\n",
+    "    else:\n",
+    "        violin_kwargs = merge_two_dicts(default_violin_kwargs, violin_kwargs)\n",
     "\n",
-    "    # Adjust the halfviolin function call based on 'horizontal'\n",
-    "    if horizontal:\n",
-    "        half = \"top\"\n",
+    "    # zeroline kwargs\n",
+    "    default_zeroline_kwargs = {\n",
+    "        \"linewidth\": 1,\n",
+    "        \"color\": \"black\"\n",
+    "    }\n",
+    "    if zeroline_kwargs is None:\n",
+    "        zeroline_kwargs = default_zeroline_kwargs\n",
     "    else:\n",
-    "        half = \"right\"  # Assuming \"right\" is the default or another appropriate value\n",
+    "        zeroline_kwargs = merge_two_dicts(default_zeroline_kwargs, zeroline_kwargs)\n",
     "\n",
-    "    # Assuming halfviolin has been updated to accept a 'half' parameter\n",
-    "    halfviolin(v, alpha=alpha_violin_plot, half=half)\n",
-    "    \n",
-    "    # Handle the custom color palette\n",
-    "    if custom_palette:\n",
+    "    # Effect size marker kwargs\n",
+    "    default_marker_kwargs = {\n",
+    "                'marker': 'o',\n",
+    "                'markersize': marker_size,\n",
+    "                'color': 'black',\n",
+    "                'alpha': 1,\n",
+    "                'zorder': 2,\n",
+    "    }\n",
+    "    if marker_kwargs is None:\n",
+    "        marker_kwargs = default_marker_kwargs\n",
+    "    else:\n",
+    "        marker_kwargs = merge_two_dicts(default_marker_kwargs, marker_kwargs)\n",
+    "\n",
+    "    # Effect size error bar kwargs\n",
+    "    default_errorbar_kwargs = {\n",
+    "                'color': 'black',\n",
+    "                'lw': 2.5,\n",
+    "                'linestyle': '-',\n",
+    "                'alpha': 1,\n",
+    "                'zorder': 1,\n",
+    "    }\n",
+    "    if errorbar_kwargs is None:\n",
+    "        errorbar_kwargs = default_errorbar_kwargs\n",
+    "    else:\n",
+    "        errorbar_kwargs = merge_two_dicts(default_errorbar_kwargs, errorbar_kwargs)\n",
+    "\n",
+    "    # Delta text kwargs\n",
+    "    default_delta_text_kwargs = {\n",
+    "                \"color\": None, \n",
+    "                \"alpha\": 1,\n",
+    "                \"fontsize\": 10, \n",
+    "                \"ha\": 'center', \n",
+    "                \"va\": 'center', \n",
+    "                \"rotation\": 0, \n",
+    "                \"x_coordinates\": None, \n",
+    "                \"y_coordinates\": None,\n",
+    "                \"offset\": 0\n",
+    "    }\n",
+    "    if delta_text_kwargs is None:\n",
+    "        delta_text_kwargs = default_delta_text_kwargs\n",
+    "    else:\n",
+    "        delta_text_kwargs = merge_two_dicts(default_delta_text_kwargs, delta_text_kwargs)\n",
+    "\n",
+    "    # Contrast bars kwargs.\n",
+    "    default_contrast_bars_kwargs = {\n",
+    "                    \"color\": None, \n",
+    "                    \"zorder\":-3,\n",
+    "                    'alpha': 0.15\n",
+    "    }\n",
+    "    if contrast_bars_kwargs is None:\n",
+    "        contrast_bars_kwargs = default_contrast_bars_kwargs\n",
+    "    else:\n",
+    "        contrast_bars_kwargs = merge_two_dicts(default_contrast_bars_kwargs, contrast_bars_kwargs)\n",
+    "\n",
+    "    # reference band kwargs.\n",
+    "    default_reference_band_kwargs = {\n",
+    "                    \"span_ax\": False,\n",
+    "                    \"color\": None, \n",
+    "                    \"alpha\": 0.15,\n",
+    "                    \"zorder\":-3\n",
+    "    }\n",
+    "    if reference_band_kwargs is None:\n",
+    "        reference_band_kwargs = default_reference_band_kwargs\n",
+    "    else:\n",
+    "        reference_band_kwargs = merge_two_dicts(default_reference_band_kwargs, reference_band_kwargs)\n",
+    "\n",
+    "    return (violin_kwargs, zeroline_kwargs, marker_kwargs, errorbar_kwargs, \n",
+    "            delta_text_kwargs, contrast_bars_kwargs, reference_band_kwargs)\n",
+    "\n",
+    "def color_palette(\n",
+    "        custom_palette, \n",
+    "        labels, \n",
+    "        number_of_curves_to_plot,\n",
+    "        contrast_desat\n",
+    "    ):\n",
+    "    if custom_palette is not None:\n",
     "        if isinstance(custom_palette, dict):\n",
     "            violin_colors = [\n",
-    "                custom_palette.get(c, sns.color_palette()[0]) for c in contrasts\n",
+    "                custom_palette.get(c, sns.color_palette()[0]) for c in labels\n",
     "            ]\n",
     "        elif isinstance(custom_palette, list):\n",
-    "            violin_colors = custom_palette[: len(contrasts)]\n",
+    "            violin_colors = custom_palette[: number_of_curves_to_plot]\n",
     "        elif isinstance(custom_palette, str):\n",
     "            if custom_palette in plt.colormaps():\n",
-    "                violin_colors = sns.color_palette(custom_palette, len(contrasts))\n",
+    "                violin_colors = sns.color_palette(custom_palette, number_of_curves_to_plot)\n",
     "            else:\n",
     "                raise ValueError(\n",
     "                    f\"The specified `custom_palette` {custom_palette} is not a recognized Matplotlib palette.\"\n",
     "                )\n",
     "    else:\n",
-    "        violin_colors = sns.color_palette()[: len(contrasts)]\n",
+    "        violin_colors = sns.color_palette(n_colors=number_of_curves_to_plot)\n",
+    "    violin_colors = [sns.desaturate(color, contrast_desat) for color in violin_colors]\n",
+    "    return violin_colors\n",
     "\n",
-    "    for patch, color in zip(v[\"bodies\"], violin_colors):\n",
-    "        patch.set_facecolor(color)\n",
-    "        patch.set_alpha(alpha_violin_plot)\n",
+    "def forest_plot(\n",
+    "    data: list,\n",
+    "    idx: Optional[list[int]] = None,\n",
+    "    ax: Optional[plt.Axes] = None,\n",
+    "    fig_size: tuple[int, int] = None,\n",
+    "    effect_size: str = \"mean_diff\",\n",
+    "    ci_type='bca',\n",
+    "    horizontal: bool = False, \n",
+    "\n",
+    "    marker_size: int = 10,\n",
+    "    custom_palette: Optional[Union[dict, list, str]] = None,\n",
+    "    contrast_alpha: float = 0.8,\n",
+    "    contrast_desat: float = 1,\n",
+    "\n",
+    "    labels: list[str] = None,\n",
+    "    labels_rotation: int = None,\n",
+    "    labels_fontsize: int = 10,\n",
+    "    title: str = None,\n",
+    "    title_fontsize: int = 16,\n",
+    "    ylabel: str = None,\n",
+    "    ylabel_fontsize: int = 12,\n",
+    "    ylim: Optional[list[float, float]] = None,\n",
+    "    yticks: Optional[list[float]] = None,\n",
+    "    yticklabels: Optional[list[str]] = None,\n",
+    "    remove_spines: bool = True,\n",
+    "\n",
+    "    delta_text: bool = True,\n",
+    "    delta_text_kwargs: dict = None,\n",
+    "\n",
+    "    contrast_bars: bool = True,\n",
+    "    contrast_bars_kwargs: dict = None,\n",
+    "    reference_band: list|tuple = None,\n",
+    "    reference_band_kwargs: dict = None,\n",
+    "\n",
+    "    violin_kwargs: Optional[dict] = None,\n",
+    "    zeroline_kwargs: Optional[dict] = None,\n",
+    "    marker_kwargs: Optional[dict] = None,\n",
+    "    errorbar_kwargs: Optional[dict] = None,\n",
+    ")-> plt.Figure:\n",
+    "    \"\"\"  \n",
+    "    Custom function that generates a forest plot from given contrast objects, suitable for a range of data analysis types, including those from packages like DABEST-python.\n",
+    "\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    data : List\n",
+    "        List of contrast objects.\n",
+    "    idx : Optional[List[int]], default=None\n",
+    "        List of indices to select from the contrast objects if delta-delta experiment. \n",
+    "        If None, only the delta-delta objects are plotted.\n",
+    "    ax : Optional[plt.Axes], default=None\n",
+    "        Matplotlib Axes object for the plot; creates new if None.\n",
+    "        additional_plotting_kwargs : Optional[dict], default=None\n",
+    "        Further customization arguments for the plot.\n",
+    "    fig_size : Tuple[int, int], default=None\n",
+    "        Figure size for the plot.\n",
+    "    effect_size : str\n",
+    "        Type of effect size to plot (e.g., 'mean_diff', `hedges_g` or 'delta_g').\n",
+    "    ci_type : str\n",
+    "        Type of confidence interval to plot (bca' or 'pct')\n",
+    "    horizontal : bool, default=False\n",
+    "        If True, the plot will be horizontal.\n",
+    "    marker_size : int, default=12\n",
+    "        Marker size for plotting effect size dots.\n",
+    "    custom_palette : Optional[Union[dict, list, str]], default=None\n",
+    "        Custom color palette for the plot.\n",
+    "    contrast_alpha : float, default=0.8\n",
+    "        Transparency level for violin plots.\n",
+    "    contrast_desat : float, default=1\n",
+    "        Saturation level for violin plots.\n",
+    "    labels : List[str]\n",
+    "        Labels for each contrast. If None, defaults to 'Contrast 1', 'Contrast 2', etc.\n",
+    "    labels_rotation : int, default=45 for vertical, 0 for horizontal\n",
+    "        Rotation angle for contrast labels.\n",
+    "    labels_fontsize : int, default=10\n",
+    "        Font size for contrast labels.\n",
+    "    title : str\n",
+    "        Plot title, summarizing the visualized data.\n",
+    "    title_fontsize : int, default=16\n",
+    "        Font size for the plot title.\n",
+    "    ylabel : str\n",
+    "        Label for the y-axis, describing the plotted data or effect size.\n",
+    "    ylabel_fontsize : int, default=12\n",
+    "        Font size for the y-axis label.\n",
+    "    ylim : Optional[Tuple[float, float]]\n",
+    "        Limits for the y-axis.\n",
+    "    yticks : Optional[List[float]]\n",
+    "        Custom y-ticks for the plot.\n",
+    "    yticklabels : Optional[List[str]]\n",
+    "        Custom y-tick labels for the plot.\n",
+    "    remove_spines : bool, default=True\n",
+    "        If True, removes plot spines (except the relevant dependent variable spine).\n",
+    "    delta_text : bool, default=True\n",
+    "        If True, it adds text next to each curve representing the effect size value.\n",
+    "    delta_text_kwargs : dict, default=None\n",
+    "        Additional keyword arguments for the delta_text.\n",
+    "    contrast_bars : bool, default=True\n",
+    "        If True, it adds bars from the zeroline to the effect size curve.\n",
+    "    contrast_bars_kwargs : dict, default=None\n",
+    "        Additional keyword arguments for the contrast_bars.\n",
+    "    reference_band: list | tuple, default=None,\n",
+    "        It adds reference bands to the relevant effect size curves.\n",
+    "    reference_band_kwargs : dict, default=None,\n",
+    "        Additional keyword arguments for the reference_band.\n",
+    "    violin_kwargs : Optional[dict], default=None\n",
+    "        Additional arguments for violin plot customization.\n",
+    "    zeroline_kwargs : Optional[dict], default=None\n",
+    "        Additional arguments for the zero line customization.\n",
+    "    marker_kwargs : Optional[dict], default=None\n",
+    "        Additional arguments for the effect size marker customization.\n",
+    "    errorbar_kwargs : Optional[dict], default=None\n",
+    "        Additional arguments for the effect size error bar customization.\n",
     "\n",
-    "    # Flipping the axes for plotting based on 'horizontal'\n",
-    "    for k in range(1, len(contrasts) + 1):\n",
+    "    Returns\n",
+    "    -------\n",
+    "    plt.Figure\n",
+    "        The matplotlib figure object with the generated forest plot.\n",
+    "    \"\"\"\n",
+    "    from .plot_tools import halfviolin\n",
+    "\n",
+    "    # Check for errors in the input arguments\n",
+    "    all_kwargs = locals()\n",
+    "    contrast_type = check_for_errors(**all_kwargs)\n",
+    "\n",
+    "    # Load plot data and extract info\n",
+    "    bootstraps, differences, bcalows, bcahighs = load_plot_data(\n",
+    "                                                        data = data, \n",
+    "                                                        effect_size = effect_size, \n",
+    "                                                        contrast_type = contrast_type,\n",
+    "                                                        ci_type = ci_type,\n",
+    "                                                        idx = idx\n",
+    "    )\n",
+    "    # Adjust figure size based on orientation\n",
+    "    number_of_curves_to_plot = len(bootstraps)\n",
+    "    if ax is not None:\n",
+    "        fig = ax.figure\n",
+    "    else:\n",
+    "        if fig_size is None:\n",
+    "            fig_size = (4, 1.3 * number_of_curves_to_plot) if horizontal else (1.3 * number_of_curves_to_plot, 4)\n",
+    "        fig, ax = plt.subplots(figsize=fig_size)\n",
+    "\n",
+    "    # Get Kwargs\n",
+    "    (violin_kwargs, zeroline_kwargs, marker_kwargs, errorbar_kwargs, \n",
+    "     delta_text_kwargs, contrast_bars_kwargs, reference_band_kwargs) = get_kwargs(\n",
+    "                                                                        violin_kwargs = violin_kwargs,\n",
+    "                                                                        zeroline_kwargs = zeroline_kwargs,\n",
+    "                                                                        horizontal = horizontal,\n",
+    "                                                                        marker_kwargs = marker_kwargs,\n",
+    "                                                                        errorbar_kwargs = errorbar_kwargs,\n",
+    "                                                                        delta_text_kwargs = delta_text_kwargs,\n",
+    "                                                                        contrast_bars_kwargs = contrast_bars_kwargs,\n",
+    "                                                                        reference_band_kwargs = reference_band_kwargs,\n",
+    "                                                                        marker_size = marker_size\n",
+    "    )\n",
+    "                                            \n",
+    "    # Plot the violins and make adjustments\n",
+    "    v = ax.violinplot(\n",
+    "        bootstraps, \n",
+    "        **violin_kwargs\n",
+    "    )\n",
+    "    halfviolin(\n",
+    "            v, \n",
+    "            alpha = contrast_alpha, \n",
+    "            half = \"bottom\" if horizontal else \"right\",\n",
+    "        )\n",
+    "    \n",
+    "    ## Plotting the effect sizes and confidence intervals\n",
+    "    for k in range(1, number_of_curves_to_plot + 1):\n",
     "        if horizontal:\n",
-    "            ax.plot(differences[k - 1], k, \"k.\", markersize=marker_size)  # Flipped axes\n",
-    "            ax.plot([bcalows[k - 1], bcahighs[k - 1]], [k, k], \"k\", linewidth=ci_line_width)  # Flipped axes\n",
+    "            ax.plot(differences[k - 1], k, **marker_kwargs)  \n",
+    "            ax.plot([bcalows[k - 1], bcahighs[k - 1]], [k, k], **errorbar_kwargs) \n",
     "        else:\n",
-    "            ax.plot(k, differences[k - 1], \"k.\", markersize=marker_size)\n",
-    "            ax.plot([k, k], [bcalows[k - 1], bcahighs[k - 1]], \"k\", linewidth=ci_line_width)\n",
+    "            ax.plot(k, differences[k - 1], **marker_kwargs)\n",
+    "            ax.plot([k, k], [bcalows[k - 1], bcahighs[k - 1]], **errorbar_kwargs)\n",
+    "    \n",
+    "    # Aesthetic Adjustments\n",
+    "    ## Handle the custom color palette\n",
+    "    violin_colors = color_palette(\n",
+    "                        custom_palette = custom_palette, \n",
+    "                        labels = labels, \n",
+    "                        number_of_curves_to_plot = number_of_curves_to_plot,\n",
+    "                        contrast_desat = contrast_desat\n",
+    "                    )\n",
+    "    \n",
+    "    for patch, color in zip(v[\"bodies\"], violin_colors):\n",
+    "        patch.set_facecolor(color)\n",
     "\n",
-    "    # Adjusting labels, ticks, and limits based on 'horizontal'\n",
+    "    ## Add a zero line to the plot\n",
     "    if horizontal:\n",
-    "        ax.set_yticks(range(1, len(contrasts) + 1))\n",
-    "        ax.set_yticklabels(contrast_labels,  rotation=rotation_for_xlabels, fontsize=fontsize)\n",
-    "        ax.set_xlabel(ylabel, fontsize=fontsize)\n",
+    "        ax.plot([0, 0], [0, number_of_curves_to_plot+1], **zeroline_kwargs)   \n",
     "    else:\n",
-    "        ax.set_xticks(range(1, len(contrasts) + 1))\n",
-    "        ax.set_xticklabels(contrast_labels, rotation=rotation_for_xlabels, fontsize=fontsize)\n",
-    "        ax.set_ylabel(ylabel, fontsize=fontsize)\n",
+    "        ax.plot([0, number_of_curves_to_plot+1], [0, 0], **zeroline_kwargs)\n",
     "\n",
-    "    # Setting the title and adjusting spines as before\n",
-    "    ax.set_title(title, fontsize=fontsize)\n",
+    "    ## lims\n",
+    "    ### Indepedent variable\n",
+    "    if horizontal:\n",
+    "        ax.set_ylim([0.7, number_of_curves_to_plot + 0.2])\n",
+    "    else:\n",
+    "        ax.set_xlim([0.7, number_of_curves_to_plot + 0.5])\n",
+    "\n",
+    "    ## Depedent variable\n",
+    "    if ylim is not None:\n",
+    "        lim_key = ax.set_xlim if horizontal else ax.set_ylim\n",
+    "        lim_key(ylim)\n",
+    "\n",
+    "    ## Ticks\n",
+    "    ### Indepedent variable\n",
+    "    lim_key = ax.set_yticks if horizontal else ax.set_xticks\n",
+    "    lim_key(range(1, number_of_curves_to_plot + 1))\n",
+    "\n",
+    "    if labels_rotation == None:\n",
+    "        labels_rotation = 0 if horizontal else 45\n",
+    "    if labels is None:\n",
+    "        labels = [f\"Contrast {i}\" for i in range(1, number_of_curves_to_plot + 1)]\n",
+    "    lim_key = ax.set_yticklabels if horizontal else ax.set_xticklabels\n",
+    "    lim_key(labels, rotation=labels_rotation, fontsize=labels_fontsize, ha=\"right\")\n",
+    "\n",
+    "    ### Depedent variable\n",
+    "    if yticks is not None:\n",
+    "        lim_key = ax.set_xticks if horizontal else ax.set_yticks\n",
+    "        lim_key(yticks)\n",
+    "\n",
+    "    if yticklabels is not None:\n",
+    "        lim_key = ax.set_xticklabels if horizontal else ax.set_yticklabels\n",
+    "        lim_key(yticklabels)\n",
+    "\n",
+    "    ## y-label \n",
+    "    if ylabel is None:\n",
+    "        effect_attr_map = {\n",
+    "            \"mean_diff\": \"Mean Difference\",\n",
+    "            \"median_diff\": \"Median Difference\", \n",
+    "            \"cohens_d\": \"Cohen's d\",\n",
+    "            \"cohens_h\": \"Cohen's h\",\n",
+    "            \"cliffs_delta\": \"Cliff's delta\",\n",
+    "            \"hedges_g\": \"Hedges' g\",\n",
+    "            \"delta_g\": \"Delta g\"\n",
+    "        }\n",
+    "        if contrast_type=='delta2' and idx is None and effect_size == \"hedges_g\":\n",
+    "            ylabel = \"Delta g\"\n",
+    "        elif contrast_type=='delta2' and idx is not None and (effect_size == \"delta_g\" or effect_size == \"hedges_g\"):\n",
+    "            ylabel = \"Hedges' g with Delta g\"\n",
+    "        else:\n",
+    "            ylabel = effect_attr_map[effect_size]\n",
+    "    lim_key = ax.set_xlabel if horizontal else ax.set_ylabel\n",
+    "    lim_key(ylabel, fontsize=ylabel_fontsize)\n",
+    "\n",
+    "    ## Setting the title\n",
+    "    if title is not None:\n",
+    "        ax.set_title(title, fontsize=title_fontsize)\n",
+    "\n",
+    "    ## Adjust Spines\n",
     "    if remove_spines:\n",
-    "        for spine in ax.spines.values():\n",
-    "            spine.set_visible(False)\n",
+    "        spines = [\"top\", \"right\", \"left\"] if horizontal else [\"top\", \"right\", \"bottom\"]\n",
+    "        ax.spines[spines].set_visible(False)\n",
     "\n",
-    "    # Apply additional customizations if provided\n",
-    "    if additional_plotting_kwargs:\n",
-    "        ax.set(**additional_plotting_kwargs)\n",
+    "    # Delta Text\n",
+    "    if delta_text:\n",
+    "        if delta_text_kwargs.get('color') is not None:\n",
+    "            delta_text_colors = [delta_text_kwargs.pop('color')] * number_of_curves_to_plot\n",
+    "        else:\n",
+    "            delta_text_colors = violin_colors\n",
+    "            delta_text_kwargs.pop('color')\n",
+    "\n",
+    "        # Collect the X-coordinates for the delta text\n",
+    "        delta_text_x_coordinates = delta_text_kwargs.pop('x_coordinates')\n",
+    "        delta_text_x_adjustment = delta_text_kwargs.pop('offset')\n",
+    "\n",
+    "        if delta_text_x_coordinates is not None:\n",
+    "            if not isinstance(delta_text_x_coordinates, (list, tuple)) or not all(isinstance(x, (int, float)) for x in delta_text_x_coordinates):\n",
+    "                raise TypeError(\"delta_text_kwargs['x_coordinates'] must be a list of x-coordinates.\")\n",
+    "            if len(delta_text_x_coordinates) != number_of_curves_to_plot:\n",
+    "                raise ValueError(\"delta_text_kwargs['x_coordinates'] must have the same length as the number of ticks to plot.\")\n",
+    "        else:\n",
+    "            delta_text_x_coordinates = (np.arange(1, number_of_curves_to_plot + 1) \n",
+    "                                        + (0.5 if not horizontal else -0.4)\n",
+    "                                        + delta_text_x_adjustment\n",
+    "                                    )\n",
+    "\n",
+    "        # Collect the Y-coordinates for the delta text\n",
+    "        delta_text_y_coordinates = delta_text_kwargs.pop('y_coordinates')\n",
+    "\n",
+    "        if delta_text_y_coordinates is not None:\n",
+    "            if not isinstance(delta_text_y_coordinates, (list, tuple)) or not all(isinstance(y, (int, float)) for y in delta_text_y_coordinates):\n",
+    "                raise TypeError(\"delta_text_kwargs['y_coordinates'] must be a list of y-coordinates.\")\n",
+    "            if len(delta_text_y_coordinates) != number_of_curves_to_plot:\n",
+    "                raise ValueError(\"delta_text_kwargs['y_coordinates'] must have the same length as the number of ticks to plot.\")\n",
+    "        else:\n",
+    "            delta_text_y_coordinates = differences\n",
+    "\n",
+    "        if horizontal:\n",
+    "            delta_text_x_coordinates, delta_text_y_coordinates = delta_text_y_coordinates, delta_text_x_coordinates\n",
+    "\n",
+    "        for idx, x, y, delta in zip(np.arange(0, number_of_curves_to_plot, 1), delta_text_x_coordinates, \n",
+    "                                    delta_text_y_coordinates, differences):\n",
+    "            delta_text = np.format_float_positional(delta, precision=2, sign=True, trim=\"k\", min_digits=2)\n",
+    "            ax.text(x, y, delta_text, color=delta_text_colors[idx], zorder=5, **delta_text_kwargs)\n",
+    "\n",
+    "    # Contrast bars\n",
+    "    if contrast_bars:\n",
+    "        _bar_color = contrast_bars_kwargs.pop('color')\n",
+    "        if _bar_color is not None:\n",
+    "            bar_colors = [_bar_color] * number_of_curves_to_plot\n",
+    "        else:\n",
+    "            bar_colors = violin_colors\n",
+    "        for x, y in zip(np.arange(1, number_of_curves_to_plot + 1), differences):\n",
+    "            if horizontal:\n",
+    "                ax.add_patch(mpatches.Rectangle((0, x-0.25), y, 0.25, color=bar_colors[x-1], **contrast_bars_kwargs))\n",
+    "            else:\n",
+    "                ax.add_patch(mpatches.Rectangle((x, 0), 0.25, y, color=bar_colors[x-1], **contrast_bars_kwargs))\n",
+    "\n",
+    "    # Reference band\n",
+    "    if reference_band:\n",
+    "        _bar_color = reference_band_kwargs.pop('color')\n",
+    "        if _bar_color is not None:\n",
+    "            bar_colors = [_bar_color] * number_of_curves_to_plot\n",
+    "        else:\n",
+    "            bar_colors = violin_colors\n",
+    "\n",
+    "        span_ax = reference_band_kwargs.pop(\"span_ax\")\n",
+    "        summary_xmin, summary_xmax = ax.get_xlim()\n",
+    "        summary_ymin, summary_ymax = ax.get_ylim()\n",
+    "\n",
+    "        for summary_index in reference_band:\n",
+    "            if span_ax == True:\n",
+    "                starting_location = summary_ymin if horizontal else summary_xmin\n",
+    "            else:\n",
+    "                starting_location = summary_index+1   \n",
+    "\n",
+    "            summary_color = bar_colors[summary_index]\n",
+    "            summary_ci_low, summary_ci_high = bcalows[summary_index], bcahighs[summary_index]\n",
+    "\n",
+    "            if horizontal:\n",
+    "                ax.add_patch(mpatches.Rectangle(\n",
+    "                    (summary_ci_low, starting_location),\n",
+    "                    summary_ci_high-summary_ci_low, summary_ymax+1, \n",
+    "                    color=summary_color, \n",
+    "                    **reference_band_kwargs)\n",
+    "                    )\n",
+    "            else:\n",
+    "                ax.add_patch(mpatches.Rectangle(\n",
+    "                    (starting_location, summary_ci_low),\n",
+    "                    summary_xmax+1, summary_ci_high-summary_ci_low, \n",
+    "                    color=summary_color, \n",
+    "                    **reference_band_kwargs)\n",
+    "                    )\n",
+    "\n",
+    "    ## Invert Y-axis if horizontal \n",
+    "    if horizontal:\n",
+    "        ax.invert_yaxis()\n",
     "\n",
     "    return fig"
    ]
diff --git a/nbs/API/load.ipynb b/nbs/API/load.ipynb
index c628b30a..c71302db 100644
--- a/nbs/API/load.ipynb
+++ b/nbs/API/load.ipynb
@@ -66,6 +66,7 @@
     "    experiment_label=None,\n",
     "    x1_level=None,\n",
     "    mini_meta=False,\n",
+    "    ps_adjust=False,\n",
     "):\n",
     "    \"\"\"\n",
     "    Loads data in preparation for estimation statistics.\n",
@@ -126,6 +127,9 @@
     "        is True; otherwise it can only be a string.\n",
     "    mini_meta : boolean, default False\n",
     "        Indicator of weighted delta calculation.\n",
+    "    ps_adjust : boolean, default False\n",
+    "        Indicator of whether to adjust calculated p-value according to Phipson & Smyth (2010)\n",
+    "        # https://doi.org/10.2202/1544-6115.1585\n",
     "\n",
     "    Returns\n",
     "    -------\n",
@@ -149,6 +153,7 @@
     "        experiment_label,\n",
     "        x1_level,\n",
     "        mini_meta,\n",
+    "        ps_adjust,\n",
     "    )"
    ]
   },
diff --git a/nbs/API/misc_tools.ipynb b/nbs/API/misc_tools.ipynb
index 0395a57c..a1075102 100644
--- a/nbs/API/misc_tools.ipynb
+++ b/nbs/API/misc_tools.ipynb
@@ -49,19 +49,26 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "5f54be1c",
+   "id": "3c9a6ef1",
    "metadata": {},
    "outputs": [],
    "source": [
     "#| export\n",
+    "\n",
     "import datetime as dt\n",
-    "from numpy import repeat"
+    "import numpy as np\n",
+    "from numpy import repeat\n",
+    "import pandas as pd\n",
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib\n",
+    "import matplotlib.axes as axes"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "6b50da46",
+   "id": "5f54be1c",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -125,8 +132,1894 @@
     "    matching_vars = [k for k, v in globals().items() if v is obj]\n",
     "    if len(matching_vars) > 0:\n",
     "        return matching_vars[0]\n",
-    "    return \"\""
+    "    return \"\"\n",
+    "\t\n",
+    "\n",
+    "def get_unique_categories(names):\n",
+    "    \"\"\"\n",
+    "    Extract unique categories from various input types.\n",
+    "    \"\"\"\n",
+    "    if isinstance(names, list):\n",
+    "        return names\n",
+    "    if isinstance(names, np.ndarray):\n",
+    "        return names  # numpy.unique() returns a sorted array\n",
+    "    elif isinstance(names, (pd.Categorical, pd.Series)):\n",
+    "        return names.cat.categories if hasattr(names, 'cat') else names.unique()\n",
+    "    else:\n",
+    "        # For dict_keys and other iterables\n",
+    "        return np.unique(list(names))\n",
+    "\n",
+    "def get_params(\n",
+    "        effectsize_df: object, \n",
+    "        plot_kwargs: dict,\n",
+    "        sankey_kwargs: dict,\n",
+    "        barplot_kwargs: dict\n",
+    "    ):\n",
+    "    \"\"\"\n",
+    "    Extracts parameters from the `effectsize_df` and `plot_kwargs` objects for use in the plotter function.\n",
+    "    \n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    effectsize_df : object\n",
+    "        A `dabest` EffectSizeDataFrame object.\n",
+    "    plot_kwargs : dict\n",
+    "        Kwargs passed to the plot function.\n",
+    "    sankey kwargs : dict\n",
+    "        Kwargs relating to the sankey diagram plots\n",
+    "    barplot_kwargs : dict\n",
+    "        Kwargs relating to the barplot\n",
+    "    \"\"\"\n",
+    "    plot_data = effectsize_df._plot_data\n",
+    "    xvar = effectsize_df.xvar\n",
+    "    yvar = effectsize_df.yvar\n",
+    "    is_paired = effectsize_df.is_paired\n",
+    "    delta2 = effectsize_df.delta2\n",
+    "    is_mini_meta = effectsize_df.is_mini_meta\n",
+    "    effect_size = effectsize_df.effect_size\n",
+    "    proportional = effectsize_df.is_proportional\n",
+    "    results = effectsize_df.results\n",
+    "    dabest_obj = effectsize_df.dabest_obj\n",
+    "    all_plot_groups = dabest_obj._all_plot_groups\n",
+    "    idx = dabest_obj.idx\n",
+    "    x1_level = dabest_obj.x1_level\n",
+    "    experiment_label = dabest_obj.experiment_label\n",
+    "    \n",
+    "    if effect_size not in [\"mean_diff\", \"hedges_g\"] or not delta2:\n",
+    "        show_delta2 = False\n",
+    "    else:\n",
+    "        show_delta2 = plot_kwargs[\"show_delta2\"]\n",
+    "\n",
+    "    if effect_size != \"mean_diff\" or not is_mini_meta:\n",
+    "        show_mini_meta = False\n",
+    "    else:\n",
+    "        show_mini_meta = plot_kwargs[\"show_mini_meta\"]\n",
+    "\n",
+    "    if show_delta2 and show_mini_meta: raise ValueError(\"`show_delta2` and `show_mini_meta` cannot be True at the same time.\")\n",
+    "\n",
+    "   # Horizontal\n",
+    "    horizontal = plot_kwargs[\"horizontal\"]\n",
+    "\n",
+    "    # Disable Gardner-Altman plotting if any of the idxs comprise of more than\n",
+    "    # two groups or if it is a delta-delta plot.\n",
+    "    float_contrast = plot_kwargs[\"float_contrast\"]\n",
+    "    if len(idx) > 1 or len(idx[0]) > 2:\n",
+    "        float_contrast = False\n",
+    "\n",
+    "    if effect_size in [\"cliffs_delta\"]:\n",
+    "        float_contrast = False\n",
+    "\n",
+    "    if show_delta2 or show_mini_meta or horizontal:\n",
+    "        float_contrast = False\n",
+    "\n",
+    "    if not is_paired:\n",
+    "        show_pairs = False\n",
+    "    else:\n",
+    "        show_pairs = plot_kwargs[\"show_pairs\"]\n",
+    "\n",
+    "    # Group summaries\n",
+    "    group_summaries = plot_kwargs[\"group_summaries\"]\n",
+    "    group_summaries = None if barplot_kwargs['errorbar'] is not None else group_summaries\n",
+    "\n",
+    "    # Contrast Axes kwargs\n",
+    "    ci_type = plot_kwargs[\"ci_type\"]\n",
+    "    if ci_type not in [\"bca\", \"pct\"]:\n",
+    "        raise ValueError(\"Invalid `ci_type`. Must be either 'bca' or 'pct'.\")\n",
+    "        \n",
+    "    # Boolean for showing Baseline Curve\n",
+    "    show_baseline_ec = plot_kwargs[\"show_baseline_ec\"]\n",
+    "\n",
+    "    # Sankey details\n",
+    "    # We need to extract the `sankey` and `flow` from the kwargs\n",
+    "    # to use for varying different kinds of paired proportional plots\n",
+    "    # We also don't want to pop the parameter from the kwargs\n",
+    "    one_sankey = (\n",
+    "        False if is_paired is not None else None\n",
+    "    )  # Flag to indicate if only one sankey is plotted.\n",
+    "    two_col_sankey = (\n",
+    "        True if proportional and not one_sankey and sankey_kwargs[\"sankey\"] and not sankey_kwargs[\"flow\"] else False\n",
+    "    )\n",
+    "\n",
+    "    # Asymmetric side for swarmplots\n",
+    "    asymmetric_side = (\n",
+    "        plot_kwargs[\"swarm_side\"]  # Default asymmetric side is right\n",
+    "        if plot_kwargs[\"swarm_side\"] is not None\n",
+    "        else \"right\" if not horizontal\n",
+    "        else \"left\"\n",
+    "    )  \n",
+    "    # Whether to show sample sizes with ticklabels\n",
+    "    show_sample_size = plot_kwargs[\"show_sample_size\"]\n",
+    "        \n",
+    "    return (dabest_obj, plot_data, xvar, yvar, is_paired, effect_size, proportional, all_plot_groups, \n",
+    "            idx, show_delta2, show_mini_meta, float_contrast, show_pairs, group_summaries, \n",
+    "            horizontal, results, ci_type, x1_level, experiment_label, show_baseline_ec, \n",
+    "            one_sankey, two_col_sankey, asymmetric_side, show_sample_size)\n",
+    "\n",
+    "def get_kwargs(\n",
+    "        plot_kwargs: dict, \n",
+    "        ytick_color\n",
+    "    ):\n",
+    "    \"\"\"\n",
+    "    Extracts the kwargs from the `plot_kwargs` object for use in the plotter function.\n",
+    "\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    plot_kwargs : dict\n",
+    "        Kwargs passed to the plot function.\n",
+    "    ytick_color : str or color list\n",
+    "        Color of the yticks.\n",
+    "    \"\"\"\n",
+    "    from .misc_tools import merge_two_dicts\n",
+    "\n",
+    "    # Swarmplot kwargs\n",
+    "    default_swarmplot_kwargs = {\n",
+    "        \"size\": plot_kwargs[\"raw_marker_size\"],\n",
+    "        \"alpha\": plot_kwargs[\"raw_alpha\"],\n",
+    "        \"fontsize\": plot_kwargs.get(\"fontsize_rawxlabel\"),\n",
+    "    }\n",
+    "    if plot_kwargs[\"swarmplot_kwargs\"] is None:\n",
+    "        swarmplot_kwargs = default_swarmplot_kwargs\n",
+    "    else:\n",
+    "        swarmplot_kwargs = merge_two_dicts(\n",
+    "            default_swarmplot_kwargs, plot_kwargs[\"swarmplot_kwargs\"]\n",
+    "        )\n",
+    "\n",
+    "    # Barplot kwargs\n",
+    "    default_barplot_kwargs = {\n",
+    "        \"estimator\": np.mean, \n",
+    "        \"errorbar\": None,\n",
+    "        \"width\": plot_kwargs[\"bar_width\"],\n",
+    "        \"alpha\": plot_kwargs[\"raw_alpha\"],\n",
+    "        \"err_kws\": {'color': 'black'},\n",
+    "        \"fontsize\": plot_kwargs[\"fontsize_rawxlabel\"]\n",
+    "    }\n",
+    "    if plot_kwargs[\"barplot_kwargs\"] is None:\n",
+    "        barplot_kwargs = default_barplot_kwargs\n",
+    "    else:\n",
+    "        barplot_kwargs = merge_two_dicts(\n",
+    "            default_barplot_kwargs, plot_kwargs[\"barplot_kwargs\"]\n",
+    "        )\n",
+    "\n",
+    "    # Sankey Diagram kwargs\n",
+    "    default_sankey_kwargs = {\n",
+    "        \"width\": 0.4,\n",
+    "        \"align\": \"center\",\n",
+    "        \"sankey\": True,\n",
+    "        \"flow\": True,\n",
+    "        \"alpha\": plot_kwargs['raw_alpha'],\n",
+    "        \"rightColor\": False,\n",
+    "        \"bar_width\": 0.2,\n",
+    "        \"fontsize\": plot_kwargs.get(\"fontsize_rawxlabel\")\n",
+    "    }\n",
+    "    if plot_kwargs[\"sankey_kwargs\"] is None:\n",
+    "        sankey_kwargs = default_sankey_kwargs\n",
+    "    else:\n",
+    "        sankey_kwargs = merge_two_dicts(\n",
+    "            default_sankey_kwargs, plot_kwargs[\"sankey_kwargs\"]\n",
+    "        )\n",
+    "\n",
+    "    # Violinplot kwargs.\n",
+    "    default_contrast_kwargs = {\n",
+    "        \"widths\": 0.5,\n",
+    "        \"orientation\": 'vertical',\n",
+    "        \"showextrema\": False,\n",
+    "        \"showmedians\": False,\n",
+    "        \"alpha\": plot_kwargs[\"contrast_alpha\"],\n",
+    "        \n",
+    "    }\n",
+    "    if plot_kwargs[\"contrast_kwargs\"] is None:\n",
+    "        contrast_kwargs = default_contrast_kwargs\n",
+    "    else:\n",
+    "        contrast_kwargs = merge_two_dicts(\n",
+    "            default_contrast_kwargs, plot_kwargs[\"contrast_kwargs\"]\n",
+    "        )\n",
+    "\n",
+    "    # Slopegraph kwargs.\n",
+    "    default_slopegraph_kwargs = {\n",
+    "        \"linewidth\": 1, \n",
+    "        \"alpha\": plot_kwargs[\"raw_alpha\"],\n",
+    "        'jitter': 0, \n",
+    "        'jitter_seed': 9876543210,\n",
+    "    }\n",
+    "    if plot_kwargs[\"slopegraph_kwargs\"] is None:\n",
+    "        slopegraph_kwargs = default_slopegraph_kwargs\n",
+    "    else:\n",
+    "        slopegraph_kwargs = merge_two_dicts(\n",
+    "            default_slopegraph_kwargs, plot_kwargs[\"slopegraph_kwargs\"]\n",
+    "        )\n",
+    "\n",
+    "    # Zero reference-line kwargs.\n",
+    "    default_reflines_kwargs = {\n",
+    "        \"linestyle\": \"solid\",\n",
+    "        \"linewidth\": 0.75,\n",
+    "        \"zorder\": 2,\n",
+    "        \"color\": ytick_color,\n",
+    "    }\n",
+    "    if plot_kwargs[\"reflines_kwargs\"] is None:\n",
+    "        reflines_kwargs = default_reflines_kwargs\n",
+    "    else:\n",
+    "        reflines_kwargs = merge_two_dicts(\n",
+    "            default_reflines_kwargs, plot_kwargs[\"reflines_kwargs\"]\n",
+    "        )\n",
+    "\n",
+    "    # Legend kwargs.\n",
+    "    default_legend_kwargs = {\n",
+    "        \"loc\": \"upper left\", \n",
+    "        \"frameon\": False,\n",
+    "    }\n",
+    "    if plot_kwargs[\"legend_kwargs\"] is None:\n",
+    "        legend_kwargs = default_legend_kwargs\n",
+    "    else:\n",
+    "        legend_kwargs = merge_two_dicts(\n",
+    "            default_legend_kwargs, plot_kwargs[\"legend_kwargs\"]\n",
+    "        )\n",
+    "\n",
+    "    # Group summaries kwargs.\n",
+    "    gs_default = {\n",
+    "        \"mean_sd\", \n",
+    "        \"median_quartiles\", \n",
+    "        None\n",
+    "    }\n",
+    "    if plot_kwargs[\"group_summaries\"] not in gs_default:\n",
+    "        raise ValueError(\n",
+    "            \"group_summaries must be one of\" \" these: {}.\".format(gs_default)\n",
+    "        )\n",
+    "\n",
+    "    default_group_summaries_kwargs = {\n",
+    "                        \"zorder\": 3, \n",
+    "                        \"lw\": 2, \n",
+    "                        \"alpha\": 1,\n",
+    "                        'gap_width_percent': 1.5,\n",
+    "                        'offset': 0.1,\n",
+    "                        'color': None\n",
+    "    }\n",
+    "    if plot_kwargs[\"group_summaries_kwargs\"] is None:\n",
+    "        group_summaries_kwargs = default_group_summaries_kwargs\n",
+    "    else:\n",
+    "        group_summaries_kwargs = merge_two_dicts(\n",
+    "            default_group_summaries_kwargs, plot_kwargs[\"group_summaries_kwargs\"]\n",
+    "        )\n",
+    "\n",
+    "    # Redraw axes kwargs.\n",
+    "    redraw_axes_kwargs = {\n",
+    "        \"colors\": ytick_color,\n",
+    "        \"facecolors\": ytick_color,\n",
+    "        \"lw\": 1,\n",
+    "        \"zorder\": 10,\n",
+    "        \"clip_on\": False,\n",
+    "    }\n",
+    "    \n",
+    "    # Delta dots kwargs.\n",
+    "    default_delta_dot_kwargs = {\n",
+    "                \"color\": 'k',\n",
+    "                \"marker\": \"^\", \n",
+    "                \"alpha\": 0.5, \n",
+    "                \"zorder\": 2, \n",
+    "                \"size\": 3, \n",
+    "                \"side\": \"right\"\n",
+    "    }\n",
+    "    if plot_kwargs[\"delta_dot_kwargs\"] is None:\n",
+    "        delta_dot_kwargs = default_delta_dot_kwargs\n",
+    "    else:\n",
+    "        delta_dot_kwargs = merge_two_dicts(default_delta_dot_kwargs, plot_kwargs[\"delta_dot_kwargs\"])\n",
+    "\n",
+    "    # Delta text kwargs.\n",
+    "    default_delta_text_kwargs = {\n",
+    "                \"alpha\": 1,\n",
+    "                \"fontsize\": 10, \n",
+    "                \"ha\": 'center', \n",
+    "                \"va\": 'center', \n",
+    "                \"rotation\": 0, \n",
+    "                \"x_coordinates\": None, \n",
+    "                \"y_coordinates\": None,\n",
+    "                \"offset\": 0\n",
+    "    }\n",
+    "    if plot_kwargs[\"delta_text_kwargs\"] is None:\n",
+    "        delta_text_kwargs = default_delta_text_kwargs\n",
+    "    else:\n",
+    "        delta_text_kwargs = merge_two_dicts(default_delta_text_kwargs, plot_kwargs[\"delta_text_kwargs\"])\n",
+    "\n",
+    "    # Reference band kwargs.\n",
+    "    default_reference_band_kwargs = {\n",
+    "                    \"span_ax\": False,\n",
+    "                    \"alpha\": 0.15,\n",
+    "                    \"zorder\":-3\n",
+    "    }\n",
+    "    if plot_kwargs[\"reference_band_kwargs\"] is None:\n",
+    "        reference_band_kwargs = default_reference_band_kwargs\n",
+    "    else:\n",
+    "        reference_band_kwargs = merge_two_dicts(default_reference_band_kwargs, plot_kwargs[\"reference_band_kwargs\"])\n",
+    "\n",
+    "    # Swarm bars kwargs.\n",
+    "    default_raw_bars_kwargs = {\n",
+    "                    \"zorder\":-3,\n",
+    "                    \"alpha\": 0.2\n",
+    "    }\n",
+    "    if plot_kwargs[\"raw_bars_kwargs\"] is None:\n",
+    "        raw_bars_kwargs = default_raw_bars_kwargs\n",
+    "    else:\n",
+    "        raw_bars_kwargs = merge_two_dicts(default_raw_bars_kwargs, plot_kwargs[\"raw_bars_kwargs\"])\n",
+    "\n",
+    "    # Contrast bars kwargs.\n",
+    "    default_contrast_bars_kwargs = {\n",
+    "                    \"zorder\":-3,\n",
+    "                    \"alpha\": 0.2\n",
+    "    }\n",
+    "    if plot_kwargs[\"contrast_bars_kwargs\"] is None:\n",
+    "        contrast_bars_kwargs = default_contrast_bars_kwargs\n",
+    "    else:\n",
+    "        contrast_bars_kwargs = merge_two_dicts(default_contrast_bars_kwargs, plot_kwargs[\"contrast_bars_kwargs\"])\n",
+    "\n",
+    "    # Table axes for horizontal plot kwargs.\n",
+    "    default_table_kwargs = {\n",
+    "                'show': True,\n",
+    "                'color' : 'yellow',\n",
+    "                'alpha' : 0.2,\n",
+    "                'fontsize' : 12,\n",
+    "                'text_color' : 'black', \n",
+    "                'text_units' : '',\n",
+    "                'control_marker' : '-',\n",
+    "                'fontsize_label': 12,\n",
+    "                'label': 'Δ'\n",
+    "    }\n",
+    "    if plot_kwargs[\"horizontal_table_kwargs\"] is None:\n",
+    "        table_kwargs = default_table_kwargs\n",
+    "    else:\n",
+    "        table_kwargs = merge_two_dicts(default_table_kwargs, plot_kwargs[\"horizontal_table_kwargs\"])\n",
+    "\n",
+    "    # Gridkey kwargs.\n",
+    "    default_gridkey_kwargs = {\n",
+    "                'show_es' : plot_kwargs['gridkey_show_es'],           # If True, the gridkey will show the effect size of each comparison.\n",
+    "                'show_Ns' : plot_kwargs['gridkey_show_Ns'],           # If True, the gridkey will show the number of observations in eachgroup.\n",
+    "                'merge_pairs' : plot_kwargs['gridkey_merge_pairs'],   # If True, the gridkey will merge the pairs of groups into a single cell. This is useful for when the groups are paired.\n",
+    "                'delimiters': plot_kwargs['gridkey_delimiters'],      # Delimiters to split the group names.\n",
+    "                'marker': \"\\u25CF\",                                   # Marker for the gridkey dots.\n",
+    "    }\n",
+    "    if plot_kwargs[\"gridkey_kwargs\"] is None:\n",
+    "        gridkey_kwargs = default_gridkey_kwargs\n",
+    "    else:\n",
+    "        gridkey_kwargs = merge_two_dicts(default_gridkey_kwargs, plot_kwargs[\"gridkey_kwargs\"])\n",
+    "\n",
+    "    # Effect size marker kwargs\n",
+    "    default_contrast_marker_kwargs = {\n",
+    "                'marker': 'o',\n",
+    "                'markersize': plot_kwargs['contrast_marker_size'],\n",
+    "                'color': ytick_color,\n",
+    "                'alpha': 1,\n",
+    "                'zorder': 2,\n",
+    "    }\n",
+    "    if plot_kwargs['contrast_marker_kwargs'] is None:\n",
+    "        contrast_marker_kwargs = default_contrast_marker_kwargs\n",
+    "    else:\n",
+    "        contrast_marker_kwargs = merge_two_dicts(default_contrast_marker_kwargs, plot_kwargs['contrast_marker_kwargs'])\n",
+    "\n",
+    "    # Effect size error bar kwargs\n",
+    "    default_contrast_errorbar_kwargs = {\n",
+    "                'color': ytick_color,\n",
+    "                'lw': 2,\n",
+    "                'linestyle': '-',\n",
+    "                'alpha': 1,\n",
+    "                'zorder': 1,\n",
+    "    }\n",
+    "    if plot_kwargs['contrast_errorbar_kwargs'] is None:\n",
+    "        contrast_errorbar_kwargs = default_contrast_errorbar_kwargs\n",
+    "    else:\n",
+    "        contrast_errorbar_kwargs = merge_two_dicts(default_contrast_errorbar_kwargs, plot_kwargs['contrast_errorbar_kwargs'])\n",
+    "\n",
+    "    # Prop sample counts kwargs\n",
+    "    default_prop_sample_counts_kwargs = {\n",
+    "                                        'color': 'k', \n",
+    "                                        'zorder': 5, \n",
+    "                                        'ha': 'center', \n",
+    "                                        'va': 'center'\n",
+    "    }\n",
+    "    if plot_kwargs['prop_sample_counts_kwargs'] is None:\n",
+    "        prop_sample_counts_kwargs = default_prop_sample_counts_kwargs\n",
+    "    else:\n",
+    "        prop_sample_counts_kwargs = merge_two_dicts(default_prop_sample_counts_kwargs, plot_kwargs['prop_sample_counts_kwargs'])\n",
+    "\n",
+    "\n",
+    "    # RM Lines kwargs\n",
+    "    default_contrast_paired_lines_kwargs = {\n",
+    "        \"linestyle\": \"-\",\n",
+    "        \"linewidth\": 2,\n",
+    "        \"zorder\": -2,\n",
+    "        \"color\": 'dimgray',\n",
+    "        \"alpha\": 1\n",
+    "    }\n",
+    "    if plot_kwargs[\"contrast_paired_lines_kwargs\"] is None:\n",
+    "        contrast_paired_lines_kwargs = default_contrast_paired_lines_kwargs\n",
+    "    else:\n",
+    "        contrast_paired_lines_kwargs = merge_two_dicts(default_contrast_paired_lines_kwargs, plot_kwargs[\"contrast_paired_lines_kwargs\"])\n",
+    "\n",
+    "    # Return the kwargs.\n",
+    "    return (swarmplot_kwargs, barplot_kwargs, sankey_kwargs, contrast_kwargs, slopegraph_kwargs, \n",
+    "            reflines_kwargs, legend_kwargs, group_summaries_kwargs, redraw_axes_kwargs, delta_dot_kwargs,\n",
+    "            delta_text_kwargs, reference_band_kwargs, raw_bars_kwargs, contrast_bars_kwargs, table_kwargs, gridkey_kwargs,\n",
+    "            contrast_marker_kwargs, contrast_errorbar_kwargs, prop_sample_counts_kwargs, contrast_paired_lines_kwargs)\n",
+    "\n",
+    "\n",
+    "def get_color_palette(\n",
+    "        plot_kwargs: dict, \n",
+    "        plot_data: pd.DataFrame, \n",
+    "        xvar: str, \n",
+    "        show_pairs: bool, \n",
+    "        idx: list, \n",
+    "        all_plot_groups: list,\n",
+    "        delta2: bool,\n",
+    "        sankey: bool\n",
+    "    ):\n",
+    "    \"\"\"\n",
+    "    Create the color palette to be used in the plotter function.\n",
+    "\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    plot_kwargs : dict\n",
+    "        Kwargs passed to the plot function.\n",
+    "    plot_data : object (Dataframe)\n",
+    "        A dataframe of plot data.\n",
+    "    xvar : str\n",
+    "        The name of the x-axis variable.\n",
+    "    show_pairs : bool\n",
+    "        A boolean flag to determine if the plot is for paired data.\n",
+    "    idx : list\n",
+    "        A list of tuples containing the group names.\n",
+    "    all_plot_groups : list\n",
+    "        A list of all the group names.\n",
+    "    delta2 : bool\n",
+    "        A boolean flag to determine if the plot will have a delta-delta effect size.\n",
+    "    sankey : bool\n",
+    "        A boolean flag to determine if the plot is for a Sankey diagram.\n",
+    "    \"\"\"\n",
+    "    # Create color palette that will be shared across subplots.\n",
+    "    color_col = plot_kwargs[\"color_col\"]\n",
+    "    if color_col is None:\n",
+    "        color_groups = pd.unique(plot_data[xvar])\n",
+    "        bootstraps_color_by_group = True\n",
+    "    else:\n",
+    "        if color_col not in plot_data.columns:\n",
+    "            raise KeyError(\"``{}`` is not a column in the data.\".format(color_col))\n",
+    "        color_groups = pd.unique(plot_data[color_col])\n",
+    "        bootstraps_color_by_group = False\n",
+    "    if show_pairs:\n",
+    "        bootstraps_color_by_group = False\n",
+    "\n",
+    "    # Handle the color palette.\n",
+    "    filled = True\n",
+    "    empty_circle = plot_kwargs[\"empty_circle\"]\n",
+    "    color_by_subgroups = (\n",
+    "        True if empty_circle else False\n",
+    "    )  # boolean flag to determine if colour is being grouped by subgroup or the default\n",
+    "    if empty_circle:\n",
+    "        # Handling color_by_subgroups\n",
+    "        # For now, color_by_subgroups can only be True for multi-2-group and 2-group comparison\n",
+    "        if isinstance(idx[0], str):\n",
+    "            if len(idx) > 2:\n",
+    "                color_by_subgroups = False\n",
+    "        else:\n",
+    "            for group_i in idx:\n",
+    "                if len(group_i) > 2:\n",
+    "                    color_by_subgroups = False\n",
+    "\n",
+    "        # filled is now a list, which determines the which group in idx has their dots filled for the swarmplot\n",
+    "        filled = []\n",
+    "        for i in range(len(idx)):\n",
+    "            filled.append(False)\n",
+    "            filled.extend([True] * (len(idx[i]) - 1))\n",
+    "\n",
+    "    if color_col is not None:\n",
+    "        if sankey:\n",
+    "            names = [1, 0]\n",
+    "        else:\n",
+    "            names = color_groups if not color_by_subgroups else idx\n",
+    "    else:\n",
+    "        if sankey:\n",
+    "            names = [1, 0]\n",
+    "        else:\n",
+    "            names = all_plot_groups if not color_by_subgroups else idx\n",
+    "\n",
+    "    n_groups = len(color_groups)\n",
+    "    custom_pal = plot_kwargs[\"custom_palette\"]\n",
+    "    raw_desat = plot_kwargs[\"raw_desat\"]\n",
+    "    contrast_desat = plot_kwargs[\"contrast_desat\"]\n",
+    "\n",
+    "    if custom_pal is None:\n",
+    "        unsat_colors = sns.color_palette(n_colors=n_groups)\n",
+    "        if empty_circle and not color_by_subgroups:\n",
+    "            unsat_colors = [sns.color_palette(\"gray\")[3]] + unsat_colors\n",
+    "    else:\n",
+    "        if isinstance(custom_pal, dict):\n",
+    "            if delta2:\n",
+    "                groups_in_palette = {\n",
+    "                    k: custom_pal[k] for k in color_groups\n",
+    "                }\n",
+    "            elif sankey:\n",
+    "                groups_in_palette = {\n",
+    "                    k: custom_pal[k] for k in [1, 0]\n",
+    "                }\n",
+    "            elif color_col is None:\n",
+    "                groups_in_palette = {\n",
+    "                    k: custom_pal[k] for k in all_plot_groups if k in color_groups\n",
+    "                }\n",
+    "            else:\n",
+    "                raise ValueError(\"The `custom_palette` dictionary is not supported when `color_col` is not None.\")\n",
+    "\n",
+    "            names = groups_in_palette.keys()\n",
+    "            unsat_colors = groups_in_palette.values()\n",
+    "\n",
+    "        elif isinstance(custom_pal, list):\n",
+    "            if sankey:\n",
+    "                if len(custom_pal) != 2:\n",
+    "                    raise ValueError(\"To specify a custom palette for a Sankey diagram, you must provide exactly two colors.\")\n",
+    "                else:\n",
+    "                    groups_in_palette = {\n",
+    "                        k: custom_pal[k] for k in [1, 0]\n",
+    "                    }\n",
+    "                    names = groups_in_palette.keys()\n",
+    "                    unsat_colors = groups_in_palette.values()\n",
+    "            elif len(custom_pal) < n_groups:\n",
+    "                err1 = \"The specified `custom_palette` has fewer colors than the number of groups.\"\n",
+    "                err2 = \" Please specify a custom palette with at least {} colors.\".format(n_groups)\n",
+    "                raise ValueError(err1 + err2)\n",
+    "            else:\n",
+    "                unsat_colors = custom_pal[0:n_groups]\n",
+    "\n",
+    "        elif isinstance(custom_pal, str):\n",
+    "            # check it is in the list of matplotlib palettes.\n",
+    "            if custom_pal in plt.colormaps():\n",
+    "                unsat_colors = sns.color_palette(custom_pal, n_groups)\n",
+    "            else:\n",
+    "                err1 = \"The specified `custom_palette` {}\".format(custom_pal)\n",
+    "                err2 = \" is not a matplotlib palette. Please check.\"\n",
+    "                raise ValueError(err1 + err2)\n",
+    "\n",
+    "    if custom_pal is None and color_col is None:\n",
+    "        categories = get_unique_categories(names)\n",
+    "        raw_colors = [sns.desaturate(c, raw_desat) for c in unsat_colors]\n",
+    "        contrast_colors = [sns.desaturate(c, contrast_desat) for c in unsat_colors]\n",
+    "        if color_by_subgroups:\n",
+    "            plot_palette_raw = dict()\n",
+    "            plot_palette_contrast = dict()\n",
+    "            for i in range(len(idx)):\n",
+    "                for names_i in idx[i]:\n",
+    "                    plot_palette_raw[names_i] = raw_colors[i]\n",
+    "                    plot_palette_contrast[names_i] = contrast_colors[i]\n",
+    "        else:\n",
+    "            plot_palette_raw = dict(zip(categories, raw_colors))\n",
+    "            plot_palette_contrast = dict(zip(categories, contrast_colors))\n",
+    "    else:\n",
+    "        raw_colors = [sns.desaturate(c, raw_desat) for c in unsat_colors]\n",
+    "        contrast_colors = [sns.desaturate(c, contrast_desat) for c in unsat_colors]\n",
+    "        if color_by_subgroups:\n",
+    "            plot_palette_raw = dict()\n",
+    "            plot_palette_contrast = dict()\n",
+    "            for i in range(len(idx)):\n",
+    "                for names_i in idx[i]:\n",
+    "                    plot_palette_raw[names_i] = raw_colors[i]\n",
+    "                    plot_palette_contrast[names_i] = contrast_colors[i]\n",
+    "        else:\n",
+    "            plot_palette_raw = dict(zip(names, raw_colors))\n",
+    "            plot_palette_contrast = dict(zip(names, contrast_colors))\n",
+    "            plot_palette_sankey = dict(zip(names, unsat_colors))\n",
+    "\n",
+    "    # For Sankey Diagram plot, each bar will have the same two colors if custom_pal is None\n",
+    "    # default color palette will be set to \"hls\"\n",
+    "    if custom_pal is None:\n",
+    "        plot_palette_sankey = None\n",
+    "\n",
+    "    return (color_col, bootstraps_color_by_group, n_groups, filled, raw_colors,\n",
+    "            plot_palette_raw, plot_palette_contrast, plot_palette_sankey)\n",
+    "\n",
+    "def initialize_fig(\n",
+    "        plot_kwargs: dict, \n",
+    "        dabest_obj: object, \n",
+    "        show_delta2: bool, \n",
+    "        show_mini_meta: bool, \n",
+    "        is_paired: bool, \n",
+    "        show_pairs: bool, \n",
+    "        proportional: bool,\n",
+    "        float_contrast: bool,\n",
+    "        effect_size_type: str, \n",
+    "        yvar: str, \n",
+    "        horizontal: bool, \n",
+    "        show_table: bool,\n",
+    "        color_col: str,\n",
+    "    ):\n",
+    "    \"\"\"\n",
+    "    Initialize the figure and axes for the plotter function.\n",
+    "\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    plot_kwargs : dict\n",
+    "        Kwargs passed to the plot function.\n",
+    "    dabest_obj : object (EffectSizeDataFrame)\n",
+    "        A `dabest` EffectSizeDataFrame object.\n",
+    "    show_delta2 : bool\n",
+    "        A boolean flag to determine if the plot will have a delta-delta effect size.\n",
+    "    show_mini_meta : bool\n",
+    "        A boolean flag to determine if the plot will have a mini-meta effect size.\n",
+    "    is_paired : bool\n",
+    "        A boolean flag to determine if the plot is for paired data.\n",
+    "    show_pairs : bool\n",
+    "        A boolean flag to determine if the plot will show the paired data.\n",
+    "    proportional : bool\n",
+    "        A boolean flag to determine if the plot is for proportional data.\n",
+    "    float_contrast : bool\n",
+    "        A boolean flag to determine if the plot is for floating contrast data.\n",
+    "    effect_size_type : str\n",
+    "        The type of effect size to be plotted.\n",
+    "    yvar : str\n",
+    "        The name of the y-axis variable.\n",
+    "    horizontal : bool\n",
+    "        A boolean flag to determine if the plot is for horizontal plotting.\n",
+    "    show_table : dict\n",
+    "        A boolean flag to determine if the table will be shown in horizontal plot.\n",
+    "    color_col : str\n",
+    "        The column name for coloring the data points.\n",
+    "    \"\"\"\n",
+    "    # Params\n",
+    "    fig_size = plot_kwargs[\"fig_size\"]\n",
+    "    face_color = plot_kwargs[\"face_color\"]\n",
+    "    if plot_kwargs[\"face_color\"] is None:\n",
+    "        face_color = \"white\"\n",
+    "\n",
+    "    # Create Figure and Axes\n",
+    "    if fig_size is None:\n",
+    "        all_groups_count = np.sum([len(i) for i in dabest_obj.idx])\n",
+    "        # Increase the width (vertical layout) or height (horizontal layout) for delta-delta or mini-meta graph\n",
+    "        if show_delta2 or show_mini_meta:\n",
+    "            all_groups_count += 1\n",
+    "   \n",
+    "        if horizontal:\n",
+    "            frac = 0.3 if is_paired or show_mini_meta else 0.5\n",
+    "            fig_size = (7, 1 + (frac * all_groups_count))\n",
+    "        else:\n",
+    "            if is_paired and show_pairs and proportional is False:\n",
+    "                if color_col is not None and float_contrast:\n",
+    "                    frac = 0.9\n",
+    "                else:\n",
+    "                    frac = 0.8\n",
+    "            else:\n",
+    "                frac = 1\n",
+    "            if float_contrast:\n",
+    "                height_inches = 4\n",
+    "                each_group_width_inches = 2.5 * frac\n",
+    "            else:\n",
+    "                height_inches = 6\n",
+    "                each_group_width_inches = 1.5 * frac\n",
+    "\n",
+    "            width_inches = each_group_width_inches * all_groups_count\n",
+    "            fig_size = (width_inches, height_inches) \n",
+    "\n",
+    "    init_fig_kwargs = dict(figsize=fig_size, dpi=plot_kwargs[\"dpi\"], tight_layout=True)\n",
+    "\n",
+    "    width_ratios_ga = [2.5, 1]\n",
+    "    h_space_cummings = (0.1 if plot_kwargs[\"gridkey\"] is not None\n",
+    "                        else 0.3)\n",
+    "\n",
+    "    if plot_kwargs[\"ax\"] is not None:\n",
+    "        # New in v0.2.6.\n",
+    "        # Use inset axes to create the estimation plot inside a single axes.\n",
+    "        # Author: Adam L Nekimken. (PR #73)\n",
+    "        rawdata_axes = plot_kwargs[\"ax\"]\n",
+    "        ax_position = rawdata_axes.get_position()  # [[x0, y0], [x1, y1]]\n",
+    "\n",
+    "        fig = rawdata_axes.get_figure()\n",
+    "        fig.patch.set_facecolor(face_color)\n",
+    "\n",
+    "        if horizontal:\n",
+    "            plot_width_ratios = [1, 0.7, 0.3]\n",
+    "            contrast_wspace = 0.05\n",
+    "            contrast_axes = rawdata_axes.inset_axes(\n",
+    "                    [1+contrast_wspace, 0, (plot_width_ratios[1]/plot_width_ratios[0]), 1]\n",
+    "                    )\n",
+    "            if show_table:\n",
+    "                table_axes = rawdata_axes.inset_axes(\n",
+    "                        [1+contrast_wspace+(plot_width_ratios[1]/plot_width_ratios[0]), 0, (plot_width_ratios[2]/plot_width_ratios[0]), 1]\n",
+    "                        )\n",
+    "            else:\n",
+    "                table_axes = None\n",
+    "\n",
+    "            rawdata_axes.set_position(\n",
+    "                    [ax_position.x0,\n",
+    "                     ax_position.y0,\n",
+    "                     (ax_position.x1 - ax_position.x0) * (plot_width_ratios[0] / sum(plot_width_ratios)),\n",
+    "                     (ax_position.y1 - ax_position.y0)]\n",
+    "                     )\n",
+    "            rawdata_axes.contrast_axes = contrast_axes\n",
+    "            rawdata_axes.table_axes = table_axes\n",
+    "        \n",
+    "        else:\n",
+    "            if float_contrast:\n",
+    "                axins = rawdata_axes.inset_axes(\n",
+    "                    [1, 0, width_ratios_ga[1] / width_ratios_ga[0], 1]\n",
+    "                )\n",
+    "                rawdata_axes.set_position(  # [l, b, w, h]\n",
+    "                    [\n",
+    "                        ax_position.x0,\n",
+    "                        ax_position.y0,\n",
+    "                        (ax_position.x1 - ax_position.x0)\n",
+    "                        * (width_ratios_ga[0] / sum(width_ratios_ga)),\n",
+    "                        (ax_position.y1 - ax_position.y0),\n",
+    "                    ]\n",
+    "                )\n",
+    "\n",
+    "                contrast_axes = axins\n",
+    "            else:\n",
+    "                axins = rawdata_axes.inset_axes([0, -1 - h_space_cummings, 1, 1])\n",
+    "                plot_height = (ax_position.y1 - ax_position.y0) / (2 + h_space_cummings)\n",
+    "                rawdata_axes.set_position(\n",
+    "                    [\n",
+    "                        ax_position.x0,\n",
+    "                        ax_position.y0 + (1 + h_space_cummings) * plot_height,\n",
+    "                        (ax_position.x1 - ax_position.x0),\n",
+    "                        plot_height,\n",
+    "                    ]\n",
+    "                )\n",
+    "\n",
+    "            # Set axes\n",
+    "            contrast_axes = axins\n",
+    "            rawdata_axes.contrast_axes = axins\n",
+    "            table_axes = None\n",
+    "\n",
+    "    else:\n",
+    "        # Here, we hardcode some figure parameters.\n",
+    "        if horizontal:\n",
+    "            if show_table:\n",
+    "                fig, axx = plt.subplots(\n",
+    "                    ncols=3, gridspec_kw={'width_ratios' : [1,0.7,0.3], 'wspace' : 0.05}, **init_fig_kwargs\n",
+    "                )\n",
+    "            else:\n",
+    "                fig, axx = plt.subplots(\n",
+    "                    ncols=2, gridspec_kw={'width_ratios' : [1,0.7], 'wspace' : 0.05}, **init_fig_kwargs\n",
+    "                )\n",
+    "        else:\n",
+    "            if float_contrast:\n",
+    "                fig, axx = plt.subplots(\n",
+    "                    ncols=2,\n",
+    "                    gridspec_kw={\"width_ratios\": width_ratios_ga, \"wspace\": 0},\n",
+    "                    **init_fig_kwargs\n",
+    "                )\n",
+    "            else:\n",
+    "                fig, axx = plt.subplots(\n",
+    "                    nrows=2, gridspec_kw={\"hspace\": h_space_cummings}, **init_fig_kwargs\n",
+    "                )\n",
+    "        fig.patch.set_facecolor(face_color)\n",
+    "\n",
+    "        # Set axes   \n",
+    "        rawdata_axes = axx[0]\n",
+    "        contrast_axes = axx[1]\n",
+    "        table_axes = axx[2] if horizontal and show_table else None\n",
+    "\n",
+    "\n",
+    "    # Title\n",
+    "    title, fontsize_title = plot_kwargs[\"title\"], plot_kwargs[\"fontsize_title\"]\n",
+    "    if title is not None:\n",
+    "        if plot_kwargs[\"ax\"] is not None:\n",
+    "            rawdata_axes.set_title(title, fontsize=fontsize_title)\n",
+    "        else:\n",
+    "            fig.suptitle(title, fontsize=fontsize_title)\n",
+    "\n",
+    "    rawdata_axes.set_frame_on(False)\n",
+    "    contrast_axes.set_frame_on(False)\n",
+    "    if horizontal and show_table:\n",
+    "        table_axes.set_frame_on(False)\n",
+    "    \n",
+    "    # Swarmplot ylim (Vertical) or xlim (Horizontal)\n",
+    "    raw_ylim = plot_kwargs[\"raw_ylim\"]\n",
+    "    if raw_ylim is not None:\n",
+    "        if not isinstance(raw_ylim, list) and not isinstance(raw_ylim, tuple) or len(raw_ylim) != 2:\n",
+    "            raise ValueError(\"`raw_ylim` must be a tuple/list of the lower and upper bound.\")\n",
+    "        if horizontal:\n",
+    "            rawdata_axes.set_xlim(raw_ylim)\n",
+    "        else:\n",
+    "            rawdata_axes.set_ylim(raw_ylim)\n",
+    "\n",
+    "    # Contrastplot ylim (Vertical) or xlim (Horizontal)\n",
+    "    if horizontal or not float_contrast:\n",
+    "        contrast_ylim, delta2_ylim = plot_kwargs[\"contrast_ylim\"], plot_kwargs[\"delta2_ylim\"]\n",
+    "        if contrast_ylim is not None or (delta2_ylim is not None and show_delta2):\n",
+    "            if contrast_ylim is not None:\n",
+    "                if delta2_ylim is not None and show_delta2:\n",
+    "                    if contrast_ylim != delta2_ylim:\n",
+    "                        raise ValueError(\"Please check if `contrast_ylim` and `delta2_ylim` are assigned with same values.\")\n",
+    "            else:\n",
+    "                contrast_ylim = delta2_ylim\n",
+    "\n",
+    "            if not isinstance(contrast_ylim, list) and not isinstance(contrast_ylim, tuple) or len(contrast_ylim) != 2:\n",
+    "                raise ValueError(\"`contrast_ylim` must be a tuple/list of the lower and upper bound.\")\n",
+    "\n",
+    "            if effect_size_type == \"cliffs_delta\":\n",
+    "                # Ensure the ylims for a cliffs_delta plot never exceed [-1, 1].\n",
+    "                l = contrast_ylim[0]\n",
+    "                h = contrast_ylim[1]\n",
+    "                low = -1 if l < -1 else l\n",
+    "                high = 1 if h > 1 else h\n",
+    "                if horizontal:\n",
+    "                    contrast_axes.set_xlim(low, high)\n",
+    "                else:\n",
+    "                    contrast_axes.set_ylim(low, high)\n",
+    "            else:\n",
+    "                if horizontal:\n",
+    "                    contrast_axes.set_xlim(contrast_ylim)\n",
+    "                else:\n",
+    "                    contrast_axes.set_ylim(contrast_ylim)\n",
+    "\n",
+    "    # Set raw axes y-label.\n",
+    "    raw_label = plot_kwargs[\"raw_label\"]\n",
+    "    if raw_label is None:\n",
+    "        if proportional:\n",
+    "            raw_label = \"Proportion of Success\" if effect_size_type != \"cohens_h\" else \"Value\"\n",
+    "        else:\n",
+    "            raw_label = yvar \n",
+    "\n",
+    "    fontsize_rawylabel = plot_kwargs[\"fontsize_rawylabel\"]\n",
+    "    if horizontal:\n",
+    "        rawdata_axes.set_xlabel(raw_label, fontsize=fontsize_rawylabel)\n",
+    "        rawdata_axes.set_ylabel(\"\")\n",
+    "    else:\n",
+    "        rawdata_axes.set_ylabel(raw_label, fontsize=fontsize_rawylabel)\n",
+    "        rawdata_axes.set_xlabel(\"\")\n",
+    "\n",
+    "    # Set contrast axes y-label.\n",
+    "    contrast_label_dict = {\n",
+    "        \"mean_diff\": \"Mean Difference\",\n",
+    "        \"median_diff\": \"Median Difference\",\n",
+    "        \"cohens_d\": \"Cohen's d\",\n",
+    "        \"hedges_g\": \"Hedges' g\",\n",
+    "        \"cliffs_delta\": \"Cliff's delta\",\n",
+    "        \"cohens_h\": \"Cohen's h\",\n",
+    "    }\n",
+    "\n",
+    "    if proportional and effect_size_type != \"cohens_h\":\n",
+    "        default_contrast_label = \"Proportion Difference\"\n",
+    "    else:\n",
+    "        default_contrast_label = contrast_label_dict[effect_size_type]\n",
+    "\n",
+    "    if plot_kwargs[\"contrast_label\"] is None:\n",
+    "        if is_paired:\n",
+    "            contrast_label = \"Paired\\n{}\".format(default_contrast_label)\n",
+    "        else:\n",
+    "            contrast_label = default_contrast_label\n",
+    "    else:\n",
+    "        contrast_label = plot_kwargs[\"contrast_label\"]\n",
+    "\n",
+    "    fontsize_contrastylabel = plot_kwargs[\"fontsize_contrastylabel\"]\n",
+    "\n",
+    "    if horizontal:\n",
+    "        contrast_axes.set_xlabel(contrast_label, fontsize=fontsize_contrastylabel)\n",
+    "    else:\n",
+    "        contrast_axes.set_ylabel(contrast_label, fontsize=fontsize_contrastylabel)\n",
+    "        if float_contrast:\n",
+    "            contrast_axes.yaxis.set_label_position(\"right\")\n",
+    "\n",
+    "    return fig, rawdata_axes, contrast_axes, table_axes\n",
+    "\n",
+    "def get_plot_groups(\n",
+    "        is_paired: bool, \n",
+    "        idx: list, \n",
+    "        proportional: bool, \n",
+    "        all_plot_groups: list\n",
+    "    ):\n",
+    "    \"\"\"\n",
+    "    Extract the plot groups from the `idx` object for use in the plotter function.\n",
+    "\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    is_paired : bool\n",
+    "        A boolean flag to determine if the plot is for paired data.\n",
+    "    idx : list\n",
+    "        A list of tuples containing the group names.\n",
+    "    proportional : bool\n",
+    "        A boolean flag to determine if the plot is for proportional data.\n",
+    "    all_plot_groups : list\n",
+    "        A list of all the group names.\n",
+    "    \"\"\"\n",
+    "\n",
+    "    if is_paired == \"baseline\":\n",
+    "        idx_pairs = [\n",
+    "            (control, test)\n",
+    "            for i in idx\n",
+    "            for control, test in zip([i[0]] * (len(i) - 1), i[1:])\n",
+    "        ]\n",
+    "        temp_idx = idx if not proportional else idx_pairs\n",
+    "    else:\n",
+    "        idx_pairs = [\n",
+    "            (control, test) for i in idx for control, test in zip(i[:-1], i[1:])\n",
+    "        ]\n",
+    "        temp_idx = idx if not proportional else idx_pairs\n",
+    "\n",
+    "    # Determine temp_all_plot_groups based on proportional condition\n",
+    "    plot_groups = [item for i in temp_idx for item in i]\n",
+    "    temp_all_plot_groups = all_plot_groups if not proportional else plot_groups\n",
+    "    \n",
+    "    return temp_idx, temp_all_plot_groups\n",
+    "\n",
+    "\n",
+    "def add_counts_to_ticks(\n",
+    "        plot_data: pd.DataFrame, \n",
+    "        xvar: str, \n",
+    "        yvar: str, \n",
+    "        rawdata_axes: axes.Axes, \n",
+    "        plot_kwargs: dict, \n",
+    "        flow: bool, \n",
+    "        horizontal: bool\n",
+    "    ):\n",
+    "    \"\"\"\n",
+    "\n",
+    "    Add the counts to the raw data axes labels.\n",
+    "\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    plot_data : object (Dataframe)\n",
+    "        A dataframe of plot data.\n",
+    "    xvar : str\n",
+    "        The name of the x-axis variable.\n",
+    "    yvar : str\n",
+    "        The name of the y-axis variable.\n",
+    "    rawdata_axes : object (Axes)\n",
+    "        The raw data axes.\n",
+    "    plot_kwargs : dict\n",
+    "        Kwargs passed to the plot function.\n",
+    "    flow : bool\n",
+    "        Whether sankey flow is enabled or not.\n",
+    "    horizontal : bool\n",
+    "        A boolean flag to determine if the plot is for horizontal plotting.\n",
+    "    \"\"\"\n",
+    "\n",
+    "    # Add the counts to the rawdata axes xticks.\n",
+    "    counts = plot_data.groupby(xvar, observed=False).count()[yvar]\n",
+    "    \n",
+    "    def lookup_value(text):\n",
+    "        try:\n",
+    "            return str(counts.loc[text])\n",
+    "        except KeyError:\n",
+    "            try:\n",
+    "                numeric_key = pd.to_numeric(text, errors='coerce')\n",
+    "                if pd.notnull(numeric_key):\n",
+    "                    return str(counts.loc[numeric_key])\n",
+    "            except (ValueError, KeyError):\n",
+    "                pass\n",
+    "        print(f\"Key '{text}' not found in counts.\")\n",
+    "        return \"N/A\"\n",
+    "    \n",
+    "    ticks_with_counts = []\n",
+    "    if horizontal:\n",
+    "        get_label, get_ticks = rawdata_axes.get_yticklabels, rawdata_axes.get_yticks\n",
+    "        set_label, set_major_loc_method= rawdata_axes.set_yticklabels, rawdata_axes.yaxis.set_major_locator\n",
+    "    else:\n",
+    "        get_label, get_ticks = rawdata_axes.get_xticklabels, rawdata_axes.get_xticks\n",
+    "        set_label, set_major_loc_method = rawdata_axes.set_xticklabels, rawdata_axes.xaxis.set_major_locator\n",
+    "    \n",
+    "    for ticklab in get_label():\n",
+    "        t = ticklab.get_text()\n",
+    "\n",
+    "        if horizontal and not flow:\n",
+    "            te = t.split('v.s. ')[-1]  # Get the last line of the label\n",
+    "        else:\n",
+    "            te = t.split('\\n')[-1]  # Get the last line of the label\n",
+    "\n",
+    "        value = lookup_value(te)\n",
+    "        if horizontal:\n",
+    "            ticks_with_counts.append(f\"{t} (N={value})\")\n",
+    "        else:\n",
+    "            ticks_with_counts.append(f\"{t}\\n(N={value})\")\n",
+    "\n",
+    "    set_major_loc_method(plt.FixedLocator(get_ticks()))\n",
+    "\n",
+    "    # label = ticks_with_counts if plot_kwargs['show_sample_size'] else get_label()\n",
+    "    # set_label(label, fontsize=plot_kwargs.get(\"fontsize_rawxlabel\"))\n",
+    "\n",
+    "    set_label(ticks_with_counts, fontsize=plot_kwargs.get(\"fontsize_rawxlabel\"))\n",
+    "\n",
+    "    # Ensure ticks are at the correct locations\n",
+    "    set_major_loc_method(plt.FixedLocator(get_ticks()))\n",
+    "\n",
+    "def extract_contrast_plotting_ticks(\n",
+    "        is_paired: bool, \n",
+    "        show_pairs: bool, \n",
+    "        two_col_sankey: bool, \n",
+    "        plot_groups: list, \n",
+    "        idx: list, \n",
+    "        sankey_control_group: list\n",
+    "    ):\n",
+    "    \"\"\"\n",
+    "    Extract the contrast plotting ticks from the `idx` object for use in the plotter function.\n",
+    "\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    is_paired : bool\n",
+    "        A boolean flag to determine if the plot is for paired data.\n",
+    "    show_pairs : bool\n",
+    "        A boolean flag to determine if the plot will show the paired data.\n",
+    "    two_col_sankey : bool\n",
+    "        A boolean flag to determine if the plot will show a two-column sankey diagram.\n",
+    "    plot_groups : list\n",
+    "        A list of the plot groups.\n",
+    "    idx : list\n",
+    "        A list of tuples containing the group names.\n",
+    "    sankey_control_group : list\n",
+    "        A list of the control group names.\n",
+    "    \"\"\"\n",
+    "    # Take note of where the `control` groups are.\n",
+    "    ticks_to_skip_contrast = None\n",
+    "    ticks_to_start_twocol_sankey = None\n",
+    "    if is_paired == \"baseline\" and show_pairs:\n",
+    "        if two_col_sankey:\n",
+    "            ticks_to_skip = []\n",
+    "            ticks_to_plot = np.arange(0, len(plot_groups) / 2).tolist()\n",
+    "            ticks_to_start_twocol_sankey = np.cumsum([len(i) - 1 for i in idx]).tolist()\n",
+    "            ticks_to_start_twocol_sankey.pop()\n",
+    "            ticks_to_start_twocol_sankey.insert(0, 0)\n",
+    "        else:\n",
+    "            ticks_to_skip = np.cumsum([len(t) for t in idx])[:-1].tolist()\n",
+    "            ticks_to_skip.insert(0, 0)\n",
+    "            # Then obtain the ticks where we have to plot the effect sizes.\n",
+    "            ticks_to_plot = [\n",
+    "                t for t in range(0, len(plot_groups)) if t not in ticks_to_skip\n",
+    "            ]\n",
+    "            ticks_to_skip_contrast = np.cumsum([(len(t)) for t in idx])[:-1].tolist()\n",
+    "            ticks_to_skip_contrast.insert(0, 0)\n",
+    "    else:\n",
+    "        if two_col_sankey:\n",
+    "            ticks_to_skip = [len(sankey_control_group)]\n",
+    "            # Then obtain the ticks where we have to plot the effect sizes.\n",
+    "            ticks_to_plot = [\n",
+    "                t for t in range(0, len(plot_groups)) if t not in ticks_to_skip\n",
+    "            ]\n",
+    "            ticks_to_skip = []\n",
+    "            ticks_to_start_twocol_sankey = np.cumsum([len(i) - 1 for i in idx]).tolist()\n",
+    "            ticks_to_start_twocol_sankey.pop()\n",
+    "            ticks_to_start_twocol_sankey.insert(0, 0)\n",
+    "        else:\n",
+    "            ticks_to_skip = np.cumsum([len(t) for t in idx])[:-1].tolist()\n",
+    "            ticks_to_skip.insert(0, 0)\n",
+    "            # Then obtain the ticks where we have to plot the effect sizes.\n",
+    "            ticks_to_plot = [\n",
+    "                t for t in range(0, len(plot_groups)) if t not in ticks_to_skip\n",
+    "            ]\n",
+    "    \n",
+    "    ticks_for_baseline_ec = ticks_to_skip\n",
+    "    \n",
+    "    return ticks_to_skip, ticks_to_plot, ticks_for_baseline_ec, ticks_to_skip_contrast, ticks_to_start_twocol_sankey\n",
+    "\n",
+    "def set_xaxis_ticks_and_lims(\n",
+    "        show_delta2: bool, \n",
+    "        show_mini_meta: bool, \n",
+    "        rawdata_axes: axes.Axes, \n",
+    "        contrast_axes: axes.Axes, \n",
+    "        show_pairs: bool, \n",
+    "        float_contrast: bool,\n",
+    "        ticks_to_skip: list, \n",
+    "        contrast_xtick_labels: list, \n",
+    "        plot_kwargs: dict, \n",
+    "        proportional: bool, \n",
+    "        horizontal: bool\n",
+    "    ):\n",
+    "    \"\"\"\n",
+    "    Set the x-axis/yaxis ticks and limits for the plotter function.\n",
+    "\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    show_delta2 : bool\n",
+    "        A boolean flag to determine if the plot will have a delta-delta effect size.\n",
+    "    show_mini_meta : bool\n",
+    "        A boolean flag to determine if the plot will have a mini-meta effect size.\n",
+    "    rawdata_axes : object (Axes)\n",
+    "        The raw data axes.\n",
+    "    contrast_axes : object (Axes)\n",
+    "        The contrast axes.\n",
+    "    show_pairs : bool\n",
+    "        A boolean flag to determine if the plot will show the paired data.\n",
+    "    float_contrast : bool\n",
+    "        A boolean flag to determine if the plot is a GA or Cumming design.\n",
+    "    ticks_to_skip : list\n",
+    "        A list of ticks to skip.\n",
+    "    contrast_xtick_labels : list\n",
+    "        A list of contrast xtick labels.\n",
+    "    plot_kwargs : dict\n",
+    "        Kwargs passed to the plot function.\n",
+    "    proportional: bool\n",
+    "        A boolean flag to determine if the plot is a proportional plot.\n",
+    "    horizontal : bool\n",
+    "        A boolean flag to determine if the plot is for horizontal plotting.\n",
+    "    \"\"\"\n",
+    "\n",
+    "    if horizontal:\n",
+    "        # Ticks\n",
+    "        if show_delta2 is False and show_mini_meta is False:\n",
+    "            contrast_axes.set_yticks(rawdata_axes.get_yticks())\n",
+    "        else:\n",
+    "            temp = rawdata_axes.get_yticks()\n",
+    "            temp = np.append(temp, [max(temp) + 0, max(temp) + 1])\n",
+    "            contrast_axes.set_yticks(temp)       \n",
+    "\n",
+    "        # Lims\n",
+    "        if show_pairs:\n",
+    "            max_x = contrast_axes.get_ylim()[1]\n",
+    "            rawdata_axes.set_ylim(-0.375, max_x)\n",
+    "\n",
+    "            if proportional:\n",
+    "                rawdata_axes.set_ylim(-0.375, max_x+0.1)\n",
+    "\n",
+    "        if show_delta2 or show_mini_meta:\n",
+    "            # Increase the ylim of raw data by 2\n",
+    "            temp = rawdata_axes.get_ylim()\n",
+    "            if show_pairs:\n",
+    "                rawdata_axes.set_ylim(temp[0], temp[1] + 0.00)\n",
+    "            else:\n",
+    "                rawdata_axes.set_ylim(temp[0], temp[1] + 1)\n",
+    "            contrast_axes.set_ylim(rawdata_axes.get_ylim())\n",
+    "        else:\n",
+    "            contrast_axes.set_ylim(rawdata_axes.get_ylim())\n",
+    "    # Vertical\n",
+    "    else:\n",
+    "        # Ticks\n",
+    "        if show_delta2 is False and show_mini_meta is False:\n",
+    "            contrast_axes.set_xticks(rawdata_axes.get_xticks())\n",
+    "        else:\n",
+    "            temp = rawdata_axes.get_xticks()\n",
+    "            temp = np.append(temp, [max(temp) + 1])\n",
+    "            contrast_axes.set_xticks(temp)\n",
+    "\n",
+    "        # Lims\n",
+    "        if show_pairs:\n",
+    "            max_x = contrast_axes.get_xlim()[1]\n",
+    "            rawdata_axes.set_xlim(-0.375, max_x)\n",
+    "\n",
+    "        if float_contrast:\n",
+    "            contrast_axes.set_xlim(0.5, 1.5)\n",
+    "\n",
+    "        elif show_delta2:\n",
+    "            if show_pairs:\n",
+    "                rawdata_axes.set_xlim(-0.375, 4.75)\n",
+    "            else:\n",
+    "                rawdata_axes.set_xlim(-0.5, 4.75)\n",
+    "            contrast_axes.set_xlim(rawdata_axes.get_xlim())\n",
+    "\n",
+    "        elif show_mini_meta:\n",
+    "            # Increase the xlim of raw data by 2\n",
+    "            temp = rawdata_axes.get_xlim()\n",
+    "            if show_pairs:\n",
+    "                rawdata_axes.set_xlim(temp[0], temp[1] + 0.5)\n",
+    "            else:\n",
+    "                rawdata_axes.set_xlim(temp[0], temp[1] + 1)\n",
+    "            contrast_axes.set_xlim(rawdata_axes.get_xlim())\n",
+    "        else:\n",
+    "            contrast_axes.set_xlim(rawdata_axes.get_xlim())\n",
+    "\n",
+    "        # Properly label the contrast ticks.\n",
+    "        for t in ticks_to_skip:\n",
+    "            contrast_xtick_labels.insert(t, \"\")\n",
+    "\n",
+    "        contrast_axes.set_xticklabels(\n",
+    "            contrast_xtick_labels, fontsize=plot_kwargs[\"fontsize_contrastxlabel\"]\n",
+    "        )\n",
+    "\n",
+    "\n",
+    "def show_legend(\n",
+    "        legend_labels: list, \n",
+    "        legend_handles: list, \n",
+    "        rawdata_axes: axes.Axes, \n",
+    "        contrast_axes: axes.Axes, \n",
+    "        table_axes: axes.Axes, \n",
+    "        float_contrast: bool, \n",
+    "        show_pairs: bool, \n",
+    "        horizontal: bool, \n",
+    "        legend_kwargs: dict, \n",
+    "        table_kwargs: dict\n",
+    "    ):\n",
+    "    \"\"\"\n",
+    "    Show the legend for the plotter function.\n",
+    "\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    legend_labels : list\n",
+    "        A list of legend labels.\n",
+    "    legend_handles : list\n",
+    "        A list of legend handles.\n",
+    "    rawdata_axes : object (Axes)\n",
+    "        The raw data axes.\n",
+    "    contrast_axes : object (Axes)\n",
+    "        The contrast axes.\n",
+    "    table_axes : object (Axes)\n",
+    "        The table axes.\n",
+    "    float_contrast : bool\n",
+    "        A boolean flag to determine if the plot is GA or Cumming format.\n",
+    "    show_pairs : bool\n",
+    "        A boolean flag to determine if the plot will show the paired data.\n",
+    "    horizontal : bool\n",
+    "        A boolean flag to determine if the plot is for horizontal plotting.\n",
+    "    legend_kwargs : dict\n",
+    "        Kwargs passed to the legend function.\n",
+    "    \"\"\"\n",
+    "\n",
+    "    legend_labels_unique = np.unique(legend_labels)\n",
+    "    unique_idx = np.unique(legend_labels, return_index=True)[1]\n",
+    "    legend_handles_unique = (\n",
+    "        pd.Series(legend_handles, dtype=\"object\").loc[unique_idx]\n",
+    "    ).tolist()\n",
+    "\n",
+    "    # Location of the legend\n",
+    "    if \"bbox_to_anchor\" not in legend_kwargs.keys():\n",
+    "        if horizontal:\n",
+    "            bta = (1,1)\n",
+    "        else:\n",
+    "            if float_contrast:\n",
+    "                bta = (2.00, 1.02) if show_pairs else (1.5, 1.02)\n",
+    "            else:\n",
+    "                bta = (1.02, 1.0) if show_pairs else (1.0, 1.0)\n",
+    "        legend_kwargs.update({'bbox_to_anchor': bta})\n",
+    "\n",
+    "    # Pick the ax to plot\n",
+    "    if horizontal:\n",
+    "        if table_kwargs['show']:\n",
+    "            axes_with_legend = table_axes\n",
+    "        else:\n",
+    "            axes_with_legend = contrast_axes\n",
+    "    elif float_contrast:\n",
+    "        axes_with_legend = contrast_axes\n",
+    "    else:\n",
+    "        axes_with_legend = rawdata_axes\n",
+    "\n",
+    "    # Plot the legend\n",
+    "    if len(legend_handles_unique) > 0:\n",
+    "        leg = axes_with_legend.legend(\n",
+    "                            legend_handles_unique,\n",
+    "                            legend_labels_unique,\n",
+    "                            handlelength=0.5,\n",
+    "                            **legend_kwargs\n",
+    "        )\n",
+    "        if show_pairs:\n",
+    "            for line in leg.get_lines():\n",
+    "                line.set_linewidth(3.0)\n",
+    "    \n",
+    "def gardner_altman_adjustments(\n",
+    "        effect_size_type: str, \n",
+    "        plot_data: pd.DataFrame, \n",
+    "        xvar: str, \n",
+    "        yvar: str, \n",
+    "        current_control: str, \n",
+    "        current_group: str,\n",
+    "        rawdata_axes: axes.Axes, \n",
+    "        contrast_axes: axes.Axes, \n",
+    "        results: pd.DataFrame, \n",
+    "        current_effsize: float, \n",
+    "        is_paired: bool, \n",
+    "        one_sankey: bool,\n",
+    "        reflines_kwargs: dict, \n",
+    "        redraw_axes_kwargs: dict\n",
+    "    ):\n",
+    "    \"\"\"\n",
+    "    Aesthetic adjustments specific to Gardner-Altman plots (float_contrast=True).\n",
+    "    \n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    effect_size_type : str\n",
+    "        The type of effect size.\n",
+    "    plot_data : object (Dataframe)\n",
+    "        A dataframe of plot data.\n",
+    "    xvar : str\n",
+    "        The name of the x-axis variable.\n",
+    "    yvar : str\n",
+    "        The name of the y-axis variable.\n",
+    "    current_control : str\n",
+    "        The name of the current control group.\n",
+    "    current_group : str\n",
+    "        The name of the current test group.\n",
+    "    rawdata_axes : object (Axes)\n",
+    "        The raw data axes.\n",
+    "    contrast_axes : object (Axes)\n",
+    "        The contrast axes.\n",
+    "    results : object (DataFrame)\n",
+    "        A dataframe of the results.\n",
+    "    current_effsize : float\n",
+    "        The current effect size.\n",
+    "    is_paired : bool\n",
+    "        A boolean flag to determine if the plot is for paired data.\n",
+    "    one_sankey : bool\n",
+    "        A boolean flag to determine if the plot is for a single sankey diagram.\n",
+    "    reflines_kwargs : dict\n",
+    "        Kwargs passed to the reference lines.\n",
+    "    redraw_axes_kwargs : dict\n",
+    "        Kwargs passed to the redraw axes.\n",
+    "    \"\"\"\n",
+    "    from ._stats_tools.effsize import (\n",
+    "        _compute_standardizers,\n",
+    "        _compute_hedges_correction_factor,\n",
+    "    )\n",
+    "\n",
+    "    og_xlim_raw, og_ylim_raw = rawdata_axes.get_xlim(), rawdata_axes.get_ylim()\n",
+    "    \n",
+    "    # Normalize ylims and despine the floating contrast axes.\n",
+    "    # Check that the effect size is within the swarm ylims.\n",
+    "    if effect_size_type in [\"mean_diff\", \"cohens_d\", \"hedges_g\", \"cohens_h\"]:\n",
+    "        control_group_summary = (\n",
+    "            plot_data.groupby(xvar, observed=False)\n",
+    "            .mean(numeric_only=True)\n",
+    "            .loc[current_control, yvar]\n",
+    "        )\n",
+    "        test_group_summary = (\n",
+    "            plot_data.groupby(xvar, observed=False).mean(numeric_only=True).loc[current_group, yvar]\n",
+    "        )\n",
+    "    elif effect_size_type == \"median_diff\":\n",
+    "        control_group_summary = (\n",
+    "            plot_data.groupby(xvar, observed=False).median(numeric_only=True).loc[current_control, yvar]\n",
+    "        )\n",
+    "        test_group_summary = (\n",
+    "            plot_data.groupby(xvar, observed=False).median(numeric_only=True).loc[current_group, yvar]\n",
+    "        )\n",
+    "\n",
+    "    _, contrast_xlim_max = contrast_axes.get_xlim()\n",
+    "\n",
+    "    difference = float(results.difference[0])\n",
+    "\n",
+    "    if effect_size_type in [\"mean_diff\", \"median_diff\"]:\n",
+    "        # Align 0 of contrast_axes to reference group mean of rawdata_axes.\n",
+    "        # If the effect size is positive, shift the contrast axis up.\n",
+    "        rawdata_ylims = np.array(rawdata_axes.get_ylim())\n",
+    "        if current_effsize > 0:\n",
+    "            rightmin, rightmax = rawdata_ylims - current_effsize\n",
+    "        # If the effect size is negative, shift the contrast axis down.\n",
+    "        elif current_effsize < 0:\n",
+    "            rightmin, rightmax = rawdata_ylims + current_effsize\n",
+    "        else:\n",
+    "            rightmin, rightmax = rawdata_ylims\n",
+    "\n",
+    "        contrast_axes.set_ylim(rightmin, rightmax)\n",
+    "\n",
+    "        og_ylim_contrast = rawdata_axes.get_ylim() - np.array(control_group_summary)\n",
+    "\n",
+    "        contrast_axes.set_ylim(og_ylim_contrast)\n",
+    "        contrast_axes.set_xlim(contrast_xlim_max - 1, contrast_xlim_max)\n",
+    "\n",
+    "    elif effect_size_type in [\"cohens_d\", \"hedges_g\", \"cohens_h\"]:\n",
+    "\n",
+    "        which_std = 1 if is_paired else 0   ############################ Unused line of code\n",
+    "        temp_control = np.array(plot_data[plot_data[xvar] == current_control][yvar])\n",
+    "        temp_test = np.array(plot_data[plot_data[xvar] == current_group][yvar])\n",
+    "\n",
+    "        stds = _compute_standardizers(temp_control, temp_test)\n",
+    "        if is_paired:\n",
+    "            pooled_sd = stds[1]\n",
+    "        else:\n",
+    "            pooled_sd = stds[0]\n",
+    "\n",
+    "        if effect_size_type == \"hedges_g\":\n",
+    "            gby_count = plot_data.groupby(xvar, observed=False).count()\n",
+    "            len_control = gby_count.loc[current_control, yvar]\n",
+    "            len_test = gby_count.loc[current_group, yvar]\n",
+    "\n",
+    "            hg_correction_factor = _compute_hedges_correction_factor(\n",
+    "                len_control, len_test\n",
+    "            )\n",
+    "\n",
+    "            ylim_scale_factor = pooled_sd / hg_correction_factor\n",
+    "\n",
+    "        elif effect_size_type == \"cohens_h\":\n",
+    "            ylim_scale_factor = (\n",
+    "                np.mean(temp_test) - np.mean(temp_control)\n",
+    "            ) / difference\n",
+    "\n",
+    "        else:\n",
+    "            ylim_scale_factor = pooled_sd\n",
+    "\n",
+    "        scaled_ylim = (\n",
+    "            (rawdata_axes.get_ylim() - control_group_summary) / ylim_scale_factor\n",
+    "        ).tolist()\n",
+    "\n",
+    "        contrast_axes.set_ylim(scaled_ylim)\n",
+    "        og_ylim_contrast = scaled_ylim\n",
+    "\n",
+    "        contrast_axes.set_xlim(contrast_xlim_max - 1, contrast_xlim_max)\n",
+    "\n",
+    "    if one_sankey is None:\n",
+    "        # Draw summary lines for control and test groups..\n",
+    "        for jj, axx in enumerate([rawdata_axes, contrast_axes]):\n",
+    "            # Draw effect size line.\n",
+    "            if jj == 0:\n",
+    "                ref = control_group_summary\n",
+    "                diff = test_group_summary\n",
+    "                effsize_line_start = 1\n",
+    "\n",
+    "            elif jj == 1:\n",
+    "                ref = 0\n",
+    "                diff = ref + difference\n",
+    "                effsize_line_start = contrast_xlim_max - 1.1\n",
+    "\n",
+    "            xlimlow, xlimhigh = axx.get_xlim()\n",
+    "\n",
+    "            # Draw reference line.\n",
+    "            axx.hlines(\n",
+    "                ref,  # y-coordinates\n",
+    "                0,\n",
+    "                xlimhigh,  # x-coordinates, start and end.\n",
+    "                **reflines_kwargs\n",
+    "            )\n",
+    "\n",
+    "            # Draw effect size line.\n",
+    "            axx.hlines(diff, effsize_line_start, xlimhigh, **reflines_kwargs)\n",
+    "    else:\n",
+    "        ref = 0\n",
+    "        diff = ref + difference\n",
+    "        effsize_line_start = contrast_xlim_max - 0.9\n",
+    "        xlimlow, xlimhigh = contrast_axes.get_xlim()\n",
+    "        # Draw reference line.\n",
+    "        contrast_axes.hlines(\n",
+    "            ref,  # y-coordinates\n",
+    "            effsize_line_start,\n",
+    "            xlimhigh,  # x-coordinates, start and end.\n",
+    "            **reflines_kwargs\n",
+    "        )\n",
+    "\n",
+    "        # Draw effect size line.\n",
+    "        contrast_axes.hlines(diff, effsize_line_start, xlimhigh, **reflines_kwargs)\n",
+    "    rawdata_axes.set_xlim(og_xlim_raw)  # to align the axis\n",
+    "    # Despine appropriately.\n",
+    "    sns.despine(ax=rawdata_axes, bottom=True)\n",
+    "    sns.despine(ax=contrast_axes, left=True, right=False)\n",
+    "\n",
+    "    # Insert break between the rawdata axes and the contrast axes\n",
+    "    # by re-drawing the x-spine.\n",
+    "    rawdata_axes.hlines(\n",
+    "        og_ylim_raw[0],  # yindex\n",
+    "        rawdata_axes.get_xlim()[0],\n",
+    "        1.3,  # xmin, xmax\n",
+    "        **redraw_axes_kwargs\n",
+    "    )\n",
+    "    rawdata_axes.set_ylim(og_ylim_raw)\n",
+    "\n",
+    "    contrast_axes.hlines(\n",
+    "        contrast_axes.get_ylim()[0],\n",
+    "        contrast_xlim_max - 0.8,\n",
+    "        contrast_xlim_max,\n",
+    "        **redraw_axes_kwargs\n",
+    "    )\n",
+    "\n",
+    "def draw_zeroline(\n",
+    "        ax : axes.Axes,\n",
+    "        horizontal : bool,\n",
+    "        reflines_kwargs : dict,\n",
+    "        extra_delta : bool,\n",
+    "    ):\n",
+    "    \"\"\"\n",
+    "    Draw the independent axis spine lines.\n",
+    "\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    ax : object (Axes)\n",
+    "        The contrast data axes.\n",
+    "    horizontal : bool\n",
+    "        A boolean flag to determine if the plot is for horizontal plotting.\n",
+    "    reflines_kwargs : dict\n",
+    "        Additional keyword arguments to be passed to the zeroline.\n",
+    "    extra_delta : bool\n",
+    "        A boolean flag to determine if the plot includes an extra delta (delta-delta or mini-meta).\n",
+    "    \"\"\"\n",
+    "    # If 0 lies within the ylim of the contrast axes, draw a zero reference line.\n",
+    "    if extra_delta and not horizontal:\n",
+    "        contrast_xlim = [-0.5, 3.4]\n",
+    "        delta2_xlim = [3.6, 4.75]\n",
+    "        \n",
+    "        if ax.get_ylim()[0] < ax.get_ylim()[1]:\n",
+    "            contrast_lim_low, contrast_lim_high = ax.get_ylim()\n",
+    "        else:\n",
+    "            contrast_lim_high, contrast_lim_low = ax.get_ylim()\n",
+    "\n",
+    "        if contrast_lim_low < 0 < contrast_lim_high:\n",
+    "            ax.hlines(y=0, xmin=contrast_xlim[0], xmax=contrast_xlim[1], **reflines_kwargs)\n",
+    "            ax.hlines(y=0, xmin=delta2_xlim[0], xmax=delta2_xlim[1], **reflines_kwargs)\n",
+    "    else:\n",
+    "        ax_lim = ax.get_xlim() if horizontal else ax.get_ylim()\n",
+    "        method = ax.axvline if horizontal else ax.axhline\n",
+    "\n",
+    "        if ax_lim[0] < ax_lim[1]:\n",
+    "            contrast_lim_low, contrast_lim_high = ax_lim\n",
+    "        else:\n",
+    "            contrast_lim_high, contrast_lim_low = ax_lim\n",
+    "\n",
+    "        if contrast_lim_low < 0 < contrast_lim_high:\n",
+    "            method(0, **reflines_kwargs)\n",
+    "\n",
+    "def redraw_independent_spines(\n",
+    "        rawdata_axes : axes.Axes,\n",
+    "        contrast_axes : axes.Axes,\n",
+    "        horizontal : bool,\n",
+    "        two_col_sankey : bool,\n",
+    "        ticks_to_start_twocol_sankey : list,\n",
+    "        idx : list,\n",
+    "        is_paired : str,\n",
+    "        show_pairs : bool,\n",
+    "        proportional : bool,\n",
+    "        ticks_to_skip : list,\n",
+    "        temp_idx : list,\n",
+    "        ticks_to_skip_contrast : list,\n",
+    "        redraw_axes_kwargs : dict\n",
+    "    ):\n",
+    "    \"\"\"\n",
+    "    Draw the independent axis spine lines.\n",
+    "\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    rawdata_axes : object (Axes)\n",
+    "        The raw data axes.\n",
+    "    contrast_axes : object (Axes)\n",
+    "        The contrast axes.\n",
+    "    horizontal : bool\n",
+    "        A boolean flag to determine if the plot is for horizontal plotting.\n",
+    "    two_col_sankey : bool\n",
+    "        A boolean flag to determine if the plot is for two-col sankey.\n",
+    "    ticks_to_start_twocol_sankey : list\n",
+    "        A list of ticks to start for sankey plot.\n",
+    "    idx : list\n",
+    "        A list of indices.\n",
+    "    is_paired : bool\n",
+    "        A boolean flag to determine if the data is paired.\n",
+    "    show_pairs : bool\n",
+    "        A boolean flag to determine if pairs should be shown.\n",
+    "    proportional : bool\n",
+    "        A boolean flag to determine if the plot is proportional/binary.\n",
+    "    ticks_to_skip : list,\n",
+    "        A list of ticks to be skipped in the raw data axes.\n",
+    "    temp_idx : list,\n",
+    "        A temporary list of indices to be used for skipping ticks in the raw data axes.\n",
+    "    ticks_to_skip_contrast : list,\n",
+    "        A list of ticks to be skipped in the contrast axes.\n",
+    "    redraw_axes_kwargs : dict\n",
+    "        Kwargs passed to the redraw axes.\n",
+    "    \"\"\"\n",
+    "    # Extract the ticks\n",
+    "    if two_col_sankey:\n",
+    "        rightend_ticks_raw = rightend_ticks_contrast = np.array([len(i) - 2 for i in idx]) + np.array(ticks_to_start_twocol_sankey)\n",
+    "        starting_ticks_raw = starting_ticks_contrast = ticks_to_start_twocol_sankey\n",
+    "    else:\n",
+    "        if is_paired == \"baseline\" and show_pairs:\n",
+    "            if proportional and is_paired is not None:\n",
+    "                rightend_ticks_raw = rightend_ticks_contrast = np.array([len(i) - 1 for i in idx]) + np.array(ticks_to_skip)\n",
+    "            else:\n",
+    "                rightend_ticks_raw = np.array([len(i) - 1 for i in temp_idx]) + np.array(ticks_to_skip)\n",
+    "                temp_length = [(len(i) - 1) * 2 - 1 for i in idx] if proportional else [(len(i) - 1) for i in idx]\n",
+    "                rightend_ticks_contrast = np.array(temp_length) + np.array(ticks_to_skip_contrast)\n",
+    "            starting_ticks_raw, starting_ticks_contrast = ticks_to_skip, ticks_to_skip_contrast\n",
+    "        else:\n",
+    "            rightend_ticks_raw = rightend_ticks_contrast = np.array([len(i) - 1 for i in idx]) + np.array(ticks_to_skip)\n",
+    "            starting_ticks_raw = starting_ticks_contrast = ticks_to_skip\n",
+    "\n",
+    "    # Plot the spines\n",
+    "    if horizontal:\n",
+    "        sns.despine(ax=rawdata_axes, left=True)\n",
+    "        xlim, ylim = rawdata_axes.get_xlim(), rawdata_axes.get_ylim()\n",
+    "        redraw_axes_kwargs[\"x\"] = xlim[0]\n",
+    "        for k, start_tick in enumerate(starting_ticks_raw):\n",
+    "            end_tick = rightend_ticks_raw[k]\n",
+    "            rawdata_axes.vlines(\n",
+    "                    ymin = start_tick, \n",
+    "                    ymax = end_tick, \n",
+    "                    **redraw_axes_kwargs\n",
+    "            )\n",
+    "        rawdata_axes.set_xlim(xlim)\n",
+    "        rawdata_axes.set_ylim(ylim)\n",
+    "        del redraw_axes_kwargs[\"x\"] \n",
+    "\n",
+    "        # Remove y ticks and labels from the contrast axes.\n",
+    "        sns.despine(ax=contrast_axes, left=True)\n",
+    "        contrast_axes.set_yticks([])\n",
+    "        contrast_axes.set_yticklabels([])\n",
+    "    \n",
+    "    else:\n",
+    "        for ax, starting_ticks_current, rightend_ticks_current in zip(\n",
+    "                [rawdata_axes, contrast_axes],\n",
+    "                [starting_ticks_raw, starting_ticks_contrast],\n",
+    "                [rightend_ticks_raw, rightend_ticks_contrast],\n",
+    "            ):\n",
+    "            sns.despine(ax=ax, bottom=True)\n",
+    "            xlim, ylim = ax.get_xlim(), ax.get_ylim()\n",
+    "            redraw_axes_kwargs[\"y\"] = ylim[0]\n",
+    "            for k, start_tick in enumerate(starting_ticks_current):\n",
+    "                end_tick = rightend_ticks_current[k]\n",
+    "                ax.hlines(\n",
+    "                    xmin=start_tick, \n",
+    "                    xmax=end_tick, \n",
+    "                    **redraw_axes_kwargs\n",
+    "                    )\n",
+    "            ax.set_xlim(xlim)\n",
+    "            ax.set_ylim(ylim)\n",
+    "            del redraw_axes_kwargs[\"y\"]\n",
+    "            \n",
+    "def redraw_dependent_spines(\n",
+    "        rawdata_axes: axes.Axes, \n",
+    "        contrast_axes: axes.Axes, \n",
+    "        redraw_axes_kwargs: dict, \n",
+    "        float_contrast: bool, \n",
+    "        horizontal: bool,\n",
+    "        show_delta2: bool, \n",
+    "        delta2_axes: axes.Axes\n",
+    "    ):\n",
+    "    \"\"\"\n",
+    "    Draw the dependent axis spine lines.\n",
+    "\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    rawdata_axes : object (Axes)\n",
+    "        The raw data axes.\n",
+    "    contrast_axes : object (Axes)\n",
+    "        The contrast axes.\n",
+    "    redraw_axes_kwargs : dict\n",
+    "        Kwargs passed to the redraw axes.\n",
+    "    float_contrast : bool\n",
+    "        A boolean flag to determine if the plot is GA or Cum\n",
+    "    horizontal : bool\n",
+    "        A boolean flag to determine if the plot is for horizontal plotting.\n",
+    "    show_delta2 : bool\n",
+    "        A boolean flag to determine if the plot will have a delta-delta effect size.\n",
+    "    delta2_axes : object (Axes)\n",
+    "        The delta2 axes.\n",
+    "    \"\"\"\n",
+    "\n",
+    "    # Because we turned the axes frame off, we also need to draw back the x-spine for both axes.\n",
+    "    og_xlim_raw, og_ylim_raw = rawdata_axes.get_xlim(), rawdata_axes.get_ylim()\n",
+    "    og_xlim_contrast, og_ylim_contrast = contrast_axes.get_xlim(), contrast_axes.get_ylim()\n",
+    "    if horizontal:\n",
+    "        for current_ax, current_ylim, current_xlim in zip((rawdata_axes, contrast_axes), (og_ylim_raw, og_ylim_contrast), \n",
+    "                                                          (og_xlim_raw, og_xlim_contrast)):\n",
+    "            current_ax.hlines(\n",
+    "                current_ylim[0], \n",
+    "                current_xlim[0], \n",
+    "                current_xlim[1], \n",
+    "                **redraw_axes_kwargs\n",
+    "            )    \n",
+    "    else:\n",
+    "        for current_ax, current_ylim, current_xlim in zip((rawdata_axes, contrast_axes), (og_ylim_raw, og_ylim_contrast), \n",
+    "                                                          (og_xlim_raw[0], og_xlim_contrast[1] if float_contrast else og_xlim_contrast[0])):\n",
+    "            current_ax.vlines(\n",
+    "                current_xlim, \n",
+    "                current_ylim[0], \n",
+    "                current_ylim[1], \n",
+    "                **redraw_axes_kwargs\n",
+    "            )\n",
+    "\n",
+    "        if show_delta2:\n",
+    "            og_xlim_delta, og_ylim_delta = contrast_axes.get_xlim(), contrast_axes.get_ylim()\n",
+    "            delta2_axes.set_ylim(og_ylim_delta)\n",
+    "            \n",
+    "            delta2_axes.vlines(\n",
+    "                og_xlim_delta[1], \n",
+    "                og_ylim_delta[0], \n",
+    "                og_ylim_delta[1], \n",
+    "                **redraw_axes_kwargs\n",
+    "            )\n",
+    "\n",
+    "    for current_ax, xlim, ylim in zip([rawdata_axes, contrast_axes], [og_xlim_raw, og_xlim_contrast], [og_ylim_raw, og_ylim_contrast]):\n",
+    "        current_ax.set_xlim(xlim)\n",
+    "        current_ax.set_ylim(ylim)\n",
+    "\n",
+    "def extract_group_summaries(\n",
+    "        proportional: bool, \n",
+    "        rawdata_axes: axes.Axes, \n",
+    "        asymmetric_side: str, \n",
+    "        horizontal: bool, \n",
+    "        bootstraps_color_by_group: bool, \n",
+    "        plot_palette_raw: list, \n",
+    "        all_plot_groups: list,\n",
+    "        n_groups: int, \n",
+    "        color_col, \n",
+    "        ytick_color, \n",
+    "        group_summaries_kwargs: dict\n",
+    "    ):\n",
+    "    \"\"\"\n",
+    "    Extract the group summaries for the plotter function.\n",
+    "\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    proportional : bool\n",
+    "        A boolean flag to determine if the plot is for proportional data.\n",
+    "    rawdata_axes : object (Axes)\n",
+    "        The raw data axes.\n",
+    "    asymmetric_side : str\n",
+    "        The side of the asymmetric error bars.\n",
+    "    horizontal : bool\n",
+    "        A boolean flag to determine if the plot is for horizontal plotting.\n",
+    "    bootstraps_color_by_group : bool\n",
+    "        A boolean flag to determine if the bootstraps are colored by group.\n",
+    "    plot_palette_raw : list\n",
+    "        A list of the plot palette colors.\n",
+    "    all_plot_groups : list\n",
+    "        A list of all the plot groups.\n",
+    "    n_groups : int\n",
+    "        The number of groups.\n",
+    "    color_col : str\n",
+    "        The name of the color column.\n",
+    "    ytick_color : str\n",
+    "        The color of the y-ticks.\n",
+    "    group_summaries_kwargs : dict\n",
+    "        Kwargs passed to the group summaries.\n",
+    "    \"\"\"\n",
+    "    \n",
+    "    from .plot_tools import get_swarm_spans\n",
+    "\n",
+    "    if proportional:\n",
+    "        group_summaries_method = \"proportional_error_bar\"\n",
+    "        group_summaries_offset = 0\n",
+    "        group_summaries_line_color = \"black\"\n",
+    "    else:\n",
+    "        # Create list to gather xspans.\n",
+    "        xspans = []\n",
+    "        line_colors = []\n",
+    "        for jj, c in enumerate(rawdata_axes.collections):\n",
+    "            try:\n",
+    "                if asymmetric_side == \"right\":\n",
+    "                    # currently offset is hardcoded with value of -0.2\n",
+    "                    x_max_span = -0.2\n",
+    "                else:\n",
+    "                    if horizontal:\n",
+    "                        x_max_span = 0.1 # currently offset is hardcoded with value of 0.1\n",
+    "                    else:\n",
+    "                        _, x_max, _, _ = get_swarm_spans(c)\n",
+    "                        x_max_span = x_max - jj\n",
+    "                xspans.append(x_max_span)\n",
+    "            except TypeError:\n",
+    "                # we have got a None, so skip and move on.\n",
+    "                pass\n",
+    "\n",
+    "            if bootstraps_color_by_group:\n",
+    "                line_colors.append(plot_palette_raw[all_plot_groups[jj]])\n",
+    "\n",
+    "            # Break the loop since hue in Seaborn adds collections to axes and it will result in index out of range\n",
+    "            if jj >= n_groups - 1 and color_col is None:\n",
+    "                break\n",
+    "\n",
+    "        if len(line_colors) != len(all_plot_groups):\n",
+    "            line_colors = ytick_color\n",
+    "            \n",
+    "        # hue in swarmplot would add collections to axes which will result in len(xspans) = len(all_plot_groups) + len(unique groups in hue)\n",
+    "        if len(xspans) > len(all_plot_groups):\n",
+    "            xspans = xspans[:len(all_plot_groups)]\n",
+    "\n",
+    "        group_summaries_method = \"gapped_lines\"\n",
+    "        group_summaries_offset = xspans + np.array(group_summaries_kwargs[\"offset\"])\n",
+    "        group_summaries_line_color = line_colors\n",
+    "\n",
+    "    if group_summaries_kwargs['color'] is not None:\n",
+    "        group_summaries_line_color = group_summaries_kwargs.pop(\"color\")\n",
+    "    group_summaries_kwargs.pop(\"offset\")\n",
+    "\n",
+    "    return group_summaries_method, group_summaries_offset, group_summaries_line_color\n",
+    "\n",
+    "def color_picker(color_type: str,\n",
+    "                 kwargs: dict, \n",
+    "                 elements: list, \n",
+    "                 color_col: str, \n",
+    "                 show_pairs: bool, \n",
+    "                 color_palette: dict) -> list:\n",
+    "    num_of_elements = len(elements)\n",
+    "    colors = (\n",
+    "        [kwargs.pop('color')] * num_of_elements\n",
+    "        if kwargs.get('color', None) is not None\n",
+    "        else ['black'] * num_of_elements\n",
+    "        if color_col is not None or show_pairs \n",
+    "        else list(color_palette.values())\n",
+    "    )\n",
+    "    if color_type in ['contrast', 'summary', 'delta_text']:\n",
+    "        if len(colors) == num_of_elements:\n",
+    "            final_colors = colors\n",
+    "        else:\n",
+    "            final_colors = []\n",
+    "            for tick in elements:\n",
+    "                final_colors.append(colors[int(tick)])\n",
+    "    else:\n",
+    "        final_colors = colors\n",
+    "    return final_colors\n",
+    "\n",
+    "\n",
+    "def prepare_bars_for_plot(bar_type, bar_kwargs, horizontal, plot_palette_raw, color_col, show_pairs,\n",
+    "                          plot_data = None, xvar = None, yvar = None,  # Raw data\n",
+    "                          results = None, ticks_to_plot = None, extra_delta = None, # Contrast data\n",
+    "                          reference_band = None, summary_axes = None, ci_type = None  # Summary data\n",
+    "                          ):\n",
+    "    from .misc_tools import color_picker\n",
+    "    bar_dict = {}\n",
+    "    if bar_type in ['raw', 'contrast']:\n",
+    "        if bar_type == 'raw':\n",
+    "            if isinstance(plot_data[xvar].dtype, pd.CategoricalDtype):\n",
+    "                order = pd.unique(plot_data[xvar]).categories\n",
+    "            else:\n",
+    "                order = pd.unique(plot_data[xvar])\n",
+    "            means = plot_data.groupby(xvar, observed=False)[yvar].mean().reindex(index=order).values\n",
+    "            ticks = list(range(len(order)))\n",
+    "        elif bar_type == 'contrast':\n",
+    "            means = results.difference.to_list()\n",
+    "            ticks = ticks_to_plot.copy()\n",
+    "            if extra_delta is not None:\n",
+    "                ticks.append(ticks[-1]+1)  # Add an extra tick\n",
+    "                means.append(extra_delta)\n",
+    "\n",
+    "        num_of_bars = len(means)\n",
+    "        y_start_values, y_distances = [0]*num_of_bars, means\n",
+    "        x_start_values, x_distances = [num - (0.5 if horizontal else 0.25) for num in ticks], [0.5,]*num_of_bars\n",
+    "\n",
+    "    elif bar_type == 'summary':\n",
+    "        # Begin checks        \n",
+    "        if not isinstance(reference_band, list):\n",
+    "            raise TypeError(\"reference_band must be a list of indices (ints).\")\n",
+    "        if not all(isinstance(i, int) for i in reference_band):\n",
+    "            raise TypeError(\"reference_band must be a list of indices (ints).\")\n",
+    "        if any(i >= len(results) for i in reference_band):\n",
+    "            raise ValueError(\"Index {} chosen is out of range for the contrast objects.\".format([i for i in reference_band if i >= len(results)]))\n",
+    "\n",
+    "        ticks = [ticks_to_plot[tick] for tick in reference_band]\n",
+    "        summary_xmin, summary_xmax = summary_axes.get_xlim()\n",
+    "        summary_ymin, summary_ymax = summary_axes.get_ylim()\n",
+    "        span_ax = bar_kwargs.pop(\"span_ax\")\n",
+    "\n",
+    "        x_start_values, y_start_values, x_distances, y_distances = [], [], [], []\n",
+    "        for summary_index in reference_band:\n",
+    "            summary_ci_low = results.get(ci_type+'_low')[summary_index]\n",
+    "            summary_ci_high = results.get(ci_type+'_high')[summary_index]   \n",
+    "\n",
+    "            if span_ax == True:\n",
+    "                starting_location = summary_ymax if horizontal else summary_xmin\n",
+    "            else:\n",
+    "                starting_location = ticks_to_plot[summary_index]  \n",
+    "            x_distance = summary_ymin if horizontal else summary_xmax \n",
+    "\n",
+    "            x_start_values.append(starting_location)\n",
+    "            y_start_values.append(summary_ci_low)\n",
+    "            x_distances.append(x_distance + 1)\n",
+    "            y_distances.append(summary_ci_high - summary_ci_low)\n",
+    "    else:\n",
+    "        raise ValueError(\"Invalid bar_type. Must be 'raw' or 'contrast'.\")\n",
+    "    \n",
+    "    if horizontal:\n",
+    "        x_start_values, y_start_values = y_start_values, x_start_values\n",
+    "        x_distances, y_distances = y_distances, x_distances\n",
+    "\n",
+    "    for name, values in zip(['x_start_values', 'x_distances', 'y_start_values', 'y_distance'],\n",
+    "                            [x_start_values, x_distances, y_start_values, y_distances]\n",
+    "                        ):\n",
+    "        bar_dict[name] = values\n",
+    "\n",
+    "    # Colors\n",
+    "    colors = color_picker(\n",
+    "                color_type = bar_type,\n",
+    "                kwargs = bar_kwargs, \n",
+    "                elements = ticks_to_plot if bar_type=='contrast' else ticks, \n",
+    "                color_col = color_col, \n",
+    "                show_pairs = show_pairs, \n",
+    "                color_palette = plot_palette_raw\n",
+    "            )\n",
+    "    if bar_type == 'contrast' and extra_delta is not None:\n",
+    "        colors.append('black')\n",
+    "    bar_dict['colors'] = colors\n",
+    "\n",
+    "    return bar_dict, bar_kwargs"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4c5fe4f1",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
diff --git a/nbs/API/plot_tools.ipynb b/nbs/API/plot_tools.ipynb
index 7187025b..5d7d83a2 100644
--- a/nbs/API/plot_tools.ipynb
+++ b/nbs/API/plot_tools.ipynb
@@ -64,6 +64,7 @@
     "import matplotlib.pyplot as plt\n",
     "import matplotlib.lines as mlines\n",
     "import matplotlib.axes as axes\n",
+    "import matplotlib.patches as mpatches\n",
     "from collections import defaultdict\n",
     "from typing import List, Tuple, Dict, Iterable, Union\n",
     "from pandas.api.types import CategoricalDtype\n",
@@ -130,6 +131,7 @@
     "        1,\n",
     "    ],  # The positions of the error bars for the sankey_error_bar method.\n",
     "    method: str = \"gapped_lines\",  # The method to use for drawing the error bars. Options are: 'gapped_lines', 'proportional_error_bar', and 'sankey_error_bar'.\n",
+    "    horizontal: bool = False,  # If True, the error bars will be horizontal. If False, the error bars will be vertical.\n",
     "    **kwargs: dict,\n",
     "):\n",
     "    \"\"\"\n",
@@ -151,7 +153,11 @@
     "\n",
     "    if ax is None:\n",
     "        ax = plt.gca()\n",
-    "    ax_ylims = ax.get_ylim()\n",
+    "\n",
+    "    if horizontal:\n",
+    "        ax_ylims = ax.get_xlim()\n",
+    "    else:\n",
+    "        ax_ylims = ax.get_ylim()\n",
     "    ax_yspan = np.abs(ax_ylims[1] - ax_ylims[0])\n",
     "    gap_width = ax_yspan * gap_width_percent / 100\n",
     "\n",
@@ -170,15 +176,15 @@
     "    else:\n",
     "        group_order = pd.unique(data[x])\n",
     "\n",
-    "    means = data.groupby(x)[y].mean().reindex(index=group_order)\n",
+    "    means = data.groupby(x, observed=False)[y].mean().reindex(index=group_order)\n",
     "\n",
     "    if method in [\"proportional_error_bar\", \"sankey_error_bar\"]:\n",
     "        g = lambda x: np.sqrt(\n",
     "            (np.sum(x) * (len(x) - np.sum(x))) / (len(x) * len(x) * len(x))\n",
     "        )\n",
-    "        sd = data.groupby(x)[y].apply(g)\n",
+    "        sd = data.groupby(x, observed=False)[y].apply(g)\n",
     "    else:\n",
-    "        sd = data.groupby(x)[y].std().reindex(index=group_order)\n",
+    "        sd = data.groupby(x, observed=False)[y].std().reindex(index=group_order)\n",
     "\n",
     "    lower_sd = means - sd\n",
     "    upper_sd = means + sd\n",
@@ -186,9 +192,9 @@
     "    if (lower_sd < ax_ylims[0]).any() or (upper_sd > ax_ylims[1]).any():\n",
     "        kwargs[\"clip_on\"] = True\n",
     "\n",
-    "    medians = data.groupby(x)[y].median().reindex(index=group_order)\n",
+    "    medians = data.groupby(x, observed=False)[y].median().reindex(index=group_order)\n",
     "    quantiles = (\n",
-    "        data.groupby(x)[y].quantile([0.25, 0.75]).unstack().reindex(index=group_order)\n",
+    "        data.groupby(x, observed=False)[y].quantile([0.25, 0.75]).unstack().reindex(index=group_order)\n",
     "    )\n",
     "    lower_quartiles = quantiles[0.25]\n",
     "    upper_quartiles = quantiles[0.75]\n",
@@ -228,7 +234,8 @@
     "\n",
     "    kwargs[\"zorder\"] = kwargs[\"zorder\"]\n",
     "\n",
-    "    for xpos, central_measure in enumerate(central_measures):\n",
+    "    for xpos, val in enumerate(central_measures.index):\n",
+    "        central_measure = central_measures[val]\n",
     "        kwargs[\"color\"] = custom_palette[xpos]\n",
     "\n",
     "        if method == \"sankey_error_bar\":\n",
@@ -236,30 +243,36 @@
     "        else:\n",
     "            _xpos = xpos + offset[xpos]\n",
     "\n",
-    "        low = lows[xpos]\n",
-    "        high = highs[xpos]\n",
-    "        if low == high == central_measure:\n",
-    "            low_to_mean = mlines.Line2D(\n",
-    "                [_xpos, _xpos], [low, central_measure], **kwargs\n",
-    "            )\n",
-    "            ax.add_line(low_to_mean)\n",
+    "        # Fix for the non-string x-axis issue #108\n",
+    "        if central_measures.index.dtype.name == \"category\":\n",
+    "            low = lows[xpos]\n",
+    "            high = highs[xpos]\n",
+    "        else: \n",
+    "            low = lows[val]\n",
+    "            high = highs[val]\n",
     "\n",
-    "            mean_to_high = mlines.Line2D(\n",
-    "                [_xpos, _xpos], [central_measure, high], **kwargs\n",
-    "            )\n",
-    "            ax.add_line(mean_to_high)\n",
+    "        if low == high == central_measure:\n",
+    "            if horizontal:\n",
+    "                low2mean_x, low2mean_y = [low, central_measure], [_xpos, _xpos]\n",
+    "                mean2high_x, mean2high_y = [central_measure, high], [_xpos, _xpos]\n",
+    "            else:\n",
+    "                low2mean_x, low2mean_y = [_xpos, _xpos], [low, central_measure]\n",
+    "                mean2high_x, mean2high_y = [_xpos, _xpos], [central_measure, high]\n",
     "        else:\n",
-    "            low_to_mean = mlines.Line2D(\n",
-    "                [_xpos, _xpos], [low, central_measure - gap_width], **kwargs\n",
-    "            )\n",
-    "            ax.add_line(low_to_mean)\n",
-    "\n",
-    "            mean_to_high = mlines.Line2D(\n",
-    "                [_xpos, _xpos], [central_measure + gap_width, high], **kwargs\n",
-    "            )\n",
-    "            ax.add_line(mean_to_high)\n",
-    "\n",
-    "\n",
+    "            if horizontal:\n",
+    "                low2mean_x, low2mean_y = [low, central_measure - gap_width], [_xpos, _xpos]\n",
+    "                mean2high_x, mean2high_y = [central_measure + gap_width, high], [_xpos, _xpos]\n",
+    "            else:\n",
+    "                low2mean_x, low2mean_y = [_xpos, _xpos], [low, central_measure - gap_width]\n",
+    "                mean2high_x, mean2high_y = [_xpos, _xpos], [central_measure + gap_width, high]\n",
+    "        # Add lines\n",
+    "        ax.add_line(mlines.Line2D(\n",
+    "                    low2mean_x, low2mean_y, **kwargs\n",
+    "                ))\n",
+    "        ax.add_line(mlines.Line2D(\n",
+    "                    mean2high_x, mean2high_y, **kwargs\n",
+    "                ))\n",
+    "        \n",
     "def check_data_matches_labels(\n",
     "    labels,  # list of input labels\n",
     "    data,  # Pandas Series of input data\n",
@@ -408,11 +421,14 @@
     "    strip_on: bool = True,  # if True, draw strip for each group comparison\n",
     "    one_sankey: bool = False,  # if True, only draw one sankey diagram\n",
     "    right_color: bool = False,  # if True, each strip of the diagram will be colored according to the corresponding left labels\n",
-    "    align: bool = \"center\",  # if 'center', the diagram will be centered on each xtick,  if 'edge', the diagram will be aligned with the left edge of each xtick\n",
+    "    align: str = \"center\",  # if 'center', the diagram will be centered on each xtick,  if 'edge', the diagram will be aligned with the left edge of each xtick\n",
+    "    horizontal: bool = False,  # if True, the horizontal format for the sankey diagram will be used\n",
     "):\n",
     "    \"\"\"\n",
     "    Make a single Sankey diagram showing proportion flow from left to right\n",
+    "\n",
     "    Original code from: https://github.com/anazalea/pySankey\n",
+    "    \n",
     "    Changes are added to normalize each diagram's height to be 1\n",
     "\n",
     "    \"\"\"\n",
@@ -489,6 +505,7 @@
     "    if align not in (\"center\", \"edge\"):\n",
     "        err = \"{} assigned for `align` is not valid.\".format(align)\n",
     "        raise ValueError(err)\n",
+    "    \n",
     "    if align == \"center\":\n",
     "        try:\n",
     "            leftpos = xpos - width / 2\n",
@@ -568,16 +585,24 @@
     "\n",
     "    # Plot vertical bars for each label\n",
     "    for left_label in left_labels:\n",
-    "        ax.fill_between(\n",
-    "            [leftpos + (-(bar_width) * xMax * 0.5), leftpos + (bar_width * xMax * 0.5)],\n",
-    "            2 * [leftWidths_norm[left_label][\"bottom\"]],\n",
-    "            2 * [leftWidths_norm[left_label][\"top\"]],\n",
-    "            color=colorDict[left_label],\n",
-    "            alpha=0.99,\n",
-    "        )\n",
+    "        if horizontal:\n",
+    "            fill_method = ax.fill_betweenx\n",
+    "        else:\n",
+    "            fill_method = ax.fill_between\n",
+    "        fill_method(\n",
+    "                [leftpos + (-(bar_width) * xMax * 0.5), leftpos + (bar_width * xMax * 0.5)],\n",
+    "                2 * [leftWidths_norm[left_label][\"bottom\"]],\n",
+    "                2 * [leftWidths_norm[left_label][\"top\"]],\n",
+    "                color=colorDict[left_label],\n",
+    "                alpha=0.99,\n",
+    "            )\n",
     "    if (not flow and sankey) or one_sankey:\n",
     "        for right_label in right_labels:\n",
-    "            ax.fill_between(\n",
+    "            if horizontal:\n",
+    "                fill_method = ax.fill_betweenx\n",
+    "            else:\n",
+    "                fill_method = ax.fill_between\n",
+    "            fill_method(\n",
     "                [\n",
     "                    xMax + leftpos + (-bar_width * xMax * 0.5),\n",
     "                    leftpos + xMax + (bar_width * xMax * 0.5),\n",
@@ -590,16 +615,29 @@
     "\n",
     "    # Plot error bars\n",
     "    if error_bar_on and strip_on:\n",
-    "        error_bar(\n",
-    "            concatenated_df,\n",
-    "            x=\"groups\",\n",
-    "            y=\"values\",\n",
-    "            ax=ax,\n",
-    "            offset=0,\n",
-    "            gap_width_percent=2,\n",
-    "            method=\"sankey_error_bar\",\n",
-    "            pos=[leftpos, leftpos + xMax],\n",
-    "        )\n",
+    "        if horizontal:\n",
+    "            error_bar(\n",
+    "                concatenated_df,\n",
+    "                x=\"groups\",\n",
+    "                y=\"values\",\n",
+    "                ax=ax,\n",
+    "                offset=0,\n",
+    "                gap_width_percent=2,\n",
+    "                method=\"sankey_error_bar\",\n",
+    "                pos=[leftpos, leftpos + xMax],\n",
+    "                horizontal=True,\n",
+    "            )\n",
+    "        else:\n",
+    "            error_bar(\n",
+    "                concatenated_df,\n",
+    "                x=\"groups\",\n",
+    "                y=\"values\",\n",
+    "                ax=ax,\n",
+    "                offset=0,\n",
+    "                gap_width_percent=2,\n",
+    "                method=\"sankey_error_bar\",\n",
+    "                pos=[leftpos, leftpos + xMax],\n",
+    "            )\n",
     "\n",
     "    # Determine widths of individual strips, all widths are normalized to 1\n",
     "    ns_l = defaultdict()\n",
@@ -657,7 +695,11 @@
     "                rightWidths_norm[right_label][\"bottom\"] += ns_r_norm[left_label][\n",
     "                    right_label\n",
     "                ]\n",
-    "                ax.fill_between(\n",
+    "                if horizontal:\n",
+    "                    fill_method = ax.fill_betweenx\n",
+    "                else:\n",
+    "                    fill_method = ax.fill_between\n",
+    "                fill_method(\n",
     "                    np.linspace(\n",
     "                        leftpos + (bar_width * xMax * 0.5),\n",
     "                        leftpos + xMax - (bar_width * xMax * 0.5),\n",
@@ -670,13 +712,13 @@
     "                    edgecolor=\"none\",\n",
     "                )\n",
     "\n",
-    "\n",
     "def sankeydiag(\n",
     "    data: pd.DataFrame,\n",
     "    xvar: str,  # x column to be plotted.\n",
     "    yvar: str,  # y column to be plotted.\n",
-    "    left_idx: str,  # the value in column xvar that is on the left side of each sankey diagram\n",
-    "    right_idx: str,  # the value in column xvar that is on the right side of each sankey diagram, if len(left_idx) == 1, it will be broadcasted to the same length as right_idx, otherwise it should have the same length as right_idx\n",
+    "    temp_all_plot_groups: list,\n",
+    "    idx: list,\n",
+    "    temp_idx: list,\n",
     "    left_labels: list = None,  # labels for the left side of the diagram. The diagram will be sorted by these labels.\n",
     "    right_labels: list = None,  # labels for the right side of the diagram. The diagram will be sorted by these labels.\n",
     "    palette: str | dict = None,\n",
@@ -688,6 +730,7 @@
     "    right_color: bool = False,  # if True, each strip of the diagram will be colored according to the corresponding left labels\n",
     "    align: str = \"center\",  # the alignment of each sankey diagram, can be 'center' or 'left'\n",
     "    alpha: float = 0.65,  # the transparency of each strip\n",
+    "    horizontal: bool = False,  # if True, the horizontal format for the sankey diagram will be used\n",
     "    **kwargs,\n",
     "):\n",
     "    \"\"\"\n",
@@ -697,7 +740,6 @@
     "    right_idx in the column xvar is on the right side of each sankey diagram\n",
     "\n",
     "    \"\"\"\n",
-    "\n",
     "    if \"width\" in kwargs:\n",
     "        width = kwargs[\"width\"]\n",
     "\n",
@@ -719,9 +761,38 @@
     "    if \"flow\" in kwargs:\n",
     "        flow = kwargs[\"flow\"]\n",
     "\n",
+    "    fontsize = kwargs.pop(\"fontsize\")\n",
+    "\n",
     "    if ax is None:\n",
     "        ax = plt.gca()\n",
     "\n",
+    "    left_idx = []\n",
+    "    right_idx = []\n",
+    "    # Design for Sankey Flow Diagram\n",
+    "    sankey_idx = (\n",
+    "    [\n",
+    "        (control, test)\n",
+    "        for i in idx\n",
+    "        for control, test in zip(\n",
+    "            i[:],\n",
+    "            (tuple(i[1:]) + (i[0],)) if isinstance(i, tuple) else (list(i[1:]) + [i[0]])\n",
+    "        )\n",
+    "    ]\n",
+    "    if flow\n",
+    "    else temp_idx   \n",
+    "    )\n",
+    "\n",
+    "    for i in sankey_idx:\n",
+    "        left_idx.append(i[0])\n",
+    "        right_idx.append(i[1])\n",
+    "\n",
+    "    if len(temp_all_plot_groups) == 2:\n",
+    "        one_sankey = True\n",
+    "        left_idx.pop()\n",
+    "        right_idx.pop()  # Remove the last element from two lists\n",
+    "\n",
+    "    # two_col_sankey = True if proportional == True and one_sankey == False and sankey == True and flow == False else False\n",
+    "\n",
     "    allLabels = pd.Series(np.sort(data[yvar].unique())[::-1]).unique()\n",
     "\n",
     "    # Check if all the elements in left_idx and right_idx are in xvar column\n",
@@ -791,6 +862,7 @@
     "                flow=flow,\n",
     "                align=align,\n",
     "                alpha=alpha,\n",
+    "                horizontal=horizontal,\n",
     "            )\n",
     "            xpos += 1\n",
     "        else:\n",
@@ -814,24 +886,1230 @@
     "                flow=False,\n",
     "                align=\"edge\",\n",
     "                alpha=alpha,\n",
+    "                horizontal=horizontal,\n",
     "            )\n",
     "\n",
     "    # Now only draw vs xticks for two-column sankey diagram\n",
+    "\n",
     "    if not one_sankey or (sankey and not flow):\n",
-    "        sankey_ticks = (\n",
+    "        sankey_tick_vals = (\n",
     "            [f\"{left}\" for left in broadcasted_left]\n",
     "            if flow\n",
-    "            else [\n",
-    "                f\"{left}\\n v.s.\\n{right}\"\n",
+    "            else [f\"{left} v.s. {right}\" if horizontal\n",
+    "                else f\"{left}\\n v.s.\\n{right}\"\n",
     "                for left, right in zip(broadcasted_left, right_idx)\n",
     "            ]\n",
     "        )\n",
-    "        ax.get_xaxis().set_ticks(np.arange(len(right_idx)))\n",
-    "        ax.get_xaxis().set_ticklabels(sankey_ticks)\n",
+    "        sankey_tick_locs = np.arange(len(right_idx))\n",
+    "    else:\n",
+    "        sankey_tick_vals, sankey_tick_locs = [broadcasted_left[0], right_idx[0]], [0, 1]\n",
+    "\n",
+    "    if horizontal:\n",
+    "        ax.set_yticks(sankey_tick_locs)\n",
+    "        ax.set_yticklabels(sankey_tick_vals, fontsize = fontsize)\n",
+    "    else:\n",
+    "        ax.set_xticks(sankey_tick_locs)\n",
+    "        ax.set_xticklabels(sankey_tick_vals, fontsize = fontsize)\n",
+    "\n",
+    "    return (left_idx, right_idx)\n",
+    "\n",
+    "def add_bars_to_plot(bar_dict: dict, ax: axes.Axes, bar_kwargs: dict):\n",
+    "    \"\"\"\n",
+    "    Add bars to the relevant axes.\n",
+    "\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    bar_dict : dict\n",
+    "        Dictionary of bar values.\n",
+    "    ax : axes.Axes\n",
+    "        Matplotlib axis object to plot on.\n",
+    "    bar_kwargs : dict\n",
+    "        Keyword arguments for the bars.\n",
+    "    \"\"\"\n",
+    "    og_xlim, og_ylim = ax.get_xlim(), ax.get_ylim()\n",
+    "\n",
+    "    x_start_values, x_distances, y_start_values, y_distances, colors = bar_dict.values()\n",
+    "\n",
+    "    for start_x, start_y, distance_x, distance_y, current_color in zip(\n",
+    "                                                                        x_start_values, \n",
+    "                                                                        y_start_values, \n",
+    "                                                                        x_distances, \n",
+    "                                                                        y_distances, \n",
+    "                                                                        colors\n",
+    "                                                                    ):\n",
+    "        ax.add_patch(mpatches.Rectangle((start_x, start_y), \n",
+    "                                        distance_x, distance_y, \n",
+    "                                        color=current_color, **bar_kwargs\n",
+    "                                        )\n",
+    "                    )\n",
+    "    ax.set_xlim(og_xlim)\n",
+    "    ax.set_ylim(og_ylim) \n",
+    "\n",
+    "def delta_text_plotter(\n",
+    "        results: pd.DataFrame, \n",
+    "        ax_to_plot: object, \n",
+    "        ticks_to_plot: list, \n",
+    "        delta_text_kwargs: dict, \n",
+    "        color_col: str, \n",
+    "        plot_palette_raw: dict, \n",
+    "        show_pairs: bool,\n",
+    "        float_contrast: bool,\n",
+    "        extra_delta: float,\n",
+    "    ):\n",
+    "    \"\"\"\n",
+    "    Add delta text to the contrast plot.\n",
+    "\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    results : object (Dataframe)\n",
+    "        Dataframe of contrast object comparisons.\n",
+    "    ax_to_plot : axes.Axes\n",
+    "        Matplotlib axis object to plot on.\n",
+    "    ticks_to_plot : list\n",
+    "        List of indices of the contrast objects.\n",
+    "    delta_text_kwargs : dict\n",
+    "        Keyword arguments for the delta text.\n",
+    "    color_col : str\n",
+    "        Column name of the color column.\n",
+    "    plot_palette_raw : dict\n",
+    "        Dictionary of colors used in the plot.\n",
+    "    show_pairs : bool\n",
+    "        Whether the data is paired and show pairs.\n",
+    "    float_contrast : bool\n",
+    "        Whether the DABEST plot uses Gardner-Altman or Cummings.\n",
+    "    extra_delta : float or None\n",
+    "        The extra mini-meta or delta-delta value if applicable.\n",
+    "    \"\"\"\n",
+    "    # Colors\n",
+    "    from .misc_tools import color_picker\n",
+    "    delta_text_colors = color_picker(color_type = 'delta_text',\n",
+    "                                     kwargs = delta_text_kwargs, \n",
+    "                                     elements = ticks_to_plot, \n",
+    "                                     color_col = color_col, \n",
+    "                                     show_pairs = show_pairs, \n",
+    "                                     color_palette = plot_palette_raw\n",
+    "                                    )\n",
+    "\n",
+    "    num_of_elements = len(ticks_to_plot) + 1 if extra_delta is not None else len(ticks_to_plot)\n",
+    "\n",
+    "    # Collect the means for the delta text\n",
+    "    delta_values = []\n",
+    "    for j, tick in enumerate(ticks_to_plot):\n",
+    "        delta_values.append(results.difference[int(j)])\n",
+    "    if extra_delta is not None: \n",
+    "        delta_values.append(extra_delta)\n",
+    "        delta_text_colors.append('black')\n",
+    "\n",
+    "    # Collect the X-coordinates for the delta text\n",
+    "    delta_text_x_coordinates = delta_text_kwargs.pop('x_coordinates')\n",
+    "    delta_text_x_offset = delta_text_kwargs.pop('offset')\n",
+    "\n",
+    "    if delta_text_x_coordinates is not None:\n",
+    "        if not isinstance(delta_text_x_coordinates, (list, tuple)) or not all(isinstance(x, (int, float)) for x in delta_text_x_coordinates):\n",
+    "            raise TypeError(\"delta_text_kwargs['x_coordinates'] must be a list of x-coordinates.\")\n",
+    "        if len(delta_text_x_coordinates) != num_of_elements:\n",
+    "            raise ValueError(\"delta_text_kwargs['x_coordinates'] must have the same length as the number of ticks to plot.\")\n",
+    "    else:\n",
+    "        x_adjust = (-0.4 if float_contrast else 0.48) + delta_text_x_offset\n",
+    "        delta_text_x_coordinates = [x+x_adjust for x in ticks_to_plot]\n",
+    "        if extra_delta is not None: delta_text_x_coordinates.append(max(ticks_to_plot)+1+x_adjust)\n",
+    "\n",
+    "    # Collect the Y-coordinates for the delta text\n",
+    "    delta_text_y_coordinates = delta_text_kwargs.pop('y_coordinates')\n",
+    "    if float_contrast: delta_text_kwargs[\"va\"] = 'bottom' if results.difference[0] >= 0 else 'top'\n",
+    "\n",
+    "    if delta_text_y_coordinates is not None:\n",
+    "        if not isinstance(delta_text_y_coordinates, (list, tuple)) or not all(isinstance(y, (int, float)) for y in delta_text_y_coordinates):\n",
+    "            raise TypeError(\"delta_text_kwargs['y_coordinates'] must be a list of y-coordinates.\")\n",
+    "        if len(delta_text_y_coordinates) != num_of_elements:\n",
+    "            raise ValueError(\"delta_text_kwargs['y_coordinates'] must have the same length as the number of ticks to plot.\")\n",
+    "    else:\n",
+    "        delta_text_y_coordinates = delta_values\n",
+    "\n",
+    "    # Plot the delta text\n",
+    "    for x, y, text, color in zip(delta_text_x_coordinates, delta_text_y_coordinates, delta_values, delta_text_colors):\n",
+    "        delta_text = np.format_float_positional(text, precision=2, sign=True, trim=\"k\", min_digits=2)\n",
+    "        ax_to_plot.text(x, y, delta_text, color=color, zorder=5, **delta_text_kwargs)\n",
+    "\n",
+    "def delta_dots_plotter(\n",
+    "        plot_data: pd.DataFrame, \n",
+    "        contrast_axes: axes.Axes, \n",
+    "        delta_id_col: str, \n",
+    "        idx: list, \n",
+    "        xvar: str, \n",
+    "        yvar: str, \n",
+    "        is_paired: bool, \n",
+    "        color_col: str, \n",
+    "        float_contrast: bool, \n",
+    "        plot_palette_raw: dict, \n",
+    "        delta_dot_kwargs: dict, \n",
+    "        horizontal: bool\n",
+    "    ):\n",
+    "    \"\"\"\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    plot_data : object (Dataframe)\n",
+    "        Dataframe of the plot data.\n",
+    "    contrast_axes : axes.Axes\n",
+    "        Matplotlib axis object to plot on.\n",
+    "    delta_id_col : str\n",
+    "        Column name of the delta id column.\n",
+    "    idx : list\n",
+    "        List of indices of the contrast objects.\n",
+    "    xvar : str\n",
+    "        Column name of the x variable.\n",
+    "    yvar : str\n",
+    "        Column name of the y variable.\n",
+    "    is_paired : bool\n",
+    "        Whether the data is paired.\n",
+    "    color_col : str\n",
+    "        Column name of the color column.\n",
+    "    float_contrast : bool\n",
+    "        Whether the DABEST plot uses Gardner-Altman or Cummings\n",
+    "    plot_palette_raw : dict\n",
+    "        Dictionary of colors used in the plot.\n",
+    "    delta_dot_kwargs : dict\n",
+    "        Keyword arguments for the delta dots.\n",
+    "    horizontal : bool\n",
+    "        If the rawplot is horizontal.\n",
+    "    \"\"\"\n",
+    "\n",
+    "    # Checks and initializations\n",
+    "    # from .plot_tools import swarmplot\n",
+    "    delta_dot_color = delta_dot_kwargs.pop('color')\n",
+    "    if color_col is not None:\n",
+    "        plot_palette_deltapts = plot_palette_raw\n",
+    "        delta_plot_data = plot_data[[xvar, yvar, delta_id_col, color_col]]\n",
+    "    else:\n",
+    "        plot_palette_deltapts = delta_dot_color\n",
+    "        delta_plot_data = plot_data[[xvar, yvar, delta_id_col]]\n",
+    "\n",
+    "    # TODO: to make jitter value more accurate and not just a hardcoded eyeball value\n",
+    "    jitter = 0.6 if float_contrast else 1 \n",
+    "\n",
+    "    # Create dataframe of delta values\n",
+    "    final_deltas = pd.DataFrame()\n",
+    "    for i in idx:\n",
+    "        for j in i:\n",
+    "            if i.index(j) != 0:\n",
+    "                temp_df_exp = delta_plot_data[\n",
+    "                    delta_plot_data[xvar].str.contains(j)\n",
+    "                ].reset_index(drop=True)\n",
+    "                if is_paired == \"baseline\":\n",
+    "                    temp_df_cont = delta_plot_data[\n",
+    "                        delta_plot_data[xvar].str.contains(i[0])\n",
+    "                    ].reset_index(drop=True)\n",
+    "                elif is_paired == \"sequential\":\n",
+    "                    temp_df_cont = delta_plot_data[\n",
+    "                        delta_plot_data[xvar].str.contains(\n",
+    "                            i[i.index(j) - 1]\n",
+    "                        )\n",
+    "                    ].reset_index(drop=True)\n",
+    "                delta_df = temp_df_exp.copy()\n",
+    "                delta_df[yvar] = temp_df_exp[yvar] - temp_df_cont[yvar]\n",
+    "                final_deltas = pd.concat([final_deltas, delta_df])\n",
+    "\n",
+    "    if horizontal:\n",
+    "        delta_dot_kwargs.update({'side': 'left'})\n",
+    "    # Plot the delta dots\n",
+    "    swarmplot(\n",
+    "        data=final_deltas,\n",
+    "        x=xvar,\n",
+    "        y=yvar,\n",
+    "        ax=contrast_axes,\n",
+    "        order=None,\n",
+    "        hue=color_col,\n",
+    "        palette=plot_palette_deltapts,\n",
+    "        jitter=jitter,\n",
+    "        is_drop_gutter=True,\n",
+    "        gutter_limit=1,\n",
+    "        horizontal=horizontal,\n",
+    "        **delta_dot_kwargs\n",
+    "    )\n",
+    "    contrast_axes.legend().set_visible(False)\n",
+    "\n",
+    "\n",
+    "def slopegraph_plotter(\n",
+    "        dabest_obj: object, \n",
+    "        plot_data: pd.DataFrame, \n",
+    "        xvar: str, \n",
+    "        yvar: str, \n",
+    "        color_col: str, \n",
+    "        plot_palette_raw: dict, \n",
+    "        slopegraph_kwargs: dict, \n",
+    "        rawdata_axes: axes.Axes, \n",
+    "        ytick_color: str, \n",
+    "        temp_idx: list, \n",
+    "        horizontal: bool,\n",
+    "        temp_all_plot_groups: list,\n",
+    "        plot_kwargs: dict\n",
+    "    ):\n",
+    "    \"\"\"\n",
+    "    Add slopegraph to the rawdata axes.\n",
+    "\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    dabest_obj : object\n",
+    "        DABEST object.\n",
+    "    plot_data : object (Dataframe)\n",
+    "        Dataframe of the plot data.\n",
+    "    xvar : str\n",
+    "        Column name of the x variable.\n",
+    "    yvar : str\n",
+    "        Column name of the y variable.\n",
+    "    color_col : str\n",
+    "        Column name of the color column.\n",
+    "    plot_palette_raw : dict\n",
+    "        Dictionary of colors used in the plot.\n",
+    "    slopegraph_kwargs : dict\n",
+    "        Keyword arguments for the slopegraph.\n",
+    "    rawdata_axes : axes.Axes\n",
+    "        Matplotlib axis object to plot on.\n",
+    "    ytick_color : str\n",
+    "        Color of the yticks.\n",
+    "    temp_idx : list\n",
+    "        List of indices of the contrast objects.\n",
+    "    horizontal : bool\n",
+    "        If the plotting will be in horizontal format.\n",
+    "    temp_all_plot_groups : list\n",
+    "        List of all plot groups.\n",
+    "    plot_kwargs : dict\n",
+    "        Keyword arguments for the plot.\n",
+    "\n",
+    "    \"\"\"\n",
+    "    # Jitter Kwargs \n",
+    "    # With help from GitHub user: devMJBL\n",
+    "    jitter = slopegraph_kwargs.pop(\"jitter\")\n",
+    "    if jitter > 1:\n",
+    "        err0 = \"Jitter value is too high. Defaulting to 1.\"\n",
+    "        warnings.warn(err0)\n",
+    "        jitter = 1\n",
+    "    rng = np.random.default_rng(slopegraph_kwargs.pop(\"jitter_seed\"))\n",
+    "\n",
+    "    # Pivot the long (melted) data.\n",
+    "    if color_col is None:\n",
+    "        pivot_values = [yvar]\n",
+    "    else:\n",
+    "        pivot_values = [yvar, color_col]\n",
+    "    pivoted_plot_data = pd.pivot(\n",
+    "        data=plot_data,\n",
+    "        index=dabest_obj.id_col,\n",
+    "        columns=xvar,\n",
+    "        values=pivot_values,\n",
+    "    )\n",
+    "\n",
+    "    x_start = 0\n",
+    "    for ii, current_tuple in enumerate(temp_idx):\n",
+    "        current_pair = pivoted_plot_data.loc[\n",
+    "            :, pd.MultiIndex.from_product([pivot_values, current_tuple])\n",
+    "        ].dropna()\n",
+    "\n",
+    "        # Check for correct pairing\n",
+    "        if len(current_pair) == 0:\n",
+    "            raise ValueError('There are no pairs to plot... check original dataframe for correct ID pairing')\n",
+    "\n",
+    "        current_pair = pivoted_plot_data.loc[\n",
+    "            :, pd.MultiIndex.from_product([pivot_values, current_tuple])\n",
+    "        ]\n",
+    "        grp_count = len(current_tuple)\n",
+    "\n",
+    "        # Iterate through the data for the current tuple.\n",
+    "        for ID, observation in current_pair.iterrows():\n",
+    "            x_points = [t + 0.15*jitter*rng.standard_t(df=6, size=None) for t in range(x_start, x_start + grp_count)] # devMJBL\n",
+    "            y_points = observation[yvar].tolist()\n",
+    "\n",
+    "            if color_col is None:\n",
+    "                slopegraph_kwargs[\"color\"] = ytick_color\n",
+    "            else:\n",
+    "                color_key = observation[color_col].iloc[0]\n",
+    "                if isinstance(color_key, (str, np.int64, np.float64)):\n",
+    "                    slopegraph_kwargs[\"color\"] = plot_palette_raw[color_key]\n",
+    "                    slopegraph_kwargs[\"label\"] = color_key\n",
+    "\n",
+    "            x_points, y_points = (y_points, x_points) if horizontal else (x_points, y_points)\n",
+    "            rawdata_axes.plot(x_points, y_points, **slopegraph_kwargs)\n",
+    "\n",
+    "        x_start = x_start + grp_count\n",
+    "\n",
+    "    # Set the tick labels, because the slopegraph plotting doesn't.\n",
+    "    if horizontal:\n",
+    "        rawdata_axes.set_yticks(np.arange(0, len(temp_all_plot_groups)))\n",
+    "        rawdata_axes.set_yticklabels(temp_all_plot_groups, fontsize = plot_kwargs.get(\"fontsize_rawxlabel\"))\n",
+    "    else:\n",
+    "        rawdata_axes.set_xticks(np.arange(0, len(temp_all_plot_groups)))\n",
+    "        rawdata_axes.set_xticklabels(temp_all_plot_groups, fontsize = plot_kwargs.get(\"fontsize_rawxlabel\"))\n",
+    "    \n",
+    "\n",
+    "def plot_minimeta_or_deltadelta_violins(\n",
+    "        dabest_obj: object,\n",
+    "        type: str,\n",
+    "        ci_type: str, \n",
+    "        rawdata_axes: axes.Axes,\n",
+    "        contrast_axes: axes.Axes, \n",
+    "        contrast_kwargs: dict, \n",
+    "        contrast_xtick_labels: list, \n",
+    "        effect_size: str, \n",
+    "        plot_kwargs: dict, \n",
+    "        horizontal: bool, \n",
+    "        show_pairs: bool,\n",
+    "        contrast_marker_kwargs: dict, \n",
+    "        contrast_errorbar_kwargs: dict,\n",
+    "    ):\n",
+    "    \"\"\"\n",
+    "    Add mini meta-analysis or delta-delta violin plots to the contrast plot.\n",
+    "\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    dabest_obj : object\n",
+    "        DABEST Effectsize object delta-delta or mini_meta\n",
+    "    type: str\n",
+    "        mini_meta or delta_delta\n",
+    "    ci_type : str\n",
+    "        Type of confidence interval to plot.\n",
+    "    rawdata_axes : axes.Axes\n",
+    "        Matplotlib axis object to plot on.\n",
+    "    contrast_axes : axes.Axes\n",
+    "        Matplotlib axis object to plot on.\n",
+    "    contrast_kwargs : dict\n",
+    "        Keyword arguments for the violinplot.\n",
+    "    contrast_xtick_labels : list\n",
+    "        List of xtick labels for the contrast plot.\n",
+    "    effect_size : str\n",
+    "        Type of effect size to plot.\n",
+    "    plot_kwargs : dict\n",
+    "        Keyword arguments for the plot.\n",
+    "    horizontal : bool\n",
+    "        If the plot is horizontal.\n",
+    "    show_pairs : bool\n",
+    "        Whether the data is paired and shown in pairs.\n",
+    "    contrast_marker_kwargs: dict\n",
+    "        Keyword arguments for the effectsize marker.\n",
+    "    contrast_errorbar_kwargs: dict\n",
+    "        Keyword arguments for the effectsize errorbar.\n",
+    "    \"\"\"\n",
+    "\n",
+    "    # Plot the curve\n",
+    "    def extract_curve_data(dabest_object):\n",
+    "        try:\n",
+    "            data = dabest_object.bootstraps_weighted_delta\n",
+    "        except AttributeError:\n",
+    "            data = dabest_object.bootstraps_delta_delta\n",
+    "\n",
+    "        ci_low, ci_high = dabest_object.results.get(ci_type+'_low')[0], dabest_object.results.get(ci_type+'_high')[0]\n",
+    "        return data, dabest_object.difference, ci_low, ci_high\n",
+    "\n",
+    "    data, difference, ci_low, ci_high = extract_curve_data(dabest_obj)\n",
+    "\n",
+    "    if contrast_kwargs.get('alpha') is not None:\n",
+    "        contrast_alpha = contrast_kwargs.pop('alpha')\n",
+    "\n",
+    "    if horizontal:  \n",
+    "        contrast_kwargs.update({'orientation': 'horizontal', 'widths': 1})\n",
+    "        position = max(rawdata_axes.get_yticks()) + 1\n",
+    "        half = \"bottom\"\n",
+    "        effsize_x, effsize_y = difference, [position]\n",
+    "        ci_x, ci_y = [ci_low, ci_high], [position, position]\n",
+    "    else:\n",
+    "        position = max(rawdata_axes.get_xticks()) + 1\n",
+    "        half = \"right\"\n",
+    "        effsize_x, effsize_y = [position], difference\n",
+    "        ci_x, ci_y = [position, position], [ci_low, ci_high]\n",
+    "\n",
+    "    v = contrast_axes.violinplot(\n",
+    "        data[~np.isinf(data)], positions=[position], **contrast_kwargs\n",
+    "        )\n",
+    "\n",
+    "    halfviolin(v, fill_color=\"grey\", alpha=contrast_alpha, half=half)\n",
+    "\n",
+    "    # Plot the effect size.\n",
+    "    contrast_axes.plot(\n",
+    "        effsize_x,\n",
+    "        effsize_y,\n",
+    "        **contrast_marker_kwargs\n",
+    "    )\n",
+    "    # Plot the confidence interval.\n",
+    "    contrast_axes.plot(\n",
+    "        ci_x,\n",
+    "        ci_y,\n",
+    "        **contrast_errorbar_kwargs\n",
+    "    )\n",
+    "\n",
+    "    # Add labels and ticks\n",
+    "    if horizontal:\n",
+    "        current_ylabels = rawdata_axes.get_yticklabels()\n",
+    "        if type == 'mini_meta':\n",
+    "            current_ylabels.extend([\"Weighted Delta\"])\n",
+    "        elif effect_size == \"hedges_g\":\n",
+    "            current_ylabels.extend([\"Delta g\"])\n",
+    "        else:\n",
+    "            current_ylabels.extend([\"Delta-Delta\"])\n",
+    "\n",
+    "        rawdata_axes.set_yticks(np.append(rawdata_axes.get_yticks(), position))\n",
+    "        rawdata_axes.set_yticklabels(current_ylabels)\n",
+    "    else:\n",
+    "        if type == 'mini_meta':\n",
+    "            if show_pairs:\n",
+    "                contrast_xtick_labels.extend([\"Weighted\\n Delta\"])\n",
+    "            else:\n",
+    "                contrast_xtick_labels.extend([\"Weighted Delta\"])\n",
+    "        elif effect_size == \"hedges_g\":\n",
+    "            contrast_xtick_labels.extend([\"Delta g\"])\n",
+    "        else:\n",
+    "            contrast_xtick_labels.extend([\"Delta-Delta\"])\n",
+    "\n",
+    "    # Create the delta-delta axes.\n",
+    "    if type == 'delta_delta' and not horizontal:\n",
+    "        if plot_kwargs[\"delta2_label\"] is not None:\n",
+    "            delta2_label = plot_kwargs[\"delta2_label\"]\n",
+    "        elif effect_size == \"mean_diff\":\n",
+    "            delta2_label = \"Delta-Delta\"\n",
+    "        else:\n",
+    "            delta2_label = \"Delta g\"\n",
+    "        fontsize_delta2label = plot_kwargs[\"fontsize_delta2label\"]\n",
+    "        delta2_axes = contrast_axes.twinx()\n",
+    "        delta2_axes.set_frame_on(False)\n",
+    "        delta2_axes.set_ylabel(delta2_label, fontsize=fontsize_delta2label)\n",
+    "        og_xlim_delta, og_ylim_delta = contrast_axes.get_xlim(), contrast_axes.get_ylim()\n",
+    "        delta2_axes.set_ylim(og_ylim_delta)\n",
+    "    else:\n",
+    "        delta2_axes = None\n",
+    "\n",
+    "    return delta2_axes, contrast_xtick_labels\n",
+    "\n",
+    "\n",
+    "def effect_size_curve_plotter(\n",
+    "        ticks_to_plot: list, \n",
+    "        ticks_for_baseline_ec: list, \n",
+    "        results: pd.DataFrame, \n",
+    "        ci_type: str, \n",
+    "        contrast_axes: axes.Axes, \n",
+    "        contrast_kwargs: dict, \n",
+    "        bootstraps_color_by_group: bool, \n",
+    "        plot_palette_contrast: dict,\n",
+    "        horizontal: bool, \n",
+    "        contrast_marker_kwargs: dict, \n",
+    "        contrast_errorbar_kwargs: dict,\n",
+    "        idx: list, \n",
+    "        is_paired: bool, \n",
+    "        contrast_paired_lines: bool, \n",
+    "        contrast_paired_lines_kwargs: dict,\n",
+    "        show_baseline_ec: bool = False\n",
+    "    ):\n",
+    "    \"\"\"\n",
+    "    Add effect size curves to the contrast plot.\n",
+    "\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    ticks_to_plot : list\n",
+    "        List of indices of the contrast objects.\n",
+    "    ticks_for_baseline_ec : list\n",
+    "        List of indices of the baseline effect curve objects.\n",
+    "    results : object (Dataframe)\n",
+    "        Dataframe of contrast object comparisons.\n",
+    "    ci_type : str\n",
+    "        Type of confidence interval to plot.\n",
+    "    contrast_axes : axes.Axes\n",
+    "        Matplotlib axis object to plot on.\n",
+    "    contrast_kwargs : dict\n",
+    "        Keyword arguments for the violinplot.\n",
+    "    bootstraps_color_by_group : bool\n",
+    "        Whether to color the bootstraps by group.\n",
+    "    plot_palette_contrast : dict\n",
+    "        Dictionary of colors used in the contrast plot.\n",
+    "    horizontal : bool\n",
+    "        If the plot is horizontal.\n",
+    "    contrast_marker_kwargs: dict\n",
+    "        Keyword arguments for the effectsize marker.\n",
+    "    contrast_errorbar_kwargs: dict\n",
+    "        Keyword arguments for the effectsize errorbar.\n",
+    "    idx : list\n",
+    "        List of indices of the raw groups.\n",
+    "    is_paired : bool\n",
+    "        Whether the data is paired.\n",
+    "    contrast_paired_lines : bool\n",
+    "        Whether to add lines for repeated measures data.\n",
+    "    contrast_paired_lines_kwargs : dict\n",
+    "        Keyword arguments for the repeated measures lines.\n",
+    "    show_baseline_ec : bool\n",
+    "        Whether to show the baseline effect curve.\n",
+    "    \"\"\"\n",
+    "\n",
+    "    def plot_effect_size(tick, group, control, bootstrap, effsize, ci_low, ci_high):\n",
+    "        # Create the violinplot\n",
+    "        if horizontal:  \n",
+    "            contrast_kwargs.update({'orientation': 'horizontal', 'widths': 1})\n",
+    "            \n",
+    "        v = contrast_axes.violinplot(\n",
+    "            bootstrap[~np.isinf(bootstrap)],\n",
+    "            positions=[tick],\n",
+    "            **contrast_kwargs\n",
+    "        )\n",
+    "        \n",
+    "        # Color the violin plot\n",
+    "        fc = plot_palette_contrast[group] if bootstraps_color_by_group else \"grey\"\n",
+    "        half = \"bottom\" if horizontal else \"right\"\n",
+    "        halfviolin(v, fill_color=fc, alpha=contrast_alpha, half=half)\n",
+    "\n",
+    "        # Plot the confidence interval\n",
+    "        if horizontal:\n",
+    "            ci_x, ci_y = [ci_low, ci_high], [tick, tick]\n",
+    "        else:\n",
+    "            ci_x, ci_y = [tick, tick], [ci_low, ci_high]\n",
+    "            \n",
+    "        contrast_axes.plot(ci_x, ci_y, **contrast_errorbar_kwargs)\n",
+    "        \n",
+    "        return \"{}\\nminus\\n{}\".format(group, control)\n",
+    "    \n",
+    "    if contrast_kwargs.get('alpha') is not None:\n",
+    "        contrast_alpha = contrast_kwargs.pop('alpha')\n",
+    "\n",
+    "    # Plot the curves\n",
+    "    contrast_xtick_labels = []\n",
+    "    for j, tick in enumerate(ticks_to_plot):\n",
+    "        current_group = results.test[int(j)]\n",
+    "        current_control = results.control[int(j)]\n",
+    "        current_bootstrap = results.bootstraps[int(j)]\n",
+    "        current_effsize = results.difference[int(j)]\n",
+    "        current_ci_low = results.get(ci_type+'_low')[int(j)]\n",
+    "        current_ci_high = results.get(ci_type+'_high')[int(j)]\n",
+    "\n",
+    "        # Plot the effect size marker\n",
+    "        if horizontal:\n",
+    "            effsize_x, effsize_y = current_effsize, [tick]\n",
+    "        else:\n",
+    "            effsize_x, effsize_y = [tick], current_effsize\n",
+    "\n",
+    "        contrast_axes.plot(\n",
+    "            effsize_x,\n",
+    "            effsize_y,\n",
+    "            **contrast_marker_kwargs\n",
+    "        )\n",
+    "\n",
+    "        label = plot_effect_size(tick, current_group, current_control, current_bootstrap,\n",
+    "                               current_effsize, current_ci_low, current_ci_high)\n",
+    "        contrast_xtick_labels.append(label)\n",
+    "\n",
+    "    # Add baseline effect curve plotting\n",
+    "    bec_results = results.drop_duplicates(subset='control', keep='first').reset_index(drop=True)\n",
+    "    for j, tick in enumerate(ticks_for_baseline_ec):\n",
+    "        bec_group = bec_results.control[j]\n",
+    "        bec_control = bec_results.control[j]\n",
+    "        bec_bootstrap = bec_results.bec_bootstraps[j]\n",
+    "        bec_effsize = bec_results.bec_difference[j]\n",
+    "        bec_ci_low = bec_results.get('bec_'+ci_type+'_low')[j]\n",
+    "        bec_ci_high = bec_results.get('bec_'+ci_type+'_high')[j]\n",
+    "        \n",
+    "        # Plot the effect size marker regardless of show_baseline_ec\n",
+    "        if horizontal:\n",
+    "            effsize_x, effsize_y = bec_effsize, [tick]\n",
+    "        else:\n",
+    "            effsize_x, effsize_y = [tick], bec_effsize\n",
+    "            \n",
+    "        contrast_axes.plot(effsize_x, effsize_y, **contrast_marker_kwargs)\n",
+    "        \n",
+    "        if show_baseline_ec:\n",
+    "            _ = plot_effect_size(tick, bec_group, bec_control, bec_bootstrap, \n",
+    "                               bec_effsize, bec_ci_low, bec_ci_high)\n",
+    "            # Baseline Curve doesn't need tick text\n",
+    "\n",
+    "    # Add lines for repeated measures data\n",
+    "    if is_paired and contrast_paired_lines:\n",
+    "        temp_num = 0\n",
+    "        lines_to_plot_list = []\n",
+    "\n",
+    "        for group in idx:\n",
+    "            new_group = []\n",
+    "            if len(group) >= 2:\n",
+    "                new_group.append(temp_num)\n",
+    "                for i in range(1, len(group)):\n",
+    "                    new_group.append(temp_num+i)\n",
+    "            temp_num += len(group)\n",
+    "            lines_to_plot_list.append(new_group)\n",
+    "\n",
+    "        for group in lines_to_plot_list:\n",
+    "            if len(group) > 0:\n",
+    "                mean_diffs_for_lines = []\n",
+    "                for ticks in group:\n",
+    "                    if ticks in ticks_to_plot:\n",
+    "                        mean_diffs_for_lines.append(results.loc[ticks_to_plot.index(ticks)][\"difference\"])\n",
+    "                    else:\n",
+    "                        mean_diffs_for_lines.append(int(0))\n",
+    "\n",
+    "                x_data = mean_diffs_for_lines if horizontal else group\n",
+    "                y_data = group if horizontal else mean_diffs_for_lines\n",
+    "\n",
+    "                contrast_axes.plot(\n",
+    "                    x_data, \n",
+    "                    y_data,\n",
+    "                    **contrast_paired_lines_kwargs\n",
+    "                )\n",
+    "\n",
+    "    contrast_kwargs['alpha'] = contrast_alpha\n",
+    "    return current_group, current_control, current_effsize, contrast_xtick_labels\n",
+    "\n",
+    "def gridkey_plotter(\n",
+    "        is_paired: bool, \n",
+    "        idx: list,\n",
+    "        all_plot_groups: list, \n",
+    "        gridkey: list, \n",
+    "        rawdata_axes: axes.Axes, \n",
+    "        contrast_axes: axes.Axes, \n",
+    "        plot_data: pd.DataFrame, \n",
+    "        xvar: str, \n",
+    "        yvar: str, \n",
+    "        results: pd.DataFrame, \n",
+    "        show_delta2: bool, \n",
+    "        show_mini_meta: bool, \n",
+    "        x1_level: list, \n",
+    "        experiment_label: list, \n",
+    "        float_contrast: bool, \n",
+    "        horizontal: bool, \n",
+    "        delta_delta: object, \n",
+    "        mini_meta: object, \n",
+    "        effect_size: str, \n",
+    "        gridkey_kwargs: dict,\n",
+    "    ):\n",
+    "    \"\"\"\n",
+    "    Add gridkey to the contrast plot.\n",
+    "\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    is_paired : bool\n",
+    "        Whether the data is paired.\n",
+    "    idx : list\n",
+    "        List of indices of the contrast objects.\n",
+    "    all_plot_groups : list\n",
+    "        List of all plot groups.\n",
+    "    gridkey : list\n",
+    "        List of gridkey rows.\n",
+    "    rawdata_axes : axes.Axes\n",
+    "        Matplotlib axis object for the raw data.\n",
+    "    contrast_axes : axes.Axes\n",
+    "        Matplotlib axis object for the contrast data.\n",
+    "    plot_data : object (Dataframe)\n",
+    "        Dataframe of the plot data.\n",
+    "    xvar : str\n",
+    "        Column name of the x variable.\n",
+    "    yvar : str\n",
+    "        Column name of the y variable.\n",
+    "    results : object (Dataframe)\n",
+    "        Dataframe of contrast object comparisons.\n",
+    "    show_delta2 : bool\n",
+    "        Whether to show the delta-delta.\n",
+    "    show_mini_meta : bool\n",
+    "        Whether to show the mini meta-analysis.\n",
+    "    x1_level : list\n",
+    "        List of x1 levels.\n",
+    "    experiment_label : list\n",
+    "        List of experiment labels.\n",
+    "    float_contrast : bool\n",
+    "        Whether the DABEST plot uses Gardner-Altman or Cummings\n",
+    "    horizontal : bool\n",
+    "        If the plot is horizontal.\n",
+    "    delta_delta : object\n",
+    "        delta-delta object.\n",
+    "    mini_meta : object\n",
+    "        Mini meta-analysis object.\n",
+    "    effect_size : str\n",
+    "        Type of effect size to plot\n",
+    "    gridkey_kwargs : dict\n",
+    "        Keyword arguments for the gridkey.\n",
+    "    \"\"\"\n",
+    "    # Extract relevant kwargs\n",
+    "    gridkey_show_Ns = gridkey_kwargs[\"show_Ns\"]\n",
+    "    gridkey_show_es = gridkey_kwargs[\"show_es\"]\n",
+    "    gridkey_merge_pairs = gridkey_kwargs[\"merge_pairs\"]\n",
+    "    gridkey_marker = gridkey_kwargs[\"marker\"]\n",
+    "    gridkey_delimiters = gridkey_kwargs[\"delimiters\"] \n",
+    "    labels_fontsize = gridkey_kwargs.get('labels_fontsize')\n",
+    "    fontsize = gridkey_kwargs.get('fontsize')\n",
+    "\n",
+    "    # Auto parser for gridkey - implemented by SangyuXu\n",
+    "    if gridkey == \"auto\" or gridkey == True:\n",
+    "        if experiment_label is not None:\n",
+    "            gridkey = list(np.concatenate([experiment_label, x1_level]))\n",
+    "        else:\n",
+    "            temp_groups = \";\".join(all_plot_groups)\n",
+    "            for delimiter in gridkey_delimiters:\n",
+    "                temp_groups = temp_groups.replace(delimiter, \";\")\n",
+    "            temp_groups = [i.strip() for i in temp_groups.split(';')]\n",
+    "            unique_groups = list(set(temp_groups))\n",
+    "            rank = [sum([temp_groups.index(i) for i in temp_groups if(j in i)]) for j in unique_groups]\n",
+    "            gridkey = [x for _,x in sorted(zip(rank,unique_groups))]\n",
+    "    \n",
+    "    # Raise error if there are more than 2 items in any idx and gridkey_merge_pairs is True and is_paired is not None\n",
+    "    if gridkey_merge_pairs and is_paired is not None:\n",
+    "        for i in idx:\n",
+    "            if len(i) > 2:\n",
+    "                warnings.warn(\n",
+    "                    \"gridkey_merge_pairs=True only works if all idx in tuples have only two items. gridkey_merge_pairs has automatically been set to False\"\n",
+    "                )\n",
+    "                gridkey_merge_pairs = False\n",
+    "                break\n",
+    "    elif gridkey_merge_pairs and is_paired is None:\n",
+    "        warnings.warn(\n",
+    "            \"gridkey_merge_pairs=True is only applicable for paired data.\"\n",
+    "        )\n",
+    "        gridkey_merge_pairs = False\n",
+    "\n",
+    "    # Checks for gridkey_merge_pairs and is_paired; if both are true, \"merges\" the gridkey per pair\n",
+    "    if gridkey_merge_pairs and is_paired is not None:\n",
+    "        groups_for_gridkey = []\n",
+    "        for i in idx:\n",
+    "            groups_for_gridkey.append(i[1])\n",
+    "    else:\n",
+    "        groups_for_gridkey = all_plot_groups\n",
+    "\n",
+    "    # raise errors if gridkey is not a list, or if the list is empty\n",
+    "    if isinstance(gridkey, list) is False:\n",
+    "        raise TypeError(\"gridkey must be a list (or a string 'auto').\")\n",
+    "    if any(isinstance(i, str) is False for i in gridkey):\n",
+    "        raise TypeError(\"gridkey must contain only strings.\")\n",
+    "    if len(gridkey) == 0:\n",
+    "        warnings.warn(\"gridkey is an empty list.\")\n",
+    "\n",
+    "    # raise Warning if an item in gridkey is not contained in any idx\n",
+    "    for i in gridkey:\n",
+    "        in_idx = 0\n",
+    "        for j in groups_for_gridkey:\n",
+    "            if i in j:\n",
+    "                in_idx += 1\n",
+    "        if in_idx == 0:\n",
+    "            if is_paired is not None:\n",
+    "                warnings.warn(\n",
+    "                    i\n",
+    "                    + \" is not in any idx. Please check. Alternatively, merging gridkey pairs may not be suitable for your data; try passing gridkey_merge_pairs=False.\"\n",
+    "                )\n",
+    "            else:\n",
+    "                warnings.warn(i + \" is not in any idx. Please check.\")\n",
+    "\n",
+    "    # Populate table: checks if idx for each column contains rowlabel name\n",
+    "    # IF so, marks that element as present w black dot (default \"\\u25CF\"), or space if not present\n",
+    "    table_cellcols = []\n",
+    "    for i in gridkey:\n",
+    "        thisrow = []\n",
+    "        for q in groups_for_gridkey:\n",
+    "            if str(i) in q:\n",
+    "                thisrow.append(gridkey_marker)\n",
+    "            else:\n",
+    "                thisrow.append(\"\")\n",
+    "        table_cellcols.append(thisrow)\n",
+    "\n",
+    "    # Adds a row for Ns with the Ns values\n",
+    "    if gridkey_show_Ns:\n",
+    "        gridkey.append(\"Ns\")\n",
+    "        list_of_Ns = []\n",
+    "        for i in groups_for_gridkey:\n",
+    "            list_of_Ns.append(str(plot_data.groupby(xvar, observed=False).count()[yvar].loc[i]))\n",
+    "        table_cellcols.append(list_of_Ns)\n",
+    "\n",
+    "    # Adds a row for effectsizes with effectsize values\n",
+    "    if gridkey_show_es and not horizontal:\n",
+    "        gridkey.append(\"\\u0394\")\n",
+    "        effsize_list = []\n",
+    "        results_list = results.test.to_list()\n",
+    "\n",
+    "        # get the effect size, append + or -, 2 dec places\n",
+    "        for i in enumerate(groups_for_gridkey):\n",
+    "            if i[1] in results_list:\n",
+    "                curr_esval = results.loc[results[\"test\"] == i[1]][\"difference\"].iloc[0]\n",
+    "                curr_esval_str = np.format_float_positional(\n",
+    "                    curr_esval,\n",
+    "                    precision=2,\n",
+    "                    sign=True,\n",
+    "                    trim=\"k\",\n",
+    "                    min_digits=2,\n",
+    "                )\n",
+    "                effsize_list.append(curr_esval_str)\n",
+    "            else:\n",
+    "                effsize_list.append(\"-\")\n",
+    "\n",
+    "        table_cellcols.append(effsize_list)\n",
+    "\n",
+    "    # Set the axes to plot on\n",
+    "    if float_contrast or horizontal:\n",
+    "        ax_to_plot = rawdata_axes\n",
+    "    else:\n",
+    "        ax_to_plot = contrast_axes\n",
+    "\n",
+    "    # Add delta-delta or mini_meta details to the table\n",
+    "    if show_mini_meta or show_delta2:\n",
+    "        if show_delta2:\n",
+    "            added_group_name = [\"Deltas' g\"] if effect_size == \"hedges_g\" else [\"Delta-Delta\"]\n",
+    "        else:\n",
+    "            added_group_name = [\"Weighted Delta\"]\n",
+    "        gridkey = added_group_name + gridkey\n",
+    "        table_cellcols = [[\"\"]*len(table_cellcols[0])] + table_cellcols\n",
+    "\n",
+    "        if not horizontal and show_delta2:\n",
+    "            extra_table_cellcols = [[] for i in range(len(table_cellcols))]\n",
+    "\n",
+    "            for group_idx, group_vals in enumerate(extra_table_cellcols):\n",
+    "                if group_idx == 0:\n",
+    "                    added_group = [gridkey_marker]\n",
+    "                elif gridkey_show_es and (group_idx == len(extra_table_cellcols)-1) and not horizontal:\n",
+    "                    added_delta_effectsize = delta_delta.difference\n",
+    "                    added_delta_effectsize_str = np.format_float_positional(\n",
+    "                                                                            added_delta_effectsize,\n",
+    "                                                                            precision=2,\n",
+    "                                                                            sign=True,\n",
+    "                                                                            trim=\"k\",\n",
+    "                                                                            min_digits=2,\n",
+    "                                                                        )\n",
+    "                    added_group = [added_delta_effectsize_str]\n",
+    "                else:\n",
+    "                    added_group = ['']\n",
+    "                for n in added_group:\n",
+    "                    group_vals.append(n)\n",
+    "\n",
+    "        elif horizontal or show_mini_meta:\n",
+    "            for group_idx, group_vals in enumerate(table_cellcols):\n",
+    "                if group_idx == 0:\n",
+    "                    added_group = [gridkey_marker]\n",
+    "                elif gridkey_show_es and (group_idx == len(table_cellcols)-1) and not horizontal:\n",
+    "                    added_delta_effectsize = delta_delta.difference if show_delta2 else mini_meta.difference\n",
+    "                    added_delta_effectsize_str = np.format_float_positional(\n",
+    "                                                                            added_delta_effectsize,\n",
+    "                                                                            precision=2,\n",
+    "                                                                            sign=True,\n",
+    "                                                                            trim=\"k\",\n",
+    "                                                                            min_digits=2,\n",
+    "                                                                        )\n",
+    "                    added_group = [added_delta_effectsize_str]\n",
+    "                else:\n",
+    "                    added_group = ['']\n",
+    "                for n in added_group:\n",
+    "                    group_vals.append(n)\n",
+    "\n",
+    "    # Create the table object\n",
+    "    def add_table(celltext, bbox, rowlabels=None):\n",
+    "        gridkey_to_plot = ax_to_plot.table(\n",
+    "                    cellText=celltext,\n",
+    "                    rowLabels=rowlabels,\n",
+    "                    cellLoc=\"center\",\n",
+    "                    bbox=bbox,\n",
+    "        )\n",
+    "        return gridkey_to_plot\n",
+    "\n",
+    "    if horizontal:\n",
+    "        # Convert the cells format for horizontal table plotting\n",
+    "        converted_list = []\n",
+    "        for j in range(0, len(table_cellcols[0])):\n",
+    "            temp_list = []\n",
+    "            for i in table_cellcols:\n",
+    "                temp_list.append(i[j])\n",
+    "            converted_list.append(temp_list)\n",
+    "\n",
+    "        gridkey_to_plot = add_table(celltext = converted_list, bbox = [-len(gridkey) * 0.2, 0, len(gridkey) * 0.2, 1])\n",
+    "\n",
+    "        # Add the column labels as text below the table\n",
+    "        text_locs = np.arange((-len(gridkey)*0.2) +0.1, 0, 0.2)\n",
+    "        for loc, txt in zip(text_locs, gridkey):\n",
+    "            ax_to_plot.text(\n",
+    "                        loc+0.04, \n",
+    "                        -0.01, \n",
+    "                        txt, \n",
+    "                        transform=ax_to_plot.transAxes, \n",
+    "                        ha='right',\n",
+    "                        rotation=45,\n",
+    "                        fontsize=labels_fontsize if labels_fontsize is not None else 10,\n",
+    "                        va='top',\n",
+    "                    )\n",
     "    else:\n",
-    "        sankey_ticks = [broadcasted_left[0], right_idx[0]]\n",
-    "        ax.set_xticks([0, 1])\n",
-    "        ax.set_xticklabels(sankey_ticks)"
+    "        # Plot the table for vertical format\n",
+    "        if show_mini_meta:\n",
+    "            gridkey_to_plot = add_table(celltext = table_cellcols, rowlabels=gridkey, bbox = [0, -len(gridkey) * 0.1 - 0.05, 1, len(gridkey) * 0.1])\n",
+    "        elif show_delta2:\n",
+    "            gridkey_to_plot = add_table(celltext = table_cellcols, rowlabels=gridkey, bbox = [0, -len(gridkey) * 0.1 - 0.05, 0.75, len(gridkey) * 0.1])\n",
+    "            extra_gridkey  =  add_table(celltext = extra_table_cellcols, bbox = [0.78, -len(gridkey) * 0.1 - 0.05, 0.15, len(gridkey) * 0.1])\n",
+    "        else:\n",
+    "            gridkey_to_plot = add_table(celltext = table_cellcols, rowlabels=gridkey, bbox = [0, -len(gridkey) * 0.1 - 0.05, 1, len(gridkey) * 0.1]) \n",
+    "\n",
+    "        # modifies row label cells\n",
+    "        for cell in gridkey_to_plot._cells:\n",
+    "            if cell[1] == -1:\n",
+    "                gridkey_to_plot._cells[cell].visible_edges = \"open\"\n",
+    "                gridkey_to_plot._cells[cell].set_text_props(**{\"ha\": \"right\"})\n",
+    "\n",
+    "    if fontsize is not None:\n",
+    "        gridkey_to_plot.auto_set_font_size(False)\n",
+    "        gridkey_to_plot.set_fontsize(fontsize)\n",
+    "        if show_delta2 and not horizontal:\n",
+    "            extra_gridkey.auto_set_font_size(False)\n",
+    "            extra_gridkey.set_fontsize(fontsize)\n",
+    "\n",
+    "    if labels_fontsize is not None and not horizontal:\n",
+    "        gridkey_to_plot.auto_set_font_size(False)\n",
+    "        for cell in gridkey_to_plot._cells:\n",
+    "            if cell[1] == -1:\n",
+    "                gridkey_to_plot._cells[cell].set_text_props(**{\"fontsize\": labels_fontsize})\n",
+    "\n",
+    "    # turns off both x axes\n",
+    "    if horizontal:\n",
+    "        rawdata_axes.get_yaxis().set_visible(False)\n",
+    "        contrast_axes.get_yaxis().set_visible(False)\n",
+    "    else:\n",
+    "        rawdata_axes.get_xaxis().set_visible(False)\n",
+    "        contrast_axes.get_xaxis().set_visible(False)\n",
+    "\n",
+    "def barplotter(\n",
+    "        xvar: str, \n",
+    "        yvar: str, \n",
+    "        all_plot_groups: list, \n",
+    "        rawdata_axes: axes.Axes, \n",
+    "        plot_data: pd.DataFrame, \n",
+    "        raw_colors: str, \n",
+    "        plot_palette_raw: dict, \n",
+    "        color_col: str,\n",
+    "        barplot_kwargs: dict, \n",
+    "        horizontal: bool\n",
+    "    ):\n",
+    "    \"\"\"\n",
+    "    Add bars to the raw data plot.\n",
+    "\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    xvar : str\n",
+    "        Column name of the x variable.\n",
+    "    yvar : str\n",
+    "        Column name of the y variable.\n",
+    "    all_plot_groups : list\n",
+    "        List of all plot groups.\n",
+    "    rawdata_axes : object\n",
+    "        Matplotlib axis object to plot on.\n",
+    "    plot_data : object (Dataframe)\n",
+    "        Dataframe of the plot data.\n",
+    "    raw_colors : str\n",
+    "        Color of the bar.\n",
+    "    plot_palette_raw : dict\n",
+    "        Dictionary of colors used in the bar plot.\n",
+    "    color_col : str\n",
+    "        Column name of the color column.\n",
+    "    barplot_kwargs : dict\n",
+    "        Keyword arguments for the barplot.\n",
+    "    horizontal : bool\n",
+    "        If the plot is horizontal.\n",
+    "    \"\"\"\n",
+    "    bar_width = barplot_kwargs.get('width', 0.5)\n",
+    "    fontsize = barplot_kwargs.pop('fontsize')\n",
+    "\n",
+    "    x_label, y_label = rawdata_axes.get_xlabel(), rawdata_axes.get_ylabel()\n",
+    "    if horizontal:\n",
+    "        x_var, y_var, orient = np.ones(len(all_plot_groups)), all_plot_groups, \"h\"\n",
+    "    else:\n",
+    "        x_var, y_var, orient = all_plot_groups, np.ones(len(all_plot_groups)), \"v\"\n",
+    "\n",
+    "    # Create bar1_df with basic columns\n",
+    "    bar1_df = pd.DataFrame({\n",
+    "        xvar: x_var, \n",
+    "        \"proportion\": y_var\n",
+    "    })\n",
+    "\n",
+    "    # Handle colors\n",
+    "    if color_col:\n",
+    "        # Get first color value for each group\n",
+    "        color_mapping = plot_data.groupby(xvar, observed=False)[color_col].first()\n",
+    "        bar1_df[color_col] = [color_mapping.get(group) for group in all_plot_groups]\n",
+    "        \n",
+    "        # Map colors, defaulting to bar_color if no match\n",
+    "        edge_colors = [\n",
+    "            plot_palette_raw.get(hue_val, raw_colors) \n",
+    "            for hue_val in bar1_df[color_col]\n",
+    "        ]\n",
+    "    else:\n",
+    "        edge_colors = raw_colors\n",
+    "\n",
+    "    bar1 = sns.barplot(\n",
+    "        data=bar1_df,\n",
+    "        x=xvar,\n",
+    "        y=\"proportion\",\n",
+    "        ax=rawdata_axes,\n",
+    "        order=all_plot_groups,\n",
+    "        linewidth=2,\n",
+    "        facecolor=(1, 1, 1, 0),\n",
+    "        edgecolor=edge_colors,\n",
+    "        zorder=1,\n",
+    "        orient=orient,\n",
+    "    )\n",
+    "\n",
+    "    bar2 = sns.barplot(\n",
+    "        data=plot_data,\n",
+    "        x=yvar if horizontal else xvar,\n",
+    "        y=xvar if horizontal else yvar,\n",
+    "        hue=xvar if color_col is None else color_col,\n",
+    "        ax=rawdata_axes,\n",
+    "        order=all_plot_groups,\n",
+    "        palette=plot_palette_raw,\n",
+    "        dodge=False,\n",
+    "        zorder=1,\n",
+    "        orient=orient,\n",
+    "        **barplot_kwargs\n",
+    "    )\n",
+    "\n",
+    "    # adjust the width of bars\n",
+    "    if horizontal:\n",
+    "        for bar in bar1.patches:\n",
+    "            y = bar.get_y()\n",
+    "            height = bar.get_height()\n",
+    "            centre = y + height / 2.0\n",
+    "            bar.set_y(centre - bar_width / 2.0)\n",
+    "            bar.set_height(bar_width)\n",
+    "    else:\n",
+    "        for bar in bar1.patches:\n",
+    "            x = bar.get_x()\n",
+    "            width = bar.get_width()\n",
+    "            centre = x + width / 2.0\n",
+    "            bar.set_x(centre - bar_width / 2.0)\n",
+    "            bar.set_width(bar_width)\n",
+    "\n",
+    "    # reset the x and y labels\n",
+    "    rawdata_axes.set_xlabel(x_label)\n",
+    "    rawdata_axes.set_ylabel(y_label)\n",
+    "\n",
+    "    if horizontal:\n",
+    "        rawdata_axes.set_yticks(rawdata_axes.get_yticks())\n",
+    "        rawdata_axes.set_yticklabels(rawdata_axes.get_yticklabels(), fontsize = fontsize)\n",
+    "    else:\n",
+    "        rawdata_axes.set_xticks(rawdata_axes.get_xticks())\n",
+    "        rawdata_axes.set_xticklabels(rawdata_axes.get_xticklabels(), fontsize = fontsize)\n",
+    "\n",
+    "def table_for_horizontal_plots(\n",
+    "        effectsize_df: object, \n",
+    "        ax: axes.Axes, \n",
+    "        contrast_axes: axes.Axes, \n",
+    "        ticks_to_plot: list, \n",
+    "        show_mini_meta: bool, \n",
+    "        show_delta2: bool, \n",
+    "        table_kwargs: dict,\n",
+    "        ticks_to_skip: list\n",
+    "    ):\n",
+    "    \"\"\"\n",
+    "    Add table axes for showing the deltas for horizontal plots.\n",
+    "\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    effectsize_df : object\n",
+    "        Effect size DABEST object.\n",
+    "    ax : object\n",
+    "        Matplotlib axis object to plot the table axes.\n",
+    "    contrast_axes : object\n",
+    "        Matplotlib axis object to plot the contrast axes.\n",
+    "    ticks_to_plot : list\n",
+    "        List of indices of the contrast objects.\n",
+    "    show_mini_meta : bool\n",
+    "        Whether to show the mini meta-analysis.\n",
+    "    show_delta2 : bool\n",
+    "        Whether to show the delta-delta.\n",
+    "    table_kwargs : dict\n",
+    "        Keyword arguments for the table.\n",
+    "    ticks_to_skip:  list\n",
+    "        List of ticks to skip in the table.\n",
+    "    \"\"\"\n",
+    "\n",
+    "    table_color = table_kwargs['color']\n",
+    "    table_alpha = table_kwargs['alpha']\n",
+    "    table_font_size = table_kwargs['fontsize']\n",
+    "    table_text_color = table_kwargs['text_color']\n",
+    "    text_units = table_kwargs['text_units']\n",
+    "    table_font_size -= 2 if text_units != '' else 0\n",
+    "    control_marker = table_kwargs['control_marker']  \n",
+    "    fontsize_label = table_kwargs['fontsize_label']\n",
+    "    label = table_kwargs['label']\n",
+    "\n",
+    "    ### Create a table of deltas\n",
+    "    cols=['Δ','N']\n",
+    "    lst = []\n",
+    "    for n in np.arange(0, len(effectsize_df.results.difference), 1):\n",
+    "        lst.append([effectsize_df.results.difference[n], 0])\n",
+    "    if show_mini_meta:\n",
+    "        lst.append([effectsize_df.mini_meta.difference, 0])\n",
+    "    elif show_delta2:\n",
+    "        lst.append([effectsize_df.delta_delta.difference, 0])\n",
+    "    tab = pd.DataFrame(lst, columns=cols)\n",
+    "\n",
+    "    ### Plot the text\n",
+    "    if show_mini_meta or show_delta2:\n",
+    "        new_ticks = ticks_to_plot + [max(ticks_to_plot)+1]\n",
+    "    else:\n",
+    "        new_ticks = ticks_to_plot.copy()\n",
+    "    for i,loc in zip(tab.index, new_ticks):\n",
+    "        ax.text(0.5, loc, \"{:+.2f}\".format(tab.iloc[i,0])+text_units, ha=\"center\", va=\"center\", color=table_text_color, size=table_font_size)\n",
+    "\n",
+    "    # Plot the dashes\n",
+    "    if control_marker is not None:\n",
+    "        for loc in ticks_to_skip:\n",
+    "            ax.text(0.5, loc, control_marker, ha=\"center\", va=\"center\", color=table_text_color, size=table_font_size)\n",
+    "\n",
+    "    ### Parameters for table\n",
+    "    ax.axvspan(0, 1, facecolor=table_color, alpha=table_alpha)  #### Plot the background color\n",
+    "    ax.set_xticks([0.5])\n",
+    "    ax.set_xticklabels([])\n",
+    "    ax.set_ylim(contrast_axes.get_ylim())\n",
+    "    ax.set_yticks([])\n",
+    "    ax.set_yticklabels([])\n",
+    "    ax.tick_params(left=False, bottom=False)\n",
+    "    ax.set_xlabel(label, fontsize=fontsize_label) # Set the x-axis label - hardcoded for now\n",
+    "    sns.despine(ax=ax, left=True, bottom=True)\n",
+    "\n",
+    "\n",
+    "def add_counts_to_prop_plots(\n",
+    "        plot_data: pd.DataFrame, \n",
+    "        xvar: str, \n",
+    "        yvar: str, \n",
+    "        rawdata_axes: axes.Axes, \n",
+    "        horizontal: bool, \n",
+    "        is_paired: bool, \n",
+    "        prop_sample_counts_kwargs: dict\n",
+    "    ):\n",
+    "    \"\"\"\n",
+    "    Add counts to the proportion plots.\n",
+    "\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    plot_data : object (Dataframe)\n",
+    "        Dataframe of the plot data.\n",
+    "    xvar : str\n",
+    "        Column name of the x variable.\n",
+    "    yvar : str\n",
+    "        Column name of the y variable.\n",
+    "    rawdata_axes : axes.Axes\n",
+    "        Matplotlib axis object to plot on.\n",
+    "    horizontal : bool\n",
+    "        If the plot is horizontal.\n",
+    "    is_paired : bool\n",
+    "        Whether the data is paired.\n",
+    "    prop_sample_counts_kwargs : dict\n",
+    "        Keyword arguments for the sample counts.\n",
+    "    \"\"\"\n",
+    "\n",
+    "    # Group orders\n",
+    "    if isinstance(plot_data[xvar].dtype, pd.CategoricalDtype):\n",
+    "        sample_size_text_order = pd.unique(plot_data[xvar]).categories\n",
+    "    else:\n",
+    "        sample_size_text_order = pd.unique(plot_data[xvar])\n",
+    "\n",
+    "    # Get the sample size values\n",
+    "    ones, zeros = plot_data[plot_data[yvar] == 1], plot_data[plot_data[yvar] == 0]\n",
+    "\n",
+    "    sample_size_val1 = ones.groupby(xvar, observed=False)[yvar].count().reindex(index=sample_size_text_order)\n",
+    "    sample_size_val0 = zeros.groupby(xvar, observed=False)[yvar].count().reindex(index=sample_size_text_order)\n",
+    "\n",
+    "    if \"fontsize\" not in prop_sample_counts_kwargs.keys():\n",
+    "        fontsize = 8 if horizontal else 10\n",
+    "        fontsize -= 2 if is_paired else 0\n",
+    "        prop_sample_counts_kwargs.update({'fontsize': fontsize})\n",
+    "\n",
+    "    for sample_text_x, sample_text_y0, sample_text_y1 in zip(\n",
+    "                                                            np.arange(0, len(sample_size_text_order) + 1, 1), \n",
+    "                                                            sample_size_val0,\n",
+    "                                                            sample_size_val1,\n",
+    "                                                            ):\n",
+    "        if horizontal:\n",
+    "            rawdata_axes.text(0.05, sample_text_x, sample_text_y1, **prop_sample_counts_kwargs)\n",
+    "            rawdata_axes.text(0.95, sample_text_x, sample_text_y0, **prop_sample_counts_kwargs)\n",
+    "        else:\n",
+    "            rawdata_axes.text(sample_text_x, 0.05, sample_text_y1, **prop_sample_counts_kwargs)\n",
+    "            rawdata_axes.text(sample_text_x, 0.95, sample_text_y0, **prop_sample_counts_kwargs)"
    ]
   },
   {
@@ -846,7 +2124,7 @@
     "    data: pd.DataFrame,\n",
     "    x: str,\n",
     "    y: str,\n",
-    "    ax: axes.Subplot,\n",
+    "    ax: axes.Axes,\n",
     "    order: List = None,\n",
     "    hue: str = None,\n",
     "    palette: Union[Iterable, str] = \"black\",\n",
@@ -854,8 +2132,10 @@
     "    size: float = 5,\n",
     "    side: str = \"center\",\n",
     "    jitter: float = 1,\n",
+    "    filled: Union[bool, List, Tuple] = True,\n",
     "    is_drop_gutter: bool = True,\n",
     "    gutter_limit: float = 0.5,\n",
+    "    horizontal: bool = False,\n",
     "    **kwargs,\n",
     "):\n",
     "    \"\"\"\n",
@@ -869,8 +2149,8 @@
     "        The column in the DataFrame to be used as the x-axis.\n",
     "    y : str\n",
     "        The column in the DataFrame to be used as the y-axis.\n",
-    "    ax : axes._subplots.Subplot | axes._axes.Axes\n",
-    "        Matplotlib AxesSubplot object for which the plot would be drawn on. Default is None.\n",
+    "    ax : axes.Axes\n",
+    "        Matplotlib axes.Axes object for which the plot would be drawn on. Default is None.\n",
     "    order : List\n",
     "        The order in which x-axis categories should be displayed. Default is None.\n",
     "    hue : str\n",
@@ -886,20 +2166,27 @@
     "        The side on which points are swarmed (\"center\", \"left\", or \"right\"). Default is \"center\".\n",
     "    jitter : int | float\n",
     "        Determines the distance between points. Default is 1.\n",
+    "    filled : bool | List | Tuple\n",
+    "        Determines whether the dots in the swarmplot are filled or not. If set to False,\n",
+    "        dots are not filled. If provided as a List or Tuple, it should contain boolean values,\n",
+    "        each corresponding to a swarm group in order, indicating whether the dot should be\n",
+    "        filled or not.\n",
     "    is_drop_gutter : bool\n",
     "        If True, drop points that hit the gutters; otherwise, readjust them.\n",
     "    gutter_limit : int | float\n",
     "        The limit for points hitting the gutters.\n",
+    "    horizontal : bool\n",
+    "        If True, the swarm plot is drawn horizontally. Default is False.\n",
     "    **kwargs:\n",
     "        Additional keyword arguments to be passed to the swarm plot.\n",
     "\n",
     "    Returns\n",
     "    -------\n",
-    "    axes._subplots.Subplot | axes._axes.Axes\n",
-    "        Matplotlib AxesSubplot object for which the swarm plot has been drawn on.\n",
+    "    axes.Axes\n",
+    "        Matplotlib axes.Axes object for which the swarm plot has been drawn on.\n",
     "    \"\"\"\n",
-    "    s = SwarmPlot(data, x, y, ax, order, hue, palette, zorder, size, side, jitter)\n",
-    "    ax = s.plot(is_drop_gutter, gutter_limit, ax, **kwargs)\n",
+    "    s = SwarmPlot(data, x, y, ax, order, hue, palette, zorder, size, side, jitter, horizontal)\n",
+    "    ax = s.plot(is_drop_gutter, gutter_limit, ax, filled, horizontal, **kwargs)\n",
     "    return ax\n",
     "\n",
     "\n",
@@ -909,7 +2196,7 @@
     "        data: pd.DataFrame,\n",
     "        x: str,\n",
     "        y: str,\n",
-    "        ax: axes.Subplot,\n",
+    "        ax: axes.Axes,\n",
     "        order: List = None,\n",
     "        hue: str = None,\n",
     "        palette: Union[Iterable, str] = \"black\",\n",
@@ -917,6 +2204,7 @@
     "        size: float = 5,\n",
     "        side: str = \"center\",\n",
     "        jitter: float = 1,\n",
+    "        horizontal: bool = False,\n",
     "    ):\n",
     "        \"\"\"\n",
     "        Initialize a SwarmPlot instance.\n",
@@ -929,8 +2217,8 @@
     "            The column in the DataFrame to be used as the x-axis.\n",
     "        y : str\n",
     "            The column in the DataFrame to be used as the y-axis.\n",
-    "        ax : axes.Subplot\n",
-    "            Matplotlib AxesSubplot object for which the plot would be drawn on.\n",
+    "        ax : axes.Axes\n",
+    "            Matplotlib axes.Axes object for which the plot would be drawn on.\n",
     "        order : List\n",
     "            The order in which x-axis categories should be displayed. Default is None.\n",
     "        hue : str\n",
@@ -946,6 +2234,8 @@
     "            The side on which points are swarmed (\"center\", \"left\", or \"right\"). Default is \"center\".\n",
     "        jitter : int | float\n",
     "            Determines the distance between points. Default is 1.\n",
+    "        horizontal : bool\n",
+    "            If True, the swarm plot is drawn horizontally. Default is False.\n",
     "\n",
     "        Returns\n",
     "        -------\n",
@@ -988,21 +2278,28 @@
     "\n",
     "        x_min = min(x_vals)\n",
     "        x_max = max(x_vals)\n",
-    "        ax.set_xlim(left=x_min - 0.5, right=x_max + 0.5)\n",
-    "\n",
     "        y_range = max(y_vals) - min(y_vals)\n",
     "        y_min = min(y_vals) - 0.05 * y_range\n",
     "        y_max = max(y_vals) + 0.05 * y_range\n",
     "\n",
-    "        # ylim is set manually to override Axes.autoscale if it hasn't already been scaled at least once\n",
-    "        if ax.get_autoscaley_on():\n",
-    "            ax.set_ylim(bottom=y_min, top=y_max)\n",
+    "        if horizontal:\n",
+    "            ax.set_ylim(bottom=x_min - 0.5, top=x_max + 0.5)\n",
+    "            # ylim is set manually to override Axes.autoscale if it hasn't already been scaled at least once\n",
+    "            if ax.get_autoscalex_on():\n",
+    "                ax.set_xlim(left=y_min, right=y_max)\n",
+    "        else:\n",
+    "            ax.set_xlim(left=x_min - 0.5, right=x_max + 0.5)\n",
+    "            # ylim is set manually to override Axes.autoscale if it hasn't already been scaled at least once\n",
+    "            if ax.get_autoscaley_on():\n",
+    "                ax.set_ylim(bottom=y_min, top=y_max)\n",
     "\n",
     "        figw, figh = ax.get_figure().get_size_inches()\n",
     "        w = (ax.get_position().xmax - ax.get_position().xmin) * figw\n",
     "        h = (ax.get_position().ymax - ax.get_position().ymin) * figh\n",
     "        ax_xspan = ax.get_xlim()[1] - ax.get_xlim()[0]\n",
     "        ax_yspan = ax.get_ylim()[1] - ax.get_ylim()[0]\n",
+    "        if horizontal:\n",
+    "            ax_xspan, ax_yspan = ax_yspan, ax_xspan\n",
     "\n",
     "        # increases jitter distance based on number of swarms that is going to be drawn\n",
     "        jitter = jitter * (1 + 0.05 * (math.log(ax_xspan)))\n",
@@ -1017,7 +2314,7 @@
     "        self.__dsize = dsize\n",
     "\n",
     "    def _check_errors(\n",
-    "        self, data: pd.DataFrame, ax: axes.Subplot, size: float, side: str\n",
+    "        self, data: pd.DataFrame, ax: axes.Axes, size: float, side: str\n",
     "    ) -> None:\n",
     "        \"\"\"\n",
     "        Check the validity of input parameters. Raises exceptions if detected.\n",
@@ -1026,8 +2323,8 @@
     "        ----------\n",
     "        data : pd.Dataframe\n",
     "            Input data used for generation of the swarmplot.\n",
-    "        ax : axes.Subplot\n",
-    "            Matplotlib AxesSubplot object for which the plot would be drawn on.\n",
+    "        ax : axes.Axes\n",
+    "            Matplotlib axes.Axes object for which the plot would be drawn on.\n",
     "        size : int | float\n",
     "            scalar value determining size of dots of the swarmplot.\n",
     "        side: str\n",
@@ -1040,9 +2337,9 @@
     "        # Type enforcement\n",
     "        if not isinstance(data, pd.DataFrame):\n",
     "            raise ValueError(\"`data` must be a Pandas Dataframe.\")\n",
-    "        if not isinstance(ax, (axes._subplots.Subplot, axes._axes.Axes)):\n",
+    "        if not isinstance(ax, axes.Axes):\n",
     "            raise ValueError(\n",
-    "                f\"`ax` must be a Matplotlib AxesSubplot. The current `ax` is a {type(ax)}\"\n",
+    "                f\"`ax` must be a Matplotlib axes.Axes. The current `ax` is a {type(ax)}\"\n",
     "            )\n",
     "        if not isinstance(size, (int, float)):\n",
     "            raise ValueError(\"`size` must be a scalar or float.\")\n",
@@ -1059,7 +2356,9 @@
     "        if not isinstance(self.__jitter, (int, float)):\n",
     "            raise ValueError(\"`jitter` must be a scalar or float.\")\n",
     "        if not isinstance(self.__palette, (str, Iterable)):\n",
-    "            raise ValueError(\"`palette` must be either a string indicating a color name or an Iterable.\")\n",
+    "            raise ValueError(\n",
+    "                \"`palette` must be either a string indicating a color name or an Iterable.\"\n",
+    "            )\n",
     "        if self.__hue is not None and not isinstance(self.__hue, str):\n",
     "            raise ValueError(\"`hue` must be either a string or None.\")\n",
     "        if self.__order is not None and not isinstance(self.__order, Iterable):\n",
@@ -1089,7 +2388,6 @@
     "            err = \"`palette` cannot be an empty string. It must be either a string indicating a color name or an Iterable.\"\n",
     "            raise ValueError(err)\n",
     "        if isinstance(self.__palette, dict):\n",
-    "            # TODO: to add detection of when dict length is less than size of unique_items\n",
     "            for group_i, color_i in self.__palette.items():\n",
     "                if group_i not in pd.unique(data[color_col]):\n",
     "                    err = (\n",
@@ -1099,8 +2397,10 @@
     "                    )\n",
     "                    raise IndexError(err)\n",
     "                if isinstance(color_i, str) and color_i.strip() == \"\":\n",
-    "                    err = \"The color mapping for {0} in `palette` is an empty string. It must contain a color name.\".format(group_i)\n",
-    "                    raise ValueError(err) \n",
+    "                    err = \"The color mapping for {0} in `palette` is an empty string. It must contain a color name.\".format(\n",
+    "                        group_i\n",
+    "                    )\n",
+    "                    raise ValueError(err)\n",
     "\n",
     "        if side.lower() not in [\"center\", \"right\", \"left\"]:\n",
     "            raise ValueError(\n",
@@ -1199,9 +2499,10 @@
     "            raise ValueError(\"`dsize` must be a scalar or float.\")\n",
     "\n",
     "        # Sorting algorithm based off of: https://github.com/mgymrek/pybeeswarm\n",
-    "        points_data = pd.DataFrame(\n",
-    "            {\"y\": [yval * 1.0 / dsize for yval in values], \"x\": [0] * len(values)}\n",
-    "        )\n",
+    "        points_data = pd.DataFrame({\n",
+    "            \"y\": [yval * 1.0 / dsize for yval in values],\n",
+    "            \"x\": np.zeros(len(values), dtype=float)  # Initialize with float zeros\n",
+    "        })\n",
     "        for i in range(1, points_data.shape[0]):\n",
     "            y_i = points_data[\"y\"].values[i]\n",
     "            points_placed = points_data[0:i]\n",
@@ -1236,7 +2537,7 @@
     "                        bad_x_offsets.append(True)\n",
     "                    else:\n",
     "                        bad_x_offsets.append(False)\n",
-    "                potential_x_offsets[bad_x_offsets] = np.infty\n",
+    "                potential_x_offsets[bad_x_offsets] = np.inf\n",
     "                abs_potential_x_offsets = [abs(_) for _ in potential_x_offsets]\n",
     "                valid_x_offset = potential_x_offsets[\n",
     "                    abs_potential_x_offsets.index(min(abs_potential_x_offsets))\n",
@@ -1302,8 +2603,14 @@
     "        return points_data\n",
     "\n",
     "    def plot(\n",
-    "        self, is_drop_gutter: bool, gutter_limit: float, ax: axes.Subplot, **kwargs\n",
-    "    ) -> axes.Subplot:\n",
+    "        self,\n",
+    "        is_drop_gutter: bool,\n",
+    "        gutter_limit: float,\n",
+    "        ax: axes.Subplot,\n",
+    "        filled: Union[bool, List, Tuple],\n",
+    "        horizontal: bool,\n",
+    "        **kwargs,\n",
+    "    ) -> axes.Axes:\n",
     "        \"\"\"\n",
     "        Generate a swarm plot.\n",
     "\n",
@@ -1313,28 +2620,50 @@
     "            If True, drop points that hit the gutters; otherwise, readjust them.\n",
     "        gutter_limit : int | float\n",
     "            The limit for points hitting the gutters.\n",
-    "        ax : axes.Subplot\n",
+    "        ax : axes.Axes\n",
     "            The matplotlib figure object to which the swarm plot will be added.\n",
+    "        filled : bool | List | Tuple\n",
+    "            Determines whether the dots in the swarmplot are filled or not. If set to False,\n",
+    "            dots are not filled. If provided as a List or Tuple, it should contain boolean values,\n",
+    "            each corresponding to a swarm group in order, indicating whether the dot should be\n",
+    "            filled or not.\n",
     "        **kwargs:\n",
     "            Additional keyword arguments to be passed to the scatter plot.\n",
     "\n",
     "        Returns\n",
     "        -------\n",
-    "        axes.Subplot:\n",
-    "            The matplotlib figure containing the swarm plot.\n",
+    "        axes.Axes:\n",
+    "            The matplotlib axes containing the swarm plot.\n",
     "        \"\"\"\n",
     "        # Input validation\n",
     "        if not isinstance(is_drop_gutter, bool):\n",
     "            raise ValueError(\"`is_drop_gutter` must be a boolean.\")\n",
     "        if not isinstance(gutter_limit, (int, float)):\n",
     "            raise ValueError(\"`gutter_limit` must be a scalar or float.\")\n",
+    "        if not isinstance(filled, (bool, list, tuple)):\n",
+    "            raise ValueError(\"`filled` must be a boolean, list or tuple.\")\n",
+    "        \n",
+    "        fontsize = kwargs.pop('fontsize', 12)\n",
+    "\n",
+    "        # More thorough input validation checks\n",
+    "        if isinstance(filled, (list, tuple)):\n",
+    "            if len(filled) != len(self.__order):\n",
+    "                err = (\n",
+    "                    \"There are {0} unique values in `x` column in `data` \"\n",
+    "                    \"but `filled` has a length of {1}. If `filled` is a list \"\n",
+    "                    \"or a tuple, it must have the same length as the number of \"\n",
+    "                    \"unique values/groups in the `x` column of data.\"\n",
+    "                ).format(len(self.__order), len(filled))\n",
+    "                raise ValueError(err)\n",
+    "            if not all(isinstance(_, bool) for _ in filled):\n",
+    "                raise ValueError(\"All values in `filled` must be a boolean.\")\n",
     "\n",
     "        # Assumptions are that self.__data_copy is already sorted according to self.__order\n",
     "        x_position = (\n",
     "            0  # x-coordinate of center of each individual swarm of the swarm plot\n",
     "        )\n",
     "        x_tick_tabels = []\n",
-    "        for group_i, values_i in self.__data_copy.groupby(self.__x):\n",
+    "        for group_i, values_i in self.__data_copy.groupby(self.__x, observed=False):\n",
     "            x_new = []\n",
     "            values_i_y = values_i[self.__y]\n",
     "            x_offset = self._swarm(\n",
@@ -1355,8 +2684,8 @@
     "\n",
     "            if values_i.empty:\n",
     "                ax.scatter(\n",
-    "                    values_i[\"x_new\"],\n",
-    "                    values_i[self.__y],\n",
+    "                    values_i[\"x_new\"] if not horizontal else values_i[self.__y],\n",
+    "                    values_i[self.__y] if not horizontal else values_i[\"x_new\"],\n",
     "                    s=self.__size,\n",
     "                    zorder=self.__zorder,\n",
     "                    **kwargs,\n",
@@ -1371,10 +2700,11 @@
     "                cmap = []\n",
     "                for cmap_group_i in cmap_values:\n",
     "                    cmap.append(self.__palette[cmap_group_i])\n",
+    "\n",
     "                cmap = ListedColormap(cmap)\n",
     "                ax.scatter(\n",
-    "                    values_i[\"x_new\"],\n",
-    "                    values_i[self.__y],\n",
+    "                    values_i[\"x_new\"] if not horizontal else values_i[self.__y],\n",
+    "                    values_i[self.__y] if not horizontal else values_i[\"x_new\"],\n",
     "                    s=self.__size,\n",
     "                    c=index,\n",
     "                    cmap=cmap,\n",
@@ -1382,21 +2712,48 @@
     "                    edgecolor=\"face\",\n",
     "                    **kwargs,\n",
     "                )\n",
+    "\n",
     "            else:\n",
     "                # color swarms based on `x` column\n",
+    "                if not isinstance(filled, bool):\n",
+    "                    facecolor = (\n",
+    "                        \"none\"\n",
+    "                        if not filled[x_position - 1]\n",
+    "                        else self.__palette[group_i]\n",
+    "                    )\n",
+    "                else:\n",
+    "                    facecolor = \"none\" if not filled else self.__palette[group_i]\n",
+    "\n",
     "                ax.scatter(\n",
-    "                    values_i[\"x_new\"],\n",
-    "                    values_i[self.__y],\n",
+    "                    values_i[\"x_new\"] if not horizontal else values_i[self.__y],\n",
+    "                    values_i[self.__y] if not horizontal else values_i[\"x_new\"],\n",
     "                    s=self.__size,\n",
-    "                    c=self.__palette[group_i],\n",
     "                    zorder=self.__zorder,\n",
-    "                    edgecolor=\"face\",\n",
+    "                    facecolor=facecolor,\n",
+    "                    edgecolor=self.__palette[group_i],\n",
+    "                    label=group_i,\n",
     "                    **kwargs,\n",
     "                )\n",
     "\n",
-    "        ax.get_xaxis().set_ticks(np.arange(x_position))\n",
-    "        ax.get_xaxis().set_ticklabels(x_tick_tabels)\n",
+    "        # Handling of legends\n",
+    "        # This is currently a workaround because c and cmap is unable to map the labels when calling scatter()\n",
+    "        # labels has to be used to designate legend labels and handles in scatter() due to the potential calling of ax.get_legend_handles_labels()\n",
+    "        if self.__hue is not None:\n",
+    "            for cmap_group_i in self.__palette:\n",
+    "                ax.scatter(\n",
+    "                    [],\n",
+    "                    [],\n",
+    "                    color=self.__palette[cmap_group_i],\n",
+    "                    label=cmap_group_i,\n",
+    "                )\n",
     "\n",
+    "        if horizontal:\n",
+    "            ax.get_yaxis().set_ticks(np.arange(x_position))\n",
+    "            ax.get_yaxis().set_ticklabels(x_tick_tabels, fontsize = fontsize)\n",
+    "        else:\n",
+    "            ax.get_xaxis().set_ticks(np.arange(x_position))\n",
+    "            ax.get_xaxis().set_ticklabels(x_tick_tabels, fontsize = fontsize)\n",
+    "            \n",
     "        return ax"
    ]
   }
diff --git a/nbs/API/plotter.ipynb b/nbs/API/plotter.ipynb
index 7e054ea4..faefacd2 100644
--- a/nbs/API/plotter.ipynb
+++ b/nbs/API/plotter.ipynb
@@ -59,6 +59,8 @@
     "import seaborn as sns\n",
     "import matplotlib\n",
     "import matplotlib.pyplot as plt\n",
+    "import matplotlib.patches as mpatches\n",
+    "from matplotlib.lines import Line2D\n",
     "import pandas as pd\n",
     "import warnings\n",
     "import logging"
@@ -73,7 +75,7 @@
    "source": [
     "# | export\n",
     "# TODO refactor function name\n",
-    "def effectsize_df_plotter(effectsize_df, **plot_kwargs):\n",
+    "def effectsize_df_plotter(effectsize_df: object, **plot_kwargs) -> matplotlib.figure.Figure:\n",
     "    \"\"\"\n",
     "    Custom function that creates an estimation plot from an EffectSizeDataFrame.\n",
     "    Keywords\n",
@@ -84,46 +86,88 @@
     "        A `dabest` EffectSizeDataFrame object.\n",
     "    plot_kwargs\n",
     "        color_col=None\n",
-    "        raw_marker_size=6, es_marker_size=9,\n",
-    "        swarm_label=None, contrast_label=None, delta2_label=None,\n",
-    "        swarm_ylim=None, contrast_ylim=None, delta2_ylim=None,\n",
-    "        custom_palette=None, swarm_desat=0.5, halfviolin_desat=1,\n",
-    "        halfviolin_alpha=0.8,\n",
-    "        face_color = None,\n",
-    "        bar_label=None, bar_desat=0.8, bar_width = 0.5,bar_ylim = None,\n",
-    "        ci=None, ci_type='bca', err_color=None,\n",
+    "        raw_marker_size=6, contrast_marker_kwargs=9,\n",
+    "        raw_label=None, contrast_label=None, delta2_label=None,\n",
+    "        raw_ylim=None, contrast_ylim=None, delta2_ylim=None,\n",
+    "        custom_palette=None, \n",
+    "        swarm_side=None, \n",
+    "        empty_circle=False,\n",
+    "        face_color=None,\n",
+    "        raw_desat=0.5, contrast_desat=1,\n",
+    "        raw_alpha=None, contrast_alpha=0.8,\n",
+    "        bar_width = 0.5,\n",
+    "        ci_type='bca',\n",
     "        float_contrast=True,\n",
     "        show_pairs=True,\n",
-    "        show_delta2=True,\n",
-    "        group_summaries=None,\n",
-    "        group_summaries_offset=0.1,\n",
+    "        show_sample_size=True,\n",
+    "        show_delta2=True, show_mini_meta=True,\n",
+    "        group_summaries=\"mean_sd\",\n",
     "        fig_size=None,\n",
     "        dpi=100,\n",
     "        ax=None,\n",
-    "        gridkey_rows=None,\n",
     "        swarmplot_kwargs=None,\n",
-    "        violinplot_kwargs=None,\n",
     "        slopegraph_kwargs=None,\n",
+    "        barplot_kwargs=None,\n",
     "        sankey_kwargs=None,\n",
+    "        contrast_kwargs=None,\n",
     "        reflines_kwargs=None,\n",
-    "        group_summary_kwargs=None,\n",
+    "        group_summaries_kwargs=None,\n",
     "        legend_kwargs=None,\n",
     "        title=None, fontsize_title=16,\n",
     "        fontsize_rawxlabel=12, fontsize_rawylabel=12,\n",
     "        fontsize_contrastxlabel=12, fontsize_contrastylabel=12,\n",
-    "        fontsize_delta2label=12\n",
+    "        fontsize_delta2label=12,\n",
+    "\n",
+    "        raw_bars=True, raw_bars_kwargs=None,\n",
+    "        contrast_bars=True, contrast_bars_kwargs=None,\n",
+    "        reference_band=None, reference_band_kwargs=None,\n",
+    "        delta_text=True, delta_text_kwargs=None,\n",
+    "        delta_dot=True, delta_dot_kwargs=None,\n",
+    "\n",
+    "        horizontal=False, horizontal_table_kwargs=None,\n",
+    "        gridkey=None, \n",
+    "        gridkey_merge_pairs=False,\n",
+    "        gridkey_show_Ns=True,\n",
+    "        gridkey_show_es=True,\n",
+    "        gridkey_delimiters=[';', '>', '_'],\n",
+    "        gridkey_kwargs=None,\n",
+    "        contrast_marker_kwargs=None, contrast_errorbar_kwargs=None,\n",
+    "        prop_sample_counts=False, prop_sample_counts_kwargs=None, \n",
+    "        contrast_paired_lines=True, contrast_paired_lines\n",
+    "\t\tshow_baseline_ec=False,\n",
+    "\n",
     "    \"\"\"\n",
-    "    from .misc_tools import merge_two_dicts\n",
+    "    from .misc_tools import (\n",
+    "        get_params,\n",
+    "        get_kwargs,\n",
+    "        get_color_palette,\n",
+    "        initialize_fig,\n",
+    "        get_plot_groups,\n",
+    "        add_counts_to_ticks,\n",
+    "        extract_contrast_plotting_ticks,\n",
+    "        set_xaxis_ticks_and_lims,\n",
+    "        show_legend,\n",
+    "        gardner_altman_adjustments,\n",
+    "        extract_group_summaries,\n",
+    "        draw_zeroline,\n",
+    "        redraw_dependent_spines,\n",
+    "        redraw_independent_spines,\n",
+    "        prepare_bars_for_plot\n",
+    "    )\n",
     "    from .plot_tools import (\n",
-    "        halfviolin,\n",
-    "        get_swarm_spans,\n",
     "        error_bar,\n",
     "        sankeydiag,\n",
     "        swarmplot,\n",
-    "    )\n",
-    "    from ._stats_tools.effsize import (\n",
-    "        _compute_standardizers,\n",
-    "        _compute_hedges_correction_factor,\n",
+    "        delta_text_plotter,\n",
+    "        delta_dots_plotter,\n",
+    "        slopegraph_plotter,\n",
+    "        plot_minimeta_or_deltadelta_violins,\n",
+    "        effect_size_curve_plotter,\n",
+    "        gridkey_plotter,\n",
+    "        barplotter,\n",
+    "        table_for_horizontal_plots,\n",
+    "        add_counts_to_prop_plots,\n",
+    "        add_bars_to_plot\n",
     "    )\n",
     "\n",
     "    warnings.filterwarnings(\n",
@@ -141,1513 +185,492 @@
     "        original_rcParams[parameter] = plt.rcParams[parameter]\n",
     "\n",
     "    plt.rcParams[\"axes.grid\"] = False\n",
-    "\n",
     "    ytick_color = plt.rcParams[\"ytick.color\"]\n",
-    "    face_color = plot_kwargs[\"face_color\"]\n",
-    "\n",
-    "    if plot_kwargs[\"face_color\"] is None:\n",
-    "        face_color = \"white\"\n",
-    "\n",
-    "    dabest_obj = effectsize_df.dabest_obj\n",
-    "    plot_data = effectsize_df._plot_data\n",
-    "    xvar = effectsize_df.xvar\n",
-    "    yvar = effectsize_df.yvar\n",
-    "    is_paired = effectsize_df.is_paired\n",
-    "    delta2 = effectsize_df.delta2\n",
-    "    mini_meta = effectsize_df.mini_meta\n",
-    "    effect_size = effectsize_df.effect_size\n",
-    "    proportional = effectsize_df.proportional\n",
-    "\n",
-    "    all_plot_groups = dabest_obj._all_plot_groups\n",
-    "    idx = dabest_obj.idx\n",
-    "\n",
-    "    if effect_size not in [\"mean_diff\", \"delta_g\"] or not delta2:\n",
-    "        show_delta2 = False\n",
-    "    else:\n",
-    "        show_delta2 = plot_kwargs[\"show_delta2\"]\n",
     "\n",
-    "    if effect_size != \"mean_diff\" or not mini_meta:\n",
-    "        show_mini_meta = False\n",
-    "    else:\n",
-    "        show_mini_meta = plot_kwargs[\"show_mini_meta\"]\n",
-    "\n",
-    "    if show_delta2 and show_mini_meta:\n",
-    "        err0 = \"`show_delta2` and `show_mini_meta` cannot be True at the same time.\"\n",
-    "        raise ValueError(err0)\n",
-    "\n",
-    "    # Disable Gardner-Altman plotting if any of the idxs comprise of more than\n",
-    "    # two groups or if it is a delta-delta plot.\n",
-    "    float_contrast = plot_kwargs[\"float_contrast\"]\n",
-    "    effect_size_type = effectsize_df.effect_size\n",
-    "    if len(idx) > 1 or len(idx[0]) > 2:\n",
-    "        float_contrast = False\n",
-    "\n",
-    "    if effect_size_type in [\"cliffs_delta\"]:\n",
-    "        float_contrast = False\n",
-    "\n",
-    "    if show_delta2 or show_mini_meta:\n",
-    "        float_contrast = False\n",
-    "\n",
-    "    if not is_paired:\n",
-    "        show_pairs = False\n",
-    "    else:\n",
-    "        show_pairs = plot_kwargs[\"show_pairs\"]\n",
-    "\n",
-    "    # Set default kwargs first, then merge with user-dictated ones.\n",
-    "    # Swarmplot kwargs\n",
-    "    default_swarmplot_kwargs = {\"size\": plot_kwargs[\"raw_marker_size\"]}\n",
-    "    if plot_kwargs[\"swarmplot_kwargs\"] is None:\n",
-    "        swarmplot_kwargs = default_swarmplot_kwargs\n",
-    "    else:\n",
-    "        swarmplot_kwargs = merge_two_dicts(\n",
-    "            default_swarmplot_kwargs, plot_kwargs[\"swarmplot_kwargs\"]\n",
-    "        )\n",
-    "    asymmetric_side = (\n",
-    "        \"left\"  # TODO: allow users to control side for swarms of swarmplot.\n",
+    "    # Extract parameters and set kwargs\n",
+    "    (swarmplot_kwargs, barplot_kwargs, sankey_kwargs, contrast_kwargs, \n",
+    "     slopegraph_kwargs, reflines_kwargs, legend_kwargs, group_summaries_kwargs, \n",
+    "     redraw_axes_kwargs, delta_dot_kwargs, delta_text_kwargs, reference_band_kwargs, \n",
+    "     raw_bars_kwargs, contrast_bars_kwargs, table_kwargs, gridkey_kwargs, contrast_marker_kwargs, \n",
+    "     contrast_errorbar_kwargs, prop_sample_counts_kwargs, contrast_paired_lines_kwargs) = get_kwargs(\n",
+    "                                                                                                plot_kwargs = plot_kwargs, \n",
+    "                                                                                                ytick_color = ytick_color\n",
     "    )\n",
     "\n",
-    "    # Barplot kwargs\n",
-    "    default_barplot_kwargs = {\"estimator\": np.mean, \"errorbar\": plot_kwargs[\"ci\"]}\n",
-    "\n",
-    "    if plot_kwargs[\"barplot_kwargs\"] is None:\n",
-    "        barplot_kwargs = default_barplot_kwargs\n",
-    "    else:\n",
-    "        barplot_kwargs = merge_two_dicts(\n",
-    "            default_barplot_kwargs, plot_kwargs[\"barplot_kwargs\"]\n",
-    "        )\n",
-    "\n",
-    "    # Sankey Diagram kwargs\n",
-    "    default_sankey_kwargs = {\n",
-    "        \"width\": 0.4,\n",
-    "        \"align\": \"center\",\n",
-    "        \"sankey\": True,\n",
-    "        \"flow\": True,\n",
-    "        \"alpha\": 0.4,\n",
-    "        \"rightColor\": False,\n",
-    "        \"bar_width\": 0.2,\n",
-    "    }\n",
-    "    if plot_kwargs[\"sankey_kwargs\"] is None:\n",
-    "        sankey_kwargs = default_sankey_kwargs\n",
-    "    else:\n",
-    "        sankey_kwargs = merge_two_dicts(\n",
-    "            default_sankey_kwargs, plot_kwargs[\"sankey_kwargs\"]\n",
-    "        )\n",
-    "    # We also need to extract the `sankey` and `flow` from the kwargs for plotter.py\n",
-    "    # to use for varying different kinds of paired proportional plots\n",
-    "    # We also don't want to pop the parameter from the kwargs\n",
-    "    sankey = sankey_kwargs[\"sankey\"]\n",
-    "    flow = sankey_kwargs[\"flow\"]\n",
-    "\n",
-    "    # Violinplot kwargs.\n",
-    "    default_violinplot_kwargs = {\n",
-    "        \"widths\": 0.5,\n",
-    "        \"vert\": True,\n",
-    "        \"showextrema\": False,\n",
-    "        \"showmedians\": False,\n",
-    "    }\n",
-    "    if plot_kwargs[\"violinplot_kwargs\"] is None:\n",
-    "        violinplot_kwargs = default_violinplot_kwargs\n",
-    "    else:\n",
-    "        violinplot_kwargs = merge_two_dicts(\n",
-    "            default_violinplot_kwargs, plot_kwargs[\"violinplot_kwargs\"]\n",
-    "        )\n",
-    "\n",
-    "    # Slopegraph kwargs.\n",
-    "    default_slopegraph_kwargs = {\"linewidth\": 1, \"alpha\": 0.5}\n",
-    "    if plot_kwargs[\"slopegraph_kwargs\"] is None:\n",
-    "        slopegraph_kwargs = default_slopegraph_kwargs\n",
-    "    else:\n",
-    "        slopegraph_kwargs = merge_two_dicts(\n",
-    "            default_slopegraph_kwargs, plot_kwargs[\"slopegraph_kwargs\"]\n",
-    "        )\n",
-    "\n",
-    "    # Zero reference-line kwargs.\n",
-    "    default_reflines_kwargs = {\n",
-    "        \"linestyle\": \"solid\",\n",
-    "        \"linewidth\": 0.75,\n",
-    "        \"zorder\": 2,\n",
-    "        \"color\": ytick_color,\n",
-    "    }\n",
-    "    if plot_kwargs[\"reflines_kwargs\"] is None:\n",
-    "        reflines_kwargs = default_reflines_kwargs\n",
-    "    else:\n",
-    "        reflines_kwargs = merge_two_dicts(\n",
-    "            default_reflines_kwargs, plot_kwargs[\"reflines_kwargs\"]\n",
-    "        )\n",
-    "\n",
-    "    # Legend kwargs.\n",
-    "    default_legend_kwargs = {\"loc\": \"upper left\", \"frameon\": False}\n",
-    "    if plot_kwargs[\"legend_kwargs\"] is None:\n",
-    "        legend_kwargs = default_legend_kwargs\n",
-    "    else:\n",
-    "        legend_kwargs = merge_two_dicts(\n",
-    "            default_legend_kwargs, plot_kwargs[\"legend_kwargs\"]\n",
-    "        )\n",
-    "\n",
-    "    ################################################### GRIDKEY WIP - extracting arguments\n",
-    "\n",
-    "    gridkey_rows = plot_kwargs[\"gridkey_rows\"]\n",
-    "    gridkey_merge_pairs = plot_kwargs[\"gridkey_merge_pairs\"]\n",
-    "    gridkey_show_Ns = plot_kwargs[\"gridkey_show_Ns\"]\n",
-    "    gridkey_show_es = plot_kwargs[\"gridkey_show_es\"]\n",
-    "\n",
-    "    if gridkey_rows is None:\n",
-    "        gridkey_show_Ns = False\n",
-    "        gridkey_show_es = False\n",
-    "\n",
-    "    ################################################### END GRIDKEY WIP - extracting arguments\n",
-    "\n",
-    "    # Group summaries kwargs.\n",
-    "    gs_default = {\"mean_sd\", \"median_quartiles\", None}\n",
-    "    if plot_kwargs[\"group_summaries\"] not in gs_default:\n",
-    "        raise ValueError(\n",
-    "            \"group_summaries must be one of\" \" these: {}.\".format(gs_default)\n",
-    "        )\n",
-    "\n",
-    "    default_group_summary_kwargs = {\"zorder\": 3, \"lw\": 2, \"alpha\": 1}\n",
-    "    if plot_kwargs[\"group_summary_kwargs\"] is None:\n",
-    "        group_summary_kwargs = default_group_summary_kwargs\n",
-    "    else:\n",
-    "        group_summary_kwargs = merge_two_dicts(\n",
-    "            default_group_summary_kwargs, plot_kwargs[\"group_summary_kwargs\"]\n",
-    "        )\n",
-    "\n",
-    "    # Create color palette that will be shared across subplots.\n",
-    "    color_col = plot_kwargs[\"color_col\"]\n",
-    "    if color_col is None:\n",
-    "        color_groups = pd.unique(plot_data[xvar])\n",
-    "        bootstraps_color_by_group = True\n",
-    "    else:\n",
-    "        if color_col not in plot_data.columns:\n",
-    "            raise KeyError(\"``{}`` is not a column in the data.\".format(color_col))\n",
-    "        color_groups = pd.unique(plot_data[color_col])\n",
-    "        bootstraps_color_by_group = False\n",
-    "    if show_pairs:\n",
-    "        bootstraps_color_by_group = False\n",
-    "\n",
-    "    # Handle the color palette.\n",
-    "    names = color_groups\n",
-    "    n_groups = len(color_groups)\n",
-    "    custom_pal = plot_kwargs[\"custom_palette\"]\n",
-    "    swarm_desat = plot_kwargs[\"swarm_desat\"]\n",
-    "    bar_desat = plot_kwargs[\"bar_desat\"]\n",
-    "    contrast_desat = plot_kwargs[\"halfviolin_desat\"]\n",
-    "\n",
-    "    if custom_pal is None:\n",
-    "        unsat_colors = sns.color_palette(n_colors=n_groups)\n",
-    "    else:\n",
-    "        if isinstance(custom_pal, dict):\n",
-    "            groups_in_palette = {\n",
-    "                k: v for k, v in custom_pal.items() if k in color_groups\n",
-    "            }\n",
-    "\n",
-    "            names = groups_in_palette.keys()\n",
-    "            unsat_colors = groups_in_palette.values()\n",
-    "\n",
-    "        elif isinstance(custom_pal, list):\n",
-    "            unsat_colors = custom_pal[0:n_groups]\n",
-    "\n",
-    "        elif isinstance(custom_pal, str):\n",
-    "            # check it is in the list of matplotlib palettes.\n",
-    "            if custom_pal in plt.colormaps():\n",
-    "                unsat_colors = sns.color_palette(custom_pal, n_groups)\n",
-    "            else:\n",
-    "                err1 = \"The specified `custom_palette` {}\".format(custom_pal)\n",
-    "                err2 = \" is not a matplotlib palette. Please check.\"\n",
-    "                raise ValueError(err1 + err2)\n",
-    "\n",
-    "    if custom_pal is None and color_col is None:\n",
-    "        swarm_colors = [sns.desaturate(c, swarm_desat) for c in unsat_colors]\n",
-    "        plot_palette_raw = dict(zip(names.categories, swarm_colors))\n",
-    "\n",
-    "        bar_color = [sns.desaturate(c, bar_desat) for c in unsat_colors]\n",
-    "        plot_palette_bar = dict(zip(names.categories, bar_color))\n",
-    "\n",
-    "        contrast_colors = [sns.desaturate(c, contrast_desat) for c in unsat_colors]\n",
-    "        plot_palette_contrast = dict(zip(names.categories, contrast_colors))\n",
-    "\n",
-    "        # For Sankey Diagram plot, no need to worry about the color, each bar will have the same two colors\n",
-    "        # default color palette will be set to \"hls\"\n",
-    "        plot_palette_sankey = None\n",
-    "\n",
-    "    else:\n",
-    "        swarm_colors = [sns.desaturate(c, swarm_desat) for c in unsat_colors]\n",
-    "        plot_palette_raw = dict(zip(names, swarm_colors))\n",
-    "\n",
-    "        bar_color = [sns.desaturate(c, bar_desat) for c in unsat_colors]\n",
-    "        plot_palette_bar = dict(zip(names, bar_color))\n",
-    "\n",
-    "        contrast_colors = [sns.desaturate(c, contrast_desat) for c in unsat_colors]\n",
-    "        plot_palette_contrast = dict(zip(names, contrast_colors))\n",
-    "\n",
-    "        plot_palette_sankey = custom_pal\n",
-    "\n",
-    "    # Infer the figsize.\n",
-    "    fig_size = plot_kwargs[\"fig_size\"]\n",
-    "    if fig_size is None:\n",
-    "        all_groups_count = np.sum([len(i) for i in dabest_obj.idx])\n",
-    "        # Increase the width for delta-delta graph\n",
-    "        if show_delta2 or show_mini_meta:\n",
-    "            all_groups_count += 2\n",
-    "        if is_paired and show_pairs and proportional is False:\n",
-    "            frac = 0.75\n",
-    "        else:\n",
-    "            frac = 1\n",
-    "        if float_contrast:\n",
-    "            height_inches = 4\n",
-    "            each_group_width_inches = 2.5 * frac\n",
-    "        else:\n",
-    "            height_inches = 6\n",
-    "            each_group_width_inches = 1.5 * frac\n",
+    "    (dabest_obj, plot_data, xvar, yvar, is_paired, effect_size, proportional, \n",
+    "     all_plot_groups, idx, show_delta2, show_mini_meta, float_contrast, \n",
+    "     show_pairs, group_summaries, horizontal, results, ci_type, x1_level, experiment_label, \n",
+    "     show_baseline_ec, one_sankey, two_col_sankey, asymmetric_side, show_sample_size) = get_params(\n",
+    "                                                                                            effectsize_df = effectsize_df, \n",
+    "                                                                                            plot_kwargs = plot_kwargs,\n",
+    "                                                                                            sankey_kwargs = sankey_kwargs,\n",
+    "                                                                                            barplot_kwargs = barplot_kwargs\n",
+    "    )\n",
     "\n",
-    "        width_inches = each_group_width_inches * all_groups_count\n",
-    "        fig_size = (width_inches, height_inches)\n",
+    "    # Extract Color palette\n",
+    "    (color_col, bootstraps_color_by_group, n_groups, filled, raw_colors,\n",
+    "     plot_palette_raw, plot_palette_contrast, plot_palette_sankey) = get_color_palette(\n",
+    "                                                                                plot_kwargs = plot_kwargs, \n",
+    "                                                                                plot_data = plot_data, \n",
+    "                                                                                xvar = xvar, \n",
+    "                                                                                show_pairs = show_pairs,\n",
+    "                                                                                idx = idx,\n",
+    "                                                                                all_plot_groups = all_plot_groups,\n",
+    "                                                                                delta2 = effectsize_df.delta2,\n",
+    "                                                                                sankey = True if proportional and show_pairs else False,\n",
+    "    )\n",
     "\n",
     "    # Initialise the figure.\n",
-    "    init_fig_kwargs = dict(figsize=fig_size, dpi=plot_kwargs[\"dpi\"], tight_layout=True)\n",
-    "\n",
-    "    width_ratios_ga = [2.5, 1]\n",
-    "\n",
-    "    ###################### GRIDKEY HSPACE ALTERATION\n",
-    "\n",
-    "    # Sets hspace for cummings plots if gridkey is shown.\n",
-    "    if gridkey_rows is not None:\n",
-    "        h_space_cummings = 0.1\n",
-    "    else:\n",
-    "        h_space_cummings = 0.3\n",
-    "\n",
-    "    ###################### END GRIDKEY HSPACE ALTERATION\n",
-    "\n",
-    "    if plot_kwargs[\"ax\"] is not None:\n",
-    "        # New in v0.2.6.\n",
-    "        # Use inset axes to create the estimation plot inside a single axes.\n",
-    "        # Author: Adam L Nekimken. (PR #73)\n",
-    "        rawdata_axes = plot_kwargs[\"ax\"]\n",
-    "        ax_position = rawdata_axes.get_position()  # [[x0, y0], [x1, y1]]\n",
-    "\n",
-    "        fig = rawdata_axes.get_figure()\n",
-    "        fig.patch.set_facecolor(face_color)\n",
-    "\n",
-    "        if float_contrast:\n",
-    "            axins = rawdata_axes.inset_axes(\n",
-    "                [1, 0, width_ratios_ga[1] / width_ratios_ga[0], 1]\n",
-    "            )\n",
-    "            rawdata_axes.set_position(  # [l, b, w, h]\n",
-    "                [\n",
-    "                    ax_position.x0,\n",
-    "                    ax_position.y0,\n",
-    "                    (ax_position.x1 - ax_position.x0)\n",
-    "                    * (width_ratios_ga[0] / sum(width_ratios_ga)),\n",
-    "                    (ax_position.y1 - ax_position.y0),\n",
-    "                ]\n",
+    "    fig, rawdata_axes, contrast_axes, table_axes = initialize_fig(\n",
+    "                                                            plot_kwargs = plot_kwargs, \n",
+    "                                                            dabest_obj = dabest_obj, \n",
+    "                                                            show_delta2 = show_delta2, \n",
+    "                                                            show_mini_meta = show_mini_meta, \n",
+    "                                                            is_paired = is_paired, \n",
+    "                                                            show_pairs = show_pairs, \n",
+    "                                                            proportional = proportional, \n",
+    "                                                            float_contrast = float_contrast,\n",
+    "                                                            effect_size_type = effect_size,\n",
+    "                                                            yvar = yvar,\n",
+    "                                                            horizontal = horizontal,\n",
+    "                                                            show_table = table_kwargs['show'],\n",
+    "                                                            color_col = color_col\n",
+    "    )\n",
+    "    \n",
+    "    # Plotting the rawdata.\n",
+    "    if show_pairs:  ## Paired plots!\n",
+    "        temp_idx, temp_all_plot_groups = get_plot_groups(\n",
+    "                                                    is_paired = is_paired, \n",
+    "                                                    idx = idx, \n",
+    "                                                    proportional = proportional, \n",
+    "                                                    all_plot_groups = all_plot_groups\n",
+    "        )\n",
+    "        if proportional:  ## Plot the raw data as a set of Sankey Diagrams aligned like barplot.\n",
+    "            if sankey_kwargs[\"flow\"] == False and len(temp_all_plot_groups) == 2: \n",
+    "                sankey_kwargs[\"flow\"], two_col_sankey = True, False\n",
+    "                warnings.warn(\"Sankey flow must be true for singular two-group sankey plots\")\n",
+    "            sankey_control_test_groups = sankeydiag(\n",
+    "                                                    plot_data,\n",
+    "                                                    xvar = xvar,\n",
+    "                                                    yvar = yvar,\n",
+    "                                                    temp_all_plot_groups = temp_all_plot_groups,\n",
+    "                                                    idx = idx,\n",
+    "                                                    temp_idx = temp_idx,\n",
+    "                                                    palette = plot_palette_sankey,\n",
+    "                                                    ax = rawdata_axes,\n",
+    "                                                    horizontal = horizontal,\n",
+    "                                                    **sankey_kwargs\n",
     "            )\n",
-    "\n",
-    "            contrast_axes = axins\n",
-    "\n",
-    "        else:\n",
-    "            axins = rawdata_axes.inset_axes([0, -1 - h_space_cummings, 1, 1])\n",
-    "            plot_height = (ax_position.y1 - ax_position.y0) / (2 + h_space_cummings)\n",
-    "            rawdata_axes.set_position(\n",
-    "                [\n",
-    "                    ax_position.x0,\n",
-    "                    ax_position.y0 + (1 + h_space_cummings) * plot_height,\n",
-    "                    (ax_position.x1 - ax_position.x0),\n",
-    "                    plot_height,\n",
-    "                ]\n",
+    "        else: ## Plot the raw data as a slopegraph.\n",
+    "            slopegraph_plotter(\n",
+    "                dabest_obj = dabest_obj, \n",
+    "                plot_data = plot_data, \n",
+    "                xvar = xvar, \n",
+    "                yvar = yvar, \n",
+    "                color_col = color_col, \n",
+    "                plot_palette_raw = plot_palette_raw, \n",
+    "                slopegraph_kwargs = slopegraph_kwargs, \n",
+    "                rawdata_axes = rawdata_axes, \n",
+    "                ytick_color = ytick_color, \n",
+    "                temp_idx = temp_idx,\n",
+    "                horizontal = horizontal,\n",
+    "                temp_all_plot_groups = temp_all_plot_groups, \n",
+    "                plot_kwargs = plot_kwargs,\n",
     "            )\n",
-    "\n",
-    "        contrast_axes = axins\n",
-    "        rawdata_axes.contrast_axes = axins\n",
-    "\n",
-    "    else:\n",
-    "        # Here, we hardcode some figure parameters.\n",
-    "        if float_contrast:\n",
-    "            fig, axx = plt.subplots(\n",
-    "                ncols=2,\n",
-    "                gridspec_kw={\"width_ratios\": width_ratios_ga, \"wspace\": 0},\n",
-    "                **init_fig_kwargs\n",
+    "            \n",
+    "            ## Add delta dots to the contrast axes for paired plots.\n",
+    "            show_delta_dots = plot_kwargs[\"delta_dot\"]\n",
+    "            unavailable_effect_sizes = [\"hedges_g\", \"delta_g\", \"cohens_d\"]\n",
+    "            if show_delta_dots and is_paired and not any([es in effect_size for es in unavailable_effect_sizes]):\n",
+    "                delta_dots_plotter(\n",
+    "                    plot_data = plot_data, \n",
+    "                    contrast_axes = contrast_axes, \n",
+    "                    delta_id_col = dabest_obj.id_col, \n",
+    "                    idx = idx, \n",
+    "                    xvar = xvar, \n",
+    "                    yvar = yvar, \n",
+    "                    is_paired = is_paired, \n",
+    "                    color_col = color_col, \n",
+    "                    float_contrast = float_contrast, \n",
+    "                    plot_palette_raw = plot_palette_raw, \n",
+    "                    delta_dot_kwargs = delta_dot_kwargs,\n",
+    "                    horizontal = horizontal,\n",
+    "                    )\n",
+    "                \n",
+    "    else:  ## Unpaired plots!\n",
+    "        if proportional:  # Plot the raw data as a barplot.\n",
+    "            barplotter(\n",
+    "                xvar = xvar, \n",
+    "                yvar = yvar, \n",
+    "                all_plot_groups = all_plot_groups, \n",
+    "                rawdata_axes = rawdata_axes, \n",
+    "                plot_data = plot_data, \n",
+    "                raw_colors = raw_colors, \n",
+    "                plot_palette_raw = plot_palette_raw, \n",
+    "                color_col = color_col,\n",
+    "                barplot_kwargs = barplot_kwargs,\n",
+    "                horizontal = horizontal,\n",
     "            )\n",
-    "            fig.patch.set_facecolor(face_color)\n",
-    "\n",
-    "        else:\n",
-    "            fig, axx = plt.subplots(\n",
-    "                nrows=2, gridspec_kw={\"hspace\": h_space_cummings}, **init_fig_kwargs\n",
+    "        else:   ## Plot the raw data as a swarmplot.\n",
+    "            ## swarmplot() plots swarms based on current size of ax\n",
+    "            ## Therefore, since the ax size for show_mini_meta and show_delta changes later on, there has to be increased jitter\n",
+    "            rawdata_plot = swarmplot(\n",
+    "                            data = plot_data,\n",
+    "                            x = xvar,\n",
+    "                            y = yvar,\n",
+    "                            ax = rawdata_axes,\n",
+    "                            order = all_plot_groups,\n",
+    "                            hue = color_col,\n",
+    "                            palette = plot_palette_raw,\n",
+    "                            zorder = 1,\n",
+    "                            side = asymmetric_side,\n",
+    "                            jitter = 1.25 if show_mini_meta else 1.4 if show_delta2 else 1, # TODO: to make jitter value more accurate and not just a hardcoded eyeball value\n",
+    "                            filled = filled,\n",
+    "                            is_drop_gutter = True,\n",
+    "                            gutter_limit = 0.45,\n",
+    "                            horizontal = horizontal,\n",
+    "                            **swarmplot_kwargs\n",
     "            )\n",
-    "            fig.patch.set_facecolor(face_color)\n",
-    "\n",
-    "        # Title\n",
-    "        title = plot_kwargs[\"title\"]\n",
-    "        fontsize_title = plot_kwargs[\"fontsize_title\"]\n",
-    "        if title is not None:\n",
-    "            fig.suptitle(title, fontsize=fontsize_title)\n",
-    "        rawdata_axes = axx[0]\n",
-    "        contrast_axes = axx[1]\n",
-    "    rawdata_axes.set_frame_on(False)\n",
-    "    contrast_axes.set_frame_on(False)\n",
-    "\n",
-    "    redraw_axes_kwargs = {\n",
-    "        \"colors\": ytick_color,\n",
-    "        \"facecolors\": ytick_color,\n",
-    "        \"lw\": 1,\n",
-    "        \"zorder\": 10,\n",
-    "        \"clip_on\": False,\n",
-    "    }\n",
-    "\n",
-    "    swarm_ylim = plot_kwargs[\"swarm_ylim\"]\n",
-    "\n",
-    "    if swarm_ylim is not None:\n",
-    "        rawdata_axes.set_ylim(swarm_ylim)\n",
-    "\n",
-    "    one_sankey = (\n",
-    "        False if is_paired is not None else None\n",
-    "    )  # Flag to indicate if only one sankey is plotted.\n",
-    "    two_col_sankey = (\n",
-    "        True if proportional and not one_sankey and sankey and not flow else False\n",
-    "    )\n",
-    "\n",
-    "    if show_pairs:\n",
-    "        # Determine temp_idx based on is_paired and proportional conditions\n",
-    "        if is_paired == \"baseline\":\n",
-    "            idx_pairs = [\n",
-    "                (control, test)\n",
-    "                for i in idx\n",
-    "                for control, test in zip([i[0]] * (len(i) - 1), i[1:])\n",
-    "            ]\n",
-    "            temp_idx = idx if not proportional else idx_pairs\n",
-    "        else:\n",
-    "            idx_pairs = [\n",
-    "                (control, test) for i in idx for control, test in zip(i[:-1], i[1:])\n",
-    "            ]\n",
-    "            temp_idx = idx if not proportional else idx_pairs\n",
-    "\n",
-    "        # Determine temp_all_plot_groups based on proportional condition\n",
-    "        plot_groups = [item for i in temp_idx for item in i]\n",
-    "        temp_all_plot_groups = all_plot_groups if not proportional else plot_groups\n",
-    "\n",
-    "        if not proportional:\n",
-    "            # Plot the raw data as a slopegraph.\n",
-    "            # Pivot the long (melted) data.\n",
     "            if color_col is None:\n",
-    "                pivot_values = [yvar]\n",
-    "            else:\n",
-    "                pivot_values = [yvar, color_col]\n",
-    "            pivoted_plot_data = pd.pivot(\n",
-    "                data=plot_data,\n",
-    "                index=dabest_obj.id_col,\n",
-    "                columns=xvar,\n",
-    "                values=pivot_values,\n",
-    "            )\n",
-    "            x_start = 0\n",
-    "            for ii, current_tuple in enumerate(temp_idx):\n",
-    "                current_pair = pivoted_plot_data.loc[\n",
-    "                    :, pd.MultiIndex.from_product([pivot_values, current_tuple])\n",
-    "                ].dropna()\n",
-    "                grp_count = len(current_tuple)\n",
-    "                # Iterate through the data for the current tuple.\n",
-    "                for ID, observation in current_pair.iterrows():\n",
-    "                    x_points = [t for t in range(x_start, x_start + grp_count)]\n",
-    "                    y_points = observation[yvar].tolist()\n",
-    "\n",
-    "                    if color_col is None:\n",
-    "                        slopegraph_kwargs[\"color\"] = ytick_color\n",
-    "                    else:\n",
-    "                        color_key = observation[color_col][0]\n",
-    "                        if isinstance(color_key, (str, np.int64, np.float64)):\n",
-    "                            slopegraph_kwargs[\"color\"] = plot_palette_raw[color_key]\n",
-    "                            slopegraph_kwargs[\"label\"] = color_key\n",
-    "\n",
-    "                    rawdata_axes.plot(x_points, y_points, **slopegraph_kwargs)\n",
-    "\n",
-    "                x_start = x_start + grp_count\n",
-    "\n",
-    "            ##################### DELTA PTS ON CONTRAST PLOT WIP\n",
-    "\n",
-    "            contrast_show_deltas = plot_kwargs[\"contrast_show_deltas\"]\n",
-    "\n",
-    "            if is_paired is None:\n",
-    "                contrast_show_deltas = False\n",
-    "\n",
-    "            if contrast_show_deltas:\n",
-    "                delta_plot_data_temp = plot_data.copy()\n",
-    "                delta_id_col = dabest_obj.id_col\n",
-    "                if color_col is not None:\n",
-    "                    plot_palette_deltapts = plot_palette_raw\n",
-    "                    delta_plot_data = delta_plot_data_temp[\n",
-    "                        [xvar, yvar, delta_id_col, color_col]\n",
-    "                    ]\n",
-    "                    deltapts_args = {\n",
-    "                        \"marker\": \"^\",\n",
-    "                        \"alpha\": 0.5,\n",
-    "                    }\n",
-    "\n",
-    "                else:\n",
-    "                    plot_palette_deltapts = \"k\"\n",
-    "                    delta_plot_data = delta_plot_data_temp[[xvar, yvar, delta_id_col]]\n",
-    "                    deltapts_args = {\"marker\": \"^\", \"alpha\": 0.5}\n",
-    "\n",
-    "                final_deltas = pd.DataFrame()\n",
-    "                for i in idx:\n",
-    "                    for j in i:\n",
-    "                        if i.index(j) != 0:\n",
-    "                            temp_df_exp = delta_plot_data[\n",
-    "                                delta_plot_data[xvar].str.contains(j)\n",
-    "                            ].reset_index(drop=True)\n",
-    "                            if is_paired == \"baseline\":\n",
-    "                                temp_df_cont = delta_plot_data[\n",
-    "                                    delta_plot_data[xvar].str.contains(i[0])\n",
-    "                                ].reset_index(drop=True)\n",
-    "                            elif is_paired == \"sequential\":\n",
-    "                                temp_df_cont = delta_plot_data[\n",
-    "                                    delta_plot_data[xvar].str.contains(\n",
-    "                                        i[i.index(j) - 1]\n",
-    "                                    )\n",
-    "                                ].reset_index(drop=True)\n",
-    "                            delta_df = temp_df_exp.copy()\n",
-    "                            delta_df[yvar] = temp_df_exp[yvar] - temp_df_cont[yvar]\n",
-    "                            final_deltas = pd.concat([final_deltas, delta_df])\n",
-    "\n",
-    "                # swarmplot() plots swarms based on current size of ax\n",
-    "                # Therefore, since the ax size for Gardner-Altman plot changes later on, there has to be decreased jitter\n",
-    "                # TODO: to make jitter value more accurate and not just a hardcoded eyeball value\n",
-    "                if float_contrast:\n",
-    "                    jitter = 0.6\n",
-    "                else:\n",
-    "                    jitter = 1\n",
-    "\n",
-    "                # Plot the raw data as a swarmplot.\n",
-    "                deltapts_plot = swarmplot(\n",
-    "                    data=final_deltas,\n",
-    "                    x=xvar,\n",
-    "                    y=yvar,\n",
-    "                    ax=contrast_axes,\n",
-    "                    order=None,\n",
-    "                    hue=color_col,\n",
-    "                    palette=plot_palette_deltapts,\n",
-    "                    zorder=2,\n",
-    "                    size=3,\n",
-    "                    side=\"right\",\n",
-    "                    jitter=jitter,\n",
-    "                    is_drop_gutter=True,\n",
-    "                    gutter_limit=1,\n",
-    "                    **deltapts_args\n",
-    "                )\n",
-    "                contrast_axes.legend().set_visible(False)\n",
-    "\n",
-    "            ##################### DELTA PTS ON CONTRAST PLOT END\n",
-    "\n",
-    "            # Set the tick labels, because the slopegraph plotting doesn't.\n",
-    "            rawdata_axes.set_xticks(np.arange(0, len(temp_all_plot_groups)))\n",
-    "            rawdata_axes.set_xticklabels(temp_all_plot_groups)\n",
-    "\n",
-    "        else:\n",
-    "            # Plot the raw data as a set of Sankey Diagrams aligned like barplot.\n",
-    "            group_summaries = plot_kwargs[\"group_summaries\"]\n",
-    "            if group_summaries is None:\n",
-    "                group_summaries = \"mean_sd\"\n",
-    "            err_color = plot_kwargs[\"err_color\"]\n",
-    "            if err_color is None:\n",
-    "                err_color = \"black\"\n",
-    "\n",
-    "            if show_pairs:\n",
-    "                sankey_control_group = []\n",
-    "                sankey_test_group = []\n",
-    "                # Design for Sankey Flow Diagram\n",
-    "                sankey_idx = (\n",
-    "                    [\n",
-    "                        (control, test)\n",
-    "                        for i in idx\n",
-    "                        for control, test in zip(i[:], (i[1:] + (i[0],)))\n",
-    "                    ]\n",
-    "                    if flow\n",
-    "                    else temp_idx\n",
-    "                )\n",
-    "                for i in sankey_idx:\n",
-    "                    sankey_control_group.append(i[0])\n",
-    "                    sankey_test_group.append(i[1])\n",
-    "\n",
-    "            if len(temp_all_plot_groups) == 2:\n",
-    "                one_sankey = True\n",
-    "                sankey_control_group.pop()\n",
-    "                sankey_test_group.pop()  # Remove the last element from two lists\n",
-    "\n",
-    "            # two_col_sankey = True if proportional == True and one_sankey == False and sankey == True and flow == False else False\n",
-    "\n",
-    "            # Replace the paired proportional plot with sankey diagram\n",
-    "            sankeyplot = sankeydiag(\n",
-    "                plot_data,\n",
-    "                xvar=xvar,\n",
-    "                yvar=yvar,\n",
-    "                left_idx=sankey_control_group,\n",
-    "                right_idx=sankey_test_group,\n",
-    "                palette=plot_palette_sankey,\n",
-    "                ax=rawdata_axes,\n",
-    "                one_sankey=one_sankey,\n",
-    "                **sankey_kwargs\n",
-    "            )\n",
-    "\n",
-    "    else:\n",
-    "        if not proportional:\n",
-    "            # Plot the raw data as a swarmplot.\n",
-    "            asymmetric_side = (\n",
-    "                plot_kwargs[\"swarm_side\"] if plot_kwargs[\"swarm_side\"] is not None else \"right\"\n",
-    "            )  # Default asymmetric side is right\n",
-    "\n",
-    "            # swarmplot() plots swarms based on current size of ax\n",
-    "            # Therefore, since the ax size for mini_meta and show_delta changes later on, there has to be increased jitter\n",
-    "            # TODO: to make jitter value more accurate and not just a hardcoded eyeball value\n",
-    "            if show_mini_meta:\n",
-    "                jitter = 1.25\n",
-    "            elif show_delta2:\n",
-    "                jitter = 1.4\n",
-    "            else:\n",
-    "                jitter = 1\n",
-    "\n",
-    "            if color_col is None: # Determine the use of hue\n",
-    "                rawdata_plot = swarmplot(\n",
-    "                    data=plot_data,\n",
-    "                    x=xvar,\n",
-    "                    y=yvar,\n",
-    "                    ax=rawdata_axes,\n",
-    "                    order=all_plot_groups,\n",
-    "                    hue=xvar,\n",
-    "                    palette=plot_palette_raw,\n",
-    "                    zorder=1,\n",
-    "                    side=asymmetric_side,\n",
-    "                    jitter=jitter,\n",
-    "                    is_drop_gutter=True,\n",
-    "                    gutter_limit=0.45,\n",
-    "                    **swarmplot_kwargs\n",
-    "                )\n",
     "                rawdata_plot.legend().set_visible(False)\n",
-    "            else:\n",
-    "                rawdata_plot = swarmplot(\n",
-    "                    data=plot_data,\n",
-    "                    x=xvar,\n",
-    "                    y=yvar,\n",
-    "                    ax=rawdata_axes,\n",
-    "                    order=all_plot_groups,\n",
-    "                    hue=color_col,\n",
-    "                    palette=plot_palette_raw,\n",
-    "                    zorder=1,\n",
-    "                    side=asymmetric_side,\n",
-    "                    jitter=jitter,\n",
-    "                    is_drop_gutter=True,\n",
-    "                    gutter_limit=0.45,\n",
-    "                    **swarmplot_kwargs\n",
-    "                )\n",
-    "        else:\n",
-    "            # Plot the raw data as a barplot.\n",
-    "            bar1_df = pd.DataFrame(\n",
-    "                {xvar: all_plot_groups, \"proportion\": np.ones(len(all_plot_groups))}\n",
-    "            )\n",
-    "            bar1 = sns.barplot(\n",
-    "                data=bar1_df,\n",
-    "                x=xvar,\n",
-    "                y=\"proportion\",\n",
-    "                ax=rawdata_axes,\n",
-    "                order=all_plot_groups,\n",
-    "                linewidth=2,\n",
-    "                facecolor=(1, 1, 1, 0),\n",
-    "                edgecolor=bar_color,\n",
-    "                zorder=1,\n",
-    "            )\n",
-    "            bar2 = sns.barplot(\n",
-    "                data=plot_data,\n",
-    "                x=xvar,\n",
-    "                y=yvar,\n",
-    "                ax=rawdata_axes,\n",
-    "                order=all_plot_groups,\n",
-    "                palette=plot_palette_bar,\n",
-    "                zorder=1,\n",
-    "                **barplot_kwargs\n",
+    "            \n",
+    "        ## Plot the error bars on unpaired plots.\n",
+    "        if group_summaries is not None:\n",
+    "            (group_summaries_method, \n",
+    "             group_summaries_offset, group_summaries_line_color) = extract_group_summaries(\n",
+    "                                                                            proportional = proportional, \n",
+    "                                                                            rawdata_axes = rawdata_axes, \n",
+    "                                                                            asymmetric_side = asymmetric_side if not proportional else None, \n",
+    "                                                                            horizontal = horizontal, \n",
+    "                                                                            bootstraps_color_by_group = bootstraps_color_by_group, \n",
+    "                                                                            plot_palette_raw = plot_palette_raw, \n",
+    "                                                                            all_plot_groups = all_plot_groups,\n",
+    "                                                                            n_groups = n_groups, \n",
+    "                                                                            color_col = color_col, \n",
+    "                                                                            ytick_color = ytick_color, \n",
+    "                                                                            group_summaries_kwargs = group_summaries_kwargs\n",
     "            )\n",
-    "            # adjust the width of bars\n",
-    "            bar_width = plot_kwargs[\"bar_width\"]\n",
-    "            for bar in bar1.patches:\n",
-    "                x = bar.get_x()\n",
-    "                width = bar.get_width()\n",
-    "                centre = x + width / 2.0\n",
-    "                bar.set_x(centre - bar_width / 2.0)\n",
-    "                bar.set_width(bar_width)\n",
-    "\n",
-    "        # Plot the gapped line summaries, if this is not a Cumming plot.\n",
-    "        # Also, we will not plot gapped lines for paired plots. For now.\n",
-    "        group_summaries = plot_kwargs[\"group_summaries\"]\n",
-    "        if group_summaries is None:\n",
-    "            group_summaries = \"mean_sd\"\n",
-    "\n",
-    "        if group_summaries is not None and not proportional:\n",
-    "            # Create list to gather xspans.\n",
-    "            xspans = []\n",
-    "            line_colors = []\n",
-    "            for jj, c in enumerate(rawdata_axes.collections):\n",
-    "                try:\n",
-    "                    if asymmetric_side == \"right\":\n",
-    "                        # currently offset is hardcoded with value of -0.2\n",
-    "                        x_max_span = -0.2\n",
-    "                    else:\n",
-    "                        _, x_max, _, _ = get_swarm_spans(c)\n",
-    "                        x_max_span = x_max - jj\n",
-    "                    xspans.append(x_max_span)\n",
-    "                except TypeError:\n",
-    "                    # we have got a None, so skip and move on.\n",
-    "                    pass\n",
-    "\n",
-    "                if bootstraps_color_by_group:\n",
-    "                    line_colors.append(plot_palette_raw[all_plot_groups[jj]])\n",
-    "\n",
-    "                # Break the loop since hue in Seaborn adds collections to axes and it will result in index out of range\n",
-    "                if jj >= n_groups - 1 and color_col is None:\n",
-    "                    break\n",
-    "\n",
-    "            if len(line_colors) != len(all_plot_groups):\n",
-    "                line_colors = ytick_color\n",
-    "\n",
+    "            ## Plot the error bar\n",
     "            error_bar(\n",
     "                plot_data,\n",
-    "                x=xvar,\n",
-    "                y=yvar,\n",
-    "                # Hardcoded offset...\n",
-    "                offset=xspans + np.array(plot_kwargs[\"group_summaries_offset\"]),\n",
-    "                line_color=line_colors,\n",
-    "                gap_width_percent=1.5,\n",
-    "                type=group_summaries,\n",
-    "                ax=rawdata_axes,\n",
-    "                method=\"gapped_lines\",\n",
-    "                **group_summary_kwargs\n",
-    "            )\n",
-    "\n",
-    "        if group_summaries is not None and proportional:\n",
-    "            err_color = plot_kwargs[\"err_color\"]\n",
-    "            if err_color is None:\n",
-    "                err_color = \"black\"\n",
-    "            error_bar(\n",
-    "                plot_data,\n",
-    "                x=xvar,\n",
-    "                y=yvar,\n",
-    "                offset=0,\n",
-    "                line_color=err_color,\n",
-    "                gap_width_percent=1.5,\n",
-    "                type=group_summaries,\n",
-    "                ax=rawdata_axes,\n",
-    "                method=\"proportional_error_bar\",\n",
-    "                **group_summary_kwargs\n",
+    "                x = xvar,\n",
+    "                y = yvar,\n",
+    "                offset = group_summaries_offset,\n",
+    "                line_color = group_summaries_line_color,\n",
+    "                type = group_summaries,\n",
+    "                ax = rawdata_axes,\n",
+    "                method = group_summaries_method,\n",
+    "                horizontal = horizontal,\n",
+    "                **group_summaries_kwargs\n",
     "            )\n",
     "\n",
     "    # Add the counts to the rawdata axes xticks.\n",
-    "    counts = plot_data.groupby(xvar).count()[yvar]\n",
-    "    ticks_with_counts = []\n",
-    "    ticks_loc = rawdata_axes.get_xticks()\n",
-    "    rawdata_axes.xaxis.set_major_locator(matplotlib.ticker.FixedLocator(ticks_loc))\n",
-    "    for xticklab in rawdata_axes.xaxis.get_ticklabels():\n",
-    "        t = xticklab.get_text()\n",
-    "        if t.rfind(\"\\n\") != -1:\n",
-    "            te = t[t.rfind(\"\\n\") + len(\"\\n\") :]\n",
-    "            N = str(counts.loc[te])\n",
-    "            te = t\n",
-    "        else:\n",
-    "            te = t\n",
-    "            N = str(counts.loc[te])\n",
-    "\n",
-    "        ticks_with_counts.append(\"{}\\nN = {}\".format(te, N))\n",
-    "\n",
-    "    if plot_kwargs[\"fontsize_rawxlabel\"] is not None:\n",
-    "        fontsize_rawxlabel = plot_kwargs[\"fontsize_rawxlabel\"]\n",
-    "    rawdata_axes.set_xticklabels(ticks_with_counts, fontsize=fontsize_rawxlabel)\n",
-    "\n",
-    "    # Save the handles and labels for the legend.\n",
-    "    handles, labels = rawdata_axes.get_legend_handles_labels()\n",
-    "    legend_labels = [l for l in labels]\n",
-    "    legend_handles = [h for h in handles]\n",
-    "    if bootstraps_color_by_group is False:\n",
-    "        rawdata_axes.legend().set_visible(False)\n",
-    "\n",
-    "    # Enforce the xtick of rawdata_axes to be 0 and 1 after drawing only one sankey\n",
-    "    if one_sankey:\n",
-    "        rawdata_axes.set_xticks([0, 1])\n",
-    "\n",
-    "    # Plot effect sizes and bootstraps.\n",
-    "    # Take note of where the `control` groups are.\n",
-    "    if is_paired == \"baseline\" and show_pairs:\n",
-    "        if two_col_sankey:\n",
-    "            ticks_to_skip = []\n",
-    "            ticks_to_plot = np.arange(0, len(temp_all_plot_groups) / 2).tolist()\n",
-    "            ticks_to_start_twocol_sankey = np.cumsum([len(i) - 1 for i in idx]).tolist()\n",
-    "            ticks_to_start_twocol_sankey.pop()\n",
-    "            ticks_to_start_twocol_sankey.insert(0, 0)\n",
-    "        else:\n",
-    "            # ticks_to_skip = np.arange(0, len(temp_all_plot_groups), 2).tolist()\n",
-    "            # ticks_to_plot = np.arange(1, len(temp_all_plot_groups), 2).tolist()\n",
-    "            ticks_to_skip = np.cumsum([len(t) for t in idx])[:-1].tolist()\n",
-    "            ticks_to_skip.insert(0, 0)\n",
-    "            # Then obtain the ticks where we have to plot the effect sizes.\n",
-    "            ticks_to_plot = [\n",
-    "                t for t in range(0, len(all_plot_groups)) if t not in ticks_to_skip\n",
-    "            ]\n",
-    "            ticks_to_skip_contrast = np.cumsum([(len(t)) for t in idx])[:-1].tolist()\n",
-    "            ticks_to_skip_contrast.insert(0, 0)\n",
-    "    else:\n",
-    "        if two_col_sankey:\n",
-    "            ticks_to_skip = [len(sankey_control_group)]\n",
-    "            # Then obtain the ticks where we have to plot the effect sizes.\n",
-    "            ticks_to_plot = [\n",
-    "                t for t in range(0, len(temp_idx)) if t not in ticks_to_skip\n",
-    "            ]\n",
-    "            ticks_to_skip = []\n",
-    "            ticks_to_start_twocol_sankey = np.cumsum([len(i) - 1 for i in idx]).tolist()\n",
-    "            ticks_to_start_twocol_sankey.pop()\n",
-    "            ticks_to_start_twocol_sankey.insert(0, 0)\n",
-    "        else:\n",
-    "            ticks_to_skip = np.cumsum([len(t) for t in idx])[:-1].tolist()\n",
-    "            ticks_to_skip.insert(0, 0)\n",
-    "            # Then obtain the ticks where we have to plot the effect sizes.\n",
-    "            ticks_to_plot = [\n",
-    "                t for t in range(0, len(all_plot_groups)) if t not in ticks_to_skip\n",
-    "            ]\n",
-    "\n",
-    "    # Plot the bootstraps, then the effect sizes and CIs.\n",
-    "    es_marker_size = plot_kwargs[\"es_marker_size\"]\n",
-    "    halfviolin_alpha = plot_kwargs[\"halfviolin_alpha\"]\n",
-    "\n",
-    "    ci_type = plot_kwargs[\"ci_type\"]\n",
-    "\n",
-    "    results = effectsize_df.results\n",
-    "    contrast_xtick_labels = []\n",
-    "\n",
-    "    for j, tick in enumerate(ticks_to_plot):\n",
-    "        current_group = results.test[j]\n",
-    "        current_control = results.control[j]\n",
-    "        current_bootstrap = results.bootstraps[j]\n",
-    "        current_effsize = results.difference[j]\n",
-    "        if ci_type == \"bca\":\n",
-    "            current_ci_low = results.bca_low[j]\n",
-    "            current_ci_high = results.bca_high[j]\n",
-    "        else:\n",
-    "            current_ci_low = results.pct_low[j]\n",
-    "            current_ci_high = results.pct_high[j]\n",
-    "\n",
-    "        # Create the violinplot.\n",
-    "        # New in v0.2.6: drop negative infinities before plotting.\n",
-    "        v = contrast_axes.violinplot(\n",
-    "            current_bootstrap[~np.isinf(current_bootstrap)],\n",
-    "            positions=[tick],\n",
-    "            **violinplot_kwargs\n",
-    "        )\n",
-    "        # Turn the violinplot into half, and color it the same as the swarmplot.\n",
-    "        # Do this only if the color column is not specified.\n",
-    "        # Ideally, the alpha (transparency) fo the violin plot should be\n",
-    "        # less than one so the effect size and CIs are visible.\n",
-    "        if bootstraps_color_by_group:\n",
-    "            fc = plot_palette_contrast[current_group]\n",
-    "        else:\n",
-    "            fc = \"grey\"\n",
-    "\n",
-    "        halfviolin(v, fill_color=fc, alpha=halfviolin_alpha)\n",
-    "\n",
-    "        # Plot the effect size.\n",
-    "        contrast_axes.plot(\n",
-    "            [tick],\n",
-    "            current_effsize,\n",
-    "            marker=\"o\",\n",
-    "            color=ytick_color,\n",
-    "            markersize=es_marker_size,\n",
+    "    if show_sample_size:\n",
+    "        add_counts_to_ticks(\n",
+    "                plot_data = plot_data, \n",
+    "                xvar = xvar, \n",
+    "                yvar = yvar, \n",
+    "                rawdata_axes = rawdata_axes, \n",
+    "                plot_kwargs = plot_kwargs,\n",
+    "                flow = sankey_kwargs[\"flow\"],\n",
+    "                horizontal = horizontal,\n",
     "        )\n",
     "\n",
-    "        ################## SHOW ES ON CONTRAST PLOT WIP\n",
-    "\n",
-    "        contrast_show_es = plot_kwargs[\"contrast_show_es\"]\n",
-    "        es_sf = plot_kwargs[\"es_sf\"]\n",
-    "        es_fontsize = plot_kwargs[\"es_fontsize\"]\n",
-    "\n",
-    "        if gridkey_show_es:\n",
-    "            contrast_show_es = False\n",
-    "\n",
-    "        effsize_for_print = current_effsize\n",
-    "\n",
-    "        printed_es = np.format_float_positional(\n",
-    "            effsize_for_print, precision=es_sf, sign=True, trim=\"k\", min_digits=es_sf\n",
+    "    # Add counts to prop plots (embedded in the plot bars)\n",
+    "    if proportional and plot_kwargs['prop_sample_counts'] and sankey_kwargs[\"flow\"]:\n",
+    "        add_counts_to_prop_plots(\n",
+    "                        plot_data = plot_data, \n",
+    "                        xvar = xvar, \n",
+    "                        yvar = yvar, \n",
+    "                        rawdata_axes = rawdata_axes, \n",
+    "                        horizontal = horizontal,\n",
+    "                        is_paired = is_paired,\n",
+    "                        prop_sample_counts_kwargs = prop_sample_counts_kwargs,\n",
     "        )\n",
-    "        if contrast_show_es:\n",
-    "            if effsize_for_print < 0:\n",
-    "                textoffset = 10\n",
-    "            else:\n",
-    "                textoffset = 15\n",
-    "            contrast_axes.annotate(\n",
-    "                text=printed_es,\n",
-    "                xy=(tick, effsize_for_print),\n",
-    "                xytext=(\n",
-    "                    -textoffset - len(printed_es) * es_fontsize / 2,\n",
-    "                    -es_fontsize / 2,\n",
-    "                ),\n",
-    "                textcoords=\"offset points\",\n",
-    "                **{\"fontsize\": es_fontsize}\n",
-    "            )\n",
-    "\n",
-    "        ################## SHOW ES ON CONTRAST PLOT END\n",
     "\n",
-    "        # Plot the confidence interval.\n",
-    "        contrast_axes.plot(\n",
-    "            [tick, tick],\n",
-    "            [current_ci_low, current_ci_high],\n",
-    "            linestyle=\"-\",\n",
-    "            color=ytick_color,\n",
-    "            linewidth=group_summary_kwargs[\"lw\"],\n",
-    "        )\n",
+    "    ## Swarm bars\n",
+    "    raw_bars = plot_kwargs[\"raw_bars\"]\n",
+    "    if raw_bars and not proportional and not horizontal: #Currently not supporting swarm bars for horizontal plots (looks weird)\n",
+    "        raw_bars_dict, raw_bars_kwargs = prepare_bars_for_plot(\n",
+    "                                                    bar_type = 'raw', \n",
+    "                                                    bar_kwargs = raw_bars_kwargs, \n",
+    "                                                    horizontal = horizontal,\n",
+    "                                                    plot_palette_raw = plot_palette_raw,\n",
+    "                                                    color_col = color_col, \n",
+    "                                                    show_pairs = show_pairs, \n",
+    "                                                    plot_data = plot_data,\n",
+    "                                                    xvar = xvar, \n",
+    "                                                    yvar = yvar,      \n",
+    "                                        )\n",
+    "        add_bars_to_plot(bar_dict = raw_bars_dict, \n",
+    "                         ax = rawdata_axes, \n",
+    "                         bar_kwargs = raw_bars_kwargs\n",
+    "                        )\n",
+    "\n",
+    "    # Plot the contrast axes - effect sizes and bootstraps!\n",
+    "    plot_groups = (temp_all_plot_groups if (is_paired == \"baseline\" and show_pairs and two_col_sankey) \n",
+    "                   else temp_idx if two_col_sankey \n",
+    "                   else all_plot_groups\n",
+    "    )\n",
     "\n",
-    "        contrast_xtick_labels.append(\n",
-    "            \"{}\\nminus\\n{}\".format(current_group, current_control)\n",
-    "        )\n",
+    "    ## Extract ticks for contrast plot\n",
+    "    (ticks_to_skip, ticks_to_plot, ticks_for_baseline_ec,\n",
+    "     ticks_to_skip_contrast, ticks_to_start_twocol_sankey) = extract_contrast_plotting_ticks(\n",
+    "                                                                                    is_paired = is_paired, \n",
+    "                                                                                    show_pairs = show_pairs, \n",
+    "                                                                                    two_col_sankey = two_col_sankey, \n",
+    "                                                                                    plot_groups = plot_groups,\n",
+    "                                                                                    idx = idx,\n",
+    "                                                                                    sankey_control_group = sankey_control_test_groups[0] if two_col_sankey else None,\n",
+    "    )                                                                  \n",
+    "\n",
+    "    ## Adjust contrast tick locations to account for different plotting styles in horizontal plots\n",
+    "    table_axes_ticks_to_plot = ticks_to_plot\n",
+    "    if (horizontal and proportional and not show_pairs) or (horizontal and plot_kwargs[\"swarm_side\"] == \"right\"):\n",
+    "        ticks_to_plot = [x+0.25 for x in ticks_to_plot]\n",
+    "\n",
+    "    ## Plot the bootstraps, then the effect sizes and CIs.\n",
+    "    contrast_paired_lines = False if float_contrast or not sankey_kwargs[\"flow\"] else plot_kwargs[\"contrast_paired_lines\"]\n",
+    "    (current_group, current_control,\n",
+    "     current_effsize, contrast_xtick_labels) = effect_size_curve_plotter(\n",
+    "                                                                ticks_to_plot = ticks_to_plot, \n",
+    "                                                                ticks_for_baseline_ec = ticks_for_baseline_ec,\n",
+    "                                                                results = results, \n",
+    "                                                                ci_type = ci_type, \n",
+    "                                                                contrast_axes = contrast_axes, \n",
+    "                                                                contrast_kwargs = contrast_kwargs, \n",
+    "                                                                bootstraps_color_by_group = bootstraps_color_by_group,\n",
+    "                                                                plot_palette_contrast = plot_palette_contrast,\n",
+    "                                                                horizontal = horizontal,\n",
+    "                                                                contrast_marker_kwargs = contrast_marker_kwargs,\n",
+    "                                                                contrast_errorbar_kwargs = contrast_errorbar_kwargs,\n",
+    "                                                                idx = idx,\n",
+    "                                                                is_paired = is_paired,\n",
+    "                                                                contrast_paired_lines = contrast_paired_lines,\n",
+    "\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcontrast_paired_lines_kwargs = contrast_paired_lines_kwargs,\n",
+    "\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tshow_baseline_ec = show_baseline_ec,\n",
+    "    )\n",
     "\n",
-    "    # Plot mini-meta violin\n",
+    "    ## Plot mini-meta or delta-delta violin\n",
+    "    delta2_axes = None\n",
     "    if show_mini_meta or show_delta2:\n",
-    "        if show_mini_meta:\n",
-    "            mini_meta_delta = effectsize_df.mini_meta_delta\n",
-    "            data = mini_meta_delta.bootstraps_weighted_delta\n",
-    "            difference = mini_meta_delta.difference\n",
-    "            if ci_type == \"bca\":\n",
-    "                ci_low = mini_meta_delta.bca_low\n",
-    "                ci_high = mini_meta_delta.bca_high\n",
-    "            else:\n",
-    "                ci_low = mini_meta_delta.pct_low\n",
-    "                ci_high = mini_meta_delta.pct_high\n",
-    "        else:\n",
-    "            delta_delta = effectsize_df.delta_delta\n",
-    "            data = delta_delta.bootstraps_delta_delta\n",
-    "            difference = delta_delta.difference\n",
-    "            if ci_type == \"bca\":\n",
-    "                ci_low = delta_delta.bca_low\n",
-    "                ci_high = delta_delta.bca_high\n",
-    "            else:\n",
-    "                ci_low = delta_delta.pct_low\n",
-    "                ci_high = delta_delta.pct_high\n",
-    "        # Create the violinplot.\n",
-    "        # New in v0.2.6: drop negative infinities before plotting.\n",
-    "        position = max(rawdata_axes.get_xticks()) + 2\n",
-    "        v = contrast_axes.violinplot(\n",
-    "            data[~np.isinf(data)], positions=[position], **violinplot_kwargs\n",
-    "        )\n",
-    "\n",
-    "        fc = \"grey\"\n",
-    "\n",
-    "        halfviolin(v, fill_color=fc, alpha=halfviolin_alpha)\n",
-    "\n",
-    "        # Plot the effect size.\n",
-    "        contrast_axes.plot(\n",
-    "            [position],\n",
-    "            difference,\n",
-    "            marker=\"o\",\n",
-    "            color=ytick_color,\n",
-    "            markersize=es_marker_size,\n",
+    "        delta2_axes, contrast_xtick_labels = plot_minimeta_or_deltadelta_violins(\n",
+    "                                                                dabest_obj = effectsize_df.mini_meta if show_mini_meta else effectsize_df.delta_delta,\n",
+    "                                                                type = 'mini_meta' if show_mini_meta else 'delta_delta',\n",
+    "                                                                ci_type = ci_type, \n",
+    "                                                                rawdata_axes = rawdata_axes,\n",
+    "                                                                contrast_axes = contrast_axes, \n",
+    "                                                                contrast_kwargs = contrast_kwargs, \n",
+    "                                                                contrast_xtick_labels = contrast_xtick_labels, \n",
+    "                                                                effect_size = effect_size,\n",
+    "                                                                plot_kwargs = plot_kwargs, \n",
+    "                                                                horizontal = horizontal,\n",
+    "                                                                show_pairs = show_pairs,\n",
+    "                                                                contrast_marker_kwargs = contrast_marker_kwargs,\n",
+    "                                                                contrast_errorbar_kwargs = contrast_errorbar_kwargs,\n",
     "        )\n",
-    "        # Plot the confidence interval.\n",
-    "        contrast_axes.plot(\n",
-    "            [position, position],\n",
-    "            [ci_low, ci_high],\n",
-    "            linestyle=\"-\",\n",
-    "            color=ytick_color,\n",
-    "            linewidth=group_summary_kwargs[\"lw\"],\n",
+    "    ## Contrast bars\n",
+    "    contrast_bars = plot_kwargs[\"contrast_bars\"]\n",
+    "    if contrast_bars:\n",
+    "        contrast_bars_dict, contrast_bars_kwargs = prepare_bars_for_plot(\n",
+    "                                                    bar_type = 'contrast', \n",
+    "                                                    bar_kwargs = contrast_bars_kwargs, \n",
+    "                                                    horizontal = horizontal,\n",
+    "                                                    plot_palette_raw = plot_palette_raw,\n",
+    "                                                    color_col = color_col, \n",
+    "                                                    show_pairs = show_pairs, \n",
+    "                                                    results = results, \n",
+    "                                                    ticks_to_plot = ticks_to_plot, \n",
+    "                                                    extra_delta = (effectsize_df.mini_meta.difference if show_mini_meta \n",
+    "                                                                  else effectsize_df.delta_delta.difference if show_delta2\n",
+    "                                                                  else None)\n",
+    "                                                )\n",
+    "        add_bars_to_plot(bar_dict = contrast_bars_dict, \n",
+    "                         ax = contrast_axes, \n",
+    "                         bar_kwargs = contrast_bars_kwargs\n",
+    "                        )\n",
+    "    \n",
+    "    ## Delta text\n",
+    "    delta_text = plot_kwargs[\"delta_text\"]\n",
+    "    if delta_text and not horizontal: \n",
+    "        delta_text_plotter(\n",
+    "                    results = results, \n",
+    "                    ax_to_plot = contrast_axes, \n",
+    "                    ticks_to_plot = ticks_to_plot, \n",
+    "                    delta_text_kwargs = delta_text_kwargs, \n",
+    "                    color_col = color_col, \n",
+    "                    plot_palette_raw = plot_palette_raw, \n",
+    "                    show_pairs = show_pairs,\n",
+    "                    float_contrast = float_contrast, \n",
+    "                    extra_delta = (effectsize_df.mini_meta.difference if show_mini_meta \n",
+    "                                                                  else effectsize_df.delta_delta.difference if show_delta2\n",
+    "                                                                  else None),\n",
     "        )\n",
-    "        if show_mini_meta:\n",
-    "            contrast_xtick_labels.extend([\"\", \"Weighted delta\"])\n",
-    "        elif effect_size == \"delta_g\":\n",
-    "            contrast_xtick_labels.extend([\"\", \"deltas' g\"])\n",
-    "        else:\n",
-    "            contrast_xtick_labels.extend([\"\", \"delta-delta\"])\n",
-    "\n",
-    "    # Make sure the contrast_axes x-lims match the rawdata_axes xlims,\n",
-    "    # and add an extra violinplot tick for delta-delta plot.\n",
-    "    if show_delta2 is False and show_mini_meta is False:\n",
-    "        contrast_axes.set_xticks(rawdata_axes.get_xticks())\n",
-    "    else:\n",
-    "        temp = rawdata_axes.get_xticks()\n",
-    "        temp = np.append(temp, [max(temp) + 1, max(temp) + 2])\n",
-    "        contrast_axes.set_xticks(temp)\n",
-    "\n",
-    "    if show_pairs:\n",
-    "        max_x = contrast_axes.get_xlim()[1]\n",
-    "        rawdata_axes.set_xlim(-0.375, max_x)\n",
     "\n",
-    "    if float_contrast:\n",
-    "        contrast_axes.set_xlim(0.5, 1.5)\n",
-    "    elif show_delta2 or show_mini_meta:\n",
-    "        # Increase the xlim of raw data by 2\n",
-    "        temp = rawdata_axes.get_xlim()\n",
-    "        if show_pairs:\n",
-    "            rawdata_axes.set_xlim(temp[0], temp[1] + 0.25)\n",
-    "        else:\n",
-    "            rawdata_axes.set_xlim(temp[0], temp[1] + 2)\n",
-    "        contrast_axes.set_xlim(rawdata_axes.get_xlim())\n",
-    "    else:\n",
-    "        contrast_axes.set_xlim(rawdata_axes.get_xlim())\n",
-    "\n",
-    "    # Properly label the contrast ticks.\n",
-    "    for t in ticks_to_skip:\n",
-    "        contrast_xtick_labels.insert(t, \"\")\n",
-    "\n",
-    "    if plot_kwargs[\"fontsize_contrastxlabel\"] is not None:\n",
-    "        fontsize_contrastxlabel = plot_kwargs[\"fontsize_contrastxlabel\"]\n",
-    "\n",
-    "    contrast_axes.set_xticklabels(\n",
-    "        contrast_xtick_labels, fontsize=fontsize_contrastxlabel\n",
+    "    ## Make sure the contrast_axes x-lims match the rawdata_axes xlims,\n",
+    "    ## and add an extra violinplot tick for delta-delta plot.\n",
+    "    ## Name is xaxis but it is actually y-axis for horizontal plots\n",
+    "    set_xaxis_ticks_and_lims(\n",
+    "                    show_delta2 = show_delta2, \n",
+    "                    show_mini_meta = show_mini_meta, \n",
+    "                    rawdata_axes = rawdata_axes, \n",
+    "                    contrast_axes = contrast_axes, \n",
+    "                    show_pairs = show_pairs, \n",
+    "                    float_contrast = float_contrast,\n",
+    "                    ticks_to_skip = ticks_to_skip, \n",
+    "                    contrast_xtick_labels = contrast_xtick_labels, \n",
+    "                    plot_kwargs = plot_kwargs,\n",
+    "                    proportional = proportional,\n",
+    "                    horizontal = horizontal,\n",
     "    )\n",
-    "\n",
-    "    if bootstraps_color_by_group is False:\n",
-    "        legend_labels_unique = np.unique(legend_labels)\n",
-    "        unique_idx = np.unique(legend_labels, return_index=True)[1]\n",
-    "        legend_handles_unique = (\n",
-    "            pd.Series(legend_handles, dtype=\"object\").loc[unique_idx]\n",
-    "        ).tolist()\n",
-    "\n",
-    "        if len(legend_handles_unique) > 0:\n",
-    "            if float_contrast:\n",
-    "                axes_with_legend = contrast_axes\n",
-    "                if show_pairs:\n",
-    "                    bta = (1.75, 1.02)\n",
-    "                else:\n",
-    "                    bta = (1.5, 1.02)\n",
-    "            else:\n",
-    "                axes_with_legend = rawdata_axes\n",
-    "                if show_pairs:\n",
-    "                    bta = (1.02, 1.0)\n",
-    "                else:\n",
-    "                    bta = (1.0, 1.0)\n",
-    "            leg = axes_with_legend.legend(\n",
-    "                legend_handles_unique,\n",
-    "                legend_labels_unique,\n",
-    "                bbox_to_anchor=bta,\n",
-    "                **legend_kwargs\n",
-    "            )\n",
-    "            if show_pairs:\n",
-    "                for line in leg.get_lines():\n",
-    "                    line.set_linewidth(3.0)\n",
-    "\n",
-    "    og_ylim_raw = rawdata_axes.get_ylim()\n",
-    "    og_xlim_raw = rawdata_axes.get_xlim()\n",
-    "\n",
-    "    if float_contrast:\n",
-    "        # For Gardner-Altman plots only.\n",
-    "\n",
-    "        # Normalize ylims and despine the floating contrast axes.\n",
-    "        # Check that the effect size is within the swarm ylims.\n",
-    "        if effect_size_type in [\"mean_diff\", \"cohens_d\", \"hedges_g\", \"cohens_h\"]:\n",
-    "            control_group_summary = (\n",
-    "                plot_data.groupby(xvar)\n",
-    "                .mean(numeric_only=True)\n",
-    "                .loc[current_control, yvar]\n",
-    "            )\n",
-    "            test_group_summary = (\n",
-    "                plot_data.groupby(xvar).mean(numeric_only=True).loc[current_group, yvar]\n",
-    "            )\n",
-    "        elif effect_size_type == \"median_diff\":\n",
-    "            control_group_summary = (\n",
-    "                plot_data.groupby(xvar).median().loc[current_control, yvar]\n",
-    "            )\n",
-    "            test_group_summary = (\n",
-    "                plot_data.groupby(xvar).median().loc[current_group, yvar]\n",
-    "            )\n",
-    "\n",
-    "        if swarm_ylim is None:\n",
-    "            swarm_ylim = rawdata_axes.get_ylim()\n",
-    "\n",
-    "        _, contrast_xlim_max = contrast_axes.get_xlim()\n",
-    "\n",
-    "        difference = float(results.difference[0])\n",
-    "\n",
-    "        if effect_size_type in [\"mean_diff\", \"median_diff\"]:\n",
-    "            # Align 0 of contrast_axes to reference group mean of rawdata_axes.\n",
-    "            # If the effect size is positive, shift the contrast axis up.\n",
-    "            rawdata_ylims = np.array(rawdata_axes.get_ylim())\n",
-    "            if current_effsize > 0:\n",
-    "                rightmin, rightmax = rawdata_ylims - current_effsize\n",
-    "            # If the effect size is negative, shift the contrast axis down.\n",
-    "            elif current_effsize < 0:\n",
-    "                rightmin, rightmax = rawdata_ylims + current_effsize\n",
-    "            else:\n",
-    "                rightmin, rightmax = rawdata_ylims\n",
-    "\n",
-    "            contrast_axes.set_ylim(rightmin, rightmax)\n",
-    "\n",
-    "            og_ylim_contrast = rawdata_axes.get_ylim() - np.array(control_group_summary)\n",
-    "\n",
-    "            contrast_axes.set_ylim(og_ylim_contrast)\n",
-    "            contrast_axes.set_xlim(contrast_xlim_max - 1, contrast_xlim_max)\n",
-    "\n",
-    "        elif effect_size_type in [\"cohens_d\", \"hedges_g\", \"cohens_h\"]:\n",
-    "            if is_paired:\n",
-    "                which_std = 1\n",
-    "            else:\n",
-    "                which_std = 0\n",
-    "            temp_control = plot_data[plot_data[xvar] == current_control][yvar]\n",
-    "            temp_test = plot_data[plot_data[xvar] == current_group][yvar]\n",
-    "\n",
-    "            stds = _compute_standardizers(temp_control, temp_test)\n",
-    "            if is_paired:\n",
-    "                pooled_sd = stds[1]\n",
-    "            else:\n",
-    "                pooled_sd = stds[0]\n",
-    "\n",
-    "            if effect_size_type == \"hedges_g\":\n",
-    "                gby_count = plot_data.groupby(xvar).count()\n",
-    "                len_control = gby_count.loc[current_control, yvar]\n",
-    "                len_test = gby_count.loc[current_group, yvar]\n",
-    "\n",
-    "                hg_correction_factor = _compute_hedges_correction_factor(\n",
-    "                    len_control, len_test\n",
-    "                )\n",
-    "\n",
-    "                ylim_scale_factor = pooled_sd / hg_correction_factor\n",
-    "\n",
-    "            elif effect_size_type == \"cohens_h\":\n",
-    "                ylim_scale_factor = (\n",
-    "                    np.mean(temp_test) - np.mean(temp_control)\n",
-    "                ) / difference\n",
-    "\n",
-    "            else:\n",
-    "                ylim_scale_factor = pooled_sd\n",
-    "\n",
-    "            scaled_ylim = (\n",
-    "                (rawdata_axes.get_ylim() - control_group_summary) / ylim_scale_factor\n",
-    "            ).tolist()\n",
-    "\n",
-    "            contrast_axes.set_ylim(scaled_ylim)\n",
-    "            og_ylim_contrast = scaled_ylim\n",
-    "\n",
-    "            contrast_axes.set_xlim(contrast_xlim_max - 1, contrast_xlim_max)\n",
-    "\n",
-    "        if one_sankey is None:\n",
-    "            # Draw summary lines for control and test groups..\n",
-    "            for jj, axx in enumerate([rawdata_axes, contrast_axes]):\n",
-    "                # Draw effect size line.\n",
-    "                if jj == 0:\n",
-    "                    ref = control_group_summary\n",
-    "                    diff = test_group_summary\n",
-    "                    effsize_line_start = 1\n",
-    "\n",
-    "                elif jj == 1:\n",
-    "                    ref = 0\n",
-    "                    diff = ref + difference\n",
-    "                    effsize_line_start = contrast_xlim_max - 1.1\n",
-    "\n",
-    "                xlimlow, xlimhigh = axx.get_xlim()\n",
-    "\n",
-    "                # Draw reference line.\n",
-    "                axx.hlines(\n",
-    "                    ref,  # y-coordinates\n",
-    "                    0,\n",
-    "                    xlimhigh,  # x-coordinates, start and end.\n",
-    "                    **reflines_kwargs\n",
-    "                )\n",
-    "\n",
-    "                # Draw effect size line.\n",
-    "                axx.hlines(diff, effsize_line_start, xlimhigh, **reflines_kwargs)\n",
-    "        else:\n",
-    "            ref = 0\n",
-    "            diff = ref + difference\n",
-    "            effsize_line_start = contrast_xlim_max - 0.9\n",
-    "            xlimlow, xlimhigh = contrast_axes.get_xlim()\n",
-    "            # Draw reference line.\n",
-    "            contrast_axes.hlines(\n",
-    "                ref,  # y-coordinates\n",
-    "                effsize_line_start,\n",
-    "                xlimhigh,  # x-coordinates, start and end.\n",
-    "                **reflines_kwargs\n",
-    "            )\n",
-    "\n",
-    "            # Draw effect size line.\n",
-    "            contrast_axes.hlines(diff, effsize_line_start, xlimhigh, **reflines_kwargs)\n",
-    "        rawdata_axes.set_xlim(og_xlim_raw)  # to align the axis\n",
-    "        # Despine appropriately.\n",
-    "        sns.despine(ax=rawdata_axes, bottom=True)\n",
-    "        sns.despine(ax=contrast_axes, left=True, right=False)\n",
-    "\n",
-    "        # Insert break between the rawdata axes and the contrast axes\n",
-    "        # by re-drawing the x-spine.\n",
-    "        rawdata_axes.hlines(\n",
-    "            og_ylim_raw[0],  # yindex\n",
-    "            rawdata_axes.get_xlim()[0],\n",
-    "            1.3,  # xmin, xmax\n",
-    "            **redraw_axes_kwargs\n",
+    "    # Plot aesthetic adjustments.\n",
+    "    if float_contrast: # For Gardner-Altman (float contrast) plots only.\n",
+    "        gardner_altman_adjustments(\n",
+    "                                effect_size_type = effect_size, \n",
+    "                                plot_data = plot_data, \n",
+    "                                xvar = xvar, \n",
+    "                                yvar = yvar, \n",
+    "                                current_control = current_control, \n",
+    "                                current_group = current_group,\n",
+    "                                rawdata_axes = rawdata_axes, \n",
+    "                                contrast_axes = contrast_axes, \n",
+    "                                results = results, \n",
+    "                                current_effsize = current_effsize, \n",
+    "                                is_paired = is_paired, \n",
+    "                                one_sankey = one_sankey,\n",
+    "                                reflines_kwargs = reflines_kwargs, \n",
+    "                                redraw_axes_kwargs = redraw_axes_kwargs, \n",
     "        )\n",
-    "        rawdata_axes.set_ylim(og_ylim_raw)\n",
-    "\n",
-    "        contrast_axes.hlines(\n",
-    "            contrast_axes.get_ylim()[0],\n",
-    "            contrast_xlim_max - 0.8,\n",
-    "            contrast_xlim_max,\n",
-    "            **redraw_axes_kwargs\n",
+    "    else: # For Cumming plots only.\n",
+    "        ## Add Zero line if lies within the ylim of contrast axes\n",
+    "        draw_zeroline(\n",
+    "                ax = contrast_axes,\n",
+    "                horizontal = horizontal,\n",
+    "                reflines_kwargs = reflines_kwargs,\n",
+    "                extra_delta = True if show_delta2 else False,\n",
     "        )\n",
+    "        ## Axes independent spine lines\n",
+    "        is_gridkey = True if plot_kwargs[\"gridkey\"] is not None else False\n",
+    "        if not is_gridkey:\n",
+    "            redraw_independent_spines(\n",
+    "                        rawdata_axes = rawdata_axes,\n",
+    "                        contrast_axes = contrast_axes,\n",
+    "                        horizontal = horizontal,\n",
+    "                        two_col_sankey = two_col_sankey,\n",
+    "                        ticks_to_start_twocol_sankey = ticks_to_start_twocol_sankey,\n",
+    "                        idx = idx,\n",
+    "                        is_paired = is_paired,\n",
+    "                        show_pairs = show_pairs,\n",
+    "                        proportional = proportional,\n",
+    "                        ticks_to_skip = ticks_to_skip,\n",
+    "                        temp_idx = temp_idx if is_paired == \"baseline\" and show_pairs else None,\n",
+    "                        ticks_to_skip_contrast = ticks_to_skip_contrast,\n",
+    "                        redraw_axes_kwargs = redraw_axes_kwargs\n",
+    "            )\n",
     "\n",
-    "    else:\n",
-    "        # For Cumming Plots only.\n",
-    "\n",
-    "        # Set custom contrast_ylim, if it was specified.\n",
-    "        if plot_kwargs[\"contrast_ylim\"] is not None or (\n",
-    "            plot_kwargs[\"delta2_ylim\"] is not None and show_delta2\n",
-    "        ):\n",
-    "            if plot_kwargs[\"contrast_ylim\"] is not None:\n",
-    "                custom_contrast_ylim = plot_kwargs[\"contrast_ylim\"]\n",
-    "                if plot_kwargs[\"delta2_ylim\"] is not None and show_delta2:\n",
-    "                    custom_delta2_ylim = plot_kwargs[\"delta2_ylim\"]\n",
-    "                    if custom_contrast_ylim != custom_delta2_ylim:\n",
-    "                        err1 = \"Please check if `contrast_ylim` and `delta2_ylim` are assigned\"\n",
-    "                        err2 = \"with same values.\"\n",
-    "                        raise ValueError(err1 + err2)\n",
-    "            else:\n",
-    "                custom_delta2_ylim = plot_kwargs[\"delta2_ylim\"]\n",
-    "                custom_contrast_ylim = custom_delta2_ylim\n",
-    "\n",
-    "            if len(custom_contrast_ylim) != 2:\n",
-    "                err1 = \"Please check `contrast_ylim` consists of \"\n",
-    "                err2 = \"exactly two numbers.\"\n",
-    "                raise ValueError(err1 + err2)\n",
-    "\n",
-    "            if effect_size_type == \"cliffs_delta\":\n",
-    "                # Ensure the ylims for a cliffs_delta plot never exceed [-1, 1].\n",
-    "                l = plot_kwargs[\"contrast_ylim\"][0]\n",
-    "                h = plot_kwargs[\"contrast_ylim\"][1]\n",
-    "                low = -1 if l < -1 else l\n",
-    "                high = 1 if h > 1 else h\n",
-    "                contrast_axes.set_ylim(low, high)\n",
-    "            else:\n",
-    "                contrast_axes.set_ylim(custom_contrast_ylim)\n",
-    "\n",
-    "        # If 0 lies within the ylim of the contrast axes,\n",
-    "        # draw a zero reference line.\n",
-    "        contrast_axes_ylim = contrast_axes.get_ylim()\n",
-    "        if contrast_axes_ylim[0] < contrast_axes_ylim[1]:\n",
-    "            contrast_ylim_low, contrast_ylim_high = contrast_axes_ylim\n",
-    "        else:\n",
-    "            contrast_ylim_high, contrast_ylim_low = contrast_axes_ylim\n",
-    "        if contrast_ylim_low < 0 < contrast_ylim_high:\n",
-    "            contrast_axes.axhline(y=0, **reflines_kwargs)\n",
-    "\n",
-    "        if is_paired == \"baseline\" and show_pairs:\n",
-    "            if two_col_sankey:\n",
-    "                rightend_ticks_raw = np.array([len(i) - 2 for i in idx]) + np.array(\n",
-    "                    ticks_to_start_twocol_sankey\n",
-    "                )\n",
-    "            elif proportional and is_paired is not None:\n",
-    "                rightend_ticks_raw = np.array([len(i) - 1 for i in idx]) + np.array(\n",
-    "                    ticks_to_skip\n",
-    "                )\n",
-    "            else:\n",
-    "                rightend_ticks_raw = np.array(\n",
-    "                    [len(i) - 1 for i in temp_idx]\n",
-    "                ) + np.array(ticks_to_skip)\n",
-    "            for ax in [rawdata_axes]:\n",
-    "                sns.despine(ax=ax, bottom=True)\n",
-    "\n",
-    "                ylim = ax.get_ylim()\n",
-    "                xlim = ax.get_xlim()\n",
-    "                redraw_axes_kwargs[\"y\"] = ylim[0]\n",
-    "\n",
-    "                if two_col_sankey:\n",
-    "                    for k, start_tick in enumerate(ticks_to_start_twocol_sankey):\n",
-    "                        end_tick = rightend_ticks_raw[k]\n",
-    "                        ax.hlines(xmin=start_tick, xmax=end_tick, **redraw_axes_kwargs)\n",
-    "                else:\n",
-    "                    for k, start_tick in enumerate(ticks_to_skip):\n",
-    "                        end_tick = rightend_ticks_raw[k]\n",
-    "                        ax.hlines(xmin=start_tick, xmax=end_tick, **redraw_axes_kwargs)\n",
-    "                ax.set_ylim(ylim)\n",
-    "                del redraw_axes_kwargs[\"y\"]\n",
-    "\n",
-    "            if not proportional:\n",
-    "                temp_length = [(len(i) - 1) for i in idx]\n",
-    "            else:\n",
-    "                temp_length = [(len(i) - 1) * 2 - 1 for i in idx]\n",
-    "            if two_col_sankey:\n",
-    "                rightend_ticks_contrast = np.array(\n",
-    "                    [len(i) - 2 for i in idx]\n",
-    "                ) + np.array(ticks_to_start_twocol_sankey)\n",
-    "            elif proportional and is_paired is not None:\n",
-    "                rightend_ticks_contrast = np.array(\n",
-    "                    [len(i) - 1 for i in idx]\n",
-    "                ) + np.array(ticks_to_skip)\n",
-    "            else:\n",
-    "                rightend_ticks_contrast = np.array(temp_length) + np.array(\n",
-    "                    ticks_to_skip_contrast\n",
-    "                )\n",
-    "            for ax in [contrast_axes]:\n",
-    "                sns.despine(ax=ax, bottom=True)\n",
-    "\n",
-    "                ylim = ax.get_ylim()\n",
-    "                xlim = ax.get_xlim()\n",
-    "                redraw_axes_kwargs[\"y\"] = ylim[0]\n",
-    "\n",
-    "                if two_col_sankey:\n",
-    "                    for k, start_tick in enumerate(ticks_to_start_twocol_sankey):\n",
-    "                        end_tick = rightend_ticks_contrast[k]\n",
-    "                        ax.hlines(xmin=start_tick, xmax=end_tick, **redraw_axes_kwargs)\n",
-    "                else:\n",
-    "                    for k, start_tick in enumerate(ticks_to_skip_contrast):\n",
-    "                        end_tick = rightend_ticks_contrast[k]\n",
-    "                        ax.hlines(xmin=start_tick, xmax=end_tick, **redraw_axes_kwargs)\n",
-    "\n",
-    "                ax.set_ylim(ylim)\n",
-    "                del redraw_axes_kwargs[\"y\"]\n",
-    "        else:\n",
-    "            # Compute the end of each x-axes line.\n",
-    "            if two_col_sankey:\n",
-    "                rightend_ticks = np.array([len(i) - 2 for i in idx]) + np.array(\n",
-    "                    ticks_to_start_twocol_sankey\n",
-    "                )\n",
-    "            else:\n",
-    "                rightend_ticks = np.array([len(i) - 1 for i in idx]) + np.array(\n",
-    "                    ticks_to_skip\n",
-    "                )\n",
-    "\n",
-    "            for ax in [rawdata_axes, contrast_axes]:\n",
-    "                sns.despine(ax=ax, bottom=True)\n",
-    "\n",
-    "                ylim = ax.get_ylim()\n",
-    "                xlim = ax.get_xlim()\n",
-    "                redraw_axes_kwargs[\"y\"] = ylim[0]\n",
-    "\n",
-    "                if two_col_sankey:\n",
-    "                    for k, start_tick in enumerate(ticks_to_start_twocol_sankey):\n",
-    "                        end_tick = rightend_ticks[k]\n",
-    "                        ax.hlines(xmin=start_tick, xmax=end_tick, **redraw_axes_kwargs)\n",
-    "                else:\n",
-    "                    for k, start_tick in enumerate(ticks_to_skip):\n",
-    "                        end_tick = rightend_ticks[k]\n",
-    "                        ax.hlines(xmin=start_tick, xmax=end_tick, **redraw_axes_kwargs)\n",
-    "\n",
-    "                ax.set_ylim(ylim)\n",
-    "                del redraw_axes_kwargs[\"y\"]\n",
-    "\n",
-    "    if show_delta2 or show_mini_meta:\n",
-    "        ylim = contrast_axes.get_ylim()\n",
-    "        redraw_axes_kwargs[\"y\"] = ylim[0]\n",
-    "        x_ticks = contrast_axes.get_xticks()\n",
-    "        contrast_axes.hlines(xmin=x_ticks[-2], xmax=x_ticks[-1], **redraw_axes_kwargs)\n",
-    "        del redraw_axes_kwargs[\"y\"]\n",
-    "\n",
-    "    # Set raw axes y-label.\n",
-    "    swarm_label = plot_kwargs[\"swarm_label\"]\n",
-    "    if swarm_label is None and yvar is None:\n",
-    "        swarm_label = \"value\"\n",
-    "    elif swarm_label is None and yvar is not None:\n",
-    "        swarm_label = yvar\n",
-    "\n",
-    "    bar_label = plot_kwargs[\"bar_label\"]\n",
-    "    if bar_label is None and effect_size_type != \"cohens_h\":\n",
-    "        bar_label = \"proportion of success\"\n",
-    "    elif bar_label is None and effect_size_type == \"cohens_h\":\n",
-    "        bar_label = \"value\"\n",
-    "\n",
-    "    # Place contrast axes y-label.\n",
-    "    contrast_label_dict = {\n",
-    "        \"mean_diff\": \"mean difference\",\n",
-    "        \"median_diff\": \"median difference\",\n",
-    "        \"cohens_d\": \"Cohen's d\",\n",
-    "        \"hedges_g\": \"Hedges' g\",\n",
-    "        \"cliffs_delta\": \"Cliff's delta\",\n",
-    "        \"cohens_h\": \"Cohen's h\",\n",
-    "        \"delta_g\": \"mean difference\",\n",
-    "    }\n",
-    "\n",
-    "    if proportional and effect_size_type != \"cohens_h\":\n",
-    "        default_contrast_label = \"proportion difference\"\n",
-    "    elif effect_size_type == \"delta_g\":\n",
-    "        default_contrast_label = \"Hedges' g\"\n",
-    "    else:\n",
-    "        default_contrast_label = contrast_label_dict[effectsize_df.effect_size]\n",
-    "\n",
-    "    if plot_kwargs[\"contrast_label\"] is None:\n",
-    "        if is_paired:\n",
-    "            contrast_label = \"paired\\n{}\".format(default_contrast_label)\n",
-    "        else:\n",
-    "            contrast_label = default_contrast_label\n",
-    "        contrast_label = contrast_label.capitalize()\n",
-    "    else:\n",
-    "        contrast_label = plot_kwargs[\"contrast_label\"]\n",
-    "\n",
-    "    if plot_kwargs[\"fontsize_rawylabel\"] is not None:\n",
-    "        fontsize_rawylabel = plot_kwargs[\"fontsize_rawylabel\"]\n",
-    "    if plot_kwargs[\"fontsize_contrastylabel\"] is not None:\n",
-    "        fontsize_contrastylabel = plot_kwargs[\"fontsize_contrastylabel\"]\n",
-    "    if plot_kwargs[\"fontsize_delta2label\"] is not None:\n",
-    "        fontsize_delta2label = plot_kwargs[\"fontsize_delta2label\"]\n",
-    "\n",
-    "    contrast_axes.set_ylabel(contrast_label, fontsize=fontsize_contrastylabel)\n",
-    "    if float_contrast:\n",
-    "        contrast_axes.yaxis.set_label_position(\"right\")\n",
-    "\n",
-    "    # Set the rawdata axes labels appropriately\n",
-    "    if not proportional:\n",
-    "        rawdata_axes.set_ylabel(swarm_label, fontsize=fontsize_rawylabel)\n",
-    "    else:\n",
-    "        rawdata_axes.set_ylabel(bar_label, fontsize=fontsize_rawylabel)\n",
-    "    rawdata_axes.set_xlabel(\"\")\n",
-    "\n",
-    "    # Because we turned the axes frame off, we also need to draw back\n",
-    "    # the y-spine for both axes.\n",
-    "    if not float_contrast:\n",
-    "        rawdata_axes.set_xlim(contrast_axes.get_xlim())\n",
-    "    og_xlim_raw = rawdata_axes.get_xlim()\n",
-    "    rawdata_axes.vlines(\n",
-    "        og_xlim_raw[0], og_ylim_raw[0], og_ylim_raw[1], **redraw_axes_kwargs\n",
-    "    )\n",
-    "\n",
-    "    og_xlim_contrast = contrast_axes.get_xlim()\n",
-    "\n",
-    "    if float_contrast:\n",
-    "        xpos = og_xlim_contrast[1]\n",
-    "    else:\n",
-    "        xpos = og_xlim_contrast[0]\n",
-    "\n",
-    "    og_ylim_contrast = contrast_axes.get_ylim()\n",
-    "    contrast_axes.vlines(\n",
-    "        xpos, og_ylim_contrast[0], og_ylim_contrast[1], **redraw_axes_kwargs\n",
+    "    # Modify ylims of axes to flip the plot for horizontal format\n",
+    "    if horizontal:\n",
+    "        if not proportional or (proportional and show_pairs):\n",
+    "            raw_ylim, contrast_ylim = rawdata_axes.get_ylim(), contrast_axes.get_ylim()\n",
+    "            rawdata_axes.set_ylim(raw_ylim[1], raw_ylim[0])\n",
+    "            contrast_axes.set_ylim(contrast_ylim[1], contrast_ylim[0])\n",
+    "\n",
+    "        ## Modify the ylim to reduce whitespace in specific plots.\n",
+    "        if show_delta2 or show_mini_meta or (proportional and show_pairs):\n",
+    "            raw_ylim, contrast_ylim = rawdata_axes.get_ylim(), contrast_axes.get_ylim()\n",
+    "            rawdata_axes.set_ylim(raw_ylim[0]-0.5, raw_ylim[1])\n",
+    "            contrast_axes.set_ylim(contrast_ylim[0]-0.5, contrast_ylim[1])\n",
+    "\n",
+    "    # Add the dependent axes spines back in.\n",
+    "    redraw_dependent_spines(\n",
+    "        rawdata_axes = rawdata_axes, \n",
+    "        contrast_axes = contrast_axes, \n",
+    "        redraw_axes_kwargs = redraw_axes_kwargs, \n",
+    "        float_contrast = float_contrast, \n",
+    "        horizontal = horizontal,\n",
+    "        show_delta2 = show_delta2, \n",
+    "        delta2_axes = delta2_axes\n",
     "    )\n",
     "\n",
-    "    if show_delta2:\n",
-    "        if plot_kwargs[\"delta2_label\"] is not None:\n",
-    "            delta2_label = plot_kwargs[\"delta2_label\"]\n",
-    "        elif effect_size == \"mean_diff\":\n",
-    "            delta2_label = \"delta - delta\"\n",
-    "        else:\n",
-    "            delta2_label = \"deltas' g\"\n",
-    "        delta2_axes = contrast_axes.twinx()\n",
-    "        delta2_axes.set_frame_on(False)\n",
-    "        delta2_axes.set_ylabel(delta2_label, fontsize=fontsize_delta2label)\n",
-    "        og_xlim_delta = contrast_axes.get_xlim()\n",
-    "        og_ylim_delta = contrast_axes.get_ylim()\n",
-    "        delta2_axes.set_ylim(og_ylim_delta)\n",
-    "        delta2_axes.vlines(\n",
-    "            og_xlim_delta[1], og_ylim_delta[0], og_ylim_delta[1], **redraw_axes_kwargs\n",
+    "    # Table Axes for horizontal plots\n",
+    "    if horizontal and table_kwargs['show']:\n",
+    "        table_for_horizontal_plots(\n",
+    "                            effectsize_df = effectsize_df,\n",
+    "                            ax = table_axes,\n",
+    "                            contrast_axes = contrast_axes,\n",
+    "                            ticks_to_plot = table_axes_ticks_to_plot, \n",
+    "                            show_mini_meta = show_mini_meta,\n",
+    "                            show_delta2 = show_delta2,\n",
+    "                            table_kwargs = table_kwargs,\n",
+    "                            ticks_to_skip = ticks_to_skip\n",
     "        )\n",
     "\n",
-    "    ################################################### GRIDKEY MAIN CODE WIP\n",
-    "\n",
-    "    # if gridkey_rows is None, skip everything here\n",
-    "    if gridkey_rows is not None:\n",
-    "        # Raise error if there are more than 2 items in any idx and gridkey_merge_pairs is True and is_paired is not None\n",
-    "        if gridkey_merge_pairs and is_paired is not None:\n",
-    "            for i in idx:\n",
-    "                if len(i) > 2:\n",
-    "                    warnings.warn(\n",
-    "                        \"gridkey_merge_pairs=True only works if all idx in tuples have only two items. gridkey_merge_pairs has automatically been set to False\"\n",
-    "                    )\n",
-    "                    gridkey_merge_pairs = False\n",
-    "                    break\n",
-    "        elif gridkey_merge_pairs and is_paired is None:\n",
-    "            warnings.warn(\n",
-    "                \"gridkey_merge_pairs=True is only applicable for paired data.\"\n",
-    "            )\n",
-    "            gridkey_merge_pairs = False\n",
-    "\n",
-    "        # Checks for gridkey_merge_pairs and is_paired; if both are true, \"merges\" the gridkey per pair\n",
-    "        if gridkey_merge_pairs and is_paired is not None:\n",
-    "            groups_for_gridkey = []\n",
-    "            for i in idx:\n",
-    "                groups_for_gridkey.append(i[1])\n",
-    "        else:\n",
-    "            groups_for_gridkey = all_plot_groups\n",
-    "\n",
-    "        # raise errors if gridkey_rows is not a list, or if the list is empty\n",
-    "        if isinstance(gridkey_rows, list) is False:\n",
-    "            raise TypeError(\"gridkey_rows must be a list.\")\n",
-    "        elif len(gridkey_rows) == 0:\n",
-    "            warnings.warn(\"gridkey_rows is an empty list.\")\n",
-    "\n",
-    "        # raise Warning if an item in gridkey_rows is not contained in any idx\n",
-    "        for i in gridkey_rows:\n",
-    "            in_idx = 0\n",
-    "            for j in groups_for_gridkey:\n",
-    "                if i in j:\n",
-    "                    in_idx += 1\n",
-    "            if in_idx == 0:\n",
-    "                if is_paired is not None:\n",
-    "                    warnings.warn(\n",
-    "                        i\n",
-    "                        + \" is not in any idx. Please check. Alternatively, merging gridkey pairs may not be suitable for your data; try passing gridkey_merge_pairs=False.\"\n",
-    "                    )\n",
-    "                else:\n",
-    "                    warnings.warn(i + \" is not in any idx. Please check.\")\n",
-    "\n",
-    "        # Populate table: checks if idx for each column contains rowlabel name\n",
-    "        # IF so, marks that element as present w black dot, or space if not present\n",
-    "        table_cellcols = []\n",
-    "        for i in gridkey_rows:\n",
-    "            thisrow = []\n",
-    "            for q in groups_for_gridkey:\n",
-    "                if str(i) in q:\n",
-    "                    thisrow.append(\"\\u25CF\")\n",
-    "                else:\n",
-    "                    thisrow.append(\"\")\n",
-    "            table_cellcols.append(thisrow)\n",
-    "\n",
-    "        # Adds a row for Ns with the Ns values\n",
-    "        if gridkey_show_Ns:\n",
-    "            gridkey_rows.append(\"Ns\")\n",
-    "            list_of_Ns = []\n",
-    "            for i in groups_for_gridkey:\n",
-    "                list_of_Ns.append(str(counts.loc[i]))\n",
-    "            table_cellcols.append(list_of_Ns)\n",
-    "\n",
-    "        # Adds a row for effectsizes with effectsize values\n",
-    "        if gridkey_show_es:\n",
-    "            gridkey_rows.append(\"\\u0394\")\n",
-    "            effsize_list = []\n",
-    "            results_list = results.test.to_list()\n",
-    "\n",
-    "            # get the effect size, append + or -, 2 dec places\n",
-    "            for i in enumerate(groups_for_gridkey):\n",
-    "                if i[1] in results_list:\n",
-    "                    curr_esval = results.loc[results[\"test\"] == i[1]][\n",
-    "                        \"difference\"\n",
-    "                    ].iloc[0]\n",
-    "                    curr_esval_str = np.format_float_positional(\n",
-    "                        curr_esval,\n",
-    "                        precision=es_sf,\n",
-    "                        sign=True,\n",
-    "                        trim=\"k\",\n",
-    "                        min_digits=es_sf,\n",
-    "                    )\n",
-    "                    effsize_list.append(curr_esval_str)\n",
-    "                else:\n",
-    "                    effsize_list.append(\"-\")\n",
-    "\n",
-    "            table_cellcols.append(effsize_list)\n",
-    "\n",
-    "        # If Gardner-Altman plot, plot on raw data and not contrast axes\n",
-    "        if float_contrast:\n",
-    "            axes_ploton = rawdata_axes\n",
-    "        else:\n",
-    "            axes_ploton = contrast_axes\n",
-    "\n",
-    "        # Account for extended x axis in case of show_delta2 or show_mini_meta\n",
-    "        x_groups_for_width = len(groups_for_gridkey)\n",
-    "        if show_delta2 or show_mini_meta:\n",
-    "            x_groups_for_width += 2\n",
-    "        gridkey_width = len(groups_for_gridkey) / x_groups_for_width\n",
-    "\n",
-    "        gridkey = axes_ploton.table(\n",
-    "            cellText=table_cellcols,\n",
-    "            rowLabels=gridkey_rows,\n",
-    "            cellLoc=\"center\",\n",
-    "            bbox=[\n",
-    "                0,\n",
-    "                -len(gridkey_rows) * 0.1 - 0.05,\n",
-    "                gridkey_width,\n",
-    "                len(gridkey_rows) * 0.1,\n",
-    "            ],\n",
-    "            **{\"alpha\": 0.5}\n",
+    "    # Gridkey\n",
+    "    gridkey = plot_kwargs[\"gridkey\"]\n",
+    "    if gridkey is not None:\n",
+    "        gridkey_plotter(\n",
+    "                is_paired = is_paired, \n",
+    "                idx = idx, \n",
+    "                all_plot_groups = all_plot_groups, \n",
+    "                gridkey = gridkey, \n",
+    "                rawdata_axes = rawdata_axes,\n",
+    "                contrast_axes = contrast_axes,\n",
+    "                plot_data = plot_data, \n",
+    "                xvar = xvar, \n",
+    "                yvar = yvar, \n",
+    "                results = results, \n",
+    "                show_delta2 = show_delta2, \n",
+    "                show_mini_meta = show_mini_meta, \n",
+    "                x1_level = x1_level,\n",
+    "                experiment_label = experiment_label,\n",
+    "                float_contrast = float_contrast,\n",
+    "                horizontal = horizontal,\n",
+    "                delta_delta = effectsize_df.delta_delta if show_delta2 else None,\n",
+    "                mini_meta = effectsize_df.mini_meta if show_mini_meta else None,\n",
+    "                effect_size = effect_size,\n",
+    "                gridkey_kwargs = gridkey_kwargs,\n",
+    "        )\n",
+    "    \n",
+    "    #  Reference band\n",
+    "    reference_band = plot_kwargs[\"reference_band\"]\n",
+    "    if reference_band is not None and not float_contrast:\n",
+    "        reference_band_dict, reference_band_kwargs = prepare_bars_for_plot(bar_type = 'summary', \n",
+    "                                                                       bar_kwargs = reference_band_kwargs, \n",
+    "                                                                       horizontal = horizontal, \n",
+    "                                                                       plot_palette_raw = plot_palette_raw, \n",
+    "                                                                       color_col = color_col, \n",
+    "                                                                       show_pairs = show_pairs,\n",
+    "                                                                       results = results,    \n",
+    "                                                                       ticks_to_plot = ticks_to_plot, \n",
+    "                                                                       reference_band = reference_band, \n",
+    "                                                                       summary_axes = contrast_axes, \n",
+    "                                                                       ci_type = ci_type,\n",
+    "                                                                    )\n",
+    "        \n",
+    "        add_bars_to_plot(bar_dict = reference_band_dict,\n",
+    "                        ax = contrast_axes,\n",
+    "                        bar_kwargs = reference_band_kwargs\n",
     "        )\n",
     "\n",
-    "        # modifies row label cells\n",
-    "        for cell in gridkey._cells:\n",
-    "            if cell[1] == -1:\n",
-    "                gridkey._cells[cell].visible_edges = \"open\"\n",
-    "                gridkey._cells[cell].set_text_props(**{\"ha\": \"right\"})\n",
-    "\n",
-    "        # turns off both x axes\n",
-    "        rawdata_axes.get_xaxis().set_visible(False)\n",
-    "        contrast_axes.get_xaxis().set_visible(False)\n",
+    "    # Legend\n",
+    "    handles, labels = rawdata_axes.get_legend_handles_labels()\n",
+    "    legend_labels = [l for l in labels]\n",
+    "    legend_handles = [h for h in handles]\n",
     "\n",
-    "    ####################################################### END GRIDKEY MAIN CODE WIP\n",
+    "    if bootstraps_color_by_group is False and color_col is not None:\n",
+    "        rawdata_axes.legend().set_visible(False)\n",
+    "        show_legend(\n",
+    "            legend_labels = legend_labels, \n",
+    "            legend_handles = legend_handles, \n",
+    "            rawdata_axes = rawdata_axes, \n",
+    "            contrast_axes = contrast_axes, \n",
+    "            table_axes = table_axes,\n",
+    "            float_contrast = float_contrast, \n",
+    "            show_pairs = show_pairs, \n",
+    "            horizontal = horizontal,\n",
+    "            legend_kwargs = legend_kwargs,\n",
+    "            table_kwargs = table_kwargs\n",
+    "        )\n",
     "\n",
     "    # Make sure no stray ticks appear!\n",
     "    rawdata_axes.xaxis.set_ticks_position(\"bottom\")\n",
@@ -1661,16 +684,78 @@
     "        plt.rcParams[parameter] = original_rcParams[parameter]\n",
     "\n",
     "    # Return the figure.\n",
-    "    return fig\n"
+    "    return fig"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7355251f",
+   "metadata": {},
+   "source": [
+    "For details on how to control the aesthetic of the generated estimation plot by modifying the **plot_kwargs**, please refer to [Controlling Plot Aesthetics](../tutorials/09-plot_aesthetics.ipynb)\n",
+    "\n",
+    "- **effectsize_df**: A `dabest` `EffectSizeDataFrame` object.\n",
+    "- **plot_kwargs**:\n",
+    "    - color_col=None\n",
+    "    - raw_marker_size=6, contrast_marker_size=9,\n",
+    "    - raw_label=None, contrast_label=None, delta2_label=None,\n",
+    "    - raw_ylim=None, contrast_ylim=None, delta2_ylim=None,\n",
+    "    - custom_palette=None, swarm_side=None, empty_circle=False,\n",
+    "    - face_color = None,\n",
+    "    - raw_desat=0.5, contrast_desat=1,\n",
+    "    - raw_alpha=None, contrast_alpha=0.8,\n",
+    "    - bar_width=0.5, \n",
+    "    - ci_type='bca',\n",
+    "    - float_contrast=True,\n",
+    "    - show_pairs=True,\n",
+    "    - show_sample_size=True\n",
+    "    - show_delta2=True, show_mini_meta=True,\n",
+    "    - group_summaries=\"mean_sd\",\n",
+    "    - fig_size=None, dpi=100,\n",
+    "    - ax=None,\n",
+    "    - swarmplot_kwargs=None,\n",
+    "    - slopegraph_kwargs=None,\n",
+    "    - barplot_kwargs=None,\n",
+    "    - sankey_kwargs=None,\n",
+    "    - contrast_kwargs=None,\n",
+    "    - reflines_kwargs=None,\n",
+    "    - group_summaries_kwargs=None,\n",
+    "    - legend_kwargs=None,\n",
+    "  \n",
+    "    - title=None, fontsize_title=16,\n",
+    "    - fontsize_rawxlabel=12, fontsize_rawylabel=12,\n",
+    "    - fontsize_contrastxlabel=12, fontsize_contrastylabel=12,\n",
+    "    - fontsize_delta2label=12,\n",
+    "    - raw_bars=True, raw_bars_kwargs=None,\n",
+    "    - contrast_bars=True, contrast_bars_kwargs=None,\n",
+    "    - reference_band=None, reference_band_kwargs=None,\n",
+    "    - delta_text=True, delta_text_kwargs=None,\n",
+    "    - delta_dot=True, delta_dot_kwargs=None,\n",
+    "  \n",
+    "    - horizontal=False, horizontal_table_kwargs=None,\n",
+    "    - gridkey=None, gridkey_merge_pairs=False,\n",
+    "    - gridkey_show_Ns=True, gridkey_show_es=True,\n",
+    "    - gridkey_delimiters=[';', '>', '_'],\n",
+    "    - gridkey_kwargs=None,\n",
+    "    - contrast_marker_kwargs=None, contrast_errorbar_kwargs=None\n",
+    "    - prop_sample_counts=False, prop_sample_counts_kwargs=None\n",
+    "    - contrast_paired_lines=True, contrast_paired_lines_kwargs=None,\n",
+    "    - show_baseline_ec=False"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "7355251f",
+   "id": "7d23e292",
    "metadata": {},
    "outputs": [],
    "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f1cc27d4",
+   "metadata": {},
+   "source": []
   }
  ],
  "metadata": {
diff --git a/nbs/API/precompile.ipynb b/nbs/API/precompile.ipynb
new file mode 100644
index 00000000..223c4ce9
--- /dev/null
+++ b/nbs/API/precompile.ipynb
@@ -0,0 +1,111 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# precompile\n",
+    "\n",
+    "> A tool to pre-compile Numba functions for speeding up DABEST bootstrapping\n",
+    "\n",
+    "- order: 10"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#| default_exp _stats_tools/precompile"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#| hide\n",
+    "from __future__ import annotations"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#| hide\n",
+    "from nbdev.showdoc import *\n",
+    "import nbdev\n",
+    "nbdev.nbdev_export()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#| export\n",
+    "import numpy as np\n",
+    "from tqdm import tqdm\n",
+    "from dabest._stats_tools import effsize\n",
+    "from dabest._stats_tools import confint_2group_diff"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#| export\n",
+    "\n",
+    "_NUMBA_COMPILED = False\n",
+    "\n",
+    "def precompile_all():\n",
+    "    \"\"\"Pre-compile all numba functions with dummy data\"\"\"\n",
+    "    global _NUMBA_COMPILED\n",
+    "    \n",
+    "    if _NUMBA_COMPILED:\n",
+    "        return\n",
+    "        \n",
+    "    print(\"Pre-compiling numba functions for DABEST...\")\n",
+    "    \n",
+    "    # Create dummy data\n",
+    "    dummy_control = np.array([1.0, 2.0, 3.0])\n",
+    "    dummy_test = np.array([4.0, 5.0, 6.0])\n",
+    "    \n",
+    "    funcs = [\n",
+    "        # effsize.py functions\n",
+    "        (effsize.cohens_d, (dummy_control, dummy_test)),\n",
+    "        (effsize._mann_whitney_u, (dummy_control, dummy_test)),\n",
+    "        (effsize._cliffs_delta_core, (dummy_control, dummy_test)),\n",
+    "        (effsize._compute_standardizers, (dummy_control, dummy_test)),\n",
+    "        (effsize.weighted_delta, (np.array([1.0, 2.0]), np.array([0.1, 0.2]))),\n",
+    "        \n",
+    "        # confint_2group_diff.py functions\n",
+    "        (confint_2group_diff.create_jackknife_indexes, (dummy_control,)),\n",
+    "        (confint_2group_diff.create_repeated_indexes, (dummy_control,)),\n",
+    "        (confint_2group_diff.bootstrap_indices, (True, 3, 3, 10, 12345)),\n",
+    "        (confint_2group_diff.delta2_bootstrap_loop, \n",
+    "            (dummy_control, dummy_test, dummy_control, dummy_test, 10, 1.0, 12345, False)),\n",
+    "        (confint_2group_diff._compute_quantile, (0.5, 0.1, 0.1)),\n",
+    "        (confint_2group_diff.calculate_group_var, (1.0, 3, 1.0, 3))\n",
+    "    ]\n",
+    "    \n",
+    "    for func, args in tqdm(funcs, desc=\"Compiling numba functions\"):\n",
+    "        func(*args)\n",
+    "    \n",
+    "    _NUMBA_COMPILED = True\n",
+    "    \n",
+    "    print(\"Numba compilation complete!\")"
+   ]
+  }
+ ],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/nbs/blog/posts/robust-beautiful/robust-beautiful.ipynb b/nbs/blog/posts/robust-beautiful/robust-beautiful.ipynb
index 703526fb..6672337e 100644
--- a/nbs/blog/posts/robust-beautiful/robust-beautiful.ipynb
+++ b/nbs/blog/posts/robust-beautiful/robust-beautiful.ipynb
@@ -172,7 +172,7 @@
     "the original Gardner-Altman design. Here, the 95% CI is computed through\n",
     "parametric methods, and displayed as a vertical error bar.\n",
     "\n",
-    "Read more about this technique at [bootstraps](03-bootstraps.ipynb). "
+    "Read more about this technique at [bootstraps](../bootstraps/bootstraps.ipynb). \n"
    ]
   },
   {
diff --git a/nbs/read_me.ipynb b/nbs/read_me.ipynb
index 216b7426..2f9d8600 100644
--- a/nbs/read_me.ipynb
+++ b/nbs/read_me.ipynb
@@ -13,7 +13,7 @@
    "id": "5164f940",
    "metadata": {},
    "source": [
-    "[![minimal Python version](https://img.shields.io/badge/Python%3E%3D-3.8-6666ff.svg)](https://www.anaconda.com/distribution/)\n",
+    "[![minimal Python version](https://img.shields.io/badge/Python%3E%3D-3.10-6666ff.svg)](https://www.anaconda.com/distribution/)\n",
     "[![PyPI version](https://badge.fury.io/py/dabest.svg)](https://badge.fury.io/py/dabest)\n",
     "[![Downloads](https://img.shields.io/pepy/dt/dabest.svg\n",
     ")](https://pepy.tech/project/dabest)\n",
@@ -29,15 +29,49 @@
    "source": [
     "## Recent Version Update\n",
     "\n",
-    "We are proud to announce **DABEST Version Ondeh (v2024.03.29)**. This new version of the DABEST Python library provides several new features and includes performance improvements.\n",
+    "We are proud to announce **DABEST Version Dadar (v2025.03.27)** This new version of the DABEST Python library includes several new features and performance improvements. It’s a big one!\n",
     "\n",
-    "1. **New Paired Proportion Plot**: This feature builds upon the existing proportional analysis capabilities by introducing advanced aesthetics and clearer visualization of changes in proportions between different groups, inspired by the informative nature of Sankey Diagrams. It's particularly useful for studies that require detailed examination of how proportions shift in paired observations.\n",
+    "1. **Python 3.13 Support**: DABEST now supports Python 3.10—3.13.\n",
     "\n",
-    "2. **Customizable Swarm Plot**: Enhancements allow for tailored swarm plot aesthetics, notably the adjustment of swarm sides to produce asymmetric swarm plots. This customization enhances data representation, making visual distinctions more pronounced and interpretations clearer.\n",
+    "2. **Horizontal Plots**: Users can now create horizontal layout plots, providing compact data visualization. This can be achieved by setting `horizontal=True` in the `.plot()` method. See the [Horizontal Plots tutorial](https://acclab.github.io/DABEST-python/tutorials/08-horizontal_plot.html) for more details.\n",
     "\n",
-    "3. **Standardized Delta-delta Effect Size**: We added a new metric akin to a Hedges’ g for delta-delta effect size, which allows comparisons between delta-delta effects generated from metrics with different units. \n",
+    "3. **Forest Plots**: Forest plots provide a simple and intuitive way to visualize many delta-delta (or delta *g*), mini-meta, or regular delta effect sizes at once from multiple different dabest objects without presenting the raw data. See the [Forest Plots tutorial](https://acclab.github.io/DABEST-python/tutorials/07-forest_plot.html) for more details.\n",
     "\n",
-    "4. **Miscellaneous Improvements**: This version also encompasses a broad range of miscellaneous enhancements, including bug fixes, Bootstrapping speed improvements, new templates for raising issues, and updated unit tests. These improvements are designed to streamline the user experience, increase the software's stability, and expand its versatility. By addressing user feedback and identified issues, DABEST continues to refine its functionality and reliability.\n"
+    "4. **Gridkey**: Users can now represent experimental labels in a ‘gridkey’ table. This can be accessed with the `gridkey` parameter in the `.plot()` method. See the gridkey section in the [Plot Aesthetics tutorial](https://acclab.github.io/DABEST-python/tutorials/09-plot_aesthetics.html) for more details.\n",
+    "\n",
+    "5. **Other Visualization Improvements**:\n",
+    "   - **Comparing means and effect sizes**: The estimation plots now include three types of customizable visual features to enhance contextualization and comparison of means and effect sizes:\n",
+    "     - **Bars for the mean of the observed values (`raw_bars`)**: Colored rectangles that extend from the zero line to the mean of each group's raw data. These bars visually highlight the central tendency of the raw data.\n",
+    "     - **Bars for effect size/s (`contrast_bars`)**: Similar to raw bars, these highlight the effect-size difference between two groups (typically test and control) in the contrast axis. They provide a visual representation of the differences between groups.\n",
+    "     - **Summary bands (`reference_band`)**: An optional band or ribbon that can be added to emphasize a specific effect size’s confidence interval that is used as a reference range across the entire contrast axis. Unlike raw and contrast bars, these span horizontally (or vertically if `horizontal=True`) and are not displayed by default.\n",
+    "\n",
+    "          Raw and contrast bars are shown by default. Users can customize these bars and add summary bands as needed. For detailed customization instructions, please refer to the [Plot Aesthetics tutorial](https://acclab.github.io/DABEST-python/tutorials/09-plot_aesthetics.html).\n",
+    "\n",
+    "   - **Tighter spacing in delta-delta and mini-meta plots**: We have adjusted the spacing of delta-delta and mini-meta plots to reduce whitespace. The new format brings the overall effect size closer to the two-groups effect sizes. In addition, delta-delta plots now have a gap in the zero line to separate the delta-delta from the ∆ effect sizes.\n",
+    "\n",
+    "   - **Delta-delta effect sizes for proportion plots**: In addition to continuous data, delta-delta plots now support binary data (proportions). This means that 2-way designs for binary outcomes can be analyzed with DABEST.\n",
+    "\n",
+    "   - **Proportion plots sample sizes**: The sample size of each binary option for each group can now be displayed. These can be toggled on/off via the `prop_sample_counts` parameter.\n",
+    "\n",
+    "   - **Effect size lines for paired plots**: Along with lines connecting paired observed values, the paired plots now also display lines linking the effect sizes within a group in the contrast axes. These lines can be toggled on/off via the `contrast_paired_lines` parameter.\n",
+    "\n",
+    "   - **Baseline error curves**: To represent the baseline/control group in the contrast axes, it is now possible to plot the baseline dot and the baseline error curve. The dot is shown by default, while the curve can be toggled on/off via the `show_baseline_ec` parameter. This dot helps make it clear where the baseline comes from i.e. the control minus itself. The baseline error curve can be used to show that the baseline itself is an estimate inferred from the observed values of the control data. \n",
+    "\n",
+    "   - **Delta text**: Effect-size deltas (e.g. mean differences) are now displayed as numerals next to their respective effect size. This can be toggled on/off via the `delta_text` parameter.\n",
+    "\n",
+    "   - **Empty circle color palette**: A new swarmplot color palette modification is available for unpaired plots via the `empty_circle` parameter in the `.plot()` method. This option modifies the two-group swarmplots to have empty circles for the control group and filled circles for the experimental group.\n",
+    "\n",
+    "6. **Miscellaneous Improvements & Adjustments**\n",
+    "    - **Numba for speed improvements**: We have added [Numba](https://numba.pydata.org/) to speed up the various calculations in DABEST. Precalculations will be performed during import, which will help speed up the subsequent loading and plotting of data.\n",
+    "  \n",
+    "    - **Terminology/naming updates**: During the refactoring of the code, we have made several updates to the documentation and terminology to improve clarity and consistency. For example:\n",
+    "      - Plot arguments have been adjusted to bring more clarity and consistency in naming. Arguments relating to the rawdata plot axis will now be typically referred to with `raw` while arguments relating to the contrast axis will be referred to with `contrast`. For example, `raw_label` replaces `swarm_label` and `bar_label`. The various kwargs relating to each different type of plot (e.g., `swarmplot_kwargs`) remain unchanged.\n",
+    "  \n",
+    "      - The method to utilise the Delta *g* effect size is now via the .hedges_g.plot() method rather than creating a whole new Delta_g object as before. The functionality remains the same, it plots hedges_g effect sizes and then the Delta *g* effect size alongside these (if a delta-delta experiment was loaded correctly).\n",
+    "\n",
+    "    - **Updated tutorial pages**: We have updated the tutorial pages to reflect the new features and changes. The tutorial pages are now more comprehensive and (hopefully!) more intuitive!\n",
+    "\n",
+    "    - **Results dataframe for delta-delta and mini-meta plots**: A results dataframe can now be extracted for both the delta-delta and mini-meta effect size data (similar to the results dataframe for the regular effect sizes). These can be found via the `.results` attribute of the `.delta_delta` or `.mini_meta` object."
    ]
   },
   {
@@ -89,7 +123,7 @@
    "source": [
     "## Installation\n",
     "\n",
-    "This package is tested on Python 3.8 and onwards.\n",
+    "This package is tested on Python 3.11 and onwards.\n",
     "It is highly recommended to download the [Anaconda distribution](https://www.continuum.io/downloads) of Python in order to obtain the dependencies easily.\n",
     "\n",
     "You can install this package via `pip`.\n",
@@ -164,9 +198,9 @@
    "source": [
     "## Contributing\n",
     "\n",
-    "All contributions are welcome; please read the [Guidelines for contributing](CONTRIBUTING.md) first.\n",
+    "All contributions are welcome; please read the [Guidelines for contributing](../CONTRIBUTING.md) first.\n",
     "\n",
-    "We also have a [Code of Conduct](CODE_OF_CONDUCT.md) to foster an inclusive and productive space.\n"
+    "We also have a [Code of Conduct](../CODE_OF_CONDUCT.md) to foster an inclusive and productive space.\n"
    ]
   },
   {
@@ -175,7 +209,7 @@
    "metadata": {},
    "source": [
     "### A wish list for new features\n",
-    "If you have any specific comments and ideas for new features that you would like to share with us, please read the [Guidelines for contributing](CONTRIBUTING.md), create a new issue using Feature request template or create a new post in [our Google Group](https://groups.google.com/g/estimationstats)."
+    "If you have any specific comments and ideas for new features that you would like to share with us, please read the [Guidelines for contributing](../CONTRIBUTING.md), create a new issue using Feature request template or create a new post in [our Google Group](https://groups.google.com/g/estimationstats)."
    ]
   },
   {
@@ -197,18 +231,12 @@
     "\n",
     "The test suite ensures that the bootstrapping functions and the plotting functions perform as expected.\n",
     "\n",
-    "For detailed information, please refer to the [test folder](nbs/tests/README.md)\n",
+    "For detailed information, please refer to the [test folder](../nbs/tests/README.md)\n",
     "\n",
     "## DABEST in other languages\n",
     "\n",
     "DABEST is also available in R ([dabestr](https://github.com/ACCLAB/dabestr)) and Matlab ([DABEST-Matlab](https://github.com/ACCLAB/DABEST-Matlab)).\n"
    ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7106313a",
-   "metadata": {},
-   "source": []
   }
  ],
  "metadata": {
diff --git a/nbs/tests/data/mocked_data_test_01.py b/nbs/tests/data/mocked_data_test_01.py
index 196d66a3..c6bd49ab 100644
--- a/nbs/tests/data/mocked_data_test_01.py
+++ b/nbs/tests/data/mocked_data_test_01.py
@@ -67,4 +67,5 @@
     experiment_label=None,
     x1_level=None,
     mini_meta=False,
+    ps_adjust=False,
 )
diff --git a/nbs/tests/data/mocked_data_test_06.py b/nbs/tests/data/mocked_data_test_06.py
index 5a43b75e..fec52abb 100644
--- a/nbs/tests/data/mocked_data_test_06.py
+++ b/nbs/tests/data/mocked_data_test_06.py
@@ -62,4 +62,5 @@
     idx=None,
     proportional=False,
     mini_meta=False,
+    ps_adjust=False,
 )
diff --git a/nbs/tests/data/mocked_data_test_08.py b/nbs/tests/data/mocked_data_test_08.py
index 450b1665..b87724d0 100644
--- a/nbs/tests/data/mocked_data_test_08.py
+++ b/nbs/tests/data/mocked_data_test_08.py
@@ -28,4 +28,5 @@
     x1_level=None,
     paired=None,
     id_col=None,
+    ps_adjust=False,
 )
diff --git a/nbs/tests/data/mocked_data_test_forestplot.py b/nbs/tests/data/mocked_data_test_forestplot.py
index 3509c64d..9c766d87 100644
--- a/nbs/tests/data/mocked_data_test_forestplot.py
+++ b/nbs/tests/data/mocked_data_test_forestplot.py
@@ -37,24 +37,18 @@
 
 # Default forestplot params for unit testing
 default_forestplot_kwargs = {
-    "contrasts": dummy_contrasts,  # Ensure this is a list of contrast objects.
-    "selected_indices": None,  # Valid as None or a list of integers.
-    "contrast_type": "delta2",  # Ensure it's a string and one of the allowed contrast types.
-    "xticklabels": None,  # Valid as None or a list of strings.
+    "data": dummy_contrasts,  # Ensure this is a list of contrast objects.
+    "idx": None,  # Valid as None or a list of lists of integers.
     "effect_size": "mean_diff",  # Ensure it's a string.
-    "contrast_labels": ["Drug1"],  # This should be a list of strings.
+    "labels": ["Drug1"],  # This should be a list of strings.
     "ylabel": "Effect Size",  # Ensure it's a string.
-    "plot_elements_to_extract": None,  # No specific checks needed based on your tests.
     "title": "ΔΔ Forest Plot",  # Ensure it's a string.
     "custom_palette": None,  # Valid as None, a dictionary, list, or string.
-    "fontsize": 20,  # Ensure it's an integer or float.
     "violin_kwargs": None,  # No specific checks needed based on your tests.
     "marker_size": 20,  # Ensure it's a positive integer or float.
-    "ci_line_width": 2.5,  # Ensure it's a positive integer or float.
-    "zero_line_width": 1,  # Ensure it's a positive integer or float.
     "remove_spines": True,  # Ensure it's a boolean.
-    "additional_plotting_kwargs": None,  # No specific checks needed based on your tests.
-    "rotation_for_xlabels": 45,  # Ensure it's an integer or float between 0 and 360.
-    "alpha_violin_plot": 0.4,  # Ensure it's a float between 0 and 1.
+    "labels_rotation": 45,  # Ensure it's an integer or float between 0 and 360.
+    "contrast_alpha": 0.8,  # Ensure it's a float between 0 and 1.
     "horizontal": False,  # Ensure it's a boolean.
 }
+
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diff --git a/nbs/tests/mpl_image_tests/baseline_images/test_99_style_sheets.png b/nbs/tests/mpl_image_tests/baseline_images/test_99_style_sheets.png
index dd9a202a..75dca995 100644
Binary files a/nbs/tests/mpl_image_tests/baseline_images/test_99_style_sheets.png and b/nbs/tests/mpl_image_tests/baseline_images/test_99_style_sheets.png differ
diff --git a/nbs/tests/mpl_image_tests/test_03_plotting.py b/nbs/tests/mpl_image_tests/test_03_plotting.py
index 7cc9b3ab..3bb6c446 100644
--- a/nbs/tests/mpl_image_tests/test_03_plotting.py
+++ b/nbs/tests/mpl_image_tests/test_03_plotting.py
@@ -131,64 +131,75 @@ def create_demo_dataset(seed=9999, N=20):
     id_col="ID",
 )
 
-
 @pytest.mark.mpl_image_compare(tolerance=8)
 def test_01_gardner_altman_unpaired_meandiff():
+    plt.rcdefaults()
     return two_groups_unpaired.mean_diff.plot()
 
 
 @pytest.mark.mpl_image_compare(tolerance=8)
 def test_02_gardner_altman_unpaired_mediandiff():
+    plt.rcdefaults()
     return two_groups_unpaired.median_diff.plot()
 
 
 @pytest.mark.mpl_image_compare(tolerance=8)
 def test_03_gardner_altman_unpaired_hedges_g():
+    plt.rcdefaults()
     return two_groups_unpaired.hedges_g.plot()
 
 
 @pytest.mark.mpl_image_compare(tolerance=8)
 def test_04_gardner_altman_paired_meandiff():
+    plt.rcdefaults()
     return two_groups_paired.mean_diff.plot()
 
 
 @pytest.mark.mpl_image_compare(tolerance=8)
 def test_04_gardner_altman_paired_hedges_g():
+    plt.rcdefaults()
     return two_groups_paired.hedges_g.plot()
 
 
 @pytest.mark.mpl_image_compare(tolerance=8)
 def test_05_cummings_two_group_unpaired_meandiff():
+    plt.rcdefaults()
     return two_groups_unpaired.mean_diff.plot(fig_size=(4, 6), float_contrast=False)
 
 
 @pytest.mark.mpl_image_compare(tolerance=8)
 def test_06_cummings_two_group_paired_meandiff():
+    plt.rcdefaults()
     return two_groups_paired.mean_diff.plot(fig_size=(6, 6), float_contrast=False)
 
 
 @pytest.mark.mpl_image_compare(tolerance=8)
 def test_07_cummings_multi_group_unpaired():
+    plt.rcdefaults()
     return multi_2group.mean_diff.plot()
 
 
 @pytest.mark.mpl_image_compare(tolerance=8)
 def test_08_cummings_multi_group_paired():
+    plt.rcdefaults()
     return multi_2group_paired.mean_diff.plot(fig_size=(6, 6))
 
 
 @pytest.mark.mpl_image_compare(tolerance=8)
 def test_09_cummings_shared_control():
+    plt.rcdefaults()
     return shared_control.mean_diff.plot()
 
 
 @pytest.mark.mpl_image_compare(tolerance=8)
 def test_10_cummings_multi_groups():
+    plt.rcdefaults()
     return multi_groups.mean_diff.plot()
 
 
 @pytest.mark.mpl_image_compare(tolerance=8)
 def test_11_inset_plots():
+    plt.rcdefaults()
     # Load the iris dataset.
     try:
         # parent directory of the current working directory
@@ -244,23 +255,27 @@ def test_11_inset_plots():
 
 @pytest.mark.mpl_image_compare(tolerance=8)
 def test_12_gardner_altman_ylabel():
+    plt.rcdefaults()
     return two_groups_unpaired.mean_diff.plot(
-        swarm_label="This is my\nrawdata", contrast_label="The bootstrap\ndistribtions!"
+        raw_label="This is my\nrawdata", contrast_label="The bootstrap\ndistribtions!"
     )
 
 
 @pytest.mark.mpl_image_compare(tolerance=8)
 def test_13_multi_2group_color():
+    plt.rcdefaults()
     return multi_2group.mean_diff.plot(color_col="Gender")
 
 
 @pytest.mark.mpl_image_compare(tolerance=8)
 def test_14_gardner_altman_paired_color():
+    plt.rcdefaults()
     return two_groups_paired.mean_diff.plot(fig_size=(6, 6), color_col="Gender")
 
 
 @pytest.mark.mpl_image_compare(tolerance=8)
 def test_15_change_palette_a():
+    plt.rcdefaults()
     return multi_2group.mean_diff.plot(
         fig_size=(8, 6), color_col="Gender", custom_palette="Dark2"
     )
@@ -268,6 +283,7 @@ def test_15_change_palette_a():
 
 @pytest.mark.mpl_image_compare(tolerance=8)
 def test_16_change_palette_b():
+    plt.rcdefaults()
     return multi_2group.mean_diff.plot(custom_palette="Paired")
 
 
@@ -281,28 +297,33 @@ def test_16_change_palette_b():
 
 @pytest.mark.mpl_image_compare(tolerance=8)
 def test_17_change_palette_c():
+    plt.rcdefaults()
     return multi_2group.mean_diff.plot(custom_palette=my_color_palette)
 
 
 @pytest.mark.mpl_image_compare(tolerance=8)
 def test_18_desat():
+    plt.rcdefaults()
     return multi_2group.mean_diff.plot(
-        custom_palette=my_color_palette, swarm_desat=0.75, halfviolin_desat=0.25
+        custom_palette=my_color_palette, raw_desat=0.75, contrast_desat=0.25
     )
 
 
 @pytest.mark.mpl_image_compare(tolerance=8)
 def test_19_dot_sizes():
-    return multi_2group.mean_diff.plot(raw_marker_size=3, es_marker_size=12)
+    plt.rcdefaults()
+    return multi_2group.mean_diff.plot(raw_marker_size=3, contrast_marker_size=12)
 
 
 @pytest.mark.mpl_image_compare(tolerance=8)
 def test_20_change_ylims():
-    return multi_2group.mean_diff.plot(swarm_ylim=(0, 5), contrast_ylim=(-2, 2))
+    plt.rcdefaults()
+    return multi_2group.mean_diff.plot(raw_ylim=(0, 5), contrast_ylim=(-2, 2))
 
 
 @pytest.mark.mpl_image_compare(tolerance=8)
 def test_21_invert_ylim():
+    plt.rcdefaults()
     return multi_2group.mean_diff.plot(
         contrast_ylim=(2, -2), contrast_label="More negative is better!"
     )
@@ -310,6 +331,7 @@ def test_21_invert_ylim():
 
 @pytest.mark.mpl_image_compare(tolerance=8)
 def test_22_ticker_gardner_altman():
+    plt.rcdefaults()
     f = two_groups_unpaired.mean_diff.plot()
 
     rawswarm_axes = f.axes[0]
@@ -326,7 +348,8 @@ def test_22_ticker_gardner_altman():
 
 @pytest.mark.mpl_image_compare(tolerance=8)
 def test_23_ticker_cumming():
-    f = multi_2group.mean_diff.plot(swarm_ylim=(0, 6), contrast_ylim=(-3, 1))
+    plt.rcdefaults()
+    f = multi_2group.mean_diff.plot(raw_ylim=(0, 6), contrast_ylim=(-3, 1))
 
     rawswarm_axes = f.axes[0]
     contrast_axes = f.axes[1]
@@ -360,6 +383,7 @@ def test_23_ticker_cumming():
 
 @pytest.mark.mpl_image_compare(tolerance=8)
 def test_24_wide_df_nan():
+    plt.rcdefaults()
     wide_df_dabest = load(wide_df, idx=("Control", "Test 1", "Test 2", "Test 3"))
 
     return wide_df_dabest.mean_diff.plot()
@@ -367,6 +391,7 @@ def test_24_wide_df_nan():
 
 @pytest.mark.mpl_image_compare(tolerance=8)
 def test_25_long_df_nan():
+    plt.rcdefaults()
     long_df_dabest = load(
         long_df, x="group", y="value", idx=("Control", "Test 1", "Test 2", "Test 3")
     )
@@ -376,16 +401,19 @@ def test_25_long_df_nan():
 
 @pytest.mark.mpl_image_compare(tolerance=8)
 def test_26_slopegraph_kwargs():
+    plt.rcdefaults()
     return two_groups_paired.mean_diff.plot(slopegraph_kwargs=dict(linestyle="dotted"))
 
 
 @pytest.mark.mpl_image_compare(tolerance=8)
 def test_27_gardner_altman_reflines_kwargs():
+    plt.rcdefaults()
     return two_groups_unpaired.mean_diff.plot(reflines_kwargs=dict(linestyle="dotted"))
 
 
 @pytest.mark.mpl_image_compare(tolerance=8)
 def test_28_unpaired_cumming_reflines_kwargs():
+    plt.rcdefaults()
     return two_groups_unpaired.mean_diff.plot(
         fig_size=(12, 10),
         float_contrast=False,
@@ -396,6 +424,7 @@ def test_28_unpaired_cumming_reflines_kwargs():
 
 @pytest.mark.mpl_image_compare(tolerance=8)
 def test_29_paired_cumming_slopegraph_reflines_kwargs():
+    plt.rcdefaults()
     return two_groups_paired.mean_diff.plot(
         float_contrast=False,
         color_col="Gender",
@@ -407,17 +436,20 @@ def test_29_paired_cumming_slopegraph_reflines_kwargs():
 
 @pytest.mark.mpl_image_compare(tolerance=8)
 def test_30_sequential_cumming_slopegraph():
+    plt.rcdefaults()
     return multi_groups_sequential.mean_diff.plot()
 
 
 @pytest.mark.mpl_image_compare(tolerance=8)
 def test_31_baseline_cumming_slopegraph():
+    plt.rcdefaults()
     return multi_groups_baseline.mean_diff.plot()
 
 
 @pytest.mark.mpl_image_compare(tolerance=8)
 def test_99_style_sheets():
     # Perform this test last so we don't have to reset the plot style.
+    plt.rcdefaults()
     plt.style.use("dark_background")
 
-    return multi_2group.mean_diff.plot()
+    return multi_2group.mean_diff.plot(face_color="black")
diff --git a/nbs/tests/mpl_image_tests/test_05_forest_plot.py b/nbs/tests/mpl_image_tests/test_05_forest_plot.py
index 430a4eb3..578258bf 100644
--- a/nbs/tests/mpl_image_tests/test_05_forest_plot.py
+++ b/nbs/tests/mpl_image_tests/test_05_forest_plot.py
@@ -153,46 +153,304 @@ def create_mini_meta_dataset(N=20, seed=9999, control_locs=[3, 3.5, 3.25], contr
 contrasts_mini_meta = [contrast_mini_meta01, contrast_mini_meta02, contrast_mini_meta03]    
 
 
+delta1 = load(data = df_mini_meta01,
+                                   idx=(("Control 1", "Test 1"), ("Control 2", "Test 2"), ("Control 3", "Test 3")))
+delta2 = load(data = df_mini_meta02,
+                                    idx=(("Control 1", "Test 1"), ("Control 2", "Test 2"), ("Control 3", "Test 3")))
+delta3 = load(data = df_mini_meta03,
+                                   idx=(("Control 1", "Test 1"), ("Control 2", "Test 2"), ("Control 3", "Test 3")))
+contrasts_deltas = [delta1, delta2, delta3]  
+
 # Import your forest_plot function here
 from dabest.forest_plot import forest_plot
 
 @pytest.mark.mpl_image_compare(tolerance=8)
-def test_201_forest_plot_no_colorpalette():
-    return forest_plot(contrasts, 
-                       contrast_labels=['Drug1', 'Drug2', 'Drug3'])
+def test_500_deltadelta_forest():
+    plt.rcdefaults()
+    return forest_plot(
+                contrasts, 
+                labels=['Drug1', 'Drug2', 'Drug3']
+            )
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_501_deltadelta_with_deltas_idx_forest():
+    plt.rcdefaults()
+    return forest_plot(
+                contrasts, 
+                labels=['Drug1 Delta', 'Drug1 Delta-Delta', 
+                        'Drug2 Delta', 'Drug2 Delta-Delta',
+                        'Drug3 Delta', 'Drug3 Delta-Delta'
+                        ],
+                idx = [(0, 2), (0, 2), (0, 2)]
+            )
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_502_minimeta_forest():
+    plt.rcdefaults()
+    return forest_plot(
+                contrasts_mini_meta, 
+                labels=['mini_meta1', 'mini_meta2', 'mini_meta3']
+            )
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_503_deltadelta_custompalette_forest():
+    plt.rcdefaults()
+    return forest_plot(
+                contrasts, 
+                labels=['Drug1', 'Drug2', 'Drug3'],
+                custom_palette=['gray', 'blue', 'green']
+            )
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_504_deltadelta_horizontal_forest():
+    plt.rcdefaults()
+    return forest_plot(
+                contrasts, 
+                labels=['Drug1', 'Drug2', 'Drug3'],
+                horizontal=True
+            )
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_505_deltadelta_insert_ax_forest():
+    plt.rcdefaults()
+    f_forest_drug_profiles, axes  = plt.subplots(2, 2, figsize=[15, 14])
+    f_forest_drug_profiles.subplots_adjust(hspace=0.3, wspace=0.3)
+
+    for ax, contrast in zip(axes.flatten(), [unpaired_delta_01, unpaired_delta_02, unpaired_delta_03]):
+        contrast.mean_diff.plot(                  
+                        contrast_label='Mean Diff',
+                        raw_marker_size = 1,
+                        contrast_marker_size = 5,
+                        color_col='Genotype',
+                        ax = ax
+        )
+        forest_plot(
+                data = contrasts, 
+                labels = ['Drug1', 'Drug2', 'Drug3'], 
+                ax = axes[1,1], 
+                )
+    
+    for ax, title in zip(axes.flatten(), ['Drug 1', 'Drug 2', 'Drug 3', 'Forest plot']):
+        ax.set_title(title, fontsize = 12)
+
+    return f_forest_drug_profiles
+
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_506a_deltadelta_delta_g_using_hedges_g_forest():
+    plt.rcdefaults()
+    return forest_plot(
+                contrasts, 
+                labels=['Drug1', 'Drug2', 'Drug3'],
+                effect_size='hedges_g'
+            )
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_506b_deltadelta_delta_g_using_delta_g_forest():
+    plt.rcdefaults()
+    return forest_plot(
+                contrasts, 
+                labels=['Drug1', 'Drug2', 'Drug3'],
+                effect_size='delta_g'
+            )
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_507_deltadelta_fig_size_forest():
+    plt.rcdefaults()
+    return forest_plot(
+                contrasts, 
+                labels=['Drug1', 'Drug2', 'Drug3'],
+                fig_size=[6, 6]
+            )
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_508_deltadelta_fig_size_forest():
+    plt.rcdefaults()
+    return forest_plot(
+                contrasts, 
+                labels=['Drug1', 'Drug2', 'Drug3'],
+                fig_size=[6, 6]
+            )
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_509_deltadelta_halfviolin_aesthetics_forest():
+    plt.rcdefaults()
+    return forest_plot(
+                contrasts, 
+                labels=['Drug1', 'Drug2', 'Drug3'],
+                contrast_alpha=0.2,
+                contrast_desat=0.2
+            )
+
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_510_deltadelta_labels_and_title_aesthetics_forest():
+    plt.rcdefaults()
+    return forest_plot(
+                contrasts, 
+                labels=['Drug1', 'Drug2', 'Drug3'],
+                labels_fontsize=12,
+                labels_rotation=0,
+                ylabel='Effect Size',
+                ylabel_fontsize=14,
+                title='Drug Efficacy',
+                title_fontsize=20
+            )
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_511_deltadelta_lims_and_ticks_forest():
+    plt.rcdefaults()
+    return forest_plot(
+                contrasts, 
+                labels=['Drug1', 'Drug2', 'Drug3'],
+                ylim=[-1, 1],
+                yticks=[-1, 0, 1],
+                yticklabels=['Negative', 'Zero', 'Positive']
+            )
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_512_deltadelta_spines_forest():
+    plt.rcdefaults()
+    return forest_plot(
+                contrasts, 
+                labels=['Drug1', 'Drug2', 'Drug3'],
+                remove_spines=False
+            )
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_513_deltadelta_violinkwargs_forest():
+    plt.rcdefaults()
+    return forest_plot(
+                contrasts, 
+                labels=['Drug1', 'Drug2', 'Drug3'],
+                violin_kwargs={
+                    "widths": 0.8, "showextrema": True, 
+                    "showmedians": True, "orientation": 'vertical'
+                }
+            )
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_514_deltadelta_zerolinekwargs_forest():
+    plt.rcdefaults()
+    return forest_plot(
+                contrasts, 
+                labels=['Drug1', 'Drug2', 'Drug3'],
+                zeroline_kwargs={"linewidth": 2, "color": "red"}
+            )   
 
 @pytest.mark.mpl_image_compare(tolerance=8)
-def test_202_forest_plot_with_colorpalette():
-    return forest_plot(contrasts, 
-                       contrast_labels=['Drug1', 'Drug2', 'Drug3'],
-                       custom_palette=['gray', 'blue', 'green']) 
+def test_515_deltadelta_esmarkerkwargs_forest():
+    plt.rcdefaults()
+    return forest_plot(
+                contrasts, 
+                labels=['Drug1', 'Drug2', 'Drug3'],
+                marker_kwargs={
+                    'marker': '^', 'markersize': 15,'color': 'blue',
+                    'alpha': 0.5,
+                    }
+            )
 
 @pytest.mark.mpl_image_compare(tolerance=8)
-def test_203_horizontal_forest_plot_no_colorpalette():
-    return forest_plot(contrasts, 
-                       contrast_labels=['Drug1', 'Drug2', 'Drug3'],
-                       horizontal=True)
+def test_516_deltadelta_eserrorbarkwargs_forest():
+    plt.rcdefaults()
+    return forest_plot(
+                contrasts, 
+                labels=['Drug1', 'Drug2', 'Drug3'],
+                errorbar_kwargs={
+                    'color': 'red', 'lw': 4, 'linestyle': '--', 'alpha': 0.6,
+                }
+            )
+
 
 @pytest.mark.mpl_image_compare(tolerance=8)
-def test_204_horizontal_forest_plot_with_colorpalette():
-    return forest_plot(contrasts, 
-                       contrast_labels=['Drug1', 'Drug2', 'Drug3'], 
-                       custom_palette=['gray', 'blue', 'green'], 
-                       horizontal=True)
+def test_517_regular_delta_no_idx():
+    plt.rcdefaults()
+    return forest_plot(
+                contrasts_deltas,
+            )
 
 @pytest.mark.mpl_image_compare(tolerance=8)
-def test_206_forest_mini_meta():
-    return forest_plot(contrasts_mini_meta, 
-                       contrast_type='mini_meta', 
-                       contrast_labels=['mini_meta1', 'mini_meta2', 'mini_meta3'])
+def test_518_regular_delta_idx():
+    plt.rcdefaults()
+    return forest_plot(
+                contrasts_deltas,
+                idx = [(0,), (0,), (0,)],
+                labels=['Drug1 \nTest 1 - Control 1', 'Drug2 \nTest 2 - Control 2', 'Drug3 \nTest 3 - Control 3']
+            )
+
+
 
 @pytest.mark.mpl_image_compare(tolerance=8)
-def test_205_forest_mini_meta_horizontal():
-    return forest_plot(contrasts_mini_meta, 
-                       contrast_type='mini_meta', 
-                       contrast_labels=['mini_meta1', 'mini_meta2', 'mini_meta3'], 
-                       horizontal=True)
+def test_519_minimeta_with_deltas_forest():
+    plt.rcdefaults()
+    return forest_plot(
+                contrasts_mini_meta, 
+                idx=[(0, 3),(0, 3),(0, 3)],
+                labels=['Contrast A1', 'Mini_Meta A', 'Contrast B1', 'Mini_Meta B', 'Contrast C1', 'Mini_Meta C']
+            )
 
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_520_minimeta_with_deltas_and_delta_text_kwargs_forest():
+    plt.rcdefaults()
+    return forest_plot(
+                contrasts_mini_meta, 
+                idx=[(0, 3),(0, 3),(0, 3)],
+                labels=['Contrast A1', 'Mini_Meta A', 'Contrast B1', 'Mini_Meta B', 'Contrast C1', 'Mini_Meta C'],
+                delta_text_kwargs={'color': 'black','fontsize': 8, 'rotation': 45, 'va': 'bottom',
+                                   'x_coordinates': [1.4, 2.4, 3.4, 4.4, 5.4, 6.4], 
+                                   'y_coordinates': [0.6, 0.1, -2, -1.5, -1.5, -1.5]}
+            )
 
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_521_minimeta_with_deltas_with_contrast_bars_kwargs_forest():
+    plt.rcdefaults()
+    return forest_plot(
+                contrasts_mini_meta, 
+                idx=[(0, 3),(0, 3),(0, 3)],
+                labels=['Contrast A1', 'Mini_Meta A', 'Contrast B1', 'Mini_Meta B', 'Contrast C1', 'Mini_Meta C'],
+                contrast_bars_kwargs={'color': 'red', 'alpha': 0.4}
+            )
 
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_522a_minimeta_with_deltas_with_summary_bars():
+    plt.rcdefaults()
+    return forest_plot(
+                contrasts_mini_meta, 
+                idx=[(0, 3),(0, 3),(0, 3)],
+                labels=['Contrast A1', 'Mini_Meta A', 'Contrast B1', 'Mini_Meta B', 'Contrast C1', 'Mini_Meta C'],
+                reference_band=[0, 2],
+            )
 
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_522b_minimeta_with_deltas_with_summary_bars_horizontal():
+    plt.rcdefaults()
+    return forest_plot(
+                contrasts_mini_meta, 
+                idx=[(0, 3),(0, 3),(0, 3)],
+                labels=['Contrast A1', 'Mini_Meta A', 'Contrast B1', 'Mini_Meta B', 'Contrast C1', 'Mini_Meta C'],
+                reference_band=[0, 2],
+                horizontal=True
+            )
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_522c_minimeta_with_deltas_with_summary_bars_kwargs():
+    plt.rcdefaults()
+    return forest_plot(
+                contrasts_mini_meta, 
+                idx=[(0, 3),(0, 3),(0, 3)],
+                labels=['Contrast A1', 'Mini_Meta A', 'Contrast B1', 'Mini_Meta B', 'Contrast C1', 'Mini_Meta C'],
+                reference_band=[0, 2],
+                reference_band_kwargs={'span_ax': True, 'color': 'grey', 'alpha': 0.1}
+            )
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_522d_minimeta_with_deltas_with_summary_bars_kwargs_horizontal():
+    plt.rcdefaults()
+    return forest_plot(
+                contrasts_mini_meta, 
+                idx=[(0, 3),(0, 3),(0, 3)],
+                labels=['Contrast A1', 'Mini_Meta A', 'Contrast B1', 'Mini_Meta B', 'Contrast C1', 'Mini_Meta C'],
+                reference_band=[0, 2],
+                horizontal=True,
+                reference_band_kwargs={'span_ax': True, 'color': 'grey', 'alpha': 0.1}
+            )
\ No newline at end of file
diff --git a/nbs/tests/mpl_image_tests/test_07_delta-delta_plots.py b/nbs/tests/mpl_image_tests/test_07_delta-delta_plots.py
index 9dd63e1e..e3d3eac1 100644
--- a/nbs/tests/mpl_image_tests/test_07_delta-delta_plots.py
+++ b/nbs/tests/mpl_image_tests/test_07_delta-delta_plots.py
@@ -26,6 +26,9 @@ def create_demo_dataset_delta(seed=9999, N=20):
     y = norm.rvs(loc=3, scale=0.4, size=N*4)
     y[N:2*N] = y[N:2*N]+1
     y[2*N:3*N] = y[2*N:3*N]-0.5
+    ind = np.random.binomial(1, 0.5, size=N*4)
+    ind[N:2*N] = np.random.binomial(1, 0.2, size=N)
+    ind[2*N:3*N] = np.random.binomial(1, 0.7, size=N)
 
     # Add drug column
     t1 = np.repeat('Placebo', N*2).tolist()
@@ -54,10 +57,11 @@ def create_demo_dataset_delta(seed=9999, N=20):
 
     # Combine all columns into a DataFrame.
     df = pd.DataFrame({'ID'        : id_col,
-                      'Rep'      : rep,
+                      'Rep'        : rep,
                        'Genotype'  : genotype, 
-                       'Treatment': treatment,
-                       'Y'         : y
+                       'Treatment' : treatment,
+                       'Y'         : y,
+                       'Cat'       :ind
                     })
     return df
 
@@ -81,87 +85,151 @@ def create_demo_dataset_delta(seed=9999, N=20):
                 experiment = "Genotype",
                 paired="sequential", id_col="ID")
 
+unpaired_prop = load(data = df, proportional=True,
+                            # id_col="index", paired='baseline', 
+                            x = ["Genotype", "Genotype"], 
+                            y = "Cat", delta2=True,
+                            experiment="Treatment",)
+
+unpaired_specified_prop = load(data = df, proportional=True,
+                            # id_col="index", paired='baseline', 
+                            x = ["Genotype", "Genotype"], 
+                            y = "Cat", delta2=True,
+                            experiment="Treatment",
+                            experiment_label = ["Drug", "Placebo"],
+                            x1_level = ["M", "W"])
+
+paired_prop = load(data = df, proportional=True,
+                            id_col="ID", paired='baseline', 
+                            x = ["Genotype", "Genotype"], 
+                            y = "Cat", delta2=True,
+                            experiment="Treatment",)
+
+paired_specified_prop = load(data = df, proportional=True,
+                            id_col="ID", paired='baseline', 
+                            x = ["Genotype", "Genotype"], 
+                            y = "Cat", delta2=True,
+                            experiment="Treatment",
+                            experiment_label = ["Drug", "Placebo"],
+                            x1_level = ["M", "W"])
+
 
 @pytest.mark.mpl_image_compare(tolerance=8)
 def test_47_cummings_unpaired_delta_delta_meandiff():
+    plt.rcdefaults()
     return unpaired.mean_diff.plot();
 
 
 @pytest.mark.mpl_image_compare(tolerance=8)
 def test_48_cummings_sequential_delta_delta_meandiff():
+    plt.rcdefaults()
     return sequential.mean_diff.plot();
 
 
 @pytest.mark.mpl_image_compare(tolerance=8)
 def test_49_cummings_baseline_delta_delta_meandiff():
+    plt.rcdefaults()
     return baseline.mean_diff.plot();
 
 
 @pytest.mark.mpl_image_compare(tolerance=8)
 def test_50_delta_plot_ylabel():
-    return baseline.mean_diff.plot(swarm_label="This is my\nrawdata",
+    plt.rcdefaults()
+    return baseline.mean_diff.plot(raw_label="This is my\nrawdata",
                                    contrast_label="The bootstrap\ndistribtions!", 
                                    delta2_label="This is delta!");
 
 
 @pytest.mark.mpl_image_compare(tolerance=8)
 def test_51_delta_plot_change_palette_a():
+    plt.rcdefaults()
     return sequential.mean_diff.plot(custom_palette="Dark2");
 
 
 @pytest.mark.mpl_image_compare(tolerance=8)
 def test_52_delta_specified():
+    plt.rcdefaults()
     return unpaired_specified.mean_diff.plot();
 
 
 @pytest.mark.mpl_image_compare(tolerance=8)
 def test_53_delta_change_ylims():
-    return sequential.mean_diff.plot(swarm_ylim=(0, 9),
+    plt.rcdefaults()
+    return sequential.mean_diff.plot(raw_ylim=(0, 9),
                                        contrast_ylim=(-2, 2),
                                        fig_size=(15,6));
 
 
 @pytest.mark.mpl_image_compare(tolerance=8)
 def test_54_delta_invert_ylim():
+    plt.rcdefaults()
     return sequential.mean_diff.plot(contrast_ylim=(2, -2),
                                        contrast_label="More negative is better!");
 
 
 @pytest.mark.mpl_image_compare(tolerance=8)
 def test_55_delta_median_diff():
+    plt.rcdefaults()
     return sequential.median_diff.plot();
 
 
 @pytest.mark.mpl_image_compare(tolerance=8)
 def test_56_delta_cohens_d():
+    plt.rcdefaults()
     return unpaired.cohens_d.plot();
 
 
 @pytest.mark.mpl_image_compare(tolerance=8)
 def test_57_delta_show_delta2():
+    plt.rcdefaults()
     return unpaired.mean_diff.plot(show_delta2=False);
 
 
 @pytest.mark.mpl_image_compare(tolerance=8)
 def test_58_delta_axes_invert_ylim():
+    plt.rcdefaults()
     return unpaired.mean_diff.plot(delta2_ylim=(2, -2),
                                    delta2_label="More negative is better!");
 
                             
 @pytest.mark.mpl_image_compare(tolerance=8)
 def test_59_delta_axes_invert_ylim_not_showing_delta2():
+    plt.rcdefaults()
     return unpaired.mean_diff.plot(delta2_ylim=(2, -2),
                                    delta2_label="More negative is better!",
                                    show_delta2=False);
 
 @pytest.mark.mpl_image_compare(tolerance=8)
 def test_71_unpaired_delta_g():
-    return unpaired.delta_g.plot();
+    plt.rcdefaults()
+    return unpaired.hedges_g.plot();
 
 @pytest.mark.mpl_image_compare(tolerance=8)
 def test_72_sequential_delta_g():
-    return sequential.mean_diff.plot();
+    plt.rcdefaults()
+    return sequential.hedges_g.plot();
 
 @pytest.mark.mpl_image_compare(tolerance=8)
 def test_73_baseline_delta_g():
-    return baseline.mean_diff.plot();
\ No newline at end of file
+    plt.rcdefaults()
+    return baseline.hedges_g.plot();
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_74_unpaired_prop_delta2():
+    plt.rcdefaults()
+    return unpaired_prop.mean_diff.plot()
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_75_unpaired_specified_prop_delta2():
+    plt.rcdefaults()
+    return unpaired_specified_prop.mean_diff.plot()
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_76_paired_prop_delta2():
+    plt.rcdefaults()
+    return paired_prop.mean_diff.plot()
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_77_paired_specified_prop_delta2():
+    plt.rcdefaults()
+    return paired_specified_prop.mean_diff.plot()
\ No newline at end of file
diff --git a/nbs/tests/mpl_image_tests/test_09_mini_meta_plots.py b/nbs/tests/mpl_image_tests/test_09_mini_meta_plots.py
index 2ba4ad5e..4e510ac2 100644
--- a/nbs/tests/mpl_image_tests/test_09_mini_meta_plots.py
+++ b/nbs/tests/mpl_image_tests/test_09_mini_meta_plots.py
@@ -73,59 +73,70 @@ def create_demo_dataset(seed=9999, N=20):
 
 @pytest.mark.mpl_image_compare(tolerance=8)
 def test_60_cummings_unpaired_mini_meta_meandiff():
+    plt.rcdefaults()
     return unpaired.mean_diff.plot();
 
 
 @pytest.mark.mpl_image_compare(tolerance=8)
 def test_61_cummings_sequential_mini_meta_meandiff():
+    plt.rcdefaults()
     return sequential.mean_diff.plot();
 
 
 @pytest.mark.mpl_image_compare(tolerance=8)
 def test_62_cummings_baseline_mini_meta_meandiff():
+    plt.rcdefaults()
     return baseline.mean_diff.plot();
 
 
 @pytest.mark.mpl_image_compare(tolerance=8)
 def test_63_mini_meta_plot_ylabel():
-    return baseline.mean_diff.plot(swarm_label="This is my\nrawdata",
+    plt.rcdefaults()
+    return baseline.mean_diff.plot(raw_label="This is my\nrawdata",
                                    contrast_label="The bootstrap\ndistribtions!");
 
 
 @pytest.mark.mpl_image_compare(tolerance=8)
 def test_64_mini_meta_plot_change_palette_a():
+    plt.rcdefaults()
     return unpaired.mean_diff.plot(custom_palette="Dark2");
 
 
 @pytest.mark.mpl_image_compare(tolerance=8)
 def test_65_mini_meta_dot_sizes():
+    plt.rcdefaults()
     return sequential.mean_diff.plot(show_pairs=False,raw_marker_size=3,
-                                       es_marker_size=12);
+                                       contrast_marker_size=12);
 
 
 @pytest.mark.mpl_image_compare(tolerance=8)
 def test_66_mini_meta_change_ylims():
-    return sequential.mean_diff.plot(swarm_ylim=(0, 5),
+    plt.rcdefaults()
+    return sequential.mean_diff.plot(raw_ylim=(0, 5),
                                     contrast_ylim=(-2, 2),
                                     fig_size=(15,6));
 
 
 @pytest.mark.mpl_image_compare(tolerance=8)
 def test_67_mini_meta_invert_ylim():
+    plt.rcdefaults()
     return sequential.mean_diff.plot(contrast_ylim=(2, -2),
                                        contrast_label="More negative is better!");
 
 
 @pytest.mark.mpl_image_compare(tolerance=8)
 def test_68_mini_meta_median_diff():
+    plt.rcdefaults()
     return sequential.median_diff.plot();
 
 
 @pytest.mark.mpl_image_compare(tolerance=8)
 def test_69_mini_meta_cohens_d():
+    plt.rcdefaults()
     return unpaired.cohens_d.plot();
 
 
 @pytest.mark.mpl_image_compare(tolerance=8)
 def test_70_mini_meta_not_show():
+    plt.rcdefaults()
     return unpaired.mean_diff.plot(show_mini_meta=False);
diff --git a/nbs/tests/mpl_image_tests/test_10_proportion_plot.py b/nbs/tests/mpl_image_tests/test_10_proportion_plot.py
index 5a75aa86..68ba23eb 100644
--- a/nbs/tests/mpl_image_tests/test_10_proportion_plot.py
+++ b/nbs/tests/mpl_image_tests/test_10_proportion_plot.py
@@ -177,43 +177,51 @@ def create_demo_prop_dataset(seed=9999, N=40):
 
 @pytest.mark.mpl_image_compare(tolerance=8)
 def test_101_gardner_altman_unpaired_propdiff():
+    plt.rcdefaults()
     return two_groups_unpaired.mean_diff.plot()
 
 
 @pytest.mark.mpl_image_compare(tolerance=8)
 def test_103_cummings_two_group_unpaired_propdiff():
+    plt.rcdefaults()
     return two_groups_unpaired.mean_diff.plot(fig_size=(4, 6), float_contrast=False)
 
 
 @pytest.mark.mpl_image_compare(tolerance=8)
 def test_105_cummings_multi_group_unpaired_propdiff():
+    plt.rcdefaults()
     return multi_2group.mean_diff.plot()
 
 
 @pytest.mark.mpl_image_compare(tolerance=8)
 def test_106_cummings_shared_control_propdiff():
+    plt.rcdefaults()
     return shared_control.mean_diff.plot()
 
 
 @pytest.mark.mpl_image_compare(tolerance=8)
 def test_107_cummings_multi_groups_propdiff():
+    plt.rcdefaults()
     return multi_groups.mean_diff.plot()
 
 
 @pytest.mark.mpl_image_compare(tolerance=8)
 def test_109_gardner_altman_ylabel():
+    plt.rcdefaults()
     return two_groups_unpaired.mean_diff.plot(
-        bar_label="This is my\nrawdata", contrast_label="The bootstrap\ndistribtions!"
+        raw_label="This is my\nrawdata", contrast_label="The bootstrap\ndistribtions!"
     )
 
 
 @pytest.mark.mpl_image_compare(tolerance=8)
 def test_110_change_fig_size():
+    plt.rcdefaults()
     return two_groups_unpaired.mean_diff.plot(fig_size=(6, 6), custom_palette="Dark2")
 
 
 @pytest.mark.mpl_image_compare(tolerance=8)
 def test_111_change_palette_b():
+    plt.rcdefaults()
     return multi_2group.mean_diff.plot(custom_palette="Paired")
 
 
@@ -227,23 +235,27 @@ def test_111_change_palette_b():
 
 @pytest.mark.mpl_image_compare(tolerance=8)
 def test_112_change_palette_c():
+    plt.rcdefaults()
     return multi_2group.mean_diff.plot(custom_palette=my_color_palette)
 
 
 @pytest.mark.mpl_image_compare(tolerance=8)
 def test_113_desat():
+    plt.rcdefaults()
     return multi_2group.mean_diff.plot(
-        custom_palette=my_color_palette, bar_desat=0.1, halfviolin_desat=0.25
+        custom_palette=my_color_palette, raw_desat=0.1, contrast_desat=0.25
     )
 
 
 @pytest.mark.mpl_image_compare(tolerance=8)
 def test_114_change_ylims():
+    plt.rcdefaults()
     return multi_2group.mean_diff.plot(contrast_ylim=(-2, 2))
 
 
 @pytest.mark.mpl_image_compare(tolerance=8)
 def test_115_invert_ylim():
+    plt.rcdefaults()
     return multi_2group.mean_diff.plot(
         contrast_ylim=(2, -2), contrast_label="More negative is better!"
     )
@@ -251,6 +263,7 @@ def test_115_invert_ylim():
 
 @pytest.mark.mpl_image_compare(tolerance=8)
 def test_116_ticker_gardner_altman():
+    plt.rcdefaults()
     fig = two_groups_unpaired.mean_diff.plot()
 
     rawswarm_axes = fig.axes[0]
@@ -266,11 +279,13 @@ def test_116_ticker_gardner_altman():
 
 @pytest.mark.mpl_image_compare(tolerance=8)
 def test_117_err_color():
-    return two_groups_unpaired.mean_diff.plot(err_color="purple")
+    plt.rcdefaults()
+    return two_groups_unpaired.mean_diff.plot(barplot_kwargs={"err_kws": {"color" : "purple"}})
 
 
 @pytest.mark.mpl_image_compare(tolerance=8)
 def test_118_cummings_two_group_unpaired_meandiff_bar_width():
+    plt.rcdefaults()
     return two_groups_unpaired.mean_diff.plot(bar_width=0.4, float_contrast=False)
 
 
@@ -295,6 +310,7 @@ def test_118_cummings_two_group_unpaired_meandiff_bar_width():
 
 @pytest.mark.mpl_image_compare(tolerance=8)
 def test_119_wide_df_nan():
+    plt.rcdefaults()
     wide_df_dabest = load(
         wide_df, idx=("Control", "Test 1", "Test 2", "Test 3"), proportional=True
     )
@@ -304,6 +320,7 @@ def test_119_wide_df_nan():
 
 @pytest.mark.mpl_image_compare(tolerance=8)
 def test_120_long_df_nan():
+    plt.rcdefaults()
     long_df_dabest = load(
         long_df,
         x="group",
@@ -317,81 +334,134 @@ def test_120_long_df_nan():
 
 @pytest.mark.mpl_image_compare(tolerance=8)
 def test_121_cohens_h_gardner_altman():
+    plt.rcdefaults()
     return two_groups_unpaired.cohens_h.plot()
 
 
 @pytest.mark.mpl_image_compare(tolerance=8)
 def test_122_cohens_h_cummings():
+    plt.rcdefaults()
     return two_groups_unpaired.cohens_h.plot(float_contrast=False)
 
 
 @pytest.mark.mpl_image_compare(tolerance=8)
 def test_123_sankey_gardner_altman():
+    plt.rcdefaults()
     return two_groups_paired.mean_diff.plot()
 
 
 @pytest.mark.mpl_image_compare(tolerance=8)
 def test_124_sankey_cummings():
+    plt.rcdefaults()
     return two_groups_paired.mean_diff.plot(float_contrast=False)
 
 
 @pytest.mark.mpl_image_compare(tolerance=8)
 def test_125_sankey_2paired_groups():
+    plt.rcdefaults()
     return multi_2group_paired.mean_diff.plot()
 
 
 @pytest.mark.mpl_image_compare(tolerance=8)
 def test_126_sankey_2sequential_groups():
+    plt.rcdefaults()
     return multi_2group_sequential.mean_diff.plot()
 
 
 @pytest.mark.mpl_image_compare(tolerance=8)
 def test_127_sankey_multi_group_paired():
+    plt.rcdefaults()
     return multi_groups_paired.mean_diff.plot()
 
 
 @pytest.mark.mpl_image_compare(tolerance=8)
 def test_128_sankey_transparency():
+    plt.rcdefaults()
     return two_groups_paired.mean_diff.plot(sankey_kwargs={"alpha": 0.2})
 
 
 @pytest.mark.mpl_image_compare(tolerance=8)
 def test_129_zero_to_zero():
+    plt.rcdefaults()
     return zero_to_zero.mean_diff.plot()
 
 
 @pytest.mark.mpl_image_compare(tolerance=8)
 def test_130_zero_to_one():
+    plt.rcdefaults()
     return zero_to_one.mean_diff.plot()
 
 
 @pytest.mark.mpl_image_compare(tolerance=8)
 def test_131_one_to_zero():
+    plt.rcdefaults()
     return one_to_zero.mean_diff.plot()
 
 
 @pytest.mark.mpl_image_compare(tolerance=8)
 def test_132_shared_control_sankey_off():
+    plt.rcdefaults()
     return shared_control_paired.mean_diff.plot(sankey_kwargs={"sankey": False})
 
 
 @pytest.mark.mpl_image_compare(tolerance=8)
 def test_133_shared_control_flow_off():
+    plt.rcdefaults()
     return shared_control_paired.mean_diff.plot(sankey_kwargs={"flow": False})
 
 
 @pytest.mark.mpl_image_compare(tolerance=8)
 def test_134_separate_control_sankey_off():
+    plt.rcdefaults()
     return multi_groups_sequential.mean_diff.plot(sankey_kwargs={"sankey": False})
 
 
 @pytest.mark.mpl_image_compare(tolerance=8)
 def test_135_separate_control_flow_off():
+    plt.rcdefaults()
     return multi_groups_sequential.mean_diff.plot(sankey_kwargs={"flow": False})
 
+# Show sample counts
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_137_multi_2group_show_sample_counts():
+    plt.rcdefaults()
+    return multi_2group.mean_diff.plot(prop_sample_counts=True)
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_138_multi_groups_paired_show_sample_counts():
+    plt.rcdefaults()
+    return multi_groups_paired.mean_diff.plot(prop_sample_counts=True)
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_139_multi_2group_show_sample_counts_and_kwargs():
+    plt.rcdefaults()
+    return multi_2group.mean_diff.plot(prop_sample_counts=True, prop_sample_counts_kwargs={
+        "color": "red", "fontsize": 12, "fontweight": "bold"})
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_140_multi_groups_paired_show_sample_counts_with_sankey_off():
+    plt.rcdefaults()
+    return multi_groups_paired.mean_diff.plot(prop_sample_counts=True, sankey_kwargs={"sankey": False})
+
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_141_sankey_change_palette_a():
+    plt.rcdefaults()
+    return multi_groups_paired.mean_diff.plot(custom_palette="Dark2")
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_142_sankey_change_palette_b():
+    plt.rcdefaults()
+    return multi_groups_paired.mean_diff.plot(custom_palette={1: 'red', 0: 'blue'})
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_143_sankey_change_palette_c():
+    plt.rcdefaults()
+    return multi_groups_paired.mean_diff.plot(custom_palette=['red', 'blue'])
 
 @pytest.mark.mpl_image_compare(tolerance=8)
 def test_136_style_sheets():
     # Perform this test last so we don't have to reset the plot style.
+    plt.rcdefaults()
     plt.style.use("dark_background")
     return multi_2group.mean_diff.plot(face_color="black")
diff --git a/nbs/tests/mpl_image_tests/test_Gridkey.py b/nbs/tests/mpl_image_tests/test_Gridkey.py
new file mode 100644
index 00000000..3a36374e
--- /dev/null
+++ b/nbs/tests/mpl_image_tests/test_Gridkey.py
@@ -0,0 +1,353 @@
+import pytest
+import numpy as np
+import pandas as pd
+from scipy.stats import norm
+
+
+import matplotlib as mpl
+
+mpl.use("Agg")
+import matplotlib.pyplot as plt
+import matplotlib.ticker as Ticker
+import seaborn as sns
+
+from dabest._api import load
+
+def create_demo_dataset(seed=9999, N=20):
+    import numpy as np
+    import pandas as pd
+    from scipy.stats import norm  # Used in generation of populations.
+
+    np.random.seed(9999)  # Fix the seed so the results are replicable.
+    # pop_size = 10000 # Size of each population.
+
+    # Create samples
+    c1 = norm.rvs(loc=3, scale=0.4, size=N)
+    c2 = norm.rvs(loc=3.5, scale=0.75, size=N)
+    c3 = norm.rvs(loc=3.25, scale=0.4, size=N)
+
+    t1 = norm.rvs(loc=3.5, scale=0.5, size=N)
+    t2 = norm.rvs(loc=2.5, scale=0.6, size=N)
+    t3 = norm.rvs(loc=3, scale=0.75, size=N)
+    t4 = norm.rvs(loc=3.5, scale=0.75, size=N)
+    t5 = norm.rvs(loc=3.25, scale=0.4, size=N)
+    t6 = norm.rvs(loc=3.25, scale=0.4, size=N)
+
+    # Add a `gender` column for coloring the data.
+    females = np.repeat("Female", N / 2).tolist()
+    males = np.repeat("Male", N / 2).tolist()
+    gender = females + males
+
+    # Add an `id` column for paired data plotting.
+    id_col = pd.Series(range(1, N + 1))
+
+    # Combine samples and gender into a DataFrame.
+    df = pd.DataFrame(
+        {
+            "Control 1": c1,
+            "Test 1": t1,
+            "Control 2": c2,
+            "Test 2": t2,
+            "Control 3": c3,
+            "Test 3": t3,
+            "Test 4": t4,
+            "Test 5": t5,
+            "Test 6": t6,
+            "Gender": gender,
+            "ID": id_col,
+        }
+    )
+
+    return df
+
+def create_demo_dataset_delta(seed=9999, N=20):
+
+    import numpy as np
+    import pandas as pd
+    from scipy.stats import norm  # Used in generation of populations.
+
+    np.random.seed(seed)  # Fix the seed so the results are replicable.
+    # pop_size = 10000 # Size of each population.
+
+    from scipy.stats import norm  # Used in generation of populations.
+
+    # Create samples
+    y = norm.rvs(loc=3, scale=0.4, size=N * 4)
+    y[N : 2 * N] = y[N : 2 * N] + 1
+    y[2 * N : 3 * N] = y[2 * N : 3 * N] - 0.5
+
+    # Add drug column
+    t1 = np.repeat("Placebo", N * 2).tolist()
+    t2 = np.repeat("Drug", N * 2).tolist()
+    treatment = t1 + t2
+
+    # Add a `rep` column as the first variable for the 2 replicates of experiments done
+    rep = []
+    for i in range(N * 2):
+        rep.append("Rep1")
+        rep.append("Rep2")
+
+    # Add a `genotype` column as the second variable
+    wt = np.repeat("W", N).tolist()
+    mt = np.repeat("M", N).tolist()
+    wt2 = np.repeat("W", N).tolist()
+    mt2 = np.repeat("M", N).tolist()
+
+    genotype = wt + mt + wt2 + mt2
+
+    # Add an `id` column for paired data plotting.
+    id = list(range(0, N * 2))
+    id_col = id + id
+
+    # Combine all columns into a DataFrame.
+    df = pd.DataFrame(
+        {"ID": id_col, "Rep": rep, "Genotype": genotype, "Treatment": treatment, "Y": y}
+    )
+    return df
+
+
+def create_demo_prop_dataset(seed=9999, N=40):
+    np.random.seed(9999)  # Fix the seed so the results are replicable.
+    # Create samples
+    n = 1
+    c1 = np.random.binomial(n, 0.2, size=N)
+    c2 = np.random.binomial(n, 0.2, size=N)
+    c3 = np.random.binomial(n, 0.8, size=N)
+
+    t1 = np.random.binomial(n, 0.5, size=N)
+    t2 = np.random.binomial(n, 0.2, size=N)
+    t3 = np.random.binomial(n, 0.3, size=N)
+    t4 = np.random.binomial(n, 0.4, size=N)
+    t5 = np.random.binomial(n, 0.5, size=N)
+    t6 = np.random.binomial(n, 0.6, size=N)
+    t7 = np.zeros(N)
+    t8 = np.ones(N)
+    t9 = np.zeros(N)
+
+    # Add a `gender` column for coloring the data.
+    females = np.repeat("Female", N / 2).tolist()
+    males = np.repeat("Male", N / 2).tolist()
+    gender = females + males
+
+    # Add an `id` column for paired data plotting.
+    id_col = pd.Series(range(1, N + 1))
+
+    # Combine samples and gender into a DataFrame.
+    df = pd.DataFrame(
+        {
+            "Control 1": c1,
+            "Test 1": t1,
+            "Control 2": c2,
+            "Test 2": t2,
+            "Control 3": c3,
+            "Test 3": t3,
+            "Test 4": t4,
+            "Test 5": t5,
+            "Test 6": t6,
+            "Test 7": t7,
+            "Test 8": t8,
+            "Test 9": t9,
+            "Gender": gender,
+            "ID": id_col,
+        }
+    )
+
+    return df
+
+df = create_demo_dataset()
+df_delta = create_demo_dataset_delta()
+df_prop = create_demo_prop_dataset()
+
+# Two group
+two_groups_unpaired = load(df, idx=("Control 1", "Test 1"))
+two_groups_paired = load(df, idx=("Control 1", "Test 1"), paired='baseline', id_col='ID')
+
+# Multi two group
+multi_2group_unpaired = load(df, idx=(("Control 1","Test 1",),("Control 2", "Test 2"),),)
+multi_2group_paired = load(df, idx=(("Control 1","Test 1",),("Control 2", "Test 2"),), paired='baseline', id_col='ID')
+
+# Multi-group
+shared_control = load(df, idx=("Control 1", "Test 1", "Test 2", "Test 3", "Test 4", "Test 5", "Test 6"))
+repeated_measures = load(df, idx=("Control 1", "Test 1", "Test 2", "Test 3", "Test 4", "Test 5", "Test 6"), paired='baseline', id_col='ID')
+
+# Mixed multi group and two group
+multi_groups_unpaired = load(df,idx=(("Control 1","Test 1",),("Control 2", "Test 2", "Test 3"),("Control 3", "Test 4", "Test 5", "Test 6"),),)
+multi_groups_paired = load(df,idx=(("Control 1","Test 1",),("Control 2", "Test 2", "Test 3"),("Control 3", "Test 4", "Test 5", "Test 6"),),
+                           paired='baseline', id_col='ID')
+
+
+# Proportions
+multi_groups_unpaired_prop = load(df_prop, idx=(("Control 1","Test 1",),("Control 2", "Test 2", "Test 3"),("Control 3", "Test 4", "Test 5", "Test 6"),),
+                                    proportional=True,)
+
+multi_groups_paired_baseline_prop = load(df_prop, idx=(("Control 1","Test 1",),("Control 2", "Test 2", "Test 3"),("Control 3", "Test 4", "Test 5", "Test 6"),),
+                            paired="baseline", id_col="ID", proportional=True,)
+
+# delta-delta
+delta_delta_unpaired = load(data=df_delta, x=["Genotype", "Genotype"], y="Y", delta2=True, experiment="Treatment")
+delta_delta_paired = load(data = df_delta, x = ["Treatment", "Rep"], y = "Y", delta2 = True, experiment = "Genotype", paired="baseline", id_col="ID")
+
+# mini_meta
+mini_meta_unpaired = load(df, idx=(("Control 1", "Test 1"), ("Control 2", "Test 2"), ("Control 3", "Test 3")),mini_meta=True)
+mini_meta_paired = load(df, idx=(("Control 1", "Test 1"), ("Control 2", "Test 2"), ("Control 3", "Test 3")),mini_meta=True, paired='baseline', id_col='ID')
+
+
+# Two Group
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_250_2group_unpaired_meandiff_gridkey_autoparser():
+    plt.rcdefaults()
+    return two_groups_unpaired.mean_diff.plot(gridkey='auto');
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_251_2group_unpaired_meandiff_gridkey_userdefinedrows():
+    plt.rcdefaults()
+    return two_groups_unpaired.mean_diff.plot(gridkey=['Control', 'Test']);
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_252_2group_paired_meandiff_gridkey_autoparser():
+    plt.rcdefaults()
+    return two_groups_paired.mean_diff.plot(gridkey='auto');
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_253_2group_paired_meandiff_gridkey_userdefinedrows():
+    plt.rcdefaults()
+    return two_groups_paired.mean_diff.plot(gridkey=['Control', 'Test']);
+
+# Multi 2 Group
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_254_multi_2group_unpaired_meandiff_gridkey_autoparser():
+    plt.rcdefaults()
+    return multi_groups_unpaired.mean_diff.plot(gridkey='auto');
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_255_multi_2group_unpaired_meandiff_gridkey_userdefinedrows():
+    plt.rcdefaults()
+    return multi_groups_unpaired.mean_diff.plot(gridkey=['Control', 'Test']);
+
+# Shared Control and Repeated Measures
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_256_shared_control_meandiff_gridkey_autoparser():
+    plt.rcdefaults()
+    return shared_control.mean_diff.plot(gridkey='auto');
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_257_shared_control_meandiff_gridkey_userdefinedrows():
+    plt.rcdefaults()
+    return shared_control.mean_diff.plot(gridkey=['Control', 'Test']);
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_258_repeated_measures_meandiff_gridkey_autoparser():
+    plt.rcdefaults()
+    return repeated_measures.mean_diff.plot(gridkey='auto');
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_259_repeated_measures_meandiff_gridkey_userdefinedrows():
+    plt.rcdefaults()
+    return repeated_measures.mean_diff.plot(gridkey=['Control', 'Test']);
+
+
+# Multi groups
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_260_multigroups_unpaired_meandiff_gridkey_userdefinedrows():
+    plt.rcdefaults()
+    return multi_groups_unpaired.mean_diff.plot(gridkey=['Control', 'Test']);
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_261_multigroups_unpaired_meandiff_gridkey_autoparser():
+    plt.rcdefaults()
+    return multi_groups_unpaired.mean_diff.plot(gridkey='auto');
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_262_multigroups_paired_meandiff_gridkey_userdefinedrows():
+    plt.rcdefaults()
+    return multi_groups_paired.mean_diff.plot(gridkey=['Control', 'Test']);
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_263_multigroups_paired_meandiff_gridkey_autoparser():
+    plt.rcdefaults()
+    return multi_groups_paired.mean_diff.plot(gridkey='auto');
+
+# Proportions
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_264_multigroups_prop_unpaired_meandiff_gridkey_userdefinedrows():
+    plt.rcdefaults()
+    return multi_groups_unpaired_prop.mean_diff.plot(gridkey=['Control', 'Test']);
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_265_multigroups_prop_unpaired_meandiff_gridkey_autoparser():
+    plt.rcdefaults()
+    return multi_groups_unpaired_prop.mean_diff.plot(gridkey='auto');
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_266_multigroups_prop_paired_meandiff_gridkey_userdefinedrows():
+    plt.rcdefaults()
+    return multi_groups_paired_baseline_prop.mean_diff.plot(gridkey=['Control', 'Test']);
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_267_multigroups_prop_paired_meandiff_gridkey_autoparser():
+    plt.rcdefaults()
+    return multi_groups_paired_baseline_prop.mean_diff.plot(gridkey='auto');
+
+
+# delta-delta
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_268_delta_delta_unpaired_meandiff_gridkey_autoparser():
+    plt.rcdefaults()
+    return delta_delta_unpaired.mean_diff.plot(gridkey='auto');
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_269_delta_delta_paired_meandiff_gridkey_autoparser():
+    plt.rcdefaults()
+    return delta_delta_paired.mean_diff.plot(gridkey='auto');
+
+
+# mini-meta
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_270_mini_meta_unpaired_meandiff_gridkey_userdefinedrows():
+    plt.rcdefaults()
+    return mini_meta_unpaired.mean_diff.plot(gridkey=['Control', 'Test']);
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_271_mini_meta_unpaired_meandiff_gridkey_autoparser():
+    plt.rcdefaults()
+    return mini_meta_unpaired.mean_diff.plot(gridkey='auto');
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_272_mini_meta_paired_meandiff_gridkey_userdefinedrows():
+    plt.rcdefaults()
+    return mini_meta_paired.mean_diff.plot(gridkey=['Control', 'Test']);
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_273_mini_meta_paired_meandiff_gridkey_autoparser():
+    plt.rcdefaults()
+    return mini_meta_paired.mean_diff.plot(gridkey='auto');
+
+
+# Gridkey kwargs
+multi_2group_paired_test = load(df, idx=(("Control 1","Control 2",),("Test 1", "Test 2"),), paired='baseline', id_col='ID')
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_274_gridkey_merge_pairs_and_autoparser():
+    plt.rcdefaults()
+    return multi_2group_paired_test.mean_diff.plot(gridkey=['Control', 'Test'], gridkey_kwargs={'merge_pairs': True});
+
+gridkey_kwargs = {'show_es': False, 'show_Ns': False, 'marker': '√'}
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_275_gridkey_kwargs_and_autoparser():
+    plt.rcdefaults()
+    return two_groups_unpaired.mean_diff.plot(gridkey='auto', gridkey_kwargs=gridkey_kwargs);
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_276_gridkey_fontsize_and_autoparser():
+    plt.rcdefaults()
+    return two_groups_unpaired.mean_diff.plot(gridkey='auto', gridkey_kwargs={'fontsize': 15});
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_277_gridkey_labels_fontsize_and_autoparser():
+    plt.rcdefaults()
+    return two_groups_unpaired.mean_diff.plot(gridkey='auto', gridkey_kwargs={'labels_fontsize': 15});
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_278_gridkey_labels_fontsize_and_fontsize_and_autoparser():
+    plt.rcdefaults()
+    return two_groups_unpaired.mean_diff.plot(gridkey='auto', 
+                                              gridkey_kwargs={'fontsize': 8, 'labels_fontsize': 15});
\ No newline at end of file
diff --git a/nbs/tests/mpl_image_tests/test_Horizontal_Plots.py b/nbs/tests/mpl_image_tests/test_Horizontal_Plots.py
new file mode 100644
index 00000000..b260be08
--- /dev/null
+++ b/nbs/tests/mpl_image_tests/test_Horizontal_Plots.py
@@ -0,0 +1,1038 @@
+import pytest
+import numpy as np
+import pandas as pd
+from scipy.stats import norm
+
+
+import matplotlib as mpl
+
+mpl.use("Agg")
+import matplotlib.pyplot as plt
+import matplotlib.ticker as Ticker
+import seaborn as sns
+
+from dabest._api import load
+
+def create_demo_dataset(seed=9999, N=20):
+    import numpy as np
+    import pandas as pd
+    from scipy.stats import norm  # Used in generation of populations.
+
+    np.random.seed(9999)  # Fix the seed so the results are replicable.
+    # pop_size = 10000 # Size of each population.
+
+    # Create samples
+    c1 = norm.rvs(loc=3, scale=0.4, size=N)
+    c2 = norm.rvs(loc=3.5, scale=0.75, size=N)
+    c3 = norm.rvs(loc=3.25, scale=0.4, size=N)
+
+    t1 = norm.rvs(loc=3.5, scale=0.5, size=N)
+    t2 = norm.rvs(loc=2.5, scale=0.6, size=N)
+    t3 = norm.rvs(loc=3, scale=0.75, size=N)
+    t4 = norm.rvs(loc=3.5, scale=0.75, size=N)
+    t5 = norm.rvs(loc=3.25, scale=0.4, size=N)
+    t6 = norm.rvs(loc=3.25, scale=0.4, size=N)
+
+    # Add a `gender` column for coloring the data.
+    females = np.repeat("Female", N / 2).tolist()
+    males = np.repeat("Male", N / 2).tolist()
+    gender = females + males
+
+    # Add an `id` column for paired data plotting.
+    id_col = pd.Series(range(1, N + 1))
+
+    # Combine samples and gender into a DataFrame.
+    df = pd.DataFrame(
+        {
+            "Control 1": c1,
+            "Test 1": t1,
+            "Control 2": c2,
+            "Test 2": t2,
+            "Control 3": c3,
+            "Test 3": t3,
+            "Test 4": t4,
+            "Test 5": t5,
+            "Test 6": t6,
+            "Gender": gender,
+            "ID": id_col,
+        }
+    )
+
+    return df
+
+def create_demo_dataset_delta(seed=9999, N=20):
+
+    import numpy as np
+    import pandas as pd
+    from scipy.stats import norm  # Used in generation of populations.
+
+    np.random.seed(seed)  # Fix the seed so the results are replicable.
+    # pop_size = 10000 # Size of each population.
+
+    from scipy.stats import norm  # Used in generation of populations.
+
+    # Create samples
+    y = norm.rvs(loc=3, scale=0.4, size=N * 4)
+    y[N : 2 * N] = y[N : 2 * N] + 1
+    y[2 * N : 3 * N] = y[2 * N : 3 * N] - 0.5
+
+    # Add drug column
+    t1 = np.repeat("Placebo", N * 2).tolist()
+    t2 = np.repeat("Drug", N * 2).tolist()
+    treatment = t1 + t2
+
+    # Add a `rep` column as the first variable for the 2 replicates of experiments done
+    rep = []
+    for i in range(N * 2):
+        rep.append("Rep1")
+        rep.append("Rep2")
+
+    # Add a `genotype` column as the second variable
+    wt = np.repeat("W", N).tolist()
+    mt = np.repeat("M", N).tolist()
+    wt2 = np.repeat("W", N).tolist()
+    mt2 = np.repeat("M", N).tolist()
+
+    genotype = wt + mt + wt2 + mt2
+
+    # Add an `id` column for paired data plotting.
+    id = list(range(0, N * 2))
+    id_col = id + id
+
+    # Combine all columns into a DataFrame.
+    df = pd.DataFrame(
+        {"ID": id_col, "Rep": rep, "Genotype": genotype, "Treatment": treatment, "Y": y}
+    )
+    return df
+
+def create_demo_prop_dataset(seed=9999, N=40):
+    np.random.seed(9999)  # Fix the seed so the results are replicable.
+    # Create samples
+    n = 1
+    c1 = np.random.binomial(n, 0.2, size=N)
+    c2 = np.random.binomial(n, 0.2, size=N)
+    c3 = np.random.binomial(n, 0.8, size=N)
+
+    t1 = np.random.binomial(n, 0.5, size=N)
+    t2 = np.random.binomial(n, 0.2, size=N)
+    t3 = np.random.binomial(n, 0.3, size=N)
+    t4 = np.random.binomial(n, 0.4, size=N)
+    t5 = np.random.binomial(n, 0.5, size=N)
+    t6 = np.random.binomial(n, 0.6, size=N)
+    t7 = np.zeros(N)
+    t8 = np.ones(N)
+    t9 = np.zeros(N)
+
+    # Add a `gender` column for coloring the data.
+    females = np.repeat("Female", N / 2).tolist()
+    males = np.repeat("Male", N / 2).tolist()
+    gender = females + males
+
+    # Add an `id` column for paired data plotting.
+    id_col = pd.Series(range(1, N + 1))
+
+    # Combine samples and gender into a DataFrame.
+    df = pd.DataFrame(
+        {
+            "Control 1": c1,
+            "Test 1": t1,
+            "Control 2": c2,
+            "Test 2": t2,
+            "Control 3": c3,
+            "Test 3": t3,
+            "Test 4": t4,
+            "Test 5": t5,
+            "Test 6": t6,
+            "Test 7": t7,
+            "Test 8": t8,
+            "Test 9": t9,
+            "Gender": gender,
+            "ID": id_col,
+        }
+    )
+
+    return df
+
+df = create_demo_dataset()
+df_delta = create_demo_dataset_delta()
+df_prop = create_demo_prop_dataset()
+
+# Two group
+two_groups_unpaired = load(df, idx=("Control 1", "Test 1"))
+two_groups_paired = load(df, idx=("Control 1", "Test 1"), paired='baseline', id_col='ID')
+
+# Multi two group
+multi_2group_unpaired = load(df, idx=(("Control 1","Test 1",),("Control 2", "Test 2"),),)
+multi_2group_paired = load(df, idx=(("Control 1","Test 1",),("Control 2", "Test 2"),), paired='baseline', id_col='ID')
+
+# Multi-group
+shared_control = load(df, idx=("Control 1", "Test 1", "Test 2", "Test 3", "Test 4", "Test 5", "Test 6"))
+repeated_measures = load(df, idx=("Control 1", "Test 1", "Test 2", "Test 3", "Test 4", "Test 5", "Test 6"), paired='baseline', id_col='ID')
+
+
+# Mixed multi group and two group
+multi_groups_unpaired = load(df,idx=(("Control 1","Test 1",),("Control 2", "Test 2", "Test 3"),("Control 3", "Test 4", "Test 5", "Test 6"),),)
+multi_groups_paired_baseline = load(df,idx=(("Control 1","Test 1",),("Control 2", "Test 2", "Test 3"),("Control 3", "Test 4", "Test 5", "Test 6"),),
+                                    paired='baseline', id_col='ID')
+multi_groups_paired_sequential = load(df,idx=(("Control 1","Test 1",),("Control 2", "Test 2", "Test 3"),("Control 3", "Test 4", "Test 5", "Test 6"),),
+                                                paired='sequential', id_col='ID')
+
+# Proportion plots
+df_prop = create_demo_prop_dataset()
+
+two_groups_unpaired_prop = load(df_prop, idx=("Control 1", "Test 1"), proportional=True)
+
+two_groups_paired_baseline_prop = load(df_prop, idx=("Control 1", "Test 1"), paired="baseline", id_col="ID", proportional=True)
+
+two_groups_paired_sequential_prop = load(df_prop, idx=("Control 1", "Test 1"), paired="sequential", id_col="ID", proportional=True)
+
+multi_2group_unpaired_prop = load(df_prop, idx=(("Control 1","Test 1",),("Control 2", "Test 2"),), proportional=True,)
+
+multi_2group_paired_baseline_prop = load(df_prop, idx=(("Control 1", "Test 1"), ("Control 2", "Test 2")), paired="baseline", id_col="ID", proportional=True,)
+
+multi_2group_paired_sequential_prop = load(df_prop, idx=(("Control 1", "Test 1"), ("Control 2", "Test 2")), paired="sequential", id_col="ID", proportional=True,)
+
+shared_control_prop = load(df_prop, idx=("Control 1", "Test 1", "Test 2", "Test 3", "Test 4", "Test 5", "Test 6"), proportional=True,)
+
+repeated_measures_baseline_prop = load(df_prop, idx=("Control 1", "Test 1", "Test 2", "Test 3", "Test 4", "Test 5", "Test 6"), 
+                              paired="baseline", id_col="ID", proportional=True,)
+
+repeated_measures_sequential_prop = load(df_prop, idx=("Control 1", "Test 1", "Test 2", "Test 3", "Test 4", "Test 5", "Test 6"), 
+                              paired="sequential", id_col="ID", proportional=True,)
+
+multi_groups_unpaired_prop = load(df_prop, idx=(("Control 1","Test 1",),("Control 2", "Test 2", "Test 3"),("Control 3", "Test 4", "Test 5", "Test 6"),),
+                                    proportional=True,)
+
+multi_groups_paired_baseline_prop = load(df_prop, idx=(("Control 1","Test 1",),("Control 2", "Test 2", "Test 3"),("Control 3", "Test 4", "Test 5", "Test 6"),),
+                            paired="baseline", id_col="ID", proportional=True,)
+
+multi_groups_paired_sequential_prop = load(df_prop, idx=(("Control 1","Test 1",),("Control 2", "Test 2", "Test 3"),
+                                                        ("Control 3", "Test 4", "Test 5", "Test 6"),), paired="sequential",
+                                                        id_col="ID", proportional=True,)
+
+zero_to_zero_prop = load(df_prop, idx=("Test 7", "Test 9"), proportional=True, paired="sequential", id_col="ID")
+zero_to_one_prop = load(df_prop, idx=("Test 7", "Test 8"), proportional=True, paired="sequential", id_col="ID")
+one_to_zero_prop = load(df_prop, idx=("Test 8", "Test 7"), proportional=True, paired="sequential", id_col="ID")
+one_in_separate_control_prop = load(df_prop,idx=((("Control 1", "Test 1"), ("Test 2", "Test 3"), ("Test 4", "Test 8", "Test 6"))),
+                                proportional=True, paired="sequential", id_col="ID",)
+
+
+
+# delta-delta
+delta_delta_unpaired = load(data = df_delta, x = ["Genotype", "Genotype"], y = "Y", delta2 = True, experiment = "Treatment")
+delta_delta_unpaired_specified = load(data = df_delta, x = ["Genotype", "Genotype"], y = "Y", 
+                          delta2 = True, experiment = "Treatment",
+                                        experiment_label = ["Drug", "Placebo"],
+                                        x1_level = ["M", "W"])
+delta_delta_paired_baseline = load(data = df_delta, x = ["Treatment", "Rep"], y = "Y", delta2 = True, experiment = "Genotype", paired="baseline", id_col="ID")
+delta_delta_paired_sequential = load(data = df_delta, x = ["Treatment", "Rep"], y = "Y", delta2 = True, experiment = "Genotype", paired="sequential", id_col="ID")
+
+# mini_meta
+mini_meta_unpaired = load(df, idx=(("Control 1", "Test 1"), ("Control 2", "Test 2"), ("Control 3", "Test 3")), mini_meta=True)
+mini_meta_paired_baseline = load(df, id_col = "ID", idx=(("Control 1", "Test 1"), ("Control 2", "Test 2"), ("Control 3", "Test 3")), 
+                                 paired = "baseline", mini_meta=True)
+mini_meta_paired_sequential = load(df, id_col = "ID", idx=(("Control 1", "Test 1"), ("Control 2", "Test 2"), ("Control 3", "Test 3")), 
+                                   paired = "sequential", mini_meta=True)
+
+
+# Tests
+# Two Group
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_300_2group_unpaired_meandiff():
+    plt.rcdefaults()
+    return two_groups_unpaired.mean_diff.plot(horizontal=True)
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_301_2group_unpaired_mediandiff():
+    plt.rcdefaults()
+    return two_groups_unpaired.median_diff.plot(horizontal=True)
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_302_2group_unpaired_hedges_g():
+    plt.rcdefaults()
+    return two_groups_unpaired.hedges_g.plot(horizontal=True)
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_303_2group_paired_meandiff():
+    plt.rcdefaults()
+    return two_groups_paired.mean_diff.plot(horizontal=True)
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_304_2group_paired_hedges_g():
+    plt.rcdefaults()
+    return two_groups_paired.hedges_g.plot(horizontal=True)
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_305_2group_cummings_unpaired_meandiff():
+    plt.rcdefaults()
+    return two_groups_unpaired.mean_diff.plot(horizontal=True, fig_size=(6, 4), float_contrast=False)
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_306_2group_cummings_paired_meandiff():
+    plt.rcdefaults()
+    return two_groups_paired.mean_diff.plot(horizontal=True, float_contrast=False)
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_307_multi2group_unpaired():
+    plt.rcdefaults()
+    return multi_2group_unpaired.mean_diff.plot(horizontal=True)
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_308_multi2group_paired():
+    plt.rcdefaults()
+    return multi_2group_paired.mean_diff.plot(horizontal=True)
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_309_sharedcontrol():
+    plt.rcdefaults()
+    return shared_control.mean_diff.plot(horizontal=True)
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_310_repeatedmeasure():
+    plt.rcdefaults()
+    return repeated_measures.mean_diff.plot(horizontal=True)
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_311_multigroups_unpaired():
+    plt.rcdefaults()
+    return multi_groups_unpaired.mean_diff.plot(horizontal=True)
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_312_multigroups_paired_baseline():
+    plt.rcdefaults()
+    return multi_groups_paired_baseline.mean_diff.plot(horizontal=True)
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_313_multigroups_paired_sequential():
+    plt.rcdefaults()
+    return multi_groups_paired_sequential.mean_diff.plot(horizontal=True)
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_314_2group_unpaired_ylabel():
+    plt.rcdefaults()
+    return two_groups_unpaired.mean_diff.plot(horizontal=True,
+        raw_label="This is my\nrawdata", contrast_label="The bootstrap\ndistribtions!"
+    )
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_315_multi2group_color():
+    plt.rcdefaults()
+    return multi_2group_unpaired.mean_diff.plot(horizontal=True, color_col="Gender")
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_316_2group_paired_color():
+    plt.rcdefaults()
+    return two_groups_paired.mean_diff.plot(horizontal=True, color_col="Gender")
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_317_multi2group_unpaired_change_palette_a():
+    plt.rcdefaults()
+    return multi_2group_unpaired.mean_diff.plot(horizontal=True, color_col="Gender", custom_palette="Dark2")
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_318_multi2group_unpaired_change_palette_b():
+    plt.rcdefaults()
+    return multi_2group_unpaired.mean_diff.plot(horizontal=True, custom_palette="Paired")
+
+my_color_palette = {
+    "Control 1": "blue",
+    "Test 1": "purple",
+    "Control 2": "#cb4b16",  # This is a hex string.
+    "Test 2": (0.0, 0.7, 0.2),  # This is a RGB tuple.
+}
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_319_multi2group_unpaired_change_palette_c():
+    plt.rcdefaults()
+    return multi_2group_unpaired.mean_diff.plot(horizontal=True, custom_palette=my_color_palette)
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_320_multi2group_unpaired_desat():
+    plt.rcdefaults()
+    return multi_2group_unpaired.mean_diff.plot(horizontal=True, 
+        custom_palette=my_color_palette, raw_desat=0.75, contrast_desat=0.25
+    )
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_321_multi2group_unpaired_dot_sizes():
+    plt.rcdefaults()
+    return multi_2group_unpaired.mean_diff.plot(horizontal=True, raw_marker_size=3, contrast_marker_size=12)
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_322_multi2group_unpaired_change_ylims():
+    plt.rcdefaults()
+    return multi_2group_unpaired.mean_diff.plot(horizontal=True, raw_ylim=(0, 5), contrast_ylim=(-2, 2))
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_323_2group_unpaired_ticker():
+    plt.rcdefaults()
+    f = two_groups_unpaired.mean_diff.plot(horizontal=True)
+
+    rawswarm_axes = f.axes[0]
+    contrast_axes = f.axes[1]
+
+    rawswarm_axes.xaxis.set_major_locator(Ticker.MultipleLocator(1))
+    rawswarm_axes.xaxis.set_minor_locator(Ticker.MultipleLocator(0.5))
+
+    contrast_axes.xaxis.set_major_locator(Ticker.MultipleLocator(0.5))
+    contrast_axes.xaxis.set_minor_locator(Ticker.MultipleLocator(0.25))
+
+    return f
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_324_multi2group_unpaired_ticker():
+    plt.rcdefaults()
+    f = multi_2group_unpaired.mean_diff.plot(horizontal=True, raw_ylim=(0, 6), contrast_ylim=(-3, 1))
+
+    rawswarm_axes = f.axes[0]
+    contrast_axes = f.axes[1]
+
+    rawswarm_axes.xaxis.set_major_locator(Ticker.MultipleLocator(2))
+    rawswarm_axes.xaxis.set_minor_locator(Ticker.MultipleLocator(1))
+
+    contrast_axes.xaxis.set_major_locator(Ticker.MultipleLocator(0.5))
+    contrast_axes.xaxis.set_minor_locator(Ticker.MultipleLocator(0.25))
+
+    return f
+
+np.random.seed(9999)
+Ns = [20, 10, 21, 20]
+c1 = pd.DataFrame({"Control": norm.rvs(loc=3, scale=0.4, size=Ns[0])})
+t1 = pd.DataFrame({"Test 1": norm.rvs(loc=3.5, scale=0.5, size=Ns[1])})
+t2 = pd.DataFrame({"Test 2": norm.rvs(loc=2.5, scale=0.6, size=Ns[2])})
+t3 = pd.DataFrame({"Test 3": norm.rvs(loc=3, scale=0.75, size=Ns[3])})
+wide_df = pd.concat([c1, t1, t2, t3], axis=1)
+
+long_df = pd.melt(
+    wide_df,
+    value_vars=["Control", "Test 1", "Test 2", "Test 3"],
+    value_name="value",
+    var_name="group",
+)
+long_df["dummy"] = np.repeat(np.nan, len(long_df))
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_325_wide_df_nan():
+    plt.rcdefaults()
+    wide_df_dabest = load(wide_df, idx=("Control", "Test 1", "Test 2", "Test 3"))
+
+    return wide_df_dabest.mean_diff.plot(horizontal=True)
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_326_long_df_nan():
+    plt.rcdefaults()
+    long_df_dabest = load(
+        long_df, x="group", y="value", idx=("Control", "Test 1", "Test 2", "Test 3")
+    )
+
+    return long_df_dabest.mean_diff.plot(horizontal=True)
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_327_2group_paired_slopegraph_kwargs():
+    plt.rcdefaults()
+    return two_groups_paired.mean_diff.plot(horizontal=True, slopegraph_kwargs=dict(linestyle="dotted"))
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_328_2group_unpaired_reflines_kwargs():
+    plt.rcdefaults()
+    return two_groups_unpaired.mean_diff.plot(horizontal=True, reflines_kwargs=dict(linestyle="dotted"))
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_329_2group_unpaired_cumming_reflines_kwargs():
+    plt.rcdefaults()
+    return two_groups_unpaired.mean_diff.plot(horizontal=True,
+        fig_size=(12, 10),
+        float_contrast=False,
+        reflines_kwargs=dict(linestyle="dotted", linewidth=2),
+        contrast_ylim=(-1, 1),
+    )
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_330_2group_paired_cumming_slopegraph_reflines_kwargs():
+    plt.rcdefaults()
+    return two_groups_paired.mean_diff.plot(horizontal=True,
+        float_contrast=False,
+        color_col="Gender",
+        slopegraph_kwargs=dict(linestyle="dotted"),
+        reflines_kwargs=dict(linestyle="dashed", linewidth=2),
+        contrast_ylim=(-1, 1),
+    )
+
+
+# Proportion plots
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_331_2group_unpaired_propdiff():
+    plt.rcdefaults()
+    return two_groups_unpaired_prop.mean_diff.plot(horizontal=True)
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_332_2group_unpaired_cummings_propdiff():
+    plt.rcdefaults()
+    return two_groups_unpaired_prop.mean_diff.plot(horizontal=True, fig_size=(6,4), float_contrast=False)
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_333_multi2group_unpaired_propdiff():
+    plt.rcdefaults()
+    return multi_2group_unpaired_prop.mean_diff.plot(horizontal=True)
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_334_shared_control_propdiff():
+    plt.rcdefaults()
+    return shared_control_prop.mean_diff.plot(horizontal=True)
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_335_repeated_measures_baseline_propdiff():
+    plt.rcdefaults()
+    return repeated_measures_baseline_prop.mean_diff.plot(horizontal=True)
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_336_repeated_measures_sequential_propdiff():
+    plt.rcdefaults()
+    return repeated_measures_sequential_prop.mean_diff.plot(horizontal=True)
+
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_337_multi_groups_unpaired_propdiff():
+    plt.rcdefaults()
+    return multi_groups_unpaired_prop.mean_diff.plot(horizontal=True)
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_338_multi_groups_paired_baseline_propdiff():
+    plt.rcdefaults()
+    return multi_groups_paired_baseline_prop.mean_diff.plot(horizontal=True)
+
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_339_multi_groups_paired_sequential_propdiff():
+    plt.rcdefaults()
+    return multi_groups_paired_sequential_prop.mean_diff.plot(horizontal=True)
+
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_340_2group_unpaired_prop_change_fig_size_and_palette_a():
+    plt.rcdefaults()
+    return two_groups_unpaired_prop.mean_diff.plot(horizontal=True, fig_size=(6, 6), custom_palette="Dark2")
+
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_341_multi2group_unpaired_prop_change_palette_b():
+    plt.rcdefaults()
+    return multi_2group_unpaired_prop.mean_diff.plot(horizontal=True, custom_palette="Paired")
+
+my_color_palette = {
+    "Control 1": "blue",
+    "Test 1": "purple",
+    "Control 2": "#cb4b16",  # This is a hex string.
+    "Test 2": (0.0, 0.7, 0.2),  # This is a RGB tuple.
+}
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_342_multi2group_unpaired_prop_change_palette_c():
+    plt.rcdefaults()
+    return multi_2group_unpaired_prop.mean_diff.plot(horizontal=True, custom_palette=my_color_palette)
+
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_343_multi2group_unpaired_prop_desat():
+    plt.rcdefaults()
+    return multi_2group_unpaired_prop.mean_diff.plot(horizontal=True,
+        custom_palette=my_color_palette, raw_desat=0.1, contrast_desat=0.25
+    )
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_344_2group_unpaired_prop_err_color():
+    plt.rcdefaults()
+    return two_groups_unpaired_prop.mean_diff.plot(horizontal=True, barplot_kwargs={"err_kws": {"color": "purple"}})
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_345_2group_unpaired_cummings_meandiff_bar_width():
+    plt.rcdefaults()
+    return two_groups_unpaired_prop.mean_diff.plot(horizontal=True, bar_width=0.4, float_contrast=False)
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_346_2group_unpaired_prop_cohens_h():
+    plt.rcdefaults()
+    return two_groups_unpaired_prop.cohens_h.plot(horizontal=True)
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_347_2group_unpaired_prop_cummings_cohens_h():
+    plt.rcdefaults()
+    return two_groups_unpaired_prop.cohens_h.plot(horizontal=True, float_contrast=False)
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_348_2group_sankey():
+    plt.rcdefaults()
+    return two_groups_paired_baseline_prop.mean_diff.plot(horizontal=True)
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_349_2group_sankey_cummings():
+    plt.rcdefaults()
+    return two_groups_paired_baseline_prop.mean_diff.plot(horizontal=True, float_contrast=False)
+
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_350_multi2group_sankey_baseline():
+    plt.rcdefaults()
+    return multi_2group_paired_baseline_prop.mean_diff.plot(horizontal=True)
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_351_multi2group_sankey_sequential():
+    plt.rcdefaults()
+    return multi_2group_paired_sequential_prop.mean_diff.plot(horizontal=True)
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_352_multigroups_sankey_baseline():
+    plt.rcdefaults()
+    return multi_groups_paired_baseline_prop.mean_diff.plot(horizontal=True)
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_353_multigroups_sankey_sequential():
+    plt.rcdefaults()
+    return multi_groups_paired_sequential_prop.mean_diff.plot(horizontal=True)
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_354_2group_sankey_transparency():
+    plt.rcdefaults()
+    return two_groups_paired_baseline_prop.mean_diff.plot(horizontal=True, sankey_kwargs={"alpha": 0.2})
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_355_zero_to_zero():
+    plt.rcdefaults()
+    return zero_to_zero_prop.mean_diff.plot(horizontal=True)
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_356_zero_to_one_prop():
+    plt.rcdefaults()
+    return zero_to_one_prop.mean_diff.plot(horizontal=True)
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_357_one_to_zero():
+    plt.rcdefaults()
+    return one_to_zero_prop.mean_diff.plot(horizontal=True)
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_358_repeated_measures_baseline_sankey_off():
+    plt.rcdefaults()
+    return repeated_measures_baseline_prop.mean_diff.plot(horizontal=True, sankey_kwargs={"sankey": False})
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_359_repeated_measures_baseline_flow_off():
+    plt.rcdefaults()
+    return repeated_measures_baseline_prop.mean_diff.plot(horizontal=True, sankey_kwargs={"flow": False})
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_360_multigroups_paired_sequential_sankey_off():
+    plt.rcdefaults()
+    return multi_groups_paired_sequential_prop.mean_diff.plot(horizontal=True, sankey_kwargs={"sankey": False})
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_361_multigroups_paired_sequential_flow_off():
+    plt.rcdefaults()
+    return multi_groups_paired_sequential_prop.mean_diff.plot(horizontal=True, sankey_kwargs={"flow": False})
+
+
+
+# delta-delta
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_362_cummings_unpaired_delta_delta_meandiff():
+    plt.rcdefaults()
+    return delta_delta_unpaired.mean_diff.plot(horizontal=True);
+
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_363_cummings_sequential_delta_delta_meandiff():
+    plt.rcdefaults()
+    return delta_delta_paired_sequential.mean_diff.plot(horizontal=True);
+
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_364_cummings_baseline_delta_delta_meandiff():
+    plt.rcdefaults()
+    return delta_delta_paired_baseline.mean_diff.plot(horizontal=True);
+
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_365_delta_plot_ylabel():
+    plt.rcdefaults()
+    return delta_delta_paired_baseline.mean_diff.plot(horizontal=True,
+                                                    raw_label="This is my\nrawdata",
+                                                    contrast_label="The bootstrap\ndistribtions!", 
+                                                    delta2_label="This is delta!");
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_366_delta_plot_change_palette_a():
+    plt.rcdefaults()
+    return delta_delta_paired_sequential.mean_diff.plot(horizontal=True, custom_palette="Dark2");
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_367_delta_specified():
+    plt.rcdefaults()
+    return delta_delta_unpaired_specified.mean_diff.plot(horizontal=True);
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_368_delta_change_ylims():
+    plt.rcdefaults()
+    return delta_delta_paired_sequential.mean_diff.plot(horizontal=True, raw_ylim=(0, 9),
+                                                        contrast_ylim=(-2, 2),
+                                                        fig_size=(15,6));
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_369_delta_invert_ylim():
+    plt.rcdefaults()
+    return delta_delta_paired_sequential.mean_diff.plot(horizontal=True, 
+                                                        contrast_ylim=(2, -2),
+                                                        contrast_label="More negative is better!");
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_370_delta_median_diff():
+    plt.rcdefaults()
+    return delta_delta_paired_sequential.median_diff.plot(horizontal=True);
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_371_delta_cohens_d():
+    plt.rcdefaults()
+    return delta_delta_unpaired.cohens_d.plot(horizontal=True);
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_372_delta_show_delta2():
+    plt.rcdefaults()
+    return delta_delta_unpaired.mean_diff.plot(horizontal=True, show_delta2=False);
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_373_delta_axes_invert_ylim():
+    plt.rcdefaults()
+    return delta_delta_unpaired.mean_diff.plot(horizontal=True, delta2_ylim=(2, -2),
+                                   delta2_label="More negative is better!");
+                
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_374_delta_axes_invert_ylim_not_showing_delta2():
+    plt.rcdefaults()
+    return delta_delta_unpaired.mean_diff.plot(horizontal=True, delta2_ylim=(2, -2),
+                                                delta2_label="More negative is better!",
+                                                show_delta2=False);
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_375_unpaired_delta_g():
+    plt.rcdefaults()
+    return delta_delta_unpaired.hedges_g.plot(horizontal=True);
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_376_sequential_delta_g():
+    plt.rcdefaults()
+    return delta_delta_paired_sequential.hedges_g.plot(horizontal=True);
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_377_baseline_delta_g():
+    plt.rcdefaults()
+    return delta_delta_paired_baseline.hedges_g.plot(horizontal=True);
+
+
+# mini_meta
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_378_cummings_unpaired_mini_meta_meandiff():
+    plt.rcdefaults()
+    return mini_meta_unpaired.mean_diff.plot(horizontal=True);
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_379_cummings_sequential_mini_meta_meandiff():
+    plt.rcdefaults()
+    return mini_meta_paired_sequential.mean_diff.plot(horizontal=True);
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_380_cummings_baseline_mini_meta_meandiff():
+    plt.rcdefaults()
+    return mini_meta_paired_baseline.mean_diff.plot(horizontal=True);
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_381_mini_meta_plot_ylabel():
+    plt.rcdefaults()
+    return mini_meta_paired_baseline.mean_diff.plot(horizontal=True, raw_label="This is my\nrawdata",
+                                                    contrast_label="The bootstrap\ndistribtions!");
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_382_mini_meta_plot_change_palette_a():
+    plt.rcdefaults()
+    return mini_meta_unpaired.mean_diff.plot(horizontal=True, custom_palette="Dark2");
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_383_mini_meta_dot_sizes():
+    plt.rcdefaults()
+    return mini_meta_paired_sequential.mean_diff.plot(horizontal=True, show_pairs=False,raw_marker_size=3,
+                                                    contrast_marker_size=12);
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_384_mini_meta_change_ylims():
+    plt.rcdefaults()
+    return mini_meta_paired_sequential.mean_diff.plot(horizontal=True, raw_ylim=(0, 5),
+                                                        contrast_ylim=(-2, 2),
+                                                        fig_size=(15,6));
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_385_mini_meta_invert_ylim():
+    plt.rcdefaults()
+    return mini_meta_paired_sequential.mean_diff.plot(horizontal=True, contrast_ylim=(2, -2),
+                                                        contrast_label="More negative is better!");
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_386_mini_meta_median_diff():
+    plt.rcdefaults()
+    return mini_meta_paired_sequential.median_diff.plot(horizontal=True);
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_387_mini_meta_cohens_d():
+    plt.rcdefaults()
+    return mini_meta_unpaired.cohens_d.plot(horizontal=True);
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_388_mini_meta_not_show():
+    plt.rcdefaults()
+    return mini_meta_unpaired.mean_diff.plot(horizontal=True, show_mini_meta=False);
+
+
+# Aesthetic kwargs
+# Swarm_Side
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_389_Swarm_Side_Center():
+    plt.rcdefaults()
+    return two_groups_unpaired.mean_diff.plot(horizontal=True, swarm_side='center');
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_390_Swarm_Side_Right():
+    plt.rcdefaults()
+    return two_groups_unpaired.mean_diff.plot(horizontal=True, swarm_side='right');
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_391_Swarm_Side_Left():
+    plt.rcdefaults()
+    return two_groups_unpaired.mean_diff.plot(horizontal=True, swarm_side='left');
+
+# Empty Circle
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_392_Empty_Circle():
+    plt.rcdefaults()
+    return multi_2group_unpaired.mean_diff.plot(horizontal=True, empty_circle=True);
+
+# Table kwargs
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_393_Horizontal_Table_Kwargs():
+    plt.rcdefaults()
+    return multi_2group_unpaired.mean_diff.plot(horizontal=True, horizontal_table_kwargs={'color': 'red', 'alpha': 0.5, 'text_color': 'white',
+                                                                       'text_units':'mm', 'label': 'delta mm', 'control_marker': 'o',});
+
+# Gridkey
+# Two Group
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_394_2group_unpaired_meandiff_gridkey_autoparser():
+    plt.rcdefaults()
+    return two_groups_unpaired.mean_diff.plot(horizontal=True, gridkey='auto');
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_395_2group_paired_meandiff_gridkey_autoparser():
+    plt.rcdefaults()
+    return two_groups_paired.mean_diff.plot(horizontal=True, gridkey='auto');
+
+# Multi 2 Group
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_396_multi_2group_unpaired_meandiff_gridkey_userdefinedrows():
+    plt.rcdefaults()
+    return multi_groups_unpaired.mean_diff.plot(horizontal=True, gridkey=['Control', 'Test']);
+
+# Shared Control and Repeated Measures
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_397_shared_control_meandiff_gridkey_autoparser():
+    plt.rcdefaults()
+    return shared_control.mean_diff.plot(horizontal=True, gridkey='auto');
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_398_repeated_measures_meandiff_gridkey_autoparser():
+    plt.rcdefaults()
+    return repeated_measures.mean_diff.plot(horizontal=True, gridkey='auto');
+
+
+# Multi groups
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_399_multigroups_unpaired_meandiff_gridkey_userdefinedrows():
+    plt.rcdefaults()
+    return multi_groups_unpaired.mean_diff.plot(horizontal=True, gridkey=['Control', 'Test']);
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_400_multigroups_unpaired_meandiff_gridkey_autoparser():
+    plt.rcdefaults()
+    return multi_groups_unpaired.mean_diff.plot(horizontal=True, gridkey='auto');
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_401_multigroups_paired_meandiff_gridkey_userdefinedrows():
+    plt.rcdefaults()
+    return multi_groups_paired_baseline.mean_diff.plot(horizontal=True, gridkey=['Control', 'Test']);
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_402_multigroups_paired_meandiff_gridkey_autoparser():
+    plt.rcdefaults()
+    return multi_groups_paired_baseline.mean_diff.plot(horizontal=True, gridkey='auto');
+
+# Proportions
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_403_multigroups_prop_unpaired_meandiff_gridkey_userdefinedrows():
+    plt.rcdefaults()
+    return multi_groups_unpaired_prop.mean_diff.plot(horizontal=True, gridkey=['Control', 'Test']);
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_404_multigroups_prop_unpaired_meandiff_gridkey_autoparser():
+    plt.rcdefaults()
+    return multi_groups_unpaired_prop.mean_diff.plot(horizontal=True, gridkey='auto');
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_405_multigroups_prop_paired_meandiff_gridkey_userdefinedrows():
+    plt.rcdefaults()
+    return multi_groups_paired_baseline_prop.mean_diff.plot(horizontal=True, gridkey=['Control', 'Test']);
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_406_multigroups_prop_paired_meandiff_gridkey_autoparser():
+    plt.rcdefaults()
+    return multi_groups_paired_baseline_prop.mean_diff.plot(horizontal=True, gridkey='auto');
+
+
+# delta-delta
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_407_delta_delta_unpaired_meandiff_gridkey_autoparser():
+    plt.rcdefaults()
+    return delta_delta_unpaired.mean_diff.plot(horizontal=True, gridkey='auto');
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_408_delta_delta_paired_meandiff_gridkey_autoparser():
+    plt.rcdefaults()
+    return delta_delta_paired_baseline.mean_diff.plot(horizontal=True, gridkey='auto');
+
+
+# mini-meta
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_409_mini_meta_unpaired_meandiff_gridkey_userdefinedrows():
+    plt.rcdefaults()
+    return mini_meta_unpaired.mean_diff.plot(horizontal=True, gridkey=['Control', 'Test']);
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_410_mini_meta_unpaired_meandiff_gridkey_autoparser():
+    plt.rcdefaults()
+    return mini_meta_unpaired.mean_diff.plot(horizontal=True, gridkey='auto');
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_411_mini_meta_paired_meandiff_gridkey_userdefinedrows():
+    plt.rcdefaults()
+    return mini_meta_paired_baseline.mean_diff.plot(horizontal=True, gridkey=['Control', 'Test']);
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_412_mini_meta_paired_meandiff_gridkey_autoparser():
+    plt.rcdefaults()
+    return mini_meta_paired_baseline.mean_diff.plot(horizontal=True, gridkey='auto');
+
+# Gridkey kwargs
+multi_2group_paired_test = load(df, idx=(("Control 1","Control 2",),("Test 1", "Test 2"),), paired='baseline', id_col='ID')
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_413_gridkey_merge_pairs_and_autoparser():
+    plt.rcdefaults()
+    return multi_2group_paired_test.mean_diff.plot(horizontal=True, gridkey=['Control', 'Test'], gridkey_kwargs={'merge_pairs': True});
+
+gridkey_kwargs = {'show_es': False, 'show_Ns': False, 'marker': '√'}
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_414_gridkey_kwargs_and_autoparser():
+    plt.rcdefaults()
+    return two_groups_unpaired.mean_diff.plot(horizontal=True, gridkey='auto', gridkey_kwargs=gridkey_kwargs);
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_429_gridkey_fontsize_and_autoparser():
+    plt.rcdefaults()
+    return two_groups_unpaired.mean_diff.plot(horizontal=True, gridkey='auto', gridkey_kwargs={'fontsize': 15});
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_430_gridkey_labels_fontsize_and_autoparser():
+    plt.rcdefaults()
+    return two_groups_unpaired.mean_diff.plot(horizontal=True, gridkey='auto',
+                                               gridkey_kwargs={'labels_fontsize': 15});
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_431_gridkey_labels_fontsize_and_fontsize_and_autoparser():
+    plt.rcdefaults()
+    return two_groups_unpaired.mean_diff.plot(horizontal=True, gridkey='auto', 
+                                              gridkey_kwargs={'fontsize': 8, 'labels_fontsize': 15});
+
+# Table hide
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_415_Horizontal_Table_hide():
+    plt.rcdefaults()
+    return multi_2group_unpaired.mean_diff.plot(horizontal=True, horizontal_table_kwargs={'show': False});
+
+# Delta-dots
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_416_delta_dot_hide():
+    plt.rcdefaults()
+    return multi_2group_paired.mean_diff.plot(horizontal=True, delta_dot=False);
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_417_delta_dot_kwargs():
+    plt.rcdefaults()
+    return multi_2group_paired.mean_diff.plot(horizontal=True, delta_dot_kwargs={"color":'red', "alpha":0.1, 'zorder': 2, 'size': 5, 'side': 'left'});
+
+# Contrast bars
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_418_shared_control_meandiff_showcontrastbars():
+    plt.rcdefaults()
+    return shared_control.mean_diff.plot(horizontal=True, contrast_bars=True, raw_bars=False);
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_419_shared_control_meandiff_hidecontrastbars():
+    plt.rcdefaults()
+    return shared_control.mean_diff.plot(horizontal=True, contrast_bars=False, raw_bars=False);
+
+contrast_kwargs = {'color': "red", 'alpha': 0.2}
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_420_shared_control_meandiff_contrastbars_kwargs():
+    plt.rcdefaults()
+    return shared_control.mean_diff.plot(horizontal=True, contrast_bars=True, contrast_bars_kwargs = contrast_kwargs, raw_bars=False);
+
+# reference_band
+reference_band=[0, 1]
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_421_shared_control_meandiff_summarybars():
+    plt.rcdefaults()
+    return shared_control.mean_diff.plot(horizontal=True, reference_band=[0, 1], raw_bars=False, contrast_bars=False,);
+
+reference_band_kwargs = {'color': "black", 'alpha': 0.2, 'span_ax': True}
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_422_shared_control_meandiff_summarybars_kwargs():
+    plt.rcdefaults()
+    return shared_control.mean_diff.plot(horizontal=True, reference_band=[0, 1], reference_band_kwargs = reference_band_kwargs,
+                                         contrast_bars=False, raw_bars=False);
+
+# Add counts to prop plots
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_423_shared_control_propdiff_show_counts():
+    plt.rcdefaults()
+    return shared_control_prop.mean_diff.plot(horizontal=True, prop_sample_counts=True,)
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_424_repeated_measures_baseline_propdiff_show_counts():
+    plt.rcdefaults()
+    return repeated_measures_baseline_prop.mean_diff.plot(horizontal=True, prop_sample_counts=True,)
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_425_repeated_measures_baseline_propdiff_show_counts_and_kwargs():
+    plt.rcdefaults()
+    return repeated_measures_baseline_prop.mean_diff.plot(horizontal=True,
+            prop_sample_counts=True, prop_sample_counts_kwargs={"color": "red", "fontsize": 12, "fontweight": "bold"})
+
+# Effect size paired lines
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_426_repeatedmeasures_meandiff_show_es_paired_lines():
+    plt.rcdefaults()
+    return repeated_measures.mean_diff.plot(horizontal=True, contrast_paired_lines=True);
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_427_repeatedmeasures_meandiff_hide_es_paired_lines():
+    plt.rcdefaults()
+    return repeated_measures.mean_diff.plot(horizontal=True, contrast_paired_lines=False);
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_428_multigroups_paired_meandiff_es_paired_lines_kwargs():
+    plt.rcdefaults()
+    return multi_groups_paired_baseline.mean_diff.plot(horizontal=True, contrast_paired_lines=True, contrast_paired_lines_kwargs={'color':'red', 'linestyle': '--', 'linewidth': 2, 'alpha': 0.5});
diff --git a/nbs/tests/mpl_image_tests/test_plot_aesthetics.py b/nbs/tests/mpl_image_tests/test_plot_aesthetics.py
new file mode 100644
index 00000000..b85d85e7
--- /dev/null
+++ b/nbs/tests/mpl_image_tests/test_plot_aesthetics.py
@@ -0,0 +1,347 @@
+import pytest
+import numpy as np
+import pandas as pd
+from scipy.stats import norm
+
+
+import matplotlib as mpl
+
+mpl.use("Agg")
+import matplotlib.pyplot as plt
+import matplotlib.ticker as Ticker
+import seaborn as sns
+
+from dabest._api import load
+
+
+def create_demo_dataset(seed=9999, N=20):
+    import numpy as np
+    import pandas as pd
+    from scipy.stats import norm  # Used in generation of populations.
+
+    np.random.seed(9999)  # Fix the seed so the results are replicable.
+    # pop_size = 10000 # Size of each population.
+
+    # Create samples
+    c1 = norm.rvs(loc=3, scale=0.4, size=N)
+    c2 = norm.rvs(loc=3.5, scale=0.75, size=N)
+    c3 = norm.rvs(loc=3.25, scale=0.4, size=N)
+
+    t1 = norm.rvs(loc=3.5, scale=0.5, size=N)
+    t2 = norm.rvs(loc=2.5, scale=0.6, size=N)
+    t3 = norm.rvs(loc=3, scale=0.75, size=N)
+    t4 = norm.rvs(loc=3.5, scale=0.75, size=N)
+    t5 = norm.rvs(loc=3.25, scale=0.4, size=N)
+    t6 = norm.rvs(loc=3.25, scale=0.4, size=N)
+
+    # Add a `gender` column for coloring the data.
+    females = np.repeat("Female", N / 2).tolist()
+    males = np.repeat("Male", N / 2).tolist()
+    gender = females + males
+
+    # Add an `id` column for paired data plotting.
+    id_col = pd.Series(range(1, N + 1))
+
+    # Combine samples and gender into a DataFrame.
+    df = pd.DataFrame(
+        {
+            "Control 1": c1,
+            "Test 1": t1,
+            "Control 2": c2,
+            "Test 2": t2,
+            "Control 3": c3,
+            "Test 3": t3,
+            "Test 4": t4,
+            "Test 5": t5,
+            "Test 6": t6,
+            "Gender": gender,
+            "ID": id_col,
+        }
+    )
+
+    return df
+
+
+def create_demo_dataset_delta(seed=9999, N=20):
+
+    import numpy as np
+    import pandas as pd
+    from scipy.stats import norm  # Used in generation of populations.
+
+    np.random.seed(seed)  # Fix the seed so the results are replicable.
+    # pop_size = 10000 # Size of each population.
+
+    from scipy.stats import norm  # Used in generation of populations.
+
+    # Create samples
+    y = norm.rvs(loc=3, scale=0.4, size=N * 4)
+    y[N : 2 * N] = y[N : 2 * N] + 1
+    y[2 * N : 3 * N] = y[2 * N : 3 * N] - 0.5
+
+    # Add drug column
+    t1 = np.repeat("Placebo", N * 2).tolist()
+    t2 = np.repeat("Drug", N * 2).tolist()
+    treatment = t1 + t2
+
+    # Add a `rep` column as the first variable for the 2 replicates of experiments done
+    rep = []
+    for i in range(N * 2):
+        rep.append("Rep1")
+        rep.append("Rep2")
+
+    # Add a `genotype` column as the second variable
+    wt = np.repeat("W", N).tolist()
+    mt = np.repeat("M", N).tolist()
+    wt2 = np.repeat("W", N).tolist()
+    mt2 = np.repeat("M", N).tolist()
+
+    genotype = wt + mt + wt2 + mt2
+
+    # Add an `id` column for paired data plotting.
+    id = list(range(0, N * 2))
+    id_col = id + id
+
+    # Combine all columns into a DataFrame.
+    df = pd.DataFrame(
+        {"ID": id_col, "Rep": rep, "Genotype": genotype, "Treatment": treatment, "Y": y}
+    )
+    return df
+
+
+df = create_demo_dataset()
+df_delta = create_demo_dataset_delta()
+
+two_groups_unpaired = load(df, idx=("Control 1", "Test 1"))
+
+multi_2group = load(df, idx=(("Control 1", "Test 1",), ("Control 2", "Test 2"),),)
+
+multi_2group_paired = load(df, idx=(("Control 1", "Test 1"),
+                                         ("Control 2", "Test 2")),paired='baseline', id_col='ID')
+
+shared_control = load(df, idx=("Control 1", "Test 1", "Test 2", "Test 3", "Test 4", "Test 5", "Test 6"))
+
+repeated_measures = load(df, idx=("Control 1", "Test 1", "Test 2", "Test 3", "Test 4", "Test 5", "Test 6"), paired='baseline', id_col='ID')
+
+multi_groups_paired_baseline = load(df,idx=(("Control 1","Test 1", "Test 2"),("Control 2", "Test 3"),("Control 3", "Test 4", "Test 5", "Test 6"),),
+                                    paired='baseline', id_col='ID')
+
+multi_groups = load(df, idx=(("Control 1", "Test 1",), ("Control 2", "Test 2", "Test 3"),
+                              ("Control 3", "Test 4", "Test 5", "Test 6"),),)
+
+unpaired_delta_delta = load(data=df_delta, x=["Genotype", "Genotype"], y="Y", delta2=True, experiment="Treatment")
+
+unpaired_mini_meta = load(df, idx=(("Control 1", "Test 1"), ("Control 2", "Test 2"), ("Control 3", "Test 3")), mini_meta=True)
+
+multi_groups_change_idx_original = load(df,
+    idx=(
+        ("Control 1", "Test 1", "Test 2"),
+        ("Control 2", "Test 3", "Test 4"),
+        ("Control 3", "Test 5", "Test 6"),
+    ),
+)
+multi_groups_change_idx_new = load(
+    df,
+    idx=(
+        ("Control 1", "Control 2", "Control 3"),
+        ("Test 1", "Test 3", "Test 5"),
+        ("Test 2", "Test 4", "Test 6"),
+    ),
+)
+palette = {"Control 1": sns.color_palette("magma")[5],
+           "Test 1": sns.color_palette("magma")[3],
+           "Test 2": sns.color_palette("magma")[1],
+           "Control 2": sns.color_palette("magma")[5],
+           "Test 3": sns.color_palette("magma")[3],
+           "Test 4": sns.color_palette("magma")[1],
+           "Control 3": sns.color_palette("magma")[5],
+           "Test 5": sns.color_palette("magma")[3],
+           "Test 6": sns.color_palette("magma")[1]}
+
+# Jitter tests
+np.random.seed(9999) # Fix the seed to ensure reproducibility of results.
+Ns = 20 # The number of samples taken from each population
+# Create samples
+c1 = [0.5]*Ns + [1.5]*Ns
+c2 = [2]*Ns + [1]*Ns
+t1 = [1]*Ns + [2]*Ns
+t2 = [1.5]*Ns + [2.5]*Ns
+t3 = [2]*Ns + [1]*Ns
+t4 = [1]*Ns + [2]*Ns
+t5 = [1.5]*Ns + [2.5]*Ns
+id_col = pd.Series(range(1, 2*Ns+1))
+df_jittertest= pd.DataFrame({'Control 1' : c1,     'Test 1' : t1,
+                 'Control 2' : c2,     'Test 2' : t2, 'Test 3' : t3,
+                    'Test 4'    : t4,     'Test 5' : t5, 'ID'  : id_col})
+multi_2group_jitter = load(df, idx=(("Control 1","Test 1",), ("Control 2", "Test 2"), ), paired='baseline', id_col = 'ID')
+
+
+# Tests
+
+# Empty circle
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_207_gardner_altman_meandiff_empty_circle():
+    plt.rcdefaults()
+    return two_groups_unpaired.mean_diff.plot(empty_circle=True);
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_208_cummings_two_group_unpaired_meandiff_empty_circle():
+    plt.rcdefaults()
+    return two_groups_unpaired.mean_diff.plot(empty_circle=True, float_contrast=False);
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_209_cummings_shared_control_meandiff_empty_circle():
+    plt.rcdefaults()
+    return shared_control.mean_diff.plot(empty_circle=True);
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_210_cummings_multi_groups_meandiff_empty_circle():
+    plt.rcdefaults()
+    return multi_groups.mean_diff.plot(empty_circle=True);
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_211_cummings_multi_2_group_meandiff_empty_circle():
+    plt.rcdefaults()
+    return multi_2group.mean_diff.plot(empty_circle=True);
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_212_cummings_unpaired_delta_delta_meandiff_empty_circle():
+    plt.rcdefaults()
+    return unpaired_delta_delta.mean_diff.plot(empty_circle=True);
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_213_cummings_unpaired_mini_meta_meandiff_empty_circle():
+    plt.rcdefaults()
+    return unpaired_mini_meta.mean_diff.plot(empty_circle=True);
+
+
+# Change palette 
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_214_change_idx_order_custom_palette_original():
+    plt.rcdefaults()
+    return multi_groups_change_idx_original.mean_diff.plot(custom_palette=palette);
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_215_change_idx_order_custom_palette_new():
+    plt.rcdefaults()
+    return multi_groups_change_idx_new.mean_diff.plot(custom_palette=palette);
+
+# Swarm bars
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_216_cummings_shared_control_meandiff_showswarmbars():
+    plt.rcdefaults()
+    return shared_control.mean_diff.plot(raw_bars=True, contrast_bars=False);
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_217_cummings_shared_control_meandiff_hideswarmbars():
+    plt.rcdefaults()
+    return shared_control.mean_diff.plot(raw_bars=False, contrast_bars=False);
+
+raw_kwargs = {'color': "red", 'alpha': 0.2}
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_218_cummings_shared_control_meandiff_swarmbars_kwargs():
+    plt.rcdefaults()
+    return shared_control.mean_diff.plot(raw_bars=True, raw_bars_kwargs = raw_kwargs, contrast_bars=False);
+
+
+# Contrast bars
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_219_cummings_shared_control_meandiff_showcontrastbars():
+    plt.rcdefaults()
+    return shared_control.mean_diff.plot(contrast_bars=True,raw_bars=False);
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_220_cummings_shared_control_meandiff_hidecontrastbars():
+    plt.rcdefaults()
+    return shared_control.mean_diff.plot(contrast_bars=False, raw_bars=False);
+
+contrast_kwargs = {'color': "red", 'alpha': 0.2}
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_221_cummings_shared_control_meandiff_contrastbars_kwargs():
+    plt.rcdefaults()
+    return shared_control.mean_diff.plot(contrast_bars=True, contrast_bars_kwargs = contrast_kwargs, raw_bars=False);
+
+
+#  reference_band
+reference_band=[0, 1]
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_222_cummings_shared_control_meandiff_summarybars():
+    plt.rcdefaults()
+    return shared_control.mean_diff.plot(reference_band=[0, 1], raw_bars=False, contrast_bars=False,);
+
+reference_band_kwargs = {'color': "black", 'alpha': 0.2, 'span_ax': True}
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_223_cummings_shared_control_meandiff_summarybars_kwargs():
+    plt.rcdefaults()
+    return shared_control.mean_diff.plot(reference_band=[0, 1], reference_band_kwargs = reference_band_kwargs,
+                                         contrast_bars=False, raw_bars=False);
+
+
+# Delta text
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_224_multi_2group_meandiff_showdeltatext():
+    plt.rcdefaults()
+    return multi_2group.mean_diff.plot(delta_text=True);
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_225_multi_2group_meandiff_hidedeltatext():
+    plt.rcdefaults()
+    return multi_2group.mean_diff.plot(delta_text=False);
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_226_multi_2group_meandiff_deltatext_kwargs():
+    plt.rcdefaults()
+    return multi_2group.mean_diff.plot(delta_text=True, delta_text_kwargs={"color":"red", "rotation":45, "va":"bottom", "alpha":0.7});
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_227_multi_2group_meandiff_deltatext_kwargs_specificy_coordinates():
+    plt.rcdefaults()
+    return multi_2group.mean_diff.plot(delta_text=True, delta_text_kwargs={"x_coordinates":(0.5, 2.75), "y_coordinates":(0.5, -1.7)});
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_228_multi_2group_meandiff_deltatext_kwargs_x_adjust():
+    plt.rcdefaults()
+    return multi_2group.mean_diff.plot(delta_text=True, delta_text_kwargs={"offset":0.1});
+
+# Jitter
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_229_samevalues_jitter():
+    plt.rcdefaults()
+    return multi_2group_jitter.mean_diff.plot(slopegraph_kwargs={'jitter': 1});
+
+# Delta-dots
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_230_delta_dot_hide():
+    plt.rcdefaults()
+    return multi_2group_paired.mean_diff.plot(delta_dot=False);
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_231_delta_dot_kwargs():
+    plt.rcdefaults()
+    return multi_2group_paired.mean_diff.plot(delta_dot_kwargs={"color":'red', "alpha":0.1, 'zorder': 2, 'size': 5, 'side': 'left'});
+
+# Effect size paired lines
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_232_repeatedmeasures_meandiff_show_es_paired_lines():
+    plt.rcdefaults()
+    return repeated_measures.mean_diff.plot(contrast_paired_lines=True);
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_233_repeatedmeasures_meandiff_hide_es_paired_lines():
+    plt.rcdefaults()
+    return repeated_measures.mean_diff.plot(contrast_paired_lines=False);
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_234_multigroups_paired_meandiff_es_paired_lines_kwargs():
+    plt.rcdefaults()
+    return multi_groups_paired_baseline.mean_diff.plot(contrast_paired_lines=True, contrast_paired_lines_kwargs={'color':'red', 'linestyle': '--', 'linewidth': 2, 'alpha': 0.5});
+
+# Baseline Error Curve
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_235_cummings_multi_groups_meandiff_show_baseline_ec():
+    plt.rcdefaults()
+    return multi_groups.mean_diff.plot(show_baseline_ec=True);
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_236_cummings_multi_2_group_meandiff_show_baseline_ec():
+    plt.rcdefaults()
+    return multi_2group.mean_diff.plot(show_baseline_ec=True);
\ No newline at end of file
diff --git a/nbs/tests/test_01_effsizes_pvals.ipynb b/nbs/tests/test_01_effsizes_pvals.ipynb
index f2997a42..d499f31b 100644
--- a/nbs/tests/test_01_effsizes_pvals.ipynb
+++ b/nbs/tests/test_01_effsizes_pvals.ipynb
@@ -132,7 +132,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "cohens_d = effsize.cohens_d(wellbeing.control, wellbeing.expt,\n",
+    "cohens_d = effsize.cohens_d(np.array(wellbeing.control), np.array(wellbeing.expt),\n",
     "                            is_paired=False)\n",
     "assert np.round(cohens_d, 2) == pytest.approx(0.47)"
    ]
@@ -152,7 +152,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "hedges_g = effsize.hedges_g(wellbeing.control, wellbeing.expt,\n",
+    "hedges_g = effsize.hedges_g(np.array(wellbeing.control), np.array(wellbeing.expt),\n",
     "                                is_paired=False)\n",
     "assert np.round(hedges_g, 2) == pytest.approx(0.45)"
    ]
@@ -172,7 +172,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "cohens_d = effsize.cohens_d(paired_wellbeing.pre, paired_wellbeing.post,\n",
+    "cohens_d = effsize.cohens_d(np.array(paired_wellbeing.pre), np.array(paired_wellbeing.post),\n",
     "                                is_paired=\"baseline\")\n",
     "assert np.round(cohens_d, 2) == pytest.approx(0.34)\n"
    ]
@@ -192,7 +192,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "hedges_g = effsize.hedges_g(paired_wellbeing.pre, paired_wellbeing.post,\n",
+    "hedges_g = effsize.hedges_g(np.array(paired_wellbeing.pre), np.array(paired_wellbeing.post),\n",
     "                            is_paired=\"baseline\")\n",
     "assert np.round(hedges_g, 2) == pytest.approx(0.33)"
    ]
@@ -212,7 +212,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "cohens_h = effsize.cohens_h(smoke.low, smoke.high)\n",
+    "cohens_h = effsize.cohens_h(np.array(smoke.low), np.array(smoke.high))\n",
     "assert np.round(cohens_h, 2) == pytest.approx(0.17)"
    ]
   },
@@ -231,10 +231,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "likert_delta = effsize.cliffs_delta(likert_treatment, likert_control)\n",
+    "likert_delta = effsize.cliffs_delta(np.array(likert_treatment), np.array(likert_control))\n",
     "assert likert_delta == pytest.approx(-0.25)\n",
     "\n",
-    "scores_delta = effsize.cliffs_delta(b_scores, a_scores)\n",
+    "scores_delta = effsize.cliffs_delta(np.array(b_scores), np.array(a_scores))\n",
     "assert scores_delta == pytest.approx(0.65)"
    ]
   },
diff --git a/nbs/tests/test_02_edge_cases.ipynb b/nbs/tests/test_02_edge_cases.ipynb
index 27821eee..42fb6377 100644
--- a/nbs/tests/test_02_edge_cases.ipynb
+++ b/nbs/tests/test_02_edge_cases.ipynb
@@ -49,7 +49,7 @@
     "random_seed=12345\n",
     "\n",
     "# rng = RandomState(MT19937(random_seed))\n",
-    "rng = RandomState(PCG64(12345))\n",
+    "rng = RandomState(PCG64(random_seed))\n",
     "# rng = np.random.default_rng(seed=random_seed)\n",
     "\n",
     "df = pd.DataFrame(\n",
@@ -63,19 +63,10 @@
     "            idx=['Group 1', 'Group 2'])\n",
     "\n",
     "md = test.mean_diff.results\n",
-    "\n",
     "assert md.difference[0] == pytest.approx(-0.0322, abs=1e-4)\n",
-    "assert md.bca_low[0]    == pytest.approx(-0.2279, abs=1e-4)\n",
-    "assert md.bca_high[0]   == pytest.approx(0.1613, abs=1e-4)"
+    "assert md.bca_low[0]    == pytest.approx(-0.2268, abs=1e-4)\n",
+    "assert md.bca_high[0]   == pytest.approx(0.1524, abs=1e-4)"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "afc96b46",
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
diff --git a/nbs/tests/test_08_mini_meta_pvals.ipynb b/nbs/tests/test_08_mini_meta_pvals.ipynb
index 464d3524..0c38d9b3 100644
--- a/nbs/tests/test_08_mini_meta_pvals.ipynb
+++ b/nbs/tests/test_08_mini_meta_pvals.ipynb
@@ -74,7 +74,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "mini_meta_delta = unpaired.mean_diff.mini_meta_delta\n",
+    "mini_meta_delta = unpaired.mean_diff.mini_meta\n",
     "\n",
     "control_var    = mini_meta_delta.control_var\n",
     "np_control_var = [np.var(rep1_no, ddof=1),\n",
@@ -108,12 +108,12 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "difference = unpaired.mean_diff.mini_meta_delta.difference\n",
+    "difference = unpaired.mean_diff.mini_meta.difference\n",
     "\n",
-    "np_means = [np.mean(rep1_yes)-np.mean(rep1_no), \n",
-    "            np.mean(rep2_yes)-np.mean(rep2_no)]\n",
-    "np_var   = [np.var(rep1_yes, ddof=1)/N+np.var(rep1_no, ddof=1)/N,\n",
-    "            np.var(rep2_yes, ddof=1)/N+np.var(rep2_no, ddof=1)/N]\n",
+    "np_means = np.array([np.mean(rep1_yes)-np.mean(rep1_no), \n",
+    "            np.mean(rep2_yes)-np.mean(rep2_no)])\n",
+    "np_var   = np.array([np.var(rep1_yes, ddof=1)/N+np.var(rep1_no, ddof=1)/N,\n",
+    "            np.var(rep2_yes, ddof=1)/N+np.var(rep2_no, ddof=1)/N])\n",
     "\n",
     "np_difference = effsize.weighted_delta(np_means, np_var)\n",
     "\n",
@@ -135,7 +135,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "mini_meta_delta   = unpaired.mean_diff.mini_meta_delta\n",
+    "mini_meta_delta   = unpaired.mean_diff.mini_meta\n",
     "pvalue             = mini_meta_delta.pvalue_permutation\n",
     "permutations_delta = mini_meta_delta.permutations_weighted_delta\n",
     "\n",
diff --git a/nbs/tests/test_99_confidence_intervals.ipynb b/nbs/tests/test_99_confidence_intervals.ipynb
index 2475793b..2926a5c7 100644
--- a/nbs/tests/test_99_confidence_intervals.ipynb
+++ b/nbs/tests/test_99_confidence_intervals.ipynb
@@ -55,8 +55,9 @@
     "             paired=\"baseline\", id_col=\"subject_id\")\n",
     "paired_mean_diff = ex_bp.mean_diff.results\n",
     "\n",
-    "assert pytest.approx(3.875) == paired_mean_diff.bca_low[0]\n",
-    "assert pytest.approx(9.5) == paired_mean_diff.bca_high[0]"
+    "\n",
+    "assert pytest.approx(3.625) == paired_mean_diff.bca_low[0]\n",
+    "assert pytest.approx(9.125) == paired_mean_diff.bca_high[0]"
    ]
   },
   {
@@ -198,14 +199,6 @@
     "assert error_count_median_diff  <= max_errors\n",
     "assert error_count_cliffs_delta <= max_errors\n"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "9da1b76d",
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
diff --git a/nbs/tests/test_forest_plot.py b/nbs/tests/test_forest_plot.py
index 6b57c6e6..d8d28642 100644
--- a/nbs/tests/test_forest_plot.py
+++ b/nbs/tests/test_forest_plot.py
@@ -2,36 +2,55 @@
 import pandas as pd
 import numpy as np
 import matplotlib.pyplot as plt
-from dabest.forest_plot import load_plot_data, extract_plot_data, forest_plot
-from data.mocked_data_test_forestplot import dummy_contrasts, default_forestplot_kwargs
+from dabest.forest_plot import forest_plot
+from data.mocked_data_test_forestplot import default_forestplot_kwargs
 
 def test_forest_plot_no_input_parameters():
-    error_msg = "The `contrasts` parameter cannot be None"
+    error_msg = "The `data` argument must be a non-empty list of dabest objects."
     with pytest.raises(ValueError) as excinfo:
-        forest_plot(contrasts = None)
+        forest_plot(data = None)
     
     assert error_msg in str(excinfo.value)
 
+idx_msg1 = "The `idx` argument must have the same length as the number of dabest objects. "
+idx_msg2 = "E.g., If two dabest objects are supplied, there should be two lists within `idx`. "
+idx_msg3 = "E.g., `idx` = [[1,2],[0,1]]."
+    
 @pytest.mark.parametrize("param_name, param_value, error_msg, error_type", [
-    ("contrasts", None, "The `contrasts` parameter cannot be None", ValueError),
-    ("contrasts", [], "The `contrasts` argument must be a non-empty list.", ValueError),
-    ("selected_indices", "not a list or None", "The `selected_indices` must be a list of integers or `None`.", TypeError),
-    ("contrast_type", 123, "The `contrast_type` argument must be a string.", TypeError),
-    ("xticklabels", [123, 456], "The `xticklabels` must be a list of strings or `None`.", TypeError),
-    ("effect_size", 456, "The `effect_size` argument must be a string.", TypeError),
-    ("contrast_labels", ["valid", 123], "The `contrast_labels` must be a list of strings or `None`.", TypeError),
-    ("ylabel", 789, "The `ylabel` argument must be a string.", TypeError),
-    ("custom_palette", 123, "The `custom_palette` must be either a dictionary, list, string, or `None`.", TypeError),
-    ("fontsize", "big", "`fontsize` must be an integer or float.", TypeError),
+    ("data", [], "The `data` argument must be a non-empty list of dabest objects.", ValueError),
+    ("idx", 123, "`idx` must be a tuple or list of integers.", TypeError),
+    ("idx", ((0,1),(0,1),(0,1),(0,1),(0,1)), idx_msg1+idx_msg2+idx_msg3, ValueError),
+    ("ax", "axes", "The `ax` must be a `matplotlib.axes.Axes` instance or `None`.", TypeError),
+    ("fig_size", "huge", "`fig_size` must be a tuple or list of two positive integers.", TypeError),
+    ("effect_size", 456, "The `effect_size` argument must be a string and please choose from the following effect sizes: 'mean_diff', 'median_diff', 'cohens_d', 'cohens_h', 'cliffs_delta', 'hedges_g', 'delta_g'.", TypeError),
+    ("ci_type", 'linear', "`ci_type` must be either 'bca' or 'pct'.", TypeError),
+    ("horizontal", "sideways", "`horizontal` must be a boolean value.", TypeError),
     ("marker_size", "large", "`marker_size` must be a positive integer or float.", TypeError),
-    ("ci_line_width", "thick", "`ci_line_width` must be a positive integer or float.", TypeError),
-    ("zero_line_width", "thin", "`zero_line_width` must be a positive integer or float.", TypeError),
+    ("custom_palette", 123, "The `custom_palette` must be either a dictionary, list, string, or `None`.", TypeError),
+    ("custom_palette", "test_palette", "The specified `custom_palette` test_palette is not a recognized Matplotlib palette.", ValueError),
+    ("contrast_alpha", "opaque", "`contrast_alpha` must be a float between 0 and 1.", TypeError),
+    ("contrast_desat", "yes", "`contrast_desat` must be a float between 0 and 1 or an int (1).", TypeError),
+    ("labels", ["valid", 123], "The `labels` must be a list of strings or `None`.", TypeError),
+    ("labels", ['valid', 'valid'], "`labels` must match the number of `data` provided.", ValueError),
+    ("labels_fontsize", "big", "`labels_fontsize` must be an integer or float.", TypeError),
+    ("labels_rotation", "right", "`labels_rotation` must be an integer or float between 0 and 360.", TypeError),
+    ("title", 123, "The `title` argument must be a string.", TypeError),
+    ("title_fontsize", "big", "`title_fontsize` must be an integer or float.", TypeError),
+    ("ylabel", 789, "The `ylabel` argument must be a string.", TypeError),
+    ("ylabel_fontsize", "big", "`ylabel_fontsize` must be an integer or float.", TypeError),
+    ("ylim", "auto", "`ylim` must be a tuple or list of two floats.", TypeError),
+    ("ylim", [0, 1, 2], "`ylim` must be a tuple or list of two floats.", ValueError),
+    ("yticks", "auto", "`yticks` must be a tuple or list of floats.", TypeError),
+    ("yticklabels", "auto", "`yticklabels` must be a tuple or list of strings.", TypeError),
+    ("yticklabels", [532, 123], "`yticklabels` must be a list of strings.", TypeError),
     ("remove_spines", "yes", "`remove_spines` must be a boolean value.", TypeError),
-    ("rotation_for_xlabels", "right", "`rotation_for_xlabels` must be an integer or float between 0 and 360.", TypeError),
-    ("alpha_violin_plot", "opaque", "`alpha_violin_plot` must be a float between 0 and 1.", TypeError),
-    ("horizontal", "sideways", "`horizontal` must be a boolean value.", TypeError),
-    ("contrast_type", "unknown", "Invalid contrast_type: unknown. Available options: [`delta2`, `mini_meta`]", ValueError),
+    ("reference_band", "yes", "`reference_band` must be a list/tuple of indices (ints).", TypeError),
+    ("reference_band", [0.1, 0.5], "`reference_band` must be a list/tuple of indices (ints).", TypeError),
+    ("reference_band", [10,], "Index [10] chosen is out of range for the contrast objects.", ValueError),
+    ("delta_text", "auto", "`delta_text` must be a boolean value.", TypeError),
+    ("contrast_bars", "auto", "`contrast_bars` must be a boolean value.", TypeError),
 ])
+
 def test_forest_plot_input_error_handling(param_name, param_value, error_msg, error_type):
     # Setup: Define a base set of valid inputs to forest_plot
     valid_inputs = default_forestplot_kwargs.copy()
diff --git a/nbs/tests/test_load_errors.py b/nbs/tests/test_load_errors.py
index eb598796..9084b6bb 100644
--- a/nbs/tests/test_load_errors.py
+++ b/nbs/tests/test_load_errors.py
@@ -35,18 +35,6 @@ def test_wrong_params_combinations():
 
     assert error_msg in str(excinfo.value)
 
-    error_msg = "`proportional` and `delta2` cannot be True at the same time."
-    with pytest.raises(ValueError) as excinfo:
-        my_data = load(
-            dummy_df,
-            x=["Control 1", "Control 1"],
-            y="Test 1",
-            delta2=True,
-            proportional=True
-        )
-
-    assert error_msg in str(excinfo.value)
-
     error_msg = "`idx` should not be specified when `delta2` is True.".format(N)
     with pytest.raises(ValueError) as excinfo:
         my_data = load(
@@ -105,7 +93,7 @@ def test_param_validations():
     assert error_msg in str(excinfo.value)
 
     wrong_paired = 'not_valid'
-    error_msg = "{} assigned for `paired` is not valid.".format(wrong_paired)
+    error_msg = "'{}' assigned for `paired` is not valid. Please use either 'baseline' or 'sequential'.".format(wrong_paired)
     with pytest.raises(ValueError) as excinfo:
         my_data = load(
             dummy_df, idx=("Control 1", "Test 1"), paired=wrong_paired, id_col="ID"
@@ -115,7 +103,7 @@ def test_param_validations():
 
 
     wrong_id_col = 'not_valid'
-    error_msg = "{} is not a column in `data`. ".format(wrong_id_col)
+    error_msg = "`id_col` was given as '{}'; however, '{}' is not a column in `data`.".format(wrong_id_col, wrong_id_col)
     with pytest.raises(IndexError) as excinfo:
         my_data = load(
             dummy_df, idx=("Control 1", "Test 1"), paired="baseline", id_col=wrong_id_col
diff --git a/nbs/tests/test_plot_tools.py b/nbs/tests/test_plot_tools.py
index b47dba7f..70f73640 100644
--- a/nbs/tests/test_plot_tools.py
+++ b/nbs/tests/test_plot_tools.py
@@ -84,7 +84,7 @@ def test_check_data_matches_labels():
     ("data", None, "`data` must be a Pandas Dataframe.", ValueError),
     ("x", None, "`x` must be a string.", ValueError),
     ("y", None, "`y` must be a string.", ValueError),
-    ("ax", None, "`ax` must be a Matplotlib AxesSubplot. The current `ax` is a <class 'NoneType'>", ValueError),
+    ("ax", None, "`ax` must be a Matplotlib axes.Axes. The current `ax` is a <class 'NoneType'>", ValueError),
     ("order", 5, "`order` must be either an Iterable or None.", ValueError),
     ("hue", 5, "`hue` must be either a string or None.", ValueError),
     ("palette", None, "`palette` must be either a string indicating a color name or an Iterable.", ValueError),
@@ -94,6 +94,7 @@ def test_check_data_matches_labels():
     ("jitter", None, "`jitter` must be a scalar or float.", ValueError),
     ("is_drop_gutter", None, "`is_drop_gutter` must be a boolean.", ValueError),
     ("gutter_limit", None, "`gutter_limit` must be a scalar or float.", ValueError),
+    ("filled", 1, "`filled` must be a boolean, list or tuple.", ValueError),
 
     # More thorough input validation checks
     ("x", "a", "a is not a column in `data`.", IndexError),
@@ -104,7 +105,9 @@ def test_check_data_matches_labels():
     ("palette", {"Control 1": " "}, "The color mapping for Control 1 in `palette` is an empty string. It must contain a color name.", ValueError),
     ("palette", {"Control 3": "black"}, "Control 3 in `palette` is not in the 'group' column of `data`.", IndexError),
     # TODO: to add palette validation testing for when color_col is hue
-    ("side", "top", "Invalid `side`. Must be one of 'center', 'right', or 'left'.", ValueError)
+    ("side", "top", "Invalid `side`. Must be one of 'center', 'right', or 'left'.", ValueError),
+    ("filled", [True, "a"], "All values in `filled` must be a boolean.", ValueError),
+    ("filled", [True], "There are 2 unique values in `x` column in `data` but `filled` has a length of 1.", ValueError),
 ])
 def test_swarmplot_input_error_handling(param_name, param_value, error_msg, error_type):
     with pytest.raises(error_type) as excinfo:
@@ -120,6 +123,7 @@ def test_swarmplot_input_error_handling(param_name, param_value, error_msg, erro
             size=5 if param_name != "size" else param_value,
             side="center" if param_name != "side" else param_value,
             jitter=1 if param_name != "jitter" else param_value,
+            filled=True if param_name != "filled" else param_value,
             is_drop_gutter=True if param_name != "is_drop_gutter" else param_value,
             gutter_limit=0.5 if param_name != "gutter_limit" else param_value,
         )
diff --git a/nbs/tutorials/01-basics.ipynb b/nbs/tutorials/01-basics.ipynb
index 23c16177..21c63817 100644
--- a/nbs/tutorials/01-basics.ipynb
+++ b/nbs/tutorials/01-basics.ipynb
@@ -2,7 +2,6 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "5b32febf",
    "metadata": {},
    "source": [
     "# Basics\n",
@@ -14,7 +13,6 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c964abcb",
    "metadata": {},
    "source": [
     "## Load libraries"
@@ -23,14 +21,35 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "38b902ea",
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "We're using DABEST v2024.03.29\n"
+      "Pre-compiling numba functions for DABEST...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Compiling numba functions: 100%|██████████| 11/11 [00:00<00:00, 44.16it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Numba compilation complete!\n",
+      "We're using DABEST v2025.03.27\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
      ]
     }
    ],
@@ -45,7 +64,6 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "11eb9759",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -56,7 +74,6 @@
   },
   {
    "cell_type": "markdown",
-   "id": "61f4ab6b",
    "metadata": {},
    "source": [
     "## Create dataset for demo"
@@ -64,7 +81,6 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c45f63cd",
    "metadata": {},
    "source": [
     "Here, we create a dataset to illustrate how ``dabest`` works. In\n",
@@ -74,78 +90,6 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "9c459d31",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from scipy.stats import norm # Used in generation of populations.\n",
-    "\n",
-    "np.random.seed(9999) # Fix the seed to ensure reproducibility of results.\n",
-    "\n",
-    "Ns = 20 # The number of samples taken from each population\n",
-    "\n",
-    "# Create samples\n",
-    "c1 = norm.rvs(loc=3, scale=0.4, size=Ns)\n",
-    "c2 = norm.rvs(loc=3.5, scale=0.75, size=Ns)\n",
-    "c3 = norm.rvs(loc=3.25, scale=0.4, size=Ns)\n",
-    "\n",
-    "t1 = norm.rvs(loc=3.5, scale=0.5, size=Ns)\n",
-    "t2 = norm.rvs(loc=2.5, scale=0.6, size=Ns)\n",
-    "t3 = norm.rvs(loc=3, scale=0.75, size=Ns)\n",
-    "t4 = norm.rvs(loc=3.5, scale=0.75, size=Ns)\n",
-    "t5 = norm.rvs(loc=3.25, scale=0.4, size=Ns)\n",
-    "t6 = norm.rvs(loc=3.25, scale=0.4, size=Ns)\n",
-    "\n",
-    "\n",
-    "# Add a `gender` column for coloring the data.\n",
-    "females = np.repeat('Female', Ns/2).tolist()\n",
-    "males = np.repeat('Male', Ns/2).tolist()\n",
-    "gender = females + males\n",
-    "\n",
-    "# Add an `id` column for paired data plotting.\n",
-    "id_col = pd.Series(range(1, Ns+1))\n",
-    "\n",
-    "# Combine samples and gender into a DataFrame.\n",
-    "df = pd.DataFrame({'Control 1' : c1,     'Test 1' : t1,\n",
-    "                 'Control 2' : c2,     'Test 2' : t2,\n",
-    "                 'Control 3' : c3,     'Test 3' : t3,\n",
-    "                 'Test 4'    : t4,     'Test 5' : t5, 'Test 6' : t6,\n",
-    "                 'Gender'    : gender, 'ID'  : id_col\n",
-    "                })"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "142607a1",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "id": "51097f12",
-   "metadata": {},
-   "source": [
-    "Note that we have 9 groups (3 Control samples and 6 Test samples). Our\n",
-    "dataset has also a non\\-numerical column indicating gender, and another\n",
-    "column indicating the identity of each observation."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e975d14a",
-   "metadata": {},
-   "source": [
-    "This is known as a *wide* dataset. See this \n",
-    "[writeup](https://sejdemyr.github.io/r-tutorials/basics/wide-and-long/) \n",
-    "for more details."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "48be62cf",
    "metadata": {},
    "outputs": [
     {
@@ -279,12 +223,62 @@
     }
    ],
    "source": [
+    "from scipy.stats import norm # Used in generation of populations.\n",
+    "\n",
+    "np.random.seed(9999) # Fix the seed to ensure reproducibility of results.\n",
+    "\n",
+    "Ns = 20 # The number of samples taken from each population\n",
+    "\n",
+    "# Create samples\n",
+    "c1 = norm.rvs(loc=3, scale=0.4, size=Ns)\n",
+    "c2 = norm.rvs(loc=3.5, scale=0.75, size=Ns)\n",
+    "c3 = norm.rvs(loc=3.25, scale=0.4, size=Ns)\n",
+    "\n",
+    "t1 = norm.rvs(loc=3.5, scale=0.5, size=Ns)\n",
+    "t2 = norm.rvs(loc=2.5, scale=0.6, size=Ns)\n",
+    "t3 = norm.rvs(loc=3, scale=0.75, size=Ns)\n",
+    "t4 = norm.rvs(loc=3.5, scale=0.75, size=Ns)\n",
+    "t5 = norm.rvs(loc=3.25, scale=0.4, size=Ns)\n",
+    "t6 = norm.rvs(loc=3.25, scale=0.4, size=Ns)\n",
+    "\n",
+    "# Add a `gender` column for coloring the data.\n",
+    "females = np.repeat('Female', Ns/2).tolist()\n",
+    "males = np.repeat('Male', Ns/2).tolist()\n",
+    "gender = females + males\n",
+    "\n",
+    "# Add an `id` column for paired data plotting.\n",
+    "id_col = pd.Series(range(1, Ns+1))\n",
+    "\n",
+    "# Combine samples and gender into a DataFrame.\n",
+    "df = pd.DataFrame({'Control 1' : c1,     'Test 1' : t1,\n",
+    "                 'Control 2' : c2,     'Test 2' : t2,\n",
+    "                 'Control 3' : c3,     'Test 3' : t3,\n",
+    "                 'Test 4'    : t4,     'Test 5' : t5, 'Test 6' : t6,\n",
+    "                 'Gender'    : gender, 'ID'  : id_col\n",
+    "                })\n",
     "df.head()"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "7dd2c3f4",
+   "metadata": {},
+   "source": [
+    "Note that we have 9 groups (3 Control samples and 6 Test samples). Our\n",
+    "dataset has also a non\\-numerical column indicating gender, and another\n",
+    "column indicating the identity of each observation."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This is known as a *wide* dataset. See this \n",
+    "[writeup](https://sejdemyr.github.io/r-tutorials/basics/wide-and-long/) \n",
+    "for more details."
+   ]
+  },
+  {
+   "cell_type": "markdown",
    "metadata": {},
    "source": [
     "## Loading data"
@@ -292,7 +286,6 @@
   },
   {
    "cell_type": "markdown",
-   "id": "eda4a39f",
    "metadata": {},
    "source": [
     "Before creating estimation plots and obtaining confidence intervals for our effect sizes, we need to load the data and specify the relevant groups.\n",
@@ -303,7 +296,6 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "dfb7a0a1",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -312,7 +304,6 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3befeecd",
    "metadata": {},
    "source": [
     "Calling this ``Dabest`` object gives you a gentle greeting, as well as\n",
@@ -322,17 +313,16 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "4503730b",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "DABEST v2024.03.29\n",
+       "DABEST v2025.03.27\n",
        "==================\n",
        "                  \n",
        "Good afternoon!\n",
-       "The current time is Tue Mar 19 15:35:21 2024.\n",
+       "The current time is Tue Mar 25 16:02:11 2025.\n",
        "\n",
        "Effect size(s) with 95% confidence intervals will be computed for:\n",
        "1. Test 1 minus Control 1\n",
@@ -351,7 +341,6 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3565f8d1",
    "metadata": {},
    "source": [
     "### Changing statistical parameters"
@@ -359,7 +348,6 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f71a2c3d",
    "metadata": {},
    "source": [
     "You can change the width of the confidence interval by manipulating the ``ci`` argument."
@@ -368,27 +356,16 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "407f6d9b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "two_groups_unpaired_ci90 = dabest.load(df, idx=(\"Control 1\", \"Test 1\"), ci=90)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "aeb436f4",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "DABEST v2024.03.29\n",
+       "DABEST v2025.03.27\n",
        "==================\n",
        "                  \n",
        "Good afternoon!\n",
-       "The current time is Tue Mar 19 15:35:21 2024.\n",
+       "The current time is Tue Mar 25 16:02:11 2025.\n",
        "\n",
        "Effect size(s) with 90% confidence intervals will be computed for:\n",
        "1. Test 1 minus Control 1\n",
@@ -402,12 +379,12 @@
     }
    ],
    "source": [
+    "two_groups_unpaired_ci90 = dabest.load(df, idx=(\"Control 1\", \"Test 1\"), ci=90)\n",
     "two_groups_unpaired_ci90"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "5084cfcb",
    "metadata": {},
    "source": [
     "## Effect sizes"
@@ -415,13 +392,12 @@
   },
   {
    "cell_type": "markdown",
-   "id": "837ffe5c",
    "metadata": {},
    "source": [
     "The **dabest** library now features a range of effect sizes:\n",
     "\n",
-    "  - the mean difference (`mean＿diff`)\n",
-    "  - the median difference (`median＿diff`)\n",
+    "  - Mean difference (`mean＿diff`)\n",
+    "  - Median difference (`median＿diff`)\n",
     "  - [Cohen's d](https://en.wikipedia.org/wiki/Effect_size#Cohen's_d) (`cohens＿d`)\n",
     "  - [Hedges' g](https://en.wikipedia.org/wiki/Effect_size#Hedges'_g) (`hedges＿g`)\n",
     "  - [Cohen's h](https://en.wikipedia.org/wiki/Cohen's_h) (`cohens＿h`)\n",
@@ -435,19 +411,18 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "782ff891",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "DABEST v2024.03.29\n",
+       "DABEST v2025.03.27\n",
        "==================\n",
        "                  \n",
        "Good afternoon!\n",
-       "The current time is Tue Mar 19 15:35:22 2024.\n",
+       "The current time is Tue Mar 25 16:02:11 2025.\n",
        "\n",
-       "The unpaired mean difference between Control 1 and Test 1 is 0.48 [95%CI 0.221, 0.768].\n",
+       "The unpaired mean difference between Control 1 and Test 1 is 0.48 [95%CI 0.205, 0.774].\n",
        "The p-value of the two-sided permutation t-test is 0.001, calculated for legacy purposes only. \n",
        "\n",
        "5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.\n",
@@ -469,7 +444,6 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1ae5b84f",
    "metadata": {},
    "source": [
     "For each comparison, the type of effect size is reported (here, it's the\n",
@@ -480,7 +454,7 @@
     "\n",
     "Since v0.3.0, DABEST will report the p-value of the [non-parametric two-sided approximate permutation t-test](https://en.wikipedia.org/wiki/Resampling_(statistics)#Permutation_tests). This is also known as *the Monte Carlo permutation test*.\n",
     "\n",
-    "For unpaired comparisons, the p-values and test statistics of [Welch's t test](https://en.wikipedia.org/wiki/Welch%27s_t-test>), \n",
+    "For unpaired comparisons, the p-values and test statistics of [Welch's t test](https://en.wikipedia.org/wiki/Welch%27s_t-test), \n",
     "[Student's t test](https://en.wikipedia.org/wiki/Student%27s_t-test), \n",
     "and [Mann-Whitney U test](https://en.wikipedia.org/wiki/Mann%E2%80%93Whitney_U_test) can be found. For paired comparisons, the p-values and test statistics of the \n",
     "[paired Student's t](https://en.wikipedia.org/wiki/Student%27s_t-test#Paired_samples)\n",
@@ -490,7 +464,6 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "8f6884f3",
    "metadata": {},
    "outputs": [
     {
@@ -541,6 +514,14 @@
        "      <th>statistic_students_t</th>\n",
        "      <th>pvalue_mann_whitney</th>\n",
        "      <th>statistic_mann_whitney</th>\n",
+       "      <th>bec_difference</th>\n",
+       "      <th>bec_bootstraps</th>\n",
+       "      <th>bec_bca_interval_idx</th>\n",
+       "      <th>bec_bca_low</th>\n",
+       "      <th>bec_bca_high</th>\n",
+       "      <th>bec_pct_interval_idx</th>\n",
+       "      <th>bec_pct_low</th>\n",
+       "      <th>bec_pct_high</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
@@ -554,25 +535,33 @@
        "      <td>None</td>\n",
        "      <td>0.48029</td>\n",
        "      <td>95</td>\n",
-       "      <td>0.220869</td>\n",
-       "      <td>0.767721</td>\n",
-       "      <td>(140, 4889)</td>\n",
-       "      <td>0.215697</td>\n",
-       "      <td>0.761716</td>\n",
+       "      <td>0.205161</td>\n",
+       "      <td>0.773647</td>\n",
+       "      <td>(145, 4893)</td>\n",
+       "      <td>0.197427</td>\n",
+       "      <td>0.758752</td>\n",
        "      <td>(125, 4875)</td>\n",
-       "      <td>[0.6686169333655454, 0.4382051534234943, 0.665...</td>\n",
+       "      <td>[0.6148498102262239, 0.6752095203445543, 0.300...</td>\n",
        "      <td>5000</td>\n",
        "      <td>12345</td>\n",
        "      <td>[-0.17259843762502491, 0.03802293852634886, -0...</td>\n",
        "      <td>0.001</td>\n",
        "      <td>5000</td>\n",
-       "      <td>[0.026356588154404337, 0.027102495439046997, 0...</td>\n",
+       "      <td>[0.26356588154404337, 0.2710249543904699, 0.26...</td>\n",
        "      <td>0.002094</td>\n",
        "      <td>-3.308806</td>\n",
        "      <td>0.002057</td>\n",
        "      <td>-3.308806</td>\n",
        "      <td>0.001625</td>\n",
        "      <td>83.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>[-0.09732932551566487, 0.08087009665445155, -0...</td>\n",
+       "      <td>(127, 4877)</td>\n",
+       "      <td>-0.256862</td>\n",
+       "      <td>0.259558</td>\n",
+       "      <td>(125, 4875)</td>\n",
+       "      <td>-0.25826</td>\n",
+       "      <td>0.25759</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -583,10 +572,10 @@
        "0  Control 1  Test 1         20      20  mean difference      None   \n",
        "\n",
        "   difference  ci   bca_low  bca_high bca_interval_idx   pct_low  pct_high  \\\n",
-       "0     0.48029  95  0.220869  0.767721      (140, 4889)  0.215697  0.761716   \n",
+       "0     0.48029  95  0.205161  0.773647      (145, 4893)  0.197427  0.758752   \n",
        "\n",
        "  pct_interval_idx                                         bootstraps  \\\n",
-       "0      (125, 4875)  [0.6686169333655454, 0.4382051534234943, 0.665...   \n",
+       "0      (125, 4875)  [0.6148498102262239, 0.6752095203445543, 0.300...   \n",
        "\n",
        "   resamples  random_seed                                       permutations  \\\n",
        "0       5000        12345  [-0.17259843762502491, 0.03802293852634886, -0...   \n",
@@ -595,13 +584,19 @@
        "0               0.001               5000   \n",
        "\n",
        "                                    permutations_var  pvalue_welch  \\\n",
-       "0  [0.026356588154404337, 0.027102495439046997, 0...      0.002094   \n",
+       "0  [0.26356588154404337, 0.2710249543904699, 0.26...      0.002094   \n",
        "\n",
        "   statistic_welch  pvalue_students_t  statistic_students_t  \\\n",
        "0        -3.308806           0.002057             -3.308806   \n",
        "\n",
-       "   pvalue_mann_whitney  statistic_mann_whitney  \n",
-       "0             0.001625                    83.0  "
+       "   pvalue_mann_whitney  statistic_mann_whitney  bec_difference  \\\n",
+       "0             0.001625                    83.0             0.0   \n",
+       "\n",
+       "                                      bec_bootstraps bec_bca_interval_idx  \\\n",
+       "0  [-0.09732932551566487, 0.08087009665445155, -0...          (127, 4877)   \n",
+       "\n",
+       "   bec_bca_low  bec_bca_high bec_pct_interval_idx  bec_pct_low  bec_pct_high  \n",
+       "0    -0.256862      0.259558          (125, 4875)     -0.25826       0.25759  "
       ]
      },
      "execution_count": null,
@@ -617,7 +612,6 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "07ddee85",
    "metadata": {},
    "outputs": [
     {
@@ -671,8 +665,8 @@
        "      <td>None</td>\n",
        "      <td>0.48029</td>\n",
        "      <td>95</td>\n",
-       "      <td>0.220869</td>\n",
-       "      <td>0.767721</td>\n",
+       "      <td>0.205161</td>\n",
+       "      <td>0.773647</td>\n",
        "      <td>0.001</td>\n",
        "      <td>0.002094</td>\n",
        "      <td>-3.308806</td>\n",
@@ -690,7 +684,7 @@
        "0  Control 1  Test 1         20      20  mean difference      None   \n",
        "\n",
        "   difference  ci   bca_low  bca_high  pvalue_permutation  pvalue_welch  \\\n",
-       "0     0.48029  95  0.220869  0.767721               0.001      0.002094   \n",
+       "0     0.48029  95  0.205161  0.773647               0.001      0.002094   \n",
        "\n",
        "   statistic_welch  pvalue_students_t  statistic_students_t  \\\n",
        "0        -3.308806           0.002057             -3.308806   \n",
@@ -710,7 +704,110 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2548d82c",
+   "metadata": {},
+   "source": [
+    "**Note:**\n",
+    "A research paper [Phipson & Smyth (2010)](https://doi.org/10.2202/1544-6115.1585) suggested that permutation p-values should never be zero, and provided a slightly adjusted formula to compute permutation p-values. \n",
+    "\n",
+    "Since **v2025.03.27**, DABEST provides a `ps_adjust` parameter in the `.load()` function. This parameter allows you to adjust the permutation p-values using the formula suggested by Phipson & Smyth (2010). By default, DABEST uses the unadjusted p-values."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>control</th>\n",
+       "      <th>test</th>\n",
+       "      <th>control_N</th>\n",
+       "      <th>test_N</th>\n",
+       "      <th>effect_size</th>\n",
+       "      <th>is_paired</th>\n",
+       "      <th>difference</th>\n",
+       "      <th>ci</th>\n",
+       "      <th>bca_low</th>\n",
+       "      <th>bca_high</th>\n",
+       "      <th>pvalue_permutation</th>\n",
+       "      <th>pvalue_welch</th>\n",
+       "      <th>statistic_welch</th>\n",
+       "      <th>pvalue_students_t</th>\n",
+       "      <th>statistic_students_t</th>\n",
+       "      <th>pvalue_mann_whitney</th>\n",
+       "      <th>statistic_mann_whitney</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Control 1</td>\n",
+       "      <td>Test 1</td>\n",
+       "      <td>20</td>\n",
+       "      <td>20</td>\n",
+       "      <td>mean difference</td>\n",
+       "      <td>None</td>\n",
+       "      <td>0.48029</td>\n",
+       "      <td>95</td>\n",
+       "      <td>0.205161</td>\n",
+       "      <td>0.773647</td>\n",
+       "      <td>0.0012</td>\n",
+       "      <td>0.002094</td>\n",
+       "      <td>-3.308806</td>\n",
+       "      <td>0.002057</td>\n",
+       "      <td>-3.308806</td>\n",
+       "      <td>0.001625</td>\n",
+       "      <td>83.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     control    test  control_N  test_N      effect_size is_paired  \\\n",
+       "0  Control 1  Test 1         20      20  mean difference      None   \n",
+       "\n",
+       "   difference  ci   bca_low  bca_high  pvalue_permutation  pvalue_welch  \\\n",
+       "0     0.48029  95  0.205161  0.773647              0.0012      0.002094   \n",
+       "\n",
+       "   statistic_welch  pvalue_students_t  statistic_students_t  \\\n",
+       "0        -3.308806           0.002057             -3.308806   \n",
+       "\n",
+       "   pvalue_mann_whitney  statistic_mann_whitney  \n",
+       "0             0.001625                    83.0  "
+      ]
+     },
+     "execution_count": null,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "two_groups_unpaired_adjusted = dabest.load(df, idx=(\"Control 1\", \"Test 1\"), resamples=5000, ps_adjust=True)\n",
+    "two_groups_unpaired_adjusted.mean_diff.statistical_tests"
+   ]
+  },
+  {
+   "cell_type": "markdown",
    "metadata": {},
    "source": [
     "Let's compute the *Hedges'g*   for our comparison."
@@ -719,19 +816,18 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "e302c877",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "DABEST v2024.03.29\n",
+       "DABEST v2025.03.27\n",
        "==================\n",
        "                  \n",
        "Good afternoon!\n",
-       "The current time is Tue Mar 19 15:35:23 2024.\n",
+       "The current time is Tue Mar 25 16:02:11 2025.\n",
        "\n",
-       "The unpaired Hedges' g between Control 1 and Test 1 is 1.03 [95%CI 0.349, 1.62].\n",
+       "The unpaired Hedges' g between Control 1 and Test 1 is 1.03 [95%CI 0.317, 1.62].\n",
        "The p-value of the two-sided permutation t-test is 0.001, calculated for legacy purposes only. \n",
        "\n",
        "5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.\n",
@@ -754,7 +850,6 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "3980ba80",
    "metadata": {},
    "outputs": [
     {
@@ -805,6 +900,14 @@
        "      <th>statistic_students_t</th>\n",
        "      <th>pvalue_mann_whitney</th>\n",
        "      <th>statistic_mann_whitney</th>\n",
+       "      <th>bec_difference</th>\n",
+       "      <th>bec_bootstraps</th>\n",
+       "      <th>bec_bca_interval_idx</th>\n",
+       "      <th>bec_bca_low</th>\n",
+       "      <th>bec_bca_high</th>\n",
+       "      <th>bec_pct_interval_idx</th>\n",
+       "      <th>bec_pct_low</th>\n",
+       "      <th>bec_pct_high</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
@@ -818,25 +921,33 @@
        "      <td>None</td>\n",
        "      <td>1.025525</td>\n",
        "      <td>95</td>\n",
-       "      <td>0.349394</td>\n",
-       "      <td>1.618579</td>\n",
-       "      <td>(42, 4724)</td>\n",
-       "      <td>0.472844</td>\n",
-       "      <td>1.74166</td>\n",
+       "      <td>0.316506</td>\n",
+       "      <td>1.616235</td>\n",
+       "      <td>(42, 4725)</td>\n",
+       "      <td>0.44486</td>\n",
+       "      <td>1.745146</td>\n",
        "      <td>(125, 4875)</td>\n",
-       "      <td>[1.1337301267831184, 0.8311210968422604, 1.539...</td>\n",
+       "      <td>[1.469217954462509, 1.5972518056777079, 0.6051...</td>\n",
        "      <td>5000</td>\n",
        "      <td>12345</td>\n",
-       "      <td>[-0.3295089865590538, 0.07158401210924781, -0....</td>\n",
+       "      <td>[-0.329508986559053, 0.07158401210924781, -0.2...</td>\n",
        "      <td>0.001</td>\n",
        "      <td>5000</td>\n",
-       "      <td>[0.026356588154404337, 0.027102495439046997, 0...</td>\n",
+       "      <td>[0.26356588154404337, 0.2710249543904699, 0.26...</td>\n",
        "      <td>0.002094</td>\n",
        "      <td>-3.308806</td>\n",
        "      <td>0.002057</td>\n",
        "      <td>-3.308806</td>\n",
        "      <td>0.001625</td>\n",
        "      <td>83.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>[-0.2669450878059954, 0.21187593591106418, -0....</td>\n",
+       "      <td>(127, 4877)</td>\n",
+       "      <td>-0.642387</td>\n",
+       "      <td>0.629464</td>\n",
+       "      <td>(125, 4875)</td>\n",
+       "      <td>-0.643604</td>\n",
+       "      <td>0.627968</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -846,23 +957,29 @@
        "     control    test  control_N  test_N effect_size is_paired  difference  ci  \\\n",
        "0  Control 1  Test 1         20      20   Hedges' g      None    1.025525  95   \n",
        "\n",
-       "    bca_low  bca_high bca_interval_idx   pct_low  pct_high pct_interval_idx  \\\n",
-       "0  0.349394  1.618579       (42, 4724)  0.472844   1.74166      (125, 4875)   \n",
+       "    bca_low  bca_high bca_interval_idx  pct_low  pct_high pct_interval_idx  \\\n",
+       "0  0.316506  1.616235       (42, 4725)  0.44486  1.745146      (125, 4875)   \n",
        "\n",
        "                                          bootstraps  resamples  random_seed  \\\n",
-       "0  [1.1337301267831184, 0.8311210968422604, 1.539...       5000        12345   \n",
+       "0  [1.469217954462509, 1.5972518056777079, 0.6051...       5000        12345   \n",
        "\n",
        "                                        permutations  pvalue_permutation  \\\n",
-       "0  [-0.3295089865590538, 0.07158401210924781, -0....               0.001   \n",
+       "0  [-0.329508986559053, 0.07158401210924781, -0.2...               0.001   \n",
        "\n",
        "   permutation_count                                   permutations_var  \\\n",
-       "0               5000  [0.026356588154404337, 0.027102495439046997, 0...   \n",
+       "0               5000  [0.26356588154404337, 0.2710249543904699, 0.26...   \n",
        "\n",
        "   pvalue_welch  statistic_welch  pvalue_students_t  statistic_students_t  \\\n",
        "0      0.002094        -3.308806           0.002057             -3.308806   \n",
        "\n",
-       "   pvalue_mann_whitney  statistic_mann_whitney  \n",
-       "0             0.001625                    83.0  "
+       "   pvalue_mann_whitney  statistic_mann_whitney  bec_difference  \\\n",
+       "0             0.001625                    83.0             0.0   \n",
+       "\n",
+       "                                      bec_bootstraps bec_bca_interval_idx  \\\n",
+       "0  [-0.2669450878059954, 0.21187593591106418, -0....          (127, 4877)   \n",
+       "\n",
+       "   bec_bca_low  bec_bca_high bec_pct_interval_idx  bec_pct_low  bec_pct_high  \n",
+       "0    -0.642387      0.629464          (125, 4875)    -0.643604      0.627968  "
       ]
      },
      "execution_count": null,
@@ -876,7 +993,6 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5f1eb018",
    "metadata": {},
    "source": [
     "## Producing estimation plots"
@@ -884,7 +1000,6 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b451ab38",
    "metadata": {},
    "source": [
     "To generate a **Gardner-Altman estimation plot**, simply use the\n",
@@ -897,12 +1012,11 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "0a929da1",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 500x400 with 2 Axes>"
       ]
@@ -918,12 +1032,11 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "238bbeea",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 500x400 with 2 Axes>"
       ]
@@ -938,11 +1051,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5b566185",
    "metadata": {},
    "source": [
     "Instead of a Gardner-Altman plot, you can generate a **Cumming estimation\n",
-    "plot** by setting ``float_contrast=False`` in the ``plot()`` method.\n",
+    "plot** by setting ``float_contrast=False`` in the ``.plot()`` method.\n",
     "This will plot the bootstrap effect sizes below the raw data, and also\n",
     "displays the the mean (gap) and ± standard deviation of each group\n",
     "(vertical ends) as gapped lines. This design was inspired by Edward\n",
@@ -952,12 +1064,11 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "e4b10d17",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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UuKwgM41TP31K8r41AKQcWMu1Y1uB0haVCqgU5WVx4sePAcg4ux9Ug9n3KMrL5MbVCxRkpFImyP6iIDOtWj4T1GCYxcfHV6mfWfPmzUlISGDXrl0899xzjBkzhqNHj5pdNyoqitGjRxMREUF0dDQ//PADvr6+zJs3r9z9T5s2jczMTONj06ZNla5RCFt2Ydt3GIqLzAZR4savMeiLSNn3CyUtqpuoBnIunyTvShIot44PRaPF2SvA/H7+4OzpX8nqy1flw8y33nrL7PKMjAw2b97M/v37eeWVVyq9XycnJ5o2bQpAZGQke/bsYfbs2bcMqFKOjo506NCB06dPl7uOTqdDp/tzkEI3N7dK1yiEJeVdSSJ572pyUs/iVM8b/4h+eDftbHLxK+vCUS7tWkn25ZM4unjgH9GPgA4D0Tg4kn5qT7ktquIb2eSmnKEg5xq3alEVZl+jQcvupB3aaHZfzl4BuDRoTGDnBzj98z/L7kDR4B0eic7Tt5KfvnxVDrMZM2aYXe7t7U14eDhz585l/PjxVd29kcFgMDnHdSt6vZ5Dhw4xaJDM1C3s07UTOzj2/Xt/HPUZQNFw7cR2AjoOJHzgRBRFIe3wRk6u+hhFo0E16CnMusrZuPlcO7GT1qNi4HZX/BUtrvWbkHXjWLmh5+zTiCY9R3HtxE70hfl/rqcooEJo//Eoigb/iP7kJJ8mZf8a0GhQUFANelwbNKHZA1Oq9d+mymFmMJj/kHdi2rRpDBw4kKCgILKzs1myZAkbN24kLi4OgNGjR9OoUSNiY2OBktZht27daNq0KRkZGXz00UckJiYybty4aq9NCEvTF94oOV/119+9P0IkZf8v+NzVDc+gNpxe8xmgmlw9BJXM8wdJO7SB+s27k3ow3mxQOdbzxi0gjIZdh5F14UjZIhQNPk074+xVctI+4qlZJG5czLUT21ENetwbtSQ4+jG8QiNKVlcUmg6aSECHAVw9tg1DcQEeQa2pf1e3Mv3R7pRVTWiSlpbG6NGjSU5OxtPTk3bt2hEXF0e/fiV9VpKSktBo/jxOT09PZ/z48aSkpODt7U1kZCTbt28v94KBELbs2sldGArL6RCuaEj7fR2GooJbrKNw5fBGmg2ezLXj2yguyLupRaUS2m8cikZLgxbdCYp+gqRN/y055aUoYDDgFtiMZkP+n3GXLvUb0eLhV1DVkqua5QWUW2BT3AKb3sGnv70Kh1lSUlKV3qAyY5rd7m6BjRs3mjz/9NNP+fTTT6tSlhA2pzgvyxg6ZagGinIz0Bfmlb8DVaU4Pxdn7wDaj/2E8xsWce3EDlAN1PMPI6jXY9S/q6tx9aC7H8Wv3b1cO74NfWE+Hk1a4RnczmzHdEXR3Oo8f62ocJiFhIRUqXe9Xq+//UpCiNuq5x9qPsgAFA1ugeG4N2pR/g4UDZ5BJX0wXeo3ouXwVzHoi1ENerSO5mfucvb0o1HXB++09FpR4TD76quv5FYhIWqQk5u3yX9v5hHUhnr+YeSmnb/pfJeCotEQ0HEwLj6B+NzVles3X7FUFDRaBwI7md5lo9E6gNaqzjZVmaKq5UV93bB//34iIyPZt28fHTt2tHQ5QtxSQdZVjn73lvFeSAAHZzeaP/gy3uGRQMmFglOr/8nVo1so7V6h8/LnriEvGFtmqkFPcX4OWidXNA7Ve8O3pUiYSZgJG6OqKtkXj5Gbdh7Hep74NO2MxsGpzHoFmVfITTuHg4s77o2aoyglXTUu7viey7tWUZSXiaJ1xK/tPYTcOxZHVw8LfJrqc8fty23btrF//34yMzPLdNdQFIU33njjTt9CCPEXiqLg0aQVHk1ufdVe5+lbplPqmbVzSNn/i/G5qi8i9eBvZF08RsTTs9A6OtdIzbWhymF2/fp1Bg8ezO7du1FVFUVRjBMBl/6/hJn1S0q9zpL43ew6eg6AHm3DGdW3C418vSxbWB2U8J/nKcxJx8nNm4inZ1f7/m9cTzYJMiPVwI2rF7hyaCMBHe+r9vetLVW+N/Oll17i999/Z8mSJZw9exZVVYmLi+PkyZM8++yzREREcPny5eqsVVSz05fSmPjJt2w4cIKcGwXk3Cggfu8x/j5zCYkp1yxdXp1TmJNOYfY1CnPS72g/qmogM+kwV45sLrlY8If0M3spv/+EwvVTu+/ofS2tyi2zNWvWMGHCBEaOHMm1ayU/+BqNhqZNm/Kvf/2Lhx56iClTpvDtt99WW7HC1N9nLiE9Ow9vd1e+eOFvld5+/o9bKCwqxvCX06YGg0p+URH/Xr2Vt8cNrc5yRS3IvnyK4z/E/jFaRQmPoNa0eOgVbnWvJYB6m9etXZVbZhkZGcZxw0pv1v7r2GD9+/c33oYkakZ6dh5XM3NIz75FR8ly5NzI58CpCyZBVspgUNl19BwFhcXVUaaoJYW5GRz+5lUKMq+YLM+6cIwj307HK6wj5Qeaik/TLjVeY02qcpg1bNiQlJQUoGQkCj8/Pw4ePGh8/dKlS9Ivzcr89cJ1UfGtOzOrKhQbpMOzLUk9EIe+8EbZey5VA7mpZynMuop/hwGUOdRUNLjUb4xf23tqrdaaUOXDzLvvvpv4+Hhee+01AEaOHMmHH36IVqvFYDAwa9YsBgwYUG2FiqopKCxm6bo9rN7+O5k5N2jYwIvh93RkULc2NGzgyeWrmWW2URQI9q9PPWfzvcKFdcq+fPKWdwhkXz5J04ETcfb059KulRTfyEbROuDbujehfZ9C62S7VzLhDsLshRdeID4+noKCAnQ6HTNmzODIkSPGq5e9evXis88+q7ZCReXp9QZenb+SQ2cvG1tll69mMHv5ei6kpTPmvihi/7u2zHaqCmMGlj9UubCswtwM0g7Gk5t6Hkc3L/za9cHNPwwH53p/DHFtpkWtqjg4u6FotDTpOZLG3YdTdCMbB52r2T5qtqjKYabVapk6darxube3N7/99hsZGRlotVrc3d2rpUBRddsOneH3M5dMlpX+3f5h0wEWvfYkU0f25T8/byMz5wYA3u6uPDPkbnq2q9kRDkTVZCYd4ci3b2IoLgAUFEXh8q5VBPcejW+be0j7fZ3Z7RSNhgYtevzluRanel61U3QtqXKYtWnThrZt2zJy5EgeeeQR4+iwXl5e1VWbuEPbDp1Go1EwGMoeeiiKwrZDZxhxTyT9OrfkzKUrKIpCeENftFqZG9oaGYqLOLbinZIgU0vG5C89qkzc+DVtnvgAv/Z9STv4W8mQ1qrB2FILG/AcjvU8LVp/TavyT+2cOXNo0KABb775Js2bNycyMtI4OKKwDkV6A+XdraYAxX+MaOKg1dI8KIC7mvhLkFmx66d3lwwDZOY7VTRa0g7+SrP7n6f5gy/jGdwWZ5+G+DSPou3oDwmMND+Rtj2p8k/uhAkTWLduHZcuXWL27NnUq1ePV155hbCwMKKiopg9e7Z0mrWwjnc1Kfd8sEFV6XhXxceaE5ZXmHWt3CGvVYOegswrKIoG39bRtH38PTr9/UtaPjzNeHO5vbvjP8P+/v5MmjSJzZs3k5SUxMyZM1EUhRdeeIHg4ODqqFFUUZ/IlgT4eKDRmP4CaBSFLi1DaB4kkyfbEmefhre8WulSv3HtFmRlqvWYIjAwkNatW9OyZUtcXV1rZJ4AUXEuOkc+nfwIXVuGGv+gOzpoGdy9LW88OdiyxYlK8w7rgM7T3/wUb6pKYGTdnsjnjkfNUFWVjRs3smzZMlauXMnVq1fx9vbm0UcfZeTIkdVRo7gDDTzdeGvcENKz88jIycPP2136j9koRaOl9agZHP7mdQqzr5Wc3FcNKIqGpoMnl4xEW4dVOcy2bNnCd999x4oVK0hLS8PDw4Nhw4YxcuRI+vbti4ODfYxeaS+83V3xdq/8pMzCurg2CKLTxP9w7cT2P8Yz88K3dbTddbOoiionTnR0NG5ubjzwwAOMHDmS++67Dycn++h8J4Q10zg44ts6Gt/W0ZYuxapUOcyWL1/O4MGDcXa27VsghBB/0hcVkJV0CIO+GI/GLXF0tZ2+aVUOs4cffrg66xBC1LD8jFSundyJqi/GKzQCt4Bwk9dTEn7l3K9fGqerUzRaArsMJbTP2JKp5KycnNiyc6qqsvd4Imt2HuZqZg4hAfUZ2rM9TRv7Wbo0Uc0KczMwFBei82hgEj6qqnJ+/QIu7fj+j35qCqgGfJp1pflD/0DrqOP6qd2cXm06uq1q0HN55w84OLkQ1Kvy4+XVNgkzOzf/f1tYsXG/8bamUxdSidt9hJdGDaBf55aWLk9Ug+zLpzj76zyyLx4DQOfhS1D0Y/i37wdAasKvJUEGxtugoOSOgnO//YemA//Oha3Lyp1g+NLOH2gU9XC5c2taCwkzO3b47CVWbNwPYLw/U//Hfz9ZFk/XViF41HOp0L5UVWX/ySTidh3hWlYuYQ19eaBHO4L8fWqmeFEhuVcSOfT1yxj0fw6kWZB1hVM/zcJQXExg5EAu7fzB/MaqSmrCr4Tc+yQ5yafK7ZCrL7zBjeuXcPMPq4mPUG2s/0BYVFn8nmNoNeZvf9HrDWxKOFWh/aiqytwfN/PK3JVsOniK389c4n/bDvLMh4vZduh0dZYsKunitu9KguzmARkpufncoC/ixvVLZrYsoeqLKMhMQ3ObscwcdPXuuNaaJmFmxzJzbxhbYjfTaBSy8vKNz1VV5eDpi/xv60G2/n6awuI//9InnL7ID5sOAH+28AwGFb1B5f3/xnGjoLAGP4W4leundpsNMoDiG1nkpp7D0eXW82E6unri17aP+TsLFA1uDe/C2cu/OsqtUXKYacfCG/my4/BZs+P86w0q4Q0bAJB6PYvXv/yR83+Zkcnd1Zk3nhxMh2ZNiN99tNyhhPILi9h26Ax9O8n5N0u43VVGRdEQ0HEgF7YtK3sYqWjwDuuIk5s3QXc/SvrpPeRnpPy5nqJB4+hE00GTaqj66mVVLbM5c+bQrl07PDw88PDwICoqil9+MTPP318sX76cFi1a4OzsTNu2bVmzZk0tVWv9BnVri6OjtsxcDBqNQqMGXnRuGYLBoPLKvJVcSLtusk7OjXxen7+KtPQsrmfnmg0yKLlpvSoTqojqUb9Fd/MtKsDJzYd6/qE07jECj8Z//LEpvZoJ6NzrG4PK0dWTiKc+JajX47j6BuPs3ZDAyEF0HP95mS4c1sqqWmaNGzfm/fffp1mzZqiqyqJFixg6dCgHDhwwzgT1V9u3b2fUqFHExsZy//33s2TJEoYNG8b+/ftp06aNBT6BdanvWY/3JzxIzILVZPwxkixAkJ8Pb48fglajYc/x81xMKztPo6pCsd7AzzsOE97It2QmJzOBZlBVwhv6llkuakeTHo9w7fh2igvy/nK4qQAqof3GoWi0aDVa2jwey7Xj27l2YjsGfTFeIe3xa9cHB92ft7g5uLgTdPejBN39qEU+y52yqjB74IEHTJ6/++67zJkzh507d5oNs9mzZ3Pffffx0ksvAfD2228THx/P559/zty5c2ulZmvXJqwR384Yx97jiVzLzCXI34c2YQ2NrbWzl66WewhpUFXOXLrCxAd788OmA6g3zayo1Sg08vUmolmT2vkwwqgg6yo3rl7AsZ4X7Z6cSeLGr7l2YgeoBtwCwwnq9Rg+zf6cOk6jdcC3dS98W/eyYNU1y6rC7K/0ej3Lly8nNzeXqCjzk2vs2LHDZB4CgAEDBrBq1apy91tQUEBBQYHx+V/n+rRXDlot3VqXXFbX6w3kFxbh7OSIoih4urmUewip1Sh4ubkQ2MCTt8YN4d2vfyH3RoGxO1JjPx/eHT+0zHhpouYUF+Rx+ufPuHp0C6X9xVz9Qrhr6Au0eOgfqAa93UxQUllWF2aHDh0iKiqK/Px83NzcWLlyJa1atTK7bkpKCv7+pldZ/P39jfN5mhMbG0tMTEy11mwLMnNusGDNNuL3HqOwSI+ftzuP3NuJPh2b89n3GygsKjvhr96g0q9zyb995xYhLJsxnp1Hz5KelUdIYH3aN20sc6PWsuPfx5JxLoG/TuabdyWJQ4unEfnsHJzc6m6/P6u6AADQvHlzEhIS2LVrF8899xxjxozh6NGj1bb/adOmkZmZaXxs2rSp2vZtrW4UFPL/PvuOX3YdobCoZNz/tPRsPv9+A0vX7eXlUf3RKIqxT1ppS+vh6A60C29k3I/OyYHoiLsY1iuCiGZNJMhqWU7KGTLO7jc7ya++II+U/WWnDaxLrK5l5uTkZJzpKTIykj179jB79mzmzZtXZt2AgABSU1NNlqWmphIQUP5w0DqdDp3uz9sy3Nzcqqly67V21xEupqVj7mDyuw37WDL9aea+9Bg/bfudc5evUt/Tjfu6tiayeZAElhXJunCU0pP7ZagGMpMO13ZJVsXqwuxmBoPB5BzXX0VFRbFu3TqmTJliXBYfH1/uOba6asfhs2aDDP68Ef2+rq2ZPPzeWq1LVI7W0RmzQQagaNA6VezWNHtlVWE2bdo0Bg4cSFBQENnZ2SxZsoSNGzcSFxcHwOjRo2nUqBGxsbEAPP/880RHRzNz5kwGDx7M0qVL2bt3L/Pnz7fkx7A65jrNVuZ1YR187uqKonFANZQ9v4lqsOsrlRVhVefM0tLSGD16NM2bN6dPnz7s2bOHuLg4+vUrufs/KSmJ5ORk4/rdu3dnyZIlzJ8/n/bt27NixQpWrVolfcxu0qVlSHkzlAEQKVPO2QRHVw9C+40reWLSUVbBK6wjDVr2tEhd1sKqWmb/+c9/bvn6xo0byywbMWIEI0aMqKGK7MOgqDb8uPUgVzNzynTDeKBHO/x9bn3vXnXJvVFAVm4+9T3r4eRoVT96NqNh5wdw8Qnk4o4fyE07h5OrF/4dBhDYaTCKRmvp8ixKfqJsWOkEJbebqMTNxZlZkx9h7qrNbP39NAZVxd3VmYejO/Bo384AFBXr2XP8vLHbRauQwGo7+X89K5cvVm5iy8FTGFQVF50jQ3q0Z8zAKBwd6vYvYFV4h3fCO7yTpcuwOhJmNuyLFyo3+ucj90byzJCeKIoGHw9XHLQlQXLw9AXeXriGzNw/b3lq2siXmKeH4Oftfkc13igo4v99tpyU65nGc3M3Cor4bsNeUtOzeG103Z7rUVQfCTM7l3o9i0+W/cb+k0kAaDUa+kS2YNLDvXHQaklLz+LV+asoKjbtu3Qu+Sqvzl/F/Jcev6Me/r/tPcblqxlllqsqbDxwkr/160JoYIMq71+IUlZ1AUBUr7z8ks6yCacvGJfpDQZ+23uMGV+tRlVVVm8/RHGxAfWmK5p6g0piyjUSTl24ebeVsu9EYrkXHxRFYe/xxDvavxClpGVmw/4+cwnp2Xl4u7uaPeSM33uMKxll7z01/DEE9rHEZM5culJu1wyNonDm8hU6Nq/Y1U5VVcnOy8dBq8XV2cm4j3I7eqKi1cjf01JObt4m/xWVI2Fmw9Kz87iaWf6N8gdPXyxvjgo0isLB0xfxrOdyy1Ez3F1LhlPOLyziXPJVdI4OhAY2KHNxYHPCKRb+sp0LfwwnFHlXEM8MvZuoNmFs+d380NqqCt1ah1b049q9iKdn336lCiouyOPK4Y3kpp3D0dUL/3b34uwdWG37t0YSZnbMyaFkYMabDyGhpJ3k6OBA384tid97zPz2jg70bBvOt7/t5tvf9nCjoAiAwPqe/N/we+jcIgQomWvgwyVx/DXeDpy6wPOzv2PW5Ee4q4kfpy+WbQEO7dmehg28quGT1i0lQbWB3LTzOLp64df2Xlx8/gyqnORTHP7mdYrzc1A0WlRV5cKWbwkbMIGGnR+4xZ5tm7Tx7VjPdk3LHd5HVVV6tA2nQ7MmDO3ZHvjzBnOtRkFRFF58tB8/bf+dr37ebgwygJTrmbzx5Y8cS0xGrzfw5U9bSvb5l/0bVJXC4mKWb9jHh39/mAd7ReCicwTAz8ud54ZF8/cHe1f7Z7Z3Ocmn2PvZWM788gWpB+K4sHUp+74Yx+U9/wPAoC/myLK3SgZrpGTuy5Ib01XOxs0l+3LFJrGxRdIys2NRbcKIvCuI/aeS/hzW/Y/DzpH3diKwvicAEx/qTecWIazddZgrmTmEN/Rl6N0lraZH3ix7a5iqgqrAt7/tYfSAbuUOm20wqOw4fJZXHr+PZ4dF88yQXhTr9Tg6lB3KW9yeQV/M0ZuD6g9n4+bh3qgFhdnXKMq5bn4HGi0pB37BvWGz2ii31kmY2TGtRsNb44fww8YD/LT9d9Kz82js583w6I4mEwArikLX1qF0ven81bHEZJMW2V8ZDCoJpy4wekC3Ctej0Sg4aeRHrqrSz+ylsLygUjSk7F+Lq19wuZP5YtCTn17+WH+2Tn6y7JyTgwOP9u1s7OlvTmkwbTt8Br1eT2TzYKLahKG7zS1HTg4lFwO83V3Nts40GoWoNtY9cawtKchIKz+oVAP56cn43NWl3Ml80Whx9i5/eCxbJ2FWxxUWFxOzYDW7j543dpMoncTkg2cfIrC+JynXM8v8fmg0Cvd0bI5Wq2H8A3cbLwCUrqZRFJwcHPhbvy6I6qHz8i8/qBQNzt6BeId3wsnNh8LcjLKDOBr0BHYcWON1WopcAKhDzF0MWLZuL3uOnQdKOtTqDSW/AOeSrzJn1Sb+b/g9aBTF5C4AjUbBx70ej/Ypae3169ySN58cTJD/n0M2d2wexOznHzFZJu6MT9OSoDI7tZxqIKDjQDRaB1qNnI6Dc8kM5IpG+8f6CuH3PYdboH2eLwNpmdm9/MIilsTv5ucdh8jKzSewvicP9+7IkB7tUBSF/209aP70ikFl44GTTHq4N59OfoRvf9tDwqkLODk4cE/H5jzapzP1PesZ17+7fTN6tmtKdl4Bjg4aXHR1c1KNmqRotLR6dAaHv3mN4hvZxm4XqCph9z1rPLHvFtiUTpO+4srhTeRdOf/HjOX32vUhJkiY2bVivZ5X563kyLlkYx+v5GuZfP79Bi6mpfPs0F4m82neTG8wkJ6dR8vgQN56esht309RFDzqOVdb/aIst4DwCgWVg86VwEj7PaQ0R8LMjm37/QyHzl42+9qqLQkMvbs99T3rcS0z1+w6DloNPh71zL4mLKcuBlVFyDkzO7bt8JlyR7xQFIUdh88y7O4IzK2hURT6dW5FPWedmVeFsD4SZnZMry87GkYp5Y/XR/SOpHfH5kBJz//SK5qtQwN5dmjdHlPemuWnp3Dt5C6yL50o9zuua+Qw0451vCuIzQfN375iUFU63BWEVqvh1ScG8nB0B7YdOoNebyCyeTAd7pJ5Ma1RcX4OJ//3CddP7jIuc/ZpRPNhL9ltz/6KkjCzY/dGtuC79XtJSc8y6ZahKApdW4XQPOjP2eCbBwXQPMi+r3bZg2PL3y0zP2Z+ejKH/zuNjs/ORedRdwe6lMNMO+aic+ST/3uEqNZhxlaWk4OWoT3b8/rowRauTlRW9uWTZCb+bn5G86ICUvb/YpnCrIS0zOxcfc96zHjqAbJyb5CecwM/LzfpA2ajsi8d51YzmmddND+UU10hYVZHeNRzwaNe3Z7x2taVzFhe/u1MDrpbz9Jl7+QwUwgbUf+ubihaR/MvqgZ8W0fXbkFWRsJMCBvh4OJO+IBnS54Y788sORfq06wr9Vt0t0xhVkIOM4WwIQEd78PZpyGXd60kJ/UsTvW88Y8YQECH/jKjuaULEDWrsKiYZev38vP2Q2Tk5NHI15uHe3dkYNfW0o/MRnmFtMMrpJ2ly7A6EmZ2TK838NqXP3Lw9EVjL/ELqdf5dNlvXExL55khd1u4QiGqj5wzs2PbD58h4dQFk9tdSv9v+YZ9ZmcaF8JWWVWYxcbG0rlzZ9zd3fHz82PYsGGcOHHiltssXLgQRVFMHs7OMgwNwNbfT/8xCW9ZiqKw/fDZWq5IiJpjVWG2adMmJk6cyM6dO4mPj6eoqIj+/fuTm2t+iJpSHh4eJCcnGx+JiYm1VLF1K9IbUMvpl6QARcXFtVuQEDXIqs6ZrV271uT5woUL8fPzY9++ffTqVf4IDoqiEBAg9xXerEOzxmy5xY3mEc2a1HJFQtQcq2qZ3SwzMxMAH59bjyOfk5NDcHAwTZo0YejQoRw5cqQ2yrN6fTu1xN/bHe1NY5ppFIVOLYJpITeWCztitWFmMBiYMmUKPXr0oE2bNuWu17x5c7766it+/PFH/vvf/2IwGOjevTsXL140u35BQQFZWVnGR05OTk19BItz0Tnx6eRHiGwebFzmoNVwX9fWvPnk/WW6ZhQWFbN+33Hm/biZb3/bTer1rNouWYgqs6rDzL+aOHEihw8fZuvWrbdcLyoqiqioKOPz7t2707JlS+bNm8fbb79dZv3Y2FhiYmKqvV5r5evlzrvPDONaZi7pObkE+Hjg5lL2AsnFK+m89MX3XM3IQavRoKoqC9Zs59mhvXgouqMFKheicqyyZTZp0iRWr17Nhg0baNy4caW2dXR0pEOHDpw+fdrs69OmTSMzM9P42LRpU3WUbNX0BgOJKdc4d/kal65klBmZVFVVpv/nJ65n5RrXN6gqqgpzVm3m8NlLlihbiEqxqpaZqqr83//9HytXrmTjxo2EhoZWeh96vZ5Dhw4xaNAgs6/rdDp0uj/HtXdzc6tyvZbm7e5q8l9zjiUm8/bCn7mS8efhdPMgf6aPvR9fL3cADp+9TFLqdbPbazUK/9v6O23CGlVj5UJUP6sKs4kTJ7JkyRJ+/PFH3N3dSUlJAcDT0xMXl5Lha0aPHk2jRo2IjY0F4K233qJbt240bdqUjIwMPvroIxITExk3bpzFPkdt+eKFv93y9fTsXP4x5wcKCk27YJy+mMa0uSuZ//ITaDQKl27ReVZvUElKMx90QlgTqwqzOXPmANC7d2+T5QsWLODJJ58EICkpCY3mz6Pj9PR0xo8fT0pKCt7e3kRGRrJ9+3ZatWpVW2VbrTU7j5BfWFzmsFJvUElMvc6+E4l0bhmCv49HufvQaBQC63vWdKlC3DGrCrOKzDKzceNGk+effvopn376aQ1VZNtOX0zD7HTllBw+nryYRueWIbQPb0yAjwdp6dnGyYJLGQwq93dvWxvlCnFHrPICgKgebi66cufNNBhU3F1Kzh1qNAoznnqAen88L5lyrmS7JwZ0NenaIYS1sqqWmahefTu1YO0u8x2INRoNvSL+nJosvJEvi98Yy297j3P6Yhoers707dSS0IZ1d7YfYVskzOxYu/DGDOnRjv9t+x2NomBQVbQaBb1BZfKIe/FyK7kKWlBYzIYDJ9h3IhGtRkNUmzB6tm2KVisNd2E7JMzsmKIoTHr4Hjo2D2LNjsNcycgmJLABw+5uT6uQhgCkZ+fxwufLuZCWXjLChgLr9h2ndWhDYic8iIuunDHnhbAyEmZ2TlEUerRtSo+2Tc2+/q8fNhq7ZhhU1Tjg2bHzyXzz6y7GPdCzlioV4s5ImNUBZy5dYe2uI1zLyiXI34dB3Vrj5+1Bzo18thw8VeYKJpQE2887DvH0/T1keG1hEyTM7NzyDfuY/78taDUl58wUFJat20PM0w/QqIG32SArlXOjgGK9AUeHuj1RhrANcobXjp2+mMb8/20BSjrKqmpJi0uvN/D2wjW46hzROZb/98zXy02CTNgMCTM79suuI2XGMoOS02L5hUXsOnaewVFtyz2MHN5bRssQtkPCzI5dy8xBbyj/DoBrmTk8fX8PurUquaFfoyjGOQMGR7Vl2N0daq1WIe6UnDOzY038vNFoFAxmAk1vUGns542TowNvjRvCiaRUk35mQf63Ht1XCGsjYWbHBnVry/IN++GmSU00ioKXuyvd24QblzUP8qd5kH8tVyhE9ZHDTDsW2MCTN58cjNMfJ/m1f4w24uXmQuyEYXJyX9gVaZnZue5tw1kWM45NB05xLSuHIH8furcNx8lBvnphX+Qnug5wc3FmsAzjI+ycHGYKIeyChJkQwi5ImAkh7IKcM6tDkpOTSU5OtnQZohoFBgYSGBho6TKsQp0Ps8DAQKZPn273PxAFBQWMGjWqTswTWpdER0cTFxdnMn1iXaWoFZlFRNi8rKwsPD092bRpk03PFSr+lJOTQ3R0NJmZmXh4lD/DVl1R51tmdU1ERIT84NuJrKwsS5dgVeQCgBDCLkiYCSHsgoRZHaHT6Zg+fbqcKLYj8p2akgsAQgi7IC0zIYRdkDATQtgFCTMhhF2QMBNVcv78eRRFYeHChZYuRQhAwqxWnDlzhgkTJhAWFoazszMeHh706NGD2bNnc+PGjRp736NHjzJjxgzOnz9fY+9REe+++y5DhgzB398fRVGYMWOGReupLYqiVOixcePGO36vvLw8ZsyYUal92dv3IncA1LCff/6ZESNGoNPpGD16NG3atKGwsJCtW7fy0ksvceTIEebPn18j73306FFiYmLo3bs3ISEhNfIeFfH6668TEBBAhw4diIuLs1gdtW3x4sUmz7/++mvi4+PLLG/ZsuUdv1deXh4xMTEA9O7du0Lb2Nv3ImFWg86dO8ejjz5KcHAw69evN7mZfeLEiZw+fZqff/7ZghX+SVVV8vPzcXFxqfZ9nzt3jpCQEK5evYqvr2+1799aPf744ybPd+7cSXx8fJnllmJv34scZtagDz/8kJycHP7zn/+YHZWjadOmPP/888bnxcXFvP3224SHh6PT6QgJCeHVV1+loKDAZLuQkBDuv/9+tm7dSpcuXXB2diYsLIyvv/7auM7ChQsZMWIEAPfcc0+ZQ5rSfcTFxdGpUydcXFyYN28eAGfPnmXEiBH4+Pjg6upKt27d7ih0LdkqtHYGg4FZs2bRunVrnJ2d8ff3Z8KECaSnp5ust3fvXgYMGECDBg1wcXEhNDSUp556Cig5f1kaRjExMcbv+naHjfb2vUjLrAb99NNPhIWF0b179wqtP27cOBYtWsTw4cN54YUX2LVrF7GxsRw7doyVK1earHv69GmGDx/O008/zZgxY/jqq6948skniYyMpHXr1vTq1YvJkyfzz3/+k1dffdV4KPPXQ5oTJ04watQoJkyYwPjx42nevDmpqal0796dvLw8Jk+eTP369Vm0aBFDhgxhxYoVPPjgg9X3DySYMGECCxcuZOzYsUyePJlz587x+eefc+DAAbZt24ajoyNpaWn0798fX19fXnnlFby8vDh//jw//PADAL6+vsyZM4fnnnuOBx98kIceegiAdu3aWfKj1T5V1IjMzEwVUIcOHVqh9RMSElRAHTdunMnyF198UQXU9evXG5cFBwergLp582bjsrS0NFWn06kvvPCCcdny5ctVQN2wYUOZ9yvdx9q1a02WT5kyRQXULVu2GJdlZ2eroaGhakhIiKrX61VVVdVz586pgLpgwYIKfT5VVdUrV66ogDp9+vQKb2NPJk6cqP71V27Lli0qoH7zzTcm661du9Zk+cqVK1VA3bNnT7n7vpN/W3v5XuQws4aUDs/i7u5eofXXrFkDwNSpU02Wv/DCCwBlDvNatWrF3XffbXzu6+tL8+bNOXv2bIVrDA0NZcCAAWXq6NKlCz179jQuc3Nz45lnnuH8+fMcPXq0wvsXt7Z8+XI8PT3p168fV69eNT4iIyNxc3Njw4YNAHh5eQGwevVqioqKLFixdZMwqyGlY4ZlZ2dXaP3ExEQ0Gg1NmzY1WR4QEICXlxeJiYkmy4OCgsrsw9vbu8y5llsJDQ01W0fz5s3LLC89PL25DlF1p06dIjMzEz8/P3x9fU0eOTk5pKWlASWjyT788MPExMTQoEEDhg4dyoIFC8qcS63r5JxZDfHw8KBhw4YcPny4UtspilKh9bRa87ORq5UYN6AmrlyKijMYDPj5+fHNN9+Yfb30pL6iKKxYsYKdO3fy008/ERcXx1NPPcXMmTPZuXOnjBz8BwmzGnT//fczf/58duzYQVRU1C3XDQ4OxmAwcOrUKZOT9KmpqWRkZBAcHFzp969oMN5cx4kTJ8osP378uPF1UT3Cw8P57bff6NGjR4X+sHTr1o1u3brx7rvvsmTJEh577DGWLl3KuHHjqvRd2xs5zKxBL7/8MvXq1WPcuHGkpqaWef3MmTPMnj0bgEGDBgEwa9Ysk3U++eQTAAYPHlzp969Xrx4AGRkZFd5m0KBB7N69mx07dhiX5ebmMn/+fEJCQmjVqlWl6xDmPfLII+j1et5+++0yrxUXFxu/t/T09DIt7oiICADjoaarqytQue/a3kjLrAaFh4ezZMkSRo4cScuWLU3uANi+fTvLly/nySefBKB9+/aMGTOG+fPnk5GRQXR0NLt372bRokUMGzaMe+65p9LvHxERgVar5YMPPiAzMxOdTse9996Ln59fudu88sorfPvttwwcOJDJkyfj4+PDokWLOHfuHN9//z0aTeX//i1evJjExETy8vIA2Lx5M++88w4ATzzxRJ1t7UVHRzNhwgRiY2NJSEigf//+ODo6curUKZYvX87s2bMZPnw4ixYt4osvvuDBBx8kPDyc7OxsvvzySzw8PIx/BF1cXGjVqhXLli3jrrvuwsfHhzZt2tCmTZty39/uvhdLX06tC06ePKmOHz9eDQkJUZ2cnFR3d3e1R48e6meffabm5+cb1ysqKlJjYmLU0NBQ1dHRUW3SpIk6bdo0k3VUtaRbxeDBg8u8T3R0tBodHW2y7Msvv1TDwsJUrVZr0k2jvH2oqqqeOXNGHT58uOrl5aU6OzurXbp0UVevXm2yTmW6ZkRHR6uA2Ye5biP26uauGaXmz5+vRkZGqi4uLqq7u7vatm1b9eWXX1YvX76sqqqq7t+/Xx01apQaFBSk6nQ61c/PT73//vvVvXv3muxn+/btamRkpOrk5FShrhb29r3ISLNCCLsg58yEEHZBwkwIYRckzIQQdkHCTAhhFyTMhBB2QcJMCGEXJMwsaOHChSiKgrOzM5cuXSrzeu/evW/Z6bEmrFu3jqeeeoq77roLV1dXwsLCGDduHMnJyWbX3759Oz179sTV1ZWAgAAmT55MTk5OrdZsTeQ7tRwJMytQUFDA+++/b+kyAPjHP/7Bxo0befDBB/nnP//Jo48+ynfffUeHDh1ISUkxWTchIYE+ffqQl5fHJ598wrhx45g/f75xhNu6TL5TC7B0r926bMGCBSqgRkREqDqdTr106ZLJ69HR0Wrr1q1rtaZNmzYZB2D86zJAfe2110yWDxw4UA0MDFQzMzONy7788ksVUOPi4mqlXmsj36nlSMvMCrz66qvo9Xqr+Eveq1evMvdf9urVCx8fH44dO2ZclpWVZZyco3TsNoDRo0fj5ubGd999V2s1WyP5Tmuf3GhuBUJDQxk9ejRffvklr7zyCg0bNqzU9nl5ecabhW9Fq9Xi7e1d6fpycnLIycmhQYMGxmWHDh2iuLiYTp06mazr5OREREQEBw4cqPT72BP5TmuftMysxGuvvUZxcTEffPBBpbf98MMPy4xUau7RoUOHKtU2a9YsCgsLGTlypHFZ6cljc7NOBQYGcvny5Sq9lz2R77R2ScvMSoSFhfHEE08wf/58XnnlFbM/UOUZPXq0yZj95anKyLKbN28mJiaGRx55hHvvvde4vHQmdp1OV2YbZ2fnGp2p3VbId1q7JMysyOuvv87ixYt5//33jYM2VkRYWBhhYWHVXs/x48d58MEHadOmDf/+979NXiv9JTI3Dn1NTSZsi+Q7rT0SZlYkLCyMxx9/3PiXvKJKz3/cjlarrfDM1RcuXKB///54enqyZs2aMrNMlbYyzPVVSk5OrvQ5Insl32ntkXNmVub111+v9HmWjz/+mMDAwNs+OnfuXKH9Xbt2jf79+1NQUEBcXJzZw6M2bdrg4ODA3r17TZYXFhaSkJBgHNZZyHdaW6RlZmXCw8N5/PHHmTdvHsHBwTg43P4rqs7zK7m5uQwaNIhLly6xYcMGmjVrZnY9T09P+vbty3//+1/eeOMN41/5xYsXk5OTYxudLGuJfKe1Q0aataCFCxcyduxY9uzZY3I5/PTp07Ro0QK9Xk/r1q0rPV3dnRg2bBg//vgjTz31VJl5B9zc3Bg2bJjx+f79++nevTutWrXimWee4eLFi8ycOZNevXoRFxdXazVbE/lOLcjSvXbrstLe4nv27Cnz2pgxY1Sg1nuLBwcHlzsufHBwcJn1t2zZonbv3l11dnZWfX191YkTJ6pZWVm1WrM1ke/UcqRlJoSwC3IBQAhhFyTMhBB2QcJMCGEXJMyEEHZBwkwIYRckzIQQdkHCTAhhFyTMhBB2QcJMCGEXJMyEEHZBwkwIYRckzIQQdsGqwiw2NpbOnTvj7u6On58fw4YN48SJE7fcpnQG6b8+nJ2da6liIYS1sKow27RpExMnTmTnzp3Ex8dTVFRE//79yc3NveV2Hh4eJCcnGx+JiYm1VLEQwlpY1Uiza9euNXm+cOFC/Pz82LdvH7169Sp3O0VRCAgIqOnyhBBWzKpaZjfLzMwEwMfH55br5eTkEBwcTJMmTRg6dChHjhyp8HskJyczY8YMs5M4CCFsh9UOzmgwGBgyZAgZGRls3bq13PV27NjBqVOnaNeuHZmZmXz88cds3ryZI0eO0Lhx4zLrFxQUmEyllZCQQHR0NPv27aNjx4418lmEEDXPasPsueee45dffmHr1q1mQ6k8RUVFtGzZklGjRvH222+XeX3GjBnExMSUWS5hJoRts8rDzEmTJrF69Wo2bNhQqSADcHR0pEOHDpw+fdrs69OmTSMzM9P42LRpU3WULISwMKsKM1VVmTRpEitXrmT9+vWEhoZWeh96vZ5Dhw6ZnRcQSqae9/DwMD7c3NzutGwhhBWwqquZEydOZMmSJfz444+4u7uTkpIClMznVzo/4OjRo2nUqBGxsbEAvPXWW3Tr1o2mTZuSkZHBRx99RGJiIuPGjbPY5xBC1D6rCrM5c+YA0Lt3b5PlCxYs4MknnwQgKSkJjebPBmV6ejrjx48nJSUFb29vIiMj2b59O61ataqtsoUQVsBqLwDUlv379xMZGSkXAISwcVZ1zkwIIapKwkwIW1ZcaOkKrIaEmRC2zFBs6QqshoSZEDatTp/yNiFhJoSwCxJmQtiyut0ZwYSEmRA2TcKslISZELZMWmZGEmZCCLsgYSaELVMNlq7AakiYCWHLJMyMJMyEsGUGvaUrsBoSZkLYMkORpSuwGhJmQtgyvdybWUrCTAhbJjeaG0mYCWHL9AW3X6eOkDATwpYV5lm6AqshYSaELSuSMCslYSaELSvMsXQFVkPCTAhbViBhVkrCTAhbJi0zIwkzIWxZfqalK7AaEmZC2DIJMyOrCrPY2Fg6d+6Mu7s7fn5+DBs2jBMnTtx2u+XLl9OiRQucnZ1p27Yta9asqYVqhbACEmZGVhVmmzZtYuLEiezcuZP4+HiKioro378/ubm55W6zfft2Ro0axdNPP82BAwcYNmwYw4YN4/Dhw7VYuRAWUpAlAzT+wapnNL9y5Qp+fn5s2rSJXr16mV1n5MiR5Obmsnr1auOybt26ERERwdy5c2/7HjKjubBpSx+Dh+aDUz1LV2JxVtUyu1lmZkkT2sfHp9x1duzYQd++fU2WDRgwgB07dphdv6CggKysLOMjJ0euBgkbl59l6QqsgtWGmcFgYMqUKfTo0YM2bdqUu15KSgr+/v4my/z9/UlJSTG7fmxsLJ6ensZHdHR0tdYtRK3Lz7B0BVbBasNs4sSJHD58mKVLl1brfqdNm0ZmZqbxsWnTpmrdvxC1TlpmADhYugBzJk2axOrVq9m8eTONGze+5boBAQGkpqaaLEtNTSUgIMDs+jqdDp1OZ3zu5uZ25wULYUlyRROwspaZqqpMmjSJlStXsn79ekJDQ2+7TVRUFOvWrTNZFh8fT1RUVE2VKYR1kTADrKxlNnHiRJYsWcKPP/6Iu7u78byXp6cnLi4uAIwePZpGjRoRGxsLwPPPP090dDQzZ85k8ODBLF26lL179zJ//nyLfQ4hapWcMwOsrGU2Z84cMjMz6d27N4GBgcbHsmXLjOskJSWRnJxsfN69e3eWLFnC/Pnzad++PStWrGDVqlW3vGgghF2RMAOsrGVWkS5vGzduLLNsxIgRjBgxogYqEsIGyGEmYGUtMyFEFdzIsHQFVkHCTAhbdyPd0hVYBQkzIWxd7lW5PxMJMyFsX3F+yQ3ndZyEmRD2IPOSpSuwOAkzIezB9bOWrsDiJMyEsAdXjlu6AouTMBPCHlw+YOkKLE7CTAh7kHkRMi5YugqLkjATwl6cWW/pCixKwkwIe3HiFzAYLF2FxUiYCWEvspPh4m5LV2ExEmZC2JPfv7N0BRYjYSaEPbm0D67cfq5ZeyRhJoS9OfBfS1dgERJmQtibc5vh+jlLV1HrJMyEsEcHFlu6glonYSaEjerUqRONJ66k03v7y754Zj1kJNV+URYkYSaEjUpJSeHS9RukZBWWfVFV69y5syrNAfDUU0/d8nVFUXB2dqZx48b07t1bpn0TwhJOxUPkk+DR0NKV1Ioqhdn69eu5ceMGV65cAcDb2xuA9PSS4Xt9fX0xGAxcu3YNRVEYMGAAK1aswNXVtZrKFkLclmqAhCXQ60VLV1IrqnSY+csvv6DT6ZgxYwbXrl0zPq5evcr06dNxcXFh27ZtpKen88Ybb7B27VreeOON6q5dCHE7J9dC7jVLV1ErqhRmkyZNYtCgQbz55pvGVhmAj48P06dP57777mPSpEl4enoyY8YMHn30UVasWHHb/W7evJkHHniAhg0boigKq1atuuX6GzduRFGUMo/SyYOFqPP0RXBouaWrqBVVCrOdO3fSvn37cl9v374927dvNz6/++67SU1Nve1+c3Nzad++Pf/6178qVc+JEydITk42Pvz8/Cq1vRB27dj/oCDH0lXUuCqdM/Py8uLXX3/lueeeM/v62rVr8fT0ND7PycnBw8PjtvsdOHAgAwcOrHQ9fn5+eHl5VXo7IeqEwlw4+iN0eMzSldSoKrXMxo8fz48//sjw4cNZt24diYmJJCYmsm7dOoYPH87q1asZP368cf01a9YQERFRXTWXERERQWBgIP369WPbtm23XLegoICsrCzjIyfH/v9iCcHvS6Ewz9JV1KgqtcymT5/OjRs3+PTTT1m5cqXJa1qtlqlTpzJ9+nQA8vPzefLJJ2nXrt2dV3uTwMBA5s6dS6dOnSgoKODf//43vXv3ZteuXXTs2NHsNrGxscTExFR7LUJYtfws+H0ZdBpr6UpqjKKqVZ89NC0tzdgyAwgODqZPnz7Vcs5KURRWrlzJsGHDKrVddHQ0QUFBLF5s/naOgoICCgoKjM8TEhKIjo5m37595QagENaocePGXLp0iUZeTlx8v9vtN9A6wSOL7LbfWZVaZqX8/PwYNWpUddVSLbp06cLWrVvLfV2n06HT6YzP3dzcaqMsISxPXwhbZsKgj0FRLF1NtbO725kSEhIIDAy0dBlCWKeLe+HIytuvZ4PuqGVW3XJycjh9+rTx+blz50hISMDHx4egoCCmTZvGpUuX+PrrrwGYNWsWoaGhtG7dmvz8fP7973+zfv16fv31V0t9BCGs3845ENge6odbupJqZVVhtnfvXu655x7j86lTpwIwZswYFi5cSHJyMklJf44EUFhYyAsvvMClS5dwdXWlXbt2/Pbbbyb7EELcRF8I8W/CQ1+Ck/3cYnhHFwDswf79+4mMjJQLAMLmVPoCwM3CekPfGXZz/szuzpkJISro7Ea7mgBFwkyIumzXXLicYOkqqkWFz5nde++9ZZatX1+3Z1AWwuapBvhtBjz8b6jXwNLV3JEKh1lwcHBN1iGEsJQb6bAuBu6fBRqtpaupsgqH2YIFC2qyDiGEJSX/Dnv+DV0nWLqSKpNzZkKIEge/hcsHLF1FlVUpzBISEvj2229NlsXFxdGrVy+6du3K7Nmzq6U4IUQtUlXY8J7Njq5RpTB7+eWXWbZsmfH5uXPnePDBBzl3rmTi0alTpzJ//vzqqVAIUXty0mDvV5auokqqFGYHDx6kZ8+exudff/01Wq2WAwcOsGvXLoYPH87cuXOrrUghRC06/L1NzohepTDLzMykfv36xudr1qyhX79+NGhQcmm3X79+JvdYCiFsiGoouRhgY6oUZoGBgRw7dgyA5ORk9u3bR//+/Y2v5+TkoNHItQUhbNb5rXDlpKWrqJQq3Wg+dOhQPvvsM/Lz89m1axc6nY4HH3zQ+PrBgwcJCwurtiKFEBaQ8A30s51RmasUZu+88w5Xrlxh8eLFeHl5sXDhQvz9/QHIyspixYoVTJw4sVoLFULUsnObISsZPGxjfMAqhZmbmxvffPNNua9dvHhRZi8XwtapBji6CrqZn4XN2lTLia3MzEz0en3JDjUaPD09cXR0rI5dCyEs6fjPUFxo6SoqpMphtnfvXu677z5cXV2pX78+mzZtAuDq1asMHTqUjRs3VleNQghLKciGc5ssXUWFVCnMtm/fTs+ePTl16hSPP/44BoPB+FqDBg3IzMxk3rx51VakEMKCjq+2dAUVUqUwe/XVV2nZsiVHjx7lvffeK/P6Pffcw65du+64OCGEeUlJSeTlldx2lFdoIOl6fs292eUEyLhQc/uvJlUKsz179jB27Fh0Oh2KmSF3GzVqREpKyh0XJ4QwtXv3bh544AFCQkJIT08HID2vmJDXdjPki8PsOZ9dM2989Mea2W81qlKYOTo6mhxa3uzSpUsyH6UQ1eyHH36gR48e/PLLL9w8dYeqwprD1+n+YQI/HLha/W9+cq3V34BepTDr1q0bK1asMPtabm4uCxYsIDo6+o4KE0L8affu3YwcORK9Xm/sOXAzvQH0BpWRXx6r/hZaQXZJoFmxKoVZTEwMe/fuZfDgwfzyyy9ASa//f//730RGRnLlyhXeeOONai1UiLrsnXfeQVXVMi2ym6mAiso7axKrv4jD38MtjsgsrUph1rVrV9asWcPp06cZPXo0AC+88ALPPPMMer2eNWvW0K5du0rvd/PmzTzwwAM0bNgQRVFYtWrVbbfZuHEjHTt2RKfT0bRpUxYuXFjp9xXCmiUlJbF69epyW2Q30xvgp0PXq/+iQOZFuLinevdZjarcz+zee+/lxIkT7N+/n2XLlvHtt9+ye/duTp48WeVDzNzcXNq3b8+//vWvCq1/7tw5Bg8ezD333ENCQgJTpkxh3LhxxMXFVen9hbBG69atu22L7GaqCuuPZ1R/Maes93frjmc0j4iIICIiohpKgYEDBzJw4MAKrz937lxCQ0OZOXMmAC1btmTr1q18+umnDBgwoFpqEsLSsrOz0Wg0t7zodjONAln5FWvJVUrSLtAXg/aOo6PaVamizZs33/J1RVFwdnamcePGBAbW3E2qO3bsoG/fvibLBgwYwJQpU8rdpqCggIKCAuPznJycmipPiGrh7u5eqSADMKjg4VwDMy0V5sCN6+DmV/37vkNVCrPevXub7V9mTrNmzYiJiWHkyJFVeatbSklJMY7WUcrf35+srCxu3LiBi4tLmW1iY2OJibGdYU2E6NOnD4qiVOpQU1Hg3hZeNVNQfpZVhlmVzpmtXbuWdu3a0bx5cz7++GNWrVrFqlWr+Oijj2jevDkRERGsWLGCmTNnoigKf/vb38rtylHbpk2bRmZmpvFRek+pENYqKCiI+++/H622Yi0trQYeaOtDkI9zzRRkpUMCValltnbtWpydndm1axdOTk4mr/3973+nd+/e7Ny5kw8++IBnn32WTp068cEHHzB8+PBqKbpUQEAAqampJstSU1Px8PAw2yoD0Ol06HQ643Pp3CtswRtvvMEvv/xy2xaaAigovD6ohibt9gkDp3o1s+87VKWW2TfffMPf/va3MkEG4OzszGOPPcaiRYuMzx9//HGOHj16Z5WaERUVxbp160yWxcfHExUVVe3vJYQlde7cmWXLlqHVasttoWk1oNUofDe+JZ1D3GumkLYjama/1aBKYZabm1umRfRXycnJJifWvby8KtREzsnJISEhgYSEBKCk60VCQgJJSUlAySFiab82gGeffZazZ8/y8ssvc/z4cb744gu+++47/t//+39V+VhCWLWHHnqI7du3M2jQoDLnrBUFBrfxYfvLETzYoUHNFODZGJr1q5l9V4Mqhdm9997LrFmzWL267NAgP/30E7Nnz+bee+81LktISCAkJOS2+927dy8dOnSgQ4cOQMn8mx06dODNN98ESkKyNNgAQkND+fnnn4mPj6d9+/bMnDmTf//739ItQ9itzp0787///Y/z58/j7e0NgLerA+ff7cKPf29Tcy0yRYHer4DWegddrdI5s88//5x77rmHoUOH0qhRI8LDwwE4c+YMly5dIjg4mM8++wyA/Px8kpKSGDdu3G3327t371ueDzDXu793794cOGC7U8oLURVBQUG4urqSnp6Oq5Om5k72l2r3KAS0rdn3uENVCrOgoCAOHTrE3LlziYuLIzGx5D6wli1bMmXKFCZMmEC9eiUnCZ2dnVmzZk31VSyEqF1+raDz7Rsjllblbryurq5MnTqVqVOnVmc9Qghr4uQGfd60yh7/N5OZeoUQ5Yt+2Wr7ld2sQnH715P5FaUoSpluE0IIG9JqKITZzriEFQozg8FQ5lLwhQsXOHv2LJ6ensbZy8+dO0dGRgbh4eE0adKk+qsVQtQO7xCIsq2JvCsUZjdPG7d161aGDBnCl19+yZgxY3BwKNlNcXExCxYs4B//+IeMKyaErdI6wr1vgIPu9utakSqd1XvxxRcZO3YsTz/9tOnOHBwYP348x48fZ+rUqTJDkxC2qNPT0KCppauotCpdAPj999+Nh5bmhIaGcujQoSoXJYSwkMD20K76R7ipDVUKs4YNG7Js2TKKi4vLvFZcXMyyZcto2LDhHRcnhKhFTm7QexpobLOTQ5UOM19++WWeffZZunXrxrPPPkvTpiVN0lOnTjF37lwSEhL44osvqrVQIUQN6/WizXTDMKdKYfbMM8+g1Wp57bXXeOaZZ4xXOlVVxdfXl7lz5zJ+/PhqLVQIUYPaPATh91i6ijtS5W69Tz/9NGPGjGHPnj3Gm7+Dg4Pp1KmT8eqmEMIGNO4MUZMsXcUdu6PUcXBwICoqSsYPE8JW+baAvjNAUwPzBdSyKp/py8rK4v3332fAgAF06NCB3bt3A3D9+nU++eQTTp8+XW1FCiFqQINmMOgj0NnHaMtVapldvHiR6OhoLly4QLNmzTh+/LhxMEYfHx/mzZtHYmIis2fPrtZihRDVxL8N3BcLzh6WrqTaVCnMXnrpJbKzs0lISMDPzw8/P9OZWoYNG2Z24EYhhBVo0gX6vQWO5ufJsFVVOsz89ddfmTx5Mq1atTI75VxYWBgXLly44+KEENUstBf0f9fuggyq2DK7ceMGvr6+5b6enZ1d5YKEEDWkWf+Soa/t4GS/OVVqmbVq1eqWs5qvWrXKOI6/EMIKhPS06yCDKobZlClTWLp0KR988AGZmZlAyTBBp0+f5oknnmDHjh0yQ5IQ1iKgDfSZbtdBBlU8zHz88cdJTEzk9ddf57XXXgPgvvvuQ1VVNBoN7733HsOGDavOOoUQVeHiBX1mgEPZOW7tTZU7zb722ms88cQTfP/995w+fRqDwUB4eDgPPfTQLUfUEELUol4vg1v557ftyR3dARAUFFQjh5P/+te/+Oijj0hJSaF9+/Z89tlndOnSxey6CxcuZOzYsSbLdDod+fn51V6XEDYlpCeE9LB0FbXG6sb6WLZsGVOnTmX69Ons37+f9u3bM2DAANLS0srdxsPDg+TkZOOjdOo7IeosjRa6/d3SVdSqCrfM2rVrV6kdK4rCwYMHK13QJ598wvjx442trblz5/Lzzz/z1Vdf8corr5T7XgEBAZV+LyHsVvNB4NnI0lXUqgqHmY+Pj0kH2aKiIrZv3067du2M08TfqcLCQvbt28e0adOMyzQaDX379mXHjh3lbpeTk0NwcDAGg4GOHTvy3nvv0bp1a7PrFhQUUFBQYLKtEHZF0UDEY5auotZVOMxuntTk6tWr+Pn58cknn1RpKjpzrl69il6vx9/f32S5v78/x48fN7tN8+bN+eqrr2jXrh2ZmZl8/PHHdO/enSNHjtC4ceMy68fGxhITE1Mt9QphlcJ62/Qgi1VV5XNm5m5jsoSoqChGjx5NREQE0dHR/PDDD/j6+jJv3jyz60+bNo3MzEzjY9OmTbVcsRA1rM3Dlq7AIqxqFMUGDRqg1WpJTU01WZ6amlrhc2KOjo506NCh3CGIdDodOt2fU2i5udnH8CdCAFA/HPzNn2Kxd1Z1NdPJyYnIyEiTmdANBgPr1q2r8ACQer2eQ4cOERhY95rZQnDXQLCSo6baZlUtM4CpU6cyZswYOnXqRJcuXZg1axa5ubnGq5ujR4+mUaNGxMbGAvDWW2/RrVs3mjZtSkZGBh999BGJiYmMGzfOkh9DiNqnaKBpH0tXYTEVDrP9+/ebPC+9J/PUqVN4eXmZ3aZjx46VLmjkyJFcuXKFN998k5SUFCIiIli7dq3xokBSUhKav0yFlZ6ezvjx40lJScHb25vIyEi2b99Oq1atKv3eQti0wPbg6mPpKixGUVVVrciKGo2mzEl/VVXNXggoXa7X66unyhq0f/9+IiMj2bdvX5XCVwhLady4MZcuXaKRlxMX3+8GPadA6wctXZbFVLhltmDBgpqsQwhxp4J7WroCi6pwmI0ZM6Ym6xBC3Anf5nXmhvLyWNXVTCFEFTXpaukKLE7CTAh70LiTpSuwOKvrmiGEqJiAgAC4cZ0AdwfwbWnpcixOwkwIG7V3715Y+ljJaLJ1YCTZ25HDTCFsXYO7LF2BVZAwE8LWeYdaugKrIGEmhK3zqFuDMJZHwkwIW+cuoyyDhJkQtk1RoF7d7ixbSsJMCFvm7ClXMv8gYSaELXOtb+kKrIaEmRC2TMLMSMJMCFvm7GXpCqyGhJkQtszFy9IVWA0JMyFsmc7d0hVYDQkzIWyZk8wuVkrCTAhb5uhi6QqshoSZELZMq7v9OnWEhJkQtkzraOkKrIaEmRC2TCNDEpayyjD717/+RUhICM7OznTt2pXdu3ffcv3ly5fTokULnJ2dadu2LWvWrKmlSoWwMAkzI6sLs2XLljF16lSmT5/O/v37ad++PQMGDCAtLc3s+tu3b2fUqFE8/fTTHDhwgGHDhjFs2DAOHz5cy5ULYQGK1f0KW0yFJwGuLV27dqVz5858/vnnABgMBpo0acL//d//8corr5RZf+TIkeTm5rJ69Wrjsm7duhEREcHcuXNv+34yCbCwablXoV4DS1dhFawq1gsLC9m3bx99+/Y1LtNoNPTt25cdO3aY3WbHjh0m6wMMGDCg3PWFsCvSMjOyqgPuq1evotfr8ff3N1nu7+/P8ePHzW6TkpJidv2UlBSz6xcUFFBQUGB8npOTA0BxcTFFRUV3Ur4Qta9YD3Xg59bR8fZXba0qzGpDbGwsMTExZZZ37SqTqAphrSpyNsyqwqxBgwZotVpSU1NNlqemppbMEWhGQEBApdafNm0aU6dONT5PSEggOjqaXbt20aFDhzv8BELUsvwscPawdBVWwarCzMnJicjISNatW8ewYcOAkgsA69atY9KkSWa3iYqKYt26dUyZMsW4LD4+nqioKLPr63Q6dLo/e027uZXc2+bg4FChpqwQVsWgA/m5BawszACmTp3KmDFj6NSpE126dGHWrFnk5uYyduxYAEaPHk2jRo2IjY0F4Pnnnyc6OpqZM2cyePBgli5dyt69e5k/f74lP4YQtUNRLF2B1bC6MBs5ciRXrlzhzTffJCUlhYiICNauXWs8yZ+UlIRG8+cVnO7du7NkyRJef/11Xn31VZo1a8aqVato06aNpT6CELVHrmYaWV0/s9om/cyETSvKB0dnS1dhFSTWhbBl0jIzkn8JIWyZnDMzkjATwqZJmJWSMBPClknLzEjCTAibJmFWSsJMCGEXJMyEsGVymGkkYSaEsAsSZkIIuyBhJoSwCxJmQtiyun03ogkJMyGEXZAwE0LYBQkzIWyaHGaWkjATwpbJOTMjCTMhbJqEWSkJMyFsmbTMjCTMhBB2QcJMCFumlZmZSkmYCWHL5EZzIwkzIYRdkDATQtgFCTMhhF2QMBNC2AUJMyGEXZAwE0LYBQdLFyBqT3JyMsnJyZYuQ1SjwMBAAgMDLV2GVajzYRYYGMj06dPt/geioKCAUaNGsWnTJkuXIqpRdHQ0cXFx6HQ6S5dicYqqys1ddUFWVhaenp5s2rQJNzc3S5cjqkFOTg7R0dFkZmbi4eFh6XIsrs63zOqaiIgI+cG3E1lZWZYuwarIBQAhhF2QMBNC2AUJszpCp9Mxffp0OVFsR+Q7NSUXAIQQdkFaZkIIuyBhJoSwCxJmQgi7IGEmhLALEmZC1BBFUSr02Lhx4x2/V15eHjNmzKjUvt59912GDBmCv78/iqIwY8aMO67DkuQOACFqyOLFi02ef/3118THx5dZ3rJlyzt+r7y8PGJiYgDo3bt3hbZ5/fXXCQgIoEOHDsTFxd1xDZYmYSZEDXn88cdNnu/cuZP4+Pgyyy3l3LlzhISEcPXqVXx9fS1dzh2Tw0whLMhgMDBr1ixat26Ns7Mz/v7+TJgwgfT0dJP19u7dy4ABA2jQoAEuLi6Ehoby1FNPAXD+/HljGMXExBgPX2932BgSElITH8lipGUmhAVNmDCBhQsXMnbsWCZPnsy5c+f4/PPPOXDgANu2bcPR0ZG0tDT69++Pr68vr7zyCl5eXpw/f54ffvgBAF9fX+bMmcNzzz3Hgw8+yEMPPQRAu3btLPnRap8qhKgVEydOVP/6K7dlyxYVUL/55huT9dauXWuyfOXKlSqg7tmzp9x9X7lyRQXU6dOnV7quO9nWmshhphAWsnz5cjw9PenXrx9Xr141PiIjI3Fzc2PDhg0AeHl5AbB69WqKioosWLF1kzATwkJOnTpFZmYmfn5++Pr6mjxycnJIS0sDSkaTffjhh4mJiaFBgwYMHTqUBQsWUFBQYOFPYF3knJkQFmIwGPDz8+Obb74x+3rpSX1FUVixYgU7d+7kp59+Ii4ujqeeeoqZM2eyc+dOGTn4DxJmQlhIeHg4v/32Gz169MDFxeW263fr1o1u3brx7rvvsmTJEh577DGWLl3KuHHjUBSlFiq2bnKYKYSFPPLII+j1et5+++0yrxUXF5ORkQFAeno66k0jdUVERAAYDzVdXV0BjNvURdIyE8JCoqOjmTBhArGxsSQkJNC/f38cHR05deoUy5cvZ/bs2QwfPpxFixbxxRdf8OCDDxIeHk52djZffvklHh4eDBo0CAAXFxdatWrFsmXLuOuuu/Dx8aFNmza0adOm3PdfvHgxiYmJ5OXlAbB582beeecdAJ544gmCg4Nr/h+hOln6cqoQdcXNXTNKzZ8/X42MjFRdXFxUd3d3tW3bturLL7+sXr58WVVVVd2/f786atQoNSgoSNXpdKqfn596//33q3v37jXZz/bt29XIyEjVycmpQl0toqOjVcDsY8OGDdX1sWuNjDQrhLALcs5MCGEXJMyEEHZBwkwIYRckzIQQdkHCTAhhFyTMhBB2QcJMCCt1/vx5FEVh4cKFli7FJkiYCSHsgnSaFcJKqapKQUEBjo6OaLVaS5dj9STMhBB2QQ4zhahBM2bMQFEUTp48yeOPP46npye+vr688cYbqKrKhQsXGDp0KB4eHgQEBDBz5kzjtubOmT355JO4ublx6dIlhg0bhpubG76+vrz44ovo9Xrjehs3bjQ7J6e5faakpDB27FgaN26MTqcjMDCQoUOHcv78+Rr6V6kZEmZC1IKRI0diMBh4//336dq1K++88w6zZs2iX79+NGrUiA8++ICmTZvy4osvsnnz5lvuS6/XM2DAAOrXr8/HH39MdHQ0M2fOZP78+VWq7eGHH2blypWMHTuWL774gsmTJ5OdnU1SUlKV9mcxlrvHXQj7N336dBVQn3nmGeOy4uJitXHjxqqiKOr7779vXJ6enq66uLioY8aMUVVVVc+dO6cC6oIFC4zrjBkzRgXUt956y+R9OnTooEZGRhqfb9iwwezoFzfvMz09XQXUjz76qHo+sAVJy0yIWjBu3Djj/2u1Wjp16oSqqjz99NPG5V5eXjRv3pyzZ8/edn/PPvusyfO77767QtvdzMXFBScnJzZu3Fhmrk5bI2EmRC0ICgoyee7p6YmzszMNGjQos/x2oeLs7FxmBnJvb+8qhZFOp+ODDz7gl19+wd/fn169evHhhx+SkpJS6X1ZmoSZELXAXNeK8rpbqLfpYFCRbhrlzQnw14sEpaZMmcLJkyeJjY3F2dmZN954g5YtW3LgwIHbvo81kTATwg55e3sDZecESExMNLt+eHg4L7zwAr/++iuHDx+msLDQ5MqqLZAwE8IOBQcHo9Vqy1wZ/eKLL0ye5+XlkZ+fb7IsPDwcd3d3m5uXUyY0EcIOeXp6MmLECD777DMURSE8PJzVq1cbJxYudfLkSfr06cMjjzxCq1atcHBwYOXKlaSmpvLoo49aqPqqkTATwk599tlnFBUVMXfuXHQ6HY888ggfffSRyYxNTZo0YdSoUaxbt47Fixfj4OBAixYt+O6773j44YctWH3lye1MQgi7IOfMhBB2QcJMCGEXJMyEEHZBwkwIYRckzIQQdkHCTAhhF/MNSJgJUUlnzpxhwoQJhIWF4ezsjIeHBz169GD27NncuHGjxt736NGjzJgxw+KDJr777rsMGTIEf39/FEVhxowZFq2nlHSaFaISfv75Z0aMGIFOp2P06NG0adOGwsJCtm7dyksvvcSRI0eqPEji7Rw9epSYmBh69+5NSEhIjbxHRbz++usEBATQoUMH4uLiLFbHzSTMhKigc+fO8eijjxIcHMz69esJDAw0vjZx4kROnz7Nzz//bMEK/6SqKvn5+bi4uFT7vs+dO0dISAhXr14tMxSRJclhphAV9OGHH5KTk8N//vMfkyAr1bRpU55//nnj8+LiYt5++23Cw8PR6XSEhITw6quvlrmBOyQkhPvvv5+tW7fSpUsXnJ2dCQsL4+uvvzaus3DhQkaMGAHAPffcg6IoJmP8l+4jLi6OTp064eLiwrx58wA4e/YsI0aMwMfHB1dXV7p163ZHoWvJVuGtSJgJUUE//fQTYWFhdO/evULrjxs3jjfffJOOHTvy6aefEh0dTWxsrNkbuE+fPs3w4cPp168fM2fOxNvbmyeffJIjR44A0KtXLyZPngzAq6++yuLFi1m8eDEtW7Y07uPEiROMGjWKfv36MXv2bCIiIkhNTaV79+7ExcXx97//nXfffZf8/HyGDBnCypUrq+FfxYpYdNBuIWxEZmamCqhDhw6t0PoJCQkqoI4bN85k+YsvvqgC6vr1643LgoODVUDdvHmzcVlaWpqq0+nUF154wbhs+fLlZsf1/+s+1q5da7J8ypQpKqBu2bLFuCw7O1sNDQ1VQ0JCVL1er6qq+fkGbufKlSsqoE6fPr3C29QkaZkJUQFZWVkAuLu7V2j9NWvWADB16lST5S+88AJAmcO8Vq1acffddxuf+/r6Vng+gFKhoaEMGDCgTB1dunShZ8+exmVubm4888wznD9/nqNHj1Z4/9ZOwkyICvDw8AAgOzu7QusnJiai0Who2rSpyfKAgAC8vLzKjPh68xwBUPlx/UNDQ83W0bx58zLLSw9Pyxt51hZJmAlRAR4eHjRs2JDDhw9XarvyxuK/WVXnA/irmrhyaUskzISooPvvv58zZ86wY8eO264bHByMwWDg1KlTJstTU1PJyMggODi40u9f0WC8uY4TJ06UWX78+HHj6/ZCwkyICnr55ZepV68e48aNIzU1tczrZ86cYfbs2QAMGjQIgFmzZpms88knnwAwePDgSr9/vXr1gLKTlNzKoEGD2L17t0kA5+bmMn/+fEJCQmjVqlWl67BW0mlWiAoKDw9nyZIljBw5kpYtW5rcAbB9+3aWL1/Ok08+CUD79u0ZM2YM8+fPJyMjg+joaHbv3s2iRYsYNmwY99xzT6XfPyIiAq1WywcffEBmZiY6nY57770XPz+/crd55ZVX+Pbbbxk4cCCTJ0/Gx8eHRYsWce7cOb7//ns0msq3ZxYvXkxiYiJ5eXkAbN68mXfeeQeAJ554wnKtPUtfThXC1pw8eVIdP368GhISojo5Oanu7u5qjx491M8++0zNz883rldUVKTGxMSooaGhqqOjo9qkSRN12rRpJuuoakm3isGDB5d5n+joaDU6Otpk2ZdffqmGhYWpWq3WpJtGeftQVVU9c+aMOnz4cNXLy0t1dnZWu3Tpoq5evdpkncp0zYiOjlYBsw9z3UZqi8wBIISwC3LOTAhhFyTMhBB2QcJMCGEXJMyEEHZBwkwIYRckzIQQdkHCTAhhFyTMhBB2QcJMCGEXJMyEEHZBwkwIYRckzIQQdkHCTAhhF/4/nXZIguOCGEgAAAAASUVORK5CYII=",
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",
       "text/plain": [
        "<Figure size 300x600 with 2 Axes>"
       ]
@@ -972,295 +1083,23 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6af069aa",
-   "metadata": {},
-   "source": [
-    "The ``dabest`` package also implements a range of estimation plot\n",
-    "designs aimed at depicting common experimental designs.\n",
-    "\n",
-    "The **multi-two-group estimation plot** tiles two or more Cumming plots\n",
-    "horizontally, and is created by passing a *nested tuple* to ``idx`` when\n",
-    "``dabest.load()`` is first invoked.\n",
-    "\n",
-    "Thus, the lower axes in the Cumming plot is effectively a [forest\n",
-    "plot](https://en.wikipedia.org/wiki/Forest_plot), commonly used in\n",
-    "meta-analyses to aggregate and to compare data from different experiments."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "1caaaff8",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 600x600 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "multi_2group = dabest.load(df, idx=((\"Control 1\", \"Test 1\",),\n",
-    "                                         (\"Control 2\", \"Test 2\")\n",
-    "                                       ))\n",
-    "\n",
-    "multi_2group.mean_diff.plot();"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "cc59ca70",
-   "metadata": {},
-   "source": [
-    "The **shared control plot** displays another common experimental\n",
-    "paradigm, where several test samples are compared against a common\n",
-    "reference sample.\n",
-    "\n",
-    "This type of Cumming plot is automatically generated if the tuple passed\n",
-    "to ``idx`` has more than two data columns."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "1ab93ba9",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "shared_control = dabest.load(df, idx=(\"Control 1\", \"Test 1\",\n",
-    "                                          \"Test 2\", \"Test 3\",\n",
-    "                                          \"Test 4\", \"Test 5\", \"Test 6\")\n",
-    "                                 )"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "688420ee",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DABEST v2024.03.29\n",
-       "==================\n",
-       "                  \n",
-       "Good afternoon!\n",
-       "The current time is Tue Mar 19 15:35:26 2024.\n",
-       "\n",
-       "Effect size(s) with 95% confidence intervals will be computed for:\n",
-       "1. Test 1 minus Control 1\n",
-       "2. Test 2 minus Control 1\n",
-       "3. Test 3 minus Control 1\n",
-       "4. Test 4 minus Control 1\n",
-       "5. Test 5 minus Control 1\n",
-       "6. Test 6 minus Control 1\n",
-       "\n",
-       "5000 resamples will be used to generate the effect size bootstraps."
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "shared_control"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "cc3cc602",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DABEST v2024.03.29\n",
-       "==================\n",
-       "                  \n",
-       "Good afternoon!\n",
-       "The current time is Tue Mar 19 15:35:31 2024.\n",
-       "\n",
-       "The unpaired mean difference between Control 1 and Test 1 is 0.48 [95%CI 0.221, 0.768].\n",
-       "The p-value of the two-sided permutation t-test is 0.001, calculated for legacy purposes only. \n",
-       "\n",
-       "The unpaired mean difference between Control 1 and Test 2 is -0.542 [95%CI -0.914, -0.211].\n",
-       "The p-value of the two-sided permutation t-test is 0.0042, calculated for legacy purposes only. \n",
-       "\n",
-       "The unpaired mean difference between Control 1 and Test 3 is 0.174 [95%CI -0.295, 0.628].\n",
-       "The p-value of the two-sided permutation t-test is 0.479, calculated for legacy purposes only. \n",
-       "\n",
-       "The unpaired mean difference between Control 1 and Test 4 is 0.79 [95%CI 0.306, 1.31].\n",
-       "The p-value of the two-sided permutation t-test is 0.0042, calculated for legacy purposes only. \n",
-       "\n",
-       "The unpaired mean difference between Control 1 and Test 5 is 0.265 [95%CI 0.0137, 0.497].\n",
-       "The p-value of the two-sided permutation t-test is 0.0404, calculated for legacy purposes only. \n",
-       "\n",
-       "The unpaired mean difference between Control 1 and Test 6 is 0.288 [95%CI -0.00441, 0.515].\n",
-       "The p-value of the two-sided permutation t-test is 0.0324, calculated for legacy purposes only. \n",
-       "\n",
-       "5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.\n",
-       "Any p-value reported is the probability of observing theeffect size (or greater),\n",
-       "assuming the null hypothesis of zero difference is true.\n",
-       "For each p-value, 5000 reshuffles of the control and test labels were performed.\n",
-       "\n",
-       "To get the results of all valid statistical tests, use `.mean_diff.statistical_tests`"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "shared_control.mean_diff"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "278fa389",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1050x600 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "shared_control.mean_diff.plot();"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0848f20b",
-   "metadata": {},
-   "source": [
-    "Thus ``dabest`` empowers you to perform robust analyses and present complex visualizations of your statistics elegantly."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "a638c960",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "multi_groups = dabest.load(df, idx=((\"Control 1\", \"Test 1\",),\n",
-    "                                         (\"Control 2\", \"Test 2\",\"Test 3\"),\n",
-    "                                         (\"Control 3\", \"Test 4\",\"Test 5\", \"Test 6\")\n",
-    "                                       ))"
+    "The confidence interval shown on the contrast axis is a BCa confidence interval by default.\n",
+    "This can be modified using the `ci_type` parameter in the `.plot()` method, whereby you can \n",
+    "select between `bca` and `pct` (percentile)."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "854216aa",
    "metadata": {},
    "outputs": [
     {
      "data": {
+      "image/png": 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",
       "text/plain": [
-       "DABEST v2024.03.29\n",
-       "==================\n",
-       "                  \n",
-       "Good afternoon!\n",
-       "The current time is Tue Mar 19 15:35:32 2024.\n",
-       "\n",
-       "Effect size(s) with 95% confidence intervals will be computed for:\n",
-       "1. Test 1 minus Control 1\n",
-       "2. Test 2 minus Control 2\n",
-       "3. Test 3 minus Control 2\n",
-       "4. Test 4 minus Control 3\n",
-       "5. Test 5 minus Control 3\n",
-       "6. Test 6 minus Control 3\n",
-       "\n",
-       "5000 resamples will be used to generate the effect size bootstraps."
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "multi_groups"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "8a3b6380",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DABEST v2024.03.29\n",
-       "==================\n",
-       "                  \n",
-       "Good afternoon!\n",
-       "The current time is Tue Mar 19 15:35:37 2024.\n",
-       "\n",
-       "The unpaired mean difference between Control 1 and Test 1 is 0.48 [95%CI 0.221, 0.768].\n",
-       "The p-value of the two-sided permutation t-test is 0.001, calculated for legacy purposes only. \n",
-       "\n",
-       "The unpaired mean difference between Control 2 and Test 2 is -1.38 [95%CI -1.93, -0.895].\n",
-       "The p-value of the two-sided permutation t-test is 0.0, calculated for legacy purposes only. \n",
-       "\n",
-       "The unpaired mean difference between Control 2 and Test 3 is -0.666 [95%CI -1.3, -0.103].\n",
-       "The p-value of the two-sided permutation t-test is 0.0352, calculated for legacy purposes only. \n",
-       "\n",
-       "The unpaired mean difference between Control 3 and Test 4 is 0.362 [95%CI -0.114, 0.887].\n",
-       "The p-value of the two-sided permutation t-test is 0.161, calculated for legacy purposes only. \n",
-       "\n",
-       "The unpaired mean difference between Control 3 and Test 5 is -0.164 [95%CI -0.404, 0.0742].\n",
-       "The p-value of the two-sided permutation t-test is 0.208, calculated for legacy purposes only. \n",
-       "\n",
-       "The unpaired mean difference between Control 3 and Test 6 is -0.14 [95%CI -0.398, 0.102].\n",
-       "The p-value of the two-sided permutation t-test is 0.282, calculated for legacy purposes only. \n",
-       "\n",
-       "5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.\n",
-       "Any p-value reported is the probability of observing theeffect size (or greater),\n",
-       "assuming the null hypothesis of zero difference is true.\n",
-       "For each p-value, 5000 reshuffles of the control and test labels were performed.\n",
-       "\n",
-       "To get the results of all valid statistical tests, use `.mean_diff.statistical_tests`"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "multi_groups.mean_diff"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "c91c9d44",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 1350x600 with 2 Axes>"
+       "<Figure size 500x400 with 2 Axes>"
       ]
      },
      "metadata": {},
@@ -1268,12 +1107,11 @@
     }
    ],
    "source": [
-    "multi_groups.mean_diff.plot();"
+    "two_groups_unpaired.mean_diff.plot(ci_type='pct');"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "66ca9423",
    "metadata": {},
    "source": [
     "### Using long (aka 'melted') data frames"
@@ -1281,7 +1119,6 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1f532032",
    "metadata": {},
    "source": [
     "``dabest`` can also handle 'melted' or 'long' data. This term is used because each row now corresponds to a single data point, with one column carrying the value and other columns containing 'metadata'\n",
@@ -1289,14 +1126,13 @@
     "\n",
     "For more details on wide vs long or 'melted' data, refer to this\n",
     "[Wikipedia article](https://en.wikipedia.org/wiki/Wide_and_narrow_data). The\n",
-    "[pandas documentation](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.melt.html)\n",
+    "[pandas documentation](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.melt.html)\n",
     "provides recipes for melting dataframes.\n"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "f529430a",
    "metadata": {},
    "outputs": [
     {
@@ -1397,7 +1233,6 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1ffb38fa",
    "metadata": {},
    "source": [
     "When your data is in this format, you need to specify the ``x`` and\n",
@@ -1407,17 +1242,16 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "fdee72da",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "DABEST v2024.03.29\n",
+       "DABEST v2025.03.27\n",
        "==================\n",
        "                  \n",
        "Good afternoon!\n",
-       "The current time is Tue Mar 19 15:35:38 2024.\n",
+       "The current time is Tue Mar 25 16:02:12 2025.\n",
        "\n",
        "Effect size(s) with 95% confidence intervals will be computed for:\n",
        "1. Test 1 minus Control 1\n",
@@ -1438,24 +1272,34 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "9a6f8668",
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Dabest estimation plot designs"
+   ]
+  },
+  {
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 500x400 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
    "source": [
-    "analysis_of_long_df.mean_diff.plot();"
+    "The ``dabest`` package implements a range of estimation plot\n",
+    "designs aimed at depicting common experimental designs:\n",
+    "\n",
+    "1. [Two-Group](02-two_group.html)\n",
+    "   \n",
+    "2. [Shared Control (Unpaired) and Repeated Measures (Paired)](03-shared_control_and_repeated_measures.html)\n",
+    "   \n",
+    "3. [Proportion Plots](04-proportion_plot.html)\n",
+    "   \n",
+    "4. [Mini-Meta](05-mini_meta.html)\n",
+    "   \n",
+    "5. [Delta-Delta](06-delta_delta.html)\n",
+    "   \n",
+    "6.  [Forest Plots](07-forest_plot.html)\n",
+    "   \n",
+    "In addition, as of Dabest **v2025.03.27**, we introduce a new plotting orientation: **[Horizontal Plots](08-horizontal_plot.html)**. \n",
+    "\n",
+    "Lastly, we have a whole tutorial page for making [aesthetic changes to dabest plots](09-plot_aesthetics.html).\n"
    ]
   }
  ],
@@ -1467,5 +1311,5 @@
   }
  },
  "nbformat": 4,
- "nbformat_minor": 5
+ "nbformat_minor": 2
 }
diff --git a/nbs/tutorials/02-repeated_measures.ipynb b/nbs/tutorials/02-repeated_measures.ipynb
deleted file mode 100644
index 67ca28f9..00000000
--- a/nbs/tutorials/02-repeated_measures.ipynb
+++ /dev/null
@@ -1,590 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "5a4db386",
-   "metadata": {},
-   "source": [
-    "# Repeated measures\n",
-    "\n",
-    "> Explanation of how to use dabest for repeated measures analysis.\n",
-    "\n",
-    "- order: 2"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9081a4df",
-   "metadata": {},
-   "source": [
-    "DABEST version 2023.02.14 expands the repertoire of plots for experiments with repeated-measures designs. DABEST now allows the visualization of paired experiments with one control and multiple test \n",
-    "groups, as well as repeated measurements of the same group. This is an improved version of paired data plotting in previous versions, which only supported computations involving one test group and one control group.\n",
-    "\n",
-    "The repeated-measures function supports the calculation of effect sizes for\n",
-    "paired data, either based on sequential comparisons (group i vs group i + 1) \n",
-    "or baseline comparisons (control vs group i). To use these features, \n",
-    "you can simply declare the argument ``paired = \"sequential\"`` or ``paired = \"baseline\"`` \n",
-    "correspondingly while running ``dabest.load()``. As in the previous version, you must also pass a column in the dataset that indicates the identity of each observation, using the \n",
-    "``id_col`` keyword. \n",
-    "\n",
-    "<div style=\"padding: 15px; border: 1px solid transparent; border-color: transparent; margin-bottom: 20px; border-radius: 4px; color: #8a6d3b;; background-color: #fcf8e3; border-color: #faebcc;\">\n",
-    " **(Please note that** ``paired = True`` **and** ``paired = False`` **are no longer valid since v2023.02.14)**\n",
-    "</div>\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "eecb3c0a",
-   "metadata": {},
-   "source": [
-    "## Load Libraries"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "ea25e869",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "We're using DABEST v2024.03.29\n"
-     ]
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "import dabest\n",
-    "\n",
-    "print(\"We're using DABEST v{}\".format(dabest.__version__))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "d20f817b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#| hide\n",
-    "import warnings\n",
-    "warnings.filterwarnings(\"ignore\", category=UserWarning) # to suppress warnings related to points not being able to be plotted due to dot size"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1d78dd2c",
-   "metadata": {},
-   "source": [
-    "## Creating a demo dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "9906d636",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from scipy.stats import norm # Used in generation of populations.\n",
-    "\n",
-    "np.random.seed(9999) # Fix the seed so the results are reproducible.\n",
-    "Ns = 20 # The number of samples taken from each population\n",
-    "\n",
-    "# Create samples\n",
-    "c1 = norm.rvs(loc=3, scale=0.4, size=Ns)\n",
-    "c2 = norm.rvs(loc=3.5, scale=0.75, size=Ns)\n",
-    "c3 = norm.rvs(loc=3.25, scale=0.4, size=Ns)\n",
-    "\n",
-    "t1 = norm.rvs(loc=3.5, scale=0.5, size=Ns)\n",
-    "t2 = norm.rvs(loc=2.5, scale=0.6, size=Ns)\n",
-    "t3 = norm.rvs(loc=3, scale=0.75, size=Ns)\n",
-    "t4 = norm.rvs(loc=3.5, scale=0.75, size=Ns)\n",
-    "t5 = norm.rvs(loc=3.25, scale=0.4, size=Ns)\n",
-    "t6 = norm.rvs(loc=3.25, scale=0.4, size=Ns)\n",
-    "\n",
-    "\n",
-    "# Add a `gender` column for coloring the data.\n",
-    "females = np.repeat('Female', Ns/2).tolist()\n",
-    "males = np.repeat('Male', Ns/2).tolist()\n",
-    "gender = females + males\n",
-    "\n",
-    "# Add an `id` column for paired data plotting.\n",
-    "id_col = pd.Series(range(1, Ns+1))\n",
-    "\n",
-    "# Combine samples and gender into a DataFrame.\n",
-    "df = pd.DataFrame({'Control 1' : c1,     'Test 1' : t1,\n",
-    "                   'Control 2' : c2,     'Test 2' : t2,\n",
-    "                   'Control 3' : c3,     'Test 3' : t3,\n",
-    "                   'Test 4'    : t4,     'Test 5' : t5, 'Test 6' : t6,\n",
-    "                   'Gender'    : gender, 'ID'  : id_col\n",
-    "                  })"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "76040145",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "two_groups_paired_sequential = dabest.load(df, idx=(\"Control 1\", \"Test 1\"),\n",
-    "                                               paired=\"sequential\", id_col=\"ID\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "2774e88a",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DABEST v2024.03.29\n",
-       "==================\n",
-       "                  \n",
-       "Good afternoon!\n",
-       "The current time is Tue Mar 19 15:36:05 2024.\n",
-       "\n",
-       "Paired effect size(s) for the sequential design of repeated-measures experiment \n",
-       "with 95% confidence intervals will be computed for:\n",
-       "1. Test 1 minus Control 1\n",
-       "\n",
-       "5000 resamples will be used to generate the effect size bootstraps."
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "two_groups_paired_sequential"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "d6c78841",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "two_groups_paired_baseline = dabest.load(df, idx=(\"Control 1\", \"Test 1\"),\n",
-    "                                  paired=\"baseline\", id_col=\"ID\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "c64388d3",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DABEST v2024.03.29\n",
-       "==================\n",
-       "                  \n",
-       "Good afternoon!\n",
-       "The current time is Tue Mar 19 15:36:05 2024.\n",
-       "\n",
-       "Paired effect size(s) for repeated measures against baseline \n",
-       "with 95% confidence intervals will be computed for:\n",
-       "1. Test 1 minus Control 1\n",
-       "\n",
-       "5000 resamples will be used to generate the effect size bootstraps."
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "two_groups_paired_baseline"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "17eae308",
-   "metadata": {},
-   "source": [
-    "When dealing with only 2 paired data groups, assigning either ``baseline`` or ``sequential`` to the ``paired`` parameter will yield the same numerical results"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "27a891ac",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DABEST v2024.03.29\n",
-       "==================\n",
-       "                  \n",
-       "Good afternoon!\n",
-       "The current time is Tue Mar 19 15:36:07 2024.\n",
-       "\n",
-       "The paired mean difference for the sequential design of repeated-measures experiment \n",
-       "between Control 1 and Test 1 is 0.48 [95%CI 0.237, 0.73].\n",
-       "The p-value of the two-sided permutation t-test is 0.001, calculated for legacy purposes only. \n",
-       "\n",
-       "5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.\n",
-       "Any p-value reported is the probability of observing theeffect size (or greater),\n",
-       "assuming the null hypothesis of zero difference is true.\n",
-       "For each p-value, 5000 reshuffles of the control and test labels were performed.\n",
-       "\n",
-       "To get the results of all valid statistical tests, use `.mean_diff.statistical_tests`"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "two_groups_paired_sequential.mean_diff"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "bb9f8761",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DABEST v2024.03.29\n",
-       "==================\n",
-       "                  \n",
-       "Good afternoon!\n",
-       "The current time is Tue Mar 19 15:36:08 2024.\n",
-       "\n",
-       "The paired mean difference for repeated measures against baseline \n",
-       "between Control 1 and Test 1 is 0.48 [95%CI 0.237, 0.73].\n",
-       "The p-value of the two-sided permutation t-test is 0.001, calculated for legacy purposes only. \n",
-       "\n",
-       "5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.\n",
-       "Any p-value reported is the probability of observing theeffect size (or greater),\n",
-       "assuming the null hypothesis of zero difference is true.\n",
-       "For each p-value, 5000 reshuffles of the control and test labels were performed.\n",
-       "\n",
-       "To get the results of all valid statistical tests, use `.mean_diff.statistical_tests`"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "two_groups_paired_baseline.mean_diff"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "47395e35",
-   "metadata": {},
-   "source": [
-    "For paired data, we use\n",
-    "[slopegraphs](https://www.edwardtufte.com/bboard/q-and-a-fetch-msg?msg_id=0003nk>)\n",
-    "(another innovation from Edward Tufte) to connect paired observations.\n",
-    "Both Gardner-Altman and Cumming plots support this.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "10e1951a",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 375x400 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "two_groups_paired_sequential.mean_diff.plot();"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "93c49b60",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 225x600 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "two_groups_paired_sequential.mean_diff.plot(float_contrast=False);"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "00d966f2",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 375x400 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "two_groups_paired_baseline.mean_diff.plot();"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "ab0cf2e3",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 225x600 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "two_groups_paired_baseline.mean_diff.plot(float_contrast=False);"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e86d261e",
-   "metadata": {},
-   "source": [
-    "When creating repeated-measures plots with multiple test groups, declaring ``paired`` as ``sequential`` or ``baseline`` will generate different results."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "59fdde69",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sequential_repeated_measures = dabest.load(df, idx=(\"Control 1\", \"Test 1\", \"Test 2\", \"Test 3\"),\n",
-    "                                               paired=\"sequential\", id_col=\"ID\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "f3b41638",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DABEST v2024.03.29\n",
-       "==================\n",
-       "                  \n",
-       "Good afternoon!\n",
-       "The current time is Tue Mar 19 15:36:12 2024.\n",
-       "\n",
-       "The paired mean difference for the sequential design of repeated-measures experiment \n",
-       "between Control 1 and Test 1 is 0.48 [95%CI 0.237, 0.73].\n",
-       "The p-value of the two-sided permutation t-test is 0.001, calculated for legacy purposes only. \n",
-       "\n",
-       "The paired mean difference for the sequential design of repeated-measures experiment \n",
-       "between Test 1 and Test 2 is -1.02 [95%CI -1.36, -0.716].\n",
-       "The p-value of the two-sided permutation t-test is 0.0, calculated for legacy purposes only. \n",
-       "\n",
-       "The paired mean difference for the sequential design of repeated-measures experiment \n",
-       "between Test 2 and Test 3 is 0.716 [95%CI 0.14, 1.22].\n",
-       "The p-value of the two-sided permutation t-test is 0.022, calculated for legacy purposes only. \n",
-       "\n",
-       "5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.\n",
-       "Any p-value reported is the probability of observing theeffect size (or greater),\n",
-       "assuming the null hypothesis of zero difference is true.\n",
-       "For each p-value, 5000 reshuffles of the control and test labels were performed.\n",
-       "\n",
-       "To get the results of all valid statistical tests, use `.mean_diff.statistical_tests`"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "sequential_repeated_measures.mean_diff"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "d1e57580",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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6cRgYGDB//nykUilHjx5lw4YNREdHExoaSnp6Ounp6eTl5bFgwQJCQ0Nf67PxN5GOjg727NmDTCbD3NycefPm6ezFNTQ0cOjQIYKCgliyZMmw/v59fX2kpqaSm5uLi4sLGzZsGOSj+bpjIGgnTeoR2bBhA46Ojvz+978nMTGR8ePH84c//GHI28rlcuRyufj/vLw8Zs6cya1bt5g4ceILWrGeu3fvcuzYMcLCwli2bBlXr17l6tWr4j7Y0xShNDU1kZycTFVVFYGBgcybN2/QVIT7uXXrFmfOnCE4OJgVK1Zgamr6LC8JlUrF3r17aW1tZcuWLaJ/Z3t7O+fOnaOiooKQkBAWLFiAk5PTMz2XntFBd3c3O3bsQKlUIpPJCAkJYfXq1aKgdXR0sH37dhwdHfnggw8e+xnTaDTk5ORw+fJlAJKSkpg8efIznYy9qujF7gG+/PJL/vM//5OcnBzMzc0fK3Y///nP+cUvfjHo93qxe3Hk5+dz4sQJoqKimDNnDsePH6euro6kpCSmTZv2xJFPd3c3qamp3LlzBxcXF+bNm0dwcPCwHqe8vJzDhw/j7OzMmjVrnrmxd2BggJ07d6JQKNiyZQs2NjbANxFnSUkJFy5cQCqVMnXqVGbMmPHMAqvn5dHb28uOHTtQqVQYGBhgZGTE1q1bxX46qVTK9u3bMTY2ZvPmzVhaWj7y8aqqqjh37hxtbW1MmjSJpKSkx97ndUYvdvdRV1fH5MmTSUlJEffq9JHd6Ob27ducOnWK8ePHEx4ezsmTJzEyMuLdd9994rSlXC4nIyODrKwszMzMmDVrFhMnTnzis+Dm5mb279+PkZERa9eu1ZkY/TR0d3ezfft2LC0t2bRpk44FlFKpJDMzk4yMDCwtLZk/fz4RERH61OYrRn9/P7t27aK/vx93d3dqamrYsmWLmEmQyWTs2rWLvr4+Pvzww0fus0kkElJSUrh79y4+Pj4sWrRoWL13KpWKpqam13aKuV7s7uOrr75i2bJlGBkZib9Tq9UYGBhgaGiIXC7XuW4ocnNzmTRpkl7sXgA3b97k66+/ZuLEiVhaWpKRkUFwcPATpy01Gg25ublcvnwZuVzO1KlTmTZt2jP5CnZ3d7N//356enpYvXr1M3tttra2smPHDjw9PVm7du2gz2FXVxfnz5+ntLSUgIAAFi1a9Mwiq+fFIJfL2b17NxKJhIkTJ5KRkcGyZcvEfjqVSsW+fftobm5m8+bNg+YualEqlVy7do2MjAzMzc2ZO3cu0dHRwz7xSU1NJTMzk3/4h394La3G9GJ3H1KplJqaGp3fbdq0ibCwMH7yk58Mq49FL3YvhuvXr3P+/HnGjh2LRCKhvr7+idOWgiBQVlZGSkoK7e3tjB07ltmzZ+sUnzwLMpmMw4cPU1NTw9KlS4mOjn6mx6uurmbv3r1ERESwfPnyIV9nWVkZ586dQyKREBcXR2Jiot4MeBSjVCrZt28fLS0tLFiwgNOnTzNhwgRxkoFGo+Ho0aPcu3eP9evXD5mteDClPWXKFGbMmPFEf/eWlha2bdvGjBkzmDVr1oi9vtGEvhrzPmxsbAYJmpWVFU5OTk/csPkyaWlpoaioCB8fH7y9vXU89F4HMjMzSUlJwd/fn7KyMkxMTNi4ceMTNXY3Nzdz4cIFqqqqCAgIYPny5U9ts/QwzM3NWbt2LadPn+bYsWNIJJKnbn0A8Pf3Z/ny5Rw9ehRbW1vmzp076DYhISEEBASQlZVFeno6BQUFzJ07l7Fjx+pTm6MMlUrFl19+SVNTEytXruTMmTO4u7uzYMEC4BsRO3/+PMXFxaxatWpIoWtra+PcuXNUVlYSGhrK+vXrn7hYSaPRcOrUKZycnJgxY8aIvLbRiF7sXkM6OjrIycnhypUrALi4uIjC5+Pjg7Oz8yt74Lty5Yo45buqqoqQkBCWL18+7I33np4esfjEycmJNWvWEBIS8tzeDyMjI9555x3s7e25dOkSEomExYsXP3U1XGRkJL29vZw7dw4bG5shm4mNjY2ZMWMGY8eOJSUlhRMnTnDz5s1h793oef5oNBpxSPD7779PVlYWCoWCTZs2if2TV69eJTs7myVLlhAWFqZzf5lMRlpaGtnZ2djb27NmzRpCQ0Ofai3Z2dk0NjayefPm17p38/V9ZSNEWlray17CExMREUF4eDidnZ3U19dTV1dHXV0dt2/fRhAEzM3N8fb2FsXPy8tr1Ed/giBw+fJlUlJSEASBvr4+5s6dO+y0pVwuJzMzk6ysLExNTVm0aBGTJk16ISXYBgYGJCYmYm9vz6lTp+ju7mblypVPnV6Mi4ujp6eHCxcuYGNjQ2Rk5JC3s7OzY8WKFUyaNImzZ8/y+eefM3nyZJKSkrCwsHiWl6TnGRAEga+++orS0lJWr15NbW0tFRUVrFu3Tiw8yc3NJTU1lVmzZjFp0iSd++bl5XHx4kWUSiVJSUnEx8c/tUhJJBIuXbpETEzMa1uYokW/ZzfCjOY9O7lcTkNDgyiA9fX1DAwMYGBggIuLiyh+Pj4+ODk5jZroTxAEUlJS+Prrr1GpVGIf23DSlhqNhtu3b3P58mVkMhlTpkxh+vTpL20fq7KykkOHDuHg4MDatWvFVoInRRAETpw4wd27d1m/fv1jC2DUarXYb2VkZMTs2bOZMGHCG9lv9TIRBIEzZ85w69YtsRdz//79JCUlkZCQAEBpaSlffvklkydPZtGiReL3sL6+nrNnz9LY2Cjayj3t50e7lv3799Pa2sonn3zy2u/t6sVuhBnNYvcggiDQ0dGhE/21tbUhCAIWFhaDor+X8WXQHhwOHz6MmZkZiYmJLFu27LFpS0EQKC8vJyUlhdbWVrH4ZDRYI7W0tLB//34MDAxYu3btQ6vrHodarRa9Ezdt2oSbm9tj79Pb28vFixfJy8vD09OTRYsW4e3t/VTPr+fJ0J60Xbt2jXfeeQd/f38+//xzfH19xcbx2tpa9uzZQ2hoKCtWrMDQ0FDnb+bh4cHChQtHxHj8zp07nDhx4plSoK8SerEbYV4lsRsKmUw2KPqTyWQYGBjg6uqqE/05Ojo+1+hPEAQOHz7MgQMH8PDwYN26dcNKWzY3N5OcnExlZSX+/v7MmzcPT0/P57bOp6Gnp4cDBw7Q1dXFqlWrnmo0C3wTre/cuZP+/n62bNkybDGvq6vj7NmzNDU1MWHCBObMmTMqRh29zqSlpZGWlsaiRYuYMGECO3bsQCaTsXXrViwsLMT2End3d9atW4eBgQE3btzgypUrIx6N9/X18emnnxIUFMSKFStG4NWNfvRiN8K86mL3IIIg0N7ePij6A7C0tBwU/Y2Ug4dGo2Hbtm0cP36csWPH8r3vfe+xZ7M9PT1cvnyZvLw8nJycmDt37qj2jpTL5Rw5coTKykreeeedQXPKhovWWcPExITNmzcPez9O21946dIlBEFg1qxZxMTE6FObz4GsrCwuXLjA7NmzmTFjBidPnqSgoIAPP/wQd3d30TjAwsKCTZs2UV9fz/nz5+no6CA2NpbExMQR3Wc9duwY5eXlfPe7331jTnL0YjfCjAax6+7uFp0QnscHWSaTUV9frxP9yeVyDAwMcHNzE8XP29v7qaI/lUrFf/zHf5Cens78+fP5+7//+0emLRUKBZmZmVy7dg0TExMSExOZNGnSYw0ARgNqtZozZ86Qm5vLrFmzSEhIeCpxbm9vZ/v27bi6urJ+/fonKljo7+8nNTWVW7du4erqysKFC5+5CV7P/3Hr1i1Onz7NjBkzmD17Nrm5uZw6dYqlS5cyfvx4BgYGRD/M5cuXk5mZSWlpKf7+/ixcuHBY6ekn4d69exw4cECncf1NQC92I8xoEDvtlwnAyckJX19f8fI8Uo+CINDW1qYT/bW3twPfRH/3tz14eno+Mvrr6uriX/7lXygtLWXz5s2sXbv2oevVaDTk5eWRmpqKTCYjPj6e6dOnj/rK0gcRBIGrV6+SmprK+PHjWbJkyVMJtXYy+/37PU9CY2MjZ8+epb6+nujoaObOnTtiDfZvKlrf1tjYWBYsWEBTUxM7duxg3LhxLFmyBKVSyZ49e2hpaSE8PJy7d+9iZWXFvHnznovtm1wu589//jPOzs5iqvRNQS92I8xoEDv4Jrqrra2ltraWuro6WlpaEAQBKysrfHx8RPHz8PB4LhHQwMCATvTX0NCAXC7H0NBwUPTn4OCAgYEBxcXF/Md//AddXV38+Mc/fqSTQ3l5OcnJyWLxSVJSEvb29iP+Ol4k+fn5nDx5En9/f957772nKgjSVvLFxMSwcOHCJz6YCYLAnTt3SElJQalUMnPmTOLj41+JKHm0UVJSwuHDhxk3bhxvv/02MpmMbdu2YWlpyebNmzE0NOTgwYPcuHEDBwcHTExMmDZtGtOmTXtuht7nzp0jNzeX73znOzg4ODyX5xit6MVuhBktYvcg2tSjVvzq6+tRKpUYGxvj5eUlip+3t/dz6cHSaDSDor+Ojg7gm+ivs7OTa9euYW1tzS9/+cuHvnctLS0kJydTUVGBn58f8+bNw8vLa8TX+7Koqqri0KFD2NnZsXbt2qeKrLSeoXPmzGH69OlPtY77m5YdHBxYtGgRQUFBT/VYbyIVFRUcOHCAsLAw3n33XQwMDDhw4AD19fV8/PHH2NnZsXv3bk6ePImPjw/Tpk1j3rx5z1WA6urq2LFjB/PmzWPKlCnP7XlGK3qxG2FGq9g9iFqtprm5WRS/2tpaent7xZ67+1OfdnZ2zyXd0d/fT0lJCfv27ePKlSsYGhoydepUnJyccHd314n+jIyMxOITR0dH5s6dy5gxY17LNExrayv79+9Ho9Gwdu3aR87QexiXL1/mypUrz7wv09LSwrlz56iuriY8PJz58+e/8hH086ampoZ9+/YREBDAqlWrMDIyIi0tjStXrrBu3To8PDz4/e9/T0pKClOmTOHDDz987icSKpWKbdu2YWpqypYtW97IIiS92I0wr4rYPYggCHR1demIn7bq0sbGRkf83NzcRuTLUlZWxpEjR7hz5w5+fn58+9vfxsLCYlDlpzYN6+joSGJiInPmzMHb2xsTE5NnXsNoRSqVcuDAATo7O3nvvfee+GAoCAKnTp3izp07rF279pkOpoIgcPfuXS5cuMDAwAAzZsxg6tSpr/X7/7Q0Njaye/duPD09WbNmDSYmJpSXl7N//34SEhKwtrZm165dlJSUsGrVKrZs2fJCUsRpaWmkp6fz8ccfj3jBy6uCXuxGmFdV7Iaiv79fFJ3a2loaGhpQq9WYmpri7e0tit+TNpyr1WouX75MWloaLS0t+Pj4sHnzZp3mZm3xyYULF2hubsbLywsHBwdaW1tRKpUYGhri4eGhE/09rwj0ZaFQKDhy5AgVFRUsWbKECRMmPNH91Wo1X375JTU1NWzatOmZfTEVCgXp6elkZWVha2vLggULRnVrx4umtbWVnTt34uTkJE4Rl0gkbNu2DXNzc0xNTSkoKKCrq4vVq1fzzjvvvLB1bdu2jWnTppGUlPRCnnM0ohe7EeZ1ErsHUalUNDY26kR/Wrsxd3d3Ufx8fHweutfU3d3NsWPHqKysRC6XiweG+5u+KyoqSE5OpqWlhejoaGbPni2mzjQaDS0tLTrRX1dXF/BNBHp/07uHh8crb2yr0Wg4e/YsN2/eJCEhgVmzZj2RuCgUCnbv3k13dzdbtmwZkT2h9vZ2zp07R0VFBSEhISxYsOCJnfaHQqPRoFAoUCgUyOVynZ+WlpYj4hryvOjo6GDnzp1YW1uzYcMGLCwsUKlUfPbZZ9y5cwcPDw+srKzo6upiypQpLFu27IWcJGg0GrF5/Vvf+tYr/314FvRiN8K8zmL3INqG8/vFr7OzEwB7e3sd8XN1daW8vJwTJ06g0WjQaDSYmJiwfv16cU+qtbWV5ORkysvL8fX1Zd68ecOysurt7dWp/GxsbESpVGJkZDRk9PeqIQgC165dIyUlRazse5LUV19fH9u3b8fAwIAtW7YMe0LE49ZUWlrKmTNnkEgkTJo0iUmTJiEIwiChksvlw/qdQqF45HO+++67zzwT8HnQ3d3Njh07MDExYdOmTVhZWaFSqfj1r39Namoq8fHxJCQkkJOTg4+PD2vWrHlh1a3Z2dmcPXuWTZs2DTki6E1CL3YjzJskdkPR29urI35NTU1iRNjZ2UlISAimpqZYW1uzefNmXFxckEqlpKWlkZubi4ODA3PnziUsLOypz3zVavWg6E8ikQBga2urE/25u7u/Mme7hYWFnDhxAl9fX1atWvVE/YSdnZ1s375dHAej0WiGFKAnESftT+3f2sTEhKCgIFxcXMS/nYmJCWZmZpiZmWFqaqrzc6jfPey6CxcuUFxczObNm0fVmKLe3l527NiBIAhs2rQJGxsb7t27x/bt28nJyWHFihUsX76c/fv3Y2dnx4YNG55bW8GDdHd389lnnzF27FhxGOybjF7sRpg3XewepK2tje3bt3Pv3j1cXFwoKipCoVAwceJE/Pz8xKjMycmJefPmMXny5Ody1tvb2yu2XGijP5VKhZGREZ6enjrR32hrpFar1aK4VFRUcPToUczMzFiwYAFmZmbDFqr29nZx/llUVNSQJxOGhobDFqD7fyeTybh27RrV1dUEBgayePFivL29R6zqT6lUsmPHDvr7+9m6deuosLjq7+9n165dDAwMsHnzZtRqNefPn+f27dvU1NTwzjvvsGzZMnbs2IGBgQGbN29+YesWBIEDBw7Q0tLCd77znVfOaOF5oBe7EUYvdv9HWVkZx48fx9TUlLlz53Lp0iXUajULFiwgNzdXnEjg7OyMr6+vuO+nbXp/nmOGtK0X90d/3d3dwDdz4B6M/p5EgAVBQKVSPXWk9OB1KpVK5/H7+/vJz89Ho9EQHR2Ng4PDsEWptbWVlJQUoqOjRbG8/3bGxsbP9J6Xl5dz7tw5urq6iIuLY+bMmSN2oO3u7ubzzz/H1dWVdevWvdRGd7lczu7du5FIJKxZs4bi4mKuX7+OhYUFHR0d+Pv7s27dOvbt24dUKh2x/dLhUlBQwLFjx3j//fcZM2bMC3ve0Yxe7EYYvdh9IySpqalkZmYSGhpKQkICR44cwcjIiJkzZ5KVlUVzczNRUVHMnj0bQ0NDMRVWW1srur1oixK04ve8C06kUqlO9KdNwRobGw+K/qysrMjPz6ewsBCZTDZIsDQazUOfx8DA4Kmip/t/KhQKTp48KU5NCAkJGfbrzMvL46uvviIxMZHExMQReOd0UalUXL9+nStXrmBmZsbcuXMZO3bsiJy41NTUsHv3btF+62WgVCrZt28fzc3NxMbGcvv2beRyOdOnT6empoampiY+/PBDzpw5Q0NDA5s2bXqqXsmnpb+/n08//ZSAgABWrlw57PsJgoBUKh11mY2RQi92I8ybLnbd3d0cPXqUhoYG5syZQ0hICHv27EGhUODi4kJ9fT0+Pj7Mnz//ocUncrlcdHupra0d0u1FG3U9z4nbKpVqyOivo6ODpqYment7cXJyYtq0aQQEBGBubj4swTIxMRmRA79CoeDYsWPcu3ePxYsXM3ny5GHf9+rVq1y6dIm33377uX1Oe3p6SE5OprCwEB8fHxYtWjQi+23aogutkfKLRKVScfDgQe7evYuzszO9vb1ERUUxd+5c8vLySEtLY82aNeTn51NUVDSswbojzYkTJ7h37x6ffPIJ1tbWj7ytIAg0NDRQUlJCcXEx3d3d/OhHP3otB7nqxW6EeZPF7t69e5w4cQJTU1NWrFiBmZkZn3/+OQ0NDTg6OuLm5sacOXMIDw9/ooO9tuBEK35atxcAV1dXnejP3t7+uaU+a2pqOH78ONevXxenvBsZGTFmzBgmTZpETEwMEyZMeKH7SRqNhvPnz5Odnc306dOZPXv2sF6/IAicPXuWW7dusXr16uc6vLOqqopz587R1tbGpEmTSEpKeqaKUG3DfEFBAZs2bXphdnEajYa9e/eSnJyMi4sLY8aMESdE3N84LpfLuXHjBitXriQiIuKFrE1LeXk5+/bt45133nloX6Zaraa6upqSkhJKSkqQSqVYWVkxZswYwsLCCAoKei29UPViN8K8iWL3YNpy2bJltLW18atf/YqmpiYmT57M3LlziYmJGZEvkSAISCQSHfF70O1FK37u7u7DKpIQBAGFQoFEIqGxsZHm5mZaWlpoa2ujurqaoqIimpqa6O/vRxAEzM3NcXZ2Ri6XAzB+/Hjc3d2xsLAgMjKSmJgYvL29X0gvlSAIXL9+neTkZCIjI1m6dOmw0r0ajYbDhw9TUVHBhg0bnuvEcrVaTU5ODpcvX8bQ0JDZs2czceLEpy5gUalU7Nq1i56eHrZu3frYCOZZUalU/OY3v+HSpUuMGzeO9957j8mTJ2NoaCg2jnt7e+Pn58fFixdZvHgxMTExz3VND6JQKPjzn/+Mo6Mj69ev1/nsKRQKysvLKS4upqysDJlMhr29PeHh4YSFheHj4/PaW4jpxW6EedPE7sG0ZXx8PBcvXuSPf/wjxsbGbNmyhblz5z7XdCN8M2Xh/n0/ba+doaEhLi4uODg4YG9vL5pOt7W10dbWRkdHB11dXUgkEnp7e1EqleJjasvz5XI5xsbGmJub4+LiQnx8PDNmzMDJyYnk5GSOHz+OXC4nKCgIT09P5HI5FhYW+Pv7ExMTQ1RU1AspNy8qKuL48eN4eXmxevXqYb3nSqWSvXv30t7ezpYtW0akOfxR9Pb2cvHiRfLy8vDw8GDRokX4+Pg81WP19PTw+eef4+joyIYNG55bNFJRUcFvfvMbSkpKWLFiBZs2bRIjU5VKJVaJxsfHc/78eRISEl6KU8n58+e5desW3/72t3F0dKSvr4/S0lJKSkqorKxEpVLh7u5OWFgYYWFhuLm5vVHuN3qxG2HeJLF7MG2p3c+4dOkSoaGh/PSnP9VxRhkJBEFAJpPR29tLX1/foJ89PT10dHTQ2toqemtKJBK6u7uRy+VoNBosLCywsbHBxsYGT09P3NzccHFxwcXFBTc3N+zs7KipqaGsrEwsNjExMWHixIkkJSVRU1PDmTNnyMzMRCqVYm5uLgpcSEgINjY2SKVS8fm8vb2JjY1l8uTJODs7j+j78SB1dXUcPHgQS0tL1q5dO6wKwIGBAbZv345arWbLli3PPUrSrvPs2bM0NTUxfvx45syZ81TPW1dXx65du5g4cSKLFy8e0TVKJBIuXLjA119/TU9PD5988gnz5s3Tuc3p06e5c+cOiYmJOvMIX7SI1NfXs337duLi4rCzs6OkpITa2loAfH19RYF708b63I9e7EaYN0Hs7k9bjhkzhmnTppGRkUF2djY1NTVMnz6d7373u8Pe5BYEgYGBAR3hepiY9fb2MjAwgEwmEy8KhQJBENBoNKjVaoyNjTE1NcXExAQHBwdRyCwsLFCr1fT29tLV1UV/fz+GhobY29uLLQYtLS0UFxeLBTFyuRxvb2/GjRtHYWEhaWlpNDQ0YGlpSVxcHLa2tuTk5NDR0UF/fz8GBgYEBQVhb2+PXC6nu7ub/v5+jIyMcHJyIiYmhqlTpzJmzJjnljbq6Ohg//79yOVy1qxZM6w9LYlEwvbt27G2tmbjxo0vpEBBo9GQm5vLpUuX0Gg0zJo1i9jY2Cd+X7STwJcsWcKkSZOeeV1KpZLMzEwyMjJobGxEEAQ2btxIXFyczu20Va1xcXHk5uYSGBjIqlWrXmg6UBAEGhsb+fWvf01bWxvBwcGYmJgQGBhIWFgYY8aMGRU9iaMBvdiNMK+72N2ftpw2bRr9/f3cvn0bpVJJd3c3kyZNEu2Q+vv7HypY9//s6+sTS/UFQUCpVKJWqzEwMEAQBLFvTalUijZgJiYmmJqaYmdnp5Om1P7UXh61d6VtNK+qquLq1avcunULhUKBlZUVpqamottKZ2cnlZWVAISGhjJ//nyxyEIQBC5cuMCFCxfo7u7Gw8MDiURCdHQ0VlZWVFdXU1tbS3NzMx0dHSiVSqysrAgNDWXp0qXMmDEDGxubEf879fX1cfDgQVpaWlixYsWweq1aWlrYsWMH3t7eL9TSqr+/n9TUVG7duoWLiwuLFi164grGr7/+mtu3b7Nx48anTosKgkBxcTHJyclIpVLs7Oxoa2tj3rx5zJgxQ+e2zc3N/O1vf8Pf35+GhgZcXFxYv379C5kEodFoqKurEwtM8vLyqKurY/Xq1UyZMoXg4ODXspryWdGL3QjzOouddvKySqXC1dWV0tJSlEoljo6OVFRU4ODgQFRUFAMDA2Ihx/1obcJMTU1FIVOr1ahUKtEbUWssrT3Qmpubi8J1v5Bp//0se2GCIFBQUEBqaird3d04OjpSXl5OaWkp3d3dYurTxsaGsWPHMm/ePCZOnIivr69Oyk0QBNLS0ti/fz8KhYK4uDja29tZvnw5UVFRYtFLWVkZN2/e5NatW9TV1TEwMICdnR3jx49nwYIFTJ48GU9PzxFrwlYqlZw4cYLi4mIWLlxIbGzsY+9TVVXFvn37iIqKYunSpS80HdfU1MSZM2eor68nKiqKefPmDbvnS61Ws3v3bjo7O/n444+f+ASitbWVc+fOUVVVRWhoKG5ubly9epUZM2Ywe/ZsndsODAzw+eefiydhlpaWbNq06bm3wVRWVlJSUkJpaSl9fX3Y2Njg7u7OzZs3WbBgAfPnz3+m59BoNLS3t+Pq6jpCqx5d6MVuhHnVxE6tVj82+pJKpeTl5VFeXi5GSmq1Gi8vL1HofHx8mDlzJnZ2dpiamooHAq2TiFwuF9OHMplMfH4TE5OHCpm9vf1zOYAIgkB5eTkXL16kpaUFJycnamtruXPnDn19fWKlZXR0NJMnT8bGxobm5mYdj01HR0d8fX0JCQkhIiICAwMDMjMz+eMf/4iFhQWzZs2itraWFStWDCo/FwSBsrIyLl++THJyMmVlZfT392Nvb09ISAgxMTH4+fnh6emJp6cnHh4eTy3qgiCQnJxMVlYWU6dOZe7cuY8VsMLCQo4ePcr06dOZM2fOUz3v0yIIAnfu3CElJQWlUklCQgLx8fHDqi7t7e3l888/x9bWlo0bNw7rPjKZjMuXL5OTk4ODgwMLFixgYGCAEydOiI3r979fgiBw8OBBKisrMTc3x8jIiC1btjyXRmyZTEZZWRnFxcWUl5ejUChwcnIiLCyM8PBwPD092bVrF319fXzrW9965qgyNTWVa9eu8b3vfe+5ZBteNnqxG2FGg9hp96WGswc2MDAw6P4WFhZYWVlhbW2NgYEBt2/fprGxUSzsCA8PZ+rUqTQ0NHD69GlcXV2Jioqip6cHiURCX1+f+FhGRkY6acUHRc3S0vKFRg91dXVcvHiRsrIy4JtxNXl5ecjlctzd3YmIiCApKYmYmJghXS96enp0Wh6am5tJSkoiISEBgKysLH75y1/i5eVFYmIi5eXlj+xjEwSBpqYmkpOTOXv2LFVVVQB4enri6+uLo6MjhoaGODs7i+Ln6emJu7v7Ex3cbty4wfnz5wkPD2fZsmWPvW9WVhYXLlxg0aJFw4oIRxqZTEZaWhrZ2dk4ODgwb948goKCHitgDQ0N7Ny5k7Fjxz6yUEQ7L/HixYuoVCpmzpxJfHw89+7d48iRI+J0iQfvn56ezsWLF8XPrdbMfKSQSqWUlpZSXFxMdXU1arUaT09PsUXA2dlZXFNOTg5nzpxh48aNz9y4XlxczKFDh5gzZw7Tp08fgVcy+njlxa6hoYH09HRaW1t599138fb2Rq1W093djZ2d3QtvjhwNYnf79m1Onjyp8ztLS0usra1FEXvUTwMDA3p6erh16xaHDh2iqqoKc3NznJycxAnhzc3NFBcX4+7uTmxsLI6OjkNGZzY2NqOivLmtrY1z586RmZkpFsPU1taiUCjw8/Nj4cKFTJs2jYiIiGGLiEaj4dKlS2RmZuqIwpUrV/iP//gPoqOjiYmJoaKigjVr1jx2WrggCBQVFXHq1Cmx0tPJyYno6GhCQkIYGBigubkZtVottlTcL4Bubm6PFIOSkhKOHTuGu7s777///mMbuy9cuMD169dfSnO0ltbWVs6ePUt1dTUmJib4+voSEBBAQEAAHh4eQxaDaAtHHtbrVldXx7lz52hsbGTcuHHMmTMHGxsbKioqOHDgAGFhYbz77ruDHruiooK9e/eiVqsxNzfngw8+eOr9wfvp6OiguLiYkpIS6uvrMTQ0xN/fXywwGWosVU9PD5999hlRUVEsWbLkmZ6/ra2NL774guDgYBYvXvzaFrS8smInCAL/+I//yKeffopKpcLAwICUlBSSkpLo7u7Gx8eHf//3f+f73//+C13XaBC7np4eWltbRfGysrLS+eJqPfAkEonYY6a9aP9fWlpKYWEharWawMBAYmNjiYyMxMHBgZaWFm7cuEFMTAzvvffeC9mUf1ra2to4ePAg6enpSKVSzMzMxOgzODiYlStXMnPmzMeenff19dHa2kpLS4t4aW1tRa1Wo9FoGBgYYO3atYwbNw6AU6dO8fvf/56EhAQCAwOpr69n7dq1wz4DVyqVXLx4kXPnznH37l0MDQ1FW6qgoCB6e3tpbGyksbGRlpYWNBoNRkZGuLq66gigq6urzglfQ0MDBw4cwMzMjHXr1uHo6PjQNQiCwLFjxygpKWH9+vUvbR6aIAg0NzdTVVVFVVUVNTU1KBQKzM3N8fPzE8XP1dVVPLE6d+4cOTk5bNiwQVy3VCrl4sWL4jDV+3v8ampq2LdvHwEBAaxatWrQSbK2cbylpQVbW1vWrFnz1K4z2mheK3BtbW2YmJgQHBxMWFgYoaGhj0zfa1OpTU1NfPLJJ8+0xyuRSPjNb35DZ2cnoaGh9Pb28pOf/GRUf6eflldW7P73f/+Xf/7nf+YnP/kJs2fPZu7cuVy8eFFs5ty4cSMVFRVcvXr1ha5rNIidIAj09/frCNmDoqZWq8XbW1lZiRGZgYEBp0+fpqysjLCwMDZu3MjUqVPFiEGbOomJiWHRokWjImp7EJVKRUFBAcePHycrK0v05RwYGEAqleLv78/777/PvHnzBkVCarWa9vZ2HVFraWlBKpUCYGxsjKurK25ubri5uaFWq8nKyuLWrVsIgsDf/d3fMW3aNAB27tzJ/v37mTdvHk5OTrS0tLB+/fonjgaampo4deoUaWlptLS04OjoyOTJk5k1axZRUVGYmZnR0tIiil9jYyNtbW1oNBqMjY1xc3PTEUBjY2MOHDjAwMAA77///iPXo1Kp2L9/P01NTWzZsmVEU3ZPi1qtprGxURS/2tpa1Go1VlZWovD5+vry9ddfi83yxcXFpKenY2RkxOzZs5kwYYJ4AtjY2Mju3bvx9PRkzZo1gw70KpWKnTt3cufOHezs7FixYsVDrbgeteaamhqxgrKnpwcLCwsdi67hCszdu3c5cuQIq1atIjw8/InWoVKpqK+vp7KykoqKCs6fP093dzdJSUlER0cTGBjImDFjXpkZj0/CKyt2ISEhTJ8+nZ07d9LR0YGLi4uO2P3ud7/jf/7nf2hpaXmh6xoNYpebm8upU6fE/2srGh9WBKItKDl9+jSff/45arWaNWvW8O677+qkuq5fv8758+eJj49n/vz5o0roNBoNVVVV3L59m5SUFO7du4dGo8HDwwMTExM6Oztxd3dnxYoVzJs3D0NDQ6RS6SBRa29vF9sg7O3tRVHTXrR7aPBNybxUKsXe3p68vDz+/Oc/U1dXx7Jly1i5ciUuLi787ne/Iy0tjblz52Jubk5XVxcbNmx4qmZ7hULBrVu3+PrrrykuLkYmk+Hl5UV8fDyTJk0iLCxMPMtXKpU0NzfrCGB7ezuCIGBiYoKjoyP37t1DoVCwZs0apkyZ8tD+MJlMxs6dO5HJZM+tGONZUCqVYgtJVVUVDQ0NoqXbzZs36e3tZfz48cycOZPExESdqKm1tZWdO3fi5OTEBx98MGQh0Ndff83XX3+NlZWV2C4yHBQKBRUVFZSUlHDv3j2x+lbb4O3n5/fEPXn9/f189tln4gDfx6GNiisrK8WoWKlUYmlpSX9/P01NTXz00UdPZCL+qvLKip25uTl/+tOf+Oijj4YUu23btvH9739/yAKM58loEDuJREJTU5Moao9Lc1RUVPCnP/2JvLw8xo0bx49+9KNBPokZGRlcvHjxicyGnzeCIFBXV0dhYSEFBQWUlZVRV1eHWq3G2dkZV1dX+vr6MDU1ZcKECYSHh9Pb2ysKm/azYWZmhpubm07E5urqqvO+9ff309jYSFNTkyge2vl3JiYmeHt74+7uzvnz5ykpKSE8PFw0ht6/fz/37t0jPj4eExMT+vr62LBhw1OPfdG+7oyMDHG/2szMDB8fHyZNmkRUVBShoaGDIgW5XK4jgHV1dVy7do3W1lbCwsKIi4vTiQAdHR3Fv3NPTw/bt2/HzMyMzZs3j+phoDKZjPz8fI4fP87NmzdpaGggNDSUOXPmEBgYSEBAAP7+/gwMDLBz506sra3ZsGHDkKnDO3fu8Pnnn6NSqVi6dOmg6swH6e/v5969e5SUlFBRUYFSqcTV1VUUOA8Pj2f67nz11VeUlJTwySefPLRisquri8rKSlHg+vv7MTExwc/Pj8DAQAIDA+nq6uLQoUPMnj172OL9qvPKip2vry8bN27k3//934cUu61bt3LlyhVKS0tf6LpGg9gNl/b2dk6ePCkWs2zatGlQb5UgCKSnp3P58mUSExOZOXPmSxU67ZlqYWEhhYWFohWYdvyOtbW16E/Z0dGBubk5Hh4e2NnZYWBggJOT06BoTXudFq2w3S9uWmHTPp6Hhweenp5YW1vT2NgoVmdKpVKxhUE7987b25u6ujoAgoODMTQ0RKVSsXHjxmdOC/b19ZGbm0tGRoZYnm5lZYWXlxeRkZFiauphhVoDAwMcOXKEtLQ03N3dcXV1FdsrtK9VK36mpqZigcu6detGZapLoVBw9epVrl27hrW1NfPnz6enp4c9e/YQGBiImZkZHR0dyOVyysvLcXV1ZevWrYSFhQ1qxG5ububXv/41zc3NLF26lBUrVgz52e/u7hbTkzU1NQiCgLe3tyhwI+U3WllZyZ49ewaNZerv76eqqkoUuK6uLgwMDPDy8hLFzdvbW/x7tbe388UXXxAYGMh77703Kk5cXwSvrNh9//vf58CBA1y/fl100bh06RKzZs0iOTmZt956ix//+Mf88pe/fKHrehXErq+vjytXrnD+/HmqqqoIDw/n7/7u7wbt3QiCQGpqKlevXn3pZ4Dt7e0UFBRQWFhIR0cHxsbG9Pb2kpeXR2NjoxhdaVOW5ubmxMbGEhsbi4eHh+h/+WC009fXJwraw4RN2+vm6emJg4PDQw8OgiDQ0dFBWVkZu3fvpqmpSdyra29vR6VSERgYSGhoqLg/umnTphE5GGo0GsrKysjJySE/P5/u7m5MTEywsrLCycmJiIgIoqKi8PPzG3L9OTk5nD17Vhxb097erpMC1b4ncrmcsrIyIiIiWL16NV5eXtja2r70A6YgCBQWFpKSkkJ/fz/Tp09n2rRp4t87OTmZ69evs379ekxNTfnjH/9IR0cHQUFBKBQKDA0N8fLyEvf8XFxc+OMf/0hOTg5vv/02H3zwgSgWgiDQ1tYmzoBramrCyMiIgIAAwsPDGTNmzIj7iyoUCv7yl79gZ2fHmjVrqK2tFQWuqakJAGdnZ1Hc/P39h4y+5XI5X3zxBQYGBnz44YdvlNPKKyt23d3dJCQkUFVVxYwZMzh//jxz586lt7eXrKwsJkyYQHp6+jPNzXoaRrPY3T9BuqKiAoDZs2ezfPnyQSmc+5uR58+fz5QpU174eiUSCfn5+WRnZ1NZWYlCocDS0pKmpiZKS0vp6enBxsaG+Ph4wsPDqa+vR61WM336dBYvXjzobz+SwvY4+vr6RDf8xMRE8vLy2Lt3L/X19VhaWuLo6CiK8Mcff0x4ePiIRUqdnZ3cvHmT3Nxc2tvbMTY2xtDQEBMTE2xtbYmKiiIqKgpPT0+d16ftMXN1dWXNmjU6Jeh9fX2i8OXk5JCSkoKLiwtBQUFiNH3/5UU2JTc3N3P27Flqa2sJDw9n/vz52Nvb69xGo9Gwf/9+ampqMDc3F3vk7O3t6ezsFPf7qqqq6Ovr486dO+Kx5R/+4R/w9/ensbFRrKDs7OzE1NSUkJAQwsPDCQkJeW7CodFoOHjwIJcvX2b8+PF0dXWhVquxsbERxS0gIOCxe6mCIIitRFu3bn3uEy5GG6+s2ME3KZjf/va3HD16lLKyMjQaDUFBQbz33nv86Ec/eu5jZYZiNIqd9qz34sWLtLW10dfXh62tLYsXLyYuLm7QAV0QBM6dO0d2dvYLbSru7++nsrKSGzducPv2bWpqahgYGBCdVGQymWizFRQUxOrVq5k1axapqakUFxeL/XLu7u46wqYVt+clbA+ju7ubHTt2YGpqyqZNmwD493//d+7evUtbWxtyuRxzc3NsbW2JiYkhMDAQX19fcR7fs35+lUold+/eJScnh/r6egCsra1RKpUoFAocHR2JiooiOjpaTKc2NjZy4MABTExMWLt27UOnNGRnZ3PixAnGjh2Lu7u7+D5rDQW0EyXuv4x0/1Z/fz+XL1/m5s2bODs7s3DhQgIDAx96e4lEwscff4xSqeTTTz8dskhIEAQOHz7MH/7wB1xcXPD396etrY3u7m4sLCzw8PAgNjaW+Ph4AgMDn0sqV5sh0KYl8/PzuXbtGmPGjCExMVEUuPsbzIfDlStXuHz58kPbJhQKBU1NTS+txeR580qL3WhktIldTU0NycnJNDQ0YGtri0QiwdHRkZUrVw7phq+tyrx9+zZvvfXWiLjIP8iD5f11dXUUFBRQXV1NV1eXmBIKDQ3F0tKSuro6Kisr6e/vJzg4mFWrVjFp0iSysrLIyMjA2NiYqKgobGxsaGpqeqiwacXteQjbw+jo6GDHjh3Y2dmxYcMGOjs7+dvf/oajoyO3bt2irKxMbJaeNWsW7e3tYpuDdgq79vLg3uKT0NDQwM2bNykoKECtVuPi4oKZmRmtra3I5XLc3NyIjo4mKioKgP3799Pb28v777+Pr6/vkI956dIlrl69yrvvvkt0dDSCINDT06OT/mxsbBQLgezs7AYJ4NMIukaj4datW6SmporTEh43GFipVLJv3z4qKipQqVRMnjyZd999d9D7mZ+fz9///d9jYmJCfHw8hoaGGBoaYm1tLVbwajQaLC0txZRnQECATjHP0yCVSnX23Xp6ejAyMsLT05PCwkJcXFz40Y9+9NT9b/fu3ePgwYPivjt88z42NjZSUVFBZWUldXV1CILAj3/845cSKDxv9GI3wowWsevo6ODixYuiy4mFhQVVVVWEhYXxzjvvDPlh1mg0nDx5kvz8fJYuXSo2SD8tgiDoVD9qL21tbSiVStrb2+nt7UUmk2FpacmYMWOIiYnBxcWF4uJiioqKqK+vRy6X4+fnx5IlS4iIiCArK4uzZ8+KvpbOzs4YGRlhbm6uE62NlLBp+xY7Ozvp7Oykq6uLzs5OmpqaaGtrw9vb+5GuNL29vRw4cAAPDw/Wrl1LYWEhJ0+eZNGiRWRkZHD58mU6Ozvx8vLiH//xHwkODqa+vn7QFHZbW1txAruvry9ubm5PXLo+MDBAXl4eOTk5dHZ24uLigru7O0qlUqwe9PHxITg4mOLiYtrb21m2bBmRkZFDvi9fffUVhYWFrF27dsioSjtV/kEB1E54d3Bw0BE/Dw+PR1Z61tTUcO7cOVpaWpgwYQKzZ89+bMSonbNYV1fHBx98QE9PD4cPH2bu3LlMmzaN3t5eSktLuXnzJp9++ilqtZr169czdepUwsLCdJrVVSrVoDYHjUaDra2tjvgN5XpyP3K5nOrqalHctH9jd3d3MXLz9fXlxo0bXL58mY8++ggPD49HPubD6Ojo4PPPP8ff35+5c+dSVVVFRUUF1dXVyGQyzMzMCAgIIDAwkKCgoGcW7tHKKyt2mzdvfuxtDAwM2L59+7Af8y9/+Qt/+ctfqK6uBiAyMpKf/exnLFy4cNiPMRrErqCggBMnTmBjY0NMTAzFxcU0Nzczd+7cIdOW8E20deLECYqKikS3/idBqVTS1tY2SNj6+/uBbyYeODs7o1arkUgkdHZ2il+yqKgovLy8KCsr4/bt26JZdE9Pj3gbJycnqqurxevd3d1JSEggJCRkRIRNo9HQ09OjI2b3/1uhUOi8Vx0dHdTW1qJSqUhISMDPz09nZNH9TfvwjUlxcXExHh4ezJo1i6KiIpqamli2bBmlpaXcuXOHpqYmTExMmDNnDrNnzyY6OloclfTgFHa1Wo2ZmRne3t6i+Hl5eQ3bMFoQBCorK8nJyaG0tBRTU1MiIyOxs7OjoaFBjIC0r33VqlXMmjVr0PurVqtFIdm0adOw2ikEQaCzs1NH/JqamsT32MnJaZAADgwMkJKSQmFhId7e3ixcuHBYc/o0Gg1HjhyhrKyMtWvXEhAQAMCJEyc4c+aMWKCiVqvJyMhgYGCA3/3ud8Peo5bL5WKxSFVVFc3NzQiCgJOTkyh82mIRbTN3ZWWlKJL29vY6+273C3dHRwd/+ctfiIuLY+7cucNaz4N0dnby29/+lvb2doKDg+nr68PQ0BAfHx/xeb28vDA0NKSjo4OCgoKXXnH9vHhlxc7f33/IL15TU5OYprGyshLnkA2H06dPY2RkREhICIIgsHv3bn79619z+/btIc9sh2I0iN3du3c5fvw4gYGBFBYW4uzsLFbODYVarebo0aPcu3ePFStWPNKVQRAEuru7B4laR0cHgiBgYGCAo6OjWNavdS6pra2lpKRENFyOiooiPDyc1tZWbt26RXFxMf39/eK+QV9fH05OTvj5+WFpaSlaoHl6erJ8+XImT578xF9IlUoliteDgna/q4yBgYHYo6j1/LSzs6OlpYXMzEyKioro6+vD1dVV7F374IMPWLVqFWZmZjrT1O833r537x7nz5/HxcWF0NBQ0tLS6OvrY8KECZSXl4v7kUZGRuJzjx07lvDwcOzs7MRo0dzcHKlUSmdnpziNQSaTYWhoiIeHh86+33CqAiUSCbdu3SI3N5e+vj4CAgKIjo5GrVZz9+5d0tLSqKmpISYmhnXr1hEWFqYjqgqFgl27diGVStmyZcug4pDhoNFo6OjoGCSAWitAQ0NDLCwsmDNnDuPGjRvW314QBE6cOEFhYSGrVq3C1tZWrKBsaWmhqKgIY2NjPv74Y7Hq+N/+7d8GjfR5Evr7+8WILT8/n4qKCp2CEnd3dyZMmMCYMWMIDAx86AmaIAjie/rtb3972OlLpVJJbW2t6JBy8eJFurq6mDdvHuPGjSMwMBA/Pz+dYhqZTMaJEye4ePEiCoWCP//5z/qpB68CSqWSbdu28Yc//IGUlBTxTO5pcXR05Ne//jVbtmwZ1u1Hg9jdu3ePv/71r+Tl5eHo6EhkZCS+vr54e3vj7e2Nj4+PWLmlUqk4fPgwFRUVrFq1SmfjWi6XD/KDbGlpEVNQFhYWg3rWtOX9tbW1FBYWisLg5OQkVgEODAyQmprKjRs3aGtrE1NxPT09qFQqwsPDmTVrFqGhoXR0dHDjxg0xgnrcuJeBgYFBgqb92dPTI95OO8X8fkFzdHTE0dFRx0C8p6eHM2fOkJKSQl1dHVZWVkRGRjJr1ixxQOt//dd/cf36dWJjY3n77beJiYl56BqLioo4cuQIkyZNYurUqWzbtg1PT08WLlzImTNnSEtLo7e3Fzc3NywsLKipqcHQ0FAsonnw62pkZCQOkR0YGKCvr4/u7m6USiUmJia4ubkREBBAcHAwoaGhjyxqUKlUFBcXk5OTQ21tLTY2NkyaNInQ0FBSUlI4fvw4xsbGjBs3jsjISKKioggODsbIyIje3l62b9+OkZERmzdvHpEqaI1GQ1tbG42NjchkMiZOnDjsikft3vPly5cJCwtjYGAAiUSCubk5oaGhhIeH4+Xlxe7du7l58yZVVVVs2rRJLCR6GiQSidjIXVlZKUb45ubmGBoaIpfLEQRB3IvTRn4+Pj6DxEw7fX3Dhg2PPIZp+061+27abIO1tTUymYz6+no+/PDDQQ4p/f39lJWVkZKSwuXLlxkYGCA4OJjExESWL18+qk0DnpbXTuy0fOc736GmpoYzZ8481f3VajVHjhxhw4YN3L59+6Gu79pZbVry8vKYOXPmSxW7mzdvcu7cOWbPno2vry8NDQ3U1dVRX18vNgzb2tri4eFBcXExfX19rFy5EltbWx1R6+rqAhBHzDwobPdPNNCa22qbvXt6erCzsxOrxmQyGTdv3iQvL4+mpiaMjY3FCKSnpweZTMbYsWN5++238fT0pKGhgXPnzlFfX090dDRz587F1tZW3Ad8MM2o/Xm/Y46FhYWOkN3/81HTGDQaDTdv3uTkyZPcvHkTpVJJUFAQ8+bNY/r06bi7u+vcV6VS8cUXX5CVlYW7uzvu7u7MnDmT8ePHD1k0oZ1KMX36dHx8fDh48CBz5sxh2rRpXLp0ibNnzyKVSpk6dSrz58/n+vXrFBQUYGlpSUxMDGPGjEGhUDxyfFNHRwcdHR3iENre3l4MDAywsrLC3d0dLy8vfHx88Pb2xs7OTtxf1EaPUqmUmzdvkp+fj0qlIiwsTOxllcvleHp60t3djbm5udjDZ2Njo2O99TLMhLV7j19++SWZmZliX6N2Bpyfn5/O3+TEiRP88Ic/ZOLEiRw6dOiJ9kAHBgZ0iko6OzsxMDDA09NTTBH6+Pjo9Od1dXUNanMwMjLCx8dH3Deztrbmr3/9KxEREbzzzjuDnlcrqhUVFToOKf7+/uLzdnd3c/DgQRISEpg1a5ZYjFJWVkZ5eTnFxcWUlZUhCAKTJ08WMzovekrMi+S1Fbtt27bxwx/+UKxsGy4FBQVMmTIFmUyGtbU1Bw4cYNGiRQ+9/c9//nN+8YtfDPr9yxS7R00cbmlpoaCggIKCAs6ePUt9fT0ODg6YmZlhY2ODq6srQUFBhIWFERAQgLu7O87Ozg+NVNra2kSB06adnJycsLS0RKFQ0NzcTFNTk+hmEhQUxNSpUwkODiYvL4/Kykq8vLyYM2cOAQEB9PX1kZKSwvXr17GwsGDChAmYmZmJgtbV1YVSqRSf39bWVicqu1/QnqSiTBAEsfjh8uXLtLa2Ym9vz8yZM1myZAlBQUGPTJ3JZDJ27dpFW1sbvr6+VFVV4ejoKJo1P3hf7by4OXPmIJfLycjI4IMPPiAgIICrV69y7NgxJBIJSUlJrFmzhu7ubjIyMrhz5w7m5uZMnTqVmJiYh0Y6giDoCOL9vWR1dXVilaQ28rC2tsbOzg5bW1uxL8/S0lJ0HNHe3srKip6eHnx9fVm0aBE9PT2Ul5cjkUiwtrbGzc2NgoICJkyYwKpVq564gOZpGBgY0BlyWlZWRmtrK4sXL2bp0qWD+gm1FBUV8cMf/hAbGxtCQ0OZO3euOJdwKLQenFpxa2pqEvfn7m/mHu7nTtucrhVMbcFIaWkphoaGfPTRR4SFhWFvby+mRisqKnRENSgoSBRVrVB1dnby+eef4+rqysSJEykvL6eiokI8EZRKpfT19REREcGyZctGZEzRq8BrK3YrVqzg6tWrT2wErVAoqK2tpbu7m6NHj/K3v/2NK1euvFKRHfxfef+DaUhtqvDu3bsIgsDChQvx8PBArVbT399Pe3u7GNHZ2NiIZ/8+Pj54eHhgbGxMV1cX2dnZZGVliV9Q7bw8BwcHTE1N0Wg0SCQSZDIZrq6uolmxoaEhqamp4kF77NixODo60tHRIe4byWQy/P39RYf+B/fPtMJmb2//TNGDIAg6TdLaga7R0dG89dZbJCQkiAcQQRDo6+sbNEFCo9EQHx+Pm5sbUqmU7du3Y2JiwqJFi8jKyuLevXu4ubmRlJREaGiozkH38uXLXLlyhUWLFlFcXExraysff/wxtra2ZGdns3//fjo6Oli4cCGrV6/GyMgIiURCRkYGt2/fxtTUlPj4eOLi4p447aRWq2lubhaLXiorK5FIJCiVSmxsbHB0dMTW1hYrKytxJFRdXR337t2jvr6e1tZWLCwsiImJwcvLC5VKhUQiEaPJtrY2UfD8/PweOm7qaenp6REtuqqrq9FoNHh7e6NSqaisrGTJkiWPdPypr6/nhz/8IYIg8Omnn3L37l2uXLnC+++/L6byNRoNTU1NohjdnyLURmGBgYGPrbwcLhqNhitXrrB7927c3Nxoa2ujvb1dNJD28/Nj0qRJjB8/noCAgEGiqtFoqKys5E9/+hMtLS0EBwdjbGyMp6cn/v7+4ntmZmbGnDlzGD9+/GtZiPIwXlmx+/d///chfy+RSEhPTyc3N5d/+qd/4le/+tUzPc+cOXMICgpi27Ztw7r9aNizKygo4KuvvhILLuzs7HR8INPT05HJZHzwwQeDDJ/hm8rB+vp66uvrqauro7q6WjQQ1s6C036JAgMDGTt2rM5Q14qKCuRyuZiacXR0pLm5mYyMDO7evYtarRb9JQ0MDOjt7aWhoQGVSsXYsWOZNWuWaERsa2s7otHB/d6a165do6ioCIlEgqurK1OnTmXq1KmYmJgMORbp/ojSwsICe3t7ZDIZEomEqKgoMV20Y8cOMZXX3NzMpUuXqK6uxtvbm9mzZ4t7MIIgcP78ebKzs1mwYAEZGRk4ODiwYcMGjIyMyMvLY8eOHbS2trJ06VLee+89nf3NzMxMbt26hZGREXFxccTHxz/1Xpm2QvL+KewdHR3AN5+f+/v9jI2NxYNyc3MzEyZMIC4uDicnJ/r7+6mpqeHGjRvk5+djbW2Nl5cXrq6uuLq6YmlpiYWFhU7K9GE/raysdNJqWouukpISGhoaMDQ0JCAgQBxyWlZWxunTp5kxY8Yji0w6Ojr4t3/7NxoaGvjVr35FVFSUOCOuuLiYhIQEMRKWyWSYmprqpAhdXFxGVCS0EV5RURGff/45Go2GMWPGYGFhga2tLUZGRiiVSjo7O9FoNNjY2Ij7fc7OzrS3t1NeXk55eTm5ublIpVLee+89Jk2aRFBQELW1tSQnJyOVSomLi2PmzJmDMgLaYqSSkhJWrlz5WorgKyt2DzsAOjg4EBQUxIcffshHH330zH+0pKQkfH192bVr17BuPxrErr29ncrKSlHgtGf9/f397N27F4lEwgcffDBk345UKhWdR6qrq7lz5w6VlZW0trai0Wh0RgPZ2dlhYWFBX18fLS0t9PX1YW5ujqurKzY2NqLhcV1dHS0tLVhZWTFx4kTi4uJwcXHB2NiY3NxcysvLxbTY0/YSPQpBEGhtbSU3N5ecnByxFUMQBBwcHAgICMDe3l4c7QPftEpoX+tQo5G0Bwu1Wk1eXh5Xrlyht7eXiRMnEhQUJFbDrlq1CgMDAyorK0lNTaWhoYHAwECSkpLw9vYWe9W0Jd9XrlwhLi6O+fPnA9+k2v7617/S1NTEe++9N2iCdm9vL9euXePmzZsAxMTEMGXKlBHxZuzr6xvU8qDRaDA3N8fHxwd3d3cyMjLIzc3F0dGR8PBwJk2axKRJk7C3tyc1NZWjR4/i4eEhplPt7e3x8vLCxcVF3H/VXieTyQatwdLSEisrK9RqNZ2dnZiYmBASEkJYWBghISFidJOfn8+JEyeIjY195GQCqVTK//7v/3Lr1i2++93vipaDlZWV3Lt3j8uXL2NgYMDbb78tVkx6eXmN+F6WVCoV05KVlZX09vZSXl6OXC7nww8/JDo6etCEBIVCQVVVFdnZ2eTm5lJWVkZvby8WFhYEBwdjbW0tVm9GRkbS0tIi+t+GhoYyf/78QRZhcrmc3Nxcrl+/Tnd3N8HBwSxfvvyF2yy+CF5ZsXse/PM//zMLFy7E19cXqVTKgQMH+J//+R8uXLgw7D6X0SB2Q9HX18eePXvo7e3lgw8+ENNuD3pFdnV10dHRgUQiQaFQYGFhgb+/P+Hh4Xh7e9Pf309HRwelpaXcvn2bhoYGsVze0dERZ2dnsfhBa3tkZWUlGvNaWFigUqnIzMwkIyMDc3Nz5s6dS3R09DOfmCiVSp1IrKqqirt373Lv3j2am5vp6ekR+9O8vLyIiIhgzJgxYkr0flGzsLB4ovUolUpycnLIyMhAoVDg7e1NRUUFsbGxvPXWWxgYGCAIAqWlpaSmpopjdWbNmoWLiwuHDx+mvLycyMhI7ty5w8qVK8V2l/Lycv7f//t/1NXVsW7dOpYvXz5obf39/WRlZZGdnY1arWbSpElMmzZtRGfPKZVKGhoaRPHTtjzU1dXR0NCAq6srzs7OYsXq5MmTKS4uJi8vj3fffVe0rdNa+/n5+REdHU14eDiWlpaoVKpBhTbanxqNhuDgYAIDAwelrouLizly5Ajjxo3j7bfffujfTSaT8ac//YmzZ88SHR3NmDFjxGZuV1dXsRUgOTmZ4OBgVq9ePWIRjkKh0Nl3u7+JXDu49dKlS7zzzjuDKie7u7vFyK2yshK5XI6VlZVoKadWq8nPzyctLQ0/Pz8mTJhAd3c37e3tBAYG8tZbbxESEqLzmFKplBs3bnDz5k0UCgXR0dFMnToVNze3EXm9oxG92N3Hli1buHTpEk1NTdjZ2TF27Fh+8pOfPFFD52gUO6lUyrZt22hvbyc+Pp6BgQEaGxvF4h1tWXRHR4c44NPOzg47OzssLS3Fg4t236qjowOlUomLiwuTJ09m2rRpeHl50d7eTm1tLRkZGWRkZCCRSHB3dycyMpLg4GC8vb2Ry+XiCJz4+HgSEhKGXU6uVqvp7u5+6AT23t5e+vv7xb0OlUolOqsYGBjg5ubGxIkTSUxMJCAg4LmkauRyOdeuXSMrK4umpiakUilr167V+QxpNBoKCwu5fPkyEomE6Ohopk+fzvnz56mrq8PZ2ZmOjg62bt0qelPW1NTw29/+lurq6iFHMWkZGBjgxo0bXL9+HaVSyYQJE5g+ffpT9b49Do1GQ2trK7W1tVy5coVLly5hbm6Ok5MTfX19GBgY4O3tjZGREaampnz88cd4eXkhk8koLi6msLCQyspKDAwMCA4OJioqalAP3+OoqKjgwIEDhIWFDYp64ZvPTENDA2VlZezdu5fs7Gzc3NxYsGABYWFhYjP3/ZGw1lpLW8n4tO9NQ0ODWMxSV1eHRqMRK5SDgoLEJnKlUslf/vIXbGxs2LhxI2q1mtraWlHgWltbMTAwEJ1tgoODdaK+rq4uPv/8cxwdHbGwsCAlJYWOjg7c3Nzw9vYWR/0EBASIA23z8/MxNjZm0qRJxMfHj7qBvM+DV0bsamtrn+p+D/P1e16MBrErKiri/PnzSKVS2tvbuXnzJjKZDF9fXywsLLCwsMDMzIyBgQGxylEQBKytrfHx8SEiIgIfHx8x4tGWWFdXVyMIAmFhYUycOJHAwEDx4KKNWi5dukRbWxsRERHExcUhl8upq6ujuLhYHDTq5OTEtGnTCAsLEwtg7O3tRW/FofbLurq6kEqlYp+ZgYGBWImpLZppaWmht7dXLJSRyWTI5XIcHByIjY1l4sSJL8zzr6+vj6tXr3LkyBFqa2tZs2YN69ev16lqVavV3L59mytXrtDX10d0dDT19fVIpVLx9X300Ufiwb+xsZH//u//prKykq1bt7JkyZKHCrZcLicnJ4dr164hk8kYN24cM2bMwNHR8bm95oqKCnbv3o1Go2Hs2LGUlZWRn58vjjhycnJi69atzJo1C3d3dwwNDent7aWoqIiCggLq6uowMTEhNDSU6OhoscDiYdTU1LBv3z4CAgJYtWoVRkZG4v6XVmSqq6vFsUT19fX4+PjwT//0T0NWyN7P1atXuXTpEqtWrXqkyYIW7Z6nNi35JFZcKSkppKWlkZiYSHt7O1VVVSgUCmxsbERxCwwMHPKzq1Ao2L59O01NTTg6OtLd3c3EiRNJSkoS056VlZXcvn1bnNbg6urK9OnTmTdvntgn+SbwyoidoaHhU52JP2jb9LwZDWK3Z88ePv/8c2Qymehs4u7ujrm5OXK5XBQBrcGtm5ubWBBibGyMgYEBarVatP+Sy+XY2tqKX1hbW1tMTEwwNTXF1NSUzs5O8vLyaG9vx9fXl4SEBLy9vTE1NUUQBLKzs7l9+zb29vZER0ejUCgoLy+nurqalpYWZDIZgiBgamqKjY0Ntra22NjYDNonu///Go2G0tJSCgsLxXl22hObpqYm+vv78ff3Jy4ujjFjxryQEvihkEgk/O53v+Pq1avExcWxatUqxo0bp7Oe+1OgfX19dHZ2ioU548eP10lbtra28stf/pKysjK+/e1v8/bbbz/y+RUKBbdu3SIzM1MU1ISEhIdOM3hWWlpa2L9/PwYGBqxduxYbGxvu3bvHhQsXOHToED09PQQHBxMUFCSeMGkND/r7+8U2lubmZszNzQkPDycqKoqAgACd96yxsZHdu3fj6enJ4sWLday4ent7MTIywtfXl4CAAKqrq8nKysLIyIgNGzYQExPz2NchCAJHjhyhvLycDz/8cMg2nr6+PlFMKioq6O7ufqgV14OoVCqxkGf//v24urqKDeYhISEEBwfj5ub2yGOe1uXp/Pnz+Pj4EB4eLlZXA+J3JDMzk7q6OiwtLcX9x5qaGmQymTjBXFvwoj0JeR15ZcRu165dTyV2GzZseA6reTijQezy8vI4ceIEZWVlWFtbM2vWLPr6+sSKR3t7e7GCzd7eHqVSiVKpRC6XU1FRIdocacu5/f39cXR0FEfDaH+2t7dz9+5dmpubsbS0xNfXV3RuGBgYoKmpiZqaGuRyOTY2NuKZqZGREUZGRmKUqd3PUqvVyOVyTExMsLCwwNXVVfRH9Pb2xszMTCyTb2trw9TUlNDQUDw9Penq6hJbB8aNG0dsbOyo2X/QaDTs3LmTlJQUPDw8CAoKIikpifDwcJ3PtFwuJysri7S0NG7evImNjQ3e3t6sXLlSZ8xSZ2cnP//5z7l37x7f/e53eeuttx67BqVSye3bt8nIyEAqlRIREUFCQsJzeY96eno4cOAAXV1drFq1SjSI7uzs5Fe/+pV44NW2rGj3+dzd3UWja0tLS6qrqykoKBDFf8GCBYSHh1NXV8cf//hHVCoVAQEBSCQSDAwM8PDw0GnmNjExIT09nVOnTtHf38/cuXMfmv4dCoVCwd/+9jdUKhUfffQRxsbG4ufv/qGp2rl+Q1lx3U9nZ6fYB1hdXY1CoaCoqAhnZ2c++eQTgoODh91ColAo+Otf/yoW5axfv57IyEgMDAxQKpXcuXOHa9eu0dnZib+/P1OnTiUkJER87RqNhubmZrH3sqamBqVSibm5Od///vf1Dip6Hs9oELv29nY+++wzOjo6xDNma2tr0eLJ29tb5wvf09NDXl4eubm5Yhn+xIkTGTdu3KDUiUwmo6qqipSUFDHvr93z0PbVdXd3c+/ePfr6+vDx8WHcuHE4OTlhbm6OhYUF5ubm4gFBoVCIF6VSiUwmo7Ozk9bWVrFPsLm5md7eXvHL6OLigpeXF1ZWVrS2ttLd3Y2ZmRm+vr5iT9f9kaepqanO/5/0OiMjoyEPkD09PXR1delUvD4MlUrFvn37uHfvHl5eXrS1teHp6cns2bMJDAzUefz+/n7Onz/PF198gUQiISwsjJ/97Gc6tlE9PT389Kc/pbi4mO9973ssXrx4WJ8NlUrFnTt3uHr1qvjYCQkJQ852exbkcjlHjhyhsrKSt99+m/HjxwPfRN1alxU/Pz/y8vLo6OgQU9JKpVIcz+Tg4ICPjw+WlpYUFxdz+/ZtAHEAbmJiorjv5u/vP6iCMDc3l+PHj9PT00N4eDgffvjhE/VlCoJAcXExf/rTnxAEAS8vL9RqNdbW1jr7bg/b71IqlVRXV4sC19nZKUacwcHBdHV1cfPmTT766KNhmVpr15Sfn8+hQ4e4fv06ixcvFkcS9ff3k5OTQ3Z2Nv39/URERDB16tRhPbZ2b7OxsZH4+Phhv0evEnqxG2FGg9idPHmSv/3tb0yePJkJEyYQFRWFv7+/TnpCo9FQVlZGbm4u9+7dE2fCRUVFicL1YBFIa2srpaWlYtowKCiIyMhIcW/PwsKC4uJiKisr8fHx4e23334qb9K+vj6xgEF7xqm1tNKOqKmtrUUul+Ph4SFW9GkFVaVSDRLRof6tUChQqVSPXY+hoaGYku3p6RH3FbVVqNqJDl5eXmJKyM/PT4xmtUImk8nYuXMnAwMDzJkzh5ycHOrq6vD392fOnDmDeh4rKyv5xS9+wZ07d/D29ubHP/4x06ZNE/dYent7+bd/+zcKCwv5/ve/P6wIT4taraagoICrV6/S0dFBSEgICQkJI+qmoVarOXPmDLm5ucyaNYuEhAQMDAyoqKhg//79jBs3joULF4p+nPX19djZ2RERESEaDdTW1tLU1CQaRV+/fh0rKyt+/OMfs2jRoodGaaWlpRw8eJD+/n4cHBz4+OOPh7VfOZQVl1Qqpbq6mpkzZ7J69eqH9tlpq4+1hSXV1dViJiU4OJiQkBD8/f1FR6A///nPxMTEiG0mj0Nroafdf5wxYwZbt26lu7ubrKws8WRg/PjxTJky5bnuz76KvPJil5mZSW5uLt3d3Tp9UvBNEcNPf/rTF7qe0SB2UqmUhoaGITf429rayMjIIDs7m46ODnH6sq2trVjNqMXIyAh7e3usrKxoaWmhqqoKS0tLsUrN3t4eAwMD0Uvy8uXLCIIgDtN8ktx/f38/JSUlFBYWiiOWtON/wsLCkEgk3Lhxg8LCQgRBwM/PD3d3dwYGBqirq6O9vR34pi9L6/iirUR7VHWfRqMZlJ7VXiQSCTU1NdTU1FBfX09nZydqtRorKyucnJxEt5iWlhaam5tpa2sTi2i0+6F2dnZiOtbZ2RlDQ0OuXr2KlZUVK1asoKenR/z8RkZGMn/+fJ0xOY2NjfzhD38gMzMTFxcXEhMTRRNqQ0NDBgYG+Jd/+Rfy8/P5wQ9+MOwI7/7Xf/fuXdLT02lrayMwMJCEhAT8/f2f6HEehiAIXL16ldTUVMaPH8+SJUswMjIiPz+f48ePk5CQQFJSkvhac3JyKCgoQBAEIiIiiImJwc3NjebmZr766itx3l5xcTG+vr4sWbJEnLKupba2lj179qDRaFCr1axZs4YxY8YMuT5tpuJBKy5tBaM2JXrjxg2Sk5N1WkLg/3rftAKnHT7s7+8vCpyTk5OOOAqCwJ49e5BIJHz7299+bPWpVCrl0qVL5OXl4eLiIlqzvfXWW+Tm5lJUVISFhQWxsbHExsa+lj1yI8ErK3adnZ0sXryY7OxscbTM/ZV62t+9iQUq/f39NDc3i5FZe3u76DzR2NiIkZERbm5ueHl5iZWQQzVPm5ubk5ubS3p6OnK5nNjYWKZPn67zZaqpqeHs2bO0trYOe5imFplMJgpcZWUlgiDg7+8vCpy5uTklJSXcuHGD2tpa7OzsiImJYeLEiYO+0AMDAzqG19qhr9qWg/snPjxsOGVfXx/V1dXiRdsL5ezsjL+/v3h5WMO2tkn6/krA+vp6sVVDa8QM30Qe1tbWTJgwAUNDQ1pbW8XKQV9fX8aNGyfuZfX29nLmzBkaGxuJiooST1DmzZtHdHQ0SqWSf/7nfyYvL48f/OAHTxTh3b92bcVsc3Mzfn5+JCQkDEqxPi35+fmcPHkSPz8/3nvvPczNzcnMzCQlJYW33npLp7dMG73fvHlTLKHX7udu3rwZBwcHqqurOX36NBKJhBkzZjB9+nSMjY1pbW1lx44d4iSGhIQEHTcVtVqt42/Z0NCAIAg4OjqK+25D+VsKgsDx48cpLi5m6dKloidoTU0NarUaBwcHsbDE39//kQKWm5vLqVOnWL9+PUFBQQ+9nUql4vr166Snp2NsbMysWbOoqanh2rVr+Pv709nZiaOjI1OmTGH8+PEvxXj7VeKVFbstW7bw5ZdfsmPHDuLi4ggMDOTChQsEBATw+9//nqysLM6dO/fCixRGg9jl5eXx1VdfiWNNurq6MDAwwNfXl4kTJzJp0iRcXV0fasUlCAIFBQWkpqbS3d3N+PHjSUxM1PEA7OnpITk5+YmHacrlcrGKUlsE4+vrS2RkJBEREVhbW9PX1ye6nfT09DxVVaW2BF1reVZfXy+Klzb601YkaoWytbUV+GZ46P3i9qyzvbTFQfdfWlpauH37Np6eniQlJeHo6IiNjQ01NTXcvHmTnp4esThIrVZTU1MjzhuLjY0V/6729vZERkbi5eXF119/TV1dHStXrhRPOu6/3N8z+aj37d69e6Snp9PQ0IC3t7c4JPdZRa+qqopDhw5hZ2cnVmpq7dJWrVpFWFjYoLVoB8y2trby/vvv60RxKpWK9PR0MjIycHJyYubMmSQnJwPfnEh5e3uzdu1aOjo6xJaAmpoaFAoFlpaWBAQEiAL3qD5EuVxOZWUlJSUlHDhwAKlUSmxsLGPGjBFbAx50JnkYUqmUzz77jLCwMJYuXTrkbbR/gwsXLiCRSMSTzOPHj3Po0CGxcVzbvjOS1ZPa1p3XkVdW7Dw8PHj//ff53e9+R0dHBy4uLqSkpIhnccuXL8fMzIyDBw++0HWNBrHTflHa2tqwsrJi3LhxTJw4ccjy6fsRBIGysjIuXbpES0sLYWFhzJ49e9ABJisri/T0dExNTZk7d+5jh2kqFAru3btHYWEh5eXlqFQqfHx8RIHTbvA3NTWRnZ1NQUEBAGPHjiU2NnZY06+Hg9bAOi8vj+LiYurr61GpVDouMRMnTiQsLGxQ6mkk0fZkZWZmcvjwYRwcHHBxcUGj0WBkZISrqytSqZT6+nqsrKxITExk+vTplJaW8rOf/QxLS0t+8pOf0NzczNWrV6mvr8fR0ZGAgADOnTtHRUUF8fHxQ0YNpqamQ4rgg7+zsLCgubmZzMxMamtr8fDwICEhgbCwsGd6X1pbW9m/fz8ajYa1a9fi6uoqDg7esGHDU+0ZtrS0cOzYMb7++ms8PT0JDg6mvb2diRMn0tjYSG9vrzhSSltY8uCYpvvR2stpx+HU1tai0WhwcnLCw8OD7OxswsLC2Lhx4xMLzeHDh6mpqeGTTz4ZMt3Y1tbG+fPnqaioICgoiMTERGprazl79izXrl0jLi6Ob3/72/j6+o7o51OpVJKcnExBQQHf/e53X0vBe2XFzsLCgk8//ZQtW7Ygl8uxsLDgxIkT4vynv/71r/zLv/wLnZ2dL3Rdo0HsqqurSUtLY9KkSYSHhz+yMVdLXV0dFy9epKamBj8/P+bMmTPowKOdtC2RSERD2YdVISqVSsrKykS7LqVSiZeXF5GRkURGRopRokajobi4mOzsbGpqah6ZqnxSZDIZNTU1VFdXU1VVRUtLC4IgYG9vr1NE0tPToxP9CYKAhYWFTurTy8tr2E4vT4J2tl1CQgKhoaFi5KedLKD1FbWzs2Pq1Kk4OTmxe/duoqOj+d///V+MjIx0TlACAwO5ceMGRUVFfOtb32LhwoX09fWJl/7+fp3/3/97hUIxaH1mZmaiMUB3d7fomhMREYGNjY2OUFpaWg7r4K+14uvs7GTlypX4+/uzd+9eWltb2bJlyxP3APb29vKHP/yBGzdu0NjYSHd3N9OnT2fKlCnivpuvr+8jo1qZTEZlZaUocFKpFBMTEwICAsT0pIODA/BNhLp3714dD9PhUFxczKFDh1ixYgVRUVE61w0MDHDlyhWys7Oxt7dn2rRp4iQQ7R58TEwM3/nOd0a8D66trY2jR4/S0dHB/PnzmTx58mtpBP34o+AoxdPTk+bmZuCbL6Srqyt37twRxa6hoeG1/IMNB39/fzZu3Dis27a1tXHp0iVKSkpwc3Nj7dq1BAcH67x3HR0dnD9/nrKyMgIDAwelk7SoVCrKy8u5e/cupaWlKBQKPDw8mDlzJpGRkeLBAr7ZV7x165aYqtTu5TxLWkYmk1FbWyuKm9bs2c7OjoCAAOLj4/H39x8yZTVhwgTxMbRiU1dXR1ZWlmgO7OLiIha+aNOgz/oZmzBhAlKplNTUVOzs7IiLixOv6+/vFwduXr58mTNnzoj70qdOnaKtrY1PPvkEb29vPvzwQ0pLS7l8+bJYHfvpp58C8N577w1rnUql8pHCWFNTQ35+PocPH8bExARvb2+dxmcDAwMsLCweGi3e/7tVq1Zx5swZDhw4wFtvvcXq1avZuXMne/fu5cMPP3xk6vh+K67y8nLOnTtHZ2cnY8aMwdTUVCy4cnBwID4+fsjH0k6/0LYF1NfXo9FocHFxISoqipCQEHHCw4MEBAQwf/58zp07h4eHB2PHjn3seyuTyTh79iyhoaE6BS4ajYbc3FxSU1NRqVRMnDgRuVzO2bNnMTExYcKECZSWluLq6srmzZtHfALI7du3OXfuHPb29nz00Uejpjf1efDKRnYbN24UIxiA733ve2zfvp1//ud/RqPR8L//+7/Mnz+fo0ePvtB1jYbIbjh0d3dz+fJl7ty5g729vVjh96DLenp6OllZWdjY2DB//vxBaSy1Wk1FRYU4HkQul+Pm5iZGcA/uZTQ3N3Pjxg0xVRkdHU1cXNxTpSrlcrmOuGmHadra2uLv709AQIAobk8rStpy8vsLX1pbWxEEAXNzc3F8jYuLC87Ozri4uDyxJZkgCJw7d46cnBxWr1790MrBrq4uzpw5w7Vr18jPz6e+vp6YmBgiIyPFoiNPT096e3spKSnh0qVLdHd38/d///ds2bJlxE7+GhoauHLlCkVFRVhZWTF+/Hj8/PyQyWSPjCAfrJaGbyonW1paiIqKIjIykqtXr2JjY8O7776Lo6OjKI4ajWbQkFOte09PTw8rVqwgOzubiIgIli5dSlFREefOnUOtVjN37lwmTpyITCajoqJCrJzs7e3F1NSUwMBAce9tuB6igiBw8uRJCgsL2bx582P7FE+fPk1hYSHf+c53xKxGdXU158+fp6mpSXRaqa+vx9bWlilTpjBhwgTOnTtHUVERW7ZsGbF0PnwjvqdPn+bu3btMmjSJBQsWvPYFLq+s2BUUFJCSksInn3yCmZkZXV1drFy5ktTUVAASEhI4ePDgcxkZ8yhGu9j19/dz9epVcnJyMDMzIyEhgcmTJ+v442nd6ZOTkxkYGBAnFmi/DGq1WpwoUFxcjEwmw8XFRRS4B6M+jUYjVlXW1NRga2srelU+SapSO1hXWy2pHTljY2OjI24ODg7PNaqXy+Vi9NfQ0EB7ezudnZ1i1GVtbY2Li8ugi6Wl5UPXpdFohr13pY3G//znP9PU1MQnn3zClClTaGxsFKs/tSbN6enpdHV1sXDhQn71q1+N6AGzubmZ9PR0ioqKsLOzY/r06UyYMGHIaEgQBORy+SAB7O3t5ebNm6JBs6enJ1evXsXMzExsr9ByvxVXUFAQxcXFZGZmsnjxYq5fv46RkREffvihWAnZ39/Pl19+SXp6uhiZW1pa4ubmJrYF3D/h+0lRqVTs3LmT3t5etm7d+tAq5Orqanbt2sXixYuJiYlBIpGQkpJCYWEhhoaGmJubMzAwgJubG1OnTiUqKgojIyNu3LjBuXPnePfdd4mOjn6qNQ5FfX09R48eRSaTsWTJEp1I83XmlRW7oqKiIaeHSyQSjIyMnrmC7mkZrWKnUCi4fv06mZmZCILA1KlTmTJlyqB9qObmZs6dO0dNTQ3h4eHMnz9fnPVWXV0tClx/fz9OTk6iwLm6ug45dubBVGVcXNywU5UKhUIcHltdXU1DQwMajQZra2uxUlI7HPZlp6xVKhUdHR20tbXpXLS9efDNPvNQImhjY4OBgQEqlUrcu9q8efOQqeL70U5AqK+vZ926daxfv57AwEBxqkV9fT0VFRV89tlnVFVV4ebmxqxZs5g+fToBAQF4e3vj7u7+zGf0ra2tXL16lcLCQqytrZk2bRqTJk16osctLCzkxIkT+Pr6Eh8fz6FDhwgODmbOnDn09/ej0Wh09k21QjB37lzRF3Pr1q2Ym5vrRG/aCLO9vR0LCwuWLFnCvHnzRsz8uKenh23btuHs7MwHH3ww6HFVKhV/+ctfsLS0ZP369WRmZpKenk5nZyfm5uaYm5sTGBjItGnTCAoKEj/H1dXV7Nmz54n3BR+FIAhkZmaSmpqKp6cnK1aseC7TMEYrr6zYGRoaEh0dzapVq3jvvfcIDg5+2UsCRp/YqdVqcnNzuXLlCgMDA8TExDBjxoxBZ6EDAwOkpqZy8+ZNnJ2dWbhwIf7+/tTW1nL37l2Kioro6+vDwcFBFLiHVbQ9bapSqVQOEjdtE/f94vY8KyVHGrVaTVdX1yAR1I4ggm/2nO8XvitXrmBqasp3v/tdnXaPoWhra2PTpk20tLQwbdo0sdfxfjcWbeN5cnIydnZ2uLu74+joiIeHByYmJjqjYLy8vJ56L7Kjo4OrV6+Sn5+PhYUFU6dOJSYmZtgje2pqavjyyy+xtrZm4sSJJCcnM2XKFObNm6dzu7t373L06FFxMvuJEycYN24cCoWCxsZGBEHAw8NDTE16e3uj0Wi4cuUK165dw9nZmbfffnuQY83TUltby65du5g8eTKLFi3Sue7SpUtkZmaSmJjItWvXRLciNzc3xo0bx7Rp0wZln7q7u/n8889xcXHhgw8+GJF9OqlUyokTJ6iqqmL69OkkJiYOEmbtXuhIuuiMJl5Zsdu2bRuHDx/mypUrCILA+PHjWb16Ne+99x5+fn4vbV2jRey0qcjLly/T1dXF2LFjRdeT+9FukF+6dAmNRsPMmTPx8PCgpKSEoqIipFIpdnZ2osB5enoOeSAcKlUZExPDpEmTHpqq1E4x14pbfX09arUaS0tLHXEbiUKQ0YZGo0EikYjCd78QSqVScnNzMTMzY+7cueJkb+2eoKOjo84BsLCwkH/913/FwsKCcePGIZfLCQsLIykpSWw3GRgY4H/+53/IyMhgzJgxODk5IQgCgYGB2Nraig4wAObm5qL5tlYAn6QUvauri4yMDPLy8jA1NWXKlCnExsYOy1y4vb2d/fv3o1QqCQ8PJycnhwULFoh+jVVVVezYsQMHBwfMzMz46quv8PDwIDIykqCgIHGiwsMyO83NzZw6dYqmpiZiY2NJSkoakSrbmzdv8vXXX/POO++IxU7Nzc389re/RS6Xiyc3oaGhTJs2jSlTpgwZVQ03NfoklJWV8dVXX2FgYMDy5ctFY24tgiCIe7xdXV384Ac/GJHnHW28smKnpaWlhSNHjnD48GEyMzMBiI2NZfXq1axcuXLEDW4fx2gQu7q6Os6ePUtTUxOhoaHMnj17yCorbf9OU1MTPj4+ODs7U1lZSU9PDzY2NqLAPWgcfT/9/f1iA3h3dzd+fn7ExsYSFhY2ZEqnoaFBnI33YJ+b9jJUSvRNQeu/WVJSwu7duzE2NiY6OpqOjg5kMhnwjY2bk5OTTir0zp07HDhwgMjISBYtWkReXh4SiYSxY8eSmJiIg4MDfX19/OY3v+HGjRviANuioiJsbW1JTExkzJgxNDc3i3uRDQ0N9Pb2AmBvby8Kn7e3txgVPoru7m7Rzs/Y2Ji4uDji4+MfW8DT29vLwYMHRbPsmpoaMV2p7ZsNDQ2lvLyckJAQvv3tb+Pj4zPsCEij0XDjxg1SU1OxtLRk8eLFhIaGDuu+D0MQBL7++mvy8vLYtGkTdnZ2fPe736WgoAB3d3fGjh3LokWLmDx58kNP/u4vetmyZcsz1xuo1WouXrxIVlYWISEhLF26dJCIVVVVcfHiRdFecPbs2S+8zuFF8cqL3f00NDSIwpednS2Ou3iRjAax07ptzJkzZ8goVyqVkpyczLVr11Cr1eIZprW1NREREURGRj62abW5uZns7Gzy8/OBb1KVsbGxOl8UrZO6Vtzq6upQqVSYm5vriNvj5na9qdTV1bFnzx6Cg4NZsWIFAwMDg9KhbW1t9PX1iWXk1dXVRERE8N5774l2bIaGhsTGxjJjxgwAfv/735OTk8Ps2bNZtmwZ2dnZ3L17F2dnZ2bNmkVERIRoudfd3a3T+9fU1IRSqcTQ0BBXV1ed6O9hBslSqZRr165x8+ZNDAwMiI2NZcqUKY+MHhQKBcePH6ekpARLS0s6OjooLCzEx8eHzZs3c+vWLQYGBvj444+fOgrRVreWl5cTFRXFggULnqmZWqVSsWPHDq5evSqm4efOncuHH3740MKd+8nJyeHMmTMsW7aMcePGPfU64Bs7xaNHj9LS0sKcOXOIj4/X+ds0NjZy6dIlKioq8PLyYs6cOU9l2v4q8VqJnUaj4dKlS3z55ZccOXKEvr6+N9IbExC9Qe9HpVJx9uxZTp48SXt7O+7u7gQGBooRnJ+f3yPPjh+Wqpw4cSJWVlao1WoaGxt1xE07lsfPz09H3F7XAZEjTWlpKV9++SWTJk1i8eLFQ4pJf38/7e3t1NTU8Le//Y3S0lLc3d2JiIjAyMiIhoYG2trasLCwEO3iLly4QGlpKYmJiXz00Uf09fWRmppKWVkZ7u7uzJ49e1C/JSBWed4f/Wkb8c3MzPD09BSjPy8vL510Yl9fH1lZWaKf7eTJk5k6depDU44ajYYLFy5w/fp14Bsbty1btojVxBs3bnzm/SWtNd758+cRBIF58+Yxfvz4Jz75UqlUnDlzhl27dpGXl4ehoSHf+973+Lu/+7thPZZ23y8mJoaFCxc+7csBvvEh/frrr7G2tmbFihU62a329nYuX77M3bt3cXFxISkp6ZldcV4VXnmxEwSBtLQ0Dh06xIkTJ2hvb8fBwYHly5ezatUqHRPYF8FoEbv7aW1tJTk5WRS5gIAAFi1axIQJEwZNgB6KB1OVvr6+xMXFERISIhoYV1VVUVtbi1KpxMzMTEfcXufpxy8CrXFwUlISCQkJj7xtbW0tf/nLX+jo6CA6OpoFCxaI3pyZmZncuXMHuVyOq6srDQ0NdHV1ERERwbvvvktgYCByuZz8/Hyam5vx9fVl9uzZj90Dl8vlNDY26rRjSKVSAGxtbXWiP09PT9RqNdevX+fGjRuoVComTJjA9OnThyzGEQSBGzdukJeXx6pVq2hoaODo0aMsWrRIZ6Dts9Lf38+FCxe4c+cOAQEBLFmyZFgjcmQyGZcuXeLAgQM0NDQQHh6OpaUlRUVFbN26lWXLlj32MXp6evj8889xcnIasqJzuGib0e/cucO4ceNYtGiRuB/Z09PDlStXuH37NjY2NiQmJjJu3Lg36nv5yord1atXOXz4MEePHqW1tRVbW1uWLl3KqlWrmDNnzrAssp4Ho0XstFPEs7OzuX79Ot3d3URERPD+++8TGxs7rC9US0sLN27cEFOV2uhvYGBAFDeFQoGpqSm+vr5in5uHh8cb9SV6EaSnp5Oamsrbb7/92M/V9evXOXbsGGq1msjISNavXy9WRPb29pKamkpGRga9vb3U1tbS1taGm5sb4eHhWFhYIAgCMpmM5uZmlEolY8aMYf78+URFRWFlZfXYKEAQBNHbUxv9NTY2olAoMDAwENOfzs7OtLa2UlxcjFKpZNy4ccyYMUPHaed+2tra+OKLLxgzZgzLly9/LtFIRUUFX3/9NVKplMTERKZMmTLkd6W7u5v09HSOHz9ObW0tAQEBbN68GQsLC06ePMmECRO4ffv2oIkOD6JSqdi1axc9PT1s3br1qdOoTU1NHD16FKlUyuLFi8U06MDAABkZGdy4cQNTU1NmzJhBTEzMSzs+vkxeWbHTzgtbsmQJq1atYsGCBcMucX6ejAaxy83N5cSJEzQ2NjIwMICvry/vv/8+Y8eOfewBQqPRUFpayo0bN6iqqgIQJ3G3tLQgl8sxMTEZJG4j1bekZ2gEQeDs2bPcvHnzkS4r2tsePXqUW7duYWxszJgxY3j//fd1DnBdXV2kpaWRlZVFQUGBuJf27rvvYmhoSFtbG62trRQUFHD79m16e3txdnYmPDwcf3//Qb2Ctra2j/xsaTQacQqFVgC1TjRGRkbIZDLa2towMzMjPj6e+fPn63hkyuVyvvjiCwwNDXUax58HCoVCfG/c3Nx4++23xVRgS0sLGRkZpKSkUFtbi7u7O++99x6zZ89GLpfz2WefERISwvLlyzl79iy3bt1iw4YN+Pr6Dvlcp0+f5s6dO2zatGnY08rvRxv5pqSk4OrqyooVK3ByckKhUHDjxg0yMzPRaDRMmTKFqVOnPhd/11eFV1bsjh07xuLFi4dVzvwiGQ1iV1BQwL59+7C0tBRnfT3u4NDf3y8OYK2trcXY2BhLS0tsbGwwMzPDx8dHFDdPT0+9uL0ENBoNR44coby8nA8++OCR+1VacdA6u4SHh7NixYpBEXdraysnTpzgyy+/pL+/n7i4OP7xH/9RJ3WpUqnIyMjgwoULtLS04OLigru7O/39/WIBmKmpqU57hPZib2//0Chf2xenTX/W1tZSWlpKXV0dABERESQlJREVFUVOTg4VFRVs3bp12ON0npXGxkZOnTpFc3Mz/v7+CIJAfn4+DQ0N2NraMm/ePBYsWCBO7Thy5AhVVVV897vfxdLSErVazZ49e+jo6GDr1q3i7bRo2xWWLl3K+PHjn3h9fX19fPXVV5SVlTFlyhRmz56NgYGBTl/t5MmTSUhIeC1bCZ6UV1bsRiujQeyam5tJS0tj/vz5D00Jwf/14p0/f56cnBwkEgmOjo74+fkREREh9rl5eXnpxW2U8CQuK62trXzxxRfY2trS2dnJuHHjeOedd4aMwG7evMl//dd/UVVVhY+PD//wD/9AYmKizm0eNCiYMGECY8eORSaTDeoVlMvlABgbG+sI4P29gkN9pqRSKdXV1aSnp5OZmUlLSwsODg74+vry4YcfDuma9LzQaDTk5+ezf/9+bty4gVKpJCAggISEBBYvXqxzslFaWsrBgwdZvny5jjF0X18f27Ztw8bGhk2bNonRdV1dHbt27WLSpEmDGtGHQ1VVFcePH0etVrNs2TKCg4MpLCwkNTVVbDkZqq/2TUYvdiPMaBC7h6Gd01VZWUlWVhZZWVlild748eNJSEggIiICLy+vNzKn/6owMDDAzp07kcvlbNmyZVDEcD8FBQUcO3aM0NBQysrKRPupoQSvtraW3//+9+Tm5mJgYMCCBQtYv379oPSaUqkkOzubjIwMVCoVsbGxTJs2Tewf0+7ZPegY09raysDAAPDNNsSDvYIuLi44OTmJnz21Wk1eXh7nz5+nvb0dT0/PF+Kko1AoyM3N5fr166LPaFdXF52dndjZ2YkRnTZa0qYv3dzcWLNmzaA1NTY2smPHDqKionjnnXfo7e1l27ZtODo6smHDhic6kVSr1aSlpZGRkYG/vz/Lli2jpaWFS5cu0dzczJgxY5g9e/ZjZ1e+iejFboQZTWKnndatdSgpKyujoqKCpqYmTE1NGTNmDElJSSQmJo66dLCeR9PT08P27dsxNzdn06ZNj/z7afeOJk6cSE5ODrNmzWLmzJlD3raqqoo9e/ZQXl5OR0cHfn5+JCYmkpSUNCiKlMlk4kmTgYEBU6dOJT4+/qH7QoIg0N/fP2SvoLZ53cDAQBxme78AagteampqBhmAa8XvWQ3Ae3t7yc7OJicnB5lMhr29Pd3d3RgYGBAXF0dCQgIlJSVcuHABAwMD5s+fz9ixY8UKyO985zsPjaTu3LnDiRMnmD9/PkVFRUgkEj7++OMnKkiRSCQcO3aMhoYGZs2aha+vL6mpqY+cQann/9CL3QgzGsSurq6O69evU11dTV9fHwMDAwwMDNDX14etrS3Tpk1j6tSpL9xdRs/I0tbWxo4dO3B1dWX9+vUPjca1FX9SqZSoqCgyMzN1LLgepLy8nAMHDtDT04NUKsXKygo7OzvGjRtHYmLioAN6X18fGRkZ5OTkiBV/kydPfiIj6IGBgUGp0La2Nrq7u3VuZ2BggKGhIVKplO7ubnGNBgYGWFlZ4e7ujru7O56entjZ2WFsbIyJiQnGxsaD/q39f09PD/n5+aJvZUBAAFKplI6ODsaMGcOiRYt0imX6+vo4f/48BQUF2Nvb09TUxPLly3VmEQ7FhQsX2LNnD25ubvzgBz94Im/OoqIiTp06hbm5OTNnzqSkpETsp3xYT6QeXfRiN8KMBrErLy/n8uXLGBkZ0d7ejlQqxd7eXvSq1G9Wvz7U1tayZ88eQkNDhyxA0dLd3c22bdtwd3fHzc2NrKysRxZGaJvZlUolBgYG+Pr60t3dLRY9zJgxY1BUcn8vl7W1NTNnzmT8+PHPtN+r9ZWUSCQolUpUKhUqlUrn3319fTQ1NdHY2Ehzc7M4acLc3BwHBwfs7e2xtbXF2NhYvK9Go6G7u5u6ujra29sxNTXF3d0duVxOS0sLlpaWBAcHi712RkZGg8Syvb2d06dPA7B48WIiIiIwNTUdUlCNjY25d+8eO3fuxMvLi+9///s4OjoOKcLafxsZGaFSqTh//jy3bt3C398fCwsLSkpKsLe3F4t39CI3PPRiN8KMBrErKyvjzJkzSCQSfHx8iIuLIzw8XF9k8pqiFSat+8bDDn4VFRXs27ePhIQEent7yc3N5b333iM8PHzI2xcVFXH48GGMjY1RKpVMnToVCwsL0WYuPj6eadOmDUqhdnR0kJaWRmFhIQ4ODsyaNeuFHpQHBgaoqakRnXxaWlqAbxxYtFWmNTU1tLa24uDgwPjx45HL5WRmZqJWq8WBuBqNZkhx1f771q1b3LlzB09PT+rq6rC1tSU6Ohpra+tB92lvbxcnivT09GBsbMyECRMe+Z3s7++nuLiYgYEBrK2tUSgUmJubExYWRmBgIGZmZo8Uy6f5t1ZkX0f0YjfCjAaxa2pq4vr168TFxelTlW8It27d4vTp08yePVv0wByKK1eukJaWxvvvv09+fj7FxcWsWbOGoKCgIW+fn5/PiRMnsLS0pLe3l/j4eGbOnMm1a9e4ceMGxsbGTJs2jbi4uEFpy5aWFlJTUyktLcXNzY2kpCRCQ0NfeCTS19dHeXm5WNjR1tYmpmV9fX2prq4GIC4ujqSkpGFlPlpaWti2bRsJCQkkJib+f+3dd1RU1/o38O/QO6iAYKGqCIIiKFJULIgNG4IVsUs00SQm8adJbDfxenNTrsZcDRpFxN67IiJFBURULBQLTQWkSAeZAea8f+TlXEdmkDaF4fmsNWs555w555nNOM/sfXZBdnY2zp8/j4KCAri4uGDEiBFsedR3SNHV1YW/vz9ycnIQFBSEXr16YezYsairqxNIjjU1NXj48CEiIyNRVlYGFRUVqKqqwsbGhl3KrLEk/OG/65eSaqrvv/9eLjuoUbJrY7KQ7EjHFBUVhYiICIFlZj7EMAwOHz6M169fY8mSJbh69SoyMzMbHbdXP11Z586dUVRUhIEDB2LSpEmorKxEdHQ07t27B01NTQwfPhwODg4NagavX79GeHg4MjIy0KNHD4wePVpikw6/e/cOd+/eRXx8PCorK9G3b18MGDAAhYWF7AoDKioq7Np39Z1dTE1NRa7OwOfzsXfvXvB4PAQEBAj0Ho2JiUFUVBS0tbUxadIkmJqaIjg4GMXFxVi2bBk7D+iTJ09w8uRJjB07Fi4uLgLxnjlzhl1yy8zMDK6urgK9XZuLYRiBxPexBDlgwAC5bBqlZNfGKNkRaWEYBpcuXcL9+/cxa9YskcvWvHv3DoGBgezq2UePHkVeXh4WLlwodCkoAIiPj8fly5fRvXt35OTkwMbGBt7e3lBUVERxcTEiIiLYDhv1zZYf3j9MT09HeHg4srOzYW5u3mCR2bZUUlKC2NhYPHjwAHw+H/b29nBxcYGWlhZu3ryJ2NhYaGlpYcyYMejRoweysrLYOV5LSkrA4XBgZGTETqRgamrK9jKNi4tDaGgoFi1aJPQHwtu3b3HhwgVkZmairq4OHA4HS5cubXDs9evXcfv2bXaF+czMTOzYsQNPnz6Fubk5PD094e7u3ujQEtJ0lOzaGCU7Ik18Ph/Hjx9HWloa5s+fLzKZ5OTkYO/evRg4cCA8PDwQHByM8vJyLFy4UOQMJbGxsQgNDYWFhQWysrJgYWGBGTNmsM11eXl5iIiIQGpqKgwNDTF69OgGzZYMw+Dp06e4ceMG8vPz2eEvopJsc+Xm5iImJgZJSUlQVVWFk5MTnJycoKGhgUePHuH69et49+4dhg4dCjc3N6E9RktKStj7fRkZGSgrK4OCggKMjY1hYGCA6OhouLu7Y8qUKSLjYBgGR44cwb59+2BjY4OlS5c2uG/J5/Nx+PBhvHr1Cnp6erh48SKUlJTg7e0NLy8vic0U01FQsmtjlOyItNXU1CAkJAQFBQVYvHixQLf599Xf56ufgSMoKAg1NTVYtGiR0BUIgP9NSG1jY4Pnz5+je/fumD17tsDYuqY0W/L5fCQlJSEiIgLFxcWwtbXFyJEjm7TSwIcYhkF6ejpu376N9PR0dOrUCS4uLrC3t4eKigpev36Nq1ev4vXr1+jXrx/GjBnT5JlFGIZBUVERMjMzkZ6ejtOnT6OoqAhDhgyBqakpW/Pr2bOnQOLMzs5GUFAQevfuDQ6Hg+TkZPTu3RsTJ04UuHZ8fDxWrlyJ4uJiTJ06FatWrRJbbbejo2TXxijZEVlQP8sKj8fD4sWLha4ZV78ydlJSEpYsWQJ1dXXs27cPSkpKWLhwociOGjdu3EB0dDQcHByQnJyMLl26YO7cuQL3lOoTUHh4OHJycmBpaYnRo0c36DBVP0tKVFQUKioqMHDgwCY33dXV1SEpKQkxMTF48+YNunXrBjc3N1hbW7Nj8cLDw5GYmAgjIyOMHz/+o8sVNebhw4c4ffo0xo8fD0VFRbb2V1VVBUVFRfTo0QPm5ubo2rUrLl68iE6dOmHBggVQUlLC06dPcenSJVRXV2PUqFHo0aMHDh8+jLCwMKirq6Nr167w9PSEj4+PXN4vkwWU7NoYJTsiK0pLS7F3716oq6uLnGWlpqYGf/31F2pra7F06VJUVVVh37590NbWxvz584W+hmEYhIWFISYmBi4uLnj48CG0tLQwb968BkmVYRikpKTgxo0bKCwshI2NDUaOHNlgNpaamhokJCTg5s2b4PF4GDx4MIYOHSo04XK5XHY6r9LSUvTu3Ruurq4wMzMDh8NBbW0t4uLiEB0dDSUlJYwaNQoODg6tWnaqsrISf/zxB3r16oXp06cLvL/313TMyMhAXFwcuFwupk2bBmtra5ibm6Nbt26ora3FmTNncPLkSbx69QpaWloYP348li9fjpcvX+LYsWPw8PDA0KFDWxwnEY2SXRujZEdkSX5+Pvbt2wcjIyP4+fkJ7VJeVFSEwMBA9h5cfn4+goKC0LVrV/j5+Qm9r8UwDK5cuYK7d+/C3d0d9+/fh5KSEvz9/YU2EdZPqhwREYGysjLY29vD3d29wbFcLhdxcXGIiYkBwzBwcXGBi4sL1NTUUF5ejjt37iAhIQE1NTWws7ODq6srOw9k/f3Aa9euoaSkBE5OTnB3dxfZq7I5Tp06hRcvXuCzzz5rdGjCpUuXEBkZCRcXF1RXVyMrKwtcLhd8Ph8VFRXIz89HTk4Oampq0LdvX8ycORPu7u5QVlbGjRs3cPPmTcyZMwe9e/dudcxEECW7NkbJjsiapsyykpqaiqNHj8LT0xOurq54/fo1Dhw4AFNTU8yaNUvoQGOGYXDhwgU8ePAAHh4eSEhIAJ/Ph7+/v8jOFfWDsaOjo1FdXS1yCZqqqircvn0b8fHx7BReT58+hZKSEhwdHeHs7CzQ1FlQUICrV68iLS0NlpaWGDduXKMrQjTHs2fPcPjwYUybNo1dFFWY+vkvJ06ciMGDBwP4u0Z44cIFXL9+Ha9evUJxcTE6d+4Me3t7KCsr482bNzA1NcWcOXNgbm6OI0eO4OXLl1i6dCl1UGljlOzaGCU7IotSU1Nx7NixRmdZCQsLQ2xsLObPnw9TU1Okp6fj0KFD6Nu3L7uo64f4fD7Onj2LJ0+eYOLEiYiLi0NVVRXmzZsHIyMjkfHweDzExcXh9u3bYBgGzs7OcHV1bdBsWl5ejujoaGRlZWHAgAFwdHQUOObdu3eIjIzE3bt3oaenh7Fjx7bpwHUul4udO3dCX18ffn5+Is9bv7KBnZ0dJk+ejJqaGnbxVB6PB4ZhUFNTAwcHBwwYMADZ2dnIzMxESkoKkpOTUVlZCXt7e3h6eiIhIQE6OjpYunRph15sta1RsmtjlOyIrKpfLFTUfSE+n48DBw6gsLAQAQEB0NbWRkpKCo4fP84OJBf2Zc/n83Hq1CmkpqZiypQpiIuLQ1FREfz8/D7as7C+Bnfnzh0oKytj6NChcHJy+ugk0nw+H/fv38eNGzdQW1sLd3d3DBkypM1n/rhy5Qru37+PFStWiFwbsrKyErt374ampibmz5+Px48fIzIyEu/evYOZmRlyc3NRV1cHLy8v2NnZCby2trYWr1+/RmhoKMLCwlBeXs6OZbSyssLcuXNhbm4OfX196rjSSpTs3rN161acPn0aqampUFdXh6urK3766SdYWVk1+RyU7Igsi4yMRGRkpMhJoN9fa83f3x+KiopITEzE2bNn4erqijFjxgj90q2rq2PH902fPh2xsbF48+YNZs+e3aTZUsrLyxEVFYX79+9DU1MT7u7uIueOzMzMxJUrV5CXlwd7e3t4eHg0a6mcpnr16hX27dsHT09PgVlO3sfn8xESEoK8vDwMGzYMd+/eRXFxMezs7KChoYH4+HgYGxtj+vTpHx1WUVZWhgsXLiA+Ph48Hg+ZmZkwNjaGiYkJtLS02NldzMzM0LlzZ0p+zUTJ7j3jxo3DrFmzMHjwYNTW1uLbb7/FkydPkJyc3OSVAijZEVnGMAwuXryIBw8eYPbs2UI7QmRlZSE4OBjOzs7w9PQEANy5cwdXrlxpdO7N2tpaHD16FFlZWZg5cyZiY2ORlZWFGTNmiJzN5UNFRUWIiIgQOol0SUkJrl27huTkZPTo0QPjx49vsLBsW6mtrUVgYCBUVFSwePFikT05r169iitXrsDQ0BA1NTWwsrLCkCFDEBMTgxcvXsDNzQ2jRo1q1uTKKSkpuHz5MlJTU1FXV4eZM2eyQx1ycnLAMAx0dHQEkp+oWif5H0p2jSgoKIChoSGioqIwfPjwJr2Gkh2RdXw+H8eOHUN6errIWVbqZ0uZOXMmuypC/dybEyZMgJOTk9Bz19TU4PDhw8jOzsacOXNw584dPH36FN7e3rC1tW1yjB9OIm1ubo6EhASoq6tjzJgxsLOzE2vNJjIyEtHR0QgICBA5u0tYWBj++9//Ql9fH66urvDw8EBNTQ3OnDkDhmHg7e0tcoLtj6murkZYWBiCg4PBMAx++OEH2NjYgMvlCkxt9ubNGzAMAz09PTb5mZub0xRjQlCya8SLFy/Qu3dvPH78WOR/VC6XCy6Xyz5PTEyEu7s7JTsi02pqanDgwAG8ffsWixcvbtDzj2EYnDhxAmlpaVi2bBm6dOkChmFw7do1xMbGwtvbG/379xd6bh6Ph4MHDyI/Px/z5s1DfHw8Hj16hEmTJjX7/8SrV68QHh6OV69ewdXVFcOGDYOKikqL33dT5OfnIzAwkK2VCdt/6tQpnDhxAr1798ZXX30FCwsLRERE4Pbt27C0tMS0adPapGn12bNn+P7771FZWYlVq1Zh9OjRAvclRS1n1LlzZ7bWZ2ZmJnRSgY6Gkp0IfD4fkydPRklJCW7duiXyuE2bNmHz5s0NtlOyI7Lu3bt32LdvH2pqaoTOssLlcrF7924oKipiyZIlUFFRAcMwOH/+PB4+fIiZM2eKvJ/N5XJx4MABFBcXY/78+UhISMDdu3cbzPLfFPVfUZK4R8Xn87Fv3z5UV1fjk08+EUgsJSUlbM/P5ORk2NnZsYno5MmTyM3NxejRo+Hq6tqmsebn5+O7775DcXExOyeniYmJ0GMrKysFkl9BQQEAQF9fXyD5dcQFnCnZibB8+XJcuXIFt27darRHGdXsSHtWP8uKhoYGFixY0KDrf35+Pvbs2QMbGxtMnToVHA4HfD4fJ0+exLNnz9jegsK8e/eOnWB6wYIFePjwIW7duoWRI0di+PDhMtnBov7e5MKFC9mpxSorK3Hz5k3cvXsXqqqqqKqqgpqaGj755BO8fv0aFy5cgIaGBnx8fMR2D/HFixcIDAwEn8+HtrY2Bg0aBA8PD6Ez3LyvoqKCbfLMzMzE27dvAQCGhoYCya8tBt7LOkp2Qnz22Wc4d+4coqOjm73uFt2zI+1N/SwrxsbGmDt3boPu+48ePcLp06fh5eWFQYMGAfhfZ5SXL19i/vz5Ir/kKysrsX//flRXV2PhwoVISkpCeHh4oz07paW0tBT//e9/0b9/f3h5eYHL5SI2NhYxMTHgcDhwc3NDRUUFEhISMHPmTKSmpuLBgwews7ODl5eX2MfE3bp1C2FhYbC2tkZ6ejpUVVUxYcIEkSvNC1NWViaQ/IqLi8HhcNj7omZmZujVq5dcrlZOye49DMNg5cqVOHPmDCIjI1s0ZQ8lO9IeZWVlISQkBFZWVkInI65fJ2/RokVsYqu/N1dQUICFCxey03Z9qKKiAkFBQaitrcXChQvx7NkzXL58GY6Ojpg4cWKr5qxsK/WL2ubl5WHZsmV48uQJoqOjwePx4OTkhKFDhyI9PR0nT57EoEGDkJmZidLSUkycOFFii50yDIOTJ0/i+fPn8PX1RUJCAp4+fQpra2tMmDChRfflPlzOqLKyEmvXrv3oOMf2iJLde1asWIHDhw/j3LlzAvcidHV1m1zNp2RH2qv6AeROTk4YN26cwBd4bW0tgoKCUFFRgYCAAHaFg+rqauzfvx+VlZVYuHChyLFkZWVlCAoKAofDwYIFC5Ceno5z587B1tYWU6dOlXpN4vHjxzh58iQGDhzIrmE3cOBAjBgxAjo6OsjLy8OePXugrKwMLpcLQ0ND+Pj4iFw+SVx4PB67SvrSpUuRkZGBK1euoKamBmPGjIGjo2OLEy/DMCgvL5fbnpyU7N4j6kMSFBSEBQsWNOkclOxIe9bYLCulpaUIDAxEt27dMHfuXPb/S2VlJfbt2wc+n49FixaJrGGUlJQgKCgIysrKWLBgAV6+fIlTp06hV69e8PX1bfPZT5qqsrISGzduRHFxMXr27AkbGxuMGjWKTWTv3r3DH3/8gdTUVHTr1o1tgpVWvMXFxdi9ezf7d+ByuQgLC8P9+/dhYmKCyZMnSzwJtwfSbz+QIQzDCH00NdER0t4NGjQI7u7uuH79OhITEwX26erqwtvbG2lpaYiOjma3a2pqwt/fn51urKqqSui59fT04O/vDy6Xi5CQEJiZmWH27NnsHJw8Hk+cb02ozMxMfPPNN0hMTMSQIUOwdOlSzJgxg00WfD4fu3btQlRUFExMTODn54fx48dLLdEBQKdOneDr68uuF6iuro7JkydjwYIFqKysZOOtq6uTWoyyiJIdIUTAiBEj4ODggPPnz+P58+cC+3r16oURI0YgMjISL168YLfr6upi3rx5qKqqwsGDBwV6KL+vS5cu8Pf3R0VFBUJCQtCjRw/4+fkhJycHBw4cwLt378T63url5ubi4MGD+O233/Dq1St8/vnnWLZsmUBHGz6fj99++w0XLlzA8OHD8eWXXzZr6kBxsrCwgKenJ27fvo3Hjx8DAMzMzPDJJ5/A1dUVUVFRCAwMxKtXr6QcqeygZEcIEcDhcODl5YXevXvj+PHjyM7OFtg/fPhwWFpa4tSpUygtLWW3168MUFRUhCNHjqCmpkbo+Q0MDODv74+SkhIcPHgQRkZGmD9/PoqKihAcHIzKykqxvbeioiKcPHkSgYGBKCwsZIcMeHl5CRxXWlqKf/7zn7h48SJ8fHzw9ddfy9y9LGdnZ/Tv3x/nz59Hbm4uAEBZWRmjR49GQEAAlJWVsW/fPly+fFnkj4+OhO7ZtTG6Z0fkRWOzrFRVVSEwMBBaWlpYuHChQLPey5cvERISAnNzc3ZeR2FycnIQHBwMIyMjzJ07FyUlJThw4ADU1NQwb9486Orqttl7eX+iaS0tLYwYMQJv3rzBgwcPsHz5coGONampqTh06BDu378PLy8vfPLJJzI1ROJ9NTU12LdvH6qqqrBs2TKBweJ8Ph/x8fEIDw+HsrIyPDw8YGpqCi6XCx6Pxz4+fO7h4SGz77c1KNm1MUp2RJ5UVVVh3759qK2txZIlSwSmwMrOzsa+ffvg4OCAiRMnCrzuxYsXOHLkCGxsbODt7S3yy/PVq1dsc+bs2bNRXl6OAwcOAAD8/f0/ulLAx7x7945dQkhJSQnDhg3D4MGDkZeXh71792LMmDFwdXUF8HfiuHbtGmJiYpCdnY2BAwdi+fLlYp+e7EN8Pl8gAYn6d/3z4uJiXL58GRoaGnByckJtba3AsWVlZXj27BmKiopgYGCA3r17N3hPKioqUFVVhYqKCpYvXy7Ve5LiQsmujVGyI/KmtLQUf/31FzQ1NbFw4UKBwdP1vTeFzZWZlJTEjkubMGGCyISXmZmJQ4cOwczMDLNmzUJlZSUOHDiA6upq+Pv7ixy/15j6xVNv3bqFuro6uLi4sIvD1tXVITAwEEpKSliyZAkUFBRQUFCAkydPoqCgABwOB8rKyggICPjoagIMw7DJ5WPJqbEa1fv/rq2tbfSaCgoKAslJRUUF5eXliI2NRd++fTFkyBCBfaqqqlBWVkZmZiY7QN7DwwODBg2CmpoalJWV5bIm9yFKdm2Mkh2RR3l5eQgKCkK3bt0wZ84c9pc/wzA4e/YskpOTsXTp0gaJ6cGDBzh37hyGDRuG0aNHizx/Wloajhw5gt69e8PHxwfV1dUICQlBaWkp5s2bh27dujUpzrq6Ojx48ABRUVGorKzEoEGDMHz4cLZGyjAMwsPDERERgblz50JXVxcPHjxAeHg4NDQ0oKuri2fPnrFDD5qSqPh8fqMxKSkpNUhOov7dlH2KiopCk1N8fDwuX76MadOmYcCAAUJjqaqqwrVr15CYmAgzMzNMmjSpwSTg8oqSXRujZEfkVWZmJg4ePIi+ffti+vTp7BduTU0N/vrrL9TW1mLZsmUNps2qXy5ozJgxcHNzE3n+Z8+e4dixY7C2toa3tzd4PB4OHTqE/Px8+Pj4QE9Pr9Fa07Nnz3Dv3j2UlZXB2NgYvXr1YgeB1x9XXFyMhIQE9OzZEyYmJnj69CkKCgpgbGwMPT09pKSkoE+fPrCysmpWAhL1b2VlZYkNmK+fpPvx48dYtGhRoz8Q0tPTceHCBZSXl8Pd3R2urq5SH9gvbpTs2hglOyLPkpOTceLECQwZMgRjx45lE97bt2+xe/duWFpawtfXt0HNIyIiAlFRUZg0aRIcHR1Fnj8lJQUnTpxA//79MWXKFNTU1ODo0aNIT08XejyHw0F5eTmysrJQWVmJbt26wc7ODl27dm2QgJSVlREaGgoej4fRo0cjOjoatbW1mDhxIrp164aDBw+iT58+mDFjRrtt1qutrcX+/ftRVlaGZcuWNbrMUE1NDSIjIxEbGwsDAwNMnjxZbBNZywJKdm2Mkh2Rd3fv3sWlS5ca1NRSUlJw7Ngxocv4MAyDq1evIj4+HtOnT290IdfHjx/j9OnT7NyZfD4fr169gqKiokCNqqCgABEREcjKyoKJiQlGjx7NrlQgKu6LFy+iX79+SE1NRffu3TF9+nSoqalhz549AksZtWdlZWXYvXs3OnfujPnz53+0xpabm4vz58/jzZs3GDJkCEaNGtXuy0AY+etyQwgRq8GDB6O8vBxhYWHQ0tJi7w9ZW1vD1dUVYWFh6Natm0Di4XA4GDduHKqrq3H69GmoqKigT58+Qs9vZ2eHuro6nD17FkpKShg7dizMzMzY/QUFBQgNDUVqaioMDQ0xZ84c9O7du9HaWFlZGS5duoSysjKkpKRg6NChGDFiBBQUFHDkyBFUVlZi2bJlcvElr6Ojg5kzZ2L//v24evVqg56yHzI2NsbSpUsRFxeH+/fvY+TIkRKKVLIo2RFCmm3kyJEoLy/HuXPnoKmpiV69egEAPDw8kJ2djZMnTyIgIECgGY3D4WDKlCngcrk4fvw45s2bJ7ImZm9vj9raWly8eBFKSkoYPXo0SktLERkZiYcPH7JTl9na2n501QSGYfDXX38hISEBQ4cOxYwZM2BhYQEAiIyMxPPnzzFnzpxWD3OQJT179sSECRNw4cIFGBsbf7SVSUFBAa6urnB2dpaJVSjEQT7fFSFErDgcDiZNmoRevXoJzLKioKAAHx8fdjmaD3sq1u83MTHB4cOHkZOTI/IagwYNwrhx43Dr1i0cPHgQO3bswPPnzzF+/HisXLkS/fv3/+gXc11dHfbs2YPz589j+PDhWLlyJZvonj59isjISIwcObJFy3nJOkdHRwwaNAiXLl1q8rRh8proAEp2hJAWUlBQgK+vLwwNDXH48GF2FWxtbW34+vri5cuXCA8Pb/A6JSUlzJo1CwYGBux6eKI4OztjzJgxyMnJgbu7Oz7//HM4OTk1qefg27dvsWvXLpw6dQqjR4/GN998w84wUlhYiNOnT8Pa2hrDhg1rYQnIvvHjx6N79+44fvw4ysvLpR2OVFGyI4S0mLKyMubMmQN1dXUcPHgQFRUVAABTU1N4eHjg9u3bSE1NbfA6FRUVzJ07F9ra2ggJCUFJSYnIa7i5uWHNmjUYPnx4k++pPXr0CIGBgUhMTISjoyO++OIL9p4el8vF0aNHoaOjg6lTp7bbnpdNoaioyPYuPXbs2EcHrMszSnaEkFbR0NCAn58famtrcejQIXbSYRcXF1hbW+PMmTMoKipq8Dp1dXXMmzcPSkpKOHDgQKM1j6YmJC6XizNnzuD06dPo1KkTjIyM4OPjw66xxzAMzpw5g/LycsyaNavBmEB5pKWlhZkzZ+LNmze4fPkyOmoHfEp2hJBW09PTY1c8OHbsGOrq6tgOKVpaWjh27JjQVRC0tLTg7++P2tpahISEtGqJn9zcXOzevRspKSmYNGkSeDweevXqhYEDB7LHREdHIzU1FdOnT+8wM4cAQPfu3eHl5YX79+8jISFB2uFIBSU7Qkib6Nq1K2bPno2srCycPXsWDMNATU0NM2bMQFFRES5evCi0VqGnp4d58+ahoqKiRYu4MgyDuLg4/PXXX1BVVUVAQADevn2L8vJyTJo0ia0VPnv2jO2QImrYgzyzt7fHkCFDcOXKFWRlZUk7HImjZEcIaTNmZmaYPn06njx5gmvXrgH4Owl6eXnh4cOHuH//vtDXGRgYwM/PDwUFBTh69GiT7y1VVlbi8OHDuHr1KpycnLB48WJUV1cjNjYWI0aMYGtvb9++xalTp2BlZYXhw4e3zZtthzw9PWFiYoLjx48LrEXYEVCyI4S0KRsbG4wfPx6xsbGIiYkBAAwYMACDBg3C5cuXRQ43qJ9k+uXLl0KHLXwoIyMDf/75J3JycjB37lyMHTsWAHD+/HkYGRmxS/fUd0jR1tbGtGnT5LpDyscoKirC19cXSkpKIpuW5RUlO0JIm3NycsKwYcNw7do1PHr0CAAwbtw4dO3aFcePHxd5b87U1BQzZ87Es2fPcO7cOaHNnnV1dQgPD8eBAwdgYGCATz75hB0nFxMTg4KCAkyePBkKCgrsqgxlZWUdpkPKx2hqamLWrFkoKCgQ2bQsjyjZEULEYtSoUbC3t8fZs2eRlpYGJSUlzJgxA1wuF6dPnxb5Jdu7d294e3vj0aNHuHr1qsBxJSUl2L9/P27fvo1Ro0Zh3rx5bE/LwsJCREVFwcXFBcbGxgCAW7duISUlBdOmTYO+vr7433Q7YWxsjMmTJ+Phw4e4c+eOtMORCEp2hBCxqJ9lxdLSEseOHUNOTg709PQwffp0vHjxAjdv3hT5WltbW3h5eeHOnTuIjIwE8PdisH/++SfKy8uxcOFCDBs2jG2SZBgGFy5cgI6ODkaMGAEAeP78OW7cuAF3d3f07dtX3G+33bGzs4OrqyuuXbuGjIwMaYcjdpTsCCFiU3+PyNDQEIcOHUJRURF69eoFd3d3REREIC0tTeRrHR0dMWbMGERFRWH//v04ceIELC0t8cknn6Bnz54Cx96/fx9ZWVmYNGkSlJWVUVRUhFOnTqF3795s8iMNeXh4wNzcHCdOnGh0YL88oGRHCBErFRUVzJkzB2pqauwsK8OHD4elpSVOnTrVaK9ANzc3DB8+HNnZ2Zg8eTJ8fHygpqYmcExZWRmuXbuGgQMHwtzcHDweD0ePHoWmpia8vb07dIeUj6mfq1RVVRVHjx6V6w4rlOwIIWKnoaGBefPmgcfj4fDhw6ipqYG3tzeUlZVx4sQJ1NXViXztqFGjsHbtWjg4ODRIXAzD4PLly1BWVoanpycYhsG5c+dQUlKCWbNmNUiMpCF1dXXMmjULb9++FdkpSB5QsiOESET9LCtv377F8ePHoaqqihkzZiA3NxehoaGNvlbUxM8pKSlITU3FhAkToK6ujtu3byMpKQnTpk2DgYGBON6GXOratSumTp2K169fo7KyUtrhiAUlO0KIxBgZGWHWrFnIzMzEuXPn0K1bN4wbNw7x8fF4/Phxs8717t07XL58GX379oW1tTVevHiB8PBwDB8+HNbW1mJ6B/KrX79++PTTTwXWIJQnlOwIIRJlbm4Ob29vPH78GGFhYRg0aBD69++P8+fPIz8/v8nnCQsLQ01NDSZMmICSkhKcOnUKvXr1og4praCsrCztEMSGkh0hROL69euHcePGISYmBnFxcfDy8kKnTp1w/PhxdtWExmRkZOD+/fsYM2YM1NTUcPToUairq8Pb21uuFyAlLUefCkKIVAwZMgRDhw5FaGgonj59ihkzZqC8vBznz59vtJNETU0NLly4AFNTUzg4OOD8+fMoLi7GrFmzoK6uLsF3QNoTSnaEEKkZPXo0BgwYgLNnz6K0tBRTpkxBUlJSo7N6REZGoqysDJMmTUJcXByePHmCKVOmwNDQUIKRk/aGkh0hRGo4HA4mT54MCwsLHDt2DJ06dYKLiwuuXbuGly9fNjg+NzcXsbGxcHd3R1lZGcLCwjB06FD069dPCtGT9oSSHSFEqupnWTEwMMDBgwfh4OCAHj164MSJE6ioqGCP4/P5OH/+PAwMDGBjY4OTJ0/CwsICo0aNkmL0pL2gZEcIkbr3Z1k5cuQIJkyYAIZhcOrUKXapn9jYWLx58wbjx4/HiRMnoKqqCh8fH+qQQpqEPiWEEJmgqakJPz8/8Hg8nD9/HpMnT0ZWVhZu3LiBoqIiREREYMiQIbh//z7evn1LHVJIs1CyI4TIjE6dOmHu3Ll4+/Yt7ty5gxEjRuDWrVs4dOgQtLW1oampiUePHmHKlCno2rWrtMMl7QglO0KITDE2NmZnWSkoKICVlRXevn2LAQMGICIiAq6urrC1tZV2mKSdoWRHCJE55ubmmDZtGh4/fgw9PT3MmDED8fHxMDMzg4eHh7TDI+2QkrQDIIQQYWxtbVFRUYGrV68iKSkJKioq1CGFtBh9aj4QHR2NSZMmoVu3buBwODh79qy0QyKkw3J2dsbQoUPB4/Ewc+ZMaGhoSDsk0k5Rze4DlZWVGDBgABYtWgRvb29ph0NIh+fh4YERI0ZASYm+rkjL0afnA+PHj8f48eOlHQYh5D2U6Ehr0SeolbhcrsAs7e/P+EAIIUQ20D27Vtq6dSt0dXXZh7u7u7RDIoQQ8gFKdq20bt06lJaWso+oqChph0QIIeQD1IzZSqqqqlBVVWWfy+uS9oQQ0p5RzY4QQojco5rdByoqKvDixQv2eUZGBhITE9G5c2eYmJhIMTJCCCEtRcnuAwkJCRg5ciT7fPXq1QCA+fPnY//+/VKKqvlyc3ORm5sr7TAIIe2IsbExjI2NpR2GWHAYhmGkHYQ8yc3NRWBgIAICAqT2oeFyuRg7dix1liGENIu7uztCQ0MF+iHIC0p2cqisrAy6urqIioqiDjNiVFFRAXd3dypnMaNyloz6ci4tLYWOjo60w2lz1Iwpx+zt7eXyQysrysrKAFA5ixuVs2TUl7O8ot6YhBBC5B4lO0IIIXKPkp0cUlVVxcaNG+XyJrMsoXKWDCpnyZD3cqYOKoQQQuQe1ewIIYTIPUp2hBBC5B4lO0IIIXKPkh1pVGZmJjgcTruaKo0QQj5Eya4NpaWlISAgABYWFlBTU4OOjg7c3Nywfft2vHv3TmzXTU5OxqZNm5CZmSm2azTFli1bMHnyZHTt2hUcDgebNm2SajwAwOFwmvSIjIxs9bWqqqqwadOmZp1LFsusJWS5nFNTU7FmzRrY29tDW1sbxsbGmDhxIhISElodi6TJcjnn5OTAz88PVlZW0NbWhp6eHpycnBAcHAxZ6AdJM6i0kUuXLsHX1xeqqqrw9/eHra0teDwebt26hW+++QZJSUnYvXu3WK6dnJyMzZs3Y8SIETAzMxPLNZri+++/h5GREQYOHIjQ0FCpxfG+kJAQgecHDhxAWFhYg+3W1tatvlZVVRU2b94MABgxYkSTXiOLZdYSslzOf/31F/bu3Yvp06djxYoVKC0tRWBgIJydnXH16lV4eHi0OiZJkeVyLiwsxOvXr+Hj4wMTExPU1NQgLCwMCxYswNOnT/HPf/6z1TG1CkNaLT09ndHS0mL69u3L5OTkNNj//PlzZtu2bWK7/okTJxgATERExEeP5fP5TFVVVZPPnZGRwQBggoKCmnQswzBMQUEBA4DZuHFjk68jKZ9++ikjro99S953eyizlpClck5ISGDKy8sFthUWFjIGBgaMm5ubGCKUHFkqZ1G8vLwYTU1Npra2tm0CayFqxmwD//73v1FRUYG9e/cKXemgV69e+Pzzz9nntbW1+OGHH2BpaQlVVVWYmZnh22+/BZfLFXidmZkZvLy8cOvWLTg5OUFNTQ0WFhY4cOAAe8z+/fvh6+sLABg5cmSDZoz6c4SGhmLQoEFQV1dHYGAgACA9PR2+vr7o3LkzNDQ04OzsjEuXLrW4HKRZq2wNPp+Pbdu2oV+/flBTU0PXrl0REBCA4uJigeMSEhIwduxY6OvrQ11dHebm5li0aBGAv+9tGhgYAAA2b97M/h0+1izZXsusJaRVzo6Ojg0mkO7SpQuGDRuGlJSUtn2TMkCan2dhzMzMUFVVBR6P1+r31hrUjNkGLly4AAsLC7i6ujbp+CVLliA4OBg+Pj746quvcOfOHWzduhUpKSk4c+aMwLEvXryAj48PFi9ejPnz52Pfvn1YsGABHB0d0a9fPwwfPhyrVq3C77//jm+//ZZtvni/GePp06eYPXs2AgICsHTpUlhZWSEvLw+urq6oqqrCqlWr0KVLFwQHB2Py5Mk4efIkpk2b1nYFJOMCAgKwf/9+LFy4EKtWrUJGRgb++OMPPHjwALdv34aysjLy8/Ph6ekJAwMDrF27Fnp6esjMzMTp06cBAAYGBti1axeWL1+OadOmwdvbGwDQv39/ab41mSJr5fzmzRvo6+u36XuUBdIu53fv3qGyshIVFRWIiopCUFAQXFxcoK6uLtb3/VFSrVfKgdLSUgYAM2XKlCYdn5iYyABglixZIrD966+/ZgAwN27cYLeZmpoyAJjo6Gh2W35+PqOqqsp89dVX7LbGmjHrz3H16lWB7V988QUDgLl58ya7rby8nDE3N2fMzMyYuro6hmGa14xZT5ab5D5s9rl58yYDgDl06JDAcVevXhXYfubMGQYAc/fuXZHnbs37luUyawlZLed60dHRDIfDYdavX9/ic8gCWSznrVu3MgDYx+jRo5mXL1826xziQM2YrVS/LIa2tnaTjr98+TKA/62AXu+rr74CgAbNiDY2Nhg2bBj73MDAAFZWVkhPT29yjObm5hg7dmyDOJycnDB06FB2m5aWFpYtW4bMzEwkJyc3+fzt2YkTJ6Crq4sxY8agsLCQfdQ3fUVERAAA9PT0AAAXL15ETU2NFCNun2SpnPPz8zFnzhyYm5tjzZo1YrmGtMhCOc+ePRthYWE4fPgw5syZAwBi7Y3eVJTsWql+fa3y8vImHZ+VlQUFBQX06tVLYLuRkRH09PSQlZUlsN3ExKTBOTp16tSg/b0x5ubmQuOwsrJqsL2++fPDOOTV8+fPUVpaCkNDQxgYGAg8KioqkJ+fD+DvFZynT5+OzZs3Q19fH1OmTEFQUFCD+6xEOFkp58rKSnh5eaG8vBznzp2Tu8VgZaGcTU1N4eHhgdmzZ+PQoUOwsLCAh4eH1BMe3bNrJR0dHXTr1g1Pnjxp1us4HE6TjlNUVBS6nWnGuBWpt5XLMD6fD0NDQxw6dEjo/vqb9BwOBydPnkRcXBwuXLiA0NBQLFq0CL/++ivi4uLk7kuzrclCOfN4PHh7e+PRo0cIDQ2Fra1ti88lq2ShnD/k4+ODPXv2IDo6ukELkyRRsmsDXl5e2L17N2JjY+Hi4tLosaampuDz+Xj+/LlAJ5K8vDyUlJTA1NS02ddvauL8MI6nT5822J6amsru7wgsLS1x/fp1uLm5NelHgbOzM5ydnbFlyxYcPnwYc+fOxdGjR7FkyZIW/R06CmmXM5/Ph7+/P8LDw3H8+HG4u7u35G3IPGmXszD1NbrS0tI2OV9LUTNmG1izZg00NTWxZMkS5OXlNdiflpaG7du3AwAmTJgAANi2bZvAMb/99hsAYOLEic2+vqamJgCgpKSkya+ZMGEC4uPjERsby26rrKzE7t27YWZmBhsbm2bH0R7NmDEDdXV1+OGHHxrsq62tZcu0uLi4QW3a3t4eANimHw0NDQDN+zt0FNIu55UrV+LYsWPYuXMn27NQHkmznAsKCoRu37t3LzgcDhwcHJp0HnGhml0bsLS0xOHDhzFz5kxYW1sLzKASExODEydOYMGCBQCAAQMGYP78+di9ezdKSkrg7u6O+Ph4BAcHY+rUqRg5cmSzr29vbw9FRUX89NNPKC0thaqqKkaNGgVDQ0ORr1m7di2OHDmC8ePHY9WqVejcuTOCg4ORkZGBU6dOQUGh+b+DQkJCkJWVhaqqKgBAdHQ0fvzxRwDAvHnzZLK26O7ujoCAAGzduhWJiYnw9PSEsrIynj9/jhMnTmD79u3w8fFBcHAwdu7ciWnTpsHS0hLl5eXYs2cPdHR02B8w6urqsLGxwbFjx9CnTx907twZtra2jTaXtccyawlplvO2bduwc+dOuLi4QENDAwcPHhTYP23aNPYHY3snzXLesmULbt++jXHjxsHExARFRUU4deoU7t69i5UrVzbopyBx0u0MKl+ePXvGLF26lDEzM2NUVFQYbW1txs3NjdmxYwdTXV3NHldTU8Ns3ryZMTc3Z5SVlZmePXsy69atEziGYf4eNjBx4sQG13F3d2fc3d0Ftu3Zs4exsLBgFBUVBYYhiDoHwzBMWloa4+Pjw+jp6TFqamqMk5MTc/HiRYFjmjP0wN3dXaDL8fuPpszuIgmiZpzYvXs34+joyKirqzPa2tqMnZ0ds2bNGnZGnPv37zOzZ89mTExMGFVVVcbQ0JDx8vJiEhISBM4TExPDODo6MioqKk3qtt0eyqwlZKmc58+fL7KMAbCz2LRHslTO165dY7y8vJhu3boxysrK7PdfUFAQw+fz2/R9twStVE4IIUTu0T07Qgghco+SHSGEELlHyY4QQojco2RHCCFE7lGyI4QQIvco2RFCCJF7lOwkZP/+/eBwOFBTU0N2dnaD/SNGjJD4XH3h4eFYtGgR+vTpAw0NDVhYWGDJkiXIzc0VenxMTAyGDh0KDQ0NGBkZYdWqVaioqJBozB9D5SwZVM6SQeXcdijZSRiXy8W//vUvaYcBAPi///s/REZGYtq0afj9998xa9YsHD9+HAMHDsSbN28Ejk1MTMTo0aNRVVWF3377DUuWLMHu3bvZVdJlDZWzZFA5SwaVcxuQ9qj2jiIoKIgBwNjb2zOqqqpMdna2wH53d3emX79+Eo0pKiqKXaT1/W0AmO+++05g+/jx4xljY2OmtLSU3bZnzx4GABMaGiqReJuCylkyqJwlg8q57VDNTsK+/fZb1NXVycSvtOHDhzeYA3P48OHo3LkzUlJS2G1lZWUICwuDn58fu34fAPj7+0NLSwvHjx+XWMxNReUsGVTOkkHl3Ho0EbSEmZubw9/fH3v27MHatWvRrVu3Zr2+qqqKnTS4MYqKiujUqVOz46uoqEBFRQX09fXZbY8fP0ZtbS0GDRokcKyKigrs7e3x4MGDZl9H3KicJYPKWTKonFuPanZS8N1336G2thY//fRTs1/773//u8EKxMIeAwcObFFs27ZtA4/Hw8yZM9lt9TeejY2NGxxvbGyMnJycFl1L3KicJYPKWTKonFuHanZSYGFhgXnz5mH37t1Yu3at0A+DKP7+/hg6dOhHj2vJ6uTR0dHYvHkzZsyYgVGjRrHb6xdfVFVVbfAaNTU1dr+soXKWDCpnyaBybh1KdlLy/fffIyQkBP/617/YhV2bwsLCAhYWFm0eT2pqKqZNmwZbW1v89ddfAvvq/wPUL+r4vurq6hb9B5EUKmfJoHKWDCrnlqNkJyUWFhbw8/Njf6U1VX3b+McoKirCwMCgSed89eoVPD09oauri8uXL0NbW1tgf/0vSGHjaHJzc5t9/0CSqJwlg8pZMqicW47u2UnR999/3+w2+F9++QXGxsYffQwePLhJ53v79i08PT3B5XIRGhoqtGnE1tYWSkpKSEhIENjO4/GQmJgIe3v7JscvDVTOkkHlLBlUzi1DNTspsrS0hJ+fHwIDA2FqagolpY//Odqy7b2yshITJkxAdnY2IiIi0Lt3b6HH6erqwsPDAwcPHsT69evZX3AhISGoqKiQ2YG49aicJYPKWTKonFtIoqP6OrD6waF3794V2P78+XNGUVGRASDxwaFTpkxhADCLFi1iQkJCBB5nzpwROPbevXuMqqoqM3DgQGbXrl3Md999x6ipqTGenp4SjfljqJwlg8pZMqic2w4lOwkR9aFlGIaZP3++VD60pqamDAChD1NT0wbH37x5k3F1dWXU1NQYAwMD5tNPP2XKysokGvPHUDlLBpWzZFA5tx0OwzCMeOqMhBBCiGygDiqEEELkHiU7Qgghco+SHSGEELlHyY4QQojco2RHCCFE7lGyI4QQIvco2RFCCJF7lOwIIYTIPUp2hBBC5B4lO0IIIXKPkh0hhBC5R8mOEEKI3KNkRwghRO5RsnvPrl270L9/f+jo6EBHRwcuLi64cuWKtMMihBDSSrTEz3suXLgARUVF9O7dGwzDIDg4GD///DMePHiAfv36STs8QgghLUTJ7iM6d+6Mn3/+GYsXL27S8bm5uQgMDERAQACMjY3FHB0hhJCmoGZMEerq6nD06FFUVlbCxcWlya/Lzc3F5s2bkZubK8boCCGENIeStAOQNY8fP4aLiwuqq6uhpaWFM2fOwMbGRuTxXC4XXC6XfV5RUSGJMAkhhDQD1ew+YGVlhcTERNy5cwfLly/H/PnzkZycLPL4rVu3QldXl324u7tLMFpCCCFNQffsPsLDwwOWlpYIDAwUuv/Dml1iYiLc3d1x7949ODg4SCpMQgghjaBmzI/g8/kCyexDqqqqUFVVZZ9raWlJIixCCCHNQMnuPevWrcP48eNhYmKC8vJyHD58GJGRkQgNDZV2aIQQIlY8Hg8JCQkYNGgQVFRUpB1Om6Nk9578/Hz4+/sjNzcXurq66N+/P0JDQzFmzBhph0YIIWIVFxeHkJAQ1NXVYdiwYdIOp81RsnvP3r17pR0CIYRIHJfLRWhoKDIyMnD16lU4OTkJ3J6RB9QbkxBCOrg7d+7g2bNn6N+/P549e4b4+Hhph9TmKNkRQkgHVl+rU1FRgY6ODlRUVHD16tVGO+a1R5TsCCGkA3vw4AHS0tJQWVmJpKQkVFZWIi0tDQ8ePJB2aG2K7tkRQkgH1rNnT8ydO1fodnlCyY4QQjqw7t27o3v37tIOQ+yoGZMQQojco2RHCCFE7lGyI4QQIvco2RFCCJF7lOwIIYTIPUp2hBBC5B4lO0IIIXKPkh0hhBC5R8mOEEKI3KNkRwghpFE8Hg8xMTHg8XjSDqXFKNkRQghpVFxcHIKCgnDnzh1ph9JilOwIIYSIrL19uLBre136h5IdIYQQkbU3eVnYlZIdIYR0cKJqb/K0sCslO0II6eBE1d7kaWFXWs+OEEI6MFG1NycnJ7la2JWSHSGEdGD1tbfq6mokJSWhpqaGrb05OzvLzcKulOwIIaQDk6faW2Mo2RFCSAfWvXt3uam9NYY6qBBCCJF7MpnscnNz8fDhQ1RWVkr0ulu3bsXgwYOhra0NQ0NDTJ06FU+fPpVoDIQQQtqeTCW7c+fOoW/fvujRowccHBzYwY2FhYUYOHAgzp49K9brR0VF4dNPP0VcXBzCwsJQU1MDT09PiSddQgiRFfIwLyYgQ8nuwoUL8Pb2hr6+PjZu3AiGYdh9+vr66N69O4KCgsQaw9WrV7FgwQL069cPAwYMwP79+/Hy5Uvcu3dPrNclhBBZJQ/zYgIylOz+8Y9/YPjw4bh16xY+/fTTBvtdXFwkPpCxtLQUANC5c2eRx3C5XJSVlbGPiooKSYVHCCFiJS/zYgIylOyePHmCGTNmiNzftWtX5OfnSywePp+PL774Am5ubrC1tRV53NatW6Grq8s+3N3dJRYjIYS0FWHNlfIyLyYgQ8lOQ0Oj0Xtj6enp6NKli8Ti+fTTT/HkyRMcPXq00ePWrVuH0tJS9hEVFSWhCAkhpO182FwpT/NiAjKU7EaOHIng4GDU1tY22PfmzRvs2bMHnp6eEonls88+w8WLFxEREYEePXo0eqyqqip0dHTYh5aWlkRiJISQtiKsuVKe5sUEZGhQ+ZYtW+Ds7IzBgwfD19cXHA4HoaGhuHHjBgIDA8EwDDZu3CjWGBiGwcqVK3HmzBlERkbC3NxcrNcjhBBZIKy50sLCQq5mVpGZZGdlZYVbt27h888/x/r168EwDH7++WcAwIgRI/Df//4XZmZmYo3h008/xeHDh3Hu3Dloa2vjzZs3AABdXV2oq6uL9dqEECINoporN2zYgEmTJkk7vDYjM8kOAPr164fr16+juLgYL168AJ/Ph4WFBQwMDCRy/V27dgH4O7m+LygoCAsWLJBIDKT94PF4SEhIwKBBg6CioiLtcAhpkY9NBC0vZCrZ1evUqRMGDx4s8eu+P7aPkI+Ji4tDSEgI6urqMGzYMGmHQ0iL0ETQEvb777/j0qVLCA0NFbp//PjxmDx5MpYvXy7hyAhp6MMb+k5OTlBVVZV2WIQ0G00ELWF79+6FjY2NyP02NjbYvXu3BCMiRDR5Gn9ESEcgM8kuLS0N1tbWIvf37dsXaWlpEoyIEOHkbfwRIR2BzCQ7FRUVtvejMLm5uVBQkJlwSQcmb+OPCOkIZOaenbOzM/bv348vv/wS2traAvtKS0sRFBQkVz2DSPvVUW7ok46Hz+fLbaVCZpLdxo0b4e7uDnt7e3zxxRfo168fgL/nzNy2bRtyc3Nx+PBhKUdJSMe5oU86ntraWrkdRiMzyW7IkCG4cOECAgIC8Pnnn4PD4QD4eziAubk5zp8/DxcXFylHSQgh8kueh1/JTLIDgDFjxuDFixfsPREAsLS0hIODA5v8CCGEkOaSqWQHAAoKCnB0dISjo6O0QyGEkA6FanYSlJycjPT0dBQXFwsteH9/fylERQgh8q+urk7aIYiNzCS7tLQ0+Pn5IT4+XuSvCw6HQ8mOEELEhMfjQV1dXS57ZMpMsgsICMDjx4+xbds2DBs2DJ06dZJ2SIQQ0qEwDIPy8nLo6upKO5Q2JzPJ7vbt2/j222+xcuVKaYdCCCEdVlFRkVwmO5mpq+rr68tlARP5xePxEBMTAx6PJ+1QCGkzjc1k1Z7JTLL75JNPcPDgQbm+QUrkS1xcHIKCgnDnzh1ph0JIq/F4PMTHx+Ply5fSDkUsZKYZs0+fPqirq8OAAQOwaNEi9OzZE4qKig2O8/b2lkJ0hAiiJX6IvImLi8Phw4dhZGSEKVOmQE1NTdohtSmZSXYzZ85k//31118LPYbD4VDNj8gEYUv80AKupL2q//GWlZWFwsJCpKWlsVM2yguZSXYRERHSDoGQJhG1xA/V7kh7Vf/jrV+/frhz5w4uXrxIyU5c3N3dpR0CIU1SP51ddXU1kpKSUFNTwy7xQytzkPbm/R9v2traUFRURFRUFObNm4du3bpJO7w2IzPJrh6Xy8X9+/eRn58PNzc36OvrSzskQgTQEj9Enrz/4y0lJQU8Hg8FBQU4cuQIVq9eLTfzEstUsvv999+xadMmlJaWAgDCwsIwatQoFBYWom/fvvj3v/+NRYsWSTlK0tHREj9EntT/eFu9ejXevn0LJSUl+Pr6ora2FsnJyXLTnCkzQw+CgoLwxRdfYNy4cdi7d6/AlGH6+voYNWoUjh49KsUICSFE/nTv3h2TJk3Cu3fvUFxcjJqaGgwYMACdOnVCbGwssrOzpR1im5CZZPfrr79iypQpOHz4MCZNmtRgv6OjI5KSkqQQGSGEdEx8Ph+hoaF49eqVtENpNZlJdi9evMD48eNF7u/cuTPevn0rwYgIaXs06wppb2pra3H16lU8fPiwSUsAyepnXGaSnZ6eHgoLC0XuT05OhpGRkdjjiI6OxqRJk9CtWzdwOBycPXtW7NckHQfNukLaI4ZhcOfOHYSGhuLdu3eNJjRZ/YzLTLKbMGECdu/ejZKSkgb7kpKSsGfPHkyePFnscVRWVmLAgAH473//K/ZrEfkl7Mvgw1lXuFyuFCMkpPlevnyJkydP4vz580ITmix/xmUm2f3444+oq6uDra0tvv/+e3A4HAQHB8PPzw+DBg2CoaEhNmzYIPY4xo8fjx9//BHTpk0T+7VI+9bcX7fCZl0hpL0pKyvDvn378PDhQ1y5ckUgocnyZ1xmkl23bt1w7949jBs3DseOHQPDMAgJCcGFCxcwe/ZsxMXFyeSYOy6Xi7KyMvZRUVEh7ZCIhIhqrhH261bUrCuy9MuXkA/V1tYiLS0NtbW17LaMjAzk5eVBR0cH0dHRuHnzJgDRMwvJymdcJpIdl8vF+fPn8ebNG/z1118oKipCXl4ecnNzUVxcjH379sHQ0FDaYQq1detW6Orqsg+aCaZjaKy5Rtiv2/qBu5WVlUhKSkJlZSU76wohsio9PR0xMTHIyMgAANTU1CA5ORmKiopQV1cHj8fDH3/8gaysLJn/jMvEoHIVFRX4+vpi+/bt6N+/PwDAwMBAylE1zbp167B69Wr2eWJiIiW8DkDURNCift0uXryYZl0h7Up9YissLERSUhLMzMzw6tUrFBQUoKamBjk5Oairq0N2djb+/PNPuLm5YdasWQ1Wq5GVz7hMJDsOh4PevXs32htTVqmqqgpM/qulpSXFaIgkvJ/QtLS0BCaCFjVvZn5+vtDxo4TIqvrmyu7duyMvLw+ZmZnQ19eHk5NTg2P19PSQk5ODLl26YPjw4ejcubMUIm6cTCQ7APj222+xevVq+Pr6wsrKStrhECLS+wntyZMn4PP5bHMNzZtJ2qOXL1+isrISwN8dr/Ly8gSaK8vKypCUlISJEydiwIABIs/z9u1bnDlzBkOHDpW573GZSXZxcXHo0qULbG1tMWLECJiZmUFdXV3gGA6Hg+3bt4s1joqKCrx48YJ9npGRgcTERHTu3BkmJiZivTZpH95PaO/evWM/pz179qR5M0m7Eh8fjx9++AGXLl1iB4xXVVVhw4YN0NXVhaGhIerq6lBXV4eCggK8evUKFhYWjZ6zrq4OUVFRKCoqgrOzs8xMJM1hmjIkXgIUFD7eV0YSi7dGRkZi5MiRDbbPnz8f+/fv/+jr79+/D0dHR9y7dw8ODg5iiJDIkqKiImhqakJVVRU8Hg8JCQkYNGgQVFRUpB0aIY06ffo0Zs6cCYZhhH6vcjgccDgcjBkzBpaWlgAAExMTdOrUqcnX6NWrF0aOHCkTCa9ZNTtzc/NmB83hcJCWlvbR4/h8frPOKy4jRoxo0pQ4hNTLzc2FmZkZ4uLiEBISgrq6Olq1nMi0+Ph4zJw5E3V1dSK/7xiGAcMwCAsLg4ODA8zMzJp9nRcvXkBJSQnDhg2TesJr1tADd3f3Bg9NTU1kZmZCQ0MDAwcOxMCBA6GhoYHMzExoaWlh+PDh4oqdEJnw9OnTZs8cIavzB5KO4ccff2STWVNcvny5xddKTU1tUoVH3JpVs/uwGe/s2bM4e/YswsLCMHr0aIF9YWFhmDFjBn744YdmBRQXF4eIiAjk5+djxYoV6N27N6qqqpCamoo+ffpQb0cic16/fo2oqCihQxFEoVogkZaXL1/i4sWLTU50fD4fjx49QlFRUYt7WSYkJMDS0lKqtbtWDSrfsGEDVq5c2SDRAcCYMWPw2Wef4fvvv2/SuXg8Hry9veHm5obvvvsOv//+O7ushIKCAjw9PcXeOYWQ5hg0aBBsbW2xefNm7NmzBwoKCk2aOUKW5w8k8i88PLzZt2oYhkFqamqLr1lWViZ03mNJalWye/78Obp06SJyf5cuXZpcfV2/fj0uXryIXbt24enTpwJ/DDU1Nfj6+uLcuXOtCZeQNvXmzRt2lp/MzEw8f/4ct2/fRkVFRaMzR8jy/IFE/pWXlzepQ+D7OBwOqqurW3XdsrKyVr2+tVqV7CwtLREUFCR0Psjy8nLs27fvo91U6x05cgTLly/HsmXLhFaVra2tkZ6e3ppwCRELBQUFODk5wcXFBX369IGlpSWcnZ1hYGDQ4N6crM8fSOSftrZ2szsEMgwDNTW1Vl1X2h3/WjXO7scff4SPjw/69u2LBQsWoFevXgD+rvEFBwcjLy8PJ06caNK58vPzYWdnJ3K/oqIiqqqqWhMuIW3m5cuXKC0tBfB3AuvZs2eDH2mRkZEoKytDQkICAgICMHz4cJEzrDx48ADOzs7SeCukgxk9ejQ4HE6zkg+Hw0Hfvn1bdd3mDFkQh1Ylu6lTp+Ly5cv4v//7P/zzn/8U2Gdvb4+9e/di7NixTTpXz549G20Tvn37NptMCZEWYYNwuVwuvv32W9jZ2WHixIlsF+3q6mqEhYUhIyMD27Ztg7a2NgwNDWmGFSJVJiYm8PLywuXLl5s0bllBQQF2dnbN7pxSW1uLrKwsmJqawtzcHLq6ui0NuU20egYVT09PeHp64s2bN8jKygIAmJqaNntV8Tlz5uC3337D9OnT0adPHwBge+7s2bMHx48fx7/+9a/WhktIi70/CPfDX8UMw+DJkyd48uQJli5dCgcHB4G5BbOysnD48GH07dsXJiYm6N27N3r27Nlg0lxCJGH9+vW4cuVKk2t4EyZMELnv/aSmpPS/lJKeno47d+5AWVkZ8+fPb5O4W6PNpgszMjJqdoJ733fffYe4uDgMHz4c1tbW4HA4+PLLL1FUVITXr19jwoQJ+PLLL9sqXEKapSmDcOvvg+zZswdfffWV0LkFzczMkJGRgYyMDKirq8PKygr9+vWDpqamJN8O6eAGDx6MY8eONTqDSn0nlmXLlqFHjx5IS0trkNCA/yU1Pp+P3r17A/jfiglv375FRUVFg9dIQ6vXs3v58iU++eQTWFlZoXPnzoiOjgYAFBYWYtWqVU1ey6j+Rn1QUBAsLCzQt29fcLlc9O/fH/v378eFCxfoVzCRmuYOwj1z5gwKCgrA4/GQk5MDHo/Hzi1Y7927d0hMTMSRI0dw/fp1ZGdnS/0mPuk4vL29ERMTgwkTJjQY/8bhcGBnZ4f/+7//w8CBAxusa1fvw2WAampqAPw9p3BBQQGGDRuG7Oxsmehx3Kp0m5ycjGHDhoHP52PIkCF48eIFu6Ktvr4+bt26hcrKSuzdu7fBa1evXo158+Zh4MCBAP5OmgYGBvDz84Ofn19rwiKkTbVkEO6LFy8wd+7cBvcphN2k5/P5SE9PR3p6OnR1dWFjY4O+fftCWVm5TeInRJTBgwfj/PnzePnyJWxtbVFeXg5VVVVs2rSJvUcnbF27+s+msGWAzMzMkJ6eDjMzMxgbG6OiooJdAuv95dAkrVU1uzVr1kBPTw/Pnj3DwYMHG3wZTJw4kV2y/UPbtm1DSkoK+9zc3BxnzpxpTTiEiEVLBuECfy93MmDAAIHHx3qklZaWIjY2FidPnqTex0RiunbtKvBcW1ub/bewhAY0XLVcUVERSUlJUFRUhLKyMmpqamRqxfJW1eyio6OxYcMGGBgY4O3btw32m5iYIDs7W+hru3btKjBujppviKyqH4Tb3LFJWVlZqKmpaVENrby8HAUFBTA1NW32awlprjt37rCtcnw+H5mZmejdu3eDhPb+vecPVy1XVFSEqqoqunXrJrR1Tto9jluV7Ph8PjQ0NETuLygoEFltnThxIv7xj3/g2rVr0NPTAwD8+uuvOHr0qMjzcTgcmkWFSFxLBuECf68LVv+l0Vympqa0Lh6RiPqJDt4nKqG9v65dp06d4OTkBA6HAwsLC1hYWEBRUREODg4y+dltVbJzcHDApUuXsGLFigb7amtrcfToUZEDZbdv3w5DQ0NEREQgKSkJHA4Hr169QlFRkcjrSXuJCNIxtWQQLgDo6uo2uMfRGA6HAzMzM9jZ2TXo2Uxr5RFxqZ/o4P3P94cJ7UOdOnVCp06d0K1bN4waNUomk9uHWpXs1q1bBy8vLyxfvhyzZs0CAOTl5eH69ev45z//iZSUFPzxxx9CX6upqSkwEF1BQQHbtm3DnDlzWhMSIW2uuYNwgb8THYfDadLqzp07d4alpSX69OkjcggCrZJAxKVnz56YO3curl+/jurqaqioqMDJyYlNaKLuMxsYGGDMmDHtZiWaViW78ePHY//+/fj888+xe/duAICfnx8YhoGOjg4OHDggcj07b29vfPnll+x/3IiICNjY2LQmHELEpjmDcBUUFODh4cHe9P/wy0JBQQFGRkYwMTGBiYkJ24wvyoerJEi7VxuRL927d0f37t3ZuS+VlZUxYMCARl/Tu3dvDBs2TCbGzzVVqyOdN28evL29ERYWhufPn4PP58PS0hJjx44V6NHzoXPnzmH69Ons81GjRiEkJIRqdkQmNXcQbv2QmnocDgfdu3dH7969YWRkhMePH6Nv374CTZKimiqFrZJAtTsiDQoKCnBxcYGNjU27u63U4mRXVVWFnj17Yu3atfjmm28wderUZr2+e/fuePDgATtPIMMw7a7wSMdSPwh39erVuHXrVoP9FhYW8PX1hZmZGTuFkpWVFaytrdGvXz/o6OgA+LsXs7AmSWFNlaJWSaDaHZE0VVVVeHp6wtjYWNqhtEiLx9lpaGhASUmpxdMczZo1C7/99htMTEzQv39/AMDatWvRv39/kY+PVa0JEbfBgwfj6NGj2Lt3L3uvQlVVFfPmzcOSJUtgZmYGDoeDd+/eISsrCxYWFnBxcWETnaiFW0Vtr+88UFlZKVNjlkjHoq6ujsmTJ7fbRAe0shlz+vTpOHnyJJYvX97sWtnWrVvRq1cvREREID8/HxwOB5qamo0uBkuILOjevTsWLVqEDRs2oKKiAurq6hg6dCiAv4cMODg4YNu2bSgsLERYWBhcXFzYWpioJklR2+s7D3xI2mOWSMehqKiIcePGSX2JntZqVbKbNWsWVqxYgZEjR2Lp0qUwMzODurp6g+McHBwabFNUVMSyZcuwbNkyAH+3BX///fd0z460G1paWtDQ0GATWf/+/TFkyBDcvHlTaOIS1SQ5YMAAkU2V9Z0HCJEWFxcXGBgYSDuMVmtVshsxYgT7b2HTgtXfh2tKd+2MjAy5KFDSMXC5XEyfPh23bt2CpqYmLC0tMWTIEPB4PJGJS9TCrcePH6cFXRtBYwylx9jYGNbW1tIOo020KtkFBQW1VRw0LRJpV+qbHfv164cHDx5ASUkJHA6n0ZXIRTVJGhsbN5ibEKCmyno0xlA66urq2Dku5eFHRquSXWsW5FNQUICCggKqqqqgoqICBQWFj97343A47Pxt4vTf//4XP//8M968eYMBAwZgx44dQmcRIB3T+82R2tra6Ny5M65duwZnZ+dG77FRk2Tz0RhD6amtrcWpU6egqakpFz8ypDYicMOGDeBwOOygxPrn0nbs2DGsXr0af/75J4YMGYJt27Zh7NixePr0KQwNDaUdHpEB79feUlJSwDCMQLMjJbS2Q2MMJcfIyAh8Ph/Kysrs0Bl5+pHBYZox4d+iRYvA4XCwe/duKCoqYtGiRR+/AIcjdD07WTVkyBAMHjyYneaMz+ejZ8+eWLlyJdauXfvR19+/fx+Ojo64d++e0I45pP3Lzs7G/fv3AQCVlZXQ0NAAh8OR2Qlw2ysul4t//OMf7GTaz58/h5mZGTZs2NDuv3hlVVFREU6ePIni4mIkJCSgZ8+eePXqFVatWtXuf2Q0q2Z348YNdqkTRUVF3Lhxo0lNj+0Fj8fDvXv3sG7dOnZb/dRPsbGxUoyMyJL3myO5XC598YpJY/c/qeOO+NTU1CAjI0PuJjJoVrKrX7RP1PPmOHDgQIte5+/v3+JrfkxhYSHq6uoadBbo2rUrUlNThb6Gy+WyA4ABoKKiAsDf7d31S9QT+cXn8+nvLCZGRkaYOXOm0O1U5m2Px+MhJiYG6enpKCwsBI/Hw+PHj1FTU4Pnz5/j7t27GDJkiLTDbKCp60VK7Z7dggULGmyrrwV+2LL6fu1QnMmuJbZu3YrNmzc32C6LHwpCCGmpEydOSDsEoZp6J05qyS4jI0PgeUlJCebPnw9dXV2sXLkSVlZWAIDU1FTs2LED5eXlCA4OFmtM+vr6UFRURF5ensD2vLy8BuuL1Vu3bh1Wr17NPk9MTIS7uzvu3LnTYDJgIl9u3ryJQ4cOwc/Pj51BhZD2iMvl4scff8Tt27fRq1cv7Nixo103WQrT6mR35coV/Pbbb7h//z5KS0uFZllhg8o/HFe3adMmGBgY4Nq1awI1OTs7O0yfPh2enp74z3/+06Zj+z6koqICR0dHhIeHsxNb8/l8hIeH47PPPhP6GlVVVYEPRf18iUpKSk2uXpP2h8vlIjw8HFlZWbh+/TpcXV3l7stBVjQ2qJwGnLeN2NhYpKWlwc7ODllZWXjw4EG775DyoRZPBA0Ap06dgpeXF/Ly8jBr1izw+XzMnj0bs2bNgrq6Ovr3748NGzY06Vxnz57FtGnThHZoUVBQgLe3N86dO9eacJtk9erV2LNnD4KDg5GSkoLly5ejsrISCxcuFPu1SfshrEs8EY+4uDgEBQXhzp07zdpHmubDcaPq6uoCk5HLi1Ylu61bt7LTINXft1q0aBEOHTqEJ0+eIDc3F+bm5k06F8MwIjuBAEBycnKT22ZbY+bMmfjll1+wYcMG2NvbIzExEVevXhU6wwXpmETNcSlvXw6yQNRqEB/bR5ru/ZU1UlJSwOVy5XJljVY1YyYnJ2Pr1q1QVFRkB4fX95IyMzPDihUr8NNPPzWpU8nUqVOxa9cumJmZ4ZNPPoGGhgaAv9fN27VrFwIDA4XOTCEOn332mchmS0KoS7zkNDaonAact433Z/2pqKiAhoYGFBQU5G66ulYlOw0NDbadXE9PD6qqqsjNzWX3d+3atUFHFFG2b9+OjIwMfP3111i3bh27blJubi5qamrg5uaGbdu2tSZcQtoELbsjGY0tXAuAFrVtI++PG62srGzxGqWyrlXJzsrKCsnJyexze3t7hISEwM/PD7W1tTh8+DBMTEyadC5dXV1ERUXh3LlzuHLlCrKysgAA48aNw4QJEzBp0qR2NUCdyC+a41IyGqtBA2hx7Zo6tYhW30Inj1r1zry9vfH777/jl19+gaqqKr777jtMmTIFenp64HA4qKysxL59+5p1zilTpmDKlCmtCYsQIgc+VoNuae2aVlEQTVFRUdohiE2Lkl11dTXOnTuHmpoafP/99ygqKoKxsTG8vLwQGRmJ06dPQ1FRERMnTsTIkSPbOmZCSAfwsRp0S2rXtIpC4yTRCVBamp3s8vPz4erqioyMDHZxVnV1dZw9exYeHh4YNmwY/VoihEhEc8fgUaeWxvH5fGmHIDbNHnrwww8/IDMzE19++SUuXryI//znP1BXV0dAQIA44iOkXaqfZ5DH4zVrH2me5ozBoyEjH0fJ7j3Xrl2Dv78/fvnlF0yYMAGrVq3CH3/8gczMTDx9+lQcMRIikxpLWjQQuu2IKufmjsF7fzxZUlISKisr5XI8GRGu2cnu5cuXDeYBHDp0KBiGaTCnJCHyTFTSooHQbUtUOTc2i42wffUdXhYvXgx/f38sXrwYc+fOpSEj/x+Px8Pdu3fltjWi2cmOy+VCTU1NYFv989ra2raJihAZ11jSau6XMBGtvpzT09MFyrmxJklR+/T19TFp0qQGDxpG8rf6Xqry2hrRot6YmZmZ7ErNAFBaWgoAeP78OfT09Boc39QVu1NSUhAUFIT09HQUFxcLXeonPDy8JSET0qZEdXSggdBtS1Q5i2sMXkfVWC9VeenB2qJkt379eqxfv77B9hUrVgg8r++tKWzVgw+FhIRg4cKFUFZWhpWVFTp16tTgGHnuFkvaj8YSGn0Jt53GyllcY/A6qo4wLVuzk524ltjZtGkTBg4ciCtXrkBfX18s1yCkLTSW0OhLuO28X85PnjxBXV2dwI+Dth6D11F1lNaIZie7+fPniyMO5OTk4Ouvv6ZER2ReYwlNHAOhO6r3y7mqqgrq6urgcDj046CNdZTWCJmZCK1///7IycmRdhiEfBTNjSkZ75dzUVERFBQUBPoE0ByXbaOjtEbITLL77bff4Ovri/Hjx8PV1VXa4RBCZEx+fr5AsqM5LttGR2mNkJlk99NPP0FXVxfDhg2DjY0NTExMGkxKyuFwJLJaOSFE9uTm5qJPnz4A5KeHIJEcmUl2jx49AofDgYmJCSoqKgSWDqpHS/wQ0nGlp6fDwcEB2tractNDkEiOzCS7zMxMaYdACJFBPB4P8fHxePfuHS5duoSRI0fKTQ9BIjkyk+wIIUSY/v3749WrV9DW1sY//vEP7NixA4mJiVBWVm73PQSJ5MhksisvL0dpaanQGbibuvI5IaT943K5yMnJQVVVFerq6lBTUwNtbW2YmZnByMgINjY2bE/M9thDkEiOTCW7Xbt24bfffkN6errIY5oyGwshRD7cuXOHnXOXz+cjMzMTvXv3ZmdYqqqqgq2tLfr06UP39Emjmj0RtLj8+eef+PTTT9GrVy/8+OOPYBgGX3zxBdauXQsjIyMMGDAAe/fulXaYhBAJqe9x+b76Zst67969Q1RUFE6ePIm0tDSaUpCIJDPJbseOHRg7diyuXLmCZcuWAQAmTpyILVu2IDk5GeXl5Xj79q2UoySESEr9zB7vJ7CCggK8evWqwbHFxcUIDw/HsWPH8PjxY5FLJ8nDUjWkZWQm2aWlpWHSpEkAAGVlZQBgP5C6urpYsmQJdu7cKbX4CCGS1bNnT3h6erLjbRUUFGBtbS10kvh6ZWVliI2NxaFDh3Dz5s0GP5DlYaka0jIyc89OV1eXbZvX0dGBhoaGwC84bW1tvHnzRlrhEUIkKD4+Hj/88AMuXbrE1uy4XC4OHjwIOzs7TJw4ET169EBWVhZMTU2hpPS/r7La2lp2PseUlBQYGxtjwIABMDQ0pIHoHZjMJDtbW1s8fPiQfe7s7Ixdu3ZhwoQJ4PP5CAwMZGdPEJctW7bg0qVLSExMhIqKCkpKSsR6PUJIQ6dPn8bMmTPBMEyDe3AMw+DJkyd48uQJvLy8UFRUBD6fj969e7PHpKen486dO+z23Nxc5ObmIi8vD4mJiTQQvYOSmWZMPz8/PHnyhG1r37x5M1JSUmBiYgIzMzM8ffoUP/74o1hj4PF48PX1xfLly8V6HUKIcPHx8Zg5cybq6upE9rzm8/ng8/m4cOECXr16JdBppaamBsnJySgsLGywPTY2FgUFBcjPzweHw2mwwjyRbzJTs1u4cCEWLlzIPndzc0NSUhIuXLgARUVFeHp6ir1mt3nzZgDA/v37xXodQohw9T2xm9KrkmEYVFZWIi8vjx2SkJGRgby8PHTv3l1g+6tXr1BQUICamhqkpKQA+HsYEw1E7zhkJtkJY2Fhgc8//1zaYTSKy+UK/DqsqKiQYjSEtF8vX77ExYsXmzV8oLCwEN26dUNSUhK6d++O5ORkKCoqQl1dHWVlZUhKSoKZmRk6derELkZar0uXLujRo0dbvw0io2Qu2cXFxSEiIgL5+flYsWIFevfujaqqKqSmpqJPnz7Q0tKSdogCtm7dytYICSEtFx4e3qJxcsXFxVBRUcG9e/fY2ltOTg7q6urYoQoWFhZCe3G+fv2aEl4HITP37Hg8Hry9veHm5obvvvsOv//+O9sbU0FBAZ6enti+fXuzz7t27VpwOJxGH6mpqS2Oe926dSgtLWUfUVFRLT4XIR1ZeXk5FBSa/5VkamoKJycn9OzZE05OTnBzc4OzszPc3Nzg5OTU6FCFR48eUS/vDkJmanbr16/HxYsXsWvXLowcORJWVlbsPjU1Nfj6+uLcuXP47rvvmnXer776CgsWLGj0GAsLi5aEDABQVVUV6L4sazVPQtoLbW1tofPhfoylpSUGDBjQ4mu+vyBsRybvK7/LTLI7cuQIli9fjmXLlgmdKcXa2honTpxo9nkNDAxgYGDQFiESQsRo9OjR4HA4zWrK5HA46Nu3b7OvxeFw0KdPHzg7O9NYu/9P3ld+l5lkl5+fDzs7O5H7FRUVUVVVJdYYXr58iaKiIrx8+RJ1dXVITEwEAPTq1YtqbISImYmJCby8vHD58uUmTfiuoKAAOzs7dO7cucnX4HA4sLS0xMCBAxtt3uxoOsLK7zKT7Hr27NnovbPbt2+jV69eYo1hw4YNCA4OZp8PHDgQABAREYERI0aI9dqEkL9vZ1y5cqXJNbwJEyY06bzKysqwsrKCnZ0dtLW1Wxum3OkIK7/LTAeVOXPmIDAwELGxsey2+iU79uzZg+PHj8Pf31+sMezfv58d4/P+gxIdIZIxePBgHDt2DIqKiuycmB9SUFCAgoICli1bBjMzs0bPp6enB1dXV8ydOxeurq6U6ISor9V9uPK7vA24l5ma3XfffYe4uDgMHz4c1tbW4HA4+PLLL1FUVITXr19jwoQJ+PLLL6UdJiFEzLy9vRETE4Mffvihwbg7DocDOzs7TJgwQWSi43A4MDExga2tLbp160br3H1E/eoS1dXVcr3yO4eRoQWgGIbBoUOHcPLkSTx//hx8Ph+WlpaYMWMG5s2b1y4+tPfv34ejoyPu3bsHBwcHaYdDSLv28uVL2Nvbo7i4GBoaGli/fn2j9+h69OgBFxcXuh/XDNnZ2bh//36D7Q4ODujevbsUIhIPmanZAX//IvPz84Ofn5+0QyGEyAATExNoaGiwA8dFJTpFRUW4uLiwrUKk6bp37y5XSU0UmUp2hBDSXPr6+nB3d0eXLl2kHQqRYTKV7G7duoV9+/YhPT0dxcXFDXpjcTgcgWWACCEdl7q6OgYNGoS+fftSbY58lMwku99++w3ffPMN1NTUYGVl1ayxM4SQjqVfv34YPHiwXM70QcRDZpLdzz//DDc3N1y4cAG6urrSDocQIqNGjBgh9uW+iPyRmWRXVVWFuXPnUqIjhAgwMjICn8+HsrIyzM3NKdGRFpGZZDdy5Eg8fvxY2mEQQmRMQkICioqKcPLkSXZWI0KaS2ZmUNmxYwfCw8Pxyy+/oKioSNrhEEJkTNeuXaGvry/tMEg7JTPJrmfPnggICMDatWthYGAATU1N6OjoCDyoiZOQjsvS0lLaIZB2TGaaMTds2IAtW7age/fuGDRoECU2QoiA9wc+y/vaa6TtyUyy+/PPPzFx4kScPXu2RasVE0Lkl6qqKjQ0NNjn8r72Gml7MpNVeDweJk6cSImOENKApqYmO3D8w7XX5G12fiIeMpNZvLy8cPPmTWmHQQiRccLWXiPkY2Qm2W3cuBHJyclYsWIF7t27h4KCAhQVFTV4EEI6Fh6Ph5iYGPB4vA6z9hppezJzz87KygoAkJiYiMDAQJHH1dXVSSokQogMeP/+nLKycodYe420PZlJdhs2bKDJXAkhAj68P7d48WLMnTu3wXE9e/aUQnSkPZGZZLdp0yZph0AIkTEf3p/Lzs7GpEmTpB0WaYdk5p4dIYS8j+7PkbZEyY4QIpMePHiAtLQ0VFZWIikpCZWVlez9OUKaS2aaMQkh5H09e/YUy/05mn2lY6JkRwiRSd27dxeYIqyt0OwrHRM1YxJC5M77Y/PeR7OvdFyU7P6/zMxMLF68GObm5lBXV4elpSU2btzY4D8LIUT2xcXFISgoCHfu3BHYTrOvdFyU7P6/1NRU8Pl8BAYGIikpCf/5z3/w559/4ttvv5V2aISQZhBVe6PenR0bJbv/b9y4cQgKCoKnpycsLCwwefJkfP311zh9+rS0QyOkQxPVJClqn6jaG/Xu7Niog0ojSktL0blz50aP4XK5Ar8MKyoqxB0WIR1KYx1KPtwnqvbm5OQktt6dpH2gZCfCixcvsGPHDvzyyy+NHrd161Zs3rxZQlER0rF82CTp5OQEVVVVkfvqa2+i5s4UR+9O0j7IfbJbu3Ytfvrpp0aPSUlJQd++fdnn2dnZGDduHHx9fbF06dJGX7tu3TqsXr2afZ6YmAh3d/fWBU0IASC8SbK+didsn4WFBdXeiFByn+y++uorLFiwoNFjLCws2H/n5ORg5MiRcHV1xe7duz96flVVVfaXJgBoaWm1OFZCyP801iQJQOi+DRs20NyZRCi5T3YGBgYwMDBo0rHZ2dkYOXIkHB0dERQURKumEyJFjTVJAqClfkizyH2ya6rs7GyMGDECpqam+OWXX1BQUMDuMzIykmJkhHRMH+tQQs2VpDko2f1/YWFhePHiBV68eIEePXoI7GMYRkpREdJxfWy6MOpsQpqD2un+vwULFoBhGKEPQgiRd42NZ5QHlOwIIYSInGJNXlCyI4SQDq4jTJBNyY4QQjq4jjBBNiU7QohMk/d7SdLWUSbIpmRHCJFp8n4vSdo6ygTZNPSAECKzGpsbk7SNjjJBNiU7QojMamxuTNI2PjaeUV5QMyYhRCZ1lHtJRDIo2RFCZFJHuZdEJIOaMQkhMqmj3EsikkHJjhAikzrKvSQiGdSMSQghRO5RsiOEECL3KNkRQgiRe3TPTk7l5uYiNzdX2mEQQtoRY2NjGBsbSzsMsaBk18aMjY2xceNGqX5guFwuZs+ejaioKKnFQAhpf9zd3REaGiqXs9RwGFqdVO6UlZVBV1cXUVFR0NLSknY4cquiogLu7u5UzmJG5SwZ9eVcWloKHR0daYfT5qhmJ8fs7e3l8kMrK8rKygBQOYsblbNk1JezvKIOKoQQQuQeJTtCCCFyj5KdHFJVVcXGjRvl8iazLKFylgwqZ8mQ93KmDiqEEELkHtXsCCGEyD1KdoQQQuQeJTtCCCFyj5IdIYQQuUfJjsg1DofTpEdkZGSrr1VVVYVNmzY161xbtmzB5MmT0bVrV3A4HGzatKnVcUiDLJdzamoq1qxZA3t7e2hra8PY2BgTJ05EQkJCq2ORNFku55ycHPj5+cHKygra2trQ09ODk5MTgoODIQv9IGkGFSLXQkJCBJ4fOHAAYWFhDbZbW1u3+lpVVVXYvHkzAGDEiBFNes33338PIyMjDBw4EKGhoa2OQVpkuZz/+usv7N27F9OnT8eKFStQWlqKwMBAODs74+rVq/Dw8Gh1TJIiy+VcWFiI169fw8fHByYmJqipqUFYWBgWLFiAp0+f4p///GerY2oVhpAO5NNPP2XE9bEvKChgADAbN25s8msyMjJa/FpZJkvlnJCQwJSXlwtsKywsZAwMDBg3NzcxRCg5slTOonh5eTGamppMbW1t2wTWQtSMSTo8Pp+Pbdu2oV+/flBTU0PXrl0REBCA4uJigeMSEhIwduxY6OvrQ11dHebm5li0aBEAIDMzEwYGBgCAzZs3s81JH2uWNDMzE8dbkknSKmdHR8cGE0h36dIFw4YNQ0pKStu+SRkgzc+zMGZmZqiqqgKPx2v1e2sNasYkHV5AQAD279+PhQsXYtWqVcjIyMAff/yBBw8e4Pbt21BWVkZ+fj48PT1hYGCAtWvXQk9PD5mZmTh9+jQAwMDAALt27cLy5csxbdo0eHt7AwD69+8vzbcmU2StnN+8eQN9ff02fY+yQNrl/O7dO1RWVqKiogJRUVEICgqCi4sL1NXVxfq+P0qq9UpCJOzDZp+bN28yAJhDhw4JHHf16lWB7WfOnGEAMHfv3hV57tY0+8h7M6aslHO96OhohsPhMOvXr2/xOWSBLJbz1q1bGQDsY/To0czLly+bdQ5xoGZM0qGdOHECurq6GDNmDAoLC9lHfdNXREQEAEBPTw8AcPHiRdTU1Egx4vZJlso5Pz8fc+bMgbm5OdasWSOWa0iLLJTz7NmzERYWhsOHD2POnDkA/q7tSRslO9KhPX/+HKWlpTA0NISBgYHAo6KiAvn5+QD+XsF5+vTp2Lx5M/T19TFlyhQEBQWBy+VK+R20D7JSzpWVlfDy8kJ5eTnOnTsnd4vBykI5m5qawsPDA7Nnz8ahQ4dgYWEBDw8PqSc8umdHOjQ+nw9DQ0McOnRI6P76m/QcDgcnT55EXFwcLly4gNDQUCxatAi//vor4uLi5O5Ls63JQjnzeDx4e3vj0aNHCA0Nha2tbYvPJatkoZw/5OPjgz179iA6Ohpjx45ts/M2FyU70qFZWlri+vXrcHNza9INdGdnZzg7O2PLli04fPgw5s6di6NHj2LJkiXgcDgSiLh9knY58/l8+Pv7Izw8HMePH4e7u3tL3obMk3Y5C1NfoystLW2T87UUNWOSDm3GjBmoq6vDDz/80GBfbW0tSkpKAADFxcUNZoGwt7cHALbpR0NDAwDY15D/kXY5r1y5EseOHcPOnTvZnoXySJrlXFBQIHT73r17weFw4ODg0KTziAvV7EiH5u7ujoCAAGzduhWJiYnw9PSEsrIynj9/jhMnTmD79u3w8fFBcHAwdu7ciWnTpsHS0hLl5eXYs2cPdHR0MGHCBACAuro6bGxscOzYMfTp0wedO3eGra1to81lISEhyMrKQlVVFQAgOjoaP/74IwBg3rx5MDU1FX8hSIA0y3nbtm3YuXMnXFxcoKGhgYMHDwrsnzZtGjQ1NcVeBpIgzXLesmULbt++jXHjxsHExARFRUU4deoU7t69i5UrV6JXr16SLIqGpNwblBCJEjXjxO7duxlHR0dGXV2d0dbWZuzs7Jg1a9YwOTk5DMMwzP3795nZs2czJiYmjKqqKmNoaMh4eXkxCQkJAueJiYlhHB0dGRUVlSZ123Z3dxfopv3+IyIioq3etsTJUjnPnz9fZBkDYGexaY9kqZyvXbvGeHl5Md26dWOUlZUZbW1txs3NjQkKCmL4fH6bvu+WoJXKCSGEyD26Z0cIIUTuUbIjhBAi9yjZEUIIkXuU7AghhMg9SnaEEELkHiU7Qgghco+SHSGNyMzMBIfDwf79+6UdilyjcpaMjlzOlOwIIYTIPRpUTkgjGIYBl8uFsrIyFBUVpR2O3KJyloyOXM6U7AghhMg9asYkcm/Tpk3gcDh49uwZ/Pz8oKurCwMDA6xfvx4Mw+DVq1eYMmUKdHR0YGRkhF9//ZV9rbB7HAsWLICWlhays7MxdepUaGlpwcDAAF9//TXq6urY4yIjI8HhcBAZGSkQj7BzvnnzBgsXLkSPHj2gqqoKY2NjTJkyBZmZmWIqlbZH5SwZVM4tQ8mOdBgzZ84En8/Hv/71LwwZMgQ//vgjtm3bhjFjxqB79+746aef0KtXL3z99deIjo5u9Fx1dXUYO3YsunTpgl9++QXu7u749ddfsXv37hbFNn36dJw5cwYLFy7Ezp07sWrVKpSXl+Ply5ctOp80UTlLBpVzM0ln/mlCJGfjxo0MAGbZsmXsttraWqZHjx4Mh8Nh/vWvf7Hbi4uLGXV1dWb+/PkMwzBMRkYGA4AJCgpij6mfRf8f//iHwHUGDhzIODo6ss8jIiKErl7w4TmLi4sZAMzPP//cNm9YSqicJYPKuWWoZkc6jCVLlrD/VlRUxKBBg8AwDBYvXsxu19PTg5WVFdLT0z96vk8++UTg+bBhw5r0ug+pq6tDRUUFkZGRKC4ubvbrZQ2Vs2RQOTcPJTvSYZiYmAg819XVhZqaGvT19Rts/9h/UjU1NRgYGAhs69SpU4v+c6uqquKnn37ClStX0LVrVwwfPhz//ve/8ebNm2afSxZQOUsGlXPzULIjHYawrtaiul8zH+mk3JRu2xwOR+j292/61/viiy/w7NkzbN26FWpqali/fj2sra3x4MGDj15H1lA5SwaVc/NQsiNETDp16gQAKCkpEdielZUl9HhLS0t89dVXuHbtGp48eQIejyfQk44IR+UsGe29nCnZESImpqamUFRUbNATbufOnQLPq6qqUF1dLbDN0tIS2tra4HK5Yo+zvaNyloz2Xs5KUrsyIXJOV1cXvr6+2LFjBzgcDiwtLXHx4kXk5+cLHPfs2TOMHj0aM2bMgI2NDZSUlHDmzBnk5eVh1qxZUoq+/aByloz2Xs6U7AgRox07dqCmpgZ//vknVFVVMWPGDPz888+wtbVlj+nZsydmz56N8PBwhISEQElJCX379sXx48cxffp0KUbfflA5S0Z7LmeaLowQQojco3t2hBBC5B4lO0IIIXKPkh0hhBC5R8mOEEKI3KNkRwghRO5RsiNEBghbE4wQ0nYo2ZF2Jy0tDQEBAbCwsICamhp0dHTg5uaG7du34927d2K7bnJyMjZt2iT1hT63bNmCyZMno2vXruBwONi0aZNU4wH+njexKY8PF/5siaqqKmzatKlZ55LFMmsJWS7n1NRUrFmzBvb29tDW1oaxsTEmTpyIhISEVsfSFmhQOWlXLl26BF9fX6iqqsLf3x+2trbg8Xi4desWvvnmGyQlJbV4wcmPSU5OxubNmzFixAiYmZmJ5RpN8f3338PIyAgDBw5EaGio1OJ4X0hIiMDzAwcOICwsrMF2a2vrVl+rqqoKmzdvBgCMGDGiSa+RxTJrCVku57/++gt79+7F9OnTsWLFCpSWliIwMBDOzs64evUqPDw8Wh1Ta1CyI+1GRkYGZs2aBVNTU9y4cQPGxsbsvk8//RQvXrzApUuXpBjh/zAMg+rqaqirq7f5uTMyMmBmZobCwsIGy7JIi5+fn8DzuLg4hIWFNdguLbJYZi0hy+U8e/ZsbNq0CVpaWuy2RYsWwdraGps2bZJ6sqNmTNJu/Pvf/0ZFRQX27t0rkOjq9erVC59//jn7vLa2Fj/88AMsLS2hqqoKMzMzfPvttw0mozUzM4OXlxdu3boFJycnqKmpwcLCAgcOHGCP2b9/P3x9fQEAI0eObNBcVH+O0NBQDBo0COrq6ggMDAQApKenw9fXF507d4aGhgacnZ1blZSlWatsDT6fj23btqFfv35QU1ND165dERAQ0GDNtISEBIwdOxb6+vpQV1eHubk5Fi1aBODve5v1yWrz5s3s3+FjzZLttcxaQlrl7OjoKJDoAKBLly4YNmwYUlJS2vZNtgDV7Ei7ceHCBVhYWMDV1bVJxy9ZsgTBwcHw8fHBV199hTt37mDr1q1ISUnBmTNnBI598eIFfHx8sHjxYsyfPx/79u3DggUL4OjoiH79+mH48OFYtWoVfv/9d3z77bdsM9H7zUVPnz7F7NmzERAQgKVLl8LKygp5eXlwdXVFVVUVVq1ahS5duiA4OBiTJ0/GyZMnMW3atLYrIBkXEBCA/fv3Y+HChVi1ahUyMjLwxx9/4MGDB7h9+zaUlZWRn58PT09PGBgYYO3atdDT00NmZiZOnz4NADAwMMCuXbuwfPlyTJs2Dd7e3gCA/v37S/OtyRRZK+c3b940WFBWKhhC2oHS0lIGADNlypQmHZ+YmMgAYJYsWSKw/euvv2YAMDdu3GC3mZqaMgCY6Ohodlt+fj6jqqrKfPXVV+y2EydOMACYiIiIBterP8fVq1cFtn/xxRcMAObmzZvstvLycsbc3JwxMzNj6urqGIZhmIyMDAYAExQU1KT3xzAMU1BQwABgNm7c2OTXSMqnn37KvP/1cvPmTQYAc+jQIYHjrl69KrD9zJkzDADm7t27Is/dmvcty2XWErJazvWio6MZDofDrF+/vsXnaCvUjEnahbKyMgCAtrZ2k46/fPkyAGD16tUC27/66isAaNCMaGNjg2HDhrHPDQwMYGVlhfT09CbHaG5ujrFjxzaIw8nJCUOHDmW3aWlpYdmyZcjMzERycnKTz9+enThxArq6uhgzZgwKCwvZR33TV0REBABAT08PAHDx4kXU1NRIMeL2SZbKOT8/H3PmzIG5uTnWrFkjlms0ByU70i7o6OgAAMrLy5t0fFZWFhQUFNCrVy+B7UZGRtDT02uwurKJiUmDc3Tq1KnBfY7GmJubC43Dysqqwfb65k9RqzzLm+fPn6O0tBSGhoYwMDAQeFRUVLBrorm7u2P69OnYvHkz9PX1MWXKFAQFBdHiqk0kK+VcWVkJLy8vlJeX49y5cw3u5UkD3bMj7YKOjg66deuGJ0+eNOt1HA6nSccpKioK3c40YwUscfS8lBd8Ph+GhoY4dOiQ0P31nSE4HA5OnjyJuLg4XLhwAaGhoVi0aBF+/fVXxMXFycSXpiyThXLm8Xjw9vbGo0ePEBoaKrDWnTRRsiPthpeXF3bv3o3Y2Fi4uLg0eqypqSn4fD6eP38u0IkkLy8PJSUlMDU1bfb1m5o4P4zj6dOnDbanpqay+zsCS0tLXL9+HW5ubk36UeDs7AxnZ2ds2bIFhw8fxty5c3H06FEsWbKkRX+HjkLa5czn8+Hv74/w8HAcP34c7u7uLXkbYkHNmKTdWLNmDTQ1NbFkyRLk5eU12J+Wlobt27cDACZMmAAA2LZtm8Axv/32GwBg4sSJzb6+pqYmAKCkpKTJr5kwYQLi4+MRGxvLbqusrMTu3bthZmYGGxubZsfRHs2YMQN1dXX44YcfGuyrra1ly7S4uLhBbdre3h4A2CY2DQ0NAM37O3QU0i7nlStX4tixY9i5cyfbg1NWUM2OtBuWlpY4fPgwZs6cCWtra4EZVGJiYnDixAksWLAAADBgwADMnz8fu3fvRklJCdzd3REfH4/g4GBMnToVI0eObPb17e3toaioiJ9++gmlpaVQVVXFqFGjYGhoKPI1a9euxZEjRzB+/HisWrUKnTt3RnBwMDIyMnDq1CkoKDT/92ZISAiysrJQVVUFAIiOjsaPP/4IAJg3b55M1hbd3d0REBCArVu3IjExEZ6enlBWVsbz589x4sQJbN++HT4+PggODsbOnTsxbdo0WFpaory8HHv27IGOjg77A0ZdXR02NjY4duwY+vTpg86dO8PW1rbR5rL2WGYtIc1y3rZtG3bu3AkXFxdoaGjg4MGDAvunTZvG/mCUCin3BiWk2Z49e8YsXbqUMTMzY1RUVBhtbW3Gzc2N2bFjB1NdXc0eV1NTw2zevJkxNzdnlJWVmZ49ezLr1q0TOIZh/h42MHHixAbXcXd3Z9zd3QW27dmzh7GwsGAUFRUFhiGIOgfDMExaWhrj4+PD6OnpMWpqaoyTkxNz8eJFgWOaM/TA3d2dASD0IWxYhDR82CW+3u7duxlHR0dGXV2d0dbWZuzs7Jg1a9YwOTk5DMMwzP3795nZs2czJiYmjKqqKmNoaMh4eXkxCQkJAueJiYlhHB0dGRUVlSZ1j28PZdYSslTO8+fPF1nGAJiMjIy2fOvNxmGYZtyBJ4QQQtohumdHCCFE7lGyI4QQIvco2RFCCJF7lOwIIYTIPUp2hBBC5B4lO0IIIXKPkh0hhBC5R8mOEEKI3KNkRwghRO5RsiOEECL3KNkRQgiRe5TsCCGEyD1KdoQQQuTe/wMjvN5kF/G4gQAAAABJRU5ErkJggg==",
-      "text/plain": [
-       "<Figure size 450x600 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "sequential_repeated_measures.mean_diff.plot();"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "469088f9",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "baseline_repeated_measures = dabest.load(df, idx=(\"Control 1\", \"Test 1\", \"Test 2\", \"Test 3\"),\n",
-    "                                               paired=\"baseline\", id_col=\"ID\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "7fdd662a",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DABEST v2024.03.29\n",
-       "==================\n",
-       "                  \n",
-       "Good afternoon!\n",
-       "The current time is Tue Mar 19 15:36:16 2024.\n",
-       "\n",
-       "The paired mean difference for repeated measures against baseline \n",
-       "between Control 1 and Test 1 is 0.48 [95%CI 0.237, 0.73].\n",
-       "The p-value of the two-sided permutation t-test is 0.001, calculated for legacy purposes only. \n",
-       "\n",
-       "The paired mean difference for repeated measures against baseline \n",
-       "between Control 1 and Test 2 is -0.542 [95%CI -0.975, -0.198].\n",
-       "The p-value of the two-sided permutation t-test is 0.014, calculated for legacy purposes only. \n",
-       "\n",
-       "The paired mean difference for repeated measures against baseline \n",
-       "between Control 1 and Test 3 is 0.174 [95%CI -0.297, 0.706].\n",
-       "The p-value of the two-sided permutation t-test is 0.505, calculated for legacy purposes only. \n",
-       "\n",
-       "5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.\n",
-       "Any p-value reported is the probability of observing theeffect size (or greater),\n",
-       "assuming the null hypothesis of zero difference is true.\n",
-       "For each p-value, 5000 reshuffles of the control and test labels were performed.\n",
-       "\n",
-       "To get the results of all valid statistical tests, use `.mean_diff.statistical_tests`"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "baseline_repeated_measures.mean_diff"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "8c152456",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 450x600 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "baseline_repeated_measures.mean_diff.plot();"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "139563f1",
-   "metadata": {},
-   "source": [
-    "Similar to unpaired data, DABEST empowers you to perform complex \n",
-    "visualizations and statistics for paired data."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "9043f11a",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 900x600 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "multi_baseline_repeated_measures = dabest.load(df, idx=((\"Control 1\", \"Test 1\", \"Test 2\", \"Test 3\"),\n",
-    "                                                      (\"Control 2\", \"Test 4\", \"Test 5\", \"Test 6\")),\n",
-    "                                               paired=\"baseline\", id_col=\"ID\")\n",
-    "multi_baseline_repeated_measures.mean_diff.plot();"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "python3",
-   "language": "python",
-   "name": "python3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/nbs/tutorials/02-two_group.ipynb b/nbs/tutorials/02-two_group.ipynb
new file mode 100644
index 00000000..68e7c195
--- /dev/null
+++ b/nbs/tutorials/02-two_group.ipynb
@@ -0,0 +1,656 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Two-Group Experiments\n",
+    "\n",
+    "> Explanation of how to use dabest for two-group and multi two-group analysis.\n",
+    "\n",
+    "- order: 2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Pre-compiling numba functions for DABEST...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Compiling numba functions: 100%|██████████| 11/11 [00:00<00:00, 30.12it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Numba compilation complete!\n",
+      "We're using DABEST v2025.03.27\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import dabest\n",
+    "\n",
+    "print(\"We're using DABEST v{}\".format(dabest.__version__))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#| hide\n",
+    "import warnings\n",
+    "warnings.filterwarnings(\"ignore\", category=UserWarning)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Creating a demo dataset"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here, we create a dataset to illustrate how to perform Two-Group analyses using dabest."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Control 1</th>\n",
+       "      <th>Test 1</th>\n",
+       "      <th>Control 2</th>\n",
+       "      <th>Test 2</th>\n",
+       "      <th>Control 3</th>\n",
+       "      <th>Test 3</th>\n",
+       "      <th>Test 4</th>\n",
+       "      <th>Test 5</th>\n",
+       "      <th>Test 6</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>ID</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2.793984</td>\n",
+       "      <td>3.420875</td>\n",
+       "      <td>3.324661</td>\n",
+       "      <td>1.707467</td>\n",
+       "      <td>3.816940</td>\n",
+       "      <td>1.796581</td>\n",
+       "      <td>4.440050</td>\n",
+       "      <td>2.937284</td>\n",
+       "      <td>3.486127</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>3.236759</td>\n",
+       "      <td>3.467972</td>\n",
+       "      <td>3.685186</td>\n",
+       "      <td>1.121846</td>\n",
+       "      <td>3.750358</td>\n",
+       "      <td>3.944566</td>\n",
+       "      <td>3.723494</td>\n",
+       "      <td>2.837062</td>\n",
+       "      <td>2.338094</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3.019149</td>\n",
+       "      <td>4.377179</td>\n",
+       "      <td>5.616891</td>\n",
+       "      <td>3.301381</td>\n",
+       "      <td>2.945397</td>\n",
+       "      <td>2.832188</td>\n",
+       "      <td>3.214014</td>\n",
+       "      <td>3.111950</td>\n",
+       "      <td>3.270897</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2.804638</td>\n",
+       "      <td>4.564780</td>\n",
+       "      <td>2.773152</td>\n",
+       "      <td>2.534018</td>\n",
+       "      <td>3.575179</td>\n",
+       "      <td>3.048267</td>\n",
+       "      <td>4.968278</td>\n",
+       "      <td>3.743378</td>\n",
+       "      <td>3.151188</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2.858019</td>\n",
+       "      <td>3.220058</td>\n",
+       "      <td>2.550361</td>\n",
+       "      <td>2.796365</td>\n",
+       "      <td>3.692138</td>\n",
+       "      <td>3.276575</td>\n",
+       "      <td>2.662104</td>\n",
+       "      <td>2.977341</td>\n",
+       "      <td>2.328601</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Control 1    Test 1  Control 2    Test 2  Control 3    Test 3    Test 4  \\\n",
+       "0   2.793984  3.420875   3.324661  1.707467   3.816940  1.796581  4.440050   \n",
+       "1   3.236759  3.467972   3.685186  1.121846   3.750358  3.944566  3.723494   \n",
+       "2   3.019149  4.377179   5.616891  3.301381   2.945397  2.832188  3.214014   \n",
+       "3   2.804638  4.564780   2.773152  2.534018   3.575179  3.048267  4.968278   \n",
+       "4   2.858019  3.220058   2.550361  2.796365   3.692138  3.276575  2.662104   \n",
+       "\n",
+       "     Test 5    Test 6  Gender  ID  \n",
+       "0  2.937284  3.486127  Female   1  \n",
+       "1  2.837062  2.338094  Female   2  \n",
+       "2  3.111950  3.270897  Female   3  \n",
+       "3  3.743378  3.151188  Female   4  \n",
+       "4  2.977341  2.328601  Female   5  "
+      ]
+     },
+     "execution_count": null,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from scipy.stats import norm # Used in generation of populations.\n",
+    "\n",
+    "np.random.seed(9999) # Fix the seed to ensure reproducibility of results.\n",
+    "\n",
+    "Ns = 20 # The number of samples taken from each population\n",
+    "\n",
+    "# Create samples\n",
+    "c1 = norm.rvs(loc=3, scale=0.4, size=Ns)\n",
+    "c2 = norm.rvs(loc=3.5, scale=0.75, size=Ns)\n",
+    "c3 = norm.rvs(loc=3.25, scale=0.4, size=Ns)\n",
+    "\n",
+    "t1 = norm.rvs(loc=3.5, scale=0.5, size=Ns)\n",
+    "t2 = norm.rvs(loc=2.5, scale=0.6, size=Ns)\n",
+    "t3 = norm.rvs(loc=3, scale=0.75, size=Ns)\n",
+    "t4 = norm.rvs(loc=3.5, scale=0.75, size=Ns)\n",
+    "t5 = norm.rvs(loc=3.25, scale=0.4, size=Ns)\n",
+    "t6 = norm.rvs(loc=3.25, scale=0.4, size=Ns)\n",
+    "\n",
+    "\n",
+    "# Add a `gender` column for coloring the data.\n",
+    "females = np.repeat('Female', Ns/2).tolist()\n",
+    "males = np.repeat('Male', Ns/2).tolist()\n",
+    "gender = females + males\n",
+    "\n",
+    "# Add an `id` column for paired data plotting.\n",
+    "id_col = pd.Series(range(1, Ns+1))\n",
+    "\n",
+    "# Combine samples and gender into a DataFrame.\n",
+    "df = pd.DataFrame({'Control 1' : c1,     'Test 1' : t1,\n",
+    "                 'Control 2' : c2,     'Test 2' : t2,\n",
+    "                 'Control 3' : c3,     'Test 3' : t3,\n",
+    "                 'Test 4'    : t4,     'Test 5' : t5, 'Test 6' : t6,\n",
+    "                 'Gender'    : gender, 'ID'  : id_col\n",
+    "                })\n",
+    "df.head(5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Loading data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "First, we need to load the data and specify the relevant groups. \n",
+    "\n",
+    "We can achieve this by supplying the dataframe to `dabest.load()`. Additionally, we must provide the groups to be compared in the `idx` argument as a tuple or list.\n",
+    "\n",
+    "For this tutorial, we will create two separate analyses: \n",
+    "\n",
+    "- A singular two-group comparison between Control 1 and Test 1.\n",
+    "  \n",
+    "- A multi two-group comparison between Control 1 and Test 1, and between Control 2 and Test 2. \n",
+    "  \n",
+    "The **multi two-group estimation plot** tiles two or more Cumming plots\n",
+    "horizontally, and is created by passing a *nested tuple* to ``idx`` when\n",
+    "``dabest.load()`` is first invoked."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "two_groups_unpaired = dabest.load(df, idx=(\"Control 1\", \"Test 1\"))\n",
+    "multi_two_groups_unpaired = dabest.load(df, idx=((\"Control 1\", \"Test 1\"),(\"Control 2\", \"Test 2\"),(\"Control 3\", \"Test 3\")))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In addition, we can specify the `paired` argument to indicate paired data.\n",
+    "\n",
+    " `paired` can be set as `'baseline'` or `'sequential'` or left as `None` (unpaired). \n",
+    " \n",
+    " **Note: For two-group, both `'baseline'` and `'sequential'` are equivalent.**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "two_groups_paired = dabest.load(df, idx=(\"Control 1\", \"Test 1\"), paired='baseline', id_col='ID')\n",
+    "multi_two_groups_paired = dabest.load(df, idx=((\"Control 1\", \"Test 1\"),(\"Control 2\", \"Test 2\"),(\"Control 3\", \"Test 3\")), \n",
+    "                                      paired='baseline', id_col='ID')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The **dabest** library features a range of effect sizes. In this case, we shall proceed with the default effect size, which is the mean difference.\n",
+    "\n",
+    "Here we will show the two-group unpaired analysis as an example."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "DABEST v2025.03.27\n",
+       "==================\n",
+       "                  \n",
+       "Good afternoon!\n",
+       "The current time is Tue Mar 25 17:21:58 2025.\n",
+       "\n",
+       "The unpaired mean difference between Control 1 and Test 1 is 0.48 [95%CI 0.205, 0.774].\n",
+       "The p-value of the two-sided permutation t-test is 0.001, calculated for legacy purposes only. \n",
+       "\n",
+       "5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.\n",
+       "Any p-value reported is the probability of observing theeffect size (or greater),\n",
+       "assuming the null hypothesis of zero difference is true.\n",
+       "For each p-value, 5000 reshuffles of the control and test labels were performed.\n",
+       "\n",
+       "To get the results of all valid statistical tests, use `.mean_diff.statistical_tests`"
+      ]
+     },
+     "execution_count": null,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "two_groups_unpaired.mean_diff"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A dataframe of the mean_diff results can be extracted by calling the `results` attribute of the `dabest.mean_diff` object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>control</th>\n",
+       "      <th>test</th>\n",
+       "      <th>control_N</th>\n",
+       "      <th>test_N</th>\n",
+       "      <th>effect_size</th>\n",
+       "      <th>is_paired</th>\n",
+       "      <th>difference</th>\n",
+       "      <th>ci</th>\n",
+       "      <th>bca_low</th>\n",
+       "      <th>bca_high</th>\n",
+       "      <th>...</th>\n",
+       "      <th>pvalue_mann_whitney</th>\n",
+       "      <th>statistic_mann_whitney</th>\n",
+       "      <th>bec_difference</th>\n",
+       "      <th>bec_bootstraps</th>\n",
+       "      <th>bec_bca_interval_idx</th>\n",
+       "      <th>bec_bca_low</th>\n",
+       "      <th>bec_bca_high</th>\n",
+       "      <th>bec_pct_interval_idx</th>\n",
+       "      <th>bec_pct_low</th>\n",
+       "      <th>bec_pct_high</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Control 1</td>\n",
+       "      <td>Test 1</td>\n",
+       "      <td>20</td>\n",
+       "      <td>20</td>\n",
+       "      <td>mean difference</td>\n",
+       "      <td>None</td>\n",
+       "      <td>0.48029</td>\n",
+       "      <td>95</td>\n",
+       "      <td>0.205161</td>\n",
+       "      <td>0.773647</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.001625</td>\n",
+       "      <td>83.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>[-0.09732932551566487, 0.08087009665445155, -0...</td>\n",
+       "      <td>(127, 4877)</td>\n",
+       "      <td>-0.256862</td>\n",
+       "      <td>0.259558</td>\n",
+       "      <td>(125, 4875)</td>\n",
+       "      <td>-0.25826</td>\n",
+       "      <td>0.25759</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1 rows × 35 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     control    test  control_N  test_N      effect_size is_paired  \\\n",
+       "0  Control 1  Test 1         20      20  mean difference      None   \n",
+       "\n",
+       "   difference  ci   bca_low  bca_high  ... pvalue_mann_whitney  \\\n",
+       "0     0.48029  95  0.205161  0.773647  ...            0.001625   \n",
+       "\n",
+       "   statistic_mann_whitney  bec_difference  \\\n",
+       "0                    83.0             0.0   \n",
+       "\n",
+       "                                      bec_bootstraps bec_bca_interval_idx  \\\n",
+       "0  [-0.09732932551566487, 0.08087009665445155, -0...          (127, 4877)   \n",
+       "\n",
+       "   bec_bca_low  bec_bca_high bec_pct_interval_idx  bec_pct_low  bec_pct_high  \n",
+       "0    -0.256862      0.259558          (125, 4875)     -0.25826       0.25759  \n",
+       "\n",
+       "[1 rows x 35 columns]"
+      ]
+     },
+     "execution_count": null,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "two_groups_unpaired.mean_diff.results"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Producing estimation plots\n",
+    "\n",
+    "We can now call the `.plot()` method to generate the estimation plot."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "two_groups_unpaired.mean_diff.plot();"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For singular two-group comparisons, the plot will display the effect size curve by default to the right of the raw data.\n",
+    "We term this a **Gardner-Altman plot**.\n",
+    "\n",
+    "This can be changed by setting the `float_contrast` argument to `False`. Here, the effect size curve will be displayed below the raw data - a **Cumming estimation plot**.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 300x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "two_groups_unpaired.mean_diff.plot(float_contrast=False);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For multi two-group comparisons, the effect size curves will always be displayed below the raw data.\n",
+    "\n",
+    "The lower axes in the Cumming plot is effectively a [forest\n",
+    "plot](https://en.wikipedia.org/wiki/Forest_plot), commonly used in\n",
+    "meta-analyses to aggregate and to compare data from different experiments.\n",
+    "\n",
+    "**Note: If you're interested in just plotting the contrast ax (the violin plots), you may be interested in the new [forest plot](07-forest_plot.html) feature added in v2025.03.27!**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 900x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "multi_two_groups_unpaired.mean_diff.plot();"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For paired data, we use\n",
+    "[slopegraphs](https://www.edwardtufte.com/bboard/q-and-a-fetch-msg?msg_id=0003nk>)\n",
+    "(another innovation from Edward Tufte) to connect paired observations.\n",
+    "Both Gardner-Altman and Cumming plots support this.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 400x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "two_groups_paired.mean_diff.plot();"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 240x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "two_groups_paired.mean_diff.plot(float_contrast=False);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 720x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "multi_two_groups_paired.mean_diff.plot();"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For further aesthetic changes, the [Plot Aesthetics Tutorial](09-plot_aesthetics.html) provides detailed examples of how to customize the plot.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "python3",
+   "language": "python",
+   "name": "python3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/nbs/tutorials/03-proportion_plot.ipynb b/nbs/tutorials/03-proportion_plot.ipynb
deleted file mode 100644
index 3ec2f34e..00000000
--- a/nbs/tutorials/03-proportion_plot.ipynb
+++ /dev/null
@@ -1,1252 +0,0 @@
-{
- "cells": [
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "29d885e4",
-   "metadata": {},
-   "source": [
-    "# Proportion plots\n",
-    "\n",
-    "> A guide to plot proportion plots with binary data.\n",
-    "\n",
-    "- order: 3"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "098387ff",
-   "metadata": {},
-   "source": [
-    "<div style=\"padding: 15px; border: 1px solid transparent; border-color: transparent; margin-bottom: 20px; border-radius: 4px; color: #31708f; background-color: #d9edf7; border-color: #bce8f1;\"> As of v2023.02.14, DABEST can be used to generate Cohen's *h* and the corresponding proportion plot for binary data. It's important to note that the code we provide only supports numerical proportion data, \n",
-    "where the values are limited to 0 (failure) and 1 (success). This means that the code is not suitable for \n",
-    "analyzing proportion data that contains non-numeric values, such as strings like 'yes' and 'no'. </div>\n"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "325366c2",
-   "metadata": {},
-   "source": [
-    "## Load libraries"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "04166b0a",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "We're using DABEST v2024.03.29\n"
-     ]
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "import dabest\n",
-    "\n",
-    "print(\"We're using DABEST v{}\".format(dabest.__version__))"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "7942a214",
-   "metadata": {},
-   "source": [
-    "## Creating a demo dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "fca1a99f",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Control 1</th>\n",
-       "      <th>Test 1</th>\n",
-       "      <th>Control 2</th>\n",
-       "      <th>Test 2</th>\n",
-       "      <th>Control 3</th>\n",
-       "      <th>Test 3</th>\n",
-       "      <th>Test 4</th>\n",
-       "      <th>Test 5</th>\n",
-       "      <th>Test 6</th>\n",
-       "      <th>Test 7</th>\n",
-       "      <th>Test 8</th>\n",
-       "      <th>Test 9</th>\n",
-       "      <th>Gender</th>\n",
-       "      <th>ID</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   Control 1  Test 1  Control 2  Test 2  Control 3  Test 3  Test 4  Test 5  \\\n",
-       "0          1       0          0       0          1       0       0       1   \n",
-       "1          0       1          0       1          1       1       0       0   \n",
-       "2          0       1          0       0          1       0       1       1   \n",
-       "3          0       1          0       0          1       0       0       1   \n",
-       "4          0       0          0       0          1       0       0       0   \n",
-       "\n",
-       "   Test 6  Test 7  Test 8  Test 9  Gender  ID  \n",
-       "0       0     1.0     0.0     0.0  Female   1  \n",
-       "1       0     1.0     0.0     0.0  Female   2  \n",
-       "2       0     1.0     0.0     0.0  Female   3  \n",
-       "3       0     1.0     0.0     0.0  Female   4  \n",
-       "4       1     1.0     0.0     0.0  Female   5  "
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "def create_demo_prop_dataset(seed=9999, N=40):\n",
-    "    import numpy as np\n",
-    "    import pandas as pd\n",
-    "\n",
-    "    np.random.seed(9999)  # Fix the seed to ensure reproducibility of results.\n",
-    "    # Create samples\n",
-    "    n = 1\n",
-    "    c1 = np.random.binomial(n, 0.2, size=N)\n",
-    "    c2 = np.random.binomial(n, 0.2, size=N)\n",
-    "    c3 = np.random.binomial(n, 0.8, size=N)\n",
-    "\n",
-    "    t1 = np.random.binomial(n, 0.6, size=N)\n",
-    "    t2 = np.random.binomial(n, 0.2, size=N)\n",
-    "    t3 = np.random.binomial(n, 0.3, size=N)\n",
-    "    t4 = np.random.binomial(n, 0.4, size=N)\n",
-    "    t5 = np.random.binomial(n, 0.5, size=N)\n",
-    "    t6 = np.random.binomial(n, 0.6, size=N)\n",
-    "    t7 = np.ones(N)\n",
-    "    t8 = np.zeros(N)\n",
-    "    t9 = np.zeros(N)\n",
-    "\n",
-    "    # Add a `gender` column for coloring the data.\n",
-    "    females = np.repeat('Female', N / 2).tolist()\n",
-    "    males = np.repeat('Male', N / 2).tolist()\n",
-    "    gender = females + males\n",
-    "\n",
-    "    # Add an `id` column for paired data plotting.\n",
-    "    id_col = pd.Series(range(1, N + 1))\n",
-    "\n",
-    "    # Combine samples and gender into a DataFrame.\n",
-    "    df = pd.DataFrame({'Control 1': c1, 'Test 1': t1,\n",
-    "                       'Control 2': c2, 'Test 2': t2,\n",
-    "                       'Control 3': c3, 'Test 3': t3,\n",
-    "                       'Test 4': t4, 'Test 5': t5, 'Test 6': t6,\n",
-    "                       'Test 7': t7, 'Test 8': t8, 'Test 9': t9,\n",
-    "                       'Gender': gender, 'ID': id_col\n",
-    "                       })\n",
-    "\n",
-    "    return df\n",
-    "df = create_demo_prop_dataset()\n",
-    "df.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7070baac",
-   "metadata": {},
-   "source": [
-    "### Convenient funtion to create a dataset for Unpaired Proportion Plot"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "aa0a822c",
-   "metadata": {},
-   "source": [
-    "In DABEST v2024.3.29, we incorporated feedback from biologists who may not have tables of 0’s and 1’s readily available. As a result, a convenient function to generate a binary dataset based on the specified sample sizes is provided. Users can generate a pandas.DataFrame containing the sample sizes for each element in the groups and the group names (optional if the sample sizes are provided in a dict)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "4da428be",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "True\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>a</th>\n",
-       "      <th>b</th>\n",
-       "      <th>ID</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   a  b  ID\n",
-       "0  0  0   1\n",
-       "1  0  0   2\n",
-       "2  0  1   3\n",
-       "3  1  1   4\n",
-       "4  1  1   5"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "sample_size_1 = {'a':[3, 4], 'b':[2, 5]}\n",
-    "sample_size_2 = [3, 4, 2, 5]\n",
-    "names = ['a', 'b']\n",
-    "sample_df_1 = dabest.prop_dataset(sample_size_1)\n",
-    "sample_df_2 = dabest.prop_dataset(sample_size_2, names)\n",
-    "print(all(sample_df_1 == sample_df_2))\n",
-    "sample_df_1.head()"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "b08c7276",
-   "metadata": {},
-   "source": [
-    "## Loading data"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "a10dd2b3",
-   "metadata": {},
-   "source": [
-    "When loading data, you need to set the parameter ``proportional=True``."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "85d38228",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "two_groups_unpaired = dabest.load(df, idx=(\"Control 1\", \"Test 1\"), proportional=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "092e4f0e",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DABEST v2024.03.29\n",
-       "==================\n",
-       "                  \n",
-       "Good afternoon!\n",
-       "The current time is Tue Mar 19 15:37:29 2024.\n",
-       "\n",
-       "Effect size(s) with 95% confidence intervals will be computed for:\n",
-       "1. Test 1 minus Control 1\n",
-       "\n",
-       "5000 resamples will be used to generate the effect size bootstraps."
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "two_groups_unpaired"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "217bf60d",
-   "metadata": {},
-   "source": [
-    "## Effect sizes"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "32a8ce9b",
-   "metadata": {},
-   "source": [
-    "To generate a proportion plot, the **dabest** library features two effect sizes:\n",
-    "\n",
-    "  - the mean difference (``mean_diff``)\n",
-    "  - [Cohen's h](https://en.wikipedia.org/wiki/Cohen%27s_h) (``cohens_h``)\n",
-    "\n",
-    "These are attributes of the ``Dabest`` object."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "f9405f04",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DABEST v2024.03.29\n",
-       "==================\n",
-       "                  \n",
-       "Good afternoon!\n",
-       "The current time is Tue Mar 19 15:37:30 2024.\n",
-       "\n",
-       "The unpaired mean difference between Control 1 and Test 1 is 0.575 [95%CI 0.35, 0.725].\n",
-       "The p-value of the two-sided permutation t-test is 0.0, calculated for legacy purposes only. \n",
-       "\n",
-       "5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.\n",
-       "Any p-value reported is the probability of observing theeffect size (or greater),\n",
-       "assuming the null hypothesis of zero difference is true.\n",
-       "For each p-value, 5000 reshuffles of the control and test labels were performed.\n",
-       "\n",
-       "To get the results of all valid statistical tests, use `.mean_diff.statistical_tests`"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "two_groups_unpaired.mean_diff"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "103bfed3",
-   "metadata": {},
-   "source": [
-    "Let's compute the *Cohen's h* for our comparison."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "241db548",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DABEST v2024.03.29\n",
-       "==================\n",
-       "                  \n",
-       "Good afternoon!\n",
-       "The current time is Tue Mar 19 15:37:31 2024.\n",
-       "\n",
-       "The unpaired Cohen's h between Control 1 and Test 1 is 1.24 [95%CI 0.769, 1.66].\n",
-       "The p-value of the two-sided permutation t-test is 0.0, calculated for legacy purposes only. \n",
-       "\n",
-       "5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.\n",
-       "Any p-value reported is the probability of observing theeffect size (or greater),\n",
-       "assuming the null hypothesis of zero difference is true.\n",
-       "For each p-value, 5000 reshuffles of the control and test labels were performed.\n",
-       "\n",
-       "To get the results of all valid statistical tests, use `.cohens_h.statistical_tests`"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "two_groups_unpaired.cohens_h"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "f8e4b193",
-   "metadata": {},
-   "source": [
-    "## Generating proportion plots"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "66e29a7e",
-   "metadata": {},
-   "source": [
-    "To generate a **Gardner-Altman estimation plot**, simply use the\n",
-    "``.plot()`` method. \n",
-    "\n",
-    "Each effect size instance has access to the ``.plot()`` method, allowing you to quickly create plots for different effect sizes with ease."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "b8c30a86",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 500x400 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "two_groups_unpaired.mean_diff.plot();"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "4096bdb3",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 500x400 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "two_groups_unpaired.cohens_h.plot();"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "36f8b4c0",
-   "metadata": {},
-   "source": [
-    "In the bar plot, the white portion represents the proportion of observations in the dataset that do not belong to the category, equivalent to the proportion of 0 in the data. Conversely, the colored portion represents the proportion of observations belonging to the category, equivalent to the proportion of 1 in the data. By default, the value of ‘group_summaries’ is set to “mean_sd,” displaying the mean and ± standard deviation of each group as gapped lines in the plot. The gap represents the mean, while the vertical ends represent the standard deviation. Alternatively, if the value of ‘group_summaries’ is set to “median_quartiles,” the median and 25th and 75th percentiles of each group are plotted. By default, the bootstrap effect sizes are plotted on the right axis.\n",
-    "\n",
-    "Instead of a Gardner-Altman plot, you can generate a **Cumming estimation plot** by setting ``float_contrast=False`` in the ``plot()`` method. This will plot the bootstrap effect sizes below the raw data."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "d4d75d09",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 300x600 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "two_groups_unpaired.mean_diff.plot(float_contrast=False);"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "78740e4c",
-   "metadata": {},
-   "source": [
-    "You can also modify the width of bars by setting the parameter ``bar_width`` in the ``plot()`` method. \n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "20997acf",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 500x400 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "two_groups_unpaired.mean_diff.plot(bar_width=0.3);"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "6d16d8cd",
-   "metadata": {},
-   "source": [
-    "The ``bar_desat`` is used to control the amount of desaturation applied to the bar colors. A value of 0.0 means full desaturation (i.e., grayscale), \n",
-    "while a value of 1.0 means no desaturation (i.e., full color saturation). The default one is 0.8.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "8903725f",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 500x400 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "two_groups_unpaired.mean_diff.plot(bar_desat=1.0);"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "2bd92f6a",
-   "metadata": {},
-   "source": [
-    "The parameters ``bar_label`` and ``contrast_label`` can be used to set labels for the y-axis of the bar plot and the contrast plot."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "5fdb7ad6",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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6qVg/gk4MaqIq7syZM2KXQHq6ffs29uzZ88qQfk6lBnb/9gC3HzyFW02r8hdQzQaoXrv841QAfWZgBIA2bdrg1KlTFVxV+TCoiQgqlUrrOp0pFarUKFSpIVep8ezZM1FqqGz2799f5pB+ThCAAxcfYlgb51d3fhVHD0ClKvpUoMLCwgodv7JgUBNJwCOZPZBbiL0LBouy/e8P/4YffpbA+5snfy92BWZt5HfXMPK7a0YY6QQAaT3CZM4Y1ESE9zs3QkinRqJt3154BMvqr6PVP9eJVkNlsn79eowaNUrv9eKHvGGcI+p2EwHf3uUf5xXOnz+P1q1bV/h2pI5BTURQyMV9AMRCkMNCIde8b5lKFxQUBJlMptfpb5kM6ObrgGoKI+xrJ2/ABPvKwoIRBfDxLCKiSsfNzQ09e/bUelFHaRRyoFeTmsa5kQwA7OsZZxwqEwY1URU3ceV+DPt8Fyau3C92KaSHWbNmQSaTQSaTldpPBkAGGSK7G+lxKoUlYFPTOGNRmTCoiaq4h7lP8Ff2EzzMfSJ2KaSHVq1aYcuWLVAoFCUeWSvkgEIuw48jGxrvNaLVXYrOo5PJMKiJiCqpfv364eTJk+jevXuxI2uZDOjRuCZOTvND3+aOxtuoXR3jjUVlwiv1RESVWKtWrfDTTz/h9u3b8PPzw8OHD+FgY4HkyBbGuyb9ohpuxh+TSsUjaiIiM+Dm5gYbGxsAgI2lvGJCGgAc36iYcalEDGoiIio7Z/Get6+qGNRERFQ21V0BO2m+49ucMaiJiKhsPNqLXUGVxKAmIqKyqd9F7AqqJAY1ERG9mlMDwMlH7CqqJAY1ERG9mt8gvuhEJAxqIiIqnXMjwKOj2FVUWQxqIiIqmUwGtB0PiDzDWlXGP3kiIipZ4wFArQZiV1GlMaiJiEg3+7pAqxFiV1EpZGdnY9GiRQgKCkLz5s1x+vRpAMCDBw+wdOlSXL9+3eCx+a5vIiLSLXAaUK2CXkVqRu7cuYPAwED88ccfeOONN3D58mXk5uYCAGrWrInVq1fj1q1biI2NNWh8BjURERXXqC/g2kzsKiqFqVOnIicnB8nJyahVqxZq1aqltTw4OBh79uwxeHye+iaq4hxsrfG6nTUcbK3FLoWkwroG0Gq42FVUGgcOHMD48ePh6+tbbLpRAPDy8sIff/xh8Pg8oiaq4mI+CRK7BJKaViMBZXWxq6g0njx5AicnpxKX5+TklGt8HlETEdHf7OsCPu+JXUWl4uvri2PHjpW4fNeuXWjevLnB4zOoiYjoby0/BOQKsauoVCZOnIjNmzdj8eLFePToEQBArVbj+vXrGDp0KJKSkjBp0iSDx+epbyIiKmLrDHi/LXYVlc6QIUNw69YtREZGYubMmQCAd999F4IgQC6XY+HChQgODjZ4fAY1EREVadyfR9MGmjlzJoYOHYrt27fj+vXrUKvV8Pb2Rr9+/eDl5VWusY0a1IIg4Oeff0Z+fj7at2+P6tV5MwIRUaVgoeS16XJyc3Mr1ynukhh8jXrmzJno3Lmz5rsgCOjWrRu6du2KHj16oEmTJrhx44ZRiiQiogpWvwtgZSd2FZXSuXPnsHLlyhKXr1y5EsnJyQaPb3BQb9++HQEBAZrv27Ztw+HDhzF//nzs2bMHKpUKc+bMMbgwIiIyocb9xa6g0po5cyYOHTpU4vIjR44gMjLS4PENDuq7d++ifv36mu87duyAr68vIiIi0L17d4wZMwaJiYkGF0ZERCZSuznwurfYVVRaZ8+eRYcOHUpc3qFDB5w5c8bg8Q0OagsLC+Tn5wMoOu19+PBhvPvuu5rlzs7OyMzM1HvcFStWwMPDA1ZWVmjdurXmxeYlycrKwtixY+Hq6gqlUok333wT+/bt03u7RERVlt9gsSuo1HJycmBhUfItX3K5XPPYliEMDurGjRvju+++w8OHD7Fu3Tr89ddf6NGjh2b5rVu34OjoqNeYW7ZsQXh4OKKionDu3Dk0a9YMQUFByMjI0Nm/oKAAXbt2RWpqKrZt24YrV64gPj4ederUMfTHIiKqWpx8gLqtxK6iUnvjjTdw4MCBEpcnJCSU685vg4N69uzZSE5OhqOjI0aOHIl27dpp3Vy2d+9etGql385funQpRo4cibCwMPj6+mLVqlWwsbHB2rVrdfZfu3YtHjx4gF27dqFdu3bw8PBAYGAgmjXji+SJiMqkZRig4/3UVHbDhw/H3r17ER4ejqysLE17VlYWJk2ahISEBAwfbvi70w1+PKtr1644d+4cDh48iBo1aiAkJESz7OHDh+jYsSP69OlT5vEKCgpw9uxZREREaNrkcjm6dOmCpKQknev89NNPaNOmDcaOHYt///vfcHJywuDBg/Hpp59CoeCzgEREpXJuBLi9JXYVld748eORnJyMmJgYfP3116hduzYA4M8//4RarcbQoUPFezOZr68vfH19i7U7ODjgq6++0muszMxMqFQqODs7a7U7Ozvj8uXLOtdJSUnBkSNH8MEHH2Dfvn24fv06PvnkEzx79gxRUVE618nPz9dcWwegmTOUiKjKaf0xj6aNQCaTYd26dQgNDcX27duRkpICAOjTpw/69++PTp06lWt8g4M6JycHWVlZqFevnqbtzz//xKpVq5Cfn48BAwbofepbX2q1GrVq1UJcXBwUCgVatmyJu3fv4osvvigxqKOjozF37twKrYuISPI8OwCuTcWuwqx07txZ6xKwsRh8jXrUqFEYOHCg5nt2djbeeustzJ8/H0uWLEGHDh30ejzL0dERCoUC6enpWu3p6elwcXHRuY6rqyvefPNNrdPcDRs2RFpaGgoKCnSuExERgUePHmk+R48eLXONRERmQWEJvDVW7CqojAwO6hMnTqBnz56a79999x3+/PNPnDx5Eg8fPkTTpk0xf/78Mo9naWmJli1b4vDhw5o2tVqNw4cPo02bNjrXadeuneadqs9dvXoVrq6usLS01LmOUqmEnZ2d5mNra1vmGomIzEKz9wE7V7GrMBuCIGD16tUICAjQHHS+/Cnt8a1XMXjNzMxMrcegfvrpJ7Rv3x5vvVV0Y0JoaKjep5jDw8MxbNgw+Pv7IyAgADExMXj8+DHCwsI0Y9apUwfR0dEAgDFjxmD58uWYMGEC/vnPf+LatWtYuHAhxo8fb+iPRURk3mydAb8PxK7CrEybNg1Lly6Fn58fhgwZAgcHB6OOb3BQ16hRA2lpaQCAJ0+e4Pjx45rpvYCiF6Lk5eXpNWZISAju37+P2bNnIy0tDX5+fkhISNDcYHb79m3I5X+fBKhXrx7279+PSZMmoWnTpqhTpw4mTJiATz/91NAfi4jIvLUeDVSzErsKs7Jhwwb0798fP/74Y4WMb3BQt23bFitXrkSDBg2QkJCAp0+faj2OdfXqVYNePDJu3DiMGzdO5zJd17zbtGmDU6dO6b0dIqIqx8kH8DL+zU5V3ZMnT9ClS5cKG9/ga9SLFy9GtWrV0L9/f8THxyM8PByNGjUCAKhUKmzduhWBgYFGK5SIiMqpZRggN/iffSrBO++8g19//bXCxjf4iLp+/fq4cuUKLl68CHt7e3h4eGiW5eXlYfny5XxDGBGRVNSoB9RrLXYVZmnlypUICgrCwoULMXr0aLz++utGHb9cLzypVq2azjCuXr26Xm8lIyKiCtawN4+mK4iPjw/UajVmzZqFWbNmwcrKqtjbMWUymcETc5QrqLOzs7Fy5Ur8/PPPyMjI0Nye/uDBA6xfvx69e/fWmgqTiIhE4tVJ7ArMVv/+/SGrwDe8GRzUd+7cQWBgIP744w+88cYbuHz5suZ1nDVr1sTq1atx69YtxMbGGq1YIiIygOMbgG0tsaswiRUrVuCLL75AWloamjVrhmXLliEgIOCV623evBmDBg1Cnz59sGvXLr22uX79esOKLSODz4NMnToVOTk5SE5OxtGjRyEIgtby4OBgHDp0qNwFEhFROdVpKXYFJqHvVMnPpaamYsqUKejQoYOJKtWPwUF94MABjB8/Hr6+vjoP+b28vPDHH3+UqzgiIjICl6rxTm99p0oGip5S+uCDDzB37txyzRl9+/ZtfPzxx/Dx8YGDgwOOHTsGoOjlYOPHj8f58+cNHtvgoH7y5AmcnJxKXJ6Tk2Po0EREZEy1GopdQbnk5uYiOztb83lxBsTnnk+V/OLzzK+aKhkAPvvsM9SqVatc80VfvHgRzZs3x5YtW+Dp6Yns7GwUFhYCKJrH4sSJE1i+fLnB4xsc1L6+vprfGHTZtWsXmjdvbujwRERkDNVdAJuaYldRLoGBgbC3t9d8nr9G+kWlTZX8/C2aLztx4gTWrFmD+Pj4ctU3bdo01KhRA1evXsV3331X7FJwjx49cPz4cYPHN/hmsokTJ2LYsGFo2rSpZhYttVqN69evY+7cuUhKSsL27dsNLoyIiIzAyUfsCsrt6NGj8PPz03xXKpXlHjMnJwdDhw5FfHw8HB0dyzXWsWPHMHv2bDg5OeGvv/4qttzNzQ137941eHyDg3rIkCG4desWIiMjNe/4fvfddyEIAuRyORYuXIjg4GCDCyMiIiNwqtynvQHA1tYWdnZ2pfbRd6rkGzduIDU1Fb169dK0PZ+J0cLCAleuXIG3t3eZ6lOr1bCxsSlx+f3798v1y0W5nqOeOXMmhg4diu3bt2umm/T29ka/fv3KdVGeiIiMpFYDsSswiRenSn5+kPh8qmRd80c0aNAAv/32m1ZbZGQkcnJyEBsbi3r16pV52y1atMDevXvxySefFFtWWFiIzZs3a2aWNES5ghooOqSfNGlSeYchIiJjk8kAx8p/6rus9Jkq2crKCo0bN9Zav0aNGgBQrP1VIiIi0LNnT4wZMwbvv/8+gKIj+UOHDmHhwoW4dOlSuW4mMzioz507h1OnTun8DQIoevdp27Ztta4rEBGRCdVwAyxLPiVrbvSdKtlY3nvvPaxfvx4TJkxAXFwcgKLLw4IgwM7ODt9++y06duxo8PgGB/XMmTNhbW1dYlAfOXIE+/btw549ewwujoiIyqEKHU0/p+9UyS8qzxvGhg4din79+uHgwYO4du2a5lJwUFAQqlevbvC4QDmC+uzZs4iIiChxeYcOHXTeQk9ERCbi+IbYFZi9vLw81KtXD9OnT8fUqVMr5CZqg88B5OTkwMKi5JyXy+UGzxRCRERGULNsdy2T4WxsbGBhYYHXXnutwrZhcFC/8cYbOHDgQInLExISeOc3EZGYanqKXUGV0L9/f2zbtq3Yi06MxeCgHj58OPbu3Yvw8HBkZWVp2rOysjBp0iQkJCSU65VsRERUDlZ2lf6NZJXF+++/j4yMDHTu3Bnff/89/vvf/+LcuXPFPoYy+Br1+PHjkZycjJiYGHz99deoXbs2AODPP/+EWq3G0KFD+dgWEZFYariJXUGV0alTJ83/1/WqUEEQIJPJoFKpDBrf4KCWyWRYt24dQkNDsX37dqSkpAAA+vTpg/79+2sVTkREJmbPoDaVdevWVej45X7hSefOndG5c2dj1EJERMZiV1vsCqqMYcOGVej4Bl+jvnnzJnbv3l3i8t27dyM1NdXQ4YmIqDyqu4pdQZV07949/O9//8Pjx4+NNqbBQT1lyhR8/fXXJS5fsWIFpk+fbujwRESkJxcXF9Sp7QoXO8ui6S3JZP7973+jQYMGqFu3Llq0aIFffvkFQNH0m82bN8fOnTsNHtvgoE5KSkLXrl1LXP7OO++Ua/5NIiLSz5kzZ3Dn0lmcmdECsHV+9QpkFLt370a/fv3g6OiIqKgorce0HB0dUadOnXK99czgoH748GGpr0WztbXVOS8nERFVMJmcj2aZ0GeffYaOHTvixIkTGDt2bLHlbdq0wfnz5w0e3+CgdnNzw3//+98Slx8/fhx169Y1dHgiIjKUtQMgV4hdRZVx4cIF/OMf/yhxubOzMzIyMgwe3+CgHjRoEH744Qd8/fXXmsm2AUClUiE2NhZbtmzB4MGDDS6MiIgMxKNpk7KxsSn15rGUlBS8/vrrBo9vcFBHRESgc+fOmDhxIlxdXdGxY0d07NgRtWvXxqRJkxAYGIiZM2caXBgRERnI2kHsCqqUzp07Y8OGDSgsLCy2LC0tDfHx8ejWrZvB4xsc1EqlEgcOHMCaNWsQEBCAzMxMZGZmIiAgAGvXrsWhQ4egVCoNLoyIiAxkZS92BVXKggULcOfOHbRq1QqrV6+GTCbD/v37ERkZiSZNmkAQBERFRRk8frleeCKXyxEWFoawsLDyDENERMZkaSt2BVWKj48PTpw4gQkTJmDWrFkQBAFffPEFgKLXi65YsQIeHh4Gj1/uN5MREZHEWFbclIsE/N///R/c3d1hb//3mYtGjRrh0KFDePjwIa5fvw61Wg0vLy84OTmVe3sGB/Xbb7/9yj4ymQyHDx82dBNERGQIBnWFat68OTZu3Ki5Yfrtt9/GzJkz8c4778DBwQGtWrUy6vYMvkatVqshCILWp7CwEDdu3EBiYiLu3LmjdTc4ERGZiIWV2BWYNWtra+Tl5Wm+JyYmIj09vcK2Z/ARdWJiYonL9uzZg1GjRmHp0qWGDk9ERIZiUFeoZs2aYenSpVAoFJrT37/++iusrEr/c+/Xr59B26uQa9Q9e/bEkCFDMHHiRBw9erQiNkFERCWxsBS7ArMWExODgQMHYvjw4QCKLvPGxsYiNja2xHVEmY/6Vby9vbF8+fKKGp6IiEoiryZ2BWatVatWuH79Om7cuIH09HR06tQJM2bMKHX+i/KokKAuLCzEjz/+CEdHx4oYnoiISqPgEXVF+umnn+Dv7w8fHx/4+Phg2LBh6NWrF1q3bl0h2zM4qD/66COd7VlZWTh16hTS0tJ4jZqISAwM6grVt29frbu+jx49ir59+1bY9gwO6iNHjkAmk2m1yWQyODg4oH379hgxYkS5XplGREQGUvDUd0WqXr06srKyNN9TU1ORm5tbYdszOKhTU1ONWAYRERkNj6grVEBAABYsWID09HTNXd/79u1DWlpaievIZDJMmjTJoO0ZHNTJycm4dOkSBg0apGnbv38/FixYgPz8fAwePBgTJkwwdHgiIjIUj6gr1MqVKxEaGop58+YBKArhTZs2YdOmTSWuI0pQT5s2DTY2NpqgvnnzJvr27YvXX38dtWvXRnh4OKytrTFq1ChDN0FERIZgUFeo+vXr4+TJk3j69CkyMjLg4eGBmJgY9OnTp0K2Z3BQ/+9//8PUqVM137/99lsoFAqcP38ejo6OCAkJwapVqxjURESmxsezTMLKygpubm6IiorC22+/DXd39wrZjsFB/ejRI62JsPft24euXbtqHsnq2rUr/vOf/5S/QiIi0o8Fpxg2pfJMYVkWBge1q6srLl26BAC4d+8ezp49qzXdZW5uLuRyg18lTkREhpJzYsSK9NFHH0EmkyEuLg4KhaLEx5VfJJPJsGbNGoO2Z/De7NOnD5YtW4anT5/il19+gVKp1HqO7H//+x+8vLwMHZ6IiAzFa9QV6siRI5DL5VCr1VAoFDofV37Zq5aXxuBD3vnz56Nfv37YuHEjMjIysH79ejg7OwMAsrOzsW3bNoOfo34+ybaVlRVat26N06dPl2m9zZs3QyaTITg42KDtEhGZBT6eVaFSU1ORkpKCatWqab7fvHmz1E9KSorB2zP4iNrW1hbff/99icvu3LkDGxsbvcfdsmULwsPDsWrVKrRu3RoxMTEICgrClStXUKtWrRLXS01NxZQpU9ChQwe9t0lEZDbkiqIPmY0KuYgsl8thb2+v+W1DH0uXLsXIkSMRFhYGX19frFq1CjY2Nli7dm2J66hUKnzwwQeYO3cuT7cTUdXG69MVTi6XQ6FQ6P0xlKT2aEFBAc6ePYuIiAhNm1wuR5cuXZCUlFTiep999hlq1aqF4cOH4/jx46VuIz8/H/n5+ZrvFfnaNyIik+PRdIWbPXt2sWvOO3fuxO+//46goCD4+PgAAC5fvowDBw6gcePG5bokK6mgzszMhEql0lzrfs7Z2RmXL1/Wuc6JEyewZs0aJCcnl2kb0dHRmDt3bnlLJSKSJh5RV7g5c+ZofY+Li0NGRgYuXLigCennLl26hLfffhu1a9c2eHuV+vmpnJwcDB06FPHx8WWeUjMiIgKPHj3SfI4ePVrBVRIRmZCsUv+zXil98cUXGDduXLGQBoCGDRti3Lhx+Pzzzw0eX1K/ejk6OkKhUCA9PV2rPT09HS4uLsX637hxA6mpqejVq5emTa1WAwAsLCxw5coVeHt7a62jVCqhVP79MgBbW1tj/ghEROJiUJvcnTt3Sr0nq1q1arhz547B40tqj1paWqJly5Y4fPiwpk2tVuPw4cNo06ZNsf4NGjTAb7/9huTkZM2nd+/e6Ny5M5KTk1GvXj1Tlk9EJAGGP69LhmncuDFWrlyJu3fvFlt2584drFy5Ek2aNDF4fEkdUQNAeHg4hg0bBn9/fwQEBCAmJgaPHz/WvPUsNDQUderUQXR0NKysrNC4cWOt9WvUqAEAxdqJiIgqwldffYWgoCC8+eab6Nu3L+rXrw8AuHbtGnbt2gVBEPDdd98ZPL7kgjokJAT379/H7NmzkZaWBj8/PyQkJGhuMLt9+zZfTUpERJLRvn17/PLLL5g1axZ27tyJJ0+eAACsra0RFBSEuXPnmtcRNQCMGzcO48aN07ksMTGx1HXXr19v/IKIiIhK0bhxY+zcuRNqtRr3798HADg5ORnlwFKSQU1ERAYqxzulqfzkcnmxR4zLPaZRRyMiInFxQg6zw6AmIiKSMAY1ERGZDX1mX4yPj0eHDh3g4OAABwcHdOnSpcyzNZoSg5qIiMzC89kXo6KicO7cOTRr1gxBQUHIyMjQ2T8xMRGDBg3Czz//jKSkJNSrVw/dunXT+Ty0mBjURERkFvSdffH777/HJ598Aj8/PzRo0AD/+te/NC/ZkhIGNRERSVpubi6ys7M1nxdnQHzu+eyLXbp00bSVZfbFF+Xl5eHZs2eoWbOm0Wo3BgY1ERFJWmBgIOzt7TWf6OjoYn1Km30xLS2tTNv59NNPUbt2ba2wlwI+R01ERJJ29OhR+Pn5ab6/OLGSsSxatAibN29GYmIirKysjD5+eTCoiYhI0mxtbWFnZ1dqH31nX3zRl19+iUWLFuHQoUNo2rRpues1Np76JiKiSk/f2Ref+/zzzzFv3jwkJCTA39/fFKXqjUfURERkFvSZfREAFi9ejNmzZ2PTpk3w8PDQXMu2tbWFra2taD/HyxjURERkFvSdffGbb75BQUEBBgwYoDVOVFQU5syZY8rSS8WgJiIis6HP7IupqakVX5AR8Bo1ERGRhDGoiYiIJIxBTUREJGEMaiIiIgljUBMREUkYg5qIiEjCGNREREQSxqAmIiKSMAY1ERGRhDGoiYiIJIxBTUREJGEMaiIiIgljUBMREUkYg5qIiEjCGNREREQSxqAmIiKSMAY1ERGRhDGoiYiIJIxBTUREJGEMaiIiIgljUBMREUkYg5qIiEjCGNREREQSxqAmIiKSMAY1ERGRhDGoiYiIJIxBTUREJGEMaiIiIgljUBMREUkYg5qIiEjCGNREREQSxqAmIiKSMEkG9YoVK+Dh4QErKyu0bt0ap0+fLrFvfHw8OnToAAcHBzg4OKBLly6l9iciIqpMJBfUW7ZsQXh4OKKionDu3Dk0a9YMQUFByMjI0Nk/MTERgwYNws8//4ykpCTUq1cP3bp1w927d01cORERkfFJLqiXLl2KkSNHIiwsDL6+vli1ahVsbGywdu1anf2///57fPLJJ/Dz80ODBg3wr3/9C2q1GocPHzZx5ZWTv78/6tatC39/f7FLISIiHSzELuBFBQUFOHv2LCIiIjRtcrkcXbp0QVJSUpnGyMvLw7Nnz1CzZk2dy/Pz85Gfn6/5npubW76iK7m0tDSefSAikjBJHVFnZmZCpVLB2dlZq93Z2RlpaWllGuPTTz9F7dq10aVLF53Lo6OjYW9vr/kEBgaWu24iIqKKIqmgLq9FixZh8+bN2LlzJ6ysrHT2iYiIwKNHjzSfo0ePmrhKIiKispPUqW9HR0coFAqkp6drtaenp8PFxaXUdb/88kssWrQIhw4dQtOmTUvsp1QqoVQqNd9tbW3LVzQREVEFktQRtaWlJVq2bKl1I9jzG8PatGlT4nqff/455s2bh4SEBN4URUREZkVSR9QAEB4ejmHDhsHf3x8BAQGIiYnB48ePERYWBgAIDQ1FnTp1EB0dDQBYvHgxZs+ejU2bNsHDw0NzLdvW1pZHy0REVOlJLqhDQkJw//59zJ49G2lpafDz80NCQoLmBrPbt29DLv/7RMA333yDgoICDBgwQGucqKgozJkzx5SlExERGZ3kghoAxo0bh3HjxulclpiYqPU9NTW14gsiIiISiaSuURMREZE2BjUREZGEMaiJiIgkjEFNRERmQ5/ZFwFg69ataNCgAaysrNCkSRPs27fPRJWWHYO6inNxcUGdOnVe+UIZIiKp03f2xZMnT2LQoEEYPnw4zp8/j+DgYAQHB+PChQsmrrx0DOoq7syZM7hz5w7OnDkjdilEROWi7+yLsbGxePfddzF16lQ0bNgQ8+bNQ4sWLbB8+XITV146BjUREVV6z2dffHFCplfNvpiUlFRsAqegoKAyz9ZoKpJ8jppMS6VSQa1Wi7Z9taoQapUKalUhnj17JlodYilUqVGoEu/PXwoKBTXkKnWV3P9UssLCQgBF0xFnZ2dr2l+eswEoffbFy5cv6xw/LS2tXLM1mgqDWgJshDzkZeWhz8SFomz/8qnDuPrLEVG2/bIf548RuwQS0+Tvxa6AJOjl6Yir2psnGdQEn4DOeLNVJ1FryJPZwNH+NXw3e7iodYjh12VhuJ9bKHYZorIXHsGy+uto9c91YpdCEnL+/Hm0bt0aR48ehZ+fn6b95aNpwLDZF11cXAyardHUGNQEmVwOmcg1yGUKyBUWqFatmsiVmJ6FQg4LRdW+XcRCKPozqIr7n0pmYVEUUba2trCzsyu174uzLwYHBwP4e/bFkl5J3aZNGxw+fBgTJ07UtB08eLDU2RrFwKCu4o7+sAL5eblQ2tgicNBYscshIjKYvrMvTpgwAYGBgViyZAl69OiBzZs348yZM4iLixPzxyiGQV3F5efl4mlu9qs7EhFJnL6zL7Zt2xabNm1CZGQkZsyYgTfeeAO7du1C48aNxfoRdGJQExGR2dBn9kUAGDhwIAYOHFjBVZVP1b4wRkREJHEMaiIiIgljUBMREUkYg5qIiEjCGNREREQSxqAmIiKSMAY1ERGRhDGoiYiIJIxBTUREJGEMaiIiIgljUBMREUkY3/VdxSltbLX+l4iIpIVBXcVxaksiImnjqW8iIiIJY1ATERFJGIOaiIhIwhjUREREEsagJiIikjAGNRERkYQxqImIiCSMQU1ERCRhDGoiIiIJY1ATERFJGIOaiIhIwhjUREREEsagJiIikjAGNRERkYQxqImIiCSMQU1ERCRhDGoiIiIJY1ATERFJGIOaiIhIwiQZ1CtWrICHhwesrKzQunVrnD59utT+W7duRYMGDWBlZYUmTZpg3759JqqUiIioYkkuqLds2YLw8HBERUXh3LlzaNasGYKCgpCRkaGz/8mTJzFo0CAMHz4c58+fR3BwMIKDg3HhwgUTV05ERGR8kgvqpUuXYuTIkQgLC4Ovry9WrVoFGxsbrF27Vmf/2NhYvPvuu5g6dSoaNmyIefPmoUWLFli+fLmJKyciIjI+SQV1QUEBzp49iy5dumja5HI5unTpgqSkJJ3rJCUlafUHgKCgoBL7ExERVSYWYhfwoszMTKhUKjg7O2u1Ozs74/LlyzrXSUtL09k/LS1NZ//8/Hzk5+drvufm5gIALl26VJ7SDfbg3i08zXkgyral5KnMCvI8G5w7d07sUkzu8h9/4eETldhliMpWyEU1G8Dipf3v6uoKV1dXkaoqn3v37uHevXtil1GpifXvstRIKqhNITo6GnPnztVqc3d3x5AhQ0SqiF50IG6+2CWQmJZo3wgaFRWFOXPmiFNLOa1evbrYvzWkv8DAwEr7y5qxSCqoHR0doVAokJ6ertWenp4OFxcXneu4uLjo1T8iIgLh4eFabQ8ePMCDB1XzqDY3NxeBgYE4evQobG1txS6HRCDlvwOV+R/o0aNHo3fv3ibdppT3paEq81kVY5EJgiCIXcSLWrdujYCAACxbtgwAoFar4ebmhnHjxmH69OnF+oeEhCAvLw+7d+/WtLVt2xZNmzbFqlWrTFZ3ZZWdnQ17e3s8evQIdnZ2YpdDIuDfAfPBfWmeJHVEDQDh4eEYNmwY/P39ERAQgJiYGDx+/BhhYWEAgNDQUNSpUwfR0dEAgAkTJiAwMBBLlixBjx49sHnzZpw5cwZxcXFi/hhERERGIbmgDgkJwf379zF79mykpaXBz88PCQkJmhvGbt++Dbn875vV27Zti02bNiEyMhIzZszAG2+8gV27dqFx48Zi/QhERERGI7lT32Ra+fn5iI6ORkREBJRKpdjlkAj4d8B8cF+aJwY1ERGRhEnqhSdERESkjUFNREQkYQxqMqrU1FTIZDKsX79e7FKIiMwCg1pEN27cwOjRo+Hl5QUrKyvY2dmhXbt2iI2NxZMnTypsuxcvXsScOXOQmppaYdsoiwULFqB3795wdnaGTCartG+gqmgymaxMn8TExHJvKy8vD3PmzNFrLO5H/XB/kr4k93hWVbF3714MHDgQSqUSoaGhaNy4MQoKCnDixAlMnToVv//+e4U9C37x4kXMnTsXnTp1goeHR4VsoywiIyPh4uKC5s2bY//+/aLVIXUbN27U+v7tt9/i4MGDxdobNmxY7m3l5eVpXnvZqVOnMq3D/agf7k/SF4NaBDdv3sT7778Pd3d3HDlyROv1eGPHjsX169exd+9eESv8myAIePr0KaytrY0+9s2bN+Hh4YHMzEw4OTkZfXxz8fJ76E+dOoWDBw9K5v303I/64f4kffHUtwg+//xz5ObmYs2aNTrfYVu/fn1MmDBB872wsBDz5s2Dt7c3lEolPDw8MGPGDK1ZwADAw8MDPXv2xIkTJxAQEAArKyt4eXnh22+/1fRZv349Bg4cCADo3LlzsdNsz8fYv38//P39YW1tjdWrVwMAUlJSMHDgQNSsWRM2NjZ46623yvULhZhH8+ZGrVYjJiYGjRo1gpWVFZydnTF69Gg8fPhQq9+ZM2cQFBQER0dHWFtbw9PTEx999BGAovsLnv/DPHfuXM3fjVed+uR+ND7uT3oRj6hFsHv3bnh5eaFt27Zl6j9ixAhs2LABAwYMwOTJk/HLL78gOjoaly5dws6dO7X6Xr9+HQMGDMDw4cMxbNgwrF27Fh9++CFatmyJRo0aoWPHjhg/fjy+/vprzJgxQ3N67cXTbFeuXMGgQYMwevRojBw5Ej4+PkhPT0fbtm2Rl5eH8ePH4/XXX8eGDRvQu3dvbNu2DX379jXeHxDpbfTo0Vi/fj3CwsIwfvx43Lx5E8uXL8f58+fx3//+F9WqVUNGRga6desGJycnTJ8+HTVq1EBqaip27NgBAHBycsI333yDMWPGoG/fvujXrx8AoGnTpmL+aFUS9ydpEcikHj16JAAQ+vTpU6b+ycnJAgBhxIgRWu1TpkwRAAhHjhzRtLm7uwsAhGPHjmnaMjIyBKVSKUyePFnTtnXrVgGA8PPPPxfb3vMxEhIStNonTpwoABCOHz+uacvJyRE8PT0FDw8PQaVSCYIgCDdv3hQACOvWrSvTzycIgnD//n0BgBAVFVXmdaqysWPHCi/+p3v8+HEBgPD9999r9UtISNBq37lzpwBA+PXXX0scuzz7gvvRMNyf9Co89W1i2dnZAIDq1auXqf++fUXz8748NefkyZMBoNipZ19fX3To0EHz3cnJCT4+PkhJSSlzjZ6enggKCipWR0BAANq3b69ps7W1xahRo5CamoqLFy+WeXwyrq1bt8Le3h5du3ZFZmam5tOyZUvY2tri559/BgDUqFEDALBnzx48e/ZMxIqpNNyf9DIGtYk9n3ouJyenTP1v3boFuVyO+vXra7W7uLigRo0auHXrlla7m5tbsTEcHByKXdsqjaenp846fHx8irU/P2X+ch1kOteuXcOjR49Qq1YtODk5aX1yc3ORkZEBAAgMDET//v0xd+5cODo6ok+fPli3bl2xex1IXNyf9DJeozYxOzs71K5dGxcuXNBrPZlMVqZ+CoVCZ7ugxyvdK+IOb6o4arUatWrVwvfff69z+fMbimQyGbZt24ZTp05h9+7d2L9/Pz766CMsWbIEp06dgq2trSnLphJwf9LLGNQi6NmzJ+Li4pCUlIQ2bdqU2tfd3R1qtRrXrl3TuuErPT0dWVlZcHd313v7ZQ39l+u4cuVKsfbLly9rlpM4vL29cejQIbRr165Mv2S99dZbeOutt7BgwQJs2rQJH3zwATZv3owRI0YY9HeDjIv7k17GU98imDZtGl577TWMGDEC6enpxZbfuHEDsbGxAIDu3bsDAGJiYrT6LF26FADQo0cPvbf/2muvAQCysrLKvE737t1x+vRpJCUladoeP36MuLg4eHh4wNfXV+86yDj+8Y9/QKVSYd68ecWWFRYWavbzw4cPi51Z8fPzAwDN6VIbGxsA+v3dIOPi/qSX8YhaBN7e3ti0aRNCQkLQsGFDrTeTnTx5Elu3bsWHH34IAGjWrBmGDRuGuLg4ZGVlITAwEKdPn8aGDRsQHByMzp076719Pz8/KBQKLF68GI8ePYJSqcTbb7+NWrVqlbjO9OnT8cMPP+C9997D+PHjUbNmTWzYsAE3b97E9u3bIZfr/zvfxo0bcevWLeTl5QEAjh07hvnz5wMAhg4dyqP0MgoMDMTo0aMRHR2N5ORkdOvWDdWqVcO1a9ewdetWxMbGYsCAAdiwYQNWrlyJvn37wtvbGzk5OYiPj4ednZ3mF0Jra2v4+vpiy5YtePPNN1GzZk00btwYjRs3LnH73I/Gxf1JxYh813mVdvXqVWHkyJGCh4eHYGlpKVSvXl1o166dsGzZMuHp06eafs+ePRPmzp0reHp6CtWqVRPq1asnREREaPURhKJHq3r06FFsO4GBgUJgYKBWW3x8vODl5SUoFAqtR7VKGkMQBOHGjRvCgAEDhBo1aghWVlZCQECAsGfPHq0++jyeFRgYKADQ+dH16BgVeflxnufi4uKEli1bCtbW1kL16tWFJk2aCNOmTRP+/PNPQRAE4dy5c8KgQYMENzc3QalUCrVq1RJ69uwpnDlzRmuckydPCi1bthQsLS3L9HgO92P5cH/Sq8gEQY+7jIiIiMikeI2aiIhIwhjUREREEsagJiIikjAGNRERkYQxqImIiCSMQU1ERCRhDGoiokokNTUVMpkM69evF7sUMhEGtUStX78eMpkMVlZWuHv3brHlnTp1KvXtQqYwcuRIyGQy9OzZU+fyn376CS1atICVlRXc3NwQFRWFwsJCE1dZOXH/E9FzDGqJy8/Px6JFi8Quo5gzZ85g/fr1sLKy0rn8P//5D4KDg1GjRg0sW7YMwcHBmD9/Pv75z3+auNLKjfufXubu7o4nT55g6NChYpdCJsJ3fUucn58f4uPjERERgdq1a4tdDoCiKTPHjx+P0NBQHD58WGefKVOmoGnTpjhw4AAsLIr+mtnZ2WHhwoWYMGECGjRoYMqSKy3uf3rZ8zMtVHXwiFriZsyYAZVKJamjqo0bN+LChQtYsGCBzuUXL17ExYsXMWrUKM0/0gDwySefQBAEbNu2zVSlVnrc/+Zpzpw5kMlkuHr1KoYMGQJ7e3s4OTlh1qxZEAQBf/zxB/r06QM7Ozu4uLhgyZIlmnV1XaP+8MMPYWtri7t37yI4OBi2trZwcnLClClToFKpNP0SExMhk8mQmJioVY+uMdPS0hAWFoa6detCqVTC1dUVffr0QWpqagX9qVBJGNQS5+npidDQUMTHx+PPP//Ue/28vDxkZma+8vPw4cMyjZeTk4NPP/0UM2bMgIuLi84+58+fBwD4+/trtdeuXRt169bVLKdX4/43byEhIVCr1Vi0aBFat26N+fPnIyYmBl27dkWdOnWwePFi1K9fH1OmTMGxY8dKHUulUiEoKAivv/46vvzySwQGBmLJkiWIi4szqLb+/ftj586dCAsLw8qVKzF+/Hjk5OTg9u3bBo1HhmNQVwIzZ85EYWEhFi9erPe6n3/+OZycnF75ad68eZnG++yzz2BtbY1JkyaV2OfevXsAAFdX12LLXF1dDQqcqoz733wFBARg06ZNGDNmDP7973+jbt26mDx5siYcx4wZgz179sDa2hpr164tdaynT58iJCQEa9aswccff4xt27ahefPmWLNmjd51ZWVl4eTJk4iMjMS8efMwfPhwRERE4MiRI+jYsaOhPy4ZiNeoKwEvLy8MHToUcXFxmD59us5/AEsSGhqK9u3bv7KftbX1K/tcvXoVsbGx+OGHH6BUKkvs9+TJEwDQ2cfKygrZ2dmv3Bb9jfvffI0YMULz/xUKBfz9/XHnzh0MHz5c016jRg34+PggJSXlleN9/PHHWt87dOiAjRs36l2XtbU1LC0tkZiYiOHDh8PBwUHvMch4GNSVRGRkJDZu3IhFixYhNja2zOt5eXnBy8vLKDVMmDABbdu2Rf/+/Uvt9/wf/fz8/GLLnj59WqZQIG3c/+bJzc1N67u9vT2srKzg6OhYrP2vv/4qdSwrKys4OTlptTk4OJT5ssaLlEolFi9ejMmTJ8PZ2RlvvfUWevbsidDQ0BIveVDFYVBXEl5eXhgyZIjmqKqscnNzkZub+8p+CoWi2H/kLzpy5AgSEhKwY8cOrZtJCgsL8eTJE6SmpqJmzZqws7PTHPHdu3cP9erV0xrn3r17CAgIKHP9VIT73zwpFIoytQFFd9vrO9bLZDKZzvYXbzh7buLEiejVqxd27dqF/fv3Y9asWYiOjsaRI0fKfKmEjIPXqCuRyMhIva9Vfvnll3B1dX3lp1WrVqWO8/wGkn79+sHT01PzuXv3Lo4cOQJPT0/NNTQ/Pz8ARc/avujPP//EnTt3NMtJP9z/VF7PT2FnZWVptd+6dUtnf29vb0yePBkHDhzAhQsXUFBQoHUHOpkGj6grEW9vbwwZMgSrV6+Gu7u71qMvJTHWNcq3334bO3fuLNY+atQouLu7Y+bMmWjSpAkAoFGjRmjQoAHi4uIwevRozW/633zzDWQyGQYMGPDKeqg47n8qL3d3dygUChw7dgzBwcGa9pUrV2r1y8vLg1wu13pe29vbG9WrV9d5SYMqFoO6kpk5cyY2btyIK1euoFGjRq/sb6xrlG5ubsWupwFFp8ecnZ21/qMHgC+++AK9e/dGt27d8P777+PChQtYvnw5RowYgYYNG5a7nqqK+5/Kw97eHgMHDsSyZcsgk8ng7e2NPXv2ICMjQ6vf1atX8c477+Af//gHfH19YWFhgZ07dyI9PR3vv/++SNVXXTz1XcnUr18fQ4YMEbuMV+rZsyd27NiBBw8e4J///Cd27NiBGTNmYMWKFWKXVqlx/1N5LVu2DH369MGqVasQGRkJNzc3bNiwQatPvXr1MGjQICQmJiIiIgIRERHIzs7Gjz/++MqbCcn4ZMKr7lAgIiIi0fCImoiISMIY1ERERBLGoCYiIpIwBjUREZGEMaiJiIgkjEFNREQkYQxqIiIqJjU1FTKZDOvXrxe7lCqPQU1EVE43btzA6NGj4eXlBSsrK9jZ2aFdu3aIjY3VTPtZES5evIg5c+ZoTZQihgULFqB3795wdnaGTCbDnDlzRK3H3PAVokRE5bB3714MHDgQSqUSoaGhaNy4MQoKCnDixAlMnToVv//+O+Li4ipk2xcvXsTcuXPRqVMneHh4VMg2yiIyMhIuLi5o3rw59u/fL1od5opBTURkoJs3b+L999+Hu7s7jhw5opniEwDGjh2L69evY+/evSJW+DdBECpsPvCbN2/Cw8MDmZmZpU6XSobhqW8iIgN9/vnnyM3NxZo1a7RC+rn69etjwoQJmu+FhYWYN28evL29oVQq4eHhgRkzZhSbkcrDwwM9e/bEiRMnEBAQACsrK3h5eeHbb7/V9Fm/fj0GDhwIAOjcuTNkMhlkMhkSExO1xti/fz/8/f1hbW2N1atXAwBSUlIwcOBA1KxZEzY2NnjrrbfK9QuFmEfzVQGDmojIQLt374aXlxfatm1bpv4jRozA7Nmz0aJFC3z11VcIDAxEdHS0zhmprl+/jgEDBqBr165YsmQJHBwc8OGHH+L3338HAHTs2BHjx48HAMyYMQMbN27Exo0btWYnu3LlCgYNGoSuXbsiNjYWfn5+SE9PR9u2bbF//3588sknWLBgAZ4+fYrevXvrnMqUJEAgIiK9PXr0SAAg9OnTp0z9k5OTBQDCiBEjtNqnTJkiABCOHDmiaXN3dxcACMeOHdO0ZWRkCEqlUpg8ebKmbevWrQIA4eeffy62vedjJCQkaLVPnDhRACAcP35c05aTkyN4enoKHh4egkqlEgRBEG7evCkAENatW1emn08QBOH+/fsCACEqKqrM69Cr8YiaiMgA2dnZAIDq1auXqf++ffsAAOHh4VrtkydPBoBip559fX3RoUMHzXcnJyf4+PggJSWlzDV6enoiKCioWB0BAQFo3769ps3W1hajRo1CamoqLl68WObxyTQY1EREBrCzswMA5OTklKn/rVu3IJfLUb9+fa12FxcX1KhRA7du3dJqd3NzKzaGg4MDHj58WOYaPT09ddbh4+NTrP35KfOX6yDxMaiJiAxgZ2eH2rVr48KFC3qtJ5PJytRPoVDobBcEoczbqog7vMn0GNRERAbq2bMnbty4gaSkpFf2dXd3h1qtxrVr17Ta09PTkZWVBXd3d723X9bQf7mOK1euFGu/fPmyZjlJC4OaiMhA06ZNw2uvvYYRI0YgPT292PIbN24gNjYWANC9e3cAQExMjFafpUuXAgB69Oih9/Zfe+01AEBWVlaZ1+nevTtOnz6t9cvF48ePERcXBw8PD/j6+updB1UsvvCEiMhA3t7e2LRpE0JCQtCwYUOtN5OdPHkSW7duxYcffggAaNasGYYNG4a4uDhkZWUhMDAQp0+fxoYNGxAcHIzOnTvrvX0/Pz8oFAosXrwYjx49glKpxNtvv41atWqVuM706dPxww8/4L333sP48eNRs2ZNbNiwATdv3sT27dshl+t//LZx40bcunULeXl5AIBjx45h/vz5AIChQ4fyKL28xL7tnIiosrt69aowcuRIwcPDQ7C0tBSqV68utGvXTli2bJnw9OlTTb9nz54Jc+fOFTw9PYVq1aoJ9erVEyIiIrT6CELRo1U9evQotp3AwEAhMDBQqy0+Pl7w8vISFAqF1qNaJY0hCIJw48YNYcCAAUKNGjUEKysrISAgQNizZ49WH30ezwoMDBQA6PzoenSM9CMTBD3uTCAiIiKT4jVqIiIiCWNQExERSRiDmoiISMIY1ERERBLGoCYiIpIwBjUREZGEMaiJiIgkjEFNREQkYQxqIiIiCWNQExERSRiDmoiISMIY1ERERBLGoCYiIpKw/wf8LbPRoHn0LgAAAABJRU5ErkJggg==",
-      "text/plain": [
-       "<Figure size 500x400 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "two_groups_unpaired.mean_diff.plot(bar_label=\"success\",contrast_label=\"difference\");"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "411d9947",
-   "metadata": {},
-   "source": [
-    "The color of the error bar can be modified by setting ``err_color``."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "ef6ba800",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 500x400 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "two_groups_unpaired.mean_diff.plot(err_color=\"purple\");"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "67dbf66e",
-   "metadata": {},
-   "source": [
-    "## Generating results"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "37e94f00",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>control</th>\n",
-       "      <th>test</th>\n",
-       "      <th>control_N</th>\n",
-       "      <th>test_N</th>\n",
-       "      <th>effect_size</th>\n",
-       "      <th>is_paired</th>\n",
-       "      <th>difference</th>\n",
-       "      <th>ci</th>\n",
-       "      <th>bca_low</th>\n",
-       "      <th>bca_high</th>\n",
-       "      <th>...</th>\n",
-       "      <th>pct_high</th>\n",
-       "      <th>pct_interval_idx</th>\n",
-       "      <th>bootstraps</th>\n",
-       "      <th>resamples</th>\n",
-       "      <th>random_seed</th>\n",
-       "      <th>permutations</th>\n",
-       "      <th>pvalue_permutation</th>\n",
-       "      <th>permutation_count</th>\n",
-       "      <th>permutations_var</th>\n",
-       "      <th>proportional_difference</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Control 1</td>\n",
-       "      <td>Test 1</td>\n",
-       "      <td>40</td>\n",
-       "      <td>40</td>\n",
-       "      <td>Cohen's h</td>\n",
-       "      <td>None</td>\n",
-       "      <td>1.242163</td>\n",
-       "      <td>95</td>\n",
-       "      <td>0.769088</td>\n",
-       "      <td>1.659486</td>\n",
-       "      <td>...</td>\n",
-       "      <td>1.72357</td>\n",
-       "      <td>(125, 4875)</td>\n",
-       "      <td>[1.4827506328621212, 1.0122770907407532, 1.491...</td>\n",
-       "      <td>5000</td>\n",
-       "      <td>12345</td>\n",
-       "      <td>[-0.25268025514207904, 0.050400851615126196, -...</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>5000</td>\n",
-       "      <td>[0.012419871794871796, 0.012612179487179487, 0...</td>\n",
-       "      <td>1.242163</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>1 rows × 22 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "     control    test  control_N  test_N effect_size is_paired  difference  ci  \\\n",
-       "0  Control 1  Test 1         40      40   Cohen's h      None    1.242163  95   \n",
-       "\n",
-       "    bca_low  bca_high  ... pct_high  pct_interval_idx  \\\n",
-       "0  0.769088  1.659486  ...  1.72357       (125, 4875)   \n",
-       "\n",
-       "                                          bootstraps resamples random_seed  \\\n",
-       "0  [1.4827506328621212, 1.0122770907407532, 1.491...      5000       12345   \n",
-       "\n",
-       "                                        permutations  pvalue_permutation  \\\n",
-       "0  [-0.25268025514207904, 0.050400851615126196, -...                 0.0   \n",
-       "\n",
-       "  permutation_count                                   permutations_var  \\\n",
-       "0              5000  [0.012419871794871796, 0.012612179487179487, 0...   \n",
-       "\n",
-       "   proportional_difference  \n",
-       "0                 1.242163  \n",
-       "\n",
-       "[1 rows x 22 columns]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "two_groups_unpaired.cohens_h.results"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "845b7224",
-   "metadata": {},
-   "source": [
-    "## Producing estimation plots"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "1d0e1042",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 500x400 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "two_groups_unpaired.mean_diff.plot();"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "c012a174",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 500x400 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "two_groups_unpaired.cohens_h.plot();"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "5f33004b",
-   "metadata": {},
-   "source": [
-    "The white part in the bar represents the proportion of observations in the dataset that do not belong to the category, which is \n",
-    "equivalent to the proportion of 0 in the data. The colored part, on the other hand, represents the proportion of observations \n",
-    "that belong to the category, which is equivalent to the proportion of 1 in the data. By default, the value of \"group_summaries\"\n",
-    "is set to \"mean_sd\". This means that the error bars in the plot display the mean and ± standard deviation of each group as \n",
-    "gapped lines. The gap represents the mean, while the vertical ends represent the standard deviation. Alternatively, if the \n",
-    "value of \"group_summaries\" is set to \"median_quartiles\", the median and 25th and 75th percentiles of each group are plotted instead. \n",
-    "By default, the bootstrap effect sizes is plotted on the right axis."
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "f3865a7a",
-   "metadata": {},
-   "source": [
-    "Instead of a Gardner-Altman plot, you can generate a **Cumming estimation\n",
-    "plot** by setting ``float_contrast=False`` in the ``plot()`` method.\n",
-    "This will plot the bootstrap effect sizes below the raw data."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "1e8639a1",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 300x600 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "two_groups_unpaired.mean_diff.plot(float_contrast=False);"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "3e649272",
-   "metadata": {},
-   "source": [
-    "## Generating Sankey plots for paired proportions and repeated-measures proportions"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "e6c37cd5",
-   "metadata": {},
-   "source": [
-    "For the paired version of the proportion plot, we adopt the style of a Sankey Diagram. The width of each bar in each xtick represents the proportion of the corresponding label in the group, and the strip denotes the paired relationship for each observation.\n",
-    "\n",
-    "Starting from v2024.3.29, the paired version of the proportion plot receives a major upgrade. We introduce the ``sankey`` and ``flow`` parameters to control the plot. By default, both ``sankey`` and ``flow`` are set to True to cater the needs of repeated measures. When ``sankey`` is set to False, DABEST will generate a bar plot with a similar aesthetic to the paired proportion plot. When ``flow`` is set to False, each group of comparsion forms a Sankey diagram that does not connect to other groups of comparison.\n",
-    "\n",
-    "Similar to the unpaired version, the ``.plot()`` method is used to produce a **Gardner-Altman estimation plot**, the only difference is that\n",
-    "the ``is_paired`` parameter is set to either ``baseline`` or  ``sequential`` when loading data.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "fafe0150",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 500x400 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "two_groups_baseline = dabest.load(df, idx=(\"Control 1\", \"Test 1\"), \n",
-    "                                  proportional=True, paired=\"baseline\", id_col=\"ID\")\n",
-    "    \n",
-    "two_groups_baseline.mean_diff.plot();"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "6984eaf5",
-   "metadata": {},
-   "source": [
-    "The Sankey plots for paired proportions also supports the ``float_contrast`` parameter, which can be set to ``False`` to produce a **Cumming estimation plot**.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "c0c9d25e",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 300x600 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "two_groups_baseline.mean_diff.plot(float_contrast=False);"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "c9cea250",
-   "metadata": {},
-   "source": [
-    "The upper part (grey section) of the bar represents the proportion of observations in the dataset that do not belong to the category, equivalent to the proportion of 0 in the data. The lower part, conversely, represents the proportion of observations that belong to the category, synonymous with **success**, equivalent to the proportion of 1 in the data. \n",
-    "\n",
-    "Repeated measures are also supported in the Sankey plots for paired proportions. By adjusting the ``is_paired`` parameter, two types of plot can be generated.\n",
-    "\n",
-    "By default, the raw data plot (upper part) in both ``baseline`` and ``sequential`` repeated measures remains the same; the only difference is the lower part. For detailed information about repeated measures, please refer to [repeated measures](02-repeated_measures.ipynb) ."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "e949b002",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1050x600 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "multi_group_baseline = dabest.load(df, idx=(((\"Control 1\", \"Test 1\",\"Test 2\", \"Test 3\"),\n",
-    "                                (\"Test 4\", \"Test 5\", \"Test 6\"))),\n",
-    "                    proportional=True, paired=\"baseline\", id_col=\"ID\")\n",
-    "\n",
-    "multi_group_baseline.mean_diff.plot();"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "611f1567",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1050x600 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "multi_group_sequential = dabest.load(df, idx=(((\"Control 1\", \"Test 1\",\"Test 2\", \"Test 3\"),\n",
-    "                                (\"Test 4\", \"Test 5\", \"Test 6\"))),\n",
-    "                    proportional=True, paired=\"sequential\", id_col=\"ID\")\n",
-    "\n",
-    "multi_group_sequential.mean_diff.plot();"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "539a0a22",
-   "metadata": {},
-   "source": [
-    "If you want to specify the order of the groups, you can use the ``idx`` parameter in the ``.load()`` method.\n",
-    "\n",
-    "To compare all groups together, you can include all the groups in the ``idx`` parameter of the ``load()`` method without using subbrackets.\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "e8b6e61c",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1050x600 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "multi_group_baseline_specify = dabest.load(df, idx=((\"Control 1\", \"Test 1\",\"Test 2\", \"Test 3\",\n",
-    "                                \"Test 4\", \"Test 5\", \"Test 6\")),\n",
-    "                    proportional=True, paired=\"baseline\", id_col=\"ID\")\n",
-    "\n",
-    "multi_group_baseline_specify.mean_diff.plot();"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "f7600b4d",
-   "metadata": {},
-   "source": [
-    "By changing the ``sankey`` and ``flow`` parameters, you can generate different types of Sankey plots for paired proportions."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "5675c0d8",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1050x600 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1050x600 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1050x600 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "separate_control = dabest.load(df, idx=(((\"Control 1\", \"Test 1\"),\n",
-    "                                (\"Test 2\", \"Test 3\"),\n",
-    "                                (\"Test 4\", \"Test 7\", \"Test 6\"))),\n",
-    "                    proportional=True, paired=\"sequential\", id_col=\"ID\")\n",
-    "\n",
-    "separate_control.mean_diff.plot();\n",
-    "separate_control.mean_diff.plot(sankey_kwargs={'sankey':False});\n",
-    "separate_control.mean_diff.plot(sankey_kwargs={'flow':False});"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "e686109e",
-   "metadata": {},
-   "source": [
-    "Several exclusive parameters can be provided to the ``plot()`` method to customize the Sankey plots for paired proportions.\n",
-    "By modifying the sankey_kwargs parameter, you can customize the Sankey plot. The following parameters are supported:\n",
-    "\n",
-    "- **width**: The width of each Sankey bar. Default is 0.5.\n",
-    "- **align**: The alignment of each Sankey bar. Default is \"center\".\n",
-    "- **alpha**: The transparency of each Sankey bar. Default is 0.4.\n",
-    "- **bar_width**: The width of each bar on the side in the plot. Default is 0.1.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "c7b25c1d",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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traVDee25uLjg/PnzWLx4MXbu3Am5XI6TJ0/C19cXs2bNwueffw57e3uDytDrE+7EiRPo27evenvLli24evUqfvzxRzRo0AAff/wxQkNDsXfvXp3u26NHD8THx2PmzJmIjY2Fn58fDh06pO5g9uDBA40a9PTp0yEIAqZPn45Hjx7Bw8MDnTt3xrx58/T5sagEUalUSEtLQ1paGuRyORwdHTWaNKyBmJOD3ATO0EfGp1Kp8OjRI/j4+Fg6FAJgb2+P6dOnY/r06Sa5v16JOjY2VuMNsnfvXvj7+6NXr14AgKFDh+Krr77SK6AxY8ZgzJgxWo9FRkZqbNvY2GDWrFmYNWuWXmVR6ZBXy7azs4OTkxPkcrmlQwIA5CTEl6hH9FSyPH78mInaCuTm5iIjIwPOzs5aj6ekpBj85E+vNmpHR0ckJSWpg4yMjNQYnlWmTBkkJyfrHRSRPhQKBRITExEfH2/x6RZzk5OgyuaUj2Q6+TvekmWMHTu2yOFZLVq0wMSJEw0qQ69E3ahRI6xduxa///475s2bh9TUVHTu3Fl9/O7duwXGQxOZS05ODhITE/Hs2TMoFAqzly/m5kKZ9Nzs5dLr5dmzZwbPIU2GO3ToELp161bo8W7duuHgwYMGlaFXXXzevHkICgqCv78/RFFEt27dEBAQoD6+Z88etGjRwqDAiAyVN8zLwcEBZcqUMVsHw9zniXzkTSanUqmQmJgIDw8PS4fyWnv8+HGR07pWqFABjx49MqgMvWrU/v7+uHnzJnbv3o0TJ07gp59+Uh9LSkrCqFGjMGnSJIMCIzKWjIwMxMfHIz093eQJVJWVBWU6p6Ul89A2ZaWl6boCYlJSEkaPHo3y5ctDJpOhRo0aBtdAzalcuXKIiooq9PiNGzcKbb8uLr1btz08PDQW4sjj6uqKcePGGRQUkbGpVCokJycjIyMDrq6uJhvWkptofR+cVHpZW6LWdQVEhUKB9u3bw9PTEzt37kTFihURExOj81oRlvTuu+9izZo16NOnT4EhyZcvX0Z4eDi6d+9uUBl6J2qlUokdO3bgxIkTePr0KebMmYN69eohOTkZx44dQ4sWLdhOTVYnJycHCQkJcHR0RJkyZYw6PakyIwMqhXFXwiIqyvPn1tUXQtcVENevX4/ExEScPXtW/eW5pPVkDwsLw6FDhxAQEIAPPvgAb731FgDg6tWr+OWXX+Dp6YmwsDCDytDr0XdSUpJ6ecmtW7di3759iI+PB/BiSs6xY8di2bJlBgVGZCqiKCItLQ1Pnz5FZmam0e7LDmRkbuZI1HlTy+a9sgtZljVvBcR27dqp971qBcR9+/ahWbNmGD16NLy8vFC3bl3Mnz+/RHWSq1ChAi5evIjevXvj2LFjmDt3LubOnYvjx4+jT58++O233/DGG28YVIZeiXrKlCm4du0aIiIicO/ePY12P6lUapRebkSmplQq8fz5cyQkJBRYHETne6WnszZNBTx48AAZGRkA/h0+aEyZmZkmH9kQGBgIFxcX9StvZsj8iloBMTY2Vus19+7dw86dO6FUKnHw4EHMmDEDixcvxty5c43+c5hS+fLlsXHjRjx//hyxsbGIjY3F8+fPsWHDBlSoUMHg++uVqPfu3YvPPvsM7du31/rosEaNGoiOjjY0NiKzUCgUiI+Px/Pnz5Gbm6vXPZScJpRecuHCBXTu3Bk+Pj7qWm9GRgamTZuGlStXGvXz0dQLqZw8eRLJycnq19SpU412b5VKBU9PT4SHh6Nx48bo0aMHQkJCsHr1aqOVYU6CIMDT0xOenp5GbVbTq406OTm5yJVbcnJy9P7AI7KUzMxMZGVlwd7eHk5OTsWeSUiVlVUqatN5K8lxRTnD7N69Gz169IAoigVGGYiiiKtXr+Lq1asYOnQoGjVqZHB5aWlpcHd3N/g+hXFycipWr2V9VkAsX748bG1tNYZO1q5dG7GxsVAoFLCzszMseDN5/vw5tm7dinv37uH58+cF/u6CIGDdunV631+vRO3r64vLly8Xevzw4cOoU6eO3kERWYooisjIyEBGRgbs7e3h6Oj4yg8LZUqSeYLTgy7D0YYNG6bXdfSvCxcuoEePHlAqlYX+DlUqFQBg7dq1mDx5ssGdp9LT0w263lheXgGxS5cuAP5dAbGwaaFbtGiBLVu2QKVSqddxuHXrFsqXL19iknRERAS6deuG9PR0ODs7o2zZsgXOMbR2rVeiHjJkCCZPnow2bdrgnXfeUQeSnZ2NOXPm4NChQwgPDzcoMCJLy8zMRGZmJuzs7ODo6Ai5XF7gH5yYkwPlP22QuhEgdXKCxN4eEplx5yaXOThBEASzJltBEErMB6spzZ07V2tNujAHDx7EqFGjDCrTErPvFUbXFRBHjhyJFStWYNy4cfjss89w+/ZtzJ8/H2PHjrXkj6GTiRMnwtvbG7t370a9evVMUoZeiXrcuHG4du0aevXqpR7v1rt3bzx79gy5ubkYPnw4Bg8ebMw4iSxGoVBAoVCol9a0t7dXDyXJTdF9TnupoxNsyrpBMNHynO6enhg9erRZP8Dt7OxQrlw5s5VnjR48eID9+/cXO0mrVCr8+eefSExMhJubm97lWlOi1nUFxEqVKiEiIgITJkxA/fr1UbFiRYwbNw6TJ0+21I+gszt37uCrr74yWZIG9EzUgiBg7dq1GDBgAHbu3Inbt29DpVLB19cXn3zyCVq3bm3sOIks7uWlNW1sbCCXySBJSoKNtHh9MgVBApty7pCaoQ34dU2aSqVS/WjZ3CIiInR+iiGKIq5fv45mzZrpXW5OTo7Boxa00befkS4rIAJAs2bNcP78eb3KsgZvvvkmUlNTTVqGQV/pW7ZsiZYtWxorFqISIzc3FympKchNeg6pRAI7W1vIbG1hZ2urUWPIIwgS2Hp7G/0xN2kKCwtDaGiopcPQyebNm7F582ZLh0F6mjt3LkaPHo3evXubbLIWvRL1/fv3cfXqVY0Vs172yy+/oF69eiVuhhkiXSjTXnTiUapUyMzORuY/E0FIJRLY2tjA1sYGNlIpbG3tIC9fHhKZzJLhvhZmzJiBkJAQi5S9YcMGjQ55xdWvXz+DatSNGjUySu/x/H7//Xc0adLE6PctbY4dOwYPDw/Url0b7du3R6VKlQosACQIgkGTgOmVqCdNmoSUlJRCE/XKlSvh6uqKbdu26R0YkTUTc3Ig5mpvG1SqVFAqFMj6p+3Qpmw5SBMTIZVK1S9BECCRSCAIglHHWwIvZoOSJidBNON62IJMDjvv8mYrrzB5v19LCAoK0rkTnyAIqFOnjkExOzs7m2Tu+uIOT3zdrVixQv3/+/fv13qORRL1uXPnMH78+EKPv/POO1i6dKmeIRFZP2VG8YbESJ2cIZHLIYoicnNzzTK/gJj0HJlfmX9mp8oLllhFsraUypUro1OnTjh48GCxpsCUSCSoV6+eQR3JAKBMmTIGXU+GMUefCL1mJnv+/HmRbw4nJyerW9WFyFhEUYRYjDnCBTsZpBb4EBUt1AvYnDV4azVjxgydnpJ07NjR4DJL0kpTpB+9EnXlypXxv//9r9Djp0+fNngSciJrJWZnQxSL/hYtQAIb14ITH5idIBT79dEvh9Hyp5/x0S+HdbqO/vWf//wH27dvL/IRvEQigUQiwbBhwwzux5M3KQ9Z3vnz57FgwQJMmDABt2/fBvBi2tjLly8jLc2wNer1StS9evXC1q1b8c0332hU+5VKJZYtW4bt27ejd+/eBgVGZK1Uma+e4ETq6gLBQm2l+orPzERcRibijbii2Ouoa9euOHv2LDp27FigZi0IAurVq4fJkycXWLtYH15eXkbv40C6USgU6Nq1K1q0aIGQkBB88803+PvvvwG8+FLWoUMHg1eT1CtRT506FW3btsX48eNRvnx5tG7dGq1bt0aFChUwYcIEBAYGWqznJZEpiaIIVVbRj3gFWztI7B3MFBFZo//85z/Yt28foqOj1VNKOjg4YP78+Rg1apTRRsRUqlTJKPch/c2YMQP79+/HqlWrEBUVpdGZUC6Xo3v37vj5558NKkOvRC2TyXD48GGsW7cOAQEBSEhIQEJCAgICArB+/XocPXoUMg5FoVJIzMoEUHSvXhsXV7PEQtavcuXKcHB48aXNzs7O4I5j+TFRW97WrVsxcuRIDBs2TOvft3bt2rh3755BZejd/14ikWDQoEHqOVyJXgevmtdbYu8AwQRDZYjy8/Ly4kpnVuDp06dFTh8qlUrVa5LrS68adWJiIv78889Cj//111/qNViJSgtRpSqyR7UAAdIyr14OkMgYfH19LR0C4cVTjZs3bxZ6/H//+x+qV69uUBl6JeoJEyYUOQPP8OHDMWnSJL2DIrJGr3rsLXF0LHEdyKhkkkgkBn/4k3H07t0ba9aswblz59T78jr4rV27Fj/99BP69+9vUBl6Pfo+fvw4Ro4cWejxzp07Y/Xq1XoHRWSNlEX09hYgQOLEiSfIPCpXrgy5nPPGW4OQkBCcP38erVu3Ru3atSEIAiZMmIDExEQ8fPgQHTt2xIQJEwwqQ68adXx8PNzd3Qs9Xq5cOTx9+lTvoIiszYvH3oWvUCRxdISgZTEOIlOoWbOmpUOgf9jZ2eHQoUP4/vvvUa1aNdSqVQvZ2dmoX78+NmzYgF9++cXgaW31qlGXL18ev//+e6HHL126BA8PD72DIrI2L2bdKuyxtwCJIzv1kHk4ODiwt7eVyMzMREhICNq2bYu+ffuib9++JilHrypAly5dsG7dOuzbt6/AsZ9//hnff/89PvroI4ODI7IWqqzCJwGRyO3ZNk1mU6NGDa1LqZL52dvbY82aNYiLizNpOXrVqGfPno2jR4/io48+QoMGDVC3bl0AwNWrV/HHH3+gdu3aJW5NWKLCiKIIMSu70ONSDpEhMxEEAbVr17Z0GPSSxo0b4+rVqyYtQ6+vZS4uLjh//jymT5+OnJwc7Ny5Ezt37kROTg5mzJiBX3/9lRPFU6khZmVBLOSxt2An47hpMptKlSpxtSwrs3TpUmzbtg3fffedyVbH03vCE0dHR4SGhrLmTKVeUVOGSh24IAKZT1ETa5BlDBw4EBKJBMOHD8fYsWNRsWJF2Nvba5wjCAL++OMPvcvgyuBEr1DY8o2CRAqBQ2TITNzd3VGhQgVLh0H5uLm5oVy5cibtia9Xov70009feY4gCFi3bp0+tyeyGmKOotAlLSUOjqVq5SKPf2oBHvlqA2Qd/P39S9X7rbSIjIw0eRl6T3iS/w2jVCrx5MkTKJVKeHh4cI1UKhUKf+wtQOJQulbI2vNBkKVDoEJ4eXlxSNZrTK/OZNHR0bh//77G68GDB8jIyMA333yDMmXK4NixY8aOlcjsVIX09pbIZRySRWbTvHlz1qatWEpKChYuXIigoCA0bNgQFy5cAPBiXYwlS5bgzp07Bt3fqIPxbG1tMWbMGHTo0AFjxowx5q2JzE5UKiHmal+EQyIvXbVpsl41a9bkBFJW7OHDh2jYsCFmzpyJhw8f4s8//0RaWhqAF+3Xa9aswfLlyw0qwySj5hs0aIBTp06Z4tZEZiMqtNemBUHCTmRkFjKZDAEBAZYOg4rw+eefIzU1FVeuXMHJkychippDObt06YKjR48aVIZJEvWRI0fUi6UTlVSFtU8L9vZ8DElm0aRJkwJDfci6HD58GGPHjkWdOnW0fi5Uq1YNf//9t0Fl6NWZbM6cOVr3JyUl4dSpU7h8+TKmTJliUGBEliZma69Rc+w0mYOXlxcX3ygBMjMzi2yaSE1NNbgMvacQ1aZs2bLw9fXF6tWrMXToUEPiIrKowoZlCTY2nImMTE4QBLRs2ZJPbkqAOnXq4NSpUxg+fLjW43v37kXDhg0NKkOvRK1SaR9XSlRaFN7bm48hyfRq166NcuXKWToMKobx48djwIABqF+/Prp37w7gRY68c+cOQkNDce7cOezatcugMjgzGZEWKoX29mkmajI1mUwGf39/S4dBxdS3b1/ExMRg+vTpCAkJAQC8++67EEUREokE8+fPR5cuXQwqQ6/OZA8ePMCZM2c09v3xxx/o378/evTogb179+od0MqVK+Hj4wO5XI4mTZqox6MVJikpCaNHj0b58uUhk8lQo0YNHDx4UO/yiURRhKjIKbBfsLHlY28yOT8/P8g5qqBECQkJwd27d/HVV19h5MiRGDp0KBYtWoSoqChMnjzZ4PvrVaMeO3Ys0tLS1F3O4+Li0LZtWygUCpQpUwY7d+7Ejh070LVrV53uu337dgQHB2P16tVo0qQJli5diqCgIERFRcHT07PA+QqFAu3bt4enpyd27tyJihUrIiYmhit3kUFedCIruFoWa9Nkag4ODnjrrbcsHQYVoVGjRpg/fz7effddAMCmTZvQunVr+Pj4YMKECSYpU68a9YULF9C+fXv19qZNm5CZmYk//vgDjx49wjvvvIOvv/5a5/suWbIEQ4cOxaBBg1CnTh2sXr0aDg4OWL9+vdbz169fj8TEROzduxctWrSAj48PAgMD0aBBA31+LCIAhY+flnCYDJlYgwYNYGPDFklr9ueffyIhIUG9PWjQIJw9e9akZeqVqBMTEzVquPv370dgYCB8fX0hkUjQtWtX3Lx5U6d7KhQKXLp0Ce3atfs3OIkE7dq1w7lz57Res2/fPjRr1gyjR4+Gl5cX6tati/nz50OpVBZaTnZ2NlJSUtSvvBlkiPKotAzLEmxsIfADlExILpejdu3alg6DXqFKlSo4evSoOs+Iomjy3vl6JWoPDw/ExMQAeNFGfP78eQQF/Tuhf25urs4LaCckJECpVMLLy0tjv5eXF2JjY7Vec+/ePezcuRNKpRIHDx7EjBkzsHjxYsydO7fQchYsWAAXFxf1KzAwUKc4qXQTVSqIuQXbpyVsMyQTe+utt1ibLgFGjBiBTZs2QS6Xw9nZGYIgYPDgwXB2di705eLiYlCZer0r2rVrh2+++QbOzs6IjIyESqXS6NV2/fp1s6z0olKp4OnpifDwcEilUjRu3BiPHj3CV199hVmzZmm9ZurUqQgODlZvX7lyhcma1Aqb5ITt02RKEokEderUsXQYVAyff/45GjRogBMnTiAuLg4bNmzAf/7zH1SrVs1kZeqVqBcuXIhbt25h0qRJsLOzw9dff42qVasCePFo+aeffkLv3r11uqe7uzukUini4uI09sfFxcHb21vrNeXLl4etrS2kL61iVLt2bcTGxkKhUMDOzq7ANTKZDDKZTL3t5OSkU5xUuqm0tE8LEil7e5NJVatWjVOFliAdOnRAhw4dAAAbNmzA8OHDdc55utDr0beXlxf+97//4fnz50hJScG4cePUx1QqFY4dO1bo7GWFsbOzQ+PGjTWWx8y7V7NmzbRe06JFC9y5c0djApZbt26hfPnyWpM00ato60jGBTjI1Ng2XXK4ublh586d6u1Zs2ahfv36Ji3ToEU5XFxcCiREe3t7NGjQAG5ubjrfLzg4GGvXrsXGjRtx48YNjBw5Eunp6Rg0aBAAoH///pg6dar6/JEjRyIxMRHjxo3DrVu3cODAAcyfPx+jR4825Mei19SLZS0L9q2Q8rE3mZCLi0uhTw3J+qSlpSEjI0O9PWfOHPz5558mLdOqei706NED8fHxmDlzJmJjY+Hn54dDhw6pO5g9ePAAEsm/3y0qVaqEiIgITJgwAfXr10fFihUxbtw4owwwp9ePqCi49rQACcCnM2RCNWrU4JzeJYivry927tyJVq1awdnZGaIoIj09HYmJiUVep0/lNY9VJWoAGDNmDMaMGaP1WGRkZIF9zZo1w/nz500cFb0OtLZPy2X8ECWTEQQBb775pqXDKPWqVq2q879jQRBw9+7dAvunTZuGQYMG4cCBA+rzRowYgREjRhR5v6KGDb+K1SVqIkvRVqPmsCwypfLly7NDqxkEBgYWSNQXL17EtWvXUKdOHfVyolFRUbh+/Trq1q2Lxo0ba71Xv379EBAQgMjISMTFxWH27Nn46KOPTNpOzURNhH/m99Yyflqwk2k5m8g4fH19LR3Ca2HDhg0a23v37sXevXtx5MgRvPPOOxrHjhw5gk8++QRhYWGF3q9mzZrq5P79999jwIAB+OCDD4wed55idSbL38ttzpw5uHr1qsmCIjI3beOnBRs7CC8N/SMyJolEYtKxt5ai68JKebZt2wZBEAxeaao4Zs6cic8++6xAkgaA9u3bY8yYMZg+fXqx7nX//n2TJmmgmDXq/L3cZs+ejerVq6Nu3bomC4zInMQcbY+9WZsm06lYsaLGnA6lga4LK+WJjo7GpEmT0KpVK7PEefv27SLX+y5XrpzW9mkAOHXqFACgdevWGtuvkne+PoqVqPP3cgNg8l5uROakyipYo2b7NJmSj4+PpUMwupcXVgKA1atX48CBA1i/fj2mTJmi9RqlUok+ffogNDQUp0+fRlJSksnj9PX1xffff4/BgwcX6COQmpqK9evXF/q0o02bNhAEAZmZmbCzs1NvFyZvLnCTdyazRC83InPR1j4tQALYcDYyMp0qVapYOgSjyltY6eW5Ll61sBLwoinV09MTgwcPxunTp80RKubOnYtu3bqhVq1aGDhwIKpXrw7gRU1748aNiIuLw44dO7Ree+LECQBQzyGSt21KxUrUlujlRmQ2CgXyrz8tyO04LItMxsPDAw4ODpYOo1jS0tKQkpKi3s4/DXOeohZWKmw1xTNnzmDdunW4cuWKUWN+lS5duuDgwYOYPHky5s+fr3HMz88P69at01ho6mX514Ywx1oRxe71be5ebkTmotI2LMuOj73JdN544w1Lh1Bs+RPRrFmzdJ4iWpvU1FT069cPa9euhbu7u8H301XefN2xsbHq1SCrVKlilbPE6TU86/79+8aOg8hiVFo6kgmlrJMPWZeKFStaOoRiO3nyJPz8/NTbhXWA03Vhpbt37yI6OhqdO3dW78tbt8HGxgZRUVFmGb7m7e2tU3L+9NNPdS5DEASsW7dO5+vy6D2OWqlU4ocffsCBAwc0vo106tQJffr00VjRisiq5atRC1IbCFwXmExEKpUW2QPa2jg5Oak7ERfl5YWV8oZY5S2spG22yVq1auGvv/7S2Dd9+nSkpqZi2bJlJl8q+cGDB5g/fz5OnDiB+Ph47N27F61bt0ZCQgLmzJmDQYMGoWHDhgWuO378eIFmsYyMDMTHxwMAypYtCwB4/vw5gBfNHI6OjgbFqtenUXJyMoKCgvDbb7+hTJky6t5xR44cwa5du7Bq1SpEREQU649LZEliTg5EUaWxj7VpMiV3d3fYlNIvgsHBwRgwYAD8/f0REBCApUuXFlhYqWLFiliwYAHkcnmBIb6urq4AYPKhv9evX0erVq2gUqnQpEkT3LlzB7n/LMjj7u6OM2fOID09XWstODo6usC9OnTogGnTpmH8+PHqx/gJCQn4v//7P2zatEndEVtfer1bQkJCcOnSJSxfvhxDhw6F7T9r9ebk5OC7777D2LFjERISguXLlxsUHJGpaR0/zdnIyIQ8PDwsHYLJ6LqwkqV88cUXcHV1xfnz5yEIQoEnHO+//z62b99erHt99tlneO+99zB37lyN/e7u7pg3bx6ePn2Kzz77DEePHtU7Xr0S9Z49ezBq1CiMGjVKY7+trS1GjhyJGzduYOfOnUzUZPW0dSRjjZpMqTQnakD3hZVeln+qT1M5deoUZs6cCQ8PDzx79qzA8cqVK+PRo0fFutf58+fRrVu3Qo83bNgQW7du1TtWQM/1qJ89e6buAa5NrVq1XjkZCpE1yF+jFmxsIVjBN34qvYqaEYvMQ6VSFTk8Lj4+vtizxrm5ueG///1voccPHjyofqSvL70+kapXr459+/YVenzfvn2cbJ6s3ouJTnI19glce5pMSCKRwMXFxdJhvPYaNWpUaLtxbm4utm3bhqZNmxbrXsOHD8f+/fvx4Ycf4ujRo4iOjkZ0dDSOHDmCDz74AP/9739fOTnYq+j16HvUqFEYM2YMOnbsiPHjx6NGjRoAXiwR9s033+DIkSNYsWKFQYERmZyW9afZPk2m5OLiwhExVmDq1Kno1KkTRo4ciZ49ewJ4MYzs6NGjmD9/Pm7cuFHsHDZ9+nRkZ2fjq6++wv79+zWO2djYYMqUKcVe4KMweifqp0+fYuHChYiIiNA4Zmtri5kzZ2LkyJEGBUZkaiqFlmUtOb83mRBr09bhvffew4YNGzBu3DiEh4cDAPr27QtRFOHs7IxNmzbptIhGWFgYxo0bh6NHj2oMV27Xrp1RJnPRe4zA7NmzMWbMGJMFRmRq+Sc6EWw4bSiZFoesWo9+/fqha9euOHLkCG7fvg2VSgVfX18EBQWhTJkyOt/P3d1dXTs3NoMG85kyMCKTy9fjWyJj+zSZlj4JgIwrIyMDlSpVwpQpU/D555+bZf1rQ7F7K72WxNzcghOd2DJRk2kxUVueg4MDbGxsDJ4tzJyYqOm1JOZoaZ/m+GkysZKUHEqzjz/+GDt37oQoiq8+2QqUznnsiF5Bla/HN8dPkzmUlKUtS7uePXti1KhRaNu2LYYOHQofHx/Y29sXOK9Ro0YWiK4gJmp6LeWvUXP8NJmDnKMKrEKbNm3U/3/69OkCx0VRhCAIUCqVZoyqcEzU9NoRRRHIl6g5fppMTRAEjiqwEt9//72lQ9AJEzW9fnJzIUKzbYo1ajI1JmnrMWDAAKPdSxRFhIeHY926dbh37556ecuXCYKgXp1LH3olanMERmQqBeb3ltpA4GxRZGJM1KXTF198gSVLlsDPzw99+/ZVr0dtTHolanMERmQqqvzt0xyWRVSqffrppxAEAeHh4ZBKpfj0009feY0gCFrXo85v48aN+Pjjj/HTTz8ZI1St9ErU5giMyFRERf72aSZqMj3WqC3n+PHjkEgkUKlUkEqlOH78+Cv/HsX9e2VmZqJdu3bGCLNQeiVqcwRGZAovVsxij28yPyZqy4mOji5y2xDvvPMOfvvtNwwbNsxo98xPr4GjeYERlTi5ucBLHckESAAb9qkkIv18++23OH/+PObPn49nz56ZpAy9ErU5AiMyBTHf/N6ws2VNh8yC77PSqWbNmrh37x5mzJgBT09PODo6wtnZWeNl6KppelUlatasCZVKhRkzZmDGjBmQy+UF1lgVBAHJyckGBUdkbPlXzGL7NNHr6b///S+WLFmCy5cvIzk5Wet0osWZ8OTjjz82+ZcwvRK1OQIjMoX8M5IxUZO58DPTeuzatQuffPIJ3nrrLfTs2ROrVq1C7969IYoifv75Z7z55pvFXlVrw4YNJo0V0DNRmyMwImPT1pEMnJGMzISJ2nosWLAAAQEBOHPmDJ4/f45Vq1bh008/xdtvv43o6Gg0bdoUVatWtXSYalyFgF4f+dqnBRu2TxO9jq5fv46ePXtCKpXC5p/OpDn/PG3z8fHBqFGjsGjRomLfLyUlBaGhoQgICICXlxe8vLwQEBCAOXPmICUlxeB49U7Upg6MyNhU+RO1ra2FIqHXibe3N9zc3ODl5WXpUOgfDg4OsPun2cvV1RUymQxPnjxRH/fy8sL9+/eLda/Hjx+jYcOGCA0NRVpaGlq0aIEWLVogPT0ds2fPRqNGjTTurQ+9ErU5AiMyNjGXHcnI/C5evIjw8HCcPXvW0qHQP2rWrInr16+rt/38/LB582bk5uYiKysLW7ZsQeXKlYt1r8mTJyM2Nhb79+/H9evXsXv3buzevRvXrl3DgQMHEBsbiylTphgUr16J2hyBERlb/qFZnDqUzEUQBC5xaUW6du2Kffv2ITv7xbr0ISEhiIyMhKurKzw8PHD69Oli57BDhw5h/Pjx6NixY4Fj7733HsaOHYuDBw8aFK9encmKE9jatWsNCozImESlEqJKpd4WIHCiEzIbmUwGiYRdgiwtKysLP//8M3JycjB9+nQkJiaifPny6NSpEyIjI7F7925IpVK8//77aNu2bbHumZ6eXmSzhre3N9LT0w2KW69PKnMERmRMBSY6sWVHMjIfe3t7S4fw2nv69CmaN2+O+/fvQxRFCIIAe3t77N27F+3atUOrVq3QqlUrne9bp04dbN26FSNGjFC3e+fJycnB1q1bUadOHYNi1+srXl5givwffkYMjMiY8g/LYvs0mZNMxmGAlhYWFobo6GhMmDAB+/fvx//93//B3t4ew4cPN+i+kydPxq+//oqAgACEh4cjMjISkZGRWLNmDQICAnDhwgWDm4L1qlFPnjwZPXr0QEBAAEaNGoUaNWoAAKKiorB69Wr8+eef2L59u0GBERmT6p+2qDxsnyZzYvu05R0+fBj9+/fH119/rd7n5eWF3r17IyoqCjVr1tTrvt27d0d6ejqmTJmCESNGqJ/UiaIIT09PrF+/Ht26dTModr0StTkCIzIWURSB/GtQs0ZNZpT/kSiZ34MHDzB58mSNfS1btoQoioiLi9M7UQPAwIED0bdvX1y8eBExMTEAgCpVqsDf3189TtsQevduGDhwIB4+fIizZ89iy5Yt2LJlC86ePYuHDx9iwIABBgW1cuVK+Pj4QC6Xo0mTJrhw4UKxrtu2bRsEQSj21G/0msjNgfjyilkSCYR8c9MTmRITteVlZ2cXeLKRt52bm2vw/W1sbNC0aVP06NEDPXr0QNOmTY2SpAE9a9T5A2vatKlRggGA7du3Izg4GKtXr0aTJk2wdOlSBAUFISoqCp6enoVeFx0djUmTJunVGYBKt/wTnYCPvcnMjPWBTYaJjo7G5cuX1dt5C0fdvn0brq6uBc5v1KhRgX2nTp0CALRu3Vpj+1XyztdHsd495gxsyZIlGDp0KAYNGgQAWL16NQ4cOID169cX2iCvVCrRp08fhIaG4vTp00hKStK5XCq98vf4ljBRk5kxUVuHvBUf8xs1apTGdl6vcG2rZ7Vp0waCICAzMxN2dnbq7cIUda/iKta7x1yBKRQKXLp0CVOnTlXvk0gkaNeuHc6dO1fodXPmzIGnpycGDx6M06dP61QmlX4FJzrh1KFkXvmXASbz+/77741ynxMnTgD4tzkjb9uUipWozRVYQkIClEplgTHaXl5euHnzptZrzpw5g3Xr1uHKlSvFKiM7O1s9Gw0ApKWl6R0vWb8XE51ofmFkoiZzY6K2PEP7TuUJDAwsctsUipWoLRFYcaSmpqJfv35Yu3Yt3N3di3XNggULEBoaauLIyFoUqE1LpOxIRmbHWclKr7fffhshISF45513tB4/ceIEwsLCcPz4cb3L0Ovd8/bbb+PYsWOFHj9x4gTefvttne/r7u4OqVSKuLg4jf1xcXHw9vYucP7du3cRHR2Nzp07w8bGBjY2Nti0aRP27dsHGxsb3L17t8A1U6dORXJysvp18uRJneOkkkOlyDd+2o61aTI/1qhLr8jIyAI562VPnz41OM/olahNFZidnR0aN26s8SVApVLh2LFjaNasWYHza9Wqhb/++gtXrlxRvz744AO0bdsWV65cQaVKlQpcI5PJ4OzsrH45OTnpHCeVHAVq1DbsSEbmxxp16VZUn607d+6gTJkyBt1f766IpgosODgYAwYMgL+/PwICArB06VKkp6ere4H3798fFStWxIIFCyCXy1G3bl2N6/O62OffT68fURS1TB3KGjWZHxN16bJx40Zs3LhRvT137lytC1ElJSXhzz//1LqAlS6KnajNFViPHj0QHx+PmTNnIjY2Fn5+fjh06JC6g9mDBw/4pqdiEfNNGwoAsOOcy2R+/MwqXTIyMhAfH6/eTk1NLfA3FgQBjo6OGDFiBGbOnGlQecVO1OYMbMyYMRgzZozWY5GRkUVeu2HDBr3LpdJFzMn/2NuGK2aRRTBRly4jR47EyJEjAQBVq1bFsmXL8MEHH5isvGInanMHRmSoAgtxsH2aLIRfEEunzMxMdOnSxeR/X52/5pkrMCJDiKIIMYdLW5J14Odl6WRvb4/w8PAiO1cbg86J2lyBERlEoQBeWogD4IpZZDlM1KVX48aNcfXqVZOWoVfDiTkCIzJEgfHTEADOt0xkcrqsfrh27Vq0atUKZcuWRdmyZdGuXbtir5ZoLZYuXYpt27bhu+++M8oqXNrolajNERiRIfK3T8PWlrUaspjX5b2Xt/rhrFmzcPnyZTRo0ABBQUF4+vSp1vMjIyPRq1cvnDhxAufOnUOlSpXQoUMHPHr0yMyR62/gwIGQSCQYPnw4nJ2d8eabb6J+/foarwYNGhhUhl5VjJcDGzt2LCpWrAh7e3uNcwRBwB9//GFQcET6YPs0WZvXJVHruvrhjz/+qLH93XffYdeuXTh27Bj69+9vlpgN5ebmhnLlyqFmzZomK0OvRG2OwIj0pshGgfZpLm1JpJe0tDSkpKSot2UyGWSygvMR6Lv64csyMjKQk5MDNzc3wwM3k1cNGTYGvRK1OQIj0pcq37ShADuSkWWV5Bp1/kWYZs2ahdmzZxc4T5/VD/ObPHkyKlSogHbt2ukdb2nE3jVU6qiy8nUk44pZZGF2JfiL4smTJ+Hn56fe1labNoaFCxdi27ZtiIyMhFwuN0kZpqJUKvHDDz/gwIEDiImJAQBUqVIFnTp1Qp8+fQxelEXvRG3qwIj0oW1+b4HThhLpzcnJCc7Ozq88T9fVD1/29ddfY+HChTh69Cjq169vULzmlpycjKCgIPz2228oU6YMqlWrBgA4cuQIdu3ahVWrViEiIqJYv8PC6NXrOzk5GS1atMCnn36Kw4cPIycnBzk5OThy5AgGDRqEli1barRpEJnLi/m984+f5kIcRKam6+qHeb788kuEhYXh0KFD8Pf3N0eoRhUSEoJLly5h+fLliI+Px+XLl3H58mU8ffoUK1aswMWLFxESEmJQGXolanMERqQPVXZWgX0S1qiJzCI4OBhr167Fxo0bcePGDYwcObLA6ocvdzZbtGgRZsyYgfXr18PHxwexsbGIjY1FWlqapX4Ene3ZswejRo3CqFGjYGv7b6XA1tZWPfX2rl27DCpDr0ffLwf2srzAbty4gZ07d2L58uUGBUekK5ETnRBZjK6rH65atQoKhQLdunXTuE9hHdas0bNnz4ocAVWrVi0kJiYaVIZen2DmCIxIV6JSCTH/BDx2nOiEyJx0Wf0wOjra9AGZWPXq1bFv374CFdc8+/btg6+vr0Fl6PXoOy+wwhgjMCJd5a9NA3zsTUSmNWrUKBw+fBgdO3bE4cOHER0djejoaEREROD999/HkSNHCv3iUlx61ahHjRqFMWPGoGPHjhg/fjxq1KgBAIiKisI333yDI0eOYMWKFQYFRqSr/MOyAM5IRkSmNWrUKDx9+hQLFy5ERESExjFbW1vMnDlTvUS0vvRO1KYOjEhXYoGOZALAGjURmdjs2bMxZswYHDlyBA8ePADwYrhyu3bt4O7ubvD99e5lY+rAiHQh5iggiiqNfYIN26eJyDzc3d3Rq1cvk9zboO6wpgyMSBdaH3vL+NibiMxj//79OHjwoLqDnI+PDzp27IhOnToZfG+DErUpAyPShSqr4PhpLsRBRKaWlJSEjz76CKdOnYJUKkX58uUBAEePHsWaNWvQqlUr7N27F66urnqXoVev76SkJLRt2xYffvghvvvuO1y7dg3Xrl3Dd999hw8//BBt2rRBUlKS3kER6UJUqSDmalmIw0RzEhMR5Rk3bhxOnz6NRYsW4fnz54iJiUFMTAyeP3+OhQsX4syZMxg3bpxBZeiVqM0RGFFxFexEBgg2NhAker29iYiKbe/evRg1ahQmTZoER0dH9X5HR0d8/vnnGDlyJPbu3WtQGXo9+n45sJflBfbgwQNs2rTJoMCIikvrY2/29iYiM7C1tX3lBGAvTy2qD72qHOYIjKg4RFGEqHX8NBM1EZnexx9/jB07dkCpVBY4lpubi59++gndu3c3qAy9atR5gY0YMaLAcpbGCoyoWBTZEKEqsJvt00RkDn379sWYMWPQvHlzDBs2DNWrVwcA3L59G+Hh4VAoFOjTpw8uX76scV2jRo2KXYZeidocgREVh1LbY28bW7ZPE5FZBAYGqv//t99+U8/dIIqi1nNEUYQgCFpr4IXRK1GbIzCi4hCzMgvsEzhtKBGZyffff2/yMvRK1OYIjOhVxBwFRFXBx95snyYicxkwYIDJy9ArUZsjMKJX0dbbGwAEudzMkRARAWlpafj7778BAJUqVYKTk5NR7mtwQ15aWhpu3LiBGzduIC0tzRgxERWLSttjbxs7zu9NRGb122+/oW3btihbtizq1q2LunXromzZsnj77bdx8eJFg++v9xSiv/32G7744gucOXMGqn8eP0okErRq1Qpffvkl/P39DQ6OqDBiTg7E3NwC+yVyPvYmIvP59ddf0aZNG9jZ2WHIkCGoXbs2AODGjRvYunUrWrdujcjISAQEBOhdhl6J2hyBERVFW20aYPs0EZlXSEgIKlasiDNnzsDb21vj2OzZs9GiRQuEhITgyJEjepehV6I2R2BERVFlannsDQFgj28iMqNff/0VM2fOLJALAcDLywvDhg1DWFiYQWXo1Ub966+/Yvjw4UUGdv78eYMCIyqMmJMDUVnwsTdkMrZPE5FZSSQS5GpphsujVCohMXBeB72uNkdgRIVRZWZo3S/hbGREZGbNmzfHypUrERMTU+DYgwcP8O2336JFixYGlaHXo++8wHr37o0qVaqYJDAibURRLDxRs32aiMxs/vz5aNWqFWrVqoWPPvoINWrUAABERUXh559/ho2NDRYsWGBQGXolanMERqSVIlvrJCeCRAqBC8EQkZk1bNgQFy5cQEhICPbt24eMjBcVCQcHB7z77ruYO3cu6tSpY1AZeiVqcwRGpI0yQ3tvb05yQkTmlp2djYiICPj4+GDPnj1QqVSIj48HAHh4eBitCVjnRG2uwIjye7GkZSHDsmRM1ERkXnZ2dujevTuWLVuG+vXrQyKRwMvLy+jl6JxV8wI7e/bsixv8E5iXlxeTNJmUmJkBEWKB/QIELmtJRGYnCALefPNNJCQkmLQcnTOruQIjyk+Zrr0TGYdlEZGlTJs2DStWrEBUVJTJytCrjXratGkIDg5G9+7dUbNmTWPHRFTAiylDFVqPcVgWEVnK+fPnUa5cOdStWxdt2rSBj48P7O3tNc4RBAHLli3Tuwy9ErU5AiN6mTK98AVf2D5NRJayYsUK9f8fO3ZM6zkWSdTmCIwojyiKELVMGQoAgo0tBBu915YhIjKISstwUWPTq/eXSqV65UupVOod1MqVK+Hj4wO5XI4mTZrgwoULhZ67du1atGrVCmXLlkXZsmXRrl27Is+nkkeVka61ExkASDgsi4hKOaurimzfvh3BwcFYvXo1mjRpgqVLlyIoKAhRUVHw9PQscH5kZCR69eqF5s2bQy6XY9GiRejQoQOuXbuGihUrWuAnIGMSRRGqItY5l8jtCz1GRGQuV69excGDBxEdHQ0A8PHxwXvvvYd69eoZfG+DErUpAluyZAmGDh2KQYMGAQBWr16NAwcOYP369ZgyZUqB83/88UeN7e+++w67du3CsWPH0L9/f73jKMnWrFmDtLQ0ODk5Yfjw4ZYOxyBiVhZElfanM4LUhrOREZFFZWdnY/jw4di8eTNEUVQPU1apVJg6dSr69OmD7777DnYGrOynV6I2VWAKhQKXLl3C1KlT1fskEgnatWuHc+fOFeseGRkZyMnJgZubW6GxZ2dnq7fTiqitlVRpaWlISUmxdBhGoSyyNs3H3kRkWZMnT8amTZswatQofPbZZ/D19YUgCLhz5w6++eYbrFq1Cm5ubli6dKneZejVRp0X2MiRI3Hjxg1kZWUhOzsbN27cwIgRI/DDDz/giy++0Pm+CQkJUCqVBWZ28fLyQmxsbLFjq1ChAtq1a6f1+IIFC+Di4qJ+BQYG6hwnmYeYnV3okCyAj72JyPJ++OEH9OvXDytWrEDNmjVhY2MDqVSKmjVrYuXKlejTpw9++OEHg8rQK1GbIzB9LFy4ENu2bcOePXsgL6S2NXXqVCQnJ6tfJ0+eNHOUVFy5qYU/FRAkEggGPEoiIjKGnJwcNG3atNDjzZs3L3JZ6OLQK1GbKjB3d3dIpVLExcVp7I+Li4O3t3eR13799ddYuHAhDh8+jPr16xd6nkwmg7Ozs/rl5OSkc5xkemJ2FsScwmvTAmvTRGQFgoKCEBERUejxQ4cOoUOHDgaVoVeiNlVgdnZ2aNy4scbYbJVKhWPHjqFZs2aFXvfll18iLCwMhw4dgr+/v87lkvXJTUkt8rjUwcFMkRARFS4sLAz3799H165dcezYMcTExCAmJgZHjx7FRx99hJiYGISFhSExMVHjpQu9OpOFhYXhk08+QdeuXTF69GhUr14dAHD79m2sXLkSMTEx2L59e4FgCuvg9bLg4GAMGDAA/v7+CAgIwNKlS5Genq7uBd6/f39UrFhRvd71okWLMHPmTGzZsgU+Pj7qtmwnJyfWlksoVWZmkW3TL9ae5mNvIrK82rVrAwD++usv/PzzzxrHRPHF/A/aln3WZa4RvRK1KQPr0aMH4uPjMXPmTMTGxsLPzw+HDh1SdzB78OCBxipdq1atgkKhQLdu3TTuM2vWLMyePVunn4ssTxRFKFOSizxHwto0EVmJmTNnmnxRIL0StakDGzNmDMaMGaP1WGRkpMZ23hhuKh1UaWmFjpvOw97eRGQtzFEh1CtRs6ZKpiDm5kKVVnTbtGBjx0lOiOi1oldnMiJjE0URuc+fFzqndx6JA2vTRPR6sbq5vslweZ3oSlJnOlVaWpEdyF4QILFn+zQRvV5Yoy4hRFEs9mvYsGEIDg7GsGHDdLouryOg2X82hQLKVzzyBgCJXAZBwrcskTXTZfVDANixYwdq1aoFuVyOevXq4eDBg2aKtORgjdqK2dnZQRAEiyRQDw+PYg2nK478XwaUSqV6KdSc7GxkJz4DXvHIGwAk9o5GiYeITEPX1Q/Pnj2LXr16YcGCBejUqRO2bNmCLl264PLly6hbt64FfgLrJIiWqkZZicuXL6Nx48a4dOkSGjVqZOlwCnj27BkUilc9EjYuOzs7lCtXzuTliEolcmKfQKnIRq5SiZzcXOTmKqHIzUFuvqF8gkQKW6+iZ6ejF1RP45C17MsXGyYeNgIA+OcjpNLs+ZBVqWr68sgs9PlsbNKkCf7zn/9gxYoVAF5MWFWpUiV89tlnWlc/7NGjB9LT07F//371vqZNm8LPzw+rV682zg9SCrBGbeXMkTAtQczNhSLuCcScHAiCAFsbG9ja/Pt2VKlUyMnNRbYiB9k5CsCBtWl6tbynNWS4vGmg86/GJ5PJIJPJCpyvz+qH586dQ3BwsMa+oKAg7N271wg/QenBRE1mp8rKQk78U4jKwueDl0gkkNnZQWZnB8AJEi9vZOfmIjMz0+AJ7qn0CgsLQ2hoqKXDKFXyrzBY2GRSRa1+ePPmTa33jo2NNWi1xNcFEzWZjahUQpmchNyUFBSnTTqP1MEBtvb2sANQpkwZ5OTkICMjA5mZmaw9kYYZM2YgJCTE0mGUCr///juaNGmCkydPws/PT71fW22aTIuJ2sopYp9AzM4yW3libi5gYwM7T+O1B4u5OVBlZUGVka5Xxzipi6vGtq2tLVxcXODs7IzMzExkZGSYvR2frJNUKoVUKrV0GKWCzT9NUU5OTnB2dn7l+fqsfujt7a3XaomvGyZqK6aIfYIHU4NffaIx/ZNIvccEw8bd3bxlayGRyyEp5Bu8IAhwcHCAg4MDFAoF0tLSkJVlvi81RPSvl1c/7NKlC4B/Vz8sbEroZs2a4dixYxg/frx635EjR4pcLfF1xERtxcxZky5QdhFrQZuT1Nm1WOfZ2dnBzc0NOTk5SEtLQ2ZmpmkDI6ICdF39cNy4cQgMDMTixYvx/vvvY9u2bbh48SLCw8Mt+WNYHSbqkkKHYTYf7YtAfGYmPOztseeDIN3KsaLRehJbO53Xnba1tUXZsmXh5OSE1NRU1rCJzEjX1Q+bN2+OLVu2YPr06Zg2bRrefPNN7N27l2Oo82GiLoXiMzMRl1Hya5RSV1e9r7W1tYWbmxsUCgWSk5ORk5NjvMCIqFC6rH4IAN27d0f37t1NHFXJxvkYySpJbO0gdTR8rnI7Ozt4eHjA1dVV45s8EVFJwRo1WSWpa1mj3s/BwQFyuRypqalIT0836r2JiEyJVQyyOhKZHFJH489EJpFI4OLiAnd3d9hyTWsiKiGYqMnq2LiZdtpUOzs7uLu7w9nZmY/Dicjq8VOKrIrUqUyh46aNSRAEODk5wdPTEw4ODhDMsXgFEZEemKjJaggSKWzKGmdpzeKSSCRwdXWFh4cH5HK5WcsmIioOdiYjq2FTrhwEC03/aGNjox7OxRnOiMiaMFGTVZA6OhllOJah8s9wlpWVpdf85ERExsJETRYn2NrCppzl5xV/Wd4MZ0qlEunp6cjIyOBKXURkEUzUpZCHvb3Gf62ZIEhg5+kNwUp7X0ulUjg7O6NMmTLIyspCRkYGsrOzLR0WEb1GmKhLIZ3n97YQQRBg6+kFoQSMaRYEAfb29rC3t4dSqURWVhYyMzO5vCYRmRwTNVnEiyTtDUkJqPXnJ5VK4ejoCEdHRyiVSmRnZyM7OxsKhQJKpdLS4RFRKcNETWYn2NjC1sPTLOOlTU0qlarXxAaA3NxcKBQKKBQK5ObmIicnh53RiMggTNRkRgKkZcrApqyb1bZJv4ooiv8uBZr/vwCkogh7W1vIbf79p6VUKpGbmwtlbi6USiVUKhVUogiVSgVRVP17XyMR+MWAqFRhoibtRBGAcWbrEmxsIJHLIXV0gmBjAyiVEJXKwpOTKALIS4Ka+8R82y+Ov0ie6iT60j4AEPN6a+f9V1T9c7mo8RLz7vfyvTXK1T8BCnjxj80c/+BycxXgsiNEpQcTNWknCDAkMb1MzM2BMi0HyrRUo9yPiOh1UjKfPxIREb0mmKiJiIisGBM1ERGRFWOiJiIismJM1ERERFaMiZqIiMiKMVETERFZMSZqIiIiK8ZETUREZMWYqImIiKwYEzUREZEVY6ImIiKyYkzUREREVoyJmoiIyIpZZaJeuXIlfHx8IJfL0aRJE1y4cKHI83fs2IFatWpBLpejXr16OHjwoJkiJSIiMi2rS9Tbt29HcHAwZs2ahcuXL6NBgwYICgrC06dPtZ5/9uxZ9OrVC4MHD8bvv/+OLl26oEuXLrh69aqZIyciIjI+q0vUS5YswdChQzFo0CDUqVMHq1evhoODA9avX6/1/GXLluHdd9/F559/jtq1ayMsLAyNGjXCihUrzBw5ERGR8VlVolYoFLh06RLatWun3ieRSNCuXTucO3dO6zXnzp3TOB8AgoKCCj0/OzsbKSkp6ldaWprxfgBTEkXzvKh04XuGqMSzsXQAL0tISIBSqYSXl5fGfi8vL9y8eVPrNbGxsVrPj42N1Xr+ggULEBoaapyATUyQyS1QqACIIux8qsHOu7z5yyeDSeT2FinXIu9XoteAVSVqc5g6dSqCg4PV21euXEFgYKAFIyqcnXd5VF6wBGJ2llnLFWRyJukSzBLvG75niEzHqhK1u7s7pFIp4uLiNPbHxcXB29tb6zXe3t46nS+TySCTydTbTk5OBkZtWvzwI33wfUNUelhVG7WdnR0aN26MY8eOqfepVCocO3YMzZo103pNs2bNNM4HgCNHjhR6PhERUUliVTVqAAgODsaAAQPg7++PgIAALF26FOnp6Rg0aBAAoH///qhYsSIWLFgAABg3bhwCAwOxePFivP/++9i2bRsuXryI8PBwS/4YRERERmF1ibpHjx6Ij4/HzJkzERsbCz8/Pxw6dEjdYezBgweQSP59ENC8eXNs2bIF06dPx7Rp0/Dmm29i7969qFu3rqV+BCIiIqMRRPH1Hl9x+fJlNG7cGJcuXUKjRo0sHQ4RkVXgZ6P1sKo2aiIiItLERE1ERGTFmKiJiMjsEhMT0adPHzg7O8PV1RWDBw8ucqbIxMREfPbZZ6hZsybs7e1RuXJljB07FsnJyWaM2jKYqImIyOz69OmDa9eu4ciRI9i/fz9OnTqFYcOGFXr+48eP8fjxY3z99de4evUqNmzYgEOHDmHw4MFmjNoyrK7XNxERlW43btzAoUOH8Ntvv8Hf3x8AsHz5cnTs2BFff/01KlSoUOCaunXrYteuXeptX19fzJs3D3379kVubi5sbEpvOmONmoiIzOrcuXNwdXVVJ2kAaNeuHSQSCX799ddi3yc5ORnOzs6lOkkDrFGr3bhxw9IhEFExlS9fHuXL6z9N6pMnT/DkyRMjRlT65H0mpqWlISUlRb0//zTM+oiNjYWnp6fGPhsbG7i5uRW6oFJ+CQkJCAsLK/JxeakhvuYeP34sBgYGigD44ouvEvKaNWuWQf/uZ82aZfGfoSS8qlSpotPvfvLkya+8540bN8R58+aJNWrUKHC9h4eH+O23377y75ecnCwGBASI7777rqhQKAx5K5QIr/2EJ0Dp+3adlpaGwMBAnDx50uoXHSHrUZLeNyWpRl2Sfq/5ubm5wc3NTWNfUTXq+Ph4PHv2rMh7VqtWDT/88AMmTpyI58+fq/fn5uZCLpdjx44d+Oijjwq9PjU1FUFBQXBwcMD+/fshl5f+5VWZqEuhlJQUuLi4qNtviIqD7xvT4O+1oBs3bqBOnTq4ePEiGjduDAA4fPgw3n33XTx8+FBrZzLgxe8yKCgIMpkMBw8ehIODgznDthh2JiMiIrOqXbs23n33XQwdOhQXLlzA//73P4wZMwY9e/ZUJ+lHjx6hVq1auHDhAoAXSbpDhw5IT0/HunXrkJKSgtjYWMTGxkKpVFryxzE5diYjIiKz+/HHHzFmzBi88847kEgk+Pjjj/HNN9+oj+fk5CAqKgoZGRkAXsw9ntcjvHr16hr3un//Pnx8fMwWu7kxUZdCMpkMs2bNMrhnJr1e+L4xDf5etXNzc8OWLVsKPe7j44OXW2bbtGmD17Wllm3UREREVoxt1ERERFaMiZqIiMiKMVHTK0VHR0MQBGzYsMHSoRARvXaYqI3s7t27GD58OKpVqwa5XA5nZ2e0aNECy5YtQ2ZmpsnKvX79OmbPno3o6GiTlVEc8+bNwwcffAAvLy8IgoDZs2dbNJ7SRhCEYr0iIyMNLisjIwOzZ8/W6V4l8e/P3ylZO/b6NqIDBw6ge/fukMlk6N+/P+rWrQuFQoEzZ87g888/x7Vr1xAeHm6Ssq9fv47Q0FC0adPGosMUpk+fDm9vbzRs2BAREREWi6O02rx5s8b2pk2bcOTIkQL7a9eubXBZGRkZCA0NBfCix21xlMS/P3+nZO2YqI3k/v376NmzJ6pUqYLjx49rTG84evRo3LlzBwcOHLBghP8SRRFZWVmwt7c3+r3zxjMmJCTAw8PD6Pd/3fXt21dj+/z58zhy5EiB/ZZSEv/+/J2SteOjbyP58ssvkZaWhnXr1mmdg7h69eoYN26cejs3NxdhYWHw9fWFTCaDj48Ppk2bhuzsbI3rfHx80KlTJ5w5cwYBAQGQy+WoVq0aNm3apD5nw4YN6N69OwCgbdu2BR7V5d0jIiIC/v7+sLe3x5o1awAA9+7dQ/fu3eHm5gYHBwc0bdrUoC8UpXnSgZJCpVJh6dKleOuttyCXy+Hl5YXhw4drzKsMABcvXkRQUBDc3d1hb2+PqlWr4tNPPwXwol9CXlIIDQ1Vv6de9di1tP79+TslS2KN2kh++eUXVKtWDc2bNy/W+UOGDMHGjRvRrVs3TJw4Eb/++isWLFiAGzduYM+ePRrn3rlzB926dcPgwYMxYMAArF+/HgMHDkTjxo3x1ltvoXXr1hg7diy++eYbTJs2Tf2I7uVHdVFRUejVqxeGDx+OoUOHombNmoiLi0Pz5s2RkZGBsWPHoly5cti4cSM++OAD7Ny5s8iJ8cl6DR8+HBs2bMCgQYMwduxY3L9/HytWrMDvv/+O//3vf7C1tcXTp0/RoUMHeHh4YMqUKXB1dUV0dDR2794NAPDw8MCqVaswcuRIfPTRR+jatSsAoH79+pb80SyGv1OyKIut21WKJCcniwDEDz/8sFjnX7lyRQQgDhkyRGP/pEmTRADi8ePH1fvylpk7deqUet/Tp09FmUwmTpw4Ub1vx44dIgDxxIkTBcrLu8ehQ4c09o8fP14EIJ4+fVq9LzU1Vaxataro4+MjKpVKURRF8f79+yIA8fvvvy/WzyeKohgfH2+U5QipaKNHjxZf/md8+vRpEYD4448/apx36NAhjf179uwRAYi//fZbofc25G9Ykv/+/J2SteGjbyPIW1S9TJkyxTr/4MGDAIDg4GCN/RMnTgSAAo+e69Spg1atWqm3PTw8ULNmTdy7d6/YMVatWhVBQUEF4ggICEDLli3V+5ycnDBs2DBER0fj+vXrxb4/WYcdO3bAxcUF7du3R0JCgvrVuHFjODk54cSJEwAAV1dXAMD+/fuRk5NjwYitH3+nZGlM1EaQt3Rdampqsc6PiYmBRCIpMLG8t7c3XF1dERMTo7G/cuXKBe5RtmzZAu1jRalatarWOGrWrFlgf94j8/xxkPW7ffs2kpOT4enpCQ8PD41XWloanj59CgAIDAzExx9/jNDQULi7u+PDDz/E999/X6CPBPF3SpbHNmojcHZ2RoUKFXD16lWdrhMEoVjnSaVSrftFHaZpN0UPb7I+KpUKnp6e+PHHH7Uez+vMJAgCdu7cifPnz+OXX35BREQEPv30UyxevBjnz5+Hk5OTOcO2avydkqUxURtJp06dEB4ejnPnzqFZs2ZFnlulShWoVCrcvn1bo8NXXFwckpKSUKVKFZ3LL27Szx9HVFRUgf03b95UH6eSxdfXF0ePHkWLFi2K9eWsadOmaNq0KebNm4ctW7agT58+2LZtG4YMGaLXe6o04u+ULI2Pvo3kiy++gKOjI4YMGYK4uLgCx+/evYtly5YBADp27AgAWLp0qcY5S5YsAQC8//77Opfv6OgIAEhKSir2NR07dsSFCxdw7tw59b709HSEh4fDx8cHderU0TkOsqxPPvkESqUSYWFhBY7l5uaq3x/Pnz8v8ETGz88PANSPah0cHADo9p4qjfg7JUtjjdpIfH19sWXLFvTo0QO1a9fWmJns7Nmz2LFjBwYOHAgAaNCgAQYMGIDw8HAkJSUhMDAQFy5cwMaNG9GlSxe0bdtW5/L9/PwglUqxaNEiJCcnQyaT4e2334anp2eh10yZMgVbt27Fe++9h7Fjx8LNzQ0bN27E/fv3sWvXLkgkun+P27x5M2JiYtSLvZ86dQpz584FAPTr14+1dBMLDAzE8OHDsWDBAly5cgUdOnSAra0tbt++jR07dmDZsmXo1q0bNm7ciG+//RYfffQRfH19kZqairVr18LZ2Vn9RdLe3h516tTB9u3bUaNGDbi5uaFu3bqoW7duoeWXxr8/f6dkcRbudV7q3Lp1Sxw6dKjo4+Mj2tnZiWXKlBFbtGghLl++XMzKylKfl5OTI4aGhopVq1YVbW1txUqVKolTp07VOEcUXwytev/99wuUExgYKAYGBmrsW7t2rVitWjVRKpVqDNUq7B6iKIp3794Vu3XrJrq6uopyuVwMCAgQ9+/fr3GOLsOzAgMDRQBaX9qGjpFh8g8lyhMeHi42btxYtLe3F8uUKSPWq1dP/OKLL8THjx+LoiiKly9fFnv16iVWrlxZlMlkoqenp9ipUyfx4sWLGvc5e/as2LhxY9HOzq5YQ4NKw9+fv1OyNoIo6tAjiYiIiMyKbdRERERWjImaiIjIijFRExERWTEmaiIiIivGRE1ERGTFmKiJiIisGBM1EZEBoqOjIQgCNmzYYOlQqJRiojaTDRs2QBAEyOVyPHr0qMDxNm3aFDk7kTkMHToUgiCgU6dOWo/v27cPjRo1glwuR+XKlTFr1izk5uaaOcrXC983RMREbWbZ2dlYuHChpcMo4OLFi9iwYQPkcrnW4//973/RpUsXuLq6Yvny5ejSpQvmzp2Lzz77zMyRvp74vrFeVapUQWZmJvr162fpUKiU4lzfZubn54e1a9di6tSpqFChgqXDAfBiucyxY8eif//+OHbsmNZzJk2ahPr16+Pw4cOwsXnxtnF2dsb8+fMxbtw41KpVy5whv3b4vrFeeU88iEyFNWozmzZtGpRKpVXVjjZv3oyrV69i3rx5Wo9fv34d169fx7Bhw9QftgAwatQoiKKInTt3mivU1xbfN6Y1e/ZsCIKAW7duoW/fvnBxcYGHhwdmzJgBURTx999/48MPP4SzszO8vb2xePFi9bXa2qgHDhwIJycnPHr0CF26dIGTkxM8PDwwadIkKJVK9XmRkZEQBAGRkZEa8Wi7Z2xsLAYNGoQ33ngDMpkM5cuXx4cffojo6GgT/VbIWjBRm1nVqlXRv39/rF27Fo8fP9b5+oyMDCQkJLzy9fz582LdLzU1FZMnT8a0adPg7e2t9Zzff/8dAODv76+xv0KFCnjjjTfUx8l0+L4xjx49ekClUmHhwoVo0qQJ5s6di6VLl6J9+/aoWLEiFi1ahOrVq2PSpEk4depUkfdSKpUICgpCuXLl8PXXXyMwMBCLFy9GeHi4XrF9/PHH2LNnDwYNGoRvv/0WY8eORWpqKh48eKDX/ajkYKK2gJCQEOTm5mLRokU6X/vll1/Cw8Pjla+GDRsW635z5syBvb09JkyYUOg5T548AQCUL1++wLHy5cvrlThId3zfmF5AQAC2bNmCkSNH4ueff8Ybb7yBiRMnqpPjyJEjsX//ftjb22P9+vVF3isrKws9evTAunXrMGLECOzcuRMNGzbEunXrdI4rKSkJZ8+exfTp0xEWFobBgwdj6tSpOH78OFq3bq3vj0slBNuoLaBatWro168fwsPDMWXKFK0fZIXp378/WrZs+crz7O3tX3nOrVu3sGzZMmzduhUymazQ8zIzMwFA6zlyuRwpKSmvLIsMx/eN6Q0ZMkT9/1KpFP7+/nj48CEGDx6s3u/q6oqaNWvi3r17r7zfiBEjNLZbtWqFzZs36xyXvb097OzsEBkZicGDB6Ns2bI634NKLiZqC5k+fTo2b96MhQsXYtmyZcW+rlq1aqhWrZpRYhg3bhyaN2+Ojz/+uMjz8j68s7OzCxzLysoq1oc7GQffN6ZVuXJljW0XFxfI5XK4u7sX2P/s2bMi7yWXy+Hh4aGxr2zZssVuXniZTCbDokWLMHHiRHh5eaFp06bo1KkT+vfvX2jTA5UeTNQWUq1aNfTt21ddOyqutLQ0pKWlvfI8qVRa4EPiZcePH8ehQ4ewe/dujc4oubm5yMzMRHR0NNzc3ODs7KyuuT158gSVKlXSuM+TJ08QEBBQ7PjJMHzfmJZUKi3WPuBFr3dd75WfIAha97/c4SzP+PHj0blzZ+zduxcRERGYMWMGFixYgOPHjxe7yYJKJrZRW9D06dN1bnP8+uuvUb58+Ve+/vOf/xR5n7wOKF27dkXVqlXVr0ePHuH48eOoWrWqug3Oz88PwIsxsy97/PgxHj58qD5O5sH3TemR9wg7KSlJY39MTIzW8319fTFx4kQcPnwYV69ehUKh0OiBTqUTa9QW5Ovri759+2LNmjWoUqWKxhCWwhirrfHtt9/Gnj17CuwfNmwYqlSpgpCQENSrVw8A8NZbb6FWrVoIDw/H8OHD1TWFVatWQRAEdOvW7ZXxkPHwfVN6VKlSBVKpFKdOnUKXLl3U+7/99luN8zIyMiCRSDTGa/v6+qJMmTJamxaodGGitrCQkBBs3rwZUVFReOutt155vrHaGitXrlygPQ548XjNy8tL40MDAL766it88MEH6NChA3r27ImrV69ixYoVGDJkCGrXrm1wPKQbvm9KBxcXF3Tv3h3Lly+HIAjw9fXF/v378fTpU43zbt26hXfeeQeffPIJ6tSpAxsbG+zZswdxcXHo2bOnhaInc+GjbwurXr06+vbta+kwXqlTp07YvXs3EhMT8dlnn2H37t2YNm0aVq5caenQXkt835Qey5cvx4cffojVq1dj+vTpqFy5MjZu3KhxTqVKldCrVy9ERkZi6tSpmDp1KlJSUvDTTz+9slMflXyC+KoeEURERGQxrFETERFZMSZqIiIiK8ZETUREZMWYqImIiKwYEzUREZEVY6ImIiKyYkzUREQlRHR0NARBwIYNGywdCpkREzURlUp3797F8OHDUa1aNcjlcjg7O6NFixZYtmyZeglOU7h+/Tpmz56tsWiJJcybNw8ffPABvLy8IAgCZs+ebdF4SH+cQpSISp0DBw6ge/fukMlk6N+/P+rWrQuFQoEzZ87g888/x7Vr1xAeHm6Ssq9fv47Q0FC0adMGPj4+JimjOKZPnw5vb280bNgQERERFouDDMdETUSlyv3799GzZ09UqVIFx48fVy+3CQCjR4/GnTt3cODAAQtG+C9RFE22Nvf9+/fh4+ODhISEIpcuJevHR99EVKp8+eWXSEtLw7p16zSSdJ7q1atj3Lhx6u3c3FyEhYXB19cXMpkMPj4+mDZtWoFVqXx8fNCpUyecOXMGAQEBkMvlqFatGjZt2qQ+Z8OGDejevTsAoG3bthAEAYIgIDIyUuMeERER8Pf3h729PdasWQMAuHfvHrp37w43Nzc4ODigadOmBn2hsGRtnoyLiZqISpVffvkF1apVQ/PmzYt1/pAhQzBz5kw0atQI//d//4fAwEAsWLBA66pUd+7cQbdu3dC+fXssXrwYZcuWxcCBA3Ht2jUAQOvWrTF27FgAwLRp07B582Zs3rxZY6WwqKgo9OrVC+3bt8eyZcvg5+eHuLg4NG/eHBERERg1ahTmzZuHrKwsfPDBB1qXFaXXjEhEVEokJyeLAMQPP/ywWOdfuXJFBCAOGTJEY/+kSZNEAOLx48fV+6pUqSICEE+dOqXe9/TpU1Emk4kTJ05U79uxY4cIQDxx4kSB8vLucejQIY3948ePFwGIp0+fVu9LTU0Vq1atKvr4+IhKpVIURVG8f/++CED8/vvvi/XziaIoxsfHiwDEWbNmFfsasi6sURNRqZGSkgIAKFOmTLHOP3jwIAAgODhYY//EiRMBoMCj5zp16qBVq1bqbQ8PD9SsWRP37t0rdoxVq1ZFUFBQgTgCAgLQsmVL9T4nJycMGzYM0dHRuH79erHvT6UPEzURlRrOzs4AgNTU1GKdHxMTA4lEgurVq2vs9/b2hqurK2JiYjT2V65cucA9ypYti+fPnxc7xqpVq2qNo2bNmgX25z0yzx8HvV6YqImo1HB2dkaFChVw9epVna4TBKFY50mlUq37RVEsdlmm6OFNpRsTNRGVKp06dcLdu3dx7ty5V55bpUoVqFQq3L59W2N/XFwckpKSUKVKFZ3LL27Szx9HVFRUgf03b95UH6fXFxM1EZUqX3zxBRwdHTFkyBDExcUVOH737l0sW7YMANCxY0cAwNKlSzXOWbJkCQDg/fff17l8R0dHAEBSUlKxr+nYsSMuXLig8eUiPT0d4eHh8PHxQZ06dXSOg0oPTnhCRKWKr68vtmzZgh49eqB27doaM5OdPXsWO3bswMCBAwEADRo0wIABAxAeHo6kpCQEBgbiwoUL2LhxI7p06YK2bdvqXL6fnx+kUikWLVqE5ORkyGQyvP322/D09Cz0milTpmDr1q147733MHbsWLi5uWHjxo24f/8+du3aBYlE9zrV5s2bERMTg4yMDADAqVOnMHfuXABAv379WEsvSSzd7ZyIyBRu3bolDh06VPTx8RHt7OzEMmXKiC1atBCXL18uZmVlqc/LyckRQ0NDxapVq4q2trZipUqVxKlTp2qcI4ovhla9//77BcoJDAwUAwMDNfatXbtWrFatmiiVSjWGahV2D1EUxbt374rdunUTXV1dRblcLgYEBIj79+/XOEeX4VmBgYEiAK0vbUPHyHoJoqhDLwgiIiIyK7ZRExERWTEmaiIiIivGRE1ERGTFmKiJiIisGBM1ERGRFWOiJiIismJM1ERERFaMiZqIiMiKMVETERFZMSZqIiIiK8ZETUREZMWYqImIiKwYEzUREZEV+3/wndFE+LYG6wAAAABJRU5ErkJggg==",
-      "text/plain": [
-       "<Figure size 500x400 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "two_groups_baseline.mean_diff.plot(sankey_kwargs = {\"alpha\": 0.2});"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "python3",
-   "language": "python",
-   "name": "python3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/nbs/tutorials/03-shared_control_and_repeated_measures.ipynb b/nbs/tutorials/03-shared_control_and_repeated_measures.ipynb
new file mode 100644
index 00000000..566fea45
--- /dev/null
+++ b/nbs/tutorials/03-shared_control_and_repeated_measures.ipynb
@@ -0,0 +1,633 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Shared Control & Repeated Measures\n",
+    "\n",
+    "> Explanation of how to use dabest for shared control and repeated measures analyses.\n",
+    "\n",
+    "- order: 3"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The **shared control plot** and **repeated measures plot** display common experimental\n",
+    "paradigms, where several test samples are compared against a common\n",
+    "reference sample. The shared control plot is for unpaired data, while the\n",
+    "repeated measures plot is for paired data.\n",
+    "\n",
+    "These types of Cumming plots are automatically generated if the tuple passed\n",
+    "to ``idx`` has more than two data columns."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Pre-compiling numba functions for DABEST...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Compiling numba functions: 100%|██████████| 11/11 [00:00<00:00, 25.48it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Numba compilation complete!\n",
+      "We're using DABEST v2025.03.27\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import dabest\n",
+    "\n",
+    "print(\"We're using DABEST v{}\".format(dabest.__version__))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#| hide\n",
+    "import warnings\n",
+    "warnings.filterwarnings(\"ignore\", category=UserWarning) # to suppress warnings related to points not being able to be plotted due to dot size"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Creating a demo dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from scipy.stats import norm # Used in generation of populations.\n",
+    "\n",
+    "np.random.seed(9999) # Fix the seed so the results are reproducible.\n",
+    "Ns = 20 # The number of samples taken from each population\n",
+    "\n",
+    "# Create samples\n",
+    "c1 = norm.rvs(loc=3, scale=0.4, size=Ns)\n",
+    "c2 = norm.rvs(loc=3.5, scale=0.75, size=Ns)\n",
+    "c3 = norm.rvs(loc=3.25, scale=0.4, size=Ns)\n",
+    "\n",
+    "t1 = norm.rvs(loc=3.5, scale=0.5, size=Ns)\n",
+    "t2 = norm.rvs(loc=2.5, scale=0.6, size=Ns)\n",
+    "t3 = norm.rvs(loc=3, scale=0.75, size=Ns)\n",
+    "t4 = norm.rvs(loc=3.5, scale=0.75, size=Ns)\n",
+    "t5 = norm.rvs(loc=3.25, scale=0.4, size=Ns)\n",
+    "t6 = norm.rvs(loc=3.25, scale=0.4, size=Ns)\n",
+    "\n",
+    "\n",
+    "# Add a `gender` column for coloring the data.\n",
+    "females = np.repeat('Female', Ns/2).tolist()\n",
+    "males = np.repeat('Male', Ns/2).tolist()\n",
+    "gender = females + males\n",
+    "\n",
+    "# Add an `id` column for paired data plotting.\n",
+    "id_col = pd.Series(range(1, Ns+1))\n",
+    "\n",
+    "# Combine samples and gender into a DataFrame.\n",
+    "df = pd.DataFrame({'Control 1' : c1,     'Test 1' : t1,\n",
+    "                   'Control 2' : c2,     'Test 2' : t2,\n",
+    "                   'Control 3' : c3,     'Test 3' : t3,\n",
+    "                   'Test 4'    : t4,     'Test 5' : t5, 'Test 6' : t6,\n",
+    "                   'Gender'    : gender, 'ID'  : id_col\n",
+    "                  })"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Shared control plot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "DABEST v2025.03.27\n",
+       "==================\n",
+       "                  \n",
+       "Good afternoon!\n",
+       "The current time is Tue Mar 25 16:03:03 2025.\n",
+       "\n",
+       "Effect size(s) with 95% confidence intervals will be computed for:\n",
+       "1. Test 1 minus Control 1\n",
+       "2. Test 2 minus Control 1\n",
+       "3. Test 3 minus Control 1\n",
+       "4. Test 4 minus Control 1\n",
+       "5. Test 5 minus Control 1\n",
+       "6. Test 6 minus Control 1\n",
+       "\n",
+       "5000 resamples will be used to generate the effect size bootstraps."
+      ]
+     },
+     "execution_count": null,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "shared_control = dabest.load(df, idx=(\"Control 1\", \"Test 1\",\n",
+    "                                          \"Test 2\", \"Test 3\",\n",
+    "                                          \"Test 4\", \"Test 5\", \"Test 6\")\n",
+    "                                 )\n",
+    "shared_control"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "DABEST v2025.03.27\n",
+       "==================\n",
+       "                  \n",
+       "Good afternoon!\n",
+       "The current time is Tue Mar 25 16:03:04 2025.\n",
+       "\n",
+       "The unpaired mean difference between Control 1 and Test 1 is 0.48 [95%CI 0.205, 0.774].\n",
+       "The p-value of the two-sided permutation t-test is 0.001, calculated for legacy purposes only. \n",
+       "\n",
+       "The unpaired mean difference between Control 1 and Test 2 is -0.542 [95%CI -0.915, -0.206].\n",
+       "The p-value of the two-sided permutation t-test is 0.0042, calculated for legacy purposes only. \n",
+       "\n",
+       "The unpaired mean difference between Control 1 and Test 3 is 0.174 [95%CI -0.273, 0.647].\n",
+       "The p-value of the two-sided permutation t-test is 0.479, calculated for legacy purposes only. \n",
+       "\n",
+       "The unpaired mean difference between Control 1 and Test 4 is 0.79 [95%CI 0.325, 1.33].\n",
+       "The p-value of the two-sided permutation t-test is 0.0042, calculated for legacy purposes only. \n",
+       "\n",
+       "The unpaired mean difference between Control 1 and Test 5 is 0.265 [95%CI 0.0115, 0.497].\n",
+       "The p-value of the two-sided permutation t-test is 0.0404, calculated for legacy purposes only. \n",
+       "\n",
+       "The unpaired mean difference between Control 1 and Test 6 is 0.288 [95%CI 0.00913, 0.524].\n",
+       "The p-value of the two-sided permutation t-test is 0.0324, calculated for legacy purposes only. \n",
+       "\n",
+       "5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.\n",
+       "Any p-value reported is the probability of observing theeffect size (or greater),\n",
+       "assuming the null hypothesis of zero difference is true.\n",
+       "For each p-value, 5000 reshuffles of the control and test labels were performed.\n",
+       "\n",
+       "To get the results of all valid statistical tests, use `.mean_diff.statistical_tests`"
+      ]
+     },
+     "execution_count": null,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "shared_control.mean_diff"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1050x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "shared_control.mean_diff.plot();"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "``dabest`` allows for combining both two-group and shared control experiments into the same plot. This empowers you to perform robust analyses and present complex visualizations of your statistics elegantly."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "DABEST v2025.03.27\n",
+       "==================\n",
+       "                  \n",
+       "Good afternoon!\n",
+       "The current time is Tue Mar 25 16:03:05 2025.\n",
+       "\n",
+       "Effect size(s) with 95% confidence intervals will be computed for:\n",
+       "1. Test 1 minus Control 1\n",
+       "2. Test 2 minus Control 2\n",
+       "3. Test 3 minus Control 2\n",
+       "4. Test 4 minus Control 3\n",
+       "5. Test 5 minus Control 3\n",
+       "6. Test 6 minus Control 3\n",
+       "\n",
+       "5000 resamples will be used to generate the effect size bootstraps."
+      ]
+     },
+     "execution_count": null,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "multi_groups = dabest.load(df, idx=((\"Control 1\", \"Test 1\",),\n",
+    "                                         (\"Control 2\", \"Test 2\",\"Test 3\"),\n",
+    "                                         (\"Control 3\", \"Test 4\",\"Test 5\", \"Test 6\")\n",
+    "                                       ))\n",
+    "multi_groups"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "DABEST v2025.03.27\n",
+       "==================\n",
+       "                  \n",
+       "Good afternoon!\n",
+       "The current time is Tue Mar 25 16:03:06 2025.\n",
+       "\n",
+       "The unpaired mean difference between Control 1 and Test 1 is 0.48 [95%CI 0.205, 0.774].\n",
+       "The p-value of the two-sided permutation t-test is 0.001, calculated for legacy purposes only. \n",
+       "\n",
+       "The unpaired mean difference between Control 2 and Test 2 is -1.38 [95%CI -1.93, -0.905].\n",
+       "The p-value of the two-sided permutation t-test is 0.0, calculated for legacy purposes only. \n",
+       "\n",
+       "The unpaired mean difference between Control 2 and Test 3 is -0.666 [95%CI -1.29, -0.0788].\n",
+       "The p-value of the two-sided permutation t-test is 0.0352, calculated for legacy purposes only. \n",
+       "\n",
+       "The unpaired mean difference between Control 3 and Test 4 is 0.362 [95%CI -0.111, 0.901].\n",
+       "The p-value of the two-sided permutation t-test is 0.161, calculated for legacy purposes only. \n",
+       "\n",
+       "The unpaired mean difference between Control 3 and Test 5 is -0.164 [95%CI -0.398, 0.0747].\n",
+       "The p-value of the two-sided permutation t-test is 0.208, calculated for legacy purposes only. \n",
+       "\n",
+       "The unpaired mean difference between Control 3 and Test 6 is -0.14 [95%CI -0.4, 0.0937].\n",
+       "The p-value of the two-sided permutation t-test is 0.282, calculated for legacy purposes only. \n",
+       "\n",
+       "5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.\n",
+       "Any p-value reported is the probability of observing theeffect size (or greater),\n",
+       "assuming the null hypothesis of zero difference is true.\n",
+       "For each p-value, 5000 reshuffles of the control and test labels were performed.\n",
+       "\n",
+       "To get the results of all valid statistical tests, use `.mean_diff.statistical_tests`"
+      ]
+     },
+     "execution_count": null,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "multi_groups.mean_diff"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1350x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "multi_groups.mean_diff.plot();"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Repeated measures plot"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "DABEST **v2023.02.14** expands the repertoire of plots for experiments with repeated-measures designs. DABEST now allows the visualization of paired experiments with one control and multiple test \n",
+    "groups, as well as repeated measurements of the same group. This is an improved version of paired data plotting in previous versions, which only supported computations involving one test group and one control group.\n",
+    "\n",
+    "The repeated-measures function supports the calculation of effect sizes for\n",
+    "paired data, either based on sequential comparisons (group i vs group i + 1) \n",
+    "or baseline comparisons (control vs group i). To use these features, \n",
+    "you can simply declare the argument ``paired = \"sequential\"`` or ``paired = \"baseline\"`` \n",
+    "correspondingly while running ``dabest.load()``. As in the previous version, you must also pass a column in the dataset that indicates the identity of each observation, using the \n",
+    "``id_col`` keyword. \n",
+    "\n",
+    "<div style=\"padding: 15px; border: 1px solid transparent; border-color: transparent; margin-bottom: 20px; border-radius: 4px; color: #8a6d3b;; background-color: #fcf8e3; border-color: #faebcc;\">\n",
+    " **(Please note that** ``paired = True`` **and** ``paired = False`` **are no longer valid since v2023.02.14)**\n",
+    "</div>\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "DABEST v2025.03.27\n",
+       "==================\n",
+       "                  \n",
+       "Good afternoon!\n",
+       "The current time is Tue Mar 25 16:03:06 2025.\n",
+       "\n",
+       "Paired effect size(s) for repeated measures against baseline \n",
+       "with 95% confidence intervals will be computed for:\n",
+       "1. Test 1 minus Control 1\n",
+       "2. Test 2 minus Control 1\n",
+       "3. Test 3 minus Control 1\n",
+       "\n",
+       "5000 resamples will be used to generate the effect size bootstraps."
+      ]
+     },
+     "execution_count": null,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "baseline_repeated_measures = dabest.load(df, idx=(\"Control 1\", \"Test 1\", \"Test 2\", \"Test 3\"),\n",
+    "                                               paired=\"baseline\", id_col=\"ID\")\n",
+    "baseline_repeated_measures"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "DABEST v2025.03.27\n",
+       "==================\n",
+       "                  \n",
+       "Good afternoon!\n",
+       "The current time is Tue Mar 25 16:03:07 2025.\n",
+       "\n",
+       "The paired mean difference for repeated measures against baseline \n",
+       "between Control 1 and Test 1 is 0.48 [95%CI 0.241, 0.749].\n",
+       "The p-value of the two-sided permutation t-test is 0.001, calculated for legacy purposes only. \n",
+       "\n",
+       "The paired mean difference for repeated measures against baseline \n",
+       "between Control 1 and Test 2 is -0.542 [95%CI -0.977, -0.179].\n",
+       "The p-value of the two-sided permutation t-test is 0.014, calculated for legacy purposes only. \n",
+       "\n",
+       "The paired mean difference for repeated measures against baseline \n",
+       "between Control 1 and Test 3 is 0.174 [95%CI -0.303, 0.702].\n",
+       "The p-value of the two-sided permutation t-test is 0.505, calculated for legacy purposes only. \n",
+       "\n",
+       "5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.\n",
+       "Any p-value reported is the probability of observing theeffect size (or greater),\n",
+       "assuming the null hypothesis of zero difference is true.\n",
+       "For each p-value, 5000 reshuffles of the control and test labels were performed.\n",
+       "\n",
+       "To get the results of all valid statistical tests, use `.mean_diff.statistical_tests`"
+      ]
+     },
+     "execution_count": null,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "baseline_repeated_measures.mean_diff"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 480x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "baseline_repeated_measures.mean_diff.plot();"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "DABEST v2025.03.27\n",
+       "==================\n",
+       "                  \n",
+       "Good afternoon!\n",
+       "The current time is Tue Mar 25 16:03:07 2025.\n",
+       "\n",
+       "Paired effect size(s) for the sequential design of repeated-measures experiment \n",
+       "with 95% confidence intervals will be computed for:\n",
+       "1. Test 1 minus Control 1\n",
+       "2. Test 2 minus Test 1\n",
+       "3. Test 3 minus Test 2\n",
+       "\n",
+       "5000 resamples will be used to generate the effect size bootstraps."
+      ]
+     },
+     "execution_count": null,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sequential_repeated_measures = dabest.load(df, idx=(\"Control 1\", \"Test 1\", \"Test 2\", \"Test 3\"),\n",
+    "                                               paired=\"sequential\", id_col=\"ID\")\n",
+    "sequential_repeated_measures"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "DABEST v2025.03.27\n",
+       "==================\n",
+       "                  \n",
+       "Good afternoon!\n",
+       "The current time is Tue Mar 25 16:03:08 2025.\n",
+       "\n",
+       "The paired mean difference for the sequential design of repeated-measures experiment \n",
+       "between Control 1 and Test 1 is 0.48 [95%CI 0.241, 0.749].\n",
+       "The p-value of the two-sided permutation t-test is 0.001, calculated for legacy purposes only. \n",
+       "\n",
+       "The paired mean difference for the sequential design of repeated-measures experiment \n",
+       "between Test 1 and Test 2 is -1.02 [95%CI -1.35, -0.709].\n",
+       "The p-value of the two-sided permutation t-test is 0.0, calculated for legacy purposes only. \n",
+       "\n",
+       "The paired mean difference for the sequential design of repeated-measures experiment \n",
+       "between Test 2 and Test 3 is 0.716 [95%CI 0.153, 1.2].\n",
+       "The p-value of the two-sided permutation t-test is 0.022, calculated for legacy purposes only. \n",
+       "\n",
+       "5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.\n",
+       "Any p-value reported is the probability of observing theeffect size (or greater),\n",
+       "assuming the null hypothesis of zero difference is true.\n",
+       "For each p-value, 5000 reshuffles of the control and test labels were performed.\n",
+       "\n",
+       "To get the results of all valid statistical tests, use `.mean_diff.statistical_tests`"
+      ]
+     },
+     "execution_count": null,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sequential_repeated_measures.mean_diff"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 480x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sequential_repeated_measures.mean_diff.plot();"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Similar to unpaired data, DABEST empowers you to perform complex \n",
+    "visualizations and statistics for paired data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 960x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "multi_baseline_repeated_measures = dabest.load(df, idx=((\"Control 1\", \"Test 1\", \"Test 2\", \"Test 3\"),\n",
+    "                                                      (\"Control 2\", \"Test 4\", \"Test 5\", \"Test 6\")),\n",
+    "                                               paired=\"baseline\", id_col=\"ID\")\n",
+    "multi_baseline_repeated_measures.mean_diff.plot();"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For further aesthetic changes, the [Plot Aesthetics Tutorial](09-plot_aesthetics.html) provides detailed examples of how to customize the plot.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "python3",
+   "language": "python",
+   "name": "python3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/nbs/tutorials/04-mini_meta_delta.ipynb b/nbs/tutorials/04-mini_meta_delta.ipynb
deleted file mode 100644
index 595432d9..00000000
--- a/nbs/tutorials/04-mini_meta_delta.ipynb
+++ /dev/null
@@ -1,996 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "2985aa19",
-   "metadata": {},
-   "source": [
-    "# Mini-Meta Delta\n",
-    "\n",
-    "> Explanation of how to compute the meta-analyzed weighted effect size using dabest.\n",
-    "\n",
-    "- order: 4"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a9ca4dd5",
-   "metadata": {},
-   "source": [
-    "When scientists conduct replicates of the same experiment, the effect size of each replicate often varies, complicating the interpretation of the results. Starting from v2023.02.14, DABEST can now compute the meta-analyzed weighted effect size given multiple replicates of the same experiment. This can help resolve differences between replicates and simplify interpretation.\n",
-    "\n",
-    "This function employs the generic *inverse-variance* method to calculate the effect size, as follows:\n",
-    "\n",
-    "$\\theta_{\\text{weighted}} = \\frac{\\Sigma\\hat{\\theta_{i}}w_{i}}{{\\Sigma}w_{i}}$\n",
-    "\n",
-    "where:\n",
-    "\n",
-    "\n",
-    "$\\hat{\\theta_{i}} = \\text{Mean difference for replicate }i$\n",
-    "\n",
-    "\n",
-    "$w_{i} = \\text{Weight for replicate }i = \\frac{1}{s_{i}^2}$ \n",
-    "\n",
-    "\n",
-    "$s_{i}^2 = \\text{Pooled variance for replicate }i = \\frac{(n_{test}-1)s_{test}^2+(n_{control}-1)s_{control}^2}{n_{test}+n_{control}-2}$\n",
-    "\n",
-    "\n",
-    "$n = \\text{sample size and }s^2 = \\text{variance for control/test.}$\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5fb1dc0f",
-   "metadata": {},
-   "source": [
-    "Note that this utilizes the fixed-effects model of meta-analysis, in contrast to the random-effects model. In the fixed-effects model, all variation between the results of each replicate is assumed to be solely due to sampling error. Therefore, we recommend using this function exclusively for replications of the same experiment, where it can be safely assumed that each replicate estimates the same population mean $\\mu$.\n",
-    "\n",
-    "Additionally, be aware that as of v2023.02.14, DABEST can only compute weighted effect size *for mean difference only*, and not for standardized measures such as Cohen's *d*.\n",
-    "\n",
-    "For more information on meta-analysis, please refer to Chapter 10 of the Cochrane handbook: https://training.cochrane.org/handbook/current/chapter-10\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "12c4d226",
-   "metadata": {},
-   "source": [
-    "## Load libraries"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "5c7d7eaa",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "We're using DABEST v2024.03.29\n"
-     ]
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "import dabest\n",
-    "\n",
-    "print(\"We're using DABEST v{}\".format(dabest.__version__))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "05e75af8",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#| hide\n",
-    "import warnings\n",
-    "warnings.filterwarnings(\"ignore\", category=UserWarning) # to suppress warnings related to points not being able to be plotted due to dot size"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4a4f0bde",
-   "metadata": {},
-   "source": [
-    "## Create dataset for mini-meta demo"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "09a9b692",
-   "metadata": {},
-   "source": [
-    "Let´s create a dataset to demonstrate the mini-meta function."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "6b80a5aa",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from scipy.stats import norm # Used in generation of populations.\n",
-    "\n",
-    "np.random.seed(9999) # Fix the seed to ensure reproducibility of results.\n",
-    "Ns = 20 # The number of samples taken from each population\n",
-    "\n",
-    "# Create samples\n",
-    "c1 = norm.rvs(loc=3, scale=0.4, size=Ns)\n",
-    "c2 = norm.rvs(loc=3.5, scale=0.75, size=Ns)\n",
-    "c3 = norm.rvs(loc=3.25, scale=0.4, size=Ns)\n",
-    "\n",
-    "t1 = norm.rvs(loc=3.5, scale=0.5, size=Ns)\n",
-    "t2 = norm.rvs(loc=2.5, scale=0.6, size=Ns)\n",
-    "t3 = norm.rvs(loc=3, scale=0.75, size=Ns)\n",
-    "\n",
-    "\n",
-    "# Add a `gender` column for coloring the data.\n",
-    "females = np.repeat('Female', Ns/2).tolist()\n",
-    "males = np.repeat('Male', Ns/2).tolist()\n",
-    "gender = females + males\n",
-    "\n",
-    "# Add an `id` column for paired data plotting.\n",
-    "id_col = pd.Series(range(1, Ns+1))\n",
-    "\n",
-    "# Combine samples and gender into a DataFrame.\n",
-    "df = pd.DataFrame({'Control 1' : c1,     'Test 1' : t1,\n",
-    "                   'Control 2' : c2,     'Test 2' : t2,\n",
-    "                   'Control 3' : c3,     'Test 3' : t3,\n",
-    "                   'Gender'    : gender, 'ID'  : id_col\n",
-    "                  })"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e5b9dbbd",
-   "metadata": {},
-   "source": [
-    "We now have three *Control* and three *Test* groups, simulating three replicates of the same experiment. Our\n",
-    "dataset has also a non-numerical column indicating gender, and another\n",
-    "column indicating the identity of each observation."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a9e6d91f",
-   "metadata": {},
-   "source": [
-    "This is known as a 'wide' dataset. See this\n",
-    "[writeup](https://sejdemyr.github.io/r-tutorials/basics/wide-and-long/)\n",
-    "for more details."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "ef976b60",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Control 1</th>\n",
-       "      <th>Test 1</th>\n",
-       "      <th>Control 2</th>\n",
-       "      <th>Test 2</th>\n",
-       "      <th>Control 3</th>\n",
-       "      <th>Test 3</th>\n",
-       "      <th>Gender</th>\n",
-       "      <th>ID</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>2.793984</td>\n",
-       "      <td>3.420875</td>\n",
-       "      <td>3.324661</td>\n",
-       "      <td>1.707467</td>\n",
-       "      <td>3.816940</td>\n",
-       "      <td>1.796581</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>3.236759</td>\n",
-       "      <td>3.467972</td>\n",
-       "      <td>3.685186</td>\n",
-       "      <td>1.121846</td>\n",
-       "      <td>3.750358</td>\n",
-       "      <td>3.944566</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>3.019149</td>\n",
-       "      <td>4.377179</td>\n",
-       "      <td>5.616891</td>\n",
-       "      <td>3.301381</td>\n",
-       "      <td>2.945397</td>\n",
-       "      <td>2.832188</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>2.804638</td>\n",
-       "      <td>4.564780</td>\n",
-       "      <td>2.773152</td>\n",
-       "      <td>2.534018</td>\n",
-       "      <td>3.575179</td>\n",
-       "      <td>3.048267</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>2.858019</td>\n",
-       "      <td>3.220058</td>\n",
-       "      <td>2.550361</td>\n",
-       "      <td>2.796365</td>\n",
-       "      <td>3.692138</td>\n",
-       "      <td>3.276575</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   Control 1    Test 1  Control 2    Test 2  Control 3    Test 3  Gender  ID\n",
-       "0   2.793984  3.420875   3.324661  1.707467   3.816940  1.796581  Female   1\n",
-       "1   3.236759  3.467972   3.685186  1.121846   3.750358  3.944566  Female   2\n",
-       "2   3.019149  4.377179   5.616891  3.301381   2.945397  2.832188  Female   3\n",
-       "3   2.804638  4.564780   2.773152  2.534018   3.575179  3.048267  Female   4\n",
-       "4   2.858019  3.220058   2.550361  2.796365   3.692138  3.276575  Female   5"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "21171074",
-   "metadata": {},
-   "source": [
-    "## Loading data"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "adc6d626",
-   "metadata": {},
-   "source": [
-    "Next, we load data as usual using ``dabest.load()``. However, this time, we also specify the argument ``mini_meta=True``. Since we are loading data from three experiments, ``idx`` is passed as a tuple of tuples, as shown below:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "e6701c83",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "unpaired = dabest.load(df, idx=((\"Control 1\", \"Test 1\"), (\"Control 2\", \"Test 2\"), (\"Control 3\", \"Test 3\")), mini_meta=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1a3bcd5c",
-   "metadata": {},
-   "source": [
-    "When this `dabest` object is invoked, it should indicate that effect sizes will be calculated for each group, along with the weighted delta. It is important to note once again that the weighted delta will only be calculated for mean differences"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "cafcafd2",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DABEST v2024.03.29\n",
-       "==================\n",
-       "                  \n",
-       "Good afternoon!\n",
-       "The current time is Tue Mar 19 15:39:44 2024.\n",
-       "\n",
-       "Effect size(s) with 95% confidence intervals will be computed for:\n",
-       "1. Test 1 minus Control 1\n",
-       "2. Test 2 minus Control 2\n",
-       "3. Test 3 minus Control 3\n",
-       "4. weighted delta (only for mean difference)\n",
-       "\n",
-       "5000 resamples will be used to generate the effect size bootstraps."
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "unpaired"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f52ddc8e",
-   "metadata": {},
-   "source": [
-    "By calling the ``mean_diff`` attribute, you can view the mean differences for each group as well as the weighted delta.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "535d1163",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DABEST v2024.03.29\n",
-       "==================\n",
-       "                  \n",
-       "Good afternoon!\n",
-       "The current time is Tue Mar 19 15:39:47 2024.\n",
-       "\n",
-       "The unpaired mean difference between Control 1 and Test 1 is 0.48 [95%CI 0.221, 0.768].\n",
-       "The p-value of the two-sided permutation t-test is 0.001, calculated for legacy purposes only. \n",
-       "\n",
-       "The unpaired mean difference between Control 2 and Test 2 is -1.38 [95%CI -1.93, -0.895].\n",
-       "The p-value of the two-sided permutation t-test is 0.0, calculated for legacy purposes only. \n",
-       "\n",
-       "The unpaired mean difference between Control 3 and Test 3 is -0.255 [95%CI -0.717, 0.196].\n",
-       "The p-value of the two-sided permutation t-test is 0.293, calculated for legacy purposes only. \n",
-       "\n",
-       "The weighted-average unpaired mean differences is -0.0104 [95%CI -0.222, 0.215].\n",
-       "The p-value of the two-sided permutation t-test is 0.937, calculated for legacy purposes only. \n",
-       "\n",
-       "5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.\n",
-       "Any p-value reported is the probability of observing theeffect size (or greater),\n",
-       "assuming the null hypothesis of zero difference is true.\n",
-       "For each p-value, 5000 reshuffles of the control and test labels were performed.\n",
-       "\n",
-       "To get the results of all valid statistical tests, use `.mean_diff.statistical_tests`"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "unpaired.mean_diff"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0de6f65c",
-   "metadata": {},
-   "source": [
-    "You can view the details of each experiment by accessing the property `mean_diff.results` as follows."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "c9cb2caa",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>control</th>\n",
-       "      <th>test</th>\n",
-       "      <th>control_N</th>\n",
-       "      <th>test_N</th>\n",
-       "      <th>effect_size</th>\n",
-       "      <th>is_paired</th>\n",
-       "      <th>difference</th>\n",
-       "      <th>ci</th>\n",
-       "      <th>bca_low</th>\n",
-       "      <th>bca_high</th>\n",
-       "      <th>bca_interval_idx</th>\n",
-       "      <th>pct_low</th>\n",
-       "      <th>pct_high</th>\n",
-       "      <th>pct_interval_idx</th>\n",
-       "      <th>bootstraps</th>\n",
-       "      <th>resamples</th>\n",
-       "      <th>random_seed</th>\n",
-       "      <th>permutations</th>\n",
-       "      <th>pvalue_permutation</th>\n",
-       "      <th>permutation_count</th>\n",
-       "      <th>permutations_var</th>\n",
-       "      <th>pvalue_welch</th>\n",
-       "      <th>statistic_welch</th>\n",
-       "      <th>pvalue_students_t</th>\n",
-       "      <th>statistic_students_t</th>\n",
-       "      <th>pvalue_mann_whitney</th>\n",
-       "      <th>statistic_mann_whitney</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Control 1</td>\n",
-       "      <td>Test 1</td>\n",
-       "      <td>20</td>\n",
-       "      <td>20</td>\n",
-       "      <td>mean difference</td>\n",
-       "      <td>None</td>\n",
-       "      <td>0.480290</td>\n",
-       "      <td>95</td>\n",
-       "      <td>0.220869</td>\n",
-       "      <td>0.767721</td>\n",
-       "      <td>(140, 4889)</td>\n",
-       "      <td>0.215697</td>\n",
-       "      <td>0.761716</td>\n",
-       "      <td>(125, 4875)</td>\n",
-       "      <td>[0.6686169333655454, 0.4382051534234943, 0.665...</td>\n",
-       "      <td>5000</td>\n",
-       "      <td>12345</td>\n",
-       "      <td>[-0.17259843762502491, 0.03802293852634886, -0...</td>\n",
-       "      <td>0.0010</td>\n",
-       "      <td>5000</td>\n",
-       "      <td>[0.026356588154404337, 0.027102495439046997, 0...</td>\n",
-       "      <td>0.002094</td>\n",
-       "      <td>-3.308806</td>\n",
-       "      <td>0.002057</td>\n",
-       "      <td>-3.308806</td>\n",
-       "      <td>0.001625</td>\n",
-       "      <td>83.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Control 2</td>\n",
-       "      <td>Test 2</td>\n",
-       "      <td>20</td>\n",
-       "      <td>20</td>\n",
-       "      <td>mean difference</td>\n",
-       "      <td>None</td>\n",
-       "      <td>-1.381085</td>\n",
-       "      <td>95</td>\n",
-       "      <td>-1.925232</td>\n",
-       "      <td>-0.894537</td>\n",
-       "      <td>(108, 4857)</td>\n",
-       "      <td>-1.903964</td>\n",
-       "      <td>-0.875420</td>\n",
-       "      <td>(125, 4875)</td>\n",
-       "      <td>[-1.1603841133810318, -1.6359724856206515, -1....</td>\n",
-       "      <td>5000</td>\n",
-       "      <td>12345</td>\n",
-       "      <td>[0.015164519971271773, 0.017231919606192303, -...</td>\n",
-       "      <td>0.0000</td>\n",
-       "      <td>5000</td>\n",
-       "      <td>[0.12241741427801064, 0.12241565174150129, 0.1...</td>\n",
-       "      <td>0.000011</td>\n",
-       "      <td>5.138840</td>\n",
-       "      <td>0.000009</td>\n",
-       "      <td>5.138840</td>\n",
-       "      <td>0.000026</td>\n",
-       "      <td>356.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Control 3</td>\n",
-       "      <td>Test 3</td>\n",
-       "      <td>20</td>\n",
-       "      <td>20</td>\n",
-       "      <td>mean difference</td>\n",
-       "      <td>None</td>\n",
-       "      <td>-0.254831</td>\n",
-       "      <td>95</td>\n",
-       "      <td>-0.717337</td>\n",
-       "      <td>0.196121</td>\n",
-       "      <td>(115, 4864)</td>\n",
-       "      <td>-0.710346</td>\n",
-       "      <td>0.206131</td>\n",
-       "      <td>(125, 4875)</td>\n",
-       "      <td>[-0.09556572841011901, 0.35166073097757433, -0...</td>\n",
-       "      <td>5000</td>\n",
-       "      <td>12345</td>\n",
-       "      <td>[-0.05901068591042824, -0.13617667681797307, 0...</td>\n",
-       "      <td>0.2934</td>\n",
-       "      <td>5000</td>\n",
-       "      <td>[0.058358897501663703, 0.05796253365278035, 0....</td>\n",
-       "      <td>0.294766</td>\n",
-       "      <td>1.069798</td>\n",
-       "      <td>0.291459</td>\n",
-       "      <td>1.069798</td>\n",
-       "      <td>0.285305</td>\n",
-       "      <td>240.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "     control    test  control_N  test_N      effect_size is_paired  \\\n",
-       "0  Control 1  Test 1         20      20  mean difference      None   \n",
-       "1  Control 2  Test 2         20      20  mean difference      None   \n",
-       "2  Control 3  Test 3         20      20  mean difference      None   \n",
-       "\n",
-       "   difference  ci   bca_low  bca_high bca_interval_idx   pct_low  pct_high  \\\n",
-       "0    0.480290  95  0.220869  0.767721      (140, 4889)  0.215697  0.761716   \n",
-       "1   -1.381085  95 -1.925232 -0.894537      (108, 4857) -1.903964 -0.875420   \n",
-       "2   -0.254831  95 -0.717337  0.196121      (115, 4864) -0.710346  0.206131   \n",
-       "\n",
-       "  pct_interval_idx                                         bootstraps  \\\n",
-       "0      (125, 4875)  [0.6686169333655454, 0.4382051534234943, 0.665...   \n",
-       "1      (125, 4875)  [-1.1603841133810318, -1.6359724856206515, -1....   \n",
-       "2      (125, 4875)  [-0.09556572841011901, 0.35166073097757433, -0...   \n",
-       "\n",
-       "   resamples  random_seed                                       permutations  \\\n",
-       "0       5000        12345  [-0.17259843762502491, 0.03802293852634886, -0...   \n",
-       "1       5000        12345  [0.015164519971271773, 0.017231919606192303, -...   \n",
-       "2       5000        12345  [-0.05901068591042824, -0.13617667681797307, 0...   \n",
-       "\n",
-       "   pvalue_permutation  permutation_count  \\\n",
-       "0              0.0010               5000   \n",
-       "1              0.0000               5000   \n",
-       "2              0.2934               5000   \n",
-       "\n",
-       "                                    permutations_var  pvalue_welch  \\\n",
-       "0  [0.026356588154404337, 0.027102495439046997, 0...      0.002094   \n",
-       "1  [0.12241741427801064, 0.12241565174150129, 0.1...      0.000011   \n",
-       "2  [0.058358897501663703, 0.05796253365278035, 0....      0.294766   \n",
-       "\n",
-       "   statistic_welch  pvalue_students_t  statistic_students_t  \\\n",
-       "0        -3.308806           0.002057             -3.308806   \n",
-       "1         5.138840           0.000009              5.138840   \n",
-       "2         1.069798           0.291459              1.069798   \n",
-       "\n",
-       "   pvalue_mann_whitney  statistic_mann_whitney  \n",
-       "0             0.001625                    83.0  \n",
-       "1             0.000026                   356.0  \n",
-       "2             0.285305                   240.0  "
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pd.options.display.max_columns = 50\n",
-    "unpaired.mean_diff.results"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c581f3fa",
-   "metadata": {},
-   "source": [
-    "Note, however, that this does not contain the relevant information for our weighted delta. The details of the weighted delta are stored as attributes of the ``mini_meta_delta`` object, such as:\n",
-    "\n",
-    "  - ``group_var``: the pooled group variances of each set of 2 experiment groups.\n",
-    "  - ``difference``: the weighted mean difference calculated based on the raw data.\n",
-    "  - ``bootstraps``: the deltas of each set of 2 experiment groups calculated based on the bootstraps.\n",
-    "  - ``bootstraps_weighted_delta``: the weighted deltas calculated based on the bootstraps.\n",
-    "  - ``permutations``: the deltas of each set of 2 experiment groups calculated based on the permutation data.\n",
-    "  - ``permutations_var``: the pooled group variances of each set of 2 experiment groups calculated based on permutation data.\n",
-    "  - ``permutations_weighted_delta``: the weighted deltas calculated based on the permutation data.\n",
-    "\n",
-    "You can call each of the above attributes individually:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "1c867467",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "-0.01035228770106855"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "unpaired.mean_diff.mini_meta_delta.difference"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5eafcc8e",
-   "metadata": {},
-   "source": [
-    "Attributes of the weighted delta can also be recorded in a `dict` using the ``to_dict()`` function. Here, we demonstrate this process and then convert the generated dictionary into a dataframe for enhanced readability:\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "d30d3b9b",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>0</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>acceleration_value</th>\n",
-       "      <td>0.000193</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>alpha</th>\n",
-       "      <td>0.05</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>bca_high</th>\n",
-       "      <td>0.215037</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>bca_interval_idx</th>\n",
-       "      <td>(128, 4878)</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>bca_low</th>\n",
-       "      <td>-0.221666</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>bias_correction</th>\n",
-       "      <td>0.005013</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>bootstraps</th>\n",
-       "      <td>[[0.6686169333655454, 0.4382051534234943, 0.66...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>bootstraps_weighted_delta</th>\n",
-       "      <td>[0.1771640316740503, 0.05505265333097302, 0.16...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>ci</th>\n",
-       "      <td>95</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>control</th>\n",
-       "      <td>[Control 1, Control 2, Control 3]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>control_N</th>\n",
-       "      <td>[20, 20, 20]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>control_var</th>\n",
-       "      <td>[0.17628013404546258, 0.9584767911266554, 0.16...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>difference</th>\n",
-       "      <td>-0.010352</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>group_var</th>\n",
-       "      <td>[0.021070042637349427, 0.07222883451891535, 0....</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>jackknives</th>\n",
-       "      <td>[-0.008668330406027476, -0.00864390324492664, ...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>pct_high</th>\n",
-       "      <td>0.213769</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>pct_interval_idx</th>\n",
-       "      <td>(125, 4875)</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>pct_low</th>\n",
-       "      <td>-0.222307</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>permutation_count</th>\n",
-       "      <td>5000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>permutations</th>\n",
-       "      <td>[[-0.17259843762502491, 0.03802293852634886, -...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>permutations_var</th>\n",
-       "      <td>[[0.026356588154404337, 0.027102495439046997, ...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>permutations_weighted_delta</th>\n",
-       "      <td>[-0.11757207833491819, -0.012928679700934625, ...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>pvalue_permutation</th>\n",
-       "      <td>0.9374</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>test</th>\n",
-       "      <td>[Test 1, Test 2, Test 3]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>test_N</th>\n",
-       "      <td>[20, 20, 20]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>test_var</th>\n",
-       "      <td>[0.24512071870152594, 0.4860998992516514, 0.96...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                                                             0\n",
-       "acceleration_value                                                    0.000193\n",
-       "alpha                                                                     0.05\n",
-       "bca_high                                                              0.215037\n",
-       "bca_interval_idx                                                   (128, 4878)\n",
-       "bca_low                                                              -0.221666\n",
-       "bias_correction                                                       0.005013\n",
-       "bootstraps                   [[0.6686169333655454, 0.4382051534234943, 0.66...\n",
-       "bootstraps_weighted_delta    [0.1771640316740503, 0.05505265333097302, 0.16...\n",
-       "ci                                                                          95\n",
-       "control                                      [Control 1, Control 2, Control 3]\n",
-       "control_N                                                         [20, 20, 20]\n",
-       "control_var                  [0.17628013404546258, 0.9584767911266554, 0.16...\n",
-       "difference                                                           -0.010352\n",
-       "group_var                    [0.021070042637349427, 0.07222883451891535, 0....\n",
-       "jackknives                   [-0.008668330406027476, -0.00864390324492664, ...\n",
-       "pct_high                                                              0.213769\n",
-       "pct_interval_idx                                                   (125, 4875)\n",
-       "pct_low                                                              -0.222307\n",
-       "permutation_count                                                         5000\n",
-       "permutations                 [[-0.17259843762502491, 0.03802293852634886, -...\n",
-       "permutations_var             [[0.026356588154404337, 0.027102495439046997, ...\n",
-       "permutations_weighted_delta  [-0.11757207833491819, -0.012928679700934625, ...\n",
-       "pvalue_permutation                                                      0.9374\n",
-       "test                                                  [Test 1, Test 2, Test 3]\n",
-       "test_N                                                            [20, 20, 20]\n",
-       "test_var                     [0.24512071870152594, 0.4860998992516514, 0.96..."
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "weighted_delta_details = unpaired.mean_diff.mini_meta_delta.to_dict()\n",
-    "weighted_delta_df = pd.DataFrame.from_dict(weighted_delta_details, orient = 'index')\n",
-    "weighted_delta_df"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7797244d",
-   "metadata": {},
-   "source": [
-    "## Generating estimation plots - unpaired data"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d51a505d",
-   "metadata": {},
-   "source": [
-    "Calling the ``plot()`` method produces a **Cumming estimation plot** showing the data for each experimental replicate as well as the calculated weighted delta.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "3ffaa157",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1200x600 with 2 Axes>"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1200x600 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "unpaired.mean_diff.plot()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a382d182",
-   "metadata": {},
-   "source": [
-    "You can also hide the weighted delta by passing the argument ``show_mini_meta=False``. In this case, the resulting graph would be identical to a multiple two-groups plot:\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "91488409",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 900x600 with 2 Axes>"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 900x600 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "unpaired.mean_diff.plot(show_mini_meta=False)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e132dfd1",
-   "metadata": {},
-   "source": [
-    "## Producing estimation plots - paired data"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9103409b",
-   "metadata": {},
-   "source": [
-    "The tutorial up to this point has focused on unpaired data. If your data is paired, the process for loading, plotting, and accessing the data is similar to that for unpaired data, with the exception that the argument ``paired=\"sequential\"`` or ``paired=\"baseline\"`` and an appropriate ``id_col`` are passed during the ``dabest.load()`` step, as shown below:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "4b0feff8",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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NDWXGjBmcOXOG8PBw/Pz8RryeCEGCMLY0Gg2FhYWcOnWKy5cvYzQaWbhwIVu2bMHf3/+ev28ymcjPz+fs2bMMDAyQmpqK0WgUAUgQnkEiAAnCfWpubmb37t309/ezadMmoqOjH/cpCQ9h+vTpQ0KQuaBBeHg4r7zyCp9++inbtm1jy5YtuLu7j3iM9PR0Kisr2b9/P6+//jpWVlYjXk+EIEH4ZoxGIxUVFeTn53Pr1i0qKyvR6/XMnDmTrVu34uvre89jSJJERUUFmZmZtLW1MWXKFObPnz/iPj5BEJ4NogrcGBNV4J4+kiRx7do1vvzyS3x8fFi/fj1ubm6P+7SEb0CSJA4dOsSNGzd44YUXiIyMlH+mUqnYsWMHAwMDbN68edSR5ba2Nt577z2SkpJYunTpPW/v2LFjXL16lZUrV4oQJAj30NbWRn5+Pjdv3qSvrw+j0Yharcbb25vly5czderU+9q/09LSwsmTJ6mqqiI0NJTFixff12yRIAhPNzEDJAh3odPpOHLkCIWFhaSkpJCRkYGl5fA/G7GZdnwxNy/t7+9n9+7dvPzyywQFBQHg5ubGa6+9xs6dO9m+fTsbN24kIiJi2DG8vb1ZtGgRX375JZGRkUyYMOGut7ds2TLgf6rDiQESQRiqv7+fwsJCCgoKaGpqwt7entDQUJqbm1GpVKSnp5ORkYGjo+M9j9XT08OZM2e4ceMGHh4evPDCC0RFRYnnaUEQADEDNObEDNDTo62tjd27d9PT08PKlStHbJhpXlN+9epVXnvttVGXQglPJr1ez44dO2hra+PVV1/F29tb/tng4CB79uyhsrKS1atXj9j7R5Ik+ffffPNN7O3t73p75pmgvLw8Vq5cKZ4jhGeeyWSisrKSgoICSktLkSSJyMhIJk+eTHt7O1lZWTg7O7N8+fK7DjKY6XQ6srKyuHz5MjY2NsybN4+pU6eKvZqCIAwhAtAYEwHo6VBQUMDRo0dxd3dn/fr1eHp6DrtOXV0dX375Jc3NzSQkJJCRkYGdnd1jONtny8DAAFZWVmP2hmZgYICPPvqI/v5+tm7dOqSin9Fo5PDhwxQUFLB48WJmzJgx7Pd7e3v505/+RGhoKBs2bLjnCLMIQYIAHR0dFBQUcOPGDXp7e/H29iYhIYEpU6bQ2dnJ4cOH6erqYsaMGcydO/eeg0tGo5Hr169z7tw5dDod06dPZ9asWaLpqSAIIxJL4AThDnq9nmPHjpGfn09iYiLLli0b9sLb09NDZmYmhYWFBAQE8N3vfpfAwMDHdMbPFqPRyAcffEBkZCQZGRljckxbW1u2bNnChx9+yJ///Gdee+01eSZHqVSyatUqnJycOHnyJL29vSxevHhIyHFycmLFihXs3r2bGzdukJCQcNfbu3M53KFDh5AkiaSkpDG5L4LwJBsYGODWrVsUFBRQX1+Pra0tcXFxJCYm4ufnR39/P5mZmeTn5xMUFMQbb7yBj4/PXY8pSRK3b98mMzOTzs5O4uPjSU9PF60JBEG4KxGABOH/19nZye7du+nq6mL16tXD3sgaDAays7O5ePEiVlZWrFq1ioSEBLGm/BFSKpWkpKTw5ZdfEhAQMOKyxIfh5OTESy+9xLZt2/j000/5zne+I/cCUigULFiwAEdHR44fP05fXx+rV68eMgMVExNDQkICx44dIyQk5J5FMswhSKFQcPjwYQARgoSnkiRJVFdXU1BQQElJCQaDgYiICNavX8/EiROxtLREkiRu3rzJiRMnMJlMPPfccyQlJd3zubWpqYmTJ09SU1NDeHg469atG7UsvSAIwp1EABIEoKioiEOHDuHs7Mx3v/vdIaOO5hHGEydOoFarSU1NZe7cufdstid8O6ZNm0ZjYyNffPEFXl5e9xwhvl/u7u5s2bKF7du3s2vXLl588cUhISc1NRVHR0f279+PRqNh48aNQ5bXLF26lNraWvbv38+rr756z94iCoVCrh4nQpDwtOnq6uLGjRsUFBTQ3d2Nh4cHc+fOZcqUKTg7O8vX6+zs5OjRo1RVVTF58mQyMjJwcnK667HVajVnzpzh5s2beHl5sXnzZiZMmCAGowRBuG9iD9AYE3uAxheDwcDJkye5cuUKkydPZsWKFUPe1HZ0dHD8+HEqKiqIiIhgyZIleHl5PcYzFuCrpYrbtm1jcHCQ119/fUzDaHV1NTt27GDSpEmsW7du2JuqmpoaPvvsM9zd3dm8efOQilR1dXV89NFHpKenM2fOnPu6PUmS+PLLL7ly5QorVqwQIUgYtwYHBykuLiY/P5/a2lpsbGyYPHkyCQkJBAYGDvlbMhqNZGVlceHCBRwdHVm+fPmQcvQjGRgY4OLFi+Tm5mJra0t6ejqJiYmikakgCA9MBKAxJgLQ+KFSqdizZw+tra0sXbp0yJILnU7H+fPnycnJwcXFhYyMDCZOnChGGJ8gKpWKd999l+DgYF544YUx/b8pKSlh9+7dpKSksHTp0mHHbm1tZceOHVhaWrJlyxY8PDzkn505c4ZLly6xdetWAgIC7uv27gxBzz33HMnJyWN2XwTh2yRJEnV1deTn51NcXMzg4CDh4eEkJCQwadKkEYsX1NbWcuTIETo7O++ryIHRaOTq1aucP39eboI6Y8YMeZmqIAjCgxIBaIyJADQ+lJWVceDAAezs7NiwYYO8blySJAoKCjh9+jQ6nY45c+Ywffr0EXv/CI9feXk5O3fuZN68ecydO3dMj33t2jUOHz5Menr6iMdWq9Xs2LEDrVbL5s2b5bBjNBrZtm0bOp2ON954477fpEmSxPHjx8nNzRUhSHjidXd3U1BQQEFBASqVCjc3NxISEoiPj8fV1XXE3zEXObh+/TqBgYGsWLHirktYJUmitLSUzMxMVCoViYmJpKen33OJnCAIwr2Id3XCM8VoNHL69GkuX75MdHQ0q1evlpdPNTQ08OWXX9LY2EhcXByLFi0aslZdeDJUVlbi6+uLg4MDkZGRpKenc/bsWfz8/IiKihqz20lKSkKj0XDmzBkcHByGBRJXV9dhDVMnTJiAUqlk7dq1vPvuu2RmZrJ8+fL7uj2FQsGSJUsAOHLkCIAIQcITRa/XU1JSQkFBAdXV1VhaWhIbG8uqVasICQkZdRZWkiQKCws5ceIEBoOB5cuXk5ycfNdZ24aGBk6ePEldXR0TJkxg48aNY7bfTxAEQQQg4ZnR09PDnj17aGxsJCMjg7S0NBQKBX19fZw6dYqCggJ8fX159dVXCQkJedynK4zA3JdHkiQ2bNhAQEAAs2fPprGxkf379/P666/j7u4+Zrc3e/ZsNBoNR48exd7enpiYmCE/t7e35zvf+Q579uxh586drFq1ivj4eDw9PVm8eDFHjx4lMjLyvoOZCEHCk0aSJBoaGigoKKCoqAidTkdISAgrV64kJibmnn12urq6OHr0KJWVlcTGxrJkyZK7zuCoVCpOnz5NUVERPj4+vPTSS0RERIz13RIE4RknlsCNMbEE7slUUVHB/v37sbS0ZP369QQFBWE0GsnNzeX8+fMolUrmz5/P1KlTxYbaJ1xPTw+7d++mublZ3rul0+l4//33sbS0ZOvWrWO6N0CSJPbv309xcTFbtmwhLCxs2HVMJhNHjhzh+vXrLFy4kJkzZwKwc+dOmpqa+P73v4+Dg8MD3aZ5Odzy5ctJSUkZs/sjCPejt7dXruLW0dGBi4sL8fHxJCQk3Ncgg9Fo5PLly5w/f/6+ihz09/dz4cIFrly5gr29PfPnzyc+Pv5bez5ub2+nqqqK1NTUb+X4giA82UQAGmMiAD1ZTCYT586d4+LFi0yYMIE1a9Zgb29PRUUFx48fp7Ozk5SUFNLT07Gzs3uo25AkiY6ODlEd7hEyGAycOHGCvLw8EhISWL58OSqVig8++ICJEyeydu3aMS2KYDQa2blzJ/X19bzyyiv4+/sPu44kSZw7d47z58+TmprKkiVL0Gg0vPPOOwQGBrJp06YHOidJkjhx4gQ5OTkiBD0j+vr6cHBweGzFVgwGA2VlZRQUFFBRUYFSqWTSpEkkJiYSGhp632Gkrq6Ow4cP09nZyfTp05k7d+6ogxIGg4G8vDwuXLiA0Whk1qxZpKWlfWsFDjo6Ojh//jxFRUW4uLjw/e9/XxRTEIRnkAhAd/F//+//5ec//zl/8zd/wx/+8If7+h0RgJ4cfX197Nu3j5qaGubPn8+sWbNQqVScOHGCsrIyQkNDWbp06UOvKzcYDBQWFpKTk0N7ezs//OEPxebcR+zGjRscOXIEDw8PNm7cSFNTE3v27GHJkiWkpaWN6W0NDg7y8ccfo1aree2114ZUfrtTXl4ex44dIyYmhjVr1lBZWclnn332UCWuRQh6duj1ev74xz/i5+fH8uXLH9n+Q0mSaG5upqCggMLCQvr7+wkMDCQxMZHY2NgHKjHf39/PqVOnuHbtGgEBAaxYsQJfX99Rb7e4uJhTp06hVqtJSkpi3rx5Q8rKj6XOzk7Onz9PYWEhTk5OzJkzh8TExCG9vgRBeHaIADSKvLw8NmzYgLOzM+np6SIAjTM1NTXs3bsXgOeffx5/f38uXrzI5cuXcXR0JCMjg0mTJj3USGtvby95eXlcvXoVrVZLVFQUaWlphIWFiTLZj0FLSwu7du1iYGCAtWvXUl1dTU5ODi+//DKhoaFjeltarZYPP/wQg8HA1q1bRw28JSUl7Nu3T575yczM5ObNm/zFX/zFqMFpNHeGoGXLljFt2rSxuCvCE6ikpISjR4+i1+tZvHgxU6dO/daeUzQaDTdv3iQ/P5+2tjacnJzkJW6enp4PdCxJkigqKuL48eMYDAYWLFhAcnLyqDNGdXV1nDx5koaGBqKioli0aNG3NoPe1dXF+fPnuXnzJk5OTsyePZvExERR2VMQnnEiAI2gr6+PqVOn8qc//Yn/83/+DwkJCSIAjROSJHHp0iXOnDlDaGio/IY4MzOT/v5+Zs2axcyZM+/ac2I0jY2N5OTkcOvWLSwtLUlMTGTatGkP/IZWGHv9/f0cOHCA8vJyZs+eTV1dHe3t7bzxxhtjPpLe3d3Ntm3bsLOz49VXXx11hLy2tpbPPvsMFxcXNmzYwKeffoqdnR2vvfbaA486S5LEyZMnyc7OFiHoKXdnqejQ0FBWrlw5ZoU9jEYj5eXl5OfnU15ejkKhIDo6moSEBCIiIh5qv82dRQ5iYmJYunTpqAMDnZ2dnDp1ipKSEvz8/Fi8ePGIe+rGgkqlkoOPg4MDs2fPZurUqSL4CIIAiAA0ou985zu4u7vz+9//nnnz5t01AOl0OnQ6nfx1QUEBc+fOFQHoMdBqtRw4cICKigpmz57NxIkTOXHiBHV1dcTExLB48eJR+1OMxmg0UlJSQm5uLvX19bi5uZGamkpCQsIDLQ0Rvn2SJHHx4kXOnj1LUFAQ7e3teHh48Morr4z5m5729nY+/PBDvLy8eOmll0YN1G1tbezYsQMLCwvmz5/PwYMHmT17Nunp6Q98myIEPVuqqqo4fPgwvb29zJ8/n7S0tIcuCNDa2kp+fj6FhYVoNBr8/f1JSEhg8uTJ2NvbP9Qx7yxy4ODgwPLly0etdqjVajl//jx5eXk4OTmxYMEC4uLivpXZLZVKxYULF7hx4wb29vbMmjWLpKSkhxr0EgTh6SUC0Nd8/vnn/OY3vyEvLw9bW9t7BqBf/vKX/OpXvxr2fRGAHq36+nr27t2LXq9nyZIl1NbWcv36dby8vFi6dOkDjzJqtVquX7/OlStX6OnpISwsjNTUVKKiokSVuCdcRUUF+/btY2BgAI1Gw5w5c3juuefG/Hbq6+v55JNPCA8PZ+PGjaM+Lrq7u9mxYwcajYawsDBKSkp49dVXCQoKeuDbFCHo2TI4OMjZs2fJycnBz8+PVatW3feeRa1WS2FhIQUFBTQ3N+Pg4MCUKVNISEj4xv106uvrOXz4MB0dHaSlpTFv3rwRCwkYDAZyc3O5cOEC8FVZ+dTU1G8ljKjVai5evEh+fj729vbMnDmT5ORkEXwEQRiRCEB3qK+vJzk5mczMTKZMmQIgZoCecJIkkZOTQ2ZmJn5+foSHh5OXlwdAeno6KSkpDxRY2trayM3N5ebNm0iSRFxcHGlpaaIB3zijVqvZvXs3BQUFGI1G/uIv/oLExMQxv53y8nI+++wz4uPjWbly5agj2v39/Xz22Wc0NjZiYWGBk5MTb7zxxj17qIxEhKBnT0NDA1988QWdnZ3Mnj2b2bNnjziraTKZqKiooKCggLKyMiRJIioqioSEBCIjI7/xhv/+/n5Onz7N1atXCQgI4LnnnsPPz2/Y9cx7gk6dOkVvby/JycnMnTv3gUrB36/u7m45+Nja2jJz5kxSUlJQKpVotVq0Wi0ajUa+3Pm10WjkxRdfHPNzEgThyScC0B0OHjzImjVrhrxIGI1GFAoFFhYW6HS6e76AiD1Aj87AwABffPEFJSUlhIeH09PTQ2dnJ1OnTmX+/Pn3/WIrSRLl5eXk5ORQVVWFk5MTKSkpJCUlfSsv2MKjYTAYOHbsGJ999hlKpZJ//ud/Jjg4eMxv5+bNm+zfv59Zs2axcOHCUa+n1+vZt28fBQUF9PX1sXjxYlauXPlQtylJEpmZmVy+fFmEoGeEwWDg0qVLXLx4EXd3d1auXCnPIra3t1NQUMCNGzfo6+vDx8eHhIQEpkyZMibPYZIkcevWLY4fP45er2f+/PmjDi7V1NRw8uRJmpqaiI6OZuHChQ9cVGEkJpOJ/v5+Oby0tbWRlZVFUVERkiQRFhaGn58fOp0OrVZLf38/X397o1QqcXBwwMHBAXt7exwdHVm9erUoXiMIzyARgO7Q29tLbW3tkO+9+uqrREdH89Of/pTJkyff8xhPSgA6f/48JpMJb29vvL298fDweKqWbpnLHXd1deHq6kp3dzdBQUEsW7ZsxBHJkeh0OgoKCsjNzaWrq4uAgADS0tKIiYkRpVGfIleuXOGf//mfsba25g9/+MOIPXy+qZycHI4fP87ixYuZMWPGqNczmUwcO3aMw4cPMzg4yN/+7d8yadKkh7rNO0PQ0qVLRUPHZ0RrayuHDh2itrYWb29vrK2taWlpwc7Ojri4OBITE/H19R2zN/UqlYqjR49SUVHBpEmTWLp06YiFRTo6OsjMzKSsrIyAgAAWL15MSEjIqMc1B5o7Z2TuNltjDjQ6nY66ujqampqwsrIiKiqK6OhoXFxcsLe3lwOOOeTc+bmNjY0IO4IgAE9BAGpsbOTChQu0tbWxbt06AgMDMRqNdHd34+Li8o3fyN5rCdzXPSkBaN++fVRXV9PX1weApaUlnp6eeHt74+PjIwcjZ2fncfWCIEkS165d48iRI/T29mJra4u7uzuLFy9m8uTJ93VfVCoVubm55Ofno9friYmJIS0tjcDAwEdwD4Rv6tq1awQFBeHt7X3fv1NaWsrf/u3fYmdnx29+8xsmTJgw5ud1+vRpLl68yJo1a4iPjx/1epIkcf78ed5++22cnJz4wx/+gIuLy0PdpghBzxaTyUR1dTXXr18nMzOTyspK/P392bx5MwsXLhzTYh9Go5Hs7GzOnz+Pvb09y5YtY+LEicOup9FoOHfuHFevXsXOzo60tDSCg4NHDTbmz7Va7bAZGgsLi2HBxfw1fPV3fPv2bbmq26xZs0QxGkEQHsq4DUCSJPGjH/2It99+G4PBgEKhIDMzk/nz58uzAb/+9a956623vtHtjNcAZGZeKtDW1kZra6v8+eDgIAC2trZyGLozHNnZ2T3mMx9Op9Nx+PBhzpw5g16vJyAgQF4Pf69O3pIkUVNTQ05ODrdv38bOzo6kpCRSUlIeWcNB4ZszGo28//77dHV1sXr1amJiYu77d4uLi/mHf/gHHBwc+N73vsesWbPGNPxLksThw4cpKChg06ZNo1bEMrt06RK//vWviYqK4ve///1Db9YWIejp19XVJS9x6+7uxtPTk8TERAIDAzl37hzV1dUkJCSQkZExJs/d5iIHTU1NTJkyhcTERPR6/ZAg09PTw82bN7l16xZ6vR5/f3/8/f2HrDSwsLAYNgtztxkaW1vbYX+TfX19XLp0iatXr2JpacmMGTNITU19qP1zgiAIZuM2AP3ud7/j5z//OT/96U9ZsGABixYt4tSpU8yfPx+AV155hcrKSi5evPhIz+tJC0AjkSQJtVothyFzOOro6MBkMgHg5OQ0ZKbIx8cHT0/Px1ZRp7W1lW3btnH9+nW8vb2ZPXs2GRkZ9+yPodfrKSwsJDc3l9bWVry9vUlLSyMuLk5UBxqnBgcHOXToEEVFRcyaNYv58+ff9/LOrKws3nvvPXkEec2aNWM6gmwymdi9ezeVlZW8/PLL96z0duLECX77298yc+ZM/u7v/u6hz0WSJE6dOkVWVpYIQeOUyWRi3759xMXFER0djU6no7i4mPz8fOrq6rCxsSEuLo6EhAQCAgLkoCBJEvn5+Zw8eRJLS0uWLVs24sCAJElyZcTRZmdUKhX5+flUVFRga2tLZGTkkJ4+FhYW2NnZ0dXVRU1NDZIkERsbK/dD+3qoGSnQ3K++vj6ysrLIy8vD0tKS6dOnk5qaKmZ8BEEYE+M2AEVGRjJr1iw++ugjOjs78fLyGhKA/uM//oPf/va3tLa2PtLzGg8BaDRGo5HOzs4hM0Wtra2o1WoAFAoF7u7uw5bRubu7f6v7i3JycvjTn/5EV1cXM2fO5PnnnycyMvKuv9PT00NeXh7Xrl2jv7+fqKgoUlNTCQsLG1dL/oSRSZJEdnY2mZmZhIeH8/zzz9/XyLckSezbt4/Lly/j4OCAl5cXGzduHNMqfwaDgR07dtDS0sJrr712z6V6H3/8MZ9++ikLFizgzTfffOgZyTtD0JIlS0hLS3uo4wiPh/lxU1BQgK+vL0ajEYPBQFhYGImJiURHRw8ZtDHvhzGHl/b2dk6cOEF5eTk+Pj5MnjwZk8k0JOSYB7jMFAoF9vb22Nvb093dTUlJCRYWFqSlpTF16lScnJyGzNA0NzeTmZlJS0sLsbGxLFiwYMyatJppNBo5+JjPZfr06SL4CIIwpsZtS+T6+vq7bjZ2cHCgp6fnEZ7Rk0WSpAd+o69UKuVQcyedTkd7e/uQZXRXrlxBq9UCX+0v8vLyGraMzsnJ6RuFDZ1Ox9tvv83x48fx9fXlb//2b5k5c+Zd93U1NDSQm5vLrVu3sLKyIjExkWnTpo35i7TweCkUCmbMmIGvry979uzhvffeY9OmTfcMMgqFgpUrV9LW1kZPTw8KhYIPPviAFStWyKXvvylLS0s2bdrE9u3b+fOf/8zWrVvv2oD3xRdfpLm5matXr/L+++/z8ssv4+Xl9cC3q1Ao5Cp0x48fBxAhaBwxGo3s37+fxsZGFAoF8+bNY/ny5VhaWtLY2Mjt27eHzdwYjcZhx3F3d6esrIzKykpSUlKIjY0dddmZnZ0d3d3dHD16lPb2dp577rkRixy0tbVx4MABysvLCQoKYuvWrQ/Vx+putFotWVlZXLlyBQsLC6ZPn8706dOfyOXYgiCMf+M2AHl7e1NfXz/qz69du/atlLwdLz755BN0Oh2+vr74+PjIl4d5MbGxsSEwMHBYkYC+vr5h+4uKi4vR6/UA2NnZjbi/6H5G8vLz8/nd735Ha2srK1as4Hvf+x6Ojo4jXtdoNFJcXExubi4NDQ1yUYTExESxTvwpFx4ezuuvv86uXbv44IMPWLVq1T2rNVpbW7Nx40bef/993NzcCAgIYP/+/TQ0NJCRkTEmFQBtbW3ZsmUL27Zt489//jOvvfbaqOWIraysePnll+UqlB9++CEvvPDCQz1/mUOQQqEQIWicsba2xsXFhcHBQVpbWzl48CDXr18nPT0dd3d3Obzc+fnXg42dnR0WFhZotVpOnjxJQUEBnZ2dzJgxAzc3tyG3ZzQauXz5MufOncPOzo5NmzYRHR095Dq9vb2cO3eO69ev4+bmxoYNG5g0adKYzqJrtVouX77MlStXgK8erzNmzBDBRxCEb9W4XQL31ltvsXPnTnJycnBxccHLy4vTp0+Tnp7OyZMnee655/jbv/1b/s//+T+P9LyelCVwV69epaGhgdbWVtrb2zEYDAC4uLgMCUQ+Pj5jWiLbvL/o68voOjs75eUXzs7OQ5bQeXt74+XlhaWlJT09PWzbto0jR47g7e3NT3/6UxISEka8La1Wy7Vr18jLy6Onp4fw8HBSU1OJjIx8qkp+C0N1dXXh5OQ0ZDmQXq/n8OHD3Lx5kxkzZrBw4cJ7PgZu377Nzp07mT9/PnZ2dhw/fhx/f3/Wr18/ZoUxurq6+PDDD3F2duY73/nOXQP5xYsXOXnyJM7Ozuh0Op5//vlhb0jvlyRJnD59mkuXLpGRkcH06dMf9i4Ij1B5eTmWlpZoNBpOnjzJkSNHcHNz49VXX2XevHmjDgKNpqKigsOHD6PValmwYAHTpk3DwsKChoYGDh8+TFtbG6mpqaSnpw95bA4ODpKdnU1WVhZKpZK5c+fKzUXHSn9/P5cvXyY3NxeAadOmMWPGDLnimyAIwrdp3Aag7u5u5syZQ3V1NbNnz+b48eMsWrSIvr4+srOzSUxM5MKFC4/8yfRJCUB3MplMdHZ20tLSQmtrq3wxLxG0tLSUZ2h8fHzkWaOxHIEzGAzD9he1tbXJ+4skSUKlUlFQUIBWqyU9PZ233noLPz+/YW9kW1tbyc3N5ebNmwBMmTKF1NTUMd3HITyZjEYjP/rRj5g4cSJvvPHGkMeGJEnk5uZy8uRJQkNDef755+/593/u3DnOnz/P5s2bsbW1Zffu3RiNRtavX09oaOiYnHNzczPbt28nICCAF198cdRSxSaTie3bt6NSqfDx8aGyspLnnnuOpKSkh7pdEYLGnx07dmBjYyM/F3d3d/P+++/T3NxMXFwc06dPZ8aMGQ/UWFSn03HmzBmuXLmCt7c3Li4ulJeX4+vry4oVK4b0xTKZTBQUFHD27Fm0Wi2pqanMnj17TF8L+vv7yc7OJjc3F5PJJAcf0XRaEIRHadwGIPjqifTf//3f2bt3L+Xl5ZhMJiIiItiwYQM/+clPHssU+pMYgEaj1WqHBKKWlpYhs0XmmZo7l9GNdUPVgYEBsrOz2bdvn1ztJz4+Hi8vLxQKBVZWVnh5eeHp6YlOp6OhoYGuri48PDxITU0lKSlJjBg+Q4xGI//yL//ChQsXeO2119i4ceOw5Tg1NTXs3r1bXup2t8a4kiSxc+dOGhoaeP3117G2tmbv3r3U1taycOFCpk+fPibLfWpqatixYwfR0dGsXbt21L8htVrNO++8Q1RUFHZ2dly5coV58+Yxd+7chzoPEYLGD0mSOHHihDxQ1d/fD3wVSm7fvk13dzd+fn64uLiQmJjIvHnz7nuZpPlx8N///d/09vaydu1avve97w2ZRa2oqODkyZO0tbURFxfHggUL7rp37UENDAyQk5NDdnY2JpOJlJQUZs6cKYKPIAiPxbgOQE+i8RSARmKeLTIHopFmi7y8vIbtLXqYENLe3s7x48fJzc2lvb2dKVOm8Nprr+Hr6yvvLzIXNbh27Rrt7e04OjoSGBhIUFAQfn5+Q/YXeXl5iUpBzwCtVssvf/lLbt68yY9+9CMWLVo07Drd3d3s2rWLtrY2Vq5cedcCB/39/bz33nvY2NiwdetWlEolZ86c4dKlS8TExLBq1aox2UtWUlLC7t27SUlJYenSpaMGmhs3bnDgwAGef/55VCoVp0+fJikpieXLlz/U4IMkSZw5c4aLFy+KEDROSJJEb2+v/PxbX1/Pl19+SV1dHV5eXnJFt+DgYKZPn05ycjJ+fn4jVuRUq9UcO3aM27dvM2HCBJydnSkoKMDT05NVq1ahVCrlpqohISEsXryYgICAMbsvAwMD5Obmkp2djcFgkIPPgy7nEwRBGEsiAI2x8R6ARqPVamlraxsSitra2obNFt25jG602aKBgQHOnz9PdnY2ra2tKJVKZs2axerVq+UA09XVRW5uLgUFBej1ernXhIODw7D9RV1dXfL+IhcXl2Fluj09Pce0Q7rw+KnVan72s5/R0NDAr371qxGXien1eo4ePUpBQQFpaWksWrRo1D0Mra2tfPDBB8TExLB69WoUCgUlJSUcPHgQJycnNm7c+FCV2b7u2rVrHD58mPT0dObOnTvidSRJYu/evVRVVfHmm29SWVnJ4cOHiYqKYt26dQ/Vv+rOELR48eIhFTSNRiN6vZ7BwcERP+r1+jGrkCc8PKPRyIEDB8jJySE2NhZJksjJyaGmpgalUklQUBABAQHywJCXlxdNTU0UFRXh4uLCsmXL5D1lLS0tfP755+Tm5mJpaUlSUhJLlixh4sSJY1bgQKfTycFHr9eTnJzMzJkzh/QVEgRBeFzGbQB67bXX7nkdhULBtm3bHsHZ/I+nNQCN5M7ZojuX0X19tujOYNTS0kJWVhY9PT0MDg5iZ2dHRkaGXKmqurqa3Nxcbt++jZ2dHcnJySQnJ991U7rBYKCjo2NINbq2tja6u7uBr5r3eXh4DAtGbm5uoifQONbS0sJPfvITNBoNv/vd75gwYcKw60iSRF5eHsePHyc4OJj169ePuuSmsLCQffv2sWzZMqZNmwZAR0cHu3btoru7m1WrVhEbG/uNz/vixYucPn2a5cuXk5KSMuJ1+vv7+dOf/oSXlxdbtmyRZ4/uHLW/W2gZ6ePg4CCFhYWUlZURGRmJv78/er1+xFLKX/eP//iPorDII9Da2oqbmxvW1tYj/lySJHnfWnJyMsuWLaOpqYmzZ89y7do1DAYD/v7+mEwmrly5Qk9PDwEBAcTFxREQEICPjw9ubm7U1tZSXFxMS0sLJpOJiRMnsnr1asLCwr7xfdDpdFy5coXLly+j1+tJSkpi1qxZIvgIgvBEGbcBKDQ0dNibV6PRSHNzM0ajES8vLxwcHKiqqnqk5/UsBaDR9Pf3DwtF5eXllJaW0tvbi5ubGxYWFnh5ebFu3TpiYmJobGzk6tWrtLW14ePjQ2pqKnFxcQ812m02MDAwrEx3W1ubvLbeyspqxDLdDg4OIhiNE1VVVfzkJz/B1taWf//3f8fX13fE69XW1rJ7926USiUbN24cdYnP8ePHuXLlCq+88oq8v2JwcJBDhw5RVFQ0aoU5k8l0X0FEr9ej0+m4fPkyt27dIi0tDV9f3xGv29zcTF5eHuHh4QQEBNDT00NhYSFWVlZMmTJl2HJPpVKJtbU1VlZWo360srLi1q1blJSUkJaWRlJS0rCfj/S7SqVS/E18yyRJ4ne/+53cviAoKEi+uLi4DLlufn4+hw8fJiIigueffx4bGxs6Ozu5cOECBw4coLGxkUmTJvHiiy/i6upKa2srzc3NXLt2jZs3b2I0GgkODiYxMREXFxfKysro6+tjxowZrF69+qH2zw4ODsrBR6fTycFnrCoqCoIgjKVxG4BGo9freffdd/nDH/5AZmbmmIxoPQgRgIbq7e3l9OnT5Ofny8GiqKgIR0dHvL29qayspKmpCZPJRHh4OCkpKcTFxeHr64uvr++YFziQJIm+vr5hy+juLP5gb28/rEy3t7e36Cn0hLp58ya/+MUv8PPz49///d9HfMNlrjL42Wef0dzczIIFC5g4ceKw4DEwMMChQ4dQqVRkZGRgZWUlz56UlpZSUFCAi4sLcXFxWFhYyL9rfuzcizlQWFpaUlxcTFtbG7NmzSIgIGDE4HHt2jVu377Nhg0b8PPzo7e3lyNHjqBQKNi0aRMBAQHyde93hkaSJM6ePcuFCxeGLYcTHh9Jkmhvb6e+vl6+dHZ2Al8tMb4zEPn6+srFPtzd3XnhhRdoaGjgyy+/pKenBy8vL/r6+hgcHGTSpEn4+flx48YN2tvbiYmJITY2dshAVUtLCzU1NVRWVmJra8vMmTNJSEiQZ+69vb1HnZUaHBwkLy+PrKwsdDodiYmJzJ49e1hoEwRBeJI8dQHI7Pvf/z61tbUcPXr0kd6uCEBfMRqN5OTkcOHCBZRKJWlpaVRWVlJXV8fkyZMxmUyUlJQAEBISgp+fHwMDA3IwMTdTdXJyGnFv0Vj2o4CvRvBVKtWwMt2dnZ2Y/0RcXV2HLaNzcXGhtLSUW7du8cILL4hlQo/ABx98gFKplKu76fV68vPz2bt3L/7+/qxevRpJkobNvJhMJkwmE+Xl5TQ3NxMQEEBERMSQ/zMLCwskSeLq1as4OTkxa9YsbGxs5FDS3d0t75tIT08nKCjorjMud86sWFlZDZlFMRqNfPbZZ9TV1fHKK68MKUdsZjAYeO+991AoFHzve9/D0tKS3t5ePv30U9RqNS+88AIhISEP/G94ZwhatGgRM2fOfIj/CeHbptFoaGhokANRY2MjBoMBKysr/P39cXR05PLly7S0tBAYGEhiYiLLli3D1dWVwcFBTp06xaeffkpjYyNRUVG88sorI1Y2lCSJ7u5uKisr+eKLLygtLZWfe83Bx93dfchzsZubG5WVlVy+fJn+/n45+Ixl5bhvk8lkoq2tbdRZY0EQnm5PbQB69913+fGPf0xvb+8jvV0RgL5q5nf8+HFUKhUpKSmEhYVx6NAhWltb5XLW7u7upKamkpCQMGxmxWQy0dXVNWwZnXlPj1KplPcW3VmN7tsop6rX60fcX9Ta2kpTUxP19fVotVpsbGz4/PPPx7R6kjCcJEnMmDGD7u5u7Ozs8PPzIyIigokTJ1JeXs6pU6eIi4tj06ZN2NnZjRpKSktLuXDhAgEBAaxfv17ed2EO1g0NDXz00UckJSWxbNmyIefQ29vLnj17aGhoICMjg2nTpj308rDBwUE++eQTVCoVr732Gh4eHsOu09LSwvvvv09qaiqLFy8GvlreuWvXLurr61m7di0xMTEPfNsiBD15Ojs75SXCIzEv866vr6e2tpasrCxu3rxJe3s7Pj4+bNy4kWnTpuHi4kJBQQGFhYV4enoSFhZGQ0MDzc3N+Pr6MmPGDGJjY0ccSJIkiaKiIr788ksMBgPTpk3Dw8NjyPNeeXk59fX1GI1GoqOjmTlzJhEREfJz8eNoQXG/NBoN169fJy8vD51Ox49//ONvtNRaEITx6akNQM8//zwXL16ktbX1kd7usxyAurq6OH78OLdv3yYsLIyMjAxu3rzJZ599Rn9/P+Hh4URHR5OWlkZkZOQDv2k0zxDdWaL7ztkiR0fHYeW5PT09v9Fskclkoqenh/b2djo6OigsLOT69euUlJTQ1tbGwMAACoUCa2trsrOzcXNze+jbEu5Pbm4uFRUV3Lhxg9u3b6NWq7GwsCAoKIj+/n5u377NmjVr+PGPf3zXUFxfX8+uXbtQKBRs3LiRwMDAIT+/evUqR44cYc2aNcTHxw/5mdFo5NSpU2RnZxMXF8eKFStGXSJ0L1qtlo8++gi9Xs/WrVtH3Cx++fJlMjMzefnll+VlvQaDgYMHD3Lr1i2WLVs2akGFu7lzU70IQY+XXq/nP//zP/Hx8WHdunV3Xf7b1NTE4cOHaW5uJiYmhqCgIPbt20dxcTFKpRKNRoOtrS0zZsxg1qxZhISE4O/vT1NTE5cvX6a8vBwXFxfS0tKYOnXqiMt7NRoNx48fp7CwkAkTJpCRkUFlZSWXLl1CrVYTGBhIaGgo/f398sCQuaDG16uCmnvIjfXM/YNobm4mNzeXoqIiAOLi4khNTRUzQILwjBq3AejXv/71iN9Xq9VcuHCB69ev87Of/Yx//ud/fqTn9SwGoMHBQS5cuEB2djaOjo5kZGRga2vLH//4R27cuEFoaCirVq1i+vTpeHt7j+ltm5eu3Vmeu7W1FbVaDfzPbJGXlxfu7u44OzvLe0TUajWdnZ10dXWhUqno7u5GrVbT09NDb28vfX198jr6vr4+enp6GBgYwGQyYWVlhbu7OyEhIYSGhmJtbc0//dM/id4Wj8C+ffvw8/OTK7VVVlaSk5NDfn4+VVVVlJSUoFarmTJlCosXLyY4OBhfX1/8/Pzw9fXFxcVFDt+9vb3s3r2bpqYmli1bNqSctiRJHDp0iMLCQrZu3TpiQ9WioiIOHTqEq6srGzduHHEG5350d3ezbds2bG1tefXVV4eNoEuSxCeffEJnZydvvvmm/HNz88ycnBzmzJlDenr6Aw8s3BmCFi5cyKxZsx7qPgjfXFVVFXv37sXKyor169cPC+U6nY4zZ85w5coVfHx8WLFiBQEBARiNRq5cucK7775LbW0tM2fOZO7cubS1tVFfX8/AwAAWFhb4+PgQFBSEvb09DQ0NVFZWYmNjQ3JyMqmpqSOG7+LiYt577z1u375NYGAgS5cuZe7cubi7uw+5ntFoHDZz39raOmTm3tPTc1gwcnR0/NYKbBiNRkpKSsjNzaW+vh4XFxdSUlKYOnWqaKAtCM+4cRuARlsi4ObmRkREBN/97nf53ve+98grFz1LAUiSJAoLC8nMzKS/v58ZM2bg5eVFZmYmJ06cwMrKii1btrBy5coxe7ExGo0MDAzQ398/4kWr1dLd3U1bWxuNjY20tLTQ0dGBSqWir69vSNlfS0tLbGxscHBwwN7eHldXVzkgmd8kd3V10dHRwcDAAFZWVri5uRETE8O0adPQaDRkZ2dz48YNOjs72b9//7hZ/z5emUwmjh8/ztWrV3F1dWXRokVER0cPCTQ3btzg17/+NcXFxUyaNImIiAhsbGxwdHTEysoKW1vbIYHIy8uLq1evyn+7S5culftGGQwGPvzwQ7RaLa+//vqIj+O2tjZ27dpFX18fa9askXutPKj29nY+/PBDvLy8eOmll4Yty+nu7uadd95hwoQJPP/88/L3JUmSZ4gSExNZsWLFA+9FEyHoydHd3c2ePXtobm5myZIlJCcny32pvvzyS/r7+0lPTyctLQ2FQkFZWRmZmZl0dXURHx+Pra0tOTk5JCQkyI+F0YorWFpa0t/fT1dXF46OjsycOZNZs2bh6emJwWAgPz+fixcvolKpMJlMGAwGJk6cyMqVK/H09Lyv+3PnzP2dPeQGBweB/yk6c+fFy8vrGy1L6+vr49q1a1y9epXe3l7CwsKYNm0aEydOFPs0BUEAxnEAelI9KwGoqamJL7/8kvr6eiZMmIC3tzfFxcUUFhbS0dFBUlISf/VXfzXqkjCDwTBqiBkt2JhnZsxVuXQ6nfzRvMHdZDJhYWGBlZUVlpaWWFlZ4eDggIuLCy4uLlhbW6NQKORN8lqtFp1Oh7W1NdbW1ri5uaHT6Whvb5f3jymVSuzt7fHw8EChUFBTU0NFRQUDAwO4uroyefJkpk+fzoIFC4aVJha+He3t7Zw8eZLy8nJCQ0NZsmTJkKUs/f39/PVf/zVFRUUsWbJE/v92cXHB1dUVW1tb1Go1XV1dwFdvBAcGBqiuriY0NJQtW7YQERGBtbU1arWa9957Dz8/PzZv3jziGyidTsfBgwcpKSlh9uzZpKenP9QbrYaGBj7++GPCwsLYuHHjsCVDRUVF7N27l3Xr1hEXFzfkZzdu3OCLL76QA9KDLskTIejJYTQaOXHiBFeuXCEiIgKFQkFFRQWRkZEsX74cV1dXGhsbOXHiBHV1dURERLB48WJ8fHyAr3paHTx4kODgYDZu3DjseenrxRVqamqor6+nubkZKysrPDw8sLOzw83NjaSkJObMmYOnpyc1NTUcPnwYtVrNvHnzmDFjxkMta5MkCbVaPSwYdXV1IUkSCoUCDw8PueCMORi5urredVCzoaGBK1eucOvWLSwsLIiPj2fatGljvvJAEITxTwSgMfa0ByCNRsOZM2e4fv06tra2uLu7U19fT19fH93d3fKm2cmTJzM4ODhqoDHv27mzWpd5RND8fXOgMRgMGAyGYcHGyckJNzc33NzccHV1xcnJCScnJxwdHYd8bh7NH83AwADl5eWcO3eOa9eu0dbWRl9f35DzNFcHs7W1JTg4mFmzZjF37txRNxILj0Z5eTknT56ko6ODxMRE5s+fLy9D7O7u5q/+6q9QqVT85je/YXBwkOLiYqqrq4GveolFRETg7u5Ob28vzc3NlJaWcu7cOUwmE5MnTyY8PBxfX19MJhOXLl1i8eLFPPfccyOei3km5tSpU4SFhbFu3bqHKsxRUVHBzp07mTJlCqtWrRr2hm/fvn2Ul5fzF3/xF8NmHCsqKti9ezfe3t68+OKLDzXzeu7cOc6dOydC0GPQ3NyMl5cXlpaWmEwmdu7cyccff4yTkxM//vGPmT59Omq1mtOnT1NUVISPjw+LFi0asQlwbW0tn3/+OU5OTmzevPmuZamNRiONjY1kZmZy8OBBKioqsLCwIDAwkOTkZJKSkggJCSEoKAhXV1cuXLjA5cuX8fb2ZtWqVSMuD30Yg4ODtLe3DwtG5t5tNjY2QwKRj48P7u7uVFZWcuXKFRobG3Fzc2PatGkkJCTcsxiDOWwJgvDsGTcBqK6u7qF+z9zM8FEZjwFIkiR0Ot1dZ2A0Gg1FRUXcuHGD7u5ulEolg4ODWFpa4urqikqlAiA6OhovLy9sbW2HNE80Bxqj0YjRaMRgMMih585go1AosLe3lwPM1wON+ev7CTb3c7/NL5zl5eVotVp6e3tpaGigra0Nk8kkhx5zCWxXV1ccHR2HVKIzX0JDQ7/xOQn3VlVVNaTqn9Fo5Nq1a5w9exaj0cjs2bOZPn06lpaWtLS08Jd/+ZcoFArefvttfHx80Gg0lJaWymFIkiRCQkKIiYkhJiYGg8HARx99xO3bt4mOjsbR0ZG2tjbKy8upqqpi2rRpxMfHy0vofH19cXNzkx/r1dXV7N27F0tLSzZs2PBQlQELCwvZt28fM2fOZNGiRUN+NjAwwDvvvIObmxsvv/zysJmmxsZGdu7ciZ2dHVu2bHmoZZnmELRgwQJmz579wL8vPDi9Xs8f/vAHPD09mTNnDqdOnaKlpYXIyEhaWlro6+vD39+f5uZm7O3tmT9/PvHx8Xedaezo6GDHjh0YDAY2b948YlAxGo3cuHGDCxcu0N3dTWxsLLNnz5b7CpWUlGA0GnFxcZEbRQcGBmJnZ0dJSQmDg4PMnj2befPmfSvV1O7s3XbnxVwWvKmpCaVSSXh4OKmpqSQmJuLn54eHh8eIDYvNPY9qampQq9W8+eabIgQJwjNo3AQgCwuLh3qSMu/3eFSelADU3Nwsz2Lcz2Wkh4FSqcTOzg6NRkNZWRmtra1YWVnh6OiIh4cHEyZMQKfTcePGDRwdHUlISMDKykoOTCaTacjxHBwcRgwzX//6255R6e/vJz8/n5ycHKqrq+nt7aWlpYXm5mYGBwdxdXUlPDycpKQk0tLSiI2NlUdPBwYG5HKw5mp05vXsP//5z0Wz1G+Z0Wjkj3/8IwaDgVWrVhEZGSn/rL+/nwsXLpCbm4uzszOLFi0iJiaGyspK3nrrLTw8PPjv//7vIYUqtFqtHIaqqqqQJIng4GCio6NpamqisLBQ7q3S09PD9u3bKS4uJiUlBY1GIy+TtLGxkcOQr68vjo6OnD17ltbWVpYuXUpSUtIDP3/l5uby5ZdfjtistKamho8//piFCxeOWLmts7NzyBvfh6l0JULQo1dRUcG//Mu/0NzczPz589m0aRN+fn5kZWXx7rvv0tLSwrJly/jBD35w38tt+/r6+Oyzz2hvb2f9+vXy34zRaOTmzZtcuHABlUpFbGwsc+fOHbZcrLGxkaysLIqKitDr9fj5+cmDAlqtloaGBjo6OuQeXGlpaUMKjYwlSZKor68nNzeXW7duMTg4SEBAAD4+PvJeI/PfpKWlJR4eHlhbW8tLrru7u5EkCSsrK4KCgggJCWHWrFliFl8QnkHjJgBt3779oZ5Qv/Od73wLZzO6JyUA/b//9/9oaWkBvuo+b2dnd9eL+Y27eXbGYDDQ1tbG6dOnuXbtGlqtFicnJ7y8vAgICMDBwYHy8nLa2trksGCeHRkp4DyKYHMv5jXz2dnZtLe3093dTXd3N1qtFnt7eyZOnMjSpUuZOnUqEyZMuOcbDPPm4c7OTurr61m2bJkYSXwEent7+eKLL6ioqCA5OZnFixcP2e/S2dnJyZMnKSsrIzg4mCVLltDU1MQvfvELoqKi+P3vfz/iSLVWq6WsrIxbt27JYUiSJJqbm4mNjeU73/kOtra2fPDBB0iSxPe+9z30ej0tLS1ygG5paRmyj8HcTHfq1KmsXbuWoKCgBwrJp0+f5uLFi6xevZqEhIQhP8vMzCQnJ4fvfe97Iwacvr4+Pv30U7q6uti0aZNcPvtBnDt3jitXrvCDH/zgW+mzJfwPcxlstVqNVqvF29ub5ORkiouLUalUJCYm4uDgQFZWFkFBQaxfv/6+q04ODg6yf/9+ysrKWLp0KdbW1ly4cIGuri4mTZrEvHnz5P1Do+nq6iI7O5v8/HwUCgWJiYlERkbS29vLrVu3OH78OI2Njfj7+xMfH09YWBhBQUEEBQXh6+v7jWbH9Xo9RUVF5Obm0tLSgoeHh7zM7c6/J6PRSFVVFQUFBRQXF1NZWYlKpWJgYABHR0dcXV3x9/cnKioKPz8/fHx8iIuLE4URBOEZNG4C0HjxpAQglUqFpaUl1tbWchnn3t5e+fL1r++csTEYDJSVlVFeXo5erycoKEjuF+Hn54dOp+Ps2bPo9XrWrVs3rEfKk6S9vZ3Tp09z9uxZysvL5TLW5v1GwcHBZGRksHDhQsLDw4eENEmS0Gg0qFQqurq6hl3M69Lhq9mtv/zLv3yiGwA+LcxPWVevXuXkyZM4Ozuzdu3aYUvNqqqqOH78OG1tbfJSof/8z/9k5syZ/PrXv75rWO3v75fDUEFBATdv3sTZ2ZkXX3yR+Ph4du3aRUREBOvXrx92HJ1OJ88OtrS0kJeXR3Z2Nra2tsTGxhIQEDCkCp2vr++I5YfN9/Xw4cMUFBSwadMmoqKi5J8ZDAY++OADjEYjr7/++oihTqfTsWvXLmpra1m7di2xsbH3/e9s1tfXJ8q7PwJGo5Gf/exnTJkyhdjYWN5++22qq6vJyMjgtddek2dmamtr2bNnDwDr168nJCTkvo5vMBh49913OXLkCJ6enixbtoz09PQHnh3UaDTk5eVx5coV+vv7iY2NZebMmfj6+nLx4kUOHjyIVqslJCREHlCztLTE399fDkRBQUH3Fai7u7vJy8vj+vXr9Pf3M2HCBFJTU+XCEEajkaamJmpra6mpqaGuro7BwUGsra0JCgoiNDSU0NBQfH196enpGbaMTqfT8ZOf/EQMXAnCM0gEoDH2pASgXbt20dDQQF9f35DlbQqFAgcHhxGXoNnZ2XH58mWOHTuGWq0mNjaWF154geTkZHmUraCggKNHj+Lu7s6GDRseuu/Jt8VcyevGjRtcvHiR4uJienp6sLe3R6lUYjKZcHJyIiEhgVWrVpGUlIRGoxkx4HR1dQ0pzODk5IS7u/uwi4ODAxqNZsw2Agt39/7772NpaUlYWBiurq7k5ubS2trKnDlzmD179pAQazKZuH79OmfPnpUrBp4/f57Vq1fzwx/+8L5ub2BggIKCArZv387t27cJDw8nJCSEmpoaNmzYQEZGxj2P0djYyEcffUR7eztTpkxBqVTS0tLCwMAA8D9NfM0XPz8/3N3dUSgUmEwm9uzZQ3l5OS+//PKQfY1tbW289957cvnukRiNRg4ePChXxEtNTb2v+y08WiaTiR/84Afk5uai1WqJiIggLCwMnU5Heno6CxcuxM3NDSsrK/r6+tizZw/19fUsWrRILok92nGLioo4f/48nZ2dWFhY0NPTQ1paGqtXr37omRm9Xk9+fj7Z2dmoVCrCwsKYOXMmHh4eHD16lIqKCmJjY0lISJBnyevr6+np6QHA3d19SCDy9vaWK3TW1taSm5tLaWkp1tbWJCYmMm3aNFxcXGhqapL38NTX18uBJzg4WA48fn5+97XiwBzOBEF49oz7AJSVlcX169fp7u4etudEoVDwD//wD4/0fJ6UAJSVlYVerx+2JM3BwWHYdL9Go+HkyZN8/vnntLa2EhMTw6uvvir3n4CvXuyOHTtGfn4+U6dOZenSpd/KhtcHZTQaaWhooKqqioqKCgoLC6msrKSjowMLCwtcXV1xcHDAYDBga2tLTEwMEydORJIkuQGqudKbQqHAxcVlWMAxV5nT6XR0dHQMu5hf0H/2s5+JMtjfMkmSuHr1KlVVVVRXVzMwMIClpSVarZa2tjYmTpzIq6++OqxHycDAABcvXpQbptbV1fHWW2+xZcuW+75tk8nE0aNHOXbsGHZ2dnR2dlJbW0tGRgZz584lJibmrgUH+vv7OXDgAOXl5cydO5c5c+bQ3d09bAmd+fFkbW2Nj48Pfn5+eHp6kpWVhVar5bvf/e6Q5UrmvUIvvfQSERERo/67ZWZmcvnyZWbNmsWCBQvEqPcTxmAw8JOf/IT+/n4sLCxoaWnBZDLJ/cj8/PyIiorC2dlZrnpZXV1NVVUVkydP5vnnn8fb21t+Q28ymbh16xbnz5+no6ODqKgo5s2bh7+/PyUlJezbtw9/f382bdr0jfq0mUwmSkpKyMrKoqmpCR8fH6ZPn47JZCIzMxOFQsHSpUuZPHkyCoWC7u7uIT2JzPfTHFi6urowmUyEh4czbdo0vLy85NBTX1+PXq/HxsZmWOARy9gEQXgQ4zYAdXV1sXz5cq5cuSKvtzffFfPn5inyR+lJCUD3o6WlhfPnz3PkyBGampqIjIzk1VdfHTZC3NHRwe7du1GpVCxfvnzYXoRHSZIkOjs7qayspLKykpqaGvr6+qitraWpqQm1Wo21tTWurq5YWVnR09ODUqkkMDCQgIAAueHpSDM55jevnZ2dw0JOZ2enPBukVCrx8PDA09NzyMXHx+ex73N6lpgrOpnDUGFhIYWFhZhMJubNm8eCBQvkMtfmN/tdXV2cPHlS3iP3d3/3d7z00ksPdLuFhYUcOnQIJycn2tvbqaysJDw8HEtLSwICAuRqciP1wJIkiYsXL3L27FkmTJjA2rVrhy2b1Gg0cigyB6POzk70ej03btzA2tqaNWvWEBERIe9j2Lt3L21tbbz55pt3fTN7+fJlTp48SXx8PCtXrhSP1ydMfX09vr6+WFlZyY/VkpIS9Ho9nZ2dBAYGMm3aNPr7+1Gr1ajVaioqKigpKcHGxobY2Fh8fX3RaDTy7EhkZCQLFiwgOjoaZ2dn+f+8oaFBrha4efNm3N3dv9G5m2dusrKyKC8vx9nZmfj4eFpbW7l9+zZRUVE899xzODs7D/m91tZWTpw4QVZWllx909LSEqPRiCRJODg44OXlxeTJk4mJiSEsLAxfX18ReARB+EbGbQDaunUrn3/+OR9++CGpqamEh4dz4sQJwsLC+P3vf092djZffvnlPTd2jrUnPQCZTCbKysrIzs4mOzub5uZmAgMD2bRp07DlQ/BV48VDhw7h7OzMhg0bHktDOY1GQ1VVFeXl5RQVFcnVh3p6emhqaqKtrW1ImVbzfXB3dycxMZHU1FR5SZGLiwsWFhZotdoRZ3PUarUcpO3t7YeFHE9PT1xdXcWL7xPIYDBQVVXF3r17ycnJwdramqioKDw9PQkPDycsLIywsDCcnZ2pqKjg9ddfp6amhtdff50333zzrn1Svq6lpYXPP/+cvr4+dDodPj4+zJgxg/LycsrLyzEYDPj7+8th6OtvLisqKti3bx82NjZs3LjxnssnBwcHaWtro6qqij//+c/09fURGRkpPw5tbW25efMmkZGRvPDCC/j7++Pk5DTiLI+5SWZYWBgbNmx44IapwqNl3stWXl5OV1cXcXFxbN26VQ7YRqORmpoaduzYQWlpKRYWFhgMBpydneWlYHcODjo7O8uz2hYWFmRlZWFhYcFLL71EdHT0mDy3tbW1cfnyZQoLC7GyssLb25uWlhYUCgWLFi1i6tSpVFdXk52dzdWrV9Fqtbi4uGBjYyMvVbazs0OpVMotExQKBY6OjkOWzfn5+YklbIIgPJRxG4D8/Px44YUX+I//+A86Ozvx8vIiMzOTBQsWALB27VpsbGz47LPPHul5PakBaGBggPz8fHJzc6mpqaGrqwtHR0cWLVrEokWLhm1yNhgMnDhxgry8POLi4lixYsUjeaNkboR369YtiouLKS8vp7GxcUjBgZ6eHlQqFUajUQ45sbGxaDQa1Go1AQEBzJw5k8mTJ9Pb2zti0DEfT6FQ4O7ujqen57BZnW+yLER4vG7fvs2+ffvo6uoiKioKo9EoV0X09PSUR5H/6Z/+iYqKCpYsWcKqVauYOXPmfT/OtVot+/bt48aNG/T09LB06VJWr16NXq/n9u3b8uPXXDo4JiaG2NhYOQyp1Wp2795NW1sby5cvJzEx8b5ut6uriw8//BBHR0eWL1+OSqWipaWFq1evcubMGSZMmICvry/29vbDii2Ye6NUVVXx+eef4+XlxYsvvigqvD3hzHvZDh8+zPXr1wkKCuInP/kJYWFhSJJEaWkpp06d4tKlSwwODrJ69Wq2bNkiB4ju7m55xkitVqNSqYZ8XlRURG9vL7GxsUyYMAFXV1c5JJkvbm5uODo6PtDSyZ6eHnJycrh27ZrccqG8vJy+vj6sra2xtLTEz8+PkJAQwsPDCQ0NJSQkBB8fnyFBzFxu27xsrrGxEb1ej1KplIsrBAcHExgYeM+CHQaDgYGBAQYGBuS/TUEQnj3jNgDZ2dnx9ttvs3XrVnQ6HXZ2dhw4cIBVq1YBX5WB/sUvfkFXV9cjPa8nLQB1dnaSm5tLQUEBGo2GwcFBTCYT0dHRLFu2bMQmjSqVij179nyjHiZ3MzAwMKTQgHkvRUVFBY2NjfJ+Ljs7O7y8vOQ9PM3NzfT09ODs7ExiYiJLlizB1taWCxcuUF9fj7OzMyEhIfL+jK6uLnkJpI2NzYizOW5ubt94BHFwcJCenp5h+06Eb8dHH30k92tycXGRP5o/t7OzQ6FQoNFoOHz4MKWlpSQmJjJ79myam5vlJXPmSn6ZmZnodDpmzpxJREQEGRkZxMfH39dj3mQycfr0aTls/a//9b9IS0uTfz44OEh5eTnFxcXcvn0bvV6Pr6+vHIZcXFw4duyY/LyxdOnS+3o8trS08NFHHxEQEMCLL74o/86BAwfIz89n+fLl9Pf3y8vo1Go18FVJfB8fH7kscVZWFu7u7rzyyisjLtkTniwDAwOcOHGC7du3MzAwwIoVK1AoFLS2thIeHs7cuXNpa2vj+PHj+Pn5sX79+nvObOr1ejo6OtizZw9FRUXExsbi5+cnByStVitf19LSUv47GykkOTg4DPm70ev11NfXc/XqVfbt20d+fj5arRYrKyt5BnLlypX4+fnd92uMyWRCo9FQV1dHdXU1tbW11NXVoVKpMBgM2NnZyc8Jjo6OWFlZMTg4OCT0mFlbW/OLX/ziAf8XBEF4GozbABQREcFrr73G3/3d3wHg6+vL97//ff7xH/8RgH/4h3/gT3/6E52dnfd9zHfeeYd33nmHmpoaAGJjY/nHf/zHUasrjeRJCECSJFFVVUVOTg7l5eVyn5+uri6cnZ1ZuHDhqG/wSktLOXjwIHZ2dmzYsOGhRsckSZJ75Ix00Wq16HQ6VCoVfX19DAwMYGFhgZOTE6Ghofj4+GBlZYVaraampobW1laMRiMeHh7Ex8cTEBBAWVkZ165dk+9TUFCQ/CZ4pKDzoCOX5vthXmrX29tLT0/PsM9bWlrkRoC7d+8WZbAfgcuXL9PZ2YlarZZHtg0Gg/xza2tr+bHg4uJCW1sbN27cwM3Njeeff57Y2FgsLCxQq9VUV1dz5coV/uu//gtJkpg0aRIGg4HIyEjWrVvHtGnT7mufzK1bt/jtb3+LWq3mN7/5DXFxccOuo9frh4ShwcFBfHx8iImJwWAwkJ2djY+PDxs2bLiv5XjmZU9RUVE8//zzWFhYoNPpeOedd3B2duaVV16RR9HvDEPmYgsdHR1oNBpu3ryJra0tzz33HJMmTZJni8QM6KN36NAhdDodCoVi2AWQP5aWlrJz506am5sJDQ3lxRdfJDo6Wr5OR0cHFy9exGg0MmfOHAICAoYcY7TjX716lZs3bzJ58mTS0tJQKpXo9Xq56W9fX9+QFgp9fX3y3sg7j6PX6+nv76ejo4Pu7m70ej1eXl5MnTqVgIAA6uvrKSgooLe3lwkTJrBkyRKcnJwYHByUqzXqdDo5uNz5Pb1eP+S53Py5+ecDAwNoNBr6+/tRKpXY2tri6+uLv78/wcHBBAUF4ezsLPfAG2kQUBCEp9+4DUCvvPIKNTU1nDt3DoC/+Zu/Ydu2bfz85z/HZDLxu9/9joyMDPbu3Xvfxzx8+DBKpZLIyEgkSeLjjz/mX//1X8nPz7/v/hlPSgD61a9+hUKhYMKECbS0tKDRaEhLS2Pu3LkjNmI0Go2cOnWK7OxsJk2axKpVq+5a0UySJPr6+kbtkWMu7wtflfh1dnbGYDDIy9T6+/uxt7eXX5CUSiU9PT3cvn2btrY22tvb6e3txWAw4OTkhIeHB46OjvKbN3NFt+nTpxMRESEvYbvf5UtGo5G+vr4RQ82dX99ZREOhUODk5IRSqaS9vZ3q6mra2trQ6XQ4Ozvz0Ucf4eXl9QD/U8JYMAdVcxi686P5866uLkpLS+nu7iYkJIT4+Hh59NrFxYX29nZ+//vf4+7uzoIFC8jKypIrb6WnpzN58mTCw8Px8fEZNUg3NTXx4x//mO7ubv7lX/6FKVOmjHrOer2eiooKiouLKSsrk0v5NjU1yTMy4eHh97zvpaWl7Nq1i+TkZLkRb11dHR999BHp6enMmTPnrufQ1tZGdXW1XDY/PDxc7knk4uIih6FZs2Y9EVUfn3Z79uyhv79fbsILyJ+bi36UlZWhVqtxc3NDq9VSWlqKnZ0diYmJREdHY21tjSRJ6HQ6CgoK6OzsZMKECXIjXPPx7rzc+f2GhgZKS0vx8PAgJiZGDtF3Xt/c30en09HZ2UlbWxtdXV10d3cPCS3mggZ2dnby40epVKJUKjEajWg0Gvr6+lAoFHh6ehIYGIiNjQ2WlpYPdLGyskKpVA752zQajfT29tLd3U1PTw/d3d3yQImDgwMuLi54eHjwn//5n2JPpyA8g8ZtACosLCQzM5Mf/OAH2NjYoFKpWL9+PWfOnAFgzpw5fPbZZ994fa+7uzv/+q//ytatW+/r+k9CAAL4zW9+Q15eHmq1Gm9vb2bMmEFERATe3t74+Pjg7e0tj/B2d3ezd+9eGhsbWbx4MampqXIlvZ6enhEDjkqlGtIjx9nZeVhFtcHBQbq6uqivr6ehoQGj0YirqythYWHym87S0lJ5Tbh5pFGn02FlZUVQUBBTpkzBx8eHpqYmWlpacHFxYebMmUyfPn3U5pHmJWmjhZqenh40Gs2Q/khWVlY4OTnh7OwsX+78WqfTceXKFXJycqioqKC/vx93d3eioqJITU0lKiqKCRMmiA25TyjzjOOZM2c4f/48tra2xMfHI0kS3d3d9Pb2ygMq/v7+LF++nLa2NkpKStBoNLi6uhIUFISnpycTJ05k4sSJhIeHD6kwB19t/n7rrbfQarX88Ic/ZM6cOfecedTr9VRWVlJcXExhYSE3btxAp9ORkZHBxo0b5f4oo7l+/TqHDh1i3rx5zJs3D4AzZ85w6dIlvvvd7+Lv73/Pf5/BwUF2795NZWUl6enpuLu7y4MNXV1d/PVf/7Uom/2YSJJERUUF586do7GxkeDgYNLT0wkNDUWhUHD9+nXeffddVCoVkydPZsGCBaSmpmJpaYnJZOL8+fOcP3+eqKgo1qxZg62t7ZB9MP39/fLn5ktlZSVnzpzB3t6epKQk4KtZxN7eXtra2uQ9RL29vZhMJqysrLCzs5NDkblMdWRkJO7u7hiNRvR6vTyTY74d88BFWVkZjY2N2Nvbk5KSQmpqKp6envLzsIuLC05OTnKIGinEjRTm7ryYTCZUKpX8uG5ubkan0/Fv//Zv4rEtCM+gcRuAiouLiYmJGfZ9tVqNUqkc9c3x/TIajezZs4fvfOc75Ofnj3hb8D/T7mYFBQXMnTv3sc8Avf3222g0GhITE7G3t6e9vV2eWTHPajg6OmIymbh58ybW1tbMnTsXDw8Pent75ZBjHjEbrUeOuU+OlZUVKpWKyspKqqqqqKqqQqvVYjKZ5M2zBoNB3uvT1dUlH9PZ2RkLCwuMRiNeXl7yTNXAwABZWVncvn0bZ2dnUlNTiYmJQafT3TXg3Dn7BF/tFxsp1Nz5ua2t7ZAXQXNvi3PnzpGXl0dtbS0KhYKAgACSk5OZOXOm3JNDGF+am5vZv38/KpWKhQsXkpqaislkoqenh927d/P+++/L+3G6urooKCigqKgIvV4/pIqgjY0Nbm5uhIaGMmHCBKKjowkICECtVvNv//ZvGI1GVq1axapVq+57ZtJgMFBeXs7u3bu5cOECrq6uzJo1i4SEBGJiYkYNQxcvXuT06dMsX76clJQUjEYj27ZtQ6fT8cYbb9zX7RuNRg4dOsSNGzfIyMhg+vTpD/xvK3wzWVlZeHh4EB4ejpWVFZWVlZw7d46GhgaCgoJIT08nLCwMo9E4JLBUVFSwd+9eeXbc2dmZuLg4PDw80Ol01NbWcuXKFRQKBRMnThx1eaN5yZitra086GMwGIiKipKfd5VKpbzfMjQ0FEtLS+rq6mhsbMTZ2Znk5GSSkpLu6zVYkiR5VYC52XB5eTlOTk5MmjQJLy+vIY9dcxuDkQo0uLi4PPAspV6vFzObgvCMGrcByMLCgri4ODZu3MiGDRuYMGHCmBy3sLCQ6dOnMzAwgKOjIzt37mTZsmWjXv+Xv/wlv/rVr4Z9/3HPAHV0dODq6ir3UzAvVTMXHDDvfbh9+7a8sdX84ufp6Ym/vz+BgYGEhoYSERFBRETEsKVz/f39VFdXU1paSmFhIc3NzfT398svoOZlDp2dnXR3d8tLyKKiouQSvnV1dWi1WoKDg4mNjcXb25uioiKys7Opr6/H1taWwMBAnJ2d0Wg0Q/Z6mMuijjZrY/76fl7gJEmio6ODmzdvkpWVRUFBAV1dXVhZWTFp0iQ5lPn7+w95A6rT6eQ18SEhIWIkcZzQ6/WcPn2anJwcwsPDWb16tRxm33//fT799FNeeukleeZXrVZz9OhRrl27hqOjI5MmTaKvr4/Kykpqa2tpb2/HYDBgb2+Pm5ubvPnb0dGR8PBwli1bRnBwsLyB3NnZ+Z6zhcXFxWzbtg21Wi2X+/Xw8CA2NpaYmJghy/EkSeLEiRPk5ubK+5w6Ojp49913SUhIYPny5ff17yJJEqdPn+bSpUvMmDGDRYsWicf0I2Iymfj7v/97udiLRqPBaDTi7+9PdHQ0Tk5OcuC583nQTKvVUlhYKJfAHhwcxM/Pj+TkZHx9fdHr9WRnZ9PX10d6ejpTp06Vn6vNF4PBQH19PTU1NdTU1FBdXc3NmzcBeO6555g2bRqhoaHY2dlRUFDAlStXUKvVBAYGygNU36S3lMlk4sKFC+zYsYPW1lbCwsJISUkhOjoaGxubYVXsvt4A3dHRcdQCDS4uLmKGXhAE2bgNQO+++y67d+/m/PnzSJJEQkICmzZtYsOGDYSEhDz0cQcHB6mrq5OXhX3wwQecP39+XM0AAZw+fZrGxkZ5Xbb5v9m8Hru8vByNRsPs2bOZP38+zs7O6PV6eabIfDF3pVcoFNjb26PValGpVHR0dNDV1YVer8fS0lKeCQoICMDCwoK+vj66u7uxsLDAw8ODiIgIfHx80Gq1XL9+Xe6V4uXlhYeHh7y+vb6+Xl5eFhMTQ0RExJBAc2fIcXR0/EZrt3t7e6mqqqKwsJDc3Fxqa2vp7e3Fzc2NmJgYEhMT5Q3q5k2/5o2/5s/vXAb4s5/97K77poQnT1VVFQcPHmRwcJDly5cTFxeHJEn83//7fzl58iRvvfWWXFkSvmoeefz4cRoaGoiOjmbx4sW4u7uj0WgoLS2V9/S0tLRQUVFBd3c39vb2WFlZyTM4ZuY3ayNVsnNxccHW1pauri65VHZiYiJGo5HS0lIGBgbkPRoxMTH4+voCX1WBu3XrFps3byY8PJy8vDyOHj3Kiy++SFRU1H3/u+Tm5nL8+HEmT57M6tWrRcPUR0CSJN544w3Ky8tRqVRyYRhnZ2f8/f2JiIggKioKX19f7OzshoUXW1tb9Ho9u3btorm5maSkJHm2PTk5mfT0dKytrfnyyy/l16f09HSamprkwNPS0oIkSTg7OxMaGkpoaCi+vr6cPHmS+vp6Zs+eTW9vLzdv3sRkMjF58mRSU1Pva5nlg1CpVBw4cIDs7GxMJhO+vr5ERUXJlRrNodxkMtHb2zukrPedAamnp2dIDyQnJ6dhM0cJCQki5AvCM2jcBiCz1tZW9uzZw+7du8nKygJg2rRpbNq0ifXr13/jJ+aFCxcSERHBu+++e1/Xf1L2AB08eBCdTjdsuVpnZyf79+8H4Pnnnyc0NHTI75k7jnd0dNDe3k5ZWRkFBQWUl5fT0dGBTqfDZDLh5OSEm5sbXl5eeHl5yZXfzGHLvMnUXH2tra2NxsZGtFotrq6uREVFMWnSJBwdHWlqaqKqqgpJkpg8eTLz588f8iI3Vvr7+7l9+zYlJSXcuHGD0tJS2tvb0ev12NnZ4efnR2BgIE5OTkNGFeGrMtqOjo44OTnJl69/7ebmJjbTjkP9/f0cPXqUoqIi4uLiWLZsGVZWVvzsZz+joKCAf/zHfxxSTECSJG7dukVmZiZ9fX2kpqYyZ86cIeFXrVZz+/ZtPvjgA5qamrC0tEStVpOQkEBqaqrc9NG8Sdv8Zu3Oohu2tra4uLjg4ODA7du3aWpqIiUlhYyMDHp6eqipqaGsrGzIgMHEiRM5f/48dXV1vPLKK/j5+bFz506ampr4/ve//0D9fm7dusX+/fsJCQlh48aNIxZPEcaOwWBg1apVWFhYEBoairu7OwaDgc7OTrmsvyRJODo6ys9Vvr6+2NraYm1tjbW1tdxINDc3l/r6etLS0rCxsaGgoAClUklqaiq+vr5kZ2dz9uxZ4Ktqp76+vnLgCQkJwc3NbUjIuHXrFu+99x6FhYXExsayYcMGkpOTv9X+UZIkUVBQwPHjx+no6MDZ2VkOQzNmzGDy5Mn3DOZGo5Genp5hvY/MXxsMBn76059+a/dBEIQn17gPQHdqbGyUw5B5vfOdNf8fxvz58wkODmb79u33df0nJQB9nSRJXLx4kbNnzxISEkJGRgYDAwNyY1Bz6DFvcL1z5sja2hovLy98fX3x8fHBwcGBuro6SktLaWpqQq1Wy03pHB0dcXNzw8PDAycnJ7mwgaOjIykpKSxYsIAJEybQ3d1NTk4O169fx2QykZiYyPTp0+UmkQ9633Q63bBZGnMZ7ZqaGhoaGmhqahoya2OuQhcVFUV4eDhubm6jBpxH0QRWeLwKCws5evQo1tbWrF69Gn9/f/76r/+a+vp6fve73w0rbW1eUnTp0iUsLS1JT08nKSlpSAhWqVS8++678vK6M2fOYG1tTXh4OPb29nIDyLCwMLy9veX9EF+vZKdSqSguLqa4uBgnJydiY2NxcHCQ99aZZ2XNjX3VajUuLi786Ec/wtbWlnfeeYfAwEA2bdr0QAML1dXVXL58mQ0bNoi9Eo9Ae3s7dnZ2csEAc9GAwcFBtFotNTU1VFZWUl1dTW9vL5aWlnh6euLl5YWbm5v8XKjT6eQCM87Ozjg4OFBdXU17eztWVlb4+vri7u5OR0cHtra2JCcnyxXYzGFKkiTq6+uprq5mYGAAf39/rK2taWlpIT4+nkWLFmFvby9f3xzAxnoQqLe3l2PHjlFcXIynpyd2dnY0NDTg7OxMWloaSUlJDx3OjUajmN0UhGfUUxWAzE0JP//8c/bs2SOvob5fP//5z1m6dCnBwcH09vayc+dOfvvb33LixAkWLVp0X8d4UgJQQ0MDg4OD9PX1yY3xqqurCQgIwN3dHa1WK5cr1el0cr8bc9M7e3t7PDw8cHd3lzeXmktHq1Qq9Ho9Dg4OREZGEhcXx5QpU/D09ESlUpGTk0NWVhZVVVXo9XpcXV3x9fWVX6TMI3Fubm7MmjWLhQsX4urqOuw+SJLEwMDAsGVnI31tMBiGlObWaDRyEQZzKValUomXlxdxcXEkJiYSHx8vihgIQ3R3d3Pw4EGqq6vlN1d/8zd/g0aj4b/+679GXF7b29vL6dOnKSgowNvbm4yMDCIiIuSfV1RU8OmnnzJnzhz8/f3l0vzx8fF0dXVRV1cn7x8KCwsjLCxMDuR3hhVzNbBPP/0UjUbDjBkzcHBwkIOSSqWioaGB9vZ2Wltbqa+vx8bGhrlz5+Lt7U1FRQWLFi1i+vTp8nI7MaszPkmSRHNzM2VlZZSWltLa2opSqcTPz29Iqf78/HzKysrw8/Nj8eLF8mxia2srAQEBTJkyhatXr1JdXU1MTAzR0dG0tbVRXFxMbW0tRqMRb29v/P39sbW1ZXBwkKamJsrKyuSlwl/fV2NpaTkkEH09IN3t65G+Z2VlhUKhoLi4mGPHjmEwGEhOTqa3t5eioiKsrKxITk4mNTX1Gxc/EgTh2THuA5AkSZw7d45du3Zx4MABOjo6cHNzY+3atWzcuJEFCxbc97G2bt3K6dOnaW5uxsXFhSlTpvDTn/70vsMPPDkBaN26ddTW1qLVauWlE66urtjY2MihwGAwyCNgdnZ2eHh44O3tjZ+fH+7u7tjZ2dHf309PTw8dHR0YDAYcHBzkwghBQUHY2dlhaWkpjziWlJSg1WoJCQkhOTmZyZMnY2FhQWFhIWfPnqW0tFQ+FysrKwwGA4ODg/IadnMPCHMZ7q8HWFtb2yGzNHeW6m5vb5fDjrn7t16vx8nJiQkTJhAbG8vEiRPFPh3hriRJIicnh9OnT+Pm5kZaWhq/+c1vsLa25u2338bT03PE32tqauLEiRPU1tYSFRXF4sWL5euaq7S98MILeHh48Pnnn9Pb28vatWuJiIiQR9qrq6tpbGzEZDLh4uIih6GwsDD5zZ1Go2Hv3r3U1taycOFCpk+fPqQBpfnv4dq1a3zyySd0dnbi7e1Ne3s7Op2OtLQ0uZqcnZ3dqHuQXF1dsbe3F/sjnmAajYaamhpu3bpFXl4eFRUVqNVqbG1tCQ0NJSkpidDQUPLy8nB1deXFF1/E2dmZsrIyTp48iVqtJjk5GaVSKTdh9fHxwdPTk5SUFKZOnTqkYpz5Obm0tJTdu3fj6OjIc889h7W1tTxjdees1f18fa9VGgqFQg5DAJWVlTQ1NeHr68uUKVPo6OiguroahUJBVFQUSUlJ+Pj43DV0iaXKgiCM2wB08eJFdu/ezd69e2lra8PZ2ZnVq1ezceNGFi5c+NiqvTwpAeh//a//RXFxMWq1Wi5CMDg4iFqtxmg0YmNjI3fGDggIwMHBQV4vXV9fT21tLS0tLfJMj7nRqPl6er0evV6PWq2msbFRXn7j6emJp6enHIpaW1tpamqir68PKysreXO3+U2VwWDAYDDIe27MH62trbGzs8Pd3V0egTTPXvX19dHS0kJTUxMajQalUomnpydKpRKdTkd/fz/W1tZERkYyefJkEXqEh9LW1sb+/ftpb28nNDRU7iv2X//1X6PufZAkiZKSEk6ePElPTw8pKSnMmzcPW1tbdu3aRXV1Na+//jqOjo4cOHCA0tJS5s2bx9y5c4d0tK+traWqqorq6mpaW1sB8PLykgNRUFCQvPwuJiaGVatWjTib09HRwQcffABAZGQkn3zyCTqdjqSkJAIDA/Hy8sLGxkbeJ2FuZGlmrhD55ptvigpaT4C+vj5qa2vlpb3t7e0AeHh4yGWpfX19aW1tpaysjPLycnQ6HUqlkvr6ejw8PPj+979PUFAQBoOBs2fPsmfPHhobG3F0dESSJMLDw/n+979PcHDwXc+ltbWVnTt3IkkSmzdvxsfH56Huk8lkGhKO7idA1dbWkpubi0ajITIyEi8vL2pqaqiqqkKj0eDh4UFQUBAuLi4jBnjzLJW9vT1/+Zd/+VDnLQjC+DZuA5CFhQWOjo6sWLGCjRs3smTJkidin8aTEIBMJhP/9m//Rn5+Pu7u7nh4eKBUKvH39yc8PJyIiAgCAwOxtLSUyz+bl1I0NjYCEBwczMSJE4mOjpY7jpuXnnV2dlJQUEBBQQGtra1YWVnJ4Qi+GoVubW2lsbERg8GAp6cnERER+Pn5yR3B71zeYD4PvV6PwWCQl7GpVCo6OztpaWmhra2N3t5edDqdXP7a1dVV/j8370Fyc3PD29sbDw8P+dh3dgt/mI+j/Qy+2uze0dGBSqVixYoVYmTxKWMwGDh37hxZWVkMDAyQnZ1NYmIiv/3tb+/6fGMwGMjJyeHixYtYWFgwb9484uLi2LZtG0qlku9+97tYWVnJ+/LubFL5dRqNRp4dqqqqQqVSoVAo8PPzQ6lUUlJSQlBQEC+++CJeXl7Dfr+hoYGPP/6YsLAwpk+fzn/913/h4+ODpaUlfX19ODs7M2nSJGJjY+U+Ro2NjTQ1NdHa2opKpeKtt94ay39W4T719vYOCTwdHR3AV4HnzqIFoy3lNRqN1NbWUlZWJpf4HxgYYMaMGbi4uKBSqYD/2Ufp7e3N4OAgRqORJUuWkJycfNcZQPNS8a6uLjZs2DBk6ee3bXBwkNOnT3PlyhX8/f1ZtWoVHh4e3Lx5k0uXLtHc3Iy3tzdTp04lODhYXm1wZ6CSJGlIgRNBEJ4d4zYA7du3j+XLlz9xI/tPQgAC+PGPf4yVlRUzZsyQl9CYlzKYTCYaGhooKSmhsLCQlpYWTCYT3t7eeHl54erqil6vH7LXxmQyodFoaGpqoqWlBaVSSVBQELGxsYSFhcl9TcyVqQASEhKYPXs2fn5+933eRqNRrgpXVVVFQ0MDRqMRJycnfH19USqVtLS0UFJSQk1NDVqtVq7AFhAQgKenp7yEx1wy21wMwzzbZP78fj+aTCaMRiMajWbIv4lGo0GSJLlE+I4dO3Bzc/tW/j+F/3HhwgUkSZKXQZr/n+3s7L615Vq1tbUcOHCA69evU1payvLly/n7v//7e26g7uvr4+zZs1y/fh0PDw9SUlI4deoUEydOZN26dSgUCsrLy9m3bx8ODg5s2rRpxBBzJ7VaLc8OVVdXy3s2bG1tee6551iwYAH+/v5Dzq2iooIdO3YwYcIEOXgtWLAAjUZDSUkJlZWVdHd3A+Du7o6Xl5fc9NXBwYG33npLzAA9Aj09PUMCT2dnJwCenp5DAs/D7HXR6/WcOXOGt99+m9u3b+Pm5iaXsY6Li8PFxYWcnBy56AFAWlqavMRtNIODg+zZs4fKykpWrFhBYmLiw935h1RfX8+hQ4fo6upi9uzZzJo1C6VSSUVFBVlZWdTU1ODu7s706dNJSEgQxTwEQQDGcQB6Uj0pAaizsxOdTie/aVepVNy+fZuKigpqamro6+sDvnqzYw4NSqUSBweHIRXQ7O3t6erqorKyko6ODtzd3UlNTSU1NVUedezq6iI7O5v8/HwUCgVTp05l+vTpIxY2+DpJkmhvb5dHt2tqatDpdPIa9vDwcHx8fOQ3eTU1NSgUCsLDw4mNjSUqKor+/n7a2tpobW2V+xeZ9z0B8qzQnRfzkrmRDA4O0tLSQnNzM83NzTQ0NNDW1iYXWvDw8JCX+pmb7QFMmjRJzAA9An/+859pbW2VA6iZUqkcsj/MHIy+/r2HnSnW6XQcO3aM7du3U1FRwauvvsqPfvSj+wpdLS0tnDhxgurqamxtbens7GTt2rVMnz4d+Opv6PPPP0etVrNmzRomTZp0X+dk/vspKipi165dFBYW4ubmhp+fH25ubjg6OsoDH9XV1RQXFxMYGCiX3F6wYAHu7u44Ojqi0+loa2ujubkZvV6Ph4cH8fHxxMXFERwcLB7b3zJJkvjtb3/LwMAAXl5eQwKPo6PjQx+3t7eXq1evcvXqVTQaDeHh4RiNRrk4gpubm1zwwMvLCzs7O+rq6mhra0Or1ZKYmMiWLVvw8PAY9TZMJhPHjh3j6tWrzJkzh/T09Ee6d8xgMHDx4kUuXryIp6cnK1euJDAwEPhqb15WVhbFxcXY2dmRmppKSkrKkL1NgiA8e0QAGmNPSgD605/+RENDg9xDoq+vTy6ZGh4eLpd+Nr9JdHR0xNHRUQ4Fvb29XL9+natXr9Lb20twcDApKSlDOn0/7AtLT0+PPMNTVVVFX18fSqWS4OBgwsPD5fO6ffs2t27dkje4hoWFERsbS3R09D1vQ6/Xy2W97wxG5sau5gatbm5uKJVKeR26eQO5JEkolUp8fHzw9/fHz88PPz8/vL29xUj4E8JclfDOaoA9PT1Dvu7t7ZVHs81sbGzuGZTMlbRGUlRUxE9/+lPKysp48803+dGPfnRf5ytJkrz5/Nq1axiNRn7+858TGxsLfBW8Dx48SHFxMXPmzGHevHnAV0vgzPerp6dnyOfmj+blPOaZXfMev8HBQSwtLXF1dZV7ftXV1bFw4UKKi4uJi4tj5cqVw86zoaFBLrut0Wj4yU9+IirGPQJ1dXVDlhM/LHMJ6ytXrlBcXIylpSUJCQlMmzZNLsxx7do1jh49SkREBCtWrKChoYGysjJu376NRqOhs7NTHkyKioriBz/4AZMnT77rbV6+fJnMzEymTJnCqlWrHnmJ6dbWVr744guam5tJS0uTm7/CV9VHzQN1gNx6QczaC8KzSQSgMfakBKA//vGP1NTUYGNjQ3h4uNwk8W6jeJIkUVdXR15eHsXFxSiVSqZMmUJKSorcaV6SJCorK8nKyqK6uhp3d3dmzJhBfHz8qEsL+vv7qampkWd5zAUTfH195cATHByMXq+npKSEW7duUVNTgyRJcuiZNGnSNx6xM59HcXExFRUVVFdX09zcjEajwWQyyfuKgoODmTBhAhMmTJBDj7mhqzD+DA4ODgtFIwUlg8Ew5PfMs6EjBSWDwcBPfvITSkpK+M53vsPPf/7z+55ZMhqN5OTk8Pvf/x61Ws3LL79MXFycHHTMo/VOTk6Eh4cPeRNpnuUyn8udH82fd3V1ceDAARQKBWvWrEGhUMh/e01NTVRWVtLS0kJsbCz9/f1897vfJSkpacRzlSSJrq6uuz5vCE8OvV5PUVERubm5tLS04OHhwbRp00hISBgxwFZWVrJ7927c3NzkCnEmk4n6+npKS0u5ceMGeXl5lJSUYGVlxdq1a/nhD3941xmpoqIiDhw4QFBQEBs3bsTOzu7bvMvDmEwmcnJyOHPmDE5OTqxYsYLw8HD551qtlitXrnDlyhUMBgM//vGPn4j9w4IgPFoiAI2xJyUAZWdnY2trS1RU1D1HEwcHB7l58yZ5eXm0trbKexUSEhLkPVZGo5GioiIuX74s95CYOXMm0dHRw5bGGAwG6uvr5RmepqYmJEnC3d1dDjyhoaHY29uj1WopLS2VZ3ruDD3R0dEPPRKq0WjkJWzNzc1yw1b4qsKcr6+vPLNj7qZunjEyzxq1t7fLJVrt7Ozk5XM+Pj7y50/aHjTh4UiSRH9//7BQ9PWw1NfXJy+7Gxwc5IsvvqCtrY2EhATWrFlDaGgojo6O8hsqSZIwmUwMDAzQ19c3ZNamo6ODM2fOoNFoCAsLIyYmhoiICFxcXOjr6+P69es4Ozuzbt06QkNDcXZ2vu+y1L29vezZs4eGhgYyMjKYNm0aCoWCgYEBampq+Pzzz7l69Sp6vR6TycSSJUuYNGmS/LcpHtfjS3d3N3l5eVy/fp3+/n4mTJhAamoqERER93y8tLW18emnn2Iymdi8ebM82AXIRXIuXrzItm3buHXrFp6enmzcuJEZM2YQHR09Yjiuq6vjs88+w9HRkc2bN9/Xcuix1tXVxaFDh6ipqSExMZHFixcPCWN6vZ6mpqYRe3sJgvD0EwFojD0pAeh+dHR0kJeXR0FBAYODg0ycOJGUlBTCw8OHlOS9fv06OTk5dHd3ExkZycyZMwkJCZGvYzKZaGlpkQOPubGjg4ODXLY3PDxcfhHs7++XQ09VVRWSJBEaGirP9Dxo6Ont7R0WdsxL3WxsbPDz8xuyjM3d3f2+9jNIkoRKpZJDkfnS0dEhl+t2dnaWw9CcOXPEG8en3J37w1pbW6murubDDz+kubkZT09PvLy8sLa2HtLb5Ov9dsz77szl4jMzM7G2tsbb25uIiAgyMjLw9fVFpVLx+eefo1KpWLVqlbxU7n4ZjUZOnTpFdnY2cXFxrFixQg5mJpOJPXv2kJ+fT2dnJ05OTgQHB6NWq1EoFPj7+w8puS02jj95JEmSy0GXlpZibW1NYmIi06ZNw93d/YGO1dvby2effUZHRwfPP/88UVFRI97eoUOH+I//+A9UKhUTJ05k4sSJ+Pr6yp8HBgbKz60dHR18+umn6PV6XnzxRfz9/cfkfj8ISZK4fv06J0+exMrKiuXLl9/3/jpBEJ5uIgCNsSc9AJlMJsrKysjLy6OqqgoHBwemTp1KcnIyLi4u8vX6+vrIzc0lLy+PwcFB4uLimDFjBj4+PnIwMAee6upqufdOSEiIHHjMzRZh5NATEhIih5772eRrbnr69bBjLuhgZ2c3LOy4ubmN+dI1o9E4ZLbIHIq+//3vP/I178LYuHMGaKQ9NubPtVrtkN+zsbHBaDRy6NAhtFot0dHRREVFsXTpUjw8POTGkeZiJF+fUdLr9TQ3N1NaWoqvry8DAwMYDAYmTpzItGnTcHNzo6CggPr6embNmsXSpUtxcnJ6oIIERUVFHDp0CFdXVzZu3CiP2BsMBnbs2EFRUREAGzduZMKECXJ1uerqannvoNgD9OQYHByksLCQ3Nxc2tra8PLyYtq0acTHx3+jpVyDg4Ps37+fsrIyli5dyrRp00a8nkql4re//S1XrlzBz8+PpKQkDAYDWq0WBwcHoqKimDhxIuHh4ej1ej777DNaW1tZv379iMHqUejp6eHo0aOUlZURExPDsmXLvlFhCUEQxj8RgMbYkxqAzEtqrl69Sk9PD0FBQXJRgzs39Xd0dJCdnU1BQQFKpZLk5GRSU1NRKpXyPoKqqiq6u7uxsLAgMDBQHikODAwcEgD6+/spKyuTQ4/JZLrv0CNJEmq1ekjYMe/Xga/2Z3w97IzW9E54upj3LKSkpNzX0hqj0TgsfIz08c49QOZeU6PttzF/NIeCW7du8Ytf/AJbW1umTJkCQEZGBklJSaM+Js29V3p7ezl48CD5+fmkp6dTX1/P1atXGRgYwN/fH3d3dxobG6msrMTNzY2YmBjc3d3vWunOyclpSMPhtrY2du3aRV9fH2vWrCE6Ohr4aoZ3+/bt5OXl4ePjw1tvvSWX4TZXmGtubiY+Pv6h/7+EsaFSqeRlbjqdTg7JYWFhY/a8ZzKZOHXqFJcvXyYtLY3FixePGLZNJhOHDh2SG6HOnj2blJQUNBoNZWVltLe3Y2lpKfedKy0tpaamhmXLlpGSkjIm5/qgJEni1q1bfPnll5hMJjIyMoiPjxevGYLwjBIBaIw9SQHIXAnIXNTAwsKCuLg4UlJShvXmqa+v5/Lly5SWluLg4EBSUhJeXl40NjZSVVUld6P39vaWZ3hCQkKGjQoPDAzIoaeyshKTyURwcLAcekbqX2HeaP31sNPf3w+Ak5PTsLBj7u8jPFvMb9CuX7/OwMAA4eHhTJo0CTc3t1FDztfLZVtZWY1aQODOqogPOpuXlZXFb37zG8LCwpg7dy7FxcVERkayatWqe442GwwGtm/fTk9PD2+88QYWFhacO3eOvLw8nJ2dmTVrFgaDgX379mE0Gpk5cyZWVlZD7vPXZ6csLS2HhCIbGxvy8/Npbm5m1qxZLF68GFdXV3Q6He+//z5ZWVnMmTOHH/zgB2Im8wkhSRJVVVVcuXKF27dvY2try9SpU+87/D+svLw8jh07RlRUFOvWrRt1ZqmkpITt27dTV1fH/8fefYdFeaX/438PSO9IEZGOUuyAWBDR2FvsWGOLZdPLZhNTjLopbjblY5JNUWPUEI29RBNbbEQREAUVkCKCIB2RDjPAPL8//M78GAGlzgzM+3Vdc+3OU2+eHGHuOefcp0ePHvD398fYsWPlZbYTExORnp4OqVSK4uJiFBcXY+LEiZg9e7bKyqpXVFTg5MmTuHnzJtzc3BAcHMzeTSINxASojalDAiQIAqKjoxEZGYmcnBxYWlrKixrUnQQqCAKSkpLki8V16dIF3bp1g0gkQnZ2NqRSKczMzOQLqbq4uDSYwDSU9MgWSfX29lY4RyqV4sGDB/WSHbFYDAAwMzOTJzmyhIdDFaiuffv2ITMzU76mVXl5OQwNDeW9kRYWFk+skla3Z6St/f777/juu+/g7++P4OBgnDp1ClKpFFOnTn3q3IOSkhJs3rwZNjY2eO6556ClpYWCggKcPn0aSUlJcHJywpAhQxAaGoqCggI8++yz6Nu3r/z8mpoahbLgDVW6Ky4uRkpKCu7evQsLCwt4eXnB1NQU2trauHTpErKysjBz5kyMHj26Xq8Sv3BQHrFYjBs3biAyMhIFBQWwtbWVL1iqrPlYycnJ2L9/P7p27YoFCxY0uvjqgwcPsHfvXsTHx0NPTw8WFhYYNmwYhg8fDl1dXVRUVCA5ORmJiYk4f/48bt++DWdnZ8yePRt9+vRR2RpTycnJiI+Px7PPPsu2TaSBmAC1MXVIgABg586d0NXVxaBBg+pVAqqpqcHNmzdx6tQp3LlzR74AqqmpKQwMDBQKF1haWjb4x0EsFsuTnjt37qC2tlahp0dWTlU2hEb2ysnJgUQiAQD5go2yZKdbt26tXv+COr8zZ85AKpXC1NQUxsbGKC4uRlJSEu7duwcDAwMMHDgQgwYNavZE8LYgCAJ27tyJ3bt3Y+zYsVi5ciVOnjyJhIQEDBw4EBMmTHjit8337t3Dzp075cOPZFJSUnDq1Cnk5+ejd+/ekEgkSEpKwtChQzF27NhmfYAUi8WIjY3FgQMHUFNTg4CAAOjp6SEjIwO7du1CYWEhxowZI58TKBKJ8MEHH7BXSAkePHiAyMhIxMTEoLq6Gp6enhg8eDAcHR1V8iE9JycHu3fvhkgkwoIFC2Bra9vgcdXV1Th+/DiuX78OQ0NDVFdXw8jICKNHj1YYZlZTU4O//voLu3btglgsRs+ePWFqaoqePXvCw8MD7u7u7I0hIqVgAtTG1CUBqq2trfeBJTc3F3/++ScuXLiA7OxsmJqawtnZGf369ZP38tjZ2TX6YUosFssXJ71z5w5qamrkPT0eHh6oqqqql+zI5lV07dq1XrKj7PUhqHMrKipCVFQUrl27hqqqKvTs2RP+/v5NKgXclmpra/HNN9/gxIkTmD17Np5//nncvHkTJ06cgKGhIWbMmPHE0rvh4eE4efIk5syZo1D5TSqV4tq1azh//jyqq6vRtWtXZGdnw9XVFbNnz272lwclJSXYt28fsrOzMXHiRPj4+CA+Ph5vvfUWunTpgmXLlskXUl6zZg2/JW9ntbW1+OKLLyASieDr61uvMI2qlJSU4LfffkNhYSHmzJkDd3f3Bo8TBAHXrl3DiRMnYGZmBjMzM6SmpsLe3h4TJkyAg4OD/NjMzEx5EtSvXz9kZWUhNzcX2tracHFxgYeHB3r16qUWPz8RdU5MgNqYuiRAwKMiBKmpqYiNjcWFCxeQmJgIQRDg5eWFZ555BgMHDnxqiduGkh5ZImNqaiovQZ2bm4va2lqIRCJYWVnVS3b4rR4pS3MXg2wPVVVV+M9//oPw8HAsW7YMwcHBKCoqwuHDh5GRkYGAgACMGjWqwV4VQRDk1bhWrFgBGxubetcODQ1FREQEJBIJysvL0aNHD8yfP7/e3L6GVFRUoLCwEA8ePEB+fj7Onj2LW7duwcLCAs7OzsjJycHZs2fh5uaGhQsXwsrKChMmTFAolkLtIysrCzY2Nmr3rCUSCQ4cOIA7d+5g0qRJ8PPza/TYzMxM7Nu3D9XV1Rg6dCji4+ORnZ2Nvn37KvQsPnz4ELt27UJFRQXmz58PExMT+byhtLQ0SKVS2NnZKZTbZhJORG2FCVAbU5cE6Ndff8WNGzeQkZGB0tJSWFtbIzAwEFOnTq33gepxsuE1cXFxSEhIQHFxMQwMDGBqagpdXV2UlpZCKpVCS0sL1tbWCsmOra0tV9UmtSArAhIRESFfyb5///7w9/eHlZVVu9+/pKQEGzZsQGJiIl599VWMGzcOUqkUYWFhOH/+PKytrTFz5swG/z1KJBJs27YNNTU1WLlyZYPrSxUWFuL06dOIiYlBdnY2bGxssGjRIvTv3x9VVVXyJOfBgwfy/19YWCgvLgI8KjBiaWmJkpISxMbGonv37li4cCGuXr2KrVu3YsmSJVi2bFm7PifqGKRSKU6dOoWIiAgMGzYMY8eObTQhqaiowMGDB3H37l2MHDkSJiYmOHfuHMRiMQICAuRFPCorK7Fnzx5kZmZi5syZ8Pb2BvAoyZfNG0pOToZYLIaZmZk8GXJ2duaQTCJqFSZAbUwdEiBBEPDpp58iPT0djo6OGDVqFHx9fZ/47bdEIkF8fDzCwsJw48YNFBUVoUuXLtDX14e1tTWMjIxgY2OjkOzY2NhwgUTqEEpKSuTD48rLy+Hm5obBgwejZ8+e7fqtcm5uLj788EPk5eXhnXfewZAhQwAA2dnZOHToEB4+fIgxY8Zg8ODB9eIoLCzEli1b4OTkhHnz5tXbL5FIUFhYiBs3buDkyZPy0tmenp5wcXGRD2U1MjKCpaUlunbtiq5du8r/v6WlpcKXFTk5Odi7dy+qqqowY8YM7N27FxcvXsT777+PUaNGtdszoo4lIiICJ0+ehKenJ2bOnNno3wCpVIqLFy/i4sWL6NWrFyZNmoSoqChcuXIFRkZGGDt2LPr06YPa2locPXoUsbGxGDt2LIYOHarQ1mtra3Hv3j0kJibKv5DT09ODu7s7PD094e7uzuHURNRsTIDamDokQAAQGhoKMzMz9OnTp8FvyqqqqpCeno7IyEhER0cjKSkJpaWlMDExQbdu3dC7d2+4u7vLkx1ra2u1G5ZB1Fw1NTWIi4tDZGQkMjMz5RUSBw4c2GAvS1u4e/cu1q9fD4lEgrVr18rn9VRXV+Ps2bMIDw+Hq6srpk+fDlNTU4Vz4+PjsX37dvTt2xeurq4KPTmlpaXy4/T19eVrfeXm5qJfv35466234Obm1qyfq7KyEocPH0ZycjIGDx6MgwcPoqioCNu3b2c1RpJLSkrCgQMHYG1tjfnz5z+xbSQnJ+PQoUPQ19dHcHAw9PX1cfr0ady+fRsODg6YMGECunfvjnPnzuHvv/+Gv78/JkyY0OBcVEEQkJubKx8ql5WVBS0tLTg5Ocl7hywsLNrzRyeiToIJUBtTlwSoroqKCuTk5CArKwsZGRm4desWkpOT8eDBAwBAjx490K9fPwwaNAienp6wsrLi8ALq9O7fv4+IiAj5Glmy4XFPGyLaEjdu3MAnn3wCIyMjrFu3Ds7OzvJ9SUlJ2LNnD4qLizFgwABYWFjIh66VlJQgLS0NaWlp8PX1hYeHh0IPjux/DQ0NATyas3fw4EHs3LkTenp6ePnll584VKkhgiDg77//xvnz52FiYoLq6mq88sor8nsQAY96MXfv3g1tbW0sWLDgif9uioqKsG/fPuTl5WHSpEnw8fFBamoqTp48idzcXPTv3x9jxoxBUlIS/vjjD/Ts2fOJ6w/JlJSUICkpCQkJCUhNTUVtbS1sbW3lyVD37t05b4iIGsQEqI2pSwJ05coVpKenIzs7W/6N8cOHD1FVVQVDQ0O4uLjA398fQ4cOVcp8CCJ1VVZWhqioKERFRaGsrAwuLi4YPHgwevXq1Wbrk9TW1uLPP//EN998AzMzM8yYMUO+JlZxcTEkEgmSk5NRUFAAd3d3jBgxAnZ2drC0tISlpSXOnj2L3NxcrF69uknlvdPT0/Hpp58iMTERAQEBWLVqFRwdHZsV8507d3Dw4EHo6upi1apVLFFP9RQXF2P37t0oKirC3Llz4erq2uixNTU1OHHiBK5du4aBAwdi0qRJ0NbWRnR0NM6dO4fq6moMHz4cVlZWOHLkCKysrLBgwYIm9zyKxWKkpKQgMTERSUlJqKyshImJCXr16gUPDw+4urpyFAMRyTEBamPqkgD98ssvyMzMREVFBYqLi6Gvrw8XFxf06dMHvXv3VskaKUTqrLa2FvHx8YiMjERGRgbMzc3lw+Oa0vshW+2+oeIDRUVFqK2tRUJCAm7cuAFHR0cEBwfDyclJYX5Oamoq/vzzT+jq6mL69OnyD5RVVVXYsmULdHR0sGLFiibNvaupqcHOnTtx+PBhmJqaYtq0aRg/fjzMzc2b/EyKiopw8+ZNBAYG8pt0apBYLMaBAweQkpKCKVOmPPXvXkxMDI4fPw5ra2sEBwfDwsJCobKhiYkJBg4ciKtXr6JLly5YuHAhrK2tmxWTVCpFRkYGEhISkJiYiMLCQujo6MDd3V1eYps9mkSajQlQG1OXBOi7775Dfn6+fD6Pt7c3unbtqrJ4iDqSrKwsREZG4tatWxCJROjXrx/8/f1ha2uLkpKSepXVHjx4gIcPH6K2thYAoK2tDQsLi3rFBywsLHD48GEcOnQIQ4YMwRtvvFGvZ6W4uBhHjhxBamoqhgwZgtGjR0NHRwd5eXnYunWrfPJ5UxOSqKgo/PzzzygoKICHhwdGjhyJ4cOHszQ9tRmpVIoTJ07g6tWrGD58OEaPHv3E9pmTk4N9+/ahoqICM2fORK9evQA8Wgj29OnTSExMhLW1NUpKSiASiTBv3jyFYaPNIQgCCgoK5POG7t+/DwBwcHCAh4cHBg8ezJ4hIg3EBKiNqUsClJKSAjMzMw5vI2omQRBQVlaGBw8eIDMzExEREYiOjkZhYSH09PRgZ2cnnydnbm5ebz5O165dYWZm1ujwOYlEgu+//x5//fUXxowZgxdffLHeXAdBEBAeHo6zZ8/CwsICM2fOhJ2dHWJjY3HgwAFMnDgRgwcPbvLPdP/+fezatQupqakwMTGBra0tRo8ejf79+7fZMD/SbLI2e/r0aXh7e2P69OlP7KmsqqrC4cOHkZiYiBEjRmDkyJHytpiSkoJTp04hOzsbpaWlMDY2RnBwMPr169fqOMvKypCUlITExEQUFBTg5ZdfZu8mkQZiAtTG1CUBIqLGCYKAioqKBntyCgsLIZFIAAAikQhmZmawsLBARUUFMjMzUVxcDBsbGwQGBmLQoEEtmhtTXl6Or776ClevXsW0adOwdOnSBguP5OXl4dChQ8jPz8eoUaMwbNgw/PXXXwgPD8eSJUvg5OTU5HuWlZVh37598i9HKioq0K1bN0yYMKHF364TPS4hIQEHDx6Era0t5s+f/8R/H4Ig4PLlyzh79ixcXFwwa9Ys+fFSqRRRUVE4e/Ys4uPjoaOjg0WLFmHUqFFtlrDI1rMjIs3DBKiNMQEiUh+VlZUKyU3d/y8Wi+XHmZqaNtiTY2FhUW94TE5ODiIjI3Hz5k0AQJ8+feDv74/u3bs3K7YHDx7g888/R2JiIubPn485c+Y0+MGupqYGFy5cwOXLl+Hg4IBp06bh2LFjKCgowKpVq+qVzn6S2tpanDp1CpGRkXB0dER1dTWys7Ph5eWFsWPHcm4gtYmsrCzs3r0bOjo6WLBgwVPn8KSmpuLAgQPQ1tZGcHAwevToId9XWVmJ8+fP48CBA8jKysLUqVOxevVqDlsjolZhAtTGmAARKZdYLK6X3Mj+t7KyUn6csbGxQnIj+/+WlpYtWtC3oqIC0dHRuHr1KoqKiuDg4AB/f394e3s3uYx8RkYGPv/8c+Tk5GD58uUYP358o99u37t3D4cPH0ZFRQVGjhyJK1euwMzMDEuXLm32h0HZRHRbW1v07t0b4eHhKC8vx5AhQxAYGNhuayKR5igqKsLu3btRUlKCuXPnwsXF5YnHl5SUYP/+/cjKysL48eMxaNAghX8L+fn52Lp1K86cOYOePXvigw8+aHZlQyIiGSZAbYwJEFH7unLlCnJzc+VJTnl5uXyfoaGhQnJTN8lpr0n/UqkUSUlJiIiIQGpqKoyNjeHn5wc/P78mlfC9ffs2/u///g+VlZX4xz/+gYCAgEaPFYvFOHHiBGJiYmBjY4Ps7GwMGTIEkydPbnbcWVlZ2LNnD6RSKaZPn47MzExcunQJurq6GDVqFHx8fDg8iFqlqqoK+/fvR2pqKp599lkMGDDgicfX1tbizJkzCA8PR9++fTF16tR68+POnTuHTZs2QSqV4rnnnsOUKVNYop2Imo0JUBtjAkTUvrZv346ampoGh6ypuuciLy8PkZGRuHHjBqRSKby9vTF48GCFIT0NiYiIwPfffw8dHR28/PLLT/2gePv2bRw7dgxZWVmora3F8uXLMXDgwGbHW15ejv379yM9PR0TJkyAh4cHzp07hxs3bsDW1hbjx49/4touRE8jWwPr2rVrGDFiRJPm8MTGxuL333+Hubk5goOD6xXzyc7Oxn/+8x/cuXMH/fv3x5QpUzB48GAu4E1ETcYEqI0xASKiqqoqREdHIzIyEg8fPoS9vT38/f3Ru3fvRoernTp1Cjt37kTXrl3x6quvomfPnk+8R2lpKX7//XccP34cXbp0wUcffdSiYgZ1v3UfMGAApkyZgtzcXJw6dQrp6enw8PDA9OnTYWBg0OxrEwGPih2EhYXhzJkz6Nu3L6ZNm/bUYZv5+fnYu3cvSktLMW3aNHh7eyvsLy0txY4dOxAVFQVzc3O4u7tj3Lhx6NWrF6u6EdFTMQGqY+PGjTh06BASEhJgYGCAYcOG4bPPPoOHh0eTr8EEiIhkBEFAcnIyIiMjcefOHRgZGcHX1xd+fn71ihcIgoD9+/fjwIEDcHJywmuvvfbUniNZ6eHPPvsMWlpa+Oyzz56aODXm5s2b+P3332FjY4O5c+fC1NQU8fHxuHXrFoKDgzkcjlotPj4ehw4dQvfu3TFv3rynLkYqFovx+++/Iy4uDsOGDcPo0aMVenkkEgkOHDiAmJgYGBsbQyqVws3NDePHj4eNjU17/zhE1IExAapjwoQJmDdvHgYNGoSamhq89957iI2NRXx8fJPHGDMBIqKGFBQUIDIyEjExMaipqYGXlxcGDx4MBwcH+TfWNTU12LFjB06ePAlvb2+88sorT62gBTyqovWvf/0LUqkUL7/8MoKCglo0HCg7Oxt79+5FdXU15syZw/LY1Obu37+P3377DXp6eli4cOFTF+gWBAERERE4ffo0HBwcMHv2bJiYmMj3yxZhjYyMhKurK4qKilBcXAw/Pz+MHDnyqUkWEWkmJkBPkJ+fDxsbG1y8eBEjRoxo0jlMgIjoScRiMWJiYhAZGYkHDx6gW7duGDx4MPr06QMdHR1UVlbixx9/RFhYGHx9ffHiiy82qdT1nTt38Mknn0AqlWL48OGYOXPmUz9cNqSiogIHDhxAWloaxo0bh8GDB3NIEbWphw8fYvfu3SgrK8O8efOatJ5Veno69u/fD0EQMGfOHIVz6g6x8/b2Rrdu3XD58mVoaWlh5MiR8PPz4/wgIlLABOgJ7ty5g549e+LWrVvo06dPk85hAkRETSEIAlJSUhAZGYnk5GQYGBjAx8cHgwYNgiAI+N///oebN29i+PDhWLlyZZPm4ISFheHAgQPQ19eHkZERxo0bBz8/v2YnMFKpFH/99RfCwsLQr18/TJ06tUWlwokaU1VVhb179yI9PR3Tpk1Dv379nnpOWVkZDhw4gPT0dIwZMwZDhw5VaNtxcXE4fPgwevTogSlTpuDKlSu4fv06rKysMH78eLi7u7fnj0REHQgToEZIpVI8++yzKCoqwqVLlxo9TiwWKyyoGBMTg6CgICZARNRkhYWFuHr1KqKjoyEWi+Hp6QkXFxccPXoUd+/exejRo7FkyZKnJiGCIODAgQNISEiAq6srkpOT0bNnT0ybNq1JJbkfFxsbi/DwcCxevLheOWKi1qqtrcXx48cRHR2NkSNHIigo6KnJulQqxblz53Dp0iV4eXlh+vTpCiXu09PTsWfPHhgaGmLhwoUQi8U4efIk0tLS0LNnT4wfP75eVTki0jxMgBrxwgsv4MSJE7h06dITJyKvX78eGzZsqLedCRARNZdEIsHNmzcRERGB/Px8AI96oqVSKaZMmYJ58+Y9tRiBRCLB1q1bIQgCRo4ciZMnT0IqlWLq1Knw8vJqdkyCIHAIHLUbQRBw6dIlnD17Fv369cOzzz7bpIV9ExIScPjwYRgbG2Pu3LkKRQ8ePHiAXbt2QSwWY8GCBejevTsSEhJw+vRpFBcXw9/fH0FBQaxsSKTBmAA14OWXX8bRo0cRGhr61NWr2QNERG1NEASkpaUhIiICFy5cwM2bN2FiYoIlS5Zg/vz5T01IHjx4gC1btsDFxQVTpkzB8ePHkZCQgAEDBmDixInttigsUUvFxsbiyJEjsLe3x7x585qUnBQWFmLv3r0oLCzE1KlTFYbRlZeX47fffkNubi5mzZoFT09P1NTUIDw8HKGhoejSpQtGjRoFX19fVjgk0kBMgOoQBAGvvPIKDh8+jAsXLrSonCznABFRWyoqKsJPP/2EAwcOoKamBhMmTMCyZcvg6ur6xEQoMTERv/32G0aPHo3hw4cjJiYGJ06cgKGhIWbMmNGkiedEypSRkYHffvsNBgYGWLhwISwtLZ96TnV1NY4fP44bN25g0KBBGD9+vLwHqbq6GocPH8bt27cxYcIEDB48GMCjNYTOnTuH1NRUvPTSS5zfRqSBmADV8eKLL2L37t04evSowto/ZmZmTe4qZwJERG1NEAQcOnQIISEhKC4uhru7O/r37w9/f3/079+/0R6d8+fPIzQ0FIsWLYKbmxsePnyIw4cPIyMjAwEBARg1ahSrY5FaKSwsxO7du1FRUYF58+bB0dHxqecIgoDr16/jzz//RLdu3RAcHAwzMzP5vtOnT+PKlSsYOnQoxo0bJ//iQCKRcG4bkYZiAlRHY9+mbt++HUuXLm3SNZgAEVF7qK2txe7du3Hu3Dno6OjAw8MD5eXl0NHRwYABA+Dv71+v7LVUKsVvv/2G+/fvY/Xq1TA3N4dUKkVYWBjOnz8Pa2trzJw5k4tGklqprKzE3r17kZGRgRkzZjS5CmtWVhb27dsHiUSCWbNmwc3NTb4vMjISJ06cgKenJ2bOnMleHyINxwSojTEBIqL2IhaLsX37doSHh8Pe3h7BwcEoKCjAtWvXUFFRAXd3dwwePBju7u7yL3QqKyuxZcsW6OvrY/ny5fIPftnZ2Th06BAePnyIMWPGcL0fUiu1tbX4/fffcePGDTzzzDMIDAxsUvusqKjAoUOHkJKSglGjRimcl5iYiAMHDsDW1hbz589v8gLnRNT5MAFqY0yAiKg9lZaWYsuWLbh58ybc3NywcuVKWFtbIzY2FpGRkcjKyoKlpSX8/f0xYMAA6OvrIycnB9u2bYO3tzemT58u/0BYXV2Ns2fPIjw8HK6urpg+fXqTFl0lUgZBEBAaGorz589jwIABmDp1apOGbAqCgIsXL+LixYtwd3fHzJkz5cPYMzMzsXv3bujp6WHhwoUtWiyYiDo+JkBtjAkQEbW3/Px8bNmyBXfu3IGXlxdWrlwJCwsLCIKA+/fvIyIiAvHx8ejSpYt8rpCsx2fy5MkYNGiQwvXu3r2LI0eOQCKRYPLkyejbt6+KfjKi+m7evImjR4/C0dERwcHBTZ6Te+fOHRw8eBB6enqYO3cu7OzsAAAPHz7Erl27UFlZiVdeeQX6+vrtGT4RqSEmQG2MCRARKcO9e/fw008/ITs7G3379sXzzz+vsNhpaWkpoqKicO3aNZSVlcHV1RVisRiZmZl4/vnn4eDgoHC9yspK/PHHH4iNjUWfPn0wefJkrpNCauPevXvYs2cPjIyMsHDhQlhYWDTpvKKiIuzbtw95eXmYNGmS/O9yZWUlUlNT4e3t3Z5hE5GaYgLUxpgAEZGyxMbGIiQkBEVFRfDx8cHSpUvrVYSrra1FXFwcIiMjkZ6ejjt37qBr165Yu3Ztg8UPbt26hT/++AO6urqYPn06XF1dlfXjED1R3QVO58+f/8RFyuuqqanByZMnERUVhYEDB2LSpEksgkCk4ZgAtTEmQESkTGFhYTh06BAqKysxePBgLFy4sNF5EpmZmbh48SJ++eUXGBoaYvHixRg6dChsbW0VjisuLsaRI0eQmpqKIUOGYPTo0fzASGqhoqICe/bsQVZWFmbMmIHevXs3+dwbN27g2LFjsLKywty5c5vci0REnQ8ToDbGBIiIlEkQBJw8eRKnT5+GVCpFYGAgZs2a9cSKWQkJCfj888+hpaUFe3t7ODs7w9/fH56entDS0pJfNyIiAteuXcOKFSsaXWuISNlqampw9OhR3Lp1C2PGjEFAQECTKxjm5ORg3759qKiowIwZMxTW/CMizcEEqI0xASIiZZNKpdi/fz/CwsLQpUsXjBkzBhMmTHjih8KrV6/i2LFj6N+/P4qLi5Geng4zMzMMGjQIPj4+MDQ0lF9blhQRqQtBEHDhwgVcvHgRPj4+mDx5cpMX9a2qqsKRI0dw7949vPbaayyCQKSBuqg6ACIiah0tLS3MnDkTZWVluHnzJi5cuAATExMMHz680XP8/PyQmZmJ2NhYrFixAoIgIDIyEhcuXMCFCxfQt29f+Pv7yytnEakTkUiEUaNGwcLCAseOHUNxcTHmzJnTpGRGX18fc+fOxcOHD5n8EGko9gC1MfYAEZGqVFRUYNu2bUhOToaJiQlmz56NgQMHNnp8dXU1fv75Z1RVVWHVqlUwMDBARUUFrl+/jqtXr6K4uBiOjo6YP38+K8KR2kpNTcXevXthamqKBQsWwNzcXNUhEZGa47gGIqJOwtDQEAsXLkSPHj1QWlqKI0eOICkpqdHjdXR0MHfuXIjFYhw8eBBSqRSGhoYYPnw4XnvtNcydOxfW1tb8lpzUmouLC1asWIHq6mr89NNPyMzMVHVIRKTmmAAREXUilpaWWLhwIWxsbFBUVIS9e/ciIyOj0ePNzc0xa9YspKSk4MKFC/LtWlpa8PLywtSpU5s8wZxIVaysrLBixQpYWFhgx44duH37tqpDIiI1xgSIiKiTsbe3R3BwMMzNzVFYWIhdu3YhPz+/0ePd3NwwevRohIaGIiEhQYmRErUdIyMjLF68GL169cK+ffsQFhYGjvInooYwASIi6oR69eqFqVOnwsjICAUFBQgJCUFxcXGjxwcEBMDLywuHDx9GQUGBEiMlajs6OjqYPXs2hg8fjtOnT+OPP/6AVCpVdVhEpGaYABERdVJ+fn4YNWoUdHR0kJubi19//RWVlZUNHisSiTB9+nSYmJhg7969EIvFSo6WqG2IRCKMHj0a06ZNw/Xr17F79262ZyJSwASIiKgTe+aZZzBo0CBoaWnh/v372L17N6qrqxs8Vk9PD/PmzUNJSQmOHj3K4UPUoQ0cOBCLFi1CRkYGfv755yf2gBKRZmECRETUiYlEIkybNg2enp4AgJSUFOzfv7/RYUFWVlaYPn064uPjERYWpsxQidqcq6srnn/+eYjFYvz000/Izs5WdUhEpAaYABERdXLa2tqYO3cuHB0dIRKJEBcXh2PHjjXaw+Pl5YXAwED89ddfuHv3rpKjJWpbNjY2WLFiBUxNTfHzzz8jMTFR1SERkYoxASIi0gD6+vpYuHAhunbtCpFIhKtXr+LcuXONHj9q1Ci4urriwIEDKC8vV2KkRG3P2NgYS5cuhbu7O/bs2YOIiAhVh0REKsQEiIhIQ5iammLhwoUwMjKCtrY2Ll682OgHQS0tLcyaNQtjxoyBoaGhkiMlans6OjoIDg7GsGHDcOLECfz555+sEEekoZgAERFpEFtbW8ydOxddunSBlpYWTpw4gdjY2AaPNTQ0hI+PDxdCpU5DJBJh7NixmDJlCu7fv4+amhpVh0REKsAEiIhIw7i6uuLZZ5+FIAjQ1tbG4cOHOdeHNIqfnx9WrFgBXV1dVYdCRCrABIiISAP1798fY8aMQXV1NbS0tLBnzx5WyCKNoqXFj0BEmor/+omINNTw4cPh7+8PiUQCQRDw66+/orCwUNVhERERtSsmQEREGkokEmHSpEnw8PBATU0NJBIJQkJCUFZWpurQiIiI2g0TICIiDaalpYXZs2fDzs4OUqkUpaWl+PXXXyEWi1UdGhERUbtgAkREpOF0dXWxYMECmJqaQltbG/n5+dizZw8rZBERUafEBIiIiGBsbIxFixZBW1sb+vr6SEtLw+HDh7lOChERdTpMgIiICADQtWtXzJ8/H2KxGGZmZoiLi8PJkychCIKqQyMiImozTICIiEjOwcEBs2bNQnFxMaytrZGQkICKigpVh0VERNRmmAA9JjQ0FFOnTkX37t0hEolw5MgRVYdERKRUXl5emDBhAvLz8zFo0CAYGRmpOiQiIqI2wwToMeXl5ejfvz++++47VYdCRKQygwcPxrBhw/D333+jvLxc1eEQERG1mS6qDkDdTJw4ERMnTlR1GEREKjd27Fj4+PiwB4iIiDoVJkCtJBaLFdbL4AKCRNRZiEQiWFlZqToMIiKiNsUhcK20ceNGmJmZyV9BQUGqDomIiIiIiBrBBKiV3n33XRQXF8tfFy9eVHVIRERERETUCA6BayU9PT3o6enJ3xsbG6swGiIiIiIiehL2ABERERERkcZgD9BjysrKcOfOHfn71NRUxMTEwNLSEo6OjiqMjIiIiIiIWosJ0GOioqIwatQo+fs333wTALBkyRLs2LFDRVEREREREVFbYAL0mJEjR0IQBFWH0Says7ORnZ2t6jA0gp2dHezs7FQdhsZg21Yetm3lYttWHrZtIs3FBKiN2dnZYd26dSr/pSoWizF//nxWpVOSoKAgnDp1SqEgBrUPtm3lYttWHrZt5WLbJtJcIqGzdHeQgpKSEpiZmeHixYusTNfOysrKEBQUhOLiYpiamqo6nE6PbVt52LaVi21bedi2iTQbe4A6uQEDBvCXezsrKSlRdQgaiW27/bFtqwbbdvtj2ybSbCyDTUREREREGoMJEBERERERaQwmQJ2Unp4e1q1bx8mdSsBnrVx83srDZ61cfN7Kw2dNpNlYBIGIiIiIiDQGe4CIiIiIiEhjMAEiIiIiIiKNwQSIiIiIiIg0BhMgeqq0tDSIRCLs2LFD1aEQtSm2beqs2LaJiBrHBKiNpaSkYPXq1XB1dYW+vj5MTU0REBCAr7/+GpWVle123/j4eKxfvx5paWntdo+m+OSTT/Dss8/C1tYWIpEI69evV2k8MiKRqEmvCxcutPpeFRUVWL9+fbOupa7PrS5NbtsJCQl4++23MWDAAJiYmMDOzg6TJ09GVFSUymKSUee2rc7PrS5NbttZWVlYtGgRPDw8YGJiAnNzc/j7+2Pnzp1QdY0kdW7b6vzciOjpuqg6gM7kjz/+wJw5c6Cnp4fFixejT58+kEgkuHTpEv71r38hLi4OW7ZsaZd7x8fHY8OGDRg5ciScnZ3b5R5N8cEHH6Bbt24YOHAgTp06pbI4HhcSEqLw/pdffsGZM2fqbffy8mr1vSoqKrBhwwYAwMiRI5t0jro+NxlNb9s//fQTtm3bhlmzZuHFF19EcXExNm/ejCFDhuDkyZMYM2aMSuIC1Lttq/Nzk9H0tl1QUID79+9j9uzZcHR0RHV1Nc6cOYOlS5ciMTERn376qUriAtS7bavzcyOiJhCoTdy9e1cwNjYWPD09haysrHr7k5OThU2bNrXb/ffv3y8AEM6fP//UY6VSqVBRUdHka6empgoAhO3btzfpWEEQhPz8fAGAsG7duibfR5leeuklob2af0t+dnV+bmzbghAVFSWUlpYqbCsoKBCsra2FgICAJt9PGdSpbav7c2PbbtyUKVMEIyMjoaampkXntwd1atuNUcfnRkT1cQhcG/nvf/+LsrIybNu2DXZ2dvX2u7u747XXXpO/r6mpwUcffQQ3Nzfo6enB2dkZ7733HsRiscJ5zs7OmDJlCi5dugR/f3/o6+vD1dUVv/zyi/yYHTt2YM6cOQCAUaNG1RsWILvGqVOn4OfnBwMDA2zevBkAcPfuXcyZMweWlpYwNDTEkCFD8Mcff7T4Oaiy96m1pFIpNm3ahN69e0NfXx+2trZYvXo1Hj58qHBcVFQUxo8fDysrKxgYGMDFxQXLly8H8GjcvbW1NQBgw4YN8v8WTxvSps7PjW0b8PX1hbGxscK2rl27IjAwELdv327RNZVJVW1b3Z8b23bjnJ2dUVFRAYlE0qbXbWuq/L3dkI7y3Ig0nqozsM7C3t5ecHV1bfLxS5YsEQAIs2fPFr777jth8eLFAgBh+vTpCsc5OTkJHh4egq2trfDee+8J//vf/wQfHx9BJBIJsbGxgiAIQkpKivDqq68KAIT33ntPCAkJEUJCQoScnBz5Ndzd3QULCwthzZo1wo8//iicP39eyMnJEWxtbQUTExPh/fffF7766iuhf//+gpaWlnDo0CF5DC35JlEdezLqauibxBUrVghdunQRVq5cKfz444/CO++8IxgZGQmDBg0SJBKJIAiCkJubK1hYWAi9evUSPv/8c2Hr1q3C+++/L3h5eQmCIAhlZWXCDz/8IAAQZsyYIf9vcePGjSbFpY7PjW27ccOGDRN69erVonPbi7q27brU5bmxbf//KioqhPz8fCE1NVXYsWOHYGRkJAwbNqzJz0YZ1LFtd4TnRkT1MQFqA8XFxQIAYdq0aU06PiYmRgAgrFixQmH7W2+9JQAQzp07J9/m5OQkABBCQ0Pl2/Ly8gQ9PT3hn//8p3zbk4ZSyK5x8uRJhe2vv/66AED4+++/5dtKS0sFFxcXwdnZWaitrRUEQTMSoL///lsAIOzatUvhuJMnTypsP3z4sABAuHr1aqPXbs3Prm7PjW27caGhoYJIJBLWrl3b7HPbk7q2bRl1eW5s24o2btwoAJC/Ro8eLaSnpzfpXGVRx7bdEZ4bEdXHIXBtoKSkBABgYmLSpOP//PNPAMCbb76psP2f//wnANQbyuDt7Y3AwED5e2tra3h4eODu3btNjtHFxQXjx4+vF4e/vz+GDx8u32ZsbIxVq1YhLS0N8fHxTb5+R7d//36YmZlh7NixKCgokL9kQ3jOnz8PADA3NwcAHD9+HNXV1SqMWDnYthuWl5eHBQsWwMXFBW+//XarrtXe1Kltq9NzY9tWNH/+fJw5cwa7d+/GggULAKBdK+C1BXVo2x3xuRERy2C3CVNTUwBAaWlpk46/d+8etLS04O7urrC9W7duMDc3x7179xS2Ozo61ruGhYVFvTHOT+Li4tJgHB4eHvW2yyrqPB5HZ5acnIzi4mLY2NjA2tpa4VVWVoa8vDwAQFBQEGbNmoUNGzbAysoK06ZNw/bt2+vNAegs2LbrKy8vx5QpU1BaWoqjR4/Wm+OibtSlbavbc2PbVuTk5IQxY8Zg/vz52LVrF1xdXTFmzBi1/jCvDm27Iz43ImIZ7DZhamqK7t27IzY2tlnniUSiJh2nra3d4HahGWsNGBgYNPlYTSSVSmFjY4Ndu3Y1uF82QVYkEuHAgQMIDw/HsWPHcOrUKSxfvhxffvklwsPDVf6hrq2xbSuSSCSYOXMmbt68iVOnTqFPnz5Ku3dLqUPbVsfnxrb9ZLNnz8bWrVsRGhparxdKXahD235cR3huRMQEqM1MmTIFW7ZswZUrVzB06NAnHuvk5ASpVIrk5GSF9Qtyc3NRVFQEJyenZt+/qX+UH48jMTGx3vaEhAT5fk3h5uaGv/76CwEBAU360DFkyBAMGTIEn3zyCXbv3o2FCxdiz549WLFiRYv+W6gztu1HpFIpFi9ejLNnz2Lfvn0ICgpq9jVUQdVtW52fG9t242Q9GMXFxW1yvfag6rbdkI7w3IiIQ+DazNtvvw0jIyOsWLECubm59fanpKTg66+/BgBMmjQJALBp0yaFY7766isAwOTJk5t9fyMjIwBAUVFRk8+ZNGkSIiMjceXKFfm28vJybNmyBc7OzvD29m52HB1VcHAwamtr8dFHH9XbV1NTI3+uDx8+rPcN7oABAwBAPpzC0NAQQPP+W6gztu1HXnnlFezduxfff/89Zs6c2ezzVUXVbVudnxvbNpCfn9/g9m3btkEkEsHHx6dZ11MmVbbtjvzciIg9QG3Gzc0Nu3fvxty5c+Hl5aWwonhYWBj279+PpUuXAgD69++PJUuWYMuWLSgqKkJQUBAiIyOxc+dOTJ8+HaNGjWr2/QcMGABtbW189tlnKC4uhp6eHp555hnY2Ng0es6aNWvw22+/YeLEiXj11VdhaWmJnTt3IjU1FQcPHoSWVvPz45CQENy7dw8VFRUAgNDQUHz88ccAgOeee05te5WCgoKwevVqbNy4ETExMRg3bhx0dHSQnJyM/fv34+uvv8bs2bOxc+dOfP/995gxYwbc3NxQWlqKrVu3wtTUVP4BycDAAN7e3ti7dy969eoFS0tL9OnT54nDftT5ubFtP/rQ+/3332Po0KEwNDTEr7/+qrB/xowZ8g+z6kaVbVvdnxvbNvDJJ5/g8uXLmDBhAhwdHVFYWIiDBw/i6tWreOWVV+rNeVInqmzbHfm5ERG4DlBbS0pKElauXCk4OzsLurq6gomJiRAQECB8++23QlVVlfy46upqYcOGDYKLi4ugo6MjODg4CO+++67CMYLwqBTq5MmT690nKChICAoKUti2detWwdXVVdDW1lYordrYNQTh0VoUs2fPFszNzQV9fX3B399fOH78uMIxzSmnGhQUpFAStO6rKaudK0tjK4pv2bJF8PX1FQwMDAQTExOhb9++wttvvy1fJf769evC/PnzBUdHR0FPT0+wsbERpkyZIkRFRSlcJywsTPD19RV0dXWbVFq1Izw3TW7bsvVfGnulpqY+8XxlUqe23VGemya37dOnTwtTpkwRunfvLujo6Mh/9u3btwtSqfSJ5yqbOrXtjvTciKg+kSA0Y0YmERERERFRB8Y5QEREREREpDGYABERERERkcZgAkRERERERBqDCRAREREREWkMJkBERERERKQxmAAREREREZHGYAKkRDt27IBIJIK+vj4yMzPr7R85cuQTF8tsD2fPnsXy5cvRq1cvGBoawtXVFStWrEB2dnaDx4eFhWH48OEwNDREt27d8Oqrr6KsrEypMTcFn7Vy8XkrD5+1cvF5Kw+fNREpCxMgFRCLxfjPf/6j6jAAAO+88w4uXLiAGTNm4JtvvsG8efOwb98+DBw4EDk5OQrHxsTEYPTo0aioqMBXX32FFStWYMuWLZgzZ46Kon86Pmvl4vNWHj5r5eLzVh4+ayJqd6peiVWTbN++XQAgDBgwQNDT0xMyMzMV9gcFBQm9e/dWakwXL14Uamtr620DILz//vsK2ydOnCjY2dkJxcXF8m1bt24VAAinTp1SSrxNxWetXHzeysNnrVx83srDZ01EysIeIBV47733UFtbqxbfcI0YMQJaWlr1tllaWuL27dvybSUlJThz5gwWLVoEU1NT+fbFixfD2NgY+/btU1rMzcFnrVx83srDZ61cfN7Kw2dNRO2ti6oD0EQuLi5YvHgxtm7dijVr1qB79+7NOr+iogIVFRVPPU5bWxsWFhbNjq+srAxlZWWwsrKSb7t16xZqamrg5+encKyuri4GDBiA6OjoZt9HGfislYvPW3n4rJWLz1t5+KyJqL2xB0hF3n//fdTU1OCzzz5r9rn//e9/YW1t/dTXwIEDWxTbpk2bIJFIMHfuXPk22YRPOzu7esfb2dkhKyurRfdSBj5r5eLzVh4+a+Xi81YePmsiak/sAVIRV1dXPPfcc9iyZQvWrFnT4C/NxixevBjDhw9/6nEGBgbNjis0NBQbNmxAcHAwnnnmGfn2yspKAICenl69c/T19eX71RGftXLxeSsPn7Vy8XkrD581EbUnJkAq9MEHHyAkJAT/+c9/8PXXXzf5PFdXV7i6urZ5PAkJCZgxYwb69OmDn376SWGf7A+FWCyud15VVVWL/pAoE5+1cvF5Kw+ftXLxeSsPnzURtRcmQCrk6uqKRYsWyb/hairZ+OOn0dbWhrW1dZOumZGRgXHjxsHMzAx//vknTExMFPbLvn1raO2D7OzsZo/RVjY+a+Xi81YePmvl4vNWHj5rImovnAOkYh988EGzxzl/8cUXsLOze+pr0KBBTbregwcPMG7cOIjFYpw6darBoQZ9+vRBly5dEBUVpbBdIpEgJiYGAwYMaHL8qsJnrVx83srDZ61cfN7Kw2dNRO2BPUAq5ubmhkWLFmHz5s1wcnJCly5P/0/SluOby8vLMWnSJGRmZuL8+fPo2bNng8eZmZlhzJgx+PXXX7F27Vr5t18hISEoKyvrEAu98VkrF5+38vBZKxeft/LwWRNRexAJgiCoOghNsWPHDixbtgxXr15VKJV5584deHp6ora2Fr1790ZsbKzSYpo+fTqOHj2K5cuXY9SoUQr7jI2NMX36dPn769evY9iwYfD29saqVatw//59fPnllxgxYgROnTqltJibgs9aufi8lYfPWrn4vJWHz5qIlEbVK7FqEtkq11evXq23b8mSJQIApa9y7eTkJABo8OXk5FTv+L///lsYNmyYoK+vL1hbWwsvvfSSUFJSotSYm4LPWrn4vJWHz1q5+LyVh8+aiJSFPUBERERERKQxWASBiIiIiIg0BhMgIiIiIiLSGEyAiIiIiIhIYzABIiIiIiIijcEEiIiIiIiINAYTICIiIiIi0hhMgIiIiIiISGMwASIiIiIiIo3BBIiIiIiIiDQGEyAiIiIiItIYTICIiIiIiEhjMAGqY+PGjRg0aBBMTExgY2OD6dOnIzExUdVhERERERFRG2ECVMfFixfx0ksvITw8HGfOnEF1dTXGjRuH8vJyVYdGRERERERtQCQIgqDqINRVfn4+bGxscPHiRYwYMULV4RARERERUSt1UXUA6qy4uBgAYGlp2egxYrEYYrFYYZuenh709PTaNTYiIiIiImo+DoFrhFQqxeuvv46AgAD06dOn0eM2btwIMzMzhdf48eORnZ2txGiJiIiIiKgpOASuES+88AJOnDiBS5cuoUePHo0e93gPUExMDIKCgnDt2jX4+PgoI1QiIiIiImoiDoFrwMsvv4zjx48jNDT0ickPUH+4m7GxcXuHR0RERERELcQEqA5BEPDKK6/g8OHDuHDhAlxcXFQdEhERERERtSEmQHW89NJL2L17N44ePQoTExPk5OQAAMzMzGBgYKDi6IiIiIiIqLVYBKGOH374AcXFxRg5ciTs7Ozkr71796o6NCIiIiIiagPsAaqD9SCoJSQSCaKiouDn5wddXV1Vh0NERERET8AeIKJWCg8Px/bt2xEREaHqUIiIiIjoKZgAEbWCWCzGqVOnkJqaipMnT9ZbFJeIiIiI1AsTIKJWiIiIQFJSEvr164ekpCRERkaqOiQiIiIiegImQEQtJOv90dXVhampKXR1ddkLRERERKTmmAARtVB0dDRSUlJQXl6OuLg4lJeXIyUlBdHR0aoOjYiIiIgawSpwRC3k4OCAhQsXNridiIiIiNQTEyCiFrK3t4e9vb2qwyAiIiKiZuAQOCIiIiIi0hhMgIiIiIiISGMwASIiIiIiIo3BBIiIiIg0ikQiQVhYGCQSiapDISIVYAJEREREGiU8PBzbt29HRESEqkMhIhVgAkREREQaQ7aIdWpqKhevJtJQTICIWolDKYiIOo6IiAgkJSWhX79+SEpKQmRkpKpDIiIlYwJE1EocSkFE1DHIen90dXVhamoKXV1d9gIRaSAmQEStwKEUREQdR3R0NFJSUlBeXo64uDiUl5cjJSUF0dHRqg6NiJSoi6oDIOrIGhpKERgYqOqwiIioAQ4ODli4cGG97ba2tggLC4Ofnx90dXVVEBkRKRMTIKIWamwohb+/P/T09FQdHhERPcbe3h729vb1toeGhiIkJAS1tbX8EotIA3AIHFELcSgFEVHHx6HMRJqHPUBELdTYUAoHBwcVRENERC3BocxEmocJEFELNTaUgoiI1JtEIkFUVBT69u3LocxEGohD4IiIiEijyJYv2LVrF4cyE2kg9gARERGRxqg758fAwADBwcHQ0dFROIZDmYk6NyZAREREpDHqzvnJyMiAtbU15/wQaRgOgSNSIolEgrCwMEgkElWHQkSkcRpbvoCV34g0CxOgx4SGhmLq1Kno3r07RCIRjhw5ouqQqBORjTuPiIhQdShERBqHyxcQEcAhcPWUl5ejf//+WL58OWbOnKnqcKgDk1UZkq0s/vhaE6wyRESkXFy+gIgANU2AsrOzkZeXB3d3dxgZGSn13hMnTsTEiROVek/q2B5PdGTCw8MVVhbnWhNERKr1pOULGvtdTkSdj1oNgTt69Cg8PT3Ro0cP+Pj4yIcJFRQUYODAgWo5HE0sFqOkpET+KisrU3VIpGQNDWt7vLenpKSE486JiNQYhygTaQ61SYCOHTuGmTNnwsrKCuvWrYMgCPJ9VlZWsLe3x/bt21UYYcM2btwIMzMz+SsoKEjVIZESPZ7oyBKax3t7du/ezXHnRERqqrHf5UTUOanNELh///vfGDFiBM6fP48HDx5g/fr1CvuHDh2KzZs3qya4J3j33Xfx5ptvyt/HxMQwCdIgDQ1r8/f3r9fbEx8fz7UmiIjUFIcoE2kWtUmAYmNj8dVXXzW639bWFnl5eUqMqGn09PQUJrIbGxurMBpSprrlVI2NjeXD2gRBQEpKCqqqqhAXF4fq6mrU1taie/fuGDJkiKrDJiLSeHXn+wiC0OAQZRaqIeq81CYBMjQ0RHl5eaP77969i65duyoxIqInk5VTraqqQmxsLKRSKVJSUlBUVMQqQ0REaqxukRodHZ16X1rJhijzSyuizkltEqBRo0Zh586deP311+vty8nJwdatWzFlypR2j6OsrAx37tyRv09NTUVMTAwsLS3h6OjY7venjqNuOdXKykro6+tDJBLBx8en0SpDRESkWo/P93n++ef5pRWRhlGbBOiTTz7BkCFDMGjQIMyZMwcikQinTp3CuXPnsHnzZgiCgHXr1rV7HFFRURg1apT8vWx+z5IlS7Bjx452vz91HHXLqRYXF0NXVxfa2tqIioqCtbU1y6gSEamhx+f7ZGZmYurUqaoOi4iUSG0SIA8PD1y6dAmvvfYa1q5dC0EQ8PnnnwMARo4cie+++w7Ozs7tHsfIkSMVKtARNVVeXh7u3bunsPYPERGpj7pzNznfh0hzqU0CBAC9e/fGX3/9hYcPH+LOnTuQSqVwdXWFtbW1qkMjeqrY2FiEhYXJh1XwDyoRkXqpO3eT832INJdaJUAyFhYWGDRokKrDIGqW0NBQJCcnP7WMKlcbJyJSjbpzNx/fTkSaQ20SoG+++QZ//PEHTp061eD+iRMn4tlnn8ULL7yg5MiInszPzw9ZWVkoLy+Hr6+vQknshnqB6lYf4jA5IiLlqTt3k4g0l5aqA5DZtm0bvL29G93v7e2NLVu2KDEioqbJyclBdnY2KisrUVhYiNOnT6OgoEA+rKIurjZORKQepFKpqkMgIhVRmwQoJSUFXl5eje739PRESkqKEiMiah5dXV0EBATA19cX7u7ucHd3R3V1NS5fvgyJRAKg4dXGiYiIiEh51GYInK6uLnJychrdn52dDS0ttcnXiAAA6enpKC4uBgDU1NTAwcEBlpaW8v1Hjx7F9evX8dxzz2HGjBmsPkREpCZEIpGqQyAiFVGbjGLIkCHYsWMHSktL6+0rLi7G9u3bWaGF1EZkZCSmTp0KZ2dnlJWVAXg0vO29997Dd999h7S0NFRXVyM+Ph7Z2dnYv38/Pv30U4SFhaGwsBCxsbEoLy9vcJgcERG1L4lEgrCwMHnvPBFpFrXpAVq3bh2CgoIwYMAAvP766+jduzeAR6WFN23ahOzsbOzevVvFURIBhw4dwty5cyEIQr01owRBQGxsLGJjYzFlyhTk5ubC3t4eubm5cHR0lM9zMzY2Rvfu3WFnZ8fqQ0RESiYrRiOVSlmMhkgDqU0CNHjwYBw7dgyrV6/Ga6+9Ju+aFgQBLi4u+P333zF06FAVR0maLjIyEnPnzkVtbW2jC+bKJtYeO3YMnp6e6N69O0pKSpCeno7JkydDR0dHfmxxcTGio6MhFovh7OzMYZ5ERO1MVozm7t27HIZMpKHUJgECgLFjx+LOnTvyhcoAwM3NDT4+PhyrS2rh448/brDnpyGCICAzMxMGBgaora1Ffn4+MjIy4OrqqnBMVlYWsrKyYGxsDG9vb3h4eMDAwKA9fwwiIo0lK0bTu3fvJ67ZRkSdl1olQACgpaUFX19f+Pr6qjoUIgXp6ek4fvx4k5IfmZKSEvTu3RsmJiYAHi3y25iysjJERkYiKioKLi4u8Pb2Rrdu3Zj8ExG1EVnvj66uLoyMjFiMhkhDqV0CFB8fj7t37+Lhw4cNftBcvHixCqIiAs6ePdus5EdGS0sL/fv3b/LxUqkUKSkpSElJgY2NDUaNGgUzM7Nm35eIiBTJRphUVVUhNjYW2tra8mI0LLREpDnUJgFKSUnBokWLEBkZ2eiHTJFIxASIVKa0tBRaWlrNXjyvvLy8xffMy8tDZGQkxo4d2+JrEBHRIw4ODli4cCEAoLCwEEZGRtDT02MxGiINozYJ0OrVq3Hr1i1s2rQJgYGBTxwqRKQKJiYmLVo5XFYmu6XqritEREQtZ29vD3t7ewCP1hcUiUTo1q2biqMiImVTmwTo8uXLeO+99/DKK6+oOhSiBo0ePRoikajZw+CqqqpQXV2tUP2tKSwtLTFw4ECFoglE6kQikSAqKgp+fn7Q1dVVdThEzfbw4UMmQNTpSKVSVpV9CrV5OlZWVpznQGrN0dERU6ZMgba2dpPPMTU1RVlZGTIyMpp0vJaWFlxdXTF27Fj5GkEsgkDqKjw8HNu3b0dERISqQyFqkaKiIlWHQNTmKisrVR2C2lObBOgf//gHfv31V9TW1qo6FKJGrV27FiKRqElJiZaWFsaOHQt/f/8nDunU1taGo6MjRowYgeeeew5jxoxBRkYGduzYwQ+WpLZk1bRSU1Nx8uRJiMViVYdE1GwPHjxQdQhEba6mpkbVIag9tRkC16tXL9TW1qJ///5Yvnw5HBwcGvymfebMmSqIjuiRQYMGYe/evZg7dy4EQWgwYZd1O69atQoDBw5s8DoGBgZwcnKCk5MTrK2tERMTA1dXV+jq6tb7YMnyrKSOZGup9OvXj2upUIeVm5vboiHKROqspKQEBgYGHJr8BGqTAM2dO1f+/996660GjxGJROwhIpWbOXMmwsLC8Oabb+LSpUv19ru6usoT9ZqaGnTp8v//M7O1tUVtbS0mTZoEfX19AEBoaChCQkJQW1uLwMBAfrAktSWb89O3b1/5WiqmpqZcS4U6rNraWqSkpMDT01PVoRC1GUEQkJGRATc3N1WHorbUJgE6f/68qkMgarJBgwZhz549OHXqFF577TWUlZVBT08PwcHB6N27N/Lz8xEREQGpVIoBAwagZ8+e8PDwwI0bNxASEgJra2sEBgbW6+3p378/P1iS2goPD0dISAh8fX3la6nExcWhurqaa6lQhxUTE4NevXqhpqaGRT2o04iPj4erqyvnETdCbRKgoKAgVYdA1Cz29vZYvnw5PvzwQ5SVlcHAwADDhw9HdXU1Ll68iNLSUlRWVmLWrFnQ19dvcGjb4709u3fv5gdLUkt126+BgQGCg4PrDRviWirUUfj5+SErKws6Ojp4//33ERcXh4cPHyr0xhN1RH5+fsjMzISuri4cHBwwYMAAVYekltQmAZIRi8W4fv068vLyEBAQACsrK1WHRNQssopvzzzzDDIyMnD16tUGh7ZdunQJ586dU+jtiY+P5wdLUkt1229GRoa8F5OoI8rJyUF2djbMzc0BAFeuXEFSUhLnXlKHl5OTg5ycHJibmyMyMhLl5eUYMmRIsyrYagK1qQIHAN988w3s7OwwfPhwzJw5Ezdv3gQAFBQUwMrKCj///LOKIySqz9jYGIaGhtDT04NUKkVFRQUsLCwUhrCVlJTUG9oWEhKC5ORklJeXIy4uDuXl5cjLy0P37t0xdepUhZds4T4iVZD1/jw+NJOV36ijkkgkAP7/allJSUm4fPkyevfuLZ97SdQZxMXF4cCBA0hNTW32Ooadmdr0AG3fvh2vv/465s2bh3HjxmH58uXyfVZWVnjmmWewZ88ehe1EqiYWizFr1ixcvnwZhoaGsLCwQF5eXr0hbPv27as3tE0qlSIwMLDe5Fv29pC6kBU9kE0U59BM6gzEYjGqqqoAANXV1aioqEB8fDxqa2uRn5+PLl26sBeIOqT09HRUVFQAePT7u7CwEJaWliguLsaZM2dgamoKDw8P9OzZE8bGxiqOVrXUJgH68ssvMW3aNOzevbvBuvy+vr745ptvVBAZUeNkw4J69+6NmzdvQkdHBwsXLqx3nJ2dHWxtbett9/HxYe8OqS1Z0YOJEyc22K6ZrFNHFBERIe/5kUqluHr1KvLz81FdXY3ExETo6upCKpUywacOIzIyEh999BH++OMPeS9PRUUF3nvvPfTt2xeTJ0+Gs7MzSkpKcPXqVURFRcHW1hZubm5wcXGBoaGhin8C5VObBOjOnTt49dVXG91vaWmptAXLvvvuO3z++efIyclB//798e2338Lf318p96aOo+6wIBMTE5ibm+PatWv48MMP+a0hdXh1ix6wXVNnIWvXdWVnZ8PX11dhjoSVlRW6d++u7PCImu3QoUPytQkfH+ImCAJiY2MRGxuLlStXwsfHR75dNlcoLCwMNjY2cHV1hbOzM0xMTFTxYyid2swBMjc3R0FBQaP74+Pj0a1bt3aPY+/evXjzzTexbt06XL9+Hf3798f48eORl5fX7vemjiU6OhopKSkoLy9HQkICBEGQDwsi6ugaWo+KqKOT/d6u+0GxtLQU5ubm6N+/v/xlb28vHxZHpK4iIyMxd+5c1NbWNtpWpVIppFIptm7dirS0tHr7BUFAbm4urly5gt9++w0HDhxAREQEsrKyIJVK2/knUB21SYAmTZqELVu2oKioqN6+uLg4bN26Fc8++2y7x/HVV19h5cqVWLZsGby9vfHjjz/C0NCQBRioHgcHByxcuBDPP/88FixYgFWrVmHhwoUcFkQdHoseUGcl+71tZGQEANDV1YW/vz8sLCwAPCqKkJKSgpqaGqSnp+P48eMoLy9XZchEjfr4448b7PlpzJ9//vnUYwoLC3Hjxg0cP34cISEhuHz5MkpLS1sbqtoRCWpSEiIrKwuDBw+GIAiYOnUqtmzZgkWLFqG2thYHDx6EnZ0dIiMj27UstkQigaGhIQ4cOIDp06fLty9ZsgRFRUU4evRovXPEYrHCh4KYmBgEBQUhIiICAwcObLdYSb1IJBIunEedRkREBL755htUVVVBR0cH1dXV0NfXx6uvvorBgwerOjyiVnN2dkZWVhbMzMywceNG+XZZb+fgwYPRs2dPAI+SpMGDB6NXr15cVJLURnp6Otzd3Zt93scffwxLS8tmnaOlpYUBAwagf//+0NfXb/Y91ZKgRnJzc4Xnn39esLCwEEQikSASiQRTU1Nh2bJlQm5ubrvfPzMzUwAghIWFKWz/17/+Jfj7+zd4zrp16wQAfPHFF1988cUXX3zx1alfhYWF7f55XBnUogiCbLiFs7MzfvrpJ/z000/Iz8+HVCqFtbU1tLTUZqRePe+++y7efPNN+Xv2AGmev//+G7t27cKiRYswfPhwVYdDRERPUbcHaMWKFXBycsLdu3dx8eJFmJubo6ioCCNHjpT3Aj1+7pAhQzS+jDCp1v/+9z/885//bPbaPnPmzMGoUaNafN/MzEz5kNGOTC0SIF1dXcyZMwdff/01+vXrBwCwtrZWehxWVlbQ1tZGbm6uwvbc3NxGCzDo6ekpVEaS/ULs0qULdHR02i9YUgtisRhnz57FvXv38Ndff2HYsGGslEVEpOZkQ9lqa2sRERGB2tpaJCYmokuXLjAyMkJZWRkSEhLg5uZW7295RkYGcnJyEBAQgF69eqkifCKYm5u3aGFTQ0NDhYqHTaWrq4shQ4bAw8Oj2eeqI7XoWhGJROjZs+cTq8Apg66uLnx9fXH27Fn5NqlUirNnz2Lo0KEqjIzUFStlERF1LHUXi6yqqkJ2djbCw8ORm5sLiUSCrKwsSCQS5OfnIyMjo8FrVFdX48KFCwgNDWWlOFKJ0aNHN3tOmkgkqrf4+tN06dIFXl5eCA4OhqenZ6eZB6cWPUAA8N577+HNN9/EnDlzVJpdvvnmm1iyZAn8/Pzg7++PTZs2oby8HMuWLVNZTKSeGquUxdXDiYjUT0OLRcqqvuXl5cHX1xfe3t4K5zxtqE9CQgIKCgowevRomJmZtVvsRI9zdHTElClT8OeffzYpCdfS0kLfvn2bVABBS0sL9vb2cHV1hYuLS6cs8qQ2CVB4eDi6du2KPn36YOTIkXB2doaBgYHCMSKRCF9//XW7xjF37lzk5+fjww8/RE5ODgYMGICTJ0/C1ta2Xe9LHY9sPYmqqirExcWhurpavg4QVw8nIlIfT1osEni0FtDFixfh7u6OQYMGNevaBQUFOHToEAIDA1tUlYuopdauXYsTJ05AJBI1aTjcpEmTGt2no6MDBwcHODs7w9HRsVMmPXWpTRnsphQ6EIlEat/VfP36dfj6+uLatWvyFXepc8rMzMT169frbffx8YG9vb0KIiJSLolEgqioKPj5+XX6P5bUcUVGRiIgIAC1tbVP/ZCopaWFd955B87Ozi26l6+vL3x8fDrNMCFSf3WT+4Y+I8s+X69atapecS6RSAQHBwf06tULjo6O6NJFbfpF2l2zflIXF5cWjTdMSUl56nGdebVZ6pzs7e2Z6JDGaCjZCQ8PR0hICGpraxEYGKjiCIka1pLFIl988cUW3evatWswNDSEl5dXi84naq6ZM2ciLCwMH330EY4fP67QzkUiEfr27YtJkyYpJPW2trZwdXWFm5sbDA0NVRC16jUrAQoKCqqXAEVFRSEuLg7e3t7yuTuJiYmIj49Hnz594Ovr23bREhGRSjye7MjmwKWmpnLuG6mt9PT0eh8Kn0QqleLmzZsoLCxs9mKRMuHh4Q0O4ydqL4MGDcLvv/+O9PR0DBgwAA8fPoShoSHWrl0rb8cmJibw8vKCu7s7S7ijmQnQjh07FN4fOXIER44cwZkzZzB69GiFfWfOnEFwcDA++uijZgUUHh6O8+fPIy8vDy+++CJ69uyJiooKJCQkoFevXvyPRkSkZA0lOw1VQGQvEKmbs2fPNrtUsCAISEhIwLBhw1p0z+rqaiQlJaF///4tOp+opRwdHWFoaIiHDx9CV1cXlpaW0NbWhp+fH/r27avW62oqW6uexIcffohXXnmlXvIDAGPHjsXLL7+MDz74oEnXkkgkmDlzJgICAvD+++/jm2++kZef1NLSwrhx49q9AAIREdX3eLJz6dKlBisgisViVYdKpKC0tLTZH/pEIhGqqqpadd/i4uJWnU/UUt26dUO3bt1gamoKAwMDPPvss+jfvz+Tn8e06mkkJyeja9euje7v2rVrk+b/AI8qWRw/fhw//PADEhMTFb6x0dfXx5w5c3D06NHWhEtERM3UULn3kJAQJCcno7y8HHFxcSgvL5dXQCRSJyYmJs2eYywIAvT19Vt136eVzyZqL1FRUYiIiMAHH3yA8ePHw9raWtUhqaVWlXtwc3PD9u3b8fzzz9cbmlZaWoqff/4Zrq6uTbrWb7/9hhdeeAGrVq3CgwcP6u338vLC/v37WxMukVI1NGmcVbOoo2mo3LtUKkVgYGC9BfUcHBxUFCVRw2SLRTZ3GFxrylkbGBiodD1DIgBwdXWFjY2NqsNQW61KgD7++GPMnj0bnp6eWLp0qfwXRnJyMnbu3Inc3NwmJy15eXno27dvo/u1tbXlKzcTqZPGkpqGKmSxahZ1JBKJBLm5uQgODoaOjo7CPpZ7p46guYtFikQiWFhYoLi4uEUfHrW1tTFmzBh+wUUqx0qET9aqBGj69On4888/8c477+DTTz9V2DdgwABs27YN48ePb9K1HBwckJCQ0Oj+y5cvc4ExUksNJTUNTRoHwKpZ1KGEh4fj+PHjWLx4MRN26rCau1ikiYkJ4uLi4OzsXC/xfxJdXV2MHTsWdnZ2rQmXqNV0dHRga2ur6jDUWqtnRI0bNw7R0dHIysrClStXcOXKFWRlZeH69etNTn4AYMGCBdi8eTOuXLki3yYrub1161bs27cPixcvbm24RG3q8URHNgm8oQpZDW0jUleNtW2ijmbQoEHYu3cvtLW1oa2t3eAxWlpa0NLSgouLC9zd3ZGbm4u0tLQm38PCwgIzZsxgryipBSsrKxY9eIo2W/JVVnWipd5//32Eh4djxIgR8PLygkgkwhtvvIHCwkLcv38fkyZNwhtvvNFW4RK1iYaSGn9//3qTxo8fPw4A9apmsReI1BXLXFNn8rTFInv37g1jY2PU1NTAwMAAJSUlTe4FcnNzw4gRI5rVW0TUntgWn67V6WF6ejr+8Y9/wMPDA5aWlggNDQUAFBQU4NVXX21yVSDZB8Lt27fD1dUVnp6eEIvF6NevH3bs2IFjx441+s0NkSo0VB3r5MmTiIiIQEpKikKFrGvXruH69eusmkUdQmNtm71A1JHJFotMS0uTV2nr0qULVq5ciUmTJqG6uhoSiQRZWVmQSCTIz8+XL8fREG1tbQQEBOCZZ57hB06iDqZVPUDx8fEIDAyEVCrF4MGDcefOHdTU1AB41P126dIllJeXY9u2bfXOffPNN/Hcc89h4MCBAB4lUtbW1li0aBEWLVrUmrCIlKKh6lgpKSkoKirCwoULFY6VVTZ8vGw8q2aROmqsbUdHR2PIkCGqDo+oVeouFgkAGRkZcHR0lM/VrKuxctZWVlYYOXIkLC0t2zVWImofrUqA3n77bZibmyM8PBwikahexZTJkydj7969DZ67adMm+Pn5yRMgFxcXhISEYMGCBa0JiUhpHBwc6iU6AKtjUcfXUNuurq5Gbm4uJBIJK1xRhyeRSAA86sXJzc1FUVER+vfv/9Tz9PT04OvrC29vb86xIOrAWpUAhYaG4sMPP4S1tXWDa/c4OjoiMzOzwXNtbW1x9+5d+fvm1ugnUjV7e3smOtQp1W3bUqkUWlpaCA0NRUhICCwtLTkXiDo0sViMqqoqAI/m/2hraz91vo9IJIK3tzd8fX1bvUgqEaleqxIgqVQKQ0PDRvfn5+c3OsF78uTJ+Pe//43Tp0/D3NwcAPDll19iz549jV5PJBLh6NGjrQmZiIiaQSqVorq6miXcqdOIjo6WD9evra1VmO/T0OLttra2CAwM5HA3ok6kVQmQj48P/vjjD7z44ov19tXU1GDPnj2Njhf/+uuvYWNjg/PnzyMuLg4ikQgZGRkoLCxs9H6ysthERKQctbW1rAhHnYqDgwMMDQ1RWVkJXV1dBAQEAKg/30ckEsHPzw8DBgzg5w+iTqZVCdC7776LKVOm4IUXXsC8efMAALm5ufjrr7/w6aef4vbt2/jf//7X4LlGRkYKi6dqaWlh06ZNnANERKRGSktLG6wIx14g6qjs7e3lw9h0dHQanPtjZGSE0aNHt2p5DyJSX62awTdx4kTs2LEDe/fuxTPPPAMAWLRoEcaNG4fr16/jl19+wYgRIxo8d+bMmfj777/l78+fP4+xY8e2JhwiImpjUVFRSE5OZgl36lRkRRBkQ+Hq6t69O2bOnMnkh6gTa/VCqM899xxmzpyJM2fOIDk5GVKpFG5ubhg/fjxMTEwaPe/o0aOYNWuW/P0zzzzDKnBERGpEIpEgLy8PkydPrjf/gSXcqaOqWwShuroa1dXV8uIH3t7eGDZsGCu8EXVyLU6AKioq4ODggDVr1uBf//oXpk+f3qzz7e3tER0dLS+1KggCx9gSEamR8PBwnDlzBuPHj8fUqVNVHQ5Rm4iIiJD3/EilUqSlpaFnz57o27cvhgwZws8iRBqgxQmQoaEhunTpAiMjoxadP2/ePHzxxRfYt2+fvArcmjVrsHHjxkbPEYlEuHHjRovuR0RETScWi3Hq1Cmkp6fjxIkTmDNnDuf8UIcna9cGBgbQ1taGVCpFXFwchg0bxuSHSIO0agjcrFmzcODAAbzwwgvN/qWxceNGuLu74/z588jLy4NIJIKRkRG6du3ampCIiKgNyCq/eXt7IyoqCleuXMHIkSNVHRZRq0RHRyMlJQWBgYGoqanB/fv3UVBQABMTEyY/RBqkVQnQvHnz8OKLL2LUqFFYuXIlnJ2dYWBgUO84Hx+fetu0tbWxatUqrFq1CsCjKnAffPAB5wAREamY7FtyXV1d+QfDvXv3YujQoewFog7NwcFBPvS+sLAQkZGR6NGjB7y8vFQcGREpU6sSoLrfBtat6CYjm9dTW1v71GulpqbC2tq6NeEQEVEbkH1LXlVVhYSEBEgkEty6dQvXr1/H0KFDVR0eUYvZ29vD3t4eAJCdnQ2xWIwZM2bw8weRhmlVArR9+/a2igNOTk5tdi0iImq5ut+Sl5SUICwsDMCjL7WIOhNTU1NYWVmpOgwiUrJWJUBLlixp8blaWlrQ0tJCRUUFdHV1oaWl9dTxtyKRqMGa/W3lk08+wR9//IGYmBjo6uqiqKio3e5FRKSuZN+S+/n5ISsrCzo6Onj//feRkpICHx8f+SKSRB1djx49OPeHSAO1eh2glvrwww8hEonQpUsXhfeqJJFIMGfOHAwdOhTbtm1TaSxERKqWk5OD7OxseaXOyspKXLx4EePGjVP572uitmBjY6PqEIhIBZqVAC1fvhwikQhbtmyBtrY2li9f/tRzRCJRg8nE+vXrn/heFTZs2AAA2LFjh2oDISJSAxKJBAAUet7v3buHuLg49OnTR1VhEbUZMzMzVYdARCrQrATo3Llz0NLSglQqhba2Ns6dO9ekYWudmVgshlgslr8vKytTYTRERG1DLBajqqoKAFBdXY3q6mro6OgAAK5cuQIjIyO4uLioMkSiVjM0NFR1CESkAs1KgNLS0p74vjl++eWXFp23ePHiFt+zPWzcuFHec0RE1FlERETIe36kUinS0tLQs2dPAI+KIfz1118ICAiAt7e3KsMkahGJRIKrV68iKChI1aEQkQqobA7Q0qVL622T9RY9Xmmobi9ScxOgNWvW4LPPPnviMbdv34anp2ezrivz7rvv4s0335S/j4mJ4S9UIurQZOsA1RUXFwdnZ2d5L5AgCLh06RLy8vIQEBAg307UEYSHh2Pv3r0wMTHBqFGjVB0OESmZyhKg1NRUhfdFRUVYsmQJzMzM8Morr8DDwwMAkJCQgG+//RalpaXYuXNns+/zz3/+s8Fkqy5XV9dmX1dGT09PYWFAY2PjFl+LiEgdyNYBqvtlVH5+PjIyMur9vkxKSkJeXh6eeeYZlhOmDkGW4GdkZODMmTMYNmwYF/gl0jCtToBOnDiBr776CtevX0dxcXGD60Q0tBDq4+v+rF+/HtbW1jh9+rRCj0/fvn0xa9YsjBs3Dv/3f//X7LWHrK2tucAZEVEzyNYB+uuvv1BVVQVdXV34+/vDwsKiweOLiopw5MgRDBo0CP369ev0cz+pY4uIiEBSUhL69OmD5ORkREZGIjAwUNVhEZESabXm5IMHD2LKlCnIzc3FvHnzIJVKMX/+fMybNw8GBgbo168fPvzwwyZd68iRI5gxY0aDfzi1tLQwc+ZMHD16tDXhPlV6ejpiYmKQnp6O2tpaxMTEICYmhoUNiEij2NvbY+rUqfL1fnR0dNC/f/9GEyDg0TyhiIgI/P7771xDjdSWrPdHV1cX5ubm0NXVxcmTJxWKGRFR59eqBGjjxo3w9/dHdHS0vBDA8uXLsWvXLsTGxiI7O7vJVYIEQUBCQkKj++Pj49t9FfIPP/wQAwcOxLp161BWVoaBAwdi4MCBiIqKatf7EhF1Frm5uThw4ACuXLkiryJHpC5kwzvLy8uRkJCA8vJypKSkIDo6WtWhEZEStWoIXHx8PDZu3AhtbW35gqbV1dUAAGdnZ7z44ov47LPPmlS4YPr06fjhhx/g7OyMf/zjH/LSlBUVFfjhhx+wefNmLFy4sDXhPtWOHTu4BhARUStJpVLcunULiYmJ6Nu3L/r168ciCaQWZMM7G9pORJqjVQmQoaEhdHV1AQDm5ubQ09NDdna2fL+trW29YgeN+frrr5Gamoq33noL7777Luzs7AAA2dnZqK6uRkBAADZt2tSacImIqInS09NRUVEB4FHJ4MLCQlhaWjbrGhKJBNeuXUN8fDwGDRoEDw8Pzg8ilbK3t4e9vb2qwyAiFWvVEDgPDw/Ex8fL3w8YMAAhISGoqalBVVUVdu/eDUdHxyZdy8zMDBcvXsThw4exbNkyeHl5wcvLC8uWLcORI0cQGhoKc3Pz1oRLRERPERkZialTp8LZ2RkPHz4E8Kgn/r333sN3333XovXfKisrERoailOnTslHCRAREamKSGjFxJovv/wS33zzDZKSkqCnp4fjx49j2rRpMDAwgEgkQnl5OX7++eenlqHuTK5fvw5fX19cu3YNPj4+qg6HiKjJDh06hLlz50IQhAard2ppPfrObOXKlS3+/ebg4IAJEyawJ4jUgkQiQVRUFPz8/OQjWoio82tRD1BVVRX27t2L6upqfPDBBygsLAQATJkyBRcuXMDKlSuxevVqnD17VqOSHyKijioyMhJz585FbW1tg8kP8Ghuj1QqxdatW1vUEwQAGRkZiI2NbUWkRG0nPDwc27dvR0REhKpDISIlavYcoLy8PAwbNgypqakQBAEikQgGBgY4cuQIxowZg8DAQNbTJyLqYD7++GMIgtDkapt//vknXnzxxRbdKyoqCs7OzjAxMWnR+URtQVYSOzU1FSdPnoS/vz8XRCXSEM3uAfroo4+QlpaGN954A8ePH8f//d//wcDAAKtXr26P+IiIqJ2lp6fj+PHjjfb8PE4qleLmzZvy3v/mqq6uxt9//93uSxsQPYlsQdR+/fohKSkJkZGRqg6JiJSk2T1Ap0+fxuLFi/HFF1/It9na2mLBggVITEyEh4dHmwZIRETt6+zZs81ORmRrtw0bNqxF97x//z7u3bsHZ2fnFp1P1Bp1F0Q1NTWVL4jKXiAizdDsHqD09HQMHz5cYdvw4cMhCAJyc3PbLDAiIlKO0tJSeYGDphKJRK1e6LSpyyQQtbW6C6LGxcVxQVQiDdPsHiCxWAx9fX2FbbL3NTU1bRMVEREpjYmJCaRSabPOEQSh3t+C5mLVLVIVLohKpNlatBBqWloarl+/Ln9fXFwMAEhOTm5wrZ6mlku9ffs2tm/fjrt37+Lhw4f1hmSIRCKcPXu2JSETEVEjRo8eDZFI1KxhcCKRCJ6enq26L4dMk6pYW1uja9euLH9NpKFalACtXbsWa9eurbf98YpAsipxTZlYGxISgmXLlkFHRwceHh6wsLCodwwnzBIRtT1HR0dMmTIFf/75Z5N+X2tpaaFv376wtLRs8T1dXFxgZWXV4vOJWiM8PBwhISGora1l5VoiDdTsBGj79u3tEQfWr1+PgQMH4sSJE/yjSESkZGvXrsWJEyea3BM0adKkFt/L0NAQAQEBLT6fqDVY/pqImp0ALVmypD3iQFZWFt566y0mP0REKjBo0CDs3bsXc+fOhSAIDfYEyQolrFq1qsXV2/T09DBx4kQYGhq2JlyiFmuo/DV7gYg0S7OrwLWXfv36ISsrS9VhEBFprJkzZyIsLAyTJk2CSCRS2CcSidC3b1+88847GDhwYIuub2xsjGeffRZdu3Zti3CJmq2x8tdisVjVoRGREqlNAvTVV19h27ZtCAsLU3UoREQaa9CgQfj999+RlpYGExMTAI96bT799FO8+OKLLe75sbW1xfTp0xuc30mkLCx/TURAC4sgtIfPPvsMZmZmCAwMhLe3NxwdHaGtra1wjEgkwtGjR1UUIRGR5rC1tVV4L0uGmktLSwv9+/eHr69vs9caImprLH9NRIAaJUA3b96ESCSCo6MjysrKEB8fX++Yx4dkEBFR+4iIiJCv7SaVSpGWloaePXs26xo9evTA0KFD2etDasPe3h729vaqDoOIVExtEqC0tDRVh0BERPj/50nUFRcXB2dnZ+jo6Dz1/K5du2Lw4MHo0aNHe4VIRETUYmqTABERkXqQzZOoWw47Pz8fGRkZcHV1bfQ8fX19DBo0CJ6enuyxJyIitaWWCVBpaSmKi4shlUrr7XN0dFRBREREmkM2T+Kvv/5CVVUVdHV14e/v/8ShbD179sTQoUOhr6+vxEiJWkYikSAqKgp+fn7Q1dVVdThEpGRqlQD98MMP+Oqrr3D37t1Gj2nKKuVERNRysnkSsmRGR0cH/fv3l++vqanBvXv34OTkBGNjYwQFBcHJyUlV4RI1W3h4OEJCQlBbW8s1gIg0kNqU5Pnxxx/x0ksvwd3dHR9//DEEQcDrr7+ONWvWoFu3bujfvz+2bdum6jCJiDTe3bt3ERYWhqKiIsyaNYvJD3UosjluqampXAOISEOpTQL07bffYvz48Thx4gRWrVoFAJg8eTI++eQTxMfHo7S0FA8ePFBxlEREmkMikQCAvBocAFRXVyM+Ph6VlZUoLS1Fly5qNZCA6KkiIiKQlJSEfv36ISkpCZGRkaoOiYiUTG0SoJSUFEydOhUA5FWGZH98zczMsGLFCnz//fcqi4+ISJOIxWJUVVUBeJT0VFdXAwBSU1NRXV2NwMBA3Llzhx8eqUOR9f7o6urC1NQUurq67AUi0kBqkwCZmZnJv2U0NTWFoaEhMjIy5PtNTEyQk5OjqvCIiDRKQ+sAVVdXo6ioCDY2NjAzM+OHR+pwZBUOy8vLERcXh/LycqSkpCA6OlrVoRGREqnN2IU+ffrgxo0b8vdDhgzBDz/8gEmTJkEqlWLz5s3o1atXu90/LS0NH330Ec6dO4ecnBx0794dixYtwvvvv88KMUSkUWTfkhsYGEBbWxtSqRRxcXHo1q0bysrKUFVVhbi4OFRXV8s/PA4ZMkTVYRM9lazCYUPbiUhzqE0CtGjRIvz4448Qi8XQ09PDhg0bMGbMGHnZax0dHRw8eLDd7p+QkCBPtNzd3REbG4uVK1eivLwcX3zxRbvdl4hI3ci+JQ8MDIRUKkV6ejry8/PRo0ePBr+I4odH6ihkFQ4bw/LYRJpBJNRd6U7N3L17F8eOHYO2tjbGjRvXrj1ADfn888/xww8/PLEs9+OuX78OX19fXLt2DT4+Pu0YHRFR+8jMzMT169cBACUlJQgLC0PXrl2xevXqJ354JOooGkt0QkNDERISgsWLF7M8NlEnpjY9QA1xdXXFa6+9prL7FxcXw9LSUmX3JyJShbrfkhcUFKC8vByDBw9m8kOdRkPrAD1eHtvf3x96enoqjpSI2oPaFEGQCQ8Px8aNG/HGG28gOTkZAFBRUYHr16+jrKxMaXHcuXMH3377LVavXv3E48RiMUpKSuQvZcZIRKQsHOZGnUVj6wCxPDaR5lCbBEgikWDmzJkICAjA+++/j2+++UZeBU5LSwvjxo3D119/3ezrrlmzBiKR6ImvhIQEhXMyMzMxYcIEzJkzBytXrnzi9Tdu3AgzMzP5KygoqNkxEhGpI4lEgsjISBgYGMDCwkLV4RC1iYYSHZbHJtIsapMArV27FsePH8cPP/yAxMRE1J2apK+vjzlz5uDo0aPNvu4///lP3L59+4kvV1dX+fFZWVkYNWoUhg0bhi1btjz1+u+++y6Ki4vlr4sXLzY7RiIidRQeHo7ffvsNpaWlEIlEqg6HqNUaS3QiIiJYHptIg6jNHKDffvsNL7zwAlatWoUHDx7U2+/l5YX9+/c3+7rW1tawtrZu0rGZmZkYNWoUfH19sX37dmhpPT0/1NPTUxgjbGxs3OwYiYjUjeyDYnp6OkxMTOQVOok6MlmFw8dLuRcVFbE8NpEGUZsEKC8vD3379m10v7a2NioqKtrt/pmZmRg5ciScnJzwxRdfID8/X76vW7du7XZfIiJ1JBsm5O3tjczMTERGRrIqFnV4ja0D5OPjwyIfRBpEbRIgBweHenNx6rp8+TLc3d3b7f5nzpzBnTt3cOfOHfTo0UNhnxpXCicianN1hwmZmJjAwMCAVbGoU3jaOkBEpBnUZg7QggULsHnzZly5ckW+TTbmfOvWrdi3bx8WL17cbvdfunQpBEFo8EVEpElkw4TKy8uRkJCAqqoqzocgIqJOQ216gN5//32Eh4djxIgR8PLygkgkwhtvvIHCwkLcv38fkyZNwhtvvKHqMImIOr26w4QqKythYGAg305ERNTRqU0CJKvEsmvXLhw4cAC1tbUQi8Xo168fPv74Yzz33HOsQkREpAR1hwnVTYCIiIg6A7VJgIBHQ94WLVqERYsWqToUIiKNJ1sHaOjQodDV1VV1OERERG1CbeYAERGRegkPD8cvv/yCiIgIVYdCRETUZtSqB+jSpUv4+eefcffuXTx8+LBeAQKRSIQbN26oKDoiIs0hqwSXlpbGCnBERNSpqE0C9NVXX+Ff//oX9PX14eHhAUtLS1WHRESksWTrAPXt2xdJSUlcB4iIiDoNtUmAPv/8cwQEBODYsWMwMzNTdThERBqr7jpAZmZmyM/PZy8QERF1GmozB6iiogILFy5k8kNEpGJ11wGKj49HeXk51wEiIqJOQ216gEaNGoVbt26pOgwiIo1Xdx2gx7cTERF1dGqTAH377bcYN24cvvjiCyxfvpxzgIiIVKTuOkBERESdjdoMgXNwcMDq1auxZs0aWFtbw8jICKampgovDo8jIlI/EokEYWFhkEgkqg6FiIjoqdSmB+jDDz/EJ598Ant7e/j5+THZISJSQxKJBFFRUfDz85MvjhoeHo6QkBDU1tayUhwREak9tUmAfvzxR0yePBlHjhyBlpbadEwREVEdjyc7sopxqamprBRHREQdgtpkGhKJBJMnT2byQ0SkJh4f2vZ4siMWi+XrBfXr10++XhAREZE6U5tsY8qUKfj7779VHQYREf0/4eHh2L59OyIiIgCgXrJz6dIl+XpBpqam0NXVlSdGRERE6kptEqB169YhPj4eL774Iq5du4b8/HwUFhbWexERUft7vLenpKSkXrITEhKC5ORklJeXIy4ujusFERFRh6A2c4A8PDwAADExMdi8eXOjx9XW1iorJCIijfV4b8/u3buRkpKCqqoqxMXFobq6GlKpFIGBgfD09FQ4l+sFERGROlObBOjDDz+ESCRSdRhERBpP1vtTt7cnPj4ewcHB0NHRUTjWx8eHawYREVGHojYJ0Pr161UdAhERAYiOjq7X21NbW4vu3btjyJAhqg6PiIioVdQmASIiIvXg4OCAhQsXNridiIioo2MCRERECuzt7TmsjYiIOi21qQJHRERERETU3pgAERERERGRxmACREREREREGoMJEBERNUgikSAsLAwSiUTVoRAREbUZJkB1PPvss3B0dIS+vj7s7Ozw3HPPISsrS9VhERGpRHh4OLZv346IiAhVh0JERNRmmADVMWrUKOzbtw+JiYk4ePAgUlJSMHv2bFWHRUSkdLLFUFNTU3Hy5EmIxWJVh0RERNQmWAa7jjfeeEP+/52cnLBmzRpMnz4d1dXV9VY/JyLqzCIiIpCUlIR+/fohKSkJkZGRCAwMVHVYRERErcYeoEYUFhZi165dGDZsGJMfItIost4fXV1dmJqaQldXl71ARETUaTABesw777wDIyMjdO3aFenp6Th69OgTjxeLxSgpKZG/ysrKlBQpEVH7iI6ORkpKCsrLyxEXF4fy8nKkpKQgOjpa1aERERG1mkgQBEHVQbSnNWvW4LPPPnviMbdv34anpycAoKCgAIWFhbh37x42bNgAMzMzHD9+HCKRqMFz169fjw0bNtTbfu3aNfj4+LT+ByAiUrLMzExcv3693nYfHx/Y29urICIiIqK20+kToPz8fDx48OCJx7i6ukJXV7fe9vv378PBwQFhYWEYOnRog+eKxWKFYSExMTEICgpiAkREREREpIY6fREEa2trWFtbt+hcqVQKAE8c966npwc9PT35e2Nj4xbdi4iIiIiI2l+nT4CaKiIiAlevXsXw4cNhYWGBlJQUrF27Fm5ubo32/hARERERUcfCIgj/j6GhIQ4dOoTRo0fDw8MDzz//PPr164eLFy8q9PAQEREREVHHxR6g/6dv3744d+6cqsMgIiIiIqJ2xB4gIiIiIiLSGEyAiIiIiIhIYzABIiKiBkkkEoSFhUEikag6FCIiojbDBIiIiBoUHh6O7du3IyIiQtWhEBERtRkmQEREVI9YLMapU6eQmpqKkydPPnE9NCIioo6ECRAREdUTERGBpKQk9OvXD0lJSYiMjFR1SERERG2CCRARESmQ9f7o6urC1NQUurq67AUiIqJOgwkQEREpiI6ORkpKCsrLyxEXF4fy8nKkpKQgOjpa1aERERG1GhdCJSIiBQ4ODli4cGGD24mIiDo6JkBERKTA3t4e9vb2qg6DiIioXXAIHBERERERaQwmQEREREREpDGYABERERERkcbgHKBOLDs7G9nZ2aoOQyPY2dnBzs5O1WFoDLZt5WHbVi62beVh21Yutm3lYdt+OiZAbczOzg7r1q1TecMTi8WYP38+Ll68qNI4NEVQUBBOnToFPT09VYfS6bFtKxfbtvKwbSsX27bysG0rF9v204kEQRBUHQS1vZKSEpiZmeHixYswNjZWdTidWllZGYKCglBcXAxTU1NVh9PpsW0rD9u2crFtKw/btnKxbSsP23bTsAeokxswYAD/AbSzkpISVYegkdi22x/btmqwbbc/tm3VYNtuf2zbTcMiCEREREREpDGYABERERERkcZgAtRJ6enpYd26dZwApwR81srF5608fNbKxeetPHzWysXnrTx81k3DIghERERERKQx2ANEREREREQagwkQERERERFpDCZARERERESkMZgAERERERG10NKlS+Hs7Nzic1W9OOyOHTsgEomQlpbWovNb8/OPHDkSI0eObNG5rcEEiDSCSCRq0uvChQutvldFRQXWr1/frGt98sknePbZZ2FrawuRSIT169e3Og7SDOrcthMSEvD2229jwIABMDExgZ2dHSZPnoyoqKhWx0Kdnzq37aysLCxatAgeHh4wMTGBubk5/P39sXPnTrC2lHrYt28fRCIRDh8+XG9f//79IRKJcP78+Xr7HB0dMWzYMGWE2Cwt+WzREWVlZWH9+vWIiYlp1/t0aderE6mJkJAQhfe//PILzpw5U2+7l5dXq+9VUVGBDRs2AECTv9X44IMP0K1bNwwcOBCnTp1qdQykOdS5bf/000/Ytm0bZs2ahRdffBHFxcXYvHkzhgwZgpMnT2LMmDGtjok6L3Vu2wUFBbh//z5mz54NR0dHVFdX48yZM1i6dCkSExPx6aeftjomap3hw4cDAC5duoQZM2bIt5eUlCA2NhZdunTB5cuXMWrUKPm+jIwMZGRkYN68ec2619atWyGVStsm8Ea05LNFR5SVlYUNGzbA2dkZAwYMaLf7MAEijbBo0SKF9+Hh4Thz5ky97aqSmpoKZ2dnFBQUwNraWtXhUAeizm17/vz5WL9+vcLwjuXLl8PLywvr169nAkRPpM5tu1+/fvW+iX/55ZcxdepUfPPNN/joo4+gra2tmuAIANC9e3e4uLjg0qVLCtuvXLkCQRAwZ86cevtk72XJU1Pp6Oi0LlhSOg6BI/p/pFIpNm3ahN69e0NfXx+2trZYvXo1Hj58qHBcVFQUxo8fDysrKxgYGMDFxQXLly8HAKSlpckTmA0bNsiHaDxtSFtLx84SNYWq2ravr2+9se1du3ZFYGAgbt++3bY/JGkkVf7eboizszMqKiogkUha/bNR6w0fPhzR0dGorKyUb7t8+TJ69+6NiRMnIjw8XKHn5vLlyxCJRAgICJBv+/XXX+Hr6wsDAwNYWlpi3rx5yMjIULhPQ3NgHjx4gOeeew6mpqYwNzfHkiVLcOPGDYhEIuzYsaNerJmZmZg+fTqMjY1hbW2Nt956C7W1tQCa1kYTEhIwe/ZsWFpaQl9fH35+fvj999/r3ScuLg7PPPMMDAwM0KNHD3z88cfN6r06cuQI+vTpA319ffTp06fBIYZA0/9t1nXhwgUMGjQIALBs2TL5zyl7Xn///TfmzJkDR0dH6OnpwcHBAW+88YbCf9+mYg8Q0f+zevVq7NixA8uWLcOrr76K1NRU/O9//0N0dDQuX74MHR0d5OXlYdy4cbC2tsaaNWtgbm6OtLQ0HDp0CABgbW2NH374AS+88AJmzJiBmTNnAnj0bSGRqqhb287JyYGVlVWb/oykmVTdtisrK1FeXo6ysjJcvHgR27dvx9ChQ2FgYNCuPzc1zfDhwxESEoKIiAj5sLHLly9j2LBhGDZsGIqLixEbGyv/b3358mV4enqia9euAB7Nz127di2Cg4OxYsUK5Ofn49tvv8WIESMQHR0Nc3PzBu8rlUoxdepUREZG4oUXXoCnpyeOHj2KJUuWNHh8bW0txo8fj8GDB+OLL77AX3/9hS+//BJubm544YUXntpG4+LiEBAQAHt7e6xZswZGRkbYt28fpk+fjoMHD8qHAObk5GDUqFGoqamRH7dly5Ymt9fTp09j1qxZ8Pb2xsaNG/HgwQMsW7YMPXr0qHdsU/5tPs7Lywv//ve/8eGHH2LVqlUIDAwEAPmcrP3796OiogIvvPACunbtisjISHz77be4f/8+9u/f36SfQU4g0kAvvfSSULf5//333wIAYdeuXQrHnTx5UmH74cOHBQDC1atXG712fn6+AEBYt25ds+NqzblEgqC+bVsmNDRUEIlEwtq1a1t8DdJM6ti2N27cKACQv0aPHi2kp6c36xrUfuLi4gQAwkcffSQIgiBUV1cLRkZGws6dOwVBEARbW1vhu+++EwRBEEpKSgRtbW1h5cqVgiAIQlpamqCtrS188sknCte8deuW0KVLF4XtS5YsEZycnOTvDx48KAAQNm3aJN9WW1srPPPMMwIAYfv27QrnAhD+/e9/K9xn4MCBgq+vr/z9k9ro6NGjhb59+wpVVVXybVKpVBg2bJjQs2dP+bbXX39dACBERETIt+Xl5QlmZmYCACE1NbXB5ygzYMAAwc7OTigqKpJvO336tABA4edv6r9NQRCEoKAgISgoSP7+6tWr9Z6RTEVFRb1tGzduFEQikXDv3r0nxv44DoEjwqNvFczMzDB27FgUFBTIX7IhPLJKMbJve44fP47q6moVRkzUNOrUtvPy8rBgwQK4uLjg7bffbpd7kOZQh7Y9f/58nDlzBrt378aCBQsAoEXDcah9eHl5oWvXrvK5PTdu3EB5ebm8R2HYsGG4fPkygEdzg2pra+Xzfw4dOgSpVIrg4GCF9tWtWzf07NmzwQpyMidPnoSOjg5Wrlwp36alpYWXXnqp0XP+8Y9/KLwPDAzE3bt3n/ozFhYW4ty5cwgODkZpaak8zgcPHmD8+PFITk5GZmYmAODPP//EkCFD4O/vLz/f2toaCxcufOp9srOzERMTgyVLlsDMzEy+fezYsfD29lY4tqn/Npurbk9VeXk5CgoKMGzYMAiCgOjo6GZdi0PgiAAkJyejuLgYNjY2De7Py8sDAAQFBWHWrFnYsGED/u///g8jR47E9OnTsWDBAujp6SkzZKImUZe2XV5ejilTpqC0tBSXLl1S+boX1PGpQ9t2cnKCk5MTgEfJ0KpVqzBmzBgkJiZyGJwaEIlEGDZsGEJDQyGVSnH58mXY2NjA3d0dwKME6H//+x8AyBMhWQKUnJwMQRDQs2fPBq/9pMIH9+7dg52dHQwNDRW2y+77OH19/XoFkCwsLJ44X0bmzp07EAQBa9euxdq1axs8Ji8vD/b29rh37x4GDx5cb7+Hh8dT73Pv3j0AaPB5eHh44Pr16/L3Tf232Vzp6en48MMP8fvvv9d7NsXFxc26FhMgIjwar2tjY4Ndu3Y1uF/2i0kkEuHAgQMIDw/HsWPHcOrUKSxfvhxffvklwsPD+aGO1I46tG2JRIKZM2fi5s2bOHXqFPr06dPiaxHJqEPbftzs2bOxdetWhIaGYvz48W12XWq54cOH49ixY7h165Z8/o/MsGHD8K9//QuZmZm4dOkSunfvDldXVwCP2pdIJMKJEycarOjXlu2mNRUDZQUM3nrrrUbbXGOJV3tp6r/N5qitrcXYsWNRWFiId955B56enjAyMkJmZiaWLl3a7DLkTICIALi5ueGvv/5CQEBAk761GzJkCIYMGYJPPvkEu3fvxsKFC7Fnzx6sWLECIpFICRETNY2q27ZUKsXixYtx9uxZ7Nu3D0FBQS35MYjqUXXbbohs+Ftzv42m9lN3PaDLly/j9ddfl+/z9fWFnp4eLly4gIiICEyaNEm+z83NDYIgwMXFBb169WrWPZ2cnHD+/HlUVFQo9ALduXOnxT9HY21UlrDp6Og8dWkBJycnJCcn19uemJj4xM5k/AAAEZBJREFU1PvLejqbcn5z/23W1djPeevWLSQlJWHnzp1YvHixfPuZM2eadX0ZzgEiAhAcHIza2lp89NFH9fbV1NSgqKgIAPDw4cN6q3zLFuoSi8UAIP9lJzuHSJVU3bZfeeUV7N27F99//728chFRW1Bl287Pz29w+7Zt2yASieDj49Ok61D78/Pzg76+Pnbt2oXMzEyFHiA9PT34+Pjgu+++Q3l5ucL6PzNnzoS2tjY2bNhQr/0IgoAHDx40es/x48ejuroaW7dulW+TSqX47rvvWvxzNNZGbWxsMHLkSGzevBnZ2dn1zqvbVidNmoTw8HBERkYq7G+sp6YuOzs7DBgwADt37lRI8M+cOYP4+HiFY5v6b7MhRkZGAOr/nLJesrr/LQRBwNdff/3U2BvCHiAiPBojvnr1amzcuBExMTEYN24cdHR0kJycjP379+Prr7/G7NmzsXPnTnz//feYMWMG3NzcUFpaiq1bt8LU1FT+zZGBgQG8vb2xd+9e9OrVC5aWlujTp88Th/2EhITg3r17qKioAACEhobi448/BgA899xz8m9eiJpLlW1706ZN+P777zF06FAYGhri119/Vdg/Y8YM+R87ouZSZdv+5JNPcPnyZUyYMAGOjo4oLCzEwYMHcfXqVbzyyitKH3JEjdPV1cWgQYPw999/Q09PD76+vgr7hw0bhi+//BKA4gKobm5u+Pjjj/Huu+8iLS0N06dPh4mJCVJTU3H48GGsWrUKb731VoP3nD59Ovz9/fHPf/4Td+7cgaenJ37//XcUFhYCaLyX40me1Ea/++47DB8+HH379sXKlSvh6uqK3NxcXLlyBffv38eNGzcAAG+//TZCQkIwYcIEvPbaa/Iy2E5OTrh58+ZTY9i4cSMmT56M4cOHY/ny5SgsLMS3336L3r17o6ysTH5cU/9tNsTNzQ3m5ub48ccfYWJiAiMjIwwePBienp5wc3PDW2+9hczMTJiamuLgwYNNmifVoGbVjCPqJB4vpyqzZcsWwdfXVzAwMBBMTEyEvn37Cm+//baQlZUlCIIgXL9+XZg/f77g6Ogo6OnpCTY2NsKUKVOEqKgoheuEhYUJvr6+gq6ubpNKqwYFBSmUUq37On/+fFv92KQB1Klty8q7NvZ6WslVorrUqW2fPn1amDJlitC9e3dBR0dHMDExEQICAoTt27cLUqm0TX9uar13331XACAMGzas3r5Dhw4JAAQTExOhpqam3v6DBw8Kw4cPF4yMjAQjIyPB09NTeOmll4TExET5MY+XwRaER2WrFyxYIJiYmAhmZmbC0qVLhcuXLwsAhD179iica2RkVO++69atq9fen9RGU1JShMWLFwvdunUTdHR0BHt7e2HKlCnCgQMHFK5x8+ZNISgoSNDX1xfs7e2Fjz76SNi2bVuTfycfPHhQ8PLyEvT09ARvb2/h0KFDDf78gvD0f5uCUL8MtiAIwtGjRwVvb2+hS5cuCiWx4+PjhTFjxgjGxsaClZWVsHLlSuHGjRuNls1+EpEgPNavR0REREREberIkSOYMWMGLl26hICAAFWHo9GYABERERERtaHKykqFAgC1tbUYN24coqKikJOTwzLpKsY5QEREREREbeiVV15BZWUlhg4dCrFYjEOHDiEsLAyffvopkx81wB4gIiIiIqI2tHv3bnz55Ze4c+cOqqqq4O7ujhdeeAEvv/yyqkMjMAEiIiIiIiINwnWAiIiIiIhIYzABIiIiIiIijcEEiKgJ0tLSIBKJsGPHDlWHQtRm2K6ps2LbJqInYQJEREREREQag0UQiJpAEASIxWLo6OhAW1tb1eEQtQm2a+qs2LaJ6EmYABERERERkcbgEDjSGOvXr4dIJEJSUhIWLVoEMzMzWFtbY+3atRAEARkZGZg2bRpMTU3RrVs3fPnll/JzGxpPvnTpUhgbGyMzMxPTp0+HsbExrK2t8dZbb6G2tlZ+3IULFyASiXDhwgWFeBq6Zk5ODpYtW4YePXpAT08PdnZ2mDZtGtLS0trpqVBHx3ZNnRXbNhG1FyZApHHmzp0LqVSK//znPxg8eDA+/vhjbNq0CWPHjoW9vT0+++wzuLu746233kJoaOgTr1VbW4vx48eja9eu+OKLLxAUFIQvv/wSW7ZsaVFss2bNwuHDh7Fs2TJ8//33ePXVV1FaWor09PQWXY80B9s1dVZs20TU5gQiDbFu3ToBgLBq1Sr5tpqaGqFHjx6CSCQS/vOf/8i3P3z4UDAwMBCWLFkiCIIgpKamCgCE7du3y49ZsmSJAED497//rXCfgQMHCr6+vvL358+fFwAI58+fVzju8Ws+fPhQACB8/vnnbfMDk0Zgu6bOim2biNoLe4BI46xYsUL+/7W1teHn5wdBEPD888/Lt5ubm8PDwwN379596vX+8Y9/KLwPDAxs0nmPMzAwgK6uLi5cuICHDx82+3zSbGzX1FmxbRNRW2MCRBrH0dFR4b2ZmRn09fVhZWVVb/vT/qjp6+vD2tpaYZuFhUWL/hjq6enhs88+w4kTJ2Bra4sRI0bgv//9L3Jycpp9LdI8bNfUWbFtE1FbYwJEGqehkqiNlUkVnlIksSnlVUUiUYPb6066lXn99deRlJSEjRs3Ql9fH2vXroWXlxeio6Ofeh/SbGzX1FmxbRNRW2MCRNTOLCwsAABFRUUK2+/du9fg8W5ubvjnP/+J06dPIzY2FhKJRKG6EZE6YLumzoptm6jzYwJE1M6cnJygra1drzrR999/r/C+oqICVVVVCtvc3NxgYmICsVjc7nESNQfbNXVWbNtEnV8XVQdA1NmZmZlhzpw5+PbbbyESieDm5objx48jLy9P4bikpCSMHj0awcHB8Pb2RpcuXXD48GHk5uZi3rx5KoqeqGFs19RZsW0TdX5MgIiU4Ntvv0V1dTV+/PFH6OnpITg4GJ9//jn69OkjP8bBwQHz58/H2bNnERISgi5dusDT0xP79u3DrFmzVBg9UcPYrqmzYtsm6txEwtNmDBIREREREXUSnANEREREREQagwkQERERERFpDCZARERERESkMZgAERERERGRxmACREREREREGoMJEJEaSUtLg0gkwo4dO1QdClGbYtumzoptm6jjYQJEHVZKSgpWr14NV1dX6Ovrw9TUFAEBAfj6669RWVnZbveNj4/H+vXrkZaW1m73aIpPPvkEzz77LGxtbSESibB+/XqVxkNtR5PbdkJCAt5++20MGDAAJiYmsLOzw+TJkxEVFaWymKjtaHLbzsrKwqJFi+Dh4QETExOYm5vD398fO3fuBFckIVIuLoRKHdIff/yBOXPmQE9PD4sXL0afPn0gkUhw6dIl/Otf/0JcXBy2bNnSLveOj4/Hhg0bMHLkSDg7O7fLPZrigw8+QLdu3TBw4ECcOnVKZXFQ29L0tv3TTz9h27ZtmDVrFl588UUUFxdj8+bNGDJkCE6ePIkxY8aoJC5qPU1v2wUFBbh//z5mz54NR0dHVFdX48yZM1i6dCkSExPx6aefqiQuIk3EBIg6nNTUVMybNw9OTk44d+4c7Ozs5Pteeukl3LlzB3/88YcKI/z/CYKAqqoqGBgYtPm1U1NT4ezsjIKCAlhbW7f59Un52LaB+fPnY/369TA2NpZvW758Oby8vLB+/XomQB0U2zbQr18/XLhwQWHbyy+/jKlTp+Kbb77BRx99BG1t7Ta9JxE1jEPgqMP573//i7KyMmzbtk3hj6iMu7s7XnvtNfn7mpoafPTRR3Bzc4Oenh6cnZ3x3nvvQSwWK5zn7OyMKVOm4NKlS/D394e+vj5cXV3xyy+/yI/ZsWMH5syZAwAYNWoURCIRRCKR/I+a7BqnTp2Cn58fDAwMsHnzZgDA3bt3MWfOHFhaWsLQ0BBDhgxp1R98VfY+Uftg2wZ8fX0Vkh8A6Nq1KwIDA3H79u0WXZNUj227cc7OzqioqIBEImnT6xLR/9fe/YU01YdxAP9uYkZrokEZRp6dJQ0lMLwQsuJQCZUOMhmEQtqFdadEhpgRERTRjRSVhBEmq0WIgRARRQmLCuwmQqI/rLW6UQe1NZQo2vNeSOd9zztnW1m6zvcD52LP+f3beGB7tp3fmYEQZZgVK1aI0+lMuX1TU5MAEI/HI+fPn5fGxkYBILW1tYZ2iqKIy+WSgoIC6ezslHPnzkl5eblYLBYZGRkREZFAICCtra0CQDo7O8Xr9YrX65XR0VF9jOLiYsnPz5eOjg65cOGCDA0NyejoqBQUFIjdbpfDhw9LV1eXlJWVidVqlRs3buhrCAaDAkB6e3tTfn7hcFgAyNGjR1PuQ/MTczu5yspKWb169U/1pbnH3P7X5OSkhMNhCQaDcvnyZbHZbFJZWZnya0NEv44FEGWUaDQqAGTHjh0ptX/69KkAkObmZkP84MGDAkDu37+vxxRFEQDi9/v12Pj4uOTk5EhbW5se6+/vFwAyNDSUMN/3MW7fvm2I79+/XwDIgwcP9FgsFhNVVcXhcMi3b99EhAWQmTG3k/P7/WKxWOTIkSNp96W5x9w2OnnypADQjy1btsi7d+9S6ktEs4N/gaOM8unTJwCA3W5Pqf2tW7cAAAcOHDDE29raACDhrwylpaXYuHGj/njp0qVwuVx48+ZNymtUVRVbt25NWEdFRQU2bNigxxYvXox9+/bh7du3eP78ecrj09+JuT298fFxNDQ0QFVVtLe3/9JYNDeY20b19fW4e/cufD4fGhoaAOC37oBHRIlYAFFGyc3NBQDEYrGU2odCIVitVhQXFxviy5cvR15eHkKhkCFeVFSUMEZ+fj4+fvyY8hpVVZ12HS6XKyFeUlKinydzY24nmpiYgNvtRiwWw+DgYMK1QZQZmNtGiqKgqqoK9fX1uHr1KpxOJ6qqqlgEEf1BLIAoo+Tm5qKwsBAjIyNp9bNYLCm1S7YDj6Rxj4bfseMb/f2Y20ZfvnxBXV0dnj17hsHBQaxZs+aPzU2zi7k9M4/Hg/fv38Pv98/ZGojMhgUQZRy3241AIIDHjx//sK2iKIjH43j9+rUhPjY2hkgkAkVR0p4/1Tfl/6/j5cuXCfEXL17o54mY21Pi8TgaGxtx7949+Hw+aJqW9hg0vzC3k/v+y080Gp2V8Yjox1gAUcZpb2+HzWZDc3MzxsbGEs4HAgGcOXMGAFBdXQ0AOH36tKFNV1cXAKCmpibt+W02GwAgEomk3Ke6uhrDw8OGN/+JiQn09PTA4XCgtLQ07XXQ34e5PaWlpQXXr19Hd3c36urq0u5P8w9zGwiHw9PGL126BIvFgvLy8rTGI6KfxxuhUsZZtWoVfD4fdu3ahZKSEsMdxR89eoT+/n7s2bMHAFBWVoampib09PQgEolA0zQMDw+jr68PtbW12LRpU9rzr127FllZWTh16hSi0ShycnKwefNmLFu2LGmfjo4OXLt2Ddu3b0drayuWLFmCvr4+BINBDAwMwGpN/7sIr9eLUCiEyclJAIDf78fx48cBALt37+avShmIuT31obe7uxvr1q3DokWLcOXKFcP5nTt36h9mKXMwt4ETJ07g4cOH2LZtG4qKivDhwwcMDAzgyZMnaGlpSbjmiYh+oznehY7op7169Ur27t0rDodDFixYIHa7XdavXy9nz56Vz58/6+2+fv0qx44dE1VVJTs7W1auXCmHDh0ytBGZ2gq1pqYmYR5N00TTNEPs4sWL4nQ6JSsry7C1arIxRKbuReHxeCQvL08WLlwoFRUVcvPmTUObdLZT1TTNsJXqf4/ptnqlzGHm3P5+/5dkRzAYnLE/zW9mzu07d+6I2+2WwsJCyc7O1p97b2+vxOPxGfsS0eyyiKRxlSAREREREVEG4zVARERERERkGiyAiIiIiIjINFgAERERERGRabAAIiIiIiIi02ABREREREREpsECiIiIiIiITIMFEBERERERmQYLICIiIiIiMg0WQEREREREZBosgIiIiIiIyDRYABERERERkWmwACIiIiIiItNgAURERERERKbxD8UM2gWuiN2XAAAAAElFTkSuQmCC",
-      "text/plain": [
-       "<Figure size 900x600 with 2 Axes>"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 900x600 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "paired = dabest.load(df, idx=((\"Control 1\", \"Test 1\"), (\"Control 2\", \"Test 2\"), (\"Control 3\", \"Test 3\")), mini_meta=True, id_col=\"ID\", paired=\"baseline\")\n",
-    "paired.mean_diff.plot()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "1c5081cc",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "python3",
-   "language": "python",
-   "name": "python3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/nbs/tutorials/04-proportion_plot.ipynb b/nbs/tutorials/04-proportion_plot.ipynb
new file mode 100644
index 00000000..f9665aad
--- /dev/null
+++ b/nbs/tutorials/04-proportion_plot.ipynb
@@ -0,0 +1,1922 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Proportion Plots\n",
+    "\n",
+    "> A guide to plot proportion plots with binary data.\n",
+    "\n",
+    "- order: 4"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div style=\"padding: 15px; border: 1px solid transparent; border-color: transparent; margin-bottom: 20px; border-radius: 4px; color: #31708f; background-color: #d9edf7; border-color: #bce8f1;\"> As of v2023.02.14, DABEST can be used to generate Cohen's *h* and the corresponding proportion plot for binary data. It's important to note that the code we provide only supports numerical proportion data, \n",
+    "where the values are limited to 0 (failure) and 1 (success). This means that the code is not suitable for \n",
+    "analyzing proportion data that contains non-numeric values, such as strings like 'yes' and 'no'. </div>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Pre-compiling numba functions for DABEST...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Compiling numba functions: 100%|██████████| 11/11 [00:00<00:00, 34.27it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Numba compilation complete!\n",
+      "We're using DABEST v2025.03.27\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import dabest\n",
+    "\n",
+    "print(\"We're using DABEST v{}\".format(dabest.__version__))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#| hide\n",
+    "import warnings\n",
+    "warnings.filterwarnings(\"ignore\", category=UserWarning) # to suppress warnings related to points not being able to be plotted due to dot size\n",
+    "warnings.filterwarnings(\"ignore\", category=FutureWarning) # to suppress warnings related to points not being able to be plotted due to dot size"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Creating a demo dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Control 1</th>\n",
+       "      <th>Test 1</th>\n",
+       "      <th>Control 2</th>\n",
+       "      <th>Test 2</th>\n",
+       "      <th>Control 3</th>\n",
+       "      <th>Test 3</th>\n",
+       "      <th>Test 4</th>\n",
+       "      <th>Test 5</th>\n",
+       "      <th>Test 6</th>\n",
+       "      <th>Test 7</th>\n",
+       "      <th>Test 8</th>\n",
+       "      <th>Test 9</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>ID</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Control 1  Test 1  Control 2  Test 2  Control 3  Test 3  Test 4  Test 5  \\\n",
+       "0          1       0          0       0          1       0       0       1   \n",
+       "1          0       1          0       1          1       1       0       0   \n",
+       "2          0       1          0       0          1       0       1       1   \n",
+       "3          0       1          0       0          1       0       0       1   \n",
+       "4          0       0          0       0          1       0       0       0   \n",
+       "\n",
+       "   Test 6  Test 7  Test 8  Test 9  Gender  ID  \n",
+       "0       0     1.0     0.0     0.0  Female   1  \n",
+       "1       0     1.0     0.0     0.0  Female   2  \n",
+       "2       0     1.0     0.0     0.0  Female   3  \n",
+       "3       0     1.0     0.0     0.0  Female   4  \n",
+       "4       1     1.0     0.0     0.0  Female   5  "
+      ]
+     },
+     "execution_count": null,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def create_demo_prop_dataset(seed=9999, N=40):\n",
+    "    import numpy as np\n",
+    "    import pandas as pd\n",
+    "\n",
+    "    np.random.seed(9999)  # Fix the seed to ensure reproducibility of results.\n",
+    "    # Create samples\n",
+    "    n = 1\n",
+    "    c1 = np.random.binomial(n, 0.2, size=N)\n",
+    "    c2 = np.random.binomial(n, 0.2, size=N)\n",
+    "    c3 = np.random.binomial(n, 0.8, size=N)\n",
+    "\n",
+    "    t1 = np.random.binomial(n, 0.6, size=N)\n",
+    "    t2 = np.random.binomial(n, 0.2, size=N)\n",
+    "    t3 = np.random.binomial(n, 0.3, size=N)\n",
+    "    t4 = np.random.binomial(n, 0.4, size=N)\n",
+    "    t5 = np.random.binomial(n, 0.5, size=N)\n",
+    "    t6 = np.random.binomial(n, 0.6, size=N)\n",
+    "    t7 = np.ones(N)\n",
+    "    t8 = np.zeros(N)\n",
+    "    t9 = np.zeros(N)\n",
+    "\n",
+    "    # Add a `gender` column for coloring the data.\n",
+    "    females = np.repeat('Female', N / 2).tolist()\n",
+    "    males = np.repeat('Male', N / 2).tolist()\n",
+    "    gender = females + males\n",
+    "\n",
+    "    # Add an `id` column for paired data plotting.\n",
+    "    id_col = pd.Series(range(1, N + 1))\n",
+    "\n",
+    "    # Combine samples and gender into a DataFrame.\n",
+    "    df = pd.DataFrame({'Control 1': c1, 'Test 1': t1,\n",
+    "                       'Control 2': c2, 'Test 2': t2,\n",
+    "                       'Control 3': c3, 'Test 3': t3,\n",
+    "                       'Test 4': t4, 'Test 5': t5, 'Test 6': t6,\n",
+    "                       'Test 7': t7, 'Test 8': t8, 'Test 9': t9,\n",
+    "                       'Gender': gender, 'ID': id_col\n",
+    "                       })\n",
+    "\n",
+    "    return df\n",
+    "df = create_demo_prop_dataset()\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Helper function to create a binary table - `dabest.prop_dataset` "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In DABEST **v2024.3.29**, we incorporated feedback from biologists who may not have tables of 0’s and 1’s readily available. As a result, a convenient function  - `dabest.prop_dataset` - to generate a binary dataset based on the specified sample sizes is provided. Users can generate a pandas.DataFrame containing the sample sizes for each element in the groups and the group names (optional if the sample sizes are provided in a dict)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "True\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>a</th>\n",
+       "      <th>b</th>\n",
+       "      <th>ID</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   a  b  ID\n",
+       "0  0  0   1\n",
+       "1  0  0   2\n",
+       "2  0  1   3\n",
+       "3  1  1   4\n",
+       "4  1  1   5"
+      ]
+     },
+     "execution_count": null,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sample_size_1 = {'a':[3, 4], 'b':[2, 5]}\n",
+    "sample_size_2 = [3, 4, 2, 5]\n",
+    "names = ['a', 'b']\n",
+    "sample_df_1 = dabest.prop_dataset(sample_size_1)\n",
+    "sample_df_2 = dabest.prop_dataset(sample_size_2, names)\n",
+    "print(all(sample_df_1 == sample_df_2))\n",
+    "sample_df_1.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Loading data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "When loading data, you need to set the parameter ``proportional=True``."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "DABEST v2025.03.27\n",
+       "==================\n",
+       "                  \n",
+       "Good afternoon!\n",
+       "The current time is Tue Mar 25 17:22:24 2025.\n",
+       "\n",
+       "Effect size(s) with 95% confidence intervals will be computed for:\n",
+       "1. Test 1 minus Control 1\n",
+       "\n",
+       "5000 resamples will be used to generate the effect size bootstraps."
+      ]
+     },
+     "execution_count": null,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "two_groups_unpaired = dabest.load(df, idx=(\"Control 1\", \"Test 1\"), proportional=True)\n",
+    "two_groups_unpaired"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Effect sizes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To generate a proportion plot, the **dabest** library features two effect sizes:\n",
+    "\n",
+    "  - Mean difference (``mean_diff``)\n",
+    "  - [Cohen's h](https://en.wikipedia.org/wiki/Cohen's_h) (`cohens＿h`)\n",
+    "\n",
+    "These are attributes of the ``Dabest`` object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "DABEST v2025.03.27\n",
+       "==================\n",
+       "                  \n",
+       "Good afternoon!\n",
+       "The current time is Tue Mar 25 17:22:24 2025.\n",
+       "\n",
+       "The unpaired mean difference between Control 1 and Test 1 is 0.575 [95%CI 0.35, 0.725].\n",
+       "The p-value of the two-sided permutation t-test is 0.0, calculated for legacy purposes only. \n",
+       "\n",
+       "5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.\n",
+       "Any p-value reported is the probability of observing theeffect size (or greater),\n",
+       "assuming the null hypothesis of zero difference is true.\n",
+       "For each p-value, 5000 reshuffles of the control and test labels were performed.\n",
+       "\n",
+       "To get the results of all valid statistical tests, use `.mean_diff.statistical_tests`"
+      ]
+     },
+     "execution_count": null,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "two_groups_unpaired.mean_diff"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's compute the *Cohen's h* for our comparison."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "DABEST v2025.03.27\n",
+       "==================\n",
+       "                  \n",
+       "Good afternoon!\n",
+       "The current time is Tue Mar 25 17:22:25 2025.\n",
+       "\n",
+       "The unpaired Cohen's h between Control 1 and Test 1 is 1.24 [95%CI 0.784, 1.66].\n",
+       "The p-value of the two-sided permutation t-test is 0.0, calculated for legacy purposes only. \n",
+       "\n",
+       "5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.\n",
+       "Any p-value reported is the probability of observing theeffect size (or greater),\n",
+       "assuming the null hypothesis of zero difference is true.\n",
+       "For each p-value, 5000 reshuffles of the control and test labels were performed.\n",
+       "\n",
+       "To get the results of all valid statistical tests, use `.cohens_h.statistical_tests`"
+      ]
+     },
+     "execution_count": null,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "two_groups_unpaired.cohens_h"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Generating proportion plots"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To generate an **estimation plot**, simply use the\n",
+    "``.plot()`` method. \n",
+    "\n",
+    "Each effect size instance has access to the ``.plot()`` method, allowing you to quickly create plots for different effect sizes with ease."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Unpaired proportion plots"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Unpaired proportion plots utilise the common bar plot. The bar plot displays the proportion of observations in the dataset that belong to the category of interest: \n",
+    "\n",
+    "- The white portion represents the proportion of observations that do not belong to the category (proportion of 0s in the data). \n",
+    "- The colored portion represents the proportion of observations belonging to the category (proportion of 1s in the data)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Two-Group"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "two_groups_unpaired.mean_diff.plot();"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "two_groups_unpaired.cohens_h.plot();"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Instead of a Gardner-Altman plot, you can generate a **Cumming estimation plot** by setting ``float_contrast=False`` in the ``.plot()`` method. This will plot the bootstrap effect sizes below the raw data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 300x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "two_groups_unpaired.mean_diff.plot(float_contrast=False);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Multi Two-Group, Shared-Control, and Multi Groups\n",
+    "As with regular (non-binary) unpaired data, multi two-group, shared-control, and multi group plots can be generated for binary data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "DABEST v2025.03.27\n",
+       "==================\n",
+       "                  \n",
+       "Good afternoon!\n",
+       "The current time is Tue Mar 25 17:22:25 2025.\n",
+       "\n",
+       "Effect size(s) with 95% confidence intervals will be computed for:\n",
+       "1. Test 1 minus Control 1\n",
+       "2. Test 2 minus Control 2\n",
+       "3. Test 3 minus Control 3\n",
+       "\n",
+       "5000 resamples will be used to generate the effect size bootstraps."
+      ]
+     },
+     "execution_count": null,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "multi_two_groups_unpaired = dabest.load(df, idx=((\"Control 1\", \"Test 1\"),(\"Control 2\", \"Test 2\"),(\"Control 3\", \"Test 3\")),\n",
+    "                                        proportional=True)\n",
+    "multi_two_groups_unpaired"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "DABEST v2025.03.27\n",
+       "==================\n",
+       "                  \n",
+       "Good afternoon!\n",
+       "The current time is Tue Mar 25 17:22:26 2025.\n",
+       "\n",
+       "The unpaired mean difference between Control 1 and Test 1 is 0.575 [95%CI 0.35, 0.725].\n",
+       "The p-value of the two-sided permutation t-test is 0.0, calculated for legacy purposes only. \n",
+       "\n",
+       "The unpaired mean difference between Control 2 and Test 2 is 0.025 [95%CI -0.15, 0.15].\n",
+       "The p-value of the two-sided permutation t-test is 0.535, calculated for legacy purposes only. \n",
+       "\n",
+       "The unpaired mean difference between Control 3 and Test 3 is -0.6 [95%CI -0.75, -0.425].\n",
+       "The p-value of the two-sided permutation t-test is 0.0, calculated for legacy purposes only. \n",
+       "\n",
+       "5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.\n",
+       "Any p-value reported is the probability of observing theeffect size (or greater),\n",
+       "assuming the null hypothesis of zero difference is true.\n",
+       "For each p-value, 5000 reshuffles of the control and test labels were performed.\n",
+       "\n",
+       "To get the results of all valid statistical tests, use `.mean_diff.statistical_tests`"
+      ]
+     },
+     "execution_count": null,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "multi_two_groups_unpaired.mean_diff"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 900x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "multi_two_groups_unpaired.mean_diff.plot();"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "DABEST v2025.03.27\n",
+       "==================\n",
+       "                  \n",
+       "Good afternoon!\n",
+       "The current time is Tue Mar 25 17:22:26 2025.\n",
+       "\n",
+       "Effect size(s) with 95% confidence intervals will be computed for:\n",
+       "1. Test 1 minus Control 1\n",
+       "2. Test 2 minus Control 1\n",
+       "3. Test 3 minus Control 1\n",
+       "4. Test 4 minus Control 1\n",
+       "\n",
+       "5000 resamples will be used to generate the effect size bootstraps."
+      ]
+     },
+     "execution_count": null,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "shared_control = dabest.load(df, idx=(\"Control 1\", \"Test 1\", \"Test 2\", \"Test 3\", \"Test 4\"),\n",
+    "                                        proportional=True)\n",
+    "shared_control"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "DABEST v2025.03.27\n",
+       "==================\n",
+       "                  \n",
+       "Good afternoon!\n",
+       "The current time is Tue Mar 25 17:22:27 2025.\n",
+       "\n",
+       "The unpaired mean difference between Control 1 and Test 1 is 0.575 [95%CI 0.35, 0.725].\n",
+       "The p-value of the two-sided permutation t-test is 0.0, calculated for legacy purposes only. \n",
+       "\n",
+       "The unpaired mean difference between Control 1 and Test 2 is 0.025 [95%CI -0.15, 0.15].\n",
+       "The p-value of the two-sided permutation t-test is 0.539, calculated for legacy purposes only. \n",
+       "\n",
+       "The unpaired mean difference between Control 1 and Test 3 is 0.125 [95%CI -0.025, 0.325].\n",
+       "The p-value of the two-sided permutation t-test is 0.0936, calculated for legacy purposes only. \n",
+       "\n",
+       "The unpaired mean difference between Control 1 and Test 4 is 0.15 [95%CI -0.05, 0.3].\n",
+       "The p-value of the two-sided permutation t-test is 0.0604, calculated for legacy purposes only. \n",
+       "\n",
+       "5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.\n",
+       "Any p-value reported is the probability of observing theeffect size (or greater),\n",
+       "assuming the null hypothesis of zero difference is true.\n",
+       "For each p-value, 5000 reshuffles of the control and test labels were performed.\n",
+       "\n",
+       "To get the results of all valid statistical tests, use `.mean_diff.statistical_tests`"
+      ]
+     },
+     "execution_count": null,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "shared_control.mean_diff"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 750x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "shared_control.mean_diff.plot();"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "DABEST v2025.03.27\n",
+       "==================\n",
+       "                  \n",
+       "Good afternoon!\n",
+       "The current time is Tue Mar 25 17:22:27 2025.\n",
+       "\n",
+       "Effect size(s) with 95% confidence intervals will be computed for:\n",
+       "1. Test 1 minus Control 1\n",
+       "2. Test 2 minus Control 2\n",
+       "3. Test 3 minus Control 2\n",
+       "4. Test 4 minus Control 2\n",
+       "\n",
+       "5000 resamples will be used to generate the effect size bootstraps."
+      ]
+     },
+     "execution_count": null,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "multi_groups_unpaired = dabest.load(df, idx=((\"Control 1\", \"Test 1\"),(\"Control 2\", \"Test 2\", \"Test 3\", \"Test 4\")),\n",
+    "                                        proportional=True)\n",
+    "multi_groups_unpaired"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "DABEST v2025.03.27\n",
+       "==================\n",
+       "                  \n",
+       "Good afternoon!\n",
+       "The current time is Tue Mar 25 17:22:28 2025.\n",
+       "\n",
+       "The unpaired mean difference between Control 1 and Test 1 is 0.575 [95%CI 0.35, 0.725].\n",
+       "The p-value of the two-sided permutation t-test is 0.0, calculated for legacy purposes only. \n",
+       "\n",
+       "The unpaired mean difference between Control 2 and Test 2 is 0.025 [95%CI -0.15, 0.15].\n",
+       "The p-value of the two-sided permutation t-test is 0.535, calculated for legacy purposes only. \n",
+       "\n",
+       "The unpaired mean difference between Control 2 and Test 3 is 0.125 [95%CI -0.05, 0.325].\n",
+       "The p-value of the two-sided permutation t-test is 0.099, calculated for legacy purposes only. \n",
+       "\n",
+       "The unpaired mean difference between Control 2 and Test 4 is 0.15 [95%CI -0.05, 0.3].\n",
+       "The p-value of the two-sided permutation t-test is 0.0604, calculated for legacy purposes only. \n",
+       "\n",
+       "5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.\n",
+       "Any p-value reported is the probability of observing theeffect size (or greater),\n",
+       "assuming the null hypothesis of zero difference is true.\n",
+       "For each p-value, 5000 reshuffles of the control and test labels were performed.\n",
+       "\n",
+       "To get the results of all valid statistical tests, use `.mean_diff.statistical_tests`"
+      ]
+     },
+     "execution_count": null,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "multi_groups_unpaired.mean_diff"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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PUjLljkFERESkQ/Ji5PDhw/lG0xIEAZUrV0a7du0wevRodO3aVeoYRERERERkZCQvRqKjo6U+BRERERERlUOyd2AnIiIiIqKKSZJiJCMjAzExMcjJycm3buPGjXjllVfg5eWF/v37cyQtIiIiIqIKSpJi5JNPPkHTpk3zFSPz58/HmDFjcOzYMTx+/Bg7d+5Ex44d8ccff0gRg4iIiIiIjJgkxciRI0fQs2dP2NjYaJelpKRg/vz5cHd3x40bN/D48WOcOXMG5ubm+PTTT6WIQURERERERkySYiQ6OhpNmzbVWbZ//37k5OTgo48+gqenJwDA19cXI0eOxIkTJ6SIQURERERERkySYiQ1NRVVq1bVWXb8+HEIgoDAQN0J17y8vPD48WMpYhARERERkRGTpBipVasWrl+/rrPs6NGjcHFxQd26dXWW5+TkwM7OTooYRERERERkxCQpRrp27YqNGzfi7NmzAIBvvvkG169fR79+/fJte/HiRXh4eEgRg4iIiIiIjJgkkx7OmjULO3fuRJs2baBUKpGXlwcnJyfMnj1bZ7uMjAzs2LEDY8eOlSIGEf2jso2lzq9ERERExkCSYsTR0RGRkZFYv349bt++jVq1amHUqFFwdnbW2e7PP//EG2+8gWHDhkkRg4j+sXxC4Is3IiIiIipjkhQjAFC5cmV88MEHRW7j6+sLX19fqSIQEREREZERk6TPCBERERER0YuwGCEiIiIiIlmwGCEiIiIiIlmU+2Jk1apV8PDwgIWFBVq1aoVz584VuX1SUhLeeecduLm5QaVSoX79+ti/f38ZpSUiIiIioucMXozs3r0bDx8+NPRhC7R9+3YEBQUhODgYERERaNasGQIDAxEfH1/g9jk5OejSpQuio6Px888/IyoqCuvWrYO7u3uZ5CUiIiIiov8xeDHSr18/HD16VPu+du3a2L17t6FPAwBYunQpxowZg5EjR8LLywtr1qyBlZUVNm7cWOD2GzduxNOnT7Fz5060bdsWHh4e8Pf3R7NmzSTJR0REREREhTN4MWJra4ukpCTt++joaKSlpRn6NMjJycHFixcREBCgXaZQKBAQEIDw8PAC99m9ezf8/PzwzjvvwMXFBY0bN8bChQuhVqsLPU92djZSUlK0Lyk+CxERERFRRWTweUZ8fX2xYMECxMXFwd7eHgCwf/9+xMbGFrqPIAiYOnVqic6TkJAAtVoNFxcXneUuLi64fv16gfvcvn0bhw8fxhtvvIH9+/fj5s2bmDBhAnJzcxEcHFzgPiEhIZg7d26JshERERER0YsZvBhZvXo1hg8fjnnz5gF4Vmhs3boVW7duLXQffYoRfWg0Gjg7O2Pt2rVQKpXw8fHBgwcP8NlnnxVajEybNg1BQUHa95GRkfD395c8KxERERGRqTN4MVK3bl2cPn0aWVlZiI+Ph4eHB5YvX44+ffoY9DyOjo5QKpWIi4vTWR4XFwdXV9cC93Fzc0OlSpWgVCq1yxo1aoTY2Fjk5OTA3Nw83z4qlQoqlUr73sbGxkCfgIiIiIioYjN4MfKchYUFatasieDgYHTu3Bm1atUy6PHNzc3h4+ODsLAw9O3bF8CzOx9hYWGYOHFigfu0bdsWW7duhUajgULxrLvM33//DTc3twILESIiIiIiko7k84wEBwejcePGAIC0tDRcu3YN165dM0hH8KCgIKxbtw5btmzBtWvXMH78eKSnp2PkyJEAgOHDh2PatGna7cePH4+nT59i8uTJ+Pvvv7Fv3z4sXLgQ77zzTqmzEBERERFRyUh2Z+Tfzp8/jw8//BAnT56ERqMB8Gzkq/bt22Px4sVo2bKlXscdNGgQHj9+jNmzZyM2Nhbe3t4IDQ3VdmqPiYnR3gEBgBo1auDAgQOYOnUqmjZtCnd3d0yePBkfffRR6T8kERERERGViOTFyNmzZ9GxY0eYm5tj9OjRaNSoEQDg2rVr+OGHH9ChQwccPXoUvr6+eh1/4sSJhT6W9e/5Tp7z8/PDmTNn9DoXEREREREZjuTFyIwZM+Du7o6TJ0/m61g+Z84ctG3bFjNmzMChQ4ekjkJEREREREZE8j4jZ8+exbhx4woc4crFxQVjx47lnQoiIiIiogpI8mJEoVAgLy+v0PVqtVqnXwcREREREVUMklcBbdq0wapVq3D37t1862JiYrB69Wq0bdtW6hhERERERGRkJO8zsnDhQnTo0AENGzZEv379UL9+fQBAVFQUdu3aBTMzM4SEhEgdg4iIiIiIjIzkxUjz5s1x9uxZzJgxA7t370ZGRgYAwMrKCt26dcP8+fPh5eUldQwiIiIiIjIyZTLPiJeXF3bs2AGNRoPHjx8DAJycnNhXhIiIiIioAiuTYuQ5hUKhnZCQiIiIiIgqNt6aICIiIiIiWbAYISIiIiIiWbAYISIiIiIiWbAYISIiIiIiWbAYISIiIiIiWZTZaFpXr17F7du3kZiYCFEU860fPnx4WUUhIiIiIiIjIHkxcuvWLQwdOhTnzp0rsAgBAEEQWIwQEREREVUwkhcj48aNw5UrV7B8+XK0b98elStXlvqURERERERUDkhejJw6dQrTp0/Hu+++K/WpiIiIiIioHJG8A7ujoyPs7e2lPg0REREREZUzkhcjb7/9Nr777juo1WqpT0VEREREROWI5I9p1a9fH2q1Gs2aNcOoUaNQo0YNKJXKfNv1799f6ihERERERGREJC9GBg0apP39+++/X+A2giDwzgkRERERUQUjeTFy5MgRqU9BRERERETlkOTFiL+/v9SnICIiIiKicqjMZmAHns3CfvfuXQBArVq14OXlVZanJyIiIiIiI1ImxciuXbsQFBSE6OhoneWenp5YunQpevfuXRYxiIiIiIjIiEg+tO/+/fsxYMAAAMDChQuxY8cO7NixAwsXLoQoiujfvz9CQ0OljkFEREREREZG8jsj8+bNQ9OmTXHixAlYW1trl/fu3RsTJ05Eu3btMHfuXHTr1k3qKEREREREZEQkvzNy+fJljBgxQqcQec7a2hpvvvkmLl++rPfxV61aBQ8PD1hYWKBVq1Y4d+5csfbbtm0bBEFA37599T43ERERERHpT/JixMLCAk+fPi10/dOnT2FhYaHXsbdv346goCAEBwcjIiICzZo1Q2BgIOLj44vcLzo6Gu+//z7at2+v13mJiIiIiKj0JC9GOnfujBUrViA8PDzfurNnz2LlypUICAjQ69hLly7FmDFjMHLkSHh5eWHNmjWwsrLCxo0bC91HrVbjjTfewNy5c1G7dm29zktERERERKUneZ+RxYsXw8/PD+3atYOvry8aNGgAAIiKisK5c+fg7OyMRYsWlfi4OTk5uHjxIqZNm6ZdplAoEBAQUGDh89wnn3wCZ2dnvPXWWzhx4sQLz5OdnY3s7Gzt+7S0tBJnJSIiIiKi/CS/M+Lp6YnLly9j0qRJSExMxPbt27F9+3YkJiZi8uTJ+OOPP+Dh4VHi4yYkJECtVsPFxUVnuYuLC2JjYwvc5+TJk9iwYQPWrVtX7POEhITA3t5e++IkjkREREREhlEm84w4Oztj2bJlWLZsWVmcrkCpqakYNmwY1q1bB0dHx2LvN23aNAQFBWnfR0ZGsiAhIiIiIjKAMp2B3ZAcHR2hVCoRFxenszwuLg6urq75tr916xaio6PRq1cv7TKNRgMAMDMzQ1RUFOrUqZNvP5VKBZVKpX1vY2NjqI9ARERERFShGbwYGTVqFARBwNq1a6FUKjFq1KgX7iMIAjZs2FCi85ibm8PHxwdhYWHa4Xk1Gg3CwsIwceLEfNs3bNgQV65c0Vk2c+ZMpKamYsWKFahRo0aJzk9ERERERKVj8GLk8OHDUCgU0Gg0UCqVOHz4MARBKHKfF60vTFBQEEaMGIGWLVvC19cXy5cvR3p6OkaOHAkAGD58ONzd3RESEgILCws0btxYZ38HBwcAyLeciIiIiAgAWrZsidjYWLi6uuLChQtyxzE5Bi9GoqOji3xvSIMGDcLjx48xe/ZsxMbGwtvbG6GhodpO7TExMVAoJO+jT0REREQmKjY2Fg8ePJA7hsmSvM9ITEwMnJycYGlpWeD6zMxMPH78GDVr1tTr+BMnTizwsSwAOHr0aJH7bt68Wa9zEhERERFR6ZXJ0L47duwodP3u3bvh6ekpdQwiIiIiIjIykhcjoigWuT43N5ePUhERERERVUCSPKaVkpKCpKQk7fsnT54gJiYm33ZJSUnYtm0b3NzcpIhBREREVObY4Zmo+CQpRpYtW4ZPPvkEwLORsqZMmYIpU6YUuK0oipg/f74UMYiIiIjKHDs8ExWfJMVI165dYWNjA1EU8eGHH2Lw4MFo0aKFzjaCIMDa2ho+Pj5o2bKlFDGIiIiIiMiISVKM+Pn5wc/PDwCQnp6OAQMGcC4PIiIiIiLSIenQvhkZGVi5ciWsrKxYjBARERERkQ5Jh7GysrKCmZkZrK2tpTwNERERERGVQ5KPqTtgwAD8/PPPLxzil4iIiIiIKhbJZ2B//fXXMWHCBHTq1AljxoyBh4dHgbOx/7eDOxERERERmTbJi5GOHTtqf3/ixIl860VRhCAIUKvVUkchIiIiIioRV1dXnV/JsCQvRjZt2iT1KYiIiIiIJMGJK6UleTEyYsQIqU9BRERERBVEnsIMT5NTMW30m3JHkZTkP6QbiTL9nGlpabh37x4AoEaNGrCxsSnL0xMRERERkRGRfDQtADh//jw6deqEypUro3HjxmjcuDEqV66Mzp0789YXERERERmtzbv2YtUPP2Hzrr1yRzFJkt8ZOXv2LDp27Ahzc3OMHj0ajRo1AgBcu3YNP/zwAzp06ICjR4/C19dX6ihEREREkmOHZ9OSnpGJ1IwMuWOYLMmLkRkzZsDd3R0nT57Md1HOmTMHbdu2xYwZM3Do0CGpoxARERFJjk99EBVfmdwZmT17doHfDri4uGDs2LGYN2+e1DGIiIioojLPQXpWDr6c963cSSRmLncAohKTvM+IQqFAXl5eoevVajUUijLpukJEREREREZE8jsjbdq0wapVqzBkyBDUqlVLZ11MTAxWr16Ntm3bSh2DiIiIqEwsXjMbqWnJsLWxx4dvfyJ3HCKjJnkxsnDhQnTo0AENGzZEv379UL9+fQBAVFQUdu3aBTMzM4SEhEgdg4iIiKhMpKYlIyklUe4YROWC5MVI8+bNcfbsWcyYMQO7d+9Gxj+jEVhZWaFbt26YP38+vLy8pI5BRERERERGpkwmPfTy8sKOHTug0Wjw+PFjAICTkxP7ihARERERVWBlOgO7IAgQBEH7eyIiIiIiqrjK5NbE1atXMXDgQNjZ2cHNzQ1ubm6ws7PDwIED8eeff5ZFBCIiIqPVsmVLVK9eHS1btpQ7ChFRmZL8zsiJEyfw6quvQqPRoE+fPjod2Hfv3o3ffvsNoaGhaN++vdRRiIiIjFJsbCwePHggdwwiojIneTEydepUODs749ixY6hRo4bOunv37qFDhw4ICgrC+fPnpY5CRERERERGRPLHtP766y9MmDAhXyECADVq1MD48ePx119/6X38VatWwcPDAxYWFmjVqhXOnTtX6Lbr1q1D+/btUblyZVSuXBkBAQFFbk9EREREFZu1lSVsraxgbWUpdxSTJPmdkVq1aiE7O7vQ9Tk5OQUWKsWxfft2BAUFYc2aNWjVqhWWL1+OwMBAREVFwdnZOd/2R48exeDBg9GmTRtYWFhg0aJF6Nq1K/766y+4u7vrlYGIiIiITNebfXrKHcGkSX5nZPbs2Vi5ciUiIyPzrbt06RK++OILzJkzR69jL126FGPGjMHIkSPh5eWFNWvWwMrKChs3bixw+++//x4TJkyAt7c3GjZsiPXr10Oj0SAsLEyv85sydqYkMl68PomIyFRIfmfkzJkzcHFxgY+PD9q0aYO6desCAG7cuIHw8HA0btwY4eHhCA8P1+4jCAJWrFhR5HFzcnJw8eJFTJs2TbtMoVAgICBA51hFycjIQG5uLqpUqVLoNtnZ2Tp3dtLS0op17PKOnSmJjBevTyIiMhWSFyNffvml9venTp3CqVOndNZfuXIFV65c0VlWnGIkISEBarUaLi4uOstdXFxw/fr1YmX76KOPUK1aNQQEBBS6TUhICObOnVus4xERERHZ2tjr/EpEhZO8GNFoNFKfQi+ffvoptm3bhqNHj8LCwqLQ7aZNm4agoCDt+8jISPj7+5dFRCIiIiqHPnz7E7kjEJUbZToDuyE5OjpCqVQiLi5OZ3lcXBxcXV2L3HfJkiX49NNP8fvvv6Np06ZFbqtSqaBSqbTvbWxs9A9NRERERERaZVaM3LlzB7/99hvu3r0L4NkoW6+++io8PT31Op65uTl8fHwQFhaGvn37AoC2M/rEiRML3W/x4sVYsGABDhw4wM6fRERkFJ5/ifaiL9OIiExNmRQj7733HlasWJHvkS2FQoEpU6ZgyZIleh03KCgII0aMQMuWLeHr64vly5cjPT0dI0eOBAAMHz4c7u7uCAkJAQAsWrQIs2fPxtatW+Hh4YHY2FgAz+528I4HERHJ5cKFC3JHICKSheTFyOeff45ly5Zh4MCBeO+999CoUSMAwLVr17Bs2TIsW7YM7u7umDp1aomPPWjQIDx+/BizZ89GbGwsvL29ERoaqu3UHhMTA4Xif6MXf/XVV8jJycHAgQN1jhMcHKz38MJERGR60q2tkZ6djc0jRsgdRVLWcgcgogpP8mJk3bp16N27N3788Ued5a1atcK2bduQlZWFr7/+Wq9iBAAmTpxY6GNZR48e1XkfHR2t1zmIiIiIiMjwJJ/0MDo6GoGBgYWuDwwMZJFAREQV2tx9+xD088+Yu2+f3FGIiMqU5HdGnJ2d8ccffxS6/o8//oCTk5PUMaiE2JmSyHjx+jQ9yZmZSMzIkDsGEVGZk7wYee2117BixQp4eHjg3XffhbX1sydU09PT8eWXX2L9+vWYMmWK1DGohNiZksh48fokIiJTIXkxMm/ePERGRmL69OmYPXs2qlWrBgB4+PAh8vLy0KlTJ3zyCScHehErMQMZSRnoM2Wh3FGkJVjJnYCoxFTZKqRlp6HfrH5yR5GW6sWbEBERlYTkxYiVlRXCwsKwa9cunXlGunXrhu7du6NXr14QBEHqGEREREREZGQkLUYyMjIwdOhQDBgwAG+88Qb69Okj5enIgI79sArZGWlQWdnAf/A7cschon85uuEostKyYGFjgY5vdZQ7DhERkd4kHU3LysoKv//+OzLYKa/cyc5IQ1ZaCrIz0uSOQkT/kZWWhazULGSlZckdhYiIqFQkH9q3Xbt2CA8Pl/o0RERERERUzkhejHz55Zc4ceIEZs6cifv370t9OiIiIiIiKickL0aaNWuG+/fvIyQkBLVq1YJKpYKdnZ3Oy97eXuoYRERERERkZCQfTWvAgAEcLYuIiIiIiPKRvBjZvHmz1KcgIiIq1+wtLXV+JSKqKCQrRrKysrBr1y7cuXMHjo6O6NGjB9zc3KQ6HRERUbkV3KOH3BGIiGQhSTESHx+PNm3a4M6dOxBFEcCzYX537tyJgIAAKU5JRERERETljCQd2OfNm4fo6GhMnToVe/fuxfLly2FpaYlx48ZJcToiIiIiIiqHJLkzcvDgQQwfPhxLlizRLnNxccGQIUMQFRWFBg0aSHFaMiCVlY3Or0RkPCxsLHR+JSIiKq8kKUZiYmLw0Ucf6Sxr164dRFFEXFwci5FywH/wO3JHIKJCdHyro9wRiIiIDEKSx7Sys7NhYaH7jd3z93l5eVKckoiIiIiIyhnJRtOKjo5GRESE9n1ycjIA4MaNG3BwcMi3fYsWLaSKQkRERERERkiyYmTWrFmYNWtWvuUTJkzQeS+KIgRBgFqtlioKEREREREZIUmKkU2bNklxWCIiIiIiMiGSFCMjRoyQ4rBERERERGRCJOnATkRERERE9CIsRoiIiIiISBYsRoiIiIiISBYsRoiIiIiISBYsRoiIiIiISBblvhhZtWoVPDw8YGFhgVatWuHcuXNFbv/TTz+hYcOGsLCwQJMmTbB///4ySkpERERERP9WrouR7du3IygoCMHBwYiIiECzZs0QGBiI+Pj4Arc/ffo0Bg8ejLfeeguXLl1C37590bdvX/z5559lnJyIiIiIiMp1MbJ06VKMGTMGI0eOhJeXF9asWQMrKyts3LixwO1XrFiBbt264YMPPkCjRo0wb948tGjRAl9++WUZJyciIiIionJbjOTk5ODixYsICAjQLlMoFAgICEB4eHiB+4SHh+tsDwCBgYGFbk9ERERERNKRZAb2spCQkAC1Wg0XFxed5S4uLrh+/XqB+8TGxha4fWxsbKHnyc7ORnZ2tvZ9WlpaKVKXL1npKchKTy3bcwoWUGRYISIiokzPW5au33uCxEx1mZ+3iq0lqthalvl5SRpZqVnISssq03PmmOcAVjDp6xMAbiYkICknR+4YZcIyMxMqjcak2/Tu/VvIzKkg/3fnVoJlisqk2/N+XDxS0jLK/Lw2VpawsbIq8/NWBOW2GCkrISEhmDt3rs4yf39/uLm5lVmGH+aMLrNzPZednY3AwEAcP3aszM8NAAfXzpflvKbM398fBw5sg0qlkjuKSfl28rdlfs7n1+cxma7P3z7/TZbzkoR27JA7ARnQ3JVyJzA9z/4PPcD/QyVQbosRR0dHKJVKxMXF6SyPi4uDq6trgfu4urqWaHsAmDZtGoKCgnSWqVQqk//LmJ2djWPHjuHYsWOwsbGROw6VUlpaGvz9/ZGdnW3yf3crAl6fpuf5Nco2NQ1sT9PC/0OlVW6LEXNzc/j4+CAsLAx9+/YFAGg0GoSFhWHixIkF7uPn54ewsDBMmTJFu+zQoUPw8/Mr9DwVofAoire3N+zs7OSOQaWUkpIidwSSAK9P0/H8GmWbmga2p2nh/6HSKrfFCAAEBQVhxIgRaNmyJXx9fbF8+XKkp6dj5MiRAIDhw4fD3d0dISEhAIDJkyfD398fn3/+OXr06IFt27bhwoULWLt2rZwfg4iIiIioQirXxcigQYPw+PFjzJ49G7GxsfD29kZoaKi2k3pMTAwUiv8NGNamTRts3boVM2fOxPTp01GvXj3s3LkTjRs3lusjEBERERFVWOW6GAGAiRMnFvpY1tGjR/Mte+211/Daa69JnKr8U6lUCA4OrtCPqJkStqdpYXuaHrapaWF7mha2p7QEURRFuUMQEREREVHFU24nPSQiIiIiovKNxQgREREREcmCxQgREREREcmCxQhJLjo6GoIgYPPmzXJHIaIC8BolIiK5sBgxMrdu3cK4ceNQu3ZtWFhYwM7ODm3btsWKFSuQmZkp2XmvXr2KOXPmIDo6WrJzFMeCBQvQu3dvuLi4QBAEzJkzR9Y8ZUkQhGK9CholrqQyMjIwZ86cEh2rIrfNv1Xka/T69ev48MMP4e3tDVtbW7i5uaFHjx64cOGCbJnKijFfnxW5XUrDmNv04cOHGDp0KBo0aABbW1s4ODjA19cXW7ZsAccdKpgxt+d/ff/99xAEATY2NqXOYgrK/dC+pmTfvn147bXXoFKpMHz4cDRu3Bg5OTk4efIkPvjgA/z111+STdB49epVzJ07Fx07doSHh4ck5yiOmTNnwtXVFc2bN8eBAwdkyyGHb7/9Vuf9N998g0OHDuVb3qhRo1KfKyMjA3PnzgUAdOzYsVj7VOS2ea6iX6Pr16/Hhg0bMGDAAEyYMAHJycn4+uuv0bp1a4SGhiIgIECWXGXBmK/PitwupWHMbZqQkID79+9j4MCBqFmzJnJzc3Ho0CG8+eabiIqKwsKFC0udydQYc3v+W1paGj788ENYW1uXOofJEMko3L59W7SxsREbNmwoPnz4MN/6GzduiMuXL5fs/D/99JMIQDxy5MgLt9VoNGJGRkaxj33nzh0RgLhp06ZibSuKovj48WMRgBgcHFzs85iad955R5TqEtXnz7eitw2vUVG8cOGCmJqaqrMsISFBdHJyEtu2bVvs85kCY7o+2S6GYUxtWpiePXuK1tbWYl5enmGCmTBjbc+PPvpIbNCggfjGG2+I1tbWhg9XDvExLSOxePFipKWlYcOGDXBzc8u3vm7dupg8ebL2fV5eHubNm4c6depApVLBw8MD06dPR3Z2ts5+Hh4e6NmzJ06ePAlfX19YWFigdu3a+Oabb7TbbN68WTsRZKdOnfLdynx+jAMHDqBly5awtLTE119/DQC4ffs2XnvtNVSpUgVWVlZo3bo19u3bp/efg5x3ZcoDjUaD5cuX46WXXoKFhQVcXFwwbtw4JCYm6mx34cIFBAYGwtHREZaWlvD09MSoUaMAPOsf4OTkBACYO3eutr1f9NhVRW8bXqOAj49PvscKqlativbt2+PatWt6HdOUyHV9sl2kI+e/uQXx8PBARkYGcnJySv3ZKiK52/PGjRtYtmwZli5dCjMzPpz0HP8kjMSePXtQu3ZttGnTpljbjx49Glu2bMHAgQPx3nvv4ezZswgJCcG1a9ewY8cOnW1v3ryJgQMH4q233sKIESOwceNGvPnmm/Dx8cFLL72EDh06YNKkSVi5ciWmT5+uvYX571uZUVFRGDx4MMaNG4cxY8agQYMGiIuLQ5s2bZCRkYFJkyahatWq2LJlC3r37o2ff/4Z/fr1M9wfEAEAxo0bh82bN2PkyJGYNGkS7ty5gy+//BKXLl3CqVOnUKlSJcTHx6Nr165wcnLCxx9/DAcHB0RHR+PXX38FADg5OeGrr77C+PHj0a9fP/Tv3x8A0LRpUzk/mtHjNVq42NhYODo6GuRY5ZmxXZ9sl9KTu00zMzORnp6OtLQ0HDt2DJs2bYKfnx8sLS0l/dymSu72nDJlCjp16oTu3bvjxx9/lPSzlity35ohUUxOThYBiH369CnW9pGRkSIAcfTo0TrL33//fRGAePjwYe2yWrVqiQDE48ePa5fFx8eLKpVKfO+997TLinoE5PkxQkNDdZZPmTJFBCCeOHFCuyw1NVX09PQUPTw8RLVaLYpiyR7Teq6iPgr0b/+9xXzixAkRgPj999/rbBcaGqqzfMeOHSIA8fz584UeuzR/vhWxbXiNFu748eOiIAjirFmzSrxveWas1+dzFbVdSsMY2zQkJEQEoH298sorYkxMTImOUVEZW3vu3btXNDMzE//66y9RFEVxxIgRfEzrH3xMywikpKQAAGxtbYu1/f79+wEAQUFBOsvfe+89AMj3CIaXlxfat2+vfe/k5IQGDRrg9u3bxc7o6emJwMDAfDl8fX3Rrl077TIbGxuMHTsW0dHRuHr1arGPTy/2008/wd7eHl26dEFCQoL29fwRjSNHjgAAHBwcAAB79+5Fbm6ujIlNB6/RgsXHx2PIkCHw9PTEhx9+WKpjlXfGdH2yXQzDGNp08ODBOHToELZu3YohQ4YAgKSj9pkyOdszJycHU6dOxdtvvw0vLy+DHNOUsBgxAnZ2dgCA1NTUYm1/9+5dKBQK1K1bV2e5q6srHBwccPfuXZ3lNWvWzHeMypUr53tGsiienp4F5mjQoEG+5c8fHflvDiqdGzduIDk5Gc7OznByctJ5paWlIT4+HgDg7++PAQMGYO7cuXB0dESfPn2wadOmfH0VqPh4jeaXnp6Onj17IjU1Fbt27arwQ1Qay/XJdjEcY2jTWrVqISAgAIMHD8b333+P2rVrIyAggAWJHuRsz2XLliEhIUE7AhfpYp8RI2BnZ4dq1arhzz//LNF+giAUazulUlngcrEEY5Xz+VT5aTQaODs74/vvvy9w/fMOdYIg4Oeff8aZM2ewZ88eHDhwAKNGjcLnn3+OM2fO8IcTPfAa1ZWTk4P+/fvj8uXLOHDgABo3blxm5zZWxnB9sl0Myxja9L8GDhyIdevW4fjx4/nuhFLR5GrP5ORkzJ8/HxMmTEBKSor2TntaWhpEUUR0dDSsrKzg7Oxcug9YjrEYMRI9e/bE2rVrER4eDj8/vyK3rVWrFjQaDW7cuKHTgTUuLg5JSUmoVatWic9f3B+a/psjKioq3/Lr169r15Ph1KlTB7///jvatm1brB88W7dujdatW2PBggXYunUr3njjDWzbtg2jR4/Wq70rOl6jz2g0GgwfPhxhYWH48ccf4e/vX+JjmCK5r0+2i+HJ3aYFeX5HJDk52SDHq0jkas/ExESkpaVh8eLFWLx4cb71np6e6NOnD3bu3FmSj2NS+JiWkXg+Ac7o0aMRFxeXb/2tW7ewYsUKAED37t0BAMuXL9fZZunSpQCAHj16lPj8zyffSUpKKvY+3bt3x7lz5xAeHq5dlp6ejrVr18LDw4PPRRrY//3f/0GtVmPevHn51uXl5WnbLjExMd836t7e3gCgvc1sZWUFoGTtXdHxGn3m3Xffxfbt27F69WrtKDIk//XJdjE8Odv08ePHBS7fsGEDBEFAixYtinUc+h+52tPZ2Rk7duzI9+rUqRMsLCywY8cOTJs2Tf8PZgJ4Z8RI1KlTB1u3bsWgQYPQqFEjndmdT58+jZ9++glvvvkmAKBZs2YYMWIE1q5di6SkJPj7++PcuXPYsmUL+vbti06dOpX4/N7e3lAqlVi0aBGSk5OhUqnQuXPnIm8bfvzxx/jhhx/w6quvYtKkSahSpQq2bNmCO3fu4JdffoFCUfJa99tvv8Xdu3eRkZEBADh+/Djmz58PABg2bFiFvtvi7++PcePGISQkBJGRkejatSsqVaqEGzdu4KeffsKKFSswcOBAbNmyBatXr0a/fv1Qp04dpKamYt26dbCzs9P+kGxpaQkvLy9s374d9evXR5UqVdC4ceMiH+uo6G3Da/RZcbV69Wr4+fnBysoK3333nc76fv36VdhZheW8Ptku0pCzTRcsWIBTp06hW7duqFmzJp4+fYpffvkF58+fx7vvvpuvPxq9mFztaWVlhb59++ZbvnPnTpw7d67AdRWOjCN5UQH+/vtvccyYMaKHh4dobm4u2traim3bthW/+OILMSsrS7tdbm6uOHfuXNHT01OsVKmSWKNGDXHatGk624jisyE/e/Toke88/v7+or+/v86ydevWibVr1xaVSqXOEKKFHUMURfHWrVviwIEDRQcHB9HCwkL09fUV9+7dq7NNSYYN9ff31xnG8N+v4sw8bUoKmz127dq1oo+Pj2hpaSna2tqKTZo0ET/88EPtrOARERHi4MGDxZo1a4oqlUp0dnYWe/bsKV64cEHnOKdPnxZ9fHxEc3PzYg1RyLZ5piJfoyNGjCj07wAA8c6dO0Xub0qM6fpkuxiGMbXpwYMHxZ49e4rVqlUTK1WqpP13ZtOmTaJGozHo5zZVxtSeBeHQvv8jiGIJekgSEREREREZCPuMEBERERGRLFiMEBERERGRLFiMEBERERGRLFiMEBERERGRLFiMEBERERGRLFiMlDOLFy9Gw4YNodFo5I5Saq+//jr+7//+T+4YsmJ7mh62qWlhe5oWtqdpYXuaCLnHFqbiS05OFqtUqSJu3LhRuwz/jCO/ZMmSfNtv2rRJBCCeP3/e4FkCAgJEAOI777xT4Pr169eLDRs2FFUqlVi3bl1x5cqV+baJiIgQFQqFGBkZafB85QHb0/SwTU0L29O0sD1NC9vTdPDOSDmyceNG5OXlYfDgwfnWffbZZ9qZsaX266+/Ijw8vND1X3/9NUaPHo2XXnoJX3zxBfz8/DBp0iQsWrRIZ7vmzZujZcuW+Pzzz6WObJTYnqaHbWpa2J6mhe1pWtieJkTuaoiKr2nTpuLQoUN1lgEQvb29RQDi559/rrNOim8BMjMzRQ8PD/GTTz4p8FuAjIwMsWrVqvlmg37jjTdEa2tr8enTpzrLlyxZIlpbW4upqakGy1hesD1ND9vUtLA9TQvb07SwPU0H74yUE3fu3MHly5cREBCQb13btm3RuXNnLF68GJmZmZLmWLx4MTQaDd5///0C1x85cgRPnjzBhAkTdJa/8847SE9Px759+3SWd+nSBenp6Th06JBkmY0R29P0sE1NC9vTtLA9TQvb07SwGCknTp8+DQBo0aJFgevnzJmDuLg4fPXVV0UeJzs7GwkJCcV6/VdMTAw+/fRTLFq0CJaWlgUe/9KlSwCAli1b6iz38fGBQqHQrn/Oy8sLlpaWOHXqVJG5TQ3b0/SwTU0L29O0sD1NC9vTtJjJHYCK5/r16wAAT0/PAte3b98enTp1wmeffYbx48cXemH88MMPGDlyZLHOKYqizvv33nsPzZs3x+uvv17oPo8ePYJSqYSzs7POcnNzc1StWhUPHz7UWW5mZoYaNWrg6tWrxcpkKtiepodtalrYnqaF7Wla2J6mhcVIOfHkyROYmZnBxsam0G3mzJkDf39/rFmzBlOnTi1wm8DAQL1u/x05cgS//PILzp49W+R2mZmZMDc3L3CdhYVFgbdMK1euXOC3DqaM7Wl62Kamhe1pWtiepoXtaVpYjJiQDh06oFOnTli8eDHefvvtArdxc3ODm5tbiY6bl5eHSZMmYdiwYXj55ZeL3NbS0hI5OTkFrsvKyirw2wlRFCEIQokyVQRsT9PDNjUtbE/TwvY0LWzP8oPFSDlRtWpV5OXlITU1Fba2toVuFxwcjI4dO+Lrr7+Gg4NDvvWZmZlITk4u1jldXV0BAN988w2ioqLw9ddfIzo6Wmeb1NRUREdHw9nZGVZWVnBzc4NarUZ8fLzObcmcnBw8efIE1apVy3eexMRE1KtXr1iZTAXb0/SwTU0L29O0sD1NC9vTtLADeznRsGFDAM9GkCiKv78/OnbsiEWLFhV4+2/79u3abwJe9HouJiYGubm5aNu2LTw9PbUv4NlF6enpiYMHDwIAvL29AQAXLlzQOe+FCxeg0Wi065/Ly8vDvXv30KhRoxL9eZR3bE/TwzY1LWxP08L2NC1sT9PCOyPlhJ+fH4Bnf4GbNm1a5LZz5sxBx44dsXbt2nzr9Hk+8vXXX893wQBAv3790L17d4wZMwatWrUCAHTu3BlVqlTBV199he7du2u3/eqrr2BlZYUePXroHOPq1avIyspCmzZtSpSpvGN7mh62qWlhe5oWtqdpYXuaGHmmNyF9NG7cWBw8eLDOMhQwyY4oiqK/v78IwOAT/BTn3KtWrRIBiAMHDhTXrVsnDh8+XAQgLliwIN+2S5YsEa2srMSUlBRJMhoztqfpYZuaFranaWF7mha2p+lgMVKOLF26VLSxsREzMjK0ywr7y3/kyBHZLjxRFMW1a9eKDRo0EM3NzcU6deqIy5YtEzUaTb7tWrVqlW8G1YqC7Wl62Kamhe1pWtiepoXtaTpYjJQjSUlJYpUqVcT169fLHcUgLl26JAqCIF66dEnuKLJge5oetqlpYXuaFranaWF7mg5BFP8ziwsZtUWLFmHTpk24evUqFIryPf7A66+/Do1Ggx9//FHuKLJhe5oetqlpYXuaFranaWF7mgYWI0REREREJIvyXUYSEREREVG5xWKEiIiIiIhkwWKEiIiIiIhkwWKEiIiIiIhkwWKEiIiIiIhkwWKEiIiIiIhkwWKEiIiIiIhkwWKEiIiIiIhkwWKEiIiIiIhkwWKEiIiIiIhkwWKEiIiIiIhkwWKEiIiIiIhkwWKEiIiIiIhkwWKkhB49eoQ5c+bg0aNHckchIiIiIirXWIyU0KNHjzB37lwWI0REREREpcRihIiIiIiIZMFihIiIiIiIZMFihIiIiIiIZMFihIiIiIiIZMFihIiIiIiIZMFihIiIiIiIZMFihIiIiIiIZMFihKiiyHgqdwIiIiIiHSxGiCqK9MdyJyAiIiLSwWKEqKLITpU7AREREZEOFiNEFUV2CqBRy52CiIiISKvUxcijR4/wxx9/ID093RB5SmzVqlXw8PCAhYUFWrVqhXPnzhW5/fLly9GgQQNYWlqiRo0amDp1KrKyssooLZGMRJH9RoiIiMio6F2M7Nq1Cw0bNkT16tXRokULnD17FgCQkJCA5s2bY+fOnYbKWKjt27cjKCgIwcHBiIiIQLNmzRAYGIj4+PgCt9+6dSs+/vhjBAcH49q1a9iwYQO2b9+O6dOnS56VyCikPpI7AREREZGWXsXInj170L9/fzg6OiI4OBiiKGrXOTo6wt3dHZs2bTJYyMIsXboUY8aMwciRI+Hl5YU1a9bAysoKGzduLHD706dPo23bthgyZAg8PDzQtWtXDB48+IV3U4hMRsoDuRMQERERaelVjHzyySfo0KEDTp48iXfeeSffej8/P1y6dKnU4YqSk5ODixcvIiAgQLtMoVAgICAA4eHhBe7Tpk0bXLx4UVt83L59G/v370f37t0LPU92djZSUlK0r7S0NMN+EKKylBQjdwIiIiIiLTN9dvrzzz+xdOnSQte7uLgU+qiUoSQkJECtVsPFxSXfua9fv17gPkOGDEFCQgLatWsHURSRl5eHt99+u8jHtEJCQjB37lyDZieSTWK03AmIiIiItPS6M2JlZVVkh/Xbt2+jatWqeoeSytGjR7Fw4UKsXr0aERER+PXXX7Fv3z7Mmzev0H2mTZuG5ORk7evYsWNlmJjIwJ7clDsBERERkZZexUinTp2wZcsW5OXl5VsXGxuLdevWoWvXrqUOVxRHR0colUrExcXpLI+Li4Orq2uB+8yaNQvDhg3D6NGj0aRJE/Tr1w8LFy5ESEgINBpNgfuoVCrY2dlpXzY2Ngb/LERlJi0eSE+QOwURERERAD2LkQULFuD+/ft4+eWX8fXXX0MQBBw4cAAzZ85EkyZNIIoigoODDZ1Vh7m5OXx8fBAWFqZdptFoEBYWBj8/vwL3ycjIgEKh+5GVSiUA6HTCJzJp9zhgAxERERkHvYqRBg0a4OTJk6hatSpmzZoFURTx2WefYeHChWjSpAlOnDgBDw8PA0fNLygoCOvWrcOWLVtw7do1jB8/Hunp6Rg5ciQAYPjw4Zg2bZp2+169euGrr77Ctm3bcOfOHRw6dAizZs1Cr169tEUJkcn7+ze5ExAREREB0LMDOwC89NJL+P3335GYmIibN29Co9Ggdu3acHJyMmS+Ig0aNAiPHz/G7NmzERsbC29vb4SGhmo7tcfExOjcCZk5cyYEQcDMmTPx4MEDODk5oVevXliwYEGZZSaS3aPLwMNIoJq33EmIiIioghNEPp9UIhEREfDx8cHFixfRokULueMQFd/NMCDsk2e/r+wB9F8HmJnLGomIiIgqNr0e01q5ciUCAwMLXf/qq6/iq6++0jsUEUksMRqI2CJ3CiIiIqrg9CpGNmzYAC8vr0LXe3l5Ye3atXqHIqIyEPn9s0e2iIiIiGSiVzFy69YtNGrUqND1DRs2xK1bt/QORURlQBSBoyFAbqbcSYiIiKiC0qsYMTc3R2xsbKHrHz16lG8IXSIyQikPgbNfy52CiIiIKii9KobWrVtj8+bNSE1NzbcuOTkZmzZtQuvWrUsdjojKwF87gJizcqcgIiKiCkivoX2Dg4Ph7+8Pb29vTJkyBS+99BIA4M8//8Ty5cvx6NEjbN261aBBiUh/LVu2ROz9u3BVZeHC9AJGgTs8D+i/FrCrVvbhiIiIqMLSqxhp1aoV9uzZg3HjxmHy5MkQBAHAs1nMPT09sXv37kJnQSeishcbG4sHcQmAQyFD+WanAvs/AHp/AVhVKdtwREREVGHpPelhly5dcPPmTVy6dEnbWb1OnTpo0aKFtjghonIk+T6wdyrQ43PA2lHuNERERFQB6F2MAIBCoYCPjw98fHwMlYeI5JQYDex6B3h1MVC5ltxpiIiIyMSVqhi5evUqbt++jcTERBQ0kfvw4cNLc3gikkNq7LOCpOt8oJq33GmIiIjIhOlVjNy6dQtDhw7FuXPnCixCAEAQBBYjROVVdiqw/32g03SgTme50xAREZGJ0qsYGTduHK5cuYLly5ejffv2qFy5sqFzEZHc1LlA2CdAZiLQeIDcaYiIiMgE6VWMnDp1CtOnT8e7775r6DxEZExEETi1EkhPAF4eA3AyUyIiIjIgvX6ycHR0hL29vaGzEJGxitwKHP4EyMuWOwkRERGZEL2Kkbfffhvfffcd1Gq1ofMQkbG6dQTY/S6Q9ljuJERERGQi9HpMq379+lCr1WjWrBlGjRqFGjVqQKlU5tuuf//+pQ5IREbkcRTw6xggYA5H2iIiIqJS06sYGTRokPb377//foHbCILAOydEpigz8dnkiK3GAU0HAZzklIiIiPSkVzFy5MgRQ+cgovJE1ABnvnp2p6Tjx4CZSu5EREREVA7pVYz4+/sbOgcRlUe3DgMZCUC3TwFza7nTEBERUTlTqnE6s7OzER4ejl27diEhIcFQmYioPHl0GTg4E1DnyZ2EiIiIyhm9i5GVK1fCzc0N7dq1Q//+/XH58mUAQEJCAhwdHbFx40aDhSQiI/cgAjj7ldwpiIiIqJzRqxjZtGkTpkyZgm7dumHDhg0QRVG7ztHREZ07d8a2bdsMFpKIyoErPwPRp+ROQUREROWIXsXI559/jj59+mDr1q3o1atXvvU+Pj7466+/Sh2uOFatWgUPDw9YWFigVatWOHfuXJHbJyUl4Z133oGbmxtUKhXq16+P/fv3l0lWIpN37FMg/YncKYiIiKic0KsYuXnzJl599dVC11epUgVPnkj/A8n27dsRFBSE4OBgREREoFmzZggMDER8fHyB2+fk5KBLly6Ijo7Gzz//jKioKKxbtw7u7u6SZyWqELJSgGOLgH/dLSUiIiIqjF7FiIODQ5Ed1q9evQpXV1e9QxXX0qVLMWbMGIwcORJeXl5Ys2YNrKysCu2vsnHjRjx9+hQ7d+5E27Zt4eHhAX9/fzRr1kzyrERyiYmJQXp6OgAgPVuNmKdZ0p7w3lng6k5pz0FEREQmQa+hfbt37461a9diwoQJ+db99ddfWLduHUaNGlXqcEXJycnBxYsXMW3aNO0yhUKBgIAAhIeHF7jP7t274efnh3feeQe7du2Ck5MThgwZgo8++qjAGeSBZyOGZWdna9+npaUZ9oMQSeTcuXOYN28e9u3bp+3XlZSphseMc+jZpApmda+Flz1spTn5mTVAdV/A3jB3HXMzU3E7dA2e3jgLCAo4NmyD2oHjoDS3LHSfy998jJSYKzrLXFu8irrdJwIA4v44hBt7lhe4r+/U72Fu7WCQ7ERERFQ4vYqR+fPno1WrVmjcuDF69eoFQRCwZcsWbNy4Eb/88gvc3Nwwe/ZsQ2fVkZCQALVaDRcXF53lLi4uuH79eoH73L59G4cPH8Ybb7yB/fv34+bNm5gwYQJyc3MRHBxc4D4hISGYO3euwfMTSenXX3/FoEGDIIqizgATwLMnqPb/+RS//ZmI7WMaoX9zR8MHyMsCzqwGAhcUe5fL33wMl2avwKVZl3zr/t75GXLSnqLxG/OhUatxY89y3Nz3BRr0+7DIY7o0D0Qt/6Ha94pKFtrfO3p1QOU6Prrn2b0MmrxcFiJERERlRK/HtKpVq4aLFy+iW7du2L59O0RRxLfffos9e/Zg8ODBOHPmDBwdJfgBp5Q0Gg2cnZ2xdu1a+Pj4YNCgQZgxYwbWrFlT6D7Tpk1DcnKy9nXs2LEyTExUcufOncOgQYOgVquhVqsL3EatAdQaEYPWXcP56FRpgkSfBFIelfowGQkxSLx1EXV7TIate0PY13wJdbqNw+O/jiM7tei+acpKFjC3qaJ9mams/rVOpbNOEJRIjr4MV++upc5MRERExVPiOyPZ2dk4cOAAPDw8sH79eqxfvx6PHz+GRqOBk5MTFIpSzaNYbI6OjlAqlYiLi9NZHhcXV2h/FTc3N1SqVEnnkaxGjRohNjYWOTk5MDc3z7ePSqWCSqXSvrexsTHQJyCSxvz58wu8I/JfIgARIubvv4tdExpLE+bBRcCuZ6kOkXL/OpQW1rCtVk+7zMGzOSAISH0QBVXDNoXuG//nEcRfOQJzm8qoUs8XNdq/DuW/7o78W9yVMCgqqVC1UdtS5SUiIqLiK3HlYG5ujtdeew2nT5/WLnNycoKLi0uZFSLPc/j4+CAsLEy7TKPRICwsDH5+fgXu07ZtW9y8eRMajUa77O+//4abm1uBhQhReRMTE4O9e/cWekfkv9QaYM+Vp9J1an96u9SHyE1LhLmVg84yQaFEJUtb5KYnFrqfc2N/NOjzPpoMC0H1Nq8h/sph/L1zSaHbx0UehFNjfygrqQrdhoiIiAyrxHdGBEFAvXr1ihxNq6wEBQVhxIgRaNmyJXx9fbF8+XKkp6dj5MiRAIDhw4fD3d0dISEhAIDx48fjyy+/xOTJk/Huu+/ixo0bWLhwISZNmiTnx6AKSKPOhagpXsFQEocOhr7wjsh/iSIQdj0Jb/q5vHjjkkqLg1DIqnsnt+PeqR+17zV5OUh9cB23Qv/32GSLt/Wf1d21xf+GH7d29oC5TRX8+f10ZD59BMsqbjrbpty/hsyEe2jQ5z29z0dEREQlp1cH9unTpyMoKAivvfYaGjRoYOhMxTZo0CA8fvwYs2fPRmxsLLy9vREaGqrt1B4TE6Nzt6ZGjRo4cOAApk6diqZNm8Ld3R2TJ0/GRx99JNdHoApIo85F6oO/oc7JNPix4+/+DYVCoXP370UUApCUlilJHnVyLCqpc6FQVsq3ztWnOxy92mvfR+38DI4N26Lqvx67UtlWRSWbysjJSNLZV9SokZuZikrWlYudxdb92b9VWYkP8xUjcZcOwNqlNmzc6hW0KxEREUlEr2LkzJkzqFq1Kho3boyOHTvCw8MDlpa6Q2wKgoAVK1YYJGRRJk6ciIkTJxa47ujRo/mW+fn54cyZMxKnIiqcqFFDnZMJhZlZgT+kl4a9g0OJChEA0IiAvaUZBAM/ZimKIjRQPLsDVMDnrGRpi0qW/xtaWGGmQiVre1hWqaaznV31hlBnpSPt0Q1tsZB05w9AFLUFRnGkxz17ZMzcporOcnVOJhKunUStTiOKfSwiIiIyDL2KkS+//FL7+3/32fi3sipGiMorhbISFGaG7avUqUN7CIJQoke1BAHoVN8eKPSBKv0IAiCq7Et9HCvHmqhcxwc39n2Buq++A1Gjxq0DX8HppQ5Q2VYFAGSnJODP72egfu8g2Lo3QObTR3j811FUqdsSZpZ2SI+/gzsH18GuZmNYu3jqHP/xX8chatRwbtKp1FmJiIioZPQqRkr6zSsRlY0a1auhW0AnHDx8rFid2JUKoPtLDqhZRZpO22q76gY5Tv2+H+B26Ff48/sZgCCgasO2qBM4Trte1KiR+eQ+1LnPJihVKM2QdCcSD8/tgjonCyo7J1Rt1BY12r2e79hxfxxE1QZtYGbBkfKIiIjKmiCWtLdrBRcREQEfHx9cvHgRLVq0kDsOlUPq3Cwk3/0TZipLg98ZAYCLkZcR0Pt1qNXqIu+QCACUCgEngrzwci3D/yAuCgqkdlwAu/p+hQ6nS0RERBVbqR4SP3PmDEJCQjB16lTcuHEDAJCRkYGIiAikpaUZJCARlYyPd1NsXrMMSqVSZ06df1MqnhUi20bVlaQQAYDcuoEQVbYv3pCIiIgqLL2KkZycHPTv3x9t27bFjBkzsHLlSty7d+/ZARUKdO3alf1FiGTUp3sgft+9DV07+0MQdPuCCMKzR7NOBHmhb7MqhRyhdDSVPZDboI8kxyYiIiLToVcxMmvWLOzduxdfffUVoqKidB4FsbCwwGuvvYZdu3YZLCQRlZyPd1P8uGUN/jp7BA72dgAAB0slbs3xxo6xDSS7IyJaOyHb770CR9AiIiIi+je9ipEffvgB48ePx9ixY1GlSv5vVhs1aoTbt0s/8zIRlV6N6tVgZfVs6G1rlUKyzuoAoHGohayOswFLB8nOQURERKZDr9G04uPj0aRJk0LXK5VKZGRk6B2KiMqfvFrtkes9AjCTrtghIiIi06JXMVKjRg1cv3690PWnTp1C3bp19Q5FROWImTlyvEdCXaud3EmIiIionNHrMa0hQ4bg66+/Rnh4uHbZ806y69atw48//ojhw4cbJiERGS3RxgVZHeeyECEiIiK96HVnZMaMGThz5gw6dOiARo0aQRAETJ06FU+fPsX9+/fRvXt3TJ061dBZiciIaKrUQXab9wAO30tERER60uvOiLm5OUJDQ7Fp0ybUrl0bDRs2RHZ2Npo2bYrNmzdjz549hc5vQETln6ZKbWS3+4iFCBEREZVKse6MBAUFYdiwYWjevDkAICYmBk5OThg6dCiGDh0qaUAiMi6ijQuy27wPVLKUOwoRERGVc8W6M7J8+XJcu3ZN+97T0xM7duyQLBQRGSfR3PpZIcI7IkRERGQAxSpGXFxcdOYN+fckh0RUQZhZIKftBxBtXeVOQkRk9NRqjdwRyIA0arXcEUxWsR7T6tGjBz755BMcPHgQDg4OAIDPP/8c27ZtK3QfQRA4CzuRiRAt7JHd9n2IDh5yRyEiKhdys9RQWuvVNZeMUG52NlRWVnLHMEnFKkZWrFgBZ2dnHDlyBH/99RcEQcC9e/fw9OnTQvd5PtQvEZVvmiq1kdN6MkTLKnJHISIqN/gUiWkRNbzTJZViFSPW1tZYuHCh9r1CocDy5csxZMgQyYIRkfzyPDog1/tNQFlJ7ihEROWKRs1ixJSIIosRqRTr/mH//v1x4sQJ7fsjR46gS5cukoUiIpkJAnKbDUNui9EsRIiI9MFaxKSwz4h0ilWM7Nq1CzExMdr3nTt3xqFDhyQLRUQyMjNHdpsg5NXtCvBxSyIivfApLdPCYkQ6xSpG3N3dcenSJe17URTZJ4TIFFWyRHa7j6Fx9ZY7CRERkdFQ5+XJHcFkFavPyOuvv44lS5bgxx9/1I6m9fHHHyMkJKTQfQRBwB9//GGQkERUBsxUyG77ATRV68mdhIio3ON3tqYlLztL7ggmq1h3RkJCQrBmzRq0bdsWTk5OEAQB1tbWqFq1aqGvKlXKZuSdVatWwcPDAxYWFmjVqhXOnTtXrP22bdsGQRDQt29faQMSlQeCgOxWE1mIEBERFSAnK1PuCCarWHdGlEolxo4di7FjxwJ4NprWzJkzZR9Na/v27QgKCsKaNWvQqlUrLF++HIGBgYiKioKzs3Oh+0VHR+P9999H+/btyzAtkfHKbTyIj2YRERkS74yYlJzMTGjUaiiUSrmjmBy9ZuO5c+eOUdxRWLp0KcaMGYORI0fCy8sLa9asgZWVFTZu3FjoPmq1Gm+88Qbmzp2L2rVrl2FaIuOkrtYCefW6yx2DiMi0sAO7aRFFZKamyJ3CJOlVjNSqVQtWMs9CmZOTg4sXLyIgIEC7TKFQICAgAOHh4YXu98knn8DZ2RlvvfVWWcQkMmqihR1yWozmw81ERAam0bAaMTVpiYVP9k36K9ZjWgqFAgqFAhkZGTA3N4dCoXjhaFqCICBPwpEHEhISoFar4eLiorPcxcUF169fL3CfkydPYsOGDYiMjCz2ebKzs5Gdna19n5aWpldeImOU2+QNQGUrdwwiIpOjzuMkeaYm7WkCXDzryB3D5BSrGJk9ezYEQYCZmZnO+/IkNTUVw4YNw7p16+Do6Fjs/UJCQjB37lwJkxHJQ1O1LtQ1/OSOQURkkvJyWIyYmtSEBLkjmKRiFSNz5swp8r0cHB0doVQqERcXp7M8Li4Orq6u+ba/desWoqOj0atXL+0yjebZPxRmZmaIiopCnTr5q91p06YhKChI+z4yMhL+/v6G+hhEssl9aRAfzyIikkhOZq7cEcjAkh/HvXgjKjG9+owYA3Nzc/j4+CAsLEy7TKPRICwsDH5++b/tbdiwIa5cuYLIyEjtq3fv3ujUqRMiIyNRo0aNAs+jUqlgZ2enfdnY2Ej2mYjKiqZqXWicGsodg4jIZGWmshgxNUmxD+WOYJKKdWfk37Kzs/Hdd9/h4MGDuHXrFlJTU2Fra4u6deuiW7duGDJkCMzNzaXImk9QUBBGjBiBli1bwtfXF8uXL0d6ejpGjhwJABg+fDjc3d0REhICCwsLNG7cWGf/5xM4/nc5kanLq/2K3BGIiExaenKO3BHIwFKfJCAnMwPmlvIO4mRqSlSMXLlyBX369MHdu3chiiLs7e1hY2OD+Ph4RERE4KeffsKCBQuwe/duNGrUSKrMWoMGDcLjx48xe/ZsxMbGwtvbG6GhodpO7TExMVAoyu3NHyJpKCtBXa2l3CmIiExaTlYesjNyobKqJHcUMqD4u3dQveFLcscwKcUuRtLS0tC7d2/Ex8djwYIFGDZsGNzd3bXrHzx4gG+++Qbz589Hr1698Mcff8Da2lqS0P82ceJETJw4scB1R48eLXLfzZs3Gz4QkZFTO78EmFnIHYOIyOQlP86Ecy0WI6Yk9ubfLEYMrNi3DTZt2oSYmBjs27cPH3/8sU4hAgDu7u6YNm0a9uzZgzt37vAHfSIjpXHmP6JERGUh8VG63BHIwO5f+wuiyDlkDKnYxci+ffvQtWtXdOzYscjtOnfujC5dumDPnj2lzUZEElBXrS93BCKiCuHJAxYjpibtaQISH7EjuyEVuxi5cuXKCwuR5zp37owrV67om4mIJCNAtCt45Dgikpco4UTBJI/H91IhciZ2kxP9x0W5I5iUYhcjT58+LXD+joK4uLjg6dOneociIsNycXJCNafKcHGwBpR8fpnIKGk4SZ6pyc7IQ1J8htwxyMBuR5yHRq2WO4bJKHYH9uzsbFSqVLwfYszMzJCTwyHtiIzF8dBfobx3BspHF8Erk8hIcRJSk/TwRhIqu0o/oA+Vnay0VMT8+Qc8mrWQO4pJKNHQvtHR0YiIiHjhdnfu3NE7EBFJR2Pr/uKNiIioVFq2bIl70Q9go3LAKsef4NWuGgQWmyblz6O/o1YTbwicQqLUSlSMzJo1C7NmzXrhdqIo8qIjMkKiTfEetSQiGXCEHpMRGxuL+CexyLPTICUhE4mPMlClGu+OmJKk2Ie4fekC6vj4yh2l3Ct2MbJp0yYpcxBRGRCtneSOQESFENVq8Gs803TrUjyqVPOUOwYZWMT+XXBv4AULGxu5o5RrxS5GRowYIWUOIioDGquqckcgokKI7BBrsmL+eoIm/tVhYcMBRExJdkY6wn/5AR2Hj+YTQaXAB92IKgoBgMpO7hREVAgxN1fuCCQRdZ6I62ceyR2D9NSyZUv4BXbH7M0/5Fv34PpfuHbyaNmHMiEsRogqCNHMEhB4yRMZKxYjpu1WRDzSk7PljkF6iI2NRWx8PJLSCx6m+dJvuxEffbuMU5kO/mRCVFEolHInIKKicNJDk6bOE3H5yH25Y5AERFHEiR+2IDMtVe4o5RKLEaKKggP1EBk19hkxffeuPkXsnWS5Y5AEMlOScXLrFk6GqAcWI0QVhchvXYmMmcg7IxXCxf3RyMliW5uiuDs3cX73LxA5THeJsBghqig0GrkTEFERxJwcuSNQGUhPzsHF0Lv8gdVE3Th3GlcOH5A7RrlSokkP/+vq1au4ffs2EhMTC7yohg8fXprDE5EhcdhBIqMmZmbKHYHKyL2rT1HV3Rr1X+ZEtKbo8u+hUCiUaNypi9xRygW9ipFbt25h6NChOHfuXKGVvSAILEaIjImC49sTGTN1errcEagM/fH7PdhWsYRbHXu5o5AEIg/uQ05WJpp368U5SF5Ar2Jk3LhxuHLlCpYvX4727dujcuXKhs5FRIZmppI7AREVQZOSIncEKkOiCITvuImOQxqiSjVrueOQBK4eP4yM5GT4DXwdSjN+IVgYvYqRU6dOYfr06Xj33XcNnYeIJCKaWcodgYiKkPfkqdwRqIzl5WhwfPvf6DS0Ieyd+G+0KYr+4yIyUpLgP/QtqKys5I5jlPTqwO7o6Ah7e95WJCpPRLtqckcgoiLkPX4sdwSSQU5mHo5vi0JaYpbcUUgi8Xdu4eDXK5GRnCR3FKOkVzHy9ttv47vvvoOaYykTlR+cfZ3IqOU+egiRo95VSJmpuTi6lQWJKUuOj8WBNSuQkhAvdxSjo9djWvXr14darUazZs0watQo1KhRA0pl/tmd+/fvX+qAREREFYGYmYXchw9hXr263FFIBhnJOTjy3XV0eL0BH9kyUelJiTiwZiVeGfU2qlTjdf6cXsXIoEGDtL9///33C9xGEATeOSEiIiqBrCtXWIxUYJmpuTj8zTW0GVAXLh52cschCWSnp+HQ2i/R6c2xcPaoLXcco6BXMXLkyBFD59DbqlWr8NlnnyE2NhbNmjXDF198AV9f3wK3XbduHb755hv8+eefAAAfHx8sXLiw0O2JiIjKUvrpcNi9+qrcMUhGudlqHP8hCs0CaqBeSxcOC2uCcrOzELZxDToOewtu9RrIHUd2ehUj/v7+hs6hl+3btyMoKAhr1qxBq1atsHz5cgQGBiIqKgrOzs75tj969CgGDx6MNm3awMLCAosWLULXrl3x119/wd3dXYZPQERE9D9ZV68i5949mNeoIXcUkpEoApGH7uHJ/XS07O6BSqr8j8JT+abOzcGRLWvRfvAI1HipqdxxZFXqHq1Xr17Fb7/9ht9++w1Xr141RKZiW7p0KcaMGYORI0fCy8sLa9asgZWVFTZu3Fjg9t9//z0mTJgAb29vNGzYEOvXr4dGo0FYWFiZ5iYiIipM8o4dckcgI3Hv2lMc3PAXnj7khJimSKNW4/j3m3Dzwlm5o8hK72Jk165dqFOnDpo0aYKePXuiZ8+eaNKkCerWrYvdu3cbMmOBcnJycPHiRQQEBGiXKRQKBAQEIDw8vFjHyMjIQG5uLqpUqVLoNtnZ2UhJSdG+0tLSSp2diIioMGknTyHvyRO5Y5CRSE/KxuFvr+HGhTiIoih3HDIwURRx5pcf8NexsArbvnoVI/v378eAAQMAAAsXLsSOHTuwY8cOLFy4EKIoon///ggNDTVo0P9KSEiAWq2Gi4uLznIXFxfExsYW6xgfffQRqlWrplPQ/FdISAjs7e21L2N5RI2IiEyUWo204yfkTkFGRKMWcelgDM7tuQN1Hod/NkWXQvfg0m+7K2RBolcxMm/ePDRt2hSXL1/GRx99hN69e6N379746KOPcPnyZTRp0gRz5841dFaD+vTTT7Ft2zbs2LEDFhYWhW43bdo0JCcna1/Hjh0rw5RERFQRZUZclDsCGaG7fz7B8R+ikJOVJ3eUCiMmJgbp6c8ek8vOzUVCcopk57p64gjO7fq5ws03pFcH9suXL2PhwoWwtrbOt87a2hpvvvkmpk+fXupwRXF0dIRSqURcXJzO8ri4OLi6uha575IlS/Dpp5/i999/R9OmRXcaUqlUUKlU2vc2Njb6hyYiIiqGrKi/oU5Lh9Im//+zVLE9vpeGo99HwX9wfaisKskdx2SdO3cO8+bNw759+7R3KzKyshH01SZ41/VE37a+qO1W9M+b+rhx9hQUSiVa9uxnsJHUMtLSsG/rN4iKjIAgKODl8zK6DxkGVRFfxp8/ehiXz57Go7vRyM7KwvQvv4alle6/R59/MAVJTxJ0lnUZ8H/o0KN3ifLpdWfEwsICT58+LXT906dPi7zbYAjm5ubw8fHR6Xz+vDO6n59fofstXrwY8+bNQ2hoKFq2bClpRiIiIr2o1Ug/eVLuFGSkkuIycHzb37xDIpFff/0Vbdu2xW+//ZbvsSkRwB+3ovHJtz/hfNRNSc4fdfo4/j5Tsut/w6L5iDh5vMB1P69djfgH9zHivY8xdPJ7iP77OnZt2VDk8XJzclCvcdMXFhad+w7Ah8u+1L5aB3QtUW5Az2Kkc+fOWLFiRYEdxc+ePYuVK1cW2Q/DUIKCgrBu3Tps2bIF165dw/jx45Geno6RI0cCAIYPH45p06Zpt1+0aBFmzZqFjRs3wsPDA7GxsYiNjWWndCIiMjpJv/4CTWam3DHISCXGZuD0r7egVlesR3qkdu7cOQwaNAhqtbrQybs1ogi1RoNVu37D7UfF66dcUhH7dyMjOanUx4l/+AA3/ryMvm+ORo06dVGrfgP0fGM4/jx3BimJiYXu16ZrN3To0Rs16tQt8vgqC0vY2jtoX+aqkt+M0KsYWbx4MSwsLNCuXTv4+fnhzTffxJtvvgk/Pz+dOTykNmjQICxZsgSzZ8+Gt7c3IiMjERoaqu3UHhMTg0ePHmm3/+qrr5CTk4OBAwfCzc1N+1qyZInkWYmIiEpC/eQpHn/5ZYV7fry80+ljkJOJhCRpflgFgPjoFEQeipHs+BXR/PnzIYpisTqSi6KIXafOS5JDnZeLO5Gl7zt279ZNWFhZwd3zf7O91/ZqDEEQcP926e/snNi/BwvffRur5szAyd/2FlrAFUWvPiOenp64fPkyQkJC8Ntvv2H79u0AgFq1amHy5Mn4+OOPC5x0UAoTJ07ExIkTC1x39OhRnffR0dHSByIiIjKQjDNnkfDlKji+MwGCkhPfGbOC+hikZ6Vi4pI+aNGgHfp3egt1q3sZ/Ly3Ih7DuaYdangVPk0BFU9MTAz27t1b7BGtNKKISzdvIyE5BY72dgbP8/Th/VIfIy05Cda2utmUSiUsrW2QlpJcqmO3DuiKarU8YGltg5ibN3Dol+1ITU7Cq68PLdFx9CpGAMDZ2RnLli3DsmXL9D0EERERAWjZsiUeXL8Ox0qVsLvzKzrr0o4dgyY9HU5BU6H414AqZDx+/fVXDBo0qMBv1EVRxKWo04j8+zQmv74QrV7qZPDzXzwQDWcPW4N1aM/KyMbxfRG4E/UQgiCgjld1tO/eHOaqwo//5/lb+PvyXTx+lIjc7DyMmd4PKktznW32fncCCbFJyEzPgsrCHDXquMCvazPY2FmWKF9eXh40enwD/yIHQkNLPLSuCOBq9D20b2L4QjM7I6PQdcf27sLxff+b1y83Jwf3b93Cvu+3aJe9O1/ap5TaBnbX/t61Rk0ozcyw+5uN6DJgEMwqFf/vot7FCBERERlGbGwsYtPTAYuCfyjLuHABscFz4DJ9GpR2hv8GlvT37z4Ghf0gqxHVgChgxbbp+GTcBoPfIcnJVON6eCyavVKj2Pv8uuEwGjX3RKMWnvnWHfz5DDJSs9BnRMdngwP9eg5Hdl1A4P8VPkBQXm4eatVzQ616bgg/dLnAbarXdkZL/0awsrVEekomToVGInTbKQwcW/x+xnl5eXhw+xays7KKvU9x3b11CwqFApoSPBopCEBqejqyswzfvysrMwN5eXkwM8v/4/rLHV9B45dbad//vPYreLV8GV4t/jc4k61DZdjYOyA9VXc4YrVajcz0NNjY2Rs0b/XadaBRq5GY8BhObtWKvV+xipFRo0ZBEASsXbsWSqUSo0aNeuE+giBgw4aie+oTERFR8WTfuIFHM2bAZeZMVPrPhL8kn+L3MRAhisCOIxvxwTDD91WNvpKAJp2qQ6Eo3XCwT+NTEHMjFq+93QUu7s8e/erQswX2fHscbbt5F3oXw7tNAwDA/TvxhR77+TYAYOdgDZ/2jbDvh5NQqzVQKovXjVmjViM7KwvKSmZQFvBDemnYV7YvUSECAKIIWKpUEBR6dcMu8sBmllbP7gAV8DmtbGxg9a/pJszMK8Ha1g5VXXSHG65Rpy6yMjLwIPoO3D2eFZ53rl2FKIqoXrvozuklFRtzF4IglLjIKVYrHj58WFspKpVKHD58+IVjHxtqbGQiIiJ6JvfhIzz8+GO4fPghLBo1kjtOhVfyPgZqXIw6gYSkWDg6GHaOiuyMPKQ8zoSDi1WpjhN7LwEqi0raQgQAatR2gSAIiLv/BDZe1UsbFcCzR8GiLt+FWw3HYhci/6Y0M0OlSuYv3rAE/Nt3gCAIJXpUSwDQqKa7wX/uFQHYOpb+Swfnau6o17gpdm1ej97DR0GtVmPv91vQ2Lc17CpXBgCkJD7Fps9CMGD026heuw4AIDU5CWnJyXgS/2w+v7j796CysIR9laqwsnnWR+T+7VvwbNgIKgtL3Lt1A79t+x7N/NrCsoB5CItSrGLkvx2/2RGciIhIHpqUVDwKnoOqI9+Ebbdu/PKvGNR5amg0JesLUBwHDxwseR8DUcSVW+fRsUVPg+dJjE0rdTGSkZYFS2vd4VkVSgUsLM2RkVb6R6NOH/gDl8/eQF6uGi41qqLn0PalPqahuLu7I6BzZxw+erRYo0IpBAFNPWuiqp2twbMolEo4VHM3yLEGjp2Avd9vwabPQiAoBLzk8zK6DxmuXa9Wq5EQ+wi5OdnaZeePhOHI7h3a9xs+nQ8A6DdqLFq06wCzSma4ci4cR3b9iry8XFR2dIJf125o2/XVEufT6/5WTEwMnJycYGlZ8K26zMxMPH78GDVr1tTn8ERERFQUtRpP1m9A1rXrcBz/NhSF/H9MzwqRuAdPkZNt+AkC7919pEcfAwFp6anIzTF8nvj7Saj5UlUozfKPvHbh2FVcPH5N+z4vV424+09wfF+EdtmQd7sZPNN/NW/XEI18aiM1KR3nj/yF3385i55D2xtNUT353Yk4cuxY8e6QCEDPVj6S5HCuUx+VSjBnx1sfzSx0nZWNDf5v3DuFrq/s6IR5G7/TWda57wB07jug0H2q1fLEuJlzi52vKHoP7fvtt99iyJAhBa7fvXs3hgwZotdYw0RERFQ86adOIefObThNnQpV7dov3qEC0mhE5GTnQalU6PU4UFEc7PXpYyDCysLa8I/1iCJyczXQaEQUNAh045froG7j/3VwP/TzGdTxqo7a/3rsytrWElY2FshM170DolFrkJWZAyubkk9o91+W1ipYWqtQ2dEWVZzssHnJHsTeewK3mo6lPrYheDdrhtVfrMSEdydBFMUCf5ZVCAIgAON7dIWnq+GnsjAzV6FO67YGP66x0qsYeVGlmJubC4WhO/IQERFRPrkPH+HhtGmo/H//B/u+fTkfSSGUSgXMKhn2z8bfv6MefQwEvOTpA8PfCBBgblH457OwUsHC6n9DQyvNlLC0toBDVd1HjFxrOCI7KxfxD57C+Z9+I/fvxEMURbhUr2rQxM//3IxtFvnu3bph588/YcUXX+L3w4d12lcA0NSzJnq28pGkEAGAxgHdYGFji5xMw48YZoyKXYykpKQgKSlJ+/7JkyeIick/62dSUhK2bdsGNzc3gwQkIiKiF8hTI3HrD8g4dw6O774L8+qG6WRMRavuXh1dAroi7PDvxexjoIB3/TYG77z+nFXl0s8zUsXZDjXrueLIrvPo2LslNGoNju29iHqNa2pH0kpLycDOTUfRZUArbYGSnpqJjLQsJD9JBQA8iUtGJZUZbO2tYGGlQuy9J4h/8BRutRyhsjRHytM0nAm7AvsqNnCrYdgixxC8mzXDpvXr8ODBA3Tp3gMpKSmwUpljztDXJOkj8lz9tv5wqdsAubk5kp3D2BS7GFm2bBk++eQTAM+ed5wyZQqmTJlS4LaiKGL+/PkGCUhERETFk33zFh6+/wGqvDUKdl26yB2nQgia/D4OHwkrxh0SAYIgoG+HNyXJYWFjBmsHw4wu1XVgaxzbG4Gdm44+m/TwpWeTHj6nUYtISkhFbu7/CrA/z9/C+SN/ad//uuEwAOCVfr5o1MITZpWUuHX1Ps4e/hN5uXmwsrFErXquaDnIq8A+LsbC3d0dVlZWSElJgapSJUkLEU8fX3i08JXs+Maq2MVI165dYWNjA1EU8eGHH2Lw4MFo0aKFzjaCIMDa2ho+Pj5o2bJlIUciIiIiqYi5uXiy5mvkPXyIysOHG03HYFPV3LsF1q5ej7ETRhfRx0ABQRDw7mvzUcfd8DN1A0CtJpVL1Nb93+pc6DoLK1WRExzaVbbGxHmDdJa16twYrTo3LnQfR1cH9Btl+NnnTYWnjy/qtfGvkNdrsYsRPz8/+Pk9+4uZnp6OAQMGoHHjwv/SERERkXySd++BJjsHVUe/ZfgJ2UhHj+69sHfnb1i6YgkO/X7wP30MBHjXb4O+Hd6UrBBxcLWCW307nTsVVH7UftkPdVu3q5CFCKBHB/aMjAysXLkSVlZWLEaIiIiMWOqBAxBzc+D49tvs2C6x5t4t8O2mrbj/4D46d+mA5JRkWFnYIuTtLZL1EQEAcyszNO7kWmF/kC3v6vm1R+2XC78LVRGU+KsSKysrmJmZwbqEsysSERFR2Us7fATxny+FmFNxOsTKqbp7dVhZPZt4UFXJQtJCRKFUoFlANVhYl77jOpW9em06VPhCBNCjGAGAAQMG4Oeffy7xrKNERERU9jLOnkXswoXQZGbKHYUMRRDQpLMb7J054WV5VLdVW9Ru2VruGEZBr3lGXn/9dUyYMAGdOnXCmDFj4OHhUeBs7P/t4E5ERES6YmJikJ6eDgDIyMvDg4wMuP/zzbohZV35E7Fz5sJlxnQo7ewMfnwqW17tXeBUy0buGKSH2i+3Rp1WFWdSwxfRqxjp2LGj9vcnTpzIt14URQiCwBnYiYiICnHu3DnMmzcP+/bt0z5pkJKXiw6hv6GzqxsmNmyIZlWqGPSc2Tdv4tGMGXCZOQuVXAw3YVtOaioubdiAhxcvQhAEuLdujeYjR8KsgC8qn1Pn5OCPLVtw79QpqPPy4NqsGVqMGQMLBwcAQFJ0NK7v2IGE69eRnZoKaycn1OnaFfV69DBY7vKqfmtnVKtvL3cM0oOnjy/qtm4vdwyjolcxsmnTJkPnICIiqjB+/fVXDBo0CKIo5nvkWQRwNC4Wx+JisdK3Fbq5uxv03LkPHyF27lxUW7gAyn9+8C+Oo7Nnw6NTJ3h0yj8869kVK5CZlIQOs2ZBVKtxftUqXPj6a7QuZD4yAIjcvBmPIiLg9957qGRlhYgNG3D6s8/QecECAEDirVtQ2duj1aRJsHR0xJOoKFxcswaCQoG6r75a0o9tMmq3qIqajSvLHYP04NmyFer5deBgA/+hVzEyYsQIQ+cgIiKqEM6dO4dBgwZBrVYX2vdSLYoQAEw6dxY/+Xc0+B2SvLg4JO/ZiyrDhpb6WCn37yM2MhKvfPopqtStCwBo/tZbOLFwIZoNHw7LArLnpqfjzuHDaD15MpybNAEAvPzOOzgweTKe/P03qtavD89XXtHZx8bFBU+ionD/7NkKW4zUeKkyPJsb32zl9GL12/rDo4UvC5EC6FWM/FtaWhru3bsHAKhRowZsbPj8YnkXExODsLAwpKamwtbWFq+88gpq1qwpdywiAq9PUzB//vwC74j8l/jPa1XUdaz1a2PwHLmxjwxynCdRUahkba0tRADAuWlTCIKApzduwL1Vq3z7JN6+DTEvD85Nm2qX2bm7w+qfOyBV69cvOHNGBswr6M8Z7g0dUL+1E3+YLWcUSjM07vIq3Oo3kjuK0dK7GDl//jw+/PBDnDx5EhqNBgCgUCjQvn17LF68mDOwl0P/fX5ZoVBAo9FAEAT07NkTs2bNwssvvyx3TKIKidenaYiJicHevXuLPRqlWhQR9uiRJJ3aFQY6XlZSElT2uv0XFEolzG1skJWUVOg+CjMzmP9nmgCVg0Oh+yRcv457p0+j3bRphohdrrg3dEDDts4sRMoZcytrNO/ZDw6u1eSOYtT0KkbOnj2Ljh07wtzcHKNHj0ajRs+qvWvXruGHH35Ahw4dcPToUfj6+ho0LEmnoOeXnxeZoihi//79+O2337B9+3b0799fzqhEFQ6vz7Knyc3V/hkb0sHQ0BIPiy8COB0fj4G1ahk0iygI0OTmQlGp4Dkqrv3yC67t2KF9r87JwZMbNxCxYYN2WbdlywyaqTDJMTE4tXgxvF57Da7e3mVyTmNRs0ll1PPlHZHyxqaqI1r0HghLW45c9yJ6FSMzZsyAu7s7Tp48CVdX3cl85syZg7Zt22LGjBk4dOiQQUIWZdWqVfjss88QGxuLZs2a4YsvviiyCPrpp58wa9YsREdHo169eli0aBG6d+8ueU5jVqznl9VqCIKAQYMG4fTp0/wGlqiM8Pose5rcXDy5eRN5WVkGP/ajmze1d7WKSwEgJSMDeQaeIyTt3j0IN2+iat26BRYkdbp2RY02/3s87OyKFXBv3RrV//XYlUWVKrBwcEB2crLOvhq1GjlpadqRsf7LwsEBmrw85KSn69wdyU5KyrdPyr17ODZ3LmoHBMBr4EA9Pmk5JQio38qJndXLocrVqqN5r/6opLKQO0q5oNekh2fPnsW4cePyFSIA4OLigrFjx+LMmTOlDvci27dvR1BQEIKDgxEREYFmzZohMDAQ8fHxBW5/+vRpDB48GG+99RYuXbqEvn37om/fvvjzzz8lz2rMiv388j/bzJ8/v4ySERGvz7Kn0WiQl5UFpZkZKllYGPRlb29f4jsuGgC25pUgKBQGfSElBXlZWYXmMbe1hY2bm/alNDeHhb29zjKFUomqDRogNz0dibduafeNv3IFoiiiSr16BR67cu3aEMzMEH/linZZ6oMHyEhIQNUGDbTLku/dw9E5c+DRsSOaDBlSoj+38sxMpUTzQHcWIuWQY00P+PR5jYVICehVjCgUCuTl5RW6Xq1WQ6HQ69AlsnTpUowZMwYjR46El5cX1qxZAysrK2zcuLHA7VesWIFu3brhgw8+QKNGjTBv3jy0aNECX375peRZjdXz55eLOyeMWq3Gnj17EBMTI3EyIuL1KS+FmRmU5uYGfXXqUPJhPQUArR2dAEEw6Ev98BFEAzyKZle9Oly9vXFhzRo8vXEDCdev49KGDajRtq12JK3MJ08QOmkSnt64AQCoZG0Nz86d8cfmzYj/808k3rqF86tWoWr9+trO68kxMTgWHAyXZs1Qv2dPZCUmIisxMd9dGFNjU0WFVn1qomp16xdvTEalao1a8O7ZH8pCHn2kgun1mFabNm2watUqDBkyBLX+8wxrTEwMVq9ejbZtpZ1ZMicnBxcvXsS0f3VkUygUCAgIQHh4eIH7hIeHIygoSGdZYGAgdu7cWeh5srOzkZ2drX2flpYGAMjLy0Nubm4pPoFxOHDgQMmfXxZFHDx4kEM860mdm4vc3DyoxQwolOX/71BhNOpcaPLUyM3NhQZKueOUS7w+5ZGXm/vs760oQmHgf+cd7e0R0L49wv41+EtRFAA6ODnDydwcuQaeSFjMy4NZXBxyGzSAWIwvEDWiiDy1usD/+1q88w4ub9qEY3PnAoKAar6+aPrmm9pts7OzkfrwIbIyMrTLXnrjDYiiiNOffQbNPyNrNRs1Srv+7qlTyE5JQczx44g5flx7LktHRwR+8UWxP2du7rP/rzUaNZS50n9RCjz7swKeXY+5RXx5+1/OHtZo0MYZMNMgM6Nkjwmq1Rqo1Zpnf35Cyf7dKE9yn1+fEMvs5zDxX330CmtPexc3NOgciOycbCAnu8BtSkKdlwf1P393hTL4gv+5SnIUUqIeIiIiRBsbG9HCwkIcPHiwGBwcLAYHB4uvv/66aGlpKdra2oqRkZH6HLrYHjx4IAIQT58+rbP8gw8+EH19fQvcp1KlSuLWrVt1lq1atUp0dnYu9DzBwcHPR1fkiy+++OKLL7744osvk33JQa87I82bN8fZs2cxY8YM7N69GxkZGQAAKysrdOvWDfPnz4eXl5c+hzY606ZN07mbEhkZCX9/f5w9exbNmzeXMZlhbN68GWPHji3xfuvWreM3r6WgUedC1Bj2W05jJCiUUCh5u1pfvD7lI9VoWs/t3LULw0aMgCiKBZ5HgWePZy1/+WV0qWbYGdifcxgyGA49exY6mpYpUeepodGIZXa+evXr4uHDh6hs64gv3t9V5LaCIKB51xqo1bj0kxkqFAKUZqZ/JzovLw8aA98pLEr9Bg3w8OFDOFhbYenbI3XWmVta4pW3JsDW0cng51UolTAzK/WUgEZP70/o5eWFHTt2QKPR4PHjxwAAJyenMukrAgCOjo5QKpWIi4vTWR4XF1dgx3oAcHV1LdH2AKBSqaBSqbTvn0/qaGZmJs+tLAMLDAyEIAglehREEAR07drVJD6/bPhnR8XA61NGEv/5DX7jDdStXx/z5s3LN++IAKCzmxveadDQ4DOvP1d5yBDY9+9XYYaLLevr4fmfqyAoUMms8HMLAtC6Tx3U8JKmnU2VbO2pUKBSpX//6Cyg07C34OrhWaZ5TE2pKwdBEHReZcXc3Bw+Pj4ICwvTLtNoNAgLC4Ofn1+B+/j5+elsDwCHDh0qdPuKoGbNmujZsyeUyuJ9k6JUKtGrVy/O+ExUBnh9mraXX34Zu3fvRnR0NBz+Gc7WzqwSjnd7FWv92khTiCiVcHznHTgM6F9hChFjJQiAb6/aLETKMa8OnVCtfkO5Y5R7ehcjV69excCBA2FnZwc3Nze4ubnBzs4OAwcOLLOhcoOCgrBu3Tps2bIF165dw/jx45Geno6RI5/dQhs+fLhOB/fJkycjNDQUn3/+Oa5fv445c+bgwoULmDhxYpnkNVazZs0qVjH5fJuZM2eWUTIi4vVp+mrWrAnrf+basDIzM/hM688p7e3gGjwbtp07SXJ8KplmAYZ5NIvkYWXvgKYBr8odwyTo9ZjWiRMn8Oqrr0Kj0aBPnz6o/88wfFFRUdi9ezd+++03hIaGon379gYN+1+DBg3C48ePMXv2bMTGxsLb2xuhoaFwcXEB8Gxkr38/NtamTRts3boVM2fOxPTp01GvXj3s3LkTjRs3ljSnsXv55Zexfft27QzPBQ0jqlQqIQgCfvzxR06oRlSGeH2SIVg0bQKnSZNgVpnzVhiDei1dUP/lwh8RJ+Pn1b4TzPg4rEEIYkkeRv5Hy5Yt8fTpUxw7dgw1atTQWXfv3j106NABjo6OOH/+vMGCGouIiAj4+Pjg4sWLaNGihdxxDOr8+fM6zy8/nyVYEAT06tULM2fO5A86RDLh9WnaqlevjgcPHsDVwhKnu3c32HEFMzNUHvoG7Hr0KNPhQSu65+1Zxc4ZX320V2edi6cdOgyqD0HBx+TKi+ftWdnWBivfeQuCQoGBM+ZDJdFdzIpGrzsjf/31F+bNm5evEAGAGjVqYPz48ZgzZ05ps1EZe/78ckxMDA4fPoyUlBTY2dmhc+fOfAadSGa8PqmkzFxd4fxeEFS1a8sdhf5haVMJrfvUZiFSzrl41mEhYkB6FSO1atXSmQjwv3JycgosVKh8qFmzJt588025YxBRAXh9UnFYtW4FpwkToLDmLN7GpGV3D6is+GhPeedWj53WDUmvYmT27NmYOnUqevToAW9vb511ly5dwhdffIHly5cbIB4REREVl2Bujioj34Rtly4cLUtGrq6uyM1Sw0bloF1Wra4D3Oo6FLoPGS9XV1eoc3Nh+c+Tju4NX5I3kInRqxg5c+YMXFxc4OPjgzZt2qBu3boAgBs3biA8PByNGzdGeHg4wsPDtfsIgoAVK1YYJjURERHpUNWtA6dJk1DJXZpJEqn4Lly4gAu/ReP2pcfaZV7tqsmYiErjwoULiI68iJPbv4V15Sqwd3aRO5JJ0asY+fLLL7W/P3XqFE6dOqWz/sqVK7hy5YrOMhYjRERE0rDv0weVB78OgaP7GKWq7taoUo2PzJmCGo0a866jgelVjGg0GkPnICIiohJS2NjAafJkWLVoLncUKkKtxo5yRyADqdbAS+4IJkevYoSIiIjkZeHVCE5TpsCsKifOM3bu9R3kjkAGoFAo4exZR+4YJqdUxcidO3fw22+/4e7duwCejbL16quvwtPT0yDhiIiI6D8EAQ4DB8LhtYEQlEq509AL2DtbwtLWXO4YZABV3KtzokMJ6F2MvPfee1ixYkW+R7YUCgWmTJmCJUuWlDocERER/Y/CygpOU6fysaxyxKmGrdwRyEAqV6sudwSTpNd0rJ9//jmWLVuG/v37Izw8HElJSUhKSkJ4eDgGDhyIZcuWYdmyZYbOSkREVGEp7Gzh+slcFiLlTNXqNnJHIANxcHGVO4JJ0uvOyLp169C7d2/8+OOPOstbtWqFbdu2ISsrC19//TWmTp1qkJBEREQVmpkSLh9/DBUfgy53qrhxFC1TYVOFAxFIQa87I9HR0QgMDCx0fWBgIKKjo/XNRERERP9SedDrsGjQQO4YVEJm5grYOKjkjkEGYm3vIHcEk6RXMeLs7Iw//vij0PV//PEHnJyc9A5FREREz5i5ucK+dy+5Y5Ae7J0sISg4J4WpUNnwkTsp6FWMvPbaa1i/fj0+/fRTpKena5enp6dj0aJFWL9+PQYNGmSwkERERBVV5UGDIJhxJP7yyN7JUu4IZCiCAJWVldwpTJJe/7rNmzcPkZGRmD59OmbPno1q1aoBAB4+fIi8vDx06tQJn3zyiUGDEhERVTTmHh6wbttW7hikJztHFiOmwqySORQKDqUtBb2KESsrK4SFhWHXrl0684x069YN3bt3R69evSAIvC1JRERUGlXHjIag0OshBjICLEZMh5k554qRSomLkYyMDAwdOhQDBgzAG2+8gT59+kiRi4iIqEKz7doVFg0byh2DSsG2qoXcEchAlCxGJFPir1usrKzw+++/IyMjQ4o8REREFZ7CxgaV3xgidwwqBaVSASvOvG4ylGaceV0qet37bdeuHcLDww2dhYiIiADY9+0LJUfuKdes7M05kpYJUZqxv4hU9CpGvvzyS5w4cQIzZ87E/fv3DZ2JiIioQnF1dYWrtTUcLVQQLC1gF9hV7khUShbW/CbdlCiUHNFOKnr9yTZr1gx5eXkICQlBSEgIzMzMoFLpTuojCAKSk5MNEpKIiMiUXbhwAfffnYTchw9h0649FBxCtNwzt+QPr6ZEyeG1JaPXn+yAAQM4WhYREZEErNu3kzsCGUAlFR/rMSWCku0pFb2Kkc2bNxs4Rsk9ffoU7777Lvbs2QOFQoEBAwZgxYoVsCnkGdunT58iODgYBw8eRExMDJycnNC3b1/MmzcP9vb2ZZyeiIgoP4WNDSwaNZI7BhmAWSUOyWxKFBxiWzIlKkaysrKwa9cu3LlzB46OjujRowfc3NykylakN954A48ePcKhQ4eQm5uLkSNHYuzYsdi6dWuB2z98+BAPHz7EkiVL4OXlhbt37+Ltt9/Gw4cP8fPPP5dxeiIiovwsmzblvCImQmHGJ0hMC9tTKsUuRuLj49GmTRvcuXMHoigCeDbM786dOxEQECBZwIJcu3YNoaGhOH/+PFq2bAkA+OKLL9C9e3csWbJEOyP8vzVu3Bi//PKL9n2dOnWwYMECDB06FHl5eTDjs4BERCQziyaN5Y5ABqJUsqg0JRwZTTrFvlLmzZuH6OhoTJ06FXv37sXy5cthaWmJcePGSZmvQOHh4XBwcNAWIgAQEBAAhUKBs2fPFvs4ycnJsLOzK7IQyc7ORkpKivaVlpZWquxERESF4SNapoM/vJoWgXdGJFPs2wEHDx7E8OHDsWTJEu0yFxcXDBkyBFFRUWjQoIEkAQsSGxsLZ2dnnWVmZmaoUqUKYmNji3WMhIQEzJs3D2PHji1yu5CQEMydO1fvrERERMUhqFSo5O4udwwyEAWLEZPCDuzSKfadkZiYGLRrpzvCR7t27SCKIuLi4gwS5uOPP4YgCEW+rl+/XurzpKSkoEePHvDy8sKcOXOK3HbatGlITk7Wvo4dO1bq8xMREf1XpWrV2F/ElLAWMSlmlThvjFSKfWckOzsbFhYWOsuev8/LyzNImPfeew9vvvlmkdvUrl0brq6uiI+P11mel5eHp0+fwtXVtcj9U1NT0a1bN9ja2mLHjh2o9IK/XCqVSmcOlcJG6yIiIioNM1cXuSMQEZW5EvXajo6ORkREhPb980kNb9y4AQcHh3zbt2jRokRhnJyc4OTk9MLt/Pz8kJSUhIsXL8LHxwcAcPjwYWg0GrRq1arQ/VJSUhAYGAiVSoXdu3fnK66IiIjkYlalitwRiIjKnCA+HxrrBRQKRYETHYqimG/582VqtdowKQvw6quvIi4uDmvWrNEO7duyZUvt0L4PHjzAK6+8gm+++Qa+vr5ISUlB165dkZGRgR07dsDa2lp7LCcnJyiL+SxgREQEfHx8cPHixRIXW0RERIVJP30a1m3ayB2DDCQnM4+zsBMVQ7Gvkk2bNkmZo8S+//57TJw4Ea+88op20sOVK1dq1+fm5iIqKgoZGRkAnhURz0faqlu3rs6x7ty5Aw8PjzLLTkRE9F8KKyu5I5ABsRAhKp5i3xmhZ3hnhIiIpJB19SosvLzkjkFEVKY4bAcREZEREMzN5Y5ARFTmWIwQEREZA85jQEQVEIsRIiIiI2Beq5bcEYiIyhyLESIiIiPACQ+JqCLiv3xERERERCQLFiNERERERCQLFiNERERERCQLFiNERERERCQLFiNERERERCQLFiNERERERCQLM7kDkPF69OgRHj16JHcMMhA3Nze4ubnJHYMMhNcnEVHZ4f+h0mExUkJubm4IDg42+b+Q2dnZGDx4MI4dOyZ3FDIQf39/HDhwACqVSu4oVEq8PomIyhb/D5WOIIqiKHcIMj4pKSmwt7fHsWPHYGNjI3ccKqW0tDT4+/sjOTkZdnZ2csehUuL1aXqeX6NsU9PA9jQt/D9UWrwzQkXy9vbmhWcCUlJS5I5AEuD1aTqeX6NsU9PA9jQt/D9UWuzATkREREREsmAxQkREREREsmAxQgVSqVQIDg5mRy0TwfY0LWxP08M2NS1sT9PC9pQWO7ATEREREZEseGeEiIiIiIhkwWKEiIiIiIhkwWKEiIiIiIhkwWKEiIiIiIhkwWKEyEgIglCs19GjR0t9royMDMyZM6dEx1qwYAF69+4NFxcXCIKAOXPmlDoHUXlhzNfn9evX8eGHH8Lb2xu2trZwc3NDjx49cOHChVJnMWXG3KYPHz7E0KFD0aBBA9ja2sLBwQG+vr7YsmULOO5QwYy5Pf/r+++/hyAIsLGxKXUWU8AZ2ImMxLfffqvz/ptvvsGhQ4fyLW/UqFGpz5WRkYG5c+cCADp27FisfWbOnAlXV1c0b94cBw4cKHUGovLEmK/P9evXY8OGDRgwYAAmTJiA5ORkfP3112jdujVCQ0MREBBQ6kymyJjbNCEhAffv38fAgQNRs2ZN5Obm4tChQ3jzzTcRFRWFhQsXljqTqTHm9vy3tLQ0fPjhh7C2ti51DlPBYoTISAwdOlTn/ZkzZ3Do0KF8y+Vy584deHh4ICEhAU5OTnLHISpTxnx9Dh48GHPmzNH5lnXUqFFo1KgR5syZw2KkEMbcpk2bNs33rfvEiRPRq1cvrFy5EvPmzYNSqZQnnJEy5vb8t/nz58PW1hadOnXCzp075Y5jFPiYFlE5otFosHz5crz00kuwsLCAi4sLxo0bh8TERJ3tLly4gMDAQDg6OsLS0hKenp4YNWoUACA6OlpbTMydO1d76/pFj115eHhI8ZGITIZc16ePj0++xz2qVq2K9u3b/3979x5TZf3HAfx95HIAuZqA98NtEozmhdLM7PzMWyGLqxdcipqp6WSWxtRiouHUzNs0MsghQ2xIii4ssCmGaU5dOqdmEB6w1RBZgAgqCJ/fH85jR0C5+pxzeL+2s3m+53m+z/c5733P+Pjc8Pvvv3fuTnYzSv7mNsfDwwO1tbWoq6vr8L51R0rnWVhYiK1bt2LLli2wtOTxgEf4TRCZkAULFmDPnj2YM2cOYmJioNPpsHPnTly4cAGnTp2ClZUVysrKMHHiRLi6umLFihVwdnZGcXExDh48CABwdXXFV199hQ8++ABhYWEIDw8H8PB/4oio/YxtfpaWlqJ3796duo/djdKZ3r17FzU1Nbhz5w5+/vlnpKSkYNSoUbC1te3S/TZXSue5dOlSjB07FkFBQdi/f3+X7qtJESIySosXL5b/TtGTJ08KAElPTzdYLicnx6A9KytLAMi5c+da7PvWrVsCQFavXt3mcXVkXSJzYazz85H8/HxRqVQSFxfX7j66G2PMdP369QJA/xo3bpzcuHGjTX10V8aWZ3Z2tlhaWsqVK1dERCQ6Olp69uzZhj0yXzxNi8hEZGZmwsnJCRMmTEB5ebn+9egUjby8PACAs7MzACA7Oxv19fUKjpio+zCm+VlWVoYZM2bA09MTsbGxXbKN7sAYMo2KisJPP/2Effv2YcaMGQAeHi2htlMyz7q6Onz44YdYuHAh/P39O6VPc8JihMhEFBYWoqqqCm5ubnB1dTV43blzB2VlZQAArVaLiIgIrFmzBr1790ZISAhSUlJw//59hfeAyHwZy/ysqalBcHAwqqurcfjwYd46tAOMIVONRoPx48cjKioK6enp8PLywvjx41mQtIOSeW7duhXl5eX6O3CRIV4zQmQiGhsb4ebmhvT09GY/f3RBnUqlwnfffYczZ87g+++/R25uLubOnYvNmzfjzJkz/OOEqAsYw/ysq6tDeHg4Ll26hNzcXAQEBLS7LzKOTJ8UGRmJ5ORk5OfnY9KkSZ3Wb3egVJ5VVVVISEjAokWLcPv2bdy+fRvAw1v8igiKi4thZ2cHNze3ju2gKVP6PDEiat6T57suWrRILCwspLa2ts19paenCwBJTk4WEZHy8nJeM0LUAcY2PxsaGmTatGliYWEhBw4caPMYyPgybc6hQ4cEgGRkZHSon+7AWPLU6XQG1/009woJCWnzmMwJT9MiMhFTp05FQ0MDPvvssyafPXjwAJWVlQCAioqKJk/oHTp0KADoDzPb2dkBgH4dIuoYpefnkiVLkJGRgcTERP3dfahjlMz01q1bzbbv3r0bKpUKw4cPb1U/9JhSebq5uSErK6vJa+zYsbCxsUFWVhZWrlzZ/h0zAzxNi8hEaLVaLFiwAOvXr8fFixcxceJEWFlZobCwEJmZmdi+fTsiIyORmpqKxMREhIWFwdvbG9XV1UhOToajoyOCgoIAALa2tvD390dGRgYGDx6MXr16ISAg4KmndaSlpaGkpAS1tbUAgPz8fCQkJAAAZs6cCY1G0/VfApGRUnJ+btu2DYmJiRg1ahTs7Oywd+9eg8/DwsL4tOd2UDLTdevW4dSpU3jrrbcwaNAg/Pvvvzhw4ADOnTuHJUuWwMfH53l+FWZBqTzt7OwQGhrapP3QoUM4e/Zss591O8oemCGiljx5iPmRpKQkCQwMFFtbW3FwcJCXXnpJYmNj5Z9//hERkd9++02ioqJk0KBBolarxc3NTYKDg+X8+fMG/Zw+fVoCAwPF2tq6VYebtVpti4eY8/LyOmu3iUyCMc3P6Ojop54CotPpOnPXzZYxZXr06FEJDg6Wfv36iZWVlTg4OMjo0aMlJSVFGhsbO3W/zZUx5dkc3tr3MZXIE8eiiIiIiIiIngNeM0JERERERIpgMUJERERERIpgMUJERERERIpgMUJERERERIpgMUJERERERIpgMUJERERERIpgMUJkBoqLi6FSqbBnzx6lh0JEzeAcNS/M07wwT2WxGCEiIiIiIkXwoYdEZkBEcP/+fVhZWcHCwkLp4RDREzhHzQvzNC/MU1ksRoiIiIiISBE8TYvISMTHx0OlUqGgoADvvvsunJyc4Orqiri4OIgI/vrrL4SEhMDR0RF9+vTB5s2b9es2d77r7NmzYW9vj7///huhoaGwt7eHq6srli9fjoaGBv1yJ06cgEqlwokTJwzG01yfpaWlmDNnDgYMGAC1Wo2+ffsiJCQExcXFXfStEBkPzlHzwjzNC/M0XSxGiIzMtGnT0NjYiA0bNmDkyJFISEjAtm3bMGHCBPTv3x8bN26Ej48Pli9fjvz8/Kf21dDQgEmTJuGFF17AF198Aa1Wi82bNyMpKaldY4uIiEBWVhbmzJmDxMRExMTEoLq6Gjdu3GhXf0SmiHPUvDBP88I8TZAQkVFYvXq1AJD58+fr2x48eCADBgwQlUolGzZs0LdXVFSIra2tREdHi4iITqcTAJKSkqJfJjo6WgDI2rVrDbYzbNgwCQwM1L/Py8sTAJKXl2ew3JN9VlRUCADZtGlT5+wwkYnhHDUvzNO8ME/TxSMjREZm3rx5+n9bWFjg5Zdfhojgvffe07c7OzvD19cX169ff2Z/CxcuNHg/ZsyYVq33JFtbW1hbW+PEiROoqKho8/pE5oJz1LwwT/PCPE0PixEiIzNo0CCD905OTrCxsUHv3r2btD/rB83Gxgaurq4GbS4uLu36IVSr1di4cSN+/PFHuLu744033sDnn3+O0tLSNvdFZMo4R80L8zQvzNP0sBghMjLN3VawpVsNyjNuhteaWxSqVKpm2/97gd4jS5cuRUFBAdavXw8bGxvExcXBz88PFy5ceOZ2iMwF56h5YZ7mhXmaHhYjRN2ci4sLAKCystKgvaSkpNnlvb29sWzZMhw9ehSXL19GXV2dwV1JiKhzcY6aF+ZpXphnx7EYIermNBoNLCwsmtxVJDEx0eB9bW0t7t27Z9Dm7e0NBwcH3L9/v8vHSdRdcY6aF+ZpXphnx1kqPQAiUpaTkxOmTJmCHTt2QKVSwdvbG9nZ2SgrKzNYrqCgAOPGjcPUqVPh7+8PS0tLZGVl4ebNm5g+fbpCoycyf5yj5oV5mhfm2XEsRogIO3bsQH19PXbt2gW1Wo2pU6di06ZNCAgI0C8zcOBAREVF4dixY0hLS4OlpSVefPFF7N+/HxEREQqOnsj8cY6aF+ZpXphnx6jkWVfvEBERERERdQFeM0JERERERIpgMUJERERERIpgMUJERERERIpgMUJERERERIpgMUJERERERIpgMUJEbVJcXAyVSoU9e/YoPRQiagbnqHlhnuaFeTbFYoSoCxUVFWHBggXw8vKCjY0NHB0dMXr0aGzfvh13797tsu1evXoV8fHxKC4u7rJttMa6devwzjvvwN3dHSqVCvHx8YqOh+hJ3XmOXrt2DbGxsRg6dCgcHBzQt29fTJ48GefPn1dsTB3FPJlnZ2CezxcfekjURY4cOYIpU6ZArVZj1qxZCAgIQF1dHX755Rd8/PHHuHLlCpKSkrpk21evXsWaNWvwv//9Dx4eHl2yjdb49NNP0adPHwwbNgy5ubmKjYOoOd19jn7zzTfYvXs3IiIisGjRIlRVVeHrr7/Gq6++ipycHIwfP16RcbUX82SenYV5Pl8sRoi6gE6nw/Tp06HRaHD8+HH07dtX/9nixYvx559/4siRIwqO8DERwb1792Bra9vpfet0Onh4eKC8vByurq6d3j9Re3GOAlFRUYiPj4e9vb2+be7cufDz80N8fLxJ/bHDPJmnUphnJxAi6nQLFy4UAHLq1KlWLV9fXy9r164VLy8vsba2Fo1GIytXrpR79+4ZLKfRaGTy5Mly8uRJeeWVV0StVounp6ekpqbql0lJSREATV55eXkGfeTk5EhgYKCo1WrZunWriIgUFRVJZGSkuLi4iK2trYwcOVKys7MNxqDT6QSApKSktPr7uHXrlgCQ1atXt3odoq7EOdqy8PBw6dWrV7vWVQrzbBnzfIx5GicWI0RdoH///uLl5dXq5aOjowWAREZGypdffimzZs0SABIaGmqwnEajEV9fX3F3d5dVq1bJzp07Zfjw4aJSqeTy5csi8vDHMCYmRgDIqlWrJC0tTdLS0qS0tFTfh4+Pj7i4uMiKFStk165dkpeXJ6WlpeLu7i4ODg7yySefyJYtW2TIkCHSo0cPOXjwoH4MLEbIHHCOtuy1116TwYMHt2tdpTDPljHPx5incWIxQtTJqqqqBICEhIS0avmLFy8KAJk3b55B+/LlywWAHD9+XN+m0WgEgOTn5+vbysrKRK1Wy7Jly/RtmZmZBv+T81+P+sjJyTFoX7p0qQCQkydP6tuqq6vF09NTPDw8pKGhQURYjJDp4xxtWX5+vqhUKomLi2vzukphni1jnszTFPBuWkSd7Pbt2wAABweHVi3/ww8/AAA++ugjg/Zly5YBQJPzYv39/TFmzBj9e1dXV/j6+uL69eutHqOnpycmTZrUZBwjRozA66+/rm+zt7fH/PnzUVxcjKtXr7a6fyJjxjnavLKyMsyYMQOenp6IjY3tUF/PE/NsHvNknqaCxQhRJ3N0dAQAVFdXt2r5kpIS9OjRAz4+Pgbtffr0gbOzM0pKSgzaBw0a1KQPFxcXVFRUtHqMnp6ezY7D19e3Sbufn5/+cyJzwDnaVE1NDYKDg1FdXY3Dhw8bXDRr7JhnU8yTeZoS3k2LqJM5OjqiX79+uHz5cpvWU6lUrVrOwsKi2XYRafW2uuLOWUSmgnPUUF1dHcLDw3Hp0iXk5uYiICDguW27MzBPQ8zz6Zin8eGREaIuEBwcjKKiIvz666/PXFaj0aCxsRGFhYUG7Tdv3kRlZSU0Gk2bt9/aH+Unx/HHH380ab927Zr+cyJzwTn6UGNjI2bNmoVjx45h37590Gq1be7DGDDPh5jnY8zTdLAYIeoCsbGx6NmzJ+bNm4ebN282+byoqAjbt28HAAQFBQEAtm3bZrDMli1bAACTJ09u8/Z79uwJAKisrGz1OkFBQTh79qzBj39NTQ2SkpLg4eEBf3//No+DyFhxjj60ZMkSZGRkIDExEeHh4W1e31gwz4eY52PM03TwNC2iLuDt7Y19+/Zh2rRp8PPzM3h67OnTp5GZmYnZs2cDAIYMGYLo6GgkJSWhsrISWq0WZ8+eRWpqKkJDQzF27Ng2b3/o0KGwsLDAxo0bUVVVBbVajTfffBNubm4trrNixQp8++23ePvttxETE4NevXohNTUVOp0OBw4cQI8ebf+/i7S0NJSUlKC2thYAkJ+fj4SEBADAzJkzebSFFMM5+vCPt8TERIwaNQp2dnbYu3evwedhYWH6P8qMHfNknszThCl7My8i81ZQUCDvv/++eHh4iLW1tTg4OMjo0aNlx44dBg9jqq+vlzVr1oinp6dYWVnJwIEDn/rApidptVrRarUGbcnJyeLl5SUWFhbNPrCpOY8e2OTs7Cw2NjYyYsSIDj2wSavVNvvwKLRwy0Si5607z9FHz2Zo6aXT6Z66vjFinsyTeZoelUgbrtghIiIiIiLqJLxmhIiIiIiIFMFihIiIiIiIFMFihIiIiIiIFMFihIiIiIiIFMFihIiIiIiIFMFihIiIiIiIFMFihIiIiIiIFMFihIiIiIiIFMFihIiIiIiIFMFihIiIiIiIFMFihIiIiIiIFMFihIiIiIiIFMFihIiIiIiIFPF/huhLZfRTV/kAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 900x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "multi_groups_unpaired.mean_diff.plot();"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Paired proportion plots"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For the paired version of the proportion plot, we adopt the style of a Sankey Diagram. The width of each bar in each xtick represents the proportion of the corresponding label in the group, and the strip denotes the paired relationship for each observation.\n",
+    "\n",
+    "Starting from **v2024.3.29**, the paired version of the proportion plot receives a major upgrade. We introduce the ``sankey`` and ``flow`` parameters to control the plot. By default, both ``sankey`` and ``flow`` are set to True to cater the needs of repeated measures. When ``sankey`` is set to False, DABEST will generate a bar plot with a similar aesthetic to the paired proportion plot. When ``flow`` is set to False, each group of comparsion forms a Sankey diagram that does not connect to other groups of comparison.\n",
+    "\n",
+    "Similar to the unpaired version, the ``.plot()`` method is used to produce an **estimation plot**.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Two-Group"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "DABEST v2025.03.27\n",
+       "==================\n",
+       "                  \n",
+       "Good afternoon!\n",
+       "The current time is Tue Mar 25 17:22:28 2025.\n",
+       "\n",
+       "Paired effect size(s) for repeated measures against baseline \n",
+       "with 95% confidence intervals will be computed for:\n",
+       "1. Test 1 minus Control 1\n",
+       "\n",
+       "5000 resamples will be used to generate the effect size bootstraps."
+      ]
+     },
+     "execution_count": null,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "two_groups_paired = dabest.load(df, idx=(\"Control 1\", \"Test 1\"), \n",
+    "                                  proportional=True, paired=\"baseline\", id_col=\"ID\")\n",
+    "two_groups_paired"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "DABEST v2025.03.27\n",
+       "==================\n",
+       "                  \n",
+       "Good afternoon!\n",
+       "The current time is Tue Mar 25 17:22:28 2025.\n",
+       "\n",
+       "The paired mean difference for repeated measures against baseline \n",
+       "between Control 1 and Test 1 is 0.575 [95%CI 0.325, 0.725].\n",
+       "The p-value of the two-sided permutation t-test is 0.0, calculated for legacy purposes only. \n",
+       "\n",
+       "5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.\n",
+       "Any p-value reported is the probability of observing theeffect size (or greater),\n",
+       "assuming the null hypothesis of zero difference is true.\n",
+       "For each p-value, 5000 reshuffles of the control and test labels were performed.\n",
+       "\n",
+       "To get the results of all valid statistical tests, use `.mean_diff.statistical_tests`"
+      ]
+     },
+     "execution_count": null,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "two_groups_paired.mean_diff"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "two_groups_paired.mean_diff.plot();"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The Sankey plots for paired proportions also supports the ``float_contrast`` parameter, which can be set to ``False`` to produce a **Cumming estimation plot**.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 300x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "two_groups_paired.mean_diff.plot(float_contrast=False);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Multi Two-Group, Repeated Measures, and Multi Groups\n",
+    "As with regular (non-binary) unpaired data, multi two-group, repeated-measures, and multi group plots can be generated for binary data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "DABEST v2025.03.27\n",
+       "==================\n",
+       "                  \n",
+       "Good afternoon!\n",
+       "The current time is Tue Mar 25 17:22:28 2025.\n",
+       "\n",
+       "Paired effect size(s) for repeated measures against baseline \n",
+       "with 95% confidence intervals will be computed for:\n",
+       "1. Test 1 minus Control 1\n",
+       "2. Test 2 minus Control 2\n",
+       "3. Test 3 minus Control 3\n",
+       "\n",
+       "5000 resamples will be used to generate the effect size bootstraps."
+      ]
+     },
+     "execution_count": null,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "multi_two_groups_paired = dabest.load(df, idx=((\"Control 1\", \"Test 1\"),(\"Control 2\", \"Test 2\"),(\"Control 3\", \"Test 3\")),\n",
+    "                                        proportional=True, paired=\"baseline\", id_col=\"ID\")\n",
+    "multi_two_groups_paired"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "DABEST v2025.03.27\n",
+       "==================\n",
+       "                  \n",
+       "Good afternoon!\n",
+       "The current time is Tue Mar 25 17:22:29 2025.\n",
+       "\n",
+       "The paired mean difference for repeated measures against baseline \n",
+       "between Control 1 and Test 1 is 0.575 [95%CI 0.325, 0.725].\n",
+       "The p-value of the two-sided permutation t-test is 0.0, calculated for legacy purposes only. \n",
+       "\n",
+       "The paired mean difference for repeated measures against baseline \n",
+       "between Control 2 and Test 2 is 0.025 [95%CI -0.15, 0.175].\n",
+       "The p-value of the two-sided permutation t-test is 0.571, calculated for legacy purposes only. \n",
+       "\n",
+       "The paired mean difference for repeated measures against baseline \n",
+       "between Control 3 and Test 3 is -0.6 [95%CI -0.775, -0.425].\n",
+       "The p-value of the two-sided permutation t-test is 0.0, calculated for legacy purposes only. \n",
+       "\n",
+       "5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.\n",
+       "Any p-value reported is the probability of observing theeffect size (or greater),\n",
+       "assuming the null hypothesis of zero difference is true.\n",
+       "For each p-value, 5000 reshuffles of the control and test labels were performed.\n",
+       "\n",
+       "To get the results of all valid statistical tests, use `.mean_diff.statistical_tests`"
+      ]
+     },
+     "execution_count": null,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "multi_two_groups_paired.mean_diff"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 900x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "multi_two_groups_paired.mean_diff.plot();"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "DABEST v2025.03.27\n",
+       "==================\n",
+       "                  \n",
+       "Good afternoon!\n",
+       "The current time is Tue Mar 25 17:22:29 2025.\n",
+       "\n",
+       "Paired effect size(s) for repeated measures against baseline \n",
+       "with 95% confidence intervals will be computed for:\n",
+       "1. Test 1 minus Control 1\n",
+       "2. Test 2 minus Control 1\n",
+       "3. Test 3 minus Control 1\n",
+       "4. Test 4 minus Control 1\n",
+       "\n",
+       "5000 resamples will be used to generate the effect size bootstraps."
+      ]
+     },
+     "execution_count": null,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "repeated_measures_baseline = dabest.load(df, idx=(\"Control 1\", \"Test 1\", \"Test 2\", \"Test 3\", \"Test 4\"),\n",
+    "                                        proportional=True, paired=\"baseline\", id_col=\"ID\")\n",
+    "repeated_measures_baseline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "DABEST v2025.03.27\n",
+       "==================\n",
+       "                  \n",
+       "Good afternoon!\n",
+       "The current time is Tue Mar 25 17:22:31 2025.\n",
+       "\n",
+       "The paired mean difference for repeated measures against baseline \n",
+       "between Control 1 and Test 1 is 0.575 [95%CI 0.325, 0.725].\n",
+       "The p-value of the two-sided permutation t-test is 0.0, calculated for legacy purposes only. \n",
+       "\n",
+       "The paired mean difference for repeated measures against baseline \n",
+       "between Control 1 and Test 2 is 0.025 [95%CI -0.15, 0.175].\n",
+       "The p-value of the two-sided permutation t-test is 0.555, calculated for legacy purposes only. \n",
+       "\n",
+       "The paired mean difference for repeated measures against baseline \n",
+       "between Control 1 and Test 3 is 0.125 [95%CI -0.075, 0.275].\n",
+       "The p-value of the two-sided permutation t-test is 0.277, calculated for legacy purposes only. \n",
+       "\n",
+       "The paired mean difference for repeated measures against baseline \n",
+       "between Control 1 and Test 4 is 0.15 [95%CI -0.05, 0.325].\n",
+       "The p-value of the two-sided permutation t-test is 0.075, calculated for legacy purposes only. \n",
+       "\n",
+       "5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.\n",
+       "Any p-value reported is the probability of observing theeffect size (or greater),\n",
+       "assuming the null hypothesis of zero difference is true.\n",
+       "For each p-value, 5000 reshuffles of the control and test labels were performed.\n",
+       "\n",
+       "To get the results of all valid statistical tests, use `.mean_diff.statistical_tests`"
+      ]
+     },
+     "execution_count": null,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "repeated_measures_baseline.mean_diff"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 750x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "repeated_measures_baseline.mean_diff.plot();"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "DABEST v2025.03.27\n",
+       "==================\n",
+       "                  \n",
+       "Good afternoon!\n",
+       "The current time is Tue Mar 25 17:22:31 2025.\n",
+       "\n",
+       "Paired effect size(s) for the sequential design of repeated-measures experiment \n",
+       "with 95% confidence intervals will be computed for:\n",
+       "1. Test 1 minus Control 1\n",
+       "2. Test 2 minus Test 1\n",
+       "3. Test 3 minus Test 2\n",
+       "4. Test 4 minus Test 3\n",
+       "\n",
+       "5000 resamples will be used to generate the effect size bootstraps."
+      ]
+     },
+     "execution_count": null,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "repeated_measures_sequential = dabest.load(df, idx=(\"Control 1\", \"Test 1\", \"Test 2\", \"Test 3\", \"Test 4\"),\n",
+    "                                        proportional=True, paired=\"sequential\", id_col=\"ID\")\n",
+    "repeated_measures_sequential"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "DABEST v2025.03.27\n",
+       "==================\n",
+       "                  \n",
+       "Good afternoon!\n",
+       "The current time is Tue Mar 25 17:22:32 2025.\n",
+       "\n",
+       "The paired mean difference for the sequential design of repeated-measures experiment \n",
+       "between Control 1 and Test 1 is 0.575 [95%CI 0.325, 0.725].\n",
+       "The p-value of the two-sided permutation t-test is 0.0, calculated for legacy purposes only. \n",
+       "\n",
+       "The paired mean difference for the sequential design of repeated-measures experiment \n",
+       "between Test 1 and Test 2 is -0.55 [95%CI -0.725, -0.4].\n",
+       "The p-value of the two-sided permutation t-test is 0.0, calculated for legacy purposes only. \n",
+       "\n",
+       "The paired mean difference for the sequential design of repeated-measures experiment \n",
+       "between Test 2 and Test 3 is 0.1 [95%CI -0.075, 0.225].\n",
+       "The p-value of the two-sided permutation t-test is 0.342, calculated for legacy purposes only. \n",
+       "\n",
+       "The paired mean difference for the sequential design of repeated-measures experiment \n",
+       "between Test 3 and Test 4 is 0.025 [95%CI -0.2, 0.2].\n",
+       "The p-value of the two-sided permutation t-test is 0.624, calculated for legacy purposes only. \n",
+       "\n",
+       "5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.\n",
+       "Any p-value reported is the probability of observing theeffect size (or greater),\n",
+       "assuming the null hypothesis of zero difference is true.\n",
+       "For each p-value, 5000 reshuffles of the control and test labels were performed.\n",
+       "\n",
+       "To get the results of all valid statistical tests, use `.mean_diff.statistical_tests`"
+      ]
+     },
+     "execution_count": null,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "repeated_measures_sequential.mean_diff"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 750x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "repeated_measures_sequential.mean_diff.plot();"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "DABEST v2025.03.27\n",
+       "==================\n",
+       "                  \n",
+       "Good afternoon!\n",
+       "The current time is Tue Mar 25 17:22:32 2025.\n",
+       "\n",
+       "Paired effect size(s) for repeated measures against baseline \n",
+       "with 95% confidence intervals will be computed for:\n",
+       "1. Test 1 minus Control 1\n",
+       "2. Test 2 minus Control 2\n",
+       "3. Test 3 minus Control 2\n",
+       "4. Test 4 minus Control 2\n",
+       "\n",
+       "5000 resamples will be used to generate the effect size bootstraps."
+      ]
+     },
+     "execution_count": null,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "multi_groups_baseline = dabest.load(df, idx=((\"Control 1\", \"Test 1\"),(\"Control 2\", \"Test 2\", \"Test 3\", \"Test 4\")),\n",
+    "                                        proportional=True, paired=\"baseline\", id_col=\"ID\")\n",
+    "multi_groups_baseline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "DABEST v2025.03.27\n",
+       "==================\n",
+       "                  \n",
+       "Good afternoon!\n",
+       "The current time is Tue Mar 25 17:22:33 2025.\n",
+       "\n",
+       "The paired mean difference for repeated measures against baseline \n",
+       "between Control 1 and Test 1 is 0.575 [95%CI 0.325, 0.725].\n",
+       "The p-value of the two-sided permutation t-test is 0.0, calculated for legacy purposes only. \n",
+       "\n",
+       "The paired mean difference for repeated measures against baseline \n",
+       "between Control 2 and Test 2 is 0.025 [95%CI -0.15, 0.175].\n",
+       "The p-value of the two-sided permutation t-test is 0.571, calculated for legacy purposes only. \n",
+       "\n",
+       "The paired mean difference for repeated measures against baseline \n",
+       "between Control 2 and Test 3 is 0.125 [95%CI -0.075, 0.3].\n",
+       "The p-value of the two-sided permutation t-test is 0.309, calculated for legacy purposes only. \n",
+       "\n",
+       "The paired mean difference for repeated measures against baseline \n",
+       "between Control 2 and Test 4 is 0.15 [95%CI -0.025, 0.3].\n",
+       "The p-value of the two-sided permutation t-test is 0.0362, calculated for legacy purposes only. \n",
+       "\n",
+       "5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.\n",
+       "Any p-value reported is the probability of observing theeffect size (or greater),\n",
+       "assuming the null hypothesis of zero difference is true.\n",
+       "For each p-value, 5000 reshuffles of the control and test labels were performed.\n",
+       "\n",
+       "To get the results of all valid statistical tests, use `.mean_diff.statistical_tests`"
+      ]
+     },
+     "execution_count": null,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "multi_groups_baseline.mean_diff"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 900x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "multi_groups_baseline.mean_diff.plot();"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Aesthetic adjustments\n",
+    "\n",
+    "Here we demonstrate a few proportion plot specific aesthetic adjustments."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Bar Width\n",
+    "\n",
+    "You can modify the width of the bar plot bars (unpaired data) by setting the parameter ``bar_width`` in the ``.plot()`` method. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "two_groups_unpaired.mean_diff.plot(bar_width=0.3);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Bar desaturation\n",
+    "\n",
+    "The ``raw_desat`` is used to control the amount of desaturation applied to the bar plot bar colors (specific to unpaired data). A value of 0.0 means full desaturation (i.e., grayscale), \n",
+    "while a value of 1.0 means no desaturation (i.e., full color saturation). The default one is 0.8.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "two_groups_unpaired.mean_diff.plot(raw_desat=1.0);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Raw Label and Contrast Label\n",
+    "The parameters ``raw_label`` and ``contrast_label`` can be used to set labels for the y-axis of the bar plot and the contrast plot."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "two_groups_unpaired.mean_diff.plot(raw_label=\"success\",contrast_label=\"difference\");\n",
+    "two_groups_paired.mean_diff.plot(raw_label=\"success\",contrast_label=\"difference\");"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Barplot kwargs\n",
+    "The parameters ``barplot_kwargs`` can be used to alter the aesthetics of the bar plot. This is a dictionary that can be used to pass additional arguments to the bar plot."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "two_groups_unpaired.mean_diff.plot(barplot_kwargs={\"alpha\":0.5, \"edgecolor\":\"red\", \"linewidth\":2, 'errorbar': ('sd', 0.1)});"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Sankey and Flow\n",
+    "\n",
+    "By changing the ``sankey`` and ``flow`` parameters, you can generate different types of Sankey plots for paired proportions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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",
+      "text/plain": [
+       "<Figure size 1050x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1050x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1050x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "separate_control = dabest.load(df, idx=(((\"Control 1\", \"Test 1\"),\n",
+    "                                (\"Test 2\", \"Test 3\"),\n",
+    "                                (\"Test 4\", \"Test 7\", \"Test 6\"))),\n",
+    "                    proportional=True, paired=\"sequential\", id_col=\"ID\")\n",
+    "\n",
+    "separate_control.mean_diff.plot();\n",
+    "separate_control.mean_diff.plot(sankey_kwargs={'sankey':False});\n",
+    "separate_control.mean_diff.plot(sankey_kwargs={'flow':False});"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Sankey kwargs\n",
+    "Several exclusive parameters can be provided to the ``.plot()`` method to customize the Sankey plots for paired proportions.\n",
+    "By modifying the `sankey_kwargs` parameter, you can customize the Sankey plot. The following parameters are supported:\n",
+    "\n",
+    "- **align**: The alignment of each Sankey bar. Default is \"center\".\n",
+    "- **alpha**: The transparency of each Sankey bar. Default is 0.4.\n",
+    "- **bar_width**: The width of each bar on the side in the plot. Default is 0.1."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 750x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "repeated_measures_baseline.mean_diff.plot(sankey_kwargs = {\"alpha\": 0.2,\n",
+    "                                                  \"bar_width\": 0.4});"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Custom Palette\n",
+    "\n",
+    "The `custom_palette` parameter functions in a similar way for proportion plots as for other plots - however, there are some differences!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A `custom_palette` dict can be passed for sankey plots, whereby two keys used are 0 and 1. The color associated with these keys will be used to color the bars in the sankey plot.\n",
+    "\n",
+    "For bar plots, the `custom_palette` dict can be passed like a regular plot, with a color associated to each group. The chosen color will then be used to color the filled portion of the bar plot."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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tWgAAIiMjkZiYiN69ezNBJu1JSBBH11lbA35+4qC+Ss7ad/myOHU0VVJcHLBtG9ClizhwUiI5OUBqqnjv0tPFJSNDTIxra2JIIiLSHXqZIJuamuLAgQPYuHEjtm3bhtu3bwMAAgMD8dxzz2HChAllTtBBVKsyM8XOxOfOiROPtGghdgko5/9eTg5w6VIdx/i4SjZdmwHqqaYrdYwgVLk1vdKUSuDECTE77dZNe9eB+GOkpor9w5OSgORkICVFbAUmIqL6Qy8TZACQy+WYNGmSuj4xkWRUKuDuXXExMxOrX/j4iLWVSyTL586Jj9grw8hI7MHh6Sn2Lqjp5z2Te/9MhFLJ5DK7qhcsPu+wYeLEK9VVUCBmpffuiXNvl0zOr10T/wGrOVNmWbKyxFLYCQli3/CkJLHrMRER1W96myAT6aS8POD6dXExMREzXC8vpFl44OZNmyceLpeLvTYCAsSSzLXGqRlw44aYEWqLlRXQrFnNz+PhAbRuLfZhuHBBnGqw2M2bYqWM7t2rdEqlUmyATkkRl+RkMRnOy6t5uEREZHj0MkHu27fvE/eRyWQICwurg2iIylFQoG5Zjr0ONMmwRI6NG/KsnZFn5YQ8S0eojP/Ngq2sgP79tVJ2WVQbyWtdsrEB+vQBmjcX5+LOzhbXX7sG2NoC/v4auxcUiPl/VpaYWxcvaWlibxgOjCQiosrSywRZpVJB9tijYqVSiXv37uH+/fto2rQpPD09JYqOSFPxY3wjZMMm8TZsEm+rtylNzFFgbgsLNxsEtrKCaaIFkGkmdtUwNhanva6Nqa8VCkTFWmmtAVmpFFu/a9K7Avg3iS2upFZUBBSpPKHsMALGh/ZDSEhAUSFQeOsUYjs4IN3SQ105orLdV4iIiJ5ELxPkx2ezK2n37t149dVXsXTp0roLiKgC9++Xv01RkAtXs1z4O8bB6Jr2Yogq8oHv2/21dv7ixHbBAuCfWdlrmTlk8qFokHIAlqkPAAgoSjqEhICRUBpXroIIERFRZRlcqYehQ4di3LhxFU5FTVRXcnIrnjHPwkKsXmak5Y+qWXniBSrbzUClsoRKpYBKZVmp/YvPq81qD4LCCA/8BiDHzgMAYFSQA/ebR7V3QSIiqrcMLkEGgCZNmuDcuXNSh0GEB/fLT0qNjMXk2LgOp0CTySq3iEXeVADyqnCM9hUnyQUWdgAAq+R7sEm4VTcXJyKiesPgEuSioiL8+uuvcHJykjoUqucKCsTyYeVp0RwwN6+7eAyFysgE91sNgvKfAY6ut09CUchyFEREVHv0sg/y5MmTy1yflpaG06dPIy4ujn2QSXKxceVPK+zpCTg61m08hqTQ3AaxzYLQIOIAFIV5cL57FnHNe0kdFhERGQi9TJAPHTpUqoqFTCaDvb09evTogVdeeQUDBgyQKDoiMTGOfVT2NnNzcRIQqpksJ2+kevjB/lEE7OIjkerhh3wrPjkiIqKa08sEOTo6WuoQiCqUlCR2sShLs+a1U7mNgASfLrBKfQDj3Ay43T6Je22fljokIiIyAAbXB5lIF8TGlr3e1RWws63bWAyZoDBCbDOxa4V5ehyskqKlDYiIiAyC3iTIOTk5iImJQUEZzXLr1q1Dv3794OfnhxEjRrCCBUkqJwdITy+9XqEAvH3qPh5Dl2PngXS3FgAA53vnOGUeERHVmN4kyB9//DHatGlTKkFeuHAhpkyZgqNHjyIxMRE7duxA79698ffff0sUKdV35bUeN2wImJrUbSz1RYJ3IFRGJjDNTmXZNyIiqjG9SZAPHz6MoUOHwsrKSr0uIyMDCxcuhKenJ6KiopCYmIjTp0/DxMQEn376qYTRUn2lUpVd2s3UVKxcQdqhNDFHUsMOAACnmItsRSYiohrRmwQ5Ojoabdq00Vi3d+9eFBQU4MMPP4SPj/jsOjAwEJMmTcKxY8ekCJPqueRkoLCw9HovL0CuN79t+inFwx+FZtYwyU2HddIdqcMhIiI9pjdv2ZmZmXB8rHDsX3/9BZlMhoEDB2qs9/PzQ2JiYl2GRwQAiI8vvc7MDHBzq/tYasYM4p8HM6kDqTy5HImNOgIAnGIuSRwMERHpM71JkBs1aoQbN25orDty5AhcXV3RtGlTjfUFBQWwsbGpy/CIUFAApKaVXu/VUP9ajxWKbCgUSigU2VKHUiUZLk2Rb+kA0+wUWKbESB0OERHpKb152x4wYADWrVuHM2fOAAB+/PFH3LhxA88++2ypfS9cuABvzsRAdSwhERAemznPxARwdZEmnnpJJkNSowAAgOODyxIHQ0RE+kpvEuQ5c+bAysoK3bp1g4mJCV566SU4Oztj7ty5Gvvl5ORg+/bt6Nevn0SRUn2VUEb3igYN9K/1WN9lOvkg39IeFmmPYJqVLHU4RESkh/RmJj0nJyeEh4fjhx9+wJ07d9CoUSNMnjwZLi6azXNXr17Fiy++iPHjx0sUKdVHOblAVpbmOiMjwM1dmnjqu2Sv9vC4cQgOD68gtkVvqcMhIiI9o1dtW/b29vjvf/+LlStX4qOPPiqVHANiFYtvvvkGrVq1qvZ1VqxYAW9vb5iZmaFz5844e/ZshfunpaXhrbfegru7O0xNTdG8eXPs3bu32tcn/VPWmFBXV8CIU0pLIsO5CQrNbWCTeBuKwjypwyEiIj2jVwlyXdiyZQumT5+OefPm4eLFi2jbti0GDhyIhLKK20IcENi/f39ER0dj69atiIyMxJo1a+DJorf1yuMJskwGeHhIEwsBkMmQ4uEPmUoJ27hIqaMhIiI9wwT5MUuXLsWUKVMwadIk+Pn5YdWqVbCwsMC6devK3H/dunVISUnBjh070L17d3h7eyMoKAht27at48hJKtnZQM5jxR4cHABzc2niIVGaWwuojExgHxvBiUOIiKhKmCCXUFBQgAsXLiA4OFi9Ti6XIzg4GKdOnSrzmF27dqFr165466234OrqCn9/fyxevBhKpbLc6+Tn5yMjI0O9ZD3eeZX0SlJS6XXu7HssOUFhjFT3ljDOy4Rl6gOpwyEiIj3CBLmEpKQkKJVKuLq6aqx3dXVFXFxcmcfcuXMHW7duhVKpxN69ezFnzhx8+eWXWLhwYbnXCQkJga2trXoJCgqq1Z+D6tbjCbKZGWBvL00spCnVvRUgk8Eu7saTdyYiIvqHXiTIu3btwqNHj6QOo0wqlQouLi5YvXo1AgICMGrUKMyaNQurVq0q95gZM2YgPT1dvRw9erQOI6balJMrdrEoydVN7INM0isys0KWQ0NYpdzjYD0iIqo0vUiQn332WRw5ckT9unHjxti1a1etX8fJyQkKhQLxj80XHB8fD7dy5gp2d3dH8+bNoVD8W66gZcuWiIuLQ0FBQZnHmJqawsbGRr1YWVnV3g9BdSr5sdZjmVwfp5U2bKkerSBTqWAbf1PqUIiISE/oRYJsbW2NtLQ09evo6Git9Ns1MTFBQEAAwsLC1OtUKhXCwsLQtWvXMo/p3r07bt26BZXq3ynUbt68CXd3d5iYmNR6jKRbHu9eYW8HmPK265Rs+wYoNLeBbTyrWRARUeXoxUQhgYGBWLRoEeLj42FrawsA2Lt3b7n9ggFAJpNh2rRpVb7W9OnTMXHiRHTs2BGBgYFYtmwZsrOzMWnSJADAhAkT4OnpiZCQEADAG2+8gW+//Rbvvvsu3n77bURFRWHx4sV45513qvGTkj7JLwAyMzXXPdZ9nXREmpsvnO+ehWlWEvKtnKQOh4iIdJxeJMjfffcdJkyYgE8++QSAmPxu2rQJmzZtKveY6ibIo0aNQmJiIubOnYu4uDi0a9cOoaGh6oF7MTExkJeYO9jLywv79+/HtGnT0KZNG3h6euLdd9/Fhx9+WOVrk35JeWwWYyNjwNFRmlioYmmuzeEcfQ628VFIYIJMRERPoBcJctOmTXHy5Enk5eUhISEB3t7eWLZsGYYPH66V602dOhVTp04tc1vJvtDFunbtitOnT2slFtJdyY8lyM7OgFwvOi3VP0oTC2Q5NIRN4m0kNO7CUZRERFQhvUiQi5mZmaFhw4aYN28e+vbti0aNGkkdEtVTSiVQols8AMDFWZJQqJLSXFugQfI9WKY+QLaDl9ThEBGRDtOrBLnYvHnz1N9nZWXh/v37AMTuDqwIQXUhNRUoMS4TpmbAP93jSUdlOTSE0tgMNom3mSATEVGF9PaB8Llz59CnTx/Y29vD398f/v7+sLe3R9++fXH+/HmpwyMDl5Ki+dqZrce6Ty5HhnMTWCdHQ6Yqf6ZLIiIivWxBPnPmDHr37g0TExO88soraNmyJQDg+vXr+N///odevXrhyJEjCAwMlDhSMlSpqZqvmSDrh3SXZrB/dA2WKfeR5eQtdThERKSj9DJBnjVrFjw9PXH8+PFSE3jMnz8f3bt3x6xZs3Dw4EGJIiRDlp0N5Of/+9rcHLBmzx69kGfjggJzW9gk3maCTERE5dLLLhZnzpzBa6+9Vubsdq6urnj11VdZVYK0ht0r9Fu6azNYp9yDTFkkdShERKSj9DJBlsvlKCoq/81NqVRq1Comqk2PJ8iOLKurVzKdGkOmLIJl6gOpQyEiIh2ll1lkt27dsGLFCty7d6/UtpiYGHz33Xfo3r27BJGRoStSAhklZs8zM2P3Cn1TYGGHfCtHWCfflToUIiLSUXrZB3nx4sXo1asXfH198eyzz6J58+YAgMjISOzcuRNGRkbqqaCJalNaKiCUKO/mxNZjvZTh3ASO98MRq1JxdhciIipFLxPk9u3b48yZM5g1axZ27dqFnJwcAICFhQUGDRqEhQsXws/PT+IoyRA9PjkIE2T9lOHcBM53z8Iy7SFrIhMBgCAARkaAsXHNzlN8jurOVlnZ48rbryrXreGMmvkFQFGhOHFUTciN0mEJgPN76ha9TJABwM/PD9u3b4dKpUJiYiIAwNnZmX2PSatKlnczMQGsraWLhaqv0MwaeVZOsE6OZoJMBIjJYlERUFhYs/MUFoplfqysAAcHwMUF8PAQRzPr8RTv+fnAgwfAo0dAYqLYWFLBUKgqcSy6hBFMj3WO3ibIxeRyOVxdXaUOg+qBvDwgN/ff1w4Oev33vt7LdPKB/aNrQLOeUodCZFhUKiAjQ1yio8V15uaAjw/QvLmYNOsBQQDu3QNu3BCT45Kzp5Lh0/sEmaiupKZpvnZwkCQMqiWZTj5wjj4Hs4wE5Nnoxxs2kd7KzQUiIsTFyQlo0wZo0kQnWxlUKjEp/vtvIDPzyfuTYWKCTFRJaSW6VygUgL29dLFQzRVY2CHf0h7WKfeYIBPVpaQk4NAh4MIFoFMnoHFjqSNSu3sXOHNGbPym+o0JMlEllRygZ2cnJsmk3zIdfWCdHI1E705Sh0JU/6SnA3/+Cbi5AT17StrqkJUFHD8OxMRIFgLpGI5oI6qE7GzNsStsPTYMWY6NYJqdAuM8PkclkkxcHPD772KLsgQdfaOigK1bmRyTJibIRJXA/seGKc/aGUWmlrBK4TsjkaRUKjFB3rmzzvo3KJXA0aPA4cNAQUGdXJL0iF53sYiIiMCdO3eQmpoKQRBKbZ8wYYIEUZEhSk/793sLS3EGPTIMmY6NYJlyH6keraQOhYgSE4Ft24DevQFvb61dJicH2L9fvBxRWfQyQb59+zbGjRuHs2fPlpkYA4BMJmOCTLVCEMSucsXYemxYshwaocH1g5ApiyAo9PJPIpFhKSgADhwAOnYEOnSo9dOnpAD79old54jKo5fvBq+99hquXLmCZcuWoWfPnrBnh1DSosxMzYLwDvzvZlCy7TwhQAaL9EfIdmgodThEVOz8ebF1Iiio1qaEj4sDQkPZpYKeTC8T5BMnTmDmzJl4++23pQ6F6oGS/Y8VCsDGRrJQSBvkcmTbe8Iq5T4TZCJdExUl1lAeMECcxroGHj4Uu1XU1gx4ZNj0cpCek5MTbG1tpQ6D6omS9Y/t7GqtIYN0SJZDQ1il3JM6DCIqy4MHwJ49NWr2ffhQbDlmckyVpZdv9a+//jp+/vlnKJVKqUMhA1ekBDJKVABjbx7DlOXQEMZ5WTDNSpY6FCIqS3w8sHdvtZLkuDix5ZgpA1WFXnaxaN68OZRKJdq2bYvJkyfDy8sLijJmbRgxYoQE0ZEhSU8DhBJlOe2YIBskpYkF8q0cYZVyD/lWjlKHQ0RlSUgQk+QhQwBj40odkpLClmOqHr1MkEeNGqX+/v333y9zH5lMxhZmqrHUEt0rzMwAC3PpYiHtynJoCOukaCQ3rP1R80RUSxISxAoXgwY9cTrT7GyxWgUH5FF16GWCfPjwYa2ef8WKFfj8888RFxeHtm3b4ptvvkFgYOATj9u8eTPGjBmD4cOHY8eOHVqNkepG6mP9j8lwZTk0hGPMJRjlZ6PI1FLqcIioPA8firN7BAeXu0tRkdhyzFJuVF16mSAHBQVp7dxbtmzB9OnTsWrVKnTu3BnLli3DwIEDERkZCRcXl3KPi46Oxvvvv4+ePXtqLTaqWzm54uDpYux/bNhyrV2gNDGHdXI0Jw0h0nV37gCnTgFdu5a5+dAhIJlDCqgG9HKQXkkRERHYt28f9u3bh4iIiBqfb+nSpZgyZQomTZoEPz8/rFq1ChYWFli3bl25xyiVSrz44otYsGABGjduXOMYSDekpGi+ZguygZPJ/ulmcVfqSIioMq5cAcp4379wAYiOrvtwyLDobYK8c+dONGnSBK1bt8bQoUMxdOhQtG7dGk2bNsWuXbuqdc6CggJcuHABwSUe28jlcgQHB+PUqVPlHvfxxx/DxcUFL7/8cqWuk5+fj4yMDPWSlZVVrXhJu1JLJMiWlpUeE0J6LNOhESzSY6EozJM6FCKqjJMnxS4X/4iJERNkoprSywR57969eO655wAAixcvxvbt27F9+3YsXrwYgiBgxIgRCA0NrfJ5k5KSoFQq4erqqrHe1dUVcXFxZR5z/PhxrF27FmvWrKn0dUJCQmBra6tetNllhKpHqdScXpqtx/VDtn0DCHIFrJKjpQ6FiCpDpQLCwoDMTGRliV2TiWqDXibIn3zyCdq0aYPLly/jww8/xNNPP42nn34aH374IS5fvozWrVtjwYIFWo8jMzMT48ePx5o1a+Dk5FTp42bMmIH09HT1cvToUS1GSdWRlib+3S3GBLl+EBRGyLJvABt2syDSH3l5UIUeQNj+IuTnSx0MGQq9HKR3+fJlLF68GJaWpUeaW1pa4qWXXsLMmTOrfF4nJycoFArEx8drrI+Pj4ebm1up/W/fvo3o6GgMGzZMvU71T1ZlZGSEyMhINGnSpNRxpqamMDU1Vb+2srKqcqykXSUHd8hkACdurD+yHL3hFvUX5IX5UBmbPvkAIpLcvYvJMMo9DPj1lzoUMhB62YJsZmaGlMdHUJWQkpICMzOzKp/XxMQEAQEBCAsLU69TqVQICwtD1zJGyvr6+uLKlSsIDw9XL08//TT69OmD8PBweHl5VTkG0g0l/3tZWwNGevlRkqoj09EbAGCdzFZkIn2Qli7ORm2ddBfO0eekDocMhF6+7fft2xfLly/HoEGDSiWuZ86cwddff40BAwZU69zTp0/HxIkT0bFjRwQGBmLZsmXIzs7GpEmTAAATJkyAp6cnQkJCYGZmBn9/f43j7f55Fv/4etIfGRmaheVZ3q1+URmZIMfWAzaJt5Hu5it1OERUgcJCIPIGIAjia8eYSyg0tUKae0tpA9Oijkol4gC4ATj/hMlSqPr0MkH+7LPP0LVrV/To0QOBgYFo0aIFACAyMhJnz56Fi4sLlixZUq1zjxo1ComJiZg7dy7i4uLQrl07hIaGqgfuxcTEQC7Xy4Z3qqTHa2ey/3H9k+HcGO5Rx6AoyIXShNMnEumqyEiU6nfsdus4iozNkeXkLUlM2hYH4OET96Ka0ssE2cfHB5cvX0ZISAj27duHLVu2AAAaNWqEd999Fx999FGFk3o8ydSpUzF16tQytx05cqTCYzds2FDt65JuSC7RvcLISOxiQfVLlqM3EHUMNkl3OGkIkY66F1O6Xj0AQBDgeSMM91sNQo69Z53HRYZBLxNkAHBxccFXX32Fr776SupQyIDk5AI5JaYmtbUD+MCg/lEamyHbzhM2CVFMkPWRTFb6a3nfV/S1ou8rWvePoiJxUQlVjP/xSygsYQag/CvVPykpQMy98rfLVEp4RexHjP9TyLV1r7vAyGDobYJMpA3JSZqv7e0kCYN0QIZzE7jfPArj3HQUmrOMCQRBM7msDplMfCxjZCTOvGNmBlhYiDPxWFmJ5WIcHMT1xfuXdY6KvkpApQLi4sQlKUksE5mZKdZTrw2OyksYgdm1czIDkJMD3CjR77g8MmURGl7dxySZqkUvEuTJkydDJpNh9erVUCgUmDx58hOPkclkWLt2bR1ER4YkMVHztYODNHGQ9DIdveEmPwbb+CgkeXeUOhzpyWRiRvKkrKQigiCOgC0eBVtyNp6SLCwAJyfA2RlwcwNcXXWulIxSKU5nfOcOcP++2FKsKyyVSuQBMAOQbWCDuAoLgWvXKv/vXZwkP2jZH9kOrCxFladbf3HKcejQIcjlcqhUKigUChw6dAiyJ7QWPGk70ePy8oCSs35bWPzbkEX1j8rYFNn2XrBNYIJc53JyxDmDY2LE13K5mCR7eQENG0r6yTUrC7h6tezBYboiD4Dqn6+GRKUCIiKA3NyqHSdTFqFBxH7ENgtChmsz7QRHBkcvEuTo6OgKXxPVhqTHulew9ZjSXZrC83oYLNIeIcfOQ+pw6i+VCoiNFZezZ8WuGD4+QNOmdfaLmpkJXLgA3LqlOcsm1Q1BELtVlPfQ4UlkKhU8Ig/DOD8TyQ071G5wZJD0IkF+XExMDJydnWFuXnb5pdzcXCQmJqJhw4Z1HBnpM3avoMdlOXpDZWQC2/hIJsi6JD0dCA8XF0dHwNcXaNYMMDGp9UsVFAAXL4qtxkyMpRMVVboRozqco8/DLDsFj5r3hqDQyxSI6ohejs/38fHB9u3by92+a9cu+Pj41GFEpO9yc8UWomIKBWBjI108pBsEuQKZTj6wSboLeVHBkw+gupecDJw4Afz8M/DXX+XU/aqeO3eAX38FLl9mciylW7fEAZC1xTrxDnwubYNJdmrtnZQMjl4myMITBokUFhZyMg+qkoTHWo/t7VnejUTpLs0gUxbBJuGW1KFQRYqKxGfwW7cCe/eK3TGqKS8P+PNPccnJqcUYqcqibgGPHtX+eU1y0uBzaRvsH16t/ZOTQdCb5wsZGRlIS0tTv05OTkZM8QCOEtLS0rB582a4u7OkC1Ve0mMJsqOjNHGQ7smx80ChmTXsYyOQ5uEndThUGQ8eiIubG9CxI+BR+e4xjx4Bhw4xMZaaSgXcjAIS4rV3DZlKCdfbJ2GTdAexzXqhwMJOexcjvaM3CfJXX32Fjz/+GIBYoeK9997De++9V+a+giBg4cKFdRgd6bOsLCC7xOQgMhn7H5OmNLcWcI4+D/P0OOTaukkdDlVWXBywezfg6QkEBopl4ypw6RJw/nzNKtlRzRUpgRvXa7W3TIXM0+Pgc3ErUt1bIalhB6iMTSt3nDlgaip2yasJ2+KBh5X8j+dW8mtljimuYU5VojcJ8oABA2BlZQVBEPDBBx9gzJgx6NBBcySqTCaDpaUlAgIC0LEjyzJR5cQ/1kJhYyPOYUBULN21BZzvXYB9bAQTZH308CGwfTvg7S22KD/2CbioCDhyROxzTNLKyxPrHJdstKgLMpUKDg+vwC4+EimerZHq0QpK43/rfJqZAQ0aiA8jnJ3FQiq1Vpo7ygr4CKjsXInnq9r/rzg5trKq2nH1nN4kyF27dkXXrl0BANnZ2Xjuuefg7+8vcVSk71QqVq+gJysytUS2fQNYJ92BoqArlCZlV9AhHRcdLS7e3kC7doCLC3JzgdDQ0n8HqO6lpAA3IoGiQulikBcVwOneBTg++BuZrk1hG9gCPl1c4empxUbYZs3E/vMlC/HXNisr8TpUaXqTIBfLycnB119/DQsLCybIVGOpaf9O6lXM0UmSUEjHpbq1hGXKfdg/usaJQ/TdP4lyjo0bwuL9kWjmw0fQElKpgLt3xYZ+XaBQAO7uRfBscAOmKTeAv6zED1WenuKENVqYQSoKzaDF9BhWAJgeV43eJcgWFhYwMjKCpaWl1KGQAYh/rHSQpSVgwcZBKkOWYyMUmVrCPjYCyV7tWENVz2VnA1dOx8G2IA6WppZIc22BdLcWKDSzljq08lWyj6oZoJ5qutJ9VCWSni7WONaFQZEymZj/NvIGTEuW1C6ePvHqPxUvLC3FPhZWVmLt7Rp2Qo6KMYXv6LaobBeL6rpxg43IVaGXf+Gfe+45bN26FW+88QanlKZqyy8Akh8bBOLE1mMqj0yGVPeWcI4+D9uEKKS5t5Q6IqqmzCzg6hWg8J9H+Ub52XCKuQinmIvIsXVHhktTZDr5aPRBrQy5XCwRaWcHWFuLg7iMjWteMtI42goIASqbQGVXp4+qIAC9eoktpdUlCOI/ak6OWFg+JQVISyszAc/LExvyExKqf7naZGkpJo+Vqn+fnV2rnaSzYhwBoS0EaOdBRvEYPW324DBEepkgjx49Gm+++Sb69OmDKVOmwNvbu8xZ9R4fxEdUUkI8IDxW/J8JMlUkzc0XTjEX4fDgMtLcfPlYXg9lZQFXrpTfz9UiPRYW6bFwu3UcOTZuyHJshCx7LxRY2pe5v5MT0KiR+PTd2bnmFQ3K1FyP+6gWForVRO7fB+7eRW5SNh48FJ/e6cLkKzKZOPiuUSPpa9/LZJX7k6JUdgQQB8ANCsV5bYdVb+llgty7d2/198eOHSu1XRAEyGQyKJXKOoyK9M3jMzNZWIitCETlUZpYIMO5CWzjo2CTeBsZLk2lDomqICen4uRYgyCok2UXnEaRiQVybN2Ra+sGmasLvDs4oHlLRd3NuKmvz8aNjVHk7oV7BV6IyuiG5EePYFcYAStEQwZpM2RjY3GWcvuyP/vosDgAOtJh24DpZYK8fv16qUMgPZeSIk4vXRJbj6kyUjzbwDY+Co73w5kg65G8PDE5LqxmhQSjghy459yGl+NtOMkA+RU5EGMrZlc2NuJiaSn2qyjuW2FsXGtPGaKi9KMBubBQ7H2QkSFWBomPFyc1VLdX2Xog29YDRnlZcHwQDru4G5BJ0JRsZQX4+WllvB0ZCL1MkCdOnCh1CKTnypq69AlzCBABAPKtHJFj5wGLtEewTrqLTCcfqUOiJygsFMdX5edX73hzc7FrrsbfCJUKSE0Vl4rUwnP7qHgb+M59HtocxCUIwCefiIPUaqKyuW6RmRXim/ZAimcbuESfhXVi3RWhdnAAWrbUUncYMhh6mSCXlJWVhfv37wMAvLy8YMVC2PQEObmlZ2iytGT3Cqq85AZtYZH2CM7R55Dp6M2+yDpMqQQiIqpXJUGhABo2FPsXVzvPrYXW0axcBSCg0oO4VCpLFNexkMufPJiseAxdbm7d9wsuNLfBw5bBsHR7ALdbx2Gcm6HV67m5iS3l/JWlJ5G4S3r1nTt3Dn369IG9vT38/f3h7+8Pe3t79O3bF+fPs9M6le9RGV232HpMVZHt4IU8KyeY5KTBNiFK6nCoHCoVcP26WEqsquztgYAAwMtL+sFbxYoHcT1pEZNjFYC8KhwjrWz7BrjTYSRSPfy0dg1PT6B5c934eUn36WUL8pkzZ9C7d2+YmJjglVdeQcuWYrml69ev43//+x969eqFI0eOIDAwUOJISdcUFpaeWhpggkxVl9ywPTwjDsLp3nlkODVmXWQdo1KJhR8ef1r0JAoF4OMjTilMdUtQGCG+aQ9k23vB/eYRKAqr2SemDJ6eQJMmtXY6qgf08i/6rFmz4OnpiePHj8PNzU1j2/z589G9e3fMmjULBw8elChC0lUPH5YYLPIPGxuxjyFRVWQ6+SDf0h6m2alweHgZyQ31p6ykpVKpnkgi2wA7YqpUQGQkkJRUteMsLMS+qexuJa0sx0aIbj8CDSIOwDQrucbnc/dgckxVpyMPjqrmzJkzeO2110olxwDg6uqKV199FadPn5YgMtJlRUrgUWzp9TUdlEL1V1Ijccppp/vhMMrTnyr8/z6ANzwqFXD9hlhBoSqcnYF27Zkc64pCM2tEtx1e40Gwzs5AMxaboWrQywRZLpejqKio3O1KpRJyXek0Rjoj9lHp+qdyObtXUPVlOvkg18YFMmURXO+clDqceq9ICVy9BiRXseW4USOx5djI8BrT9ZqgMMLDlsFI8WxdrePt7IEWLWo5KKo39DKL7NatG1asWIF79+6V2hYTE4PvvvsO3bt3lyAy0lVFRcD9B6XXOzoCRnrZ0Yh0RaK3ONbBOika1kl3JY6m/iooAC7/DaQ9oepaSTK5mEA1aqS9uKiGZDIkNOmKRJ+qjSmysAT8WurOAEvSP3r5X2fx4sVIT0+Hr68vxo4di/nz52P+/PkYM2YMfH19kZ6ejpCQkGqff8WKFfD29oaZmRk6d+6Ms2fPlrvvmjVr0LNnT9jb28Pe3h7BwcEV7k/SePiw7NmzyuilQ1QlOXYeyHTyBgC43joBRaEhdlzQbVlZwKXwqk2koVAA/q3YxUpfJHu1Q3zTyjV8GRuL99ZwGz/cAHj+85W0RS//+7Rv3x5nzpzBrFmzsGvXLuT8U+DSwsICgwYNwsKFC+HnV71SMVu2bMH06dOxatUqdO7cGcuWLcPAgQMRGRkJFxeXUvsfOXIEY8aMQbdu3WBmZoYlS5ZgwIABuHbtGjw9PWv0c1LtKCgAHpTRemxmpo9TjJIuSvDpAquU++JsazeP4kGrgVKHVG8kJAJRN0sPvq2IkRHQqhVga6u9uKj2pXq0ggAZ3G4dL3cfmVzsLmPIM+QpFCxlWxf0sgUZAPz8/LB9+3ZkZGQgNjYWsbGxyMjIwLZt26qdHAPA0qVLMWXKFEyaNAl+fn5YtWoVLCwssG7dujL3/+WXX/Dmm2+iXbt28PX1xQ8//ACVSoWwsLBqx6APOiqVaKBUomNV3pUkEh1d9punu3udh0IGqtDcBikN2gAArJLvwf7hVYkjMnwqFXD7NnDjetWTY39/Jsf6Ks3DD/FNupa73ccHsLOru3jIcOltglxMJpNpLDVRUFCACxcuIDg4WL1OLpcjODgYp06dqtQ5cnJyUFhYCAcHh3L3yc/PR0ZGhnrJqspzQR0RB+DhP191WWZW2XWPZXLAhY9WqRYlNeyAAgs7AIDL3dOwSCtjPnOqFdnZQHi42HWqKhQKMTm2sdFKWFRHUj1bI6mMsopOTkADPrilWqK3CXJERARGjhwJGxsbuLu7w93dHTY2Nhg5ciSuXq1e601SUhKUSiVcH+uU5urqiri4yqWCH374ITw8PDSS7MeFhITA1tZWvQQFBVUrXqqYIAC3ov6dRrUkJ0fA1KTuYyLDJcgViG3WC5DJIFOp4Hn9IIxzqzGFG5VLpQJiYoCLl6rW3xgQk+NW9SY5NoP49m64/QySvDsizb2l+rW5uThLHlFt0cs+yMeOHcNTTz0FlUqF4cOHo/k/vxWRkZHYtWsX9u3bh9DQUPTs2bNO4/r000+xefNmHDlyBGYVdICaMWMGpk+frn4dHh7OJFkLHj4EMjPL3sbu4aQNubZuSG7QFo73w6EozEejy7txr+3TKDSzljo0DWaAeqIQfZGaKnap+GfISZXI5IBvS8CunnSrUCiypQ6hTsQ17QGj/GxYp8XA19eQB+WRFPTyv9O0adPg4uKCo0ePwsvLS2Pb/fv30atXL0yfPh3nzp2r0nmdnJygUCgQ/9gz+fj4+DInJSnpiy++wKeffoo///wTbdq0qXBfU1NTmJqaql9bWVlVKU56spwcoIwqgAAAa+v60opEUkhs1BEW6Y9gnpEA06JsNLvxBxI7PgXY28PYWGzJVCjE8lNyOVDDnmEAALviv+RlPS4pQ3bJ2leVOUYQxEAbNgSaNat6gCUplWLdxYICIC8PyM0F8sufUjgzUxxHkFqF8m2Pa94McCy/1xvpK5kMD1v2Q//snbC2ruKc4kRPoJcJ8rVr1/DJJ5+USo4BwMvLC2+88Qbmz59f5fOamJggICAAYWFheOaZZwBAPeBu6tSp5R732WefYdGiRdi/fz86duxY5etS7VKpgOsVDNxh6zHVlEIhftCysvp3sbQUpyo2N5fD7JlgmIduh6IgF0AWIN8JBPQVE0xtaPDPh+zayLbLUpxQ9+lT8wS5LEqlmAlnZABpaUBqKpJuJuPR1RSkJatqdOomTVjKzZA1bGwM724Dge3bxQ9cRLVELxPkRo0aIb+CFoeCgoIyk+fKmD59OiZOnIiOHTsiMDAQy5YtQ3Z2NiZNmgQAmDBhAjw9PdV1lpcsWYK5c+di06ZN8Pb2VvdVtrKyYsuwRG7dEgfxlMXMTBzIQVQZZmaAg4NYDtDeXqx8YGsrJsMV56JWwOCBwB9/iMlfQQEQGirWnwoMBEo8QaoVzZoBN25UvWNuVVhZaSc5BsRPHHZ2SIMd7iQ1xM1YIEMAZC2VMMtKgnl6HCzTH8EiPRYyZfmzqD7Ox4cfiA2ZtbX4mQ0m1kBwMLB3r9hCQlQL9DJBnjt3LqZNm4YhQ4agXbt2GtsuXbqEb775BsuWLavWuUeNGoXExETMnTsXcXFxaNeuHUJDQ9UD92JiYjSmsV65ciUKCgowcuRIjfPMmzevWq3YVDP37wMVjads0IAzK1HZrKzEacedncUPUQ4OYotwtbm4AP37AwcO/Pumff262JHWz08cUVSL9aii0AzarIdjBaA202OVSmwwTkoSK83cvy++LkmQK5Br44pcG1ekeLWFTKWEeXocrFPuwSo5GsZ55f/EPj5ANdtJSA8YGQEDBwImxYOtPTyAHj2Av/6SNC4yHHqZIJ8+fRqurq4ICAhAt27d0LRpUwBAVFQUTp06BX9/f5w6dUqjNJtMJsPy5csrdf6pU6eW26XiyJEjGq+jo6Or9TPorEr2YXQr+bUqfRi1KD4euFvBTL8mJnzUSiJLS3GacSenf5PiGiXD5WnYUGzZCgv7t89PQYFYoyw8XLyonZ3YgloDUfE28H25crOMVUfxr/jq1TVrkRUE8cfPzRWf8lS1sU+QK5Bj74kce0/EN+kG84x42CTcgk3ibY0ZDJs0YcuxoevdW/wQq8HXV/yUFR4uQURkaPQyQf7222/V3584cQInTpzQ2H7lyhVcuXJFY11VEuR6yapqfRjPV7UZtvi8w4aJ717VpVKJS2GhxiCf6GvZuBGfCWOzjHJblRo0qHEeQnpEJhMTYTs7sVtEcTcJB4fa7+FQIW9vYMgQsSX58T6SOTnVK8vwmKw4cdabyn4OVaksUVzHQi5/csWD4vNGR+vWE+zi1uX4xl1hnXIP9vHX0d7pAT8IG7hOnYDGjcvZGBgofvqKiqrTmMjw6GWCrNKlv9CGQk/7MKpUwMmTQEQ+gH+Kh8iURTDNSYVpdjLMMxNhnhEP66JUeHhUrnWcdJ9cLvYPNjcXF0vLfxdr638H0OnMByI3N2DkSPHxb0yM1i4jk1X2M24eABWAvErtr+WHPzUnlyPfwwetJ/rA1ToDuHoViIwUP0iTQWnRAmjf/gk7BQWJlVG0+LtGhk8vE2TSEm0NwNGSBw/E5DgtTXO9oDBCnrUz8qydke7mCwDoEVgAuUOceNCDB6UPohopfgRvaiomrNVVnIjJ5f+WQ1MoAGPjfxdTU/FryTJpxd+rVGIxhOzsf0uplSyrZmSked7i10ZGddA33cICGDTon5kuLgIJCVq+YP1hYyP+04pdum2Abt2Ajh2Ba9eAK1dY3cBANGwIVGp6A7lc7P8fGlr16RaJ/qHXCfLdu3exb98+3Pun4G2jRo3w1FNPwcfHR+LI9FNUlPYakAsLxSSkJr0rADHxSUwUY02pZNlLOzvAt40JIG/4b5mt9HSxUPKdO0xUakFxYpufL/Yv1UfFyXTJpLlkMl1yKU7IK1qKW3OLl+IEXCZrCDRrCLl7BowTHkKRmVZ+TcJKemRSfwt7e3kBffuW0W3GxERsamzdWkyU//6bibIec3cXu/NX+oOsQiGO4gsNBR5x2neqOr1NkP/zn/9g+fLlpbpbyOVyvPfee/jiiy8kikw/RUWJ4xu0pbiFccGCuh8o17VrGX9UbW2BNm3EJSNDrA1382bpYfRUb5Ts3l43bP5Zai6uoFZOo1fkciAgoBKP242MgLZtxcoh164Bly8zUdYzrq7iE4Iqz5RnZCQeeOCA+OSQqAr0suDVl19+ia+++gojRozAqVOnkJaWhrS0NJw6dQojR47EV199ha+++krqMPVKcctxJYtYQKXqCJWqAVSqyk2MUnzeCspXa0XDhpUo9WRjA3ToAIweLQ6matyYteCIdJidHfD005VIjksyNgbatQPGjhXLgdnWk3mn9ZybGzB4sHj7qqU4SS53VB9R2fSyBXnNmjV4+umn8euvv2qs79y5MzZv3oy8vDx8//33mDZtmkQR6q/KD/KJA/BQfUxlzlvXjI3F98Eq8fQUl+xssbUpIkKslkFEkpPLxYc+AQE1GIBpZCS2Jvv5if3Br10TWxcr2zpAdcbLS+xKXOWW48fJ5UC/fuIo3scqXBGVRy8T5OjoaLz77rvlbh84cCBCQ0PrMCLSRZ06/Vu9rsosLcVyQe3bi0ny5cv627mWyAB4eYndpWpxbhXxEVPDhuIjtJs32c1Kh7RoIQ7Iq7WHeTKZ+B/I1lYc3c1qWPQEepkgu7i44O+//y53+99//w1nZ+c6jIh0jacn0KpVLZzI2Fjsv9iq1b+Jci3UrSWiynFzEwtSeHho8SJWVmI3qw4dxEG7d+6IRZ+ZLNc5mUxsm2jbVksX8PMTi6H/+Sf/llOF9DJBfv7557F8+XJ4e3vj7bffhqWlJQAgOzsb3377LX744Qe899570gZJkjE3F0e112q3DiMj8dmuv784ovHyZSA1tRYvQFSXzFA8UYguksvFht02bcQEuU65uIhLly7i7/j9+2IXjLg4oKiojoOpX8zMxL/dDRpo+ULFdcmPHGGtZCqXXibIn3zyCcLDwzFz5kzMnTsXHv80LTx69AhFRUXo06cPPv74Y4mjJCkUdzWrSS3eJ16gRQtxefRI7L947x4f15FeUSiePHueFFxcxLFUTZtqaervqiqefrFNG/F3PClJbGFOTASSk8V66vzdrxWenuL00f+0d2mfmZk4eO/6deD0aU4qQ6XoZYJsYWGBsLAw7Ny5U6MO8qBBgzB48GAMGzYMMp2f+om0oVcvLT+KLcnDQ1xyc8VW5Zs3K1+cmaieMzMT+xM7OYllvDw9xXXaJAiVW4B/895/18kBMxcIXi4Qils4VSqxG0ZmBpCZBVlONmS5OUBBPmQFBWKLs1IJoGYDALONbSEAMMR3NRMTsUuFn59EAbRsKT6uOHkSuHtXoiBIF+ldgpyTk4Nx48bhueeew4svvojhw4dLHRLpiG7dgObNJbiwufm/NZVTUsT+i3fvsgsGGRRBELvkm5hU/xwymdhbycRErEJRVCT2XIiNBcLDNQtJFH9f1teKEtnHF5VKc//aJQdg98+iPTEG2Lgpk4l/rzt10oGnBZaWYrmM2FjgzBlOHkUA9DBBtrCwwJ9//omnnnpK6lDqObfHvkpHLhfLuWlzopNKc3AQl44dxZal4v6LsbEsF0d6TSYTn0LX9L9xfr5YRZHqJ5lM7EbToYPYe0WnuLsDzzwj/s2+fJmTi9RzepcgA0CPHj1w6tQpTJkyRepQ6i2F4rzUIQAQB5/361f3s/NVio2NWP2iVSux+So5GYiPF/svJiWx/yIR1RvW1kCzZmJDRrXLb9aVBg3EJS1N7Dp3+zaQmSl1VFTH9DJB/vbbbzFw4EDMnj0br7/+Ohpofcgr6RoTE7GgRLt2tVBEvi7IZBAcnSA4OEHw/eeRr1IFIT0DSE8X//hmZYllh3JzIeTlQ1ZUKPZfVCprfPlCeQ2eixMRVZGFxb/9y728xO/1jp2d2EE6MFDsMvfwodjIkZws/s2uhb/NpLv0IbUopW3btigqKkJISAhCQkJgZGQEU1NTjX1kMhnS09MlipAeV9z/z9S0+gNxZDKxu6+9vTg2TqEQu/s+3jex5PcV9Uks+fXx75XKf78vuZS1vuRxJdc9uf9j3fRfBIAsNlQTUQVqs4+5mZn49zkvTyzy8884+krHURv7PGm/qvdJt/9n8Rf/dNsC8vxcKIrya/wk8I6RwmAHYeozvUyQn3vuOVap0DPFtys/X/yjWV25ueI4uNu3aycuIiJiH/PqMf9nqZlkltfWSXqZIG/YsEHqEIiIiIjIQOlVgpyXl4edO3fi7t27cHJywpAhQ+Du7i51WERERERkQPQmQU5ISEC3bt1w9+5dCP90HrKwsMCOHTsQHBwscXREREREZCjkUgdQWZ988gmio6Mxbdo07N69G8uWLYO5uTlee+01qUMjIiIiIgOiNy3IBw4cwIQJE/DFF1+o17m6umLs2LGIjIxEixYtJIyOiIiIiAyF3rQgx8TEoEePHhrrevToAUEQEB8fL1FURERERGRo9CZBzs/Ph9ljBXSLXxcVsUYKEREREdUOvUmQASA6OhoXL15UL5cvXwYAREVFaawvXqprxYoV8Pb2hpmZGTp37oyzZ89WuP9vv/0GX19fmJmZoXXr1ti7d2+1r01ERERE0tKbPsgAMGfOHMyZM6fU+jfffFPjtSAIkMlkUFZjGsgtW7Zg+vTpWLVqFTp37oxly5Zh4MCBiIyMhIuLS6n9T548iTFjxiAkJARDhw7Fpk2b8Mwzz+DixYvw9/ev8vWJiIiISFp6kyCvX7++Tq6zdOlSTJkyBZMmTQIArFq1Cnv27MG6devw0Ucfldp/+fLlGDRoEP773/8CEKttHDx4EN9++y1WrVpVJzETERERUe3RmwR54sSJWr9GQUEBLly4gBkzZqjXyeVyBAcH49SpU2Uec+rUKUyfPl1j3cCBA7Fjx45yr5Ofn4/8/Hz166ysrJoFXouqPj995c/L2cGlx/tr2Hh/DZs27y9Jj7+/ukVvEuS6kJSUBKVSCVdXV431rq6uuHHjRpnHxMXFlbl/XFxcudcJCQnBggULah5wLbKyEr9q65eo+LzdugGNGmnnGlS+4kIvvL+G6f598Svvr2Fq0EC7CY5MJiZRvL/SqIv7C/z7Pk+VwwRZAjNmzNBodQ4PD0dQUJCEEQHNmgE3bgDabMy2shKvQ3WvVSveX0PG+2vYeH8NG++vbmKCXIKTkxMUCkWpusrx8fFwc3Mr8xg3N7cq7Q8ApqamMDU1Vb+20pGPdfzlMWy8v4aN99ew8f4aNt5f3aNXZd60zcTEBAEBAQgLC1OvU6lUCAsLQ9euXcs8pmvXrhr7A8DBgwfL3Z+IiIiIdBtbkB8zffp0TJw4ER07dkRgYCCWLVuG7OxsdVWLCRMmwNPTEyEhIQCAd999F0FBQfjyyy8xZMgQbN68GefPn8fq1aul/DGIiIiIqJqYID9m1KhRSExMxNy5cxEXF4d27dohNDRUPRAvJiYGcvm/De/dunXDpk2bMHv2bMycORPNmjXDjh07WAOZiIiISE/JBIEFXqR28eJFBAQE4MKFC+jQoYPU4RARERHVa+yDTERERERUAhNkIiIiIqIS2AeZqi02NhaxsbFSh0FERFSvuLu7w93dXeowDBoTZB3g7u6OefPm6dV/9vz8fIwZMwZHjx6VOhQiIqJ6JSgoCPv379eYU4FqFwfpUbVkZGTA1tYWR48e1ZmJTqj2ZGVlISgoiPfXQPH+GjbeX8NWfH/T09NhY2MjdTgGiwkyVUtxgsxfUMPE+2vYeH8NG++vYeP9rRscpEdEREREVAITZCIiIiKiEpggU7WYmppi3rx5HCBgoHh/DRvvr2Hj/TVsvL91g32QiYiIiIhKYAsyEREREVEJTJCJiIiIiEpggkxEREREVAITZJJcdHQ0ZDIZNmzYIHUoREREREyQ9c3t27fx2muvoXHjxjAzM4ONjQ26d++O5cuXIzc3V2vXjYiIwPz58xEdHa21a1TGokWL8PTTT8PV1RUymQzz58+XNB6pyGSySi1Hjhyp8bVycnIwf/78Kp2L96nmdPke37hxAx988AHatWsHa2truLu7Y8iQITh//nyNY6kvdPn+Pnr0COPGjUOLFi1gbW0NOzs7BAYGYuPGjeC4/srR5fv7uF9++QUymYyzLj7GSOoAqPL27NmD559/HqamppgwYQL8/f1RUFCA48eP47///S+uXbuG1atXa+XaERERWLBgAXr37g1vb2+tXKMyZs+eDTc3N7Rv3x779++XLA6p/fTTTxqvf/zxRxw8eLDU+pYtW9b4Wjk5OViwYAEAoHfv3pU6hvep5nT5Hv/www9Yu3YtnnvuObz55ptIT0/H999/jy5duiA0NBTBwcE1jsnQ6fL9TUpKwoMHDzBy5Eg0bNgQhYWFOHjwIF566SVERkZi8eLFNY7J0Ony/S0pKysLH3zwASwtLWsch6Fhgqwn7t69i9GjR6NRo0Y4dOgQ3N3d1dveeust3Lp1C3v27JEwwn8JgoC8vDyYm5vX+rnv3r0Lb29vJCUlwdnZudbPry/GjRun8fr06dM4ePBgqfVS4X2qOV2+x2PGjMH8+fM1WpwmT56Mli1bYv78+UyQK0GX72+bNm1KtUZOnToVw4YNw9dff41PPvkECoVCmuD0hC7f35IWLlwIa2tr9OnTBzt27JA6HJ3CLhZ64rPPPkNWVhbWrl2rkRwXa9q0Kd59913166KiInzyySdo0qQJTE1N4e3tjZkzZyI/P1/jOG9vbwwdOhTHjx9HYGAgzMzM0LhxY/z444/qfTZs2IDnn38eANCnT59Sj4aKz7F//3507NgR5ubm+P777wEAd+7cwfPPPw8HBwdYWFigS5cuNUrkpWy91jcqlQrLli1Dq1atYGZmBldXV7z22mtITU3V2O/8+fMYOHAgnJycYG5uDh8fH0yePBmA2D+8OMFdsGCB+t4/qcsE71PdkOoeBwQElHoc6+joiJ49e+L69eu1+0PWY1L+DpfF29sbOTk5KCgoqPHPRtLf36ioKHz11VdYunQpjIzYXvo4/ovoiT/++AONGzdGt27dKrX/K6+8go0bN2LkyJH4z3/+gzNnziAkJATXr1/H9u3bNfa9desWRo4ciZdffhkTJ07EunXr8NJLLyEgIACtWrVCr1698M477+Drr7/GzJkz1Y+ESj4aioyMxJgxY/Daa69hypQpaNGiBeLj49GtWzfk5OTgnXfegaOjIzZu3Iinn34aW7duxbPPPlt7/0BUymuvvYYNGzZg0qRJeOedd3D37l18++23uHTpEk6cOAFjY2MkJCRgwIABcHZ2xkcffQQ7OztER0dj27ZtAABnZ2esXLkSb7zxBp599lmMGDECgNjCRNLTtXscFxcHJyenWv0Z6zOp729ubi6ys7ORlZWFo0ePYv369ejatatWng7WR1Lf3/feew99+vTB4MGD8euvv2r1Z9VLAum89PR0AYAwfPjwSu0fHh4uABBeeeUVjfXvv/++AEA4dOiQel2jRo0EAMJff/2lXpeQkCCYmpoK//nPf9TrfvvtNwGAcPjw4VLXKz5HaGioxvr33ntPACAcO3ZMvS4zM1Pw8fERvL29BaVSKQiCINy9e1cAIKxfv75SP58gCEJiYqIAQJg3b16ljzFkb731llDy1/nYsWMCAOGXX37R2C80NFRj/fbt2wUAwrlz58o9d03+rXmfao+u3uNif/31lyCTyYQ5c+ZU+xz1mS7e35CQEAGAeunXr58QExNTpXOQSNfu7+7duwUjIyPh2rVrgiAIwsSJEwVLS8sq/ESGj10s9EBGRgYAwNraulL77927FwAwffp0jfX/+c9/AKBUFwc/Pz/07NlT/drZ2RktWrTAnTt3Kh2jj48PBg4cWCqOwMBA9OjRQ73OysoKr776KqKjoxEREVHp81PV/Pbbb7C1tUX//v2RlJSkXoofjR8+fBgAYGdnBwDYvXs3CgsLJYyYqkqX7nFCQgLGjh0LHx8ffPDBB1q5Rn2jC/d3zJgxOHjwIDZt2oSxY8cCgFarJdUnUt7fgoICTJs2Da+//jr8/Pxq5ZyGiAmyHrCxsQEAZGZmVmr/e/fuQS6Xo2nTphrr3dzcYGdnh3v37mmsb9iwYalz2Nvbl+oHVREfH58y42jRokWp9cVdMx6Pg2pPVFQU0tPT4eLiAmdnZ40lKysLCQkJAICgoCA899xzWLBgAZycnDB8+HCsX7++VF910j26co+zs7MxdOhQZGZmYufOnSwVVUt04f42atQIwcHBGDNmDH755Rc0btwYwcHBTJJrgZT396uvvkJSUpK68gWVjX2Q9YCNjQ08PDxw9erVKh0nk8kqtV95o5GFKtS7ZJ803aJSqeDi4oJffvmlzO3FgzpkMhm2bt2K06dP448//sD+/fsxefJkfPnllzh9+jSTHR2mC/e4oKAAI0aMwOXLl7F//374+/tX+1ykSRfu7+NGjhyJNWvW4K+//ir1xJCqRqr7m56ejoULF+LNN99ERkaG+gl1VlYWBEFAdHQ0LCws4OLiUrMf0AAwQdYTQ4cOxerVq3Hq1Cl07dq1wn0bNWoElUqFqKgojYF08fHxSEtLQ6NGjap8/com24/HERkZWWr9jRs31NtJO5o0aYI///wT3bt3r9SHly5duqBLly5YtGgRNm3ahBdffBGbN2/GK6+8Uq17T9on9T1WqVSYMGECwsLC8OuvvyIoKKg6PwaVQ+r7W5biluP09PRaOV99JtX9TU1NRVZWFj777DN89tlnpbb7+Phg+PDhLPkGdrHQG8WFvF955RXEx8eX2n779m0sX74cADB48GAAwLJlyzT2Wbp0KQBgyJAhVb5+cRHxtLS0Sh8zePBgnD17FqdOnVKvy87OxurVq+Ht7c2+T1r0wgsvQKlU4pNPPim1raioSH0fU1NTSz0paNeuHQCoH+FZWFgAqNq9J+2T+h6//fbb2LJlC7777jv1yHmqPVLe38TExDLXr127FjKZDB06dKjUeah8Ut1fFxcXbN++vdTSp08fmJmZYfv27ZgxY0b1fzADwhZkPdGkSRNs2rQJo0aNQsuWLTVm0jt58iR+++03vPTSSwCAtm3bYuLEiVi9ejXS0tIQFBSEs2fPYuPGjXjmmWfQp0+fKl+/Xbt2UCgUWLJkCdLT02Fqaoq+fftW+Bjmo48+wv/+9z889dRTeOedd+Dg4ICNGzfi7t27+P333yGXV/3z2U8//YR79+4hJycHAPDXX39h4cKFAIDx48ezVfofQUFBeO211xASEoLw8HAMGDAAxsbGiIqKwm+//Ybly5dj5MiR2LhxI7777js8++yzaNKkCTIzM7FmzRrY2NioP2iZm5vDz88PW7ZsQfPmzeHg4AB/f/8KH6fzPmmflPd42bJl+O6779C1a1dYWFjg559/1tj+7LPPcmauGpLy/i5atAgnTpzAoEGD0LBhQ6SkpOD333/HuXPn8Pbbb5ca30JVJ9X9tbCwwDPPPFNq/Y4dO3D27Nkyt9VbUpbQoKq7efOmMGXKFMHb21swMTERrK2the7duwvffPONkJeXp96vsLBQWLBggeDj4yMYGxsLXl5ewowZMzT2EQSxRNuQIUNKXScoKEgICgrSWLdmzRqhcePGgkKh0Cj5Vt45BEEQbt++LYwcOVKws7MTzMzMhMDAQGH37t0a+1SlzFtQUJBG2aGSS1kl6OqLx0sIFVu9erUQEBAgmJubC9bW1kLr1q2FDz74QHj06JEgCIJw8eJFYcyYMULDhg0FU1NTwcXFRRg6dKhw/vx5jfOcPHlSCAgIEExMTCpVToj3qfbp0j2eOHFiufcXgHD37t3a/NHrBV26vwcOHBCGDh0qeHh4CMbGxur3mfXr1wsqlapWf+76Qpfub1lY5q00mSBUYSQWEREREZGBYx9kIiIiIqISmCATEREREZXABJmIiIiIqAQmyEREREREJTBBJiIiIiIqgQmygfnss8/g6+sLlUoldSg1Nnr0aLzwwgtSh6FTeH8NG++vYeP9NXy8xwZE6jpzVHvS09MFBwcHYd26dep1+Kcu6RdffFFq//Xr1wsAhHPnztV6LMHBwQIA4a233ipz+w8//CD4+voKpqamQtOmTYWvv/661D4XL14U5HK5EB4eXuvx6SPeX8PG+2vYeH8NH++xYWELsgFZt24dioqKMGbMmFLbPv/8c/WsZtq2bds2jemlH/f999/jlVdeQatWrfDNN9+ga9eueOedd7BkyRKN/dq3b4+OHTviyy+/1HbIeoH317Dx/ho23l/Dx3tsYKTO0Kn2tGnTRhg3bpzGOgBCu3btBADCl19+qbFNG59ec3NzBW9vb+Hjjz8u89NrTk6O4OjoWGrmvRdffFGwtLQUUlJSNNZ/8cUXgqWlpZCZmVlrMeor3l/Dxvtr2Hh/DR/vsWFhC7KBuHv3Li5fvozg4OBS27p3746+ffvis88+Q25urlbj+Oyzz6BSqfD++++Xuf3w4cNITk7Gm2++qbH+rbfeQnZ2Nvbs2aOxvn///sjOzsbBgwe1FrM+4P01bLy/ho331/DxHhseJsgG4uTJkwCADh06lLl9/vz5iI+Px8qVKys8T35+PpKSkiq1PC4mJgaffvoplixZAnNz8zLPf+nSJQBAx44dNdYHBARALpertxfz8/ODubk5Tpw4UWHcho7317Dx/ho23l/Dx3tseIykDoBqx40bNwAAPj4+ZW7v2bMn+vTpg88//xxvvPFGub88//vf/zBp0qRKXVMQBI3X//nPf9C+fXuMHj263GNiY2OhUCjg4uKisd7ExASOjo549OiRxnojIyN4eXkhIiKiUjEZKt5fw8b7a9h4fw0f77HhYYJsIJKTk2FkZAQrK6ty95k/fz6CgoKwatUqTJs2rcx9Bg4cWK1HKYcPH8bvv/+OM2fOVLhfbm4uTExMytxmZmZW5uMne3v7Mj8t1ye8v4aN99ew8f4aPt5jw8MEuR7p1asX+vTpg88++wyvv/56mfu4u7vD3d29SuctKirCO++8g/Hjx6NTp04V7mtubo6CgoIyt+Xl5ZX5qVoQBMhksirFVB/x/ho23l/Dxvtr+HiP9QsTZAPh6OiIoqIiZGZmwtrautz95s2bh969e+P777+HnZ1dqe25ublIT0+v1DXd3NwAAD/++CMiIyPx/fffIzo6WmOfzMxMREdHw8XFBRYWFnB3d4dSqURCQoLGI56CggIkJyfDw8Oj1HVSU1PRrFmzSsVkqHh/DRvvr2Hj/TV8vMeGh4P0DISvry8AcSRtRYKCgtC7d28sWbKkzEcpW7ZsUX+CfdJSLCYmBoWFhejevTt8fHzUCyD+4vr4+ODAgQMAgHbt2gEAzp8/r3Hd8+fPQ6VSqbcXKyoqwv3799GyZcsq/XsYGt5fw8b7a9h4fw0f77HhYQuygejatSsA8T95mzZtKtx3/vz56N27N1avXl1qW3X6P40ePbrULxUAPPvssxg8eDCmTJmCzp07AwD69u0LBwcHrFy5EoMHD1bvu3LlSlhYWGDIkCEa54iIiEBeXh66detWpZgMDe+vYeP9NWy8v4aP99gASVWAmWqfv7+/MGbMGI11KGeqyaCgIPUUmNqY5rKia69YsUIAIIwcOVJYs2aNMGHCBAGAsGjRolL7fvHFF4KFhYWQkZGhlRj1Ce+vYeP9NWy8v4aP99iwMEE2IEuXLhWsrKyEnJwc9bryfkEOHz4s2S+nIAjC6tWrhRYtWggmJiZCkyZNhK+++kpQqVSl9uvcuXOpmYnqK95fw8b7a9h4fw0f77FhYYJsQNLS0gQHBwfhhx9+kDqUWnHp0iVBJpMJly5dkjoUncD7a9h4fw0b76/h4z02LDJBeKzSNOm1JUuWYP369YiIiIBcrt9jMEePHg2VSoVff/1V6lB0Bu+vYeP9NWy8v4aP99hwMEEmIiIiIipBvz/eEBERERHVMibIREREREQlMEEmIiIiIiqBCTIRERERUQlMkImIiIiISmCCTERERERUAhNkIiIiIqISmCATEREREZXABJmIiIiIqAQmyEREREREJTBBJiIiIiIqgQkyEREREVEJTJCJiIiIiEpggqwDYmNjMX/+fMTGxkodChEREVG9xwRZB8TGxmLBggVMkImIiIh0ABNkIiIiIqISmCATEREREZXABJmIiIiIqAQmyEREREREJTBBJiIiIiIqgQkyEREREVEJTJCJiIiIiEpggkxEZSoqKpI6BCIiIkkwQSaiMjFBJiKi+ooJMhGVSalUSh0CERGRJJggE1GZmCATEVF9pZMJcmxsLP7++29kZ2dLHQpRvcUuFkREVF/pVIK8c+dO+Pr6okGDBujQoQPOnDkDAEhKSkL79u2xY8cOaQMkqkeYIBMRUX2lMwnyH3/8gREjRsDJyQnz5s2DIAjqbU5OTvD09MT69esljJCofiksLJQ6BCIiIknoTIL88ccfo1evXjh+/DjeeuutUtu7du2KS5cuSRAZUf1UUFAgdQhERESS0JkE+erVq3jhhRfK3e7q6oqEhIQ6jIiofsvLy5M6BCIiIknoTIJsYWFR4aC8O3fuwNHRsQ4jIqrfmCATEVF9pTMJcp8+fbBx48YyBwbFxcVhzZo1GDBggASREdVPrCJDRET1lc4kyIsWLcKDBw/QqVMnfP/995DJZNi/fz9mz56N1q1bQxAEzJs3r05iWbFiBby9vWFmZobOnTvj7NmzFe6/bNkytGjRAubm5vDy8sK0adPY+kZ6Lysri7WQiYioXtKZBLlFixY4fvw4HB0dMWfOHAiCgM8//xyLFy9G69atcezYMXh7e2s9ji1btmD69OmYN28eLl68iLZt22LgwIHl9n/etGkTPvroI8ybNw/Xr1/H2rVrsWXLFsycOVPrsRJpkyAISElJkToMIiKiOmckdQAltWrVCn/++SdSU1Nx69YtqFQqNG7cGM7OznUWw9KlSzFlyhRMmjQJALBq1Srs2bMH69atw0cffVRq/5MnT6J79+4YO3YsAMDb2xtjxoxR13Am0mfx8fF1+vtHRESkC3SmBbkke3t7dOrUCZ07d67TN+eCggJcuHABwcHB6nVyuRzBwcE4depUmcd069YNFy5cUHfDuHPnDvbu3YvBgweXe538/HxkZGSol6ysrNr9QYhqyYMHD6QOgYiIqM7pTIL89ddfY+DAgeVuf+qpp7By5UqtxpCUlASlUglXV1eN9a6uroiLiyvzmLFjx+Ljjz9Gjx49YGxsjCZNmqB3794VdrEICQmBra2tegkKCqrVn4Ootjx8+JAThhARUb2jMwny2rVr4efnV+52Pz8/rF69ug4jqpwjR45g8eLF+O6773Dx4kVs27YNe/bswSeffFLuMTNmzEB6erp6OXr0aB1GTFR5SqUS0dHRUodBRERUp3QmQb59+zZatmxZ7nZfX1/cvn1bqzE4OTlBoVAgPj5eY318fDzc3NzKPGbOnDkYP348XnnlFbRu3RrPPvssFi9ejJCQEKhUqjKPMTU1hY2NjXqxsrKq9Z+FqLbcvHlT6hCIiIjqlM4kyCYmJuV2YwCA2NhYyOXaDdfExAQBAQEICwtTr1OpVAgLC0PXrl3LPCYnJ6dUXAqFAoBYBYBI3z18+BCpqalSh0FERFRndCZB7tKlCzZs2IDMzMxS29LT07F+/Xp06dJF63FMnz4da9aswcaNG3H9+nW88cYbyM7OVle1mDBhAmbMmKHef9iwYVi5ciU2b96Mu3fv4uDBg5gzZw6GDRumTpSJ9N3ly5elDoGIiKjO6EyZt3nz5iEoKAjt2rXDe++9h1atWgEArl69imXLliE2NhabNm3SehyjRo1CYmIi5s6di7i4OLRr1w6hoaHqgXsxMTEaLcazZ8+GTCbD7Nmz8fDhQzg7O2PYsGFYtGiR1mMlqis3b95E27ZtYWdnJ3UoREREWicTdKgfwMGDB/Haa68hOjoaMpkMgNhNwcfHBytXrjTYqaYvXryIgIAAXLhwAR06dJA6HCIAwL59+3D//n31a09PTwwePFj9u0lERGSodKYFGQD69++PW7du4dKlS+oBeU2aNEGHDh34pkwksYcPH+LKlSto06aN1KEQERFplU4lyIA4MUdAQAACAgKkDoWIHnPmzBnY2NjUybTvREREUtG5BDkiIgJ37txBampqmVUgJkyYIEFURASIXZ7CwsLQr18/JslERGSwdCZBvn37NsaNG4ezZ8+WWx5NJpMxQSaSmFKpxMGDB9GjR48Ka5cTERHpK51JkF977TVcuXIFy5YtQ8+ePWFvby91SET1VseOHREdHQ1LS0vMmjWr1HZBEHDs2DGkpaWhc+fOWq9RTkREVJd0JkE+ceIEZs6cibffflvqUIjqvbi4OCQnJ0OpVFa435UrV5CcnIzg4GCYmZnVUXRERETapTPNPk5OTrC1tZU6DCKqokePHmH79u1ISkqSOhQiIqJaoTMJ8uuvv46ff/75iS1WRKR7MjMzsXPnTty6dUvqUIiIiGpMZ7pYNG/eHEqlEm3btsXkyZPh5eVV5lTNI0aMkCA6InoSpVKJQ4cOISUlBZ06dWLtciIi0ls6kyCPGjVK/f37779f5j4ymYwtzEQ6Ljw8HBkZGejTp0+ZH3KJiIh0nc4kyIcPH5Y6BCKqJXfu3EFeXh4GDhwIY2NjqcMhIiKqEp1JkIOCgqQOgYhq0aNHj7B371489dRTMDExkTocIiKiStOZQXrF8vPzcerUKezcuZOj4on0XHx8PPbt24eCggKpQyEiIqo0nUqQv/76a7i7u6NHjx4YMWIELl++DABISkqCk5MT1q1bJ3GERFRV8fHx2L9/P8cPEBGR3tCZBHn9+vV47733MGjQIKxdu1ZjumknJyf07dsXmzdvljBCIqqu2NhYhIWFlTuNPBERkS7RmQT5yy+/xPDhw7Fp0yYMGzas1PaAgABcu3ZNgsiIqDZER0fj1KlTUodBRET0RDqTIN+6dQtPPfVUudsdHByQnJxchxERUW27evUqrl69KnUYREREFdKZBNnOzq7CQXkRERFwc3Orw4iISBtOnTqFmJgYqcMgIiIql84kyIMHD8bq1auRlpZWatu1a9ewZs0aPP3003UfGBHVKkEQEBYWxio1RESks3QmQV64cCGUSiX8/f0xe/ZsyGQybNy4EePGjUPHjh3h4uKCuXPn1kksK1asgLe3N8zMzNC5c2ecPXu2wv3T0tLw1ltvwd3dHaampmjevDn27t1bJ7ES6aPCwkLs27evzA/EREREUtOZBNnDwwMXLlzAoEGDsGXLFgiCgJ9++gl//PEHxowZg9OnT8PJyUnrcWzZsgXTp0/HvHnzcPHiRbRt2xYDBw5EQkJCmfsXFBSgf//+iI6OxtatWxEZGYk1a9bA09NT67ESaUNMTAyys7MBiHXJU1JStHKd3Nxc7NmzB+np6Vo5PxERUXXJBB2ou5Sfn4/9+/fD29sbbdq0AQAkJiZCpVLB2dkZcnnd5fGdO3dGp06d8O233wIAVCoVvLy88Pbbb+Ojjz4qtf+qVavw+eef48aNG9WeUvfixYsICAjAhQsX0KFDhxrFT1RdZ8+exSeffII9e/ZolGOTyWRo3bo1hgwZAm9v71q/rqWlJYYOHQpbW9taPzcREVF16EQLsomJCZ5//nmcPHlSvc7Z2Rmurq51mhwXFBTgwoULCA4OVq+Ty+UIDg4utzzVrl270LVrV7z11ltwdXWFv78/Fi9ezEkRSK9s27YN3bt3x759+0rVKhYEAVevXsWSJUtw8eLFWr92dnY29uzZg6ysrFo/d0pKCl588UXY2NjAzs4OL7/88hOv07t3b8hkMo3l9ddfV2/fsGFDqe3FS3lPmoiISL/oRIIsk8nQrFkzyQftJCUlQalUwtXVVWO9q6sr4uLiyjzmzp072Lp1K5RKJfbu3Ys5c+bgyy+/xMKFC8u9Tn5+PjIyMtSLNhIDoso6e/YsRo0aBaVSWe4HO5VKBZVKhTVr1iA6OrrWY8jKykJoaCiKioqqfGzv3r2xYcOGMre9+OKLuHbtGg4ePIjdu3fjr7/+wquvvvrEc06ZMgWxsbHq5bPPPlNvGzVqlMa22NhYDBw4EEFBQXBxcaly/EREpHt0IkEGgJkzZ+Lbb79FZGSk1KFUiUqlgouLC1avXo2AgACMGjUKs2bNwqpVq8o9JiQkBLa2tuolKCioDiMm0rRw4UIIglDpWe60NQA1JSUFFy5cqLXzXb9+HaGhofjhhx/QuXNn9OjRA9988w02b96MR48eVXishYUF3Nzc1IuNjY16m7m5ucY2hUKBQ4cO4eWXX6612ImISFpGUgdQ7PTp03B0dIS/vz969+4Nb29vmJuba+wjk8mwfPlyrcXg5OQEhUKB+Ph4jfXx8fHl1mB2d3eHsbExFAqFel3Lli0RFxeHgoICmJiYlDpmxowZmD59uvp1eHg4k2SSRExMDHbv3l3p5FilUuHy5ctISUmBg4NDrccTERGBjh07avw+VdepU6dgZ2eHjh07qtcFBwdDLpfjzJkzePbZZ8s99pdffsHPP/8MNzc3DBs2DHPmzIGFhUWZ+/7444+wsLDAyJEjaxwzERHpBp1JkIsHxQFAWFhYmftoO0E2MTFBQEAAwsLC8MwzzwAQE4KwsDBMnTq1zGO6d++OTZs2QaVSqftL37x5E+7u7mUmxwBgamoKU1NT9WsrK6va/UHIoBQUFFSr60Fl7N27t9LJcTFBEHD9+nV07dq11uPJz8/Ho0eP4OXlVeNzxcXFleryYGRkBAcHh3K7TAHA2LFj0ahRI3h4eODy5cv48MMPERkZiW3btpW5/9q1azF27NhSH+iJiEh/6UyCrFKppA4BADB9+nRMnDgRHTt2RGBgIJYtW4bs7GxMmjQJADBhwgR4enoiJCQEAPDGG2/g22+/xbvvvou3334bUVFRWLx4Md555x0pfwwyEAUFBTh79qzW+qn//fffkMlkVUqSZTIZ0tPTtRZTeHg4XF1dy/2AuXjxYixevFj9Ojc3F6dPn9b4EBsREVHt65fso9y6dWu4u7ujX79+uH37Npo0aaKx76lTp3D9+nX89NNP1b4eERHpHp1JkHXFqFGjkJiYiLlz5yIuLg7t2rVDaGioeuBeTEyMRmUNLy8v7N+/H9OmTUObNm3g6emJd999Fx9++KFUPwIZkKKiImRlZcHExETjqUNtsbe3r1YLspmZmVYqzAiCoG4xLy9Bfv311/HCCy+oX7/44ot47rnnMGLECPU6Dw8PuLm5laoqUVRUhJSUlCpNW9+5c2cAwK1bt0olyD/88APatWuHgICASp+PiIh0n84lyKdPn8bhw4eRkJCAN998E82aNUNOTg5u3LiB5s2b10l3hKlTp5bbpeLIkSOl1nXt2hWnT5/WclRUn5mamsLMzKzWz9u1a9dqtSC3aNFCayUYn9RVwcHBQaP/s7m5OVxcXNC0aVON/bp27Yq0tDRcuHBBncAeOnQIKpVKnfRWRnh4OABxvEFJWVlZ+PXXX9VPk4iIyHDoTBWLgoICjBgxAt27d8esWbPw9ddf4/79+wDEWsQDBgzQav9jovomOzsb4eHhaNiwIWQyWaWOkclkaN68Oezs7LQSk6OjY60l3i1btsSgQYMwZcoUnD17FidOnMDUqVMxevRoeHh4AAAePnwIX19f9XTyt2/fxieffIILFy4gOjoau3btwoQJE9CrVy/1JEbFtmzZgqKiIowbN65W4iUiIt2hMwnynDlzsHv3bqxcuRKRkZEaLVpmZmZ4/vnnsXPnTgkjJDIcMTEx2LFjB2JjYzWqPDyJTCZDt27dkJqaisLCwlqNycjICA0aNKjVc/7yyy/w9fVFv379MHjwYPTo0QOrV69Wby8sLERkZCRycnIAiAN1//zzTwwYMAC+vr74z3/+g+eeew5//PFHqXOvXbsWI0aM0NqHBSIiko7OdLH43//+hzfeeAOvvvoqkpOTS21v2bIlfvvtNwkiIzIcSqUS586dw7Vr19TrvL298dFHH6knwyhrspDiVt0RI0bAw8MDKpUK6enpMDc3h4WFRaVboMsjk8ng6+sLExMT5OfnV+nYsro9FXNwcMCmTZvK3e7t7a3xYdzLywtHjx6t1HVLzvxJRESGRWcS5ISEBLRu3brc7QqFQt3KQ0RVl5aWhsOHDyMlJUW9rmHDhujZsyfMzMzg6+uLtWvX4tixYxpJo0wmQ8uWLTFgwAB4enoiKytL3Xqcm5uLgoICWFtbw8ioen9OipNje3t75OXl1eyHJCIiqgU6kyB7eXnhxo0b5W4/ceJEqUE4RPRkgiAgKioKp06dUtdTVigUCAwMRMuWLdWtv61atcLSpUsRFxeHMWPGIDMzE+bm5vjvf/8Le3t79flsbGyQl5eH7OxsAGKLc1paGiwtLatcC1gul6Nly5ZamXSEiIiounSmD/LYsWPx/fff49SpU+p1xW/ca9aswa+//ooJEyZIFR6RXiooKMCRI0dw7NgxdXJsa2uLYcOGwc/Pr8yuEW5ubupE18TERCM5BsTfS3Nzc9jZ2WnMeJednY309PQyu2iUxcjICK1bt2ZyTEREOkdnWpBnzZqF06dPo1evXupWrWnTpiElJQUPHjzA4MGDMW3aNKnDJNIbiYmJOHz4MDIzM9Xrmjdvji5dusDY2LjG5zcyMoKdnR1ycnKQm5sLQBz0lpaWBisrqwrrNpuamsLf37/c6ZuJiIikpDMJsomJCUJDQ/HLL79g69atUCqVyM/PR5s2bbBw4UKMHz++xgOBiOoDQRBw5coVnD9/Xt2X2MTEBN27d0fjxo1r9VoymQyWlpYwMTFBZmYmVCoVBEFAZmYmCgoKYGlpWapsm4WFBfz9/bUy8QkREVFtkCxBnj59OsaPH4/27dsDEMtOOTs7Y9y4cawrSlRNOTk5OHr0KB49eqRe5+zsjD59+sDa2lpr1zU2NoadnR2ys7PVVSjy8/NRWFgIKysr9ax4tra2aNmyZa20YBMREWmLZH2Qly1bhuvXr6tf+/j4YPv27VKFQ6T37t+/j+3bt2skx23btsXQoUO1mhwXk8vlsLa2hrW1tfppj0qlQkZGBrKysuDo6Ah/f38mx0REpPMka0F2dXXFnTt31K+rMtUtEf1LqVTi/PnzuHr1qnqdhYUFgoKC1DPG1SVTU1MYGxsjMzNTXQ4uNTUV2dnZcHFxgZOTU53HREREVBWSJchDhgzBxx9/jAMHDqhnovryyy+xefPmco+RyWScTY+ohPT0dBw+fFhjch0vLy/07NmzyiXXapNcLoetrS1yc3ORlJSE3Nxc5ObmYteuXWjfvj3atm1ba1NKExER1TbJEuTly5fDxcUFhw8fxrVr1yCTyXD//n2NSQwex0F6RP+KiorCyZMn1eXb5HI5OnXqhFatWunM70rbtm1hamqKI0eOIDk5GYIg4OLFi3jw4AGCgoJgY2MjdYhERESlSJYgW1paYvHixerXcrkcy5Ytw9ixY6UKiUgvFBQU4OTJk7h9+7Z6na2tLXr37q1T3ReaNGkCV1dXAMCwYcNw6dIlXL58GYIgICEhAdu3b0fnzp3RokULnUnoiYj0QXZ2NiwtLaUOw6BJ9oxzxIgROHbsmPr14cOH0b9/f6nCIdILiYmJ2LFjh0Zy3KxZMwwfPlynkmN3d3eN/s8KhQIdO3bEkCFD1AMGi4qKcOLECfz555/qOspERPRkxeM7SHskS5B37tyJmJgY9eu+ffvi4MGDUoVDpNOKaxv/8ccf6ok/jI2N0bt3b/Tq1UunKkPY2NiUW2/Z1dUVzz77LJo3b65eFxMTg23btuHevXt1FSIRkV5jgqx9kiXInp6euHTpkvq1IAh8zEpUhry8PBw+fBhnz55VV3txdnbGM888gyZNmkgcnSYzMzO0bNmywgF4xsbG6NmzJ4KDg2FmZgZA/Bn//PNPnD59Wt2nmoiqj5WhDFtRURGUSqXUYRg0yfogjx49Gl988QV+/fVXdRWLjz76CCEhIeUeI5PJ8Pfff9dRhETSi4yMxJEjR1BQUKBe16ZNGwQEBOhcFQgTExP4+/urJwV5kkaNGsHFxQXHjx9XP026ffs2YmNj0bhxY/j5+WkzXCKDlpuby6ncDVxeXh77IWtRlRJkHx+fKrfyymQyjf6SxUJCQtC0aVMcPnwYCQkJ6ilrHR0dq3R+IkNUVFSEXbt24c8//1SvMzc3R1BQEDw9PSWMrGympqZo3bp1lUvLmZubIzg4GDdv3lS3Hufk5GDFihUYOHAgBg8eDCMjyT7HE+kttiAbPg7U064qvfMEBQWVSpDPnz+Pa9euwc/PDy1atAAgtnpFRETA398fAQEBZZ5LoVDg1VdfxauvvgpArGIxe/ZsVrGgei8xMRHr1q3T6JPr4eGB3r17S1rbuDzm5uZo3bo1TE1Nq3W8TCZDixYt4O7ujsOHDyMpKQmCICA0NBTXrl3DSy+9BHd391qOmsiwqVQqqUMgLcvKyoKLi4vUYRisKj2j3bBhA9avX69ehg8fjgcPHuDgwYO4evUqfv/9d/z++++4evUq9u/fj/v37+OZZ56p1Lnv3r1b6X21bcWKFfD29oaZmRk6d+6Ms2fPVuq4zZs3QyaT6czPQfrn7NmzCAkJUSfHCoUCrVq10tnk2MzMrEbJcUk2Njbo378/fH191d1H7t+/j08//RSHDx/mGz5RFfD3xfAVD9gm7ahRJ8a5c+fi7bffRr9+/Upt69+/P6ZOnYrZs2dX6lyNGjXSif5SW7ZswfTp0zFv3jxcvHgRbdu2xcCBA5GQkFDhcdHR0Xj//ffRs2fPOoqUDEleXh42btyIDRs2IC8vD4A4EO/tt99GkyZNdHIAq5GREfz9/WslOS4ml8vRvHlzvPPOO+oayoWFhfjtt9/w7bffIi0trdauRWTImCAbPibI2lWjBDkqKqrCPsOOjo5l9j8GxDdCIyMj9eAjuVwOhUJR4VIXfRGXLl2KKVOmYNKkSfDz88OqVatgYWGBdevWlXuMUqnEiy++iAULFpRb3oqoPDExMfj0009x5swZ9bouXbpgxowZaNCggYSRVaxFixZaa9Vu0KABZsyYgd69e6vX3bhxAwsXLsSFCxe0ck0iQ8JqMIYvIyND6hAMWo0yziZNmmD9+vV4+eWXYWVlpbEtMzMT69atKzdhnDt3LmQymTrpLX4tpYKCAly4cAEzZsxQr5PL5QgODsapU6fKPe7jjz+Gi4sLXn75ZY3JT8qTn5+P/Px89eusrKyaBU56SaVS4fDhw9ixY4e6XI+pqSnGjBmDwMBAAEBOTo6UIZbL1dUVDg4OWr2GiYkJXnjhBfj7++Onn35Ceno6cnJysHbtWly+fBmjRo3SiadORLqoZOUbMkx8oqZdNUqQFy5ciJEjR8LX1xcvvfQSmjZtCkBsWd64cSPi4+Px22+/lXns/PnzK3wthaSkJCiVSvWj3WKurq64ceNGmcccP34ca9euRXh4eKWvExISggULFtQkVNJzmZmZ+PHHH3Ht2jX1uoYNG2Ly5Mk6P+hCoVDA29u7zq7n5+eH2bNn43//+x8uXrwIADh37hyioqIwceJE9eBgIvpXcVctMlxZWVnIy8tT15On2lWjBPmZZ57B3r178eGHH2Lx4sUa29q1a4e1a9di4MCBNQpQl2VmZmL8+PFYs2ZNlab5nTFjBqZPn65+HR4ejqCgIG2ESDroxo0b2LBhg8bjseDgYDz99NN6UdLMw8Oj0rWOa4ulpSVefvlltGnTBlu2bEFubi7S0tKwfPly9OvXD08//bROzSZIJLWCggIolUooFAqpQyEtiouLq9MGi/qkxu/GAwYMwIABAxAXF6ceed+oUSO4ublV+hz5+fn4+eefceDAAdy+fRuZmZmwtrZG06ZNMWjQIIwdO7ZO3pCdnJygUCgQHx+vsT4+Pr7Mn+f27duIjo7GsGHD1OuKB0YYGRkhMjKyzJnOTE1NNQY2Pd49hQyTUqnEH3/8gYMHD6prlFpbW2PixIl6MymGQqGQrA6zTCZDYGAgmjZtih9//BE3b94EAISFhSEiIgKTJk3S6T7bRHUtKysLtra2UodBWnT//n0myFpSa81Vbm5uVUqKi125cgXDhw/HvXv3IAgCbG1tYWVlhYSEBFy8eBG//fYbFi1ahF27dqFly5a1FW6ZTExMEBAQgLCwMHWpNpVKhbCwMEydOrXU/r6+vrhy5YrGutmzZyMzMxPLly+Hl5eXVuMl/ZGUlIR169YhOjpava5ly5aYOHEibGxspAusitzc3CRvqXVwcMA777yDw4cPY+fOnSgqKkJsbCyWLFmCYcOGITg4WOdmGSSSQmZmJhNkAxcTEwNBECQfw2WIavwuEhMTg9dffx0tWrSAg4MD/vrrLwBiQvDOO+/g0qVL5R6blZWFp59+GvHx8Vi0aBHu37+P1NRUja8LFy7Eo0ePMGzYMGRnZ9c03CeaPn061qxZg40bN+L69et44403kJ2djUmTJgEAJkyYoB7EZ2ZmBn9/f43Fzs4O1tbWVZpylwzb+fPnsXjxYnVyLJfL8eyzz+Ktt97Sq+RYLpfrTAutXC5Hv3798OGHH6pbtJVKJXbs2IFly5YhOTlZ4giJpMdBXIYvOzsbSUlJUodhkGrUghwREYGePXtCpVKhc+fOuHXrlrq0jJOTE44fP47s7GysXbu2zOPXr1+PmJgYhIWFaZRzKubp6YkZM2agc+fO6N+/PzZs2IC33nqrJiE/0ahRo5CYmIi5c+ciLi4O7dq1Q2hoqHrgXkxMDFunqFLy8/Px66+/alRAcXZ2xqRJk/TykZi7u7vOfejz9PTEBx98gN27d+PPP/+EIAi4desWFi1ahBdeeAGdO3dmywrVW6mpqVKHQHXg7t27cHZ2ljoMg1OjBPmDDz6AnZ0dTp8+DZlMVmr0/ZAhQ7Bly5Zyj9+zZw8GDBhQZnJcUt++fdG/f3/88ccfWk+QAWDq1KlldqkAgCNHjlR47IYNG2o/INI79+/fx7p16zT6s3fq1AmjR4/WyRnxnkShUOhM6/HjjI2N8eyzz8Lf3x8bN25ESkoK8vLy8OOPP+Ly5csYO3Ys+/lTvZSSkiJ1CFQHbt26hU6dOrExoJbVqCn0r7/+whtvvAFnZ+cyb0zDhg3x8OHDco+/cuXKE5PjYn379i3V35dI1wiCgMOHD+Pzzz9XJ8empqaYOHEiJk2apJfJMSBN5YqqatasGWbNmoUuXbqo14WHh2PhwoUa5fSI6ouUlBT1gGAyXFlZWeoiCVR7apQgq1SqCgv1JyYmVjgNbUpKSqUH9rm6uvLTMOm0zMxMrFy5Er/99pu6q5GXl5e6m5C+ksvl8PDwkDqMSjE3N8eECRMwZcoUWFpaAhBnm1qxYgX+97//aUzQQ2ToCgsLdXayIapdly5d4oehWlajBLlDhw7Ys2dPmduKioqwefNmjdacx+Xn51d6RHzJaamJdE1kZCQWL16Mq1evqtf169cP77//vs5P/PEkzs7OOt96/Lj27dtj9uzZaNWqlXrdsWPHEBISolFJhMjQsR9y/ZCYmIioqCipwzAoNeqDPGPGDAwdOhRvvPEGRo8eDUCsGfznn39i8eLFuH79Or799tsKzxEdHa2eHasid+/erUmoRFqhVCqxe/duHDhwQP3p3crKChMnTtRIzvRZdco36gJbW1u8+eabOHbsGH7//XcUFhYiISEBX3zxBZ566ikMGjSIkyiQwUtMTNTZ8QNUPR07dsSjR49gbGyMWbNmqdefPn0aXl5eetuVT9fUKEF+6qmnsGHDBrz77rtYvXo1AGDcuHEQBAE2Njb48ccf0atXrwrPMWfOHMyZM+eJ12KdP9I1ycnJWLduncaHN19fX0ycONFgao/KZDJYW1tLHUa1yWQy9OrVCy1atMCGDRtw7949qFQq7NmzB9euXcPEiRNLTS1PZEgePHiA9u3bSx0G1aK4uDjExsbCzs5OY31eXh6OHz+O4OBg5ku1oMYThYwfPx4jRozAwYMHERUVBZVKhSZNmmDgwIFPfGNdv359TS9PJIkLFy5g06ZNyM3NBSD203366acNZpIKR0dHFBYWwt7e3iD+0Lq6uuL999/Hvn37EBoaCpVKhejoaISEhGDEiBHo2bOnQfycRI+LjY1Fenq6wXxop4rdvXsXkZGR8PX1lToUvVftBDknJwdeXl746KOP8N///lc981xVTJw4sbqXJ5JEfn4+tm7dihMnTqjXOTo6YvLkyfDx8ZEwstr1008/4erVq3rff7okhUKBoUOHolWrVti4cSMSEhJQUFCAzZs348qVKxg3bhyTCDJIFy9eRJ8+faQOg+rIyZMn4eLiAgcHB6lD0WvVbuqysLCAkZGReqQ4kaF78OABPv30U43kuGPHjpg5c6ZBJcclGWL9YB8fH8yYMQM9e/ZUr7t27RoWLlyI8PBw6QIj0pJbt24hMTFR6jCojhQVFWH//v3qJ5xUPTV6Fvzcc89h69atLC1CBk0QBBw5cgSfffaZuraxiYkJxo8fr9e1jZ9ELpfDzMxM6jC0wtTUFGPGjMEbb7yhnu47Ozsbq1evxk8//VQv3lhY8q7+EAQBJ0+e5Ht1PZKZmYl9+/ax+lcN1ChBHj16NBISEtCnTx/88ssvOHHiBC5evFhqIdJXWVlZ+P777/Hrr7+qaxs3aNAAM2bMQNeuXQ2636qJiYlB9KeuSOvWrTFr1iy0bdtWve7UqVNYvHgxbt26JWFk2ldylkcyfPHx8bh9+7bUYVAdSkpKwu7du+vFB35tqNEgvZKz4B07dqzU9uLKE0qlsiaXIZLEzZs3sWHDBqSlpanX9enTB88880yl63frs4om+TEk1tbWePXVV3H69Gn89ttvyMvLQ3JyMr766iv0798fQ4cOhZFRjccz6xy2INc/xWXA6svvNolJ8s6dOzFo0KBSVS+oYjX6q88qFGSIlEol9u7di9DQUI3axuPHj0fr1q0ljq7u1IcPAcVkMhm6du2KZs2aYePGjbh9+zYEQcCBAwcQERGBl156SW9mE6wsJsiGq2PHjnjw4AFMTU016uTm5OTg2LFj6Nevn0E//SJNGRkZ2LFjB/r16wcvLy+pw9EbNUqQWYWCDE1ycjI2bNig8SiyefPmeOmll+rdp+/6OImGk5MTpk2bhj///BN//PEHlEqlenDm8OHD0adPH4PpdpKXl8f68gYqLi4O8fHxZf7NunPnDmxsbNCpUyfe+3qkoKAAoaGh6NKlC/z9/XnvK0HnnhtGRETgzp07SE1NLXNAwYQJEySIiuqDS5cu4eeff9aobTx06FAMGDDAYJKiqqiPPzMg/twDBgxAy5YtsWHDBsTGxqKoqAi///47rl69ivHjxxtE+aTCwkIUFhbq3TTiVHPh4eEoKChAt27d6u3veX0kCAJOnTqFlJQU9OzZk/f+CaqUIE+ePBkymQyrV6+GQqHA5MmTn3iMTCbD2rVrn7jf7du3MW7cOJw9e7bckbYymYwJMtW6goICbN26FcePH1evc3BwwOTJk9G4cWMJI5NWff/jWVznfefOnTh06BAAIDIyEosWLcLo0aPRqVMniSOsuczMTDg6OkodBkkgIiICqamp6NevHywsLKQOh+pQZGQksrKy0L9/f35ArkCVEuRDhw5BLpdDpVJBoVDg0KFDT2ymr2wz/muvvYYrV65g2bJl6NmzJ+zt7asSGlG1PHz4EOvWrUNsbKx6XYcOHTB27Fi+aRCMjY0xcuRI+Pv748cff0RaWhpyc3Oxfv16XLlyBaNHj9br/ydMkOu32NhYbNu2DX369IGnp6fU4VAdevjwIXbt2oWnnnqK81mUo0oJcnR0dIWva+LEiROYOXMm3n777Vo7J1F5BEHAsWPH8Pvvv6OwsBCAmAy98MIL6NatG/tnofIfbusDX19fzJ49G1u2bMG5c+cAAOfPn8etW7cwYcIEvZ3WNT09XeoQSGI5OTnYu3cvOnTogA4dOvD3vh5JSUnBzp07MWTIEM4iWgadeYbq5OTEG0R1onhCiM2bN6uTY09PT3z00Ufo3r073yD+wX8HTRYWFpg0aRImT56snhwmLS0NX3/9NX777Te9LMhfsoQh1V+CIODChQsIDQ1ldZN6JisrC7t27UJSUpLUoegcnUmQX3/9dfz888+smUxaFRUVhUWLFuHvv/9Wr+vduzc++OADuLu7SxiZ7qnvfZDL07FjR8yePRstWrRQrzt8+DA+/fRTxMTESBhZ1SUnJ0sdAumQ+/fvY8eOHUhNTZU6FKpDubm5+OOPP/Do0SOpQ9EpNX4H3LdvH/r37w9HR0cYGRlBoVCUWiqjefPmUCqVaNu2LZYuXYrffvsN27ZtK7XUhRUrVsDb2xtmZmbo3Lkzzp49W+6+a9asUfeZtre3R3BwcIX7kzSUSiV2796NZcuWqVvNLC0t8frrr+OFF16oVzV/K4styOWzt7fH22+/jeeff179fycuLg6fffYZQkNDoVKpJI6wcpKTk9liSBrS09Oxc+dOPHjwQOpQqA4VFhZi3759uHPnjtSh6IwalXn7/fff8cILL6BVq1YYPXo0Vq5cibFjx0IQBOzcuRPNmjXDM888U6lzjRo1Sv39+++/X+Y+dTEr35YtWzB9+nSsWrUKnTt3xrJlyzBw4EBERkbCxcWl1P5HjhzBmDFj0K1bN5iZmWHJkiUYMGAArl27xkEPOiIlJQUbNmzQmDq4WbNmmDRpUr2rbVwV9bEOclXI5XL06dMHvr6+2LBhA+7fvw+VSoVdu3bh2rVrmDhxIpycnKQOs0KCICA6OlqjNZyouGZuz549+X+jHlEqlQgLC0Nubi5atWoldTiSq1GCHBISgsDAQBw/fhypqalYuXIlJk+ejL59+yI6OhpdunSBj49Ppc51+PDhmoRSa5YuXYopU6Zg0qRJAIBVq1Zhz549WLduHT766KNS+//yyy8ar3/44Qf8/vvvCAsLY0k6HRAeHo6ff/4ZOTk5AMSkZsiQIRg4cCC7EDwBW5Arx93dHf/973+xZ88eHDhwAIIg4Pbt21i0aBGef/55dO3aVaf/LSMiItC8eXOdjpHqnkqlwtGjR5GTk4N27drx/0c9IQgCTpw4gdzcXAQEBNTr+16jBDkiIgIhISFQKBQwMhJPVTzoydvbG2+++SaWLFlSqUQxKCioJqHUioKCAly4cAEzZsxQr5PL5QgODsapU6cqdY6cnBwUFhZWOJFAfn6+xmPNrKys6gdNZSooKMDvv/+OY8eOqdc5ODhg0qRJaNKkiYSR6Q9+gKg8IyMjDB8+HP7+/tiwYYO668LPP/+My5cv48UXX4S1tbXUYZYpMTER9+/fR8OGDaUOhXTQuXPnkJmZiR49evBvQj1y8eJFqFQqBAYGSh2KZGqUIFtYWKiLTNvZ2cHU1FSjnqyrqyvu3r1b5fNGRETg3r17AIBGjRrBz8+vJmFWWlJSEpRKJVxdXTXWu7q64saNG5U6x4cffggPDw8EBweXu09ISAgWLFhQo1ipfI8ePcK6des0Bhy0b98eL774ol7XrK1rfDOsuiZNmmDWrFnYunUrTp48CQC4fPky7t69i3HjxqF169YSR1i2M2fOoEGDBrznVKYbN24gMzMTwcHBMDU1lTocqiPh4eGwtLSst90tavTXsEWLFoiIiFC/bteuHX766ScUFRUhLy8PmzZtqlKrxM6dO9GkSRO0bt0aQ4cOxdChQ9G6dWs0bdoUu3btqkmodeLTTz/F5s2bsX37dpiZmZW734wZM5Cenq5ejh49WodRGi5BEHD8+HEsWbJEnRwbGxtj7NixeOWVV5gcV1F9frRWE2ZmZhg3bhxeffVVWFlZARAn5Fi5ciU2bdqEvLw8iSMsLTU1VeNvOdHjHj58iO3btyMlJUXqUOq1mJgYZGdnAxCfRmv7fpw+fbre3vMaJcgjRozArl271N0FZs2ahSNHjsDOzg7Ozs44duxYmf12y7J3714899xzAIDFixdj+/bt2L59OxYvXgxBEDBixAiEhobWJNwncnJygkKhQHx8vMb6+Ph4uLm5VXjsF198gU8//RQHDhxAmzZtKtzX1NQUNjY26qX4TZSqLycnBz/88AM2bdqk7ubj4eGBDz/8ED169GCyR3WuXbt2mD17Nvz9/dXrjh8/jpCQkGo9WdO2c+fOsbsXVSgjIwM7d+7E7du3pQ6l3jl79iyGDRsGb29vdSWm3NxczJw5EytWrKjVidtKUiqVOH36tFbOnZKSghdffBE2Njaws7PDyy+//MS/QatXr0bv3r1hY2MDmUxWZi13b29vyGQyjeXTTz+tcnwyQRCEqh6Ul5eHnTt34u7du3B0dMTQoUPVNWSPHTuGbdu2QaFQYMiQIejTp0+lztm1a1fk5+fj2LFjpaY9zM7ORo8ePWBmZlbpvsDV1blzZwQGBuKbb74BIA5UaNiwIaZOnVpusv/ZZ59h0aJF2L9/P7p06VLla168eBEBAQG4cOECOnToUKP466Pbt29j/fr1Gp9ye/XqhREjRuj9PPM5OTn466+/YG1tXeFTCW1QKpV1XskiLy8PmZmZ6NWrl8G0+BcPetm6dat6MhGZTIZBgwZh8ODBklQL6dixI2JiYmBubo5Zs2ap13t4eGDIkCH8QKnnGjRogIcPH8LOzg5LlizRyjX8/f3RuXNnVrupA9u2bcOoUaMgCEKZlbyKu0ZNmTJFaznEM888U2Ylryfp3bs3XnrpJbz00kultj311FOIjY3F999/j8LCQkyaNAmdOnXCpk2byj3fsmXL1E/hZsyYgdTU1FLVqLy9vfHyyy9jypQp6nXW1tZVnlK7yn2QExIS0K1bN9y9exeCIEAmk8Hc3Bw7duxAcHAwevbsiZ49e1b1tLh8+TIWL15c5g9gaWmJl156CTNnzqzyeatq+vTpmDhxIjp27IjAwEAsW7YM2dnZ6qoWEyZMgKenJ0JCQgAAS5Yswdy5c7Fp0yZ4e3sjLi4OAGBlZcWWYS1TqVQIDQ3Fnj17UPw5z8LCAuPGjUO7du2kDc4AMEmqHTKZDD169EDz5s2xceNG9d/Offv24dq1a5g0aVKpcQ/aFhcXh8TExFJvLI8ePcKFCxfQsWPHOo2H9M/Vq1eRlJSE/v37q2eWpNp39uxZjBo1CkqlEuW1ZxbXXV+zZg0+/PBDeHt713ocly5dwsCBA2vtfNevX0doaCjOnTun/nvzzTffYPDgwfjiiy/g4eFR5nHvvfceALHEbkWsra2f+OT/SarcxeKTTz5BdHQ0pk2bht27d+Orr76Cubk5XnvttRoFYmZmVmE/l5SUlDppQRs1ahS++OILzJ07F+3atUN4eDhCQ0PVb2AxMTEaAxFXrlyJgoICjBw5Eu7u7urliy++0Hqs9VlqaiqWL1+O3bt3q/9oNG3aFDNnzmRyXEs4YKt2ubi4YPr06Rg6dKj63zYmJgaLFy/G0aNHy33zq2sXL17kI3Q9Vpd9VOPi4rBz505kZmZq7Rr13cKFCyEIQqX/Puzdu1crcdy7d69U99OaOHXqFOzs7DQ+jAcHB0Mul+PMmTM1Pv+nn34KR0dHtG/fHp9//jmKioqqfI4qvwMeOHAAEyZMwBdffIHBgwfjnXfewbfffovo6GhERkZWOYBiffv2xfLly8vsQnHmzBl8/fXXFVaGqE1Tp07FvXv3kJ+fjzNnzqBz587qbUeOHMGGDRvUr6Ojo9X/eUsu8+fPr5NY66O///4bixcvRlRUFACxhW7IkCF47733KiyvRyQ1hUKBwYMH4/3331d/6C4sLMSWLVuwYsWKMvvTSeHIkSO4f/++1GFQFUjVRzUjIwN//PGHVvqva6OPanR0NF5++WX4+PjA3Nz8/+3deVxUZfs/8M8sMAw7KDCaCgiJIGlKX5VQccfUckFzzX3LzFzSsjT1cWux1Cwfc8ktLbPENbfcNRSXFvclQUpFVGQTBIa5f3/4Yx5GFtmGMxw+79frvGTuOXPmGi6Hueace4GPjw+mT59u7P4ktezsbDx+/Bipqak4d+4cduzYUeQF0gwGA/766y+zfSk6efJkmX2Rj4uLy9NlQ61Ww9XV1XglvqTGjh2LH374AQcPHsTIkSMxd+5cTJ48udjHKXYXi9jYWLz33nsmbc2aNYMQAnfv3i3xqjuffvopgoOD0axZMzRu3Nh4nCtXriAqKgru7u5m60tFFUNWVhY2b95sMuuHi4sLBg8eDF9fXwkjIyoeLy8vTJkyBREREcb/zxcvXsScOXPQp08fycciZGdnY+/evQgLC0ONGjUkjYWeLXcf1acLGCEEzp8/j/Pnz5utj2pqair27t2Lrl27FvvKU2F9VPv164c7d+5g3759xj6qI0aMKLSPalpaGjp06IAOHTqYrGmQ4/LlyzAYDPjmm2/g6+tr/L08evQI8+fPhxACer0eWVlZJlvuttw/p6enIyMjA3q93tj+9M+5H1fQPjk/516m/vLly8UuSIUQuHTpEoKDg4v1uKK4ffs2bt68WWgXjrlz52Lu3LnG2+np6Thx4gTGjBljbDP3jDkTJkww/ly/fn1YW1tj5MiRmDdvXrGmKSx2gZyRkZGnq0PO7ZKcws7h7e2Nv/76C/PmzcOuXbuwceNGAE/mQX7nnXfw/vvvl6iDOMnDnTt38O233+LWrVvGtgYNGqB///7F7nhPZAmsra3Rq1cvBAYG4rvvvkNSUhIePXqEFStWoHHjxujVq5ekfTuzs7OxZ88etGvXjouIWDBL6aN6//59XLlyBf7+/qU6jhACBoMB586dw+7du/Hrr7/C29sbmZmZmDp1qrFIdnV1zbdgff7555GVlYU///wTAPD9999Do9GYFLv+/v44ffo0IiMjkZWVhYCAAKxYsQKZmZmlqmPKWmZmJhQKRbGKZIVCgaSkJLPNSHPkyBFUr169wAHwo0aNwuuvv2683a9fP4SHh6N79+7GturVq0On0yE+Pt7ksXq9HgkJCaXuO/y0Jk2aQK/XIyYmplgncUu0UEhMTAzOnj1rvJ2UlAQAuHbtWp5BHwCK/I3V3d0dCxYswIIFC0oSFsmQEAK//fYbfvzxR+P0bVZWVggPD0fz5s05kIwqvHr16uHDDz/E999/j99//x3Ak6Ln+vXrGDBgAOrUqSNZbDlnklu1asUVKC1USfqojh49+pn75dd18Fnb0aNHERMTU6wzsDExMdi0aRMuXLhgbMs5C2ptbY2ff/4ZP//8M4D/Ffoff/wxateuXWj8OSdTTp8+/cyzhsnJybC2ti7X4lilUhW6KZVKxMfHl+gMso2NjVnGkAghkJKSAr1eX2CB7OrqatLVUavVwt3dPc9V3uDgYCQmJuLMmTMICgoCABw4cAAGg8GkW2tZ+OOPP6BUKot9krVEBfK0adMwbdq0PO1Pv+lyZrkoav8ZotzS0tKwYcMGky9j1apVw9ChQwsc4UpUEdnb22PYsGGIiorCxo0b8fjxYyQkJGDRokVo06YNXn31VVhZWUkSm8FgwP79+5GSkoIGDRrwS6kFiY2NNRmo/CwGgwF//vknrly5AicnpwIL3ZK6d+8e/vjjj2I9JqebQs7AwhxpaWl5rqAolUpoNBqkpaWVOMYcarUaVlZWSE1NxYULF9C+fXvUqFEDVlZWxvtytty31Wo1rK2toVarIYRAdHQ07OzsYGNjY1LgqtXqfAtftVptnJv3WRo2bIjNmzcX+wyyn5+fWQpkg8EAtbpUCzAb+fv7o0OHDhg+fDiWLl2KrKwsjBkzBr179zZ+vt+6dQtt2rTB2rVrjUtex8XFIS4uDtevXwcAnDt3Dg4ODqhVqxZcXV0RGRmJkydPolWrVnBwcEBkZCTGjx+P/v37w8XFpVgxFvuVrlq1qrgPydeQIUOgUCiwbNkyqFQqDBky5JmPUSgUWLlyZZk8P1m2Gzdu4NtvvzUZbNCsWTP06NGjws9tTJQfhUKBJk2awNfXF2vXrsW1a9cghMCvv/6KixcvYtCgQZL2B46KisK9e/fQokULLjdcDGV12T49PR0JCQl4+PAhEhISkJCQgF9++aVEBe3Vq1fRoEGDUsf0tKIUZWfOnDGe9FAoFNDr9bh79y6OHTtmLBonT54MNzc3xMTEoFGjRiYF6vr161GvXj289tprhRaxZ86cwbZt2zBp0iS4ubmZ3J9zhvbWrVsIDQ1F//79sWLFimK/3rS0NAghzDZPvU6nQ/PmzXH8+PEinWhUKpXw9/cvdiFYHI6OjmV2rPXr12PMmDFo06YNlEolwsPD8eWXXxrvz8rKwpUrV0y+EC1duhQzZ8403m7RogWAJ7XpoEGDoNFo8MMPP2DGjBnIyMiAt7c3xo8fb9IvuaiKXSAPHDiw2E+SnwMHDkCpVMJgMEClUuHAgQPP/EbFMxfyZzAYsHfvXuzYscN4OU2r1aJ///5o2LChxNERmV+VKlXwzjvvYP/+/di+fTv0ej1u376NTz/9FK+++qrxw0QK0dHRiI+PR4sWLVCzZk1JYqhIMjMzERUVVaT+oHq9Hmlpaflu6enpxi5mucXExJSoj2ruGRtyPlfL6t8XXngBgYGB0Gq1+RavKSkpSE5ONi4wkl8fVS8vL6xduxbHjx/HsGHDTH5Hffv2Rfv27dGhQ4dCX2fOdKw6nS7frp+3b99Gq1at8PLLL2PZsmWF/9IkNHToUBw/frzIeW7fvr3ZYtFqtfn+LgtT2HzFrq6uhQ649PLyyvOaZ8yYUegsYY0aNSqzlf/K5lx5CTw95Yy5pqChiiMxMRGrV6/G1atXjW0+Pj4YNGgQqlSpImFkROVLqVSiXbt28Pf3x+rVq3H79m3o9XpERETg/PnzGDBggGTviUePHmHXrl3w9fVF06ZNZbPioTno9XqkpqbC2toaKpUKjx49Qmpqar7/ZmRkFPv41tbWJeqj6uLiYpYpMYUQsLa2ho+PT4H/L+zt7Y0r7wLS9FG9desWWrVqhaCgIKxatcqi53yvV68e5s2bZ5yRo7CV9AYOHGi2AbVKpRK+vr6VqsusZAXy02JjY+Hm5lbgqO309HTcu3ePo6ll6ty5c1i7dq2xH5rUS/ESWYIaNWrgvffew/bt27F//34IIXDt2jXMmTMHvXr1QuPGjSW7snb9+nXcvHkTQUFBCAwMtOgiozxkZWUhISEBDx48MG7x8fG4efMm0tPTjcvjFpdCoTCuzOrg4GDyb6tWrXDo0CGL6qP6dF/ikjJXH9Vbt26hZcuW8PT0xPz583Hv3j3jc5b17AllpXXr1vj222+xcuVKHD161CTfCoUC/v7+aN++vVnrIz8/P9ja2laqRWEspkD29vbGunXr0Ldv33zv37ZtG/r27Vupvr1UBllZWYiIiDC5DOPs7IxBgwZJOnqfyFJYWVmhe/fuCAwMxNq1a5GQkIDHjx9jzZo1+Ouvv9CnTx/JlrXPysrCiRMncOXKFbRs2RJubm6SxFEesrOz8fDhQ9y/f9+kCM7ZcmZzKi6FQgFbW1s4ODjkKYDt7e1hZ2dXaDFraX1U8+sKUlLm6KO6b98+XL9+HdevX8/Tp99SVrPMT7169fDFF18gLi4Offr0QUpKCrRaLSZNmmTWfAJPruRWrVq1xF/yKiqLKZCf9R8zKyur0p+hkJu7d+9i5cqV+Pfff41t9evXR//+/SX7wCeyVHXq1MGHH36IjRs3IioqCgDw+++/48aNG3jjjTcQEBAgWWwPHz7Eli1bEBISImkcpZGdnY3ExMR8i98HDx4gMTGxxAWUVquFo6NjvmeB7ezsSnWVzJL6qAIo9iDq8u6jWtCiJBWFTqeDVqtFSkoKrK2tzV4c16hRo9LOGiVpgZycnGyyDOSDBw8QGxubZ7/ExET88MMPJv2WqOISQuDEiRPYuHGjcbCIWq1GeHg4WrRowcGYRAXQarUYNGgQ6tevj++//x6PHj1CUlISvvrqK4SGhqJbt26SzfIihMCxY8fw+PFjyVcCzI/BYEBSUlKBBfDDhw9NVjErDkdHR7i6uqJq1aom/9rZ2eH8+fNwdnY2yywHgOX0UQWeXO0wd8FG5cfNzc0si8pUFJIWyAsWLMB//vMfAE8uM40bNw7jxo3Ld18hBGbPnl2O0ZE5pKen4/vvv8fp06eNbTqdDkOGDOGStkRF1KhRI9SuXRvfffedcdnWw4cP4/Llyxg0aBA8PT0li+306dNQqVRmmUasMEIIJCcnF1oAl3S6NXt7e7i6uqJKlSrGLacIrlKlSoFfStLS0splDIUl9FFVKpV4/vnnzXZ8Kl+Ojo6oU6dOpT5hJWmB3L59e9jb20MIgcmTJ6NPnz55zjwoFArY2dkhKCgIL730kkSRUlmIjo7Gt99+iwcPHhjbQkJC0KNHD86rSlRMzs7OeOutt3DkyBFs3rwZWVlZuHv3Lj777DN07NgRYWFhkg1wPXnyJFQqFQIDA8vsmEIIpKamFlgAJyQklLj/q1arNSl4ny6CzXX2tyxJ2UdVpVIhICAANjY2lWoQl1zZ2NjA39+/0ndrlbRADg4ORnBwMIAnUweFh4eX6R9UsgwGgwH79u3D9u3bTeY27tu3r3H6HiIqPoVCgdDQUPj5+WH16tWIjY2FwWDAjh07cOHCBQwcODDP8qqxsbHGmQYyMjKQkJBglim/fvvtN6hUKvj7+xdpfyEE0tLSCi2ASzIVGgBoNJo8Z31zb3Kaqq68+6ja2NggICAAdnZ2lW4QlxxZWVkhMDCQC3LBQgbppaWl4csvv4StrS0LZJlJSkrCmjVrcPnyZWObt7c3hgwZwrmNicqITqfDpEmT8Msvv2D37t3GJXDnzp2LHj16ICQkBKdOncKsWbOwc+dO4yX49PR0fPDBB3jhhRfQqVOnMu9vePToUQAwFsnp6ekFFsAPHjwodoH1+PFjHDt2DDExMVCpVGjYsCFGjhyJ6tWrmxTEdnZ2xkvFjx8/xsSJE/HDDz8gIyMDYWFhWLJkCTw8PAA8GQvTr18//PXXX3jw4AHc3d3RpUsXzJ07t0xXEZODKlWq4Pnnn5dsGXQqWzlXfQqabreysYgC2dbWFmq1GnZ2dlKHQmXowoULWLNmjXEVKYVCgbCwMHTq1IlzGxOVMZVKhVdffRWBgYFYvXo17t27h8zMTGzYsAFr167FqlWrIITIM+pfCIHz58/j/PnzGD58eKkG2AkhoNfrTbaIiAhs3rwZDx8+NJmOq6i2bt2KgIAAhISE5BkE9+6778LGxgZHjhyBXq/H4MGDsWfPnkJnPhg/fjx27tyJTZs2wcnJCWPGjEH37t1x/PhxAE/60nbp0gWzZ8+Gm5sbrl+/jrfeegsJCQmFHrey8fb2xnPPPVep+6jKSU5xzBmk/sciCmQACA8Px08//YQ333yTb7gKLisrC1u3bsWBAweMbU5OThg0aBD8/PwkjIxI/ry9vTFlyhRs3rwZx44dw927dxEREVHoFGA5XZ+WL1+O9957r8AzyTkFcHZ2dp5CWK/XFzoLRO7ljZ+mUqng6uqa70wQOXM9DxkyxOQxly5dwuHDh3Hq1Cnj+JTFixejY8eOmD9/fr5TUyUlJWHlypXYsGEDWrduDeDJ/Lj+/v44ceIEmjZtChcXF7z55pvGx3h6emL06NH47LPPCoy/MlGr1fD39y/2ksNkuVQqFerVq8crJE+xmAK5d+/eGD16NFq1aoXhw4fDy8sr39P8ljh9UGUTGxuL/fv3IyUlBQ4ODmjTpo1xdPTdu3fx7bff4p9//jHuHxgYiAEDBvCbKVE5sbGxQd++ffHCCy+gW7duxXrsjh07MGzYMJPCN6cgLs1CTS4uLnB2djbp+pDzr7Ozc4EDgtRqdb73RUZGwtnZ2WTwdtu2baFUKnHy5Ml8X/eZM2eQlZWFtm3bGtvq1q2LWrVqITIyEk2bNs3zmNu3b2Pz5s0IDQ0tycuWFa1Wi3r16vESvIzkFMdOTk5Sh2JxLKZAbtmypfHnnH5ruQkhoFAouJKehKKiokz6MCqVShgMBigUCnTu3Bldu3bFuXPnjANp1Go1unXrhpYtW/KqAJEEnJyccOPGjSIvcGEwGHDu3Dlcu3at2B+YKpUKarXaZMvdplAoMGDAgDKbsSYuLi7PAES1Wg1XV1fExcUV+Bhra+s8Zz89PDzyPKZPnz7YunUr0tPT8eqrr2LFihVlEndF5ezsjLp167K/sYyo1WqeOS6ExRTIq1atkjoEKsTmzZvRq1cvkz6MOZdThRDYuXMnduzYgfbt26N27drw8PDAkCFDULNmTSnDJrJ4mZmZJZ6f91l++eWXEq3+FhMTk2ceY5VKZSx48/u3sC/BOX8rbt68CS8vr0JHyM+dOxdz58413k5PT8eJEycwZswYY1vO3M/mtGDBAkyfPh1Xr17FlClTMGHCBCxZssTsz2uJqlevDm9v70o/7ZecaDQa1KtXj2O/CmExBfLAgQOlDsHo66+/xmeffYa4uDg0aNAAixcvRuPGjQvcf9OmTZg2bRpiYmLw/PPP45NPPkHHjh3LMWLzioqKQq9evZCdnV3gh23OB+DevXsxffp0vP/++5zbmOgZMjMzERUVZRzIWtb+/PPPIi9BnEOhUMBgMECr1UKpVBq3pwvg3APyiuro0aOIj49H48aNCyySR40ahddff914u1+/fggPD0f37t2NbdWrV4dOp0N8fLzJY/V6PRISEqDT6fI9tk6nQ2ZmJhITE03OIt+9ezfPY3Q6HXQ6HerWrQtXV1c0b94c06ZNq1QrulpbW8PX15czDsmMg4MD/P39+Rn9DBZTIOeWmppq7MNas2bNcu27unHjRkyYMAFLly5FkyZNsHDhQoSFheHKlSt5LucBT+b67NOnD+bNm4fOnTtjw4YN6Nq1K86ePSubKetmz56d7+j3/CiVSpw9e5ZvPKIi0Ov1SE1NhbW1tVneMy4uLsU+gyyEgIODg1n6maampiIlJQV6vb7AAjlnsF4OrVYLd3d3+Pr6muwXHByMxMREnDlzxjif+oEDB2AwGNCkSZN8jx0UFAQrKyvs378f4eHhAIArV64gNjbWOCd/fnJOAJR0HuaKqFq1avD09GSXCpnR6XTw8fHh1YAisKjf0KlTp9CqVSu4uLggMDAQgYGBcHFxQevWrU2WJjanL774AsOHD8fgwYMREBCApUuXwtbWFt9++22++y9atAgdOnTApEmT4O/vj1mzZqFRo0b46quvyiVec4uNjcWOHTuK3Pc7Ozsb27dvR2xsrJkjI5IPjUYDGxubMt9CQkKK3f9foVDAz8/P5OxxWW3Z2dnGRUpKy9/fHx06dMDw4cMRFRWF48ePY8yYMejdu7dxBotbt26hbt26iIqKAvCkT/bQoUMxYcIEHDx4EGfOnMHgwYMRHBxsHKD3yy+/YNWqVTh//jxiYmKwc+dOjBo1CiEhIWU+T7QlcnR0RMOGDeHr68viWEZylgJ//vnnWRwXkcWcQT558iRatmwJa2trDBs2zDix/KVLl/D999+jRYsWOHToUKFdHUorMzMTZ86cwZQpU4xtSqUSbdu2RWRkZL6PiYyMxIQJE0zawsLCsGXLlgKfJyMjw+RMRM7lVb1eX+KlUs1lz549JToDtXfvXovqNlNRZWVlQa/X49GjR2brp2pJMjIyjO8DS3svmIO582tvb4+mTZvixIkTRXofKxQK+Pv7w97e3iy/fyEEUlJSipVfIQSys7Pz3X/16tV455130KZNGyiVSnTr1g0LFiww7puWloYrV64gOTnZ2Pbpp58CeDK1aEZGBtq1a4fFixcb77eyssKyZcswfvx4ZGRkoEaNGujatSsmT55c7N+JVO/f3ONDihqzSqVCzZo14ebmBgAl6vbD92/5KG5+NRoNfH19YWdnV6ruXOWZX4v4ciYsRJs2bYSPj4+4c+dOnvvi4uKEj4+PaNu2rVljuHXrlgAgfvvtN5P2SZMmicaNG+f7GCsrK7FhwwaTtq+//lq4u7sX+DzTp08XALhx48aNGzdu3Lg9tVkCizqD/NFHH+U7uMLDwwMjRozArFmzJIis7OWMiM7xxx9/IDQ0FCdPnkTDhg0ljCyv1atXY8SIEcV+3PLly3kGuYyYc5YDS6RWqwud5UBuyiO/27Ztw+DBgyGEyHcxj5xuGEOHDsWLL75olhiUSiWaNGkCPz8/5tfM/P39cefOHTg5OWHOnDmF7uvk5IT27duXWR94vn/Nr6j59fLyQnBwcJmuXFuZ8msxBbJSqSz0P1l2drbZ+81UrVoVKpUKd+/eNWnPb4RzDp1OV6z9gSeXO3L/McoZhKhWqy3jskIuYWFhJRoF3759e4t7LRUVf4/yVh75feONN1C3bl3MmjULO3bsMHk/KxQK1K9fHx07djRbH1s7Ozu0bdsWHh4eZjm+JZPi/ZvzWalQKAp9fmtra7z22mucB7cULDW/derUQWhoKNcgKAWL6an98ssv4+uvv8bNmzfz3BcbG4slS5YgJCTErDFYW1sjKCgI+/fvN7YZDAbs37+/wBHOwcHBJvsDwL59+wodEV2R1KpVC507dy7yN1CVSoVXX33VuLIeEVmG//u//8O2bdsQExNjnOJMq9Vi7ty5GD16tNmK45o1ayI8PLxSFseWLjg4mMWxDHl6erI4LgMWcwZ57ty5aNGiBerWrYtu3bqhTp06AJ5MwbN161ao1WrMmzfP7HFMmDABAwcOxEsvvYTGjRtj4cKFePToEQYPHgwAGDBgAJ577jljLO+88w5CQ0Px+eefo1OnTvjhhx9w+vRpLFu2zOyxlpdp06Zh165dzzyTrFAooFAoMHXq1HKMjoiKo1atWrCzs0NiYiI0Go3JlGplrVGjRggKCuIHtQWqXbu28XOW5CNn5i++50rPYgrkhg0b4uTJk/jwww+xbds2pKWlAQBsbW3RoUMHzJ49GwEBAWaPo1evXrh37x4++ugjxMXF4cUXX8Tu3buNZz9iY2NNunq8/PLL2LBhA6ZOnYoPPvgAzz//PLZs2SKbOZCBJ2eeNm7caFxJL78p33JW0vrxxx/xf//3fxJESUSWQqVSoWXLlvDx8ZE6FMqHi4sLzzDKkEqlQtu2bdktr4xYTIEMAAEBAYiIiIDBYMC9e/cAAG5ubuU+Z9+YMWNMljXN7dChQ3naevbsiZ49e5o5Kml1794dv/32m0kfRqVSCYPBAIVCgU6dOmHq1KksjokqOWtra4SFhVWqFecqEq1Wiw4dOrCIkqFGjRrBxcVF6jBkw6IK5Bw5l+pzfibLkNOHMTY2FgcOHEBycjIcHR3RunVr9jkmItjb2yMsLIxLE1solUqF9u3bw8HBQepQqIw5OTmhfv36UochKxZVIF+8eBEfffQR9uzZY9LFIiwsDDNmzJBVt4WKrFatWhg0aJDUYRCRBalZsyZatmxpliWqqWy8/PLLHCwpU02aNCnT6dzIggrko0eP4pVXXoHBYECXLl1MBult27YNu3btwu7du9G8eXOJIyUiohzW1tZo2rQp/Pz8eMXPgnl5eaFu3bpSh0FlQKfTwWAwGLvJVK1aFZ6enhJHJT8WUyCPHz8e7u7uOHz4MGrWrGly3z///IMWLVpgwoQJOHXqlEQREhFRbl5eXggJCYGdnZ3UoVAhrKysEBISwi8wMnH69GncuXMH27dvBwA0aNCAuTUDi5kH+cKFCxg9enSe4hh4cunuzTffxIULFySIjIiIctNoNGjdujXatWvH4rgCqFevHvMkU1qtFt7e3lKHIUsWcwbZ09MTGRkZBd6fmZmZb/FMRETlp2bNmggNDYWtra3UoVABdDod9Ho9NBoNFAoF6tWrJ3VIZCbe3t7lPtNXZWExBfJHH32E8ePHo1OnTnjxxRdN7vv999+xePFiLFy4UJLYiIgqO7VajeDgYNStW5eXcy3c6dOncenSJRw9ehQ6nY5nj2WMJw7Nx2IK5BMnTsDDwwNBQUF4+eWX4evrCwC4du0aIiMjERgYiMjISERGRhofo1AosGjRIqlCJiKqFJycnNCuXTuzrrpH5sEpOOVNp9NJHYJsWUyB/NVXXxl/Pn78OI4fP25y/7lz53Du3DmTNhbIRETm9dxzz6Ft27bQaDRSh0IlUL16dalDIDOxs7Pj+9KMLKZANhgMUodARES51KlTBy1atGAfxwrK2toaVatWlToMMhMnJyepQ5A1iymQiYjIcrzwwgto2rQp+xtXYDqdjvmTMXt7e6lDkDWLK5Cjo6Oxa9cu3Lx5E8CT2S1eeeUVTmNCRFRO/P39WRzLAFfNkzcOvjQviyqQJ06ciEWLFuXpbqFUKjFu3DjMnz9fosiIiCqHGjVqcFEJmWD3CnnjVIvmZTEdyz7//HMsWLAA3bt3R2RkJBITE5GYmIjIyEj06NEDCxYswIIFC6QOk4hItuzt7dG6dWv2OZYJFxcXqUMgM2KBbF4WcwZ5+fLleO211/Djjz+atDdp0gQ//PADHj9+jG+++Qbjx4+XKEIiInlr2bIlbGxspA6DyoBSqeQleJljgWxeFnOaICYmBmFhYQXeHxYWhpiYmPILiIioEvHz8+OUYDJiY2PDbjIyxwLZvCymQHZ3d8eff/5Z4P1//vkn3NzcyjEiIqLKQaPRoEmTJlKHQWWI8+PKHwtk87KYArlnz55YsWIFPv74Yzx69MjY/ujRI3zyySdYsWIFevXqJWGERETyoNPp4ObmBkdHRwBAUFAQu1bIjFptMT0oyQysrKygUqmkDkPWFEIIIXUQAJCWloZXX30VBw8ehFqtNl7qu337NvR6PVq1aoXt27fL8hvT2bNnERQUhDNnzqBRo0ZSh0NElUBkZCTOnTsHBwcHvP766/ywlZmHDx9ykJ6MpaSkwMHBQeowZM1iziDb2tpi//79iIiIwJAhQ+Dv7w9/f38MGTIEW7Zswa+//mr24jghIQH9+vWDo6MjnJ2dMXToUKSmpha6/9tvvw0/Pz9otVrUqlULY8eORVJSklnjJCIqK4GBgSyOZYgzkciblZWV1CHInkVcg0lLS0P//v0RHh6Ofv36oUuXLpLE0a9fP9y5cwf79u1DVlYWBg8ejBEjRmDDhg357n/79m3cvn0b8+fPR0BAAG7evIlRo0bh9u3b+Omnn8o5eiKi4lEoFPD19ZU6DDIDFsjyxi+15mcRBbKtrS1+/fVXvPLKK5LFcOnSJezevRunTp3CSy+9BABYvHgxOnbsiPnz5+c7ujswMBA///yz8baPjw/mzJmD/v37Q6/Xsw8YEVk0V1dXaLVaqcMgM2ABJW/Mr/lZzFfMZs2aITIyUrLnj4yMhLOzs7E4BoC2bdtCqVTi5MmTRT5OUlISHB0dCy2OMzIykJycbNwK68ZBRGQunBlIvlhAyRun8DM/iymQv/rqKxw9ehRTp07Fv//+W+7PHxcXB3d3d5M2tVoNV1dXxMXFFekY9+/fx6xZszBixIhC95s3bx6cnJyMW2hoaInjJiIqKVdXV6lDIDNhFwui0rGYd1CDBg3w77//Yt68efD09IRGo4Gjo6PJ5uTkVOzjvv/++1AoFIVuly9fLnX8ycnJ6NSpEwICAjBjxoxC950yZQqSkpKM2+HDh0v9/ERExcUCWb54hlHemF/zs5hOsuHh4WZJ+MSJEzFo0KBC96lduzZ0Oh3i4+NN2vV6PRISEqDT6Qp9fEpKCjp06AAHBwdEREQ8c3SpRqMxmcTd3t6+8BdBRGQGVapUkToEMhMWUPImhGCOzcxiCuTVq1eb5bhubm5F6mcXHByMxMREnDlzBkFBQQCAAwcOwGAwFLrCVHJyMsLCwqDRaLBt2zZOtk9EFYKdnR1XW5MxC1nigKjCkrxAfvz4MbZu3Yro6GhUrVoVnTp1QrVq1co9Dn9/f3To0AHDhw/H0qVLkZWVhTFjxqB3797GGSxu3bqFNm3aYO3atWjcuDGSk5PRvn17pKWl4bvvvjMOugOeFOYcJEFElipnFT2SJ55dJCodSQvk+Ph4vPzyy4iOjjZ+27W1tcWWLVvQtm3bco9n/fr1GDNmDNq0aQOlUonw8HB8+eWXxvuzsrJw5coVpKWlAXiyAl7ODBdPzyUaHR0NLy+vcoudiKg45LgqKf0PT9DIG7tYmJ+kS02//fbb+O9//4tx48ahdevWuH79OmbNmgVHR0f8/fffUoVV7rjUNBGVt8TERDg7O0sdBhGVQHZ2Nr8EmZmkZ5D37t2LAQMGYP78+cY2Dw8P9O3bF1euXIGfn5+E0RERyRc/XImICibpNG+xsbFo1qyZSVuzZs0ghMDdu3clioqISP54eZao4uL71/wkLZAzMjLyzPqQc1uv10sREhFRpcAPWKKKiwvBmJ/ks1jExMTg7NmzxttJSUkAgGvXruXbP459dImISo8FMhFRwSQdpKdUKvP9I53f6Myctuzs7PIKr9xwkB4Rlbe0tDTOZEFEVABJzyCvWrVKyqcnIqq0eAaZiKhgkhbIAwcOlPLpiYgqLRbIREQFYy9vIqJKiAUyEVHBWCATEVVCLJCJiArGApmIqBKysrKSOgQiIovFApmIqBLiGWQiooKxQCYiIiIiyoUFMhERERFRLiyQiYiIiIhyYYFMRERERJQLC2QiIiIiolxYIBMRERER5SLpUtNUsd25cwd37tyROgwiIqJKpVq1aqhWrZrUYcgaC2QLUK1aNUyfPr1C/WfPyMhAnz59cPjwYalDISIiqlRCQ0OxZ88eaDQaqUORLYUQQkgdBFU8ycnJcHJywuHDh2Fvby91OFTGUlNTERoayvzKFPMrb8yvvOXkNykpCY6OjlKHI1sskKlEcgpkvkHlifmVN+ZX3phfeWN+ywcH6RERERER5cICmYiIiIgoFxbIVCIajQbTp0/nAAGZYn7ljfmVN+ZX3pjf8sE+yEREREREufAMMhERERFRLiyQiYiIiIhyYYFMRERERJQLC2QiIiIiolxYIBNVQAqFokjboUOHSv1caWlpmDFjRrGONWfOHLz22mvw8PCAQqHAjBkzSh1HZWPJOb58+TImT56MF198EQ4ODqhWrRo6deqE06dPlzqWysKS83v79m30798ffn5+cHBwgLOzMxo3bow1a9aA4/qLxpLz+7T169dDoVBw1cWnqKUOgIiKb926dSa3165di3379uVp9/f3L/VzpaWlYebMmQCAli1bFukxU6dOhU6nQ8OGDbFnz55Sx1AZWXKOV6xYgZUrVyI8PByjR49GUlISvvnmGzRt2hS7d+9G27ZtSx2T3Flyfu/fv49///0XPXr0QK1atZCVlYV9+/Zh0KBBuHLlCubOnVvqmOTOkvObW2pqKiZPngw7O7tSxyE7gogqvLfeekuY6+187949AUBMnz69yI+Jjo4u8WMpf5aU49OnT4uUlBSTtvv37ws3NzcREhJihgjlz5LyW5DOnTsLOzs7odfryyawSsRS8/vee+8JPz8/0a9fP2FnZ1f2wVVg7GJBJFMGgwELFy5EvXr1YGNjAw8PD4wcORIPHz402e/06dMICwtD1apVodVq4e3tjSFDhgAAYmJi4ObmBgCYOXOm8bLgs7pMeHl5meMl0VOkynFQUFCey7FVqlRB8+bNcenSpbJ9kZWYlO/h/Hh5eSEtLQ2ZmZmlfm0kfX6vXbuGBQsW4IsvvoBazQ4FT+NvhEimRo4cidWrV2Pw4MEYO3YsoqOj8dVXX+H333/H8ePHYWVlhfj4eLRv3x5ubm54//334ezsjJiYGGzevBkA4Obmhv/+979488030a1bN3Tv3h0AUL9+fSlfGv1/lpbjuLg4VK1atUxfY2UmdX7T09Px6NEjpKam4vDhw1i1ahWCg4Oh1WrN+rorC6nzO27cOLRq1QodO3bEjz/+aNbXWiFJfQqbiErv6ct3R48eFQDE+vXrTfbbvXu3SXtERIQAIE6dOlXgsUtz+Y5dLMqOpeY4x5EjR4RCoRDTpk0r8TEqM0vM77x58wQA49amTRsRGxtbrGPQE5aW3x07dgi1Wi0uXLgghBBi4MCB7GLxFHaxIJKhTZs2wcnJCe3atcP9+/eNW86l8YMHDwIAnJ2dAQA7duxAVlaWhBFTcVlSjuPj49G3b194e3tj8uTJZnmOysYS8tunTx/s27cPGzZsQN++fQE8OatMpSdlfjMzMzF+/HiMGjUKAQEBZXJMOWKBTCRD165dQ1JSEtzd3eHm5maypaamIj4+HgAQGhqK8PBwzJw5E1WrVkWXLl2watUqZGRkSPwK6FksJcePHj1C586dkZKSgq1bt3KqqDJiCfn19PRE27Zt0adPH6xfvx61a9dG27ZtWSSXASnzu2DBAty/f9848wXlj32QiWTIYDDA3d0d69evz/f+nEEdCoUCP/30E06cOIHt27djz549GDJkCD7//HOcOHGCxY4Fs4QcZ2Zmonv37vjrr7+wZ88eBAYGlvhYZMoS8vu0Hj16YPny5Thy5AjCwsLK7LiVkVT5TUpKwuzZszF69GgkJycjOTkZwJPp3oQQiImJga2tLdzd3Uv3AuVA6j4eRFR6T/dvGz16tFCpVCItLa3Yx1q/fr0AIJYvXy6EeDJ9F9gHWXKWluPs7GzRq1cvoVKpxM8//1zsGMiUpeU3P1u2bBEAxMaNG0t1nMrIUvIbHR1t0q88v61Lly7FjkmO2MWCSIZef/11ZGdnY9asWXnu0+v1SExMBAA8fPgwz8pYL774IgAYL+HZ2toCgPExZBmkzvHbb7+NjRs3YsmSJcaR81R2pMzvvXv38m1fuXIlFAoFGjVqVKTjUMGkyq+7uzsiIiLybK1atYKNjQ0iIiIwZcqUkr8wGWEXCyIZCg0NxciRIzFv3jz88ccfaN++PaysrHDt2jVs2rQJixYtQo8ePbBmzRosWbIE3bp1g4+PD1JSUrB8+XI4OjqiY8eOAACtVouAgABs3LgRderUgaurKwIDAwu9nL5u3TrcvHkTaWlpAIAjR45g9uzZAIA33ngDnp6e5v8lyJyUOV64cCGWLFmC4OBg2Nra4rvvvjO5v1u3blyZq5SkzO+cOXNw/PhxdOjQAbVq1UJCQgJ+/vlnnDp1Cm+//TZ8fX3L81chS1Ll19bWFl27ds3TvmXLFkRFReV7X6Ul7QlsIioLBa3StGzZMhEUFCS0Wq1wcHAQL7zwgpg8ebK4ffu2EEKIs2fPij59+ohatWoJjUYj3N3dRefOncXp06dNjvPbb7+JoKAgYW1tXaRLeaGhoQVevjt48GBZvexKxZJyPHDgwEIv0easpEhFZ0n53bt3r+jcubOoXr26sLKyEg4ODiIkJESsWrVKGAyGMn3dlYUl5Tc/nOYtL4UQT527JyIiIiKqxNgHmYiIiIgoFxbIRERERES5sEAmIiIiIsqFBTIRERERUS4skImIiIiIcmGBTERERESUCwtkokooJiYGCoUCq1evljoUMgPmV96YX3ljfi0DC2QiIiIioly4UAhRJSSEQEZGBqysrKBSqaQOh8oY8ytvzK+8Mb+WgQUyEREREVEu7GJBVEHNmDEDCoUCV69eRf/+/eHk5AQ3NzdMmzYNQgj8888/6NKlCxwdHaHT6fD5558bH5tfH7dBgwbB3t4et27dQteuXWFvbw83Nze8++67yM7ONu536NAhKBQKHDp0yCSe/I4ZFxeHwYMHo0aNGtBoNKhWrRq6dOmCmJgYM/1W5IP5lTfmV96Y34qPBTJRBderVy8YDAZ8/PHHaNKkCWbPno2FCxeiXbt2eO655/DJJ5/A19cX7777Lo4cOVLosbKzsxEWFoYqVapg/vz5CA0Nxeeff45ly5aVKLbw8HBERERg8ODBWLJkCcaOHYuUlBTExsaW6HiVEfMrb8yvvDG/FZggogpp+vTpAoAYMWKEsU2v14saNWoIhUIhPv74Y2P7w4cPhVarFQMHDhRCCBEdHS0AiFWrVhn3GThwoAAg/vOf/5g8T8OGDUVQUJDx9sGDBwUAcfDgQZP9nj7mw4cPBQDx2Weflc0LrmSYX3ljfuWN+a34eAaZqIIbNmyY8WeVSoWXXnoJQggMHTrU2O7s7Aw/Pz/cuHHjmccbNWqUye3mzZsX6XFP02q1sLa2xqFDh/Dw4cNiP56eYH7ljfmVN+a34mKBTFTB1apVy+S2k5MTbGxsULVq1Tztz/pDaGNjAzc3N5M2FxeXEv0B1Wg0+OSTT7Br1y54eHigRYsW+PTTTxEXF1fsY1VmzK+8Mb/yxvxWXCyQiSq4/KYBKmhqIPGMSWuKMqWQQqHItz33QJEc48aNw9WrVzFv3jzY2Nhg2rRp8Pf3x++///7M56EnmF95Y37ljfmtuFggE1GxuLi4AAASExNN2m/evJnv/j4+Ppg4cSL27t2L8+fPIzMz02TENlkW5lfemF95Y37LDgtkIioWT09PqFSqPCOulyxZYnI7LS0Njx8/Nmnz8fGBg4MDMjIyzB4nlQzzK2/Mr7wxv2VHLXUARFSxODk5oWfPnli8eDEUCgV8fHywY8cOxMfHm+x39epVtGnTBq+//joCAgKgVqsRERGBu3fvonfv3hJFT8/C/Mob8ytvzG/ZYYFMRMW2ePFiZGVlYenSpdBoNHj99dfx2WefITAw0LhPzZo10adPH+zfvx/r1q2DWq1G3bp18eOPPyI8PFzC6OlZmF95Y37ljfktG1xqmoiIiIgoF/ZBJiIiIiLKhQUyEREREVEuLJCJiIiIiHJhgUxERERElAsLZCIiIiKiXFggE5FZxcTEQKFQYPXq1VKHQmbA/Mob8ytvzG/BWCATWZC///4bI0eORO3atWFjYwNHR0eEhIRg0aJFSE9PN9vzXrx4ETNmzEBMTIzZnqMo5syZg9deew0eHh5QKBSYMWOGpPGUNeaX+TUH5rd8ML/yzu/TuFAIkYXYuXMnevbsCY1GgwEDBiAwMBCZmZk4duwYJk2ahAsXLmDZsmVmee6LFy9i5syZaNmyJby8vMzyHEUxdepU6HQ6NGzYEHv27JEsDnNgfplf5rfiYn7lnd/8sEAmsgDR0dHo3bs3PD09ceDAAVSrVs1431tvvYXr169j586dEkb4P0IIPH78GFqttsyPHR0dDS8vL9y/fx9ubm5lfnypML9PML/SY36Lj/l9Qq75LQi7WBBZgE8//RSpqalYuXKlyR/fHL6+vnjnnXeMt/V6PWbNmgUfHx9oNBp4eXnhgw8+QEZGhsnjvLy80LlzZxw7dgyNGzeGjY0NateujbVr1xr3Wb16NXr27AkAaNWqFRQKBRQKBQ4dOmRyjD179uCll16CVqvFN998AwC4ceMGevbsCVdXV9ja2qJp06al+qCQ8uyIOTG//4tXjpjf/8UrR8zv/+KtVAQRSe65554TtWvXLvL+AwcOFABEjx49xNdffy0GDBggAIiuXbua7Ofp6Sn8/PyEh4eH+OCDD8RXX30lGjVqJBQKhTh//rwQQoi///5bjB07VgAQH3zwgVi3bp1Yt26diIuLMx7D19dXuLi4iPfff18sXbpUHDx4UMTFxQkPDw/h4OAgPvzwQ/HFF1+IBg0aCKVSKTZv3myMITo6WgAQq1atKvLru3fvngAgpk+fXuTHWDLm1xTzy/xWJMyvKbnltyAskIkklpSUJACILl26FGn/P/74QwAQw4YNM2l/9913BQBx4MABY5unp6cAII4cOWJsi4+PFxqNRkycONHYtmnTJgFAHDx4MM/z5Rxj9+7dJu3jxo0TAMTRo0eNbSkpKcLb21t4eXmJ7OxsIQT/ADO/eTG/zG9FwfzmJaf8FoZdLIgklpycDABwcHAo0v6//PILAGDChAkm7RMnTgSAPJfQAgIC0Lx5c+NtNzc3+Pn54caNG0WO0dvbG2FhYXniaNy4MZo1a2Zss7e3x4gRIxATE4OLFy8W+fhyxvzKG/Mrb8xv5cUCmUhijo6OAICUlJQi7X/z5k0olUr4+vqatOt0Ojg7O+PmzZsm7bVq1cpzDBcXFzx8+LDIMXp7e+cbh5+fX552f39/4/3E/Mod8ytvzG/lxQKZSGKOjo6oXr06zp8/X6zHKRSKIu2nUqnybRdCFPm5zDEiurJgfuWN+ZU35rfyYoFMZAE6d+6Mv//+G5GRkc/c19PTEwaDAdeuXTNpv3v3LhITE+Hp6Vns5y/qH/On47hy5Uqe9suXLxvvpyeYX3ljfuWN+a2cWCATWYDJkyfDzs4Ow4YNw927d/Pc//fff2PRokUAgI4dOwIAFi5caLLPF198AQDo1KlTsZ/fzs4OAJCYmFjkx3Ts2BFRUVEmHxqPHj3CsmXL4OXlhYCAgGLHIVfMr7wxv/LG/FZOXCiEyAL4+Phgw4YN6NWrF/z9/U1Wavrtt9+wadMmDBo0CADQoEEDDBw4EMuWLUNiYiJCQ0MRFRWFNWvWoGvXrmjVqlWxn//FF1+ESqXCJ598gqSkJGg0GrRu3Rru7u4FPub999/H999/j1deeQVjx46Fq6sr1qxZg+joaPz8889QKov//XvdunW4efMm0tLSAABHjhzB7NmzAQBvvPFGhT3rwfw+wfwyvxUR8/uEXPNbIGkn0SCi3K5evSqGDx8uvLy8hLW1tXBwcBAhISFi8eLF4vHjx8b9srKyxMyZM4W3t7ewsrISNWvWFFOmTDHZR4gnUwB16tQpz/OEhoaK0NBQk7bly5eL2rVrC5VKZTKlUEHHEOLJHJ09evQQzs7OwsbGRjRu3Fjs2LHDZJ/iTCMUGhoqAOS75TfFUUXD/DK/QjC/FRXzK+/8Pk0hRDF6ghMRERERyRz7IBMRERER5cICmYiIiIgoFxbIRERERES5sEAmIiIiIsqFBTIRERERUS4skImIiIiIcmGBTERERESUCwtkIiIiIqJcWCATEREREeXCApmIiIiIKBcWyEREREREubBAJiIiIiLKhQUyEREREVEu/w9jOHjelIkoqQAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 750x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 750x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "repeated_measures_baseline.mean_diff.plot(custom_palette={0: \"red\", 1: \"blue\"});\n",
+    "shared_control.mean_diff.plot(custom_palette={'Control 1': \"red\", 'Test 1': \"blue\", 'Test 2': \"green\", 'Test 3': \"purple\", 'Test 4': \"orange\"});"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Similarly, premade matplotlib/seaborn color palette can be passed. For sankey plots, the first two colors in the palette will be used to color the bars in the sankey plot. For bar plots, the colors will be used to color the filled portion of the bar plot."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 750x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 750x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "repeated_measures_baseline.mean_diff.plot(custom_palette='Set1');\n",
+    "shared_control.mean_diff.plot(custom_palette='Set1');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Passing a custom palette list functions differently for bar plots and sankey plots:\n",
+    "\n",
+    "- For bar plots, the list should contain the colors associated with each group. \n",
+    "- For sankey plots, the list should contain two colors, the first color will be used to color the binary '1's, and the second color will be used to color the '0's.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 750x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 750x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "repeated_measures_baseline.mean_diff.plot(custom_palette=['red', 'blue']);\n",
+    "shared_control.mean_diff.plot(custom_palette=['red', 'blue', 'green', 'purple', 'orange']);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Add counts to proportion plots\n",
+    "\n",
+    "By default, the sample counts for each bar in proportion plots are not shown.\n",
+    "\n",
+    "This feature can be turned on by setting `prop_sample_counts=True` in the `.plot()` method.\n",
+    "\n",
+    "**Note**: This feature is not compatible with `flow=False` in `sankey_kwargs`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "two_groups_unpaired = dabest.load(df, idx=(\"Control 1\", \"Test 1\"), proportional=True)\n",
+    "two_groups_unpaired.mean_diff.plot(prop_sample_counts=True);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The sample counts kwargs can be utilised via `prop_sample_counts_kwargs` in the `.plot()` method."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "two_groups_unpaired.mean_diff.plot(prop_sample_counts=True, prop_sample_counts_kwargs={\"color\":\"red\"});"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For further aesthetic changes, the [Plot Aesthetics Tutorial](09-plot_aesthetics.html) provides detailed examples of how to customize the plot.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "python3",
+   "language": "python",
+   "name": "python3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/nbs/tutorials/05-delta_delta.ipynb b/nbs/tutorials/05-delta_delta.ipynb
deleted file mode 100644
index 9dfe1c32..00000000
--- a/nbs/tutorials/05-delta_delta.ipynb
+++ /dev/null
@@ -1,842 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "cf1612f8",
-   "metadata": {},
-   "source": [
-    "# Delta-Delta\n",
-    "\n",
-    "> Explanation of how to calculate delta-delta using DABEST.\n",
-    "\n",
-    "- order: 5"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "cfdb7e31",
-   "metadata": {},
-   "source": [
-    "Since version 2023.02.14, DABEST also supports the calculation of delta-delta, an experimental function that facilitates the comparison between two bootstrapped effect sizes computed from two independent categorical variables. \n",
-    "\n",
-    "Many experimental designs investigate the effects of two interacting independent variables on a dependent variable. The delta-delta effect size enables us distill the net effect of the two variables. To illustrate this, let's explore the following  problem. \n",
-    "\n",
-    "Consider an experiment where we test the efficacy of a drug named ``Drug`` on a disease-causing mutation ``M`` based on disease metric ``Y``. The greater the value ``Y`` has, the more severe the disease phenotype is. Phenotype ``Y`` has been shown to be caused by a gain-of-function mutation ``M``, so we expect a difference between wild type (``W``) subjects and mutant subjects (``M``). Now, we want to know whether this effect is ameliorated by the administration of ``Drug`` treatment. We also administer a placebo as a control. In theory, we only expect ``Drug`` to have an effect on the ``M`` group, although in practice, many drugs have non-specific effects on healthy populations too.\n",
-    "\n",
-    "Effectively, we have four groups of subjects for comparison."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7a202204",
-   "metadata": {},
-   "source": [
-    "|       | Wildtype | Mutant   |\n",
-    "|-------|---------|----------|\n",
-    "| Drug  | X<sub>D, W</sub> | X<sub>D, M</sub> |\n",
-    "| Placebo | X<sub>P, W</sub> | X<sub>P, M</sub> |"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "be4d9084",
-   "metadata": {},
-   "source": [
-    "There are two ``Treatment`` conditions, ``Placebo`` (control group) and ``Drug`` (test group). There are two ``Genotype``\\s: ``W`` (wild type population) and ``M`` (mutant population). Additionally, each experiment was conducted twice (``Rep1`` and ``Rep2``). We will perform several analyses to visualise these differences in a simulated dataset. \n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9ec30d58",
-   "metadata": {},
-   "source": [
-    "## Load libraries"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "0fdd66d0",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "We're using DABEST v2024.03.29\n"
-     ]
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "import dabest\n",
-    "\n",
-    "print(\"We're using DABEST v{}\".format(dabest.__version__))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "9c8a33e6",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#| hide\n",
-    "import warnings\n",
-    "warnings.filterwarnings(\"ignore\", category=UserWarning) # to suppress warnings related to points not being able to be plotted due to dot size"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "96a35aa6",
-   "metadata": {},
-   "source": [
-    "## Simulate a dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "729207f7",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from scipy.stats import norm # Used in generation of populations.\n",
-    "np.random.seed(9999) # Fix the seed to ensure reproducibility of results.\n",
-    "\n",
-    "# Create samples\n",
-    "N = 20\n",
-    "y = norm.rvs(loc=3, scale=0.4, size=N*4)\n",
-    "y[N:2*N] = y[N:2*N]+1\n",
-    "y[2*N:3*N] = y[2*N:3*N]-0.5\n",
-    "\n",
-    "# Add a `Treatment` column\n",
-    "t1 = np.repeat('Placebo', N*2).tolist()\n",
-    "t2 = np.repeat('Drug', N*2).tolist()\n",
-    "treatment = t1 + t2 \n",
-    "\n",
-    "# Add a `Rep` column as the first variable for the 2 replicates of experiments done\n",
-    "rep = []\n",
-    "for i in range(N*2):\n",
-    "    rep.append('Rep1')\n",
-    "    rep.append('Rep2')\n",
-    "\n",
-    "# Add a `Genotype` column as the second variable\n",
-    "wt = np.repeat('W', N).tolist()\n",
-    "mt = np.repeat('M', N).tolist()\n",
-    "wt2 = np.repeat('W', N).tolist()\n",
-    "mt2 = np.repeat('M', N).tolist()\n",
-    "\n",
-    "\n",
-    "genotype = wt + mt + wt2 + mt2\n",
-    "\n",
-    "# Add an `id` column for paired data plotting.\n",
-    "id = list(range(0, N*2))\n",
-    "id_col = id + id \n",
-    "\n",
-    "\n",
-    "# Combine all columns into a DataFrame.\n",
-    "df_delta2 = pd.DataFrame({'ID'        : id_col,\n",
-    "                  'Rep'      : rep,\n",
-    "                   'Genotype'  : genotype, \n",
-    "                   'Treatment': treatment,\n",
-    "                   'Y'         : y\n",
-    "                })"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "0c00f10e",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>ID</th>\n",
-       "      <th>Rep</th>\n",
-       "      <th>Genotype</th>\n",
-       "      <th>Treatment</th>\n",
-       "      <th>Y</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0</td>\n",
-       "      <td>Rep1</td>\n",
-       "      <td>W</td>\n",
-       "      <td>Placebo</td>\n",
-       "      <td>2.793984</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1</td>\n",
-       "      <td>Rep2</td>\n",
-       "      <td>W</td>\n",
-       "      <td>Placebo</td>\n",
-       "      <td>3.236759</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>2</td>\n",
-       "      <td>Rep1</td>\n",
-       "      <td>W</td>\n",
-       "      <td>Placebo</td>\n",
-       "      <td>3.019149</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>3</td>\n",
-       "      <td>Rep2</td>\n",
-       "      <td>W</td>\n",
-       "      <td>Placebo</td>\n",
-       "      <td>2.804638</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>4</td>\n",
-       "      <td>Rep1</td>\n",
-       "      <td>W</td>\n",
-       "      <td>Placebo</td>\n",
-       "      <td>2.858019</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   ID   Rep Genotype Treatment         Y\n",
-       "0   0  Rep1        W   Placebo  2.793984\n",
-       "1   1  Rep2        W   Placebo  3.236759\n",
-       "2   2  Rep1        W   Placebo  3.019149\n",
-       "3   3  Rep2        W   Placebo  2.804638\n",
-       "4   4  Rep1        W   Placebo  2.858019"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df_delta2.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "50d94de3",
-   "metadata": {},
-   "source": [
-    "## Unpaired data"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f4315e6f",
-   "metadata": {},
-   "source": [
-    "To create a delta-delta plot, you simply need to set ``delta2=True`` in the \n",
-    "``dabest.load()`` function. However, in this case,``x`` needs to be declared as a list consisting of 2 elements, unlike most cases where it is a single element. The first element in ``x`` will represent the variable plotted along the horizontal axis, and the second one will determine the color of dots for scattered plots or the color of lines for slope graphs. We use the ``experiment`` input to specify the grouping of the data."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "36a5e3fd",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "unpaired_delta2 = dabest.load(data = df_delta2, x = [\"Genotype\", \"Genotype\"], y = \"Y\", delta2 = True, experiment = \"Treatment\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "3018f94e",
-   "metadata": {},
-   "source": [
-    "The above function creates the following object: "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "a5499575",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DABEST v2024.03.29\n",
-       "==================\n",
-       "                  \n",
-       "Good afternoon!\n",
-       "The current time is Tue Mar 19 15:44:01 2024.\n",
-       "\n",
-       "Effect size(s) with 95% confidence intervals will be computed for:\n",
-       "1. M Placebo minus W Placebo\n",
-       "2. M Drug minus W Drug\n",
-       "3. Drug minus Placebo (only for mean difference)\n",
-       "\n",
-       "5000 resamples will be used to generate the effect size bootstraps."
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "unpaired_delta2"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f720abcf",
-   "metadata": {},
-   "source": [
-    "\n",
-    "We can quickly check out the effect sizes:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "5e9cc16d",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DABEST v2024.03.29\n",
-       "==================\n",
-       "                  \n",
-       "Good afternoon!\n",
-       "The current time is Tue Mar 19 15:44:04 2024.\n",
-       "\n",
-       "The unpaired mean difference between W Placebo and M Placebo is 1.23 [95%CI 0.948, 1.52].\n",
-       "The p-value of the two-sided permutation t-test is 0.0, calculated for legacy purposes only. \n",
-       "\n",
-       "The unpaired mean difference between W Drug and M Drug is 0.326 [95%CI 0.0934, 0.584].\n",
-       "The p-value of the two-sided permutation t-test is 0.0122, calculated for legacy purposes only. \n",
-       "\n",
-       "The delta-delta between Placebo and Drug is -0.903 [95%CI -1.27, -0.522].\n",
-       "The p-value of the two-sided permutation t-test is 0.0, calculated for legacy purposes only. \n",
-       "\n",
-       "5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.\n",
-       "Any p-value reported is the probability of observing the effect size (or greater),\n",
-       "assuming the null hypothesis of zero difference is true.\n",
-       "For each p-value, 5000 reshuffles of the control and test labels were performed.\n",
-       "\n",
-       "To get the results of all valid statistical tests, use `.mean_diff.statistical_tests`"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "unpaired_delta2.mean_diff"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "7dbda11b",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 900x600 with 3 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "unpaired_delta2.mean_diff.plot();"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1a3e7ca1",
-   "metadata": {},
-   "source": [
-    "In the above plot, the horizontal axis represents the ``Genotype`` condition\n",
-    "and the dot colour is also specified by ``Genotype``. The left pair of \n",
-    "scattered plots is based on the ``Placebo`` group while the right pair is based\n",
-    "on the ``Drug`` group. The bottom left axis contains the two primary deltas: the ``Placebo`` delta \n",
-    "and the ``Drug`` delta. We can easily see that when only the placebo was \n",
-    "administered, the mutant phenotype is around 1.23 [95%CI 0.948, 1.52]. This difference was shrunken to around 0.326 [95%CI 0.0934, 0.584] when the drug was administered. This gives us some indication that the drug is effective in amiliorating the disease phenotype. Since the ``Drug`` did not completely eliminate the mutant phenotype, we have to calculate how much net effect the drug had. This is where ``delta-delta`` comes in. We use the ``Placebo`` delta as a reference for how much the mutant phenotype is supposed to be, and we subtract the ``Drug`` delta from it. The bootstrapped mean differences (delta-delta) between the ``Placebo`` \n",
-    "and ``Drug`` group are plotted at the right bottom with a separate y-axis from other bootstrap plots. \n",
-    "This effect size, at about -0.903 [95%CI -1.28, -0.513], is the net effect size of the drug treatment. That is to say that treatment with drug A reduced disease phenotype by 0.903.\n",
-    "\n",
-    "The mean difference between mutants and wild types given the placebo treatment is:\n",
-    "\n",
-    "$\\Delta_{1} = \\overline{X}_{P, M} - \\overline{X}_{P, W}$\n",
-    "\n",
-    "The mean difference between mutants and wild types given the drug treatment is:\n",
-    "\n",
-    "\n",
-    "$\\Delta_{2} = \\overline{X}_{D, M} - \\overline{X}_{D, W}$\n",
-    "\n",
-    "The net effect of the drug on mutants is:\n",
-    "    \n",
-    "\n",
-    "\n",
-    "$\\Delta_{\\Delta} = \\Delta_{2} - \\Delta_{1}$\n",
-    "    \n",
-    "\n",
-    "where $\\overline{X}$ is the sample mean, $\\Delta$ is the mean difference."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "054d04d2",
-   "metadata": {},
-   "source": [
-    "## Specifying grouping for comparisons"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "58c98331",
-   "metadata": {},
-   "source": [
-    "In the example above, we used the convention of *test - control* but you can manipulate the orders of the experiment groups as well as the horizontal axis variable by setting the paremeters ``experiment_label`` and ``x1_level``.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "c9398a01",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 900x600 with 3 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "unpaired_delta2_specified = dabest.load(data = df_delta2, \n",
-    "                                            x = [\"Genotype\", \"Genotype\"], y = \"Y\", \n",
-    "                                            delta2 = True, experiment = \"Treatment\",\n",
-    "                                            experiment_label = [\"Drug\", \"Placebo\"],\n",
-    "                                            x1_level = [\"M\", \"W\"])\n",
-    "\n",
-    "unpaired_delta2_specified.mean_diff.plot();"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d513187c",
-   "metadata": {},
-   "source": [
-    "## Paired data"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "fdc663cb",
-   "metadata": {},
-   "source": [
-    "The delta-delta function also supports paired data, providing a useful alternative visualization of the data. Assuming that the placebo and drug treatment were administered to the same subjects, our data is paired between the treatment conditions. We can specify this by using ``Treatment`` as ``x`` and ``Genotype`` as ``experiment``, and we further specify that ``id_col`` is ``ID``, linking data from the same subject with each other. Since we have conducted two replicates of the experiments, we can also colour the slope lines according to ``Rep``. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "0949bfea",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 675x600 with 3 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "paired_delta2 = dabest.load(data = df_delta2, \n",
-    "                                paired = \"baseline\", id_col=\"ID\",\n",
-    "                                x = [\"Treatment\", \"Rep\"], y = \"Y\", \n",
-    "                                delta2 = True, experiment = \"Genotype\")\n",
-    "paired_delta2.mean_diff.plot();"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5c7868a7",
-   "metadata": {},
-   "source": [
-    "We see that the drug had a non-specific effect of -0.321 [95%CI -0.498, -0.131] on wild type subjects even when they were not sick, and it had a bigger effect of -1.22 [95%CI -1.52, -0.906] in mutant subjects. In this visualisation, we can see the delta-delta value of -0.903 [95%CI -1.21, -0.587] as the net effect of the drug accounting for non-specific actions in healthy individuals. \n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "3b07192c",
-   "metadata": {},
-   "source": [
-    "The mean difference between drug and placebo treatments in wild type subjects is:\n",
-    "\n",
-    "$$\\Delta_{1} = \\overline{X}_{D, W} - \\overline{X}_{P, W}$$\n",
-    "\n",
-    "The mean difference between drug and placebo treatments in mutant subjects is:\n",
-    "\n",
-    "$$\\Delta_{2} = \\overline{X}_{D, M} - \\overline{X}_{P, M}$$\n",
-    "\n",
-    "The net effect of the drug on mutants is:\n",
-    "\n",
-    "$$\\Delta_{\\Delta} = \\Delta_{2} - \\Delta_{1}$$\n",
-    "\n",
-    "where $\\overline{X}$ is the sample mean, $\\Delta$ is the mean difference."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ea1da476",
-   "metadata": {},
-   "source": [
-    "## Standardising delta-delta effect sizes with Deltas' g"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1429f772",
-   "metadata": {},
-   "source": [
-    "Standardized mean difference statistics like Cohen's d and Hedges' g quantify effect sizes in terms of the sample variance. We have introduced a metric, *Deltas' g*, to standardize delta-delta effects. This metric enables the comparison between measurements of different dimensions.\n",
-    "\n",
-    "The standard deviation of the delta-delta value is calculated from a pooled variance of the 4 samples:\n",
-    "\n",
-    "$$s_{\\Delta_{\\Delta}} = \\sqrt{\\frac{(n_{D, W}-1)s_{D, W}^2+(n_{P, W}-1)s_{P, W}^2+(n_{D, M}-1)s_{D, M}^2+(n_{P, M}-1)s_{P, M}^2}{(n_{D, W} - 1) + (n_{P, W} - 1) + (n_{D, M} - 1) + (n_{P, M} - 1)}}$$\n",
-    "\n",
-    "where $s$ is the standard deviation and $n$ is the sample size.\n",
-    "\n",
-    "A deltas' g value is then calculated as delta-delta value divided by pooled standard deviation $s_{\\Delta_{\\Delta}}$:\n",
-    "\n",
-    "\n",
-    "$\\Delta_{g} = \\frac{\\Delta_{\\Delta}}{s_{\\Delta_{\\Delta}}}$"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "8b156226",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DABEST v2024.03.29\n",
-       "==================\n",
-       "                  \n",
-       "Good afternoon!\n",
-       "The current time is Tue Mar 19 15:44:15 2024.\n",
-       "\n",
-       "The unpaired deltas' g between W Placebo and M Placebo is 2.54 [95%CI 1.68, 3.28].\n",
-       "The p-value of the two-sided permutation t-test is 0.0, calculated for legacy purposes only. \n",
-       "\n",
-       "The unpaired deltas' g between W Drug and M Drug is 0.793 [95%CI 0.152, 1.34].\n",
-       "The p-value of the two-sided permutation t-test is 0.0122, calculated for legacy purposes only. \n",
-       "\n",
-       "The deltas' g between Placebo and Drug is -2.11 [95%CI -2.97, -1.22].\n",
-       "The p-value of the two-sided permutation t-test is 0.0, calculated for legacy purposes only. \n",
-       "\n",
-       "5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.\n",
-       "Any p-value reported is the probability of observing the effect size (or greater),\n",
-       "assuming the null hypothesis of zero difference is true.\n",
-       "For each p-value, 5000 reshuffles of the control and test labels were performed.\n",
-       "\n",
-       "To get the results of all valid statistical tests, use `.delta_g.statistical_tests`"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "unpaired_delta2.delta_g"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e53154bb",
-   "metadata": {},
-   "source": [
-    "We see the standardised delta-delta value of -2.11 standard deviations [95%CI -2.98, -1.2] as the net effect of the drug accounting for non-specific actions in healthy individuals. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "1645b2e9",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 900x600 with 3 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "unpaired_delta2.delta_g.plot();"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e33f0064",
-   "metadata": {},
-   "source": [
-    "## Connection to ANOVA"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "647eaa00",
-   "metadata": {},
-   "source": [
-    "The configuration of comparison we performed above is reminiscent of a two-way ANOVA. In fact, the delta - delta is an effect size estimated for the interaction term between ``Treatment`` and ``Genotype``. Main effects of ``Treatment`` and ``Genotype``, on the other hand, can be determined by simpler, univariate contrast plots. "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "044a5fab",
-   "metadata": {},
-   "source": [
-    "## Omitting delta-delta plot"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "226337e9",
-   "metadata": {},
-   "source": [
-    "If for some reason you don't want to display the delta-delta plot, you can easily do so by \n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "3230fae7",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 600x600 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "unpaired_delta2.mean_diff.plot(show_delta2=False);"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0b3a3da4",
-   "metadata": {},
-   "source": [
-    "## Other effect sizes"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5cb9650b",
-   "metadata": {},
-   "source": [
-    "\n",
-    "Since the delta-delta function is only applicable to mean differences, plots \n",
-    "of other effect sizes will not include a delta-delta bootstrap plot."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "d7b6b505",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 600x600 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "unpaired_delta2.cohens_d.plot();"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b71ec4b4",
-   "metadata": {},
-   "source": [
-    "## Statistics"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4ed26036",
-   "metadata": {},
-   "source": [
-    "You can find all outputs of the delta-delta calculation by assessing the attribute named ``delta_delta`` of the effect size object."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c1a0cada",
-   "metadata": {},
-   "source": [
-    "### Delta-delta statistics"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "205b0b55",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DABEST v2024.03.29\n",
-       "==================\n",
-       "                  \n",
-       "Good afternoon!\n",
-       "The current time is Tue Mar 19 15:44:20 2024.\n",
-       "\n",
-       "The delta-delta between Placebo and Drug is -0.903 [95%CI -1.27, -0.522].\n",
-       "The p-value of the two-sided permutation t-test is 0.0, calculated for legacy purposes only. \n",
-       "\n",
-       "5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.\n",
-       "Any p-value reported is the probability of observing the effect size (or greater),\n",
-       "assuming the null hypothesis of zero difference is true.\n",
-       "For each p-value, 5000 reshuffles of the control and test labels were performed."
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "unpaired_delta2.mean_diff.delta_delta"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "75dde9a4",
-   "metadata": {},
-   "source": [
-    "### Standardised delta-delta statistics "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "b71c96a6",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DABEST v2024.03.29\n",
-       "==================\n",
-       "                  \n",
-       "Good afternoon!\n",
-       "The current time is Tue Mar 19 15:44:20 2024.\n",
-       "\n",
-       "The deltas' g between Placebo and Drug is -2.11 [95%CI -2.97, -1.22].\n",
-       "The p-value of the two-sided permutation t-test is 0.0, calculated for legacy purposes only. \n",
-       "\n",
-       "5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.\n",
-       "Any p-value reported is the probability of observing the effect size (or greater),\n",
-       "assuming the null hypothesis of zero difference is true.\n",
-       "For each p-value, 5000 reshuffles of the control and test labels were performed."
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "unpaired_delta2.delta_g.delta_delta"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "3ba800cc",
-   "metadata": {},
-   "source": [
-    "The ``delta_delta`` object has its own attributes, containing various information of delta - delta.\n",
-    "\n",
-    "  - ``difference``: the mean bootstrapped differences between the 2 groups of bootstrapped mean differences \n",
-    "  - ``bootstraps``: the 2 groups of bootstrapped mean differences \n",
-    "  - ``bootstraps_delta_delta``: the bootstrapped differences between the 2 groups of bootstrapped mean differences \n",
-    "  - ``permutations``: the mean difference between the two groups of bootstrapped mean differences calculated based on the permutation data\n",
-    "  - ``permutations_var``: the pooled group variances of two groups of bootstrapped mean differences calculated based on permutation data\n",
-    "  - ``permutations_delta_delta``: the delta-delta calculated based on the permutation data\n",
-    "\n",
-    "``delta_delta.to_dict()`` will return all the attributes in a dictionary format."
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "python3",
-   "language": "python",
-   "name": "python3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/nbs/tutorials/05-mini_meta.ipynb b/nbs/tutorials/05-mini_meta.ipynb
new file mode 100644
index 00000000..153d26b9
--- /dev/null
+++ b/nbs/tutorials/05-mini_meta.ipynb
@@ -0,0 +1,876 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Mini-Meta\n",
+    "\n",
+    "> Explanation of how to compute the meta-analyzed weighted effect size using dabest.\n",
+    "\n",
+    "- order: 5"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "When scientists conduct replicates of the same experiment, the effect size of each replicate often varies, complicating the interpretation of the results. Starting from **v2023.02.14**, DABEST can now compute the meta-analyzed weighted effect size given multiple replicates of the same experiment. This can help resolve differences between replicates and simplify interpretation.\n",
+    "\n",
+    "This function employs the generic *inverse-variance* method to calculate the effect size, as follows:\n",
+    "\n",
+    "$\\theta_{\\text{weighted}} = \\frac{\\Sigma\\hat{\\theta_{i}}w_{i}}{{\\Sigma}w_{i}}$\n",
+    "\n",
+    "where:\n",
+    "\n",
+    "\n",
+    "$\\hat{\\theta_{i}} = \\text{Mean difference for replicate }i$\n",
+    "\n",
+    "\n",
+    "$w_{i} = \\text{Weight for replicate }i = \\frac{1}{s_{i}^2}$ \n",
+    "\n",
+    "\n",
+    "$s_{i}^2 = \\text{Pooled variance for replicate }i = \\frac{(n_{test}-1)s_{test}^2+(n_{control}-1)s_{control}^2}{n_{test}+n_{control}-2}$\n",
+    "\n",
+    "\n",
+    "$n = \\text{sample size and }s^2 = \\text{variance for control/test.}$\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note that this utilizes the fixed-effects model of meta-analysis, in contrast to the random-effects model. In the fixed-effects model, all variation between the results of each replicate is assumed to be solely due to sampling error. Therefore, we recommend using this function exclusively for replications of the same experiment, where it can be safely assumed that each replicate estimates the same population mean $\\mu$.\n",
+    "\n",
+    "Additionally, be aware that as of **v2023.02.14**, DABEST can only compute weighted effect size *for mean difference only*, and not for standardized measures such as Cohen's *d*.\n",
+    "\n",
+    "For more information on meta-analysis, please refer to [Chapter 10 of the Cochrane handbook](https://training.cochrane.org/handbook/current/chapter-10)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Pre-compiling numba functions for DABEST...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Compiling numba functions: 100%|██████████| 11/11 [00:00<00:00, 69.63it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Numba compilation complete!\n",
+      "We're using DABEST v2025.03.27\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import dabest\n",
+    "\n",
+    "print(\"We're using DABEST v{}\".format(dabest.__version__))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#| hide\n",
+    "import warnings\n",
+    "warnings.filterwarnings(\"ignore\", category=UserWarning) # to suppress warnings related to points not being able to be plotted due to dot size"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Creating a demo dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Control 1</th>\n",
+       "      <th>Test 1</th>\n",
+       "      <th>Control 2</th>\n",
+       "      <th>Test 2</th>\n",
+       "      <th>Control 3</th>\n",
+       "      <th>Test 3</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>ID</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2.793984</td>\n",
+       "      <td>3.420875</td>\n",
+       "      <td>3.324661</td>\n",
+       "      <td>1.707467</td>\n",
+       "      <td>3.816940</td>\n",
+       "      <td>1.796581</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>3.236759</td>\n",
+       "      <td>3.467972</td>\n",
+       "      <td>3.685186</td>\n",
+       "      <td>1.121846</td>\n",
+       "      <td>3.750358</td>\n",
+       "      <td>3.944566</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3.019149</td>\n",
+       "      <td>4.377179</td>\n",
+       "      <td>5.616891</td>\n",
+       "      <td>3.301381</td>\n",
+       "      <td>2.945397</td>\n",
+       "      <td>2.832188</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2.804638</td>\n",
+       "      <td>4.564780</td>\n",
+       "      <td>2.773152</td>\n",
+       "      <td>2.534018</td>\n",
+       "      <td>3.575179</td>\n",
+       "      <td>3.048267</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2.858019</td>\n",
+       "      <td>3.220058</td>\n",
+       "      <td>2.550361</td>\n",
+       "      <td>2.796365</td>\n",
+       "      <td>3.692138</td>\n",
+       "      <td>3.276575</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Control 1    Test 1  Control 2    Test 2  Control 3    Test 3  Gender  ID\n",
+       "0   2.793984  3.420875   3.324661  1.707467   3.816940  1.796581  Female   1\n",
+       "1   3.236759  3.467972   3.685186  1.121846   3.750358  3.944566  Female   2\n",
+       "2   3.019149  4.377179   5.616891  3.301381   2.945397  2.832188  Female   3\n",
+       "3   2.804638  4.564780   2.773152  2.534018   3.575179  3.048267  Female   4\n",
+       "4   2.858019  3.220058   2.550361  2.796365   3.692138  3.276575  Female   5"
+      ]
+     },
+     "execution_count": null,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from scipy.stats import norm # Used in generation of populations.\n",
+    "\n",
+    "np.random.seed(9999) # Fix the seed to ensure reproducibility of results.\n",
+    "Ns = 20 # The number of samples taken from each population\n",
+    "\n",
+    "# Create samples\n",
+    "c1 = norm.rvs(loc=3, scale=0.4, size=Ns)\n",
+    "c2 = norm.rvs(loc=3.5, scale=0.75, size=Ns)\n",
+    "c3 = norm.rvs(loc=3.25, scale=0.4, size=Ns)\n",
+    "\n",
+    "t1 = norm.rvs(loc=3.5, scale=0.5, size=Ns)\n",
+    "t2 = norm.rvs(loc=2.5, scale=0.6, size=Ns)\n",
+    "t3 = norm.rvs(loc=3, scale=0.75, size=Ns)\n",
+    "\n",
+    "\n",
+    "# Add a `gender` column for coloring the data.\n",
+    "females = np.repeat('Female', Ns/2).tolist()\n",
+    "males = np.repeat('Male', Ns/2).tolist()\n",
+    "gender = females + males\n",
+    "\n",
+    "# Add an `id` column for paired data plotting.\n",
+    "id_col = pd.Series(range(1, Ns+1))\n",
+    "\n",
+    "# Combine samples and gender into a DataFrame.\n",
+    "df = pd.DataFrame({'Control 1' : c1,     'Test 1' : t1,\n",
+    "                   'Control 2' : c2,     'Test 2' : t2,\n",
+    "                   'Control 3' : c3,     'Test 3' : t3,\n",
+    "                   'Gender'    : gender, 'ID'  : id_col\n",
+    "                  })\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We now have three *Control* and three *Test* groups, simulating three replicates of the same experiment. Our\n",
+    "dataset has also a non-numerical column indicating gender, and another\n",
+    "column indicating the identity of each observation."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This is known as a 'wide' dataset. See this\n",
+    "[writeup](https://sejdemyr.github.io/r-tutorials/basics/wide-and-long/)\n",
+    "for more details."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Loading data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Next, we load data as usual using ``dabest.load()``. However, this time, we also specify the argument ``mini_meta=True``. Since we are loading data from three experiments, ``idx`` is passed as a tuple of tuples, as shown below.\n",
+    "\n",
+    "When this `dabest` object is invoked, it should indicate that effect sizes will be calculated for each group, along with the weighted delta. It is important to note once again that the weighted delta will only be calculated for mean differences."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "DABEST v2025.03.27\n",
+       "==================\n",
+       "                  \n",
+       "Good afternoon!\n",
+       "The current time is Tue Mar 25 16:03:08 2025.\n",
+       "\n",
+       "Effect size(s) with 95% confidence intervals will be computed for:\n",
+       "1. Test 1 minus Control 1\n",
+       "2. Test 2 minus Control 2\n",
+       "3. Test 3 minus Control 3\n",
+       "4. weighted delta (only for mean difference)\n",
+       "\n",
+       "5000 resamples will be used to generate the effect size bootstraps."
+      ]
+     },
+     "execution_count": null,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "unpaired = dabest.load(df, idx=((\"Control 1\", \"Test 1\"), (\"Control 2\", \"Test 2\"), (\"Control 3\", \"Test 3\")), mini_meta=True)\n",
+    "unpaired"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "By calling the ``mean_diff`` attribute, you can view the mean differences for each group as well as the weighted delta.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "DABEST v2025.03.27\n",
+       "==================\n",
+       "                  \n",
+       "Good afternoon!\n",
+       "The current time is Tue Mar 25 16:03:09 2025.\n",
+       "\n",
+       "The unpaired mean difference between Control 1 and Test 1 is 0.48 [95%CI 0.205, 0.774].\n",
+       "The p-value of the two-sided permutation t-test is 0.001, calculated for legacy purposes only. \n",
+       "\n",
+       "The unpaired mean difference between Control 2 and Test 2 is -1.38 [95%CI -1.93, -0.905].\n",
+       "The p-value of the two-sided permutation t-test is 0.0, calculated for legacy purposes only. \n",
+       "\n",
+       "The unpaired mean difference between Control 3 and Test 3 is -0.255 [95%CI -0.696, 0.208].\n",
+       "The p-value of the two-sided permutation t-test is 0.293, calculated for legacy purposes only. \n",
+       "\n",
+       "The weighted-average unpaired mean differences is -0.0104 [95%CI -0.226, 0.213].\n",
+       "The p-value of the two-sided permutation t-test is 0.937, calculated for legacy purposes only. \n",
+       "\n",
+       "5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.\n",
+       "Any p-value reported is the probability of observing theeffect size (or greater),\n",
+       "assuming the null hypothesis of zero difference is true.\n",
+       "For each p-value, 5000 reshuffles of the control and test labels were performed.\n",
+       "\n",
+       "To get the results of all valid statistical tests, use `.mean_diff.statistical_tests`"
+      ]
+     },
+     "execution_count": null,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "unpaired.mean_diff"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can view the details of each experiment by accessing the property `mean_diff.results` as follows."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>control</th>\n",
+       "      <th>test</th>\n",
+       "      <th>control_N</th>\n",
+       "      <th>test_N</th>\n",
+       "      <th>effect_size</th>\n",
+       "      <th>is_paired</th>\n",
+       "      <th>difference</th>\n",
+       "      <th>ci</th>\n",
+       "      <th>bca_low</th>\n",
+       "      <th>bca_high</th>\n",
+       "      <th>bca_interval_idx</th>\n",
+       "      <th>pct_low</th>\n",
+       "      <th>pct_high</th>\n",
+       "      <th>pct_interval_idx</th>\n",
+       "      <th>bootstraps</th>\n",
+       "      <th>resamples</th>\n",
+       "      <th>random_seed</th>\n",
+       "      <th>permutations</th>\n",
+       "      <th>pvalue_permutation</th>\n",
+       "      <th>permutation_count</th>\n",
+       "      <th>permutations_var</th>\n",
+       "      <th>pvalue_welch</th>\n",
+       "      <th>statistic_welch</th>\n",
+       "      <th>pvalue_students_t</th>\n",
+       "      <th>statistic_students_t</th>\n",
+       "      <th>pvalue_mann_whitney</th>\n",
+       "      <th>statistic_mann_whitney</th>\n",
+       "      <th>bec_difference</th>\n",
+       "      <th>bec_bootstraps</th>\n",
+       "      <th>bec_bca_interval_idx</th>\n",
+       "      <th>bec_bca_low</th>\n",
+       "      <th>bec_bca_high</th>\n",
+       "      <th>bec_pct_interval_idx</th>\n",
+       "      <th>bec_pct_low</th>\n",
+       "      <th>bec_pct_high</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Control 1</td>\n",
+       "      <td>Test 1</td>\n",
+       "      <td>20</td>\n",
+       "      <td>20</td>\n",
+       "      <td>mean difference</td>\n",
+       "      <td>None</td>\n",
+       "      <td>0.480290</td>\n",
+       "      <td>95</td>\n",
+       "      <td>0.205161</td>\n",
+       "      <td>0.773647</td>\n",
+       "      <td>(145, 4893)</td>\n",
+       "      <td>0.197427</td>\n",
+       "      <td>0.758752</td>\n",
+       "      <td>(125, 4875)</td>\n",
+       "      <td>[0.6148498102262239, 0.6752095203445543, 0.300...</td>\n",
+       "      <td>5000</td>\n",
+       "      <td>12345</td>\n",
+       "      <td>[-0.17259843762502491, 0.03802293852634886, -0...</td>\n",
+       "      <td>0.0010</td>\n",
+       "      <td>5000</td>\n",
+       "      <td>[0.26356588154404337, 0.2710249543904699, 0.26...</td>\n",
+       "      <td>0.002094</td>\n",
+       "      <td>-3.308806</td>\n",
+       "      <td>0.002057</td>\n",
+       "      <td>-3.308806</td>\n",
+       "      <td>0.001625</td>\n",
+       "      <td>83.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>[-0.09732932551566487, 0.08087009665445155, -0...</td>\n",
+       "      <td>(127, 4877)</td>\n",
+       "      <td>-0.256862</td>\n",
+       "      <td>0.259558</td>\n",
+       "      <td>(125, 4875)</td>\n",
+       "      <td>-0.258260</td>\n",
+       "      <td>0.257590</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Control 2</td>\n",
+       "      <td>Test 2</td>\n",
+       "      <td>20</td>\n",
+       "      <td>20</td>\n",
+       "      <td>mean difference</td>\n",
+       "      <td>None</td>\n",
+       "      <td>-1.381085</td>\n",
+       "      <td>95</td>\n",
+       "      <td>-1.934192</td>\n",
+       "      <td>-0.905164</td>\n",
+       "      <td>(94, 4838)</td>\n",
+       "      <td>-1.901802</td>\n",
+       "      <td>-0.877098</td>\n",
+       "      <td>(125, 4875)</td>\n",
+       "      <td>[-1.7266697532252988, -1.7990605927248775, -1....</td>\n",
+       "      <td>5000</td>\n",
+       "      <td>12345</td>\n",
+       "      <td>[0.015164519971271773, 0.017231919606192303, -...</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>5000</td>\n",
+       "      <td>[1.2241741427801065, 1.2241565174150129, 1.128...</td>\n",
+       "      <td>0.000011</td>\n",
+       "      <td>5.138840</td>\n",
+       "      <td>0.000009</td>\n",
+       "      <td>5.138840</td>\n",
+       "      <td>0.000026</td>\n",
+       "      <td>356.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>[-0.7109511916465152, -0.3436697507223183, -0....</td>\n",
+       "      <td>(126, 4876)</td>\n",
+       "      <td>-0.578621</td>\n",
+       "      <td>0.598647</td>\n",
+       "      <td>(125, 4875)</td>\n",
+       "      <td>-0.579306</td>\n",
+       "      <td>0.598009</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Control 3</td>\n",
+       "      <td>Test 3</td>\n",
+       "      <td>20</td>\n",
+       "      <td>20</td>\n",
+       "      <td>mean difference</td>\n",
+       "      <td>None</td>\n",
+       "      <td>-0.254831</td>\n",
+       "      <td>95</td>\n",
+       "      <td>-0.696379</td>\n",
+       "      <td>0.207659</td>\n",
+       "      <td>(123, 4873)</td>\n",
+       "      <td>-0.694790</td>\n",
+       "      <td>0.208585</td>\n",
+       "      <td>(125, 4875)</td>\n",
+       "      <td>[0.3059887140714319, -0.22727011648745288, 0.0...</td>\n",
+       "      <td>5000</td>\n",
+       "      <td>12345</td>\n",
+       "      <td>[-0.05901068591042824, -0.13617667681797307, 0...</td>\n",
+       "      <td>0.2934</td>\n",
+       "      <td>5000</td>\n",
+       "      <td>[0.5835889750166371, 0.5796253365278035, 0.581...</td>\n",
+       "      <td>0.294766</td>\n",
+       "      <td>1.069798</td>\n",
+       "      <td>0.291459</td>\n",
+       "      <td>1.069798</td>\n",
+       "      <td>0.285305</td>\n",
+       "      <td>240.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>[0.07996849455952271, 0.24534680794041375, 0.0...</td>\n",
+       "      <td>(124, 4874)</td>\n",
+       "      <td>-0.243754</td>\n",
+       "      <td>0.240283</td>\n",
+       "      <td>(125, 4875)</td>\n",
+       "      <td>-0.243713</td>\n",
+       "      <td>0.240490</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     control    test  control_N  test_N      effect_size is_paired  \\\n",
+       "0  Control 1  Test 1         20      20  mean difference      None   \n",
+       "1  Control 2  Test 2         20      20  mean difference      None   \n",
+       "2  Control 3  Test 3         20      20  mean difference      None   \n",
+       "\n",
+       "   difference  ci   bca_low  bca_high bca_interval_idx   pct_low  pct_high  \\\n",
+       "0    0.480290  95  0.205161  0.773647      (145, 4893)  0.197427  0.758752   \n",
+       "1   -1.381085  95 -1.934192 -0.905164       (94, 4838) -1.901802 -0.877098   \n",
+       "2   -0.254831  95 -0.696379  0.207659      (123, 4873) -0.694790  0.208585   \n",
+       "\n",
+       "  pct_interval_idx                                         bootstraps  \\\n",
+       "0      (125, 4875)  [0.6148498102262239, 0.6752095203445543, 0.300...   \n",
+       "1      (125, 4875)  [-1.7266697532252988, -1.7990605927248775, -1....   \n",
+       "2      (125, 4875)  [0.3059887140714319, -0.22727011648745288, 0.0...   \n",
+       "\n",
+       "   resamples  random_seed                                       permutations  \\\n",
+       "0       5000        12345  [-0.17259843762502491, 0.03802293852634886, -0...   \n",
+       "1       5000        12345  [0.015164519971271773, 0.017231919606192303, -...   \n",
+       "2       5000        12345  [-0.05901068591042824, -0.13617667681797307, 0...   \n",
+       "\n",
+       "   pvalue_permutation  permutation_count  \\\n",
+       "0              0.0010               5000   \n",
+       "1              0.0000               5000   \n",
+       "2              0.2934               5000   \n",
+       "\n",
+       "                                    permutations_var  pvalue_welch  \\\n",
+       "0  [0.26356588154404337, 0.2710249543904699, 0.26...      0.002094   \n",
+       "1  [1.2241741427801065, 1.2241565174150129, 1.128...      0.000011   \n",
+       "2  [0.5835889750166371, 0.5796253365278035, 0.581...      0.294766   \n",
+       "\n",
+       "   statistic_welch  pvalue_students_t  statistic_students_t  \\\n",
+       "0        -3.308806           0.002057             -3.308806   \n",
+       "1         5.138840           0.000009              5.138840   \n",
+       "2         1.069798           0.291459              1.069798   \n",
+       "\n",
+       "   pvalue_mann_whitney  statistic_mann_whitney  bec_difference  \\\n",
+       "0             0.001625                    83.0             0.0   \n",
+       "1             0.000026                   356.0             0.0   \n",
+       "2             0.285305                   240.0             0.0   \n",
+       "\n",
+       "                                      bec_bootstraps bec_bca_interval_idx  \\\n",
+       "0  [-0.09732932551566487, 0.08087009665445155, -0...          (127, 4877)   \n",
+       "1  [-0.7109511916465152, -0.3436697507223183, -0....          (126, 4876)   \n",
+       "2  [0.07996849455952271, 0.24534680794041375, 0.0...          (124, 4874)   \n",
+       "\n",
+       "   bec_bca_low  bec_bca_high bec_pct_interval_idx  bec_pct_low  bec_pct_high  \n",
+       "0    -0.256862      0.259558          (125, 4875)    -0.258260      0.257590  \n",
+       "1    -0.578621      0.598647          (125, 4875)    -0.579306      0.598009  \n",
+       "2    -0.243754      0.240283          (125, 4875)    -0.243713      0.240490  "
+      ]
+     },
+     "execution_count": null,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.options.display.max_columns = 50\n",
+    "unpaired.mean_diff.results"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note, however, that this does not contain the relevant information for our weighted delta. The details of the weighted delta are stored as attributes of the ``mini_meta`` object, such as:\n",
+    "\n",
+    "  - ``group_var``: the pooled group variances of each set of 2 experiment groups.\n",
+    "  - ``difference``: the weighted mean difference calculated based on the raw data.\n",
+    "  - ``bootstraps``: the deltas of each set of 2 experiment groups calculated based on the bootstraps.\n",
+    "  - ``bootstraps_weighted_delta``: the weighted deltas calculated based on the bootstraps.\n",
+    "  - ``permutations``: the deltas of each set of 2 experiment groups calculated based on the permutation data.\n",
+    "  - ``permutations_var``: the pooled group variances of each set of 2 experiment groups calculated based on permutation data.\n",
+    "  - ``permutations_weighted_delta``: the weighted deltas calculated based on the permutation data.\n",
+    "\n",
+    "A dataframe of this mini meta dabest object can also be called via the `mini_meta.results` attribute."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>control</th>\n",
+       "      <th>test</th>\n",
+       "      <th>control_N</th>\n",
+       "      <th>test_N</th>\n",
+       "      <th>control_var</th>\n",
+       "      <th>test_var</th>\n",
+       "      <th>group_var</th>\n",
+       "      <th>difference</th>\n",
+       "      <th>ci</th>\n",
+       "      <th>bca_low</th>\n",
+       "      <th>bca_high</th>\n",
+       "      <th>bca_interval_idx</th>\n",
+       "      <th>pct_low</th>\n",
+       "      <th>pct_high</th>\n",
+       "      <th>pct_interval_idx</th>\n",
+       "      <th>bootstraps_deltas</th>\n",
+       "      <th>bootstraps_weighted_delta</th>\n",
+       "      <th>permutations</th>\n",
+       "      <th>permutations_var</th>\n",
+       "      <th>permutations_weighted_delta</th>\n",
+       "      <th>pvalue_permutation</th>\n",
+       "      <th>permutation_count</th>\n",
+       "      <th>bias_correction</th>\n",
+       "      <th>jackknives</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>[Control 1, Control 2, Control 3]</td>\n",
+       "      <td>[Test 1, Test 2, Test 3]</td>\n",
+       "      <td>[20, 20, 20]</td>\n",
+       "      <td>[20, 20, 20]</td>\n",
+       "      <td>[0.17628013404546258, 0.9584767911266554, 0.16...</td>\n",
+       "      <td>[0.24512071870152594, 0.48609989925165153, 0.9...</td>\n",
+       "      <td>[0.2107004263734943, 0.7222883451891535, 0.567...</td>\n",
+       "      <td>-0.010352</td>\n",
+       "      <td>95</td>\n",
+       "      <td>-0.22563</td>\n",
+       "      <td>0.212955</td>\n",
+       "      <td>(132, 4882)</td>\n",
+       "      <td>-0.227045</td>\n",
+       "      <td>0.209665</td>\n",
+       "      <td>(125, 4875)</td>\n",
+       "      <td>[[0.6148498102262239, 0.6752095203445543, 0.30...</td>\n",
+       "      <td>[0.1351632773105745, 0.03969128532968254, -0.0...</td>\n",
+       "      <td>[[-0.17259843762502491, 0.03802293852634886, -...</td>\n",
+       "      <td>[[0.26356588154404337, 0.2710249543904699, 0.2...</td>\n",
+       "      <td>[-0.11757207833491819, -0.01292867970093462, -...</td>\n",
+       "      <td>0.9374</td>\n",
+       "      <td>5000</td>\n",
+       "      <td>0.012533</td>\n",
+       "      <td>[-0.011161759003339202, -0.011142660785299416,...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                             control                      test     control_N  \\\n",
+       "0  [Control 1, Control 2, Control 3]  [Test 1, Test 2, Test 3]  [20, 20, 20]   \n",
+       "\n",
+       "         test_N                                        control_var  \\\n",
+       "0  [20, 20, 20]  [0.17628013404546258, 0.9584767911266554, 0.16...   \n",
+       "\n",
+       "                                            test_var  \\\n",
+       "0  [0.24512071870152594, 0.48609989925165153, 0.9...   \n",
+       "\n",
+       "                                           group_var difference  ci  bca_low  \\\n",
+       "0  [0.2107004263734943, 0.7222883451891535, 0.567...  -0.010352  95 -0.22563   \n",
+       "\n",
+       "   bca_high bca_interval_idx   pct_low  pct_high pct_interval_idx  \\\n",
+       "0  0.212955      (132, 4882) -0.227045  0.209665      (125, 4875)   \n",
+       "\n",
+       "                                   bootstraps_deltas  \\\n",
+       "0  [[0.6148498102262239, 0.6752095203445543, 0.30...   \n",
+       "\n",
+       "                           bootstraps_weighted_delta  \\\n",
+       "0  [0.1351632773105745, 0.03969128532968254, -0.0...   \n",
+       "\n",
+       "                                        permutations  \\\n",
+       "0  [[-0.17259843762502491, 0.03802293852634886, -...   \n",
+       "\n",
+       "                                    permutations_var  \\\n",
+       "0  [[0.26356588154404337, 0.2710249543904699, 0.2...   \n",
+       "\n",
+       "                         permutations_weighted_delta pvalue_permutation  \\\n",
+       "0  [-0.11757207833491819, -0.01292867970093462, -...             0.9374   \n",
+       "\n",
+       "  permutation_count bias_correction  \\\n",
+       "0              5000        0.012533   \n",
+       "\n",
+       "                                          jackknives  \n",
+       "0  [-0.011161759003339202, -0.011142660785299416,...  "
+      ]
+     },
+     "execution_count": null,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "unpaired.mean_diff.mini_meta.results"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Generating mini meta plots"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1050x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "unpaired.mean_diff.plot();"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can also hide the weighted delta by passing the argument ``show_mini_meta=False``. In this case, the resulting graph would be identical to a multiple two-groups plot.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 900x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "unpaired.mean_diff.plot(show_mini_meta=False);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As with regular two-groups plots, you can also analyse paired mini meta experiments via the `paired=baseline` argument."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 840x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "paired = dabest.load(df, idx=((\"Control 1\", \"Test 1\"), (\"Control 2\", \"Test 2\"), (\"Control 3\", \"Test 3\")), mini_meta=True, id_col=\"ID\", paired=\"baseline\")\n",
+    "paired.mean_diff.plot();"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For further aesthetic changes, the [Plot Aesthetics Tutorial](09-plot_aesthetics.html) provides detailed examples of how to customize the plot.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "python3",
+   "language": "python",
+   "name": "python3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/nbs/tutorials/06-delta_delta.ipynb b/nbs/tutorials/06-delta_delta.ipynb
new file mode 100644
index 00000000..cf1dcbc6
--- /dev/null
+++ b/nbs/tutorials/06-delta_delta.ipynb
@@ -0,0 +1,1032 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Delta-Delta\n",
+    "\n",
+    "> Explanation of how to calculate delta-delta using DABEST.\n",
+    "\n",
+    "- order: 6"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Since v2023.02.14, DABEST also supports the calculation of delta-delta, an experimental function that facilitates the comparison between two bootstrapped effect sizes computed from two independent categorical variables.** \n",
+    "\n",
+    "**Since v2025.03.27, DABEST also supports the calculation of delta-delta for binary data (proportion plots).**\n",
+    "\n",
+    "Many experimental designs investigate the effects of two interacting independent variables on a dependent variable. The delta-delta effect size enables us distill the net effect of the two variables. To illustrate this, let's explore the following  problem. \n",
+    "\n",
+    "Consider an experiment where we test the efficacy of a drug named ``Drug`` on a disease-causing mutation ``M`` based on disease metric ``Y``. The greater the value ``Y`` has, the more severe the disease phenotype is. Phenotype ``Y`` has been shown to be caused by a gain-of-function mutation ``M``, so we expect a difference between wild type (``W``) subjects and mutant subjects (``M``). Now, we want to know whether this effect is ameliorated by the administration of ``Drug`` treatment. We also administer a placebo as a control. In theory, we only expect ``Drug`` to have an effect on the ``M`` group, although in practice, many drugs have non-specific effects on healthy populations too.\n",
+    "\n",
+    "Effectively, we have four groups of subjects for comparison."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "|       | Wildtype | Mutant   |\n",
+    "|-------|---------|----------|\n",
+    "| Drug  | X<sub>D, W</sub> | X<sub>D, M</sub> |\n",
+    "| Placebo | X<sub>P, W</sub> | X<sub>P, M</sub> |"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "There are two ``Treatment`` conditions, ``Placebo`` (control group) and ``Drug`` (test group). There are two ``Genotype``\\s: ``W`` (wild type population) and ``M`` (mutant population). Additionally, each experiment was conducted twice (``Rep1`` and ``Rep2``). We will perform several analyses to visualise these differences in a simulated dataset. \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Pre-compiling numba functions for DABEST...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Compiling numba functions: 100%|██████████| 11/11 [00:00<00:00, 38.44it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Numba compilation complete!\n",
+      "We're using DABEST v2025.03.27\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import dabest\n",
+    "\n",
+    "print(\"We're using DABEST v{}\".format(dabest.__version__))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#| hide\n",
+    "import warnings\n",
+    "warnings.filterwarnings(\"ignore\", category=UserWarning) # to suppress warnings related to points not being able to be plotted due to dot size"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Creating a demo dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>ID</th>\n",
+       "      <th>Rep</th>\n",
+       "      <th>Genotype</th>\n",
+       "      <th>Treatment</th>\n",
+       "      <th>Y</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>Rep1</td>\n",
+       "      <td>W</td>\n",
+       "      <td>Placebo</td>\n",
+       "      <td>2.793984</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>Rep2</td>\n",
+       "      <td>W</td>\n",
+       "      <td>Placebo</td>\n",
+       "      <td>3.236759</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>Rep1</td>\n",
+       "      <td>W</td>\n",
+       "      <td>Placebo</td>\n",
+       "      <td>3.019149</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>Rep2</td>\n",
+       "      <td>W</td>\n",
+       "      <td>Placebo</td>\n",
+       "      <td>2.804638</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4</td>\n",
+       "      <td>Rep1</td>\n",
+       "      <td>W</td>\n",
+       "      <td>Placebo</td>\n",
+       "      <td>2.858019</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   ID   Rep Genotype Treatment         Y\n",
+       "0   0  Rep1        W   Placebo  2.793984\n",
+       "1   1  Rep2        W   Placebo  3.236759\n",
+       "2   2  Rep1        W   Placebo  3.019149\n",
+       "3   3  Rep2        W   Placebo  2.804638\n",
+       "4   4  Rep1        W   Placebo  2.858019"
+      ]
+     },
+     "execution_count": null,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from scipy.stats import norm # Used in generation of populations.\n",
+    "np.random.seed(9999) # Fix the seed to ensure reproducibility of results.\n",
+    "\n",
+    "# Create samples\n",
+    "N = 20\n",
+    "y = norm.rvs(loc=3, scale=0.4, size=N*4)\n",
+    "y[N:2*N] = y[N:2*N]+1\n",
+    "y[2*N:3*N] = y[2*N:3*N]-0.5\n",
+    "\n",
+    "# Add a `Treatment` column\n",
+    "t1 = np.repeat('Placebo', N*2).tolist()\n",
+    "t2 = np.repeat('Drug', N*2).tolist()\n",
+    "treatment = t1 + t2 \n",
+    "\n",
+    "# Add a `Rep` column as the first variable for the 2 replicates of experiments done\n",
+    "rep = []\n",
+    "for i in range(N*2):\n",
+    "    rep.append('Rep1')\n",
+    "    rep.append('Rep2')\n",
+    "\n",
+    "# Add a `Genotype` column as the second variable\n",
+    "wt = np.repeat('W', N).tolist()\n",
+    "mt = np.repeat('M', N).tolist()\n",
+    "wt2 = np.repeat('W', N).tolist()\n",
+    "mt2 = np.repeat('M', N).tolist()\n",
+    "\n",
+    "\n",
+    "genotype = wt + mt + wt2 + mt2\n",
+    "\n",
+    "# Add an `id` column for paired data plotting.\n",
+    "id = list(range(0, N*2))\n",
+    "id_col = id + id \n",
+    "\n",
+    "\n",
+    "# Combine all columns into a DataFrame.\n",
+    "df_delta2 = pd.DataFrame({'ID'        : id_col,\n",
+    "                  'Rep'      : rep,\n",
+    "                   'Genotype'  : genotype, \n",
+    "                   'Treatment': treatment,\n",
+    "                   'Y'         : y\n",
+    "                })\n",
+    "df_delta2.head(5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Loading data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To create a delta-delta plot, you simply need to set ``delta2=True`` in the \n",
+    "``dabest.load()`` method. However, in this case,``x`` needs to be declared as a list consisting of 2 elements, unlike most cases where it is a single element. The first element in ``x`` will represent the variable plotted along the horizontal axis, and the second one will determine the color of dots for scattered plots or the color of lines for slope graphs. We use the ``experiment`` input to specify the grouping of the data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "DABEST v2025.03.27\n",
+       "==================\n",
+       "                  \n",
+       "Good afternoon!\n",
+       "The current time is Tue Mar 25 16:03:11 2025.\n",
+       "\n",
+       "Effect size(s) with 95% confidence intervals will be computed for:\n",
+       "1. M Placebo minus W Placebo\n",
+       "2. M Drug minus W Drug\n",
+       "3. Drug minus Placebo (only for mean difference)\n",
+       "\n",
+       "5000 resamples will be used to generate the effect size bootstraps."
+      ]
+     },
+     "execution_count": null,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "unpaired_delta2 = dabest.load(data = df_delta2, x = [\"Genotype\", \"Genotype\"], y = \"Y\", delta2 = True, experiment = \"Treatment\")\n",
+    "unpaired_delta2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "DABEST v2025.03.27\n",
+       "==================\n",
+       "                  \n",
+       "Good afternoon!\n",
+       "The current time is Tue Mar 25 16:03:12 2025.\n",
+       "\n",
+       "The unpaired mean difference between W Placebo and M Placebo is 1.23 [95%CI 0.937, 1.51].\n",
+       "The p-value of the two-sided permutation t-test is 0.0, calculated for legacy purposes only. \n",
+       "\n",
+       "The unpaired mean difference between W Drug and M Drug is 0.326 [95%CI 0.0956, 0.574].\n",
+       "The p-value of the two-sided permutation t-test is 0.0122, calculated for legacy purposes only. \n",
+       "\n",
+       "The delta-delta between Placebo and Drug is -0.903 [95%CI -1.27, -0.522].\n",
+       "The p-value of the two-sided permutation t-test is 0.0, calculated for legacy purposes only. \n",
+       "\n",
+       "5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.\n",
+       "Any p-value reported is the probability of observing the effect size (or greater),\n",
+       "assuming the null hypothesis of zero difference is true.\n",
+       "For each p-value, 5000 reshuffles of the control and test labels were performed.\n",
+       "\n",
+       "To get the results of all valid statistical tests, use `.mean_diff.statistical_tests`"
+      ]
+     },
+     "execution_count": null,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "unpaired_delta2.mean_diff"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Generating delta-delta plots"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 750x600 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "unpaired_delta2.mean_diff.plot();"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In the above plot, the horizontal axis represents the ``Genotype`` condition\n",
+    "and the dot colour is also specified by ``Genotype``. The left pair of \n",
+    "scattered plots is based on the ``Placebo`` group while the right pair is based\n",
+    "on the ``Drug`` group. The bottom left axis contains the two primary deltas: the ``Placebo`` delta \n",
+    "and the ``Drug`` delta. We can easily see that when only the placebo was \n",
+    "administered, the mutant phenotype is around 1.23 [95%CI 0.948, 1.52]. This difference was shrunken to around 0.326 [95%CI 0.0934, 0.584] when the drug was administered. This gives us some indication that the drug is effective in amiliorating the disease phenotype. Since the ``Drug`` did not completely eliminate the mutant phenotype, we have to calculate how much net effect the drug had. This is where ``delta-delta`` comes in. We use the ``Placebo`` delta as a reference for how much the mutant phenotype is supposed to be, and we subtract the ``Drug`` delta from it. The bootstrapped mean differences (delta-delta) between the ``Placebo`` \n",
+    "and ``Drug`` group are plotted at the right bottom with a separate y-axis from other bootstrap plots. \n",
+    "This effect size, at about -0.903 [95%CI -1.28, -0.513], is the net effect size of the drug treatment. That is to say that treatment with drug A reduced disease phenotype by 0.903.\n",
+    "\n",
+    "The mean difference between mutants and wild types given the placebo treatment is:\n",
+    "\n",
+    "$\\Delta_{1} = \\overline{X}_{P, M} - \\overline{X}_{P, W}$\n",
+    "\n",
+    "The mean difference between mutants and wild types given the drug treatment is:\n",
+    "\n",
+    "\n",
+    "$\\Delta_{2} = \\overline{X}_{D, M} - \\overline{X}_{D, W}$\n",
+    "\n",
+    "The net effect of the drug on mutants is:\n",
+    "    \n",
+    "\n",
+    "\n",
+    "$\\Delta_{\\Delta} = \\Delta_{2} - \\Delta_{1}$\n",
+    "    \n",
+    "\n",
+    "where $\\overline{X}$ is the sample mean, $\\Delta$ is the mean difference."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The configuration of comparison we performed above is reminiscent of a two-way ANOVA. In fact, the delta - delta is an effect size estimated for the interaction term between ``Treatment`` and ``Genotype``. Main effects of ``Treatment`` and ``Genotype``, on the other hand, can be determined by simpler, univariate contrast plots. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Specifying grouping for comparisons"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In the example above, we used the convention of *test - control* but you can manipulate the orders of the experiment groups as well as the horizontal axis variable by setting the paremeters ``experiment_label`` and ``x1_level``.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 750x600 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "unpaired_delta2_specified = dabest.load(data = df_delta2, \n",
+    "                                            x = [\"Genotype\", \"Genotype\"], y = \"Y\", \n",
+    "                                            delta2 = True, experiment = \"Treatment\",\n",
+    "                                            experiment_label = [\"Drug\", \"Placebo\"],\n",
+    "                                            x1_level = [\"M\", \"W\"])\n",
+    "\n",
+    "unpaired_delta2_specified.mean_diff.plot();"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Utilising the `show_delta2` argument within the `.plot()` method allows for control of whether the delta-delta effect size is displayed on the plot. By default, this is set to `True`. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "unpaired_delta2_specified.mean_diff.plot(show_delta2=False);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The delta-delta function also supports paired data, providing a useful alternative visualization of the data. Assuming that the placebo and drug treatment were administered to the same subjects, our data is paired between the treatment conditions. We can specify this by using ``Treatment`` as ``x`` and ``Genotype`` as ``experiment``, and we further specify that ``id_col`` is ``ID``, linking data from the same subject with each other. Since we have conducted two replicates of the experiments, we can also colour the slope lines according to ``Rep``. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x600 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "paired_delta2 = dabest.load(data = df_delta2, \n",
+    "                                paired = \"baseline\", id_col=\"ID\",\n",
+    "                                x = [\"Treatment\", \"Rep\"], y = \"Y\", \n",
+    "                                delta2 = True, experiment = \"Genotype\")\n",
+    "paired_delta2.mean_diff.plot();"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We see that the drug had a non-specific effect of -0.321 [95%CI -0.498, -0.131] on wild type subjects even when they were not sick, and it had a bigger effect of -1.22 [95%CI -1.52, -0.906] in mutant subjects. In this visualisation, we can see the delta-delta value of -0.903 [95%CI -1.21, -0.587] as the net effect of the drug accounting for non-specific actions in healthy individuals. \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The mean difference between drug and placebo treatments in wild type subjects is:\n",
+    "\n",
+    "$$\\Delta_{1} = \\overline{X}_{D, W} - \\overline{X}_{P, W}$$\n",
+    "\n",
+    "The mean difference between drug and placebo treatments in mutant subjects is:\n",
+    "\n",
+    "$$\\Delta_{2} = \\overline{X}_{D, M} - \\overline{X}_{P, M}$$\n",
+    "\n",
+    "The net effect of the drug on mutants is:\n",
+    "\n",
+    "$$\\Delta_{\\Delta} = \\Delta_{2} - \\Delta_{1}$$\n",
+    "\n",
+    "where $\\overline{X}$ is the sample mean, $\\Delta$ is the mean difference."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Standardising delta-delta effect sizes with Delta g"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Standardized mean difference statistics like Cohen's d and Hedges' g quantify effect sizes in terms of the sample variance. We have introduced a metric, *Delta g*, to standardize delta-delta effects. This metric enables the comparison between measurements of different dimensions.\n",
+    "\n",
+    "The standard deviation of the delta-delta value is calculated from a pooled variance of the 4 samples:\n",
+    "\n",
+    "$$s_{\\Delta_{\\Delta}} = \\sqrt{\\frac{(n_{D, W}-1)s_{D, W}^2+(n_{P, W}-1)s_{P, W}^2+(n_{D, M}-1)s_{D, M}^2+(n_{P, M}-1)s_{P, M}^2}{(n_{D, W} - 1) + (n_{P, W} - 1) + (n_{D, M} - 1) + (n_{P, M} - 1)}}$$\n",
+    "\n",
+    "where $s$ is the standard deviation and $n$ is the sample size.\n",
+    "\n",
+    "A delta g value is then calculated as delta-delta value divided by pooled standard deviation $s_{\\Delta_{\\Delta}}$:\n",
+    "\n",
+    "\n",
+    "$\\Delta_{g} = \\frac{\\Delta_{\\Delta}}{s_{\\Delta_{\\Delta}}}$\n",
+    "\n",
+    "This metric can be accessed via the 'hedges_g' effect size when utilising `delta2=True` in `dabest.load()`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "DABEST v2025.03.27\n",
+       "==================\n",
+       "                  \n",
+       "Good afternoon!\n",
+       "The current time is Tue Mar 25 16:03:15 2025.\n",
+       "\n",
+       "The unpaired Hedges' g between W Placebo and M Placebo is 2.54 [95%CI 1.71, 3.31].\n",
+       "The p-value of the two-sided permutation t-test is 0.0, calculated for legacy purposes only. \n",
+       "\n",
+       "The unpaired Hedges' g between W Drug and M Drug is 0.793 [95%CI 0.17, 1.33].\n",
+       "The p-value of the two-sided permutation t-test is 0.0122, calculated for legacy purposes only. \n",
+       "\n",
+       "The delta g between Placebo and Drug is -2.11 [95%CI -2.97, -1.22].\n",
+       "The p-value of the two-sided permutation t-test is 0.0, calculated for legacy purposes only. \n",
+       "\n",
+       "5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.\n",
+       "Any p-value reported is the probability of observing the effect size (or greater),\n",
+       "assuming the null hypothesis of zero difference is true.\n",
+       "For each p-value, 5000 reshuffles of the control and test labels were performed.\n",
+       "\n",
+       "To get the results of all valid statistical tests, use `.hedges_g.statistical_tests`"
+      ]
+     },
+     "execution_count": null,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "unpaired_delta2.hedges_g"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We see the standardised delta-delta (delta *g*) value of -2.11 standard deviations [95%CI -2.98, -1.2] as the net effect of the drug accounting for non-specific actions in healthy individuals. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 750x600 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "unpaired_delta2.hedges_g.plot();"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Delta-delta for binary data\n",
+    "Since **v2025.03.27**, the delta-delta function also supports binary data (proportion plots). In this case, the delta-delta value is calculated as the difference between the two proportions. This can be used for both unpaired and paired binary data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 750x600 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def create_demo_dataset_delta(seed=9999, N=20):\n",
+    "    \n",
+    "    import numpy as np\n",
+    "    import pandas as pd\n",
+    "    from scipy.stats import norm # Used in generation of populations.\n",
+    "\n",
+    "    np.random.seed(seed) # Fix the seed so the results are replicable.\n",
+    "    # pop_size = 10000 # Size of each population.\n",
+    "\n",
+    "    from scipy.stats import norm # Used in generation of populations.\n",
+    "\n",
+    "    # Create samples\n",
+    "    y = norm.rvs(loc=3, scale=0.4, size=N*4)\n",
+    "    y[N:2*N] = y[N:2*N]+1\n",
+    "    y[2*N:3*N] = y[2*N:3*N]-0.5\n",
+    "    ind = np.random.binomial(1, 0.5, size=N*4)\n",
+    "    ind[N:2*N] = np.random.binomial(1, 0.2, size=N)\n",
+    "    ind[2*N:3*N] = np.random.binomial(1, 0.1, size=N)\n",
+    "\n",
+    "    # Add drug column\n",
+    "    t1 = np.repeat('Placebo', N*2).tolist()\n",
+    "    t2 = np.repeat('Drug', N*2).tolist()\n",
+    "    treatment = t1 + t2 \n",
+    "\n",
+    "    # Add a `rep` column as the first variable for the 2 replicates of experiments done\n",
+    "    rep = []\n",
+    "    for i in range(N*2):\n",
+    "        rep.append('Rep1')\n",
+    "        rep.append('Rep2')\n",
+    "\n",
+    "    # Add a `genotype` column as the second variable\n",
+    "    wt = np.repeat('WT', N).tolist()\n",
+    "    mt = np.repeat('Mut', N).tolist()\n",
+    "    wt2 = np.repeat('WT', N).tolist()\n",
+    "    mt2 = np.repeat('Mut', N).tolist()\n",
+    "\n",
+    "\n",
+    "    genotype = wt + mt + wt2 + mt2\n",
+    "\n",
+    "    # Add an `id` column for paired data plotting.\n",
+    "    id = list(range(0, N*2))\n",
+    "    id_col = id + id \n",
+    "\n",
+    "\n",
+    "    # Combine all columns into a DataFrame.\n",
+    "    df_prop = pd.DataFrame({'ID'        : id_col,\n",
+    "                      'Rep'        : rep,\n",
+    "                       'Genotype'  : genotype, \n",
+    "                       'Treatment' : treatment,\n",
+    "                       'Y'         : y,\n",
+    "                       'Cat'       :ind\n",
+    "                    })\n",
+    "    return df_prop\n",
+    "\n",
+    "df_prop = create_demo_dataset_delta()\n",
+    "\n",
+    "unpaired_prop = dabest.load(data = df_prop, proportional=True,\n",
+    "                            # id_col=\"index\", paired='baseline', \n",
+    "                            x = [\"Treatment\", \"Treatment\"], \n",
+    "                            y = \"Cat\", delta2=True,\n",
+    "                            experiment=\"Genotype\",)\n",
+    "\n",
+    "unpaired_prop.mean_diff.plot();"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Statistics"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can find all outputs of the delta-delta calculation by assessing the attribute named ``delta_delta`` of the effect size object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "DABEST v2025.03.27\n",
+       "==================\n",
+       "                  \n",
+       "Good afternoon!\n",
+       "The current time is Tue Mar 25 16:03:16 2025.\n",
+       "\n",
+       "The delta-delta between Placebo and Drug is -0.903 [95%CI -1.27, -0.522].\n",
+       "The p-value of the two-sided permutation t-test is 0.0, calculated for legacy purposes only. \n",
+       "\n",
+       "5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.\n",
+       "Any p-value reported is the probability of observing the effect size (or greater),\n",
+       "assuming the null hypothesis of zero difference is true.\n",
+       "For each p-value, 5000 reshuffles of the control and test labels were performed."
+      ]
+     },
+     "execution_count": null,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "unpaired_delta2.mean_diff.delta_delta"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The ``delta_delta`` object has its own attributes, containing various information of delta-delta.\n",
+    "\n",
+    "  - ``difference``: the mean bootstrapped differences between the 2 groups of bootstrapped mean differences \n",
+    "  - ``bootstraps``: the 2 groups of bootstrapped mean differences \n",
+    "  - ``bootstraps_delta_delta``: the bootstrapped differences between the 2 groups of bootstrapped mean differences \n",
+    "  - ``permutations``: the mean difference between the two groups of bootstrapped mean differences calculated based on the permutation data\n",
+    "  - ``permutations_var``: the pooled group variances of two groups of bootstrapped mean differences calculated based on permutation data\n",
+    "  - ``permutations_delta_delta``: the delta-delta calculated based on the permutation data\n",
+    "\n",
+    "A dataframe of this delta delta dabest object can also be called via the `delta_delta.results` attribute."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>control</th>\n",
+       "      <th>test</th>\n",
+       "      <th>difference</th>\n",
+       "      <th>ci</th>\n",
+       "      <th>bca_low</th>\n",
+       "      <th>bca_high</th>\n",
+       "      <th>bca_interval_idx</th>\n",
+       "      <th>pct_low</th>\n",
+       "      <th>pct_high</th>\n",
+       "      <th>pct_interval_idx</th>\n",
+       "      <th>bootstraps_control</th>\n",
+       "      <th>bootstraps_test</th>\n",
+       "      <th>bootstraps_delta_delta</th>\n",
+       "      <th>permutations_control</th>\n",
+       "      <th>permutations_test</th>\n",
+       "      <th>permutations_delta_delta</th>\n",
+       "      <th>pvalue_permutation</th>\n",
+       "      <th>permutation_count</th>\n",
+       "      <th>bias_correction</th>\n",
+       "      <th>jackknives</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Placebo</td>\n",
+       "      <td>Drug</td>\n",
+       "      <td>-0.903179</td>\n",
+       "      <td>95</td>\n",
+       "      <td>-1.271483</td>\n",
+       "      <td>-0.521935</td>\n",
+       "      <td>(124, 4874)</td>\n",
+       "      <td>-1.270426</td>\n",
+       "      <td>-0.519652</td>\n",
+       "      <td>(125, 4875)</td>\n",
+       "      <td>[1.0890043559982234, 1.1472720447282119, 1.072...</td>\n",
+       "      <td>[0.6003430615628478, 0.6547912656551773, 0.294...</td>\n",
+       "      <td>[-0.43421309034304567, -0.7324573148122022, -1...</td>\n",
+       "      <td>[-0.15899787281865496, 0.23958268043726694, 0....</td>\n",
+       "      <td>[-0.036113268018566735, -0.05491466432013192, ...</td>\n",
+       "      <td>[0.12288460480008823, -0.29449734475739886, -0...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>5000</td>\n",
+       "      <td>-0.000501</td>\n",
+       "      <td>[-0.9006797310317582, -0.9006200702547091, -0....</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   control  test difference  ci   bca_low  bca_high bca_interval_idx  \\\n",
+       "0  Placebo  Drug  -0.903179  95 -1.271483 -0.521935      (124, 4874)   \n",
+       "\n",
+       "    pct_low  pct_high pct_interval_idx  \\\n",
+       "0 -1.270426 -0.519652      (125, 4875)   \n",
+       "\n",
+       "                                  bootstraps_control  \\\n",
+       "0  [1.0890043559982234, 1.1472720447282119, 1.072...   \n",
+       "\n",
+       "                                     bootstraps_test  \\\n",
+       "0  [0.6003430615628478, 0.6547912656551773, 0.294...   \n",
+       "\n",
+       "                              bootstraps_delta_delta  \\\n",
+       "0  [-0.43421309034304567, -0.7324573148122022, -1...   \n",
+       "\n",
+       "                                permutations_control  \\\n",
+       "0  [-0.15899787281865496, 0.23958268043726694, 0....   \n",
+       "\n",
+       "                                   permutations_test  \\\n",
+       "0  [-0.036113268018566735, -0.05491466432013192, ...   \n",
+       "\n",
+       "                            permutations_delta_delta pvalue_permutation  \\\n",
+       "0  [0.12288460480008823, -0.29449734475739886, -0...                0.0   \n",
+       "\n",
+       "  permutation_count bias_correction  \\\n",
+       "0              5000       -0.000501   \n",
+       "\n",
+       "                                          jackknives  \n",
+       "0  [-0.9006797310317582, -0.9006200702547091, -0....  "
+      ]
+     },
+     "execution_count": null,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "unpaired_delta2.mean_diff.delta_delta.results"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Similarly, for the standardised delta-delta effect size, the `hedges_g` object has its own delta delta (Delta *g*) results attribute."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>control</th>\n",
+       "      <th>test</th>\n",
+       "      <th>difference</th>\n",
+       "      <th>ci</th>\n",
+       "      <th>bca_low</th>\n",
+       "      <th>bca_high</th>\n",
+       "      <th>bca_interval_idx</th>\n",
+       "      <th>pct_low</th>\n",
+       "      <th>pct_high</th>\n",
+       "      <th>pct_interval_idx</th>\n",
+       "      <th>bootstraps_control</th>\n",
+       "      <th>bootstraps_test</th>\n",
+       "      <th>bootstraps_delta_delta</th>\n",
+       "      <th>permutations_control</th>\n",
+       "      <th>permutations_test</th>\n",
+       "      <th>permutations_delta_delta</th>\n",
+       "      <th>pvalue_permutation</th>\n",
+       "      <th>permutation_count</th>\n",
+       "      <th>bias_correction</th>\n",
+       "      <th>jackknives</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Placebo</td>\n",
+       "      <td>Drug</td>\n",
+       "      <td>-2.106681</td>\n",
+       "      <td>95</td>\n",
+       "      <td>-2.965759</td>\n",
+       "      <td>-1.217424</td>\n",
+       "      <td>(124, 4874)</td>\n",
+       "      <td>-2.963294</td>\n",
+       "      <td>-1.212099</td>\n",
+       "      <td>(125, 4875)</td>\n",
+       "      <td>[2.3610871907095192, 2.7764672664031567, 2.350...</td>\n",
+       "      <td>[1.549355181508767, 1.7247260954921417, 0.6471...</td>\n",
+       "      <td>[-1.0128104949604284, -1.708470960574333, -2.7...</td>\n",
+       "      <td>[-0.1986457235842243, 0.3014021841951519, 0.31...</td>\n",
+       "      <td>[-0.08117530110708499, -0.12358349103957916, -...</td>\n",
+       "      <td>[0.11747042247713932, -0.4249856752347311, -0....</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>5000</td>\n",
+       "      <td>-0.000501</td>\n",
+       "      <td>[-2.1008530246437576, -2.10071386471865, -2.10...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   control  test difference  ci   bca_low  bca_high bca_interval_idx  \\\n",
+       "0  Placebo  Drug  -2.106681  95 -2.965759 -1.217424      (124, 4874)   \n",
+       "\n",
+       "    pct_low  pct_high pct_interval_idx  \\\n",
+       "0 -2.963294 -1.212099      (125, 4875)   \n",
+       "\n",
+       "                                  bootstraps_control  \\\n",
+       "0  [2.3610871907095192, 2.7764672664031567, 2.350...   \n",
+       "\n",
+       "                                     bootstraps_test  \\\n",
+       "0  [1.549355181508767, 1.7247260954921417, 0.6471...   \n",
+       "\n",
+       "                              bootstraps_delta_delta  \\\n",
+       "0  [-1.0128104949604284, -1.708470960574333, -2.7...   \n",
+       "\n",
+       "                                permutations_control  \\\n",
+       "0  [-0.1986457235842243, 0.3014021841951519, 0.31...   \n",
+       "\n",
+       "                                   permutations_test  \\\n",
+       "0  [-0.08117530110708499, -0.12358349103957916, -...   \n",
+       "\n",
+       "                            permutations_delta_delta pvalue_permutation  \\\n",
+       "0  [0.11747042247713932, -0.4249856752347311, -0....                0.0   \n",
+       "\n",
+       "  permutation_count bias_correction  \\\n",
+       "0              5000       -0.000501   \n",
+       "\n",
+       "                                          jackknives  \n",
+       "0  [-2.1008530246437576, -2.10071386471865, -2.10...  "
+      ]
+     },
+     "execution_count": null,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "unpaired_delta2.hedges_g.delta_delta.results"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For further aesthetic changes, the [Plot Aesthetics Tutorial](09-plot_aesthetics.html) provides detailed examples of how to customize the plot.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "python3",
+   "language": "python",
+   "name": "python3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/nbs/tutorials/06-plot_aesthetics.ipynb b/nbs/tutorials/06-plot_aesthetics.ipynb
deleted file mode 100644
index fc4141ce..00000000
--- a/nbs/tutorials/06-plot_aesthetics.ipynb
+++ /dev/null
@@ -1,850 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "2f833a32",
-   "metadata": {},
-   "source": [
-    "# Controlling Plot Aesthetics\n",
-    "\n",
-    "- order: 6"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4b12cf7c",
-   "metadata": {},
-   "source": [
-    " **Since v2024.03.29, swarmplots are, by default, plotted asymmetrically to the right side. For detailed information, please refer to [Swarm Side](#changing-swarm-side)**\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "5d374d47",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "We're using DABEST v2024.03.29\n"
-     ]
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "import dabest\n",
-    "\n",
-    "print(\"We're using DABEST v{}\".format(dabest.__version__))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "baf2ec0c",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#| hide\n",
-    "import warnings\n",
-    "warnings.filterwarnings(\"ignore\") # to suppress warnings related to points not being able to be plotted due to dot size"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "ab12ec7f",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from scipy.stats import norm # Used in generation of populations.\n",
-    "\n",
-    "np.random.seed(9999) # Fix the seed to ensure reproducibility of results.\n",
-    "\n",
-    "Ns = 20 # The number of samples taken from each population\n",
-    "\n",
-    "# Create samples\n",
-    "c1 = norm.rvs(loc=3, scale=0.4, size=Ns)\n",
-    "c2 = norm.rvs(loc=3.5, scale=0.75, size=Ns)\n",
-    "c3 = norm.rvs(loc=3.25, scale=0.4, size=Ns)\n",
-    "\n",
-    "t1 = norm.rvs(loc=3.5, scale=0.5, size=Ns)\n",
-    "t2 = norm.rvs(loc=2.5, scale=0.6, size=Ns)\n",
-    "t3 = norm.rvs(loc=3, scale=0.75, size=Ns)\n",
-    "t4 = norm.rvs(loc=3.5, scale=0.75, size=Ns)\n",
-    "t5 = norm.rvs(loc=3.25, scale=0.4, size=Ns)\n",
-    "t6 = norm.rvs(loc=3.25, scale=0.4, size=Ns)\n",
-    "\n",
-    "\n",
-    "# Add a `gender` column for coloring the data.\n",
-    "females = np.repeat('Female', Ns/2).tolist()\n",
-    "males = np.repeat('Male', Ns/2).tolist()\n",
-    "gender = females + males\n",
-    "\n",
-    "# Add an `id` column for paired data plotting.\n",
-    "id_col = pd.Series(range(1, Ns+1))\n",
-    "\n",
-    "# Combine samples and gender into a DataFrame.\n",
-    "df = pd.DataFrame({'Control 1' : c1,     'Test 1' : t1,\n",
-    "                   'Control 2' : c2,     'Test 2' : t2,\n",
-    "                   'Control 3' : c3,     'Test 3' : t3,\n",
-    "                   'Test 4'    : t4,     'Test 5' : t5, 'Test 6' : t6,\n",
-    "                   'Gender'    : gender, 'ID'  : id_col\n",
-    "                  })"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "1e3b1021",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "two_groups_unpaired = dabest.load(df, idx=(\"Control 1\", \"Test 1\"), resamples=5000)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "eea91eac",
-   "metadata": {},
-   "source": [
-    "## Changing y-axes labels"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "54a3445d",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 500x400 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "two_groups_unpaired.mean_diff.plot(swarm_label=\"This is my\\nrawdata\",\n",
-    "                                       contrast_label=\"The bootstrap\\ndistribtions!\");"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8d0f7aed",
-   "metadata": {},
-   "source": [
-    "## Changing the graph colours\n",
-    "\n",
-    "### Colour categories from another variable\n",
-    "Use the parameter `color_col` to specify which column in the dataframe will be used to create the different colours for your graph."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "527b475b",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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bkgQM7RoK32YuJoqaqGlzatG29o2SAs5+7Y0XDJGeGKVEY/ny5Zg+fTr+85//IDw8vMb2sLAwTQkH6cfqbUfwbcwBTZIBAAXFZVj83RbEp2bi42cnIyy4hWabytoKkwZE4oXJg0wRLhEBcPRtDZeAsJoTrkkKKKxs4BM5AkIIFKScweVd3yN5z08oykgwTbBEdWSUEo2UlBT06tWr1u0ODg4oKCgwRihNQkVlFX7aprueXyFJ+OHPQ/jvc5Px/tMTkZVfhMKSMvi4u8BOZW3kSIksk4+Pj9b/66JWy9h04CQ27juJ7PxiBPi4Y0J0F3SbOA/nN3yA3Pibz7DK2QNt738Z1vYuOPPTfOReOqpJRpJ3rYJX2CC0HvU8e6RQo2SURMPLywspKSm1bj969Cj8/f2NEUqTkHotD8VluhuUyULg7OWbE3B5uDjCw6VpdvMlMpS7NeiVZYFF327CnhPxkFDdr+RUwlWcuJSKx0b2wrSpC1Gak4rizMuwtneBc8v2kCQFLsUsRW5CbPVJxM1G25kntsHeMwB+PScY7k0RNZBRqk7Gjx+PL7/8EgkJN4v4pOuNEf/880+sXLkSkyZNMkYoTYKd7Z1LJlTWLLkgMqXD55Kw50Q8AGh6gN0YPO/rTftxLa8Qdu4t4NGuF1z8O0CSFFBXliHj2JZaZ/G9emiDMUInqjejJBoLFy6Er68vwsPDMX36dEiShHfffRd9+vTB8OHDERYWhnnz5hkjlCbBx90Frf28dPYsUSgkDIy8Q4MzIjK4XccuQKGoreeXwJ7j8TXWVhTmQK6qqPWcFYXZkKsq9RQhNURpdioS/lyGE9++gnO/vIvcS0chakkMmxKjJBouLi44cOAA5syZg9TUVNja2mLXrl3Iy8vD/PnzsWfPHtjb2xsjlCbjuYkDYW2lhPKWDzOFJKGZswMeHtbDhJERWb6oqCj4+fkhKipK5/ayikoIWfcXkCRJKKuomTBYO7gCito/sq1sHSFx9leTyb5wELFL/4mrh39HQfIpZJ39G6d//DcSYr5s8smG0f4q7ezs8Prrr+P111831iWbtHYBPvj8xQewdsdRHDmXBCulEv0j2mB8dBe4OTGpIzKkG+Oi1CY0qDn+PhkPXSPnybJAh6DmNdZbqezhGdoP107v1mqfAQCQFPDpMkJTJU3Gpa4ow4Vf34eQZWhu6vV7lHZkI9xbd4dbSJfaT2DhmP5aMH9vd7w4dcjddyQioxrWLRQ/bTtcYywbhUJC25beCAtpofO44KFPojgjESXXLlf3OpEkQFbDuWUHtOw71Vjh021yLh6EuqJU90ZJgYwTfzHRMLTHHnvsrvtIkoQVK1YYIRoiMjUhBErLK6GytoJSabZTLjWYk70tPnpmEt75fgsupmRq1ncPDcJLU4fWKJkoyUpBWV4GbF29Ef74EmSd/Ru5CbGQFEo0a9Md7q27aXVtrSwpQNqRjcg6tw8Qari37gbfrmOgcmqm2UdWV+Lq4d+RfnQzKopzYd/MDy16jINHaD+WjNRTZXF+ddKnq4pEyKgsyjV+UI2IURKN7du31/jDVavVSEtLg1qthqenJxwcHIwRCpHZk6sqIKuroLSxM9gXglot4/ilKygqKUcrP08093DVy3llWWD97mNYt/MosvKLobK2wtBuoXh0RC842dvq5Rrmwt/bHZ/PfgBJadnILiiCn6cbvN2dtfYpz7+G87++j4KU05p1Ti1D0fb+l+HVaYDO81YU5uD417NRXpitKb4vybqC9GN/ovMjH8CuWQsIWY2zaxddH6uj+suxKD0e59e/h5KsFAREP2SYN22h7L0Ca+0NBEkBB59go8bT2Bgl0UhKStK5vrKyEkuXLsXHH3+MrVu3GiMUIrNVkpWCpG1fISf+MCAE7D0D4B/9IDza9dbrdWLPJ+Od77cgt7BEs653pxDMeWAY7G1t7uncX/66C+v3xGmWyyur8Mf+kzh5KRWf/GsqbG2aXtdrb3cn2Ntaw/W2tlNyVSVOfvcqyvIytNYXXjmHU6teRZd/fAmFVc1/r6Qd32glGQAAIaOqrAiX/lyKjtPeRE78YeTGH9Y+8PoXZcqen+AdPhS2Ll76eYNNgEtAJ9h7BaLkWvJt7WckSJICvpEjTBZbY2DSMktra2s888wzGDp0KJ555hlThkLUqJXmpOH417ORE39E84VQci0Z59b9BxknttXrXBXFeUiP+xNpR/+o/mC8xeWMHLy2/FfkFZVord9/KgHvfLflrudWyzIOnE7AD1sPYfOBUygqLdNsS8/J10oybpBlgaT0bGw/eq5e78Pc5ReV4t3vYzBu3pd48M2vMOn1ZVjxx17N5IfZ5/aiLDetZsNPIaMsLx3Z5/bWOKeQ1bh2elfNY64fl3fpKCpLC5F1Zk/NYc5vkMDp6OtJkiR0mLIA9p43Bp6sLmlUquzQftJrsHPX3eamqWgUjUE7d+6MVatWmToMokYrZe8aqCvKbvsCqU44kv5aAc8O0VDUoWvjlX1rkbTzW0C+eZ5m7Xuj7diXoLCywa+7j0EIUaMUWBYC+08nICUzBy293HWe+2pWHuZ+uR5p2flQKiSoZYFPf9mJVx4Yhn7hrXHoTJJmFMzbSRKw79QljOjZ6a7vwRKUV1ThxU/XIuVarqYxaEl5BVZvO4y0rDy8PmMkCq6chaRQQsjqGsdLCiUKrpyFZ8f+WutldRWE+s5jaajLS6rH46i1y6V0x/E6SDeViycinvgUBcmnUJyZCGsHN7i37galtcrUoZlco2iFtXXrVo6jQXQHORf26/6VCqCyJB/F6Zfueo5rZ/YgaftKrSQDqP71mrD1/wAAZxLToK5lfAcAOJ+coXO9LAu8tuxXZOZWz1l04xwVlVVY9O0mXM7I0Yx8qYsQNcKyaDuOna/+N7nt31oIYFfcRVxKvQaljR2EzrSsOllT2tRs06K0VsGumR9u/KK+nbW9C1TOHnDxv0NCJ2S4+Hes61uhW0iSBJeATmjedQw8Q/syybjOKCUab775ps71eXl52L17N2JjYzF37lxjhEJknu464M/dBwRK3f+z7pbxQiAjLgaBA6bD0V5Va+N5AHC00/3BGXshGVeu5ek+SAI27j2BsX073zHK7h2C7voeLMWRc0lQSJLO5EshSTh8LgmjOvbDlX1rdZ9AVsMjtJ/OTS37TMGFDR/q3Nai10RICiW8wgbhyr61qCjO005gJQVc/DvAidPRkx4ZJdFYsGCBzvVubm4ICQnBl19+iSeeeMIYoRA1Sneb7dO9TXdkntyhs1TDys4JDj4hAKqrLzYfOIUr1/Lg6eqI4d07Iqi5BwCgODOp1gxCqKtQmpOGIV1DceKS7oGmHO1U6NJW9+SHyRk5kCRJ5wiIN9pg+Hm6YVi3UPx56IxWwqFQSPBxc8aQqKbz5Xa33kIKSYKjdzCad78fVw/+Wt2eQsiaRNG321g4+oSgoqoKu45dxKGziRBCoFv7IPSP6Ieg4nxc3vmtpgpEUlihRc/xaNF9HADAytYBYTPew4Xf/nuzR4ukgGdoX4SMeIbdW0mvjJJoyE2pTNTMyOpKZJ/bj7yEWEChQLM2PeDWKgpSbQ3FyCDuNtunX+/JyDq793rduvbzFDjgESiU1thz/CIWfbsJAoCQBRQKCet3x+HZCQMwpk9nWNs7o6Iwu9ZrWNu7YHBUM+w8dgFHz1/WtKdQKiQIAbw0bQhsrHR/ZDRzdqh1mGWFQoKHS3X39X9NHgwvNyf8sisOxWXlUCok9O3cGv+8P/qee7SYkx4dgrHz2AWd22Qh0L1DdXfIoMEz4dS8Da4e2Yiy3DTYuvmieeRIeHSIRlFpGV769GdcunpNM6/RrriLWLczFh8+MxHdIoYhP+k4hJDh4t8J1vbaXWdt3XwRNuM9lOWmoaIoF7ZuvrBxdDPsG7dwSWnZ+G3vcVy8kolmzg4Y1q0DenQIavKJW6NoDEqmUVlaiJOrXkVJZiJwfbCfjGMxcAkMR4ep86Gwajof/I2dfTM/hD3yPhK3rkB+UhwAwNbVB/7RD8Kr00DkF5Vi8XdbtNpX3PjvT37egYg2LeEdPgwpf/9Ys1RDUsDZrz1sXau7M779xBjEHDyDmEOnUVBchvaBvhjfLwKtW3qhtLwCv/19AtuOnkNpeQXCW7fExP5d0KNDMBztVCguK6/ZkFQWuK97BwCAUqnA9Pt6YtrgbsgtLIGTvQp2qqb3d9avc2v8susY4q9k1qg+ua97BwR4Vze4lSQJnh2i4dkhusY5Vmzci8S0LADQOsfljGws/20PZk8dgmZte941Fls3X9i6+d7L2yEAf5+Ix9vf/AGg+tlTKCTsPXkJI3p2xAuTBjXpZMMgiUZycvLdd9LB3193sSw1XEVVFRKvZsPaSoFAHw+tGSMT/lxaPZQxANzSsj3/8nGk/L0aAf0fNna4dAeO3sHo9NAiVJUVQa6qgLWDm+bDa0fseVSpa/ZOAKpLFGIOnsGj901AXkIsClPP3WyrIUmwtnNC61HPa/a3UioxslcnjOyl3WCwpKwC//pkDRLTsjTJxNbDZ7Dt6Dm8849xeGPGSLzxfxtQpZYhC6HpeTJtcFd0btVS61zWVkp4uTnp8V/HvFhbKfHeU+Px7eYD2HzgFEorKuHmZI8J0V0wccDNoaqFkFGYeh5VpYVw8A6Gyrm6GqyySo0/D5/R2cZDlgX+OnoOz0wYABtr/pY0htLyCrz7fYxWon+joe+m/afQu1MIurVvOm2QbmeQv8LAwMAGZW/qWj4oSVtUVBTS09Ph4+NTa5G7EALrd8dhVcwBFJWWAwC83Zzw9PgB6NkxGFXlJcjSNTlT9cFIO/oH/KMfatJZuDHV5Z4CwKXUa9h9/CLKK6sQFtwC3TsEQalQICu/CApJAbXO+wlk5RdBaW2LTg+/g2und2qqYVwDw+ATMRzWDi53jfHnnbFISsvWKrFQywKyUOPDn7Zi5bxH8PW8R7Bp/0kkpmXB1dEeQ7uF6pwgjAAHWxX+OS4aT47ti/KKKtiprLWet/zk07iw4UOU59/o6VNdutFq5LMorRSoqKz987KySo3isnImGkay/1SCzhl3gepE/89DZ5lo6NtXX33FLygDutvMkACw4e/j+OLXXVrrMnMLMf+r3/H+UxPQxl3S2T//hqrSQgi5CpKy6Y3UaAp3u6dCCHz68w78tvcElAoJgISfd8YiuLkH3v3nePh7u0N9h7ZQ/teL4hVW1vDuPATenes/2V5tv6CFAK5m5SP+yjW0bumFR0b0qve5mzKlQlGjfUppzlWc/uF1yFpjYghcO7MbsroCbca/CmcHWxQUl0EXRzsVnO3tDBg13Sq/uLTW3lqyLJBfVMuEa02EQRKNRx55xBCnpTqqUqvxXczBGusFqgdO+f7Pg1g8cyQkpXWtg/vYOLpDwSSj0Yg5dAa/7T0B4Ebbi+uDdaVn48OftuK16SOw9LfdKCop10oGJAmwUio0bSTuRUnZnQdxKi4rv+drULWrh3+HrK7S0RVZRva5fajIS8O4fhH4dvP+Gl2GJQDj+oU3ycnqTCWkhWetXcIVCgmt/DyNG1Ajw79EC5SSmYv8Yt0ZtCwETlxKhdLGFt7hQ2sZhlhC825jDRsk1cv63cd0DsEkywIHTieisKQMi2eNg7ND9SBOSkX1fbW1scabM8fA3fneJy0MDfLVauNzKyulAsHNm/aHqT4VJJ+qdYA2ACi4cg7TBnXF4K7VXYIVkqTpeTIwsh0eHNLdKHFStU7BLRDSwvN6aeNNklT9LI7uHWaiyBoHo1bg7d27F7GxscjPz6/R5VWSJLzxxhvGDMdi1dYF8QZrq+oeJkGDHkNp9hXkJx2vHuoY1cMzenbsjxY9xhkjVKqj9OyCOw52lZFTgI7BLfD9/Mex98QlpGZVj6PRr3NrvfXqmDKwKw6eSayxXpKAUb3CNEkO1U9JVjIqCnNg594CKpfqZE2pcgBqHbAdsFLZQ6lUYM4DwzB5QNT1cTSAbqGBCPL1MF7wBKD6++vtJ8bi3yt+w8WUTM16RztbzHt4uN5mPzZXRkk0cnJyMHLkSBw6dAhCCK2BfW78NxMN/Wnu4YIAn2ZIzsiuUZynUEiIDm8NoHoI444PLkJBymnkXYoFFEo0a9Mdjr6tTBA13YmXmxMup2fXmmzc6MFhY2WFAV3a1umclcX5EBCwcXDVuT23sAQ7j51HbmEJAn2boU9YK7w+fSSWrNumaRugkCSM6NkRs8b2re9bavJKs1NxfsMHKLp6czwN97Y90WbU8/Dq2B8FySd1Hqe0sYdrcIRmOdC3GQJ9mxk8XrozDxdHfPavaTh7OQ2JV7Ph6mSHru0D7/rDrykwyr/Ayy+/jBMnTuCHH35A9+7dERwcjJiYGAQFBeG///0v9u/fj82bNxsjlCZBkiQ8O6E/5n65HrIQmm5WCoUEJ3tbPDysh9a+Lv4djTK3gayuwrXTu3Dt1E6oK0rh4t8BvpGjNL/iqHZj+nTG/9Ztr7FeoZAQ0dofXm7VgzHJskBcfAoupV6Di4MdeoeFwMFWe9jwvMTjSNy2QjM/ioN3EAIHPgq3kEjNPn8dOYsPf9oKtSxDoVBArZbh7uyAd/8xHj8teAInL6WirKIS7QJ89FIt09RUlRXhxLdzUFlSoLU+58JBnF69EB0f+g8yT+2srkK5kV5KCkAItBrxNJTWLD1qjCRJQmhgc4QGsqfVrSRR23B+euTr64tp06bho48+QnZ2Njw9PbF161YMGjQIADB+/HioVCr8+OOPhg5FS2xsLCIjI3H06FF06dLl7gc0En5+fkhNTUWLFi1w5cqVWve7kJKBH7cewtELybBSKhEd3hrTBnfVfCnpU0VRLtKObkJeQiwkK2t4tu8Dr86DNR+IclUlTv80H/lJx28Zw0EBpbUKHR/6D5yat9F7TObkbvdUlgXe//FP/HXkrKYeWC0LtPBwxQfPTISHiyOu5RVi3tJfkZSerZlHQ2VthZenDUV0RPW/b17SCZz6/jUA4paGhtXn6zBtIdxCIpGUlo0n3/+uxkifCkmCh6sjvn3tUTY0vIu73c/Ug+uRuHUFaqsa6fTwu3Bq0RZpsZuQGbcVlaUFcGreFi16jINzy1ADR0+GkhN/GKn7f0FxRgKsHVzhHT4UzbuOgcLKshveG6VEIy8vDx06VLd6d3R0BAAUFRVptg8dOhTz5s0zRihNSpuW3pj/2Oh7Pk9OQTG2HDyN5IwcNHN2wNBuoQjwuVlUW5KVghPfzEFVWdH1BmwSCi6fRPqxGHSa/i6sVPZIO7oR+UnVvSY0X3BChrqyHBc2fIAu/1jKLtF3oFBImPPAUIzq1Qm74i6gorIKnUL80LdzK9hYWUEIgX//329IycwBcHOkyPLKKvxn1Wa09HZDcHNPXN75LbSTDFQvQ0LSjm/gFhKJjftOQCEBah1TxWfmFuLIuctNagI0QyhIOVv7RkmBgpTTcAnoiBbdxqIFG2ZbhKuHfkPCn0s189ZUlRUhadvXyL10FB2mvQmF0nKrWIzys6R58+ZIT08HAKhUKnh5eeH48eOa7ampqfX+kvniiy8QFhYGZ2dnODs7o2fPnqx+MYDYC8l4+O2vsXLzfuyIPY91u2Ix891V+GXXMc0+8X98ckuSAdz4lVacmYQre9cAADLitkLnrzchozQ7tU7TnDd1kiShQ1BzPDWuP16YPBiDIttp6n/PJKUhPvWazineJQn47e/jUFeUovDK2VomVhMoTr+EypJ8pGTm1jpVvEKSkHItV4/vqmlSWttW3xidBBQ6poAn81VZko/Ev1ZUL2j1JhLITzqOrDN7TBKXsRgl0ejbty+2bt2qWZ4yZQree+89LFq0CG+99RY+/vhjDBgwoF7n9PPzwzvvvIOjR4/iyJEjGDhwIMaOHYvTp0/rO/wmobJKXaOovLS8Em9+vRGVVVUQQmi19/ji112IT81Eef616tkfdY5IKSM97s/q85fk3/n6t9VVU01CCOQnn8KlmC9xceP/kHliu2Z2zqS02idLU8sCl65mATo7yN5OgpebU41uejfIQsDT1bEB0dOtPEL71N59VQAe7XobNyC6o6ioKPj5+SEqKqpBx+dcOAQhV+neKEnIOmvZiYZRympefPFFbN26FeXl5VCpVFiwYAFOnz6t6WXSr18/fPLJJ/U65+jR2lUCixYtwhdffIEDBw5oqmnozoQQ+G3vCazbcRTpOQVwsLXBfT06YvqwHrC3tcHfJ+JRXMsgTUqFhC0HTuOR3neen0ZdVl1F5ujTCrkJsbV8uEqw9+Q8N3ciZDUu/PZfXDu14+YEeHExSN7zAzo9/A7cnO1rPVYhSWjm7ACljS2c/TvpTgwlCY6+bWBt74zhPTpiy8GaCbtCkuBgp0LP6zOLUsO5tYpCs3a9kH1u382V14vUA/o/pJnThBqHuozGfIMsCxSVlsNOZa0ZSkBddYfB7ISAukL3CK+WwiiJhlKpxOzZszXLbm5u+Ouvv5CXlwelUgknp3ubXEmtVmPt2rUoLi5Gz561z1ZYXl6O8vKbN/zWdiJN0dLf9uDnnbGa5eKyCqzfdQwnL6Xiv89NwrW8Qs3EWLdTywLX8gph594cCisbzS9rLZIEB+/quvwWPcYj95KOOTwkBTxC+/KD9S7Sj22pTjIArQnwyvIycPH3j9F1ykK4ONihoKS05uyp4ubsqYEDZ+Dkt3OrK7FuJBuSBEgSAgc+AgAIDfTF4yN7Y8UfezVTxAsA1tZKLHhsFOfP0ANJUqDd+LlIj92MtNjNqCjMhr2HP5p3vx8e7TiEuzmSZYGfd8Vi7Y6jyC0sgbWVEoMj2+HxUX3u3IBXUhil158pGeUTo2PHjujUqROmTJmCyZMno1Wr6nEaXF1d7+m8J0+eRM+ePVFWVgZHR0esX78eoaG139DFixdj4cKF93RNS5Gek6+VZNwgC4ELKRnYdewi/Dzdaq+rV0ho4ekGpY0dfKNGIvXAr6jRBkMI+PWcCABwDeqMViOfQ0LMl1pJiXurKLQe+ay+3pbFSju8UfcGISMv8RjURdl4bcYIvL7s1xqzp47pHYbuodUJn7Nfe3R6eDEu7/wW+ZdPatYF9J8Bl4CbH3ZTB3dFt9BA/HnojGYcjfu6d4CbE7uy6oukUMI3ahR8o0aZOhTSgy9+3YVf98Rpliur1Ig5fAanEq/is9nT4NYqCrmXbivVlRSwUtnDp8t9xg/YiIySaHzxxRdYs2YN/v3vf+ONN95AeHg4pk6dismTJyMgIKDB523bti3i4uKQn5+PdevWYcaMGdi1a1etycarr76qVbISFxeH6OjoBl+/sZOrKnDt9G7kJR2HQmmFZm17wq1VFCRJgQOnEmudBEiSgH2nLmHew8Ph7mSPvOJSTdsMDQGM6Fn9xRQwYAYqSwuRefyvm+dQWiFgwAx4hN4cyMknYhg82vdBbvxhqCvK4OTXDg5egYZ46xanvODaXbdHtO6Ar16dgY37TuBS6jU4O9hhaNdQRLRpqdXY2rllKDo9/A7UlWWAqB64TZfg5p74x/2W+3wQ6Ut6Tr5WknGDLAukZOZi6+GzGDl+LuI3f4Zrp3Zpkg0H7yC0GTMbNo7uRo7YuIySaMyaNQuzZs1CRkYG1q5dizVr1mDu3LmYO3cuunXrhqlTp2LSpElo3rx+g5zY2NhoSkciIyNx+PBhLFmyBEuXLtW5v0qlgkp1c/CiG11tzY2Pj4/W/+tSUZSDk9/ORWlOanXdryQhI+5PuIVEov3kNyBf74aquycIoFbLsLZSYtGs+/Hql+uRV1QKK6UCalmGUqHAKw8Og5+nGwBAobRGm9H/gn+faci/fBKSlTXcQiJhbVezSszK1gGeHfvrjDkztxB/7D+J+CuZcHGww5BuoQhv5cdurwBs3XxRnJGI2sZdsHX1BgB4uzvj8VF96nRODvpEpB+Hz16udZsEYP/pBIzp0xltx76EoEGPoSQrBdYOrnDwbPgPbSEESrKSUVmcD3uPlrBxdGvwuQzNqJWt3t7eeOaZZ/DMM88gNTVVk3S8+OKLeOmll1BZqXsm0bqSZVmrDYalOnJER1uH28Rv+gyluWnVC0LWfD/lXopF6v6f0aXNEAixW+exAkBku+oHoFULL3z3xuPYc+IiLqdno5mzIwZ0aQsXx5pTUNu6+cDWrfbkJ6+oBNn5xfByc4KTvfaX3PH4FMxb9iuqqqqL/RUKCVuPnMXYPp3x9Pj+TT7ZaN51DC5u/LjmBkkB91Zd2caFyKRqH/eyuj3Uze02ju73XIJRnJGIC799eP3HBwBJAa9OAxBy31O1llCaksladfn6+qJDhw5o3749Tp06heLi4nod/+qrr2L48OHw9/dHYWEhfvjhB+zcuRMxMTEGith8VBTnIefCQej+4xdIO7oJ3fpMRf+INtgVd0Gr+kShkODl6oQhUe0161Q2Vhh8y/KtyiuqsPXIGew5fhEVlWp0aeOPUb07adXl5xaW4H/rtmPvyUsQQkCpUGBgl7Z4ekJ/ONiqUFmlxlvfbEJllazpYnujqmbD38fRtV1gkx8gyqvzYBRlXELa4d8BSVE9R5CshoNXIFqPfh7A9e6vl0+iKD0e1rZOaNauJ6xstUvtynLTkbJ3DbLP7YUQMtxCotCyzxRWYRHdg8i2dy6Z6B5a955a+UWlWL8nDrvjLqBKLaNr+0BMjO4CXw8XAEBFYQ5OfPsK1BW3zNAtZGSe3IGqsiKETv53g96DIRk10RBCYOfOnVi9ejXWr1+PrKwsuLm5YerUqZgyZUq9zpWZmYnp06cjLS0NLi4uCAsLQ0xMDIYMGWKg6M1HZVEu7pRhVxRVD7g054Fh8HJ1wm97T6CsohKSJKFXxxA8Pb4/7G3vPuNnSVkFXv58HS6kZGoqYc4kpWHD38fx3+cmoaWXOyqr1Hjps3W4ci1Xk0SoZRnbjp7D1ex8/PfZSThy7jLyi3RPa69QSNh88FSTTzQkSULIsH/AJ+I+ZJ39G3JlOVwCOsEtJBKSQomKwhyc/mk+ijMSNN0k47d8htYjn4NXp4EAgNKcqzj+1b9QVV6iqSPOOvs3ci4cQKeH34FTi7pNxkaNw+WMHPy49ZBmRt3enUIwbXA3tPB0NW1gTVBzD1eM6tUJG/dpT4SnUEjwcXfG0G51GzY+p6AYz338E67lFWlG99247wS2Hj6D/z47GSEtPJEWu6k6ybi9i7qQkXPhIIozkxrdDwejJBp79uzBmjVrsG7dOmRmZsLZ2Rn3338/pkyZgsGDB8OqAbPbrVixwgCRmoeoqCikp6fDx8dHZzWKysWzetr3W7pB3srWtbp6w9pKiSfG9MX0+3riWn4hnO3takz1XZSRgJwLhwAhwy0kUuvL6MdthxF/pbqR4o20RhYChaVl+O/qv/DRs5Ox5/hFJGfk1IhBFgKnE6/i2IUU5BbWXpolywJZ+ZbfDbku7W4AwMErUOeHyNl1i1CcmVS9cP0DSFRV4sKGj2Dv0RKOvq1xeee3WknGjX1ldRUS/lyGzo9+qI+3QkYQfyUTL3yyBpVVak3p39YjZ7HnRDz+98JUBHhbduPCxuiZCQPg6eqEn3fFoqC4DEqFAv0j2mDW2L6aH26yuhJZZ/ciN/4wAAnurbuiWbteUCir5zpZFXMA1/JvJhlA9WdgeUUV/rduO5Y8P6W6t1htg70BKEg53TQTjejoaDg6OmL06NGYMmUK7rvvPtjY3P0XM+l2t8FjrGwd4dlpIDJPbNP5B9mie/XcCVVlRSjOvAwrlT1aeAVqtYMQshoXfv8vrp3cUf0LGUDy7u/h3qYH2o2fC4WVNWIOnNZ6IG6QZYGTCVeRmVuAYxdTah2LQ6lQ4NjF5DsWKyoVEoKbW/7srnVpd1OborSLKEw9p3ujQoGrh39D61EvVA8OVcsIroWp51BRlGPxrd8txZcbdqOyUl3jC6msohIrNv6NNx8fY8LomialQoEHhnTDlIFRyC8uhb2tDWxtbk6WVlVWhJOrXr1Z6gjg2qkdcPRtjY4P/QdKGzv8deRczR5+qP5hdiYpDVl5RTeHr69lPlRFI2zkbZREY+3atRg5ciRsbRvfP4ClCh76JMrz0quzX4Wy+o9SyPDpMgJe4UORsHU50o78AaGuboBr18wPrUc9rxlY5sq+dbh2cmf1yW75csq5eBCXd61C0KDHUFh659HsCorLYKVUoLbeLQICVkolOgT5IqSFJxLTsmo8ZLIAxvbp3NB/hiahOLP2Fu+Q1ShKT4QQotYSLs2ulbpHgaXGpbCkDMfjdc/aLMsCB04noKKyigOrmYhSqYC7c83xZhK3fV2j1BEAitIv4fKObxA87B8or7hzh4ji8gp4dojWPfghqocVaNa6W4NjNxSjzHUyYcIEJhlGZqWyR8eHFqPTw++gZa9J8O/3ICJmfY5WI55G0l8rcPXgBk2SAQClOak49f1rKMm+AiEErh7aAN1dXwXSjvwBuaoSQb4etc4LZWOlhK+HC/qEtYJa1l3MJ8sCvcNCIEkS3nx8DFp6Vf+aVlw/qY21FV596D6EtLD8Eo17ccdubZICKqdmUCit4Ni8ba0Tedk4eUDlwn9nc1BReeeEUQigSl170ToZn1xVUWsJM4SMjONbIWQ1Wrf01nz+3c7RTgXfZs7w6NAPLgGdoDV30fUSkqDBM2GlY1gBU2PKa8EkSYJLQKfrf5TVKopykXZ0E3SN4inLVbh6YD0CBz9+x0nQ5MoyVBTnYsqgKLz9zSYd1wVG9w6Dg60KXdr4o2eHYBw4nVAjbbmvewe0auEFAPByc8Kylx9C3MUUxKdmwtnBDn3DWsHBTlXj/E1VRVEOUvauxbXTOyGqKuEc0Akte0+Ga1A4rB3dUVmcW7M4VcjwDh8KAPDv9wDO/DRf57n9+z0A6focKtS4uTvbw7eZC9Kyaz6jkgQE+XrUqTE36Z8QAgUpp1GcmQQbexe4te4GpbUKVWVFWj/sbidXlkNdXoIHh3TD/K9+17nPlIFRmtmaO0x7E1cP/Yb0uBhUlRTAwTsYLXpOgHurhk36ZmhMNJqYwtRztTckkmXkJcZBaa2C0sZOu/vULSSlFaztnBEd7oWMnAJ8vWmf1i+ogV3aaQaNkiQJ/350JNbtiMVve08gO78IPs2cMa5fBMb01q4SUSgkdGnrjy5tOcHa7SqKchG34l+oKMrR3L/c+CPIjT+C0Cnz0W78Kzj9478hV1VWb7/e88Q7fCiaXZ87w71VFNqOn4vEP5dVnweAlZ0TAqIfgk/EMJO9N0tU18a9dVVSVoHsgiK4OdnD0c4WM4b3xDvfbamxnxDA9Pt66OWaVD/lBVk4s3phdRuM65QqB7QdNweuQeFQqhygLtfd8N3KzglWtg7o1ckZ/5oyGMs27NZMaGmlVGDywChMHngziVBY2cCv10T49Zpo2DelJ0w0mhiF1Z1LCBQ2tpAUSvh0uQ+pBzfomOVTAc+OAzSDwkweGIX7unfAwTNJqFSr0TnEr0b3OiulElMHd8XUwV31+VaalCv71molGQCu/7eES5s/Q9QzKxD5z2VIj92MorSLsLJzglengXAN7qLVyNcztC882vVCcUZi9Tgc3sFQWFnXvCDdk3tp3Hur0vIKLN2wG38ePovKKjWUCgnREW3w9LgB+NeUwfhq417kF1f/IHB3dsCTY/qid6dWerk21Z0QAqd/mo+Sa8la69XlJTi75i10+ccXaB41Cil716BmlbSE5l3HaEoUR/ToiEFd2uFkQirUahntA31r9AY0N0w0mhiXgE6wsnVEVZmOLqOSBK/rw4P793sQBVfOofDKWU39H4QMe88ABA9+XOswZwc7DOmqe0Av0o9rp3fVUhIlUJ6fieKMRDj6hCCg/8N3PZekUMLRl19GjUVBcSlyi0rh5eoIO9XNKg8hBN74v99w8lKqpneJWhbYeewCktKy8em/pmFo1/a4lJoFSQJCmntCqTRKszu6Tf7lkyi50dBTi4AQMtKObkLgwBkoybmC7LN7tT5TPTv0Q8s+2uNIqWysENWu4cOTNzZMNCxYSfYVpO7/GbmXjkKhtIZHaF+06D4OIcOfwvn171/vInVjqnAF7D1awidyJABAaWOHsOnvIvv8AeRcPAghBNxDItGsfW9Nn28ynltnvNW5vdLyh963NNn5xfjk5+3YdyoBQgjYWCkxomcnzBzVByobKxyPv6Kzd4ksCyRczcLek/HoH9EWbf29TRA93aokM6n2LqdCRnH6JSiU1mg/YR6K0uKRE38EkgS4teoKR58Qg8aWnJGDvSfjUVGpRnjrlggLaWH0KR2YaFiowtTzOLnqVQi5StOt8cr+n5F5aifCH/sInR5ejJR9a1F45SyUKgd4hw1Cix7jYaWy15xDUijh0b43PNr3NtXboOtcAjtXDyuvo1RDYW0LB++6D3FMpldaXonZn65Bek6BZsTciio1fvv7ONJz8vHWzLE4eiEZSoVCZ68tpULC0fPJ6B/B0VwbA2t751rHtYCkgPUtPcMcfVvprURRyGqU5qZBobSBrauX9jYh8MX6XVi/Jw4KSYIkAd/9eRCdglvgrSfGwMHWeA3tmWhYqPjNn0FWV9ao068ozEby7h/RasTTWr1RqHFr2Xsyci8egkDNX00te09qlBMpUe22Hz2Hq1k1e43IQuDA6UScT86AUqHAnaYSqN5OjYF7mx7XG9CXoWaPPhneYYP0ej0hBNJjNyN59/eoLM4DADj4hCBk2D80YyFtPnAK669PXS8LoQnrdOJVfPrzTrzyoPEagPMv1QKV5aajOP1SrX22r53aaZDryrLA+eQMHI9PQdFdBvOi+nFq3gahUxdqho8Hqqu3AvpPh1/v+s0TRKYXeyG51jFoFJKEYxeS0atjsM4RdYHqthq9Oxm2yJ3qTmljizb3vwRJobjZ/uL6//tEjoRrcBe9Xi/tyO+4tPkzTZIBAMUZCTj53TwUpV8CAPyy6xh0/YnJQmBH7DkUFOvuVWgILNGwQOrKO3/Jy1X1r8+X1VUozUqBpLSCXTO/GnV8sReS8dHqv5CRUwCgeh6VsX06Y+aoPmygpiduwRGIfGo5Sq5dhlxZBnuvwOrhiMnsKJXXZ+DVUdwurm9v09IbgyLbYfvRc1q/kSUJiGwTcNcZQ8m4mrXpgYgnP0Pa0T9QnJ4Aa0c3eHceUj3xoR7bRMhVlUje9X3NDddH/03ZuwbtJ7yK1Ky8WsvD1LJAek4BnB3s9BbXnTDRsEB27i1q77MtKeBYz1k6045uQvKu7zSDeNm6N0fIsFlwC6nu1x2fmonXlv2qVZdcWaXGzztjIQuBf94f3fA3Q1okSWp0EyZR/fXuGIIdsed1bhNCoGfH6jY3Lz8wFCEtPPHLrmPIyi+Ci6MdxvQOw9TBXaFQGLdBH92dvUdLhAz7xz2fRwgZuZdikXVmD+TKMji1DIV32CBY2TqiODNRd69BABAy8i4dBVDd3Tkzt7DWa+gaJt1QmGhYIIWVNfx6TcTlHd/U3Chk+Pepe1F7euxmXNr8mda6spw0nP5pITo9/A5c/Dtg7faj1XNp3F41CeC3v4/jwSHdjJY5E5mD3mEh6BjUHGeS0mpMTDi2T2f4eVY3HlQqFJg0IBKTBkRCrZZZOmgB4i6m4Ls/D+JkQiqsrZQYENEWDw/rDi83ZwDVDTzPrX+3uhusQgEIgayze3Fl7xqETX/vriP4Sorqr/XRvcPw1ca9NUo1FAoJUW0D4OHiaIi3pxP/ai2UX6+JaNn3ASisbvbLt7JzQpuxL2lKIu5GyGpc3vWdri0AgOQ9PwAAjsdfqbUuuUot43xKRv2CJ7JwVkolFv9jHKYO7qoZjMm3mQuemTAAT4/vr/MYJhnmoSwvEzkXD6Hw6sUaVWN7T8Zjzhc/V4+Ncn369z8Pn8HTH/2Ia3nVpQ/px7ZUJxkAIMvXG38LVJYU4PyGD+DgFQgbZw/dF5cU8AitHpV5QnQXdA0NBFCdsN6YQ8XbzRn/mjxY7+/7TliiYYbqMryxJCkQEP0gWnS/H4VXL0ChtIKTX7saY2BUFOXiyv6fkXV6F+SqCrgEhqFl78lw9G2N0uxUrcZGWoSMgssnIYSArerO42rY2XDcDaLb2dpY49ERvfDoiF5QyzJ7kZi5qrJiXPj9v8g5v1+zzq5ZC7S9/2U4+raGWpbx6c87qyfSvqWcQZYFCkrK8NO2I3h2wgCkHd0MnTNeCxlFVy+gNCcVwUNn4dy6/9QYC8nK1hF+vSYDqG4n99bjY3H0/GXsPn4RlVXV42gMiGgLlY1xv/qZaJih+gxvXJafgZLMJEhKK6hcvWHrcrOvdUVRLuK++hcqCrM1f6zZFw4g58JBdJi6ECrXO8/TICmUkCQJg7q0w3d/HqxRBCwBcHN2QPsA37q/OaImiEmG+Tu7bhHyL5/UWleak4aTq+ahyz+/RHKeGln5uttWyLLArmMX8OyEAdWfx3fo1lxRkA2Pdr3Q8cG3kbznRxSknIaksIJnh77w7/ug1ngaCoWEru0D0bV9oD7eYoMx0bBQ6spynF//HnIuHLie9QIJMUvh12sSAgZMhyRJ1fNn3JJkAABkGUKSEL/5M3T55zLYe/qj5FoKavzh31JENz46AjvjLuBKZq4m2VAoqq/5wqRBLPIlIrN2t1LkwqsXkZ90vOYGIUNdWYb02M2oDLjzWBpV6uqBFe09W6Ig5Wytk1/aNmsBAHANCodrUDjE9TmPjD3aZ33wG8BCJf61AjkXD1YvXK/jAwSu7FuDzON/AUD1eBo6x9oQKMtNQ2lWMoKH/gNQSDf7hgPVRXQqe/j3fQAA4GCnwpLnJ2Pa4K7wdneGs4MtenYIxn+fm6xpPU9EZK6OHDmCK1eu1FqaXJh6FtA5agWqq5lTzqBVC0842Nro3EWhkDTdlZt3u1/357KkgHubHlql0kB1NXljTjIAlmhYpKryEmTE/VnLkLgSrhz4Gd7hQ+owf0YZXIM6o/OM95G8+0fkJR2/Pix5H/j3nQpbt5tVIo52tnhkRC88MqKXnt8NEVHjprSxQ63VHZIEpY0dbKyt8NCwHli6YfdtmyUoJAnThlTPbu3Rrhf8ox9G8q7vrucu1e0wnJq3RuvRLxjybRgMEw0LVJ6XAaGurGWrQGl2KgDAOaATcuOP1Dp/hv318RqcWrRDh2kLDRQtEZF5c2/TA5LSCkJdVXOjEPC8Piv2hOgIKBUSvvvzIAqKqwdWDPJthmcnDECrFjdLKvz7ToVX2EBkn9sLdUU5nFu2h0tAWKMvuagNEw0LZO3gcuft9tX9tVv2nlydaOho4ezXaxJHnSQiAhAVFYX09HT4+PjorD6xtnNC8NB/4NLmT6urma+3mwAE3Fp1g0e76pJeSZIwrl8ERvcOw5XMPKisreDTzFlnAmHr4oUW3ccZ+J0ZBxMNC2Tj6A7XkEjkJRyrWVohKeATcR8AwNmvPUKn/BuXNn+O8vxMANUlGX69JqJln8nGDpuoSRGyGumxm5EWuxkVhTmw9/RHi+73o1nbnqYOjW6Tnp6O1NTUO+7jGzkcdu7NkXpwPYozEmDt4AqfiGHwDh9WY5AtK6USgb7NDBlyo8JEw0K1Gv4MTnzz8s2uUtezbKcWbeHXe5JmP/dWXeH2zAoUpydArqqAg3cwZwIlMjAhZJz75R1kn9unWVeQcgYFyacQ0H86WtZj9F5qPFyDOsM1qPNd9ysuLYdaFnCyV5ltdUh9MNGwULauXugy63NknPgLeYlxUCit0axdL3i07wOFUvu2S5ICjr6tTBQpUdOTe+moVpIBQFP6eHnnKniFDYKqttEfyWydSbqK5b/9jVOJVwEAIc098OjI3ugeGmTiyAyLiYYFs7J1QItuY9Gi21hTh0JEt8g6veeWuvzbSEDWub18bi3M+eR0vPjpOsi3TNeQkJaF15dvwJuPj7HooQA4jgYRkZGpK8tq6X4OABLkijKjxkOGt3Lzfsiy0BpBWYjqJqPLf99TY14US8JEg4jIyJxbtq99o5Dh3DLUeMGQwallGUfPX64xTQNQ3d8vJTP3jlO6mzuzTTQWL16Mrl27wsnJCV5eXrj//vtx/vx5U4dFRHRX3mGDq7uhS7d9BEsKOPm1h7N/R9MERg0mhEDGiW2IXfoU/l40Cgc+egBJ27+BuqK0TsdbcptQs000du3ahaeffhoHDhzA1q1bUVlZiaFDh6K4uNjUoRER3ZGVnRM6TX8Xjj4hWuvdW3dDhynzm0RPBEuTsudHXPztI5RcSwaEQFVJPq7sX4eT382DJFchql2AZqr2W0kA/L3d4enqZPygjcRsG4Nu2bJFa3nlypXw8vLC0aNH0a9fPxNFRURUN/bN/BD++McouZaMisJs2DXzg8rF09RhUQOUF2Yjec+P15duqR65PrX7tVO7MOO+Xoi7eAVQy5oqlBt5xxOj+1h0cmm2JRq3y8/PBwC4u7ubOBIiorqz9/SHa3AEkwwzlnvxcK2zrUKSkHVuL9r6e+PDZyaiU0gLzaZWLbyw6In70aOD5fY4Acy4RONWsizjhRdeQO/evdGxY+11m+Xl5SgvL9csFxUVGSM8IiKyYLJcBV1TOQAAhNDMPdU+wBcfPD0RpeUVkGUBBzuVUeM0FYtINJ5++mmcOnUKf//99x33W7x4MRYu5ORgRESkP66BYah19lZIcA2K0Fpjp9I9XbylMvuqk2eeeQYbN27Ejh074Ofnd8d9X331VeTn52teu3btMlKURERkqew9/OER2q9m1xFJAWtHN3hHDDNNYI2E2ZZoCCHw7LPPYv369di5cyeCgu4+hKtKpYJKdbOoytHR0ZAhEhFRE9FmzGzYOLohPXYz5KoKAIBrYGe0GvE0rO0st0dJXZhtovH000/jhx9+wIYNG+Dk5IT09HQAgIuLC+zs7EwcHRERNSUKK2sED30SAf0fRlleBqztnGHjxM4JgBlXnXzxxRfIz89H//794evrq3mtXr3a1KEREVETpbSxg4NXIJOMW5htiYYljwtPRERkKcy2RIOIiIgaP7Mt0SAiIjJXarWM3/eewO97TyArvwgtPF0xoX8XDOzS1uJGCWWiQUREZERCCCz6dhP2nIjXrItPzcQ7321BwtVreGJ0XxNGp3+sOiEiIjKio+eTtZIMALjR7HDN9qO4ci3XBFEZDhMNIiIiA6hSq3E5PRtpWflaHRh2H78IpUL3169CkvD38Xid28wVq06IiIj0SAiB3/eewLcxB5BfVAoACPL1wDMT+iMsxA8VlVW19pyUJAlllVXGDNfgWKJBRESkRxv2HMcnP+/QJBkAkJSejVe++AUXUzLRKbiFZqr426llGWHBLXRuM1dMNIiIiPSkoqoK32zZX2O9EAKyEPhh60EMjGwHLzcnKBTavUsUCgntA3wQ0aalscI1CiYaREREd+Dj44MWLVrAx8fnrvsmXs1GUWm5zm2yLHDkfDLsVNb46JlJ6HRLyYUkAb06hmDRk/ezeysREVFTcuTIkTrva21159/vVsrq7d7uzvjg6YlIy85HVl4RfD1c4OFimRN9MtEgIiLSk0AfD3i5OSEzt7DGNoVCQnR4G611vs1c4NvMxVjhmQSrToiIiPREoZDw9Pj+kKTqrqq3rneys8UDQ7qaMDrTYKJBRESkR706huD9pyagU0gLKBQSVNZWGBzZDp/OngovN2dTh2d0rDohIiLSAyEEzidnIDEtC66O9lg8axyslAqLa9xZX0w0iIiI7lF2fjEWfP07zl1O16xzcbDDvIeHo0tbfxNGZnqsOiEiIroHQgi88X8bcDElQ2t9QUkpXv+/DUjLyjdRZI0DEw0iIqJ7cCrhKi5eyYRa1h7tU4jqkT5/33fCRJE1Dkw0iIiI7kF8aiZqa4YhywIXUzKNG1Ajw0SDiIjoHjg72KGWqUugUEhwcbQzbkCNDBMNIiKie9CzQzBsbax1bpNlgSFR7Y0cUePCRIOIiOge2Nva4OVpQ6GQJCivT5R2Y7CuYd1C0S000ITRmR67txIREd2jfuGt4ef1AH7dcxyXUjPh7uyAYd06oHenEI6jYeoAiIiILEFwc0/MnjLY1GE0Oqw6ISIiIoNhiQYREZGRxKdm4uedx3A68Sqc7G0xpGt7jOjRETbWlvt1bLnvjIiIqBE5cDoBC776HQCglgXSsvNxMSUDu+Mu4p1/joONlWV+JVvmuyIiIjKyisIcpMfFoDgzETYObvAKGwSn5m0AAJVVanzw41bIssCtQ24IACcTUrH5wGmM7dPZJHEbGhMNIiKie5R/+SRO/zgfsroCACBJCqQd2YiWfR9AQPSDOHHpCvKLS3UeKwHYeviMxSYabAxKRER0D+SqCpxdt6g6yRACEAJCVgMAUvb8gPzLp1BcWlHr8QJAUWm5kaI1PrNONHbv3o3Ro0ejefPmkCQJv/76q6lDIiKiJibn4iFUlRZC5zjkkgLpcTFo4+9V6/FKhYSOQc0NGKFpmXWiUVxcjM6dO+Ozzz4zdShERNREVRTmoNZZ1YSMioIs+Li7oH9EG82IoTdI1/93QnQXQ4dpMmbdRmP48OEYPny4qcMgIqImzK5ZC92lGQCgUMDeoyUA4MWpQyBJEnbEntdsdnWyx8vThiKouYcxQjUJs0406qu8vBzl5TfrwYqKikwYDRERWQLXoHDYuvmiLC8DELL2RiHgEzkCAGBrY415Dw/H4yN748KVTDjaqRAW3AJKpVlXLtyVZb+72yxevBguLi6aV3R0tKlDIiIiMycplOgwdSFUzh6aZUCCpLRCm7EvwcErUGt/b3dn9A1rhYjWLS0+yQAASYjaynvMiyRJWL9+Pe6///5a97m9RCMuLg7R0dE4evQounSx3PoxIiIyPFldhZyLB1GckQQbR1d4tO8La3tnU4dlck2q6kSlUkGlUmmWHR0dTRgNERFZEoXSCh7tesOjXW9Th9KoWH6ZDREREZmMWZdoFBUVIT4+XrOcmJiIuLg4uLu7w9/f34SREREREWDmicaRI0cwYMAAzfLs2bMBADNmzMDKlStNFBURERHdYNaJRv/+/WEhbVkNLi0tDWlpaaYOg/TE19cXvr6+pg6D9ITPp+XhM3qTWSca98rX1xfz58+3+D+G8vJyTJs2Dbt27TJ1KKQn0dHRiImJ0WrcTOaJz6dl4jN6k8V0b6XaFRQUwMXFBbt27WJPGwtQVFSE6Oho5Ofnw9mZXefMHZ9Py8NnVFuTLtFoasLDw/lHbwEKCgpMHQIZAJ9Py8FnVBu7txIREZHBMNEgIiIig2Gi0QSoVCrMnz+fjZIsBO+nZeH9tDy8p9rYGJSIiIgMhiUaREREZDBMNIiIiMhgmGgQERGRwTDRoHpJSkqCJEmcS4aokeIzSo0NEw0DunTpEmbNmoXg4GDY2trC2dkZvXv3xpIlS1BaWmqw6545cwYLFixAUlKSwa5RF4sWLcKYMWPg7e0NSZKwYMECk8ZjTJIk1em1c+fOe75WSUkJFixYUK9zNeV7c6um/IyeO3cOc+bMQXh4OJycnODr64uRI0fiyJEjJovJWBrz82mJ94UjgxrIH3/8gUmTJkGlUmH69Ono2LEjKioq8Pfff+Pll1/G6dOnsWzZMoNc+8yZM1i4cCH69++PwMBAg1yjLl5//XX4+PggIiICMTExJovDFFatWqW1/O2332Lr1q011rdv3/6er1VSUoKFCxcCqJ5osC6a8r25oak/o//3f/+HFStWYMKECXjqqaeQn5+PpUuXokePHtiyZQsGDx5skriMoTE/n5Z4X5hoGEBiYiKmTp2KgIAAbN++XWvStqeffhrx8fH4448/TBjhTUIIlJWVwc7OTu/nTkxMRGBgILKysuDp6an38zdmDz30kNbygQMHsHXr1hrrTaUp3xuAzygATJs2DQsWLNCaX+Wxxx5D+/btsWDBArP8Qqurxvx8WuJ9YdWJAbz33nsoKirCihUrdM4M26pVKzz//POa5aqqKrz11lsICQmBSqVCYGAg5s2bh/Lycq3jAgMDMWrUKPz999/o1q0bbG1tERwcjG+//Vazz8qVKzFp0iQAwIABA2oUAd44R0xMDKKiomBnZ4elS5cCABISEjBp0iS4u7vD3t4ePXr0uKcPW1OWppgDWZbx8ccfo0OHDrC1tYW3tzdmzZqF3Nxcrf2OHDmCYcOGwcPDA3Z2dggKCsJjjz0GoLo+/kaisHDhQs39vltVSFO/N3xGgcjIyBqTuDVr1gx9+/bF2bNnG3ROS2Kq59Mi74sgvWvRooUIDg6u8/4zZswQAMTEiRPFZ599JqZPny4AiPvvv19rv4CAANG2bVvh7e0t5s2bJz799FPRpUsXIUmSOHXqlBBCiEuXLonnnntOABDz5s0Tq1atEqtWrRLp6emac7Rq1Uq4ubmJuXPnii+//FLs2LFDpKenC29vb+Hk5CRee+018dFHH4nOnTsLhUIhfvnlF00MiYmJAoD4+uuv6/z+rl27JgCI+fPn1/kYS/P000+L2x+3mTNnCisrK/HEE0+IL7/8UrzyyivCwcFBdO3aVVRUVAghhMjIyBBubm6iTZs24v333xfLly8Xr732mmjfvr0QQoiioiLxxRdfCABi3Lhxmvt9/PjxOsXVVO8Nn9Ha9erVS7Rp06ZBx5qrxvp83sqc7wsTDT3Lz88XAMTYsWPrtH9cXJwAIGbOnKm1/qWXXhIAxPbt2zXrAgICBACxe/duzbrMzEyhUqnEiy++qFm3du1aAUDs2LGjxvVunGPLli1a61944QUBQOzZs0ezrrCwUAQFBYnAwEChVquFEEw0Gur2D7I9e/YIAOL777/X2m/Lli1a69evXy8AiMOHD9d67nv5922K94bPaO12794tJEkSb7zxRr2PNWeN9fm8wdzvC6tO9OzG9MBOTk512n/Tpk0AgNmzZ2utf/HFFwGgRrFoaGgo+vbtq1n29PRE27ZtkZCQUOcYg4KCMGzYsBpxdOvWDX369NGsc3R0xJNPPomkpCScOXOmzuenu1u7di1cXFwwZMgQZGVlaV43ik137NgBAHB1dQUAbNy4EZWVlSaM2HLwGdUtMzMTDzzwAIKCgjBnzpx7Ope5a0zPpyXcFyYaeubs7AwAKCwsrNP+ly9fhkKhQKtWrbTW+/j4wNXVFZcvX9Za7+/vX+Mcbm5uNeoN7yQoKEhnHG3btq2x/kar69vjoHtz8eJF5Ofnw8vLC56enlqvoqIiZGZmAgCio6MxYcIELFy4EB4eHhg7diy+/vrrGm0DqO74jNZUXFyMUaNGobCwEBs2bKjRRqCpaSzPp6XcF/Y60TNnZ2c0b94cp06dqtdxkiTVaT+lUqlzvajH3HiG6GFC9SPLMry8vPD999/r3H6jAZkkSVi3bh0OHDiA33//HTExMXjsscfw4Ycf4sCBA2b7wWNKfEa1VVRUYPz48Thx4gRiYmLQsWNHo127sWoMz6cl3RcmGgYwatQoLFu2DPv370fPnj3vuG9AQABkWcbFixe1+mxnZGQgLy8PAQEB9b5+XT8Qb4/j/PnzNdafO3dOs530JyQkBH/99Rd69+5dpy+VHj16oEePHli0aBF++OEHPPjgg/jpp58wc+bMBt3vpo7PaDVZljF9+nRs27YNa9asQXR0dL3PYYlM/Xxa2n1h1YkBzJkzBw4ODpg5cyYyMjJqbL906RKWLFkCABgxYgQA4OOPP9ba56OPPgIAjBw5st7Xd3BwAADk5eXV+ZgRI0bg0KFD2L9/v2ZdcXExli1bhsDAQISGhtY7Dqrd5MmToVar8dZbb9XYVlVVpbl3ubm5NX4Jh4eHA4CmeNbe3h5A/e53U8dntNqzzz6L1atX4/PPP8f48ePrfbylMvXzaWn3hSUaBhASEoIffvgBU6ZMQfv27bVGHdy3bx/Wrl2LRx55BADQuXNnzJgxA8uWLUNeXh6io6Nx6NAhfPPNN7j//vsxYMCAel8/PDwcSqUS7777LvLz86FSqTBw4EB4eXnVeszcuXPx448/Yvjw4Xjuuefg7u6Ob775BomJifj555+hUNQ/J121ahUuX76MkpISAMDu3bvx9ttvAwAefvjhJl1KEh0djVmzZmHx4sWIi4vD0KFDYW1tjYsXL2Lt2rVYsmQJJk6ciG+++Qaff/45xo0bh5CQEBQWFmL58uVwdnbWfAHa2dkhNDQUq1evRps2beDu7o6OHTvesai1qd8bPqPVidPnn3+Onj17wt7eHt99953W9nHjxmkSoqbGlM+nRd4X03Z6sWwXLlwQTzzxhAgMDBQ2NjbCyclJ9O7dW3zyySeirKxMs19lZaVYuHChCAoKEtbW1qJly5bi1Vdf1dpHiOpubyNHjqxxnejoaBEdHa21bvny5SI4OFgolUqtbnS1nUOI6v79EydOFK6ursLW1lZ069ZNbNy4UWuf+nSdi46OFgB0vnR167NkuvrpCyHEsmXLRGRkpLCzsxNOTk6iU6dOYs6cOeLq1atCCCFiY2PFtGnThL+/v1CpVMLLy0uMGjVKHDlyROs8+/btE5GRkcLGxqZOXel4b6o15Wf0xtggtb0SExPveLwlaUzPpyXeF0mIerRQIiIiIqoHttEgIiIig2GiQURERAbDRIOIiIgMhokGERERGQwTDSIiIjIYJhpERERkMEw0TGTlypWQJAm2trZITU2tsb1///5GH9t+27ZteOyxx9CmTRvY29sjODgYM2fORFpams799+3bhz59+sDe3h4+Pj547rnnUFRUZNSYGwveT8vC+2l5eE9Nh4mGiZWXl+Odd94xdRgAgFdeeQU7d+7EuHHj8L///Q9Tp07FmjVrEBERgfT0dK194+LiMGjQIJSUlOCjjz7CzJkzsWzZMkyaNMlE0TcOvJ+WhffT8vCemoCpRwxrqr7++msBQISHhwuVSiVSU1O1tkdHR4sOHToYNaZdu3YJtVpdYx0A8dprr2mtHz58uPD19RX5+fmadcuXLxcARExMjFHibUx4Py0L76fl4T01HZZomNi8efOgVqsbRYbdr1+/GvMl9OvXD+7u7jh79qxmXUFBAbZu3YqHHnoIzs7OmvXTp0+Ho6Mj1qxZY7SYGxveT8vC+2l5eE+Nj5OqmVhQUBCmT5+O5cuXY+7cuWjevHm9ji8pKdFMjHUnSqUSbm5u9Y6vqKgIRUVF8PDw0Kw7efIkqqqqEBUVpbWvjY0NwsPDcezYsXpfx1LwfloW3k/Lw3tqfCzRaARee+01VFVV4d133633se+99x48PT3v+oqIiGhQbB9//DEqKiowZcoUzbobDZV8fX1r7O/r64urV6826FqWgvfTsvB+Wh7eU+NiiUYjEBwcjIcffhjLli3D3Llzdf4x1Wb69Ono06fPXfezs7Ord1y7d+/GwoULMXnyZAwcOFCzvrS0FACgUqlqHGNra6vZ3lTxfloW3k/Lw3tqXEw0GonXX38dq1atwjvvvIMlS5bU+bjg4GAEBwfrPZ5z585h3Lhx6NixI/7v//5Pa9uNB6i8vLzGcWVlZQ16wCwN76dl4f20PLynxsNEo5EIDg7GQw89pMmw6+pGfd7dKJVKeHp61umcKSkpGDp0KFxcXLBp0yY4OTlpbb+R/evq652WllbvOk9LxPtpWXg/LQ/vqfGwjUYj8vrrr9e73vCDDz6Ar6/vXV9du3at0/mys7MxdOhQlJeXIyYmRmeRYseOHWFlZYUjR45ora+oqEBcXBzCw8PrHL8l4/20LLyflof31DhYotGIhISE4KGHHsLSpUsREBAAK6u73x591hcWFxdjxIgRSE1NxY4dO9C6dWud+7m4uGDw4MH47rvv8MYbb2iy71WrVqGoqMg8BpAxAt5Py8L7aXl4T41DEkIIUwfRFK1cuRKPPvooDh8+rNVlKT4+Hu3atYNarUaHDh1w6tQpo8V0//33Y8OGDXjssccwYMAArW2Ojo64//77NcuxsbHo1asXQkND8eSTT+LKlSv48MMP0a9fP8TExBgt5saC99Oy8H5aHt5TEzL1iGFN1Y1R6g4fPlxj24wZMwQAo49SFxAQIADofAUEBNTYf8+ePaJXr17C1tZWeHp6iqeffloUFBQYNebGgvfTsvB+Wh7eU9NhiQYREREZDBuDEhERkcEw0SAiIiKDYaJBREREBsNEg4iIiAyGiQYREREZDBMNIiIiMhgmGkRERGQwTDSIiIjIYJhoEBERkcEw0SAiIiKDYaJBREREBsNEg4iIiAyGiQYREREZDBMNIiIiMhgmGkRERGQwTTrRSEtLw4IFC5CWlmbqUIiIiCxSk080Fi5cyESDiIjIQJp0okFERESGxUSDiIiIDMasE43du3dj9OjRaN68OSRJwq+//mrqkIiIiOgWZp1oFBcXo3Pnzvjss89MHQoRERHpYGXqAO7F8OHDMXz4cFOHQURERLUw60SjvsrLy1FeXq5ZLioqMmE0REREls+sq07qa/HixXBxcdG8oqOjTR0SERGRRWtSicarr76K/Px8zWvXrl2mDomoQaqqqkwdAhFRnTSpqhOVSgWVSqVZdnR0NGE0RA2nVqthZdWkHl8iMlNNqkSDyFIIIUwdAhFRnZj1T6KioiLEx8drlhMTExEXFwd3d3f4+/ubMDIiw6qoqICtra2pwyAiuiuzTjSOHDmCAQMGaJZnz54NAJgxYwZWrlxpoqiIDK+wsBDOzs6mDoOI6K7MOtHo378/i5CpSSotLUVRURHbGRFRo8c2GkRm6sqVK6YOgYjorphoEJmphIQEU4dARHRXTDSIzFRqaioKCgpMHQYR0R0x0SAyU0IIHDt2zNRhEBHdERMNIjN24cIFpKWlmToMIqJaMdEgMjNRUVHo06cPFi1aBCEEtm/fjtLSUlOHRUSkExMNIjOTnp6OjIwMTfuM4uJixMTEoLKy0sSRERHVxESDyAJkZmbijz/+QElJialDISLSwkSDyEJkZmZi3bp1iI+P50B2RNRoMNEgsiBlZWXYvn07Nm7ciOzsbFOHQ0TERIPIEqWlpeGXX37Bnj17UFZWZupwiKgJM+u5ToiodkIInD17FgkJCejSpQtCQ0OhVCpNHRYRNTEs0SCycOXl5di/fz/WrFmDM2fOoKqqytQhEVETwhINoiaisLAQf//9N44cOYLQ0FB06NABdnZ2pg6LiCwcEw2iJqasrAyxsbE4fvw4Wrdujc6dO8PFxcXUYRGRhWKiQWRGkpOTNWNlVFRUICcnB+7u7g06l1qtxrlz53D+/Hm0atUKERERcHV11WO0RERso0FkFg4dOoTRo0cjMDAQubm5AICSkhLMmzcPn332GZKSkhp8biEELl68iLVr12L79u3Iy8vTT9BERGCJBlGj98svv2DKlCkQQtQYiEsIgVOnTuHUqVN44okn0KVLlwZfRwiB+Ph4XLp0Ca1atUJUVBScnJzuNXwiauJYokHUiB06dAhTpkyBWq2GWq3WuY8sy5BlGcuXL7+nko0bbpRwrFmzBidPnuQoo0R0T5hoEDVib7/9ts6SjNps2rRJb9dWq9XYv38/Tpw4obdzElHTc0+Jxo3++Rs2bEBWVpa+YiIiVDf83LhxY60lGbeTZRknTpxATk6OXuOIjY3l2BtE1GANTjT+97//wdfXF3369MH48eM1v3qysrLg4eGBr776Sm9BEjVF27Ztq3e1hRAC586d02sclZWVSExM1Os5iajpaFCi8fXXX+OFF17AfffdhxUrVmh9GHp4eGDgwIH46aef9BYkUVNUWFgIhaJ+j6gkSQaZ2+TQoUOcM4WIGqRBicaHH36IsWPH4ocffsDo0aNrbI+MjMTp06fvOTiipszJyQmyLNfrGCEEbG1t9R5LcXExYmNj9X5eIrJ8DUo04uPjMXz48Fq3u7u7c4pqons0aNAgSJJUr2MkSUK7du0MEg+7uhJRQzQo0XB1db1j488zZ87Ax8enwUEREeDv749Ro0bVecZVhUKBsLCwBo8UWhtra2v06dMHHTt21Ot5iahpaFCiMWLECCxbtkznCIKnT5/G8uXLMWbMmHuNjajJe+ONNyBJUp1LNkaMGKHX6wcEBGDSpEkIDQ2td+kKERHQwETj7bffhlqtRseOHfH6669DkiR88803eOihhxAVFQUvLy/8+9//1nesRE1O165dsXr1aiiVylpLNhQKBRQKBZ588kkEBgbq5bqOjo4YMmQIhg4dCkdHR72ck4iapgYlGs2bN8fRo0dx3333YfXq1RBCYNWqVfj9998xbdo0HDhwAB4eHvqOlahJGj9+PPbt24cRI0bUKFWQJAmdOnXCK6+8goiIiHu+loODA3r27IkpU6YgKCiIpRhEdM8koYfxha9duwZZluHp6Vnv7nimFBsbi8jISBw9evSe5oggMpbk5GSEh4cjNzcX9vb2eOONN/TSJsPb2xsdOnRAUFBQnduEEBHVhV4mVfP09NTHaYjoLvz9/WFvb4/c3FzY2NjcU5JhZWWF1q1bIzQ0FM2aNdNjlERENzWo+OH1119HeHh4rdsjIiKwcOHChsZERAbk5OSE7t2748EHH0Tfvn2ZZBCRQTWoRGPdunUYN25crdtHjBiB1atXY/78+Q0OjIj0q0WLFujQoQMCAgLY9oKIjKZBiUZycjJCQkJq3R4UFITLly83OCgi0g+VSoU2bdogNDQULi4upg6HiJqgBiUajo6Od0wkEhMTDTIMMhHVjYODA8LDw9G2bVtYWemlKRYRUYM0qI1G//79sXTpUqSmptbYlpKSgmXLlmHAgAH3HBwR1Y8kSYiIiMCUKVPQoUMHJhlEZHIN+hR666230K1bN3To0AGPP/44OnToAAA4deoUvvrqKwgh8NZbb+k1UCK6Mzs7OwwZMoTD/xNRo9KgRKNt27bYs2cPnn32Wfz3v//V2tavXz/873//Q/v27fUSIBFp8/HxQVVVFVQqlWadi4sLRowYwYnPiKjRaXC5alhYGHbt2oWsrCwkJCQAAIKDgzkiKJGBHTlyBPHx8di+fTsAwNnZGaNHj4a9vb2JIyMiqumeK3A9PDyYXBCZiFKpxLBhw5hkEFGj1eBEQ61WIyYmBgkJCcjNzcXtI5lLkoQ33njjngMkotqFhYXBzc3N1GEQEdWqQYnGkSNHMGHCBFy5cqVGgnEDEw0iw5IkSdMQm4iosWpQ99annnoKpaWl+PXXX5GTkwNZlmu81Gq1vmMlols0b96cVSZE1Og1qETjxIkTWLRoEUaPHq3veIiojgIDA00dAhHRXTWoRMPPz6/WKhNj++yzzxAYGAhbW1t0794dhw4dMnVIREbRsmVLU4dARHRXDUo0XnnlFSxfvhwFBQX6jqdeVq9ejdmzZ2P+/PmIjY1F586dMWzYMGRmZpo0LiJDU6lUHDODiMxCg6pOCgsL4ejoiFatWmHq1Klo2bIllEql1j6SJOFf//qXXoKszUcffYQnnngCjz76KADgyy+/xB9//IGvvvoKc+fONei1iUzJ3t6eM7ASkVmQRAPqQBSKuxeESJJk0AahFRUVsLe3x7p163D//fdr1s+YMQN5eXnYsGHDXc8RGxuLyMhIHD16FF26dDFYrET6VlBQAGdnZ1OHQUR0Vw0q0UhMTNR3HPWWlZUFtVoNb29vrfXe3t44d+6czmPKy8tRXl6uWS4qKgIAVFVVobKy0nDBEumZLMv8myUik7O2tr7rPg1KNAICAhpymMktXrwYCxcurLG+e/fuJoiGiIjIvNWlUuSehiBPTU3F7t27kZmZiQkTJsDPzw9qtRr5+flwcXGp0W5Dnzw8PKBUKpGRkaG1PiMjo9bZK1999VXMnj1bsxwXF4fo6GgcPHgQERERBouVSN8qKyvr9EuCiMjUGpRoCCHw4osv4tNPP0VVVRUkSUKnTp3g5+eHoqIiBAYG4s0338QLL7yg53BvsrGxQWRkJLZt26ZpoyHLMrZt24ZnnnlG5zEqlUprxktHR0cAgJWVFT+0yawoFAqDJvJERPrSoO6t77//PpYsWYKXXnoJW7du1So6cXFxwfjx4/Hzzz/rLcjazJ49G8uXL8c333yDs2fP4p///CeKi4s1vVCILBV7nBCRuWhQicby5csxffp0/Oc//0F2dnaN7WFhYdi8efM9B3c3U6ZMwbVr1/Dvf/8b6enpCA8Px5YtW2o0ECWyNEw0iMhcNCjRSElJQa9evWrd7uDgYLTBvJ555plaq0qIiIjItBpUdeLl5YWUlJRatx89ehT+/v4NDoqIiIgsQ4MSjfHjx+PLL79EQkKCZt2Notw///wTK1euxKRJk/QTIRHVIMuyqUMgIqqTBo0Mmp+fj379+iExMRF9+/bFli1bMGTIEBQVFWH//v2IiIjA7t27G/0U1hwZlMyVWq1mrxMiMgsNKtFwcXHBgQMHMGfOHKSmpsLW1ha7du1CXl4e5s+fjz179jT6JIPInDHJICJzUe/GoGVlZVi2bBnCw8Px+uuv4/XXXzdEXERERGQB6l2iYWtri1deeQXnz583RDxERERkQRpUddKxY0ckJSXpORQiIiKyNA1KNBYtWoSlS5fir7/+0nc8REREZEEaNGDXp59+Cnd3dwwbNgxBQUEICgqCnZ2d1j6SJGHDhg16CZKIiIjMU4MSjRMnTkCSJPj7+0OtViM+Pr7GPhwimYiIiBqUaLB9BhEREdVFg9poEBEREdVFgxMNtVqNn376CbNmzcK4ceNw8uRJANWjhv7yyy/IyMjQW5BERERknhqUaOTl5aF379544IEH8OOPP+K3337DtWvXAACOjo547rnnsGTJEr0GSkREROanQYnG3Llzcfr0acTExCAhIQG3TpeiVCoxceJEbNq0SW9BEhERkXlqUKLx66+/4tlnn8WQIUN09i5p06YNG4wSERFRwxKN/Px8BAUF1bq9srISVVVVDQ6KiIiILEODEo2QkBDExsbWuv3PP/9EaGhog4MiIiIiy9CgRGPmzJn46quvsHr1ak37DEmSUF5ejtdeew1btmzBrFmz9BooERERmZ8GDdj1/PPP4/Tp05g2bRpcXV0BAA888ACys7NRVVWFWbNm4fHHH9dnnERERGSGGpRoSJKE5cuXY8aMGVi3bh0uXrwIWZYREhKCyZMno1+/fvqOk4iIiMxQnRKN8ePH41//+hf69u0LANi9ezfat2+PPn36oE+fPgYNkIiIiMxXndpobNiwAcnJyZrlAQMGYOvWrQYLioiIyJzJsmzqEBqNOiUaLVq0wLFjxzTLQgjOzkpERFSLiooKU4fQaNSp6mTq1Kn44IMPsGbNGk3jz7lz52Lx4sW1HiNJEo4fP66XIImIiMyJWq02dQiNRp0SjcWLF6NVq1bYsWMHMjMzIUkSHBwc0KxZM0PHR0REZHY4aOVNkrh1opI6UigU+O677/DAAw8YIiajiY2NRWRkJI4ePYouXbqYOhwiIrIQWVlZ8PDwMHUYjUKd2mh06dIFW7Zs0Sx//fXXiIiIMFhQRERE5qysrMzUITQadUo0Tpw4gaysLM3yY489ptU4lIiIiG4qKSkxdQiNRp0SjYCAAPz111+axi3sdUJERFS7oqIiU4fQaNQp0fjHP/6Bb7/9Fra2tnB2doYkSXj88cfh7Oxc68vFxcXQsRMRETVK+fn5pg6h0ahTr5OXX34ZnTt3xo4dO5CRkYFvvvkGXbt2RXBwsKHjIyIiMjsFBQUs/b+uznOdDB06FEOHDgUArFy5ErNmzTL7XidERESGUFZWhrKyMtjZ2Zk6FJNr0KRqHFqViIjozvLy8phooI6Jxo15Tvz9/bWW7+bG/kRERE1NTk4OfH19TR2GydUp0QgMDIQkSSgtLYWNjY1m+W44BCsRETVV6enp6NChg6nDMLk6JRpfffUVJEmCtbW11jIRERHplpKSArVaDaVSaepQTKpOicYjjzxyx2UiIiLSVlFRgcTERLRq1crUoZhUncbRICIiovo7fvw4GjClmEWpU4nGm2++We8TS5KEN954o97HERERWYrs7GxcvnwZgYGBpg7FZOqUaCxYsKDGuhttNG7P1CRJ0gxSwkSDiIiauiNHjiAgIKDJtm2sU9WJLMtar5SUFHTq1AnTpk3DoUOHkJ+fj/z8fBw8eBBTp05F586dkZKSYujYiYiIGr2cnBxcuHDB1GGYjCQaUHl0//33w9raGmvXrtW5feLEiVCr1Vi/fv09B2hIsbGxiIyMxNGjR9GlSxdTh0NERBYgKioKiYmJcHR0xGuvvQYAsLOzw+TJk6FSqUwcnfE1qDHo9u3bMXDgwFq3Dxo0CNu2bWtwUEREROYqPT0dOTk5KCgo0KwrLS3F7t27m2TD0AYlGra2tti/f3+t2/ft2wdbW9sGB0VERGRpEhMTcfz4cVOHYXQNSjQefPBBfP/993juuedw8eJFTduNixcv4tlnn8UPP/yABx98UN+xalm0aBF69eoFe3t7uLq6GvRaRERE+nDo0CGcP3/e1GEYVYMmVXv33XeRlZWFTz/9FJ999hkUiup8RZZlCCEwbdo0vPvuu3oN9HYVFRWYNGkSevbsiRUrVhj0WkRERPqye/duKBQKtG7d2tShGEWDEg0bGxusWrUKL7/8MjZt2oTLly8DAAICAjB8+HB07txZr0HqsnDhQgDVU9YTERGZCyEEdu7cCQBNItloUKJxQ1hYGMLCwvQVi8GVl5ejvLxcs1xUVGTCaIiIqKm6kWwoFAqEhISYOhyDalJDkC9evBguLi6aV3R0tKlDIiKiJkoIgR07dlj8uFONKtGYO3cuJEm64+vcuXMNPv+rr76qGVwsPz8fu3bt0mP0RERE9SPLMrZu3YrMzExTh2Iw91R1om8vvvjiXWeGDQ4ObvD5VSqV1mApjo6ODT4XERGRPlRVVWHLli0YM2aMRfaibFSJhqenJzw9PU0dBhERkVGVlZXhjz/+wOjRo+Hs7GzqcPSqUVWd1EdycjLi4uKQnJwMtVqNuLg4xMXFsYEnERGZpeLiYvz+++/Izc01dSh6ZbaJxr///W9ERERg/vz5KCoqQkREBCIiInDkyBFTh0ZERNQgxcXF2LBhg0U1EG1w1UlMTAxWrFiBhIQE5Obm6pwu/tKlS/ccYG1WrlzJMTSIiMjiVFRUYMuWLejSpQu6dOli9tPLNyjReP/99zF37lx4e3ujW7du6NSpk77jIiIiarKEEDh69ChSU1MxYMAAODk5mTqkBmtQorFkyRIMHDgQmzZtgrW1tb5jIiIiIlTPBLtu3Tr06NED7dq1M8vSjQa10cjNzcXEiROZZBARERlYZWUl9uzZgy1btqCkpMTU4dRbgxKNbt26NbnZ54iIiEwpJSUFP//8M1JTU00dSr00KNH4/PPP8csvv+CHH37QdzxERERUi9LSUmzatAknT540dSh11qA2GlOmTEFVVRUefvhh/POf/4Sfnx+USqXWPpIk4fjx43oJkoiIiKoJIbB//36UlZWha9eupg7nrhqUaLi7u6NZs2ZNYnpbIiKixujYsWOwt7dHhw4dTB3KHTUo0di5c6eewyAiIjJ/ycnJmgabFRUVyMnJgbu7u8Gut3//fvj6+hr0GvfKbEcGJSIiaiwOHTqE0aNHIzAwUDOEeElJCebNm4fPPvsMSUlJBrmuLMs4fPiwQc6tL/c0qVplZSXOnTuH/Px8yLJcY3u/fv3u5fRERESN3i+//IIpU6ZACFFjlGwhBE6dOoVTp07hiSeeQJcuXfR+/eTkZBQXF8PBwUHv59aHBiUasizj1Vdfxeeff37HPr1qtbrBgRERETV2hw4dwpQpU6BWq2skGTfc+CG+fPlyvPLKKwgMDNRrDEIIJCcno3379no9r740qOrkP//5D95//3089NBD+PbbbyGEwDvvvIMvv/wSYWFh6Ny5M2JiYvQdKxERUaPy9ttv6yzJqM2mTZsMEkdjHlujQYnGypUrMXnyZHzxxRe47777AACRkZF44okncPDgQUiShO3bt+s1UCIiosYkOTkZGzdurHPpvSzLOHHiBHJycvQeS3p6ut7PqS8NSjSuXLmCgQMHAgBUKhUAoKysDABgY2ODhx56CKtWrdJTiERERI3Ptm3b6lyScYMQAufOndN7LCUlJSgtLdX7efWhQYlGs2bNUFRUBABwdHSEs7MzEhIStPa50eqWiIjIEhUWFkKhqN/XqCRJmh/m+lZcXGyQ896rBjUGjYiI0OpOM2DAAHz88ceIiIiALMv43//+h86dO+stSCIiosbGyclJZ4/LOxFCwNbW1iDx3D5Cd2PRoBKNJ598EuXl5SgvLwcALFq0CHl5eejXrx+io6NRUFCADz/8UK+BEhERNSaDBg2q97TtkiShXbt2eo/F2toazs7Oej+vPjSoRGPMmDEYM2aMZjk0NBSXLl3Czp07oVQq0atXr0Y9ShkREdG98vf3x6hRo7Bp06Y6NQhVKBTo1KmTQb4fg4ODG22Jxj0N2HUrFxcXjB07Vl+nIyIiavTeeOMNbN68GZIk1alh6IgRI/Qeg5WVFSIjI/V+Xn1p8BDkarUaP/30E2bNmoVx48ZppqzNz8/HL7/8goyMDL0FSURE1Bh17doVq1evhlKprLVEQaFQQKFQ4Mknn9T7YF0A0KtXLzg6Our9vPrSoEQjLy8PvXv3xgMPPIAff/wRv/32G65duwaguhfKc889hyVLlug1UCIiosZo/Pjx2LdvH0aMGFGjzYYkSejUqRNeeeUVRERE6P3a7dq1Q9u2bfV+Xn1qUKIxd+5cnD59GjExMUhISNAqLlIqlZg4caLBRj8jIiJqbLp27YrffvsNSUlJcHNzAwDY29vjP//5D5566imDlGT4+Pigd+/e9W6QamwNSjR+/fVXPPvssxgyZIjON9imTRuDzVRHRETUWPn7+8Pe3h5A9QCWhuoYYW9vj8GDBzfaBqC3alCikZ+fj6CgoFq3V1ZWoqqqqsFBERERkW6SJGHgwIGahKaxa1CiERISgtjY2Fq3//nnnwgNDW1wUERERKRbREQEmjdvbuow6qxBicbMmTPx1VdfYfXq1Zr2GZIkoby8HK+99hq2bNmCWbNm6TVQIiKips7Pz69Rd2XVpUHjaDz//PM4ffo0pk2bBldXVwDAAw88gOzsbFRVVWHWrFl4/PHH9RknERFRk+bp6YnBgwc3+saft2tQoiFJEpYvX44ZM2Zg3bp1uHjxImRZRkhICCZPnox+/frpO04iIqImy9fXF8OGDYONjY2pQ6m3exoZtE+fPujTp4++YiEiIqLbtGvXDr179zaLHia66G0IciIiItIfpVKJPn36NPoBue6mzonGrZOo1YUkSdiwYUO9AyIiImrqXFxcMHjwYDRr1szUodyzOicaGzduhK2tLXx8fOo0cYy5NVYhIiJqDIKCghAdHW2W7TF0qXOi0aJFC6SmpsLDwwMPPPAApk6dCh8fH0PGRkRE1GRIkoTu3bujU6dOFvVjvc7jaKSkpGDHjh2IiIjAW2+9hZYtW2Lw4MH4+uuvUVhYaMgYiYiILJpKpcLw4cMRFhZmUUkGUM8Bu6Kjo7F06VKkp6dj3bp1aNasGZ555hl4eXlh/PjxWLduHcrLyw0VKxERkcVxdHTEmDFj4OfnZ+pQDKJBI4NaW1tj7NixWL16NTIyMjTJx5QpU/Dee+/pO0YiIiKL5OzsjDFjxmhmfLVEDUo0bigvL0dMTAw2bNiAY8eOwdbW1iBT4RIREVkaOzs7jBgxAo6OjqYOxaDqnWjIsoyYmBg88sgj8Pb2xrRp01BaWorly5cjMzMTDz/8sCHiJCIishgKhQJDhw6Fs7OzqUMxuDr3Otm3bx9++OEHrF27FtnZ2ejRowf+85//YPLkyfDw8DBkjERERBalZ8+e8Pb2NnUYRlHnRKNPnz6aYp5p06ZpqkiSk5ORnJys85guXbroJUgiIiJLERgYiNDQUFOHYTT1GoK8tLQUP//8M3755Zc77ieEgCRJUKvV9xQcERGRJXF0dER0dLTFdWG9kzonGl9//bUh4yAiIrJokiRhwIABUKlUpg7FqOqcaMyYMcOQcRAREVm0sLAw+Pr6mjoMo7un7q1ERER0d05OToiMjDR1GCZhlolGUlISHn/8cQQFBcHOzg4hISGYP38+KioqTB0aERFRDVFRUbCyqlezSIthlu/63LlzkGUZS5cuRatWrXDq1Ck88cQTKC4uxgcffGDq8IiIqAnz8fFBaWmpZiAuJycntGrVysRRmY5ZJhr33Xcf7rvvPs1ycHAwzp8/jy+++IKJBhERmdSRI0ewevVq5OfnAwDat2/fpHqZ3M4sq050yc/Ph7u7u6nDICIi0pAkCa1btzZ1GCZlliUat4uPj8cnn3xy19KM8vJyrdlli4qKDB0aERE1Yb6+vnBwcDB1GCbVqEo05s6dC0mS7vg6d+6c1jGpqam47777MGnSJDzxxBN3PP/ixYvh4uKieUVHRxvy7RARURMXEhJi6hBMThJCCFMHccO1a9eQnZ19x32Cg4NhY2MDALh69Sr69++PHj16YOXKlVAo7pw33V6iERcXh+joaBw9epTDpRMRkd6sXr0aBQUFeOihh2BnZ2fqcEyqUVWdeHp6wtPTs077pqamYsCAAYiMjMTXX3991yQDAFQqldaIbJY+NS8REZmOt7d3k08ygEaWaNRVamoq+vfvj4CAAHzwwQe4du2aZpuPj48JIyMiIqrWsmVLU4fQKJhlorF161bEx8cjPj4efn5+WtsaUU0QERE1Yc2bNzd1CI1Co2oMWlePPPIIhBA6X0RERKZmZWVV56YAls4sEw0iIqLGzN3dvU5tB5sC/isQERHpmaurq6lDaDSYaBAREekZezXexESDiIhIz2xtbU0dQqPBRIOIiEjPbgwsSUw0iIiI9I4NQW/ivwQREZGeMdG4if8SREREZDBMNIiIiPSMJRo38V+CiIhIzyRJMnUIjQYTDSIiIj1TKpWmDqHRYKJBRESkZxyw6yYmGkRERHrGqpObmGgQERGRwTDRICIiIoNhokFEREQGw0SDiIiIDIaJBhERERkMEw0iIiIyGCtTB0DGkZaWhrS0NFOHQXri6+sLX19fU4dBesLn0/LwGb2pSScavr6+mD9/vsX/MZSXl2PatGnYtWuXqUMhPYmOjkZMTAxUKpWpQ6F7xOfTMvEZvUkSQghTB0GGVVBQABcXF+zatYuj1VmAoqIiREdHIz8/H87OzqYOh+4Rn0/Lw2dUW5Mu0WhqwsPD+UdvAQoKCkwdAhkAn0/LwWdUGxuDEhERkcEw0SAiIiKDYaLRBKhUKsyfP5+NkiwE76dl4f20PLyn2tgYlIiIiAyGJRpERERkMEw0iIiIyGCYaBAREZHBMNEgIiIig2GiQWQAkiTV6bVz5857vlZJSQkWLFhQr3MtWrQIY8aMgbe3NyRJwoIFC+45DiJz0Zifz3PnzmHOnDkIDw+Hk5MTfH19MXLkSBw5cuSeYzEVjgxKZACrVq3SWv7222+xdevWGuvbt29/z9cqKSnBwoULAQD9+/ev0zGvv/46fHx8EBERgZiYmHuOgcicNObn8//+7/+wYsUKTJgwAU899RTy8/OxdOlS9OjRA1u2bMHgwYPvOSZjY6JBZAAPPfSQ1vKBAwewdevWGutNJTExEYGBgcjKyoKnp6epwyEyqsb8fE6bNg0LFizQmvfmscceQ/v27bFgwQKzTDRYdUJkIrIs4+OPP0aHDh1ga2sLb29vzJo1C7m5uVr7HTlyBMOGDYOHhwfs7OwQFBSExx57DACQlJSkSRQWLlyoKfK9W1VIYGCgId4SkcUw1fMZGRlZY3K9Zs2aoW/fvjh79qx+36SRsESDyERmzZqFlStX4tFHH8Vzzz2HxMREfPrppzh27Bj27t0La2trZGZmYujQofD09MTcuXPh6uqKpKQk/PLLLwAAT09PfPHFF/jnP/+JcePGYfz48QCAsLAwU741IrPX2J7P9PR0eHh46PU9Go0gIoN7+umnxa2P2549ewQA8f3332vtt2XLFq3169evFwDE4cOHaz33tWvXBAAxf/78esd1L8cSWYrG+nzesHv3biFJknjjjTcafA5TYtUJkQmsXbsWLi4uGDJkCLKysjSvG8WmO3bsAAC4uroCADZu3IjKykoTRkzUdDSm5zMzMxMPPPAAgoKCMGfOHINcw9CYaBCZwMWLF5Gfnw8vLy94enpqvYqKipCZmQkAiI6OxoQJE7Bw4UJ4eHhg7Nix+Prrr1FeXm7id0BkuRrL81lcXIxRo0ahsLAQGzZsqNF2w1ywjQaRCciyDC8vL3z//fc6t99oQCZJEtatW4cDBw7g999/R0xMDB577DF8+OGHOHDggNl+8BA1Zo3h+ayoqMD48eNx4sQJxMTEoGPHjg0+l6kx0SAygZCQEPz111/o3bs37Ozs7rp/jx490KNHDyxatAg//PADHnzwQfz000+YOXMmJEkyQsRETYepn09ZljF9+nRs27YNa9asQXR0dEPeRqPBqhMiE5g8eTLUajXeeuutGtuqqqqQl5cHAMjNzYUQQmt7eHg4AGiKZ+3t7QFAcwwR3RtTP5/PPvssVq9ejc8//1zTU8WcsUSDyASio6Mxa9YsLF68GHFxcRg6dCisra1x8eJFrF27FkuWLMHEiRPxzTff4PPPP8e4ceMQEhKCwsJCLF++HM7OzhgxYgQAwM7ODqGhoVi9ejXatGkDd3d3dOzY8Y5FratWrcLly5dRUlICANi9ezfefvttAMDDDz+MgIAAw/8jEDVSpnw+P/74Y3z++efo2bMn7O3t8d1332ltHzduHBwcHAz+b6BXpu72QtQU3N597oZly5aJyMhIYWdnJ5ycnESnTp3EnDlzxNWrV4UQQsTGxopp06YJf39/oVKphJeXlxg1apQ4cuSI1nn27dsnIiMjhY2NTZ260kVHRwsAOl87duzQ19smMguN6fmcMWNGrc8mAJGYmKjPt24UkhC3lfsQERER6QnbaBAREZHBMNEgIiIig2GiQURERAbDRIOIiIgMhokGERERGQwTDSIiIjIYJhpEjUxSUhIkScLKlStNHQoR6cBntH6YaBAREZHBcMAuokZGCIHy8nJYW1tDqVSaOhwiug2f0fphokFEREQGw6oTIgNYsGABJEnChQsX8NBDD8HFxQWenp544403IIRASkoKxo4dC2dnZ/j4+ODDDz/UHKur/veRRx6Bo6MjUlNTcf/998PR0RGenp546aWXoFarNfvt3LkTkiRh586dWvHoOmd6ejoeffRR+Pn5QaVSwdfXF2PHjkVSUpKB/lWIGg8+o8bDRIPIgKZMmQJZlvHOO++ge/fuePvtt/Hxxx9jyJAhaNGiBd599120atUKL730Enbv3n3Hc6nVagwbNgzNmjXDBx98gOjoaHz44YdYtmxZg2KbMGEC1q9fj0cffRSff/45nnvuORQWFiI5OblB5yMyR3xGjcBUs7kRWbL58+cLAOLJJ5/UrKuqqhJ+fn5CkiTxzjvvaNbn5uYKOzs7MWPGDCGEEImJiQKA+PrrrzX73JjR8c0339S6TkREhIiMjNQs79ixQ+cMrLefMzc3VwAQ77//vn7eMJGZ4TNqPCzRIDKgmTNnav5bqVQiKioKQgg8/vjjmvWurq5o27YtEhIS7nq+f/zjH1rLffv2rdNxt7Ozs4ONjQ127tyJ3Nzceh9PZCn4jBoeEw0iA/L399dadnFxga2tLTw8PGqsv9uHia2tLTw9PbXWubm5NehDSKVS4d1338XmzZvh7e2Nfv364b333kN6enq9z0VkzviMGh4TDSID0tX1rbbucOIuHcDq0o1OkiSd629tjHbDCy+8gAsXLmDx4sWwtbXFG2+8gfbt2+PYsWN3vQ6RpeAzanhMNIgsiJubGwAgLy9Pa/3ly5d17h8SEoIXX3wRf/75J06dOoWKigqt1vVEpF9N8RllokFkQQICAqBUKmu0jv/888+1lktKSlBWVqa1LiQkBE5OTigvLzd4nERNVVN8Rq1MHQAR6Y+LiwsmTZqETz75BJIkISQkBBs3bkRmZqbWfhcuXMCgQYMwefJkhIaGwsrKCuvXr0dGRgamTp1qouiJLF9TfEaZaBBZmE8++QSVlZX48ssvoVKpMHnyZLz//vvo2LGjZp+WLVti2rRp2LZtG1atWgUrKyu0a9cOa9aswYQJE0wYPZHla2rPKIcgJyIiIoNhGw0iIiIyGCYaREREZDBMNIiIiMhgmGgQERGRwTDRICIiIoNhokHUhCUlJUGSJKxcudLUoRCRDpbwjDLRIKqjS5cuYdasWQgODoatrS2cnZ3Ru3dvLFmyBKWlpQa77pkzZ7BgwQIkJSUZ7Bp1sWjRIowZMwbe3t6QJAkLFiwwaTxEt2vKz+i5c+cwZ84chIeHw8nJCb6+vhg5ciSOHDlisphu4IBdRHXwxx9/YNKkSVCpVJg+fTo6duyIiooK/P3333j55Zdx+vRpLFu2zCDXPnPmDBYuXIj+/fsjMDDQINeoi9dffx0+Pj6IiIhATEyMyeIg0qWpP6P/93//hxUrVmDChAl46qmnkJ+fj6VLl6JHjx7YsmULBg8ebJK4ACYaRHeVmJiIqVOnIiAgANu3b4evr69m29NPP434+Hj88ccfJozwJiEEysrKYGdnp/dzJyYmIjAwEFlZWTWmwiYyJT6jwLRp07BgwQI4Ojpq1j322GNo3749FixYYNJEg1UnRHfx3nvvoaioCCtWrND6ALuhVatWeP755zXLVVVVeOuttxASEgKVSoXAwEDMmzevxkRIgYGBGDVqFP7++29069YNtra2CA4OxrfffqvZZ+XKlZg0aRIAYMCAAZAkCZIkYefOnVrniImJQVRUFOzs7LB06VIAQEJCAiZNmgR3d3fY29ujR48e9/Rha8rSFKI74TMKREZGaiUZANCsWTP07dsXZ8+ebdA59YWJBtFd/P777wgODkavXr3qtP/MmTPx73//G126dMF///tfREdHY/HixTonQoqPj8fEiRMxZMgQfPjhh3Bzc8MjjzyC06dPAwD69euH5557DgAwb948rFq1CqtWrUL79u015zh//jymTZuGIUOGYMmSJQgPD0dGRgZ69eqFmJgYPPXUU1i0aBHKysowZswYrF+/Xg//KkSNB5/R2qWnp8PDw0Nv52sQQUS1ys/PFwDE2LFj67R/XFycACBmzpyptf6ll14SAMT27ds16wICAgQAsXv3bs26zMxMoVKpxIsvvqhZt3btWgFA7Nixo8b1bpxjy5YtWutfeOEFAUDs2bNHs66wsFAEBQWJwMBAoVarhRBCJCYmCgDi66+/rtP7E0KIa9euCQBi/vz5dT6GyFD4jNZu9+7dQpIk8cYbb9T7WH1iiQbRHRQUFAAAnJyc6rT/pk2bAACzZ8/WWv/iiy8CQI1i0dDQUPTt21ez7OnpibZt2yIhIaHOMQYFBWHYsGE14ujWrRv69OmjWefo6Ignn3wSSUlJOHPmTJ3PT9SY8RnVLTMzEw888ACCgoIwZ86cezrXvWKiQXQHzs7OAIDCwsI67X/58mUoFAq0atVKa72Pjw9cXV1x+fJlrfX+/v41zuHm5obc3Nw6xxgUFKQzjrZt29ZYf6M49/Y4iMwVn9GaiouLMWrUKBQWFmLDhg012m4YG3udEN2Bs7MzmjdvjlOnTtXrOEmS6rSfUqnUuV4IUedrGaKHCZG54DOqraKiAuPHj8eJEycQExODjh07Gu3atWGJBtFdjBo1CpcuXcL+/fvvum9AQABkWcbFixe11mdkZCAvLw8BAQH1vn5dPxBvj+P8+fM11p87d06znchS8BmtJssypk+fjm3btuGHH35AdHR0vc9hCEw0iO5izpw5cHBwwMyZM5GRkVFj+6VLl7BkyRIAwIgRIwAAH3/8sdY+H330EQBg5MiR9b6+g4MDACAvL6/Ox4wYMQL/384ds7QOhWEcf65BWwqCdOhWaOtkF50KxSHUTVzaIjhJuzjWuY6Ci3OhQ5yK0iLSzU/QwSEfoVCC38AOSqHDewdBCNfeS4aj93L/P8iSnJOT5YQnyXkThmHsxvv6+qogCFQoFFQulxNfB/C3Yo6+63Q6ur+/V7/fV7PZTNzfFT6dAH+wvb2t4XCok5MT7ezsxP46+PT0pIeHB7XbbUnS7u6uWq2WgiDQy8uLfN9XGIYaDAaq1+uq1WqJx9/b25Pnebq+vtZ8PlcqldLBwYFyudzKPt1uV6PRSIeHhzo/P1c2m9VgMFAURRqPx1pbS/6McXt7q+fnZ729vUmSJpOJrq6uJEmnp6e8JcG3YY6+B6d+v69qtapMJqO7u7vY8Uaj8RGIvty31rwA/5DpdGpnZ2dWKBRsY2PDNjc3bX9/33q9ni0Wi492y+XSLi8vrVgs2vr6uuXzebu4uIi1MXsvezs6OvplHN/3zff92L6bmxsrlUrmeV6sjG7VOczMZrOZHR8f29bWlqXTaatUKvb4+Bhrk6R0zvd9k/Tp9llZH/DV/uc52mq1Vs5PSRZF0W/7u/TDLMGKFgAAgARYowEAAJwhaAAAAGcIGgAAwBmCBgAAcIagAQAAnCFoAAAAZwgaAADAGYIGAABwhqABAACcIWgAAABnCBoAAMAZggYAAHCGoAEAAJz5CQKY3wHjPJ3SAAAAAElFTkSuQmCC",
-      "text/plain": [
-       "<Figure size 600x600 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "multi_2group = dabest.load(df, idx=((\"Control 1\", \"Test 1\"),\n",
-    "                                         (\"Control 2\", \"Test 2\")\n",
-    "                                       ))\n",
-    "multi_2group.mean_diff.plot(color_col=\"Gender\");"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "562245e3",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 375x400 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "two_groups_paired_baseline = dabest.load(df, idx=(\"Control 1\", \"Test 1\"),\n",
-    "                                  paired=\"baseline\", id_col=\"ID\")\n",
-    "two_groups_paired_baseline.mean_diff.plot(color_col=\"Gender\");"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "bccd01be",
-   "metadata": {},
-   "source": [
-    "### Adding a custom palette\n",
-    "The colour palette for the graph can be changed using the parameter `custom_palette`. All values from matplotlib or seaborn color palettes are accepted."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "8a6a82fd",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 600x600 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "multi_2group.mean_diff.plot(color_col=\"Gender\", custom_palette=\"Dark2\");"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "c87743ed",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 600x600 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "multi_2group.mean_diff.plot(custom_palette=\"Paired\");"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5d1c2921",
-   "metadata": {},
-   "source": [
-    "Additionally, a customized color palette can be defined by creating a dictionary where the keys are group names, and the values are valid matplotlib colours.\n",
-    "\n",
-    "There are [many ways](https://matplotlib.org/users/colors.html) to specify matplotlib colours. Find one example below using accepted colour names, hex strings (commonly used on the web), and RGB tuples."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "33271a43",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 600x600 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "my_color_palette = {\"Control 1\" : \"blue\",\n",
-    "                        \"Test 1\"    : \"purple\",\n",
-    "                        \"Control 2\" : \"#cb4b16\",     # This is a hex string.\n",
-    "                        \"Test 2\"    : (0., 0.7, 0.2) # This is a RGB tuple.\n",
-    "                       }\n",
-    "\n",
-    "multi_2group.mean_diff.plot(custom_palette=my_color_palette);"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "032b975b",
-   "metadata": {},
-   "source": [
-    "## Changing colour saturation\n",
-    "\n",
-    "By default, ``dabest.plot()`` [desaturates](https://en.wikipedia.org/wiki/Colorfulness#Saturation)\n",
-    "the colour of the dots in the swarmplot by 50%. This draws attention to the effect size bootstrap curves.\n",
-    "\n",
-    "You can alter the default values with the parameters ``swarm_desat`` and ``halfviolin_desat``.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "3db70141",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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Ou7i4oH///vj9999L/T4HDhzA/PnzS30eSyCKIhYsSEXjxvcwYsRDDBkSh2bN7mHbtkwMG+YENzcJnm1AEgTgpZccUaUK5/ITmYJb1ecUHZRI4FYjxGixEOmLURKN5cuXY+TIkfj8888REhJSYH/9+vU1LRykH0uWpGH+/FTI5U+SupQUFSZOTMSlS3nYuNEXTZo8abmwsxPw+uvOmDOHhZ+ITMUluDYq1GmsKUmuIUggtZbBv/MgiKKI5GuRuL5xEW78vgzp0ddMEyxRERlljMa9e/fQooXuyoaAemxFejqbBPVFLldh8WLdi2xJJHhUP8MX69b54OHDfKSmqlClihXs7c12bDBRmSJz9dD6ry4qZT7u7f0dd/ZshjwlHo6VgxHYcwTCPvgO576fioRzhzXH2nl4I+TdL2Hj7IZTn49HQuQRTTJyff0PqNy2D+q/MYszUqhMMkqi4eXlhXv37hW6/8yZM/Dz8zNGKOVCVFQ+MjJ0D+hUqYBz5+Sa597eVvD2NlZkROVDqy/XP3e/qFLh3HeTEffvXqhXcBWRfPUski+fRo1h76DJtB+RFXsHGXdvwsbZDW41QiBIJLi08gsk/Hfs0TmUmvPdP7ANjlWCEdx7tOE+FFEJGeVP2P79+2PJkiW4ffu2ZpsgqEso7969G6tXr8bAgQONEUq54OhYcDnxp9nZPX8/ERlWQuTRR0kGoFkp71H1vGu//YCcpDg4+PrDp2kHuNdqCEEigVKeg7t7fwdElc5zRv31qxEiJyo+oyQas2fPhq+vL0JCQjBy5EgIgoAvv/wSrVq1Qrdu3VC/fn1MmzbNGKGUC5UrW6NuXRudM0ukUqBPn/JZqIyorHhwbFfBcRgaIuJO7C2wNTc5ASqFXMfxavLkeCgVeXqKkEriVto9TD/xPfr+9TbG/TML+++f1Jr8UF4ZJdFwcXHBiRMnMHnyZMTExMDW1hYHDx5EamoqZs6cicOHD8Pe3t4YoZQbn35aATY2gtbMEqkU8PKS4t13WWKcyJCOfDgE+8Z1xJEPh+jcr8zNgVhIy4QgqFsvnmXj4v6c5ASwdnDm6q8mFHH3KNr8PhIrL2/B8bj/8Gf0AQyJ+ADTjs8v98mG0Ub/2dnZYfr06YiMjERWVhZycnJw8eJFfPzxx7CzszNWGOVGSIgMO3b4YsAAR3h7S1GlihVef90F27dXhKen2VaeJzIL8tRE5CbHQ56aqHP/86apiiol3GqEFthube8I3xZddScbggR+nQdquqTJuLIUOXjrwKdQiiooHyWQj/+78spWHIwp2sKfloq/cSxYcLANvvyy8FHvRGQaVdr1xa0/VkCRma41qFOQSOFStQ7ca4fpfF2dVyYj/c41ZN67qUk4RJUS7rUaotqAcUaJnQraffcYMhXZOvdJBQnW3/gbbSs3MXJUZYdREo0xY8a88BhBELBixQojRENEpiaKIuSZcljbWUNqVf6mZFo7OqP5J6sR+f1UpN2+/GirAK+wNqj/5icFWiYyY6KQHX8f9l6V0erL9Yg7sQcJkUchSK3g3bgdvBu20ZrampeRiju71iP2370QVUp4NwxHQPdhsHX30hyjUigQvWsd7uzeBHlqIhwrBSGo1wh1qwlbRoolMTcFAgSIKNhFohRViM9JNkFUZYdREo1//vmnwA+uUqlEbGwslEolPD094eDgYIxQiMyeIlcBZZ4SMieZwX4hKPOVuHX0FrJTs1G5fmV4BOqnZUylUuHQkkM48MMBpMWmwdrOGk2GNkH3Gd1h71q+xmk5VgpEqy/XI+PeTeQmJ8Choj/sPStqHZOTGIdz309BypWzmm1uNUMR+s4XqNS6h87z5qYk4Oi04chNeqiZoZIZE4W7/2xBi89+gWPFAIhKJU5/MxEJ545oFj5Ku30Z5+Z/iMz7t1F98HgDfWrLVNs9WGeSAQBSQYq67tWMHFHZYpREIzo6Wud2hUKBpUuXYv78+dizZ48xQiEyWw+vP8SfM//E5YjLEEURPjV90G1aN9Tv9Zyy1SVw7cA1rH1jLTIePll3o16Pehi2eBhsnUq3Ds4f0/7A4aVPClEpchQ4/vNx3Dp+C+/tfQ829jalOr85svOoCCtbB9i4alflVSrycGLWq8hJeKC1PfX6eZyY/SrafPcHpNYF/72urfse8uR47WmwKhXyszJwedWXaPLRYsSfPYSEs4e1X/jo+Bu/L0OV9v1h5+mrnw9YDrTwCUEttyBcT70DpfhUVxgESAQBo2v1MWF0pmfSUpDW1taYMGECOnfujAkTJpgyFKIyLTEqEfM7zceVPVc0I9jjrsVh1chVOLW+eAPNMhIy8O+v/+LoiqOIuxqnte/htYdYPng5MhMytbZf3HURv77+4joNKqUKl3Zdwp55e3BizQnkpD2ZPZF8NxmHlx3W+Zq4K3E4s+lMsT6HuctLT0HkwmnY/Uor/PNWF+x9NRxX1y2ASqEAAMT9uxfZD+9pjeEA1GMysh/ef6oOx1P7lEo8OPJ3gdc8fl1C5DHkZaThwfHdz53BEney4LmpcIIgYG3nL1HDLUD9HOqWRkdre6zqMAdBLlVMGJ3plYnBoA0aNMCaNWtMHQZRmbX3273Iy8qDSvnUX6mPWmq3f7wdDQc0hNT6xWMd9s3fh52f7VSfR12QEg16N8DLS1+Gta01Di09BFElQlRpNwOLShGXdl1C/I14eFXz0nnuxKhELOm/BEnRSZBIJVApVdgyeQuGLRmGkD4huLz7ss7XAeov6os7L6L5qOYv/AyWQCnPxfGZY5D1IFqTFOTnZOHWHyuRHXcfDSd9jZRr/0GQWkFU5hd4vSC1QsrVSFRq1V1ruypfAVX+82ppiMjPyYQqL/c502sFKPMKr9dBulVy9MY/fVfieNx/uJx8C552bujs1xJ2VjJTh2ZyZWJxiz179rCOBtFzXNh5QTvJeEpmQibun7//wnOc23oOO2bveHKeR7nE+R3nsW3GNgBA1MkoqPJ1vw8A3D17V+d2lUqFZQOXIeVeivr5o/dQyBX45dVf8PDawwLJy9NEiFCpCn9fS/Pg2C5k3r9VsOVBFBF7PALp0ddgZWuvGT9RgCiq9z9DKrOFQ8VAALrH7tg4u8G2gjfcazcqNDZRpXrufiqcIAho4RuCsXUGoE9QeyYZjxilReOTTz7RuT01NRWHDh3C2bNnMWXKFGOEQmSeXlTvpwj1gPYv3A9BIhRsrVCJOPHLCfSY3gP2rvY6j3nMzkV3zZvrB64j4VaCzrgECDi68ihav976uXHW6VznxR/CQiScOwoIEt3lxCUSJEQeRcWWXXHrD90z8USVEr4tu+rcV3XAa/hvoe5Ky8F9x0AitULl8N649cdK5KUlayc7EgncazaEW/UGxf5MRIUxSqIxa9Ysndvd3NwQHByMJUuW4LXXXjNGKERlkpOXk9Z/n1Wnax2c2XhGZ6uGg7sDKtWvBEDdfXHilxNIuJUA10quaDq8KSrWUc9kiL0UW2gCocxTIuF2AhoPaYxbR28VPEAA7JztUKNdDZ2vf3j9YaEJikqpQtzVOHgGe6LJy01wct1JrYRDIpXAzc8NjYaUn7+iBckLZgsJApwDaiCwxwhE/bXmSVLy6L8B3YfDJbAmlIo8xB6LQPzZw4AowqthK/i26Iq8tGRcW78QqkddIILUCkG9RyOw50gAgLWDE1p8+jMif5z+ZEaLRIKKLbqg7mszOL2V9MooiUZ5ahI1N3l5InbvzsbhwzmQSIBOnezRtq0dJC/6IiS9en//+8/d3/G9jvhv23/Il+cXSDZ6fNwDVjZW+G/7f/hlzC8A1PecRCLBoSWHMODrAWg1thUcKjggLTat0PdwrOCIRoMb4dzWc7j2zzXNGA6JVAIRIob+OBRWMt1fGc7ezoUmMRKpBC6+6rL3g+YPgltlNxxcchC5abmQSCVo0KcB+n7et9QzWsyJV1g4HhzdpXunSgWvsHAAQK1RH8C1Wl1E71qP7Lh7sPepAv8ug1GxZTcostJxfOaryLhzDZBIABGIPR6B23/+guazV6FKh/5IunQKokqJCrUbwcbJVett7L0ro8Unq5H98D5yUxLg4OMH2TMzX6h4rqZEYdXlrTifdB0+9hUwpHp3dK7SotwnboJYjouwnz17FmFhYThz5gwaNmxo6nCMLjVViaFD43D1qkKzJopSCbRsaYsVK7wgk5WJITz0SMyFGGybsQ03Dt4AAFQIqICuU7qi0eBGyEzKxOw6s5Gfl1+we0IApv47FWd/P4vdX+8ukBBIpBIENAnA2zvfBgAoFUr8u/ZfnFx3EllJWQhoHIA2b7ZBlQZVIM+U48iKIziz6QzkmXJUa10Nbce3hbu/O2bVnoXctFyd6zqM/3M8qraqqnmen5ePjPgM2LvaQ+Zoef3Y+8Z1RG5yPGzdvdBhacEZHCqFAsdmjERa1BXNqq2PVWnfD/XfnP3C97iw7FPc3fd7gdcLEikqt+2D+m/OKtVnoOLZGX0IY/+ZCQBQikpIBQmUogojavTC1y0/KNfJhkESjbt3dQ8YexE/Pz89R/J85SHRkMtFXLuWB2trATVqWGu1VLz3XgL+/DMLymfGo0kkwPjxLpg0yc3I0VJR5KTlQJGrgJOXk+bL69CyQ/hjyh86f8lLpBK0ndAWXSZ3weK+ixF9KlrTzSFIBDi4O+Dtv9+GV1Xds0key83IxcJuCxF7OVbzPhKpBBKpBG9seQP5inz8NOQnKPOVEJUiJFYSqPJV6DipI3rM0F1cylK9KNEAAEV2Jm5sXIS7+7ZAmZsNmasHAnsOR1DPUZoqn6JKhdQbF5CXmQbngOqwq+ADQJ2oRIxuoekaeZbEygadfz4KqY3lJXFlUZYiG3XX9UVOfq7OYUjrOn+FDlWaGT2ussIgXScBAQElyt6Uz/7GI516936AhAQlPD2l2L69os5jRFHE6tUZmD8/Fenp6r94KlWSYtasCujY0R4ZGSrs2FEwyQDUfyD9+msG3nvPtVxn4cY0r908ZMRnwMnL6bndKDEXYvDftv+gyFUgqEUQ6nSpA4lUgrQHaRCsBIiKgl9zIkSkxabBxt4G4/8cj7O/n0Xktkjk5+ajWutqaD66ORw9HF8Y44FFBxB7JVYrmVEpVRBFEevfWY9pp6dh2ulpOP7zccRejoWjpyOaDG2CwKaBJftHsXDW9o6oPXoyao14H8q8XEht7bXut+QrZxG5cNqTgl2CgIotu6HeuI+hUuQVmmQAgCo/D/nZmUw0jGTX3aPIzs/VuU8qSLDhxt9MNPRt5cqV/AVlQAkJSsTFPT8p++WXDHzyiXZ9/QcPlBg3Lh5r1/rA21uK/ILT8zVSUlRQKACb8leo0SQy4jOQ9qDw8ROiKOL3yb/j6E9HIbFSd2kd+PEAKtapiDf/eBPeNbyhUhQyFkoEvKt7AwCsZFZoMqwJmgwr/gJPp347pXMchqgSkXg7EffP30eVBlXQ/aPuOl5NhRGkUljZaS/BkBV7F/9+Nk5TvAsAIIp4cGwXVIo8NHzva1g7uUKRkarznFYOTrB2cjFg1PS05Ny05651kpRb+L1dHhgk0Rg9erQhTktFpFCI+P771ALbRREQBGDhwlQsX+4FmQyQF/JHkbe3FDY2TBbLipNrT+LoT0cBQKvORdzVOKx/ez1GrhiJ7dO3Izs1WysZEAQBUmspmg5vWuoYcjN0/8Wm2Z/+/P1UdNF/r4OYn19w+qtKpa4YGn8fgd1fxvUNi1BgUI4gILD7y5BIy0Q9xnKhrnvV56x1IkG9CuV7rROO9rNAt28rkJys+69blQo4eTIX9vYSDBzoCImOnwBBAEaPdjZwlFQch5Ye0lmDSaVU4VLEJWSnZGPc7+Pg4K7+y/hxq4eNgw3G/jYWzt6lv56BTQIhker+ypBaS1Gxru5uPCq+5CtndZYRfyzl+n8I7vcqKoX3Um+QSPD4Zq7UqjuqDnjdGGHSI818GqCue1VIBe3qvAIESAUpRtfqa5rAygijprxHjx7F2bNnkZaWVmDKqyAImDFjhjHDsVgy2fNbIh63VEyd6o6oqHwcPZqrNeukTx8HjB3LRKMsSYpOKrzYlaheRySoeRBmXpyJ8zvOI/F2IlwruaJB7wZ6m9XR/t32uBxRsIy4IAho8UoLOLhxBeaSyLh/G/KUeDj4BsDOQz3Y08reUZ3xFzJW38rOERKpFUImfIbgPqPVdTQgwiu0NZz8yvdfz6agXuvkK4zcOxX/JV7TbHeVOWFJ248R4FzJhNGZnlESjeTkZPTo0QMnT56EKIoQBEEzoOzx/2eioT/+/laoXt0aN28qnp35BqkU6NFDXbrY3l6CNWu8ceqUHIcO5cDKCujY0R5163IAWVnjVsVNvQBaIcmGW2X1DCErmRUaDijaDKrMxEyIoggnT91FwjISMnBuyzlkxGfAt5Yv6veqj5GrRmLzpM3ISs4CAAhSAc1HNkefz8r36pQlkfkgGpELpyHt5sVHWwR4N2mHBm9+gkqtuyP58mmdr7Oyc4BngydrwjhVqQqnKlV1HkvG4+PggYjey3A6/hKupNyGh60bOlRpCpmUA92Mkmj873//w/nz57Fu3To0bdoUQUFBiIiIQGBgIL777jscP34cf//9tzFCKRcEQcDs2e4YMeIhBAGamSVSKeDqKsE777hpHdukiS2aNDF8sSSFQsT27VnYvj0TWVkiGjeWYcQIZ1SsyL7kF2k1thU2v7+5wHaJVIJq4dXgVkV9TVUqFW4evomYCzFwqOCA+j3qw9ZZ+9reOHQD2z/ejvv/qddHqVi3InrN6oWaHWpqjjm94TTWv70eqnwVJFYSKBVKOPs4440tb2DWlVm4dewW8rLz4B/mr5dumfJGkZWO4x+PhiLj6UGCIuJPH8TJuePRbNYKxBzeieQrZzStGoJEClFUoe7rMyCV6S4FT6YlCAIae9dFY++6pg6lTDHKN/zOnTsxbtw4DB48GElJSQAAiUSCqlWr4scff0T//v0xceJE/Pbbb8YIp1xo1swOW7b44scf03DkSA6srQX06OGAN990QaVK+r/sCQlKrF2bjkOHcmFjA/To4YCXXnKEnZ2631guF/Hqqw9x9GguJBL1WJHISDnWrMnA2rU+aNCArSjP03x0c0Sfisbp9ac14y9U+Sq4B7hj6A9DAQCpMalYOnAp4q7EaepkbLbbjKE/DkVov1AAwM0jN7G4/2KtlpHYS7FYNnAZXt/0Omp2qInYK7FY99Y6zaBSpUKdqWYkZGD5oOX46NxHqNFWdylyKpp7+7chLz2lQNeIqFIi9fp/SL1xHk2mL8Hd3Rtxb/8fUGSkwaVqXQT1Ggn3mqEmippKa++941h0YT0uJt2Ep50bXq7RA6/WHmDxrR5GSTRSU1NRp456wSRHR/V8/czMTM3+zp07Y9o03YsAUcnVqyfDkiXPL8JUFAkJ+di4MRM3bijg7S3FSy85olq1JzfGrVt5GDgwDmlpKqhU6q7lkyflWL8+A+vX+8LJSYJff03HsWPqWQmPu3OUSiAnR8SkSQnYu7cSp0Q/h0QiwbBFw9BidAtE/hEJRY4CwS2C0aBPA1jJrCCKIn4a9hPir8cDgCZJUOQqsOa1NfCu5o2KdSvir0//AkRozUx53HW545MdqNmhJo6tOqZ78TWliJT7Kbi67yrqdCk/C6AZQsrVc4XuEyRSpFw9hwq1GyGwx3AE9hhuxMjIUJZf2ozpJ76HRJBAJaqQlpeBT04uwT/3T+K3Ll/DWmK5LbtGmXVSsWJFxMXFAQBkMhm8vLzw33//afbHxMQU+5fM4sWLUb9+fTg7O8PZ2RnNmzdn94sBHD2agzZtYvDtt6nYsSMLK1ako3PnB1i1Kl1zzNSpSUhPV2kSCFFUP65eVWDxYnXT8MaNmbpOD5UKuH07H5cu5Rn8s5g7QRAQ2DQQ/eb2w6D5gxA2KEyz9kj0yWjEnI8puOjao9VTj6w4AnmmHNEno3XXwhBFxJyPQWZSJuJvxBe6VLwgEZBwU8cqrVQsUls7CILur19RVLFrxMIk5aZi9slFAADVU1OWRYg4/OAMtt3+x1ShGYVRUqjWrVtjz549+OijjwAAgwcPxldffQWpVAqVSoX58+ejS5cuxTpn5cqV8cUXX6BatWoQRRE///wz+vTpg3PnzmlaT6jo8vJEWFtDK+HLzlbhzTfjIZeLBQa/f/JJMpo2lcHVVYpTp3QX41CpgA0bMjB5shuSk5WFDaAHgEKn49IToiji9vHbTyqDNg9CSN8QWNtaI/ZKbKGvUylVeHDxgc7psc8SBAFuVdwgkUp0rhQrqkS4VnItxacgAPBt3gUxB/8sdL9Ps45GjIZepPO21xCfnQwve3fs7rO82K/fffcYFCrdFRIlkGB71H68VLVzacMss4ySaLz//vvYs2cP5HI5ZDIZZs2ahUuXLmlmmbRp0wYLFy4s1jl79eql9XzOnDlYvHgxTpw4wUSjiERRxJo1GVi+PB337+fDyUnAoEFOmDjRFY6OEuzalY2MjEKK0EiBDRsyMXjw80tXPy5/XqeODEeO5OgseS4IQPXq1qX+PJZMpVRh3VvrcGbjGc0YjRO/nMDur3dj/J/j4exV+IBMQSrA2dsZMgcZglsG4/bx2wVaNQSJgCqhVeDg7oBmI5rh3zX/FjyPRICdix3qdOX9VVpeoa3g06QD4k7uw5NlctWDl6oPHq9Z04TKhvjsZMRmF60lT90tkgkHKzvYSNXfazn5uYVWDlVBhSxFjl7jLWuM0nUilUoxadIkyGTqAX9ubm7Yu3cvkpOTkZaWhgMHDsDX17fE51cqlVi/fj2ysrLQvHnzQo+Ty+VIT0/XPJ4eJ1IezZmTgpkzk3H/vjrTzsgQsWpVOl5+OQ5yuYjY2HxNfY1nKZVAbGw+/P2tYWur+09liQSoVUs9luP11511JhkSCdCrlwN8fCy3f1Ifjq8+jjMbzwBQDwJ93LWRfCcZv034DTU71oRDBQcIkoLXQlSKmsqgPT/uCYlUonWcIBEgCAJ6zVIn7wGNA9DjY/UiaBIrCQSpAEEiwNrWGmPWjIG1LZPC0hIkEoRO+hp1xk6Dk19VWDu6wK1GCMI++A7VWGzLLKlEFRZfWI8Gv/VHzV97IviXrph0+Esk5aaiqXf9QiuHSgQJmvs0MHK0xmWUb/e6deuiXr16GDx4MAYNGoSqVdVzvl1dXUt13gsXLqB58+bIzc2Fo6Mjtm7ditq1axd6/Ny5czF79ouXXy4P7t9XYOXK9ALbVSrg/Pk8/PVXFgIDrXUmB4C6RSMgwBoODhIMH+6EFSvSC3SNqFTAuHHq9RZatLDDF19UwMyZyZDLnxzYrp0dPv+8gt4+l6U68tMRzR++T1MpVbi+/zrS49IxauUoLBu07MnqqY+6P1qObYnaXdT3RUCTAIz/czx2frYTN4/c1GzrMb0HglsGa87b8b2OqN2pNk7+dlJTR6Pp8KZw8tJdc4OKTyK1QkCXIQjoMsTUoZAezDixED9d/l3zPE+lwPobf+Pfhxewu88ydKzcDP/EnNQaoyEVJHCydsDImr1NEbLRGCXRWLx4MTZu3IiPP/4YM2bMQEhICIYMGYJBgwbB39+/xOetUaMGIiMjkZaWhs2bN2PUqFE4ePBgocnG1KlTMWnSJM3zyMhIhIeHl/j9yzq5XIUdO7Jx9Kh6emuXLvZo29YOEomAvXsLb6qTSIA9e7KxYIEnPD2lSE5W6kw4hg5V/9L53//ckJqqxO+/Z2mSDWtr9fYePZ5Uixw82AndujngwIFsZGWJaNhQhho1LHtal76k3E8pvDIo1FNbq7Wphqknp+LYqmOaOhqNhzZG9fDqWmNvApsGYvyf45GXnQdRFCFz0D21uGLdiug7p6+ePwmR5bmbEYsVTyUZjylFFW6l3cPGGxFY1n4WJh/9Fltv74XyUbJR2z0Y37eZBi97y/5jyyiJxrhx4zBu3Dg8fPgQmzZtwsaNGzFlyhRMmTIFTZo0wZAhQzBw4EBUrFi8tRJsbGw0rSNhYWE4deoUFixYgKVLl+o8XiaTabpvgCdTbc2Np6dU67+6JCTkY/DgOERFPen+2LgxE+Hhtli2zBsqlVhohWNRBPLzRdjYCFi92gsjRz5EUpIKVlbqLhMrK+Dbbz0RGKhuQrexEfD115545x1XnDiRCxsbAeHhdnB1LRifs7MEvXvr/nd/8CAf69Zl4PLlPLi5STBggCOaN7fltFcAHoEeeHDpgc4ZIxAAdz93AOr/9pzZs0jntLFnkkekD/vvn3zu/oi7R/FK7X74se10zGzyJq6n3oGHnRtqugWW+D1FUcS11Ggk5aaimqs/vOzcS3wuQzNqx7i3tzcmTJiACRMmICYmRpN0vP/++/jggw+geHpJ5BJQqVSQF7YcqQXZvv3FCdlHHyXh7l312IunWyMOHcrFsmVp6NzZHipVSqGvb91aPb2udm0ZjhypjL//zsaNGwp4eUnRu7cD3N0LJhFVqlijSpXC+++TkpR4+FCJSpWkcHHRfv2JEzkYPToeCoUIlUrdNbNlSxZGjXLCzJnu5T7ZaD2uNdZPWF9gu0QqQa3OtTgThMiECht/oWuvl32FUrdgXEq+hbcPzsGlZHX3p1SQYEBwZ3zR4j04WJe9qdEmG4Hn6+uLOnXqoFatWrh48SKysrKK9fqpU6eiW7du8PPzQ0ZGBtatW4cDBw4gIiLCQBGbj8REJfbuzSm0teLXXzMwYYIreva0x19/ZWsdJ5UCFStaoX//J60OtrYS9OunuxUiN1eFLVuysHNnFuRyEa1a2WHYMCet1pbERCVmzEjC7t3ZUKnULSK9eztg1qwKcHKSIC9PxIQJCZokA3iSHP38cwbatLFD+/b2pf53MWdNhjVBzPkYHF52WL2CqqAeFOpb2xdDFqr7+EVRxK2jt3Av8h7s3exRv2d92Llof+kk3UnC3m/34vyf56FSqlCrYy10er8TfGuXfDA2UXnXtlLj5+7vVKXwSQrPSspNxU+Xfsf2qP1QqPLRoXJTjKs7GAHO6j8wH2Ynou9fbyNLka15jVJUYfOt3UjLy8AvneaW7EMYkFETDVEUceDAAWzYsAFbt25FYmIi3NzcMGTIEAwePLhY54qPj8fIkSMRGxsLFxcX1K9fHxEREejUqZOBojcfCQnPr1mRkKD+LT5vnicqVkzBr79mIDtbhEQCdOpkj1mz3OHo+OIJSZmZKgwbFocLF/I03TBnz8rxyy/p2LjRF8HB1sjLEzF0aByiop4s8JafD2zbloU7d/KxaZMPDh3KQVKS7joaj6fRlvdEQxAE9P+yP5qPao7IbZFQZCsQ3DIYtTrVgkQqQVpcGpYPWo6YCzFPyo+/vxmDFwxGo8GNAAAJtxMwv8N85Gbkampk/LftP1zceRHjd4yHf1jJx0uR8WXcv41bW37CwzMHIQgCvJu0R9V+Y+Hg62fq0MqdAOdKGFmzD365uk2r9UIqSODn5Ish1boV6Tzx2Uno9ucbeJCVoBk0+vPVbdh4MwLbe/yAOhWq4uer25GpyNYaVAqoZ71E3D2KK8m3Ucs9SF8fTS+MkmgcPnwYGzduxObNmxEfHw9nZ2f07dsXgwcPRseOHWFlVfwwVqxYYYBIzUPv3g+QkKCEp6dUZzdKpUpSWFmpf6E/SxAAPz/1v7eNjYCpU93x3nuuiI1Vws1NUmBcxeXLedi3T93q0aaNHUJCnoxxWbw4TVPR83Fio1IBaWkqTJ2aiI0bffH331m4ebNgl5hSCZw5I8fRo7maxEcXpRJ4+FB3oRtL8ng2x4tmdfjW9tXZ+rBq5CrEXlYX7Xo8jiNfno91b66Ddw1vVAmpgp2f7dRKMgD1rBVRFPHH1D/w7u539fVxyMDSoq7g+IxRUCkUEFXq+yfm4J+IO7EHLT5fC6fKZesXTXkwt/lEVHTwwpKLG5AiT4eVIEXfoA6Y2fQtONqo/1DKUyrwZ/QB7Lt3AgIEdKzSHD0C2mjqbXxzbjVisxK1kgilqEJOfi4+PPYtdvRahGOx5wokGU/79+H58plohIeHw9HREb169cLgwYPRtWtX2NhwIFpJJSQoERdX+C9nZ2cp+vVzwO+/ZxVYJl4UgTFj1MWd0tOVuHZNAUdHCWrWtNYaB5GfL2Ly5ERs3ZqlGUz63Xep6NTJDgsXekEmE7BxY0aB8wPq5ODUKTliYvJx7FgupFLonLViZQUcO5aL9u0L71OUSp/U4rBk7+9/v8SvvRd5D3dO3dG5T5AKOLT0EIb+MFTTXfIsUSUi+lQ00h+mcyVWM3Hl52+gVOTh6RtQVCmhlOfi2tr5aPTh9yaMrnySSqSYGDICE+oPRXJuGhxtHGBv9WTl5DR5BvrvnIiLyTcgfVR+fvOt3QjxqInN3b6Do7U9Nt2MgFIs+GWpFFU4FX8RsVkJsLey06yXosvT71lWGKVg16ZNmxAfH4+1a9eid+/eTDKM4OOPK6BJE3Xrg1QKTbLw8suOGDTIEZ99loTGje9h0KA4dO/+AJ06xeD06VzN65cuTcMff6jHzSiVTxKFffty8O236kGkaWnPLxuemqqEtbWAwsZxiqJ6GmxYmAy1a1vrLA4misCIEfzl9zzPLT+er8KDC+rZKoWtX/KYIrd0g7HJOBSZ6Ui6dAq6snxRpcTDMwehzLP8QfFllZXECl72FQr8wv/s1FJcSbkFQJ04PJ7ieiHpOj4/oy5rnpP//OuWochCv6AOhSYZ1hJrdPZrWdqPoHdGSTQGDBgAW9uyl2VZMkdHCdat88Fvv/ngrbdc8O67roiIqIjPPvPAnDkpWLkyA3lPrWMWFZWP4cMf4tYtBURRXSFU1zgPlUo9mFQuF1Gjhk2hSYRMJsDPzxpdutjr7MIB1MlLly72EAQBy5d7IyhI3XwokTw5x/z5Hqhdm4np8zy3/LhEgIuvC6TWUviF+emsHAoALhVd4FbZzVAhkh4pFS9IIkQRotLyuxvNSW6+HBtu7tIkF09Tiir8dn0n8kUlGnjUgKSQxfZcbBzh71QRfYM7oIVPCISnFi963EIyu+lbcJWVvaJ6rPtswQRBQLNmtmjW7EmSl5CgxLp1GTqreObni1ixIg3TprkXOjgTALKzRSQmKvHGGy6YMKFg/X9BAIYPd4KTkwStWtmiY0c77Nv3ZBbM44GjgwY5onZtdatLxYpW2LWrIo4dy8WlS3lwd5egSxcHODsbJRc2C+kP07H3u704u/ks8uX5CG4ZjE6TOqFaeDU4ezsjIyGj4NLuKhFNR6jLj3f9sCuWDV6m89xdp3RVz2ahMk/m6gF778rIfni/4E5BgJNfNVjZORTcRwYniiL+fXgel5NvwcPODZ2qtICdlQzpeZmQKwtfoTonPxcZeVmYGDICo/d+pPOYCfWHQSZV/9H1W5ev8dPl37H22g4k56ahToWqGF9vKDpUaWaQz1VaTDTKmXPn5IWWFVcqgaNHc2FnJ8DBQUBWlu6pK9bWgJubBD16OOD+/Xx8802KVqtFnz4OmDxZ/dexIAhYtMgLP/2Uhl9/zUBcnBJVqljhlVecMWKEduYtkQho1coOrVqVvXngppYRn4Hv2n+H9IfpmnEWV/dexZW9V/Dab69h5MqRWDpwKfLl+Vrlx5uOaIr6veoDAGp1qoWRK0bij2l/ID1OXX7e3s0e3aZ1Q7MRZfMLylzJXD20/lta+TlZyE2Oh8y1AqwdnFF98HhEfj+14IGiiOqD3tLLe1LxPMiKx4jdU3Ax+aZmATVnG0csafsx2lRqBGcbR6Tn6V5fy03mDBcbR3Tzb415rSZj1r8/IkOh7rq2llhhfL2hmFB/mOZ4WysZJtQfprWtLGOiUc4UtgDaY/b2AqRSAUOHOmHlyvQC3cBSKdC3rwPs7dV//Y4b54JBgxyxf38O8vJENGtmi4AA7aJd1tYC3nzTFW++6arPj1Ku7J2/VyvJANQzRiAAmz/YjI/OfYRpJ6fh2OpjuBd5Dw5uDmg0uBFqtK+hNcg3tF8oGvRugJiLMVDlq1CpbiVYyfg1oG+tvixYXK0k8nOycfmXbxBzYBtU+QoIEil8W3RFnTFTUO+Nmbi27nvkpavHTMlcPVBr5PvwadJeL+9NRSeKIl7e/SGupUSrnz+a5JqRl4VRe6fh8IBfMKZWPyz479cCxb0ECHitzkuQStSD1IbX6IkBwZ3wb9x/UKiUaORdB24y8x6nxm+YcqZpU1u4uEh0DuSUSIA+fdSFuSZOdMXZs3KcPSvXDNJUKtXLuX/0kXapWzc3qVaBL9K/c7+f0zljBCKQfDcZDy4+QOX6ldH9o+4vPJdEKkGVBlUMECWVRF5GKuRpybCr4AMruyf1YkRRxKkvJyD5ylnNwE9RpUTssV3IuHcDreb+hsrhvZF+5zoECHAKqA6JlF/ppnAsLhKXk28V2C5ChEpUYfWVP/BRo3G4lXYPf0Yf0IypUIoq9AvqgHcbDNd6nZ2VDG0rNzFK7MbAn0oLduuWAsuXp+HgwRzY2Ajo0cMBr77qjE8/dce77yZCInkym0QiAapWtcbw4eruDAcHCTZs8MGePdnYt09d0bNtWzt07eoAG5vyXQ7cFF40I0SRwxkj5iY3JQEXf/ocD0/tB0QVJNYy+HUcgJovT4RUZoukS6eQfOl0gdeJKiUy7lxH3Ml9qNiyK1yD65ggenra5eRbmu6SZylFFS4m3YSN1Bo/dfgE5xOvYe+9ExAEAZ2qNEfdCtUMGtuN1DvYeecw5Mo8tPJtiOY+DYy+pAMTDQsVGSnH0KFxUChETTKxbFka/vgjE9u2+WLdOh8sWZKKs2flcHKSoH9/R7z2motWRVArKwHdujmgWzcOLDO1aq2r4dKuSzpbNWwcbFCxbvEWJCTTys/NxvGPX0FOfAzwaCaCSiFH9K71yI6PQeMpC5F4/jgEqRSijkFVgkSKxPPHUbFlV2OHTjpUsHUtdL0TqSCBp92TGV31PWqgvkcNvbxvviof0ekPIJPaoIqTj9Y+URQx48RCLL+8GVJBAgEC5p1bjeY+DbCm0xdwsjHe9zoTDQs1Y0YS8vJErTEWSiUQH6/EggVp+OyzCmjWzKfwE1CZ0nFSR1yKuKQpL661b2LHQpd6p7LpweGdyI67W3CHqEL8mYNIvXUJgsQKha7VJQACu0nKjC5+LeFobY8sRU6BhEMpqjC4iCXIi0oURfxydTu+PrcSCTnqMTr1KlTD580nool3PQDAr9d2YPnlzZoYHjv58AKmHZ+PheG6Z7cYAuezWaB79xS4eDGv0Kqd27bpHvlcWiqViPPn5ThxIgfp6YVXLqXi82voh9c3vo4KAU9WfZQ5ydB9end0fL+jCSOjkki8cAKFFqGRSJB4/gR8GrfTlBd/lqhUwrtxOwNGSMXhYG2HH8OnQypIIBXUg9oej8N4pVa/Fy66VlwrLm/B5GPzNEkGAFxKvokBOyfiYtINAMCyS5u0am08phRV+P3WXiTnpuk1pudhSmyBCpuW+lhOzvP366JQiLh5UwEbGyAoyLpAH9/RozmYMiUJ9++r57na2AAjRzrjww/dYGXFMR36UKNdDUw7PQ2xl2OhyFHAt7YvbOxZzMwcCRIpBEGAqHOJZUBiZQWX4Nqo1LoHYg7vhFbThiDAo35zeDZoYbR46cW6+rfCgf6rserKH7iUdANe9hUwpFo3tK/cVK9jIuTKPHx9dmWB7SpRhBIqzI9cg586fIKo9PuFducoRSXuZcbB3dZFb3E9DxMNCxQYaA1nZwHp6QV/yCQSIDS0eM3sa9em49tvU5GcrG4iCQiwwqxZ7ggPV4+Qv3xZjtGjH2rV58jLA1asSIdKJWLGjAq6TkslIAgCKtbheAxz592kPR4c/Vv3TlEF70ZtAQANxn8Gp4AaiP5rLXKTH8LG2Q3+XQYjuN9YCBI2SJc11Vz98Xnz0i9OqBJV2H//JLZH7Ud2fi4ae9XF4Gpd4SJzwuXkW0jNy9D5OqWoxP6YkwAAb/sKuJ/5UOdxAgBvO+N9LzPRsEAymYA33nDBV1+lFtinUgETJhQ9i123LgPTpydrbbtzJx9jxsRj/XofNG5si2XL1OXKn/3jTBSBX37JwIQJrnBz07GQCVE55dOkPdxqhiLl2n+awaDqr38R/l2HwsHXX71FKkVw79EI7j0aKmU+p69agCMPzuLbyJ9xIu4/2Eht0C+oA94PHY3Kjt4A1AM839j/yaNpsFKIogp/Rh3A9+d/xbYeP8Ba8vyfAatH9ThG1+yLOaeXFWjVkApStK3UGD4O+ikmVxRMiS3UuHEuePddF8hkT5rs3Nwk+O47D01LxIvk54v47ruUAttFUd29/P33qQCA48dzC602mp8PnD9feOldovJIYmWNptOXoGq/V2Ht5AoAsPeqhDqvTkOdMVN0v4ZJhlm4lxGHPXePIzLhaoGusZ3Rh/HS3+/heNx/muXfN9zYhS7bXseDrHgA6kGcO6IPAFC3UKggQoSI5Nw0TDj4GWq5BaGig5eO0RfqJKJ3oHrszri6gzQlya0EqWbMSGVHb8xr9T/DfPhC8CfXDHl6SrX+q4tEImDiRDeMGeOC//6Tw9paQMOGsgI1MBISlFi6NA1//pmF3FwRLVrY4q23XFCvngxRUQokJupe80SpVCcYoijC3v7F1UaJSJtUZocaQ99GjaFvQ1QqIehavpjMRnpeJt45NBe77hzWtCEEu1TB4rYfo4FHDShVSkw7MR+ACNVTCYhSVCJFnoaF/63F3Bbv4eer2/C4detpSlGFswlXcCv9HuY0ewdj9s2ARBA0K7lKBQlcbBzxTn118S8bqTXWdJqLAzGn8GfUAXUdjYoN0S+oI+ysjDtLjYmGGdq+veh99Pfv5+PatTxYWwuoUsUKlSo9ueQJCUr06fMA8fFKTYvEnj3Z2Ls3G6tWeaNKlef/eKiXgBfQt68jvv8+tcAsF0FQJ0PFHRNCVN4wyTB/r+ydjuNxkVrpQXR6DAbsfBdHBvyK+JwkxGYVXIQSUCcR26L+wdwW7yEuK6HQQZwAEJuViO4BbbCx27f49txqnIg7D2uJFfoGtccHDV/RqqchESRoX7kp2lduqq+PWSJMNCxUbq4K77yTiD17siGRqLs7Zs9OxptvuuCDD1whCAKWLEnTSjIAdUuFIADTpyfhn38qolo1a9y8qSgw/kIqBbp3V3fBvPKKM3bsyMLt2wpNsiGVqt9zzpwKnHVCRGbNy95d67/Piky4iiOxZwtsV4oqZOXn4pdr21/4y16hVM/Yq+4aiJPxFzQtFU8ToG4lAYA2FcPQpmIYVKIKAgSjV/ssDo7RsFCff56CffuyAagHgD4erLloURo2b1bX0di2LVPn2ApRVA/4vH49H7NmuUMiAZ7+g0sqBRwdJXj3XVcAgLOzBL//7oO33nJB5cpSuLlJ0KGDPTZv9kHHjkUbD0JEVFbt7rMckUN/x+4+y3XuPx1/SWfNCkA9g+TfuPOo614VTta6q3FKBSnCH9XaGFd3oM4kQypI0MWvlWbQ6GMSQVKmkwyAiYZFyshQYcOGDJ0FuwQBWLZMvUR4bu6L6220aGGHTZt80bq1HWxs1OMt+vVzwPbtvvDze7JKq7OzFO+/74bDh6vg7Fk/LF3qhdBQW71+LiKissjR2r7Q7g6JIIGTtQNsrWT4IHR0wf0QIBEETAwZAQDoHtAGHzZ8FQIESCBoCoCFeNTE/Da6BwqXdew6sUD37+cjr5CJHqII3L6tXoCraVNbHDyYo7NVw95eQM2a6kQiNFSGVau8Cx5ERETo4t8SNketkacquLihSlShX3AHAOqZIFKJFN+e+xnJcnVlzpruQfii+Xtai6tNCh2FQdW6Ykf0QWQrctDEux5a+oaW+ZaLwjDRsEAeHhIIQsG6Fo+5u6sbssaPd8GBAzk6j33jDRfY2bHBi4io87bXEJ+dDC97d53dJ24yZ8xp/g7+d3QepIIEykfjJgARHas0R8+AcADqgnuv1XkJo2v1xa20e7CVyuDv5Kszgajs6I036g4y9EczCiYaFsjT0wpt2tjiyJGC9S0kEmDIEPVS8A0b2uKnn7wwY0YSYmLUB9rZqYt9jR9vnNK0ROWVSpmPu3s34+7uTchNSYBT5WAE9hwBnybtTR0aPSM+Oxmx2bpnjDw2smYfBDpXxpKLG3Ex6Qa87Nzxco2eeLlGT0gl2rOKrCVWqOkWaMiQyxQmGhbqs888MGhQLOLi1AmEIKgHhYaGyvDWW0+SiHbt7HHokB0uX85Dbq6I2rVtYG/PlgwiQxJVKpz77n+I+3cfHtdMSL52DslXzqDG0LdRtf9rpg6RSqB1xTC0rhj2wuPS8zKhFFVwtXEy2+6Q4mCiYaEqV7bCrl2V8PvvmTh6NAfW1gK6drVH9+4OsLbW/sGWSATUrctaF0TGkhB59FGSAWgKMz0avX1t/Q+oFN4LdhV8dL+YzNaphxfx6anF+PfhBQBAHfeqmNboNXSs0tzEkRkWEw0L5uwswSuvOOOVV5xNHQoRPeXBsV0QJNJCl4GPO7EXgT2GGzkqMqRzCVfQb+c7UD41dfVK8i0M3/0hfu40F138WpowOsNiGzkRkZEpc3Mg6qiVAACCIIFSnmPkiMjQvjyzAipRpVUjQwURgIBPTi4usC6KJWGiQURkZG41QwvdJ6qUcKtR+H4yP0qVEgdiTmm1ZjwmQsTNtLuFLuluCcw20Zg7dy4aN24MJycneHl5oW/fvrh27ZqpwyIieqEqbfvAxtkNwjOzEQSJFK7VG8C99osHFFLZIooiNt7YhfAto+CzIhy11/bGnNPLkKXILtLrLXlQqNkmGgcPHsT48eNx4sQJ7NmzBwqFAp07d0ZWVpapQyMiei5rR2c0n70KzoE1n9oqwCusDZpM/dGif+lYqnnnVuPtQ5/jWko0RIhIyk3FD+fXYcDf70Ghyke7yk00S7U/TYCAaq7+qOTgZYKojcNsB4Pu2rVL6/nq1avh5eWFM2fOoE2bNiaKioioaBwrBaLVF78h494tyFMS4FAxAHYenGlijuKyEvFt5M8AoFWKXCWqcC7hCrbe3ocPG76KIw/OQny0HVCXHxcBfNz4TYtOLs22ReNZaWnqcq7u7rpX1yMiKoucqgTDo34zJhlmbM+94zrHXwDqZGJH9AGEeNbEHz0Wopl3fc2+uhWqYV2Xr9DZr4WxQjUJs23ReJpKpcLEiRPRsmVL1K1bt9Dj5HI55HK55nlmZqYxwiMiIgumUCkgQNC5sJoKIvKU6jVQwrxqY2uP75GlyIZSVMHZxtHYoZqERSQa48ePx8WLF3HkyJHnHjd37lzMnj3bSFEREVF50Kpiw0JXbxUgaJaAf8zB2t4YYZUZZt91MmHCBOzYsQP79+9H5cqVn3vs1KlTkZaWpnkcPHjQSFESEZGlqu4agD5B7SGB9jgLqSDRrHlSnplti4Yoinj77bexdetWHDhwAIGBL16gRiaTQSZ7Umrb0bF8NFsREZFhLWwzDd52FfDL1W3IVeYBAFpVDMNXLd6Hm6x8V2c220Rj/PjxWLduHbZt2wYnJyfExcUBAFxcXGBnZ2fi6IiIqDyRSW3wabO3MSXsVdzJiEUFWxd423uYOqwywWwTjcWLFwMA2rZtq7V91apVGD16tPEDIiKics/B2h613YNNHUaZYraJhiXXhSciIrIUZj8YlIiIiMous23RICIiMlf5qnysvrINq6/8gdjsBAS7VMHrdQZiQHAni6sSykSDiIjIiERRxLj9s/FX9EHgUaGvC4nXMf7gZ7iSfAszmrxp6hD1il0nRERERnQw5hR2RB+EiCdro6ge/feHC7/hdto9E0anf0w0iIiIDEChyse1lGhEpz/QmsCwPeoArASpztdIBAl2RFtWMUl2nRAREemRKIpYfeUPfH1uFZJyUwEAtdyCMLf5RDT3DUGuUl5oyXIJBOTky3XuM1ds0SAiItKjFZe3YMrx7zRJBgBcS4nGwF2TcD7xGpr5NCh0tdd8UYlmPg2MFKlxMNEgIiLSE7kyD1+fXVlguwoqqEQR30WuwYDgTqjk4A3pM90nUkGCMM/aaFMxzFjhGgUTDSIioufwsneHr70nvOzdX3jsleTbSM3L0LlPKSpxMOYUHKztsK3HQjT1rqfZJ0BAN//WWNfla05vJSIiKk9291le5GOtpdbP3y9R/9qt4uSDrT2+x52MB4jNSkCAUyX4OFjm2ihMNIiIiPSkllsgKjt6IybzYYHhnlJBit5B7bS2+TtVhL9TReMFaALsOiEiItITiSDBnGbvAhAgFZ78ipUKUrjIHPFugxGmC85EmGgQERHpUVf/Vvi9+3w082kAqSCBnVSGl6p2QkTvZajs6G3q8IyOXSdERER6IIoiziVewZXk2/Cwc8P6Lt/AWmJlcYM7i4uJBhERUSk9zE7E6L3TcTbhsmabu60LlrSdifBKjUwYmemx64SIiKgURFHE8N1T8F/iNa3tqbnpGL77Q0SnPzBRZGUDEw0iIqJS+PfheZxPug6lqNTaroIIpajEz1f/ME1gZQQTDSIiolK4kHQDAnSPw1CKKpxPvG7kiMoWJhpERESl4CZzLnSRNKkgQQVbFyNHVLYw0SAiIiqFrn6tYG9lq7NVQymqMLBqVxNEVXYw0SAiIioFRxt7fN9mGiSCoFko7XGxrqHVuqNjlWamDM/kOL2ViIiolHoFtkWQywqsvPQ7LiTfgLddBQyt3h3d/FuzjoapAyAiIrIEddyDMa/1ZFOHUeaw64SIiIgMhi0aRERERnIx6QaWXNyIkw8vwFXmhMHVuuLl6j1hayUzdWgGw0SDiIjICHbfPYbRez8CAChFJe5mAOcTr+PPqAPY0HUeZFIbE0doGEw0iIiI9OBhdiLWXvsLl5NvwdPODYOqdUWoZy0AQJ5SgXcPz4VKVGlqbjz+3+Nx/2HttR0YU7u/yWI3JCYaREREpXQsNhLDIv4HuUoBiCIkggQrr2zFB6Gj8b+GY3A8LhLJuWk6XytAwMabEUw0iIiIqKDcfDnG7JsOuTIPqkftFKpH6558c241Wvk2RHpeVqGvFyEiTZ5hlFhNwaxnnRw6dAi9evVCxYoVIQgC/vjjD1OHRERE5cyee8eRIk/XJBlPkwpSrL3+Fxp41ChkNRT1MU296xs2SBMy60QjKysLDRo0wI8//mjqUIiIqJyKy058zqJqSjzIioefky96B7WHRND+tSs8euXrdQcaIVLTMOuuk27duqFbt26mDoOIiMqxqi5+z1lUTYrqrv4AgPmtp0ACCf64vVdztKedGxa0mYra7sFGitb4zDrRKC65XA65XK55npmZacJoiIjIErSpGIYAp0q4lxkLpajS2idCxKiafQEA9la2WNLuY3zU6HWcT7oGZxtHNPdpACuJZf8qNuuuk+KaO3cuXFxcNI/w8HBTh0RERGZOKpFiXZev4OvgCQCwEqQQIMBGYo0fwj9CLfcgreOrOPmgR0A4WlcMs/gkAwAEURR1t/eYGUEQsHXrVvTt27fQY55t0YiMjER4eDjOnDmDhg0bGiFKIiKyVApVPnbfPfqojoY7ege2g7uti6nDMjnLT6WeIpPJIJM9KfPq6OhowmiIiMiSWEus0CMgHD0C2Fr+tHLVdUJERETGZdYtGpmZmbh586bmeVRUFCIjI+Hu7g4/Pz8TRkZERESAmScap0+fRrt27TTPJ02aBAAYNWoUVq9ebaKoiIiI6DGzTjTatm0LCxnLanCxsbGIjY01dRikJ76+vvD19TV1GKQnvD8tD+/RJ8w60SgtX19fzJw50+J/GORyOYYOHYqDBw+aOhTSk/DwcERERGgNbibzxPvTMvEefcJiprdS4dLT0+Hi4oKDBw9ypo0FyMzMRHh4ONLS0uDs7GzqcKiUeH9aHt6j2sp1i0Z5ExISwh96C5Cenm7qEMgAeH9aDt6j2ji9lYiIiAyGiQYREREZDBONckAmk2HmzJkclGQheD0tC6+n5eE11cbBoERERGQwbNEgIiIig2GiQURERAbDRIOIiIgMhokGFUt0dDQEQeBaMkRlFO9RKmuYaBjQrVu3MG7cOAQFBcHW1hbOzs5o2bIlFixYgJycHIO97+XLlzFr1ixER0cb7D2KYs6cOejduze8vb0hCAJmzZpl0niMSRCEIj0OHDhQ6vfKzs7GrFmzinWu8nxtnlae79GrV69i8uTJCAkJgZOTE3x9fdGjRw+cPn3aZDEZS1m+Py3xurAyqIH89ddfGDhwIGQyGUaOHIm6desiLy8PR44cwf/+9z9cunQJy5YtM8h7X758GbNnz0bbtm0REBBgkPcoiunTp8PHxwehoaGIiIgwWRymsGbNGq3nv/zyC/bs2VNge61atUr9XtnZ2Zg9ezYA9UKDRVGer81j5f0e/emnn7BixQoMGDAAb731FtLS0rB06VI0a9YMu3btQseOHU0SlzGU5fvTEq8LEw0DiIqKwpAhQ+Dv749//vlHa9G28ePH4+bNm/jrr79MGOEToigiNzcXdnZ2ej93VFQUAgICkJiYCE9PT72fvywbPny41vMTJ05gz549BbabSnm+NgDvUQAYOnQoZs2apbW+ypgxY1CrVi3MmjXLLH+hFVVZvj8t8bqw68QAvvrqK2RmZmLFihU6V4atWrUq3n33Xc3z/Px8fPrppwgODoZMJkNAQACmTZsGuVyu9bqAgAD07NkTR44cQZMmTWBra4ugoCD88ssvmmNWr16NgQMHAgDatWtXoAnw8TkiIiLQqFEj2NnZYenSpQCA27dvY+DAgXB3d4e9vT2aNWtWqi9bU7ammAOVSoX58+ejTp06sLW1hbe3N8aNG4eUlBSt406fPo0uXbrAw8MDdnZ2CAwMxJgxYwCo++MfJwqzZ8/WXO8XdYWU92vDexQICwsrsIhbhQoV0Lp1a1y5cqVE57Qkpro/LfK6iKR3lSpVEoOCgop8/KhRo0QA4ksvvST++OOP4siRI0UAYt++fbWO8/f3F2vUqCF6e3uL06ZNE3/44QexYcOGoiAI4sWLF0VRFMVbt26J77zzjghAnDZtmrhmzRpxzZo1YlxcnOYcVatWFd3c3MQpU6aIS5YsEffv3y/GxcWJ3t7eopOTk/jRRx+J3377rdigQQNRIpGIW7Zs0cQQFRUlAhBXrVpV5M+XkJAgAhBnzpxZ5NdYmvHjx4vP3m5jx44VraysxNdee01csmSJ+OGHH4oODg5i48aNxby8PFEURfHhw4eim5ubWL16dfHrr78Wly9fLn700UdirVq1RFEUxczMTHHx4sUiALFfv36a6/3ff/8VKa7yem14jxauRYsWYvXq1Uv0WnNVVu/Pp5nzdWGioWdpaWkiALFPnz5FOj4yMlIEII4dO1Zr+wcffCACEP/55x/NNn9/fxGAeOjQIc22+Ph4USaTie+//75m26ZNm0QA4v79+wu83+Nz7Nq1S2v7xIkTRQDi4cOHNdsyMjLEwMBAMSAgQFQqlaIoMtEoqWe/yA4fPiwCENeuXat13K5du7S2b926VQQgnjp1qtBzl+bftzxeG96jhTt06JAoCII4Y8aMYr/WnJXV+/Mxc78u7DrRs8fLAzs5ORXp+J07dwIAJk2apLX9/fffB4ACzaK1a9dG69atNc89PT1Ro0YN3L59u8gxBgYGokuXLgXiaNKkCVq1aqXZ5ujoiNdffx3R0dG4fPlykc9PL7Zp0ya4uLigU6dOSExM1DweN5vu378fAODq6goA2LFjBxQKhQkjthy8R3WLj4/HsGHDEBgYiMmTJ5fqXOauLN2flnBdmGjombOzMwAgIyOjSMffuXMHEokEVatW1dru4+MDV1dX3LlzR2u7n59fgXO4ubkV6Dd8nsDAQJ1x1KhRo8D2x6Oun42DSufGjRtIS0uDl5cXPD09tR6ZmZmIj48HAISHh2PAgAGYPXs2PDw80KdPH6xatarA2AAqOt6jBWVlZaFnz57IyMjAtm3bCowRKG/Kyv1pKdeFs070zNnZGRUrVsTFixeL9TpBEIp0nFQq1bldLMbaeIaYYULFo1Kp4OXlhbVr1+rc/3gAmSAI2Lx5M06cOIE///wTERERGDNmDObNm4cTJ06Y7RePKfEe1ZaXl4f+/fvj/PnziIiIQN26dY323mVVWbg/Lem6MNEwgJ49e2LZsmU4fvw4mjdv/txj/f39oVKpcOPGDa052w8fPkRqair8/f2L/f5F/UJ8No5r164V2H716lXNftKf4OBg7N27Fy1btizSL5VmzZqhWbNmmDNnDtatW4eXX34Z69evx9ixY0t0vcs73qNqKpUKI0eOxL59+7Bx40aEh4cX+xyWyNT3p6VdF3adGMDkyZPh4OCAsWPH4uHDhwX237p1CwsWLAAAdO/eHQAwf/58rWO+/fZbAECPHj2K/f4ODg4AgNTU1CK/pnv37jh58iSOHz+u2ZaVlYVly5YhICAAtWvXLnYcVLhBgwZBqVTi008/LbAvPz9fc+1SUlIK/CUcEhICAJrmWXt7ewDFu97lHe9RtbfffhsbNmzAokWL0L9//2K/3lKZ+v60tOvCFg0DCA4Oxrp16zB48GDUqlVLq+rgsWPHsGnTJowePRoA0KBBA4waNQrLli1DamoqwsPDcfLkSfz888/o27cv2rVrV+z3DwkJgVQqxZdffom0tDTIZDK0b98eXl5ehb5mypQp+O2339CtWze88847cHd3x88//4yoqCj8/vvvkEiKn5OuWbMGd+7cQXZ2NgDg0KFD+OyzzwAAI0aMKNetJOHh4Rg3bhzmzp2LyMhIdO7cGdbW1rhx4wY2bdqEBQsW4KWXXsLPP/+MRYsWoV+/fggODkZGRgaWL18OZ2dnzS9AOzs71K5dGxs2bED16tXh7u6OunXrPreptbxfG96j6sRp0aJFaN68Oezt7fHrr79q7e/Xr58mISpvTHl/WuR1Me2kF8t2/fp18bXXXhMDAgJEGxsb0cnJSWzZsqW4cOFCMTc3V3OcQqEQZ8+eLQYGBorW1tZilSpVxKlTp2odI4rqaW89evQo8D7h4eFieHi41rbly5eLQUFBolQq1ZpGV9g5RFE9v/+ll14SXV1dRVtbW7FJkybijh07tI4pztS58PBwEYDOh65pfZZM1zx9URTFZcuWiWFhYaKdnZ3o5OQk1qtXT5w8ebL44MEDURRF8ezZs+LQoUNFPz8/USaTiV5eXmLPnj3F06dPa53n2LFjYlhYmGhjY1OkqXS8Nmrl+R59XBuksEdUVNRzX29JytL9aYnXRRDFYoxQIiIiIioGjtEgIiIig2GiQURERAbDRIOIiIgMhokGERERGQwTDSIiIjIYJhpERERkMEw0TGT16tUQBAG2traIiYkpsL9t27ZGr22/b98+jBkzBtWrV4e9vT2CgoIwduxYxMbG6jz+2LFjaNWqFezt7eHj44N33nkHmZmZRo25rOD1tCy8npaH19R0mGiYmFwuxxdffGHqMAAAH374IQ4cOIB+/frh+++/x5AhQ7Bx40aEhoYiLi5O69jIyEh06NAB2dnZ+PbbbzF27FgsW7YMAwcONFH0ZQOvp2Xh9bQ8vKYmYOqKYeXVqlWrRABiSEiIKJPJxJiYGK394eHhYp06dYwa08GDB0WlUllgGwDxo48+0trerVs30dfXV0xLS9NsW758uQhAjIiIMEq8ZQmvp2Xh9bQ8vKamwxYNE5s2bRqUSmWZyLDbtGlTYL2ENm3awN3dHVeuXNFsS09Px549ezB8+HA4Oztrto8cORKOjo7YuHGj0WIua3g9LQuvp+XhNTU+LqpmYoGBgRg5ciSWL1+OKVOmoGLFisV6fXZ2tmZhrOeRSqVwc3MrdnyZmZnIzMyEh4eHZtuFCxeQn5+PRo0aaR1rY2ODkJAQnDt3rtjvYyl4PS0Lr6fl4TU1PrZolAEfffQR8vPz8eWXXxb7tV999RU8PT1f+AgNDS1RbPPnz0deXh4GDx6s2fZ4oJKvr2+B4319ffHgwYMSvZel4PW0LLyelofX1LjYolEGBAUFYcSIEVi2bBmmTJmi84epMCNHjkSrVq1eeJydnV2x4zp06BBmz56NQYMGoX379prtOTk5AACZTFbgNba2tpr95RWvp2Xh9bQ8vKbGxUSjjJg+fTrWrFmDL774AgsWLCjy64KCghAUFKT3eK5evYp+/fqhbt26+Omnn7T2Pb6B5HJ5gdfl5uaW6AazNLyeloXX0/LwmhoPE40yIigoCMOHD9dk2EX1uD/vRaRSKTw9PYt0znv37qFz585wcXHBzp074eTkpLX/cfava653bGxssfs8LRGvp2Xh9bQ8vKbGwzEaZcj06dOL3W/4zTffwNfX94WPxo0bF+l8SUlJ6Ny5M+RyOSIiInQ2KdatWxdWVlY4ffq01va8vDxERkYiJCSkyPFbMl5Py8LraXl4TY2DLRplSHBwMIYPH46lS5fC398fVlYvvjz67C/MyspC9+7dERMTg/3796NatWo6j3NxcUHHjh3x66+/YsaMGZrse82aNcjMzDSPAjJGwOtpWXg9LQ+vqXEIoiiKpg6iPFq9ejVeeeUVnDp1SmvK0s2bN1GzZk0olUrUqVMHFy9eNFpMffv2xbZt2zBmzBi0a9dOa5+joyP69u2reX727Fm0aNECtWvXxuuvv4779+9j3rx5aNOmDSIiIowWc1nB62lZeD0tD6+pCZm6Ylh59bhK3alTpwrsGzVqlAjA6FXq/P39RQA6H/7+/gWOP3z4sNiiRQvR1tZW9PT0FMePHy+mp6cbNeaygtfTsvB6Wh5eU9NhiwYREREZDAeDEhERkcEw0SAiIiKDYaJBREREBsNEg4iIiAyGiQYREREZDBMNIiIiMhgmGkRERGQwTDSIiIjIYJhoEBERkcEw0SAiIiKDYaJBREREBsNEg4iIiAyGiQYREREZDBMNIiIiMhgmGkRERGQw5TrRiI2NxaxZsxAbG2vqUIiIiCxSuU80Zs+ezUSDiIjIQMp1okFERESGxUSDiIiIDMasE41Dhw6hV69eqFixIgRBwB9//GHqkIiIiOgpZp1oZGVloUGDBvjxxx9NHQoRERHpYGXqAEqjW7du6Natm6nDICIiokKYdaJRXHK5HHK5XPM8MzPThNEQERFZPrPuOimuuXPnwsXFRfMIDw83dUhEREQWrVwlGlOnTkVaWprmcfDgQVOHRFQiijyFqUMgIiqSctV1IpPJIJPJNM8dHR1NGA1RySnyFLC2sTZ1GEREL1SuWjSILIUoiqYOgYioSMy6RSMzMxM3b97UPI+KikJkZCTc3d3h5+dnwsiIDCs3JxcOTg6mDoOI6IXMukXj9OnTCA0NRWhoKABg0qRJCA0Nxccff2ziyIgMKyk+ydQhEBEViVm3aLRt25ZNyFQuZaVnISUxBW4ebqYOhYjoucy6RYOoPLt+4bqpQyAieiEmGkRm6vy/500dAhHRCzHRIDJT1y9cR+LDRFOHQUT0XEw0iMyUKIrY98c+U4dBRPRcTDSIzNjpQ6dx++ptU4dBRFQoJhpEZqZRo0bo2rcrfo34FaIoYu0Pa5GRlmHqsIiIdGKiQWRm4uLiEJ8Qj6ycLABAWnIaVs1bBXmO/AWvJCIyPiYaRBbg7s27WDp3KdJT0k0dChGRFiYaRBbi7s27mDdlHs4dO8dCdkRUZjDRILIgWRlZWPvDWiyZswQxd2JMHQ4RERMNIkt06/ItzP9oPjav2IysjCxTh0NE5RgTDSILJapEnNh3Al9M+gKH/z4MhUJh6pCIqBxiokFk4XKycrBtzTZ89cFXOLbnGBR5TDiIyHiYaBCVEykJKdiyags+e/szRGyOYO0NIjIKs14mnoiKLysjC3u27MH+P/cjrHUY2vVsBw8fD1OHRUQWiokGkRm5e/cusrOzAQCKfAXSs9Lh7OBconPlK/Lx7z//4uSBkwhtEYqOfTvCq6KXPsMlImLXCZE5OHnyJHr16oWAgACkpKQAAOQKOZZvX46tB7ciLimuxOcWVSLOHjmLryd/jXU/rkP8g3h9hU1ExBYNorJuy5YtGDx4MERR1FmIKyo2CtGx0ejZsieqValW4vcRVSLOHj2Lc8fPIbRFKLoO7Ap3T/fShE5ExBYNorLs5MmTGDx4MJRKJZRKpc5jRFGESlRhx9EdpWrZ0JzvUQvHVx98hcN/H2aVUSIqFSYaRGXYZ599VmhLxrNEiDhx6YTe3jtfkY9ta7bh4F8H9XZOIip/SpVoyOVyHD9+HNu2bUNiYqK+YiIiqAd+7tixo9CWjGeJoohbMbeQnqXfhdX2bNnD2htEVGIlTjS+//57+Pr6olWrVujfvz/Onz8PAEhMTISHhwdWrlyptyCJyqN9+/aVqNvi7sO7eo1DnivH+X/P6/WcRFR+lCjRWLVqFSZOnIiuXbtixYoVWl+GHh4eaN++PdavX6+3IInKo4yMDEgkxbtFBQjIU+TpPZa/1v/FNVOIqERKlGjMmzcPffr0wbp169CrV68C+8PCwnDp0qVSB0dUnjk5OUGlUhXrNSJE2Fjb6D2W9JR07Nm6R+/nJSLLV6JE4+bNm+jWrVuh+93d3ZGUlFTioIgI6NChAwRBKPbr/Lz9DBANONWViEqkRImGq6vrcwd/Xr58GT4+PiUOiogAPz8/9OzZE1KptEjHC4KA4ErBJa4UWhgbWxv0f6U/WndtrdfzElH5UKJEo3v37li2bBlSU1ML7Lt06RKWL1+O3r17lzY2onJvxowZEAShSC0bAgQ0q9NMr+9fu2FtTP56Mlp0alGi1hUiohIlGp999hmUSiXq1q2L6dOnQxAE/Pzzzxg+fDgaNWoELy8vfPzxx/qOlajcady4MTZs2ACpVFpoy4YgCJAIEvRs2RM+FfTTkuhawRUjJ47EK++/AtcKrno5JxGVT4JYwrJ/8fHxmDZtGrZs2aJp2XBycsKAAQPwxRdfwMur7C/OdPbsWYSFheHMmTNo2LChqcMhKtSpU6fw6aefYseOHQWmvAZXCkazOs30kmS4uLsgvEc4mndsDmtr61Kfj4ioxInG0xISEqBSqeDp6Vns6XimxESDzM3du3cREhKClJQUyKxlGNltpF7GZPhX90erzq1Qr3E9WFlzCSQi0h+9fKN4enrq4zRE9AJ+fn6wt7dHSkoKrK2sS5VkWMusEdYqDM07Nkcl/0p6jJKI6IkSJRrTp0/Hjh07EBkZqXN/aGgo+vbti5kzZ5YmNiIyAHdPd7To1AJN2jaBvaO9qcMhIgtXokRj8+bN6NevX6H7u3fvjg0bNjDRICpDqtWthpadW6J2w9pm1cVJROatRInG3bt3ERwcXOj+wMBA3Llzp8RBEZF+2DnYoXF4YzTv0ByevuziJCLjK1Gi4ejo+NxEIioqCra2tiUOiohKx8XdBe17t0fj8Mawkem/JDkRUVGVKNFo27Ytli5dijfeeAOVKmkPIrt37x6WLVuGdu3a6SVAIio6QSKgQ+8O6NC3A6xtOD2ViEyvRInGp59+iiZNmqBOnTp49dVXUadOHQDAxYsXsXLlSoiiiE8//VSvgRLR8zm6OGLUxFEIrBFo6lCIiDRKlGjUqFEDhw8fxttvv43vvvtOa1+bNm3w/fffo1atWnoJkIi0+fj4QJGngKB8UhLcw9cDr3/4Oty9uPAZEZUtJa6jUb9+fRw8eBCJiYm4ffs2ACAoKAgeHh56C46ICjp9+jTOHTuHtT+sBQBU8K6At2a8BWdX/S6mRkSkD6Uu2OXh4cHkgshErKyt8Mr7rzDJIKIyq8SJhlKpREREBG7fvo2UlJQC6y8IgoAZM2aUOkAiKlx4j3D4VNbPQmpERIZQokTj9OnTGDBgAO7fv18gwXiMiQaRYQkSAS07tTR1GEREz1Wi8oBvvfUWcnJy8McffyA5ORkqlarAQ6lU6jtWInpK1dpV4ezGLhMiKttK1KJx/vx5zJkzB7169dJ3PERURHUb1zV1CEREL1SiFo3KlSsX2mVibD/++CMCAgJga2uLpk2b4uTJk6YOicgoajaoaeoQiIheqESJxocffojly5cjPT1d3/EUy4YNGzBp0iTMnDkTZ8+eRYMGDdClSxfEx8ebNC4iQ7Ozt4O7J2tmEFHZV6Kuk4yMDDg6OqJq1aoYMmQIqlSpAqlUqnWMIAh477339BJkYb799lu89tpreOWVVwAAS5YswV9//YWVK1diypQpBn1vIlNydnOGIAgvPpCIyMQEsQR9IEVZYloQBIMOCM3Ly4O9vT02b96Mvn37araPGjUKqamp2LZt2wvPcfbsWYSFheHMmTNo2LChwWIl0rek+CRU8Kpg6jCIiF6oRC0aUVFR+o6j2BITE6FUKuHt7a213dvbG1evXtX5GrlcDrlcrnmemZkJAMjPz4dCoTBcsER6Jooif2aJyOSsrV+8eGOJEg1/f/+SvMzk5s6di9mzZxfY3rRpUxNEQ0REZN6K0ilSqhLkMTExOHToEOLj4zFgwABUrlwZSqUSaWlpcHFxKTBuQ588PDwglUrx8OFDre0PHz6Ej4/uSolTp07FpEmTNM8jIyMRHh6Of//9F6GhoQaLlUjf5DlyyOxkpg6DiOiFSpRoiKKI999/Hz/88APy8/MhCALq1auHypUrIzMzEwEBAfjkk08wceJEPYf7hI2NDcLCwrBv3z7NGA2VSoV9+/ZhwoQJOl8jk8kgkz35cnZ0dAQAWFlZFan5h6gs4c8sEZmDEk1v/frrr7FgwQJ88MEH2LNnj1bTiYuLC/r374/ff/9db0EWZtKkSVi+fDl+/vlnXLlyBW+++SaysrI0s1CILFVRBmQTEZUFJWrRWL58OUaOHInPP/8cSUlJBfbXr18ff//9d6mDe5HBgwcjISEBH3/8MeLi4hASEoJdu3YVGCBKZGmYaBCRuShRonHv3j20aNGi0P0ODg5GK+Y1YcKEQrtKiIiIyLRK9GeRl5cX7t27V+j+M2fOwM/Pr8RBEdHzlZUlAIiIXqREiUb//v2xZMkS3L59W7PtcZXC3bt3Y/Xq1Rg4cKB+IiSiAlRKlalDICIqkhJVBk1LS0ObNm0QFRWF1q1bY9euXejUqRMyMzNx/PhxhIaG4tChQ7C3tzdEzHrDyqBkrhQKBWedEJFZKFGLhouLC06cOIHJkycjJiYGtra2OHjwIFJTUzFz5kwcPny4zCcZROaMSQYRmYtiDwbNzc3FsmXLEBISgunTp2P69OmGiIuIiIgsQLFbNGxtbfHhhx/i2rVrhoiHiIiILEiJuk7q1q2L6OhoPYdCRERElqZEicacOXOwdOlS7N27V9/xEBERkQUpUcGuH374Ae7u7ujSpQsCAwMRGBgIOzs7rWMEQcC2bdv0EiQRERGZpxIlGufPn4cgCPDz84NSqcTNmzcLHPO4rgYRERGVXyVKNDg+g4iIiIqCKzMRERGRwZQ40VAqlVi/fj3GjRuHfv364cKFCwDUVUO3bNmChw8f6i1IIiIiMk8lSjRSU1PRsmVLDBs2DL/99hu2b9+OhIQEAICjoyPeeecdLFiwQK+BEhERkfkpUaIxZcoUXLp0CREREbh9+7bWSpJSqRQvvfQSdu7cqbcgiYiIyDyVKNH4448/8Pbbb6NTp046Z5dUr16dA0aJiIioZIlGWloaAgMDC92vUCiQn59f4qCIiIjIMpQo0QgODsbZs2cL3b97927Url27xEERERGRZShRojF27FisXLkSGzZs0IzPEAQBcrkcH330EXbt2oVx48bpNVAiIiIyPyUq2PXuu+/i0qVLGDp0KFxdXQEAw4YNQ1JSEvLz8zFu3Di8+uqr+oyTiIiIzFCJEg1BELB8+XKMGjUKmzdvxo0bN6BSqRAcHIxBgwahTZs2+o6TiIiIzFCREo3+/fvjvffeQ+vWrQEAhw4dQq1atdCqVSu0atXKoAESERGR+SrSGI1t27bh7t27muft2rXDnj17DBYUERGROVMqlaYOocwoUqJRqVIlnDt3TvNcFEWuzkpERFSInLxcU4dQZhSp62TIkCH45ptvsHHjRs3gzylTpmDu3LmFvkYQBPz33396CZKIiMicKPIVpg6hzChSojF37lxUrVoV+/fvR3x8PARBgIODAypUqGDo+IiIiMyOgkUrNYqUaEilUrz++ut4/fXXAQASiQTTp0/HsGHDDBocERGROcpl14lGkcZoNGzYELt27dI8X7VqFUJDQw0WFBERkTnLys02dQhlRpESjfPnzyMxMVHzfMyYMVqDQ4mIiOiJtMx0U4dQZhQp0fD398fevXs103U464SIiKhwyRkppg6hzChSovHGG2/gl19+ga2tLZydnSEIAl599VU4OzsX+nBxcTF07ERERGVSQkriiw8qJ4o0GPR///sfGjRogP379+Phw4f4+eef0bhxYwQFBRk6PiIiIrMTn5rI1v9HirzWSefOndG5c2cAwOrVqzFu3DjOOiEiItIhKycLmTlZcLJ3NHUoJleiRdVUKpW+4yAiIrIocUkPmWigiInG43VO/Pz8tJ6/yOPjiYiIypuYxDhUqxJs6jBMrkiJRkBAAARBQE5ODmxsbDTPX4SLyhARUXl160EU2oa2NHUYJlekRGPlypUQBAHW1tZaz4mIiEi3S1FXochXwNrK2tShmFSREo3Ro0c/9zkRERFpy5Xn4tyNC2hSq6GpQzGpItXRICIiouLbc/oARFE0dRgmVaQWjU8++aTYJxYEATNmzCj264iIiCxFTPwD/HfrEkKq1jV1KCZTpERj1qxZBbY9HqPxbKYmCIKmSAkTDSIiKu/+PLoL9YNqQyIpn50IRfrUKpVK63Hv3j3Uq1cPQ4cOxcmTJ5GWloa0tDT8+++/GDJkCBo0aIB79+4ZOnYiIqIyLzYxDicunzZ1GCYjiCXoPOrbty+sra2xadMmnftfeuklKJVKbN26tdQBGtLZs2cRFhaGM2fOoGHD8j1Yh4iI9KNRo0a4fvsGbB3s8NLE4QAAR3tHzHxlMhxs7U0cnfGVqB3nn3/+Qfv27Qvd36FDB+zbt6/EQREREZmruLg4ZKSkIzsjS7MtMzsT6/ZsLpcDQ0uUaNja2uL48eOF7j927BhsbW1LHBQREZGlOXf9PHaf2m/qMIyuRInGyy+/jLVr1+Kdd97BjRs3NGM3bty4gbfffhvr1q3Dyy+/rO9YtcyZMwctWrSAvb09XF1dDfpeRERE+rDt8E4cu3jS1GEYVYkWVfvyyy+RmJiIH374AT/++KNmJK1KpYIoihg6dCi+/PJLvQb6rLy8PAwcOBDNmzfHihUrDPpeRERE+vLr7k2QSqRoWjvM1KEYRYkSDRsbG6xZswb/+9//sHPnTty5cwcA4O/vj27duqFBgwZ6DVKX2bNnA1AvWU9ERGQ2RBG/RKyHIABNall+slGiROOx+vXro379+vqKxeDkcjnkcrnmeWZmpgmjISKi8kpUifh513pIJVKE1QgxdTgGVa6qh8ydOxcuLi6aR3h4uKlDIiKickpUiVi1cx0uRV01dSgGVaYSjSlTpkAQhOc+rl4t+QWZOnWqprhYWloaDh48qMfoiYiIikelUmHZnz8jKvaOqUMxmFJ1nejb+++//8KVYYOCgkp8fplMBplMpnnu6OhY4nMRERHpg0KhwKKtK/D+kAnwcfcydTh6V6YSDU9PT3h6epo6DCIiIqPKysnG95uXYtKgt+DhWsHU4ehVmeo6KY67d+8iMjISd+/ehVKpRGRkJCIjIznAk4iIzFJqRhq+3bgIDxLjTB2KXpltovHxxx8jNDQUM2fORGZmJkJDQxEaGorTp8vvwjVERGTeUjPS8M36H3A52nIGiJa46yQiIgIrVqzA7du3kZKSonO5+Fu3bpU6wMKsXr2aNTSIiMji5Mpz8cOWFejerBO6N+to9svLlyjR+PrrrzFlyhR4e3ujSZMmqFevnr7jIiIiKr9EETuP78a1uzcwqttQeLi4mzqiEitRorFgwQK0b98eO3fuhLW1tb5jIiIiIgC3YqIw55d56B/eE63qNYMgCKYOqdhK1B6TkpKCl156iUkGERGRgcnz5Phtz+9YtHUF0rLSTR1OsZUo0WjSpAmuXbum71iIiIioEJeiruLzX77F1TvXTR1KsZQo0Vi0aBG2bNmCdevW6TseIiIiKkRGdiYWblmOf84eNnUoRVaiMRqDBw9Gfn4+RowYgTfffBOVK1eGVCrVOkYQBPz33396CZKIiIjURJWIzfu3ISM7E31adTN1OC9UokTD3d0dFSpUQLVq1fQdDxERERVBxL/74OLgjLahLU0dynOVKNE4cOCAnsMgIiIyf3fv3kV2djYAID9PgYyUdDi5ORvs/TYf3I7qVYJQ0cPXYO9RWuZdBYSIiKgMOHnyJHr16oWAgACkpKQAAOQ5cvz6+XLsXLkV8XcNU1ZcpVRi+9FdBjm3vpRqUTWFQoGrV68iLS0NKpWqwP42bdqU5vRERERl3pYtWzB48GCIoligSjZE4O7VKNy9Go3OI3oiqJ7+hxycv3UZqZlpcHV00fu59aFEiYZKpcLUqVOxaNEiTRORLkqlssSBERERlXUnT57E4MGDoVQqCyYZj4gqEYCI3Wt2oP+EofDy89FvEKKIC7cvo3X95vo9r56UqOvk888/x9dff43hw4fjl19+gSiK+OKLL7BkyRLUr18fDRo0QEREhL5jJSIiKlM+++wz3S0ZOok4s/eEQeK4dvemQc6rDyVKNFavXo1BgwZh8eLF6Nq1KwAgLCwMr732Gv79918IgoB//vlHr4ESERGVJXfv3sWOHTuK3HovqkREX7mFjBT9V/e8GROl93PqS4kSjfv376N9+/YAAJlMBgDIzc0FANjY2GD48OFYs2aNnkIkIiIqe/bt21fEloyniEDMzbt6jyU9Mx3pWRl6P68+lCjRqFChAjIzMwEAjo6OcHZ2xu3bt7WOeTzqloiIyBJlZGQUfwl3QUBebp5B4imr66CUaDBoaGgoTp06pXnerl07zJ8/H6GhoVCpVPj+++/RoEEDvQVJRERU1jg5OemccflcoggbWxuDxCOVSF98kAmUqEXj9ddfh1wuh1wuBwDMmTMHqampaNOmDcLDw5Geno558+bpNVAiIqKypEOHDsVftl0AKlX103ssNtY28HLz0Pt59aFELRq9e/dG7969Nc9r166NW7du4cCBA5BKpWjRogXc3d31FiQREVFZ4+fnh549e2Lnzp1FGhAqSAT41wwySKXQRjVDYCUtVWksg9FbVC4uLujTp4++TkdERFTmzZgxA3///TcEQSjCwFABYR2b6T0Ga2trdG/WSe/n1ZcSlyBXKpVYv349xo0bh379+uHChQsAgLS0NGzZsgUPHz7UW5BERERlUePGjbFhwwZIpdICq5g/JkgECBIJOo/oqf9iXQAGtesLd2c3vZ9XX0qUaKSmpqJly5YYNmwYfvvtN2zfvh0JCQkA1LNQ3nnnHSxYsECvgRIREZVF/fv3x7Fjx9C9e/eCYzYEwL9mEPpPGGqQ8uMt6jVFi7pN9H5efSpRojFlyhRcunQJERERuH37tlZzkVQqxUsvvYSdO3fqLUgiIqKyrHHjxti+fTuio6Ph5qZuXZDZyTB82mvoNqavQVoygisFYkiHfsUfkGpkJUo0/vjjD7z99tvo1KmTzg9YvXp1REdHlzY2IiIis+Ln5wd7e3sAgJWNtcGWiHd2dMbYXiPK7ADQp5Uo0UhLS0NgYGCh+xUKBfLz80scFBERERVCEDCm+8twcTBMEqNvJUo0goODcfbs2UL37969G7Vr1y5xUERERKRbt6YdUL1KsKnDKLISJRpjx47FypUrsWHDBs34DEEQIJfL8dFHH2HXrl0YN26cXgMlIiIq72oFVEeP5p1NHUaxlKhz591338WlS5cwdOhQuLq6AgCGDRuGpKQk5OfnY9y4cXj11Vf1GScREVG55udTBWN7jiz++iomVqJEQxAELF++HKNGjcLmzZtx48YNqFQqBAcHY9CgQWjTpo2+4yQiIiq3qlUJxht9RsNOZmvqUIqtVMNVW7VqhVatWukrFiIiInpGy/pNMahdX1hbWZs6lBIp+/NiiIiIyiErKysM6dC/zBfkepEiJxpPL6JWFIIgYNu2bcUOiIiIqLzzcvPA2J4jUNmrkqlDKbUiJxo7duyAra0tfHx8irBwDMp8pTIiIqKyKLR6fQzvPMgsx2PoUuREo1KlSoiJiYGHhweGDRuGIUOGwMdH/yVViYiIyiNBIqBf6x7oEBZuUX+sF3mOzL1797B//36Ehobi008/RZUqVdCxY0esWrUKGRkZhoyRiIjIotnZ2mFC/9fQsVFbi0oygGIW7AoPD8fSpUsRFxeHzZs3o0KFCpgwYQK8vLzQv39/bN68GXK53FCxEhERWRxXJ1e8P3g8avlXN3UoBlGiqh/W1tbo06cPNmzYgIcPH2qSj8GDB+Orr77Sd4xEREQWqYKrOz4YMh4VPSx3KEKpyovJ5XJERERg27ZtOHfuHGxtbREQEKCn0IiIiCyXk70j3h0wDu7ObqYOxaCKnWioVCpERERg9OjR8Pb2xtChQ5GTk4Ply5cjPj4eI0aMMEScREREFkMilWJcn9HwcK1g6lAMrsizTo4dO4Z169Zh06ZNSEpKQrNmzfD5559j0KBB8PDwMGSMREREFuWl8N4Iqhhg6jCMosiJRqtWrWBnZ4fu3btj6NChmi6Su3fv4u7duzpf07BhQ70ESUREZCkaVKuL8JAWpg7DaIpVgjwnJwe///47tmzZ8tzjRFGEIAhQKpWlCo6IiMiSuDq5YnjnQRY3hfV5ipxorFq1ypBxEBERWTZBwCvdh8HB1t7UkRhVkRONUaNGGTIOIiIii9apUTiqVQ4ydRhGV6rprURERPRi7s5u6NGii6nDMAmzTDSio6Px6quvIjAwEHZ2dggODsbMmTORl5dn6tCIiIgK6N2qK2ysrE0dhkkUazBoWXH16lWoVCosXboUVatWxcWLF/Haa68hKysL33zzjanDIyKicszHxwfp2RmwdbADAFRwcUejGqEmjsp0zDLR6Nq1K7p27ap5HhQUhGvXrmHx4sVMNIiIyKROnz6NWSu/QHxKIgCgVf1mkEjMsgNBLyzmk6elpcHd3d3UYRARET0hCGhaO8zUUZiUWbZoPOvmzZtYuHDhC1sz5HK51uqymZmZhg6NiIjKsepVguHq6GLqMEyqTLVoTJkyBYIgPPdx9epVrdfExMSga9euGDhwIF577bXnnn/u3LlwcXHRPMLDww35cYiIqJwLq9HA1CGYnCCKomjqIB5LSEhAUlLSc48JCgqCjY0NAODBgwdo27YtmjVrhtWrV7+wD+zZFo3IyEiEh4fjzJkzLJdORER6M2vlF0hIS8IX42bCyd7R1OGYVJnqOvH09ISnp2eRjo2JiUG7du0QFhaGVatWFWmgjUwmg0wm0zx3dCzfF5+IiAwnyDeg3CcZQBlLNIoqJiYGbdu2hb+/P7755hskJCRo9vn4+JgwMiIiIrXagTVMHUKZYJaJxp49e3Dz5k3cvHkTlStX1tpXhnqCiIioHKtRpZqpQygTytRg0KIaPXo0RFHU+SAiIjI1a2sb+HtXfvGB5YBZJhpERERlWWUPX0ilUlOHUSYw0SAiItIzb3cvU4dQZjDRICIi0jN3Z1dTh1BmMNEgIiLSM0c7B1OHUGYw0SAiItIzWxtbU4dQZjDRICIi0jMOBH2CiQYREZGeSQX+en2M/xJERET6JgimjqDMYKJBRESkZxK2aGjwX4KIiEjPpEVY6LO84L8EERGRnkmlZrmUmEEw0SAiItIzdydXU4dQZjDRICIi0jMJu040+C9BREREBsNEg4iIiAyGiQYREREZDBMNIiIiMhgmGkRERGQwTDSIiIjIYFhRpJyIjY1FbGysqcMgPfH19YWvr6+pwyA94f1peXiPPlGuEw1fX1/MnDnT4n8Y5HI5hg4dioMHD5o6FNKT8PBwREREQCaTmToUKiXen5aJ9+gTgiiKoqmDIMNKT0+Hi4sLDh48CEdHR1OHQ6WUmZmJ8PBwpKWlwdnZ2dThUCnx/rQ8vEe1lesWjfImJCSEP/QWID093dQhkAHw/rQcvEe1cTAoERERGQwTDSIiIjIYJhrlgEwmw8yZMzkoyULweloWXk/Lw2uqjYNBiYiIyGDYokFEREQGw0SDiIiIDIaJBhERERkMEw0iIiIyGCYaRAYgCEKRHgcOHCj1e2VnZ2PWrFnFOtecOXPQu3dveHt7QxAEzJo1q9RxEJmLsnx/Xr16FZMnT0ZISAicnJzg6+uLHj164PTp06WOxVRYGZTIANasWaP1/JdffsGePXsKbK9Vq1ap3ys7OxuzZ88GALRt27ZIr5k+fTp8fHwQGhqKiIiIUsdAZE7K8v35008/YcWKFRgwYADeeustpKWlYenSpWjWrBl27dqFjh07ljomY2OiQWQAw4cP13p+4sQJ7Nmzp8B2U4mKikJAQAASExPh6elp6nCIjKos359Dhw7FrFmztNa9GTNmDGrVqoVZs2aZZaLBrhMiE1GpVJg/fz7q1KkDW1tbeHt7Y9y4cUhJSdE67vTp0+jSpQs8PDxgZ2eHwMBAjBkzBgAQHR2tSRRmz56tafJ9UVdIQECAIT4SkcUw1f0ZFhZWYHG9ChUqoHXr1rhy5Yp+P6SRsEWDyETGjRuH1atX45VXXsE777yDqKgo/PDDDzh37hyOHj0Ka2trxMfHo3PnzvD09MSUKVPg6uqK6OhobNmyBQDg6emJxYsX480330S/fv3Qv39/AED9+vVN+dGIzF5Zuz/j4uLg4eGh189oNCIRGdz48ePFp2+3w4cPiwDEtWvXah23a9cure1bt24VAYinTp0q9NwJCQkiAHHmzJnFjqs0ryWyFGX1/nzs0KFDoiAI4owZM0p8DlNi1wmRCWzatAkuLi7o1KkTEhMTNY/Hzab79+8HALi6ugIAduzYAYVCYcKIicqPsnR/xsfHY9iwYQgMDMTkyZMN8h6GxkSDyARu3LiBtLQ0eHl5wdPTU+uRmZmJ+Ph4AEB4eDgGDBiA2bNnw8PDA3369MGqVasgl8tN/AmILFdZuT+zsrLQs2dPZGRkYNu2bQXGbpgLjtEgMgGVSgUvLy+sXbtW5/7HA8gEQcDmzZtx4sQJ/Pnnn4iIiMCYMWMwb948nDhxwmy/eIjKsrJwf+bl5aF///44f/48IiIiULdu3RKfy9SYaBCZQHBwMPbu3YuWLVvCzs7uhcc3a9YMzZo1w5w5c7Bu3Tq8/PLLWL9+PcaOHQtBEIwQMVH5Yer7U6VSYeTIkdi3bx82btyI8PDwknyMMoNdJ0QmMGjQICiVSnz66acF9uXn5yM1NRUAkJKSAlEUtfaHhIQAgKZ51t7eHgA0ryGi0jH1/fn2229jw4YNWLRokWamijljiwaRCYSHh2PcuHGYO3cuIiMj0blzZ1hbW+PGjRvYtGkTFixYgJdeegk///wzFi1ahH79+iE4OBgZGRlYvnw5nJ2d0b17dwCAnZ0dateujQ0bNqB69epwd3dH3bp1n9vUumbNGty5cwfZ2dkAgEOHDuGzzz4DAIwYMQL+/v6G/0cgKqNMeX/Onz8fixYtQvPmzWFvb49ff/1Va3+/fv3g4OBg8H8DvTL1tBei8uDZ6XOPLVu2TAwLCxPt7OxEJycnsV69euLkyZPFBw8eiKIoimfPnhWHDh0q+vn5iTKZTPTy8hJ79uwpnj59Wus8x44dE8PCwkQbG5siTaULDw8XAeh87N+/X18fm8gslKX7c9SoUYXemwDEqKgofX50oxBE8Zl2HyIiIiI94RgNIiIiMhgmGkRERGQwTDSIiIjIYJhoEBERkcEw0SAiIiKDYaJBREREBsNEg6iMiY6OhiAIWL16talDISIdeI8WDxMNIiIiMhgW7CIqY0RRhFwuh7W1NaRSqanDIaJn8B4tHiYaREREZDDsOiEygFmzZkEQBFy/fh3Dhw+Hi4sLPD09MWPGDIiiiHv37qFPnz5wdnaGj48P5s2bp3mtrv7f0aNHw9HRETExMejbty8cHR3h6emJDz74AEqlUnPcgQMHIAgCDhw4oBWPrnPGxcXhlVdeQeXKlSGTyeDr64s+ffogOjraQP8qRGUH71HjYaJBZECDBw+GSqXCF198gaZNm+Kzzz7D/Pnz0alTJ1SqVAlffvklqlatig8++ACHDh167rmUSiW6dOmCChUq4JtvvkF4eDjmzZuHZcuWlSi2AQMGYOvWrXjllVewaNEivPPOO8jIyMDdu3dLdD4ic8R71AhMtZobkSWbOXOmCEB8/fXXNdvy8/PFypUri4IgiF988YVme0pKimhnZyeOGjVKFEVRjIqKEgGIq1at0hzzeEXHTz75ROt9QkNDxbCwMM3z/fv361yB9dlzpqSkiADEr7/+Wj8fmMjM8B41HrZoEBnQ2LFjNf9fKpWiUaNGEEURr776qma7q6sratSogdu3b7/wfG+88YbW89atWxfpdc+ys7ODjY0NDhw4gJSUlGK/nshS8B41PCYaRAbk5+en9dzFxQW2trbw8PAosP1FXya2trbw9PTU2ubm5laiLyGZTIYvv/wSf//9N7y9vdGmTRt89dVXiIuLK/a5iMwZ71HDY6JBZEC6pr4VNh1OfMEEsKJMoxMEQef2pwejPTZx4kRcv34dc+fOha2tLWbMmIFatWrh3LlzL3wfIkvBe9TwmGgQWRA3NzcAQGpqqtb2O3fu6Dw+ODgY77//Pnbv3o2LFy8iLy9Pa3Q9EelXebxHmWgQWRB/f39IpdICo+MXLVqk9Tw7Oxu5ubla24KDg+Hk5AS5XG7wOInKq/J4j1qZOgAi0h8XFxcMHDgQCxcuhCAICA4Oxo4dOxAfH6913PXr19GhQwcMGjQItWvXhpWVFbZu3YqHDx9iyJAhJoqeyPKVx3uUiQaRhVm4cCEUCgWWLFkCmUyGQYMG4euvv0bdunU1x1SpUgVDhw7Fvn37sGbNGlhZWaFmzZrYuHEjBgwYYMLoiSxfebtHWYKciIiIDIZjNIiIiMhgmGgQERGRwTDRICIiIoNhokFEREQGw0SDiIiIDIaJBlE5Fh0dDUEQsHr1alOHQkQ6WMI9ykSDqIhu3bqFcePGISgoCLa2tnB2dkbLli2xYMEC5OTkGOx9L1++jFmzZiE6Otpg71EUc+bMQe/eveHt7Q1BEDBr1iyTxkP0rPJ8j169ehWTJ09GSEgInJyc4Ovrix49euD06dMmi+kxFuwiKoK//voLAwcOhEwmw8iRI1G3bl3k5eXhyJEj+N///odLly5h2bJlBnnvy5cvY/bs2Wjbti0CAgIM8h5FMX36dPj4+CA0NBQREREmi4NIl/J+j/70009YsWIFBgwYgLfeegtpaWlYunQpmjVrhl27dqFjx44miQtgokH0QlFRURgyZAj8/f3xzz//wNfXV7Nv/PjxuHnzJv766y8TRviEKIrIzc2FnZ2d3s8dFRWFgIAAJCYmFlgKm8iUeI8CQ4cOxaxZs+Do6KjZNmbMGNSqVQuzZs0yaaLBrhOiF/jqq6+QmZmJFStWaH2BPVa1alW8++67muf5+fn49NNPERwcDJlMhoCAAEybNq3AQkgBAQHo2bMnjhw5giZNmsDW1hZBQUH45ZdfNMesXr0aAwcOBAC0a9cOgiBAEAQcOHBA6xwRERFo1KgR7OzssHTpUgDA7du3/9/evYREuYdxHP/qUIamqF2IoGbGAlGCpEDKspcCiVS6iBEFpYupRZBEhlRQERTRoosELmwRMmGEREQRuajAoMBVRDeraZpdY4vGBkOKfM4izsCc8TJz6u1yzu8Ds/B5/7cRnpdn/vP+lc2bN1NcXExubi7Lli37rpvtr9xNEZmIchSWLl2aVGQAzJgxg+rqap4/f/6vxvxRVGiITOLGjRuUlJRQVVWVVvtAIMCRI0dYsmQJZ8+exXEcTp48OeY/Qnr9+jWNjY3U1NRw+vRpioqKaG5u5unTpwCsWrWKlpYWAA4dOkQwGCQYDFJWVpYYY2BggK1bt1JTU0N7ezsVFRVEo1Gqqqro7e1l9+7dnDhxgpGREdavX8+1a9d+wG9F5PehHB3fu3fvmDlz5g8b718xERnX0NCQAbZhw4a02j969MgACwQCSfH9+/cbYHfv3k3EvF6vAdbX15eIDQ4OWk5OjrW2tiZiPT09Bti9e/dS5vt7jNu3byfF9+7da4Ddv38/EYvH4+b3+83n89nXr1/NzCwcDhtgFy9eTOv9mZm9f//eADt69GjafUTcohwdX19fn2VlZdnhw4cz7vsjaUdDZAIfP34EID8/P632t27dAmDfvn1J8dbWVoCUbdHy8nKqq6sTP8+aNYvS0lLevHmT9hr9fj9r165NWUdlZSUrV65MxKZPn86uXbt4+/Ytz549S3t8kd+ZcnRsg4ODbNu2Db/fT1tb23eN9b1UaIhMoKCgAIB4PJ5W+0gkQnZ2NgsXLkyKz5kzh8LCQiKRSFJ8/vz5KWMUFRXx4cOHtNfo9/vHXEdpaWlK/O/t3H+uQ+RPpRxNNTw8TH19PfF4nOvXr6c8u/Gz6dSJyAQKCgqYO3cuT548yahfVlZWWu08Hs+YcTNLey43TpiI/CmUo8k+f/5MQ0MDjx8/pre3l0WLFv20ucejHQ2RSdTX1xMKhXj48OGkbb1eL6Ojo7x69SopHo1GicVieL3ejOdP94b4z3UMDAykxF+8eJG4LvJfoRz9ZnR0lB07dnDnzh26u7txHCfjMdygQkNkEm1tbeTl5REIBIhGoynXQ6EQ7e3tANTW1gJw7ty5pDZnzpwBoK6uLuP58/LyAIjFYmn3qa2tpb+/P+nGOzw8TGdnJz6fj/Ly8ozXIfK7Uo5+s2fPHq5cuUJHRwcNDQ0Z93eLvjoRmcSCBQvo7u5my5YtlJWVJf3VwQcPHtDT00NzczMAixcvpqmpic7OTmKxGI7j0N/fT1dXFxs3bmT16tUZz19RUYHH4+HUqVMMDQ2Rk5PDmjVrmD179rh9Dhw4wOXLl1m3bh0tLS0UFxfT1dVFOBzm6tWrZGdn/hkjGAwSiUT49OkTAH19fRw/fhyA7du3a5dEfhnl6LfCqaOjg+XLl5Obm8ulS5eSrm/atClREP10v/TMi8gf5OXLl7Zz507z+Xw2depUy8/PtxUrVtj58+dtZGQk0e7Lly927Ngx8/v9NmXKFJs3b54dPHgwqY3Zt2NvdXV1KfM4jmOO4yTFLly4YCUlJebxeJKO0Y03hplZKBSyxsZGKywstGnTplllZaXdvHkzqU0mR+ccxzFgzNdYx/pEfrb/c442NTWNm5+AhcPhCfu7KcssgydaRERERDKgZzRERETENSo0RERExDUqNERERMQ1KjRERETENSo0RERExDUqNERERMQ1KjRERETENSo0RERExDUqNERERMQ1KjRERETENSo0RERExDUqNERERMQ1KjRERETENX8Bv0teE0VeBjsAAAAASUVORK5CYII=",
-      "text/plain": [
-       "<Figure size 600x600 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "multi_2group.mean_diff.plot(custom_palette=my_color_palette,\n",
-    "                                swarm_desat=0.75,\n",
-    "                                halfviolin_desat=0.25);"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9547d1aa",
-   "metadata": {},
-   "source": [
-    "## Changing size\n",
-    "It is possible change the size of the dots used in the rawdata swarmplot, as well as those to indicate the effect sizes, by using the parameters `raw_marker_size` and `es_marker_size` respectively.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "2e964805",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 600x600 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "multi_2group.mean_diff.plot(raw_marker_size=3,\n",
-    "                                es_marker_size=12);"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "21949c5f",
-   "metadata": {},
-   "source": [
-    "## Changing axes\n",
-    "\n",
-    "To change the y-limits for the rawdata axes, and the contrast axes, use the parameters `swarm_ylim` and `contrast_ylim`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "97d2052e",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 600x600 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "multi_2group.mean_diff.plot(swarm_ylim=(0, 5),\n",
-    "                                contrast_ylim=(-2, 2));"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4688b5c9",
-   "metadata": {},
-   "source": [
-    "If the effect size is qualitatively inverted (ie. a smaller value is a\n",
-    "better outcome), you can simply invert the tuple passed to\n",
-    "``contrast_ylim``."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "63e2465a",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 600x600 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "multi_2group.mean_diff.plot(contrast_ylim=(2, -2),\n",
-    "                                contrast_label=\"More negative is better!\");"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5c0f96f8",
-   "metadata": {},
-   "source": [
-    "The contrast axes share the same y-limits as those of the delta-delta plot. Thus, the y axis of the delta-delta plot changes as well."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "d588b8d3",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 675x600 with 3 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "np.random.seed(9999) # Fix the seed so the results are replicable.\n",
-    "\n",
-    "# Create samples\n",
-    "N = 20\n",
-    "y = norm.rvs(loc=3, scale=0.4, size=N*4)\n",
-    "y[N:2*N] = y[N:2*N]+1\n",
-    "y[2*N:3*N] = y[2*N:3*N]-0.5\n",
-    "\n",
-    "# Add a `Treatment` column\n",
-    "t1 = np.repeat('Placebo', N*2).tolist()\n",
-    "t2 = np.repeat('Drug', N*2).tolist()\n",
-    "treatment = t1 + t2 \n",
-    "\n",
-    "# Add a `Rep` column as the first variable for the 2 replicates of experiments done\n",
-    "rep = []\n",
-    "for i in range(N*2):\n",
-    "    rep.append('Rep1')\n",
-    "    rep.append('Rep2')\n",
-    "\n",
-    "# Add a `Genotype` column as the second variable\n",
-    "wt = np.repeat('W', N).tolist()\n",
-    "mt = np.repeat('M', N).tolist()\n",
-    "wt2 = np.repeat('W', N).tolist()\n",
-    "mt2 = np.repeat('M', N).tolist()\n",
-    "\n",
-    "\n",
-    "genotype = wt + mt + wt2 + mt2\n",
-    "\n",
-    "# Add an `id` column for paired data plotting.\n",
-    "id = list(range(0, N*2))\n",
-    "id_col = id + id \n",
-    "\n",
-    "\n",
-    "# Combine all columns into a DataFrame.\n",
-    "df_delta2 = pd.DataFrame({'ID'        : id_col,\n",
-    "                  'Rep'      : rep,\n",
-    "                   'Genotype'  : genotype, \n",
-    "                   'Treatment': treatment,\n",
-    "                   'Y'         : y\n",
-    "                })\n",
-    "\n",
-    "paired_delta2 = dabest.load(data = df_delta2, \n",
-    "                                paired = \"baseline\", id_col=\"ID\",\n",
-    "                                x = [\"Treatment\", \"Rep\"], y = \"Y\", \n",
-    "                                delta2 = True, experiment = \"Genotype\")\n",
-    "paired_delta2.mean_diff.plot(contrast_ylim=(3, -3),\n",
-    "                                 contrast_label=\"More negative is better!\");"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7682de82",
-   "metadata": {},
-   "source": [
-    "You can also change the `*y-limits* and *y-label* for the delta-delta plot."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "856301bb",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 675x600 with 3 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "paired_delta2.mean_diff.plot(delta2_ylim=(3, -3),\n",
-    "                                 delta2_label=\"More negative is better!\");"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a60c4367",
-   "metadata": {},
-   "source": [
-    "### Axes ticks\n",
-    "You can add minor ticks and also change the tick frequency by accessing\n",
-    "the axes directly.\n",
-    "\n",
-    "Each estimation plot produced by ``dabest`` has two axes. The first one\n",
-    "contains the rawdata swarmplot while the second one contains the bootstrap\n",
-    "effect size differences.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "8c2f3504",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 500x400 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import matplotlib.ticker as Ticker\n",
-    "\n",
-    "f = two_groups_unpaired.mean_diff.plot()\n",
-    "\n",
-    "rawswarm_axes = f.axes[0]\n",
-    "contrast_axes = f.axes[1]\n",
-    "\n",
-    "rawswarm_axes.yaxis.set_major_locator(Ticker.MultipleLocator(1))\n",
-    "rawswarm_axes.yaxis.set_minor_locator(Ticker.MultipleLocator(0.5))\n",
-    "\n",
-    "contrast_axes.yaxis.set_major_locator(Ticker.MultipleLocator(0.5))\n",
-    "contrast_axes.yaxis.set_minor_locator(Ticker.MultipleLocator(0.25))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "fc0f29ec",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 600x600 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "f = multi_2group.mean_diff.plot(swarm_ylim=(0,6),\n",
-    "                                   contrast_ylim=(-3, 1))\n",
-    "\n",
-    "rawswarm_axes = f.axes[0]\n",
-    "contrast_axes = f.axes[1]\n",
-    "\n",
-    "rawswarm_axes.yaxis.set_major_locator(Ticker.MultipleLocator(2))\n",
-    "rawswarm_axes.yaxis.set_minor_locator(Ticker.MultipleLocator(1))\n",
-    "\n",
-    "contrast_axes.yaxis.set_major_locator(Ticker.MultipleLocator(0.5))\n",
-    "contrast_axes.yaxis.set_minor_locator(Ticker.MultipleLocator(0.25))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2bb38d27",
-   "metadata": {},
-   "source": [
-    "## Changing swarm side\n",
-    "In `dabest`, swarmplots are, by default, plotted asymmetrically to the right side. You may change this by using the parameter `swarm_side`. \n",
-    "\n",
-    "There are only three valid values: \"right\" (default), \"left\", \"center\"."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "593f5923",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 600x600 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "multi_2group.mean_diff.plot(swarm_side=\"left\");"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "1f1d5107",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 600x600 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "multi_2group.mean_diff.plot(swarm_side=\"center\");"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ec7f5271",
-   "metadata": {},
-   "source": [
-    "## Hiding options \n",
-    "For mini-meta plots, it is possible to hide the weighted average plot by setting the parameter ``show_mini_meta=False`` in the ``plot()`` function."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "337fa39d",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 675x600 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "np.random.seed(9999) # Fix the seed so the results are replicable.\n",
-    "# pop_size = 10000 # Size of each population.\n",
-    "Ns = 20 # The number of samples taken from each population\n",
-    "\n",
-    "# Create samples\n",
-    "c1 = norm.rvs(loc=3, scale=0.4, size=Ns)\n",
-    "c2 = norm.rvs(loc=3.5, scale=0.75, size=Ns)\n",
-    "c3 = norm.rvs(loc=3.25, scale=0.4, size=Ns)\n",
-    "\n",
-    "t1 = norm.rvs(loc=3.5, scale=0.5, size=Ns)\n",
-    "t2 = norm.rvs(loc=2.5, scale=0.6, size=Ns)\n",
-    "t3 = norm.rvs(loc=3, scale=0.75, size=Ns)\n",
-    "\n",
-    "\n",
-    "# Add a `gender` column for coloring the data.\n",
-    "females = np.repeat('Female', Ns/2).tolist()\n",
-    "males = np.repeat('Male', Ns/2).tolist()\n",
-    "gender = females + males\n",
-    "\n",
-    "# Add an `id` column for paired data plotting.\n",
-    "id_col = pd.Series(range(1, Ns+1))\n",
-    "\n",
-    "# Combine samples and gender into a DataFrame.\n",
-    "df = pd.DataFrame({'Control 1' : c1,     'Test 1' : t1,\n",
-    "                   'Control 2' : c2,     'Test 2' : t2,\n",
-    "                   'Control 3' : c3,     'Test 3' : t3,\n",
-    "                   'Gender'    : gender, 'ID'  : id_col\n",
-    "                  })\n",
-    "mini_meta_paired = dabest.load(df, idx=((\"Control 1\", \"Test 1\"), (\"Control 2\", \"Test 2\"), (\"Control 3\", \"Test 3\")), mini_meta=True, id_col=\"ID\", paired=\"baseline\")\n",
-    "mini_meta_paired.mean_diff.plot(show_mini_meta=False);"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "659d880a",
-   "metadata": {},
-   "source": [
-    "Similarly, you can also hide the delta-delta plot by setting \n",
-    "``show_delta2=False`` in the ``plot()`` function."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "d2984546",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 450x600 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "paired_delta2.mean_diff.plot(show_delta2=False);"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "aa66a227",
-   "metadata": {},
-   "source": [
-    "## Creating estimation plots in existing axes"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ba3ebef2",
-   "metadata": {},
-   "source": [
-    "*Implemented in v0.2.6 by Adam Nekimken*.\n",
-    "\n",
-    "``dabest.plot`` has an ``ax`` parameter that accepts Matplotlib\n",
-    "``Axes``. The entire estimation plot will be created in the specified\n",
-    "``Axes``.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "9a2aa538",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "two_groups_paired_baseline = dabest.load(df, idx=(\"Control 1\", \"Test 1\"),\n",
-    "                                  paired=\"baseline\", id_col=\"ID\")\n",
-    "multi_2group_paired = dabest.load(df,\n",
-    "                            idx=((\"Control 1\", \"Test 1\"),\n",
-    "                                 (\"Control 2\", \"Test 2\")),\n",
-    "                            paired=\"baseline\", id_col=\"ID\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "9624ce3b",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 1500x1500 with 4 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from matplotlib import pyplot as plt\n",
-    "f, axx = plt.subplots(nrows=2, ncols=2,\n",
-    "                      figsize=(15, 15),\n",
-    "                      gridspec_kw={'wspace': 0.25} # ensure proper width-wise spacing.\n",
-    "                     )\n",
-    "\n",
-    "two_groups_unpaired.mean_diff.plot(ax=axx.flat[0]);\n",
-    "\n",
-    "two_groups_paired_baseline.mean_diff.plot(ax=axx.flat[1]);\n",
-    "\n",
-    "multi_2group.mean_diff.plot(ax=axx.flat[2]);\n",
-    "\n",
-    "multi_2group_paired.mean_diff.plot(ax=axx.flat[3]);"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c793b67c",
-   "metadata": {},
-   "source": [
-    "In this case, to access the individual rawdata axes, use\n",
-    "``name_of_axes`` to manipulate the rawdata swarmplot axes, and\n",
-    "``name_of_axes.contrast_axes`` to gain access to the effect size axes."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "ad858bba",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Text(642.3472222222223, 0.5, 'New y-axis label for effect size')"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "topleft_axes = axx.flat[0]\n",
-    "topleft_axes.set_ylabel(\"New y-axis label for rawdata\")\n",
-    "topleft_axes.contrast_axes.set_ylabel(\"New y-axis label for effect size\")"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "python3",
-   "language": "python",
-   "name": "python3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/nbs/tutorials/07-forest_plot.ipynb b/nbs/tutorials/07-forest_plot.ipynb
new file mode 100644
index 00000000..36a2d33b
--- /dev/null
+++ b/nbs/tutorials/07-forest_plot.ipynb
@@ -0,0 +1,1101 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Forest Plots\n",
+    "\n",
+    "> Explanation of how to use forest_plot for contrast objects e.g delta-delta and mini-meta or regular deltas.\n",
+    "\n",
+    "- order: 7"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In DABEST **v2025.03.27**, we introduce a new function to plot separately calculated effect sizes in the same axes to allow direct visual comparisons. \n",
+    "\n",
+    "Currently you can make a forest plot for delta-delta, mini-meta, or standard delta effect sizes. In addition, for delta-delta and mini-meta experiments, you can also plot the effect sizes of the original comparisons alongside the delta-delta/mini-meta measurement."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Pre-compiling numba functions for DABEST...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Compiling numba functions: 100%|██████████| 11/11 [00:00<00:00, 49.90it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Numba compilation complete!\n",
+      "We're using DABEST v2025.03.27\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import dabest\n",
+    "import matplotlib.pyplot as plt\n",
+    "import dabest \n",
+    "print(\"We're using DABEST v{}\".format(dabest.__version__))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Delta-delta effects"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "First please revisit the notebook [Delta-Delta Tutorial](06-delta_delta.html) for how to generate a delta-delta effect size. We will generate three of them plot them into the same axes. Here we test the efficacy of 3 drugs named ``Drug1``, ``Drug2`` , and ``Drug3`` on a disease-causing mutation ```M``` based on disease metric ```Tumor Size```.  We want to know how the three drugs fare in ameliorating the phenotype metric ```Tumor Size```. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "|       | Wildtype | Mutant   |\n",
+    "|-------|---------|----------|\n",
+    "| Drug1  | X<sub>D<sub>1, W</sub> | X<sub>D<sub>1, M</sub> |\n",
+    "| Placebo | X<sub>P<sub>1, W</sub> | X<sub>P<sub>1, M</sub> |"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "|       | Wildtype | Mutant   |\n",
+    "|-------|---------|----------|\n",
+    "| Drug2  | X<sub>D<sub>2, W</sub> | X<sub>D<sub>2, M</sub> |\n",
+    "| Placebo | X<sub>P<sub>2, W</sub> | X<sub>P<sub>2, M</sub> |"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "|       | Wildtype | Mutant   |\n",
+    "|-------|---------|----------|\n",
+    "| Drug3  | X<sub>D<sub>3, W</sub> | X<sub>D<sub>3, M</sub> |\n",
+    "| Placebo | X<sub>P<sub>3, W</sub> | X<sub>P<sub>3, M</sub> |"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In each scenario, there are two ``Treatment`` conditions, ``Placebo`` (control group) and ``Drug`` (test group). There are two ``Genotype``\\'s: ``W`` (wild type population) and ``M`` (mutant population). Additionally, each experiment was conducted twice (``Rep1`` and ``Rep2``). We will perform several analyses to visualise these differences in a simulated dataset. We will simulate three separte datasets below. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Creating a demo dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from scipy.stats import norm\n",
+    "def create_delta_dataset(N=20, \n",
+    "                        seed=9999, \n",
+    "                        second_quarter_adjustment=3, \n",
+    "                        third_quarter_adjustment=-0.1):\n",
+    "    np.random.seed(seed)  # Set the seed for reproducibility\n",
+    "\n",
+    "    # Create samples\n",
+    "    y = norm.rvs(loc=3, scale=0.4, size=N*4)\n",
+    "    y[N:2*N] += second_quarter_adjustment\n",
+    "    y[2*N:3*N] += third_quarter_adjustment\n",
+    "\n",
+    "    # Treatment, Rep, Genotype, and ID columns\n",
+    "    treatment = np.repeat(['Placebo', 'Drug'], N*2).tolist()\n",
+    "    rep = ['Rep1', 'Rep2'] * (N*2)\n",
+    "    genotype = np.repeat(['W', 'M', 'W', 'M'], N).tolist()\n",
+    "    id_col = list(range(0, N*2)) * 2\n",
+    "\n",
+    "    # Combine all columns into a DataFrame\n",
+    "    df = pd.DataFrame({\n",
+    "        'ID': id_col,\n",
+    "        'Rep': rep,\n",
+    "        'Genotype': genotype,\n",
+    "        'Treatment': treatment,\n",
+    "        'Tumor Size': y\n",
+    "    })\n",
+    "\n",
+    "    return df\n",
+    "\n",
+    "# Generate the first dataset with a different seed and adjustments\n",
+    "df_delta2_drug1 = create_delta_dataset(seed=9999, second_quarter_adjustment=1, third_quarter_adjustment=-0.5)\n",
+    "\n",
+    "# Generate the second dataset with a different seed and adjustments\n",
+    "df_delta2_drug2 = create_delta_dataset(seed=9999, second_quarter_adjustment=0.1, third_quarter_adjustment=-1)\n",
+    "\n",
+    "# Generate the third dataset with the same seed as the first but different adjustments\n",
+    "df_delta2_drug3 = create_delta_dataset(seed=9999, second_quarter_adjustment=3, third_quarter_adjustment=-0.1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Loading data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "unpaired_delta_01 = dabest.load(data = df_delta2_drug1, \n",
+    "                                x = [\"Genotype\", \"Genotype\"], \n",
+    "                                y = \"Tumor Size\", delta2 = True, \n",
+    "                                experiment = \"Treatment\")\n",
+    "unpaired_delta_02 = dabest.load(data = df_delta2_drug2, \n",
+    "                                x = [\"Genotype\", \"Genotype\"], \n",
+    "                                y = \"Tumor Size\", delta2 = True, \n",
+    "                                experiment = \"Treatment\")\n",
+    "unpaired_delta_03 = dabest.load(data = df_delta2_drug3, \n",
+    "                                x = [\"Genotype\", \"Genotype\"], \n",
+    "                                y = \"Tumor Size\", \n",
+    "                                delta2 = True, \n",
+    "                                experiment = \"Treatment\")\n",
+    "contrasts = [unpaired_delta_01, unpaired_delta_02, unpaired_delta_03]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Generate delta-delta plots for each datasets "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To create a delta-delta plot, you simply need to set ``delta2=True`` in the \n",
+    "``dabest.load()`` function and ``mean_diff.plot()``\n",
+    "\n",
+    "In this case,``x`` needs to be declared as a list consisting of 2 elements, unlike most cases where it is a single element. \n",
+    "The first element in ``x`` will represent the variable plotted along the horizontal axis, and the second one will determine the \n",
+    "color of dots for scattered plots or the color of lines for slope graphs. We use the ``experiment`` input to specify the grouping of the data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 700x400 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 700x400 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 700x400 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "f1 = unpaired_delta_01.mean_diff.plot(\n",
+    "    contrast_label='Mean Diff',\n",
+    "    fig_size = (7, 4),\n",
+    "    raw_marker_size = 1,\n",
+    "    contrast_marker_size = 5,\n",
+    ");\n",
+    "f1.suptitle('Delta-delta plot for Drug 1');\n",
+    "\n",
+    "\n",
+    "f2 = unpaired_delta_02.mean_diff.plot(                  \n",
+    "            contrast_label='Mean Diff',\n",
+    "            fig_size = (7, 4),\n",
+    "            raw_marker_size = 1,\n",
+    "            contrast_marker_size = 5,\n",
+    ");\n",
+    "f2.suptitle('Delta-delta plot for Drug 2');\n",
+    "\n",
+    "\n",
+    "f3 = unpaired_delta_03.mean_diff.plot(                  \n",
+    "                    contrast_label='Mean Diff',\n",
+    "                    fig_size = (7, 4),\n",
+    "                    raw_marker_size = 1,\n",
+    "                    contrast_marker_size = 5,\n",
+    ");\n",
+    "f3.suptitle('Delta-delta plot for Drug 3');\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Generate a forest plot"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This will allow for comparisons of different ``Drug`` effects.\n",
+    "\n",
+    "Key Parameters:\n",
+    "\n",
+    "- ``data``: A list of dabest objects \n",
+    "\n",
+    "- ``labels``: A list of labels for the dabest objects. E.g., ``['Drug1', 'Drug2', 'Drug3']``\n",
+    "\n",
+    "- ``effect_size``: For delta-delta experiments, you can select the effect size metric from ``\"mean_diff\", or \"hedges_g\" / \"delta_g\"``. The default is ``\"mean_diff\"``.\n",
+    "\n",
+    "- ``ci_type``: A string specifying the confidence interval type to use. The options are either `bca` or `pct`. Default is `bca`.\n",
+    "  \n",
+    " **Note: \"hedges_g\" and \"delta_g\" can be used interchangeably for delta-delta experiments - both plot hedges_g regular effect sizes and our `Delta g` delta-delta effect size.**\n",
+    "\n",
+    "- ``horizontal``: A boolean input (``True``/ ``False``) to adjust the plot orientation. The default is vertical orientation (``False``) \n",
+    "\n",
+    "- ``ax``: Optional argument to specify an existing matplotlib axes (otherwise a standalone figure will be created) \n",
+    "\n",
+    "See the [Controlling aesthetics](#controlling-aesthetics) section for more information on how to alter the aesthetics of the plots.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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sbKuLckcHU25CbxCrfiERkQNZFRIRERH45ZdfzD5/8OBBhIWFWV2UO1Cr1fjl0CGcO3kMeWl/IvNmDo5d5NEEEdUuVoVEXFwcNm/ejBUrVsBgMJge1+v1WL58ObZs2YIJEybYrUhXdOrUKTzQLxaLnhyP4x/MRsG1NOw6eRWlOkOV2xIROYpVA9fz58/HoUOH8Mwzz2DBggVo2bIlACA5ORlZWVmIjY1FfHy8XQt1BzmFpdh58irGduZRGBHVDlYdSSiVSuzbtw9r1qxB165dcfPmTdy8eRNdu3bF2rVr8f3330Op5Jk61vjhbCZOXsmTugwiIgA2XEwnk8kwdepUTJ061Z71EIC1hy7iuYEtEBHsI3UpROTmuMZ1LaTV6bHypxRkanglNhFJy+ojiYSEBKxZswZpaWnIzc0ttzodYJzfKTU11eYC3VV+SRn+7/vzmPtgC4TwIjsikohVIbF48WLMmzcP9erVQ9euXdGuXTt710UA8opKsfS7c5gzoAVC/RkUROR4VoXE8uXL0b9/f+zZswcKhcLeNdEdcgpL8d6+ZMzq3xyN6nhLXQ4RuRmrr7geM2YMA8JB8orL8G5CMv64nCt1KUTkZqwKia5duyI5OdnetVAltDo9Pv45DRuPXEIxlzslIgexKiRWrVqF7du3Y9OmTfauh6pw8PxNvL7rNI5eyLnnZAEiInuzakxi/Pjx0Ol0mDRpEv79738jLCwMcrm83GsEQcDJkyftUiSVl1dchrW/XMD+5CyM7RKGJiFc+pSIaoZVIREUFITg4GA0b97c3vVQNaTdLMA7e5Nwf2QQRkY3QpCvp9QlEZGLsSok9u/fb+cyyBbHLuYgMT0PD99XHwPa1INCzmskicg++G0ikbvHE2wdXyjTG7Dz5FUs2HMWaVwOlYjsxOqQ0Gg0ePvttzFo0CBER0fj6NGjAICcnBwsXboUKSkpdivSleTl5WH58uWIi4sr9/hfn72Jyz9vRVlxvk37v64uweJ957AzMYOLGBGRzazqbrpy5Qr69u2L9PR0NG/eHElJSSgoMP56DQoKwkcffYRLly5h+fLldi3W2SUkJGD06NEoKiq65zmt5iaSd3yAlD2foP2UNxDSqqvV7yOKIr796zrOXsvH9N5N7DKthyiK+L/vzmHzsXRoisvQJbIO3hrRrtJB8wKtDu/tS8a+05m4WaBF24YBeHVoW3QID7Rpv0TkOFYdSTz//PPIz89HYmIiDhw4cE9XyYgRI/D999/bpUBXkZCQgCFDhqC4uBiiKJrpXhKhL9PixOp5uJl01Ob3vJhdiLe/TcKFm4U27+vDA2lY9+tFLBhxH3Y82QveCg/8c+0RlFRyzcZ/tv2JQ+dvYum4Dkh4pg96Nw/FxE+O4Lq6xKb9EpHjWBUS+/btw+zZs9GmTRsIgnDP802bNkV6errNxbmKvLw8jB49GqIollvJr0KiCEDEn+tfsbnrCTD+ml/+w3lk5Wut3ocoilj7ywXM6t8MD7atj9YNArB0fAdkarTYdyazwm1KyvTY+9d1zH+4Fbo1DUZkiC/mDGyBxiE++Py3S1bvl4gcy6qQKC4uRmhoqNnn8/Nt/3JzJRs2bEBRUVHVAXGLKEJfqsW1Y/vs8v4lZXrsPnXN6u3Tc4qRla9Fr2YhpscCvBToGB6IPy5VPFWIziBCbxCh9Ch//YyXh9y0lrc1+yUix7IqJNq0aYOff/7Z7PM7duxAdHS01UW5ElEUsXLlSqu2vXxwm92uqj5zTQODlfvKKjB2D4XeNbYR6qdEVkHFRyh+Sg90igjEih/OI1NTAr1BxFcnruCPy7mmoxpr9ktEjmXVwPUzzzyDyZMno3379hg7diwAwGAwICUlBa+//joOHz6Mbdu22bVQZ5WdnW3luhoiirOvorRQA09flc11FGl1Fnc57TiRgRe/OmW6v3bK/Va95/+N74jnt/6Jbgt/gFwm4L6GARjWoSFOZait2h8ROZ5VITFx4kRcunQJL730EuLj4wEAgwcPhiiKkMlkWLhwIUaMGGHPOp3WrbO+rFVWUgQPnwC71HIppwjyCsaQ7jagTT10vOMMpFK9sZssq0CLugFepsezCrRo08B8bY2DffG/mT1QVKpDQYkOdQO88OSmPxARZFyWNdTPy6r9EpHjWL0yXXx8PCZNmoRt27YhJSUFBoMBUVFRGDVqFJo2bWrPGiu0YMEC7N69G4mJifD09EReXl6Nv6c1/Pz8bNre188Pngp51S+0QJFWDw8Lrsb2U3rAT3n7oyGKIkL9lfg1JRttGxqPavJLypCYnoeJ3RtXuT8fTw/4eHpAXVSGn89lYf5DrQEA4UHeNu2XiGpetUOiqKgIvXv3xowZM/D4449jzpw5NVFXlUpLSzF27Fj06NEDa9askaQGSwQHByMqKgppaWnVHF8Q4BPSEEpfVYVnkFnDz8u63wSCIOBfvZpg5Y/nERnii/Agb7y37xzqBSjxYJt6ptdNWP0bBrWtj8k9IwEAB85lQRRFRIX64WJ2IRbuSUJUqB/Gdgmr1n6JSDrV/tbw8fHBhQsX7PbFZa3XX38dALB+/XpJ66iKIAiYNWuWVWHapO8Yu/09ywTggdbWf/E+3rcpikt1mL/9FDQlZbg/sg42TO0KrzuOci5lFyGnsNR0P7+kDO/uTcZ1dQlUPgo8dF99zB3UstzcUpbsl4ikI4hWnD4zYcIElJSUYPv27TVRU7WsX78ezzzzjFXdTRqNBiqVCmq1GgEBNdcHnpeXh7CwMBQXF1t2GqwgQK7wwoA3tkHh42+XGiZ1b4xx94fbZV9EzqBUX4qknCR4y72hkNfsKppl+jIU64vRKqgVPOWuNRuzVafAvvzyyzh37hwmTZqEQ4cOISMjAzk5OffcahutVguNRlPu5giBgYHYtm0bBEGATFbFX7kgQICALtPesltAPNyugamLh4ioOqwKibZt2+LMmTPYuHEj+vbti4iICISGht5zq6558+ZBEIRKb0lJSdaUDABYtGgRVCqV6RYe7rhf1oMGDcLu3bvh7e1t+n+5l/EIouvjixHa2vq5m+40MroRHu/bVPLuQSJyTlaNZL7yyis18qXz3HPPYcqUKZW+xpYzp+bPn49nn33WdF+j0Tg8KK5cuYJPP/0U7777LjIyMkzPKVUhaDbgUYR1HQyFt21nRAHGMYjH+kRhSPsGNu+LiNyXVSHx2muv2bkMI2uPQCylVCqhVNo+I6otAgMDMXv2bERHR6NPnz6mxztNfg3BzTrY5T38vTzwwuBW5a51ICKyhtXXSdxJrVbDz8/vnnWua9Lly5eRk5ODy5cvQ6/XIzExEQDQrFkzm69NcIS7j8TsdWTWqr4/nh/cEnX9vap+MRFRFaxedOj48eMYPHgwfHx8EBwcjAMHDgAAbt68ieHDh9f4EqevvPIKoqOj8eqrr6KgoADR0dGIjo7G8ePHa/R9ayuFXMCUnpF4Z3R7BgQR2Y1VIfHrr78iJiYG58+fx8SJE8ud1hkSEgK1Wo2PPvrIbkVWZP369aZ1Ge68xcbG1uj71kbdmgRh1aOdMbpzGGQyDlATkf1Y1d304osvonXr1vjtt9+Qn5+PTz75pNzz/fr1w4YNG+xSIJnXONgH03s35dgDEdUYq0Li2LFjWLRoEZRKZYUT2DVq1AjXr1+3uTiqmJ/SA//s0RiD2tbnkQMR1SirQkKhUFR65XBGRoZTDB47o36t6mJaTBOovGv2ClIiIsDKMYnu3btj69atFT5XWFiIdevWoW/fvjYVRuX5KT3w4sOt8ezAFgwIInIYq0Li9ddfx/HjxzFkyBB8++23AICTJ0/ik08+QefOnZGVlYWXX37ZroW6s8gQXyx7pCN6RAVLXQoRuRmrupu6deuGPXv24N///jf++c9/AjBeLQ0AUVFR2LNnD9q3b2+/Kt1Yz2bBmDOgBWdFJSJJWBQSGo0Gvr6+5S6W69+/P5KTk5GYmIjz58+bFh3q3Lkz5wmyk7iuEYjrGs6/TyKSjEXdTXXq1MGWLVtM9//1r3/hyJEjAICOHTti7NixGD9+PLp06cIvNDuQCcCzA1tgQrcI/n0SkaQsCglPT09otVrT/fXr1yM1NbXGinJnggA892BL9GtVV+pSiIgs625q1aoVPvnkE0RGRkKlMq5FfPHiRfzxxx+VbtepUyfbK3Qz47qEo0+LmpvkkIioOiwKiUWLFmH8+PEYMGAAAONkdC+//LLZM5hEUYQgCNDr9far1A00DvbBI1w9johqEYtCYvDgwbhw4QKOHTuGzMxMTJkyBY899hh69OhR0/W5rHbt2uHgwYM4mZ6LTUfS4d+wKab0jISH3Oo5F4mI7M6ikPjzzz/RuHFjDBo0CACwbt06jB07Fg888ECNFufKVCoVYmJiIJ7Pwt6bdRAZ4ovOjetIXRYRUTkW/WyNjo7G7t27a7oWtzaobT2eyUREtY5FIeHt7Y2ioiLT/QMHDiAzM7PGinJHMc1CpC6BiOgeFnU3dejQAUuXLoVcLjed3XTs2DF4eVW+uM2oUaNsr9ANNAnxRaCPp9RlEBHdQxBFUazqRcePH8eYMWNw+fJl40aCgKo2c4azmzQaDVQqFdRqNQICAiSp4eD5LJy+qsHjfaMkeX8iV1WqL0VSThK85d5QyGt2UswyfRmK9cVoFdQKnnLX+sFn0ZFEly5dkJKSgtTUVGRmZiI2Nhbx8fGmU2LJNi3qcVp1IqqdLJ7gz8PDAy1btkTLli0xefJk/OMf/0C3bt1qsja3ERHkK3UJREQVsmoW2HXr1tm7DrdWL0ApdQlERBWyKCTeeOMNCIKA+Ph4yGQyvPHGG1Vuc+uqbKqcXCbAT2lVVhMR1TiLBq5lMhkEQUBxcTE8PT0hk1V95iwHri3z55U8tA8LlOS9iVwZB67tw6KfsHevZ13Z+tZUPd5cTIiIajFOFCQxrjhHRLWZ1Z3hZ8+eRWpqKvLz8+Hv749mzZqhVatW9qzNLSg4oR8R1WLVDomPPvoICxYsQEZGxj3PRUREID4+HtOnT7dLce7AguEdIiLJVCsk5s6di6VLlyIoKAj/+te/cN9998HPzw8FBQU4deoUduzYgZkzZ+L8+fN45513aqpmlxLiy9Nfiaj2sujsJgA4evQounfvjpEjR+LTTz+Fr++9F4AVFhZi4sSJ2LVrF44cOYIuXbrYvWB7qg1nNxFRzeDZTfZhcWfHmjVr0KBBA2zatKnCgAAAX19fbN68GfXq1cOaNWvsViQREUnD4pA4fPgwxo4dC6Wy8u4RLy8vjB07Fr/88ovNxRERkbQsDon09HS0bt3aote2adMG6enpVhdFRES1g8UhodFo4O/vb9Fr/fz8kJ+fb3VRRERUO1gcEqIoVmt5TQvHw4mIqBar1imwS5YswebNm6t8XUXXUBARkfOxOCQiIiKQk5ODnJwci19PRETOzeKQuHjxYg2WQUREtREnhSAiIrMYEkREZBZDgoiIzGJIEBGRWQwJIiIyiyFBRERmMSSIiMgsq5cvTUhIwJo1a5CWlobc3Nx7puEQBAGpqak2F0hERNKxKiQWL16MefPmoV69eujatSvatWtn77oqdfHiRbz55pv48ccfcf36dTRs2BATJ05EfHw8PD1da8EPIiIpWRUSy5cvR//+/bFnzx4oFDW74lNFkpKSYDAY8NFHH6FZs2b466+/MGPGDBQWFmLJkiUOr4eIyFVZFRK5ubkYM2aMJAEBAIMHD8bgwYNN95s2bYrk5GT897//ZUgQEdmRVQPXXbt2RXJysr1rsYlarUZQUJDUZRARuRSrQmLVqlXYvn07Nm3aZO96rJKSkoKVK1di5syZlb5Oq9VCo9GUuxERkXlWhcT48eOh0+kwadIkqFQqtG3bFu3bty9369ChQ7X3O2/ePAiCUOktKSmp3DYZGRkYPHgwxo4dixkzZlS6/0WLFkGlUplu4eHh1a6RiMidCKIVS8jFxsZatErdTz/9VK39ZmVlITs7u9LXNG3a1HQG09WrVxEbG4vu3btj/fr1kMkqzzytVgutVmu6r9FoEB4eDrVajYCAgGrVSkS1W6m+FEk5SfCWe0Mhr9nx0zJ9GYr1xWgV1Aqectc6w9Kqgev9+/fbuQyj0NBQhIaGWvTajIwM9OvXD507d8a6deuqDAgAUCqVUCqVtpZJROQ2rL6YTkoZGRmIjY1F48aNsWTJEmRlZZmeq1+/voSVERG5FptCoqysDElJSVCr1TAYDPc836dPH1t2b9Z3332HlJQUpKSkICwsrNxzVvSeERGRGVaNSRgMBsyfPx+rVq1CUVGR2dfp9XqbiqtpGo0GKpWKYxJELohjEvZh1dlNCxcuxOLFizFx4kR8+umnEEURb7/9Nj788EPTmU0JCQn2rpWIiBzMqpBYv349xo0bh//+97+mK587d+6MGTNm4MiRIxAEAT/++KNdCyUiIsezKiSuXLmC/v37A4DpbKGSkhIAgKenJyZOnIjPPvvMTiUSEZFUrAqJ4OBgFBQUAAD8/PwQEBCAtLS0cq/Jzc21vToiIpKUVWc3RUdH49ixY6b7/fr1w7JlyxAdHQ2DwYAVK1ZYdcU1ERHVLlYdSTz22GPlrl5esGAB8vLy0KdPH/Tt2xcajQbvvfeeXQslIiLHs+oU2Iqo1Wrs378fcrkcPXv2dIoZWXkKLJHr4imw9mG3K65VKhWGDx9ur90REVEtYFV3E2C8UO6LL77AzJkzMXLkSJw6dQqA8Yhi+/btyMzMtFuRREQkDatCIi8vD7169cKECROwefNm7Nq1yzR/kp+fH2bPno3ly5fbtVAiInI8q0Ji3rx5OH36NBISEpCWllZuviS5XI4xY8Zgz549diuSiIikYVVI7NixA7NmzcLAgQMrXFeiRYsWuHjxoq21ERGRxKwKCbVajSZNmph9vqysDDqdzuqiiIiodrAqJKKiovDHH3+YfX7fvn1o06aN1UUREVHtYFVITJ8+HWvXrsWWLVtM4xGCIECr1SI+Ph579+7FzJkz7VooERE5nlXXSTz99NM4ffo04uLiEBgYCACYMGECsrOzodPpMHPmTEybNs2edRIRkQSsCglBELB69WpMnjwZW7duxfnz52EwGBAVFYVx48bV2Ip0RETkWDZdcR0TE4OYmBh71UJERLWM1VdcExGR67P4SGLYsGHV2rEgCNi5c2e1CyIiotrD4pD45ptv4OXlhfr168OSiWMrusiOiIici8Uh0ahRI2RkZCAkJAQTJkzAI488gvr169dkbUREJDGLxyTS09Px008/ITo6Gm+++SbCw8MxYMAArFu3Dvn5+TVZIxERSaRaA9d9+/bFRx99hOvXr2Pr1q0IDg7GU089hbp162LUqFHYunWrabU6IiJyflad3aRQKDB8+HBs2bIFmZmZpuAYP3483n33XXvXSEREErHpFFitVouEhATs3LkTJ06cgJeXFyIjI+1UGhERSa3aIWEwGJCQkIApU6agXr16iIuLQ3FxMVavXo0bN25g0qRJNVEnERFJwOKzm3799Vds2rQJX375JbKzs9G9e3csXLgQ48aNQ0hISE3WSEREErE4JGJiYuDt7Y2HH34YcXFxpm6ly5cv4/LlyxVu06lTJ7sUSURE0hBES66MAyCT3e6ZqupCOVEUIQgC9Hq9bdXVMI1GA5VKBbVajYCAAKnLISI7KtWXIiknCd5ybyjkihp9rzJ9GYr1xWgV1Aqecs8afS9Hs/hIYt26dTVZBxER1UIWh8TkyZNrsg4iIqqFOAssERGZxZAgIpeVnJMsdQlOjyFBRC4ruyQbZYYyqctwagwJInJZoihCrVVLXYZTY0gQkUtjSNiGIUFELi27JFvqEpwaQ4KIXFp6frrUJTg1hgQRubTk3GSLllymijEkiMil5Zbk4pLmktRlOC2GBBG5vN+u/SZ1CU6LIUFELu/3G78jvzRf6jKcEkOCiFyOWq3GL4d+QcqJFGSfyUZxfjG+u/Sd1GU5JYsn+CMichanTp1C/9j+pvu9F/bGId9D6NagGxr5NZKwMufjtEcSw4YNQ0REBLy8vNCgQQNMmjQJV69elbosIqqlRIj47Mxn0Oq1UpfiVJw2JPr164f//e9/SE5OxrZt25CamooxY8ZIXRYR1WKZRZnYdHYTDKJB6lKchtN2N82ZM8f058aNG2PevHkYMWIEysrKoFDU7CpUROS8/rz5J75K+Qqjmo2qcpVNcuKQuFNOTg42btyInj17VhoQWq0WWu3tQ02NRuOI8oioljmUcQheci8MaTpE6lJqPaftbgKA//znP/D19UVwcDAuX76MnTt3Vvr6RYsWQaVSmW7h4eEOqpSIapvvL3+PvRf3Sl1GrVerQmLevHkQBKHSW1JSkun1zz//PE6cOIF9+/ZBLpfjn//8Z6WX38+fPx9qtdp0S0/nnC5E7izhYgJ2p+3mtB2VqFXdTc899xymTJlS6WuaNm1q+nNISAhCQkLQokULtG7dGuHh4fjtt9/Qo0ePCrdVKpVQKpX2LJmInNz3l7+HWqvG2JZjoZBxPPNutSokQkNDERoaatW2BoPxbIU7xxyIiCxxLPMYrhVew8TWE1HPt57U5dQqtaq7yVJHjhzB+++/j8TERFy6dAk//vgj4uLiEBUVZfYogoioMlcKrmDJ70vw3aXvuOTpHZwyJHx8fLB9+3Y88MADaNmyJaZNm4b27dvjwIED7E4iIqvpDDrsubAH7x57F6dunuJYBWpZd5Ol2rVrhx9//FHqMojIRd0svom1f61FVGAURkSNQJh/mNQlScYpjySIiBwhNS8V7/3+HrYkb0FBaYHU5UiCIUFEVIXfrv2GRUcX4ci1I27XBcWQICKXc/cXuT2+2It0Rfgi+Qt89OdHyNPm2bw/Z8GQICKXkZeXh+XLlyMuLq7c48ffO47Ur1NRWlBq83sk5yZj8bHFOJl10uZ9OQNBdLdjpztoNBqoVCqo1WoEBARIXQ4R2SAhIQGjR49GUVERgAqOHgRArpSj63+6ol60fa6F6BvWF8OihkFv0KNYX4xWQa3gKfes9n5WJa7Ctxe+RWZRJjxkHmgT3Aazo2ejfWh7s9tsSdqCLee24GqBcYmEqMAoPN7+cfQO6216jVavxeJji7H34l6U6kvRq2EvxHePR4h3iMW1MSQYEkROLyEhAUOGDIEoiqYLayskAIIgoPvL3e0WFJ3rdca45uNQYiixOiR2p+1GkFcQwvzDoNVp8dnZz7Dv4j7sHmV8vCL70/dDJsjQOKAxRFHErtRdWHd6Hb78x5doVqcZAODNw2/i54yf8Vavt+Dn6YeFRxZCBhk+e/gzi2tjSDAkiJxaXl4ewsLCUFxcXHlA3PL3EcWgTwbB06/6X+gVeaTFI2gd0trqkLhbQWkBemzugdUPrkb3Bt0t3q7X5l54rstzGNV8FPJL89FnSx+80/sdPBj5IAAgTZ2G4TuG4/OHP0eH0A4W7ZNjEkTk1DZs2ICioiLLAgIARECv1SN9v/0m+DyWecxu+yrTl2Hrua3wV/ijZZ2WFm2jN+jx7YVvUawrNn35n8k+A51Bh+4Nb4dMU1VTNPBtgJM3LB9PccqL6YiIAOO4w8qVK63aNu2bNDQd0tQuCw+l56fbvNrdgfQDeP7n51GiK0Godyg+fvBj1PGqU+k253LPYeKeiSjVl8LHwwfL+i1DVGAUAOMFgQqZAgGe5XtJgr2CcbPkpsV18UiCiJxWdnY2UlNTq3+KqwgUXi9EqaYUoijafCvRl5gGkKvyTdo36Lqxq+n2e+bvAID769+PrUO34rOHP0OvRr0w98BcZBdnV7qvJgFNsHXoVmwcshHjWo7DS4deQmpeavX+LqrAIwkicloFBbZdBV1aXAoPf/t8DV4tuAqZUPXv7n7h/dA+5PZZS3V96gIAfBQ+iFBEIAIR6BDaAUO2D8FXKV9hervpZvelkCsQERABAGgb3BZ/Zf+Fz89+jld7vIoQ7xCUGcqgKdWUO5rILslGiJflZzcxJIjIafn5+dm0va+fL5Qe9pkU1NfTFx6yqr9SfRW+8FX4Vvk6g2hAqb5613WIomjapk1wG3jIPHDk2hEMbDwQAHBBfQHXCq+hQ13LBq0BhgQRObHg4GBERUUhLS2tel1OAuBbzxfKAKVdxiQAlDs6qI6isiKsPrUaseGxCPUORa42F18kfYEbRTfwYOMHTa+bnjAd/SP6Y0LrCQCAZb8vQ0yjGDTwa4DCskLsSduDY9eP4cOBHwIA/D39MarZKCw+thgqTxV8PX2x6MgidAjtYPGZTQBDgoicmCAImDVrFubMmVPtbZsPbW63gOhavyvaBLexalu5TI4L6gvYlbILudpcBCoD0TakLTY8tMF0vQNgHBy/czqQnJIcxB+KR1ZxFvw9/dG8TnN8OPBD9GzY0/SaF7q+AOGYgDn756DMUIaeDXvipe4vVas+XifB6ySInJq110kMXTfULtdJNA5ojNd6vAYfhY/N+6qNeHYTETm1wMBAbNu2DYIgQCar4ivt7yuue83vZZeAaKpqipe7v+yyAQEwJIjIBQwaNAi7d++Gt7c3BEGouBvp7yOI3q/2Rv1O9W1+z46hHfFKj1fg7+lv875qM3Y3sbuJyGXk5eXh008/xbvvvouMjAzT495B3mg5uiUiH4iEp6/tRxAPN3kYE1tPhFwmt3lftR1DgiFB5HIOHjyIPn36mO7HLopF3fvq2rxfpVyJ6e2mo09Yn6pf7CJ4dhMRuZy7u5vscRZTs8BmeLLjk2jo19DmfTkThgQRUSW8PbwxvuV4DIocZNEV1a6GIUFEVAEBAvqF98MjrR6BSqmSuhzJMCSIiO7SJrgNJreZjEhVpNSlSI4hQUT0t0BlICa3mYweDXvY7WpsZ8eQICICENMoBlPbToWfp22TBroahgQRuTWFTIFp901Dv4h+UpdSKzEkiMht1fGqg+e7PG9azY3uxZAgIrfULLAZ5naZW+USoe6OIUFEbqdHgx54suOTUMgVUpdS6zEkiMitPBDxAKa3m+6WF8ZZg39LROQ2OtXtxICoJv5NEZFbUHmq8ETHJxgQ1cTuJiJyOe3atcPBgwfx69VfsffCXqgiVRjfcrzLr/1QExgSRORyVCoVYmJioE5T43ef3xHqHYrY8Fipy3JKPO4iIpf3YOSDbrFAUE1gSBCRy+vVsJfUJTgthgQRubTGAY0R7B0sdRlOiyFBRC6tbXBbqUtwagwJInJpzQObS12CU2NIEJFL48JBtmFIEJHLEiCgrk9dqctwagwJInJZAcoAeMh4OZgtGBJE5LL8FFxlzlYMCSJyWT4KH6lLcHpOHxJarRYdO3aEIAhITEyUuhwiqkW85F5Sl+D0nD4kXnjhBTRs2FDqMoioFlLIuKiQrZw6JL799lvs27cPS5YskboUIqqFOGhtO6f9G8zMzMSMGTOwY8cO+PhY1u+o1Wqh1WpN9zUaTU2VR0S1gJ8nB65t5ZRHEqIoYsqUKXj88cfRpUsXi7dbtGgRVCqV6RYeHl6DVRKR1Lw9vKUuwenVqpCYN28eBEGo9JaUlISVK1ciPz8f8+fPr9b+58+fD7Vabbqlp6fX0P8JEZFrEERRFKUu4pasrCxkZ2dX+pqmTZti3Lhx+PrrryEIgulxvV4PuVyORx99FBs2bLDo/TQaDVQqFdRqNQICAmyqnYjIFdWqkLDU5cuXy40nXL16FYMGDcLWrVvRrVs3hIWFWbQfhgQRUeWccuA6IiKi3H0/P+PgVFRUlMUBQUREVatVYxJERFS7OOWRxN0iIyPhhL1mRES1Ho8kiIjILIYEERGZxZAgIiKzGBJERGQWQ4KIiMxiSBARkVkMCQlptVq89tpr5WampdqL7eVc2F724ZTTctiLKIrIz8+Hv79/uXmgHIXTgjgXtpdzYXvZh0tcTGctQRD44SEiqgS7m4iIyCyGBBERmcWQkJBSqcSrr74KpVIpdSlkAbaXc2F72YdbD1wTEVHleCRBRERmMSSIiMgshgQREZnFkCAiIrMYEkREZBZDgoiIzGJIEJHTMBgM5e7zDP6a59ZzN9nbuXPnUKdOHYSGhkpdClngwIEDOHPmDC5fvoxHH30ULVq0gKenp9RlkRnffPMNfvjhB1y6dAkjR47E8OHDOfeaA/BIwk4+//xz9OzZE2vXrsXNmzelLoeqsHbtWowbNw47d+7Ehg0bMGLECBw+fBjAvb9WSXpr167FxIkTodfrcfPmTbz99tv4888/pS7LPYhks59++kls1qyZ2LlzZ7F58+bie++9J2ZlZUldFpnx9ddfiyEhIeL27dtFvV4viqIoxsbGioMHD5a4MqrI9u3bxaCgIHH79u2mx9q2bSt+8cUXoiiKosFgEEVRNLUl2RePJGxUVlaGpKQkdO/eHd999x1GjRqFFStW4NNPP+URRS2kVquxc+dOPP744xg6dCh0Oh0A4KmnnsK1a9eg1+slrpDulJeXh4MHD+KFF17AsGHDTI+rVCps27YNsbGxmDt3Li5evAiZTMYxihrAMQkbKRQKDB48GF26dEGdOnXw9ttvw2AwYMWKFQCASZMmmcYoDAaD6YMsxSJHZPxyCQ0NRadOneDhcfvjHxgYiCtXriAvLw916tSBTMbfT7VBYGAg4uLiEBoaCrlcDgB48MEHkZ6ejgkTJuDChQs4duwY5s+fjzVr1sDHx0fiil0PQ8JKt77wASAyMhKRkZGmL/93330XgiCYgmLKlClQKBR4++238fTTT6Nu3bpSlu6W7myvBQsWmEL6Vpv5+fnB398fXl5eptft3LkTAwYMgK+vr2R1u6s72+v+++83PX7p0iXo9Xr8+OOPaNasGQBg4cKF+Pjjj6HRaBgSNYAhYaVbH+Dk5GS0bNnS9Lher4dcLsc777wDAFi5ciUKCwuxa9culJaW4o033pCkXnd3q73Onj2L1q1bA7jdVgDg6ekJT09PUyD0798fCoWiXBcHOU5F/750Oh0aN26MhIQEeHh4QKfTwcPDA02aNEFERAS8vb2lLNll8Zi6mu4882X//v2IjY3F3r17ARiXQ5XJZKZ+7XfeeQdDhw7Fq6++Cr1ej+PHj0Mul/PsGQe6u7369+9vai+5XG7qwy4qKkJ+fj5u3LiBIUOGICMjA9988w0EQWA/twNV9u/Lw8MDoiiagt3DwwMlJSX4/PPPERUVBZVKJUnNro4hUQ13HgJv2bIFX375JXJzc/H0009jz549AG4HhcFgQG5uLk6ePIn7778fR48ehUKhgE6nY3+3g1jaXoDxSEKhUGDo0KE4d+4c/vrrL1N7cfzIMSxtL0EQUFJSgpSUFIwaNQoZGRlYvXo1AF5cVxP4bVUNtz7AL7zwAubOnYvmzZtjzpw58Pf3x9y5c7Fr1y4Atz/IW7duRWpqKg4dOlTu8Jgcw9L2AoxdGenp6VAqlThz5owpINhejlOd9tq1axeeeeYZaLVaHDt2DB4eHtDr9Qz0miDd2bfO6cyZM2KzZs3E3bt3mx775ZdfxLi4OLFly5bi3r17TY/rdDrTudtlZWUOr5Usby+dTifOnTvX1E5sL2lY2l56vV7cu3evqNPpRFFke9UkHklUQbzr8FUulyMzM9N0fj0A9OzZE48//jg0Gg2efPJJ06GxXC43nfLKX6SOYU177dq1C3K5HIsXL+YRn4NZ015ff/01ZDIZBg0aZBrjY3vVHIZEFe4+fPX19UXHjh2RmJgIjUZjerxPnz7o2LEj6tatizfffBNHjx41uw+qOda016JFi8q1F79wHMea9lq4cGG59uIYX83i364F3nvvPQwdOhQA0KhRI/Tu3Rvvv/8+vvrqK9MHWa1Ww9fXF48++ij0ej0OHDggZcluje3lXNhetZsg3n28R+UYDAZ8+eWXmDZtGoYNG4ZNmzYBAJ544gns2LEDvXv3RuPGjfHrr79Cp9Pht99+wz/+8Q/I5XLs3LlT4urdD9vLubC9aj8eSdzl7msYZDIZRo4ciY0bN2LPnj0YN24cAGDVqlV46aWX4O/vj8OHD6NFixbYv38/AGO/6n333cfT8RyA7eVc2F5OSLIh81ruu+++K3dfq9WKX331lahSqcTx48ebHtfpdKYzLPLy8sSXXnpJDA4OFs+ePevQet0d28u5sL2cB0Pib3dOM3zixAnRw8NDnDNnTrnXlJSUiOvXrxcFQRCfeOKJcs9dv35dnDBhghgZGSmeOHHCESW7NbaXc2F7OS+GxF1Wr14tbt68WXz//ffFunXrinPnzi33fEpKihgeHi4KgiC+9NJL5Z47ffq0ePHiRUeW6/bYXs6F7eV83D4k7vyFs2LFCjE0NFQ8e/asmJ2dLX7wwQdiUFBQuQ/y9evXxWnTpon79+83HQaT47C9nAvby/m5/Qnht86xPnHiBK5du4Zly5ahVatWAIAJEyYAAF588UVcuXIFw4cPx9q1a6FQKNCnTx8IgsALrxyM7eVc2F4uQOqUqg2OHDkiCoIgyuVycd26deWey8/PF3ft2iVGRUWJ0dHRYv/+/cXS0lJRFG8vm0iOxfZyLmwv5+aWIXHt2jXxzz//FD/77DPx1KlToiga19EVBEGcOnWqeOPGjXu2KSkpEa9du2b64HKuGMdhezkXtpdrcbuQ2LZtm/jwww+L9evXFwMCAkRPT09x2LBhYnp6urht2zZREATxrbfeEvPy8kzb3N03ygXXHYft5VzYXq7HrULi448/FuvUqSMuWbJE/P7778Xc3FzxjTfeEJs3by62aNFCTE9PFzdu3CgKgiAuXLhQVKvVUpfs1thezoXt5ZrcJiQ+/vhj0dPTU9y2bds9z/3vf/8T77vvPrF3796iwWAQV61aJcrlcvHFF18UCwoKJKiW2F7Ohe3lutwiJH766SdREATx9ddfF0XROCBmMBjK9Xu+//77olKpFLdu3SqKoii++eabYs+ePTl4JgG2l3Nhe7k2t5jg7/z585g2bRqCgoLw3HPPoXfv3qbn7lwysX379ujVqxf++9//AjDOdX9rjWNO9+04bC/nwvZybW4xwV/z5s2xZs0aaLVaLFiwAIcOHTI9d+vDqdFoUFxcjIYNG5Z7jh9gx2N7ORe2l2tzi5AAjB/kFStWQBAEvPXWW/jll1/KPZ+WloawsDB0794dwO0Vs/gBlgbby7mwvVyXW3Q33en8+fOYPXs2RFFEfHw8evfuDZ1Oh+HDh0Mmk2Hnzp1c6aoWYXs5F7aX63G7kABuf5BlMhlefPFFLF26FElJSUhMTIRCoSjXj0rSY3s5F7aXa3HLkACMH+Q5c+Zg3759aNq0KU6dOgWFQsG5YmoptpdzYXu5DrcNCQBISkrCqlWrsHTpUnh4ePADXMuxvZwL28s1uHVI3IkfYOfC9nIubC/nxZAgIiKzOHpERERmMSSIiMgshgQREZnFkCAiIrMYEkREZBZDgoiIzGJIEBGRWQwJIiIyiyFBRERmMSSIiMis/weXZb3o4FD+jQAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 390x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "f_forest_delta2 = dabest.forest_plot(\n",
+    "                        data = contrasts, \n",
+    "                        labels = ['Drug1', 'Drug2', 'Drug3']\n",
+    ");"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Generate a forest plot with delta effect sizes alongside the delta-delta effect sizes\n",
+    "\n",
+    "If you want to plot the original effect sizes alongside the delta-delta effect sizes, you can do so by utilising the `idx` parameter. This parameter takes a tuple/list of indices of the original effect sizes you want to plot. \n",
+    "\n",
+    "For example, if you want to plot only the first effect size and the delta-delta effect size for each of the three dabest object supplied, you can do so by setting `idx=[[0, 2],[0, 2],[0, 2]]`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 780x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "f_forest_delta2 = dabest.forest_plot(\n",
+    "                        data = contrasts, \n",
+    "                        labels = ['Drug1 Delta1', 'Drug1 Delta-Delta', 'Drug2 Delta1', 'Drug2 Delta-Delta', 'Drug3 Delta1', 'Drug3 Delta-Delta'],\n",
+    "                        idx=[[0, 2], [0, 2], [0, 2]]\n",
+    ");"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Selecting normalised effect sizes via `hedges_g` or `delta_g`\n",
+    "\n",
+    "Remember, `hedges_g` and `delta_g` are interchangeable for delta-delta experiments. However, when plotting the original effect sizes alongside the delta-delta effect sizes, you should note that hedges_g effect sizes will be plotted alongside the Delta g effect sizes. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 390x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 390x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 780x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "f_forest_delta2 = dabest.forest_plot(\n",
+    "                            data = contrasts, \n",
+    "                            labels = ['Drug1', 'Drug2', 'Drug3'],\n",
+    "                            effect_size='hedges_g');\n",
+    "f_forest_delta2 = dabest.forest_plot(\n",
+    "                            data = contrasts, \n",
+    "                            labels = ['Drug1', 'Drug2', 'Drug3'],\n",
+    "                            effect_size='delta_g');\n",
+    "\n",
+    "f_forest_delta2 = dabest.forest_plot(\n",
+    "                            data = contrasts, \n",
+    "                            labels = ['Drug1 Delta1', 'Drug1 Delta-Delta', 'Drug2 Delta1', 'Drug2 Delta-Delta', 'Drug3 Delta1', 'Drug3 Delta-Delta'],\n",
+    "                            effect_size='hedges_g',\n",
+    "                            idx=[[0, 2], [0, 2], [0, 2]]);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Mini-meta effects\n",
+    "Next we will generate a similar forest plot for mini-meta effect sizes. Please revisit the notebook [Mini-Meta Tutorial](05-mini_meta.html) on how to generate a mini-meta effect size. We will generate three mini-meta effect sizes for three separate mini-meta analyses:\n",
+    "\n",
+    "**Note: the only effect size metric currently available for mini-meta is ``\"mean_diff\"``.**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Creating a demo dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def create_mini_meta_dataset(N=20, seed=9999, control_locs=[3, 3.5, 3.25], control_scales=[0.4, 0.75, 0.4], \n",
+    "                             test_locs=[3.5, 2.5, 3], test_scales=[0.5, 0.6, 0.75]):\n",
+    "    np.random.seed(seed)  # Set the seed for reproducibility\n",
+    "\n",
+    "    # Create samples for controls and tests\n",
+    "    controls_tests = []\n",
+    "    for loc, scale in zip(control_locs + test_locs, control_scales + test_scales):\n",
+    "        controls_tests.append(norm.rvs(loc=loc, scale=scale, size=N))\n",
+    "\n",
+    "    # Add a `Gender` column for coloring the data\n",
+    "    gender = ['Female'] * (N // 2) + ['Male'] * (N // 2)\n",
+    "\n",
+    "    # Add an `ID` column for paired data plotting\n",
+    "    id_col = list(range(1, N + 1))\n",
+    "\n",
+    "    # Combine samples and gender into a DataFrame\n",
+    "    df_columns = {f'Control {i+1}': controls_tests[i] for i in range(len(control_locs))}\n",
+    "    df_columns.update({f'Test {i+1}': controls_tests[i + len(control_locs)] for i in range(len(test_locs))})\n",
+    "    df_columns['Gender'] = gender\n",
+    "    df_columns['ID'] = id_col\n",
+    "\n",
+    "    df = pd.DataFrame(df_columns)\n",
+    "\n",
+    "    return df\n",
+    "\n",
+    "# Customizable dataset creation with different arguments\n",
+    "df_mini_meta01 = create_mini_meta_dataset(seed=9999, \n",
+    "                                          control_locs=[3, 3.5, 3.25], \n",
+    "                                          control_scales=[0.4, 0.75, 0.4], \n",
+    "                                          test_locs=[3.5, 2.5, 3], \n",
+    "                                          test_scales=[0.5, 0.6, 0.75])\n",
+    "\n",
+    "df_mini_meta02 = create_mini_meta_dataset(seed=9999, \n",
+    "                                          control_locs=[4, 2, 3.25], \n",
+    "                                          control_scales=[0.3, 0.75, 0.45], \n",
+    "                                          test_locs=[2, 1.5, 2.75], \n",
+    "                                          test_scales=[0.5, 0.6, 0.4])\n",
+    "\n",
+    "df_mini_meta03 = create_mini_meta_dataset(seed=9999, \n",
+    "                                          control_locs=[6, 5.5, 4.25], \n",
+    "                                          control_scales=[0.4, 0.75, 0.45], \n",
+    "                                          test_locs=[4.5, 3.5, 3], \n",
+    "                                          test_scales=[0.5, 0.6, 0.9])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Loading data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "contrast_mini_meta01 = dabest.load(data = df_mini_meta01,\n",
+    "                                   idx=((\"Control 1\", \"Test 1\"), (\"Control 2\", \"Test 2\"), (\"Control 3\", \"Test 3\")), \n",
+    "                                   mini_meta=True)\n",
+    "contrast_mini_meta02 = dabest.load(data = df_mini_meta02,\n",
+    "                                    idx=((\"Control 1\", \"Test 1\"), (\"Control 2\", \"Test 2\"), (\"Control 3\", \"Test 3\")), \n",
+    "                                    mini_meta=True)\n",
+    "contrast_mini_meta03 = dabest.load(data = df_mini_meta03,\n",
+    "                                   idx=((\"Control 1\", \"Test 1\"), (\"Control 2\", \"Test 2\"), (\"Control 3\", \"Test 3\")),\n",
+    "                                    mini_meta=True)\n",
+    "contrasts_mini_meta = [contrast_mini_meta01, contrast_mini_meta02, contrast_mini_meta03]    \n",
+    "                              "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Generate a forest plot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 390x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "f_forest_minimeta = dabest.forest_plot(\n",
+    "                            data = contrasts_mini_meta, \n",
+    "                            labels=['mini_meta1', 'mini_meta2', 'mini_meta3']\n",
+    ");"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Generate a forest plot with delta effect sizes alongside the mini-meta effect sizes\n",
+    "\n",
+    "If you want to plot the original effect sizes alongside the mini-meta effect sizes, you can do so by utilising the `idx` parameter. This parameter takes a tuple/list of indices of the original effect sizes you want to plot.  \n",
+    "\n",
+    "For example, if you want to plot only the first effect size and the mini-meta effect size for each of the three dabest object supplied, you can do so by setting `idx=[[0, final_idx],[0, final_idx],[0, final_idx]]` (where `final_idx` is the index of the last contrast object which will be the  mini-meta effect size.)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 780x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "f_forest_minimeta = dabest.forest_plot(\n",
+    "                        data = contrasts_mini_meta, \n",
+    "                        idx = [[0, 3],[0, 3], [0, 3]],\n",
+    "                        labels=['Contrast 1A', 'mini_meta1', 'Contrast 2A', 'mini_meta2', 'Contrast 3A', 'mini_meta3']\n",
+    ");"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Delta effects\n",
+    "Next we will generate a similar forest plot of regular delta effect sizes. In the example below, we will generate three regular `mean_diff` experiments. Here, we will only plot the effect size between the first group (Test 1 - Control 1) for each of the three dabest object supplied."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "delta1 = dabest.load(data = df_mini_meta01,\n",
+    "                                   idx=((\"Control 1\", \"Test 1\"), (\"Control 2\", \"Test 2\"), (\"Control 3\", \"Test 3\")))\n",
+    "delta2 = dabest.load(data = df_mini_meta02,\n",
+    "                                    idx=((\"Control 1\", \"Test 1\"), (\"Control 2\", \"Test 2\"), (\"Control 3\", \"Test 3\")))\n",
+    "delta3 = dabest.load(data = df_mini_meta03,\n",
+    "                                   idx=((\"Control 1\", \"Test 1\"), (\"Control 2\", \"Test 2\"), (\"Control 3\", \"Test 3\")))\n",
+    "contrasts_deltas = [delta1, delta2, delta3]         "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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SjgFxsDQTOgRCCCkTJRwDYkPNaYQQHcZrwlEoFDhw4ACmTp2KwYMHIzw8HAAgkUhw9OhRpKSk8FlcubZs2QIPDw9YWFigQ4cOuHHjRrnbHzp0CF5eXrCwsICPjw9Onz6tpUj5YyU2EToEQggpE28JJzMzE2+99RZGjx6N/fv348SJE3j58iUAwMbGBrNmzcLGjRv5Kq5cBw8exLx587B06VLcuXMHrVq1QmBgIF68eFHq9teuXcOoUaMwceJE3L17F4MGDcKgQYNw//59rcTLFyu6wiGE6DDeEs7ChQvx4MEDhISEIDY2Fowx1ToTExMMGzZMa1cNGzZswOTJk/H++++jWbNm2LZtG6ysrLBz585St9+4cSN69+6N+fPnw9vbG1988QXatGmDb775Rivx8sXKjK5wCCG6i7efxMeOHcPMmTPxzjvvIC0t7bX1TZo0we7du/kqrkwFBQW4ffs2goKCVMtEIhECAgIQGhpa6j6hoaGYN2+e2rLAwEAcO3as1O1lMhlkMpnqvVQqBQCEhYXBxkaYyc4K5Ao8TciE5JkYpqLqHbRTrmRQKhnyk60hNqUkV2UKOcCU2imLEwEmdAWsCblSrvZDurpwHAdTkXbqqk2bNloppxhvZyWRSNCgQYMy1xcWFkIul/NVXJlSU1OhUCjg6qo+8ZirqysiIiJK3Sc5ObnU7ZOTk0vdftWqVVi+fPlry7t3717FqAkhRPu0kUBL4i3heHp64s6dO2Wu//3339GsWTO+ihNUUFCQ2hWRVCqFu7s7/vzzT8GucAAgK68Q5mba6Xgo4jia3E0T8gIgLRoQmQCiau7OriwElArAqRFgKq7esgxUoaIQcdI4mMCkWq8+5Eo5FFDAw84DZiaG95gDb//nJk2ahAULFsDf3x89e/YEUHRpKJPJsGLFCpw9exbfffcdX8WVydnZGSYmJq/1iEtJSYGbm1up+7i5uVVqe3Nzc5ibvz5AZuvWrWFnZ1fFyDWXWyCHlZiaTfSCXAYkmwJmVoBJNScBRQFQmAu4+QCmNLBrVRQoCmCZbglLE8tqTQSFikLkKfLg5egFcXX/XQiAt5+os2fPxrhx4zBq1Cg0adIEADB69GjY2tpi1apVmDJlCiZOnMhXcWUSi8Xw8/PD+fPnVcuUSiXOnz+PTp06lbpPp06d1LYHgHPnzpW5va7iQBOuEUJ0F28/hzmOw/fff4/x48fj8OHDiIqKglKphKenJ959911069aNr6LeaN68eRg/fjzatm2L9u3bIzg4GDk5OXj//fcBAOPGjUOdOnWwatUqAEXJsnv37li/fj369euHAwcO4NatW1q5IuMTTfBJCNFlvLe/dOnSBV26dOH7sJUyYsQIvHz5EkuWLEFycjJat26Ns2fPqjoGxMfHQyT67+Kuc+fO2LdvHz777DMsWrQIjRs3xrFjx9CiRQuhTqFKzE3pngohRHdxjKduCk+ePMH9+/fRv3//Utf/9ttv8PHxgYeHBx/F6RSpVAp7e3tIJBJB7+EQPSKXAcnhdA9HTxQoChCRHkH3cDTE2xXOJ598AqlUWmbC2bJlCxwcHHDgwAG+iiSEEKJHeGuDCQ0NxTvvvFPm+p49e+LKlSt8FUcIIUTP8JZwMjIyYGtb9myTNjY2pY5AQAghxDjwlnDq1auHq1evlrn+ypUrqFu3Ll/FEUII0TO8JZxRo0Zh//792LRpE5TK/8aHUigU2LhxIw4ePIjRo0fzVRwhhBA9w1svNZlMhn79+uHChQtwcXFB06ZNAQCRkZF4+fIl/P39cebMmVKf0Nd31EuNVBr1UtMr1EuNH7xd4Zibm+P333/Hjh070L59e6SmpiI1NRXt27fHzp078ccffxhksiGEEFIxvD74KRKJ8P7776ue6CeEEEKK0aPphBBCtILXK5yQkBDs2LEDsbGxyMjIeG2uBY7jEBMTw2eRhBBC9ARvCWft2rVYuHAhXF1d0b59e/j4+PB1aEIIIQaAt4SzceNGvP322zh9+jTMzAxv4iBCCCGa4XWkgWHDhlGyIYQQUireEk779u0RGRnJ1+EIIYQYGN4SzrfffoujR49i3759fB2SEEKIAeHtHs6IESMgl8sxduxYTJ8+HXXr1oWJiYnaNhzH4Z9//uGrSEIIIXqEt4Tj6OgIJycnNG7cmK9DEkIIMSC8JZxLly7xdShCCCEGiEYaIIQQohW8JhypVIqvvvoKgYGB8PX1xY0bNwAA6enp2LBhA6Kjo/ksjhBCiB7hrUnt2bNn6N69OxISEtC4cWNEREQgOzsbQNH9ne3bt+Pp06fYuHEjX0USQgjRI7wlnPnz5yMrKwthYWGoWbMmatasqbZ+0KBBOHnyJF/FEUII0TO8Nan9/vvvmDVrFpo1awaO415b37BhQyQkJPBVHCGEED3DW8LJy8uDi4tLmeuzsrL4KooQQoge4i3hNGvWDJcvXy5z/bFjx+Dr68tXcYQQQvQMbwlnzpw5OHDgAFavXg2JRAIAUCqViI6OxtixYxEaGoq5c+fyVRwhhBA9w1ungTFjxuDp06f47LPPsHjxYgBA7969wRiDSCTCypUrMWjQIL6KI4QQomd4nfFz8eLFGDt2LI4cOYLo6GgolUp4enpiyJAhaNiwIZ9FEUII0TO8JJzc3Fx07doVkydPxrRp06jpjBBCyGt4uYdjZWWFJ0+elNodWpvS09Px3nvvwc7ODg4ODpg4caLq4dOy+Pv7g+M4tde0adO0FDEhhBgP3joN9O7dGyEhIXwdrkree+89PHjwAOfOncPJkydx+fJlTJky5Y37TZ48Gc+fP1e91qxZo4VoCSHEuPB2D+fzzz/H8OHDMXbsWEydOhUNGjSApaXla9s5OjryVaSaR48e4ezZs7h58ybatm0LANi8eTP69u2LdevWoXbt2mXua2VlBTc3t2qJixBCSBHeEk7z5s0BAA8fPix31k+FQsFXkWpCQ0Ph4OCgSjYAEBAQAJFIhOvXr2Pw4MFl7vvzzz/jp59+gpubG/r374/PP/8cVlZWZW4vk8kgk8lU76VSKT8nQQghBoy3hLNkyRJB7+EkJye/Nn6bqakpHB0dkZycXOZ+o0ePRv369VG7dm3cu3cPCxYsQGRkJI4ePVrmPqtWrcLy5ct5i50QQowBbwln2bJlfB1KzcKFC7F69epyt3n06FGVj1/yHo+Pjw9q1aqFnj17IiYmBp6enqXuExQUhHnz5qneS6VSuLu7VzkGQggxBrw+h1OSRCKBjY0NTExMNDrOxx9/jAkTJpS7TcOGDeHm5oYXL16oLZfL5UhPT6/U/ZkOHToAAKKjo8tMOObm5jA3N6/wMQkhhPA8AdutW7fQu3dvWFlZwcnJCX/++ScAIDU1FQMHDqzSNNQuLi7w8vIq9yUWi9GpUydkZmbi9u3bqn0vXLgApVKpSiIVERYWBgCoVatWpWMlhBBSNt4SzrVr19ClSxdERUVhzJgxUCqVqnXOzs6QSCTYvn07X8W9xtvbG71798bkyZNx48YNXL16FTNmzMDIkSNVPdQSExPh5eWlmok0JiYGX3zxBW7fvo24uDicOHEC48aNQ7du3dCyZctqi5UQQowRbwln0aJF8Pb2xsOHD7Fy5crX1vfo0QPXr1/nq7hS/fzzz/Dy8kLPnj3Rt29fdOnSBd99951qfWFhISIjI5GbmwsAEIvF+OOPP9CrVy94eXnh448/xtChQ/Hbb79Va5yEEGKMeLuHc/PmTaxatQrm5ualPt1fp06dcnuL8cHR0bHcLtkeHh5gjKneu7u7q5r9CCGEVC/ernDMzMzUmtFelZiYCBsbG76KI4QQomd4SzgdO3bE4cOHS12Xk5ODXbt2oXv37nwVRwghRM/wlnCWL1+OW7duoV+/fjhz5gwA4J9//sEPP/wAPz8/vHz5Ep9//jlfxRFCCNEzvN3D6dChA06fPo3p06dj3LhxAIqeoQEAT09PnD59mnp+EUKIEatywpFKpbC2tlZ7sPPtt99GZGQkwsLCEBUVpZqAzc/PT/CpCwghhAiryk1qNWrUwMGDB1XvP/jgA1W359atW2P48OEYMWIE2rZtS8mGEEJI1ROOWCxWGzF59+7diImJ4SUoQgghhqfKTWpeXl744Ycf4OHhAXt7ewBAXFwc7ty5U+5+bdq0qWqRhBBC9BjHSj4JWQlnz57FiBEj3jiFczHGGDiOq7b5cIQklUphb28PiUQCOzs7ocMh+kAuA5LDATMrwERcvWUpCoDCXMDNBzClQWerokBRgIj0CFiaWMLMxKzayilUFCJPkQcvRy+Iq/vvQgBVvsLp3bs3njx5gps3byIlJQUTJkzAlClT0KlTJz7jI4QQYiCqnHDu3buH+vXrIzAwEACwa9cuDB8+HD179uQtOEIIIYajyp0GfH19cerUKT5jIYQQYsCqnHAsLS1Voy4DwJ9//omUlBRegiKEEGJ4qtyk1qpVK2zYsAEmJiaqXmo3b96EhYVFufsNGTKkqkUSQgjRY1XupXbr1i0MGzYM8fHxRQfiOLzpUNRLjZB/US81vUK91PhR5Suctm3bIjo6GjExMUhJSYG/vz8WL16MgIAAPuMjhBBiIDQavNPU1BRNmzZF06ZNMX78ePzf//0fOnTowFdshBBCDAhvo0Xv2rWLr0MRQggxQFVOOCtWrADHcVi8eDFEIhFWrFjxxn04jqM5cQghxEhVudOASCQCx3HIy8uDWCyGSPTmHtbUaYCQf1GnAb1CnQb4UeUrHKVSWe57QgghpCTeppgmhBBCysNbpwEAePToEWJiYpCVlQVbW1s0atQIXl5efBZBCCFET/GScLZv344vv/wSiYmJr62rV68eFi9ejEmTJvFRFCGEED2lccL55JNPsGHDBjg6OuKDDz5AixYtYGNjg+zsbISHh+PYsWOYOnUqoqKisHr1aj5iJoQQooc0Sjg3btzAhg0bMHjwYPz444+wtrZ+bZuNGzdizJgxWLduHYYPH462bdtqUiQhhBA9pVGngR07dqBWrVrYt29fqckGAKytrbF//364urpix44dmhRHCCFEj2mUcEJDQzF8+HCYm5fft9/CwgLDhw/H1atXNSmOEEKIHtMo4SQkJMDb27tC2zZr1gwJCQmaFEcIIUSPaZRwpFIpbG1tK7StjY0NsrKyNCnujb788kt07twZVlZWcHBwqNA+jDEsWbIEtWrVgqWlJQICAhAVFVWtcRJCiDHSKOEwxsBxXKW2r04FBQUYPnw4pk+fXuF91qxZg02bNmHbtm24fv06rK2tERgYiPz8/GqMlBBCjI/G3aLXrVuH/fv3v3G70p7R4dvy5csBALt3767Q9owxBAcH47PPPsPAgQMBAD/++CNcXV1x7NgxjBw5srpCJYQQo6NRwqlXrx7S09ORnp5e4e11yZMnT5CcnKw2aZy9vT06dOiA0NDQMhOOTCaDTCZTvZdKpdUeKyGE6DuNEk5cXBxPYQgjOTkZAODq6qq23NXVVbWuNKtWrVJdTRFCCKkYnR+8c+HCheA4rtxXRESEVmMKCgqCRCJRvaj3HSGEvBmvg3dWh48//hgTJkwod5uGDRtW6dhubm4AgJSUFNSqVUu1PCUlBa1bty5zP3Nz8zc+e0QIIUSdziccFxcXuLi4VMuxGzRoADc3N5w/f16VYKRSKa5fv16pnm6EEMMnU8hgaWIpdBh6Teeb1CojPj4eYWFhiI+Ph0KhQFhYGMLCwpCdna3axsvLC7/++iuAohlI58yZg//97384ceIEwsPDMW7cONSuXRuDBg0S6CwIIbroRe4LoUPQezp/hVMZS5YswZ49e1TvfX19AQAXL16Ev78/ACAyMhISiUS1zaeffoqcnBxMmTIFmZmZ6NKlC86ePQsLCwutxk4I0W2FikKhQ9B7HKvupzGNgFQqhb29PSQSCezs7IQOh+gDuQxIDgfMrIDqnrteUQAU5gJuPoAp3XusigJFAY5HH0drl9YwMzGrtnIKFYXIU+TBy9EL4ur+uxCAQTWpEUJIdZEpZG/eiJSL1ya1kJAQ7NixA7GxscjIyHhtKBuO4xATE8NnkYQQohV58jyhQ9B7vCWctWvXYuHChXB1dUX79u3h4+PD16EJIURwWQXVO/iwMeAt4WzcuBFvv/02Tp8+DTOz6mvjJIQQIaTnV2wIL1I23u7hZGRkYNiwYZRsCCEGKSknSegQ9B5vCad9+/aIjIzk63CEEKJT0vLSqFlNQ7wlnG+//RZHjx7Fvn37+DokIYTolBgJdXrSBG/3cEaMGAG5XI6xY8di+vTpqFu3LkxMTNS24TgO//zzD19FEkKIVj3OeIx2bu2EDkNv8ZZwHB0d4eTkhMaNG/N1SEII0SmP0h5ByZQQcfQIY1XwlnAuXbrE16EIIURnSCQS3Am7g7SHaUhHOsLqhqGNRxuhw9JLBjWWGiGE8C08PBxv+7+tet/IoRElnCriPeEUFhYiIiICEokESqXytfXdunXju0hCCNGaGEkMUnJS4Grt+uaNiRreEo5SqURQUBC+/fZb5ObmlrmdQqHgq0hCCBHE8ZjjmOwzGRzHCR2KXuHtztfKlSuxdu1ajBkzBj/++CMYY/jqq6+wbds2tGzZEq1atUJISAhfxRFCiGAepT/CzeSbQoehd3hLOLt378a7776LrVu3onfv3gAAPz8/TJ48GdevXwfHcbhw4QJfxRFCiKAORx1GQlaC0GHoFd4SzrNnz/D220U31szNi+bcyM/PBwCIxWKMGTMGe/fu5as4QggRVKGyEN/d+45mAq0E3hKOk5OTaipnGxsb2NnZITY2Vm2bjIwMvoojhBDBZRdmY/PdzXiW9UzoUPQCbwnH19cXN2/+16bZo0cPBAcH4+rVq7hy5Qo2bdqEVq1a8VUcIYTohOzCbGy6uwl3XtwROhSdx1vCmTJlCmQyGWSyolnxvvzyS2RmZqJbt27o3r07pFIp1q9fz1dxhBCiMwqVhdj7cC+ORB1BobJQ6HB0Fm/dogcMGIABAwao3jdr1gwxMTG4dOkSTExM0LlzZzg6OvJVHCGE6Jy/Ev9CvDQe77d4Hw7mDkKHo3OqdaQBe3t7DBw4sDqLIIQQnRKfFY/gO8GY3nI6PRz6Cl5HoFMoFDhw4ACmTp2KwYMHIzw8HEDRWERHjx5FSkoKn8URQohOksgk+Pafb/Ey96XQoegU3hJOZmYm3nrrLYwePRr79+/HiRMn8PJl0f9sGxsbzJo1Cxs3buSrOEII0WnSAim23duGjHzqnVuMt4SzcOFCPHjwACEhIYiNjQVjTLXOxMQEw4YNw+nTp/kqjhBCdF56fjo2392M5JxkoUPRCbwlnGPHjmHmzJl45513Sh1fqEmTJoiLi+OrOEII0QsZsgwE3wnGPy9p8kneEo5EIkGDBg3KXF9YWAi5XM5XcYQQojdkChl2P9iN/RH7kSfPEzocwfCWcDw9PXHnTtkPPv3+++9o1qwZX8URQojeuZF8A1/d+Ar3U+8LHYogeEs4kyZNws6dO3Hw4EHV/RuO4yCTybB48WKcPXsWU6dO5as4QgjRipL3o0t7X1nSAil23N+B3Q92Q1og1ehY+oZjmv7f+xdjDFOmTMGOHTvg4OCAzMxMuLq6Ii0tDXK5HFOnTsXWrVv5KErnSKVS2NvbQyKRwM7OTuhwiD6Qy4DkcMDMCjARV29ZigKgMBdw8wFMzau3LAOSmZmJPXv2YO3atUhMTFQtt3C0QOPBjeHewx1iG83qztLUEkMaDYGfqx84jkOhohB5ijx4OXpBXN1/FwLgLeEU++uvv3D48GFERUVBqVTC09MT7777rlZm+vzyyy9x6tQphIWFQSwWIzMz8437TJgwAXv27FFbFhgYiLNnz1a4XEo4pNIo4ei0kJAQDB06VDWZ5GtfkxxgYm6C9gvaw9VX84c7fWv64t0m78KEM9Eo4fzx9A/8EvkLHqY/hEQmwaH+h+Dl6FXuPseij+Hzq5+rLROLxLg99jaAomF7Nt/djCvPriAxOxE2ZjboWKsj5vjNQU2rmpWKj/eRBrp06YIuXbrwfdgKKSgowPDhw9GpUyfs2LGjwvv17t0bu3btUr0vnl6BEGJ8QkJC0K9fPzDGym4+Y4BCpsDfX/yNjp931Djp3H1xFyk5KZjYYiJMTar+tZwnz4Ovqy8CPQKxLHRZhfezMbPBb4N/K3Vdvjwfj9IeYWqrqWhaoymkBVKsvrEaMy/MxMH/O1ip+Kp1aBttW758OYCiyeAqw9zcHG5ubtUQESFEn2RmZmLo0KFgjEGpVJa/MQMYGG6svoHAHwI1bl5LyknCjvs7MNFnYpWP0d+zPwAgMTvxDVuq48DB2dK51HW2Ylt83+t7tWWLOizCqFOj8Dz7OWrZ1KpwORolnJKDdVYEx3E4fvy4JkVWi0uXLqFmzZqoUaMG3n77bfzvf/+Dk5NTmduXHBUbKGpSI4Tovz179iA3N7fiHQP+vdJJuJQAz//z1Lj8xOxEXH9+HS2cW2h8rMrIleei1+FeUDIlvJ28Mdt3NhrVaFTm9lkFWeDAwVZsW6lyNEo4J0+ehIWFBdzc3CpUQaU9ECq03r17Y8iQIWjQoAFiYmKwaNEi9OnTB6GhoTAxMSl1n1WrVqmupgghhoExhs2bN1dp39iTsWjYryEv33HafkDUw84DK95agSY1miCrIAt7HuzB2DNj8evAX+Fm/XrLj0whw9e3v0afBn1gI7apVFkaJZw6deogMTERzs7OGD16NEaOHMl709TChQuxevXqcrd59OgRvLzKvzFWlpEjR6r+7ePjg5YtW8LT0xOXLl1Cz549S90nKCgI8+bNU72XSqVwd3evUvmEEN2QlpaGmJiYyu/IgJzkHBRICyC206xZjYFVeMrqk7EnsSJ0her91oCt8HP1q3SZrWu2RuuardXeDzw2EIceH8JM35lq2xYqC/HJpU8AAJ93VO9oUBEaJZyEhAT8+eef2LdvH7744gvMnz8f3bt3x3vvvYdhw4bB1rZyl1ul+fjjjzFhwoRyt2nYsKHG5ZQ8lrOzM6Kjo8tMOObm5tSxgBADk52drdH+BXkFMLXV/LY4J+Ig4t78iGQP9x5o6dxS9b6yPcbKYiYyg5ejFxKkCWrLi5NNUk4SdvTaUemrG4CHTgPdu3dH9+7d8c033+D06dPYt28fZsyYgQ8//BB9+vTB6NGj0b9//yp/Qbu4uMDFxUXTMCvs2bNnSEtLQ61aFb8RRgjRfzY2lf8CLcnaxhrmPHQ7b+jQEKaiN381W5tZw9rMWuPyXqVQKhCVEYWudbuqlhUnm/iseOwI3AEHC4cqHZu3kQbMzMwwcOBAHDx4ECkpKdi+fTuSk5MxYsQIrFmzhq9iyhUfH4+wsDDEx8dDoVAgLCwMYWFhar9cvLy88OuvvwIo+kUzf/58/P3334iLi8P58+cxcOBANGrUCIGBgVqJmRCiG5ycnODp6Vn5+zAcYO1mDXM7c3Acp/Grt0fvKp+DRCZBRHoEYjKLmgbjJHGISI9Aal6qaptFVxYh+Haw6v3Wf7biWuI1JGQl4GHaQwRdCcLznOcY2ngogKJkM+/SPDxIe4Cvun4FJVMiNS8VqXmpKFRUbjpt3rtFy2QyhISE4Pjx47h79y4sLCzg4eHBdzGlWrJkidpDnL6+vgCAixcvwt/fHwAQGRkJiUQCoGjahHv37mHPnj3IzMxE7dq10atXL3zxxRfUZEaIkeE4DjNnzsTcuXMrvW/j/o156TDQ2qU1utWt+kPyFxMuqj3EOf/yfADA9FbT8WHrDwEAz3Oeq8UqlUmxLHQZUvNSYSe2QzOnZtjbZy88HYp63b3IfYFLCZcAAMN+G6ZW3s7AnWjn1q7C8fEy0oBSqcS5c+ewf/9+HDt2DLm5uQgICMDo0aMxePBgWFvzf9mnS2ikAVJpNNKATsrMzETdunWRl5f35udwANWIA/139df4OZw6NnWwovOKKt0b0RcaXeFcu3YN+/btw6FDh5CWloaOHTti5cqVePfdd+HsXPpDRISQfykKihIO0RkODg44cuQI+vXrB5FIVH7S4Yquit4KekvjZGMntsPC9gsNOtkAGl7hiEQiWFpaom/fvhg1alSFms7atGlT1eJ0Fl3hkEqTy4DHIYBTI7rC0UEVHUvtraC34NZGs0dBzERm+Lzj52jq2FSj4+gDjROO6kBvaL9kjIHjOCgUiqoWp7Mo4ZBKk8uAR78BNZtRwtFRmZmZ+PHHH7FmzRq10aItHS3RdGhTePT0gNhas7rjwGGO3xx0rNVR03D1gkZNaiUHvCSEVFIle/gQ7XJwcMCsWbPg6+urNtp9h/kdULOF5s+8iCDC9NbTjSbZABomnPHjx/MVByHGR1EgdASkAl5tveGjN5qFiQVmtZlVpZEB9JlBjRZNiF4pNN657Y1ZHZs6mOs3F+62xjccFiUcQoSSLxE6AqJl/u7+mNB8AixNLYUORRCUcAgRSl660BEQLbE1s8XklpPRoVYHoUMRFCUcQoSSXbFRgYl+6+DWARN9JsLe3F7oUARHCYcQoUieAawCT7MTveRg7oAPWnxg9Fc1JVHCIUQocllR0nEqe2ZFop8C6gVgtPfoahnNWZ9RwiFESC8eUcIxII4Wjvio9UdanyJaX/A2PQEhpApS7gsdAeFJm5ptsLbbWko25aArHEK0TCKRIPzubSBaAjz+Gz7NEmFfs47QYREN9KrfC++3eL9CM3UaM0o4hGhZeHg4uvb4b/ryKw0OoMuojwWMiGiiZ72e+KDFB7yMQGDoKB0TIrSE64AsS+goSBU0rdGUkk0lUMIhRGhyGXD/iNBRkEoyE5nhw9YfwlREDUUVRQmHEF0Qcx5IjRI6ClIJfRr0gZu1ZnPhGBtKOIToAsaA0G+AfKnQkZAKsDGzwaBGg4QOQ+/QtSAhuiI3DbiyDui+EBDT1NO6wsfHB1euXMGyq8ughBL2HvYY0ngIPdRZBXSFQ4guSY8FLv4PyMsQOhLyL3t7e3Tp0gUuzV3g0twFDVwbINAjUOiw9BIlHEJ0TWY8cG4JkBYjdCSkFO95v0cdBaqIEg4huigvA7jwBRD9R9H9HaITPOw80KZmG6HD0FuUcAjRVUo5cHs38PcWmh1UR/Ss15OeudEAJRxCdF3838Dvi4vu7xBB+bn6CR2CXqOEQ4g+yH4BnF8ORJyiOXQE4mrtCidLJ6HD0GuUcAjRF0oF8M9+4PI6IF8idDRGx8POQ+gQ9B4lHEL0TfI9ICQISA4XOhKjUtOqptAh6D1KOIToo3wp8Odq4O5PgKJQ6GiMgqOFo9Ah6D2DSThxcXGYOHEiGjRoAEtLS3h6emLp0qUoKCgod7/8/Hx89NFHcHJygo2NDYYOHYqUlBQtRU2MEXulm/Or7yvl8Vng3GdAxhMNoyJvYiu2FToEvWcwCSciIgJKpRLbt2/HgwcP8PXXX2Pbtm1YtGhRufvNnTsXv/32Gw4dOoQ///wTSUlJGDJkiJaiJsYkMzMTGzduxKhRo9SWj9oVjY0Xk5GZK6/agSWJwB9LgYfHqENBNbIR2wgdgt7jmEY/r3Tb2rVrsXXrVsTGlt6dVCKRwMXFBfv27cOwYcMAFCUub29vhIaGomPHjhUqRyqVwt7eHhKJBHZ2drzFTwxHSEgIhg4ditzcXADqVzXFT3VYiUU4MqkxAps5VL0gl6ZAx48AqxLNP4oCoDAXcPMBTM0rfixFYdHDp1HngIw4wNwOaOgPBCwD7GqVv++N74Grm4DsFMCtBdBnLVC3RJfiXf2Ap3+p7+P3PtA/uOLxaVlMZgw8HTyFDkOvGcwVTmkkEgkcHctud719+zYKCwsREBCgWubl5YV69eohNDS0zP1kMhmkUqnai5CyhISEoF+/fsjLywNj7PUmtX9feYVK9NsaiZCHmVUv7GVkURPby0hNQi5SmAs8/wfoNh+YehkY8ROQFgXsH1n+fvePACGLAP8FRfu5tgB+Ggxkv1Tfrs144OPH/73eWaF5zNXIypQGVNWUwSac6OhobN68GVOnTi1zm+TkZIjFYjg4OKgtd3V1RXJycpn7rVq1Cvb29qqXu7s7X2ETA5OZmYmhQ4eCMQalsvzmLiUrSjxDf4iqevMaUNSh4NIqIOFG1Y8BABb2wLjjQIshgHNjwL0d0Hct8DwMyEwoe7/QLUXJxHcMUNML+L9gwMwKuLtXfTszK8DW9b+XhW63DliYWggdgt7T+YSzcOFCcBxX7isiIkJtn8TERPTu3RvDhw/H5MmTeY8pKCgIEolE9UpIKOfDR4zanj17kJub+8ZkU0zJgNwCJX68kapZwUp50fw6miadV+VLAXBFyag08gIgKayo6a2YSFT0/tlN9W3DfwFWNwC2dAT+WAYU5PIbK89owE7N6fz/wY8//hgTJkwod5uGDRuq/p2UlIQePXqgc+fO+O6778rdz83NDQUFBcjMzFS7yklJSYGbW9kz+Zmbm8PcvBJt4cQoMcawefPmKu276VIyZnZ31WzcLqYErm8FeiwGrJ2rfpxihflFnRN8hpV9NZKbBjAFYPPKMyvWLkDq4//e+wwDHNwB21pAygPg3NKiGU9H/qx5nNXEhDMROgS9p/MJx8XFBS4uLhXaNjExET169ICfnx927doFkaj8Czg/Pz+YmZnh/PnzGDp0KAAgMjIS8fHx6NSpk8axE+OWlpaGmJjKTzHAAMSkypCeUwgnGzPNglAUAP/sAzrPevO2934Bfpvz3/sxh4H6nf89TiFwaELRyNX9NmgWEwC0ff+/f7s2B2xcgR8HFI0X59iw7P0ERIN2ak7nE05FJSYmwt/fH/Xr18e6devw8uV/NyiLr1YSExPRs2dP/Pjjj2jfvj3s7e0xceJEzJs3D46OjrCzs8PMmTPRqVOnCvdQI6Qs2dnZGu2flS+HkzUPv6pfRACcKfCmX+hN+wB1SvQks6td9N/iZCNJAMb/Vv69FiunonKyX6gvz3lZlFTKUrdt0X91OOEQzRlMwjl37hyio6MRHR2NunXrqq0r7hVUWFiIyMhIVddUAPj6668hEokwdOhQyGQyBAYG4ttvv9Vq7MQw2dho9tyGrY01YKbhFQ4AmJgDrs0Akzd83M1ti14lFSebtBhgwkn17talMRUDtVsDT/4EvP+vaJlSCcT+CbQv535q8TA9NmU3ZQvNlDOYr0vBGPRzONpCz+GQ0jDG0LhxY8TGxlZqNAEOQEMXC0StaMdPM453f6DbJ5XfT1EI/DKuqGv06IOAdYn7MpY1ipILAOzpD3j1BzpMKXp//wjw6/SiZ2rq+AF/fws8+BWYcavo3k56LBB+GGj8DmDpWHQPJyQIsKsDvH9a49MluotSNiHVhOM4zJw5E3Pnzq30vrN61OEn2TjUA9pPqdq+0iQg8t8EsK2L+rrxJ4EGXYv+nR5X1FmgWIuhQE4acHHlvw9++gBjjv7XkcBEDMReKkpEBbmAfR3Ae0DR8z7EoNEVDg/oCoeUJTMzE3Xr1kVeXl6FukaLOMBSLMKzVR3hYKXh70GnRkCfNYA1zeFCdIPOP4dDiD5zcHDAkSNHwHHcG3tNijiA44CjU5tpnmzqdwYGbKZkQ3QKJRxCqllgYCBOnToFS0tL1cPKJXH/vizFIpye0QK9mmk4DH6zAUCvLwExDcVCdAslHEK0IDAwEM+ePUNwcDBq166ttq62gxjB73oi8auOmiebVqOALvOKnu4nRMfQPRwe0D0cUhlXrlxBt27dVO8vf9wSXRs7aH7gtu8XjWFGDygSHUW91AjRstea1DRNECIT4K05RU1phOgwSjiE6DMbV+Dtz4BaLYWOhJA3ooRDiD7iREXTBrSdSJ0DiN6ghEOIvqnXCegwFXBsIHQkhFQKJRxC9IWLF9BxGlDbV+hICKkSSjiE6DoLe6DDNKBJb+ruTPQaJRxCdFnD7kCXuUWDZRKi5yjhEKKLOBHQeQbQfAg9V0MMBiUcQnSNyBR4Zzng0eXN2xKiR6hBmBBd02MRJRtikCjhEKJL2k0CGvUUOgpCqgUlHEJ0RaMAwHeM0FEQUm3oHg4hWubj44MrV64Ap+YD8jz41LEGXJsD3RdQBwFi0CjhEKJl9vb26NKlCxDlCBRkFz1n884KwFQsdGiEVCtqUiNEaG/NBqydhY6CkGpHCYcQITk1AjzfFjoKQrSCEg4hQmrah+7bEKNBCYcQIbm3FzoCQrSGEg4hQjG3AezdhY6CEK2hhEOIUGo0oOY0YlQo4RAiFId6QkdAiFZRwiFEKPZ1hY6AEK2ihEOIUOzqCB0BIVplMAknLi4OEydORIMGDWBpaQlPT08sXboUBQUF5e7n7+8PjuPUXtOmTdNS1MSoWTkKHQEhWmUwQ9tERERAqVRi+/btaNSoEe7fv4/JkycjJycH69atK3ffyZMnY8WKFar3VlZW1R0uIUVD2hBiRAwm4fTu3Ru9e/dWvW/YsCEiIyOxdevWNyYcKysruLm5VXeIhKgztRA6AkK0ymCa1EojkUjg6PjmZouff/4Zzs7OaNGiBYKCgpCbm1vu9jKZDFKpVO1FSKVxBv3xI+Q1BnOF86ro6Ghs3rz5jVc3o0ePRv369VG7dm3cu3cPCxYsQGRkJI4ePVrmPqtWrcLy5cv5DpkYG6YUOgJCtIpjjDGhgyjPwoULsXr16nK3efToEby8vFTvExMT0b17d/j7++OHH36oVHkXLlxAz549ER0dDU9Pz1K3kclkkMlkqvdSqRTu7u6QSCSws7OrVHnEiBXkAmK6X0iMh84nnJcvXyItLa3cbRo2bAixuGgukaSkJPj7+6Njx47YvXs3RKLKNVvk5OTAxsYGZ8+eRWBgYIX2kUqlsLe3p4RDKocSDjEyOt+k5uLiAhcXlwptm5iYiB49esDPzw+7du2qdLIBgLCwMABArVq1Kr0vIZVCnQaIkTGYu5aJiYnw9/dHvXr1sG7dOrx8+RLJyclITk5W28bLyws3btwAAMTExOCLL77A7du3ERcXhxMnTmDcuHHo1q0bWrZsKdSpEGNRhR9EhOgznb/Cqahz584hOjoa0dHRqFtXfciQ4lbDwsJCREZGqnqhicVi/PHHHwgODkZOTg7c3d0xdOhQfPbZZ1qPnxBCDJ3O38PRB3QPhxBC3oyu6QkhhGgFJRxCCCFaQQmHEEKIVlDCIYQQohWUcAghhGgFJRxCCCFaQQmHEEKIVlDCIYQQohWUcAghhGgFJRxCCCFaQQnHQMhkMixbtkxtnh6iu6i+9AvVFz9oLDUeMMaQlZUFW1tbcBwnSAw0npt+ofrSL1Rf/DCY0aKFxHEc/RESQsgbUJMaIYQQraCEQwghRCso4RgIc3NzLF26FObm5kKHQiqA6ku/UH3xgzoNEEII0Qq6wiGEEKIVlHAIIYRoBSUcQgghWkEJhxBCiFZQwiGEEKIVlHAIIYRoBSUcQgghWkEJhxBCiFZQwqlmSqUSQNGI0kT35eTkID09XegwSAUlJibi0aNHePHiheqzRnQXJZxqFBISgt27dyMnJwccx1HS0XEHDx7E0KFD0bZtWwwcOBCXL18WOiRSjr1792LgwIHo3r07BgwYgP379wsdEnkDSjjV5MiRI+jTpw9WrlyJX3/9Fbm5uZR0dNiPP/6IKVOmoFu3bli8eDGePn2KpUuX0q9mHbVnzx58+OGHmDp1Ko4dOwZHR0fs2bOHPl+6jhHePX78mHXq1IktWbKEDR8+nHl7e7M9e/awnJwcxhhjSqVS4AhJSbdu3WLe3t5sx44dqmXJycnM2tqanTp1SsDISGlCQ0OZh4cH27t3r2rZ77//ziZMmMBu3rzJYmJimEwmEzBCUhaagK0aiMViBAQEYNiwYfDx8cHIkSOxevVqAMCwYcNgZWUlcISkGGMMERER8Pb2Rp8+fQAAcrkc9vb28PT0RGFhocARkpIYY8jJycGcOXPQt29f1fL169cjPDwcJ0+eRIMGDdCgQQP8+OOPNLqzjqGEUw3c3d0xc+ZMuLi4AAAOHDiglnSGDx8OS0tLSKVSWFpawszMTMhwjRrHcQgMDISlpSVq1aoFABCJRLCwsICDgwPy8vLUtlcqlRCJqCVaKBzHoWvXrmjWrBkcHR0BFH2e7t+/j6NHj8LV1RXnz59HcHAwzp49i4EDBwocMSmJPjk8YSXajkUiEZydnQFA9Qv5wIEDaNGiBVavXo0jR47gyZMnGDVqFFatWiVIvMauZH05OztjyJAhqvfFCSU7OxsvXrxQLZ87dy5OnjypvSCJSsn6EovFcHNzU72fPXs2rl69ig4dOsDDwwMDBgxAUlISkpOThQiVlIMSDg8YY+A4DgCwc+dO3Lx5U9VBwMzMDAqFAkBRL6iWLVti5cqV6NatG2JjYxEUFCRk6EaptPoCXu/Cbm1trfoV3bt3b/z6669qzTivHpNUj7I+X0qlEowxdOnSBfXr11dtn5OTgxYtWsDT01OokEkZKOFoSKlUqj4Md+/exQ8//IBp06bh4cOHqqRjYmKiSjqbN29GTEwM6tWrh3v37sHMzAxyuVzteKT6lFdfIpEISqVSVVe2trbgOA5DhgxBXFwcoqKiYGpqqlpffDwAqmMWowTEjzfVF2NM7TOTm5uLWbNmQSQSoUePHqUer/i/VEfaRzN+aqDkL68vvvgC//zzD548eYLw8HC0bt0a3333HVq3bq3aLj09HQEBAcjLy0N4eDhMTU0hl8thalp0K634/sCzZ88gk8noFxrPKlpfxbp164a//voLTZs2Vftx8Gp9PXnyBAcOHEBaWhrc3d0xe/bs18ojlVeZ+srJycHZs2exY8cOJCYm4tatW6rWBRMTEwD/1dfjx4+xbt06JCQkoH79+ggODoaFhYVQp2lctNgjzmBt2rSJWVtbswsXLrD4+Hi2a9cu9vbbb7O2bduysLAwxlhRV+icnBz26aefqrpsFhYWqo5R3FX60aNHTCQSsaZNm7KoqCjtn4wRqEh95eXlsT59+rD27dur6qlkfSkUCsYYY/fu3WNubm6sX79+rHv37szOzo59+OGH2j8pA1aR+srMzGSLFi1iU6dOfWN9ubi4sFGjRrF58+axmjVrskmTJmn/pIwUJRwNFRYWslGjRrFp06apLT916hRr06YNa9++PXvw4EGp+73q5cuXrGfPnmzo0KHMz8+PNWvWjD1+/LjaYjdGFamv8PBwxljRszilfXkVe/r0KWvSpAn79NNPGWOM5ebmsl9++YU1adKE3b9/v5rPxDhUpr6ys7NViUUul792rNjYWObp6ckWLFigWrZ+/Xo2ffp01X6ketE9nEp69cayqakpbGxsEBUVBZlMptqub9++6Nu3L27evImJEyfi3r17ascpbpYpKTY2Fg0bNsSMGTMQEhICGxsbDBo0CFFRUdV4RoatKvU1efJk3L17F66urqp7Nq/WF2MMJ06cgLu7OxYsWAAAsLS0RKtWrZCRkYHs7GwtnaFhqWp9hYWFwdraWnVfp7gZraSjR4+ia9euWLRokWpZVFQUrly5gs6dO2Pw4MEICQlRu0dH+EUJp5KKu8xevXpVtaxVq1ZISEhASEgI8vPzVcu9vb0xaNAg1K1bF19//TVycnLKPbavry8mT54Mf39/ODk54eTJk7C2tsagQYPw+PFj1XZKpVKtowEpW1Xra9OmTar6Ku3Li+M4tG/fHr1791b1ZFMoFGjcuDEcHBwglUqr87QMVlXra+PGjar6Kuu+2fTp0zF16lTY2dkBANauXYvt27dj6NChmDNnDp4/f45PP/0UmZmZ1XR2hJrUquDWrVuM4zj22WefqZb16dOHNW7cmP3000/s6dOnLDMzkw0YMIB99dVXbP369axmzZosPj6+zGO+OtxN8SV+amoqa9eunap5TS6Xs1WrVrFdu3ZVy7kZouqoL8aYWjNMyfpr1qwZ+/XXX1Xvjx8/zvLy8vg7IQOnjc/Xixcv2Keffsp+//131bKsrCzGcRw7cOAA/ydFGGN0D6dKpFIp27hxIxOLxWofiqFDhzIfHx/m6OjIvL29WZMmTRhjjN2+fZt5enqy2NjYKpVXnHR8fHzYyJEjGcdx7NGjR7ycizHQVn3J5XIml8tZo0aN2OnTpxljjH3++eeM4zj29OlT/k7IwGmrvrKzs1X/lsvl7P79+6xNmzbs9u3b/JwIeQ0NbVMFtra2mDhxIkQiEebMmQOlUokvv/wShw8fxl9//YWnT59CLBarnl7fuXMnHB0dUaNGDQDArVu30LZt2wqX5+TkhOPHj6Nhw4ZISkrC7du34eXlVS3nZog0ra+oqCg0btz4jd2cWYlnQmxtbbFmzRps2LABN2/eRL169ar/RA2Etuqr5JiGJiYmOHDgAExMTFCnTp3qPUFjJnDC02klL8PXr1/PNmzYoLY+Ozubbd68mXEcx/73v/+9tv9ff/3FpkyZwpycnFTdN69fv844jmPBwcEVjqOgoIBNmzaNWVhYUO+nclRHfZ07d45xHMeOHj36Whll6dChA2vevDkzNzdnN2/e1OSUDJqu1Nfdu3fZokWLmK2treo4pHpQwilDyfb5+/fvs4ULFzJLS0v23XffqW0nkUjY4MGDGcdxbOHChWrrfv/9d/bOO++we/fuqS1fs2YNE4vFbNOmTRWKJSIigvXt25e+vMpRXfWVkZHBZs6cySwsLNixY8cYY2V/iRU/a1WvXj0mEolU3XXJ63ShvhhjLD4+nk2dOpX5+PhQstECSjhvsHDhQjZt2jQWFhbGlixZwmxtbdm2bdvUtgkKCmLdu3dnPXr0YEqlUu0PvHgOnFetW7eOiUSiCiUduVzOsrKyNDsRI1Ed9SWRSNicOXOYiYlJuV9ixcuOHz/OIiMj+TwtgyVkfRUvj4mJYUlJSTyeFSkLJZxXlPzDvHbtGvP29mY3btxgjDH2/PlztnjxYmZnZ6f6JZaTk8NGjBjB9u/fX+oxylOZpENKp636yszMrNSXGCmdLtYX0R5KOGXYsGEDmzNnzmvDlCQlJbFly5YxjuOYn58f8/b2Zq1atVI9iV7ZL6HVq1dT0uEB3/VVGqlUymbOnElfYjyg+jJOlHCY+h9h8b/Hjh3LOI5jHTp0YBkZGWrb5+fns0uXLrGPP/6YrVy5UvVhKG04jZLHDA0NZdu3b2dfffUVu3XrlurZjK+++oqSTiVoq75u377Njhw5wn744QeWlJTE5HI5UygU9CVWSVRfpBglHFbUCyw3N5c9e/aMFRQUqJbPnz+fcRzHtm/fznJzc1XLS/uDLW2srZIOHz7MbGxsWEBAAHN2dmY+Pj5s0qRJqnszq1evZubm5mz16tU8nZXh0kZ9HTp0iDk4OLD27dszCwsL1rJlS7Zy5UqWn5/P5HI5mzlzJrO0tGQHDx7k78QMFNUXKWb0CSckJIRNnz6dubu7MwcHB9avXz+1m5ZTp05lFhYWbO/evVV+WjwyMpK5u7uz7du3M4VCwRQKBVu/fj3r2rUrmzJlCsvPz2eMMfbFF18wR0dHlp6ezsu5GSJt1Nc///zDXF1d2Y4dO1hWVhbLy8tjM2bMYF26dGGrV69mCoWCSSQSNmnSJObs7EwdOspB9UVKMuqEs2PHDlanTh02b948tnr1arZ9+3bWokULVqtWLTZ//nzVdlOnTmVWVlbsp59+UvslVpZXf6GFhoayOnXqqE03kJOTw9asWcNatmzJIiIiVMvT0tJ4ODPDpK36OnnyJPP09GTPnz9XLZNKpWz69OnM19eXSSQSxlhRb6jk5GSezs7wUH2RVxltwtm2bRsTi8Vs//79apf5jx8/ZuPHj2eurq5qzVszZsxgHMexs2fPvvHYxR+Ic+fOsTNnzrDQ0FDWsGFDdunSJcbYf88g5OXlMWtra7Z161Y+T80gaaO+Ll26xB4+fMiOHTvG6tevrxoqpbi8zMxMZmpqyn755Rc+T80gUX2R0hhlwvn1118Zx3HsxIkTjDH22k3J6Oho5u/vz9q1a8fi4uJU+61bt+6NbcnFrly5wjiOY8eOHWOpqanM29ub9e/fn6Wmpqq2yc7OZp07d2aHDh3i69QMkjbq68KFC4zjOBYSEsKSkpKYk5MTmzx5sto2z58/Z61atWIXLlzg47QMFtUXKYvRJZz8/Hw2bdo05unpyTZv3qxaXvxhKP71FBISwkQiEbty5cprx3jThyIqKor9/PPPbPny5apljx49YjVq1GD9+vVjJ0+eZGFhYSwoKIg5OTlVeVBPY6CN+oqLi2NHjhxhX331lWpZSEgIs7GxYR988AG7d+8ei42NZZ999hmrXbv2G0eRNmZUX6Q8RpdwGCvq6z979mzWoUMHtT9ahUKh+kBERkYyCwuLCl/iF+/38uVLJhaLGcdx7JNPPlHbLiIigrVu3Zo1aNCA1a9fn3l5ebE7d+7weGaGqTrrKz4+nnEcx6ytrdWOzRhjf/zxB6tVqxZzd3dnDRs2ZB4eHjSScAVQfZGyGGXCYazocnvGjBmvfSiKf10dPnyYvfXWW2X+Oiq+D1PyJmfxfDVnz55lrq6urFevXqpOAMXbZ2dns4iICHbnzh2WkpJSLedmiPiqL5lMplqWmJjIlEol+/bbb5mjoyN7//33VeuKv+DS09PZ5cuX2aVLl1hiYiLv52WoqL5IaYw24TBW9odCKpWyvn37sokTJ5b7kFhCQgIbMWIEi46OZsePH2eWlpbs4cOHjDHGzpw5wywtLdm0adNU3T3pgTPNaFpfcXFxbN68eSwjI4MdPnyYWVtbs2fPnrHc3Fy2bds2ZmpqylasWKHavqL3E0jpqL7Iq4w64TCm/qFYu3YtY4yxgQMHVmg4jVOnTjF/f3/Wtm1bZm5uzvbt28cY++/X2alTp5iFhQWbPn266lkbohlN6mvXrl2sadOmrHfv3szCwoLt2bNHtU4mk7Fvv/2WmZiYlDoUPqkaqi9SktEnHMaKPhQzZ85knTt3ZjVr1mRNmjRRda0saziNYitXrmQcx7GWLVuq5qpRKpVqScfW1paNHTuWkg5PNKmvuXPnMo7jWI8ePV5rcin+ErOwsGBBQUHVFr+xofoixURCTwCnC9zc3LBo0SI0atQIfn5+uH//PszMzCCXy2FiYlLqPoWFhQAAV1dXfP7553B3d8f8+fNx/fp1cBwHjuOgVCrRt29f7N27F3/88QcyMjK0eVoGqyr1VVBQAACws7PD1KlTkZ2djRUrViAiIgJA0WydYrEYEydOxJdffonvvvsOqampWjsnQ0b1RVSEzni6JD09XXVlUlZ7cPHl/6tPRB86dIj16tWL9enTh12/fl21/Pr160wul6vNn074UZn6KnnzmTHGvvnmG+br68umTp2qNtJD8WgQNLwQ/6i+CMcYY0InPV2jVCohEr1+8cf+nSP99OnTCA4Ohq2tLVq2bImlS5cCAI4cOYLvv/8eHMdh5syZuHnzJr7++mtERUXBxcVF26dhNN5UXyEhIfjhhx/g5OQEPz8/TJ48GQCwZcsW7Nq1C35+fpg4cSLOnDmDjRs3Ii4uDnZ2dto+DaNB9WW8KOFU0pUrVxAQEIBJkyYhPT0dV65cQceOHXH48GEAwPHjx7Fz507cuXMH5ubm2L9/P9q1aydw1MbrwoULCAwMxOjRoxEbG4uMjAz07NkTGzduBABs374du3btwsuXL1FYWIjDhw+jffv2AkdtvKi+DBslnEp4/PgxoqKiEBUVhTlz5iAnJwfnz5/HhAkT0KNHDxw5cgQAEBcXh5ycHNSoUQO1a9cWOGrjFRsbiwsXLiA/Px8zZszAixcvcODAAQQHB6Nv37745ptvAAB3795FTk4O6tWrh3r16gkctfGi+jICgjXm6ZmEhATm4uLCbGxs1CZKk8lk7MSJE8zR0ZENHz5cwAhJSY8fP2bNmzdnderUUZsDJS0tjW3cuJE1aNCAzZo1S8AISUlUX8aBeqlVkLW1NZYsWQJ7e3vcuHFDtVwsFqN379748ccfcfjwYUyYMEG4IImKmZkZAgMDkZ+fr1Zfjo6OGDt2LD7++GPs2bMHCxcuFDBKUozqyziYCh2ArmL/3sAsVqNGDYwZMwampqaYP38+5syZg+DgYABFH5Z33nkHp0+fRoMGDQSK2Li9Wl8eHh6YM2cOxGIx9u3bBxcXFyxYsABAUV2OHDkSZmZmePvtt4UK2ahRfRknuodTiuIPw7Vr13Dnzh08e/YM7777Lry8vGBubo7vv/8en332GcaMGaNKOkQ4xfV169YtREREID09HQMGDICHhwdevHiB4OBgHDlyBB988IHqSwwou7cUqV5UX0ZMuNY83Xb48GFmY2PDunXrxry9vZmDgwNbsGABi4+PZ4WFhWzbtm3Mzc2NTZo0SehQCSt6DqpGjRqq0bhtbW3Zli1bWH5+Pnv+/DkLCgpiLVq0YEuXLhU6VMKovowVJZxSPH78mNWvX5/t3LlTNQRHcHAwa9myJVu0aBErKChgGRkZLDg4mHl6etK0tQILDw9nNWvWZLt371ZNJ7xw4ULm4uLCtm/fzhhj7MmTJ2z27NmsXbt2apPgEe2j+jJeRp1wip96ftWdO3dY/fr12d27d9UGFtywYQOzt7dnjx49YowVzZGekZGhjVAJK7u+Lly4wJo0acLi4+PVtpk/fz6zt7dnSUlJjLGinoYvXrzQSqyE6ou8zmgbRIvbg+Pj47Fnzx6sWrVKtS4nJwdZWVkwMTEBx3HIzc0FAMydOxc1atTAb7/9BqBonCcHBwchwjc6xfX17NkzHDt2DFu2bFGtk0gkePbsGaytrSESiZCXlwcAWL58OWxsbHD+/HkAQN26dWnEBy2h+iKlMcqEU/xhuH//Pnr37o2LFy/iwYMHkMlkAIAuXbqgVatWeO+996BUKmFlZQUAyM7OhqOjIz3MqWUl66t///44dOgQLl++jPz8fABAv3794OXlhTFjxkAul8PS0hKMMWRnZ8PW1pZ+FGgZ1Rcpk9CXWEKJiIhgjo6OLCgoSDVBGmP/DZd+9+5d1rJlS9asWTN29epVduXKFfb5558zZ2dnFhMTI1TYRqe4SfPhw4fMwcGBLVq0SK0Zs3j64aNHjzI/Pz/Wq1cvFh8fzx4+fMiWLl3KateuzZ4+fSpQ9MaH6ouUx+i6RTPGIJPJMHHiRJibm+O7776Dqampal3xswFKpRIPHz7EJ598grt378LGxgYWFhbYu3cv2rRpI+QpGJ2cnByMHj0atWrVwrZt21TLS9aXTCZDSEgI/ve//+H+/fuoW7cu5HI5Dh8+TPWlZVRfpCxGl3CAork2WrZsiY8++ggzZ858bb1CoVCbp+Pu3buwtbWFvb09tSkLIDMzE+3atcOyZcswevRotQcGAUAul6t+NADAuXPn4OTkhFq1aqFWrVraDtfoUX2RshjdPRzGGJ49e4aYmBh4e3sDKLqaKcnExASFhYVYvXo1AMDX1xeNGjWiZCOQuLg4xMTEoFmzZuA4Dq/+RjI1NUVeXh42bdoEAHjnnXfQpk0b+vISCNUXKYvRJRyO4+Dq6opGjRph9+7dkEgkpT69fOvWLfzyyy9ISkoSIEpSkouLC1xcXHD48GHk5eW99osZAK5evYoDBw7QrKo6gOqLlMXoEg5QNBCnv78/Tpw4gaNHjyInJ+e1bc6cOYN69erB3t5egAhJSXXq1EGnTp2wc+dOhIaGQi6Xv7bNhQsX0LBhQ1haWgoQISmJ6ouUSaDOCoIpftBMqVSyLl26MEdHR/b111+zlJQUxhhjcXFxbN68eczFxYXdv39fyFAJ+6++nj9/zlq0aMEaN27Mjh07ppqyOzk5mX366afM1dWVPXjwQMhQCaP6IuUzyk4Dxc8JSCQSvPvuu/jrr79ga2sLNzc3mJqaQiqV4pdffkHr1q2FDpWUEBYWhkmTJuHBgwdo2LAhnJ2dIZfLkZSUhKNHj8LX11foEEkJVF/kVUaZcAD1Lpr79u3D48ePkZqais6dO6Nr165wd3cXOEJSGsYY1q5diydPnkAqlaJbt24IDAyEh4eH0KGRUlB9kZKMNuEAr3d/JrqNhqfXL1Rf5FVG8ddQfNOytO7PrzLi/KszXr3JXFxvpX15UX0Jj+qLVJTBJxyFQgFTU1M8efIEgwcPfmM3zNK6cBLtKa6v2NhYjBw5EkDpX1zFqL6ERfVFKsPgE46JiQni4uLQrVs3GhhQxzHGYGJigqdPn8Lf3x8ikYh+Eeswqi9SWQZ/Dyc3NxejRo1CzZo18d1339EvLB2XlZWFvn37olmzZti2bRvVl46j+iKVYfAJhzGG27dvw8/Pjz4MAirZK7BYaTeVFQoFLl68iJ49e1J9Cai0+iqtkw3VF6kMvUw4r35RvToYYLHSPjRE+4rrSyqVIjc3F3l5eWjQoAEA9S8x6tWkG4rr4cWLF0hKSkJqaioCAgIAUH0RzejdX0vxH/mDBw9UE6SZmpqWOnwGJRvhlZyMa/DgwfD398f//d//YdasWQDUewrSl5fwiusrPDwcvXr1wtixYzF48GAMGjQIANUX0Yze/cWIRCLExsaib9++2L9/P3r16lVu0iHCYYxBJBLh0aNH6N69O9q0aYNVq1Zh6tSpCAkJwe7du4UOkZRQsr78/f3Rr18/7N27F4cOHcKVK1dw+/ZtoUMkek7vmtRycnIwf/58vHjxAgMHDsSaNWvg6OiIixcvQiQSldm8RoSRmZmJ9957D56enqrh6LOysvDuu++iUaNG2Lx5s8ARkpJSU1PRt29f+Pv7Y82aNQCK6mvgwIH49NNPkZOTg4CAABrUllSJ3l3hWFtbo3Hjxhg+fDhGjx6NDRs2IC0tDT169FBd6SgUCqHDJP/Kzs6Gvb09/P39ART9ira1tUXPnj0RExMDACgsLFRtr2e/fwyOo6Mj+vXrh8GDB6uWBQcH4+rVq1i0aBHmzJmDdu3a4d69ewBef5iakPLo1RVOcSeAkp0BCgoKcPHiRXzyySdqVzr5+flIS0tD7dq16V6OgHJzc3Hv3j107NgRwH91uGbNGoSEhOD8+fPUuUNHlNYJ4MSJE5g+fTq+/fZbdOrUCTVr1kSrVq3QqFEjHDlyRKBIib7Sqyuc4i+l4v8qlUqIxWL07NkTa9euRXp6Onr06IG8vDx8/PHHmDJlCmQymZAhGz0rKytVslEqlaq6K3klynEc5s2bhylTpggWJym9E0D9+vVx9uxZDBw4UDXjbbdu3UqdQ4qQN9Hrmx3FHxBTU1MEBARg3bp1WLBgAWrWrAmFQoFLly7BwsJC4ChJsZJfaLa2tqp/L1q0CFu3bsXFixeFCIuUo1WrVqp/F/9YSE9PR8uWLVXNn3R1SipK5xNOycv88kZ3NjU1RZcuXeDm5oaEhARcvnwZzZs312aoBBWvL5lMBltbW3z55ZdYv349QkND0aZNG22GSlDx+iredunSpbhw4QL+/PNPSjSk0nSuSa34JqRMJlN9GMLCwgCUPrpzMYVCgY0bN+LChQs4f/48JRstqWp9FRQU4NSpU1i7di2uXbtGyUZLqlpfly5dwvjx4/H999/j9OnTaNKkiTbCJQZG5xKOSCRCTEwMRo4cqZp5s02bNrh582a5+5mYmMDW1hZhYWE0U6cWVbW+WrVqhZYtW+Kvv/6Cn5+flqIlVamv/Px8cByn6pRDM3WSqtLJXmoJCQlo1KgRmjdvjnv37mHHjh0YP3489WbSUVWpr8zMTCiVSjg6Omo5WlKV+lIqlZDL5RCLxVqOlhgSnbvCUSgUcHd3xzfffIOwsDB4eXnhnXfeUesSTXRHVevLwcGBko0AqlpfIpGIkg3RmE4lnOL5NRhjcHV1RXBwMFJTUzF27FhERkYCwGsfCnrIUzhUX/qF6osIjukIpVLJGGPs4sWL7KuvvmIvXrxgjDEWHx/PXF1dWY8ePVhkZKRq+7NnzwoSJylC9aVfqL6ILtCJKxz27+X8kSNHMHDgQOTn5yMpKQmMMbi7u+PmzZt48OABPvzwQ5w5cwZLlizBgAED8OzZM6FDN0pUX/qF6ovoDCGzXUnXrl1jjo6O7Pvvv1dbnpaWxhgr+iXWtGlT1rp1a1avXj12+/ZtIcIk/6L60i9UX0QX6MyDn6GhoWjdujUmTZqE7OxsXLx4EXv37kVsbCxmz56NsWPH4u+//0Z8fDxcXV3h6uoqdMhGjepLv1B9EV2gMwnHxcUFT548werVq3Hp0iWYmprC1NQUXbt2xfjx49GuXTt4eXnBwcFB6FAJqL70DdUX0QVaTziMMdVET3l5eRCLxTAxMUFAQABu3ryJXbt2oVu3bhg3bhy6dOmCiIgIXL16FWZmZtoOlYDqS99QfRFdppWEUzyERmFhIczMzMBxHM6cOYO9e/ciKioK7dq1w+jRo7Fp0yakp6erPZ+xd+9e5OXlwc7OThuhElB96RuqL6Ivqr2XWvGH4cGDB1i1ahUA4Pjx4xgyZAiaN2+OiRMnIi0tDd27d0dERITqw3D58mV8+OGH2Lp1K/bu3asaGp1UL6ov/UL1RfRKdfZIUCgUjDHGwsLCGMdxbOXKlSwnJ4cFBASwr7/+mjHG2IsXL1jt2rXZRx99pNovPT2dBQUFsQEDBrDw8PDqDJGUQPWlX6i+iL6ptoRT/GF48OABs7S0ZEuXLmWMMZaamso8PT3Z7du3WWJiIqtTpw6bPHmyar9Dhw6x5ORklpGRwTIzM6srPPIKqi/9QvVF9FG1NKkVX+bfv38f3bt3h4eHB5YtW6Za7+3tjTt37uCtt95C3759sXXrVgDAs2fPcPr0afz9999wcHCAvb19dYRHXkH1pV+ovoi+4j3hFH8Y/vnnH3To0AEtWrSARCLB7NmzAQBOTk6oW7cupkyZAl9fX2zfvl01D8eWLVtw/fp1mhtFi6i+9AvVF9Fr1XHZdPPmTWZmZsaWLVvG5HI52759O3N2dlZrRx46dChzdnZmK1euZGvWrGFTpkxhtra2LCwsrDpCIuWg+tIvVF9EX1VLwvnzzz/ZrFmzVO8zMzNL/VB89NFHrEePHszX15eNHTuWbmAKhOpLv1B9EX1V7WOpFY9SK5FIVB+KGTNmqNZnZGSwvLw8JpPJqjsUUgFUX/qF6ovok2p/8LN4BkE7OzuMHDkSALB48WKIRCJs3LiRhtLQMVRf+oXqi+gTrQ5tU/yhEIlEmDJlCqysrFQPqxHdQ/WlX6i+iK7T+lhqdnZ2GD58OMzMzNCpUydtF08qiepLv1B9EV3GMVbGJObVjP07KRTRD1Rf+oXqi+giwRIOIYQQ46ITU0wTQggxfJRwCCGEaAUlHEIIIVpBCYcQQohWUMIhhBCiFZRwCCGEaAUlHEIIIVpBCYcQQohWUMIhhBCiFZRwCCGEaAUlHEIIIVpBCYcQQohW/D9h34r82vSIQAAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 390x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "dabest.forest_plot(contrasts_deltas, idx=((0,),(0,), (0,)), \n",
+    "            labels=['Drug1 \\nTest 1 - Control 1', 'Drug2 \\nTest 2 - Control 2', 'Drug3 \\nTest 3 - Control 3']);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Unlike delta-delta and mini-meta experiments, here you can choose between more effect size metrics (where applicable): `mean_diff`, `cohens_d`, `cohens_h`, `hedges_g`, and `cliffs_delta`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 390x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "dabest.forest_plot(contrasts_deltas, idx=((0,),(0,), (0,)), effect_size = 'cohens_d',\n",
+    "            labels=['Drug1 \\nTest 1 - Control 1', 'Drug2 \\nTest 2 - Control 2', 'Drug3 \\nTest 3 - Control 3']);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Controlling aesthetics"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The main aesthetic parameters for the forest_plot function are:\n",
+    "\n",
+    "- `fig_size`: The size of the figure\n",
+    "\n",
+    "- ``horizontal``: A boolean input (``True``/ ``False``) to adjust the plot orientation. The default is vertical orientation (``False``) \n",
+    "\n",
+    "- `custom_palette`: A list or dictionary of colors, one for each contrast object. E.g., `['gray', 'blue', 'green']` or `{'Drug1':'gray', 'Drug2':'blue', 'Drug3':'green'}` or a set of colors from seaborn color palettes.\n",
+    "\n",
+    "- `marker_size`: The size of the markers for the effect sizes. The default is 10.\n",
+    "\n",
+    "- `contrast_alpha`: Transparency level for violin plots. The default is 0.8.\n",
+    "\n",
+    "- `contrast_desat`: Saturation level for violin plots. The default is 1.\n",
+    "\n",
+    "- `labels_rotation`: Rotation angle for contrast labels. The default is 45 (for `horizontal=False`).\n",
+    "\n",
+    "- `labels_fontsize`: Font size for contrast labels. The default is 10.\n",
+    "\n",
+    "- `title`: The plot title. The default is None.\n",
+    "\n",
+    "- `title_fontsize`: Font size for the plot title. The default is 16.\n",
+    "\n",
+    "- `ylabel`: The axis label of dependent variable (Y-axis for vertical layout, X-axis for horizontal layout). The default will be given via the effect size selected. (eg., `\"Mean Difference\"` for `\"mean_diff\"`)\n",
+    "\n",
+    "- `ylabel_fontsize`: Font size for the axis label (Y-axis for vertical layout, X-axis for horizontal layout). The default is 12.\n",
+    "\n",
+    "- `ylim`: Limits for the dependent variable (Y-axis for vertical layout, X-axis for horizontal layout). The default is None.\n",
+    "\n",
+    "- `yticks`: Custom ticks (Y-axis for vertical layout, X-axis for horizontal layout) for the plot. The default is None.\n",
+    "\n",
+    "- `yticklabels`: Custom tick labels (Y-axis for vertical layout, X-axis for horizontal layout) for the plot. The default is None.\n",
+    "\n",
+    "- `remove_spines`: If True, removes plot spines (except the relevant dependent variable spine). The default is True.\n",
+    "\n",
+    "- `violin_kwargs`: A dictionary of keyword arguments for the violin plots. \n",
+    "  \n",
+    "        The default violin_kwargs = {\"widths\": 0.5, \"showextrema\": False, \"showmedians\": False, \"vert\": not horizontal}\n",
+    "\n",
+    "- `zeroline_kwargs`: A dictionary of keyword arguments for the zero line. The default is None.\n",
+    "  \n",
+    "        The default zeroline_kwargs = {\"linewidth\": 1, \"color\": \"black\"}\n",
+    "\n",
+    "- `marker_kwargs`: A dictionary of keyword arguments for the effect size markers. The default is None.\n",
+    "  \n",
+    "        The default marker_kwargs = {'marker': 'o', 'markersize': 12, 'color': 'black', 'alpha': 1, 'zorder': 2}\n",
+    "\n",
+    "- `errorbar_kwargs`: A dictionary of keyword arguments for the effect size error bars. The default is None.\n",
+    "  \n",
+    "        The default errorbar_kwargs = {'color': 'black', 'lw': 2.5, 'linestyle': '-', 'alpha': 1, 'zorder': 1}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Changing layout with `horizontal`\n",
+    "Forest plot assumes a vertical layout by default, but you can change it to a horizontal layout by setting ```horizontal``` to be ```True```:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 400x390 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "f_forest_minimeta = dabest.forest_plot(\n",
+    "                        data = contrasts_mini_meta, \n",
+    "                        labels=['mini_meta1', 'mini_meta2', 'mini_meta3'],\n",
+    "                        horizontal=True,)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Using a custom palette \n",
+    "You can color the half-violins with ```custom_palette```:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 390x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "f_forest_minimeta = dabest.forest_plot(\n",
+    "                        data = contrasts_mini_meta, \n",
+    "                        labels=['mini_meta1', 'mini_meta2', 'mini_meta3'],\n",
+    "                        custom_palette=['#FF0000', '#00FF00', '#0000FF'],)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Plotting other effect sizes \n",
+    "Forest plots can be drawn for effect sizes other than mean_difference, such as `hedges_g`, by setting `effect_size`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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6r2qqsWzn79h8LI8PMSIiu7nl4abMzEwYjUZ8+OGHSExMxKlTpzBt2jRUVFRg+fLlUpdnVVpaGsaNG4fKyjs779NpC3H2u/dxfsf/4b7JixDRtpfN2xFFEd+fuooz+WV4amBrRASq7CkbtQYjlu88i32Z13G5uBJBvgoMSIzA//6pLZoF+1pd7kBWET76KQsn8zS4VqbDh092R0qH5hbzpJ7KxxcHLuNkngallbXY/twAdIhW21UvETmOW+5JjBgxAmvXrsXw4cMRHx+P0aNHY86cOdi4caPUpVmVlpaGkSNHoqqqCqIoWjm8JMJQq8PRj+eiMPOg3dvMLqrAm99n2n0FVFWtAb/lafHs0ERse24A/vlkd1worMBT6/5T73KVtQa0axGMRWM6Wp+nxoAecWGYO6KtXTUSUdNwyz2Jumg0GoSFhUldRp1KS0sxbtw4iKIIo9FY/8yiCAjAiU9fxcCF30LpF2TXtst1eqzcfQ5/Hd4GQb62NXewrxKfP9XbYtyi0R0w5v2fkVdahZYhfnUuN7hNFAa3iap33Y90iwEA5BSza3QiV+SWexK3O3/+PFavXo2nn3663vl0Oh20Wq3FyxnWrVuHysrKuwfEDaIIQ40O+Yd2OmT71bUGbD+Z75B13VBWrYcgAME2Bg8RuQeXCom5c+dCEIR6X5mZmRbL5OXlYcSIEfjzn/+MadOm1bv+pUuXQq1Wm1+xsbFN+esAMJ0fWL16tU3LXk7fYPdVTzecztfC6KB1Vdca8GbqGYzuHI0gX6VD1klErsmlvga++OKLmDx5cr3zxMfHm/995coVDB48GP369cNHH3101/XPmzcPs2fPNg9rtdomD4qioiJcuHDBhiVFVBVdQU2FFj4B9p/IrdTpG9zl+HdH8/DyppPm4U+n9EKv1qZDebUGI2Z+eQSiCLwx1vq5BiLyDC4VEpGRkYiMjGzQvHl5eRg8eDC6d++OtWvXQia7+06RSqWCSmXflT6NVV5u3z0LtdWVUPgHO6SWS8WVkDegr6dh7ZuhS2yIebi52nQFU63BiBlfHEFuSRW+mtaHexFEXsClQqKh8vLykJycjLi4OCxfvhzXr183T2vevHk9SzpfYGCgXcsHBAbCRyl3SC2VOgMUDbgbO1ClQKDK8q1xIyCyiyrw1bQ+CA3wcUhNROTabAqJv/zlL/VOFwQBvr6+iImJQXJyMvr27WtTcdbs2rUL58+fx/nz5xETE2MxzVHH8B0lPDwcCQkJyMrKamRtAvwjoqEKUDusp9dAG08y1xqM+J/Pj+C3Kxp8MqknDKKIa2Wm52+H+PnAR2EKnokf70dKh+aY1K8VAKBCp0d20c3Lb3OKK/HbFQ1C/H3MV0SVVtYgr7QK17SmQ2FZ103zRwapEBVk/R4MInIOmz419uzZg6qqKvM3+NDQUABASYnpoTiRkZEwGo0oKiqCIAhISUnB+vXr4e/v75CiJ0+efNdzF65CEAQ8++yzmDVrVqOXbX3/ow4LCJkADG3XzKZlr2qq8cOZAgDAg6vSLaZ9Na0P+iaEAwAuFVVadDR4IleDCR/vNw+/sf0MAGBctxi8Pb4zAGDX6QL8df0J8zzPfnUUAPD80CTMeuBem+olIscRRBu+ep85cwYpKSl46qmn8Oyzz5pDori4GKtXr8batWuxc+dONGvWDO+++y4WLVqEWbNm4e2333b4L2APrVYLtVoNjUaD4GDHHPevS2lpKWJiYlBVVdWwy2AFAXKlL4Yt2gClv333SdzwZJ84jO/Z9FdzEbmKGkMNMosz4Sf3g1LetOfPag21qDJUoW1YW/jIPetQrE0hMXToUCQlJeGf//xnndOnT5+OrKws7Nxpus5/4sSJ+Pnnn3Hp0iX7qnUwZ4UEcPOO67veUCcIECCg1/RliGxne9cct3qwUwtMvz+eDygir8KQcAyb7pPYv38/OnfubHV6586d8csvv5iHBw4ciIKCAls25TFSUlKwfft2+Pn5me/5uJNpD8KRAfFw15YMCCKymU0hERISYt5LqEtqairU6pvX9peXlzf5N3V3kJKSgtzcXKxYsQLR0dEW01TqCHQY9xyGvb7RIQEhE4Dp9yfgLwNaMyCIyGY2hcS0adOwefNmPProo9i9ezcuXbqES5cuYffu3Xj00Uexbds2i7ufd+zYgS5dujiqZrcWEhKC5557Dl999ZXF+G6TXkPr+x+F0s++S2YBIMhXgb+N6YiR97Wwe11E5N1surpp4cKFqKqqwrvvvotNmzZZTJPL5Zg9ezYWLlwIAKiursbkyZNx33332V+tB7n9272jvu23bR6Ev45ow8tHicghbAoJQRDw1ltv4cUXXzTvSQBAXFwchg4diqiomz1/+vr6YtKkSY6plqxSygU83jsOD3dtCZmMh5eIyDHsuuM6KirqjieskfP1bh2GpwbGm7vPICJyFLfsloNM4sL98dTAeIt+loiIHIkh4YYCVQr8v75xSOnQnIeWiKhJMSTczOC2UZg6oDXUfuyBlYiaHkPCTQSqFHhuaJK5nyQiImdgSLiBVhEBWDCyHZoF88Q0ETkXQ8LF9UsMx6xh98LXQc+UICJqDJtD4sSJE1i9ejWOHDkCjUZzR6d1giDY+NhOumFCr3swoVcsu9UgIsnY1C3Hvn370KtXL2zbtg3R0dHIyspCfHw8oqOjcenSJQQGBmLQoEGOrtVryARg9gP3YmLvexgQRCQpm0Li1VdfRXx8PM6ePYu1a9cCAF5++WVkZGTgl19+QW5uLsaPH+/QQr2FIAAvDm+DwW2j7j4zEVETsykkjhw5gqlTpyI4OBhyuelYucFgAAD07t0bTz/9NF555RXHVelFxveIxaB7I6Uug4gIgI0hoVAoEBRkemJaSEgIlEolrl27Zp4eHx+P06dPO6ZCLxIX7o//4tPjiMiF2HTiOjExEefOnQNgOkHdtm1bbNq0CY8//jgAYPv27WjevLnjqvRAnTp1Qnp6Oo7nlODLAzkIio7H5H6toJDblNtERE3Cpk+kBx98EF999RX0ej0AYPbs2di4cSOSkpKQlJSELVu24Omnn3ZooZ5GrVZjwIABuK9HH4Ql3Iek2GboHhcqdVlERBZs2pN45ZVX8Pzzz5vPR0yaNAlyuRwbNmyAXC7H/PnzMXnyZEfW6fFSOjTjlUxE5HJsCgmlUonwcMvuIZ544gk88cQTDinKGw1IjJC6BCKiO9h0uCk+Ph5btmyxOn3btm2Ij4+3uShv0zoiACH+PlKXQUR0B5tCIjs7G+Xl5Vanl5eXm59WR3fXPjpY6hKIiOpk86U09R0/P3ToEEJCQmxdtde5t1mg1CUQEdWpweckVq5ciZUrVwIwBcQLL7yA+fPn3zGfRqNBaWkpJk6c6LgqPdw9YQFSl0BEVKcGh0RUVBQ6dOgAwHS4qWXLlmjZsqXFPIIgICAgAN27d8czzzzj2Eo9WLNgldQlEBHVqcEhMWHCBEyYMAEAMHjwYCxYsABDhw5tssK8hVwmIFDFHtuJyDXZ9Om0d+9eR9fhtQJVCt4fQUQuq0Eh8dNPP9m0cnYXfnd+fJgQEbmwBoVEcnKyxbddURTr/fZ7Y/qNnmHJOj5xjohcWYNCgoeXmo6SHfoRkQtrUEjcf//9TV2H15IxI4jIhdn9EZWfn4/jx4+joqLCEfV4nYgAXv5KRK7L5pDYvHkz2rZti5iYGHTr1g0HDhwAABQWFqJr16747rvvHFWjR5PJeGUTEbkum0Ji69ateOSRRxAREYGFCxdCFEXztIiICLRs2dL87GsiInJfNoXEokWLMGjQIGRkZGDGjBl3TO/bty+OHj1qd3ENodPp0KVLFwiCgGPHjjllm0RE3sKmkDh16hTGjx9vdXqzZs0snnndlF566SVER0c7ZVtERN7GppDw9/ev90R1VlbWHQ8lagrff/89du7cieXLlzf5toiIvJFNITF48GCsW7fO/IzrW129ehUff/wxhg8fbndx9SkoKMC0adPw2Wefwd/fv0HL6HQ6aLVaixcREVlnU0gsXrwYubm56NmzJz788EMIgoC0tDQsWLAAnTp1giiKWLhwoaNrNRNFEZMnT8b06dPRo0ePBi+3dOlSqNVq8ys2NrbJaiQi8gQ2hUSbNm2QkZGB8PBwvPLKKxBFEcuWLcOSJUvQqVMnpKeno1WrVo1e79y5cyEIQr2vzMxMrF69GmVlZZg3b16j1j9v3jxoNBrzKycnp9E1EhF5E0G89fpVG5SUlOD8+fMwGo2Ij49HZGSkzeu6fv06ioqK6p0nPj4e48ePx9atWy36jzIYDJDL5Xj88cexbt26Bm1Pq9VCrVZDo9EgOJiPECXyJDWGGmQWZ8JP7gelXNmk26o11KLKUIW2YW3hI/es59XbHRJSuHz5ssX5hCtXriAlJQXr169H7969ERMT06D1MCSIPBdDwjEa/TwJnU6Hzz//HDt37sSFCxdQVlaGoKAgJCYmYsSIEZg4cSJ8fJr2P+mee+6xGA4MND0jOiEhocEBQUREd9eokDh58iTGjBmDS5cuQRRFqNVqBAYG4tq1azhy5Ai+/fZbLF68GFu2bEG7du2aqmYiInKSBp+4Li8vx+jRo1FQUIDFixcjJycHJSUlFj/feOMNXLlyBaNGjXJqh3+tWrWCKIro0qWL07ZJROQNGhwSa9euxeXLl7F9+3bMnTsXLVu2tJjesmVLzJs3D1u3bsXFixfx6aefOrpWIiJysgaHxPbt2zF8+HAkJyfXO9+QIUPwwAMPYOvWrfbWRkREEmtwSJw8efKuAXHDkCFDcPLkSVtrIiIiF9HgkCguLkbz5s0bNG+zZs1QXFxsc1FEROQaGhwSOp0OSmXDrjVWKBSoqamxuSgiInINjboENjs7G0eOHLnrfBcvXrS5ICIich0NvuNaJpNZdINRH1EUIQgCDAaDXcU1Nd5xTeS5eMe1YzR4T4KPIyUi8j4NDolJkyY1ZR1EROSCbOoqnIiIvANDgoiIrGJIEBGRVQwJIiKyiiFBRERWMSSIiMgqhgQREVnFkCAiIqsYEkREZBVDgoiIrGJIEBGRVQwJIiKyiiFBRERWMSSIiMgqhgQREVnFkCAiIqsYEkREZBVDgoiIrGJIEBGRVQwJIiKyiiFBRERWMSSIiMgqhgQREVnFkCAiIqsYEkREZBVDgoiIrGJIEBGRVW4dEtu3b0fv3r3h5+eH0NBQjB07VuqSiIg8ikLqAmy1YcMGTJs2DUuWLMGQIUOg1+tx6tQpqcsiIvIobhkSer0ezz//PJYtW4apU6eax7dv317CqoiIPI9bHm46cuQI8vLyIJPJ0LVrV7Ro0QJ/+tOfuCdBRORgbhkSWVlZAIDXXnsNCxYswLZt2xAaGork5GQUFxdbXU6n00Gr1Vq8iIjIOpcKiblz50IQhHpfmZmZMBqNAID58+dj3Lhx6N69O9auXQtBEPDtt99aXf/SpUuhVqvNr9jYWGf9akREbsmlzkm8+OKLmDx5cr3zxMfHIz8/H4DlOQiVSoX4+HhcvnzZ6rLz5s3D7NmzzcNarZZBQURUD5cKicjISERGRt51vu7du0OlUuHs2bMYMGAAAKC2thbZ2dmIi4uzupxKpYJKpXJYvUREns6lQqKhgoODMX36dCxcuBCxsbGIi4vDsmXLAAB//vOfJa6OiMhzuGVIAMCyZcugUCjw5JNPoqqqCr1798aePXsQGhoqdWlERB5DEEVRlLoIqWi1WqjVamg0GgQHB0tdDhE5UI2hBpnFmfCT+0EpVzbptmoNtagyVKFtWFv4yH2adFvO5lJXNxERkWthSBCRxzpbfFbqEtweQ4KIPFZRdRFqjbVSl+HWGBJE5LFEUYRGp5G6DLfGkCAij8aQsA9Dgog8WlF1kdQluDWGBBF5tJyyHKlLcGsMCSLyaGdLzsKLbwezG0OCiDxaSXUJLmkvSV2G22JIEJHH25+/X+oS3BZDgog83uFrh1FWUyZ1GW6JIUFEHkej0eDnjJ9x/uh5FJ0uQlVZFXZd2iV1WW7JbXuBJSKy5uTJkxiSPMQ8PHDJQGQEZKB3i95oGdhSwsrcD/ckiMgriBDx2enPoDPopC7FrTAkiMhrFFQW4MszX8IoGqUuxW0wJIjIq5woPIFN5zfx3okGYkgQkdfJyMvAjos7pC7DLTAkiMgr/XD5B6Rmp0pdhstjSBCR10rLTsP2rO089FQPhgQRebUfLv+ArzK/4sOJrGBIEJHXO1RwCKuOrEJBRYHUpbgchgQREYDc8lwsP7wcuy7t4l7FLRgSRER/0Bv12HFxB/5+6O84WXiS5yrAkCAiukNhVSHWnFqD94+/j9yyXKnLkRRDgojIigulF/D24bfxzdlvUF5TLnU5kmBIEBHdxf78/Vh6cCkO5B/wukNQDAki8ji3f5A74oO9Ul+Jr89+jQ9PfIhSXand63MXDAki8hilpaVYuXIlJkyYYDH+P2//Bxe2XkBNeY3d2zhbchbLDi3D8evH7V6XOxBEb9t3uoVWq4VarYZGo0FwcLDU5RCRHdLS0jBu3DhUVlYCqGPvQQDkKjl6/W8vNOvazCHbvD/mfoxOGA2D0YAqQxXahrWFj9yn0evptK5TneNnd5+NKR2n1DktZX0KrlRcuWP8Y20ew4I+CwAAOoMOyw4tQ2p2KmoMNegf3R/z+8xHhF9Eg2tjSDAkiNxeWloaRo4cCVEUYTTW0w24AAiCgD6v9HFYUHRv1h3jk8aj2lhtc0gUVhVaDKfnpmPhLwux/ZHtiA2KrXOZ4upiiy7Pz5Wcw3/v+m+sSVmDns17AgBe//V1/JT3E97o/wYCfQKx5MASyCDDZw9+1uDaeLiJiNxaaWkpxo0bd/eAAADRtIdx8K2DDjn0BACHCw7j6LWjdq0jwi/C4rU3Zy96Ne9lNSAAIMw3zGKZn3J/QmxQLHo06wEAKKspw8bzG/HXHn9F7xa90SG8A17v/zqOXT/WqENlDAkicmvr1q1DZWXl3QPiBhEw6AzI2ZfjsBoOFRxy2LoKqwqRnpuOh5MebvAytYZabMvahocTH4YgCACA00WnoTfq0Se6j3m+eHU8WgS0wPFrDAki8gKiKGL16tU2LZu1Lcthl7PmlOU47Gl3Wy5sgb/SH8PihjV4md05u1FWU4YxiWPM4wqrCqGUKRHsY3koPdw3HIXVhbevwiqGBBG5raKiIly4cKHxH/YiUHG1AjXaGoiiaPer2lCNK+V3nkSuy7asbej1RS/z63DBYYvpm85twsj4kVDJVQ3+dTad24QBLQcgyj+qUf8NDaFw+BqJiJykvNy+u6BrqmqgCHLMx+CV8iuQCXf/3j04djDui7jPPHzrB/vhgsPI1mZj+f3LG7Xd/fn78W7yuxbjI/wiUGushbZGa7E3UVRdhAjfhl/dxJAgIrcVGBho1/IBgQFQKRr+jb3edfkEQCG7+0dqgDIAAcqAOqdtPLcR7cPbo01YmwZv97vz3yHMNwyDYgZZjG8f3h4KmQIH8g/ggbgHAAAXNReRX5GPzlGdG7x+tz3c9Pvvv2PMmDGIiIhAcHAwBgwYgL1790pdFhE5UXh4OBISEswnaxtMAAKaB0AVrIIgCA553bp3YIvymnLsurQL45LG1Tn9qbSn8OWZLy3GGUUjvjv/HUYnjL4joIJ8gvBI4iNYdmgZDuYfxG9Fv+GVn19B58jO6BzpBSHx0EMPQa/XY8+ePTh8+DA6d+6Mhx56CFevXpW6NCJyEkEQ8Oyzz9q0bNKopMaHixW9mvdC+/D2dq3j++zvIYoi/tT6T3VOzynLuaM7kP1X9iO/Ih8PJ9Z9JdRLvV7CoJhBmLVvFqakTkGEXwRWDF7RqLrc8ma6wsJCREZG4qeffsLAgQMBAGVlZQgODsauXbswbFjDrgrgzXRE7q+0tBQxMTGoqqpq2GWwf9x5PWrtKPgENv7Gt9vFBcfhtb6vwV/pb/e6XJFb7kmEh4ejTZs2+Ne//oWKigro9Xp8+OGHiIqKQvfu3aUuj4icKCQkBBs2bIAgCJDJ7vKR9scd1/3n9XdIQMSr4/FKn1c8NiAANz1xLQgCfvjhB4wdOxZBQUGQyWSIiopCamoqQkNDrS6n0+mg0+nMw1qt1hnlElETS0lJwfbt2xvUd1P/ef3RvFtzu7fZJbILXuj+AvwUfnavy5W51J7E3Llz73pyKDMzE6IoYsaMGYiKikJ6ejoOHjyIsWPHYtSoUcjPz7e6/qVLl0KtVptfsbHWb3knIveSkpKC3NxcrFixAtHR0RbT/ML80OWpLhj16SiHBMSDrR/ESz1f8viAAFzsnMT169dRVFRU7zzx8fFIT0/H8OHDUVJSYnEuISkpCVOnTsXcuXPrXLauPYnY2FiekyDyMOnp6Rg06OYloclLkxHV0f4bzVRyFZ7q9NQdl5t6Mpc63BQZGYnIyMi7zndjd/L2448ymazeE1cqlQoqlWOuiSYi13X7VUuOuIopMSQRM7rMQHRg9N1n9iAuFRIN1bdvX4SGhmLSpEl49dVX4efnh48//hgXL17EyJEjpS6PiDyIn8IPj7V5DCmtUhp0R7WnccuQiIiIQGpqKubPn48hQ4agtrYWHTp0wObNm9G5c8NvEiEiskaAgMGxg/Ffbf8LapVa6nIk45YhAQA9evRAWlqa1GUQkQdqH94ek9pPQit1K6lLkZzbhgQRkaOFqEIwqf0k9I3u67C7sd0dQ4KICMCAlgMwpcMUBPrY12mgp2FIEJFXU8qUmNpxKgbfM1jqUlwSQ4KIvFaobyj+2uOvSAhJkLoUl8WQICKvlBiSiDk95iDU13pXPsSQICIv1LdFX8zoMgNKuVLqUlweQ4KIvMrQe4biqU5PeeWNcbbg/xIReY1uUd0YEI3E/yki8gpqHzWe6fIMA6KReLiJiDxOp06dkJ6ejl+u/ILUi6lQt1LjsTaPIcgnSOrS3A5Dgog8jlqtxoABA6DJ0uCw/2FE+kUiOTZZ6rLcEve7iMjjDW81HHKZXOoy3BJDgog8Xv/o/lKX4LYYEkTk0eKC4xDuFy51GW6LIUFEHq1DeAepS3BrDAki8mhJIUlSl+DWGBJE5NH44CD7MCSIyGMJEBDlHyV1GW6NIUFEHitYFQyFjLeD2YMhQUQeK1DJp8zZiyFBRB7LX+kvdQlujyFBRB7LV+4rdQlujyFBRB5LKeNDhezFkCAij8WT1vZjSBCRxwr04YlrezEkiMhj+Sn8pC7B7TEkiIjIKoYEERFZxZAgIiKrGBJERGQVQ4KIiKxiSBARkVUMCSIisoohQUREVjEkiIjIKoYEERFZxZAgIiKrGBJERGQVQ0JCOp0Or732GnQ6ndSlUAOwvdwL28sxBFEURamLkIooiigrK0NQUBAEQXD69rVaLdRqNTQaDYKDg52+fWoctpd7YXs5hlc/kUMQBL55iIjqwcNNRERkFUOCiIisYkhISKVSYeHChVCpVFKXQg3A9nIvbC/H8OoT10REVD/uSRARkVUMCSIisoohQUREVjEkiIjIKoYEERFZxZAgIiKrGBJE5DaMRqPFMK/gb3pe3XeTo/3+++8IDQ1FZGSk1KVQA/z44484ffo0Ll++jMcffxz33nsvfHx8pC6LrNi2bRt2796NS5cu4eGHH8aYMWPY95oTcE/CQT7//HP069cPa9asQWFhodTl0F2sWbMG48ePx+bNm7Fu3TqMHTsWv/76K4A7v62S9NasWYMnnngCBoMBhYWFePPNN3HixAmpy/IOItlt7969YmJioti9e3cxKSlJfPvtt8Xr169LXRZZsXXrVjEiIkLcuHGjaDAYRFEUxeTkZHHEiBESV0Z12bhxoxgWFiZu3LjRPK5Dhw7i119/LYqiKBqNRlEURXNbkmNxT8JOtbW1yMzMRJ8+fbBr1y488sgjWLVqFf71r39xj8IFaTQabN68GdOnT8eoUaOg1+sBADNnzkR+fj4MBoPEFdKtSktLkZ6ejpdeegmjR482j1er1diwYQOSk5MxZ84cZGdnQyaT8RxFE+A5CTsplUqMGDECPXr0QGhoKN58800YjUasWrUKAPDkk0+az1EYjUbzG1mKhxyR6cMlMjIS3bp1g0Jx8+0fEhKC3NxclJaWIjQ0FDIZvz+5gpCQEEyYMAGRkZGQy+UAgOHDhyMnJwcTJ07ExYsXcejQIcybNw+ffPIJ/P39Ja7Y8zAkbHTjAx8AWrVqhVatWpk//P/+979DEARzUEyePBlKpRJvvvkmnn/+eURFRUlZule6tb0WL15sDukbbRYYGIigoCD4+vqa59u8eTOGDRuGgIAAyer2Vre2V8+ePc3jL126BIPBgD179iAxMREAsGTJEnz00UfQarUMiSbAkLDRjTfw2bNn0aZNG/N4g8EAuVyOt956CwCwevVqVFRUYMuWLaipqcGiRYskqdfb3WivM2fOoF27dgButhUA+Pj4wMfHxxwIQ4YMgVKptDjEQc5T19+XXq9HXFwc0tLSoFAooNfroVAo0Lp1a9xzzz3w8/OTsmSPxX3qRrr1ypd9+/YhOTkZqampAEyPQ5XJZObj2m+99RZGjRqFhQsXwmAw4D//+Q/kcjmvnnGi29tryJAh5vaSy+XmY9iVlZUoKyvDtWvXMHLkSOTl5WHbtm0QBIHHuZ2ovr8vhUIBURTNwa5QKFBdXY3PP/8cCQkJUKvVktTs6RgSjXDrLvA333yDb7/9FiUlJXj++eexY8cOADeDwmg0oqSkBMePH0fPnj1x8OBBKJVK6PV6Hu92koa2F2Dak1AqlRg1ahR+//13nDp1ytxePH/kHA1tL0EQUF1djfPnz+ORRx5BXl4ePv74YwC8ua4p8NOqEW68gV966SXMmTMHSUlJmDVrFoKCgjBnzhxs2bIFwM038vr163HhwgVkZGRY7B6TczS0vQDToYycnByoVCqcPn3aHBBsL+dpTHtt2bIFL7zwAnQ6HQ4dOgSFQgGDwcBAbwrSXX3rnk6fPi0mJiaK27dvN4/7+eefxQkTJoht2rQRU1NTzeP1er352u3a2lqn10oNby+9Xi/OmTPH3E5sL2k0tL0MBoOYmpoq6vV6URTZXk2JexJ3Id62+yqXy1FQUGC+vh4A+vXrh+nTp0Or1WLGjBnmXWO5XG6+5JXfSJ3DlvbasmUL5HI5li1bxj0+J7OlvbZu3QqZTIaUlBTzOT62V9NhSNzF7buvAQEB6NKlC44dOwatVmseP2jQIHTp0gVRUVF4/fXXcfDgQavroKZjS3stXbrUor34geM8trTXkiVLLNqL5/iaFv93G+Dtt9/GqFGjAAAtW7bEwIED8d5772HTpk3mN7JGo0FAQAAef/xxGAwG/Pjjj1KW7NXYXu6F7eXaBPH2/T2yYDQa8e2332Lq1KkYPXo0vvzySwDAM888g++++w4DBw5EXFwcfvnlF+j1euzfvx8PPfQQ5HI5Nm/eLHH13oft5V7YXq6PexK3uf0eBplMhocffhhffPEFduzYgfHjxwMAPvjgAyxYsABBQUH49ddfce+992Lfvn0ATMdVO3bsyMvxnIDt5V7YXm5IslPmLm7Xrl0WwzqdTty0aZOoVqvFxx57zDxer9ebr7AoLS0VFyxYIIaHh4tnzpxxar3eju3lXthe7oMh8Ydbuxk+evSoqFAoxFmzZlnMU11dLX766aeiIAjiM888YzHt6tWr4sSJE8VWrVqJR48edUbJXo3t5V7YXu6LIXGbjz/+WPzqq6/E9957T4yKihLnzJljMf38+fNibGysKAiCuGDBAotpv/32m5idne3Mcr0e28u9sL3cj9eHxK3fcFatWiVGRkaKZ86cEYuKisT3339fDAsLs3gjX716VZw6daq4b98+824wOQ/by72wvdyf118QfuMa66NHjyI/Px8rVqxA27ZtAQATJ04EALz88svIzc3FmDFjsGbNGiiVSgwaNAiCIPDGKydje7kXtpcHkDqlXMGBAwdEQRBEuVwurl271mJaWVmZuGXLFjEhIUHs2rWrOGTIELGmpkYUxZuPTSTnYnu5F7aXe/PKkMjPzxdPnDghfvbZZ+LJkydFUTQ9R1cQBHHKlCnitWvX7limurpazM/PN79x2VeM87C93Avby7N4XUhs2LBBfPDBB8XmzZuLwcHBoo+Pjzh69GgxJydH3LBhgygIgvjGG2+IpaWl5mVuPzbKB647D9vLvbC9PI9XhcRHH30khoaGisuXLxd/+OEHsaSkRFy0aJGYlJQk3nvvvWJOTo74xRdfiIIgiEuWLBE1Go3UJXs1tpd7YXt5Jq8JiY8++kj08fERN2zYcMe0f//732LHjh3FgQMHikajUfzggw9EuVwuvvzyy2J5ebkE1RLby72wvTyXV4TE3r17RUEQxL/97W+iKJpOiBmNRovjnu+9956oUqnE9evXi6Ioiq+//rrYr18/njyTANvLvbC9PJtXdPB37tw5TJ06FWFhYXjxxRcxcOBA87RbH5l43333oX///vjHP/4BwNTX/Y1nHLO7b+dhe7kXtpdn84oO/pKSkvDJJ59Ap9Nh8eLFyMjIME+78ebUarWoqqpCdHS0xTS+gZ2P7eVe2F6ezStCAjC9kVetWgVBEPDGG2/g559/tpielZWFmJgY9OnTB8DNJ2bxDSwNtpd7YXt5Lq843HSrc+fO4bnnnoMoipg/fz4GDhwIvV6PMWPGQCaTYfPmzXzSlQthe7kXtpfn8bqQAG6+kWUyGV5++WW88847yMzMxLFjx6BUKi2Oo5L02F7uhe3lWbwyJADTG3nWrFnYuXMn4uPjcfLkSSiVSvYV46LYXu6F7eU5vDYkACAzMxMffPAB3nnnHSgUCr6BXRzby72wvTyDV4fErfgGdi9sL/fC9nJfDAkiIrKKZ4+IiMgqhgQREVnFkCAiIqsYEkREZBVDgoiIrGJIEBGRVQwJIiKyiiFBRERWMSSIiMgqhgQREVn1/wFuo6Rza2uCrQAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 390x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "f_forest_hedgesg = dabest.forest_plot(\n",
+    "                            data = contrasts, \n",
+    "                            labels =['Drug1', 'Drug2', 'Drug3'], \n",
+    "                            effect_size='hedges_g',\n",
+    ");"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Delta text\n",
+    "You can add/remove delta text via the `delta_text` argument. It is on by default."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 390x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "f_forest_minimeta = dabest.forest_plot(\n",
+    "                        data = contrasts_mini_meta, \n",
+    "                        labels=['mini_meta1', 'mini_meta2', 'mini_meta3'],\n",
+    "                        custom_palette=['#FF0000', '#00FF00', '#0000FF'],\n",
+    "                        delta_text=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can set a variety of kwargs to customize the delta text via `delta_text_kwargs`.\n",
+    "\n",
+    "The relevant inputs to `delta_text_kwargs` are:\n",
+    "\n",
+    "- `'color'` - Color. If color is not specified, the color of the effect size curve will be used. \n",
+    "- `'alpha'`- Alpha (transparency)\n",
+    "- `'fontsize'` - Font size\n",
+    "- `'ha'` - Horizontal alignment\n",
+    "- `'va'` - Vertical alignment \n",
+    "- `'rotation'` - Text rotation\n",
+    "- `'x_coordinates'` - Specify the x-coordinates of the text\n",
+    "- `'y_coordinates'` - Specify the y-coordinates of the text\n",
+    "- `'offset'` - Am x-axis coordinate adjuster for minor movement of all text\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 390x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "f_forest_minimeta = dabest.forest_plot(\n",
+    "                        data = contrasts_mini_meta, \n",
+    "                        labels=['mini_meta1', 'mini_meta2', 'mini_meta3'],\n",
+    "                        custom_palette=['#FF0000', '#00FF00', '#0000FF'],\n",
+    "                        delta_text=True,\n",
+    "                        delta_text_kwargs={'color': 'red', 'offset': 0.1,\n",
+    "                                           'fontsize': 8, 'rotation': 45,\n",
+    "                                           'va': 'bottom',\n",
+    "                                           'x_coordinates': [1.4,2.4,3.4], \n",
+    "                                           'y_coordinates': [0,-1.4,-1.6]})          "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Contrast bars\n",
+    "You can add/remove contrast bars via the `contrast_bars` argument. It is on by default."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 390x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "f_forest_minimeta = dabest.forest_plot(\n",
+    "                        data = contrasts_mini_meta, \n",
+    "                        labels=['mini_meta1', 'mini_meta2', 'mini_meta3'],\n",
+    "                        custom_palette=['#FF0000', '#00FF00', '#0000FF'],\n",
+    "                        contrast_bars=True,)          "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can set a variety of kwargs to customize the delta text via `contrast_bars_kwargs`.\n",
+    "\n",
+    "Pass any keyword arguments accepted by matplotlib.patches.Rectangle here, as a string.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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9e/Zg9erVCAsLw61bt3Dr1i2EhYVhzZo1+Omnn+Dk5CR3rUREpBImX0Sp0WgwbNgwDBs2TM56iIjICnDIERERyc7kI5fo6GisXr0aly5dwp07dwrcjRLQzz928eJFswskIiL1MSlcFi5ciKlTp8LX1xdhYWEICQmRuy4iIlIxk8IlKioKnTp1wq5du+Dg4CB3TUREpHImX6H//PPPM1iIiKhIJoVLWFgYYmNj5a6lTJKTkzFw4EC4ubnBw8MDkZGRuHv3bonbdOzYEZIkFXi88sor5VQxEZHtMClcli1bhm3btmHTpk1y12O0gQMH4q+//sKPP/6I77//Hr/++itGjBhR6nbDhw/HjRs3DI8FCxaUQ7VERLbFpD6Xvn37Ijc3F4MGDcKoUaNQq1Yt2BWakkSSJJw6dUqWIgs7e/Ysdu/ejePHj+PRRx8FACxZsgRPPfUUPvjgA9Qo4S6NlStXhp+fn9HvlZWVhaysLMPz1NRU0wsnIrIRJh25eHp6IigoCB06dECLFi3g4+MDLy+vAg9PT0+5azU4cuQIPDw8DMECAF26dIFGo8HRo0dL3Hbjxo2oVq0amjRpgmnTpiEjI6PE9efPnw93d3fDw5+TRhIRlcqkI5d9+/bJXEbZJCQkwMfHp8Br9vb28PT0REJCQrHbDRgwALVr10aNGjXw559/YsqUKYiNjcW2bduK3WbatGmYNGmS4XlqaioDhoioFCZfRGkJU6dOxfvvv1/iOmfPnjV5//n7ZEJCQlC9enV07twZFy9eRGBgYJHbODk5cZ40IqIyMjlcUlNTsWzZMvzyyy9ISkrCypUrERYWhuTkZKxbtw49evRA/fr1y7TPyZMnY+jQoSWuU69ePfj5+SEpKanA67m5uUhOTi5Tf0rr1q0BABcuXCg2XIiIqOxMCpdr167h8ccfx9WrVxEUFIRz584ZhgF7enpi5cqV+PfffxEVFVWm/Xp7e8Pb27vU9dq2bYuUlBScOHECLVu2BAD8/PPP0Ol0hsAwRkxMDACgevXqZaqTiIhKZlKH/uuvv460tDTExMRg//79D80r1rNnT/z000+yFFiURo0aoVu3bhg+fDiOHTuGQ4cOYcyYMejXr59hpFh8fDyCg4Nx7NgxAMDFixfx7rvv4sSJE4iLi8OOHTswePBgdOjQAU2bNrVYrUREtsikcNmzZw/GjRuHxo0bQ5Kkh5bXq1cPV69eNbu4kmzcuBHBwcHo3LkznnrqKYSHh+PTTz81LM/JyUFsbKxhNJijoyN++uknPPnkkwgODsbkyZPRu3dvfPfddxatk4jIFpl0WiwzM7PE01dpaWkmF2QsT0/PEi/irFOnToEjKn9/f+zfv9/idRERkYlHLo0bN8avv/5a7PLt27cjNDTU5KKIiEjdTAqXCRMm4Msvv8T7778PrVYLANDpdLhw4QIGDRqEI0eOYOLEibIWSkRE6mHSabEXX3wR//77L2bMmIHp06cDALp16wYhBDQaDebNm4eePXvKWScREamIyde5TJ8+HYMGDcLWrVtx4cIF6HQ6BAYGolevXqhXr56cNRIRkcqUOVwyMjLQvn17DB8+HK+88gpPfxER0UPK3OdSuXJlXL58ucghyERERICJHfrdunVDdHS03LUQEZGVMClcZs6ciX/++QeDBg3CwYMHER8fj+Tk5IceRERkm0zq0H/kkUcAAH///XeJFzLm5eWZVhUREamaSeEya9Ys9rkQEVGxTAqXt956S+YyiIjImpjU51KYVqvlKTAiIjIwOVx+//13dOvWDZUrV4aXl5dhUshbt27h2WefVfxWyEREpByTwuXw4cMIDw/H+fPn8eKLL0Kn0xmWVatWDVqtFitXrpStSCIiUheTwuXNN99Eo0aN8Pfff2PevHkPLX/iiSdw9OhRs4sjIiJ1Milcjh8/jmHDhsHJyanIUWM1a9ZEQkKC2cUREZE6mRQuDg4OBU6FFRYfHw8XFxeTiyIiInUzKVzatGmDb775pshl6enpWLt2LR5//HGzCiMiIvUyKVzefvtt/P777+jevTt++OEHAMCpU6fw2WefoWXLlrh58yZmzpwpa6FERKQeJl1E2bp1a+zatQujRo3C4MGDAQCTJ08GAAQGBmLXrl1o2rSpfFUSEZGqGBUuqampqFKlCuzs7AyvderUCbGxsYiJicH58+cNNwtr2bIlp4YhIrJxRp0Wq1q1KrZs2WJ4/tJLLxmGGjdv3hx9+vRB37598eijjzJYiIjIuHBxdHREVlaW4fm6detw8eJFixVFRETqZtRpseDgYHz22WeoU6cO3N3dAQBxcXE4efJkidu1aNHC/AqJiEh1jAqX+fPno2/fvujSpQsAQJIkzJw5s9gRYUIISJLEySyJiGyUUeHSrVs3XL58GcePH0diYiKGDh2KESNGoG3btpauj4iIVMiocPnzzz9Ru3ZtREREAADWrl2LPn36oHPnzhYtjoiI1MmoDv3Q0FDs3LnT0rUQEZGVMCpcnJ2dkZGRYXi+f/9+JCYmWqwoIiJSN6NOizVr1gwfffQR7OzsDKPFjh8/jkqVKpW4Xa9evcyvkIiIVMeocImKisLzzz+PyMhIAPrRYlFRUYiKiip2G44WIyKyXUaFy6OPPooLFy7g4sWLSExMRMeOHTF9+nTD0GQiIqL8jJ640t7eHg0bNkTDhg0xZMgQPP3002jdurUlayMiIpUyaVbktWvXyl0HERFZEaPC5Z133oEkSZg+fTo0Gg3eeeedUrd5cBU/ERHZHqPC5a233oIkSZgyZQocHR3x1ltvlboNw4WIyHYZFS46na7E50RERPmZdJtjIiKikpjUoQ8AZ8+excWLF5GWlgZXV1fUr18fwcHBctZGREQqVeZwWblyJebOnYv4+PiHlgUEBGD69Ol4+eWXZSmOiIjUqUzh8tprr+Gjjz6Cp6cnXnrpJTRp0gQuLi64e/cuTp8+je3bt2PkyJE4f/483n//fUvVTEREFZzR4XLs2DF89NFHeO6557BhwwZUqVLloXWioqLw4osv4oMPPkCfPn3w6KOPylosERGpg9Ed+qtXr0b16tWxadOmIoMFAKpUqYLNmzfD19cXq1evlq1IIiJSF6PD5ciRI+jTpw+cnJxKXK9SpUro06cPDh06ZHZxRESkTkaHy9WrV9GoUSOj1m3cuDGuXr1qclFERKRuRodLamoqXF1djVrXxcUFaWlpJhdFRETqZnS4CCEgSZLROxZCmFQQERGpX5mGIn/wwQfYvHlzqesVdQ2M3ObOnYudO3ciJiYGjo6OSElJKXUbIQRmz56NVatWISUlBY899hiWL1+OoKAgi9dLRGRLjA6XgIAAJCcnIzk52ej1LSk7Oxt9+vRB27ZtjR6ZtmDBAixevBjr169H3bp1MXPmTERERODvv/8u9ZbNRERkPKPDJS4uzoJllN3bb78NAFi3bp1R6wshsGjRIsyYMQPPPvssAGDDhg3w9fXF9u3b0a9fP0uVSkRkc2xm4srLly8jISGhwK2Z3d3d0bp1axw5cqTY7bKyspCamlrgQUREJbOZcElISAAA+Pr6Fnjd19fXsKwo8+fPh7u7u+Hh7+9v0TqJiKxBhQqXqVOnQpKkEh/nzp0r15qmTZsGrVZrePD6HSKi0pk85b4lTJ48GUOHDi1xnXr16pm0bz8/PwBAYmIiqlevbng9MTERzZs3L3Y7JyenUmclICKigipUuHh7e8Pb29si+65bty78/Pywd+9eQ5ikpqbi6NGjGDVqlEXek4jIVlWo02JlceXKFcTExODKlSvIy8tDTEwMYmJicPfuXcM6wcHB+N///gcAkCQJEyZMwJw5c7Bjxw6cPn0agwcPRo0aNdCzZ0+FfgoiIutUoY5cymLWrFlYv3694XloaCgA4JdffkHHjh0BALGxsdBqtYZ13njjDaSnp2PEiBFISUlBeHg4du/ezWtcyKIy7XPgrHQRROXM5HCJjo7G6tWrcenSJdy5c+eh6V4kScLFixfNLrA469atK/Ual6Jqeuedd/DOO+9YrC6iwm5VyQTHGJKtMSlcFi5ciKlTp8LX1xdhYWEICQmRuy4iq3GrcgZqZuvUew6ayAQmhUtUVBQ6deqEXbt2wcHBQe6aiKxKjp0Ot5zT4QM3pUshKjcmfZm6c+cOnn/+eQYLkZGuuWpLX4nIipgULmFhYYiNjZW7FiKrlViZ9zci22JSuCxbtgzbtm3Dpk2b5K6HyCrdqZSpdAlE5cqkPpe+ffsiNzcXgwYNwqhRo1CrVi3Y2dkVWEeSJJw6dUqWIonUTut4T+kSiMqVSeHi6ekJLy8v3mSLyEi3nNOVLoGoXJkULvv27ZO5DCLr9q+bcTfZI7IWHHpPVA7OeSYhW5OrdBlE5cas6V9ycnJw7tw5aLVa6HS6h5Z36NDBnN0TWY17drn4zecSOiQ0ULoUonJhUrjodDpMmzYNy5YtQ0ZGRrHr5eXlmVwYkbXZXP84w4VshkmnxebNm4eFCxfixRdfxIYNGyCEwHvvvYcVK1agadOmaNasGaKjo+WulUjVjnvH4bCv5ebbI6pITAqXdevW4YUXXsDy5cvRrVs3AEDLli0xfPhwHD16FJIk4eeff5a1UCK10Wq1OPjbb4i5kIi7R7OQl6rD/OY/IMMuW+nSiCzOpHC5du0aOnXqBACGuzTeu6cfx+/o6IgXX3wRn3/+uUwlEqnT6dOn0f6ZZzDyox8Q2/MmMv/Oxo3KWnzY9EelSyOyOJPCxcvLy3BTLhcXF7i5ueHSpUsF1rlz54751RFZoW/rxOCnmmeVLoPIokzq0A8NDcXx48cNz5944gksWrQIoaGh0Ol0WLx4MZo1ayZbkUTW5t3QnWiQ4ouAdE+lSyGyCJOOXEaMGIGsrCxkZWUBAObOnYuUlBR06NABjz/+OFJTU/Hhhx/KWiiRNUl3yMKEdlugdeCcY2SdTDpy6dGjB3r06GF43rhxY1y8eBH79u2DnZ0d2rVrB09PfiMjKskVl2RMaLcFSw8OQOU8R6XLIZKVWRdR5ufu7o5nn31Wrt0R2YTTnvEYHb4JUYf7wi3HWelyiGRj8vQveXl5+PLLLzFy5Eg899xzOH36NAD98Mtt27YhMTFRtiKJrNlpz3i89PgGXKnC+cfIepgULikpKXjssccwYMAAbN68GTt27MDNmzcB6EePjRs3DlFRUbIWSmTN4lxvYdATa7Cn5t9Kl0IkC5PCZerUqfjrr78QHR2NS5cuQQhhWGZnZ4fnn38eu3btkq1IIluQ7pCFN8P+hzdb/Q93HDlFP6mbSeGyfft2jB07Fl27doUkSQ8tb9CgAeLi4sytjcgm7an1N57vuhLfB/wJAVH6BkQVkEnhotVqUbdu3WKX5+TkIDeX04sTmUrrmIm3Wn6HUeGb2BdTzu5kVlK6BKtgUrgEBgbi5MmTxS7fs2cPGjdubHJRRKT3u3cc+nVehfVBR5AnPXxbC5LfnXsctScHk8Ll5Zdfxpo1a7BlyxZDf4skScjKysL06dOxe/dujBw5UtZCiWxVtl0uljT5GcMeX49LrjeVLsfq3c3hNUdyMOk6l/Hjx+Ovv/5C//794eHhAQAYMGAAbt++jdzcXIwcORKRkZFy1klk8/6ueh0vPrEGk053Qe/LLSDh4f5OMp/2XiUIcZd/u2YyKVwkScKqVaswZMgQfPPNNzh//jx0Oh0CAwPxwgsv8A6URECBUZRFPTdFtl0u3mu+G39XvYE3//g/2As7s/dJBeXo7KC95wQPV6UrUTezrtAPDw9HeHi4XLUQWYWUlBSsX78eCxcuLPD65ZG34TvGDV59XWDvbvL1ywCAHbVPIUeTh3d+78EjGAtITKsMD+8cpctQNfM+4URUQHR0NGrVqoWJEyfi+vXrBZblJOpwbWYKTjeLh/Zn8yes/MH/DDbVP2b2fuhh8VoXpUtQPaOPXPJPVGkMSZLw7bfflrkgIrWKjo5G9+7dIYQo+hTY/Zd09wQuDLyJ+hu94d7JvJFJSx75BS1v1kaw1s+s/QDANmzDCqzACZxAMpLxB/5AczQvdZt5mIcLuIAc5CAIQZiMyRiEQQXWO4uzmIIp2I/9yEUuGqMxtmIrAhBgdt2WcE3rAoD3pDKH0eHy/fffo1KlSvDz8zPq3HFRF1cSWauUlBT07t0bQgjodKUMGdYB0ACXXrqFkFM1zTpFlqvJwxtttuLzn1+Cu8l70UtHOsIRjhfwAoZjuFHbeMIT0zEdwQiGIxzxPb7HMAyDD3wQgQgAwEVcRDjCEYlIvI234QY3/IW/UAkV93qS6zxyMZvR4VKzZk3Ex8ejWrVqGDBgAPr16wc/P/O/LRFZg/Xr1yMjI8P4TnsdoMsUSP4qHT7Dzes5vl45BVNbb8Mn0c/AnO79B0cbcYgzepuO6Fjg+XiMx3qsx0EcNITLdEzHU3gKC7DAsF4gAs2o1PKupzJczGX0V6arV6/il19+QWhoKN599134+/ujS5cuWLt2LdLS0ixZI1GFJoTAkiVLTNo2aVWaLKPIjnvH4dOQ38zejzkEBPZiL2IRiw7QjxjVQYed2IkGaIAIRMAHPmiN1tiO7YrWWpprPHIxW5mOxx9//HGsXLkSCQkJ+Oabb+Dl5YUxY8bAx8cHvXr1wjfffGO4OyWRrbh9+zYuXrxY9pAQQFZcLvJu5QJ5OrMf64OPIrHKXcv8kCXQQgsXuMARjuiO7liCJeiKrgCAJCThLu7iPbyHbuiGPdiD5/AceqEX9mN/uddqrGsprsjM5jBvc5h0stfBwQHPPvsstmzZgsTEREPg9O3bFwsWLCh9B0RW5O5d8/5Bz0vNAfJyzX7kSrmIDr0JVCq9L2MjNsIl338HcMDk+l3hihjE4DiOYy7mYhImYR/2AdAfuQDAs3gWEzERzdEcUzEVT+NprMAKk9/T0nQCOB3Pu+maw6zrXLKyshAdHY1vv/0Wf/zxBypVqoQ6derIVBqROri4mHcKxa6OL+Alz01hz3T2ARxLr6cHeqA1Whue10RNk99TAw3qoz4AoDma4yzOYj7moyM6ohqqwR72aIyCcw02QiMcxEGT37M87P+nOsLqcrodU5X5E63T6fDjjz9i8+bN2L59OzIyMtClSxesWrUKzz33HKpUqWKJOokqLC8vLwQGBj50b6NSSYBTPSfY+ToBMo2uzDOyS9/1/n+WoIMOWdCfHneEI1qhFWIRW2Cdf/APaqO2Rd5fLnvO1sKELqfhYMfbHpjC6HA5fPgwNm3ahK+//hq3b99GmzZtMG/ePLzwwguoVq2aJWskqtAkScLYsWMxceLEMm/rM85H1mH7bdHW5G2TkYwruILr0F/8+SAQ/O7/BwCDMRg1URPzMR8AMB/z8SgeRSACkYUs7MIufI7PsRzLDft9Ha+jL/qiAzrgCTyB3diN7/Cd4dRZRXUn3Qn7Ymuga+N4pUtRJaPDJTw8HM7OznjqqafQv39/w+mvK1eu4MqVK0Vu06JFC1mKJKrohgwZgunTpyMzM7P061wAQANonDXwHCzfef2WaIme6Gny9juwA8MwzPC8H/oBAGZjNt7CWwCAK7gCTb6u2nSk41W8imu4Bmc4IxjB+AJfoC/6GtZ5Ds9hBVZgPuZjHMahIRpiK7YiHBVr6iitVovTv/2G00nncTfrKJydmmDTsfro0ihergNLmyIJI4/jNZr/PlClfdMSQkCSJOTl5ZlXXQWUmpoKd3d3aLVauLm5KV0OVSD5r9AvMWA0ACQgaFcQ3J6U5zPUDu3wHt5DZVSWZX+26ODBg2jfvr3heUOfHXCp1Aaf9D+ENvWS5H/Du3eB27eBiRMBKzz7Y/SRy9q1ay1ZB5HqRUREYOfOnejduzcyMjIAFJoJ+f53Mo2zBoHbAmUJFgkSXsJLGImRBY4oSD4f/tgUGyN/hqM9b9ZWFkaHy5AhQyxZB5FViIiIwLVr17BhwwYsWLAA8fH/na93qOEAvzf84DXEC3bu5l9D4Q9/zMbsUuf/IvNcvuWKqL1N8HrEn0qXoiryjH8kIgMPDw+MGzcOoaGhBe5tVHdzXbi2N3+ElgYaDMIgjMAIOMHJ7P1R6bb8Hoh63mno3eKy0qWoBsOFyEIK903KMSqsMRpjJmYiCEFm74vK5v3oZnB3zkaXRhw9ZgyGC5EKaKDBSIzEUAyFnVnTU5KpdDoJ07e3QmXHXLQLTFS6nAqPPYBEFZwb3LAMyxCJSAaLwvJ0Et7Y2hrnEsy9wYH1Y7gQVWA+8MEarMGjeFTpUui+ezl2mPR1WySns7+rJKoNl7lz56Jdu3aoXLkyPDw8jNpm6NChkCSpwKNbt26WLZTIRAEIwGqsRh3UUboUKiQp1RnTt7dCno5XVxZHteGSnZ2NPn36YNSoUWXarlu3brhx44bhsXnzZgtVSGS6JmiC1ViN6qiudClUjONx3oja20TpMios1Xbov/322wCAdevWlWk7Jycn3kGTKrSn8BRmYAYc4ah0KVSKTcfqw9v1Hga1Oa90KRWOao9cTLVv3z74+PigYcOGGDVqFG7fvl3i+llZWUhNTS3wILIEDTSYgAl4G28zWFQkam8TrDnUEDLcUNSq2FS4dOvWDRs2bMDevXvx/vvvY//+/fi///u/EudAmz9/Ptzd3Q0Pf3//cqyYbEUlVMKH+BAv4kVI4Hl8tVm2rzHm/9AcuXlsuwcqVLhMnTr1oQ73wo9z586ZvP9+/fqhR48eCAkJQc+ePfH999/j+PHj2LdvX7HbTJs2DVqt1vC4evWqye9PVBQ3uGEFVqA92pe+MlVY2/6oi3Fb2iHtnoPSpVQIFarPZfLkyRg6dGiJ69SrV0+296tXrx6qVauGCxcuoHPnzkWu4+TkBCcnDjkky/CCF5ZhGQIRqHQpJINjl33w0vrHsbjfIVR3z1S6HEVVqHDx9vaGt7d3ub3ftWvXcPv2bVSvzhE5VP5qoAaWYRlqoZbSpZCMLt9yxUvrH8fS/odQzztN6XIUU6FOi5XFlStXEBMTgytXriAvLw8xMTGIiYnB3bt3DesEBwfjf//7HwDg7t27eP311/Hbb78hLi4Oe/fuxbPPPov69esjIiJCqR+DbFQt1MJn+IzBYqVupjlj5Mb2uHTTMreSVgPVhsusWbMQGhqK2bNn4+7duwgNDUVoaCh+//13wzqxsbHQarUAADs7O/z555/o0aMHGjRogMjISLRs2RIHDhzgaS8qVw9OhfnAR+lSyILupDvhlY3tcTW5itKlKKJCnRYri3Xr1pV6jUv+GzU5OzsjOjrawlURlUwDDRZiIWqghtKlUDlITnfC6M3hWD14P7xd7yldTrlS7ZELUUUXEhKCAwcOYOWBlWh4oCGcQ5wxBEPQFE2VLo3K0fWUyjY5iozhQmQh7u7uCA8PR/Pw5nAJd4GnuyeGYZjSZVExRKGrIAs/N8f5RHdM+roNsnJs559c2/lJiRT2LJ5FZVRWugwqJCUlBVFRUejfv3+B1y/fHonE1E+Rq9PK8j5/XKmGN7a1tpkLLRkuROWkC7ooXQIVEh0djVq1amHixIm4fv16gWU5ukRcS5mJ0/HNoM38WZb3O3TBD2991xI6nfn72rYNePJJwMsLkCQgJsa47VJSgNGjgerVAScnoEEDYNeuotd97z39vidMKHt9DBeicuAGNwQjWOkyKJ/o6Gh0794dmZmZEEIUcRpMABDQiXu4cHOgbAGz+y9/fLLvEbP3k54OhIcD779v/DbZ2UDXrkBcHPDNN0BsLLBqFVCz5sPrHj8OrFwJNDWxi1C1o8WI1KQ5mkPD73IVRkpKCnr37g0hBHSlHkboAGhw6dZLCKl5CvYa8+9CueFIAzzicR2dvUqeOLckgwbp/4yLM36bNWuA5GTg8GHA4f74gjp1Hl7v7l1g4EB98MyZY1p9/LQTlYMm4H0/KpL169cjIyPDiGB5QAedyERy+ley1fD+L61wL7d8v9/v2AG0bas/LebrCzRpAsybBxSeu3f0aKB7d6CLGWdyGS5E5aA+6itdAt0nhMCSJUtM2jYpbZVso8iSMyrhp8vlO6fcpUv602F5efp+lpkzgQ8/LHh08uWXwMmTwPz55r0Xw4WoHHCal4rj9u3buHjxogkhIZCVG4e83FuALk+Ghw5/Jhl348KNGwEXl/8eBw6U/ecGAJ0O8PEBPv0UaNkS6NsXmD4dWLFCv/zqVWD8eP37Vapk2ns8wD4XonJQDdWULoHuyz//oCnyclJhL8zvdwGANDsPo/4V79EDaN36v+dFdcAbo3p1fV+Lnd1/rzVqBCQk6Dv7T5wAkpKAFi3+W56XB/z6K/DJJ0BWVsFtS8JwIbIwDTRwgYvSZdB9Li7mtYWdbx3A3kuWWjwerwa4lD63oaur/mGuxx4DNm3SH8Fo7p+3+ucffeg4OgKdOwOnTxfcZtgwIDgYmDLF+GABGC5EFucCF44Uq0C8vLwQGBiIS5culfHUmAQnp3qwc/LVX/whg5BHTd82ORm4cgV4cHlObKz+Tz8//QMABg/WH+U86D8ZNUp/BDJ+PDB2LHD+vL5Df9w4/XJXV30nf35VquivpSn8emn4iSeyMF6VX7FIkoSxY8eatK2PzzhIMgWLmxvQsaPp2+/YAYSG6kd1AUC/fvrnD/pPAH343Ljx33N/fyA6Wn8NS9Om+lAZPx6YOtX0OoojCTkn0LEBqampcHd3h1arhZubm9LlkArcxm14QZ7TKCSPlJQU1KpVC5mZmUYOR9ZAo3FGSMg12Nt7yFLDjBlAz56y7KpC4pELkYUxWCoeDw8PbN26FZIkQaMp7Z9BDQAJgYHbZAuWp58Gnn1Wll1VWAwXIrJJERER2LlzJ5ydnSFJUhGnuyQAEjQaZwQF7YKb25OyvG+7dvqjFpnOrlVYDBcislkRERG4du0aFi1ahBo1Ct7AzcGhBvz9F6Fp03jZgqVFC2DhQsDeBoZSsc+ljNjnQmSdDhw4gA4dOhieN2jwK1xd28u2/8aN9Z3tlW1kfAePXIiIgIdOi8k1KgwA6tUDliyxnWABGC5ERBZVs6b+2hJ3eS7qVw2GCxGRhfj4AMuX6/+0NQwXIiIL8PbW32yr0DgBm8FwISKSmaenvvPe31/pSpTDcCEikpGbG7B0KVC7ttKVKIvhQkQkk8qV9aPCgoKUrkR5DBciIhm4uuqPWB55ROlKKgaGCxGRmXx8gFWrgJAQpSupOBguRERmCAkBNmwA6tdXupKKheFCRGSi557TDzeuxrtYP8QGpk8jIpKXRgNMngz07at0JRUXw4WIqAzs7PS3Bu7cWelKKjaeFiMiKoN332WwGIPhQkRkpOHDgSflubWL1WO4EBEZoVUrfbiQcRguRESlcHMD3n5b35FPxuFfFRFRKWbMsM1p883BcCEiKkGPHkCnTkpXoT4cikxEBCAkJAQHDhzAiRP6G3w5O4fA21t/PQuVHcOFiAiAu7s7wsPDkZ0NuLjoX3v1VaBKFWXrUiueFiMiKkK1asD//Z/SVagXw4WIqAidOgH2PLdjMoYLEVERHn1U6QrUjeFCRFSEhg2VrkDdGC5ERIU4OQHVqytdhboxXIiICqlTh1fjm4t/fUREhdSrp3QF6sdwISIqpG5dpStQP4YLEVEhAQFKV6B+qgyXuLg4REZGom7dunB2dkZgYCBmz56N7OzsEre7d+8eRo8eDS8vL7i4uKB3795ITEwsp6qJSC3YmW8+VYbLuXPnoNPpsHLlSvz111/4+OOPsWLFCrz55pslbjdx4kR89913+Prrr7F//35cv34dvXr1KqeqiUgtqlVTugL1k4QQQuki5LBw4UIsX74cly5dKnK5VquFt7c3Nm3ahOeffx6APqQaNWqEI0eOoE2bNkVul5WVhaysLMPz1NRU+Pv7Q6vVws3NTf4fhIgU9fPPQHg44OiodCXqpsojl6JotVp4enoWu/zEiRPIyclBly5dDK8FBwcjICAAR44cKXa7+fPnw93d3fDw9/eXtW4iqlgqVWKwyMEqwuXChQtYsmQJRo4cWew6CQkJcHR0hIeHR4HXfX19kZCQUOx206ZNg1arNTyuXr0qV9lEVAE9mBGZzFOhwmXq1KmQJKnEx7lz5wpsEx8fj27duqFPnz4YboEbXDs5OcHNza3Ag4isF8NFHhVqzs/Jkydj6NChJa5TL9/VTdevX8cTTzyBdu3a4dNPPy1xOz8/P2RnZyMlJaXA0UtiYiL8/PzMKZuIrAi/P8qjQoWLt7c3vL29jVo3Pj4eTzzxBFq2bIm1a9dCU8pcDS1btoSDgwP27t2L3r17AwBiY2Nx5coVtG3b1uzaicg6cKSYPCrUaTFjxcfHo2PHjggICMAHH3yAmzdvIiEhoUDfSXx8PIKDg3Hs2DEA+rvMRUZGYtKkSfjll19w4sQJDBs2DG3bti12pBgREZmmQh25GOvHH3/EhQsXcOHCBdSqVavAsgcjq3NychAbG4uMjAzDso8//hgajQa9e/dGVlYWIiIisGzZsnKtnYjIFljNdS7lJTU1Fe7u7rzOhYioBKo8LUZERBUbw4WIiGTHcCEiItkxXIiISHYMFyIikh3DhYiIZMdwISIi2TFciIhIdgwXIiKSHcNFZbKysvDWW28VuDsmVVxsL3Vhe8mH07+UkRACaWlpcHV1hSRJ5f7+nH5GXdhe6sL2ko8qJ65UkiRJ/NAREZWCp8WIiEh2DBciIpIdw0VlnJycMHv2bDg5OSldChmB7aUubC/5sEOfiIhkxyMXIiKSHcOFiIhkx3AhIiLZMVyIiEh2DBciIpIdw4WIiGTHcCEiItkxXIjMwMvEiIrGiSsrgIyMDFSuXFnpMshIN27cQEZGBqpVq8ZJTFXg7NmziI+PR9WqVVG3bl14enoqXZJN4JGLwrZt24a3334bly9fVroUMsIXX3yB7t2744knnkCTJk2wZs0a3vujAlu3bh2eeeYZvPLKK3jmmWewePFiZGdnK12WbRCkmO3btwtJkoSLi4uYMWOG+Pfff5UuiUqwceNG4ebmJj799FNx5MgRMWbMGOHr6ysuX76sdGlUhM8//1y4urqKzz//XCQlJYlZs2aJGjVqiJSUFKVLswmcW0whN27cQGRkJFq1agVXV1d8/PHHGDJkCF555RUEBAQoXR4Vcu7cOQwZMgRDhgzBq6++anj9kUcewaBBgzB16lQFq6PCzpw5g4EDB2LMmDEYPnw4ACA+Ph6vvvoqBg0aBE9PT/j7+yMoKAhCCEVu/Gft2OeikMqVK6NXr15o0KABOnToAGdnZ8yfPx8AGDAVUFJSEry8vNClSxcAQF5eHuzs7FC7dm2kpaUpXB0VptFoMG7cODz99NOG10aNGoVDhw4hLi4O9vb6f/rWrFmDZs2aKVWmVWO4KMTd3R39+vWDi4sLAGD06NEAgPnz50MIgVGjRiEgIAApKSm4desW6tevr2S5Nq9Dhw6QJAkNGjQA8N8osYCAADg6OhZY986dO6hatWq510j/ady4Mfz8/Ayd92+88QaOHj2Kn3/+GY0aNcKJEycwfvx4REdHo1mzZjx6sQCGiwIefJAfBMuDb8GjR4+GEALvvfceNBoNevXqhSlTpqBGjRrYsGGDwlXbrgft0759ewD69nvwzTc9PR03btwwvD5o0CC0bt0aY8eOVaxeW/fg9yv/qLCpU6di4sSJqF69OgCgbdu2EELg5s2bAMBgsQCGSznR6XTQaPSD8wp/kDUajWH5mDFjoNFoMG/ePCxbtgzVq1fHDz/8oETJNi1/e9nZ2ZW47oMbSz399NM4ffo01q1bZ+nyqJCSfr9yc3MfGn6clJQENzc3NGnSpNxqtDXs0C8H+T/4X331Ff788094eXmhZcuW6NChg2EdSZIgSRJ0Oh1q166NgIAA7N+/H/b29sjNzTV8WybLMqa9srOz4ejoiAkTJqBKlSo4d+4czpw5gzNnzsDBwcFwtEOWZ0x75W+P1NRUvPjii7h9+zZ+/fVXtpOFMFwsLP+53DfeeANffPEFWrVqhbt37yI1NRXjxo3DoEGDAOh/STIzM9G1a1dcu3YNly5dYrCUs7K0FwCMGDECn332GUJCQvD777/DwcGB7VWOytJe6enp2LJlC7Zu3YobN27g6NGj/CJgSeU89NlmLV26VNSpU0ccOXJECCHE8uXLhaOjo6hbt65YsWJFgXW//PJLkZ2dLYQQIicnp9xrJePba+bMmSIsLMzQTmwvZRjbXqtXrxZvvvkm26scMFwsJDc31/D/9+7dE6NHjxYLFiwQQgjx7bffCnd3dzF79mwxcOBAUaNGDbFhw4aH9sEPfvkpa3utW7fOsH5eXp4Qgu1Vnsxpr6L2QfJjuFhAcnKy4f9PnDghhBAiKSlJXLx4UZw/f17Ur19ffPzxx0II/S9CpUqVRJUqVcRXX32lRLk2z9T22rx5s2G7BwFDlmdqe23ZskWJcm0W5xaT2Z49ezBhwgRcv34d48aNw5NPPonk5GR4e3ujXr16OHbsGDw8PDB06FAAgLOzM7p3746oqCj06tVL2eJtkDnt1adPH8N+HnQok2WZ0169e/dWtngbw98Imd24cQMxMTH4v//7P2zatAmHDx+Gp6cndDodAMDR0RHXrl3Dvn37kJ6ejsWLFyMgIAAvvfQS7OzskJeXp/BPYFvYXurC9lIRpQ+drNHAgQOFJEmiZ8+e4tKlSwWWxcbGir59+4qqVauKunXripCQEEPnvU6nU6Jcm8f2Uhe2lzpwKLIMxP3hkDk5OdBoNPjkk0+QmZmJbdu24ZFHHsHEiRPRtGlTw3rnz59HXFwcEhMT0b9/f9jZ2XH4ajlie6kL20ullMs165C/IzclJaXAiKENGzaI0NBQMWzYMHH69GnD6wcOHCiwD45aKT9sL3Vhe6kXj1zMkP/K4AULFmDXrl1ISUmBr68vFi9ejIYNG2Ljxo2IiopCUFAQ+vbti+XLlyMuLg5///035zMqZ2wvdWF7qZzS6WYNZsyYIXx9fcVnn30m/vjjD+Hj4yPatGkjkpKShBBCbN68WXTq1EkEBQWJDh06GM4BkzLYXurC9lInhouZLl++LEJDQ8UPP/wghBBiz549wtXV9aGr7uPi4sQ///zDC+4UxvZSF7aXejFczHTq1ClRp04dIYQQ33//vXBxcTF88LVa7UO/BELwgjslsb3Uhe2lXgyXMihqKGNmZqZo06aNGD16tHB1dRWffvqpYdmZM2dE27Ztxf79+8uzTLqP7aUubC/rwosojfRgSnwAyMzMRE5OjuH1sLAwfPHFF+jXr5/hft337t3DlClT4OnpifDwcMXqtlVsL3Vhe1kfjhYrozlz5uC3335DSkoK5syZg44dO+LSpUsYPXo0kpKS0KJFC9SsWRP79u1DcnIyTpw4AQcHhwIjX6j8sL3Uhe1lPdgaZfDJJ59gyZIlaNGiBdzd3REREYFly5ahXr16WLJkCfr164dTp07hzJkzCA0NxcmTJw339+AHv/yxvdSF7WVllD4vV5EV7hiMiooSX3/9teH5jBkzhEajEZ988onhfHHh88a8gKv8sL3Uhe1l3TgfQjGEEIZvQzt27MDNmzfxyy+/GGZbBYB3330XADBhwgTY2dmhf//+cHd3L7Af3uGufLC91IXtZQMUDrcKKf+3oylTpohKlSqJpk2bCkmSRGRkpLh27VqB9WfNmiUkSRLbtm0r71JJsL3Uhu1lGxguJfjtt9/E008/LQ4dOiQyMzPFvHnzRI0aNcTcuXPF9evXC6y7atUqXrilMLaXurC9rBtPixXj888/x9atW+Ho6IjWrVvDzs4O06ZNQ15eHpYvXw4hBCIjI+Hn5wcAePnllwGAs68qhO2lLmwv68dWKsbVq1dx/PhxODg44N9//0W9evUAADNmzIAkSVi1ahXS0tLw+uuvw8vLy7AdP/jKYHupC9vLBih85FShrVq1SjRo0ECMGjVKXLx4scCyN954Q/Ts2ZM3IKpA2F7qwvaybryIsgj5L8iKiorC+vXrER4ejokTJ6Ju3bqG9cT9mxM9+JOUwfZSF7aXbbDpY8ziPrQajcbwCzB+/HgAwIYNG2BnZ4dXX30VQUFBAMAPfjlje6kL28u22eRlrV9//TUAlPihffALAADjx4/HkCFD8PXXX2Pnzp0F1uMH3/LYXurC9iLABucWW7BgAU6fPo3169cbDs1Lmpco/7KvvvoKvXv35oVb5YjtpS5sL3rA5sLlxo0b8Pb2hr29PY4fP45WrVoBMP4XAADy8vL4C1BO2F7qwvaiB2zutFj16tVhb2+PXbt2YdCgQfj4448BFDxMLyz/oXlSUhI/+OWI7aUubC96wObC5YGGDRsiPDwcW7duRVRUFICifwHydyhGRUWhffv2uHPnTrnXa+vYXurC9iKbuM6l8OyrD55fuHBBjBgxQrRp00YsWrTooeX5x9ivWLFCeHp6ik2bNpVDxbaN7aUubC8qitWHS+EP8KRJk8SsWbPElStXhBBCnD9/3vALEBUVZVg3/zxGK1asEG5ubuKbb74pv8JtFNtLXdheVByrDpf836imTJkivL29RZcuXUTTpk2Fv7+/OHfunBBC/w1r5MiRol27dmLOnDkF9rFy5Urh7u7OD345YHupC9uLSmK14ZL/G1ViYqIYO3asOHnypBBCiDNnzoinnnpKeHh4FPgFeOGFF8Tw4cMN23755ZdCkiSxdevW8v8BbAzbS13YXlQaqwuX/HeyE0KIzz//XLi4uIiwsDDDoboQ+sP1p556SlStWtXwC3Dt2rUC38b+/vtvER0dXT6F2yi2l7qwvchYVhUuGzduFM2aNRN5eXmGb0d79+4VERERwsXFRfz7779CiP++dV24cEE888wzQpIkwzIhHu6gJMtge6kL24vKwqrCJSsry3BP7aNHjwoh9B/kQ4cOibCwMBEUFCSSkpKEEP/9Apw7d0689tprvBGRAthe6sL2orKwqnB54PDhw0KSJPHRRx8JIfQf9IMHD4rw8HDRuHFjkZiYaHg9P/4CKIPtpS5sLzKGVYRL4cNsnU4n5syZIxwcHMTHH39seO3gwYOiffv2IiQkRNy4cUOBSkkItpfasL3IFKoPl/wf/B9++EFs2bJFxMbGCiGE+PDDD4UkSQV+AQ4dOiSCg4PFwIEDlSjX5rG91IXtRaZSfbg8MHXqVFGlShVRv359YW9vL5YuXSoSEhLERx99JCRJMlwhnJeXJ/7880/DuWNSBttLXdheVFaqvVmYyHeXun///RcHDx7Ejz/+iIYNG2LNmjUYM2YM0tLSMGTIEEiShNdffx1paWmYMWMGQkJCAHD21fLE9lIXtheZS5Xhkn+K7jt37iAnJwfh4eEICwuDnZ0dXnvtNTg4OGDixImQJAmDBw9GWloaoqOjMX36dMNEefzglw+2l7qwvUgWSh42mevNN98UrVq1Eu7u7qJp06aGi7UeWLRokbC3txczZswQt2/fNoxeKTyKhcoH20td2F5kDlWFS/7Oxc2bN4vq1auLxYsXiwkTJojKlSuL1157TcTFxRXYZs6cOeKxxx7jB18BbC91YXuRnFQVLg/s27dPvPrqq2L9+vWG15YuXSpq1aolpkyZ8tAvAD/4ymJ7qQvbi+Sguj6XhIQEREZGIjExEQ0aNDC8/uqrr0IIgffeew92dnaIjIxEvXr1AMDQMZn/jndUPthe6sL2ItkomWymOnXqlGjQoIHo2rWr+PPPPwssW7ZsmbCzsxPLly9XqDoqjO2lLmwvkoMqw0UIIWJiYkRoaKgYPny4OHPmTIFlW7du5Tj7CobtpS5sLzKXJIQQSh89meqPP/7Ayy+/jJYtW2LChAlo3LhxgeUcZ1+xsL3Uhe1F5lB1uAD6X4CRI0eidu3aWLBgAerWrat0SVQCtpe6sL3IVBqlCzBXaGgoPvnkE7i6uqJ27dpKl0OlYHupC9uLTKX6I5cHxP3RKvmvLqaKi+2lLmwvKiurCRcAHA6pMmwvdWF7UVlYVbgQEVHFwONbIiKSHcOFiIhkx3AhIiLZMVyIiEh2DBciIpIdw4WIiGTHcCEiItkxXIiISHb/D3mTMmb0q7TAAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 390x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "f_forest_minimeta = dabest.forest_plot(\n",
+    "                        data = contrasts_mini_meta, \n",
+    "                        labels=['mini_meta1', 'mini_meta2', 'mini_meta3'],\n",
+    "                        custom_palette=['#FF0000', '#00FF00', '#0000FF'],\n",
+    "                        contrast_bars=True,\n",
+    "                        contrast_bars_kwargs={'color': 'red', 'alpha': 0.4})    "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Reference band\n",
+    "You can add reference bands by supplying a list/tuple to the `reference_band` argument, indicating the contrast to highlight. None are displayed by default."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 390x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "f_forest_minimeta = dabest.forest_plot(\n",
+    "                        data = contrasts_mini_meta, \n",
+    "                        labels=['mini_meta1', 'mini_meta2', 'mini_meta3'],\n",
+    "                        custom_palette=['#FF0000', '#0000FF', '#00FF00'],\n",
+    "                        reference_band=[1,])    "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can set a variety of kwargs to customize the reference bands via `reference_band_kwargs`.\n",
+    "\n",
+    "Pass any keyword arguments accepted by matplotlib.patches.Rectangle here, as a string.\n",
+    "\n",
+    "In addition, the `span_ax` keyword argument can be used to expand the reference band across the whole plot. \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 390x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "f_forest_minimeta = dabest.forest_plot(\n",
+    "                        data = contrasts_mini_meta, \n",
+    "                        labels=['mini_meta1', 'mini_meta2', 'mini_meta3'],\n",
+    "                        custom_palette=['#FF0000', '#0000FF', '#00FF00'],\n",
+    "                        reference_band=[1,],\n",
+    "                        reference_band_kwargs={'span_ax': True, 'color': 'grey', 'alpha': 0.2})    "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Embedding forest plots into an existing Axes \n",
+    "\n",
+    "You can plot a forest plot into an existing Axes as a subplot by using the with the ``ax`` parameter. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x1400 with 7 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "f_forest_drug_profiles, axes  = plt.subplots(2, 2, figsize=[15, 14])\n",
+    "f_forest_drug_profiles.subplots_adjust(hspace=0.3, wspace=0.3)\n",
+    "\n",
+    "for ax, contrast in zip(axes.flatten(), [unpaired_delta_01, unpaired_delta_02, unpaired_delta_03]):\n",
+    "    contrast.mean_diff.plot(                  \n",
+    "                    contrast_label='Mean Diff',\n",
+    "                    raw_marker_size = 1,\n",
+    "                    contrast_marker_size = 5,\n",
+    "                    color_col='Genotype',\n",
+    "                    ax = ax\n",
+    "    )\n",
+    "\n",
+    "dabest.forest_plot(\n",
+    "    data = contrasts, \n",
+    "    labels = ['Drug1', 'Drug2', 'Drug3'], \n",
+    "    ax = axes[1,1], \n",
+    "    )\n",
+    "\n",
+    "for ax, title in zip(axes.flatten(), ['Drug 1', 'Drug 2', 'Drug 3', 'Forest plot']):\n",
+    "    ax.set_title(title, fontsize = 12)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "python3",
+   "language": "python",
+   "name": "python3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/nbs/tutorials/08-horizontal_plot.ipynb b/nbs/tutorials/08-horizontal_plot.ipynb
new file mode 100644
index 00000000..40c7ed61
--- /dev/null
+++ b/nbs/tutorials/08-horizontal_plot.ipynb
@@ -0,0 +1,674 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Horizontal Plots\n",
+    "\n",
+    "> A guide to plot data in a horizontal format.\n",
+    "\n",
+    "- order: 8"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In DABEST **v2025.03.27**, we introduce a new plotting orientation: **horizontal plots**. \n",
+    "\n",
+    "To access this, provide `horizontal=True` to the `.plot()` method."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Pre-compiling numba functions for DABEST...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Compiling numba functions: 100%|██████████| 11/11 [00:00<00:00, 30.36it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Numba compilation complete!\n",
+      "We're using DABEST v2025.03.27\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import dabest\n",
+    "\n",
+    "print(\"We're using DABEST v{}\".format(dabest.__version__))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#| hide\n",
+    "import warnings\n",
+    "warnings.filterwarnings(\"ignore\") # to suppress warnings related to points not being able to be plotted due to dot size"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Creating a demo dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from scipy.stats import norm # Used in generation of populations.\n",
+    "\n",
+    "np.random.seed(9999) # Fix the seed to ensure reproducibility of results.\n",
+    "\n",
+    "Ns = 20 # The number of samples taken from each population\n",
+    "\n",
+    "# Create samples\n",
+    "c1 = norm.rvs(loc=3, scale=0.4, size=Ns)\n",
+    "c2 = norm.rvs(loc=3.5, scale=0.75, size=Ns)\n",
+    "c3 = norm.rvs(loc=3.25, scale=0.4, size=Ns)\n",
+    "\n",
+    "t1 = norm.rvs(loc=3.5, scale=0.5, size=Ns)\n",
+    "t2 = norm.rvs(loc=2.5, scale=0.6, size=Ns)\n",
+    "t3 = norm.rvs(loc=3, scale=0.75, size=Ns)\n",
+    "t4 = norm.rvs(loc=3.5, scale=0.75, size=Ns)\n",
+    "t5 = norm.rvs(loc=3.25, scale=0.4, size=Ns)\n",
+    "t6 = norm.rvs(loc=3.25, scale=0.4, size=Ns)\n",
+    "\n",
+    "\n",
+    "# Add a `gender` column for coloring the data.\n",
+    "females = np.repeat('Female', Ns/2).tolist()\n",
+    "males = np.repeat('Male', Ns/2).tolist()\n",
+    "gender = females + males\n",
+    "\n",
+    "# Add an `id` column for paired data plotting.\n",
+    "id_col = pd.Series(range(1, Ns+1))\n",
+    "\n",
+    "# Combine samples and gender into a DataFrame.\n",
+    "df = pd.DataFrame({'Control 1' : c1,     'Test 1' : t1,\n",
+    "                 'Control 2' : c2,     'Test 2' : t2,\n",
+    "                 'Control 3' : c3,     'Test 3' : t3,\n",
+    "                 'Test 4'    : t4,     'Test 5' : t5, 'Test 6' : t6,\n",
+    "                 'Gender'    : gender, 'ID'  : id_col\n",
+    "                })"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Generating two-group plots"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 700x200 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 700x160 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "two_groups_unpaired = dabest.load(df, idx=(\"Control 1\", \"Test 1\"))\n",
+    "two_groups_unpaired.mean_diff.plot(horizontal=True);\n",
+    "\n",
+    "two_groups_paired = dabest.load(df, idx=(\"Control 1\", \"Test 1\"), paired='baseline', id_col='ID')\n",
+    "two_groups_paired.mean_diff.plot(horizontal=True);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Generating shared-control and repeated-measures plots"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 700x400 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 700x280 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 700x280 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "shared_control = dabest.load(df, idx=(\"Control 1\", \"Test 1\", \"Test 2\", \"Test 3\", \"Test 4\", \"Test 5\"))\n",
+    "shared_control.mean_diff.plot(horizontal=True);\n",
+    "\n",
+    "repeated_measures_baseline = dabest.load(df, idx=(\"Control 1\", \"Test 1\", \"Test 2\", \"Test 3\", \"Test 4\", \"Test 5\"), paired='baseline', id_col='ID')    \n",
+    "repeated_measures_baseline.mean_diff.plot(horizontal=True);\n",
+    "\n",
+    "repeated_measures_sequential = dabest.load(df, idx=(\"Control 1\", \"Test 1\", \"Test 2\", \"Test 3\", \"Test 4\", \"Test 5\"), paired='sequential', id_col='ID')    \n",
+    "repeated_measures_sequential.mean_diff.plot(horizontal=True);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Generating multi-group plots"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 700x500 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "multi_2group = dabest.load(df, idx=((\"Control 1\", \"Test 1\"),(\"Control 2\", \"Test 2\"),(\"Control 3\", \"Test 3\", \"Test 4\", \"Test 5\")))\n",
+    "multi_2group.mean_diff.plot(horizontal=True);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Generating proportion plots\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def create_demo_prop_dataset(seed=9999, N=40):\n",
+    "    import numpy as np\n",
+    "    import pandas as pd\n",
+    "\n",
+    "    np.random.seed(9999)  # Fix the seed to ensure reproducibility of results.\n",
+    "    # Create samples\n",
+    "    n = 1\n",
+    "    c1 = np.random.binomial(n, 0.2, size=N)\n",
+    "    c2 = np.random.binomial(n, 0.2, size=N)\n",
+    "    c3 = np.random.binomial(n, 0.8, size=N)\n",
+    "\n",
+    "    t1 = np.random.binomial(n, 0.6, size=N)\n",
+    "    t2 = np.random.binomial(n, 0.2, size=N)\n",
+    "    t3 = np.random.binomial(n, 0.3, size=N)\n",
+    "    t4 = np.random.binomial(n, 0.4, size=N)\n",
+    "    t5 = np.random.binomial(n, 0.5, size=N)\n",
+    "    t6 = np.random.binomial(n, 0.6, size=N)\n",
+    "    t7 = np.ones(N)\n",
+    "    t8 = np.zeros(N)\n",
+    "    t9 = np.zeros(N)\n",
+    "\n",
+    "    # Add a `gender` column for coloring the data.\n",
+    "    females = np.repeat('Female', N / 2).tolist()\n",
+    "    males = np.repeat('Male', N / 2).tolist()\n",
+    "    gender = females + males\n",
+    "\n",
+    "    # Add an `id` column for paired data plotting.\n",
+    "    id_col = pd.Series(range(1, N + 1))\n",
+    "\n",
+    "    # Combine samples and gender into a DataFrame.\n",
+    "    df = pd.DataFrame({'Control 1': c1, 'Test 1': t1,\n",
+    "                       'Control 2': c2, 'Test 2': t2,\n",
+    "                       'Control 3': c3, 'Test 3': t3,\n",
+    "                       'Test 4': t4, 'Test 5': t5, 'Test 6': t6,\n",
+    "                       'Test 7': t7, 'Test 8': t8, 'Test 9': t9,\n",
+    "                       'Gender': gender, 'ID': id_col\n",
+    "                       })\n",
+    "\n",
+    "    return df\n",
+    "df_prop = create_demo_prop_dataset()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 700x500 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 700x310 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "multi_two_groups_unpaired = dabest.load(df_prop, idx=((\"Control 1\", \"Test 1\"), (\"Control 2\", \"Test 2\"), (\"Test 3\", \"Test 4\"), (\"Test 5\", \"Test 6\")), proportional=True)\n",
+    "multi_two_groups_unpaired.mean_diff.plot(horizontal=True);\n",
+    "\n",
+    "multi_group_baseline = dabest.load(df_prop, idx=(((\"Control 1\", \"Test 1\",\"Test 2\", \"Test 3\"),(\"Test 4\", \"Test 5\", \"Test 6\"))),proportional=True, paired=\"baseline\", id_col=\"ID\")\n",
+    "multi_group_baseline.mean_diff.plot(horizontal=True);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Generating delta-delta plots"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from scipy.stats import norm # Used in generation of populations.\n",
+    "np.random.seed(9999) # Fix the seed to ensure reproducibility of results.\n",
+    "\n",
+    "# Create samples\n",
+    "N = 20\n",
+    "y = norm.rvs(loc=3, scale=0.4, size=N*4)\n",
+    "y[N:2*N] = y[N:2*N]+1\n",
+    "y[2*N:3*N] = y[2*N:3*N]-0.5\n",
+    "\n",
+    "# Add a `Treatment` column\n",
+    "t1 = np.repeat('Placebo', N*2).tolist()\n",
+    "t2 = np.repeat('Drug', N*2).tolist()\n",
+    "treatment = t1 + t2 \n",
+    "\n",
+    "# Add a `Rep` column as the first variable for the 2 replicates of experiments done\n",
+    "rep = []\n",
+    "for i in range(N*2):\n",
+    "    rep.append('Rep1')\n",
+    "    rep.append('Rep2')\n",
+    "\n",
+    "# Add a `Genotype` column as the second variable\n",
+    "wt = np.repeat('W', N).tolist()\n",
+    "mt = np.repeat('M', N).tolist()\n",
+    "wt2 = np.repeat('W', N).tolist()\n",
+    "mt2 = np.repeat('M', N).tolist()\n",
+    "\n",
+    "\n",
+    "genotype = wt + mt + wt2 + mt2\n",
+    "\n",
+    "# Add an `id` column for paired data plotting.\n",
+    "id = list(range(0, N*2))\n",
+    "id_col = id + id \n",
+    "\n",
+    "\n",
+    "# Combine all columns into a DataFrame.\n",
+    "df_delta2 = pd.DataFrame({'ID'        : id_col,\n",
+    "                  'Rep'      : rep,\n",
+    "                   'Genotype'  : genotype, \n",
+    "                   'Treatment': treatment,\n",
+    "                   'Y'         : y\n",
+    "                })"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 700x350 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "unpaired_delta2 = dabest.load(data = df_delta2, x = [\"Genotype\", \"Genotype\"], y = \"Y\", delta2 = True, experiment = \"Treatment\")\n",
+    "unpaired_delta2.mean_diff.plot(horizontal=True);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Generating mini-meta plots"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 700x310 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "unpaired = dabest.load(df, idx=((\"Control 1\", \"Test 2\"), (\"Control 2\", \"Test 3\"), (\"Test 4\", \"Test 5\")), mini_meta=True)\n",
+    "unpaired.mean_diff.plot(horizontal=True);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Controlling aesthetics\n",
+    "\n",
+    "As with the vertical plots, horizontal plots can be customized using the same options. Shown below are a few cases where the aesthetics are modified, added functionality, or just less intuitive."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Swarm side\n",
+    "\n",
+    "As with the vertical plots, you can specify the side of the swarms via `swarm_side` in the `.plot()` method. \n",
+    "\n",
+    "In this case, `swarm_side='left'` would plot the swarms upwards, and `swarm_side='right'` would plot the swarms downwards."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Default is `swarm_side='left'`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 700x250 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "two_groups_unpaired = dabest.load(df, idx=(\"Control 1\", \"Test 1\", 'Test 2'), resamples=5000)\n",
+    "two_groups_unpaired.mean_diff.plot(swarm_side='left', horizontal=True);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`swarm_side='center'`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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ihdIx3333HQCgR48eOgdboUL+14lF3RlOk3fffRfnz5/HmTNnFNsyMjKwceNGeHh44M0339Q5jtIo+HCh7Z3zAgMDER8fr/EGLQU1ydo8CouPj0fXrl0hkUhw6NChItd6btiwIerXr4+NGzcqLe+zbt06CIKgNMkPAFJSUnDv3j2tVz8h03J2qIDOLeoj6s7fkMtFvZYgK5jYN6l/Rwzw8y72VtKGqAUu6O/Dbm8h4vJt5Mrkei+dZsi2Squk62hOddREqlq2bImdO3dCKpUWOaJcsO+XX37hjUSo3NBpJNnLywuhoaEYNGgQGjRooHTHvcjISISFhWH48OEAgKZNm2LYsGHYuHEjkpOT4ePjg/Pnz+PHH39Enz59lCbtaatZs2aQSqVYvHgxUlJSYG1tjY4dO6Jq1apFnjNjxgxs374d77zzDiZNmoRKlSrhxx9/RGxsLH799Ve9VrbYtm0bHj58iMzMTADAn3/+iQULFgAAPvzww2JHpz09PdGoUSMcOXIEI0eOLLEvHx8f+Pj44MSJE2r79K1J7t69O+7fv4/p06fj1KlTOHXqlGJftWrV0KVLF8XPS5Ysgb+/P7p27YrBgwfj+vXrWLNmDUaPHq30DQEAHDlyBKIoar0CCpmeNl/ja3s3vJIUV38rlQjIfJmDVeERWq3aYMi4y/Ia6Muc6qiJNOnXrx8iIyMxf/587N+/X2lEWRAE9OjRA7Nnz2aCTOWKzrel9vf3x9WrV7FkyRLs2bMH69atg7W1NZo0aYJly5bh448/Vhy7efNmeHp6Ijg4GLt374arqytmzpyJgIAAvYJ1dXXF+vXr8c0332DUqFGQyWQ4duxYsUlytWrVEBkZiS+//BKrV6/Gy5cv0aRJE+zbt0+v0WwA2LJli1LSeuzYMRw7dgwA0K5duxJLOEaOHIk5c+YgKytLqzrqwMBAvT5UFCU6OhpA/s1hVPn4+CglyT179sSuXbswd+5cTJw4ES4uLpg1a5bG8o+wsDC0a9cOXl5eBouVjKssv8ZXXQquoKxBIggQRSDk8HmtSx0MGXd5KGUo6tpZSCVF1oATlbWWLVti7969ePToEf744w98/PHH2LRpE7p27coaZCqXBFHXVcCp1FJSUuDp6YmgoCCMGjXK1OEYRHx8PGrXro0dO3boNJJ8+fJleHt749KlS8XeYIWM45eIi9i8/7TGOkJBEPBxr3YGH0VNSstQ1N9WrGCLnUcvIFfDSK6lhbTIVRsMGbcproG+Cl+7kmrAiUwpNzcXVlZWyMnJgaWlpanDeU1cMnUAZk739/Gyu4sGKVSsWBHTp0/HkiVLdF5f0lytWLECjRs3ZqlFOWOIu+HpqnD9ra21JfLkmj+nF7dqgyHjNsU10JcuNeBERFQ6TJJN5Msvv8StW7fK9G5/xvTtt9/i/Pnzpg6DdKTPUm6GVFDqoElxpQ6GjNvU14CIiMyTzjXJRPRq0XUpN0MqzaoNhozblNeAiIjME5NkIlJ8jV+S8ctCkZSWabB+5aJY9Lqqoqix1KG4GHadKPqW6LradeIKnB3ssPazIQZrk4iIyg8myUSktaS0TDxPSS+TvhzsbDSO5JZlDERE9PpikkxEWjNW+YFcFJGdkwuZXIRUIsDayhKVHTWvk1yWJRAstyAien0xSSYirZlD6YE5xEBERK++V2NpBSIiIiIiA2KSTERERESkgkkyEREREZEKJslERERERCqYJBMRERERqWCSTERERESkgkkyEREREZEKJslERERERCqYJBMRERERqWCSTERERESkgkkyEREREZEKJslERERERCqYJBMRERERqWCSTERERESkgkkyEREREZEKC1MHQERERK8mWe5LSCAzdRgAAEEihURqaeowqBxhkkxEREQGJZflAgBSHt6ApaUZpBqiHBZiLuxrNYXE2t7U0VA5YQavXCIiInqViPL80WOJhRQW1ramDASWdw7C4s4BCDkZEKztgJqtgLfHA5Vqmy4uKheYJBMREZFRSKSWkFhYmaZzuQxWFzZB+vgsABEiAIgi8Pd5IP460HM5ULW+aWKjcoET94iIiOjVIoqwvPLD/yfIGuRmAn98BbxMLdu4qFxhkkxERESvFMsbYbB48GfxB2U8B86uK5uAqFxikkxERESvDIs7B2Fxe592B98+ACTcMm5AVG4xSSYiIqJXgvTvs7C8GqrbSec3GicYKveYJBMREVG5JyQ/hNUlPRLeJ5eAp1EGj4fKPybJREREVL7JcmB9fg3w/+sz6+ziD4aNh14JTJKJiIioXLOM2QUhLV7/BuKigadXDBcQvRKYJBMREVG5JaQ8hsWdA6Vv6OLW/HWUif4fk2QiIiIqn0QRVle3GSa5jYsGnlwufTv0ymCSTEREROWSJO4KJAkxhmvw/EaOJpMCk2QiIiIqf+QyWN3Yadg2/7kF3D1q2Dap3GKSTEREROWO9NFJCKlPDd/w2bW8XTUBYJJMRERE5Y0sF5Y3dxun7cxE4PRKll0QLEwdABGRIeWkJyHhWgSyk5/B2qkaqjbuCCt7Z1OHRUQGZPHgOITMF8br4O4RoFpDoFE/4/VBZo9JMhG9MhL/Oodbv34DUZ4HQZBAFOV4eHwbGvSfhUp1Wpk6PCIyBFkuLG7vM34/kasBp1pADW/j90VmieUWRPRKyElPyk+QZbmAKEKUy/L/K8vFzfBFyElPMmhfj8/8insH1+LxmV8N2jYRFc/iwXEIWWXwOyfKgaOBQPo/xu+LzBJHkonolZBwLQKiPE/jPlGeh4RrEajR+r1S98PRaiITKqtR5AIvU4Hj3wA9lgGCUHb9klngSDIRvRKyk59BEDS/pQmCBNnJz0rdR1mOVhOROovYiLIZRS7sySXgr9/Ltk8yC0ySieiVYO1UDaIo17hPFOWwdqpW6j60Ga0mIiPJzYTFrT2m6fvcei4L9xpikkxEr4SqjTtCkGiuIBMkFqjapFOp+yiL0Woi0szy5i4I2Wmm6TwrGbi01TR9k8kwSSaiV4KVvTPe6DEJKEhiBQkgCBCklmjQfxasKjhp1U5xk/LKYrSaiNRJXtyDxd0/TBvEjf8B/9w2bQxUppgkE9ErIfGvc7j726r8GemCkP9fCKjTc7LWE+oS/zqHC6tH4EHEVsRf+R0PIrbiwuoReHHnPICyGa0mIhU56bC6sNb0N/cQ5cCJxUBejmnjoDLDJJmITMoQy6kpTagD/v1jKspxZ/9KrdrUZlKelb0zGvSfBUFqmT9KLZHqNVpNRFqS5cD67EoI6Ql6N/HoRTaCz/6D1SeeYWtkPB69eKl/PIn3gLPf63/+KyI5OQ1jxiyEi0tnVKjQDn5+Y3H58i2d28nNzcObbw6AILTA0qXb1PbHxT3HmDELUbu2P2xt28LLqzemTfsOiYnJBngWJeMScCYSFBSEH374ATExMZBIyv9nlRkzZuDYsWM4d+6cqUOhcsRQy6kZYvk3bduoVKcVWk7cqnxXvyadmCATGVpOOqzProTkH92TLwA4/yAdCw89wYEbyRBFQCIAcjH/i6aejSvh63droaWHg+4N3/gf4OwBNOyrV1zlnVwuR48ekxEdfQdffPEhqlRxwtq14fD1HYtLl7ahTh13rdtavXoHHj2K17gvPT0TrVuPQEZGFsaPH4CaNashOvovrFnzC44du4hLl342ev5kltmZIAhaPY4fP17qvjIzMxEYGKhTWwsXLoS/vz+qVasGQRAQGBioU5+pqalYvHgxvvzyS6V/4ILntWzZMrVzgoODIQgCLl68qFNfmuzatQuDBg2Cp6cn7OzsUK9ePXz22WdITk7WePzevXvRvHlz2NjYwN3dHQEBAcjLU04mpkyZgujoaOzdu7fU8dHrwZDLqRliQp0ubVjZO6NG6/fg9c541Gj9HhNkIgOTPL8Nm6Nf650g7456gQ7LY/B7TLLiiyV5wRdMInDg+gu0CYrCrivP9Qvw9Erg1gH9zjVzvr5jMHx4YJH7w8OPIjLyKoKDAxAQMAYTJgzE8eMbIJVKEBCwQet+EhJeYN68zfjyy2Ea9+/d+ycePoxDcHAg5s4di9Gj+2D16umYOXMEoqL+QnT0X7o+NZ2Z5Ujytm3KQ+4//fQTDh8+rLa9QYMGpe4rMzMTc+fOBQD4+vpqdc7s2bPh6uqK//73vzh06JDOff7www/Iy8vD+++/r3H/kiVLMG7cONjZ2enctjbGjBmD6tWrY+jQoXB3d8e1a9ewZs0aHDhwAJcvX4atra3i2IMHD6JPnz7w9fXF6tWrce3aNSxYsAAJCQlYt26d4jhXV1f07t0bS5cuhb+/v1HipvIlJz1JebS1cUdY2Tsr9us7+qupXV0n1BmijfKipH+HsmjP0DHQK0yWB8ub4bD464DeNcjnH6Tj/a13IZOLKKoFmRwQIGLQppuInN5M9xFlUcyvT05/BjQfBrwC3whrKzz8KKpVq4x+/Toqtrm4OGPgwM74+eeDyM7OgbW1VYntzJixGvXq1cLQoe9gzpz1avtTU9MBANWqVVLa7uZWGQBga2tTmqehFbNMkocOHar089mzZ3H48GG17aYSGxsLDw8PPH/+HC4uLjqfv3XrVvj7+8PGRv0fuFmzZoiKisL69esxbdo0Q4SrJjw8XO0Dgbe3N4YNG4aQkBCMHj1asf3zzz9HkyZN8Mcff8DCIv/l4ujoiEWLFmHy5MmoX7++4tiBAwdiwIABuH//Pjw9PY0SO5UP2pRRFIzciqJM7fyiRn+LardOz0kQJBb/1iQXbktlQl1p2ihvyZ6h7w6oT3u8QyFpS8j4B1bn10Dy4n6p2ll06AlEFJ0gFxABiBCx4MBD7BnfSL/OLgXn32yk3VSgspd+bZQzV67cRvPm9dRKHVq1aoiNG3fjr78eoXHjN4pt4/z56/jxx99w6tRmCEXcybBDh+aQSCSYPHkpli2biho1quLq1TtYuHAr+vTxRf36HoZ6SkUqtx995HI5VqxYgYYNG8LGxgbVqlXD2LFjkZSk/BXtxYsX0a1bN1SpUgW2traoXbs2Ro4cCQB48OCBIsmdO3euotyhpPIJDw8PveOOjY3F1atX0blzZ43727Zti44dOyIoKAhZWVl691McTSPmffvm11bdvHlTsS0mJgYxMTEYM2aMIkEGgPHjx0MURYSHhyu1UfCc9uwx0WLvZBa0LaPQZ/S3qHbv7F+FN3pMKnFCXWnaSHtyu9iVL8yNoe8OqE97vEMhaUWUQxp7DDZHvyp1gvzoRTZ+u5EMmea3FjUyObDv2ovSTeaLvwb8Ogo4EgjEXzf9KhxGFhf3HG5uVdS2F2x7+vSfYs8XRRETJy7BoEFd0Lp1kyKPe/NNT2zcOAsxMbFo3XoEatbsgR49pqBTp5YIC/u2dE9CS2Y5kqyNsWPHIjg4GCNGjMCkSZMQGxuLNWvW4MqVKzh9+jQsLS2RkJCArl27wsXFBTNmzICTkxMePHiAXbt2AQBcXFywbt06jBs3Dn379kW/fv0AAE2aFP2PVlqRkZEAgObNmxd5TGBgIDp06IB169YVO5qcnZ2NtDTtFlavUkX9BV1YfHy82nFXrlwBALRo0ULp2OrVq6NGjRqK/QUqVqwILy8vnD59GlOnTi0y5uzsbMXP6enpWsVP5Ye2ZRRVG3fEw+PbtBr91abd3IykEifU6dsGRBEXVo9QxFow+l2Q7LWcuNXsRpQNMZmxtO0ZOgYqf+SyXMiLWDJNeJkCadwlWN4/Ckl6nGL7W0HXEZ+m/r6gjYxsuc45qigCTedfQgVrqV59AoCroxUuzhKBe8cAJ3fAqyPg0T5/dLmIkVJzkJubh5SUdLVt2dk5eP48WWl7pUqOkEgkyMrK1lhOYWNjDQDIyspW21dYcPA+XLt2F+Hhi0uM7z//qYpWrRri3XfbolYtN5w8eQWrVu1AlSpOWLp0Sonnl1a5TJJPnTqFzZs3IyQkBEOGDFFs9/PzQ/fu3REWFoYhQ4YgMjISSUlJ+OOPP5QSvQULFgAAKlSogP79+2PcuHFo0qRJmZRz3LqVPwmhdu3aRR7Tvn17+Pn5KWqTC9cIF7Z9+3aMGDFCq37FEt41Fi9eDKlUiv79+yu2xcXlv2m5ubmpHe/m5oanT5+qbff09ERMTEyR/XzzzTeKGnB6NWlbRlGwnNrN8EVKX8ULEguNy6lp027BhLrSxKapjcdnfi13yZ4+5SyGbs/QMZCZy8nMX0tYlEPIycjflvwEIrIhZKdCyEmD5GUKJFnPIUl7Ckl6PIT/L4oQ8W8iGZ+WiyfJ+iXJ+krOkiE5S/11qj0BkFrm/29aHBAVkv+wrQS41AMq1gDsKgPWjoClLWBpl7/d2t4g8evr9Oko+Pl9orY9MvIqduxQvnlLbOxeeHhUh62tNbKz1T/4vHyZnxzb2loX2V9qajpmzvweX3zxEWrWdC0xtp49p+Ls2a1o0eJNAECfPr5wdKyAuXM3YeRIf7z5pnFLO8tlkhwWFoaKFSuiS5cueP7835mp3t7esLe3x7FjxzBkyBA4OTkBAPbv34+mTZvC0tLSRBH/KzExERYWFrC3L/4XIzAwED4+Pli/fn2Ro7LdunXD4cOHSx1TaGgotmzZgunTp6NOnTqK7QXlHtbW6i94GxsbpKaq38fe2dlZbYS5sJkzZyqNjkdFRcHHx6c04ZOZ0aWMwt6tDmq07o/k2PzXjJPnf+HWoqfG1SIMMbFO3zbKY7Jn6ImI+rT3qk6GpCJY/TvZXCLNH9xxaNhJ57+9rlvbAs80LwtWkoyMzCJXaiqOk5MTKlSooFefQP7kdYw+ovf5ptK0aV0cPqy87vNnn62Aq2tlfPHFh0rbXV3zJ8y5uVVBXJz6qiAF26pXL3qu1tKlPyMnJxeDBnXBgwf5A22PH+evgZ2UlIoHD56ienUXWFlZYsOGXahWrZIiQS7g798BgYEbERl5lUmyJnfu3EFKSgqqVq2qcX9CQv4F9/HxwXvvvYe5c+di+fLl8PX1RZ8+fTBkyBCNiZ856dChA/z8/BAUFIRPPlH/lAfkj+ZqGuXVxcmTJzFq1Ch069YNCxcuVNpXMIJduDyiwMuXLzWOcIuiWGQRPpCfcBe+9iV9WKDyR9syCk0TutLj78Ghel2NE7pKatfZqwUen/m12El1upZ4FCiPyZ6+z9WQ7Rk6Bip/pJY2kOqYJF+8dEnv/h49egQPD48Svz0tTBAEREdHw91d+/V9XxXOzo7o3PktlW0OcHOrora9QLNmdXHyZBTkcrnS5L1z527Azs4GdesWfR0fPYpHUlIqGjYcqLZv0aKtWLRoK65cCUGzZvXw7NkLyDQUl+fm5n+rp7oUrTGUy4l7crkcVatWxeHDhzU+5s2bByD/hR8eHo4zZ87g008/xZMnTzBy5Eh4e3ubrBa2cuXKyMvL06qWOCAgAPHx8diwQfO6g1lZWYiPj9fqoUl0dDT8/f3RqFEjhIeHK03OA/4tsygouygsLi4O1atXV9uelJRUYv0zvdq0uSudPhO6imu3Ruv3ELVlcomT6opqAxILuDTywd9/hmq86195vB21oe8OqE97vEMhlTV3d3f07NkTUql29cVSqRS9evV6LRNkffXv3wnPniVi164Ixbbnz5MRFnYEvXq1V6pXvnfvMe7de6z4edKkwdi9e6nSY8OGWQCA4cN7Yffupahd+z8AgLp13fHsWSKOH1e+P8T27flL7/73v/VhbOVyJNnLywtHjhxB27Zti6zXLeztt9/G22+/jYULFyI0NBQffPABduzYgdGjRxc76mkMBUumxcbGljhB0MfHB76+vli8eDHmzJmjtn/nzp161yTfu3cP3bt3R9WqVXHgwAGNI7rNmjUDkL9CSKtW/47sPX36FI8fP8aYMWPUzomNjUXTpk21ioleXSXdlU7fCV2a2nX2aoGoLZO1nlSn2oYsNxv/3DiOhKtHi1yiTNf6aXNh6LsD6tMe71BIZe3rr7/GwYMHIQhCsSPKBStazZ49uwyjK//69++Et99ujBEj5iEmJvb/77gXBplMjrlzxyod26nTOADAgwf7AADNm9dH8+bKyW1B2UXDhp7o08dXsf3TTwdi69Z96NVrGiZOHIRatVxx4sRlbN9+CF26vIW33tJz2T4dlMskeeDAgVi7di3mz5+PRYsWKe3Ly8tDeno6nJyckJSUBCcnJ6VEuCDxKyghKLhhhz41TPpo3bo1gPzEU5tVNAIDA+Hr64uNGzeq7dO3Jjk+Ph5du3aFRCLBoUOHilzruWHDhqhfvz42btyIsWPHKj6Zr1u3DoIgKE3yA4CUlBTcu3cP48aN0zkmevUUN4muNDW+qu3qM6muoI2c9KT/X7Ui//ziEuzymuyVNJmxLNozdAxExWnZsiV27tyJQYMGQRRFyGTq7zNSqRSCIOCXX35By5YtTRBl+SWVSnHgwEp88cVKrFq1A1lZ2WjZ8k0EBweiXj0Pg/VTr54HLl3ahtmz1+Hnnw8gPj4R1au74PPPP1RLxo2lXCbJPj4+GDt2LL755htERUWha9eusLS0xJ07dxAWFoaVK1eif//++PHHH7F27Vr07dsXXl5eSEtLw6ZNm+Do6Ih3330XQH7d7ZtvvomdO3eibt26qFSpEho1aoRGjYr+hLJt2zY8fPgQmZmZAIA///xTsWLGhx9+iFq1ahV5rqenJxo1aoQjR44o1msu6bn6+PjgxIkTavv0rUnu3r077t+/j+nTp+PUqVM4deqUYl+1atXQpUsXxc9LliyBv78/unbtisGDB+P69etYs2YNRo8erXbHwyNHjkAURfTu3VvnmOj1Ysga39Ik3LqOaDPZIyof+vXrh8jISMyfPx/79++HKIqQSCSQy+UQBAE9evTA7NmzmSBrcPy4+qCcKmdnR2ze/DU2b/662OMKRpCL4+FRHaJ4UeO+evU8EBZW8lJxxlIuk2QAWL9+Pby9vbFhwwbMmjULFhYW8PDwwNChQ9G2bVsA+Qnm+fPnsWPHDjx79gwVK1ZEq1atEBISorQE2+bNmzFx4kRMnToVOTk5CAgIKDZJ3rJli1LSeuzYMRw7dgwA0K5du2KTZAAYOXIk5syZg6ysLK3KRQIDA+Hn51ficdqKjo4GAAQFBant8/HxUUqSe/bsiV27dmHu3LmYOHEiXFxcMGvWLI3lH2FhYWjXrh28vF6Puw6R/gw5oas0CbeuCXZ5u+Me0eusZcuW2Lt3Lx49eoSIiAikpqbC0dERHTt2ZA0yaUUQdZkCSgaRkpICT09PBAUFYdSoUaYOxyDi4+NRu3Zt7NixQ6eR5MuXL8Pb2xuXLl0q9gYr9Op5ced8kTW+utyu+N+SCQ0Jt9QSLScFF1kS8fjMr3gQsVXzHbIEAR4dRyhGjjWtxqFPvESvg9zcXFhZWSEnJ8csll99Pei/KsjrwVvnM8rl6hblXcWKFTF9+nQsWbIEcrmW9840cytWrEDjxo1ZakFaK6jx9eg4Aq7/7Q6PjiPQclKwzglnaVZQ0HbVCt5emYjo9cORZDIpjiSToaiVQmg5qU6bEW1dRpyJiCPJpsGR5OLpPpJcbmuSiYgK03dSnTarVpTHO+4REVHpMEkmIrMVtWWySUoZnp7fAyA/8W42amXxkwPlMiRcP4bEv86WSWwFMRERkXExSSYis5WTnoSctERTh1HsahwAIMvOhCw7s4yjIiIiY2KSTERmy9TLqxX0r3rHvcK1yRa2DpBYWBXVhNFiIiIi42KSTERmy5zKCsrrHfeIiEg/TJKJiLTEO+4REb0+uE4yEREREZEKJslERERERCqYJBMRERERqWCSTERERESkgkkyEREREZEKJslERERERCqYJBMRERERqWCSTERERESkgkkyEREREZEKJslERERERCqYJBMRERERqWCSTERERESkgkkyEREREZEKJslERERERCqYJBMRERERqbAwdQBERESku1xZLmRymanD0Cg3N9fUIRCVGpNkIiKiciQlOwW77uzC8QfHIYEEraq0QovKLSAIgqlDU8jLzQOQn8hbWlqaOBoi/TBJJiIiKgdEUcTxv4/jp5ifkJGbgazsLAiCgN+f/o747Hj0r90fEsE8qigzxUwAMNuRbiJtMEkmIiIyczmyHKyPXo/TT08rbRcEAYIgIPpFNCwkFhjoNdAsEuVcKcstqPxjkkxERGTGXua9xLfnv8XNFzeLPe7S80uoaFUR77i/U0aREb3aTP9xk4iIiDTKledi2cVlJSbIBSKeRuB8wnkjR0X0emCSTEREZIbkohwbojfg6vOrOp336/1fcSfljpGiInp9MEkmIiIyQ9tvbcfJJyd1Pk8OObb9tQ3/ZP1jhKiIXh9MkomIiMzMnrt7sPfeXr3Pz5JlYevtrcjKyzJgVESvFybJREREZuTYo2MIvRVa6nb+efkPQu+GQi7KDRAV0euHSTIREZGZuJl4E5uubTJYe7eSb+HY02MGa4/odcIkmYiIyAykZKdg5eWVkImGvQHHob8P4V7qPYO2SfQ6YJJMRERkYqIoYuPVjUjKTjJ82xCx4+4O1icT6YhJMhERkYmdfHISF59dNFr7yTnJ+N+D/xmtfaJXEZNkIiIiE3rx8gWCbwQbvZ/Lzy/jaqJuay4Tvc6YJBMREZmIKIrYEL0BGbkZZdJf+P1wJGcnl0lfROUdk2QiIiITORh7EFH/RJVZf1myLITcDTH45ECiVxGTZCIiIhO4/eI2Qm6GlHm/D9IeYN/DfWXeL1F5wySZiIiojP2T+Q+WXVyGPDHPJP2fjj+NyPhIk/RNVF4wSSYiIipDGbkZWHxhMVJyUkwax+4Hu3Et8ZpJYyAyZ0ySiYiIykiuLBfLLi7D32l/mzoUAMDPd3/GX8l/mToMIrPEJJmIiKgMyEU51kStwY3EG6YORUEuyvHjXz/iYdpDU4dCZHaYJBORRknpSQg/E47vD36P8DPhSEo3/J3AiF4XclGODdEbcDburKlDUZMjz8EPt39AQlaCqUMhMisWpg6AiMzPub/OYdGvi5Anz4NEkEAuyrHt+DbM6j8Lb9V5yyh9JqUn4ei1o3iW/AzVnKqhU+NOcLZ3NkpfRGUpT56HtVFrcfrpaVOHUqTMvExsvrUZnzb8FI5WjqYOh8gsMEkmIiVJ6UlY9Osi5MpyAUCxnmquLBeLwhcheGJwicmrrglvaZNyJthkrjJzM7H80nJcfW7+d7pLyk7Clltb8Mmbn8DWwtbU4RCZHMstiEjJ0WtHkSfXvCxVnjwPEdciij3/3F/nMHz1cGyN2Irfr/yOrRFbMXz1cJy7c07j8YWTclEUIZPLIIqiIikvqcxD1/6IykpiViICIwPLRYJc4GnmU2y5tQXZsmxTh0JmJDk5DWPGLISLS2dUqNAOfn5jcfnyLa3OHT48EILQQu1Rv/57xZ4XEnIQgtAC9vbtDfEU9MIk2USCgoJQv359yOVyU4diEDNmzMBbbxnna3gqW8+Sn0EiaH5rkAgSxCfHF3muPglvaZLy0ibYRMbyIOUBZp+ebZQJcRkJGYg9EovYA7F4FPEImf9kGrT9h+kPsenmJmTmGbZdKp/kcjl69JiM0NDf8emnAxEUNAkJCUnw9R2LO3ceadWGtbUVtm2bp/RYsmRykcenp2di+vRVqFDBtN9omGWSLAiCVo/jx4+Xuq/MzEwEBgZq3datW7cwffp0NGvWDA4ODnBzc0OPHj1w8eJFrftMTU3F4sWL8eWXX0Ii+fefoOB5LVu2TO2c4OBgCIKgUz9F2bVrFwYNGgRPT0/Y2dmhXr16+Oyzz5CcnKzx+L1796J58+awsbGBu7s7AgICkJennNRMmTIF0dHR2Lt3b6njI9Oq5lQNclHzhze5KIerk2uR5+qT8JYmKS/tqDeRMVyMv4iAyAC8ePnCoO0m/pWIk/NO4rfRv+Hiqou4EXwDUd9H4fAnh3F20Vkk3THch8KH6Q+x9sZaJL5MNFibVD6Fhx9FZORVBAcHICBgDCZMGIjjxzdAKpUgIGCDVm1YWEgxdOi7So9evToUefyCBVvg4GCHPn18DfQs9GOWNcnbtm1T+vmnn37C4cOH1bY3aNCg1H1lZmZi7ty5AABfX98Sj9+8eTO2bNmC9957D+PHj0dKSgo2bNiAt99+G7///js6d+5cYhs//PAD8vLy8P7772vcv2TJEowbNw52dnY6PRdtjRkzBtWrV8fQoUPh7u6Oa9euYc2aNThw4AAuX74MW9t/P7kdPHgQffr0ga+vL1avXo1r165hwYIFSEhIwLp16xTHubq6onfv3li6dCn8/f2NEjeVjU6NO2Hb8W2KmuTCLCQW6NSkU5HnFiS8BXXMhRWV8JYmKdenPyJjyZXl4pe/fsG+e/sgQjRo248jH+NM0BlABBRNF/pvwuUEJFxJQIvPWqD629UN0uezrGdYeW0l3vN8D00rNzVIm1T+hIcfRbVqldGvX0fFNhcXZwwc2Bk//3wQ2dk5sLa2KrEdmUyGjIwsODraF3vcnTuPsHx5KHbvXoJffjlS6vhLwyyT5KFDhyr9fPbsWRw+fFhtuym8//77CAwMhL39v//II0eORIMGDRAYGKhVkrx161b4+/vDxsZGbV+zZs0QFRWF9evXY9q0aQaNvUB4eLjaBwJvb28MGzYMISEhGD16tGL7559/jiZNmuCPP/6AhUX+y8XR0RGLFi3C5MmTUb9+fcWxAwcOxIABA3D//n14enoaPG5jTc4qTbtlPWHMmP0VbtunkQ+OXz8OmVymmEhnIbHArP6z4FTBqchYtEl4Vc9r4dmixKS8qOddmgS7uOdvqGvLCYWvhzx5HiKfRiLsrzAkZBp+GbXEvxJxJugMRFnRibcoz993cdlFtF/UHs51DPM6y5Jl4ec7PyPqeRR61eqFSjaVDNIulR9XrtxG8+b1lL75BoBWrRpi48bd+OuvR2jc+I1i28jMfAlHRx9kZr6Es7Mj3n+/GxYvngh7e/XBwClTlsHPrwXefbcdk2R9yeVyrFq1Cps2bcK9e/dQsWJF9OnTB99++y2cnf99c7h48SK++uorXLp0CRkZGXB1dYWfnx9++OEHPHjwALVr1wYAzJ07VzGiHBAQgMDAQI39ent7q22rXLky2rdvr1XJRmxsLK5evVpkAty2bVtUqlQJQUFBGDdunNKorqFoGjHv27cvhg0bhps3byq2xcTEICYmBt9//70iQQaA8ePHY+HChQgPD8fs2bMV2ws+IOzZswdTp041aMzGWpKsNO2W9TJpxuxPU9tSQYrOTTrDysIKrk6u6NSkkyJBLiqWST0nwUJiUWTC61TBCcNXD1c7r3+b/giPDFfaXpCU335yu8jnXZpRb2NfW1Mso0dl63HaY5x6cgrHHx9H0kvj1b/H7IyB1gPTIvBX+F94a6ZhX2PXk64jJjkGrVxaob1be1S1rWrQ9sl8xcU9R4cO/1Xb7uZWBQDw9Ok/xSbJbm5VMH36R2jePH8e1u+/n8HatWGIjv4Lx49vUMovfvvtFP744yyio7cb/onoodwmyWPHjkVwcDBGjBiBSZMmITY2FmvWrMGVK1dw+vRpWFpaIiEhAV27doWLiwtmzJgBJycnPHjwALt27QIAuLi4YN26dRg3bhz69u2Lfv36AQCaNGmiczzx8fGoUqVKicdFRkYCAJo3b17kMYGBgejQoQPWrVtX7GhydnY20tLStIqvpNji4+PVjrty5QoAoEWLFkrHVq9eHTVq1FDsL1CxYkV4eXnh9OnTBk2SDbEkmaHbNVZMxohV37bzxDwcv35cre3iYlm1fxUm9ZiEVb+tUkt4J/WchFX7V2k8LzwyHCtHrsSl+5cQnxyvSMpFUcTw1cOLfd6z+s/CovBFGhPsgqS+rK9tWb8+qGxk5mbidtJtXH9+HVcSruBJ+hOj95mRkIG4C3FaJ8miXET8xXhk/pMJOxfDluzJRTnOJpzF2YSzqFGhBupWrAt3e3e42bnBydqpyLkFVL5lZWVrLKewsbFW7C/ON998qvTz4MHdULeuO776ai3Cw49i8OBuAICcnFxMnfodPvnkPbz5puG/jdZHuUyST506hc2bNyMkJARDhgxRbPfz80P37t0RFhaGIUOGIDIyEklJSfjjjz+UEr0FCxYAACpUqID+/ftj3LhxaNKkid7lHCdPnsSZM2eURlWLcutW/pIpBSPYmrRv3x5+fn6K2uSiRpO3b9+OESNGaBWjKBb/Drt48WJIpVL0799fsS0uLg4A4Obmpna8m5sbnj59qrbd09MTMTExRfaTnZ2N7Ox/f6HS09NLjF2byVnvtS5+KRlDt2usmIwRq6HbLun45IxkBE8MRsS1CKWE98jVI8Wed+n+JbXnEH4mXKvYNPWnTYKszfPR59qW9euD9CeKIvLEPOTJ8yCTyyATZXiZ9xLpuelIzEpEfGY8HqY+RGxKLJ6mPy2y1vjw1MN4mfTS4PHlvczTfhS5gAgcm3YMFtaG+RNv7WwNnyAfpW1/p/+Nv9P/VvxsKbGEq60r3Ozc4GrnCkch/4Ykmr7lIfOUk5OLFy9SlLa5uDjD1tYa2dk5ase/fJn/t9zW1lrnvqZOHYKvv16PI0fOK5Lk5ctD8Px5MubOHatH9MZRLpPksLAwVKxYEV26dMHz588V2729vWFvb49jx45hyJAhcHJyAgDs378fTZs2haWlpcFjSUhIwJAhQ1C7dm1Mnz69xOMTExNhYWGhVNOsSWBgIHx8fLB+/foiR2W7deuGw4cP6xV3YaGhodiyZQumT5+OOnXqKLZnZWUBAKyt1X8BbGxskJqaqrbd2dlZbYS5sG+++UZR1lLAx8dHYyJewFiTs0rTbllPGDNmf7q2rc3xzvbOakmgPs9B23M09actY1xbTig0H7myXIgQIUAAhPxtclGOPHkecmW5yJHnaKxrr2BZARUsK8Dd0R2tXFuV2E/zzOZISjSfJQfzMvKQl6H5g5qu7C3tMb7eeN36z83vOzknGfIsOSwllpAIEgjIX8XJ1sKWI89mJjIyGn5+nyhti43dCze3KoiLe652fMG26tVddO7L1tYGlStXxIsX+XlESko6Fiz4AePH90dqagZSUzMA5C8FJ4oiHjx4Cjs7G1StWrY18eUySb5z5w5SUlJQtarmmqiEhPyJEz4+Pnjvvfcwd+5cLF++HL6+vujTpw+GDBmiMfHTVUZGBnr27Im0tDScOnWqxMRXFx06dICfnx+CgoLwySefaDzGzc2t2ORSGydPnsSoUaPQrVs3LFy4UGlfwQh24ZHfAi9fvtQ4wi2KIgRBKLK/mTNnqpWQWFtbF/vvYcjJWYZqV58JaqWZtGXM/nS9DvpeN33OM9a/vbH7KIu4STuWUs2DI9ZSa8CA4ybV3aobJenLyMgocnnO4jg5OcGugmHKLVyrucKnrk/JBxaSm5s/glzTsaZRBqjI8Jo2rYvDh79X2ubqWhnNmtXFyZNRkMvlSpP3zp27ATs7G9St665zX2lpGXj+PBkuLk4AgKSkVKSnZyIo6CcEBf2kdnzt2v7o3dsH//uf+hK5xlQuk2S5XI6qVasiJCRE434Xl/xPNYIgIDw8HGfPnsW+fftw6NAhjBw5EsuWLcPZs2dLldTm5OSgX79+uHr1Kg4dOoRGjRppdV7lypWRl5eHtLQ0ODg4FHtsQEAAfH19sWHDBsWoeGFZWVlISUlRP1EDV1f1P8rR0dHw9/dHo0aNEB4erlQ8D/xbZhEXF4eaNWsq7YuLi0OrVuqjK0lJScXWP5eUEGtS3OQsAQIePX+E8DPhOiehpZn0VdK5RU1Q03fSljH7K+n6ZuZkIik9SXFt9b1u+pxnqIl5xTFGH2URN5kXQ6xhr8mjR4/g4eFRYslcYYIgIDo6Gu7uuicvhiKF1GR9k36cnR3RubP634v+/TshPPwodu2KQP/++RP0nz9PRljYEfTq1V6pXvnevccAAC+vGgDySzJyc/Pg4FBBqc358zdDFEV0794GAFC1aiXs3r1Ure9Vq3bgzJlr2L59oWKiYFkql991eHl5ITExEW3btkXnzp3VHk2bKq/n+Pbbb2PhwoW4ePEiQkJCcOPGDezYsQMAih31LIpcLsdHH32Eo0ePIjQ0FD4+2n/CLlgyLTY2tsRjfXx84Ovri8WLFytKHwrbuXOnYjS5pIeqe/fuoXv37qhatSoOHDig8QNDs2bNAKi/+T99+hSPHz9W7C8sNjbWIOtXF+Zs74xZ/WfBUmoJQRAglfz75iuKIiKuReh1K2JN7QqCAEupZYmTvoo7d1LP/IlrhrwLnDH7U21bdTRsx6kdStdW3+tW1HkWEgv4NPJByJ8hCD8TrhRvaf6NtGWMPsoibno9uLu7o2fPnpBKtUs6pVIpevXqZdIEmV4t/ft3wttvN8aIEfMwb94mrF0bBl/fMZDJ5Gr1w506jUOnTuMUP8fHJ8LdvSfGj/8Wq1btwKpVO9Cjx2QsWbIN3bu3Qe/e+fmTnZ0N+vTxVXu4u7tCKpWgTx9fvPWWdoORhiSIunw8NZFPP/0U33//veKT9IkTJ+Dr64uZM2di0aJFSsfm5eUhPT0dTk5OSEpKgpOTk1IiHBMTg4YNG2LNmjWYMGECsrKyYGdnh8mTJ2PFihVaxTNhwgSsXbsWGzZswJgxY3R6Lvfv34eXlxe2bNmCkSNHKu0TBAETJkzAmjVrFNsKnmvB+skXLlxQTEKMi4vDjRs3tOq38PrN8fHxaNu2LV6+fInTp0/Dw8OjyPMaNGgAa2trXLp0SfEm/fXXX2PhwoW4ceOGUkKckpICZ2dnLF261ChrPCelJyHiWgQe/vMQEdciNH6dbSm11HnlgIJ29Zn0pencI1ePYGvEVo0jP4IgYGTHkXrXzxqzv6T0JPx28TfsOL1Dq2ur73UrfF5Obg6O3TimcT3mwiPgpfk30pYx+iiLuOnVd+HCBbRp0wYymazYEWVBECCVShEZGYmWLVuWYYTqcnNzYWVlhZycHJZblJlLRms5KSkVX3yxEv/733FkZWWjZcs3sXTpFLRo8abScR4evQAADx7sAwAkJ6dh4sQgnD17HU+f/gOZTI433qiBDz54B59//iEsLYsvaBg+PBDh4UeRnn7SAM9CfQnfkpTLJBkAPvnkE2zYsAHvvPMOunbtCktLS9y5cwdhYWFYuXIl+vfvjxUrVmDt2rXo27cvvLy8kJaWhk2bNiEuLg5RUVGKFSYaNmyIFy9e4Ouvv0alSpXQqFGjIssnVqxYgalTp6J169YYP159IkPfvn1RoUIFDWf+q3HjxmjcuDFCQ0OVtmtKkoH8dY1PnDgBAEpJsr6aNWuG6OhoTJ8+HY0bN1baV61aNXTp0kXx8/79++Hv7w8/Pz8MHjwY169fx5o1azBq1Chs3LhR6dxff/0V/fv3x927d+Hl5VWqGIsTfibcaEmoIXx/8Hv8fuV3yOTqk7akEim6/7c7JrwzwSz7K8trm5SepLS8W2H6fNghepXt2rULgwYNyv+2SKbhd12a/23FL7/8gr59+5ogQmVMkk3BeEnyq0H3JLlc1iQDwPr16+Ht7Y0NGzZg1qxZsLCwgIeHB4YOHYq2bdsCyC9XOH/+PHbs2IFnz56hYsWKaNWqFUJCQpSWYNu8eTMmTpyIqVOnIicnBwEBAUUmyVFRUQCAM2fO4MyZM2r7Y2NjS0ySR44ciTlz5iArK0urm4UEBgbCz8+vxOO0FR0dDQAICgpS2+fj46OUJPfs2RO7du3C3LlzMXHiRLi4uGDWrFmYM2eO2rlhYWFo166dURNkwPxXDijrSVuG7K8sry2XSSPSXr9+/RAZGYn58+dj//79EEUREokEcrkcgiCgR48emD17tslHkIleJeViJPlVk5KSAk9PTwQFBWHUqFGmDscg4uPjUbt2bezYsQO9e/c2al/mPpJc0gjpj5N+NOhX7obsryyvbVmPuBO9Kh49eoSIiAikpqbC0dERHTt2NLsaZI4kmwJHkoun+0hyuZy4V95VrFgR06dPx5IlSyCXax4BLG9WrFiBxo0bGz1BBvJXDrCQaP4SxBxWDijrSVuG7E+ba5uUnoTwM+H4/uD3ahPtdMFl0oj04+7ujuHDh2PSpEkYPny42SXIRK8KjiRTuXTuzrkib0WszxJrxlDWk7YM1V9x1xYisOhXw1z3sh5xJ6Kyw5FkU+BIcvFe0Yl7RJpw5QDj0XRtRVE0+ES78vBhh4h0xyTZFJgkF49JMpFJTdoySe/yA1NytnfGqlGr1LYXfj5ZOVnIzM4ssg07azvYWpU8EVWVXJQjOzdbcTcnG0sbvdYvL6yo50NEZYNJsikwSS7ea7S6BZE5SkpPQmJaoqnDMBhdnk9mdmaxSbS2DNEGERFRaTFJJjKg8rqub1FxF95urJFkYyiv/w5ERGQ+WG5BRFrhRDsi0hbLLUyB5RbF4xJwRGQkZb20HRERkSlxJJmIdMJVRYioJBxJNgWOJBePq1sQERGRiTFJNgUmycVjuQURERERUakxSSYiIiIiUsEkmYiIiIhIBZNkIiIiIiIVTJKJiIiIiFQwSSYiIiIiUsEkmYiIiIhIBZNkIiIiIiIVTJKJiIiIiFQwSSYiIiIiUsEkmYiIiIhIBZNkIiIiIiIVTJKJiIiIiFRYmDoAori4OMTFxZk6DCIyM25ubnBzcytyP987zFdeXh4A4MqVK7CwYKqhj5Je/1QGRCITCwgIEAHwwQcffCg9AgIC+N7Bx2v7KOn1T8YniKIogsiEihoNSk9Ph4+PD06cOAF7e3sTRGZ+eE2U8Xooe9Wuh7mNJL9q19eYeK20V9S14kiy6TFJJrOVmpqKihUrIiUlBY6OjqYOxyzwmijj9VDG62FcvL7a47XSHq+V+eLEPSIiIiIiFUySiYiIiIhUMEkms2VtbY2AgABYW1ubOhSzwWuijNdDGa+HcfH6ao/XSnu8VuaLNclERERERCo4kkxEREREpIJJMhERERGRCibJREREREQqmCQTEREREalgkkxm6c8//0SvXr1QvXp1CIKA//3vf6YOyWS++eYbtGzZEg4ODqhatSr69OmD27dvmzosk1q3bh2aNGkCR0dHODo6onXr1jh48KCpwzIL3377LQRBwJQpU0wdyitv4cKFaNOmDezs7ODk5GTqcMzK999/Dw8PD9jY2OCtt97C+fPnTR2S2eHfOfPHJJnMUkZGBpo2bYrvv//e1KGY3IkTJzBhwgScPXsWhw8fRm5uLrp27YqMjAxTh2YyNWrUwLfffotLly7h4sWL6NixI3r37o0bN26YOjSTunDhAjZs2IAmTZqYOpTXQk5ODgYMGIBx48aZOhSzsnPnTkybNg0BAQG4fPkymjZtim7duiEhIcHUoZkV/p0zf1wCjsyeIAjYvXs3+vTpY+pQzMI///yDqlWr4sSJE+jQoYOpwzEblSpVwpIlSzBq1ChTh2IS6enpaN68OdauXYsFCxagWbNmWLFihanDei0EBwdjypQpSE5ONnUoZuGtt95Cy5YtsWbNGgCAXC5HzZo1MXHiRMyYMcPE0Zkn/p0zTxxJJipnUlJSAOQnhQTIZDLs2LEDGRkZaN26tanDMZkJEyagR48e6Ny5s6lDoddYTk4OLl26pPQ6lEgk6Ny5M86cOWPCyIh0Z2HqAIhIe3K5HFOmTEHbtm3RqFEjU4djUteuXUPr1q3x8uVL2NvbY/fu3XjzzTdNHZZJ7NixA5cvX8aFCxdMHQq95p4/fw6ZTIZq1aopba9WrRpu3bploqiI9MORZKJyZMKECbh+/Tp27Nhh6lBMrl69eoiKisK5c+cwbtw4DBs2DDExMaYOq8z9/fffmDx5MkJCQmBjY2PqcMq9GTNmQBCEYh9M9oheDxxJJionPv30U+zfvx9//vknatSoYepwTM7KygpvvPEGAMDb2xsXLlzAypUrsWHDBhNHVrYuXbqEhIQENG/eXLFNJpPhzz//xJo1a5CdnQ2pVGrCCMuXzz77DMOHDy/2GE9Pz7IJphyqUqUKpFIpnj17prT92bNncHV1NVFUVNbWrl2LCRMmoFWrVjh37pypw9Ebk2QiMyeKIiZOnIjdu3fj+PHjqF27tqlDMktyuRzZ2dmmDqPMderUCdeuXVPaNmLECNSvXx9ffvklE2Qdubi4wMXFxdRhlFtWVlbw9vbG0aNHFZPQ5HI5jh49ik8//dS0wVGZCQkJgZWVFc6fP4+7d+8qBjTKGybJZJbS09Nx9+5dxc+xsbGIiopCpUqV4O7ubsLIyt6ECRMQGhqKPXv2wMHBAfHx8QCAihUrwtbW1sTRmcbMmTPxzjvvwN3dHWlpaQgNDcXx48dx6NAhU4dW5hwcHNTq0ytUqIDKlSu/9nXrxvbo0SO8ePECjx49gkwmQ1RUFADgjTfegL29vWmDM6Fp06Zh2LBhaNGiBVq1aoUVK1YgIyMDI0aMMHVoZuVV/TsXGxuLyMhIzJs3D/Pnz0dISAgCAgJMHZZ+RCIzdOzYMRGA2mPYsGGmDq3MaboOAMStW7eaOjSTGTlypFirVi3RyspKdHFxETt16iT+8ccfpg7LbPj4+IiTJ082dRivvGHDhmn83Tx27JipQzO51atXi+7u7qKVlZXYqlUr8ezZs6YOyey8qn/n5s+fL0qlUjE+Pl589913xTp16pg6JL1xnWQiIiIiMogGDRrAzc0NERER+PHHHzF8+HCcP38eLVu2NHVoOuPqFkRERERUapcuXcKtW7cwaNAgAECfPn1gZWWFkJAQE0emHybJRERERFRqISEhsLCwwHvvvQcgf+5M9+7dsWPHDshkMhNHpzsmyURERERUKgV3P+3YsSOqVKmi2D5o0CA8e/YMR48eNWF0+mGSTERERESlEhERgbi4OEWpRQF/f3/Y2tqWy5ILJslEREREVCohISGwtLRE3759lbbb29vj3Xffxe7du5GVlWWi6PTDJJmIiIiI9JaVlYVdu3ahS5cucHZ2Vts/cOBApKWlYe/evSaITn+8mQgRERER6W3v3r1IS0sDAHz77bdq+zMzMwHkjzarlmOYM66TTERERER68/f3x759+0o8ztLSEnFxcahcuXIZRFV6LLcgIpN78OABBEFAcHCwqUMhokIEQUBgYKDStgsXLqBNmzaoUKECBEFQ3I77999/R7NmzWBjYwNBEJCcnFzm8ZJp7N27F6IolvjIyckpNwkywCSZiPTg7+8POzs7xddrmnzwwQewsrJCYmJiGUZGZFjBwcEQBAGCIODUqVNq+0VRRM2aNSEIAnr27GmCCLXn4eGheC4SiQROTk5o3LgxxowZg3PnzmnVRm5uLgYMGIAXL15g+fLl2LZtG2rVqoXExEQMHDgQtra2+P7777Ft2zZUqFDByM+IyLhYk0xEOvvggw+wb98+7N69Gx999JHa/szMTOzZswfdu3cvV6MGREWxsbFBaGgo2rVrp7T9xIkTePz4MaytrU0UmW6aNWuGzz77DACQlpaGmzdvIiwsDJs2bcLUqVPx3XffKR2flZUFC4t/U4V79+7h4cOH2LRpE0aPHq3Y/vvvvyMtLQ3z589H586dy+bJEBkZR5KJSGf+/v5wcHBAaGioxv179uxBRkYGPvjggzKOjMg43n33XYSFhSEvL09pe2hoKLy9veHq6mqiyHTzn//8B0OHDsXQoUMxbtw4rFq1Cvfv30efPn2wfPlyrFu3Tul4GxsbpSQ5ISEBAODk5KR0XFHbSyMjI8NgbRHpg0kyEenM1tYW/fr1w9GjRxV/HAsLDQ2Fg4MD2rVrh88//xyNGzeGvb09HB0d8c477yA6OrrEPnx9feHr66u2ffjw4fDw8FDaJpfLsWLFCjRs2BA2NjaoVq0axo4di6SkJH2fIpGS999/H4mJiTh8+LBiW05ODsLDwzFkyBCN52j7utyzZw969OiB6tWrw9raGl5eXpg/f77abXx9fX3RqFEjxMTEwM/PD3Z2dvjPf/6DoKCgUj03W1tbbNu2DZUqVcLChQtReD5/4Zrk4cOHw8fHBwAwYMAACIKg+D0dNmwYAKBly5YQBAHDhw9XtHHu3Dl0794dFStWhJ2dHXx8fHD69GmlGAIDAyEIAmJiYjBkyBA4Ozsrjdr//PPP8Pb2hq2tLSpVqoTBgwfj77//1vv6vHz5EoGBgahbty5sbGzg5uaGfv364d69e4pj+L5CTJKJSC8ffPAB8vLy8Msvvyhtf/HiBQ4dOoS+ffsiLi4O//vf/9CzZ0989913+OKLL3Dt2jX4+Pjg6dOnBotl7Nix+OKLL9C2bVusXLkSI0aMQEhICLp164bc3FyD9UOvLw8PD7Ru3Rrbt29XbDt48CBSUlIwePBgjedo+7oMDg6Gvb09pk2bhpUrV8Lb2xtz5szBjBkz1NpMSkpC9+7d0bRpUyxbtgz169fHl19+iYMHD5bq+dnb26Nv37548uQJYmJiinw+s2bNAgBMmjQJ27Ztw1dffYWvvvoKY8aMAQDMmzcP27Ztw9ixYwHk34WtQ4cOSE1NRUBAABYtWoTk5GR07NgR58+fV+tjwIAByMzMxKJFi/Dxxx8DABYuXIiPPvoIderUwXfffYcpU6bg6NGj6NChg9rkQG2uj0wmQ8+ePTF37lx4e3tj2bJlmDx5MlJSUnD9+nWl58v3ldecSESkh7y8PNHNzU1s3bq10vb169eLAMRDhw6JL1++FGUymdL+2NhY0draWpw3b57SNgDi1q1bFdt8fHxEHx8ftX6HDRsm1qpVS/HzyZMnRQBiSEiI0nG///67xu1Euti6dasIQLxw4YK4Zs0a0cHBQczMzBRFURQHDBgg+vn5iaIoirVq1RJ79OihOE+X12VBe4WNHTtWtLOzE1++fKnY5uPjIwIQf/rpJ8W27Oxs0dXVVXzvvfdKfC6qMapavny5CEDcs2ePYhsAMSAgQPHzsWPHRABiWFiY0rmFr1MBuVwu1qlTR+zWrZsol8uVnm/t2rXFLl26KLYFBASIAMT3339fqd0HDx6IUqlUXLhwodL2a9euiRYWFkrbtb0+P/zwgwhA/O6779SuQUGcfF8hURRFjiQTkV6kUikGDx6MM2fO4MGDB4rtoaGhqFatGjp16gRra2tIJPlvMzKZDImJibC3t0e9evVw+fJlg8QRFhaGihUrokuXLnj+/Lni4e3tDXt7exw7dswg/RANHDgQWVlZ2L9/P9LS0rB///4iSy10eV3a2toq/j8tLQ3Pnz9H+/btkZmZiVu3bim1a29vj6FDhyp+trKyQqtWrXD//v1SPz97e3tFDIYQFRWFO3fuYMiQIUhMTFRcg4yMDHTq1Al//vkn5HK50jmffPKJ0s+7du2CXC7HwIEDla6jq6sr6tSpo/b7rc31+fXXX1GlShVMnDhRLWZBEADwfYXycXULItLbBx98gOXLlyM0NBSzZs3C48ePcfLkSUyaNAlSqRRyuRwrV67E2rVrERsbq1RjaahVL+7cuYOUlBRUrVpV435NNdNE+nBxcUHnzp0RGhqKzMxMyGQy9O/fX+Oxurwub9y4gdmzZyMiIgKpqalKx6WkpCj9XKNGDUUiV8DZ2RlXr17V5ykpSU9PBwA4ODiUui0g/xoAUNQra5KSkqJ0G+PatWurtSGKIurUqaPxfEtLS6Wftbk+9+7dQ7169ZQmJGqKne8rxCSZiPTm7e2N+vXrY/v27Zg1axa2b98OURQVq1osWrQIX3/9NUaOHIn58+ejUqVKkEgkmDJlitoIkipBEJQmEBVQncwkl8tRtWpVhISEaGzHxcVFz2dHpG7IkCH4+OOPER8fj3feeafI1Ry0fV0mJyfDx8cHjo6OmDdvHry8vGBjY4PLly/jyy+/VPs9kUqlGtvT9Luiq4J63DfeeKPUbQFQxL5kyRI0a9ZM4zEFo9cFCo+qF7QhCAIOHjyo8bmrnm+o68P3FQKYJBNRKX3wwQf4+uuvcfXqVYSGhqJOnTpo2bIlACA8PBx+fn7YsmWL0jnJycmoUqVKse06Oztr/Ar54cOHSj97eXnhyJEjaNu2rdofWCJD69u3L8aOHYuzZ89i586dRR6n7evy+PHjSExMxK5du9ChQwfF9tjYWIPGXZL09HTs3r0bNWvWRIMGDQzSppeXFwDA0dFR77WTvby8IIoiateujbp16xosrnPnziE3N1dtJLrwMXxfIdYkE1GpFIwaz5kzB1FRUUprI0ulUrURnLCwMDx58qTEdr28vHDr1i38888/im3R0dFqS0cNHDgQMpkM8+fPV2sjLy+Pt8Ylg7K3t8e6desQGBiIXr16FXmctq/LgpHPwr8nOTk5WLt2rWEDL0ZWVhY+/PBDvHjxAl999ZVauYK+vL294eXlhaVLlypKOQor/LtdlH79+kEqlWLu3Llq7yWiKOp1R8/33nsPz58/x5o1a9T2FfTB9xUCOJJMRKVUu3ZttGnTBnv27AEApSS5Z8+emDdvHkaMGIE2bdrg2rVrCAkJgaenZ4ntjhw5Et999x26deuGUaNGISEhAevXr0fDhg2V6jZ9fHwwduxYfPPNN4iKikLXrl1haWmJO3fuICwsDCtXriyybpRIH8XV2BbQ9nXZpk0bODs7Y9iwYZg0aRIEQcC2bdsMUj6hyZMnT/Dzzz8DyB89jomJQVhYGOLj4/HZZ58plm4zBIlEgs2bN+Odd95Bw4YNMWLECPznP//BkydPcOzYMTg6OmLfvn3FtuHl5YUFCxZg5syZePDgAfr06QMHBwfExsZi9+7dGDNmDD7//HOd4vroo4/w008/Ydq0aTh//jzat2+PjIwMHDlyBOPHj0fv3r35vkIAmCQTkQF88MEHiIyMRKtWrZTqGWfNmoWMjAyEhoZi586daN68OX777TeN67+qatCgAX766SfMmTMH06ZNw5tvvolt27YhNDQUx48fVzp2/fr18Pb2xoYNGzBr1ixYWFjAw8MDQ4cORdu2bQ39dIm0os3rsnLlyti/fz8+++wzzJ49G87Ozhg6dCg6deqEbt26GTymqKgofPjhhxAEAQ4ODqhZsyZ69eqF0aNHo1WrVgbvz9fXF2fOnMH8+fOxZs0apKenw9XVFW+99ZbWCfmMGTNQt25dLF++HHPnzgUA1KxZE127doW/v7/OMUmlUhw4cAALFy5EaGgofv31V1SuXBnt2rVD48aNFcfxfYUE0VgfV4mIiIiIyinWJBMRERERqWCSTERERESkgkkyEREREZEKJslERERERCqYJBMRERERqWCSTERERESkgkkyEREREZEKJslERERERCqYJBMRERERqWCSTERERESkgkkyEREREZEKJslERERERCr+D9NKib2HFCf1AAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 700x250 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "two_groups_unpaired.mean_diff.plot(swarm_side='center', horizontal=True);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`swarm_side='right'`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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otKenJxo0aICjR49ixIgRRfbl4+MDHx8f/P7772r79K1J7ty5M+7fv4/p06fj9OnTOH36tGJf5cqV0aFDB8XPS5cuhb+/Pzp27IiBAwfixo0bWLNmDUaNGqX0DQEAHD16FKIoaj0DCpleSdTnFpaUCgLQrE7R3+YYciQ1v8RhXrDyTXBSiYC2TWsj5PBZs5s5whwTe6JX9e7dG1FRUZg/fz4OHDigNKIsCAK6du2KWbNmMUGmUkXnZan9/f1x7do1LF26FHv37sW6detgbW2NRo0aYfny5fjkk08Ux27evBmenp4IDg7Gnj174Obmhq+++goBAQF6Bevm5ob169dj8eLFGDlyJGQyGSIjIwtNkitXroyoqCh8+eWXWL16NV6+fIlGjRph//79eo1mA8CWLVuUktbIyEhERkYCAFq3bl1kCceIESMwZ84cZGZmalVHHRgYqNeHioJcvXoVQN7iMKp8fHyUkuRu3bph9+7dmDt3LiZMmABXV1fMnDlTY/lHeHg4WrduDS8vL4PFSsZVEl/jv5qUqiZ5olzE+G93FFnaYeiRVNUSh+ycXBy/dAu/XfjTLGeOKCixt5BK1GquiUylefPm2LdvHx49eoTffvsNn3zyCTZt2oSOHTuyBplKJUHUdRZwKrbk5GR4enoiKCgII0eONHU4BhEfH48aNWpg586dOo0kX758Gd7e3rh06VKhC6yQcfx0/CI2H/hDYx2hIAj4pHtrg830EBv3Lz5dFqYo7XiVpYW00NKOxNR0DJ63VeNIat65I/VOFItu23xmjkhMTS+y5prIHOTk5MDKygrZ2dmwtLQ0dTiviUumDsDM6f63rORW0SAFJycnTJ8+HUuXLtV5fklztXLlSjRs2JClFqVMh+Z1YSHV/Dbw6pRrhliV7sKfeTcHalLUDA0FzQJhIZUoSiT0ja80zRyRX7s8sW/b/6/BZoJMRGQsHEkmk+JIsumdvXlf49f4/f288VPkJY1f7+tTgvBdxHEcPHMDMg0fDKUSCbq0bICJfQufO/XVkdT8EolcuVis+AwRFxEp40iyKXAkuXAcSSYiHeXX537SvTW6tGyAT7q3xvdTP8RPkZeQkyvLu1tdLocoiopZL/QZsTVEXXH+SOpHnd7B8cu3kSOTFzs+zhxBRESaMEkmIrWv8S/8+cDgJQhFlXboMkODIUskDBkXERGVHUySiUiNMValM2RdsSHj02W1PiIien3oPAUcEZV9xipBMNTUa8aeEo4zRxAREUeSiUiNMUsQDFFXXFh82i5QUlBcnDmCiIgAJslEpMGrJQhA3pzJACCRCJjav51BEsji1BWrxveq/AVKzt68X+wYiYjo9cUkmYg0ere+J6b0bweJRIAo5k2zJorAtz8dM0gCWty64nfre+L7qQMhkSi3IQLFmoWDiIgIYJJMRAVITE3Hip+OKVbIk4tisaeBe5Uh6oqLs0AJERFRYZgkE5FGxl6JrrC6YqlE0Kru2RizcBAREQFMkomoAMZOQPPriqUS9bchUQRuP4ovsg0uBEJERMbCJJmINCqJBLR2tcqQaMjDZXK5ViUdXAiEiIiMhUkyEWlUEgnokQt/IleuORHXpqSDC4EQEZGxcDERItIoPwGdF/wLcmVyxWIfFlKJwRLQ/JIOmYYRa21LOrgQCBERGQOTZCIqkLETUEOVdOQvBEJERGQoTJKJqFDGTEA7NK+L4ENnkJMrU9vHmmIiIjIl1iQTkcmwppiIiMwVR5KJyKRYU0xEROaISTIRmRxriomIyNyw3IKIiIiISAWTZCIiIiIiFUySiYiIiIhUMEkmIiIiIlLBJJmIiIiISAWTZCIiIiIiFUySiYiIiIhUMEkmIiIiIlLBJJmIiIiISAWTZCIiIiIiFUySiYiIiIhUMEkmIiIiIlLBJJmIiIiISAWTZCIiIiIiFUySiYiIiIhUMEkmIiIiIlLBJJmIiIiISAWTZCIiIiIiFRamDoCIiIjKJlnOS0ggM3UYAABBIoVEamnqMKgUYZJMREREBiWX5QAAkh/ehKWlGaQaogipBHCo3ggSC2tTR0OlhBm8comIiKgsEeV5o8cSCyksrG1NGIgIi9hjsPzrAITMJAi2jkD194B3PgWc3jBdXFQqMEkmIiIio5BILSGxsDJN56Iclpe3wOLBSQAiRACQ5QCxJ4EnV4Duq4AKXqaJjUoF3rhHREREZYsowvJqyP8nyBpkpQKHZwJZaSUbF5UqTJKJiIioTLG4vRcW944WflBqPHBufckERKUSk2QiIiIqM6T3j8Hy5s/aHXzrAPDsrnEDolKLSTIRERGVCdInF2EVvV37E0QRuLDJeAFRqcYkmYiIiEo9IeUxrC6sy0t8dfHoLBB/wzhBUanGJJmIiIhKN1kOrM59D+Rm63f+xa2GjYfKBE4BR0RmK3rLJGSnJZqsfyt7FzQZuUptuynjKigmoteZxa29kKQ81r+Bfy4B8dcBt4aGC6oMS0pKxfTp32HPnkhkZLxEixb1sXz5FDRtWkendnJyctG48Yf4889YLF06CZ9//pHS/ri4ZwgI2IAjR84hPv45qlSpiB49fPD11yNQoYKzAZ+RZkySTSQoKAhbt25FTEwMJJLSP6A/Y8YMREZG4ty5c6YOhcqQ7LREZKc+N3UYasw1LqLXkZD6BJZ/HSh+Qxe3Ad2+LX47ZZxcLkfXrpNw9eodfPHFR6hY0Rlr10bA13cMLl0KQc2a1bRua/XqnXj0KF7jvrS0DLRsORzp6ZkYN64fqlatjKtX/8KaNT8hMvIiLl360ej5k1lmZ4IgaPU4ceJEsfvKyMhAYGCgTm0tXLgQ/v7+qFy5MgRBQGBgoE59pqSkYMmSJfjyyy+VfsH5z2v58uVq5wQHB0MQBFy8eFGnvjTZvXs3BgwYAE9PT9jZ2aF27dqYNm0akpKSNB6/b98+NG3aFDY2NqhWrRoCAgKQm5urdMzkyZNx9epV7Nu3r9jxEeWzsneBlUMF0z3sXcwvrgJiInpdWV4LBf5/hb9i+ecS8CS6+O2Ucr6+ozFsWGCB+yMijiEq6hqCgwMQEDAa48f3x4kTGyCVShAQsEHrfp4+fYF58zbjyy+Haty/b99JPHwYh+DgQMydOwajRvXE6tXT8dVXwxEd/ReuXv1L16emM7McSQ4JCVH6+YcffsCRI0fUttetW7fYfWVkZGDu3LkAAF9fX63OmTVrFtzc3PD222/j8OHDOve5detW5Obm4sMPP9S4f+nSpRg7dizs7Ox0blsbo0ePRpUqVTBkyBBUq1YN169fx5o1a3Dw4EFcvnwZtrb/LSF66NAh9OzZE76+vli9ejWuX7+OBQsW4OnTp1i3bp3iODc3N/To0QPLli2Dv7+/UeKm0iU7LRFPrx9HVlICrJ0ro1LDtjoneJrKCgzRrr5t5J/nUKW23n2XNENcr+K2Z+gYiPJJ4q9BGn/NcA2e3wT0WAMIguHaLGMiIo6hcuUK6N27rWKbq6sL+vdvjx9/PISsrGxYWxe9yuKMGatRu3Z1DBnyAebMUZ+vOiUlb6GXypXLK213d68AALC1tSnO09CKWSbJQ4YMUfr57NmzOHLkiNp2U4mNjYWHhweePXsGV1dXnc/ftm0b/P39YWOj/gtu0qQJoqOjsX79ekydOtUQ4aqJiIhQ+0Dg7e2NoUOHIjQ0FKNGjVJs//zzz9GoUSP89ttvsLDIe7k4Ojpi0aJFmDRpEurU+a/+qH///ujXrx/u378PT09Po8ROpcPzv87h1s+LIcpzIQgSiKIcD0+EoG7fmShfs4VJ29W3DWM9J2MydMz6tFcarxuVEqIcljd2GLbNhBtA7O+Ap69h2y1Drly5jaZNa6uVOrRoUR8bN+7BX389QsOGbxXaxvnzN7B9+y84fXozhAI+kLRp0xQSiQSTJi3D8uVT8OablXDt2h0sXLgNPXv6ok4dD0M9pQKZZbmFNuRyOVauXIn69evDxsYGlStXxpgxY5CYqHwzzcWLF9GpUydUrFgRtra2qFGjBkaMGAEAePDggSLJnTt3rqLcoajyCQ8PD73jjo2NxbVr19C+fXuN+1u1aoW2bdsiKCgImZmZevdTGE0j5r169QIA/Pnnn4ptMTExiImJwejRoxUJMgCMGzcOoigiIiJCqY3857R3714jRE2lRXZaYl5SJMsBRBGiXJb3X1kO/oxYpPcNb4ZoV982jPWcjMnQMevTXmm8blR6SP+OgiS5GDfrFeTM90B2uuHbLSPi4p7B3b2i2vb8bU+e/Fvo+aIoYsKEpRgwoANatmxU4HH16nli48aZiImJRcuWw1G1ald07ToZ7do1R3j4N8V7EloqtUnymDFj8MUXX6BVq1ZYtWoVhg8fjtDQUHTq1Ak5OTkAgKdPn6Jjx4548OABZsyYgdWrV2Pw4ME4e/YsAMDV1VVRMtCrVy+EhIQgJCQEvXv3NlrcUVFRAICmTZsWeExgYCASEhKUyhk0ycrKwrNnz7R6FCU+Pq9wvmLF/174V65cAQA0a9ZM6dgqVargzTffVOzP5+TkBC8vL/zxxx+FxpySkqJ4pKWlFRkblS5Prx+HKM/VuE+U5+Lp9eMma1ffNoz1nIzJ0DHr015pvG5USshyYRmj5ap6ukp7CkStMU7bZiYnJxfPniUpPXJycpGVla22XS6XAwAyM7M0llPY2Fgr9hcmOHg/rl+/iyVLJhQZ3xtvVEKLFvWxcuU07NmzDFOnDkZo6CHMmFEyvx+zLLcoyunTp7F582aEhoZi0KBBiu1+fn7o3LkzwsPDMWjQIERFRSExMRG//fabUqK3YMECAEC5cuXQt29fjB07Fo0aNSqRco5bt24BAGrUqFHgMe+//z78/PwUtcmv1gi/aseOHRg+fLhW/YpFTK6+ZMkSSKVS9O3bV7EtLi4OAODu7q52vLu7O548eaK23dPTEzExMQX2s3jxYkUNOJVNWUkJ//+1uvqNNIIgQVZSgsna1bcNYz0nYzJ0zPq0VxqvG5UO0ocnIaQXPQCkt9sH86aDq9PFeH2YgT/+iIaf36dq26OirmHnzt+UtsXG7oOHRxXY2lojK0t9PuqXL/OSY1tb6wL7S0lJw1dffY8vvvgYVau6FRlbt25TcPbsNjRrVg8A0LOnLxwdy2Hu3E0YMcIf9eoZt7SzVCbJ4eHhcHJyQocOHZRGSb29vWFvb4/IyEgMGjQIzs7OAIADBw6gcePGsLS0NFHE/3n+/DksLCxgb29f6HGBgYHw8fHB+vXrMWXKFI3HdOrUCUeOHCl2TGFhYdiyZQumT5+OmjVrKrbnl3tYW6u/4G1sbJCSkqK23cXFRW2E+VVfffWVUq11dHQ0fHx8ihM+mRlr58oQRbnGfaIoh7VzZcXPutzQpUu7hojN0H2XNEPHrE97pfG6USkgy4Xl7RKYSenUcsDpTcC94JKA0q5x41o4cuR7pW3Tpq2Em1sFfPGF8pzFbm55N8y5u1dEXJz6B5T8bVWqFHyv1rJlPyI7OwcDBnTAgwd5A22PHz8FACQmpuDBgyeoUsUVVlaW2LBhNypXLq9IkPP5+7dBYOBGREVdY5KsyZ07d5CcnIxKlSpp3P/0ad4F9/HxQZ8+fTB37lysWLECvr6+6NmzJwYNGqQx8TMnbdq0gZ+fH4KCgvDpp+qf8oC80VxNo7y6OHXqFEaOHIlOnTph4cKFSvvyR7CzstS/Onn58qXGEW5RFAsswgfyEu5Xr31RHxao9KnUsC0engjJq0NVIUgsUKlROwC639BVVLsuXs3w+MzPhSbc2sam73MyJ4aOWZ/2SuN1I/MnfXgSQkYJzFMuzwWOzAH6bgXsyhd9fCnk4uKI9u3fUdnmAHf3imrb8zVpUgunTkVDLpcr3bx37txN2NnZoFatgudJfvQoHomJKahfv7/avkWLtmHRom24ciUUTZrURkLCC8hk6h+yc3LySrhUp6I1hlJZkyyXy1GpUiUcOXJE42PevHkA8uYdjoiIwJkzZ/DZZ5/hn3/+wYgRI+Dt7W2yWtgKFSogNzcXqampRR4bEBCA+Ph4bNiged7BzMxMxMfHa/XQ5OrVq/D390eDBg0QERGhdHMe8F+ZRX7Zxavi4uJQpUoVte2JiYlKdc30+rGyd0HdvjMhSC0BQYAgkeb9V2qJun1nwqqcs143dBXW7pst+yB6yyQ8OL4N8Vd+xYPj23Bh9XC8uHNeqzYgsYBrAx/8fTIMj8/8rNa/Ns/J3Bg6Zn3aK43XjcycLBeWt/eXXH+ZicCJb4AiShZfJ337tkNCwnPs3v3fPQXPniUhPPwound/X6le+d69x7h377+bKydOHIg9e5YpPTZsmAkAGDasO/bsWYYaNd4AANSqVQ0JCc9x4oTy+hA7duRNvfv227qt7qePUjmS7OXlhaNHj6JVq1YF1uu+6t1338W7776LhQsXIiwsDIMHD8bOnTsxatSoQkc9jSF/yrTY2Fg0alT4Vzg+Pj7w9fXFkiVLMGfOHLX9u3bt0rsm+d69e+jcuTMqVaqEgwcPahzRbdKkCYC8GUJatPhvZO/Jkyd4/PgxRo8erXZObGwsGjdurFVMVHaVr9kCzSdsUy6laNROkRRpc0PXmy37aNWui1czRG+ZpBitzK9/zU+4m0/YpjSirNqGLCcL/948gafXjhU6ov3qeRn/PkJORjIsyzkh49nfsHevaZbz/hb1eyiJ9gwdA73epA9PQMgwYi2yJn+fA+4dA97SPCvV66Zv33Z4992GGD58HmJiYv9/xb1wyGRyzJ07RunYdu3GAgAePMj7YNO0aR21pavzyy7q1/dEz56+iu2ffdYf27btR/fuUzFhwgBUr+6G33+/jB07DqNDh3fwzjsNjPgs85TKJLl///5Yu3Yt5s+fj0WLFinty83NRVpaGpydnZGYmAhnZ2elRDg/8csvIchfsKOg1eYMrWXLlgDyEs+ikmQgrzbZ19cXGzduVNunb01yfHw8OnbsCIlEgsOHDxc413P9+vVRp04dbNy4EWPGjIFUKgUArFu3DoIgKN3kBwDJycm4d+8exo4dq3NMVPZY2btoTHSB4t3Qpdru4zM/65xw57eRnZaIC6uHQ5TlnV9Ugm1l7wLbCm/mlRCUknl/C/s9lFR7ho6BXlM5L2H55x7T9H3me6BaS8CqnGn6NyNSqRQHD67CF1+swnff7URmZhaaN6+H4OBA1K7tYbB+atf2wKVLIZg1ax1+/PEg4uOfo0oVV3z++UdqybixlMok2cfHB2PGjMHixYsRHR2Njh07wtLSEnfu3EF4eDhWrVqFvn37Yvv27Vi7di169eoFLy8vpKamYtOmTXB0dESXLnl3rNra2qJevXrYtWsXatWqhfLly6NBgwZo0KDgTyghISF4+PAhMjIyAAAnT55UzJjx0UcfoXr16gWe6+npiQYNGuDo0aOK+ZqLeq4+Pj74/fff1fbpW5PcuXNn3L9/H9OnT8fp06dx+vRpxb7KlSujQ4cOip+XLl0Kf39/dOzYEQMHDsSNGzewZs0ajBo1Sm3Fw6NHj0IURfTo0UPnmOj1YsgbuoqTcOs6oq1UJoKik2oiMhzL23shvFS/YbxEZLwALm0HWo4zTf8l6MQJ9UE5VS4ujti8eTY2b55d6HH5I8iF8fCoAlG8qHFf7doeCA9fUmQbxlIqk2QAWL9+Pby9vbFhwwbMnDkTFhYW8PDwwJAhQ9CqVSsAeQnm+fPnsXPnTiQkJMDJyQktWrRAaGio0hRsmzdvxoQJEzBlyhRkZ2cjICCg0CR5y5YtSklrZGQkIiMjAQCtW7cuNEkGgBEjRmDOnDnIzMzUqlwkMDAQfn5+RR6nratXrwIAgoKC1Pb5+PgoJcndunXD7t27MXfuXEyYMAGurq6YOXOmxvKP8PBwtG7dGl5eXgaLlcomQ97QVZyEW9cEW98yESIqHiHpISzuHDRtEDcigFqdgAr8G/e6EMSiJtAlg0tOToanpyeCgoIwcuRIU4djEPHx8ahRowZ27typ00jy5cuX4e3tjUuXLhW6wAqVPS/unMefEYvyEmVByLsxRpCglv8UVGrYVut2/iuZ0JBwSy3RfGJwgfWvj8/8jAfHt2m+KUcQ4NF2uFLSe+/QWsRf+TXvRkPVwyVSuL3dGV4flP2RJqKivMxIhW05Rzy7dwXWtsUsUcjJhE1kAIRU9ZvItSdClMshtbIt3r1IrrWBHt8DUtNPKavukqkDMHPeOp9RKme3KO2cnJwwffp0LF26VLGCTWm3cuVKNGzYkKUWpLXyNVvgra4TAEGiSJABEXcOfKc2K0VhlGZQAPISbgAQJKjZbWKhN4hVatgWgqSgL9QEyLIzlWa64Ly/RCVMlgOr82uKmSAb0L+3gfObTB0FlRAmySby5Zdf4tatW0pzDJZm33zzDc6f1z6xIcpOS8TdX1YD+UmnKC9yGriC6Jtwq05Rlnfef/4+vVNpKrnCkmrO+0tkYDkvYXV2JaTx14rVzKMXWQg++y9W/56AbVHxePTiZfHiurYLuGXi0g8qEWUjQyOiUkeb+l5tFSfhzp+irGqrgapBqLXBeX+JSoaQeB82x2cXK0E+/yANPTbchldgNEaFxuLzPX9jZMgdeHx9Hv5rb+DCg6LXKyjQyaXA3WP6n0+lQqm9cY+ISrfizEqhqrg31FnZu0BqbQtA8y0ar7bBeX+JjEgug8XtfXlTvRXjlqk90S/w4ba7ECEqmpH//39FETh44wUO3UjErk/qovfbeiyAJcqB4/OBtKdA44H/lXlRmcIkmYhMwlymgdOnDc77S2R4QuYLWJ1fB8mzW8Vq5/yDNHy47S5kcrGAj72ATA4IEDFg05+Imt4EzT0cdO9IFIFz64F/LgKtJgPOVYsTNpkhllsQkUkYsr7XEAk3b8ojMhFRhPRRFKyPfl3sBBkAFh3+J28EuahuAYgQseDgw+J1+PgiED4UOLEEeH6veG2RWWGSTEQmYcj6XkMk3Nq0kZ2WiMdnfsa9Q2vx+MzPOt1cSEQqstMhfXgK1pFzYHVhHYTstGI3+ehFFn65mQSZlhNHyeTA/usvin8zn1wG3D4IRIwAfv4EiN4BJD8uXptkcpwnmUyK8yRTdlqiQep7DTHvsqKNV5acFiQWqNt3JkRRzFttT8M+c1yOmqjE5WblJYsAXqYnw7Z8Ffx75RfYSGQQXiZDyEqB8DIRQuYLSJL/hjT5ETTdB/BO0A3Ep6rPe66N9Cw5kjLVS6aK4mwrRTlrqV59AoCboxUuztTwN8zBHahUB3B8AyhXEbB2ACzt8h4Vaxp4mWvOk1w43edJZpJMJsUkmQwp4dox3Nm/Mu+mmv+fBk7XRFZT0g5RLHzBEi5HTaRIkAEg62UGbMo5Iv7GaVha6nb7U4M23RGX8K+hozOqN954A48f6Vi2IUgMfMMfk+TC6Z4k88Y9IioTNE4DByimcNM2kdV0U97jMz9zOWqiokj+G4mVWNoAAJxqNIKlpW6r01V5oyokFvqtaJeenoGkpCSdz3N2dka5cvqP6rq5uSk9fyobmCQTUZlQ3GngCmPI6eqIXidSSxtIdUySL17Sf0T00aNH8PDwgC5fkguCgKtXr6JatWp690tlE2/cI6IyIT+R1aS4iSxnviAqHapVq4Zu3bpBKtVuVFcqlaJ79+5MkEkjJslEVCYYM5HlctREpcfs2bMhCAKEIup984+ZNWtWCUVGpQ2TZCIqE4yZyGqcri6vYTjXaIyn145xOjgiM9G8eXPs2rULUqm0wBHl/H0//fQTmjdvXsIRUmnBJJmIygRDzrusSf5y1B5th8O5xtuK2TOS7l/Bg+PbcGH1cLy4c94gz4WIiqd3796IiopCly5dFCPKEkleyiMIArp27YqoqCj06tXLlGGSmeMUcGRSnAKODM1Q8y4X1j6ngyMqXE5ODqysrJCdna3z7BaG9ujRIxw/fhwpKSlwdHRE27Zty2gNMqeAKxyngCOi15ymKdwMyZizaBCR4VWrVg3Dhg0zdRhUCrHcgohIB8acRYOIiMwHk2QiIh1wOjgiotcDk2QiIh1wOjgiotcDk2QiIh0YexYNIiIyD7xxj4hIR/nTwRlzFg0iIjItJslERHow9iwaRERkWiy3ICIiIiJSwSSZiIiIiEgFk2QiIiIiIhVMkomIiIiIVDBJJiIiIiJSwSSZiIiIiEgFk2QiIiIiIhVMkomIiIiIVDBJJiIiIiJSwSSZiIiIiEgFk2QiIiIiIhVMkomIiIiIVDBJJiIiIiJSwSSZiIiIiEgFk2QiIiIiIhVMkomIiIiIVFiYOgAiIiLSXY4sBzK5zNRhaJSTk2PqEIiKjUkyERFRKZKWnYZ99/Yh8kEkBFFAi4ot0NilMQRBMHVoCrk5uQDyEnlLS0sTR0OkHybJREREpUTUP1HYenMrUrJSkJmVCUEQsP/xfsRnxaNH9R6QCOZRRZkhZgCA2Y50E2mDSTIREZGZy5HnYMv1LYj8O1JpuyAIEAQBF59dhIXEAr1r9DaLEeUcKcstqPRjkkxERGTGcmQ5WHZxGaL/jS70uLNPz8LJygnt32xfMoERlXHm8b0MERERqZHJZVhxeUWRCXK+w48P4/K/l40bFNFrgkkyERGRGRJFEVtvbMWlhEs6nffT/Z8QmxprpKiIXh9MkomIiMxQ+F/hOProqM7nyUQZtt/ejhcvXxghKqLXB5NkIiIiM3Mo9hB+vvOz3uen56Zj6+2tyJJlGTAqotcLb9wjMqCJWyYiMS3R1GHozMXeBd+N/E5te1l7PkSlwR///IHgm8HFbichMwG77u3CRzU/MosZL6j0SkpKxfTp32HPnkhkZLxEixb1sXz5FDRtWqfIc4cNC8T27QfUtteuXR23bhX8QTA09BCGDJmNcuVskZZ2qljx64tJsokEBQVh69atiImJgURS+gf0Z8yYgcjISJw7d87UoZhUYloinqc+N3UYBlPWng+RubuTeAfrrq4zWHvXX1zHybiT8KniY7A26fUil8vRteskXL16B1988REqVnTG2rUR8PUdg0uXQlCzZrUi27C2tsLmzbOUtjk52Rd4fFpaBqZP/w7lytkWO/7iMMskWdtPvJGRkfD19S1WXxkZGQgKCoKvr69Wbd26dQtbt27Fb7/9hnv37sHe3h5NmzbF3Llz0axZM636TElJwZIlS7Bs2TKlBDn/eS9btgzTpk1TOic4OBjDhw/HhQsXtO6nILt378auXbtw4cIFxMfHo2rVqujWrRtmz54NZ2dnteP37duHwMBAxMTEoFKlShg+fDhmz54NC4v/Xj6TJ0/GypUrsW/fPvj7+xcrvtLMxd7F1CHopaC4y9rzITJnadlpWHFpBXLkhp1j+OCjg/Bw8EB1h+oGbZdeDxERxxAVdQ3h4d+gb9+86QX79++AWrV6IyBgA8LCFhbZhoWFFEOGdNG6zwULtsDBwQ5+fs3wv/+d0Df0YjPLJDkkJETp5x9++AFHjhxR2163bt1i95WRkYG5c+cCgFZJ8ubNm7Flyxb06dMH48aNQ3JyMjZs2IB3330Xv/76K9q3L3p+yq1btyI3Nxcffvihxv1Lly7F2LFjYWdnp9Nz0dbo0aNRpUoVDBkyBNWqVcP169exZs0aHDx4EJcvX4at7X+f3A4dOoSePXvC19cXq1evxvXr17FgwQI8ffoU69b9N9rh5uaGHj16YNmyZa91klzWvuIva8+HyJxtvr4Zz18a/psbOeTYcXcHpjSaAmuptcHbp7ItIuIYKleugN692yq2ubq6oH//9vjxx0PIysqGtbVVke3IZDKkp2fC0bHgEWQAuHPnEVasCMOePUvx00+637hqSGaZJA8ZMkTp57Nnz+LIkSNq203hww8/RGBgIOzt//sljxgxAnXr1kVgYKBWSfK2bdvg7+8PGxsbtX1NmjRBdHQ01q9fj6lTpxo09nwRERFqHwi8vb0xdOhQhIaGYtSoUYrtn3/+ORo1aoTffvtNMXLs6OiIRYsWYdKkSahT5796pP79+6Nfv364f/8+PD09DR53Yloijl0/hoSkBFR2rox2DdsZZMSwOO0aKyZT9Kdr2/rGos95JXGdjdFHSb8+qPQ68+QMzsSdMVr7z7OeY//D/ejr2ddofVDZdOXKbTRtWlutNLRFi/rYuHEP/vrrERo2fKvQNjIyXsLR0QcZGS/h4uKIDz/shCVLJsDeXn0wcPLk5fDza4YuXVozSdaXXC7Hd999h02bNuHevXtwcnJCz5498c0338DF5b8/QhcvXsTXX3+NS5cuIT09HW5ubvDz88PWrVvx4MED1KhRAwAwd+5cxYhyQEAAAgMDNfbr7e2ttq1ChQp4//33ceLEiSLjjo2NxbVr1wpMgFu1aoXy5csjKCgIY8eOVRrVNRRNI+a9evXC0KFD8eeffyq2xcTEICYmBt9//71SacW4ceOwcOFCREREYNas/2qM8j8g7N27F1OmTDFozOf+OodFPy9CrjwXEkECuShHyIkQzOw7E+/UfMck7RorJlP0p2vb+saiz3klcZ2N0UdJvz6o9ErOSsaW61uM3s+5p+dQ17ku6pevb/S+qOyIi3uGNm3eVtvu7l4RAPDkyb+FJsnu7hUxffrHaNq0DuRyOX799QzWrg3H1at/4cSJDUr5xS+/nMZvv53F1as7DP9E9FBq7xgbM2YMvvjiC7Rq1QqrVq3C8OHDERoaik6dOiEnJ6+e6+nTp+jYsSMePHiAGTNmYPXq1Rg8eDDOnj0LAHB1dVWUDPTq1QshISEICQlB7969dY4nPj4eFStWLPK4qKgoAEDTpk0LPCYwMBAJCQlK5QyaZGVl4dmzZ1o9tIkfgNJzuHLlCgCo1UBXqVIFb775pmJ/PicnJ3h5eeGPP/4osj9dJKYlYtHPi5Ajy4EoipDJZRBFETmyHCyKWKT37AvFaddYMZmiP13b1jcWfc4rietsjD5K+vVBpZcoith0bRNSc1JLpL/w++FIzS6ZvqhsyMzM0lhOYWNjrdhfmMWLP8M330xA//4dMHBgJwQHB2LhwnH444+riIg4pjguOzsHU6Z8i08/7YN69Qz/bbQ+SmWSfPr0aWzevBnbt2/Hxo0bMWbMGHzzzTf4+eefceHCBYSHhwPIS0gTExMRGhqKzz//HKNGjcKCBQsQExMDAChXrhz69s376qlRo0YYMmQIhgwZgkaNGukUz6lTp3DmzBkMGDCgyGNv3boFAIoRbE3ef/99+Pn5YenSpcjMzCzwuB07dsDV1VWrR1GWLFkCqVSquB4AEBcXBwBwd3dXO97d3R1PnjxR2+7p6am4vppkZWUhJSVF8UhLSysytmPXjyFXnqtxX648F8evHy+yDUO3a6yYTNGfrm3rG4s+55XEdTZGHyX9+qDS69ijY7iQcKHE+kvPTUfY3TDIRXmJ9UmlQ3Z2DuLjnyk9ZDIZbG2tkZWVrXb8y5d5ybGtre517lOmDIJEIsHRo+cV21asCMWzZ0mYO3eM/k/CwEpluUV4eDicnJzQoUMHpVFSb29v2NvbIzIyEoMGDVLM1HDgwAE0btwYlpaWBo/l6dOnGDRoEGrUqIHp06cXefzz589hYWGhVNOsSWBgIHx8fLB+/foCSxc6deqEI0eO6BX3q8LCwrBlyxZMnz4dNWvWVGzPT9CtrdX/AdjY2CAlJUVtu4uLi9oI86sWL16sKGvJ5+PjozERz5eQlACJIIFMlKntkwgSxCfFF3huYYrTrrFiMkV/uratbyz6nFcS19kYfZT064NKp3tJ9wwyH7Ku7qbcxaG/D6Frta4l3jeZr6ioq/Dz+1RpW2zsPri7V0RcnPo30vnbqlQpeiBOla2tDSpUcMKLF3l5RHJyGhYs2Ipx4/oiJSUdKSnpAPKmghNFEQ8ePIGdnQ0qVSqvc1/FUSqT5Dt37iA5ORmVKlXSuP/p06cA8pKvPn36YO7cuVixYgV8fX3Rs2dPDBo0SGPip6v09HR069YNqampOH36dJGJry7atGkDPz8/BAUF4dNPP9V4jLu7e6HJpTZOnTqFkSNHolOnTli4UHkal/x66Kws9a9SXr58qbFeWhTFQqfw++qrr9Tqsa2trQv9fVR2rlzgqIdclMPN2a3AcwtTnHa1OdeQN20Zsz9dr4O+102f87Q9pzjX2hivL2O9ZqnsePHyBZZdXGbw6d60deLJCVSyqYTmlZqbpH8yP40b18KRI98rbXNzq4AmTWrh1KloyOVypZv3zp27CTs7G9SqVfQ8yapSU9Px7FkSXF2dAQCJiSlIS8tAUNAPCAr6Qe34GjX80aOHD/73v+U691UcpTJJlsvlqFSpEkJDQzXuzy8vEAQBEREROHv2LPbv34/Dhw9jxIgRWL58Oc6ePVuspDY7Oxu9e/fGtWvXcPjwYTRo0ECr8ypUqIDc3FykpqbCwcGh0GMDAgLg6+uLDRs2aJy/ODMzE8nJyVr16+am/kf56tWr8Pf3R4MGDRAREaFUPA/8V2YRFxeHqlWrKu2Li4tDixYt1NpMTEwstDa7qIRYk3YN2yHkRAhyZOp/TAQIePTsESLOROichBbWroXEAu0atdP7XOdyzhi2epjBbtoyZn9FXd+M7AwkpiUqrq02101T0qrP9dbmnOLeIFec10FJtkllR2ZuJoIuBOHFyxcmjSP8fjjsLe1R16X406lS6efi4oj27dXfM/v2bYeIiGPYvfu4Yp7kZ8+SEB5+FN27v69Ur3zv3mMAgJfXmwDySjJycnLh4FBOqc358zdDFEV07vweAKBSpfLYs2eZWt/ffbcTZ85cx44dCxU3CpakUlmT7OXlhefPn6NVq1Zo37692qNx48ZKx7/77rtYuHAhLl68iNDQUNy8eRM7d+4EoP3CJa+Sy+X4+OOPcezYMYSFhcHHR/uVjPKnTIuNjS3yWB8fH/j6+mLJkiUaa5N37dqlGE0u6qHq3r176Ny5MypVqoSDBw9q/MDQpEkTAHkzhLzqyZMnePz4sWL/q2JjYw0yf/WrXOxdMLPvTFhKLSEIAqQSqWKfKIo4fv04th3fhmGrh+HcHe1X/NPUriAIsJRaYmbfmXAu56zXuRO7TcR3v3xn0Ju2jNmfatsSQfltYefpnUrXtqjrdvuf2xi2ehi2Hd+GX6/8qvjd/BX3l87Xu6i+RFEs9g1yxXkdlGSbVDbkynOx8tJKxCYX/TfA2ESI+OGvH3A/5b6pQyEz1rdvO7z7bkMMHz4P8+Ztwtq14fD1HQ2ZTK5WP9yu3Vi0azdW8XN8/HNUq9YN48Z9g+++24nvvtuJrl0nYenSEHTu/B569MjLn+zsbNCzp6/ao1o1N0ilEvTs6Yt33tFuMNKQBFEUxRLvVUefffYZvv/+e+SH+vvvv8PX1xdfffUVFi1apHRsbm4u0tLS4OzsjMTERDg7OyslwjExMahfvz7WrFmD8ePHIzMzE3Z2dpg0aRJWrlypVTzjx4/H2rVrsWHDBowePVqn53L//n14eXlhy5YtGDFihNI+QRAwfvx4rFmzRrEt/7nmz5/86op7cXFxuHnzplb9vjp/c3x8PFq1aoWXL1/ijz/+gIeHR4Hn1a1bF9bW1rh06RKk0rzkdPbs2Vi4cCFu3ryplBAnJyfDxcUFy5YtM8ocz4lpiTh+/Tge/vsQx68f1/h1tqXUEsETgnUaUc5vNz4pHm7ObmjXqJ3WSYymc49eO4ptx7dB0z8tQRAwou0I9GnZR+v4Sqq/xLRE/HLxF+z8Y6dW11ZTLKIoYtjqYRpHUPPPB6Dz9S7odxRxJsJg17o4r4OSbJNKL7kox9rotTj1z6lityWKIjKzMiGRSPQa7HmVtdQa4+qNQ5VyVYodV770zHS87fU2UtJT4GBX+LemZCiXjNZyYmIKvvhiFf73vxPIzMxC8+b1sGzZZDRrVk/pOA+P7gCABw/2AwCSklIxYUIQzp69gSdP/oVMJsdbb72JwYM/wOeffwRLy8ILGoYNC0RExDGkpRX/3wygPoVvUUpluYWPjw/GjBmDxYsXIzo6Gh07doSlpSXu3LmD8PBwrFq1Cn379sX27duxdu1a9OrVC15eXkhNTcWmTZvg6OiILl3ylke0tbVFvXr1sGvXLtSqVQvly5dHgwYNCiyfWLlyJdauXYuWLVvCzs4OP/74o9L+Xr16oVy5chrPBfJmf2jQoAGOHj2qliQX9Fx9fHzw+++/q+3Ttya5c+fOuH//PqZPn47Tp0/j9OnTin2VK1dGhw4dFD8vXboU/v7+6NixIwYOHIgbN25gzZo1GDVqlNqI8dGjRyGKInr06KFzTNpwsXdBn5Z9EHEmAiI0f7bLnzlAlyQ0v93ixPQqY960Zcz+XOxdYGNto/W11RRLxJmIImd16NOyj9J5iWmJiDgTUWg9cUG/I0Ne6+K8DkqyTSqdRFHE1htbDZIgG1qWLAubb23GZ/U/Q3mbkr0xikoHFxdHbN48G5s3zy70uPzkOJ+zswNCQubr3W9wcCCCgwP1Pr+4SmWSDADr16+Ht7c3NmzYgJkzZ8LCwgIeHh4YMmQIWrVqBSAvwTx//jx27tyJhIQEODk5oUWLFggNDVWagm3z5s2YMGECpkyZguzsbAQEBBSYJEdHRwMAzpw5gzNn1FdHio2NLTRJBvJW6JszZw4yMzO1WiwkMDAQfn5+RR6nratXrwIAgoKC1Pb5+PgoJcndunXD7t27MXfuXEyYMAGurq6YOXMm5syZo3ZueHg4WrduDS8vL4PFqom5zxxQ0jdtGbK/4l5bXc8vbj0xb5Cj0kAuyrHp2iYc/9t8p/5LzUnFplub8Fn9z1DOsvC/YUSvi1JRblHWJCcnw9PTE0FBQRg5cqSpwzGI+Ph41KhRAzt37jTaSHI+Q37FbgyJaYmFlhxsn7jdoF+5G7K/4l5bXc4vKm5tymZK+loT6epl7kusvrIaFxMuFn2wDgxZbvGqquWqYky9MbCWFm8GKJZbmILxyi3KBt3LLUrljXulnZOTE6ZPn46lS5dCLi8bE7qvXLkSDRs2NHqCDOTNHGAh0fwliDnMHFDSN20Zsr/iXltdzjfEghu8QY7MWXJWMuadmWfwBNmY/k7/G9tub0O2TH3xCKLXDUeSqVQ6d+ccFkUof01vIbHQe4o1Yyjpm7YM1V/+tc2R5UAQBIiiCIkgwRT/KWjXsOgPINr+br4/9D1+vfIrZHL10gypRIrOb3fG+A/GaxUzb5Ajc/M49TGWnF+Cp5lPDd52+tN0JFxNQGZqJiztLOHayBV2rnYG7cPL0QvDaw/Xe0SZI8mmwJHkwuk+kswkmUotJkbGc+zaMazYvwJyUQ6JIIEIUacPIdr8bsy9bIZIX9f/vY4Vl1cgPSfdoO0+/+s5YnbGIO5iHCACEKD4b2XvyqjdrzZcauq3YJEmVeyqYHjt4XC2dtb5XCbJpsAkuXBMkomomAxRK2yIflhPTKVNrjwX/7v7P/z818+Qw7CldI+jHuNM0BlABES5hg+WEgEQgGbTmqHKu4abyq2cRTkM8Bqg84IjTJJNgUly4ViTTETFZIhaYW0UtIiJRJDAp4GPxhFmInMkF+U4H3ceX578EuF/hRs8QX7+13OcCToDUSZqTJCBvMRZlIm4uPwiEu/ovmBRQdJz07H19lbsuLsDydnarfBKVFYwSSYiJfnTuGli6Cn23qn5DoInBKN9w/8WuxEEAceuHdN5BUWikvY04yn23NmDSZGTsPzScjxOe2yUfmJ2xaCA6cvVicBfEX8ZPIbLzy7jm+hvsP/hfrzIMu1y2kQlpdTOk0xExmGKuYdP3Dyh6DP/Rr78paUNVd5BVFzZsmzcTbqLG89u4MrTK7ifbPzlnNOfpiPuQpzWSbIoFxF/MR4Z/2YY/Ga+XHkuTsadxMm4k/B08EQt51qoZl8NbnZucLBkSQWVPUySiUhJu4btEHIiRGOtsIXEAs28mhW5Qp4utCnv4A18ZGxyUQ6ZXIZcMRdZuVlIzUnF88zniM+Ix98pfyM2ORYPUh5oXCgHAI5MOYKXiS8NHlfuy1ztR5HziUDk1EhYWBvmT7y1izV8gnyUtt1LuYd7KfcUP5ezKAc3Oze42brB3c4djoJjXigsm6JSjEkyESnJrxXWNI1bn5Z9MHHLRL1XyNPE3FdQpNJN6cPe/6+5IYoicuW5yJHnIEuWhWxZtsbXn4uNC1xsXFC3fNE3rb2b8S4SnxuuFri4ctNzkZuu+cOnrspZlMMnNT/Rrf+cvL7jM+KRI8mBhcQCEkECAXn3H9hY2BRY1kVkLpgkE5Ga/FrhV6dxa+bVDBO3TFQkHflJRXHLIri0NBmTpdRS43YrqZVB+3nD/Q1IBalB2wSA9PR0JCUl6Xyes7Mz7MoZptzCrbIb2tdpX/SBr8jJyXufqOFcA5aWmn8HROaOSTIRaeRi76JU5hBxJsIoZRFFlXeYegVFIm1cvGicVfUePXoEDw8PncoWBEHA1atXUa1aNaPEpA0pDP+Bgaik8bsOItKKsWa94NLSRAWrVq0aunXrBqlUu6RTKpWie/fuJk2QicoKjiQTkVaMWRahqbyDKygS5Zk9ezYOHTqkWCa+IIIgQBAEzJo1qwSjIyq7uOIeEWmFK+QRmc7u3bsxYMAAiKIImUz9JkOpNO8bmJ9++gm9evUyQYTKcnJyYGVlhezsbNYklxiuuFc4rrhHREbCsggi0+nduzeioqLQpUsXCELeNB0SSd6fcEEQ0LVrV0RFRZlFgkxUVnAkmYh0kpiWyLIIIhN69OgRjh8/jpSUFDg6OqJt27ZmV4PMkWRT4Ehy4XQfSWaSTERERAbFJNkUmCQXjuUWRERERETFxiSZiIiIiEgFk2QiIiIiIhVMkomIiIiIVDBJJiIiIiJSwSSZiIiIiEgFl6Umk4uLi0NcXJypwyAiM+Pu7g53d/cC9/O9w3zl5uYCAK5cuQILC6Ya+ijq9U8lQCQysYCAABEAH3zwwYfSIyAggO8dfLy2j6Je/2R8XEyETK6g0aC0tDT4+Pjg999/h729vQkiMz+8Jsp4PZSVtethbiPJZe36GhOvlfYKulYcSTY9JslktlJSUuDk5ITk5GQ4OjqaOhyzwGuijNdDGa+HcfH6ao/XSnu8VuaLN+4REREREalgkkxEREREpIJJMpkta2trBAQEwNra2tShmA1eE2W8Hsp4PYyL11d7vFba47UyX6xJJiIiIiJSwZFkIiIiIiIVTJKJiIiIiFQwSSYiIiIiUsEkmYiIiIhIBZNkMksnT55E9+7dUaVKFQiCgP/973+mDslkFi9ejObNm8PBwQGVKlVCz549cfv2bVOHZVLr1q1Do0aN4OjoCEdHR7Rs2RKHDh0ydVhm4ZtvvoEgCJg8ebKpQynzFi5ciPfeew92dnZwdnY2dThm5fvvv4eHhwdsbGzwzjvv4Pz586YOyezw75z5Y5JMZik9PR2NGzfG999/b+pQTO7333/H+PHjcfbsWRw5cgQ5OTno2LEj0tPTTR2aybz55pv45ptvcOnSJVy8eBFt27ZFjx49cPPmTVOHZlIXLlzAhg0b0KhRI1OH8lrIzs5Gv379MHbsWFOHYlZ27dqFqVOnIiAgAJcvX0bjxo3RqVMnPH361NShmRX+nTN/nAKOzJ4gCNizZw969uxp6lDMwr///otKlSrh999/R5s2bUwdjtkoX748li5dipEjR5o6FJNIS0tD06ZNsXbtWixYsABNmjTBypUrTR3WayE4OBiTJ09GUlKSqUMxC++88w6aN2+ONWvWAADkcjmqVq2KCRMmYMaMGSaOzjzx75x54kgyUSmTnJwMIC8pJEAmk2Hnzp1IT09Hy5YtTR2OyYwfPx5du3ZF+/btTR0Kvcays7Nx6dIlpdehRCJB+/btcebMGRNGRqQ7C1MHQETak8vlmDx5Mlq1aoUGDRqYOhyTun79Olq2bImXL1/C3t4ee/bsQb169Uwdlkns3LkTly9fxoULF0wdCr3mnj17BplMhsqVKyttr1y5Mm7dumWiqIj0w5FkolJk/PjxuHHjBnbu3GnqUEyudu3aiI6Oxrlz5zB27FgMHToUMTExpg6rxP3999+YNGkSQkNDYWNjY+pwSr0ZM2ZAEIRCH0z2iF4PHEkmKiU+++wzHDhwACdPnsSbb75p6nBMzsrKCm+99RYAwNvbGxcuXMCqVauwYcMGE0dWsi5duoSnT5+iadOmim0ymQwnT57EmjVrkJWVBalUasIIS5dp06Zh2LBhhR7j6elZMsGUQhUrVoRUKkVCQoLS9oSEBLi5uZkoKippa9euxfjx49GiRQucO3fO1OHojUkykZkTRRETJkzAnj17cOLECdSoUcPUIZkluVyOrKwsU4dR4tq1a4fr168rbRs+fDjq1KmDL7/8kgmyjlxdXeHq6mrqMEotKysreHt749ixY4qb0ORyOY4dO4bPPvvMtMFRiQkNDYWVlRXOnz+Pu3fvKgY0ShsmyWSW0tLScPfuXcXPsbGxiI6ORvny5VGtWjUTRlbyxo8fj7CwMOzduxcODg6Ij48HADg5OcHW1tbE0ZnGV199hQ8++ADVqlVDamoqwsLCcOLECRw+fNjUoZU4BwcHtfr0cuXKoUKFCq993bqxPXr0CC9evMCjR48gk8kQHR0NAHjrrbdgb29v2uBMaOrUqRg6dCiaNWuGFi1aYOXKlUhPT8fw4cNNHZpZKat/52JjYxEVFYV58+Zh/vz5CA0NRUBAgKnD0o9IZIYiIyNFAGqPoUOHmjq0EqfpOgAQt23bZurQTGbEiBFi9erVRSsrK9HV1VVs166d+Ntvv5k6LLPh4+MjTpo0ydRhlHlDhw7V+G8zMjLS1KGZ3OrVq8Vq1aqJVlZWYosWLcSzZ8+aOiSzU1b/zs2fP1+USqVifHy82KVLF7FmzZqmDklvnCeZiIiIiAyibt26cHd3x/Hjx7F9+3YMGzYM58+fR/PmzU0dms44uwURERERFdulS5dw69YtDBgwAADQs2dPWFlZITQ01MSR6YdJMhEREREVW2hoKCwsLNCnTx8AeffOdO7cGTt37oRMJjNxdLpjkkxERERExZK/+mnbtm1RsWJFxfYBAwYgISEBx44dM2F0+mGSTERERETFcvz4ccTFxSlKLfL5+/vD1ta2VJZcMEkmIiIiomIJDQ2FpaUlevXqpbTd3t4eXbp0wZ49e5CZmWmi6PTDJJmIiIiI9JaZmYndu3ejQ4cOcHFxUdvfv39/pKamYt++fSaITn9cTISIiIiI9LZv3z6kpqYCAL755hu1/RkZGQDyRptVyzHMGedJJiIiIiK9+fv7Y//+/UUeZ2lpibi4OFSoUKEEoio+llsQkck9ePAAgiAgODjY1KEQ0SsEQUBgYKDStgsXLuC9995DuXLlIAiCYjnuX3/9FU2aNIGNjQ0EQUBSUlKJx0umsW/fPoiiWOQjOzu71CTIAJNkItKDv78/7OzsFF+vaTJ48GBYWVnh+fPnJRgZkWEFBwdDEAQIgoDTp0+r7RdFEVWrVoUgCOjWrZsJItSeh4eH4rlIJBI4OzujYcOGGD16NM6dO6dVGzk5OejXrx9evHiBFStWICQkBNWrV8fz58/Rv39/2Nra4vvvv0dISAjKlStn5GdEZFysSSYinQ0ePBj79+/Hnj178PHHH6vtz8jIwN69e9G5c+dSNWpAVBAbGxuEhYWhdevWStt///13PH78GNbW1iaKTDdNmjTBtGnTAACpqan4888/ER4ejk2bNmHKlCn49ttvlY7PzMyEhcV/qcK9e/fw8OFDbNq0CaNGjVJs//XXX5Gamor58+ejffv2JfNkiIyMI8lEpDN/f384ODggLCxM4/69e/ciPT0dgwcPLuHIiIyjS5cuCA8PR25urtL2sLAweHt7w83NzUSR6eaNN97AkCFDMGTIEIwdOxbfffcd7t+/j549e2LFihVYt26d0vE2NjZKSfLTp08BAM7OzkrHFbS9ONLT0w3WFpE+mCQTkc5sbW3Ru3dvHDt2TPHH8VVhYWFwcHBA69at8fnnn6Nhw4awt7eHo6MjPvjgA1y9erXIPnx9feHr66u2fdiwYfDw8FDaJpfLsXLlStSvXx82NjaoXLkyxowZg8TERH2fIpGSDz/8EM+fP8eRI0cU27KzsxEREYFBgwZpPEfb1+XevXvRtWtXVKlSBdbW1vDy8sL8+fPVlvH19fVFgwYNEBMTAz8/P9jZ2eGNN95AUFBQsZ6bra0tQkJCUL58eSxcuBCv3s//ak3ysGHD4OPjAwDo168fBEFQ/DsdOnQoAKB58+YQBAHDhg1TtHHu3Dl07twZTk5OsLOzg4+PD/744w+lGAIDAyEIAmJiYjBo0CC4uLgojdr/+OOP8Pb2hq2tLcqXL4+BAwfi77//1vv6vHz5EoGBgahVqxZsbGzg7u6O3r174969e4pj+L5CTJKJSC+DBw9Gbm4ufvrpJ6XtL168wOHDh9GrVy/ExcXhf//7H7p164Zvv/0WX3zxBa5fvw4fHx88efLEYLGMGTMGX3zxBVq1aoVVq1Zh+PDhCA0NRadOnZCTk2Owfuj15eHhgZYtW2LHjh2KbYcOHUJycjIGDhyo8RxtX5fBwcGwt7fH1KlTsWrVKnh7e2POnDmYMWOGWpuJiYno3LkzGjdujOXLl6NOnTr48ssvcejQoWI9P3t7e/Tq1Qv//PMPYmJiCnw+M2fOBABMnDgRISEh+Prrr/H1119j9OjRAIB58+YhJCQEY8aMAZC3ClubNm2QkpKCgIAALFq0CElJSWjbti3Onz+v1ke/fv2QkZGBRYsW4ZNPPgEALFy4EB9//DFq1qyJb7/9FpMnT8axY8fQpk0btZsDtbk+MpkM3bp1w9y5c+Ht7Y3ly5dj0qRJSE5Oxo0bN5SeL99XXnMiEZEecnNzRXd3d7Fly5ZK29evXy8CEA8fPiy+fPlSlMlkSvtjY2NFa2trcd68eUrbAIjbtm1TbPPx8RF9fHzU+h06dKhYvXp1xc+nTp0SAYihoaFKx/36668atxPpYtu2bSIA8cKFC+KaNWtEBwcHMSMjQxRFUezXr5/o5+cniqIoVq9eXezataviPF1el/ntvWrMmDGinZ2d+PLlS8U2Hx8fEYD4ww8/KLZlZWWJbm5uYp8+fYp8LqoxqlqxYoUIQNy7d69iGwAxICBA8XNkZKQIQAwPD1c699XrlE8ul4s1a9YUO3XqJMrlcqXnW6NGDbFDhw6KbQEBASIA8cMPP1Rq98GDB6JUKhUXLlyotP369euihYWF0nZtr8/WrVtFAOK3336rdg3y4+T7ComiKHIkmYj0IpVKMXDgQJw5cwYPHjxQbA8LC0PlypXRrl07WFtbQyLJe5uRyWR4/vw57O3tUbt2bVy+fNkgcYSHh8PJyQkdOnTAs2fPFA9vb2/Y29sjMjLSIP0Q9e/fH5mZmThw4ABSU1Nx4MCBAkstdHld2traKv4/NTUVz549w/vvv4+MjAzcunVLqV17e3sMGTJE8bOVlRVatGiB+/fvF/v52dvbK2IwhOjoaNy5cweDBg3C8+fPFdcgPT0d7dq1w8mTJyGXy5XO+fTTT5V+3r17N+RyOfr37690Hd3c3FCzZk21f9/aXJ+ff/4ZFStWxIQJE9RiFgQBAN9XKA9ntyAivQ0ePBgrVqxAWFgYZs6cicePH+PUqVOYOHEipFIp5HI5Vq1ahbVr1yI2NlapxtJQs17cuXMHycnJqFSpksb9mmqmifTh6uqK9u3bIywsDBkZGZDJZOjbt6/GY3V5Xd68eROzZs3C8ePHkZKSonRccnKy0s9vvvmmIpHL5+LigmvXrunzlJSkpaUBABwcHIrdFpB3DQAo6pU1SU5OVlrGuEaNGmptiKKImjVrajzf0tJS6Wdtrs+9e/dQu3ZtpRsSNcXO9xVikkxEevP29kadOnWwY8cOzJw5Ezt27IAoiopZLRYtWoTZs2djxIgRmD9/PsqXLw+JRILJkyerjSCpEgRB6QaifKo3M8nlclSqVAmhoaEa23F1ddXz2RGpGzRoED755BPEx8fjgw8+KHA2B21fl0lJSfDx8YGjoyPmzZsHLy8v2NjY4PLly/jyyy/V/p1IpVKN7Wn6t6Kr/Hrct956q9htAVDEvnTpUjRp0kTjMfmj1/leHVXPb0MQBBw6dEjjc1c931DXh+8rBDBJJqJiGjx4MGbPno1r164hLCwMNWvWRPPmzQEAERER8PPzw5YtW5TOSUpKQsWKFQtt18XFReNXyA8fPlT62cvLC0ePHkWrVq3U/sASGVqvXr0wZswYnD17Frt27SrwOG1flydOnMDz58+xe/dutGnTRrE9NjbWoHEXJS0tDXv27EHVqlVRt25dg7Tp5eUFAHB0dNR77mQvLy+IoogaNWqgVq1aBovr3LlzyMnJURuJfvUYvq8Qa5KJqFjyR43nzJmD6OhopbmRpVKp2ghOeHg4/vnnnyLb9fLywq1bt/Dvv/8qtl29elVt6qj+/ftDJpNh/vz5am3k5uZyaVwyKHt7e6xbtw6BgYHo3r17gcdp+7rMH/l89d9JdnY21q5da9jAC5GZmYmPPvoIL168wNdff61WrqAvb29veHl5YdmyZYpSjle9+m+7IL1794ZUKsXcuXPV3ktEUdRrRc8+ffrg2bNnWLNmjdq+/D74vkIAR5KJqJhq1KiB9957D3v37gUApSS5W7dumDdvHoYPH4733nsP169fR2hoKDw9PYtsd8SIEfj222/RqVMnjBw5Ek+fPsX69etRv359pbpNHx8fjBkzBosXL0Z0dDQ6duwIS0tL3LlzB+Hh4Vi1alWBdaNE+iisxjaftq/L9957Dy4uLhg6dCgmTpwIQRAQEhJikPIJTf755x/8+OOPAPJGj2NiYhAeHo74+HhMmzZNMXWbIUgkEmzevBkffPAB6tevj+HDh+ONN97AP//8g8jISDg6OmL//v2FtuHl5YUFCxbgq6++woMHD9CzZ084ODggNjYWe/bswejRo/H555/rFNfHH3+MH374AVOnTsX58+fx/vvvIz09HUePHsW4cePQo0cPvq8QACbJRGQAgwcPRlRUFFq0aKFUzzhz5kykp6cjLCwMu3btQtOmTfHLL79onP9VVd26dfHDDz9gzpw5mDp1KurVq4eQkBCEhYXhxIkTSseuX78e3t7e2LBhA2bOnAkLCwt4eHhgyJAhaNWqlaGfLpFWtHldVqhQAQcOHMC0adMwa9YsuLi4YMiQIWjXrh06depk8Jiio6Px0UcfQRAEODg4oGrVqujevTtGjRqFFi1aGLw/X19fnDlzBvPnz8eaNWuQlpYGNzc3vPPOO1on5DNmzECtWrWwYsUKzJ07FwBQtWpVdOzYEf7+/jrHJJVKcfDgQSxcuBBhYWH4+eefUaFCBbRu3RoNGzZUHMf3FRJEY31cJSIiIiIqpViTTERERESkgkkyEREREZEKJslERERERCqYJBMRERERqWCSTERERESkgkkyEREREZEKJslERERERCqYJBMRERERqWCSTERERESkgkkyEREREZEKJslERERERCqYJBMRERERqfg/uE6wo3JRPusAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 700x250 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "two_groups_unpaired.mean_diff.plot(swarm_side='right', horizontal=True);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Table kwargs\n",
+    "\n",
+    "The table axis can be customized using the `horizontal_table_kwargs` argument. A dict of keywords can be passed to customize the table. \n",
+    "\n",
+    "If None, the following keywords are passed:\n",
+    "\n",
+    "- `'show'` - Whether to show the table. Default is True.\n",
+    "- `'color'` - The color of the table. Default is 'yellow'.\n",
+    "- `'alpha'` - The transparency of the table. Default is 0.2.\n",
+    "- `'fontsize'` - The fontsize of the table. Default is 12.\n",
+    "- `'text_color'` - The color of the text in the table. Default is 'black'.\n",
+    "- `'text_units'` - The units of the text in the table. Default is None. \n",
+    "- `'control_marker'` - The marker for the control group. Default is '-'.\n",
+    "- `'fontsize_label'` - The fontsize of the table x-label. Default is 12.\n",
+    "- `'label'` - The table x-label."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 700x500 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "multi_2group = dabest.load(df, idx=((\"Control 1\", \"Test 1\"),(\"Control 2\", \"Test 2\"),(\"Control 3\", \"Test 3\"),(\"Test 4\", \"Test 5\")))\n",
+    "multi_2group.mean_diff.plot(horizontal=True, \n",
+    "                            horizontal_table_kwargs={'color': 'red', \n",
+    "                                                     'alpha': 0.5, \n",
+    "                                                     'text_color': \n",
+    "                                                     'white',\n",
+    "                                                     'text_units':'mm', \n",
+    "                                                     'label': 'delta mm',\n",
+    "                                                     'control_marker': 'o',\n",
+    "                                                    });"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The table axis can be hidden using the `'show':False` in the `horizontal_table_kwargs` dict."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 700x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "multi_2group = dabest.load(df, idx=((\"Control 1\", \"Test 1\"),(\"Control 2\", \"Test 2\"),(\"Control 3\", \"Test 3\"),(\"Test 4\", \"Test 5\")))\n",
+    "multi_2group.mean_diff.plot(horizontal=True, horizontal_table_kwargs={'show': False});"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Gridkey \n",
+    "\n",
+    "As with the vertical plots, you can utilise a gridkey table for representing the groupings. This can be reached via `gridkey` in the `.plot()` method. \n",
+    "\n",
+    "You can either use `gridkey='auto'` to automatically generate the gridkey, or pass a list of indexes to represent the groupings (e.g., `gridkey=['Control', 'Test']`).\n",
+    "\n",
+    "See the examples in the [Plot Aesthetics Tutorial](09-plot_aesthetics.html) for more information with regards to kwargs."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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30hQBO94Hykot74vsFq9gE5HFGrR6GBGvLTIs9Wjfs8bk1pibI5tFDgHwdxJ/+qeZBle7JZWL/mq3f4sHTRqfiMho2jK4nI+Tr7+sS8CRpUDEWPn6JLvCBJuIZOHm5a9PiI1RU111VTcn1pbEmzo+EZGx1Gn7IBVmytvpsRVAm36Ad5C8/ZJdYIkIESnCnJsjK5Lolk/8A80ih/AKNRFZX1kxXE+vlb9fbSlw8Fv5+7Vz2dl5GDfuIzRu3Av16z+G7t1fRkrKGZP70WjKcP/9z0KSHsKnny6t9Pj16zcxbtxHaNFiIDw9O6Fly6cwceLnuHUrW4ZZ1I5XsIlIEZbcHElEZCuup9ZCKqp9w6q0zBLsOpeD3KIy+Nb3QK9QfwQ38Kj5pAs7gLaDgKAH5AnWzul0OvTr9waOHTuPt99+AY0a+WH+/J/QrdvLSE5eilatgo3u67//XYG0tPQqH8vPL0Rk5BgUFBThH/94FnffHYhjx87hq69WYffuw0hOXgaVyrrXmHkFm8wya9YsREREwNvbGwEBARg0aBDOnj1r0Ka4uBgxMTFo2LAhvLy8MGTIEGRkcE1iKmfuzZFERLaiupYCl/Oba2xz8FI+nvrmLFq+fxT/tzwVb637A2OXnkfIuwcxcP4JHLqUV/Mg8f8BNMUyRq2cbt3GYfTo96t9/KefdiIx8TcsXjwN06aNQ0zMUOzZ8w3UahWmTfvG6HFu3MjEhx8uxL/+NarKxzds2IvLl69j8eL38cEHL+P//m8Q/vvff+Lf/x6Do0fP4dixc6ZOzWRMsMks8fHxiImJwYEDB7B9+3ZoNBr07t0bBQV/32H95ptvYuPGjVi9ejXi4+Nx7do1DB48WMGoyd5U1FWH9BiDoAf7IqTHGES8vthgiT4iIiWo0o/B/eBXNbZZdzQTXeacwpZT2fr9Y3R//SsEEHciE1H/OYq1R25W30l2GrB3dp3YgOann3YiMLAhBg/uoT/WuLE/hg7thfXr41FSYtzKKu+881+0bt0cI0Y8UeXjubn5AIDAwAYGx5s0aQgA8PSs5Z0FGbBEREFxcXGYO3cukpOTodVq0b59e8TExCA6Olrp0Gq1ZcsWg88XL16MgIAAJCcno0uXLsjJycF3332H2NhY9OhR/oO0aNEihIaG4sCBA3j00UeVCJssVNOmMMZsGFNdm5puTjSmX1Pa2YIlsVjyPBKRDIQOLmfWw/X0uhqT3oOX8vHcogvQ6gSqa6XVARIEor89jcR/dkREiHfVDS/sANy9gajXASuXLijpyJGzCAtrXak84+GH2+J//1uHc+fS8MAD99bYx8GDJ/DDD79g376FkKSqd/Ht0iUMKpUKb7zxKT777E00axaA3347j48+WoRBg7qhTZsQuaZULSbYCpk4cSLmzJljcCwhIQEJCQnYuHEjlixZYvX6IDnl5OQAABo0KH+1mJycDI1Gg169eunbtGnTBsHBwUhKSmKC7YBq2hRGCFHrhjHGbipj7Ji3n2NO39ZiSSzGnGtPcyVyOiX5cD/4FVQ3TtbadObWqxCoPrmuIAAICMyIu4z1/2hXfcOT64D8G0CXt4B6Dapv58CuX7+JLl0erHS8SZNGAIBr1/6sMcEWQuC112YjOvpxREa2x6VL16psd//99+B//5uMt976ApGRY/THR43qj4ULp1g4C+M4TgbnRL7//vtKyfXtli9fjk8++cSGEVlGp9NhwoQJ6NSpE9q1K//lkZ6eDjc3N/j5+Rm0DQwMRHp61TclkP2qaVOYU6s/wpk1M2vcMMaUTWWMGfP2c8zpW4nnqbZYjDnXnuZK5GxUN07AY9cUo5LrtMwS/HIyG9pqNpi9k1YHbDyeibTMWmqtL+8HVo4ADn8PyL0soMw0mjLcvJlt8KHRlKGkpLTScZ2u/IkqKiqBu7tbpb48PNz1j9dk8eKNOH78Aj755LVa47vrrgA8/HBbzJ07CevWfYqJE5/H8uWb8c47NZf9yIVXsG1Mp9NhxowZtbabPXs2JkyYAE9PTxtEZZmYmBicOHEC+/btUzoUspKaNoWBrqzaKzgVG8ZU/H9Nbe4sEzF2IxpTNqyxNktiMebciv83p38iKqfTaqArKwXKiqHKz4Aq8wJcrh6EOvM8AOCR/5xAel7l1Y1uV1CiM7lkWgigw/Rk1HdX19guyMcNhycXAClLgWYRQMhj5auM+DQFXNxNG9SK9u8/iu7dX6l0PDHxN6xYsc3gWGrqBoSENIWnp3uVddbFxeWJtadn9fPLzc3Hv/89D2+/PRJ3313z2uH79x9F//5v4sCBRXjoofsBAIMGdYOPT3188MG3ePHFgbj//ntqnaMlmGDb2MWLF5Gamlpru6ysLKSkpKBTp042iMp848ePx6ZNm7B37140a9ZMfzwoKAilpaXIzs42uIqdkZGBoCAuqu9oatoUBqiogav81+b2DWNM2VSmtjFvP8fUDWusyZJYjD3XXuZK5DA0RYAQkDSF5Z9npUHoCiGVFUMHQKf2RFlwVyC4KwDgevE/cS270CqhZBdpkV1U1e/R23j4AI9/eMeJaeUfrvUAt3qAz12Ap59VYjRWhw73Yfv2eQbHJk2ai6Cghnj77RcMjgcFld9c2KRJI1y/XvmGz4pjTZs2rna8Tz9dhtJSDaKjH9eXhly5cgMAkJWVi0uXrqFp08Zwc3PFN9+sRWBgA31yXWHgwC54//3/ITHxNybYzubWrVtWaWtr5XVQr2HdunXYs2cPWrRoYfB4eHg4XF1dsXPnTgwZUn5F7ezZs0hLS0NkZKQSIZMFatoUpqrEWv/IbRvGmLqpjLEb0ZizYY21WBKLsefay1yJHIZr+TvBKskVAODdrjdcXV2rbd7k7q8hudVcylhQUIjs7GyTQ/Hz80P9+vVrbBMUFATc09Xkvm3N398HvXo9cscxbzRp0qjS8QodO96HhISj0Ol0BveZ/frrSdSr54H77qt+Hey0tHRkZeWibduhlR6bOXMRZs5chCNHlqNjx9bIyMiEtor6HY2m/B3AsrJq3pGVERNsGwsJCbFKW1uLiYlBbGws1q9fD29vb31dta+vLzw9PeHr64uxY8di4sSJaNCgAXx8fPDaa68hMjKSNzg6oJo2hYHKBZIECG3lX1j6DWOEMHlTGWM3orGnDWssicWoc814HonIkNrVA+oaEuzDycm19pGWloaQkBAIE+pEJEnCsWPHEBxs/GYqzuaZZ3rip592Yu3aXXjmmfJFEG7ezMbq1TswYEBng/rs33+/AgBo2bL83fHXXx+GQYO6GfR340YmXn55JkaPHoCnnuqKFi3uAgDcd18wtm07gD17DqNbt4f07X/8cSsA4MEH21htjhV4k6ONBQYGomvX2l+ZhoaGon379jaIyDwLFixATk4OunXrhiZNmug/Vq5cqW8zZ84c9O/fH0OGDEGXLl0QFBSEtWutsN0sWV1Nm8Lc/+y7CH3m3Ro3jDFnUxljz7GnDWssicWYc+1prkR1WXBwMPr37w+1uuZ66gpqtRoDBgyo08k1UJ5gP/roAxgz5kN8+OG3mD9/Nbp1GwetVocPPnjZoG3Pnq+iZ89X9Z+HhbXBoEHdDD569y6/YNe27T0YNKgbfH29AADjxw9F/fqeGDBgIiZPnodvvlmD4cPfxdy5P+Lxxx/BI4/UsJqLTHgFWwGff/45oqKiUFJS9d2yKpUKX375pY2jMo0xr9o9PDwwb948zJs3r9a2ZP8qNoUxWH+5fU99UlfTY8acb86YlvRtLZbEYsy59jRXorps6tSp2Lx5MyRJqvFvoiRJkCQJU6bYZnk4e6ZWqxEX9wXefvsLfPnlChQVlSAi4n4sXvw+WrcOkW2c1q1DkJy8FFOmLMCyZXFIT7+Fpk0b4623XqiUyFuLJEx5f6MGKSkpCA8PR3JyMsLCwuToUlHLly/HiBEjrDafbdu2Yfjw4ZXqrL28vLBw4UKrbDZj7TnZmrN9zxERkfVoNBq4ubmhtLS0xhpsU6xduxbR0dEQQkCrrXzzolqthiRJWLVqFZ5++mlZxrSe2ktj6q5wk8/gFWyF9O7dGxcvXsTSpUv1Ozl26NABI0eORKNGjZQOj4iIiGoxePBgJCYmYvr06di0aROEEFCpVNDpdJAkCf369cOUKVMQERGhdKhkY0ywFeTj44OYmBilwyAiIiIzRUREYMOGDUhLS8OuXbuQm5sLHx8f9OjRo87XXNdlTLCJyKqOfveG1XYYdPPyR8exX9h0zJrGJaK6Kzg4GKNHj1Y6DLITTLCJyKpK87NQmmfbNd2VGJOIiKgCE2wisio3L3+b923NMW3RPxEROTYm2ERkVUqUUrB8g4iIlMSNZoiIiIiIZMQEm4iIiIhIRkywiYiIiIhkxASbiIiIiEhGTLCJiIiIiGTEBJuIiIiISEZMsImIiIiIZMQEm4iIiIhIRkywiYiIiIhkxASbiIiIiEhGTLCJiIiIiGTkIneHcXFxOH36tNzd2tz+/fsBOM98AOebU2pqqtIhEBEREVUiCSGEHB0lJSWhc+fO0Gq1cnRnF1QqFXQ6ndJhyMrZ5qRWq5GQkIDIyEilQyEiIjum0Wjg5uaG0tJSuLq6Kh2OHUpWOgA7Fm7yGbJdwXZ3d4dWq8WyZcsQGhoqV7eKiYuLw9SpU51mPoDzzen06dMYMWIE3N3dlQ6FiIiISE/2EpHQ0FCEhYXJ3a3NVZRQOMt8AOecExEREZG94U2OREREREQyYoJNRERERCQjJthERERERDKSvQabiMjRZOVnYefxncjIzkCgXyB6PtAT/l7+SodFREQOigk2EdXIUZNPY+P+9dyvmLlmJsp0ZVBJKuiEDkv3LMXkZybjkVaPKBA5ERE5OibYRFQtR00+jY07Kz8LM9fMhEarAQBoRfk6/hqtBjN/monFry12iBcTcnPUF1VERPaCCTYRVcnek8/qkkBT4t55fCfKdGVV9l+mK8Ou47swJHKIbSZkJ+ztRRWTfSJyREywiahK9px81pQE/nHzD6PjzsjOgEpS6ZPw26kkFdKz0606D3tjby+q7C3ZJyIyFlcRIaIqVSSfVVEy+bw9CRRCQKvTQgihTwLT/kwzOu5Av0DohK7KtjqhQ5BfkFXmYK+MeVFlK7V9nbPys2wWCxGRqZhgE1GV7DX5rC0JzCnMMTrung/0hIuq6jfyXFQu6Nm+p+UBOxB7elFlT8k+EZGpmGATUZXsNfmsLQn0redrdNz+Xv6Y/MxkuKpdIUkS1Co1JEmCq9oVk5+ZDL/6ftaYgt2ypxdV9pTsExGZigk2EVXJXpPP2pLA5o2bmxT3I60eweLXFuPFHi+i74N98WKPF/HD6z/UyRpfe3pRZU/JPhGRqXiTIxFVqyL53HV8F9Kz0xHkF4Se7XsqemW35wM9sXTPUv2NeLerSAL96vuZFLe/l3+dWy2kKhUvqmb+ZHhjoYvKxeYvqoz5OhMR2Ssm2ERUI3tLPo1NAu0tbkdhLy+q7CnZJyIyFRNsInI49pIEOit7eXHCrzPJJS0tDVu3bgUALF68GH369EFwcLDCUZEzYw02mWXWrFmIiIiAt7c3AgICMGjQIJw9e9agTXFxMWJiYtCwYUN4eXlhyJAhyMjIUChicjYVSWDMEzEYEjmESZeT4teZLHHw4EEMGDAAISEhGDduHABg3LhxCAkJwcCBA3Ho0CGFIyRnxQSbzBIfH4+YmBgcOHAA27dvh0ajQe/evVFQUKBv8+abb2Ljxo1YvXo14uPjce3aNQwePFjBqMlRZeVn4aeknzBv8zz8lPQT10AmolqtXbsWnTp1wubNmyGEMHhMCIG4uDhERUVh7dq1CkVIzsyhSkTi4uIwd+5cJCcnQ6vVon379oiJiUF0dLTSodU5W7ZsMfh88eLFCAgIQHJyMrp06YKcnBx89913iI2NRY8ePQAAixYtQmhoKA4cOIBHH31UibCdmtxbSpvSn1xjV9XPuWvnjN7Nz5w4jDnH0vnVdr7S24ErPb69xkKO6+DBg4iOjoZWq62UXFfQarWQJAnR0dFITExERESEjaMkZ+YwCfbEiRMxZ84cg2MJCQlISEjAxo0bsWTJEqhUvCCvlJycHABAgwYNAADJycnQaDTo1auXvk2bNm0QHByMpKQkJtgyk3tLaVP6k2vsqvpZsnsJBMp38QNq3rrbnDiMOcfS+dV2vtLbgSs9vr3GQo5txowZEEJUm1xXqGgzY8YMrF+/3kbRUV3gEBnp999/Xym5vt3y5cvxySef2DAiup1Op8OECRPQqVMntGvXDgCQnp4ONzc3+Pn5GbQNDAxEejo3iJCT3FtKm9KfXGNX10+ZrkyfXN/p9t38zInDmHMsnV9t51/KuKToduD2tB25PcVCji0tLQ2bNm2CVlv17447abVabNy4EWlpaVaOjOoSu0+wdTodZsyYUWu72bNno6ioyAYR0Z1iYmJw4sQJrFixQulQ6iS5t5Q2pT+5xq6pn+rcvpufOXEYc46l86vt/EW7Fym6Hbg9bUduT7GQY9u5c2etV67vJITArl38HiP52H2CffHiRaSmptbaLisrCykpKTaIiG43fvx4bNq0Cbt370azZs30x4OCglBaWors7GyD9hkZGQgK4g5scpJ7S2lT+pNr7Jr6qc7tu/mZE4cx51g6v9rOv5FzQ9HtwO1pO3J7ioUcW15enskloyqVCrm5uVaKiIxVWqpBevpNgw9j34mwN3afYN+6dcsqbckyQgiMHz8e69atw65du9CiRQuDx8PDw+Hq6oqdO3fqj509exZpaWmIjIy0dbhOTe4tpU3pT66xa+qnOrfv5mdOHMacY+n8ajs/wDdA0e3A7Wk7cnuKhRybt7c3dDrTfp/odDr4+PhYKSIyVmLiMTRp0tfg448/HHN5X7tPsENCQqzSliwTExODZcuWITY2Ft7e3khPT0d6erq+TMfX1xdjx47FxIkTsXv3biQnJ2PMmDGIjIzkDY4y6/lAT7ioqr5f2ZwtpU3pT66xa+pHrVLDRe0CSZKgVqkhSRJc1a4Gu/mZE4cx51g6v9rOf7HHi7J+7Uwl9/eOs8RCjq1nz56QJMmkcyRJ0q94Rcrp0OE+bN8+z+AjKKih0mGZxe4T7MDAQHTt2rXWdqGhoWjfvr0NIiIAWLBgAXJyctCtWzc0adJE/7Fy5Up9mzlz5qB///4YMmQIunTpgqCgIK43agUVW0q7ql1rTEKt0Z9cY9fUz5Rnp+CH137Aiz1eRN8H++LFHi/ih9d/MFhVwpw4jDnH0vnVdn7zgOayfu1MJff3jrPEQo4tODgY/fv3h1qtNqq9Wq3GgAEDuLOjHfD390GvXo8YfHh4uCsdllkkYeqdANVISUlBeHg4kpOTERYWJkeXBn1HRUWhpKSkysdVKhW2bt1qsCScpZYvX44RI0ZYZT5KcbY5WfN7zhFl5WfJuqW0Kf3JNbal/ZhzvjHnWDsuub92plJ6fHuNhRzXoUOHEBUVVeM62ED5lWu1Ws11sAEAyUoHYMfCTT7DIRJsANi2bRuGDx9eqc7ay8sLCxculH2zGWdLRgHnmxMTbCIiqs7atWsRHR1dvuxjFTfKqdXl75KsWrUKTz/9tAIR2hsm2NUzPcF2mI1mevfujYsXL2Lp0qX6nRw7dOiAkSNHolGjRkqHR0RERHZk8ODBSExMxPTp07Fp0yaDK9mSJKFfv36YMmUKr1yTVThMgg0APj4+iImJUToMIiIicgARERHYsGED0tLSsG3bNrz00kv49ttv0bt3b9Zck1XZ/U2ORERERJYIDg7GqFGjAACjRo1ick1W51BXsInIPr3+3esOv5W1v5c/vhz7ZaXjjj636uZFRETWwwSbiCyWlZ+FW3nOudGTM8+NiIisgwk2EVnM38tf6RAsVt0cHH1ujh4/EZEjYoJNRBZz5hIEZ54bERFZB29yJCIiIiKSERNsIiIiIiIZMcEmIiIiIpIRE2wiIiIiIhkxwSYiIiIikhETbCIiIiIiGTHBJiIiIiKSERNsIiIiIiIZMcEmIiIiIpIRE2wiIiIiIhnJvlV6XFwcTp8+LXe3Nrd//34AzjMfwPnmlJqaqnQIRER2Q6fRQKfTKR2G3SrTaJQOgeoQSQgh5OgoKSkJnTt3hlarlaM7u6BSqZzul5WzzUmtViMhIQGRkZFKh1LnFWdn43J8PApu3ED9gAA079oVHn5+SodFVCfoNBrcunABmuxslF28CHHzJqT69eESGgqpXj2lw7MLGo0GzR95BMUFBXDnc1KFZKUDsGPhJp8h2xVsd3d3aLVaLFu2DKGhoXJ1q5i4uDhMnTrVaeYDON+cTp8+jREjRsDd3V3pUOq8a4cPI+mzz6ArK4OkUkHodDixYgUiJ01C04ceUjo8IqdWlpWF/IOHkLd1K7QXLgC3Xegq2fQLPJ98Eu6PdYIkSQpGqTzdX9cTnekiE9kv2UtEQkNDERYWJne3NldRQuEs8wGcc06kvOLs7PLk+q+3X8Vff9x1Gg2SPvsM/RYs4JVsIpmJsjLk79uHvO3bUXL2HIROh7KiIkgqFXB7Il1WhqING6C7dg1ezw2D5CL7n32HoWKJCNlQ3f1JIyJZXI6Ph66srMrHdGVluBwfj9ZPPWXjqIicV2HKEWQu+h6aa9eNPqfk8GGIoiJ4jxkNydXVitEREcBVRIjIQgU3bpRfNauCpFKh4MYNG0dE5Jx0BQX488v/IuOjj0xKriuUnjyJvEWLIHgll8jqmGATkUXqBwRAVFPTKHQ61A8IsHFERM6n5MIFXH3rbeTHx1vUT+nJU8j7/nsm2URWxgSbiCopzs7G2fXrkfLttzi7fj2Ks7Orbdu8a1eoqqnrVLm4oHm3btYJkqiOyNu1G9ffnYIymd4NKj11GrlffwNdcbEs/RFRZazBpjqHy8mVq+55MHVFEA8/P0ROmlTpHJWLCyInTYKHr68CsyNyfEIIZK9ajexVq2TvW3PhAnLnfgHvcS9B3aCB7P0T1XVMsKlO4XJy5ap7HsJfeQXJX39t8oogTR96CP0WLDBM2Lt1Y3JNZCah0+HWd98hb8tWq41Rdv06cj6fA59x4+ASfLfVxiGqi1giQnWGwXJyQpQnj0Lok8eayiCcSU3Pw+F582pdEaQ6Hn5+aP3UUwh76SW0fuopJtdEZhJaLW5+9ZVVk+sKurw85Hz1FTQXLlh9LKK6hAk21RnGLCdXF9T0PFR3syLAFUGIbEGUleHPuXORH7/XdmOWlCD3f/+D5uJFm41J5OyYYFOdofRycqbcOGhNNT0PkCTgr93O7sQVQYisS2g0uPHpZyhITLL92CWlyP3f/1B27ZrNxyZyRqzBpjpDyeXkrFX7bc4NmzU9DxBCH9+dqloRhDeMEslDlJbixmefofBwsnIxFBUj9+tv4DthAtQN/BWLg8gZ8Ao21RlKLSdnrdrva4cP45dXX8Vvy5bh4o4d+G3ZMvzy6qu4dvhwjefV+Dy4uiJi/HioXF0BSYKkVgOSBJWra6UVQcwdn4gM6UpLkfGf2Yom1/pYcnKQu2ABdHn5SodC5NCYYFOdUbGcnDHJo5ysUfttSdJe2/PQvEsX9FuwAO1HjMA9vXqh/YgR6Pf11wZX2nnDKJE8dAUFyJjxEYqOHFE6FD3tjRvI/XoBdIWFSodC5LBYIkJ1ihLLyVXUPFcseXc7c2u/jUnaWz/1VLXn1/Y8VKwIYq3xiQjQZNxAxsezoEn7Q+lQKim7chW5X82DzysvQ+Xjo3Q4RA6HCTbVObUlj3KzRu23HEm7Jc+DXC8aWMNNdVXR0aO4MWcudPn2W4pRdvUqcubMhff/jYXLXXcpHQ45oOvXb+KLL37Er7+ewOHDp5GfX4jdu79Gt27G3Xu0bt1ufP31Ghw/fgG3buWgcWN/PPpoO7z//ji0a3evQdvi4hLMmROLpUvjcOnSNfj7+yAqqj3ef38c2rZtaY3p1YgJNpGVNe/aFSdWrNBv3nI7c2u/lbxhU67xuekP1UVCq0XWypXIWbuu2hV7THW1sBCJNzKQW1gEL1dXRDZujKaenrL0rc3MRM7cL+A19Fm4R0TI0ifVHWfPXsInn/yAVq2C8cAD9yIp6TeTzj9+/AL8/b3xxhvD0KiRH9LTb+H77zfg4YdHISlpETp0uE/f9vnnp2DDhr146aWnERb2PK5d+xPz5q1GZOSLOH58BZo3byL39GrEGmwyy6xZsxAREQFvb28EBARg0KBBOHv2rEGb4uJixMTEoGHDhvDy8sKQIUOQkZGhUMTKsUbtt1I3bMo1Pmu4qS4qu3UL16dNQ86atbIk18cyM/F/ifvRZctm/CslBbPOnMa/j/+G7rt24pVDh/CbTD9HorQUecuWI3/FCogqLhQQVSc8PBS3bu3EuXNrMXHicJPPf++9l7BixSz861+jMXbsILz77lgkJn4PjaYMCxb8pG939eoNrF27GxMmDMf8+e/g//5v0F/nzkReXgHWrt0l57SMwgSbzBIfH4+YmBgcOHAA27dvh0ajQe/evVFQUKBv8+abb2Ljxo1YvXo14uPjce3aNQwePFjBqJVTUfNc042DplDqhk25xuemP1TXFPx6EFcnTkLJ6TOy9Lfl6lU8G78H8RkZqEjVK95TEgDi/7yBYYn7sfX6dVnGA4DipAPI+fxzlF1Pl61Pcm7e3vXRoIG8f48CAhqgXj0PZGfn6Y/l5ZXfkBsY2MCgbZMmjQAAnp7ussZgDJaIKCguLg5z585FcnIytFot2rdvj5iYGERHRysdWq22bNli8PnixYsREBCA5ORkdOnSBTk5Ofjuu+8QGxuLHj16AAAWLVqE0NBQHDhwAI8++qhN45Wj1teUPqpra0ntd1V9mnrDZm1zqOlxS8a/89zcK1fMruG29GvJum+ypbKsLGQu/gEF+/bJ1uexzEy8fvBXaIVAddfBtUJAAvDmkRSs8OyE9jJ9j5ddu46czz5DvQH94dG5c/WbVhHJKDs7DxpNGdLTb2Hu3Fjk5hagZ8+H9Y+3bNkMzZoF4rPPlqF16+Z48MHWuHbtT/zzn1+iRYu7MGxYH5vHzARbIRMnTsScOXMMjiUkJCAhIQEbN27EkiVLoHKgX1w5OTkAgAYNyl89JicnQ6PRoFevXvo2bdq0QXBwMJKSkmyaYMtR62tKH9aoLa6pT2OT9triqulxAGaPX1W/kiRBmLFjpKXPLeu+yVa02dnIiYtDblwcRFGxrH1/deYMBFBtcl2hos38C+fx9UPy1U8LjQYFa9eh5OAh1OvXD66hbSBJkmz9E93p0UdH4+zZywAAL696mDJlLMaO/ftvj6urC9as+QTDh0/BwIET9cfDw0ORmPgd/Py8bR6z42RwTuT777+vlFzfbvny5fjkk09sGJFldDodJkyYgE6dOqFdu3YAgPT0dLi5ucHvjqsmgYGBSE+33duLctT6mtKHNWqLbTGHnMuXq3088dNPkfTpp2aNX924Qqertga1uhpuS58H1n2TNQkhUPbnn8iPj0fGf2Yj7eWXkbNmrezJ9dXCQuxKvw6tkTXcWiGwOyMD14qKZI0DAMquXEHuN98g+6OZKNy8BWVXr1b7wpnIEosWTcOWLf/F/PnvIDQ0BEVFJdBqDW+09/f3QceO9+Gdd0bj558/xaefTsClS9fw7LPvoLi4xOYx8wq2jel0OsyYMaPWdrNnz8aECRPgKdOd4NYUExODEydOYJ+Mb4HKRY71mk3pwxrrQ9tiDr8tX17t46KsrNorZbWNX9O4QHk5iLhte3aVi0u1NdyWPg9cu5vkoCsshCYjA2XXr0Nz/To06enl/169Cl1ueU3owF07cdNKf9ALa/h5rI4AMHBvPDyruTHZVI3d3LH2scf0n2tv3EDh5s0o3LwZkpcXXEJCoG7WDOomQVA3bgxVgwaQZBrbEjX9LiLLlJZqkJmZY3CscWN/qNVqWfqPjGyv//9hw3ojNPRZAMCnn04AAOTk5KNz55fw9tsvYNKkEfq2Dz0Uim7dXsaiRRvx6qvPyBKLsZT/jq9jLl68iNTU1FrbZWVlISUlBZ06dbJBVOYbP348Nm3ahL1796JZs2b640FBQSgtLUV2drbBVeyMjAwEBQXZLD451ms2pQ9rbCpjizkU/vlntY+j4q3fKq5M1TZ+jeOq1Qju3Bm+d99tVA25pc+DNb425Nw0GTdQduMGdAX50ObkQpuVCd1tN3IDgMrDA+4tWsC9RQv9sVu7dyG9WP4rxpbILStDrkwJpuTmBpdHHq72cQGgLCsTZVmZ5QdUKqh8fSH5+kLy8oLk4QHJ1RVQqQGVBKhUgEpVXs+tUpX/zpH+Og78/TlQ/nvott9FpiTu2r/m70jll44iMfEYund/xeBYauoGhIQ0lX0sf38f9OjxEJYv36xPsNes2YmMjFsYOLCLQduuXcPh41Mf+/cfY4Lt7G7dumWVtrYmhMBrr72GdevWYc+ePWhx2x8XAAgPD4erqyt27tyJIUOGAADOnj2LtLQ0REZG2ixOOdZrNqUPa6xPbYs51GvcGLlXrlR9cg1v+dY2fm3j+t59t9FXjS19HpReO5wcj2tgAFwDTf++aLpgAVRWKoUrKChAthnlTH5+fqhfv74sMQQGBuLuf/1Llr5sSfPXEoMqV1eFI3E+HTrch+3b5xkcCwpqaLXxiopKkJPz9yZNGRnlL+buLBsRQkCr1aFMgXcvmGDbWEhIiFXa2lpMTAxiY2Oxfv16eHt76+uqfX194enpCV9fX4wdOxYTJ05EgwYN4OPjg9deew2RkZE2vcFRjk1eTOnDGpvK2GIO7UeMwI3jx6t8XHJxgYSq316tbXw5nw9L+7LG14aoKocPH7Za32lpaQgJCTGp1lmSJBw7dgzBwcFWi8sRCF65thp/fx/06vWIRX2kpaWjsLAYbdqE6I/duJGJgADDpfcuXbqGnTsP4aGH7tcfu+++8u/tFSu24v33X9Yf37AhHgUFRXjwwdYWxWYOfrfZWGBgILp27Vpru9DQULRv377WdkpZsGABcnJy0K1bNzRp0kT/sXLlSn2bOXPmoH///hgyZAi6dOmCoKAgrF271qZxyrFetCl9WGN9alvMwTc4uNrHo956C5FvvWXW+HI+H5b2pfTa4URyCA4ORv/+/Y2ubVWr1RgwYECdT65JOTNmLMSMGQuxZk35Zi9Ll8bpj91u5Mj3EBpqWMbxwAPDMHz4u/jPf37At9+uwz//+QXCw1+ARlOGjz8er283YEAXtG17Dz78cCHGjPkA33yzBm+//QWGDXsXTZo0wtixg6w+zztJQqZbflNSUhAeHo7k5GSEhYXJ0aWili9fjhEjRlhlPikpKYiKikJJSdU3wahUKmzdutVgiTs5WHNOSjDle67S2se1rBdtaR9yjKfEHGp63JLx5Xw+LO3LGl8bIls6dOgQoqKioNVqa7ySLUkS1Go1EhMTEcFtzqHRaODm5obS0lK4skykCslW6VWSql8CVYi/3+3p1m0c4uNTDI69//43+OWX/fj99yvIyytAQEADdOnyICZPfhEPPHCvQV9ZWbmYPn0hfvllHy5fToe3dz306vUwZs6MQYsWd1k4i3CTz2CCXQ1rJ6Pbtm3D8OHDK9VZe3l5YeHChVbZbKYuJ9hERM5k7dq1iI6O/qvGtPKNu2q1GpIkYdWqVXj66acViND+MMGujXUSbOdgeoLNGmyF9O7dGxcvXsTSpUv1Ozl26NABI0eORKNGjZQOj4iI7NjgwYORmJiI6dOnY9OmTRBCQKVSQffXRk79+vXDlClTeOWaSCFMsBXk4+ODmJgYpcMgIiIHFBERgQ0bNiAtLQ27du1Cbm4ufHx80KNHD9ZcEymMCTYREZEDCw4OxujRo5UOg4huwwSbyAZ2/POfdrEVt4efH3r95z+Vjtt7fERERI6ECTaRDRRnZ6MoM1PpMKpl7/ERERE5EibYRDbgcdt28UqqLg57j4+IiMiRMMEmsgF7L3uw9/iIiIgcCXdyJCIiIiKSERNsIiIiIiIZMcEmIiIiIpIRE2wiIiIiIhkxwSYiIiIikhETbCIiIiIiGTHBJiIiIiKSERNsIiIiIiIZMcEmIiIiIpIRE2wiIiIiIhnJvlV6XFwcTp8+LXe3Nrd//34AzjMfwPnmlJqaqnQIRERERJVIQgghR0dJSUno3LkztFqtHN3ZBZVKBZ1Op3QYsnK2OanVaiQkJCAyMlLpUIiIyI5pNBq4ubmhtLQUrq6uSodjh5KVDsCOhZt8hmxXsN3d3aHVarFs2TKEhobK1a1i4uLiMHXqVKeZD+B8czp9+jRGjBgBd3d3pUMhIiIi0pO9RCQ0NBRhYWFyd2tzFSUUzjIfwDnnRERERGRveJMjEREREZGMmGATEREREcmICTYRERERkYxkr8EmIqpKYX4xzhy9hLysAnj710ebjiGo5+Vh9XOJiIhsjQk2EVld6pmr2LIiETqdDpIkQQiBX3ccR9/nOqFF66ZWO/d2TNKJiMhWmGATkVUV5hdjy4pEaLXl669XLL2v1eqw5cf9GPXWgGoTXUvOvZ2xSTqTcCIikgNrsInIqs4cvVTt5kY6nQ5njl6yyrkVbk/ShQB0OgEh/k7SC/OLAZQn4T98uhFJ247h5OHfkbTtGH74dCNSz16rdQwiIqLbMcEmIqvKyyqAJElVPiZJEvKyCqxybgVjknRjk3AiIiJjMMEmIqvy9q+vL+24kxAC3v71rXJuBWOSdDmulBMREVVggk1EVtWmYwhUqqp/1ahUKrTpGGKVcysYk6TLcaWciIioAhNsIrKqel4e6PtcJ6jVKkgSoFJJkCRArVah73OdaryJ0JJzKxiTpMtxpZyIiKgCVxEhIqtr0bopRr01wKwVOiw5F/g7Sd/y436DVURUqr+T9DYdQ/DrjuP61UpuZ+yVciIiogpMsInIJup5eSDssTY2PxeoPUk3JgknIiIyFhNsInI61a1nXVOSbumVciIiogpMsInIqViy86OlV8qJyD6lpaVh69atAIDFixejT58+CA4OVjgqcma8yZGInAbXsyai2x08eBADBgxASEgIxo0bBwAYN24cQkJCMHDgQBw6dEjhCMlZMcEms8yaNQsRERHw9vZGQEAABg0ahLNnzxq0KS4uRkxMDBo2bAgvLy8MGTIEGRkZCkVMzqgwvxgp+84gfmMyUvadwfFfz3M9ayICAKxduxadOnXC5s2bK60SJIRAXFwcoqKisHbtWoUiJGfmUCUicXFxmDt3LpKTk6HVatG+fXvExMQgOjpa6dDqnPj4eMTExCAiIgJlZWWYPHkyevfujVOnTqF+/fIlzd5880388ssvWL16NXx9fTF+/HgMHjwY+/fvVzh6qkl19cuWnlNTG3Meq6oUpJwEoPKSe9WtZ23OfC19LojIug4ePIjo6Ghotdpql+DUarWQJAnR0dFITExERESEjaMkZ+YwCfbEiRMxZ84cg2MJCQlISEjAxo0bsWTJkmrXuiX5bdmyxeDzxYsXIyAgAMnJyejSpQtycnLw3XffITY2Fj169AAALFq0CKGhoThw4AAeffRRJcKmWphTv2zMOTW1gRAmP9Z9UAR2/3xIv6ye4R9Q49eztqReW67nj4jkN2PGDAghqk2uK1S0mTFjBtavX2+j6KgucIiM9Pvvv6+UXN9u+fLl+OSTT2wYEd0pJycHANCgQQMAQHJyMjQaDXr16qVv06ZNGwQHByMpKUmRGKlm5tQvG3NOTW02x+7DZjMe27n2ILTVlIJU5871rOWu12b9N5F9SEtLw6ZNm6DVao1qr9VqsXHjRqSlpVk5MqpL7D7B1ul0mDFjRq3tZs+ejaKiIhtERHfS6XSYMGECOnXqhHbt2gEA0tPT4ebmBj8/P4O2gYGBSE9PVyBKqs2Zo5dMrl825pya2wjoqtjcpbbHhBCoemNzQJLKy0Fq2/nRnPnWRO7+iMg8O3furPXK9Z2EENi1a5eVIqK6yO5LRC5evIjU1NRa22VlZSElJQWdOnWyQVR0u5iYGJw4cQL79u1TOhSyQF5WwR21zH+rrn7Z2HOqa1P+IKqr6qj2MUkCavr7Gd41FG7urjXWQZsz35rI3R8RmScvLw8qlaraF7xVUalUyM3NtWJUZIzSUg0yM3MMjjVu7A+1Wq1QROaz+wT71q1bVmlL8hg/fjw2bdqEvXv3olmzZvrjQUFBKC0tRXZ2tsFV7IyMDAQFBSkQKdXG279+tUlwVfXLppxT49Wkmi40VfOYEICkkiB0lRuoVCq0f6RVrTcWmjNfW/ZHRObx9vY2KbkGyt9l8vHxsVJEZKzExGPo3v0Vg2OpqRsQEuJ497DYfYlISEiIVdqSZYQQGD9+PNatW4ddu3ahRYsWBo+Hh4fD1dUVO3fu1B87e/Ys0tLSEBkZaetwyQhtOoZUe6PwnfXLppxTcxsJKrXpj6nVKvR8+mGo1apaS0GqY858bdkfEZmnZ8+ekKTqisiqJkmS/oZ8Uk6HDvdh+/Z5Bh9BQQ2VDsssdn8FOzAwEF27dkV8fHyN7UJDQ9G+fXsbRUUxMTGIjY3F+vXr4e3tra+r9vX1haenJ3x9fTF27FhMnDgRDRo0gI+PD1577TVERkZyBRE7Vc/LA32f64QtP+43WAVDpao+aTX2nJraADDrsRatmyL43iCzl8QzZ7627I+IzBMcHIz+/fsjLi7OqBsd1Wo1+vXrx50d7YC/vw969XpE6TBkIQlT7wSoRkpKCsLDw5GcnIywsDA5ujToOyoqCiUlJVU+rlKpsHXrVoMVKyy1fPlyjBgxwirzUYqcc6ru6sCiRYswevRoAOUbzUyaNAk//vgjSkpK0KdPH8yfP1+2EhFrfs/VZY6yDraS87Vlf0RkukOHDiEqKqrGdbCB8r9larWa62ADAJKVDsCOhZt8ht1fwQaAsLAwbNiwAcOHD69UZ+3l5YWFCxfKmlxT7Yx5Xebh4YF58+Zh3rx5NoiI5FLPywNhj7WR/Zya2pj7mBzk7t/a8RJR7SIiIrBy5UpER0dDCFHllWy1Wg1JkrBq1Som1yQ7h0iwAaB37964ePEili5dqt/JsUOHDhg5ciQaNWqkdHhERERkRwYPHozExERMnz4dmzZtMrgwJEkS+vXrhylTpjC5JqtwmAQbAHx8fBATE6N0GEREROQAIiIisGHDBqSlpWHbtm146aWX8O2336J3796suSarsvtVRIiIiIgsERwcjFGjRgEARo0axeSarM6hrmATkWNauWCbrFuF1/PyQPSrva06TnVjEBER1YYJNhFZXWF+MQpyi5xmHCIiopowwSYiq5N7mbrq+pNzHC6tR0RE5mKCTURWZ6tSC5Z0EBGRPeBNjkREREREMmKCTUREREQkIybYREREREQyYoJNRERERCQjJthERERERDJigk1EREREJCMm2EREREREMmKCTUREREQkIybYREREREQyYoJNRERERCQj2bdKj4uLw+nTp+Xu1ub2798PwHnmAzjfnFJTU5UOgYiIFFBWVgadVmvSORqNxkrREFUmCSGEHB0lJSWhc+fO0Jr4DW/PVCoVdDqd0mHIytnmpFarkZCQgMjISKVDISIiGygpLsK55EMoLS6Bp58fXFzdjDpPo9Gg3UMRKCwshKenp5WjdETJSgdgx8JNPkO2K9ju7u7QarVYtmwZQkND5epWMXFxcZg6darTzAdwvjmdPn0aI0aMgLu7u9KhEBGRlRXn5+O3nVtw4fCvKMzNgSRJUKnVaHxPK4Q89Ai8GjSs8Xwdyq8nmnrlm8gcspeIhIaGIiwsTO5uba6ihMJZ5gM455yIiMj5XTt3GvtXLUdJQT6E7q833iUJQgjc+P0c/rx4HiFhD+PeRx+DSq2usg+WiJAtyZ5gExEREclB6HQ4vnsbftuxFUD1Fa1CCKQm/4qsa1fQsd8guNerb7sgiarABJuIFJWfk4OjiQnIunkT/o0aoWNUZ3j5+iodFhEpLO/WTSSt+RE3Un83+pzs61dxaM0KRAyOhnt9LytGR1QzJthEpJgzR1OwYv6X0Gm1kFQqCJ0OO9b9hOf+8QZad3xQ6fCISAFCp8Pp/fE4uu0X6MrKTD6/IOsWDq9bhYefeQ6uHryZkZTBdbCJSBH5OTlYMf9LaMvKIISATquFEALasjL8OP8L5OfkKB0iEdlYWWkp4pd9h5S49WYl1xXyM2/iyKZ10FrQB5ElmGATkSKOJiZUeze/TqvF0aR9No6IiJSkLdNg9+L/4crpk7L0l3XtCk7v2Q6ZViMmMglLRIhIEVk3b5aXhVSRZEsqFbL+/LPG81m7TeQ8hBBIXB2LjNQLsvZ79dRx+DQORHAHrpxFtsUEm4hs4s6EuJ6XF0Q1mx4JnQ7+jRtX2xdrt4mcy+mE3bj82xGr9H0mYRd8AgLh5utnlf6JqsIEm4isrqqEWFKpqr2CrVKr0THqsSr7ur12G4D+/Ira7bdmf8Er2UQO5M/LqTiyZaPV+hc6HY7FrUeHp5612hhEd2KCTURWVV1CLLRaqFQqqF1cDBJvlVqN5/7xBrx8qk6SjandfqxvP+tMxsGwjIbsXWlRIfatWGL1Ounignyc2LHZqmMQ3Y4JNhFZVU0JsRACXZ7sD3cPT2T9+Sf8GzdGx6jHqk2uActrt+sKltGQvRNCIGnNChRkZ9lkvOxrV/TjElkbVxEhIquqSIirIqlUKMzLx2N9+2HAC6PxWN9+NSbXAODfqJHZtdt1BZdAJEdwau8u/HHyN5uPeyZxr83HpLqHCTYRWZXcCXHHqM5QqdVVPlZT7XZdwiUQyd79cfI3HN26SZGxj2/fjAuHDigyNtUdTLCJSFb5OTnYt3kTNi5djH2bN6HVAx1kTYi9fH3x3D/egNrFBZIkQaVWQ5IkqF1caqzdrktqe9eAZTSkpGvnTiPBBnXXNTmwdiXOH0pSbHxyfqzBJlKQs92EVl3db+cn+iNh8yaTbmasSeuOD+Kt2V/gaNI+o2u36xKW0ZC9+uPkb0j48Ydq32GxHYFf165EUW4OHujRB5IkKRwPORsm2EQKcbab0GpaPi9h8ya8+t50nD/xm2wJsZevL1cLqUbHqM7Yse6nKreJZhkNKeXikUNIWh1r0ZXrmzm5OHnpD+QXFMDD3Q33BzdDQx9vs/v7bccWZF67isghz8G9Xj2z+6HqZWfn4Z///BLr1u1GYWExHn64LT777E2EhbWp8TydToclS37B2rW7cOTIOWRm5qBFi6YYNqw33nrrBXh4uBu0l6SHquxn1qzxeOed0XJNx2hMsIkU4IxrOddW93v+xG9MiG2koozmx/lfyPauAZElzh3Yh4PrfzL7/N+vpePnxIM4diEVAoAkSRBCQALQ/p7mGPBIOFoEBZjV95VTx/HL1T8Q9ezzCGrZyuwYqTKdTod+/d7AsWPn8fbbL6BRIz/Mn/8TunV7GcnJS9GqVXC15xYWFmPMmA/w6KMP4JVXBiMgoAGSkn7DtGn/w86dh7Br19eV3nl4/PFHMHKk4d+ZBx9sbZW51YYJNpll1qxZWLt2Lc6cOQNPT09ERUXhk08+QevWf38jFxcXY9KkSVixYgVKSkrQp08fzJ8/H4GBgQpGbh+ccS1nLp9nX1hGQ/ZACIETe7bj2LY4s/s4dPYC5q3fDCEEKq59V1wFFwCOp6bh+KU0vPLk4whvdY9ZYxTmZGPHwnloHdUFHfv0g6ube+0nUa1++mknEhN/w+rVH+OZZ3oBAIYOfRz33TcY06Z9g9jYj6o9183NFfv3f4eoqA76Yy+99DRCQppi2rRvsHPnQfTq9YjBOffdF4wRI560zmRMxARbQXFxcZg7dy6Sk5Oh1WrRvn17xMTEIDo6WunQahUfH4+YmBhERESgrKwMkydPRu/evXHq1CnUr18fAPDmm2/il19+werVq+Hr64vx48dj8ODB2L9/v8LRm0aOOuk7+7hx7Vqtyahc9dmW9FPTuXJsfW5qbNZubyxj+pV77Nr6q+5xR3uhRs5DU1qCgz//hNQjh8zu4/dr6Zi3fjO01fxuAQCdEIAAvo7bjsnRT5t9JRsAzibuxdUzJ/HI00PR5F5lrnw6k59+2onAwIYYPLiH/ljjxv4YOrQXli3bjJKSUri7u1V5rpubq0FyXeHpp7tj2rRvcPp0aqUEGwCKioohSVKlEhJbY4KtkIkTJ2LOnDkGxxISEpCQkICNGzdiyZIlUFWzCoA92LJli8HnixcvRkBAAJKTk9GlSxfk5OTgu+++Q2xsLHr0KP/BWrRoEUJDQ3HgwAE8+uijSoRtMjnqpKvqA3+9vVkVodNBoynFp2+/YXF9tiXx13SugLB463NTY7N2e2MZ06/cY9fWn7PV85NjEzod/jh1HMlx61GQlWlRX+sTDxpfsy2ATb8m47WnnrBozPzMW9j53QK0ePAhPNh3AOrxXR+zHTlyFmFhrSvlMw8/3Bb/+986nDuXhgceuNekPtPTbwIAGjXyq/TY4sWbMH/+TxBCIDS0BaZMGYvhw/uaHb8l7DeDc2Lff/99peT6dsuXL8cnn3xiw4gsl/PXxhUNGjQAACQnJ0Oj0aBXr176Nm3atEFwcDCSkhxjaSQ5Nuuorg+h0wHV/NGQVCr8diDR4k1CLIm/pnNj582t8jGdVgsIYdTyeabGZu32xjKmX7nHrq2/jCt/cFMZsgulRYU4d2AfNn3xH+xdvsji5PpmTi6OXkgtv0JtBJ0QOHbxMm7l5lk0boXUI4ex4bOPkLJ5A/Iyb8rSZ11z/fpNNGnSqNLximPXrpleOvif/yyBj099PPFEJ4PjUVHt8dFH/8DPP3+KBQvegVqtwvPPT8GCBebX/luCV7BtTKfTYcaMGbW2mz17NiZMmABPT08bRGUZnU6HCRMmoFOnTmjXrh0AID09HW5ubvDz8zNoGxgYiPT0dAWiNJ0cddI19QEAKpWq/Ead225Ca/9IFI4mJlg0rqXx13ZudYzd+tzU2Kzd3lhGbeBS8WJDprFrG3PbTyudrp6fHIPQ6ZB36yYyUi/g6plTuHb+DHR/3bj93uIfkV1QaFH/JRoNTF1vRAB4f9lquLu6Vn7sr0R94teLql0n/k5+9evhw9JSnNq7C76NA9GgWTB8AwLh2zgAfkFN4eXfwOi+6qKiopIqS0AqyjeKikpM6m/mzO+xY8dBzJ//Dvz8DFeP2b//e4PPX3zxKYSHj8DkyfMwenR/eHp6mBi9ZZhg29jFixeRmppaa7usrCykpKSgU6dOtbZVWkxMDE6cOIF9+5xrdzg5btqrqQ+VWo0Oj0Yh4K5mBsno7vXrZLlZ0JL4azoXkgQJf/+xurPfwrx89Bz0jKyxWbu9sYztV86xaxsz+xZvLiXLacs0KCvVQKctg1ajgbasDNoyzd//r9GgrLQUpcWFKM7PR0FWJnL+vAFNSbG+D59Gf9c+5xYVIysvX4mpoLCkFIUlpdU+bkrir1Kp4BfUVP951vWryLp+Vf+5q7s7/AKbwKdxALz8G6Cerx/c69WHq4dn+bt5qvJVLiRJVe2GW86gtFSDzEzDd8saN/aHp6c7Sqr4WhQXlyfWnp7G10mvXLkNU6YswNixT+HVV2v+GwOU13CPHz8Ur7wyC8nJZ/DYYx2NHksOTLBt7NatW1Zpq5Tx48dj06ZN2Lt3L5o1a6Y/HhQUhNLSUmRnZxtcxc7IyEBQUJACkZpOjs06ausj4K5mla4uyrVJiCX91HQubruT39z4TI3N2u2NZVS/FSVAMo1d25h+DRvhz2tXq32cm8qQMdQurlC7VL7qa66QpavgYeG7lQUFBcjOzjb5PD8/P/3N9rcTQuDatWto2rSp0RvLBAUFof8b/zQ5hromMfEYund/xeBYauoGNGnSCNevVy6vqTjWtKlxv5+2bz+AkSOnoV+/x/D11/82Oq677y5ftezO5N8WmGDbWEhIiFXa2poQAq+99hrWrVuHPXv2oEWLFgaPh4eHw9XVFTt37sSQIUMAAGfPnkVaWhoiIyOVCNlkcmzWYU4fcm0SYkk/tZ0rSZJNnxdrtzeWUf0KyDp2bWP2fjYav586wU1lyK4cPnzY4j7S0tIQEhJi0sY0kiTh2LFjCA6uvL6yRqOBm5sbLl26BNcqSkjIfB063Ift2+cZHAsKaoiOHe9DQsJR6HQ6gxsdf/31JOrV88B991W/DvbfbU/g6affxkMPhWLVqllwcTE+db14sfziQ+PG/kafIxcWDtlYYGAgunbtWmu70NBQtG/f3gYRmScmJgbLli1DbGwsvL29kZ6ejvT0dBQVFQEAfH19MXbsWEycOBG7d+9GcnIyxowZg8jISIdZQaRisw5jbtqTsw85xrW0n5rOHR4zwebPi7XbG8uYfuUeu7b+Au+62ypzJVJacHAw+vfvD7WRpRVqtRoDBgyoMrkm6/L390GvXo8YfHh4uOOZZ3oiI+MW1q7dpW9782Y2Vq/egQEDOhvUZ//++xX8/vsVg35Pn05Fv35vICSkCTZtmlttHfWff2ZVOpaXV4C5c39Eo0Z+CA8PlWmmxpOEJXuW3iYlJQXh4eFITk5GWFiYHF0qavny5RgxYoRV5pOSkoKoqCiUlFRd3K9SqbB161aDFTjkIOecqnt7bdGiRRg9ejSAvzea+fHHHw02mpGrRMRW33P5OTkWb9ZhTh9yjGtpPzWdq8TzYu32csYt99i19WetuRIp6dChQ4iKioL2r9VxqiNJEtRqNRITExEREVFlm4or2KWlpbyCXaVk2XvUarV47LH/w4kTv9+2k+NqpKVl4NChH9C6dYi+bUjIAADApUsbAZQnyG3bDsXVq39i5sx/4K67DNc3b9myGSIjyy9Evv/+N/j553gMGNAZwcFBuH79Jr7/fgPS0tKxdOmHeP55y5ZuBMJNPoMJdjWsmWADwLZt2zB8+PBKddZeXl5YuHChVTabsfacbM3ZvueIiKiytWvXIjo6unz5ySpu5lX/9a7NqlWr8PTTT1fbDxPs2sifYANAVlYu3n77C/z88x4UFZUgIuJ+fPrpBDz00P0G7e5MsC9duoYWLQZW2++oUf2xePH7AMprtGfPXorjxy/g1q0c1K/viYcfbot//WsUevSo+gWXaUxPsFmDrZDevXvj4sWLWLp0qX4nxw4dOmDkyJFo1KjympFERER10eDBg5GYmIjp06dj06ZNEEJApVJBp9NBkiT069cPU6ZMqfbKNSnL398HCxdOxcKFU2tsV5FYVwgJaQohjKvlf/zxR/H44/ZVfsoEW0E+Pj6IiYlROgwiIiK7FhERgQ0bNiAtLQ27du1Cbm4ufHx80KNHD9Zck11igk1EREQOITg4WH+fD5E9Y4JN5MAWfDAV+bnZSodhES8fP7w6bbrBMWedFxER1Q1MsIkcWH5uNnKzKi9P5OicdV5ERFQ3MMEmcmBePn5Kh2CxqubgrPMiIqK6gQk2kQNz1hIEZ50XERHVDdzJkYiIiIhIRkywiYiIiIhkxASbiIiIiEhGTLCJiIiIiGTEBJuIiIiISEZMsImIiIiIZMQEm4iIiIhIRkywiYiIiIhkxASbiIiIiEhGTLCJiIiIiGQk+1bpcXFxOH36tNzd2tz+/fsBOM98AOebU2pqqtIhEBEREVUiCSGEHB0lJSWhc+fO0Gq1cnRnF1QqFXQ6ndJhyMrZ5qRWq5GQkIDIyEilQyEiIjum0Wjg5uaG0tJSuLq6Kh2OHUpWOgA7Fm7yGbJdwXZ3d4dWq8WyZcsQGhoqV7eKiYuLw9SpU51mPoDzzen06dMYMWIE3N3dlQ6FiIiISE/2EpHQ0FCEhYXJ3a3NVZRQOMt8AOecExEREZG94U2OREREREQyYoJNRERERCQjJthERERERDKSvQabiMgRaQpL8OeZayjJLYK7jycat2kK13qOcQOtI8d+O2eZBxERE2yiOszYhMbZE5+s1Bs4v/kohE4HSZIghMCVA+fR6omO8G8RoHR4NXLk2G/nLPMgIgKYYBPVWcYmNM6e+GgKS8rnpy1fH75iawCh1eH85qN4cHRX/YsJW7/QqG08U2K3Z84yDyKiCqzBJqqDDBIaAQidKP/3r4RGU1hiUjtH9ueZaxDVbL4kdDrcPHMNQPkLjSOL4/FH4ln8efIP/JF4FkcWxyMr9YZV4jJmPGNjt3fOMg8iogpMsInqIGMTmrqQ+JTkFkGSpCofkyQJxblFNn+hYex4xsTuCJxlHkREFZhgE9VBxiY0dSHxcffx1Jck3EkIAQ8fT5u/0DB2PGNidwTOMg8iogpMsInqIGMTmrqQ+DRu0xSSqupfhZJKhUZtmtr8hYax4xkTuyNwlnkQEVVggk1UBxmb0NSFxMe1njtaPdERkloFSICkksr/VavQ6omOcK3nbvMXGsaOZ0zsjsBZ5kFEVIGriBDVQRUJzZ2rg0gqw4TG2HaOzr9FAB4c3RU3z1xDcW4RPHw80ei2FTsat2mKKwfO61e5uJ01XmiYMl5tsTsKZ5kHERHABJuoTqhquTdjE5q6kvi41nNHk7AW1T5myxcapo5XU+yOxFnmQUTEBJvIydW2jrUxCQ0TH9u/0KgrL2yIiJwRE2wiJ8YNPORl6xcafGFDJI+0tDRs3boVALB48WL06dMHwcHBCkdFzow3ORI5sbqwjjURUXUOHjyIAQMGICQkBOPGjQMAjBs3DiEhIRg4cCAOHTqkcITkrJhgk1lmzZqFiIgIeHt7IyAgAIMGDcLZs2cN2hQXFyMmJgYNGzaEl5cXhgwZgoyMDIUirpvqwjrWRERVWbt2LTp16oTNmzdXWpVHCIG4uDhERUVh7dq1CkVIzowlIgqKi4vD3LlzkZycDK1Wi/bt2yMmJgbR0dFKh1ar+Ph4xMTEICIiAmVlZZg8eTJ69+6NU6dOoX79+gCAN998E7/88gtWr14NX19fjB8/HoMHD8b+/fsVjt553Xkzo6uHq0XLy1V1c6StS0psEUNNY9jyOTB3LDlirCtfa6obDh48iOjoaGi12mp/B2q1WkiShOjoaCQmJiIiIsLGUZIzY4KtkIkTJ2LOnDkGxxISEpCQkICNGzdiyZIlUFWz/rA92LJli8HnixcvRkBAAJKTk9GlSxfk5OTgu+++Q2xsLHr06AEAWLRoEUJDQ3HgwAE8+uijSoTt1Kq6mRGS9Pf/36G25eVquznSFmwRQ01jALDZc2DuXOV4jurK15rqjhkzZkAIUW1yXaGizYwZM7B+/XobRUd1gf1mcE7s+++/r5Rc32758uX45JNPbBiR5XJycgAADRo0AAAkJydDo9GgV69e+jZt2rRBcHAwkpKSFInRmRnczCgAoROAAKATELht4w4jN/Corr+KmyM1hSWKzUnOGGoa41zcEZyPO2KT58DcucrxHNWVrzXVHWlpadi0aRO0Wq1R7bVaLTZu3Ii0tDQrR0Z1CRNsG9PpdJgxY0at7WbPno2iIseoj9XpdJgwYQI6deqEdu3aAQDS09Ph5uYGPz8/g7aBgYFIT09XIErnVtPNjBACTcPvQXBUazRuezeCo1rjwdFda7wqaA83R9oihhqfN50oT/SsOL4xcdQ0lhzPUV35WlPdsXPnzlqvXN9JCIFdu3ZZKSKqi1giYmMXL15Eampqre2ysrKQkpKCTp062SAqy8TExODEiRPYt2+f0qHUWRU3M1ZZCiJJ0BRr0OzRVrL1Z4ubI20RQ01j1ETu58DcucrxHNWVrzXVHXl5eVCpVNBV9+K5CiqVCrm5uVaMioxRWqpBZmaOwbHGjf2hVqsVish8TLBt7NatW1Zpq5Tx48dj06ZN2Lt3L5o1a6Y/HhQUhNLSUmRnZxtcxc7IyEBQUJACkTo3dx9Pi25mtHZ/5rBFDDWNURO5nwNz5yrHc1RXvtZUd3h7e5uUXAPl78T6+PhYKSIyVmLiMXTv/orBsdTUDQgJqf5+IXvFEhEbCwkJsUpbWxNCYPz48Vi3bh127dqFFi0MN8MIDw+Hq6srdu7cqT929uxZpKWlITIy0tbhOr3GbZpCquam2NpuZrRFf+awRQw1jQGVVF6zbsXxjYmjprHkeI7qytea6o6ePXtWuzxpdSRJ0t+QT8rp0OE+bN8+z+AjKKih0mGZhQm2jQUGBqJr1661tgsNDUX79u1tEJF5YmJisGzZMsTGxsLb2xvp6elIT0/X1437+vpi7NixmDhxInbv3o3k5GSMGTMGkZGRXEHEClzruaPVEx0hqVUm3cxoq/7MYYsYahrjvicfRKsnH7TJc2DuXOV4jurK15rqjuDgYPTv39/osgK1Wo0BAwZwZ0c74O/vg169HjH48PBwzJ9/SZjz/mgVUlJSEB4ejuTkZISFhcnRpaKWL1+OESNGWGU+KSkpiIqKQklJ1XfGq1QqbN261WAFDjnIOafqrg4sWrQIo0ePBlC+0cykSZPw448/oqSkBH369MH8+fNlKxFxtu85OWgKS3DzzDUU5xbBw8cTjSxcR1ju/uw1hprGsOVzYO5YcsRYV77WVDccOnQIUVFRNa6DDZT/LVOr1VwHGwCQrHQAdizc5DNYg62AsLAwbNiwAcOHD69UZ+3l5YWFCxfKnlzLzZjXZR4eHpg3bx7mzZtng4gIKL8S2CSsRe0NFerPXmOoaQxbPgfmjiVHjHXla011Q0REBFauXIno6GgIIapcsk+tVkOSJKxatYrJNcmOCbZCevfujYsXL2Lp0qX6nRw7dOiAkSNHolGjRkqHR0RE5NAGDx6MxMRETJ8+HZs2bTK4MCRJEvr164cpU6YwuSarYIKtIB8fH8TExCgdBhERkVOKiIjAhg0bkJaWhm3btuGll17Ct99+i969e7PmmqyKNzkSERGRUwsODsaoUaMAAKNGjWJyTVbHK9hEVKsTKxNRWuB821W71XdHu+ioSsfr2nyJiEheTLCJqFalBSXQOGHCWZ26Nl8iIpIXE2wiqpVbfedcKq26edW1+RIRkbyYYBNRrepaWUFdmy8REcmLNzkSEREREcmICTYRERERkYyYYBMRERERyYgJNhERERGRjJhgExERERHJiAk2EREREZGMmGATEREREcmICTYRERERkYyYYBMRERERyYgJNhERERGRjJhgExERERHJyEXuDuPi4nD69Gm5u7W5/fv3A3Ce+QDON6fU1FSlQyAiIguUlZVBp9PZZCyNRmOTcYgAQBJCCDk6SkpKQufOnaHVauXozi6oVCqb/eDbirPNSa1WIyEhAZGRkUqHQkRERtDpdLhw4QJOnjyJ69evw9XVFc2aNUOLFi2gVqutNq5Go0Hbtm1RWFgIT09Pq43juJKVDsCOhZt8hmxXsN3d3aHVarFs2TKEhobK1a1i4uLiMHXqVKeZD+B8czp9+jRGjBgBd3d3pUMhIiIjXLt2Dfv370dmZiaEECgrK4NWq8WFCxeQnp6OsLAw+Pv7W2XsiotLznSRieyX7CUioaGhCAsLk7tbm6sooXCW+QDOOSciIrJ/paWlOHDgQLXliZIkoaCgAPv27UPLli3Rpk0buLm5yRqDi4vsKQ9RtfjdRkRERFZz8+ZN7NixAzk5ObW2FULgwoULuHTpEpo1a4Z77rkHfn5+1g+SSGZMsImIiMgqLly4gPj4eJSVlZl0XllZGS5duqRPtDt27Cj7FW0ia2KCTUQ2lZ+fj2PHjiE7Oxt+fn7o0KEDvLy8lA4LgH3HRuRIhBA4fPgwUlJSLO7rypUryM3NxWOPPQYPDw8ZoiOyPibYRGQzZ8+exapVq6DT6SBJEoQQ2LVrF4YOHYrWrVvLMoa5SbItYiOqC7RaLeLj43H+/HnZ+szNzdWvVsZaanIE3GiGiGwiPz8fq1atglarhRACOp0OQghotVqsWrUK+fn5Fo9x9uxZzJkzBzt27EBycjJ27NiBOXPm4OzZs4rHRlQXlJWVYevWrbIm1xWysrJw7Ngx2fslsgYm2ERkE8eOHat2eSydTmfxH05LkmRrx0ZUF5SVlWHLli34448/rDbG5cuXrdo/kVyYYBORTWRnZ0OSpCofkyQJ2dnZFvVvSZJs7diInJ1Wq8X27dtx9epVq4919OhRFBYWWn0cIkswwSYim/Dz80N1G8cKISxeisuSJNnasRE5MyEE4uPjkZaWZpPxNBoNDh8+XO3PLJE9YIJNRLLKz8/H/v378csvv2D//v360owOHTpApar6V45KpUKHDh0sGteSJNnasRE5s+TkZKvUXNfk5s2b1W5aQ2QPmGATkWxqusnQy8sLQ4cOhVqthiRJUKlUkCQJarUaQ4cOtXg5PEuSZGvHRuSszp07h+TkZEXGPnPmDK5du6bI2ES14Vo3RCSL228yBKC/mlxxk+Gbb76J1q1b480337TKWtMVSfKdS+2pVCqjkmRrxkbkjK5fv469e/cqGsOhQ4fQuXNnNGjQQNE4iO7EBJuIZGHMTYadOnWCl5cXOnXqZJUYLE2SrRkbkTPJzMzE1q1b9S+olaLVarF//3489thj8Pf3VzQWotsxwSYiWVTcZFhVHbQtV+JgkkxkXTdv3kRcXBxKSkqUDgVA+U2P+/btQ1RUFBo2bKh0OEQAWINNRDLhShxEzu/SpUvYsGEDioqKlA7FQEWSnZ6ernQodIfs7DyMG/cRGjfuhfr1H0P37i8jJeWMUeeOHv0+JOmhSh9t2gwxaHft2p8YMWIqWrceDG/vLvDz64aHHx6JH37YpNhqM7yCTUSy6NChA3bt2lXlW8ZciYPIsZWUlODXX3+VbeWOzMxMnD59GgUFBfDw8EDr1q0trqPWarVISkpCeHg4goODZYmTLKPT6dCv3xs4duw83n77BTRq5If5839Ct24vIzl5KVq1qv3r5O7uhoULpxgc8/U1LPu7eTMbV65k4JlneiI4OAgaTRm2b/8Vo0e/j7NnL2PmzBhZ52UMJthkllmzZmHt2rU4c+YMPD09ERUVhU8++QStW7fWtykuLsakSZOwYsUKlJSUoE+fPpg/fz4CAwMVjJysxdKbDInI/gghcPHiRSQlJaGgoMDi/lJTUxEXF4cTJ05ACKH/PSFJEtq2bYu+ffuiefPmFsV7+PBh5OXl4f777692bXyyjZ9+2onExN+wevXHeOaZXgCAoUMfx333Dca0ad8gNvajWvtwcVFjxIgna2zTvn0r7NnzP4Nj48dHY8CAN/HllyswfforUKvV5k/EDEywySzx8fGIiYlBREQEysrKMHnyZPTu3RunTp1C/fr1AQBvvvkmfvnlF6xevRq+vr4YP348Bg8ejP379yscPVkLV+Igch63bt1CUlKSbLszHjlyBN9++y2Av1cZuv3fU6dO4dSpUxgzZgw6duxo0Vhnz55FZmYmwsLC9H+TyPZ++mknAgMbYvDgHvpjjRv7Y+jQXli2bDNKSkrh7u5Waz9arRYFBUXw8THtb0lISBMUFhajtFQDT08m2HVGXFwc5s6di+TkZGi1WrRv3x4xMTGIjo5WOrRabdmyxeDzxYsXIyAgAMnJyejSpQtycnLw3XffITY2Fj16lP9gLVq0CKGhoThw4AAeffRRJcKmO+Tn51uUDFd3fk03GdY0piXxWDoXufogcmRCCGRkZOD48eNITU2VrX41NTUV3377bbUrDQHQP7Zo0SJMnDjRoivZAPDnn39ix44daNmyJVq2bGlRX2SeI0fOIiysdaU9Ch5+uC3+9791OHcuDQ88cG+NfRQWFsPHpysKC4vh7++D557rg08+eQ1eXvUqtS0qKkZBQTHy8wsRH5+CRYs2IjLyAXh6esg6L2MwwVbIxIkTMWfOHINjCQkJSEhIwMaNG7FkyZJqN82wRzk5OQCgr6FLTk6GRqNBr1699G3atGmD4OBgJCUlMcG2A2fPnq1UzrFr1y4MHTrUoNRHzvNrOgeA2fFYOhe5+iByRDqdDhkZGbh8+TJSU1ORm5sr+xibN282qf3WrVsxbtw4i8fVarU4d+4czp8/r1/Gr6CggFe1beT69Zvo0uXBSsebNGkEoPzmxJoS7CZNGuGf/xyJsLA20Ol02LIlCfPnr8axY+ewZ883cHExTGO/+GIF/v3vr/Sf9+z5MBYtek+m2ZiGCbYCvv/++0rJ9e2WL1+Otm3b4t///rcNozKfTqfDhAkT0KlTJ7Rr1w4AkJ6eDjc3t0orRwQGBvIubztgzKYwNV25Nef8ms5ZuXIlJEkyKx5L5yJXH0SOpLCwEFeuXMEff/yBP/74w6pL7mVmZuL48eNGXw3X6XQ4ceIEMjMzZdtARgiBGzduAABWr16NwMBA3HXXXWjSpAkaNWqE+vXrs17bCoqKSqosAfHwcNc/XpNZs8YbfD5sWB/cd18w3n13Pn76aSeGDetj8Phzz/XBQw+F4s8/s7Bp0z5kZGTWOoa1MMG2MZ1OhxkzZtTabvbs2ZgwYQI8PT1tEJVlYmJicOLECezbt0/pUMhIxm4KI+f5tZ1TndrisXQucvVBpAStVouysjLodDqDBFYIoX+suLgYhYWFyMvLQ1ZWFm7cuKF/1/FOM2fOlP0KdklJicmlJkIIfPzxx3Bzq70+1xg+Pj6YOHGi/vNbt27h1q1b+O233wAA7u7u8PHxgY+PD7y9veHt7Q0vLy94enrCw8MDbm5uUKvVUKvVTMSrUFqqQWam4fdU48b+8PR0R0lJaaX2xcXlSa+np7vJY7355nBMnfo1duw4WCnBbt68CZo3bwIAeO65vhg37iP06vUPnD27xuZlIkywbezixYtITU2ttV1WVhZSUlLs/o/6+PHjsWnTJuzduxfNmjXTHw8KCkJpaam+lrVCRkYGgoKCFIiUbmfppjDmnF/TOTWpLR45Nrixl01yiEwlhNCvwnFn4qdWq+Hm5oZ69eoZfSX4ww8/tJvv96KiItnW2/bw8ED37t0BAM8//zw8PExLtoQQKCsrQ1lZGSRJgouLi81XpbBniYnH0L37KwbHUlM3oEmTRrh+/Wal9hXHmjZtbPJYnp4eaNjQF5mZtb8QfOaZnvj223XYu/cI+vSJNHksSzDBtrFbt25Zpa2tCSHw2muvYd26ddizZw9atGhh8Hh4eDhcXV2xc+dODBlSviD82bNnkZaWhshI236TU2WWbgpjzvk1nVOT2uKRY4MbbpJDjsrFxaVSHaolmjRpIvsV2oKCArOSdj8/P9lqpQMDA3HvveW1vvXr14erq6ss/VK5Dh3uw/bt8wyOBQU1RMeO9yEh4Sh0Op3BfWW//noS9ep54L77TF+vPC+vADdvZqNxY79a2xYVFQMAcnLyTR7HUkywbSwkJMQqbW0tJiYGsbGxWL9+Pby9vfV11b6+vvD09ISvry/Gjh2LiRMnokGDBvDx8cFrr72GyMhI3uBoByzdFMac82s75/YabFPikWODG26SQ1Tu8OHDsveZlpaGkJAQk15gS5KEY8eOybphjEajka0vMuTv74NevR6pdPyZZ3rip592Yu3aXfp1sG/ezMbq1TswYEBng/rs33+/AgBo2bL83fDi4hJoNGXw9jZ8kTV9+kIIIdC3b5T+2J9/ZqFxY/9K43/33XpIkoSwsDaWT9JETLBtLDAwEF27dkV8fHyN7UJDQ9G+fXsbRWW6BQsWAAC6detmcHzRokUYPXo0AGDOnDlQqVQYMmSIwUYzpDxLN4Ux5/zazgEqryJiTDxybHDDTXKIrCc4OBj9+/dHXFxclS9i76RWq9GvXz/uxugEnnmmJx599AGMGfMhTp1K/Wsnx9XQanX44IOXDdr27PkqAODSpY0AgPT0W3jwwefx3HN90KZNCABg69YkxMXtR9++UXjqqa76cz/66Hvs338MfftGIjg4CJmZOVizZhcOHTqF116Lxr333m2bCd9GEjItcpmSkoLw8HAkJycjLCxMji4VtXz5cowYMcIq80lJSUFUVFS1d22rVCps3brVYIk7OVhzTkpwtu85JVhrHWxzz+E62ETO6dChQ4iKioJWq63xSrYkSVCr1UhMTERERISsMWg0Gri5uaG0tJQlIlVKtkqvWVm5ePvtL/Dzz3tQVFSCiIj78emnE/DQQ/cbtAsJGQDg7wQ7OzsPr732Hxw4cALXrv0JrVaHe+9thueffwJvvfUCXF3/vka8ffsBfPnlSqSknMGff2bBw8Md7dvfi//7v0EYNaq/DGVP4SafwSvYCggLC8OGDRswfPjwSnXWXl5eWLhwoezJNVFVatsUxhrn13SOJfFYOhe5+iCiyiIiIrBy5UpER0frVzi5U8UKHatWrZI9uSbl+Pv7YOHCqVi4cGqN7SoS6wp+ft5YunS6UWM8/vijePxx+yo/ZYKtkN69e+PixYtYunSpfifHDh06YOTIkWjUqJHS4REREclq8ODBSExMxPTp07Fp0yZ9GVZFWVa/fv0wZcoUJtfkFJhgK8jHxwcxMTFKh0FERGQTERER2LBhA9LS0rBr1y7k5ubCx8cHPXr0YM01ORUm2EQki2+++Qb5+ZYvheTl5YWXX3650nFr909EthMcHKy/IZ7IGTHBJiJZ5OfnIy8vz2H7JyIikgsTbCKShVyrbVTXj7X7JyIikgsTbCKShbXLLljWQUREjkJVexMiIiIiIjIWE2wiIiIiIhkxwSYiIiIikhETbCIiIiIiGTHBJiIiIiKSERNsIiIiIiIZMcEmIiIiIpIRE2wiIiIiIhkxwSYiIiIikhETbCIiIiIiGTHBJiIiIiKSkYvcHZ4+fVruLhWRmpoKwHnmAzjfnCrm4SzzISL5NGnSBE2aNKmxzfXr13H9+nUbRURKKysrAwAcOXIELi6ypz92xZjvf7IyIZPLly+LevXqCQBO86FWqxWPgXOq+cPFxUXxGPjBD37Y38e0adNq/bs1bdo0xePkBz+s8WHM9z9ZlySEEJBJWloabt68KVd3iispKYG7u7vSYcjK2eak1WqhVqurfTw/Px9du3ZFfHw8vLy8bBiZ/JxpLoBzzceZ5gI4x3xsfQXbGZ6z2znbfADnm1NN8+EVbOXJmmAT2Zvc3Fz4+voiJycHPj4+SodjEWeaC+Bc83GmuQDONx9bcLbnzNnmAzjfnJxtPs6GNzkSEREREcmICTYRERERkYwcLsHet2+f0iFYhU6nUzoEp+Tu7o5p06Y5Rd25M80FcK75ONNcAOebjy0423PmbPMBnG9OzjYfZ+NQNdhHjx5FWFgYpk6dig8++EDpcGRRUlICV1dXqFQqHD16FB07dlQ6JCIiIiKygENdwb7//vsxf/58fPLJJ06RYP/+++8YNmwYcnNzsWrVKoSFheHQoUNKh0VEREREFnColdbd3NwwduxYqFQq/OMf/wAATJs2TeGozOfm5oa4uDj06NEDv/32GxYtWoSIiAgIISBJktLhmaUi9gsXLkCSJLRs2VLpkIiIiIhsyqGuYAOAq6srRo0ahfnz52P69OkOeyVbq9Xi7rvvxldffYWjR4+iTZs2ePzxx/UJqgNV7uhVxP7zzz/jySefxM6dO3Hjxg2lwyIiIiKyKYdIsO9MNt3d3fHiiy/iq6++csgkWwgBtVoNIQQCAwMxd+5c3Lx5Ey+88ALOnj0LAA6XZJeVlUGSJPzyyy8YMWIEYmJi8PTTTyMgIMCgHW/mJCIiImdn9wl2xVXRxMRELFiwAJMnT8axY8eQn5+PV155Bf/9738dKsmumM+ePXvwn//8B5GRkXj99ddx8OBBnDhxAjExMTh37hyA8iR769atCkdcs8uXL0MIARcXF9y8eROzZ8/GW2+9hTfeeANeXl64du0aFi1ahDVr1gAAVCqVTV447N27FwMGDEDTpk31V9Ud1axZsxAREQFvb28EBARg0KBB+hdijmbBggVo3749fHx84OPjg8jISGzevFnpsGTz8ccfQ5IkTJgwQelQzPL+++9DkiSDjzZt2igdlkO5dOkSxo4dixYtWsDT0xMtW7bEtGnTUFpaqnRoFvnoo48QFRWFevXqwc/PT+lwTDZv3jyEhITAw8MDjzzyCA4ePKh0SGZzpr9vzszuE2xJkrBmzRr06dMHa9aswfLlyzFo0CC89957uHLlCl599VV89dVX+Pjjj/Gvf/1L6XBrVJFcr1mzBk899RSKi4tx7do1CCEQHByMw4cP48SJE/jHP/6BzZs347333sPAgQNx5coVpUOvUklJCYYNG4Z77rkHQgg0atQIkiTBzc0NFy5cwHvvvYcXXngBb7/9Nt59911MnjwZAGxSX15QUIAOHTpg3rx5Vh/L2uLj4xETE4MDBw5g+/bt0Gg06N27NwoKCpQOzWTNmjXDxx9/jOTkZBw+fBg9evTAU089hZMnTyodmsUOHTqEb775Bu3bt1c6FIu0bdtWv4X49evXnXZpVGs5c+YMdDodvvnmG5w8eRJz5szB119/rf/956hKS0vx7LPP4tVXX1U6FJOtXLkSEydOxLRp05CSkoIOHTqgT58+DlvC6Ex/35yasHNnz54VzZs3FwsXLhQajUYIIcTs2bNF165dxRtvvCFyc3NFaWmp+OKLL0SjRo3En3/+qXDENUtMTBQNGjQQ3377rcHxW7duCSGESEtLE61btxYdO3YUwcHBIjk5WYkwjaLT6URCQoJo166dePDBB4UQQkyaNEncf//9ws3NTTzzzDNi0aJFIj09XYwePVq8+OKLisQJQKxbt06Rsa3hxo0bAoCIj49XOhRZ+Pv7i4ULFyodhkXy8vJEq1atxPbt2/W/mxzRtGnTRIcOHZQOw+n85z//ES1atFA6DFksWrRI+Pr6Kh2GSR5++GERExOj/1yr1YqmTZuKWbNmKRiVPJzt75szsbsr2EIIgxKCvLw8lJWV4eGHH4aLS/miJ2+99RaefPJJrF+/Hjdu3ICrqyteeeUVnD9/Ho0aNVIqdKMkJSWhY8eO+L//+z/k5+dj48aNGDp0KHr37o2lS5fi7rvvxoEDB/DDDz/g4MGDCAsLUzrkakmShKioKHz77bfIz89Ht27d8Omnn+KHH37Ajz/+iNWrV2PkyJEIDAyEEAIqlQpardahasvtUU5ODgCgQYMGCkdiGa1WixUrVqCgoACRkZFKh2ORmJgY9OvXD7169VI6FIudP38eTZs2xT333IPnn38eaWlpSofk8HJychz+59VRlZaWIjk52eBnU6VSoVevXkhKSlIwMnJ2drdMX0X5wJYtW+Dm5gZXV1dIkoSioiIA5T8sbm5u+Oc//4lPP/0UP//8MyZNmgQ3Nze4ubkpGbpRGjdujNTUVHzyySfYs2cPXFxc4OLigs6dO2PUqFGIiIhAmzZt7LbGLT09HZcuXcKjjz4KoPwXVXh4OJYtW4Zhw4bhkUcewYEDB/DQQw9BCIGbN2/i888/x/r167F//36o1WqFZ+DYdDodJkyYgE6dOqFdu3ZKh2OW48ePIzIyEsXFxfDy8sK6detw//33Kx2W2VasWIGUlBSnWMP+kUceweLFi9G6dWtcv34dH3zwATp37owTJ07A29tb6fAc0oULF/Df//4Xn376qdKh1Ek3b96EVqtFYGCgwfHAwECcOXNGoaioLrC7K9gAcPDgQTz55JPIyspCp06dEBgYiIkTJ6K4uFifROfl5eGee+5Bs2bNFI62akII/YoZRUVF0Gq1AIBevXqhf//+WLRoEe6++27861//wrp16/Dyyy/joYcegqurq5Jh1+iPP/5Au3btEBUVhe7du2Py5MnYtWsXioqK8PDDD2PlypXQaDTo0KEDhBDYv38/hg8fjp9//hm7d+926CTKXsTExODEiRNYsWKF0qGYrXXr1jh69Ch+/fVXvPrqqxg1ahROnTqldFhm+eOPP/DGG29g+fLl8PDwUDociz3xxBN49tln0b59e/Tp0wdxcXHIzs7GqlWrlA5Nce+8806lG0Dv/LgzYbt69Sr69u2LZ599Fi+99JJCkVfPnDkRWdv8+fMhSRIeeeQRpUOxiN1dwT5x4gQyMzPx4YcfYsiQIQDKrxD17dsX3bt3x4wZM1C/fn388ssv+P333xEREaFwxJVpNBr9lffNmzdj6dKlOH/+PCIiIjB8+HB8+eWXyMzMNHjLcOnSpSgqKoKPj4+CkddMp9Ph7rvvRqNGjZCfn49r166hX79+aNOmDR544AH0798f//73vzF16lQ8+eST2Lx5M9588020bdsWISEhSofv8MaPH49NmzZh7969dvvC0hhubm649957AQDh4eE4dOgQvvjiC3zzzTcKR2a65ORk3Lhxw6CUS6vVYu/evfjqq69QUlLi0O/a+Pn54b777sOFCxeUDkVxkyZNwujRo2tsc8899+j//9q1a+jevTuioqLwv//9z8rRmcfUOTmiRo0aQa1WIyMjw+B4RkYGgoKCFIqKarJ8+XK4ubnh4MGDuHDhgv7vhcNRsP5bT6vVCiGEuHnzpmjQoIGQJEm8/vrrBm0uXrwooqKiRPPmzUVwcLC4//777fIGwBMnTogPPvhACCHEzz//LDw8PMSMGTPEggULxNChQ4VKpRKnT5/Wt4+Pjxevvvqq8Pf3F0eOHFEoauOdP39ePP300+Kpp54SBw4cEJcvXxY//vij6NSpk3j44YdFvXr1xAMPPCAkSRLDhg1TOlwhhOPfBKLT6URMTIxo2rSpOHfunNLhyK579+5i1KhRSodhltzcXHH8+HGDj4ceekiMGDFCHD9+XOnwLJaXlyf8/f3FF198oXQoDuXKlSuiVatWYtiwYaKsrEzpcGTlqDc5jh8/Xv+5VqsVd911F29ytEMXL14UAMSHH34oXF1dxfvvv690SGZTJMGuSKgLCgr0x86cOSN0Op3YvHmzCA0NFZ07dxalpaUG7YUoT2BPnjwpbty4YdugjXD06FEhSZKYOXOmKCgoEL169RJz5swRQpSv/NC0aVODO5kzMzPFv//9bzFw4ECH+mN85swZ0adPH/H444+LgwcP6o9nZWWJJUuWiMmTJ4sHH3xQpKSkKBZjXl6eOHLkiDhy5IgAID7//HNx5MgRcfnyZcViMterr74qfH19xZ49e8T169f1H4WFhUqHZrJ33nlHxMfHi9TUVPHbb7+Jd955R0iSJLZt26Z0aLJx5FVEJk2aJPbs2SNSU1PF/v37Ra9evUSjRo3s8vetvbpy5Yq49957Rc+ePcWVK1cMfmYd2eXLl8WRI0fEBx98ILy8vPS/X/Py8pQOrVYrVqwQ7u7uYvHixeLUqVNi3Lhxws/PT6Snpysdmlmc6e/bnaZPny7UarVIT08XTz75pGjVqpXSIZlNsSvYly9fFqNGjRK//fabWLNmjZAkSZw6dUqUlZWJbdu2iUaNGolnn31W374i2bZXJ0+eFJ6enmLatGlCiPKr8S1bthTJycni6tWr4q677hIvvfSSvv3q1atFenq6yMrKEtnZ2QpFbb5z586JPn36iD59+og9e/ZUerxiSUWl7N69WwCo9OGIV0qrmgcAsWjRIqVDM9mLL74omjdvLtzc3ETjxo1Fz549nSq5FsKxE+zo6GjRpEkT4ebmJu666y4RHR0tLly4oHRYDmXRokXV/sw6slGjRlU5p927dysdmlH++9//iuDgYOHm5iYefvhhceDAAaVDMpsz/X27U5s2bUT37t2FEEIsXrxYADC4kOdIFPuJ3717t+jQoYOIiooSHh4eYunSpfrHdDqd2LZtm2jYsKEYOnSowXF7dPz4cdGoUSMRGhqqP3bz5k3Rv39/8e2334qQkBDx0ksv6d8q/OOPP8SYMWPEzz//rFTIsjh37pzo27ev6NOnj9i/f7/S4RAREZGDOnz4sAAgvv76ayGEENnZ2cLNzc1hL1gotopIt27d8MILLyApKQlt27ZFaGio/jFJktCrVy/8+OOP2Lt3L5544gn9cXtz7NgxPPLII2jXrh1ycnLwxhtvAAAaNmyIZs2aYdy4cXjwwQfxzTff6G92mjdvHn799Ve7XuPaGK1atcKXX34JV1dXTJo0CQcOHFA6JCIiInJAy5cvh4uLi36BC19fX/Tt2xcrVqzQr8TmSBRJsMvKygAAPj4++PDDD+Hp6YmZM2ciPj5e36YiyV64cCHOnTuHq1evKhFqjQ4fPoyIiAj885//xI4dOzBt2jTExsZi/PjxAIAFCxZg8ODBSEhIwMcff4zZs2fj5Zdfxrx58xAbG4u7775b4RlYrlWrVpg9ezaaNWuGpk2bKh0OEREROZiKjcd69OhhsGFgdHQ0MjIysHPnTgWjM48khO221RNCQJIk5OXlGWxasHnzZkyfPh1NmjTBhAkT0LlzZwDAr7/+ikceeQSFhYWoV6+ercI02t69e7FmzRp88cUXAMp361q5ciXeffddREdH46uvvgJQvrzaqVOnkJ2djXbt2uGf//ynw24SUp2KDYCIiIiITLF9+3b07t0b3333HV588UX98fz8fAQEBODZZ5/FDz/8oGCEprNpgg0Av/zyCz777DPUr18fYWFhmDJlClxdXbF582bMmDEDTZo0wbPPPouzZ8/i/fffR3p6OgICAmwZolkqXjzk5uZixYoVePfddzFs2DD897//BQBkZ2fDw8MDKpWKiSgRERHRX0aPHo3Y2FhkZGTA39/f4LFnnnkG27ZtQ0ZGBjw9PRWK0HQ2LRE5cOAAnn76aYSHh8Pd3R1xcXEYNGgQSktL8cQTT2DatGnIz8/HBx98gCVLluDgwYMOkVwDf9eH+/j4YNiwYfjoo4+wYsUKfU22n58fPDw8mFwTERER/aWoqAhr167F448/Xim5BoChQ4ciLy8PGzZsUCA689lsJ8eTJ0/i3LlzmDVrFiZNmoSSkhJs2LABH3/8MQYOHIj169ejd+/eaNWqFTQaDXx8fBx2l6WKJFulUmHcuHGoV68eZs2apXRYRERERHZlw4YNyMvLAwB8/PHHlR4vLCwEUH4TZHR0tE1js4RNSkTS0tIwePBgXLx4Ee+//z5ef/11AEBJSQk2btyImTNnokmTJli7di3c3d2tHY7N5OTk4Oeff0ZkZCTuu+8+pcMhIiIisisDBw7Exo0ba23n6uqK69evo2HDhjaIynI2KRHx8/PDsGHD0KBBA2zevFl/3N3dHQMHDsSUKVNw+vRpjBgxwhbh2Iyvry9GjhzJ5JrIii5dugRJkrB48WKlQyGi20iShPfff9/g2KFDhxAVFYX69etDkiQcPXoUALBlyxZ07NgRHh4ekCQJ2dnZNo+XlLFhwwaI8n1ZavwoLS11mOQasFKCfedFcR8fH7z88suYOHEiLl68iP/7v//TP+bm5ob+/ftj7ty5+M9//mONcBRlj2t3Eylp4MCBqFevnv4twao8//zzcHNzw61bt2wYGZG8Fi9eDEmSIEkS9u3bV+lxIQTuvvtuSJKE/v37KxCh8UJCQvRzUalU8PPzwwMPPIBx48bh119/NaoPjUaDZ599FpmZmZgzZw6WLl2K5s2b49atWxg6dCg8PT0xb948LF26FPXr17fyjIisS/Ya7IrVNPbv34/9+/cjMzMTvXr1Qq9evTB27FgIIfDNN9/g//7v/7Bw4UIA5Un2wIED5Q6FiOzQ888/j40bN2LdunUYOXJkpccLCwuxfv169O3b16GuVhBVx8PDA7GxsXjssccMjsfHx+PKlSsOUxrZsWNHTJo0CQCQl5eH06dPY/Xq1fj222/x5ptv4vPPPzdoX1RUBBeXv9OM33//HZcvX8a3335rcKFty5YtyMvLw/Tp09GrVy/bTIbIymS/gi1JEtasWYO+ffsiLi4Oe/bsQe/evTFx4kRkZ2dj7NixGDduHI4ePepQxepEJI+BAwfC29sbsbGxVT6+fv16FBQU4Pnnn7dxZETW8eSTT2L16tX6TdYqxMbGIjw83GFu6L/rrrswYsQIjBgxAq+++iq+/PJLXLx4EYMGDcKcOXOwYMECg/YeHh4GCfaNGzcAlJeN3q6645YoKCiQrS8ic1iUYFd1f+SFCxcwceJEzJkzB7t378aBAwcQGxuLJUuW4NNPP4WHhwdeeOEFDB06FNevX8f169ctCYGIHIynpycGDx6MnTt36v+w3i42Nhbe3t547LHH8NZbb+GBBx6Al5cXfHx88MQTT+DYsWO1jtGtWzd069at0vHRo0cjJCTE4JhOp8PcuXPRtm1beHh4IDAwEC+//DKysrLMnSKRgeeeew63bt3C9u3b9cdKS0vx008/Yfjw4VWeY+z35fr169GvXz80bdoU7u7uaNmyJaZPn15pa+lu3bqhXbt2OHXqFLp374569erhrrvusrg009PTE0uXLkWDBg3w0UcfGeQFt9dgjx49Gl27dgUAPPvss5AkSf9zOmrUKABAREQEJEnC6NGj9X38+uuv6Nu3L3x9fVGvXj107doV+/fvN4jh/fffhyRJOHXqFIYPHw5/f3+DdwuWLVuG8PBweHp6okGDBhg2bBj++OMPs5+f4uJivP/++7jvvvvg4eGBJk2aYPDgwfj999/1bfh7hcxOsHU6HSRJwp9//onDhw8jJSUFQPk3nouLCyIiIvRthw0bhi+//BKff/459u3bB19fX/zjH//A+vXr0aRJE8tnQUQO5fnnn0dZWRlWrVplcDwzMxNbt27F008/jevXr+Pnn39G//798fnnn+Ptt9/G8ePH0bVrV1y7dk22WF5++WW8/fbb6NSpE7744guMGTMGy5cvR58+faDRaGQbh+qukJAQREZG4scff9Qf27x5M3JycjBs2LAqzzH2+3Lx4sXw8vLCxIkT8cUXXyA8PBzvvfce3nnnnUp9ZmVloW/fvujQoQM+++wztGnTBv/6178MFh8wh5eXF55++mlcvXoVp06dqnY+kydPBgC8/vrrWLp0Kd599128++67GDduHADgww8/xNKlS/Hyyy8DAHbt2oUuXbogNzcX06ZNw8yZM5GdnY0ePXrg4MGDlcZ49tlnUVhYiJkzZ+Kll14CAHz00UcYOXIkWrVqhc8//xwTJkzAzp070aVLl0o3Uhrz/Gi1WvTv3x8ffPABwsPD8dlnn+GNN95ATk4OTpw4YTBf/l6p44QZtFqtEEKIkydPik6dOom+ffuKwYMHi7KyMnHo0CHh6uoqDhw4IIQQori4WH9eu3btxKeffmrOkETkRMrKykSTJk1EZGSkwfGvv/5aABBbt24VxcXF+t81FVJTU4W7u7v48MMPDY4BEIsWLdIf69q1q+jatWulcUeNGiWaN2+u/zwhIUEAEMuXLzdot2XLliqPE5li0aJFAoA4dOiQ+Oqrr4S3t7coLCwUQgjx7LPPiu7duwshhGjevLno16+f/jxTvi8r+rvdyy+/LOrVq2fw97dr164CgFiyZIn+WElJiQgKChJDhgypdS53xninOXPmCABi/fr1+mMAxLRp0/Sf7969WwAQq1evNjj39uepgk6nE61atRJ9+vQROp3OYL4tWrQQjz/+uP7YtGnTBADx3HPPGfR76dIloVarxUcffWRw/Pjx48LFxcXguLHPz/fffy8AiM8//7zSc1ARJ3+vkBBCmHwFWwgBlUqFkydPolOnTujatSu++eYbrF69Gmq1Gg899BAGDRqEF198ERcvXtTfvFFaWgp3d3f4+PhY+pqAiBycWq3GsGHDkJSUhEuXLumPx8bGIjAwED179oS7uztUqvJfUVqtFrdu3YKXlxdat26tf8fMUqtXr4avry8ef/xx3Lx5U/8RHh4OLy8v7N69W5ZxiIYOHYqioiJs2rQJeXl52LRpU7XlIaZ8X96+dXReXh5u3ryJzp07o7CwEGfOnDHo18vLy2A5XDc3Nzz88MO4ePGixfPz8vLSxyCHo0eP4vz58xg+fDhu3bqlfw4KCgrQs2dP7N27FzqdzuCcV155xeDztWvXQqfTYejQoQbPY1BQEFq1alXp59uY52fNmjVo1KgRXnvttUoxV6waxt8rBJixiogkScjMzMQrr7yCkSNH4qOPPtI/ptPpoFKp8MYbb2DGjBl48sknMX/+fLi4uGDbtm24fPkyevbsKesEiMgxPf/885gzZw5iY2MxefJkXLlyBQkJCXj99dehVquh0+nwxRdfYP78+UhNTTWoKZVrdZHz588jJycHAQEBVT5eVY04kTkaN26MXr16ITY2FoWFhdBqtXjmmWeqbGvK9+XJkycxZcoU7Nq1C7m5uQbtcnJyDD5v1qxZpaVj/f398dtvv5kzJQP5+fkAAG9vb4v7AsqfAwD6+uyq5OTkGGyt3aJFi0p9CCHQqlWrKs93dXU1+NyY5+f3339H69atDW7erCp2/l4hs5bpS09Px/Xr1zFkyBB9Ug1A/2+nTp3w4Ycf4osvvsCTTz6Ju+++G+7u7ti2bRvuuece+aInIocVHh6ONm3a4Mcff8TkyZPx448/QgihXz1k5syZmDp1Kl588UVMnz4dDRo0gEqlwoQJEypdubqTJElV3oR9541fOp0OAQEBWL58eZX9NG7c2MzZEVU2fPhwvPTSS0hPT8cTTzxR7aoZxn5fZmdno2vXrvDx8cGHH36Ili1bwsPDAykpKfjXv/5V6edErVZX2V9VPyumqqg/vvfeey3uC4A+9tmzZ6Njx45Vtqm4al7h9qv5FX1IkoTNmzdXOfc7z5fr+eHvFQLMTLCPHj2Ky5cvo3PnzpAkySDJ1mq1UKvVaNu2LaZMmYK5c+eisLAQ9erVQ6NGjWQNnogc2/PPP4+pU6fit99+Q2xsLFq1aqW/Qfqnn35C9+7d8d133xmck52dXevvEn9//yrf9r58+bLB5y1btsSOHTvQqVOnSn+cieT29NNP4+WXX8aBAwewcuXKatsZ+325Z88e3Lp1C2vXrkWXLl30x1NTU2WNuzb5+flYt24d7r77boSGhsrSZ8uWLQGUb1Rn7trYLVu2hBACLVq0kG1H5ZYtW+LXX3+FRqOpdAX89jb8vUJmrSISEhICFxcXrF27trwT1d/dVLwC/O677/D666/D29sbwcHBTK6JqJKKq9Xvvfcejh49arD2tVqtrnTlaPXq1bh69Wqt/bZs2RJnzpzBn3/+qT927NixSst7DR06FFqtFtOnT6/UR1lZGbdrJll5eXlhwYIFeP/99zFgwIBq2xn7fVnx9/b2n5PS0lLMnz9f3sBrUFRUhBdeeAGZmZl49913Zdu9ODw8HC1btsSnn36qLz+53e0/29UZPHgw1Go1Pvjgg0q/S4QQZu0UO2TIENy8eRNfffVVpccqxuDvFQLMvILdvHlz+Pj4YMmSJXjooYfQvHlzAH/v4giUXykKDw+Hm5ubfNESkVNp0aIFoqKisH79egAwSLD79++PDz/8EGPGjEFUVBSOHz+O5cuXG1Vm9uKLL+Lzzz9Hnz59MHbsWNy4cQNff/012rZta1Cn2rVrV7z88suYNWsWjh49it69e8PV1RXnz5/H6tWr8cUXX1RbJ0tkjppqiisY+30ZFRUFf39/jBo1Cq+//jokScLSpUtlKfmoytWrV7Fs2TIA5VetT506hdWrVyM9PR2TJk3SL68nB5VKhYULF+KJJ55A27ZtMWbMGNx11124evUqdu/eDR8fH2zcuLHGPlq2bIkZM2bg3//+Ny5duoRBgwbB29sbqampWLduHcaNG4e33nrLpLhGjhyJJUuWYOLEiTh48CA6d+6MgoIC7NixA//4xz/w1FNP8fcKlTN3+ZE1a9YId3d38cILL4iTJ0/qjxcUFIh///vfonnz5uLs2bPmdk9EdcS8efMEAPHwww8bHC8uLhaTJk0STZo0EZ6enqJTp04iKen/27l/XUPCOIzjP8XxJzIajWiEKCQiIRpE4h5E3ICoqDUuwR3QavUUopJF4wYoafRKzxYnzu4Jkl3n3eKs76d+Z+adyczked/i+XFTwXevpk+SxuOxUqmU/H6/8vm8ZrPZTU3f1XA4VLFYVCgUkud5yuVy6vV6Oh6P/+CO8Sru1c/d86gC70/ey+VyqVKppFAopHg8rl6vp9lsJjPTYrH4GFer1ZTNZm+u8eibuDdHM5OZyefzKRKJKJvNqt1ua71e3z3GvlDTd7XdblWv1xWNRhUIBJRIJNRsNjWfzz/GXGv6TqfT3XlMJhNVq1WFw2GFw2FlMhl1Op1PGeVvns/5fFa/31cymdTb25tisZgajYb2+/2ncfxXXptPem6pe7lcbDQaWbfbtXQ6beVy2YLBoB0OB1utVjadTq1QKLhZBQAAAADfxNMB+2qz2dhgMLDdbmee51mlUrFWq/WwFgcAAAD4n305YJv9ag4BAAAAXt1TLSI3J/mtRcRBXgcAAAC+LSc72AAAAADeOdnBBgAAAPCOgA0AAAA4RMAGAAAAHCJgAwAAAA4RsAEAAACHCNgAAACAQwRsAAAAwCECNgAAAOAQARsAAABwiIANAAAAOETABgAAABz6CUH/bccLP9yJAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 700x500 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "multi_2group = dabest.load(df, idx=((\"Control 1\", \"Test 1\"),(\"Control 2\", \"Test 2\"),(\"Control 3\", \"Test 3\"),(\"Test 4\", \"Test 5\")))\n",
+    "multi_2group.mean_diff.plot(horizontal=True, gridkey=['Control', 'Test']);"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "python3",
+   "language": "python",
+   "name": "python3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/nbs/tutorials/09-plot_aesthetics.ipynb b/nbs/tutorials/09-plot_aesthetics.ipynb
new file mode 100644
index 00000000..46dc3c4a
--- /dev/null
+++ b/nbs/tutorials/09-plot_aesthetics.ipynb
@@ -0,0 +1,1919 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Controlling Plot Aesthetics\n",
+    "\n",
+    "> A guide to various plot aesthetic changes that can be done.\n",
+    "\n",
+    "- order: 9"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " Since **v2024.03.29**, swarmplots are, by default, plotted asymmetrically to the right side. For detailed information, please refer to [Swarm Side](#changing-swarm-side).\n",
+    "\n",
+    " Since **v2025.03.27**, further aesthetic changes were added/updated which include:\n",
+    "\n",
+    " - [Raw bars](#raw-bars)\n",
+    "  \n",
+    " - [Contrast bars](#contrast-bars)\n",
+    "  \n",
+    " - [Reference Band](#reference-band)\n",
+    "  \n",
+    " - [Delta text](#delta-text)\n",
+    "  \n",
+    " - [Jitter](#adding-jitter-to-slopegraph-plots)\n",
+    "  \n",
+    " - [Gridkey](#gridkey)\n",
+    "  \n",
+    " - [Delta dots](#delta-dot)\n",
+    "  \n",
+    " - [Effect size paired lines](#effect-size-paired-lines)\n",
+    "  \n",
+    " - [Baseline error curve](#baseline-error-curve)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Pre-compiling numba functions for DABEST...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Compiling numba functions: 100%|██████████| 11/11 [00:00<00:00, 50.05it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Numba compilation complete!\n",
+      "We're using DABEST v2025.03.27\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import dabest\n",
+    "import seaborn as sns\n",
+    "\n",
+    "print(\"We're using DABEST v{}\".format(dabest.__version__))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#| hide\n",
+    "import warnings\n",
+    "warnings.filterwarnings(\"ignore\") # to suppress warnings related to points not being able to be plotted due to dot size"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Creating a demo dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from scipy.stats import norm # Used in generation of populations.\n",
+    "\n",
+    "np.random.seed(9999) # Fix the seed to ensure reproducibility of results.\n",
+    "\n",
+    "Ns = 20 # The number of samples taken from each population\n",
+    "\n",
+    "# Create samples\n",
+    "c1 = norm.rvs(loc=3, scale=0.4, size=Ns)\n",
+    "c2 = norm.rvs(loc=3.5, scale=0.75, size=Ns)\n",
+    "c3 = norm.rvs(loc=3.25, scale=0.4, size=Ns)\n",
+    "\n",
+    "t1 = norm.rvs(loc=3.5, scale=0.5, size=Ns)\n",
+    "t2 = norm.rvs(loc=2.5, scale=0.6, size=Ns)\n",
+    "t3 = norm.rvs(loc=3, scale=0.75, size=Ns)\n",
+    "t4 = norm.rvs(loc=3.5, scale=0.75, size=Ns)\n",
+    "t5 = norm.rvs(loc=3.25, scale=0.4, size=Ns)\n",
+    "t6 = norm.rvs(loc=3.25, scale=0.4, size=Ns)\n",
+    "\n",
+    "\n",
+    "# Add a `gender` column for coloring the data.\n",
+    "females = np.repeat('Female', Ns/2).tolist()\n",
+    "males = np.repeat('Male', Ns/2).tolist()\n",
+    "gender = females + males\n",
+    "\n",
+    "# Add an `id` column for paired data plotting.\n",
+    "id_col = pd.Series(range(1, Ns+1))\n",
+    "\n",
+    "# Combine samples and gender into a DataFrame.\n",
+    "df = pd.DataFrame({'Control 1' : c1,     'Test 1' : t1,\n",
+    "                   'Control 2' : c2,     'Test 2' : t2,\n",
+    "                   'Control 3' : c3,     'Test 3' : t3,\n",
+    "                   'Test 4'    : t4,     'Test 5' : t5, 'Test 6' : t6,\n",
+    "                   'Gender'    : gender, 'ID'  : id_col\n",
+    "                  })\n",
+    "\n",
+    "np.random.seed(9999) # Fix the seed so the results are replicable.\n",
+    "\n",
+    "# Create samples\n",
+    "N = 20\n",
+    "y = norm.rvs(loc=3, scale=0.4, size=N*4)\n",
+    "y[N:2*N] = y[N:2*N]+1\n",
+    "y[2*N:3*N] = y[2*N:3*N]-0.5\n",
+    "\n",
+    "# Add a `Treatment` column\n",
+    "t1 = np.repeat('Placebo', N*2).tolist()\n",
+    "t2 = np.repeat('Drug', N*2).tolist()\n",
+    "treatment = t1 + t2 \n",
+    "\n",
+    "# Add a `Rep` column as the first variable for the 2 replicates of experiments done\n",
+    "rep = []\n",
+    "for i in range(N*2):\n",
+    "    rep.append('Rep1')\n",
+    "    rep.append('Rep2')\n",
+    "\n",
+    "# Add a `Genotype` column as the second variable\n",
+    "wt = np.repeat('W', N).tolist()\n",
+    "mt = np.repeat('M', N).tolist()\n",
+    "wt2 = np.repeat('W', N).tolist()\n",
+    "mt2 = np.repeat('M', N).tolist()\n",
+    "\n",
+    "genotype = wt + mt + wt2 + mt2\n",
+    "\n",
+    "# Add an `id` column for paired data plotting.\n",
+    "id = list(range(0, N*2))\n",
+    "id_col = id + id \n",
+    "\n",
+    "# Combine all columns into a DataFrame.\n",
+    "df_delta2 = pd.DataFrame({'ID'        : id_col,\n",
+    "                  'Rep'      : rep,\n",
+    "                   'Genotype'  : genotype, \n",
+    "                   'Treatment': treatment,\n",
+    "                   'Y'         : y\n",
+    "                })\n",
+    "\n",
+    "def create_demo_prop_dataset(seed=9999, N=40):\n",
+    "    import numpy as np\n",
+    "    import pandas as pd\n",
+    "\n",
+    "    np.random.seed(9999)  # Fix the seed to ensure reproducibility of results.\n",
+    "    # Create samples\n",
+    "    n = 1\n",
+    "    c1 = np.random.binomial(n, 0.2, size=N)\n",
+    "    c2 = np.random.binomial(n, 0.2, size=N)\n",
+    "    c3 = np.random.binomial(n, 0.8, size=N)\n",
+    "\n",
+    "    t1 = np.random.binomial(n, 0.6, size=N)\n",
+    "    t2 = np.random.binomial(n, 0.2, size=N)\n",
+    "    t3 = np.random.binomial(n, 0.3, size=N)\n",
+    "    t4 = np.random.binomial(n, 0.4, size=N)\n",
+    "    t5 = np.random.binomial(n, 0.5, size=N)\n",
+    "    t6 = np.random.binomial(n, 0.6, size=N)\n",
+    "    t7 = np.ones(N)\n",
+    "    t8 = np.zeros(N)\n",
+    "    t9 = np.zeros(N)\n",
+    "\n",
+    "    # Add a `gender` column for coloring the data.\n",
+    "    females = np.repeat('Female', N / 2).tolist()\n",
+    "    males = np.repeat('Male', N / 2).tolist()\n",
+    "    gender = females + males\n",
+    "\n",
+    "    # Add an `id` column for paired data plotting.\n",
+    "    id_col = pd.Series(range(1, N + 1))\n",
+    "\n",
+    "    # Combine samples and gender into a DataFrame.\n",
+    "    df = pd.DataFrame({'Control 1': c1, 'Test 1': t1,\n",
+    "                       'Control 2': c2, 'Test 2': t2,\n",
+    "                       'Control 3': c3, 'Test 3': t3,\n",
+    "                       'Test 4': t4, 'Test 5': t5, 'Test 6': t6,\n",
+    "                       'Test 7': t7, 'Test 8': t8, 'Test 9': t9,\n",
+    "                       'Gender': gender, 'ID': id_col\n",
+    "                       })\n",
+    "\n",
+    "    return df\n",
+    "df_prop = create_demo_prop_dataset()\n",
+    "\n",
+    "\n",
+    "two_groups_prop_paired = dabest.load(df_prop, idx=(\"Control 1\", \"Test 1\"), proportional=True, paired=\"baseline\", id_col=\"ID\")\n",
+    "two_groups_prop = dabest.load(df_prop, idx=(\"Control 1\", \"Test 1\"), proportional=True)\n",
+    "two_groups_unpaired = dabest.load(df, idx=(\"Control 1\", \"Test 1\"))\n",
+    "multi_2group = dabest.load(df, idx=((\"Control 1\", \"Test 1\"),(\"Control 2\", \"Test 2\")))\n",
+    "repeated_measures = dabest.load(df, idx=(\"Control 1\", \"Test 1\", \"Test 2\", \"Test 3\"),paired=\"baseline\", id_col=\"ID\")\n",
+    "two_groups_paired = dabest.load(df, idx=(\"Control 1\", \"Test 1\"), paired=\"baseline\", id_col=\"ID\")\n",
+    "mini_meta_paired = dabest.load(df, idx=((\"Control 1\", \"Test 1\"), (\"Control 2\", \"Test 2\"), (\"Control 3\", \"Test 3\")), mini_meta=True, id_col=\"ID\", paired=\"baseline\")\n",
+    "paired_delta2 = dabest.load(data = df_delta2, \n",
+    "                                paired = \"baseline\", id_col=\"ID\",\n",
+    "                                x = [\"Treatment\", \"Rep\"], y = \"Y\", \n",
+    "                                delta2 = True, experiment = \"Genotype\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Changing the graph colours\n",
+    "\n",
+    "### Color categories from another variable\n",
+    "Use the parameter `color_col` to specify which column in the dataframe will be used to create the different colours for your graph."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 450x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "multi_2group.mean_diff.plot(color_col=\"Gender\");\n",
+    "\n",
+    "two_groups_paired.mean_diff.plot(color_col=\"Gender\");"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Custom palette\n",
+    "The colour palette for the graph can be changed using the parameter `custom_palette`. Multiple types of color palettes can be used:\n",
+    "\n",
+    "- A list of colors (named colors, hex, rgb, etc) e.g. `['red', 'blue', 'green']`\n",
+    "  \n",
+    "- A seaborn color palette e.g. `'Set1'`\n",
+    "  \n",
+    "- A matplotlib color map e.g. `'viridis'`\n",
+    "  - `'paired'` is an interesting option for two-group (or multi two-group) comparisons\n",
+    "  \n",
+    "- A dictionary with the keys as the column names and the values as the colors e.g. `{'Control 1': 'red', 'Test 1': 'blue', 'Test 2': 'green'}`"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### A list of colors"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "multi_2group.mean_diff.plot(custom_palette=['red', 'blue', 'green', 'purple', 'orange', 'brown']);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Seaborn color palette"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "multi_2group.mean_diff.plot(custom_palette=sns.color_palette(\"husl\", 6));"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Matplotlib color map/palette"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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cV6FVIyf/MtSaUqjcvREeGFXtvGpNGa7lXOAwZwvIyc+GQlAYalJupxAUyM7PkiEq52CTRCU5OblaoqLT6XDhwgX8/PPPaNOmDTp06GCLUIjIDuQVlWD7wRPIyitEgyB/DOicgCA/H7nDsqlgvwh0btavzmQCqJ50JMb2RF7x9RqPu1GUjZN3JDMXrp9CQlQSgv1uramWV3wdh87u5ArNFhIeGFHrGk0RgQ1sHJHzsEmismrVqhr3/f777xg4cCDGjx9vi1CIHJqtvuCteZ1fjp3Da6u3QKvTQyEI0IsiVm37Ba9MHIJureseseJMPNxUdc40W1vSYezYCq3aUB74ewizKOpx4vJhdG7WDzq9Dmez/jA6zPnw2V3o134Ua1ZMlNK2H9buXmPoo3I7N4UbUtr1kyEq5yB7H5X27dsjNTUV//znP+UOhciu/XLsHMa/9gk+3vIztv5yFB9v+RnjX/sEvx475zDXySsqwWurt0CjvTWpmk6vhyiK0Gh1eHX1FuQVlVjgEziOCq0al3MzkHHtT1zOzUCFVl1tf21Jx53lASAn/7Kh/J1EUY+c/Mu4XnClxqHJlSs0k2mCfIMwa+SLcFe6QxAEKBVKCIIAd6U7Zo18EYE+gXKH6LDsYuhHREQEjh8/LncYRHbr9i94AND99SVT+QW/ds4UyTUetdWWWOI6tZ1/+8ET0OqMf4lqdXr8cOgkRqUkSfocjk5K84yUpOPOWhW1prTWIcxqTemtkYA1leEKzWbr3KwrPn56FXb/uRPZ+VmICGyAlHb9mKTUk+yJyo0bN7BixQpERbG3OlFNLPUFX1ezS32vU9f5s/IKoRAEQwJ0O4Ug4NrNgjo/gzOQ0jzj4aaSlHTcScoQZr2o5wrNVhLkG4Th3R+QOwynYpNEpW/fvka35+fn4+TJk6ioqMCaNWtsEQqRQ7LEF7yU2pL6XEfK+RsE+de6BlVkcECdn8MZSK0pMWfelPDAKFy4fsro+SuHMOv0OlzKPW20+YcrNJO9sUkfFf1f7dC3v4Bbw5afeuopHD16FGPHjrVFKER2KcjPG6EBvgjyMz5hlylf8HlFJfhi5yH8+8ud+GLnIUO/Dym1JfVJJKScf0DnBLgpjf/acVMq0L9TQo3ndyaVNSXG3F5TImXelDv7uQBAQlSS4bjK69w+hNnDTYWmDdpBUVnmr//evkIzkb2wSY3K7t27bXEZMpNao8WV3HyUqivgrfJAo9BAzlxrAo1WB52+ftORv//UKMP/l1dUHzXQq30zrNr2i6G24nZuSgV6tW+K8goNDhzPxJvr0qo1vcwaPwhXcvNrrS25fD0PD/brXOt1akskpNTGBPn54JWJQ/DqHc1DbkoFXpk4pMZEzdlIrSmpa96UorL8Gvu51DX0Ocg3DH3aDMe1PM6jYklc68fy+G3k4rLziqpNs3/6cg46NG3MGWwl0Gh1OHUxC6Vq6662CwAP9u2MdT8cuDXvhSBAFEUoBAUe7NsZF7PzcDzzGt7+7HtD0nR708sba75D7/bNoK8hodKLeuhFEZdy8mq8zsSB3eDrpaqxs6zU2phureOwds4U/HDoJK7dLEBkcAD6d0pwmSQFkNY8U6mm+VYA4OCZHbX2c6mrCUfl7oX4yDaW+lguj2v9WIdVEpVPP/3UrOMmTJhg4UioNmqN1pCkADCstqwXRfx29hJSEpuzZqUOOr0epWoN3N0UcFdab7VdAOjYvDGaRYXh0MkLuFFUghA/H3Rq2QR+3p4AgF+Pnat1wil3pQJKpcJo84xSocBdrePgrXI3ep3EZlFQeXjgl6MZRmtsXpk4BAM6J0iujQny83GZ0T3GmDozrbH5Vi7nZpg8Ioish2v9WI9VvoUmTZpk8jGCIDBRkeinoxmo0Gjh4e6Gnm1q/0VUW7POldz8Wv8CvpKbz+n3JXJXKuFRj6TurbVpKCwtg7+3F/45fmCN5UICfDGwa2uj+wpKygw1IHdSCALKNVpMHdoTH3/7E3T6vxMNpUKBqUN7IiTg75Eed16nQqNFTl4R3l2/vdbOsmzWkU7KzLS1TYNvzoggsh4pa/1wNJB5rJKoZGZmWuO09JcKjRblGuMPxO3qatYpVVcYtt9JEIBSdYUVoidjCkvLkF9cVne5kjLsP3EeNwtLEOzvg64JMfD3udWnINjfp5ZJvESE+PugbVwjvPbIfThw4jxuFJYgxN8HXVvFGmplavPbmUuShi67erOOKWqbmbaueVa4krJ94Vo/1mOVRKVJkybWOC2ZQEqzjrfKw2iSUlneW+Vho2hJij8zruDjLbdqQyprTr7d9wemDu2JtnGN0DUhBt/u+6PGpp2urWIBAP4+XmaNrskrLpU0dNnVm3UsQco8K6b0cyHr41o/1iP7FPpkHVKadRqFBkJRw6rWCkFAo9BAK0ZIpigsKcPHW36CVqeHKAJ6vQhRvFWT8fG3P6GwpAz+Pl6YOrQn3JQKCAKgVAgQhFv9Q6YO7Smp1qQ2Qb7enAPFRqTMs1LZz6W2YchkOylt+8FNYfxvf671Uz826ymZlZWFFStW4MiRIygoKKg2+kAQBOzYscNW4Tg9Kc06Knc3dGjauFrzkEIQ0KFpY3aktSP7T5yvcQi0Tq/HgRPn0b9TQr2adurSoVlj7Dhy0qyhy2Qaqf1PTFmBmayrcq2fNzdWHfXjpnDjWj/1ZJNvoj/++AN9+vRBWVkZWrRogT///BOtWrVCfn4+rly5gvj4eDRuzGXFLUlqs05EkB9SEptzHhU7d7OwpNaOsjcK/17Mz9ymnbr4enti1vhBeHPtNnaWtZCaOsua0v9EygrMZBtc68c6bPJtNGvWLPj6+iI9PR3e3t4IDw/HokWL0LdvX2zYsAHTpk3D2rVrbRGKy2gUGojTl3OMVtXf2ayjcnez2egeTi5nHikdZU1RW6dcY2UCfLzQJrYh7mobz86yFlJbZ1n2P3FcXOvH8mzyDfHzzz9j5syZiI6Oxs2bNwHA0PQzatQo/PTTT3jhhRewZ88eW4TjEuRo1qkrCeHkcuaT2lEWqDsJqatTbk1ltv56FC+6DUbv9s3YWbaepHSWNWWeFXIcnLnWdDZJVPR6PSIibi1bHhgYCKVSaUhYAKBt27ZYsWKFLUJxKZZs1qlvEsLJ5eqnsqNsTXOgVPZBqSsJub1TLgBDLU1lp9zXHrkPAIyW0Yl6vLl2G9rGNUSQn2k1OFSV1EUJ2f/EuXDmWvPYZNRPbGysYW4VhUKB2NhY/PDDD4b9+/btQ2BgoEnnXLJkCdq1awd/f3/4+/uje/fu+O677ywZtlOobNZpE9MQcZGhZiUD2XlF2JV+GicvZePS9TycvJSNXemnkZ1XBKDuodCVSU5do5CodpUdZe/vmYgebZvi/p6JeH3qMEMtiJSRQVI65dZWpnK+FKofqYsSVvY/iY9si6jQeCYpDuz2mWtFUYROr4MoioaZa/OK8+QO0W5ZLVHJy/v7h3733Xdjw4YNhvfTpk3Dxx9/jP79+6Nfv35YvXo1xo0bZ9L5o6Ki8Oabb+Lw4cM4dOgQ+vbti2HDhuHYsWMW+wxkuSSkchSSMZxczjTibf+9vd+KlCSkslOuMZWdcusqUzlfCpmPk7U5ludWTMfkRQ/juRXTzT6HlJlryTir1bU3aNAA99xzD8aPH48ZM2Zg7Nix0Gg0cHd3x7PPPouSkhJ8+eWXUCqVmDNnDl588UWTzn/vvfdWeb9gwQIsWbIEv/76K1q3Nj7FOBlX32n2pQyF5uRy9VdXs46UkUFSOuXemQDdWYbzpdQfO8s6lrziPNwoulGvc3DmWvNZLVEZOXIk/u///g//93//Bz8/P4wYMQLjx49H3759IQgCXn75Zbz88ssWuZZOp8OGDRtQUlKC7t2711hOrVZDrVYb3hcXF1vk+o7MEtPsS0lCTBmFRNVJ6VsiJQnpIqFTbmUCZKwM50uxDFMXJSTHUVNnWc5caz6rNf2sXbsWOTk5+N///odevXph7dq1uPvuu9GoUSPMmDEDR44cqfc1/vzzT/j6+kKlUuHxxx/Hpk2b0KpVqxrLL1y4EAEBAYZXcnJyvWNwZFKadaQmIXXNcFs5CqmyXGVxTi4njZRmna4JMVAqjD/SlUmIlNlrayqjVCgwa/wgDkW2kMrJ2mLCE9AgKBox4Qno3Kwfgv0i5A6NzHTg9H5M/XASVu9cibTftmH1zpWY+uEkHDyznzPX1oNVO9N6eXlh7Nix+Oabb5CVlYXFixejWbNm+OCDD9C5c2e0bNkSr7/+Os6dO2fW+Vu0aIH09HTs378f06ZNw8SJE3H8+PEay8+ePRsFBQWGlysMh1ZrtDh3LRdHz1/FuWu5UN+2mKGlptmXmoRUjkJq2TgCjcOC0LJxBFISm3NosgRS+pZInUK/rk65xsrc270dZo4biC4JMbb4uC6DnWWdR12dZQFg1sgX4a50hyAIUCqUEAQB7kp3zlxbB5v9GRsUFITU1FSkpqbiypUrWLduHT777DO88sormDt3Lrp27Yp9+/aZdE4PDw80bdoUAJCUlISDBw9i0aJFWLp0qdHyKpUKKtXfvwh8fX2NlrN3Hn998XvUUQthiWYdqfOxSB0KXdfkcpwQzjipE75JnUJfyuy1t5ep0GhRqtZY4JMQOScpnWWHd3+AM9eaQZZvgEaNGuGFF17AoEGD8Morr2Dz5s3Yv39/vc+r1+ur9EGxV3q9vsbmFCm6J/w9uZfOSD8CoO5mnd5tm8LT3b3WZh1Pd3fodHqE+vugd9umuHqjAGUVGnh5uKNhSABU7m5Vru+mUKBJeLDhfU2jfGrCCeFqZsqEb9aaQp+Iaia1s6ylZq51pYnjbJ6oXLx40VCbcvToUYiiiLvuugvjx4836TyzZ8/G4MGDER0djaKiIqxbtw67d+9GWlqalSK3DL1ej/ySMqNfOJaUdbOw1mads1evI8Tfp9YaFS+VO24U3b6GjCf8fW79ZV5crkZx+a2kUKPV4UZhCdQaLVTubgjx94G7mxJuSgUCfbygUCjqrCnhhHC1kzrhmxRSps8nItNYsrNsXUmIq00cZ5Pf/Lm5ufjiiy+wbt06/PLLLxBFES1btsSrr76K8ePHIyYmxuRz5uTkYMKECbh27RoCAgLQrl07pKWlYcCAAZb/ABZUOQGXQhCgUJhY5WACjU4HATA6U4Pw134vlTuaNwrH6Ss5EEUYygsC0LxROLxU7nVeJ6+otNrxV27ko2nDMAT4eEEUpdWUSOkvY6v1iOyVlGYdS0yfT46jpkUNyfZS2vbD2t1roNFVbyI1pbNsXUnI7X1hABhqcCr7wnz89Cqnq1mxWqJSUlKCTZs2Yd26ddixYwc0Gg0iIyPx7LPPYvz48ejYsWO9zu/oU+4rFEKNIzTqkp5xGRqtDu5uSiTGG59vwdPDvYbppG4lE14e7lAqFAgN8IW/jyeu5xejvEILTw83hAX6wcNNWWccFVqtIUmpPC9wKxE5e/U62sU2klxTIqW/jDPz9/aq8t8ay9XSrGOJ6fNZs+I4alvUkCOHbC/INwizRr6INzdWTTLcFG5VOsvWVlsiJQmR2hfGmVgtUQkPD0d5eTl8fX0xbtw4wxwqCjO/nOlvGq0OFdrq7aC3Cw/0xcWcmzV+8YcF/t3nw8PNrcZ5TCq0WuTkF0NdoYXKww3hgb7wcLv1zyYnv7jWPi43CktQptZIqilx9Qnh/jl+YL2Ol5KESBnizL4tjkHKooasWbG9zs261tpZtq7aEilJiCtOHGe1RKV///4YP3487rvvPnh6Sm8/J8vwcHNDy8YROHkpu1qzTsvGEfBwU9aahADAzaKSasdfzLmJlo0jEOznA3WFttbmJbVGC0EQJNWUcEK4+jFl+vzaZq4lxyB1UUOyvZo6y0qpLZGShLjixHFWS1Q2b95srVOTRMF+PujUPNpos05dSUiFVmvYD1Rt1jl5KRudmkdD5eFWa/OSyt0NXh61jyyqrCmROgyajLPU9PnkGCoXNTS2XtDtixqS/ZBSWyIlCenTtq9F+sI4ErbDOLnKZp34hqFoFBpoqEmpLQmprGmpLcG4nl+M8EDfWhcaDPH3QcOQgDonjKvECeHqVlhShu2HTmD9zkPYfugECkvKAEibZ0XKzLXkGLiooeOprC0xprK2RMrstZV9YVxp4jj+meqCpCQhdTXrlFdoa21eat4oHO5uSpNrSuqaEM6V1dZZVso8K37enhYb4ky1q+wfYq1+IlzU0H7VZ60fqR1y6+oL42yYqLggKUlIXc06nh63/unU1LykVAiGL02ps9ZSzaR0lpWShEiduZbqJzGul0XPZ2wYMhc1tD+1dZaVOnxZahJiqYnjHAG/KVyQlCQkrJ6jhu7s2MmakvqROmLHUtPnk/2obRhy52b9OI+KnZDSWVZKbQngWkmIFExUnJyxkT1Shi57uCnrHDVEtiN1xA6TEMdS14RtUoYhc3SPfeBaP9bDRMWJ1TayR0oSUtuoIbItjthxPlImbOMwZMdh67V+pHKGNYGYqDgpKcOLpSQhtU0GR7ZjyqKEZP+kTtjGYciOQ475TVxlTSAOT3ZSUkb2GBu6TPapclFCN6UCggAoFbcm0nNTcsSOI5JSUwJwGLIjkTK02JIOnN6PqR9OwuqdK5H22zas3rkSUz+chINn9gOo2mdGFEXo9DqIomjoM5NXnGfReKyJiYqTqhzZY0zlyB5Lq9BqcTk3HxlXc3H1RgE0dUzzT6apHLFzf89E9GjbFPf3TMTrU4dxMUEHVFlTYsztNSXhgVEQaph7g8OQ7Yst5zeRkoRI6TPjKNj046SkDi+Wypzp9i9dz4NCEBAZEmDux6A7sLOsc5BaU+LhpuIwZAdiyflNamvWcbU1gZioOClTFiWsS32m2//93BUE+/twzhSi25gyYVuwXwSHITsQKZ1l69u3xNXWBGLTj5OqnDW2cvb6ykpmU4cX13e6/coVkonob5U1JZXNOpXNQDXVlHi4qRAVGo/4yLaICo1nkuLALNG3REoSYus+M9bEP3MdkPtfSYZ7HcmG1OHFtTXrWGK6/coVkonob6wpcU611ZZImRROSrOOlFluA30CJU8wZ++YqDigxHjpHejqGl5cV7OOJabbr1whmYiqqqwpIedQV5ONpfqWuNqaQExUnFxttSVS5lqp73T7d66QTETkaCprRGqbKE1KbYkl+5a40ppATFScWF21JVKadeo73X77uEbsSEtEstLoNNDrzZ8uYeGEtw3/r9aUGy2zPT2t1tqSH9LTEOwXUmsSEuIXgh4JPWtt1umR0MMQg7fKC/d0GmLYr1A451xY/AZxUlJqS6Q061R2yjV1un0PdyWCfL0RbsLoIiIiS9PoNDh95RTKK4wnGJZy8vLJWmcRPnH5JJLb9Km1RiUisAEu517GyLtG44ufP4derzes8aVQKDDyrtG4lHsZl3IvG43B08MTzRu1gLvS3eKfT05MVJyUlNoSqXOtSO2Ue3t/GJ1eb3S6dyIiW9LrdSivKIebm5tVv8DDAsJqXI9LFEWEB4QhLCAMD/WZgLW710Cn10FQCBD1IpQKJcb3eRihAWEAgPZxiYiLjMdvGYdxs/gmgn2D0TG+E3y9fGu8vkanQXlF+a2aIyYq5Aik1JY0Dg+UPNcK1/whIkfmrnSHh5t5Hfvf3/wuikoL4eftj+eGPW+0TLcW3bD9N+PNP0qFEl1bdIeHmwfaxyYirkEcDp09hJtFNxDsF4JOTTvDz6tq7XOIXwj6J95tUpxareVnHLcHTFSclJTaEqnNOkRkHRVaNYcnO4Ci0kIUlBbUWsbPyx8T+03G6h0rq9WWTOw3uUoi4uflj5S2fa0dttNgouKkpM5MK7VZh4gs60ZRtmEF5cq+DReun0JCVBKC/SLkDo/M0Dq6DeY8OLfO2hIyDRMVJ2VKbQmbdYhsq0KrNiQpAAwdMEVRjxOXD6Nzs36sWXFQddWWFJUV4uCZg8grvokg32B0btYZfl7+NozQ8TBRcWKsLSGyTzn5l42u8wPcSlZy8i9zIjgndOzi0WpNQ9sOb8XEfpPROrqN3OHZLa714+Qqa0viG4aiUWggkxQiO6DWlBrW97mTAAFqTamNIyJrKyorxOodK6HVayFChF6vhwgRWr0Wq3esRFFZodwh2i0mKkRENqZy9zY63wZwqxlI5e5t44jI2g6eOQhdDZPO6fQ6HDp7yMYROQ6HTVQWLlyIzp07w8/PD+Hh4bj//vtx6tQpucMiIqpTeGCUYeXkOwmCAuGB0tfzIvtSVFaInX/swJf7NmDnHzsMNSV5xTchKGqoRVMIuFl0w5ZhOhSH7aOyZ88ePPnkk+jcuTO0Wi1efPFF3H333Th+/Dh8fHzkDo+IqEYebiokRCXhxB2jfgRBgYSoJHakdVC19UEJ8g2GqK+hFk0vItgvxMbROg6HTVS2bdtW5f2qVasQHh6Ow4cPo3fv3jJFRUQkTbBfBDo368d5VJzE7X1QABiSkso+KP8YNgPbDm+tcUK4Tk072zReR+KwTT93Kii4NRlPcHCwzJEQEUnj4aZCVGg84iPbIio0nkmKA6urD8rJKycxsd9kuCncIECAQqGAAAFuCrdqE8JRVQ5bo3I7vV6PZ599Fj169ECbNjUP8VKr1VCr1Yb3xcXFtgiPiIicXGUfFGPNO5V9UFLa9uWEcGZwikTlySefxNGjR/HTTz/VWm7hwoWYP3++jaIiIiJXIbUPCqfPN53DN/089dRT+Pbbb7Fr1y5ERdXeU3727NkoKCgwvPbs2WOjKImIyJl1btYZSoXxearYB6V+HDZREUURTz31FDZt2oSdO3ciNja2zmNUKhX8/f0NL1/fmpfMJiIikqpyUUL2QbE8h236efLJJ7Fu3Tps3rwZfn5+yMrKAgAEBATAy8tL5uiIiMjVcFFC63DYRGXJkiUAgD59+lTZvnLlSkyaNMn2ARERkctjHxTLc9hERRSNd1oiIiIi5+GwfVSIiIjI+TlsjQoREZEjKyorxMEzB5FXfBNBvsHo3Kwz/Lz85Q7L7jBRISIisrHa1gVqHV3zxKWuiE0/RERENnT7ukAiROj1eogQDesCVa64TLcwUSEiIrKhutYFOnT2kI0jsm9s+iEiIrKCmvqgSFkXiP7GRIWIiMjCauuDInVdILqFTT9EREQWVFcflISoBK4LZAImKkRERLXw8/ZHgHcA/LylDR2uqw/KySsnuS6QCdj0Q0REVIvnhj1vUnkpfVBS2vblukASMVEhIiKyIKl9ULgukDRs+iEiIrKgzs06sw+KBTFRISIisiA/L3/2QbEgNv0QERFZWOvoNuyDYiFMVIiIiCyECw1aHhMVIiIiC+BCg9bBPipERET1xIUGrYeJChERUT1xoUHrYaJCRERUT5WTvBnDhQbrh4kKERFRPXGhQethokJERFRPnOTNepioEBER1RMnebMeDk8mIiKyAE7yZh1MVIiIiCyECw1aHhMVIiIiG+LstaZhokJERGQjnL3WdOxMS0REZCFFZYXY+ccOfLlvA3b+saPKjLScvdY8rFEhIiKygLpqS6TMXsv+LdWxRoWIiKiepNSWcPZa8zh0orJ3717ce++9aNiwIQRBwNdffy13SERE5IKk1JZw9lrzOHSiUlJSgvbt2+M///mP3KEQEZELk1JbwtlrzePQfVQGDx6MwYMHyx0GERG5OCm1JZWz197Zj0WpUHL22lo4dKJiKrVaDbVabXhfXFwsYzREROQsOjfrjG2Ht0Kr11bbd3ttCWevNZ1LJSoLFy7E/Pnz5Q6DiIicjCm1JZy91jQulajMnj0bzz33nOF9eno6kpOTZYyIiIicBWtLrMOlEhWVSgWVSmV47+vrK2M0RETkbFhbYnkOPeqHiIiInJtD16gUFxfj7NmzhveZmZlIT09HcHAwoqOjZYyMiIiILMGhE5VDhw4hJSXF8L6y/8nEiROxatUqmaIiIiIiS3HoRKVPnz4QRePj1qmqrKxryMrKstn19HoROr0eAd5eUCqdt4VRrdHizOUceLq7wd3N+ERO1hAWHoHwiAibXY+sKysrC9lZ2Ta7nk6vh16vRYD3VSiVDv01UKcKrRoZ1zKgclfBXelus+uGRYQhPCLcZtdzZoLowt/0165dw9KlS5GamorIyEi5w7EatVqNgQMHYs+ePXKHQhaSnJyMtLS0Kp3DyTHx+XROfEYtx6UTFVdRWFiIgIAA7NmzhyOdnEBxcTGSk5NRUFAAf39/ucOheuLz6Xz4jFqWc9f5URWJiYl8aJxAYWGh3CGQFfD5dB58Ri3LeTsPEBERkcNjokJERER2i4mKC1CpVJg7dy47dTkJ3k/nwvvpfHhPLYudaYmIiMhusUaFiIiI7BYTFSIiIrJbTFSIiIjIbjFRIZOcP38egiBwLSUiO8VnlJwNExUrysjIQGpqKuLi4uDp6Ql/f3/06NEDixYtQllZmdWue/z4ccybNw/nz5+32jWkWLBgAe677z5ERERAEATMmzdP1nhsSRAESa/du3fX+1qlpaWYN2+eSedy5XtzO1d+Rk+ePImZM2ciMTERfn5+iIyMxJAhQ3Do0CHZYrIVe34+Xfm+1IQz01rJli1bMGrUKKhUKkyYMAFt2rRBRUUFfvrpJ7zwwgs4duwYli1bZpVrHz9+HPPnz0efPn0QExNjlWtI8fLLL6NBgwbo0KED0tLSZItDDmvWrKny/tNPP8X27durbU9ISKj3tUpLSzF//nwAtxbqlMKV700lV39GP/74Y6xYsQIPPPAAnnjiCRQUFGDp0qXo1q0btm3bhv79+8sSly3Y8/PpyvelJkxUrCAzMxMPPvggmjRpgp07d1ZZ8PDJJ5/E2bNnsWXLFhkj/JsoiigvL4eXl5fFz52ZmYmYmBjk5uYiLCzM4ue3Zw899FCV97/++iu2b99ebbtcXPneAHxGAWDs2LGYN29elfWFpkyZgoSEBMybN8+pvxDt+fl05ftSEzb9WMHbb7+N4uJirFixwuiqzE2bNsUzzzxjeK/VavHaa68hPj4eKpUKMTExePHFF6FWq6scFxMTg6FDh+Knn35Cly5d4Onpibi4OHz66aeGMqtWrcKoUaMAACkpKdWqMCvPkZaWhk6dOsHLywtLly4FAJw7dw6jRo1CcHAwvL290a1bt3r9spazNscR6PV6fPDBB2jdujU8PT0RERGB1NRU5OXlVSl36NAhDBw4EKGhofDy8kJsbCymTJkC4FZ/hMpEY/78+Yb7XVdTjqvfGz6jQFJSUrVFEENCQtCrVy+cOHHCrHM6E7meT96X6lijYgXffPMN4uLicNddd0kqP3XqVKxevRojR47EjBkzsH//fixcuBAnTpzApk2bqpQ9e/YsRo4ciUceeQQTJ07EJ598gkmTJiEpKQmtW7dG7969MX36dPz73//Giy++aKi6vL0K89SpUxg7dixSU1Px6KOPokWLFsjOzsZdd92F0tJSTJ8+HSEhIVi9ejXuu+8+bNy4EcOHD7fcD4gAAKmpqVi1ahUmT56M6dOnIzMzEx999BF+++03/Pzzz3B3d0dOTg7uvvtuhIWFYdasWQgMDMT58+fx1VdfAQDCwsKwZMkSTJs2DcOHD8eIESMAAO3atZPzo9k9PqM1y8rKQmhoqEXO5cjs7fl06fsikkUVFBSIAMRhw4ZJKp+eni4CEKdOnVpl+/PPPy8CEHfu3GnY1qRJExGAuHfvXsO2nJwcUaVSiTNmzDBs27BhgwhA3LVrV7XrVZ5j27ZtVbY/++yzIgDxxx9/NGwrKioSY2NjxZiYGFGn04miKIqZmZkiAHHlypWSPp8oiuL169dFAOLcuXMlH+NsnnzySfH2x+3HH38UAYhr166tUm7btm1Vtm/atEkEIB48eLDGc9fn5+uK94bPaM327t0rCoIgzpkzx+RjHZm9Pp+VXPW+VGLTj4VVLu/t5+cnqfzWrVsBAM8991yV7TNmzACAatW6rVq1Qq9evQzvw8LC0KJFC5w7d05yjLGxsRg4cGC1OLp06YKePXsatvn6+uKxxx7D+fPncfz4ccnnp7pt2LABAQEBGDBgAHJzcw2vymrfXbt2AQACAwMBAN9++y00Go2METsPPqPG5eTkYNy4cYiNjcXMmTPrdS5HZ0/PJ+8L+6hYnL+/PwCgqKhIUvkLFy5AoVCgadOmVbY3aNAAgYGBuHDhQpXt0dHR1c4RFBRUrd20NrGxsUbjaNGiRbXtldXRd8ZB9XPmzBkUFBQgPDwcYWFhVV7FxcXIyckBACQnJ+OBBx7A/PnzERoaimHDhmHlypXV+kaQdHxGqyspKcHQoUNRVFSEzZs3V+sj4Wrs5fnkfbmFfVQszN/fHw0bNsTRo0dNOk4QBEnllEql0e2iCWtLWmOED5lGr9cjPDwca9euNbq/sgOeIAjYuHEjfv31V3zzzTdIS0vDlClT8N577+HXX3912V9c9cFntKqKigqMGDECf/zxB9LS0tCmTRubXdte2cPzyfvyNyYqVjB06FAsW7YMv/zyC7p3715r2SZNmkCv1+PMmTNVOtNlZ2cjPz8fTZo0Mfn6Un+h3hnHqVOnqm0/efKkYT9ZTnx8PH744Qf06NFD0pdSt27d0K1bNyxYsADr1q3D+PHj8fnnn2Pq1Klm3W9Xx2f0Fr1ejwkTJmDHjh344osvkJycbPI5nJHczyfvS1Vs+rGCmTNnwsfHB1OnTkV2dna1/RkZGVi0aBEA4J577gEAfPDBB1XKvP/++wCAIUOGmHx9Hx8fAEB+fr7kY+655x4cOHAAv/zyi2FbSUkJli1bhpiYGLRq1crkOKhmo0ePhk6nw2uvvVZtn1arNdy7vLy8an+JJyYmAoChetnb2xuAaffb1fEZveXpp5/G+vXrsXjxYsOIFJL/+eR9qYo1KlYQHx+PdevWYcyYMUhISKgy6+W+ffuwYcMGTJo0CQDQvn17TJw4EcuWLUN+fj6Sk5Nx4MABrF69Gvfffz9SUlJMvn5iYiKUSiXeeustFBQUQKVSoW/fvggPD6/xmFmzZuGzzz7D4MGDMX36dAQHB2P16tXIzMzEl19+CYXC9Jx2zZo1uHDhAkpLSwEAe/fuxeuvvw4AePjhh126liY5ORmpqalYuHAh0tPTcffdd8Pd3R1nzpzBhg0bsGjRIowcORKrV6/G4sWLMXz4cMTHx6OoqAjLly+Hv7+/4QvUy8sLrVq1wvr169G8eXMEBwejTZs2tVYVu/q94TN6K/FavHgxunfvDm9vb/zvf/+rsn/48OGGhMrVyPl88r4YIe+gI+d2+vRp8dFHHxVjYmJEDw8P0c/PT+zRo4f44YcfiuXl5YZyGo1GnD9/vhgbGyu6u7uLjRs3FmfPnl2ljCjeGrY4ZMiQatdJTk4Wk5OTq2xbvny5GBcXJyqVyirDIGs6hyiKYkZGhjhy5EgxMDBQ9PT0FLt06SJ+++23VcqYMvQxOTlZBGD0ZWxYpjO7c/hjpWXLlolJSUmil5eX6OfnJ7Zt21acOXOmePXqVVEURfHIkSPi2LFjxejoaFGlUonh4eHi0KFDxUOHDlU5z759+8SkpCTRw8ND0lBI3ptbXPkZnThxYo3/BgCImZmZtR7vTOzp+eR9qU4QRRN6eBERERHZEPuoEBERkd1iokJERER2i4kKERER2S0mKkRERGS3mKgQERGR3WKiIqO3334bLVu2hF6vlzuUeps1axa6du0qdxiy4v10PrynzoX300HJPT7aVRUUFIjBwcHiJ598YtiGv8bJv/vuu9XKr1y5ss7lxKX68ssvxdGjR4uxsbGil5eX2Lx5c/G5554T8/LyjJbfvHmz2KFDB1GlUomNGzcWX3nlFVGj0VQpc+3aNVGlUombN2+ud3yOiPfT+fCeOhfeT8fFREUm//rXv0R/f3+xrKzMsK3yoYmIiBBLSkqqlLfkQxMSEiK2bdtWnDNnjrh8+XJx+vTpooeHh9iyZUuxtLS0StmtW7eKgiCIKSkp4rJly8Snn35aVCgU4uOPP17tvKNHjxZ79epV7/gcEe+n8+E9dS68n46LiYpM2rVrJz700ENVtgEQExMTRQDie++9V2WfJR8aYzOPrl69WgQgLl++vMr2Vq1aie3bt6+Szb/00kuiIAjiiRMnqpTduHGjKAiCmJGRUe8YHQ3vp/PhPXUuvJ+Oi31UZJCZmYk//vgD/fv3r7avR48e6Nu3L95++22UlZVZ5fp9+vSptm348OEAgBMnThi2HT9+HMePH8djjz0GN7e/l4V64oknIIoiNm7cWOUclZ9n8+bNVojafvF+Oh/eU+fC++nYmKjIYN++fQCAjh07Gt0/b948ZGdnY8mSJbWeR61WIzc3V9KrLllZWQCA0NBQw7bffvsNANCpU6cqZRs2bIioqCjD/koBAQGIj4/Hzz//XOf1nAnvp/PhPXUuvJ+Ojasny+DkyZMAgNjYWKP7e/XqhZSUFLzzzjuYNm0avLy8jJb77LPPMHnyZEnXFOtY0umtt96CUqnEyJEjDduuXbsGAIiMjKxWPjIyElevXq22PS4uDsePH5cUk7Pg/XQ+vKfOhffTsTFRkcGNGzfg5uYGX1/fGsvMmzcPycnJ+O9//4t//OMfRssMHDgQ27dvr3c869atw4oVKzBz5kw0a9bMsL2yGlSlUlU7xtPTE4WFhdW2BwUFVcv6nR3vp/PhPXUuvJ+OjYmKnerduzdSUlLw9ttv4/HHHzdaJjIy0mjmbYoff/wRjzzyCAYOHIgFCxZU2Vf5V4Vara52XHl5udG/OkRRhCAI9YrJGfF+Oh/eU+fC+2m/mKjIICQkBFqtFkVFRfDz86ux3Ny5c9GnTx8sXboUgYGB1faXlZWhoKBA0jUbNGhQbdvvv/+O++67D23atMHGjRurdN4C/q5+vHbtGho3blxl37Vr19ClS5dq58zLy6vS5uoKeD+dD++pc+H9dGzsTCuDli1bArjVE702ycnJ6NOnD9566y2jvdHXr19vyPDret0pIyMDgwYNQnh4OLZu3Wq0SjQxMREAcOjQoSrbr169isuXLxv23y4zMxMJCQm1fi5nw/vpfHhPnQvvp2NjjYoMunfvDuDWP8Z27drVWnbevHno06cPli1bVm2fue2lWVlZuPvuu6FQKJCWloawsDCj5Vq3bo2WLVti2bJlSE1NhVKpBAAsWbIEgiBU6QQGAAUFBcjIyMC0adNMjsmR8X46H95T58L76eDkmb6F2rRpI44dO7bKNgDik08+Wa1scnKyYQZFS0w+1L59exGAOHPmTHHNmjVVXt9//32Vst98840oCILYt29fcdmyZeL06dNFhUIhPvroo9XOu3HjRhGAePbs2XrH6Gh4P50P76lz4f10XExUZPL++++Lvr6+VaZPrumh2bVrl0UfmspzGXslJydXK79p0yYxMTFRVKlUYlRUlPjyyy+LFRUV1cqNGTNG7NmzZ73jc0S8n86H99S58H46LiYqMsnPzxeDg4PFjz/+WO5QLOLatWuip6en+PXXX8sdiix4P50P76lz4f10XOxMK5OAgADMnDkT77zzjlMsOf7BBx+gbdu2GDZsmNyhyIL30/nwnjoX3k/HJYhiHdPnEREREcmENSpERERkt5ioEBERkd1iokJERER2i4kKERER2S0mKkRERGS3mKgQERGR3WKiQkRERHaLiQoRERHZLSYqREREZLeYqBAREZHdYqJCREREdouJChEREdktJipERERkt1w6Ubl27RrmzZuHa9euyR0KERERGeHyicr8+fOZqBAREdkph05U9u7di3vvvRcNGzaEIAj4+uuv5Q6JiIiILMihE5WSkhK0b98e//nPf+QOhYiIiKzATe4A6mPw4MEYPHiw3GEQERGRlTh0jQoRERE5N4euUTGVWq2GWq02vC8uLpYxGiIiIqqLS9WoLFy4EAEBAYZXcnKy3CERERFRLVwqUZk9ezYKCgoMrz179sgdEhEREdXCpZp+VCoVVCqV4b2vr6+M0RCZL6ewHOH+nnKHQURkdQ6dqBQXF+Ps2bOG95mZmUhPT0dwcDCio6NljIzIuq4WMFEhItfg0InKoUOHkJKSYnj/3HPPAQAmTpyIVatWyRQVkfWVVmghiiIEQZA7FCIiq3LoRKVPnz4QRVHuMIhsTqMTUVKhg6/KoR9hIqI6uVRnWiJnklVQJncIRERWx0SFyEGdzCqSOwQiIqtjokLkoPZl3JA7BCIiq2OiQuSg/rxSgKyCcrnDICKyKiYqRA7s2z+uyh0CEZFVMVEhcmDfHctCbrG67oJERA6KiQqRg+nUqRNG9m6PH9+eigqtHkt2Z3CYPhE5LSYqRA4mKysLudnXoC66CQA4cP4mNhy+LHNURETWwUSFyAms+fUCPjtwkTUrROR0mKgQOYl1By7izW0nUVSukTsUIiKLYaJC5ET2ZdzAE+uOYNfJHOj1rF0hIsfHRIXIyeSXavD+D6fxjy/SsT/zBpuDiMihMVEhclLnckvw+pYTmLHhdxy5mCd3OEREZmGiQuTkzuQUY+7/HcNr3x7nnCtE5HCYqBC5iAPnb+Kpz45gz+nrbA4iIofBRIXIhZSodXj3+1N4fcsJXCsokzscIqI6uckdABFJd/HiRZSUlAAAtOpylN3MhldwhMnnOXD+Jg5dyEO/luEY0bERooK8LR0qEZFFsEaFyAEcOHAA9957L2JiYpCfnw8A0JYVYce8UTi4dBbyL5ww+Zx6UcT2E9l4Yu0RzP/mGI5czOOQZiKyO6xRIbJzX331FcaMGQNRFKv3LRFF5Bz/BTnHf0XHyfMRmZhs8vlFAIcu5OHQhTw0DPTEkLYN0T8hHN4e1vn1UFhSjv9s2oVfj2VCEIBe7ZrhieHJ8FJ51B2rKOKl5V/j4MkLmDd5KHq0bWrYd+piFj7e8jPOXMqGIAhoER2BR4f2QnyjMKt8DiKyDdaoENmxAwcOYMyYMdDpdNDpdEbLiHo9RL0eR1bONatm5XZX88ux/MdzmLTyID75ORN5JRVmnWfGfzYg7cAxo/veXPsdzmfdwJuPD8frU4fhj3NX8K8vdkg671d7fwMgVNtepq7A7GVfIzzQDx8++yD+9fRoeKs8MHvZJmhr+LkRkWNgokJkx15//XXjNSnViABEnElbbZHrlml02PTbFTy65hC+OHQJOgs1CV3IvomDJy/guTEDkNAkEm3iGuGp4X2wO/0UcguKaz327JUcbNx9BM8/OKDavos5eSgqLcfEQd3RODwYMQ1C8PDd3ZBXVIrsm0UWiZ2I5GF2oqLT6fD5558jNTUVw4cPx59//gkAKCgowFdffYXs7GyLBUnkii5evIhvv/22xpqUO4l6PbKP7kPZTcs9e2qtHmt+vYB53xxDhVZf7/OdOH8Nvl4qtGj8dwfgjs2jIQgCTl7IqvG48goNFv5vG55+IAXB/j7V9jcOC4K/jye27T8KjVYHdYUW3+0/iuiIYDQI9q933EQkH7MaofPz8zFo0CAcOHAAvr6+KCkpwdNPPw0A8PX1xfTp0zFhwgS88cYbFg2WyB5ptDro9PX/Er/TtrTvTZ/vRBRx/fRhRHUdbNFYjlzMwze/X8EDSY3rdZ6bRSUI9K06wkipVMDf2xN5RSU1Hvffr/egVUwk7moTb3S/t6cH3n1iJOZ98g3Wbj8AAGgUFoiFjw2HUsmKYyJHZlaiMmvWLBw7dgxpaWno0KEDwsPDDfuUSiVGjhyJrVu3MlEhp6fR6nDqYhZK1ZZfsfhU5iUoFAroTUmCBAFlxYUoU5vXt6Q2P566hvvaN4S7m7LavnU/HMBnPxw0vK/QaHHiQhY++mq3YduKfz5s1nX3Hc3Ab2cv478zxtVYRl2hxfvrt6N1bEO8+PBg6PQiNu4+jJc/3oyPnh0LlZU6BhOR9Zn19H799dd4+umnMWDAANy4caPa/ubNm2PVqlX1jY3I7un0epSqNXB3U8BdWf0LvD4CAwNMS1IAQBTh5ukNhVC9w2l9iCLg7SZAp9fDHdU/59Du7ZDcvrnh/Ztrv0PPds3Q87ZROSH+vgj280F+cWmVY3U6PQpLyxHkV71JBwDSz1zCtRv5uP+lJVW2v7pqC9rENcR7T47CziMnkXWzCIumPwiF4tZnn/3QYIx4eQn2HctASocWZn92IpKXWYlKQUEBYmNja9yv0Wig1WrNDorI0bgrlfBwt+xf7b16J0MQBNOafwQBIc06QrBwouKnUqJP08Aa9/v7eMLfx9Pw3sPdDYG+XmgUVvWYhJhIFJepcfpSNpr/1U/lt7OXIIoiWjZpYPTcD/brjMHd2lTZ9tg7/8Pjw3qjW+s4AIBao4VCAG7/2LeSNQEi54YhcmhmNd7Gx8fjyJEjNe7//vvv0apVK7ODIiKgYaMo9Ok3AEqJNTWCQoHQVt3hGWT6TLW1aeCvwvTejRHo5V7vczWJCEbnlk3wry9+wMkLWTiaeRUffbULfRJbIDTAFwCQm1+MKW+uNnSuDfb3QWxkaJUXAIQH+SEyJADArQ65RWVqfPjlLlzIvonzWTfwzuffQ6kQ0L5Z/frVEJG8zEpUpk6dik8++QTr1683/LUnCALUajVeeuklbNu2DampqRYNlMgVPfHMPyAIgsQaEgGx/R+y6PWTGvvjhb4xCPOtezI2qWaNH4zG4cGY+d8v8dLyr9EmtiH+MbqfYb9Wr8OlnDyoNdL7/URHBOO1R+7DuWu5eGbR53juwy9wo7AEbzw2HCFGRgkRkeMQRDOWURVFEY899hhWrFiBwMBA5OfnIyIiAjdu3IBWq0VqaiqWLFlS94lkduTIESQlJeHw4cPo2LGj3OGQAyqv0OCPjCvwVrlbvOmn0vffbcE/nkyFKIpGhyoLCgUAAW0nvILwtr0sck2FIGBE+wj0aRoEQRBQodGiVK1Bu/hG8PSof80KEZFUZv1mFQQBy5cvx8SJE7Fx40acOXMGer0e8fHxGD16NHr37m3pOIlc1t2Dh+CzTd9g8aJ/YfeO7VX7rAgCQhK6Ibb/QwiIbmmR60X6q/Bw54ZoEuxlkfMREdVHvf4E7NmzJ3r27GmpWIioBu3ad8B/P/kUV69cxrCB/VBYWAA3L190m7HcYn1SvD2UGNgyFH2aBsGNc48QkZ0w67dRZmYmvvnmmxr3f/PNNzh//ry5MRFRDRo2ioKX960J05QenhZJUrzdlRjSOgzzBzdF/xYhTFKIyK6YVaPy/PPPo7CwEPfee6/R/f/5z38QGBiIzz//vF7BEZH1BHq5IaVZCHrGBcLT3bJzwBARWYpZicovv/yCZ599tsb9/fr1wwcffGBmSERkTTHBXkhpFowOUf5QKiw73woRkaWZlajk5eXBz8+vxv2+vr5GZ6wlInm4KwR0bOyP5KbB7CRLRA7FrEQlOjoaP//8M6ZNm2Z0/48//oioqKh6BUZE9efjoUSfZsHoFRcEP0+ud0NEjsesXnNjx47FZ599hn//+99V1iLR6XRYtGgR1q9fj3Hjal5AjIisy10pYEjrMLx6T1Pc0yqMSQoROSyzfnvNnj0bP/30E5599lksWLAALVrcWvDr1KlTuH79Ovr06YOXXnrJooESkTRxIV6Y2KURQi04mywRkVzMqlFRqVT4/vvvsWLFCnTp0gW5ubnIzc1Fly5d8Mknn+CHH36ASqWydKxEVIdOjf3xTHITJilE5DTMrg9WKBSYPHkyJk+ebMl4iMhMrRr4YkKXRhzJQ0ROhQ3XRA4mNCwMFTo9tKoAw7YIPw9M7sokhYicj9mJSlpaGlasWIFz584hLy8Pd65tKAgCMjIy6h0gEVX11ZbvcehiAVbuvwIA8FUp8XiPaHh7cNI2InI+ZiUq77zzDmbNmoWIiAh06dIFbdu2tXRcRCSBm0LA4z0aI9yPfVKIyDmZlagsWrQIffv2xdatW+HuziXfieQypHUYYkO85Q6DiMhqzJ6ZduTIkUxSHMDFixexY8cOFBUVwc/PD/369UN0dLTcYZEF+Hu6IaVZsNxhUD3w+SSqm1mJSpcuXXDq1ClLx0IWdODAAbz22mvYsmULRFGEQqGAXq+HIAgYOnQo5syZg86dO8sdJtVDp2h/uHOlY4fE55NIOrN+yy1evBhfffUV1q1bZ+l4yAK++uor9OjRA999952hk3PlDMKiKGLr1q2466678NVXX8kZJtVTi3AfuUMgM/D5JDKNWYnKmDFjoNVq8fDDDyMgIACtW7dGu3btqrzat29v6ViN+s9//oOYmBh4enqia9euOHDggE2ua68OHDiAMWPGQKfTQafTGS1TuW/MmDE4ePCgjSMkS4n056SKjobPJ5HpzEpUgoOD0axZM/Tu3RsdO3ZEeHg4QkJCqryCg63fdr5+/Xo899xzmDt3Lo4cOYL27dtj4MCByMnJsfq17dXrr78OURSrDRe/U2WZ119/3UaRkSUJAhDoxT5ijobPJ5HpBLGuJ8aOde3aFZ07d8ZHH30E4Fb1aePGjfH0009j1qxZdR5/5MgRJCUl4fDhw+jYsaO1w7W6ixcvIiYmps5fgrcTBAHnz59nBz4zlVdo8EfGFXir3OHhbrv5E89cL0GzMNs1/VRotChVa9AuvhE8PZggmYPPJ5F5HHZm2oqKChw+fBizZ882bFMoFOjfvz9++eUXo8eo1Wqo1WrD++LiYgCAVquFRqOxbsA2kJaWZtIvQeDWX27ff/89Jk6caKWonJtGo4FWo0GpqINGY7sJ19z0GpSUltnsehqdDlqtHhqNBkpOfmsWPp/y0eg00OuNN7U5E4VCCXelbf+QsMnoX9FMBQUF4sKFC8W7775bTExMFPfv3y+KoijeuHFDfO+998QzZ86Ye2pJrly5IgIQ9+3bV2X7Cy+8IHbp0sXoMXPnzhUB8MUXX3zxxRdfFnjZglk1KpcvX0ZycjIuXbqEZs2a4eTJk4baieDgYCxduhQXLlzAokWLzDm91cyePRvPPfec4X16ejqSk5Oxf/9+dOjQQcbILGPVqlV47LHHTD5u+fLl/IutHjRaHXR/jdqwleJyLXw9bVshqlQo4O7GafrNxedTHmpNOY5dOAalm+1rG2xJo9NAp9WhdZPWULl7yh2ORZn1m+6FF15AUVER0tPTER4ejvDw8Cr777//fnz77bcWCbAmoaGhUCqVyM7OrrI9OzsbDRo0MHqMSqWCSvX3SAlfX18AgJubm1NMXjdw4EAIgmByG/jdd9/tFJ9fLnL87JTuWnh7OGzLrUvi8ykPPXRwc3eDl8oLHm7Ou9REhbYCZeoyuLu7O92/F7NG/Xz//feYPn06WrVqBUGo3mAdFxeHS5cu1Tu42nh4eCApKQk7duwwbNPr9dixYwe6d+9u1Wvbq+joaAwdOhRKpbS/epVKJe6991521HNACiPPHdk3Pp9E5jErUSkrK0NYWFiN+4uKiswOyBTPPfccli9fjtWrV+PEiROYNm0aSkpKMHnyZJtc3x7NmTMHgiAYTSBvV1nm5ZdftlFkZEme7myCcUR8PolMZ1ai0qpVK+zdu7fG/V9//bVN+nyMGTMG7777Ll555RUkJiYiPT0d27ZtQ0REhNWvba86d+6M9evXQ6lU1viXW+W+L774gtN0E9kQn08i05mVqDz77LP4/PPP8dZbb6GgoADArWaXs2fP4uGHH8Yvv/yCf/zjHxYNtCZPPfUULly4ALVajf3796Nr1642ua49GzFiBPbt24d77rnH8JebQnHrVguCgCFDhmDfvn0YPny4nGESuSQ+n0SmMXvCtwULFmDevHkQRRF6vR4KhcKwuNbrr7+Of/7zn5aO1eKcbcI3Yy5evIidO3eisLAQ/v7+6Nu3L9u8iewEn0/rU2vKcfTCUZfpTNumSRunG/VTr5lpL168iC+//BJnz56FXq9HfHw8RowYgbi4OEvGaDWukKgQEbkyJiqOz+TxjaWlpejVqxceffRRPP744zZr4iEiIiLXY3IfFW9vb2RmZtbZa52IiIiovszqTDto0CCkpaVZOhYiIiKiKsxKVObMmYPTp0/j4Ycfxk8//YQrV67g5s2b1V5ERERE9WHWHNytW7cGABw/fhzr1q2rsZxO5/yrVRIREZH1mJWovPLKK+yjQkREVAOtXgM3hXOtuSMXsxKVefPmWTgMIiIiourM6qNyp4KCAjbzEBER/UWwzNcroR6JyqFDhzBo0CB4e3sjJCQEe/bsAQDk5uZi2LBh2L17t6ViJCIicijsHmE5ZiUq+/btQ8+ePXHmzBk89NBD0Ov1hn2hoaEoKCjA0qVLLRYkERERuSazEpUXX3wRCQkJOH78ON54441q+1NSUrB///56B0dERESuzaxE5eDBg5g8eTJUKpXR6q1GjRohKyur3sERERGRazMrUXF3d6/S3HOnK1euwNfX1+ygiIiIHJvZ6/3SHcxKVLp164aNGzca3VdSUoKVK1ciOTm5XoERERE5Kr3IRMVSzEpU5s+fj0OHDmHIkCH47rvvAAC///47Pv74YyQlJeH69euYM2eORQMlIiJyFCJrVCzGrAnfunbtiq1bt2LatGmYMGECAGDGjBkAgPj4eGzduhXt2rWzXJREREQORBRr7h5BppGUqBQWFsLHxwdKpdKwrW/fvjh16hTS09Nx5swZ6PV6xMfHIykpiePHiYjIpbE+xXIkNf0EBQVh/fr1hvdTpkwxDD9OTEzEqFGjMGbMGHTq1IlJChERuTydyNnaLUVSouLh4QG1Wm14v2rVKmRkZFgtKCIiIkcmsjOtxUhq+mnZsiU+/vhjxMTEICAgAABw/vx5HDlypNbjOnbsWP8IiYiIHAxrVCxHECWkfdu2bcOYMWNQXFws6aSiKEIQBLtfqPDIkSNISkrC4cOHmVQRETkhtaYcRy8chZfKCx5uHja7br46H4GqQJtdr0JbgTJ1Gdo0aQOVu6fNrmsLkmpUBg0ahMzMTBw8eBDZ2dmYNGkSHnvsMXTv3t3a8RERETkcPUf9WIykROWPP/5AkyZNMHDgQADAypUrMWrUKPTr18+qwRERETkiPZt+LEZSZ9oOHTpgy5Yt1o6FiIjIKbBGxXIkJSpeXl4oLS01vN+zZw+ys7OtFhQREZEj04pauUNwGpKaftq3b4/3338fSqXSMOrn4MGD8PSsvcPOiBEj6h8hERGRg1HryuUOwWlISlQWLVqEkSNH4pFHHgEACIKARYsWYdGiRTUe4wijfoiIiKyhWFMidwhOQ1Ki0qlTJ5w9exYZGRnIzs5Gnz598NJLL6F///7Wjo+IiMjhFFTkyx2C05C8KKGbmxtatGiBFi1aYOLEiRg6dCi6du1qzdiIiIgc0s3yG3KH4DTMWj155cqVlo6DiIjIaeSWX5c7BKchKVF59dVXIQgCXnrpJSgUCrz66qt1HiMIAubMmVPvAImIiBxNvjofap0aKqVK7lAcnqQp9BUKBQRBQFlZGTw8PKBQ1D2q2RE603IKfSIi5ybXFPrvp7+NUU0fRGPfaJtcz+Wn0Nfr9bW+JyIioqqulVy1WaLizCRN+EZERESmuVh8Qe4QnIJZnWkB4MSJE8jIyEBRURH8/PzQtGlTtGzZ0pKxEREROaxzBRkQRRGCIMgdikMzOVFZunQpFixYgCtXrlTbFx0djZdeeglTp061SHBERESOKr8iD9fLchDuHSF3KA7NpETl+eefx/vvv4/g4GBMmTIFbdq0ga+vL4qLi/Hnn3/i66+/RmpqKs6cOYO33nrLWjETERE5hD9v/oF+3gPkDsOhSU5UDhw4gPfffx/Dhw/Hp59+Ch8fn2plFi1ahIceegjvvvsuRo0ahU6dOlk0WCIiIkeSnnsYKY36QSGwS6i5JP/kVqxYgcjISKxbt85okgIAPj4++OyzzxAREYEVK1ZYLEgiIiJHdKP8Bk7nn5I7DIcmOVH55ZdfMGrUKKhUtU9e4+npiVGjRuHnn3+ud3BERESO7ofLadCLnNbDXJITlUuXLiEhIUFS2VatWuHSpUtmB0VEROQsrpZcwf7sX+QOw2FJTlQKCwvh5+cnqayvry+KiorMDoqIiMiZbL3wLbJKs+QOwyFJTlRMHQsuYWZ+IiIipzN80Ah8+vA6/PDCbsM2najF2tOrUaYtky8wB2XS8OR3330Xn332WZ3ljM2xQkRE5AquX7+Okhul0ItV19y5UZ6L9WfXYkKLKRwFZALJiUp0dDRu3ryJmzdvSi5vTQsWLMCWLVuQnp4ODw8P5OfnW/V6RERE9XU6/xS2XdyCe5rcK3coDkNyonL+/HkrhmG6iooKjBo1Ct27d+dQaCIichg/XduLcK8IdArvIncoDsHstX7kNn/+fADAqlWr5A2EiIjIRJvOfYkgVTDiA5rKHYrdc6lGMrVajcLCQsOruLhY7pCIiMgFidDjf6dX4WrJVblDsXsulagsXLgQAQEBhldycrLcIRERkYtS69RYcWIprjFZqZVdJSqzZs2CIAi1vk6ePGn2+WfPno2CggLDa8+ePRaMnoiIyDRl2lIsP/5fnC/MlDsUu2VXfVRmzJiBSZMm1VomLi7O7POrVKoqSwD4+vqafS4iIiJLKNeVYcWJZRgZPxrtQzvIHY7dsatEJSwsDGFhYXKHQUREZFM6UYv1Z9chuzQL/RsP5Dwrt7GrRMUUFy9exM2bN3Hx4kXodDqkp6cDAJo2bcqaEiIicki7r+7EtdJrGNN0HDzdPOs+wAWYnaikpaVhxYoVOHfuHPLy8qpNmS8IAjIyMuodYE1eeeUVrF692vC+Q4db1WW7du1Cnz59rHZdIiIiazqVfwL/PfYRJrSYgmDPYLnDkZ1Zico777yDWbNmISIiAl26dEHbtm0tHVedVq1axTlUiIjIKeWUZeO/xz7CpJZT0dCnodzhyMqsRGXRokXo27cvtm7dCnd3d0vHRERE5PKKNUVYcfy/mJLwGBr5RskdjmzM6q2Tl5eHkSNHMkkhIiKyojJdGT45sQxZpVlyhyIbsxKVLl264NSpU5aOhYiIiO5QmazklufKHYoszEpUFi9ejK+++grr1q2zdDxERER0h8pmoBsumKyY1UdlzJgx0Gq1ePjhhzFt2jRERUVBqVRWKSMIAn7//XeLBElEROTqCioKsPz4fzG55VREeDeQOxybMStRCQ4ORkhICJo1a2bpeIiIiKgGhRUFWHZsMSa0nIImfjFyh2MTZiUqu3fvtnAYREREJEWZrgwrTizFuGYT0DIoQe5wrI5z9BIRETkYrV6LNadW4ffcdLlDsbp6TaGv0Whw8uRJFBQUQK/XV9vfu3fv+pyeiIjIoVy9fBVlpWUAAK1ai9LrpfAO87bKtUTo8cXZdXBTKNHMv4VVrmEPzEpU9Ho9Zs+ejcWLF6O0tLTGcjqdzuzAiIiIHMXvv/2O//xrMXbv2G1YUkZTosWWx7cjMikCrUa1QHCzIItfV4SIL85+hkcTpsFP4W/WOfad/BnbDm9FRtZZFJUV4YOpHyKuQXydx2z8eT2u3bwGrV6LhsGNcH/X4Uhp189QpqyiDKt3rsT+U7+gqKwIEYERGNr5PgxOGmJSfGYlKm+88QbeeecdpKamomfPnnj44Yfx1ltvITAwEIsXL4YgCHj77bfNOTUREZFDSduahmcf/wdEUay27h1EIOtIDrJ+y0G3GZ0Q1c3y0+Fr9Bpsu7gFo2LGmnW8uqIcrRq3Rs9WvfDRln9LOsbP0w+jejyIqNAouCnccfDsfiz65l8I8AlEx/gkAMCK7cvxx/nf8dywFxAeGIHfzh3Bf7/7D4L9QtC1eTfJ8ZnVR2XVqlUYPXo0lixZgkGDBgEAkpKS8Oijj2L//v0QBAE7d+4059REREQO4/fffsezj/8DOp2uxlYEUS9C1In49b1DuHkmzypxnC08ixJNsVnHprTrhwd7j0P72A6Sj2kb0w7dW96FxqHRiAyOxH1d7kdMRCyOXzpmKHPy8gn0bdcPbWPaISIwAoM6DkZsRBzOXDFtwlizEpXLly+jb9++AACVSgUAKC8vBwB4eHjgoYcewpo1a8w5NRERkcNY/MES4zUpxojAiY2nrRZLibbEaueujSiK+D0zHVduXEbr6DaG7S2jEnDg9H7cKMyFKIr44/zvuHrzChLjOpp0frOafkJCQlBcfCtz8/X1hb+/P86dO1elTF6edbJGIiIie3D18lXs+mGXtCQFt2pWrh7KskoHW6WgQKBHoEXPWZeS8hJMXvQwNDoNFIICjw9+Eh1uS0JSB07DR1v+jcn/ngClQglBEPDUkGfQpklbk65jVqLSoUMHHDx40PA+JSUFH3zwATp06AC9Xo9///vfaN++vTmnJiIisjiNTmPxc/6490fJSYqBCGT9cR0xfaMtGku8fxw8lKo6y+3+cxcWb/3Q8H7u2Fer1IKYwkvlhQ8e/QjlFWX4/fzv+GT7cjQIbIC2Me0AAN8e/D+cvnISL4+ei7CAcBy7eBRLty1GsG8wEuOkNzOZlag89thjWLVqFdRqNVQqFRYsWIDevXujd+/eEEURQUFB+Oyzz8w5NRERkcUoFEp4eniivKIcWq3WoufOz8+HQqEwOj1HjQRAU6ox7RgJWge0haeHJxQKZa3lujTviuaN/h7KHOIXYvY1FYICDYNvdQ6OaxCPy7kXsXHfF2gb0w5qjRprdq3G7FEvo3OzLgCA2IhYZGZnYNOvX1k/Ubnvvvtw3333Gd63atUKGRkZ2L17N5RKJe666y4EBwebc2oiIiKLcVe6o3mjFtDrLT9dRrMmzU1POETA298bXiovi8WhUqpwf9sR8HL3hrvSvday3ipveKusM6+LXhSh0d6qudLpddDqtVAIQpUyCkEJUTTtZ1avCd9uFxAQgGHDhlnqdERERBbhrnQH6vgCN8eguwdBEATTmn8EILJdeLUv8PpIjOgAf88As48vKivC9YIc3Cy+CQC4cuMyACDINwhBvrcqHf61+V0E+4VgYt/JAIANP69H08hmiAyKhEanwaGzh7D7z52YNvhJALcSojbRbbFyxyfwcFP91fTzJ3b9uQNTBjxqUnxmJyo6nQ4bNmzArl27kJOTg1dffRVt27ZFQUEBduzYgR49eiAiIsLc0xMREdm16OhoDB06FFu3bpU0wamgENCoUyR8wn0sGkeXSOlzkhhz4PSvWPTNvwzv39n0FgDgwV7jMC75IQDA9YLrEIS/BwqrK8rx3+8W40ZRLjzcPBAV2hjPDXsevVonG8q8MOKf+HTnKry3+R0UlxUhLCAcD/WZgMEd7zEpPkE0uSfQrXa5QYMG4cCBA/D19UVJSQm2b9+Ovn37QqfToUmTJpgwYQLeeOMNU09tU0eOHEFSUhIOHz6Mjh1NGy5FRER08OBB3HXXXdDpdHXWrAhKAQPf7ofQ5pbrGuHr7odF/RfDQ+lhsXPaG7PmUZk1axaOHTuGtLQ0nDt3rsrNUSqVGDlyJLZu3WqxIImIiOxR586dsX79eiiVSiiVxjuyCgoBglJAr392t2iSAgCD4oY4dZICmJmofP3113j66acxYMAACEba2Zo3b47z58/XNzYiIiK7N2LECOzbtw/33HNP9e9EAWjUKRID3+6H6O5RFr1uqFc4BseZtm6OIzKrj0pBQQFiY2Nr3K/RaCw+DIyIiMhede7cGf/3f/+Hixcvon379sjPz4eHjzuG/Ptui/dJAQABAlITpzl9bQpgZo1KfHw8jhw5UuP+77//Hq1atTI7KCIiIkcUHR0NH59biYmbp5tVkhQAuK/pcLQMcY3vWbMSlalTp+KTTz7B+vXrDf1TBEGAWq3GSy+9hG3btiE1NdWigRIRERHQOrQtRrQYJXcYNmNW088zzzyDY8eOYezYsQgMDAQAjBs3Djdu3IBWq0VqaioeeeQRS8ZJRETk8hr4ROKpjs9CIZhVz+CQzEpUBEHA8uXLMXHiRGzcuBFnzpyBXq9HfHw8Ro8ejd69e1s6TiIiIpfm7xGA57vMgq+Hr9yh2FS9Zqbt2bMnevbsaalYiIiIyAhfdz/M6vYyInwayB2KzVlsCn0iIiKyPD8PP/yz68to7G/ZFZcdheRE5fZFCKUQBAGbN282OSAiIiK6JVAVhH92ewlRfo3lDkU2khOVb7/9Fp6enmjQoIGkBZiMTQRHRERE0oR6hWNWt5dcsrnndpITlUaNGuHKlSsIDQ3FuHHj8OCDD6JBA9f+4REREVlDQ99G+GfXlxDsFSJ3KLKTPL7p0qVL2LVrFzp06IDXXnsNjRs3Rv/+/bFy5UoUFRVZM0YiIiKXERfYFHPums8k5S8mDcROTk7G0qVLkZWVhY0bNyIkJARPPfUUwsPDMWLECGzcuBFqtdpasRIRETm1NqHtMLvby/D18JM7FLth1owx7u7uGDZsGNavX4/s7GxD8jJmzBi8/fbblo6RiIjI6XWN7I4ZXWbC081L7lDsSr2GJ6vVaqSlpWHz5s347bff4OnpiZiYGAuFRkRE5Bp6RfXB1PapLjXjrFQm/0T0ej3S0tIwadIkREREYOzYsSgrK8Py5cuRk5ODhx9+2BpxEhEROaWeUclMUmohuUZl3759WLduHTZs2IAbN26gW7dueOONNzB69GiEhoZaM0YiIiKn1CEiCVPbMUmpjeREpWfPnvDy8sI999yDsWPHGpp4Ll68iIsXLxo9pmPHjhYJkoiIyNnEBMThiQ7ToVQo5Q7FrpnUR6WsrAxffvklvvrqq1rLiaIIQRCg0+nqFRwREZEzClQF4R+dnoenm6fcodg9yYnKypUrrRkHERGRS3BXuOPZTs9znhSJJCcqEydOtGYcRERELmFKu8cQH9RU7jAcBnvvEBER2cjguKHoGdVb7jAcChMVIiIiG2gRnIAxLcfJHYbDYaJCRERkZV5uXpjW4SmO8DEDExUiIiIrezDhIYR4cc4xczhkonL+/Hk88sgjiI2NhZeXF+Lj4zF37lxUVFTIHRoREVEVUX7R6BPdV+4wHFa91vqRy8mTJ6HX67F06VI0bdoUR48exaOPPoqSkhK8++67codHRERkcH+zEZx5th4cMlEZNGgQBg0aZHgfFxeHU6dOYcmSJUxUiIhIVg0aNECBugAege4I8gxGpwZd5A7JoTlkomJMQUEBgoODay2jVquhVqsN74uLi60dFhERuZhDhw7hhV3/QFbJVXRreBc70NaTU9RFnT17Fh9++CFSU1NrLbdw4UIEBAQYXsnJyTaKkIiIXFFiONe8qy+7SlRmzZoFQRBqfZ08ebLKMVeuXMGgQYMwatQoPProo7Wef/bs2SgoKDC89uzZY82PQ0RELkyAgjPQWoBdNf3MmDEDkyZNqrVMXFyc4f+vXr2KlJQU3HXXXVi2bFmd51epVFCpVIb3vr6+ZsdKRERUm0jfSKiUqroLUq3sKlEJCwtDWFiYpLJXrlxBSkoKkpKSsHLlSigUdlU5RERELq6hb5TcITgFu0pUpLpy5Qr69OmDJk2a4N1338X169cN+xo0aCBjZERERLc09G0odwhOwSETle3bt+Ps2bM4e/YsoqKqZqyiKMoUFRER0d8a+jaSOwSn4JDtJZMmTYIoikZfRERE9qCRH5t+LMEhExUiIiJ7JggCGvhEyh2GU2CiQkREZGHBnsHwdPOUOwynwESFiIjIwkK9pI1gpboxUSEiIrKwYK/al3Qh6ZioEBERWVigKkjuEJwGExUiIiIL8/PwkzsEp8FEhYiIyMKYqFgOExUiIiILax7cUu4QnAYTFSIiIgtTCPx6tRT+JImIiMhuMVEhIiIiu8VEhYiIiOwWExUiIiKyW0xUiIiIyG4xUSEiIiK75SZ3AGQb165dw7Vr1+QOgywkMjISkZFcQt5Z8Pl0PnxGLcelE5XIyEjMnTvX6f8xqdVqjB07Fnv27JE7FLKQ5ORkpKWlQaVSyR0K1ROfT+fEZ9RyBFEURbmDIOsqLCxEQEAA9uzZA19fX7nDoXoqLi5GcnIyCgoK4O/vL3c4VE98Pp0Pn1HLcukaFVeTmJjIh8YJFBYWyh0CWQGfT+fBZ9Sy2JmWiIiI7BYTFSIiIrJbTFRcgEqlwty5c9mpy0nwfjoX3k/nw3tqWexMS0RERHaLNSpERERkt5ioEBERkd1iokJERER2i4kKERER2S0mKkRWIAiCpNfu3bvrfa3S0lLMmzfPpHMtWLAA9913HyIiIiAIAubNm1fvOIgchT0/nydPnsTMmTORmJgIPz8/REZGYsiQITh06FC9Y3FUnJmWyArWrFlT5f2nn36K7du3V9uekJBQ72uVlpZi/vz5AIA+ffpIOubll19GgwYN0KFDB6SlpdU7BiJHYs/P58cff4wVK1bggQcewBNPPIGCggIsXboU3bp1w7Zt29C/f/96x+RomKgQWcFDDz1U5f2vv/6K7du3V9sul8zMTMTExCA3NxdhYWFyh0NkU/b8fI4dOxbz5s2rsu7TlClTkJCQgHnz5rlkosKmHyKZ6PV6fPDBB2jdujU8PT0RERGB1NRU5OXlVSl36NAhDBw4EKGhofDy8kJsbCymTJkCADh//rwh0Zg/f76hyrquppyYmBhrfCQipyHX85mUlFRtccqQkBD06tULJ06csOyHdBCsUSGSSWpqKlatWoXJkydj+vTpyMzMxEcffYTffvsNP//8M9zd3ZGTk4O7774bYWFhmDVrFgIDA3H+/Hl89dVXAICwsDAsWbIE06ZNw/DhwzFixAgAQLt27eT8aEQOz96ez6ysLISGhlr0MzoMkYis7sknnxRvf9x+/PFHEYC4du3aKuW2bdtWZfumTZtEAOLBgwdrPPf169dFAOLcuXNNjqs+xxI5C3t9Pivt3btXFARBnDNnjtnncGRs+iGSwYYNGxAQEIABAwYgNzfX8Kqs9t21axcAIDAwEADw7bffQqPRyBgxkeuwp+czJycH48aNQ2xsLGbOnGmVa9g7JipEMjhz5gwKCgoQHh6OsLCwKq/i4mLk5OQAAJKTk/HAAw9g/vz5CA0NxbBhw7By5Uqo1WqZPwGR87KX57OkpARDhw5FUVERNm/eXK3viqtgHxUiGej1eoSHh2Pt2rVG91d2wBMEARs3bsSvv/6Kb775BmlpaZgyZQree+89/Prrry77i4vImuzh+ayoqMCIESPwxx9/IC0tDW3atDH7XI6OiQqRDOLj4/HDDz+gR48e8PLyqrN8t27d0K1bNyxYsADr1q3D+PHj8fnnn2Pq1KkQBMEGERO5DrmfT71ejwkTJmDHjh344osvkJycbM7HcBps+iGSwejRo6HT6fDaa69V26fVapGfnw8AyMvLgyiKVfYnJiYCgKF62dvbGwAMxxBR/cj9fD799NNYv349Fi9ebBgp5MpYo0Ikg+TkZKSmpmLhwoVIT0/H3XffDXd3d5w5cwYbNmzAokWLMHLkSKxevRqLFy/G8OHDER8fj6KiIixfvhz+/v645557AABeXl5o1aoV1q9fj+bNmyM4OBht2rSptap4zZo1uHDhAkpLSwEAe/fuxeuvvw4AePjhh9GkSRPr/xCI7JScz+cHH3yAxYsXo3v37vD29sb//ve/KvuHDx8OHx8fq/8M7Ircw46IXMGdwx8rLVu2TExKShK9vLxEPz8/sW3btuLMmTPFq1eviqIoikeOHBHHjh0rRkdHiyqVSgwPDxeHDh0qHjp0qMp59u3bJyYlJYkeHh6ShkImJyeLAIy+du3aZamPTeQQ7On5nDhxYo3PJgAxMzPTkh/dIQiieEe9FREREZGdYB8VIiIisltMVIiIiMhuMVEhIiIiu8VEhYiIiOwWExUiIiKyW0xUiIiIyG4xUSGyM+fPn4cgCFi1apXcoRCREXxGbYuJChEREdktTvhGZGdEUYRarYa7uzuUSqXc4RDRHfiM2hYTFSIiIrJbbPohsoJ58+ZBEAScPn0aDz30EAICAhAWFoY5c+ZAFEVcunQJw4YNg7+/Pxo0aID33nvPcKyx9u9JkybB19cXV65cwf333w9fX1+EhYXh+eefh06nM5TbvXs3BEHA7t27q8Rj7JxZWVmYPHkyoqKioFKpEBkZiWHDhuH8+fNW+qkQ2Q8+o46DiQqRFY0ZMwZ6vR5vvvkmunbtitdffx0ffPABBgwYgEaNGuGtt95C06ZN8fzzz2Pv3r21nkun02HgwIEICQnBu+++i+TkZLz33ntYtmyZWbE98MAD2LRpEyZPnozFixdj+vTpKCoqwsWLF806H5Ej4jPqAORaDZHImc2dO1cEID722GOGbVqtVoyKihIFQRDffPNNw/a8vDzRy8tLnDhxoiiKopiZmSkCEFeuXGkoU7mi6quvvlrlOh06dBCTkpIM73ft2mV0BeQ7z5mXlycCEN955x3LfGAiB8Nn1HGwRoXIiqZOnWr4f6VSiU6dOkEURTzyyCOG7YGBgWjRogXOnTtX5/kef/zxKu979eol6bg7eXl5wcPDA7t370ZeXp7JxxM5Cz6j9o+JCpEVRUdHV3kfEBAAT09PhIaGVtte1y8jT09PhIWFVdkWFBRk1i8xlUqFt956C9999x0iIiLQu3dvvP3228jKyjL5XESOjM+o/WOiQmRFxoYu1jScUaxjAJ6UYZCCIBjdfntnvkrPPvssTp8+jYULF8LT0xNz5sxBQkICfvvttzqvQ+Qs+IzaPyYqRE4kKCgIAJCfn19l+4ULF4yWj4+Px4wZM/D999/j6NGjqKioqDK6gYgsi8+o6ZioEDmRJk2aQKlUVhudsHjx4irvS0tLUV5eXmVbfHw8/Pz8oFarrR4nkaviM2o6N7kDICLLCQgIwKhRo/Dhhx9CEATEx8fj22+/RU5OTpVyp0+fRr9+/TB69Gi0atUKbm5u2LRpE7Kzs/Hggw/KFD2R8+MzajomKkRO5sMPP4RGo8F///tfqFQqjB49Gu+88w7atGljKNO4cWOMHTsWO3bswJo1a+Dm5oaWLVviiy++wAMPPCBj9ETOj8+oaTiFPhEREdkt9lEhIiIiu8VEhYiIiOwWExUiIiKyW0xUiIiIyG4xUSEiIiK7xUSFyIWdP38egiBg1apVcodCREbwGWWiQiRZRkYGUlNTERcXB09PT/j7+6NHjx5YtGgRysrKrHbd48ePY968eTh//rzVriHFggULcN999yEiIgKCIGDevHmyxkN0J1d+Rk+ePImZM2ciMTERfn5+iIyMxJAhQ3Do0CHZYrIUTvhGJMGWLVswatQoqFQqTJgwAW3atEFFRQV++uknvPDCCzh27BiWLVtmlWsfP34c8+fPR58+fRATE2OVa0jx8ssvo0GDBujQoQPS0tJki4PIGFd/Rj/++GOsWLECDzzwAJ544gkUFBRg6dKl6NatG7Zt24b+/fvLEpclMFEhqkNmZiYefPBBNGnSBDt37kRkZKRh35NPPomzZ89iy5YtMkb4N1EUUV5eDi8vL4ufOzMzEzExMcjNza22lD2RnPiMAmPHjsW8efPg6+tr2DZlyhQkJCRg3rx5Dp2osOmHqA5vv/02iouLsWLFiiq/ACs1bdoUzzzzjOG9VqvFa6+9hvj4eKhUKsTExODFF1+stpBYTEwMhg4dip9++gldunSBp6cn4uLi8OmnnxrKrFq1CqNGjQIApKSkQBAECIKA3bt3VzlHWloaOnXqBC8vLyxduhQAcO7cOYwaNQrBwcHw9vZGt27d6vXLWs7aHKLa8BkFkpKSqiQpABASEoJevXrhxIkTZp3TXjBRIarDN998g7i4ONx1112Syk+dOhWvvPIKOnbsiH/9619ITk7GwoULjS4kdvbsWYwcORIDBgzAe++9h6CgIEyaNAnHjh0DAPTu3RvTp08HALz44otYs2YN1qxZg4SEBMM5Tp06hbFjx2LAgAFYtGgREhMTkZ2djbvuugtpaWl44oknsGDBApSXl+O+++7Dpk2bLPBTIbIffEZrlpWVhdDQUIudTxYiEdWooKBABCAOGzZMUvn09HQRgDh16tQq259//nkRgLhz507DtiZNmogAxL179xq25eTkiCqVSpwxY4Zh24YNG0QA4q5du6pdr/Ic27Ztq7L92WefFQGIP/74o2FbUVGRGBsbK8bExIg6nU4URVHMzMwUAYgrV66U9PlEURSvX78uAhDnzp0r+Rgia+EzWrO9e/eKgiCIc+bMMflYe8IaFaJaFBYWAgD8/Pwkld+6dSsA4LnnnquyfcaMGQBQrVq3VatW6NWrl+F9WFgYWrRogXPnzkmOMTY2FgMHDqwWR5cuXdCzZ0/DNl9fXzz22GM4f/48jh8/Lvn8RPaMz6hxOTk5GDduHGJjYzFz5sx6nUtuTFSIauHv7w8AKCoqklT+woULUCgUaNq0aZXtDRo0QGBgIC5cuFBle3R0dLVzBAUFIS8vT3KMsbGxRuNo0aJFte2V1dF3xkHkqPiMVldSUoKhQ4eiqKgImzdvrtZ3xdFw1A9RLfz9/dGwYUMcPXrUpOMEQZBUTqlUGt0uiqLka1ljhA+Ro+AzWlVFRQVGjBiBP/74A2lpaWjTpo3Nrm0trFEhqsPQoUORkZGBX375pc6yTZo0gV6vx5kzZ6psz87ORn5+Ppo0aWLy9aX+Qr0zjlOnTlXbfvLkScN+ImfBZ/QWvV6PCRMmYMeOHVi3bh2Sk5NNPoc9YqJCVIeZM2fCx8cHU6dORXZ2drX9GRkZWLRoEQDgnnvuAQB88MEHVcq8//77AIAhQ4aYfH0fHx8AQH5+vuRj7rnnHhw4cKDKL+6SkhIsW7YMMTExaNWqlclxENkrPqO3PP3001i/fj0WL16MESNGmHy8vWLTD1Ed4uPjsW7dOowZMwYJCQlVZr3ct28fNmzYgEmTJgEA2rdvj4kTJ2LZsmXIz89HcnIyDhw4gNWrV+P+++9HSkqKyddPTEyEUqnEW2+9hYKCAqhUKvTt2xfh4eE1HjNr1ix89tlnGDx4MKZPn47g4GCsXr0amZmZ+PLLL6FQmP43ypo1a3DhwgWUlpYCAPbu3YvXX38dAPDwww+zloZkw2f0VuK1ePFidO/eHd7e3vjf//5XZf/w4cMNCZXDkXvYEZGjOH36tPjoo4+KMTExooeHh+jn5yf26NFD/PDDD8Xy8nJDOY1GI86fP1+MjY0V3d3dxcaNG4uzZ8+uUkYUbw1bHDJkSLXrJCcni8nJyVW2LV++XIyLixOVSmWVYZA1nUMURTEjI0McOXKkGBgYKHp6eopdunQRv/322yplTBn6mJycLAIw+jI2LJPI1lz5GZ04cWKNzycAMTMzs9bj7Zkgiib0CCIiIiKyIfZRISIiIrvFRIWIiIjsFhMVIiIisltMVIiIiMhuMVEhIiIiu8VEhYiIiOwWExUiIiKyW0xUiIiIyG4xUSEiIiK7xUSFiIiI7BYTFSIiIrJbTFSIiIjIbjFRISIiIrv1/4hLSxcoAmWEAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 600x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "multi_2group.mean_diff.plot(custom_palette=\"viridis\");\n",
+    "multi_2group.mean_diff.plot(custom_palette=\"Paired\");"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### A user-defined dictionary\n",
+    "\n",
+    "There are [many ways](https://matplotlib.org/users/colors.html) to specify matplotlib colours. Find one example below using accepted colour names, hex strings (commonly used on the web), and RGB tuples."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "my_color_palette = {\"Control 1\" : \"blue\",\n",
+    "                        \"Test 1\"    : \"purple\",\n",
+    "                        \"Control 2\" : \"#cb4b16\",     # This is a hex string.\n",
+    "                        \"Test 2\"    : (0., 0.7, 0.2) # This is a RGB tuple.\n",
+    "                       }\n",
+    "\n",
+    "multi_2group.mean_diff.plot(custom_palette=my_color_palette);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For sankey plots, a color palette dict can be supplied via `{1: first_color, 0, second_color}` where first_color and second_color are valid matplotlib colours."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "two_groups_prop_paired.mean_diff.plot(custom_palette={1: \"red\", 0: \"blue\"});"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Color saturation\n",
+    "\n",
+    "By default, ``dabest.plot()`` [desaturates](https://en.wikipedia.org/wiki/Colorfulness#Saturation)\n",
+    "the colour of the dots in the swarmplot by 50%. This draws attention to the effect size bootstrap curves.\n",
+    "\n",
+    "You can alter the default values with the parameters ``raw_desat`` and ``contrast_desat``.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "multi_2group.mean_diff.plot(custom_palette=my_color_palette,\n",
+    "                                raw_desat=0.75,\n",
+    "                                contrast_desat=0.25);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Alpha (transparency)\n",
+    "It is possible change the transparency of the raw data by using the `raw_alpha` parameter. This can also be achieved by adding\n",
+    "`alpha` to the relevant rawdata kwargs (`barplot_kwargs`, or `swarmplot_kwargs`, or `slopegraph_kwargs`, or `sankey_kwargs`)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "multi_2group.mean_diff.plot(raw_alpha=0.2);\n",
+    "\n",
+    "multi_2group.mean_diff.plot(swarmplot_kwargs={'alpha': 0.2});"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It is also possible change the transparency of the effect size curves by using the `contrast_alpha` parameter. This can also be \n",
+    "achieved via adding `alpha` to the `contrast_kwargs` parameter."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "multi_2group.mean_diff.plot(contrast_alpha=0.2);\n",
+    "\n",
+    "multi_2group.mean_diff.plot(contrast_kwargs={'alpha':0.2});"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Marker size\n",
+    "It is possible change the size of the dots used in the rawdata swarmplot, as well as those to indicate the effect sizes, by using the parameters `raw_marker_size` and `contrast_marker_size` respectively.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "multi_2group.mean_diff.plot(raw_marker_size=3,\n",
+    "                                contrast_marker_size=12);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Axes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Lims\n",
+    "\n",
+    "To change the y-limits for the rawdata axes, and the contrast axes, use the parameters `raw_ylim` and `contrast_ylim`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "multi_2group.mean_diff.plot(raw_ylim=(0, 5),\n",
+    "                                contrast_ylim=(-2, 2));"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If the effect size is qualitatively inverted (ie. a smaller value is a\n",
+    "better outcome), you can simply invert the tuple passed to\n",
+    "``contrast_ylim``."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "multi_2group.mean_diff.plot(contrast_ylim=(2, -2));"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The contrast axes share the same y-limits as those of the delta-delta plot. Thus, the y axis of the delta-delta plot changes as well."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x600 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "paired_delta2.mean_diff.plot(contrast_ylim=(3, -3));"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can also change the y-limit of the delta-delta axes and the regular delta axes via the `delta2_ylim` parameter."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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QffCrn+r4z/xYssy3vvUtvv71rzM9PY3FYmFxcfGK4d5rOXToEL29vQBks1mmp6evmOP3xhtv8LM/+7P863/9r/nbf/tvf+b2XYsIgIIg3BO8/Tup52LUMqvYw/1kLhzGZPdgcQc/8VyLJ4Snbwf5hTNYvR1YvddfSOKyW3l+/3beOnWBnxyb4KGtA4z2RjbrqQiCsAkkSfrMizo+i1/4hV/gG9/4Bi+99BK/8Ru/wa//+q+j6zqPPvoo+Xyed999F7fbza/8yq9snPMv/sW/IBAIEIlE+Kf/9J8SDAb5yle+ArSHfX/2Z3+Wr33ta3z1q18lFosBYDab8fv9n7u9YhazIAj3BElWCGx9FK1RRcLA7PSTOv82WuPGCrO6e7djcYdIT72L1qx/4vFmk8rTe8cY7Y3wwflZDk/Moeti+zhBuF+pqsrf//t/n3/zb/4N3/zmN/ln/+yf8eKLL7J161aee+45Xn75ZQYGBq4457d/+7f52te+xr59+4jFYnzve9/DbDYD7UUhlUqFF198kY6Ojo3bz//8z29KeyVDbHi5qY4fP86+ffs4duwYe/fuvd3NEYT7Tjk+R2ryPbyDeykun8dkdxPe8fQNLQpp1cqsHXsZq6+D4NZHb7jcy9RijMMT83QGvTy2axizKgZXBEH4eG+88QZPPfUU2WwWr9d7W9ogegAFQbinOCIDOCIDFBZO4x3cQz2fJDd34obOVa0OAlsOUEkuUo7N3PBjjvZG+cK+MZLZIj88dI5iZfO2gxIEQbgZRAAUBOGuYBgGmq7f0LH+4QdRzDZKK9N4B/ZQWJ6iHJ+7oXPtoV6cHcNkZ47SrORvuH2dQS/PHdiOpuv84P2zJLKFGz5XEAThVhMBUBCEu8LRyQXePDlNS9M+8VhZNREYe5hGKYPWqOKMDpKe/oBG8ca2c/MN7UWxOEhNvIuhf/LjXeJ12nn+wDgep40fH5lgdjV5w+cKgnD/ePLJJzEM47YN/4IIgIIg3CU6g17W0gV+emySRqv1icdb3EG8A7spLE9gC3RjdnhInn8LrfnJw7OyYiK49RGalTy5uZOfqp1Ws4lnH9zKYEeQd05f5MSFpU+9x6ggCMLNJgKgIAh3ha6Ql2cfGCNbqPDq4QlqjeYnnuPq3orN10HmwmF8ww9haK12r57xyUPJZqd/ffh4kmpm9VO1VZFlDo4PsndLH2dnVnjr5IUb6rkUBEG4VUQAFAThrhH2ufniQ9so1+r86PB5yrXrl2uRJInAWHurpfzCaQJjj1DPxcnNnbqhx3N1jWLzd5Keeh+tUf1UbZUkifHBTp7Ys4XlVI4ffXCeSq3xqa4hCIJws4gAKAjCXUHTdWqNJn63g+f2b6fV0vjRB+colK8/pKuYbQRGD1DNrNKs5PEO7qGwdJ5ycuETH7O91/ABANJT73+modzeiJ/nHtpOtdHkB4fOks6XP/U1BEEQNpsIgIIg3BXeOzvLT49O0tI03A4bz+3fjizL/OjwObLF64cqm78Ld/cYubkTWNxhHOF+MlOHaJRzn/i47QB5kGpmjeLK5Gdqe8Dj4IUD49jMJn54+ByL8RtbjCIIgnCziAAoCMJdYVtfB7lylffOzGIYBg6bhS89tB2bxcSPPjhPIlu87vnegd2Y7F7SU+/iHdqLanOSOvfWDe36YfN3rgfIkze8kvij7FYzX9q/je6glzdPTHN2dkUsDhEE4bYRAVAQhLtCwOPg0R1DzMdSnJ5ZAcBmMfHFB7fhddl59egEq6ncx54vyQrBrY+g1avk504S2vY4eqtOevK9GwpilwJkavJddO2TF6Bci6ooPL57hPGhLo5PL/Le2Rk07cZqGwqCIGwmEQAFQbhr9EUD7Bru4dTFJebX0kB7T95nHhgj6nfz2vEpFq4zvGqyu/ENP0ApNku9mCYw9gi17Br5hdOf+NjtAPkwWr1MdubYZ34OkiSxZ6SHR3cOM7eW5tWjN7aiWRAEYTOJACgIwl1l51AX/dEg756dIZUvAe2etSf3bKE37OetE9NcXEl87PmOyGB7DuCFw5hsLjz9u8gvnKWSWvrExzbZPfiGHqC0NkMlufi5nsdgZ4gvPriNQrnKD94/S65U+VzXEwRB+DREABQE4a4iSRIP7xjE57TxxonpjdIqiizz6M5hRnrCvHdmhvPzax97vn/kQWSThdTke7i6RrGHeklPvX9DW785okPt46cP0ap9vhW9YZ+LFw6OoyoyPzx07rpD2IIgCJtJBEBBEO467R6/UQDeODG1UWRZliX2bxtgfKCLo5PznPyYXThk1Uxw7BEaxTSFxbMEthxAsdhJnnsLvXX94dh2gHwIWTWtzx/8fHP4nDYrzx3YTsjn4qfHJplajH2u6wmCINwIEQAFQbgr2a1mntyzhWzpw5XB0A5oe0d72bull9MzyxyZnL9mCLS4g3j6d1FYOk+jmCa07XG0RpX01CcvClFMFgKjj1AvJCksnvvcz8Wsqjy1Z5Sx3igfnJ/j8MQcui5WCAuCcPOIACgIwl0hkS1cVT8v6HHyyPiVK4MvGR/sYv+2QaYW4rx7Zuaagcrdsw2LJ0xq6j1kU7tXsJJavqFQZ/WGcfduJ79whno++fmeHO3eywe39rfbvBjnteOTNJqfvOexIAjCZyECoCAId4WLK0nePDnN7OqVYau/48OVwQux9BXfG+2N8OiuYebWUrx5cvqqkivtreIeBl0nM3UIq78TT98O8gunqWauDJTX4unbgdkdbJeGaW3ONm+jvRGe2beVVK7EKx+co1i5/k4ngiAIn4UIgIIg3BUObBtksDPIu6dnmF6KX/G9SyuD3zkzc9VWawMdQZ7aO8pqKsdPj03SaF3Zq6Za7ARGD1JJr1BancbTtwObv5PUxLs0q9cvLi1JMsGxh9FbDTIXDm9aYeeOoIfnD4yj6zo/eP8siWxhU64rCIJwiQiAgiDcFWRZ4uHxIbb0Rjh0bvaKVb6XVgZ7HTZePzG1sTL4ku6Qj2ce2Eq6UOInRyaoN64MgbZAF66uUbKzJ2iWswTGHkYxW0mde/MTiz6rVif+kYcoJxYox2c37fl6nDaePzCOx2njx0cmmFn5/MPMgiAIl0iG2ItoUx0/fpx9+/Zx7Ngx9u7de7ubIwi3XKPRoNXa/LlrH662lTg1s8LEQoydg11sH+jYOKZSb/DjI5PYLSae3juKqlz5N26mUOGNk9NYzSae3DOC3WLe+J4iS2TOvY6ha0T3PodWqxA78UNs/k4CWx9FkqTrti899T6V5CLRvc9jsrs37Xlrus4H5+a4uJJgfLCLPSM9n9gWQRCET6Le7gYIgnDvaDQaHD58mFKptOnXVlMT0KrRCu/EkGT0YpXvvb7A8ZN2ev32jVBkrjU5eSHP4twMIxHXVWHJ3mhxfr7A9IULbOv0YDUpADidTvaMP0Tm7E/IzhwnsGU/gbGDJM+9jdk1gbtn23Xb5xt+gHohSXryXSK7v4gkK5vyvBVZ5uD4IB6njeNTixTKVR7ZMYxJ3ZzrC4JwfxIBUBCETdNqtSiVSpjNZiwWy6Ze28irkFsCvQL9j7GtL4LLWeJiLI/FojMc9SBJEi4XKGYb55YzZOoS/SHXFddxAW6Xi9OLaS6kauzsDWCSdEqlEpLZjm/oAdLTH2D1RXGE+vD0bic3dxKz04fV13HtxgGyYiIw9gjxkz8mN38a3+CeTXvukiSxfaATt8PK26cu8qPD53hq7ygO6+a+xoIg3D/EHMDr+O3f/m0kSeIf/sN/eLubIgh3FYvFgtVq3dzb0EHMw08iN4vIF17BVIkz0hVkR3+YeLHObKqCef1xeyM+tnQFWMpUyNe0q67lczs5MNqN3Wrh7EqOmvZhL6EjOoQj1Etm+gNatRKe/p1YfVFSE+/Qql2/Z9PiCuBdry1YzVx7J5LPoyfs57n926k1Wvzg/bNXLXgRBEG4USIAfowjR47w0ksvsXPnztvdFEEQaPeCKaEhTNv/LySzg9aF12jNH6LLa2VbT4C1bIlzi6mNen+DEQ8Rr51zS2kKlatLtFhMCvuGIjgsJk4tpMitHyNJEv4t+5FVM6nJ9wAIjD2CpJrbO4Vo15/f6Oreis3XQXrqPbTG5pdw8bsdvHBgHIfVzA8Pn2PhI7URBUEQboQIgNdQKpX4pV/6Jf7gD/4An893u5sjCMJlZJsHdfuXkYPDtOITNCd/RMRUY0dvkES+yumFJJputIdNewI4rCZOzSeoX6OoskmR2TMQxmM3M7FWYDmZaz/Gpa3iCinyC2dQTBZC2x6nVS2QufDBdcu9tGsLHgQgPX1o00rDXM5uNfPFh7bRHfLy5okpzs6u3JTHEQTh3iUC4DX8vb/39/iZn/kZnnnmmdvdFEEQ1hUzcUrZdv0/WVFRBx9F7T+IUSvSuvgagdo8O3u9pEs1Ts0n0HQdRZbZ1RfCAE7NJ9H0q/ftVRWZHb0B/A4z75yZ2Si3YvGE8PTtpLB4jloujtnpw7/lAOX4PKXVqeu2VTHbCGw5QDW98onHflaqovD4rhF2DnVzfHqRd8/MXFXoWhAE4eOIAPgR/+N//A+OHz/Oiy++eEPH1+t1CoXCxu1mrH4UBAEKFz8gd/J7LJ15m0atgiRJqJEx1OEnkVQb2uoZPOmT7Aqr5Ct1TswlaGk6VrPK7v4QxVqT88uZa/aUyZLEloiLgY4A7565yMRCe/6eu3cbFk+I9OR7aM0ajnA/7u6tZGeOU8vFr7rO5T6sLXiSRil7U14TSZLYPdLDozuHWYilefXoBNX69esWCoIggAiAV1haWuJrX/saf/zHf4zVar2hc1588UU8Hs/G7YknnrjJrRSE+1N4eDdWpx81eZ61w39BbO48mq6heDpQhx5DdkUwSknc2TPsdOYpVmocn43TbGm47RbGewLEsmXmEtfeVUOSJB4a62NbfydHJuY5dXEZkAiMPYKht8hMt4d+vYO72/sHT7xDq3b9RRi+wT2Y7K72VnGfMHfw8xjsDPHsg9solKu8cugsuVLlpj2WIAj3BhEAL3Ps2DESiQR79+5FVVVUVeXNN9/k3//7f4+qqmiadtU53/zmN8nn8xu3N9988za0XBDufXKjiNvrxdu3A5sK2tw7LB5+mVw6hmzzovbtR/Z0gq7jqq2yy7REpZTn2GycelMj4nUwGPEwE8uRyF87IEmSxL7RXvaM9HLq4hJHJxdQzDb8Ww5QSS1TWrvQ3v5t66NIskzq/NsY+tW/FzauJysExx5Fq5XIzR6/WS8NAGGfixcOjqMqMq8cOsvK+nxGQRCEaxEB8DJf+MIXOHPmDCdPnty4PfDAA/zSL/0SJ0+eRFGuLrxqsVhwu90bN6fTeRtaLgj3PjmyFcXTjarX8PdsxdW5BVs9SeH0K8yfepu6ZqD2PIDsjiLJCi6TwW7LKvXsKkcvrlBrtDZWBp9dTF1zZTC0Q+COoS72bxtgYmGN987OYPV34+ocITtznEY5h2K2Etz2OI1y7hP3ADY5PHgH91FcvUAltXizXh4AnDYrzx3YTsTn5rVjk0wuxG7q4wmCcPcSAfAyLpeL8fHxK24Oh4NAIMD4+Pjtbp4g3NfqTR3dP4QS2YZRL2Kz2ghsexKnx485M8nq0e+xOj+NER1H9nRhaC2cTje73RVa6QWOnJ+h2mh94srgS0Z7ozy6c5jZ1RRvnZrG1bcbk81JeuIddK2FxRXAP/IgpdgspbWL1227s2MYe7CnXVuwfnOHZ82qypN7Rhnrj3J4Yo4Pzs9tlMYRBEG4RATAe9RSIkOz9fFDU4Jwt5ley/Le1CorDQdy914kDCis4Bl6kMDIQ7hVHWPxEAtHf0TWcKEEhzDqBRxuN/t6XFBc48iJU1Sr1ctWBqfQrhOOBjtDPLlnCyvJHG+cmsEzcpBm9cPhXGd0CFfXFrIzR6nnkx97nXZtwYeQZJX05LuX7Wt8c8iyxINj/RzYPsj0UpyfHpukcZ2wKwjC/UcEwE/wxhtv8O/+3b+73c34VIqVGq8fn+JPXzvKGyemmFtN0WiJX/7C3W2000fQbWNyJcPhhSJ533Ykuw89MYlqdeLb+UV8Hf24mklKE68xt7hIwzOAUc1jkVo8sHUQRaty5Phx6vkEu/pDFGsNzi+nrzuE2xP284UHxkjmirwxsYazd9cVw7m+wb2YXQGS59+6bu+eYrISGHuYej5JYen8pr8+17KlJ8Iz+7aSzpd45YNzFCubX5haEIS7kwiA9yCX3crPP7GH3SM9VGoN3j59gf/52jFePz7F7GpShEHhrpQsVLGoCg8NR1AVmePzGc5WwzQ8/ejZRYzsErbBhwmMPY7P68aeu8Da1BFiTRt6o4KpuMwDO8aw2mwcPT9NKz7Ftg43sWyZxdT1yzdF/R6++NA2iuUa7y7VUb0d61vFlZFkhdC2x0CSPnFRiNUbwd2znfz8aeqF1Ga/RNfUEfTw/IFxdF3nB++fJZ659iroSwxdo1HO3fShakEQbi/JEOXjN9Xx48fZt28fx44dY+/evbe7OQCUqjUW4xnmYxlSuSKKLNMR9NAXCdAT9mE2qbe7icI9olKp8NZbb+FyuW64lNKNWkgWuLCWJeS2s73HT7pY48JajlqzxYDboFtbQpZllOg4kqzSjJ2juHaRSq1B1ewj6LTgstvQw9s4tZilkImzKwg5azcXsxoDfit/6YVnsdvtH9uGXKnCq0cmUNHYpc7jcPkI7/oCkiRTL6SIn3oVZ3QI/8hDH3sNQ9eIn/oJWrNGx94XkFXTpr5OH6fWaPLmyWmSuRL7x3ro89to1Yq0qiWa1SKtapFWrYhWr2IYBp6+HXj7xVaYgnCvEgFwk92JAfBy5WqdhXiahViGZK6ILMt0Bjz0RUUYFD6/mxkAARL5CmcXUzisJnb3h1AVmYVkkYVkHtVoMqrG8ap11NAwuLugGKe+epZieo1SU8JsNuP1uDF3jnMmLZNNrrLdWWSl4WStaef/+5d/hu5o8LptKFZq/OToBEY5w7hpmcjIXjx9OwAorV0gPX2YwOgBnNGhj71Gq1pk7fgr2AJdBMce2dTX6BK91aRVK34Y7qpFGpUis/MLZDJpQj43HQE3smpGtbow2ZyoNlf7ZnVhcrhRTJv/bygIwp1BBMBNdqcHwMuVa3UWYhkW4xkS2QKyLNMR8NAX9dMT8mMxizAofDo3MwA2WxqKLFOqNTk1nwBgV38Yt91MvdniYizHarpIh5Gg15THEehCjoyCrqOlLlJdm6ZYzNHUdGwWG66+XUzVA6QyaYaVBHO5FuGhnfzl55/GbrVc/3nWGvz02ASt+DRbnSX69/8MVk8YgPT0B5Tjc0R2PYPF/fFhspyYJzXxLsGxh3FEBj7Ta6K3GpcFvNIVgU9rfDjfT1ZNmGxuVJsLxepkMVvj3HKOzo4OHt69TfzhJwj3IREAN9ndFAAvV67VWYxn2j2D2SKSLLXDYMRPT1iEQeHG3MwAeHYxRbZcpz/kJuCycmYxRbnWZLw3SNjTHrYtVOpMrWZp5GL0GKsE/F7sPbuQzA70SoZWbIJyaplKuYBk6KjBIVad24hlS3SRpIYZfyDEzz3/JSz269f0rDda/PTYeVpz77Olw83Ioz+PYrJ8OMRbrxDd+zyK+eNfh9Tke1RTS0T3vYDJ5rrmMVqzvj48W6JZKWwM27aqRbRmfeM4xWRZ78FzrvfouTbuX6snbymR4e1TF3E7rDy1dxTHJ4ReQRDuLSIAbrK7NQBerlJrrM8ZTJPMFkFiPQwG6In4sJpvzZwl4e5zMwNgqdZgLlEgnitjVhV6Ai5ylRrpYo2RDh+9QReSJGEYBvF8hbnlNdyFi/htEoHBXZg9UQxdQ88u0ExcoJReQ6sVaVj8rLh2Ea8qPLFzgKkLU3S6VL7w+CM4I4NIkvSxbWq2NN44fJLa9BuMjG5j9MBzSJJEq14hdvwVTHY34R1PI8lXF5GH9jDt2vEfIEky/uGHaDXKH/bmXZqT1/ywYLVitqJanZjsblTretizuVCtThTTpw9wmUKZ149PoRsGT+0dJegRhewF4X4hAuAmuxMCoK4bHJteYKAj+Ll/oV8KgwvxNIlMOwxG/R76oyIMCle72XMAAcr1JvOJPGvZMqosoyoS5XqLnoCLsS4/stwObJqusxDPUVg8g7OVxRUdJDiwHUVWMOplWolJ6sk56rk1qrrCgjKAaWA/w91RZqYnGLKX2TU6gH/kIVTLxy8M0TSdt999m+LFQ4w88DRjOx8EoJZLkDj9E1xdW/AO7kNv1q4crq0WaFZL1PIxistTWNwhrL4oisWG6fJwZ3Ot33fdlAUj1XqD149PkS1VeXTHEH3RwKY/hiAIdx4RADfZnRAAi5Uarx45T6laJ+h1MdYboS8aQJE/X9Wfar2xMUwczxTWw6B7fQGJH5tFhMH73a0IgBuPVW+ykCywmilTrjeoNzV6g272DoYxqR/2uNUaTZbnpmglLyJb3QSG9hLweTEMA6OwRis+STU2TbVWI2/rJe3ZTrRvC4Vcmh2OLB1OGd/QPhzX6Q3UdYP3fvJ/SC9N0bfnKYY7/LSqRQqr0xSXJ7B6o6iXDfGqFvt6wHNjsrmo5WLteYN7voQ90H1TX7draWka752ZZT6WYs9IL+ODndft+RQE4e4nAuAmuxMCILTfkJaTWaYWY6yl89gsZka6w2zpiWC3mj/39dthMMtiPE0s/WEY7I0E6I2IMHi/upUB8JJao8VCqsCFtRyJXIWAy8oT27vxOq58/HwmQXrmOLVGCyM8Rn9vHw6LCaPVoLZylubySUxSi2TLzjIRpOh23B4fD0dbmIor2Pyd+EYeQoKN4dnmpaHaaoFGOc/01HmSZZ1w7whjXT5Um4taZpVmJU94x1PYAt2oVieycuWcWsMwSJz+Kc1qgY59L9yW1beGYXB6ZplTF5cZ7AxxcPsgiiJKxQrCvUoEwE12pwTAy+VKFaYW4sysJtF0nb5IgLG+CCGva1P+yq/WmywlMizE0sQyBTAg4nfTF/Wvh8HPHziFu8PtCICX1JsaF9YyHJ2Jo+kGu/vDjPcGsFy2wlVv1sjOnSSbTpBRO/B2DjEQ9aI1G5SSS/QpCcrxGVJljbmKnawpRKCjn6d7JRqxSbRGFas3itnpQ5JkFKvjiuFaQzeYPPIai003A7ufYN9ob3tRyMlX0Vt1onuf+9hw16pXiB37ARZ3kOD2J25bD9zcaor3zs6wta+DvaO9t6UNgiDcfCIAbrI7MQBe0mi2uLiSZGoxRrFSw+92MNYbpb8jgKpce5L6p1VrNDeGiWOZPBgQ9rvoiwToi4oweK+7nQHwknK9yVvnllnJlAi4rGztCtAfdmNdX8luGDrN5EXyS5Mk6ypVS4iIQ8ZOheHeLiqr56kk5inUDRZqdnK6HXOwj//rkd3I5Ti1QhJHqJfg1scx2a9euVtcmWL62OtM6930DW/lwLZB9EaZ2PEfYnb6Ce14Ekm6ds9aJbVE8txb+EcexNW55aa+TteTypdw262iPIwg3MNEANxkd3IAvMQwDFZSOaYW46wks1hMpvbwcG8Yp23z3rRrjSZL8SwL8TRr6XYYDPlc9Efbw8SbMRQt3FnuhAAI7SkQ55ZSTCwlkA0Ntxk6HdDr1LFTw2hWMRoltGKSchPiuo+maufhhx6ku6eXejFD/NSPKRWLzBUVMhUdS6CTh5/6GXqDdnIXjqBrTXyDe3BEh6/orTMMg9S5N1ldXuCM3k9PZweP7hymmU+QOPMa7u6teAf3fGzbMxcOU4rNEt37HGaH9xa8WoIg3I9EANxkd0MAvFyhXGNqMcbFlQStlk5PxMdob5So372pQ1D1Rqs9TBzPsJrKbYTBS8PEogbZveFWB0BD16C5HuiaFWhWMZpVaFbRGzWWSzCVk5BlCZOq0pLMRD1W+sMenE4nGAZa6iLlYp7Jihdr93YGuyLsG+3DSp2VQ39GeukiCwWDTEXH4XTg79vOrgcfxZyfoxSbxebrwL/lIVTrhyvutWaN2LFXyDdljlciRAMenti9heraFNnZEwS3PYoj1HfN56RrLeInfghAZM9zV80XFARB2AwiAG6yuy0AXtJsacyupphcjJEvVfA67Yz2RhnsDF6xonIz1BstlpLtYeK1dB5d1wn73PRG/PRFRRi8m93MAKhXcxi1PDQ+DHlGqw6s/wqTFCSTDcw2JJMNyWQHk41UTeLsSgGrWSHicbCaKVFvaYQ9dgbCHpwWhdrKWerpRQJb9jPTClFtamzti7KtJ0hu4k3Wpo8xlyhRaCkE3A4Mkw3/0D62DvVRXzyB3mrgHdyLs+PD3sBaNkbizGu0fEMcikn43Q6e2rOF4swHVNMrRPZ86WN7+BrlHLHjP8TZMYR/+MFNfR0FQRBABMBNd7cGwEsMwyCWKTC1GGMpnsWkKgx1hRjtjeJ2bH6PTqPZYimRZSGWZnU9DIa87Z7BvkgAh02EwbvJzQyAWnwSvRRHMtnbQe9SyFsPfCjmj+21LlYbnJxPYhgG471BKvUm88kC1XqLkNtGh8eCWlhiyCdh84RI2IY5t5LFpMjsGurCX5ljdeoIE/NrNAyF4Z4O8sUyDYuXvh0P02kqU43PYPVGCGzZv1HyJTd3isLSOUyDj/DmVAKH1cLTe4YoTLyBoWlE9z6HrF57KkRxZYrMxaOExp+4LaVhBEG4t4kAuMnu9gB4uVK1xvRigunlOI1mi66Qj9HeCF1B701ZobgRBuNpVlPtMBj0uuhfHybezPmJws1xMwOgoWsgyZ/5Z6/e1Dg1n6RYazDeEyDkthPLl5lPFMiXqthVgy8/ugt7dgJD07APPsj5RJPZ1SQ+p43t3jrlxdMcn15ClmT2jfaQL9dI5UvI/n62jI1jy01htJp4B/fg7BgBQ29vC9eoYt3yJK+dnMGkKDy5o4fSxGuYXUFC409e8zldmktYL6SI7nvhusWoBUEQPi0RADfZvRQAL2lpGvNraSYXY2QKZdx2G1t6Iwx3hW7aKsFGs8VyMstCrD1nUNN1gh4nfdH2amIRBu9Md8oikI+j6Trnl9LEchWGO7z0h9wYwFIiy/RyilBHF50BNz3GGq5WBu/ALuquXo5OLZLKFem1N3Dnpzl6cQ2zxcYjI0FQzSzHkhTqOs7ucQZDduTcAlZvmMCWAyBJrB37ATZ/J+beffzk6CS6YfDYsI/67Pu4e7fj7d917fY2a6wd+wEmu6e9pZwoziwIwia5qwLgwYMH+YM/+APGx8dvd1M+1r0YAC8xDINkrsjkQpyFeBpFlhnqDDHaF8HrvHm9E41Wi5VEjvlYeiMMBtwfhkGX/c4LGverOz0AQvvneDaeZzaep8PnYGt3gGajTqFQYGjbLi6spEnlizi1PN1SgoHeXvyjB1lKlTg2vUizlCZYnGJ2LY3s7ebpQRuqDMW6zsrqGlXVRbhvG51yGkWv4x3YjaRaSE++R2D0ALK3h58cnaBab7K/Q0JKThLa/jj2YM8121vLrpE48zregd24e7bd4ldLEIR71V0VADs6OshkMvyjf/SP+Of//J/fkW8w93IAvFyl1mB6Kc6F5QTVeoOOgIfR3ijdId/GXqw3Q6PVYiWZYyGWZiV5eRj00xcNiDB4m93MAJgsVGhqOiGXbVMWJsWyZc4tp3HbzIxGXdSrFR5//HFsNhurqTynZ5ZZXV3FVFphrMPJrgNPo9i9nJtb5dyFOVg6Si6XwdS1k+fGw+i5FVDNJDIF4qk0LWcnPR1hAq0ENm8ESVGp5+NE9z6Pptp57dgkuVKV3a48Li1LdM+XMNk912xrbvYEheUJIru/iMUd/NzPXRAE4a4KgIVCgW9+85u89NJLDAwM8N3vfpdnnnnmdjfrCvdLALxE03UWYxkmFmOkckWcNgtbeqIMd4ewmm/udnDNlsZyMstiLMNyKoemafjdjvWi04GbsmhFuL6bGQDPLaZYyZaQJQmfw0rIYyPstm8UeP4scuU6p+aTGHqLAb+F5555Gru93Zt9aUHUyckZZqfO4ZAbPLhvL1u376baaHB8Yo7J91+hloth6dnJV556mNbqGVq1IrrqYHFlmWypjuLvo88NHqVOq17F5u8kuudLNHWDN45Pk8jk2WZeI+oyEd3zHLJ69f+bD3cTaRDd+/w1jxEEQfg07qoAeMmRI0f4O3/n73Dy5En+2l/7a/zbf/tvCYVCt7tZwP0XAC+XypeYWowxt5ZGAgY6goz1RfG7HTf9sZstrd0zGE+znGyHQZ/LQf/6MLHbYbvpbRBucgBcSpHIVbBbTMiyRKHSQDcM3DYzIY+dsMeGw2L61PPkqo0WRy6sUihV+OUvf4GhnuhVx8TTOQ69/w4Ly6v4g2H2P3SAwe4wqVyB//fP/x+qsYtIgQG+/HM/j7cep7B0DtlspdQwWFqcp6RbcAY66JYzGMUYofEnCW17DE3TeevUBRZXY2yRlxnu6ya47fFrPodmtUjs2A+wh3oJjB78zK+jIAgC3KUBEEDXdf7Df/gP/LN/9s9QFIWenqvnz0iSxKlTp25pu+7nAHhJtd7k4nKCqaU4lVqdsM/NaG+E3ogfRb75m8s3WxqrqRwLsQzLySwtTcPnsrfnDEYCeJwiDN4sNzMAFqsNllJFYrkyhgFBtxWHxUS10SJVrNHSdOwWlZC7HQbddgvyDYbBUrnC8YureMKdPLJzhNHeq0MgwOL0WT44eoRkXSXUM8LOLf30hv38r//9F+TnjtOwBtj16Avs3xKlOHecej6J2eknlkyzGlujZvLikesEmyt0PfA8wa2PYhgS752dYXpmliF5ld27H8DTd+15zqX4LOnJ9wlufQRHuP+zvpRX0VtNtHoFrVGhVa+gNapY3CGs3simPYYgCHeWu7bEfKvVIplMUq/XCQQCBAKB290kYZ3NYmLHUBfbBzpZSmSYXIzx9qkL2CxmtvRE2NITvql7AptUZX2BSICWprGSyrOwlubs7ConLyzhc9npXd+b+GYuXhE2l8tmZltPgJEOL6vZMkupIol8FY/dzEiHF7OqkCpWieXKLCQLmFWFkNtGyGPD77ShXGduqqrIjHW4cUZDfHB+jny5ygOj/VfNZ+3dMk60o5OLx19nKj7Nu8cKnPYG2LbnAJMWJ5W5D5h4+/+wuHaQh3fvIhoukJ87QdBtIRTZzcLsDKl8jXjdQ+79V9lSzhPa/jiP7BjCbFI4eapC69hRHnb5sPm7rmqnIzxALbNG5sJhLK7ARr3Bj2MYOlqjthHutHoFrV6l1Wh/1BpVtHoFXWtdcZ5isiD1qiIACsI97K7sAfzJT37C3/27f5fZ2Vn+7t/9u/zLf/kvcbmu/4vwVhE9gNeWLZaZXIgzu5bCMAz6ogHGeiOEvLfu3+1SGFyMpVlKtHsGvU77xgISEQY/v1u5Clg3DNKFKkvpIuliDbOq0OV30OV3Um/pJAsVEvkKlXoLVZEIuNpzBgNuGyblyp7oWq1GsVjk8ccfZylV4PDEPJ1BL4/tGsasXv13stask556n+TaMityB/Gmg6amUcwkCJanaLU0qsGddPX0sW84gjlzkXJiHrPLT7UlMTd9jngyiWJ1sWOkn+7RPTi7Rjl1cYVDh95lwNni6ed+DrPdffXzbjVYPfoysmomMHYQvVFfD3fVD0Neo0qrXkFv1rn8V7wkyygWO4rZhrr+UTHb1r9mR7W0P5fkzd39RxCEO89dFQCTySS//uu/zn//7/+dHTt28J/+03/ioYceut3NuoIIgNdXb7S4uJJgajFOqVoj6HEy2hulPxpAUW7+8PAlmqazksqxEM+wnMjQbGl4nHb6IpfCoE3UXPsMblcZmHKtyXK6yGq2jKbrhD12ugMuvHYzlUaLZKFKMl8hX2kgSeB3Wgm57YTcNqxm9YoAaLfbWUnmeOvUdHvnjn2j16w7aRgGhcVz5BdOo9mDrJp6OX5hldVYnFFlFafcoBXZQcPannqwPWKmvnwKrV7BFuhmeX6a+alzVMx+QuEo2wa7iYwdZGo1y9tvvEq/R+GRx54Ardnuqbss3NULKYor01g8Iaze9nC1YrJshLsrQp7lw5Anqxbxcy0IAnCXBUC/30+j0eDb3/42X//611GUO++vVBEAb4yuG6ymckwsxFhL57CaTYysDw/f6r2ANU1nNd2eM7i0HgbdDtvGAhKv0y7eNG/Q7a4D2NJ01rJlltNFSrUmTquJnqCLqNeBqsjU1sNgolAhW6phGOCxm/HYVKxS64pVwLlShdeOTdHSNJ7cM0rYd+3e6mpmjfTku0iKin1wPz88tczxyTn6jCX8Upno6ANk1Ai1RpOxLh/dxCivTCKrJuqlPIsLs+Q1KyWsdLtkesM+ii2Z+fkFvC4Hw6NjmK3O9RBnQzG3Q101vUI5Pkd41zM4Qr2i104QhE/lrgqAL7zwAv/xP/5H+vv7b3dTPpYIgJ9evlRlainGzEqSlqbTG/Yz1hcl7HPd8uClaTpr6TzzsTTLiSyNVgu3w7a+gMSPzyXC4PXc7gB4iWEYZEo1ltNFkoUqqizT6XfSHXBit7RLqDRbGqlijWShQixTpFqrsWPrKEPdUXoiPoIeJ7VGizdPTpPOl3h4fIiBzmvX4GtWiiTOvEa9kMTVOcKJ5Qrn52MYpTj2egJfIExHwEO+UEBRZCIuK5ZGBr1VR9ea1DWZnGYhXdHA4mZ8qBtcUd46OU3fwAjPPfMU6kf+4DUMncTpn9KqlojuewHFJPbNFgThxt1VAfBuIALgZ9dotZhdSTG5GKNQruJz2RntjTLYGbzqze9W0DSdtUyehViapfh6GLTb6I366YsE8LtFGPyoOyUAXq7aaLGcLrKSKdFs6QTdNnoCLgIu68a/X7lSYTmRpaNvmES+Qr3ZxGYx0x3y0uG1M7sSY241yfZON6NhG3qztrGAoj08WwNDp5pdo1FMY3IGWKw7qegq+VqLenIes9NP9/hBkE3EC3W8Xi87ghLEzlJcncbZNUIVBwuzU6QqOt5gmLDXzZGFPH0j2/nio/uv2nqxVSsTO/4DLJ4IwW2PiZ9HQRBumAiAm0wEwM/PMAzW0nkmF2OsJHKYTArDXWFGeyO3bacPTW/3DC7GMiwmMjSaLVx260bRaREG2+7EAHiJpuvEchWWU0UK1QZ2s0yP10LUpaLVSlSKOXZuG0U2NHK5DOlMhnwuS7PRQJJkcrqFtYrKUMTF/qEgZqt9Y/HE5UOztVyc3OxxmqqTI8UAHrcbNxXmj78KqhW69uIPhKjWGpRqdfoCTrqrE5Tnj+Hp34Wrextz54+xtLRE3rDispqJNR10j+3l2QO7sVmuLAJdSS2SPPc2gS0P4ewYuU2vriAId5u7tgyMcO+SJInOoJfOoJdipcbUYpyLywkm5tfoCnsZ643SEfDc0sClyDLdIR/dIR/79QFi6QIL8TTTy3HOza/yC0/tu+k7nwg3xjAM0Jqg1TFaDWjVMVp1aNWJtOqELTVqepVCqUQp3WQesJsVLKpMLevG5vTgDwQJd/Qim22UWxKxQgNyVTLxAu8n88wbJp55cJiR7qtLGpkdXqyeMKnzb7NVWuBELIhzoJ9tj/88c0d/hD13hpK8nYpkx24xs5AusSz3MRysI61MYbQaDGzdQ+fAGNOnDrGWTCM3c1w800IzJL64fwcO24fDvfZgL86OYbIzx7C4w5gc195OThAE4XKiB3CT3Qk9gLpucHhijq6Ql86A95aurr1ZWprG7GqKqcUY2WIFt8PGWG+Uwa7gNct03CqarpMtVgh6nLetDXeSm90DaOhaO9BpVwa79ucNaNXa3zP0y86SQDUjKRZQLUiqef2jhYahslZoMpcuU6w0eHDPDnYO99IT9l9zT+tStcbpmRVeOzZJudpgoCNAT8RPb8RPT9h/xfaDeqtBeup9pmfmOVd28/D+B0FrMv3BD4nYDFyDDzKdVyhVakiSRKNRp7N4hg67TiAQwGT34OoaJTY3wYUzR1jOlFlVuhka38fPPbn/it1tdK1F7MQPkSSZ6J4viQUhgiB8IhEAN9mdEADL1To/OTZJvlTBrKr0RHz0R4NEA+5bshPHzWQYBolskcmFGIuJDKoiM9QVYrQnKnb4uAPczACoxSfQC2tXflFWkdT1YKd8GOw2PirmdviTrv9zX6lWWVhL4470kKvUsVstjPZEGO4OXzXkCu3/Yz8+cp7lZI7OgIdao4mm63iddnoifnrCPgLrWyAWlyd474PDzJXNPP/M09SaOufe/zERtcjWvY+RMkU5N7dKplChWS3iyZ4hEO5kIOTCopVwRAYxO71MvPsys3MzzLbCmDu28it/6TnCfu9GmxqlDLETP8LVOYJv6IHP/XoLgnBvEwFwk90JAfCSbLHCfCzNwlqaQqWKxWSiN+KnPxog4ndfs4fjblKu1pleTnBhKU6t0aQj4GVrX5SukFfMx7tNbmYA1CsZaDXagU61gGJBUjan9/fyOoDVpsHUUoy51RQG0B8NMNYbJei9spe32dJ4+/QFVhI5dm/pwW23spTIspzM0mi2sFst9IR99Ib9uCjxo5++RrKs8ZXnvkBJt3Di/deJGnFGx/fi37KfhXiOMzMrLC7MYGQXsIUGGO1w000ci9mMp28H8ekjXDh1iNmylYSpm5/90rM8sHPbxs97YXmS7MwxwjuevOZOIoIgCJeIALjJ7oQAaBgG1fQyqs2FanUiyQqZQoWFWJr5WIpStY7VbKIvEqC/I3Bbyq1sJk3TmY+lmVyIkS6UcNqsjPVFGOoMYzGLaa630p28COR6PloIGqDWaHJxOcn0UoxStU7Q42SsL0pf5MOi5bpucHx6kfPzq4z2RHlgax8SEvFsgaVEhqV4lnKtjtmkEnZZmTh3CpNe4y89fZCyNcIHH7xPtLHAluERwuNPIKkW5mMp3nv7dVaTGZqODkJuO7u9ZUJyEbs/Sr2YJTZ3nqlYkbW6lc4te/jZ557D63ZiGAbJs2/QKGXo2PcCiln0iguCcG0iAG6yOyEAtmplVj743xv3VYsd1eZEtTpRrE4KmomVXJPlbI1qs4XNYl4vehwg5HXe1WEwmSsyuRhnIZZGkiQGO4KM9UXwuRy3u2n3hXspAF6i6wYrqSyTC/GPLVp+YSnBofOzRP0entg9slGuxTAMMoVKOwwmMsTSeaZmZvHINZ7dPUBwYCdHT58lUrnAcG8H0Z1PY7J7aNUrnHjj/+VCXmap6aZSazDggT3uAh6zQatawDDZOTezTDydQXd2sOuRL/LArnEUvcXasZcxO3yEdjx1V/9/FgTh5hEBcJPdCQHQMAz0Zo1mtUirWqRVLdGqtT82qwX0VnPjuLxmIlZVWSsbNAwVl8NJf1eYod4ugn4f8l06Z7BabzC9lGB6KU613iDidzPWG/3Yyf3C5rgXA+DlrlW0fLQvQsTnJpYp8OaJaWxWM0/vHb1myaJCucaZmWVeefcYRiVLp9eGr2uIdDbPiLTEWJeX6PgTWH0dVDOrxE+/Ri0wxmRO4czMKo1GnZ3eOlvtWaRSAs/QA0zHyyxPHEVHwtI1zoOPfZFOu07y7Ov4hvbi7t56M186QRDuUiIAbrI7IQB+Eq1ZbwfDWolWtUizWqRRLZJI51hKl1ktatRbBk6rSm/QTX/UT8Dvw2R3o1pdqLb2tlR3Q8+CpussxjNMLcZJZAs4rBa2XGdyv/D53OsB8JKPK1oecDt469QFGk2NJ/dsIeJ3X/P82dUkP/3gNK56DMVokiDEUqbEiLzK9oiFnp2P0Tk0Tm72BMXVKSK7vkimqfLumYucvLCMSauwz7pCpxEnNHqAJbWH+eNvYKnGqJl8uEcfYVvYhqkwT3T3lzC7/Jv9kgmCcJcTAXCT3Q0B8Hr0VpNGpcBqLMHsSnulba1aw6G0iNp0utwqLouMrCioVuf6PEMXqs2Fyda+r1jsn7jq8nZI58tMLcaYW0sB0N8RYLQ3Kkq4bKL7JQBeYhgGsXSBycUYy4ksJlWhJ+IjmStRqtY5uH2Qoa7QNc89Pr3I2YtL7PZWsNfixNVO3l6o4SjM0GMuoYSGCI/sw5s5hdOs0vHA88iKiXgmz0+PTXLqwjLh2izj5jUGR7eTc48xPb+CP3+WRq1CwdGPzxdgW9RG//6fQVbEHzyCIHxIBMBNdrcHwI+6tAPGQizNQixNvVrFZZHo8qh0OiVsRm19iLnEpR8lSZZRrY6rgqFqdaFaHbe9RtlHJ/eHvC7GeqP0Rv13fZmc2+1+C4CXK1XbRcsvLCeoN1pU6g00Tefh8UH2bOm9qsfcMAzeODHNWjrHo71WiJ+jbvZxNO9GKqzSqa+Rl9zkrZ34smfxdgzQu/sJOgIeVEUhls7zg0NnmTt7mC4pyWh3AFeol4mKh25jFTlxnkRNoaD42Tq+i/2PPSN+vgVB2CAC4Ca71wLg5TRNZzWdY34tzVIiS0vT8Lsd9EcD9Ia92BR9fc7hZcPLtRKtaqldwJf2Lh+Kxb4eDF0f9iKufy5vUlmPG6HrBsvJLJMLMWKZPDaLmS09YUa6I9it5k++gHCV+zkAXtLSNObX0kwsrDG1GCdTKLN9oJO/9Phu7FbLFcc2Wxo//OAczVaLp7dFKc0colRrcbIaxUaN7dYEhslBTvaSnTlO2jGM7uqgK+ihJ+ynO+Qjlkzwyvf/N6lcgU67RofHSsHSRbijh3D6A5aX5onVLdhHn+CRhx+lM+j9XM9PEIR7gwiAm+xeDoCXa2kaK8kc87E0y8kcmqYR9Djpiwbojwau2KrKMAy0euUawbAdFnWttXGsYrFhWp9neHkwNNlcyOrNC2W5UoWphTgzq0k0XacvEmCsL3rXr4q+1UQA/JBhGCRzJd49fZH3zs1it5h4eu8YO4e78bk+fIxytc4PDp3FZbfy9M5+stPvkU3FOVEOYrZY2WVLYDWrqFYHxUyKaueDLOcapHJFJEki4nMTdUjkp9/jVFqhVMwTIY1sddK59SDbvXUWDv0f4sUWlcgeOrY9wgPbhm7bvtqCINwZRADcZPdLALxcs6WxnMwyv5ZmNZVD03VCXhf9HQH6IoHr9qZdsWJ5IxSuf6wV0ZqNjWMVk2U9FDpRrVf2IMomy6YEtUazxcWVJFOLMYqVGn63g7HeKAMdwXtiS72bTQTAa1tOZPnzt46TzJXoDnnp7wgy1tdBT8iHLEskskV+fOQ8Ax0BDm4fID9/msTsWY7l7KiuEPtcWUytMprWxOaJENn9LNWGxlIi2y4vkylQy6cwV9awBXuZSxQwpSawamU8XVt49onHWHv9D0nncxSsXdTCuxjZtpsdg12YVLFtnCDcj0QA3GR3QgA0DJ3c7AlsgW4snvAt7cFqtFosxbMsxNKspvMYukHY72oPE0cCn3rl7ZUrltvlbC6Vt9EatY3jZNW0Hgqv7DlUba7PtGLZMAxWUjmmFuOsJLNYTCZGusOM9kau6N2sNZoUKzVCXtenuv69SgTAj1epNfjpsUkW4xl8LjstTbtiy7m1dI53Tl9k32gf2wc6qaQWWT33Hh+stMDTzcFQHfKLaPUqwW2P4Rvcs3HtRrPFSirH+ZMfsLi8hinYT6FuUFidxlVexGGzsn3PQ3gy59AqafINmbS5Ezp3s3d8K/3RgOjpFoT7jAiAl/nud7/Ld7/7Xebn5wHYvn07//yf/3Oef/75G77GnRAAW7USM4dexio1US0O7OF+HOF+zE7fLW1HvdFiKZFhbi1NLJMHIOp30x8N0hvxf+5dOnSteUU43Kh7WCui1asbi1KuXrHs3AiLitXxiSuW86Uqp2eWOT+/RrlWx2Wz4nbawDCoN9vD13/1mYdETwoiAH6SlqbxzumLLMWzDHWFMYz2LjaXtpxrtjSWE1me2jtKd9hHs1Jg9cwbvDOdouXs4rFBB63FoxitJr1P/LWrtnszdI3VE68SyxaoBXcyl8gxObuIETtPSCnidnuJ+L0MhGzUkgukGypZxxCe/h08tH1YFEwXhPuICICX+d73voeiKIyMjGAYBn/0R3/E7/zO73DixAm2b99+Q9e4EwJgoVzjf791HL+pyYCzgUvLYbQamB0e7OEBHOE+VOutLX1SrTdZjGdYiKWJZwogQWfQS1/UT2/Yv7FzwmYxdO3KYFi7vCj2tVcsK1YnDclCVVcotRQKTYlCpUG+XKXZ0tB0nUK5RrFSwzAg4HGwra+T7QNRAh6XKDCNCIA3wjAMTlxY4uzsCsPdYXYNdzO3mmZ6qT3tIFOsYFIV/urTDxLwOtG1JonJQ7x+fJqyyc9TO3qpn38FWTUx9KX/Hyb7lbUGW/UKseOvYLK7CY4/TbpQ4YNzs7z+zrsE6wsE5DJ1a4jw8B5G9FlapTRpPBRcI/SP7mDPSK/YQlEQ7gMiAH4Cv9/P7/zO7/Brv/ZrN3T8nRAANV3n5XfP0NA0ytU6LpuJ0YBKUC7QyK6iaxpWTxh7uB97qBfFZPnki26iar3BQizDfCxNIltAlmU6gx4GokG6wl7M6s1982m1WuSyaXKZNIVcmmI+S7WUo14uIDUrSIaOLMtYzCasDhd2pxeHx4fbE8Dl9WOyOUlVdKZn50gtXsDayvOFr/5/rlrheT8SAfDGXVxJcOjcHCGvkyd3j2JSFVZSWc7NrvLOmYtIksTPPryT8cFO7BYz+ZUpfvzWe2SaZp7as4Xm2e9jdnoZePZvYbJdOQWhlkuQOP0TXF2j+Ib2AVAsV/mjV96hOHeCnsYMeVysOsbwWyW2qSs4TJBVI9QDW9i1bRsj3WHxR40g3MNEAPwYmqbxP//n/+RXfuVXOHHiBNu2bbvmcfV6nXq9vnH/5MmTPPHEE7c1AJardd48dYFUrojVbEJVFErVGlazidHuAL32Fs3sErVsDCQJm68De2QAm7/rlpZhASjX6izE0szHMqRyRRRZpjvkoy8aoDvsRVU++7Bqo9WiUKqRK1c2PuZLVUqVOgbtH3ur2YTHacPjsLU/2m04zQYmvY720RXLtRKtaplGJUeznMPQNXTVRtUS4oHnfwnVIobPRAD8dOKZAm+cmMZiUnl63yhuhw2AlVSO//7qYUrVOgMdQfqj7VXpTqnKj3/yKqv5Bo9v7UJaeAebv5Puh38Bizt4xbWLK5NkLh4juPURHOF+oN0T/+PD50hcPEE0d5yibiOudrDccuPQiozZMjitFor2bsb2Psq+rUO37LUQBOHWEgHwI86cOcPBgwep1Wo4nU7+5E/+hBdeeOFjj//N3/xNfuu3fuuqr9/uVcCXdig4PbtMPFPAZjFhMZkolGsoisSWnghbOrxQXKMcn6deTCOrJuzBHuyhfqy+yC3fzaNUrTG/lmE+liJTKKMqCt0hH/0dAbqC3o9dhVutNymUq+RKVfLlCvlSjXy5SqX2YTB3WC3tgLce9rxOG26HDav5kxelaI0qleQCpfg89VwcQ9cwOb2Y7B5k1YzWqBLZ9ayYRI8IgJ9FoVzjteOT1BpNnti9hY6AB4BEtsgrh85is5gwqQrFSg2fy85w1MvFs0eZj2XY32XCWVnCFugivOPpjaAH7d8B6an3qaYWiez+ImZnezu4RrPFa8enKFw8jK+2SEU30ZJMJJQIy9kG5kaaXkuJ0V0HePjpn7mlr4UgCLeOCIAf0Wg0WFxcJJ/P82d/9mf85//8n3nzzTfvqh5AaC8EUSwOJEkikS1wemaF1VQOq8WE3WKmUK6hGwZDnSG2D3RikxqUE/NUkvM0K0UUsxVHuA97eACz03/Lw02hXGM+lmIhliZbrKAqChGfC6/TjsWsUqzUyZer5EtV6s0m0C4y7bJbN3rzLoU8j8P2qRdo6K0mlfQSlcR8u6cUsPo7cYT7sQW6xLZaH0MEwM+m0Wzx5slpYpkCB7YNMtITBmBmJcm7Zy6yd0svfreDycUYK4kcqgzN3DLpZIIHvQXCLgtmdwDfwF7cvds3/r/qWov4yVfRtQbRPV9CMbX/TVqaxpvHJyhNvE7E5yLfVKhnVsARICv7WYkneHBsgGe/+Nwtfy0EQbg1RAD8BM888wxDQ0O89NJLN3T8HTEHsFFl+f0/R7XasXqjWLwRrN4ouarO6dlllhNZrGYzTruFQrlKs6nRFw2wfaATv9tOo5imkpynnFhAa9Qw2d04wv3Yw/1XzTXabLpuUKzUNsJdvlxlLZ1nMZ4hnS9Rb7YwKQrRoIeBaIDeaAC/y47HacNlt36ura4MXaOaWaWSmKeSXkbTWqjOEGZ/N4qnE11WabZ0mi2NlqbR1LT25y2dPVt6RA8gIgB+Hpquc2RinumlONsHOtkz0ossSxyfWuTc3OrGyuBipcb0UpzppTgri/M0M4s84EgyvGUrsmrCERkgsGX/xpaLrVqJ2PEfYnb6Ce14cqNnX9N13j18nPz51xjY9gCqO8jMiTfRGzVMgX527X+Kjkjwek0WBOEuJpZ6fQJd16/o4bsbSIqJ8PiT1HIxark4pdgsACa7i93eKFt9fqYzTRaTJcxmlbDPRSJXYP79FJ1BL+ODnUQG9+Ed3EstG6ecmKOwdJ7c/Gks7mC7ZzDUj2L+7G/wLU2jUK5thLxLHwuVGrquA2BWVdxOG11BL9v6O3DbrSBBIlNkMZEhX64yvRinL+rHpCrYzGYaRutjQ9rG51r7fkPTaDWbaKU0en4FoxjHaDVoqnaqlhB1axSjZoVUEZi66jkoioJJkVEVhZ3DXZ9rvqIgKLLM/m0DuB02jk0uUCjXeHTnMHu29JAvV3n79AWe2z+Oz2Vn32jf+urhPn76wSmOXvyA0omj9Gx7ED0+j1YrE9z+GIrJimp1Etz6CIkzr5OfO4V3vX6gIss8+tA+DjXyzJ07TP9Dz/OFv/y3OHP4TeIXT7E85aYj8qXb/KoIgnCziB7Ay3zzm9/k+eefp7e3l2KxyJ/8yZ/wr//1v+ZHP/oRzz777A1d407oAfworVmjnktsBMJmpdD+usnBakVhqSyjOAL4PG7K1QaFSpWQ18X4YCfdIR+SJKFrLarpZcqJeWqZVQCsviiO8AC2YPfHDok2mq2NgJcrVTfm6pUqNTRDR9cNzCYVh8WM3WrBbjVhtZiwmk0ostwOcS19/WM70LVa+sZ1k7kSqXyRar2Jqih414d+7VbzVT1yqqJgUhVUWcKsVzBXk5gqCRStjmx1oHq7Mfm7MTt8mFS5fayiYLp03vrXTEr762KF5NVED+DmWE5keevUhfb2cPtGMasqP/zgLM2WzvMHxjcKqjcaDZrNJu+fucj0u/+bQCuBGh4h5DQRCvjp2vUUqq1dJqa0OkVh/hS+0YPYAj0bj6XrOqfe/D/EY6t07nuBHSP95DNJLDY7Nvvml4tSVRWzWey1LQi3mwiAl/m1X/s1fvrTn7K2tobH42Hnzp38k3/yT244/MGnD4CNRoNWq/WJx20mrV6hXkjQyCeo5xNUSwWS+TKpuoJu9eIIdFOVbeTKdTwOG1v7onSHvBiGQUvT0VoNGtlVKskFGoUkGhKaPURZDZDHTr7coFhp9+bV6k10w0DXjXaYMn0YqCwmFYtZRb3G4g4JCVWVrw5glz5X2vfNqoIiy5Sq7b1RY9kC9UYLp81CfzTAQGeQiN+FSVHR6iUqiXnKiXmalQKK2Yo91Isj1I/ZHRRDuJtABMDNky2Wee3YFLph8NTeUaxmlR+8fxa3w8azD25Fa7U4fPgwpVIJgHimSGPpMBa9StPkBXRsJhklNIrbF0QClOwMcj1LK7gdw3TZ89QaVBaOE6spSMEx+kM3bw9sp9PJQw89JEKgINxmYgj4Mn/4h394Sx+v0Whc8Qt8sxiGwUK6jMdmwmZS0AFNN6646cb655oMLRtKq4lcK9BKrJK8eAIDiaLsYkp38aO3zaCYcFpMWEwKsqLi9QdoaiZaNTeWahJn6yQ2o44um2jYwuDqwO8O4o5acdttuJ1WLKb2akbTpWB3qTdNlTEp6hVfVxX5M70BGYZBMldkbi3NYjzD3Gocc6tMSCkTVsv4HGbsoV58Q/uw+qK3fKWzINwon8vBCwfHef3END86fJ5Hdwzx5J5RfnzkPB+cm2PXYJRSqYTZbMZiseByuUjYDlKaPYxFAb/TTqGu0YhPMlfswxvppaNjO6bkGSyVRejYg3RZ2SeneR+2+RNcKCZYsdoY7fQib3IIrNfrlEolWq2WCICCcJuJAHgbtVqtK36Bb5Z8pc7ZxXlqejtwOa0mnFb1igUSkgSqLKPIEopsQ7E6UOydKBIorSrlbBK5kqVXitNrkkhrNlIlGzVJxWK20BmJEO3y4XMN4nfZ8brsuOQGUnGVWmqJVr2CasvgCPXjiPRgsns27fldjyRJBF1W7HWdvmaOldoqq3WNpZadBUsYvzVEvxxiwOTBiujxE+5sNouZLz64lffOzPLmyWn2jPRyYNsA752dwWpq/3+2WCwbva29vX2kqJJePE+i5WDY10IzHBRKa6zGGixnO+l09dFTv4A1P4vSsePDP7SsnZi1MqbYHKcLOVbtVoY7Nn/7yEajsenXFATh0xMB8A5w+S/wzaCi8eVoljxOYkoHuXo78AVcNroCTsJuB6oioRsG5VqLcr3ZvtXaHystM4azC83ewWqtgV4r4ZEq7LS0aDTrpKp1pNwcfk+V8b4BfGEPqt3dfiOJdGIM7aWeS1BOzlNcnSK/eBaz048j0o891Idq2fwhtvYK3hXKiQWq6RUwdCyeEKO7D7A72AuKiXimwNxaiqmFNc5cXMLjtNEX8dEb9uF1WAGjvUWcYYCht0tFG/plXzPaBaQvfW4YQPtYizcqhpCFm0JVFB7bNYzHaeXEhUUGO0Ns7evg5IUlbI0Gro8szA/0bEFt5FmNJ5modrPVmiHotuPXS2SJMVPtIFUN0pOfx9Ew4e8dQ1mfzyoHBnFVc+yuprAGRBFoQbiXiQB4D1IsNvz9O/GkLtDbvEDdFWWx5WE5k2d+NYFhGNhNEjYVLGr7F79FlbGZFaJmFatNwWZRsZnMmBQLmmYnnisTy5XRpSY9lhoBn4XJ+RXOTM/Q7dAZDpjwev2YHV5MTh+yakZWVOyhXhrFNLXsGvnFM2AYmBweLO5Qu76gooKhXxGorghcRntFcDuIAXx4rGHoNCsFGsU0zXIOXWuimu0bRZobxQyNYprMxaMb53QDnSaDRENjdUXj0JTG25qB2yrT5VLodKs4zZ9+WLj3sb8CklgFLNwckiSxa7gHt93Ge2dn8LsdhLxOjp9ZxO91X/EHpCTJePp2ITffZ76Y5aypl3FlDZUWQfIEnTLZ6DDp1Rb1pUlmcxqBaDfdfidWs4oS2YZ96ShyYQmCIgQKwr1KBMB7UKNeJ3PxOI2mhtTIY2rO4kHBbPJQk23kWyayZRMVJMwWiS4ndFjBJAM6UF+/rd+VgCgSYSdkSlUyhSp6QWNbwEUVLwuZGkuLDcKxZfqs03hMBorJgmp3Y7K7Mdm82PwdWLwRGqUMzWKawtJ5JFnG7PBj8YQwOf3IigKSjAwgySDx4Rw9qT0n0AC0Wol6PkEtn0Rv1lEtDuz9O7H5OlCtLpCkdm/cpY9I6+evXxcISzLjkoRmGMSyFRaSBVYzZRZLOn6njb6wh96ID6fVsnGty68nrbcPSW4PJIu5hMItMNAZxGGz8MaJKVrNJhJwdinNw0475suKnUsmK86eHQwsnGCyVuC00sdOSxylnoFyCr/WJDg8TjUOrswyM3Ez84k8YY+dnoALd8cOZOvNrfkpCMLtJQLgPUhWTaxYh7F7zNjMKlalhau0RFCroHg6kP0DGLJKqlRjNVtlrlRnviYR9tjo8DrwO61IstwOPUA78LQ/99VqSPkCnYNjXFzLUKs3GRvy4bBZWIpnuVguE7JLDHkMHFqOZjlPq15CUtxYvRG8fTuweMMYur6xIrdRykIpjS3YiyM8gMUTumo4tVkpXLVTSWD0II5wP2ZX4HMNv7ojsGWsXZtwOZFjIZZmIp7l7FqZoNdFf9RPXzSAw7p58zQF4bMK+1y8cHCcH75/hqamU6w2Ob2QZO9A5IrSRLIziD3Ux9b0EqcaDk5Knex2u1CKi+jlNMbycazhrVhoENAzJByjLGWrHJ2J47KZGIpYCXnEjjeCcK8SAfAeZFJVHtg+esXXDKMPI7+Klp5Bi51BCW0hEggRCUC92WItW2Y1Wya2kMFqVun0OejwObBbrn4DUGSJ0d4IO0f6uLiS4OzsKovxDD1hHwMdQZaTGT6IVwh5+9k6HCRoqtHIt+sQFlcvAGB2+tqBsH8XkmqmllmhnJintHYR1WrHEerH4o3QrOSpJOapFzMbexX7hh68KXsVq4pCf0eA/o4AjVaLlUSOuViK49NLHJ1cIOxz0x8N0Bf1Y7OIFYzC7eO0WXn2gTEW52fJ1nQWk0XsZhNbu6/ctlEODmGt5tjdTHO8buNE0c3u4HbUzBRGNYe+dgrZP4BcWKNDW6ZrZDvZcp2lVJGWLiqECcK9TATAe5BuGFxYy+KxW/A5LFhMKpIkI3m7kRxBtOQ02toZdGcIJbQFi8lCf9hDX8hNodJgJVtiMVVkNp7H57TS6XcQdtuvqtenKDKjvVGGu8PMraY4M7vCUiJLV9BLXzTAairPW2fm8DrtbB8YoH/oAYxGlVo+Tj0Xp5JcpLA8iSRJmF0B7KE+kKAcmyd24oc0ynkUiw1ndBjf0AM4O4aQlVvzI2tWVQY6gwx0Bmk0WywmMizEMhyZnOfIxDwRv5u+9TBoNYteEuHWM6kKWzvcxMs6FxJFTi8ksVtU+sMfrriXZAUluh3z0lH2eYscK3g5noS9XXswpSbRizGMxBSSK4peTCBZXAT8/QRcttv4zARBuBVEALwHNZoaqUKVxWQRALtFxeuw4HVY8TksWKPjUE6hJadpLXyAEhxGcncgSRIehwWPw8Jop49EvspqpsS5xTRTSpaIx07AoSJ9pHa4IssMd4cZ7AyxEEtzenaFlQtLdAQ87B3tJZ4u8O6Zi5y6uMS2/k6Gu/twRgbbhaVrJarpFYrLEyRO/4R6MY2EhMUbwRsaQLHa0WolsjNHqaYWcUT6sQV7UUy3bjjWbFIZ7goz3BWm1miyFM8yH0tx+PwchyfmiPo9PLZrWARB4ZaTJImRDi9el4P3p1Z5e2IFq0kl6nN8eIzFgRIagcQkD0T9HI/rHFsqs6d/F1aLAz09h5FbBLMTLTWDZHEhOwK38VkJgnAriAB4D7KosN+2TMvjoSA5ybYsZCtN1rJlDAMsJqUdCJ3bcddWsccnkYsxlPAokrn9xqHIMh3rw8CVevvctWyZhUQNxdAIzcfYNtiN3frhUKgsSwx0BunvCLAYz3B6ZoXjU4uEfW4eGO0nlS9yZGKe0zPLjPaG6XdJtHLLVFJL6K0m7p5xzK4AsslMq5yjlk/QqhaRJAlZUanlYlSSCygWO7ZAF/ZwPzZ/1y3rFQSwmk2M9IQZ6QlTrTdZjKeJZQpYTOK/knD79ARdWM09vHpqgR+dmudn9g4QdH9YbklydyBVMqiZC+zr28uJxQLH5jLsGRjFYfWgxc5hVDIgq7RWTmLqP4hkvjN2RBEE4eYQ71r3Il1HsnlRy2l8rVV8soLkCqCHfBRwkq3p5Mp1LuSr6IYTVTfhzqXwJI7gi3TjjQ6gKB+uKLRbTAxFvQxGPKyl88zHMpydW2ViMUFnyMtwV4jusG+j0LQkSfRFA/RG/Kwkc5yeXeHo1DxBt4O9fW7yqzMsvvs2y3qTYChM/5ad+LuHrioWbRg6jWKGWq49ZFwvJNB1jUY5S72QIjd3EpPDh7NjGGdkAIt38+cFXo/NYmK0N8pob/SWPaYgfJyQ287P7B3k+8dmePn4HC/sHSC0HgIlSUIJj6ItHkFNT7N3cCcn55Icn0uwZyCKq99Fa/kkRnENI7dMa/EI6uAjSLJ4ixCEe5X4330PklQzSni0XU+vUWqv+CunkFJTeJDwWt1I/iBGl49Cy0Su0iBbcrGQijMzvYIym8Ab6sDn9eF1WPDYLRtbs/mdVkwRFwcO7iSerzKzmuTNk9NYTCYGOgMMd4Xxu9u9iJIk0R32EXZILF88z9z0+8xOZLHYnfSNjFO3hbmYqnFhvslQI832AQtux5X1zCzuIBZ3EHq3Y+ga9UKaWi7WnkOYWaFeSFFJzpNUTFhcAdw923B1b23XGBSFmYX7jMdh4WcfGOJ7R2f54Yl5vrCjl06/EwBJMSFHt6Etn0AtLLF3qI+TcwmOzybY3R/CO/AwrdVT6IkptMQkmB2Yeh+4zc9IEISbRQTAe5gkSWBxoVhc4O/HaNUxymn0cho9Mw/GDG6TDY8jQH9HEGNgB8VCjszyRbK5BRYLOWbNXiRZxm0z43VYsZtA1nTMJpXR3gijvRFypQoXl5PMraWYXIjhdzsYCLsJy0Va2WUapQwm1cTunTupmoNMJBuczBRwawZ7R/uoN1pMLsa4uJygLxpgfLBzI0Re8XxkBas3jNUbBkDXmtTzKWrZNcrxWcqJOdaOv0LsxI+weEK4usbwDuzG6gnf4ldeEG4fj93CC3v6+eHJBd44t8T+kSiDEW97KoXNi+HvR8/Mo9h97BkIc2o+yYm5BLv6Q/h7H0SzedAWj7ZLQQmCcM8SAfA+IqkWJE8nsqcTQ9cwqjmMcgq9lITcMsgqLrsfV08Pvc0aenaJqlEhb+8jr5uI58qUqnUajTp12zl6IkHCPhdhv4sHxvrYORBm7sIUExemef1sFgmJ3s4wW7fsYWBwC4ra/nHrHoBkrsiZ2RU+OD+H02Zl52A3OgaTCzG+/95pOoNedgx2Efa5PrYnT1ZM2Pwd2Pwd+Ib2orcaVLNrFJcnKa5MkTzzBonTP8XsCuDqGMHdN4492INi2rxt9wThTuR32XhiWxfvTq1yYi5JpaGxrTuAIkvI/j6MahY9dh6l90F2D4Q5s5Dk5FySHX1BwuExZJsPyeq+3U9DEISbSATA+5QkK0iOADgCyMYWqJfQyymMchojMQlISCYbtkYZW+ksnZ5u5JER8tUmq8ksQY+TtUyeqYUVTPUM7lYWj1TCYTGxp7uHR/buIN60MRvL8t5snpOrpxnsDDLcFcLtsBHyunh67xiZQpkzMyscmZzHbjWztb8Ds6owsbDGjw6fI+R1MT7YSXfI94lDurJqxhHqwxHqI7rnSzSrBfILZygsTZCbO0l6+gNMNhe2YC+urlHswS4s7jCy+tlW7xqGga4baLqOSVXEkLNwR+kKuNgzEObcUpr5RJ5qvcWu/hAWk4IS2UZr8QhaYgolup2dfSHOLqU4vZBkvCdI1Be53c0XBOEmEwFQaAcXqwvF6oLAwGVDxSlo1TAaZbTVU2jJC1j9I4QdAXZGzejFLAV9kZJRpmKxk5X6mNdd6GkztlKJsFdiuDuEWVVJ5kpML8Y5O7tC2OdmuCtEb9SP3+3giT1byJUqnJld5djkAlaLiW39UexWC1OLcV4/PoXXaWd8sJO+aGBjsQmwEcA2btr6fU1H00ELbsfk34pRr1FcmyOxdoHKwiqtC3MYqgXF6kR1R1DtPhS7B9nqQjdA0431a+i01j/qun7F1zVd32jHL31xP4oIgMIdZijqpVxrr+LPV+ocuRhj90AIp9WKEhlDWzuDUfAjezoZ7w0ysZzm7FIKzTDoWp87KAjCvUkEQOEqVw8VZ9Hya+ixczDzGjbDYDk/gCvaj7d3J52RISSLE03XqTYaJDIl4tk88WyR8wtraJqOIku4HXZkSWJuNcnZ2RUUWSbkdRINeHDZLaiyTMTvZn4tzZ+9cQJFloj6PVgtJqYW47x/bhazqhDyuvC6bBhGuxfuRmmagS4PIvl60ap5tFIaPVNATs8hywuYTGZUsxWL3YXF6cHi8GKxu3CYzciKhCLLKHL7o3zZ54osUa1Ur9iGa7OoqorZLHYdET4bWZIY7w1SbbSoNTUkCY5cjLOjN0jQHcLwdKElp5GsbmSLsz1MLMmcX0pjGAbdAbEfsCDcq0QAvAfpukG53kRfH6K8/KP2ka9plz6/xrG6DjQrmGspLPU0csuKRoRGo0o8XsGSm0OaS9CyHKZpDdC0BNBNV9YOs5lNVOoNStU6sUyRSq2+EdoMSWIhnkGWJDxOGx0BNxGfm4HOID0RL6vJPLFsAZOi0Bvxs3ukh7VMnkSmQLZgMNAZZKgzhNViWg9iMspGULvyvtZqcfzYMcrl8vriGBt4upCaPuRqGrmaQmpmoWFAS8UotFchG7JMw+xGt7gxzG4Mk/2yPZIBwwC9ybxyc0Ka0+nkoYceEiFQ+MxURWb3QJjDF2KYFAW72cTJ+QRbOn10B4aQqnm02HmUnn1IssJolw+T2l74JQjCvUsEwHtQU9M5NL32sd+XpHbPgCxLGx8V6cPPVb2BrZnF2khj0ioYsormDNCyhWhKNhq1Cv2dLvTMEmZJx+H2IDVLSEYBk8WNxd+Bxd+F1RNGVdQrQpmERK5cIZEtkMgWiaULZIplssUKK8kcmUKF/o4Ae0Z6+OKD26k3W5ybX2V6KUEyV2S8v5Oeh7Yzu5ri4kqCkxeX2NITYWtfxxVFqT+qorWoVMpYLBYslst3EXED0XYoreWhlIBKCrQWKCooJtA1KC2A1myfopjbX5eVdgDEgL5HN33VZL1ep1Qq0Wq1RAAUPhebWWVXf5BjMwmiPjs9QRdTK1kqtRYjka3oy8fQUzMo4S1IksRQ1Hu7mywIwk0mAuA9yKTIPDQcvTLgXfpckpAkrlqwYGhNjFICvRjHqObAIiP7w0iuCJLdjyS3C0PXajWKRZkH9j+G0qqQufABzXIeZ/9OLO4gtVyManqFZm4RTTVh83dhC3Rh8nWgrC+2CHqcBD1OtvW3h3Dz5SqJbJGVVI6phRjn51Y5MjGPw2ZhuCvEjsEuvvjgVpbiWSYXY0wsxtjSHeZL+7ezGM8wtRhjYiHGUGeI7QOdV9QS/CiLxYLV2v6+YRigNaBVx2jVMGiA1YohB9pzICtpjHoRjPawOKoZFDOS3sBolUFWkEw2ZHsA2awgq5u/PV2j0dj0awr3J6/DytZuP+eW0ox2+tja7WdyJUOlYWW7bwgyF5DsfmRn8HY3VRCEW0AEwHuQLLf39P0kht5qB51ivL0NlGEg2f0okW1IjiDSJ2yxZnEHie55jsLyJPmFM1QzKwRGHsI/sp9GMU01s0I1vUo5MY8kSVg8IWyBbmz+Lkz2dokJSZLwOu14nXa29ER4as8opWqN2ZUkp2dWubCS4NTFZaxmE10hL0OdIVqaxrm5VSYXY4x0h/nSQ9tYTeWZWLiylqDXYUGrl2nVKlTyaZTCMkZNpiXpH4Y+XQeMjbagWpEUM5IziOrpxJDkdjHtagGjUUaSZCSXF9nqBQmolzDqJZDFPsDCna/T76RUazK9lmX3QJi9gxFOzyc51pDZYQtgjU8gWR5EEqWSBOGeJwLgfcYwdIxKFqMYRy8nQdeQrB6U4BCSM9zu6foUJFnB07sde7CHzIUPiJ9+DWd0EM/Abjy947i7t9GsFammV6ill8lMf4Cua5isTiyeMBZPBLOzvQWcoesYho6k6ww6dQZ2+NG3u1lI5Dm9kGYuEWNxaQmTLOGyyJiNOpkLRzmn1OnxWNjmNZEvVYktFPnp2zVcJoOAXcVmAk3TMGXzYLagmcwgKe0hW0lpD+XKKgYyUquG0apB/VIsXH+e0B72bZTRUxcxWo12r6jZjmR2gqEDonCucOcb7vBSqTc5s5DiweEID45EOTmX4FjBy3ZTEV98AqVr1y3dVlEQhFtPBMB7kKFrGLVCO5QYBoauQaOIXkphVNLtuWyqBcnmQ3J6kVRzu/RLdnHjnI1z+cj9Rh21ViV5qopJVTEMHdaDm6FrNCt5Vo98n9Wj38fm68Tk8LAen9okGb1ZoVxIkVs4g6E1kWQV1eZCtbkw2VxIstIOg3oLQ2/h0Frst2jsDrdYLrRYyGtkMjo1Q8OjNJGNCsv5FvOLKk6Hg3DQh2zuIFWD1aaC1+qmP+yhYZpFdThRLVaQpPYbnCS3J0VKcrudl9/fOEYCrvy60aiilxMYpSS0GkjoH/OvIQh3lksrg49cjHFqPsmDw1EeHI5yeiHFqVyILbVVuuyLKP7+291UQRBuIhEA70V6C23lBEargdEoQ6OEobXaG7tbHEgWF6hW0JvtQLgRfK4MOZeHIklW2/cNBUOTMbvDWK229V40GUmW28OjsozealJcmaSaiyOrFrz9u1BtjvUwpbQDlNak1ajRyCWoZJapZddoljI0CklkkwXFbEO1uVHM7VXFisWGy+IgarGz3+KgqCksZBosZqo0dLBarNQaDWZTeU5kq7hs1o3yMqlKncXlKpWsk1FniF6v73OXbJFUC7LdixEcgUYZ6SatAhaEm+HDlcFrnF5IsXcgzN6BMBMrKpMrVaqz8wxbvSh27+1uqiAIN4kIgPcgQ9dAtYDWQrYHkMJjSO4oks3bDmmfo2CxVKuhFYt4BvZgt9uveYyha9hDfZTiM2QvHCV2/JX2QhCHF61RoVWrtNu4TlYUHOF+JFlFa1ZpVUu0akUMrYlsd+OM9GML9mJxBzeGpXxAL6BpOsvJLBdXkqwmc/R3BDEpCpliiUK5QqPZwm41I8syy9kKy/llfM40Q1EvWzq92Myfb+5eu6SMKJgr3H3aK4NDHJtJMLmaYWuXn23dfuxmhQsXppDX0owMeW93MwVBuElEALwHSSZrO/iFRpEc/k2dy9NeOdukUcpAJUWrVmkvtKhX0GplWvUyWqO2cbxsstCqlyksncfk9OHp24mzYwuq1YFqcaBY7ciq5apQqmtNatk1qukVyokFCstTKCYLVn8ntkAXNl8nsmpCUWT6ogH6ogHKtTpzqykuLiexWywoskJT09A0jY6AG6XqxZBVlrNVDk2vcuRijIjXTn/YQ9Blw+ewYDWL/xLC/cPrsDLW7ef8Uhqn1URv0M1AxIvDugOPXfRqC8K9TLzb3YMkSUaJjH2mcw1dW18hW29vA9esbayYpVmDWglzrUbqdBqTyYQkK6hWO6rFicnpxRboQrHY18OdA9ViR5IV6vkk6QsfUMusYHZ4sXWObJSWuRZZMWEP9mIP9mIYRntVcXqZamaFcnwOSZaxeMLYA13Y/F2oNhcOq4XxwS62D3SSzBW5uJJkfi1FKl8muZQknSrSF/by9I4eJGBqJctCusDxmThWs4LHbsFhNeNzWPCu3xwWk9jjV7indfmdlGtNplez2C0mgi4bYc+1e/cFQbh3iAB4HzHWd62guR7o1gPeFWFP+0jdOcXcLgmhWpEcAbCFaNZbBHc+jssbRDZd3Xt3LRZPiI69z5NfPEdh6RyV1AL+kQNYveFPPFeSJCzuIBZ3EO/Ablq1EtX0CtX0CtnZE2QuHsPs8KzXHOzG7A4Q9rkJ+9w8ONbPQjzN+dkV3k4nuRDLs5Au0+lzsq3bz57BMMvpIvPJApVaC0VpkS0bxHJlDAPMqrIRBn0OC06bGVkEQuEe89GVwc7rFFUXBOHeIALgPcjQWxil5JW9d616+77x4dw7JBlJtYLJimR2INkD6/ct7Y+q5apeOqlWwygWMTv9KOZPVytMkhW8/TtxhPpITx8ifupVnB3D+Ab3IKs3/oajWp24ukZxdY2it9aHijMrlGIz5JfOo5it2Pyd2PxdWH0dDHeF6fQ50bKrlFoSS9kKC8kCs4k8EY+dnX1BHt/WzWqmxGKqSL2pEXRZ8TttNDSdXLnOxVgOXTdQFQmv3YpnPRC67RaUm7AHsCDcStdaGWxWP76HXhCEu58IgPciQ0eLT7R77y4FOrsf+VLYUy3tVcDK7RneNDk8RHZ/kdLaBXJzJ6lmVvAPP4A92PupryWrJuyhXuyhXgxDp1H4cKi4FJtFkmWsngiSI4BNbhGOBBnrDZMt1ZhezXIxluMHx+fxOy1s6wmyfyRKslBlPlkgWcgSdNkYinhw2cyUag2y5Tq5cp2FZIGZmI4sSbjtZnb3hzCJN0zhLqYqMrv6Qxy5GNtYGfx5V8sLgnDnEgHwHmRIKnJwGMnqbvfsKXfeLhWSJOHq3IIt0E32wmGS597GHuzBN/wAquWzzT+SJBmLJ4TFE8I7uIdWtUg1s0IlvUJx4RTm+BxGxYfhieJ3BDmwpYMHhiJcjOU4t5TmnYlljs7EGIn6GO8OUGtqzCfzHJ2J43VY6A+56Q+5kcISumFQqjbJVWoUqw1URRTNFe5+douJnX0hjs8mmE3kGRZ7AgvCPUsEwHuQpDXQ0rPru1PQHso1O9ZvTiSLo72DhXz7//lVi53g9ieoppbIXDzC2tHv4x3Yg7Nj+HP3Tqo2F66uMVxdYzgKeWby38cs1dELq+jZBVDMyI4Ao94gY52DrOXqnF5IcH45zcRKmi6/k7EuP30hleV0kZPzSZxWE/1hDxGPHbfdjFuslBTuMT6nlZ39QTz2zd/b+v/P3n3HNXH/fwB/XQYhkLD3lKHgFnEiiiKK1tXh/lm1tdavnXZYa62jWmtt1WqXtUNtHdW6d60LVNx7L2TLFAgQIJDk8/uDkhLZKwnk/Xw88qi5+9zdO8c1987nPoMQYjj0nwGQBscJTSHw6QMUFYAVycGK8kr+K38KdXYiNHPfCsVAaWIokoAzMQeE4ip75zZKvBwHM3sPiKwckR1zFZkPL0CeFgPbVt0hNLNskGPwBEKoxbbgpFIIRCZghTlg8oySc5KTDHA8OIitMdDLDnm+HriRlIfYVBmOZcbD2twU3o6W8HWyRLZcgVvxGYg2EcDT3gIuNubg86j2jzQv9hbUC5iQ5o4SwGaK43j/zvphDuC/nrYl08Ll/5cYKuQl8wJnxZVuCc7ErCQZ1EoMTRt9blC+UATbVj1g7uCFzIfnkXz5YMl8wu5tGjQp5TgeOLEVILYC7HzBivJLEkF5BlTpDyAGQw8zKQJaWSImT4x76UW4EZcOsYkADpZmcLIyQ5FSjftPMvE4VQZ3Oyk87KT0GJgQQkiTQQmgkeF4fMBUCs5UqrWcqYrBivKBIjmY4t8aQ1liybzBJRuCMzEH40zAKwYKs5JhwnMCX2TW4B1JTK0c4dT5OeTE34Is7iby0+Ng07I7RJb2DXqcUiUJrxl41u4l5yE/E0z+FCbyVPihGD72AqQWmSImV4C0bBUycgogMRVCKjYBY0BCeg487KTVH4gQQggxEJQAEgAAxxeCE1sC4v8euZbM+lE6n7AcTCEH5Nng52Uh8+4p5AqF4AmEEJpZQmhuBRNzSwjNrCA0t6r1EDHP4vEFsPLqBDN7T2Q+OF8yZIxLK1i16AieoPE6tXB8ITipIyB1BI+pwQpzwMvLgFv+UzjyM5FdUIw0hQmyCiTIVVmC8UUwEfChZqzRYiKEEEIaGiWApFIcx5V0IBGIADObkmWFhSjOyYFjYBcIWBGK5TIUybNRlPsU8tQYzRy/fBPTChJDy1qN9wcAJhJrOAYMRG7SA8hir6HgaQJsfLtBbOva4J/3WVqPiuELflE+HOQZsM5JR3ZmGnLzn6AAYsDMFmBOAGgYGEIIIU0DJYCk9jgOfJE5xGb2ENv8l4gxpoayIFeTFBbny1CYlYy8Jw9KahMBCEzNNLWEQnNLmJhZQWBmAR6/8kuR43iwcPOHmZ0bMh9eQNqtCJg7eMLaJxB8E3Gjf1xNHCZm4Jt4gG/tAUe3YljnpCMjNREFOU/B03HHGUIIIaQ+KAEkDYbjeCW1fmaWMLP/b1BnplahOF+GYrkMxfJsFOXLkJ8eB2WC/N/tOAjEEk0todDcqiQxFEu0On8ITCWwb9cP+elxyHp0qWTIGO/OMHf01vmA1hxfCJG1C1ytXaBWq8GjnsCEEEKaEEoASaPjeHyYSGxgIrHRWq5WFv+bGGajOD8bRXIZ8pIfQVVU+O92PAjNLJ5JDC1hZu8JU2snZEVfwdP75yBPjYFNq+4QivXTEYOSP0IIIU0NJYBEb3gCIUQWdhBZ2GktVxUXamoLi/NLHicXZCZBrSzpkczj8zVJoZm9J+RpMUg6txvWPp1h4d660YerIYQQQpo6SgCJweELTcG3MoWplaNmGWMMqqKCkqTw38Sw9L8Ag0KWhoRTmyGU2JT0HrZz/zdJtABfWL8eyYQQQkhzQwkgaRI4joNAZAaByAxiGxfNcsYYlIV5KJZnIz8jHlmPLiPjzikIxFKILO3BcXzwRWIwgRh8WTwY7MBgYzBT4RFCCCH6QHfAMpYsWYKdO3fi3r17EIvFCAoKwtKlS+Hn56fv0EglOI6DUCyFUCyFmZ07bFv1QE7iPWTHXgfH8SBx8gVPIEReVhp4hdlAcRaU2SUdSwxlKjxCCCFE1ygBLCMyMhJvvvkmunbtCqVSiU8++QQDBw7EnTt3YG5uru/wSA1wPD4sPdrCzN4DmQ/OI/fJfZg7esHKpwuK01QQmZtBwFOVzHSikIMVyaHOTQGyFKV70NtUeIQQQoiuUAJYxt9//631fv369XBwcMDly5fRp08fPUVF6kIolsKhQ3/IUx8j+/EV5KTGgZevAiQtwJmagzO10CpfMhXefzOeVDYVHmdiXjLHssm/iaFApPMhaAghhJD6ogSwCjKZDABgY2NTTUliiDiOg8TJB2IbF6TcOQPBo9MAk4O5ti15/Fu2LF9YZtaPElpT4ZUmhUVyMHk68O+MJ+AJwJmYg+/SEVwVg1kTQgghhoTuWJVQq9WYMWMGevXqhXbt2lVaTqFQQKFQaN7n5eXpIjxSC3wTMaxb9URxbBpMCpKhjL8Avo03OCvXKh/rVjQVHvBvYqgs1DxGRnEBQO0GCSGENCGUAFbizTffxK1bt3D69Okqyy1ZsgSfffaZjqIi9cFMrQFbF/DynkCV8QhcXir4Dv7gRJJa7YfjuJLOIkIxYG5X/QaEEEKIgaFW7RV46623sH//fpw4cQJubm5Vlp09ezZkMpnmFRkZqaMoSV1wPAH4Dq3Ad+sMqNVQxl+EKiMarPSRLiGEEGIEqAawDMYY3n77bezatQsRERHw8vKqdhuRSASRSKR5L5HUrjaJ6AdPbAnOowvUmXFQZ8WB5aWD5+AHnpm1vkMjhBBCGh0lgGW8+eab2Lx5M/bs2QOpVIqUlBQAgKWlJcRicTVbk6aG43jg23qBJ3WAKvU+VElXwSxcwLPzAccX6js8QgghpNHQI+AyVq9eDZlMhr59+8LZ2Vnz2rp1q75DI42IMzEH3y0AfAc/qPPSoIw7D3VuWklnD0IIIaQZohrAMuiGb7w4jgNn6QrOzBaq9IdQpdwCZ24Pvn1LcDSXMCGEkGaGagAJKYMTmkLg0h585/ZghTIo4y9AnZ1EPw4IIYQ0K1QDSEgFeBJ7cGIrqDOioUq/Dy43FXwHP3AimhKQEEJI00c1gIRUguMLwXf0LxkyRlUEZcJFqJ7G0JAxhBBCmjxKAAmpBk9sBb5HV/CsPaDOjIUq4RLUBTJ9h0UIIYTUGSWAhNQAx+ODb+sNgUdXgCeAKvEKVGkPwFRKfYdGCCGE1BolgITUAieSlAwZY+8LdW4ylPHnoc7L0HdYhBBCSK1QAkhILXEcDzwrdwg8uoEzkUCVcgtMqdB3WIQQQkiNUS9gQuqIE4rBd+kAFOeDE4iq34AQQggxEJQAElIPHMcBJjQ0DCGEkKaFHgETQgghhBgZSgAJIYQQQowMJYCEEEIIIUaGEkBCCCGEECNDCSAhhBBCiJGhBJAQQgghxMjQMDDNWEZGBjIyGnaWCoVCgfz8fEilUojF4gbdd2MqKCjAo0ePYGZmBpGoYcfss7Ozg52dXYPuk1SsMa5poGle1415TQN0XRPS3HGMMabvIJqT5ORkrFmzBtOmTYOzs7Pe4lAoFAgPD0dkZKTeYjAWISEhOHz4cKPchMl/6JrWLbquCWneKAFspnJycmBpaYnIyEhIJBJ9h9Ns5eXlISQkBDKZDBYWFvoOp1mja1p36LompPmjR8DNXKdOnegLvBHl5OToOwSjQ9d046PrmpDmjzqBEEIIIYQYGUoACSGEEEKMDCWAzZRIJML8+fOpAXcjo/OsO3SudYfONSHNH3UCIYQQQggxMlQDSAghhBBiZCgBJIQQQggxMpQAEkIIIYQYGUoAG0lERAQ4jkNERIRBxLF9+3a9xkGaD7q2CSGk6aMEsJbWr18PjuM0L1NTU7Rq1QpvvfUWUlNT9R2eXly4cAEcx+Gbb74pt27EiBHgOA7r1q0rt65Pnz5wdXVtsDgq+tu4uLggPDwc3377LXJzcxvsWA3lr7/+Asdx2LVrV7l1HTt2BMdxOHHiRLl1Hh4eCAoKatBY6NquWGmiyXEcNm7cWGGZXr16geM4tGvXrlFiaIrXNqAd9+nTp8utZ4zB3d0dHMdh6NCheoiQEONFCWAdLVy4EBs2bMD333+PoKAgrF69Gj179kR+fr6+Q9O5zp07w8zMrMIv+DNnzkAgECAqKkpreVFRES5evIhevXo1eDylf5vVq1fj7bffBgDMmDED7du3x40bNxr8ePURHBwMAOXOXU5ODm7dulXhuUtISEBCQoJm24ZG13bFTE1NsXnz5nLLY2NjcebMGZiamjZ6DE3p2i6rsnMXGRmJxMREGm6GED2gqeDqaPDgwejSpQsA4LXXXoOtrS1WrFiBPXv2YNy4cXqOTrcEAgG6d+9eLlG5f/8+MjIyMH78+HIJzuXLl1FYWNgoSUzZvw0AzJ49G8ePH8fQoUMxfPhw3L17F2KxuNLt5XI5zM3NGzyuiri4uMDLy6vc+Tl79iwYYxg1alS5daXvGysBpGu7Ys899xz27t2LjIwM2NnZaZZv3rwZjo6OaNmyJbKysho1hqZ0bZf13HPPYdu2bfj2228hEPx329m8eTMCAwORkZGh85gIMXZUA9hAQkNDAQAxMTGVljl16hRGjRoFDw8PiEQiuLu747333kNBQUG5svfu3cPo0aNhb28PsVgMPz8/zJkzR6tMUlISXn31VTg6OkIkEqFt27ZYu3ZthcdWqVT45JNP4OTkBHNzcwwfPhwJCQnlym3btg2BgYEQi8Wws7PDhAkTkJSUVO3nDw4ORmpqKh49eqRZFhUVBQsLC7z++uuaZLDsutLtdCE0NBRz585FXFyc1mO8yZMnQyKRIDo6Gs899xykUin+7//+DwDQokULTJ48udy++vbti759+2oti4uLw/Dhw2Fubg4HBwe89957OHz4cI3aygUHB+Pq1ata10FUVBTatm2LwYMH49y5c1Cr1VrrOI5rlNrTihj7tV1qxIgREIlE2LZtm9byzZs3Y/To0eDz+TXeV0My5Gu71Lhx4/D06VMcOXJEs6yoqAjbt2/H+PHja/2ZCSH1RwlgA4mOjgYA2NraVlpm27ZtyM/Px/Tp0/Hdd98hPDwc3333HSZOnKhV7saNG+jevTuOHz+OqVOnYtWqVXj++eexb98+TZnU1FT06NEDR48exVtvvYVVq1bB19cXU6ZMwcqVK8sde/HixThw4ABmzZqFd955B0eOHEFYWJjWDXr9+vWaG9mSJUswdepU7Ny5E8HBwcjOzq7y81f0KDMqKgo9evRA9+7dIRQKcebMGa11UqkUHTt2rHK/Denll18GAPzzzz9ay5VKJcLDw+Hg4IBly5bhpZdeqtV+5XI5QkNDcfToUbzzzjuYM2cOzpw5g1mzZtVo++DgYBQXF+P8+fOaZVFRUQgKCkJQUBBkMhlu3bqltc7f37/Ka60hGfu1XcrMzAwjRozAn3/+qVl2/fp13L59W+9JjKFe26VatGiBnj17ap27Q4cOQSaTYezYsbXaFyGkgTBSK+vWrWMA2NGjR1l6ejpLSEhgW7ZsYba2tkwsFrPExETGGGMnTpxgANiJEyc02+bn55fb35IlSxjHcSwuLk6zrE+fPkwqlWotY4wxtVqt+feUKVOYs7Mzy8jI0CozduxYZmlpqTlWaRyurq4sJydHU+6vv/5iANiqVasYY4wVFRUxBwcH1q5dO1ZQUKApt3//fgaAzZs3r8rzkpOTw/h8PpsyZYpmmZ+fH/vss88YY4x169aNzZw5U7PO3t6eDRgwoMp91lbp3+bixYuVlrG0tGQBAQGa95MmTWIA2Mcff1yurKenJ5s0aVK55SEhISwkJETzfvny5QwA2717t2ZZQUEB8/f3L3cNVOT27dsMAFu0aBFjjLHi4mJmbm7Ofv/9d8YYY46OjuyHH35gjP13nqdOnVrlPuuCru2KlR5n27ZtbP/+/YzjOBYfH88YY2zmzJnM29ubMVZyXbRt27bKfdVVU722y8b9/fffM6lUqvn7jRo1ivXr108Tz5AhQ6rcFyGkYVENYB2FhYXB3t4e7u7uGDt2LCQSCXbt2lVlr9aybXPkcjkyMjIQFBQExhiuXr0KAEhPT8fJkyfx6quvwsPDQ2t7juMAlPSc27FjB4YNGwbGGDIyMjSv8PBwyGQyXLlyRWvbiRMnQiqVat6PHDkSzs7OOHjwIADg0qVLSEtLwxtvvKHVmH3IkCHw9/fHgQMHqjwfUqkUHTp00NQAZmRk4P79+5qeqr169dI89n3w4AHS09N19vi3LIlEUmGPyenTp9d5n3///TdcXV0xfPhwzTJTU1NMnTq1Rtu3bt0atra2mnN3/fp1yOVyzbkLCgrSnLuzZ89CpVI16rmja7tyAwcOhI2NDbZs2QLGGLZs2WIw7SIN8doua/To0SgoKMD+/fuRm5uL/fv3673mlBBjRp1A6uiHH35Aq1atIBAI4OjoCD8/P/B4VefT8fHxmDdvHvbu3VuusbhMJgMAPH78GACqHE4iPT0d2dnZ+Pnnn/Hzzz9XWCYtLU3rfcuWLbXecxwHX19fxMbGAihp5wMAfn5+5fbl7+9fYQ/fZwUHB+O7775DRkYGzpw5Az6fjx49egAoSWJ+/PFHKBQKnbf/KysvLw8ODg5aywQCAdzc3Oq8z7i4OPj4+GiSmFK+vr412p7jOAQFBeHkyZNQq9WIioqCg4ODZvugoCB8//33AHTTdpKu7coJhUKMGjUKmzdvRrdu3ZCQkGAwSYwhXttl2dvbIywsDJs3b0Z+fj5UKhVGjhxZ59gIIfVDCWAddevWTas3XnVUKhUGDBiAzMxMzJo1C/7+/jA3N0dSUhImT56s1ci/OqVlJ0yYgEmTJlVYpkOHDjXeX0MpTQCjoqJw5swZtG/fHhKJBEBJEqNQKHDx4kWcPn0aAoFAkxzqSmJiImQyWbmbl0gkqjDBefamV0qlUjV4g//g4GDs27cPN2/e1LT/KxUUFISZM2ciKSkJp0+fhouLC7y9vRv0+GXRtV218ePH46effsKCBQvQsWNHtGnTRq/xAIZ9bZc1fvx4TJ06FSkpKRg8eDCsrKwa7ViEkKpRAqgjN2/exIMHD/D7779rNYwv2ysOgObGXrbR/7Ps7e0hlUqhUqkQFhZWo+M/fPhQ6z1jDI8ePdLcTD09PQGUDN1S2uuz1P379zXrq1K2I8jZs2e1eqm6uLjA09MTUVFRiIqKQkBAAMzMzGoUe0PZsGEDACA8PLxG5a2trSvsIBAXF6eVgHl6euLOnTtgjGndWMv2iK5O2XMXFRWFGTNmaNYFBgZCJBIhIiIC58+fx3PPPVfj/eqCMVzbZQUHB8PDwwMRERFYunRprbZtLIZ8bZf1wgsvYNq0aTh37hy2bt1ap30QQhoGtQHUkdJf1YwxzTLGGFatWqVVzt7eHn369MHatWsRHx+vta50Wz6fj5deegk7duyo8Gaanp5ebtkff/yh1T5o+/btSE5OxuDBgwEAXbp0gYODA3766ScoFApNuUOHDuHu3bsYMmRItZ+xdEy7Y8eO4dKlS+VmqggKCsLu3btx//59nT/+PX78OBYtWgQvLy/NUBjV8fHxwblz51BUVKRZtn///nJDjISHhyMpKQl79+7VLCssLMQvv/xS4/i6dOkCU1NTbNq0CUlJSVrnTiQSoXPnzvjhhx8gl8v18ui8KsZwbZfFcRy+/fZbzJ8/X9P7Vp8M/douSyKRYPXq1ViwYAGGDRtWp30QQhoG1QDqiL+/P3x8fPDhhx8iKSkJFhYW2LFjR4UDx3777bcIDg5G586d8frrr8PLywuxsbE4cOAArl27BgD48ssvceLECXTv3h1Tp05FmzZtkJmZiStXruDo0aPIzMzU2qeNjQ2Cg4PxyiuvIDU1FStXroSvr6+mMbdQKMTSpUvxyiuvICQkBOPGjUNqaipWrVqFFi1a4L333qvR5wwODtbURjw7Tl1QUJBmGIjGTGIOHTqEe/fuQalUIjU1FcePH8eRI0fg6emJvXv31njGhtdeew3bt2/HoEGDMHr0aERHR2Pjxo3w8fHRKjdt2jR8//33GDduHN599104Oztj06ZNmuNU9ritLBMTE3Tt2hWnTp2CSCRCYGCg1vqgoCAsX74cgH7aTlbFWK7tskaMGIERI0bU/mTVU1O8tp9V2aN9QoiO6bzfcRNXk+EYGKt4qIw7d+6wsLAwJpFImJ2dHZs6dSq7fv06A8DWrVuntf2tW7fYCy+8wKysrJipqSnz8/Njc+fO1SqTmprK3nzzTebu7s6EQiFzcnJi/fv3Zz///HO5OP788082e/Zs5uDgwMRiMRsyZEi5oTgYY2zr1q0sICCAiUQiZmNjw/7v//5PM/xHTaxZs0YzNMezrly5wgAwACw1NbXG+6yp0r9N6cvExIQ5OTmxAQMGsFWrVmkNFVJq0qRJzNzcvNJ9Ll++nLm6ujKRSMR69erFLl26VG6oDMYYe/z4MRsyZAgTi8XM3t6effDBB2zHjh0MADt37lyN4p89ezYDwIKCgsqt27lzJwPApFIpUyqVNdpfbdG1XfXn3bZtW5XldDEMTFO7tmt6TdEwMIToHsdYmec2hJAGs3LlSrz33ntITEyscggVQpoaurYJafooASSkARQUFGiNhVdYWIiAgACoVCo8ePBAj5ERUj90bRPSPFEbQEIawIsvvggPDw906tQJMpkMGzduxL1797Bp0yZ9h0ZIvdC1TUjzRAkgIQ0gPDwcv/76KzZt2gSVSoU2bdpgy5YtGDNmjL5DI6Re6NompHmiR8CEEEIIIUaGxgEkhBBCCDEylADq2FdffQV/f/9aTY9lqD7++GN0795d32FUis61btB51h0614SQBqPPMWiMjUwmYzY2Nmzt2rWaZfh3XK9ly5aVK1/TMbRqYseOHWz06NHMy8uLicVi1qpVK/b++++zrKysCsvv2bNHM2aau7s7mzdvHisuLtYqk5yczEQiEduzZ0+942todK51g86z7tC5JoQ0JEoAdeibb75hFhYWrKCgQLOs9Avc0dGRyeVyrfIN+QVua2vL2rdvz+bOnct++eUX9s477zATExPm7+/P8vPztcoePHiQcRzH+vXrx37++Wf29ttvMx6Px/73v/+V2+/o0aNZ79696x1fQ6NzrRt0nnWHzjUhpCFRAqhDHTp0YBMmTNBaBoB16tSJAWDLly/XWteQX+BlZ20o9fvvvzMA7JdfftFa3qZNG9axY0etX+xz5sxhHMexu3fvapXdvn074ziORUdH1zvGhkTnWjfoPOsOnWtCSEOiNoA6EhMTgxs3biAsLKzcul69eiE0NBRfffUVCgoKGuX4ffv2LbfshRdeAADcvXtXs+zOnTu4c+cOXn/9dQgE/40S9MYbb4Axhu3bt2vto/Tz7NmzpxGirhs617pB51l36FwTQhoaJYA6cubMGQBA586dK1y/YMECpKamYvXq1VXuR6FQICMjo0av6qSkpAAA7OzsNMuuXr0KAOjSpYtWWRcXF7i5uWnWl7K0tISPjw+ioqKqPZ6u0LnWDTrPukPnmhDS0GggaB25d+8eAMDLy6vC9b1790a/fv3w9ddfY/r06VpTL5X1559/4pVXXqnRMVk1QzwuXboUfD4fI0eO1CxLTk4GADg7O5cr7+zsjCdPnpRb7u3tjTt37tQoJl2gc60bdJ51h841IaShUQKoI0+fPoVAIIBEIqm0zIIFCxASEoKffvoJ7733XoVlwsPDceTIkXrHs3nzZvz222/46KOP0LJlS83y0kdIIpGo3DampqbIyckpt9za2rrcL3t9onOtG3SedYfONSGkoVECaED69OmDfv364auvvsL//ve/Css4OztX+Ou6Nk6dOoUpU6YgPDwcixcv1lpXWnOgUCjKbVdYWFhhzQJjDBzH1SsmXaNzrRt0nnWHzjUhpDYoAdQRW1tbKJVK5ObmQiqVVlpu/vz56Nu3L9asWQMrK6ty6wsKCiCTyWp0TCcnp3LLrl+/juHDh6Ndu3bYvn27VkNt4L9HN8nJyXB3d9dal5ycjG7dupXbZ1ZWllY7IH2jc60bdJ51h841IaShUScQHfH39wdQ0puvKiEhIejbty+WLl1aYY++rVu3an7FV/d6VnR0NAYNGgQHBwccPHiwwsdJnTp1AgBcunRJa/mTJ0+QmJioWV9WTEwMWrduXeXn0iU617pB51l36FwTQhoa1QDqSM+ePQGUfDF26NChyrILFixA37598fPPP5dbV9c2PCkpKRg4cCB4PB4OHz4Me3v7Csu1bdsW/v7++PnnnzFt2jTw+XwAwOrVq8FxnFaDbwCQyWSIjo7G9OnTax1TY6FzrRt0nnWHzjUhpKFRAqgj3t7eaNeuHY4ePYpXX321yrIhISEICQlBZGRkuXV1bcMzaNAgPH78GB999BFOnz6N06dPa9Y5OjpiwIABmvdff/01hg8fjoEDB2Ls2LG4desWvv/+e7z22mvlfqkfPXoUjDGMGDGi1jE1FjrXukHnWXfoXBNCGpzux542XitWrGASiURr6iQA7M033yxX9sSJE5ppnhpiJP/SfVX0CgkJKVd+165drFOnTkwkEjE3Nzf26aefsqKionLlxowZw4KDg+sdX0Ojc60bdJ51h841IaQhUQKoQ9nZ2czGxob9+uuv+g6lQSQnJzNTU1O2e/dufYdSDp1r3aDzrDt0rgkhDYk6geiQpaUlPvroI3z99ddQq9X6DqfeVq5cifbt2xvk4xs617pB51l36FwTQhoSx1g1w70TQgghhJBmhWoACSGEEEKMDCWAhBBCCCFGhhJAQgghhBAjQwkgIYQQQoiRoQSQEEIIIcTIUAJICCGEEGJkKAEkhBBCCDEylAASQgghhBgZSgAJIYQQQowMJYBlDB8+HB4eHjA1NYWzszNefvllPHnyRN9hEUIIIaSe6B6vjRLAMvr164e//voL9+/fx44dOxAdHY2RI0fqOyxCCCGE1BPd47XRXMBV2Lt3L55//nkoFAoIhUJ9h0MIIYSQBmLs93iBvgMwVJmZmdi0aROCgoKqvDAUCgUUCoXWMpFIBJFI1NghEkIIIc2OLu6rNb3HN2f0CPgZs2bNgrm5OWxtbREfH489e/ZUWX7JkiWwtLTUeoWHhyM5OVlHERNCCCHNQ3JyMsLDw8vdV5csWdIg+6/tPb45a/YJ4McffwyO46p83bt3T1N+5syZuHr1Kv755x/w+XxMnDgRVT0lnz17NmQymeYVGRmJyMhISgAJIYSQWkpOTtbcR8veW2fPnl1h+ca+xzdnzb4NYHp6Op4+fVplGW9vb5iYmJRbnpiYCHd3d5w5cwY9e/as0fGuXLmCwMBAXL58GZ07d65TzIQQQogxqu09VNf3+Oak2bcBtLe3h729fZ22VavVAFCuLQIhhBBC9I/u8XXX7BPAmjp//jwuXryI4OBgWFtbIzo6GnPnzoWPj49R/jIghBBCmgu6x5fX7NsA1pSZmRl27tyJ/v37w8/PD1OmTEGHDh0QGRlJPXoJIYSQJozu8eVRDeC/2rdvj+PHj+s7DEIIIYQ0MLrHl0c1gIQQQgghRoYSQEIIIYQQI0MJICGEEEKIkaEEkBBCCCHEyFACSAghhBBiZCgBJIQQQggxMpQAEkIIIYQYGUoACSGEEEKMDCWAhBBCCCFGhhJAQgghhBAjQwkgIYQQQoiRoQSQEEKIlvyMeCgL87SWMcbAGNNTRISQhkYJICGEEI1iuQzJlw4g426UZhljDGk3jyHz4QU9RkYIaUiUABJCCNHIjruBwuxU5CTcQWF2KgBAkZ0CWexNZD26iGK5TM8REkIaAiWAhBBCAJTU/slir8NEYgOlQo7smGtgjCHr8TWoFHIUybORHX9D32ESQhoAJYCEEEIAlNT+FcmzIZRYw0Rig5yEO8iJv4XcpHsQSmwhFFtAFnOdagEJaQYoASSEEAJlQS5ksTegLi5CQUYCivKyoMjJQMq1IyjOl4Hj8cETiKDIyaBaQEKaAYG+AyCEEKJ/HF8Ia5/OYCqlZplapURW9GUIza2gKsoHAAjNrZCfngC01lekhJCGQAkgIYQQ8E1MYevXEwCgVOSDLxSB4/Fh69cDamWxVlme0EQfIRJCGhAlgIQQQjRUxYVIPLMdEpdWsPPrAb6JGHwTsb7DIoQ0MGoDSAghRCM38R7y02ORHXMVyoJcfYdDCGkklAASQggBUFL7lxV9GTyhGMW5T5Edd0uzjmYBIaR5oQTwX7GxsZgyZQq8vLwgFovh4+OD+fPno6ioSN+hEUKITuQm3kNhdgpElg7gm0qQHXsNyoJcxDzJwK6TV1GkVFa/E0IMEN3jy6M2gP+6d+8e1Go11qxZA19fX9y6dQtTp06FXC7HsmXL9B0eIYQ0qtLaP7WyGMV5mWCMQZGVgsyYmziTyEd0UjpaujmivY+rvkMlpNboHl8eJYD/GjRoEAYNGqR57+3tjfv372P16tVGe3EQQoyHWlkMgak5zBw8NctMpNaISc9BbIoaasZw8V4s/DwdYSKgWwdpWugeXx79X1wFmUwGGxubKssoFAooFArN+7y8vMYOixBCGpxQLIV78FitZSq1GmeOXgRjGXBzsEJiWhbux6VSLSBpdHl5ecjJydG8F4lEEIlEDXqMmtzjmzNqA1iJR48e4bvvvsO0adOqLLdkyRJYWlpqXiEhITqKkBBCGld0UjpikjMgEYugVP5XC0htAUljCwkJ0bq3LlmypEH3X9N7fHNmUAmgSqXCli1bMG3aNLzwwgu4efMmgJIsfefOnUhNTa31Pj/++GNwHFfl6969e1rbJCUlYdCgQRg1ahSmTp1a5f5nz54NmUymeUVGRtY6RkIIMUTxqZkQCYUoUqqQk18IM1MT5BcWIT2LnnSQxhUZGal1b509e3aF5Rr7Ht+cccxA+vZnZ2dj0KBBuHDhAiQSCeRyOY4cOYLQ0FCoVCp4enpi4sSJ+OKLL2q13/T0dDx9+rTKMt7e3jAxKRnZ/smTJ+jbty969OiB9evXg8erXY585coVBAYG4vLly+jcuXOttiWEEENSpFQiL1+htYzH42BpLgbHcXqKijRntb2H6voe35wYTBvAjz/+GLdv38bhw4cREBAABwcHzTo+n4+RI0fi4MGDtU4A7e3tYW9vX6OySUlJ6NevHwIDA7Fu3TqjvjAIIcREIICNhcHcJggph+7xdWcwn3737t14++23MWDAgAp/WbZq1QqxsbGNdvykpCT07dsXHh4eWLZsGdLT05GSkoKUlJRGOyYhhBBCGh/d48szmJ92MpkMXl5ela4vLi6GshEbHh85cgSPHj3Co0eP4ObmprXOQJ6SE0IIIaQO6B5fnsHUAPr4+ODKlSuVrv/nn3/Qpk2bRjv+5MmTwRir8EUIIYSQpovu8eUZTAL42muvYe3atdi6davmD8JxHBQKBebMmYO///7bqLtrE0IIIYQ0FIN5BPzuu+/i9u3bGDduHKysrAAA48ePx9OnT6FUKjFt2jRMmTJFv0ESQgghhDQDBpMAchyHX375BZMmTcL27dvx8OFDqNVq+Pj4YPTo0ejTp4++QySEEKPAGINSqYRQKNR3KISQRmIwCWCp4OBgBAcH6zsMQggxSiqVGvuibsDNTooubXz0HQ4hpJEYTBvAmJgY7Nu3r9L1+/bta9RhYAghhACPktJxKyYJp64/QFHxfyMvnLkZjYcJaXqMjBDSkAymBvDDDz9ETk4Ohg0bVuH6H374AVZWVtiyZYuOIyOEEOOgUqlx6V4slCo14lOycDcuGR193ZGSKcOZW9GwkpjB08kGJkKDuXUQQurIYGoAz549iwEDBlS6vn///jh16pQOIyKEEOPyKCkdMclP4WJnCVVxEc7eikZRsRJXHyQgN78QSRnZuBuXrO8wCSENwGASwKysLEil0krXSySSauf7I4QQUjeltX9FxUqoVGqYCDjcehCDqJuPcDvmCewsJRAJ+bh4N07r0TAhpGkymATQw8MDUVFRla4/depUudG7CSGENAyZvAA58kJYmJtCXlgEpYpBkZ+Ho+dvIkdeCHOxCayl5khKL6kFZGqVvkMmhNSDwTTkGDduHBYtWoRu3brhrbfe0kzSrFKp8P3332Pr1q2YM2eOnqMkhJDmycbCHBMH9YBSpUZYWBgSk1Ng59UaQf3CYWljjycZfACAgM/Dw/t3Yf3kJBw7hUNkYQcAUKnVuB3zBC3dHCAWmejzoxBCasBgEsDZs2fj9OnTmDFjBhYvXgw/Pz8AwP3795Geno6+fftSAkgIIY3IXCwCACQnxkFpYgmhpSNsxTzYIBuOYhHat28PK2trKKMjkRP/ACIrJzh26A8AeJyUgSMX7yIvX4Gg9jR8DCGGzmAeAYtEIvzzzz/47bff0K1bN2RkZCAjIwPdunXD2rVrcfToUYhEIn2HSQghzR5PKIKNhx8EJqbIVjCYCTkUZKfjwqnjuH36EGSxNyEQiZETfwuKnAyo1GpcvBeLtKxcXL4fj9z8Qn1/hCYlJydH3yEQI2QwNYAAwOPx8Morr+CVV17RdyiEEGKU4uPjUQw+lOlPkJcaBw8XB2SYO8BJwgcYQ37ibaQUpYOJbWBtooTgzlkoXbsjJjkDLZxtkJqZi5vRSVQLWAtyuRwWFhb6DoMYGYNKAAkhhOjHhQsXsGjRIhw4cACMMc3yHfeu4pJfW4x5fij8PewhLs4E4/hQK+TILihGxqmDuMh/gkK+BE7WEkjNRLh8Px7tvF1QnHQdplZOMHdoob8P1gTk5eXpOwRihAzmETAAHD58GKNHj0aXLl3g4+MDb29vrZePD/2iJISQhrZz50706tULhw4d0kr+SjDEP7iDZV9/hWtXLkPNCcA4PjgwqHkCZCrFSM/KQ3rGU5y+eB3RMQlISk3Hvbu38fTeWWTcOQm1qrjW8QwcOBC2trbgOA7Xrl2rdptffvkFvXv3hrW1NaytrREWFoYLFy5o1hcXF2PWrFlo3749zM3N4eLigokTJ+LJkye1iq0x0BBnRB8Mpgbw66+/xscffwxHR0d069YN7du313dIhBDS7F24cAFjxoyBSqWqIPkrwZgajAHfr9sCyUcfokULT826IhXgKuLKlC4CCjOQcPE+IMqFtKgAeU8ewsK9TY1jksvlCA4OxujRozF16tQabRMREYFx48YhKCgIpqamWLp0KQYOHIjbt2/D1dUV+fn5uHLlCubOnYuOHTsiKysL7777LoYPH45Lly7VOLbGEB0djYCAAGrnTnTKYBLAVatWITQ0FAcPHoRQKNR3OIQQYhQ+//xzMMYqTf6edeDQYbzxxhua90IB4CTRLiNUySHKS0KKXIBMWR7k7G90cvKBQFizBOfll18GgFrN/75p0yat97/++it27NiBY8eOYeLEibC0tMSRI0e0ynz//ffo1q0b4uPj4eHhUeNjNbTCwkJERERg4MCB4Diu+g0IaQAGkwBmZWVh5MiRlPwRQoiOxMfHY//+/TVO/tRqNW7cuIHMzEzY2NhUXIgxmBelg8eKUcwzRaFajdTYOzi45Tf0HjoO1tbWDfgJKpefn4/i4uLK4wQgk8nAcRysrKx0ElNFunTpgsePH0MqlWL79u3o2rWr3mIhtZebmwuZTAa1Wl1unT5/VNSEwSSA3bp1w/379/UdBiGENBlFRUVQKus+LdvBgwdrnPyVYozh7t276NmzZ4Xr+eoimBTngIGDieq/zg2K9Bjs2LEDoaGh8Pb2rnPMNTVr1iy4uLggLCyswvWFhYWYNWsWxo0bp9ceuCkpKcjKygJjDFevXoWFhYVmHFxiuFavXo0VK1bg8ePHlZZRqQx7thyDSQB//PFHDB48GF26dMH48eP1HQ4hhBi0oqIiXLhwoV49SK9fvw6O42qVBHIcB5lMVvlxGYOCOYCDdo2IUiVEcXY2Dhw4gGnTpsHEpGS2kE2bNmHatGmacocOHULv3r1r/2HK+PLLL7FlyxZERETA1NS03Pri4mKMHj0ajDGsXr26XsdqaCdPnkReXh4CAgI0M2IRw/LTTz/hzTffRHh4OF599VXMmTMH7733HkxNTbF+/Xo4OjrinXfe0XeY1TKYBHDMmDFQKpV4+eWXMX36dLi5uYHP52uV4TgO169f11OEhFSMqVXgePzqCxLSgJRKJfLy8mBiYlKnzgPFxcVQKBR1qgE0NTWtMjkpgqTC5TwANjY2UCqVmgRw+PDh6N69u6aMq6trreJ51rJly/Dll1/i6NGj6NChQ7n1pclfXFwcjh8/bnDj7zHGcPnyZcTExKBnz571Ph+k4X333XcIDw/HoUOH8PTpU8yZMwdDhgxBaGgoPvroI3Tp0qVJ9Ow2mATQxsYGtra2aNmypb5DIaTG8lKikfnwIly6DYdAZKbvcIgREolEFdZyVSY/Px937tzB3bt3UVxcu+FZgJIf4n5+frWunbKxsYGzs3O5x2JSqRRSqbTWcVTkq6++wuLFi3H48GF06dKl3PrS5O/hw4c4ceIEbG1tG+S4jSEzMxMHDhyAu7s7unXrZtCxGpvo6Gi8+eabAKDpt1BUVAQAsLS0xGuvvYYff/wRH3zwgd5irAmDSQAjIiL0HQIhtcLUKmQ+uojcJ/eRk3AHNr7lbziEGIrMzEzcunUL0dHRmgbrUqkUnp6eiI+Pr1FNII/HoXXrNrXqyGFpaQkvLy9IpVIUFhYiNze3RrHGx8drxugrbR/u5OQEJycnAMDEiRPh6uqKJUuWAACWLl2KefPmYfPmzWjRogVSUlIAABKJBBKJBMXFxRg5ciSuXLmC/fv3Q6VSacrY2NhoaiQNTUJCAhITE+Hj44PAwEBYWlrqOySjZ2lpqWl7a2FhATMzMyQkJGjWS6VSzbVlyKiBQRmLFy9GUFAQzMzM9NorjDQNeSmPkZ8WB55AhOzHV6BU5Os7JEK0MMaQlJSEw4cPY9euXXj48KEm+ePxeGjZsiU+/PBD8Hi8Gg0/wgEIH9C/dOewUD6FgBVplVEzgLGS/fv4+KBDhw61ruHbu3cvAgICMGTIEADA2LFjERAQgJ9++klTJj4+HsnJyZr3q1evRlFREUaOHAlnZ2fNa9myZQCApKQk7N27F4mJiejUqZNWmTNnztQqvoYSHx8PuVwOAFAoFMjMzKywHGMMjx49wl9//YXjx483iceLhqih7vHt2rXTao7Wo0cPrF69GklJSUhISMCaNWvQqlWrBoi4cRlUApiTk4Mvv/wS4eHhCAgI0IzinpmZiRUrVuDRo0eNevyioiKMGjUK06dPb9TjkKaPqVXIenwZYAxiGxcoZGnISbij77AIAVAyXMujR4+we/du/P3330hMTNSsMzExQYcOHTB69Gj06dMHPXv2xJIlS8Dj8cDjVZwE8ngc+DwOM8eFoI1rSZs5sToPNspkWCoz/jsuA2JygCITKQICAuDi4gIAUBQrUayseY/IyZMna8YmLPtasGCBpkxERATWr1+veR8bG1vlNi1atKhwPWMMffv2rXFsDeHChQsYNmwYWrRogezsbABAQUEBPvnkE/zwww+Vjn9Ymgju2LED+/btw8OHD+vd05Qxhnnz5sHZ2RlisRhhYWF4+PBhldvk5uZixowZ8PT0hFgsRlBQEC5evFjv/Ta2hrrHT5gwAbdu3YJCoQAAfPbZZ7h79y48PDzQokUL3L9/H59//nlDhNyoDOYRcGJiIkJCQpCQkICWLVvi3r17ml5mNjY2WLNmDeLi4rBq1apGi+Gzzz4DAK0vFUIqUlL7Fwue0BTKQjkYOGQ/vgIL9zbUFpDoTVFREe7du4fbt28jP1+7RloikaBdu3Zo1apVufFWQ0ND8dsva/DrT98j6uJ1rcfBHAd08XPDiH7d0cbNEsWqp5DzLGGhegohK4JElYUcvg2EEhuoRRbgqRUoEIjBF5Y8UlWrGc7cT4apkI9OHroZA9CQ7dy5E2PGjKlw8G3GGG7duoVbt25h6tSp6Ny5c6X7SU5ORnJyMs6cOQNfX1+0aVO7R/OlvvrqK3z77bf4/fff4eXlhblz5yI8PBx37typtG3pa6+9hlu3bmHDhg1wcXHBxo0bERYWhjt37mg6rdRlv42toe7xr7zyCl555RXN+169euH27dvYt28f+Hw+Bg4c2CRqAA0mAZw5cyZyc3Nx7do1ODg4wMHBQWv9888/j/379+spusopFArNrwCAJvU2FoqcdPBF5gAYmLoYQjMpGGMozsuiBJDoXG5uLm7fvo0HDx6U69hhZ2eHDh06wNPTs8qOG+06dMLKH39FSkoKxo0bh9zcXNhaSvDF/4bBxVIIFQTgoIapWg4bZTLMVDkoElhAIlShvbMUpt6dEXErATxeMbLlCsSm58DPxQZPsvKQnJUHPo8HDxszGGZLO92oybR7pY/of/nlF8yaNQstWrSocp8KhQK3b9/G7du34enpiZ49e9a4ZzNjDCtXrsSnn36KESNGAAD++OMPODo6Yvfu3Rg7dmy5bQoKCrBjxw7s2bMHffr0AQAsWLAA+/btw+rVqzUzy9R2v8/Ky8tDTk6O5r1IJDKYqfLi4+Nhb28PsVisWebt7Y13330XQMk50vfsMjVhMI+A//nnH7zzzjto06ZNhW1RvL29tRpZGoolS5bA0tJS8woJCdF3SEQHbP16wHvAa/AeMFXzahE6CWJbGrKB6E52djZOnz6Nbdu24fbt21rJn4eHB4YMGYLhw4fDy8urxr12nZycNDc2C3MxJDYOyONboYAvQT5fCjnPEpb8QlhaSOHk5gmJtQOE+SlISk5BqkwOa3NTiAR8PHiShQJFMR48yQJjQJFShehUWa2HnWlOajvt3sGDB2u1/7i4OOzYsQPx8fE1Kh8TE4OUlBStwbItLS3RvXt3nD17tsJtlEolVCpVuVo8sViM06dP13m/zwoJCdG6t5Z29jEEXl5e2LVrV6Xr9+7dCy8vLx1GVDcGkwAWFBTA3t6+0vU16TlWkY8//hgcx1X5unfvXl3DxuzZsyGTyTSvyMjIOu+LNB0cxwPfRKz9EurnsQYxLmq1Gjdv3sTq1atx8uRJxMXFaRIKPp8Pf39/vPTSSxgwYACcnJzqNbdsprwIaSYtkGLihRQTL8gs28C+TW/Y2drARCgEy0sDK8qHsrgA92MTUaRUoViphoDPQ2ZuIa7EpCElOx9W5iJYmomQmClHdn7th55pDkqn3atpm72y0+7VRnFxMQ4fPowbN25UW7a0p6qjo6PWckdHx0p7sUqlUvTs2ROLFi3CkydPoFKpsHHjRpw9e1bTKacu+31WZGSk1r119uzZFZZr7Ht8RapL4IuLi5vEIN4G8wi4TZs2OHnypNaI8GXt3r0bAQEBtd7vBx98gMmTJ1dZpj7TEj1bLS2RVDwAKmmeipUqZGTnwdmOhmYgjau4uBgXLlzA0aNHkZqaqrXO1NQUrVu3RuvWrbUeS9WV0EwKniBLa5mLiwu8vLzAqZVQm/JKuvr+q6hIhaKEYljwBVD/u1xiKsSjlCwoVQz8fzuXyAuLEfu0aTaTMcRp96py5swZqNVqdOrUSbPs2VlXDhw4UOv9AsCGDRvw6quvwtXVFXw+H507d8a4ceNw+fLlOu2vIhKJpEaPshv7Hl8qJydH02EHAJ4+fVphTWt2dja2bNkCZ2fneh+zsRlMAjhjxgxMmjQJHTp0wKhRowD815Pts88+w9mzZ7Fjx45a79fe3r7KmkVC6uPqw3hcvBuLUf0C4WBd8mVVpFQi5kkGfF0dwOcb/q9AYtjy8vJw8uRJREZGlnsSYm5ujrZt26J169YQCBrm67ygSAk7v65QiixRmBINjuPQsmXL/2pzeCbg27TQ2kYKINxBrUn+AEClVuNydBoUxf8lTZZiAficYc+PWhGDnXavGjdu3ECbNm0qnXWltP16amqqVsKSmpqqlTg+y8fHB5GRkZDL5cjJyYGzszPGjBmjSbRKx2qs7X7rQlf3+G+++QYLFy4EUPJ3mTFjBmbMmFFhWcYY9QKujQkTJiAuLg6ffvop5syZAwAYNGgQGGPg8Xj44osv8PzzzzdqDPHx8ZoBSFUqFa5duwYA8PX1pZo9Uk5+YREu3YtDQloWrj5MQHi3tgCA2zHJiLx6H4N7tIOfh5OeoyRNVVpaGo4dO4Zz586V69jh6+uL3r17IyMjAxYWFg2W/AFATGo2JLZOcOILka2Sw9fXt9yjvIoIyv3Y4SO4tXab2JoOBG1o6jvtHgBYW1s3yrR7VZFKpVrT7j076wpjDE5OTjh27JgmMcvJycH58+drNFSKubk5zM3NkZWVhcOHD+Orr74CUNJGrj77bSz1uccPHDgQEokEjDF89NFHGDduXLle2hzHwdzcHIGBgRXORGNoDCYBBIA5c+bg5Zdfxo4dO/Do0SOo1Wr4+PjgxRdfbJAq3OrMmzcPv//+u+Z96SPnEydO6HycKGL4bsUkIS0rF042FrgZnYSAlu6wNDfDpXuxeJIhw8V7cVQLSGqFMYbHjx/j6NGjuHHjxjPDsXAICAhAWFgYWrRogfz8fJw8ebJBj19QpMTD5GwE9+iKtKdZcOvfS1ObQ2o/7V5ZvXr1qlMNYF2m3QMAd3f3apsDlNZkff7552jZsqVmuBYXFxetCpf+/fvjhRdewFtvvQUAOHz4MBhj8PPzw6NHjzBz5kz4+/trhkap6X51rT73+J49e2oexcvlcrz44oto3759o8WqCwaRAObn56N3796YOnUq/ve//+G9997TSxzr16+nMQBJjZTW/pmbmsBaaoaY5Ke4+jABDtYWSM6QoYWzLWKTn+JRUhr8PJzwMCEN0U/SMLBr20oH2yXGS61W4/r16zh69ChiYmK01olEIgQFBaFfv36ws7Nr1DhiUrORU6CAg4U5cgQc5BAhJ78IeYoiZOYWoq27bb06lRgzJycn9O7dG1FRUTXqCMLj8dC6detaj+3HcRw8PDzg4OBQo9rWjz76CHK5HK+//jqys7MRHByMv//+WyvRjY6ORkbGfwN+l3bKSExMhI2NDV566SUsXrxYa3zJmuxX1xrqHj9//vz6B2MADCIBNDMzQ0xMDH2xkCbjTmwykjNkEAr4SEjLglKlwrWHCTA1EcJEKIBELEJmTj4u3ouDp6MtTt94hORMGVq5O8LbhdqkkhIKhQJnz57F8ePHtW6wQMmwGf369UNwcDDMzBp/bMnCf2v/ipUMT3MLoAQPBcXAw+RMZOQWQpavgLO1OWyl9e9kYqymTJmCqKioGtcEDhw4sMb75vP5sLe3h5ubG8RiMQoLC2u0HcdxWLhwoaZ9W0WenZlk9OjRGD16dL3321TU5TNwHIe5c+c2QjQNxyASQKCkvd/hw4cr7QVMiCGRiEXo1kZ7nKfkpzLEpz4Fn8dDbPJTKFVqxKdkIuLqfSSmlySJF+/FoYWTHXg8Dlm5+eDzOFiY0w3V2MhkMkRERODUqVPlZuxwcXFBWFgYunTp0qBt+2rCxUYCB8uSZDM/XwgzMzPIi5RIz8mHSs3wMDkbNhJT+rFeR23btsWSJUs0Q5pUVBNY+rh30qRJNRpI2MzMDM7OznBwcND59WIsyk5BWFOUANbC3LlzMWrUKLz88suYNm0avLy8Kmy/YGNjo4foCNHm7+kEf8+StlGMMXAch5RMGR4lpmuVU6nUuB3zBHweB3srS0QnpSM2JQMejjbYF3UdIqEAo/p1ocfCRuLJkyc4duwYLl68WG5IkdatW6N///5o3bq1XhIsUxMBuvr+195PLpdDZCrGsZvx4HM8SM0EiM/IQUtnK6oFrIfQ0FCsXbsWv/32G06dOlWunWfr1q0xcODAapM/a2truLq61mn6N1I7pbOzNDcGkwC2bVvSg/LOnTvYvHlzpeXqO/E1IQ0pPz0emY8uwjlwCJxsLOFkUzIeoFKlgoDPx63HSUiX5cHO0hw8HgdFUTEu3otDgaIYcSmZ4PM4xCRnwMeVHgs3V4wx3L9/H8eOHcPt27e11vH5fHTt2hWhoaFwc3PTU4QVMzExQeLTXKTL8mFlbgoBn4OsQEG1gA2gbdu2WLFihda0e2KxGDNnzqwyoePz+XBwcICzszPMzc11GDFpjgwmAZw3bx59oZAmhTE1nj48j9zEe5A4esPKu6RHWWJ6Fo5euouhQR3w+EkGREIBcvNLxtsSi0yQnpWL0zmPwOM4qNQMl+7Fwsu5pHH/zceJ8HG1h0RMs4o0dSqVCpcvX8bRo0eRmJiotU4sFqN3797o27cvrKys9BNgNYRCIRKe5oHH4yD79/rlczykyfKRX6SEuUhYzR5IdUqn3cvNzYWJiUmlyZ9AIIC7uzucnJzoMa8BSUpKwsmTJ5GWloaXXnoJbm5uUKlUkMlksLS0BJ/P13eIVarVleTl5VXrJI3jOERHR1dbri7P2AnRJ3lqDOSpMeA4HrKir0Dq1ho8oQgX78biQXwqrtklIKxLa3R/pq1gzJMMHLl8F07WFlAxNaKT0hGTnAGOA45cvIvM3Hz0C/DT06ci9VVQUIDTp0/jxIkTWjMHACVNWEJDQxEUFKTX3pDVic/IgbW5KTq1sEcrZyutdQI+D2YmlIToio2NDVq1aqXVw5boF2MMH3zwAb7//nsolUpwHIf27dvDzc0NeXl5aNGiBRYuXFjpQNGGolb/F4eEhJRLAC9duoTbt2+jTZs28PMruWndv38fd+7cQbt27RAYGFinwGQyGSQSicFn0MQ4MaZG1uMrYGoVOHNb5GYkIDfxLnIlnngQnwpzsQg3ohPRqZU7HG0sUJCZDFn8Tdi2DcHhxFTk5SuQihwAQG6+AhfvxoDj8ZAhy8O1Bwno5OsOa2nj9/wkDSczMxMnTpxAVFRUuR6Ynp6eCAsLQ6dOnQz+O00mV+DSo1Q4WpkhyM8FUrGJvkMySjweD56ennB1daWnYwbm66+/xqpVqzBr1iz0798fAwYM0KyztLTEiy++iB07djSvBPDZ8XN2796N3bt348iRI+jfv7/WuiNHjmD06NFYtGhRjfd/6dIlfPrppzh58iSKiorwzz//IDQ0FBkZGZgyZQree+89GpCZGITS2j++yAxKZRFycnKR+egyrgmVKCxWooWTDWKSn+LagwT07+KPzIfnkJN4D6Y2rnC3t4a1VLv9ToGiCI+S0uHpaIOUzFxce5RAtYBNRHx8PI4ePYorV66UayzeoUMH9O/fH76+vk3mJv4oJRuyfAWKVSpk5BTA3pJ+iOiatbU1fHx8GmReZ9LwfvnlF0ycOBFffPEFnj59Wm59hw4dcOjQIT1EVjv1qsefN28e3n777XLJHwAMGDAAb731Fj799FOMGDGi2n2dOXMGoaGhcHV1xYQJE/Drr79q1tnZ2UEmk2HNmjWUABKDUJCVjJSsfJy9fhU5uXIUyHMR0FWIRGtriERmkMkLIODzcCM6Ea1tgbzkR2DKYsgeX0FI77Hg8YUozs+BSiGHiaUTtkVchkqlgrlYBCupkmoBDZxarcadO3dw9OhRPHjwQGudQCBAjx49EBoa2uRm0ZDJFYhJk8HKXAR5YTEeJGfBzkLcZJLXpo7jOPj6+ja568bYJCQkICgoqNL15ubmyMnJ0WFEdVOvBPDhw4ewtbWtdL2trW2N2v8BwCeffILWrVvj3LlzyM3N1UoAAaBfv35aU7gQoi8XLlzAooVf4sDBg9pDOGyLhE+bThg8ehK8/dtBIjaFgMch7cFFmCqLIbZzR/7TROQ9eQipW2uk3TwBRU46BG2GICE1E8UqNWJTnoKxkjYmd2OTEdTeR4+flDyruLgYFy5cwLFjx5CSkqK1TiKRoE+fPggJCdGab7UpeZSSjfwiJZwszcDn8ZD4NJdqARuZra0tFAoFpFKpZg5dYtgcHByQkJBQ6frLly/XaAxHfavXJKU+Pj5Yt24d8vLyyq3Lzc3F2rVrazyH78WLF/HKK69AJBJV+GvT1dW13BcuIbq2c+dO9OrVC4f+/rvcSP6MMTy+ex0/fPY+Uq8eQ18/e7zQ0Q5mhakQmFlqymVFX4Y8LQZ5yQ9RmJUMYU4cQgP9MSK4I4b0bI+hQe0xrFcHeLnUbNovxhjmzZsHZ2dniMVihIWF4eHDh1Vus3r1anTo0AEWFhawsLBAz549tR5ZZGZm4u2334afnx/EYjE8PDzwzjvvQCaT1eJsNR95eXk4dOgQPv30U2zatEnru8jBwQHjxo3D559/jqFDhzbZ5E9eWIyEjBwwNUNaTj5yChQoUCgRm26cf3Nd2bBhAxYuXKiZK5cYvhdffBE//fQTHj9+rFlWmrf8888/WL9+PUaNGqWv8GqsXjWAn3/+OUaOHAl/f39MnjwZvr6+AEpqBn///XekpqZi27ZtNdqXUCiscrDFpKQkSCSS+oRLSL1cuHABY8aMgUqlqnQap9Jr+KuvloLjgA6OAlizTIjFpjAzM4NYLEZRfg7Sb5+EWlkEvsgcBQk30LFvR/BN6tbe56uvvsK3336L33//XTPpenh4OO7cuVNpT1M3Nzd8+eWXaNmyJRhj+P333zFixAhcvXoVbdu2xZMnT/DkyRMsW7YMbdq0QVxcHP73v//hyZMn2L59e53ibIrS0tJw/PhxnD17FsXFxVrrfH190b9/f7Rv314ze0NTZiLko72nPVRq7Wvbwow6gehCixYt6FF7E/HZZ5/hxIkT6NSpE3r37g2O47B06VLMnTsXZ8+eRUBAAD755BN9h1mteiWAzz//PA4ePIhZs2bhiy++0FrXqVMn/PbbbwgPD6/Rvnr06IHt27dX2GtGLpdj3bp1CAkJqU+4hNTL559/DsZYjebwBICDBw/C63+vo0BtDagA5Ja8rEwVsC2OgdjCFhIzMyhk6chJuANrn9r3mGeMYeXKlVptbf/44w84Ojpi9+7dGDt2bIXbDRs2TOv94sWLsXr1apw7dw5t27ZFu3btsGPHDs16Hx8fLF68GBMmTIBSqWz2Y5FFR0fj2LFjuH79ermZGgICAhAWFoYWLVroL8BGIOTz4ONkpe8wjJKpqSnNctWEWFpa4ty5c1i+fDm2b98OU1NTREZGwsfHB/Pnz8fMmTObRAeeen+LDxw4EAMHDkRKSgri4uIAlAx5UNt2DJ999hlCQkIwZMgQjBs3DgBw/fp1PH78GMuWLUN6errBz6tHmq/4+Hjs37+/xsmfWq3GjRs38DQ7p9wXuzLnMfIV6cjNyUZaIiCEGqmy3XApMoWHl0+txoeLiYlBSkoKwsLCNMssLS3RvXt3nD17ttIEsCyVSoVt27ZBLpejZ8+elZaTyWSwsLBotsmfWq3G9evXcfToUcTExGitE4lE6NmzJ0JDQ2FnV7NH84TUlI2NDdX+NTFisRiffvopPv30U32HUmcN9k3u5ORUr8ar3bt3x8GDBzF9+nRMnDgRAPDBBx8AKKl9OHjwIDp06NAgsRLjVFRUVG7+1ZpQq9XYsWNHjZO/Uowx3L17t1xSVciTQGmq3UBYLefhfkQEcPI0bG1t4erqCg8PD9jb28PEpPJHcKVt0RwdHbWWOzo6Vttm9ubNm+jZsycKCwshkUiwa9cutGnTpsKyGRkZWLRoEV5//fUq99kUKRQKnDt3DsePH0d6uvZczpaWlujbty969+4NMzPqCEEaR1NtN2rs8vLykJubC6lU2iSbqNU7AYyPj8cXX3yBEydOID09Hbt370afPn2QkZGBhQsX4pVXXkFAQEC57XJycmBubq41KGpoaCju37+Pa9eu4eHDh1Cr1fDx8UFgYCD9OiL1UlRUhAsXLlTYYUmlUqGgoAD5+fkoKCjQvMq+v379OjiOq1USyHEcZDIZ8vLyYKbOBQ9q5PEskQdTABXU8hUXACjpQBUbG4uoqCj4+vriueee0ySBmzZtwrRp0zSbHDhwoHYnogw/Pz9cu3YNMpkM27dvx6RJkxAZGVkuCczJycGQIUPQpk2bZjVjj0wmQ2RkJE6dOgW5XK61zsXFBWFhYQgMDKQZGEijM+RZYYi2mzdv4quvvsKRI0e0fjA6ODggPDwcH374Idq1a6fHCGuuXgngnTt30Lt3b6jVanTv3h2PHj3S1LDY2dnh9OnTkMvl+O2338pta21tjQ0bNmD8+PEAgFdffRXTpk1D9+7d0alTJ3Tq1Kk+oRECoKQWLi8vD0+ePMGDBw9QVFSEoqIiyOVyzUuhUFS7HxMTkzrVAJqZmkDAMdip08CDCgq+BCqu+oSC4zjY2NjAxMQESqVSkwAOHz4c3bt315QrjT01NRXOzs6a5ampqdX+P2RiYqLpuBUYGIiLFy9i1apVWLNmjaZMbm4uBg0aBKlUil27djWLZCg5ORnHjh3DhQsXytUI+/v7IywsDK1bt6YfnURnmmuziuZm69atmDx5MhQKBXx8fNCzZ09IJBLk5eXh1q1b+OOPP7B161Zs3LgRL730kr7DrVa9rrqPPvoIVlZWOHfuHDiOg4ODg9b6IUOGYOvWrRVua2JionXjXb9+PcLCwrRuboRUp7i4GNnZ2cjMzERmZiaysrLw9OlTZGVlad4/23uzNkxMTCCRSNC1a1dERETUsgYQCPBxhJTlwJQVggODhVoGmdCh0m2kUilsbW3h4OAAxhhyc3PLrS/7uIgxBicnJxw7dkyT8OXk5OD8+fOYPn16rT6rWq3W+n8yJycH4eHhEIlE2Lt3b5OupWCM4cGDBzh69Chu376ttY7H46FLly4ICwuDm5ubniIkxszQpwckJYM/T5kyBZ6enli/fj169OhRrszZs2cxefJkTJ48Gd27dzf475N6JYAnT57EvHnzYG9vX+F0KB4eHkhKSqpwW39/f/z6669o0aIFLC1LxkiLjY3FlStXqjxm586d6xMyaUIYY8jPz9ckd2WTvNL/1mdcOo7jYGZmBolEAolEAnNz83L/Ltv+7ty5c4iKioJKpap23zyOQxd/V3hJiwBlBtTggYEHC/VT5DFrTS2gQCCAlZUVrK2tNTV+pZ6dT7ayzzBjxgx8/vnnaNmypWYYGBcXFzz//POacv3798cLL7yAt956CwAwe/ZsDB48GB4eHsjNzcXmzZsRERGBw4cPAyhJ/gYOHIj8/Hxs3LgROTk5mpHt7e3tm8wNS6VS4cqVKzh69Gi5gVvFYjGCg4PRt29fWFtb6ylCQtAshhFq7n766ScAJdPcuru7V1imZ8+e+Oeff9CmTRusWbOmVlPh6kO9EkC1Wl1lw+j09HSIRKIK1y1ZsgRjxozR9F7kOA5z586ttKcvYwwcx9Xo5mvs4uPjcezYMU3j1P79+xvkqOQqlUqr9q5sclf675o8nq2MSCSCjY0NbGxsIJVKkZ2dDRsbG1hbW8Pc3Bzm5ua1+uKdMmUKoqKiatYWkOMQPiAMxZwY5upsKDhzMHAwRwHcJGoInHxgaWkJMzOzej9q/OijjyCXy/H6668jOzsbwcHB+Pvvv7Vq7KKjo5GRkaF5n5aWhokTJyI5ORmWlpbo0KEDDh8+rJnU/MqVKzh//jwAaB4Tl4qJidH5ECi1vaYLCgoQFRWFEydOICsrS2udjY0NQkNDERQU1KRrNQkhuhMREYEXX3yx0uSvlKenJ1566SUcO3aseSeAnTt3xoEDB/DGG2+UW6dUKrFly5YKq0kBYNCgQYiJicHFixeRmpqKyZMn4/XXX69yGApStQsXLmDRokU4cOAAGGPg8XhQq9XgOA5Dhw7F3Llz0bVrV53FU1BQUGnN3dOnTyGTyWrdrq4sS0tLTUJXmuiVvre1tYVY/N8cpvn5+Th58iSkUmmdb/pt27bFkiVLMHv2bACo8MdIaUI5adIk2Hh1gEgdB1POFBYmfAgEQvB4puCJVRA4OYLjNUwtGsdxWLhwIRYuXFhpmdjYWK33FbXLLatv3771+ts0lNpe05mZmThx4gSioqLK1aB6eHggLCwMAQEBTaYGkxgHQ/h/jVTtwYMHNRpWCwC6du2qNbOSoapXAjh79mwMHToU06dP15yY1NRUHD16FF988QXu3r2L77//vsJtb9y4AU9PT81A0evWrcOoUaPQv3//+oRktHbu3IkxY8ZoDVRcOisFYwwHDx7EoUOHsHXrVrz44ov1Pp5arYZMJitXa1c22SsoKKjz/oVCYZXJnaWlpV46JISGhmLt2rX47bffcOrUqXKDBLdu3RrPPfccevToAQcHB4hU/oDymUe5fBOAo0c+1anNNd21a1ccPXoUly9fLjejULt27RAWFoaWLVtSxw5ikOgHieGTyWQ1bipiZWWlaTJjyOqVAA4ePBjr16/Hu+++i59//hkAMGHCBDDGYGFhgT/++AN9+vSpcNuAgACtXsCG5IcffsDXX3+NlJQUdOzYEd999x26deum77AqVZMpylQqFTiOw5gxY3DmzJlqawIVCkWVyV1WVlaVU/dVRyqVVprcWVtbQyKRGOzNum3btlixYgVSUlIwbtw45ObmQiwWY+bMmfD19UWrVq3K9Ooz/NHgDVFtrulRo0bhhRde0OqEJhAI0L17d/Tv379e45MSoguG+l3XVOjinq1UKmvcZIjH49VpzFldq3ff85dffhkvvvgijhw5ojV2X3h4eJWDW4rFYuTn52veR0ZGYurUqfUNp962bt2K999/Hz/99BO6d++OlStXIjw8HPfv3y/Xy9lQ1HSKstIyixYtwoYNGypN7jIzM8uNi1YbAoEA1tbWWgle2eTO2tq6ysGNmwonJye4ubkhO/Mp+EITdOzYEZ6envoOq1mozTUNAJcvX8bgwYNhbm6OkJAQhISE0OC6hBgBXd6zDx48WO0A+0DJ91FTUOcEMD8/H+7u7vj4448xc+ZMrR6HNdGxY0esWLECfD5f0wv44sWL1bbPaojHl1VZsWIFpk6dildeeQVASc+fAwcOYO3atfj4448b9dh1UdspylQqFfbt24e33nqrzjdIc3PzSpO70g4XxtKr7ffvvkRB0k3EKu01yR9Tq8Dy0sBJHcGVedRb2pGJVK221zRjDLGxsejXrx9GjBjRLH5cEEJqRpf37M2bN2Pz5s01KtsUvuvrnACamZlBIBDA3Ny8TtuvWrUKI0eOxJQpUwCUnKxVq1Zh1apVlW7T2L2Ai4qKcPnyZU0jf6CkKjcsLAxnz56tcBuFQqHVU7V0pgmlUlmv8edq6vDhw3VqQJyQkAA/P79yy3k8HqysrDSJXWlyV3ZZdUm6SqUyuN7axcXFUCqVkMvlDVY1z9QqIOEGVNmJcLCz1PztWXY8kHoHcO4AzsIFAFBYrMSl6HT4u1rDTvrf+asqKVQoFJrrSBfXkqGo6zUtk8nAcZzRnKvGuKYbW32uaR6P1yBt5VQqVa2brxjbudan0vObl5en1Y5OJBKVG1WkLvfsunp2fvBmgdXD9OnTWb9+/Zhara7T9sXFxezevXssMjKScRzHPv30UxYREVHlqzElJSUxAOzMmTNay2fOnMm6detW4Tbz589nAOhFL3rRi16N+Jo/f36DfM/Td3bTfFX096/LPZv8p15tAMeOHYs33ngD/fr1w9SpU9GiRQuIxeUbvVc2eLNAIICfnx/8/PwwadIkDB06tMnNBDJ79my8//77mvfXrl1DSEgIzp8/X+EcyA1t/fr1eP3112u93S+//IJJkyY1QkSGq6ioqOFq/1RKJF/cBUVmMiC2hjovDbZt+kIgMkfatUMQmFtDmS+DY+AQwMoDW45dQmZOPoQCPl4M6QQPB2vciUnG/rO34Olsi5F9OoHPL//YXCAQGN0jTbqma64hr2ldqes13VDNSubOnYs5c+bUejtjOtf6dPXqVXTv3h2RkZFa01lWNqawIZDL5Vi+fDkmTpyo8zFS66NeCWDfvn01/z516lS59awWgzevW7euPqE0CDs7O/D5fKSmpmotT01NrbQn4bPV0hKJBEDJ/3i6GKYkPDy8ZgMTl8FxHAYOHNgs5nWtjYb8vLlJ94C8dIjNzAGeGiqRCYpT70IlEEFsZgaxtR0K1AVQpj1AYpEFcgqV8PFwQkJaFh4kZaJVC1fcTXoKE5EIabJ8pOUq4O9JvVUBuqZrw9g+b0Pg8/l1epRM51o3SkdQkEgksLCwqLJsXe7ZjSEvLw+fffYZgoODjScBrE/StnDhQnAchzlz5oDH41U5iG2p0tlCGouJiQkCAwNx7NgxTacWtVqNY8eOaabQMjQeHh4YOnQoDh48WKNEm8/nY8iQIQY5M0hTUpSXDaGZJUqeTgB8iTWUBXlQFqaAxxcgPy0WalUxnibH4UKsGHyeEAWKYpiJTPAgMRUnr5kiMS0Lrg5WSH2ai4v3YtHSzQEc1OB4PK3OI8aGrmn9Yowh/XYkzB1awNyhhb7DIaRShnTPrs0PVkPBMT1FzePxwHEcCgoKYGJiUqPqfV1MBbd161ZMmjQJa9asQbdu3bBy5Ur89ddfuHfvHhwdHavd/sqVKwgMDMTly5d1Nm/xxYsXERQUVOWYaUDJ+ePz+TUaB5BUjTEGMO2G5IVZKchLidZaFp9ZgNMJKih5//3WUjOgQFEMtVoNR2spFMVK5OYrMLJvACxSL8BEagv7Nr118jkMFV3T+iNPj0Ni1DaIbVzgHjymwWasIaQmansPre89uyGkpqbC2dkZR48eRWhoqE6O2RDqPQ5gXT3bC6s+gwo3pDFjxiA9PR3z5s1DSkoKOnXqhL///ltnF1JddO3aFVu3btXMmlBRkszn88FxHP766y+6UTYAjuMATvvGKLZ1hdjWVWuZHWNoVaBA2RwmNTMHhy/cRmFRMeSFRSXbioR4EnMXXPYD8E3EsPRoBxNJzUadb47omtYPxhiyoy9DVZSP/PR45CU/hNTVH0ytQsa9M5C6+sHU0jDHQyXGyRDu2Xw+H56enhX2gTBktaoBfPXVV8FxHH7++Wfw+Xy8+uqr1R+A46qdd7Q50UcNYKmLFy9i0aJFmjHUys6bOmzYMHz66ad0o2xgsrwCPExMQ+dWHuDxaj7uU25+IVRlfvQwpkbO9UPIT3kIxtSwb90b9u1CGiPkJoWuad0qqf37CwIzSxTnZcHMzh3uwWMgT32MxHO7YeHmB+cuw5rEGGekadLnPdTY1KoG8Pjx45ovYD6fj+PHj1f7RVCbL4q7d+8iOjoaubm5kEql8PX1hb+/f21CNGpdu3bF3r17ER8fj+PHjyMnJwcWFhYIDQ2l9lGN5MLdGFx9mAArqRi+rjWvGZGaaY+lKE+NQUF6DEQW9lAVFyI77gYsW3Qw6lpAgK5pXSqt/VMXK8ATmEBobg15ehxyk+4jO+4GVAo5cp88hFVmEsxs3fQdLiGknmqVAMbGxlb5vq7WrFmDxYsXIykpqdw6Dw8PzJkzB6+99lqDHMsYeHh4YPLkyfoOo9nLyM7DjegkZOXm4/K9OHg729eqFrAUY2pkPb4CZaEcfJEZAA6K3HTIYm9QLeC/6JpufEV5mSjMTgPHF0KRXdKrkuPx8fTBeShkaRDbukMhS0H24ysQ27hSLSAxajdu3MB3332HK1euQCaTlWvGxnEcoqOjK9naMOitDWCpDz/8ECtWrICNjQ1effVVtGvXDhKJBHl5ebh58yZ2796NadOm4eHDh1i6dKm+wyVE4+rDeOTmF8LT0QbRSel4nJxeq1rAUmplEZSK/P/aVnGAqYUDiuSZDRwxIZUzkdjAtfvzJTPc/Iup1Ui7dRxMrQJPaAKhxJZqAYnRi4iIwKBBg2BtbY0uXbrg6tWrCA0NRWFhIc6ePYu2bdsiMDBQ32FWS68J4IULF7BixQq88MIL+OOPPyqcVm7VqlWYMGECli1bhlGjRqFLly56iJQQbaW1f9ZSM5iZmiA9O6/OtYB8oSk8+4wv19uVM5L5lIlh4DgOptbaY6cVZCZBWZBb8u+MhJJyPB7ykh9SAkiM1rx58+Dt7Y1z586hqKgIDg4O+OSTTxAaGorz589j8ODBTaLCqt4J4KFDh7BixQpNNWhFfUoqG7rlt99+g7OzMzZv3lzpKN/m5ub4888/4e3tjd9++40SQGIQ7sYlQ15QMge0LK8AasaQlJ6NJxnZcHOofbs9jscHPVAjhkZk6QjnrsOAZx5vmUht9BQRIfp35coVfPbZZ7CwsEBWVhaA//Kc7t27Y9q0aZg7dy4GDx6szzCrVa8EcMeOHRg9ejTatm2LsWPHYvXq1Rg/vqQmY8+ePWjZsqVmcMaKnD17FqNGjap2ihdTU1OMGjUKJ06cqE+4hDQYPw8nWEq0u/zzOA62lhI9RURIw+PxBTC399R3GIQYFIFAAKlUCgCwsrKCUChEWlqaZr23tzfu3Lmjr/BqrF4J4JIlS9CtWzecPn0aWVlZWL16NV599VWEhoYiNjYWPXr0gJeXV6XbJyQkoHXr1jU6Vps2bfDHH3/UJ1xCGoyDtRQO1lJ9h0EIIUTHfH198fDhQwAlTSf8/f2xa9cu/N///R8A4MCBAzqdiq6u6tXI6M6dOxg7diz4fL5m/r7i4mIAQIsWLfDGG29U+Rw8JydHk0VXRyKRIDc3tz7hEkIIIYTUy3PPPYc///wTSqUSAPD+++9j586daNmyJVq2bIm9e/di2rRpeo6yevWqATQzM4OJiQmAkmpQkUiE5ORkzXpHR0fExMRUuj1jrFZDCTTFufYIIYQQ0nzMnTsX7777Lvj8ktmgJk2aBD6fjx07doDP52POnDlNYtiqeiWAfn5+Ws+5O3XqhA0bNmDChAlQKpXYvHlztYO1Llu2DH/++We1x6pojEBCCCGEEF0SCoWwtbXVWjZhwgRMmDBBTxHVTb0SwBdffBHffvstli1bBpFIhDlz5mDEiBGwsrICx3GQy+VYu3Ztpdt7eHggMzMTmZk1G++MRv4nhkaRkwFZwm3Y+fcCj6/3YTUJIYQ0Mm9vb6xcuRLDhw+vcP3+/fvxzjvv4PHjxzqOrHbqdMcqLCzEnj17UFxcjE8//RSZmZlwdnbG0KFDERERgZ07d4LP52PIkCHo169fpftpqJlECNEHxhgyH16ALP4WTC0dYeFG0xYSQkhzFxsbi7y8vErX5+XlIS4uTocR1U2tE8C0tDQEBQUhJiZG04ZPLBZj9+7dCAsLQ+/evdG7d+/GiJUQg1KYlYKcxHtQKfKRFX0ZEmdfqgUkhBAjUFX/hYsXL8LKykp3wdRRrXsBL1q0CLGxsXjvvfewf/9+fPPNNxCLxU2ixwshDYUxhuyYq1AV5UNs54GCjHjkJT/Sd1iEEEIawapVq+Dt7Q1vb29wHIcZM2Zo3pd92draYuXKlXjuuef0HXK1al1d8c8//2DixIlYtmyZZpmjoyPGjx+P+/fvw8/Pr0EDJMQQldb+mUhswOMLAY6jWkBCCGmmHBwc0LZtWwAlj4BdXV3h6uqqVYbjOJibmyMwMBBvvPGGPsKslVrfqeLj4zFr1iytZcHBwWCMITU1lRJAYhTyUh6BqZUozpehOF8GACjKy0Rh5hOY2VNnJUIIaU7GjRuHcePGAQD69euHTz/9FP3799dzVPVT6wRQoVDA1NRUa1np+9JBEQlp7qy8OsHMzl17IcfB1MZFPwERQgjRieYyLW2dnlXFxsbiypUrmvcyWUkNyMOHDyts+Ni5c+e6RUeIgRKKpRCKaSo4Qghp7k6ePFmn7fr06dPAkTQsjtVyeg0ej1dh75eKZvUoXaZSqeoXZRNy5coVBAYG4vLly5T4EkIIIbVgiPfQZ/Oe6mYxayq5T61rANetW9cYcQAADh8+jN9++w2PHz9GVlZWuanfOI5DdHR0ox2fEEIIIaSs5vLI91m1TgAnTZrUGHHg66+/xscffwxHR0d069YN7du3b5TjEEIIIYTUVEhIiL5DaBQGM17FqlWrEBoaioMHD0IoFOo7HEIIIYSQKiUnJyMtLQ2+vr4wNzfXdzi1UuuBoBtLVlYWRo4cSckfIYQQQgzanj174O/vDzc3N3Tu3Bnnz58HAGRkZCAgIAC7d+/Wb4A1YDAJYLdu3XD//n19h4GTJ09i2LBhcHFxAcdxTeKPSAghhBgjfdyz9+3bhxdffBF2dnaYP3++Vn8FOzs7uLq6Nmp/iYZiMAngjz/+iJ07d2Lz5s16jUMul6Njx4744Ycf9BoHIYQQQqqmj3v2woUL0adPH5w+fRpvvvlmufU9e/bE1atXdRZPXRlMG8AxY8ZAqVTi5ZdfxvTp0+Hm5gY+n69VhuM4XL9+vVHjGDx4MAYPHtyoxyCEEEJI/enjnn3r1i2sWLGi0vWOjo5IS0vTYUR1YzAJoI2NDWxtbdGyZUt9h1IrCoUCCoVC8z4vL0+P0RBCCCFNX15eHnJycjTvRSIRRCKRHiP6j5mZGeRyeaXrHz9+DFtbWx1GVDcGkwBGREToO4Q6WbJkCT777DN9h0EIIYQ0G88OvTJ//nwsWLBAP8E8o1+/fvj9998xY8aMcutSUlLwyy+/YOjQoboPrJYMpg1gUzV79mzIZDLNKzIyUt8hEUIIIU1aZGSk1r119uzZ+g5JY/HixUhMTETXrl2xZs0acByHw4cP49NPP0X79u3BGMP8+fP1HWa1DKYGsFRxcTHu3bsHmUwGtVpdbr2hza33bLW0RCLRYzSEEEJI0yeRSGBhYaHvMCrk5+eH06dP491338XcuXPBGMPXX38NAOjbty9++OEHtGjRQr9B1oDBJIBqtRqzZ8/Gjz/+iPz8/ErLGfrceoQQQghp3tq2bYujR48iKysLjx49glqthre3N+zt7fUdWo0ZTAL4xRdf4Ouvv8a0adMQHByMl19+GUuXLoWVlRV+/PFHcByHr776qtHjyMvLw6NHjzTvY2JicO3aNdjY2MDDw6PRj08IIYSQmtH1PVuhUGDjxo34559/EB0djdzcXEilUvj6+mLQoEEYP348TExMGvy4jYIZCB8fHzZmzBjGGGMZGRmM4zh27NgxxhhjCoWCde7cmc2ePbvR4zhx4gQDUO41adKkGm1/+fJlBoBdvny5cQMlhBBCmpna3kPre8+ujRs3bjAvLy/G4/EYx3HMysqKubm5MSsrK8ZxHOPxeMzX15fduXOnwY/dGAymE0hiYiJCQ0MBQNOmrrCwEABgYmKCCRMmYMOGDY0eR9++fcEYK/dav359ox+bEEIIITWnq3t2Xl4ehg8fjtTUVCxevBgJCQnIysrS+u/nn3+OJ0+eYNiwYVUOE2MoDCYBtLW11YyhV9r48/Hjx1plsrKy9BEaIYQQQozYunXrEB8fjwMHDuDjjz+Gq6ur1npXV1fMnj0b+/btQ0xMTJOoNDKYBDAgIAAXL17UvO/Xrx9WrlyJqKgonDp1Ct9++y06duyoxwgJIYQQYowOHDiAgQMHom/fvlWWCw0NxYABA7Bv3z7dBFYPBpMAvv7661qzaixevBjZ2dno06cPQkJCkJOTg+XLl+s5SkIIIYQYm5s3b1ab/JUKDQ3FzZs3GzegBmAwvYCHDx+O4cOHa963adMG0dHRiIiIAJ/PR1BQEGxsbPQYISGEEEKMUWZmJpycnGpU1tHREZmZmY0cUf0ZTAJYEUtLS4wYMULfYRBCCCHEiCkUCgiFwhqVFQgEKCoqauSI6s+gEkCVSoVt27bhxIkTSEtLw8KFC9G+fXvIZDIcO3YMvXr1gqOjo77DJIQQQoiRiY2NxZUrV6otFxMTo4No6s9gEsDs7GwMGjQIFy5cgEQigVwux9tvvw2gpFfwO++8g4kTJ+KLL77Qc6SEEEIIMTZz587F3Llzqy3HGAPHcTqIqH4MJgH8+OOPcfv2bRw+fBgBAQFwcHDQrOPz+Rg5ciQOHjxICSAhhBBCdGrdunX6DqHBGUwCuHv3brz99tsYMGAAnj59Wm59q1atmsS4OoQQQghpXiZNmqTvEBqcwQwDI5PJ4OXlVen64uJiKJVKHUZECCGEENI8GUwC6OPjU2Xjyn/++Qdt2rTRYUSEEEIIIc2TwSSAr732GtauXYutW7eCMQYA4DgOCoUCc+bMwd9//41p06bpOUpCCCGEkKbPYNoAvvvuu7h9+zbGjRsHKysrAMD48ePx9OlTKJVKTJs2DVOmTNFvkIQQQgghzYDBJIAcx+GXX37BpEmTsH37djx8+BBqtRo+Pj4YPXo0+vTpo+8QCSGEEEKaBYNJAEsFBwcjODhY32EQQgghhDRbBtMGkBBCCCGE6IZeawCHDx9eq/Icx2HPnj2NFA0hhBBCiHHQawK4f/9+mJqawsnJSdPztypNYWoVQgghhBBDp9cE0NXVFUlJSbCzs8P48eMxduxYODk56TMkQgghhJBmT69tABMSEnDixAkEBARg0aJFcHd3R1hYGNatW4fc3Fx9hkYIIYQQ0mzpvRNISEgI1qxZg5SUFGzfvh22trZ466234ODggBdffBHbt2+HQqHQd5iEEEIIIc2G3hPAUkKhECNGjMDWrVuRmpqqSQrHjBmDr776St/hEUIIIYQ0GwaTAJZSKBQ4fPgw9uzZg6tXr8LU1BQtWrTQybGXLFmCrl27QiqVwsHBAc8//zzu37+vk2MTQgghpObonl0/BpEAqtVqHD58GJMnT4ajoyPGjRuHgoIC/PLLL0hLS8PLL7+skzgiIyPx5ptv4ty5czhy5AiKi4sxcOBAyOVynRyfEEIIITVD9+z60Wsv4DNnzmDz5s3Ytm0bnj59ih49euCLL77A6NGjYWdnp/N4/v77b63369evh4ODAy5fvkxT0RFCCCEGhO7Z9aPXBDA4OBhisRjPPfccxo0bp3nUGx8fj/j4+Aq36dy5s87ik8lkAAAbG5tKyygUCq1OKnl5eY0eFyGEENKc5eXlIScnR/NeJBJBJBJVuU1N7tnkPxyryQjMjYTH++8JdHWDPDPGwHEcVCpVY4cFoOSx9PDhw5GdnY3Tp09XWm7BggX47LPPyi2/fPmyTpNVQgghpKm7cuUKAgMDyy2fP38+FixYUOl2Nb1nk//otQZw3bp1+jx8ld58803cunWr2gtp9uzZeP/99zXvr127hpCQkMYOjxBCCGm2IiMj0alTJ8376mr/anrPJv/RawI4adIkfR6+Um+99Rb279+PkydPws3Nrcqyz1ZLSySSxg6PEEIIadYkEgksLCxqVLY292zyH70mgIaGMYa3334bu3btQkREBLy8vPQdEiGEEEIqQPfs+qEEsIw333wTmzdvxp49eyCVSpGSkgIAsLS0hFgs1nN0hBBCCClF9+z6MYhxAA3F6tWrIZPJ0LdvXzg7O2teW7du1XdohBBCCCmD7tn1QzWAZeixQzQhhBBCaoHu2fVDNYCEEEIIIUaGEkBCCCGEECNDCSAhhBBCiJGhBJAQQgghxMhQAkgIIYQQYmQoASSEEEIIMTKUABJCCCGEGBlKAAkhhBBCjAwlgIQQQgghRoYSQEIIIYQQI0MJICGEEEKIkaEEkBBCCCHEyFACSAghhBBiZCgBJIQQQggxMpQAEkIIIYQYGUoACSGEEEKMDCWAhBBCCCFGhhJAQgghhBAjQwkgIYQQQoiRoQSQEEIIIcTIUAJICCGEEGJkKAEkhBBCCDEylAASQgghhBgZSgDLWL16NTp06AALCwtYWFigZ8+eOHTokL7DIoQQQsgz6J5dP5QAluHm5oYvv/wSly9fxqVLlxAaGooRI0bg9u3b+g6NEEIIIWXQPbt+BPoOwJAMGzZM6/3ixYuxevVqnDt3Dm3bttVTVIQQQgh5Ft2z64cSwEqoVCps27YNcrkcPXv2rLScQqGAQqHQvM/Ly9NFeDWSnJyM5ORkfYfR7Dk7O8PZ2VnfYRgFuqZ1h65rok95eXnIycnRvBeJRBCJRJWWr+k9m5TBiJYbN24wc3NzxufzmaWlJTtw4ECV5efPn88AaL1CQkLYkydPdBRxxQoLC1lISEi52OjV8K+QkBBWWFio17+3MaBrmq7r5qiwsJDNnz+fzvW/njx5UuH/5/Pnz6+wfG3v2eQ/HGOMgWgUFRUhPj4eMpkM27dvx6+//orIyEi0adOmwvLP1gAC1f9S0YWcnBxYWloiMjISEolEr7E0Z3l5eQgJCYFMJoOFhYW+w2nW6JrWHbqudaf0uqZz/Z/a3Fdre88m/6EEsBphYWHw8fHBmjVr9B1KrdCXim7QedYdOte6Q+dad+hcN6ymes/WB+oFXA21Wl3ulwghhBBCDA/ds2uOOoGUMXv2bAwePBgeHh7Izc3F5s2bERERgcOHD+s7NEIIIYSUQffs+qEEsIy0tDRMnDgRycnJsLS0RIcOHXD48GEMGDBA36HVmkgkwvz58/XeFrG5o/OsO3SudYfOte7Qua675nTP1gdqA0gIIYQQYmSoDSAhhBBCiJGhBJAQQgghxMhQAkgIIYQQYmQoASSEEEKqEBERAY7jEBERoe9Q6o3jOCxYsEDfYRADQAkgaTbWr18PjuM0L1NTU7i4uCA8PBzffvstcnNz9R1iOX/99Rc4jsOuXbvKrevYsSM4jsOJEyfKrfPw8EBQUJAuQiQGoCle24B23KdPny63njEGd3d3cByHoUOHNujxGvs8bd68GStXrmyw/ZWKjY3V+gxCoRB2dnYICgrCJ598gvj4+AY93pkzZ7BgwQJkZ2c36H6J4aMEkDQ7CxcuxIYNG7B69Wq8/fbbAIAZM2agffv2uHHjhp6j0xYcHAwA5W6OOTk5uHXrFgQCAaKiorTWJSQkICEhQbMtMR5N6douy9TUFJs3by63PDIyEomJiQ0+BIouzlNjJYClxo0bhw0bNuC3337D3Llz4e3tjZUrV6J169bYsmVLgx3nzJkz+OyzzygBNEI0DiBpdgYPHowuXbpo3s+ePRvHjx/H0KFDMXz4cNy9exdisbjS7eVyOczNzXURKlxcXODl5VUuATx79iwYYxg1alS5daXvKQE0Pk3p2i7rueeew7Zt2/Dtt99CIPjvtrN582YEBgYiIyOjQY9X3/NkCDp37owJEyZoLYuLi8PAgQMxadIktG7dGh07dtRTdKQ5oBpAYhRCQ0Mxd+5cxMXFYePGjZrlkydPhkQiQXR0NJ577jlIpVL83//9HwCgRYsWmDx5crl99e3bF3379tVaFhcXh+HDh8Pc3BwODg547733cPjw4Rq1GwoODsbVq1dRUFCgWRYVFYW2bdti8ODBOHfuHNRqtdY6juPQq1ev2p8I0uwY8rVdaty4cXj69CmOHDmiWVZUVITt27dj/Pjxtf7MdVHZebp37x5GjhwJGxsbmJqaokuXLti7d2+V++rbty8OHDiAuLg4zaPaFi1aACj5XPPmzUNgYCAsLS1hbm6O3r17V9iUo7Y8PT2xfv16FBUV4auvvtJal52djRkzZsDd3R0ikQi+vr5YunSp1nfHsxYsWICZM2cCALy8vDSfJTY2FgCwbt06hIaGwsHBASKRCG3atMHq1avr/TmIYaAEkBiNl19+GQDwzz//aC1XKpUIDw+Hg4MDli1bhpdeeqlW+5XL5QgNDcXRo0fxzjvvYM6cOThz5gxmzZpVo+2Dg4NRXFyM8+fPa5ZFRUUhKCgIQUFBkMlkuHXrltY6f39/2Nra1ipO0nwZ6rVdqkWLFujZsyf+/PNPzbJDhw5BJpNh7NixtdpXfTx7nm7fvo0ePXrg7t27+Pjjj7F8+XKYm5vj+eefr7Bdbqk5c+agU6dOsLOzw4YNG7BhwwbN4+CcnBz8+uuv6Nu3L5YuXYoFCxYgPT0d4eHhuHbtWr0/Q8+ePeHj46OVTOfn5yMkJAQbN27ExIkT8e2336JXr16YPXs23n///Ur39eKLL2LcuHEAgG+++UbzWezt7QEAq1evhqenJz755BMsX74c7u7ueOONN/DDDz/U+3MQ/aNHwMRouLm5wdLSEtHR0VrLFQoFRo0ahSVLltRpv2vWrMHjx4+xe/dujBgxAgAwbdo0BAQE1Gj7su0A+/btC6VSifPnz2PSpEnw8fGBo6MjTp8+jQ4dOiA3Nxc3b97Eq6++WqdYSfNkqNd2WePHj8fs2bNRUFAAsViMTZs2ISQkBC4uLnWKrS6ePU/vvvsuPDw8cPHiRU07xDfeeAPBwcGYNWsWXnjhhQr3M2DAALi6uiIrK6vcY1pra2vExsbCxMREs2zq1Knw9/fHd999h99++63en6Ndu3bYs2cPcnJyYGFhgRUrViA6OhpXr15Fy5YtAZT8nVxcXPD111/jgw8+gLu7e7n9dOjQAZ07d8aff/6J559/XlOLWSoyMlLrUflbb72FQYMGYcWKFXjzzTfr/TmIflENIDEqEomkwp6A06dPr/M+//77b7i6umL48OGaZaamppg6dWqNtm/dujVsbW01bfuuX78OuVyu6eUbFBSk6Qhy9uxZqFQqav9HyjHEa7us0aNHo6CgAPv370dubi7279+vs8e/ZZWep8zMTBw/fhyjR49Gbm4uMjIykJGRgadPnyI8PBwPHz5EUlJSrffP5/M1yZ9arUZmZiaUSiW6dOmCK1euNNhnAKD5e2/btg29e/eGtbW15nNkZGQgLCwMKpUKJ0+erNNxyiZ/MpkMGRkZCAkJwePHjyGTyer/QYheUQ0gMSp5eXlwcHDQWiYQCODm5lbnfcbFxcHHxwccx2kt9/X1rdH2HMchKCgIJ0+ehFqtRlRUFBwcHDTbBwUF4fvvvwcATSJICSB5liFe22XZ29sjLCwMmzdvRn5+PlQqFUaOHFnn2Oqq9Dw9evQIjDHMnTsXc+fOrbBsWloaXF1da32M33//HcuXL8e9e/dQXFysWe7l5aX5d3p6OlQqlea9RCLRJHY1+QwAIJVKAQAPHz7EjRs3NI9uK/ocdREVFYX58+fj7NmzyM/P11onk8lgaWlZp/0Sw0AJIDEaiYmJkMlk5W5eIpEIPF75yvBnb3qlVCoV+Hx+g8YWHByMffv24ebNm5r2f6WCgoIwc+ZMJCUl4fTp03BxcYG3t3eDHp80bYZ8bZc1fvx4TJ06FSkpKRg8eDCsrKwa7VgVKXueSjtHfPjhhwgPD6+wfF0S3Y0bN2Ly5Ml4/vnnMXPmTDg4OIDP52PJkiVaj+i7du2KuLg4zfv58+fXeIDmW7duwcHBARYWFgBKahoHDBiAjz76qMLyrVq1qvXniI6ORv/+/eHv748VK1bA3d0dJiYmOHjwIL755psqO5eQpoESQGI0NmzYAACVftk/y9rausKxseLi4rQSME9PT9y5cweMMa0b66NHj2ocW9l2gFFRUZgxY4ZmXWBgIEQiESIiInD+/Hk899xzNd4vMQ6GfG2X9cILL2DatGk4d+4ctm7dWqd91EfZ81T6OYVCIcLCwmq9r8qS6O3bt8Pb2xs7d+7UKjN//nytcps2bdLq+V/TH3Vnz55FdHS0VttDHx8f5OXlNejn2LdvHxQKBfbu3QsPDw/N8obozUwMA7UBJEbh+PHjWLRoEby8vDRDYVTHx8cH586dQ1FRkWbZ/v37kZCQoFUuPDwcSUlJWkNHFBYW4pdffqlxfF26dIGpqSk2bdqEpKQkrRpAkUiEzp0744cffoBcLqfHv0SLoV/bZUkkEqxevRoLFizAsGHD6rSPunr2PDk4OKBv375Ys2YNkpOTy5VPT0+vcn/m5uYVtoMrrUFljGmWnT9/HmfPntUq16tXL4SFhWleNUkA4+LiMHnyZJiYmGiGbwFK2leePXsWhw8fLrdNdnY2lEpllZ+jtFx1n0Mmk2HdunXVxkmaBqoBJM3OoUOHcO/ePSiVSqSmpuL48eM4cuQIPD09sXfvXpiamtZoP6+99hq2b9+OQYMGYfTo0YiOjsbGjRvh4+OjVW7atGn4/vvvMW7cOLz77rtwdnbGpk2bNMep7Bd2WSYmJujatStOnToFkUiEwMBArfVBQUFYvnw5AGr/Z8ya4rX9rEmTJtV6m9qq6Xn64YcfEBwcjPbt22Pq1Knw9vZGamoqzp49i8TERFy/fr3SYwQGBmLr1q14//330bVrV0gkEgwbNgxDhw7Fzp078cILL2DIkCGIiYnBTz/9hDZt2mja7tXElStXsHHjRqjVamRnZ+PixYvYsWMHOI7Dhg0b0KFDB03ZmTNnYu/evRg6dCgmT56MwMBAyOVy3Lx5E9u3b0dsbCzs7Owq/RxAydA2Y8eOhVAoxLBhwzBw4ECYmJhg2LBhmDZtGvLy8vDLL7/AwcGhwoSZNEGMkGZi3bp1DIDmZWJiwpycnNiAAQPYqlWrWE5OTrltJk2axMzNzSvd5/Lly5mrqysTiUSsV69e7NKlSywkJISFhIRolXv8+DEbMmQIE4vFzN7enn3wwQdsx44dDAA7d+5cjeKfPXs2A8CCgoLKrdu5cycDwKRSKVMqlTXaH2k+muq1XRr3xYsXqyzn6enJhgwZUmWZmqjLeYqOjmYTJ05kTk5OTCgUMldXVzZ06FC2fft2TZkTJ04wAOzEiROaZXl5eWz8+PHMysqKAWCenp6MMcbUajX74osvmKenJxOJRCwgIIDt37+fTZo0SVOmKjExMVqfQSAQMBsbG9a9e3c2e/ZsFhcXV+F2ubm5bPbs2czX15eZmJgwOzs7FhQUxJYtW8aKioo05QCw+fPna227aNEi5urqyng8HgPAYmJiGGOM7d27l3Xo0IGZmpqyFi1asKVLl7K1a9dqlSFNF8dYmfpdQkiDWblyJd577z0kJibWqSchIYaKrm1Cmj5KAAlpAKWD25YqLCxEQEAAVCoVHjx4oMfICKkfurYJaZ6oDSAhDeDFF1+Eh4cHOnXqBJlMho0bN+LevXvYtGmTvkMjpF7o2iakeaIEkJAGEB4ejl9//RWbNm2CSqVCmzZtsGXLFowZM0bfoRFSL3RtE9I80SNgQgghhBAjQ+MAEkIIIYQYGUoACSGEEEKMDCWAhFQjNjYWHMdh/fr1+g6FkAZB1zQhhBJAQgghhBAjQ51ACKkGYwwKhQJCoVAzPyYhTRld04QQSgAJIYQQQowMPQImRmHBggXgOA4PHjzAhAkTYGlpCXt7e8ydOxeMMSQkJGDEiBGwsLCAk5MTli9frtm2ovZSkydPhkQiQVJSEp5//nlIJBLY29vjww8/hEql0pSLiIgAx3GIiIjQiqeifaakpOCVV16Bm5sbRCIRnJ2dMWLECMTGxjbSWSFNGV3ThJD6oASQGJUxY8ZArVbjyy+/RPfu3fH5559j5cqVGDBgAFxdXbF06VL4+vriww8/xMmTJ6vcl0qlQnh4OGxtbbFs2TKEhIRg+fLl+Pnnn+sU20svvYRdu3bhlVdewY8//oh33nkHubm5iI+Pr9P+iHGga5oQUieMECMwf/58BoC9/vrrmmVKpZK5ubkxjuPYl19+qVmelZXFxGIxmzRpEmOMsZiYGAaArVu3TlNm0qRJDABbuHCh1nECAgJYYGCg5v2JEycYAHbixAmtcs/uMysriwFgX3/9dcN8YNLs0TVNCKkPqgEkRuW1117T/JvP56NLly5gjGHKlCma5VZWVvDz88Pjx4+r3d///vc/rfe9e/eu0XbPEovFMDExQUREBLKysmq9PTFedE0TQuqCEkBiVDw8PLTeW1pawtTUFHZ2duWWV3fTMjU1hb29vdYya2vrOt3sRCIRli5dikOHDsHR0RF9+vTBV199hZSUlFrvixgXuqYJIXVBCSAxKhUNeVHZMBismg7yNRk+g+O4CpeXbVRfasaMGXjw4AGWLFkCU1NTzJ07F61bt8bVq1erPQ4xXnRNE0LqghJAQhqRtbU1ACA7O1treVxcXIXlfXx88MEHH+Cff/7BrVu3UFRUpNV7kxB9o2uakOaBEkBCGpGnpyf4fH653pc//vij1vv8/HwUFhZqLfPx8YFUKoVCoWj0OAmpKbqmCWkeBPoOgJDmzNLSEqNGjcJ3330HjuPg4+OD/fv3Iy0tTavcgwcP0L9/f4wePRpt2rSBQCDArl27kJqairFjx+opekLKo2uakOaBEkBCGtl3332H4uJi/PTTTxCJRBg9ejS+/vprtGvXTlPG3d0d48aNw7Fjx7BhwwYIBAL4+/vjr7/+wksvvaTH6Akpj65pQpo+mgqOEEIIIcTIUBtAQgghhBAjQwkgIYQQQoiRoQSQEEIIIcTIUAJICCGEEGJkKAEkhBBCCDEylACSJi8iIgIcxyEiIsIg4ti+fbte4yDNB13bhJDGQgkgMVjr168Hx3Gal6mpKVq1aoW33noLqamp+g5PLy5cuACO4/DNN9+UWzdixAhwHId169aVW9enTx+4urrqIkRSA3RtV6w00eQ4Dhs3bqywTK9evcBxnNaYg4SQ2qMEkBi8hQsXYsOGDfj+++8RFBSE1atXo2fPnsjPz9d3aDrXuXNnmJmZ4fTp0+XWnTlzBgKBAFFRUVrLi4qKcPHiRfTq1UtXYZIaomu7Yqampti8eXO55bGxsThz5gxMTU31EBUhzQvNBEIM3uDBg9GlSxcAwGuvvQZbW1usWLECe/bswbhx4/QcnW4JBAJ07969XJJ3//59ZGRkYPz48eWSw8uXL6OwsBDBwcG6DJXUAF3bFXvuueewd+9eZGRkwM7OTrN88+bNcHR0RMuWLZGVlaXHCAlp+qgGkDQ5oaGhAICYmJhKy5w6dQqjRo2Ch4cHRCIR3N3d8d5776GgoKBc2Xv37mH06NGwt7eHWCyGn58f5syZo1UmKSkJr776KhwdHSESidC2bVusXbu2wmOrVCp88skncHJygrm5OYYPH46EhIRy5bZt24bAwECIxWLY2dlhwoQJSEpKqvbzBwcHIzU1FY8ePdIsi4qKgoWFBV5//XVNMlh2Xel2xLAZ+7VdasSIERCJRNi2bZvW8s2bN2P06NHg8/k13hchpGJUA0ianOjoaACAra1tpWW2bduG/Px8TJ8+Hba2trhw4QK+++47JCYmat1Ubty4gd69e0MoFOL1119HixYtEB0djX379mHx4sUAgNTUVPTo0QMcx+Gtt96Cvb09Dh06hClTpiAnJwczZszQOvbixYvBcRxmzZqFtLQ0rFy5EmFhYbh27RrEYjGAkjZgr7zyCrp27YolS5YgNTUVq1atQlRUFK5evQorK6tKP1tpInf69Gn4+voCKEnyevToge7du0MoFOLMmTMYPny4Zp1UKkXHjh1rd6KJzhn7tV3KzMwMI0aMwJ9//onp06cDAK5fv47bt2/j119/xY0bN2pzWgkhFWGEGKh169YxAOzo0aMsPT2dJSQksC1btjBbW1smFotZYmIiY4yxEydOMADsxIkTmm3z8/PL7W/JkiWM4zgWFxenWdanTx8mlUq1ljHGmFqt1vx7ypQpzNnZmWVkZGiVGTt2LLO0tNQcqzQOV1dXlpOToyn3119/MQBs1apVjDHGioqKmIODA2vXrh0rKCjQlNu/fz8DwObNm1flecnJyWF8Pp9NmTJFs8zPz4999tlnjDHGunXrxmbOnKlZZ29vzwYMGFDlPolu0bVdsdLjbNu2je3fv59xHMfi4+MZY4zNnDmTeXt7M8YYCwkJYW3btq1yX4SQqtEjYGLwwsLCYG9vD3d3d4wdOxYSiQS7du2qsldraW0EAMjlcmRkZCAoKAiMMVy9ehUAkJ6ejpMnT+LVV1+Fh4eH1vYcxwEAGGPYsWMHhg0bBsYYMjIyNK/w8HDIZDJcuXJFa9uJEydCKpVq3o8cORLOzs44ePAgAODSpUtIS0vDG2+8odWYfciQIfD398eBAweqPB9SqRQdOnTQtPXLyMjA/fv3ERQUBKCkl2TpY98HDx4gPT2dHv8aKLq2Kzdw4EDY2Nhgy5YtYIxhy5YtRt0ukpCGRo+AicH74Ycf0KpVKwgEAjg6OsLPzw88XtW/XeLj4zFv3jzs3bu3XGNxmUwGAHj8+DEAVDmcRHp6OrKzs/Hzzz/j559/rrBMWlqa1vuWLVtqvec4Dr6+voiNjQUAxMXFAQD8/PzK7cvf37/CHr7PCg4OxnfffYeMjAycOXMGfD4fPXr0AAAEBQXhxx9/hEKhoPZ/Bo6u7coJhUKMGjUKmzdvRrdu3ZCQkIDx48fXeHtCSNUoASQGr1u3bpqekjWhUqkwYMAAZGZmYtasWfD394e5uTmSkpIwefJkqNXqGu+rtOyECRMwadKkCst06NChxvtrKKUJYFRUFM6cOYP27dtDIpEAKEkAFQoFLl68iNOnT0MgEGiSQ2JY6Nqu2vjx4/HTTz9hwYIF6NixI9q0aaPXeAhpTigBJM3OzZs38eDBA/z++++YOHGiZvmRI0e0ynl7ewMAbt26Vem+7O3tIZVKoVKpEBYWVqPjP3z4UOs9YwyPHj3S3Ew9PT0BlAzdUtrrs9T9+/c166tStiPI2bNntcb4c3FxgaenJ6KiohAVFYWAgACYmZnVKHZi2Izh2i4rODgYHh4eiIiIwNKlS2u1LSGkatQGkDQ7pUNEMMY0yxhjWLVqlVY5e3t79OnTB2vXrkV8fLzWutJt+Xw+XnrpJezYsaPCm2l6enq5ZX/88Qdyc3M177dv347k5GQMHjwYANClSxc4ODjgp59+gkKh0JQ7dOgQ7t69iyFDhlT7GV1cXODl5YVjx47h0qVLmvZ/pYKC/r+9O3ZJLQzjOP5ziBJCQkJchMTAaLNFkAMNujmIo0SDw2lxEloawjU3ccvFwUEkgpa2oD9AEIUQ3FwazuK/8DRcrtgtxLhwL/h+P/NzXs57eIcf533Pc3J6enrSbDZj+3eLuLC2V4VCIbXbbTUaDV1eXv7oWgDr8QYQW+fk5ESpVErX19d6f39XJBLR4+Pjt41j2+22PM/T2dmZrq6ulEwmNZ/P9fz8rPF4LEm6u7vT6+urstmsfN/X6empFouFRqORXl5etFgsPo0ZjUbleZ6q1aqCIFCr1dLx8bF835f062xTs9lUtVrV+fm5KpXKslXG0dGR6vX6RvP0PE+9Xk+SvvzlI5fLqd/vL+uwHVxZ26tKpZJKpdLPHxaA9f7Dl8fARn63yhgOh2vrvmuVMZ1OrVAo2P7+vh0eHprv+zaZTEySdbvdT9e/vb1ZuVy2g4MD29vbs3Q6bbe3t59qgiCwWq1miUTCdnZ2LB6PWz6ft06n8+U++v2+3dzcWCwWs3A4bMVi8UsrDjOzwWBgmUzGdnd3LRqN2sXFxbL9xybu7++XrTn+NBqNTJJJsiAINh4T/wZre/18Hx4e1tbRBgb4eyGzlb0EAAAAbD3OAAIAADiGAAgAAOAYAiAAAIBjCIAAAACOIQACAAA4hgAIAADgGAIgAACAYwiAAAAAjiEAAgAAOIYACAAA4BgCIAAAgGMIgAAAAI4hAAIAADjmA4OGiIhzT52VAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 600x600 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "paired_delta2.mean_diff.plot(delta2_ylim=(3, -3));"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Labels\n",
+    "\n",
+    "- `raw_label` - label the raw data y-axis\n",
+    "- `contrast_label` - label the contrast y-axis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "two_groups_unpaired.mean_diff.plot(raw_label=\"This is my\\nrawdata\", \n",
+    "                                   contrast_label=\"The bootstrap\\ndistribtions!\"\n",
+    "                                );"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Unique for delta-delta:\n",
+    "- `delta2_ylim` - to label the delta-delta y-axis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x600 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "paired_delta2.mean_diff.plot(delta2_label='delta-delta label');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Axes ticks\n",
+    "You can add minor ticks and also change the tick frequency by accessing\n",
+    "the axes directly.\n",
+    "\n",
+    "Each estimation plot produced by ``dabest`` has two axes. The first one\n",
+    "contains the rawdata swarmplot while the second one contains the bootstrap\n",
+    "effect size differences.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.ticker as Ticker\n",
+    "\n",
+    "f = two_groups_unpaired.mean_diff.plot()\n",
+    "\n",
+    "rawswarm_axes = f.axes[0]\n",
+    "contrast_axes = f.axes[1]\n",
+    "\n",
+    "rawswarm_axes.yaxis.set_major_locator(Ticker.MultipleLocator(1))\n",
+    "rawswarm_axes.yaxis.set_minor_locator(Ticker.MultipleLocator(0.5))\n",
+    "\n",
+    "contrast_axes.yaxis.set_major_locator(Ticker.MultipleLocator(0.5))\n",
+    "contrast_axes.yaxis.set_minor_locator(Ticker.MultipleLocator(0.25))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Add counts to tick labels\n",
+    "By default, the tick labels include the sample size for each group. This can be switched off via\n",
+    "setting `show_sample_size=False` in the `.plot()` method. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "two_groups_unpaired.mean_diff.plot(show_sample_size=False\n",
+    "                                );"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Changing swarm side\n",
+    "In `dabest`, swarmplots are, by default, plotted asymmetrically to the right side. You may change this by using the parameter `swarm_side`. \n",
+    "\n",
+    "There are only three valid values: `\"right\"` (default), `\"left\"`, `\"center\"`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "multi_2group.mean_diff.plot(swarm_side=\"right\");\n",
+    "multi_2group.mean_diff.plot(swarm_side=\"left\");\n",
+    "multi_2group.mean_diff.plot(swarm_side=\"center\");"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Creating estimation plots in existing axes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "*Implemented in v0.2.6 by Adam Nekimken*.\n",
+    "\n",
+    "``dabest.plot`` has an ``ax`` parameter that accepts Matplotlib\n",
+    "``Axes``. The entire estimation plot will be created in the specified\n",
+    "``Axes``.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "two_groups_paired_baseline = dabest.load(df, idx=(\"Control 1\", \"Test 1\"),\n",
+    "                                  paired=\"baseline\", id_col=\"ID\")\n",
+    "multi_2group_paired = dabest.load(df,\n",
+    "                            idx=((\"Control 1\", \"Test 1\"),\n",
+    "                                 (\"Control 2\", \"Test 2\")),\n",
+    "                            paired=\"baseline\", id_col=\"ID\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1500x1500 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from matplotlib import pyplot as plt\n",
+    "f, axx = plt.subplots(nrows=2, ncols=2,\n",
+    "                      figsize=(15, 15),\n",
+    "                      gridspec_kw={'wspace': 0.25} # ensure proper width-wise spacing.\n",
+    "                     )\n",
+    "\n",
+    "two_groups_unpaired.mean_diff.plot(ax=axx.flat[0]);\n",
+    "\n",
+    "two_groups_paired_baseline.mean_diff.plot(ax=axx.flat[1]);\n",
+    "\n",
+    "multi_2group.mean_diff.plot(ax=axx.flat[2]);\n",
+    "\n",
+    "multi_2group_paired.mean_diff.plot(ax=axx.flat[3]);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this case, to access the individual rawdata axes, use\n",
+    "``name_of_axes`` to manipulate the rawdata axes, and\n",
+    "``name_of_axes.contrast_axes`` to gain access to the effect size axes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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ERGDs2LEDkJCIiB6FBU4iIiIiIiIa1s6dO4fKykpMnjz5iUdvtra2IiYmBhYWFggNDR2ghERE9CgscBIREREREdGw1dHRgcTERDg5OcHKyuqJ24uLi8O9e/cwf/58qKnxIzcRkTLwpy0RERERERENW3l5eaipqUFUVNQTt1VSUoLMzExMmTIFo0aNGoB0RETUGyxwEhERERER0bDU1taG5ORkeHh4wNzc/Inaun//Pg4dOgR7e3sEBAQMUEIiIuoNFjiJiIiIiIhoWJJKpWhqakJkZOQTt/Xzzz+jtbUVc+fOHZBd2ImIqPdY4CQiIiIiIqJh5/79+0hNTYWfnx+MjY2fqK3z58/j7NmzmDFjBkaOHDlACYmIqLdY4CQiIiIiIqJhJy0tDR0dHQgLC3uidhoaGnD06FG4uLjA09NzgNIREVFfsMBJREREREREw0p9fT2kUimCgoKgr6/f73bkcjmOHDkCkUiE2bNnc2o6EZFAWOAkIiIiIiKiYSUlJQUaGhqYOHHiE7WTn5+PS5cu4amnnoKent4ApSMior5igZOIiIiIiIiGjbt37yInJwehoaHQ0dHpdzu1tbU4ceIEfHx84OTkNIAJiYior1jgJCIiIiIiomEjMTERenp6CAwM7HcbcrkcBw8exIgRIzB9+vQBTEdERP3BAicRERERERENC5WVlSgoKEBERAQ0NTX73Y5EIsHVq1cxb948aGtrD2BCIiLqDxY4iYiIiIiIaFhISEiAiYkJvL29+93GrVu3EB8fj6CgINja2g5YNiIi6j8WOImIiIiIiGjIKysrQ1FREaKioqCurt6vNjo6OhATEwMjIyNER0cPcEIiIuovFjiJiIiIiIhoSJPL5YiLi8OYMWPg5ubW73ZOnz6NyspKLFiw4ImmuBOpitbWVqSnp6O1tVXoKESPxAInERERERERDWmXL19GWVkZJk+eDJFI1K82rl+/jpSUFISFhWHcuHEDnJBocJJIJNi6dSukUqnQUYgeiQVOIiIiIiIiGrLkcjni4+NhY2MDBweHfrXR1taGmJgYjBkzBpMmTRrghESDU0tLC2JjY1FaWooTJ06gpaVF6EhEPWKBk4iIiIiIiIasc+fOoaqq6olGb8bHx6O2thbz58/v9/qdRKpGKpWiqKgInp6eKCoqQmZmptCRiHrEAicRERERERENSR0dHUhISICTkxOsrKz61UZpaSkkEgkmT56M0aNHD3BCosHpl9GbWlpaMDQ0hJaWFkdx0qDGAicRERERERENSbm5uaitre33jufNzc04ePAgbG1tIRaLBzgd0eCVl5eHkpISNDY2orCwEI2NjSgpKUFeXp7Q0Yi6pSF0ACIiIiIiIqKB1traiuTkZHh6esLMzKxfbcTGxqK5uRnz5s3r9/R2IlVkZWWFZcuWdfs+0WDEAicRERERERENOZmZmbh//z4iIiL6df3FixeRl5eHuXPnwsjIaECzEQ12FhYWsLCwEDoGUa9xijoRERERERENKffv30dqair8/PxgbGzc5+sbGxtx5MgRODk5wdvbe+ADEhHRgGKBk4iIiIiIiIaUtLQ0yGQyhIWF9flauVyOo0ePQi6XY86cOZyaTkSkAljgJCIiIiIioiGjvr4eUqkUQUFB0NfX7/P1Z8+exYULFzB79ux+XU9ERMrHAicRERERERENGcnJydDQ0EBISEifr62rq8Px48fh5eUFV1dXBaQjIiJFYIGTiIiIiIiIhoS7d+8iNzcXoaGh0NHR6dO1crkchw4dgra2NmbMmKGghEREpAgscBIREREREdGQkJiYCD09PQQGBvb52szMTFy5cgVz587tc3GUiIiExQInERERERERqbzKykoUFBQgIiICmpqafbr29u3biIuLQ2BgIBwcHBSUkIiIFIUFTiIiIiIiIlJ58fHxGDVqFLy9vft0nUwmQ0xMDAwNDTFlyhTFhCMiIoVigZOIiIiIiIhU2rVr13D58mVERUVBXV29T9empqbixo0bmD9/fp9HfhIR0eDAAicRERERERGpLLlcjvj4eIwdO7bPO5/fvHkTSUlJmDRpEiwtLRWUkIiIFI0FTiIiIiIiIlJZly9fRllZGaKjoyESiXp9XXt7O2JiYmBmZobw8HAFJiQiIkVjgZOIiIiIiIhU0i+jN21tbfu8OVBCQgLu3LmDBQsW9HlaOxERDS4scBIREREREZFKKigoQFVVVZ9Hb167dg0ZGRmIioqCmZmZAhMSEZEysMBJREREREREKqejowOJiYlwdnaGlZVVr69raWnBwYMHYWVlheDgYAUmJCIiZWGBk4iIiIiIiFRObm4uamtrERUV1afrTp48icbGRsyfPx9qavxITEQ0FPCnOREREREREamU1tZWJCcnw9PTs09TzIuKipCTk4Np06bB2NhYgQmJiEiZWOAkIiIiIiIilSKVSnH//n1ERkb2+pqmpiYcPnwYjo6O8PX1VWA6IiJSNhY4iYiIiIiISGXcv38faWlp8Pf3h5GRUa+ukcvlOHbsGDo6OvDUU0/1aUMiIiIa/FjgJCIiIiIiIpWRlpYGmUyGSZMm9fqac+fOobCwELNnz4aBgYEC0xERkRBY4CQiIiIiIiKVcO/ePUgkEgQFBUFfX7/X1xw7dgzu7u5wc3NTcEIiIhICC5xERERERESkElJSUqCpqYmQkJBenS+Xy3H48GFoampi1qxZCk5HRERCYYGTiIiIiIiIBr07d+4gNzcXkyZNgo6OTq+uycnJQXFxMebOnYsRI0YoOCEREQmFBU4iIiIiIiIa9BITE6Gvr4+AgIBenX/37l3ExsbC398f48ePV3A6IiISEgucRERERERENKhVVlbi3LlzCA8Ph6am5mPPl8lkiImJgb6+PqZOnaqEhEREJCQWOImIiIiIiGhQi4+Px6hRo+Dj49Or89PT01FRUYH58+dDS0tLwemIiEhoLHASERERERHRoHXt2jVcvnwZUVFRUFN7/EfYqqoqJCYmYuLEibC2tlZCQiIiEhoLnERERERERDQoyeVyxMXFYezYsXB1dX3s+e3t7Thw4ABMTU0RERGh+IBERDQosMBJREREREREg1JRURHKy8sxefJkiESix56flJSE27dvY/78+dDQ0FBCQiIiGgz4E5+ISCCtDTWoLkhAS20VtI3MYeYRBS19Y6FjEREREQ0KMpkM8fHxsLW1hb29/WPPLy8vR1paGqKiojBmzBglJCQiosGCBU4iIgHcKZLi4v6NkMvaIRKpQS6X4VrSTrgsfBcmjoFCxyMiIiIS3Llz51BdXY0XXnjhsaM3W1tbERMTA0tLS0ycOFFJCYmIaLDgFHUiIiVrbah5UNzsaAPkcshlHQ/+7GjDhX0b0NpQI3REIiIiIkF1dHQgMTERzs7OsLS0fOz5p06dQn19PebNm9erjYiEVFtbK3QEIqIhZ3D/5CciGoKqCxIgl7V3e0wua0d1QYKSExERERENLjk5OaitrUVUVNRjzy0uLkZWVhamTp2KUaNGKSFd/6Wnp+Orr75ikZOIaICxwElEpGQttVUQibr/8SsSqaGltkrJiYiIiIgGj9bWVqSkpMDLywtmZmaPPPf+/fs4dOgQHBwc4O/vr6SE/ZOXl4eTJ08iODgYRkZGQschIhpSWOAkIlIybSNzyOWybo/J5TJoG5krORERERHR4CGVSnH//n1EREQ89tzjx4+jra0Nc+fO7dUu60K5cOECDh8+DH9/f0RGRgodh4hoyGGBk4hIycw8oiBS636PN5GaBsw8o5WciIiIiGhwuH//PtLS0uDv7//YUY6FhYUoKCjAzJkzYWhoqJyA/VBaWop9+/bBzc0NM2fOHNSFWCIiVcUCJxGRkmnpG8Nl4bsQqWsCIhFEauoP/lTXhMvCd6GlZyR0RCIiIiJBpKamQiaTISws7JHn1dfX49ixY3B1dYWHh4eS0vXdjRs3sHv3btjZ2WH+/PmDfgMkIiJV1f0QIiIiUigTx0AEvLYV1QUJaKmtgraROcw8o1ncJCIiomHr3r17kEqlmDhxIvT09Ho8Ty6X48iRI1BTU8Ps2bMH7YjI27dv4/vvv4eZmRmeeeYZqKurCx2JiGjIYoGTiEggWvrGsAx+WugYRERERINCcnIytLS0EBwc/Mjz8vLyUFRUhKVLl0JXV1dJ6fqmrq4OO3bsgL6+PpYtWwYtLS2hIxERDWkcH09ERERERESCunPnDvLy8hAaGgodHZ0ez6upqcGJEyfg6+uLCRMmKDFh7zU2NmLnzp1QV1fHihUrMGLECKEjERENeSxwEhERERERkaASExOhr6+PgICAHs+RyWQ4ePAgdHV1MW3aNCWm672Wlhb88MMPaG5uxooVK2BgYCB0JCKiYYEFTiIiIiIiIhLMzZs3ce7cOUREREBTU7PH8yQSCcrKyjBv3jxoa2srMWHvtLe3Y8+ePbh79y6WL18OExMToSMREQ0bLHASERERERGRYOLj4zFq1Ch4e3v3eE51dTXi4+MRFBQEW1tbpWXrLZlMhn379qGiogJLly7FmDFjhI5ERDSssMBJREREREREgrh69SqKi4sRFRUFNbXuP552dHQgJiYGJiYmiI6OVnLCx5PL5Th8+DCKiorwzDPPwNraWuhIRETDDgucRERERAQAuHfvHj7++GNMmzYNPj4+yMzMBADcvXsXn376KYqLiwVOSERDiVwuR3x8PMaNGwdXV9cez0tJSUFVVRXmz58PDQ0NJSZ8PLlcjlOnTiE/Px/z5s2Do6Oj0JGIiIalwfXbgYiIiIgEUVFRgfDwcJSXl8PR0REXL15EQ0MDAMDExAT//Oc/ce3aNWzatEngpEQ0VBQVFaG8vBwrVqyASCTq9pyKigqcPn0a4eHhGDdunJITPl5qairS09Mxc+ZMeHp6Ch2HiGjYGjIjOD/++GOIRCKsXbv2keft3bsXzs7O0NHRgYeHB44fP66cgERERESD2Jtvvon6+nrk5+cjOTkZcrm8y/F58+YhLi5OoHR9w/tCosFPJpMhPj4ednZ2sLe37/actrY2xMTEYOzYsZg0aZKSEz5ednY24uPjERERgcDAQKHjEBENa0OiwJmVlYV//vOfj31ilp6ejiVLluD5559HXl4e5s2bh3nz5uHcuXNKSkpEREQ0OJ08eRK//e1v4erq2u1IKnt7e5SXlwuQrG94X0ikGgoKClBdXY3o6OgeR2/GxcWhrq4O8+fP73F9TqEUFhbi2LFjEIvFCA8PFzoOEdGwN7h+S/RDQ0MDli1bhm+//RbGxsaPPHfTpk2YPn063nzzTbi4uODDDz+Er68vvvzySyWlJSIiIhqc7t+/j9GjR/d4vL6+Xolp+of3hUSqoaOjA4mJiXB2doalpWW351y5cgVSqRRTpkyBqampkhM+WklJCQ4cOAAPDw9Mnz69xwItEREpj8oXOH/9619j1qxZmDx58mPPzcjIeOi8adOmISMjo8drWlpacO/evc6vX9aiIiIiIhpKXF1dkZKS0uPxgwcPwsfHR4mJ+o73hUSqIScnB3V1dYiKiur2eHNzMw4ePAg7O7tBN/W7oqICe/bsgYODA+bOncviJhHRIKHSmwzt2bMHubm5yMrK6tX5lZWVMDc37/Keubk5Kisre7xm48aNeP/9958oJxEREdFgt3btWqxatQqenp5YtGgRgAdr5BUXF+P9999HRkYG9u/fL3DKnvG+kEg1tLa2Ijk5GV5eXjAzM+v2nJ9//hktLS2YN2/eoCogVldX44cffsDYsWOxaNEiqKurCx2JiIj+RWVHcJaXl+P111/HDz/8AB0dHYX1884776Curq7zKzk5WWF9EREREQll+fLl+OCDD/Dee+9hwoQJAIDp06fDyckJe/bswYYNGzBv3jxhQ/aA94VEqkMikaC5uRkRERHdHr9w4QLOnDmDGTNmYOTIkcoN9wi1tbXYuXMnRo4ciaVLl0JTU1PoSERE9B9UdgRnTk4Oqqur4evr2/leR0cHUlJS8OWXX6KlpeWhJ2pjxoxBVVVVl/eqqqowZsyYHvvR1taGtrZ252t9ff0B+g6IiIiIBpd169ZhxYoV2L9/P4qLiyGTyeDg4IAFCxb0uMvxYMD7QiLV0NTUhLS0NAQEBMDIyOih442NjTh69CicnZ3h5eWl/IA9aGhowI4dO6CpqYnly5cr9EEKERH1j9IKnM3Nzdi/fz9yc3NRV1cHmUzW5bhIJMJ3333X6/aio6NRUFDQ5b3nnnsOzs7OeOutt7qdLhAcHIz4+HisXbu2871Tp04hODi4b98MERER0RBlbW2NN954Q+gYfcL7QiLVkJaWBrlcjkmTJj10TC6X48iRIwCAOXPmDJqp6c3Nzfj+++/R1taGNWvW8MEGEdEgpZQC57Vr1xAZGYmrV6/CyMgIdXV1MDExQW1tLTo6OmBqatrnXxQGBgZwd3fv8p6enh5GjRrV+f7KlSthYWGBjRs3AgBef/11hIeH45NPPsGsWbOwZ88eZGdn45tvvhmYb5SIiIhIRUVFRcHc3BybNm3qdl28uLg4bNiwAQkJCQKkezTeFxINfvfu3YNUKsXEiROhp6f30PEzZ87g4sWLePbZZ7s9LoS2tjbs3r0bdXV1eO6552BsbCx0JCKla21tRXZ2Nvz9/aGlpSV0HKIeKWUNzjfffBN1dXWQSCQoKiqCXC7Hjz/+iIaGBvzP//wPRowYgdjY2AHvt6ysDDdv3ux8HRISgl27duGbb76Bl5cX9u3bh4MHDz50Q0xEREQ03CQlJeHHH3+En59ftxv1VFVVqfSak7wvJBJWcnIytLS0EBIS8tCx2tpa/Pzzz/D29oaLi4sA6R7W0dGBvXv34saNG1i6dGmPGyIRDXUSiQRbt26FVCoVOgrRIyllBGdCQgJeffVVBAYG4u7duwAeTEHQ1tbGm2++iQsXLmDt2rU4duzYE/WTlJT0yNcAsGjRos6dQYmIiIjo3z766CMcOHAAYWFh+PLLL/H8888LHanfeF9INHjcuXMHeXl5mDJlSpd1bIEHnwsPHToEHR0dTJ8+XaCEXf2SqaSkBEuXLoWVlZXQkYgE0dLSgtjYWJSWluLEiRMIDAx86L9hosFCKSM4m5qaYGtrCwAwNDSESCRCXV1d5/Hg4GCkpqYqIwoRERER9cDW1hZpaWlYsmQJXnzxRfzqV79Ce3u70LGISMUlJCRAX18fAQEBDx2TSqUoLS3FvHnzBsXmPXK5HCdOnEBBQQEWLFgABwcHoSMRCUYqlaKoqAienp4oKipCZmam0JGIeqSUAqe1tTUqKioAABoaGrCwsIBEIuk8fv78+UHxy4yIiIhouNPW1saWLVvw9ddfY/v27QgPD+8ytZuIqC9u3ryJwsJCREREQEOj6wTC27dvIy4uDmKxGHZ2dgIl7ColJQVSqRSzZs2Cm5ub0HGIBPPL6E0tLS0YGhpCS0sLJ06cQEtLi9DRiLqllAJnVFQUDh061Pl69erV+Oyzz/Diiy/i+eefx1dffYU5c+YoIwoRERER9cLLL7+M5ORklJeXw8/PD+np6UJHIiIVFB8fD1NTU3h7e3d5v6OjAwcOHICRkREmT54sTLj/kpmZicTERERHR8Pf31/oOESCysvLQ0lJCRobG1FYWIjGxkaUlJQgLy9P6GhE3VLKGpxvv/02srKy0NLSAm1tbbz77ru4ceMG9u3bB3V1dSxduhSffPKJMqIQERERUS8FBgYiNzcXzz77LL7++muIRCKhIxGRCrl69SqKi4vxzDPPQE2t69ia1NRUVFZW4vnnn4empqZACf+toKAAx48fR3BwMEJDQ4WOQyQ4KysrLFu2rNv3iQYjpRQ4ra2tYW1t3flaR0cHmzdvxubNm5XRPRERERE9xtatW7vd3djU1BSnTp3C119/jdu3bwuQjIhUkVwuR1xcHMaNG/fQzug3btxAcnIyJk2aBAsLC4ES/tvly5cRExMDb29vTJ06lQ9ziABYWFgMiv8+iXpLKVPU16xZA6lU2uPxzMxMrFmzRhlRiIiIiKgbq1at6twU8r+pqanh17/+Nf785z8rNxQRqaxLly6hoqIC0dHRXQqGbW1tiImJgbm5OcLCwgRM+EBZWRl++uknTJgwAU899RSLm0REKkopIzi3bduGyZMnQywWd3u8tLQU27dvx5YtW5QRh4iIiGjYS0lJAYDOAsMvrx9nMBQkiGhwk8lkSEhIgJ2dHezt7bscS0hIQE1NDV566SWoq6sLlPCByspK7Nq1CxYWFli4cOFD0+iJiEh1KKXA+Tg3btzAiBEjhI5BRERENGxERERAJBLh/v370NLS6nzdE7lcDpFIhI6ODiWmJCJVVFBQgOrqarz44otdfq5cvXoVEokEU6ZMgZmZmYAJgbt37+L777+HsbExlixZ8tAO70REpFoU9lP80KFDXXZO/+abbxAXF/fQebW1tYiLi0NAQICiohARERHRf0lMTAQAaGlpdXlNRPQk2tvbkZiYCBcXly7r97W0tODgwYOwtrZGUFCQgAmB+vp67Ny5E9ra2li+fDm0tbUFzUNERE9OYQXO8+fPY+/evQAAkUgEqVSKnJycLueIRCLo6ekhLCwMn376qaKiEBEREdF/CQ8Pf+RrIqL+yMnJQV1d3UO7L8fGxqKpqQmrVq0SdCr4/fv3sXPnTnR0dGD16tXQ09MTLAsREQ0chf1meeedd1BfX4/6+nrI5XJ89913na9/+bp37x5u3ryJo0ePYsKECYqKQkRERCS4lJQUzJkzB+PGjYNIJMLBgwcfe01SUhJ8fX2hra2N8ePHY9u2bQrP+d+uXLmCCxcuKL1fIlI9ra2tSElJgZeXF0aPHt35/qVLl5Cbm4vp06fD2NhY0Hy7du1CQ0MDVqxYgZEjRwqWhUhVtLa2Ij09Ha2trUJHGVJU9b5wMFPKozOZTIalS5cqoysiIiKiQamxsRFeXl746quvenV+aWkpZs2ahcjISOTn52Pt2rV44YUXEBsbq5B8X3zxBRYvXtzlveeeew6Ojo5wd3eHv78/qqurFdI3EQ0NEokEzc3NiIiI6HyvqakJR44cwYQJE+Dj4yNYto6ODvz000+oqqrCsmXLuhRgiahnEokEW7duhVQqFTrKkDLY7wtVEVdSpkGrpr4Rp7IuoPLuPYwxMcSUABcYG3AKCRERqaYZM2ZgxowZvT7/H//4B+zs7PDJJ58AAFxcXJCamorPPvsM06ZNG/B8mzdvRmRkZOfr2NhYbN++HS+//DI8PDzw3nvv4f333+/1jTgRDS9NTU1IS0tDQEAAjIyMADzYnOzo0aOQyWR46qmnHrmRmSLJZDLExMSgtLQUy5Yt67I2KBH1rKWlBbGxsSgtLcWJEycQGBjINWsHyGC/L1RFSitw/vzzz/j000+Rm5uLuro6yOXyh87hrpz0i4xzV/Dh9mNo75BBTSSCTC7Htp8zsH71LAS52Qsdj4iISOEyMjIwefLkLu9NmzYNa9euVUh/165dg4uLS+frn376CXZ2dvj6668BAJWVldi5c6dC+iYi1Zeamgq5XI5JkyZ1vldQUIDz58/jmWeegb6+viC55HI5jh8/jsLCQjzzzDOwt+dnCaLekkqlKCoqgqenJ4qKipCZmdnlv3FSHmXfF6oipUxR379/P2bPno2qqiosXrwYMpkMS5YsweLFizFixAh4enpi/fr1yohCKqCmvhEfbj+GtvYOyOVydMhkkMvlaGvvwAfbjqGmvlHoiERERJ0aGhpw7969zq+WlpYBabeyshLm5uZd3jM3N8e9e/dw//79AenjP/33w+eTJ092GVlga2uLysrKAe+Xhr7uBjbQ0HLv3j1kZmYiJCSkc9Oee/fu4fjx4/Dw8ICrq6tg2RITE5GdnY05c+Z0eYhDRI/2y+hNLS0tGBoaQktLCydOnBiw+5yhSFH3hIDy7wtVkVIKnBs3bkRgYCDy8vLw/vvvAwDWrFmDH374AefOncPNmzdhZ2enjCikAk5lXUB7h6zbY+0dMsRlX1RyIiIiop6Fh4dj5MiRnV8bN24UOlK/TJgwATExMQAeTE+/ceNGlwJnRUVF57RTot5qamrC119/3bk2Iw1NycnJ0NLSQnBwMIAHRe1Dhw5BU1MTM2fOFCyXRCJBSkoKpkyZAl9fX8FyEKmivLw8lJSUoLGxEYWFhWhsbERJSQny8vKEjjZoDZV7QlWllCnq58+fx8aNG6Gurg4NjQddtrW1AXgwGuDVV1/F//zP/2DlypXKiEODXOXde1ATidDRzdN+NZEIN+/UCZCKiIioe8nJyfD29u58PVBrU40ZMwZVVVVd3quqqoKhoSFGjBgxIH38pz/84Q9YunQpjI2N0djYCBcXly5rOiUkJHT5Pol6o62tDWPGjMHJkyeRkJAAHx8fBAYGYtSoUUJHowFy+/Zt5OXlYcqUKZ0//7Kzs1FSUoIVK1Yo5OdVb5w5cwYnTpxAaGgoJk6cKEgGIlVmZWWFZcuWdfs+dU9R94SA8u8LVZFSCpy6urrQ0tICABgZGUFbWxs3b97sPG5ubo7S0lJlRCEVMMbEELIepjLJ5HKMHTVSyYmIiIh6pq+vD0NDwwFvNzg4GMePH+/y3qlTpzpHSA20xYsXY9SoUTh+/DiMjIzw6quvdj6Yvnv3LkxMTLBixQqF9E1D18iRI7FgwQJMmTIFWVlZyMnJgVQqhaOjI4KCgmBvby/YxjM0MBITE2FgYICAgAAAwJ07d3Dy5EkEBATAwcFBkEyXLl3CoUOH4Ovri+joaEEyEKk6CwsLbsjVR4q6JwSUf1+oipRS4HRycsL58+c7X3t7e2Pnzp1Yvnw52tvbsWvXLlhbWysjCqmAKQEu2PZzBtraH950SkNdDZP9uXYOERGpnoaGBhQXF3e+Li0tRX5+PkxMTGBtbY133nkH169fx44dOwAAv/rVr/Dll1/ij3/8I9asWYOEhAT89NNPOHbsmMIyTpkyBVOmTHnofRMTExw4cEBh/dLQZ2BggKioKISFhaGgoABSqRQ7d+7E6NGjIRaL4eXlBU1NTaFjUh/duHEDhYWFmDt3LjQ0NDp3KzcwMOj2Z4kyXL16FXv37oWzszNmz57NAjrRE2ptbYVUKoVYLO4cuEZPThXuC1WNUtbgnD9/Pg4dOtS5wOq6deuQlJQEIyMjjB49GqdPn8bbb7+tjCikAowN9LB+9SxoaqhDJBJBXU0NIpEImhrqWL96FowNdIWOSMNc/nevI3PTSuR/97rQUYhIhWRnZ8PHxwc+Pj4AgN/97nfw8fHp3Gjx5s2bKCsr6zzfzs4Ox44dw6lTp+Dl5YVPPvkEmzdv7jJtnEjVaGhowMfHBy+//DKee+45mJqa4tixY/j0009x6tQp1NVxKSJVEh8fD1NTU3h5eQEA0tLScP36dcyfP1+QQsjNmzexe/duWFtbY8GCBVBTU8rHXaIhTSKRYNu2bZBKpUJHGVJ4XzjwlDKC8w9/+AP+8Ic/dL6ePXs2kpKScODAAairq2PWrFmIjIxURhRSEUFu9vhh/RrEZV/EzTt1GDtqJCb7u7C4SYNCa0MNWuvvCB2DiFRMRETEI3eT3rZtW7fXcDF/GopEIhFsbGxgY2OD2tpaZGZmIicnB+np6XBxcUFQUBCsrKw4+m4QKy0tRUlJCZ555hmoqamhsrISSUlJCA0NFWSNvjt37uD777+HqakpFi9e3LnEBhH13y87qZeWluLEiRMIDAwc0HUlhzPeFw48wX7qT5o0CZMmTRKqe1IBxgZ6WBTpJ3QMIiIiIlIgIyMjTJ06FREREThz5gykUim2bNmCsWPHIigoCG5ubixWDTJyuRzx8fEYN24cXFxc0N7ejgMHDmD06NGIiIhQep579+5hx44d0NXVxbJlyziNlmiASKVSFBUVwcPDA0VFRcjMzGQdhwYtjtknIiIiIiLBaWlpISAgAL/+9a+xfPly6OnpISYmBp9//jmSkpLQ0NAgdET6l0uXLqGiogKTJ0+GSCRCYmIi7ty5g/nz50NdXV2pWZqamrBz504AwIoVK6CryxlfRAPhl9GbWlpaMDQ0hJaWFk6cONG59CDRYKOQR6F2dnZ9nk4iEolQUlKiiDhERETUV7XlgJHypxgSEYlEIowfPx7jx4/H7du3IZVKkZ6ejtOnT8Pd3R1isRjjxo0TOuawJZPJEB8fD3t7e9jb26OsrAzp6emIjo6Gubm5UrO0tLTghx9+QFNTE9asWaOw3YuJhqO8vDyUlJSgubkZ58+fR0dHB0pKSpCXl4egoCCh4xE9RCEFzvDw8IcKnNnZ2SgsLISrqyucnJwAPHjyd/78ebi7u8PPj1ORiYiIBo2GKhY4h5moqCisW7cO0dHR3R5PTEzEhx9+iISEBCUno+HM1NQUs2bNQnR0NHJzc5GZmYkzZ87A2toaYrEYLi4u3EhGyc6ePYtbt25h3rx5aG1tRUxMDCwtLRESEqLUHO3t7fjxxx9x+/ZtrF69GqNGjVJq/0RDnZWVFZYtWwbgwX9vvywVIsQau0S9oZAC538vhnrw4EEcPHgQp06deuim+dSpU3jmmWfw4YcfKiIKERER9UdHq9AJSMmSkpLwwgsv9Hi8uroaycnJSkxE9G86OjoICQlBUFAQLl26BKlUir1792LkyJEICAiAn58fRowYIXTMIa+9vR1JSUlwcXGBhYUFjh49ioaGBqxYsUKphWaZTIYDBw6grKwMy5cvx9ixY5XWN9FwYWFhAQsLCwBAa2sr17alQU8pq3WvX78er732WrcjAqZMmYLf/OY3eO+99zB37lxlxCEiIqLHkbULnYAE8KglhoqLi2FgYKDENEQPU1NTg4uLC1xcXFBZWQmpVIqkpCQkJyfD09MTYrEYZmZmQsccsnJyclBXV4dly5bh8uXLyM7OxuzZs2FiYqK0DHK5HEePHsXFixfx7LPPwtbWVml9Ew1Xj9rtm2iwUEqB8/Lly4+cMjBq1Ciuv0lERDSYdLQJnYCUYPv27di+fXvn648++gjffvvtQ+fV1tbi7NmzmDlzpjLjET3SmDFjMHfuXEyePBk5OTnIyspCTk4O7O3tERQUBEdHxz7vC0A9a2lpQUpKCry9vaGvr48dO3Zg/PjxSl9qLD4+Hrm5uZg/f37n0mdEpFgymUzoCESPpZQCp4ODA7Zu3Yrnn38e+vr6XY7V19djy5YtsLe3V0YUIiIi6g05b2SHg6amJty6davzdX19/UPTTEUiEfT09PCrX/0K69evV3ZEosfS09NDWFgYJk6ciPPnz0MikWDXrl0wMTGBWCyGt7c3tLW1hY6p8iQSCZqbmxEREYFjx46hvb0dTz31lFKLyGlpaUhNTcX06dPh5eWltH6JhjuO4CRVoJQC50cffYSFCxfC2dkZq1evxvjx4wE8GNm5fft2VFVVYe/evcqIQkRERL3BKerDwiuvvIJXXnkFAGBnZ4dNmzbhqaeeEjgVUf+oq6vDw8MDHh4eqKiogEQiQWxsLBISEuDj44PAwEClTqUeSpqampCeno6AgACUl5fj3LlzePrpp5W6a3lubi5OnTqFsLAw7uBMpGQcwUmqQCkFznnz5uH48eN46623sGHDhi7HvL298d1332HatGnKiEJERES9wQLnsFNaWip0BKIBY2lpiYULF+LevXudU9elUikmTJgAsVgMOzs7Tl/vg9TUVMjlcnh7e2P79u1wc3ODu7u70vq/cOECjhw5goCAAERGRiqtXyJ6gAVOUgVKKXACwNSpUzF16lRUVlbi2rVrAAAbGxuMGTNGWRGIiIiot7gG57ATFxeHhISEhx5G/2LdunWIjo5GVFSUkpMR9Z+hoSGio6MRFhaGgoICSKVS7NixA2ZmZhCLxfD09ISmpqbQMQe1uro6ZGZmYuLEiYiPj4e6ujpmzZqltALxlStXsG/fPri5uWHGjBksTBMJgAVOUgVKKXA2NTVBV1cXwIPFwFnUJCIiGuQ4gnPY+fDDD2Ftbd3j8evXr+Ojjz5igZNUkqamJnx9feHj44OrV69CIpHg6NGjiIuLg5+fHwIDA5U63VqVJCcnQ0tLCzo6Orh8+TKWLVvW+dlO0a5fv449e/bAzs4O8+fPf2iNYCJSDhY4SRUopcA5cuRI+Pr6YtKkSZg0aRJCQ0Mfuas6ERERCayjVegEpGQFBQVYtGhRj8cDAgJw9OhRJSYiGngikQh2dnaws7PD3bt3kZWVhaysLKSnp8PFxQVBQUGwtLTkKMF/uX37NvLy8hAUFITExET4+fnB0dFRKX3funULP/zwA8zNzfHMM89AXV1dKf0S0cNY4CRVoLRNhlJTU7FlyxZ8+umnEIlEcHZ2RlhYWGfR08rKShlRiIiIqDdY4Bx2Wlpa0Nra8//vLS0taGpqUmIiIsUyMTHBtGnTEBERgTNnzkAqleK7777DuHHjEBQUBDc3t2FfVEtMTISBgQHKysqgp6eHqVOnKqXf2tpa7Ny5E/r6+li6dCm0tLSU0i8RdY8FTlIFShnj/9Zbb+HIkSO4c+cO8vPz8cUXX8DT0xNHjhzBihUrYGtrCzs7O2VEISIiot5oZ4FzuHF3d0dMTEy3x+RyOQ4cOABXV1clpyJSPG1tbQQGBuI3v/kNli5dihEjRuDAgQP47LPPkJycjMbGRqEjCuLGjRsoLCyEsbExbty4gXnz5kFbW1vh/TY2NmLnzp1QV1fHihUrMGLECIX3SUSPxgInqQKlbTIEPJgS4unpCWdnZ3h6esLd3R07d+5EUVERysrKlBmFiIiIHqWNI/WGm9deew0rV67EokWLsH79eri4uAAAzp8/jw8++AAZGRnYsmWLwCmJFEckEmHChAmYMGECbt26BalUitTUVKSkpMDDwwNisRhjx44VOqbSxMfHQ1tbG2VlZQgODoaNjY3C+2xpacH333+PlpYWrFmzBgYGBgrvk4gejwVOUgVKKXDeu3cPaWlpOH36NE6fPo3s7Gy0tbXBzc0N0dHReP/99zFp0iRlRCEiIqLeaLsvdAJSsuXLl6OkpAQffvghDhw40LmZh0wmg0gkwnvvvYdVq1YJnJJIOUaPHo3Zs2cjOjoaubm5yMzMRH5+PmxsbCAWi+Hs7DykN7wpLS3F5cuXIRKJYG5urpTNxdra2rB7927U1NTgueeeg4mJicL7JKLeYYGTVIFSCpy/bCjk7++PSZMm4Y9//CNCQ0NhbGysjO6JiIiorziCc1j685//jOXLlyMmJgZXrlwBADg4OGDevHlwcHAQOB2R8o0YMQITJ05EcHAwLl68CIlEgp9++glGRkYICAiAr6/vkJtCLZfLERcXh8bGRhgaGmL+/PnQ0FDsx0aZTIZ9+/bh+vXrWLFiBczNzRXaHxH1TUdHh9ARiB5LKQVOPT093Lt3D1VVVZ1ft27dYoGTiIhosGprAjraAXWlrmZDg4CDgwP+8Ic/CB2DaFBRU1ODq6srXF1dcfPmTUgkEiQkJCApKQleXl4Qi8UYPXq00DEHxKVLl3DhwgV0dHQgMjJS4dPy5XI5Dh8+jMuXL2PJkiWwtrZWaH9E1Hft7e1CRyB6LKV8aqmpqcHZs2c7p6ivX78elZWVGD16NEJDQzFp0iSEhobC399fGXGIiIioN1ruAbqcIjjcSCQSJCYmorq6Gq+++iocHR3R1NSEixcvYsKECdDX1xc6IpGgxo4di/nz52PKlCnIzs7u/Bo/fjzEYjHGjx8PkUgkdMx+kclkiI2Nxa1btxAeHo7Q0FCF9ieXy3Hy5EmcOXMGCxYsgKOjo0L7I6L+aW3l5pM0+CmlwCkSieDl5QUvLy/85je/AQCUlJQgLi4On3/+OQ4ePAiRSMSnAkRERINJcy0LnMNIa2srFi9ejEOHDkEul0MkEmHOnDlwdHSEmpoapk6dijfeeAPr1q0TOirRoKCvr4+IiAiEhoaisLAQUqkUP/zwA0aNGgWxWAxvb29oaWkJHbNPzp49i8zMTFhaWmLevHkKX2c0NTUVGRkZmDlzJjw8PBTaFxH1HwucpAqUOu+ssbER6enpOH36NFJSUpCZmYnm5mZoaGjAx8dHmVFIBdTUN+JU1gVU3r2HMSaGmBLgAmMDPaFjERENH001AOubw8af/vQnHD16FF9//TUiIyPh5OTUeUxHRweLFi3CoUOHWOAk+i8aGhrw8vKCp6cnysvLIZVKceLECcTHx8PX1xeBgYEqsTRXe3s79u7di9bWVjz99NMwNTVVaH/Z2dmIj49HZGQkAgMDFdoXET2ZlpYWoSMQPZZSCpx/+MMfkJKSgvz8fLS3t0NXVxdisRh//OMfMWnSJAQHB0NXV1cZUUhFZJy7gg+3H0N7hwxqIhFkcjm2/ZyB9atnIcjNXuh4RETDQ+MtoROQEu3evRuvvPIKXnrpJdy5c+eh4y4uLti7d68AyYhUg0gkgrW1NaytrVFXV4esrCzk5ORAIpHAyckJQUFBsLGxGbTT19PS0pCdnY2nnnoKAQEBCu3r3LlzOHbsGMRiMcLCwhTaFxE9uebmZqEjED2WUgqc27dvx8SJE/Hss89i0qRJ8PX1VfhOfKS6auob8eH2Y2hrf7BTW4dcDgBoa+/AB9uO4Yf1aziSk4hIGRqrhU5ASlRdXf3IKaLq6upoampSYiIi1TVy5EhMnjwZ4eHhOHv2LCQSCbZt2wZzc3MEBQXB3d0dmpqaQsfs1NLSgu+++w7m5uZYvny5QouwxcXFiImJgYeHB6ZPnz5oC75E9G/8/U+qQClVxlu3OAKEeu9U1gW0d8i6PdbeIUNc9kUsivRTcioiomGooUroBKREVlZWuHjxYo/H09LSMH78eCUmoqGitLQUtra2w7KQpampCT8/P/j6+qK0tBQSiQSHDx/GqVOn4O/vj4CAABgYGAgdE3v27EFFRQU++OADjBw5UmH9lJeX48cff4SDgwPmzp07LP9NEKmi+/fvCx2B6LE4jJIGncq796AmEnWO3PxPaiIRbt6pEyAVEdEwVM8C53CydOlSfPrpp3j66acxYcIEAOgsPnz77bf46aef8PHHHwsZkVRQZWUltm/fDlNTUwQHB8PT03NQjVxUFpFIBHt7e9jb2+Pu3buQSqWQSCRITU2Fm5sbxGIxLC0tBclWXV2NPXv2ICgoCBMnTlRoP7t27cK4ceOwaNEiqKurK6wvIhpYTU1NnRsQEj2Jjo4O7N27F4mJiaiursYHH3wADw8P1NXVIT4+HhMnToS5uXm/2lZagbOyshLfffcdcnNzUVdXB5ms6wg9kUiE+Ph4ZcWhQWyMiSFk3RQ3AUAml2PsKMU9VSYiov9Qf1PoBKRE69atg0QiQVhYGFxcXCASifDGG2/g7t27qKiowMyZM/HGG28IHZNUzJgxY/D8888jPT0dR48eRUJCAgICAhAQEAA9veG55JCJiQlmzJiBqKgo5OXlITMzE5s3b4alpSXEYjFcXV2VVvyTy+X49NNPAQBr165VWPGipqYGO3fuxMiRI7FkyZJhWeQmUmUymQzNzc0YMWKE0FFIhdXW1mL69OnIzMyEvr4+Ghsb8dprrwEA9PX18dvf/hYrV67Ehg0b+tW+2kCG7cnZs2fh6uqKjz76CCUlJUhMTMStW7dw+fJlJCUloby8HPIeClo0/EwJcIGGevf/NDXU1TDZ30XJiYiIhqn6SkDW/ZIhNPRoaWnhxIkT2Lp1K+zt7eHs7IyWlhZ4enpi27ZtOHLkCEdcUb9YWVnh2WefxWuvvQY3NzekpaXhs88+w9GjR7vd0Gq40NbWRlBQEH7zm99gyZIl0NLSwv79+/H5558jJSUFjY2NCs9w+vRpZGZmYsmSJTAzM1NIHw0NDdi5cyc0NTWxfPly6OjoKKQfIlIsrsNJT+rtt99GYWEhYmNjceXKlS51QHV1dSxcuBDHjx/vd/tKGcH59ttvQ19fH/n5+dDV1YWZmRk2bdqEqKgo7N27F6+88gp++OEHZUQhFWBsoIf1q2fhg21dd1HXUFfD+tWzYGygK3REIqLhQdb+YB1Ow7FCJyEF+N3vfocVK1bAx8cHAFBWVobRo0dj+fLlWL58ucDpaCgyMTHBzJkzERERgezsbGRmZiInJwdOTk4ICQmBlZXVsJz+qKamBicnJzg5OaG6uhpSqRQpKSlISUmBh4cHgoKC+j1d71Fqa2uxefPmzgK0IjQ3N+P7779HW1sbnn/+eejr6yukHyJSvKamJowaNUroGKTCDh48iNdeew1Tpkzp9gHnhAkTsG3btn63r5QCZ1paGv74xz/C2toad+/eBYDOKeqLFi1Camoq3nzzTSQnJysjDqmAIDd7/LB+DeKyL+LmnTqMHTUSk/1dWNwkIlK2e9dZ4ByiPv/8c/j7+3cWOO3s7LBz504sXbpU4GQ01Onq6iIsLAwhISE4e/YsMjIysGXLFlhYWCAkJAQuLi5QU1PKRLNBx8zMDHPmzEF0dDRycnKQlZWFvLw82NraIigoCBMmTBiQvxu5XI6dO3eiuroa7777LrS1tQcgfVdtbW3YtWsX6urq8Nxzz8HIyGjA+yAi5eEITnpSdXV1sLOz6/F4W1sb2tvb+92+UgqcMpms86mjkZER1NXVOwudAODh4YHvvvtOGVFIhRgb6HG3dCIiodWVA5b+QqcgBTA3N8eVK1c6X3O5IFI2DQ0N+Pr6wsfHB8XFxUhPT8fevXthbGyMoKAg+Pj4QEtLS+iYgtDV1cWkSZMQEhKCCxcuQCqVYs+ePTA2NkZgYCB8fHyeaKq3RCJBSkoK/P39ERISMoDJH+jo6MBPP/2EyspKrFy5UmHT34lIebiTOj0pBwcH5Obm9nj85MmTcHV17Xf7Silw2tnZobS0FMCDKRh2dnaIi4vDM888AwBIT0/nEz0iIqLBqK5C6ASkILNmzcIHH3yAkydPdt6HffLJJ9izZ0+P14hEIhw6dEhJCWm4EIlEcHR0hKOjI27evIn09HTExsYiMTERAQEBCAwMhIGBgdAxBaGurg53d3e4u7vj+vXrkEqliIuLQ2JiIry9vSEWi/s8ZfTWrVuIiYmBtrY2Fi5cCA2Ngf1IKJfLcfDgQVy5cgVLly4VbHd4IhpYLHDSk3rhhRfw1ltvISIiAtHR0QAe3AO0tLTggw8+wIkTJ/DNN9/0u32lFDinTp2KvXv34q9//SsA4JVXXsHvf//7zkVFk5KS8Pvf/14ZUYiIiKgvaq4JnYAUZNOmTTAzM0NiYiIKCwshEolQXl7eZZbNfxuO6yOSco0dOxZPP/00Jk+eDIlEgszMTKSnp8PDwwMhISHDeiSghYUFFixYgClTpiA7O7tzHVNHR0eIxWI4ODg89r/Rjo4OxMTEoLKyEoGBgfD09BzQjHK5HD///DPOnTuHhQsXwsHBYUDbJyLhcIo6PanXX38dhYWFWLJkSefD9aVLl+LOnTtob2/Hyy+/jOeff77f7SulwLlu3TosWbIEbW1t0NTUxNq1a9HY2Ij9+/dDXV0df/rTn/Duu+8qIwoRERH1Rc1VoROQgujp6WHDhg2dr9XU1PD5559zDU4aFEaOHIlp06YhPDwcubm5kEgkyM/Px/jx4xESEgI7O7thW3A3MDBAZGQkJk2ahHPnzkEikeD777+HqakpxGIxvLy8epzaf/r0aZw/fx6jR4/GlClTBnyt0+TkZGRmZmLOnDlwc3Mb0LaJSFiNjY1CRyAVJxKJ8O2332LVqlXYt28fLl++DJlMBgcHBzzzzDMICwt7ovYVXuCUy+VQV1eHm5sbNDU1ATz4pt577z289957iu6eiIiInkTjLaD5HqBjKHQSGmALFizAG2+8gUmTJgEAEhMTn2jdIyJF0NHRQUhICMRiMQoLC5Geno4dO3ZgzJgxCAkJgZubG9TV1YWOKQgNDQ14e3vDy8sLZWVlkEgkOH78OOLj4+Hr64vAwMAuy4Bdv34dycnJ0NDQgJOTE5ydnQc0j1QqRVJSEqKjo+Hnx3X0iYYaFjhpoISGhiI0NHTA21X49oStra0wMTHBF198oeiuiIiISBHulgidgBTg0KFDKCsr63wdFRWFU6dOCZiIqGfq6urw9PTEyy+/jJUrV8LAwAAHDhzApk2bkJaWhubmZqEjCkYkEsHGxgbPPvssXn/9dfj6+iI3NxebNm3Cjz/+iGvXrqG1tRUxMTEAHmxgNHny5AEdAXv27Fn8/PPPCAkJUciHViISXkNDA2QymdAxSIWVlpbiyJEjPR4/cuQIrl692u/2FT6CU1tbG2PGjIG2traiuyIiIiJFuH0ZGOcjdAoaYBYWFsjLy8OyZcsAPJh1M1yn/JLqEIlEsLe3h729Paqrq5GRkYGEhAQkJyfDz88PYrF4WG9eamRkhKlTpyIiIgJnzpyBVCrF1q1bcffuXbS1tcHS0hL29vaws7MbsD6Liopw8OBBeHt7Y8qUKfw5QjREyeVyNDQ0wNCQs3qof/7whz/g3r17mDNnTrfHv/rqKxgZGT1yw8tHUfgITgBYvXo1duzYgdbWVmV0R0RERAOp+oLQCUgBFi9ejE8//RTW1tadG428/fbb8PT07PHLy8tL4NRE/2ZmZoa5c+di7dq1EIvFyM/PxxdffIH9+/fjxo0bQscTlJaWFgICAvDrX/8a4eHhqKiowM2bN5GUlAQdHR3U19cPSD/Xrl3DTz/9hAkTJuCpp55icZNoiKurqxM6AqmwjIwMTJkypcfj0dHROH36dL/bV8omQx4eHjh48CDc3NywevVq2NraYsSIEQ+dt2DBAmXEISIior5ggXNI2rhxI8aPH4/ExERUV1dDJBJBT08Po0aNEjoaUZ8YGBggOjoakyZNQl5eHiQSCb755hvY2toiJCQEjo6Ow7bw1tLSgvz8fMyaNQvXr1/H/fv3cfnyZVy8eBFubm4Qi8WwsLDoV9uVlZXYvXs3rKyssHDhwgHfsIiIBofW1lZcvnwZY8aMQU1NDaysrISORCqqpqYGBgYGPR7X19fHnTt3+t2+UgqcS5Ys6fzff/rTn7o9RyQSoaOjQxlxiIiIqC/qbwKNdwA9Fr6GEnV1dbz00kt46aWXADzYRf29997jLuqksrS0tCAWixEQEICLFy8iPT0du3btgqmpKUJCQuDp6QkNDaV8/Bk0YmNj0dzcDHd3d1RUVOD3v/899PX1kZeXB6lUirNnz8LKygpisRguLi693rDp7t27+P7772FiYoLFixcPu79XouFEIpEgJSUFAQEB3IyQnoi1tTXS0tLwyiuvdHv89OnTsLS07Hf7SvlNlJiYqIxuiIiIaAD4+/uj8uoljNEDst/1ffDmzXxgfLSguUixSktLMXr0aKFjED0xNTU1uLq6wsXFBeXl5UhPT8eRI0cQHx+PwMBABAQEQFdXV+iYCnfx4kXk5eVhxowZSE5Oho+PD0xNTQEAwcHBEIvFKCoqgkQiwb59+2BoaIiAgAD4+fk98u/n3r172LFjB3R0dLBs2TLutUA0hLW0tCA2Nha3bt1CYWEh/Pz8hI5EKmzJkiX48MMPERgYiN/85jedI/87Ojrw5Zdf4scff8S6dev63b5SCpzh4eHK6IaIiIgGQGVlJa7faQA6tP795o1cFjiHOBsbGwAPRmr8Mm391VdfhaOjI5qamnDx4kVMmDAB+vr6Aicl6h2RSARra2tYW1vjzp07kEgkOH36NFJTU+Ht7Y3g4GCYmJgIHVMhGhsbceTIETg5OaGpqQmtra0PfSZTU1ODs7MznJ2dUVlZCalUiuTkZCQnJ8PT0xNBQUEwMzPrcs39+/fx/fffQyaTYcWKFdDT01Pmt0VESiaVSlFUVAQrKytUVVUhNzcXCxcu5Kht6pd33nkHqampWLt2Lf7617/CyckJAHDp0iXcunULERERg7/ASURERCquTArI5cAwXcduOGhtbcXixYtx6NChzh3V58yZA0dHR6ipqWHq1Kl44403nujGk0goo0aNwqxZsxAREYHs7GxkZmYiOzsbzs7OCAkJGVJrysnlchw9ehRyuRxRUVHYsmULAgICMHLkyB6vGTNmDObOnYvJkycjJycHWVlZyM3NhZ2dHYKCguDo6Ij29nb88MMPaGhowJo1ax7ZHhGpvl9Gb2ppaWHEiBFQV1fHuXPnUF5eDjs7O6HjkQrS1tbGyZMnsX37dhw4cAAlJSUAgMDAQDz99NNYuXLlE63nzAInERERPV7jLeBOCWA6XugkpCB/+tOfcPToUXz99deIjIzsfKoOADo6Oli0aBEOHTrEAiepND09PYSHhyMkJARnz55FRkYGvvvuO1hZWSE4OBjOzs4qv1nO2bNnceHCBTzzzDPIz88HAEyaNKlX1+rp6SEsLAwTJ07E+fPnIZVKsXv3bhgaGqKurg7q6up44YUXOqe6E9HQlZeXh5KSEjQ3N+PGjRtobW3FrVu3kJyczAIn9Zuamhqee+45PPfccwPeNgucRERE1DulSSxwDmG7d+/GK6+8gpdeeqnbHSxdXFywd+9eAZIRDTxNTU34+fnB19cXly9fRnp6On766ScYGxsjODgY3t7e0NLSenxDg0xdXR2OHz8OT09PWFhY4MCBA5g0aVKf1xxVV1eHh4cHPDw8UFZWhk2bNiEvLw++vr44c+YMdHR0huz0fiJ6wMrKCsuWLQPwYF+VlpYWAA9GiRMNRixwEhERUe+UJAL+z3Oa+hBVXV0NDw+PHo+rq6ujqalJiYmIFE8kEmHChAmYMGECbty4gfT0dJw4cQKJiYkICAhAYGCgyqw7K5fLcejQIWhra2PmzJmIjY2FtrY2goKCnqjNs2fPwtDQEP/zP/+D+vr6zin+jo6OCAoKgp2dHUT8vUA05FhYWMDCwgIAUFtbi/v37wMA2tvb0dLSwg3GqF9iY2Px3Xff4cqVK6ipqXmoYC4SiTqnrvcVC5xERETUO3UVQFUhMMZd6CSkAFZWVrh48WKPx9PS0jB+PEfw0tA1btw4LFy4ELW1tZBIJJBIJEhLS4OnpydCQkIwevRooSM+UmZmJq5cuYIVK1agvr4e+fn5mD59+hMVIRISEpCdnY25c+fCx8cHwIPp7ufOnYNEIsGOHTtgZmYGsVgMT09PaGpqDtS3Q0SDhL+/P65cuQIDAwOsW7cOcrkc165dw4QJE4SORirm//7v//D222/D3NwcgYGBj3yw3h8KL3A2NTXBysoKb7/9Nt58801Fd0dERESKdOk4C5xD1NKlS/Hpp5/i6aef7vzQ8suorG+//RY//fQTPv74YyEjEimFkZERpk+fjoiICOTk5EAikSAvLw+Ojo4ICQmBra3toBuxePv2bcTFxSEwMBAODg746aefMHLkSPj5+fW7zYyMDJw+fRpTp07tLG4CD6b3+/j4wNvbG1evXoVUKsXRo0cRFxcHPz+/x25oRESqpbKy8qGRdiUlJSxwUp9t2rQJUVFROH78uEIeiCm8wKmrqwsNDQ3o6ekpuisiIiJStOJ4IOgVQNtA6CQ0wNatWweJRIKwsDC4uLhAJBLhjTfewN27d1FRUYGZM2fijTfeEDomkdLo6Ohg4sSJCAoKwrlz55Ceno7t27dj7NixCAkJgaurK9TV1YWOCZlMhpiYGBgaGmLKlCm4fv06zp8/j3nz5kFDo38f9/Lz8xEbG4vQ0FCEhIR0e45IJIKdnR3s7OxQU1ODzMxMZGVlIT09HS4uLhCLxbCyshp0xWAienIVFRVobGxknYf6pKamBgsXLlTYaH+lbBH49NNPY9++fVyMloiISNW1NwMXjwudghRAS0sLJ06cwNatW2Fvbw9nZ2e0tLTA09MT27Ztw5EjRwZFMYdI2dTV1eHl5YVf/epXWLFiBXR1dbF//3588cUXSE9P79x4Qyipqam4ceMG5s+fD01NTcTHx2P06NHw9PTsV3sXL17E4cOH4efnh+jo6F5dY2xsjGnTpuF3v/sdpk+fjsrKSmzZsgXffvstzpw5g/b29n5lIaLBSS6Xo6ioSOgYpGICAwNx6dIlhbWvlDU4Fy9ejFdffRWRkZF48cUXYWtrixEjRjx0nq+vrzLiEBER0ZM4tw9wXwCoc621oUYkEmH58uVYvny50FGIBh2RSAQHBwc4ODigqqoKGRkZiI+PR3JyMvz8/CAWi5U+NfvmzZtISkrCpEmTYGlpiStXruDKlStYvHgx1NT6Ppbl6tWr2LdvH5ydnTFr1qw+j77U1tZGYGAgAgICUFxcDIlEgpiYGJw6dQr+/v7w9/dXmU2biOjRLly4AC8vr379rKHh6e9//ztmzJgBf39/LF26dMDbV0qBMyIiovN/nz59+qHjcrkcIpEIHR0dyohDRERET6KhGrh8EnCeJXQSIiJBmJubY968eYiOjoZUKkV2djYkEgnc3d0RHByMsWPHKjxDe3s7YmJiYGZmhvDwcMjlcsTHx8PS0hJOTk59bu/GjRvYvXs3bGxssGDBgicqWohEIjg6OsLR0RG3bt2CVCpFWloaTp8+DXd3dwQFBSnl74iIntwvM3H/e0ZuQ0MDrl27Bjs7OyFikQp69tln0d7ejhUrVuCVV16BpaXlQ7ODRCIRzpw506/2lVLg3Lp1qzK6ISIiImXJ3Qk4TuUoTiIa1gwMDDB58mRMmjQJeXl5kEgkOHv2LOzs7BASEoLx48crbA3KhIQE3LlzBy+//DLU1dVx4cIFXL9+HatWrepzn7dv38b3338PU1NTPPvss/1eu7M7o0ePxuzZsxEdHY3c3FxkZmbizJkzsLa2RlBQEJydnTkCjGgQa21tBYBuB6SdO3eOBU7qNRMTE4waNQqOjo4KaV8pBc5Vq1YpoxsiIiJSlvqbQOFBwHOR0EmIiASnra2NoKAgBAYG4sKFC0hPT8cPP/wAMzMzBAcHw8PDY0CLhteuXUNGRgYmT54MMzMzyGQyxMfHw8HBoc/Fhrq6OuzcuRN6enpYtmwZtLS0BiznfxoxYgQmTpyI4OBgXLx4EVKptHO398DAQPj6+na7jBkRCaelpQXNzc0AgLa2NrS1tXXZIObmzZu4desWRo8eLVREUiFJSUkKbV8pBc7/1NDQgPLycgCAlZUV12AhIiJSVbnbgQlTAR3lrjlHRDRYqampwc3NDa6urigrK0N6ejoOHTqE+Ph4iMVi+Pv7P3ERr6WlBQcPHoSVlRWCg4MBAGfOnMHt27exYMGCPrXV1NSEnTt3QiQSdW6gpGhqampwdXWFq6srbt68CalUioSEBCQlJcHLywtisZjFEqJBQiqVdm4SJpPJcPXq1YdG3505cwaTJ08WIh5RF0qbC5CVlYXIyEgYGxvD3d0d7u7uMDY2RlRUFLKzs5UVg4iIiAZKSz0g/afQKYiIBh2RSAQbGxssWbIEv/nNb+Ds7Izk5GR8+umnOH78OO7evdvvtk+ePInGxkbMnz8fampqaG9vR1JSEtzc3DBu3Lhet9PS0oLvv/8e9+/fx4oVK2BoaNjvTP01duxYzJs3D2+88QZCQ0Nx8eJFfPXVV9i5cyeKiooeWvOPiJSnpaUFsbGxnUteyOVyFBYWoq2trct5paWlqKurEyIiqaB79+7h448/xrRp0+Dj44PMzEwAwN27d/Hpp5+iuLi4320rZQSnVCpFREQEtLS08MILL8DFxQXAg123du/ejbCwMCQlJSEwMFAZcYiIiGigXDz2YC3Ocd5CJ6En0NTUBCsrK7z99tt48803hY5DNKSYmppi9uzZiIyMRFZWFjIzM5GVlQUXFxeEhITA0tKy120VFRUhJycHc+bMgbGxMQAgOzsb9fX1iIyM7HU77e3t2LNnD+7cuYPVq1dj1KhRff6+BpK+vj7Cw8MRGhqKwsJCSCQS7Nq1C6NGjUJgYCC8vb2hra0taEai4SYvLw8lJSVdHjTcunUL5eXlsLe373xPLpcjPz8f4eHhQsQkFVJRUYHw8HCUl5fD0dERFy9eRENDA4AH63P+85//xLVr17Bp06Z+ta+UAue6detgYWGB1NRUjBkzpsuxv/zlL5g4cSLWrVuHU6dOKSMOERERDaTk/wUWfgdocu00VaWrqwsNDQ3o6ekJHYVoyNLT00NERAQmTpyIM2fOICMjA5s3b4a1tTWCg4Ph5OT0yM12mpqacPjwYTg6OsLX1xfAgxFWKSkp8Pb2hqmpaa9yyGQy7N+/H+Xl5VixYsWg2s1cXV0dnp6e8PDwQEVFBSQSCWJjY5GQkAAfHx+IxeLOwi4RKZaVlRWWLVuGU6dOobm5Gdra2ggMDOz2v8GioiJ4e3tj5EguW0Q9e/PNN1FfX4/8/HyYmZnBzMysy/F58+bh6NGj/W5faSM4169f/1BxEwDMzc3x0ksv4cMPP1RGFCIiIhpo964/mKoeulboJPQEnn76aezbtw+vvPKKwnZ9JiJAU1MT/v7+8PPzw6VLl5CRkYEff/wRJiYmCA4Ohre3d5dNPIAHI6SOHTuGjo4OPPXUU53/jWZkZKC1tRURERG96lsul+PIkSO4dOkSnn32WdjY2Az0tzcgRCIRrKysYGVlhbq6OmRlZSEnJwdSqRQTJkxAUFAQbG1t+bOKSIEsLCxgYWHRuW6wpqYmvLy8uj1XLpcjJycHUVFRyoxIKubkyZN444034Orqijt37jx03N7evnPPnv5QSoHzl7VhetLR0fHIp5VEREQ0yBXGALahgKW/0EmonxYvXoxXX30VkZGRePHFF2Fra9vtZii/jBwjoicjEong7OwMZ2dnVFRUICMjA8ePH0diYiICAgIQEBDQuSHruXPnUFhYiIULF8LAwAAA0NjYiPT0dAQGBvZ6/cy4uDjk5eVh/vz5cHJyUtj3NpBGjhyJyZMnIzw8HGfPnoVUKsX27dthbm4OsVgMDw+PhwrCRKR8xcXFcHd3f2hUHtEv7t+//8hN5Orr65+ofaUUOENCQvDVV19h6dKlDz0lLCsrw9///ndMnDhRGVGIiIhIUZI+BhZuAXSUv1EFPbn/HAF2+vTph47L5XKIRCJ0dHQoMRXR8GBpaYlFixahpqYGEokEGRkZSEtLg6enJ9zd3XHs2LHOjVp/cfr0aYhEIoSGhvaqj9TUVKSlpWH69Ok9jsIazDQ1NeHn5wdfX1+UlpZCKpXiyJEjiIuLg5+fHwICAgTZKImI/i09PR1z587l6GrqlqurK1JSUvDyyy93e/zgwYPw8fHpd/tKKXBu2LABYWFhcHZ2xvz58zFhwgQAwKVLl3Do0CFoaGhg48aNyohCREREitJ4C8j4Eoh8V+gk1A9bt24VOgLRsGdsbIwZM2YgIiICOTk5kEgk2LZtG0aMGIF58+Z1Pmj4Zdp2WFgYdHV1H9tuTk4O4uLiEB4ejqCgICV8J4ojEolgb28Pe3t73L17F5mZmcjMzERaWhpcXV0RFBTUp42biGjgVFdX4/z583BzcxM6Cg1Ca9euxapVq+Dp6YlFixYBeLAudHFxMd5//31kZGRg//79/W5fKQVOHx8fSCQSvPfeezh8+DCampoAPFjQfvr06fjoo4/g6uqqjChERESkSEWxwPjJgFWg0Emoj1atWiV0BCL6lxEjRiA0NBQaGhooLi7G2LFjsWfPHowbNw4hISG4fPkydHR0EBwc/Ni2zp8/j6NHjyIgIKDXa3WqChMTE0yfPh2RkZHIy8tDZmYmNm/eDAsLC4jFYri5uUFdXV3omETDSmZmJiwtLbnhED1k+fLluHbtGt577z2sW7cOADB9+nTI5XKoqalhw4YNmDdvXr/bV0iB8/Dhw/D398e4ceM633Nzc0NMTAxkMhlu3boFABg9ejTX3iQiIhpqTn8KPLMd0NAWOgn1U0NDQ+ci71ZWVp3rABKR8ty9exfx8fGYPXs2Zs2ahZKSEqSnp2P79u0oKCjAwoULIZfLH9nGlStXsH//fri5uWHmzJlDdtqotrY2goKCEBgYiMuXL0MqleLAgQM4deoU/P394e/vDz09PaFjEg0LbW1tSEhIwFNPPcUHDPSQdevWYcWKFdi/fz+Ki4shk8ng4OCABQsWwN7e/onaVkh1cf78+UhKSup8bW9vj8OHDz/oUE0N5ubmMDc3Z3GTiIhoKKq/CRTsFToF9UNWVhYiIyNhbGzcud6fsbExoqKikJ2dLXQ8omFDJpMhJiYG+vr6mDp1KkQiEcaPH4+VK1di/PjxGDduHCoqKvDZZ5/h1KlTuHfv3kNtVFRUYM+ePbCzs8P8+fOHbHHzP6mpqcHJyQkrV67Eq6++igkTJuD06dP47LPPcOjQIVRWVgodkWhYuHXrFjIzM4WOQYNIU1MT/Pz88I9//APW1tZ444038NVXX+Hrr7/GH/7whycubgIKGsFpYGCA2traztdXr15FQ0ODIroiIiKiwSjvB8BpJqBrInQS6iWpVIqIiAhoaWnhhRdegIuLCwDgwoUL2L17N8LCwpCUlITAQC4/QKRo6enpqKiowHPPPQctLa3O969fv46bN2/i9ddfh52dHaRSKbKzs5GRkQEPDw8EBwdjzJgxuHXrFn744QeYm5vj2WefHZajqMzMzDBnzhxER0cjNzcXmZmZyMvLg62tLcRiMZycnDjghkiBCgoKYGZmBgcHB6Gj0CCgq6uL0tJShT5sU0iBMzAwEH/9619RVVXVue7C8ePHH/nETCQS4Y033lBEHCIiIlK2tibgzG4g+NdCJ6FeWrduHSwsLJCamooxY8Z0OfaXv/wFEydOxLp163Dq1CmBEhIND1VVVUhMTERISAisra27HIuPj4eZmRk8PDygpqaGKVOmICwsDLm5uZBIJDhz5gzMzc1RXl4OS0tLLF26FJqamgJ9J4ODrq4uQkNDERwcjIsXL0IikeDHH3+EkZERAgMD4evrCx0dHaFjEg1JycnJMDIywqhRo4SOQoPA9OnTERsb2+Mu6k9KIQXOv//971i5ciU+/PBDAA+Kl7t27cKuXbt6vIYFTiIioiGm8CDgtYSjOFWEVCrF+vXrHypuAoC5uTleeumlzns7IlKM9vZ2HDhwAKNGjUJkZGSXY1euXMGVK1ewePHiLiMPtbW1ERwcDLFYjOzsbHz66aeora2FqakpLl26BHd3d2hoKGVv2UFNXV0dbm5ucHNzw40bNyCRSBAfH4+kpCR4eXlBLBbD1NRU6JhEQ0p7eztiY2Mxf/58jBgxQug4JLA//elPWLRoEVasWIGXX34ZdnZ23f67MDHp32cHhfymGz9+PNLT09Hc3Izq6mrY2tri888/x9y5cxXRHREREQ1GHa3A2Z+AoF8JnYR6QU1NDe3t7T0e7+jo4HROIgVLSkrC7du38eKLL3YpSsrlcsTFxcHS0hJOTk7dXtva2oq8vDz4+flh6tSpKCwsxMGDBxEfHw+xWAw/Pz8WGP5l3LhxWLBgAaZMmYLs7GxkZ2cjKysL48ePR1BQEBwcHIbFmqVEytDQ0IBTp05h1qxZw3K5DPo3Nzc3AMD58+cfOQCyo6OjX+0r9FGejo4OrK2t8ec//xlRUVGwsbFRZHdEREQ02Jw/CHgvAXRGCp2EHiMkJARfffUVli5d+tA9W1lZGf7+979j4sSJAqUjGvrKy8uRlpaGqKioh0ZSX7hwATdu3MDq1au7Lby1tbVh9+7dqKmpwXPPPQdzc3N4eXnh1q1byMjIQFJSElJSUuDj44OgoCAYGxsr69sa1AwMDBAZGYlJkybh3LlzkEql+P7772FqagqxWAwvL68ua6ASDXft7e24du0abGxs+jQyvLKyEhKJhPcRw9z69etVbw3O//bnP/9ZGd0QERHRYNN2HzizBxArZq0dGjgbNmxAWFgYnJ2dMX/+fEyYMAEAcOnSJRw6dAgaGhrYuHGjwCmJhqbW1lbExMTAwsLioQKATCZDQkICxo8fD1tb24eu7ejowL59+3Djxg2sWLEC5ubmncdGjx6Np556ClFRUcjKykJmZiYyMzPh6uqKkJAQWFhYKPpbUwkaGhrw9vaGl5cXysrKIJVKcfz4ccTHx8PX1xeBgYEwMjISOiaR4K5cuQKpVAqZTAZHR8c+XVtYWAhzc3OMHz9eQeloINjZ2fW5CCkSiVBSUvLY8/7yl7/0M1XvcDEWIiKBtDbUoLogAS21VdA2MoeZRxS09Dmigoagc/sB94WAHheYH8x8fHwgkUjw3nvv4fDhw2hqagLwYIOO6dOn46OPPoKrq+uA9NXR0YG9e/ciMTER1dXV+OCDD+Dh4YG6ujrEx8dj4sSJXYo0REPdqVOnUF9fj2XLlj20FMSZM2dw+/ZtPP300w9dJ5fLcfjwYVy+fBlLly59aFOiX+jr6yMyMhKhoaHIz89HRkYGvv32W9jY2CAkJAQTJkzglGw8+JBuY2MDGxsb1NbWIisrCzk5OcjIyICzszPEYjFsbGz4d0XDklwux/nz53H79m0UFhbC1ta2z5uYnT59GqNHj+7cjJoGn/Dw8Id+xmVnZ6OwsBCurq6dy6RcunQJ58+fh7u7O/z8/PrVV11dHfT19Qds6QIWOImIBHCnSIqL+zdCLmuHSKQGuVyGa0k74bLwXZg4Bgodj2hgtbcAWZuBiLeETkL/4fDhw/D398e4ceM633Nzc0NMTAxkMhlu3boF4MEIsIFce7O2thbTp09HZmYm9PX10djYiNdeew3AgyLMb3/7W6xcuRIbNmwYsD6JBrPi4mJkZWVh1qxZD+003N7ejsTERLi5uWHs2LFdjsnlcsTGxuLs2bNYsGBBr0ZFaWpqIiAgAH5+frh06RIyMjKwe/dujBo1CsHBwfDy8hr2u67/wsjICFOmTEF4eDjOnj0LqVSKbdu2YcyYMRCLxfDw8ODmTTQsyOVyAA9+HlVVVcHCwgJVVVW4evVqn0dxtrW1ISEhAXPnzuW63oPUtm3burw+ePAgDh48iFOnTiE6OrrLsVOnTuGZZ57p0yaU2dnZeO+995CSkoLW1lacPHkSUVFRuH37Np5//nm88cYbiIiI6Fd2lf0X9fXXX8PT0xOGhoYwNDREcHAwfv755x7P37ZtG0QiUZcvHR0dJSYmInqgtaHmQXGzow2QyyGXdTz4s6MNF/ZtQGtDjdARiQbepeNA5TmhU9B/mD9/PpKSkjpf29vb4/DhwwAebDhkbm4Oc3PzAf8A8vbbb6OwsBCxsbG4cuVK5wcn4MEuxwsXLsTx48f71CbvC0lV3b9/H4cOHYKDgwP8/f0fOp6VlYWGhgZERUU9dOz06dOQSCSYOXMmPDw8+tSvmpoaXFxcsGbNGjz//PMwNzfHsWPH8NlnnyEpKQmNjY39/p6GGi0tLfj7++PVV1/FihUrYGBggEOHDuGzzz5DQkIC6uvr+9xmfzfQIBJCa2tr55/q6uoYMWIE1NXVUVhYiLa2tj63d+vWLZw9e3agY5KCrF+/Hq+99tpDxU0AmDJlCn7zm9/gvffe61Vb6enpCA0NxeXLl7F8+XLIZLLOY6ampqirq8M///nPfmdV2UdOlpaW+Pjjj+Ho6Ai5XI7t27dj7ty5yMvL69yZ6b8ZGhri0qVLna85tYCIhFBdkAC5rPudiuWydlQXJMAy+OFpaEQq7/QnwPx/AhrcsGEwMDAwQG1tbefrq1evoqGhQeH9Hjx4EK+99hqmTJmCO3fuPHR8woQJD40eeBzeF5KqOn78ONra2jB37tyH/g22tLTg9OnT8PHxeWhkZ1ZWFhISEhAZGYmAgIAnymBlZQUrKyvcvXsXEokEaWlpSE1Nhbe3N4KCgmBqavpE7Q8VIpEIDg4OcHBwwJ07dyCVSiGRSJCamgo3NzcEBQX1ak3Ty5cv48SJE1i1ahUMDQ2VkJyo/1paWtDc3AzgwQjO5uZm3LhxAx0dHbh16xbKy8thb2/f53ZzcnJga2vLtW1VwOXLlx/6HfSfRo0a1av1NwHg3XffhYuLCyQSCerr67F58+YuxyMjI7F9+/Z+Z1XZAuecOXO6vP7rX/+Kr7/+GhKJpMcbWZFI9NCOhEREytZSW/WvaekPP70XidTQUlulkH655icJ7u4VIHc7EPii0EkIQGBgIP7617+iqqqqcy2s48ePo7KyssdrRCIR3njjjSfqt66uDnZ2dj0eb2trQ3t79w+BesL7QlJFhYWFKCgowIIFC7otdKWnp6O1tRXh4eFd3j937hyOHz+OoKAghIWFDVgeExMTzJw5ExEREcjOzkZmZiays7Ph5OSEkJAQWFtb80HAv4waNQozZ85EVFQU8vLykJmZiW+//RaWlpYICgqCi4tLt2vKlZaW4scff4SDgwP09PQESE7UN1KptPN3spqaGsaPH99luQxj4/59lujo6EBaWhpmzpzJnyuDnIODA7Zu3Yrnn38e+vr6XY7V19djy5YtvS5yZ2VlYePGjdDW1u72obqFhcUj70MfRyEFzh07dvTrupUrV/brul8Wqm9sbERwcHCP5zU0NMDGxgYymQy+vr7YsGFDjze9v2hpaUFLS0uXNoiInoS2kTnkclm3x+RyGbSN+r6xxuOKl1zzkwaN/F2AVSAw1kvoJMPe3//+d6xcubJz3SSRSIRdu3Zh165dPV4zEAVOBwcH5Obm9nj85MmTT7SZEe8LSRXU19fj2LFjcHV17XZ6eWNjIzIyMhAYGNil+FlcXIwDBw7Aw8MD06ZNU0hhQFdXF2FhYQgJCUFBQQHS09OxdetWWFhYICQkBC4uLlw77190dHQQHBwMsViMoqIiSKVS7Nu3D4aGhp1rnerq6gIAysrKsHv3btjY2GDRokUDtqkGkaK0tLQgNja28+eMSCRCc3MzXF1dB2St3uvXr6OkpIS7qg9yH330ERYuXAhnZ2esXr268/+vy5cvY/v27aiqqsLevXt71ZampmaXaen/7fr16w8VUftCIQXO1atX9/kakUjU5wJnQUEBgoOD0dzcDH19fcTExPR4Q+zk5IQtW7bA09MTdXV1+Nvf/oaQkBAUFhbC0tKyxz42btyI999/v0+5iIgexcwjCteSdj5Yg/O/iNQ0YOb58Pomj/K44mWXNT+BzpGjv6z5GfDaVo7kJOWRy4D4D4AF3wK6JkKnGdbGjx+P9PR0NDc3o7q6Gra2tvj8888xd+5chfb7wgsv4K233kJERETnek4ikQgtLS344IMPcOLECXzzzTd9bpf3haQq5HI5jhw5AjU1NcyePbvbIuXp06chEokQGhra+V55eTl+/PFHODo6djulfaBpaGjAx8cH3t7eKC4uRnp6Ovbu3QsjIyMEBQXB19cXWlpccgR4MLLN2dkZzs7OqKqqglQqRXJyMpKTk+Hp6QkbGxscP34c48aNw+LFi7k5EamEvLw8lJSUdFkr+0mmpXdHKpXCxsaGm5sNYvPmzcPx48fx1ltvPbQBpLe3N7777jtMmzatV20FBQVh3759WLt27UPHGhsbsXXr1odmLfSFSP6f/1oHyLVr1/p1nY2NTZ/Ob21tRVlZGerq6rBv3z5s3rwZycnJvXrq39bWBhcXFyxZsuSROz7995P6/Px8hIeHIycnB76+vn3KS0RDQ+amlWitvwMtg1EIfL1/I9bvXs7EhX0buhQlRWoafR5R2dpQg6z/91z3xVJ1TQS8thXVBQm4mrAV6O7HvUgE26jnuOYndWFpaYnr16/DwkgLFR8HKaYTCz9g5t8AFR4FlJubCz8/vyFzT/D+++/j6aefhru7u0L7kcvleOmll/Ddd9/ByMgItbW1MDc3x507d9De3o6XX34ZX3/9dZ/b5X0hqYrc3FwcPnwYS5YsgZOT00PHa2tr8f/+3/9DeHh45xT0qqoqbN26Febm5li+fLlgxYCbN28iIyMD586d69x8RywWw8DAQJA8g1lTUxNycnIQHx+P1NRUWFtbY+3atbCwsEBKSgpmzZrFAjENatevX0dubi5Wr16Nu3fvQk9PD6tWrYK1tXW/p6Z3x8vLC2KxeMDaE8JQuyfsSWVlZWe9z8bGps9L/UilUoSHhyM6OhpLlizBypUr8cknn8DAwAB/+9vfUF5ejoyMDHh6evYrn0IeHfW1UNlfWlpancNj/fz8kJWVhU2bNvVq1yVNTU34+PiguLj4kedpa2tDW1u78/WTDJclIvqFiWNgZ/Gxc1q5ZzS09Iz61E5vNiwSas1Poke6ngNkfQuIXxY6Cf3Ln//8Z6X0IxKJ8O2332LVqlXYt28fLl++DJlMBgcHBzzzzDP9XlOQ94WkCmpqanDixAn4+vp2W9wEgKSkJOjo6CAoKKjzmp07d8LIyAhLliwRdKTT2LFjsWDBAkRHR0MqlSIrKwsZGRnw8PBAcHAwzM37vszOUKWrqwtnZ2ekp6cjJCQEY8eOxY8//ohLly7BysoKkZGRLHDSoGZhYQELCwuMGDECwIPflV5eA7/EUEFBAZydnTvXA6fBa8yYMU+0frlYLMbx48fxyiuvdM7g/v3vfw/gwRJGx48f73dxE1DyJkMtLS3Izc1FdXU1Jk6cOOA78slksi5P1R+lo6MDBQUFmDlz5oBmICLqLS19416PnOxpjc3eFC8VseYn0YDI3wWYOgIOUUInIQGEhoZ2mX470HhfSIONTCbDwYMHoaur2+N0vlu3buHMmTOYPn06tLS0UF9fjx07dkBLSwvLly+Hjo6OklN3b+TIkZg6dSrCwsKQm5sLiUSC/Px8jB8/HsHBwbC3tx/2G4fcvXsXO3bsgIGBAV577TVoaWlh06ZNGDFiBAwMDLo8LCEazmQyGdLT0zF9+vRh/3NjsCorK8OGDRuQmJiIW7du4eDBgwgLC8Pt27fxwQcf4LnnnoOPj89D1927dw96enpd1hyOiorCpUuXkJ+f3+Uht5+f3xP//6+0AucXX3yBv/zlL6irqwMAnDp1ClFRUbh9+zacnZ3xv//7v1izZk2v23vnnXcwY8YMWFtbo76+Hrt27UJSUhJiY2MBPNiwyMLCAhs3bgQAfPDBBwgKCsL48eNRW1uL//u//8O1a9fwwgsvDPw3S0Q0gB61xmZvipcDveYn0YBK+hgYafWg0EnUT7wvJFUgkUhQVlaGVatW9VjcSkhIwMiRI+Hn54f79+/j+++/R0dHB9asWTMoRwzr6OggJCQEYrEY58+fR3p6Onbu3Alzc3OEhITA3d19WG6mU1tbi+3bt0NLSwsrV67EiBEjsH//fty/fx8bNmyAubk5R28S/Yfy8nKUlZUpbTYw9d758+cxadIkyGQyiMViFBcXo739wQxCU1NTpKamorGxEd99991D1xobG2Pnzp1YunQpAGDNmjV4+eWXIRaL4e3tDW9v7wHNqpQC59atW7F27VosXrwYU6dO7VLINDU1RVRUFPbs2dOnAmd1dTVWrlyJmzdvYuTIkfD09ERsbCymTJkC4EGF+T9396upqcGLL76IyspKGBsbw8/PD+np6U+0SycR0UB41A7oj9sgyHvN548tXmrpGcFl4bs9rvnZ12nxRAOqvQWIfReY/09uOjRM2NnZPfYJvUgkQklJSa/b5H0hDXbV1dWIj49HUFAQbG1tuz2noqICFy5cwPz58yGXy7Fr1y7cu3cPa9asgZGRkVLz9pW6ujo8PDzg7u6Oq1evIj09HTExMYiLi0NQUBD8/PwGzehTRftl1K2amhpWrVoFfX19xMXFobCwEIsWLYKVlZXQEYkGpfT0dFhYWHATrkHmj3/8I4yMjCCRSCASiWBmZtbl+KxZs/Djjz92e62WllaX2TTbtm3D5MmTFbbmqlL+5XzyySeYO3cudu3ahTt37jx03M/PD1988UWf2uyuOvyfkpKSurz+7LPP8Nlnn/WpDyIiRXvcDuiPW2Oz5kpOr4qXA7XmJ5FCNFQDcX8GZn0KqHMXzaEuPDz8oQJnR0cHrl27hrS0NLi7u3c7zelReF9Ig1lHRwdiYmJgYmKC6OjuZ03I5XLEx8fDzMwMrq6u+Omnn1BVVYWVK1di9OjRSk7cfyKRCHZ2drCzs0N1dTUyMjKQkJCA5ORk+Pr6IigoaNAXa59EY2Mjtm/fjvb2dqxZswaGhobIzs5Gamoqpk6dijFjxiAvLw8VFRWYPXs2p+MS/Yf6+nqcOXMGfn5+Qkeh/5CSkoL169dj9OjR3dbzrK2tcf369W6vdXZ2xubNm2Fra9u5xurVq1eRm5v7yD77u1GTUgqcxcXF+O1vf9vjcRMTk27/ooiIhrLHjc4MeG1rr9bYtAx+ulfFy76s+UmkdDfPAun/D5j0O6GTkIJt27atx2NnzpzBtGnTsGzZMuUFIlKwlJQUVFVV4YUXXuhxZNKVK1dQWlqKxYsX4/Dhw7hy5QqWLl0KS0tLJacdOGZmZpg7dy6io6ORmZmJrKwsZGZmwtXVFSEhIRg3bpzQEQfU/fv3sWPHDjQ3N+O5557DyJEjIZVKsXXrVpiamiIjIwMnT56EXC6HgYEBIiMjB+WyA0RCys/Ph6OjIwwNDYWOQv8ik8mgq6vb4/Fbt271uOzKxo0b8eyzz2Ly5MkAHjwE+9Of/oQ//elP3Z4vl8shEonQ0fHwZ9/eUEqB08jICLdv3+7x+Pnz559oJyYiIlXUmx3Qe7tBEIuXNCScPwSMdgacudGLMuzYsaNf1/2y66UieHl54eWXX8Zbb72FnJwchfVDpCwVFRU4ffo0wsPDeyzo/TJ609LSEsXFxTh37hwWLVoEBwcHJadVDH19fURFRSE0NBT5+fnIyMjAN998A1tbW4SEhMDR0VHlRzI2Nzdjx44dqKysRGhoKJKSkpCfn4/k5GSMGDECenp6EIlEnUtlNDQ0DMu1SYkep6OjAxkZGT1uxEbK5+vri2PHjuHVV1996Fh7ezv27NmDoKCgbq+dPn06SktLkZWVhaqqKqxevRovvfQSgoODFZJVKQXOmTNn4ptvvun2L6SwsBDffvttn9bfJCIaCnozOtNq0hJuEETDS+pngOkEwHS80EmGvNWrV/f5GpFIpNACJwCYm5vj/PnzCu2DSBna2toQExODsWPHIjQ0tMfzLly4gBs3bsDJyQnZ2dmYM2fOkFwPVktLC4GBgfD398fFixeRnp6OXbt2wdTUFMHBwfDy8lKptffkcjlu376N4uJibNmyBZcvX8a4ceNQXFwMmUyGkpIS6Ovrw9fXFyNHjoSZmRnMzMxgbm4OMzMzbjJE1INr166hvLyc69UOEu+88w5mz56NV155BYsXLwYAVFVVIS4uDhs2bMCFCxfw5Zdfdnvt2bNnYWNj01mw3rp1KxYtWtTjci1PSim/QT766COIxWK4u7tjzpw5EIlE2L59O7Zs2YL9+/dj7NixWL9+vTKiEBENGr0Znamlb8wNgmh46Wh9sB7ngm8BrZ6nw9CTKy0tFTrCQ+7cuYPvvvtOpaflEv0iLi4OdXV1WLJkSY+j9WQyGRISEgAAly5dwuTJk4f8+nNqampwdXWFq6srysvLkZ6ejqNHjyIhIQGBgYEICAh45HRIocjlcty4cQNnzpxBYWEhioqKUF1djStXrqCtrQ1ubm4wMzODtbU1Lly4gODgYKxZswa2trYwNDRU+VGqRMqUkZGBcePGcaTzIDBjxgxs27YNr7/+Or755hsAwPLlyyGXy2FoaIgdO3YgLCys22t9fHy67KKuaEopcI4bNw45OTl499138eOPP0Iul2Pnzp0wMDDAkiVL8PHHH8PU1FQZUYiIBg0zj6hejc7kBkE07NRVABlfAeFvCp1kSLOxsRGk36ioqG7fr62txcWLF9Ha2oqdO3cqORXRwLpy5QqkUimmT5/+yM85+fn5OHfuHPT09DBt2jRMnDhRiSmFZ2VlhWeffRZ37tyBRCJBamoqUlNT4e3tjaCgIIwaNUqQXDKZDHfv3sX58+c7i5mlpaWora2FSCSCgYEBLCwsYGBgAF9fXyxbtgw+Pj4wNDTE7t27MX78eDz//PP8jEvUT7W1tSgsLISnp6fQUQjAihUrsGDBApw6dQqXL1+GTCaDg4MDpk2bBgMDgx6vGzFiBJqamjpfJycn48UXX1RYTqXNATAzM8PmzZuxefNm3Lp1CzKZDKNHj+5ch4SIaLjpy+hMrrFJw87Fo4BtKGCjmDV6qGctLS3Izc1FdXU1Jk6cOOAf0GUy2UMjmX7ZeXny5MlYs2YNnJ2dB7RPImVqbm7GwYMHYWdnB7FY3ON57e3t+Omnn1BTU4MZM2ZgypQpw3aU36hRozBr1ixERkZ2bkaUnZ0NJycnhISEwMrKSiF/N3K5HA0NDaiurkZlZSWKioo6i5l37txBW1sbtLW1YWlpiaCgILi4uMDDwwNjx47F4cOHcfnyZSxduhT29vaQy+WIiYlBRUUFVq5cyeIm0RPKzc3FhAkToKOjI3SUYaupqQlWVlZ4++238eabb2LevHl9ut7Lywuffvop1NXVO3dRz8rKeuz/pwsWLOhXXqUvciKXyzt3Rhquv8CJiIAHu6g33S7HaLdwtDXVQVN3JHRHW3cZndnaUNN15KZHFLT0jYUNTqQsaZsACz9Ag+uUKcsXX3yBv/zlL6irqwMAnDp1ClFRUbh9+zacnZ3xv//7v0+8bnpSUtIAJCUavH7++We0tLRg3rx5j/y8c/jwYWRlZWHx4sWdy3gNd7q6uggPD8fEiRNx9uxZpKenY8uWLbC0tERISAicnZ37PUCmpaUF1dXVqK6uRlVVFSorK3HlyhXcvHkTdXV1qK+vh5aWFgwMDODg4IA5c+bA09MTzs7O0NTU7GxHJpMhJiYGly5dwuLFi2Fvbw8ASExMxNmzZ7Fo0SJYW1sPyN8H0XDW2tqKnJycYTeyfTDR1dWFhoYG9PT0+nX9pk2bsHDhQjz//PMAHjzQ3rRpEzZt2tTjNYN+F3XgwU7p69evR2xsbOcQVV1dXUybNg1/+ctf4O7urqwoRESCu1MkxcX9Gx85crO7c64l7YTLwndh4hgo7DcwBLXdr8eVE//A3ctSQKQGU+cQ2E97GepaIx57rVwux/k9f0ZNSQ5cFr2HUU7/HnVYf6MIVxO2oeFmMfD/2bvvuKru+/Hjr8ueyrwXROCyRJaCqCiKC7eJI2rcM8Nv0qbNaNo0s03apmnSNEnTjOZnYhJHonHvKIqKQlBBBRUUGQoyZG/uOr8/rLehojLvBf08H48+mnvPvee8r1wu57zv+/N+y8C+TyDK2BXYKXy78uXcH2oK4dx3MKhrh9oIN3311Vc8++yzzJ8/n4kTJzZLZLq4uDBu3Di+++47MRhSEO7i4sWLnD17lpkzZ+qrVVqSm5vLF198QVhYGCtWrBCr2v6HmZkZgwYNIiIigsuXL3PixAk2btyIo6Mjw4cPJzw8/I5DerRaLWVlZRQXF+uTmSUlJVRUVFBbW0tVVRUajQa1Wo25uTm9evUiIiKCkJAQlEolffv2bZbQ/DlJkti5c6d+0n2/fv0AOH36NEePHmXixImEhIR02b+LYDjl5eU888wz7Ny5ExMTE2bPns2HH36InZ3dPZ8rSRJTp05l3759bN26tVnV28mTJ3nppZc4ffo0MpmMoUOH8re//Y2BAwd24avpuS5cuEBwcDCOjqLAw1hmz57NDz/8wFNPPdXmL+IGDx5MVlYWV65cobi4mDFjxvDKK68wfvz4LonVIAnOY8eOMWXKFHQ6HTNmzND/IcjMzGTHjh3s3buXffv2ERMTY4hwBEEQjEpVW3Ezcfmf3pu3pqhLWjUXf/gLQ575CuCejxGVnG137puXUAyMRTFwwm3bLm17F1VtOaGL/oROq+Xyzg/I2v1PAmf99p77vZ68Dbj9D75W1cD5Da/jFBCF35SnkXRarh5Zx/n1rzHkV19jYtpzpsUazbmNEDpHDBwygL///e/MmDGD9evXU1ZWdtv2yMhIPvroozbv95tvvmlXPF09rV0QOltdXR27du2if//+d01WlJWV8fbbb2NhYcErr7zSoyaHG5pMJqNfv37069eP69evc+LECfbt28fhw4eJjIwkMDCQhoaGZsnMsrIytFotkiQBN5NNarWauro6zM3N8fT0RKlUolQq8fb2pm/fvq36GUiSxJ49ezhz5gyzZs3ST7rPyspi9+7dDBkyhOHDRVuVnmTMmDEsX76c5cuX37Zt0aJFFBYWcuDAAdRqNStWrODJJ59k/fr199zvBx980GIiqLa2lsmTJzN9+nQ++eQTNBoNb7zxBpMmTeLatWt3TKw/yCRJIikpiSlTphg7lAfW/Pnzefrppxk7dixPPPEESqUSa+vbC0AGDRrU4vPNzMwIDAwkMDCQZcuW8dBDD921fUtHGOSv6XPPPYdcLufIkSN4eno223bt2jVGjRrF888/z8mTJw0RjiAIglGVpB1C0mla3CbpNJSkHdL/990eI3pydp760qtUXDnNwJUfYN8nAAC/yas4v+EPKMc/hqX9nYcc1BZdoSBpK+GPfUDyB0v+Z7/5aBpq8B69GMvergB4jVpI6r9/QVNVCdZOfbruRd0vmmpu9uMc8KixI7nvZWVl8atf/eqO252cnFpMfN5LSxeO9yKTyUSCU+hRblX2AXddbl5dXc3q1au5fv06TzzxBK6uroYMs8dqaGhApVLh5eWFWq0mOTmZd999F5VKhUKhwM/PDx8fH2xsbDA3N6e+vp6qqip0Oh0WFhb4+/vrE5oeHh5tTipLksSBAwc4efIk06dP1w8+KSoqYuPGjQQEBDBlypRWVTfl5OTwww8/8Pzzz4sJ0d3UxYsX2bdvHydPnmTw4MEA/POf/2Tq1Km899579Olz5/O3M2fO8Pe//51Tp07h7u7ebFtGRgbl5eW8+eab+rzIG2+8wYABA8jLy8Pf37/rXlQPdu3aNa5evSpaPxjJmDFj9P997Nix27bfakHZmmXlX331VWeGdhuDJDjPnz/PW2+9dVtyE25Oznvqqaf4wx/+YIhQBEEQjK6psvg/S85v/yMgk5nQVFms/+97PUboHNX5GZha2eqTmwAOPhEgk1FTkIll/+gWn6dVN5K57V38Jj+FhZ3TbdutnT0ws+5F0Zkf8Rz5KJJOR/GZH7F28cTKQdFlr+e+k7lXJDgNwMHBgdLS0jtuv3DhAm5ubm3eb05OTkfCEoQe4ezZs2RkZDBv3rw79iqrr6/n22+/JSsri0GDBnXZEr2eTK1WU1paetvy8pqaGgBMTU1xcXEhJiaGqVOnkpmZydmzZ7lw4QKXL1/G3d0dV1dXvL29GTRokD6h2dFEYnx8PCdOnGDKlCn6KqWqqirWrVuHi4sLs2fPvmebAY1Gw44dO/jyyy8xMzNj/vz5LV4fC8aXmJiIg4ODPrkJMH78eExMTPjpp5+YNWtWi8+rr69n4cKF/Otf/2rx72VgYCDOzs6sXr2al19+Ga1Wy+rVqwkKCkKpVHbVy7kvJCYmdsrvstB2HUlKvvnmm8hkMl555RVMTEx488037/kcmUzGa6+91q7jGSTB6e3tTVNT0x23q1Qq8eEuCMIDw9JBgSTpWtwmSTos/5P4as1jhM6hrq3Awsah2X0yE1PMre1R11Xc8Xk5P35Br75BzXpu/pyZpQ1hS97m4qY/cS3hOwCsnfoQsuAtZCbiBK3VyrOhIg8cvY0dSYf961//4t1336WoqIiBAwfyz3/+k6FDW+6pu2bNGlasWNHsPktLSxobG7sktqlTp/Lvf/+bp59++rZt58+f54svvmhX/01v757/cxOEu6msrGTv3r2Eh4cTFBTU4mNUKhXr16+ntLQUJycnYmNjsbF5cFtv6HQ6KioqmiUxi4uLKS8v1y8td3R0RC6XExERgVwux8XFhYaGBq5du0ZeXh4XL15ErVbj7e2NUqmkoqICrVaLk5MTAwcOJCQkpFOSIceOHePIkSNMmDBBv6yysbGRdevWYWpqysKFC+/YD/SWwsJCPvnkExITEwkNDeWll15q1xdGgmEUFRUhl8ub3WdmZoaTkxNFRUV3fN5zzz1HdHQ0M2bMaHG7vb098fHxzJw5k7feeguAgIAA9u/fL1pV3ENVVRVpaWmEh4cbO5RO1Z3PC29ZtmxZu5/7hz/8AZlMxu9+9zssLCxaVdjY7ROcr7/+Os899xzTpk277Q2ZmprKP//5Tz744ANDhCIIgtBht3pftrcHpjxsHHnx3+r7a/6czMQM+YBYkKR7P0a4p2sJ33Pt+Eb9bZ1GRU1BBlf2faa/b9D/fdqufZddSqIy9xwRT9y5J6FW3cTlXR/Sq28wgbN+i6TTUZC0hQvf/4GBK/+Bqbllu47dla5evUpdXR0AdU1arpY34uVkZeSogPyTPT7B+f333/P888/z2WefERUVxQcffMCkSZPIzMy87ULqll69epGZmam/3ZVTlv/0pz8RFRVFaGiofont119/zZdffsnmzZtxd3fn9ddf77LjC0JPJEkS27dvx8rKismTJ7f4GI1Gw/fff09JSQmenp6Ul5czbNgwA0dqHJIkUVtb22x6eUlJCTdu3ECtvnmOY2Njg0KhwN/fH4VCgVwuRy6XY2JiQkFBAbm5uaSkpHDt2jXUajWWlpZ4eXkxZswYvL29cXd3x9TUFEmSyMnJ4cSJE2zZsoWDBw8ybNgwBg0ahJVV+/6OJSUlERcXx5gxY/STnLVaLRs3bqSmpoaVK1fedeiMVqslPj6eNWvWUFFRwfz581m6dOk9E6JC1/jLX/7CX/7yF/3thoYGkpKS+OUvf6m/78KFC+3a944dOzh06BCpqal3fExDQwOPPfYYI0aMYMOGDWi1Wt577z2mTZvGyZMnW+xraGw/Py9samqivLwcJ6fbVy0ZQkpKCgEBAe2e6N3ddPfzws6g0+nueruzdUmCs6X+TQqFgsjISKKjo/W9JS5fvqz/FispKYkFCxZ0RTiCIAidKvyxDzv0fAs7R4LmvMzFH/5y1ynqrXmMcHdukVNxCf7vALvMbe/i0n8Ezj9bcm5p74y5nSOq+spmz5V0WtQNNZjbtpzIrso9R2NFIYnvNl86ffGHv9DLM4QBS//KjfR4mqpKGLji78hkN5eu2c16kaT35lF+KQnXkNGd9Eo7Ljk5mbfeeovdu3frq2cqG7QoX0nmoTAnXpvqzRClvfECLLlovGN3kvfff58nnnhC/+37Z599xu7du/nyyy956aWXWnyOTCYzWJVPnz59OH36NC+//DLff/89kiTx7bffYm9vz4IFC/jrX/+Ki4tLpxyrqKiI1atXk5KSou+T93MymYy4uLhOOZYgdKWffvqJnJwcli5d2mISTafTsXXrVvLy8pg0aRJ79uxhypQp92WCq6mpSZ/I/Hkys76+HgBzc3NcXV1RKBSEhYXpk5m2trbIZDI0Gg35+flkZWURFxfHtWvX0Gg0WFpa4u3tzdixY/UJzZaWg8tkMnx9ffH19aW4uJjExETi4uI4cuQIkZGRREVF3XWy/f86deoU+/btY8SIEYweffPvtSRJ7Nixg7y8PJYuXXrXHqrFxcWsX7+e+Ph4nJ2deeWVVxg2bFi3T0jcz/7v//6PRx/973nbokWLmD17No888oj+vj59+uDm5kZJSUmz52o0GsrLy+/4N/nQoUNcuXIFBweHZvfPnj2bmJgY4uPjWb9+Pbm5uSQmJurfw+vXr8fR0ZHt27czf/78TnqlHdfSeWFDQwMvv/wyYWFhTJs2zeDL6jUaDcnJyYwdO9agx+0q3fW8cOXKlchkMv79739jamraqtU7MpmM1atXd2lcrdElCc6PP/74jtuOHz/O8ePHm92XlpZGeno6H37YsaSBIAhCT+EUMJQhz3xFSdohmiqLsXRQIB8Q2yxx2ZrHCHdnbm2PufV/k3ImZpaY2/a+bbhPr7790TbWUVt4GTv3m304K3POgiRh7xHY4r77Rs9BET6x2X2p//4FvhOewCng5tISnaYJZDJ+PmH9ZqJTpj9Z7A62bNnCvHnzkCTptrgkCfakl7M3vYLvnwjikYjOSXC1WVmWcY7bCrW1tVRXV+tvW1paYmnZvDpXpVJx+vRpfv/73+vvMzExYfz48SQmJt51397e3uh0OgYNGsRf/vIXQkJCOv9F/IdcLuf//b//x//7f/+PGzduoNPpcHV1vWdvubY4d+4cY8aMoaGhgcDAQNLS0ggODqayspKCggL8/PxE6yKhRygtLeXgwYNERUXh6+t723ZJkti9ezcXLlxg3rx5nDlzht69exMZGWmEaDuPVqulrKxMn8C8lcysrKwEbl7oOjs7I5fLiYqKQi6Xo1AocHBwaPZZolaryc/P5+TJk+Tl5ZGfn49Go8HKygpvb29iY2Px9vbGzc2tzZ9BCoWCmTNnEhsbS3JyMidPniQpKYmQkBCio6NvG/7yv86ePcvu3buJiopi/Pjx+qRkfHw8Z8+eZfbs2Xdsv6HT6UhISGDLli3k5eUxaNAgHn/8cTw8PNr0GoTO5+Tk1Kz60NraGrlcfttwn+HDh1NZWcnp06f1v6+HDh1Cp9PdcfrzSy+9xOOPP97svrCwMP7xj3/w8MMPAzd7dJqYmDRLct+63dWVbW1x9/NCifT0dNLT03niiSfuODm7q1y+fJnQ0NBuO6CtNeeE0L3PCw8dOoSJiQk6nQ5TU1MOHTp0zy9m2vLFzcWLF7ly5Qo1NTXY29vj7+9P//79Oxo20EUJzu70yykIgtBdWdg53nMSemseI3ScjYsXjn6RXN79T/yn/AJJp+XK/k9xDRmln6DeVF1K+rpX6Df9eew9ArGwc2pxsJBlb1esHG9+s+rgE0HOwS+5su8T+gx5GEmSyD++CZmJKQ7eAwz6Gu8kOTmZefPmodVq75h01epAhsS8Ly5y4rfhxqnkrC25mW3thpUvtyp7bnnjjTdu6zFUWlqKVqtFoWjeP1ehUJCRkdHifgMDA/nyyy8ZMGAAVVVVvPfee0RHR3P+/Hn69u3bqa/hf926qJHJZJ1ebfTSSy9hZ2fHmTNnsLGxQS6X8+GHHzJu3Dg2bdrEU089xbp16zr1mILQ2bRaLVu2bKF37953HBZ06NAhTp8+zYwZM7CzsyMjI4NZs2b1mCEZkiRRVVXVrBqzpKRE/3kGN5dLyuVygoOD9RWZrq6uLfYTVKvV5Obmkpubq09oarVarK2t8fb2Zvz48Xh7e6NQKDrtSxV7e3tiY2OJiYkhNTWVxMREPv/8c3x8fPQrC//3M+78+fNs27aNiIgIJk+erN+emprKkSNHGD9+PGFhYS0er6SkhC1btpCUlIQkScyaNYs5c+Y80P1We6KgoCAmT57ME088wWeffYZareaXv/wl8+fP109QLygoIDY2lm+++YahQ4fi5ubWYmWdl5cXPj4+AEyYMIEXX3yRX/ziFzzzzDPodDr++te/YmZm1m2qEltzXngr3/PFF1/wu9/9zuCVnKmpqUycOPHeDzSC1pwTQvc+L8zNzb3r7fb6/PPP+fOf/0xBQcFt27y8vHjllVdu+5KgrUQnW0EQBEEA+s18kex9n5K+7hWQyXDuPwK/Sav02yWdloayfLTqOw/N+182Lp4Ez3uDa0fXc/ar3yCTybB18yNkwZtY2Bunf9H/+tOf/tTiN/T/SwIkJP60J4/tT4caJrifU9ff/J9F9+u7dOTIkWY9xlv6pr49hg8fzvDh/x1gFR0dTVBQEJ9//rl+OEFnu3DhAq+//jr79+/XLyu1sbFh0qRJ/OEPfyA0tOM/++PHj/Pb3/4WLy8vysvLgf9eLM2dO5eEhARefPFFjhw50uFjCUJXSUhIoKioiMceewxzc/Pbtp84cYJjx44xceJEwsPD+frrr5HL5XdMjBlbQ0PDbRWZJSUl+kGxlpaWKBQKPD09iYyM1Ccz79YzUKVSce3aNX1Cs6CgAK1Wi42NDd7e3kycOBGlUolcLu/yZdsWFhZERUUxZMgQLl68yIkTJ1i3bh2urq4MHz6cAQMGYGZmRmZmJps3byYsLIyHHnpIH9eVK1fYuXMngwcP1vfi/DmdTseJEyfYv38/V69excXFhRkzZhATEyOWpPdQ69at45e//CWxsbGYmJgwe/ZsPvrov33X1Wo1mZmZ+r+VrdG/f3927tzJH//4R4YPH46JiQkRERHs27fvnlXFhtLa88Jb9uzZ0+Jwwq6Ul5dHVVVVm1pOGEpXnROCcc4LO8tvfvMb3n//fZycnFi5ciWhoaHY2dlRW1tLWloa27ZtY9WqVVy+fJl33nmn3ccRCU5BEAThgTFg6V/vuM3c2p7AWb+943YrBwUjX9191/23tN3RNwJH34jWB2lAV69eZdeuXa0+idXqYGdaufEGD6m6Z4LTzs6OXr163fUxLi4umJqaUlxc3Oz+4uLiVvdSMjc3JyIigqysrlmuf+zYMaZMmYJOp2PGjBn069cPgMzMTHbs2MHevXvZt28fMTEx99jT3el0On3FgoODA6ampvpEJ9xc0tcd+jgJwp1cv36dI0eOEBMT0+Ky49TUVH788UdiYmKIjo7mypUr5ObmsmDBgk5t99AearWa0tLS25KZNTU1AJiamuLi4oJCoSAwMFC/vLxXr173TNSpVCquXr3aLKGp0+mwsbFBqVQyadIklEolrq6uRkv6mZiYEBISQnBwMNeuXePEiRPs3LmTQ4cO0adPHzIyMggNDWXmzJn6n1VxcTEbN27Ez8+PqVOn3hZ7aWkp27Zt48KFC9TV1REUFMSjjz7aYtsCoXuJj4+/4zYnJyfWr19/x+1KpfLeXw63sH3ChAlMmDCh1TEaUlvPC3U6HefOnTP44KFbA8W640T11pwTQs84L+wsycnJvP/++8yaNYtvvvmmxSFRH374IYsXL+a9995j7ty5DB48uF3HMliCc+/evbz//vv6ZvIt/dLcWuogCILQHjqtGknX8z5HtFptj2ztITMxxcT09qoVoe2M9d7du2d3m3uBShLsv1DBsmEtT3fsSlJtGSaWDgY/7p1oNJpWP9bCwoLIyEji4uKYOXMmcPPCIC4urtn01rvRarWkpaUxderU9oR7T8899xxyuZwjR47c1gPz2rVrjBo1iueff56TJ0926Dg+Pj7k5OQAN5MNPj4+HDx4UD/44cSJE7cNaRCE7kKtVrN161YUCgWjRo26bfvFixfZsWMHkZGRjBs3DkmSiIuLw9PTU/+lgSHodDoqKipuW15eVlam/9x3dHRELpcTERGhn1zu7Ozc6iX0TU1NzRKa169fR6fTYWtri1KpZMCAASiVSlxcXDo1oalSqdr0+XsnLi4uTJ8+nbKyMnbt2sUXX3yBk5MTYWFhXL9+HScnJ6qrq1mzZg12dnZMmzaNxsZG/fN1Oh3JycnEx8fr+4+Gh4cza9YsevXq1abKvp7KzMzsvhyY1VpddQ7fWe/xttq5c2c7zgslzp8/z7Bhw7ooqpZlZWV1aU/ytmrrz6snnBf+XEfyeatXr8bd3Z3169ffsaLV1taWDRs24Ovry+rVq7t3gnPz5s08+uijhISEMH/+fD799FMWLlyIJEls376dgIAA/Q9VEAShPXRaNTUFl9CqGowdSpu989G/+ds/vzB2GILQak+uvcyTay8b/sC/DDL8MTvR888/z7Jlyxg8eDBDhw7lgw8+oK6uTj89c+nSpXh4ePD2228D8OabbzJs2DD8/f2prKzk3XffJS8vr8P9ie7k/PnzvPXWWy0O+PH09OSpp55qsY9Ua1RUVODo6AjAxIkT2bRpE3/+858BeOqpp3jhhRfIzs5GkiTi4+N54YUX2v06BKErHTp0iIqKCp588snbEoE5OTn88MMPBAcHM23aNGQyGefPn+f69eusWLGiS6oWJUmirq5On8S89f83btxArVYDN9tMKBQK/Pz8iI6O1vfJbOvSycbGxtsSmpIkYWdnh1KpJDw8HKVSibOzc5dVaKpUKpKTk6mtre20fd64cYOffvqJAQMG4Obmxv79+9mxYwfu7u6UlJRgaWnJpEmTSEpK0j+nurqaxMREiouL9f2KQ0JC8PLy4syZM50WW3dnZ2fH0KFDH9gk51tvvcUf//hHY4dhdGvXrmXt2rXGDqPH6e7nhbd0NJ+XmJjI3Llz7/k3x8rKirlz53L48OF2x2qQBOfbb7/N0KFDSUhIoKKigk8//ZSVK1cybtw4cnNzGTZsmL7xriAIQntIOi1aVQMmZmY9rqrwped/yW+fNWzvmo7SadXoNFp6e4dgam6Epcr3Ea26kaq885iYGb4idv0P2/nV7/7Q5ud9vkDJ0ijDTlOXJImakW/QK3hMt3nPpaam3nGaakvmzZvHjRs3eP311ykqKiI8PJx9+/bpl2tfvXq12fLViooKnnjiCYqKinB0dCQyMpITJ04QHBzc6a8FwNvbW99vryUqlard083d3NyYOnUqixYt4oUXXmDBggWo1WrMzc159tlnqaurY/PmzZiamvLaa6/x8ssvt/dlCEKXyc3NJSkpiQkTJiCXN69iv379Ohs2bECpVDJr1iz9BNpDhw7h7+9/x4nbbdHU1MSNGzduS2beqhQ0NzfH1dUVhUJBWFiYvk+mra1tuxKOjY2N5OXl6ROahYWFSJKEvb09SqWSQYMGoVQqcXJyMtiSc41GQ21tLRYWFp3S2660tJTjx4/Tp08fJk6ciLm5OdHR0Vy6dInNmzdTWlpKdHQ0DQ0N+qX1Fy5c4NSpU8DNKdzm5uaMHDnygbuebWpqora2Fo1G88AmOF977TVeeeWVTt1nfX09x44d67T3eFvs2bOHv/3tb21+3ty5cxkyZEgXRNSyW9WDq1at6jYDvNp6Tgjd/7zwlo7m865du0ZQUOuKFIKDg/nmm2/aHatBEpwXLlzg7bffxtTUVD9R79Y3ikqlkqeffpp33nmHpUuXGiIcQRDuYyam5piY9ayTLJMe2A1Zp1GhaWrA3Nwc0xaGKwitZ4IWc3MzzCytDf7eHT92DDKZrE3LkWQymNDfAXODTwGWMLN16FbvuZamBN/LL3/5yzsuPfrfXmD/+Mc/+Mc//tGe0Nrl9ddf57nnnmPatGm39bVKTU3ln//8Jx988EG79j1nzhx27NjBjh07sLe355FHHmHRokWMGzcOmUzGq6++yquvvtrxFyEIXaSpqYlt27bh5eV121LM0tJS1q5di1wuZ968efrPhjNnzlBWVsacOXPadCytVktZWVmzJGZxcbF+GbRMJsPZ2Rm5XE5UVJR+ebmjo2OHenw2NDQ0S2gWFRUhSRK9evVCqVQyePBglEoljo6ORh+cY2lpiZVVx77sKi8v5+DBg7i6ujJ16lR9kk6SJKqrq1EqlUyfPp3i4mKOHDmCpaUlDQ03VwrdWr7u7OzM+PHjH9i2GiqVytghGJWpqWmrWzq0lrm5OWZmZtja2nb4Pd5Wo0aN4t13323jeaGMoKCgFoetdRWdToeJiQnm5uYGPe7dtOecELr3eeEtHc3nVVdXY29v36pj2dnZ6XtCt4dBLqttbGz0fzAcHBywtLSksLBQv12hUOh7MQmCIAiCYBieffswefxYfjx0pFV9sE1NYGqIA15Ohq0oAJBMzMCse1Ru3i9+9atf3XafQqEgMjKS6Oho/P39Abh8+TKJiYmEhoaSlJTEggUL2nysdevW0dDQwLZt21i/fj3r1q3j66+/RqFQsGDBAhYtWsSgQYM6/JoEoavs37+f+vp6li1b1iyJWFVVpR+asHDhQv01j1qtJj4+ntDQ0DtOR76VSPvfiszS0lL9Z7K9vT0KhYLg4GB9RaaLi0unXNTX19c3S2jeWm7du3dvlEolQ4cORalU4uDgYPSEZmerrKxk165d2NnZMXny5GYViCkpKWRmZjJu3DgCAgKQJImEhAT27t1LdXU1NjY2FBUVER0dTWxsbLsTG4LQ3bi5uRETE8Px48dbdV5oYmJCUFCQvgWNIT2oVcPG0NF83q02Hq3V1j6wP2eQT+PAwEAuXLigvx0eHs63337L4sWL0Wg0rF+/Hi8vL0OEIgiCIAjCz/zuuac5cPjoPSs5ZYAMGS9Pun1isCFIdu43y0eFTvPxxx/fcdvx48c5fvx4s/vS0tJIT0/nww8/bNfxrK2tWbBgAQsWLKCiooKNGzeyfv16PvjgAz744AMCAgJYvHgxCxcuFNOHhW4lMzOTlJQUpk+f3uxCvq6ujm+//RYTExOWLFnSbKnkyZMnqa2tZezYscDN6sj/rcgsKSnRt4WwtLRELpfj6elJZGSkPplpbW3daa+jrq7utoQm3LxgVSqVDBs2TJ/QvJ9VV1eza9curKysmDZtWrMquUuXLnHq1CmGDBlCQEAANTU1HDlyhIKCAkaPHk1RURHZ2dnY2Nhw9epVjh8/zoABA4yS4BGErvDYY49x/PjxVq/wmThxogGiul1Lk7iFrtEZ+bz33nuPDRs23PNYBQUFHYrVIAnOWbNm8dFHH/Hee+9haWnJK6+8wowZM/TfBtbV1fHll18aIhRBEARBEH4mMnwAaz77B8v/7zkkSWrxG3tTk5vJze9W+jPE284IUYLWQWmU497PumLya2s5OjqyatUqVq1aRUFBAevXr2fDhg28/vrrvPHGG0RFRXHixAmjxScIt9TX17Nz50769etHRESE/v6mpiZ9ZfLKlSvp1asXcLNH5LVr1/j+++9xdnZm7969FBcX65fcmZqa4uLiglwuJzAwELlcjkKhoFevXp1eJVlbW9ssoVlSUgLc/P1TKpVER0fj7e193yc0f662tpZdu3ZhZmbGtGnTmiWQ8/PzOXLkCP379yc8PJyLFy+SmJiIpaWl/ralpSXPPPMMdnZ2ZGRkkJ6eTkZGBt7e3gwYMAB3d/f7rtpVeLCEhITw9ttv8/vf/x5oeTL2rSr2ZcuWGa1QrbVLnoWOe+SRRzqUz/Py8qK8vJzy8vJWHa8j7ymDJDh/85vf8Jvf/EZ/+6GHHiI+Pp4tW7ZgamrKtGnT9N9uCoIgCIJgWDOmTuLgju945x+fsO/g4Wbf2MtkN5elvzzJw2jJTQCdS6DRji10LQ8PD1588UUmT57M66+/zvbt2/npp5+MHZYgIEkSu3btQqfTMX36dH3i6lbFyrVr15gwYQLp6en6isyysjKys7O5du0akyZNwtTUlPDwcH1FprOzc6f37LulpqamWULzxo0bwM1+kUqlkpEjR+Lt7U3v3r275PjdXV1dHbt27QJuXo/+vAKsvLycAwcO4OHhQXh4OHv37iU/P59+/fphbm7OmTNnUCqVjBkzRj/4JTw8nLCwMLKyskhLS2Pnzp24uLgwcOBAfHx8uuznLAhdbdy4cXz55Zd88cUXJCQk3La9f//+TJo0yWjJTSsrq06tbhda1tjYyPbt21Gr1bz66quUl5fj7u7e5nxebm6uwWI2WsOQmJgYYmJijHV4QRCEdsvc/nc09dWY2fQicMYL7d6PuqGGissnUdWWY2HnhGPAEMytxbeRgnFEhg9g49efcS3/OtETplNZVY2DtSkpL4UZpedmMzIZGsVAaH9LHqGbunr1qr56Mz09HUmSiI6OZtGiRcYOTRBIS0vj/PnzPPTQQxQXF5OWlkZRURF79uwhJyeH0NBQDh06hI2NDQqFAj8/PwYOHEh9fT2PPvoo06ZN69L4qqur9QnN3NxcysrKAHB2dkapVDJq1Ci8vb311aUPsoaGBnbv3o1Go2H69OnY2f33C7u6ujr27t2Lvb09np6ebNmyBXNzc8aMGcOlS5coKioiKiqKgQMH3ladaWpqSmBgIP369aOgoICzZ88SFxeHnZ0dYWFh9O/fX/QKFHqkkJAQHnvsMQASExPRarWYmJjQv39/JkyYYNQWg3K5XFRKd7GSkhKio6PJycnR99C0trZm27ZtjB8/vtvm80RHZEEQhDbS1Fejrq/q0D6qrqaTG7cGSacFExnoJApP70EZu4LeXiGdFKkgtJ1n3z7Y2FhTWVWNraWJ8ZObgFYRBha20NRg7FDue3v37uX9998nJSWFqqqqFvtvtWbwwN2Ulpbq+28mJiYiSRL9+/fnzTffZNGiRSiVyg7tXxDaS6VS6ftj5ubm8t1332FlZcXu3buBm1Nyr1+/Tl1dHU888QSDBw9GoVBga2urv9jeu3cvjo6OjBs3rtPjq66u1iczc3Nz9cv9XFxc8PHxYezYsXh7e4ulm/+jsbGR3bt309jYyPTp05slfFUqFXv37qWpqQkbGxtOnDhBYGAgSqWSY8eOATerPe80KOoWmUxG37596du3L6WlpaSlpfHTTz9x+vRpgoKCCA0NbZZUFYSeQKFQ8Mgjj5CWlkZVVRVWVlYMHz7cqG0tTE1NcXV1paFBnBN2pbfeeovc3Fyee+45xo0bR1ZWFm+99RarVq3iypUrxg7vjrokwenj49PmjLpMJuvW/1CCIAidRd1Q85/kpubmHbqbCQRJpyE37iuC578hKjkF4We0StHGxhA2b97Mo48+SkhICPPnz+fTTz9l4cKFSJLE9u3bCQgIYObMme3ad11dHVu3bmX9+vXExcWhVqtxd3fn2WefFRPUBYPTarWUlZU1G/ZTUlJCRUWF/jHZ2dlYWVmxePFivLy8cHV15dSpUyQlJfF///d/DBgw4Lb9VlZWcurUKcaMGdMpyyerqqqaJTRvxefq6oqfnx+xsbF4e3uLxNld3Epg1tbW8vDDDzdLzGi1Wg4cOEBOTg62trbU1tYyceJEqqqqOHDgAG5ubsTGxjYbHtUaLi4ujB07liFDhuh7dKalpeHn58eAAQNwcXHp5FcpCF1DLpcjl8v1bRnMzc0JDQ01ekxmZqJOr6v9+OOPLF26lPfee09/n0KhYOHChWRmZhIY2D1bR3XJO2P06NGiZFgQBOEOKi6fvFm52QJJp6Ui6yTysM6v/BCEnkiydkTrHg5GHIjzoHj77bcZOnQoCQkJVFRU8Omnn7Jy5UrGjRtHbm4uw4YNw8fHp137lsvlNDY2Ymdnx8KFC1m0aBHjxo3TDyoQhK4gSRLV1dW3TS4vLS3VVyLb29ujUCgICgrSD/zJzc1l//79LFmyBD8/PwASEhJISkpiypQpLSY3AQ4fPoy1tTVRUVHtireysrJZQrOyshK4+fsTEBCAUqnE29tbTA9uJbVazb59+6iqqmLatGk4Ozvrt0mSRFxcHEePHsXFxYX+/fsTGRlJYmIieXl5REREMHjw4A59RtnZ2TFs2DAGDRpEZmYm586d4/Lly3h4eDBw4ED69u0rrpkFoY08PDyMHcID4erVq/zud79rdt/IkSORJIni4uIHK8G5Zs2artitIAjCfUFVW65fln4bExmqmtZNmBOEB4HGbwKYmIFOZexQ7nsXLlzg7bffxtTUVF8doVarAVAqlTz99NO88847LF26tM37Hj9+PIsWLWL69OlYWVl1atyCADd7LP48iXnr/5uamgCwtLRELpfj6elJZGSkPpn5v5WWZWVlHDp0iCFDhuiTm6dPn+bgwYOMHj36jsnLkpISzp07x5QpU1rVc1GSpGYJzby8PH1CU6FQ6JdJe3t7t7mCULg5CGr//v2UlpYydepUXF1d9dtuDY/as2cP/v7+PPLII9jb27Nnzx6ampqYPHky3t7enRaLhYUFYWFhhISEkJ2dzblz59i3bx8LFiwQ1beC0AZOTk5YW1vT2Nho7FDue01NTbedr926rdFojBFSq4jaXqFLPP339VTU1ONob8MnLyw0djiC0K1Y2Dm1nNwE0ElY2DsZNiBB6K7MLND4iOXphmJjY6NPzDg4OGBpaUlhYaF+u0KhICcnp1373r59e6fEKAgajYYbN27ctry8uroauNmfzcXFBblcTmBgoH6JZe/eve9ZLafT6di6dSv29vZMmDABgPPnz7Nr1y6GDh3KmDFj7vjcQ4cO4eDgQGRkZIvbJUmioqKiWUKzqqoKmUyGQqGgf//+KJVKvLy8REKzg7RaLQcPHqSoqIipU6fi5uam39bQ0MCmTZtISEggMjKSJUuWkJeXR1xcHI6OjkybNq3LhjKZmJjg7++Pn58fVVVVIrkpCG10r164QufKzc0lJSVFf7uq6uYMisuXL7fYh7U7tBsSCU6hS1TU1FNaVdvBfdRx4ORFisqrcXPqxYQhQTjaiyU5Qs/nGDCEwtN7/tuD82dkJqY4+g8xQlSC0P1ovEeBhbgANJTAwEAuXLigvx0eHs63337L4sWL0Wg0rF+/3qhTU4UHy62E4P9WZJaVlemHXzk4OKBQKBg4cCAKhQK5XI6zszOmpqbtOubx48cpKChg5cqVWFhYcOXKFbZs2UJoaChTpky5Y4I0Pz+fjIwMHnnkEf2xJUmivLy82ZLzmpoaZDIZbm5uBAcH6xOandGvU7hJp9Nx6NAh8vPzmTRpEn369NFvy87OZvfu3WRkZDBu3Dhmz57N8ePHyczMJDg4mOHDhxukt59MJjPqkBZB6ImsrKxwdHQ0dhgPlNdee43XXnvttvuffvrpZrdvTVnv6BDKziASnEK3lJiezVtf70aj1WEik6GTJNbsTeT15dMYFuJr7PAEoUPMre1Rxq4gN+6rZlPUZSamKGNXiAFDggAgk6Hxn2TsKB4os2bN4qOPPuK9997D0tKSV155hRkzZuDg4IBMJqOuro4vv/zS2GEK9xlJkqirq7ttefmNGzf0LRJsbGyQy+X4+fkxfPhwFAoFrq6u+sEXnaGoqIj4+HhGjhyJp6cn+fn5fPfdd/j6+jJz5sw7JjclSeLgwYPI5XLc3Nw4deqUPqFZW1uLTCbD3d2dsLAwfUJTtGnoGjqdjvj4eHJzc5kwYQKenp7AzSnqCQkJpKenc+PGDWJjYxkzZgzbt2+nqqqKsWPH0q9fPyNHLwjC3SgUCtGz1oC++uqrLtv3/v37Wb16NdnZ2VRUVOi/tLylIwPIRYJT6HYqaup46+vdqDU3vwHQ/ucNr9ZoeXPNbta9vlJUcgo9Xm+vEILnv0FF1klUNeVY2Dvh6D9EJDcF4T+07hFIdm73fqDQaX7zm9/wm9/8Rn/7oYceIj4+ni1btmBqasq0adMYO1a0DBDaT6VS6ZeU/zyZWV9fD9yc0Ovq6opcLicsLEzfJ9PW1rZLL2w1Gg1btmzBxcWF0aNHU1JSwrp163B3d+fRRx9tsSJUkiRKS0s5duwYu3fvxsfHh08++QQTExPc3d0ZOHCgPqHZmYlYoWWSJHHs2DGysrKIjY1FqVQCN5dYHjt2jIaGBkxMTAgPDyc4OJjt27djY2PDrFmzcHISrYEEobtTKBTGDuGBsmzZsi7Z77vvvstLL72EQqFg6NChhIWFder+RYJT6HYOnLyIRtvytFyNVsfBUxnMHdtyfyNB6EnMre3FtHRBuAON/xRjhyAAMTExxMTEGDsMoQerrKxk7969lJSUUFFRAdysznByctJf4NxaXu7o6NihqdXtdfjwYcrKynjyySepra3l22+/pVevXixcuBBzc3PgZgLtxo0bzXpo1tbWkpKSgpOTExMmTMDHxwdPT0+R0DQwSZI4ceIEGRkZjB07Fj8/PxobG0lMTOTSpUt4eHhgZmaGlZUVTk5OHDlyBD8/P0aNGtWqgVCCIBiXk5OT+Fy9T3z44YeMGzeOPXv26P++diaR4BS6naLyakxkMn3l5s+ZyGQUllUZISpB6HzqhhoqLp9EVVuOhZ0TjgGiglMQAHQOSnQugcYOQxCETmBhYYFGoyEoKEhfkeni4tIlFzbtcfXqVU6cOEFsbCy2trZ8+eWXmJmZsWjRIqqqqjh79qw+oVlfX4+pqSl9+vRh0KBBqFQqdDodjz/+eKdO3RZaT5IkfvrpJ9LT04mJiaFfv37k5eVx7NgxNBoNo0ePJicnh+rqamxtbcnJyWHEiBGEhISI5a6C0EP8fFCY0LNVVFQwZ86cLjsHMEiCs6amhsrKSn0fFIDr16/z2Wef0dTUxOzZsxk6dKghQhF6ADenXuhaSG4C6CQJd+feBo5IEDpf1dV0cuPWNOvBWXh6D8rYFfT2CjF2eIJgVJqAySAuPLucj49Pmy/wO9IXSXgw2djYsGTJEmOH0SKVSsXWrVvp27cvERERfPzxxxQUFDBgwAA+/fRTGhoaMDU1xcPDg8GDB6NUKunbty8WFhZotVo++eQT+vfvL5KbRnT69GnOnj1LdHQ0fn5+xMfHk5mZiZeXFyNHjuTMmTOcP38eCwsL7OzsePjhh8VSV0HoQSwtLUUbifvI0KFDyczM7LL9GyTB+eSTT5KTk0NSUhIA1dXVDBs2jPz8fExMTPjwww/Zt28fY8aMMUQ4Qjc3YUgQa/Ym6ntw/pyZqQnjBwcZISpB6Dzqhpr/JDf/M0VddzOhL+k05MZ9RfD8N0Qlp/DAkmyc0HpEGTuMB8Lo0aNFBZPwwNLpdHz33XdcuHCB8PBwli1bRmVlJZGRkZiZmTF06FB9QrOlSpMzZ85QVlbG3LlzjRC9ADd/BqdPn2bo0KE4ODiwadMm1Go1o0ePJjAwkDNnzhAXF4eFhQVBQUHExsaKAU+C0MO4ubmJc5X7yCeffMKUKVMYPHgwCxcu7PT9GyTBmZCQwKpVq/S3165dy/Xr1zlx4gQhISHExsbypz/9SSQ4BQAc7W15ffk03lzTfIq6makJry+fhqO9jbFDFIQOqbh88mblZgsknZaKrJOiN6fwwNL0ewhMRQcdQ1izZo2xQxAEg9HpdBQVFel7aKakpHDq1Cl9IszBwYHnn3+eIUOG3HPpnFqtJj4+ntDQULF00kjOnz/PqVOnGDBgADU1NSQnJ+Ph4cGYMWOws7Pj/PnzrFu3Djs7OyZNmkRERIRR+rv+L0mSKCoq4tq1awwZMkQkbgThLkxNTXF3dzd2GEInmjdvHhqNhiVLlvDUU0/Rt2/f2wb5yWQyzp492679G+QKorS0FA8PD/3tHTt2MHLkSIYNGwbA0qVL+eMf/2iIUIQeYliIL+teX8nBUxkUllXh7tyb8YODRHJTuC+oasv1y9JvYyJDVVNu+KAEoRuQrB3RKEcbOwxBEO4DOp2OwsJCfULz6tWrNDU1YW5ujlwup76+nhkzZuDh4UFmZiYLFizA39+/Vfs+efIkdXV1jBsnvow0hqysLFJSUvDy8uLKlSuoVCpiYmIICgpCJpORlpbGv//9bxwdHXnssceatUkzBkmSKCsrIysri6ysLOrq6rCzsyMkJARbW1ujxiYI3Zm7u3u36dcsdA4nJyecnZ0JCAjokv0bJMHp4OBAUVERAA0NDRw7doxXXnnlv0GYmVFfX2+IUIQexNHeVkxLF+5LFnZOLSc3AXQSFvaiz4zwYFIHzQJTMdFWEIS202q1tyU0VSoV5ubmeHp6MmLECJRKJX369GHbtm14enri5eVFeno6s2fPbnVys7GxkWPHjjFo0CDRF84I0tLSOHHiBFZWVhQUFNC3b19GjRpFr169kCSJxMRE1q5di1wu59lnn6V3b+P17q+srNQnNauqqrC2tsbX1xd/f38UCoWo3hR6DOk/8zGkO8zJ6Aq3PruF+0t8fHyX7t8gCc7o6Gh9E+59+/bR2NjIjBkz9NsvXbrUrMJTEAThfuYYMITC03v+24PzZ2Qmpjj6DzFCVIJgXFKvPmi9Rxk7DEEQegitVsv169f1Cc1r166hUqmwsLDA09OTmJgYfULz58vf0tPTSU9Px9vbm7S0NKZNm0ZoaGirj3vixAn9dG7BsC5cuMDatWspLy/H09OTkSNH6qehq1QqDh48yJ49e/Dy8uKZZ57BxsbwK79qa2u5cuUKWVlZlJaWYmFhgVKpZMSIEXh4eHSLZfKC0FZqtRq4+blrKH5+fpiZiZZFQtsY5B3zzjvvMHHiRGbPng3ACy+8QEjIzSnBWq2WTZs2MXnyZEOEIgiCYHTm1vYoY1eQG/dVsynqMhNTlLErxIAh4YGkGrgUTEzv/UBBEB5IGo3mtoSmWq3GwsICLy8vRo0ahVKpxN3d/bZ+XrfU1NSwe/duLC0tyc3NJTY2liFDWv+lYm1tLUlJSURFRWFvL/5WG1J6ejrvvPMO9fX19O/fn0mTJiGXywEoLy9n3759pKSkEBAQwGOPPWbQ5GZjYyPZ2dlkZWVRVFSEiYkJXl5eRERE4OXlJZI0Qo+mUqlQqVTAzUSnRqPp8ve0XC7HxcWlS48hGJdarSYjI4Oqqip0Ot1t20eNal/Rg0E+bf39/cnMzOTChQv07t0bpVKp31ZfX8/HH3/MwIEDDRGKIAhCt9DbK4Tg+W9QkXUSVU05FvZOOPoPEclN4YGk7TsMnTzE2GEIgtCNaDQaCgoKmiU0NRoNlpaWeHt7M2bMGH1CszVVcZIksWPHDkpKSrC2tmbkyJHExMS0KaZjx45hYmLCiBEj2vuyhHZISEjg7bffxs7OjqeffhqVSkWvXr0AuHz5MkeOHKGgoAAfHx/mzZuHnZ1dl8ekVqvJzc3lypUrXLt2DUmS8PDwYPTo0SiVSiwtLbs8BkEwhPT0dH3lpk6nIy8vDz8/vy47np2dHf7+/qKFw31Kp9Px+9//nk8++eSubSrbWy1ssK+TzM3NW0xi2tvbN1uuLgi3VNTUceDkRYrKq3Fz6sWEIUE42otG3ML9w9zaXkxLFx54koUdqoGLjR2GIAhGplarmyU08/Pz0Wg0WFlZ4e3tzbhx41Aqlbi5ubVrmW9KSgpJSUmYmJgQGRnJpEmT2nQBXVFRwalTpxgzZgzW1tZtPr7Qdmq1mu+++441a9bg6+vLn/70J+zt7Tl69CgajYZjx45x4cIFtFotCoWCqVOndmnVl0ajIT8/n6ysLPLy8tBoNCgUCoYPH46vr69RlsQLQldSqVQkJiY2uy8jIwNvb+8uqeK0trYmJCTkjlX4Qs/3l7/8hXfffZdVq1YxcuRIlixZwjvvvIODgwOffPIJMpmMv/3tb+3ef5ckOI8ePQr8t6z01u17aW8ZqnD/SUzP5q2vd6PR6jCRydBJEmv2JvL68mkMC/E1dniCIAhCJ1EPXAxWxhsCIfxXTU0NlZWVzZr6X79+nc8++4ympiZmz57N0KFDjRihcD9Rq9Xk5+c3S2hqtVqsra3x9vYmNjYWpVKJQqHocN/C8vJyvv/+eyorK5k6dSrTp09vc3VQfHw81tbWREVFdSgWoXXy8/P56quvOHr0KMOHD+e1117D0tKS+vp6amtrOXToEDU1Nbi5uVFYWMjo0aPx8vLq9Dh0Oh3Xr1/nypUr5OTk0NTUhLOzM5GRkfj5+YlWBcJ9LTMzk/z8/GbDhUpLS8nPz2+2KrczWFlZERYWhoWFGDZ5P1uzZg2PPvoon376KWVlZQBERkYybtw4li1bxvDhwzl06BDjx49v1/67JME5ZswYZDIZDQ0NWFhY6G/fiSRJyGQygzatFbqvipo63vp6N2rNzfeD9j8fqGqNljfX7Gbd6ytFJacgCMJ9QOsxBK1ntLHDEP7jySefJCcnh6SkJACqq6sZNmwY+fn5mJiY8OGHH7Jv3z7GjBlj3ECFHkmlUjVLaBYUFOgTmkqlkgkTJugTmp25NFGn0/H1119z8eJFHn74YebMmdPm6qCSkhLOnTvH1KlTxcV3F9NoNMTHx7Nv3z6ys7N56KGHeOqpp/T/7pcvX2bPnj3Y29szcOBAUlJSGDRoEEFBQZ0WgyRJlJSUcOXKFa5cuUJ9fT29evUiJCQEPz8/nJycOu1YgtCdKRQKpkyZQnJysn6IW2RkJA4ODp16HBsbG0JDQ0VrhwdAfn4+v/3tbwH0P+/GxkYALCwsWLx4Me+//z5/+ctf2rX/LklwHj58GED/h+jWbUFojQMnL6LR3t5oFkCj1XHwVAZzx0YaOCpB6HzqhhoqLp9EVVuOhZ0TjgGiB6fw4JCsHFBFrATRY6nbSEhIYNWqVfrba9eu5fr165w4cYKQkBBiY2P505/+JBKcQpuUl5ezdetWCgoK0Ol02NjYoFQqmThxIkqlErlc3qW91vbv38/evXsZM2YMS5cuxdzcvM37iIuLw8HBgUGDBnVBhMIt169fZ+vWrVy9epW6ujomTpzI8uXLsbCwQKfTcfjwYQ4dOoRcLmfAgAEcO3YMf3//Ng2Kupvy8nL9BPTq6mpsbGzw8/PDz8+vy9+ngtAdyeVy5HK5PhFlbm5OaGhopx7D3t6ekJCQdn02Cz2Ps7MztbW1wM1+q7169SI7O7vZYyoqKtq9/y5JcI4ePfqutwXhborKqzGRyfSVmz9nIpNRWFZlhKgEoXNVXU0nN25Nsynqhaf3oIxdQW8vMWxFuM/JZKiGrALLrh8EIbReaWkpHh4e+ts7duxg5MiRDBs2DIClS5fyxz/+0VjhCT2Ura0tvXr1IiwsDKVSiaurq8ESRVeuXOGf//wnAQEB/PrXv8bKyqrN+7h27RqZmZk88sgjoi9cF9FoNBw9epSEhARsbW31Vb3Lli3DysqK2tpaNm/eTG5uLuPGjaO0tJT4+HgUCgWjR4/u0PuppqZGn9QsKyvD0tISHx8fYmJi6NOnT4fbIwjC/eDWEnWphevzjnB0dCQoKEh8tj5AIiIiOHnypP722LFj+eCDD4iIiECn0/HRRx91aAC5QYYMFRUV4ebmdtfHJCcni75OAgBuTr3Q3eHDUydJuDuLXm1Cz6ZuqPlPclNz8w7df04adBpy474ieP4bopJTuK+pAx9GJ+/cCgCh4xwcHCgqKgKgoaGBY8eO8corr+i3m5mZ3XXipSC0xNLSkrlz5xr8uLW1tbz66qtYW1vzxz/+EVvbtrc3kiSJgwcPolAoCAsL64IohcLCQrZt28aNGzcYNGgQGRkZuLi4sHTpUqytrcnLy+OHH35AkiSWLVuGra0tr732GjY2NkyYMKFdg04aGhr0Sc3i4mLMzMzw9vZm8ODB9O3bt0uGpwhCT6ZWq4H2T7ZuiVwuJyAgQHyJ8IB58sknWbNmDU1NTVhaWvLnP/+ZUaNGMWrUKCRJwtHRkQ0bNrR7/wb59A4JCeHjjz9mwYIFt21Tq9W8+uqrvP/++/pfHOHBNmFIEGv2Jup7cP6cmakJ4wd3Xo8dQTCGissnb1ZutkDSaanIOimmqwv3LZ1zAJqgWcYOQ2hBdHQ0n3zyCf3792ffvn00NjYyY8YM/fZLly41q/AUhO5KpVLxhz/8gdLSUv7xj3+0e7L2lStXyMvLY+HChWJ5cifTarUcO3aMo0ePIpfLWbBgAXv27MHCwoJly5ZhY2PDiRMnOHjwIJ6ensyZMwcrKyu++OILNBoNEydObFNFrkqlIicnh6ysLK5fvw6Ap6cn48aNQ6lUGnR5rEajobi4WHyeCj2CSqVCpVIBN3M3Go2mw18CuLu74+fnJz5XH0DTp09n+vTp+tvBwcFcuXKF+Ph4TE1NiY6O7lCfY4MkOAcPHszixYvZvHkzn332mf4k4/Tp0yxbtoyMjAx+/etfGyIUoQdwtLfl9eXTeHNN8ynqZqYmvL58Go72NsYOURA6RFVbrl+WfhsTGaqacsMHJQgGIFnao4r6JZiI6pju6J133mHixInMnj0bgBdeeIGQkJstM7RaLZs2bWLy5MnGDFEQ7kmr1fLZZ5+RmprK008/3e5+cZIkERcXh5eXFwEBAZ0c5YOtuLiYbdu2UVxcTExMDIMGDeKbb75Bp9OxYsUKzM3N+f7778nIyGDkyJGMG3fzS99NmzZRUlLC2LFjWzW9XKPRcPXqVbKysrh69So6nQ53d3dGjBiBr69vu1oWdER5eTkZGRlcvnyZxsZGFi1ahJ2daNUidG/p6en6yk2dTkdeXh5+fn7t3l+fPn3w9fUVyU1Br3fv3s2+UO8Ig1xh7N+/n88//5wXX3yRkJAQPvroI9LS0njnnXdQKpXEx8czcuRIQ4Qi9BDDQnxZ9/pKDp7KoLCsCnfn3owfHCSSm8J9wcLOqeXkJoBOwsJeTOcU7kMyGaohTyFZi/d3d+Xv709mZiYXLlygd+/eKJVK/bb6+no+/vjjDvVFEoSuptPp2LRpE/v37yc2NpZZs9pfLX7+/HkKCwtZuXKluBDvJDqdjoSEBI4cOYKzszOPP/44Dg4OrFmzBpVKxYoVK2hsbOSbb76hvr6eBQsWEBgYCMC+ffvIyMhg1qxZFBYW3vUYBQUFZGVlkZubi0qlwtXVlSFDhuDn52fwhKJKpeLKlStkZmZSXFyMtbU1gYGBBAYGiuSm0O2pVCoSExOb3ZeRkYG3t3e7qjjd3d1FclPQf2l++PBhSkpKePPNNwkLC6Oqqoq4uDhGjBiBQqFo174NVkKxatUqJk2axCOPPMLChQuBm+vv//73v2NjI5JWwu0c7W3FtHThvuQYMITC03v+24PzZ2Qmpjj6d840UEHoTtTBs9EpRA+77s7c3LzFJKa9vX2nfbsuCF1BkiT27t3Lrl278Pf358knn2x3bzetVsvhw4cJCAjAy8urkyN9MN24cYOtW7dSWFjIyJEjGT16NBqNhq+//pq6ujqWL19Obm4ue/bswdXVlSVLluDo6AhAUlISSUlJTJs2jYCAgNsSnJIkUVxcTFZWFtnZ2TQ0NODg4EBYWBj+/v44ODgY9LXeiicjI4Ps7Gw0Gg2enp5MnDgRLy8vMVBF6DEyMzPJz89vNlyotLSU/Pz8Zl+Ctoazs7NYli5QWVnJ5MmTSU5Oxs7Ojrq6Op555hng5lT1X/3qVyxdupS//OUv7dq/wRKckiSxYcMGLly4gEKhoKSkhBMnTnD58mVRDSAIwgPF3NoeZewKcuO+ajZFXWZiijJ2hRgwJNx3tO4RaAIfNnYYwv84evQoAKNGjWp2+15uPV4QupPDhw+zf/9+bG1tWbRoUbv7bgKcOXOGsrIyowxHut/odDoSExM5dOgQjo6OPP7443h4eNDU1MTatWuprKxk4cKFnDhxgtTUVCIjI5kyZYq+OuzixYvs37+fESNGMGTIEP2gM0mSKC0tJSsriytXrlBbW4utrS39+vXD398fZ2dngydSGhoauHTpEhkZGVRWVmJvb094eDj9+vUT1ZpCj6RQKJgyZQrJycmoVCosLCyIjIxs85cGtra2BAYGiuSmwEsvvcT58+fZv38/ERERyOVy/TZTU1PmzJnDnj17uneCMzMzk2XLlpGcnMyqVat47733SElJYcWKFURFRfHqq6/y8ssviwlagiA8MHp7hRA8/w0qsk6iqinHwt4JR/8hIrkp3HekXn1QDXkKZOJvfHczZswYZDIZDQ0NWFhY6G/fiSRJyGSyTp2iKgidISkpibi4OHQ6nT4R1l5qtZr4+HjCwsJwc3PrxCgfPKWlpWzbto2CggKGDx/O2LFjMTc3R61Ws2HDBm7cuMHDDz/Mrl27KC8vZ9asWc0KX65du8bmzZsJCQlh/PjxwM0+lmlpaRQWFlJbW4uVlRW+vr74+/vj5uZm8ASKTqcjPz+fzMxMcnNzkclkKJVKRowYgYeHh0joCD2aXC5HLpdjaWkJ3Fzl0da+xqampgQFBYnKZQGAbdu28cwzzzBhwgTKyspu296vXz/WrFnT7v0bJMEZHh6Oq6sr+/fvZ8KECQDExMRw7tw5fvvb3/LGG2+wY8cOkpOTDRGOIAhCt2BubS+mpQv3NcnChqZhz4G5tbFDEVpw+PBhACwsLJrdFoSe5OzZs+zbtw8zMzP69u3LjBkzOpRUSk5Opq6ujrFjx3ZilA8WnU7HTz/9RFxcHL1792blypV4enoCNwf/fPfddxQUFBAVFcXOnTuxs7Pj8ccfb9Zzrby8nA0bNtCnTx9iY2P56aefSEtLIzc3l4KCAvr160dMTAweHh5GSZxUV1frqzXr6upwdnZm+PDh+Pv7G3x4kSB0Z35+flhbi/NA4aaqqip8fHzuuF2tVqPR3N7GrbUMkuCcN28eH374Ib179252v42NDR9//DGPPPIIjz32mCFCEQRBEATBEGQyVEOfQbIXFVDd1ejRo+96WxC6u8zMTLZv346Liws3btxgypQpt11vtEVjYyMJCQkMGjQIJycxEK09ysvL2bZtG9euXSMqKorY2FjMzc2B/w6WyMnJwdvbm4SEBIKDg5kxY4a+QgxuDjX76quvKCsrw9HRkY8++ggTExMCAgKYNWsWRUVFODo6GjyRqNFoyM3NJSMjg4KCAiwsLPD39ycwMBBXV1dRrSkI/8PJyanZEmRB8PPzIyUl5Y7bf/zxR4KDg9u9f4MkOO9VYjpu3DjS0tIMEYogCIIgCAagHrAQnaJty5gE4yoqKrrnktzk5GSGDh1qoIgE4c5yc3PZtGkT3t7eFBYWEhwczIABAzq0zxMnTqDRaESyvx0kSSI5OZmDBw9iZ2fH8uXL8fb21m/X6XRs2bKF9PR0bG1tycvLY/LkyURFRekTgyqVivPnz/Ppp5+Sl5dHREQElpaWTJ8+naCgIKysrKivr6e0tNSgr62srIyMjAwuX75MU1MT7u7ujBkzBl9fX33yVhCE5kxNTfH39xeJf6GZxx9/nN/97neMGTOG2NhYAGQyGU1NTbz55pvs27ePf//73+3ev8GGDN2LaLwsCIIgCPcHrfdINH6TjB2G0EYhISF8/PHHLFiw4LZtarWaV199lffffx+1Wm2E6AThvwoLC9mwYQN9+/bF1NQUU1NTHnrooQ5dSNfW1pKYmEhUVBT29qIfdltUVFSwfft2cnNzGTp0KOPHj9e3voCbyc/t27dz/PhxzMzMcHZ2Zu7cuXh6eqLVarly5QppaWlkZGRw5swZNBoNv/zlLxk7dqzRrhFVKhVZWVlkZmZSUlKCtbU1/fv3p3///gafyi4IPZGXl1ezymxBAPj1r3/N+fPnWbBggf6zdOHChZSVlaHRaFi1alWHVncbLMFZVFTE6tWrSUlJoaqqCp1O12y7TCYjLi7OUOEIgiAIgtAFdI5KVBErQHxj3+MMHjyYxYsXs3nzZj777DP9FOrTp0+zbNkyMjIy+PWvf23kKIUHXVlZGWvXrsXFxYWgoCD27t3L/PnzsbW17dB+jx49iqmpKSNGjOikSO9/kiRx6tQpDhw4gI2NDcuWLbutt5okSezatYsdO3ZgY2NDZGQks2bNorS0lJ07d3LhwgUaGhqQy+VYWVnh5+fH8uXL6d+/v1FeT1FREZmZmVy5cgWtVouXlxeTJk3C09NTDEkRhFaytramT58+xg5D6IZkMhlffPEFy5Yt44cffuDy5cvodDr8/Px49NFHGTVqVIf2b5AE57lz5xgzZgwNDQ0EBgaSlpZGcHAwlZWVFBQU4Ofnp288LQiCIAhCD2VujSrqV2Bqce/HCt3O/v37+fzzz3nxxRcJCQnho48+Ii0tjXfeeQelUkl8fDwjR440dpjCA6y6uppvvvkGa2trpk2bxtdff01ERESHk2EVFRWcPn2asWPHimEYrVRZWcmOHTvIzs5m8ODBTJgw4bZqrVuVm9988w1yuZyxY8dia2vL559/Tk1NDQ4ODgwePJjQ0FByc3PZu3cvjzzyiMGTm/X19fqBQVVVVfTq1YtBgwYREBAgVhkKQjv4+vpiYmJi7DCEbmzkyJFdck5pkATnSy+9hJ2dHWfOnMHGxga5XM6HH37IuHHj2LRpE0899RTr1q0zRCiCIAiCIHQRVeQTSLauxg5D6IBVq1YxadIkHnnkERYuXAjAk08+yd///ndsbGyMHJ3wIKuvr+fbb78FYPHixWzbtg0rKysmT57c4X0fPnwYa2troqKiOryv+50kSaSmprJ//36srKxYsmQJfn5+LT5248aNfP755/Tu3Ru5XK7vvxkaGkpoaCh9+/ZFJpORkZHBvn37iI6ONliPX51Ox7Vr18jIyODq1avIZDJ8fX2JiYmhT58+om+gILRT7969cXR0NHYYwgPKIAnO48eP89vf/hYvLy/Ky8sB9EvU586dS0JCAi+++CJHjhwxRDiCIAiCIHQyjXI0Wo8hxg5D6CBJktiwYQMXLlxAoVBQUlLCiRMnuHz5MgMHDjR2eMIDqqmpiXXr1lFfX8+KFSu4ePEiubm5LFu2rMM93oqLi0lLS2Pq1KliYMw9VFdXs2PHDrKysoiIiGDSpEktTjKvrKzkz3/+Mzt27MDV1ZUBAwYQERFBWFgYPj4+zSq7CgoK2Lx5M0FBQUyYMKHLX0NVVRWZmZlcunSJuro6nJ2dGT58OP7+/gafyi4I9yMfHx/xBYHQzPTp09v0eJlMxvbt29t1LIMkOHU6HQqFAgAHBwdMTU31iU6AsLAwVq9ebYhQBEEQBEHoZJK1I+oBC40dhtBBmZmZLFu2jOTkZFatWsV7771HSkoKK1asICoqildffZWXX35ZLDsTDEqj0fD9999TWlrKsmXLkCSJuLg4hg0bdlu/x/Y4dOgQDg4ODBo0qBOivT9JksTZs2fZt28f5ubmLFq0iICAgGaPqa+v5/z585w5c4bvvvuO7OxsoqOj+f3vf0///v0xM7v9srOiooL169fj5ubGrFmzuiwpotFoyMnJITMzk4KCAiwtLfH396d///76XsOCINyZJEnN/v9OXFxcxJA24Ta7du3CysoKNze3e76HgA79LTBIgtPHx4ecnBwATExM8PHx4eDBgzz66KMAnDhxQkyjEwRBEIQeSh2+FMzF8uWeLjw8HFdXV/bv36+vpIqJieHcuXP89re/5Y033mDHjh0kJycbOVLhQaHT6diyZQtXr15l8eLFKBQKVq9ejYODA7GxsR3e/7Vr18jMzGT27NligMwd1NTUsHPnTi5dusTAgQOZPHmyvk9pU1MTGRkZpKenc+XKFerq6khPT6empobf/e53/N///d8d91tfX8+6deuwsrJiwYIFXVI9W1paSkZGBllZWTQ1NdGnTx/Gjh2Lr69viwlXQRBaplarAdBqtXd9nLe3tyHCEXoYDw8PCgoKcHFxYeHChcyfPx83N7cuOZZBPtknTpzIpk2b+POf/wzAU089xQsvvEB2djaSJBEfH88LL7xgiFAEQRAEQehEOnkIWvdIY4chdIJ58+bx4Ycf0rt372b329jY8PHHH/PII4/w2GOPGSk64UFza/p2RkYGjz76qH7QVVFREY8//niHE2KSJHHw4EEUCgWhoaGdFPX9Q5Ik0tLS2Lt3L6ampixYsIDAwEA0Gg0XL14kPT2dzMxMNBoNXl5eBAQEEB8fD8ALL7ygL2RpiUaj4bvvvqO+vp7HH3+8U/v7NjU1kZWVRUZGBqWlpdjY2BAUFERgYKAoqBGEdlCpVKhUKuBmolOj0bT4BYFcLhe9uoUWXbt2jSNHjrB+/XreeustXnzxRUaPHs2iRYuYM2dOp1b9GiTB+corr7BgwQLUajXm5uY8++yz1NXVsXnzZkxNTXnttdd4+eWXDRGKIAiCIAidSBW2AESvpfvCmjVr7rp93LhxpKWlGSYY4YEXFxdHSkoKM2fOpH///hQUFHD06FFGjRpFnz59Orz/K1eukJeXx8KFC0W/uP9RW1urTy6HhYUxadIkiouL2b59OxcvXqSxsRE3NzfGjh1L//79SUpKYteuXVRWVvL4448zc+bMO/6bSpLE1q1buX79OsuXL8fJyanD8UqSRGFhIZmZmVy5cgVJkvD09CQyMhIvLy/RVkMQOiA9PV1fuanT6cjLy7ttsJhMJhPVm8JdjR49mtGjR/Pxxx+zZ88e1q9fzy9/+UuefvpppkyZwsKFC3n44Yc73FfbIAlOR0dHIiP/W90hk8l49dVXefXVVw1xeEEQBEEQuoDWYzCSgzihfZDY2dkZOwThAXD8+HESEhKYNGkS4eHhqNVqtm7dipubGzExMR3e/63qzVuVh8J/paens2fPHuDmBWljYyOfffYZtbW1ODk5ERUVRWhoKK6urlRWVrJx40bS09PR6XTMmzfvrslNgIMHD3LhwgUeffRR+vbt26FY6+rquHz5MhkZGVRVVdG7d28GDx5MQEAAtra2Hdq3IAg3qzcTExOb3ZeRkaFPZl67dg1PT0/69u0rhnQJrWJubs6MGTOYMWMGtbW1bNmyhc8++4x58+bxhz/8gddee61D+zdK85HGxkY2btzIpEmT9MOHBEEQBEHoWdT9Zxo7BKGTFRUVsXr1alJSUqiqqkKn0zXbLpPJiIuLM1J0woMgJSWFAwcOEBMTw/Dhw4Gb1ZyVlZWsWrWqU3plnj9/nqKiIlauXCmqN/+jrq6OPXv2kJycjLW1NXZ2dhw5cgR7e3vCwsIIDQ2lT58++n+vy5cvs2XLFqqrq7G0tCQmJuaeyc2TJ09y/PhxJk+eTFBQULvi1Ol05Ofnk5+fT1FRETKZDD8/P0aNGoW7u7v4eQpCJ8rMzCQ/P19fwSlJEqWlpfr7Tp06hSRJ+s9qQWitpqYm9u/fz/bt20lNTcXKygqlUtnh/RolwVlVVcWKFSs4cOCASHAKgiAIQg+kcw0S1Zv3mXPnzjFmzBgaGhoIDAwkLS2N4OBgKisrKSgowM/PD09PT2OHKdzHLl68yM6dOxk8eDDjxo0DICcnh6SkJCZNmoSrq2uHj6HVajl06BD9+vXDy8urw/u7HyQlJfHNN99QWFiIu7s7Li4uBAUFERYWdtsSb51OR3x8PEePHsXBwQEzMzPCwsJ45JFH7roU/NKlS+zZs4dhw4YxbNiwNsdYVlZGamoqycnJXLhwgT59+hAdHY2/v3+HlzQKgtAyhULBhAkTSEpKoq6uDgsLCyIiIrC3tycpKYmysjKuXr0q2kAIraLT6Thw4AAbNmxg27Zt1NfXM378eL744gtmzZrVKZX3Rhsf15rx8IIgCIIgdE8avwnGDkHoZC+99BJ2dnacOXMGGxsb5HI5H374IePGjWPTpk089dRTrFu3zthhCvep7OxsfvjhB4KDg5k6dSoymYzGxka2bduGUqlsV1KsJampqVRUVDBv3rxO2V9PVVtby+nTp/nuu++4ePEiCoWCOXPmMHjwYPz9/VuslK2rq+OHH34gNzeX0NBQMjIy8Pf3Z86cOXetrL1+/TqbNm2if//+TJw4sdUxqtVqLly4QGpqKrm5uVhZWdG/f3+USiXe3t5iSawgdDG5XI6DgwNDhw5Fp9NRVlaGra0tlZWVlJSU0KdPH6qqqjh//jwRERHGDlfopk6cOMH69evZtGkTZWVlDBs2jL/85S88+uijuLi4dOqxjJbgFMsHBEEQBKFnkizs0LqLE9n7zfHjx/ntb3+Ll5cX5eXlAPol6nPnziUhIYEXX3yRI0eOGDNM4T5UUFDAd999h1KpbFYJuH//fhobG++59Lm11Go1R44cITQ09IFcRdbY2MjFixdJS0vj5MmTXLp0CScnJ5555hkeeuihu1ZCXr16lU2bNqHT6Zg4cSLx8fH07duX+fPntzhR+ZbKykrWr1+PQqG4Z5XnLYWFhaSkpJCWlkZjYyM+Pj7Mnj2b/v37o1arOXr0aLtevyAIbXOrB6eZmRkmJiaYmZlx4cIFAMzMzHBycqK+vp4TJ04QEhKChYWFkSMWuqORI0dibW3N1KlTWbBggX4p+tWrV7l69WqLzxk0aFC7jiUqOAVBEARBaBNt3ygwMdophNBFdDqdPunj4OCAqampPtEJEBYWxurVq40VnnCfunHjBuvWrUMulzNv3jx9JWBGRgapqanMmDEDBweHTjlWcnIydXV1jB07tlP21xOo1WouXbpEWloaly9fpqmpierqapqamliwYAGzZ8/G3t7+js+XJInExEQOHjyIp6cnMTExbN68GblczoIFCzA3N7/jcxsaGli7di0WFhatemxaWhopKSkUFRVhb2/PkCFDiIiIaDZpXa1Wt+8fQhCENrvVg1OlUlFVVYVGo+HatWsAWFpaUlZWhkwmIz8/n8zMTMLCwowcsdBdNTQ0sHnzZrZs2XLXx0mShEwm0/d9bSujXJ0oFIrbmtYLgiAIbaduqKHi8klUteVY2DnhGDAEc+s7X6gIQmfQ9o0ydghCF/Dx8SEnJwcAExMTfHx8OHjwII8++ihwc4lRZyWaBAFu9uX/9ttvsbOzY9GiRfrqn7q6Onbu3ElgYCDh4eGdcqzGxkYSEhKIjIxsljC7H2m1WrKzs0lPT+fixYuoVCo8PDwIDAzk8uXL2NnZMXnyZAYOHHjXytjGxka2b9/OxYsXGTFiBAMGDOCbb77BwcGBRYsW3bXiU6PR8P3331NXV8fjjz/eYm81SZLIzc0lJSWFixcvotPp6NevH+PGjcPf31/09RMEI1MoFEyZMgW4mezUaDTU1dUB4OzsjK+vr/4z5EGsihda56uvvjLYsQyS4AwKCmLJkiUsWrQIb28xkEAQBKEzVF1NJzduDZJOCyYy0EkUnt6DMnYFvb1CjB2ecJ+SLGzROfczdhhCF5g4cSKbNm3iz3/+MwBPPfUUL7zwAtnZ2UiSRHx8PC+88IKRoxTuF3V1dXzzzTeYmJiwZMkSrK2tgZtJr127diFJEg8//HCntbU6fvw4Go2GUaNGdcr+uhtJkrh69Srp6emcP3+e+vp6XFxcGDFiBP7+/pw6dYrU1FT8/f2ZPn06vXr1uuv+ioqK2LhxI/X19cyfPx+5XM5XX32Fra0tS5YsuWv/S0mS2L59O/n5+SxduhRnZ+dm26urqzlz5oy+H6qzszNjx45l4MCB2NnZdcq/hyAIHSeXy5HL5QBYWFg0q6D28/OjT58+xgpN6EGWLVtmsGMZJMHp6enJG2+8weuvv050dDRLly5l7ty59O7d2xCHFwRBuO+oG2r+k9zU3LxDd7Pth6TTkBv3FcHz3xCVnEKX0CnCwOTOwySEnuuVV15hwYIFqNVqzM3NefbZZ6mrq2Pz5s2Ympry2muv8fLLLxs7TOE+0NTUxNq1a2lqamLlypXNlkifO3eOixcv8uijj3Zasqu2tpakpCSioqLuuhy7p5EkiaKiItLT00lPT6eqqorevXsTERFBWFgYCoWC7Oxsvv/+e5qampg+fToRERH3TBqnpqaye/duXFxcWLx4MSYmJnz11VdYWFiwdOlSbGxs7vr8Q4cOkZ6ezty5c/WT6rVaLZcvXyYlJYXLly9jZmZGSEgIs2bNwtPTU8xnEIQextXV1dghCMJtDJLg/PHHHykuLmb9+vWsX7+eJ598kmeeeYZp06axZMkSpk6deteeLIIgCA+iuy0/r7h88mblZgsknZaKrJPIw8YZMlzhPqJwdUXWVIOb7e0XnFq3gUaISDAER0dHIiMj9bdlMhmvvvoqr776qhGjEu43Go2GDRs2UFFRwfLly5stF6+qqmLPnj0MGDCA4ODgTjvm0aNHMTU1ZcSIEZ22T2MqKysjPT2dtLQ0SktLsbGxISQkhLCwMH2ysKmpiV27dnH69Gl8fX2ZPn36PVtMqNVq9uzZQ2pqKoMGDWLKlCk0NDSwZs0aTExMWLZs2T2TzqdPn+bYsWNMnDiR4OBgSktLSU1N5ezZs9TW1uLh4cG0adMIDQ19YKegNzU1cePGDfr27WvsUAShXRwcHET+RuiWDNaDU6FQ8Nxzz/Hcc8+RmZnJ2rVr2bBhA1u3bsXBwYF58+axePFioqOjDRWSIAhCt3Wv5eeq2nL9/bcxkaGqKb/9fkFopaP7tmB57G1MSi7ctk0rFw3kHxSNjY1s3LiRSZMmid5aQqfQ6XRs2rSJ/Px8lixZgpubm37brWXNlpaWTJ06tdOOWVFRwalTpxg3bpx+GXxPVF1dzfnz50lLS+P69etYWFgQFBTE5MmT8fHx0Q9nAsjJyWH79u3U19fz0EMPERkZec8KyfLycjZu3EhpaSkzZ84kPDxc30ZAo9GwcuXKey5rv3z5Mrt37yYiIgJra2u+/PJLrl69irW1NQMGDGDQoEEP5GeJVqulpKSEgoIC8vPzuXHjBjqdjsWLF7fYm1QQurv/bTshCN2FUYYMBQYG8tZbb/GLX/yCX//612zatInPPvuMzz//HF9fX5599lmeeuop0VhaaJeKmjoOnLxIUXk1bk69mDAkCEd7cfIg9BytWX5uYefUcnLzP4+3sL+/BygIxqFz8AYr0V7mQVFVVcWKFSs4cODAA5mUEDqXJEns2LGDy5cvM3/+/Nv68icnJ5OdnX3P/o5tdfjwYWxtbYmK6nnD0err67l48SJpaWnk5eVhampKQEAAI0eOJCAg4LYKKpVKxcGDB0lOTkapVLJs2TIcHR3veZyLFy+ybds2bG1teeKJJ1AoFDQ0NPDNN9/Q2NjIihUr7ln9ef36dVavXo1WqyU9PZ3U1FR8fX2ZM2cO/fv3x8zMKJedRiFJEhUVFfqEZmFhIWq1GktLS/r27UtgYCCOjo4iuSn0WPf7oDah5zL4X5q6ujq2bt3K2rVrOXToEAAPPfQQS5cuxcLCgn//+9/86le/4ty5c3z++eeGDk/o4RLTs3nr691otDpMZDJ0ksSavYm8vnwaw0J8jR2eIDRzpyXorVl+7hgwhMLTe/6bBP0ZmYkpjv5Dujp84QGkU4jqzQeNJN3hixRBaANJkvjxxx85c+YMjzzyCP36NR9UVlpaysGDBxk6dCh+fn6ddtzi4mLS0tKYNm1aj1lOqVKpyMzMJC0tjaysLCRJwtfXlxkzZtC/f/87Jn/z8vLYtm0btbW1TJ06lSFDhtyzalOr1RIXF8eJEycIDg5m+vTpWFlZ0djYyLfffktNTQ0rVqy4a7VWfX09J06c4JNPPkGtVhMTE8OQIUMIDw9vVXL1flFbW0tBQYH+f/X19ZiamuLm5sagQYPo27ev/t/x9OnTJCYmMnfu3PuqJ6zwYLCysnpg20sI3Z9BEpxarZb9+/ezdu1aduzYQX19PZGRkfz9739nwYIFuLi46B87ffp0Xn75Zf71r3+JBKfQJhU1dbz19W7UmpuJIe1/LsrUGi1vrtnNutdXikpOodu42xL01iw/N7e2Rxm7gty4r5rtQ2ZiijJ2hRgwJHQJ0X/zwSMGfwidISEhgcTERKZMmcKAAQOabdPpdGzdupVevXoxfvz4Tj1uXFwcjo6OREREdOp+O5tWqyUrK4u0tDQyMzNRq9V4enoyefJkgoOD79r3Uq1WExcXx08//YSnpydLlixpVXVVTU2Nvl3ApEmTGDZsGDKZDJVKxbp16ygvL2fZsmUtDhKRJImcnBxSUlI4d+4cp0+fxtXVlWeffZYBAwY8EKvwVCoV169f1yc0KyoqkMlkODs7069fPzw8PHBzc2tWuapWq4mPjycnJ4ehQ4eKifFCj3SvVhWCYEwGSXC6ublRXl6Oh4cHzzzzDEuXLiUoKOiOjx8wYAA1NTWGCE24jxw4eRGNVtfiNo1Wx8FTGcwdG9nidkEwpHstQZcPiG3V8vPeXiEEz3+DiqyTqGrKsbB3wtF/iEhuCl1CsrBF5xRg7DAEAxMVnEJHFRYWEhcXx5gxY1pcJp6QkMD169d57LHHsLCw6LTjXr16lUuXLjF79uxm/Sm7C51OR15eHmlpaVy4cIHGxkYUCgWjR48mNDT0nkvC4eZr3L59O1VVVUycOJGoqKhWJRdzcnL44YcfMDExYfny5fpJ52q1mg0bNlBSUsKSJUtwd3dv9ryqqirOnDlDamoqlZWVODk5odPpGDZsGE899VSzopX7jVar5caNG+Tn5zfro2lvb0/fvn2JjIzEw8PjjpVttbW17N+/X/+zUiqVhn0BgtABarWa7Oxs3N3dRWJe6NYMkuC8NS193LhxraoEmD9/PvPnzzdAZML9pKi8GhOZTF+5+XMmMhmFZVVGiEoQbnevJejIblZitmb5ubm1vZiWLhiEzm0gmHS/JIHQdRQKBTpdy18cCkJrubu7s2LFCn0S7ecKCwuJj48nJiamUydKS5JEXFwcbm5uhIaGdtp+O0qSJK5fv05aWhrnz5+npqYGR0dHhg4dSmhoKHK5vFX7UavVHD58mMTERDw8PG5bEXe34yckJHDo0CF8fHyYPXu2vg+kRqPh+++/Jz8/n8WLF+t/HlqtlszMTFJTU8nKysLMzIzQ0FAiIiI4deoUVVVVLF269L5LbkqSRGVlJfn5+RQUFHD9+nV9H00PDw99lWbv3vfuS11SUsKPP/6ITCZj+vTpWFhYkJSURFRUlKiSF3qEtLQ0kpKSGDRoULeviBcebAZJcK5Zs8YQhxEeIC0NEnJz6oXuDpUmOknC3VkMxhC6h3stQdc21ovl50K3o3UTJ7T3u6CgIJYsWcKiRYtuGwAjCB3R0vtJo9GwdetW5HI5o0eP7tTjZWVlkZeXx6JFi7pFAunGjRukpaWRnp5OeXk5dnZ2hIaGEhoaioeHR5tizM/PZ9u2bVRWVjJ+/HiGDx/eqqrNhoYGtmzZwuXLlxk9ejSjR4/WP0+r1bJ582Zyc3NZuHAh3t7e3Lhxg9TUVM6ePUtdXR19+/bl4YcfJiQkBEtLSw4fPsy5c+eYM2fOffN5UVdX16yPZl1dXbM+mh4eHjg7O7dpCX5WVhbx8fG4uLgwYcIEcnJySE5OxtLSktDQUFENJ3R7KpWKxMRESktLycjI6DH9jIUH04Mzzk64b9xpkNDzj8ZiZmqi78H5c2amJowffOe2CIJgSK2ZgC6WnwvdiokJWrcB936c0KN5enryxhtv8PrrrxMdHc3SpUuZO3duqyqUBKGtDh06RFlZGU8++WSnLiG/Vb3p7e2Nv79/p+23rSorK0lPTyc9PZ2ioiKsrKwIDg7moYceQqlUtrlPpUajIT4+nuPHj9OnTx9WrVrVYn/Mlly/fp2NGzfS1NTEokWLCAj4b7uRWz1QL126xKxZs6iqqmL16tVcu3YNGxsbBgwYwKBBg5pVl6ampnLkyBEmTJjQrSpk20qlUlFYWKhPaJaXlwPg4uKCv78/ffv2va2PZmtJksTp06c5ffo0/fr1Y8CAAcTFxVFYWEhISAhDhw7t1JYMgtBV0tPTuXr1Kh4eHpSWlnLp0iVRxSl0WyLBKfQodxsk9P7GOJ6fF8v738c1S36amZrw+vJpONrbGDN0QdBr7QR0sfxc6C50LkFgLj5D73c//vgjxcXFrF+/nvXr1/Pkk0/yzDPP6FsNTZ06VVRuCO2i0+maJfTy8vJITExk/PjxKBSKTj3WrYTiypUrDV69WVdXx/nz5/UJAXNzcwIDAxkzZgz+/v7tSpTBzQTltm3bKCsrY9y4cYwYMaJVCVJJkjh16hT79u3Dzc2N5cuXN+vtKUkS27dvJykpCaVSyY4dO1Cr1fj6+jJ37lwCAwNvizkrK4udO3cyePBgoqOj2/V6jOXnfTQLCgooKSnR99H08PAgIiICDw8PrK2tO3QcjUbD4cOHyc7OZsiQIZiYmLB161ZsbW15+OGH6dOnTye9IkHoWreqN83MzDAxMcHCwoITJ04QEhIiEvRCtyQSnEKPcq9BQhU1Dax7fSUHT2VQWFaFu3Nvxg8OEslNoVsRE9CFnkZMT39wKBQKnnvuOZ577jkyMzNZu3YtGzZsYOvWrTg4ODBv3jwWL17c4xIbgvFUV1fz+eefEx4ezpAhQ7C2tmbbtm14enoyfPjwTj2WVqvl8OHD9OvXr8Wen12hqamJixcvkp6eTnZ2NgB+fn488sgjBAYGYmlp2e59a7Vajh49yrFjx1AoFDz55JOtTgirVCp27drFuXPnGDp0KBMnTmyWrKyrq+PTTz/l8OHDeHl5oVariY6OJjw8/I4DjoqKiti4cSP+/v5MnTq1Wyz/v5tbfTQLCgrIz8+nsLAQlUqFpaUlffr0ITo6mr59+9KrV69Oey21tbX8+OOPVFRUEBUVRW5uLiUlJYSGhjJkyBDxJZHQo2RmZpKfn49KpaKqqgpTU1Py8/PJzMwkLCzM2OEJwm1EglPoUVozSMjR3lZMSxe6PbEEXehJtPKeuwRRaL/AwEDeeustfvGLX/DrX/+aTZs28dlnn/H555/j6+vLs88+y1NPPdXmpbbCg8XU1JTw8HBOnz7NiRMnqKurQ5IkXnrppU5/76SmplJRUcG8efM6db//S6PRcOnSJdLT07l06RIajQZvb2+mTp1KcHAwNjYd/2K9qKiIrVu3cuPGDUaPHs3IkSNbvZT/xo0bbNy4kaqqKmbPnq1PREiSRHZ2NqdPn2bXrl1cu3aNyZMnM2fOHHx8fO7686iqqmLdunU4OzszZ86cbvt7X19fr19ynp+fr++jqVAoCA8Px8PDAxcXly6J/9YwIbiZ5D516hT29vZMnz4dNze3Tj+eIHQ1hULBlClTgJvJTkdHR+RyeadX3gtCZxEJTqFHEYOEhPuJWIIu9ASSVS+kXp033VjoGerq6ti6dStr167l0KFDADz00EMsXboUCwsL/v3vf/OrX/2Kc+fO8fnnnxs5WqE7s7W1ZcKECYwePZqdO3eyevVq+vbty8aNG4mKiiIsLKxTqtrUajVHjhwhLCysSy6+dTodOTk5pKWlcfHiRZqamnB3d2fcuHGEhobSq1evTjmOVqslISGBI0eO4OrqypNPPtmm5Fh6ejo7duygd+/ePPHEE7i6ulJVVUVqaiqpqalUVVVRWVmJubk5f/jDH1o14KmxsZH169djamrKokWLutXSVLVare+jmZ+fr++j6ezsjL+/Px4eHri5uXV55eStYUJWVlaYmppy6dIlBgwYwODBg9vdmkAQjE0ul+v771pYWKBUKkWyXujWDPJp+/vf/57Ro0cTHR3daX/8hftfS5PSJwwJYs3eRDFISBAEwUB0Tv2gmy9DFDqHVqtl//79rF27lh07dlBfX09kZCR///vfWbBgAS4uLvrHTp8+nZdffpl//etfIsEptIpGoyEnJ4eFCxcyfPhwkpOT2blzJwcPHiQyMpLBgwd3aKDVTz/9RF1dHWPHju20mCVJIj8/n7S0NM6fP09dXR3Ozs4MHz6c0NDQZr8TnaGkpIStW7dSXFzMyJEjGT16dKurNjUaDT/++CPJycmEhYUxZcoUcnJy2LdvH9nZ2ZibmxMaGoparebcuXPMnTuXESNG3HO/Wq1WXw362GOPGX3qt06n48aNG2RlZXHjxg1KSkrQarXY2dl1ah/N1ro1TOjUqVOYmppSV1eHk5MTM2fObDaYSRDuBx1puSEIhmCQBOfHH3/M3/72N0xMTAgNDSUmJoaYmBhGjRolypuFFt1pUvrry6fx+vJpvLlmtxgkJAiCYAA6Zz9jhyAYiJubG+Xl5Xh4ePDMM8+wdOlSgoLu/MXhgAEDqKmpMWCEQk8lSRK7d+9Gq9UyY8YM7O3t8fPzo7y8nJMnT5KcnMzx48cJCgoiKioKT0/PNvVEbGhoICEhgcjISBwdHTsca0lJCWlpaaSnp1NZWUmvXr0YOHAgYWFhuLm5dXrvSZ1Ox/Hjx4mPj8fJyYnHH3+8TYNoKisr2bRpE0VFRQwfPhxJkvj444+pr6/H09OT6dOnExISQkpKCvv27WPs2LGtSm5KksTOnTvJy8tjyZIlrZ7a3pkkSaKsrIzs7Gyys7PJzMzk8uXL2NnZ4eXlxfDhw/Hw8KB3794G7wl6a7J9WloacLNaOTIykvDwcFG1KdyXulP1tiC0xCCfvLeWRRw9epSEhAS+//57Pv74Y2QyGX5+fvpk57JlywwRjtDN3W1S+ptrdrPu9ZVikJAgCIKB6ByUxg5BMJBb09LHjRvXqkTB/PnzmT9/vgEiE3q69PR0zp8/z5w5c7C3/2+vaScnJyZNmsSYMWM4e/YsP/30E19++SXu7u5ERUURGhraqkTRiRMn0Gq1jBo1qt0xVlRUkJaWRlpaGjdu3MDa2pqQkBDCwsLw8vLqsuTZjRs32LZtG9evX2fEiBGMGTOmTcmxrKwsvv/+e8rLy3FzcyMxMREbGxvCw8OJiIjQJyVvTVO/VRnaGkeOHOHMmTPMnj0bpVLZnpfXLrW1teTk5OiTmreGm9waTOXr64tSqeyUXqcdiXHfvn2cP38ea2trfH19GTNmTKdX9QpCdyIqOIXuziAJThMTEyIjI4mMjOS5554DbjapjYuL46OPPuKrr75izZo1IsEpAPeelH7wVAZzx0aKQUKCIAgGoOvtbewQBANZs2aNsUMQ7kPV1dXs3r2b0NBQQkNbHlhmaWnJ0KFDGTJkCFeuXOGnn35i27ZtHDhwgMGDBzN48OBmidGfq6mpISkpiWHDht3xMXdSU1PD+fPnSUtLo6CgAAsLC/r378+ECRPw8/Nr9fLw9tDpdCQmJnL48GEcHBx47LHH6Nu39f2OtVotP/zwA9u2bUOr1RIQEECfPn2IiIggMDCwWexnzpxh165dREVFERsb26pk7ZkzZ4iPjyc2NrbLpyWrVCquXr3KlStXyM7Opri4GLg54CQ4OBhfX1+8vb2xsLCgvr6eo0ePGnXI0Y0bN/jhhx/Izs6mb9++xMTEMHDgwC59vwiCsZmYmIj3uNDtGbR2vr6+nsTERI4dO8axY8dISkqioaGBwMBAYmJi2rSvTz/9lE8//ZTc3FwAQkJCeP311/VTvlqyadMmXnvtNXJzcwkICOCdd95h6tSpHXlJQhdozaR0QRAEoetJlvZg2baEgSAYgzgv7L5MTU0JCgpi4sSJ93ysTCbD398ff39/ysrKSE5O1l87hISEEBUVdVsS8OjRo5iZmbVqyTXcXM5+8eJF0tLSyM3NxcTEhICAAObMmUNgYGCXD6MBKC0tZfv27eTn5zN8+HDGjh3b6uPW1dWRlJTE119/zdWrVwkLC2P27NlERES02MM0PT2d7du3M2jQICZPntyq5GZ2djY7duwgMjKSkSNHtvn13YtOp+P69ev6Cs1r166h1Wrp1asXvr6+jBw5Eh8fH6P3+2zJpUuXWL9+PdXV1URFRTFhwgScnJyMHZYgdDlzc3ODt4EQhLYySILzN7/5DceOHSM1NRWdTsfAgQOJiYnhF7/4BTExMe3q59K3b1/++te/EhAQgCRJfP3118yYMYPU1FRCQkJue/yJEydYsGABb7/9Ng899BDr169n5syZpKSk3PHbZME4xKR0QRCE7kGydzd2CILQKuK8sPuytbVlxowZbX6es7MzU6ZMYezYsZw5c4bk5GT+3//7f3h4eBAVFUVISAhVVVWcPn2a2NhYrKys7rgvtVpNZmYm6enpXL58GZ1Oh4+PDw8//DBBQUEGHUiTlJREXFwcvXr1YsWKFXh5ed3zeTqdjitXrpCamkpSUhIXLlzA1dWVP/7xj4waNeqOSYeMjAy2bNlCWFgYDz30UKuSE8XFxXz//ff4+voybdq0TkloSJJEeXm5PqGZk5NDY2MjlpaW+Pj4MGnSJHx9fXF2du62CRRJkjhw4AA7d+7E3t6eRYsWERERYdRKUkEwJNFXVugJDPIuff/99zE1NWX27Nm89NJLhIeHd3ifDz/8cLPbf/7zn/n0009JSkpq8UT2ww8/ZPLkybz44osAvPXWWxw4cICPP/6Yzz77rMPxCJ1HTEoXBEHoHiRbMQFW6BnEeeH9y8rKimHDhhEVFcXly5f56aef2LJlCz/++CP19fWYmZkxdOjQ256n1Wq5cuUK6enpZGRkoFKp6Nu3LxMmTCAkJKTNy9k7qry8nO3bt5OXl8ewYcOIjY29Z9VmZWUlqamppKamUlVVRX19PbW1tcyYMYNFixbRq1evOz43KyuLTZs20b9/f2bOnNmqRFx1dTXr1q3D0dGRuXPndih5V1dXR05Ojn7ZeVVVFSYmJs36aHp4ePSIBGFDQwOrV6/m3LlzhIaGsmTJkg4PsxKEnkYsTxd6AoMkON99910SEhKIi4tj06ZNuLu76yepx8TEdLivi1arZdOmTdTV1TF8+PAWH5OYmMjzzz/f7L5Jkyaxbdu2u+67qamJpqYm/e3a2toOxSrcm6O9rZiULgiC0A3obMSwBKHnEeeF9yeZTEa/fv3o168fN27cYP/+/Xz11VcEBATo+0u6u7uTl5enH2rU0NCAq6srI0eOJDQ01ChLiSVJ4uTJkxw4cAA7OzuWL19+14E9Go2GjIwMUlJSyMnJwcLCgsDAQEpKSigsLGTChAnExsbeNdmQm5vLd999h5+fH7Nnz25VErGpqYn169cjk8lYtGhRm4eJqNVq8vLy9FWaRUVFAMjlcoKCgvSDgXraFOZLly7x5ZdfUl1dzezZs5kwYUKPSMoKQmcTFZxCT2CQd+kLL7zACy+8AMCFCxc4duwYCQkJ/O1vf+OZZ56hd+/ejBgxgp07d7Zpv2lpaQwfPpzGxkbs7OzYunUrwcHBLT62qKgIhULR7D6FQqH/43snb7/9Nn/84x/bFJfQccNCfMWkdEEQBCOTrJ2NHYIgtJo4L3xwuLq6YmJiwpQpUxg6dCiHDx9my5YtqFQqnJ2d8ff3JzIykrCwMORyudGWPVdUVLB9+3Zyc3MZMmQIEyZMuGOCr7i4mJSUFM6dO0dDQwNeXl7MmDEDZ2dntm3bRm1tLfPnzyco6O4rma5du8b69evx8vLi0UcfbVXV1a0vBSoqKnjsscdaVd2q0+koLCzUJzSvXr2KVqvF3t4ePz8/oqOj8fHxMXilbGdRqVTs37+fffv20bt3b37zm9/g6+tr7LAEwWhEYl/oCQyehg8ODsbPz4/AwEACAgLYsGEDmZmZ7Nmzp837CgwM5MyZM1RVVfHDDz+wbNkyjhw5cseT2fb4/e9/3+wb/jNnzjB69OhO279wZ472tmJSuiAIghFJ1mIJ3oPk97//PaNHjyY6OvquS1+7K3Fe+OC4evUqZ86cISgoiLNnz6LT6XBzc0Or1SJJEjqdDgsLC+zs7IyS3JQkidOnT/Pjjz9ibW3N0qVLW0yONTU1kZaWRmpqKgUFBdja2jJo0CAiIiJwcXHhzJkzfP3117i4uLBq1ap7VqBev36dtWvX4u7uzvz581tVcSVJErt37yYnJ4fFixcjl7fcmkSSJCoqKsjOzubKlSvN+mgqlUomTpyIr68vLi4u3baPZmtdu3aNLVu2kJmZSWhoKEuXLu2WA48EwZDEEnWhJzBIgrO6upqEhASOHTvG0aNHOX36NGq1GnNzc4YMGcKsWbPaPEUdwMLCAn9/fwAiIyM5efIkH374IZ9//vltj3Vzc6O4uLjZfcXFxbi5ud31GJaWls2WaIg/boIgCMKDQrIUQ90eJB9//DF/+9vfMDExITQ0VN9KaNSoUbdVO3ZH4rzw/ldVVUV6ejqff/455eXlODg4EBwczNSpU/Hx8cHExITi4mKSk5M5evQoR48eJSwsjKioqHv+bDszxu3bt5OdnU1kZCQTJ05s9p6RJIlr166RkpLC+fPn0Wg0BAQEMG/ePPr164epqSlqtZodO3aQkpJCREQEU6dOvWe/zuLiYr799ltcXV1ZuHBhq5eCHzt2jJSUFGbNmoWPj0+zbfX19foKzezsbCorKzExMaFv374MGzZM30fzfkl8NDY2kpiYSHx8PHV1dUydOpXJkyeLpbmCgKjgFHoGg3xaOzk5IUkS9vb2REdH88YbbzBy5EiGDh3a5v4ud6PT6Zr1Rfq54cOHExcXx7PPPqu/78CBA3fszSQIgtDV1A01VFw+iaq2HAs7JxwDhmBu3TOXcgn3KUvxfnyQVFVVkZqaytGjR0lISOD777/n448/RiaT4efnp092Llu2zNihtoo4L7w/1NfXc+HCBdLS0sjLy6OqqoqGhgZ+8YtfMGHChNuSTwqFgocffpjY2FhSUlI4efIkqampeHt7ExUVRf/+/bvkQl2SJFJTU9m/fz+WlpYsWbIEPz8//fba2lrOnj1LamoqpaWlODo6EhMTQ3h4eLOK6fLycjZu3EhpaSkzZswgIiLinscuLS3lm2++oXfv3m3qn3nu3DkOHTrE2LFjGThwIGq1mqtXr+oTmoWFhcDNlgCBgYH4+fnh7e3dqddv3UVubi5Hjhzh8uXLWFtbM336dAYNGtTjq1EFobPcL19kCPc3gyQ4//GPfxATE8PAgQM77Y/E73//e6ZMmYKXlxc1NTWsX7+e+Ph49u/fD8DSpUvx8PDg7bffBuDXv/41o0eP5u9//zvTpk3ju+++49SpU/z73//ulHgEQRDaoupqOrlxa5B0WjCRgU6i8PQelLEr6O11+8RfQTAGycLW2CEIBmRiYkJkZCSRkZE899xzAGRmZhIXF8dHH33EV199xZo1a7plglOcF95fmpqayMzMJC0tjStXrgDg6+vLzJkzOXbsGAMGDGDy5Ml3va6wsbFh5MiRREdHk5GRwU8//cTGjRvp3bs3Q4YMITIyEmtr606Jt7q6mh07dpCVlUVERASTJk3CysoKnU5HVlYWKSkpXLp0CRMTE4KCgpg2bRpKpfK2+DMyMti2bRs2NjY8/vjjrao6raio4JtvvsHGxoalS5e2+jXl5OSwbds2vL29MTEx4ZtvvuHq1atoNBrs7e3x9fXVV2n21D6ardHY2Mjx48e5cOEClZWVeHh4MGnSJNFvUxD+h0j2Cz2BQRKczzzzTKfvs6SkhKVLl1JYWEjv3r0ZMGAA+/fvZ8KECcDN3jw//3Y2Ojqa9evX8+qrr/Lyyy8TEBDAtm3bCA0N7fTYBEEQ7kbdUPOf5Kbm5h06CQBJpyE37iuC578hKjkFo5NkpmB6/1XpCPdWX19PYmIix44d49ixYyQlJdHQ0EBgYGC7WgoZgjgv7Pk0Gg1ZWVmkpaVx6dIl1Go1Xl5eTJkyheDgYGxtbUlLS6OsrIyZM2e2+mLbxMSE4OBggoODKSwsJDk5mfj4eI4cOcKAAQMYOnRou1swSJLE2bNn2bdvH+bm5ixatIiAgAAqKio4ceIEqamp1NTU4ObmxuTJkwkLC2sxAXOq7PkAAQAASURBVKnT6YiLi+P48eMEBQUxY8YMrKys7nn8qqoqvv76a8zNzVm6dCk2NvcexllRUcHJkydZs2YNWq0WjUZDYWEhSqWS8ePH4+vri6ur6wORzLhy5QrHjx+nuroajUaDj48PkydPxsXFpcuPrdFoxNJ3oUcRS9SFnsBgn6rV1dV88sknHD58mJKSEj7//HOGDh1KeXk5a9asYfr06fq+Sa2xevXqu26Pj4+/7b65c+cyd+7ctoYuGElFTR0HTl6kqLwaN6deTBgShKO9qCYSer6KyydvVm62QNJpqcg6iTxsnIGjEoT/YW4DD8AFrvBfv/nNbzh27BipqanodDoGDhxITEwMv/jFL4iJicHV1dXYId6ROC/smXQ6Hbm5uaSlpXHx4kUaGxtxc3NjzJgxhISE4ODgoH+sVqvl0KFDBAYG4unp2a7jubu7M2PGDMaPH09KSgrJycmcPn0aHx8foqKi6NevX6sv4mtqati1axeZmZkMHDiQ8ePHk5uby9dff01OTg6WlpaEhYUxaNAg3N3d75gwrKmp4YcffuDatWtMnDiR4cOHtyq5WFNTw9dffw3crFC+U5VlfX09OTk5+mXnRUVFpKam4uLiwrJlywgKCrqv+mi2Rn19PcePHyc7OxsbGxvMzMzw8PBg4sSJ2Np2zbWGSqWiqKiI69evU1hYSFlZGUuXLm11r1RBMDaR4BR6AoMkOPPz8xk9ejTXrl0jICCAjIwMamtrgZv9OT///HPy8vL48MMPDRGO0AMkpmfz1te70Wh1mMhk6CSJNXsTeX35NIaFiCUjQs+mqi3XL0u/jYkMVU254YMShP8hmd27eki4v7z//vuYmpoye/ZsXnrpJcLDw40dknAfkiSJgoIC0tLSOH/+PLW1tTg5OREVFUVoaOgdE+kpKSlUVlYyf/78Dsdga2tLTEwM0dHRXLx4kZ9++onvvvsOBwcHhg4dSkRExB2XekuSRHp6Onv27MHU1JTY2Fhqamr417/+RWNjI97e3syaNYvg4OB7DgbKyclh8+bNyGQyli9fjpeXV6vir6ur45tvvkGj0bBixQp69/7vQDiNRnNbH01JknBxccHHx4fKykrGjh3LU0891az354NAkiSysrI4fvw4MpmMPn36cP36dfz9/RkzZkynVlQ2NTVRVFREYWEh169fp7S0FEmSsLW1xd3dncDAwAeiSla4f4gEp9ATGCTB+eKLL1JTU8OZM2eQy+XI5fJm22fOnMmuXbsMEYrQA1TU1PHW17tRa25WuGmlm0kgtUbLm2t2s+71laKSU+jRLOycWk5uAugkLOydDBuQILTETCxPf9C8++67JCQkEBcXx6ZNm3B3d9dPUo+JiSEsLMzYIQo9WElJCWlpaaSnp1NRUYG9vT1hYWGEhobSp0+fuyZ71Go1R44cISwsrN3LyVtiampKaGgooaGhFBQUkJycTFxcHIcPHyY8PJyhQ4c2S7jW1taye/du0tLSsLe3x8rKiri4OOzs7Bg8eDARERE4Ozvf87iSJJGQkMChQ4dQKpXMnj0bOzu7VsXc0NDAt99+S0NDA8uXL8fBwYHC/8/encdHVd7t479mJvu+75DJAiELIWQbAiFhiwQCARRURBYravtofWztqvZRH9vHql207a+t1irWCu6K7IYtCyE7ELIQSEhC9n2drDNzfn/wzZSYAFkmmZnker9eeWnOnDnnk3CSObnmvu9PXR2uX7+OsrIy9TqaFhYW8Pb2RmRkJLy9vWFhYYEDBw7AyMgIe/bsmXXhZnd3N9LS0lBZWQkvLy8oFApUVVUhIiICixcvnnTY2NfXNyzQbGlpUQeabm5uCAgIgIuLC6ytrRlskl7idUv6YFoCzm+//RY/+tGPEBAQgJaWlhGPe3t7o6qqajpKIT2QlF0MhVI16mMKpQonc65g28qwaa6KSHNs50WgLvfof9bgvIVILIGtb4QWqiL6Dq6/Oes8++yzePbZZwEARUVFSE1NRVpaGl5//XX88Ic/hLW1NZYtW4ZDhw5puVLSJ83Nzfjss8/Q0NAAU1NTBAQEICgoSN3cZiwyMzPR29uLlStXTlmd7u7u2LJlC+Li4pCbm4vs7GxkZ2fDx8cHMpkM/f39+Oijj1BXVwcbGxsYGRlh7ty5iIuLg6+v75inePf29uKrr77C1atXERMTgxUrVoz5+9Df349///vfaGhowNKlS3HmzBmUl5ejp6cHRkZG8PT0xOrVq+Ht7Q0nJyd1ICEIAg4fPoyysjLs2LFDoyGxrhMEASUlJcjIyIBEIkFMTAyuXLmC1tZWxMXFTbiZUF9fH+rq6tSBZmtrKwRBgIWFBdzc3BAYGAhXV1dYWVkxGKIZgSM4SR9MS8DZ29t7x3Wburq6pqMM0hP1rZ0Qi0TqkZu3EotEqGvp0EJVRJpjaGoJ6epHUHHq/WFd1EViCaSrH2GDIdIJgoTrgs1mAQEB8PHxgZ+fH+bNm4cDBw6gpKQER48e1XZppGesra3h4uKCVatWjSsIHNLb24u0tDSEhYXB1tZ2iqr8DwsLC8TGxiI6OhqFhYVISkrCE088gfr6eri6uiIuLg7R0dEICQkZd3fx2tpafPrpp+jv78dDDz2E+fPnj+l5vb29KCkpwb59+1BWVob58+cjPT0d7u7uiIiIgLe3Nzw8PG77vT137hxyc3OxadMm+Pj4jKtmfdbd3Y3k5GRUV1fDz88Pvr6+6vV4N23aNK5mQr29vepAc2gNTQCwtLSEm5sbFi5cCDc3txndcZ5mNwb1pA+mJeAMCAhASkoKnnjiiVEf//rrr7F48eLpKIX0gIudFVSjhJsAoBIEuNpbj/oYkT6xnhuIgAdfRFtpNga6WmFkaQdb3wiGm6Q7OEV91uns7ERaWhpSU1ORkpKC3NxcDA4OwtDQEBEREdiyZYvOdlEn3WVoaIgtW7ZM+Pnnzp2DUqlETEyMBqu6M5VKhdLSUnz77bf49ttvoVQqsWTJEtjY2EClUqG7uxsDAwNjPp4gCMjNzcWxY8fg7Oysnlp+O0PTp4fW0ayqqsLly5ehUCiwbds2REZGQiqVjqnT+uXLl3Hy5EnExsbOmr+3BEFAcXExMjIyYGRkhHXr1kGhUODEiROws7MbUzOhnp6eYYFma+vN9dGHAvvg4GC4ubmNeWkBIn3HgJP0wbQEnM888wx2796N4OBgdbfKoRuHl19+GefPn8cXX3wxHaWQHoiL8Me+Y+fVa3DeykAixppwfy1URaR5hqaW7JZOOosjOGcfOzs7CIIAS0tLLF26FC+++CKio6MRGRkJY2MG3jT9urq6kJmZiSVLlkxLkNTS0oILFy4gOzsbFy5cQE9PD2JjY/HEE0/AwcEBXV1dyMnJQU5ODjIzMzFv3jzIZDL4+Pjc9o//gYEBHD58GPn5+YiIiMDatWtHNLMRBAENDQ0oKyvD9evXcePGDQwODsLc3Byenp5oampCYGAg9u7dC6lUOuavp6KiAl9//TUWLVqEFStWTOI7oz86OzuRkpKCmpoa+Pv7IzIyEkVFRerlBm7XTEgulw8LNNva2gDcDDTd3NwQEhICV1dXBpo0a3GKOumDaQk4H374YVRWVuKFF17A888/DwCIj4+HIAgQi8X4v//7P2zevHk6SiE9YGtpjv/Zk4D/3Te8i7qBRIz/2ZMAW0szbZdIRDTzMeCcdf74xz9i+fLlWLRoEUdqkE5ISUmBgYEBli1bNmXnGBwcRHFxMfLy8lBRUYGuri60t7fD19cXDz74IBYuXKj+ebC0tMTKlSuxfPlyFBQUIDMzE//+97/h4OCAyMhILFq0aNibAc3Nzfj000/R1taG++67b1ijrvb2dvUIzevXr6OnpweGhobw9PTEqlWr4O3tDQcHB3zxxRdQKpV45JFHxhVuNjU14eOPP8bcuXORmJg443+mh7rbZ2VlwdTUFAkJCXBxcUFycjJKS0sRHh6O0NBQ9fehu7sb9fX1qK2tRW1tLTo6bi6BZWNjAzc3N4SGhsLV1fWuIz2JZouZ/juEZoZpCTgB4Pnnn8fOnTvxxRdfoLS0FCqVCj4+Prj33nsnvLgzzVxLAr3x0f98DydzrqCupQOu9tZYE+7PcJOIaLow4Jx1fvjDH2q7BCK11tZW5ObmYvXq1WOaij1edXV1yMvLw+XLl9HX1wcPDw/Y2NhAqVQiLCwMGzduvO16igYGBggJCcGiRYtQVVWFzMxMHD9+HKdOnUJoaCgiIiJQW1uLb775BlZWVnj88cdhYWGB4uJidaDZ0tICkUgEd3d3hIeHq9fRHBpdqFKp8PXXX+PKlSt44IEHxrV2Znd3Nz766CNYWVnhgQceGPe6p/qmvb0dycnJqK+vR2BgIGQyGQYGBnDo0CF1MyEnJyeUlpaitrYWdXV16kDT1tYWHh4eiIiIgKurK8zM+LcG0Wg4gpP0wbQFnAAwd+5c/OhHP5rOU5KWDAWRkwkkbS3N2S2diEhLOEV9durs7MRf//pXnDlzBo2NjXj77bcRGRmJ1tZW7Nu3D4mJifD19dV2mTQLnDlzBubm5oiMjNTYMXt7e3H58mXk5eWhvr4elpaWiIiIgJWVFVJSUjAwMID77rtvzKOYRSIR5s6di7lz56KjowM5OTnIzs7Ghx9+iJ6eHoSEhMDHxwdff/01amtrIQgC7O3t4e3tjTVr1kAqlcLU1HTEcYe6nl++fBlbt26Fn5/fmL/GgYEB7N+/H0qlEjt27JiScFhXqFQq5OfnIycnBxYWFkhMTISrqyuam5tx8OBBdHZ2wtvbG5mZmejs7ARwcykODw8PREZGwtXVddTvPxGNxNHMpA+mNeCk2eOvzz6k7RKIiGgyJIbaroCmWXV1NWJjY1FVVYV58+bhypUr6O7uBnAzFHj77bdRWVmJt956S8uV0kxXX1+Py5cvY+PGjTA0nNzvIkEQUFFRgby8PBQXF0OlUmH+/PlYtWoVPDw8cPLkSaSmpsLX1xeJiYmwsrKa0HmsrKwwZ84cnDhxAnV1dRgYGMCpU6eQl5eHqKgoxMfHw8/P747NhYbqPXbsGC5cuIDNmzcjMDBwzDWoVCp8/vnnaG5uxve+9z1YW8/cxpxtbW3IyMhAU1MTgoKCsGDBAjQ2NiIlJQXp6ekQi8Xw9vbG4OAg5s6dCzc3N7i6us7owJeIaLabsoAzODh4XPuLRCJcunRpiqohIiKiceEIzlnnpz/9Kbq6unDx4kU4OTnByclp2OObN2/G4cOHtVQdzSanT5+GnZ0dQkJCJnyMzs5OXLp0CXl5eWhra4O9vT1WrlyJRYsWwcLCAmVlZXj77bfR19eHxMRELF68eNxrzHV0dKinnGdlZSEvLw+GhoZITExEeHg4jIyMUFZWhpKSEpw5cwYdHR2IjIy8bcgpCAJOnjyJrKwsbNy4EYsWLRpzLUPBaGlpKR566CG4uLiM62vRF0qlEvn5+bh8+TLEYjE8PDxQXl6O/Px8NDQ0oK2tDfPnz8fatWvh4eHBQJOIaBaZsoDTzs5uTDcJ9fX1KCkp4aK1REREOkQQcwTnVPj//r//D2+88Qbq6+uxaNEi/PnPf77jFNzPPvsMv/rVr1BRUYF58+bhtddew/r166ektm+//RY/+tGPEBAQgJaWlhGPe3t7o6qqakrOTTSksrISV69exdatW8e9dqRSqcS1a9eQl5eHa9euwcDAAAEBAdi8eTPmzp0LkUiE/v5+HD58GDk5OfD29kZiYuJdR1UO6evrQ0VFBa5fv46ysjK0tLRAEAR0d3ejubkZ69evx6OPPjpsFGhERATa29uRnZ2N3NxcnD9/HgsWLIBMJoOnp+ewv4GSk5Nx7tw5xMfHIyxsfMs0paenIzs7e0YuIyEIAlpbW5GTk4NPP/0UFy9ehKOjI+bMmQMAkEqlqKqqglKpxIYNG4Y1EyIi0mW6fF+oj6Ys4Dx79uwdH6+vr8drr72Gt99+GxKJBDt37pyqUoiIiGi8GHBq3CeffIIf//jH+Pvf/w6ZTIY333wTa9euRUlJyYjRksDNwGL79u149dVXsWHDBuzfvx+bN29GXl4egoKCNF5fb28vHB0db/t4V1eXxs9JdCtBEHDq1Cm4uLiMa2p2S0sL8vLycOnSJXR3d8PNzQ0JCQkICgoaNoKvvLwcBw8eRE9PDxISEhAeHn7HIEypVKKqqko9SrOmpgaCIMDOzg7e3t6IiorCpUuXUF1djcTERMTExIx6PBsbG8TFxSE2Nhb5+fnIzMzEvn374OzsDJlMhoULFyIzMxNnz57FmjVrsGTJknF93woLC5GUlISYmBiEhoaO67m6SBAENDc3o7KyUh0oFxYWoqqqCk5OToiLi8OiRYvg6ekJpVKJb7/9FnK5HPHx8eNqxkREpE26fl+oj6Z9Dc6Ghgb89re/xTvvvIPBwUE8/PDDeP755/liREREpEvEM7vrrjb84Q9/wGOPPYZHHnkEAPD3v/8dR44cwXvvvYdf/OIXI/Z/6623EB8fj5/+9KcAgFdeeQVJSUn4y1/+gr///e8ary8gIAApKSl44oknRn3866+/xuLFizV+XqIh165dw40bN/Dwww/fdQTewMAAioqKcOHCBVRWVsLU1BTBwcEIDQ2Fs7PziH2Hpn5LpVLs3r0btra2I44pCAIaGxvVgWZFRQUGBwdhZmYGLy8vhIaGwsvLC7a2tqiqqsJnn30GhUKBnTt3wtvb+65fn5GREcLDwxEWFoby8nJkZmbi0KFD+Oc//4muri5s27YN0dHR4/qe3bhxA19++SWCg4OxcuXKcT1XVwiCgKamJlRUVKhDTblcDrFYDFNTU1RXV8PGxgY7duxAVFQUzp07B0tLS3R1deH48eMAgMTExDu+QUNEpGt0/b5QH01bwDk0YvPWYPOFF14Y080AacagQgmlSqXtMsZNqVRCpYd1S8RiGBowIJguysFBDA4qIIgHIRamdlqScMt/BwcHp/RcukqlGIRiUIHBwUGowOt8MoauXaXQA7FEd64nUf8ABAO5tstQUykHoVIodeqaUygUAIDu7m51h14AMDY2hrGx8bB9BwYGkJubi1/+8pfqbWKxGGvWrMH58+dHPf758+fx4x//eNi2tWvX4uuvv9bQVzDcM888g927dyM4OBjbtm0DcLNpSWlpKV5++WWcP38eX3zxxZScm25vYGBAfa3NZENrSLq6usLV1RU9PT2j7lNXV4dLly6hoKAAAwMDkEqlSEhIgJ+fHwwMbv5pc+tzb9y4gUOHDkEul2PlypXqUZtD+3R2dqK8vBwVFRUoLy+HXC6HgYEB5syZgyVLlkAqlcLFxQUikQgGBgYwNDRERkYGvv32W7i7u2Pbtm3jbkwkEong7e0Nb29vnD59Gu+99x6MjIyQl5eHvr4+yGQyzJkz564hb3NzMw4cOIA5c+YgMTFRb6ZlDwXJtwaaPT09kEgkcHNzQ2hoKDw8PNRrmwYGBmLTpk1wcXFR/7tVVFTg3LlzsLW1xdq1a9nhmbRiKv5OHRwchEKhgFwunxW/+yeiv78fCsXNv0N05W+x8dwTAvpxX6iPpjzgrK+vx29/+1v84x//wODgIHbu3IkXXngBXl5eU31qusWgQomSG/Xo6deNXwDj8Y+/vIl3/8qOraQ7vvjJSjhZm6K2rh5RnmOfQkdEM1NsbOywz1988UW89NJLw7Y1NzdDqVSOGFnm7OyMK1eujHrc+vr6Ufevr6+ffNGjePjhh1FZWYkXXngBzz//PAAgPj4egiBALBbj//7v/7B58+YpOTeNbmBgAFlZWepu9jNZeXk5srOzsXbtWqSmpg57rL+/H+Xl5SgtLUV7ezvMzMzg7e2NgIAAWFpaoqWlBenp6cOeo1AocPHiRZSUlMDBwQFLly5Fb28vTp06hcbGRtTW1qK+vh6dnZ0QiUSwtbWFq6srfH194eTkBIlEgsHBQVy7dg3Xrl0DcHMEZn19Pa5evYqoqCisWbNm3OuE3io/Px+pqanYvn071qxZg0uXLiErKwvvvfceXF1dIZPJEBQUpA5ub9Xd3Y2PPvoIFhYWeOCBB0bdR1cIgoCGhgZUVFSoQ83e3l5IJBJ4eHggPDwcUqkUHh4eMDIyQlVVFQ4ePIi2tjasWrUKS5cuVX+fBUHA5cuXUVxcDD8/P6xYsUKnv3aa2V555RW8/PLL2i6DdMhY7gkB/bgv1EdT9mpQV1enDjYVCgV27dqF559/nsGmlihVKvT0D8JQon+jCp965sf4r6ef0XYZ4zKoUEKhVGGhjztMjLiO3XRQDvaho7IQBsYmEBtMbffnks9/DUVPB9xcXdBaWTil59JVKsUAFP19sPYMhMSQHUonS6UchKBSaruM4eRNgLluTfcTiSUQS3Tnd+qFCxcgk8mQnJw8rNvzaO/U64vnn38eO3fuxBdffIHS0lKoVCr4+Pjg3nvv5awbLVAoFOju7oaRkZFeX1d3o1QqceXKFfj6+qqvM0EQUFtbi6tXr6KyshKCIGDu3LmIjo6Gu7s7xGLxbY/X0NCA1NRUdHd3Y+nSpXBwcEB9fT3y8vLQ1NQElUoFS0tLeHp6wt3dHa6urnfttl1XV6deH/T+++9HQEDApL7moqIifPXVVwgJCcH69eshEokQGRmJiIgIlJWVITMzE19//TWSkpIQFhaGiIgIWFpaArg5wuvAgQMYHBzE7t27YWpqOqlaNE2lUg0LNG/cuIHe3l4YGBjAw8MDkZGR6kDT0PA/v9MHBwdx4sQJZGRkwN3dHd///veHTTtXKBT45ptvcOnSJchkMkRFRenNqFWamX71q1+p3xDUpNkycn8yDAwMYGQ0tX/zjcdMvCfUR1MWcPr4+KC/vx8hISF47rnn4OXlhba2NrS1td32OTNhUWxdZ2gggZGhnr3LOcl6O+W9yCyqQGunHHZW5pAFSGFlPrU3ggODipuBsqHhsBs3mjpiKGFoaAADQ0OIDab2ey665b+z9d9XJRIgUilgaGgIySz9HmiUTn4PBwEzS20XodOGRg1ZWFjcdYqqg4MDJBIJGhoahm1vaGiAi4vLqM9xcXEZ1/6aMnfuXPzoRz+a0nPQ+BgbG981gNNnhYWF6Ovrw7Jly6BQKFBSUoKSkhJ0dXXBxsYGUVFRmDdvHszMzO54HIVCgezsbGRlZUEikcDV1RUFBQUYHByEsbExPDw8EBgYCHd393FNK7969SrOnDkDIyMjfO9731N3756oq1ev4vPPP0dQUBA2btw4LKQTiUTw9fWFr68vWlpakJWVhYyMDKSlpSEwMBARERFIT09HU1MT9uzZM+YO8FNJpVKhrq5OPd38xo0b6OvrGzbV39PTEx4eHrcdbVlRUYFvvvkGnZ2diIuLw5IlS4aF2N3d3fj4449RVVWF5cuXIygoiOEmaZ1EIpnUKO7bma1/X+iz8dwTAvp1X6hPpizp6uvrA3Azyb7//vvvuK8gCBCJRFAqdWz0Cum9y2U1ePdwGpQqFUQiEQRBwOH0fOzdGI2F3u7aLo+ISHfxD0eNMjIyQlhYGE6dOqWe5q1SqXDq1Ck89dRToz4nKioKp06dwjPPPKPelpSUhKioqGmomGh6DA4OIicnB1ZWVsjMzERVVRUkEgl8fHywYMECODs73zXI6u7uRkFBAZKSktDQ0ABHR0e4ubnB2NgYoaGhcHd3h729/R1HfY5GoVAgPT0dxcXF8PLywsKFC2Fvbz+ZLxdlZWX45JNP4Ofnh82bN9+xJnt7e6xbtw6rVq3CxYsXkZGRgS+//BJdXV147LHHRkxVnC5KpXJEoNnf3w9DQ0PMmTMHS5cuVY+Ovdv08f7+fpw8eRLZ2dnw9PTEjh07RnyP6+vrceDAAahUKuzcuROlpaVT+eUREU053hdOjSkLON9///2pOjSR2p1GZ3bKe/Hu4TQolDcXfhaEm61hFEoV3j2Uhlf2Jk75SE6amQzMrIb9l2hmYsCpaT/+8Y+xe/duhIeHIzIyEm+++Sbkcrm6e+auXbvg7u6OV199FQDw3//934iNjcXvf/97JCQk4OOPP0ZOTg7eeecdjdUUHBw8rv1FIhEuXbqksfPT7Nbe3o7Dhw8jOzsb8+fPh4mJCZYvXw4fH587Tj0cGBhAbW0tampqUFVVhaKiInWwuWnTJvj7+8PFxWVSazN2dnYiKSkJbW1tiI2Nhaen56TXQq2srMTHH38Mb29vbN26dcwjv4yNjSGTyaBUKlFaWor58+cjNzcXJSUlCA8PR3h4OCwsLCZV250olUrU1taq18+8ceMGBgYGYGhoqF42QCqVws3NbVyj2crKytQNoNatW4fIyMgRYXZxcTG+/PJLODo64sEHH4SBgQEDTiKaEXTxvlDfTVnAuXv37qk6NBGAu4/OzCyquG3XeKVKhaziCqwJ95/mqmkm8Nv0rLZLIJp6HMGpcQ888ACamprwP//zP6ivr0dISAiOHz+uHoV148aNYaO5li5div379+OFF17Ac889h3nz5uHrr79GUFCQxmqys7Mb0zTP+vp6lJSUcEoojVtPTw/OnDkDMzMzmJqawsDAAC0tLaipqUFLSwuuX7+OkJAQbNu2DXZ2dqMeQ6lUoqmpCdXV1aipqUFjYyNU/+/+r6WlBRYWFli/fj0iIyPHPUpzNBUVFTh79iyMjY2xefNmODg4qGenTVR1dTU++ugjeHh44P777x/3tNaioiIkJSVh06ZNWLNmDZqampCVlYVz584hNTUVQUFBkMlkcHNzm1SdwM3vd01NzbBAc3BwEEZGRpg7dy5iYmIglUrh6uo6oem5fX19+Pbbb5GXlwcvLy/s3r0btra2w/YRBAFpaWk4deoUAgMDsXnzZhgaGqq7qBMR6TtdvC/Ud3q2GCPRTWMZndnaKVcHn98lFonQ0iGf1pqJiPQLg6yp8NRTT9126tHZs2dHbNu2bRu2bds2ZfWMds5b1dfX47XXXsPbb78NiUSCnTt3TlktNDOpVCoYGhqiuroalZWVqK+vx8DAAKysrKBUKqFSqTA4OIhvvvkGJiYmMDU1hYmJCVQqFbq7u9HR0YH29nYAgLm5OTw9PbFkyRK0traipKQE8+fPx4oVKyY9dXyo1uzsbFy8eBFSqRQrVqzQSIOIuro6/Pvf/4aLiwu2b98+7vX1qqqq8OWXXyIwMBCrV68GADg6OiIhIQGrV6/GhQsXkJmZiUuXLmHOnDmQyWTw9/cfc/ioUCjUgWZFRQWqq6vV65Z6enpixYoV6kBzsgHy1atXcfjwYfT392Pjxo0IDQ0d8cbJUDOh/Px8rFixArGxsXxzhYhmJF27L9R3DDhpSrz20Ql0ynthZW6Kn+9YO6Fj3Gn6+VhGZ9pZmY8abgKAShBgb20+obqIiIhmuoaGBvz2t7/FO++8g8HBQTz88MN4/vnn4ePjo+3SSM8oFAp0dHSgp6cHXl5eiI+Ph6+vLwYGBvDxxx/Dx8cH8+bNQ2trqzoEbWhogFwuh1KphImJCSwsLGBpaQmRSISSkhJ8++23UCgU8Pb2hqurKy5dugQzMzN1QHrrh4mJyZimqsvlcpw6dQoNDQ2QyWRYtGiRRkK1xsZGfPjhh7C3t8eOHTvG3fW3paUFBw4cgLu7OzZv3jyiJhMTE0RFRUEmk+Hq1avIzMzE559/DktLS0RERCAsLAzm5sPveRUKBaqrq4cFmgqFAiYmJvD09MTKlSshlUrh4uKikRGxANDb24vjx4/j0qVL8PX1xcaNG2FtbT1iv6FmQvX19di6dStHJhER0Zgx4KQp0SnvRXt374Sff7fp52MZnbluSSAOp+erR3neSiIWQxbgNeH6iIhmPI6WmZWGRmzeGmy+8MIL8Pb21nZppKcsLCxgb2+PJUuWwN3dXR2YnTlzBkqlEhYWFrh06RJaW1sBAG5uboiMjISHhwdcXFwgkUgwMDCA7u5u5Obm4uLFi/Dx8UFAQACMjY3R29uL7u5uNDY2oq+vD/39/SNqMDIyGhF8DoWfpqam6OzsRHZ2NgwMDJCQkKCRad7AzXDyX//6F6ysrPDwww+PezSoXC7HRx99BDMzM/X6k7cjFouxYMECLFiwAA0NDcjKykJqaipSUlLg7++POXPmQC6XqwNNpVIJU1NTeHp6Ys2aNfD09ISzs7PGAs1bXblyBYcPH4ZCocDmzZtvGx7f2kzokUcegbs7G4ISEdHYMeAknTOW6edjGZ1pZW6KvRuj8e6hm0GpWCSCShAgEYuxd2M0LM1Mpu1rIiLSPww4Z5P6+nr89re/xT/+8Q8MDg5i586deOGFF+DlxTcDaXIMDAywatUqqFQqNDY2oqamBqWlpThz5gxcXV1RXV0NDw8PLF68GO7u7jA1HdkAUi6XIzk5GS0tLVi5ciVCQ0NvO/1aqVSit7cXfX196O3tHfWjsbFR/f9DzYosLS3h5eWFI0eODAs/hz4kEglUKhXc3Nzg4OAAc3NzmJub3zZ0bGtrwwcffABTU1Ps3Llz1K/rTgYHB3HgwAH09/dj796943q+ra0tAgICIJFIkJqainfffRe9vb1wcnKCTCbDmjVr4OXlNaYO9ZMhl8tx7NgxFBQUwM/PDxs2bIClpeWo+165cgVffPEFHBwcsH37dlhZsZEjERGNDwNO0jljmX4uC5COaXTmQm93vLI3EVnFFWjpkMPe2hyyAC+Gm0RERLi5NuBQsKlQKLBr1y48//zzDDZJYzo7O5Geno66ujoMDAzA2NgYra2t8PX1xSOPPHLHRlcqlQr5+fnIycmBlZUVNm3aBCcnpzueTyKRwMLC4q5dxfv6+nD27FkMDg5i+fLl8PPzQ39/vzr4HApIu7u70dTUhM7OTnR3d6O2tnbYGprGxsYwNzeHhYWFOvQEgNOnT8PY2Bg7duxAT08PxGIxTExMxhQoqlQqfPnll2hoaMCePXtGNOD5roGBAdy4cUPdFKimpgYqlQrm5uZYvHgxNm3ahMHBQVy7dg03btxARkYGlEolrKysYGZmdtd6xksQBBQWFuLo0aMAgPvuuw9BQUGjfu23NhMKCAjAli1bxr1GKREREcCAk3TQWKafj2d0ppW5KbulExGNF6eozwo+Pj7o7+9HSEgInnvuOXh5eaGtrQ1tbW23fU5oaOg0Vkj6ztjYGAqFAosWLYKHhwcA4KuvvkJMTMwdGwO1tbXh7NmzaGpqwqJFixAWFjamtTTHorm5GUlJSejv70dCQgI8PT3v+py+vj60t7cjLCwMgiBALpcP++ju7oZcLsf169dx5swZ9PX1YdGiRfj888/VxxCLxeoQ9LsftwakGRkZKCwsxI4dO0adpt3f3z8s0KytrYVKpYKFhQU8PT0RHBwMqVQKBweHYaHi8uXLUVdXh6ysLJw9exZnz55FcHAwZDKZumvvZHV3d+PIkSMoLi5GQEAA1q9ff9uw+dZmQrGxsVixYgWbCRER0YQx4CSdM9bmQBydSUQ0lfhH5mzQ19cHALhw4QLuv//+O+4rCAJEIhGUSuV0lEYzhLGxMTZs2KD+/NixY7C2tsb8+fNH3V+lUuHy5cvIzs6GpaUlNm3apLHwTRAEXLlyBefOnYOtrS0SEhLGNRVaIpHccdRjT08P9u3bh+joaOzZswfm5uYjAtBbPzo6OlBbWwu5XI7e3ptr11dXV6O0tBTz5s3Dl19+CXNzcxgZGaGvrw+dnZ3o6OhAZ2cnDA0NYWtrCx8fH6xatQp+fn4jAs3RuLq6YtOmTVizZg3y8vKQnZ2NvLw8eHl5QSaTYf78+RNah1MQBOTn5+P48eMQi8W4//77ERAQcNv92UyIiIg0jQEn6ZyxTj8HODqTiGjKGGl+2iLpnvfff1/bJdAsUldXhxs3bmDNmjWjrqHZ3t6Os2fPorGxEQsXLkRERITGRm0ODg4iLS0NV69eRUBAAKKiojR2bODmmwUffvgh5HI59uzZox6damJicseRqkOUSiUuXLiAjz/+GNu3b4eLiwuuX7+OsrIyNDQ0YGBgABKJBObm5jA1NYWFhQUMDAxw48YN3LhxA6dPn77t6NDvjhAd+li+fDmWLl2K4uJiZGZm4uOPP4aNjQ0iIyOxePHiMa/72dnZiUOHDuHatWsIDg5GfHz8Hae+s5kQERFNBQacpHPYHIiIiGh67N69W9sl0CwhCAIyMzPh4OAAb2/vEY8VFBQgKysL5ubmSExMhIuLi8bO3d7ejqSkJHR2dmLlypW3HT06Uf39/fj3v/+N9vZ27NmzB46OjuN6fk9PDzIzM7Fv3z4YGRmhrq4OdXV1sLa2xooVKyCVSuHp6QlbW1v1Mk79/f2jTpG/0+jQWw2tHTr04ezsDCsrK5SXl+PAgQP48ssvsXjxYkRHR2Pu3Lm3XT/zwoULOHHiBIyMjLB9+3b4+fnd8Wu9cuUKvvzyS9jb27OZEJGeGBgYQE5ODsLDw2FkZKTtcohuiwEn6aSxTj/vlPcis6gCrZ1y2FmZQxYghZX5+LpUEhEREdHUunHjBhoaGrB+/fphYVlHRweSk5NRV1eHoKAgREZGarTJTFlZGZKTk2Fubo4tW7bAzs5OY8cG/tPtvKmpCbt37x7TdHq5XI7KykpUVlaioqICFRUVyMvLg4ODAxITE+Hj4wOpVAobG5tRny8SiWBiYjKu0aHfnR7/3Y+amhrI5XL09/fD0tIStbW1+OCDD/CPf/wD9vb2mDdvHjw9PdUjQYfCzaE1UuPj42Fvbw+FQjHqyFhBEHDu3DmcOnUK/v7+2Lx5M4MSIj2RkZGBDz/8EEqlEsuXL9d2OUS3xYCTdNbdpp9fLqvBu4dvjvIcejf7cHo+9m6MxkJvTnUh/TfY24W2a9kY6G6FkYUdbOdFwNDUUttlERERjYsgCMjOzoabm5u60dBQp+3MzEyYmZlh48aNcHNz09g5lUolMjMzcfnyZfj4+CAmJkbjgZpCocDHH3+M2tpa7Ny587b1d3d3q8PMyspKNDY2AgDs7Ozg4uKCyspKxMXF4amnnpqSruZDa4eOZbTkraNDOzs7cenSJWRnZ6OmpgZtbW2YM2cO5HI5CgoKIAgCvL290djYiH/961/qY3x3dKiJiQny8/NRVVWFqKgoREREoKOjQz3dno2FiHRXf38/Tpw4gfLychw/fhyRkZEwNjbWdllEo2LASTrrTqMzO+W9ePdwmnqdzqGmRAqlCu8eSsMrexM5kpP0WseNAlSc2gdBpQTEIkAloC73KKSrH4H13EBtl0dERDRmpaWlaGlpwaZNmyASidDV1YXk5GTU1NQgMDAQMplMo6M2u7u7cfLkSTQ3N2PZsmUIDAzUeIimVCrx2WefobKyEjt27MCcOXPUj3V1dQ0LNJuamgAA9vb28PT0RHR0NKRSKczMzPCvf/0L9vb2ePTRR6ck3Byv744O9fLywubNm1FdXY2TJ0/i4MGD6OzsxPLly/H444/D2dkZPT09ozZRksvlaGlpwdGjR9HU1AQfHx/cuHEDH3zwgfp8YrEYZmZmo64ROtoaokQ0vTIzM3H16lUEBwfj6tWryMrK4ihO0lkMOEkn3W10ZmZRBZSqkU2IAECpUiGruILNh0hvDfZ2/b9wU3Fzg+pmgC+oFKg49T4CHnyRIzmJiEgvKJVK5OTkQCqVwtnZGcXFxTh//jyMjY2RkJCgHtGpKdXV1Th16hQMDAywceNGjXVgv5VKpcKXX36J0tJSbN++Hfb29rh8+bJ6unlLSwsAwMHBAVKpFDExMZBKpbC0/M9rtyAI+Pzzz1FXV4fdu3drfOq8JqlUKlRVVaGmpgZLlizBnDlzUFNTg3feeQfz5s2DTCaDj4/PiBB5qJlQcHAwtm/fDjc3txFrh353DdG7rR0qEolQX18PGxsbWFpawtTU9LYfxsbGHB1KNAlDozeNjIxgZWUFIyMjjuIkncaAk3TOWEZntnbK1cHnd4lFIrR0yKe1ZqKJuN0U9LZr2TdHbo5CUCnRVpoNp4WrprlaIiKi8evo6IBSqURAQACOHj2K6upq+Pv7Y8mSJRqdMq5SqZCXl4e8vDy4u7tj9erVMDHRfGNKQRCwf/9+pKenIygoCEePHkVraysAwNHREd7e3li5ciWkUiksLCxue5ykpCQUFRXhgQce0HjIq0nNzc04ePAgqqurERkZidWrV8PIyAgKhQKFhYXIyMjAv//9bzg4OCAyMhKLFi2CsbHxbZsJjXft0J6enmEBaEtLCzIyMiASiaBUKtHc3Ize3l709vZCqRx+7yQWi2FiYjJq+Dna9tHWDp0KKpUK9fX1Gl2SgWgqXLhwAWVlZejr60NhYSEGBwdRVlaGCxcuYMmSJdouj2gEBpykc8YyOtPOynzUcBMAVIIAe2tOYSHddqcp6APdreptI4hFGOhqnf6CiYiIJsDW1hahoaE4efIkDA0NsX79+mHTuTWhr68Pp06dQk1NDcLCwrB48WKIxWKNHb+rqwsVFRW4fv06PvnkE5SXlyMgIAAA4Ovrq+5yPtYp1FlZWUhPT8e6deuwYMECjdWpSSqVCunp6Th79iysra3xyCOPYO7cuerHDQwMsGjRIgQHB6OqqgqZmZk4fvw4Tp48CSMjIzQ3NyM0NHRSzYQkEgksLS2HjXzt6enBwMAALC0thwXYgiBgYGAAvb296OvrU4eeQx99fX3o7u5GU1MTent70d/fP+J8RkZGtw0/NTE6VKlU4tq1a7h48SI6Ozvx0EMP3TEEJ9K2OXPmYMeOHaNuJ9JFDDhJ54xldOa6JYE4nJ6vHuV5K4lYDFmA13SUSjQhd5uC7hS8evRw8//ta2Spu9PYiIiIbtXS0oLU1FT4+fkhKipK49MaGxoakJSUBKVSifXr1096NKQgCOjq6kJtbS3q6+tRW1uLrq4uDA4Oorm5GWZmZnj66acRHx8/oTUzS0pKcOzYMURFRUEmk02q1qnS0NCAgwcPoq6uDlFRUVi5cuVt10gViUSYO3cu5s6di5aWFvzlL3/BmTNn4ObmBoVCgerqanh5eU35VHGRSARjY+MxX19KpVIdhA79t6enZ9jnYx0damJiAjMzs9uODjU0NMT169dx8eJFdHV1wcvLC2vWrGG4STrP3d0d7u5s3kv6gwEn6ZyxjM60MjfF3o3RePfQzXU6xSIRVIIAiViMvRujYWmm+SlJRJpytynoEAkQiSX/CUBvIRJLYOsbMdUlEhERaYSDgwO2bdum8TUmh7qwZ2RkwMHBYcKBkSAI6OzsRF1dHWpra1FXV4fu7m6IRCLY29tDKpXC1dUV169fR35+Pp544okJN9ioqanB559/jgULFuCee+6Z0DGmklKpRFpaGlJSUmBnZ4dHH310zIGxXC7H119/DYlEgtdeew0ikQgZGRn417/+BUdHR8hkMgQHB2u8k/1ESSQSdQOjuxEEAYODg+jp6bnt6FC5XD5idKhKpUJzczPq6+uhUqng4uICX19fCIKAoqIijS/TQEQ02zHgJJ0jC5COaXTmQm93vLI3EVnFFWjpkMPe2hyyAC+Gm6Tz7jYFXdnXA+nqR1Bx6v1hU9hFYgmkqx9hgyEiItIrmg43BwYGkJKSgrKyMixcuBAymQwSiWRMzxUEAR0dHcMCTbn85uwhBwcHeHt7w9XVFS4uLuop0Lm5ubhy5QrCwsIQFhY2oZrb2tqwf/9+ODs7495779W55jd1dXU4ePAgGhsbER0djZiYmDGvSdnQ0ID9+/dDqVRiz5496lA0JCQElZWVyMzMxJEjR3Dy5EmEhoYiMjISNjY2U/jVaJZIJIKRkdGYw8j+/n5cvHgRFy9ehKmpKZYuXQqpVAqJRDJsdKgml1EgIiIGnKSDxjM608rclN3SSe8YWdjddQq69dxABDz4ItpKszHQ1QojSzvY+kYw3CQiolmttbUVSUlJkMvlWLNmDXx8fO64vyAIaG9vHxZo9vT0QCwWw8HBAb6+vupAc7TpzZcuXUJOTg7CwsLueq7b6e3txUcffQRjY2Ns3779ttO9tUGhUCAlJQVpaWlwcnLCY489BldX1zE/v6SkBF988QXs7Oywfft2WFtbqx8TiUSQSqWQSqVob29HdnY2cnNzcf78eSxYsAAymQyenp46F/ZO1ODgIAoLC5Gfn4/+/n74+fkhJCREr8JcIiJ9xoCTdBJHZ9JMZjsvAnW5R+86Bd3Q1JLd0omIiP6fq1evIjU1FVZWVrj33ntHDY4EQUBbWxvq6urUoWZvby/EYjEcHR0xf/58daB5txF5Q1PgFy9ejODgYHR1dY27ZoVCgY8//hg9PT149NFHx9yIaDrU1NTg66+/RmtrK2JjYxEdHT2ukbDp6ek4efIkFixYgC1bttzx+2ljY4O4uDjExsbi8uXLyMzMxL59++Ds7AyZTIaFCxfqVPA7Hv39/SgsLMTly5cxODiI+fPnIyQkRN05noiIpgcDTtJZHJ1JM5WhqSWnoBMREY2RQqHA+fPnUVRUhPnz52P58uXq6dOCIKC1tXVYoNnX16cONBcsWAA3Nzc4OzuPK0C7cuUK0tLSEBwcjIiIiFG7bt+NIAj4+uuvUVNTg927d8Pe3n7cx5gKg4ODOHv2LNLT0+Hq6orHH38czs7OY36+QqHAoUOHcOnSJcTExGDlypVjHoVpZGSEsLAwhIaGory8HJmZmTh06BCSkpIQFhaGiIiIYaNAdVlfXx8KCgpQUFAAhUKBBQsWICQkhM2DiIi0hAEnEZEWcAo6ERHR3XV2duLkyZNobW1FTEwM/Pz81IHm0JTz/v5+SCQSODo6IiAgAK6uruMONG9VWlqKlJQUBAQEYMmSJROeQn3q1CkUFhZi27ZtmDNnzoSOoWk3btzAwYMH0dHRgdWrV2Pp0qXjWgtSLpfjk08+QW1tLe677z4sXLhwQnWIRCJ4e3vD29sbra2tyM7ORnZ2NtLT0+Hv7w+ZTIY5c+bo5PT1vr4+5Ofno7CwEEqlEgEBAVi0aJFOjc4lIpqNGHASEWkJp6ATERHdXkVFBc6cOQOlUol58+apG9YMBZrOzs4ICgpSB5pjbYpzJ+Xl5Thz5gzmz5+P6OjoCQdsOTk5SEtLw9q1axEQEDDpuiZrYGAAp06dQlZWFtzd3fHggw/C0dFxXMdoaGjAgQMHoFAohjUTmiw7OzusXbsWK1euxKVLl5CZmYn33nsPrq6ukMlkCAoK0si/7WT19PQgPz8fRUVFAICAgAAEBwfDzMxMy5URERHAgJP0UKe8F5lFFWjtlMPOyhyyACmszE21XRYRERERTZJKpUJTUxNOnz6NvLw8GBoawt3dHdeuXYOLiwuCgoLg5uYGJycnjYdeN27cwKlTp+Dl5YWYmJgJh5tXr17FkSNHIJPJsGTJEo3WOBHl5eX45ptv0N3djXvuuQcymWzcHbzv1ExIU4yMjBAREYHw8HCUlZUhMzMTX3/99bDp62NdI1ST5HK5OtgUi8UICgpCcHAwTEzYG4CISJcw4CS9crmsBu8evtldXSQSQRAEHE7Px96N0Vjo7a7t8oiIiIhoHJRKJZqbm9VraN64cQNXr16FXC5HcHAwZDIZ3Nzc4OjoOKWj+Kqrq/Htt99izpw5WLly5bgDwCG1tbX4/PPP4efnh7Vr12p1inV/fz+SkpKQk5MDT09P7Ny5E3Z2duM6xnibCWmCSCSCr68vfH190dLSgqysLGRkZCAtLQ0+Pj5QqVTTss5ld3c3Ll68iCtXrsDAwACLFi1CUFAQg00iIh3FgJP0Rqe8F+8eToNCqQJw84YLABRKFd49lIZX9iZyJCcRERGRDlMqlWhqakJ9fT1qa2tRX1+PwcFBGBgYwMTEBG1tbfDx8cHGjRunbd3K+vp6nDhxAm5ubli9evWERwm2t7dj//79cHR0xH333TfhkFQTSktLcejQIfT29iIhIQHh4eHjDlsVCgUOHz6MixcvjruZkKbY29tj3bp1WLVqFS5evIjU1FRcvHgR7u7uWLx4Mby8vDQefHd1deHChQu4evUqDAwMEBoaisDAQBgbG2v0PEREpFkMOElvZBZVQKlSjfqYUqVCVnEFu64TERER6ZChQHOoIVB9fT0UCgUMDQ3h4uKC0NBQuLi4oLa2Frm5uQgICMDq1aunrWFLY2Mjjh07BicnJ9xzzz0TDst6e3vx0UcfwdDQEA899NCEGxxNVl9fH06cOIELFy7A29sbiYmJsLGxGfdxbm0mdO+99yI4OFjzxY6DsbGxej3OAwcOoLKyEqdPn4aZmRn8/f0REBAw6bUwOzo6cOHCBVy7dg3GxsYIDw9HQEDAlI9YJSIizWDASXqjtVOunpb+XWKRCC0dci1URURERES309HRgYMHD8LIyAguLi4ICwuDm5sbHBwcIBaL0d/fj7Nnz6KiogIhISGIiIiYtpGPzc3NOHr0KGxtbREfHz/hcFOhUOCTTz5Bd3c3Hn30Ua1107569SoOHTqEgYEBJCYmYvHixRMacdnY2Ij9+/drvJmQJohEInh4eMDf3x+9vb0oLCzEpUuXcPHiRXh7e2PhwoXjbp7U3t6OvLw8lJaWwtTUFDKZDP7+/loLqYmIaGIYcJLesLMyHzXcBACVIMDeWjs3k0REREQ0OltbW9x7772wt7cfEVw2NzcjKSkJ/f39WLt2LaRS6bTV1dbWhiNHjsDKygrr1q2bcJglCAK++eYbVFdXY9euXXBwcNBwpXfX09OD48ePIz8/H/PmzcOGDRsm3ATo6tWr+Pzzz6e0mZCm2NraIjo6GhERESgpKUFhYSGuXbsGZ2dnBAUFwcvL647LDbS2tiIvLw/Xr1+HmZkZli5digULFuhEx3YiIho//vYmvSELkOJwer56Dc5bScRiyAK8tFAVEREREd2OSCQaMaJOEASUlJQgLS0Ntra2SEhIgJWV1bTV1NHRgSNHjsDc3Bzr16+f1NqKZ86cQX5+PrZt24a5c+dqsMqxKSoqwpEjR6BUKrFlyxYEBwdPaNSmIAg4f/48kpKS4Ofnh3vvvVdvpmYbGxsjODgYQUFBuHHjBgoKCnDq1CmYm5sjICAA/v7+MDX9zzr9zc3NyMvLQ3l5OSwtLREdHY358+cz2CQi0nP8LU46q1Pei8yiCrR2ymFnZQ5ZgBR7N0bj3UM3u6iLRSKoBAESsRh7N0bD0owdDYmIiIh0mUKhQFpaGkpKSuDv74+lS5dOa7DU1dWFw4cPw9DQEOvXr59UR+zc3FykpKQgLi4OgYGBGqzy7uRyOY4cOYKioiIsWLAACQkJsLS0nNCxFAoFjhw5ggsXLmD58uVYtWqVVru/T5RYLIZUKoVUKkVraysKCgpw4cIF5OXlwdfXFy4uLqioqEBlZSWsrKwQGxuLefPmTbipFBER6RYGnKSTLpfV4N3DN4PMoXU3D6fnY+/GaLyyNxFZxRVo6ZDD3tocsgAvhptEREREOq69vR0nT55ER0cHVq5cifnz50/r+bu7u3H48GGIxWIkJCRMqilNaWkpjhw5goiICCxdulSDVd6ZIAgoKCjAsWPHAABbt25FYGDghANJXWsmpCl2dnaIiYlBZGQk0tPTcfLkSTQ2NsLJyQmrVq1CTEwMR2wSEc0w/K1OOqdT3ot3D6epp6IPrbupUKrw7qE0vLI3kd3SiYiIiPTI9evXkZycDFNTU2zevBn29vbTev7e3l4cOXIEKpUKiYmJsLCwmPCx6uvr8emnn8LX1xfr1q2bttGOQ6NPS0pKEBQUhHXr1k2qodFQM6HBwUHs3r0bc+bM0WC12ldbW4u8vDzU1NRg4cKFcHFxQXt7O65du4a6ujoEBgZiwYIFkxrFS0REuoMBJ+mczKIKKFUj19kEAKVKhaziCgacRERERHpAqVQiKysL+fn58Pb2Rmxs7LSv7djX14cjR45gYGAAGzdunPBUbuDm+p379++Hg4MDtm7dOi0d3wVBwKVLl3D8+HEYGBjggQcegL//5O6Fh5oJ2draYs+ePbCxsdFMsVomCAJqamqQl5eHuro62NvbIy4uDl5eXuogurm5GQUFBcjJyUFOTg7mzZuHoKCgaQ/diYhIsxhwks5p7ZSrp6V/l1gkQkuHXAtVEREREdF4tbe3o7i4GEuXLkVQUNC0r+3Y39+Po0ePQi6XIzExcVJB3sDAAD755BNIJBI89NBD0xLUdnR04NChQygtLcWiRYsQHx8/rGHOeOlzM6E7EQQB1dXVyM3NRUNDAxwdHbF27Vp4enqOuOYcHBywYsUKyGQyXLlyBYWFhbhy5Qrc3d0RFBSEuXPnTktwTUREmsWAk3SOnZX5qOEmAKgEAfbWE5+KQ0RERETTx97eHg899JBWpgEPDg7i2LFj6OzsxIYNG2BrazvhYymVSqSkpMDKygo/+MEPJjXFfSwEQUBeXh6+/fZbGBkZ4aGHHpr0mqVKpRKHDx/GhQsXEB0djdWrV+tlM6FbCYKAyspK5OXloampCc7Ozli3bh3mzJlz16/N1NQUixcvRnBwMCoqKnD58mWcOHEClpaWCAwMhJ+fH6evExHpEQacpHNkAVIcTs9Xr8F5K4lYDFmAlxaqIiIiIqKJ0EZIpFAocOLECbS1tSEhIQEODg4TPpYgCEhLS0NjYyMeffRRODo6arDSkdra2vDNN9+gvLwcoaGhuOeeeyb9PZTL5fj0009RXV2NLVu2YNGiRRqqVjsEQcD169eRl5eHlpYWuLq6IiEhAe7u7uMObSUSCXx8fODj44OmpiYUFBQgOzsbOTk5mD9/PoKCgiYVjhMR0fRgwEk6x8rcFHs3RuPdQze7qItFIqgEARKxGHs3RrNjOhERERHdlkKhwLfffouGhgYkJCTAyclpUsfLzc1FaWkpoqKi4OnpqaEqRxIEAVlZWTh58iTMzMywc+dO+Pj4TPq4tzYT2rNnj143E1KpVKioqMC1a9fQ1dUFd3d3bNy4EW5ubho5vqOjI1auXAmZTIbi4mIUFRWhqKgIHh4e6unr+j7qlYhopmLASTppobc7XtmbiKziCrR0yGFvbQ5ZgBfDTSIiIiK6LaVSiVOnTqG2thbx8fFwcXGZ1PGuXLmC3NxchIeHw8tr6mYRtbS04ODBg7hx4wYiIiKwZs0aGBsbT/q4V69exRdffAEbGxu9biakUqlQUFCAkydPIi8vD97e3li1atWk/31vx8zMDGFhYQgJCUF5eTkuX76M48ePw9raGoGBgZg/f75G/n2IiEhzGHCSzrIyN2W3dCIiIiIaE5VKhTNnzqCqqgpxcXHw8PCY1PGqq6uRmpoKf39/BAcHo7u7W0OV/odKpUJGRgZOnz4NS0tL7NmzB1KpdNLHFQQBGRkZ+PbbbzF//nzcd999etlMSKlUIj8/H6mpqWhtbYVUKkV8fDy8vLymZekDiUQCX19f+Pr6oqGhAQUFBcjIyEB2drZ6+rq+hsZERDMNA04iIiIiItJrgiAgJSUF5eXlWL169aSnkre0tCApKQkeHh6Ijo7GwMCAhir9j6amJhw8eBA1NTWQyWRYtWqVRkLImdBMSKFQ4OLFi0hLS0N7ezv8/f2xbds2WFtbIyUlRSs1OTs7w9nZGUuWLFFPXy8sLMScOXOwcOFCeHh46N33mYhoJmHASUREREREeksQBJw7dw5Xr17FqlWr4O3tPanjdXd349ixY7CyssKaNWsgFos1VOlNKpUK586dw9mzZ2Fra4vvfe97GlsXs6enB5988oneNhNSKBTIy8tDWloaurq6EBgYiO3bt8PZ2RnAza9P28zNzREeHo6QkBBcv34dly9fxtGjR2FtbY2goCDMnz9fL0fLEhHpOwacRERERESkl4amYhcWFiI2Nha+vr6TOt7AwACOHz8OkUiE+Ph4GBoaaqjSmxoaGvD111+jvr4eS5cuxYoVKzR2jsbGRhw4cAADAwN610xocHAQOTk5SE9PR3d3NxYuXIjly5dPecf6yTAwMMD8+fMxb9489fT18+fPIzs7G35+fggMDIS1tbW2yyQimjUYcBIRaclgbxfarmVjoLsVRhZ2sJ0XAUNTS22XRUREpDdycnKQn5+PZcuWYcGCBZM6llKpRFJSErq7u5GYmAhzc3MNVXnz2KmpqUhJSYGDgwP27t0Ld3d3jR3/2rVr+Pzzz2FjY4Pdu3frzbqQAwMDyM7ORnp6Onp7e7Fo0SJER0fD3t5e26WNmUgkgouLC1xcXNDd3Y2ioiIUFxejoKAAc+fORVBQENzd3Tl9nYhoijHgJCLSgo4bBag4tQ+CSgmIRYBKQF3uUUhXPwLruYHaLo+IiEjnXbhwAXl5eZDJZAgKCprUsQRBQGpqKurq6rBu3TrY2dlpqEqgtrYWBw8eRFNTE5YvX47ly5fDwEAzf4Z9t5nQvffeqxfdvfv7+5GVlYXz58+jv78fISEhiI6Ohq2trbZLmxQLCwtERkYiNDQUpaWlKCgowJEjR2Bra4ugoCDMmzdP46OCiYjoJgacRETTbLC36/+Fm4qbG1QCAEBQKVBx6n0EPPgiR3ISERHdQX5+PrKystRrIU5WXl4eSkpKsGrVKo2NrFQoFEhOTsa5c+fg5OSExx9/HC4uLho5NnBzVOiRI0eQl5eHZcuWYfXq1RpfL1TTent7kZmZiYyMDAwODiIsLAzLli2bcVO5DQwMsGDBAvj5+aGurg4FBQVIS0tDVlYWFixYgMDAQFha8l6PiEiTGHASEU2ztmvZN0dujkJQKdFWmg2nhaumuSoiIiL9UFRUhPPnzyMkJAShoaGTPt7Vq1eRk5ODiIgIzJs3TwMVAtXV1Th48CBaW1uxYsUKLFu2DBKJRCPHBoY3E9q8ebNGQt6p1NPTg/PnzyMrKwtKpRLh4eFYtmzZjA/5RCIR3Nzc4Obmhq6uLhQWFuLKlSvIz8/HQw89BAsLC22XSEQ0YzDgJCKaZgPdrepp6SOIRRjoap3+ooiIiPRASUkJUlNTERQUhMjIyEmva1hdXY3k5GQsWLAAixcvnnR9g4ODOH36NDIyMuDm5oYnnngCTk5Okz7urZqamrB//34MDAxg9+7dmDt3rkaPr0nd3d3qxjsAEBERgaioqFkZ7FlaWmLJkiUICwtDTU3NrPweEBFNJQacRETTzMjCbvRwEwBUAowsNbfuFxER0UxRWlqK5ORk+Pv7Y+nSpZMON1tbW5GUlAR3d3dER0dP+niVlZU4ePAgOjs7sWbNGkRFRWl8yri+NBPq6upCeno6cnJyIBaLIZPJEBUVBTMzM22XpnWGhoaQSqXaLoOIaMZhwElENM1s50WgLvfof9bgvIVILIGtb4QWqiIiItJdFRUVOHPmDHx9fTUSRsrlchw7dgyWlpZYs2bNpKaPDwwM4OTJk8jKysKcOXPw0EMPwcHBYVL1fZe+NBPq6OjAuXPnkJeXBwMDAyxbtgwymQympqbaLo2IiGY4BpxERNPM0NQS0tWPoOLU+8O6qIvEEkhXP8IGQ0RERLeoqqrCyZMnIZVKsWLFikmPihwYGMCxY8cgCALi4+NhZGQ04WNdv34d33zzDeRyOeLj4xEZGanxUZv60Eyovb0dqampuHjxIoyMjBATE4PIyEiYmJhouzQiIpolGHASEWmB9dxABDz4ItpKszHQ1QojSzvY+kYw3CQiIrpFbW0tTpw4AQ8PD6xatWrSwZ5SqcTJkyfR1dWFxMTECa+D2NfXh6SkJOTm5kIqlWLXrl2ws9P8EjM9PT349NNPUVVVpZPNhFpbW5GamopLly7B1NQUq1atQnh4uE6OLiUiopmNAScRkZYYmlqyWzoREdFt1NfX4/jx43B1dZ30NHLg5jTvtLQ01NTUYN26dbC3t5/Qca5du4ZDhw6hr68PGzZsQFhY2KSnzI9mqJlQf3+/zjUTam5uRkpKCi5fvgxzc3PExcUhLCxsUqNhiYiIJoMBJxERERER6ZSmpiYcO3YMDg4OWLt2LQwMJv9ny8WLF3HlyhWsWLECHh4e435+f38/Dh06hOLiYvj4+GDjxo1T1uRnqJmQtbW1TjUTamxsREpKCgoLC2FpaYl169Zh8eLFMDQ01HZpREQ0yzHgJCIiIiIindHS0oIjR47A1tYW8fHxGgk3r127hqysLISFhcHPz2/cz6+srMTp06cxd+5cbNq0CSEhIVMyalMQBGRmZuLEiROYN28e7rvvPp2Y7l1fX4/k5GQUFxfDxsYGCQkJCAkJ0ci/DRERkSbwFYmIiIiIiHRCe3s7jhw5oh4dqIkpzzU1NUhOToafnx/CwsLG9dy+vj6kpaWhpKQEjo6OePzxx+Hi4jLpmkajVCpx9OhR5Obm6kwzoZqaGqSkpKCkpAS2trbYtGkTgoODJ71cABERkaYx4CQiIiIiIq3r6OjA4cOHYWpqioSEBI2MXGxra0NSUhJcXFywfPnycY26LCsrw7lz56BSqRAbGwsnJydYWVlNuqbR6FozoaqqKiQnJ6O0tBQODg7YsmULFi5cqPXAlYiI6HYYcBIRERERkVZ1d3fjyJEjMDAwQEJCAkxMTCZ9TLlcjmPHjsHCwgJxcXFjHnXY09ODc+fO4fr16/Dy8kJ0dDTEYjG6uromXdNompqacODAAfT19Wm9mVBFRQVSUlJw/fp1ODo6YuvWrQgICGCwSUREOo8BJxERERERaY1cLsfhw4cBABs2bICZmdmkjzk4OIjjx49DpVIhPj5+TKNBBUFAaWkpzp07B7FYjDVr1sDb2xsikQh9fX2Trmk0paWl+Oyzz2BtbY3HHnsMtra2U3KeOxEEAeXl5UhOTkZlZSVcXFxw//33w9/ff0rWGSUiIpoKDDiJiIiIiEgrent7ceTIESgUCiQmJsLCwmLSx1SpVDh16hQ6OzuxcePGMR2zu7sbaWlpqKyshK+vL5YuXQpTU9NJ13I7utBMSBAElJWVITk5GVVVVXBzc8P27dsxf/58BptERKR3GHASEREREdG06+vrw5EjR9Df34+NGzdqZH1LQRBw7tw5VFVVIT4+Hg4ODnfdv6SkBBkZGZBIJFi7di2kUumk67iTW5sJLV26FGvWrJnWKeCCIODq1atITk5GbW0tPDw8sGPHDvj6+jLYJCIivcWAk4iIiIiIptXAwACOHTsGuVyOjRs3wsbGRiPHvXTpEoqKihAbG4s5c+bccd+uri6kpKSguroafn5+WLJkiUbW/ryTW5sJbdq0CYsXL57S891KEAQUFxcjJSUF9fX18PT0xK5du+Dl5cVgk4iI9B4DTiIiIiIimjaDg4M4duwYOjo6sGHDBtjZ2WnkuKWlpcjMzERoaCgWLFhw2/0EQUBRUREyMzNhZGSEdevWTUtjn1ubCe3atQuenp5Tfk7g5pT9oqIipKSkoLGxEV5eXtizZ8+Uj1QlIiKaTgw4iYiIiIhoWigUCpw4cQItLS1ISEi46xTysaqrq8PZs2cxf/58hIeH33a/zs5OpKSkoKamBv7+/pDJZNOy9qU2mgmpVCoUFBQgJSUFzc3N8PX1xcaNG+86spWIiEgfMeAkndUp70VmUQVaO+WwszKHLEAKK/OpW+ydiIiIiKaOUqnEyZMn0dDQgHXr1sHZ2Vkjx21vb8eJEyfg4uKCmJiYUadbC4KAgoICZGVlwdTUFAkJCfDw8NDI+e9EEARkZWXh+PHj09ZMSKlUIj8/H6mpqWhtbYWfnx+2bNkCd3f3KT0vERGRNjHgJJ10uawG7x5Og1KlgkgkgiAIOJyej70bo7HQmzdnRERERPpkqLN5dXU14uPj4ebmppHj9vT04OjRozAzM0NcXBwkEsmIfdrb25GcnIz6+noEBgZCJpPB0NBQI+e/k+luJqRQKHDp0iWkpqaivb0d/v7+2LZtG1xdXafsnERERLqCASfpnE55L949nAaFUgXg5jvfAKBQqvDuoTS8sjeRIzmJiIiI9IRKpcKZM2dQWVmJuLg4jY2cHBwcxIkTJ6BSqbB+/foRIyNVKhXy8/ORk5MDCwsLJCYmTlvY19PTg88++ww3btyY8mZCCoUCeXl5SEtLQ1dXFwIDA7F9+3aNjZAlIiLSBww4SedkFlVAqVKN+phSpUJWcQXWhPtPc1VERERENF6CICA1NRVlZWVYvXq1xhrbDI0IbWtrQ2JiIiwsLIY93traiuTkZDQ1NWHhwoWIiIiAgcH0/OnT3NyM/fv3T3kzocHBQeTk5CA9PR3d3d1YuHAhli9fDkdHxyk5HxERkS5jwEk6p7VTrp6W/l1ikQgtHXItVEVERERE49XS0oJr165hxYoV8PHx0cgxBUFAeno6qqqqsHbt2mGNipRKJS5duoS8vDxYWlpi06ZN0zqSsaysDJ999hksLS2nrJnQwMAAsrOzkZ6ejt7eXixatAjR0dGwt7fX+LmIiIj0BQNO0jl2VuajhpsAoBIE2FubT3NFRERERDQRDg4OePDBB0eMsJyM/Px8FBYWIiYmBnPnzlVvb25uxtmzZ9HW1oZFixYhNDR02kZt3tpMyNfXF1u3btV4M6H+/n5kZWXh/Pnz6O/vR0hICKKjo6elIzsREZGuY8BJOkcWIMXh9Hz1Gpy3kojFkAV4aaEqIiIiIpoITYabZWVlyMjIwOLFi+Hvf3PJIqVSiQsXLuDChQuwtbXF5s2bp3WatlKpxLFjx5CTk4OoqCjExcVptJlQb28vMjMzkZGRgcHBQYSFhWHZsmWwtrbW2DmIiIj0HQNO0jlW5qbYuzEa7x662UVdLBJBJQiQiMXYuzEalmYm2i6RiIiIiKZZfX09zpw5A19fX0RERAAAGhsbkZycjPb2dixevBiLFy8etZP6VOnt7cWnn36KyspKJCYmIjQ0VGPH7unpwfnz55GVlQWlUonw8HAsW7YMlpaWGjsHERHRTMGAk3TSQm93vLI3EVnFFWjpkMPe2hyyAC+Gm0RERESzUHt7O44fPw5nZ2fExsZCqVQiNzcXly5dgr29PbZs2TJsLc7p8N1mQppqoNTd3Y3z588jOzsbABAREYGoqCiNjoQlIiKaafQ24Hz11Vfx5Zdf4sqVKzA1NcXSpUvx2muvwc/P77bP2bdvHx555JFh24yNjdHX1zfV5dIEWJmbsls6ERER3RXvC2e23t5eHDt2DGZmZoiLi0NzczOSk5PR1dWFiIgIBAcHT+uoTWBqmgl1dXUhPT0dOTk5EIvFkMlkiIqKgpmZmQYqJiIimtn0NuBMTk7Gk08+iYiICCgUCjz33HO45557UFRUBHPz2zehsbKyQklJifpzkUg0HeUSERER0RThfeHMpVAocOLECSgUCsTHxyMvLw8FBQVwcnLCPffco5UGO0PNhHx8fHDffffBxGRyM4w6Ojpw7tw55OXlwcDAAMuWLYNMJoOpqamGKiYiIpr59DbgPH78+LDP9+3bBycnJ+Tm5iImJua2zxOJRHBxcZnq8oiIiIhomvC+cGZSqVQ4ffo0WlpaEBkZiRMnTqC7uxsymQwLFy7UaCOfsVAqlTh+/Djy8/M10kyovb0dqampuHjxIoyMjBATE4PIyMhJB6ZERNNhYGAAOTk5CA8Ph5GRkbbLIdLfgPO7Ojo6AAB2dnZ33K+7uxuenp5QqVQIDQ3F//3f/yEwMPC2+/f396O/v3/Y84mIiIhId/G+cGbIyMhAWVkZnJ2dkZ6eDldXV8THx8PGxmbaa+nv78eZM2dgamqKLVu2TKqZUGtrK1JTU3Hp0iWYmppi1apVCA8Ph7GxsQYrJiLSnNHCzIyMDHz44YdQKpVYvny5liskmiEBp0qlwjPPPINly5YhKCjotvv5+fnhvffeQ3BwMDo6OvC73/0OS5cuRWFhITw8PEZ9zquvvoqXX355qkonIiIiIg3ifeHMcPnyZZw7dw4SiQRtbW1YtmwZAgMDtbKMQHt7Ow4fPoyOjg789Kc/hb//xNaIb25uRkpKCi5fvgxzc3PExcUhLCyMI5+ISOd9N8zs7+/HiRMnUF5ejuPHjyMyMpJv0pDWzYiA88knn0RBQQHS0tLuuF9UVBSioqLUny9duhT+/v54++238corr4z6nF/+8pf48Y9/rP784sWLiI2N1UzhRERERKRRvC/UfyUlJThw4AAkEglkMhliYmJgZWWllVqqq6tx8uRJGBoaYt26dfD09Bz3MRobG5GSkoLCwkJYWlpi3bp1WLx4MQwNDaegYiIizRotzMzMzMTVq1cRHByMq1evIisri6M4Sev0PuB86qmncPjwYaSkpNz23fbbMTQ0xOLFi1FaWnrbfYyNjYe9E2FhYTHhWomIiIho6vC+UP/l5uZi3759sLKywvbt2+Hv76+15k8FBQU4f/483N3dsWzZMgwMDIzr+fX19UhOTkZxcTFsbGyQkJCAkJAQGBjo/Z9gRDSLfDfMTEtLw+nTp2FkZAQrKysYGRlxFCfpBL19dRUEAT/84Q/x1Vdf4ezZs/Dy8hr3MZRKJS5fvoz169dPQYVERERENB14X6j/+vr6cOrUKRw5cgRubm546qmntLLWJnDzWjh//jwKCwuxcOFCLFmyBAMDA2MOOGtqapCSkoKSkhLY2dlh06ZNCA4OhkQimeLKiYg0q7+/H0ePHkVHRwcWLFgAIyMjfPjhh+jp6cHAwAAKCwsxODiIsrIyXLhwAUuWLNF2yTSL6W3A+eSTT2L//v04ePAgLC0tUV9fDwCwtraGqakpAGDXrl1wd3fHq6++CgD43//9XyxZsgS+vr5ob2/HG2+8gcrKSuzdu1drXwcRERERTQ7vC/VbeXk5zpw5g8uXL2PBggV47LHH1P9u062vrw8nT55EXV0dYmJixrXeZlVVFZKTk1FaWgoHBwds2bJFK93eiYg05cKFC8jOzkZpaSkGBgZgY2MDlUqF5cuXY8GCBcP2nTNnjpaqJLpJbwPOv/3tbwCAFStWDNv+/vvvY8+ePQCAGzduDLuhaGtrw2OPPYb6+nrY2toiLCwM6enpCAgImK6yiYiIiEjDeF+on3p7e3Hu3Dlcu3YNLS0tCAwMxP3336+1cLO9vR3Hjx9HX18fEhIS4ObmNqbnVVZWIjk5GdevX4ejoyO2bt2KgIAABptEpPecnJxgb2+P5uZmODk54cEHH4ShoSFCQ0Ph7u6u7fKIhtHbgFMQhLvuc/bs2WGf//GPf8Qf//jHKaqIbmVlbjrsv0RERERThfeF+kUQBJSVleHcuXMQBAEWFhYwMjLCxo0btd5MyNTUFFu2bIG1tfUd9xcEAeXl5UhOTkZlZSVcXFxw//33a3XNUCIiTauuroYgCFi9ejWqqqrg6OjIZkKks/Q24CTd9vMda6f0+J3yXmQWVaC1Uw47K3PIAqQMU4mIiIh0nFwuR1paGioqKuDt7Q1DQ0NcvXoV99xzD5ycnLRSU2FhIdLT0+Hu7o7Vq1ffsUmGIAgoLS1FcnIyqqqq4Obmhu3bt2P+/PkMNoloRhnqns5mQqQvGHCS3rlcVoN3D6dBqVJBJBJBEAQcTs/H3o3RWOjNYfJEREREukYQBFy7dg3p6ekQi8WIi4tDT08Pzp07h2XLlkEqlU57TSqVCunp6cOaCd1uWrkgCKiursb777+P5uZmeHh4YMeOHfD19WWwSUQz0oULF1BWVoa+vj42EyK9wICT9EqnvBfvHk6DQqkC8J8paQqlCu8eSsMrexM5kpOIiIhIh3R3dyM1NRU3btzAvHnzsHTpUtTX1yM9PR3BwcEICgqa9prG2kxIEARUVFQgMzMTdXV1iI6Oxq5du+Dl5cVgk4hmtDlz5mDHjh2jbifSRQw4Sa9kFlVAqVKN+phSpUJWcQXWhI+92yURERERTZ2WlhZ88803MDQ0xNq1ayGVStHY2IhTp07By8tLK6OAbm0mtH79+lEbZahUKly/fh0XLlxAa2srnJycEBcXh23btsHMzGzaayYimm7u7u5sJER6hQEn6ZXWTrl6Wvp3iUUitHTItVAVEREREY3G1tZWPUrT2NgYnZ2dOH78OOzt7bFy5cppHwV5t2ZCKpUKZWVlyMvLQ3t7O+bMmYPly5fDxsYGXV1d01orERERjR0DTtJZozUSsrMyv22nVJUgwN7afJqrJCIiIqLbEYvFCAsLA3BzWvixY8dgZGSE+Ph4GBhM758id2ompFQqce3aNVy8eBEdHR3w9PTEypUr1Y2P+vr6prVWIiIiGh8GnKSTbtdI6KG4SEjEYvUanLeSiMWQBXhpoVoiIiIiuhOFQoETJ06gr68PmzdvhomJybSd+9ZmQkFBQYiKilI3E1IoFLh27RouXLiArq4ueHl5Yc2aNXBwcJi2+oiIiGjyGHCSzrlTI6H9SVl4KC4S+5OyoFSpIBaJoBIESMRi7N0YDUuz6btZJiIiIqK7EwQBZ8+eRVNTEzZs2DBiWvhU6uvrw6lTp1BbW4vly5cjICAAwM1g88qVK7h48SJ6enrg7e2NtWvXwt7eftpqIyIiIs1hwEk6526NhLp6+vDK3kRkFVegpUMOe2tzyAK8GG4SERER6aDMzExcv34dcXFxcHFxmbbzjtZMSKFQoKioCPn5+ejp6YGvry8WL14MW1vbaauLiIiINI8BJ+mcsTQSsjI3Zbd0IiIiIh3X3NyMS5cuYenSpfDymr6lhGpqapCUlARTU1Ns3rwZ5ubmuHjxIvLz89Hf34958+YhJCQENjY201YTERERTR0GnKRz2EiIiIiIaGZwcHDAvffeC0dHx2k7Z1FREc6dOwc3NzfExMSgtLQU+fn5GBwcxPz58xESEgIrK6tpq4eIiIimnljbBRB9lyxACol49EuTjYSIiGg2aG1txY4dO2BlZQUbGxs8+uij6O7uvuNzVqxYAZFINOzj+9///jRVTHR70xVuqlQqpKWlITU1Fb6+vnB0dMQXX3yB3Nxc+Pj44MEHH0RMTAzDTSIi0iu8LxwbjuAknWNlboq9G6Px7qE0NhIiIqJZaceOHairq0NSUhIGBwfxyCOP4PHHH8f+/fvv+LzHHnsM//u//6v+3MzMbKpLJdIJ/f39OHnyJG7cuAEHBwdUVFRApVLB398fixYtgrk5ZwAREZF+4n3h2DDgJJ200NudjYSIiGhWKi4uxvHjx5GdnY3w8HAAwJ///GesX78ev/vd7+Dm5nbb55qZmU1rExciXdDe3o5Dhw6hoqIClpaW6OrqQkBAAIKDg2f8H3NERDSz8b5w7DhFnXTWUCOhB1aHY024P8NNIiLSSd3d3ejs7FR/9Pf3T+p458+fh42NjfomFgDWrFkDsViMzMzMOz73o48+goODA4KCgvDLX/4SPT09k6qFSNeVlpbiT3/6E3Jzc+Hg4IClS5fioYcewpIlSxhuEhHRtNL0PSHA+8Lx4AhOIiIiokmIjY0d9vmLL76Il156acLHq6+vh5OT07BtBgYGsLOzQ319/W2f99BDD8HT0xNubm7Iz8/Hz3/+c5SUlODLL7+ccC1Euqq7uxvffPMNUlJSYGNjgwceeACLFy+GiQnfECciIu3Q9D0hwPvC8WDASURERDQJycnJCAkJUX9ubGw86n6/+MUv8Nprr93xWMXFxROu4/HHH1f//8KFC+Hq6orVq1ejrKwMPj4+Ez4ukS7p6upCbm4uTp8+jaamJkRFReH++++HqamptksjIqJZbqz3hADvC6cCA04iIiKiSbCwsBhTV+Znn30We/bsueM+3t7ecHFxQWNj47DtCoUCra2t41pHSSaTAbg5hXem3sjS7NHR0YELFy6guLgYN27cgJmZGb7//e9j0aJF2i6NiIgIwNjvCQHeF04FBpxERERE08DR0RGOjo533S8qKgrt7e3Izc1FWFgYAOD06dNQqVTqm9OxuHjxIgDA1dV1QvUS6YL29nbk5eWhtLQUIpEIfX19mDdvHu655x54eHhouzwiIqIJ4X2h5rHJEBEREZEO8ff3R3x8PB577DFkZWXh3LlzeOqpp/Dggw+qO2XW1NRgwYIFyMrKAgCUlZXhlVdeQW5uLioqKvDNN99g165diImJQXBwsDa/HKIJaW1txcmTJ/Hpp5+itrYW8+bNg0QigaurK+69916Gm0RENCvwvnDsOIKTiIiISMd89NFHeOqpp7B69WqIxWLcd999+NOf/qR+fHBwECUlJepumEZGRjh58iTefPNNyOVyzJkzB/fddx9eeOEFbX0JRBPS3NyMvLw8lJeXw9LSEtHR0VAqlcjIyICrqyvWrFnDRkJERDSr8L5wbBhwEhEREekYOzs77N+//7aPS6VSCIKg/nzOnDlITk6ejtKIpkRjYyPy8vJQWVkJa2trxMbGwsfHB1lZWSgoKEBgYCCioqIgkUi0XSoREdG04n3h2DDgJCIiIiIiraivr0deXh6qqqpgY2ODlStXwtfXF4ODg0hKSkJNTQ2io6MRGBio7VKJiIhIhzHgJL3SKe9FZlEFWjvlsLMyhyxACitzU22XRURERETjUFdXh9zcXNTU1MDOzg6rV6+Gt7c3xGIxOjo6cPz4cfT29mLdunVcb5OIiIjuigEn6Y3LZTV493AalCoVRCIRBEHA4fR87N0YjYXe7touj4iIiIjuQBAE1NTUIC8vD3V1dbC3t0dcXBy8vLwgEokA3GyUkJSUBBMTE2zevBk2NjbaLZqIiIj0AgNO0gud8l68ezgNCqUKANTrSyiUKrx7KA2v7E3kSE4iIiIiHSQIAqqrq5Gbm4uGhgY4Ojpi7dq18PT0VAebAFBcXIy0tDQ2EyIiIqJxY8BJeiGzqAJKlWrUx5QqFbKKK7Am3H+aqyIiIiKiO2lvb8fp06fR1NQEZ2dnrFu3DnPmzBkWbKpUKmRkZODy5ctsJkREREQTwoCT9EJrp1w9Lf27xCIRWjrkWqiKiIiIiO7E3NwcZmZmSEhIgLu7+7BgEwD6+/tx6tQp1NTUYNmyZQgKCtJSpURERKTPGHCSXrCzMh813AQAlSDA3tp8misiIiIiorsxNDREfHz8qI91dHTgxIkT6OnpYTMhIiIimhSxtgsgGgtZgBQS8eiXq0QshizAa5orIiIiIqKJqq2txVdffQWVSoXNmzcz3CQiIqJJYcBJesHK3BR7N0bDQCKGSARIxCKIRICBRIy9G6NhacZF6ImIiIj0QXFxMY4cOQIHBwd2SiciIiKN4BR10lmd8l5kFlWgtVMOOytzyAKkeGVvIrKKK9DSIYe9tTlkAV4MN4mIiIj0AJsJERER0VRhwEk66XJZDd49nAalSqVuLnQ4PR97N0azWzoRERGRnunv78fp06dRXV3NZkJERESkcZyiTjqnU96Ldw+nQaFUQRAAlUqAIAAKpQrvHkpDp7xX2yUSERER0Rh1dHTg4MGDaGhowLp16xhuEhERkcYx4CSdk1lUAaVKNepjSpUKWcUV01sQEREREU1Ia2srvv76azYTIiIioinFKeqkc1o75epp6d8lFonQ0iHXQlVERERENF5WVlbw8/NDSEgITEy4bjoRERFNDQacpHPsrMxHDTcBQCUIsLc2n+aKiIiIiGgiDAwMsGTJEm2XQURERDMcp6iTzpEFSCERj35pSsRiyAK8prkiIiIiIiIiIiLSVQw4SedYmZti78ZoGEjEEIkAiVgEkQgwkIixd2M0LM04vYmIiIiIiIiIiG7iFHXSSQu93fHK3kRkFVegpUMOe2tzyAK8GG4SEREREREREdEwDDhJZ1mZm2JNuL+2yyAiIiIiIiIiIh3GKepERERERERERESktxhwEhERERERERERkd5iwElERERERERERER6iwEnERERERERERER6S0GnERERERERERERKS3GHASERERERERERGR3mLASURERERERERERHqLAScRERERERERERHpLQacREREREREREREpLcYcBIREREREREREZHeYsBJREREREREREREeosBJxEREREREREREektBpxERERERERERESktxhwEhERERERERERkd5iwElERERERERERER6iwEnERERERERERER6S0GnERERERERERERKS3DLRdgL4qLi7Wdgnj0j+owLXqRpgYGsDQQKLtcma8QYUSfYMKKDsbYWzIH7PpoFT0o7uuDBJDY4glhtouZ8ZTKQehHOyHResgJAbGt93P1dUVrq6u01iZbqqrq0NdXZ22yyAN07d7AZo6U3Ut9Pb2orS0FGZmZjA2vv3vWpp6/f396OnpgaWlJUxNTbVdzqzFnwndMdafCd4LjsT7wpmH94S6gcnLOLm6uiI2NhYPP/ywtkshItJ5L774Il566SVtl6F1b7/9Nl5++WVtl0FTIDY2ln+4zWK8LyQiujPeC47E+8KZifeE2icSBEHQdhH6hu+4TJ/u7m7ExsYiOTkZFhYW2i6HSONm+jXOd+1v0rfXjZl+XWoSr3HSt59vbeDvFKL/mG0/D3ydHEnfXjdm2zU7UbzWtY8BJ+m0zs5OWFtbo6OjA1ZWVtouh0jjeI2TLuJ1SUSaxN8pRP/BnwfSN7xmSV+wyRARERERERERERHpLQacREREREREREREpLcYcJJOMzY2xosvvsguiTRj8RonXcTrkog0ib9TiP6DPw+kb3jNkr7gGpxERERERERERESktziCk4iIiIiIiIiIiPQWA04iIiIiIiIiIiLSWww4iYiIiIiIiIiISG8x4KRZo6KiAiKRCPv27dN2KUREREQ0AbyfIyIiotEw4KRRlZWV4YknnoC3tzdMTExgZWWFZcuW4a233kJvb++UnbeoqAgvvfQSKioqpuwcY/Gb3/wGiYmJcHZ2hkgkwksvvaTVekh7RCLRmD7Onj076XP19PTgpZdeGtexeK3OTrwuiWgseD/H30U0MXydJX3C65XoJgNtF0C658iRI9i2bRuMjY2xa9cuBAUFYWBgAGlpafjpT3+KwsJCvPPOO1Ny7qKiIrz88stYsWIFpFLplJxjLF544QW4uLhg8eLFOHHihNbqIO378MMPh33+r3/9C0lJSSO2+/v7T/pcPT09ePnllwEAK1asGNNzeK3OTrwuiehueD/H30U0cXydJX3C65XoJgacNEx5eTkefPBBeHp64vTp03B1dVU/9uSTT6K0tBRHjhzRYoX/IQgC+vr6YGpqqvFjl5eXQyqVorm5GY6Ojho/PumPhx9+eNjnGRkZSEpKGrFdW3itzk68LonoTng/dxN/F9FE8XWW9AmvV6KbOEWdhnn99dfR3d2Nf/7zn8Nuhof4+vriv//7v9WfKxQKvPLKK/Dx8YGxsTGkUimee+459Pf3D3ueVCrFhg0bkJaWhsjISJiYmMDb2xv/+te/1Pvs27cP27ZtAwCsXLlyxFD6oWOcOHEC4eHhMDU1xdtvvw0AuH79OrZt2wY7OzuYmZlhyZIlk7px1+ZoA9I/KpUKb775JgIDA2FiYgJnZ2c88cQTaGtrG7ZfTk4O1q5dCwcHB5iamsLLywvf+973ANxcU2zoBf/ll19WX/93m8LBa5Vuh9cl0ezF+7n/1Es0Vfg6S/qE1yvNBhzBScMcOnQI3t7eWLp06Zj237t3Lz744ANs3boVzz77LDIzM/Hqq6+iuLgYX3311bB9S0tLsXXrVjz66KPYvXs33nvvPezZswdhYWEIDAxETEwMnn76afzpT3/Cc889px5Cf+tQ+pKSEmzfvh1PPPEEHnvsMfj5+aGhoQFLly5FT08Pnn76adjb2+ODDz5AYmIiPv/8c2zZskVz3yCiUTzxxBPYt28fHnnkETz99NMoLy/HX/7yF1y4cAHnzp2DoaEhGhsbcc8998DR0RG/+MUvYGNjg4qKCnz55ZcAAEdHR/ztb3/DD37wA2zZsgX33nsvACA4OFibXxrpMV6XRLMX7+eIph5fZ0mf8HqlWUEg+n86OjoEAMKmTZvGtP/FixcFAMLevXuHbf/JT34iABBOnz6t3ubp6SkAEFJSUtTbGhsbBWNjY+HZZ59Vb/vss88EAMKZM2dGnG/oGMePHx+2/ZlnnhEACKmpqeptXV1dgpeXlyCVSgWlUikIgiCUl5cLAIT3339/TF+fIAhCU1OTAEB48cUXx/wcmtmefPJJ4dZfnampqQIA4aOPPhq23/Hjx4dt/+qrrwQAQnZ29m2PPZnrjdfq7MbrkoiG8H5uJP4uosni6yzpE16vNFtxijqpdXZ2AgAsLS3HtP/Ro0cBAD/+8Y+HbX/22WcBYMSUooCAACxfvlz9uaOjI/z8/HD9+vUx1+jl5YW1a9eOqCMyMhLR0dHqbRYWFnj88cdRUVGBoqKiMR+faLw+++wzWFtbIy4uDs3NzeqPsLAwWFhY4MyZMwAAGxsbAMDhw4cxODioxYppNuB1STR78X6OaOrxdZb0Ca9Xmi0YcJKalZUVAKCrq2tM+1dWVkIsFsPX13fYdhcXF9jY2KCysnLY9rlz5444hq2t7Yh1P+7Ey8tr1Dr8/PxGbB+aCvXdOog06dq1a+jo6ICTkxMcHR2HfXR3d6OxsREAEBsbi/vuuw8vv/wyHBwcsGnTJrz//vsj1jcj0gRel0SzF+/niKYeX2dJn/B6pdmCa3CSmpWVFdzc3FBQUDCu54lEojHtJ5FIRt0uCMKYzzUVHTaJJkOlUsHJyQkfffTRqI8PLcQtEonw+eefIyMjA4cOHcKJEyfwve99D7///e+RkZEBCwuL6SybZjhel0SzF+/niKYeX2dJn/B6pdmCAScNs2HDBrzzzjs4f/48oqKi7rivp6cnVCoVrl27Nmzh+IaGBrS3t8PT03Pc5x/rzfV36ygpKRmx/cqVK+rHiaaKj48PTp48iWXLlo3pD7YlS5ZgyZIl+M1vfoP9+/djx44d+Pjjj7F3794JXf9Eo+F1STS78X6OaGrxdZb0Ca9Xmi04RZ2G+dnPfgZzc3Ps3bsXDQ0NIx4vKyvDW2+9BQBYv349AODNN98cts8f/vAHAEBCQsK4z29ubg4AaG9vH/Nz1q9fj6ysLJw/f169TS6X45133oFUKkVAQMC46yAaq/vvvx9KpRKvvPLKiMcUCoX6Wm5raxsxuiUkJAQA1NM+zMzMAIzv+icaDa9LotmN93NEU4uvs6RPeL3SbMERnDSMj48P9u/fjwceeAD+/v7YtWsXgoKCMDAwgPT0dHz22WfYs2cPAGDRokXYvXs33nnnHbS3tyM2NhZZWVn44IMPsHnzZqxcuXLc5w8JCYFEIsFrr72Gjo4OGBsbY9WqVXBycrrtc37xi1/gwIEDWLduHZ5++mnY2dnhgw8+QHl5Ob744guIxePP8T/88ENUVlaip6cHAJCSkoJf//rXAICdO3dyFAGpxcbG4oknnsCrr76Kixcv4p577oGhoSGuXbuGzz77DG+99Ra2bt2KDz74AH/961+xZcsW+Pj4oKurC//4xz9gZWWl/uPS1NQUAQEB+OSTTzB//nzY2dkhKCgIQUFBtz0/r1UaDa9LotmN93M38XcRTRW+zpI+4fVKs4Y2W7iT7rp69arw2GOPCVKpVDAyMhIsLS2FZcuWCX/+85+Fvr4+9X6Dg4PCyy+/LHh5eQmGhobCnDlzhF/+8pfD9hEEQfD09BQSEhJGnCc2NlaIjY0dtu0f//iH4O3tLUgkEgGAcObMmTseQxAEoaysTNi6datgY2MjmJiYCJGRkcLhw4eH7VNeXi4AEN5///27fv2xsbECgFE/huqh2enJJ58URvvV+c477whhYWGCqampYGlpKSxcuFD42c9+JtTW1gqCIAh5eXnC9u3bhblz5wrGxsaCk5OTsGHDBiEnJ2fYcdLT04WwsDDByMhIACC8+OKLd6yH1yoJAq9LIhod7+f4u4g0g6+zpE94vdJsJRKEcawITkRERERERERERKRDuAYnERERERERERER6S0GnERERERERERERKS3GHASERERERERERGR3mLASURERERERERERHqLAScRERERERERERHpLQacREREREREREREpLcYcNKEvP7661iwYAFUKpW2S5m0X/ziF5DJZNoug3QMr3GaySoqKiASibBv3z5tl0JEWsTXOqLh+DNBsxHvC2mmYMBJ49bZ2YnXXnsNP//5zyEW37yERCIRRCIRfv/734/Yf9++fRCJRMjJyZn0ub/88ks88MAD8Pb2hpmZGfz8/PDss8+ivb191P2/+eYbhIaGwsTEBHPnzsWLL74IhUIxbJ9nnnkGly5dwjfffDPp+mhm4DVOREQzHV/riIbjzwQRkX5jwEnj9t5770GhUGD79u0jHnvjjTfQ09MzZed+/PHHUVxcjIcffhh/+tOfEB8fj7/85S+IiopCb2/vsH2PHTuGzZs3w8bGBn/+85+xefNm/PrXv8YPf/jDYfu5uLhg06ZN+N3vfjdldZN+4TVOM52npyd6e3uxc+dObZdCRFrC1zqi4fgzQbMV7wtpxhCIxik4OFh4+OGHh20DIISEhAgAhN///vfDHnv//fcFAEJ2dvakz33mzJkR2z744AMBgPCPf/xj2PaAgABh0aJFwuDgoHrb888/L4hEIqG4uHjYvp9//rkgEomEsrKySddI+o/XOBERzXR8rSMajj8TRET6jSM4aVzKy8uRn5+PNWvWjHhs2bJlWLVqFV5//fUR7zRqyooVK0Zs27JlCwCguLhYva2oqAhFRUV4/PHHYWBgoN7+X//1XxAEAZ9//vmwYwx9PQcPHpyCqkmf8BonffHSSy9BJBLh6tWrePjhh2FtbQ1HR0f86le/giAIqKqqwqZNm2BlZQUXF5dh0+tGW2tpz549sLCwQE1NDTZv3gwLCws4OjriJz/5CZRKpXq/s2fPQiQS4ezZs8PqGe2Y9fX1eOSRR+Dh4QFjY2O4urpi06ZNqKiomKLvChGNBV/riIbjzwTpO94XEnGKOo1Teno6ACA0NHTUx1966SU0NDTgb3/72x2P09/fj+bm5jF93E19fT0AwMHBQb3twoULAIDw8PBh+7q5ucHDw0P9+BBra2v4+Pjg3Llzdz0fzWy8xknfPPDAA1CpVPjtb38LmUyGX//613jzzTcRFxcHd3d3vPbaa/D19cVPfvITpKSk3PFYSqUSa9euhb29PX73u98hNjYWv//97/HOO+9MqLb77rsPX331FR555BH89a9/xdNPP42uri7cuHFjQscjIs3gax3RcPyZoJmC94U0mxncfRei/7hy5QoAwMvLa9THly9fjpUrV+KNN97AD37wA5iamo6634EDB/DII4+M6ZyCINzx8ddeew0SiQRbt25Vb6urqwMAuLq6jtjf1dUVtbW1I7Z7e3ujqKhoTDXRzMVrnPRNZGQk3n77bQA31/CSSqV49tln8eqrr+LnP/85AGD79u1wc3PDe++9h5iYmNseq6+vDw888AB+9atfAQC+//3vIzQ0FP/85z/xgx/8YFx1tbe3Iz09HW+88QZ+8pOfqLf/8pe/HO+XSEQaxtc6ouH4M0EzBe8LaTZjwEnj0tLSAgMDA1hYWNx2n5deegmxsbH4+9//jh/96Eej7rN27VokJSVNup79+/fjn//8J372s59h3rx56u1D00eMjY1HPMfExASdnZ0jttva2o5415NmH17jpG/27t2r/n+JRILw8HBUV1fj0UcfVW+3sbGBn58frl+/ftfjff/73x/2+fLly/Hhhx+Ouy5TU1MYGRnh7NmzePTRR2FrazvuYxDR1OBrHdFw/JmgmYL3hTSbMeAkjYuJicHKlSvx+uuvj/iFOMTV1XXUdx7HIzU1FY8++ijWrl2L3/zmN8MeG3pXtb+/f8Tz+vr6Rn3XVRAEiESiSdVEswOvcdIlc+fOHfa5tbU1TExMhk1pG9re0tJyx2OZmJjA0dFx2DZbW1u0tbWNuy5jY2O89tprePbZZ+Hs7IwlS5Zgw4YN2LVrF1xcXMZ9PCKaXnytIxqOPxOkD3hfSLMZ1+CkcbG3t4dCoUBXV9cd93vxxRdRX1+vHh7/Xb29vaivrx/Tx2guXbqExMREBAUF4fPPPx+2yDbwn2kbQ9M4blVXVwc3N7cR29va2kb84qfZh9c46RuJRDKmbcDdp8Pd7nm3ut0fSbcuOD/kmWeewdWrV/Hqq6/CxMQEv/rVr+Dv78+RJERaxtc6ouH4M0EzBe8LaTZjwEnjsmDBAgA3Ow3eSWxsLFasWIHXXntt1G6Dn3zyifodzrt9fFdZWRni4+Ph5OSEo0ePjjqVJCQkBACQk5MzbHttbS2qq6vVj9+qvLwc/v7+d/y6aObjNU50Z0NTitrb24dtr6ysHHV/Hx8fPPvss/j2229RUFCAgYGBYZ07iWj68bWOaDj+TBBNDO8LSZdwijqNS1RUFICbL6rBwcF33Pell17CihUrRu2yNtH1aerr63HPPfdALBbjxIkTI4bMDwkMDMSCBQvwzjvv4IknnlC/+/S3v/0NIpFo2GLdANDR0YGysrJxL5ZMMw+vcaI78/T0hEQiQUpKCjZv3qze/te//nXYfj09PRCLxTAxMVFv8/HxgaWl5ahT64ho+vC1jmg4/kwQTQzvC0mXMOCkcfH29kZQUBBOnjyJ733ve3fcNzY2FrGxsUhOTh7x2ETXp4mPj8f169fxs5/9DGlpaUhLS1M/5uzsjLi4OPXnb7zxBhITE3HPPffgwQcfREFBAf7yl79g7969I97FPHnyJARBwKZNm8ZdE80svMaJ7sza2hrbtm3Dn//8Z4hEIvj4+ODw4cNobGwctt/Vq1exevVq3H///QgICICBgQG++uorNDQ04MEHH9RS9UQE8LWO6Lv4M0E0MbwvJJ0iEI3TH/7wB8HCwkLo6elRbwMgPPnkkyP2PXPmjABAACBkZ2dP+txDxxrtIzY2dsT+X331lRASEiIYGxsLHh4ewgsvvCAMDAyM2O+BBx4QoqOjJ10fzQy8xkkfvPjiiwIAoampadj23bt3C+bm5iP2j42NFQIDAwVBEITy8nIBgPD+++/f9XlD57lVU1OTcN999wlmZmaCra2t8MQTTwgFBQXDjtnc3Cw8+eSTwoIFCwRzc3PB2tpakMlkwqeffjrJr5yINIGvdUTD8WeC9BnvC4kEQSQId1lZlug7Ojo64O3tjddffx2PPvqotsuZtPr6enh5eeHjjz/mu5sEgNc4ERHNfHytIxqOPxNERPqNTYZo3KytrfGzn/0Mb7zxBlQqlbbLmbQ333wTCxcu5As/qfEaJyKimY6vdUTD8WeCiEi/cQQnERERERERERER6S2O4CQiIiIiIiIiIiK9xYCTiIiIiIiIiIiI9BYDTiIiIiIiIiIiItJbDDiJiIhoylRUVEAkEmHfvn3aLoWIiIiItIj3hTSVGHASERHpiLKyMjzxxBPw9vaGiYkJrKyssGzZMrz11lvo7e2dsvMWFRXhpZdeQkVFxZSdYyx+85vfIDExEc7OzhCJRHjppZe0Wg8RERGRtvC+kPeFND4G2i6AiIiIgCNHjmDbtm0wNjbGrl27EBQUhIGBAaSlpeGnP/0pCgsL8c4770zJuYuKivDyyy9jxYoVkEqlU3KOsXjhhRfg4uKCxYsX48SJE1qrg4iIiEibeF/I+0IaPwacREREWlZeXo4HH3wQnp6eOH36NFxdXdWPPfnkkygtLcWRI0e0WOF/CIKAvr4+mJqaavzY5eXlkEqlaG5uhqOjo8aPT0RERKTreF94E+8Labw4RZ2IiEjLXn/9dXR3d+Of//znsJvYIb6+vvjv//5v9ecKhQKvvPIKfHx8YGxsDKlUiueeew79/f3DnieVSrFhwwakpaUhMjISJiYm8Pb2xr/+9S/1Pvv27cO2bdsAACtXroRIJIJIJMLZs2eHHePEiRMIDw+Hqakp3n77bQDA9evXsW3bNtjZ2cHMzAxLliyZ1A23NkcJEBEREekC3hf+p16i8WDASUREpGWHDh2Ct7c3li5dOqb99+7di//5n/9BaGgo/vjHPyI2NhavvvoqHnzwwRH7lpaWYuvWrYiLi8Pvf/972NraYs+ePSgsLAQAxMTE4OmnnwYAPPfcc/jwww/x4Ycfwt/fX32MkpISbN++HXFxcXjrrbcQEhKChoYGLF26FCdOnMB//dd/4Te/+Q36+vqQmJiIr776SgPfFSIiIqLZh/eFRBMkEBERkdZ0dHQIAIRNmzaNaf+LFy8KAIS9e/cO2/6Tn/xEACCcPn1avc3T01MAIKSkpKi3NTY2CsbGxsKzzz6r3vbZZ58JAIQzZ86MON/QMY4fPz5s+zPPPCMAEFJTU9Xburq6BC8vL0EqlQpKpVIQBEEoLy8XAAjvv//+mL4+QRCEpqYmAYDw4osvjvk5RERERPqO94Uj8b6QxoojOImIiLSos7MTAGBpaTmm/Y8ePQoA+PGPfzxs+7PPPgsAI6YCBQQEYPny5erPHR0d4efnh+vXr4+5Ri8vL6xdu3ZEHZGRkYiOjlZvs7CwwOOPP46KigoUFRWN+fhERERExPtCoslgwElERKRFVlZWAICurq4x7V9ZWQmxWAxfX99h211cXGBjY4PKysph2+fOnTviGLa2tmhraxtzjV5eXqPW4efnN2L70BSm79ZBRERERHfG+0KiiWPASUREpEVWVlZwc3NDQUHBuJ4nEonGtJ9EIhl1uyAIYz7XVHTGJCIiIqLheF9INHEMOImIiLRsw4YNKCsrw/nz5++6r6enJ1QqFa5duzZse0NDA9rb2+Hp6Tnu84/1pvi7dZSUlIzYfuXKFfXjRERERDQ+vC8kmhgGnERERFr2s5/9DObm5ti7dy8aGhpGPF5WVoa33noLALB+/XoAwJtvvjlsnz/84Q8AgISEhHGf39zcHADQ3t4+5uesX78eWVlZw26+5XI53nnnHUilUgQEBIy7DiIiIqLZjveFRBNjoO0CiIiIZjsfHx/s378fDzzwAPz9/bFr1y4EBQVhYGAA6enp+Oyzz7Bnzx4AwKJFi7B792688847aG9vR2xsLLKysvDBBx9g8+bNWLly5bjPHxISAolEgtdeew0dHR0wNjbGqlWr4OTkdNvn/OIXv8CBAwewbt06PP3007Czs8MHH3yA8vJyfPHFFxCLx/8e6ocffojKykr09PQAAFJSUvDrX/8aALBz506++09EREQzHu8Lb+J9IY0XA04iIiIdkJiYiPz8fLzxxhs4ePAg/va3v8HY2BjBwcH4/e9/j8cee0y977vvvgtvb2/s27cPX331FVxcXPDLX/4SL7744oTO7eLigr///e949dVX8eijj0KpVOLMmTN3vJF1dnZGeno6fv7zn+PPf/4z+vr6EBwcjEOHDk1otAAA/POf/0RycrL68zNnzuDMmTMAgOjoaN7IEhER0azA+0LeF9L4iYTxrCZLREREREREREREpEO4BicRERERERERERHpLQacREREREREREREpLcYcBIREREREREREZHeYsBJREREREREREREeosBJxEREREREREREektBpxERERERERERESktxhwEhERERERERERkd5iwElERERERERERER6iwEnERERERERERER6S0GnERERERERERERKS3GHASERERERERERGR3mLASURERERERERERHqLAScRERERERERERHpLQacREREREREREREpLcYcBIREREREREREZHeYsBJREREREREREREeosBJxEREREREREREektBpxERERERERERESktxhwEhERERERERERkd5iwElERERERPT/s3ffcVHe6f7/X/fMUIehSxEQURERAXtXbNEotth7LEnMJrv73e/JaZvfOdlyzsnZc8539+ye7CYxscZu7NgL1ti7IAgWFJCiMPQ27feHyxwJoKhUuZ6PxzwE556ZzyCMN++5PtclhBBCiBZLAk4hhBBCCCGEEEIIIUSLJQGnEEIIIYQQQgghhBCixZKAUwghhBBCCCGEEEII0WJJwCmEEEIIIYQQQgghhGixJOAUQgghhBBCCCGEEEK0WBJwCiGEEEIIIYQQQgghWiwJOIUQQgghhBBCCCGEEC2WBJxCCCGEEEIIIYQQQogWSwJOIYQQQgghhBBCCCFEiyUBpxBCCCGEEEIIIYQQosWSgFMIIYQQQgghhBBCCNFiScAphBBCCCGEEEIIIYRosSTgFEIIIYQQQgghhBBCtFgScAohhBBCCCGEEEIIIVosCTiFEEIIIYQQQgghhBAtlgScQgghhBBCCCGEEEKIFksCTiGEEEIIIYQQQgghRIslAacQQgghhBBCCCGEEKLFkoBTCCGEEEIIIYQQQgjRYknA+ZIyMjL49a9/TUZGRlMvRQghhBBCNCE5LxRCCCGEaB4k4HxJGRkZ/OY3v5ETWSGEEEKIVk7OC4UQQgghmgcJOIUQQgghhBBCCCGEEC2WBJxCCCGEEEIIIYQQQogWSwJOIYQQQgghhBBCCCFEiyUBpxBCCCGEEEIIIYQQosXSNPUChBBCvFn0RXqO3jxKVl4W3q7ejAwfiZuTW1MvSwghhBBCCCHEG0oCTiGEEPXmfNJ5Pt/2OUazEZWiwmwxs/b4Wj6d9in9gvs19fKEEEIIIYQQQryBZIu6EEKIeqEv0vP5ts8xmAxYLBZMZhMWiwWDycDnWz9HX6Rv6iUKIYQQQgghhHgDScAphBCiXhy9eRSj2VjjdUazkdibsY28IiGEEEIIUR+ePHnCli1bqKioaOqlCCFEjSTgFEII8VL0RXq2nt3KX/b/ha1nt1orM7PyslApNf+3olJUZOZlNuYyhRBCCCFEPTEajdy9e5ctW7ZgMpmaejlCCFGN9OAUQghRZ8/rsent6o3ZYq7xdmaLGR9Xn0ZerRBCCCGEqA8+Pj7MmjWLdevWsWvXLt555x0URWnqZQkhhJVUcAohhKiTF/XY7N2hNxpVze+baVQaRkaMbOQVCyGEEEKI+uLj48M777zDjRs3OHLkSFMvRwghqpCAUwghRJ28qMfm5XuX+XTap9iobVAUBbVKjaIo2Kht+HTap7hqXRt3wUIIIYQQol4kJyfzpz/9CY1Gw9tvv80PP/zA2bNnm3pZQghhJVvUhRBC1Ellj02TpXrfpcoem1MHTGX1z1YTezOWzLxMfFx9GBkxUsJNIYQQQogWrF27dgQFBbFp0yYGDx7MwIEDOXjwIE5OToSHhzf18oQQQgJOIYQQdVPXHptuTm5MHTC1MZcmhBBCCCEakJ2dHTNmzODMmTMcOXKEwMBAQkJC2LlzJ1qtlg4dOjT1EoUQrZxsURdCCFEnI8NHSo9NIYQQQohWSlEUBg0axLvvvsvjx49JT0/HxcWFTZs2kZGR0dTLE0K0chJwCiGEqBM3JzfpsSmEEEII0cq1b9+epUuX4ubmRk5ODkVFRaxdu5bc3NymXpoQohWTLepCCCHqrF9wP+mxKYQQQgjRyjk7O7Nw4UIOHTrEqVOnSEpKYvXq1SxduhStVtvUyxNCtEIScAohhHgp0mNTCCGEEEKo1WrGjh2Lv78/mzdv5vTp0wB8/PHH2NnZNfHqhBCtjQScQgghhBBCCCGEeCXh4eF4e3vz7bffcvDgQQwGA3/7t3+LWq1u6qUJIVoRCTiFEEIIIYQQQgjxyry8vPjbv/1bnJ2d2b59OyUlJXz22WcScgohGo0MGRJCCCGEEEIIIcRrsbOz46c//SmLFy/m5MmT/OM//iPFxcVNvSwhRCshAacQQgghhBBCCCFem6IozJ8/n5///Odcu3aNX/7yl6SlpTX1soQQrYAEnEIIIYQQQgghhKg377zzDj/96U9JSUnhd7/7HRcuXMBisTT1soQQbzDpwSmEEEIIIYQQQoh6NXHiREwmEzt27GDdunWkpaUxfvx4bG1tm3ppQog3kFRwCiGEEEIIIYQQol4pisKkSZN4++23ATh//jwrVqwgJyeniVcmhHgTScAphBBCCCGEEEKIeqdWq5k+fToRERHY29uTn5/PN998Q2JiYlMvTQjxhpGAUwghhBBCCCGEEA3C1taWOXPm4OnpiY2NDW3btmXTpk0cOXIEs9nc1MsTQrwhJOAUQgghhBBCCCFEg3F0dGT+/PkAlJaWEhUVxQ8//MDatWspLi5u4tUJId4EEnAKIYQQQgghhBCiQbm6ujJ37lz0ej2pqanMnTuX7Oxsli1bRlpaWlMvTwjRwknAKYQQQgghhBBCiAbn4+PD7NmzefDgAdevX+eDDz7AxcWFVatWcfHiRSwWS1MvUQjRQknAKYQQQgghhBBCiEbRvn17pkyZQlxcHOfOnWPhwoX07t2bvXv3smPHDgwGQ1MvUQjRAmmaegFCCCGEEEIIIYRoPcLCwiguLmbfvn3odDrGjh2Lv78/u3fvJisri5kzZ+Lu7t7UyxRCtCBSwSmEEEIIIYQQQohG1bdvX4YMGcKhQ4e4ceMG4eHhvP/++xiNRr755htu377d1EsUQrQgEnAKIYQQQgghhBCi0Y0YMYIePXqwc+dO7t69i5eXF++//z5BQUFs3LiRo0ePYjabm3qZQogWQAJOIYQQQgghhBBCNDpFUZgwYQKdOnVi8+bNPHr0CHt7e2bMmMFbb73F6dOnWbduHcXFxU29VCFEM9eqAs5f//rXKIpS5dKlS5emXpYQQrxx9EV6tp7dyl/2/4WtZ7eiL9I39ZKEEKIKOS8UQojmQaVSMW3aNLy8vFi/fj05OTkoisKgQYNYsGABWVlZLFu2jLS0tKZeqhCiGWtVASc8bWackZFhvZw+fbqplySEEG+U80nnWfjFQlbFruLA1QOsil3Fwi8Wcj75fFMvTQghqpDzQiGEaB5sbW2ZM2cODg4OrFu3jqKiIgCCgoJYunQpzs7OrFq1iosXL2KxWJp4tUKI5qjVBZwajQYfHx/rxdPTs6mXJIQQbwx9kZ7Pt32OwWTAYrFgMpuwWCwYTAY+3/q5VHIKIZoVOS8UQoi6q6ioaND7d3R0ZN68eRiNRtavX095eTkAzs7OLFq0iN69e7N371527NiBwWBo0LUIIVqeVhdwJicn07ZtWzp06MDcuXN5+PDhc48vLy+noKDAeql8J0kIIVqr520/P3rzKEazscbbGc1GYm/GNtYyhRDiheS8UAgh6ubevXv88Y9/JCEhoUEfx9XVlXnz5qHX69m8eTMmkwkAtVrN2LFjmTp1KgkJCSxfvpzc3NwGXYsQomVpVQFnv379WL16NQcOHOCrr77i/v37DBkyhMLCwlpv8+///u+4uLhYL1FRUY24YiGEaF5etP08Ky8LlVLzfy0qRUVmXmZjLlcIIWol54VCCFF3Pj4+BAYGsnnzZnbt2mWtrmwI3t7ezJo1iwcPHrBz584qW9LDw8N5//33MRqNfPPNN9y+fbvB1iGEaFkUSytuYJGXl0dgYCB/+MMfWLJkSY3HlJeXV3nxvnbtGlFRUVy+fJmePXs21lKFEKLJ6Yv0LPxiIQZT9S1BNmobVv9sNUdvHmVV7KoaeyMpisLiEYuZOmBqYyxXCCFeipwXCiFE7SwWC6mpqeTk5LB//360Wi1TpkwhICCgwR7z1q1bfP/99/Tr148xY8agKIr1urKyMnbt2kVCQgJDhgxh+PDhqFStqn5LCPEjrfoVwNXVlc6dO3Pnzp1aj7Gzs8PZ2dl6cXJyasQVCiFE81GX7ecjw0eiUWlqPEaj0jAyYmRDLlEIIV6ZnBcKIUTtbt26xcqVKykuLubDDz/EycmJlStXEhsba91GXt+6du3KuHHjOHfuHGfOnKlynb29PTNmzOCtt97i9OnTrFu3juLi4gZZhxCiZWjVAWdRURF3797F19e3qZcihBDNXl22n7s5ufHptE+xUdugKApqlRpFUbBR2/DptE9x1bo27qKFEKKO5LxQCCFq17VrV6Kiojhy5AinTp1iwYIFDB8+nNOnT7NixQqePHnSII/bp08fhg4dyuHDh7l+/XqV6xRFYdCgQSxYsICsrCyWLVtGWlpag6xDCNH81Vxm84b627/9WyZMmEBgYCCPHj3iV7/6FWq1mtmzZzf10oQQotnzdvXGbDHXeJ3ZYsbH1QeAfsH9WP2z1cTejCUzLxMfVx9GRoyUcFMI0azIeaEQQtSdoigMHz4cd3d3du/eTV5eHjNmzKBTp05s376dZcuWMXr0aHr37l1lK3l9GD58OIWFhezatQutVkunTp2qXB8UFMTSpUvZsmULq1at4u23326QdQghmrdWVcGZlpbG7NmzCQkJYcaMGXh4eHDu3DnatGnT1EsTQohm72W2n7s5uTF1wFQ+HvsxUwdMlXBTCNHsyHmhEEK8vMjISBYsWEBmZiYrVqzA3t6epUuX0r17d/bu3cuGDRsoKiqq18dUFIUJEybQqVMntmzZQnp6erVjnJ2dWbRoEb169WLv3r3s3LkTg6F633ghxJurVQ8ZehVXrlyhV69e0kxeCNEqnU8+z+dbP8doNqJSVJgtZjQqDZ9O+5R+wf2aenlCCNGo5LxQCNFa5eTksGHDBkpLS5k1axbt2rUjOTmZXbt2YTabmThxIl26dKnXxzQYDHz33Xfk5OSwZMkSPDw8ajzuxo0bxMTE4O7uzsyZM3F3d6/XdQghmicJOF+SnMgKIVo7fZFetp8LIQRyXiiEaN1KSkrYvHkzaWlpTJ48mfDwcIqLi4mJiSExMZGePXsyZswY7Ozs6vUxV65ciclkYsmSJbUOe8vKymLz5s2UlJTwzjvvEBISUm9rEEI0TxJwviQ5kRVCCCGEECDnhUIIYTQaiYmJ4fr16wwbNoyoqCgArl27xv79+9FqtUyZMoWAgIB6e8z8/HyWL1+OVqtl0aJFtQaoZWVl7Ny5k8TERIYMGcLw4cNRqVpVlz4hWhX56RZCCCGEEEIIIcRL02g0TJ48mREjRnD8+HF27NiByWSiR48efPjhhzg5ObFy5UqOHTuGyWSql8d0cXFh3rx55OXlsWnTJoxGY43H2dvbM3PmTEaNGsXp06dZt24dxcXF9bIGIUTzIwGnEEIIIYQQQgghXomiKAwdOpRp06Zx69YtvvvuO0pKSnB3d2fRokUMHz6cU6dOsXLlSp48eVIvj+nt7c3s2bNJTU1l586d1LYxVVEUBg8ezIIFC8jKyuKbb76pcUiREKLlk4BTCCGEEEIIIYQQr6Vbt268++675OTksHz5cp48eYJKpWLo0KEsWbKEsrIyli1bxsWLF2sNJF9GYGAgU6dOJT4+noMHDz73PoOCgli6dCk6nY6VK1fW2xqEEM2HBJxCCNHK/XzFz5n/p/n8fMXPm3opQgghhBCiBQsICOC9995DrVazYsUKUlJSAPDz82Pp0qV0796dvXv3snHjRoqKil778UJDQxk3bhznzp3jzJkzzz3W2dmZRYsW0atXL/bu3cvOnTsxGAyvvQYhRPMgAacQQrRy+iI9OYU56Iv0Tb0UIYQQQgjRwrm5ubFkyRJ8fX1Zu3Yt165dA8DW1pbo6Gjmzp3Lo0eP+PLLL0lMTHztx+vTpw9RUVEcPnyY69evP/dYtVrNuHHjmDJlCrdu3WL58uXk5ua+9hqEEE1PAk4hhBBCCCGEEELUG3t7e+bOnUtkZCQ7d+7k6NGj1i3hwcHB/OQnP6Fdu3Zs2rSJ3bt3U1FR8VqPN2zYMHr27MmuXbtITk5+4fERERG89957GAwGvvnmG27fvv1ajy+EaHoScAohhBBCCCGEEKJeqdVqJkyYwFtvvcWpU6fYunWrdUu4Vqtl5syZTJw4kbi4OL7++mtSU1Nf+bEURWH8+PEEBwezZcuWOg0S8vb25oMPPqB9+/Zs3LiRo0ePYjabX3kNQoimJQGnEOK1VBTpSTu7jbv7vyTt7DYqZJuzEEIIIYQQgqfB46BBg5g5cyZJSUmsWbPG2ntTURR69uzJhx9+iKOjIytXruTYsWOYTKZXeiyVSsW0adPw8fFh/fr15OTkvPA29vb2zJw5k1GjRnH69GnWrVtHcXHxKz2+EKJpScAphHhlOUnnufjFIlJiV5F59QApsau4+MUicpMvNPXShBBCCCGEEM1EaGgoCxcuJC8vj+XLl/P48WPrde7u7ixevJhhw4Zx6tQpVq5cWadwsiY2NjbMmTMHrVbL2rVrKSwsfOFtFEVh8ODBLFiwgKysLL755ps6VYAKIZoXCTiFEK+kokhP4rZ/x2IygMWCxWx6+qfJQMLWz6WSUwghhBCiBZOtuqK++fn58f7772NnZ8fy5cu5e/eu9TqVSkVUVBRLliyhrKyMr7/+mkuXLln7dr4MBwcH5s2bh9lsZv369ZSVldXpdkFBQSxduhSdTsfKlStf+fGFEE1DAk4hxCvJvhmLxWys8TqL2Uj2zdhGXpEQQgghhHhdRUVF7Ny5k23btjX1UsQbyMXFhcWLFxMQEMD69eu5fPlylev9/PxYunQpkZGR7Nmzh40bN1q3tL/s48ybN4+8vDw2b96M0Vjz7y0/5uzszKJFi+jVqxd79uxh586d1r6hQojmTQJOIcQrKc/LQlFqfglRFBXleVmNvCIhhBBCCPGqTCYTZ8+e5YsvvuD27dsEBQVJ9ZpoEHZ2dsyZM4devXoRExPDoUOHqnyv2draMn78eObMmUN6ejpfffXVK0059/LyYvbs2aSmprJjx446fz+r1WrGjRvHlClTuHXrFsuXLyc3N/elH18I0bgk4BRCvBI7V28slpq3LlksZuxcvRt5RUIIIYQQ4lXcv3+fr7/+mkOHDhEREcHPfvYzevfujaIoTb008YZSqVSMGzeOt99+m7Nnz7J582YqKiqqHNO5c2c++ugj/P392bhxIzExMdWOeZHAwECmTp3KrVu3OHDgwEuF9hEREbz33nsYDAa++eabVwpZhRCNRwJOIcQr8QofgaLS1HidotLgFTGykVckhBBCCCFeRn5+Pt9//z1r1qzBwcGBDz74gOjoaBwdHZt6aaIVUBSF/v37M2vWLO7du8fq1aurDQXSarXMmjWLiRMncvPmTb7++mvS0tJe6nFCQ0OJjo7m/Pnz/PDDDy91W29vbz744APat2/Pxo0biY2Nlf60QjRTEnAKIV6JrZMbodM+RVHbgKKgqNRP/1TbEDrtU2y1rk29RCGEEEIIUQOj0cjJkyf585//zIMHD5gyZQqLFi3C19e3qZcmWqGQkBAWLVpEYWEh3377LZmZmVWuVxSFnj178uGHH+Lo6MjKlSs5fvw4JpOpzo/Ru3dvoqKiOHLkCNeuXXup9dnb2zNz5kxGjRrFqVOnWL9+PSUlJS91H0KIhldz+ZUQQtSiokhP9s1YyvOysHP1pvviP6K/d9n6uVfESAk3hRBCCCGaqaSkJPbv309+fj79+/cnKioKOzu7pl6WaOV8fX15//332bhxIytXrmT69OkEBwdXOcbd3Z3Fixdz6tQpTpw4QXJyMlOmTMHDw6NOjzFs2DCKiorYvXs3Wq222v0/j6IoDB48mLZt27Jt2zaWLVvGjBkz8PPze6nnKYRoOFLBKYSos5yk81z8YhEpsavIvHqAlNhVXFv5Cxw9A+g49iP8B0yVcFMIIYQQohnKyclh/fr1bNiwAXd3dz766CNGjx4t4aZoNionmAcFBbFhwwYuXLhQ7RiVSkVUVBSLFy+mrKyMr7/+mkuXLtWpt6aiKERHRxMcHMyWLVteeqs7QIcOHVi6dCk6nY6VK1fW+bGFEA1PAk4hRJ1UFOlJ3PbvWEwGsFiwmE1P/zQZSNj6ORVF+qZeomhC+iI9W89u5S/7/8LWs1vRy/eDEEII0SxUVFRw9OhRvvzySx4/fszMmTOZN28enp6eTb00IaqxtbVl5syZ9O/fn3379rF///4ae176+/uzdOlSIiMj2bNnDxs3bqSoqOiF969SqZg2bRq+vr5s2LCBJ0+evPQanZ2dWbhwIT179mTPnj3s2rULg8Hw0vcjhKhfskVdCFEn2TdjsZiNNV5nMRvJvhmL/4Cpjbwq0RycTzrP59s+x2g2olJUmC1m1h5fy6fTPqVfcL+mXp4QQgjRKlksFuLj4zl06BAlJSUMGTKEQYMGYWNj88LbPXjwgKKiIrp169ZIqxXif6lUKsaMGYO7uzv79+9Hr9czderUatXGtra2jB8/nuDgYHbv3s1XX33FxIkTCQkJee7929jYMHv2bFauXMm6detYsmQJOp3updao0WiIjo4mICCAmJgYMjMzmTFjBu7u7i/9fIUQ9UMqOIUQdVKel4Wi1PySoSgqyvOyGnlFojnQF+n5fNvnGEwGLBYLJrMJi8WCwWTg862fSyWnEEII0QSysrJYs2YNW7dupW3btnz88ccMGzbsueGm2WwmLi6Ob7/9ltWrV3PlypVGXLEQ1fXp04c5c+bw4MEDVq1aRUFBQY3HhYSE8NFHH+Hv78/GjRuJiYmhoqLiufft4ODAvHnzMJvNrFu3jrKysldaY0REBO+99x4VFRV888033L59+5XuRwjx+iTgFELUiZ2rNxZL9e0hABaLGTtX70ZekWgOjt48irGWyl6j2UjszdhGXpEQQgjRepWVlbF//36WLVtGYWEh8+bNY9asWbi5udV6m4qKCs6fP88XX3zB1q1bsbe3Z968ecyfP78RVy6aO4PBwPnz5xu932SnTp1YvHgxpaWlfPvttzx69KjG47RaLbNmzWLChAncuHGDr7/++oU9Nl1cXJg3bx75+fls2rQJo7Hmc9oX8fb25oMPPqB9+/Zs3LiR2NjYGrfVCyEalgScQog68QofgaKquauFotLgFTGykVckmoOsvCxUtVT2qhQVmXmZjbwiIYQQovWxWCxcvXqVL774gqtXrzJy5Eg++ugjOnXqVOttioqKiI2N5b//+785ePCgtafhggUL6NSpE4qiNOIzEM3d/fv3OXDgAHv37m30kNPb25v3338fZ2dnVq1aRWJiYo3HKYpCr169+PDDD3F0dGTlypUcP378uWGjl5cXc+bMIS0tje3bt79yMGlvb8/MmTMZNWoUp06dYv369ZSUlLzSfQkhXo0EnEKIOrF1ciN02qcoahtQFBSV+umfahtCp30q09NbKW9Xb8y1VPaaLWZ8XH0aeUVCCCFE65Kens7y5cvZtWsXHTt25Gc/+xmDBg1CrVbXePyTJ0+IiYnhj3/8I+fOnSMyMpKf//znTJ06FV9f30ZevWgpOnfuzMSJE7l8+TJ79uxp9JDTycmJhQsXEhwczObNmzl79myta/Dw8GDx4sUMHTqUkydPsmLFCnJycmq973bt2jFt2jQSEhI4cODAKz83RVEYPHgw8+fPJzMzk2XLlpGenv5K9yWEeHkyZEgIUWfuwX3p87NVZN+MpTwvCztXb7wiRkq42YqNDB/J2uNrMZiqT47UqDSMlMpeIYQQokEUFxdz9OhRrly5go+PD4sXL6Zdu3a1Hv/w4UPOnDnD7du30Wq1REVF0bt3bxwcHBpx1aIl69GjB4qisGvXLiwWCxMmTGjUSl8bGxumT5/O0aNHOXjwIDk5OYwdO7bGMF+lUjFs2DA6derE9u3b+frrr3n77bfp2bNnjWvu0qUL48ePJyYmBp1Ox5AhQ155nR06dGDp0qVs2bKFlStXMnbsWHr16iVV0UI0MAk4hRAvxdbJTaalCys3Jzc+nfYpn2+tOkVdo9Lw6bRPcZXwWwghhKhXZrOZixcvcuzYMRRFITo6ml69eqFSVd+cZzabuX37NmfOnCE1NRVPT08mTJhAREQEGo38KiheXvfu3VEUhZ07d2I2m5k4cWKN33sNRVEURo0ahbu7O3v27EGv1zN9+nTs7e1rPN7f358PP/yQgwcPEhMTQ1JSEhMnTkSr1VY7tlevXhQWFnL06FGcnJzo0aPHK6/T2dmZhQsXcvDgQfbs2UNaWhrR0dHPHfQlhHg98r+aEEKIl6Iv0nP05lGy8rLwdvVmZPhIVv9sNbE3Y8nMy8TH1YeRESMl3BRCCCHqWUpKCvv37yc7O5uePXsycuRIHB0dqx1nMBi4fv06Z8+eJScnh8DAQGbPnk3nzp2liky8tsjISFQqFdu3b8disTBp0qRGDTkBevbsiaurq7VKcs6cObi6utZ4rK2tLRMmTKBz587s3r2bL7/8kkmTJtG5c+dqx0ZFRVFYWEhMTAxarbbGY+pKo9EQHR1NQEAAMTExZGZmMmPGDNzd3V/5PoUQtVMsjd08o4W7cuUKvXr14vLly/Ts2bOplyOEEK9t/p/mk1OYg4fOg7X/Z+1zjz2fdJ7Pt9VcrdkvuF8jrVgIIZoHOS8UjaWgoIBDhw4RFxeHv78/48aNo23bttWOKykp4cKFC1y4cIHS0lJCQ0MZOHAg/v7+TbBq8aaLi4tj+/btdOvWjcmTJzd6yAnw+PFjNmzYQEVFBbNnz37h93pRURG7d+8mKSmJXr16MWbMGGxtbascYzab2bJlC3fv3uXdd9+tl5+frKwsNm/eTElJCVOmTHmt4FQIUTMJOF+SnMgKId40dQ049UV6Fn6xsMZ+mzZqG1b/bDVuTm4NuVQhhGhW5LxQNDSj0cjZs2c5efIkdnZ2jBo1isjIyGpVmLm5uZw9e5Zr164BT7cRDxgwQCrFRL2yWCzVvvfi4+PZtm0bXbt2ZcqUKU0SchYXF7Np0yYyMjKYMmUKXbt2fe7xFouFK1eucODAAZydnZkyZQp+fn5VjjEYDKxdu5bHjx+zZMkSPD09X3udZWVl7Ny5k8TERIYOHcqwYcOa5OslxJtKfpqEEELUydGbRzGajTVeZzQbib0Z28grEkIIId5cycnJfPnllxw7dozevXvz05/+1Nr/sFJaWhpbtmzhiy++ID4+nsGDB/N//+//JTo6WsJNUa8yMzP5+uuvSUlJqfL3YWFhTJs2jVu3brFt2zZMJlOjr02r1fLuu+/SpUsXtmzZwunTp587CV1RFHr16sWHH36Ivb09K1as4Pjx45jNZusxNjY2zJ49G51Ox9q1ayksLHztddrb2zNz5kxGjhzJqVOnWL9+PSUlJa99v0KIp6QHpxDipVQU6atOUQ8fga1U7bUKWXlZqBQVJkv1E1eVoiIzL7MJViWEEEK8WXJzczl48CC3b9+mQ4cOzJ49mzZt2livt1gsJCUlcebMGR48eIC7uzvR0dFERkbKABPRYNRqNba2tqxevZqIiAhGjx6Nk5MTAF27dmX69Ol8//33bNu2jalTp9Y42bwhaTQapk6dioeHB0eOHCEnJ4fx48c/dx0eHh4sXryYU6dOcfLkSe7cucOUKVOsbw44ODgwb948li9fzrp161i0aFGtw4zqSlEUhgwZgp+fH1u3bmXZsmXMmDGjWgWpEOLlSQWnEKLOcpLOc/GLRaTEriLz6gFSYldx8YtF5CZfaOqliUbg7eqN2WKu8TqzxYyPq08jr0gIIYR4cxgMBmJjY/nyyy+tw0jmz59vDTeNRiNXrlzhL3/5Cxs3bsRsNjNz5kx++tOf0rt3bwk3RYNq06YNixcvZtKkSdy5c4c///nPXLhwwVr1GBoaysyZM7l9+zZbt25tkkpORVEYPnw477zzDjdu3GDdunWUlpY+9zZqtZphw4axePFiSkpK+Prrr7l8+bK1AtTZ2Zn58+dTUFDAxo0bMRpr3s30sjp06MDSpUvR6XSsXLmSS5cuPbfqVAjxYhJwCiHqpKJIT+K2f8diMoDFgsVsevqnyUDC1s+pKNI39RJFAxsZPhKNqubCf41Kw8iIkY28IiGEEKLls1gsxMfH8+c//5kzZ84waNAgfvrTn9K1a1cURaG0tJRTp07xxz/+kZiYGDw9PVm8eDFLliwhNDS0QXr4lZWV8fjx43q/X9GyKYpCjx49+OlPf0pYWBj79u3j22+/JT09HYCQkBBmzpxJUlIS33//fZOEnPB0yvuCBQvIzMxkxYoV5ObmvvA2/v7+fPjhh4SHhxMTE8OmTZsoLi4Gnoa7s2fPJj09ne3bt1fZyv46XFxcWLhwIT179mTPnj3s2rULg6F6r3shRN206oDzd7/7HYqi8Itf/KKplyJEs5d9MxZLLf0XLWYj2dJ/8Y3n5uTGp9M+xUZtg6IoqFVqFEXBRm3Dp9M+xVXr2tRLFEKIVybnhaIpZGdn89133/H999/j4+PDxx9/zPDhw7GxsSEvL48DBw7w3//935w4cYKQkBA+/vhjZs2aRbt27ep9LeXl5dy8eZNNmzbxX//1X+zYsaPeH0O8GRwdHZkwYQLvvfceFouF5cuXs2fPHkpLS+ncuTOzZs3izp07bN68ud4qHl9WYGBglfU9fPjwhbextbVlwoQJzJ49m7S0NL788kuSkpIAaNeuHdOnTychIYH9+/fXW7WlRqMhOjqad955h/j4eFasWIFeL4UjQryKVtuD8+LFiyxbtoyIiIimXooQLUJ5XhaKosJSQ/9FRVFRnpfVBKsSja1fcD9W/2w1sTdjyczLxMfVh5ERIyXcFEK0aHJeKBpbWVkZx48f58KFC7i5uTF37lyCg4MByMjI4IcffuDWrVvY2dnRv39/+vbta+13WJ8qKipITk4mPj6epKQkjEYj/v7+vPXWWy+cRC2Ev78/H3zwARcvXiQ2NpZbt24xevRoIiMjmTVrFps2bWLz5s3MnDkTjabxowcPDw+WLFnC5s2bWbNmDZMnTyY8PPyFtwsJCeEnP/kJu3fvZsOGDfTu3ZvRo0cTEhLC+PHjiYmJQafTMXTo0Hpba2RkJD4+PmzevJlly5YxZcoUOnfuXG/3L0Rr0CoDzqKiIubOncu3337Lv/7rvzb1coRoEexcvbHU0n/RYjFj5+rdyCsSTcXNyY2pA6Y29TKEEKJeyHmhaEwWi4Xr169z+PBhDAYDI0aMoH///qjVau7cucMPP/zA/fv3cXV1ZcyYMfTo0QNbW9t6XYPRaLSGmrdv38ZgMNC2bVuGDx9OWFgYrq6u9fp44s2mUqno168fXbt25dChQ+zcuZMrV64QHR3N7Nmz2bhxI5s2bWLWrFlNEnI6Ojoyf/58YmJi2LZtGzk5OURFRaEoynNv5+TkxOzZs7l8+TIHDx7k/v37TJkyhV69elFUVERsbCxOTk707Nmz3tbq7e3NBx98wI4dO9iwYQNDhw5l2LBhDdKGQog3UasMOD/++GOio6MZNWrUC09ky8vLKS8vt35eVFTU0MsTolnyCh/Bg+Nrn/bg/BFFpcFL+i8KIYRogeS8UDSWR48esW/fPtLS0ujWrRujR49Gq9USFxfHmTNnyMrKom3btkyfPr3ee2sajUbu3r1rDTXLy8vx8fFh6NChhIWFWadGC/GqdDodU6dOpWfPnuzdu5dly5bRr18/pk6dyvbt29m4cSOzZs1qkmFYGo2GyZMn4+HhQWxsLLm5uUycOPGFgauiKPTu3ZugoCC2b9/OihUriIqKYvDgwRQWFhITE4NWqyUkJKTe1mpvb8+sWbM4ffo0sbGxpKenM3XqVBwdHevtMYR4U7W6gHPTpk1cuXKFixcv1un4f//3f+c3v/lNA69KiObP1smN0GmfkrD1cyxm41+3q5tRVBpCp32KrWxRFkII0cLIeaFoDCUlJRw9epQrV67g5eXFwoUL8fHx4fLly5w/f56CggKCg4MZO3YsgYGBL6wsqyuTycS9e/eIj48nMTGRsrIyvLy8GDhwIGFhYXh6etbL4wjxrKCgIH7yk59w9uxZTpw4QVxcHL179+bixYts3LiR2bNnN0nIqSgKQ4cOxd3dnZ07d5KXl8esWbPqFBx6eHiwePFiTp48yfHjx0lOTmby5MkUFxezdetWFixYQEBAQL2udciQIfj5+bF161aWLVvGjBkz8PPzq7fHEOJNpFjqqztuC5Camkrv3r05fPiwtcfSsGHD6N69O3/84x9rvM2P36m/du0aUVFRXL58uV7L0Vs6fWExhy8mkJlbgI+7M2/1CcVNp23qZYkGUFGkJ/tmLOV5Wdi5euMVMVLCzRZu/p/mk1OYg4fOg7X/Z21TL0cIIRqFnBeKhmY2m7l06RLHjh3DYrEwfPhwQkJCuHDhApcvX8ZoNBIREcGAAQPw8vKqt8e8f/8+8fHxJCQkUFpaiqenJ2FhYYSFhdXb4whRF5WDshITE3F2diY3N5fg4GBmz55d760XXkZqaiqbNm3Czs6OOXPmvFTYn5qayo4dOygqKmLUqFHExcXx+PFjFi9eTJs2bep9rfn5+WzZsoXMzEzGjh1Lr1696u1NECHeNK0q4Ny5cyfvvPMOarXa+ncmkwlFUVCpVJSXl1e5riZXrlyhV69eciL7jLNx9/iXNXsxmsyoFAWzxYJGreKzhdH0D+vQ1MsTQrxAYwac+iI9R28eJSsvC29Xb0aGj8TNya1BH1MIIWoi54WiIT148ID9+/eTlZVFjx496NatG9evX+fmzZvY2trSu3dv+vXrh06ne+3HMpvNPHjwwBpqFhcX4+7ubg01vb29JRARTSopKYl9+/aRmppKfn4+/fv3Z8GCBU0acur1ejZs2EBRUREzZ86kffv2db5tRUUFBw4c4MqVK3To0IGcnBwAlixZgrOzc72v1Wg0cvDgQS5evEj37t2Jjo5ukipYIZq7VhVwFhYW8uDBgyp/t2jRIrp06cI//MM/0K1btxfeh5zIVqUvLGbub1diMFafrG2jUbP+s8VSySlEM9dYAef5pPN8vu1zjGYjKkWF2WJGo9Lw6bRP6Rfcr8EeVwghaiLnhaIhFBYWcujQIW7evEnbtm3p1q0b9+7d486dO7i4uNC/f3969uyJnZ3daz2OxWIhNTWVuLg4bt26RVFREa6urtZQ09fXV0JN0awYDAZOnTrF3r17uX37NoMGDeJv/uZvXvtn4XWUlZWxZcsWHjx4wIQJE+jevftL3T4xMZHdu3dTUVFBSUkJ/v7+LFq0CAcHhwZZ7/Xr19mzZw8eHh7MnDkTNzcpEhDiWa2qB6dOp6t2sqrVavHw8KjTSayo7vDFBIymmidrG01mjlxKZPrwXo28KiFEc6Mv0vP5ts8x/HVIlcny9E0Rg8nA51s/Z/XPVkslpxCiUcl54f/avXs39vb2uLu74+bmhru7Oy4uLjK59yWYTCbOnTvHiRMn0Gg0dO3aldzcXA4dOoSPjw9TpkwhLCzshVXBz2OxWEhPT7eGmgUFBTg7OxMeHk5YWBh+fn4Saopmy8bGhhEjRhAZGcl3333Hnj17SElJ4V/+5V+arHWCvb09c+fOZe/evezcuZOcnBxGjBhR55+jLl264O/vz65du7h27RoXLlzAxsaGRYsWNcjE+MjISHx8fNi8eTPLli1jypQpdO7cud4fR4iWqlUFnKL+ZeYWoFIUTDUUAqsUhYyc/CZYlXgZ11b8HyqK9Ng6udF9yZ+aejniDXX05lGMZmON1xnNRmJvxjJ1wNRGXpUQQgiz2Ux+fj73798nPz8fs/npG9cqlQoXF5cqoaebm5v146bcWtrc3Llzh/379/PkyRNcXV0xGAzcunWLjh07Mn/+fDp06PDKwaPFYiEjI4O4uDji4+PJz8/HycnJWqkZEBAgoaZoFEajkfj4eCIiIl7re87Dw4Nf/OIXRERE8Pvf/56lS5fy05/+lGHDhr3WGwCvSq1WM2HCBDw8PDh8+DC5ublMnjy5zlvAnZycmDNnDiEhIWzevJldu3ZhNBpZunRpg7xJ5O3tzQcffMCOHTvYsGEDQ4cOZdiwYfKGlBBIwMnx48ebegktmo+7M+ZauhyYLRZ8PVwaeUXiZVUU6akozGnqZYg3XFZeFipFZa3cfJZKUZGZl9kEqxJCiKpa43mhSqVi/vz5wP+Gnbm5uej1euufaWlp3Lhxg4qKCuvttFpttdCz8mMnJ6dWEbrp9XoOHjzI9evXMZlM2NjYUFhYSLdu3Rg4cCA+Pj6vdL8Wi4WsrCxrqKnX69FqtXTt2pWwsDDatWsnYYZodMnJyezYsYNLly4xfvx4vL29X/m+FEVh5MiRBAUF8a//+q/8z//8Dzdv3mTSpEkEBQXV46rrvp5Bgwbh7u7O9u3bWbNmDbNmzcLJyanOt+/duzft27fnyy+/ZMuWLeTl5fH3f//3DRLa2tvbM2vWLE6fPk1sbCzp6elMnTq1ThPhhXiTtfqAU7yet/qEsnr/2Rp7cGrUKkb1Dm2CVQkhmhtvV2/MlprbWZgtZnxcX+2XQCGEEPVHpVJZQ8ofs1gslJSUWEPPZwPQ+/fvU1hYaD3WxsamxqpPNzc3XF1dm6RKqz4ZDAZOnz7NoUOHyMrKwtHREV9fX3r37k3//v1xcXm1N/izs7OtoWZOTg4ODg7WULN9+/YSaoomFRoayqJFi9izZw/Lli1jwIABREVFvVY1d4cOHfjXf/1XvvzyS65evcqTJ0/o2bMno0ePrnO4WJ9CQ0NZuHAhGzduZPny5cyZM+elts97enry//1//x/e3t5s3LgRvV7PP/7jP+Lu7l7va1UUhSFDhuDn58fWrVtZtmwZM2bMwM/Pr94fS4iWolkPGTKZTHz//fccO3aM7Oxsfvvb3xIeHk5+fj5Hjx5l0KBBr/XO0atozc3k9YXFHL6YQGZuAT7uzrzVJxQ3nZZz8ff47WqZot5SXfjTAioKc7DVedD3/3zX1MsRTaAxhgzpi/Qs/GKhtQfns2zUNqz5+Rpcta4N8thCiDeDnBc2bxUVFeTl5dUYgObl5WEyPX0zXFEUXFxcag1A7e3tm/iZ1M5isZCQkMDGjRtJTEzEwcGBsLAwBg0aRO/evV9p7U+ePCE+Pp64uDgeP36Mvb09oaGhhIWFERQU1OLDYPHmMZlM/PDDD5w8eRInJyfGjRv32n0gMzIyWLNmDUVFRTg4OKBSqRg5ciS9e/dukmA/Pz+fDRs2kJeXx4wZM+jYseNL38fWrVtZvnw5HTt2ZMmSJfTo0aPBKtvz8/PZsmULmZmZjBs3jp49e7aKKnohfqzZBpx5eXm8/fbbXLhwAScnJ4qLizl8+DAjRozAZDIRGBjIggUL+Pzzzxt1Xa31RPZs3D3+ZU3tIaa+sJgjlxLJyMnH18OFUb1DcdNJiXxLIAGnaLQp6snn+XyrTFEXQrw8OS9s2cxmMwUFBVVCz2eD0LKyMuuxjo6ONW57d3d3R6fTNdkv7dnZ2axYsYIzZ85ga2tL//79eeuttwgPD3/pEDI3N9caamZlZWFnZ0eXLl0ICwujY8eOEmqKFiE3N5e9e/dy9+5dQkNDGTt2LM7Ozq98f5mZmXz33Xc4ODjg6+tLfHw8Pj4+REdH4+/vX48rr5vy8nK+//577t27R3R0NL16vdzgXIvFwq5du9iyZQvu7u4MHjyYCRMmoNVqG2S9RqORgwcPcvHiRbp37050dHSd+4gK8aZotlvU//Ef/5H4+HgOHjxIjx49qpSGq9Vqpk2bxr59+xr9RLY10hcW8y9r9lq3oVcOFDIYTfx29V7Wf7YYN51WpqUL0UJVTi+v6xRzfZGeozePkpWXhberNyPDR1a7bU3H9Avux+qfrSb2ZiyZeZn4uPowMmKkVG4KIV5Izgsb1pMnT9DpdNjZ2TXI/atUKlxdXXF1da3WX89isVBaWlqt6jM3N5eHDx9SUFBgPVaj0VgDz2cDUHd3d1xdXRtkanFRURErV65k//79AERFRTF16lQ6der0UmGrXq/n1q1bxMXFkZGRga2tLSEhIQwfPpxOnTo1yNqFaEju7u7MmzeP+Ph4Dhw4wJ///GdGjBhB3759X6nq0sfHh3fffZc1a9bw5MkT5syZQ2xsLCtWrKBnz56MHDmyUXtM2tnZMWfOHPbv309MTAw5OTm89dZbdf65VxSFiRMnUlFRwQ8//MCNGzdIS0tj0qRJBAcH1/t6NRqNNQzes2cPmZmZzJw5s8aWI0K8qZrt/6Q7d+7kZz/7GW+99RY5OdUHoHTu3JnVq1c3/sJaocMXEzCaau6dZzSZOXIpUcJNIZqAwWTAZK7e//Zl/eeC/7R+XGYoe86RcPHORf7fzv9XpQpz7fG1/N3kv6N3p951Oia6d3SV+3zRY9ZGrVJjo5Z3poVoDeS8sOFYLBaWLVuGwWDAyckJd3f3Gi8NtXVcURQcHR1xdHSssXec0WisFn7q9Xru3r3L5cuXMRqN1vvR6XS1Tn13cHB4qXUVFRWxadMmduzYQVlZGcOGDePdd9+lXbt2db6P/Px8a6iZnp6OjY0NwcHBDBkyhODgYKmuEi+loqLC+v3enHTo0IHFixdz/Phx9uzZw8WLFxk3bhy+vr4vfV86nY4ZM2awfv169u3bx6xZs7h9+zbHjh3j+vXrjBgxgsjISGxsbF6r92ddqVQqxo0bh4eHBwcPHiQ3N5cpU6bU+bFVKhVTpkyhuLiY1NRUtFot69evp0+fPowePbpBXgMiIyPx9vZmy5YtLFu2jClTprx2CwEhWopmG3Dm5+c/d4KawWBoli/wb6LM3AJUimKt3HyWSlHIyMlvglUJ0boZTAZup9+mrOLVwsFXUVRWxO93/94aqlZORDeYDPzHjv/gk4mfALzwGCf7+mkab29rT4hfiIScQrQCcl7YsBYuXEhOTg65ubnk5uby5MkTkpKSKCkpsR7j6OiIh4dHjeHny4aHL0Oj0dCmTRvatGlT7TqLxWLd+v5sAJqdnU1iYiKlpaXWY+3t7atVfVYGoM7OztaKs5ycHPbu3cuOHTvIz8+nf//+LF26lMDAwDqtt7Cw0BpqpqamotFo6NSpE9OmTaNz586NEsqIN09FRQUXLlygqKioydZgsVjQ6/W1DsxxdHQkJCSECxcucOHCBTp37kxkZOQrfc937NiRI0eOkJyczKhRo4iIiODKlSt8+eWXtGnThuHDh/P22283ys+Toij0798fNzc3tm3bxurVq5k9ezY6na5Ot9doNMyePZtVq1ZRUlJCVFQUZ86c4f79+0yZMoW2bdvW+5p9fHz44IMP2LFjBxs2bCAqKoqoqCgZVCbeeM024OzYsSNXrlyp9fpDhw7RtWvXRlxR6+Xj7oy5llatZosFX49XmxQphHh1JrOJsooyNBpNowV855PPYzbXMgndbCY+Nd768fOOGdZt2GuvxWAyUFZRhslskoBTiFZAzgsbjqIo+Pn51Vg9Wbl1PDc3t0oAeufOHYqLi63HOTg44O7uXmMA6uDg0GB9MysHFrm4uNC+fftq15eVldU49CgtLY2CggIqRxGo1WosFguPHj0iMTGRkpISQkND+Zu/+Rv69+//wiqroqIiEhISiIuL4+HDh6hUKjp27MiUKVMICQlpsK3/ovUwGo0UFRVha2vbZN9Pt2/fZtu2bUyaNImwsLAaj9HpdLRv3574+HiuXr1KVlYW/fv3p3379i/1OqDT6Zg8eTL79+/n5MmTjB07lrfffptHjx5x6tQpvv/+eywWC2+//XajfT1CQkJYtGgRGzZs4Ntvv2XOnDn4+PjU6bb29vbMnTuXFStWkJCQwIIFC9i/fz/Lly9n2LBhDB48uN7DR3t7e2bNmsXp06eJjY0lLS2NqVOnNuo2fyEaW7MNON977z3+4R/+gWHDhjFy5Ejg6UlMeXk5v/3tbzlw4ADffPNNE6+ydXirTyir95+19uB8lkatYlTv0CZYlRACnk4gt9W83rvX/2/X/6OgpABnR2f+dtLf1npcfkk+ikrBYq7+hoeiUsgvybd+/LxjXne9laRaq+mU5eXx4MQJirOz0Xp5ERgVhb2ra1MvS7zB5LywaTg4OODg4FBjhVFZWZk1NKy85OTkcO/evSpVZvb29lUCz2dDUEdHxwYdGmRvb0/btm1rXL/RaCQvL49Lly5x8uRJrl69SnZ2Nk5OTnTv3h0fHx+OHj3K0aNH0el01ao+HRwcyMrK4u7du6SkpKAoCh06dGDixIl06dKlQataRetlZ2fXYO0iXsTFxQWLxcKmTZt45513GDBgQK0/v3369CE0NNQ6bT0lJYXBgwfXueoRwNfXlylTprBnzx6OHDnC+PHj6dChA97e3ly4cIGrV69y584dxowZQ1hYWKMMIPP19eX9999n48aNrFy5kunTp9e5n6azszPz589nxYoVHD58mAULFnDmzBmOHTtGcnIyU6ZMqfd+mYqiMGTIEPz8/Ni6dSvLli1jxowZNb6hJcSboNlOUbdYLHzwwQesWLECV1dX8vLy8Pb2JicnB6PRyNKlS/nqq68afV2tdVrmufh7/HZ17VPURcslU9RbpjJDGXEP4nCwc3jtwPCzjZ+RX5KPi6MLv53921qPO3rjKDEXY7BQQ3iJwsS+E7FYLC88ZkT4iNdaL0CFsYLS8lK6BXbD3qZpTvRbq0eXLnH297/HbDSiqFRYzGZUGg0DPvmEtr17N/XyxBtKzgtbloqKiirB57MBaGFhofU4Ozu7ahWflQGoVqttsMDCaDRy/fp1zpw5w71798jJycHZ2ZlRo0YxatQotFotRUVF1YYeZWVlkZSUxMOHD9Hr9VgsFtq0aUOnTp0IDQ3F19e3St9PFxcX2RIq6kVJSQknT55Ep9M1WcAJ8PDhQ9atW0dWVhZvv/02o0ePRq1WP/c2KSkpnD59mvLycnr16kV4ePgLb/OsvLw89uzZg42NDePHj0etVlNYWEhkZCQnT54kISGBDh06EB0djYeHx+s+xTqpqKhg27ZtJCUlMXbsWPr27Vvn26ampvLdd9/RsWNHZsyYQXp6Otu3b6e4uJixY8fSvXv3Bnnty8/PZ8uWLWRmZjJu3Dh69uzZKKGwEI2p2QaclU6fPs3WrVtJTk7GbDZbXwiGDh3aJOtpzSey+sJijlxKJCMnH18PF0b1DsVNJyXuLZ0EnC1TUwScBaUF/GbTbzCaq1dOalQafj3r11iwvPAYnUPd372vjQScTaMsL4+9P/kJZoOh2nUqGxuiv/pKKjlFg5LzwpavoqKiWuVn5SU//3/7utva2tY68Ein073SL+YlJSVcvHiRCxcuoNfrKS8vx2w2Exoayrhx4/D39692m7KyMm7fvk18fDx3797FbDbTtm1bAgIC8PT0pLy8vEoQmp+fb23VUjk9vqap725ubtKPU9RZcwk44Wmf2rVr13L37l0GDRpUp6E7BoOBS5cuERcXh6urK0OGDKnz9m54Gs7FxMSg0Wh46623MJvNDB06FEdHR5KSkti/fz8FBQUMGjSIIUOGNMoAL7PZzOHDhzl79iz9+vVjzJgxdX5DIykpiU2bNtGzZ0+io6OpqKjgwIEDXL16ldDQUCZMmNAgW8mNRiMHDhzg0qVLdO/enejoaBl2Jt4ozXaLeqXBgwczePDgpl6GANx0WpmWLkQr5uzgzKKRi1h1dBUms8m6FV2tUrNo5CJrcFmXY0TL9ODECcy1tAYwG408OHGCkEmTGnlVojWR88KWz9bWFm9vb7y9vatdZzAYagw/4+LiyM/Pt/bMtLGxqTX8dHZ2rhZ+6vV6zp49y9WrVzGZTDg4OKBWq/H29mbUqFH06NGjym0qKiqsoeadO3cwGo20a9eO0aNH07Vr1+duszWZTOTn51er/kxNTeXGjRtUVFRYj9VqtdWmvVf+2ZAVrEK8Dg8PD95//302bdrE6dOnKSgoYO7cuWi12lpvY2Njw4ABAwgODubUqVPs2rWL0NBQ+vbtW6fA1sXFhQkTJrBnzx727dvHkCFDrNd17tyZoKAgTp8+zenTp7lx4wbjxo1r8MnhKpWKMWPG4O7uzv79+9Hr9UydOrVOPUE7d+7MhAkT2LVrFzqdjqioKCZNmkTnzp2JiYnhyy+/ZNKkSXXe/l4bs9mM0Wi0DuIzGo306dMHW1tbDh48yI0bNxgzZgyOjo7W6589tq6fDx06lMjIyNdaqxD1odkHnEIIIZqPbu268atZv+LSnUvkFObgofOgT6c+VYLLuhzzrILSAi4mXyS3KBd3J3f6BPfB2cH5uce5OLoQFlBzg3vRcIqzs59uSzdV78msqFQUZ2c3waqEEG8KGxsbvLy88PLyqnZdZc/MZ4cd5ebmcuvWLfLy8qzhp0ajwc3NDQ8PDywWCw8ePCAjIwN3d3c6dOhAdnY2RUVFDBgwgOHDh1t7ZRoMBpKSkoiPjycpKQmj0Yi/vz8jR44kLCwMZ+fq/y/VRK1WW8PWjh07VrnOYrFQXFxcbehRbm5utd6lNjY2tU59d3V1faktvkLUN61Wy4IFC3BxceHEiRN8/fXXLF68+IU9JD09PZk0aRIJCQlcuHCBlJQUBgwYQKdOnV4Y6FeGnDt37uTw4cMMHDjQWuVoY2PD8OHDiYiIYN++fWzYsIEuXbrw9ttv49rAO0v69OmDm5sb33//PatWrWLOnDnVXi9qChrbtm1Lt27d2LFjB3q9ni5dumA0Ghk4cCCxsbH87ne/o3PnzvTo0QPgpYLHyo9rG/wJT/tznj17lpMnTxIaGoqHhwdqtRqNRvN0iKmNjfXjZz+3sbHBwcGhyvX13TtUiFfVbAPOoKCgF77IKYrC3bt3G2lFQoi6qijSk30zlvK8LOxcvfEKH4GtU/P+j68lrrmpODs416mPZuUvmxaLpcaenABxD+OqVXvuu7yPRSMX0a1dt+ced+DqAf7hnX9gcKhUczUWrZcXllpOli1mM9oaQgkh6oOcFwqNRoOnpyeenp7VrjOZTOTl5Vn7fN68eZPY2FhSU1NRFIU2bdqQmprK8ePH8fPzY+DAgajVaq5du0ZBQQEZGRmkpaVZQ4fhw4cTFhZW78GIoig4OTnh5OREQEBAtesrt+8/G37q9Xpu375NXl6eNayonB5fWwDa1FuYRetgY2NjHYyze/duvvjiC957770ah3o9S6VSERYWRvv27Tl79iyxsbHcvn2bwYMHv/BnztnZmbFjx7Jjxw7WrVvH+++/X+U2Hh4ezJs3j1u3bnHw4EH+8pe/EBUVxYABA1Cr1TUGjS/7eW3X2dnZcfToUQ4ePEhkZCRardZ6fW1Bo8Vi4fHjx/zxj3+kW7du1tc3lUqFvb09x44d4/z58/Tq1QsPD48XBo0/Diafd51Go8FkMrFv3z7u3r3LkCFDGD58uPQNFi1asw04o6Kiqp3ImkwmHjx4wA8//EC3bt2s72YIIRrPi4LAnKTzJG77dyxmI4qiwmIx8+D4WkKnfYp7cN0bcLf2NbdkdQ0tC0oLWHV0lbVfZ+XkdaPZyKqjq/jVrF/h7OBc63Ems4n/2vlfhAWE4SZhdKMIjIoibtOmmntwajQEDhvW+IsSrYKcF4rnUavVuLi48PDhQy5fvszjx4/p27cvH3/8MY8fPyY2NhaLxUJ4eDjOzs4kJCTw/fffk5KSgtFoxMnJCW9vb4KDg9FqtRQUFHD79m3rwCMXF5dGqZh83vZ9s9lMQUFBlapPvV7Po0ePiIuLo7y83Hqso6NjtdCz8uNX7V8qRE1UKhUjR47Ezc2NDRs28D//8z8sXrz4hdvDzWYztra2DB48GH9/f86cOcN3331HaGgoISEhKIqCyWSyhogmk8n6eWlpKV5eXiQkJPDJJ58wdOhQ7O3tqwWPiqKQmprKb3/7W+zt7enYsSMuLi4v/fxeFBhWBo1ubm4EBgZy6tQpHj58yIgRI+jYseMLw0aVSsXevXu5d+8e8+bNo0OHDtaQ8cmTJ2zfvp3MzEy6du3K4MGD6z2AfPfddzl9+jSxsbE8evSIqVOnNkj/TyEaQ7MfMlST69evM2bMGNatW8eoUaMa9bGlmbx407zMkKGagkBFpbEGgRVFei5+sQiLqXr4oaht6POzVY1eFdkS11wXTTFkqNLztpS/aBBRZWgJdZvKPiJ8xPOPUxQWj1jM1AFTX/Zpi1ckU9RFcyPnha1bWVkZly5d4vz58xQWFhISEsKAAQMoLS3l4MGDFBYW0rdvX/z9/UlOTiYxMZGysjLatGlD165dCQgIQFGUGocemf7ajqNyWFBNPT/d3NyafLu4xWKhtLS0StXns0Hos5PrK7eT1hSAVm59z8jIICEhAUVRGDHixTs2RMNrDkOGMjIyOHXqVJVet8+GkI8ePeLIkSOUlZXRr18/AgMDqwWUlcf/uKLRbDaTkZFBVlYWtra2BAYGVul1q1Kp0Gg0qNVqLBYLBoOB9u3bc+3aNQCGDRuGq6trjWFiQUEBFy5c4PHjx3Tu3JkhQ4bUeuyPP3+VMNFgMLBjxw4SEhIYPXo0/fv3f+GbCkajkXXr1pGZmcnixYurtOkwmUycOHGCU6dOERAQwDvvvNMgW8Lv3bvH1q1bsbGxYebMmS+sxBWiOWq2FZzPExkZydKlS/mHf/gHLl++3NTLEaJVqCjSPw0K/xoEWixPT/otJgMJWz+nz89WkX0zFksNwRaAxWwk+2Ys/nUMouqyZfxFxzT2mluDF1VnXky+iMlcvT8jPK24vHTnknV7e25RrvU+fkxRKeQU5rzwOJWiIjMvsx6foXiRtr17E/3VVzw4cYLi7Gy0Xl4EDhuG/UtWRQhRX+S8sHXKz8/n3LlzXL58GZPJRGRkJAMGDEBRFA4cOEBycjLOzs4EBQVx7do1zp49i4eHB/369SMsLKxan88f98t8tmLy2UtKSgpXrlzB+NeBa4qiPDf81Gga/tctRVFwdHTE0dGxxknwBoPBuoX/2fDzzp076PV6jEYjBQUFPHnyhIKCAkpKSjAajQQHB0vAKaxu3LjB999/j0qlws7ODhcXFzw9PfH19cXT0xOdTsfo0aM5ceIE586dw2Kx0KtXL2xsbFCr1db+js9+/OznarWa4uJiaxgZGBjIwIEDcXJyqhI0lpWVUVhYyNChQzEajaxZs4bi4mKmT5+Ou7t7jWufNGkS169f59ChQxw7dowRI0bQp0+fBtmObWNjw/Tp063b1XNychg7duxz3wjRaDTMmjWLVatWsW7dOpYsWWKtNlWr1YwYMYJOnTqxY8cOvvrqK8aOHUv37t3rtRq7Q4cOLF26lC1btrBixQrGjRtHz549peJbtCgtMuAE8Pb25tatW029DCFajboEgeV5WX+tkqxhAImiojwvq06PVZct43U5pjHX3BrUZUt5XUNLAHcn9xqPq7xvD53HC48zW8z4uPq81vMSL8/e1VWmpYtmRc4LW4/MzEzOnDlDXFwctra29OvXj379+mFjY8Px48c5ePAgRUVF1r58Go2G3r17ExYWhre3d51/Wa+s2nR1daVDhw5VrrNYLDWGnw8fPuTatWsY/trGo7JXZm3hp42NTb1+bWpjY2NDmzZtaNOmjXX95eXl3L59m8uXL3PlyhXu379Pbm4uxcXFmEwm67Z/ISr17NmTjz76yNqz9smTJ2RmZpKeno6dnR3e3t74+/szadIkLly4QHx8PN7e3kyfPv2lgv6goCBu377NuXPn2L59O/369aNLly41/uw6OzuzcOFC1qxZw+rVq3n33Xfx8PCodpyiKHTv3p2QkBCOHj3KgQMHuHr1KuPHj6/xTYHXpSgKo0aNwt3dnT179qDX65k+ffpzq2/t7e2ZN28eK1asYN26dSxevNg6BA2gXbt2fPjhhxw4cIBdu3aRlJTEhAkT6nU7uYuLC4sWLeLAgQPExMSQlpbGuHHjGu21SojX1SIDzpycHFasWNEgL0ZCiJrVJQi0c/XGYqmtibYZO9fqPaV+rC5Vl8ALj7F1cmu0NbcWdanOrGtoCdAnuA/7Lu+rcTu7WqWmT6c+LzxOo9IwMmLkqzwdIcQbQs4L33wWi4V79+7xww8/cO/ePVxdXRk9ejQ9e/ZEo9Fw5MgRtmzZQlpaGj4+PoSHhxMREUFYWBi+vr71XoFUGVy6uLgQFBRUba1FRUXWgUeV4WdaWho3btygoqLCeqyzszPu7u7WXp/Php+2trW3nzGbzVRUVFBeXk55eTlFRUXk5+dbL4WFhdZLUVERxcXF1j8fP35srdQsLy/HZDJhsViqVOV5eXmh0+no1KlTvX7dRMvm6upKQEAAQ4cORa1WU1ZWhl6vJzU1lXv37pGamsqNGzes1Zv5+fmsWbOGEydOMHbsWNq0aWP9uXF1dUWr1db4s6koCl26dCEwMJDz589z8uRJkpKSGDJkSI0VmjqdjnfffbdKyFnTMDIABwcHxo8fT48ePdizZw/Lly+nZ8+ejBo1qkH6Tvbs2RNXV1e2bNnCypUrmTNnznMHKel0OubNm8fKlSvZuHEj8+fPrxIu2tnZMWnSJDp37kxMTAxffvklkydPrtefVY1Gw/jx4wkICCAmJobMzExmzJghk9JFi9BsA87atkPk5eWRmJhIRUUFa9eubeRVCdF61SUI9AofwYPja2vuZ6nS4FWHIKouVZeVHz/vGP8BUxttza1FXaozx/QYU6fQEp5OY180clG1Le9qlZpFIxehc9A99ziVSsXfTf47XLWuDfachRDNg5wXtk4mk4m4uDjOnDlDVlYWvr6+TJs2jdDQUDIyMti6dSu7d+8mMzOTdu3a8f7779OvXz/8/PyabFuloijodDp0Oh2BgYFVrjMajej1ejIzM8nOziY7O5vHjx9z7949cnNzKS0txWAwUF5ejqIoqNVqVCoViqJgsVgwm82YTCbKy8sxGAzWy4/7GarVamxsbHB0dMTe3h6TyURpaSlFRUVYLBZcXV3x9vZGo9Hg4eFBaGgoPXr0oGvXrlgsFpKSkkhISMDOzq4xv3SimUtLS+Po0aNotVq6detGly5d8PX1xdfXl759n+6eMplMZGdnc//+fZKTk61vSqxZs4awsDAcHBxwcnLCzs7OWiX87MXV1RUXFxfs7e1xcHBg2LBhdO7cmVOnTrFt2zbrGxc/ptPpqlVyVlYs18TPz4/333+fS5cuERsbS2JiIqNGjaJHjx71/trRoUMHlixZwoYNG/j222+ZPXv2c9+Q8/T0ZM6cOaxZs4Zt27YxY8aMalvpQ0ND8ff3Z9euXaxbt46+ffvy1ltv1WulZWRkJN7e3mzevJlly5YxdepUgoOD6+3+hWgIzTbgNJvN1V5cFEUhKCiIUaNGsXjxYrp06dJEqxPizVHZs/JFg3TqEgTaal0JnfYpCVs/r3Goj+0zQVRt/TPrumW8LsfU95pbu7pUZ9YltPzxkKJPJn1CYnoiOYU5eOg86NOpjzXcrNStXTd+NetXXLpziZzCHFwcXQgLCKN3JxlqI0RrIOeFrUt5eTmXL1/m3LlzFBQU0KlTJ8aMGYOdnR3x8fHs27eP69ev8/jxYzp16sRPf/pTBg0a1CChpsVisYaKL3upDBUrL5UBZm0Xo9FoDSyfHchiMplQqVTY2tpia2uLk5OTtfLTy8sLX19f/P39adeuHV5eXtjZ2fHgwQMSEhJISkqitLQUs9mMSqWivLwcBwcHOnToQGhoKDqdjsTERM6fP8/atWt59OgRRqMRW1tbevToUe9fT9FyBQUFMW3aNG7evMmlS5e4fPkyISEhdOvWzVqVqFarraHnwIEDmTNnDnv37mXbtm2kpaURHh5uHRJkb2+P2WymsLCQx48fU1RUZH2symriykv37t1JT0/n2rVr3L59m4iIiGrrc3Jysoaca9aseWHIqVKp6Nu3L127duXw4cPs3r2bq1evEh0djY9P/bY/atOmDe+99x6bNm1i9erVTJkyha5du9Z6vL+/P9OnT2fTpk3s3buX8ePHV3t90+l0zJ07l4sXL3Lo0CHu3bvH1KlT8fX1rbd1+/j48MEHH7Bjxw7Wr19PVFQUUVFRDdK7VIj60CKnqDclmZYpmgOzyYCllq3CDUl/5xK3d/6/akFgyOS/xe2ZoKmiOI8ncScoy8/G3sWLNt2GYaP93z5OuXcukrTz9zXeT0lOGg9PrIOaXpoUhcCo+ViwPPeY9iMWWQcD5SZfqDW8rOzVCTUErn8NP5urppii/qIJ6b+e9WtrMFlQWmANI58NLWsaUlQZfnZr163Oa64wVlBaXkq3wG7Y2zTNNNHWrCwvr+qQoago7J+z5UqIN5WcF9avgoICzp8/z6VLlzAYDHTr1o3g4GCysrKIj48nNzeXvLw8CgoKaNOmDZMmTaJ///41Du+oDFFeJZj88aVyG3dNoWTlpPXKX6nMZrM1oLRYLFUGq1ROaNZqtdYqT2dnZ+tFq9VahwVpNBpsbW1xdHTExsbGuh342Unper2enJwcysrKMBqN1onpJSUl2NjY4OTkZN0G7OjoaO0pWlZWRkpKCg8ePKCgoMBa1RkUFERoaCiRkZF06dLludvkG0vl16G1aw5T1H+8nlu3bnHr1i3KysoIDAwkPDy81pYQt27dYvny5SiKwuTJk9FoNKSnp5OT87Q3u4uLCz4+Puh0OhwcHCgvLyc/P5+8vDzy8/MpKysDng4YevToEcXFxQwYMIBx48YREBCAh4cHbm5u1kFF3333HUVFRbz77rvVBorVJiUlhb179/LkyRP69evH8OHD672K2Wg0snPnTuLi4hg1atQL35i5evUqu3btYtiwYQwbNqzW4x4/fsz27dvJyspi+PDhDBo0qF5DSIvFwqlTpzh27BgdO3ZkypQpDbKlX4jXJQHnS5IT2efTFxZz+GICmbkF+Lg781afUNx02qZe1hvFbDJQmJ6EqaK0SR7fWFZEXsp1DEV6bJzccG0ficbeqfox969hKM7DRuuKa1B36zHGsiKSd/+hxoBWUakJGr2U+4eW1Xp98MS/ASB593/XuE1dUdvQ5+ern18t2szDy7poioATap6iXteA8kUB6a9m/QpnB+c6rVkCzqbz6NIlzv7+95iNRhSVCovZjEqjYcAnn9C2t1TUitZFzgvrR3Z2NmfOnOHmzZuo1WratWuHjY0Nd+/e5fHjx2g0GpydncnMzKS0tJSgoCDCw8OtFYm1XWr7Nafy7xVFqRIuWCyWKlvBKy+VQeaPw0qNRoNWq60STFZeHBwcqv1d5ZbxF1WaVlRUcOHChSoVbTUpLy8nPT2du3fv8uDBA0pLS1EUhYqKCvLy8igsLLT2/DQajdbnZm9vj4uLC35+fnTs2JHQ0FDatm3bKBPfX5aTkxN9+/Zt9SFncws4KxmNRu7cucONGzfQ6/V4enoSERFBhw4dqr3xkJaWxpdffmkNHnv16mUNLB89ekR6ejp5eXkAeHh40LZtW9q2bYuvr691sFd+fj7Z2dncvHmT7OxsysrKCAgIwM/PD5VKhZubG+7u7mi1Ws6fP4/FYmHJkiUEBwfXqcLbZDJx7tw5jh8/jr29PWPGjCEsLKxeq8MtFgvHjx/nxIkT9OjRg/Hjxz93wvqpU6c4evQo48ePp/dzzrNMJhPHjx/n9OnTBAQE8M4779R738y7d++ybds2bG1tmTFjBm3btq3X+xfidTWbgPO77757pdstWLCgnlfyfHIiW7uzcff4lzV7MZrMqBQFs8WCRq3is4XR9A/r8OI7EHViMpSR/yAOlUaDSt38JtoVpN7iwfF1TwNKRQGLBUWlJnDYPJwDupIdd5zMy/uBml56FHx7jcPOpc3/3odKAXPV+zCbDOQ/iCftzPcvrMx8UzVVwAm1V2e+yNEbR4m5GIOlhn97BYWJfScyIrzmPns/JgFn0yjLy2PvT36C2VC97YPKxobor76SSk5RL+S8sOUxmUxVBt+86FJWVkZ6ejq3bt3i0aNHmM1mbG1tqaiooKysDI1Gg6enJ66uruTm5pKdnY2TkxNhYWHWbdh2dnbY2tpWqVQym83WbeVmsxmj0Wi9GAwGKioqMBgMNQYW9vb2NYaStV3s7e0bZKtmZZhla2tbrYKstLSUhw8fkpKSYv26OTk5UVpaSk5ODllZWRQVFVXp4VlZNerh4YGrqyu2trYYjUZrVRyAra0tLi4u1mpSnU6Hm5tbrcNaGkN5eTkVFRUMHTq01VeLNYeA02g0kpqaSvv27av9/FgsFtLT07lx4wapqalotVrCwsIIDQ2tsl69Xs9f/vIX0tPTmTp1KiNHjqxyX8XFxdaw89GjRxQWFqIoCm3atKFt27b4+fnh6upKaWkpffr04cyZM5w5cwadTkevXr1QFIWcnBxycnLIzs7mypUrlJeX07NnTwIDA/Hw8Kh2cXR0rPZ88vPzOXDgAAkJCXTo0IFx48bV+8/C9evX2b17N+3atWPGjBlVJqb/+Gu7f/9+Ll68yMyZM1/YjuXhw4ds376d0tJSxo4dS2RkZL0GtPn5+WzZsoXMzEyio6Nb/f99onlpNgHnq5wcKIpi3RbSWN60E9mPfr8BfWEJbjpHvvxkznOPfV51pr6wmLm/XYnBWP3fw0ajZv1ni6WSs55UBpwaOwdUrxlsAdze9XuMJQVoHJ0JmfTJa92XobSQW5t+U3NlpUpD11m/IuvqQZ7cPgPmGob/qFR4hgzEf+A0DKWF6O9cpKIwF1udO26d+mDz1xDNbKzAWF6Kg6c/OQk/vFGVmXXVlAHn8/y4v2af4D7Wqszvz3zPmdtnqg1DgKf/BwwMGcj0gdPr9DgScDaN27t2cWNd7e0hIubNI2TSpMZfmHjjyHlh4youLn7tbdyGGt74qKQoijWss7W1JScnhwcPHpCTk4OiKDg4OFiH4rRv35727dvj5eVFYmIiV65cwWKxEBERQbt27aioqKCkpMR6qewv+WN2dnYvFVY6ODg0m75yPw6ziouLSUlJ4f79+6SlpVm3oJeWlpKVlUVWVhYGgwEbGxucnZ3x9PTE19eXTp060a5dO3x9fdHpdNVCjoqKCmtV3LN/FhQUUFxcjK+vLxMnTmyir8LT7ciFhYUScNI8As579+5x+PBhvLy86NevX63Ve7m5udy8eZPk5GQURanWp7O0tJTly5cTFxfHiBEjmD59eq0/ewUFBVUCz5KSEiwWCzqdjtGjRxMaGmoNALOzs6tsLTebzWRkZLBy5UoyMzPp378/FouFnJwc8vPzrY9hb29fY/Dp7u7Ow4cP2bdvHwUFBQwcOJChQ4fW6yCfBw8esGnTJrRaLXPmzKlxSjw8feNm27Zt3L59m/nz51cbYPZj5eXl7N+/n2vXrtG1a1fGjx9frz9DRqORAwcOcOnSJXr06MG4cePq9esixKtqNvsQ7t+/39RLaJX0hSU8yX/+9heouTpz9f6z1urMwxcTMJpqnlZtNJk5cimR6cN71ffyRT0wlhRgKMl/8YF/ZSgtRJ98kYqiXGyd3HEL/t/gUZ98sdbeoBazCf2di9g6uUMtg2owW7DVPf2P3cZBh9cLqvlsta7WXpui6dW0fX3f5X3W7et1GVIkmrfi7Oyn29JrCJEUlYri7OwmWJV4E8l5YeP67//+b4zG6m9OqlQqa6XksxetVou7u3uN1/24srJyUE5+fj5Xrlzhhx9+ICUlxRrIOTo64uTkhJubG46OjpSVlXH27FmSk5MpKSmhbdu2dO7cGYvFwuPHj3F0dMTFxQVfX9/nbgV/3pbPlqAy1ExOTiYlJYXi4mLUarV1YFF+fr61etPPz48uXboQHBxsHfBSlzDD1tYWT0/PapVpRqORvLw8Skubph2SaJ46dOjA+PHjOX/+PDExMQQGBtK3b99qoZy7uztRUVH07duXW7duER8fT3x8vLVPZ9u2bfnoo4/YtGkTsbGx5Obm8t5779UYkFVWFHfp0gWLxUJeXh7379/n3r17XLx4kXPnzmFjY4O/vz9eXl4cO3aMuLg4oqOj6dKlC35+fvzd3/0da9euJT09nQULFtC2bVsMBoO1h+2zl7t371JcXGx9fJ1Oh6urK0VFRWzcuJEjR44wceJE+vTpUy8tHQIDA3nvvffYsGEDy5cvZ9asWbRr167acSqVinfeeYd169axceNGFi9e/NzeonZ2dkyePJnOnTsTExPDV199xaRJk+jUqdNrrxme9sYdP348/v7+7NmzBw8PDwYPHlwv9y3E62g2AeeL3oUQTUdfWMy/rNlrrc40/bVyx2A08dvVe1n/2WIycwtQKYr1umepFIWMnLoHaKL5yn8YR8rR1VW2jmdc3kf7kYtwaRdGRVGu9e+rUSlUFObi3WMMGZf31VLlqcatU59GeCaivhWUFrDq6Cprf83KINNoNrLq6Cp+NetX9Anuw77L+2rswalWqekj//bNntbLC0tN1deAxWxGW8dG/kK8iJwXNq6ZM2ei0WiqhZQajcZa9WcwGKwVk89WUJaUlJCbm1vt70pKSjAajVRUVHD//n3u3LlDQUEBNjY2eHl50bFjR4KCgujQoQPOzs44OjpiNpu5evUqer2et956i+joaIKCglpFZZDFYiElJYXTp09z/vx5Ll++bA2BK7fcV/bkdHZ2ZuDAgURGRhIUFISPj0+dh6FUVFRQXFxMcXExRUVF1o+f/bvy8nIAvL29CQgIaLDnLFoePz8/3nnnHe7evcvFixfZunUrnTt3pnfv3jg5Ve3J7+DgQK9evYiMjOTOnTvcvHnTGoZFREQwc+ZM3N3d2bVrF3/4wx/4+OOPq93HsxRFwc3NDQcHBwICAhg8eDCFhYXcv3+f+/fvo9frURSFy5cvc/bsWSIiIpg9ezbBwcHMnz+fdevW8d133zF//nz8/Pzw8vKqMSQsLS0lNze3SvBpNBopLS3l1q1b/PKXv8TT05PevXvj7+9frfLTxcXlpbaEe3h4sGTJEjZv3syaNWuYPHky4eHh1Y7TaDTMmjWL1atXs27dOpYsWYKLi0sN9/i/unbtSkBAALt27WLdunX069ePUaNG1dtravfu3Wnbti0eHlKkIJqHZhNwiuarLtWZPu7OmGvpdmC2WPD1eP6Lr2j+DKWFfw03/xpO/TXAspiNpBxdRddZv6pTdaaNg472IxeRcnRVtR6b7UcuslaDipblYvJFTLVU75rMJi7ducSI8BEsGrmo1iFFdenjKZpWYFQUcZs21dyDU6Mh8DkTPoUQzZPFYrEOpakppKy81LQFXa1WV6ucrOxpl5eXx6lTp4iLi6OkpISOHTsyYsQIBgwYQEhISJWhMUajkTNnzvDDDz9gZ2fHkiVLCA8Pr9e+cc1NSUkJjx49Ij4+nkuXLhEfH8+TJ08wGAzWyec2NjZUVFSgKAoeHh4MGjSIHj16EBISUi2gsFgslJeX1xhYPvt55dChSo6OjtZBST4+PtbJ61qt9rlhk2h9ioqKSElJwdPTk/bt2xMUFERCQgKXL1/mzp07hIeH071792phu0ajoUuXLoSEhJCens7Nmzc5duwY58+fJywsjHnz5rFp0yZ+97vf8fOf/7zOU89VKpW1YnngwIGYTCYePXrEvXv3OHPmDCdPnuTEiROEhoYydOhQwsLCuHDhAmvWrGHBggX4+/vXeL8ODg74+fnh5+dX5e8tFguFhYWcP3+effv2kZiYaO19W1BQYG2RotFocHd3r3O/T3j6czh//nxiYmLYtm0bOTk5REVFVTvW3t6euXPnsmLFCtatW8fixYtr7d1ZSafTMXfuXC5evMihQ4e4d+8eU6ZMwdfXt05f5xep67+XEI2hWQecmZmZrFixgitXrli3YTxLURSOHj3aRKtrPepSnTl/TD9W7z9bYw9OjVrFqN6hjbFU0YDqsv3cLbhPnaozXdqF0XXWr2rtsSlantyiXGtg+WOKSiGnMAeAbu268atZv3qlIUWi6dm7ujLgk09qnaJu/4JKAiFeh5wXNgxFUdi1axdGo7FaWFm5bby2yeC2trZVfgEvKiri2LFjbN68mcTEROzs7Ojfvz/R0dFERkbW2DswKSmJ/fv3k5+fT//+/YmKiqpzNWJLYTAYyMzMJD09nbS0NBITE0lOTiYzM5PCwkJsbGysU801Gg2FhYWUlZXh6elpDY3atGlDaWkpRUVFJCUl1RhiPttmQFEU6/Z/rVaLm5tbleCycvJ75Vb+iooKCgsLKSoqorCwkJycHOzt7enevXsTfdVEc5Obm8u5c+cwmUwoioK7uzuenp5ERkby+PFjbty4QUJCAj179qRr167VtnArioK/vz/+/v7o9Xpu3rzJlStXABg8eDCnT5/mP/7jP/joo4/o2LHjS69PrVYTEBBAQEAAUVFR/OxnP2Pr1q0cO3aMmJgYvL29cXBw4M6dO/zyl79k3rx5DBgwAGdn5zrdv6IoODs789ZbbzF06FBOnDjBuXPnUKvVzJ49Gw8Pj2pb3uPi4sjPz6dy7Mmz/T5/HIJWbiv38PCwbt2fOHFita+jTqdj3rx5rFy5kg0bNrBgwYIXVmQqikLfvn0JCgpi+/btfPvttwwfPpxBgwY1m97DQtSHZhtw3rhxg2HDhlFaWkpISAg3b96ka9eu5OXlkZ6eTseOHWXLRCOpS3Wmm07LZwuj+e3qmqeou+lad2PwN0Fdtp+/THVmXXpsipbjZfprOjs413laumh+2vbuTfRXX/HgxAmKs7PRenkROGyYhJuiQcl5YcP65JNPqoWVdVVSUkJ8fDxHjhzh7NmzFBYW0q5dO5YuXcr48ePR6Wp+AysnJ4cDBw6QnJxMx44dmTt3bpNO7K4vZrOZx48fk56ebr1kZWWRn59PVlYWer2e0tJSFEXBxcWFoKAgNBoNJSUlFBUVoVar6dixI97e3tZp0UeOHKkyQEulUlnDSq1Wi6enZ5XPK8PLyuDCYrFQVlZmDS+zs7OrhJnPbkuHp0GRVqutdYiMaJ3atWvHokWL0Ov1PH782HpJTk7GbDZjMpnIyclh8+bNeHp6MmjQIHr27Fljr0o3NzeGDh1apU9nUFAQcXFx/O53v+PDDz+kV6/Xm9/g5OTEwoULGTNmDDExMaSmphIQEEDPnj3ZuXMn//mf/0lERAQdOnQgKCiIoKAg2rdvj1b74sG4dnZ2jB49msjISPbu3cu6devo1q0bY8aMqdbnsq79Pp2cnKxhZ8eOHTlx4gSpqaksWrSoWgjr6enJnDlzWLNmDVu3bmXmzJl1CirbtGnDe++9x/Hjx4mNjSU5OZl33nkHNze3On5VhWjemm3A+Y//+I84OTlx7do1HB0d8fLy4k9/+hMjRozg+++/5yc/+Qnr169v6mW2Cm/1Ca1TdWb/sA6s/2wxRy4lkpGTj6+HC6N6h0q4+Yao63Agqc5889U0KV36a7Yu9q6uMi1dNCo5L2xYL1sxWVpaSmJiItevX+fs2bM8fPgQe3t7evTowZQpU4iIiKg1LK2oqODUqVOcOXMGnU7HzJkz6dKlS4vcjm6xWMjPz68SZmZkZFBRUWHd+p+RkUF6erp1C6uTkxPu7u44OjpSUFBAQkICAC4uLnh7e+Pr64uzszMZGRm4uLjg6upapepSq9Xi4OBQ5etlNputAWlhYSGPHj2qEl4WFhZWqe7UaDTodDqcnJzw9vamU6dOODk5WS/PhqNCPEutVlsHU4WGPv0d0Gg0kpubaw08Hz58SHx8PMuXL0er1RIeHk7nzp3x9PSkTZs2uLm5Wb+/7O3t6dmzJxEREdy9excfHx/279/Pv/3bvzFx4kTmzZv32sN8fH19ee+997h06RJHjx4lPz+fX/ziF1y4cIHExEScnJxISUnh0qVLwNPes5WBZ2Bg4HOn1nt7e7No0SKuX7/O4cOH+fOf/8zw4cPp27ev9TlW9h2uaSt3WVlZteAzIyODnJwcVCoVe/bs4cCBAwwePJjAwMBqW96nTZvG5s2b2bNnDxMmTKjT66harWbkyJF06tSJHTt28PXXXzN27FgiIyNb5OuwEM9qtgHnDz/8wN///d/Trl07cnNzAaxbkaZPn87p06f5u7/7O06cONGUy2wVXqY6002nlWnpb6i6bj+HF1dnPm8S+6scJxrP8yalS39NIURDkfPCpldeXk5iYiLx8fEkJiaSmppKcXExrq6uzJ49m5EjR1brWfcsi8VCfHw8hw4doqSkhCFDhjBo0KAWNUCotLTUGmQ+fPiQe/fuodfrKS8vt4YwT548ISMjg6ysLEpLS1Gr1bi7u+Pr60ubNm1QqVTWwDEwMJBu3brRo0cP2rVrh7OzMw4ODpSWlnLy5El0Oh329vaYTCaKi4utW8crJ6lXhpfFxcVVKjzt7OxwcnJCp9Ph5+eHTqezBpo6nQ47OzsJMkS90Wg01QI8o9FIQkICJ0+eJCkpiZSUFNzd3XFwcECj0eDh4YGXlxdt2rShTZs2uLi4EBISQufOnRk0aBD/8z//w9atW7l58yazZs2iW7duL+w1+TwqlYq+ffsSGhrKgQMH2L17N+3atSM4OJiMjAzmzp2Lu7u7dWBRYmIi586dQ1EU2rZtaw08AwICqvQPhqfbv7t3705ISAixsbEcPHiQa9euER0d/cKdBfb29rX2+ywqKuL+/fts2LCB+/fv4+zsTH5+PpcuXarS77OsrIzvvvuOpKQkRowYYQ0/K3v51iYwMJCf/OQn7N+/n507d5KUlMT48eNxdJTiJNFyNduA02w24+3tDYCrqytqtdp6QgsQHh7OihUrmmp5rU5zrM7UFxZz+GICmbkF+Lg781afUNx0L95SIF5NXbefvyiUfNEk9rocp2sb3OjPX9RtUrr01xRCNAQ5L2waFRUV3L59m/j4eO7cuWPtDVleXk5AQAD9+/enf//+uLu7P/d+srKy2L9/PykpKXTp0oUxY8Y06y2RFRUV5ObmcvfuXe7du0dKSgppaWk8efKE8vJyzGazdcq8yWSioKDAOklepVLh7u7OoEGD6Nu3L2FhYTx58sQ65dnJyYlu3boRERFB27ZtURQFg8FAXl4ejx49Ii8vj8zMTGuvw7KyMkpKSqw9/ODpQJLKwNLT09MaXFb+3Y8DGCEam0ajITw8nG7dupGSksKFCxfIycnB09MTPz8/iouLefjwITdv3gTA1tbWWuHp6enJP//zP7N9+3YOHTrEt99+S69evQgNDSU8PPy1gk6dTsf06dPp3r07e/fuJT8/n/LycuvgoYiICCIiIgDQ6/XWwPPatWucPn0atVqNv7+/NfD09/e39rF1cHAgOjraet8rVqygZ8+ejBo16qVDQ0VR0Ol0RERE0LlzZ7Zs2cKDBw+YMGECERER5OfnV6n6VKlUHDlyhKSkJGtrCTs7uxoHHVX2+6w8ZvLkyXTu3JmYmBi++uorJk+e/Eo9UIVoDpptwBkUFMT9+/eBp++4BAUFceTIEWbMmAHAmTNncHV1bcIVtj6NWZ35ovDybNw9/mVN1YrS1fvP8tnCaPqHdWiUNbZGL9p+/qLwsi6T2G0cdC88rsu0X6Komu3L1xurrpPSpb+mKMvLq9qjMyoKe/k/W7wGOS9sPAaDgaSkJOLj40lKSrIOH4KnX3t/f3/69u1Lnz59XvhLe1lZGceOHePixYu4ubkxb968av3pGlt5eTkFBQVVLpW9XNPS0nj06BFPnjyhuLgYi8ViDSy9vLzo1KkTdnZ2lJeXk5aWZp08r9PpCA0NpW/fvgwePBhvb29u3brFjRs3OHToEIqiEBAQQGhoKM7OzhQUFHD69Gny8/Ot91FJpVJhb29PSUkJnp6eBAYGWoPLysvrbtkV4lU8fvyYS5cuVRk2Vvlx5Z82NjZVqgYVRbFu9U5MTOTy5cvExcURFhbG5MmTURSFJ0+eWLe337t3j+vXrwNPw7cePXpw7do1rly5gslk4tatW/j6+tK+ffsqof/LCg4O5uOPP+bEiROcOnWKO3fu8Oc//5mPPvqIDh2e/i7p5uaGm5sbPXv2xGKxWN+ouH//PufPn+f48ePY2NjQrl07a+Dp6+uLn58f7733HpcvX+bo0aMkJCTw1ltv0aNHj1eqnK6cnL5371527txJTk4OI0aMwM3Nzfp6OnbsWA4cOMDZs2cZNWpUtYFH9+7dq7XfZ+VlxowZnDhxgrVr19KvXz9GjRrVoirshYBmFnDq9Xrru7mjR4/m+++/59/+7d8A+MlPfsInn3zCvXv3sFgsHD9+nE8++aQplyte0euGl/rCYv5lzV5rT9DK6e4Go4nfrt7L+s8WSyVnA6pt+3ldwsu6TGL3Ch/xwuPy7l7BLbhv/TwhUWd1nZQuWrdHly5Vm7Iet2kTAz75hLa9ezf18kQLIueFjcdoNJKcnGwNNSsqKvDx8aFjx47k5OTw5MkTPDw8ePvtt4mMjHzhL70Wi4Vr165x5MgRDAYDI0eOpH///tZKp4ZgsVgoLS2tFl7++FJeXm4NOQsLC6moqLBuMbezs8Pb25sePXrg5+eHVqvFYrGQkZFBamoqjx8/Ji0tDaPRiFarZcCAAfTr148OHTqgUqm4efMm3333HXfu3LEGn87Ozri4uJCamkpqaioajcbaW9PHx4cuXbpYP3dxccHZ2ZmysrIqW9Tr++tUXl5OaWmp9VJWVmb9s3IYUVZWFk5OTsydO7deH1+0bCqVylptXFJSUqU1Ajyt3Hw28PzxxyNGjOD+/fvEx8dz+/ZtunfvTrdu3aps0S4rK7MGnr6+vgCcPXsWvV5PeHg4jx494syZMyQnJzN27FgGDBjwSkGcjY0No0aNIiIigl27drF//37+6Z/+iV/+8peEh4dXOVZRFOt2+r59+2I2m8nKyrIGnidPnuTIkSPY29sTGBhoDTw//vhjjhw5wu7du7ly5Qrjx4/Hx8fnpdeqVquZMGECHh4eHD58mNzcXCZPnmx93oqi8Pbbb1NUVMTx48eZP3++tUfqs1/XnJwccnNzrcFnZmYm8fHxVYaMFRQUsGLFCmJiYhg/fjwhISG4u7vj4uIivXlFs9esAk4fHx/GjRvH3Llz+eSTT5g9ezYGgwEbGxt+8YtfUFxczLZt21Cr1fzzP/8zn3766Uvd/1dffcVXX31FSkoKAGFhYXz22WeMHTu2AZ6NqEl9hJeHLyZgNJlrvH+jycyRS4nSB7QJ1CW8rMskdqjDxPai3Op/Lxrcy0xKF61TWV7e03DTYADA8tdffMwGA2d//3uiv/pKKjlFncl5YeMwGAz84Q9/oLS0FG9vbwYOHGjtl3n79m0CAgKYOXMmISEhdfrlNj09nX379pGenk5ERARvvfVWrZPU68pisVBcXPzC8PLZQTqVWzzt7e0xGo2UlZVRUVFBUVERZrMZZ2dnOnbsSEBAAH5+fri6umIwGMjIyODBgwfEx8djMBgoLS2lpKSEwsJCVCoV3t7etGnTBmdnZ0wmEwcOHODRo0c8fvwYo9ForaoKDQ3Fx8enSnhZOTCoPvtfPhtYVgaVPw4tf/xnTZVvJpOJ0tJS8vLyyMvLs4bcQlRq06YNY8aMsX5eOUir8mekpKTE+nHln5mZmdbvyWe/74xGI2lpaVy9ehWtVkuXLl3o2LEjWq3WGoYGBgYSGhrK6NGjSUhI4IsvvuD+/fsMHDiQ3NxcEhISOHv2LE5OTnTv3p1BgwbRsWNH2rZti5OTU52fl5eXF++99x6RkZH84Q9/4JNPPuHjjz9m4sSJtf6sqlQqfH198fX1ZeDAgZhMJh49emQNPI8cOWJ9I6R9+/b06tWLhIQEvv76a/r168fw4cNf+s0LRVEYNGgQ7u7ubN++nTVr1jBr1izrc1UUhXfeeYf169ezceNGFi1aZG3tAs/v91lcXFyl4vP+/fvExsby+9//nsDAQAICAtBoNLi7u9e45b2+X9eEeFXNKuCcNm0au3fvZvfu3eh0OqZMmcLcuXMZMWIEiqLwT//0T/zTP/3TK9+/v78/v/vd7wgODsZisbBmzRomTZrE1atXCQsLe/EdiNdSX+FlZm4BKkWx3v5ZKkUhIye/4Z6EqLXHZl3Cy7pOYn/hcU7P7/UlGoZMSn9zHfn7v6csLw97V1dG/ed/vvL9PDhxArOx+vcHgNlo5MGJEzJ9XdSZnBc2DhsbG3Jzc3FzcyM7O5urV6+iKAphYWFMmzaNrl271inYLC4u5ujRo1y5cgUfHx8WL15Mu3btXng7s9lMUVHRc4PLwsLCKpViarXaWh3p7OxM27ZtrQN6ysvLKSwsJDc3l4yMDLKzs4Gnv9x37NgRPz8/2rZti1arJTc3l3v37nHq1CkePXpEeXk5iqJYKzzz8/MxGo3odDpr9ZaHhwcqlcp632azmaCgIKZNm0bfvn3x9/d/rV/0KytR8/PzKS4uxmw2Vwsun/28sifosyq3uTs4OGBvb4+jo6N1wIuDgwN2dnaUlpaSkZFBSkqKdcp7RUUFDg4OeHl54efnR0hIyCs/D/HmUxQFOzs77OzsXtgipPL7+MchaHZ2NvHx8Vy/fp1bt27h7e1drfWFSqXCwcGBfv36WSslo6KiePfdd8nLy+PatWvcvHmT8+fP4+HhQUBAAG3btq1y8fX1fW5LDUVR6Nu3L8uWLePXv/41f/rTn0hISGDx4sU1Tj//MbVaTUBAAAEBAQwdOhSDwUBaWpo18ExISMBoNJKXl8e6des4fPgw06ZNY8CAAS/9ehEaGsrChQvZuHEjy5cvZ86cOdY1ajQaZs2axapVq1i3bh3vvfceLi4uz70/RVGs7S8CAwOtf79kyRKOHj1KbGwsDg4O9O7dm4qKCnJycoiPjyc/P98aWo8ZM4YBAwa81PMQoiE0q4Bz/fr1lJaWsnPnTjZs2MD69etZs2YN3t7ezJ49m7lz59KzZ89Xvv8JEyZU+fzf/u3f+Oqrrzh37lyrOpFtSM/bfl5f4aWPuzPmWnqumC0WfD2e/yIuXt3zemzWJbx061S3SewvnNjeUSp0m4Kzg7NMSn9DleXlUZr7+pXRxdnZT7elm6pXcysqFcV/DRqEqAs5L2wcZWVleHp6cuPGDUpKSnBzc8PDw4Pc3Fy2bt2KWq3G1dXV2o/O3d3d+rGbmxsajYaLFy9y7NgxFEUhOjqaXr16oVKpMJlMFBYWPje8rKyorKTRaKzBpaurq3W6+LOXyq3jT548sU41v379OllZWZjNZjQaDb6+vnTu3BlPT0+0Wi16vZ6kpCSOHz/OgwcP0Ov1lJWVYWNjg1arxWw2U15ejsFgwM7ODj8/P4YNG0Z4eDj+/v6oVCoePnxIQkICWVlZ6HQ6BgwYQERExHNDTYvFQllZGcXFxZSUlFBcXFzt42c/Lykpoby8nAcPHmBra4tGo6kWWNrb2+Pm5mb9u8pL5XU/npJusVjIysoiPj6eCxcukJqail6vx2w24+TkRJs2bejatStBQUH4+Pjg6elZ71vjReumUqmslZk/Nnr0aLKzszl//jyPHj3C29ubiIgItFpttepQd3d3du/ezf79+8nNzSUiIgJHR0e6devGo0ePSEtLIy0tDZ1Oh7u7OxqNBkVRsLW1pU2bNrRr14727dvTsWNH/P39q32fu7i48F//9V/85S9/4dChQzx69IhJkyYxdOjQl9oGb2NjY92mDk97/z58+JD79+9z69YtfvjhBz799FP8/f0ZO3Ys3bt3p3379mi1dWuz5ufnx/vvv8+GDRtYsWIFM2bMsA4GsrOzY+7cuaxYsYK1a9eyePHiV5qMrlarGT16NCEhIezYsYOLFy8ybtw4xo4di6IoGI1GcnNzyc3NpU2bNi99/0I0hGYVcMLT6WOzZ89m9uzZ6PV6tmzZwoYNG/jjH//IH//4R4KDg5k3bx5z5syxNgB+FSaTie+//57i4uLnvttQ2aenUlFR0Ss/5pvuRdvP6yu8HNW7C6v3n7VWgj5Lo1YxqndoDbcWr+tFPTY7T/oE5QXhZV0nsb/oOI2DE8by0kZ77uJ/dWvXTSali1qHCGm9vLCYa34jy2I2o61DFYQQz5LzwoZXOWl38eLF9OnTBycnJ0wmE3l5eej1enJzc9Hr9ej1eh4+fMgdp2GwAAD5kUlEQVS1a9cwGAyYzWays7NJSUmhrKyMgIAAOnXqRGxsLEePHsVoNFbZMg5PJyVXhpSenp506NChWnjp4OBQLSy0WCwUFBSQnp5OfHw86enp1orLym2gzs7O+Pv7Y2dnh8ViIS0tjcuXL5OdnU1eXh5GoxGVSoWXlxcBAQGEh4ej0WisE9Dt7e0JCQmhW7dudOnSBZ1OR1lZGQkJCfzwww+kpKSgVqvp3LkzgwYNwsfHx9qv8sqVK88NLn9cYakoCo6Ojmi1WuulTZs21o8VRSEuLg5PT09cXV2rBZYvUhmmJCYmcvfuXVJTUyksLERRFFxcXGjXrh1DhgwhODgYHx+fOocqQjQULy8vxo8fz8OHD7lw4QKHDx+mY8eO9OnTh/bt21c5dvjw4Sxbtoy0tDS0Wi3vvvsuJSUlFBUVkZ+fz61bt7h69SqZmZnY2NhYq0sfPHhAXFwcRUVFmEwmNBoNbm5ueHt707ZtW/z9/WnXrh1ubm6MHTsWW1tbzpw5w65du4iLiyM6OvqVh6TZ2dkRHBxMcHAwo0eP5oMPPuDkyZPs2LGDjRs3cvDgQQIDA2nbtq01GA0MDHzuGw0uLi4sXryY77//nvXr11vfXIKn0+Lnz5/PihUr2LhxIwsWLHjlgUGBgYF8+OGH7N+/nx07dpCUlMT48eOt1d51qXAVorEoltcZP9aI0tPT2bBhAxs3buTatWsoikK/fv04c+bMS93PzZs3GTBgAGVlZTg5ObFhwwbGjRtX6/G//vWv+c1vflPt7y9fvvxaVQPNxexfL+dJfhGeLk5s/PV7r3w/+sJi5v52ZY2ho41Gbd1+vnzPDzX2/VEUhfcnDGZU7y4vuJ8luOkcORd/j9+urhqmatSqVjFF3WQoI/9BHBo7B1Qa29e+v/iNv8JQko+Nowths6t/r1fKvhHLo4sxQE0vGQpt+07AzsW71lDSpd3/VsMYSgtrncT+rNqOMxsrMJaX4hLYDbVN66wwKDOUEfcgDgc7B2xf8/vgs42fkV+Sj4ujC7+d/dt6WmHDqjBWUFpeSrfAbti30u+B+rTngw8ozc3Fwd2d8d9889xjaxoipNJoGPDJJ7h36sTen/zE2oPzWSobG6K//hr7F2yVEqIu5LywflkslioBWkVFRa0Vl/n5+aSlpXH9+nUyMzOxtbXF09MTW1tba5BXuW1Vq9Xi5eVlDRC8vb2tFaCurq61TgMvLS21VmPduXOH+/fvk5OTQ1lZGWq1GgcHB2xsbKyVjYqiUFhYSFFRkbVvpkajQavVEhgYSOfOnQkJCcHb25t79+6RkJBAamoqKpWKoKAgOnXqhJ+fH4qiUFBQQGJiInFxcdy7d4+ysjJcXFxo06YNOp0Og8FQbbDKs4Hls8Hlj0PMys9rCnGfVVJSUuchQ0aj0VrJmpSUxP3798nMzLR+rdq0aUP79u0JDg4mNDQUDw+PWh+7sldp5UWj0TRpH86ysjIKCwsZOnToK1WgvUle5nuipTObzSQlJXHp0iVKS0sJDQ2lV69eODg4AE+/LwoKCkhISCAmJoZ+/frxm9/8Bjs7O+t9WCwWHj58yNmzZ7l9+zZ2dnZEREQQEhKCyWQiLS2Nhw8fkpqayqNHj8jKyrL26bW1tUWn0+Hk5ERmZiZ5eXm4urqiKAodOnRg4MCBtGnTxrqt+9mLra3tS70ZYTQaOX36NEePHqW8vJz27dtTUVFBXl4eiqJUCTwDAgKwta1+zm82m9m/fz8XL15k4MCBvPXWW9Y1pKens3r1aoKCgpg1a9ZrDwmKj49nz549aDQaJk+ebK0aFaK5aDEBZ6WbN2/y2WefsWvXLhRFqXaC8SIVFRU8fPiQ/Px8tm7dyvLlyzlx4gRdu3at8fgfv1N/7do1oqKiWvyJbKX6Cji3xF5q9PBSX1jMkUuJZOTk/7WyMxQ33Zt/8tNUAWfama08uX0GaqrOUqnwDBmI/8BpdQ4vX4cEnBJwSsBZv+oacJbl5T0/wPzqK3Lv3Kk1AJUp6qK+yXnh67NYLMTExFQJMcvKyqoc4+joaN0a/ujRI5KTk9HpdIwcOZJ+/frh4uJi/cXbYDBYKz6frf6svFT+G1X2fbOxsbEO8ak8prCw0Fol6uTkhLOzMzqdzlot5OjoSEVFBSUlJdbQ1dbWFicnJ9q1a0fbtm3x9PREp9ORkZHBrVu3SExMJCMjA7PZjKurK+7u7jg5OWEwGDAajRQUFJCVlUV2djZGoxF3d3dr+Onp6fnc4NLe3r5epwvXFmaZTCZyc3N5/PgxGRkZ3Lt3j7S0NPLz8ykrK8PBwYE2bdoQFBREcHAwgYGBqFQqysvLqwSXtV0MP3pt9/b2ZvLkyfX2vF6WBJz/qzUFnJWMRiNxcXFcu3YNs9lMZGQkERER1tYXQ4cOZd++fXz11Vd07NiR//iP/8DNza3a/eTm5nL+/HmuXr2KyWQiPDycAQMGVBnAYzabefz4MY8ePeLhw4ekpKSQmppKSUkJycnJFBYWEhAQYK2E7ty5Mx4eHtWqs21sbKoEnpVB6Y8vWq0WtVpdZY379u3jzp07dO7cmQEDBqDX6609PIuKilCr1fj7+1sDTz8/P+ubRBaLhfPnz3Pw4EFCQkKYMmWK9TX5zp07bNiwgcjIyOcOTqqrgoICdu3axd27d+nXrx+jRo165epQIepbs9uiXpOHDx9a36WPi4vDYrEwcOBA5s6d+9L3ZWtray0t79WrFxcvXuRPf/oTy5Ytq/H4ynegK73MRLaWoDIQrEsw+Lz+mnXZfu6m0/LZwuhaw8vKNfQP68D6zxa/MLx002lfe1r6856TqKquA4JsHHR4hY9oxJUJIRpLXYcIRX/1VdUt7MOGSeWmqDdyXli/FEUhLy8PW1tb2rdvX23LuE6nw8bGhuTkZPbv3095eTnTp08nKiqqxqDFxsbGGkQaDAby8vLIz88nLy+PnJwckpOTSU5O5sGDB2RlZVmnnxuNRuzs7NDpdLi4uODl5YW/vz8+Pj7odDpMJhMFBQWkpKSg1+upqKhAo9Hg4uKCk5MTjo7/P3v3Hd7UebcP/D6SLHlI8t574T0w2GzMDDE7KZmkSWhI0jRpmqb9pcnbNqPp+6ZN2rd522Y2CSQhhCwgwQyzN5hhjAEDBi/AE7ynbEnn94erU4yXvJDH/bkuXcZHR0ePrOOH41vP83xtIZPJkJubi8zMTFy/fh3Xr19HQ0MD5HI5PDw8EBAQAH9/f6mauV6vR1FREa5cuQJRFBEeHo4HH3wQ48aNg6enpwXejf8wGo2oqqqSgteioiIUFhaisrISdXV10Ov1UCgUsLW1ldbNNE01b25uxsmTJ3Hy5Ml2xzSNeDWt02ltbS2tt9nVjchSFAoF4uPjER4ejlOnTuHUqVPIzs5GTEyM9Pu5bNkyuLq64k9/+hOeeuop/OlPf+owpd3JyQkpKSmYOXMmTp48ifT0dGRmZiIoKAiTJk1CSEgIZDIZ3N3d4e7ujrFjxwJo+0ChvLwc165dw3fffYdz584hICAANTU1yMrKktbpDQwMhEajgVKpRFNTE+rr66VbYWEh6uvr0djY2GEgkK2tbbvQ09XVFTKZDBkZGThx4gSSk5Mxd+5cqNVqqbJ5fn4+0tPTsXfvXlhZWcHPz08KPJOSkuDo6IjvvvsOq1evxgMPPACNRoOQkBAsWbIEGzZsgEajwaxZ/fs7TavV4qGHHkJ6ejp27tyJvLw8/OhHP7LoaG8ikyEbcN64cUNaZ+nIkSPSRccf/vAHLF++vEPH1VemBcVHq3d/9aBZ+/W0vqa5hX9uV3hpTnDZ02ui9nos/MMK2kQjnrlFhKwdHAakWnpXa33S6MPrwsH18MMPd3lfZWUl0tLScPHiRQQFBeGBBx6Aq6tru2rfN4eYpq83j8Y0FRNqaWmBXC6HSqWCu7s7YmJipFDB2dkZDQ0NKC8vx9WrV5Gfn4+TJ0/i+vXrqK+vlwI9U/Dm4uICV1dXKdg0Go1oaGhAbW0tdDodXF1dMX36dMTFxSE6OloKpuvr63H27FlkZWWhuLgY1tbWmDx5MmJjY+Hv79/v0U09ubmatOnW0NCAsrIyXLt2DUVFRbh69SrOnz8vjbzU6/UQRRFyuVwqMOTg4ABnZ2c4Ojq2KzDU3a2302eJhgJra2tMmjQJ0dHROH78OA4dOgRra2t4eHhg7NixSE5OhouLC37/+9/j2WefxWuvvSaFlLceZ8qUKZg4cSLOnz+PI0eO4IsvvoCLiwsmTpyIuLi4diMR5XI5PD094enpiXHjxmHDhg3IysrCsmXL0NjYiM2bN2PDhg1wcnJCQEAAVCoVPDw8pGnlppHkN/dPN4efN99qampQVFSE+vp62NjYoKCgAG+//Tbef/99hIeHw9fXVwpCw8LC0NraipqaGpSUlODMmTMQBAEajQbBwcGIjY3FyZMn8eGHH2L58uXw8PBAXFwc6uvrsWPHDmg0GiQm9u/vNkEQMHHiRAQFBWH9+vW4ceMGA04aEoZUwNnQ0IANGzZg7dq12LVrF1pbW+Hp6Ynnnnuu35UyAeCll15CSkoK/Pz8UFdXh7Vr12Lv3r1IS0sboFdwe7TqDTB0UcRhMFTVNeL1TzdLU8tNozRb9Qb8YfVmfPziw5gWF9pt4Z9pcSFobmmb9mKjUmLRlNh2+5juM5dcJoOVQt7pfeYEl1V1Dd2+pi9e/glHct7C3AJBRDRy3c4iQp2t9Xl23TpOdR9FRst1YUtLS4diPENBS0sLdu3ahYMHD0IQBERFRUGtViM1NRW1tbWorq5GS0sLgLaRTqYK5Kaqx6b1ME2jBh0cHKSQzdraGgqFAkajEcXFxbh8+TKam5uh0+nQ1NQEvV4PpVKJqKgoaYRSQEAAbGxs0NLSgubmZlRVVSE3NxcXLlzAsWPHUF9fD4VCARcXF/j5+SE4OBj29vbSaKuKigpcvXoVRUVFkMvlCA0NxdSpUzFmzJgu1wLtialC+s1hZXe3pqYmNDU1SVOva2trpbVDTVNd5XI5BEFAa2sr1Go1fH194enpCW9vb2n6va2tba8LDxENd6bRh2PGjMHBgwfx3Xff4dSpU5g7dy6ioqLwz3/+E7/5zW/w0ksv4Ze//CXmzZvX6XHkcjmio6MRFRWFq1ev4ujRo9i8eTN27dqFxMREJCYmQqNp/3eNTCbDXXfdBZlMhkOHDuHuu+/Gu+++i6NHj2Lnzp3Q6/UICQmBUqlEXl4ejh8/DlEUYWVlBU9PT3h5eUm3kJCQbn93W1tbUV9fj4KCAmzZsgUFBQWora2Fj48PDAaD9KGPqViStbU16urqpNHrTU1NEEURFRUV+OGHHzBnzhxMmTIFXl5eCA0Nxfr162FtbY2YmJh+vydubm544oknBnSJDqL+GFIBp5ubm7TI+4MPPojly5dj1qxZA/YLU15ejocffhglJSWwt7dHbGws0tLSMHfu3AE5/u3Qqjfg4pVSNOp6Fwj2x4HTl6DvYk0rvcGAtTuOYVpcKO6fnYi1O47BKBohCAJEUYRMkOH+2Ym4UlaFK2VVPT5XfWMzTl26iqq6RjhqbDE21BdqW+sO+5zNL4bRKMLLxb7d6Exzg8sdx89Db+j8j3S9wYidJy70e/r7SGTvF4XI+1/p1xqbrU11qLp0HC31lVCqneAYOvBrdBLR4PBPTsbZdes6X4NToYD/jBlmH6u70ZnN1dVt4ea/n8c0YtTY2oojf/0rFrz3HkdyjgKj4bqwpaVFCucsRRRFrF27VlqDsaWlBRUVFSgvL5dCNjs7Oxw4cAAKhQIKhQKCIECv16OlpUW6GQwGCIIgjbLUarVwcHCAo6Mj7O3tYW1tDRsbG6hUKhgMBjQ0NKCurg41NTXQ6/XSaKnk5GSEhITA19e3w7qLRqNRCinPnz+P2tpa+Pr6Ys6cOQgMDIRWq5WqoldUVCAnJwcXLlzAlStXYDQaYW9vDy8vL4wZMwZGoxGFhYWoq6uTRkTa2NigtbXVrKDS9O/O1p+3traGra0tbG1tIZfL0dLSAp1OJ40wbW1thZWVFXx8fKT1OxsbG6XHuri4oLa2FsHBwfDz8+tzAEs0Erm4uGD27Nnw8fHBwYMH8cknnyA8PByzZ8/GP//5T/zud7/Dm2++ibKyMvz4xz/uMkwUBAF+fn7w8/NDVVUV0tPTcfToURw6dAjR0dGYNGlSu1GJMpkMS5YsgSAIWL9+PURRxJQpUxAVFYUtW7bgwoULCAsLw49//GNYW1ujpKQExcXFKC4uRk5ODo4ePQqgbamTW0NPR0dHqZ1WVlZwdHSEo6Mj4uPjkZWVhe3btyM3NxczZszAhAkTIJPJpA9Ybh0NWl1djcLCQuTm5uLIkSNYvXo1Nm7cCBcXFzg4OKCyshIHDx7ElClT4Ofn1+kaoTffeiqKxnCThpIh9b/lnDlzsHz5cixevHhQ1nz5+OOPB/yYt5vBaESjrhVW8q5HMA60usZmKbC8lSAIqGtshq3KCgljfBHq44oTFwpRUdsAZ60dxof7Q2Nr3nt5Nr8Yn249AoPxPwHpzhPn8WjKJEQFenXYRybIOozONDe4NGfN0NFCYatt97Un3a2x2VN4WXPlLAp2rW43ArTk5JYOVdZpaKttqsXxS8dRWV8JJ7UTEkMTobUx7/yh4c3awQGTfvWrLosImbvOZk+jM81d65NGttFwXajX61FfXw+lUtlubc/byWg0IisrCwaDQSre0draKgVtVlZWMBqNaG1tlULQ1tZWCIIgjc50dHSUCmrY2dlJIajp+MXFxdIf3w0NDTD++1rPVEDIFIDW1NRIgaRcLpeOU11djZKSEpSWlkKn00nV0YOCguDp6QmlUomamhrU19ejsrISeXl5uHTpEhoaGuDg4IDo6Gg4ODhAp9OhqqoKxcXFOH/+PGpqalBbW9tuFK1pOreNjQ1sbGyg1Wrh5OQEZ2dnKbD19vaWAkzTzcbGBnK5HNXV1SgtLZWCjaqqtg/4bWxs4OfnB5lMhtbWVmkEp9FohKurK8aPHy+NVDUYDFJBGYabRJ0LCgpCVFQUzpw5g927d+Pdd99FQkICXnnlFfzjH//A6tWrUV5ejl/84hc9FsFxdHTEnXfeiRkzZiAjIwPp6ek4ffo0AgMDMXHiRIwZM0bq80wh54YNG2A0GhEfH48HHngA58+fx9atW/HOO+9gxowZmDhxYrvlU5qamtqFntnZ2Th8+DCAtv7h5sDTy8sLWq0WgiAgLi4OY8aMwe7du7F9+3ZkZmZiwYIF8PPzk/opV1fXTl+X0WjE5s2bkZaWBicnJ9ja2uLatWs4d+4c9u/fj8mTJ8PT0xONjY3SFPlbl2mRy+Wws7PrNgQ1FWIjsrQh9T/m999/b+kmDBtWCjmUVv1/+/78RRpqG5qgtbPBb5Z3Pozf1UHTabgJtH3q7+aokdribK/GvAm9D6pqG5rw6dYjUjhpej69wYjVW4/g9ZWLAaDdPgax7evNozPNDS7NXTN0NAhb8qsBOU5P4WVrU92/7/93aPHvgkWiUY+CXasQef8rHMk5DJy9chardq2CwWiAIBMgGkVsObkFK2avQLRftKWbR7eB1/jx/SoiZM7oTHPX+qSRbTRdF5oKvljKPffcgytXrqC4uBi2trYIDAyEjY2NtDacaf1HBwcHuLm5wdXVFW5ubnBycoJMJpPCUaPRiKamJpSWlkqB5PXr1yGXy+Hs7IzQ0FC4uLjAyclJmgJqNBphMBjQ1NSEuro6hIaGQhAEqVr7lStX0NTUBCsrKzg5OcHLywtWVlZoaGjA8ePHodPpUFNTg/Lycty4cUMaCWmqcK5UKpGZmQmg7Q91KyurdjcnJ6d27TAajdI6mXV1dSgpKYFMJoNMJpMqtZuCXEEQYDQa0dLSIo3qFAQBSqUSrq6ucHZ2hr29PVpbW1FbW4vCwkIIggAHBwf4+fkhJiYG/v7+sLe3lwJdnU7XblSsKIqcik7UBUEQEBsbi8jISBw/fhz79+9HVlYWpk6dCkdHR/zwww8oLy/H73//+w7TzjtjWpP35nU6v/zySzg7O0vrdCqVSixevBgymQzff/89RFHE2LFjERkZieDgYOzevRs7duxAVlYWFi5cCB8fHwBtIWZQUBCCgv5T66GhoUEKPIuLi5GZmYkDBw4AaOvDbg48p0+fjrFjxyI1NRWffPIJxo4di7lz53YbLMpkMixatAgeHh7YunUr/Pz88OSTT+LKlSv417/+hby8PCgUCtTV1cHNzQ3x8fHw8fGBi4uL9AHcrbeysjLk5uZKU+QBICUlBRMmTOjPW0k0IIZUwEm3X21DE6rrm7rdZ0JkAFIPZ3U6MlIuk2FCZKDZz5WeXYDK2gY4ae0wITIAWjsbAEB6dkGX64oajEYcO18AUUSX+5hGZ5obXM5NjOh2zdA54yPMek3UxpzwsurS8bbwsxOi0YCqy8dZfX2Iq22qxapdq6D/9/ss/vt91hv1WLVrFV65/xWO5Bwleioi1N30c3NGZ97OtT6JRrumpiacPn0a1dXVcHBwgFarlaqUu7m5ISIiAq6urnB1dYVSqez08aZAs6SkBBUVFRBFUaruHRcXBw8PDykM1ev1aG5u7nDT6/UoLi6WKoabQk0HBwd4eHhArVa3C/qsra2lQhstLS3w9PTE1KlTER4eLgW0pjU/TTfT1HqDwWD218bGRlRVVeHatWu4du0aysrKcPXqVVRVVUkjnUzrimo0GmkkbmFhIRobGyGTyaBSqaSCQA4ODrCyspJ+XqYRXDdrbW1FYWEhlEolFAoF5HK5dJPJZFAoFJDJZB22d7WfOY8RRVEqeGQKMtRqNWbPnj1IZx7RwFEoFJg0aRLGjh2LgwcP4ujRo7CyssKcOXOwZ88e/OpXv8Lrr78Od3d3s44nk8kQFRXVbp3OLVu2YPfu3Rg3bhySkpKwcOFCCIIghZwJCQlQqVRISUlBXFwcUlNT8fHHH2P8+PGYPXt2px9i2dnZITQ0FKGhodK2urq6dqHniRMn0NDQAKBtHVIPDw94enriwIEDyMrKwvz585GQkNDtByGJiYlwdHTEN998g6+++goPPvgg/vSnP+Hjjz9Ga2srpk+fjtLSUly8eBHp6ekQBEEqlhQYGIioqChp+Y6GhgZpneXq6mpUVFTAzo61K2hoYMBJPdLa2WDloqn4aNPBf08NbyvcI5fJsHLRVIiiiB3Hz3caXJqcyS3CR6kH200/Tz2chZWLpiImyBuVtQ1dToOXCQIqato69e72KamowY/nTTAruHTU2OHlRxfgD6vbFyNSyGV4+dEFHaq6U/fMCS9b6iulkZ0dyAS01FUOciupv45fOg5DF++zwWjAicsnMIsh9ajX0/Rzc0ZnRt5zz4Ct9UlE3TOtFxkaGgo/Pz8pzOzqD9b6+nopnCsqKsKNGzeg1+thY2MDe3t7eHt7Q61WQy6XQ6fTobCwEBcvXmwXZJoYDAZpmnhdXR0MBgNCQ0MRHR2N4OBgeHl5wc7OTpoGbmVlhaKiIly+fBn5+flwdHREYmIiYmJiEBYW1mkA21um4hxFRUVSwFBaWgq9Xg8XFxdERkbCy8sL3t7ecHd3R11dHc6fP4/z588jLy8PdXV10Ov10Gg0UKvVcHR0lKaw29vbQ6vVStPyTaNBbWxsYDQapVC1rq4Ox44dk4ox3TxC1vRv0/d6vb7d9paWlm73M400bWhoaBcwmwpGAZCqtQcEBDDgJElVVRXOnTvXbhmHm/+tUqkgl9+eJdS6Ym1tjTlz5iAxMRF79+5FZmYmwsPDcfr0aTz33HN47bXXMGbMmF4d09fXF76+vqiurkZ6ejqOHz+Ow4cPIzo6GhMnToQgCPjhhx9gNBox/t9FEL28vLBy5UocP34cu3fvxvnz53HnnXciKiqqxxHZGo0GYWFhCAsLA9DWJ9XW1rYLPaurq6FUKnHu3DkcPHgQAQEBSElJQUxMDLy8vGBjY9PhuCEhIfjJT36CtWvX4v3338eiRYswe/ZsfP7559i8eTNmzZqF6OholJWVoaCgABkZGUhNTZWKoGm1WulDGq1WC5VKJfXPHGVOQwUDTjJLTJA3Xl+5GMfOF6CipgHO9naYEBmIgpIK/P6jH7oMLoG2kZsfpR7sdPr5R5sO4vWVi+GktetyGrxRFOFsbwdRRLf7eDrb9yq4nBgVhC9e/gl2nriAkooaeDrbY874CIabfWBOeKlUO3V+PwAYRSg1ToPbSOq3yvpKaVr6rQSZgIq6Cgu0ioYSc6afmzM6c6DW+iSingmCgJ///OcwGo3Q6XRobm5GTU0NysrK0NTUhBs3bqCkpARlZWVS9V5TlXPTqEXT6Mra2lo0NDRIoYdp6r1Wq5W2yWQyVFRUSMdzcHDAmDFj4OPjA2dnZyxatKjdlEuj0YiCggJkZWXh/Pnz0Ol08PHxkcKC/owcEkURNTU1UphZVFSEkpISaWSms7MzvLy8EB0dDS8vL3h4eKChoQH5+fnIy8vDnj170NDQAIVCAV9fX0yfPh1BQUHw8vKCIAhoampCVVUVqqqqUFlZKf378uXLqK2tla5r5XK5VFTEFIiaKsK7urr2eh1OvV6P6upq1NTUoLq6ut2/AcDW1hZ2dnbQaDTQaDTQarXSv9VqtVTh3tJhFQ0tzc3NKC0tlUJxQycfVJp+52++3RyE3vq9lZXVoIRj9vb2WLJkCSZNmiRVOT927BiefvppvPbaa5g8eXKvj+ng4IB58+ZhxowZOHXqFI4ePYqsrCz4+fnBy8sLmzZtgiiKSExMBNA2CnTChAmIiIjAtm3b8O233+LUqVNYsGCBtDSGOQRBgL29Pezt7RER0TZYRxRFVFdXo7i4GKdOncK2bdvwt7/9DU5OTvDw8JB+nzUaDWxtbaFUKtHa2oqGhgZUV1cjIyMD27ZtQ2RkJJRKJU6cOIELFy5g4sSJUKvVCAwMRHR0tFR4raqqCjdu3MD169dhMBigUCjg5+cnjfD09PTs9c+TaDAw4CSzae1s2k3dNie41NrZmDX93Jxp8KbwtLN9bh6d2Zvg0lFjx2rpA8Cc8NIxJBElJ7f8Zxr7TQSZHI4hiYPcSuovJ7VTp+Em0DZd3VnjfJtbREONOdPPza3E3t+1PonIPKIo4rPPPkNzczNEUZTWwjQVBBJFUarq6+HhAXd3d3h6erYLLW++dRZYNDc3o6CgAPn5+SgqKoLBYIC7uzumTJkiVT83rXlpalNZWRmysrJw5swZ1NXVwcnJCZMmTUJMTAycnfv2/019fX27MLO4uFhas9NUYX3atGnSmnfW1taor69Hfn4+MjMzkZeXh+rqamn6ZkJCAgIDA+Hr69tpERPTyFNvb+8O95lCyFvDz4KCApSXl+Py5cvSFHVTGGka/anVaqUgsrm5GbW1te2CzPr6eum63NraGg4ODnByckJQUBAcHBzg4OAAjUbDAJN6xdPTE8uWLQPQ9jva0tIihZ1NTU2dLj1hKhB26yhhE9No4a5Ghd56X2+Lsbm5ueHBBx/E5MmTsW7dOqxbtw5PPfUUnn/+eTz88MN9CldVKhUmTpyIpKQkXLhwAUeOHEFRURHKy8vx4YcfoqWlBVOmTJH212q1uPfee5GTk4MtW7bg3XffxfTp0zFlypROfwdNP1vTVPCbv3a2rbGxEUFBQVAqlbh8+TJKS0vh5OQElUqFlpYWyGQyWFlZwcXFBR4eHvDx8UFiYiJycnJQXFyMefPm4fHHH8c333yDoKAgLF68uMufiyiKKC0tRX5+PvLz87F//37s3LkTc+fObfeaiSyFASf1mTnB5ZzxEWZNP+9pGrypEnv7fWRdjs5kcDl4OquU7hjac3hpZaNBwOwVKNi1ql0hIkEmR8DsFSwwZEFaW227r11JDE3ElpNbpDU4byaXyZHIkHrU6GqNTXOmn/dmdGZPa30SUf8ZjUao1Wro9XppVKGzszOio6Ph4+MDLy8vuLu791iF+FaNjY3SH8ElJSUQRREeHh5SZWG1Wt3hMQ0NDTh8+DBycnJQXl4OW1tbREdHIzY2Ft7e3r0KI5qamqTpnKYws7a2FsB/inckJSVJYaapPc3NzSgsLMTevXuRl5eH8n8XNXNzc0NYWJhU6by/RaFMIzRdXFw6/TmkpaVJ62KaRtAWFBSgoqICtbW1aG5uhtFolCrZOzg4SCM+w8LC4OnpCW9vb66NR4NCEASoVCqoVCrYm/nBo16vh06n6zIMbW5uRmNjIyorK6Xvjbf8rWmqZC4IgrRMhWlEsukDhZv/bRq9GBAQgN/85jeYNm0afvvb3+IPf/gD0tPT8frrr/f5AxOZTIbIyEhERkbi2rVrOHLkCDZt2oRXXnkFixcvxqOPPgqtVgtRFKHT6aQR6nv37sXatWuxYcMGxMfHQ6PRdAgs9Z18WGxtbS0VT7O1tYWnp2e712tnZwej0YhDhw4hLy8PQUFBSElJkYq23bzkRlFREQCgpaUFH3/8MZKSkpCYmIjDhw93u/auIAjw9PSEp6cnJk+eDIPBgOLiYmi1XIOfhgYGnNRn5q6bac70c6DrafCmcNO0z8uPLsDhs3mQyQT4uDpyWvlt1F2ldHPCS3u/qLaCQ5ePt01b//fIToablvXrJb82az+tjRYrZq/oUEVdLpNjxewV0PB9HBW6W2PT3OJAHJ1JNHTIZDK0trZKIxI9PT3h4uLS62nRAKTRjvn5+SgtLZX+GJ4yZQoCAgI6rfbb3NyM/Px8nD9/HoWFhQgJCUFMTAzmzJmD4OBgs0YZtrS0oKSkpF2YWVnZtra3SqWCl5cXYmNjpbUztVqtFJbq9XpcvXoV6enpyM/PR3FxMYxGIxwcHBAYGIhp06YhMDCw00B2oIiiiLq6Oty4cQM3btxAUVERjhw5Ii0ZYOLo6IiAgABoNBqp+BDQtpZpfX29tJZpdXU1Lly4AEEQoFarpSno9vb27aak9zekJeoNhUIhjUg2hykYvDUErampQVVVFRwdHWEwGFBWViYFg52NErWysmoXfq5cuRJffPEFfvjhB5w8eRKPPPIIZs+eDWdnZ2m93O4+TBFFEc3NzR1CSXd3dyxatAgbN27EBx98gK+//hrBwcFwc3Pr8JrVajUKCgpw8eJFhISEYPz48fD29pbaeHNoaWq7uSOuQ0NDcfnyZWzZsgUffPABJk+ejOnTpyM+Ph5AW39x/fp1KfA8evQoUlNTceDAAdjb2+Nvf/sbTp06hRkzZkgfcHX13HK5HL6+vma1i+h2YMBJfWZucNmbKuy3ToPvjMbWGtPiQhEb7A1rZe9GE1DfmVMp3Zzw0spGw2rpw1i0XzReuf8VnLh8AhV1FXDWOCMxJLFDuFnbVIvjl46jsr4STmonJIYmssL6CNDTGptz/vQns4sDcXQm0dAgCALuu+++Pq+DV1tbi7y8PBQUFKCsrAxyuRze3t6YPn16lyMdTaHipUuXcOXKFRiNRri5uWHy5Ml44IEH4Ojo2OXz6fV6lJWVtQszr1+/Lk2l9/DwwJgxY6Qw08nJqd1rMxqNKCoqkoLYK1euQK/Xw87ODoGBgRg7diwCAwPh6Og44GsDtrS0oKKiAhUVFVKYafreFMzI5XKpSFNQUBDc3Nxgb28PBwcHswJJo9GIhoYG1NbWSre6ujpUVFQgPz9fWl8UaAt/TVPebw1A7ezsIJPJBvT1E/WGIAjS9PSbmZazmD59eocPTUyVvm++mQLIm/+dkpIChUKBkydP4o033sDatWvh5+cHFxcXGAwGyOVyaaSoiSiKUuEuhUIBKysrWFlZQalUQi6XS0HknDlz4Ofnh5MnT6K+vh5KpRJarRYTJ05ETEwM1Gq1VJgnIyMDO3bsQGlpKWJiYhAfHz8g/U5ISAh+9rOf4dChQzhw4ADOnDmDlJQUhIWFQS6Xw8PDAx4eHkhISMDChQuRk5OD1atXQ6/Xw8PDA3v37kVBQQFcXFwgl8vh7u4ujXb38vKCq6srl7igIYkBJ/WZucGludPPaWgzp1K6W8wshpejgNZG22219LNXznYY5bnl5BasmL0C0X7Rt7GlNNB6WmOzNDOTxYGIhqHe/kFdXV2NvLw85Ofn48aNG9IonlmzZsHPz6/TdfJMa7ddunQJeXl50Ol0cHFxQVJSkjRSs66urt1jjUajNNLIFGaWlZXBYDBAJpPB3d0dfn5+mDhxIry9veHq6tohlBNFEeXl5VJhoIKCAuh0Omna6uzZs6UgcSCCBVPFY1N4eXOQWVNTI+1nZ2cHFxcXeHp6IiYmBi4uLnB2doajoyOam5uxf//+Po2ylMlkUnGRztb+NIVDtwag5eXl7dbulMvlUjEnouHCysqq3QhnU1B5c2Bp+jpv3jxoNBppDc2GhgbY2dnB3d0d9vb2kMlk7UJOo9EIURQhCIJ0HJlMJhVduznwTExMhIODAy5cuAA/Pz/I5XJs2bIFx48fx5QpUzBhwgRYW1tj3LhxCAsLw/bt2/H9998jMzMTCxcuhKura79/FgqFAsnJyYiJicHWrVuxbt06jBkzBikpKR0+RBozZgx++ctfYu3atdDpdFi8eLG0PqdcLkdRUREKCwtx8uRJiKIIhUIBDw8P6YOkgIAAs5cqIBpMDDjJbLUNTUjPLkBlbQOctHaYEBlgdnBpzvRzGtrMqZROo0N3ozNrm2qxatcqaZ1OU1EivVGPVbtW4ZX7X+FIzmHMnDU2w5Ys4fRzohFGFEVUVlZKIWFVVRWsrKzg5+eH+Ph4+Pn5dblGZ2VlJS5fvoxLly6hvr4eGo0GkZGRCA0NbfdHdlNTE2pra3H27FlUVlaiuLgYJSUlaG1thSAIcHV1hZeXF+Lj4+Ht7Q13d/cup9HfHMDm5+ejvr5eCmFNhY28vLz6NQLJNBqzsyCz9d+j2OVyOZycnODi4oLY2Fg4OztLQaaNjU2fn7s/TCPiOgtQDAYD6urqUFdXh5qaml6vu0ojmyn8Hoyq590xGo3tChnV1NSgsrJSGlF5a9GdpqamDrMMZTJZu2nfarUa7u7uiImJwZQpU7B+/XopmDQYDPDx8cHcuXMRHBzc4fWaqpF3N0q0urpaWv9z165dCAwMhJOTEzIzM7Fjxw4oFAoEBAQgPDxcmhrv6+uLjIwMHD16FElJSZg6dSrs7e2lkaF9WTYEAJycnPDggw/iwoUL2LZtG9555x1MmzYNU6ZMaXdMV1dXrFy5EuvWrUNRURGUSiX27t2LFStWICkpCUD75UCKi4uRm5uLY8eOYf78+dI+RJbEgJPMcia3CB+ltgWZpnU3Uw9nYeWiqWYHl+ZMP6ehy5xK6TTy9TQ68/il4zB0MdLXYDTgxOUT3Y7+pKHN3DU2b/f0866KHhFR34miiBs3bkhBYU1NDVQqFfz8/JCUlAQfH58u/+BuaGiQQs2KigqoVCoEBwcjNDQU7u7u0j55eXm4fv06rl+/jpKSEtTX18Pf31+aDhkeHg5vb294enpCqVR22daGhgYpzDQFsKb1P+Pj4xEYGNhtCNvdz6CmpqbTINNUsAhoW0/P2dkZ3t7eiIuLk4JMBweHYTXNWy6XS1XWua4e3aqsrAx79uxBYGAggoKC4Orq2qew02AwdFp5vampSdp289ebl1UA2pap0Ov1aGpqgoODA+zs7KDVauHp6dlh7UrTepYqlarLtiYnJ2PSpEl46623cPbsWdxzzz1obGzEmjVrEBQUhLlz58LT01Pa38rKSvo9Mcfu3buxc+dOTJgwAXFxcSgvL8fx48eRlZWFzMxMuLu7w9fXV+pfz58/jzVr1uDbb79FaGgonJza/sZSqVSdFlHqqsDSza9ZEAREREQgODgY+/fvx759+5CVlYX58+cjODhYaqudnR0eeeQRbNy4EZmZmWhubsaaNWuwcuVKODg4QKlUwt/fH/7+/tJjmpubb3voTdQVBpzUo9qGJnyUelCaim76RExvMOKjTQfx+srFDC5HAXMqpdPIZs7ozMr6Sin4vJUgE1BRV3Fb20wDyz852ew1NgdKT+Fld0WPvMaPH/D2EI1koiiirKxMCgvr6upgbW0Nf39/TJ48Gd7e3l2OemxpaUF+fj4uXbqE4uJiyGQy+Pv7Y/z48XB2dkZlZSWuXbuGzMxMlJeXo6mpCUDbH9Surq6IiYmBjY0NlixZ0mNVY51Oh8LCQil8LSsrA9A2Aik0NBRBQUHw9/c3e5RkS0tLpyMxbx2N6ezsDGdnZ8TFxUkjMV1cXFish0YFa2tr+Pj4ICcnB6dPn4ZGo0FQUBD8/Pyg0WikgkBdBZWmIPPWwBJo+/2ysbGBtbU1bGxsoNVq4ebmJm0z3UwFgJqbm5GcnNxp4bK+SEhIwOuvv44333wTa9euxQMPPIB77rkHe/bswQcffICYmBjMmjWr2/WBuzJr1iwoFArs3r0b9vb2mDFjBpKSktDS0oLMzEwcPXoUlZWV8PX1xYIFC/D888+jrKwMGzZsQG5uLpydnTFu3DgAaDdKtKKiAlevXkVDQ0O7YmQmN68Lemv4OWHCBJw4cQJ///vfER0djQULFkjFhBQKBX70ox/B2dkZO3bswIULF/Dpp5/i8ccf7/Tnzf6PhhIGnNSj9OwCGLoYsWMwGnHsfAEDzlHAykZjVqV0GrnMGZ3ppHbqNNwE2gJRZ033f7TS0Gbt4HBb19jsKbzsqejRgvfe40hOoh4YjUaUlpZK61M2NDTA1tYWAQEB0lTurkYhGgwGXLt2DZcuXUJhYSH0ej1cXV0RFhYGW1tbVFdX49ChQ6ivrwcAaWp0REQEXF1d4erqKlUXNq0N2Vkoqdfrce3aNSnQLCoqgtFohL29PQIDA6Vp5xpN19ciptGYnQWZt47GdHFxgY+PT7sgc7iNxiQaaHq9HgaDAS4uLtIHIUeOHJHWtHV0dJRGVFpZWbULLE1Fsm4OKm++X6FQmD0KsKuQtL9CQkLwhz/8AW+99RbWrFmDqqoqrFixAhcuXMCePXvwz3/+E4mJiZ0WN+rJ9OnTIQgCdu3aBVEUMWPGDCiVSiQlJWH8+PHIycnB0aNH8fXXX8PBwQETJkzAY489hosXLyItLQ379+/H7NmzMXv27E5/TgaDAU1NTV0WVTLdrl+/Lv1br9ejpaUFX3/9NdatW4eAgAAEBwdDrVZLYaibmxvy8vLwww8/oLS0FMuXL4ejo6MUlCqVSo7epCGFASf1qLK2QZqWfiuZIKCipsECrSJLsPeLMqtSOo1M5ozOnDd2Hrac3CKN8ryZXCZHIkf6Dnte48ffljU2zQkveyp6VLhvHyu1E/XAaDQiLS0NVlZW0tRTd3f3LsM80yhP0xT0yspKKBQKqNVqWFtbS1POlUolXFxcEBwcLIWZGo3GrD+GjUYjSkpK2lU6b21tha2tLQIDAxEXFyetaXfr8XQ6XadTyisrK6XRmAqFQlobMz4+vt3amByNRNQ5vV6P6upqWFtbIzAwEBEREVCpVKivr8f169dRVlYGvV4POzs7hISEDGjxrtvFw8MDL7/8Mv7+979j8+bNqKysxDPPPIOYmBgcPXoUhw4dwqlTpzB16lRMnDixV8teTJs2DTKZDDt27IDRaMSsWbOkYkXh4eEIDw9HSUkJjhw5gh07dmDv3r1ISEjAj3/8Yxw/fhybN2/G6dOnsXDhQnh4eLQ7tlwuh1qthlqtNqstoihCp9OhsbERlZWV2LNnD06ePInKykr4+/vDzs4OjY2NUKvVCA8PR1lZGdavX48zZ84gISFB+v9BoVDA1tYWs2bNQnx8vNk/C6LBwoCTeuSktes03AQAoyjC2d5uUJ63s6JGWjvLLMhO/2Flo2Gl9FHKnNGZWhstVsxe0WGdTrlMjhWzV0DDMHxEuB1rbJoTXppT9IiIumeajthT+FhZWYmMjAycPXsW5eXlUuBoGrFlCgnd3Nzg6uoKBwcHs4MNURRRVVWF3NxclJWVoaSkBM3NzdJ6bzNnzpSCV0EQYDQaUVNTg9zc3A5BZl1dnXRcjUYDZ2dn+Pr6YuzYsVIbTRWSich8Hh4eWLp0aZf33zwa/PLly8jKyoJarUZQUNCwCjvt7e3x61//Gh999BG2bduG2tpa/OIXv8D06dMxbtw47N+/H3v27MHx48cxY8YMxMfHm92fTJkyBYIgYPv27RBFscOITE9PT9x9992YM2cOjh8/jhMnTuDo0aOIiIjAnXfeiYyMDHz44YeYOHGiNAq0LwRBkEbTOjk5ISQkBAsWLMDmzZtx6dIljB07FkuWLJFG2FdUVODtt9/GoUOHoNVqsXTpUlRWVuL69eu4ceMG+1MaMhhwUo8mRAYg9XCWtAbnzeQyGSZEBvbqeOYEl90VNQrzde/X6yGivkkMTTRrdGa0XzReuf8VnLh8AhV1FXDWOCMxJJHhJnXQ3fqa5oSX5hY9IqLuabXadt8bjUZUV1fj6tWrOHPmDHJyclBWVgaZTAYnJycEBQUhJCQE7u7ucHV1haOjY68rktfX1+PatWtSNd6amhro9XpMnjwZkyZNQmBgIFxcXFBVVYWKigpkZ2dj//790tqY+n9/AKJQKKS1MceOHdtubUyVSjVgPyMi6p5MJoOXlxe8vLwwefLkLsPOwMBA6cOKocrGxgZPPfUUHB0d8c033+CPf/wjnn32WURGRiIlJQUTJkzA7t278cMPP+DIkSOYM2cOxowZY9Zrmjx5MgRBQFpaGoxGI+bOndvhcVqtFrNnz8a0adNw+vRpHD16FNnZ2fDy8kJAQADS09Nx7tw5zJ8/H2FhYQPymr28vLBy5UqcOHECaWlpOH78OMaOHQtfX1/U1dUhMjISZ8+exQcffICDBw+2G7EZGho6IG0g6i8GnNQjrZ0NVi6aio82tQWOMkGAURQhl8mwctHUTiumd6W74DImyBtAz0WNXn50Qa8voomo/3ozOlNro2W19FGuv8WBzAkvLVH0iGikEUURtbW10tTy0tJSXL58GeXl5aitrZWKDE2ZMgVRUVHw8PDosnp6d5qbm1FUVITi4mIUFRWhpqYGgiDA2dkZXl5e8Pf3R2trK1xdXZGfn48TJ050GI3p4uICPz8/Kcg0jcYcykEJ0WjUU9hpZ2cnjewcqmGnQqHA8uXL4eTkhNWrV+NPf/oTnnjiCUyZMgVOTk5YtmwZJk2ahJ07d+LLL7+Ev78/5s6dCx8fnx6PPWnSJMhkMmzduhWiKOKOO+7o9GegVCqRmJiI8ePH49KlSzhy5Ajy8vJgZWWF6upqfP7554iOjkZKSgrszVwqSKfToaamBrW1tdLXm/9dU1ODxsZG5OXl4eDBg7C3t5eCzvvvvx/btm3D6dOnERsbiyeffBL29vbSSE8iS2PAOcqZRk72NPU7Jsgbr69cjGPnC1BR0wBneztMiAzsVbhpTjV2rZ1Nj0WNTlwoxISoILOfl4gGDkdnkjkGojiQOeGltb39bS16RDQSGY1GfP3116iqqkJTUxNaWlqgVCoRHR2NuLg4hIeH92ldytbWVpSUlKCoqAhFRUUoLy9Hc3MzrKysYGdnB7VaDUEQUFVVhRs3bkgFTJRKpRR43rw2JkdjEg1Pt4adZWVlyM3NRV5eHs6cOTOkw05BEDB//nw4ODjggw8+wD/+8Q9UVFRg0aJFkMlk8Pb2xsMPP4zLly9j586d+OijjxAZGYnZs2fD2bn7wpoTJkyAIAjYsmULRFHEvHnzunztgiBgzJgxGDNmDEpLS3HkyBGcOXMG1dXV2LVrlzSaMyEhAfX19R0CzJu/3lygSRAEqNVqaLVa2Nvbw83NDfb29tL3NTU12Lt3L27cuAFPT0/MmjUL9957L/76178iNTUVTk5OeOaZZ4bUe0ajGwPOUe43y+eZva/WzsasauldTUE3txp7j0WNalnUiMiSODqTuhudOZDFgcwJL29X0SOikUoURbS2tsLOzg7+/v4IDQ1FSEhIh2nrPTEYDCgtLcWlS5ekSudNTU0QRRFWVlZQqVTQaDRQKpVQKpWwt7eHg4MDHBwcYG9vD2tra4iiiOTk5F5XKCai4UEmk8HT0xOenp6YMmWKNLLz5rAzMDAQwcHBQyrsnDx5Muzt7fHOO+/g008/RUVFBZYvXw6VSgVBEBAaGorg4GBkZWVh9+7deOeddzBu3DgkJyd3W/gnKSkJMpkMqampMBqNSElJ6fI16/V61NXVobm5GcHBwbC1tcXJkyeRmZmJ06dP4/vvv4erqytiY2Ol0Zx2dnbQarXQarUICAiQgkvTV41G0+3MSB8fH4SHh+PYsWPYs2cPsrOzcccdd+D555+HQqHApk2boNPp8POf/5z9Ng0JDDhpQHU3Bd3cauw9FjXScgg8EdFAMoWTN08h70pPozMHsjiQueHl7Sh6RDRSKRQKTJ8+He7u7nBxcTErUNDpdKisrMSVK1eQl5eHK1euoLS0FI2NjZDJZNBoNHB0dER4eDg8PDzg6OjYLszsrPJwc3NzuynpRDSyCYIghZ03j+zMz8/H2bNnpbAzKCgIHh4eFg87o6Ki8Jvf/AbvvPMOvv/+e1RXV+Oxxx6TwkSZTIb4+HhERUXh2LFjOHDgAE6fPo3Jkydj8uTJXRYEGj9+PERRxHfffYfy8nKMGzcOdXV1HUZf1tfXt3uctbU1HB0dsWDBAlRWVuLcuXPIycnBmTNnMG/ePDzxxBNmT1vvjlwux6RJkxAVFYW0tDSsX78eGRkZuP/++wEAaWlpiIqKwsKFC/v9XET9xYCTzNZTcaCepqDPHR9hVjX2nooajQ/3H+iXRkQ0qs15802z9jNndOZAFwdieEk0+KKjoztsMxqNqKurQ3V1NWpqalBVVYWSkhJcvXoV169fR11dHQwGA6ytreHu7o6xY8ciKCgI/v7+cHR0hJ2dncUDCSIaHgRBgIeHBzw8PKSw0zSy0xR2BgQEIDg4eEBCu77y9/fHr3/9a7z//vvYv38/amtr8cQTT8DLy0vax8rKClOmTEFCQgIOHDiA/fv34+DBgxg7diz8/PzQ0NDQIbysq6tDVVUVVq9ejbS0NERHR8PBwQFarRYeHh4YM2ZMh9GXtwamoigiJycHX3zxBbZt24YDBw7gnnvuwbJly2Bj0/1ydObQarW45557kJCQgC1btuDDDz/EuHHjAAABAQH9Pj7RQGDASWYxpzhQT1PQIbQFlD1VYzenqFGjruOabEREo42xtRXGLvrdwZC3a1e3ozPzd++GtZNTt+GljbMzvCdNwtkvv+z0WDK5HD6TJkF/0xpRXZHJZJB1MhKMiMwniiLKy8ulILO6uhrV1dWora1FU1MT6urq0NDQgNbWVgiCAFtbW4SGhiIoKAihoaHw9vZm8UciGjA3h52TJk1qF3aeO3cOVlZWcHd3R0BAAMaMGQOZTHZb2+fm5oZf/vKX+OSTT7Bv3z78+c9/xrJly+Dq6tppwR6dTiet0WljYyNNZ3dwcICzszOCgoKk0PLKlSvYs2cPoqOjsXjx4h4/JGppaYH+pmspX19fvPjii7h8+TI+/vhjfPTRR9iwYQPuuusuzJw5E05OTv1+/Z6ennjkkUdw9OhRHDp0SJqa3tjY2O9j94dCoehylCyNHgw4qUfmFgfqaQp6Q1OL2dXYuytq1NLa+R/XRESjibG1FRWXL0Pf3HzbnrMyJ6fLfl4QBFRcvAjfqVO7HcFp6+aGuqIihN99N85/9x3Emz44E2QyhN99N2qLioCioh7bo7C2hnNICENOon5KTU2FXq+HSqWCKIpoaWlBY2Mj9Ho9tFotwsLC4O3tDS8vL3h6enY6xZyIaKB1FnZeuHABFy9exOeffw5HR0dERkYiMjISfn5+Axp2iqLYacVx01eDwQBRFLFz504cPnwYcXFx8Pf3h0ajkUZa+vr6SuFlc3Mzjh8/jqtXr8LOzg4zZsyAv3/7mYmhoaFwcXHBxo0bAUAqZtSZlpYWHDt2rMPUdZOUlBSEhoZi27ZtePvtt7FmzRrExcUhKioKbm5uAzLCPjo6GidOnMDx48dx7dq1fh+vP9RqNZKSkhhyjnIMOKlH5hYH6nHtTHu7XlVjN7eoERHRaGQ0GqFvboZcoYBMcXv+O7d1cemynxdFEbYuLrBzcUH0Aw/g7Jdfdggvox94AHb/rirqHhMDx8BAlGZmormqCtaOjvCIj4eym8X4b2bU66FvbobRaMTtHbtBNLIYDAb4+/ujsrIS1dXVEEUR9vb2iIyMlELNvlRRJyIaSKaw08HBAZGRkQgODkZ+fj6ys7Nx7NgxqNVqREREICoqyqyws6WlpUNoeWuQ2dLS0u75NRqNFFh6eHhgwoQJOHHiBPbv3w+dToeEhASkpKR0+dxxcXHIy8vDjh07sGrVKoSFhWHOnDlwdXVtt48gCNiwYQOMRiOWLFnS6fH0ej3q6+uhVCqhUqk6fb6xY8dKIeTBgwdx4sQJFBQUICAgAFFRUQgMDOzXCHyNRoPFixf3+fEDRafTob6+Hnq9ngHnKMeAk3pkbnGgntbOvHkKOoNLIqKBIVMoIO/nxVz63/+Olro6KDUaTHj22S7380pMRN6uXRA7mVouyOXwTkqCXKmEe0wMHAICUJqRgaaqKtg4OsJz3LgO4aWNkxMCZ83qc7sNXUyXJyLzyeVy1NfXw8XFBXFxcfD29u626i8RkaUJggAfHx+MGTMGd9xxB4qKinDu3DlkZ2fj+PHjsLGxgb+/P7y8vKDRaFBXV9chvGy+ZQaMWq2WwsugoKB2611qtVpoNJpOg8bJkycjOjoa69atw5dffonq6mrcc889XQZtQUFBeOKJJ3D27Fns2rUL7777LsaOHYuZM2dCo9EAAGJjYyEIAtavXw9RFLF06dIuQ1OVStXth1DW1taYOXMmYmJicODAAVy+fBnXrl3DjRs3kJmZiejoaISHhw/7D7JuDqNp9GLAST0yZ2QmYN7amURENPS01NVBV1vb434qjQaxDz2ErDVrIBoM/xmdKZcj9qGH2gWYKo0G/snJg9lsIhoAgiBg6dKllm4GEZFZDAYD6uvrceXKFbS2trYLLW1sbFBRUYGzZ89i9+7d0Ol0sLKygo+PD4KDgxEYGAg/P78OBXs0Gg0UfZwNIwgCZs+eDa1Wi88//xypqamoqanBj3/8Yymw7OwxMTExiIiIkEaAnjlzBhMnTsSUKVNgbW2NmJgYyGQyfPfddxBFEXfddVe/puC7uLhg6dKlOH/+PI4dO4bm5mbI5XIcP34cJ0+eRFhYmFTciGi4YsBJPTJ3ZCbQ/dqZRDQy1DbV4vil46isr4ST2gmJoYnQ2mgt3Sy6TVwjIjD1xRd7HJ1JRERE1BtGoxGNjY1oaGhAfX19p19ra2uh0+lw6tQpWFlZQaVSSWGlt7c3IiIipNCyqakJV65cQU5ODqqrq1FcXAx7e3t4e3sjICBgQNfsTExMhFarxapVq7Bv3z7U1dXh0Ucfhbu7e5ePUSgUmDhxIuLj43Ho0CEcOXIEJ0+exPTp05GYmIioqCgIgoBvv/0Woiji7rvv7lebBUFAZGQkAgICcOTIEVy+fBkuLi5wdnZGXl4esrOz4efnh9jYWHh6eg7IOp1EtxMDTupRb0dmcgo60ch19spZrNq1CgajAYJMgGgUseXkFqyYvQLRftGWbh7dJhydSURERL0hiiKampo6hJamW0NDAxobG2G8qfaDQqGAWq2GnZ0dHB0d4evrKxU5mzVrFjw9Pbtcf9IkOjoaKSkpKC4uRnZ2Ns6dO4cTJ07Azs4O4eHhiIqKGrCwMywsDM888ww++eQTnDp1Cg0NDXj44YcREhLS7eOsra0xe/ZsJCYmYu/evUhLS0N6ejpmz56NqKgo3HPPPfjmm29gNBrxox/9qF/rZgKAra0tZs+ejbCwMBw8eBCXL19GTEwM1Go1srOzsWnTJjg7OyM2NhbBwcH9fj6i24UBJ5mFIzOJqLapFqt2rYLe2LbuoWhsW7pCb9Rj1a5VeOX+VziSkwAAuro6lGRkSMWDPBMSoOpimhYRERENb6aK4zeHlZ2NvjQYDNJj5HI57OzspLUvvby8pO9NX1UqVYdRhM3Nzairq4Obm1uP4aaJIAjw9vaGt7c35syZg5KSEmnNzpMnT8LW1hYRERGIjIxEYGBgv8JOHx8fPP3001i9ejVOnjyJDz74APfddx/Gjx/f42O1Wi0WL16MiRMnYteuXfj2229x+PBhzJkzB/feey+++eYbfPvtt1i2bFmf23drW5ctW4bMzExkZmZCrVZjypQpEAQBWVlZ2LNnD44dO4aoqChEREQM+3U6aeRjwElm48hMotHt+KXjMBgNnd5nMBpw4vIJzIrpe8EYGhmuZ2cj64sv2q3Rmbt9O2IfegiuEfw/hIiIaDhqbm7G9evXuxx9qb+p8J9MJoOtra0UVrq6ukKtVrcLL62trS0yBVoQBHh5ecHLy0sKO00jO01hp2lkZ1/DTmdnZ/z0pz/FmjVrcPjwYXz22WeoqqrCnDlzzHrNbm5ueOCBB1BYWIgdO3bgs88+Q0hICGbOnIk9e/bgm2++wYIFC/ry8jtQKBQYP348goODcfDgQWzZsgWhoaGYMWMGdDodzpw5g5MnTyIjIwNjxoxBTEwM1+mkIYsBJxERmaWyvlKaln4rQSagoq7CAq2ioURXV9cWbv77jxxTgTpRr0fWmjWY+uKLHMlJREQ0DJWVlWHbtm0QBAG2trZSUOnk5NQuuLSzs4Otre2Arm85WG4OO2fPno3S0lJpZGdGRka7sDMgIKBXU7Xt7OywYsUKqNVq7N27F9999x2qqqpw1113SdPse+Lv74/HHnsM58+fx65du5CbmwsXFxecOXMGzc3NcHV17etL78DR0RELFy5ETk4Ojh49iitXriApKQnTpk1DYmIisrOzpZu/vz9iYmLg5eXFdTppSGHASUREZnFSO3UabgJt09WdNc63uUU01JRkZEA0dD7KVzQYUJqRwbU7iYiIhiEvLy88+OCDsLW1HZFrMgqCAE9PT3h6enYadtrY2LSbxm7Oz0CpVOKBBx6AVqtFWloatm/fjpqaGjz44IOws7Mzu12RkZEICwtDRkYG9u7di8bGRuzcuRNubm5YsmRJf196u+cKCwuDv78/jh49igMHDiAnJwfTpk3DuHHjEBcXh9zcXJw5cwapqalwdnZGTEwMgoOD+1yFnmgg8SwkIiKzJIYmYsvJLdIanDeTy+RIDEm0QKtoKGmuqpKmpd9KEAQ0VVVZoFVERETUX1ZWVmaPPBzuOgs7TdPYTWHnzdPYuws7ZTIZFi5cCK1Wix9++AFHjhxBXV0dfvzjH/dqBKZcLkdiYiJiY2Nx5MgRbNiwAXv37kVVVRVWrlw5oOtjWltbY8aMGQgLC8OBAwewfv16xMTEYNy4cQgLC8OYMWNQVFSEM2fOYO/evWhqakJ8fPyAPT9RXzHgJCIis2httFgxe0WHKupymRwrZq+AxoZTj0eLrooIWTs6dhpuAm3T1W0cHW9zS4mIiIj67uawc9asWSgrK5NGdp46dUoKOyMjIxEUFNRp2CkIApKTk2Fvb4+vv/4aZ86cwQcffIDly5cjMDCwV+1RqVSYMWMGIiMj8T//8z84ePAg/vjHP+LBBx9EeHj4gC4N4OnpiR/96EfIysrCyZMnkZeXhylTpsDf3x8+Pj7w8fFBdXU1iw/RkDGqAs433ngD69evx4ULF2BjY4PJkyfjz3/+M8LCwizdNCKiYSHaLxqv3P8KTlw+gYq6CjhrnJEYkshwcxTproiQZ0ICcrdvl9bgvJkgl8Nz3DgLtJioc7wuJCKi3hAEAR4eHvDw8JDCTtPIzlOnTsHa2loa2dlZ2BkfHw+1Wo21a9ciLy8PH3/8MZYtW9an0Y9qtRp33nknYmJi8PXXX+OTTz5BYmIiJk2aBF9f3wFbG1Mul2Ps2LEICgrCoUOHsG3bNgQGBmLy5MlQq9UsOERDytBf+XcA7du3D08//TSOHj2KHTt2oLW1FXfccQcaGhos3TQiomFDa6PFrJhZuGfyPZgVM4vh5ijSroiQKEI0Gtu+/ruIEADEPvQQBIUCEAQIMlnbV4UCsQ89BKVabeFXQPQfvC4kIqK+MoWds2bNwjPPPIOnnnoKSUlJuHbtGr744gu89dZb2LhxI3JycmC4aX3ykJAQPP744wgPD0dZWRm+/PJL7Nmzp8sZMD0JDw/HM888A19fX1y8eBGbN2/G5s2bcf369YF6qQAAe3t7pKSkYPbs2SgrK5NGohqNxgF9HqL+GFUjOLdt29bu+9WrV8PNzQ0nT57E9OnTLdQqIiKi4cHcIkJTX3wRpRkZaKqqgo2jIzzHjWO4SUMOrwuJiGggCIIAd3d3uLu7Y+bMmSgvL5dGdmZmZsLa2hphYWHSyE5PT0888cQT+Pzzz5GdnY1NmzahsrISS5Ys6VOxHi8vL9x7773YunUrBEFAXV0d1q9fj+DgYCQlJUGr1Q7Y6wwJCYGvry+OHz+OI0eOICcnB9OnTx/Qiu5EfTWqAs5b1dTUAACcnJy63Een00Gn00nf19fXD3q7iIiIhiJziwipNBpWS6dhh9eFRETUXzeHnTNmzMD169elNTtPnz7dLux85JFH8M033+D06dPYv38/ampqcP/998PW1rbXz+vp6Yn58+djy5YtsLW1RUxMDDIzM/H1118jIiICCQkJsLGxGZDXqFKpMHXqVISGhuLAgQOora1lwElDwqgNOI1GI5577jlMmTIF0dHRXe73xhtv4LXXXruNLSMiIhqaWESIRipeFxIR0UATBAFubm5wc3PrMLLz9OnTUKlUCAkJQWBgIAoLC5GZmYmGhgYsX7682w/buuLh4YH58+dj69atAIC7774bFy9eRGZmJnJychAXF4fY2Ng+jRLtjLu7O+6+++4BW++TqL9G1RqcN3v66adx9uxZrFu3rtv9XnrpJdTU1Ei3ffv23aYWEhERDS2eCQkQOqkOCrCIEA1vvC4kIqLB5ubmhhkzZuDpp5/Gz372M0yaNAnl5eWorq5GdXU1rly5gvT0dLz//vu4cuVKn57DFHJWVlZi+/btiIqKwv33348xY8YgIyMD69atw4ULFwZs7UyZTMaAk4aMUTmC85lnnkFqair2798PHx+fbvdVqVRQqVTS92quIUZERKOUSqNB7EMPIWvNmnZV1AW5nEWEaNjidSEREd1uppGdpmnsZ8+exebNm5Geno7c3FycPn0ay5cvx/z583s94tLd3R0LFizAli1bsGXLFqSkpGDKlCmIiYnBsWPHsG/fPmRlZSEpKQn+/v4MKGnEGFUBpyiK+PnPf44NGzZg7969CAwMtHSTiIiIhhXXiAgWEaIRgdeFREQ0FLi6umLmzJmYOXMm9u3bh9WrV6OgoAB/+ctfkJqaijvuuAPR0dEICQkxO+x0c3PDggULsHnzZmzZsgXz58+HVqvFnDlzEBcXh/T0dKSlpcHDwwMTJkyAh4fHIL9KosE3qgLOp59+GmvXrsX3338PjUaD0tJSAIC9vf2ALbhLREQ00rGIEI0EvC4kIqKhJjk5GX5+fli3bh2uXLmC2tpa7Ny5E1lZWVKBosjISHh5efV4LFdXVyxcuBCpqanYvHkz5s+fD2tra7i6umLBggW4du0a0tPT8f333yMwMBBJSUlwcHAY/BdJNEhG1Rqc7733HmpqajBjxgx4enpKt6+++srSTSMiIiKi24jXhURENBQFBgbiscceQ2hoKLy8vODg4AA3NzckJSWhvLwcX331Fd5++20cOHAABQUF0Ov1XR7LxcUFCxcuRG1tLTZv3ozm5mYAbQWQfH19cffdd2PmzJm4fv06vvnmGxw4cACNjY2366USDahRNYKzq8qvRERERDS68LqQiIiGKjc3N6xcuRJffPEFCgoKcO3aNQiCgAcffBBGoxEnT57Epk2bsGvXLhw+fBh+fn4ICgqCn59fh2nsppBz8+bNSE1NxcKFC2FtbQ2grUjQmDFjEBQUhHPnzuHUqVO4dOkSYmNjERsbC6VSaYmXT9Qno2oEJxERERERERHRUKfVarFixQpERkZCEARcu3YNH330EZqamjB16lQsWLAAy5YtQ3x8PKqrq7Fjxw589tln2LlzJ/Ly8tqN7DSFnI2NjUhNTZVGcpooFArExcXh/vvvR2RkJDIzM7Fu3TqcO3cOBoPhdr90oj5hwElERERERERENMRYW1tj+fLlGD9+PGQyGaqqqrB69WpcuHABQNu60QkJCVi2bBnuu+8+jB07Vgo7P/3003Zhp7Ozc7uQs6mpqdPnmzhxIu6//374+vri0KFD+Oabb5CXl8eZDzTkjaop6kRERDT4dHV1KMnIQHNVFawdHeGZkACVRmPpZhERERENOwqFAnfffTfs7e2xf/9+NDU14bvvvoOzszOSkpKk/RwcHDB27FiMHTsWNTU1yMvLQ15eHnbs2AGFQgF/f38EBgbizjvvRFpaGlJTU7FgwQLY2tp2eE61Wo2ZM2ciJiYGx44dw44dO+Du7o4JEybA09Pzdr58IrMx4CQiIqIBcz07G1lffAHRYIAgCBBFEbnbtyP2oYfgGhFh6eYRERERDTuCIGDOnDnQarXYsmULdDodTpw4Ab1ej5kzZ0Imaz85197evl3YmZ+fj9zcXOzcuRMKhQLOzs4oLCzE999/jyVLlnQacgJtU9vnz5+PoqIipKen44cffoC/vz+SkpLg5OR0O146kdk4RZ2IiIgGhK6uri3c1OsBUYRoNLZ91euRtWYNdHV1lm4iERER0bCVlJSE++67D0qlEjKZDNnZ2di2bRtaWlq6fIy9vT3i4+Pxox/9CPfffz/GjRsHo9EIADhy5AjeeustnDlzBq2trV0ew9vbG3fddRdmz56NqqoqfPvtt9i7dy/q6+sH/DUS9RUDTiIiIhoQJRkZELtYiF40GFCakXGbW0REREQ0skRERGD58uVQKpWQy+W4cuUKfvjhB7PCRlPYeffdd2PFihW499570djYiA8//BAfffQRtm/fjsuXL3cadgqCgJCQENx7772YPHkyCgsLsW7dOuTk5AzGyyTqNU5RJyIiol7pao3N5qoqaVr6rQRBQFNVlQVaS0RERDSy+Pj4YN68eTh8+DCam5tRWVmJjRs34s4774SLi4tZx9BqtZg6dSpiYmLw7bfforq6GpWVlcjPz4dcLoefnx+CgoLg5+cHpVIpPU4ulyM6OhqhoaHIysriVHUaMhhwEhERkdm6W2PT2tGxywqboijCxtHxNreWiIiIaGSyt7fHwoULsXfvXty4cQM6nQ4//PADZs+eDX9//14d55577sGmTZsAAIsXL0Z5eTny8vKwa9cuyOVy+Pr6Ijg4uF3YqVKpkJiYOCivjagvOEWdiIiIzNLTGpvOY8ZAkMs7fawgl8Nz3Ljb3GIiIiKikcvW1haLFi2Cj48PWltbIZfLkZaWhrNnz/bqOFqtFosXL4bRaMTevXsRFBSEu+66Cw8++CASExPR1NSEXbt24bPPPkNaWhouXbrU7bqfRJbAgJOIiGiUU2o0UGm1UGo03e7X0xqblTk5iH3oIQgKBSAIEGSytq8KBWIfeghKtXowmk9EREQ0allZWWHevHkIDw9HU1MTbG1tcfDgQRw+fFgqJmQOjUaDRYsWAQA2bdqEuro6aDQaxMXFYenSpe3Czt27d+Ozzz7Dtm3bUFJSMlgvjahXOEWdiIholJvw7LNm7WfOGpv+ycmY+uKLKM3IQFNVFWwcHeE5bhzDTSIiIqJBIpfLkZycDLVajZMnT0Kj0eDMmTOora3F7NmzYWVlZdZxTCFnamoqfvjhByxatAharVa6Ly4uDnFxcaivr0deXh7y8vLQ0NAwmC+NyGwcwUlERERmMXeNTZVGA//kZIQvXQr/5GSGm0RERESDTBAEjB8/HsnJyWhsbIRarcbVq1fxww8/9CqEVKvVWLhwIeRyOTZt2oSamppO94mNjcXSpUsREhIykC+DqM8YcBIREZFZPBMSuMYmERER0RAWHh6OefPmobm5GdbW1qitrcXGjRtRUVFh9jHUajUWLVrUbchJNNQw4CQiIiKzqDQarrFJRERENMT5+flh8eLFEEURcrkcRqMRP/zwA65du2b2Mezs7LBo0SJYWVlh06ZNqK6uHrwGEw0ABpxERERkNteICEx98UWEpqTAe8IEhKakYNpLL8E1IsLSTSMiIiKif3N1dcXSpUuhVCqh1+tha2uLrVu34vz582YfwxRyKpVKpKamMuSkIY0BJxEREfUK19gkIiIiGvq0Wi2WLl0KR0dH1NXVwcXFBfv378fRo0e7XFf9Vra2tli4cCFUKhU2bdqEqqqqQW41Ud8w4CQiIqJe0dXVoWDfPlzYuBEF+/ZBV1dn6SYRERERUSesra2xcOFC+Pr64vr16/Dy8kJWVhZ27twJvV5v1jFMIaeNjQ02bdqEysrKQW41Ue8x4CQiIiKzXc/OxsE//QmXt25FUXo6Lm/dioN/+hOu92K6ExERERHdPgqFAnPnzkVkZCSKi4vh5eWFwsJCpKamoqmpyaxj2NjYYOHChbC1tUVqamqvihYR3Q4MOImIiMgsuro6ZH3xBUS9HhBFiEZj21e9Hllr1nAkJxEREdEQJZPJMGXKFEyYMAFFRUVwdXVFTU0NNm7caPa0c9NoUFPIeePGjUFuNZH5GHASERGRWUoyMiAaDJ3eJxoMKM3IuM0tIiIiIiJzCYKA+Ph4zJo1C9evX4dGo4Eoivj+++9RVFRk1jFMIadarWbISUMKA04iIiIyS3NVFQRB6PQ+QRDQxEXniYiIiIa80NBQ3HnnnaipqYFcLodGo8HWrVtx8eJFsx5vbW2NBQsWwMHBAbW1tYPcWiLzMOAkIiIis1g7OnZZcVMURdg4Ot7mFhERERFRX/j4+GDx4sVobW1FU1MTPD09sXfvXpw4ccKsCuvW1tZYvHgxgoKCbkNriXrGgJOIiIjM4pmQAEEu7/Q+QS6H57hxt7lFRERERNRXzs7OWLp0KVQqFa5fv47g4GCcPHkSe/bsMavCukzGSImGDp6NREREZBaVRoPYhx6CoFAAggBBJmv7qlAg9qGHoFSrLd1EIiIiIuoFtVqNxYsXw9nZGQUFBQgPD0deXh62bNmC5uZmSzePyGwKSzeAiIiIhg/XiAhMffFFlGZkoKmqCjaOjvAcN47hJhEREdEwpVKpkJKSgr179+LixYsICwtDQUEBNm7ciJSUFNjb21u6iUQ9YsBJREREvaLSaOCfnGzpZhARERHRAFEoFJg9ezbUajVOnz6NkJAQlJeXY+PGjZg3bx48PDws3USibnGKOhERERERERHRKCcIAiZOnIjJkycjNzcXjo6OsLe3R2pqKi5fvmzp5hF1iwEnEREREREREREBAGJiYjBnzhxcu3YNgiDA19cXu3btwqlTp8yqsE5kCZyiTkRERANOV1eHkowMNFdVwdrREZ4JCVBpNJZuFhERERGZISgoCLa2tti2bRtsbW0RGRmJY8eOoaamBtOmTYNcLrd0E4naYcBJREREA+p6djayvvgCosEAQRAgiiJyt29H7EMPwTUiwtLNIyIiIiIzeHh4YMmSJdi6dSsKCwuRkJCA06dPo76+HnPnzoVKpbJ0E4kknKJOREREA0ZXV9cWbur1gChCNBrbvur1yFqzBrq6Oks3kYiIiIjM5OjoiCVLlsDGxgZnz55FQkICbty4ge+//x51vK6jIYQBJxEREfWKrq4OBfv24cLGjSjYt69daFmSkQHRYOj0caLBgNKMjNvVTCIiIiIaAHZ2dli0aBHc3NyQkZGB2NhYGAwGbNiwAeXl5ZZuHhEATlEnIiKiXuhp+nlzVZW0/VaCIKCpqsoCrSYiIiKi/lAqlbjzzjtx4MABHD9+HPHx8SgpKUFjY6Olm0YEYBSO4Ny/fz8WLVoELy8vCIKAjRs3WrpJREREw4I508+tHR27rK4piiJsHB1vc6uJusbrQiIiIvPJ5XIkJycjISEBmZmZcHFxgZ+fn6WbRQRgFAacDQ0NiIuLwzvvvGPpphAREQ0r5kw/90xIgNBFVU1BLofnuHGD2USiXuF1IRERUe8IgoDExERMmzYN2dnZyMrKsnSTiACMwinqKSkpSElJsXQziIiIhh1zpp+rNBrEPvQQstasaTeNXZDLEfvQQ1Cq1RZoOVHneF1IRETUN5GRkbC3t4ebm5ulm0IEYBQGnERERNQ35k4/d42IwNQXX0RpRgaaqqpg4+gIz3HjGG4SERERjSDe3t6WbgKRhAFnD3Q6HXQ6nfR9fX29BVtDRERkOZ4JCcjdvr1tDc5b3Dr9XKXRwD85+XY2j2jQ8bqQiIiIaGgadWtw9tYbb7wBe3t76ZbMP9aIiGiUMk0/FxQKQBAgyGRtXxUKTj+nUYHXhURERERDEwPOHrz00kuoqamRbvv27bN0k4iIiCzGNP08NCUF3hMmIDQlBdNeegmuERGWbhrRoON1IREREdHQxCnqPVCpVFCpVNL3ao5OISKiUY7Tz2m04nUhERER0dA06gLO+vp6XL58Wfo+Pz8fmZmZcHJygp+fnwVbRkRERES3E68LiYiIiEaGURdwnjhxAjNnzpS+f/755wEAjzzyCFavXm2hVhERERHR7cbrQiIiIqKRYdQFnDNmzIAoipZuBhERERFZGK8LiYiIiEaGURdwUv+Vl5XhenmZxZ6/VW9Ac6sehtpyqKxG3yls0OtQX5ILuZUKMrmVxdrh4e4KD3c3iz0/WV55WTmul123yHO3Glqha9WhtbIVKoWq5weMQPqWFlTl5UGhUkGusExf6O7mBg839gNEI8GNGzdw48YNiz2/TqdDY2MjNBoNbGxsLNaOoaCpqQmXL1+Gra1tuzVfbzcXFxe4uLhY7Plp6LF0PwGwrzAZKv0EwL6Chg5B5MfWvVJSUoIPPvgATz75JDw9PS3dnNtOp9Nh3rx5rBpKSE5ORlpamsX/QyXLYF9AAPsBopFyXcg+nTrDPp5uxn6CusK+goYKBpzUK7W1tbC3t8e+fftYOXQUq6+vR3JyMmpqaqDVai3dHLIA9gXEfoBo5GCfTrdiH0+3Yj9BnWFfQUPJ6JvfSwMiPj6eHdgoVltba+km0BDBvmD0Yj9ANPKwTycT9vHUFfYTdDP2FTSUyCzdACIiIiIiIiIiIqK+YsBJREREREREREREwxYDTuoVlUqFV155hQsIj3I8D4jnAPEcIBo5+PtMt+I5QbfiOUGd4XlBQwmLDBEREREREREREdGwxRGcRERERERERERENGwx4CQiIiIiIiIiIqJhiwEnERERERERERERDVsMOMmiCgoKIAgCVq9ebemmEJGFsB8gIho52KcTkTnYVxDRQGPAOYzk5ubiySefRFBQEKytraHVajFlyhT83//9H5qamgbtebOzs/Hqq6+ioKBg0J7DHP/93/+NxYsXw93dHYIg4NVXX7Voe4YyQRDMuu3du7ffz9XY2IhXX321V8fie9l3o7kfuHDhAl544QXEx8dDo9HA09MTCxYswIkTJyzWpqFuKPcFfD+JRnefDvB6oL+Gch8P8P0dSKO5r+D1Qv8M5X6C7y0NNIWlG0Dm2bx5M+655x6oVCo8/PDDiI6ORktLCw4ePIj/9//+H86dO4cPP/xwUJ47Ozsbr732GmbMmIGAgIBBeQ5z/O53v4OHhwfGjh2LtLQ0i7VjOPj888/bff/ZZ59hx44dHbZHRET0+7kaGxvx2muvAQBmzJhh1mP4XvbNaO8HPvroI3z88cf40Y9+hJ/97GeoqanBBx98gIkTJ2Lbtm2YM2eORdo1lA3lvoDvJ412o71PB3g90F9DuY8H+P4OlNHeV/B6oX+Gcj/B95YGGgPOYSA/Px/3338//P39sXv3bnh6ekr3Pf3007h8+TI2b95swRb+hyiKaG5uho2NzYAfOz8/HwEBAbhx4wZcXV0H/PgjyUMPPdTu+6NHj2LHjh0dtlsK38veYz8APPDAA3j11VehVqulbT/5yU8QERGBV199lRdBnRjKfQHfTxrN2Ke34fVA/wzlPh7g+zsQ2FfweqG/hnI/wfeWBhqnqA8Db775Jurr6/Hxxx+3+0/NJCQkBL/4xS+k7/V6PV5//XUEBwdDpVIhICAA//Vf/wWdTtfucQEBAVi4cCEOHjyIpKQkWFtbIygoCJ999pm0z+rVq3HPPfcAAGbOnNlhCLvpGGlpaRg/fjxsbGzwwQcfAADy8vJwzz33wMnJCba2tpg4cWK//gO25AiDkchoNOLtt99GVFQUrK2t4e7ujieffBJVVVXt9jtx4gTmzZsHFxcX2NjYIDAwED/5yU8AtK2dY7pgfe2116Tzo6cpSHwve4/9ADBu3Lh2F0AA4OzsjGnTpuH8+fN9OiZZri/g+0mjGfv0/7SXBhev94Y39hW8XrgdeC1IIwVHcA4DmzZtQlBQECZPnmzW/itXrsSnn36KZcuW4Ve/+hXS09Pxxhtv4Pz589iwYUO7fS9fvoxly5bhsccewyOPPIJPPvkEjz76KMaNG4eoqChMnz4dzz77LP7+97/jv/7rv6Sh6zcPYb948SIeeOABPPnkk3j88ccRFhaGsrIyTJ48GY2NjXj22Wfh7OyMTz/9FIsXL8a3336Lu+66a+B+QNQnTz75JFavXo0VK1bg2WefRX5+Pv75z3/i1KlTOHToEKysrFBeXo477rgDrq6uePHFF+Hg4ICCggKsX78eAODq6or33nsPTz31FO666y7cfffdAIDY2FhLvrQRif1A10pLS+Hi4jIgxxqNhlpfwPeTRgP26XS7DLU+nnqHfUXXeL0wcIZaP8H3lvpMpCGtpqZGBCAuWbLErP0zMzNFAOLKlSvbbf/1r38tAhB3794tbfP39xcBiPv375e2lZeXiyqVSvzVr34lbfvmm29EAOKePXs6PJ/pGNu2bWu3/bnnnhMBiAcOHJC21dXViYGBgWJAQIBoMBhEURTF/Px8EYC4atUqs16fKIri9evXRQDiK6+8YvZjRrunn35avPnX/cCBAyIA8Ysvvmi337Zt29pt37BhgwhAPH78eJfH7s/7wffSPOwHurZ//35REATx97//fa8fOxoN1b7AhO8njQbs0zvi9cDAGKp9PN/fvmFf0TVeL/TdUO0nTPjeUn9wivoQV1tbCwDQaDRm7b9lyxYAwPPPP99u+69+9SsA6DA1IDIyEtOmTZO+d3V1RVhYGPLy8sxuY2BgIObNm9ehHUlJSZg6daq0Ta1W44knnkBBQQGys7PNPj4NvG+++Qb29vaYO3cubty4Id1M0wT27NkDAHBwcAAApKamorW11YItHt3YD3SuvLwcDz74IAIDA/HCCy/061ij1VDqC/h+0mjBPp1ul6HUx1Pvsa/oHK8XBtZQ6if43lJ/MeAc4rRaLQCgrq7OrP0LCwshk8kQEhLSbruHhwccHBxQWFjYbrufn1+HYzg6OnZYb6M7gYGBnbYjLCysw3bTlIZb20G316VLl1BTUwM3Nze4urq2u9XX16O8vBwAkJycjB/96Ed47bXX4OLigiVLlmDVqlUd1vGhwcV+oKOGhgYsXLgQdXV1+P777zus30PmGSp9Ad9PGk3Yp9PtMlT6eOob9hUd8Xph4A2VfoLvLQ0ErsE5xGm1Wnh5eeHs2bO9epwgCGbtJ5fLO90uiqLZzzUYVTVpcBmNRri5ueGLL77o9H7TAtGCIODbb7/F0aNHsWnTJqSlpeEnP/kJ/vrXv+Lo0aP8j+c2YT/QXktLC+6++25kZWUhLS0N0dHRt+25R5qh0Bfw/aTRhn063S5DoY+nvmNf0R6vFwbHUOgn+N7SQGHAOQwsXLgQH374IY4cOYJJkyZ1u6+/vz+MRiMuXbrUbgHosrIyVFdXw9/fv9fPb+5/kre24+LFix22X7hwQbqfLCc4OBg7d+7ElClTzLowmThxIiZOnIj//u//xtq1a7F8+XKsW7cOK1eu7NP5Qb3HfqCN0WjEww8/jF27duHrr79GcnJyr49B/2HpvoDvJ41W7NPpdrB0H0/9x76iDa8XBo+l+wm+tzSQOEV9GHjhhRdgZ2eHlStXoqysrMP9ubm5+L//+z8AwPz58wEAb7/9drt9/vd//xcAsGDBgl4/v52dHQCgurra7MfMnz8fx44dw5EjR6RtDQ0N+PDDDxEQEIDIyMhet4MGzr333guDwYDXX3+9w316vV56r6uqqjp8ihsfHw8A0nQEW1tbAL07P6j32A+0+fnPf46vvvoK7777rlSdkfrO0n0B308ardin0+1g6T6e+o99RRteLwweS/cTfG9pIHEE5zAQHByMtWvX4r777kNERAQefvhhREdHo6WlBYcPH8Y333yDRx99FAAQFxeHRx55BB9++CGqq6uRnJyMY8eO4dNPP8XSpUsxc+bMXj9/fHw85HI5/vznP6OmpgYqlQqzZs2Cm5tbl4958cUX8eWXXyIlJQXPPvssnJyc8OmnnyI/Px/fffcdZLLeZ+uff/45CgsL0djYCADYv38//vjHPwIAfvzjH3PkQC8kJyfjySefxBtvvIHMzEzccccdsLKywqVLl/DNN9/g//7v/7Bs2TJ8+umnePfdd3HXXXchODgYdXV1+Ne//gWtVitdRNnY2CAyMhJfffUVxowZAycnJ0RHR3c7tYDvZe+xH2i7YH/33XcxadIk2NraYs2aNe3uv+uuu6QLcTKPJfsCvp80mrFPb8PrgcHF673hj30FrxcGG68FaUSxXAF36q2cnBzx8ccfFwMCAkSlUilqNBpxypQp4j/+8Q+xublZ2q+1tVV87bXXxMDAQNHKykr09fUVX3rppXb7iKIo+vv7iwsWLOjwPMnJyWJycnK7bf/617/EoKAgUS6XiwDEPXv2dHsMURTF3NxccdmyZaKDg4NobW0tJiUliampqe32yc/PFwGIq1at6vH1JycniwA6vZnaQ517+umnxc5+3T/88ENx3Lhxoo2NjajRaMSYmBjxhRdeEIuLi0VRFMWMjAzxgQceEP38/ESVSiW6ubmJCxcuFE+cONHuOIcPHxbHjRsnKpVKEYD4yiuvdNsevpd9N5r7gUceeaTL8waAmJ+f3+3jaWj1BXw/iUZ3n25qF68HBs5Q6uNFke/vQBrNfQWvFwbWUOon+N7SQBNEsRerCBMRERERERERERENIVyDk4iIiIiIiIiIiIYtBpxEREREREREREQ0bDHgJCIiIiIiIiIiomGLAScRERERERERERENWww4iYiIiIiIiIiIaNhiwElERERERERERETDFgPOEeTNN99EeHg4jEajpZvSby+++CImTJhg6WYMOzwHiOcA8RwgGjn4+0y34jlBneF5QbfiOUGjkkgjQk1Njejk5CR+8skn0jYAIgDxL3/5S4f9V61aJQIQjx8/3u/n/u6778R7771XDAwMFG1sbMQxY8aIzz//vFhVVdXp/t9//704duxYUaVSib6+vuLLL78stra2ttunpKREVKlU4vfff9/v9o0WPAeI5wDxHCAaOfj7TLfiOUGd4XlBt+I5QaMVA84R4m9/+5uo1WrFpqYmaZupE3N3dxcbGhra7T+QnZizs7MYExMj/v73vxf/9a9/ic8++6yoVCrF8PBwsbGxsd2+W7ZsEQVBEGfOnCl++OGH4s9//nNRJpOJP/3pTzsc99577xWnTZvW7/aNFjwHiOcA8RwgGjn4+0y34jlBneF5QbfiOUGjFQPOESI2NlZ86KGH2m0DIMbHx4sAxL/+9a/t7hvITmzPnj0dtn366aciAPFf//pXu+2RkZFiXFxcu09lfvvb34qCIIjnz59vt++3334rCoIg5ubm9ruNowHPAeI5QDwHiEYO/j7TrXhOUGd4XtCteE7QaMU1OEeA/Px8ZGVlYc6cOR3umzJlCmbNmoU333wTTU1Ng/L8M2bM6LDtrrvuAgCcP39e2padnY3s7Gw88cQTUCgU0vaf/exnEEUR3377bbtjmF7P999/PwitHll4DhDPAeI5QDRy8PeZbsVzgjrD84JuxXOCRjMGnCPA4cOHAQAJCQmd3v/qq6+irKwM7733XrfH0el0uHHjhlm3npSWlgIAXFxcpG2nTp0CAIwfP77dvl5eXvDx8ZHuN7G3t0dwcDAOHTrU4/ONdjwHiOcA8RwgGjn4+0y34jlBneF5QbfiOUGjmaLnXWiou3DhAgAgMDCw0/unTZuGmTNn4q233sJTTz0FGxubTvf78ssvsWLFCrOeUxTFbu//85//DLlcjmXLlknbSkpKAACenp4d9vf09ERxcXGH7UFBQcjOzjarTaMZzwHiOUA8B4hGDv4+0614TlBneF7QrXhO0GjGgHMEqKiogEKhgFqt7nKfV199FcnJyXj//ffxy1/+stN95s2bhx07dvS7PWvXrsXHH3+MF154AaGhodJ20zB4lUrV4THW1taora3tsN3R0bHDpzfUEc8B4jlAPAeIRg7+PtOteE5QZ3he0K14TtBoxoBzlJg+fTpmzpyJN998Ez/96U873cfT07PTT1B648CBA3jssccwb948/Pd//3e7+0yfDul0ug6Pa25u7vTTI1EUIQhCv9pEbXgOEM8B4jlANHLw95luxXOCOsPzgm7Fc4JGKgacI4CzszP0ej3q6uqg0Wi63O+VV17BjBkz8MEHH8DBwaHD/U1NTaipqTHrOT08PDpsO336NBYvXozo6Gh8++237RYLBv4z/LykpAS+vr7t7ispKUFSUlKHY1ZVVbVbq4M6x3OAeA4QzwGikYO/z3QrnhPUGZ4XdCueEzSascjQCBAeHg6grWJad5KTkzFjxgz8+c9/7rRq2ldffSV9UtPT7Va5ubm488474ebmhi1btnQ6JD4+Ph4AcOLEiXbbi4uLce3aNen+m+Xn5yMiIqLb10U8B4jnAPEcIBpJ+PtMt+I5QZ3heUG34jlBoxlHcI4AkyZNAtDWOcTGxna776uvvooZM2bgww8/7HBfX9fZKC0txR133AGZTIa0tDS4urp2ul9UVBTCw8Px4Ycf4sknn4RcLgcAvPfeexAEod2iwwBQU1OD3NxcPPXUU71u02jDc4B4DhDPAaKRg7/PdCueE9QZnhd0K54TNJox4BwBgoKCEB0djZ07d+InP/lJt/smJycjOTkZ+/bt63BfX9fZuPPOO5GXl4cXXngBBw8exMGDB6X73N3dMXfuXOn7t956C4sXL8Ydd9yB+++/H2fPnsU///lPrFy5ssOnMTt37oQoiliyZEmv2zTa8BwgngPEc4Bo5ODvM92K5wR1hucF3YrnBI1qIo0I//u//yuq1WqxsbFR2gZAfPrppzvsu2fPHhGACEA8fvx4v5/bdKzObsnJyR3237BhgxgfHy+qVCrRx8dH/N3vfie2tLR02O++++4Tp06d2u/2jRY8B4jnAPEcIBo5+PtMt+I5QZ3heUG34jlBoxUDzhGiurpadHJyEj/66CNLN2VAlJSUiNbW1uLGjRst3ZRhg+cA8RwgngNEIwd/n+lWPCeoMzwv6FY8J2i0YpGhEcLe3h4vvPAC3nrrLRiNRks3p9/efvttxMTEcAh6L/AcIJ4DxHOAaOTg7zPdiucEdYbnBd2K5wSNVoIoiqKlG0FERERERERERETUFxzBSURERERERERERMMWA04iIiIiIiIiIiIathhwEhERERERERER0bDFgJOIiIiIiIiIiIiGLQacRERERERERERENGwx4CQiIiIiIiIiIqJhiwEnERERERERERERDVsMOImIiIiIiIiIiGjYYsBJREREREREREREwxYDTiIiIiIiIiIiIhq2GHASERERERERERHRsMWAk4iIiIiIiIiIiIYtBpxEREREREREREQ0bDHgJCIiIiIiIiIiomGLAScRERERERERERENWww4iYiIiIiIiIiIaNhiwElERERERERERETDFgNOIiIiIhp13njjDSQmJkKj0cDNzQ1Lly7FxYsXLd0sIiIiIuoDBpxERERENOrs27cPTz/9NI4ePYodO3agtbUVd9xxBxoaGizdNCIiIiLqJUEURdHSjSAiIiIisqTr16/Dzc0N+/btw/Tp0y3dHCIiIiLqBY7g7KWSkhK8+uqrKCkpsXRTiIiIiGiA1NTUAACcnJy63Een06G2tla65eTk4He/+x2vC4mIiIgsjCM4eykjIwPjxo3DyZMnkZCQYOnmEBEREVE/GY1GLF68GNXV1Th48GCX+7366qt47bXXOmzndSERERGRZXEEJxERERGNak8//TTOnj2LdevWdbvfSy+9hJqaGum2b9++29RCIiIiIuqOwtINICIiIiKylGeeeQapqanYv38/fHx8ut1XpVJBpVJJ36vV6sFuHhERERGZYViP4Ny/fz8WLVoELy8vCIKAjRs39viYvXv3IiEhASqVCiEhIVi9evWgt5OIiIiIhhZRFPHMM89gw4YN2L17NwIDAy3dJCIiIiLqo2EdcDY0NCAuLg7vvPOOWfvn5+djwYIFmDlzJjIzM/Hcc89h5cqVSEtLG+SWEhEREdFQ8vTTT2PNmjVYu3YtNBoNSktLUVpaiqamJks3jYiIiIh6aVhPUU9JSUFKSorZ+7///vsIDAzEX//6VwBAREQEDh48iL/97W+YN2/eYDWTiIiIiIaY9957DwAwY8aMdttXrVqFRx999PY3iIiIiIj6bFgHnL115MgRzJkzp922efPm4bnnnuvyMTqdDjqdTvq+vr5+sJpHRERERLeJKIqWbgIRERERDZBhPUW9t0pLS+Hu7t5um7u7O2pra7ucjvTGG2/A3t5euiUnJ9+OphIREREREVlES0sLDh8+jJaWFks3hYiIyCyjKuDsi5deegk1NTXSbd++fZZuEhERERER0aA5evQoVq1ahfT0dEs3hYiIyCyjKuD08PBAWVlZu21lZWXQarWwsbHp9DEqlQparVa6qdXq29FUIiIiIiKi206n0yEtLQ35+fnYtm1bu+W6iIiIhqpRFXBOmjQJu3btardtx44dmDRpkoVaRDSMVeZbugVERERENMDS09ORk5OD2NhY5OTk4NixY5ZuEhERUY+GdcBZX1+PzMxMZGZmAgDy8/ORmZmJK1euAGibXv7www9L+//0pz9FXl4eXnjhBVy4cAHvvvsuvv76a/zyl7+0RPOJhrf6sp73ISIiIqJhwzR6U6lUQqvVQqlUchQnERENC8M64Dxx4gTGjh2LsWPHAgCef/55jB07Fi+//DIAoKSkRAo7ASAwMBCbN2/Gjh07EBcXh7/+9a/46KOPMG/ePIu0n2hY09VbugVERERENIBOnTqF3NxcNDQ04Ny5c2hoaEBubi5OnTpl6aYRERF1S2HpBvTHjBkzIIpil/evXr2608fwP2iiAaCrsXQLiIiIiGgA+fr6Yvny5Z1uJyIiGsqGdcBJRBZUf93SLSAiIiKiAeTt7Q1vb29LN4OIiKjXhvUUdSKyoMo8S7eAiIiIiIiIiIgBJxH1UV0xUHPN0q0gIiIiIiIiolGOAScR9V3uHku3gIiIiIiIiIhGOQacRNR3F7cCRqOlW0FEREREREREoxgDTiLqu9oi4NoxS7eCiIiIiIiIiEYxBpxE1D8nVwOiaOlWEBEREREREdEopbB0A4ho+Bk/fjxK87PhoZbjxH+hbap6+HxLN4uIiIiIiIiIRiGO4CSiXistLUVRZRNKa1vaNhz+B1BVYNE2EREREREREdHoxICTiPqvtRHY8gJQW2LplhARERFRP7W0tODw4cNoaWmxdFOIiIjMwoCTiAZGfRnw/dNA+QVLt4SIiIiI+uHo0aNYtWoV0tPTLd0UIiIiszDgJKKB01gB/PAMkPUNYDRaujVERERE1Es6nQ5paWnIz8/Htm3boNPpLN0kIiKiHjHgJKKBZWgFjvyzbTRnWbalW0NEREREvZCeno6cnBzExsYiJycHx44ds3STiIiIesSAk4gGR3k2sPEpYPvvgcp8S7eGiIiIiHpgGr2pVCqh1WqhVCo5ipOIiIYFBpxENLjy9wPfrgB2/zeLEBERERENYadOnUJubi4aGhpw7tw5NDQ0IDc3F6dOnbJ004iIiLqlsHQDiGgUEEXg0nYgbw8QvxwY+xAgt7J0q4iIiIjoJr6+vli+fHmn24mIiIYyBpxEdPsYWoGTq4HCQ8DslwEHP0u3iIiIiIj+zdvbG97e3pZuBhERUa9xijoR3X43LgHfPQ6c+ZbV1omIiIiIiIioXxhwEpFl6JuBw/8A1j8OXElvm8ZORERERERERNRLDDiJyLIqLgNbX2iruJ63jyM6iYiIiIiIiKhXGHASUa9cuXIFDQ0NAIAGnQFXKpsH5sDl54EdLwNfLQeyvgF09QNzXCIiIiIiIiIa0VhkiIjMcuzYMbz++uvYvHkzxH9PJ69uMiDgt8ewMMYJv5/vj8QATf+fqLYYOPJP4MTHwJg7gZhlgL1P/49rhtamOuRtex+Vl9IBQQaX8MkImvck5EqbHh8riiKy172CqtyTiLjnd3AOmyTdV1ecg4Ldq1FfchkQAI1XGAJmr4DaPWgwXw4RERFRn7S0tODEiRMYP348lEqlpZtDRETUo2E/gvOdd95BQEAArK2tMWHCBBw7dqzLfVevXg1BENrdrK2tb2NriYan9evXY8qUKdi6dasUbpqIIrDlbCUmv5mJ9aduDNyTtjYB5zYAXz0EbP89cOPygBw267MXUXZ6R6f35Wx8C403ChG9/I+IvO8V1Fw5h8ub/2HWcYuPbQQgdNhuaGnCuS9fhkrririf/C9iH3kLcqUNzq39PYwGfT9eCREREdHgOHr0KFatWoX09HRLN4WIiMgswzrg/Oqrr/D888/jlVdeQUZGBuLi4jBv3jyUl5d3+RitVouSkhLpVlhYeBtbTDT8HDt2DPfddx8MBgMMBkOn+xiMgMEo4r5/ncfxgrqBbYAoAvn7ge8eA3b/EWisHNjj/1vjjSuoyj2JkAW/gMY7HPZ+UQi+80lcP7cfurqKbh9bX5qLoqMbELroF50c9xr0TXXwT34Its4+sHP1h9/0B9HaUA1dTdd9FRERDb79+/dj0aJF8PLygiAI2Lhxo6WbRGRxOp0OaWlpyM/Px7Zt26DT6SzdJCIioh4N64Dzf//3f/H4449jxYoViIyMxPvvvw9bW1t88sknXT5GEAR4eHhIN3d399vYYqLh549//CNEUewwcvNWIgARIv64ZRA/NLi0A/j2J8D1nAE/dO21C5Bb20HjFSptcwgcCwgC6ooudvk4Q2szLm58C8F3PgWl2qnD/TbO3lDYaFGauR1GQysMrTqUZW6HjYsvrB3Y/xARWVJDQwPi4uLwzjvvWLopRENGeno6cnJyEBsbi5ycnHYz5FpaWnD48GG0tLRYsIVEREQdDds1OFtaWnDy5Em89NJL0jaZTIY5c+bgyJEjXT6uvr4e/v7+MBqNSEhIwP/8z/8gKiqqy/11Ol27Ty3r61n4hEaPK1euIDU1tcdw08RgBDadqcSVymb4OQ3S8g9NVUDaS8C9nwNK2wE7bGt9FZS2Du22CTI5rGw0aG2o6vJx+dv/Ba1PRLs1N2+mUNki5sdv4Pw3f8TVg+sAADZOXoh64HUIMvmAtZ+IiHovJSUFKSkplm4G0ZBhGr2pUChQU1MDhUKBbdu2ISkpCSqVCkePHsXnn38Og8GAadOmWbq5REREkmEbcN64cQMGg6HDCEx3d3dcuHCh08eEhYXhk08+QWxsLGpqavCXv/wFkydPxrlz5+Dj03kRkzfeeAOvvfbagLefqD+MhlaIxs6niw+kHdu3mR1umogisOtCNR6dNIijE+uvQyw8BFno3B53vXrwK1w99LX0vVHfgrqiC8jd9r60LeGn7/WpGRU5R1FdkIWxj/+9y30MrTpcSv0/aH0iEXbXCxCNRhQdXY/sr15F3E/+BrmVqk/PTUREtx8/+KaR7tSpU8jNzUVRURGuXbsGHx8fiKKIU6dOYezYse2mrptCTyIioqFg2AacfTFp0iRMmvSfUVaTJ09GREQEPvjgA7z++uudPuall17C888/L32fmZmJ5OTkQW8rUVeMhlbUFeXA0NI06M9VXpgDmUwGo9Fo9mNkAlBd3zTo7WspyoZ10AzI5Fbd7ucxbj5cIv8zwuDixrfgEj4FzuGTpW0qjTOs1I5oaaxu91jRaEBrUx2s7Bw7PXZNQRaaq0pw5K17220//+3/QOsbhdiH/4TrZ/dCV1OOuBV/hSC0rQqivuv/4ehf7kNlzlG4RrE/ISIaLvjBN410vr6+uPfee7Fu3TrU1tbC09MT9957L3x9fTudus5RnERENFQM24DTxcUFcrkcZWVl7baXlZXBw8PDrGNYWVlh7NixuHy56+rMKpWq3SeTarW6bw0mGiCi0QBDSxNkCkWP4V5/2Ts49CrcBACjCNjbKCDIBm+JX1EU0aLxg8poAHr4GVjZaGBlo5G+lylUsLKzh42TV7v9tD7hMDQ3oL7kEtSebetwVuefBkQRGu+wTo/tM3kZ3OPvaLft1IdPI2ju43AKTQIAGPU6QBBwc4X1tqBT6PXoWCIisix+8E0jnbe3N1xcXCCKImbOnImioiK4urrCxcUF7777LpRKJbRaLZRKJUdxEhHRkDJsiwwplUqMGzcOu3btkrYZjUbs2rWr3SjN7hgMBpw5cwaenp6D1UyiQSOTW0GmUA7qbeb0aRAEoefG3EQQgJlj7NEW6A3OrTV0Poz2vv39EbZj6+IHx+BxuLT5H6gruojaq9nITXsPrlHTodI4AwB0tTdw8r0npaJDSrUT7NwC2t0AQGXvCmvHtg9aHALHQt9Uj9xt76LxxhU0XC9Ezg9/gyCTw8E/dkBfAxERDS6VSgWtVivd+ME3jTSmNTiVSiU0Go0UZKanpyM3NxcNDQ04d+4cGhoakJubi1OnTlm6yURERACG8QhOAHj++efxyCOPYPz48UhKSsLbb7+NhoYGrFixAgDw8MMPw9vbG2+88QYA4A9/+AMmTpyIkJAQVFdX46233kJhYSFWrlxpyZdBNGT5+njhzjkzsX33PhgMPa/5KZcB86Mc4Oc0SJ/kCwJao+5Fa9BcoKV5wA8/Zun/Q96293D2i98CggDn8CkInvekdL9oNKCp4hoMrbpujtKerYsvIu97BVf3r8XpVb+GIAiw8whG1AN/gFLTseo6ERERkaWY1uBsbm7GmTNnAAC5ubmorq7G8uXLO+zv6zuwHzgTERH11bAOOO+77z5cv34dL7/8MkpLSxEfH49t27ZJhYeuXLkC2U3TZKuqqvD444+jtLQUjo6OGDduHA4fPozIyEhLvQSiIe83v/wZduzZD0Hofkp129hKAf81z3tQ2iEq7dCS+DMYPWIBfUufjxP78J+6vM/KRoOwu17o8n5rB3dM/d3mbo/f2f2OQWPhGDTW/EYSEdFtUV9f326povz8fGRmZsLJyQl+fn4WbBmRZfj6+kpBZnV1NRwcHAAACQkJ8PYenGs8IiKigSCIXASuVzIyMjBu3DicPHkSCQkJlm4OjUKG1mbUFJ6FQmUDmUJ5W57z+y1pePSnv4Qoip2O5JTL2sLNdT8JwdK4gR+VaHTwQ8vE5yDaubZ9r2+BXtcEe/9oyK2sB/z5iIhodNi7dy9mzpzZYfsjjzyC1atX9/h4XhfSSHbx4kWEhIRALpdbuilEREQ96vManAaDAevWrcOTTz6Ju+66S5rCUFNTg/Xr13co/kNEw9eS+fOw84d1uGNWcoc1OQWhbVr6gecjByXc1AcmQ5f8shRuEhHRyDAUriVnzJgBURQ73MwJN4lGutbWVuTl5aGlpQWHDx9GS0vfZ9AQERENtj5NUa+ursadd96JY8eOQa1Wo6GhAT//+c8BtFUZf/bZZ/Hwww/jf/7nfwa0sURkOePiY/H1p+/j6rViTJ67GNU1tXCwkSPjxZhBWXNTtHNBS/wjMHrED/ixiYjIsngtSTQ8nDlzBsXFxVizZg0MBgOmTZtm6SYRERF1qk8jOF988UWcO3cOaWlpyMvLa7cun1wux7Jly7Bly5YBayQRDR2+Pl6wtbUBANipZAMebopKO7TG3IfmuW8y3CQiGqF4LUk0PJSUlODrr79Gfn4+tm3bBp3O/EKLREREt1OfRnBu3LgRP//5zzF37lxUVFR0uH/MmDGc2kNEvSIq1dCHzoM+eB5gZWPp5hAR0SDitSTR0JaQkICCggIoFAr4+/sjMTEROTk5OHbsGEdxEhHRkNSngLOmpgaBgYFd3t/a2gq9Xt/nRhHR6CHaOEIfOh/6gBkACwYREY0KvJYkGtquXr2KqqoqWFlZISAgAMXFxTAYDNi6dSuSkpKgUg388kRERET90aeAMzg4GBkZGV3ev337dkRGRva5UUQ08hkdA6EPuRMGnyRA1qeuiIiIhileSxINXTqdDs3NzQAAvV6P5uZmXL16FQaDAdXV1UhLS8OiRYs6FJ4kIiKypD6lCitXrsRvfvMbzJgxA7NnzwYACIIAnU6HP/zhD9i2bRs+/PDDAW0oEY0AMhkMXknQh9wBo1NIWwl2IiIadXgtSTR0paenSyOoZTIZQkJC4OnpKd1/9uxZnDt3DosXL0Z4eDjkcrmlmkpERCTpU8D5i1/8AufOncMDDzwABwcHAMCDDz6IiooK6PV6PPnkk3jssccGsp1ENJwprKEPmg19yB0QbZws3RoiIrIwXksSDU06nQ5paWnS94IgoLm5GZGRkbCysgIA5OTkID09HZWVlYiJiUFQUBACAwPh6ekJmaxPNWyJiIj6rU8BpyAI+Ne//oVHHnkE3377LS5dugSj0Yjg4GDce++9mD59+kC3k4iGI7kVWkNToA+dDyjtLN0aIiIaIngtSTQ0nTp1CllZWWhtbQUAGAwGXLt2DVevXkVQUBBaW1uRnZ2NGzdu4Ny5cwgICEB2djays7NhZWUFLy8veHt7w9vbGw4ODpzGTkREt02/Fr6bOnUqpk6dOlBtIaIRxOgWhZaExyDauVq6KURENETxWpJo6Dh27Bh+//vfY9euXRBFEQAgiiLOnDkDvV6PpUuXoqWlBWVlZfD29kZZWRkKCgoQGhoKoK04WGFhIQoLCwEAdnZ28PHxgb+/P3x8fKBQcM11IiIaPH2aQ5Cfn49NmzZ1ef+mTZtQUFDQ1zYR0TDXGnEXdFNfYLhJRESd4rUk0dCyfv16TJkyBXv27JHCzZtdvHgRf/7zn7F7927I5XLY2NhALpfj3Llz0mjPWzU0NODixYvYvn07Pv/8c+zZswfXrl3r9Pi3W2VlJZYvXw6tVgsHBwc89thjqK+vN+uxoigiJSUFgiBg48aN7e47fvw4Zs+eDQcHBzg6OmLevHk4ffr0ILwCIiK6VZ8Czl//+tf4+9//3uX977zzDl588cU+N4qIhq/WuIegj7wbELgGExERdY7XkkRDx7Fjx3DffffBYDDAYDB0uo/RaITRaMSpU6dQXV2N4uJitLS04Pr167h69WqPz9Ha2opLly5hy5YtWLt2LY4ePYqKiopBDTtnzJiB1atXd3rf8uXLce7cOezYsQOpqanYv38/nnjiCbOO+/bbb3c69b6+vh533nkn/Pz8kJ6ejoMHD0Kj0WDevHldhsBERDRw+jRP4MiRI3juuee6vH/27Nl4++23+9gkIhqu9CF3QB8yz9LNICKiIY7XkkRDxx//+EeIomhW2CgIAlpbWzFx4kRpm6OjY6+er6GhAVlZWcjKyoKTkxNCQ0MRGhoKW1vbXre9L86fP49t27bh+PHjGD9+PADgH//4B+bPn4+//OUv8PLy6vKxmZmZ+Otf/4oTJ060qywPABcuXEBlZSX+8Ic/wNfXFwDwyiuvIDY2FoWFhQgJCRm8F0VERH0bwVlVVQWNRtPl/Wq1GhUVFX1uFBENbe6urvB2tIaHRiltM7qEozXmAQu2ioiIhgteSxINDVeuXEFqamqXIzdvJYoiCgsL4evri7i4OMTFxfU64LxZZWUl0tPT8cUXX2Dr1q3Iy8szuy19deTIETg4OEjhJgDMmTMHMpkM6enpXT6usbERDz74IN555x14eHh0uD8sLAzOzs74+OOP0dLSgqamJnz88ceIiIhAQEDAYLwUIiK6SZ9GcPr5+eHQoUN46qmnOr3/wIED8PHx6VfDiGjo2r9tPay3/z8IdaUAAFHtDt3EZwEZF48nIqKe8VqSqHstLS3Q6/WD+hyNjY1Ys2ZNr6eJi6KI8+fPY9KkSQPaHlOBIqVSCX9/f4SGhsLDwwNKpbLnB/dCaWkp3Nzc2m1TKBRwcnJCaWlpl4/75S9/icmTJ2PJkiWd3q/RaLB3714sXboUr7/+OgAgNDQUaWlpLLBERHQb9KmnfeCBB/D6668jKSkJzzzzDGSytoGgBoMB//znP/HVV1/ht7/97YA2lIiGKCsb6Cb/GlB1PRKHiIjoZryWJOpaS0sLjh07ZnbRm+7o9Xo0NDRIt/r6etTX16OhoQH/v707D4uyXP8A/n1n2HdZRWR3QXJDFHEF9zU1zaXcK7XFOqWnjpqWHjVPJyurk1kdU7MsW2xTA00FMwVcQBMVFEEUZXFhl5lh5v394WF+EqDDOMM7w3w/1zVXvvs9BA839zyLUqnEqVOnIAhCo4qcgiCgpKTEIPE15ObNm0hNTYWPjw8mT56sU5HzjTfewBtvvKHdvn37NpKSkjB//nztvjNnzugVz88//4z9+/cjNTW1wXNu376NJ598En369MFXX30FtVqNtWvXYtSoUTh69Cjs7e31ejYREelGrwLn4sWLcejQIbz44otYvXo12rdvD+DO6npFRUWIjY1lUkpkIZTdnoDoXHeYDhERUUOYSxI1rLq6GuXl5bCxsYGtre19z9doNCgvL0dZWRlKS0tRVlam/XdlZeU9r7WxsdGrB6ednZ32gwljEQQBcrkc1dXVOhU4n376aUyaNEm7PXXqVEyYMAHjx4/X7mvVqhVatmyJwsLCWtdWV1fj5s2b9Q49B4D9+/cjKysLbm5utfZPmDAB/fr1Q0JCArZt24acnBwcOXJE+7XZtm0bWrRogZ9++glTpkzR9a0TEZEe9Cpw2traYs+ePdiyZQt27NiBrKwsAEBUVBQmTJiAGTNmGP0XHhFJT+PZHmq/nlKHQUREZoa5JNH92draws7ODsCdomJFRQVKS0tRUlKCkpIS7b/LysoaXaR0cHCAq6srvL29kZCQ0OjrQ0NDjfYzKpfL0apVK3h4eKCqqkrn69zd3eHu7q7dtre3h7e3d53FfXr16oXi4mIcP34ckZGRAO4UMDUaDXr2rD+vXbRoEZ566qla+zp16oR3330XDz/8MIA7Q/5lMlmtFdZrtjUajc7vg4iI9KP3ZCAymQyzZ8/G7NmzDRkPmbDc3Fzs27cPZWVlcHZ2xqBBgxAQECB1WCSh6tAhwF1JHFkGtgVEZAjMJYlqqyli5ubmIjc3FyqVqlZRs7GL79jY2MDV1RWurq5wcXGp9W9ra2vteQcOHMAff/yh0/0FQYCbmxtKS0vh5eXV6Pd4v3v7+voiICAA1tbWqKqqalSBU1cdOnTA8OHDMWfOHGzYsAEqlQrz58/HlClTtCuo5+XlYdCgQfj8888RFRWFli1b1tu7MyAgAMHBwQCAIUOG4OWXX8Zzzz2H559/HhqNBv/6179gZWWFAQMGGPx9EJkCpVKJY8eOoXv37gafL5eosTjbMd1XSkoKVq5ciV27dkEURchkMmg0GgiCgNGjR2PZsmXo0aOH1GFSExNlVlC37CJ1GNSE2BYQERE9uKqqKhQWFtZ5FRQU4Pbt2426l5WVlbZ4+df/1vT+vJ8nn3wSf/zxh85zcTo5OeHcuXMIDAw0yOI5crkcXl5eaN26dZPNU/nll19i/vz5GDRoEGQyGSZMmID3339fe1ylUiEjI+O+Q/zvFhYWhl9++QUrVqxAr169IJPJEBERgbi4OPj6+hrjbRBJLikpCVu3boVarUa/fv2kDocsnN6/keLj47Fx40ZcvHgRt27dqvPLUBAE7XAjMl87duzA5MmTIYqi9v9xzRALURSxe/du/Prrr9i+fXut+W2o+RNd/AEr3RJnMn9sC4jI0JhLUnOmUqlw/fr1eguZJSUljbqXIAhwcXGp0wvT1dUVDg4OtYZE6+Ohhx7CmjVrsHjxYgCotydnzXB0f39/BAUFobCwEJcuXUJoaKhez3R0dISLiwtatGgBNzc3yOVy/d9AAxISEho85u7ujm3btjV4PCgo6L7F3vqODxkyBEOGDNE5RiJzplAoEB8fj+zsbMTFxSEqKkqneYOJjEWvAudbb72FRYsWwcfHB1FRUejUqZOh49LZhx9+iLfeegv5+fno0qULPvjgA0RFRTV4/rfffotly5YhJycHbdu2xZtvvomRI0c2YcTmIyUlBZMnT4ZarW7wF7xarYYgCJg8eTIOHz7M3lsWROPEhYUsBdsCIjI0U8olifSl0Whw8+bNenti3rx5s9HzWrq7u8Pb2xvu7u4oKSmBl5cXvLy84OTkZPQ5aQcOHIjPPvsMGzduxO+//14rdkEQEBYWBkdHR6jVatjZ2cHKykrnXpyOjo5wcnLSvhwdHY1S0CSippWcnIzMzEx07twZmZmZSElJYS9OkpReBc733nsPAwcOxO7du2vN39LUtm/fjgULFmDDhg3o2bMn1q1bh2HDhiEjIwPe3t51zj98+DAee+wxrFmzBqNHj8a2bdswbtw4nDhxAh07dpTgHZi2VatW1eqt1ZCac1atWoWffvqpiaIjqYkO7vc/iZoFtgVEZGimkksS3Y8oiigtLa1VvCwqKkJhYSGKiopQXV3dqPs5OzvD29u71svHxweenp7a+esqKytx8OBBODs76zzM3BAeeughvPPOO9i7dy+WLVuG6upqyGQyDBs2DKGhoTh06BCqq6uRn58PtVqNoqIipKSkICoqSlvkFAQBjo6OKCoqQrdu3dCiRQsWM4maoZrem1ZWVigpKYGVlRV7cZLk9Cpw3rp1C48++qjkCek777yDOXPmaCen37BhA3bt2oXPPvsMixYtqnP+e++9h+HDh+Pll18GAKxcuRJ79+7Ff/7zH2zYsKFJYzd1ubm52Llzp86fPKvVavzyyy/Izc3lYiMWQrRjgdMSsC0gImMwlVySqEZlZWWdAmZBQQEKCwuhUCgadS87O7s6Rcyal4ODg5HegWEolUpkZmZCLpejuroagiDg5MmT8PT01K44XiM/Px9ZWVnw8fFB9+7dtb1PT506hZSUFHh5ecHT01Oid0JExpSamoqsrCzk5eXhypUraN26NURRRGpqKqKjo6UOjyyUXgXOqKgoZGRkGDqWRlEqlTh+/Lh2rhjgztwwgwcPxpEjR+q95siRI1iwYEGtfcOGDcOPP/7Y4HMUCkWtpKa8vBwAUF1dDZVK9QDvwLTFx8c3eliNKIrYs2cPZs6caaSoCADUKhVUqmqoxUrI5NJ9D8pgA83tCkmerVGroKlWQ6VSQQP2CjAmtgWmTaNSaedCtUQymQwyiQtkLNDpxxRySbI8SqWyTvGyqKgIBQUF2hxfV1ZWVvDy8qrTE9PLywsuLi4PPC+mVDIyMnDlypVa822Xlpbi3LlzmDRpkranZnV1Na5cuQKVSoXS0lK0a9cONjY2UCqVOHLkCPLy8nD48GE89NBDXFmZqBny9/fHpEmT8PXXX6O0tBS+vr6YNGkS/P39pQ6NLJheBc7169djxIgR6N69Ox5//HFDx6ST69evQ61Ww8fHp9Z+Hx8fnDt3rt5r8vPz6z0/Pz+/weesWbMGK1asqLO/Z8+eekTd/M2ZMwdz5syROgwikhjbArIUjf0AgO4whVySmie1Wo0bN25oe2LWFDILCwtx69atRt1LEAR4eHjUO6S8RYsWRp8XUwo+Pj4YMWIEkpOToVQqIQgCAgMDUV5erl1UyM3NDTdu3IBKpUKnTp2Ql5eH9PR0RERE4PTp08jNzUXbtm2Rm5ur3U9EzYufnx88PT0hiiIGDRqEy5cvw8vLC35+flKHRhZMrwLn5MmTUV1djenTp+OZZ55B69at68ytUjOcwdwtXry4Vq/PtLQ0xMTEIDk5uVn/st68eTPmzp3b6Os+/fRT9tpqAhq1CqKm7gqXTaqqBLBzlezxgkwOmZw9p4yNbYHpqlYoUHTmDORWVpDdZ4GH5khTXQ11dTW8wsNhxbmezI4l5ZJkeBqNBiUlJbV6Yta8rl+/3uie7a6urnUKmN7e3vDw8LC4Xto1XwM3NzdUVFTAysoKrVu3RlFREbKzs/Hwww/D2dkZn376KaytreHo6AgrKyscPnwYbdu2xZEjR2BlZVVrP3txEjU/NXNw2tjYwMXFBTY2NpyDkySn119E7u7u8PDwQNu2bQ0dj848PT0hl8tRUFBQa39BQQFatqx/deeWLVs26nwAsLW1rfUD6uTkBODOsJTmnPAMGzYMgiA0qmeKIAgYOnRos/66mAxT+BrLAdg6Sx0FGRnbAtMlaDSwtraGtZ0d5Bb4h6NaqYSqqgrW1taw4vea2TGFXJJMmyiKqKioqLcnZmFhYaOnirK3t9cWLmuGktf8tykX8jEHSqUSnTp1QklJCWxsbHD16lU4OjpCqVTi6tWrAIArV65AqVTi4sWL2uHqv/32W737MzIy0KlTJ4nfFREZUs0cnFVVVUhPT4dKpUJWVhbn4CRJ6VXgTEhIMHAYjWdjY4PIyEjs27cP48aNA3Dn09x9+/Zh/vz59V7Tq1cv7Nu3Dy+++KJ23969e9GrV68miNi8BAQEYPTo0di9ezfU6vv3FJTL5Rg1ahQXFbEkQvMblkV1sS0gImMwhVyS7sjNzcW+fftQVlYGZ2dnDBo0qEnb8KqqqnoLmIWFhaisrGzUvaytrevtient7Q1HR0eznRezqZ0+fRrFxcVo3749CgsL0a1bN3Tp0gUAtNN9jRgxos51np6ecHevuwjlX6cII/MjdTtBpsff3x9Tp06FSqXChQsX0KZNG1hbW3MOTpKUWY9pW7BgAWbOnInu3bsjKioK69atQ0VFhXZV9RkzZsDPzw9r1qwBAPztb39DTEwM3n77bYwaNQpff/01jh07hk8++UTKt2Gyli1bhl9//fW+vbcEQYAgCFi6dGkTRkeSk7HHlKVgW0BE1PykpKRg5cqV2LVrF0RRhEwmg0ajgSAIGD16NJYtW4YePXoY5FkqlQrXr1+vU8AsLCxESUlJo+4lk8ng6elZpyemt7c3XF1dm+W8mE2pZpEgJycn7TyjMpkM0dHRtYaZe3t7SxglNZWmbCfIvPj5+cHPzw8HDx7Er7/+il69eqFfv35Sh0UWTu8CZ2lpKdavX48DBw6gsLAQH3/8MaKionDz5k1s3rwZY8aMQZs2bQwZax2TJ09GUVERXnvtNeTn56Nr166Ii4vTfkqYm5tbK8np3bs3tm3bhqVLl2LJkiVo27YtfvzxR3Ts2NGocZqrHj16YPv27Zg8eTJEUay395ZcLocgCPjmm2/4y83SWFnekFhLxbaAiIzBFHJJS7Vjxw5tm373atnAnaHhu3fvxq+//ort27dj/PjxOt1To9Hg1q1b9fbEvHHjRqMX5HJ3d9euUn53T0wPD48687WS4dSsoq5UKrV/S3GYuWUyRjtBzUvNPJzZ2dmcf5NMgl4FzitXriAmJgaXL19G27Ztce7cOZSXlwO4k4x8/PHHuHTpEt577z2DBluf+fPnNzgkvb7hTxMnTsTEiRONHFXzMX78eBw+fBgrV67Ezp0763xyN2rUKCxdupQFDaJmjm0BERmSKeWSH374Id566y3k5+ejS5cu+OCDDxAVFWX050olJSUFkydPhlqtbrDoqFarIQgCJk+ejMOHD2vbdlEUUVpaWm9PzKKiIlRXVzcqFicnJ23x8u6emF5eXlyURiI1q6gDwM2bN+Hm5gaZTMZh5hbmQdoJshzJycnIzMxE586dkZmZiZSUFPbiJEnpVeB8+eWXUVZWhrS0NO2nqXcbN24cdu7caZAASXo9evTAzz//jNzcXOzfvx+lpaVwcXHBwIEDOfcKkQVhW0BEhmIqueT27duxYMECbNiwAT179sS6deswbNgwZGRkNNshuKtWrarVI6shoihCo9Fg/vz5mDdvnraQWVVV1ajn2dnZ1Spe3v1ycHB4kLdCRnD3z+P169fh6ekpcUQkhca0E6IoYtWqVfjpp5+aKDoyBVxFnUyRXgXOPXv24KWXXkJ4eDhu3LhR53hISAguX778wMGRaQkICMCsWbOkDoOIJMa2gIgelKnkku+88w7mzJmjnb99w4YN2LVrFz777DMsWrTI6M9varm5udpe+LrQaDRISUlBu3bt4Ozs3OB5VlZW2uHkf325uLhwcR8zxSKFZWpsO6FWq/HLL78gNzeXH3hbEK6iTqZIrwLn7du34eXl1eDxsrIyvQMiIiIioubNFHJJpVKJ48ePY/Hixdp9MpkMgwcPxpEjR+q9RqFQQKFQaLdrhtVXV1dDpVIZN2ADiI+Pb/RcmABw+fJlhIWFwcPDQzuE/O7/uru7N7i4T2OHrdOdRZmqq6tRUVEh6ddPEATt97gUFAqF9mfLHH6+mgt92glRFLFnzx7MnDnTSFFRfZRKpWRthKurK8aNG1fv/sYuHmcIVlZWkk9tYm3NRXilpleBMzw8HAcPHsS8efPqPf7jjz8iIiLigQIjIiIioubJFHLJ69evQ61W15lb0MfHB+fOnav3mjVr1mDFihV19vfs2dMoMZqKxMREJCYmSh0GEZmwOXPmYM6cOVKHQSQZfT5AJMPSq8D54osvYubMmejcubN2wR6NRoMLFy5gxYoVOHLkCL7//nuDBkpEREREzYO55pKLFy/GggULtNtpaWmIiYlBcnKyWXy4v3nzZsydO7fR13366afsmdXEpOyZBQB//PEHtm/fjilTpqB3796SxWEKvbIsjb7txFNPPYVXX30Vfn5+RoiK/qqyshK///47bGxsLH46CYVCAaVSiX79+nFuZwunV4Fz2rRpuHTpEpYuXYpXX30VADB8+HDtqrpvvPFGvd2ViYiIiIhMIZf09PSEXC5HQUFBrf0FBQVo2bJlvdfY2trW+kPSyckJwJ0ijDkMTRs2bBgEQWhULxNBEDB06FCzeH/NiZRfb4VCgT/++AN5eXk4dOgQBg4caPEFFEuiTzsBAFVVVfj3v/+Ntm3bIjY2Fp07d4ZcLjdSlGRtbQ0rKys4OjrCzs5O6nAkZWVlBY1GA2tra/6usnB6FTgB4NVXX8X06dPx/fff48KFC9BoNAgNDcX48eMREhJiyBiJiIiIqJmROpe0sbFBZGQk9u3bpy2majQa7Nu3D/Pnzzf686UQEBCA0aNHY/fu3VCr1fc9Xy6XY9SoUVw4xMIkJycjMzMTnTt3RmZmJlJSUtCvXz+pw6Im0th2QiaTISgoSLsQ2fnz53H+/Hm0aNEC/fr1Q58+fe65SBkRkaE0usBZWVmJfv36Yc6cOXj66afx0ksvGSMuIiIiImqGTCmXXLBgAWbOnInu3bsjKioK69atQ0VFhXZV9eZo2bJl+PXXX+/bQ0sQBAiCgKVLlzZhdCQ1hUKB+Ph42NjYwMXFBTY2NoiLi0NUVBR7cVqQxrQTMpkMmzdvRnV1NRITE1FYWAgAuHXrFn7++Wfs3r0b3bt3R0xMDAIDA5vqLRCRBWp0gdPBwQHZ2dkQBMEY8RARERFRM2ZKueTkyZNRVFSE1157Dfn5+ejatSvi4uLqLDzUnPTo0QPbt2/H5MmTIYpivT205HI5BEHAN998gx49ekgQJUklNTUVWVlZqKqqQnp6OlQqFbKyspCamoro6Gipw6Mm0th2oqaHb0xMDM6dO4eEhASkp6dDFEVUV1cjKSkJSUlJCA4ORmxsLCIiImBlpfdgUiKiegmiHks9Pf7446iqqsKOHTuMEZNJO3HiBCIjI3H8+HF069ZN6nCIiMhCVSsUKDpzBtZ2dpBLtACDqFJBkGiuI7VSCVVVFbzCw2HFXkVmp7nkkuacFx49ehQrV67Ezp07tXOfajQaCIKAhx9+GEuXLmVx0wLl5eXhxIkTdfZ369aNi8dYoAdpJ65fv47ExEQcPnwYt2/frnXMxcUFffv2Rd++feHm5tYE76T5qaysxMGDB+Hs7Gzxc3BWVVWhrKwM/fv35yJDFk6vAufZs2cxceJEREREYN68eQgODoa9vX2d89zd3Q0SpCkx50SWiIiaDxY4WeA0Z80ll2wOeWFubi7279+P0tJSuLi4YODAgZxzk4hqeZB2QqFQ4OjRo0hISMDVq1drHZPJZIiIiEBsbCxCQkJMome/uTDlAqdKpcKZM2cQHh7eJIv+sMBJNfQqcMpksv+/wT0aIV0mJTY3zSGRJSIi88cCJwuc5qy55JLMC4mIdCOKIi5cuICEhAScPHkSGo2m1nF/f3/ExMSge/fusJEorzEnplLgrK+YeeLECezevRujRo1CRESE0WNggZNq6DXxxWuvvcZPV4iIiCzdXUUqosZgLklEZFkEQUDbtm3Rtm1b3Lx5E4cOHcKhQ4dQXl4OALh8+TK++OIL/PDDD+jduzf69+8PDw8PiaOm+/nzzz+xe/duaDQaREREQKlU4siRI8jLy8Phw4fx0EMPsWBNTUavAufy5csNHAYRERERWQrmkkRElsvd3R1jxozBiBEjcPz4cSQkJCA3NxcAUFFRgb179+K3335D586dERMTg/bt2/NDMRNUXzHz9OnTyM3NRdu2bZGbm4v09PQm6cVJBOhZ4PyrkpISODk5QS6XG+J2REREZA74x0azFhwc3Og/KAVBQFZWVqOfxVySiMjyWFtbIzo6GtHR0cjOzkZCQgJOnDgBtVoNURRx8uRJnDx5Er6+voiJiUFUVJTJzTdpyf5azExLS8PRo0dhZWUFR0dHWFlZsRcnNSm9x5YdO3YMw4cPh4ODAzw8PJCYmAjgzmppY8eORUJCgqFiJCIiIqImFhMTU+fl6OiInJwcODg4ICIiAhEREXBwcEBOTg6cnJzQv39/ne/PXJKIiGoEBwdj9uzZWL16NUaPHg1XV1ftsWvXruHrr7/GkiVL8O2336KwsFDCSAn4/96bdxczd+3ahdzcXFRVVeHixYuoqqrClStXkJGRIXW4ZCH06sF5+PBhDBw4EH5+fpg2bRr++9//ao95enqipKQEH3/8MWJjYw0VJxERERE1oc2bN9fa/vHHH/Hjjz9i7969GDRoUK1je/fuxaRJk7By5Uqd7s1cksh8KZVKHDt2rN7FYO51jEgXLi4uGDlyJIYNG4a0tDQkJCRoRwZUVVXhwIEDOHDgAMLDwxEbG4vw8PBaC9dR08jIyMCVK1egVCpx8eJFVFdXQ6PRoFu3bggMDKx1ro+Pj0RRkqXRq8C5ZMkSdOjQAUlJSSgrK6uVlALAgAEDsGXLFoMESERERCZKFKWOgJrQa6+9hueff75OcRMAhgwZgvnz52Pp0qUYO3bsfe/FXJLItN2rUJmUlIStW7dCrVajX79+Oh8jagy5XI7IyEhERkbi8uXLSExMxNGjR6FSqQAAZ86cwZkzZ+Dl5YX+/fujV69eXEG7Cfn4+GDEiBF19oeFhcHb21uCiIj0HKJ+9OhRzJ49G7a2tvXOzeTn54f8/PwHDo6IiIhMmEYjdQTUhM6fP3/PFW09PDx0nn+TuSSRaUtKSsKmTZuQnJxca79CoUB8fDyys7MRFxcHhUKh0zGiB+Hv749p06Zh9erVeOSRR2r9LioqKsL333+PJUuWYNu2bbh69aqEkVoOb29vbWHZ1dUVvXr1Qv/+/VncJEnpVeC0traG5h5/1OTl5cHJyUnvoIiIiIjItISGhmLTpk0oLy+vc6ysrAyfffYZQkJCdLoXc0ki03WvQmVycjIyMzPRuXNnZGZmIiUlRadjRIbg5OSEIUOGYMWKFXj66acRFhamPaZUKnHo0CGsWrUK69atQ2pqKtRqtYTRWoY///wTv/zyC06fPi11KET6DVGPjo7Gd999hxdffLHOsYqKCmzatAkxMTEPGhsRERGZMvbgtCirVq3Co48+irCwMMyaNQtt2rQBcKdn55YtW1BQUIBvv/1Wp3sxlyQyXfUVKvv166ctfNrY2MDFxQU2NjaIi4tDVFQUADR4zNbWVuJ3RM2NTCZD586d0blzZ1y7dg0HDx5EUlKSthifmZmJzMxMtGjRAv369UPfvn35oZkR1Cw0lJeXx9XSySToVeBcsWIFYmJiMGrUKDz22GMAgJMnT+LixYtYu3YtioqKsGzZMoMGSkRERCaGc3BalHHjxmH37t34xz/+gTfeeKPWsa5du2Ljxo0YNmyYTvdiLklkmu5VxExNTUVWVhaqqqqQnp4OlUqFrKwspKamAkCDx6KjoyV+V9Sc+fr6YvLkyRgzZgySkpKQmJioXWX91q1b+Pnnn7F79250794dsbGxCAgIkDji5uP06dPIzc1F27ZtkZubi/T0dEREREgdFlkwvQqcPXv2xO7du/HMM89gxowZAICFCxcCuDN8affu3ejcubPhoqzHzZs38fzzz+OXX36BTCbDhAkT8N57793zk5nY2FgkJibW2jdv3jxs2LDBqLESERE1R6JGg7qzJ1JzNnToUAwdOhT5+fm4dOkSACAwMBAtW7Zs1H1MIZckorruVcT09/fH1KlT61zj7+8PAPc8RmRs9vb2GDBgAGJiYnDu3DkkJCQgPT0doiiiuroaSUlJSEpKQnBwMGJjYxEREQErK73KIYT/771pZWUFR0dHWFlZsRcnSU6nn+jS0lI4OjpCLpdr9w0cOBAZGRlIS0vD+fPnodFoEBoaisjIyHonize0qVOn4tq1a9i7dy9UKhVmz56NuXPnYtu2bfe8bs6cOfjnP/+p3eZKa0RERHpiD06L1bJly0YVNU0xlySiuu5VxPTz84Ofn1+D197rGFFTkclkCA8PR3h4OIqKinDw4EEcPnwYt2/fBgBkZ2cjOzsb33//Pfr27Yt+/frB1dVV4qjNT0ZGBq5cuQKlUomLFy+iuroaV65cQUZGBjp16iR1eGShdCpwtmjRAlu3bsXjjz8OAHjiiScwb9489OzZE127dkXXrl2NGWMdZ8+eRVxcHI4ePYru3bsDAD744AOMHDkSa9euRatWrRq81sHBodG9DIiIiKgenIPT4uTm5uKNN97AgQMHUFRUhB9//BH9+/fH9evX8c9//hOzZ8+ud3iaqeWSRFS/+xUxicyJl5cXJkyYgNGjR+Po0aNISEjQrrJeWlqK3bt3Iy4uDt26dUNMTAxCQkL4AZuOfHx8MGLECACARqOBTCbT7ieSik6rqNvY2NRaPW/z5s3IysoyWlD3c+TIEbi5uWmLmwAwePBgyGQyJCcn3/PaL7/8Ep6enujYsSMWL16MysrKe56vUChQWlqqfdW3cigREZFFYoHTopw5cwYRERHYvn07goODUVJSgurqagCAp6cnDh06hP/85z/1XmtquSQREVkOW1tb9O3bF6+++ipefPFFREREaAtyGo0Gx44dw9tvv41//etfOHLkCJRKpcQRmz5vb2/0798fvXr1goODA3r16oX+/fvD29tb6tDIgunUgzMsLAz//e9/ERQUpO2+nZOTgxMnTtzzum7duj14hPXIz8+v84NjZWUFd3d35OfnN3jd448/jsDAQLRq1QqnTp3CP/7xD2RkZGDHjh0NXrNmzRqsWLHCYLETERE1F6JaLXUI1IReeeUVuLm5ISkpCYIg1MnFRo0ahe3bt9d7ranlkkREZHkEQUC7du3Qrl073Lx5E7///jv++OMPbSemy5cvY+vWrdixYwf69OmD/v37w93dXeKoTduff/6JXbt2wdramgsMkeR0KnCuWbMGkydPxuDBgwHcaRiWLVvW4OqWoihCEASoG/mHz6JFi/Dmm2/e85yzZ8826p53mzt3rvbfnTp1gq+vLwYNGoSsrCyEhobWe83ixYuxYMEC7XZaWhpiYmL0joGIiKjZYIHTohw8eBCvvfYavLy8cOPGjTrHAwICkJeXV++1TZVLEhER6cLd3R1jx47FyJEjcfz4cSQkJCA3NxcAUFFRgT179mDv3r3o3LkzYmNj0a5dOw5f/4uahYby8vK4wBCZBJ0KnMOHD0d2djaOHj2KgoICzJo1C3PnzkWvXr0MGszChQsxa9ase54TEhKCli1borCwsNb+6upq3Lx5s1Hza/bs2RMAcOHChQYLnLa2trC1tdVu32uVdiIiIovCRYYsikajuefijEVFRbVyprs1VS5JRETUGNbW1oiOjkbPnj2Rk5ODhIQEnDhxAmq1GqIo4uTJkzh58iR8fX0RExODqKgo2NnZSR22STh9+jRyc3Ph7++P3NxcpKensxcnSUqnAuepU6cQGBiIYcOGAQA2bdqEiRMnYtCgQQYNxsvLC15eXvc9r1evXiguLsbx48cRGRkJANi/fz80Go22aKmLtLQ0AICvr69e8RIREVkyUc05OC1Jt27dsGvXLjz77LN1jlVXV+Prr79GdHR0vdc2VS5JRESkD0EQEBwcjODgYIwfPx5//PEHfv/9d5SUlAAArl27hq+//ho//fQT55vE//fetLKy0r7Yi5OkptMiQxEREdi1a5exY9FZhw4dMHz4cMyZMwcpKSn4448/MH/+fEyZMkW7gnpeXh7CwsKQkpICAMjKysLKlStx/Phx5OTk4Oeff8aMGTPQv39/dO7cWcq3Q0REZJ40HD5sSRYvXoy4uDg888wzOH36NACgoKAAv/32G4YOHYqzZ89i0aJF9V5rarkkERFRQ1xdXTFy5EisWrUKTzzxRK3Rnrdv38b+/fuxfPlyfPjhh0hPT4fGAhddzMjIwJUrV1BVVYXc3FxUVFTgypUryMjIkDo0smA69eC0t7evtdp4YmIi5syZY7SgdPHll19i/vz5GDRoEGQyGSZMmID3339fe1ylUiEjI0Mbt42NDX777TesW7cOFRUV8Pf3x4QJE7B06VKp3gIREZFZE1UqqUOgJjRixAhs3rwZf/vb3/DJJ58AAKZNmwZRFOHi4oLPP/8c/fv3r/daU8wliYiI7kUul6N79+7o3r07Ll++jISEBBw7dgyq/+U/6enpSE9Ph5eXF2JiYtCrVy/Y29tLHHXT8PHxwYgRI1BdXY2UlBSEhobCx8cHPj4+UodGFkynAmeXLl3wzjvvQC6Xa1e+PHr06H3nnhg/fvyDR9gAd3d3bNu2rcHjQUFBEO+aG8zf3x+JiYlGi4eIiMjSiFVVUodATWz69OkYP3489u7di/Pnz0Oj0SA0NBTDhg2Ds7Nzg9eZYi5JRESkK39/f0yfPh2PPPIIDh8+jIMHD+LmzZsA7sxB/d133+GXX35BVFQUYmJitCNLmytvb294e3vjxIkTKCgoQJs2bRr8kJOoqQiieP8VAo4dO4ZHH31Uu6qYIAi432XNdeXLEydOIDIyEsePH0e3bt2kDoeIiCxUtUKBojNnYG1nB7lEcx0pjh2HbfdISZ6tViqhqqqCV3g4rBpY2IYMp7KyEv7+/li0aBFefvnlRl/fXHNJ5oVERJZJo9Hgzz//REJCQr3Dstu3b4+YmBh06tQJcrnc4M+vrKzEwYMH4ezsLNmiR0qlEp9++imSkpLg5+eHpUuXSrIoc1VVFcrKytC/f/97LoZIzZ9OPTi7d++OCxcuICsrCwUFBYiNjcWrr76KwYMHGzs+IiIiMlGakmKIoghBEKQOhYzMwcEBVlZWcHR01Ot65pJERNScyGQydOnSBV26dMG1a9eQmJiI5ORkKBQKAHfmqMzIyIC7uzv69++P3r17S1L8M6aaVdRbt26NgoIC/PHHH9rFBImkoFOBEwCsrKzQvn17tG/fHjNnzsTo0aMbtWI5ERERNS9ilQJiZSUEPYteZF4mTJiA7777Ds8884xeRW3mkkRE1Bz5+vpiypQpGDt2LJKSkpCYmIjCwkIAwM2bN/Hjjz9i165d6N69O2JjY+Hv7y9xxA/u7lXUbW1tYWVlhUOHDiE2Nha2HFlDEtG5wHm3TZs2GToOIiIiMkPqoiLIWOC0CFOmTMGzzz6LAQMGYM6cOQgKCqp3MQVdhmozlyQioubG3t4eAwYMQExMDM6ePYvExESkp6dDFEWoVCocOXIER44cQUhICGJjY9G1a1dYWelVkpFczSrqSqUS169fh1KpxLVr15Camoro6GipwyMLpdNP0z//+U8IgoBXX30VMpkM//znP+97jSAIWLZs2QMHSERERKZLU1QEBAVJHQY1gdjYWO2/f//99zrHa6YrqG/eTOaSRERkKWQyGR566CE89NBDKCwsxMGDB3HkyBHcvn0bAHDx4kVcvHgRrq6u6Nu3L/r27atdgM9c1KyiDgDZ2dmorKwEAJOfO5uaN50WGZLJZBAEAbdv34aNjQ1kMtn9b2wGE8Prg5PJExGRKTCFRYYqd+2GqFHD8eGHm/zZXGSo6W3ZskWn82bOnFlnX3PNJZkXEhGRLhQKBVJSUpCQkIBr167VOiaXyxEREYHY2FgEBwfrNA2MKSwyBADTp09Hfn4+HB0dsXDhQgBAly5d4OLi0mQxcJEhqqFTD06NRnPPbSIiIrJM6mv5UodATaS+wqWumEsSEZEls7W1Rb9+/dC3b1+cP38eCQkJOHnyJERRhFqtxrFjx3Ds2DEEBAQgJiYG3bt3h7W1tdRh39eNGzdw69atWr/Xz549i65du3IuTmpy5jnhAxEREZkEdV6e1CEQ6WX16tXYtWsX0tLSYGNjg+LiYqlDIiKiZk4QBLRr1w7t2rXDzZs38fvvv+OPP/5AeXk5ACA3Nxdbt27Fjh070KdPH/Tv3x/u7u4SR92wmgHBdw8MViqVOHXqFDp27FjvXN1ExqJ3gfPs2bPIyspCWVkZnJ2d0aZNG4SFhRkyNiIiIjJx6uJiaEpKIDOzuaPo/p544gkIgoBPPvkEcrkcTzzxxH2vEQQBGzdu1On+UueSSqUSEydORK9evXSOmYiIyFDc3d0xduxYjBw5EsePH0dCQgJyc3MBABUVFdizZw/27t2LLl26ICYmBu3atdNp+HpTUqlUAOrOvVlVVYW0tDS0b9/epAu01Lw0usD58ccfY/Xq1cirp8dGQEAAXn31VTz11FMGCY6IiIhMnyorC7acf7DZ2b9/P2QyGTQaDeRyOfbv33/fP6x0+cPLVHLJFStWAAA2b95s9GcRERE1xNraGtHR0ejZsyeys7ORmJiIEydOQK1WQxRFpKWlIS0tDa1atUJMTAyioqKkDhnAnQ8KlUolgDuFzurq6lqrwldXVyM9PR2+vr4IDg6GXC6XKlSyEI0qcP7973/HO++8A3d3dzzxxBPo2LEjnJycUF5ejj///BM//vgj5s2bh/Pnz+PNN980VsxERERkQlTnMljgbIZycnLuua0Pc88lFQoFFAqFdrtmSCEREdGDEgQBISEhCAkJwfjx4/HHH3/g999/R0lJCQDg6tWr+Oqrr/Djjz+iR48esLa2hrOzs2Txnj59WttzU6PR4NKlSwgNDa1z3rVr13Dz5k2EhITAw8PD5HqhUvOhc4EzJSUF77zzDh555BF8/vnncHR0rHPOe++9h2nTpmHt2rWYOHEiunfvbtBgiYiIyPQoz5yBqNFA0GFlbLJczSGXXLNmjbbnJxERkbG4urpi5MiRGDp0KNLS0pCYmIisrCwAwO3bt3Hw4EEAQKtWrdCpUyf4+fk1aeFQqVTiyJEjtfadO3cOgYGBtXpx1lAoFDh79izc3NwQEhJSbw5A9KB0/ktk48aN8PX1xbZt2xr8ZnR0dMRXX30FHx8fzmVERERkITRlZai+cEHqMMjENUUuuWjRIgiCcM/XuXPn9H4PixcvRklJifaVmJio972IiIjux8rKCt27d8fChQuxaNEi9OrVq1YB8erVq4iPj8d3332H06dPa4eMG1tGRgauXLlSa3Gh69ev48qVK/e8rri4GCdOnEBmZmatERFEhqBzD84jR45g4sSJsLW1ved5dnZ2mDhxIg4cOPDAwREREZF5qEpKhnW7dlKHQUb266+/4p133sGJEydQUlJS6w+bGn9daKBGU+SSCxcuxKxZs+55TkhISKPvW8PW1rZW/E5OTnrfi4iIqDECAgIwffp0PPLII0hISMCBAwdw+/ZtAEBpaSmSk5Nx/PhxtG3bFh06dECLFi2MFouPjw9GjBiBlJQUKJVK2NjYIDIyEm5ubjpdX1BQgKKiIrRq1Qr+/v719vokaiydv4suX76MDh066HRueHg4Pv/8c72DIiIiIvOiTEuD+uHRkBsxmSZpff/995g0aRIeeughTJkyBR999BEef/xxiKKIn376CW3btsW4ceMavL4pckkvLy94eXk1+joiIiJz4eTkhIEDB0Iul6OkpATnz5/HtWvXANxZ2Ofs2bM4e/YsWrVqhQ4dOiAgIAAyA08j5O3tDW9vb+2HftbW1ujYsWOj7qHRaHDlyhXk5+cjICAAvr6+Bo+TLIvOBc7S0lKdJ7B1cnJCWVmZ3kERERGReRHVatyO3wOnKZOlDoWMZM2aNYiKisKhQ4dw69YtfPTRR3jiiScwcOBA5OTkIDo6GsHBwQ1eb2q5ZG5uLm7evInc3Fyo1WqkpaUBANq0acOemUREZPIEQYC/vz/atm2LW7du4cyZM7hw4QKqq6sB3Bm+fvXqVTg5OaFDhw5o164d7OzsJI66rurqaly8eBHXrl1DSEgI3N3dpQ6JzJTO5XFRFBs1aW19Q5aIiIio+apKSoLKACttk2k6c+YMpkyZArlcrh1KplKpAABBQUF49tln77nyuanlkq+99hoiIiLw+uuvo7y8HBEREYiIiMCxY8eM+lwiIiJDa9GiBfr06YMpU6agZ8+ecHFx0R4rLy/H0aNH8fXXX+P333/HjRs3JIy0Ybdv30Z6ejrS09NRVVUldThkhho10cHatWvx1Vdf3fe8vLw8vQMiIiIiMyWKKP/iS7i9/HcI95lnkcyPg4MDbGxsAABubm6wtbXVDokD7szHlZ2dfc97mFIuuXnzZmzevNnozyEiImoqtra26NixIx566CHk5eUhPT1du/CPWq1GZmYmMjMz4ePjg/DwcAQFBZncsPCbN2+iuLgY/v7+aN26tcnFR6ZL5wJnQEAAbt68iZs3b+p8PhERETVPA8eNQ/7ly/AEsKNvP+1+dVERyr/5Fk7Tpjaqtx6Zvvbt2+PMmTPa7a5du2Lr1q2YNm0aqqursW3btnvmf8wliYiImoYgCGjdujVat26N0tJSnDlzBufPn9eusl5QUICCggI4ODggLCwM7du3h4ODg8RR/z+NRoNLly6hoKAAgYGB8PLyYl5J96VzgTOHQ86IiIjofwqKipBfUgKxnrmcFMeOwSowAPb9+0sQGRnL+PHj8f7772Pt2rWwtbXFq6++irFjx8LNzQ2CIKCiogKfffZZg9czlyQiImp6Li4uiI6ORmRkJC5cuIAzZ86guLgYAFBZWYkTJ04gLS0NwcHBCA8P17mYmJ+fr13FXalU4tatWwZfub2qqgoZGRm4cuUKAgMD4e7uzkInNahRQ9SJiIiIdFH5ww+w8m0F67ZtpA6FHlBVVRV++uknqFQqLF26FDdv3oSvry9Gjx6NhIQE7NixA3K5HKNGjcKAAQOkDpeIiIjqYW1tjQ4dOiAsLAzXrl3DmTNnkJubC1EUodFokJWVhaysLHh6eiI8PBzBwcHaObfvdvr0aWzcuBGHDh3Szpd9+/ZtrFy5EuHh4Rg6dKjBR2FUVFTgzJkzcHFxQUhIiM6LFpJlMdvJDFavXo3evXvDwcEBbm5uOl0jiiJee+01+Pr6wt7eHoMHD8b58+eNGygREZEFEjUiyjZtglrH4chkmgoLC9GxY0c8/vjjWLJkCZ5++mm0bdsWv/32GwCgX79+ePfdd7F27VoWN4mIiMyAIAho1aoVBg8ejEmTJqFz586wvWvu9OvXr+PgwYPYvn07jh07hvLycu2x/fv348knn8Thw4frLAYoiiLOnj2L9957D6dOnTJK7KWlpUhLS0N2djY0Go3B779jxw4MHToUHh4eEAQBaWlpOl3TvXt3uLm5wdHRUTuFz93Ky8sxf/58tG7dGvb29ggPD8eGDRsMHr+lM9sCp1KpxMSJE/HMM8/ofM2///1vvP/++9iwYQOSk5Ph6OiIYcOGcYUuIiIiI9BUVKDsvxshKhRSh0J6WrlyJXJycvDSSy9h586dePfdd2Fvb4958+ZJHRoRERE9ICcnJ/To0QNTpkxBv3794OHhoT1WVVWFkydP4ptvvsG+ffuQmJiIxYsXQ6PRQK1W13s/jUYDjUaDLVu2IDc312hxX7lyBX/++SdUKpVB71tRUYG+ffvizTff1Pkad3d3vPrqqzhy5AhOnTqF2bNnY/bs2YiPj9ees2DBAsTFxeGLL77A2bNn8eKLL2L+/Pn4+eefDRq/pTPbIeorVqwAAJ1XvxRFEevWrcPSpUsxduxYAMDnn38OHx8f/Pjjj5gyZYqxQiUiIrJY1Xl5KPviSzjPngWBq2CanT179mDGjBlYu3atdp+Pjw8ef/xxZGRkoH379hJGR0RERIZgZWWFdu3aoW3btigsLMSZM2eQnZ0NURQhiiJycnKwe/duaDSaOj03G7Jnzx489dRTRou5tLQUp06dQrt27Qx2z+nTpwNo3LzhsbGxtbb/9re/YcuWLTh06BCGDRsGADh8+DBmzpypPXfu3Ln4+OOPkZKSgjFjxhgidIIZ9+BsrOzsbOTn52Pw4MHafa6urujZsyeOHDnS4HUKhQKlpaXa193ds4mIiOj+lKdOofKHH3VOiMl05Obmom/fvrX29e3bF6IooqCgQKKoiIiIyBgEQYCPjw8GDBiAKVOmICIiAvb29igrK8OlS5d0zuU0Gg3OnDmDW7duGTXeyspKZGRkmEyOKYoi9u3bh4yMDPS/a7HN3r174+eff0ZeXh5EUcSBAweQmZmJoUOHShht82O2PTgbKz8/H8CdXgd38/Hx0R6rz5o1a7S9RYmIiEg/tw8ehGBnB4dRI6UOhRpBoVDAzs6u1r6a7erqailCIiIiov9RGHEaIJlMhvDwcLRv3x5ffPFFo68XRREZGRmIiooyQnT/r7y8XLsqvFRKSkrg5+cHhUIBuVyO9evXY8iQIdrjH3zwAebOnYvWrVvDysoKMpkMn376aa0iKD04kypwLlq06L5zHZw9exZhYWFNFBGwePFiLFiwQLudlpaGmJiYJns+ERFRc1G5Zw9EdTUcHn4YgiBIHQ7pKCcnBydOnNBul5SUAADOnz9f70KP3bp1a6rQiIiILJKVlRWcnJxQXl4OpVJp9OfJ5XIIgtConpKCIKCqqsooiwH9lUajqXfF93v58ssva80p/uuvv6Jfv356Pd/Z2RlpaWkoLy/Hvn37sGDBAoSEhGiHpH/wwQdISkrCzz//jMDAQBw8eBDPPfecdrEnMgy9C5zx8fHYuHEjLl68iFu3btX5RhcEAVlZWY2658KFCzFr1qx7nhMSEtLYUAEALVu2BAAUFBTA19dXu7+goABdu3Zt8DpbW9taK4o5OTnp9XwiIiICbu/bD7G8HI6TJkFoZCJK0li2bBmWLVtWZ/+zzz5ba1sURQiC0ODCA39ljFySiIjIEtjY2CAqKqrJRlPk5OQ0ehi4KIpwdXVtkhpKWFgYbGxsGnXNmDFj0LNnT+22n5+f3s+XyWRo06YNAKBr1644e/Ys1qxZg9jYWNy+fRtLlizBDz/8gFGjRgEAOnfujLS0NKxdu5YFTgPS6y+Lt956C4sWLYKPjw+ioqLQqVMngwTj5eUFLy8vg9zrr4KDg9GyZUvs27dPW9AsLS1FcnJyo1ZiJyIiogdTlZwC9a1bcJ49GzIHB6nDoXvYtGmTUe5rrFySiIjIUtjY2DS6qKevkSNH6tWDs0OHDpA1wSKT/v7+jb7G2dkZzs7ORojmTo/SmukDVCoVVCpVna+DXC5vkt6tlkSvAud7772HgQMHYvfu3bC2tjZ0TDrJzc3FzZs3kZubC7VajbS0NABAmzZttJ8QhIWFYc2aNXjkkUcgCAJefPFFrFq1Cm3btkVwcDCWLVuGVq1aYdy4cZK8ByIiIkulyjyPknfehcvcOZB7e0sdDjVg5syZRrmvKeSSREREpJuAgACMHj0au3fv1mmkhkwmQ6dOneDu7m702KytreHp6WmQe9XUmK5evQoAyMjIAHBnRHDNqOAZM2bAz88Pa9asAXBn3Zbu3bsjNDQUCoUCu3fvxtatW/HRRx8BAFxcXBATE4OXX34Z9vb2CAwMRGJiIj7//HO88847Bomb7tCrwHnr1i08+uijkiakr732GrZs2aLdjoiIAAAcOHBAO89BRkaGdp4oAHjllVdQUVGBuXPnori4GH379kVcXFydyfOJiIjI+NRFRSh+5x04z54Nm/btpQ6HmpAp5JJERESku2XLluHXX3/VuSfnyJFNs7Bk69atDdZL9Oeff8bs2bO121OmTAEAvP7661i+fDmAO53t7n5eRUUFnn32WVy5cgX29vYICwvDF198gcmTJ2vP+frrr7F48WJMnToVN2/eRGBgIFavXo2nn37aIHHTHYLY2IkUAAwYMAAREREWWW0+ceIEIiMjcfz4cU6iT0REkqlWKFB05gys7ewgb6LhSXd7qE8fXCsogI+dHX4f9GBzBwkyAY6Tp8Auuuf9T/4ftVIJVVUVvMLDYXXXXNlkHppLLsm8kIiILMmOHTswefJkiKJYb0/OmsLf3LlztZ3QjEkQBIwbN85oUx2SedGrzL1+/Xrs2LED27ZtM3Q8REREZGFEjYjyr77C7d/2SR0KNRHmkkREROZn/PjxOHz4sHZOzrsJgoBOnTrhH//4R5MUN2UyGWJjY1ncJC29hqhPnjwZ1dXVmD59Op555hm0bt0acrm81jmCIODkyZMGCZKIiIiav4pffoGoVsNh2FCpQyEjYy5JRERknnr06IGff/4Zubm56NKlC4qLi2Fvb4/XXnutSebcBIAWLVqwuEl16FXgdHd3h4eHB9q2bWvoeIiIiMiCVe7eDcHWBvb/m0+bmifmkkREROYtICAAVlZ3SkpyubxJipv29vaIiIhAeHh4k6zOTuZFrwJnQkKCgcMgIiIiuqPihx8hODjCLqqH1KGQkTCXJCIiMm8KhQJVVVUAAJVKBZVKZbTFA52cnNCpUyeEhYVxgUJqkF4FTiIiIiJjqvhqGwQ7W9h27ix1KERERET0F8nJyaiurgYAaDQa5OTkGHxkhq+vLzp27IjAwED22KT7eqACp0qlwrlz51BSUgKNRlPneP/+/R/k9kRERGShRI2I8s1bIDwxGzYdO0odDhkJc0kiIiLzo1AoEB8fr11oSBRFpKenIygo6IF7WNrY2KBdu3bo0KEDWrRoYYhwyULoVeDUaDRYvHgx1q9fj8rKygbPU6vVegdGRERElk1Uq1G28TM4TZ8G227dpA6HDIi5JBERkflKTU1FVlYWRFHU7isqKsLly5cREhKi1z3d3d3x0EMPoU2bNhyGTnrRq4/vG2+8gbfeegvTpk3D559/DlEU8a9//QsbNmxA586d0aVLF8THxxs6ViIiIrIwokaDss+3ouqPP6QOhQyIuSQREZH58vf3x9SpU+Hg4AAAsLW1RVRUlF49Llu1aoWRI0diwoQJ6NChA4ubpDe9enBu3rwZkyZNwkcffYQbN24AACIjIzFw4EDMnDkTvXr1wv79+zF48GCDBktEREQWSBRR/s230FRUwGHoUKmjIQNgLklERGS+/Pz84OfnB3t7ewCAtbU1unTp0qh7eHl5ITo6Gr6+vsYIkSyQXj04r1y5goEDBwK4U6kHoF09y8bGBtOmTcPWrVsNFCIRERERULlrNyp27qw1HIrME3NJIiKi5qu6uhpZWVnaRYjuZmtri/79+2PcuHEsbpJB6dWD08PDA+Xl5QAAJycnuLi44OLFi7XOuXXr1oNHR0RERHSX23t/g2BjA9vYWKlDoQfAXJKIiKj5unjxIpKTk6HRaGqtrB4cHIw+ffpoh7YTGZJeBc6IiAgcPXpUuz1gwACsW7cOERER0Gg0eP/99xvdPZmIiIjMw5WrV1F5+zYA4HZ1Na7evo1W/xui1BQqd+2G4O4BoV3b+59MJom5JBERkfmrGVVz9+galUqFM2fO4Pr169qV1V1dXdGnTx8EBwdLFSpZAL0KnHPnzsXmzZuhUChga2uL1atXo3///ujfvz9EUUSLFi3w1VdfGTpWIiIiktDxkyex9j//wZ6EBG0iW1pdjQH792GAtw+ebdsWnd3cmiSWym++gcOClwxyrytJSbi4Zw9uXbwIZXk5hrz1Ftzuk4BfSUrCuR07UJ6fD41aDSdfX7R/+GEExsRoz6m+fRunvvwSV1NSoCgvh6O3N9qOGIHQYcMMErc5Yy5JRERk/pRKJQBArVZr92VnZ6OgoAB+fn4oKCiAKIqYOHGidkoaImPRq8A5ZswYjBkzRrsdHh6OrKwsJCQkQC6Xo3fv3nB3dzdYkERERCStX+Lj8eTf/gZRFOvMgSkCSCwqxMGiQrwb0Q3DmmA+JbGqCsrkFKB79we+l1qhgGeHDmjduzeOb9ig0zU2Tk7oMGECnP38ILOywrXjx3H0ww9h6+qKll27AgDStmxB4enTiHrhBTh6e6Pg5Emc+PRT2Lu7o1WPHg8ctzljLklERGTeFAqFdv5slUoFlUoFADhz5gzkcjlcXV3h7OyMa9euSRkmWRC9Cpz1cXV1xdixYw11OyIiIjIRx0+exJN/+xvUanWDC/yoRRECgJdST+Br+z5N0pNTnXvJIPep6XVZUVio8zXeHTvW2m47ahRyEhJw/exZbYHzRkYGgmJitOeGDBmCrL17cfPCBYsvcNaHuSQREZH5SE5O1i4ipNFokJOTA7lcjqKiIlhZWWk/FM/KykJqaiqio6MljpiaO71WUQfudEH++uuvMW/ePDzyyCP4888/AQAlJSXYsWMHCgoKDBYkERERSeftDz+st+fmX4n/e62/cL5J4jIVoiii4NQplF29Cq/wcO1+j/btcfXYMdy+cQOiKKLw9GmUX70KH84tCYC5JBERkblSKBSIj4+Hvb097O3tYWVlhfT0dDg7O2PEiBH4xz/+gdmzZ+PJJ5/E1KlT4e/vL3XIZAH06sFZXFyM4cOHIyUlBU5OTqioqMDzzz8P4M5KmC+88AJmzJiBN954w6DBEhERUdO6cvUq4g8cuG9xs4ZaFHGgoKBJFh6S+7Q06v3vR1VRgV/mzYNGpYIgk6HbU0/VKl5GPPkkjm/YgJ3z5kGQyyEIAiKffrpWEdRSSZ1L5uTkYOXKldi/fz/y8/PRqlUrTJs2Da+++ipsbGyM8kwiIqLmIjU1FVlZWejfvz+KiopQUVGBoqIiVFVVYcmSJbBvwsUniWroVeBctGgR0tPTER8fj4iICHh7e2uPyeVyPProo9i9ezcLnEREREam+d/QIGM5cPCgzsXNGiKApOtFGN/aeJ/Wi6IIqw4dGn3dpYMHcfyTT7Tb/ZYs0bvgaGVvj6FvvYXqqioU/PknTm7ZAkcfH+2Q9Au7d+PG+fPos2gRHDw9cf3sWaT+97+wd3eHT+fOej2zuZA6lzx37hw0Gg0+/vhjtGnTBqdPn8acOXNQUVGBtWvXGuWZREREzYW/vz+mTp0K4M5Q9Vu3bgEABgwYwOImSUavAuePP/6I559/HkOGDMGNGzfqHG/Xrh02b978oLERERFRA2QyGazs7FBdVQW1EYucJSUlkMlk0Gg0uscGoEypgtiIaxpL8HCHTbu2kMkaN9tOqx494NG2rXbb/gEWshFkMjj9b0Elt+BglOXl4dwPP8C7Y0eoFQr8+dVX6PPyy/CNjLxzTlAQinNykPHzzxZf4JQ6lxw+fDiGDx+u3Q4JCUFGRgY++ugjFjiJiIjuw8/PD35+fgDufOicn58PNzc3xMbGShsYWTS9CpwlJSUIDg5u8LhKpdJONktERESGJ7O2hkebNo0qPOrDV49naAC4ODjAyoif4LsMGw73tm0hs7Zu1HXW9vawNlJcokYDzf9WENWo1RCrqwFBqHWOIJMBRv5/Zg5MMZcsKSm578rtCoUCCoVCu11eXm7ssIiIiMxCdHR0oz94JjIkvQqcoaGhOHHiRIPH9+zZg3DOL0VERGRUMmtr/VcL1NHQ4cMhCEKjhqkLAHp7e0P4S3HPkJz79ml0cbMhyrIyVF6/jtv/G15VdvUqAMDOzQ12LVoAAFLefx/2Hh7o9L/hWGd37IB7aCgcW7aERqXCtRMncOngQXSbMwcAYO3gAK/wcJzauhVyGxs4enmh6MwZ5CQmouvMmQaJ25yZWi554cIFfPDBB/ftvblmzRqsWLGiiaIiIiIybUqlEmfPnkWbNm24kBBJTq+/i5566il89tln2L59u/YPHkEQoFAo8OqrryIuLg7z5s0zaKBERETU9AICAjB69GjI5XKdzpcLAgb5+sLPwcFoMVl5e8MmKMhg97t67Bj2vvwyDv1vvsekd9/F3pdfRtaePdpz7i6AAoBaocCJTz9F/EsvYf/SpchLSkLPF15AyODB2nOiX3oJ7m3aIPn99xH30ks498MP6PTYYwgZOtRgsZsrY+WSixYtgiAI93ydO3eu1jV5eXkYPnw4Jk6ciDn/K1A3ZPHixSgpKdG+EhMTGx0jERFRc5GUlIR9+/ahurraqB9sE+lCEBu7cgDuzLEwd+5cbNy4EW5ubiguLoaPjw9u3LiB6upqzJs3Dx999JEx4tVavXo1du3ahbS0NNjY2KC4uPi+18yaNQtbtmyptW/YsGGIi4vT+bknTpxAZGQkjh8/jm7dujU2bCIiIrNz9OhR9O7dG2q1+p49OQXcKXB+GxOLLg8wt+X9uI4dC/cZ0412fzI+Y+WSRUVF9c7pebeQkBDtSulXr15FbGwsoqOjsXnz5kYPrWNeSERElkqhUOCf//wndu3ahREjRmD58uWwtbWVOiyyYHoNURcEAZ9++ilmzpyJ7777DufPn4dGo0FoaCgmTZqE/v37GzrOOpRKJSZOnIhevXph48aNOl83fPhwbNq0SbvNH0AiIqJ769GjB7Zv347JkydDFEWo1eo658gFAQKAD6J6GrW4CUGA8zD2gDR3xsolvby84OXlpdO5eXl5GDBgACIjI7Fp0ybOG0ZERNQIycnJyMzMRFhYGC5cuICUlBT069dP6rDIgulV4KzRt29f9O3b11CxNErN/EeNXWHT1tYWLVu2NEJEREREzdf48eNx+PBhrFy5Ejt37qzVk1MAMKBlSzzXPsy4xU0Ajv36wtrHx6jPoKYjVS6Zl5eH2NhYBAYGYu3atSgqKtIeY55IRER0bwqFAvHx8bCxsdGOxIiLi0NUVBQ7kZFkHqjAaY4SEhLg7e2NFi1aYODAgVi1ahU8PDwaPJ+rZRIREd3Ro0cP/Pzzz8jNzUWXLl1QXFwMFytr7Bo82KhzbtYQ7OzQ4rHHjP4cav727t2LCxcu4MKFC2jdunWtY3rM3kRERGRRUlNTkZWVhaqqKty6dQs2NjbIyspCamoqoqOjpQ6PLJTOBc4xY8Y06saCIOCnn35qdEDGNHz4cIwfPx7BwcHIysrCkiVLMGLECBw5cqTBxRO4WiYREVFtAQEBcHR0RHFxMRysrJqkuAkA7tOmwtrbu0meRYZnSrnkrFmzMGvWLKPcm4iIqLnz9/fH1KlTAQDFxcVwc3PT7ieSis4Fzp07d8LOzg4tW7bU6ZNtfVbQWrRoEd588817nnP27FmEhYU1+t4AMGXKFO2/O3XqhM6dOyM0NBQJCQkYNGhQvdcsXrwYCxYs0G6npaUhJiZGr+cTERGRfuwjIuA8fLjUYdADaIpckoiIiIzPz88Pfn5+AICSkhK4urpKHBFRIwqcfn5+yMvLg6enJx5//HFMmTLF4HMULVy48L6fpoeEhBjseSEhIfD09MSFCxcaLHDa2trWmkPCycnJYM8nIiKi+5N7uMPr+fkseJm5psgliYiIqGk1NBqWqKnpvFzk5cuXceDAAURERGDlypXw9/fH4MGDsWnTJpSVlRkkGC8vL4SFhd3zZWNjY5BnAcCVK1dw48YN+Pr6GuyeREREZDiClRW8F/4dcvYMMHtNkUsSERFR05LJdC4rERlVo74TY2Ji8PHHHyM/Px/fffcdPDw8MH/+fHh7e2P8+PH47rvvai3IY0y5ublIS0tDbm4u1Go10tLSkJaWVmsRoLCwMPzwww8A7iwO9PLLLyMpKQk5OTnYt28fxo4dizZt2mDYsGFNEjMRERE1jsczT8OufTupwyADMaVckoiIiB6MUqlEUlISlEql1KEQNa7AWcPa2hpjx47F9u3bUVBQoE1UJ0+ejH//+9+GjrFer732GiIiIvD666+jvLwcERERiIiIwLFjx7TnZGRkoKSkBMCdbtOnTp3CmDFj0K5dOzz55JOIjIzE77//XmsIOhEREZmGFo8/DufYWKnDICMwhVySiIiIHkxSUhK2bt2K5ORkqUMh0n0OzvooFArEx8fjp59+QmpqKuzs7BAUFGSg0O5t8+bN2Lx58z3PuXsCe3t7e8THxxs5KiIiIjIE17Fj4Tr+EanDICOTMpckIiIi/dX8Dr906RLi4uIQFRXFzmMkqUb34NRoNIiPj8esWbPg4+ODxx57DLdv38ann36KwsJCTJ8+3RhxEhERkYVwGT0aLaZP46JCzRRzSSIiIvOXnJyMzMxMdOrUCZmZmUhJSZE6JLJwOvfgPHz4MLZt24Zvv/0WN27cQHR0NN544w1MmjQJnp6exoyRiIiILITr2LEsbjZTzCWJiIiah5remzY2NmjRogWuX7/OXpwkOZ0LnH379oW9vT1GjhyJxx57TDt8KDc3F7m5ufVe061bN4MESURERM2f28RH4TZ5MoubzRRzSSIiouYhNTUVWVlZqKqqQnp6OlQqFbKyspCamoro6GipwyML1ag5OG/fvo3vv/8eO3bsuOd5oihCEASo1eoHCo6IiIgsQ4upU+HGOTebPeaSRERE5s/f3x9Tp06tdz+RVHQucG7atMmYcRAREZGF8njqSbiMGCF1GGRkzCWJiIiaBz8/P/j5+UkdBlEtOhc4Z86cacw4iIiIyNLIZPB89hk4DxggdSTUBJhLEhEREZGxNHoVdSIiIqIHJVhZwXvhQhY3iYiIiJoZpVKJw4cPQ6lUSh0KWRAWOImIiKhJCTY28Fm8CI7RPaUOhYiIiIj01FAhMykpCZs2bUJycrJEkZElYoGTiIiImoxgYwOfJUtg37Wr1KEQERER0QOor5CpUCgQHx+P7OxsxMXFQaFQSBghWRIWOImIiKhpWMnhs+gfsO/UUepIiIiIiOgBNFTITE5ORmZmJjp37ozMzEykpKRIHClZChY4iYiIqEl4Pf8C7Lt0kToMIiIiInpAycnJOHfuHDw9PXHu3DmkpKRoi542NjZwcXGBjY0Ne3FSk9F5FXUiIiIifbV4/HE49e0jdRhERERE9IBqCpmlpaW4ceMGPDw8EBcXB1EUkZWVhaqqKqSnp0OlUiErKwupqamIjo6WOmxq5ljgJCIiIqNy7NsXruMfkToMIiIiIjKA1NRUnD9/HpcuXUJxcTHKy8vRokUL9OzZE1OnTq1zvr+/vwRRkqVhgZOIiIiMxiY4GJ7PPgNBEKQOhYiIiIgMwN/fHxEREcjLy0OHDh1w/fp1dOvWDZGRkfDz85M6PLJQLHASERGRUchcnOH9ysuQ2dpKHQoRERERGYinpyfKy8sRFBSEtm3b4vz58ygrK4Onp6fUoZEF4yJDREREZHgyGbwX/h3W3t5SR0JEREREBpSamoqsrCxUVFQgPT0dFRUV2rk2iaTCHpxERERkcO4zpsO+40NSh0FEREREBubv78+5NsnksMBJREREBuXQvTtcRo+WOgwiIiIiMgI/Pz/OtUkmh0PUiYiIyGDkrq5cVIiIiIiIiJoUC5xERERkMO6zZ0Pu6ip1GEREREREZEFY4CQiIiKDsA1rD8e+faQOg4iIiIiILIxZFjhzcnLw5JNPIjg4GPb29ggNDcXrr78OpVJ5z+uqqqrw3HPPwcPDA05OTpgwYQIKCgqaKGoiIqLmrcVjj3NoOhERERERNTmzLHCeO3cOGo0GH3/8MdLT0/Huu+9iw4YNWLJkyT2ve+mll/DLL7/g22+/RWJiIq5evYrx48c3UdRERETNR8uWLeHr7g5PO1sAgG2bUNg9FC5xVERERETUVJRKJQ4fPnzfzmZETcEsV1EfPnw4hg8frt0OCQlBRkYGPvroI6xdu7bea0pKSrBx40Zs27YNAwcOBABs2rQJHTp0QFJSEqKjo5skdiIioubg2LFjuPXVVyj+7nsAgNOgQey9SURERGRBkpKSsHXrVqjVavTr10/qcMjCmWUPzvqUlJTA3d29wePHjx+HSqXC4MGDtfvCwsIQEBCAI0eONHidQqFAaWmp9lVeXm7QuImIiMyeIMCxZ0+poyBqlDFjxiAgIAB2dnbw9fXF9OnTcfXqVanDIiIiMgsKhQLx8fHIzs5GXFwcFAqF1CGRhWsWBc4LFy7ggw8+wLx58xo8Jz8/HzY2NnBzc6u138fHB/n5+Q1et2bNGri6umpfMTExhgqbiIioWbAJCebK6WR2BgwYgG+++QYZGRn4/vvvkZWVhUcffVTqsIiIiMxCcnIyMjMz0blzZ2RmZiIlJUXqkMjCmVSBc9GiRRAE4Z6vc+fO1bomLy8Pw4cPx8SJEzFnzhyDx7R48WKUlJRoX4mJiQZ/BhERkTmza9dO6hCIGu2ll15CdHQ0AgMD0bt3byxatAhJSUlQqVRSh0ZERGTSanpv2tjYwMXFBTY2NuzFSZIzqTk4Fy5ciFmzZt3znJCQEO2/r169igEDBqB379745JNP7nldy5YtoVQqUVxcXKsXZ0FBAVq2bNngdba2trC1tdVuOzk53ftNEBERWRiboCCpQyB6IDdv3sSXX36J3r17w9rausHzFApFrT/eOHURERFZotTUVGRlZaGqqgrp6elQqVTIyspCamoq1zchyZhUgdPLywteXl46nZuXl4cBAwYgMjISmzZtgkx2786okZGRsLa2xr59+zBhwgQAQEZGBnJzc9GrV68Hjp2IiMhSWfv7Sx0CkV7+8Y9/4D//+Q8qKysRHR2NnTt33vP8NWvWYMWKFU0UHRERkWny9/fH1KlT691PJBWTGqKuq7y8PMTGxiIgIABr165FUVER8vPza82lmZeXh7CwMO08EK6urnjyySexYMECHDhwAMePH8fs2bPRq1cvfsJARET0AKz9/KQOgQhA46c7evnll5Gamoo9e/ZALpdjxowZEEWxwftz6iIiIiLAz88PDz/8cJ2XH3NCkpBJ9eDU1d69e3HhwgVcuHABrVu3rnWsJilVqVTIyMhAZWWl9ti7774LmUyGCRMmQKFQYNiwYVi/fn2Txk5ERNScyF1dIOf0LWQiGjvdkaenJzw9PdGuXTt06NAB/v7+SEpKanB0D6cuIiIiIjJNZlngnDVr1n2T16CgoDqfwNvZ2eHDDz/Ehx9+aMToiIiILAd7b5Ipacx0R3+l0WgAgAskEBEREZkhsyxwEhERkWmw9mt9/5OITExycjKOHj2Kvn37okWLFsjKysKyZcsQGhrKudmJiIiIzJBZzsFJREREpsHar5XUIRA1moODA3bs2IFBgwahffv2ePLJJ9G5c2ckJibWGoJOREREROaBPTiJiIhIb9YtW0odAlGjderUCfv375c6DCIiIiIyEPbgJCIiIr1Z6TnfIRERERERkaGwwElERER6k3t6Sh0CERERERFZOBY4iYiISC+CtTVkjo5Sh0FEREREElAqlTh8+DCUSqXUoRCxwElERET6kbm4QBAEqcMgIiIiIgkkJSVh06ZNSE5OljoUIhY4iYiISD9yFxepQyAiIiIiCSgUCsTHxyM7OxtxcXFQKBRSh0QWjgVOIiIi0ouVh4fUIRARERGRBJKTk5GZmYnOnTsjMzMTKSkpUodEFo4FTiIiItKLbdu2UodARERERE2spvemjY0NXFxcYGNjw16cJDkWOImIiIiIiIiISCepqanIyspCRUUF0tPTUVFRgaysLKSmpkodGlkwK6kDICIiIiIiIiIi8+Dv74+pU6fWu59IKixwEhERERERERGRTvz8/ODn5yd1GES1cIg6ERERERERERERmS0WOImIiIiIiIiIiMhsscBJREREREREREREZosFTiIiIiIiIiIiIjJbLHASERERERERERGR2eIq6tRo165dw7Vr16QOgyTm6+sLX19fqcMgCbEtILYDRM0H23T6K7bx9FdsJ6g+bCvIVLDA2Ui+vr54/fXXLfYHWKFQ4LHHHkNiYqLUoZDEYmJiEB8fD1tbW6lDIQmwLSCA7QBRc8kL2aZTfdjG093YTlBD2FaQqRBEURSlDoLMR2lpKVxdXZGYmAgnJyepwyGJlJeXIyYmBiUlJXBxcZE6HJIA2wJiO0DUfLBNp79iG09/xXaC6sO2gkwJe3CSXrp27coGzIKVlpZKHQKZCLYFlovtAFHzwzadarCNp4awnaC7sa0gU8JFhoiIiIiIiIiIiMhsscBJREREREREREREZosFTmoUW1tbvP7665xA2MLx+4D4PUD8HiBqPvjzTH/F7wn6K35PUH34fUGmhIsMERERERERERERkdliD04iIiIiIiIiIiIyWyxwEhERERERERERkdligZOIiIiIiIiIiIjMFgucREREREREREREZLZY4CRqhgRB0OmVkJDwwM+qrKzE8uXLG3Wv1atXY8yYMfDx8YEgCFi+fPkDx0FEdZlyW3Du3Dm88sor6Nq1K5ydneHr64tRo0bh2LFjDxwLEZElMOU2HmC+R2QKTLmdYC5IhmYldQBEZHhbt26ttf35559j7969dfZ36NDhgZ9VWVmJFStWAABiY2N1umbp0qVo2bIlIiIiEB8f/8AxEFH9TLkt+O9//4uNGzdiwoQJePbZZ1FSUoKPP/4Y0dHRiIuLw+DBgx84JiKi5syU23iA+R6RKTDldoK5IBkaC5xEzdC0adNqbSclJWHv3r119kslOzsbQUFBuH79Ory8vKQOh6jZMuW24LHHHsPy5cvh5OSk3ffEE0+gQ4cOWL58OZNaIqL7MOU2HmC+R2QKTLmdYC5IhsYh6kQWSqPRYN26dXjooYdgZ2cHHx8fzJs3D7du3ap13rFjxzBs2DB4enrC3t4ewcHBeOKJJwAAOTk52oR1xYoV2iEO9xuCFBQUZIy3RER6kKotiIyMrJXQAoCHhwf69euHs2fPGvZNEhFZKOZ7RHQ/zAWpuWAPTiILNW/ePGzevBmzZ8/GCy+8gOzsbPznP/9Bamoq/vjjD1hbW6OwsBBDhw6Fl5cXFi1aBDc3N+Tk5GDHjh0AAC8vL3z00Ud45pln8Mgjj2D8+PEAgM6dO0v51oioEUytLcjPz4enp6dB3yMRkaUytTaeiEyPqbUTzAVJbyIRNXvPPfecePeP+++//y4CEL/88sta58XFxdXa/8MPP4gAxKNHjzZ476KiIhGA+Prrrzc6rge5logaz1TbghoHDx4UBUEQly1bpvc9iIgslam28cz3iEyHqbYTNZgL0oPgEHUiC/Ttt9/C1dUVQ4YMwfXr17WvmmECBw4cAAC4ubkBAHbu3AmVSiVhxERkDKbUFhQWFuLxxx9HcHAwXnnlFaM8g4jIkphSG09EpsmU2gnmgvSgWOAkskDnz59HSUkJvL294eXlVetVXl6OwsJCAEBMTAwmTJiAFStWwNPTE2PHjsWmTZugUCgkfgdEZAim0hZUVFRg9OjRKCsrw08//VRnPiYiImo8U2njich0mUo7wVyQDIFzcBJZII1GA29vb3z55Zf1Hq+ZIFoQBHz33XdISkrCL7/8gvj4eDzxxBN4++23kZSUxF88RGbOFNoCpVKJ8ePH49SpU4iPj0fHjh31vhcREf0/U2jjici0mUI7wVyQDIUFTiILFBoait9++w19+vSBvb39fc+Pjo5GdHQ0Vq9ejW3btmHq1Kn4+uuv8dRTT0EQhCaImIiMQeq2QKPRYMaMGdi3bx+++eYbxMTE6PM2iIioHlK38URk+qRuJ5gLkiFxiDqRBZo0aRLUajVWrlxZ51h1dTWKi4sBALdu3YIoirWOd+3aFQC0wxEcHBwAQHsNEZkPqduC559/Htu3b8f69eu1q20SEZFhSN3GE5Hpk7qdYC5IhsQenEQWKCYmBvPmzcOaNWuQlpaGoUOHwtraGufPn8e3336L9957D48++ii2bNmC9evX45FHHkFoaCjKysrw6aefwsXFBSNHjgQA2NvbIzw8HNu3b0e7du3g7u6Ojh073nNowdatW3Hp0iVUVlYCAA4ePIhVq1YBAKZPn47AwEDjfxGISNK2YN26dVi/fj169eoFBwcHfPHFF7WOP/LII3B0dDT614CIqLlivkdE98NckJoVaRdxJ6Km8Nxzz4n1/bh/8sknYmRkpGhvby86OzuLnTp1El955RXx6tWroiiK4okTJ8THHntMDAgIEG1tbUVvb29x9OjR4rFjx2rd5/Dhw2JkZKRoY2MjAhBff/31e8YTExMjAqj3deDAAUO9bSL6C1NqC2bOnNlgOwBAzM7ONuRbJyJq9kypjRdF5ntEpsiU2gnmgmRogij+pZ8xERERERERERERkZngHJxERERERERERERktljgJCIiIiIiIiIiIrPFAicRERERERERERGZLRY4iYiIiIiIiIiIyGyxwElERERERERERERmiwVOIiIiIiIiIiIiMlsscBJRHTk5ORAEAZs3b5Y6FCKSCNsBIqLmi208EemCbQWZExY4iYiIiIiIiIiIyGwJoiiKUgdBRKZFFEUoFApYW1tDLpdLHQ4RSYDtABFR88U2noh0wbaCzAkLnERERERERERERGS2OESdqJlavnw5BEFAZmYmpk2bBldXV3h5eWHZsmUQRRGXL1/G2LFj4eLigpYtW+Ltt9/WXlvfXCuzZs2Ck5MT8vLyMG7cODg5OcHLywt///vfoVarteclJCRAEAQkJCTUiqe+e+bn52P27Nlo3bo1bG1t4evri7FjxyInJ8dIXxUiy8J2gIio+WIbT0S6YFtBloIFTqJmbvLkydBoNPjXv/6Fnj17YtWqVVi3bh2GDBkCPz8/vPnmm2jTpg3+/ve/4+DBg/e8l1qtxrBhw+Dh4YG1a9ciJiYGb7/9Nj755BO9YpswYQJ++OEHzJ49G+vXr8cLL7yAsrIy5Obm6nU/Iqof2wEiouaLbTwR6YJtBTV7IhE1S6+//roIQJw7d652X3V1tdi6dWtREATxX//6l3b/rVu3RHt7e3HmzJmiKIpidna2CEDctGmT9pyZM2eKAMR//vOftZ4TEREhRkZGarcPHDggAhAPHDhQ67y/3vPWrVsiAPGtt94yzBsmojrYDhARNV9s44lIF2wryFKwBydRM/fUU09p/y2Xy9G9e3eIoognn3xSu9/NzQ3t27fHxYsX73u/p59+utZ2v379dLrur+zt7WFjY4OEhATcunWr0dcTke7YDhARNV9s44lIF2wrqLljgZOomQsICKi17erqCjs7O3h6etbZf79fKHZ2dvDy8qq1r0WLFnr9IrK1tcWbb76JX3/9FT4+Pujfvz/+/e9/Iz8/v9H3IqJ7YztARNR8sY0nIl2wraDmjgVOomZOLpfrtA8ARFFs9L3+ShCEevffPeF0jRdffBGZmZlYs2YN7OzssGzZMnTo0AGpqan3fQ4R6Y7tABFR88U2noh0wbaCmjsWOInIoFq0aAEAKC4urrX/0qVL9Z4fGhqKhQsXYs+ePTh9+jSUSmWtlfuIyPywHSAiar7YxhORLthWUFNjgZOIDCowMBByubzOynvr16+vtV1ZWYmqqqpa+0JDQ+Hs7AyFQmH0OInIeNgOEBE1X2zjiUgXbCuoqVlJHQARNS+urq6YOHEiPvjgAwiCgNDQUOzcuROFhYW1zsvMzMSgQYMwadIkhIeHw8rKCj/88AMKCgowZcoUiaInIkNgO0BE1HyxjSciXbCtoKbGAicRGdwHH3wAlUqFDRs2wNbWFpMmTcJbb72Fjh07as/x9/fHY489hn379mHr1q2wsrJCWFgYvvnmG0yYMEHC6InIENgOEBE1X2zjiUgXbCuoKQni/WaPJSIiIiIiIiIiIjJRnIOTiIiIiIiIiIiIzBYLnERERERERERERGS2WOAkIiIiIiIiIiIis8UCJxEREREREREREZktFjiJiIiIiIiIiIjIbLHASUSSysnJgSAI2Lx5s9ShEJFE2A4QETVfbOOJSBdsK+hBscBJZEaysrIwb948hISEwM7ODi4uLujTpw/ee+893L5922jPPXPmDJYvX46cnByjPUMXq1evxpgxY+Dj4wNBELB8+XJJ4yGSgiW3A+fOncMrr7yCrl27wtnZGb6+vhg1ahSOHTsmWUxERIZkyW08wFyPSFeW3FYwH6SGWEkdABHpZteuXZg4cSJsbW0xY8YMdOzYEUqlEocOHcLLL7+M9PR0fPLJJ0Z59pkzZ7BixQrExsYiKCjIKM/QxdKlS9GyZUtEREQgPj5esjiIpGLp7cB///tfbNy4ERMmTMCzzz6LkpISfPzxx4iOjkZcXBwGDx4sSVxERIZg6W08wFyPSBeW3lYwH6SGsMBJZAays7MxZcoUBAYGYv/+/fD19dUee+6553DhwgXs2rVLwgj/nyiKqKqqgr29vcHvnZ2djaCgIFy/fh1eXl4Gvz+RKWM7ADz22GNYvnw5nJyctPueeOIJdOjQAcuXL2dCS0Rmi238Hcz1iO6NbQXzQWoYh6gTmYF///vfKC8vx8aNG2v9EqvRpk0b/O1vf9NuV1dXY+XKlQgNDYWtrS2CgoKwZMkSKBSKWtcFBQVh9OjROHToEKKiomBnZ4eQkBB8/vnn2nM2b96MiRMnAgAGDBgAQRAgCAISEhJq3SM+Ph7du3eHvb09Pv74YwDAxYsXMXHiRLi7u8PBwQHR0dEP9AtXyh4FRFJjOwBERkbWSmYBwMPDA/369cPZs2f1uicRkSlgG///8RJRw9hWMB+khrHASWQGfvnlF4SEhKB37946nf/UU0/htddeQ7du3fDuu+8iJiYGa9aswZQpU+qce+HCBTz66KMYMmQI3n77bbRo0QKzZs1Ceno6AKB///544YUXAABLlizB1q1bsXXrVnTo0EF7j4yMDDz22GMYMmQI3nvvPXTt2hUFBQXo3bs34uPj8eyzz2L16tWoqqrCmDFj8MMPPxjgq0JkWdgONCw/Px+enp4Gux8RUVNjG09EumBb0TDmgwSRiExaSUmJCEAcO3asTuenpaWJAMSnnnqq1v6///3vIgBx//792n2BgYEiAPHgwYPafYWFhaKtra24cOFC7b5vv/1WBCAeOHCgzvNq7hEXF1dr/4svvigCEH///XftvrKyMjE4OFgMCgoS1Wq1KIqimJ2dLQIQN23apNP7E0VRLCoqEgGIr7/+us7XEJkztgMNO3jwoCgIgrhs2bJGX0tEZArYxtfFXI+oLrYVDWM+SKIoiuzBSWTiSktLAQDOzs46nb97924AwIIFC2rtX7hwIQDUGQoQHh6Ofv36abe9vLzQvn17XLx4UecYg4ODMWzYsDpxREVFoW/fvtp9Tk5OmDt3LnJycnDmzBmd709k6dgO1K+wsBCPP/44goOD8corrzzQvYiIpMI2noh0wbaifswHqQYLnEQmzsXFBQBQVlam0/mXLl2CTCZDmzZtau1v2bIl3NzccOnSpVr7AwIC6tyjRYsWuHXrls4xBgcH1xtH+/bt6+yvGcLw1ziIqGFsB+qqqKjA6NGjUVZWhp9++qnOXExEROaCbTwR6YJtRV3MB+luXEWdyMS5uLigVatWOH36dKOuEwRBp/Pkcnm9+0VR1PlZxlhFk4j+H9uB2pRKJcaPH49Tp04hPj4eHTt2bLJnExEZGtt4ItIF24ramA/SX7EHJ5EZGD16NLKysnDkyJH7nhsYGAiNRoPz58/X2l9QUIDi4mIEBgY2+vm6/lL8axwZGRl19p87d057nIh0x3bgDo1GgxkzZmDfvn3Ytm0bYmJiGn0PIiJTwzaeiHTBtuIO5oNUHxY4iczAK6+8AkdHRzz11FMoKCioczwrKwvvvfceAGDkyJEAgHXr1tU655133gEAjBo1qtHPd3R0BAAUFxfrfM3IkSORkpJS65dvRUUFPvnkEwQFBSE8PLzRcRBZMrYDdzz//PPYvn071q9fj/Hjxzf6eiIiU8Q2noh0wbbiDuaDVB8OUScyA6Ghodi2bRsmT56MDh06YMaMGejYsSOUSiUOHz6Mb7/9FrNmzQIAdOnSBTNnzsQnn3yC4uJixMTEICUlBVu2bMG4ceMwYMCARj+/a9eukMvlePPNN1FSUgJbW1sMHDgQ3t7eDV6zaNEifPXVVxgxYgReeOEFuLu7Y8uWLcjOzsb3338Pmazxn69s3boVly5dQmVlJQDg4MGDWLVqFQBg+vTp7ClAzRrbgTsJ+vr169GrVy84ODjgiy++qHX8kUce0SbeRETmhG38Hcz1iO6NbQXzQboHaRdxJ6LGyMzMFOfMmSMGBQWJNjY2orOzs9inTx/xgw8+EKuqqrTnqVQqccWKFWJwcLBobW0t+vv7i4sXL651jiiKYmBgoDhq1Kg6z4mJiRFjYmJq7fv000/FkJAQUS6XiwDEAwcO3PMeoiiKWVlZ4qOPPiq6ubmJdnZ2YlRUlLhz585a52RnZ4sAxE2bNt33/cfExIgA6n3VxEPU3FlyOzBz5swG2wAAYnZ29j2vJyIydZbcxtfExVyP6P4sua1gPkgNEUSxETPGEhEREREREREREZkQzsFJREREREREREREZosFTiIiIiIiIiIiIjJbLHASERERERERERGR2WKBk4iIiIiIiIiIiMwWC5xERERERERERERktljgJCIiIiIiIiIiIrPFAicRERERERERERGZLRY4iYiIiIiIiIiIyGyxwElERERERERERERmiwVOIiIiIiIiIiIiMlsscBIREREREREREZHZYoGTiIiIiIiIiIiIzBYLnERERERERERERGS2/g+fldhaNTFBrAAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 1500x1500 with 4 Axes>"
+      ]
+     },
+     "execution_count": null,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "topleft_axes = axx.flat[0]\n",
+    "topleft_axes.set_ylabel(\"New y-axis label for rawdata\")\n",
+    "topleft_axes.contrast_axes.set_ylabel(\"New y-axis label for effect size\")\n",
+    "f"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Legend\n",
+    "For plots with a `color_col` specified, a legend will be created. Utilise the `legend_kwargs` parameter to adjust the legend."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "multi_2group.mean_diff.plot(color_col=\"Gender\", \n",
+    "                            legend_kwargs={'bbox_to_anchor': [0, 1], 'fontsize':8});"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Hiding options \n",
+    "For mini-meta plots, it is possible to hide the weighted average plot by setting the parameter ``show_mini_meta=False`` in the ``.plot()`` method."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 720x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "mini_meta_paired.mean_diff.plot(show_mini_meta=False);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Similarly, you can hide the delta-delta effect size by setting \n",
+    "``show_delta2=False`` in the ``.plot()`` method."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 480x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "paired_delta2.mean_diff.plot(show_delta2=False);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Effect size error bar and marker\n",
+    "\n",
+    "Modifying the effect size marker can be done via `contrast_marker_kwargs`. This parameter accepts a dictionary of keyword arguments.\n",
+    "\n",
+    "The available options are:\n",
+    "\n",
+    "- `'marker'` - type of the marker \n",
+    "- `'markersize'` - size of the marker\n",
+    "- `'color'` - color of the marker \n",
+    "- `'alpha'` - alpha of the marker (transparency)\n",
+    "- `'zorder'` - zorder of the marker (the layering relative to other plot elements)\n",
+    "\n",
+    "**Note:\n",
+    "markersize can also be modified directly via the `contrast_marker_size` argument**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "two_groups_unpaired.mean_diff.plot(contrast_marker_kwargs={\"marker\": \"x\", 'markersize': 15, 'color': 'green', 'alpha':0.8, 'zorder': 5});"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Modifying the appearance of the effect size error bar can be done via the `contrast_errorbar_kwargs` parameter. This parameter accepts a dictionary of keyword arguments.\n",
+    "\n",
+    "The relevant inputs to `contrast_errorbar_kwargs` are:\n",
+    "\n",
+    "- `'lw'` -  width of the error bar\n",
+    "- `'linestyle'` - line style of the error bar\n",
+    "- `'color'`  - color of the error bar \n",
+    "- `'zorder'` - zorder of the error bar (the layering relative to other plot elements)\n",
+    "- `'alpha'` - alpha of the error bar (transparency)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "two_groups_unpaired.mean_diff.plot(contrast_errorbar_kwargs={'lw': 4, 'color': 'green', 'alpha':0.5, 'zorder': 2, 'linestyle': ':'});"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Group summaries\n",
+    "\n",
+    "Group summaries are included in swarmplots by default. These are the gapped lines that represent the mean and the standard deviation of the sample. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The type of group summary (gapped line) can be specified via `group_summaries` in the `.plot()` method and must be one of these: `'median_quartiles'`, `'mean_sd'`, `None`.\n",
+    "\n",
+    "By default, the group summary is set to `'mean_sd'`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "two_groups_unpaired.mean_diff.plot(group_summaries=\"mean_sd\");\n",
+    "two_groups_unpaired.mean_diff.plot(group_summaries=\"median_quartiles\");\n",
+    "two_groups_unpaired.mean_diff.plot(group_summaries=None);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Group summaries** have an associated kwargs `group_summaries_kwargs`\n",
+    "\n",
+    "The relevant inputs to `group_summaries_kwargs` are:\n",
+    "\n",
+    "- `'zorder'` - zorder of the gapped lines (the layering relative to other plot elements)\n",
+    "- `'lw'` - linewidth of the gapped lines\n",
+    "- `'alpha'` - alpha of the gapped lines (transparency)\n",
+    "- `'gap_width_percent'` - gap size\n",
+    "- `'offset'` - location adjustment of the gapped lines (x-axis)\n",
+    "- `'color'` - the shared color of the gapped lines"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "two_groups_unpaired.mean_diff.plot(group_summaries_kwargs={'gap_width_percent': 3, 'alpha': 0.5, 'lw': 4, 'offset': 0.6, 'color':'red'});"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Raw bars"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Raw bars** are included in swarmplots and slopegraph plots by default. It can be turned off by setting `raw_bars=False` in the `.plot()` method."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "multi_2group.mean_diff.plot(raw_bars=True, contrast_bars=False);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Raw bar kwargs can be utilised via `raw_bars_kwargs` in the `.plot()` method.\n",
+    "\n",
+    "Pass any keyword arguments accepted by matplotlib.patches.Rectangle here, as a string."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "multi_2group.mean_diff.plot(raw_bars=True, contrast_bars=False,\n",
+    "                            raw_bars_kwargs={'color': \"red\", 'alpha': 0.2}, \n",
+    "                            );"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Contrast bars\n",
+    "**Contrast bars** are included in all plots by default. It can be turned off by setting `contrast_bars=False` in the `.plot()` method."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "multi_2group.mean_diff.plot(contrast_bars=True, raw_bars=False);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Contrast bar kwargs can be utilised via `contrast_bars_kwargs` in the `.plot()` method.\n",
+    "\n",
+    "Pass any keyword arguments accepted by matplotlib.patches.Rectangle here, as a string."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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tJ6lUyElLUyAq52CTREWr1RZLVHQ6Ha5cuYKff/4ZjRo1QvPmzW0RChHZgbzsdKSd3I3cjFRo/IIR1LgbPFzsF2RpU5Af9WhCsXj8Yhy7fKzE4+RMYeY0Z8uqHBQEoTfdLSf0elQOCrJxRM7DJolKXFxciZ/9+uuv6N27N0aNGmWLUIgcmq1+wVvzOrd/P4xzGxdC6AsgSSoIoceVvWtQP2oWAuq2tsg1HEXRFOTSlJZQmDr24SnMAAwDb4umMMdNiQOAMvdhy0r51NZqcWrdOsMYlYep3NxQu0sX2wflJBQfo9K0aVPExMTg//7v/5QOhciu3f79MI5+PA7Ju1ch5ZcdSN69Ckc/Hoc7F444zHXystMLkxRdPiAEhF5X+F9dPs7GL3C5RRnTs9MRfzAen3z3CeIPxherg2JO3RQ5U5jl7EPl4+nnh3bTpkHl7g5IEiS1GpAkqNzd0W7aNHj6siSEuWw666ckwcHBOHPmjNJhENkto1/wAMRffwEX/YJvNWWV7BaP0lpLLHGd0s6fdnI3RAm/IIW+AGknd6OGi9QBkdP1Yk7dFLlTmDnN2fKqt2yJ/kuXGtdR6dKFSUoFKZ6o3L59GytXrkSNGjWUDoXIblnqF3xZ3S4VvU5Z58/NSP1ru4kBh5IKuRmpZX4HZyCne8bf29+suilypjALCE5zthJPPz/UGzRI6TCcik0SlW7dupncnpGRgXPnziEvLw9r1qyxRShEDskSv+DltJZU5Dpyzq/xC4Yo4RekEHpo/ILL/B7OQG5LiTl1U7o37o41e9cYkqCHFU1hFkKUuQ+RvbDJGBW9Xg8hhNELKJy2/Pe//x2nTp1CdHS0LUIhskse3v7w8KlaYrdKeX7B52Wn48+DG3HpuyX48+BGw7gPOa0lFUkk5Jw/qHE3SCXUAZFUbghykV+QRS0lpjzcUiKnbsqj41wAYFbULLir3SFJEtQqNSRJgrva3TCFuWiac2n7ENkLm7So7N271xaXITOlZ+Xgh6NnkXLnLkICqqBnq/rw96msdFgupdlzi0v9PKhxN1zZu8bQWvGwh3/Bl9b1Iqe1pGanaFnXMUXO+T28/VE/ahbOxi8wilFSuaF+1Cx4uMgvSLktJWXVTTl/7XyJ41zKmvpcnunRJB/X+rE8xceokLIOnrqMt1Ybl9mP++4g5oztzwq2dkTOL/iyul5qtBtaZmuJ3OuYGiwrtzUmoG5rtJqyyvgcTbq7TJICyOueKVJSQiGEwNiPx5Y6zqWsqc9ypkeTfFzrxzqskqh8/vnnZh03evRoC0dCpUnPysFbq7cZFi7U/dUll1+gw5tx2/DlnPFsWbEjZf2CL6vrBZAgqdzKbC0p7TqltdjIbfUBChMvV5ndY0p5K9OaSijiD8ZzJWU7wrV+rMcqicrYsWPLfYwkSUxUZHrxg7VIz7oHf59KWDJtZKn7ltat88PRsyjQmf4LuECnx87Ecyy/byMnVr6EvOx0eHj7l9oNVNov+LK6Xgru35Xd7WLqOnIGy7JbRz45XS+llbjnSsr2Rc5aP5wNZB6rJCpJSUnWOC39JT3rHm5lZpe5X1ndOil37kIlSYaWlIepJAk3bmdaI3wyIS87HXmlrLb78H4l1SiR0/VSkW4XuVOXXb1bpzxK63opq84KV1K2L1zrx3qskqjUrl3bGqelcpDTrRMSUAV6E0kKAOiFQGhVFimyJ2XVKJHb9WJut4vcqcuu3q1jCXLqrJRnnAtZH9f6sR7FS+iTdcjp1unZqj7c1Kb/L+CmVqFHy/rWDJHKQU7p+aKBsJL6rxLeKvVfpbzdLdL1whootiOnzgqnGNuX2lotVG6m//bnWj8VY7NZPykpKVi5ciWOHz+OzMxM6B/JPCVJwq5du2wVjtOT063j71MZc8b2x5txxt1DbmoV5oztD3+fSgpETqbI7Xax5oya8gyWpYqRO/6EU4ztR9FaP4/O+lG5uXGtnwqySaLy22+/oUuXLrh//z7q1auHkydPokGDBsjIyMC1a9cQERGBmjVr2iIUlyG3W6dtwzr4cs547Ew8hxu3MxFa1Rc9WtZnkmJnylMx1lpdL6yBYnklDZYtz/gTTjG2H1zrxzpskqjMmDED3t7eOHHiBCpVqoSgoCAsXrwY3bp1w4YNGzBp0iR8+eWXtgjFZfRsVR9x3x00jFF52KPdOv4+lW02u4fF5cxj6W6X0gbllrYPa6BYTmmDZTn+xHFxrR/Ls0mi8vPPP2P69OmoVasW7ty5AwCGrp9hw4bhp59+wmuvvYZ9+/bZIhyXoES3TllJCIvLma883S5lJSFlDcqVsw8Hy1aMnMGy5amzQo6DlWvLzyaJil6vR3Bw4V98fn5+UKvVhoQFABo3boyVK1faIhSXYslunYomISwuVzFyu13KSjDk1EIBUOY+Ja1JRPLIXZSQ40+cCyvXmscmiUp4eLihtopKpUJ4eDh27tyJ4cOHAwAOHDgAv3JmlEuXLsXSpUuRnJwMAGjYsCHmzJmDvn37WjJ0h2eJbh1LJCEsLldxZXW7yElC5AzKLfrfpe3DFpWKkTtYluNPnAcr15rPatOT09PTDf+7V69e2LBhg+H9pEmT8Omnn6JHjx7o3r07Vq9ejZEjS6+w+qgaNWrgn//8J44dO4bExER069YNgwYNwunTpy32Hci4HosQArq/VsIuSkKKWlrKSkKKZiGZwuJyFfDQgGk5SUjRoFxTigblytmHKobF2hzLzunTsfX557Fz+nSzzyGnci2ZZrUWlZCQEPTr1w+jRo3CtGnTEB0djfz8fLi7u+Pll19GTk4ONm7cCLVajdmzZ2PWrFnlOv/AgQON3s+fPx9Lly7FoUOH0LBhQ0t+FadX0TL7cqZCs7hcxZXVrSNnZpDcQbmsl2JdHCzrWB5kZOD+Q8MVzMHKteazWotKVFQUdu7ciREjRqB+/fr45JNPsH//fgghIEkS3njjDfzyyy9ITExEbGwsPDw8zL6WTqfDunXrkJOTg3bt2pW4X25uLu7evWt4ZWeXXYbe2R08dRmj3vwMn279GdsPnsKnW3/GqDc/w6HTlwFAVkuInCSExeUqRk7BNzlJSFDjbpBUpv8+KRqUK2cfqhgWa3NeDzIycH7LFhxfsQLnt2zBg4wMAKxcWxFWS1S+/PJLpKWl4YsvvkCnTp3w5ZdfolevXnjssccwbdo0HD9+vMLXOHnyJLy9vaHRaPDCCy9g8+bNaNCgQYn7L1y4EL6+voaXVqutcAyOTE63jqWSkKJZSO5u6r9+MKsKfzC7qVlcTgY53TpyEgw51WutXeGWChUVaxvfbTz6NO+D8d3GY/XU1WhTt43SoZGZricmYtukSfjtiy9weedO/PbFF9g2aRKuJyaycm0FWHUwrZeXF6KjoxEdHY309HR8/fXXWLt2LRYtWoRFixahbt26eOaZZzBy5EjUqVP+6an16tXDiRMnkJmZifj4eIwZMwb79u0rMVmZOXMmXnnlFcP7EydOOH2yUtFuHTn1WPx9KsmaCs3icuaT060jd2aQnFoorJdiGxws6zzkDJZl5Vrz2KyEvr+/P2JiYhATE4Nr165h7dq1+OqrrzBnzhzMnTsXbdq0wYEDB8p1Tg8PDzz++OMAgMjISBw9ehSLFy/GsmXLTO6v0Wig0WgM7729vc3/Qgoq+sVe1i94S6yeLLcei9wkpKxZSCwIZ5rcsSVyEww51Wu5uCCRfHIGy9YbNIiVa81gs0TlYY899hhee+019OnTB3PmzMGWLVtw+PDhCp9Xr9cjNzfXAhHatyXTyp4hZcnVky2VhJSFBeFKVp6Cb0wwiGxP7mBZS1WudaXCcTZPVK5evWpoTTl16hSEEGjfvj1GjRpVrvPMnDkTffv2Ra1atZCVlYW1a9di7969SEhIsFLkjsVS3TpFSktC5LSClLUPC8KVzpLr7Mgpn09E5WPJwbJlJSGuVjjOJonKrVu3DONTDh48CCEEnnzySbz55psYNWoUwsLCyn3OtLQ0jB49Gjdu3ICvry+aNGmChIQE9OzZ0/JfwAHZavVkOa0gcvZhQbiyyenWsUT5fHIcJS1qSLZXW6vFqXXrDGNUHlaewbJlJSGuWDjOaolKTk4ONm/ejLVr12LXrl3Iz89HaGgoXn75ZYwaNQotWrSo0PldueT+ix+sRXrWPfj7VCqxG8gWqyfLaQUBIKulRE5i5cyKkomyWjZK69axRPl8tqw4jtIWNeTMIdvz9POTNVi2tNYSOUmI3LEwzsRqiUpQUBAePHgAb29vjBw5EqNGjUK3bt2gUlltRrTLSM+6h1uZpdeAsUW3jpxWECGErJYSVy8I1+y5xRU63lLl8zm2xTHIWdSQLSu2V71ly1IHy5bVWiInCXHFwnFWS1R69OiBUaNG4amnnoKnp6e1LkMlkNOtU9GFBuW2gsjZpzyJFRVXnvL5pU1xJscgd1FDsr2SBsvKaS2Rk4S4YuE4qyUqW7ZssdapSabSunUssdCgnFYQIYSslhJLjJdxZZYsn0/2T+6ihmQ/5LSWyElCLDUWxpGwH8bJFXXrTI3qhmFdIw0tKZZYaFBORdrylM4vSqwmDuyIfu0aYeLAjvhyznMuPzX5YXnZ6fjz4EZc+m4J/jy4EXnZhYt/Wqp8PjkGLmroeIpaS0wpai2RU722aCyMyv2vytHqwsrRKnd3py0cp0gdFVKWpRYalNsKUp6WkorWYnFmpQ2WlVNnxaOyn8WmOFPpisaHWGucCBc1tF8lDZaV01oid0BuWWNhnA0TFRdkydWO5cwaYun8ipMzWNZS5fOp4j567iOLns/UNORZUbOwIN541o+byo2LGiqotMGycrts5CYhlioc5wiYqLggOUlIj5ZPWmTWUHn2oZLJnbFjqfL5ZD9Km4YcNyUOu0/uRkpGCkL8QtC9SXcmKQqx5Fo/rpSEyMFExcmZmtljyYUGyTbkzthhEuJYyirYJmcaMmf32Aeu9WM9TFScWGkze7jasWPhjB3nI6dgG6chOw5br/UjlzOsCcRExUnJmV5si4UGyTLKsygh2T+5Bds4DdlxKFHfxFXWBOL0ZCclZ2aPqanLZJ+KFiWU1H9NSVSp/5qa6M4ZOw5ITksJwGnIjkTO1GJLup6YiG2TJuG3L77A5Z078dsXX2DbpEm4npgI4JExM0IUtvQIYRgz8yAjw6LxWBMTFSdVNLPHFGutnZOelYOvdyfio/jd+Hp3ItKzcix+DVdWNGMnrNs4hDTvg7Bu49BqahwXE3RARS0lpjzcUtK9cXe4lVD7htOQ7Yst65vISULkjJlxFOz6cVKWXjunouX2yTI4WNY5yG0p8ff25zRkB2LJ+ialdeu42ppATFSclCXXzrFEuf2HkxoiV1eegm1t6rbhNGQHImewbEXHlrjamkDs+nFSRVVj3d3UkCQJapUKkiTB3U1drunFliq3T0T/U9RS4q52/+v5LHxO3dXuJltK/L39MbTdUEzuOxlD2w1lkuLALDG2RO6aQLYcM2NNbFFxQEVJRlnJhtzpxaV161iq3D4RGWNLiXMqrbVETlE4Od06cqrcevr6yi4wZ++YqDigJdNGyt63rOnFZXXrWLLcPhEZK2opIedQVpeNpcaWuNqaQExUnFxprSVyxpZYutw+EZGjKWoRKa1QmpzWEkuOLXGlNYGYqDixslpL5HTrsNw+Ebm6Hu++W+Y+clpL5I4tkbN4IeAcSYgcHEzrpOQMgpVTa0XuoNyi8TATB3ZEv3aNMHFgR3w55zlOTSYil1DUWmJKUWuJnAGutqzH4ijYouKk5LSWyB1bIndQLsvtE5GrktNa4mpjSyyFiYqTkjMI9tnebWSPLWESQkSuauf06XiQkQFPP78Su4Hkdtm40tgSS2Gi4qTktJYUdetwbAmRMtKz07Hr5C6kZqQi2C8Y3Rt3h7+3v9Jh0SMeZGTg/p07pe4jt7WkaF8mIfIxUXFScivTyu3WISLLOvz7YSzYaFwaf83eNZgVNQtt6rZROjwyA7tsrIOJipMqT2sJu3WIbCs9Ox0LNi4wlNDXicI/KPJ1+VgQvwBxU+LYsuKgymotKat8PhXHRMWJsbWEyD7tOrkLBXrTU1kL9AXYfXI3C8E5obIKwpFpTFScHFtLiOxPakYqVJLK0JLyMJWkQkpGigJRkTXJKQjHlhXTWEeFiMjGgv2CoRemp7LqhR4hfiE2joisTU5BODLNYROVhQsXolWrVvDx8UFQUBCefvppnD9/XumwiIjK1L1xd7ipTDdou6nc0L1JdxtHRJbyICMD57dswfEVK3B+yxY8yMgAIK8gHJnmsInKvn37MHnyZBw6dAg//PAD8vPz0atXL+Tk5CgdGhFRqfy9/TErahbc1e5/VXwurPzsrnbHrKhZXEHZQV1PTMS2SZPw2xdf4PLOnfjtiy+wbdIkXE9MlL2GDxXnsGNUduzYYfQ+Li4OQUFBOHbsGDp37qxQVERE8rSp2wZxU+Kw++RupGSkIMQvBN2bdGeS4qDKGoPS45//lL2GDxlz2ETlUZmZmQCAgIAAhSMhIpLH39ufs3ucRFljUFJOnJBdEI6MOUWiotfr8fLLL6NDhw5o1KhRifvl5uYiNzfX8D47O9sW4RERkZMrGoNS1JLysKIxKPUGDWJBODM4RaIyefJknDp1Cj/99FOp+y1cuBDz5s2zUVREROQq5I5BYfn88nPYwbRF/v73v2Pr1q3Ys2cPatSoUeq+M2fORGZmpuG1j9PBiIjIAmprtVC5mf7bn2NQKsZhExUhBP7+979j8+bN2L17N8LDw8s8RqPRoEqVKoaXt7e3DSIlIiJnV7QoocrdHZAkSGo1IElQubtzDEoFOWzXz+TJk7F27Vps2bIFPj4+SEkprOTo6+sLLy8vhaMjIiJXw0UJrcNhE5WlS5cCALo80py2atUqjB071vYBERGRy+MYFMtz2ERFCKF0CERERGRlDjtGhYiIiJyfw7aoEBERObIHGRnG41m0Wq6gbAITFSIiIhu7nphYrErtqXXr0G7aNFRv2VLp8OwKu36IiIhsyGhdICEKq9kKYVgXqGjFZSrERIWIiMiGyloX6AqLkRph1w8REZEVlDQGRc66QPQ/TFSIiIgsrLQxKHLXBaJC7PohIiKyoLLGoIQ0a8Z1gcqBiQoREVEpPP384BUQIHvqcFljUFJOnOC6QOXArh8iIqJS9Hj33XLtL2cMSr1Bg7gukExMVIiIiCxI7hgUrgskD7t+iIiILKi2VssxKBbERIWIiMiCPP38OAbFgtj1Q0REZGHVW7bkGBQLYaJCRERkIVxo0PKYqBAREVkAFxq0Do5RISIiqiAuNGg9TFSIiIgqiAsNWg8TFSIiogoqKvJmChcarBgmKkRERBXEhQath4kKERFRBbHIm/UwUSEiIqogFnmzHk5PJiIisgAWebMOJipEREQWwoUGLY+JChERkQ2xem35MFEhIiKyEVavLT8OpiUiIrKQBxkZOL9lC46vWIHzW7YYVaRl9VrzsEWFiIjIAspqLZFTvZbjW4pjiwoREVEFyWktYfVa8zh0orJ//34MHDgQ1atXhyRJ+Oabb5QOiYiIXJCc1hJWrzWPQycqOTk5aNq0KT755BOlQyEiIhcmp7WE1WvN49BjVPr27Yu+ffsqHQYREbk4Oa0lRdVrHx3HonJzY/XaUjh0olJeubm5yM3NNbzPzs5WMBoiInIWtbVanFq3rnCMyiMebi1h9dryc6lEZeHChZg3b57SYRARkZMpT2sJq9eWjySEEEoHYQmSJGHz5s14+umnS9zn0RaVEydOQKvV4tixY2jRooUNoiQiImdWrOosW0sqzKVaVDQaDTQajeG9t7e3gtEQEZGzYWuJ5Tn0rB8iIiJybg7dopKdnY2LFy8a3iclJeHEiRMICAhArVq1FIyMiIiILMGhE5XExER07drV8P6VV14BAIwZMwZxcXEKRUVERESW4tCJSpcuXeAkY4Gt7saNG7hx44bSYZCFhIaGIjQ0VOkwyEL4fDofPqOW49CJSkWFhoZi7ty5Tv9/ptzcXERHR2Pfvn1Kh0IWotVqkZCQYDQ4nBwTn0/nxGfUcpxmejKV7O7du/D19cW+ffs408kJZGdnQ6vVIjMzE1WqVFE6HKogPp/Oh8+oZbl0i4qradasGR8aJ3D37l2lQyAr4PPpPPiMWhanJxMREZHdYqJCREREdouJigvQaDSYO3cuB3U5Cd5P58L76Xx4Ty2Lg2mJiIjIbrFFhYiIiOwWExUiIiKyW0xUiIiIyG4xUaFySU5OhiRJXEuJyE7xGSVnw0TFii5duoSYmBjUqVMHnp6eqFKlCjp06IDFixfj/v37VrvumTNnEBsbi+TkZKtdQ4758+fjqaeeQnBwMCRJQmxsrKLx2JIkSbJee/furfC17t27h9jY2HKdy5XvzcNc+Rk9d+4cpk+fjmbNmsHHxwehoaHo378/EhMTFYvJVuz5+XTl+1ISVqa1km3btmHYsGHQaDQYPXo0GjVqhLy8PPz000947bXXcPr0aSxfvtwq1z5z5gzmzZuHLl26ICwszCrXkOONN95ASEgImjdvjoSEBMXiUMKaNWuM3n/++ef44Ycfim2vX79+ha917949zJs3D0DhQp1yuPK9KeLqz+inn36KlStXYujQoXjxxReRmZmJZcuWoW3bttixYwd69OihSFy2YM/Ppyvfl5IwUbGCpKQk/O1vf0Pt2rWxe/duo0UPJ0+ejIsXL2Lbtm0KRvg/Qgg8ePAAXl5eFj93UlISwsLCcOvWLQQGBlr8/PbsmWeeMXp/6NAh/PDDD8W2K8WV7w3AZxQAoqOjERsba7S+0Pjx41G/fn3ExsY69S9Ee34+Xfm+lIRdP1bw7rvvIjs7GytXrjS5MvPjjz+Ol156yfC+oKAAb731FiIiIqDRaBAWFoZZs2YhNzfX6LiwsDAMGDAAP/30E1q3bg1PT0/UqVMHn3/+uWGfuLg4DBs2DADQtWvXYk2YRedISEhAy5Yt4eXlhWXLlgEALl++jGHDhiEgIACVKlVC27ZtK/TDWsnWHEeg1+uxaNEiNGzYEJ6enggODkZMTAzS09ON9ktMTETv3r1RrVo1eHl5ITw8HOPHjwdQOB6hKNGYN2+e4X6X1ZXj6veGzygQGRlZbBHEqlWrolOnTjh79qxZ53QmSj2fvC/FsUXFCr799lvUqVMH7du3l7X/hAkTsHr1akRFRWHatGk4fPgwFi5ciLNnz2Lz5s1G+168eBFRUVF47rnnMGbMGHz22WcYO3YsIiMj0bBhQ3Tu3BlTp07FRx99hFmzZhmaLh9uwjx//jyio6MRExODiRMnol69ekhNTUX79u1x7949TJ06FVWrVsXq1avx1FNPIT4+HoMHD7bcPxABAGJiYhAXF4dx48Zh6tSpSEpKwr///W/88ssv+Pnnn+Hu7o60tDT06tULgYGBmDFjBvz8/JCcnIxNmzYBAAIDA7F06VJMmjQJgwcPxpAhQwAATZo0UfKr2T0+oyVLSUlBtWrVLHIuR2Zvz6dL3xdBFpWZmSkAiEGDBsna/8SJEwKAmDBhgtH2V199VQAQu3fvNmyrXbu2ACD2799v2JaWliY0Go2YNm2aYduGDRsEALFnz55i1ys6x44dO4y2v/zyywKA+PHHHw3bsrKyRHh4uAgLCxM6nU4IIURSUpIAIFatWiXr+wkhxM2bNwUAMXfuXNnHOJvJkyeLhx+3H3/8UQAQX375pdF+O3bsMNq+efNmAUAcPXq0xHNX5N/XFe8Nn9GS7d+/X0iSJGbPnl3uYx2ZvT6fRVz1vhRh14+FFS3v7ePjI2v/7du3AwBeeeUVo+3Tpk0DgGLNug0aNECnTp0M7wMDA1GvXj1cvnxZdozh4eHo3bt3sThat26Njh07GrZ5e3vj+eefR3JyMs6cOSP7/FS2DRs2wNfXFz179sStW7cMr6Jm3z179gAA/Pz8AABbt25Ffn6+ghE7Dz6jpqWlpWHkyJEIDw/H9OnTK3QuR2dPzyfvC8eoWFyVKlUAAFlZWbL2v3LlClQqFR5//HGj7SEhIfDz88OVK1eMtteqVavYOfz9/Yv1m5YmPDzcZBz16tUrtr2oOfrROKhiLly4gMzMTAQFBSEwMNDolZ2djbS0NACAVqvF0KFDMW/ePFSrVg2DBg3CqlWrio2NIPn4jBaXk5ODAQMGICsrC1u2bCk2RsLV2MvzyftSiGNULKxKlSqoXr06Tp06Va7jJEmStZ9arTa5XZRjbUlrzPCh8tHr9QgKCsKXX35p8vOiAXiSJCE+Ph6HDh3Ct99+i4SEBIwfPx4ffPABDh065LI/uCqCz6ixvLw8DBkyBL/99hsSEhLQqFEjm13bXtnD88n78j9MVKxgwIABWL58OQ4ePIh27dqVum/t2rWh1+tx4cIFo8F0qampyMjIQO3atct9fbk/UB+N4/z588W2nzt3zvA5WU5ERAR27tyJDh06yPql1LZtW7Rt2xbz58/H2rVrMWrUKKxbtw4TJkww6367Oj6jhfR6PUaPHo1du3bh66+/hlarLfc5nJHSzyfvizF2/VjB9OnTUblyZUyYMAGpqanFPr906RIWL14MAOjXrx8AYNGiRUb7fPjhhwCA/v37l/v6lStXBgBkZGTIPqZfv344cuQIDh48aNiWk5OD5cuXIywsDA0aNCh3HFSy4cOHQ6fT4a233ir2WUFBgeHepaenF/tLvFmzZgBgaF6uVKkSgPLdb1fHZ7TQlClTsH79eixZssQwI4WUfz55X4yxRcUKIiIisHbtWowYMQL169c3qnp54MABbNiwAWPHjgUANG3aFGPGjMHy5cuRkZEBrVaLI0eOYPXq1Xj66afRtWvXcl+/WbNmUKvVeOedd5CZmQmNRoNu3bohKCioxGNmzJiBr776Cn379sXUqVMREBCA1atXIykpCRs3boRKVf6cds2aNbhy5Qru3bsHANi/fz/efvttAMCzzz7r0q00Wq0WMTExWLhwIU6cOIFevXrB3d0dFy5cwIYNG7B48WJERUVh9erVWLJkCQYPHoyIiAhkZWVhxYoVqFKliuEXqJeXFxo0aID169fjiSeeQEBAABo1alRqU7Gr3xs+o4WJ15IlS9CuXTtUqlQJX3zxhdHngwcPNiRUrkbJ55P3xQRlJx05t99//11MnDhRhIWFCQ8PD+Hj4yM6dOggPv74Y/HgwQPDfvn5+WLevHkiPDxcuLu7i5o1a4qZM2ca7SNE4bTF/v37F7uOVqsVWq3WaNuKFStEnTp1hFqtNpoGWdI5hBDi0qVLIioqSvj5+QlPT0/RunVrsXXrVqN9yjP1UavVCgAmX6amZTqzR6c/Flm+fLmIjIwUXl5ewsfHRzRu3FhMnz5dXL9+XQghxPHjx0V0dLSoVauW0Gg0IigoSAwYMEAkJiYanefAgQMiMjJSeHh4yJoKyXtTyJWf0TFjxpT4/wEAIikpqdTjnYk9PZ+8L8VJQpRjhBcRERGRDXGMChEREdktJipERERkt5ioEBERkd1iokJERER2i4kKERER2S0mKgp699138eSTT0Kv1ysdSoXNmDEDbdq0UToMRfF+Oh/eU+fC++mglJ4f7aoyMzNFQECA+Oyzzwzb8Nc8+ffff7/Y/qtWrSpzOXG5Nm7cKIYPHy7Cw8OFl5eXeOKJJ8Qrr7wi0tPTTe6/ZcsW0bx5c6HRaETNmjXFnDlzRH5+vtE+N27cEBqNRmzZsqXC8Tki3k/nw3vqXHg/HRcTFYX861//ElWqVBH37983bCt6aIKDg0VOTo7R/pZ8aKpWrSoaN24sZs+eLVasWCGmTp0qPDw8xJNPPinu3btntO/27duFJEmia9euYvny5WLKlClCpVKJF154odh5hw8fLjp16lTh+BwR76fz4T11LryfjouJikKaNGkinnnmGaNtAESzZs0EAPHBBx8YfWbJh8ZU5dHVq1cLAGLFihVG2xs0aCCaNm1qlM2//vrrQpIkcfbsWaN94+PjhSRJ4tKlSxWO0dHwfjof3lPnwvvpuDhGRQFJSUn47bff0KNHj2KfdejQAd26dcO7776L+/fvW+X6Xbp0KbZt8ODBAICzZ88atp05cwZnzpzB888/Dze3/y0L9eKLL0IIgfj4eKNzFH2fLVu2WCFq+8X76Xx4T50L76djY6KigAMHDgAAWrRoYfLz2NhYpKamYunSpaWeJzc3F7du3ZL1KktKSgoAoFq1aoZtv/zyCwCgZcuWRvtWr14dNWrUMHxexNfXFxEREfj555/LvJ4z4f10PrynzoX307Fx9WQFnDt3DgAQHh5u8vNOnTqha9eueO+99zBp0iR4eXmZ3O+rr77CuHHjZF1TlLGk0zvvvAO1Wo2oqCjDths3bgAAQkNDi+0fGhqK69evF9tep04dnDlzRlZMzoL30/nwnjoX3k/HxkRFAbdv34abmxu8vb1L3Cc2NhZarRb/+c9/8I9//MPkPr1798YPP/xQ4XjWrl2LlStXYvr06ahbt65he1EzqEajKXaMp6cn7t69W2y7v79/sazf2fF+Oh/eU+fC++nYmKjYqc6dO6Nr165499138cILL5jcJzQ01GTmXR4//vgjnnvuOfTu3Rvz5883+qzor4rc3Nxixz148MDkXx1CCEiSVKGYnBHvp/PhPXUuvJ/2i4mKAqpWrYqCggJkZWXBx8enxP3mzp2LLl26YNmyZfDz8yv2+f3795GZmSnrmiEhIcW2/frrr3jqqafQqFEjxMfHGw3eAv7X/Hjjxg3UrFnT6LMbN26gdevWxc6Znp5u1OfqCng/nQ/vqXPh/XRsHEyrgCeffBJA4Uj00mi1WnTp0gXvvPOOydHo69evN2T4Zb0edenSJfTp0wdBQUHYvn27ySbRZs2aAQASExONtl+/fh1//vmn4fOHJSUloX79+qV+L2fD++l8eE+dC++nY2OLigLatWsHoPD/jE2aNCl139jYWHTp0gXLly8v9pm5/aUpKSno1asXVCoVEhISEBgYaHK/hg0b4sknn8Ty5csRExMDtVoNAFi6dCkkSTIaBAYAmZmZuHTpEiZNmlTumBwZ76fz4T11LryfDk6Z8i3UqFEjER0dbbQNgJg8eXKxfbVaraGCoiWKDzVt2lQAENOnTxdr1qwxen3//fdG+3777bdCkiTRrVs3sXz5cjF16lShUqnExIkTi503Pj5eABAXL16scIyOhvfT+fCeOhfeT8fFREUhH374ofD29jYqn1zSQ7Nnzx6LPjRF5zL10mq1xfbfvHmzaNasmdBoNKJGjRrijTfeEHl5ecX2GzFihOjYsWOF43NEvJ/Oh/fUufB+Oi4mKgrJyMgQAQEB4tNPP1U6FIu4ceOG8PT0FN98843SoSiC99P58J46F95Px8XBtArx9fXF9OnT8d577znFkuOLFi1C48aNMWjQIKVDUQTvp/PhPXUuvJ+OSxKijPJ5RERERAphiwoRERHZLSYqREREZLeYqBAREZHdYqJCREREdouJChEREdktJipERERkt5ioEBERkd1iokJERER2i4kKERER2S0mKkRERGS3mKgQERGR3WKiQkRERHaLiQoRERHZLZdOVG7cuIHY2FjcuHFD6VCIiIjIBJdPVObNm8dEhYiIyE45dKKyf/9+DBw4ENWrV4ckSfjmm2+UDomIiIgsyKETlZycHDRt2hSffPKJ0qEQERGRFbgpHUBF9O3bF3379lU6DCIiIrISh25RISIiIufm0C0q5ZWbm4vc3FzD++zsbAWjISIiorK4VIvKwoUL4evra3hptVqlQyIiIqJSuFSiMnPmTGRmZhpe+/btUzokIiIiKoVLdf1oNBpoNBrDe29vbwWjIaqArBTAJ0TpKIiIrM6hE5Xs7GxcvHjR8D4pKQknTpxAQEAAatWqpWBkRFaWeY2JChG5BIdOVBITE9G1a1fD+1deeQUAMGbMGMTFxSkUFZEN5GUDQgCSpHQkRERW5dCJSpcuXSCEUDoMItvT5xcmKxofpSMhIrIqlxpMS+RU7l5XOgIiIqtjokLkqFJPKx0BEZHVMVEhclRJ+5WOgIjI6pioEDmq67+w+4eInB4TFSJHdmqT0hEQEVkVExUiR3b2v0D2TaWjICKyGiYqRA6mZcuWqNExGi0XHAcKcoGf/lVYU4WIyAkxUSFyMCkpKbiWegspd/MKN1z5GfjlC2WDIiKyEiYqRM7g6KfAsTi2rBCR02GiQuQsElcBP8wBHtxVOhIiIothokLkTJL2AxvGAL9/D+j1SkdDRFRhTFSInM29O8Ce+cDm54Hkn9kdREQOjYkKkbO6dQFImAVsfgH446jS0RARmYWJCpGzu3kO2P4qsGMWa64QkcNhokLkKq78DMSPAy7uZHcQETkMJipEriQ3C9j1FpDwOpB5TeloiIjKxESFyIFcvXoVOTk5AICcXB2u3nlg3omu/Ax8/Syw710g46oFIyQisiwmKkQO4MiRIxg4cCDCwsKQkZEBAMi4r0PY60fw1JJTOJqcVf6T6nXAuW3A16OB72YUDrjllGYisjNuSgdARKXbtGkTRowYASEExCNjS4QAtp+6g+9OpWP9xPoY0rxa+S8gBHD1YOHLtwbQcDBQry/gUdlC38BY/v0sXN7xH9y5cBiQVKj2ZHvU6R0DtYeXjFAFzqybi/RLx1B/2BuoWq+d4bOs678jeXccsm9cBCTAp3o9hHUfB+/gOlb5HkRkG2xRIbJjR44cwYgRI6DT6aDT6Uzuo9MDOr3AiBVnzWtZeVjmn8CBj4EvooBDSwtrspjht89nIPXXH0x+9vs37+HerStoNOptNBgxF5lXT+Pito9lnff6kW8ASMW26/Lu4/RXc6CpEoim4z9EkzHvQe3hhdNrZ0OvKzDrOxCRfWCiQmTH3n77bZMtKY8SAAQE3t5+xTIXzr8H/LoO+CoaOL6msJvIAu7duor0S8fweP+X4PPYk/Ct1RARfWJw8/R+5GbdLvXY7JRLuHZoM+oOfMnEef9Ewf0s1NY+g0pVa6ByYG3U6jwS+TkZyM1Ms0jsRKQMsxMVnU6HdevWISYmBoMHD8bJkycBAJmZmdi0aRNSU1MtFiSRK7p69Sq2bt1aYkvKo3R64NuTd8wfYGtKwYPCBQ+/mw4U5FX4dHf/PAe1Z2X4VK9r2OYX3hyQJGRdO1/icbr8Bzj/zXuI6DMJHt4BxT73qvoY3LyqIOXE99Dr8qHLz0Xqie/hVa0mPP2CKxw3ESnHrDEqGRkZ6NOnD44cOQJvb2/k5ORgypQpAABvb29MnToVo0ePxoIFCywaLJFd0umsMgh11/ffl9mS8ighgN3nMjC2nYV/Of9xFDi1GWg2okKnyc9Oh0clP6NtkkoNdy8f5Oekl3hc0vcrUKVGfaMxKQ9z01RC42cX4uyGt/HHT+sAAF4B1dEw+i1IKnWFYiYiZZmVqMyYMQOnT59GQkICmjdvjqCgIMNnarUaUVFR2L59OxMVcn46HfDHH0BexVsbHpWVnAyVSgV9OZIglQTczc61Sjy48CPQOApQF//F/8dP6/HHz18b3usL8pB17Rwu7fiPYVuLF5aaddnbvx9CRvJvaD7xoxL30eXn4sLWxahSowHqDZ4Oodfj2qFNOLM+Fk3H/wtqd41Z1yYi5ZmVqHzzzTeYMmUKevbsidu3i/crP/HEE4iLi6tobET2T68vTArUasDNspPofHx9y5WkAIBeAFW81IBUfMBphQgBuFUp/L4mEpWQyH6o1qCT4f35b95DtSc7oOqT7Q3bND5V4e7tj7x7Gcan1uuQfz8L7pX9TV46M/k3PEi/gYPvDTfafjZ+AarUbIgmo/+Jm6f2IjczDU3HfQBJKuzR9h78Gg69PwJ3fj+EwIZac785ESnMrJ+smZmZCA8PL/Hz/Px8FBRwpD25EDc3wN3doqfs3qULJEkqV/ePJAHdnvCzfKKi8QXqDCjxY3cvH7h7+Rjeq9w0cK/sC6+A6kb7VanxJHQPcpB94wK8QwvHqWQk/QoIAZ/H6pk8d432UQhu1sto2y/LJ6NOz4kIqNsaAKAvyP3rO//vexcmLOX79yMi+2PWYNqIiAgcP368xM+///57NGjQwOygiAio9dhjGNCjB9QmWjBMUauAgY38USvAwt0cPjWADvMALzNqtDyiUrVa8I+IxIVtHyPr2nnc/eMMLiUsRWDDztD4VAUA5N69hWNLYwyDaz28A1A5KMzoBQAa30B4+ocAKByQW3A/G5d2LMG9W1eRc/MKfv/vvyCp1PCr3aTCcRORcsxKVCZMmIDPPvsM69evN/y1IkkScnNz8frrr2PHjh2IiYmxaKBErmj2yy9DkiRIZbSQFLYlSHijdw3LBlCjA9B1IeAdYrFTPvH0a6hUtQZOffk6Tq+biyo1G+Lx/lMMnwu9Dvdv/wldfq7sc1aqVhMNRszFvdRk/LrqVZxcPR152XfQMPpNePgUnyVERI5DEma0iwoh8Pzzz2PlypXw8/NDRkYGgoODcfv2bRQUFCAmJgZLl5o3cM6Wjh8/jsjISBw7dgwtWrRQOhxyRPn5QFISoNFYvOunyKbt2zFi0iQIIUxOVVarCpOUr8fXxeCmVS1zUUkNNB4NRPQr7FLJzwdyc4HwcKt9TyIiU8waoyJJElasWIExY8YgPj4eFy5cgF6vR0REBIYPH47OnTtbOk4ilzWkXz8c2LIFby1ahK07dxqNuZAkoH9Df7zRuwZa1fa2zAWr1AQiJwP+j1vmfEREFVChaQodO3ZEx44dLRULEZWgVbNm+G9cHK5eu4amPXog4+5d+Hmp8euMppYbk+LhDdQbAkT0BVRsNSEi+2DWGJWkpCR8++23JX7+7bffIjk52dyYiKgEtR57DJUrVQIAVNaoLZOkuFcG6g8Hen0C1H2KSQoR2RWzWlReffVV3L17FwMHDjT5+SeffAI/Pz+sW7euQsERkRV5BQCPDwDCewJuZa9cTESkBLNaVA4ePIiePXuW+Hn37t3x448/mh0UEVlRQF2g1ctA7yWFLShMUojIjpnVopKeng4fH58SP/f29jZZsZaIFKJ2Bx7rAET04SBZInIoZrWo1KpVCz///HOJn//444+oUcPC9RyIqPw8fArHn/T5D9Dy70xSiMjhmJWoREdH46uvvsJHH31ktBaJTqfD4sWLsX79eowcOdJiQRJROak9/kpQlhT+V+OrdERERGYxq+tn5syZ+Omnn/Dyyy9j/vz5qFevcI2O8+fP4+bNm+jSpQtef/11iwZKRDIF1ANaTQUqBysdCRFRhZnVoqLRaPD9999j5cqVaN26NW7duoVbt26hdevW+Oyzz7Bz505oNFxWncjmanQEOscySSEip2F2wTeVSoVx48Zh3LhxloyHiMwV3BxoOQVQyVvEkIjIEVSoMi0R2V5IUBCgy0OI50OL9nlXL5xyzCSFiJyM2YlKQkICVq5cicuXLyM9PR2Prm0oSRIuXbpU4QCJyFjid98Bf/wEHF1UuEHjA7SfCXhUVjQuIiJrMCtRee+99zBjxgwEBwejdevWaNy4saXjIiI5VG5Au5mAd6jSkRARWYVZicrixYvRrVs3bN++He5c8p1IOQ3+BgQ8oXQURERWY3Zl2qioKCYpDuDq1avYtWsXsrKy4OPjg+7du6NWrVpKh0WW4OkHRPRTOgqqAD6fRGUzK1Fp3bo1zp8/b+lYyIKOHDmCt956C9u2bYMQAiqVCnq9HpIkYcCAAZg9ezZatWqldJhUETU6FhZ2I4fD55NIPrPqqCxZsgSbNm3C2rVrLR0PWcCmTZvQoUMHfPfdd4ZBzkUVhIUQ2L59O9q3b49NmzYpGSZVVBDHhjkiPp9E5WNWojJixAgUFBTg2Wefha+vLxo2bIgmTZoYvZo2bWrpWE365JNPEBYWBk9PT7Rp0wZHjhyxyXXt1ZEjRzBixAjodDrodDqT+xR9NmLECBw9etTGEZLF+NRUOgIqJz6fROVnVqISEBCAunXronPnzmjRogWCgoJQtWpVo1dAQIClYy1m/fr1eOWVVzB37lwcP34cTZs2Re/evZGWlmb1a9urt99+G0KIYtPFH1W0z9tvv22jyMiiJAnwsv4zRpbF55Oo/CRR1hNjx9q0aYNWrVrh3//+N4DC5tOaNWtiypQpmDFjRpnHHz9+HJGRkTh27BhatGhh7XCt7urVqwgLCyvzh+DDJElCcnIyB/CZKz8fSEoCNBrAloPLb50GqjW03fXy84HcXCA83Lbf04nw+SQyj8NWps3Ly8OxY8cwc+ZMwzaVSoUePXrg4MGDJo/Jzc1Fbu7/qnlmZ2cDAAoKCpCfn2/dgG0gISGhXD8EgcK/3L7//nuMGTPGSlE5ufz8wpdeX/hfW9FpgHv3bHe9ggJAp7Ptd3QyfD4VpNMVPqPOTqUC1LatTm2T2b/CTJmZmWLhwoWiV69eolmzZuLw4cNCCCFu374tPvjgA3HhwgVzTy3LtWvXBABx4MABo+2vvfaaaN26tclj5s6dKwDwxRdffPHFF18WeNmCWS0qf/75J7RaLf744w/UrVsX586dM7ROBAQEYNmyZbhy5QoWL15szumtZubMmXjllVcM70+cOAGtVovDhw+jefPmCkZmGXFxcXj++efLfdyKFSv4F1tFKPHXWm5WYel8W1LgrzVnwudTIUXds2o14OawnQhlK2r1dMLuWbPu2muvvYasrCycOHECQUFBCAoKMvr86aefxtatWy0SYEmqVasGtVqN1NRUo+2pqakICQkxeYxGo4FGozG89/b2BgC4ubk5RfG63r17Q5KkcveB9+rVyym+v2KU+LdzE4BHJdtfl8zG51NB7u62H0dma0XjyNzdne57mjXr5/vvv8fUqVPRoEEDSJJU7PM6dergjz/+qHBwpfHw8EBkZCR27dpl2KbX67Fr1y60a9fOqte2V7Vq1cKAAQOglvlXr1qtxsCBAzlQzxFJbNlwNHw+icxjVqJy//59BAYGlvh5VlaW2QGVxyuvvIIVK1Zg9erVOHv2LCZNmoScnByMGzfOJte3R7Nnz4YkSSYTyIcV7fPGG2/YKDKyKHdPpSMgM/D5JCo/sxKVBg0aYP/+/SV+/s0339hkzMeIESPw/vvvY86cOWjWrBlOnDiBHTt2IDg42OrXtletWrXC+vXroVarS/zLreizr7/+mmW6iWyIzydR+ZmVqLz88stYt24d3nnnHWRmZgIo7Ha5ePEinn32WRw8eBD/+Mc/LBpoSf7+97/jypUryM3NxeHDh9GmTRubXNeeDRkyBAcOHEC/fv0Mf7mpVIW3WpIk9O/fHwcOHMDgwYOVDJPIJfH5JCofswu+zZ8/H7GxsRBCQK/XQ6VSGRbXevvtt/F///d/lo7V4pyt4JspV69exe7du3H37l1UqVIF3bp1Y583kZ3g82kDShVltDUnLspYocq0V69excaNG3Hx4kXo9XpERERgyJAhqFOnjiVjtBpXSFSIiFwaExWHV+7pyffu3UOnTp0wceJEvPDCCzbr4iEiIiLXU+4xKpUqVUJSUlKZo9aJiIiIKsqswbR9+vRBQkKCpWMhIiIiMmJWojJ79mz8/vvvePbZZ/HTTz/h2rVruHPnTrEXERERUUWYVUK/YcPC5eXPnDmDtWvXlrifTqczLyoiIiIimJmozJkzh2NUiIiISiDy8yE52ewbpZiVqMTGxlo4DCIiIqLizBqj8qjMzEx28xARERVRWeTXK6ECiUpiYiL69OmDSpUqoWrVqti3bx8A4NatWxg0aBD27t1rqRiJiIgcC4dHWIxZicqBAwfQsWNHXLhwAc888wz0er3hs2rVqiEzMxPLli2zWJBERETkmsxKVGbNmoX69evjzJkzWLBgQbHPu3btisOHD1c4OCIiInJtZiUqR48exbhx46DRaEzO/nnssceQkpJS4eCIiIjItZmVqLi7uxt19zzq2rVr8Pb2NjsoIiIih2b+er/0CLMSlbZt2yI+Pt7kZzk5OVi1ahW0Wm2FAiMiInJYTFQsxqxEZd68eUhMTET//v3x3XffAQB+/fVXfPrpp4iMjMTNmzcxe/ZsiwZKRETkKEQpvQ5UPmYVfGvTpg22b9+OSZMmYfTo0QCAadOmAQAiIiKwfft2NGnSxHJREhERORK2qFiMrETl7t27qFy5MtRqtWFbt27dcP78eZw4cQIXLlyAXq9HREQEIiMjWV6fiIhcGxMVi5HV9ePv74/169cb3o8fP94w/bhZs2YYNmwYRowYgZYtWzJJISIilydYrd1iZCUqHh4eyM3NNbyPi4vDpUuXrBYUERGRQ2OLisXI6vp58skn8emnnyIsLAy+vr4AgOTkZBw/frzU41q0aFHxCImIiBwMW1QsRxKi7LRvx44dGDFiBLKzs2WdVAgBSZLsfqHC48ePIzIyEseOHWNSRUTkjPLzgaQkQKMB3N1tdtmCO3fgFhBgs+shPx/IzQXCw236PW1BVotKnz59kJSUhKNHjyI1NRVjx47F888/j3bt2lk7PiIiIofD6cmWIytR+e2331C7dm307t0bALBq1SoMGzYM3bt3t2pwREREDsnOexQciazBtM2bN8e2bdusHQsREZFTYIuK5chKVLy8vHDv3j3D+3379iE1NdVqQRERETkykV+gdAhOQ1bXT9OmTfHhhx9CrVYbZv0cPXoUnp6epR43ZMiQikdIRETkYPQP7isdgtOQNesnMTERUVFRuHr1auFBkoSyDuOsHyIiUpxCs35yjh9HZVv+XnH1WT8tW7bExYsXcenSJaSmpqJLly54/fXX0aNHD2vHR0RE5HAK0tOVDsFpyF6U0M3NDfXq1UO9evUwZswYDBgwAG3atLFmbERERA6p4OZNpUNwGmatnrxq1SpLx0FEROQ08jnhxGJkJSpvvvkmJEnC66+/DpVKhTfffLPMYyRJwuzZsyscIBERkaMpSE+H/sEDqMqYdEJlkzWYVqVSQZIk3L9/Hx4eHlCpyp7VzMG0RESkOIUG0/45NxaBY8dCEx5mmwu6+mBa/SOFax59T0RERMZy//jDdomKE5NV8I2IiIjKJzcpSekQnIJZg2kB4OzZs7h06RKysrLg4+ODxx9/HE8++aQlYyMiInJYD86fhxACkiQpHYpDK3eismzZMsyfPx/Xrl0r9lmtWrXw+uuvY8KECRYJjoiIyFEV3LmD/JQUeISGKh2KQytXovLqq6/iww8/REBAAMaPH49GjRrB29sb2dnZOHnyJL755hvExMTgwoULeOedd6wVMxERkUPIOXYcHgP6Kx2GQ5OdqBw5cgQffvghBg8ejM8//xyVK1cuts/ixYvxzDPP4P3338ewYcPQsmVLiwZLRETkSLIPH4Zfv76QZMyWJdNk/8utXLkSoaGhWLt2rckkBQAqV66Mr776CsHBwVi5cqXFgiQiInJEBTdv4v7p00qH4dBkJyoHDx7EsGHDoNFoSt3P09MTw4YNw88//1zh4IiIiBxd+rffQrCsh9lkJyp//PEH6tevL2vfBg0a4I8//jA7KCIiImeRd/UPZO3fr3QYDkt2onL37l34+PjI2tfb2xtZWVlmB0VERORM7sRvRN6160qH4ZBkJyrlnQsuozI/ERGR02nZty9arV2Lp/bvM2wTBQVIW7YMunv3FIzMMZVrevL777+Pr776qsz9TNVYISIicgUpaWlIuZcDPLIgYX5aGm6u/AzBk1/kLKBykJ2o1KpVC3fu3MGdO3dk729N8+fPx7Zt23DixAl4eHggIyPDqtcjIiKqqPunTyN902YERA1VOhSHITtRSU5OtmIY5ZeXl4dhw4ahXbt2nApNREQOI3PnTriHhsKnQ3ulQ3EIZq/1o7R58+YBAOLi4pQNhIiIqJxuffkl3KpVhVe9ekqHYvdcqpMsNzcXd+/eNbyys7OVDomIiFyRXo+0pf9BLkt5lMmlEpWFCxfC19fX8NJqtUqHRERELkr/4AFSFi1G3p9/Kh2KXbOrRGXGjBmQJKnU17lz58w+/8yZM5GZmWl47du3r+yDiIiIrESfk4MbH36IBxcvKh2K3bKrMSrTpk3D2LFjS92nTp06Zp9fo9EYLQHg7e1t9rmIiIgsQX/vPlIWLUa1MaPh3aqV0uHYHbtKVAIDAxEYGKh0GERERDYlCgpwc+VnyLt+Hf4DB7LOykPsKlEpj6tXr+LOnTu4evUqdDodTpw4AQB4/PHH2VJCREQOKfO7Hcj/8xoCx4+DystL6XDsgtmJSkJCAlauXInLly8jPT29WMl8SZJw6dKlCgdYkjlz5mD16tWG982bNwcA7NmzB126dLHadYmIiKzp3smTuP7uewie/CLcq1VTOhzFmZWovPfee5gxYwaCg4PRunVrNG7c2NJxlSkuLo41VIiIyCnl37iBG+++h+Apf4emZk2lw1GUWYnK4sWL0a1bN2zfvh3u7u6WjomIiMjl6e7eRcq/FiHkpanQ1K6tdDiKMWu0Tnp6OqKiopikEBERWZH+3j2kLP4IedeuKx2KYsxKVFq3bo3z589bOhYiIiJ6RGGyshj5aWlKh6IIsxKVJUuWYNOmTVi7dq2l4yEiIqJH6O7exY1/LUL+zZtKh2JzZo1RGTFiBAoKCvDss89i0qRJqFGjBtRqtdE+kiTh119/tUiQRERErk6Xno4bH3yIkKlT4FG9utLh2IxZiUpAQACqVq2KunXrWjoeIiIiKoEuIwM33v8AwZNfhGdEhNLh2IRZicrevXstHAYRERHJob93DymLFiPo+YmopEB5EFtjjV4iIiIHI/Lzkbr0P8g+mqh0KFZXoRL6+fn5OHfuHDIzM6HX64t93rlz54qcnoiIyKFcvXYNOffuAQDuFRTg2r17eKxSJetcTK/Hzc8+g+SmRuVGjaxzDTtgVqKi1+sxc+ZMLFmyBPf+uiGm6HQ6swMjIiJyFEd++QVvLVqEbbt2GZaUuVtQgM67dqJbcDD+/sQTaOrnb/kLC4Gbq+Lg/uo0eAQEmHWKPw8dwuXvv0f65cvIy85Gz/feg194eJnHnNu0CdkpKdDrdPAODUW9gQNRW6s17FNw/z5++/JLXD9yBLnZ2agcFIS6ffsionfvcsVnVqKyYMECvPfee4iJiUHHjh3x7LPP4p133oGfnx+WLFkCSZLw7rvvmnNqIiIih7Jp+3aMmDQJQohi694JAHvT0rAvLQ0fRUaiT6jlZ+uIvDzc2bgJIRMnmHW8LjcX1erXR4327XHsP/+RdYyHtzfqDx0Kn8ceg8rNDTeOHcPRTz6BxtcXIc2aAQBOrF6NtFOn0HrqVFQOCkLqr7/i+IoV8AoIQPVWrWTHZ9YYlbi4OAwfPhxLly5Fnz59AACRkZGYOHEiDh8+DEmSsHv3bnNOTURE5DCO/PILRkyaBJ1OV2Ivgk4I6ITA1GPH8GtGulXiuH/uHHRZWWYdW1urRYNhwxDcpInsY4IaNcJjbdqgSo0a8A4JQd3+/eFbuzZunT1r2Of2+fMI02oR1KgRKgcFoU7PnvANC8OdixfLFZ9Zicqff/6Jbt26AQA0Gg0A4MGDBwAADw8PPPPMM1izZo05pyYiInIYby9ebLIl5VHir9cnv/9utVh0WdlWO3dphBBI/e03ZF2/jsAGDQzbq9arh+uJibh/+zaEEEg7dQrZ168juGnTcp3frK6fqlWrIju78B/E29sbVapUweXLl432SU+3TtZIRERkD65eu4atO3eWmaQU0QmBXamp1hlgq1bDrap5Y1TMlZ+Tg29jYqDPz4ekUqHFhAlGSUjz557Dsf/8B1tjYiCp1ZAkCZEvvGCUzMhhVqLSvHlzHD161PC+a9euWLRoEZo3bw69Xo+PPvoITcuZMREREVlNQYHFT7lr717ZSUoRAeDgrVuIqlnTorF41a0L1V89HKW5sn8/ji1fbnjfadascicORdy8vNDrvfdQ8OABUk+exK+rV6NycDCC/pqBdHH7dty+cAEdZsxApWrVcOvsWfzy6afwCggoVzeTWYnK888/j7i4OOTm5kKj0WD+/Pno3LkzOnfuDCEE/P398dVXX5lzaiIiIstRqQAPDyAvD7DwTNSszEyoVCqT5TlKDAdAdn4eIOQfI4d3ZIvC76kqfURH9VatUPWhqvJeZs4UAgBJpYJ3aCgAwC88HFnXruHc5s0IatQIutxcnPzqK3R47TWERkYW7hMWhozkZJz/73+tn6g89dRTeOqppwzvGzRogEuXLmHv3r1Qq9Vo3749Airw5YmIiCxCrQZq1gTKkUzI5RMWVq4kBQD0ALw9vQCPsls/5JI8Nag0YADg5VX4fUvh7uUFdy8vi137YUKvhz4/HwCg1+kgCgoASTKOVaUq972oUMG3h/n6+mLQoEGWOh0REZFlqNVl/gI3R/devSBJUrm6fyQA7YKCiv0Cr4hKLVpA5e1t9vF5WVm4d+sW7v81tjTr+nUAgKefHzz9C2u/HPnoI3hVrYrGo0YBAM5u2oSAiAhUDgmBPj8fN44fx5X9+9Fi4kQAgHulSghs0AC/rVkDtYcHKgcG4uaZM0jetw/NxowpV3xmJyo6nQ4bNmzAnj17kJaWhjfffBONGzdGZmYmdu3ahQ4dOiA4ONjc0xMREdm1WrVqYcCAAdi+fbusAqdqSULXkBCLD6St1K5dhY6/npiIo598Ynh/6F//AgA0GDYMDUeMAADcu3XLqFtJl5uL4ytW4N6dO1B7eKBK9epoM3UqanboYNin7T/+gZNr1+LwRx8hLzsblatVQ+PoaNTp1atc8UmivCOBAGRkZKBPnz44cuQIvL29kZOTgx9++AHdunWDTqdD7dq1MXr0aCxYsKC8p7ap48ePIzIyEseOHUOLFi2UDoeIiBzM0aNH0b59e+h0ulJbViQUJiobtF3Q1IJDI1Q+Pqi5fBlUHh4WO6e9MauOyowZM3D69GkkJCTg8uXLRjdHrVYjKioK27dvt1iQRERE9qhVq1ZYv3491Go11CV0L6klCWpJwset21g0SQEA34EDnDpJAcxMVL755htMmTIFPXv2hGSin+2JJ55AcnJyRWMjIiKye0OGDMGBAwfQr1+/Yr8TJQBdQ0KwQdsFvR97zKLXdQsKQpWBAy16Tntk1hiVzMxMhJeyYFF+fj4KrDBnnYiIyB61atUK//3vf3H16lU0bdoUGRkZqOLmjm09elhn9WRJQrW/T3b61hTAzBaViIgIHD9+vMTPv//+ezQws4AMERGRo6pVqxYqV64MAKjk5madJAWA39Ah8GrY0CrntjdmJSoTJkzAZ599hvXr1xvGp0iShNzcXLz++uvYsWMHYmJiLBooERERAZ5NGsPvr9k4rsCsrp+XXnoJp0+fRnR0NPz8/AAAI0eOxO3bt1FQUICYmBg899xzloyTiIjI5blXD0XQtGmFhdNchFmJiiRJWLFiBcaMGYP4+HhcuHABer0eERERGD58ODp37mzpOImIiFya2tcXwa+/DnUFirs5ogpVpu3YsSM6duxoqViIiIjIBJWPD0LmzoF7SIjSodicxUroExERkeWpqvggZM4ceNSurXQoipCdqDy8CKEckiRhy5Yt5Q6IiIiICqn9/REydw48atZUOhTFyE5Utm7dCk9PT4SEhMhagMlUITgiIiKSxy0oyGW7ex4mO1F57LHHcO3aNVSrVg0jR47E3/72N4S4+D8eERGRNbjXqIGQObPhVrWq0qEoTvb8pj/++AN79uxB8+bN8dZbb6FmzZro0aMHVq1ahaysLGvGSERE5DI0jz+O0LffYpLyl3JNxNZqtVi2bBlSUlIQHx+PqlWr4u9//zuCgoIwZMgQxMfHIzc311qxEhEROTWvpk0REjsXah8fpUOxG2ZVjHF3d8egQYOwfv16pKamGpKXESNG4N1337V0jERERE6vcvv2CJ45AyovL6VDsSsVmp6cm5uLhIQEbNmyBb/88gs8PT0RFhZmodCIiIhcg3fXrqj24iSXqjgrV7n/RfR6PRISEjB27FgEBwcjOjoa9+/fx4oVK5CWloZnn33WGnESERE5Je8uXZiklEJ2i8qBAwewdu1abNiwAbdv30bbtm2xYMECDB8+HNWqVbNmjERERE6pUsuWTFLKIDtR6dixI7y8vNCvXz9ER0cbuniuXr2Kq1evmjymRYsWFgmSiIjI2XhE1EHgP16GpFYrHYpdK9cYlfv372Pjxo3YtGlTqfsJISBJEnQ6XYWCIyIickZqf38E/9//QeXpqXQodk92orJq1SprxkFEROQSJHd3BP/fdNZJkUl2ojJmzBhrxkFEROQSqk16AZq6dZUOw2Fw9A4REZGN+D41EN5ardJhOBQmKkRERDbg2aA+/J95RukwHA4TFSIiIitTeXkh8KWXOMPHDExUiIiIrMx/9LNwY80xszhkopKcnIznnnsO4eHh8PLyQkREBObOnYu8vDylQyMiIjLiUbsWfHr0UDoMh1WhtX6Ucu7cOej1eixbtgyPP/44Tp06hYkTJyInJwfvv/++0uEREREZ+EVFsfJsBThkotKnTx/06dPH8L5OnTo4f/48li5dykSFiIgUFRISAl1GBqq5u0NdNQCV2rRROiSH5pCJiimZmZkICAgodZ/c3Fzk5uYa3mdnZ1s7LCIicjGJiYn4c8pU5F+/Du8OHTiAtoKcoi3q4sWL+PjjjxETE1PqfgsXLoSvr6/hpeVcdiIisiKvFpFKh+Dw7CpRmTFjBiRJKvV17tw5o2OuXbuGPn36YNiwYZg4cWKp5585cyYyMzMNr3379lnz6xARkStTqaB5ghVoK8quun6mTZuGsWPHlrpPnTp1DP/7+vXr6Nq1K9q3b4/ly5eXeX6NRgONRmN47+3tbXasREREpXGvXh2qh37nkHnsKlEJDAxEYGCgrH2vXbuGrl27IjIyEqtWrYKKI6qJiMiOuNd4TOkQnIJdJSpyXbt2DV26dEHt2rXx/vvv4+bNm4bPQkJCFIyMiIiokPtjTFQswSETlR9++AEXL17ExYsXUaNGDaPPhBAKRUVERPQ/Ho/8fiLzOGR/ydixYyGEMPkiIiKyB+41aiodglNwyESFiIjIrkkS3KuHKh2FU2CiQkREZGHqqgFQeXoqHYZTYKJCRERkYe5BQUqH4DSYqBAREVmYOqCq0iE4DSYqREREFqb291M6BKfBRIWIiMjC1FWqKB2C02CiQkREZGEqHx+lQ3AaTFSIiIgszLN+faVDcBpMVIiIiCxM4vpzFsN/SSIiIrJbTFSIiIjIbjFRISIiIrvFRIWIiIjsFhMVIiIisltMVIiIiMhuuSkdANnGjRs3cOPGDaXDIAsJDQ1FaCiXkHcWfD6dD59Ry3HpRCU0NBRz5851+v8z5ebmIjo6Gvv27VM6FLIQrVaLhIQEaDQapUOhCuLz6Zz4jFqOJIQQSgdB1nX37l34+vpi37598Pb2VjocqqDs7GxotVpkZmaiCtcTcXh8Pp0Pn1HLcukWFVfTrFkzPjRO4O7du0qHQFbA59N58Bm1LA6mJSIiIrvFRIWIiIjsFhMVF6DRaDB37lwO6nISvJ/OhffT+fCeWhYH0xIREZHdYosKERER2S0mKkRERGS3mKgQERGR3WKiQkRERHaLiQqRFUiSJOu1d+/eCl/r3r17iI2NLde55s+fj6eeegrBwcGQJAmxsbEVjoPIUdjz83nu3DlMnz4dzZo1g4+PD0JDQ9G/f38kJiZWOBZHxcq0RFawZs0ao/eff/45fvjhh2Lb69evX+Fr3bt3D/PmzQMAdOnSRdYxb7zxBkJCQtC8eXMkJCRUOAYiR2LPz+enn36KlStXYujQoXjxxReRmZmJZcuWoW3bttixYwd69OhR4ZgcDRMVIit45plnjN4fOnQIP/zwQ7HtSklKSkJYWBhu3bqFwMBApcMhsil7fj6jo6MRGxtrtO7T+PHjUb9+fcTGxrpkosKuHyKF6PV6LFq0CA0bNoSnpyeCg4MRExOD9PR0o/0SExPRu3dvVKtWDV5eXggPD8f48eMBAMnJyYZEY968eYYm67K6csLCwqzxlYichlLPZ2RkZLHFKatWrYpOnTrh7Nmzlv2SDoItKkQKiYmJQVxcHMaNG4epU6ciKSkJ//73v/HLL7/g559/hru7O9LS0tCrVy8EBgZixowZ8PPzQ3JyMjZt2gQACAwMxNKlSzFp0iQMHjwYQ4YMAQA0adJEya9G5PDs7flMSUlBtWrVLPodHYYgIqubPHmyePhx+/HHHwUA8eWXXxrtt2PHDqPtmzdvFgDE0aNHSzz3zZs3BQAxd+7ccsdVkWOJnIW9Pp9F9u/fLyRJErNnzzb7HI6MXT9ECtiwYQN8fX3Rs2dP3Lp1y/Aqavbds2cPAMDPzw8AsHXrVuTn5ysYMZHrsKfnMy0tDSNHjkR4eDimT59ulWvYOyYqRAq4cOECMjMzERQUhMDAQKNXdnY20tLSAABarRZDhw7FvHnzUK1aNQwaNAirVq1Cbm6uwt+AyHnZy/OZk5ODAQMGICsrC1u2bCk2dsVVcIwKkQL0ej2CgoLw5Zdfmvy8aACeJEmIj4/HoUOH8O233yIhIQHjx4/HBx98gEOHDrnsDy4ia7KH5zMvLw9DhgzBb7/9hoSEBDRq1Mjsczk6JipECoiIiMDOnTvRoUMHeHl5lbl/27Zt0bZtW8yfPx9r167FqFGjsG7dOkyYMAGSJNkgYiLXofTzqdfrMXr0aOzatQtff/01tFqtOV/DabDrh0gBw4cPh06nw1tvvVXss4KCAmRkZAAA0tPTIYQw+rxZs2YAYGherlSpEgAYjiGiilH6+ZwyZQrWr1+PJUuWGGYKuTK2qBApQKvVIiYmBgsXLsSJEyfQq1cvuLu748KFC9iwYQMWL16MqKgorF69GkuWLMHgwYMRERGBrKwsrFixAlWqVEG/fv0AAF5eXmjQoAHWr1+PJ554AgEBAWjUqFGpTcVr1qzBlStXcO/ePQDA/v378fbbbwMAnn32WdSuXdv6/whEdkrJ53PRokVYsmQJ2rVrh0qVKuGLL74w+nzw4MGoXLmy1f8N7IrS046IXMGj0x+LLF++XERGRgovLy/h4+MjGjduLKZPny6uX78uhBDi+PHjIjo6WtSqVUtoNBoRFBQkBgwYIBITE43Oc+DAAREZGSk8PDxkTYXUarUCgMnXnj17LPW1iRyCPT2fY8aMKfHZBCCSkpIs+dUdgiTEI+1WRERERHaCY1SIiIjIbjFRISIiIrvFRIWIiIjsFhMVIiIisltMVIiIiMhuMVEhIiIiu8VEhcjOJCcnQ5IkxMXFKR0KEZnAZ9S2mKgQERGR3WLBNyI7I4RAbm4u3N3doVarlQ6HiB7BZ9S2mKgQERGR3WLXD5EVxMbGQpIk/P7773jmmWfg6+uLwMBAzJ49G0II/PHHHxg0aBCqVKmCkJAQfPDBB4ZjTfV/jx07Ft7e3rh27RqefvppeHt7IzAwEK+++ip0Op1hv71790KSJOzdu9coHlPnTElJwbhx41CjRg1oNBqEhoZi0KBBSE5OttK/CpH94DPqOJioEFnRiBEjoNfr8c9//hNt2rTB22+/jUWLFqFnz5547LHH8M477+Dxxx/Hq6++iv3795d6Lp1Oh969e6Nq1ap4//33odVq8cEHH2D58uVmxTZ06FBs3rwZ48aNw5IlSzB16lRkZWXh6tWrZp2PyBHxGXUASq2GSOTM5s6dKwCI559/3rCtoKBA1KhRQ0iSJP75z38atqenpwsvLy8xZswYIYQQSUlJAoBYtWqVYZ+iFVXffPNNo+s0b95cREZGGt7v2bPH5ArIj54zPT1dABDvvfeeZb4wkYPhM+o42KJCZEUTJkww/G+1Wo2WLVtCCIHnnnvOsN3Pzw/16tXD5cuXyzzfCy+8YPS+U6dOso57lJeXFzw8PLB3716kp6eX+3giZ8Fn1P4xUSGyolq1ahm99/X1haenJ6pVq1Zse1k/jDw9PREYGGi0zd/f36wfYhqNBu+88w6+++47BAcHo3Pnznj33XeRkpJS7nMROTI+o/aPiQqRFZmauljSdEZRxgQ8OdMgJUkyuf3hwXxFXn75Zfz+++9YuHAhPD09MXv2bNSvXx+//PJLmdchchZ8Ru0fExUiJ+Lv7w8AyMjIMNp+5coVk/tHRERg2rRp+P7773Hq1Cnk5eUZzW4gIsviM1p+TFSInEjt2rWhVquLzU5YsmSJ0ft79+7hwYMHRtsiIiLg4+OD3Nxcq8dJ5Kr4jJafm9IBEJHl+Pr6YtiwYfj4448hSRIiIiKwdetWpKWlGe33+++/o3v37hg+fDgaNGgANzc3bN68Gampqfjb3/6mUPREzo/PaPkxUSFyMh9//DHy8/Pxn//8BxqNBsOHD8d7772HRo0aGfapWbMmoqOjsWvXLqxZswZubm548skn8fXXX2Po0KEKRk/k/PiMlg9L6BMREZHd4hgVIiIisltMVIiIiMhuMVEhIiIiu8VEhYiIiOwWExUiIiKyW0xUiFxYcnIyJElCXFyc0qEQkQl8RpmoEMl26dIlxMTEoE6dOvD09ESVKlXQoUMHLF68GPfv37fadc+cOYPY2FgkJydb7RpyzJ8/H0899RSCg4MhSRJiY2MVjYfoUa78jJ47dw7Tp09Hs2bN4OPjg9DQUPTv3x+JiYmKxWQpLPhGJMO2bdswbNgwaDQajB49Go0aNUJeXh5++uknvPbaazh9+jSWL19ulWufOXMG8+bNQ5cuXRAWFmaVa8jxxhtvICQkBM2bN0dCQoJicRCZ4urP6KeffoqVK1di6NChePHFF5GZmYlly5ahbdu22LFjB3r06KFIXJbARIWoDElJSfjb3/6G2rVrY/fu3QgNDTV8NnnyZFy8eBHbtm1TMML/EULgwYMH8PLysvi5k5KSEBYWhlu3bhVbyp5ISXxGgejoaMTGxsLb29uwbfz48ahfvz5iY2MdOlFh1w9RGd59911kZ2dj5cqVRj8Aizz++ON46aWXDO8LCgrw1ltvISIiAhqNBmFhYZg1a1axhcTCwsIwYMAA/PTTT2jdujU8PT1Rp04dfP7554Z94uLiMGzYMABA165dIUkSJEnC3r17jc6RkJCAli1bwsvLC8uWLQMAXL58GcOGDUNAQAAqVaqEtm3bVuiHtZKtOUSl4TMKREZGGiUpAFC1alV06tQJZ8+eNeuc9oKJClEZvv32W9SpUwft27eXtf+ECRMwZ84ctGjRAv/617+g1WqxcOFCkwuJXbx4EVFRUejZsyc++OAD+Pv7Y+zYsTh9+jQAoHPnzpg6dSoAYNasWVizZg3WrFmD+vXrG85x/vx5REdHo2fPnli8eDGaNWuG1NRUtG/fHgkJCXjxxRcxf/58PHjwAE899RQ2b95sgX8VIvvBZ7RkKSkpqFatmsXOpwhBRCXKzMwUAMSgQYNk7X/ixAkBQEyYMMFo+6uvvioAiN27dxu21a5dWwAQ+/fvN2xLS0sTGo1GTJs2zbBtw4YNAoDYs2dPsesVnWPHjh1G219++WUBQPz444+GbVlZWSI8PFyEhYUJnU4nhBAiKSlJABCrVq2S9f2EEOLmzZsCgJg7d67sY4ishc9oyfbv3y8kSRKzZ88u97H2hC0qRKW4e/cuAMDHx0fW/tu3bwcAvPLKK0bbp02bBgDFmnUbNGiATp06Gd4HBgaiXr16uHz5suwYw8PD0bt372JxtG7dGh07djRs8/b2xvPPP4/k5GScOXNG9vmJ7BmfUdPS0tIwcuRIhIeHY/r06RU6l9KYqBCVokqVKgCArKwsWftfuXIFKpUKjz/+uNH2kJAQ+Pn54cqVK0bba9WqVewc/v7+SE9Plx1jeHi4yTjq1atXbHtRc/SjcRA5Kj6jxeXk5GDAgAHIysrCli1bio1dcTSc9UNUiipVqqB69eo4depUuY6TJEnWfmq12uR2IYTsa1ljhg+Ro+AzaiwvLw9DhgzBb7/9hoSEBDRq1Mhm17YWtqgQlWHAgAG4dOkSDh48WOa+tWvXhl6vx4ULF4y2p6amIiMjA7Vr1y739eX+QH00jvPnzxfbfu7cOcPnRM6Cz2ghvV6P0aNHY9euXVi7di20Wm25z2GPmKgQlWH69OmoXLkyJkyYgNTU1GKfX7p0CYsXLwYA9OvXDwCwaNEio30+/PBDAED//v3Lff3KlSsDADIyMmQf069fPxw5csToB3dOTg6WL1+OsLAwNGjQoNxxENkrPqOFpkyZgvXr12PJkiUYMmRIuY+3V+z6ISpDREQE1q5dixEjRqB+/fpGVS8PHDiADRs2YOzYsQCApk2bYsyYMVi+fDkyMjKg1Wpx5MgRrF69Gk8//TS6du1a7us3a9YMarUa77zzDjIzM6HRaNCtWzcEBQWVeMyMGTPw1VdfoW/fvpg6dSoCAgKwevVqJCUlYePGjVCpyv83ypo1a3DlyhXcu3cPALB//368/fbbAIBnn32WrTSkGD6jhYnXkiVL0K5dO1SqVAlffPGF0eeDBw82JFQOR+lpR0SO4vfffxcTJ04UYWFhwsPDQ/j4+IgOHTqIjz/+WDx48MCwX35+vpg3b54IDw8X7u7uombNmmLmzJlG+whROG2xf//+xa6j1WqFVqs12rZixQpRp04doVarjaZBlnQOIYS4dOmSiIqKEn5+fsLT01O0bt1abN261Wif8kx91Gq1AoDJl6lpmUS25srP6JgxY0p8PgGIpKSkUo+3Z5IQ5RgRRERERGRDHKNCREREdouJChEREdktJipERERkt5ioEBERkd1iokJERER2i4kKERER2S0mKkRERGS3mKgQERGR3WKiQkRERHaLiQoRERHZLSYqREREZLeYqBAREZHdYqJCREREduv/AU97y98JMvS0AAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 600x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "multi_2group.mean_diff.plot(contrast_bars=True, raw_bars=False, \n",
+    "                            contrast_bars_kwargs={'color': \"red\", 'alpha': 0.1}\n",
+    "                            );"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Reference band\n",
+    "A **reference band** can be added for each relevant contrast object as desired via supplying a list to the argument `reference_band` in the `.plot()` method."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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KuenpCkTlGKySqERFRZVIVLRaLa5du4Yff/wRjRs3RosWLawRChHZgPycDKSf2ou8zDRo/IIR1KQb3JzsF2RZU5Af9WhCsWjMIpy4cqLUz8mZwsxpzublGRQEoTPeLSd0OngGBVk5IsdhlUQlISGh1H2//PILevXqhZEjR1ojFCK7Zq1f8Ja8zp3fjuL85gUQukJIkgpC6HBt/2o0iJmBgHptzHINe1E8BbksZSUUxj778BRmAPqBt8VTmBMmJgBAucewZaViakdF4fT69foxKg9Tubigdpcu1g/KQSg+RqVZs2aIi4vD//t//0/pUIhs2p3fjuL4J8/j6t6VSP15F67uXYnjnzyPvy4es5vr5OdkFCUp2gJACAidtui/2gKc2zTf6RZlzMjJwKYjm/Dpt59i05FNJeqgmFI3Rc4UZjnHUMW4+/mh/ZQpULm6ApIESa0GJAkqV1e0nzIF7r4sCWEqq876KU1wcDDOnj2rdBhENsvgFzwA8fdfwMW/4FtPXCm7xaOs1hJzXKes86ef2gtRyi9IoStE+qm9qOEkdUDkdL2YUjdF7hRmTnM2v+qtWqHfkiWGdVS6dGGSUkmKJyp37tzBihUrUKNGDaVDIbJZ5voFX163S2WvU9758zLT/t5uZMChpEJeZlq5X4MjkNM94+/lb1LdFDlTmAUEpzlbiLufH+oPHKh0GA7FKolKt27djG7PzMzE+fPnkZ+fj9WrV1sjFCK7ZI5f8HJaSypzHTnn1/gFQ5TyC1IIHTR+weV+HY5AbkuJKXVToptEY/X+1fok6GHFU5iFEOUeQ2QrrDJGRafTQQhh8AKKpi2/8sorOH36NGJjY60RCpFNcvPyh5t31VK7VSryCz4/JwN/HNmMy98uxh9HNuvHfchpLalMIiHn/EFNukEqpQ6IpHJBkJP8gixuKTHm4ZYSOXVTHh3nAgAzYmbAVe0KSZKgVqkhSRJc1a76KczF05zLOobIVlilRWX//v3WuAyZKCM7F98fP4fUv+4iJMAHPVo3gL+3p9JhOZXmLywqc39Qk264tn+1vrXiYQ//gi+r60VOa0nNTrGyrmOMnPO7efmjQcwMnNs03yBGSeWCBjEz4OYkvyDltpSUVzflwo0LpY5zKW/qc0WmR5N8XOvH/BQfo0LKOnL6CuauMiyzn/DtEcwa3Y8VbG2InF/w5XW91Gg/pNzWErnXMTZYVm5rTEC9Nmg9caXhOZpGO02SAsjrnilWWkIhhMDoT0aXOc6lvKnPcqZHk3xc68cyLJKofPHFFyZ97rnnnjNzJFSWjOxczF21Q79wofbvLrmCQi3eTtiBNbPGsGXFhpT3C768rhdAgqRyKbe1pKzrlNViI7fVByhKvJxldo8xFa1Mayyh2HRkE1dStiFc68dyLJKojB49usKfkSSJiYpML3+4FhnZ9+DvXQWLp4wo89iyunW+P34OhVrjfwEXanXYnXSe5fetJHnFq8jPyYCbl3+Z3UBl/YIvr+ul8P5d2d0uxq4jZ7Asu3Xkk9P1UlaJe66kbFvkrPXD2UCmsUiikpKSYonT0t8ysu/hdlZOuceV162T+tddqCRJ35LyMJUk4dadLEuET0bk52Qgv4zVdh8+rrQaJXK6XirT7SJ36rKzd+tURFldL+XVWeFKyraFa/1YjkUSldq1a1vitFQBcrp1QgJ8oDOSpACATgiEVmWRIltSXo0SuV0vpna7yJ267OzdOuYgp85KRca5kOVxrR/LUbyEPlmGnG6dHq0bwEVt/J+Ai1qF7q0aWDJEqgA5peeLB8JK6r9LeKvUf5fydjVL1wtroFiPnDornGJsW2pHRUHlYvxvf671UzlWm/WTmpqKFStW4OTJk8jKyoLukcxTkiTs2bPHWuE4PDndOv7enpg1uh/eTjDsHnJRqzBrdD/4e1dRIHIyRm63iyVn1FRksCxVjtzxJ5xibDuK1/p5dNaPysWFa/1UklUSlV9//RVdunTB/fv3Ub9+fZw6dQoNGzZEZmYmbty4gYiICNSsWdMaoTgNud067RrVwZpZY7A76Txu3clCaFVfdG/VgEmKjalIxVhLdb2wBor5lTZYtiLjTzjF2HZwrR/LsEqiMm3aNHh5eSE5ORlVqlRBUFAQFi1ahG7dumHjxo0YP3481qxZY41QnEaP1g2Q8O0R/RiVhz3arePv7Wm12T0sLmcac3e7lDUot6xjWAPFfMoaLMvxJ/aLa/2Yn1USlR9//BFTp05FrVq18NdffwGAvutn6NChOHToEN544w0cOHDAGuE4BSW6dcpLQlhcznQV6XYpLwkpb1CunGM4WLZy5AyWrUidFbIfrFxbcVZJVHQ6HYKDi/7i8/Pzg1qt1icsANCkSROsWLHCGqE4FXN261Q2CWFxucqR2+1SXoIhpxYKgHKPKW1NIpJH7qKEHH/iWFi51jRWSVTCw8P1tVVUKhXCw8Oxe/duDBs2DABw+PBh+FUwo1yyZAmWLFmCq1evAgAaNWqEWbNmoU+fPuYM3e6Zo1vHHEkIi8tVXnndLnKSEDmDcov/v6xj2KJSOXIHy3L8ieNg5VrTWWx6ckZGhv7/e/bsiY0bN+rfjx8/Hp999hm6d++O6OhorFq1CiNGlF1h9VE1atTAv/71L5w4cQJJSUno1q0bBg4ciDNnzpjtayDDeixCCGj/Xgm7OAkpbmkpLwkpnoVkDIvLVcJDA6blJCHFg3KNKR6UK+cYqhwWa7Mvu6dOxfYXX8TuqVNNPoecyrVknMVaVEJCQtC3b1+MHDkSU6ZMQWxsLAoKCuDq6orJkycjNzcXmzdvhlqtxsyZMzFjxowKnX/AgAEG7+fNm4clS5bgp59+QqNGjcz5pTi8ypbZlzMVmsXlKq+8bh05M4PkDsplvRTL4mBZ+/IgMxP3HxquYApWrjWdxVpUYmJisHv3bgwfPhwNGjTAp59+ioMHD0IIAUmS8NZbb+Hnn39GUlIS4uPj4ebmZvK1tFot1q9fj9zcXLRv377U4/Ly8nD37l39Kyen/DL0ju7I6SsY+fbn+Gz7j9h55DQ+2/4jRr79OX46cwUAZLWEyElCWFyucuQUfJOThAQ16QZJZfzvk+JBuXKOocphsTbH9SAzExe2bcPJ5ctxYds2PMjMBMDKtZVhsURlzZo1SE9Px5dffolOnTphzZo16NmzJx577DFMmTIFJ0+erPQ1Tp06BS8vL2g0Grz00kvYunUrGjZsWOrxCxYsgK+vr/4VFRVV6RjsmZxuHXMlIcWzkFxd1H//YFYV/WB2UbO4nAxyunXkJBhyqtdausItFSku1jam2xj0btEbY7qNwapJq9C2XlulQyMT3UxKwo7x4/Hrl1/iyu7d+PXLL7Fj/HjcTEpi5dpKsOhgWg8PD8TGxiI2NhYZGRn46quvsHbtWixcuBALFy5EvXr18Mwzz2DEiBGoU6fi01Pr16+P5ORkZGVlYdOmTRg1ahQOHDhQarIyffp0vPbaa/r3ycnJDp+sVLZbR049Fn/vKrKmQrO4nOnkdOvInRkkpxYK66VYBwfLOg45g2VZudY0Viuh7+/vj7i4OMTFxeHGjRtYu3Yt1q1bh1mzZmH27Nlo27YtDh8+XKFzurm5oW7dugCAyMhIHD9+HIsWLcLSpUuNHq/RaKDRaPTvvby8TP+CFFT8i728X/DmWD1Zbj0WuUlIebOQWBDOOLljS+QmGHKq13JxQSL55AyWrT9wICvXmsBqicrDHnvsMbzxxhvo3bs3Zs2ahW3btuHo0aOVPq9Op0NeXp4ZIrRti6eUP0PKnKsnmysJKQ8LwpWuIgXfmGAQWZ/cwbLmqlzrTIXjrJ6oXL9+Xd+acvr0aQgh8OSTT2LkyJEVOs/06dPRp08f1KpVC9nZ2Vi7di3279+PxMREC0VuX8zVrVOsrCRETitIecewIFzZzLnOjpzy+URUMeYcLFteEuJsheOskqjcvn1bPz7lyJEjEELgiSeewNtvv42RI0ciLCyswudMT0/Hc889h1u3bsHX1xdNmzZFYmIievToYf4vwA5Za/VkOa0gco5hQbjyyenWMUf5fLIfpS1qSNZXOyoKp9ev149ReVhFBsuWl4Q4Y+E4iyUqubm52Lp1K9auXYs9e/agoKAAoaGhmDx5MkaOHImWLVtW6vzOXHL/5Q/XIiP7Hvy9q5TaDWSN1ZPltIIAkNVSIiexcmTFyUR5LRtldeuYo3w+W1bsR1mLGnLmkPW5+/nJGixbVmuJnCRE7lgYR2KxRCUoKAgPHjyAl5cXRowYgZEjR6Jbt25QqSw2I9ppZGTfw+2ssmvAWKNbR04riBBCVkuJsxeEa/7Cokp93lzl8zm2xT7IWdSQLSvWV71VqzIHy5bXWiInCXHGwnEWS1S6d++OkSNH4qmnnoK7u7ulLkOlkNOtU9mFBuW2gsg5piKJFZVUkfL5ZU1xJvsgd1FDsr7SBsvKaS2Rk4Q4Y+E4iyUq27Zts9SpSaayunXMsdCgnFYQIYSslhJzjJdxZuYsn0+2T+6ihmQ75LSWyElCzDUWxp6wH8bBFXfrTIrphqFdI/UtKeZYaFBORdqKlM4vTqzGDeiIvu0bY9yAjlgz6wWnn5r8sPycDPxxZDMuf7sYfxzZjPycosU/zVU+n+wDFzW0P8WtJcYUt5bIqV5bPBZG5fp35Wh1UeVolaurwxaOU6SOCinLXAsNym0FqUhLSWVrsTiysgbLyqmz4ubpZ7YpzlS24vEhlhonwkUNbVdpg2XltJbIHZBb3lgYR8NExQmZc7VjObOGWDq/8uQMljVX+XyqvI9f+Nis5zM2DXlGzAzM32Q468dF5cJFDRVU1mBZuV02cpMQcxWOswdMVJyQnCSke6snzDJrqCLHUOnkztgxV/l8sh1lTUNOmJiAvaf2IjUzFSF+IYhuGs0kRSHmXOvHmZIQOZioODhjM3vMudAgWYfcGTtMQuxLeQXb5ExD5uwe28C1fiyHiYoDK2tmD1c7ti+cseN45BRs4zRk+2HttX7kcoQ1gZioOCg504utsdAgmUdFFiUk2ye3YBunIdsPJeqbOMuaQJye7KDkzOwxNnWZbFPxooSS+u8piSr131MTXTljxw7JaSkBOA3ZnsiZWmxON5OSsGP8ePz65Ze4sns3fv3yS+wYPx43k5IAPDJmRoiilh4h9GNmHmRmmjUeS2Ki4qCKZ/YYY6m1czKyc/HV3iR8vGkvvtqbhIzsXLNfw5kVz9gJ6/Y8Qlr0Rli359F6UgIXE7RDxS0lxjzcUhLdJBoupdS+4TRk22LN+iZykhA5Y2bsBbt+HJS5186pbLl9Mg8OlnUMcltK/L38OQ3ZjpizvklZ3TrOtiYQExUHZc61c8xRbv/hpIbI2VWkYFvbem05DdmOyBksW9mxJc62JhC7fhxUcdVYVxc1JEmCWqWCJElwdVFXaHqxucrtE9H/FLeUuKpd/34+i55TV7Wr0ZYSfy9/DGk/BBP6TMCQ9kOYpNgxc4wtkbsmkDXHzFgSW1TsUHGSUV6yIXd6cVndOuYqt09EhthS4pjKai2RUxROTreOnCq37r6+sgvM2TomKnZo8ZQRso8tb3pxed065iy3T0SGiltKyDGU12VjrrElzrYmEBMVB1dWa4mcsSXmLrdPRGRviltEyiqUJqe1xJxjS5xpTSAmKg6svNYSOd06LLdPRM6u+3vvlXuMnNYSuWNL5CxeCDhGEiIHB9M6KDmDYOXUWpE7KLd4PMy4AR3Rt31jjBvQEWtmvcCpyUTkFIpbS4wpbi2RM8DVmvVY7AVbVByUnNYSuWNL5A7KZbl9InJWclpLnG1sibkwUXFQcgbBPturreyxJUxCiMhZ7Z46FQ8yM+Hu51dqN5DcLhtnGltiLkxUHJSc1pLibh2OLSFSRkZOBvac2oO0zDQE+wUjukk0/L38lQ6LHvEgMxP3//qrzGPktpYUH8skRD4mKg5KbmVaud06RGReR387ivmbDUvjr96/GjNiZqBtvbZKh0cmYJeNZTBRcVAVaS1htw6RdWXkZGD+5vn6EvpaUfQHRYG2APM3zUfCxAS2rNip8lpLyiufTyUxUXFgbC0hsk17Tu1Boc74VNZCXSH2ntrLQnAOqLyCcGQcExUHx9YSItuTlpkGlaTSt6Q8TCWpkJqZqkBUZElyCsKxZcU41lEhIrKyYL9g6ITxqaw6oUOIX4iVIyJLk1MQjoyz20RlwYIFaN26Nby9vREUFISnn34aFy5cUDosIqJyRTeJhovKeIO2i8oF0U2jrRwRmcuDzExc2LYNJ5cvx4Vt2/AgMxOAvIJwZJzdJioHDhzAhAkT8NNPP+H7779HQUEBevbsidzcXKVDIyIqk7+XP2bEzICr2vXvis9FlZ9d1a6YETODKyjbqZtJSdgxfjx+/fJLXNm9G79++SV2jB+Pm0lJstfwoZLsdozKrl27DN4nJCQgKCgIJ06cQOfOnRWKiohInrb12iJhYgL2ntqL1MxUhPiFILppNJMUO1XeGJTu//qX7DV8yJDdJiqPysrKAgAEBAQoHAkRkTz+Xv6c3eMgyhuDkpqcLLsgHBlyiERFp9Nh8uTJ6NChAxo3blzqcXl5ecjLy9O/z8nJsUZ4RETk4IrHoBS3pDyseAxK/YEDWRDOBA6RqEyYMAGnT5/GoUOHyjxuwYIFmDNnjpWiIiIiZyF3DArL51ec3Q6mLfbKK69g+/bt2LdvH2rUqFHmsdOnT0dWVpb+dYDTwYiIyAxqR0VB5WL8b3+OQakcu01UhBB45ZVXsHXrVuzduxfh4eHlfkaj0cDHx0f/8vLyskKkRETk6IoXJVS5ugKSBEmtBiQJKldXjkGpJLvt+pkwYQLWrl2Lbdu2wdvbG6mpRZUcfX194eHhoXB0RETkbLgooWXYbaKyZMkSAECXR5rTVq5cidGjR1s/ICIicnocg2J+dpuoCCGUDoGIiIgszG7HqBAREZHjs9sWFSIiInv2IDPTcDxLVBRXUDaCiQoREZGV3UxKKlGl9vT69Wg/ZQqqt2qldHg2hV0/REREVmSwLpAQRdVshdCvC1S84jIVYaJCRERkReWtC3SNxUgNsOuHiIjIAkobgyJnXSD6HyYqREREZlbWGBS56wJREXb9EBERmVF5Y1BCmjfnukAVwESFiIioDO5+fvAICJA9dbi8MSipyclcF6gC2PVDRERUhu7vvVeh4+WMQak/cCDXBZKJiQoREZEZyR2DwnWB5GHXDxERkRnVjoriGBQzYqJCRERkRu5+fhyDYkbs+iEiIjKz6q1acQyKmTBRISIiMhMuNGh+TFSIiIjMgAsNWgbHqBAREVUSFxq0HCYqRERElcSFBi2HiQoREVElFRd5M4YLDVYOExUiIqJK4kKDlsNEhYiIqJJY5M1ymKgQERFVEou8WQ6nJxMREZkBi7xZBhMVIiIiM+FCg+bHRIWIiMiKWL22YpioEBERWQmr11YcB9MSERGZyYPMTFzYtg0nly/HhW3bDCrSsnqtadiiQkREZAbltZbIqV7L8S0lsUWFiIiokuS0lrB6rWnsOlE5ePAgBgwYgOrVq0OSJHz99ddKh0RERE5ITmsJq9eaxq4TldzcXDRr1gyffvqp0qEQEZETk9Nawuq1prHrMSp9+vRBnz59lA6DiIicnJzWkuLqtY+OY1G5uLB6bRnsOlGpqLy8POTl5enf5+TkKBgNERE5itpRUTi9fn3RGJVHPNxawuq1FedUicqCBQswZ84cpcMgIiIHU5HWElavrRhJCCGUDsIcJEnC1q1b8fTTT5d6zKMtKsnJyYiKisKJEyfQsmVLK0RJRESOrETVWbaWVJpTtahoNBpoNBr9ey8vLwWjISIiR8PWEvOz61k/RERE5NjsukUlJycHly5d0r9PSUlBcnIyAgICUKtWLQUjIyIiInOw60QlKSkJXbt21b9/7bXXAACjRo1CQkKCQlERERGRudh1otKlSxc4yFhgi7t16xZu3bqldBhkJqGhoQgNDVU6DDITPp+Oh8+o+dh1olJZoaGhmD17tsP/Y8rLy0NsbCwOHDigdChkJlFRUUhMTDQYHE72ic+nY+Izaj4OMz2ZSnf37l34+vriwIEDnOnkAHJychAVFYWsrCz4+PgoHQ5VEp9Px8Nn1LycukXF2TRv3pwPjQO4e/eu0iGQBfD5dBx8Rs2L05OJiIjIZjFRISIiIpvFRMUJaDQazJ49m4O6HATvp2Ph/XQ8vKfmxcG0REREZLPYokJEREQ2i4kKERER2SwmKkRERGSzmKhQhVy9ehWSJHEtJSIbxWeUHA0TFQu6fPky4uLiUKdOHbi7u8PHxwcdOnTAokWLcP/+fYtd9+zZs4iPj8fVq1ctdg055s2bh6eeegrBwcGQJAnx8fGKxmNNkiTJeu3fv7/S17p37x7i4+MrdC5nvjcPc+Zn9Pz585g6dSqaN28Ob29vhIaGol+/fkhKSlIsJmux5efTme9LaViZ1kJ27NiBoUOHQqPR4LnnnkPjxo2Rn5+PQ4cO4Y033sCZM2ewbNkyi1z77NmzmDNnDrp06YKwsDCLXEOOt956CyEhIWjRogUSExMVi0MJq1evNnj/xRdf4Pvvvy+xvUGDBpW+1r179zBnzhwARQt1yuHM96aYsz+jn332GVasWIEhQ4bg5ZdfRlZWFpYuXYp27dph165d6N69uyJxWYMtP5/OfF9Kw0TFAlJSUvCPf/wDtWvXxt69ew0WPZwwYQIuXbqEHTt2KBjh/wgh8ODBA3h4eJj93CkpKQgLC8Pt27cRGBho9vPbsmeeecbg/U8//YTvv/++xHalOPO9AfiMAkBsbCzi4+MN1hcaM2YMGjRogPj4eIf+hWjLz6cz35fSsOvHAt577z3k5ORgxYoVRldmrlu3Ll599VX9+8LCQsydOxcRERHQaDQICwvDjBkzkJeXZ/C5sLAw9O/fH4cOHUKbNm3g7u6OOnXq4IsvvtAfk5CQgKFDhwIAunbtWqIJs/gciYmJaNWqFTw8PLB06VIAwJUrVzB06FAEBASgSpUqaNeuXaV+WCvZmmMPdDodFi5ciEaNGsHd3R3BwcGIi4tDRkaGwXFJSUno1asXqlWrBg8PD4SHh2PMmDEAisYjFCcac+bM0d/v8rpynP3e8BkFIiMjSyyCWLVqVXTq1Annzp0z6ZyORKnnk/elJLaoWMA333yDOnXq4Mknn5R1/NixY7Fq1SrExMRgypQpOHr0KBYsWIBz585h69atBsdeunQJMTExeOGFFzBq1Ch8/vnnGD16NCIjI9GoUSN07twZkyZNwscff4wZM2bomy4fbsK8cOECYmNjERcXh3HjxqF+/fpIS0vDk08+iXv37mHSpEmoWrUqVq1ahaeeegqbNm3CoEGDzPcNIgBAXFwcEhIS8Pzzz2PSpElISUnBv//9b/z888/48ccf4erqivT0dPTs2ROBgYGYNm0a/Pz8cPXqVWzZsgUAEBgYiCVLlmD8+PEYNGgQBg8eDABo2rSpkl+azeMzWrrU1FRUq1bNLOeyZ7b2fDr1fRFkVllZWQKAGDhwoKzjk5OTBQAxduxYg+2vv/66ACD27t2r31a7dm0BQBw8eFC/LT09XWg0GjFlyhT9to0bNwoAYt++fSWuV3yOXbt2GWyfPHmyACB++OEH/bbs7GwRHh4uwsLChFarFUIIkZKSIgCIlStXyvr6hBDizz//FADE7NmzZX/G0UyYMEE8/Lj98MMPAoBYs2aNwXG7du0y2L5161YBQBw/frzUc1fm++uM94bPaOkOHjwoJEkSM2fOrPBn7ZmtPp/FnPW+FGPXj5kVL+/t7e0t6/idO3cCAF577TWD7VOmTAGAEs26DRs2RKdOnfTvAwMDUb9+fVy5ckV2jOHh4ejVq1eJONq0aYOOHTvqt3l5eeHFF1/E1atXcfbsWdnnp/Jt3LgRvr6+6NGjB27fvq1/FTf77tu3DwDg5+cHANi+fTsKCgoUjNhx8Bk1Lj09HSNGjEB4eDimTp1aqXPZO1t6PnlfOEbF7Hx8fAAA2dnZso6/du0aVCoV6tata7A9JCQEfn5+uHbtmsH2WrVqlTiHv79/iX7TsoSHhxuNo379+iW2FzdHPxoHVc7FixeRlZWFoKAgBAYGGrxycnKQnp4OAIiKisKQIUMwZ84cVKtWDQMHDsTKlStLjI0g+fiMlpSbm4v+/fsjOzsb27ZtKzFGwtnYyvPJ+1KEY1TMzMfHB9WrV8fp06cr9DlJkmQdp1arjW4XFVhb0hIzfKhidDodgoKCsGbNGqP7iwfgSZKETZs24aeffsI333yDxMREjBkzBh9++CF++uknp/3BVRl8Rg3l5+dj8ODB+PXXX5GYmIjGjRtb7dq2yhaeT96X/2GiYgH9+/fHsmXLcOTIEbRv377MY2vXrg2dToeLFy8aDKZLS0tDZmYmateuXeHry/2B+mgcFy5cKLH9/Pnz+v1kPhEREdi9ezc6dOgg65dSu3bt0K5dO8ybNw9r167FyJEjsX79eowdO9ak++3s+IwW0el0eO6557Bnzx589dVXiIqKqvA5HJHSzyfviyF2/VjA1KlT4enpibFjxyItLa3E/suXL2PRokUAgL59+wIAFi5caHDMRx99BADo169fha/v6ekJAMjMzJT9mb59++LYsWM4cuSIfltubi6WLVuGsLAwNGzYsMJxUOmGDRsGrVaLuXPnlthXWFiov3cZGRkl/hJv3rw5AOibl6tUqQKgYvfb2fEZLTJx4kRs2LABixcv1s9IIeWfT94XQ2xRsYCIiAisXbsWw4cPR4MGDQyqXh4+fBgbN27E6NGjAQDNmjXDqFGjsGzZMmRmZiIqKgrHjh3DqlWr8PTTT6Nr164Vvn7z5s2hVqvx7rvvIisrCxqNBt26dUNQUFCpn5k2bRrWrVuHPn36YNKkSQgICMCqVauQkpKCzZs3Q6WqeE67evVqXLt2Dffu3QMAHDx4EO+88w4A4Nlnn3XqVpqoqCjExcVhwYIFSE5ORs+ePeHq6oqLFy9i48aNWLRoEWJiYrBq1SosXrwYgwYNQkREBLKzs7F8+XL4+Pjof4F6eHigYcOG2LBhAx5//HEEBASgcePGZTYVO/u94TNalHgtXrwY7du3R5UqVfDll18a7B80aJA+oXI2Sj6fvC9GKDvpyLH99ttvYty4cSIsLEy4ubkJb29v0aFDB/HJJ5+IBw8e6I8rKCgQc+bMEeHh4cLV1VXUrFlTTJ8+3eAYIYqmLfbr16/EdaKiokRUVJTBtuXLl4s6deoItVptMA2ytHMIIcTly5dFTEyM8PPzE+7u7qJNmzZi+/btBsdUZOpjVFSUAGD0ZWxapiN7dPpjsWXLlonIyEjh4eEhvL29RZMmTcTUqVPFzZs3hRBCnDx5UsTGxopatWoJjUYjgoKCRP/+/UVSUpLBeQ4fPiwiIyOFm5ubrKmQvDdFnPkZHTVqVKn/BgCIlJSUMj/vSGzp+eR9KUkSogIjvIiIiIisiGNUiIiIyGYxUSEiIiKbxUSFiIiIbBYTFSIiIrJZTFSIiIjIZjFRUdB7772HJ554AjqdTulQKm3atGlo27at0mEoivfT8fCeOhbeTzul9PxoZ5WVlSUCAgLE559/rt+Gv+fJf/DBByWOX7lyZbnLicu1efNmMWzYMBEeHi48PDzE448/Ll577TWRkZFh9Pht27aJFi1aCI1GI2rWrClmzZolCgoKDI65deuW0Gg0Ytu2bZWOzx7xfjoe3lPHwvtpv5ioKOT//u//hI+Pj7h//75+W/FDExwcLHJzcw2ON+dDU7VqVdGkSRMxc+ZMsXz5cjFp0iTh5uYmnnjiCXHv3j2DY3fu3CkkSRJdu3YVy5YtExMnThQqlUq89NJLJc47bNgw0alTp0rHZ494Px0P76lj4f20X0xUFNK0aVPxzDPPGGwDIJo3by4AiA8//NBgnzkfGmOVR1etWiUAiOXLlxtsb9iwoWjWrJlBNv/mm28KSZLEuXPnDI7dtGmTkCRJXL58udIx2hveT8fDe+pYeD/tF8eoKCAlJQW//vorunfvXmJfhw4d0K1bN7z33nu4f/++Ra7fpUuXEtsGDRoEADh37px+29mzZ3H27Fm8+OKLcHH537JQL7/8MoQQ2LRpk8E5ir+ebdu2WSBq28X76Xh4Tx0L76d9Y6KigMOHDwMAWrZsaXR/fHw80tLSsGTJkjLPk5eXh9u3b8t6lSc1NRUAUK1aNf22n3/+GQDQqlUrg2OrV6+OGjVq6PcX8/X1RUREBH788cdyr+dIeD8dD++pY+H9tG9cPVkB58+fBwCEh4cb3d+pUyd07doV77//PsaPHw8PDw+jx61btw7PP/+8rGuKcpZ0evfdd6FWqxETE6PfduvWLQBAaGhoieNDQ0Nx8+bNEtvr1KmDs2fPyorJUfB+Oh7eU8fC+2nfmKgo4M6dO3BxcYGXl1epx8THxyMqKgr/+c9/8M9//tPoMb169cL3339f6XjWrl2LFStWYOrUqahXr55+e3EzqEajKfEZd3d33L17t8R2f3//Elm/o+P9dDy8p46F99O+MVGxUZ07d0bXrl3x3nvv4aWXXjJ6TGhoqNHMuyJ++OEHvPDCC+jVqxfmzZtnsK/4r4q8vLwSn3vw4IHRvzqEEJAkqVIxOSLeT8fDe+pYeD9tFxMVBVStWhWFhYXIzs6Gt7d3qcfNnj0bXbp0wdKlS+Hn51di//3795GVlSXrmiEhISW2/fLLL3jqqafQuHFjbNq0yWDwFvC/5sdbt26hZs2aBvtu3bqFNm3alDhnRkaGQZ+rM+D9dDy8p46F99O+cTCtAp544gkARSPRyxIVFYUuXbrg3XffNToafcOGDfoMv7zXoy5fvozevXsjKCgIO3fuNNok2rx5cwBAUlKSwfabN2/ijz/+0O9/WEpKCho0aFDm1+VoeD8dD++pY+H9tG9sUVFA+/btART9Y2zatGmZx8bHx6NLly5YtmxZiX2m9pempqaiZ8+eUKlUSExMRGBgoNHjGjVqhCeeeALLli1DXFwc1Go1AGDJkiWQJMlgEBgAZGVl4fLlyxg/fnyFY7JnvJ+Oh/fUsfB+2jllyrdQ48aNRWxsrME2AGLChAkljo2KitJXUDRH8aFmzZoJAGLq1Kli9erVBq/vvvvO4NhvvvlGSJIkunXrJpYtWyYmTZokVCqVGDduXInzbtq0SQAQly5dqnSM9ob30/HwnjoW3k/7xURFIR999JHw8vIyKJ9c2kOzb98+sz40xecy9oqKiipx/NatW0Xz5s2FRqMRNWrUEG+99ZbIz88vcdzw4cNFx44dKx2fPeL9dDy8p46F99N+MVFRSGZmpggICBCfffaZ0qGYxa1bt4S7u7v4+uuvlQ5FEbyfjof31LHwftovDqZViK+vL6ZOnYr333/fIZYcX7hwIZo0aYKBAwcqHYoieD8dD++pY+H9tF+SEOWUzyMiIiJSCFtUiIiIyGYxUSEiIiKbxUSFiIiIbBYTFSIiIrJZTFSIiIjIZjFRISIiIpvFRIWIiIhsFhMVIiIisllMVIiIiMhmMVEhIiIim8VEhYiIiGwWExUiIiKyWUxUiIiIyGY5daJy69YtxMfH49atW0qHQkREREY4faIyZ84cJipEREQ2yq4TlYMHD2LAgAGoXr06JEnC119/rXRIREREZEZ2najk5uaiWbNm+PTTT5UOhYiIiCzARekAKqNPnz7o06eP0mEQERGRhdh1iwoRERE5NrtuUamovLw85OXl6d/n5OQoGA0RERGVx6laVBYsWABfX1/9KyoqSumQiIiIqAxOlahMnz4dWVlZ+teBAweUDomIiIjK4FRdPxqNBhqNRv/ey8tLwWiIKiE7FfAOUToKIiKLs+tEJScnB5cuXdK/T0lJQXJyMgICAlCrVi0FIyOysKwbTFSIyCnYdaKSlJSErl276t+/9tprAIBRo0YhISFBoaiIrCA/BxACkCSlIyEisii7TlS6dOkCIYTSYRBZn66gKFnReCsdCRGRRTnVYFoih3L3ptIREBFZHBMVInuVdkbpCIiILI6JCpG9SjmodARERBbHRIXIXt38md0/ROTwmKgQ2bPTW5SOgIjIopioENmzc/8Fcv5UOgoiIoux6+nJRM6oVatWSP3jGkI0D3B8Rgvghw8gus9lTRUisjqVi6vFr8FEhcjOpKam4kbabcDPFbqCPODyfuRrFqGg/lNKh0ZETsbnsfoWvwYTFSJ7JhX13rqd2wKVSoXCBoPZskJEDoWJCpHdkiA9lJS4nt0M1d0/UNj6JcCNC24SkWPgYFoiB6L+4yjcvv0nVFcPAEKndDhERJXGRIXIwUgPMuF69N9w/e7/QXUjqWjxQiIiO8WuHyIHpcq8CtWhd6Hzj0Bh01iIkGZKh0REVGFsUSFycKqMy3A78A5cf3gXuHdH6XCIiCqEiQqRk1DdTILbrilQXTvE7iAishtMVIiciFSQC9efFsH10HtATqrS4RARlYuJCpEduX79OnJzcwEAuXlaXP8rz6TzqG4mQbPzVbgcWwLp7g1zhkhEZFZMVIjswLFjxzBgwACEhYUhMzMTAJB5X4s6s05i4H/O4/i1nIqfVOigTtkLt2//CdeDCyCl/sIpzURkczjrh8jGbdmyBcOHD4cQAuKRsSVCAN+eycCuM5lY90I9DG5e1YQrCKhunYTbrZMQXqHQ1usNbXgXwLWKOcIvofBBLn4/tA6ZV3+FJEnwq9MSNTsOh9rVvfxIhcClHR/j7u9nENF7PPzCW+j35aZfxY2ftuDen9cASPAMDsNj7YagSrWaFvk6iMg62KJCZMOOHTuG4cOHQ6vVQqvVGj1GqwO0OoHYFRdNa1l5iJRzCy4/r4Tbf+OgTl4N3M806TwXtn2A2+cPG92Xsvsz3P/rJh4fMBl1+76CnFsXcW3/l7LOm/7rbqNLBGgLHuDi9kVw8wrAE4Ono/6gqVC5uuPi9kUQ2kKTvgYisg1MVIhs2DvvvGO0JeVRAoCAwLxv/zDLdaXCB3C58F+47XgF6rObAZ3xJKmi7mfcwt3fz6B2l+fgGVwHXqH1ULPjP5Bx6TjyczPL/Oy9278j7ZfvEdZ1VIl9DzJSoc3LRfU2T8HdPwQeAdVRvVV/FN6/i7ycv8wSOxEpw+RERavVYv369YiLi8OgQYNw6tQpAEBWVha2bNmCtLQ0swVJ5IyuX7+O7du3l9qS8iitDth+OsPkAbbGSNo8uJxaD9eD8wFtfqXPl5t6GWq3KvAMCtNv86nRAJAk5KallPo5XUEeUnZ/hlqdRsC1im+J/e5+IVC7e+L2uUPQaQuhK8zH7XM/wt0/FBpvU7rDiMhWmJSoZGZmokOHDhgxYgTWrVuH//73v/jzzz8BAF5eXpg0aRIWLVpk1kCJnM2ePXvKbUl5lBDA3gtZZo9FlfYr1Je+q/R5Cu7dhYuHt8E2SaWGi8YThfdKj/v3w1/BMzgCfuHNje5Xu7mj/lOv46/fjuLn5RPw82cTkfX7adTtNwmSSl3puIlIOSYNpp02bRrOnDmDxMREtGjRAkFBQfp9arUaMTEx2LlzJ+bPn2+2QImcTXZ2NlQqFXQ6+TNxVBKQ/cA83TSPktLPAPX7G91368ROpJ78Vv9ep81HbtoV/P7DOv22Rv+IN+m6mSnJyL5xAQ2GvlXqMbrCfFzdvwqeIXUR3mMchE6HtF++w6Udn6BBzAyoXNxMujYRKc+kROXrr7/GxIkT0aNHD9y5U7Ik9+OPP46EhITKxkbk1Ly9vSuUpACATgDe7hZqQfAIKHVXYKMo+NdtpX+fsvsz+NdpCb86LfXbXD394FrFB4X3sw0+K3RaFOblwsVIlw4AZN+4gLysP5G8YrLB9suJ/4FXaD3UH/g6/rp4DPnZd/DE4GmQpKKG4iqBY/HL55ORmZKMgHptKvrVEpGNMClRycrKQnh4eKn7CwoKUFjIkfZElREdHQ1JkirU/SNJQLf6xn/hV4bQ+KKwwdOl7ndx94SLu6f+vcrFDS4ePnD3DTI4zjMkAtr8e8j98xo8A2sDALJvnAeEgGew8Z8pIS17o1qDjgbbzn41BzWfHAbfsKKFFnWF+X/PBvrfjCCp+D2XCyCyayaNUYmIiMDJkydL3f/dd9+hYcOGJgdFRECtWrXQv39/qNXyWkjUKqB/Y3/UCtCYNQ7h8xgKoucCnoGVPpeHfyh8ajbCtf1fIDctBTm3LuH6D+vgX7c13Dz9AAD5ORk4vW6mfnCtaxVfeFR9zOAFAG7eAdD4VANQNCBXm3cPv/+wFvczbuH+XzdxdV8CJJUK3o/Vr3TcRKQckxKVsWPH4vPPP8eGDRv0f+1JkoS8vDy8+eab2LVrF+Li4swaKJEzmjlzJiRJ+rt1oHRFbQcS3uxTw6zX19Z8EvndF0B4h5rtnOHdx8LdLxS/ffMRLu78GF6hdVG7yzP6/UKnRV5mWlEriUzu/qGo2+cV3LvzBy5s+RcufP0eCnKzULffq3D9OwEiIvskiYpOK0BRdcgXX3wRK1asgJ+fHzIzMxEcHIw7d+6gsLAQcXFxWLJkiSXiNauTJ08iMjISJ06cQMuWLcv/AJECHq5Ma2yqslpVlKSsf6EeBplUmdYISYXC5qOgrdfHaIE1IiIA+hZOSzIpUSl26NAhbNq0CRcvXoROp0NERASGDRuGzp07mzNGi2GiQvbi+PHjmDt3LrZv324wZkWSirp73uxTA61re5nlWjqfGihsOwEioK5ZzkdEjsvmExV7x0SF7M3169fRrFkzZGZmws9DjZ9nNDPbmBTh5gVtg0FFrShqV7Ock4gcmzUSFZPGqKSkpOCbb74pdf8333yDq1evmhoTEZWiVq1a8PQsml3jqVGbJUkRrp4obDQM+f3+De0TTzFJISKbYtL05Ndffx13797FgAEDjO7/9NNP4efnh/Xr11cqOCKyHOERAO3j/aCN6AG4eigdDhGRUSYlKkeOHMHkyZNL3R8dHY2FCxeaGBIRWZIuoB60j/eFrmY7QGXSjwAiIqsx6adURkYGvL29S93v5eVltGItESlDqFyhq/UktPV6c5AsEdkVk8ao1KpVCz/++GOp+3/44QfUqGHeeg5EVHHCzato/MmAJShs+wqTFCKyOyYlKrGxsVi3bh0+/vhjg7VItFotFi1ahA0bNmDEiBFmC5KIKkao3YoSlP6LoW08FHA3f1l9IiJrMKnrZ/r06Th06BAmT56MefPmoX79ohLVFy5cwJ9//okuXbrgzTffNGugRCSPrurjKGg3CfAKVjoUIqJKMylR0Wg0+O6777Bq1Sps2bIFly9fBgC0adMGQ4YMwXPPPQeVyqTGGiKSTZRYsFBb80kUtH6paIqxtkChuIiIzMfkIf8qlQrPP/88nn/+eXPGQ0QVIf7X9VoY3BR5LV4AdDpAl6dgUERE5sO5iUR2JiQkBCjMQ4jmAVSufxd8860J177vwlVT+mw8IiJ7ZHKikpiYiBUrVuDKlSvIyMgo0QQtSZK+S4iIzCcpKQm4tBvYM7dog7sv0OddSJ4BygZGRGQBJiUq77//PqZNm4bg4GC0adMGTZo0MXdcRCSH2hXovQDwZTkAInJMJiUqixYtQrdu3bBz5064unJdECLFtBoDBDdSOgoiIosxuTJtTEwMkxQ7cP36dezZswfZ2dnw9vZGdHQ0atWqpXRYZA5VAoDGMUpHQZXA55OofCYlKm3atMGFCxfMHQuZ0bFjxzB37lzs2LEDQgioVCrodDpIkoT+/ftj5syZaN26tdJhUmXU7Q64uCkdBZmAzyeRfCYVO1m8eDG2bNmCtWvXmjseMoMtW7agQ4cO+Pbbb/WDnIsrCAshsHPnTjz55JPYsmWLkmFSZVVvqXQEZAI+n0QVY1KiMnz4cBQWFuLZZ5+Fr68vGjVqhKZNmxq8mjVrZu5Yjfr0008RFhYGd3d3tG3bFseOHbPKdW3VsWPHMHz4cGi1Wmi1WqPHFO8bPnw4jh8/buUIyWz8w5SOgCqIzydRxZmUqAQEBKBevXro3LkzWrZsiaCgIFStWtXgFRBg+amSGzZswGuvvYbZs2fj5MmTaNasGXr16oX09HSLX9tWvfPOOxCiZMXSRxUf884771gpMjIrSQV4BiodBVUQn0+iipNEeU+MDWvbti1at26Nf//73wCKmk9r1qyJiRMnYtq0aeV+/uTJk4iMjMSJEyfQsqX9N6Nfv34dYWFh5f4QfJgkSbh69SoH8Nmbm8lA9eZKR0EVwOeTyDR2W5k2Pz8fJ06cwPTp0/XbVCoVunfvjiNHjhj9TF5eHvLy/ldaPCcnBwBQWFiIggL7XxclMTGxQj8EgaK/3L777juMGjXKQlGRRbh4AQ7wb9aZ8PkkR2SV2b/CRFlZWWLBggWiZ8+eonnz5uLo0aNCCCHu3LkjPvzwQ3Hx4kVTTy3LjRs3BABx+PBhg+1vvPGGaNOmjdHPzJ49WwDgiy+++OKLL77M8LIGk1pU/vjjD0RFReH3339HvXr1cP78eX3rREBAAJYuXYpr165h0aJFppzeYqZPn47XXntN/z45ORlRUVE4evQoWrRooWBk5pGQkIAXX3yxwp9bvnw5/2KzNw/uAu4+SkdBFcDnk8g0JiUqb7zxBrKzs5GcnIygoCAEBQUZ7H/66aexfft2swRYmmrVqkGtViMtLc1ge1paWtGibUZoNBpoNBr9ey8vLwCAi4uLQxSv69WrFyRJqnAfeM+ePR3i63cqwh3gPbMrfD6JTGPSrJ/vvvsOkyZNQsOGDSFJUon9derUwe+//17p4Mri5uaGyMhI7NmzR79Np9Nhz549aN++vUWvbatq1aqF/v37Q61WyzperVZjwIABHKhnjyR595hsB59PItOYlKjcv38fgYGlT43Mzs42OaCKeO2117B8+XKsWrUK586dw/jx45Gbm4vnn3/eKte3RTNnzoQkSUYTyIcVH/PWW29ZKTIyK1d3pSMgE/D5JKo4kxKVhg0b4uDBg6Xu//rrr60y5mP48OH44IMPMGvWLDRv3hzJycnYtWsXgoODLX5tW9W6dWts2LABarW61L/civd99dVXLNNNZEV8PokqzqREZfLkyVi/fj3effddZGVlASjqdrl06RKeffZZHDlyBP/85z/NGmhpXnnlFVy7dg15eXk4evQo2rZta5Xr2rLBgwfj8OHD6Nu3r/4vN5Wq6FZLkoR+/frh8OHDGDRokJJhEjklPp9EFWNywbd58+YhPj4eQgjodDqoVCr94lrvvPMO/t//+3/mjtXsHK3gmzHXr1/H3r17cffuXfj4+KBbt27s8yayEXw+icpXqcq0169fx+bNm3Hp0iXodDpERERg8ODBqFOnjjljtBhnSFSIiIjsWYWnJ9+7dw+dOnXCuHHj8NJLL1mti4eIiIicT4XHqFSpUgUpKSnljlonIiIiqiyTBtP27t0biYmJ5o6FiIiIyIBJicrMmTPx22+/4dlnn8WhQ4dw48YN/PXXXyVeRERERJVhUgn9Ro0aAQDOnj2LtWvXlnqcVqs1LSoiIiIimJiozJo1i2NUiIiISiHy8yG5uSkdhkMwKVGJj483cxhEREREJZk0RuVRWVlZ7OYhIiIqpjLLr1dCJRKVpKQk9O7dG1WqVEHVqlVx4MABAMDt27cxcOBA7N+/31wxEhER2RcmKmZj0nfy8OHD6NixIy5evIhnnnkGOp1Ov69atWrIysrC0qVLzRYkEREROSeTEpUZM2agQYMGOHv2LObPn19if9euXXH06NFKB0dERETOzaRE5fjx43j++eeh0WiMzv557LHHkJqaWungiIiIyLmZlKi4uroadPc86saNG/Dy8jI5KCIiIrtm+nq/9AiTEpV27dph06ZNRvfl5uZi5cqViIqKqlRgREREdquMP+apYkxKVObMmYOkpCT069cP3377LQDgl19+wWeffYbIyEj8+eefmDlzplkDJSIisheCLSpmY1LBt7Zt22Lnzp0YP348nnvuOQDAlClTAAARERHYuXMnmjZtar4oiYiI7AlbVMxGVqJy9+5deHp6Qq1W67d169YNFy5cQHJyMi5evAidToeIiAhERkayvD4RETk3tqiYjayuH39/f2zYsEH/fsyYMfrpx82bN8fQoUMxfPhwtGrVikkKERE5PcFq7WYjK1Fxc3NDXl6e/n1CQgIuX75ssaCIiIjsGrt+zEZW188TTzyBzz77DGFhYfD19QUAXL16FSdPnizzcy1btqx8hERERHaGLSrmIwkZQ5N37dqF4cOHIycnR9ZJhRCQJMnmFyo8efIkIiMjceLECSZVRERkNoW3b8OlWjWlw3AIslpUevfujZSUFBw/fhxpaWkYPXo0XnzxRbRv397S8REREdkdoWXXj7nISlR+/fVX1K5dG7169QIArFy5EkOHDkV0dLRFgyMiIrJL2kKlI3AYsgbTtmjRAjt27LB0LERERA5BcDCt2chKVDw8PHDv3j39+wMHDiAtLc1iQREREdkzkV+gdAgOQ1bXT7NmzfDRRx9BrVbrZ/0cP34c7u7uZX5u8ODBlY+QiIjIzuju3yv/IJJF1qyfpKQkxMTE4Pr160UfkqRy1zHgrB8iInJWuUeOwJMTTsxCVotKq1atcOnSJVy+fBlpaWno0qUL3nzzTXTv3t3S8REREdmdwtt3lA7BYchelNDFxQX169dH/fr1MWrUKPTv3x9t27a1ZGxERER2qTAtVekQHIZJqyevXLnS3HEQERE5jIKbN5UOwWHISlTefvttSJKEN998EyqVCm+//Xa5n5EkCTNnzqx0gERERPam8PYd6O7fh8rDQ+lQ7J6swbQqlQqSJOH+/ftwc3ODSlX+rGYOpiUiImf1x8RJCJw0EZp69ZQOxe7JalHRPVK45tH3REREZCgvJYWJihnIKvhGREREFZP320WlQ3AIJg2mBYBz587h8uXLyM7Ohre3N+rWrYsnnnjCnLERERHZrQenT0MIAUmSlA7FrlU4UVm6dCnmzZuHGzdulNhXq1YtvPnmmxg7dqxZgiMiIrJXhX/+iYIbN+BWo4bSodi1CiUqr7/+Oj766CMEBARgzJgxaNy4Mby8vJCTk4NTp07h66+/RlxcHC5evIh3333XUjETERHZhdzDh+E2bJjSYdg12YnKsWPH8NFHH2HQoEH44osv4OnpWeKYRYsW4ZlnnsEHH3yAoUOHolWrVmYNloiIyJ7kHDwIv5gYSDJmy5Jxsr9zK1asQGhoKNauXWs0SQEAT09PrFu3DsHBwVixYoXZgiQiIrJHhbdScf/nn5UOw67JTlSOHDmCoUOHQqPRlHmcu7s7hg4dih9//LHSwREREdm7jPUbIFjWw2SyE5Xff/8dDRo0kHVsw4YN8fvvv5scFBERkaPIv3IF2YmJSodht2QnKnfv3oW3t7esY728vJCdnW1yUERERI7kr1VfIP/6daXDsEuyB9NWdC64jMr8REREDqdVq1a4cf48qrm6YlvXbgAAkZ+PtHffQ8i8d6AqZZynPVK7ulr8GhWanvzBBx9g3bp15R5nrMYKERGRM0hNTUVqbi6Euzu0+fn67dpr13DjnXnwGjfWYWYBVbXCEgGyE5VatWrhr7/+wl9//SX7eEuaN28eduzYgeTkZLi5uSEzM9Oi1yMiIqqoR3siCi9cQN7Ob+E1eJBCEdkf2YnK1atXLRhGxeXn52Po0KFo3749p0ITEZFtMjJk4v6+fXCtUQMeHZ5UICD7Y/JaP0qbM2cOACAhIUHZQIiIiCro7po1UFerCrf69ZUOxeY5RieZTHl5ebh7967+lZOTo3RIRETkjHQ6ZC75DwpYyqNcTpWoLFiwAL6+vvpXVFSU0iEREZGTEg8eIHPhIhT88YfSodg0m0pUpk2bBkmSynydP3/e5PNPnz4dWVlZ+teBAwfMGD0REVHF6HJzkfnRR8i/dEnpUGyWTY1RmTJlCkaPHl3mMXXq1DH5/BqNxmAJAC8vL5PPRUREZA66e/eRuXARfEY9B/fWrZUOx+bYVKISGBiIwMBApcMgIiKyKlFYiKwVn6Pw5k14DhjgMHVWzMGmEpWKuH79Ov766y9cv34dWq0WycnJAIC6deuypYSIiOxS7re7UPjHDfiMeR4qDw+lw7EJJicqiYmJWLFiBa5cuYKMjIwSJfMlScLly5crHWBpZs2ahVWrVunft2jRAgCwb98+dOnSxWLXJSIisqS8U6eQ8d778JvwMtTVqikdjuJMSlTef/99TJs2DcHBwWjTpg2aNGli7rjKlZCQwBoqRETkkApv3cJf770Pv4mvwLVmTaXDUZRJicqiRYvQrVs37Ny5E65WWJCIiIjI2eju3kXm/y2E36uT4Fq7ttLhKMak0ToZGRmIiYlhkkJERGRBunv3kLnoYxTeuKl0KIoxKVFp06YNLly4YO5YiIiI6BG6e/eQsWgRCtPTlQ5FESYlKosXL8aWLVuwdu1ac8dDREREjyjuBir880+lQ7E6k8aoDB8+HIWFhXj22Wcxfvx41KhRA2q12uAYSZLwyy+/mCVIIiIiZ6fNyEDmhx/Bb9JEuFSvrnQ4VmNSohIQEICqVauiXr165o6HiIiISqHNzETGBx/Cd8LLcIuIUDocqzApUdm/f7+ZwyAiIiI5dPfuIXPhIvi+OA4aBcqDWBtr9BIREdkZUVCAzCX/wYPjSUqHYnGVKqFfUFCA8+fPIysrCzqdrsT+zp07V+b0REREduX69evIzc0FANwvLMTNe/dQvUoVy1xMp0PW558DLmq4/12d3RGZlKjodDpMnz4dixcvxr1790o9TqvVmhwYERGRvTh27Bjmzp2LHTt26JeUuVtYiC57dqNrcDAmPP44mvr5m//CQuDuygS4BAebPMA29Zdf8PuhQ7j7++8ouHcPT06dCp8aNcr9zJXvvsP9O3eg02rhFRqK+gMGoHZUlP6Ywvv38euaNbh57BjycnLgGRSEen36IKJXrwrFZ1KiMn/+fLz//vuIi4tDx44d8eyzz+Ldd9+Fn58fFi9eDEmS8N5775lyaiIiIruyZcsWDB8+HEKIEuveCQAH0tNxMD0dCyMj0SvU/LN1RH4+sjdugv+rk0z6vDYvD/516iCkRQucWb9e1mdcq1RBRM+eCGrSBCoXF9w6cQLHP/0UGl9fhDRvDgBIXrUK6adPo82kSfAMCkLaL7/g5PLl8AgIQPXWrWXHZ9IYlYSEBAwbNgxLlixB7969AQCRkZEYN24cjh49CkmSsHfvXlNOTUREZDeOHTuG4cOHQ6vVltqLoBUCWiEw+cQJ/JqZYZE48s+dg+7uXZM++1ibNqjbpw+q1q8v+zNV69VDcLNm8KlRA14hIajXrx98a9fG7XPn9MfcuXABYVFRCGrcGJ5BQajTowd8w8Lw16VLFYrPpETljz/+QLdu3QAAGo0GAPDgwQMAgJubG5555hmsXr3alFMTERHZjXfeecdoS8qjxN+vxb/9ZrFYdNnZFjt3WYQQSPv1V2TfvInAhg3126vWr4+bSUm4f+cOhBBIP30aOTdvIrhZswqd36Sun6pVqyInJwcA4OXlBR8fH1y5csXgmIwMy2SNREREtuD69evYvn17uUlKMa0Q2JuWZpEBtpJaDVXVqmY9Z3kK7t/Hlmeega6gAJJKhZZjxxokIS1eeAEn/vMfbI+Lg6RWQ5IkRL70kkEyI4dJiUqLFi1w/Phx/fuuXbti4cKFaNGiBXQ6HT7++GM0q2DGREREZE/27NkjO0kpJgAcuXMbQ6rUMmssbk88AZW7e7nH3Tx+HGc2bNC/jxw/HgEmFo5z0WjQ8/33UfjgAdJOncIvq1bBMzgYQY0bAwAu7dyJOxcvosO0aahSrRpunzuHnz/7DB4BAQhu2lT+dUwJ7sUXX0RCQgLy8vKg0Wgwb948dO7cGZ07d4YQAv7+/li3bp0ppyYiIrIL2dnZUKlURstzlEYFIKeg0OyxuLdrK+u4oCZN4BsW9r/P+fqafE1JpYJXaCgAwC88HNk3buD81q0IatwY2rw8nFq3Dh3eeAOhkZFFx4SFIfPqVVz4738tn6g89dRTeOqpp/TvGzZsiMuXL2P//v1Qq9V48sknERAQYMqpiYiI7IK3t3eFkhQA0AHwcq1UCbMSJI0bNDJ7MVzc3eEio+XFFEKng66gAACg02ohCgsBSTI4RlKpgAp+z8z23fL19cXAgQPNdToiIiKbFh0dDUmSKtT9IwFoX7WaWeNwa9QIkpubyZ/Pz83Fg4wM5GVlAQBy09MBABofH2h8fAAAv65eDY2vL+r/3Uhx+bvv4FurFiS1GrqCAtw6eRLXDh5Ey3HjABRNXw5s2BC/rl4NtZsbPAMD8efZs7h64ACajxpVofhMTlS0Wi02btyIffv2IT09HW+//TaaNGmCrKws7NmzBx06dEBwcLCppyciIrJptWrVQv/+/bFz505ZBU7VkoQuQUFmH0jr3rJlpT6ffvo0Tq9Zo3//S0ICACCid2/U69sXAHA/I8OgdUSbn4+zGzfiwWefQe3mBp/q1dF20iTU7NBBf0y7f/4Tp9auxdGPP0Z+Tg48q1VDk9hY1OnZs0LxSaKiI4EAZGZmonfv3jh27Bi8vLyQm5uL77//Ht26dYNWq0Xt2rXx3HPPYf78+RU9tVWdPHkSkZGROHHiBFpW8kYTEZHzOX78OJ588klotdoyW1YkFCUqGzp2NGuFWpWnJ6otmF+pFpXK8DaxGm5FmFRHZdq0aThz5gwSExNx5coVg5ujVqsRExODnTt3mi1IIiIiW9S6dWts2LABarUaarXa6DFqSYJakrAospXZy+hX6R6tWJJiLSYlKl9//TUmTpyIHj16QHpkoAwAPP7447h69WplYyMiIrJ5gwcPxuHDh9G3b98SvxMlAF2CgrChY0f0/HuGjLmoq1ZFlehos57TFpk0RiUrKwvh4eGl7i8oKEBhofmnXxEREdmi1q1b47///S+uX7+OZs2aITMzEz4uLvgmqotlVk+WJPiMes7hW1MAE1tUIiIicPLkyVL3f/fdd2hYwcpzRERE9q5WrVrw9PQEAHi4uFgmSQHg2bs33B5/3CLntjUmJSpjx47F559/jg0bNujHp0iShLy8PLz55pvYtWsX4uLizBooERERFVWh9RzQX+kwrMakrp9XX30VZ86cQWxsLPz8/AAAI0aMwJ07d1BYWIi4uDi88MIL5oyTiIjI6amDguA7bmxR4TQnYVKiIkkSli9fjlGjRmHTpk24ePEidDodIiIiMGzYMHTu3NnccRIRETk1lbc3/Ca+AtXfXUvOolKVaTt27IiOHTuaKxYiIiIyQuXpCf/Jr8IlMFDpUKzOvAsOEBERkVmpvLzg9+okuDz2mNKhKEJ2ovLwIoRySJKEbdu2VTggIiIiKqLy9YX/q5PgYoUKsLZKdqKyfft2uLu7IyQkRNYCTMYKwREREZE86qpV4eek3T0Pk52oPPbYY7hx4waqVauGESNG4B//+AdCQkIsGRsREZFTcgkJgd+rk6D2N2/JfXske37T77//jn379qFFixaYO3cuatasie7du2PlypXIzs62ZIxEREROwzWsNvxfn8Ik5W8VmogdFRWFpUuXIjU1FZs2bULVqlXxyiuvICgoCIMHD8amTZuQl5dnqViJiIgcmluDBvCbPBkqLy+lQ7EZJlWMcXV1xcCBA7FhwwakpaXpk5fhw4fjvffeM3eMREREDs89MhJ+E16Gyt1d6VBsSqWmJ+fl5SExMRHbtm3Dzz//DHd3d4SFhZkpNCIiIufg0b49vJ99xqkqzspV4e+ITqdDYmIiRo8ejeDgYMTGxuL+/ftYvnw50tPT8eyzz1oiTiIiIofk3q4dk5QyyG5ROXz4MNauXYuNGzfizp07aNeuHebPn49hw4ahWrVqloyRiIjIIWmaNoEPk5QyyU5UOnbsCA8PD/Tt2xexsbH6Lp7r16/j+vXrRj/TsmVLswRJRETkaFxr1YLvCy9AUquVDsWmVWiMyv3797F582Zs2bKlzOOEEJAkCVqttlLBEREROSKVry98x78ESaNROhSbJztRWblypSXjICIicgqSiwv8XnqJdVJkkp2ojBo1ypJxEBEROQXvZ56Ba3iY0mHYDY7eISIispIq3bvDo11bpcOwK0xUiIiIrMCtbl14DXpa6TDsDhMVIiIiC5Pc3eEz5nnO8DEBExUiIiIL8x48GOqAAKXDsEuVKqGvlKtXr2Lu3LnYu3cvUlNTUb16dTzzzDN488034ebmpnR4RERERYSAunp1uLVtA11BgdLR2CW7TFTOnz8PnU6HpUuXom7dujh9+jTGjRuH3NxcfPDBB0qHR0REBKCorph792hoCwuVDsVu2WWi0rt3b/Tu3Vv/vk6dOrhw4QKWLFnCRIWIiBQVEhICbWYmqrm6wi0kBIEDBnBsSiXYZaJiTFZWFgLK6f/Ly8tDXl6e/n1OTo6lwyIiIieTlJSEPyZOQsHNm/Du1BEu7u5Kh2TXHGIw7aVLl/DJJ58gLi6uzOMWLFgAX19f/SsqKspKERIRkTPyaBmpdAh2z6YSlWnTpkGSpDJf58+fN/jMjRs30Lt3bwwdOhTjxo0r8/zTp09HVlaW/nXgwAFLfjlEROTMVCpoHq+ndBR2z6a6fqZMmYLRo0eXeUydOnX0/3/z5k107doVTz75JJYtW1bu+TUaDTQPLQDl5eVlcqxERERlca1eHSouOlhpNpWoBAYGIjAwUNaxN27cQNeuXREZGYmVK1dCpbKpxiEiInJyrjUeUzoEh2BTiYpcN27cQJcuXVC7dm188MEH+PPPP/X7QkJCFIyMiIioiOtjTFTMwS4Tle+//x6XLl3CpUuXUKNGDYN9QgiFoiIiIvoft0d+P5Fp7LK/ZPTo0RBCGH0RERHZAtcaNZUOwSHYZaJCRERk0yQJrtVDlY7CITBRISIiMjN11QCoWOjNLJioEBERmZlrUJDSITgMJipERERmpg6oqnQIDoOJChERkZmp/f2UDsFhMFEhIiIyM7WPj9IhOAwmKkRERGam8vZWOgSHwUSFiIjIzNwbNFA6BIfBRIWIiMjMJK4/Zzb8ThIREZHNYqJCRERENouJChEREdksJipERERks5ioEBERkc1iokJEREQ2y0XpAMg6bt26hVu3bikdBplJaGgoQkO5hLyj4PPpePiMmo9TJyqhoaGYPXu2w/9jysvLQ2xsLA4cOKB0KGQmUVFRSExMhEajUToUqiQ+n46Jz6j5SEIIoXQQZFl3796Fr68vDhw4AC8vL6XDoUrKyclBVFQUsrKy4MP1ROwen0/Hw2fUvJy6RcXZNG/enA+NA7h7967SIZAF8Pl0HHxGzYuDaYmIiMhmMVEhIiIim8VExQloNBrMnj2bg7ocBO+nY+H9dDy8p+bFwbRERERks9iiQkRERDaLiQoRERHZLCYqREREZLOYqBAREZHNYqJCZAGSJMl67d+/v9LXunfvHuLj4yt0rnnz5uGpp55CcHAwJElCfHx8peMgshe2/HyeP38eU6dORfPmzeHt7Y3Q0FD069cPSUlJlY7FXrEyLZEFrF692uD9F198ge+//77E9gYNGlT6Wvfu3cOcOXMAAF26dJH1mbfeegshISFo0aIFEhMTKx0DkT2x5efzs88+w4oVKzBkyBC8/PLLyMrKwtKlS9GuXTvs2rUL3bt3r3RM9oaJCpEFPPPMMwbvf/rpJ3z//fcltislJSUFYWFhuH37NgIDA5UOh8iqbPn5jI2NRXx8vMG6T2PGjEGDBg0QHx/vlIkKu36IFKLT6bBw4UI0atQI7u7uCA4ORlxcHDIyMgyOS0pKQq9evVCtWjV4eHggPDwcY8aMAQBcvXpVn2jMmTNH32RdXldOWFiYJb4kIoeh1PMZGRlZYnHKqlWrolOnTjh37px5v0g7wRYVIoXExcUhISEBzz//PCZNmoSUlBT8+9//xs8//4wff/wRrq6uSE9PR8+ePREYGIhp06bBz88PV69exZYtWwAAgYGBWLJkCcaPH49BgwZh8ODBAICmTZsq+aUR2T1bez5TU1NRrVo1s36NdkMQkcVNmDBBPPy4/fDDDwKAWLNmjcFxu3btMti+detWAUAcP3681HP/+eefAoCYPXt2heOqzGeJHIWtPp/FDh48KCRJEjNnzjT5HPaMXT9ECti4cSN8fX3Ro0cP3L59W/8qbvbdt28fAMDPzw8AsH37dhQUFCgYMZHzsKXnMz09HSNGjEB4eDimTp1qkWvYOiYqRAq4ePEisrKyEBQUhMDAQINXTk4O0tPTAQBRUVEYMmQI5syZg2rVqmHgwIFYuXIl8vLyFP4KiByXrTyfubm56N+/P7Kzs7Ft27YSY1ecBceoEClAp9MhKCgIa9asMbq/eACeJEnYtGkTfvrpJ3zzzTdITEzEmDFj8OGHH+Knn35y2h9cRJZkC89nfn4+Bg8ejF9//RWJiYlo3Lixyeeyd0xUiBQQERGB3bt3o0OHDvDw8Cj3+Hbt2qFdu3aYN28e1q5di5EjR2L9+vUYO3YsJEmyQsREzkPp51On0+G5557Dnj178NVXXyEqKsqUL8NhsOuHSAHDhg2DVqvF3LlzS+wrLCxEZmYmACAjIwNCCIP9zZs3BwB983KVKlUAQP8ZIqocpZ/PiRMnYsOGDVi8eLF+ppAzY4sKkQKioqIQFxeHBQsWIDk5GT179oSrqysuXryIjRs3YtGiRYiJicGqVauwePFiDBo0CBEREcjOzsby5cvh4+ODvn37AgA8PDzQsGFDbNiwAY8//jgCAgLQuHHjMpuKV69ejWvXruHevXsAgIMHD+Kdd94BADz77LOoXbu25b8JRDZKyedz4cKFWLx4Mdq3b48qVargyy+/NNg/aNAgeHp6Wvx7YFOUnnZE5Awenf5YbNmyZSIyMlJ4eHgIb29v0aRJEzF16lRx8+ZNIYQQJ0+eFLGxsaJWrVpCo9GIoKAg0b9/f5GUlGRwnsOHD4vIyEjh5uYmaypkVFSUAGD0tW/fPnN92UR2wZaez1GjRpX6bAIQKSkp5vzS7YIkxCPtVkREREQ2gmNUiIiIyGYxUSEiIiKbxUSFiIiIbBYTFSIiIrJZTFSIiIjIZjFRISIiIpvFRIXIxly9ehWSJCEhIUHpUIjICD6j1sVEhYiIiGwWC74R2RghBPLy8uDq6gq1Wq10OET0CD6j1sVEhYiIiGwWu36ILCA+Ph6SJOG3337DM888A19fXwQGBmLmzJkQQuD333/HwIED4ePjg5CQEHz44Yf6zxrr/x49ejS8vLxw48YNPP300/Dy8kJgYCBef/11aLVa/XH79++HJEnYv3+/QTzGzpmamornn38eNWrUgEajQWhoKAYOHIirV69a6LtCZDv4jNoPJipEFjR8+HDodDr861//Qtu2bfHOO+9g4cKF6NGjBx577DG8++67qFu3Ll5//XUcPHiwzHNptVr06tULVatWxQcffICoqCh8+OGHWLZsmUmxDRkyBFu3bsXzzz+PxYsXY9KkScjOzsb169dNOh+RPeIzageUWg2RyJHNnj1bABAvvviiflthYaGoUaOGkCRJ/Otf/9Jvz8jIEB4eHmLUqFFCCCFSUlIEALFy5Ur9McUrqr799tsG12nRooWIjIzUv9+3b5/RFZAfPWdGRoYAIN5//33zfMFEdobPqP1giwqRBY0dO1b//2q1Gq1atYIQAi+88IJ+u5+fH+rXr48rV66Ue76XXnrJ4H2nTp1kfe5RHh4ecHNzw/79+5GRkVHhzxM5Cj6jto+JCpEF1apVy+C9r68v3N3dUa1atRLby/th5O7ujsDAQINt/v7+Jv0Q02g0ePfdd/Htt98iODgYnTt3xnvvvYfU1NQKn4vInvEZtX1MVIgsyNjUxdKmM4pyJuDJmQYpSZLR7Q8P5is2efJk/Pbbb1iwYAHc3d0xc+ZMNGjQAD///HO51yFyFHxGbR8TFSIH4u/vDwDIzMw02H7t2jWjx0dERGDKlCn47rvvcPr0aeTn5xvMbiAi8+IzWnFMVIgcSO3ataFWq0vMTli8eLHB+3v37uHBgwcG2yIiIuDt7Y28vDyLx0nkrPiMVpyL0gEQkfn4+vpi6NCh+OSTTyBJEiIiIrB9+3akp6cbHPfbb78hOjoaw4YNQ8OGDeHi4oKtW7ciLS0N//jHPxSKnsjx8RmtOCYqRA7mk08+QUFBAf7zn/9Ao9Fg2LBheP/999G4cWP9MTVr1kRsbCz27NmD1atXw8XFBU888QS++uorDBkyRMHoiRwfn9GKYQl9IiIislkco0JEREQ2i4kKERER2SwmKkRERGSzmKgQERGRzWKiQkRERDaLiQqRE7t69SokSUJCQoLSoRCREXxGmagQyXb58mXExcWhTp06cHd3h4+PDzp06IBFixbh/v37Frvu2bNnER8fj6tXr1rsGnLMmzcPTz31FIKDgyFJEuLj4xWNh+hRzvyMnj9/HlOnTkXz5s3h7e2N0NBQ9OvXD0lJSYrFZC4s+EYkw44dOzB06FBoNBo899xzaNy4MfLz83Ho0CG88cYbOHPmDJYtW2aRa589exZz5sxBly5dEBYWZpFryPHWW28hJCQELVq0QGJiomJxEBnj7M/oZ599hhUrVmDIkCF4+eWXkZWVhaVLl6Jdu3bYtWsXunfvrkhc5sBEhagcKSkp+Mc//oHatWtj7969CA0N1e+bMGECLl26hB07digY4f8IIfDgwQN4eHiY/dwpKSkICwvD7du3SyxlT6QkPqNAbGws4uPj4eXlpd82ZswYNGjQAPHx8XadqLDrh6gc7733HnJycrBixQqDH4DF6tati1dffVX/vrCwEHPnzkVERAQ0Gg3CwsIwY8aMEguJhYWFoX///jh06BDatGkDd3d31KlTB1988YX+mISEBAwdOhQA0LVrV0iSBEmSsH//foNzJCYmolWrVvDw8MDSpUsBAFeuXMHQoUMREBCAKlWqoF27dpX6Ya1kaw5RWfiMApGRkQZJCgBUrVoVnTp1wrlz50w6p61gokJUjm+++QZ16tTBk08+Kev4sWPHYtasWWjZsiX+7//+D1FRUViwYIHRhcQuXbqEmJgY9OjRAx9++CH8/f0xevRonDlzBgDQuXNnTJo0CQAwY8YMrF69GqtXr0aDBg3057hw4QJiY2PRo0cPLFq0CM2bN0daWhqefPJJJCYm4uWXX8a8efPw4MEDPPXUU9i6dasZvitEtoPPaOlSU1NRrVo1s51PEYKISpWVlSUAiIEDB8o6Pjk5WQAQY8eONdj++uuvCwBi7969+m21a9cWAMTBgwf129LT04VGoxFTpkzRb9u4caMAIPbt21fiesXn2LVrl8H2yZMnCwDihx9+0G/Lzs4W4eHhIiwsTGi1WiGEECkpKQKAWLlypayvTwgh/vzzTwFAzJ49W/ZniCyFz2jpDh48KCRJEjNnzqzwZ20JW1SIynD37l0AgLe3t6zjd+7cCQB47bXXDLZPmTIFAEo06zZs2BCdOnXSvw8MDET9+vVx5coV2TGGh4ejV69eJeJo06YNOnbsqN/m5eWFF198EVevXsXZs2dln5/IlvEZNS49PR0jRoxAeHg4pk6dWqlzKY2JClEZfHx8AADZ2dmyjr927RpUKhXq1q1rsD0kJAR+fn64du2awfZatWqVOIe/vz8yMjJkxxgeHm40jvr165fYXtwc/WgcRPaKz2hJubm56N+/P7Kzs7Ft27YSY1fsDWf9EJXBx8cH1atXx+nTpyv0OUmSZB2nVquNbhdCyL6WJWb4ENkLPqOG8vPzMXjwYPz6669ITExE48aNrXZtS2GLClE5+vfvj8uXL+PIkSPlHlu7dm3odDpcvHjRYHtaWhoyMzNRu3btCl9f7g/UR+O4cOFCie3nz5/X7ydyFHxGi+h0Ojz33HPYs2cP1q5di6ioqAqfwxYxUSEqx9SpU+Hp6YmxY8ciLS2txP7Lly9j0aJFAIC+ffsCABYuXGhwzEcffQQA6NevX4Wv7+npCQDIzMyU/Zm+ffvi2LFjBj+4c3NzsWzZMoSFhaFhw4YVjoPIVvEZLTJx4kRs2LABixcvxuDBgyv8eVvFrh+ickRERGDt2rUYPnw4GjRoYFD18vDhw9i4cSNGjx4NAGjWrBlGjRqFZcuWITMzE1FRUTh27BhWrVqFp59+Gl27dq3w9Zs3bw61Wo13330XWVlZ0Gg06NatG4KCgkr9zLRp07Bu3Tr06dMHkyZNQkBAAFatWoWUlBRs3rwZKlXF/0ZZvXo1rl27hnv37gEADh48iHfeeQcA8Oyzz7KVhhTDZ7Qo8Vq8eDHat2+PKlWq4MsvvzTYP2jQIH1CZXeUnnZEZC9+++03MW7cOBEWFibc3NyEt7e36NChg/jkk0/EgwcP9McVFBSIOXPmiPDwcOHq6ipq1qwppk+fbnCMEEXTFvv161fiOlFRUSIqKspg2/Lly0WdOnWEWq02mAZZ2jmEEOLy5csiJiZG+Pn5CXd3d9GmTRuxfft2g2MqMvUxKipKADD6MjYtk8janPkZHTVqVKnPJwCRkpJS5udtmSREBUYEEREREVkRx6gQERGRzWKiQkRERDaLiQoRERHZLCYqREREZLOYqBAREZHNYqJCRERENouJChEREdksJipERERks5ioEBERkc1iokJEREQ2i4kKERER2SwmKkRERGSzmKgQERGRzfr//W6o+pbBaZ8AAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 600x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "multi_2group.mean_diff.plot(reference_band=[0, 1], contrast_bars=False, raw_bars=False);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Reference band kwargs can be utilised via `reference_band_kwargs` in the `.plot()` method.\n",
+    "\n",
+    "The relevant inputs to `reference_band_kwargs` are:\n",
+    "\n",
+    "- `'span_ax'` - Whether the reference band(s) should span the entire x-axis or start from the relevant effect size curve\n",
+    "- `'color'` - Color of the reference band(s). If color is not specified, the color of the effect size curve will be used.\n",
+    "- `'alpha'` - Alpha of the reference band(s) (transparency)\n",
+    "- `'zorder'` - Zorder of the reference band(s) (the layering relative to other plot elements)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "multi_2group.mean_diff.plot(reference_band=[0,1], contrast_bars=False, raw_bars=False,\n",
+    "                            reference_band_kwargs={\"alpha\": 0.2, \"color\": 'black', 'span_ax': True}\n",
+    "                            );"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Delta text\n",
+    "**Delta text** is included in all plots by default. It can be turned off by setting `delta_text=False` in the `.plot()` method."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "multi_2group.mean_diff.plot(delta_text=True);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Delta text kwargs can be utilised via `delta_text_kwargs` in the `.plot()` method.\n",
+    "\n",
+    "The relevant inputs to `delta_text_kwargs` are:\n",
+    "\n",
+    "- `'color'` - Color. If color is not specified, the color of the effect size curve will be used. \n",
+    "- `'alpha'`- Alpha (transparency)\n",
+    "- `'fontsize'` - Font size\n",
+    "- `'ha'` - Horizontal alignment\n",
+    "- `'va'` - Vertical alignment \n",
+    "- `'rotation'` - Text rotation\n",
+    "- `'x_coordinates'` - Specify the x-coordinates of the text\n",
+    "- `'y_coordinates'` - Specify the y-coordinates of the text\n",
+    "- `'offset'` - Am x-axis coordinate adjuster for minor movement of all text\n",
+    "\n",
+    "Otherwise, pass any keyword arguments accepted by matplotlib.text.Text, as a string. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "multi_2group.mean_diff.plot(delta_text=True, \n",
+    "                            delta_text_kwargs={\"color\":\"red\", \"rotation\":45, \"va\":\"bottom\", \"alpha\":0.7});"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`'x_coordinates'` and/or `'y_coordinates'` if you would like to specify the text locations manually. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "multi_2group.mean_diff.plot(delta_text=True, \n",
+    "                            delta_text_kwargs={\"x_coordinates\":(0.5, 2.75), \n",
+    "                                               \"y_coordinates\":(0.5, -1.7)});"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`'offset'` to adjust the x location of all the texts (positive moves right, negative left)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "multi_2group.mean_diff.plot(delta_text=True, \n",
+    "                            delta_text_kwargs={\"offset\":0.1});"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Adding jitter to slopegraph plots\n",
+    "\n",
+    "For paired plots, you can add jitter to the slopegraph by adding a value for `jitter` in the `slopegraph_kwargs` parameter.\n",
+    "\n",
+    "This can be useful for specific paired plots when there are many overlapping points.\n",
+    "\n",
+    "Currently, jitter is only available for slopegraphs and only in the x-direction (vertical plots) or y-direction (horizontal plots)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Jitter tests\n",
+    "np.random.seed(9999) # Fix the seed to ensure reproducibility of results.\n",
+    "Ns = 20 # The number of samples taken from each population\n",
+    "# Create samples\n",
+    "c1 = [0.5]*Ns + [1.5]*Ns\n",
+    "c2 = [2]*Ns + [1]*Ns\n",
+    "t1 = [1]*Ns + [2]*Ns\n",
+    "t2 = [1.5]*Ns + [2.5]*Ns\n",
+    "t3 = [2]*Ns + [1]*Ns\n",
+    "t4 = [1]*Ns + [2]*Ns\n",
+    "t5 = [1.5]*Ns + [2.5]*Ns\n",
+    "id_col = pd.Series(range(1, 2*Ns+1))\n",
+    "df_jittertest= pd.DataFrame({'Control 1' : c1,     'Test 1' : t1,\n",
+    "                 'Control 2' : c2,     'Test 2' : t2, 'Test 3' : t3,\n",
+    "                    'Test 4'    : t4,     'Test 5' : t5, 'ID'  : id_col})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For the example below, there are many overlapping points for the paired plot, which makes it look like only one sample."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 840x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "multi_2group = dabest.load(df_jittertest, idx=((\"Control 1\", \"Test 1\"),(\"Control 2\", \"Test 2\", \"Test 3\", \"Test 4\", \"Test 5\")), paired='baseline', id_col='ID')\n",
+    "multi_2group.mean_diff.plot(horizontal=False);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Adding jitter can help to visualize the data better."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 840x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "multi_2group.mean_diff.plot(horizontal=False, slopegraph_kwargs={'jitter': 1});"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Gridkey\n",
+    "\n",
+    "You can utilise a gridkey table format for representing the index groupings. This can be reached via `gridkey` in the `.plot()` method. \n",
+    "\n",
+    "You can either use `gridkey='auto'` to automatically generate the gridkey, or pass a list of indexes to represent the groupings (e.g., `gridkey=['Control', 'Test']`)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x600 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "paired_delta2.mean_diff.plot(gridkey='auto');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Gridkey kwargs can be utilised via `gridkey_kwargs` in the `.plot()` method.\n",
+    "\n",
+    "The relevant inputs to `gridkey_kwargs` are:\n",
+    "\n",
+    "- `'show_es'` - Whether to show the effect size in the gridkey\n",
+    "- `'show_Ns'` - Whether to show the sample sizes in the gridkey\n",
+    "- `'merge_pairs'` - Whether to merge the pairs in the gridkey (paired data only)\n",
+    "- `'delimiters'` - Delimiters to use for the autoparser. E.g., [';', '>', '_']\n",
+    "- `'marker'` - Marker to use for filling the gridkey\n",
+    "- `'fontsize'` - Font size of the gridkey text\n",
+    "- `'labels_fontsize'` - Font size of the labels in the gridkey"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 480x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "multi_2group = dabest.load(df, idx=((\"Control 1\", \"Control 2\"), (\"Test 1\", \"Test 2\")),paired='baseline', id_col='ID')\n",
+    "multi_2group.mean_diff.plot(gridkey=['Control', 'Test'], \n",
+    "                            gridkey_kwargs={'merge_pairs': True, 'show_es': False, 'show_Ns': False, 'marker': '√',\n",
+    "                                            'fontsize': 8, 'labels_fontsize': 12});"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Delta dot\n",
+    "\n",
+    "By default, delta dots are included in paired experiment plots (excluding proportion plots). \n",
+    "\n",
+    "This feature can be turned off by setting `delta_dot=False` in the `.plot()` method."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 480x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "multi_2group_paired.mean_diff.plot(delta_dot=False);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Delta dot kwargs can be utilised via `delta_dot_kwargs` in the `.plot()` method.\n",
+    "\n",
+    "The relevant inputs to `delta_dot_kwargs` are:\n",
+    "\n",
+    "- `'color'` - Specify the color of the delta dots. If color is not specified, the color of the effect size curve will be used.\n",
+    "- `'marker'` - Marker of the dots. The default are triangles ('^')\n",
+    "- `'alpha'` - Alpha (Transparency)\n",
+    "- `'zorder'` - Zorder (the layering relative to other plot elements)\n",
+    "- `'size'` - Marker size\n",
+    "- `'side'` - Which side to plot the delta dots. The options are `'left'`, `'right'`, or `'center'`. This functions like the `swarm_side` parameter."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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Dh4QYP4UIIRYIOolarWbXrl3ExMQQGBjIa6+9hpeXV5v9ioqK2LhxI5s2bcLMzKwbWirQ09DQ0KXHc3R0ZMWKFVRVVfHDDz+0cs0yNDRk3rx5jB8/niNHjvDTTz+1O+r19PRk4sSJnDlzRnbwuh9jxowhKiqK8+fPs3fvXnQ6XZddk6D7EUIsEHSCwsJC1q5dS3JyMnPmzGH+/PmYmpq22qe2tpbdu3ezdu1aampqWLJkCStXrsTJyambWv1sU1payieffPLA5Qzvh4uLC8uXL6e0tJQtW7ag0Wjk9xQKBRMmTGDu3LkkJSW1GTnrGT16NAEBAezYsYO6urpOnXfYsGHMmTOHK1eusHPnzk4XohD0fIQQCwT3QJIkzp07xzfffIOJiQk/+9nPGDhwYKvcYLVazYkTJ/jHP/5BWloazz//PK+//jrBwcEih7gbcXR0JDAwkB07dsimHF2Fu7s7S5cuJT8/nx9//LFNfvCAAQNYvnw5RUVFfPvtt1RWVrZ6X6FQMHfuXBQKBTt27Oj0dPPAgQNZsGABN27cuG9esqD3oJDEgkOXcuXKFSIiIrh8+TKDBw/u7uYIHoG6ujp27txJRkYGo0aNYtKkSSiVSvl9nU5HUlISx44do7GxkeHDhzN27Ng2I2VB96HRaPjuu+9oaGjglVdewdLSskuPn5WVxaZNmwgICGDhwoWt7g+AsrIyNm/ejEql4oUXXmizlJGZmcnGjRuZOHEiY8eO7fR5b968ybZt2/Dx8eGFF14Q6XK9HDEiFgjaIT09nS+++ILi4mKWL1/O1KlTW/3IZmRksHbtWnbv3o2vry+/+MUvmDJlihDhHoaRkRFLlixBq9W2O3J9VPz8/Fi8eDHp6ens3LmzzdqtPqLawcGBDRs2cOPGjVbv+/v7M3bsWI4fP05ubm6nzxsUFMTSpUu5fft2m7VqQe9DCLFA0ILm5mYOHDjApk2bcHd35/XXX6dPnz7y+yUlJWzatImNGzdiYmLCmjVrmD9/Pra2tt3XaME9sba25oUXXqCwsJC9e/d2edRxYGAg8+fP58aNG/z0009tjm9ubs6KFStkt6zTp0+32icyMhJPT0+2b99OY2Njp8/r5+fH8uXLKS4uZsOGDV0emCZ4cgghFgj+f8rKyvjmm2+Ij4/nueee48UXX8TCwgK4M039008/8cUXX1BeXs7ixYtZtWoVnp6e3dxqQWfw9PRk1qxZJCYmcv78+S4/flhYGHPnziUxMZH9+/e3EWN9RHVkZCRHjx5lz549crCVvvyiWq1m9+7dD/Sg4OXlxcqVK6mqqmL9+vWdDvwS9CyEEAueeSRJ4sqVK6xdu5bm5mZeeeUVRowYgUKhQKPRcOrUKf7xj3+QnJzMtGnT+PnPf05oaKgIxOoF3LhxQ54uDg8PZ8yYMRw+fPixVDcKDw9nxowZxMXFceTIkTaCqlAoiIyMZO7cuVy9epUffvhBHgHb2Ngwe/ZsUlNTiY+Pf6Dzurm5sWrVKhobG/nuu+9a2XAKegdCiAXPNI2NjURHR7Nnzx7Cw8N59dVXcXV1RZIkkpKS+Oyzzzh58iQRERG8+eabjBgxok1AjqBnUlpayvbt21m/fr0sTpMmTSIoKIiYmJjHYrQSERHBc889x9mzZzl16lS7+wwYMIAVK1a0iagOCQlh+PDhHDx4kMLCwgc6r5OTE6tXr0aSJL777jvKy8sf+VoETw4RNd3FiKjp3kNubi7bt29HpVIxa9YswsLCgDtFHPQ/hmFhYUyePBl7e/tubq3gYbh9+zYxMTGo1WrmzJlDcHAwKpWKb7/9lubmZtasWYO5uXmXn/f06dMcPXqUqVOnMmrUqHb3KS8vZ9OmTTQ1NbFkyRK8vLxobm7m22+/Ra1W8+qrr2JiYvJA562pqeH777+nqamJFStW4Ozs3BWXI3jMiBGx4JlDp9Nx4sQJ1q1bh62tLa+//jphYWGUlZWxZcsW1q9fj1KpZPXq1SxatOiRRLi8vFy4IHUjXl5evPbaa3h7e7NlyxYOHjyIoaEhS5Ysoampiejo6MdijDF27FjGjRvHoUOHOpxqdnBwYM2aNTg5ObFhwwauX7+OoaEhCxYsoLa2ln379j3wea2trVm1ahWWlpasW7eOgoKCR70UwRNACPE9+POf/4xCoeCtt97q7qYIugh9UMvJkyeJjIxk5cqVGBoasm/fPj7//HOKi4tZsGABL7/8Mt7e3g91Dp1OR0pKCt9//z2fffYZt27d6uKrEDwIZmZmvPDCCzz33HPExcXx3XffAbB48WJycnI4cODAYznvhAkTGDlyJLGxsSQmJra7j77MYlhYGDExMZw6dQp7e3tmzJhBUlJSh5+7F3rfa33K1IOkRQm6B8PubkBPJT4+nrVr1xIeHt7dTRF0Efr0EhMTE1atWoW7uzvnzp3j9OnTcrH34cOHY2j4cF+L2tparly5wuXLl6mpqcHLy4t58+bh7+/fxVcieFAUCgUjRozAy8uLmJgY1q5dy+zZs5kxYwZ79uzB2dmZoUOHdvk5p06dikajYffu3RgaGtKvX782+xkaGjJ37lzs7e05duwYFRUVzJw5k8zMTGJjY/H09MTR0fGBzm1mZsby5cvZsmULGzdu5IUXXmiVhifoWYg14naoq6tj8ODBfP755/zhD39g4MCBfPLJJ536rFgj7nmo1WoOHDjAlStX6Nu3LzNmzODWrVscPXqUmpoahgwZQmRk5EOtFUqSRE5ODvHx8aSkpGBoaEj//v0ZOnQorq6uj+FqBI9KU1MTe/bsITk5mWHDhiFJEpcvX2bZsmWP5aFJkiR27drFtWvXWLx4McHBwR3ue/XqVXbv3o2Xlxdz585l48aNKJVKXnnllYd6QNRoNGzbto3MzEwWLlxISEjIo1yK4DEhhLgdVq5cib29PR9//DGRkZH3FGKVStXK1SYxMZHx48cLIe4hFBYWsn37dqqrq4mKisLe3p5Dhw6Rn59PSEgIkydPfuDRBtz5Mb969Srx8fGUlpbi5OTE0KFDCQ8PF+5avQBJkrh06RIHDx7EwcEBSZKora2VXbC6Gp1OR0xMDGlpabz44ov3HJ3m5OTw448/YmZmxpQpU4iJiWHQoEFMnz79oc6t1WrZvn07qampzJ07l/79+z/sZQgeE0KI72Lr1q388Y9/JD4+HlNT0/sK8fvvv88HH3zQ5nUhxN2LJElcuHCBI0eO4OTkxKRJk7hy5QopKSm4u7szdepUfH19H/i4xcXFxMfHc/XqVZqbmwkJCWHo0KH4+vqKvOJeSFFREdHR0VRWVtLY2IiHhwdr1qx5LA9TepvNrKwsli1bho+PT4f7VlRUsGnTJhobGwkNDeXy5cssXryY0NDQhzq3Tqdjz549JCUlMWPGDCIiIh72MgSPASHELbh9+zZDhgzh8OHD8tqwGBH3Purq6ti1axe3bt1i8ODBKJVKrly5gqWlJZMmTaJ///4PJJrNzc2kpKQQHx9Pbm4uVlZWREREEBERgZWV1WO8EsGTQKVSsXfvXi5evEhxcTETJkxgxYoVGBh0fSxrc3MzmzdvJi8vjxUrVtzTma2xsZEff/yR3NxczM3NaW5u5rXXXntoO1VJkti3b5/sHDdixIiHvApBVyOEuAW7du1i7ty5rQwbtFotCoUCAwMDVCrVfc0cxBpx93Lr1i127dqFVqvF39+fjIwMJElizJgxjBgx4oGq1FRVVXH58mWuXLlCfX09fn5+DB06lODgYGHq8ZQhSRIJCQls2rSJtLQ0li9fzuLFix/LudRqNT/88AMlJSW89NJL94wl0Gq17Nmzh8uXL1NVVcWAAQNYvXr1Q99/kiRx9OhRzpw5I1d8EjM53Y8Q4hbU1taSk5PT6rVVq1YREhLCr3/963YjHu+mJwmxRqNh7969ODs74+TkhJOTE7a2tk/lF6+5uZmjR49y7tw5zM3NUSgUNDY2EhERQWRkZKfL30mSREZGBvHx8dy8eRNjY2MGDhzI0KFDH2otWdC7KCkp4aOPPuLy5cu8+uqrvPDCC4/lPCqVig0bNlBVVcWqVatwcnLqcF9Jkjh9+jS7du2isLCQl156iWnTpj30ufXHO3bsGKNHj2by5MlP5W9Cb6JXpy9ptVqio6M5fvw4JSUl/P73v6d///5UV1dz9OhRRo8ejYuLS6ePZ2Vl1UZsLSwscHBw6JQI9zQaGhooLy8nNTVVnj43MjKSRVm/OTs792qBLi8vJyYmhvT0dIyNjWloaCAoKIipU6fe8weuJQ0NDSQmJnLp0iUqKipwdXVlxowZ9O/fH2Nj48d8BYLHgU6n4/Tp0wwbNgwzM7NOfcbZ2Zk//OEP/P73v2ft2rXU1NTw0ksvdfk9YGJiwvLly1m/fj3ff/89q1at6tA4RqFQMG7cOOzs7PjHP/7B559/joeHx0P/JumPZ2xszIEDB1Cr1URFRfXa7//TQK8dEVdVVckJ+paWltTX13P48GEmTpyIVqvFx8eHFStW8Kc//emRznO/NeK76UkjYj2SJFFTU0NpaSmlpaWUlJTIf28p0I6OjrIwtxxBP461sq5AkiQSExPZsWMHBQUF2NraEhgYyNSpUzuVhiJJEgUFBcTHx3P9+nUkSaJv374MHToUT09P8cPUyykrK+Pbb7/FxMSERYsW4e7u3unParVa/vjHP3L+/HkmTJjAypUrH+ihvrPU19ezbt06NBoNq1evxsbG5p775+Tk8Otf/5qmpiY+/fTTewZ8dYYrV67w008/ER4ezuzZs3vsd/1pp9cK8WuvvcamTZvYsWMHgwYNwtnZmSNHjjBx4kQA3nrrLU6cOPFQzjSPQk8U4o7Qp2y0FGa9ULcn0HePoLvzS9vU1MSOHTvYv38/Wq2WAQMGMHXqVAYMGHDfdmk0Gq5fv058fLws4EOHDmXgwIFy2UPB00FVVRXR0dEUFRUxbdo0hg4d2ukHrIaGBj7++GOuX79OSEgIM2bMYPDgwV3+gFZTU8O6detQKBSsWrXqvgGAt2/f5s0338TIyIi//vWvDxX935Jr166xc+dOQkJCmD9/voh/6AZ6rRC7urqyevVq/vSnP1FeXo6Tk1MrIf7888957733qKqqeqLt6k1C3BF6gW5vBN3U1ATccQNqbwRtZ2f32AU6OzubTz/9lNTUVLko+8iRI+87fVheXs6lS5dISEhApVIREBDA0KFDCQgIECOBpxitVsuhQ4e4ePEi/fr1Y+bMmZ0uplBSUsLatWupra3FzMyM/v37P9DnO0tlZSXr1q3DxMSEl1566b4PhMnJybz33ns4ODjwq1/96pEdAFNTU4mOjsbf359FixY9UFCj4NHptUJsZmbGP/7xD1555ZV2hfjTTz/lt7/97RMvlP00CHFHSJJEXV1duyPo9gS6pUh3hUBrtVo2bdrE1q1bMTIyYvHixcycOfOeIwidTsfNmzeJj48nIyMDc3NzBg8eTEREBHZ2do/UHkHv4saNG+zZswdLS0sWLVrU6anmmzdvsmXLFjw8PCgtLcXCwoKFCxfi5ubWpe0rLy9n3bp1WFpasnLlyvuuax8+fJjvvvsOV1dXZs+ezfjx4x9ptJ6RkcHWrVvx8PBgyZIlXf6wIeiYXivE/fr1Y+zYsXzxxRftCvH06dMpLS0lLi7uibarJwlxQ0MD//znP3F2dsbFxQVnZ2d568ovmV6g2xtB6wufGxoa4uDg0Gr07OTkhL29facEOjU1lY8++oiMjAxGjhzJm2++ec8fwrq6Oq5cucKlS5dk3+ehQ4cSFhb20F7SgseDWq2mubn5iZyrvLycnTt3Ul5ezrRp0xg4cGCnPnf+/HmOHTtGZGQkaWlplJSUMHnyZCIiIjAyMuqyYK6SkhLWrVuHg4MDy5cvv+f3VKfTsW7dOhITE7G2tiYiIoJZs2Y90v2dm5vLpk2bcHJyYunSpZ0OchM8Gr1WiD/55BN+/etf8/333zNp0iScnZ05evQoo0aN4ve//z1//vOf+eqrr3j55ZefaLt6mhDHx8dTUlJCSUlJq5J8NjY2sijrRdrR0bFLRUqSJOrr69sdQesFWqlUtjuC1gt0dXU1GzZsYM+ePdja2vLLX/6ScePGtfvkL0kSubm5xMfHk5ycjFKpJDw8nCFDhnT56EXQNajVauLi4h7bzFVVVVUbA4zm5mYuXbrErVu38Pf3Z9iwYfe97yVJ4ty5c+Tm5jJx4kRyc3NJS0vDy8uLyZMnM3bs2C4T44KCAjZs2ICbmxtLly695zRxdXU1X375JQYGBjQ1NeHp6cnixYsfqcZyQUEBP/zwA9bW1ixfvlzETTwBeq0QS5LEq6++yrfffoutrS1VVVW4uLhQXl5Oc3MzP/vZz/jiiy+eeLt6khDfTXNzM2VlZbIw6zf9OrqBgQH29vatRs4uLi5dvu6rF+j2RtANDQ3yPuXl5Vy9epWGhgbGjh3Lr371K7y8vNoEk6hUKtn3uaSkBEdHR4YOHcqAAQOE73MPp6GhgVOnTmFsbNzlU6FlZWV88cUXREVFtWvpmJ6ezrlz57CysmLixIn3daxqbm5m//791NXVMWvWLMrKyjh+/DgGBga88847BAYGdlnbb9++zcaNG/Hy8mLJkiX3fFBITU1l69atREREkJKSgqmpKUuXLn0kz+ySkhK+//57TE1NWbFiBdbW1g99LMH96bVCrOfMmTNyDqlOp6NPnz4sWrSIcePGdUt7erIQd4RKpWojzsXFxbIoGhoayqPVlpu1tXWXRpDqp7iPHTvGnj17uHbtGqampvTv3x9HR0cUCgVKpRIHBwecnJxQKpUUFxeTl5eHkZERYWFhwve5l6EXYisrqy5/aKquruaf//wnZWVlrF69ut1iBxUVFRw5coS6ujrGjRtHQEDAfdu7Y8cOzM3NmTVrFpWVlRw4cAArKyuef/55Ro4c2WX3XlZWFps2bSIgIICFCxfeM5p5//79XLp0iQULFnD06FHq6+t54YUXHim9qaKigg0bNqBQKFi5cqWIqXiM9Hoh7mn0RiHuCP20cnFxcSuRVqvVAJiamrYRZ2dn54cuJ5iens6hQ4dITEykvr6eYcOGsWzZMpycnOQRdFFRkWw7mZubi0KhwN3dHXd3dzw8PNqsQTs4OIh0jB7M4xRigNLSUv75z39SV1fHa6+91u6oVaPRcPr0adLT0+nbty8jRoy45wi0rKyM3bt34+Pjw+jRo6murkan03H58mWCgoKYM2fOI00NtyQ9PZ2tW7cSGhrKvHnzOpyZam5u5ttvv0WtVrN8+XJ2795Nbm4us2bNYsCAAQ99/urqar7//nvUajUrVqzotEGO4MEQQtzFPE1C3B6SJFFdXd1q5FxSUkJZWRlarRYAS0vLNsFhTk5OHa6hFRUVcejQIVJTUykvL8fS0pLJkyczZcoU+Qexurqay5cvc/ny5Va+z56enlRUVLRZg66vrwfuTLfrR9At16CFQPcMHrcQw51SmJ999hnNzc384he/wNvbu80+kiSRmprK2bNnsbOzY8qUKfecjs3MzOTw4cMMGDCAPn36MG7cOPLy8ti1axeGhoYsWLCg3fM8DCkpKURHRzNgwABmzZrV4Yi7vLyctWvXEhoayqxZs9i7dy8JCQmMHz+eyMjIhx6p19XV8f3331NXV8fy5cvleAtJktBqtWg0mlabWq1u9W9fX19RHOU+9Foh9vPzu++NpVAoyMjIeEItusPTLsQdodVqqaioaDO9XVlZif4Ws7OzaxUcZm5uTmJiIteuXUOn09HY2IijoyNz5swhKCgISZLIzMwkPj6etLQ02fd5yJAh930yb2hoaHcNWh8U1HI9/O4RtIiqfnI8CSGGO45U//rXvzA0NOTNN9/ssNBCWVkZR44coampicjIyHuaZVy+fJkLFy4wYsQIli1bhrm5OTU1NcTExJCXl8eECRMYM2ZMl0xVX716lZ07dzJ06FCef/75Do959epVduzYwZw5c+jfvz8nT57kyJEjBAcHM2XKFCRJalcs23ut5b/r6+s5e/YsNTU1DBgwAAsLCzQajRz8eS+WLVt23yn/Z51eK8QvvfRSm5tRq9WSk5PD2bNn6devH4MGDWLdunVPtF3PqhB3hEajkcVQvxUUFHDjxg1u376NUqnEzs4OnU5HcHAw8+fPx9XVlezsbNn32cXFhWHDhnWJ73NLgW4p1HcLdHsjaCHQXc+TEmK4U5nriy++wMLCgrfeeqtDb2eVSsXJkyfJysoiPDycYcOGtTt7IkkS+/fv59atW/zud7+TbVV1Oh0nTpzg9OnT+Pv7M2/evDaRx3ePJjsjjDdu3ODMmTMEBQXRt29fmpub2/1cQkICBQUFDB48GHNzc0pKSkhNTcXKyoq+ffu2+Q4plUqMjIzkzdjYuNW/W6ZnnTp1iurqap577jl8fHw69TlDQ0MRs3Efeq0Q34ukpCSmTZvGDz/8wOTJk5/ouYUQd4xOpyMxMZHjx49TU1ODm5sbmZmZFBQU4OPjg06nIysri5KSEgwMDAgMDCQiIoKwsDBcXFxwcXHBwsLisXypGxsb2x1B19bWAm0FWi/SQqAfjScpxHDHkeqrr76SHak6mn7WarUkJSVx4cIF7OzsGDNmDMbGxjQ3N7faampqOHLkCPb29syYMQOlUikLYl5eHhcuXECn09G/f39sbGxaiWxnfnoVCkUrYcvPzyc1NZW+ffvSv3//dkUQIDY2FmNjYxYtWoSZmRllZWXs27cPMzMzFi9ejKurq7z/gyzRaDQatm7dSk5ODosWLSIoKKjTnxV0zFMpxAC/+93v2Lt3L5cvX36i5xVC3D4ZGRkcOnSI4uJi+vXrh4uLC6dPn8bMzIy+ffuSk5NDXl4eZmZm+Pr64uTk1MoHW6PRAGBubt5ugNjj+hFvKdAthVov0AqFQhboltPc9vb2ZGVlER8fz/Tp00XEaQc8TiFuaGhg+/btODo6YmtriyRJNDc3c/PmTY4ePYqtrS0TJ07EwMAAjUbTSmD18Q719fVkZmai0+nw8/NrI9ySJNHY2EhFRQW2traMHj0aU1NTWRz1OctlZWUMGjSIiIgITExM7juK1P+9vdHkmTNnOHLkCFOnTmXUqFHtXntxcTFff/01gwcPJioqCrhjo7l582bq6upYvHjxQ3tUNzc3ExMTw82bN5k/fz59+/Z9qOMI/o+n9lHexcWF5OTk7m7GM09JSQmHDh3i1q1beHt7s2zZMpKSkti7dy9mZmZIksT58+cJCAhg6dKl7fo+63Q6qqqqWgWHZWVlcenSpXYNSvSbo6PjI3vmmpmZ4e3t3Sbwpqmpqc0IOjExkZKSEgoLC8nPz5fF2tHRkeeee+6R2iF4cIqLi9m9ezcKhQIzMzOcnJzw8PDA29ubUaNGcfbsWc6fP8+sWbMwNTXF0NCw3U2n0xEXF0dhYSHBwcEMGTJEnnJVq9XU1tbSp08ffvzxRzw8PJg9e3Yr8XzxxRc5ffo0J06cICcnh3nz5j1S8NKYMWNQq9UcOnQIIyMjhg4d2mYfFxcXpk2bRmxsLH5+foSGhmJnZ8fLL7/Mtm3b2LhxIzNnzuy0s1hLDA0NWbhwIbt37yYmJga1Ws2gQYMe+noET6kQl5eX8+233+Lp6dndTXlmqaur4/jx41y5cgU7OzvZ7Wft2rXcunULBwcH7O3tZd/njtbr4P+mhe3t7QkJCZFfb25upry8vFVw2I0bNzh79izwf6PVuyO4O2ureS8MDQ0xNTXFzMwMc3NzamtruXnzJmlpaRQXF9PY2ChPKz5pv3PBHdzc3PjNb37D7du3ycjIkKd1b968iYuLC4GBgWRkZHDhwgVee+01bGxsOlz28PPzIzExkfj4eGpra5k0aVKrtVYPDw9mzZrFjh07cHZ2bjVSNTAwYPz48fj4+LB9+3a+/PJL5s2bR58+fR762iZMmIBGoyE2NhZDQ8N2hXDIkCFkZWWxe/du3NzcsLW1lc0+YmNj2bVrFxUVFUyYMOGBl3uUSiVz587FyMiI3bt3o9FoGDZs2ENfz7NOrxVivaf03VRVVZGamoparWbjxo1PuFUCjUbD+fPnOXPmDEqlkmnTphEcHMymTZvYu3cvxsbGTJo0icjISMLCwh5pxGpoaCivHbdEpVK1CRCLj4+XU5paFqaws7PD2toaS0tLjIyMqK2tpby8nKqqKiorK6mqqqK6upra2lpqamqoq6ujvr6ehoYG1Go1dXV11NbWolKpkCQJQ0NDbG1tCQsLw8/PD1NTU7GO1k0YGBiQn5/PgAEDmDNnDs3Nzdy+fZvk5GRu3rxJfn4+zc3NHD58mOvXrzN69Gg8PT2xt7eXHxTt7e3l6eFBgwbh4uLC0aNH2b59OxMnTmzlXhUeHk5JSQmHDx/GycmpTc6yr68vr732Gjt37mTjxo2MHTuWCRMmPNRDoUKhYOrUqWg0Gvbs2YORkRH9+vVrs8+sWbP48ssv2b59Oy+99BJKpRKlUsnMmTNxcHDg8OHDVFRUMGfOnAeOdVAoFMyYMQNjY2P27duHWq1mzJgxD3wtgl4sxDqdrs1TnEKhwM/Pj8mTJ7N69epWoyfB40WSJJKSkjh27Bj19fUMHToUf39/Ll68yF//+leqq6vlAuteXl5del6VSkVjYyNNTU2t/mxsbMTQ0FAe6RQXF1NQUCDnPVdVVVFfX49arUar1cpCamxsjKmpKRYWFlhYWGBra4u1tTWOjo74+vqiUqlksdZoNBgZGWFvb0/fvn2JiIhApVIRHx9PYmIi+fn5zJw5s8uuV9B51Go1RkZGHD58GBcXF4YPH46/v78c3dzU1ERWVhZbtmzh0qVL8hKJmZkZRkZGmJmZoVAosLa2xsHBQRbnqVOnEhcXR2xsLOHh4fLxACZNmkRpaSkxMTGsWbOmTZqdhYUFS5cu5ezZsxw7doycnBzmz5+PjY3NA1+fQqFg+vTpaDQaduzYgaGhYZvfPFNTUxYsWMB3333H8ePH5eBVhULB6NGjsbOzY8eOHWzYsIEXXnjhgX2l9Q8EJiYmHDlyBJVKxcSJE0WU9APy1AZrdRfPYrBWVlYWhw4dorCwkMDAQFxdXbl58yY3btwgNzcXPz8/Xn/99Q4fjPRiereQthTU9l6rq6ujpqYGlUqFWq1us0mShE6nQ6fTyet9+gAYS0tLrK2tsba2RqlUyhGs+pSQ+vp6lEqlvK+5ublsZKLRaDA2Nkan02FhYYGDgwM6nY6cnByysrJobGzE0tKSoKAgBg8ezIQJE3B0dHxs/a9/eOiNPImo6fz8fC5evEhpaSk+Pj4MGzas1VKIJEn88MMPHDlyhJCQEFxdXWlqakKpVGJjY4O5uTmSJFFRUYFKpQKQZ0+KiopwdXXlnXfeISQkBGNjY1QqFd9++y0ajYZXXnmlQ5et27dvy2usc+fOfeiZE51Ox/bt20lNTWXJkiXt5uzqA7yWL1/eZko8Ly+PLVu2YGxszNKlSx/6Xj137hyHDh1i+PDhPPfcc0KMHwAhxF3MsyTEZWVlHD58mLS0NKysrLCxseH27dvU1tbK07ehoaGMGDECSZI6FNampiY56EqSpFZiCneeuluKqj7/UqFQtBJYExMTbG1tsbOzw87ODisrKywtLdvd9CkbHVX/0QeIpaWlkZKSQkFBgTx6VqlUaLVa+RiSJGFiYoKTkxOhoaH069cPX1/fJ5bWZGlpybBhw3qlGD+p9CVJksjIyJDXeIOCghgyZAiWlpbAnf/v9evXc/HiRdkMIzs7m+zsbOrr6zE1NcXb2xsXFxdMTU2prq6mvLyctLQ0EhMTsbe3Jzw8HF9fX1xcXDA3N+fkyZP4+/vz6quvdngvNDQ0sHv3btLS0hg5ciSTJ09+KMc3rVbLjz/+SFZWFkuXLm0TEa1/2CgqKuL111+Xr1tPVVUVmzZtora2lsWLF+Pn5/fAbQC4dOkSsbGxDBw4kJkzZ3ZpsZinmV4jxN9///1DfW7FihVd3JJ705uFWO+6c7/RaFVVFQkJCaSlpdHY2IhSqUSlUmFsbCxXwmpubiYgIAA3Nzc5XUOhUMiiqhdWvag2NzfLOZYtI1YVCgUWFhbtiundQmtiYvLAT+HtVf+prq4mLS2N9PR06uvraWpqkn949YKtUCgwMTGRH0CsrKwwMTGRR1H6hwE7OztcXV0fW5F1/WzAuHHjuszf+EnyuIU4MzMTHx8fWdy0Wi0pKSlcvnwZjUZD//79GThwICYmJmi1Wr788kuuXbvG0qVLGTt2LJIkUVpaSlZWFtnZ2VRVVWFkZISXlxe+vr44OztTVlZGZmYmOTk5BAUFYWdnJxvXJCUl4e3tzbhx4+R4Bv2mvyckSeLChQscOXIEV1dXFixY8FDpbs3NzWzevJm8vDxWrFjRJli1rq6OL7/8EmdnZ5YvX97mu9LU1ER0dDRZWVnMnDnzoSOhk5KS2LVrF3379mXu3LnCSrYT9BohftiABn0+4JOiJwlxc3Mz+fn5HQpqe2LbkWWdXkyLiopIT0+ntrZWFiIXFxf8/PxoaGggKSkJc3Nz+cdNpVLR0NAg5wHrMTIyuqeo6jcLC4vH+kXWC4GZmRlFRUUkJyeTkZFBfX09dXV1FBYW0tDQgIGBAY6Ojnh7exMaGkpoaCheXl6yeDQ1NVFVVUVFRUWrTaVSMX369McWwd/U1ERtba0Q4naorKwkJiYGe3t7IiMjWwVWqdVqrl69SlJSEkqlksGDBxMWFoYkSXz22WfcunWL1atXM2TIkDbH1I+US0pK0Ol02NnZMWPGDCorK0lMTCQkJITZs2fT3NzMsWPH2L17NyEhIVhaWlJWViZ/x+zs7FoJs06nk+01Z8+eTWho6ANfs1qt5ocffqCkpISXXnqpjZVnZmYmGzduZOLEiYwdO7bN57VaLfv27ePy5cuMHTv2odd7U1JSiImJkStHCdObe9NrhDgnJ+ehPvcoZcAehp4kxNXV1Xz88cfyv42NjTEzM5PTbu7+08zMTB7V6U0N9C5AycnJHDlyhNzcXCRJkvN23d3dMTY2lgs2+Pv7ExERgY2NzT1HscbGxj1iDSk7O5u1a9dy8+ZNKisraWhokCOjFQoFjo6O9O/fn4iICHx8fHB2dr7vQ6FKpaK2tpbq6mpKS0sJCQm5b63bh0UI8b0pLS3lxIkTVFVVMXjwYAYOHNjqwa6+vp4rV66QmpqKhYUFQ4YMwdvbm48//pi8vDxef/31NtHIeurq6uSUNTMzMzkiOT8/H29vb1566SXc3d05cOAAcXFxLFu2DG9vb8rKyiguLpa3oqIiOaJfoVBQUFBATU0Nw4YNY9asWXh4eDxQ36hUKr7//nsqKytZtWpVm4CxY8eOcebMGV566aUOC2CcP3+ew4cPExYWxpw5cx4qu+HWrVv8+OOPLFq0qEtrNT+N9Boh7i30JCHW6XRUVFTIUaD6AKeWW21tbZvXmpub5WOUl5eTlJREWVkZZmZmhIaGMnToUPr27YuDgwOVlZWcPHlSrjjzME/xTxJJkigpKeH48eOcPn2aGzduUFxcjFKpRKfT0dzcjJWVFQMHDmTUqFEEBAS0WU/T6XTU19dTU1MjpzXp/15dXS0H9MCdh58pU6aIEXEHPG4h1ns6JyQkkJiYiJ2dHZGRkW0CkqqqqoiLiyMrKwsHBwfCw8PZsmUL5eXl/PKXv+xQSPT9P3ToUPLy8khJSeH69eskJSUhSRJRUVHMnDmT48ePU1hYyJo1a1qNzPXU1dW1Eua4uDji4+Nl5zk3N7c2U9v3yodvbGxk/fr1NDQ0sGrVqlbBafr18Orqal577TXMzMzaPUZKSgo7duzAxcWFJUuWPHBEtf667v7+CNoihLiL6UlCXF1dLQdgNDY2tnrv7rXXu6eGCwsL2bVrFzdu3MDa2prnnnuO6dOn4+XlJU/5Hz9+nLNnz+Ln58fcuXN7bKmz6upqsrKySEhI4Pz586SlpVFXV4eFhQWSJFFVVSVba44cOZJhw4ZhYGDQRmj1/66trZWnFxUKhRyBbWVl1epPc3NzVCoV1tbWj+zw1RFCiDumqamJrVu34urqipubG6amply9epWqqioGDRrEoEGD2ix7FBUVcfHiRYqKirCzsyMuLo7m5mbefvvtdtPu2ut/jUZDWloaW7du5cKFC9jb2zNw4EBu376Ni4sL7777bqf+r/Ly8li/fj0lJSX069cPc3NziouLZcc2IyMjuZpZy00vrPX19axbtw6NRsOqVatazcpUV1fz5Zdf4uPjw+LFizucncrPz2fLli0YGRnx4osvinrEj4leLcRFRUV8++23XLlyRS7O3RKFQsHRo0efaJt6khCrVCqOHTvWrtiam5u3eZpubm7m8uXLbNmyhaSkJGxsbJgzZw7z589vJbIVFRVs376dwsJCJk6cyOjRo3vENLOe+vp6srKyyMrK4tatW6SmppKRkUFtba1c7cnc3BytVotCocDY2JiAgAAsLCzklKimpib5eEZGRnKq092Ca2FhQWNjI9XV1VRXV8sGINXV1dTV1cmjoq7MnW6JEOKOaWpqIjk5mYKCAoqLi2lubsbQ0FDOA/f09GT69Ok4Ozu3+pwkSeTk5BAXFycHXFlbW/Pb3/62zb736/+kpCQ2btxIfX091tbWXL58GUdHRxYtWkRYWBh9+vS550OaSqVi7969XLt2jYiICJ577jnUanWrqW297as+Hsba2hpXV1e5SMrRo0exsLDg5ZdfbvU9Tk1NZevWrURFRd3TFauqqorNmzdTU1PDokWLWuVNC7qGXivEV69eJTIyksbGRoKDg7l27RphYWFUVVWRn59Pnz598PLy4tixY0+0XT1JiDtLRUUFcXFx7Nmzh5s3b2Jvb8/s2bNZsGBBm2jfq1evEhsbi7m5OQsWLMDDw6ObWv1/NDY2yjm8+upNlZWV5OXlycFW+gcR/UjXyMgId3d3nJ2dKS0txc7ODnt7e1lk9UJrY2ODsbGxHDl9t9jW1NTIP4BKpRJra2tsbGywsbHB1tYWGxsbHBwcHltqkRDiztHc3ExZWRkFBQUUFBTID2kajYa+ffsycuRIPD09cXR0lB9QdTodN2/e5NixYxw9ehQ7Ozt+//vf4+7uLh+3M/1fVlZGdHS0LP4nTpzAwsICJycnjIyMCAgIICQkhKCgoHaniSVJIiEhgX379uHg4MCCBQvajEz19cCLiopaCbT+oTIpKQlbW1tmz54tp1i5uLhw8uRJLl26xJo1a3Bzc+uw/1pGVM+YMaPX/Lb1FnqtEEdFRXH9+nXOnDkjV+Q5cuQIEydOJDo6mtdff519+/Y9cf/T3iLEOp2OW7duERcXx/nz58nNzcXW1lZe07p7XUelUhEbG8vVq1cZMGAAUVFRjy0l536o1Wpyc3PJyMggNTWVrKwsGhoaaGpqkn9sq6ur5UhnBwcHJElCoVDg5OTEoEGDGDJkiJxWdPXqVWxtbWWRbim0+k2/7qufir5bbPXBaU86b1II8cOh1WopLi7m5MmTXLlyBa1Wi5eXFzY2Nri6uuLu7o6bmxuOjo5IksTRo0f56quvMDEx4Y033mD48OEYGRl1uv81Gg379u0jISEBCwsLampqmDBhghzomJeXh4GBAb6+voSGhhIcHNym0lNJSQnR0dFUVVUxffr0ThVsaGhooKSkhLS0NLZs2YJGoyEgIECewbKwsCAlJQUzMzNWr16Nl5cXDg4O7WYq6HQ69u3bx6VLlxgzZgyTJk3qUTNhvZleK8Q2Nja8++67/Pa3v6WiogJHR0cOHTokW7j96le/IjExkZMnTz7RdvV0Ia6vrychIYFLly6Rm5tLeXk5ZmZmjBw5kqioqHbXgPLz84mJiaGhoYHp06cTHh7+xNqrVqspLS0lNTWVlJQU0tPTycvLo6GhQTbVaGpqoqKigoaGBoyMjPD19SUiIgJ3d3eKi4tRq9UEBgYycuRI+vTpI+cEl5eXU1BQwPnz5+V8XD1mZmbtiq21tXWPSsUQQvzolJWVcezYMfLz83FxccHKykouvWlkZCQLc01NDV999RWSJDFp0iRGjBiBr68v9fX1ne7/xMRE9u7dS0FBAdbW1rzxxht4e3tTU1NDWlqa/GCp0+nw8PAgNDSUkJAQObhMrVazb98+EhMTGThwIFFRUZ2ebSkpKWH9+vXY2toyffp0qqqqKCoq4tatW+zZswdLS0tCQ0NRKpU4OTm1Wnd2dXWVYyr0EdWhoaFy4QfBo9FrhdjKyoqPP/6YNWvWoNPpMDExYdOmTSxatAiAb775hrfeeuuJV77piUIsSRK3b98mPj6e5ORkmpqaZAONoKAgpk6d2u66jyRJsieum5sb8+fPv2eVpIdtW21tLZWVlVRUVFBZWUl5ebk8zVxYWEhNTQ06nQ5TU1OcnJzk0XpJSQkVFRUYGBjg5+dHZGQkw4YNIz09nYsXL1JbW4ubmxuenp5IkkR5eTmVlZWynaWxsTFWVlayTaGTk5Mstl0hCs3NzTQ0NMhR648DIcQd09jYyPbt29vkpbf8u95PWqvVkpiYSEJCAjY2NowdOxaFQkFhYSEFBQUUFRWh0WioqKjg8uXLmJub079/f1xdXQkLC2Pp0qWdjiouLi7mxx9/5NSpU/j4+PBf//VfrQKpGhsbSU9PJzU1lfT0dDQaDY6OjrIou7u7c/XqVfbu3YuNjQ0LFy5sU/ikIwoKCtiwYQOurq4sW7ZMvi+vXr3Kjz/+yJgxY3BwcJCnuPWWrnDHwU0vzA0NDVy8eBFfX1+WLl0qIqMfkV4rxOHh4cycOZM//vGPAAQFBREZGclXX30FwOrVqzl06BB5eXmdPuYXX3zBF198QXZ2NgB9+/blv//7v3n++ec7fYyeJMRqtZpr164RHx9PUVERVlZWKJVKKioqsLGxYeLEiQwYMKDd6dTa2lp27NhBdnY2Y8aMITIy8qGNNdRqNZWVlR1uzc3N1NXVUVVVJVteGhkZYWVlhZ+fH46OjhgbG1NXV0dWVhZFRUWo1WosLCzktbW6ujouX75MRkYGzc3NuLi4yPmX+mo6d2+WlpY0NjY+sBDodDoaGhrkTW/nWV9fL/+9srKSkpISysvLWb16Nf3793+ovrsfQog7pqmpiWvXrsnmLPqtpcmPUqlsJcw6nY6UlBSampoYNGgQI0eOxNzcHJ1OJy97nD17lkOHDslmNmq1mhEjRrBw4UKGDRvWqe+JSqUiJiaGjRs30qdPHz766KN2//80Gg2ZmZmkpqaSlpZGQ0MD1tbWhISE4OzszMWLF6msrOT5559n8ODBnZoqvn37Nhs3bsTLy4slS5bIMzy7du0iOTmZV199VR6B63Q6Kisr2+Q9V1VVUVtby/Xr17G2tiYqKoqgoCBZqC0tLcW09QPQq4S4srJStn5755135OAigI8//pi3336biRMnIkkSJ06c4O233+avf/1rp4//008/oVQqCQwMRJIkNmzYwEcffURCQgJ9+/bt1DF6khBXVFTw3nvvERoaipOTE7m5uWi1WsaMGcPIkSM7nNJKS0tj9+7dKJVK5s2bd1/f2Zaj2va2lrMSRkZG2NraYmhoKItIVVUVCoUCKysrfH19ZTegyspKbt26RWlpKWVlZdTV1dHU1CQXWrC1taW6upq8vDyam5txdXVl8ODBDB48GFdXV3mfe63bthQCExMTmpqaZDHVm3vcLbiNjY20/NoYGBjIUehVVVUUFhZSXl4uV/9pz6GpqxBC/GDoPc/1ony3SOtfKywspLCwEBMTE7mQScsRdVpaGnv37sXT0xNfX185rcjFxYWxY8fKvtPu7u4dLmVIksShQ4f46KOP8PPz4y9/+cs9Z5x0Oh25ubmkpKSQmppKdXU1xsbG1NfXU1VVxdixY5k7d26nYjeysrLYtGkTffr0YdGiRSiVStRqNV999RVKpZJXXnnlnkswTU1NlJSUkJGRQXR0NMXFxfj6+spR2RYWFrIoR0REPNaiJ08DvUqITUxMiIqKYunSpYwePZqCggLCw8MxMjJCkiT++Mc/sn37dpRKJTNmzOC999575GhVe3t7PvroI15++eVO7d+ThLi8vJzf/va35OTk0NjYiK+vLyNGjMDLywsnJyd5s7CwQKFQoNFoOHz4MHFxcQQHBzN79uxWuZHtiWxFRYXsLa3Hysqqldeyra0tSqWS6upqioqKyM7Opq6uDqVSiaenJ97e3kiSREFBAampqeTk5FBfX49Wq6W+vh6VSoWhoSHu7u707dsXX19fysrKuH37NgYGBoSHhzNmzBh8fX3bfQrXV3fS5wC3zAcuKyvjypUr6HQ61Gp1qxQ4hUKBmZkZ5ubmWFhYYG5ujrm5uZz+ZWZmRl1dHTdu3CAlJYW8vDw5b9jDw4OwsDB8fHzw9vZ+bCIjhLjr0Wq1NDY2kpeXx4kTJygpKcHDwwM3NzfZFKepqYmMjAxu3LiBq6sr8+bNo6qqipSUFGpqarCyssLT0xM7Ozv69OlDcHAw/v7+eHh4tBG406dP86c//QkPDw/+4z/+g+Dg4Pu2UZIkioqKZFG+fv06t27dwt3dnaVLlzJmzJgOjTr0pKens3XrVkJDQ5k3bx4GBgYUFxfz9ddfM3jwYKKiojrVXyqViujoaDIyMhg7dixubm6tRtDz5s17bIY2Twu9SoiXLl3Knj17aGhowMrKinnz5rF06dLHUv9Sq9USHR3NypUrSUhIICwsrN39VCpVKyelxMRExo8f3yOEuLS0lM8++wwXFxf69++PTqejtLSU0tJSysvL5Sk6vbXl9evXaWxsJCIigqCgIFQqFVVVVW1GtYaGhq2E1t7evpXoGhkZUVNTQ3Z2trzWW1lZiUajaZUSVF5eTmZmJtnZ2ahUKjnH19LSUi4M4ezszPDhw2UD/gsXLnD9+nWUSiUDBw5k0KBBmJiYtBLX9ra7va7NzMywsrLC2NiY3NxcHB0dsbW1bSW4etvCluhzU5OSkkhLS6OiogKlUomLiwshISH0798fb2/vJ2ZuIoT48aLT6UhKSuLy5ctYWVkRGRmJi4uLvJwSHR3N0aNHmTJlCtOmTaOyspJr165x/fp16urq5HgD/YOnmZmZPIru06cPgYGBODg4cOXKFb799lucnZ2ZO3cukyZNeqCloPLycuLi4tiyZQu3b98mMDCQUaNGyevKHdU7TklJITo6mvDwcGbPno1CoSA+Pp7Y2FgWL17caac8nU7H/v37iY+PZ/To0UyePFlMTT8AvUqI4U4gw65du9i8eTOHDh2S1wOXLFnC0qVLH1n8rl27xsiRI2lqasLS0pLNmzff88nw/fff54MPPmjzek8QYrgTGOLs7CyPePUjWf2IMjc3l4SEBG7cuIFOp5OnjfWBTPqIUX21mT59+uDm5tZmure+vp7s7GzS0tJITk4mPz+fhoYG2d9aqVRiYGBAdXW1XAzBxMQEFxcXQkNDcXV1RaVSkZOTg06nIyAggODgYOzt7blx4wYXL14kJycHQ0NDPDw8cHBwkNeTW6Jvtz4PuKNNPyq5nxCo1WoKCgq4fv06ycnJ5ObmolarMTc3JyAggH79+jFgwABsbW1b/fDoA7UaGhqws7N7bKleQoifDBUVFZw8eZLS0lLCw8MZMmQIhoaGNDY28sMPP3Djxg1Wr17NqlWrgDu/UydOnODkyZM0NzcTHByMhYUFOTk55OTkkJ+fT11dHQqFQs49138vbWxsCAgI4LnnnsPd3V1+39ra+r4zfM3NzezevZuDBw9iYmKCvb09CoUCd3d3QkJCCA0NxdHRsdW9eu3aNXbs2MGQIUPk37ro6GgyMzN57bXXOu2TLkkSFy9e5ODBg4SEhDBv3jwRUd1Jep0Qt6SyspJt27axefNmzpw5A0BgYCDLli3jxRdffCgHGH2OanV1NTExMXzzzTecPHmyV46I6+rqOHz4sCy+ems8uDOqtbCwIDMzk4qKCgYPHsy0adPkvEl9wYKWm3762cjICENDQ+rq6uQo5+rqanlNVD8y9vDwwNjYmIaGBvl9S0tLfH198fDwwMbGhszMTBITEykuLsbQ0BBnZ2dsbW1RqVQUFRWRl5dHU1MTdnZ2hISE0KdPH7nsYHvbgwre3UKg0WgoKiri9u3bJCcnk5mZSWVlpZyTHBgYSFBQEM7OzqhUKhobG2XBbWxspL6+nsbGxlapUMJZq2N6ixDDnVHf1atXuXTpElZWVowfPx5bW1tqamq4cOECp06d4o033mDhwoXyZ2prazlx4gQJCQlYW1szceJEOXCvsLBQnlrOyMigsrKStLQ0VCqVHGkfFhbWKqWwZVpdS/MY/d/1AZkpKSlynMegQYOorKwkPT0dtVqNg4ODPFL28PBAoVBw5coV9uzZI49mVSoVX375JVZWVrz00ksPNDpPS0sjJiYGZ2dnlixZIiKqO0GvFuKW5Ofns3nzZrZs2UJiYiIKhYLhw4dz7ty5Rzru5MmT6dOnD2vXru3U/j1pjbixsZHNmze3mkbWb+Xl5ezcuRONRsPs2bMJCQlp9VmdTkdNTQ1lZWVyvu21a9dITU0lNzeXqqoq1Gq1/ERvZ2eHk5OT/ASud6LSarWYmprKhhf66OeCggJKS0sB8PT0lNfQDAwMyM3NJTc3FwMDAwYMGMC4cePo06fPY5nqqqqqYtu2bZSVlZGfn09GRgbl5eU0NDRgaGiItbU1zs7O2NnZoVQqufvrYmJi0moqW/+nPjXG3Nz8seYeCyF+8lRWVnLixAm5slZgYCDjx4/nww8/5MKFC7zzzjttZtHKyso4cuQIqampuLq6MmXKFPr06SO/ry9GkpyczLp166ipqQHu3J/h4eEMGzYMe3t7zMzMqK+vl13dqqurW80KtTScMTQ0JDExkfr6eiZOnMioUaOoqKggNzdXjsC2srIiJCSEkJAQiouLOXToEJGRkURGRpKXl8d3333HqFGjZH+GzlJQUMCWLVswMDBg6dKlbaxBBa15aoRYz7Vr1/jv//5vdu/e3SX1iCdOnIi3tzfr16/v1P4PI8RqtbpVsNPjRKvVcurUKc6fP4+npydTp05Fo9FQXl7eqo6uPudWv0bc1NSEgYEBFhYWODs74+zsjKOjI0qlktzcXDmtqKqqCo1GI+9ra2srj5AlSaKhoQGFQoG3tzfjx49n1KhRWFlZkZubKxdkMDU1ZciQIQwdOrSNu1Bn0VdIujsitqqqipycHHJzc8nLyyM/P5+CggLZd9rIyAh7e3s8PT3x8PDAycmplcjeLbjdbe4hhLh70I+OL1y4gKGhIW+99Rb+/v68++67XLt2jf/3//4fkZGRbT6Xm5vL4cOHuX37Nv7+/kyZMqWNtWRhYSHffvutvBZ97NgxDAwM6NOnD2ZmZnh4eODr64uvr6+cI68X5ZYCrbdjTUhIIDs7GwcHB0JCQjA1NcXa2hqNRiMvFWk0Grk4SUVFBUuWLCEyMpIzZ85w5MgRli9f3urBoTNUV1eza9cuZsyY0W7FKcH/8VQIcW5urjwavn79OpIkMWrUKJYuXcrrr7/e6eP853/+J88//zze3t7U1tayefNm/vKXv3Dw4EGmTJnSqWM8qBCr1Wri4uIei/GIVquVi5fr7R8vX75MeXk5Li4umJub09TUJNcdbm5ultddGxoakCSpVZ6lpaUlJiYmGBoaolar5QhSQ0NDfHx8CAkJoW/fvvTr1w9nZ2fq6upITEzk3LlzJCcnyxWP9IFRBgYGaDQaSktLUavVeHp6EhkZyZgxY9qdYpYkCbVafd8yjvoUFH0JPL1tZWNjo5x6pNVqMTQ0lKOlAwICCA0NJSgoqM0aWk9GCHH3UlRUxOHDh7G0tGTs2LGMGjWKd999l8zMTH7/+98zfPjwNp+RJIm0tDSOHDlCWVkZ/fv3Z+LEiXJqJkBycjLbtm1j4sSJeHl5ERMTQ319PQMHDkStVsuZBQYGBq2E2cvLq806siRJJCUlER0djVarlXOjW1q4FhYWUlJSQllZGXl5edTV1REWFsbw4cPlghkrVqzA1dW1RzrM9XZ6rRCXlZXJ68Pnz59HkiRCQkJYunQpS5cuxdfX94GP+fLLL3P06FEKCwuxsbEhPDycX//6150WYXhwIdb/EBkbG3d5QE9OTg4ffvghKpVKzp3UT10ZGRmh0+nkreWIUB/ZbG9vj62tLebm5igUCll4a2pq5LxfR0dHHB0dmTJlijwdptFoSE9PJzk5mZqaGhwcHOSi7NbW1lRUVHDq1CkuXLhAaWkpFhYWWFhYoFQq5QcCU1NTTE1NMTY2xtDQEAMDA7mdLTE0NGxVWcrMzExek9Y7dSmVSgwNDeVcSX30dp8+ffD19aW0tBQnJ6deKQRCiLsX/RKMqakp586dw9ramsmTJ/O///u/FBUV8ec//7lDS1idTkdCQgInTpygoaGBoUOHtvp/PHnyJMePH2fx4sV4enqyfft2cnJy5Ipn5eXlZGdny1tLYfbx8cHX1xdvb29ZmGtqaoiJiSEvL69N1TSdTifX087NzWXbtm3ExcXJvxUFBQXY2toyYsQIOS3KwsLinuvV3eG93lvpVUJcX1/Pzp072bx5M0ePHkWj0eDm5sYLL7zQJRHTXcHDCvHj+CGqqKjgr3/9q7yO5ODgIEcn6x2GjI2NcXBwwMXFBUdHR6ysrJAkSR6p6m3u9CNM/ZdNL7r60bOHh4dshKBf+7W3t8fR0RFTU1PUajXV1dUUFBTIqVP6ACulUolCoZDPq9Vq5fSllqUK9VGgeh9cffUkvetRUVERJSUlSJKEmZmZHMlcX1+PWq3G1NSU4OBgwsLCCAwMxNTU9KkQAiHE3UfL/m9oaGD37t3k5eURGhrKzp07qa+v529/+xuBgYEdHkOtVnPx4kU54HTMmDGMGDECQ0NDYmJiuHnzJi+//DLOzs6cOHGCU6dOERgYyNy5c+X/c0mSKCsrIzs7m5ycHDlX38DAAHd3d3nE7OHhwblz5zhz5gz+/v7MmzevXWtOSZKIjY3l3LlzDBgwgIKCAnbv3o2bmxt9+/bF1dVVnjnSC7g+IFOPgYGBnGbq4+PTxT3/dNGrhNjCwkJOK2qZQ9yTnrp6khCXlpayYcMGKisrcXR0lNOInJyc8PDwwN3dHRcXF3mKqb6+Xk5pysvLQ6PRYGFhgbe3N97e3nh4eGBgYNAqUri4uJjr16/T1NREVVUVRkZGODg4yF9urVYrB3vV1tZiYWEh/yhYWFhgYmKCsbExRkZG8ujX0NAQrVZLc3Mzzc3N1NfXU1FRQXV1tWy3V1JSQnV1tTzVbGJigo2NDebm5hgZGdHc3IxOp8PIyEgetesDrvTnMDQ0RJIksrOzMTc3x9TUVH695Shav7V87+73W74HyDMHVVVVcnrT40AIcfdyd//rdDouXrzI0aNH5bx3U1NT/v73v99XjPR9ER8fj7m5OZGRkfTr14/169fT0NDAK6+8gqWlJenp6ezcuRMjIyMWLlzYrlmG3lu95YhZL8xubm4YGxtz48YNHBwcWLx4cbsziJIksWvXLq5du8aiRYvIzs7mp59+ol+/flRVVaFSqbC3t2/lgV1bW0t+fn4rr+o5c+Y81Azls0SvEuLZs2ezdOlSZs2a1WO/tD1JiKurq/nzn/9M3759CQgIwN3dHVdXV3mqSpIkSktLycnJISMjQ14LsrKywtbWVnbE0q+t6ksN6i0tS0tLqaqqAmDgwIH0798fHx8frK2tMTExIS8vj9TUVBoaGvDz82PMmDGEhYU9UCqEVquloKBA/jHJzc1Fo9FgYmIiV4Spra2V/XgrKiqQJEmODvfw8MDOzk6eMrO2tpbTKfSmDAkJCRgbG6NUKmXx12q18qb/991/6kfsd6cwtbTANDEx4aWXXiIiIqJL/2/1CCHuGLVaTXx8vBzB3vJPExOTLokD6Kj/y8vL2b17N4mJiVy6dAlPT0/+8Y9/yPat96KyspJjx45x7do1HB0dGTFiBMePH8fe3p6VK1diaGhIdXU10dHRFBQUMHXqVIYPH37P65EkiYqKilbCXFZWRmpqKlqtlrFjxzJz5kx8fX1b/T/odDq2b9/O9evXiYqK4sSJE5SVlTF16lRyc3O5efMmWVlZcmqknZ1dqxgQMzMzFi1adF+b3GedXiXEvYGeJMQ6nY6KiopWYlFdXc3t27fJy8ujqKhIDspqWWNXb+hxd6Sw3gKvoKAAlUqFs7MzwcHBcjqGubk5VVVVxMXFceXKFdRqNWFhYYwYMaLTFnc6na6N8KrVakxMTPD29pan1+rr60lLS+PmzZtynnFoaKhsWFBWVtYmD7q6uhq4k+Jha2uLk5OTHLHt4eGBq6trhwYEkiRRX18vp3OVlZVRVlZGdXU1Op2ulTGDXvT10+76wLjHgRDijqmrq2Pfvn00NDS0yvUHZJer9kRaf8/rZ0nuNeN2r/7X6XSy29WRI0cIDg7mq6++6rTvcmFhIYcPHyYzMxNLS0uKi4sZPXq07ICl1Wo5cuQI58+fJzQ0lNmzZ3e6D/XCfPPmTfbs2cO5c+dQKpV4eXlhZ2eHlZUVFhYWsg9AQkIClZWVBAcHk56ejqOjI6NHj8ba2hoLCwtUKhVlZWUUFhaiUqmwsrKSAzcDAwMf2Wr4aUcIcRfTk4S4pqaGzZs3o1Kp5LSGpqYmDA0NsbW1xd3dHW9vb9zd3Vv5J+und+HOj0l+fj4pKSnk5OSgUCgICAggJCQEFxcXVCoVNTU1+Pv7k5SUREpKCqampkRERDB06NAOrfX06HQ6CgsLZeHNyclBrVZjbGwsC6+fnx+2trbcunWLlJQUMjIy0Gg0sitXaGio7B52L/Q/FiUlJbI45+fnc/XqVXla3NLSEltbW4yNjZEkiebmZrn/9D/mJiYmODo6tqrkpJ89eNIIIe4czc3NsuFKy8pZd2+NjY2tPqcf1bUn0ubm5iiVSrRaLVOnTu3Q1rSiooJPPvmELVu24O/vzw8//NBu3e+OyMjI4PDhwyQlJVFaWsrq1atb5SnrzTvMzc1ZuHAhbm5uaDQaOavgXpv+nq6srOTq1auoVCrZD1vvAeDm5oaPjw+5ublIksTkyZM5e/YsUVFRDBs2rFVb9fnQeqOSoqIili1bRkBAQKev91lECHEX05OEuKKigh9//BGVSoWpqSne3t5yEYL7eSHrR5ypqanU1tbKOYiBgYFydLdWqyUlJUX24XVzc2P48OEMGDCgwydgnU4nF37QC69KpcLIyKiV8OoN9lNTU0lJSZGLpXt6esri2xW1kSsrK9m5cyfl5eXyaL+kpEQeRZmYmGBmZoa9vb1c29jNzU2OKO/uJRIhxF3L3UsN9xJsnU5Hc3MzarUaHx8fbG1tW0Xw3/1nTEwMn3/+OW5ubnz55ZedrugGdwTu2rVrfP7559y4cYOoqCgmT54sRzsXFBRw/PhxysrK8PX1lY119OjNafTtaW8zMDAgNjaWzMxMRo8ezaBBg8jNzZW/qxUVFVy9ehUDAwMCAwNpamri3/7t3+457VxZWYm1tXW3PKT2JoQQdzE9SYi1Wi0XLlyQzSnul/en0+m4ffu27J6lNxG4e8TZ1NRESkoKN27coLq6GgcHB5YvX07//v3bjEolSaK4uJisrCxZePX1hvX+1X5+fri7u6NUKqmsrJSfpm/fvo1CocDX15fQ0FCCg4Mf2uBDv65dVFTUaisuLiYnJwczMzOcnJxwcHCQR7vW1tY0Nja2qThVU1MjrwG3NC1puT0pURFC3D3oc/MrKytlhy2tVivntbf8s2XK3fXr1zl9+jQWFhZMnz6dMWPGYGdnJ4+u9d8frVbbqmJYy+3MmTPcvn0bNzc3AgICCAsLw97eHnNzc27evElOTg79+vUjKioKBwcH2fq1M2vikiRx9uxZjh07hqenJwsWLMDa2hpJkqiqqiI9PZ3169fLS0aGhoZERUUREBCAr68vPj4+9636JGiLEOIupicJcWepq6uTC4/X1dXh4OBAWFgYAQEBrUa2FRUVXL9+Xa4BHRQURGBgIIaGhrIQ6KemWgpvY2MjhoaGbYRXH7VcWlpKSkoKKSkpFBUVYWhoKD8ABAUFPbDAtExnark1NDQAd/x6XV1dZXOCrKwsPDw82k3jaI/m5mY5gvtugdaXUTQzM5NFuW/fvq3MGroSIcTdy/36X6vVUlZWJpcELC0t5fDhwxw6dAiFQoGLiwt2dnZy4J8ehUIh26PqH/T06YDW1tacOXOG0tJSvLy8sLGxYfz48XIhimvXrvHTTz9hY2PDwoULH8peMjc3l5iYGJqbm5kzZw5BQUHye42Njaxfv56cnBzKyspwdHTE1dVVDtx0cXGRMyN8fHx65X35pBFC3MX0FiHWFxnXj34NDQ1ld6mWzlKSJHH79m2uXbtGXl4eFhYW9O3bl9DQUExNTeW6rW5ubhQVFZGTkyP7NOvLvfn5+bUakUuSRH5+vjztXF5ejomJCUFBQfL0d2eDO1QqFcXFxa0Et6SkRLYMtbOzk0VXv1lbW8vX15X9r9VqWwm03h50/Pjxj81rVwhx9yBJEo2NjVRUVFBaWkpoaCgajabVSFj/95Y/sXrr12vXrhEXFyfXKx48eDDjx4/H2NgYhUIhR/TfPbrW10JubGzkypUrGBsbY2NjQ0VFBXZ2dgwePJiwsDC0Wi3nz59HpVIxffp0RowYIRvzdJaGhgZ27drFzZs3GTVqVKvSjPX19axbt46cnByUSiVLlizBx8dHzmHOzs6msrISuFO+9l551AIhxF1OTxfi2tpaee23vr4eJycnQkND6dOnTyvx02g03Lx5k+vXr1NVVYWTkxP9+/fHz89PzhUsLCwkNzdXDtby8/OThdfT07PVVLhOpyMnJ0eedq6pqcHc3Fwuzebn53fPqfOOppYrKiqAO1Gwzs7OrQTXxcXlvn3aW4VAjxDijlGr1ezdu5eAgACCgoI6dXxJkmSTmoaGhlbBXfq/6ytstVwj9vX1xdbW9p6lN62srDA3N5dd4r788kt27NhBeHg49vb2WFhYMGvWrPt6OusFPzU1lS1btsgjz/Pnz5OZmSnHgxgbG5Oenk5RURFubm4EBQXJjlf69rS3lt3SEUufC3348GHc3d1ZsGCBnBNfU1PDunXruHbtGh4eHvzyl79sFRFeXV1NTk4OAQEBvfLefJIIs9BnAK1WK49+b9++jZGRUavRb0vq6uq4ceMGKSkp8g/MwIED0Wg05OTkcP78eRobG2VjkJCQEKytrZk7d26bCOnm5mYyMzNJSUmRq73Y2NjIwVbe3t7tpobcb2rZ1NQUV1dXgoKCcHNzk11+RECIoCVqtRpra2vi4uKIi4vDy8sLHx8fLCwsOhTaxsbGNjaq+kwCCwsL7O3t8fLyahM1PW3atAcq92dgYMCrr75KQ0MDBw8eJCoqCltbWzZu3EhERARTp07t0PJWX5hk1KhRmJqasmfPHnx8fFiwYEGrohJ+fn4sX76czMxM9u/fj0qlws/PD6VSKT9M19bWyq55ehQKBRYWFq3EOTg4mAsXLpCcnMzMmTMZNGgQlpaWrFy5kq+//pr4+Hg2bdrEz3/+c/mBWm8TLLg/QoifYpqamrh69aosgi4uLnJJwbvzZYuLi7l27RqZmZk0NzfLaTn6QCsDAwNZePWOXEZGRvKITH88lUpFeno6KSkpcv1TR0dHIiIiCA0Nxc3NrdX02P2mlm1tbXF1dWXYsGGt1nV7S1EGQfdSX1+PsbExOTk5JCUlybV+nZyccHd3l0ep+nQ+veDq/9S70XWE/v5/GHc/Q0ND3nzzTRoaGti/fz/Lli1j+vTpHD58mFu3bnVqdDx48GBKSkrYv38/Dg4O+Pv7s3r1alJTUzl69CibN28mPDycn//85xw8eJDU1FRmzZrVKmJbX6msvUCzuro6SkpKqKurw9LSkpSUFD744AM8PDzo06cPFhYWKBQK1Go1P/zwA5WVlTz33HOtRtmiQMT9Eb3zFCNJEqmpqfj7+xMSEtJm9KvVasnMzCQ+Pp6srCy0Wq38pF9TUyOv2+oduToyu2hqaiIxMZHs7GwyMjLQarW4ubkxZswYQkNDcXJyQpIk6urquHXrFoWFhe1OLTs5OeHq6kp4eLgsur1xuljQMzA2Nsba2hpXV1cGDx6MmZmZbGhTVFQE3AksCgsL67TJxuNo4zvvvINKpWLz5s28/PLLvP766/z0009s3LiRwYMHM3Xq1Ht+D6ZOnUpZWRnR0dGsWbMGBwcHOcsgISGB48ePc+PGDQYOHCg7cuXm5jJ16lSUSqXsCX2/lEa9qc3p06c5ePAgOp2Ofv36YWBggJ2dHVu3biU6OlouoqJH5BHfH7FG3MX0tDVinU7X6mldbw5/7tw5EhISKC0txdTUFBcXF9kG824rzPaoq6sjOzub9PR0cnJy8PHxaRXprNVq20wt19fXA/83tdxyc3Jy6papZbFG3L10V//X1dWRlpZGSkoK9fX1ODs7y7ESHT1wtkdX9X9NTQ0ffPABKSkpvPHGG0RFRZGQkMChQ4cwMTFh1qxZ9xSzpqYmvvnmGwDWrFnTqi/VajUXLlzg7NmzSJKEg4MDhYWFeHh4sHDhwofyQS8sLCQ6Opr6+npmzpxJ3759ycjI4J133qGuro5XX32V+vp6iouLWbhw4QPXMn7WEELcxfQ0IQbkhP/09HQSEhLIz88HwNfXl8GDBxMcHCwbwd+LqqoqsrOzycrKoqSkBKVSib29PSYmJgwfPhy1Wi3n5t49tdxy60lTy0KIu5fu7n999kBKSgq3b9/G2NiYwMDAThvGdGX/l5eX87vf/Y6cnBx+9atfMXnyZKqrq9mzZw8ZGRkMGjSIadOmddhP5eXlfP3113h6evLiiy+2mS6vr6/n1KlTXLp0SS6m4ujoyLx581qlJ3WEJEnU1NTI5UWLioo4cuQIN2/exN7eHh8fH6qqqjh48CC2trasXr0aFxcXRo0ahYuLyyP1zdOOEOIupicJcU1NDT/99JPsFqXRaHB0dGTQoEGMHj36vk/CLSu4ZGZmUlJSglqtlu0w9edQqVT4+/vLI+mWUcs9Pbm/u4XgURFC3HXU1NSQmppKamoqjY2NuLm53Teiv6v7v7CwkN/97neUlpby9ttvM2bMGCRJIiEhgYMHD2JiYsLMmTM7TAfKyMhg06ZNDB8+nGnTprW7T0VFBceOHSMhIYGCggI5Wnvy5MmycUdFRYUsuPq/V1VVyQ/YCoUCGxsbbG1tqaysJDk5GTc3NxYvXkxpaSn/8z//w/Dhw/mf//kfEUTZCYQQdzE9SYgrKir4+uuvAfDx8WHw4MH4+/vfN02osLCQ69evk5qaSmlpqVztSG8wYGZmJgdzWVlZYWxszMyZM++7xtQT6UlC8DAIIe56tFot2dnZpKSkkJ+fL9exDgkJafPw+jj6Pysriw8++ID6+np+/etfM2TIEOBOOtBPP/3ErVu3GDhwINOmTWv3QTcuLo59+/Yxa9asdn+D1Go1FRUVpKWlcejQIeLi4qioqMDT05OwsDA5WlupVMqmNPb29vJmZ2eHra1tq9+R4uJioqOjqa6uZsaMGaSkpLB27Vrmz5/PG2+80aNK1fZERLDWU4yNjQ3Dhw+X03zamw7WaDSUlJSQlpZGWloa2dnZVFdXo1QqsbW1xcPDAz8/P5ycnGTrR0tLy1Z2l7W1teKpV/DUoFQq6dOnD3369KGqqkrOfU9KSsLDw4OwsDB8fHwe2z3v5+fHb37zG/7whz/wt7/9jffee4/+/ftjY2PD0qVLSUxM5ODBg2RkZDBz5sw208pDhgwhNzeXzZs3U1ZWhomJSavRbV1dnbyvubk5Y8eOJSUlhZs3b6JWq3nttdcYP3481tbWnRZQFxcXXn31Vfbt28fOnTsZMGAAkydPZs+ePYwaNapTg5JnGSHETzFKpZLIyEjg/yIey8vLKS8vp6SkhFu3bpGXl0dVVRU6nQ5bW1v8/f0JDg4mMDAQBweHHjNKEQi6A1tbW0aOHMnQoUPJzMwkOTmZw4cPy2Y0j6vObkhICO+88w4ffvgh//u//8t7771HcHAwCoWCQYMG4e/vz7Zt2/j888/lkWx9fb0suA0NDdy6dYukpCTGjh2Lu7s79vb2+Pv7txrh6t22JEni4sWLfPbZZ3zyySckJyfzxhtv3Ld6WkuMjY2ZM2cOvr6+xMbGYmZmxsiRIx+o0tSzihDip5jGxkaSkpJk8a2traW6upr6+nq5xq+npyeTJ0+mX79+nSolKBA8ixgaGhIUFERQUBDl5eWkpKRw/fp14uPjcXR0xM3Njf79+3fpFGz//v1ZtWoVn376Kb/+9a/lesN6sdVoNDQ1NXHgwAFOnz7NyJEjCQsLo2/fvtjb27Ny5Up27dqFubk5K1as6DAYU61W09zcTHh4OP/6179Yv349e/bsIS4ujuXLlzNhwoQOzUXaIygoCDs7O3bu3CkHeOnNeB4H+vrpvRkhxE8xCoWClJQUdDodjY2NNDY2YmVlJQeg6G35BAJB53FwcGDMmDEMHz6cGzdukJSUxLZt2zh27BgREREMGjSo0/ESarVa9ia/O0CquroanU6Ho6Mjly9f5rvvvmPhwoUEBAQQEREhj2oNDAw4cOAAN2/epLm5mYiICHntePny5Xz77bfs2LGDxYsXt3nQVqvVxMXFtZquDgkJwdzcnJ07d/KnP/2JH3/8kZEjRxIYGPhA0/EBAQFcu3aNjIwMbt++3enPPSiWlpYMGzasV4uxEOKnmKamJjQajVxKUL89iBWfQCBoHyMjI9lpLjAwkBs3bnD69GlOnDhBSEgIQ4YMwc/Pj6ampnajkCsrK6mtrZWPZ2xsLE8bh4WFyX+3s7PjxIkTbNiwgezsbBYuXIibm1urtixZsoSrV6+yf/9+MjMzmTFjBiEhITg7OzN//ny2bNnCsWPHmDRpUqvP6YtLGBsbtxr19u3bFz8/Pw4cOMDVq1c5cuQImZmZDB06FD8/v07PnI0bN+4Revj+qFQq6urqaG5uFkIs6JnY2NgwceJEPD09xVqvQPAYsbKyYuDAgbi4uHD58mUOHTrExo0bAeRiJEZGRpibm8vi6ufnJ/9dX/ShI4GbPXs2TU1NbN26lU8//ZS333671dqrQqFgwIAB+Pv7s3fvXrZu3Ur//v15/vnnCQoKYvLkyRw+fBhnZ2f69+/f5vgmJiZtfiNMTU154YUX6NOnD+fPn6eoqIjjx4+TmprK8OHD8fDw6MIefHjUanV3N+GREUL8FKNQKIS1nEDQBeh0Orm0YXV1NTU1NdTU1FBeXk5ZWRlnzpyRHbmsra0ZPXo0Wq2WwsJCOYe/f//+jBo1Ci8vrweOxTAwMGDRokU0Njaye/duPvvsM9566602piNWVla88MILXLt2jf379/Ovf/2LGTNmMGrUKEpKSti9ezf29vadFlEDAwOGDx+Oq6srx48fp6mpibq6Ovbu3YuXlxfDhg3rNnvQpwkhxAKBQMCdaVq9wOq36upqWYB1Oh2A7M1sbW2Nm5sbnp6eTJgwAQ8PD2xtbdtYZNbX15OYmCiv8zo7OzNkyBDCw8MfaKZKqVSybNkyGhsbOXToEJ9//jlvvvkm1tbWrfZTKBSEh4fLo+Mff/yRfv36MXXqVCoqKti6dSuvvPJKm8/dCx8fH+bPn8/Ro0cpLS3F19eXiooKduzYQWBgIEOGDOmVPgI9BSHEAoHgmUGf964X2JajW70XOtyJxLWxscHKygpfX1+sra3lrWW9Xv3xgoKCOjT0sLCwYPTo0YwaNYrMzEwuX77MgQMHOHz4MP3792fIkCG4u7t3qv3GxsasXr2apqYmTp8+zdq1a3njjTewsLBos6+lpSWLFy/m+vXr7Nu3j7Vr1xIZGcmZM2fYunUrq1ateqC+s7KyYubMmVy8eJFr167h5+dHWFgYV69eJSMjg759+zJo0CCxDPYQCCEWCARPJU1NTVy7dq3V6FalUsnvm5iYYGNjI49sra2tZfHV59d2JQqFQjYKqa2tJSEhgcuXL3PlyhXc3d0ZMmQI/fr1u2/QkZmZGa+++ipqtZpz585hZGTEz372s3ZdthQKBf3798fPz4/Y2FhiY2Nxd3cnPz+f3bt38/zzzz/QNSiVSkaNGoWrqysnT56koqKCKVOmkJ+fT1JSEmlpaQwcOJB+/fqJ0ocPgOgpgUDwVCJJEmlpaVhbW2NnZ4evry9WVlay2HbnyM3Kyopx48YxZswYbt26xaVLl/jpp584ePAgAwYMICIi4p6FEqysrPjZz36GSqXi9OnTGBsb8/LLL3eY72tpacmiRYu4ceMG+/bto66ujmPHjj3Q9HRL/P39cXBw4PDhw+zdu5cxY8bwwgsvcOXKFeLj47l+/TpDhgwhKChI2Ft2AiHEAoHgqcTMzIxly5Z1dzPuiYGBgWwUUlVVxZUrV7hy5QpxcXF4eXkxZMgQ+vbt2+7o0t7entdee41//OMfHD9+HBMTE1asWNFhGUeFQkG/fv3k0fG+ffv44osvmDhx4kNZUNrY2DBnzhzOnTvHyZMnCQoKYsyYMfTv35/4+HhOnjzJtWvXiIyMFO5a90E8qrTgww8/ZOjQoVhZWeHs7MycOXNIS0vr7mYJBIJnAFtbWyZOnMi//du/sWjRIoyMjNi5cyd/+9vfOHjwIOXl5W0+4+rqymuvvYavry+HDh1i69ataLXae57HwsKCRYsW8c4772BiYsL69eu5cuUKD1P/x9DQkHHjxjFx4kQyMzPZuXMnOp2OyZMnM3fuXCwsLMSacScQQtyCkydP8vOf/5wLFy5w+PBhNBoNU6dObRXEIRAIBI8TpVJJWFgYK1as4Je//CWDBg0iMTGRzz77jA0bNnDjxo1WYuvt7c2rr76Ku7s7+/fvJyYmRo7wvhf9+vXj73//Oy4uLvzwww/s3bv3oa0oAwMDmTdvHgA7duwgPT0dZ2dnoqKiRDR1JxBT0y04cOBAq3+vX78eZ2dnLl++/NgdYgQCgeBuHBwcmDp1KhMnTiQ5OZlLly4RHR2NpaUlgwYNIiIiAltbWwIDA1m1ahVfffUVsbGxcgGG+wWc2dra8vLLLxMTE8OZM2coLS1l3Lhx9OnT54GD1ezs7Jg7dy6nT5/m2LFjFBYWMmrUKBG01QlED92D6upqgDZJ8wKBQPAkMTQ0JDw8nPDwcEpKSrh06RJxcXGcOXOGgIAAOeJ6xYoVrFu3jp9++gljY2OioqLuK6j6qep9+/ZRW1vLkSNHyMjIYOzYsQ9cY9nIyIgJEybg5ubG2bNnKSkpYcqUKQ9UxelZRExNd4BOp+Ott95i9OjR9OvXr8P9VCpVKwOAlubpAoFA0NXop3zffvttZs6cSX19PVu2bOHTTz+lsbGRGTNmYGRkxO7duzl69Gin1n6dnZ2ZNGkSpqam+Pr6UlxczLZt20hPT3/gtWOFQkFoaChz5swB7hilCO6NGBF3wM9//nOuX7/OmTNn7rnfhx9+yAcffPCEWiUQCAR3MDY2ZvDgwQwePJiCggIuXbrEmTNnaG5uxs7OjqysLLZv346xsXGnltYCAwOprKwkMTGRCRMmkJuby7Fjx+TRcXumIffC0dGR+fPni9KqnUCMiNvhF7/4BXv37uX48eN4enrec9///M//pLq6Wt5Onjz5hFopEAgEd3B3d2fWrFm8/fbbPP/883h5eQGQkJAgpzd1hqFDh+Lj48OZM2cYPHgw06ZNo7S0lOjoaG7evPlQo2PB/RFC3AJJkvjFL37Bzp07OXbsGH5+fvf9jImJSRv7O4FAIOgOTE1NGTZsGG+88QZ//vOfGTFiBJmZmbz33nt89NFH5OTk3FNMFQoFEyZMwMrKigMHDuDq6srChQvx9vbm+PHjHDx4UGSRPAaEELfg5z//OT/88AObN2/GysqKoqIiioqKaGxs7O6mCQQCQadRKBT4+fnx0Ucf8corr2Bvb8+BAwf4y1/+wueff87Fixc7/F0zNjZm2rRpqNVqDh8+jJGRERMnTmw1Ok5LS3uovGNB+4g14hZ88cUXAERGRrZ6fd26dbz00ktPvkECgUDwCBgZGcnFHS5evIgkSWg0Gg4ePMiRI0cIDAxErVa3mcmzsrJi6tSpxMbGcu7cOcaMGYOvry+urq6cP3+eEydOkJmZydixY8UsYBcghLgF4glPIBA8bZiamrJ8+XLUajWJiYmUl5ezaNEiqqqqOH/+PFevXsXNzY0BAwbQp08fueiEm5sbY8aM4eTJk9jZ2dGvXz9MTU2ZMGEC/v7+nD59mpiYGEaMGEFwcLBYD34EhBALBALBU46lpSUrVqxArVaTnJzM9u3befnllxk0aBA//vgjt2/f5vTp01y4cIHAwEBCQkJwdHQkJCSEiooKzp8/j62trRy86uPjg4uLCxcuXODkyZNkZmYybtw4MTp+SMQasUAgEDwD2NnZsXLlSgIDA8nNzeX777+nuLgYDw8PpkyZwosvvkj//v3Jzs5m+/bt7Nq1i7S0NIYMGYKHhwdHjhyhqqpKPp6pqSmRkZE8//zzVFRUEB0dTWpqqphZfAiEEAsEAsEzgrOzM8uXL8fHx4esrCw2bdoki6ulpSVDhgxhyZIlTJ06FSMjI06cOMHmzZuxsLBAkiQOHjzYqqYz3PG6XrhwIX5+fpw8eVIusyjoPEKIBQKB4BnCy8uLpUuX4u7uTnZ2NkeOHKGmpkZ+X6lU4ufnx/Tp03nhhRcICQkhOzubmpoarly5wubNm9FoNK2OaWJiQmRkJFFRUVRWVhIdHU1KSooYHXcSIcQCgUDwjBEQEMCiRYtwdHSksLCQ/fv3tzuKtbGxYcSIESxdupTp06cTFhbGhQsX+POf/8zFixdbCTjcEfmFCxfi7+/PqVOnZP9qwb0RQiwQCATPIP369WPWrFlYWFiQn59PbGxsh7nFhoaGBAQEsHLlSlauXIlKpeLkyZNs3bqVffv2kZ2dLZdeNDExYfz48URFRVFTU0NTU9OTvKxeiRBigUAgeEYZPHgwQ4cOxdTUlNzcXGJjY+8rnCNHjmTatGnY2NgQFhaGSqXi4MGDbN68mUuXLskjay8vLxYvXoyTk9OTuJRejUhfEggEgmeYfv36oVQqSUxMJCcnh/379zN9+nQ5n/huFAoFo0aNoqqqioyMDObNm4dKpSI5OZmrV6+SkJCAt7c3YWFh9/XqF9xBjIgFAoHgGUahUDBs2DAGDBiAUqkkOzubgwcP3rN8oVKpZMqUKRgbG3Pw4EFsbGwYN24cy5YtY9SoUdTW1rJv3z62bt1KUVHRE7ya3okQYoFAIHjGUSgUjBs3jpCQEAwMDMjMzOTw4cNotdoOP2Nqasq0adOora3l2LFjSJKEsbExffv2Zf78+cyePRtXV1dh8tEJhBALBAKBAAMDAyZPnoy/vz8A6enpHDt2TA7Cag97e3smTpxITk4O8fHx8usKhQJXV1cmTJgghLgTCCEWCAQCAXAnOnrq1Kl4enoiSRKpqamcOnXqnvnAPj4+DB8+nISEBG7duvUEW/v0IIRYIBAIBDImJiZERUXh4uKCJElcv36ds2fP3lOMw8PDCQ4O5sSJE5SUlDzB1j4dCCEWCAQCQSvMzMyYPn069vb2SJJEUlIScXFxHYqxQqFgzJgxODo6cvDgQWFx+YAIIRYIBAJBG6ysrJg+fTpWVlYAXL58mYSEhA73109rGxgYcOjQoXtGXQtaI4RYIBAIBO1iZ2dHVFQUZmZmSJLExYsXuXbtWof7m5ubM23aNCorKzlx4oTwmu4kQogFAoFA0CHOzs5MnTpVNvg4e/YsqampHe7v6OjIhAkTyMjIuOcIWvB/CCEWCAQCwT3x9PRk0qRJKJVKFAoFJ0+evGeEtL+/P0OGDCE+Pp7bt28/wZb2ToQQCwQCgeC++Pv7M27cOOBOzvHx48fJzs7ucP/BgwczevRo3NzcnlALey9CiAUCgUDQKUJCQhg+fDharRYDAwOOHDlCXl5eu/sqFAr69euHoaEoaXA/hBALBAKBoNMMHDiQgQMHotFoMDAw4ODBg8JP+hERQiwQCASCB2L48OGEhoai0WhQKBQcOHCAsrKy7m5Wr0UIsUAgEAgeCIVCwdixY/Hz80Oj0QAQGxtLZWVlN7esdyKEWCAQCAQPjIGBARMnTsTDwwONRoMkScTGxlJdXd3dTet1CCEWCAQCwUOhd9NycHBAq9Wi0WiIjY0VFpcPiBBigUAgEDw0xsbGREVFyeUOVSoVsbGxNDY2dnPLeg9CiAUCgUDwSJiamjJ9+nSMjY0xMDCgoaGB2NhYmpqaurtpvQIhxAKBQCB4ZCwtLZk+fTpwZ5RcU1PD/v37UavV3dyyno8QYoFAIBB0Cba2tkRFRaFWqzE3N6eqqkrUJ+4EQojv4tSpU8ycORN3d3cUCgW7du3q7iYJBAJBr8HJyYlp06ZRV1eHm5sb7u7u3d2kHo8Q4ruor69nwIAB/Otf/+rupggEAkGvxN3dncmTJ3P79m1yc3O7uzk9HmECehfPP/88zz//fHc3QyAQCHo1vr6+LFiwADs7u+5uSo9HCPEjolKpUKlU8r9F/pxAIBDcQYhw5xBT04/Ihx9+iI2NjbyNHz++u5skEAgEgl6EEOJH5D//8z+prq6Wt5MnT3Z3kwQCgUDQixBT04+IiYkJJiYm8r/17jICgUAgEHQGMSIWCAQCgaAbESPiu6irq+PWrVvyv7OyskhMTMTe3h5vb+9ubJlAIBAInkaEEN/FpUuXmDBhgvzvf//3fwdg5cqVrF+/vptaJRAIBIKnFSHEdxEZGYkkSd3djC6lrKyMsrKyx3JslUpFQ0MDVlZWmJmZPZZzPE4aGxu5desW5ubmrdb6uxJHR0ccHR0fy7EF90fc/x0j7v+egUJ62lSnmyksLGTt2rX87Gc/w83Nrbubg0qlYtq0aSKauxsZP348Bw8efGw/dIKOEfd/9yPu//sjhPgpp6amBhsbG06ePCkiuruBuro6xo8fT3V1NdbW1t3dnGcOcf93L+L+7xxiavoZYeDAgeKL0A3U1NR0dxMEiPu/uxD3f+cQ6UsCgUAgEHQjQogFAoFAIOhGhBA/5ZiYmPC73/1OBEp0E6L/uxfR/92L6P/OIYK1BAKBQCDoRsSIWCAQCASCbkQIsUAgEAgE3YgQYoFAIBAIuhEhxIJOk52djUKhEJ7bgmcScf8LHhdCiB8TGRkZ/OxnP8Pf3x9TU1Osra0ZPXo0n376KY2NjY/tvMnJybz//vtkZ2c/tnN0hj/+8Y/MmjULFxcXFAoF77//fre2pyMUCkWnthMnTjzyuRoaGnj//fcf6Fi9pR/v5lm+/1NTU3n33XcZOHAgVlZWuLm5MX36dC5dutRtbeqInnz/96Z+fFSEs9ZjIDY2loULF2JiYsKKFSvo168farWaM2fO8B//8R/cuHGDr7766rGcOzk5mQ8++IDIyEh8fX0fyzk6w//7f/8PV1dXBg0axMGDB7utHfdj48aNrf79/fffc/jw4Tavh4aGPvK5Ghoa+OCDD4A7xUU6Q2/px5Y86/f/N998w7fffsv8+fN54403qK6uZu3atYwYMYIDBw4wefLkbmlXe/Tk+7839eMjIwm6lMzMTMnS0lIKCQmRCgoK2ryfnp4uffLJJ4/t/NHR0RIgHT9+/L776nQ6qaGhodPHzsrKkgBp3bp1ndpXkiSptLRUAqTf/e53nT5Pd/Lzn/9celxfi4fpi97Wj+L+l6RLly5JtbW1rV4rKyuTnJycpNGjR3f6fN1BT7r/e3M/PihCiLuY1157TQKks2fPdmp/jUYj/f73v5f8/f0lY2NjycfHR/rP//xPqampqdV+Pj4+0vTp06XTp09LQ4cOlUxMTCQ/Pz9pw4YN8j7r1q2TgDab/kdJf4wDBw5IERERkomJifTxxx9LkiRJGRkZ0oIFCyQ7OzvJzMxMGj58uLR3795WbXgQIdbTWwRET3s/RFqtVvr444+lsLAwycTERHJ2dpZeffVVqaKiotV+8fHx0tSpUyUHBwfJ1NRU8vX1lVatWiVJ0v/13d1bZ/ult/SjuP87Zt68eZK9vf1DffZJ0VPv/5b0hn58UIQQdzEeHh6Sv79/p/dfuXKlBEgLFiyQ/vWvf0krVqyQAGnOnDmt9vPx8ZGCg4MlFxcX6b333pP++c9/SoMHD5YUCoV0/fp1SZLu/Ji8+eabEiC999570saNG6WNGzdKRUVF8jECAgIkOzs76Te/+Y305ZdfSsePH5eKiookFxcXycrKSvrtb38r/f3vf5cGDBggGRgYSDt27JDb8KwK8Zo1ayRDQ0PplVdekb788kvp17/+tWRhYSENHTpUUqvVkiRJUnFxsWRnZycFBQVJH330kfT1119Lv/3tb6XQ0FBJkiSprq5O+uKLLyRAmjt3rvx/k5SU1Kl29ZZ+FPd/x4waNUoKCgp6qM8+KXrq/d+S3tCPD4oQ4i6kurpaAqTZs2d3av/ExEQJkNasWdPq9XfeeUcCpGPHjsmv+fj4SIB06tQp+bWSkhLJxMREevvtt+XX7jU1pz/GgQMHWr3+1ltvSYB0+vRp+bXa2lrJz89P8vX1lbRarSRJz6YQnz59WgKkTZs2tdrvwIEDrV7fuXOnBEjx8fEdHvtR+qI39KO4/zvm1KlTkkKhkP7rv/7rgT/7JOmp97+e3tKPD4qImu5C9CW/rKysOrX/vn37APj3f//3Vq+//fbbwJ2gl5aEhYUxduxY+d9OTk4EBweTmZnZ6Tb6+fkxbdq0Nu0YNmwYY8aMkV+ztLTk1VdfJTs7m+Tk5E4f/2kjOjoaGxsbpkyZQllZmbxFRERgaWnJ8ePHAbC1tQVg7969aDSabmxx9yHu//YpKSnhxRdfxM/Pj3ffffeRjvWk6Un3f2/ux/shhLgL0dc7ra2t7dT+OTk5GBgYEBAQ0Op1V1dXbG1tycnJafW6t7d3m2PY2dlRWVnZ6Tb6+fm1247g4OA2r+sjJe9ux7NEeno61dXVODs74+Tk1Gqrq6ujpKQEgPHjxzN//nw++OADHB0dmT17NuvWrUOlUnXzFTw5xP3flvr6embMmEFtbS27d+/G0tLyoY/VHfSU+7+39+P9EOlLXYi1tTXu7u5cv379gT6nUCg6tZ9SqWz3dekB6naYmZl1el8B6HQ6nJ2d2bRpU7vvOzk5AXf+D2NiYrhw4QI//fQTBw8eZPXq1fztb3/jwoULT90PR3uI+781arWaefPmcfXqVQ4ePEi/fv2e2Lm7ip5w/z8N/Xg/hBB3MTNmzOCrr77i/PnzjBw58p77+vj4oNPpSE9Pb5WnV1xcTFVVFT4+Pg98/s7+qN3djrS0tDavp6amyu8/q/Tp04cjR44wevToTv2IjxgxghEjRvDHP/6RzZs3s3TpUrZu3cqaNWse6v+mtyHu/zvodDpWrFjB0aNH2bZtG+PHj3/gY/QEuvv+f1r68X6Iqeku5t1338XCwoI1a9ZQXFzc5v2MjAw+/fRTAKKiogD45JNPWu3z97//HYDp06c/8PktLCwAqKqq6vRnoqKiiIuL4/z58/Jr9fX1fPXVV/j6+hIWFvbA7XhaWLRoEVqtlv/5n/9p815zc7Pcz5WVlW1GZgMHDgSQp+fMzc2BB/u/6W2I+/8Ov/zlL/nxxx/5/PPPmTdv3gN/vqfQ3ff/09KP90OMiLuYPn36sHnzZhYvXkxoaGgrZ6Fz584RHR3NSy+9BMCAAQNYuXIlX331FVVVVYwfP564uDg2bNjAnDlzmDBhwgOff+DAgSiVSv7yl79QXV2NiYkJEydOxNnZucPP/OY3v2HLli08//zzvPnmm9jb27NhwwaysrLYvn07BgYP/ry2ceNGcnJyaGhoAODUqVP84Q9/AGD58uW9ZpQ9fvx4fvazn/Hhhx+SmJjI1KlTMTIyIj09nejoaD799FMWLFjAhg0b+Pzzz5k7dy59+vShtraWr7/+Gmtra1lwzMzMCAsL48cffyQoKAh7e3v69et3z6m23taP4v6/82Dx+eefM3LkSMzNzfnhhx9avT937lz5gaGn0533/9PUj/ele4O2n15u3rwpvfLKK5Kvr69kbGwsWVlZSaNHj5Y+++yzVmYFGo1G+uCDDyQ/Pz/JyMhI8vLyuqehwd2MHz9eGj9+fKvXvv76a8nf319SKpXtGhq0h97QwNbWVjI1NZWGDRv2SIYG48ePbzeBnw5SS3oKHTkLffXVV1JERIRkZmYmWVlZSf3795feffdd2T3qypUr0pIlSyRvb2/Z9GDGjBnSpUuXWh3n3LlzUkREhGRsbNypVI7e2o/P8v2vz43uaNO7pfVEetL935v78UFRSNIDRDoIBAKBQCDoUsQasUAgEAgE3YgQYoFAIBAIuhEhxAKBQCAQdCNCiAUCgUAg6EaEEAsEAoFA0I0IIe4m/vrXvxISEoJOp+vupjwyv/nNbxg+fHh3N+OBEP3fvYj+715E//cwujt/6lmkurpasre3l7777jv5Nf7/3Lj//d//bbO/vuD5vUqMdZbt27dLixYtkvz8/CQzMzMpKChI+vd//3epsrKy3f13794tDRo0SDIxMZG8vLyk//7v/5Y0Gk2rfQoLCyUTExNp9+7dj9y+J4Ho/+5F9H/3Ivq/5yGEuBv4+OOPJWtra6mxsVF+Tf9FcHFxkerr61vt35VfBAcHB6l///7Sf/3Xf0lff/219Oabb0rGxsZSSEiI1NDQ0Grfffv2SQqFQpowYYL01VdfSb/85S8lAwMD6bXXXmtz3EWLFkljx4595PY9CUT/dy+i/7sX0f89DyHE3UB4eLi0bNmyVq8B0sCBAyVA+tvf/tbqva78IrTnxrRhwwYJkL7++utWr4eFhUkDBgxo9QT629/+VlIoFFJKSkqrfWNiYiSFQiFlZGQ8chsfN6L/uxfR/92L6P+eh1gjfsJkZWVx9epVJk+e3Oa90aNHM3HiRP7617/S2Nj4WM4fGRnZ5rW5c+cCkJKSIr+WnJxMcnIyr776KoaG/2dJ/sYbbyBJEjExMa2Oob+e3bt3P4ZWdx2i/7sX0f/di+j/nokQ4ifMuXPnABg8eHC777///vsUFxfzxRdf3PM4KpWKsrKyTm33o6ioCABHR0f5tYSEBACGDBnSal93d3c8PT3l9/XY2NjQp08fzp49e9/zdSei/7sX0f/di+j/nomovvSE0dc49fPza/f9sWPHMmHCBD766CNef/31DmuAbtmyhVWrVnXqnNJ97MT/8pe/oFQqWbBggfxaYWEhAG5ubm32d3Nzo6CgoM3r/v7+JCcnd6pN3YXo/+5F9H/3Ivq/ZyKE+AlTXl6OoaEhlpaWHe7z/vvvM378eL788kv+7d/+rd19pk2bxuHDhx+5PZs3b+bbb7/l3XffJTAwUH5dPzVlYmLS5jOmpqbU1NS0ed3Ozq7Nk2pPQ/R/9yL6v3sR/d8zEULcAxk3btz/1959h0dVbQ0c/p1MKklIIw0wJAQFIj1IkRKKdASlSLlIERDB3hBUpInoFblyvaKgXFCu+CGIIjUoHUEBadIhlEAgASGZ9EmZ8/0RMmZIm0k7Q7Le55kHc+qa8SRr9j777EXnzp355z//yTPPPFPgNoGBgQV+W7TG7t27GTt2LD169GDOnDlm63K/CecW9c4rPT29wG/KqqqiKEqpYrIF8vlrSz5/bcnnX/EkEVcwHx8fsrKySEpKwt3dvdDtpk+fTqdOnVi0aBGenp751qelpaHX6y06Z0BAQL5lR48epV+/fjRq1IjVq1ebDYiAv7uErl+/zn333We27vr167Rq1SrfMePj483u89gi+fy1JZ+/tuTzt00yWKuCNWjQAMgZvViUiIgIOnXqxAcffFDgCMaVK1eavpUW97pbVFQUPXv2xM/Pj40bNxbYTdWsWTMADh48aLb82rVrXL161bQ+r4sXL9KwYcMi35fW5PPXlnz+2pLP3zZJi7iCtW3bFsi5wJo0aVLktjNmzKBTp04sXrw437qS3qOJjY2le/fu2NnZERkZia+vb4HbPfjggzRo0IDFixczYcIEdDodAJ999hmKopgNrADQ6/VERUUxceJEq2OqSPL5a0s+f23J52+jtHl8uWpr1KiROmzYMLNlgPrss8/m2zYiIsI0601ZPFDftGlTFVAnT56sLl++3Oy1ZcsWs23XrVunKoqidunSRV28eLH6wgsvqHZ2dur48ePzHXf16tUqoJ4/f77UMZY3+fy1JZ+/tuTztz2SiDUwf/581c3NzWxKt8J+EbZv316mvwi5xyroFRERkW/7H374QW3WrJnq5OSk1q5dW3377bfVjIyMfNsNGTJEbd++fanjqwjy+WtLPn9tyedveyQRayAhIUH19vZWv/zyS61DKRPXr19XnZ2d1R9//FHrUCwin7+25PPXlnz+tkcGa2nAw8ODyZMn8+GHH1aKMmQff/wxjRs3pn///lqHYhH5/LUln7+25PO3PYqqFjPtiRBCCCHKjbSIhRBCCA1JIhZCCCE0JIlYCCGE0JAkYiGEEEJDkoiFEEIIDUkiFkIIITQkiVgIIYTQkCRiIYQQQkOSiIUQQggNSSLOY+7cuTz00EO4u7vj5+fHY489xpkzZ7QOSwghRCUmiTiPnTt38uyzz/Lbb7/x888/k5mZSffu3UlJSdE6NCGEEJWUzDVdhJs3b+Ln58fOnTvp2LGj1uEIIYSohOy1DsCW6fV6ALy9vQvdxmAwYDAYzJY5OTnh5ORUrrEJIYSoHKRruhBGo5GXXnqJdu3a0ahRo0K3mzt3Lh4eHmavHj16cP369QqMVgghxL1KuqYLMXHiRDZt2sSePXuoXbt2odvd3SI+cuQIERER/PHHH7Ro0aIiQhVCCHEPk67pAjz33HOsX7+eXbt2FZmEIX83tJubW3mHJ4QQohKRRJyHqqo8//zz/PDDD+zYsYOQkBCtQxJCCFHJSSLO49lnn2XFihWsXbsWd3d3YmNjAfDw8MDFxUXj6IQQQlRGMlgrj88++wy9Xk+nTp0IDAw0vVauXKl1aOJelpWldQRCCBsmLeI8ZNyaKHNpaXDjBvj5gfSqCCEKIC1iIcqTXg/x8Tn/CiFEASQRC1Fe0tJyErCra86/aWlaRySEsEGSiIUoL3p9zv1hV9ecf6VVLIQogCRiIcpD3tYwSKtYCFEoScRClAe9HjIzwd4ejMacfzMzpVUshMhHRk0LUdYyM3NavjodJCX9vVyny1memQkODtrFJ4SwKZKIhShrDg5QsyYU9DicokgSFkKYkUQsRHmQMphCCAvJPWIhhBBCQ5KIhRBCCA1JIhZCCCE0JIlYCCGE0JAkYiHKm1RfEkIUQRKxEOUpLQ1iYmRGLSFEoSQRC1GepPqSEKIYkoiFKC9SfUkIYQFJxEKUF6m+JISwgCRiIcqDVF8SQlhIErEQ5UGqLwkhLCRzTQtR1qT6khDCCpKIhShrUn1JCGEFScRClAepviSEsJDcIxZCCCE0JIlYiNKSKSyFEKUgiViI0pApLIUQpSSJ+C67du3i0UcfpWbNmiiKwo8//qh1SMKWlcUUltKiFqJKk0R8l5SUFJo2bcqnn36qdSjC1lk6hWVRiVZa1EJUeTY1ajo7O5tVq1axfft2bty4waxZs2jcuDF6vZ6tW7fSrl07/P39yzWGXr160atXr3I9h6gkcqew9PL6u1Xs4mK+TVoa3LgBfn751+UeIz4+Z5R1QeuFEJWezbSIExISaNeuHcOHD+fbb7/lp59+4ubNmwC4ubnxwgsvsGDBAo2jzM9gMJCYmGh6JScnax2SqAiWTmFZVNe1FIUQQmBDiXjKlCmcOHGCyMhILly4gJpnMgSdTsegQYPYuHGjhhEWbO7cuXh4eJheERERWockKoIlU1gWl2ilKIQQAhtKxD/++CPPP/883bp1Q1GUfOsfeOABLl26VPGBFWPq1Kno9XrTa+fOnVqHJMrb3VNY5r7yTmEJpkRrdHHJn2ilKIQQ4g6buUes1+sJCQkpdH1mZiZZNji61MnJCac8syi5ublpGI2oEJZMYZkn0RqNRuxyE62HR8694NwWtavr3y3q5OSC7zMLISo1m0nEoaGhHDp0qND1W7ZsISwsrAIjEqIIxU1hmSfRZqalYe/i8neitbeXohBCCBObScTjxo3jjTfeoFOnTnTt2hUARVEwGAzMmjWLzZs3s3jx4nKPIzk5mfPnz5t+vnjxIkeOHMHb25ugoKByP7+oBO7qus5ITMSlevW/Ey0U36LOyspJ2EKISs9mftNffPFFTpw4wbBhw/D09ARg+PDh3Lp1i6ysLCZMmMDYsWPLPY6DBw/SuXNn08+vvPIKAKNGjWLZsmXlfn5RCdzVdX3rwgU8cm+7WFJ9qbhHnoQQlYrNJGJFUfjiiy8YNWoUq1ev5ty5cxiNRkJDQ3niiSfo2LFjhcTRqVMnsxHbQpRInq7rc1euUNea2yrybLEQVYrNJOJc7du3p3379lqHIYS5UnQVx8XFodfr8fDwKH7jux95yh3cJYSotGzm8aWLFy+ybt26QtevW7fOJh9fElVAKaahbNmyJS+99BIPPfSQZT0t8myxEFWOzSTi1157jX//+9+Frv/000+ZMmVKBUYkxB2lKOwQGxtLfHw8N2/eZP/+/UUnY3m2WIgqyWYS8b59++jWrVuh67t27cru3bsrMCIhKJvCDnccPXqUDRs2kJCQUPA+lszWJYSodGwmEcfHx+Pu7l7oejc3N27dulWBEQmBZV3FVnRdX7t2jdWrV/Pb9u2kRUX9vY+ls3UJISodmxmsFRQUxK+//srEiRMLXL97925q165dwVGJKq2wruK7B1AVMso5OjqalJQUIKc4yO3bt/H29sZoNHL2wAGup6Tg16QJIW3bEhgYiFLcs8VCiErJZlrEw4YN49tvv+Xf//43RqPRtDw7O5sFCxawcuVKhg8frmGEosopYWGH/fv38+ijjxIcHGzqhk5LS+PNN9/k008/JfrcORzT0zHY23PlxAk2rVnDN998w54DB4i5dQvVyQmcnf9+FTeLVwncvn2bf/zjH1SvXh1PT0/Gjh1rceUwVVXp1asXiqLw448/mq07cOAAXbt2xdPTEy8vL3r06MHRo0fLPH4hKhObScRTp06lc+fOvPTSSwQGBtKxY0c6duxIzZo1efnll4mIiOCtt97SOkxRVVhZ2CG363rNihW0a9eOTZs25RuYpaoqx48f5/358zlw4gRZTk4oRiMOqamkpqZy8uRJNmzYwDfffMP+/ftLXVKzU6dOhU5C849//IMTJ07w888/s379enbt2sXTTz9t0XE//vjjAguzJCcn07NnT4KCgvj999/Zs2cP7u7u9OjRg0zpWheiUDbTNe3k5MSWLVv46quvWLNmDVFRUQC0atWKgQMHMnLkSOzsbOZ7g6jsrCzsALD/3DmGPPMM2dnZhY6Ozu3tWbB6Ne/4+VEvMBDH9HQyMzLIdnQEIDU1lSNHjnD06FFCQkJo0qQJfn5+ZfbWTp06xebNmzlw4AAtW7YE4JNPPqF3797MmzePmjVrFrrvkSNH+Oijjzh48CCBgYFm606fPs3t27eZNWsW9913HwDTp0+nSZMmXL58mXr16pXZexCiMrGZRAxgZ2fHmDFjGDNmjNahCGFVYQeMRt799FNUVbXoeWEVWLt1Ky+PHIn9nVZxbiI2baOqXLhwgQsXLuDv70/jxo0JDg4u9RfSffv24enpaUrCAI888gh2dnb8/vvvPP744wXul5qayvDhw/n0008JCAjIt75+/fr4+PiwZMkS3nzzTbKzs1myZAkNGzYkODi4VDELUZnZVCIW4p5xV9d19LVrrN+61eLpUY1GI4dPnUIfF0cNT0/sMzNRsrNRdboCt4+LiyMuLg53d3eaNGlC/fr1sS/hTF+xsbH5Wtj29vZ4e3sTGxtb6H4vv/wyDz/8MP379y9wvbu7Ozt27OCxxx5j9uzZANx///1ERkaWOFYhqgKb+u2IjIxkyZIlXLhwgfj4+Hx/1BRFMXVZC1FWMjIySlbr2tMTVJW0tDSWr1xp9RzlKnDw5k0erlsXFTAqSs6gsCLo9Xp2797N/v37eeCBBwgLC8PV1RVHR0fee+893nvvPdO2aWlp/Pbbbzz33HOmZSdPnrQqxlw//fQT27Zt4/Dhw4Vuk5aWxtixY2nXrh3ffvst2dnZzJs3jz59+nDgwAFcZKpOIQpkM4n4ww8/ZMqUKfj7+9OqVSsaN26sdUiiCshITWX/oUMWDYxSVZWUlBQSExPR6/UkJiaSmJhIWloax44dQ1EUq5KxoijEJyejNxhyFuT+a4Hk5GT27dvHgQMHaNGiBW3atOGZZ57hiSeeMG3zj3/8g4EDBzJgwADTspo1axIQEMCNGzfMjpeVlcXt27cL7HIG2LZtG1FRUabKaLkGDhxIhw4d2LFjBytWrODSpUvs27fP1H2+YsUKvLy8WLt2LUOHDrX4/QlRldhMIl6wYAFdunRh48aNOMgzk6IipKWRHR1N6q1bOLq745TnnrDBYCAhIYH4+HjTv3q9nuzs7AIP5ejoaH2LWFVxdnYu8T1fOzs7AgMDSU1NJSsrC29vb7y9vU3rXVxc8PPzyzdIqm3btiQkJPDHH38QHh4O5CRao9FI69atCzzXlClTGDdunNmyxo0b869//YtHH30UyLmHbGdnZzaiOvdnYzEtfVEJSU1ti9nMpxQfH8+gQYMkCYuKo9djvHWLtNhYkpOTSUpK4vbt29y+fZvU1FSLDuHg4IC3tzd+fn7s2LHD6hZx/fr1i03EBd079vHxITQ0FFVVSUpKsvicAA0bNqRnz56MHz+ezz//nMzMTJ577jmGDh1qGjEdExND165d+frrr2nVqhUBAQEFtpaDgoIIuVNruVu3brz++us8++yzPP/88xiNRt5//33s7e3NanyLKkBqalvFZhJxq1atOHPmjNZhiEosKSmJmJgYrl69SuzFiySdPMmNmzdRs7OJd3cns5hv7x4eHnh5eZlant7e3ri5uZlagNu3b+fXX38ttNWcl52dHQ0bNsTLy6vI7XQZGVRLTibVzY1sR0ccHByoW7cuvr6+KIpCenq65R9AHt988w3PPfccXbt2xc7OjoEDB5oVXcnMzOTMmTMWfyEBaNCgAevWrWPmzJm0bdsWOzs7mjdvzubNm/M96iQqOampbRWbScQLFy6kV69etGzZUmbQEqWSmZlJXFwcV69eJSYmhpiYGK5du0ZiYqJpG/fUVFzT0kh3dsYlMxPnjAxTInZ0dMTb2xsfDw+8fH3x9vbGy8ur2JG/Y8eO5ddff7X4XnH37t2L3cYxLQ2n1FScPTxwr1uXgIAAdIWMrL7bjh07Cl3n7e3NihUrCl0fHBxc7HsoaH23bt2KLN4iqgCpqW01m0nEQ4YMISsriyeffJKJEydSu3btfH9wFEWR6fKEiaqq6PV6U7LNfcXGxhZ5T9IhKwsXg4EsJydq1KiBo6LwQPXqVKtfH8/AQKpVq4adwYB9fDxZXl6ozs6FB5HnPtiDDz7I3LlzmTp1Kqgq2QXEkNsNPWrUKIKCgoCCu55dXV3xcnbGz9UVt+rVcbC3J9PHp9DHm4SwGbmzzXl5/V0+tKBELPeQTWzmU/D29sbHx4f7779f61CEDcrIyCA2NtaslRsTE2MqqlAcNzc3atWqRe3atanj7EwtJydqhIaSlZXFb7/9hgeg8/Qk684sWbrkZHSJiagODmQVkoiV9PR8ybpLly7897//ZcmSJezevdus1agoCg0bNqR79+6mJJzb9Wzw8MAzMBAfHx88PDxwcHDA/q+/0CUmYnR3R0lKQpecXGgsQtgESwulyD1kMzaTiIvqRhNVh6qqxMfH50u4N27csKi7V6fTERAQQK1atcxe1atXz7mXm5kJV69Cejqkp5OVloYuLQ3V1RU7gwGyslCysrBLTkZ1ccEuORnFza3AVnFhyfrBBx9k/vz5xMbGMmzYMJKSknBxceH111/Pd0/YU1Go4+uLe716GPNMsqGkp+fEcOe4qrNzkbEIYRPumm0Oe3tITs7fKpZ7yGZsJhGLqic9PZ3r16/n61pOs6CuL+QMnro74fr7+xd9L/euOaTV1FTSr17F3s0NxcUF7O3RJSSgGI0YXV2xK6QlakqURSTrgIAAXFxcSE1KwtHR0SwJu7m5ERIQgF96OopOh5qaSmZ6uukYuuRklOxsjDodGI2oOh122dnSKha26+5CKbnyFkq5e452uYcM2FgiTkxMZOHChWzfvp0bN26waNEiWrVqxe3bt1m2bBn9+vWTiePvQUajkVu3buVLuH/99ZdFrVx7e3sCAwOpXbu2KeHWrFkTd3f3kgWUdw5poxGjoyOqkxOqo6PFLVFdcnKxyRrAWVWpCWTceZ8uLi4EBwfj4+ODw61bKGlpGF1czI+RlZXTOrezwy7vqGU7O1OrXe6tCZtjSaEUsPwechViM7/NV69eJSIigitXrnD//fdz+vRp02xH3t7eLFq0iMuXL7NgwQKNI713REdHs3XrVpKSknB3d6dr166me5PlJS0tLV/CvXbtGgYLZ43y8vLK18r18/OzeKRwaVnSErWm2zjIwwPS0kiuVo2aNWsSEhKSM8lFMcfI9PUt/A+aJGGLaHH9V3kFFUrJ+8XR0nvIVYzN/Ea//vrrJCUlceTIEfz8/PJNSv/YY4+xfv16jaK7t+zfv5/Zs2ezYcMGVFXFzs4Oo9GIoij07duXadOm8dBDD5XqHEajkZs3b+ZLurdu3bJof0dHR2rWrJkv6VarVq1UcZWKhS1RS7uNlfR0Fr7/PkeOHsXb05PAWrVQ74yaLu4Y6l2VmITlKuL6Fxa6e1CWpfeQqxibScRbtmzh5ZdfJiwsrMA/5nXr1uXKlSsaRHZvWbNmDUOGDDErx5f7KI+qqmzcuJFNmzaxcuVKszmIi5KSklJgK9fSYu8+Pj5m3cq1atWiRo0atldf2t6++JaoFd3Gud3XarVq1A4MBOl6Lnflcf2LUsg7KMve3rJ7yFWQzfy2p6Wl4evrW+h6a6fxK41PP/2UDz/8kNjYWJo2bconn3xCq1atKuz8JbV//36GDBlSZGH67OxsFEVhyJAh7N2716xlkJ2dTVxcXL6km5CQYNH5nZ2dTfdv897LvZeq7hTbErUkWWPefe3t7Y2dmxtI13O5Ku31L8pYQYOyLLmHXAXZzG98WFgYu3btYsKECQWu//HHH2nevHm5x7Fy5UpeeeUVPv/8c1q3bs3HH39Mjx49OHPmTL7uclvz7rvvWlSYPnebN954g7feesuUcK9fv25ROUBFUfD19c3Xrezt7W17rdxyYEm3cd6uZ1cXF+l6rgDWXv/vvvsua9euraDoqqCCBmUVUt2rqrOZRPzSSy8xatQomjRpwuDBg4GcLqXz588zc+ZM9u3bx/fff1/uccyfP5/x48czZswYAD7//HM2bNjAf//7X6ZMmVLu5y+p6Oho1q9fb3HRgezsbLZv306tWrWKHH3s4uKSr1s5MDDQrFKRuMtdXc/VsrNzuqCl67nclOT6X7duHdHR0TKAqzzIoCyr2MxfgxEjRnD58mXefvtt3nrrLQB69uxpGmzx3nvv8dhjj5VrDBkZGfzxxx85UxTeYWdnxyOPPMK+ffsK3MdgMJiNCM4d6Z2VlWXxPdSyEBkZaXUZPoArV66YKgD5+/tTs2ZN06tWrVp4eXmZlbXLVZHvrTxlZmaSlZVFSkqKRb0BllJcXODOwK0sR0cMuYO4FAW1hIUaCmIwGEzXWmX5f1ISJbn+VVVly5YtjBo1qpyiujdkZGSU6bUPQFwc3L6N4uEBd8ZBqHp9The0v3+Znsre3h7HcuxhqoiKgIpakr/e5Sg6Oprvv/+e8+fPYzQaCQ0NZcCAAdStW7fcz33t2jVq1arF3r17adu2rWn55MmT2blzJ7///nu+fWbMmMHMmTPLPTYhhBAVryJSpE20iFNTU+nQoQPjx4/nmWee4eWXX9Y6JItNnTqVV155xfTzkSNHiIiI4Pfff6+Qe9q5li1bxtNPP231fl988YW0CMqjRZArLg4lLg7V37/MWwK5yrtFcC+Q679kUlNT2b17N46OjmV6u0nJyCh0UFZZjo8wGAxkZGTQoUMHbR99LCWbSMTVqlXj4sWLBXaBVqQaNWqg0+mIi4szWx4XF1dgUXQAJycnswvYzc0NyPnjWBFdGrl69Ohhcfm9XIqi0L179wqN0xaV2/tPS8v5YxQQkPPMpKOj3B8rJyW9/lu3bl2lr38HBwfs7e1xdXXF+R6cOtXe3h6j0YiDg8M9/f/RZoa49uzZk8jISE1jcHR0JDw8nK1bt5qWGY1Gtm7datZVbYuCgoLo27evxTNQ6XQ6Hn30URmoUp5yR426uub8q9drHVGlZe31rygKderU4csvv2TNmjWmsR1CaMFmEvG0adM4e/YsTz75JHv27CEmJobbt2/ne5W3V155hS+++IKvvvqKU6dOMXHiRFJSUkyjqG3ZtGnTUBSl2J6F3G3efvvtCoqsCips1KiFBS2E9Sy9/iHndyA8PJzMzEx++eUXpk2bxrp16ywuOCJEWbKJrmnIKR0HcPLkSVasWFHodtnZ2eUax5AhQ7h58ybvvPMOsbGxNGvWjM2bN+NfTvf3ytJDDz3EypUrTTMLFfRZ6XQ6FEXhu+++k8kMypNM5VfhrLn+v/rqK5ycnNi5cydZWVkYDAY2bdrEzp076datG506dZJH9CqCPMoH2NCo6RkzZlj0TXb69OkVEE3JHTp0iPDwcP744w9atGihSQwHDhxg9uzZpucq8861++ijj/L2229LEi5PeWse383ZGWrXrtKzCJU3a67/hIQENm3axK+//mqaChPA3d2dHj160KFDh3v63mNxUlNT2bVrF+7u7mV3j9jC5Kqkp2MfH0+Wl1eJa2ynp6eTlJREx44d7+nBWlYl4pCQEKsHVCmKQlRUlNWB3atsIRHnio6OZtu2bSQmJlK9enW6dOki94QrisFQ+FR+0tKqENZc/3/99RcbN27k999/Nxvw5eXlRa9evWjbtm2FVQCrSGWdiK1JrvZ//YX9rVtk+fiQVaNGic5XJRPx6NGj8yXigwcPcuLECcLCwqhfvz4AZ86c4eTJkzRq1Ijw8HCWLl1qdWB6vR43N7d77uK3pUQshLDO9evX2bBhA4cOHTJb7uvrS58+fWjZsmWlmsa1rBOxpclVSU/HITYWxc4O1WgkMyCgRK3iypKIrbqili1bxtKlS02v/v37c/XqVX7++WeOHz/O999/z/fff8/x48eJjIzkypUrVs2GdfDgQXr27Em1atXw8fFh586dQM631f79+7Njxw5rwhVCCKsEBgYybtw4pk6dSqNGjUzLb968ybJly5gzZw6HDx+ukEke7jWmQicuLjm1tYuYQS63MpnRxQXFaERXxUetl+qr3TvvvMPzzz9P165d863r1q0bzz33nMUjc/fu3Uv79u05d+4cI0aMMLtfU6NGDfR6PYsWLSpNuEIIYZH77ruPSZMm8dprr5l6+iCnxfzFF1/wwQcfcOLECUnIeViaXPNWJgNQnZ2LTdyVXakS8blz5/Dx8Sl0vY+Pj8X3h998800aNmzIyZMnee+99/Kt79y5c4FTTAqhqbKYkau8ZvUSpVa3bl1efPFFXnzxRUJCQkzLo6Oj+fTTT5k/fz5nz57VMELbYE1yza1Mpup0YDSi6nQodyqTVVWlSsShoaEsXbq0wIfhk5KS+O9//2vxHNEHDhxgzJgxODk5FTggrFatWsTGxpYmXCHKVloaxMQU/2xwUYnW0mMITdWvX5/XXnuNiRMnUrt2bdPyqKgoPv74Y/79739z6dIl7QLUmMXJNT3drDJZ7susMlkVVKoHuN59910GDRpEgwYNGD16NPXq1QNyWspfffUVcXFxrFq1yqJjOTg4mHVH3y0mJsY0faQQNkGvz6mz6uRU+LPBaWlw4wb4+RW8jSXHEDZBURQaN27Mgw8+yJEjR1i/fr2pcXD69GlOnz5NkyZN6Nu3r1myrvTuKvtpclfZT9OIand31IKeHFCUKvtMcane9WOPPcbGjRt544038nUnN2vWjCVLltCjRw+LjtWmTRtWr17NSy+9lG9dSkoKS5cuJSIiojThClF28s6cVVSd1aISraXHEDbFzs6OFi1a0KxZM/bv38+GDRu4desWAMeOHePPP/8kPDycPn363BMTAZWavT2Zvr6FP653J7nqkpPRJSaiOjiQVb16BQdp20r99aN79+50796d2NhYLl++DECdOnUKLZJQmJkzZxIREUGfPn0YNmwYAEePHuXChQvMmzePmzdvMm3atNKGK0TZyJ1H2ssrJ9EWNGNWcYnWkmMIm2VnZ0ebNm1o2bIl+/btY9OmTSQkJKCqKgcPHuTQoUO0bt2a3r17FzmWpjIorqJSvhHVbm4lnsSjMiqzfoCAgACrk29erVu3ZuPGjUycOJGRI0cC8OqrrwI596I3btxIkyZNyiRWIUqlsHmkrUm0lh5D2Dx7e3s6dOhA69at2b17N5GRkSQnJ2M0Gtm3bx/79++nffv29OzZEw8PD63D1YRpRLWrK3ZJSeiSk8mSRGxS6ifTo6OjeeaZZ6hfvz7e3t7s2rULyHn294UXXuDw4cMF7peYmJhvLtguXbpw5swZDh06xMqVK/n222/Zv38/Z8+elW5pYTty55G2t/97HunMTPPqSsUVfbDkGOKe4ujoSNeuXZk1axaPPvooLne+UGVnZ7Nz507eeeedKlnpSR5XKl6pWsQnT56kQ4cOGI1GWrduzfnz500F1mvUqMGePXtISUlhyZIl+fb18vJi+fLlDB8+HICnnnqKCRMm0Lp1a5o1a0azZs1KE5oQ5SMzMyeZ6nSQlPT3cp0uZ3lmZs480kUVfbC3t+wY4p7k7OxMr169iIiI4JdffmH79u0YDAZTpac9e/bQpUsXunbtakrWlcpdc03njqg25hlRbXdnRLW0inOUKhFPnjwZT09PfvvtNxRFwc/Pz2x9nz59WLlyZYH7Ojo6YjAYTD8vW7aMRx55hNatW5cmJCHKl4MD1KxZ+MAUB4fikzUUfwxxz6tWrRr9+vWjc+fOREZGsmvXLrKyskhPT2fjxo2mSk8RERGVptJTvrmms7JQ09KKHVFd1ZXqE9i1axfvvPMOvr6+plGDeQUFBRETE1Pgvg0aNODLL78kODjYdN/k0qVL+eZ4vZvM3yw0V9wfTUuStagy3N3dGTRoEI888ohZpaeUlBR+/PFHtm3bRo8ePWjfvv09X+nJbGS0szPY25Pm6YlTQYO5qvDjSncr1adgNBqLnGj75s2bhX7Tmzt3LkOGDOGRRx4Bcp7RmzZtWqEjo1VVRVGUcq9HLESZqCQtHFF2PD09GTZsGN26dWPDhg3s378fVVVJTExk1apV/PLLL/Tu3Zs2bdrcc8VuoPCR0Vk6HY7y+1CkUiXiFi1asGHDBiZNmpRvXVZWFv/3f/9HmzZtCty3Z8+eXLx4kQMHDhAXF8fo0aN5+umnadu2bWlCEkIIm1ajRg1GjRpF9+7dzSo9xcfH880337Blyxb69u1LeHj4PVXpqbCR0RkZGfd0ZaSKUKpEPHXqVPr27cvEiRMZOnQoAHFxcfzyyy+89957nDp1iv/85z8F7nvs2DHq1KljmvBj6dKlDB48uMACEkIIUdnkVnq6cuUK69at4/jx40BOT+LSpUvZvHkzjz76KE2bNrW6DnxFK3RktJsbqampeHp6ahugjSvV161evXqxbNkyVq5cSZcuXQAYMWIE3bt359ChQ3z99dd07NixwH2bN2/Ohg0bSnN6IYS45+VWenr11Vd54IEHTMuvX7/O4sWL+eCDDzh58qRNV3oqaq5ps8e1quhc0sUp9Z3yJ598kgEDBvDzzz9z7tw5jEYjoaGh9OjRA3d390L3c3FxITXPKLqdO3cyfvz40oYjhBD3pNDQUF566SXOnDnDTz/9xMWLF4GcuRr+85//UK9ePR599FHuv/9+jSO9SzFzTSfHx6OqKnYGg/mIamFS4kScmprKfffdx5QpU3j99dd57LHHrNq/adOmzJ8/H51OZxo1feDAAZyL+R80YMCAkoYshBA2L7fS0/Hjx1m3bh1Xr14F4Pz58/zrX/+iYcOGPProowQHB2sbaK5i5po2REeTmpqKR1qa+YhqYVLiRFytWjXs7e1xzZ05yEoLFixg0KBBjB07FsgZNb1gwQIWLFhQ6D4yaloIURXcXelp3bp1xMXFAXDq1ClOnTpF06ZN6du3L7Vq1dI42uLnmk6Mi8PLzk7mmi5EqbqmBw4cyOrVq5k4caLVgwlatmzJ+fPniYqKIi4ujk6dOvHWW2+ZHmcSQoiqLrfSU9OmTTlw4IBZpaejR49y7NgxwsPD6du3b74JlWxJ0tWrKLVry1zThShVIh46dCiTJk2ic+fOjB8/nuDg4AKnbCtsEg57e3vq169P/fr1GTVqFH379pWZtYQQ4i46nc5U6Wnv3r1s2rQJvV5vVumpTZs29OrVy+YqPekyMjD89RcZdepgj/mIamkV5yhVIu7UqZPpv3fv3p1vvTWTcCxdurQ0oQghRKVnb29Px44dadOmTb5KT3v37mX//v20a9fOpio9OaaloWRlcUuvx9/ZWeaaLkCpEnFpkuesWbNQFIW33noLOzs7Zs2aVew+ubNvCSFEVZZb6aldu3Zs376dX375hbS0NLKysti5cyd79+6lU6dOdOvWDTc3N83iHDNiBMTE4F2tGpMmTSLAzS3nNqbMNW1GUTV6OM3Ozg5FUUhLS8PR0dGiGWTKe7DWnDlz2LBhA0eOHMHR0ZGEhASrj3Ho0CHCw8P5448/ZF5sIUSFSE1N5eeff2bHjh1mxXScnZ2LrPSUmprKrl27cHd3L/aJlZLo3bs3STdu4F69Om+//Tb16tXD398/Z6WiFDvIqzjp6ekkJSXRsWPHe3r2Ls3mTzMajWRnZ+N453+E0Wgs9lXeI6YzMjIYPHgwEydOLNfzCCFEWapWrRr9+/dn1qxZdOnSBfs7rczcSk/vvPMOW7ZsISMjo0LjclZVagAKkO3gwLkrV9AbDKhOTqVOwpWJVX0CTz31FIqisHjxYnQ6HU899VSx+yiKUmA9Yls0c+ZMIKckoxBC3GtyKz117dqVTZs2sXfv3nyVnnr27Em7du0qpNKTu9GIDkg1GoGcBtfx48cJCwuTaS/zsCoRb9u2DTs7O4xGIzqdjm3bthX72JI1jzWdOnWKqKgokpKScHd3p169ejRo0MCaECucwWAw6woym85NCCE04OXlxfDhw02Vng4cOGCq9PTdd9/xyy+/0KtXL5o0aVJuMSjp6bgbjdwEPMkZPZ3t6Eh2djbHjx8nNDSUgIAAm59HuyJYlYgvXbpU5M8ltWjRIubMmVNg7eKgoCDeeustxo0bVybnKmtz5841taSFEMKW+Pr6Mnr0aHr06MH69es5fPgwALdv3+abb74hMjKSoKCgcmnw6JKTc1rDgA85o6fT7nRHq6rK+fPnSUxMJDQ01NSVXlVpXmPrtddeY+LEiaSmpvLUU08xf/58Fi9ezPz58xkzZgxJSUlMmDCBN954o0THnzJlCoqiFPk6ffp0ieOfOnUqer3e9Nq5c2eJjyWEEOUhMDCQ8ePHM2XKFB588EHT8r/++otDhw6xYcMGLl26VGaFJXKrMaXeae2mAk4GA7q77lHfuHGDQ4cOlWhgbGWi6deQ/fv3M3/+fB5//HG+/vrrAqfLXLBgASNGjGDevHkMHjyYli1bWnWOV199ldGjRxe5Td26da06Zl5OTk445Sl6reWjAkIIUZSgoCCeffZZoqKi+Omnnzh37hwAer2erVu3UqNGDcLDw6lVq1apuoxzqzFlkzNQKwtQsrLMWsW5DAYDf/75J4GBgYSEhKDT6Ur+Bu9RpU7EmzZtYv78+Rw6dMg008vdChvtvGTJEgIDA1mxYoVZMsvL1dWVb7/9lrp167JkyRKrE7Gvry++vr5W7SOEEJVZbqWnY8eO8d133xEfHw/ktJAjIyPx9/enZcuWBAQEWH/wPNWY3FQVA+AGoNPhkJWFITsbYwHJ9vr168THx3P//fdXuYFcpeqa/v777+nbty9xcXEMHToUo9HIsGHDGDp0KC4uLjRp0oR33nmn0P337dvH4MGDC03CuZydnRk8eDC//vpracItVnR0NEeOHCE6Oprs7GyOHDnCkSNHZACWEKLSURSF+++/n/bt2xMREYG3t7dpXVxcHBs2bGDz5s3cvHnTugPfqcaUUbMmV+ztuQRE29mR6O1NiodHgUk4V3p6On/++ScXLlzAeGekdVVQqhbx3LlzadWqFXv27CE+Pp7PPvuMp556ii5dunDp0iXatGlDSEhIoftfuXKFhg0bWnSusLAwvv7669KEW6x33nmHr776yvRz8+bNAdi+fbvZdJ5CCFFZKIpC7dq1CQ0N5eLFi6beTYCYmBhiYmKoU6cOLVq0MEvWRcl9RtigKBju/JttxeNSMTExJCQk0LBhwwInIqlsStUiPnnyJEOHDkWn05lGvWVmZgIQHBzMpEmT+OCDDwrdPzExEXd3d4vO5ebmRlJSUmnCLdayZctQVTXfS5KwEKKyUxSFunXrMmDAADp06GA23uXy5cv88MMP7Nixg8TExAqJJyUlhcOHD3Pjxo0yG0Rmq0rVIq5WrZppZixPT0+cnJy4fv26ab2/vz8XL14sdP/cohCWquz/M4QQQmt2dnY88MADhIaGcubMGY4ePUpqaioAUVFRXLhwgfvvv5/mzZsXOTg1NjaWtLQ0IGfWwvj4eLy8vKyKJTs7mzNnzvDXX38RGhpa7G3Me1WpEnH9+vU5efKk6edmzZqxfPlyRowYQVZWFitWrCAoKKjIY8ybN49vv/222HMV9IyxEEKI8qHT6QgLC+OBBx7g1KlTHDt2jPT0dFRV5ezZs5w/f54GDRrQtGlTs3mejx8/zpIlS9izZ4+p8ZSWlsbs2bMJCwuje/fuxeaFu926dYuEhATq1q2Lv79/pZsEpFRd0wMGDOCnn34yzSz11ltvsWPHDjw9PfH19WX37t1MmTKl0P2DgoK4ffs2f/75Z7Gv27dvW/0/TwghROnY29vTuHFjBg8eTIsWLUxTYxqNRk6ePMmqVas4cOAABoOBbdu2MXbsWPbu3ZuvB1NVVU6dOsWCBQs4duyY1XFkZ2dz7tw5Tp06RVZWllX7rlmzhu7du+Pj44OiKBw5csSifVq2bImnpyeurq6mhmZeycnJPPfcc9SuXRsXFxfCwsL4/PPPrYoNStgiTk9PZ+3atWRmZvL2229z+/ZtAgMD6du3Lzt27GDNmjXodDr69OlD586dCz1OWc3MJYQQonw5OjrSvHlzwsLC+PPPPzlx4gRZWVlkZWVx7Ngxtm/fzurVq01jawqSOxL6q6++4sUXXyxR4+rWrVv8+eefNGrUyOJ9UlJSaN++PU888QTjx4+3aB9vb2/eeustGjRogKOjI+vXr2fMmDH4+fnRo0cPAF555RW2bdvG//73P4KDg9myZQuTJk2iZs2a9OvXz+L4rC6DeOPGDR5++GEuXrxousfr4uLCjz/+yCOPPGLNoSolKYMohLhXlKYMYlpaGkePHuXUqVMYjUY2btxIdHS0RWN57OzsaNiwYammLq5evTr3338/KSkpFpdBvHTpEiEhIRw+fJhmzZpZfc4WLVrQp08fZs+eDUCjRo0YMmQI06ZNM20THh5Or169ePfddy0+rtVd07Nnz+bSpUu8/PLLrF+/nn/961+4uLgwYcIEaw8lhBDiHuXi4kKbNm0YPHgwfn5+XL582eIBtbnd2rkTiZREYmJihY0dUlWVrVu3cubMGTp27Gha/vDDD/PTTz8RExODqqps376ds2fP0r17d6uOb3XX9JYtWxg5ciTz5s0zLfP392f48OGcOXOG+vXrW3tIIYQQGspbQc5a9vb2JapzrKoqZ86coVWrViU+9/Xr1/Hw8Cjx/sXR6/XUqlULg8GATqdj4cKFdOvWzbT+k08+4emnn6Z27drY29tjZ2fHF198YZasLWF1Io6Ojs5XgKF9+/aoqkpcXJwkYiGEuEfY29vj5uZGcnJyiZJprvj4eBRFseoRU0VRSE9PL/UMWi4uLmbVm7755huzHtpNmzbRoUOHEh3b3d3dNLvi1q1beeWVV6hbt65pbolPPvmE3377jZ9++ok6deqwa9cunn32WWrWrGnVrVqrE7HBYMh3LyH3Z2tHsgkhhNCOo6MjrVq1KvXf7pJUblJVFQ8Pj1IVynFycqJt27am+SwA+vXrR+vWrU0/16pVq8THt7Ozo169ekDO47mnTp1i7ty5dOrUibS0NN58801++OEH+vTpA0CTJk04cuQI8+bNK99EDDkf+qFDh0w/506Hdu7cuQIn65ZBS0IIYZscHR3NEllJ9O7du0Qt4oYNG2JnV/KnaP39/fNN8uHu7m7xjI3WMhqNpm78zMxMMjMz88Wv0+msbuWXKBFPmzbNbJRYrkmTJpn9nDuqurDqS0IIIe59QUFB9O3bl40bN1r0997Ozo7GjRtbPHd1Yfz8/Cza7vbt20RHR3Pt2jUAzpw5A0BAQICpwtTIkSOpVasWc+fOBXJqKbRs2ZLQ0FAMBgMbN25k+fLlfPbZZ0DOqO2IiAhef/11XFxcqFOnDjt37uTrr79m/vz5Vr0PqxPx0qVLrd3FYpGRkSxZsoQLFy4QHx+f79uVoihERUWV2/mFEEKUzLRp09i0aZPFLePevXuX6ny5c2Nb4qeffmLMmDGmn4cOHQrA9OnTmTFjBpAz/ilv6zYlJYVJkyZx9epVXFxcaNCgAf/73/8YMmSIaZv/+7//Y+rUqfzjH//g9u3b1KlThzlz5vDMM89Y916sfY64vHz44YdMmTIFf39/WrVqVeicpOX5RaAsyHPEQoiqas2aNQwZMgRVVQtsGecmuqefftpU3a6kwsPDCQ8PL9UxbEWp5pouSwsWLKBLly5s3LjRNIWaEEKIe8eAAQPYu3cvs2fPZv369WYtY0VRaPzgg/Tu25fg4OASn8POzo5WrVrRuHHjMojYNthMIo6Pj2fQoEGShIUQ4h720EMP8dNPPxEdHU3Tpk1JSEjA39mZ6ePG4XrffaQXMKDXUj4+PnTs2BFfX9+yC9gG2EwibtWqlekGuhBCiHtbUFAQrq6upCckcJ+DA15+fpCeTmZGBtlWjtKuVq0a4eHhNGjQoNJVXgIbSsQLFy6kV69etGzZkuHDh2sdjhBCiDJQHdCpKllOTjikpuKQmmpxInZxcaFp06aEhYWZTdpR2djMOxsyZAhZWVk8+eSTTJw4kdq1a6PT6cy2URSFo0ePahShEEIIazirKtWA1Dut2GxHRxwtaBVXlQScy2beobe3Nz4+Ptx///1ahyKEEKIMuGVnkwlkARiNGO3ssDcaC20V63Q6mjZtStOmTavUeCGbScQ7duzQOgQhhBBlJTMTZyAdcFdVHO7MSKUqCvaZmSjZ2ag6nenfWrVq0bFjx3KbFcuW2UwiFkIIUYk4OHBDpyMOcNfpSK5Rw7RKBVSdDl1GBi5JSTTq0oXGrVpVyoFYlrC5RJyZmcnp06fR6/UFztdpbXkpIYQQ2rADfIBMILuArmZ3o5GI8HD869SBKpqEwYYSsdFoZOrUqSxcuJDU1NRCt5N5q4UQ4t7glp2NCqQV0Kjyr16druHhuFWvDno9eHiAi0vFB2kDSl72ooy99957fPjhh4wYMYKvv/4aVVV5//33+fzzz2nSpAlNmzYlMjJS6zCFEEJYIi2N6qpKCuAB6PLUO27QoAF9O3TAzdkZXF0hKysnGVdRNpOIly1bxhNPPMFnn31Gz549gZy5RMePH8/vv/+Ooihs27ZN4yiFEEJYRK9HB6SS8xyxw52eztatW9PxoYfQJSfnJGHI+Vevh7Q0zcLVks0k4qtXr9KlSxcAU33J9PR0IKde5ogRI1i+fHm5nf/SpUuMHTuWkJAQXFxcCA0NZfr06WTk+RYnhBDCAmlpoNfj6uuLp4cHirs7junpNG/QgKZNm+Yk3cxMsLcHozHn38zMKtsqtpl7xD4+PiQnJwPg5uZG9erVuXDhgtk28fHx5Xb+06dPYzQaWbRoEfXq1eP48eOMHz+elJQU5s2bV27nFUKISudOol2/eTOrVq3CYDDg6eBAeL16OQk3LQ10OkhK+nsfnS5neWYmVKFniMGGEnHz5s05cOCA6efOnTvz8ccf07x5c4xGI//+979zvkmVk549e5q6xAHq1q3LmTNn+OyzzyQRCyGEpe5KtA7p6RgzMqjftCl2d54lpmZNKKgCr6JUuSQMNpSIn376aZYtW4bBYMDJyYk5c+bQsWNHOnbsiKqqeHl58e2331ZoTHq9Hm9v7wo9pxBC3NMcHMwSbbKvLzqdjqCOHXO6oKtgoi2OzSTifv360a9fP9PPYWFhREVFsWPHDnQ6HQ8//HCFJsXz58/zySefFNsaNhgMGHK/5YGpe10IIaqsO+N8AIyOjjRu2hSdk1NOIhb52MxgrYJ4eHjQv39/+vbtW+IkPGXKFBRFKfJ1+vRps31iYmLo2bMngwcPZvz48UUef+7cuXh4eJheERERJYpTCCEqI51OR4M6dSAmpsqOii6OoqoFddRrIzs7m1WrVrF9+3Zu3LjBrFmzaNy4MXq9nq1bt9KuXTv8/f2tOubNmze5detWkdvUrVsXxzsTkF+7do1OnTrRpk0bli1bhp1d0d9V7m4RHzlyhIiICP744w9atGhhVaxCCFHZbNu2jS5hYRAbCwEBOS9hxmb6CRISEujZsyf79+/Hzc2NlJQUnn/+eSBnFPULL7zAyJEjee+996w6rq+vL76+vhZtGxMTQ+fOnQkPD2fp0qXFJmHIedTKKU83jJubm1XxCSFEZebn7p4zijr3WeEqPINWYWyma3rKlCmcOHGCyMhILly4QN6Guk6nY9CgQWzcuLHczh8TE0OnTp0ICgpi3rx53Lx5k9jYWGJjY8vtnEIIUdm5q2rOzFkyg1ahbKZF/OOPP/L888/TrVu3AruSH3jgAZYtW1Zu5//55585f/4858+fp3bt2mbrbKj3Xggh7h1pabhmZuafQUtaxWZspkWs1+sJCQkpdH1mZiZZWVnldv7Ro0ejqmqBLyGEECWg1+Ok08kMWsWwmRZxaGgohw4dKnT9li1bCAsLq8CIhBBClNidiT10Dg4yg1YxbCYRjxs3jjfeeINOnTrRtWtXABRFwWAwMGvWLDZv3szixYs1jlIIIYRF7kzsYefuDncPYq2iM2gVxmYS8YsvvsiJEycYNmwYnp6eAAwfPpxbt26RlZXFhAkTGDt2rLZBCiGEsJzRiBIfn3NvWO4JF8pmErGiKHzxxReMGjWK1atXc+7cOYxGI6GhoTzxxBN07NhR6xCFEEJYQ6+H+Hhwd5dEXASbScS52rdvT/v27bUOQwghRGncKYWoyEjpYtnMqGkhhBCViF4PWVkobm7y/HAxNG0R5y3yYAlFUVi7dm05RSOEEKJM3GkN4+qKg4ODPD9cDE0T8fr163F2diYgIMCi53UVRamAqIQQQpSKXp/zeJKr69/PDycn5yyXRJyPpom4Vq1axMTEUKNGDYYPH87QoUMJkAnBhRDi3nXn+WF0Onl+2EKa3iO+cuUK27dvp3nz5syePZv77ruPRx55hKVLl5KU93+gEEII21PQbId3nh8mJCT/q2ZNScIF0HywVkREBIsWLSI2NpbVq1fj4+PDc889h5+fHwMGDGD16tVmZQaFEELYgLS0wmsMOzmBs3P+V55KdSblOHXxvULzRJzLwcGB/v37s3LlSuLi4kzJeciQIfzzn//UOjwhhBB55T4jXJrR0EUl8yrEZhJxLoPBQGRkJGvXruXw4cM4OzsTHBysdVhCCCFy5RkVjV5fdCItqsVbFsm8ErCJRGw0GomMjGT06NH4+/szbNgw0tLS+OKLL7hx4wZPPvmk1iEKIYTIdecZ4WJrDBfV4rUmmVdymo6a3rt3LytWrGDVqlXcunWLNm3a8N577/HEE09Qo0YNLUMTQghRkLwJFIp+Rji3xevkVPC6rCzw8vq7VVxFH23SNBG3b98eFxcXevfuzbBhw0xd0NHR0URHRxe4T4sWLSowQiGEEGYsfUb47hZv3kRtTTKvAjSfazotLY3vv/+eNWvWFLmdqqooikJ2dnYFRSaEEMKMNc8IF9XilQk/zGiaiJcuXarl6YUQQlgj9xnhgmZCzFtjuKgWr729TPhxF00T8ahRo7Q8vRBCCGsV9Czw3Ypq8QYEWJbMqxDNu6aFEEJUIpZ0X1uSzKsQScRCCCHKjqXd18JEErEQQoiyJS1eq9jEhB5CCCFEVSWJWAghRPmRog7FkkQshBCifEhRB4tIIs6jX79+BAUF4ezsTGBgIE8++STXrl3TOiwhhLg3SVEHi0gizqNz58589913nDlzhu+//56oqCgGDRqkdVhCCHHvkaIOFpNR03m8/PLLpv+uU6cOU6ZM4bHHHiMzMxMHGXIvhBCWk6IOFpMWcSFu377NN998w8MPPyxJWAghrFHYFJfSKi6QJOK7vPHGG7i6uuLj40N0dDRr164tcnuDwUBiYqLplZycXEGRCiGEjcqd4tLe/u8pLjMz5V5xISp9Ip4yZQqKohT5On36tGn7119/ncOHD7NlyxZ0Oh0jR45ELWiGmDvmzp2Lh4eH6RUREVERb0sIIWzT3VNc5r7yTnEpzChqUVmmErh58ya3bt0qcpu6devi6OiYb/nVq1e577772Lt3L23bti1wX4PBgMFgMP185MgRIiIi+OOPP6R2shCiajIYCp/iUmbdyqfSD9by9fXF19e3RPsajUYAs0R7NycnJ5zyXFhubm4lOpcQQlQakmytUukTsaV+//13Dhw4QPv27fHy8iIqKopp06YRGhpaaGtYCCGEKK1Kf4/YUtWqVWPNmjV07dqV+vXrM3bsWJo0acLOnTvNWrxCCCFEWZIW8R2NGzdm27ZtWochhBCiipEWsRBCCKEhScRCCCHKj1RfKpYkYiGEEOVDqi9ZRBKxEEKI8iHVlywiiVgIIUTZk+pLFpNELIQQouzlVl9ydc35V1rFhZJELIQQomxJ9SWrSCIWQghRtqT6klVkQg8hhBBl5+7qS7nyVl+SGu9mJBELIYQoOw4OULNm4dWXJAnnI4lYCCFE2ZL5+a0i94iFEEIIDUkiFkIIITQkXdNVwPXr17l+/brWYVRZgYGBBAYGah1GlSXXv7bk+i+eJOIyFhgYyPTp023mwjMYDAwbNoydO3dqHUqVFRERQWRkpNS11oBc/9qT6794iqoWNLRNVBaJiYl4eHiwc+dO3NzctA6nyklOTiYiIgK9Xk/16tW1DqfKketfW3L9W0ZaxFVEs2bN5BdBA4mJiVqHIJDrXyty/VtGBmsJIYQQGpJELIQQQmhIEnEl5+TkxPTp02WghEbk89eWfP7aks/fMjJYSwghhNCQtIiFEEIIDUkiFkIIITQkiVgIIYTQkCRiIYQQQkOSiEWVpiiKRa8dO3aU+lypqanMmDHDqmPNmTOHfv364e/vj6IozJgxo9RxCJHLlq//06dPM3nyZJo1a4a7uzuBgYH06dOHgwcPljoWWyMza4kqbfny5WY/f/311/z888/5ljds2LDU50pNTWXmzJkAdOrUyaJ93n77bQICAmjevDmRkZGljkGIvGz5+v/yyy9ZsmQJAwcOZNKkSej1ehYtWkSbNm3YvHkzjzzySKljshWSiEWVNmLECLOff/vtN37++ed8y7Vy8eJFgoOD+euvv/D19dU6HFHJ2PL1P2zYMGbMmGE2R/hTTz1Fw4YNmTFjRqVKxNI1LUQxjEYjH3/8MQ8++CDOzs74+/szYcIE4uPjzbY7ePAgPXr0oEaNGri4uBASEsJTTz0FwKVLl0yJdObMmaYuv+K6moODg8vjLQlhMa2u//Dw8HyFOnx8fOjQoQOnTp0q2zepMWkRC1GMCRMmsGzZMsaMGcMLL7zAxYsX+c9//sPhw4f59ddfcXBw4MaNG3Tv3h1fX1+mTJmCp6cnly5dYs2aNQD4+vry2WefMXHiRB5//HEGDBgAQJMmTbR8a0IUy9au/9jYWGrUqFGm71FzqhDC5Nlnn1Xz/lrs3r1bBdRvvvnGbLvNmzebLf/hhx9UQD1w4EChx75586YKqNOnT7c6rtLsK4SlbPX6z7Vr1y5VURR12rRpJT6GLZKuaSGKsGrVKjw8POjWrRt//fWX6ZXbbbZ9+3YAPD09AVi/fj2ZmZkaRixE2bGl6//GjRsMHz6ckJAQJk+eXC7n0IokYiGKcO7cOfR6PX5+fvj6+pq9kpOTuXHjBgAREREMHDiQmTNnUqNGDfr378/SpUsxGAwavwMhSs5Wrv+UlBT69u1LUlISa9euzXfv+F4n94iFKILRaMTPz49vvvmmwPW5A1AURWH16tX89ttvrFu3jsjISJ566ik++ugjfvvtt0r3h0NUDbZw/WdkZDBgwACOHTtGZGQkjRo1KvGxbJUkYiGKEBoayi+//EK7du1wcXEpdvs2bdrQpk0b5syZw4oVK/jHP/7B//3f/zFu3DgURamAiIUoO1pf/0ajkZEjR7J161a+++47IiIiSvI2bJ50TQtRhCeeeILs7Gxmz56db11WVhYJCQkAxMfHo95VUbRZs2YApu65atWqAZj2EcLWaX39P//886xcuZKFCxeaRlpXRtIiFqIIERERTJgwgblz53LkyBG6d++Og4MD586dY9WqVSxYsIBBgwbx1VdfsXDhQh5//HFCQ0NJSkriiy++oHr16vTu3RsAFxcXwsLCWLlyJQ888ADe3t40atSoyK625cuXc/nyZVJTUwHYtWsX7777LgBPPvkkderUKf8PQVRZWl7/H3/8MQsXLqRt27ZUq1aN//3vf2brH3/8cVxdXcv9M6gQWg/bFsKW3P34Rq7Fixer4eHhqouLi+ru7q42btxYnTx5snrt2jVVVVX10KFD6rBhw9SgoCDVyclJ9fPzU/v27asePHjQ7Dh79+5Vw8PDVUdHR4se5YiIiFCBAl/bt28vq7cthKqqtnX9jxo1qtBrH1AvXrxYlm9dU4qq3tWfIIQQQogKI/eIhRBCCA1JIhZCCCE0JIlYCCGE0JAkYiGEEEJDkoiFEEIIDUkiFkIIITQkiViIUrh06RKKorBs2TKtQxGiwsn1XzYkEQshhBAakgk9hCgFVVUxGAw4ODig0+m0DkeICiXXf9mQRCyEEEJoSLqmRZU3Y8YMFEXh7NmzjBgxAg8PD3x9fZk2bRqqqnLlyhX69+9P9erVCQgI4KOPPjLtW9A9stGjR+Pm5kZMTAyPPfYYbm5u+Pr68tprr5GdnW3abseOHSiKwo4dO8ziKeiYsbGxjBkzhtq1a+Pk5ERgYCD9+/fn0qVL5fSpiKpCrn/tSSIW4o4hQ4ZgNBp5//33ad26Ne+++y4ff/wx3bp1o1atWnzwwQfUq1eP1157jV27dhV5rOzsbHr06IGPjw/z5s0jIiKCjz76iMWLF5cotoEDB/LDDz8wZswYFi5cyAsvvEBSUhLR0dElOp4Qd5PrX0NaVZsQwlZMnz5dBdSnn37atCwrK0utXbu2qiiK+v7775uWx8fHqy4uLuqoUaNUVVXVixcvqoC6dOlS0za5VWNmzZpldp7mzZur4eHhpp+3b99eYBWlu48ZHx+vAuqHH35YNm9YiDzk+teetIiFuGPcuHGm/9bpdLRs2RJVVRk7dqxpuaenJ/Xr1+fChQvFHu+ZZ54x+7lDhw4W7Xc3FxcXHB0d2bFjB/Hx8VbvL4Ql5PrXjiRiIe4ICgoy+9nDwwNnZ2dq1KiRb3lxfxCcnZ3x9fU1W+bl5VWiPyROTk588MEHbNq0CX9/fzp27Mg///lPYmNjrT6WEIWR6187koiFuKOgxy8KeyRDLeZhA0se5VAUpcDleQe05HrppZc4e/Ysc+fOxdnZmWnTptGwYUMOHz5c7HmEsIRc/9qRRCyERry8vABISEgwW3758uUCtw8NDeXVV19ly5YtHD9+nIyMDLMRrELcS+T6/5skYiE0UqdOHXQ6Xb4RqAsXLjT7OTU1lfT0dLNloaGhuLu7YzAYyj1OIcqDXP9/s9c6ACGqKg8PDwYPHswnn3yCoiiEhoayfv16bty4Ybbd2bNn6dq1K0888QRhYWHY29vzww8/EBcXx9ChQzWKXojSkev/b5KIhdDQJ598QmZmJp9//jlOTk488cQTfPjhhzRq1Mi0zX333cewYcPYunUry5cvx97engYNGvDdd98xcOBADaMXonTk+s8hU1wKIYQQGpJ7xEIIIYSGJBELIYQQGpJELIQQQmhIErEQQgihIUnEQgghhIYkEQtxjyioTqsQVUVlvv4lEYtKKSoqigkTJlC3bl2cnZ2pXr067dq1Y8GCBaSlpZXbeU+ePMmMGTM0L1g+Z84c+vXrh7+/P4qiMGPGDE3jERWrKl//p0+fZvLkyTRr1gx3d3cCAwPp06cPBw8e1Cym4siEHqLS2bBhA4MHD8bJyYmRI0fSqFEjMjIy2LNnD6+//jonTpwocYHy4pw8eZKZM2fSqVMngoODy+Uclnj77bcJCAigefPmREZGahaHqHhV/fr/8ssvWbJkCQMHDmTSpEno9XoWLVpEmzZt2Lx5M4888ogmcRVFErGoVC5evMjQoUOpU6cO27ZtIzAw0LTu2Wef5fz582zYsEHDCP+mqirp6em4uLiU+bEvXrxIcHAwf/31V75ydKLykusfhg0bxowZM3BzczMte+qpp2jYsCEzZsywyUQsXdOiUvnnP/9JcnIyS5YsMfsjlKtevXq8+OKLpp+zsrKYPXs2oaGhODk5ERwczJtvvplvMvng4GD69u3Lnj17aNWqFc7OztStW5evv/7atM2yZcsYPHgwAJ07d0ZRFBRFYceOHWbHiIyMpGXLlri4uLBo0SIALly4wODBg/H29qZatWq0adOmVH8wtWyNC+3I9Q/h4eFmSRjAx8eHDh06cOrUqRIds7xJIhaVyrp166hbty4PP/ywRduPGzeOd955hxYtWvCvf/2LiIgI5s6dW+Bk8ufPn2fQoEF069aNjz76CC8vL0aPHs2JEycA6NixIy+88AIAb775JsuXL2f58uU0bNjQdIwzZ84wbNgwunXrxoIFC2jWrBlxcXE8/PDDREZGMmnSJObMmUN6ejr9+vXjhx9+KINPRVQVcv0XLjY2lho1apTZ8cqUKkQlodfrVUDt37+/RdsfOXJEBdRx48aZLX/ttddUQN22bZtpWZ06dVRA3bVrl2nZjRs3VCcnJ/XVV181LVu1apUKqNu3b893vtxjbN682Wz5Sy+9pALq7t27TcuSkpLUkJAQNTg4WM3OzlZVVVUvXryoAurSpUsten+qqqo3b95UAXX69OkW7yPuTXL9F27Xrl2qoijqtGnTrN63IkiLWFQaiYmJALi7u1u0/caNGwF45ZVXzJa/+uqrAPm6xsLCwujQoYPpZ19fX+rXr8+FCxcsjjEkJIQePXrki6NVq1a0b9/etMzNzY2nn36aS5cucfLkSYuPL6ouuf4LduPGDYYPH05ISAiTJ08u1bHKiyRiUWlUr14dgKSkJIu2v3z5MnZ2dtSrV89seUBAAJ6enly+fNlseVBQUL5jeHl5ER8fb3GMISEhBcZRv379fMtzu/TujkOIgsj1n19KSgp9+/YlKSmJtWvX5rt3bCtk1LSoNKpXr07NmjU5fvy4VfspimLRdjqdrsDlqhWVRMtjhLQQINf/3TIyMhgwYADHjh0jMjLSrMaxrZEWsahU+vbtS1RUFPv27St22zp16mA0Gjl37pzZ8ri4OBISEqhTp47V57f0j9rdcZw5cybf8tOnT5vWC2EJuf5zGI1GRo4cydatW1mxYgURERFWH6MiSSIWlcrkyZNxdXVl3LhxxMXF5VsfFRXFggULAOjduzcAH3/8sdk28+fPB6BPnz5Wn9/V1RWAhIQEi/fp3bs3+/fvN/vjmZKSwuLFiwkODiYsLMzqOETVJNd/jueff56VK1eycOFCBgwYYPX+FU26pkWlEhoayooVKxgyZAgNGzY0m1lo7969rFq1itGjRwPQtGlTRo0axeLFi0lISCAiIoL9+/fz1Vdf8dhjj9G5c2erz9+sWTN0Oh0ffPABer0eJycnunTpgp+fX6H7TJkyhW+//ZZevXrxwgsv4O3tzVdffcXFixf5/vvvsbOz/vvy8uXLuXz5MqmpqQDs2rWLd999F4Ann3xSWtmVlFz/OV8sFi5cSNu2balWrRr/+9//zNY//vjjpi8MNkPrYdtClIezZ8+q48ePV4ODg1VHR0fV3d1dbdeunfrJJ5+o6enppu0yMzPVmTNnqiEhIaqDg4N63333qVOnTjXbRlVzHr3o06dPvvNERESoERERZsu++OILtW7duqpOpzN7lKOwY6iqqkZFRamDBg1SPT09VWdnZ7VVq1bq+vXrzbax5vGNiIgIFSjwVdCjJaJyqcrX/6hRowq99gH14sWLRe6vBUVVrbjTLoQQQogyJfeIhRBCCA1JIhZCCCE0JIlYCCGE0JAkYiGEEEJDkoiFEEIIDUkiFkIIITQkiVgIIYTQkCRiIYQQQkOSiIUQQggNSSIWQgghNCSJWAghhNCQJGIhhBBCQ5KIhRBCCA39P+9YSKR/2Ji9AAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 480x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "multi_2group_paired.mean_diff.plot(delta_dot_kwargs={\"color\":'red', \"alpha\":0.1, 'zorder': 2, 'size': 5, 'side': 'center'});"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Effect size paired lines\n",
+    "\n",
+    "By default, effect size paired lines are included in paired experiment plots (excluding proportion plots). \n",
+    "\n",
+    "This feature can be turned off by setting `contrast_paired_lines=False` in the `.plot()` method."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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s0Wjkxo0bXL58GZ1OR1ZWFtOnT8fd3Z2ysjK++uorOjs7WbVq1YiMuLO3t2f16tXs3LmTXbt2sXHjRt577z3u3r3LmTNn+Pjjj8nMzGTWrFnSjNvvMQaDgT179lBVVYWdnR0TJkxg/vz5Vve5ceMGp0+fJicnhxkzZgz7sQVBoLKykjNnzqBUKpkwYQKzZs16oR0K30WknfIQ9PT0kJGRwUcffcQ///M/k56eLu2UX2E0Gg27d+9GpVKxbt06QkNDOX/+PFeuXCEyMpIVK1YMyq0NF5PJxK1bt7hw4QI9PT2kp6czc+bMQTm0lpYW9u3bh42NDatWrSIsLGwEXtk3Fxvbt29HrVaLE3uMRiPXr1/n4sWLCIJATk4OkydPlorBvmf09/ezZ88eGhoaAPDz8+P999+3Mr25desWR44cYfLkySxYsGDYF6VNTU3k5eXR1NREQkIC8+bNGxUFkd8FJFEegg0bNuDr68t//Md/MGvWrMeKsl6vtxpMf+vWLWbOnCmJ8ihBoVCwc+dOjEYj7777LjY2Nhw6dIiOjg5mz57N1KlTn2oSkyAIVFRUcP78eRQKBampqcyePRs/P79H/o5Go2H//v3IZDJef/11xo0b9ywvTaS3t5etW7ei1+vZtGmTONu2t7eXixcvcv36dTw8PJg7d+4zRQMkXh36+/vZvXs3ra2tuLq60t/fzw9+8AN8fX3F+1RUVHDw4EEyMjJ47bXXhnVcdHV1cebMGSorKwkODiY3N/eZ6y8krJFE+SH27dvHr3/9a27cuIGzs/O3ivIvfvELfvnLXw76viTKL5+2tjZ27dqFi4sL69ato7q6mry8PLy9vXnzzTefylRfEATu37/PuXPn6OjoICkpidmzZxMcHDys3zcajRw/fpxbt24xbdo05s6dOyLjGTUaDVu3bhUtQgeGyAeeSENDQ8nNzZXsEr/D6PV6du/eTXt7O2FhYTx48IB3333XqgCwsrKSL7/8ctiTyLRarVjp7+HhwZw5c6Rq/+eEJMoDaGpqIisri/z8fDGXLO2UX03q6+vZt28f/v7+LFu2jPz8fGpqasjOzmb+/PlPHMoVBIG6ujrOnTtHS0sLMTExzJ0796nM+S290Hl5ecTHx/Pmm2+OiI+2SqVi69atODo6snHjxkG55MbGRk6fPk1LSwtJSUnMnz9fGlD/HUOv14vjFcePH8/169dZsmQJEydOFO9TXV3Nvn37SE5O5s0333zsRaHBYKCoqIjLly9jY2PD9OnTmTRp0rALFhsaGoiMjJTE+wmQRHkAR44cYcWKFVYl/CaTCRsbG2xtbdHr9d9a3i/llF8+d+/e5eDBg0RHR5OWlsbp06extbXl9ddfHzSGcTg0NTVx9uxZHjx4QHh4OHPnzh2RtqOamhoOHDiAu7s7a9aseWzoe7h0dXWxdetW3N3d2bhx4yCxFwSBO3fucObMGdRqNVlZWcycOVMqBvsOoNPp2LVrF52dncyaNYu8vDwyMzNZsmSJeJ+6ujr27NlDfHw8K1eufOT5zNILf+7cObRaLRMnTmTGjBmiBe1wqKmpYdeuXaxevZrk5ORnfn3fFyRRHoBGoxGLIixs2rSJ5ORk/u7v/o7U1NRvfQxJlF8uN27c4MSJEyQlJeHo6EhZWRnJycksXbr0iYXH4khUVVVFUFAQc+bMITExcUSv+ru6uti7dy89PT2sXLlyREz/29vb2bZtG35+fqxfv160Dh2I0Wjk2rVrXLp0CUEQxF7q0WSWIjF8+vr62LVrFwqFgqVLl3L8+HGCg4NZt26dKLyNjY3s3LmTqKgoVq9e/ci/dW1tLfn5+chkMsaOHcvcuXOtctHDXc9HH31EUFAQ69atk3bKT4Akyt/Ct4WvH2Y0ibLJZOLMmTOEhYURGhqKj4/Pd/bDIQgCFy5coKCggLi4OLq6uujt7WXhwoVMmDDhiV53Z2cn58+f586dO/j5+TF79mzGjh373N47nU7HwYMHqampITc3d0SMRlpaWtixYwehoaGsXbv2keH63t5eLly4wI0bN/Dw8GDevHmkpqZ+Z4+T7yJ9fX3s2LEDlUrFqlWr+PrrrzGZTPzgBz8Qe+4tx0NISAjr1q0b8niQyWTk5+dTW1tLZGQkubm5Tz07+eDBg1RXV/PjH/9YapF6QqTL4u8warWae/fucfXqVeCbkYBhYWGiSIeFhX0nBhqYzWZOnjzJtWvX8PPzo7a2lvDwcNavX/9EV/gqlYoLFy5w69YtPD09RYORkSjEehzOzs6sWbOGs2fPcvr0adrb25/ZaCQsLIy1a9eya9cuvvzyS1atWjVkqNLV1ZVFixaRnZ1Nfn4+Bw8eFJ3BHnZ8khh99Pb2smPHDtRqNe+++y4FBQVoNBref/99UZBlMhk7d+4Up6A9LMgP22KuXr2apKSkp74wu3v3LuXl5bzxxhuSID8F0k55hBlNO2ULvb29tLS00NraSktLCy0tLWi1WgC8vLyshDo0NPSVcuIxGo0cOnSI4uJiHBwccHR0ZObMmUyfPn3YYtrT08PFixcpLi7G2dmZGTNmkJmZ+VJCuRajkeDg4BExGqmtrWXPnj3DKuoBePDgAXl5ebS2tjJmzBjmzZs3IrluiZFHq9WyY8cOenp6WL9+PWVlZRQWFrJu3Tri4+MB6OjoYNu2bXh7e7N+/XqrGgO9Xs/ly5dFW8xZs2Y9sy1mT08PH330EdHR0axcuVKKuDwFkiiPMKNRlB9GEAS6u7utRLq1tZX+/n5sbGzw9/e32k0HBQWNylyjXq9n7969XLt2DScnJ2JjY3njjTeGHXKz+FNfu3YNOzs7pk2bxqRJk4bMwb5IRtpo5N69e3zxxRekpaXx+uuvf+uJ0tKDfebMGTQaDRMnTmTmzJlPVOQj8XzRarVs374drVbLhg0baG1t5ciRIyxcuJDJkycD/1P05+bmxsaNG8Wds8lkori4mIKCghG1xRQEgb1799La2sqPf/xj6Xh5SiRRHmFeBVEeCrPZTFdXlyjSLS0ttLe3YzKZsLOzIzg4WBTpsLAw/P39X+pVcE9PD59//jlXr14lKCiIuXPnsmDBgmEJql6vp6ioiMLCQgRBYPLkyUydOnVE2pJGipE2GikvL+fQoUNMnDiRRYsWDetvZzAYxGIwGxsbZsyYQXZ29qi8QPs+0dPTw/bt2+nr62PDhg3odDq2bdvG+PHjWbp0KTY2NqhUKj7//HMcHR3ZtGkTbm5uCILAvXv3OHPmDAqFgvT0dGbPnj1iIebS0lKOHj3KmjVrSEpKGpHH/D4iifII86qK8lAYjUba29uthLqzsxMAJycnQkJCRJEOCwvD09PzhQi1Uqnkww8/5NatW0yYMIF33nlnWCcBiz/1pUuX0Ov1TJw4kenTp4/adqCRNhopLi7m2LFjTJs2jXnz5g37bzXQOMLT05N58+Y918I3iUej0WjYvn07er2eDRs24ODgwJ/+9Cf8/f1Zv349dnZ2qNVqtm7dio2NDZs2bcLDw8PKFjM+Pp758+ePqC2mSqXi448/JiUlhddff33EHvf7iCTKI8x3SZSHQqfT0dbWZhX27u7uBsDd3d1qNx0aGjriIaympiZ+8Ytf0NrayooVK1i7du23FqsN1596NDLSRiNFRUWcOnWKOXPmPNHwAfimKj0/P5/79+8THh5Obm4ukZGRT70WiSdDrVazfft2DAYDGzZswMPDg88//xydTscPfvAD3Nzc6OnpYevWrRiNRjZt2oTJZOLs2bPcvXv3udliCoLA9u3bUalUfPDBB69UTcpoRBLlEWY0ibIgCJSVlREWFoafn99z29n09PRYiXRLSwt9fX0A+Pj4WO2mg4ODnzpne+3aNX79619ja2vLX//1XzN9+vTHviaz2UxFRQUFBQUolUpSU1OZNWvWK1m4NJJGIxcvXuTcuXNW+ccnob6+nry8PNra2khJSWHevHlP3Mcq8WSo1Wq2bduGyWRiw4YN+Pj48MUXX1BbW8t7771HUFAQvb29bNu2jb6+PlatWkV5ebnY6vY8bTEtF3obNmyQZnmPAJIojzCjSZS7urr4z//8T+B/2qHCw8MJDw8nLCzsmeYGPw5BEFAqlVZC3dbWhsFgwMbGhsDAQCuhDggIeGzFp9lsZteuXWzfvp3IyEj++Z//+bHFT5bc2fnz50V/6jlz5rzyU2xGymhEEATOnDnDlStXWLp0KZmZmU/1GGVlZZw9e1Z0fJo5c+ZzO6a+z3R3d7Nt2zbMZjMbN27Ex8eHc+fOcenSJbF9ydKrbLn4LC8vBxBtMZ/XhLDOzk4++eQTsrKyWLhw4XN5ju8bkiiPMKNJlOGbCuPW1laam5vFm2UX6+/vL4p0eHg4gYGBz60n12w209HRYbWb7ujowGw2Y29vb5WfDg0NxdfXFxsbG5RKJb/73e+4ePEiM2bM4Oc///kjw2MP+1PHxsYyZ86cpzZAGI2MlNGIIAicOHGCmzdvirOknwaLN/KlS5ewtbVl5syZTJw4USoGGyFUKhXbt29HEAQ2btyIt7c35eXlHDx4kHnz5pGTk4Ner2f79u3cuXNH/Ny8iIp5k8nEZ599Rn9/Pz/60Y+k0aAjhCTKI8xoE+WHEQQBhUJhJdLt7e2YzWYcHBwIDQ21Eupn7ZN9HAaDgba2NqvWLIVCAXxjqGE54Xd3d7Nu3Tp+9KMfPfKiobGxkXPnzvHgwQMiIiKYM2fOdzaUZjabOXv2LFeuXCE9Pf2pjUYEQeDo0aOUlZXx9ttvP5M/cU9PDwUFBZSUlODl5cW8efNISUmRisGeAaVSyfbt27GxsWHjxo14eXnR0tLC1q1bGTt2LMuXL8doNPL//t//4+rVq8TFxTFp0qQXlk64cOECFy5c4L333hux+eASkiiPOKNdlIfCIo4DhVqtVgPfmIsMFOmQkJDnugvq7e2ltraWgwcPkp+fj1arZdy4ccTGxg5pdKJUKjl37hzV1dVia1RCQsL3QgxGwmjEbDZz4MAB7t+/z9q1a5/Ze1sul5Ofn09VVRURERHk5uYSERHxTI/5fUShULB9+3bs7OzYsGEDXl5eqNVqPv30U7y8vNi4cSPt7e386le/oqamhkWLFrF69eoXFhVqbW1ly5YtTJ8+ndmzZ7+Q5/y+IInyCPMqivJQqNVqK5FubW3FaDRiZ2dHUFCQlVCPpKd2bW0thw4d4vbt2+LJJzExcZDRiVKppL6+Hq1WS2hoKHPmzGH69OnP/aJhtDESRiMmk4l9+/bx4MED3nnnnRGx16yrqyMvL08cajBv3jx8fHye+XG/DygUCrZt24aDgwMbNmzA09MTg8HA1q1b6enpYfXq1Vy7do1du3ah1+v5q7/6K+bOnfvCLkSNRiN//OMfsbe35/33338mBzCJwUiiPMJ8V0T5YUwmEx0dHVZC3dXVBXzjnzxQpENDQ5+4bcdgMHD27FkuX76MXC4nICCAtWvXkpKSYnU/lUrF+fPnuXr1KmazmaioKBwdHeno6BCNToKCggYZnTxv/+qXyUgYjRgMBvbs2UNraysbNmwgNDT0mdclCAK3b98Wx/9NmjSJ6dOnS8Vgj6Grq4tt27bh5OQktj0JgsDBgwe5c+cOY8aMoaqqinv37uHm5sZPf/pTEhMTX+ga8/LyuH79Oj/84Q8JDAx8oc/9fUAS5RHmuyrKQ2Hx1LaIdEtLCzqdDhsbGwICAqyqvQMCAh4pjDKZjEOHDiGTyejv78fT05M1a9ZY5YQ1Gg2XLl16pD+1yWQa0uhEEAQcHR0HGZ14eXl9p0LcI2E00t/fz44dO+jq6mLjxo0jVq1uMBi4evUqly9fxs7OTiwGG6kdltlspr+/n/7+fvR6vfh1f38/APHx8a/Ebq6zs5Nt27bh4uLChg0bxP778+fPs2fPHry8vPD29sZoNGI2m1m3bt0Ld85qaGhg27ZtzJ8/n6lTp77Q5/6+IInyCPN9EuWHEQSBzs5OK6Fub28XhfHhliw3NzeuXr3K2bNncXV1Ra/X4+joyLp16wgJCQEG+1Pn5OSQnZ09bDvNh41OVCoVAG5uboOMTkars9dwGQmjkb6+PrZv305PTw+bNm0a0Z7unp4ezp07J/bO5uTkEB0djcFgGFJQHyW0D3/PaDQ+9nnHjx/P8uXLR/VFmFwuZ/v27bi6urJhwwbRFvPrr7/mv//7vwkODub1119Hp9Nx//593nrrLcaOHftC16jX6/nkk0/w9PRkw4YN3+no08tEEuUR5vssykPR398/qCWrp6cHnU5HU1MTBoOBtLQ0+vr68PPzY+PGjfj6+j43f2qtVmu1m25tbaW3txcAb29vq910SEjISx9O8TQ8q9GIVqtl27Zt6PV63n33XVxdXZ9YKB/3fa1WS21tLQqFAi8vL+Li4qz8ly3Tviw3Jycnq/8/6ntDfb+2tpbDhw8/lYPZi6Kjo4Pt27fj7u7O+vXrcXNzo6mpiQMHDnDs2DHGjh3Lz3/+c0pLS7lx4wbLly9n/PjxL3ydx44do7y8nA8++ECqD3iOSKI8wkii/HgEQeDq1at8+eWXaLVafH19KSkpwdHRkQkTJhAWFkZfXx9NTU24urqSk5PzXP2pBUFApVJZiXRra6todGIJw1tugYGBozYUajKZROGTyWR8+eWXqNVq5s2bR3Bw8BOJp1qt5tq1awCkp6c/sjfczs7uiURy4Pfa2tooLCwUDS9yc3OfS6/8hQsXOH/+PG+++eYzD/YYadrb29m+fTuenp6sX78enU7HmTNnuHXrFtXV1aSnp/O///f/pqCggMLCwqc2e3lWqqur2b1790t7/u8TkiiPMJIoPxqdTsfXX39NeXk5qampJCQkcOzYMUJDQ8nJyaGwsFB04fL29iYqKko0OLGEvl/EvGez2YxcLrcyOrH0ctvb2xMcHGwV9n4aC1NBEEZs52n5nslksnoOo9HI3bt3USgUxMXFERERgbOz87B3nXq9nhMnTuDi4sLq1avx8vIadN9nvUAxm81iMVhvby+TJ09m+vTpIzqxSxAEjhw5QkVFBRs2bBg1ft0ymYwdO3bg5eXFW2+9xfXr17lx4wYuLi5otVrc3Nz40Y9+RHFxMRcuXGDRokVMmjTpha+zt7eXjz/+mODgYNauXTuq0wDfBSRRHmEkUR6aBw8ecPjwYXQ6Ha+99hpGo5Fjx46RkJBAYmIily9fRqVSkZqayuzZs3FychpURKbX60WbzoHV3i9ijKTBYEAmk1m1Zlmqz52dnQflpz08PKivr+fGjRtotdpBgmowGL71OZ8mXPvw9+3t7bly5Qo3btwgIyODpUuXPlHLWGdnJ1u3bhXziM9rvGV/f79YDObg4MDMmTPJysoasaiEyWRi586ddHR08P777790r+62tjZ27NiBp6cniYmJXL9+HYCcnBxkMhn3799n06ZN1NXVcfbsWebPn8+0adNeyloPHDhAbW0tP/7xj5+4F76npwcXF5dRG10ajUiiPMJIomyN0Wjk/PnzFBYWEhkZyfLly7lz5w75+fkEBQVhNpvp7OwkOTmZ2bNnP7Li13K/gblpuVyOIAg4OTlZFZCFh4e/kAHrFgvTgUKtVqtRqVTIZDI0Gg2Ojo5kZ2eTkpKCu7v7sAXVwcFhRC80nsVoRCaTsW3bNgICAnj33Xefa55do9Fw/vx5SktL8fX1Zf78+SQlJY3Ie9HX18eWLVsAeO+9917IMTIUra2t4jxkd3d3dDodEydOZMaMGdy6dYv8/HzefPNNtFotp06dYtasWcyaNeulrLWiooIDBw7w1ltvkZqa+q33FwQBmUxGVVUVVVVVtLa28u677474ZKrvMpIojzCSKP8PHR0dHDp0CLlczpw5c5g8eTL5+fmcOHECBwcH3N3diY+PZ86cOU9leqHX6wcVkWm1WgB8fX2tdtNBQUHP9WpdEATq6+v56quvuH79uljQZmtrS1BQEBMnTiQtLY2MjAyioqJeSgjQYjQCsHr16id6z5ubm9mxYwfh4eGsXbv2uRu0tLe3k5eXR21tLVFRUeTm5o6IlaNCoWDLli3iBcaLNpppbm7m97//PW1tbURHR5OWlsbcuXPx8/OjqqqKvXv3kpOTg5eXF8ePH3/i2dcjiUaj4aOPPiI2NpaVK1c+8n79/f3U1dVRVVVFdXU1Go0GJycn4uPjSUhIICkpSepNfwIkUR5hJFH+RqCuX79Ofn4+Pj4+vPHGGwQGBrJlyxZOnjxJQEAAkydPZu7cuURHR4/o86pUKiuRlslkmEwm7O3trXy9w8LC8PT0HPbJzmQyodVq6ejoQCaT0d7eTmdnJ52dndTX13P//n06OjrQ6XSYzWYcHR3x9fXFwcGB7u5uEhISSE5OxtHREX9/fzIyMhg/fvxz9RYfimcxGnnw4AG7du0iLi6Ot99++4WEJGtqasjLy6Ojo4Nx48Yxd+5cvL29n+kxGxsb2b59O6mpqS+0VaqkpITf/OY36HQ6Fi9ezJIlS0QL0o6ODrZs2UJsbCxJSUkcPXqUSZMmsXDhwpciyIIgsGfPHtra2vjxj388KKqgVCpFEa6vr8dkMuHv7y+moyIjI6WQ9VMiifII830XZY1Gw5EjR6itrRXN8VtbW/nNb37DvXv3mD59Ou+8884L86c2Go3IZDIroVapVGLY28/PD19fX7GISaFQ0NnZSVdXFwqFAqVSiUqlEtu4LB8XQRAwm83odDqMRqPYxhMQEEBWVhbTp08nKCiIyspKdu3aRXNzM35+fiQlJeHg4IBer8fLy4vk5GQyMjJISEh4YX2fz2I0UlNTw969exkzZgxvvPHGC1mz2Wzm1q1bnDt3Dp1Ox+TJk8nJyXmm/LZl0tLs2bOZOXPmCK52MGq1mi+++IJ9+/YRFBTE3/7t31rNNu7t7eXTTz/F0dGRyZMn89VXX5GRkcFrr7320oqqiouLOXbsGOvWrSMhIQGTyURTUxPV1dVUVVUhl8uxs7MjKiqKxMREEhMTX3qe/ruCJMojzPdZlCsrK/nqq6+ws7Nj+fLleHl5cerUKb788ksEQeDP//zPWbBgwXM50QiCQF9fH1qtFq1WS29vr/i1Vqulp6eH7u5uurq6xIItpVJJd3c3Go0Gg8GA2WzGxcUFd3d33N3dCQoKIigoCD8/P/z9/QkICCAgIACj0cj9+/fFimyTyYSTkxPZ2dlMnz6d5uZmTp8+zZUrV+jo6MDFxQWj0YitrS0+Pj4kJSWJbUcWP/HIyEiysrKYMGHCiBp2PO79elqjkcrKSr788kvGjx/PsmXLXphw9Pf3c+XKFQoLC3FwcGDWrFlkZmY+9Y7s4sWLnDt3jjfeeIO0tLQRXu036ZXLly9z6tQpysvLyc7O5mc/+5lVKNdkMrFjxw46OzuZOXMmp06dYty4cS/V7ESpVPLxxx+LoeeqqipqamrQ6XS4u7uLu+HY2Njn3gnxfUQS5RHm+yjKer2eU6dOUVpaypgxY5g+fTrXr1/n2rVr4rSgv/mbv3miCTaCIKDX662EdSixHfg9nU4n3vR6PXq9Hvhmp2U0GrGxscHR0REHBwecnZ3x9fXF398fPz8/nJycMBqNaLVacWdsa2uLi4uLWDwGcP/+fdra2nBwcBDtDseMGUNsbCwlJSVcvnyZlpYWHB0dGT9+PCkpKaI4a7VaDAYDrq6uYvFSX18f3d3d6HQ6HBwc8PPzIyMjQywOe94zap/WaKSsrIzDhw+TnZ39wkOsarWa8+fPc+vWLfz8/Jg/fz6JiYlP1ZZ29OhRysvLWb9+/YgM4oBvhNbSxiSTyVAqlUydOpX169dbFckJgsCxY8e4ffs2M2fO5MKFCyQnJ/Pmm2++FLcsS5HWH/7wB+rr60lISMDOzo7Q0FASExNJSEggNDRUaol6zkiiPMJ830S5qamJQ4cOodVqmTFjBt3d3ZSUlGAymVCpVERERIguXf39/Y8V1Ye/N7Dv1mKMYTkhWMLHRqMRg8GAwWDA3t5eFF1PT0/8/Pzw9vbG29sbLy8v0TvYy8sLNzc38bGGsmrU6XRiVfXt27cpKipCLpfj6OiIm5sb9vb2+Pr6EhAQQFNTE62trdjZ2REfHy9Wy1ryxfX19XzxxRfU1dWJwzEMBgOZmZlotVoaGhpoa2tDLpejVCoxGo04OzsTHh7O/PnzmTlz5mMHRFhe99PS1dXF3r176enpYeXKlcMe33jz5k2OHz/O9OnTmTt37lM//9Mik8nIy8ujrq6O6OhocnNzn3iQhslkYteuXchkMt5///1nilIIgsC9e/c4c+YMCoWC4OBgmpubSUhIYM2aNYMusK5du8bJkyeZOHEipaWlLzRXb8FgMFBfXy/mhysqKnjw4AErVqxg6tSpJCQkvPC6h+87kiiPMN8XUTaZTJw9e5Zz587h6emJp6cn9+7dEws+6urqRJcuS5HUUB7Frq6uuLi4WJ2wLGI7sLfXaDRib2+PjY0NNjY2eHh4WInsw18PN6zW39/P9evX6enpGfSz9vZ2ysrKaG9vx83NDYPBQG1tLSqVit7eXnp7exEEQawiHz9+PDExMfj7+w8qjFEoFOKc4djYWJydnbG1tWXu3Lm4u7ujVqtRKpW0tbVRU1NDfX097e3t9PX14ejoSFBQEGlpaYwbN46goCA8PT3F3ZS7u/uw/cAfhU6n4+DBg9TU1JCbm8vkyZOHtSMqLCwkLy+PuXPnMn369Kd+/qdFEASxGEwul4vVzF5eXsN+jL6+Pj777DPMZjPvv//+U7VKNTc3k5eXR2NjI/Hx8cTHx3PmzBmioqJYvXr1IEGura0Vi+YaGhqIjIxkzZo1L6QavLu7W2xZqq+vx2g04uPjQ2BgIDdu3GDevHksXrx4RJ5LJpMRHBw8Io/1fUES5RHmVRZlo9FotWsdager1WqRy+XcuHEDhUKBi4uLaIRhma1cU1NDQEAAs2fPxsfHR8yhPSy2Op0OtVpNd3e3lZmGvb39I8XW29sbDw+PEdtN9Pb2cvHiRbE/GL4xdigtLaWtrQ0PDw/6+vqorq5GLpdjb2+Pi4sLfn5+jBkzhqioKGxtbens7EQul4stWe7u7gQEBBAYGEhycjL29vb09PRw5MgRSktLyczMFEPgCxcuHOQlbBnuUVZWRnFxMdXV1ajVauzs7AgICCAqKoq4uDi8vLzw8PBg8eLFREVFPVO422w2c/bsWa5cuUJ6ejqvvfbasESioKCAgoKCl+Y4Bd+svbS0lHPnzqHX65kyZQo5OTnDvjhTKpVs2bIFPz8/1q9fP2xxVCgUnDlzhrt37xIcHMz8+fMRBIF9+/YRExPDqlWrBj1WZ2cnW7Zswc3NDbVaTVhYGOvWrXtuqQqz2Uxzc7NYpNXe3o6trS2RkZFikZa3tzefffYZRqORH/3oRyNycXD//n327dvHqlWrSE5OHoFX8v1AEuURZjSJsiAIQ4rqo8LFOp1u0GNYwrVubm64urrS1tZGWVkZOp0OT09PPDw8SE9PZ8KECdTU1HDq1CkCAgJIS0sTi6vUajUDDzNXV9dBYjvw/66uri8sb2URZQ8PD5RKJcXFxTQ0NCAIgjiRR61W4+bmRkREBMnJyaSnpxMTEzNkYVRPTw9yuZz29nY6Ojro6OggODiYhQsXYm9vj06nY+/evVy7do158+aJBWpLlix5bOhUp9NRWVnJ5cuXqaioQKPR4Orqip+fn1jFbWm3CgkJITg4mODgYEJCQp64R/RJjUYEQSA/P5/CwkJef/11JkyY8ETPN5Lo9XquXLnC1atXcXR0ZNasWaSmpg7rPWhqamL79u2kpKSwYsWKxx6Dvb29XLhwgZs3b+Lm5sacOXNIS0ujtraW/fv3Exsby9tvvz1I3CwGJj09PZjNZoKDg3nnnXdGvGCqr6+P2tpasUirt7cXV1dXsUgrLi7O6vg9f/48ly5d4v333x+RWdoymYzPP/+c2NhYVq1aJeWhn4BXWpRNJhNffvml6Jf8q1/9inHjxtHd3c3Zs2eZNm3aiM2EHS6jSZSVSiW///3vrb5nb28viuzAm6ur65Dfs7e3R6vV0tbWxsGDByksLESn0+Hh4UFwcDCBgYEIgkBTUxO1tbWEhoaSmZkpthkNJb6jafJSb28vX375JRUVFdTU1GAwGNDr9chkMvr6+vD19WX8+PFkZWWRkJDwRGFRQRBobGzk7NmzBAYGisJsNBrZtm0b169fZ8mSJRiNRjQaDa+99hr+/v7f+rg6nY6SkhKuXbvGvXv30Ov1JCcnk5aWRmxsLH19fbS3t4vRBy8vr0FC/W092k9qNGIZM1hcXMybb745LPen54larebcuXPcvn0bQRAICgoiKipKvFlmFT+MxcHqUS5aBoOBa9eucenSJeAbW8zJkyfj4OBAVVUV+/fvJz4+npUrVw4SZJPJxO7du6murhY91EfKutQSWbGEpZuamkTRt+yGQ0NDhywga2lp4bPPPmPGjBkj4hym0Wj49NNPxeJBV1dXqWf5CXhlRVmlUrFw4UKuX7+Ou7s7Wq2W/Px85syZg8lkIioqivXr1/Mv//IvL3Rdo0mUjUYjtbW1VkL7sH2jyWQSQ8gqlYru7u5BX8tkMm7evIlSqcTHx4dx48aRmZlJeHg4np6eVFZWcu/ePWbPns2SJUtemQ9gRUUFu3fvpqCgAPhmZGBPTw+9vb0EBQUxa9YssrKyCAkJeayACYJAb28vSqUSpVIp9jcrlUr6+/txcXFBLpeTmJjIokWLsLe3x2w286c//YnS0lKWLl0qtkgtWrToiS4k5XI5BQUFyOVy6uvrsbe3Fy1Lk5OTMRgMtLe309bWhkwmE8dUuri4iAJt+dfPz8/qpD3QaGTZsmXf2jZkGfxQXl7OqlWrSEpKGvbreF50d3fz4MEDGhoaaGhoEP3K/fz8rER6oCHJpUuXOHv2LCtWrBBHJAqCQFlZGefOnUOj0Yi2mJbpZffu3ePLL78kMTGRt956a8jPwIkTJ7h06RI2NjZERESwYcOGZ7L6NBqNPHjwQBRilUqFg4MDsbGxYrX0wJGYQ2EwGPjjH/+Io6Mj77333jN/dtVqNb/73e9oaWkhNTUVhULB+vXriYmJeabH/T7xyoryn/3Zn7F7924OHTrEhAkTCAwM5MyZM8yZMweAv/qrv6KgoIBbt2690HWNJlGGb8J5jxJbS4/uwEPAzc1N3N26uLhw8eJFrl+/jpubG8uXL+f1118XRcNsNvPVV19x69YtFi5cyOTJk1/Wyxw2SqWSc+fO8fXXX1NbWyt+3zIoIiQkhDlz5jB16tRBO3pLqFmhUKBSqazE19J+ZWdnh7e3N76+vvj6+uLs7My9e/eora2lubmZMWPGsH79epydnTEajXzyySdUVlayYMECTCYTSqWSRYsWERISMqzXo9Pp0Gg0zJgxA4VCwddff82lS5doa2vD3d2dlJQUZs+eLRaI9fT0iAItk8loa2tDpVIB31yUBAYGWu2q/fz8OH369LCNRsxmM19++SXV1dWsXbt21HkeazQaUaAbGhro6OgAvokmREVFER0dTWRkJJcvX6a8vJx3330Xs9lMXl4eMpmMlJQU0RbTgqVv29LONJSw3bx5kwMHDtDf309sbCwbN2585G79cajVajE3XFdXh8FgwNvbWxTh6OjoJ8pNnzp1ips3b/KjH/2IgICAJ16PTqejsbGRBw8eUF9fT35+PkqlkunTpzNu3Diio6NJSkp6qtf6feWVFeXg4GA2b97Mv/zLv9DV1UVAQICVKH/00Uf8/Oc/F084L4rRJMoKhYI//OEP4v9tbW2HzOFavvb09MTBwQFBELhy5Qr/9V//RUdHB7NmzWLz5s1WfcYGg4Evv/ySmpoaVqxYMerm1A5ELpeL+dgbN24gl8vFQqz+/n4ePHhAWFgYCxcuZMKECdja2tLX1yfuegcKsCXvbhFfHx8ffHx88PX1Fd9Di2gZDAa6u7vx9PREoVBw4cIFCgoK8Pb2FkO8ZrOZTz/9lIaGBnJycrC1taWrq4uFCxcOy+t5oChbdl1Go5GKigry8vIoKSlBpVLh6+tLcnIykydPJiUlhbCwMHH3bwl3W8S6ra2Nzs5OzGYzNjY2+Pn5odFoqKmpITU1lY0bNz52yL3RaGTfvn00NDTw7rvvjppRiUPR29tLY2OjKNJtbW0IgoCrqyuVlZU0NzeTmJjIhAkTWLBggWiLaeHu3bscOHBAzEMPJcj19fVs2bKFzs5OUlJS2LRp07fuYC0IgkBLS4vYstTW1oaNjQ2RkZFifjggIOCpcrYPHjxg27ZtLFiwgClTpgzrd/r6+kQRfvDgATKZDEEQ8PT0pLu7m/b2djZv3szEiROlPPJT8sqKsouLC3/4wx/4wQ9+MKQo//73v+cf/uEfhmx1eZ6MJlE2mUzcvXtXFF93d/dvNSWor6/nj3/8I9euXSMyMpKf/OQng15HX18fe/fupa2tjVWrVhEfH/88X8YTIwgC7e3t3L17l8rKSmpqanjw4AFqtVo0A7G3txen9Li4uBAdHU1/f7+48+3r6wO+EV8vLy8r8fXx8bESX0v4uqurS7x1dnaKBW42NjZiy0lfXx/nz5/HbDaTlJQkCuThw4fp7OwkNTUVJycnurq6hhSBhxlKlAeiVCq5efMm58+fp76+Hr1ej6+vLwkJCYwfP54xY8YQEREx6LgwGo10dHRY7arv3LlDWVkZjo6OTJ06lbi4OKvw98A8tcFgYPfu3bS1tbFhw4YRKR56Eej1eu7evcuxY8e4ceMGdXV14rSqhIQEMdwdEhLC3bt3OXToEGPHjmXFihVDfrYUCgX/9V//RXV1Nenp6bz33nvf6t2t0+mora2lurqa6upqtFotLi4uxMfHk5iYSHx8/DMPeNDr9Xz88cd4e3uzYcOGRwpoX18fDQ0Nogi3t7cjCAJeXl5ER0eLt9raWo4fP/7KRMxGMy92RMoIEhcXR0lJySN/npeXR0pKygtc0ejDzs5u2DvY1tZWjh07xtdff43ZbGbTpk1DTgNSq9Xs2rWLnp4eNmzY8EQuXc8Ty46isrKSyspKFAoFvb29tLa2IpfLsbW1FVu2ZDIZOp0Ob29vHB0dxdnIfn5++Pj4kJKSIgqwp6en1e7HbDajUqmora21EmGLiFv8tCMjI8XKaJVKJVZiKxQKAgICqKmpoaKigra2NlxcXAgICBBPgKGhofj4+HD69GnmzZv3TEM7fHx8mD9/PnPnzqW2tpabN29y/fp17ty5I16whYeHk5KSwpgxY4iOjsbW1lYc4DFQTM1mMzU1NWzbto36+nqcnZ1pamp6ZJ567ty5nDhxgl27drFx40YCAwOf+nW8CAZWbjs7O/OP//iPRERE8Ic//AGtViteUBkMBhQKBc3NzWRkZJCZmYnZbB4kyjqdju3bt1NRUUFGRgabNm16pCB3dXWJueGGhgbMZjOBgYFMmDCBxMREwsPDR9Tl69SpU/T29rJx40YrQe7t7RVFuKGhQRRhb29voqOjmTx5MtHR0Vavo66ujhMnTjBx4sSX1hL3XeKV3Sn/7ne/4+/+7u/YsWMHc+fOJTAwkLNnzzJ16lR+9atf8Zvf/IY//elPvPfeey90XaNppzwc5HI5586d49y5c7S2tjJ27Fg++OCDIS0HOzs72blzJwDvvvvusCqFnydms5mmpiZxR6xWq3F0dKSvr4+ysjIaGxsRBIHAwEB8fX3RaDRotVoiIyOZNm0aiYmJuLu7i1XjlqIdC/39/VYDKixDKixOYx4eHvj5+Yne2H5+fri7uz82bNff3y+G08+cOSOGSuVyORqNBrPZTHh4OCEhIQQGBqLRaJg7d+4jc7PftlMeip6eHrH/ua6ujr6+PnGUpo+PD8nJyaJ16FDh2IFGI/Pnz2fs2LGDwt+WtJHF3MPJyYl169aRlJREUFDQc7cPfRJMJhMlJSUUFBSIPc7Tpk0Tq6Kbm5vZtm0bY8aM4fXXX+fcuXPs378fT09P/P39RTe58PBwcScdGhrK3r17+eqrrxg/fjx//ud/bpWzNZlMNDQ0iEKsUCiwt7cnJiZGzA8/6zSsR3H//n327t3LsmXLSEpKEkP3lp0wfHMxFx0dLebZH7UWuVzOZ599Jo70fBn2oN81XllRFgSBH/7wh3z22Wd4e3ujUqkICgqiq6tLbID/+OOPX/i6XhVRViqVYp9lU1MTLi4uLFiwgMWLFw/ZM9nS0sLu3btxd3fnnXfeGXZObKSxnMxu375NSUkJHR0d4sSntrY2qqur6e7uxsXFhfT0dLKysuju7kYulxMdHc3y5cutqoIt/aZ2dnb09PRY7X67u7uB/8kfW4TXMlnqWVtZWltbOXnyJF5eXqSkpHDnzh1Onz5Ne3s7zs7OYqjdwcGBOXPmMHHixEEXDk8jyhYsrWylpaWUl5ejVCpxdHTExsYGe3t70aN7zJgxxMXFDXJdO3fuHJcvXx7SaESn04kCXV9fz9GjR+nu7iY9PR0XFxf8/f2tdtXBwcHPVIn8NAiCwP3798nPz0ehUDB+/Hhmz549ZNvbnTt3+PLLLwkLC6O1tZX09HSWLVsmpkoGFo/19vZSXV3NnTt3SExM5Cc/+QmZmZkYjUarIi29Xo+np6fYshQTE/PcL1bkcjm//e1vsbW1JSoqCrlcDnwzf9wiwNHR0cNq/dNqtWzZsgUHBwc2b948Iq1dEq+wKFu4fPkyBw4coLq6GrPZLPrHzpgx46WsZ7SLskaj4eLFi5SUlIhWkUFBQSxfvvyR4X6LIUJQUBBr1659oQPLDQYDbW1t3Lp1i9LSUu7du4dSqQS+OZHY2trS09NDS0sLRqOR2NhY3njjDRYtWsT169e5ceOGlbkDfLPjt+RJGxoaKCoqQhAE7O3txfDzwJuPj89za/OyCHNQUBALFy7EbDazdetWqqqq0Gg0qNVqXF1dsbW1JSYmhujoaIKCgggMDCQoKAg3Nzf6+vqeSpQHotfrqaiooKSkhObmZgRBwMPDQ8yXOzo6kpiYyJgxY0hISBAr04drNKJWq/n000/p6+tj1qxZYqudTCaz6qd+WKi9vLyeS8HQQFvMuLg45s+f/612kJ9//jk7duxgxYoV/OVf/uWQ67IYqfz617/G2dmZlJQUsWDQbDbj7e1NUlIS2dnZjB8/nqCgoOdaENXT0yPuguvr67lw4QIqlYoFCxaQmJgoivCTXmQbjUZ27NhBV1cXP/jBD4bcSZtMJlpbWwkKChpV3gSjnVdelEcbo1WUe3t7uXLlCteuXcPW1hZHR0e6u7tJTExk+fLljzyZlpeXc/jwYdEQ4XldyRuNRjo7O8Xca1tbG5WVldTW1tLZ2YnJZMLPz080QTAYDNTU1NDS0gLAmDFjWL16NWPHjuXGjRtcvHiR/v5+UlJSCA0NFYW4o6ND9OC2FG/JZDIiIiLEEPaLrhp9WJj7+vo4fPgw9vb2dHd3U1paio2NDa6urmRkZODp6YlcLsdkMiEIAm5ubsyaNYv4+HjCw8OfWcja29spLS3l9u3b9PX14ePjg7u7O319fXR2duLg4EB8fDxjxowhMTGRrq6uYRmNKBQKtm7diouLCxs3bsTV1RWz2YxCoRB31ZZ/H5WnDg4OFod6PA0KhYKzZ89y584dgoKCyM3NHdYAjuLiYr766ivx4m39+vVD5vrv37/PX/zFX2AwGJgyZYrYzeDu7o6DgwMGg0F8bf7+/mKIOCoqakSiT5aWL0thVmdnJ4DYwnX37l02btz4TLlfQRA4dOgQlZWVbNy4UawrMZlMtLW1iRcATU1N9Pf3s2bNmlHRs/6qIInyCDPaRFmv13P16lWuXr2KIAgkJSXR1NRET08P8+fPJzs7+5EncMsUm/T0dJYuXToiu0Wj0UhXVxcdHR3I5XLxX4VCgcFgoKurSyyqcXZ2JiIigoyMDBITE5HL5ZSXl9PQ0CD6UKemprJw4ULCw8O5dOkSeXl5tLe34+Pjg5+fH46OjtjZ2REYGCie1IODgwkKCsLZ2dnKZvNpw28WO9Oenh7UajUajQaNRoNKpaKtrQ1/f3/c3NzEkLSLi8ugr1UqFfn5+aIwd3Z2cvz4cZKSknBycuLYsWNiqD4nJ4fFixeL4efGxka8vLzETgN3d3fCw8PFW2ho6FPtVCxzo0tKSqitrcXJyYmoqChcXV3p6OgQJ2PFxcURGRlJWVkZCoXisUYjcrmcrVu3ilW/Q6VKBEFAo9FYCbVlBCJ840oXFBRkJdbflqe2/J0fjpwMR9xv3LjB119/TXZ2Nrm5uezZs4fW1lbef/99/P39USgUVFdXU1xczCeffILBYGDlypXk5OSQmJhIRESE1WdHrVZbhbstIWQfHx8rQxMfH59vvbjSaDSiAD948EA0R7EIvkX0BUHgo48+IiEhgTfffPNbX/PjuHDhAufPn+fNN9/E19eX+vp6Hjx4QGNjI/39/Tg6OhIZGSlGdkJCQqRc8xPwyopyTEzMtx6wNjY2VgYR38bHH3/Mxx9/zIMHDwAYO3Ys//RP/8SiRYuG/RijSZQ7Ozv5/PPP6e/vJysrC3t7e65evUpAQABvvPHGI6thBUHg3LlzXLp0iWnTpjFv3rwn3nmZTCa6urpE4R0ovmazGfimUMrb2xudTodKpUKpVOLs7ExUVJRYaNTa2srt27dpamqir68PvV5PX18ffn5+REdH4+TkRGVlJXfu3EGj0RAaGiqOnBu4s3rUBcVwRNky29kSTraIruX/PT09VmMmBUFArVbT3t6OXq8nIiKC8ePHo9fr0el04usYai0PHjzA19eXrKwsVCoV9+7dIzMzE09PT27cuEFHRwcajYaQkBCmTJlCcnIyrq6uzJw5U6xAb25uFm+WcZdBQUFWQu3n5/dEf1OVSkVpaSmlpaWo1WqCgoJISEjA3t6euro6mpqaEARBnKD12muv8dprrw15MpbJZGzbto3AwEDeeeedYV8wWPLUA8VaLpeL/dRD5akdHBy4fv06ly5dEi9oLLaYw+H69eucOHGCyZMns2DBAmxsbNBqtXz44Ye0tbURHx+PRqPBZDJx6dIl+vv7+bd/+zdycnKG/d5qtVqrXmlL76+Hh4eVSAcEBFiJ8ECHsoCAACsRHmjWIQgCu3fvpr29nR//+MeD0k9DjS4dCrPZTEFBAV988QWhoaG4uLiIIjywyC04OBg7Ozv6+vooKipiypQpzzXf/KyjS0cbr6woP1zKD/9TBHTlyhVSU1OZMGECW7duHfZjHjt2DDs7OxISEhAEge3bt/Pv//7vlJaWMnbs2GE9xmgSZb1ez69+9SvS0tKor69HrVYze/ZsZs+e/cgpMGazmePHj1NSUkJubi5Tp0597HOYTCYUCoXVrrejo4Ouri5RfAdOTAoMDMTV1ZWuri7q6+upr69HEAQiIiJISUkhKSkJuVzO7du3uXPnDmq1GkCsgLa1tSUiIoKAgAAcHR1pb29HrVYTFxfH8uXLSU1NfSKxsYiys7MzBoNBFNmHxbe/v1/8HUdHRzw8PMSbp6cnzs7OdHZ2UlFRQX19Pb29vXh4eODi4sKDBw9ITk5m3bp14oWQyWQSBVqn04lfNzc3U1hYiKurK4mJiaKPcWxsLDY2NlRXV9Pb2yvOXHZ2dsbT05PZs2czduxY3N3dRUtVFxcXdDodSqWSzs5OsT0MEIvIwsPDiYiIICwsbFgnTrPZTF1dHSUlJdy/fx9ADGPrdDru3r3L5cuXqa2tJTExkXfeeYfx48cPCs02NzezY8cOIiIinmlkoaWfeqBQt7e3i38vBwcHTCYTWVlZzJw5c1Ch3OMoKiri1KlT4sSpmpoaqqqqxPGdFRUVxMTE8P777/P5559z584dfvnLXzJt2rSnei0WdDodTU1NNDQ0cPfuXbGOore3FwcHB7y8vIiPj2fcuHHExMQ81ssb/mfu9TvvvDPIU+Bxo0vNZjNKpZL29nba29upq6ujqqoKX19fMjMzxQiFr6+v1UWvwWCgpKSEGzduoFareeutt0hMTHym9+RxjMTo0tHEKyvKj+P27dssWLCAXbt2MW/evGd6LF9fX/793/992K1Vo0mU1Wo1H330ERcuXMBsNoujBsPCwggNDSUsLIyQkBDxZGwwGDh48CBVVVW8/vrrou8v/M8H9OGwsyXfC99YdFrEd+C/rq6uqNVqKisruXv3Lo2NjdjY2BAdHc2YMWNISkpCJpNx8eJFiouLkcvl2NjYYGdnJ4aFg4ODmTp1KhMnTsTT05P79+9TXl6Op6cn8+bNY+zYsY8VY5PJRHd3N0qlUtyVq1QqMX9qNptFYbCzs7MSXHd3d/FrDw8PnJycxOfq6Ojg8uXLlJaW0tnZiZubG9HR0UyYMIG4uDj8/f0pKCjg6NGjeHh4MGPGDDIzMx9raWjJMVtGAebn59Pe3s6CBQvo6+vj9OnTNDc3A9/kCrVaLTY2Nri4uBASEoK3tzdDfaxdXFxwdHSkv7+fvr4+8eJDEAQcHBwIDg4mOjqauLg44uLiCAwMfGzYUavVUlZWRklJCXK5HG9vb7Gv9vr16+zZswetVitebFl6oS1FQfX19ezevVusVxipYrqBeWqFQsHYsWMfO4FrKAoLCzl06BAhISF4eXnR0tKCIAiEhYWJLUsmk4lt27Zx584dZDIZf/M3f8Nrr732TGtXqVRWOWGlUonJZMLR0VH0TNfr9dja2uLk5ERkZKRVG9bD76FCoeCTTz4hLS1tyLUNHF3q4OAwKL/f39+Pvb09np6e1NTUEBYWxptvvjko7aDVamlubqakpIRbt27R29tLYGAgaWlpTJs27bm1T1rscZ+10HE08Z0UZYD/83/+D8ePH6e4uPipft8ygWrDhg2UlpY+sjJZr9dbhSJv3brFzJkzR4Uoy+VyPvroI8aOHUtOTg5dXV20trbS0tJCa2uruJvw9/cnICCA8vJy9Ho9b7zxBj4+PoPE1xLicnFxsRJey9cP70KUSqXYQ9zc3IydnZ2YY3J3d0cmk1FcXExFRQVKpRIHBwciIiKIioqip6cHjUZDVFQUixcvZty4cZhMJq5evcrly5extbVlxowZZGdnY29vL+YhHxZdy78Dx0fa2NiI1qKurq48ePCAoKAg/P398fDw+NbRkT09PRQVFXHt2jWampqwtbUlNjaWiRMnigLw8O/fu3ePw4cPIwiC2H6SkZHxyOETA4V55syZHD9+HFtbW5YvX47BYODUqVNUVVVhY2PD+PHjWbVqFbdu3eLu3bs4OzuTmZnJmDFjrMZ3DjWy09IGplQqUavVqNVqtFotgiCIpiYhISGEhoaKhigPTxSz9FmXlpZy584dDAYDcXFxxMbGUlhYSFNTE9HR0Wi1WoxGI6GhoaJAKxQK9u3bJ9pUvszco8FgoK6ujqNHj3L27FmCgoJISkoSnbQSEhIGhYX/6Z/+iZ07d/L222/zb//2b0/8nCqVyionbOnvDgoKsgpHDxQco9FIa2urGO625HIdHBwG9Urv3r0bjUbDBx98MKSX+8B2ta6uLvR6PXZ2dmIaICwsDA8PD9FUaPny5Tg7O6PX62lraxPTJQ0NDbS2tgKQmJjIrFmzSExMHJG5zI/jWVoCRyvfWVH+6KOP+OlPfyo6LQ2X8vJypkyZgk6nw93dnT179rB48eJH3v8Xv/gFv/zlLwd9fzSIMnyTvxuq1cNkMlFXV8fdu3cpLy8nLy+Pzs5OfHx8xBnKlrxtXFwcCQkJYivO4yqULcYYd+/epbm5WXTO8vDwwM7OTqywbm9vF0O8KSkpTJo0iYiICG7dukVNTQ0BAQHMnDlTvBi6desWeXl5dHV1ER8fT3x8PL29vahUKvE2MK/r7u4uelM//O9Al67hFnpptVrKy8spKiqiuroag8FAWFgYWVlZTJkyBV9f32/9W9y9e5eLFy8SFBREf38/KpWKsLAwMjMzhxxAMVCYJ02axPHjxwkODiY3Nxej0Uh+fj4lJSUYDAbee+89VqxYgVKppKioiNLSUgAmTJjAlClTHutVbcmZW4RaqVTS2NhIY2MjTU1NtLa2olarRZMMZ2dnqyiCra0tdnZ2uLq64ujoKBa4Wdq5LLuthQsXEhISQkNDA3V1dRiNRoKCgnB1daWiooKcnByWLVv2QqvfVSqV2DtcX19PbW0tMpmMmTNnsnLlSqKjo4fcwQuCwL59+/j0008ZM2YM/v7+LF++/LGzpC0594Ei3N3dLeb8LSIcGRn5RAJjNpvF9j7Lra+vj5aWFjo6Oli1ahUTJ04kIiKC7u5usTCroaEBtVpNc3OzKOahoaEEBASIYmoymTh58iRyuZypU6ei0Whobm4Wc/m2trbisZGYmEhOTs4LdW6TRPkVoauri9zcXHGiypPQ399PY2Mj3d3dHDhwgC1btnDhwoVXcqcM/1N0NDDs3NHRQWdnJ/39/fT29nL37l1cXV1ZvHgxvr6+mEwm9Hq96OFsCW9adkxhYWGEhYWJJ3qZTMaNGze4efMmjY2N6HQ6sbLYz88PBwcHHB0d0Wq1KBQKHBwcSEpKIisrSxzvVlBQQGVlJW5ubowdOxZ/f3+6u7u5f/8+RUVFyOVy/Pz8iImJESuWHyW63t7ewy7keZwoW+w0i4uLuX//Pj09PeJ85RkzZlhZUFr6eTUaDT09PeJNp9MRGxsrFiaWl5dTWFhIZmYmPj4+lJaW0tXVRWhoKBkZGYSGhlqJ0kBhTklJIT8/n7S0NCZPnozJZOLMmTNcvnyZgIAAVqxYwbJly7C1taW3t5fr169z/fp1+vr6SElJYerUqcMacvGoY6ipqYnm5max4lun02E2m60Gmri5uYm7c5lMRk1NjVhhrNPpiI6OJisrCycnJ/GiamBUIzMzkxUrVhAdHW2VHx+pHZfZbKa5uVl00uro6BCNNLRaLQ8ePGDx4sXfOlf466+/5ve//z1ZWVn88pe/5NSpU5SWlvLuu++KYwoFQUCpVFoVZllE2JIqsIjwSPb+C4LAnTt3+MMf/oCbm5vYL9zd3Y2zszM+Pj6MGTOGCRMmEBUVRV1dHd7e3lbHvyAIdHV1ceLECSoqKsTCLicnJ7HtTiaT0draiq+vL9nZ2URGRr7wdkJJlEcRlsETD2OpWO3v72fnzp2sWbPmmZ5n3rx5xMXF8cc//nFY9x9NOWWlUsknn3wiXjQ4Ojpa5XoFQeDs2bP4+Piwfv36IV18+vv7xTBVa2srTU1NtLS00N7ejlwuR61Wo9PpcHJyIiQkhNjYWMaOHUtYWJhY0GXJjbm5uREfH09YWBi2trZUV1dz9epV6uvrsbOzE6/SbWxs0Ol0tLW1odFoiIyMZNasWSQlJYnCO1LVnANF2TIIor6+nsrKSqqrq1Eqlbi6uhIXF0d6ejrBwcFiPtZys9h3DtypOzo64u7ujp2dHXK5HF9fXzIyMoiJieH27dtcv36dyZMnk5aWRkNDA8XFxXR2dhIcHExGRgbh4eHiCW6gMIeEhHDjxg1mz55NYmIifX19YmFVX18fy5cv54033rCaVHXr1i2uXr2KQqEgOjparE5/lhOo0WhEJpNZVXpbQq+enp5Wld6+vr5UVlZy6NAhCgoKcHV1FcObLi4uYkWxxZPbzc2NkJAQAgICCAgIEC+YHg6bDwyfP/z/gbvbvr4+sUirpqaGvr4+3NzcxClLsbGxFBUVUVBQwOzZs5k5c+ZjX3tBQQEffvgh0dHR/Ou//ivu7u6YTCZ2795NTU0NM2bMQK1Wi0NQbGxsCAkJsepJfh7VyIIgIJfLqa2t5fPPP6ezs5Nx48Zhb28vRsAsF0wajQYAb29v1Go1MTEx+Pn5oVQqxc96fX09LS0tTJw4kaysLMLDw3F1daW0tJTKykqcnZ3JysoiMTFxUNrB0glQUVHBtGnTHumD8KxIojyKmDVr1qCTimUaT1xcHJs3byY5OfmZn2fOnDlERkaybdu2Yd1/NImyyWSiqKhIFOKBphJ1dXXs27ePwMBA1q5dO+QBrdPpaG9vF6+ILcMeLGJpb28vDm2whKidnZ3p6+tDLpeLQ9cDAgLw8vLC3t4eGxsb1Go1jY2N9PT0EBAQwMSJE0lLS8PPzw87OzvKysq4d+8efn5+zJ8/n6SkpOd2Bd7T08OBAweQyWTU1tbS2Ngo+lu7ubkRGho6ZOGUm5sb7u7uQ948PDxEu0r4JpJQUlJCU1MTPj4+ZGRk0NnZye3bt8nJyWHs2LEIgkBjYyOlpaW0t7cTGBhIRkaGuPtoaWnh1KlTBAcH4+zsTH19PUuXLsXLywuNRoO7uzt79+5FLpezfPlyVq1aNWiQxr179ygsLKS5uZmAgACmTp0qnrRH6r0cKNItLS0YDAZsbW0JDg4mPDwcvV7PsWPH6OzsFNMQGRkZpKWl4erqysWLF/niiy8ICAjAaDTS3d2No6MjoaGhYuqkr6/PKi9ucQQbiIuLi7jDtgxVCAkJEXPDltGVgiBQUFDAhQsXmDt3LtOnT3/saywqKuLDDz/E09OTf/7nf8bBwUHcBVdXV3Pp0iXMZjNLliwRHbMiIyOfmwh3dnZahcO1Wi0NDQ0olUrWrVsniunDkaPu7m6qqqq4cuUK+fn5dHV1YTAYcHZ2JjQ0FF9fXzo7O5k1axZTp07FYDBQXl7O7du3AUhPTx/y2DEYDFRXV4t1In5+fsyYMeO5hbQlUf6O87Of/YxFixYRGRmJRqNhz549/Pa3v+X06dPMnz9/WI8xmkT5Udy5c4dDhw4RExPD22+/jYODA2q1WuwBtdy6urrEsGJfX594xZ2QkEBCQgKenp6o1Wo6Ozu5d+8e5eXltLW10dfXh52dHZ6envj5+REUFERERIQ4Mam3t1fc/Y4dOxZbW1urCT0ODg7MmjWLzMzMEanINZvNaDQauru7xdnIVVVV3Lt3j+rqaurr68UTu4ODgxgmj4yMFEdeenh4iKL7tOHU9vZ2SkpKRLMPOzs7urq6mD17tuh4ZNlhFBcXI5PJ8Pf3JyMjg+joaFpbWzl16hSBgYFi6mHx4sWYzWZmzJhBU1MTH3/8MQ0NDSxfvpx169YNWqdF/AsLC7l//z4eHh5MnjyZzMzMERcOs9lMR0eHlVB3dnai1+upqqqit7eXmJgYnJyc8PT0JDU1lYyMDBoaGrh48SILFy4kODhYLBa0XHyMGTPGaqKV5b14uIhNq9Wi1+sJCwsTj9eH3wtLP/78+fO/tZXp5s2bfPjhh3R3dzN//nxMJhM9PT3Y2tqKO2FfX1/y8/Px9/dnw4YNI+qAZwkpWxyzLCJsa2tLWFiY+F6eOnWK+fPnD7IaNhqNNDc3U1dXR11dHS0tLeLQldDQUHHHb7GetfxNLOLv5OTEhAkThjxW1Go1d+7c4f79+/T39xMdHU1qaiohISHPNaQtifJ3nPfee4+zZ8/S1taGl5cXaWlp/N3f/d2wBRmeTpSH27w/Ely/fp0jR44QFBREcnIycrmc9vZ2dDodgHiSUyqV4nAPFxcXvLy8cHV1xcXFRfyQmUwmsTDIUqySkZEhFm1pNBpaWlooKyvj4sWL1NTU4OzsTExMDCkpKURERBAcHExXVxdlZWWiNeHACT3Dob+/n+7ubvGmUqms/q9Wq60mPqnVauzs7LC3t8fW1haFQkFQUBCpqamkp6cPyuuONHK5nOLiYtGBycbGhpUrV5KQkCDeRxAEWltbKS0tpaWlBT8/PyZMmICjoyN5eXn4+fmh0WhwdHRkxowZzJkzB1dXV5qbm/nDH/7A3bt3Wbp0KZs3b36kMMjlcgoLCykrK8Pe3p7MzEwmT578XIeNWHqxGxoaOH78OBUVFQQEBODi4kJvby82NjZi6kOv17NmzRrS09MRBIHm5mbu3r3L3bt36e7uxtXVleTkZFJSUoiJiXmiCzhBEDhz5gxXrlx5ZD/+wJ1oQUEBhw8fRqPRkJWVxbRp08SccEREhFWLUEtLC9u2bSMxMZG33nrrqY8lQRBQKBSiAD948EC8CAgLC7N6fkdHRwwGA5988gnOzs6899572NjYiP3FdXV1NDQ0YDAYcHV1JTY2ltjYWIKDgykrKxOjXD09PRw5ckSsIbl69SoymQxnZ2ex3S4kJISQkBCCgoLQ6XTcuXOHxsZGHB0dGTNmDCkpKc8tXP0wkii/RHbs2PFUv7d+/foRXsnjeVJRflzz/rOi1WrJy8sTjSmampqQyWS4u7vj6emJo6Oj2P9oyZNa2mGcnJzw9fUlPDycwMBAsdLWwcFBNBRQqVQ4OjqSlJTEypUrB7mstba2UlBQQFVVFX5+fuTk5BAUFERbWxvNzc2UlpZy48YNtFotoaGhTJ48mYSEBLGYzNJb2tvbO6TYWv5v8RKGb1IYFqcwZ2dncSff1dWFvb29WFlqaZEKDg7GbDaTmpr6WAOG50FnZyc3b96koKCA3t5eli9fzsyZMweJi6V1rLm5GR8fH8LCwqisrMTDw0O8oPj5z38utqR1dXXxu9/9jps3b7Jw4cIh22EGotFouHbtGjdv3qS/v59x48YxderUR7ZrjRSCIHD16lWOHDmCl5cXSUlJVFVVUV5eTnt7O11dXTg4OLB8+XLefPNNsTVIEATa2tpEgVYoFDg7Oz9yotVQz5ufn09hYSELFy5k8uTJ4vflcrlVYZZWq6Wzs5Pq6mpMJhOvv/46P/zhD4e0Bx1IZWUlX3zxBTk5OcydO3fY74dCobAKR2s0GmxtbQkNDbUqDBvq73ny5EkuX77MnDlzUCqV1NfXo9VqcXBwICoqShTigUMwBtZU2NnZcfToUTo7O/Hz80OhUBAWFsakSZPw9vYWx3M2NzdTWVkpDhMJDg4WJ7JZ3LxeFJIov0Sepn/RxsbGqvjmRfCkojywef/bPuhPSmNjI7/5zW8wGo309PTQ19cnhmItIWODwSDm/VxdXUXPaA8PD/HEZqksViqVaDQa0dIwJCREnNQ0fvx43N3dxUEXFRUVtLS04Ovry+TJk0lJScHJyQlHR0e6urq4ePEiLS0tREVFER8fb9WqYanUNZlMODg44ObmJpp5WFqdLBW/Xl5eVl+bzWaqq6uprKykoaEBQCxQsYTonZycSEtLIzMzEw8Pj2f2vn5WOjo62LlzJ1VVVeL4wMTExEEnN4vRieV1qVQqfHx80Gg0bNq0ySqi09PTw3/+539y/vx55syZw//6X//rW48vvV5PSUkJRUVFdHd3Ex8fL+4In2fkoKamhgMHDuDu7s6aNWtwd3fnwYMHXLp0if3791NXV0dwcLAYYRkzZoxYRBYYGEhnZ6co0HK5/JETreCbY/n06dMUFRWxaNEioqOjrUS4t7cXOzs7cSdqNBo5f/48bW1tTJ48mffee2/YIenCwkLy8vJ4/fXXh2yVerg6e2Bh2MMi/Ki/XV9fH/X19RQWFnLw4EHCwsJEhzaLCIeHhz8y3WI5/7i5uXHq1Clu3rxJQEAAERERTJo0yargUK1Wiw5jOp0OX19fvL296e/vp6OjQ2yZCwoKEnfTgYGBz7VXWRLll4jlRPSkREVFjfBKHs/TivLzEIXe3l5xvFpPTw/p6el4e3ujUChQKBTY2dkREBBAZGSkGDI0Go0YjUYMBoNYyfngwQP6+vrw8PAQrfXs7OwwGAziDjs2Npbu7m7u3btHW1sbTk5ORERE4OPjQ39/PzqdTqxI7ezsFHPOluIvS6+rpd3J1tYWQRAwm82iyYnFUnLghz40NBSj0UhTU5Mo6I6OjsTGxhIVFYVOp6O6uhqNRkN4eDiZmZmMHTtWPFE/z/f/STCZTBw+fJiSkhL8/PzE3UdSUtKgk5pcLqekpEQczuHu7s6ECRPYtGmTVXFjf38/n3zyCceOHWPKlCn8/Oc/H9ZrNJlM3Llzh8LCQmQyGSEhIUybNo2UlJTnZu7R1dXF3r176enp4a233hLtIE0mE1u2bKGgoAAXFxfR3MLV1RV/f39cXFwIDQ0VRdrZ2Zmmpibu3r2LTCbDwcGBqVOnkpOTg52dHXv37uX8+fPExsaK/sx2dnZin64lHOzg4EBNTQ27du2ira2NhIQEfvjDHz5RaF8QBL7++mtKSkp45513iImJEfuULRegD1dnW2oZHiXCRqORxsZGMSTd1taGwWDg7t27REVF8cMf/pCYmJhhH8u9vb2cPn2ar7/+mlu3bjFu3Dhyc3PF6nxLGqWiooKGhgYcHR1JTk5m7NixViFqi9d9a2urWJNi+VtZBsY8DyRRlvhWRpMo9/f3c+zYMaqqqggNDaW/vx9BEAgKChJ7Zx8O2Wq1Wqqrq6murkahUODq6io6Gj1sVWi50r937x5qtZra2locHR3Fx1Wr1aK3c0NDAy0tLbi5uTFmzBji4+PFHLXF/tHJyQlBEOjv7xd38JavLWFoSyjaMjWot7cXQRBwdnYmMDCQkJAQ3Nzc6OzsFC88QkJCiIqKwtfXV+yZtvxr2VlbnLzs7e1xcHDA3t5+0NdDfc/BweGRQmXp9bbs8L9tt2k0Gjl16hQNDQ1irt3V1ZX09HSSk5MHiXNXVxcnT57k/PnzuLq6kpaWxj/+4z9a9SKbzWa2b9/O7t27SU9P55e//OWw/Z8FQaCuro4rV66IvaxTpkwRc9sjjU6n4+DBg9TU1JCbm8vkyZOxsbHBaDSyd+9eGhoamDJlCm1tbVRVVaHT6cSoTl9fn9jm4+XlRUREBB4eHnR0dHDp0iXx5K1QKEhJSSE7O1vciQ5VnfzgwQN27tyJQqHAx8eH995776l6vBUKBR9//DH37t0jMTERk8kk9ilbpig9rjpbEARkMplVXthoNOLm5ibuhC3WtR988MGQc40fhV6v5/z58/zXf/0XMpmMhQsXsnTpUuzt7cXRqBUVFSgUCnx9fUlNTRWHkHwbA61OY2Njn5tgSqIs8a2MJlFuaWnh3//934mMjCQxMVE8CTx8UjYajTx48ICqqiqam5uxtbUlOjqa2NhYfHx8RDvGh2/t7e1iC1FwcDDJyckkJiaKBh7u7u40NTVx584dHBwcmD17NtOmTXuqE7rZbKahoYHKykru3btHd3c38E2fpZOTE2q1mrKyMlpaWtDr9fj7+zNmzBjGjh0r9rqazWYroTcYDGg0Gu7cuSOeaCxRAkvEYDhYisbMZrNYBWxJF9ja2mJvby9aVlpGSFpC/y4uLjg4OIiCbTAYOHHiBEqlkunTp9PY2Eh1dTWurq6MHz+eMWPGWJ0UdTodN2/e5NKlS9TW1hIeHs5f//Vfk5OTY5V+OHz4MB9//DEJCQn89re/feJCnLa2NgoLC7lz5w5OTk5MnDiR7OzsEc/Dm81mzp07x+XLlxk/fryVSOzatYv29nY2bNiAq6srt27dorS0FJVKRUBAgNjHrlAoaG5uprW1FaPRiF6vp7S0FJlMxty5c/nf//t/D+lyZ6GpqYmdO3fS29uLra0tb7/9NqmpqcNa/1C2mSaTidraWjw9PfnhD39ISkrKYz/rSqVSFGHLcBNHR0ervHBgYCA2Njbcu3ePffv2sXz5ctLT04e1RpPJRElJCQUFBTQ2NlJZWcncuXPJzc0VPw8Wr4eoqChSU1Ofe/Hj0yKJ8ihDJpPx2WefUVJSQnd3tziVyIKNjQ1nz559oWsaTaIsCALV1dVDXombzWYaGxspLy+nuroarVaLm5sbvr6+uLm5odPprAqoAFxdXUVxa21tRalU4uXlRXJysjhb1hLyunv3LmfOnEGlUpGRkcHs2bOf+ARu8SKurKwUW2gsz2fJK9bV1VFcXExVVRX29vZERkaKNpaWUJrZbMbOzo6goCDRjSw0NBR/f390Ot0j339BEERxflisLRXqbW1toltaT08PZrMZZ2dnvLy8xClR3d3dKBQKMSdvab+yt7cX31NLLt/Sz11eXk5/f79YVV1TU0NzczPu7u5kZGSQmpqKg4ODeFKKiIjg888/5/r16/j6+jJt2jRycnLIysoSL4LOnTvHb3/7W0JCQvh//+//Dcsa9GFUKhVFRUWUlJRgNpsZP348U6ZMGfGBA2VlZXz11VcEBwezatUqPDw80Ov1bN++HZVKxaZNm0QDnLq6OtHQAiApKYmMjAyioqJQKpVcuXKF27dvM378eBoaGtBoNEydOpXp06cPukBsbW1l+/btCIKATqdj1qxZjzQqgm/6fQeKsGXm80DHrqioKPr7+/n000/FOdIDd+a9vb3U19eLQqxUKsUK64F54YdrDLRaLR999BHh4eGsXr36W0VTEAQqKys5e/YsCoWCqKgoqqqqMBqNZGZmUltbaxWiTklJea6V+COBJMqjiLKyMmbNmkVfXx9JSUmUl5eTkpKCSqWipaWFuLg4IiIiOHfu3Atd12gSZaPRSGdnp5XzlFwup76+nsbGRvr6+nB0dMTPz4+AgAD8/f2HNMKw9OaqVCqxlcfLy0t0ntJqteKHorGxkby8PJqbm0lMTGTevHlPZBxgyQFXVlZSU1NDf3+/uOsdM2YMISEhaDQaSktLxYuxkJAQMjMzGTdu3KBcnMV5auAgDot1qKOjI76+vnR1dREZGUl4eDgeHh5DntxMJhOdnZ1W4wF1Oh22trb4+flZzfB9nGWi0WhEoVDQ0tKCTCYTbU97enowGo3Y2tri7OyMvb09DQ0NCIJAcnKyOATA0j9u2TmFhYVhZ2dHRkYGOp2OkydP0traypgxY3B1dcXT05MZM2aIbWbXr1/nF7/4BV5eXnz44YdWVqFPQl9fHzdv3uTatWtotVqSkpKYOnUqkZGRT/V4Q9HS0sK+ffsAWL16NWFhYfT29rJt2zb6+vrYtGmT1YVFb2+vOLWqo6MDLy8v/Pz8qK+vZ/ny5YwfPx6DwcDly5e5cuUKbm5uLFy4kOTkZLF9aNu2bTg4ONDT00NSUhJvv/221fFgqYuw5IUtImzxrrbkhIcSiNbWVrZu3UpMTAzZ2dmiEFvmJwcEBIgi/G2uX4Ig8MUXX9DQ0MCPf/zjb73gbWhoID8/n+bmZuLj45k6dSqHDx+mra1N7OcODAxk7NixJCQkjGh/9fNEEuVRxOLFi6moqODy5cu4uroSGBjImTNnmDNnDl9++SUffPABJ06cIDs7+4WuazSJssUAxWg0WoVUXV1diYmJISkpiZiYGDw9Pa36jx+ms7NzkBjHx8dja2srfijGjh1LYWEhlZWVhISEkJubK3oAfxs9PT3cv3+fyspK6uvrMZlMhIWFiTtif39/zGaz6ENdVVWFnZ0d48aNIzMz84lDa3q9ntbWVlpbW6mrq+Py5ctWwxYCAgLw8fHBxsYGg8GASqWio6MDo9EoVpdaBDgoKGhETmBarVacxtXZ2YlcLhe9v21tbZk4cSKBgYG4u7sjCAI1NTXU19djNBrx9fVl0qRJ2NjY0NzczLlz5+jp6SEzMxOz2UxbWxv29vbEx8eTlJSEVqvl0KFDODo68ud//udifv/h23DSDEajkbKyMgoLC+ns7CQiIoJp06aNmAubRqNh//79yGQyli1bRlpaGj09PWzduhWTycTmzZuHNAVpbW2lpKSEqqoqcnNzGTdunNV9FAqFOGkrLi6O7OxsvvrqK5ycnNDr9Xh4eLB582b0er1VYZZCoQAgMDBQTAc9PMXpYSx/A8uxlp+fT3h4OGlpaaIIx8bGPtGu9Pbt2xw+fJi33377kb788E1R4JkzZ7h//z6hoaHMnz8fd3d3/vVf/5X79++L6ZDMzMxBLY0jQW9v73MVS0mURxFeXl787d/+Lf/wD/+AQqHA39+fvLw8cX7yT37yE27dusWFCxde6LpGkyir1WrOnz+PTCYTTRks1n/DEZKHxXjChAkkJCRYFTYplUoKCwsxGAz4+voyd+5cxo0b960fbkuBWGVlJU1NTQBER0eTnJxMcnKy6MM9cFesUqkICgoiKyuLcePGjcj7ZXn/LaFySwivvb1dFGp/f38iIyPFcYTBwcEj3r72MBaP4sbGRo4dO4ZWqyUqKspqfKaHhwdyuRyZTEZqaiq5ublMmjSJ5uZm/umf/gm1Ws1Pf/pT3NzcuHr1KuXl5ZhMJqKjo3FxcWHfvn3o9Xrmzp07ZCjbwcFhSLG2eE0P/L+LiwtNTU0UFRXR2NiIn58fU6dOZfz48c/cEmM0Gjl+/Di3bt1i2rRpzJ07F41Gw9atW7Gzs2PTpk1Pndu+f/8+hw4d4sKFC8THx4vOadnZ2cjlcrq6uoBvRHhgOPpxxXKWAkjL8VRfX49Op8PR0VEcYVlVVcWqVavIysp64jV3d3fz8ccfk5iYyBtvvDHkfTQaDefPn6e0tBRvb29mz56Nh4cH165d49ChQ3R3d7NhwwZmzpxJeXn5iJ9/LEMxrl27Rm5uLhERESP22AP5Lory8x12+Rwxm82iuYG3tzd2dnbiVSzAuHHj+Oyzz17W8kYFzs7OmEwmJk2aRHx8/LBPXJ2dnZSUlFBfX4+XlxezZs0aJMZGo5GKigpu3LiBXq9n3bp1zJw585FiLwgCHR0dohDLZDLs7e2Ji4tj2bJlJCUliR8qQRCora3l5s2b3L9/Hzs7O8aOHUtWVpboWfwsWEwaLEVU58+fR6/Xi8Pcp0+fTnBwsJjHlMvloqlEVVUV8M0xZync8vf3x9/ff0T7MW1sbHB3dxedz44ePYqDgwNz5swRDS3kcjn29vY4OTlRU1NDcXExXl5eTJo0idWrV/PZZ5/xySef8Ic//IHs7Gx6enooLCzkxo0bGI1G/v7v/54DBw5QXV3NP/zDPzBu3Dh6e3utbpY5zJYJWJaRm729vYNqOOCbY87Ozo6KigouXLiAu7s748aNY8KECfj4+AwSdCcnp2/9e9rb2/P6668TFBREXl4eHR0dvPnmm6xfv57PP/+cnTt3snHjxqeatOTm5iYayxQWFnLq1CmmTp1KZ2cnsbGxzJkzZ8jiyIfRarVWeWGVSoWtrS3h4eFMnjyZ2NhYMdUgCAInTpzgxIkTolf/cBEEgaNHj+Lo6DjkSFmdTseVK1coKirCwcGBuXPn4ujoyJUrV2hvb0epVOLt7c0//MM/MH78+EF1IyNBT08PBQUFtLS0iFabEsPnld0pp6WlsXTpUn79618D/zNY+09/+hMAmzdvFnObL5LRtFN+UgaKsaenJxkZGYPE2FI8duPGDXp7e8WhArm5uYOuVC3WiJaKaYVCgZOTE4mJiSQnJw8ydujp6eHWrVsUFxejVCoJDAwkKyuLtLS0Z3pvLPNmLTOCLcMwLANMVCqVmAt83IWLZR6uZQSmZSdlMpmwtbXF19fXSqgtxiojgUql4tixY7i4uPDaa6+J70dfXx/t7e3ExsbS2trKhQsXRMtSd3d37t+/T0BAAD/72c+IjY0lNDQUW1tbioqKuH79Or29vdy8eRODwcDf//3fD9tSduAM5oeF3HKTyWRUVFSIs5P9/f2JiIiw+ltaTGsetQN/+NbW1saRI0dEoxGTycS2bdvESWffFsGwjGd88OAB9+7d48yZMwiCQEJCAq2trYSHh+Pm5kZycjKLFi0iICBgyMexjHi1iLBMJgO+2VEPzAs/aj1ms5k9e/bQ1NTEe++9N+y6i+vXr3PixAneffddKzE3mUzcuHFDjPpYhkWUl5ej0+lITEzEy8uLa9euMX/+fHJycoCRPf8IgkBVVRWFhYU4Ojoyc+bM59afbOG7uFN+pURZqVSKM3z/5m/+hq+++krcufzHf/wHP/3pT5kzZ444+eWnP/0p//Zv//ZC1/gqivJQYhwfHz+o2rO5uZlr166Ju4iJEyfi7Oxs9aEwmUw8ePCAyspK7t+/j0ajEU9yycnJxMTEWO0oBUGgvr6e4uJi7t27h42NjbgrHugm9CQYDAaam5tFAW5qaqK/vx97e3vCwsKIjIwkMjKSiIgIzGbzM73/JpMJhUJhJdRKpRJBELC3t8fPz09sEbO4jnl6ej6VWCsUCo4dO4anpydLlizB0dFxyJOSRqPhzJkzXLhwgfr6esrKyggMDCQ3Nxc7Ozvc3d3Flqz29nbu37/P2bNnMRgM/K//9b949913RzS3qNVquX79OkVFRaLRzLhx43Bzc3ukoFvEfqi2NKPRSGVlJYIgMG3aNLy9vbl8+TLBwcEsX74cT09PUcQdHBxobW0V88JyuRwAd3d3qqur8fLyYunSpZw7d46pU6cyf/58qqqqOHnyJN3d3UyZMoUZM2aIj2MR4aamJkwmEx4eHmJaIyYm5olazfR6PZ9//jl6vZ7333//WyNZXV1dfPLJJ6Snp7NkyRLgf8LEZ8+eRalUEhYWhr29PY2NjeIAiezsbNRqNTt27CAtLY1ly5YNabP5LOcfy+M0NDSQlJTElClTnnuKByRRfuk4OTmxePFi1q1bx7Rp02htbSUtLQ0HBwcEQeDXv/41Bw8exM7Ojtdee42f//znz8Xk4HG8SqLc1dVFcXGxKMaWnPHDYqxQKLh27RqNjY0EBQUxefJksc9Tp9OJHrmW8K5Op8Pb21usmA4PDx8kQlqtVtwVKxQKAgICyMzMZPz48U8chuzt7bXaBbe2toqtSRYBjoyMJDQ0dFCI+Xm8/waDQQwvW0ZYdnd3i61QdnZ2oj+3xSLUcvu2NXR2dnL8+HF8fX1ZtGgRJpPpkSclrVZLYWEh+/fv5+zZs6Snp/Pzn/8cjUZDW1sbra2taLVajEYj7e3tXL58GZVKxfz58/nVr35FVFTUiIpzf3+/ONtZqVQSExPDtGnTiIuLG/J5BEHAYDAMKdpKpZIzZ87Q2NhIYmIizs7OXLlyBVdXV1JTUwcdb76+vmJhVlBQEAcPHkSr1bJs2TIOHjxIVFQUq1evtppDfeLECU6ePElPTw/+/v54eXnh7Ows9vDHxsaKbYBPS3d3N1u2bMHT05ONGzc+Mv1jNpvZunUrWq2WP/uzP8PR0ZH6+nry8/NpamrCxcUFe3t7ent78ff3Z9KkSYwfPx5HR0cUCgVbtmwhMDCQd9991+rzPRLHf11dHZcuXcLGxoYZM2YQHR39VI/zNEii/JJZt24dX331Fb29vXh4ePDGG2+wbt065syZM2oa218FUe7q6qKkpIS6urrHirFWqxV3sJ6enmRnZ4sVmjqdjsbGRqqqqqirqyM8PJywsDBRiAea3lsQBIGGhgZu3rxJZWUlNjY2pKSkkJmZKc4N/jYEQaC7u5uGhgZRhC27H09PT6KiokQRthgsPI4X9f5bCrdUKpUo0pZ/e3p6xHnNlh7ngUJtmVVt+fvIZDJOnDhBUFAQM2fOpK+v77Enpd7eXn7729+ya9cuYmNj+eu//mumT5+Om5sbarVaFOja2lr+9Kc/UVtbi6+vr9jLGx8fT2hoKKGhoSMy/cdsNlNZWcmVK1dobW0lKCiIqVOnkpqa+kTDDB42Ghk7diz79+8nOjqaBQsWoNPp0Ov1BAUFiZXNOp2OHTt2oFKpWL16NUeOHMHOzo73339fLPaz3AZOF9PpdKSlpbF27drHGo88DZZWqfj4+EEtWBYuXbrEuXPn2Lx5M46OjuTn51NRUUF/fz+Ojo64uLiQmJhIdnY2sbGx4mP09fWxZcsWAN5///1BF7zPcvxbxq1WV1cTExPD9OnTnyqv/yxIojwK6Ovr48iRI+zZs4e8vDyMRiNBQUGsWbOGdevWvfQZxqNZlIcrxgaDgdu3b1NWVoatrS2ZmZmkpKSg0+nEMGBbWxtms1nMo65ateqR+aPe3l5u377NzZs36erqws/Pj6ysLMaPH/+tHySz2YxcLrcSYbVaDXyTvxu4E34Si8GBa3vZ6QOj0Wgl0gOF2+L7bWtrK+6uLUMASkpKiIiIYOrUqaLJyKMQBIH//M//ZNu2bYSFhYmOXNOmTbMSWr1ez7/8y79w6NAhceC9t7c3QUFBuLq64u7uTmhoqOg7/ixCbblIs5zYPT09xdnOTxL6HGg0MmHCBL7++mtSU1NZsWKFlcD19/ezc+dO5HI57777LqdOneL+/ftkZ2eL/eKAaENryQs7OjpSU1PDiRMnUKlUTJo0iVmzZo1oePb+/fvs27dPDKEPRCaT8emnn5KWliamW1QqFW5uboSGhpKZmcnEiRMHVdCbTCZ27dqFTCbjBz/4wZAV9k97/Dc3N1NQUIDRaGTq1KmiV/bDyGQyAgMDn5tnuiTKowylUskXX3zBnj17uHz5MgAJCQm88847rF27ltjY2Be+ptEoysMVY7PZzP3797l58yZ6vZ7U1FSxgOjBgwe0t7cPGiNnZ2c35IdCEAQaGxu5efMmd+/eBWDMmDFkZWU9NixqNBppbW0VRbipqQmdTmflYW3JB4/Eh3A0iPKjsEznGkqwNRoNKpVKzIu++eabhIWF4e/vj5+fn1hoNjBcbzQa+b//9/+KoWwfHx/MZjMZGRnk5OSIu0mDwcAf//hHjh07RkhICDExMeh0OoKDg4mIiKC3t5fW1laxctci1APF+kmFuqOjg8LCQsrLy7G3tycrK4vJkycP+3EGGo2kp6dz+fJlMjMzWbJkidhvvnPnTu7fv8/EiRO5fPkyZWVlpKWlWbW7DeUHP/D9u3r1KhcvXsTZ2Zn58+cPq/1vuBQVFXHq1CmWLl1KZmam+Jz//d//TXV1NUajkY6ODvz8/Bg3bhxTpkwRQ9QPIwgCx44d4/bt26xfv/6Rg3me9Pg3GAxcu3aNO3fuEBYWxqxZs4Z8v1pbWykuLqa1tZUFCxY8t5C2JMqjmJaWFvbs2cPevXu5desWNjY2TJo0icLCwhe6jtEkylqtlqtXr1JbW4uHh4dYTf2wGAuCQFNTE9euXRPn81qKgBQKBfb29kRERAxpnv/wh6Kvr4/bt29TXFyMXC7Hz89PzBUP1Vai0+loamoSRbilpQWTyYSjoyMRERGiCIeFhT0Xl6HRLMqPw2g0olaruXXrFmfOnCE1NZXk5GS6urrQ6XTAN21V3t7eVkLt4uLC1q1bqaqqYunSpcTHx3P9+nX6+/tFcfby8sJoNLJt2zaOHDlCQkICc+fOpbq6GrVaTUpKihiqtJiwWELgFqH28PAQBfpJhFqtVouznY1GozjbeTjVyQONRhISEqisrCQ7OxsvLy/27NlDdXU1Y8eOpb+/n/b2dpYtW8aSJUvw9fV9ImHt7u7m9OnT4mSmxYsXj8jsaUEQOHnyJDdv3mTdunVERUXx4Ycf8sUXX4ipmdmzZ5OTk2MVoh6KK1eukJ+fz4oVKxg/fvwj7/ckx79MJqOgoACtVsukSZMYO3bsoDUMFGN/f38yMzNHvDZhIJIovwKUl5fzT//0Txw9evSVmqf8PERBr9dz/Phx0TpvqHxdZ2cnhYWFVFdXY2tri5ubG4Ig4OTkZDXK7lE9uJaRjJZpNXfu3BGtIbOysgbN4lWr1WIYuqGhgY6ODgRBwN3dncjISFGEg4KCnlvIayCvqihb0Ol0lJeX09zczOTJk1m8eDF9fX3iNK2B/yqVSsxmMz09PRQVFaHX65k6dSqLFi2io6OD6upq7OzsyMzMZPr06Xh4eLBv3z4OHDhAeHg4f/Znf4ZOp+Py5csolUqSk5OZOXOm2IdqyfdbBPpxQm0R60cJtU6nE2c7q9VqEhISmDZt2ree4Acajfj6+tLZ2cm9e/cwGo2sWrWKiIgI8vPzyc7OZtGiRc/03tfW1orDQ7Kzs5k1a9YzH0Nms5ndu3dz7tw55HI5d+7cISkpiR/96EfMnDlzWH7llZWV7N+/nxkzZjzWtxuGd/wbjUaKi4u5ffs2gYGBzJo1yypVNNBB7UWJsQVJlEcpjY2N4i65oqICQRCYOnUq69at44MPPnihaxlNogzffGCG+mCoVCry8vK4deuW6HsbGhoqVqiGhIR8a9GNXq+noqKCsrIyvLy8CAwMJDMzk/T0dNEOsrOz00qEVSoVAH5+flYibLG1fNF8F0RZo9Hg6elJXl4eU6ZMITc395H+3UqlUnRq27t3L2q1mrCwMBISEhAEgZaWFjo7O3FwcCA1NZVp06ZRVVXFmTNnCA4O5r333iMjI4Py8nIuXryIQqEgMTGRGTNmDFlT8LBQW8R6oFAP3E0/LNQmk4mKigquXLlCR0cHYWFhTJ06lTFjxjzyok0QBIqKisjLy8PW1haTycS6devw8/Pj008/JTQ0lHXr1o3IRZ/RaKSoqIgLFy7g5OTE/PnzSUtLe+pWvhMnTrB3716Ki4vp7+/ntdde4//+3/877AIqS9FYUlISb7755jMXOnZ2dlJQUIBKpRI9Ayzvm0WMi4uLaWtre6FibOG7KMqvrKNXZ2enmE++evWquDv71a9+xbp1615oWf5o5uFCl9raWgoKCqioqAC+ycFnZ2cTFxc3rGplizNXZWUltbW19Pf3ExgYyJo1a0hOTkYmk1FWViYKsWX8nWW0Y1RUFBERESM+8u/7Tnp6Ovb29pw4cUJ0/noYOzs70X0sOTmZ8PBwDh48iI2NDTExMcyaNQuVSoVMJuPGjRuUlpZy6dIlAgICsLGxoaioiDt37jBnzhxmzZrFzJkz6ezspLy8nC1bthAXF8fMmTOthlJYQuiWFjn4H6EeuJu+du0afX19wGChjouLIy0tjdraWq5cucKXX36Jj4+PONv54bSGjY0NU6ZMISAggFOnTjFv3jwiIiLYsmULrq6uvPXWWyMWhbG3tycnJ4dx48aRl5fH4cOHKS4uZvHixcOu0lar1Zw6dYqDBw/S3t5OYmIi77zzDkVFRcTFxQ3bKa67u5s9e/YQFBTE66+//kzCaDabxTSUt7c3y5cvFyeBWS7eSkpKaGtrIyAggIULFw67g0Li8bxSO2WtVsvhw4fZs2ePaHYQEhLC6tWrR0XlNYy+nbKlYtoy3q6lpQVnZ2eysrKYM2fOkK1LQ6HX66mpqaGyspKuri48PDzEQQYymQwfHx/kcjkGgwEHBwfCw8PFqujw8PAXYiTwNHxXdsqWnYIllzh37lymT5/+2N8VBIEDBw6IloxpaWm8/fbboggYDAZu3LjBmTNnkMvl4oQkQPSAtrW1RRAE0VnMYDAQHx/PnDlzGD9+PN7e3k/U6jZQqFtbWwcJtcWRzHJMu7i4iJXkj7LCtLhnNTc384Mf/AA/P78neIefjLq6Ok6cOEFXVxfZ2dnMnj17yOPKUsdx9uxZTp8+jVwuJykpifXr1+Pv78+uXbvIzs7m1q1bxMXFsXLlysdeSDypEYmFoY5/lUpFQUEBHR0dpKenk5GRgb29vSjGxcXFyGQy0VvgZYqxtFN+yQQGBqLT6XB3d2ft2rVij/KLyD2+iqhUKvbv349KpUKtVuPs7MzSpUuZPn36sD60giAgl8vFMYqW6TnBwcEYjUZu375Nf3+/aBowe/ZsIiMjhxX6lng+TJs2DYPBwNmzZ3FwcGDy5MmPvK+NjQ2vv/46XV1dNDc3c+/ePb744gtRmB0cHJg6dSrZ2dmUlJRw6dIlzGYzHR0dmEwmIiMjmT17Nj09PeJ0qzt37nD79m0uXryIl5cXcXFxxMfHizv0gUVnAy/UBu6oLVOPBgq1RayLiopEoba3t6erq4vdu3dz9OhRcVjFw3nX/Px86urqxBD28yQ2NpYPPvhADGlXVFQwf/58xo8fj42NjegZf+nSJa5du4ZSqSQuLo6f/OQnTJo0if7+fj766CNiY2NZtGgR8fHx7N27lzNnzpCbmzvkc5rNZg4ePIhKpeK999576ijUwCESbm5uLFu2jODgYNEu1yLGgYGBLFq0iIiICGln/Bx4pUR53rx5rFu3jmXLlr2Su5oXjeVixc3NjXHjxjFp0qRhnZT6+/uprq6mtLSUhoYGTCYTLi4uODs709vbi729PcHBwaSkpIjDQGbOnPmduVJ91Zk5cyYGg4FTp07h4OAgttcMhaOjI2vWrOFPf/oTZrOZmpoaK2GGb8QvOzubjIwMSkpK2Lp1K6WlpWJ1/gcffGBV4WsymSgpKeH06dOi5Wp4eLjoOW7B3d3dSqQtX3t7e2Nra/tIoVapVFa76YaGBurq6vjTn/7Eli1bSEpKYvr06aSnp6NQKLh69SqLFy9+osEPz4KdnR3Tpk0TQ9pHjhzh8uXLBAUFUVNTQ1VVFb29vURERPCDH/yAKVOmiG1NJ0+epL+/Xww/JyYmsnDhQk6ePImvr++QU6Xy8vKoqalh7dq1TzS7fCA9PT2cOXOGlpYWxo4dy6RJk7C3t6e5uZmbN2/S3t4uifEL4pUS5aNHj77sJbxSuLi4EBERQVpa2rcaw5vNZqqrq7l+/TqVlZV0d3fj6upKQEAAiYmJhISEiDOEB16JW8JHEqMHGxsb5s2bh8Fg4Pjx42Jo+lF4eXmxevVqtm3bRlBQELW1tYOEGazF+bPPPmP//v18+eWXVFRU8Ld/+7ekpqYC34jSxIkTycrKora2lgsXLtDU1ERoaCi5ubn4+fmhUCjE3XVLS4s4RMPy+76+vkMKtouLCz4+Pvj4+AwS6oaGBgoLC7l69Sp//OMfcXNzIyIigvnz5zNx4sTn+I4PjYeHB5MmTaK9vZ2vv/6arq4uvL29SU1N5e2332bGjBlWIffKykpu377NihUrxNGlAJMmTUKhUHDixAm8vb2Jj48Xf2bxFF+yZInV94eLZSJbeXk5Li4uLFmyhLCwMFpaWkQxDgoKYvHixU/tRS/xZLxSoizxZDg4OAw53g0QjQiampooKyvj3r17aDQanJycSEhIYMGCBcTExBAUFDRq88ESj8bGxoZFixZhMBg4cuQI9vb2oogNRUREBEuXLuXIkSOkpqYOCmUPxN7enh/96EckJyezbds26urq+MlPfsLy5ct56623xBYpGxsb4uPjiYuL48GDB1y4cIFDhw6J9qCzZs0ST/KCIKBWq61auCxFZN3d3eJzu7m5iUI9ULC9vb1JT08nPT2dDz74gHv37olh69u3b9PV1UVUVJRYaPg8j2lLiPr69eu0tLRgNpsZM2YMXV1dGAwGMQU0MLLU09PDsWPHGDNmzJAXUAsWLECpVPLll1+yefNmcdd98uRJJk+e/FQXHVqtlgMHDnD16lVSUlKYOXMmcrmco0ePSmL8EpFE+XuCTqdDJpOJt8bGRmQyGWq1WvTNzczMJC0t7YUP8ZB4PtjY2LB06VKMRiMHDx7E3t6exMTER94/PT2d9vZ2ioqKyMnJ4erVq48UZvgmTO7k5MRXX33F/4+9+w6L6kr/AP4dYOi99967oogoUlRAxd67JjGmmU3ZzZpN02QTN23X7G5i1BgsibElamxgAbECIhhFpHek9w7D3N8f/uauIzMw1Bng/TzPPMncNmeOw7xzzj3nPUVFRYiKikJ6ejqmTJmCwMBAmJmZseWwsbGBjY0NCgoKEBcXh2PHjsHAwADTpk2Dg4MD+Hw+uFwuTExMuq2/K8g/XV1dzf63oKAAKSkpQmlIdXR0oKurCz09Pejq6iIsLAydnZ0oLy9HSUkJ4uPjERMTAw6HAyMjI3Yw4kBnAygoKEBRURGNjY24c+cO7t69i+bmZujq6kJFRQWtra3w8PDAzJkzoampiUuXLuH06dPsKG0TExOcPXsWHA4HERERIgOgnJwclixZgh9//BGHDx/G/Pnzcfz4cTg4OIi919yTR48e4ezZs+jo6EBAQAC0tbURFRVFwVgGjKjR1yOBLI2+FiwoUVZWhtraWnR1daGtrQ1tbW3g8/kwMDDA+PHj4eLi0u8vpZE++nG0jb4WpaurC8ePH2fvO/aUfvbpkcrTp09HdHQ0O/pX3NScO3fu4MyZM+jo6GBve2hoaMDBwUHsmrpFRUW4du0a20Pj6OgIa2vrPg3aFKQhbWxsRH19PRobG9HQ0ID6+nq0tLTg2a82OTk58Hg8tLa2sstDdnR0QE5ODioqKuz9a11dXaipqUFBQQHy8vKQk5ODvLy8yIecnBw6OjrA5/NRUFAARUVF2Nraoq2tDY8fP4axsTFCQ0Ph6Ogo9N7y8/Nx/vx5VFZWQkdHB+Xl5Vi7di2cnZ17fM8NDQ349ttvkZKSgmnTpuHFF1/s04/otrY2XLhwAX/88QecnJxga2uLw4cPo7GxEWZmZpgwYQLMzMxGTDAe6d8/olBLeRTjcrkoLy9n71vV1NRAXV0dzs7OcHV1haWlJY1cHwPk5eWxZMkSHDlyBL/88gvWrl0rNJf4aYIW2Q8//ICEhAQsXLgQJ0+exPHjx8UG5okTJ0JBQQGnT5+GiooKurq6YGJigtraWrHzly0sLLB69Wrk5ORg7969SEhIQEZGBry8vESu5S2OpqamyPnAgjSkDQ0N4PF46OrqYh9PP29ubmZb4DU1NcjLy0N2dja4XC7U1dWhrq4ONTU1KCoqgs/nC12nrq4O5eXlaG5uhrGxMUxMTNDe3o7bt29DUVER1tbW4HK5OHLkCDgcDrhcLhQUFNiHnJwc6urqcPz4cWhra0NVVRX29vbdjnv6AQDl5eV4/Pgx6urqkJubCyUlJbHHCx4cDgc5OTk4ffo02traMH78eJSVleH333+HnJwcwsPDe03dSYYHtZQHmSy1lBsaGhATE4Py8nKoqqrCyckJzs7O7MIDg2Gk/1IdCy1lgc7OTvz8888oLS3F+vXrYWpqKvbY6upq7N27F+bm5vD19cWxY8d6bTHfv38fJ0+ehKKiItra2uDu7g5HR0c2G5etrS0CAwOFFkcQ1H9HRwfS0tKQm5sLdXV1jBs3Do6OjhInzhgsbW1tKC8vR1lZGUpLS1FVVYWuri4oKSnByMgI2traaGhoQElJCdra2mBgYAAjIyOYmpri3r17AIDx48fD3d2dnQIl7tHZ2YmLFy+iqqoK+vr6KCoqgpaWFtzc3KChoSHy+D/++AOVlZWwsrJCXl4ezM3Nex3g1dXVhfz8fDx+/BjKysrsv4++vj7s7e1RV1cHdXV1KCsrs70D8vLyYv+/r9vk5eUp93UfUEt5FFNVVYWqqipmzpwJKysrmjs8xnG5XKxcuRI//fQTDh06hA0bNohdSEFPTw9Lly7Fzz//DENDQ6xYsQJHjhzpscXs6ekJeXl5/Prrr9DS0sKjR4/Q3NyMjRs3Ii8vD3FxcYiMjIS1tTUCAwOFsu7p6elh5syZqKmpQUpKCm7cuIHk5GR4e3vD2dl52IKzsrIyOyAMeNLiLi8vR3p6OrvSGsMwMDQ0hIuLC7tylL29PYKCghAQECBxcEhISICWlha2bNkCW1tbFBYW4ty5c6ioqGAXAXn6WoKEScuWLYO9vT1u376NCxcuICAgAJ6eniIDf3FxMbvErampKeTl5WFgYAB3d3fo6+ujpaUF9+/fZ7OitbW1CfUGPNuzwOPxut0W6M3s2bNhYWHRp3PGMmopDzJZaikPh5H+S3Us1n9bWxsOHDiAhoYGbNy4kU2fKEpCQgIuXLiABQsWQF1dHUeOHIGdnR2WLVsm9kdeRkYGjh07Bm1tbTQ2NkJbWxurV6+GpqYmMjIyEBcXh9LSUlhZWcHX1xeFhYXQ1NQUqv+6ujqkpKQgOzsbysrK8PLygouLy5CsFCYOj8dDXl4eUlNTUVFRAS0tLbi4uEBXVxdpaWmIj49HaWkpVFRU4OvrCwcHBzaXu6WlZY/jNKqqqvD999/Dx8dHaGEMPp+PO3fuIDY2FhwOB9OnT8f48eNx//59nDp1CqGhofD392ePj4qKQkJCAlatWgUHBwehsl+9ehWnT59GfX09jI2N4eLigqCgIKFFYvrz+efz+d0CdU+B3MzMTGy2tYEa6d8/olBQHmQUlEeWsVr/LS0t2L9/P9ra2rBx40bo6OiIPO7pdXk3bNiA9vZ2iQJzdnY2jhw5Al1dXbS2toLD4WD16tUwMjICwzDIysrC1atXUVBQgJaWFvj5+cHOzq5bN2d9fT1SUlKQlZUFJSUleHp6wtXVdUhnCDQ3N+PRo0d49OgRWlpaYG5uDnd3d1haWrKj08vLy2Fubg5PT0+0t7fD3NwcFRUVKCwsRG1tLYAnrX9BkLaysmJTjvL5fOzbtw9tbW146aWXRP7QECTzuHfvHhQVFVFTU4PAwMBuo7P5fD6OHj2KvLw8PPfcczA2NkZZWRm+//57JCcnQ09PD1OnTkVISEi3FduAsfv5l2UUlAcZBeWRZSzXf1NTEyIjI9HV1YWNGzcKJax4WldXFw4cOICamhps2rQJlZWVEgXm3Nxc/PLLL9DT0wOPx0NjYyNWrFgBGxsbAE8C/oMHD/Djjz+yq1WNHz9eZMaoxsZG3Lt3DxkZGVBQUICnpyfc3NwGdb5xeXk5UlNTkZeXBzk5OTg6OsLNzQ06Ojqoq6tDQkIC8vPzoaenBz8/P5ibm4usf8HypII1wsvLywE8SSZiZWWF6upqZGdn4/XXX++1W/f+/fv46KOPAADr1q1DaGhot1ZnR0cHIiMj0djYCC0tLZw5cwZdXV0IDQ3F/Pnze1ycZyx//mUVBeVBRkF5ZBnr9V9fX4/IyEjIy8tj48aNYrtcm5ubsWfPHqiqqmLjxo0oLCzEL7/8Ant7+x4Dc0FBAX7++Wfo6emBy+WipKQECxYsgIeHB4An9R8XF4fGxkY8ePAA5eXl7FQ9UUsANjU14d69e0hPT4eCggLc3d3h7u7e73+7rq4u5Obmsl3Umpqa7AA1JSUldlphRkYG1NTUMHHiRNjb27PlkqT+W1tbUVRUhIKCAty/fx/nzp2Dubk5OwNC0JIW3PMVaGlpwb59+wA8mUN+8+ZNAMD06dPh4+MjtIRiTEwM3nnnHTQ2NiIiIgKvvPKKRBm+xvrnXxZRUB5kFJRHFqp/oLa2Fj/++CNUVFSwYcMGsdcpKyvDjz/+CAcHByxZsoTtou4tMBcXF+Onn36Cjo4OtLS0kJ6ejpkzZ8Lf3x+tra1s/SspKQktCainpwcfHx+R3a7Nzc24f/8+0tLSICcnBzc3N3h6ekr8b9jS0oK0tLRuXdQWFhbs3OP79+/jjz/+gLy8PMaPHw9XV9duA876Uv88Hg979uwBwzAIDw9HcXExCgsLUVRUhI6ODigoKLAJTczNzREXF4fa2lq88MIL0NXVRXNzMy5fvoyUlBSYmJhg1qxZaGpqwsGDB5GYmAgdHR3o6+sjKCgIy5cvl2i6I33+ZQ+NviZkjNPR0cH69esRGRmJQ4cOYf369SK/oI2NjbFw4UIcPXoUhoaGCAwMZEdlCzJ/iQrM5ubmWL9+PQ4ePAiGYeDr64tLly6hoaFBaHlJDocDc3NzmJub4/Hjx0hOTsbFixehp6eH8ePHw8bGhg3OampqmDx5Mry9vfHHH38gNTUVDx48YIOzuC/oiooKpKamIjc3FxwOB05OTmwXNfCk5Zyamork5GR0dHTAw8MD3t7eg9JNHhsbi+rqarz44oswMjJiF8jg8/koKytju7vv3LmDPXv2oKKiAqGhoUhMTGTvTc+fP5/NPb5p0ybU1dXBwMAAa9euxerVq1FQUIDDhw/j4sWLCA8PH3CZyfCjoEwIgb6+PtatW4f9+/fj559/xtq1a0UOpnJxcUFwcDBiY2NhYGAAV1fXbtOlRAVmExMTbNiwAQcPHkR+fj5CQkIQGxuLqqoqkYPMBGsnl5aW4u7du7h06RJ0dXUxbtw42Nrasq1AFRUV+Pn5wdvbGw8ePEBqaipSU1Ph6uoKLy8vqKmpoaurix1FXV5eDk1NTfj6+sLJyYkNtgzDIDc3F3fu3EFDQwMcHR0xYcKEAaXffFphYSFu3bqF6dOnd5uGJicnx77fyZMn49q1a6iqqsKCBQugqKiI9PR0xMfHg2EY8Hg8Njd4W1sb1NTU4OLiwrbiHRwcMHv2bJw7dw66urrw9fUdlPKT4UNBmRACADAyMsKaNWtw8OBB/PLLL1i1apXIkcHTpk1DRUUFTp48CV1dXTg4OEgUmI2MjNjAfP/+fURERODMmTOor6/H3LlzRbbOTUxMEBERgbKyMiQnJ+PKlStITk7G+PHjhYKzsrIyJk6cCA8PDzYwp6WlwdraGqWlpWhpaYGZmRnCwsK6ZbJ7/PgxEhISUFFRAUtLS8ycOXNQ113u6OjAyZMnYW5uLjSdSZSHDx8iJiYGc+bMQVBQEIAnPxgSExNx5swZZGVloby8HO3t7bCysoK7uzuam5uxb98+XL16FStWrMDEiRNRU1ODCxcuQFtbu8d850T2UI5FQgjLzMwMq1evRnFxMY4ePQoej9ftGA6HgwULFkBfXx+//PILmpub2cCclZWF48ePo6urS+T1DQwMsHHjRnR2duLmzZuYN28eGhoacPbs2R6XADU2Nsbs2bOxYMECaGho4MqVKzh27BgyMzPB5/PZ45SVlTFhwgSsWrUKPj4+qK6uhrW1NZYuXYqIiAih/No1NTWIiorCmTNnwDAMIiIiMGvWrEENyMCT9Y6bmpqwcOHCHu/zFhcX4+TJk/Dw8EBgYCAYhsHDhw+xa9cuXLhwARoaGrC3t8ecOXOwa9cufPzxx/Dz84O9vT00NTVx48YNPP/883jzzTcBPJmSdezYMZSVlQ3q+yFDiwZ6DTIa6DWyUP2Llpubi8OHD7ODukS1fOvr67F3717o6upi/fr1kJeXR1ZWFo4cOQIHBwexLWbgSXKQAwcOoL29Hfr6+vjjjz/Y5SZ7SmYiUFlZibt376KgoABaWlrw9vaGg4ODRFnrmpqakJSUhMzMTGhoaGDixIki50hLorf6z87Oxk8//YQ5c+b0uLxiXV0dfvjhB+jo6GDdunXIzMxEXFwc23rv6upCSUkJnJ2dERER0a1bvaOjA8XFxbh06RJiYmJQW1sLMzMzVFdXQ01NDatXr4aLiwssLCyEbkvQ51/2UEv5KTt27MDEiROhoaEBQ0NDLFiwABkZGdIuFiHDztbWFsuWLUNmZiZOnTol1BoV0NLSwooVK1BSUoJz586BYRiJW8za2trYsGED5OXlER8fj2nTpkFNTQ2///47iouLey2fgYEBwsPDsXjxYujq6rLLQaanp4t9zfb2diQkJODIkSMoKCiAv78/m7JyKHIzt7a24vTp07C3t8eECRPEHtfe3o7Dhw9DQUEBHh4e2Lt3L44fPw4NDQ3MmDED9fX17D3m5cuXi7zPLVidavPmzYiMjMSrr74KXV1dWFtbo729HT/++CP279+Pf/zjH9izZw+io6PZNKhEtlBQfkpcXBxeffVVxMfH49KlS+js7ERoaCh9cMmY5OjoiMWLFyM1NRVnz54VmfPY3Nwc8+bNQ3JyMhISEgAADg4OWL58ea+BWUtLC2vWrAGXy0VMTAymTJkCExMTXLhwAZmZmRKVUV9fH6GhoVi8eDH09fURFxeHo0ePIi0tje165/F4uH//Pn755RekpqbCy8sLK1euhLu7+5Dmgz9//jw6Ozsxb948sUFfkJErMzMTbW1tOH/+PDQ1NbFu3Tro6enh8uXL0NXVxSuvvAJvb2+Jfjyoqqpi/vz5ePvtt+Hp6QlbW1uoqKhAT08P4eHh0NfXR1paGo4ePYqdO3fi999/x40bN5CZmYnGxsY+57Ymg4sGej0lKipK6Pn+/fthaGiIu3fvYtq0aVIqFSHS4+rqyi7fqKCggFmzZnULDF5eXigvL0d0dDQMDAxgZ2cHR0dHLF++HEePHu1x8JeGhgZmzpyJGzduICoqCrNmzYKqqipiY2PR3NwscSDS19dnF7RITk5mF7RwcnJCVlYWmpqa4OzsDB8fnyHLw/y0hw8f4sGDB1i0aJHYVdn4fD6+//57nD9/HnZ2djA3N2cHd508eRL19fWYNWsWfH19+9WSNzU1xfPPP4+UlBQcOXIEZ8+eRVdXF9544w3Iy8ujvr4e6enpiIqKQnl5OXJycgA8mW5mYmLCLkepo6NDSzoOIwrKPaivrwcA6Orqij2mvb0d7e3t7POmpqYhLxchw8nT0xOdnZ04c+YMuFwuZsyY0e1LesaMGaisrMTx48exadMm6OnpCQXmEydOiL03raysjFmzZiEmJgbnz5/H7NmzoaamhsTERDQ1NWHKlCkSr/utq6uLGTNmoLa2FsnJyUhJSYGlpSVmzZolNr/3YGtqasK5c+fg6urKZi57Gp/Px8OHD3HgwAEkJSVhxowZ2LBhA0xMTHD16lXcuHEDpqamWLlypUT313vC4XAwfvx4uLi4YPfu3Th16hTKysrw6quvwtraGh4eHqitrYWGhgaAJwliBMtW5ubmgs/nQ0lJCcbGxmyQ1tfXpxXnhhAFZTH4fD7eeOMNTJkyBe7u7mKP27FjB7Zv3z6MJSNk+Pn4+KCzsxNRUVFQVFREYGCg0H45OTksXrwYP/zwAw4fPoxNmzZBWVm5T4F5zpw5uHDhAs6dO4dZs2ZBXV0d169fR3NzM2bMmNGn5Rt1dHQwffp0BAcHSxzQBwPDMPj9998hJycncvGIhw8fIi4uDhkZGXj8+DFefPFFrFq1CuXl5di7dy8qKioQHByMqVOnDmq5VVRU8MYbb8DIyAhHjhzBV199haCgIEydOpU9RllZGdbW1myu7M7OTnZt6bKyMty9exc8Hg8KCgowMjJiA7WRkdGwrt412tHoazFefvllXLhwATdu3IC5ubnY455tKd+7dw+BgYE0+nqEoPrvm+vXr+PKlSvdlhAUqK6uxg8//AAzMzOsWrWKDSyZmZk4evQoHB0dhQLzs/Xf0dGBqKgoVFVVITw8HDweD5cuXYKenh7CwsKgoqIy5O+xL56t/+TkZPz+++9YuXIlnJycADwJxqmpqWxSEENDQxQWFsLb2xtLlixBfHw8YmNjoaenh4ULF8LExGTIyiu4h33z5k3o6OhAQUEB6urqmDhxYq/d+l1dXaiqqmJb0mVlZWhvb4ecnBz09fXZlrSxsfGw/S2N9O8fUailLMJrr72Gs2fP4tq1az0GZABQUlISSsE3WBmACJFFAQEB6OzsxMWLF8HlcrtN89HT08PSpUvx008/4dKlSwgLCwMAiVvMioqKmDVrFqKjo3HhwgWEhYVh3rx5uHDhAk6fPo1Zs2aJXc1K2urq6hAVFYVx48bBycmJDcZxcXGorq6Go6MjZs6ciXPnzsHGxgZBQUE4cOAAiouL4e/vj+Dg4D71BvSHoEejsbERNTU1sLCwQFRUFEpKShAYGNjj9528vDyMjIxgZGQELy8vMAyD2tpaoe7u+/fvA3jSUyEI0hYWFiPyB6+00OjrpzAMg9deew0nT55ETEwMu8QcIeR/goODMXnyZJw7dw4pKSnd9tva2iIsLAy3b98W2i8IzJmZmThx4oTYUdlcLhfh4eEwNTVFVFQUWltbsWDBAgDA6dOnUVFRMSTvayAYhsGpU6egqqqK0NBQ/PHHH/j222/x22+/QV9fHy+++CKWLl2KuLg4MAwDR0dH7Nu3D83Nzdi4cSNmzpw55AFZQFFREatWrYKSkhKqq6sxY8YMKCkp4dy5c7h8+bLE42I4HA50dXXh6uqK6dOnY/Xq1Vi1ahVCQkLYdZ1jYmJQVVU1xO9odKGW8lNeffVVHD58GKdPn4aGhgabCUdLS0vmus0IkRYOh4PQ0FB0dnbi999/B5fL7TbuwtfXFxUVFTh79iz09PRgaWkJoHuLefbs2SJfQ0FBATNnzsSVK1dw8eJFzJgxAwsWLGAzcM2cOZO9pixITExEXl4eJk2ahL1796KmpgZOTk5YvHgxTE1NwTAMjh07hqKiIhgaGiIuLg4TJ07EzJkzReYYH2rq6upYvXo1du3aheLiYkRERKCkpAQJCQk4evQofHx84OHh0ecBXRoaGtDQ0ICDgwOAJ93Lw/VjY7SglvJTdu3ahfr6egQFBcHExIR9HD16VNpFI0SmcDgczJkzB56envjtt9+Qnp7ebf/s2bNhbm6Oo0ePsjMZAOEW88mTJ8W2mBUUFDBjxgxYW1vj0qVLbPAwNzdHdHR0t9eUlpqaGhw5cgQVFRVITEyEoaEhNm/ejJUrV8LU1BQAcPnyZcTFxaGlpQUdHR1Ys2YN5syZI5WALGBoaIhFixahtLQUiYmJcHBwwLJly+Di4oI7d+7g+PHjEiVy6YmysjIF5T6ioPwUhmFEPjZs2CDtohEiczgcDubPnw8XFxccP34c2dnZQvvl5eWxbNkycLlc/PLLL+jo6GD3CQJzdnY2bty4ITYwy8vLIyQkBHZ2doiJiUFubi5mzpwJFxcXxMXFISkpSWrJLvh8PtLT07F7927k5+djypQp2Lx5M1asWCE0WOvmzZvYvXs32tvbMWXKFLzyyiuwt7eXSpmfZWdnB19fX6SlpSE1NRVKSkrw9/fHokWLoKqqinPnzrG5u8nwoKBMCOk3OTk5LFq0CHZ2djhy5Ajy8/OF9qupqWHlypWoqanBqVOnhAKoo6MjFi1ahJKSEly9elVsYJaTk0NQUBCcnJxw9epVZGZmYsqUKfD19cXdu3cRFxcn9tyh0NXVhfT0dBw9ehS//vor+Hw+tm3bhjVr1nQbOX3lyhVs374dGhoaeOutt7Bo0SKZuxXm4OAAT09P3L59m/3309PTw9y5cxESEoLy8nIcPXoUKSkpIhcoIYOLgjIhZEAELWJLS0scPny4W5enkZERFi1ahLS0NMTFxQntc3R0xLRp01BUVIQrV670GJinTZsGV1dXxMXF4eHDhxg3bhyCg4ORlZWFixcvorOzc8jeI/C/YHzs2DHExcWBy+VCW1sbERER8PLyEjq2ra0NBw8exI4dO2Bvb4+dO3fCzc1tSMs3EBMmTIC1tTWuXLmCyspKAE96QgQpU11dXZGUlIQTJ06gqKhIyqUd3SgoE0IGTEFBAStWrICxsTF++uknlJaWCu13dnZGSEgIrl69irS0NKF95ubmCAkJQWFhIWJiYsQGZg6HgylTpsDT0xM3b97E/fv34ejoiFmzZqGsrAxnzpxBS0vLoL+3Z4Oxnp4eFixYAD6fDzMzs26D3PLy8vDNN9/g+PHjmDBhAr7++muZncYlwOFwEBISAl1dXURHRwt1VysqKmLy5MlYvHgx1NTUcO7cOZw9exalpaWora1FRUUFSkpKkJ+fj+zsbKSnp+PBgwdsPvSnxxOQ3tEdeELIoFBUVMTq1atx8OBBHDp0CBs2bIChoSG7PyAgABUVFTh58iR0dXVhbGzM7rO0tMSMGTNw+fJlxMTEICQkROTIXw6HAz8/P8jLy+P27dvo6urCuHHjMHfuXERFRbFzmbW1tQf8frq6upCVlYXk5GQ0NjbC1tYWoaGh0NPTQ3x8PBoaGjBnzhy2nJ2dnbh8+TJu376N4uJiTJw4EVu2bJFadzWfz0dnZyc6OjrEPhoaGpCamgpFRUVwOByoqanhjz/+wNdffw1PT08wDIPOzk50dnaCx+Ohs7MTtbW1ePDgAc6cOQMTExMYGRl1yz7G5XLZh6Wlpcz/KJElFJQJIYNGSUkJa9aswf79+3Hw4EFs3LgRenp6AP43MCwyMhK//PILNm3aJPRlbm1tLXFg9vX1hby8PBITE9HV1QUfHx/Mnz8f58+fx+nTpxEWFiYU9Puiq6sLmZmZSElJYYNxWFgY+z4eP36M+/fvY9KkSdDR0UFjYyNKSkoQFRWFuro6qKqqwsbGBhs2bJAo3/bTga+nACp4SHqcJPd/GYZBcXEx1NXVoaKiAi6XC2dnZ/zxxx/Izs7GxIkToaSkBC6XCwUFBTbQAk+ytGVlZYHL5cLPzw+2trbscbSARf9RUCaEDCoVFRWsW7cOkZGRbGAWtFy5XC5WrFiBPXv24OjRo1i6dKnQuZIGZuBJPm55eXkkJCSAx+Nh0qRJmD9/PqKjo3H27Fl2OpWkng7GTU1N3YIxwzBobW3F5cuXoaOjA3Nzc5SVlSEhIQGnT5+GoaEhtLW1kZqaiqlTp7LduJIE2d5GkMvJyUFRUVHkQ0VFBVpaWmL3Cx5cLrfbtra2NpFpZqdOnYoLFy5AQUEBfn5+IoOso6MjamtrcfPmTdy8eZPNTCZuVSwiGcp9PciSk5Ph4+NDua9HCKr/odPY2IjIyEgwDIONGzcKfVkXFxdj//79cHR0hIaGBjQ1NYXqPz8/H5cvX4aVlVWPgRkAUlNTcfPmTbi7u8Pf3x9dXV2IjY1FXl4e/P39YW5uzna9Pv0QbGtvb0deXh6ysrLQ0tICfX19mJubQ1FRsduxeXl5qKmpgaurK/h8PnJyctDc3AxPT0+oq6vj0aNHcHBwgJOTk0RBUdIAKi8vPyStz54+/48ePcK1a9cwefJkeHp6ir0GwzDIzc3F7du30dbWBm9vb3h7ew/L/GRZ/vz3F7WUCSFDQkNDo1uLWbDogbm5OebOnYtjx45BX18fvr6+Quf2pcXs7u4OOTk5XL9+HXw+H1OnTsX06dNx+/Zt3Lx5U2z5OBwO6urqUF5eDh6PB2NjY3h7e7MLNTx9X1RBQQHV1dWorKxEUFAQOBwOUlNT4eXlhXHjxmHixIn4/fffMWPGDCxatGhUdN+6uLigoaEB8fHx0NTUFNvrwOFwYGdnB0tLSyQnJ+PevXvIysrC5MmT+9RTQZ6goEwIGTLa2tpsYD506BDWr1/PDnzy8vJCYWEhjh07BlNT024JNfoSmF1dXSEvL8/OWZ42bRr8/f1hb28PPp/PBlbB/c68vDz88ccfAIDx48fDx8enx/u/bW1tOH78OJycnNDa2ory8nJMnjwZ7u7uqKiowPnz52FhYYH58+ePioAs4Ovri4aGBly5cgXz5s2DgYGB2GO5XC4mTZoEJycn3Lx5E9HR0bCysqIu7T6iKVGEkCGlp6eHdevWoaGhAT/99JPQUqfBwcEwNTVFbGws6urqup0rCMwFBQWIjY3tMUmIk5MTQkJCkJWVhZiYGPD5fBgZGcHExAT6+vpQV1dHQUEBTp48iVu3bsHY2BhLly7FjBkzeh2Qdf36dTx+/BiVlZVoaWnB3Llz4efnh66uLly9ehVKSkpYsWLFqEspyeFwEBwcDD09PURFRUmU2UtbWxuzZ89GaGgoampqhDK5kd5RUCaEDDlDQ0OsXbsW1dXV+Pnnn9kvajk5OUyZMgWqqqqIiopCW1tbt3MFgTk/P7/XwGxvb4/p06cjLy+PTUbC4/GQmpqKI0eO4ObNmzAxMcHSpUsxffp0iUZH379/H9HR0eDz+XBzc8OSJUtgYmLC3rtubW3FsmXLRs09zWcpKCggNDQU8vLyuHDhgkRBlsPhwMbGBitWrIC+vv4wlHL0oKBMCBkWJiYmWLNmDcrKynDkyBF2yo6ioiJmzpyJtrY2XLlyBXw+v9u5gsCcl5fXa2C2tbXFzJkzUVhYiLNnz+LIkSO4desWTE1NsWzZMomDMcMwuH//Pvbu3QtVVVWsWbMGAQEB4HK5YBgGt27dQmlpKaZNmzbqA4+qqipmzZqF5uZmXL58WeS/kSjPzl8mvaMaI4QMG3Nzc6xatYq9lywIrpqampg5cyYeP36M+Ph4kedaW1tj5syZEgVma2trhIWFoba2lg3GISEhEicVaWtrw6VLl3Do0CHo6Ojg7bffhoWFBbs/NTUVaWlp8Pf37/d86JFGR0cHM2bMQElJCW7evCm1hUBGOwrKhJBhZW1tjRUrViAnJwenT59mW11mZmaYPHkyHjx4IHZZxmcDc08tNgsLC6xfv75PwRh4Mh3r+PHj+OOPP6Crq4sXXnhBKCNVfn4+bt++DW9vbzg5OUl83dHA3NwcAQEBSEtLw/3796VdnFGJgjIhZNjZ29tj6dKlyMjIwO3bt9lWl5ubG1xdXXHjxg2UlZWJPPfpwCwY0CVOX0ZCd3R04OrVq4iOjoa6ujo0NTXh7+8PS0tL9piqqirExMTA2tq62zSuscLZ2Rne3t5ISEhAXl6etIsz6lBQJoRIhbOzM+bNm4f8/HzcunULDMOAw+HA398fRkZGuHjxIhobG0We25fALImSkhKcOHECubm5mDZtGuTk5KChoYHJkyezxzQ1NSEqKgra2toICQkZVVOf+srX1xe2traIiYlBRUWFtIszqlBQJoRIjZubG/z8/JCens62mOXl5TFz5kwoKCggOjpa7JKMgxGYeTwebt26hbNnz0JDQwNLly5FR0cHysvLERQUBEVFRQBPFpuIjo4GAISFhY26qU99xeFwEBQUBD09PURHR4v98UT6joIyIUSq7Ozs4O/vjwcPHiApKQkAoKysjLCwMDQ0NCA2NlbsoKKBBOaKigr8+uuv7ICtiIgIdHZ24s6dO/Dw8ICpqSmAJ6stxcTEoL6+HuHh4WxWsrFOQUGB/YESFRUlNP+c9B8FZUKI1Lm4uMDPzw/JyclISUkB8CTpSEhICPLz83H37l2x5z49XUqSwNzV1YWkpCScPn0aXC4XixcvhoeHB/h8PmJjY6GpqYmJEyeyxycmJqKgoADTp08f9VOf+kpFRQXh4eHsVKmeRsQTyVBQJoTIBC8vL0yYMAGJiYl48OABgCcBd+LEibh79y5ycnLEnmtjYyNRYK6pqcGpU6eQkpKC8ePHY/78+eyc5ZSUFNTU1CA4OJjtnn706BH++OMP+Pv7w8rKapDf8eigo6PDTmejqVIDR0GZECIzxo8fD29vb9y6dQuPHj0CAHh7e8Pe3h5Xr15FVVWV2HN7Csx8Ph/37t3Db7/9hq6uLixYsIBd+hF40pWdkpKCcePGsfmdi4uLcePGDbi5ucHNzW0I3/XIZ2ZmhoCAAPZHDOk/CsqEEJnB4XDg6+sLd3d3XL9+HVlZWeBwOAgMDISOjg6ioqLQ0tIi9nxRgbmhoQFnzpxBYmIi3N3dsWjRIqGFFXg8HmJjY6Gnp4dx48YBAGpra3H58mWYmZnB399/TI+0lpSzszPGjRuHhIQE5ObmSrs4IxYFZUKITBFMi3JyckJsbCxyc3PZ/MsAcPHiRTZFpyhPB+azZ8/ixIkTQotIPDtyOjExEY2NjQgODoa8vDxaW1sRFRUFNTU1zJgxg1JF9sHEiRNhZ2eHmJgYlJeXS7s4IxJ92gghMofD4SAgIAB2dna4cuUKCgoKoK6ujtDQUFRVVeHGjRs93rsUBOaKigrY29tj8eLFMDEx6XZcSUkJHjx4AF9fX+jo6IDH47HTsMLDw9kpUUQygqlS+vr6NFWqnygoE0JkkpycHIKCgmBlZYVLly6huLgYhoaGCAwMREZGRq9pHm1sbLBx40ZMmzZNZHDt6OhAXFwcTE1N4eHhAYZhEBcXh6qqKoSFhUFDQ2Oo3tqoJpgqxeVyceHCBZoq1UcUlAkhMkteXh4hISEwNTVFdHQ0ysrK4ODgwKZ5LCws7PV8cW7duoW2tjYEBQWBw+Hg7t27yM7ORnBwMIyMjAb7rYwpKioqmDVrFjo6OnocnEe6o6BMCJFpgvvJRkZGuHDhAioqKjBx4kRYWlriypUrqK2t7fM18/PzkZGRAX9/f2hoaCArKwt3796Fr68v7OzshuBdjD3a2tpYsWIFzMzMpF2UEYWCMiFE5gm6RHV0dHD+/HnU1tYiJCQE6urqiI6ORltbm8TXamtrw/Xr12FlZQUnJyeUlZUhLi4OTk5O8Pb2Hro3MQaN9XSk/UFBmRAyInC5XMyaNQsaGho4d+4cWlpaEBoaira2Nly+fFmiFJsMw+D69evo6upCQEAAGhsbER0dDSMjIwQEBNDUJyJ1FJQJISOGkpIS5syZAxUVFZw9exYAMHPmTJSWluL27du9np+dnY3c3FwEBARAQUEBFy5cgJKSEmbOnNnj/WdChgsFZULIiKKsrIw5c+ZAQUEB586dg5aWFvz9/ZGamor09HSx5zU1NeHmzZuwt7eHtbU1Ll26hNbWVoSHh0NZWXkY3wEh4lFQJoSMOKqqqoiIiAAAnD17FjY2NnB1dcWNGzdQWlra7XiGYXDt2jUoKCjA398fN27cQFlZGUJDQ6GtrT3MpSdEPArKhJARSV1dHREREeDxeDh37hzGjx8PIyMjXLx4sVvSikePHqGoqAiBgYHIzMxEeno6AgIC2OUZCZEVFJQJISOWpqYmIiIi2NSYgkQhgqxcAFBfX4/bt2/D1dUVXV1dSEhIwLhx4+Dk5CTl0hPSHQVlQsiIpq2tjTlz5qCxsRGxsbEIDg5GQ0MDYmJi0NXVhatXr0JVVZVN2Wlrayu0XjIhsoSCMiFkxNPT08OcOXNQW1uLO3fuYNq0aSgoKMDp06dRXl6OSZMmISYmBrq6umwGL0JkEQVlQsioYGBggFmzZqGiogKZmZnw8fFBZWUlXF1dkZycDA6Hg7CwMEpoQWQaBWVCyKhhbGyMsLAwPH78GFVVVZgzZw4aGhrQ2NiI8PBwqKqqSruIhPSIfjISQkYVc3NzhIaG4uLFi6ipqUFjYyNmzZoFPT09aReNkF5RS/kZ165dw9y5c2FqagoOh4NTp05Ju0iEkD6ytLRESEgImpubMWXKFFhYWEi7SIRIhFrKz2huboaXlxeee+45LFq0SNrFIYT0k62tLSwtLekeMhlR6NP6jFmzZmHWrFnSLgYhZBBQQCYjDX1iB6i9vR3t7e3s86amJimWhhBCyEhG95QHaMeOHdDS0mIfgYGB0i4SIYSQEYqC8gC9++67qK+vZx9xcXHSLhIhhJARirqvB0hJSQlKSkrsc3V1dSmWhhBCyEhGLWVCCCFERlBL+RlNTU3Izs5mn+fl5eHevXvQ1dWFpaWlFEtGCCFktKOg/IykpCQEBwezz9966y0AwPr167F//34plWpgqqqqUFVVNSTXbm9vR0tLCzQ0NKCiojIkrzGUWltbkZ2dDVVVVaHbECMF1b90Uf33Tl9fH/r6+kNy7dGIwzAMI+1CjCalpaXYvXs3Nm/eDBMTE2kXB+3t7QgLC6MBaIQQqQgMDER0dPSI/NElDRSUR7mGhgZoaWkhLi6OBqFJQVNTEwIDA6n+pYTqX7oE9V9fXw9NTU1pF2dEoO7rMcLb25v+KKSgoaEBANW/tFD9S5eg/onkaPQ1IYQQIiMoKBNCCCEygoLyKKekpISPPvqIBllICdW/dFH9SxfVf9/RQC9CCCFERlBLmRBCCJERFJQJIYQQGUFBmRBCCJERFJSJxPLz88HhcEZsulFCCJF1FJSHSE5ODjZv3gxbW1soKytDU1MTU6ZMwTfffIPW1tYhe920tDRs27YN+fn5Q/Yakvj0008xb948GBkZgcPhYNu2bVItjzgcDkeix9WrVwf8Wi0tLdi2bVufrjVS6rG/ZLn+09PT8c4778Db2xsaGhowMTHBnDlzkJSUNOCyyApZrv/Hjx9jzZo1cHJygoaGBrS1teHr64sDBw5gNI9PpoxeQ+DcuXNYunQplJSUsG7dOri7u6OjowM3btzAX/7yFzx8+BB79uwZktdOS0vD9u3bERQUBGtr6yF5DUm8//77MDY2xrhx4xAdHS21cvTm0KFDQs8PHjyIS5cuddvu4uIy4NdqaWnB9u3bAQBBQUESnTNS6rG/ZLn+f/jhB+zbtw+LFy/GK6+8gvr6euzevRt+fn6IiorCjBkzBlwmaZPl+q+qqkJxcTGWLFkCS0tLdHZ24tKlS9iwYQMyMjLw2WefDbhMMokhgyo3N5dRV1dnnJ2dmcePH3fbn5WVxezcuXPIXv/48eMMACY2NrbXY/l8PtPS0iLxtfPy8hgATGRkpETHMgzDVFZWMgCYjz76SOLXkaZXX32VGao/i/7UxUitx/6SpfpPSkpiGhsbhbZVVVUxBgYGzJQpU4aghNInS/UvTkREBKOmpsbweLzBKZiMoe7rQfbFF1+gqakJ+/btE7lKlL29Pf70pz+xz3k8Hj755BPY2dlBSUkJ1tbW+Nvf/ob29nah86ytrREREYEbN27A19cXysrKsLW1xcGDB9lj9u/fj6VLlwIAgoODu3U9Ca4RHR2NCRMmQEVFBbt37wYA5ObmYunSpdDV1YWqqir8/Pxw7ty5fteDNFvpg43P52Pnzp1wc3ODsrIyjIyMsHnzZtTW1godl5SUhLCwMOjr60NFRQU2NjZ47rnnADy5H29gYAAA2L59O/tv01t39Giqx/6SVv37+Ph0W8RCT08PAQEBePTo0eC+SRkmzc+/KNbW1mhpaUFHR8eA35ssou7rQXbmzBnY2trC399fouNfeOEFHDhwAEuWLMHbb7+NhIQE7NixA48ePcLJkyeFjs3OzsaSJUvw/PPPY/369fjxxx+xYcMG+Pj4wM3NDdOmTcPrr7+Of//73/jb3/7Gdjk93fWUkZGBlStXYvPmzdi0aROcnJxQXl4Of39/tLS04PXXX4eenh4OHDiAefPm4cSJE1i4cOHgVdAItHnzZuzfvx8bN27E66+/jry8PPz3v/9FSkoKbt68CS6Xi4qKCoSGhsLAwABbt26FtrY28vPz8dtvvwEADAwMsGvXLrz88stYuHAhFi1aBADw9PSU5lsbEWSt/svKysbU+sDSrv/W1lY0NzejqakJcXFxiIyMxOTJk0fk+tUSkXZTfTSpr69nADDz58+X6Ph79+4xAJgXXnhBaPuf//xnBgATExPDbrOysmIAMNeuXWO3VVRUMEpKSszbb7/Nbuup+1pwjaioKKHtb7zxBgOAuX79OrutsbGRsbGxYaytrZmuri6GYfrWfS0w0rpdn+2+u379OgOA+fnnn4WOi4qKEtp+8uRJBgBz584dsdceSF2MtHrsL1mtf4Fr164xHA6H+eCDD/p9DVkmi/W/Y8cOBgD7mD59OlNYWNina4wk1H09iATLlGloaEh0/Pnz5wEAb731ltD2t99+GwC6dR+7uroiICCAfW5gYAAnJyfk5uZKXEYbGxuEhYV1K4evry+mTp3KblNXV8eLL76I/Px8pKWlSXz90eb48ePQ0tLCzJkzUVVVxT4EXZuxsbEAAG1tbQDA2bNn0dnZKcUSjy6yVP8VFRVYtWoVbGxs8M477wzJa8gaWaj/lStX4tKlSzh8+DBWrVoFAEM6g0XaKCgPIsF6rY2NjRIdX1BQADk5Odjb2wttNzY2hra2NgoKCoS2W1padruGjo5Ot3s7PbGxsRFZDicnp27bBd3ez5ZjLMnKykJ9fT0MDQ1hYGAg9GhqakJFRQUAIDAwEIsXL8b27duhr6+P+fPnIzIystvYANI3slL/zc3NiIiIQGNjI06fPt3tXvNoJQv1b2VlhRkzZmDlypX4+eefYWtrixkzZozawEz3lAeRpqYmTE1NkZqa2qfzOByORMfJy8uL3M70Yc7eqL0PM0T4fD4MDQ3x888/i9wvGLzC4XBw4sQJxMfH48yZM4iOjsZzzz2Hr7/+GvHx8WPmS3ywyUL9d3R0YNGiRbh//z6io6Ph7u7e72uNNLJQ/89asmQJ9u7di2vXrnXr9RsNKCgPsoiICOzZswe3b9/G5MmTezzWysoKfD4fWVlZQoOxysvLUVdXBysrqz6/vqQB/tlyZGRkdNuenp7O7h+r7OzscPnyZUyZMkWiHzR+fn7w8/PDp59+isOHD2P16tU4cuQIXnjhhX7924x10q5/Pp+PdevW4cqVKzh27BgCAwP78zZGLGnXvyiCFnJ9ff2gXE/WUPf1IHvnnXegpqaGF154AeXl5d325+Tk4JtvvgEAzJ49GwCwc+dOoWP++c9/AgDmzJnT59dXU1MDANTV1Ul8zuzZs5GYmIjbt2+z25qbm7Fnzx5YW1vD1dW1z+UYLZYtW4auri588skn3fbxeDy2nmtra7v1WHh7ewMA24WnqqoKoG//NmOdtOt/y5YtOHr0KL777jt2xPBYIs36r6ysFLl937594HA4GD9+vETXGWmopTzI7OzscPjwYSxfvhwuLi5CGb1u3bqF48ePY8OGDQAALy8vrF+/Hnv27EFdXR0CAwORmJiIAwcOYMGCBQgODu7z63t7e0NeXh6ff/456uvroaSkhJCQEBgaGoo9Z+vWrfjll18wa9YsvP7669DV1cWBAweQl5eHX3/9FXJyff/tdujQIRQUFKClpQUAcO3aNfz9738HAKxdu3bEtL4DAwOxefNm7NixA/fu3UNoaCi4XC6ysrJw/PhxfPPNN1iyZAkOHDiA7777DgsXLoSdnR0aGxuxd+9eaGpqsj++VFRU4OrqiqNHj8LR0RG6urpwd3fvsTt0tNRjf0mz/nfu3InvvvsOkydPhqqqKn766Seh/QsXLmR/BI9W0qz/Tz/9FDdv3kR4eDgsLS1RU1ODX3/9FXfu3MGWLVu6jcUZNaQ7+Hv0yszMZDZt2sRYW1szioqKjIaGBjNlyhTmP//5D9PW1sYe19nZyWzfvp2xsbFhuFwuY2Fhwbz77rtCxzDMk+lMc+bM6fY6gYGBTGBgoNC2vXv3Mra2toy8vLzQ9Chx12AYhsnJyWGWLFnCaGtrM8rKyoyvry9z9uxZoWP6MiUqMDBQaBrD0w9Jso1Ji7iMRnv27GF8fHwYFRUVRkNDg/Hw8GDeeecdNmtbcnIys3LlSsbS0pJRUlJiDA0NmYiICCYpKUnoOrdu3WJ8fHwYRUVFiaaHjNR67C9Zqv/169eLrXsAbLa10USW6v/ixYtMREQEY2pqynC5XPY7NDIykuHz+YP6vmUJh2FGcWZvQgghZAShe8qEEEKIjKCgTAghhMgICsqEEEKIjKCgTAghhMgICsqEEEKIjKCgLCVffPEFnJ2dwefzpV2UAdu6dSsmTZok7WL0CdW/dFH9SxfVvwyT9pyssai+vp7R1dVlfvzxR3Yb/n/u41dffdXt+MjIyF6XRZPUr7/+yixbtoyxsbFhVFRUGEdHR+att95iamtrRR5/+vRpZty4cYySkhJjYWHBfPjhh0xnZ6fQMaWlpYySkhJz+vTpAZdvOFD9SxfVv3RR/cs2CspS8K9//YvR1NRkWltb2W2CPwojIyOmublZ6PjB/KPQ09NjPDw8mA8++IDZu3cv8/rrrzOKioqMs7Mz09LSInTs+fPnGQ6HwwQHBzN79uxhtmzZwsjJyTEvvfRSt+suW7aMCQgIGHD5hgPVv3RR/UsX1b9so6AsBZ6ensyaNWuEtgFgvL29GQDM119/LbRvMP8oRGWBOnDgAAOA2bt3r9B2V1dXxsvLS+iX6XvvvcdwOBzm0aNHQseeOHGC4XA4TE5OzoDLONSo/qWL6l+6qP5lG91THmZ5eXm4f/8+ZsyY0W3flClTEBISgi+++GLI1goNCgrqtm3hwoUAgEePHrHb0tLSkJaWhhdffBEKCv9Lkf7KK6+AYRicOHFC6BqC93P69OkhKPXgofqXLqp/6aL6l30UlIfZrVu3AEDsCifbtm1DeXk5du3a1eN12tvbUVVVJdGjN2VlZQAAfX19dltKSgoAYMKECULHmpqawtzcnN0voKWlBTs7O9y8ebPX15Mmqn/povqXLqp/2UerRA0zwRrFNjY2IvcHBAQgODgYX375JV5++WWxa5j+8ssv2Lhxo0SvyfSS3vzzzz+HvLw8lixZwm4rLS0FAJiYmHQ73sTEBI8fP+623dbWFmlpaRKVSVqo/qWL6l+6qP5lHwXlYVZdXQ0FBQWoq6uLPWbbtm0IDAzE999/jzfffFPkMWFhYbh06dKAy3P48GHs27cP77zzDhwcHNjtgu4rJSWlbucoKyujoaGh23YdHZ1uv2BlDdW/dFH9SxfVv+yjoCyDpk2bhuDgYHzxxRd46aWXRB5jYmIi8ldkX1y/fh3PP/88wsLC8OmnnwrtE/xCFixQ/rS2tjaRv6AZhgGHwxlQmWQB1b90Uf1LF9W/dFFQHmZ6enrg8XhobGyEhoaG2OM++ugjBAUFYffu3dDW1u62v7W1FfX19RK9prGxcbdtf/zxB+bNmwd3d3ecOHFCaDAF8L9uo9LSUlhYWAjtKy0tha+vb7dr1tbWCt0XkkVU/9JF9S9dVP+yjwZ6DTNnZ2cAT0ZB9iQwMBBBQUH4/PPPRY6EPHr0KPtrtbfHs3JychAeHg5DQ0OcP39eZFeWt7c3ACApKUlo++PHj1FcXMzuf1peXh5cXFx6fF/SRvUvXVT/0kX1L/uopTzMJk+eDODJh83T07PHY7dt24agoCDs2bOn277+3tMpKytDaGgo5OTkEB0dDQMDA5HHubm5wdnZGXv27MHmzZshLy8PANi1axc4HI7QoAwAqK+vR05ODl5++eU+l2k4Uf1LF9W/dFH9jwDSmR49trm7uzMrV64U2gaAefXVV7sdGxgYyGbbGYzJ+15eXgwA5p133mEOHTok9Lh48aLQsWfOnGE4HA4TEhLC7Nmzh3n99dcZOTk5ZtOmTd2ue+LECQYAk52dPeAyDjWqf+mi+pcuqn/ZRkFZCv75z38y6urqQmnlxP1RxMbGDuofheBaoh6BgYHdjj958iTj7e3NKCkpMebm5sz777/PdHR0dDtu+fLlzNSpUwdcvuFA9S9dVP/SRfUv2ygoS0FdXR2jq6vL/PDDD9IuyqAoLS1llJWVmVOnTkm7KBKh+pcuqn/povqXbTTQSwq0tLTwzjvv4MsvvxwVS6ft3LkTHh4emD9/vrSLIhGqf+mi+pcuqn/ZxmGYXtKtEEIIIWRYUEuZEEIIkREUlAkhhBAZQUGZEEIIkREUlAkhhBAZQUGZEEIIkREUlAkhhBAZQUGZEEIIkREUlAkhhBAZQUGZEEIIkREUlAkhhBAZQUGZEEIIkREUlAkhhBAZQUGZEEIIkREUlAdZaWkptm3bhtLSUmkXhRBCyAhDQXmQlZaWYvv27RSUCSGE9BkFZUIIIURGUFAmhBBCZAQFZUIIIURGUFAmhBBCZAQFZUIIIUOio6MDt27dQkdHh7SLMmJQUCaEEDIk4uPjERkZiYSEBGkXZcSgoEwIIWTQtbe3Izo6Gnl5eYiKikJ7e7u0izQiUFAmhBAy6BISEpCZmQlPT09kZmYiMTFR2kUaESgoE0IIGVSCVrKioiI0NTWhqKhIrWUJUVAmhBAyqFJSUpCTk4Pm5mY8fPgQzc3NyMnJQUpKirSLJvMUpF0AQggho4uFhQVWr14tcjvpGQVlQgghg8rMzAxmZmbo6OhAUlISJkyYAEVFRWkXa0Sg7mtCCCFDgqZE9R0FZUIIIYOOpkT1DwVlQgghg46mRPUPBWVCCCGDiqZE9R8FZUIIIYOKpkT1H42+JoQQMqhoSlT/UVB+yo4dO/Dbb78hPT0dKioq8Pf3x+effw4nJydpF40QQkYMwZQo0nfUff2UuLg4vPrqq4iPj8elS5fQ2dmJ0NBQNDc3S7tohBBCxgBqKT8lKipK6Pn+/fthaGiIu3fvYtq0aVIqFSGEkLGCgnIP6uvrAQC6urpij2lvbxcaUdjU1DTk5SKEEDI6Ufe1GHw+H2+88QamTJkCd3d3scft2LEDWlpa7CMwMHAYS0kIIWQ0oaAsxquvvorU1FQcOXKkx+Peffdd1NfXs4+4uLhhKiEhhJDRhrqvRXjttddw9uxZXLt2Debm5j0eq6SkBCUlJfa5urr6UBePEELIKEVB+SkMw2DLli04efIkrl69ChsbG2kXiRBCyBhCQfkpr776Kg4fPozTp09DQ0MDZWVlAAAtLS2oqKhIuXSEEEJGO7qn/JRdu3ahvr4eQUFBMDExYR9Hjx6VdtEIIYSMAdRSfgrDMNIuAiGEkDGMWsqEDKGOjg7cunULHR0d0i4KIWQEoKBMyBCKj49HZGQkEhISpF0UQsgIQEGZkCEiWFM2Ly+P1pIlhEhEpoJyV1cXjhw5gs2bN2PhwoV48OABgCfpLn/77TeUl5dLuYSESC4hIQGZmZnw9PREZmYmEhMTpV0kQoiMk5mgXFdXhylTpmDVqlX45Zdf8Pvvv6OyshLAk4Qcr7/+Or755hspl5IQyQhayYqKitDU1ISioiK1lgkhvZKZoLx161Y8fPgQ0dHRyM3NFRoJLS8vjyVLluD8+fNSLCEhkktJSUFOTg6am5vx8OFDNDc3IycnBykpKdIuGiFEhsnMlKhTp05hy5YtmDlzJqqrq7vtd3R0xP79+4e/YIT0g4WFBVavXi1yOyGEiCMzQbm+vr7HtJadnZ3g8XjDWCJC+s/MzAxmZmbSLgYhZISRme5rOzs7JCcni91/8eJFuLq6DmOJCCGEkOElM0H5hRdewI8//oijR4+y95M5HA7a29vx3nvvISoqCps3b5ZyKQkhhJChIzPd13/605/w8OFDrFy5Etra2gCAVatWobq6GjweD5s3b8bzzz8v3UISQgghQ0hmgjKHw8HevXuxfv16nDhxAllZWeDz+bCzs8OyZcswbdo0aReREEIIGVIyE5QFpk6diqlTp0q7GIQQQoZIR0cHkpKSMGHCBCgqKkq7ODJFZu4p5+Xl4cyZM2L3nzlzBvn5+cNXIEIIIQMibkEWygkvnswE5T//+c/497//LXb/t99+i61btw5jiQghhAyEqOBLOeF7JjNB+fbt25g5c6bY/dOnT8f169eHsUSEDBwt3UjGKnHBl3LC90xmgnJtbS00NDTE7ldXVxeZ6YsQWUbddGSsEhV8KSd872QmKFtaWuLmzZti91+/fh3m5ubDWCJCBoa66chYJS74JiQkUE74XsjM6OuVK1fik08+ga+vL1577TXIyT35vdDV1YX//ve/OHr0KN577z0pl5IQyYlqKQQEBEi7WIQMOcGCLG1tbXj48CE6OzuRk5ODuro6ygnfC5kJyu+++y5u3LiBN954A59++imcnJwAABkZGaisrERQUBAFZTJiiGsp+Pr6QklJSdrFI2RIiVuQZfz48ZQTvhcyE5SVlJRw8eJFHDhwAL/99htycnIAAL6+vli8eDHWrVvHtp4JkXXiWgopKSnw8/OTdvEIGVK0IEv/yUxQBgA5OTls3LgRGzdulHZRCBkQWrqRENIfMhWUCRktqKVACOkPmQrK0dHR2LdvH3Jzc1FbW8uuFiXA4XDYbm1CCCFktJGZoPzll19i69atMDIygq+vLzw8PKRdJEIIIWRYyUxQ/uabbxASEoLz58+Dy+VKuziEEELIsJOZ4cy1tbVYsmQJBWQy5lFqzsFDdUlGGpkJyr6+vsjIyJB2MQiROkrNOXioLqWro6MDV69epR9FfSAzQfm7777Db7/9hsOHD0u1HNeuXcPcuXNhamoKDoeDU6dOSbU8ZHQS14Kj1JyDh+pS+uLj43Hw4EH6UdQHMhOUly9fDh6Ph7Vr10JLSwtubm7w9PQUenh5eQ15OZqbm+Hl5YVvv/12yF+LjF3iWnC0gs7gobqULvpR1D8yM9BLV1cXenp6cHBwkGo5Zs2ahVmzZkm1DGT06OjoQFJSEiZMmABFRUUA3b+sBKk3KTXn4KG6lD7BjyI3NzfK/d4HMhOUr169Ku0i9Et7e7vQL8CmpiYplobImvj4eBw6dAhdXV3sF5K4hSooNefgobqUrqd/FKmpqdGPoj6QmaA8Uu3YsQPbt2+XdjGIDBJ8MeXm5rJfSADEtuAoNefgobqUrqd/FD169AhycnL0o0hCMhWUGxoa8N133yE2NhYVFRXYvXs3fH19UVNTg/3792PevHmwt7eXdjGFvPvuu3jrrbfY5/fu3UNgYKAUS0RkhaBF7OHhwbaIuVxujy04Ss05OCjNqXQ9/aOorq4O2tra7HbSM5kJysXFxQgMDERRUREcHByQnp7OdgXr6upi9+7dKCgowDfffCPlkgpTUlIS6o5RV1eXYmmIrHi6+05DQ4NtET///PPUgiOj3tM/ioqKiujz3QcyE5T/8pe/oLGxEffu3YOhoSEMDQ2F9i9YsABnz56VUukI6Zunu+8ePHgAAMjJyUFFRQXmzp0r5dIRMnza2tqkXYQRRWaC8sWLF/Hmm2/C1dUV1dXV3fbb2tqiqKhoyMvR1NSE7Oxs9nleXh7u3bsHXV1dWFpaDvnrk9Hh6e67+vp6aGlpsdsJGUsaGxulXYQRRWaCcmtrKwwMDMTuH65/2KSkJAQHB7PPBfeL169fj/379w9LGcjI93T3XWpqKhwdHZGUlNTjZ5yQ0aiurk7aRRhRZCYou7q64tq1a9i8ebPI/adOncK4ceOGvBxBQUHdlowkZCBycnJQXV2Nn376SWhqFCFjQW1trbSLMKLITEavN954A0eOHMHnn3+O+vp6AACfz0d2djbWrl2L27dv480335RyKQnpu6qqKpw6dYoyG5Exqbm5mbqw+0BmWspr1qxBQUEB3n//fbz33nsAgPDwcDAMAzk5OXz22WdYsGCBdAtJSB9NmDABGRkZYBgGmzZt6jWzkagMYISMdLm5uUJpkulzLp7MBGUAeO+997B27Vr8+uuvyM7OBp/Ph52dHRYtWgRbW1tpF4+QPistLUVTUxMUFBRQX18PLpfbY2YjURnACBnpHjx4AFdXV3ZpXvqciycTQbmlpQUBAQHYtGkTXnrpJeqmJqNGZ2cn+/9//PEHuFwuOjo6RGY2EpcTm5CRavz48cjOzoa2tjbs7OwwefJk+pz3QibuKauqqiIvLw8cDkfaRSFkUMnLywN4kmTGz88PPj4+MDU1RWVlJa5duya0dCOtakRGm6KiIjQ2NqK2thapqakoLS2lz3kvZCIoA0/uH0dHR0u7GIQMKjm5J39iCgoK8PLygpeXF9zd3REXF4cPP/wQhw4dQktLi9hVjWhQGBmp2tvb2cQhnZ2d6OjoQHR0NM6dO0ef8x7IRPc1AHzwwQdYunQp1q5di82bN8PGxgYqKirdjtPV1ZVC6Qjpu8LCQjQ0NAB4ktWopqYGurq66OzsRFpaGsrLy/Hrr7+yX1737t2DvLw8rWpERoWEhATweDwAT2bS5OfnQ15eHrdv34auri59zsWQmaDs5uYGAEhLS8Phw4fFHtfV1TVcRSKkXxITE/HJJ5/g3Llz7Jz39vZ2/O1vf4OHhwc8PDxQXl4OMzMzlJeXIy8vD/r6+rC2toacnByMjIxgbm4OXV3dIcsAVlNTgy1btuDMmTOQk5PD4sWL8c0330iUu51hGMyePRtRUVE4efKk0KyIO3fuYOvWrbh79y44HA58fX3xxRdfCI28JaOfoOdHcEuSYRg8fPgQU6ZMgYeHBywsLODq6srup0x3/yMzQfnDDz+ke8pkxPvtt9+wfPlyMAzTLQkNwzBITU3FgwcPYGtrC1NTUzQ0NODhw4eYM2dOtykjXV1daG9vB8Mw/frbCAoKwoYNG7Bhw4Zu+1avXo3S0lJcunQJnZ2d2LhxI1588cUefxAL7Ny5U2R5mpqaEB4ejnnz5uG7774Dj8fDRx99hLCwMBQVFbEjb8noJ8j9/vTfQGVlJRobG9nPuYmJCSZOnCitIsqsPgVlGxubPn85cDgc5OTk9Hrctm3b+nRdQmRNYmIili9fjq6uLrFZ4fh8PoAnWb4UFBSgrKyMyspKFBUVdZv2V1lZicuXL0NLSwteXl5wcHBgB44NxKNHjxAVFYU7d+5gwoQJAID//Oc/mD17Nr766iuYmpqKPffevXv4+uuvkZSUBBMTE6F96enpqKmpwccff8y2fD766CN4enqioKBA5pZdJUNHkPv90qVLaGtrg5KSEnx9faGjowMA4PF4OHHiBHg8Hvz8/KhB9pQ+BeXAwMBulZeUlISHDx/C1dUVTk5OAICMjAykpaXB3d0dPj4+/SpYfX091NXVB+VLiJDh8Pe//11kC1kUDoeD9vZ2TJ8+HQDYLytR6uvrce3aNdy9exdeXl5wdnaGgkL/O7lu374NbW1tNiADwIwZMyAnJ4eEhAQsXLhQ5HktLS1YtWoVvv32WxgbG3fb7+TkBD09Pezbtw9/+9vf0NXVhX379sHFxQXW1tb9Li8ZeQS53wXjgrhcrlBPUG5uLhISEtgfqRSY/6dPf9nPLshw6tQpnDp1CpcuXWK/XAQuXbqEZcuW4ZNPPpH4+klJSXj//ffZqSIXL15ESEgIqqqq8Pzzz+PNN99EUFBQX4pMyLAoLCzE2bNnJc6bzjAMCgoKYGFhIfHgxebmZty6dQspKSnw9PQUSsbQF2VlZd2WRlVQUICuri7KysrEnvfmm2/C398f8+fPF7lfQ0MDV69exYIFC9i/ewcHB0RHRw/oRwQZXQQDHauqqvDw4UNYW1ujra0N06ZNo0YYBjgl6sMPP8SWLVu6BWQAmDlzJl577TW8//77El3r1q1bmDp1KrKysrBmzRr2FxQA6Ovro76+Hrt37x5IcQkRqaOjAy0tLX1+NDY2Ijs7G3Fxcfj000/7vJAJwzB49OgR+Hx+nx7Nzc24ffs2fvrpJ9y7d4+d6/zZZ59BXV2dfVy/fh0vvfSS0LbCwsJ+1dHvv/+OmJgY7Ny5U+wxra2teP755zFlyhTEx8fj5s2bcHd3x5w5c9Da2tqv1yUjm+Bv4um/jby8PKGBjvn5+cjKykJUVJTQvP2xakA/X7OysqCnpyd2v56enkT3kwHgb3/7G1xcXBAfH4/Gxkb88MMPQvuDg4Nx4MCBgRSXkG46OjqQmJiIpqamHo/j8XhoaGhAfX09GhoaUFdXh8bGRvbH48OHD8HhcPocmGtqanp9bXGampoQGxuLhoYG+Pn54aWXXsKyZcvY/atXr8bixYuxaNEidpupqSmMjY1RUVHR7f3V1NSI7JYGgJiYGOTk5EBbW1to++LFixEQEICrV6/i8OHDyM/Px+3bt9n52YcPH4aOjg5Onz6NFStW9Ot9kpFLEGQFs2YErWR5eXmoqKiwAx2tra1RUlKCM2fOICwsTKJZAKPVgIKynZ0dIiMj8fzzz3erxMbGRvz4448S56y+c+cOduzYASUlJZFfUmZmZj12rRHSHzweD01NTVBUVGRT/bW3t6O2thY1NTXsfxsbG3sMuIqKiv1a8rO+vh4Mw/Sr205DQwNmZmZoaWkBj8eDrq6uUFe4iooKDA0Nuw2wmjx5Murq6nD37l12zEdMTAz4fD4mTZok8rW2bt2KF154QWibh4cH/vWvf2Hu3LkAntxzlpOTE7o3KHj+dM8XGRueTR7S2dmJoqIiVFZWorOzE48fP0ZXV5fQQMfq6mqcPHkSM2bM6DaQcKwYUFD++9//jiVLlsDZ2RkbNmxg//izsrJw4MABlJeX4/jx4xJdi8vl9viHW1JSMqZ/PZHBxzAM6urqUFZWhra2NtTX16O6ulrilquWlhb09PSgp6cHHx8fXL16tc+BuaWlBdXV1VBRUYGysjIUFRV7HfAiJycHa2trmJqaor29vc/L4rm4uCA8PBybNm3C999/j87OTrz22mtYsWIFO/K6pKQE06dPx8GDB+Hr6wtjY2ORrWhLS0vY2NgAeHLL6i9/+QteffVVbNmyBXw+H//4xz+goKCA4ODgPpVRmmgFo8EhKnmIvr4+fH19ux379EDH1tZWnD17Fr6+vvD09BxzA8AGFJQXLFiA8+fP469//Ss+++wzoX3e3t7Yt28fwsLCJLqWn58fTpw4gTfeeKPbvubmZkRGRiIwMHAgxSVjGJ/PZ3+RFxcXo6ioCEVFRRIFYDk5Oejo6LABWE9PD7q6ut0GWQUEBODmzZsSJ7hRU1NDS0sLe3+ts7MTHA4HysrKUFZWFtl61tDQgIODA9TU1IS2d3R04N69exIHkp9//hmvvfYapk+fziYP+fe//83u7+zsREZGBlpaWiR6LwDg7OyMM2fOYPv27Zg8eTLk5OQwbtw4REVFyVyrp6fASysYDZy45CHPzscXhcfjoaCgAJ2dnSgtLUVwcPCYWrBiwEMiQ0NDERoairKyMhQUFAAArKysxN6bEmf79u0IDAzEnDlzsHLlSgBPVtXJzc3FV199hcrKSnzwwQcDLS4ZA3g8HkpLS4WCb3FxsUT5dblcLnR1dYUCsLa2tkTdy88//zxu3rwp0b1lDoeDkJAQaGtrC92nZRgGra2taG1tBZfLZVvQurq6MDIygo6OjsiWQ2JiIo4fPy4USK5evSr29XV1dXtMFGJtbd3rexC1f+bMmZg5c2aP5w0nccFXXOClFYwGR0pKCu7fv8+uksbn89m/x95uaT49XUpBQQGnTp1CWFhYt/EMo9WgzVMQ170lqUmTJuH8+fN4+eWXsW7dOgDA22+/DeDJvevz58/D09NzUMpKRo+2tjaUlJQIBd/S0lK226wn6urqMDU1BZ/Ph5GREUxMTKCpqdnv7jI3Nzfs2LED7777LhiGEXk7RjAAav369UKf587OTrS1tbEZvHg8HlpbW1FdXQ0lJSWoqqpCSUlJZNk6Ojpw48YNCiQiiAq+PQVeUSsYUWu5bxITE/HBBx/gypUr7A83Pp+PBw8egMfjYcGCBTA3N0dBQQGsrKyEpsuJmi5VX1+P06dPIzw8HEZGRtJ6W8NmwEG5sLAQn332GWJjY1FZWYlTp05h2rRpqKqqwscff4yNGzdi3Lhx3c5raGiAmpqaUAskJCQEGRkZuHfvHrKyssDn82FnZwcfH58xd1+BdNfU1CQUfIuKilBRUSHRfVxBHmkLCwuYm5vDwsIC2traaG1txbVr16ChoQFlZeUBlzEkJAQ//vgj/vWvf+HevXvd9ltbW2P+/PkwNTVFXl4eLCws2Mxe5ubmUFdXR0lJCW7cuMH+ffB4PCQnJyMpKQlycnKYPn06rKys2L+JjIwMZGVlUSB5hrjgKy7wilupi37kSK6nNLPAk8/q559/joiICNTU1IDP58PBwYHdL2q6lIODA9rb23H+/HmEhYX1mHFOUv3J/b5nzx4cPnwYycnJ7HKUT7fer169KnbsRGJiosQpRQcUlNPS0hAQEMCO2szOzmZbKPr6+rhx4waam5uxb9++bufq6Ojg0KFDWLVqFQDgueeew+bNmzFp0iR4e3vD29t7IEUjIxjDMKitre0WgGtra3s9l8PhwMjISCj4WlhYdLsHO5Tc3Nzw2Wef4fbt2/jqq6/Q2toKRUVFLFy4EM7OztDW1kZOTg6SkpLYQVCGhobsD9SysjI0NTXB1dUVcnJyKCoqAsMwKCsrQ2ZmJh4/fgxnZ2c4OzvD2NgYd+/epUAigqjg6+vrKzbwCvI1t7W10QpG/dCXNLNnzpyBpaUlNDQ0YG1tDS6X2+N0KcH+CxcuIDQ0VKIFLAY793tLSwvCw8MRHh6Od999t9t+f39/lJaWCm0T9Bg8nT2vNwMKyu+88w60tbURHx8PDofTLUvQnDlzcPToUZHnKioqCt3j279/P2bMmCF2SgYZnfh8PioqKroF4Obm5l7PVVBQgKmpKRuALS0tYWpqKhPByNDQEPPnz8fu3bvR2toKFRUV9oudx+MhKysLra2tqKmpgZ6eHhuQOzo6cPv2bTx+/BjZ2dnYtGkTOjo6kJqairt376KhoQFFRUUwMjJCYmIiKioqkJmZCQMDA6SmpoLH41EgAcS2ehmGERt4Bfman0UrGEmmL2lmGYZBc3OzUGu4t+lSwJP5ztHR0QgODoadnV2/ytnf3O+CQcjixmooKioK3cLt7OzE6dOnsWXLlj719A4oKF+7dg0ffvghDAwMUF1d3W2/paUlSkpKRJ7r7OyMH374AdbW1tDS0gIA5OfnIzk5ucfXHD9+/ECKTKRIMJpSEICLiopQUlIiURYfQffu013QJiYmMp+W79mMRnJycmhra0NnZyfc3NxQVFSEhw8fsrd4UlNTUVhYCAcHBxQWFrL7FBUVoa6ujnHjxiE3Nxfl5eUwNTWFqqoqzM3NATyZl+zq6gpHR8cxH0jEtXrr6urEBl5BvmbSd31NMwsAVVVVMDU1ZVvDOjo6vU6XAp78kI+JiQGAfgXm/uZ+76vff/8d1dXV2LhxY5/OG1BQ5vP5UFVVFbu/srJSbKtlx44dWL58OWbMmAHgSbfjBx98IHaEtWD5OlpPeWRoa2vrNvr58ePHEiWR0NDQYIOvIADr6+uzg6RGElVVVXC5XCgpKUFDQwO2trY4dOgQuFwu1NTUoKCggFu3brHrid++fRsKCgpC+xwcHHD79m1wuVyYmJigs7MTampqcHJyQl5entB9MMF99o6ODgQEBLABe6wR1+odP348Bd5ndHR0SDQwsifnz5/vV/Kc2tpacLlcFBQUwNbWFh4eHiKPE/W9ERMTAzU1Nejq6vZpPnl/c7/3lWBKcF//BgcUlMePH49z587hlVde6baPx+PhyJEjYrvQwsPDkZeXhzt37qC8vBwbNmzAiy++iMmTJw+kSOQphYWFuHLlChobG6GhoYHp06fD0tJy0F+nsbFRqPVbXFzcLY2jOHp6et0GYGlpaY2KgX0dHR0ICQnBzZs3YW9vDycnJ2RnZ6O4uBgdHR3Izc0Fj8dDcXExMjIyAEDkvsuXL3fbXllZCS0tLSxcuBCZmZmoqalhpyS2t7fj+vXruH79OmxsbBAQEIDx48ePqUQY1OqVjKRpZnvS1dWFGzdu9CvNrKmpKVxdXcVmcuzN+fPn4ejoCF9fX3z11VdC+TJaW1sRHx+P1157jd2WlpbW59foj+LiYkRHR+PYsWN9PndAQfndd99FREQEXn75ZTavbXl5OS5fvozPPvsMjx49wn//+1+R596/fx9WVlZscpHIyEgsXbpU5OIWpG8SExPxySef4Ny5c2AYBnJycuDz+eBwOIiIiMAHH3zQr8XFGYZBTU2NUAAuKipCfX19r+dyOBwYGxt3C8A99bSMdIKuaDMzMzQ3NyM9PR1mZmaYNWtWt2MFUz1E7dPX1xe5kpSRkREUFBTYVmF1dTWuX7+OO3fusOM18vLykJeXh19//RV+fn4ICAjo1koYS3pKGjIWM3mJSjMriaamJjx+/BiPHz9GWVkZHj9+3K+WsrW1Ndzd3ft8nkBzczOamprA4/GGNPd7X0VGRkJPTw/z5s3r87kDCsqzZs3C/v378ac//Ql79uwBAKxZswYMw0BTUxMHDx7EtGnTRJ47btw4odHXZHCImpIg6PphGAbnz5/HhQsXcPToUaEP67P4fD7Ky8u7tYAlyfDE5XJFDsAaK190wP8GbCkoKEBVVRUtLS24desWNm3aJPZvAkCfA6YgtzDwpMt21apVWLhwIe7cuYNr167h8ePHAJ58eV25cgVXrlyBs7MzAgIC4OnpKfP35AdbT9m6xnImLyUlpR6nBHZ1daGsrIz9Hnj2h3h/eiU4HA6cnJwGdFuqs7OTvaU5lLnf+4JhGERGRmLdunX9Wlp1wPOU165di0WLFuHSpUtCc4vDwsKgoaEh9jwVFRWhL/i4uDhs2rRpoMUZFN9++y2+/PJLlJWVwcvLC//5z39EDkCQNZJMSejq6gKHw8Hy5ctx69YtTJw4EZ2dnSgpKRG6B1xSUsJm4+mJioqK0AAsCwsLGBkZjbkv+2dlZGSwXc6NjY3gcrlsN7W4+2aDRUVFBdOmTUNAQAByc3Nx/fp1JCcns/cN09PTkZ6eDi0tLfj7+2PKlCkSr+k8kvWUNIQyeXXX2NiI4uJidjyIuPvOKioqcHBwwKNHj5CSkiLRuBE5OTm4uLh0G8TVVxwOp8+3uvqT+x14ci+6rKwM2dnZAIAHDx5AQ0MDlpaWQn8/MTExyMvL67aAi6T6HZRbWlpgYWGBrVu34i9/+QsWLFjQp/O9vLzwz3/+E/Ly8uzo6zt37vSawKGn1t1gOHr0KN566y18//33mDRpEnbu3ImwsDBkZGTIfLefpFMSBNmmNm/ejEWLFqGsrEyiPyRNTU2h4GthYQE9Pb1Rcf93sBkZGbFd0U1NTexgrOHMSMThcGBnZwc7OzssWbIEt2/fxo0bN1BZWQngyQpVFy5cQFRUFDw8PBAQEAAXF5cROaCuJzU1NdDV1e0xWxdl8vpfa1gQiOvq6kQex+FwYGBgwPaECb4D9PT08Nxzz0l8bzk0NHTAZTY0NOzX57U/ud+///57bN++nX0u6PGKjIwUmgu9b98++Pv7w9nZuR/vCOAw/bkR8P+MjIzw0UcfiRzo1ZukpCQsWbKEXXRd0nzBQz36etKkSZg4cSJ7L5zP58PCwgJbtmzB1q1bez0/OTkZPj4+uHv37rBO3yosLJQoX/Gz1qxZI7JHw8DAoNsUJMGPp9GkpaVlUDN6idLc3DxkyUva2trQ2NiIadOmSXR/ns/nIyMjA9euXcODBw+6/RjT09NDQEAAJk+e3GNP10hSXl4ObW1tfPzxx+yc2KysLFhbW+PDDz8EALH7Rntrubi4GGfPnkVNTQ3KysrEtoaVlZXZ7wEzMzOx9RITE8Mm1hD1XS0uzWx/6OjowNbWFs3NzRJ//keCAXVfL168GCdOnMDLL7/c59bShAkTkJ2djZycHJSXlyMoKAjvvfceO0VKGjo6OnD37l2hbC1ycnKYMWMGbt++LfKc9vZ2oSQoghGEPB5Pou7fwRIdHd2vgRbFxcUICgpiA7CZmRksLCygoqLS7djhfD/DpbOzEzweD83NzQOeFiJOV1fXgEa39qS9vZ39rEn672Nvbw97e3vU1dXh1q1buHXrFtsqqqiowK+//orTp0/Dy8sLAQEBsLOzG9LekMGYktPTtS9dugQ9PT2kp6ejra0NKSkp4PF4SE9Px/Xr1wFA7D5JBkQqKCiMmPESgvnaaWlpSEtLEzsFSNDyNTU1hYmJCXR1ddnPQE+fNV9fX/z3v//FwYMHRX5nOjs7IyQkBJaWlgP6PtHT04OlpSVaW1v7/Pnvq/7cFx6IAbWUr127hldeeQX6+vrYtGkTrK2tRX6ZS9Ji3LhxI1566SWpZvR6/PgxzMzMcOvWLaGpWe+88w7i4uKQkJDQ7Zxt27YJdWkQQggZPQYQIvtlQEH56b58Ub+kR1rCj/4E5Wdbyvfu3UNgYCASEhJELsQxVPbv348XX3yxz+ft3bsX69evH4ISjRxD2VK7efMmjh49ihUrVsDf339IXmMwW2qVlZW4ceMG4uPju6U6VVRUxIQJEzB16tRBm+/e0tKC69ev93lKjiQ6Ojpw8OBBNp3i+vXrB71F297eziZqkZXuUx6Ph+zsbKSlpeHRo0fd8jELcDgcWFtbw8nJCY6OjjA1NR308QQuLi4oLS2FlpYWPv300wFdS0VFBf7+/t3W5h7qnorhbikPqPs6MjKy3+d+/PHH4HA4eO+99yAnJ4ePP/6413MEWb+Gir6+PuTl5VFeXi60vby8XOz8NSUlJaEvE8GAHgUFhWH9xwwLC+vz5H0Oh4PQ0NBh/9DJmqF6/+3t7bh58ya76lNISIjM36M0NTXFsmXLsHDhQqSkpOD69evIyckB8KQbPiEhAQkJCbCyssK0adPg4+MzoC9ELpfLZjAb7Hv6ycnJKC0thampKYqKilBQUDDoP5QVFBTA5/PB5XKl+ndUU1ODhw8f4uHDh8jIyBBqKDw9E0JdXR1ubm5wc3ODs7Nzj6siDQZBkOdwOP2uHw6HA1dXV0yYMEHm/34Gw4CC8kBaWNu2bQOHw8Ff//pXKCoqYtu2bb2eM9RBWVFRET4+Prhy5Qo7mpzP5+PKlStCWWFkkaWlJSIiInD+/HmJeibk5eUxZ86cIcnwRZ4YySN6uVwufH194evri5KSEly/fh2JiYnsvOiCggIcOnQIJ06cgJ+fH6ZOndqtBSNNT88Tl5OTY++fu7m5jZj7vz0RLDwiCMS9tYYFgdjCwmJEja63sLCAn5/fgKdOjSQDnqfcX8+O+pRkSs5weOutt7B+/XpMmDABvr6+2LlzJ5qbm/ucVFwaPvjgA1y4cKHXFrNgbt/7778/jKUbW0bT2rxmZmZYsWIFFixYgKSkJFy7dg3FxcUAnqQyjI2NRWxsLBwcHBAQEABvb2+hheul4el54vX19eDxeMjPzx+WeeJDpaamBmlpaUhNTe3WGn6auro6XF1d4ebmBhcXlyFvDffk2QVZJGVgYAA/Pz+Z+qE3XPr0lyOYg7Znzx7Iy8vjueee6/UcDocjcj1lWbV8+XJUVlbiww8/RFlZGby9vREVFTWs80v7a+LEiTh69Cib0UtUi1leXh4cDgfHjh3rV6pNIpnRuDavsrIypk6diilTpiA/Px/Xr1/H3bt32VGvWVlZyMrKgqamJiZPnoypU6dCT09PKmV9ep54eno6urq6oKioOKKSpPSnNezq6gpLS0uZaQ0LVoCTdFyRiooKfH194ejoOGbzH/RpoJe1tTXk5OSQkZEBLpcLa2vrXiuOw+EgNzdXous/evQIOTk57AIK9vb2/Z6ALS3Smqf8tDt37uCTTz5hl1J7Ovf13Llz8f7771NAHmIlJSUilyEdbasUNTc3IyEhAdevX+82FoPD4cDNzQ0BAQFwc3MTGSiGep742rVrUVpaCnV1dbz99ttQVlaGm5tbj4OyOjs7kZaWBldX117vg/Z1nnhvBK3hhw8fIj09fUS0hsVpb2+Hvr4+mpqaoKSkhK+//rrH+rSzs8OUKVOGLF/ASNGnlnJ+fn6Pz/tr9+7d+PTTT0WuvWxpaYn33nuv3ynLxqKJEyfi999/R2FhIWJiYtDQ0ABNTU12fiAZemNllSI1NTWEhIQgODgYmZmZuH79Ou7duwc+nw+GYZCamorU1FTo6upi6tSpmDx58rAmoamurkZdXR3bfdrW1oZ79+7B0dER+vr6Is958OABzp8/Dz6fP+QzKJ5uDaelpbG5yp/F4XBgZWXF3huWpdawOAkJCeysBj6fzyZneZa8vDymTp06plvHT5PujR8Af/7zn/HPf/4Turq6eO655+Du7g51dXU0NTXhwYMHOHXqFDZv3oysrCx8/vnn0i7uiGJpaSmU/o2QoSJYXMDJyQn19fW4desWbt68iZqaGgBPWoC///47zp49C29vbwQEBMDR0VEqZe3q6sKDBw/Q1tbWbUS8YIBYSUnJkA0Mq62tFRop/fSiIk8bCa1hcQRjKgRBlmEYPHz4ENbW1kKtZRUVFYSFhcl8CuPhJNWgnJiYiH/+859YuHAhDh48KDIV4TfffIM1a9bgq6++wtKlSzFhwgQplJQQIiktLS3MmjULYWFhSE1NxfXr15GWlsbmXE9OTkZycjKMjIzg5+fH3nccTgUFBUhKSkJDQwPmzp3Ldj0Lltt0cHBAYWEhHj58OODWcldXF3JycpCamjrqWsPiCMZUPH13tLKyEkVFRbC1tQXw5EfHnDlzRmX63oEYcFC+cOEC/vnPfyI5ORn19fUiR9mJu8m/b98+mJiY4PDhw2JHpKqpqeGXX36Bra0t9u3bR0GZkBFCTk4Onp6e8PT0RFVVFW7evIlbt26hsbERwJP5/6dPn4a8vDysrKzg7u4OfX39Qe3CFDX6V5BGs7q6GikpKTAyMoKNjQ0MDQ3ZaVRqampQUFDod2u5L61hFxcXtjU8WvKNC9b4vnTpEtra2qCkpARfX192apOamhrmzp07at7vYBpQUP7111+xbNkyuLm5YcWKFdi1axdWrVoFhmFw+vRpODg49Lh61O3bt7F06dJep4goKytj6dKliI2NHUhxCSFSoq+vj/nz52POnDm4d+8erl+/jqysLABPfrTn5uYiNzcXenp6cHZ2hp2d3aAk4xCMDH+6YVBQUICKigqYmpqioqIC+fn5kJeXR3x8PLKzsyEnJ4fc3FzweDyJl9vs6upCZmYmG4h7aw0LuqWtrKxGbGu4J4IxFYK0y1wuF15eXuz/z549mwKyGAMKyjt27ICvry9u3LiB2tpa7Nq1C8899xxCQkKQn58PPz8/2NjYiD2/qKgILi4uEr2Wq6srDh48OJDiEkKkTEFBARMmTMCECRNQWlqK2NhYxMfHswOCqqurcfPmTSQmJsLe3n5Aa+52dHSwXeOChUeAJ1OkFBQUoKysDAUFBaSnp8PKygqqqqpwdHSEsrIyjIyMel1us7m5GXl5eSgsLER0dLTYkdJqampC94bHejAKCAgYU8lA+mpAQTktLQ07duyAvLw8myxA8MvU2toar7zyCj7//HOsW7dO5PkNDQ0Sf0DV1dXZbi9CRoqOjg4kJSVhwoQJQl2g4rb3tm80MTExwYIFC6CpqYmamhpkZ2ejqqoKwJPvkUePHuHRo0cwMjKCi4sLrK2thVJG9iY1NZVtIfP5fBQUFEBeXh5VVVXg8XgoKytDV1cXqqqqUFxcDGtra2hra7Pn6+npwdbWlp2iw+fzUV5ejuLiYhQVFaG2tlbk63I4HFhaWrL3hkdra1gSxsbGaGlpYb/nzc3NYWdnJ+VSybYBBWVVVVX2S0NbWxtKSkpCE9yNjIyQl5cn9nzBghWSGu7VOggZqPj4eBw6dAhdXV1CKTbFbe9t32ikoKAAOzs7uLm5obKyEunp6cjJyWEDanl5OcrLy6GsrAwHBwc4OztDU1Ozx2sKlmx8Oijfu3cPQUFB8PHx6Xb808FYoLq6GtXV1VBWVkZ9fT0eP34sdnlAVVVVNnmHq6vrmG8NCyQlJeHgwYNoa2sDh8PBpEmTaNpTLwYUlJ2cnJCWlsY+9/b2xqFDh7BmzRrweDwcPny413mxX331FX755ZdeX0vUHGZCZJlgWkheXp5Qik1x23s6Z6wwMDCAgYEBfH19kZ2djfT0dHat57a2Njx48AAPHjyAmZkZXFxcROZyTk1Nxb/+9S/88ccf7DY+n48bN26gpKQECxYsEPu9xDAMeDwe2/Xd0dGB0tJSGBkZdXsdfX19mJiYQEtLC/Pmzet1yhLDMPjoo4+wd+9e1NXVYcqUKdi1a5fIubsCopaGdXJyQnp6usjrz549G1FRUTh58mSP43mkwdbWVmoZ3kaSAQXlRYsW4d///je++uorKCkp4b333sP8+fOhra0NDoeD5uZm/Pjjj2LPt7S0RE1NDTuXsTeU+IKMJOIWpOhpoYqRvIjFYFJSUmJbnmVlZUhPT0d+fj6bI7+kpAQlJSVQU1Njlx5UU1NDTEwM3n33XbG9avn5+fjmm2+wfv16eHp6AngSsAUBuLOzU+jckpISPHjwAB4eHrCysoKuri4MDAzg7OwMDQ0NNqOXJN3TX3zxBf7973/jwIEDsLGxwQcffICwsDCkpaX1mMXKzc0Nly9fZp+Lyyu+c+dOmW2FcjgcqWU4HGn6FZTb2tpw+vRpdHZ24v3330dNTQ1MTEwQERGBq1ev4rfffmNXIQoODhZ7ncHKCEaIrBG3IIWXl5fYhSoAjJpFLAYLh8OBiYkJTExM0NraiszMTKSnp6OpqQnAk8FWycnJSElJgZycHHbt2sVmExOFYRgwDIMDBw7gxRdfhKGhodgpmzweD3l5eWhoaEBxcTE8PDzA5XLZFrsgSEuCYRjs3LkT77//PubPnw8AOHjwIIyMjHDq1CmsWLFC7LkKCgpil44VuHfvHr7++mskJSXJ5CIOVlZWNLhLQn0OyhUVFfD390deXh57T1hFRQWnTp3CjBkzEBAQMCZ/2RPyNHELUhw7dkzsQhUARt0iFoNJRUUFXl5e8PDwQElJCdLT01FUVMQG2jNnzvQYkJ/GMAyuXLmCZcuWCW3ncDhQVFQEl8tFUVERGhoaYGFhgerqahQWFrKDlATTuFpbWyVKfpGXl4eysjLMmDGD3aalpYVJkybh9u3bPQblrKwsmJqaQllZGZMnT8aOHTuEeg1bWlqwatUqfPvtt70Gb2lxc3OTdhFGjD4H5U8++QT5+fl48803ERISguzsbHzyySfYvHkzuxg6IWOdIHnCs0xMTEROsbGwsAAAkecI9pEn5OTkYGFhAQsLCzQ1NSEjIwN37txBQUGBxNdgGAZZWVmor6+Hnp4eFBUVweFwUFpayga8zMxMkVOnnu4+rqmpkSgol5WVAeg+vcrIyIjdJ8qkSZOwf/9+ODk5obS0FNu3b0dAQABSU1PZwWRvvvkm/P392Ra4rFFWVoapqam0izFi9DkoX7x4EevWrcNXX33FbjMyMsKqVauQkZEBJyenQS0gISNRfxekGAuLWIgibo5vbxQUFODm5tbjLI+eVFRUsLkUcnJycPfuXfD5fHbqVGdnJx4/fgw+n4+qqioUFRXBysqq13L//PPP2Lx5M/v83Llz/SqfYPlJAPD09MSkSZNgZWWFY8eO4fnnn8fvv/+OmJgYtqdFFhkaGsrsvW5Z1OegXFhYiL/+9a9C26ZOnQqGYVBeXk5BmRAiMQUFBXYBmoHkwK6rqwOHw+nTtEkOh4P29nbw+Xyh1JuPHj2Cr6+vyJzXmpqa7GAz4MkgMRUVlW6Dr+bNm4dJkyaxzwXBu7y8XOieb3l5Oby9vSUus7a2NhwdHZGdnQ0AiImJQU5OTrcpXYsXL0ZAQACuXr0q8bWHiqjpZkS8Pgfl9vb2biMFBc8FGXMIIUQSioqK8PX1HfB3R35+fp/zGDAMAy0tLairqyMrKwu1tbWwsrJCTU0NOjo62MF3PdHU1ISfn1+3JC8aGhpCc5UZhoGxsTGuXLnCBuGGhgYkJCTg5ZdflrjMTU1NyMnJwdq1awEAW7du7basrYeHB/71r39h7ty5El93KIlaaIiI16/R1/n5+UILuNfX1wN4MiBB1K8iGgpPCBFHUVFxwJnLZs+e3a+WsouLC7q6uvDo0SMoKCiwrd5Hjx7B1ta2x/zb6urqmDVrlkQj4zkcDt544w38/e9/h4ODAzslytTUVGg+8fTp07Fw4UK89tprAJ4sbTt37lxYWVnh8ePH+OijjyAvL4+VK1cCeJIxS9TgLktLyx5THA8nwQpcRDL9CsoffPABPvjgg27bX3nlFaHngtHZ4qYcEELIYLC0tERERATOnz8v0fcNh8OBnZ0dNDU1UVhYiMrKSvb+cVdXV7dlBp9lZWWFwMDAHucXP+udd95Bc3MzXnzxRdTV1WHq1KmIiooSukZOTg6bahQAiouLsXLlSlRXV8PAwABTp05FfHw8DAwMJH5daRur0/n6i8P0sc/nwIEDfX6R9evXS3RcdHQ09u3bh9zcXNTW1nb71cvhcGR+hHdycjJ8fHxw9+5d6iEgZBjduXMH/v7+6Orq6rXFzOFw4OnpiZkzZ0JfXx+FhYXdjrG0tOw2t1ZeXh6TJ0+Gi4sLDV6SUH19Pa2Z3Ad9bilLGmD76ssvv8TWrVthZGQEX1/fXpdKI4SQp02cOBFHjx7F8uXLwTCMyBazIPOWl5cXWltb8fDhQ8yZM4ddVrAnampqCA0NHVGtVGkTLK4SEBAwqhdXGUwDSrM5mL755huEhITg/Pnzg7KOKiFk7Fm0aBFu3bqFTz75BGfPnhVqMXM4HHh4eMDd3R2PHj2CmZkZysvLkZ+f32P+aeDJXPGgoCB2fWAimfj4ePz0009QVFSkpFISkpn1xGpra7FkyRIKyISQAZk4cSJ+//13HD16lJ2qJC8vj02bNmHTpk2oqamBvLw8VFRUIC8vz2ZPE0VdXR3BwcEIDw+ngNxHglSzBQUFiIqK6vdc9LFGZlrKvr6+yMjIkHYxCCGjQHt7O+7duwculwsejwcOh4OioiJoaGhINKhLS0sLnp6ecHR07NMazuR/BIureHh4jOnFVfpKZoLyd999h1mzZmHChAlYtWqVtItDCBnBBLnHn+6+rqysRGtrq8j5x4IBXcbGxvDy8oKlpSUN5BqApxdk0dbWRlVV1ZhfXEVSMhOUly9fDh6Ph7Vr1+Lll1+Gubl5t1+oHA5HaI1UQggRRZB7/NKlS2hra4OSkhJ8fX1FjqgGnqQKnjRpkswu6DDSPL0gS1paGi2u0gcyE5R1dXWhp6fX64ALQgjpjSD3uOA+MJfLFTnCWk5ODpMnT4arqyu1jAeRuAVZaHGV3slMUJaFHK2EkLFDXl4e4eHhY3YRkKHU3wVZiAyNvpYFn376Kfz9/aGqqkpJ1AkZxeTl5REaGkqBg8gcmWkpC3R2diI9PR319fVCq7EITJs2bcheu6OjA0uXLsXkyZOxb9++IXsdQoj0KCgoIDQ0FObm5tIuChFBkHBkwoQJYzLhiMwEZT6fj3fffRffffcdWlpaxB43lHm0t2/fDgDYv3//kL0GIUR6FBQUMGvWLKHlE8nw6ynwxsfH49ChQ+jq6hqTU6hkpvv6s88+w5dffok1a9bg4MGDYBgG//jHP/D999/D09MTXl5eiI6OlnYxu2lvb0dDQwP7aGpqknaRCCFiBAcHU0AeRh0dHbh161a3tbLj4+MRGRmJhIQEoe2CqVR5eXljNuGIzATl/fv3Y9myZdi1axfCw8MBAD4+Pti0aRMSEhLA4XAQExMj5VJ2t2PHDmhpabGPwMBAaReJECKCjY2NzCxnOFaICr49BV5BwhFPT0824chYIzNBubi4GCEhIQD+t9RXW1sbgCfrra5ZswaHDh3q83W3bt0KDofT4yM9Pb3f5X733XdRX1/PPuLi4vp9LULI4DI2NoaOjg6boUtUq40MDXHBV1zgfTrhiKamJhQVFcdka1lm7inr6emxXb/q6urQ1NREbm6u0DG1tbV9vu7bb7+NDRs29HiMuDVTJaGkpCSUoUZdXb3f1yKEDK6kpCQcPHgQxsbGyMjIGNP3KoebqODr6+srMvD6+voKJRwR5CMfiwlHZCYojxs3Dnfu3GGfBwcHY+fOnRg3bhz4fD7+/e9/S7S82rMMDAxoqTVCxjgHBwf88MMPbKuN0j0OLXGtXoZhxAZeSjjyhMwE5RdffBH79+9He3s7lJSU8Omnn2LatGmYNm0aGIaBjo4OfvnllyEtQ2FhIWpqalBYWIiuri7cu3cPAGBvb08tYEJGKBUVFRQWFnZrtVFreeiIa/XW1dWJDbyUcOQJmQnK8+bNw7x589jnrq6uyMnJwdWrVyEvLw9/f3/o6uoOaRk+/PR4QuYAABgASURBVPBDHDhwgH0+btw4AEBsbCyCgoKG9LUJIUNDV1cXFy9eFNllSq3loSGu1evu7o7S0tIxOwdZEjITlEXR0tLC/Pnzh+319u/fT3OUCRllysrK6F7lMBPX6r127Rrd1++FTAXlrq4uHD9+HLGxsaioqMDHH38MDw8P1NfX48qVK5gyZQqMjIykXUxCyAjR0dGBpqYmLFu2DFwuV2jfWLtXKW3PjsamngrRZCYo19XVITw8HImJiVBXV0dzczO2bNkC4MmI5tdffx3r1q3DZ599JuWSEkJGivj4eFy7dg0vvfQStcykTNRobPo36U5m5ilv3boVDx8+RHR0NHJzc4UWJ5eXl8eSJUtw/vx5KZaQEDKSCFpmxcXFY3K+qyyhOciSk5mgfOrUKWzZsgUzZ84Uua6po6Mj8vPzh79ghJARSdAy8/LyGrPZoaRNkGYzMTEROTk5aG5uxsOHD9Hc3Mze1yfCZKb7ur6+vscUeJ2dneDxeMNYIkLISPV0y0xXVxfV1dV0H1MKBItLzJo1i+YgS0hmgrKdnR2Sk5PF7r948SJcXV2HsUSEkJGKskNJ39MDu+7evYsPP/yQfhBJQGaC8gsvvIC//vWvCAoKwvTp0wEAHA4H7e3t+PjjjxEVFYU9e/ZIuZSEkJGAskNJHw3s6h+ZCcp/+tOf8PDhQ6xcuRLa2toAgFWrVqG6uho8Hg+bN2/G888/L91CEkJGBMoOJV3iBnbR7YPeyUxQ5nA42Lt3L9avX48TJ04gKysLfD4fdnZ2WLZsGaZNmybtIhJCCJEA3T7oP5kJygJTp07F1KlTpV0MQggh/US3D/pP5oIyIYSQkY1uH/SfVIPy0wtQSILD4eD06dNDVBpCCCFEuqQalM+ePQtlZWUYGxsLZfASR1RSEUIIIWS0kGpQNjMzQ0lJCfT19bFq1SqsWLECxsbG0iwSIYQQIjVSTbNZVFSE2NhYjBs3Dp988gksLCwwY8YMREZGorGxUZpFI4QQMkCCNJsdHR3SLsqIIfXc14GBgdi9ezfKyspw4sQJ6Onp4bXXXoOhoSEWLVqEEydOUNJyQggZgeLj4xEZGYmEhARpF2XEkHpQFuByuZg/fz6OHj2K8vJyNlAvX74cX3zxhbSLRwghpA+eXT+ZGleSkZmgLCD4hzx9+jRSUlKgrKwMa2traReLEEJIH4hKs0l6JxNBmc/nIzo6Ghs2bICRkRFWrlyJ1tZW7N27FxUVFVi7dq20i0gIIURCtH5y/0l19PWtW7dw+PBhHD9+HNXV1fDz88Nnn32GZcuWQV9fX5pFI4QQ0k+UZrP/pBqUp06dChUVFcyePRsrV65ku6kLCwtRWFgo8pzx48cPYwkJIYT0FaXZ7D8OI0nWjiEiJ/e/3vPeEoMwDAMOh4Ourq6hLtaAJCcnw8fHB3fv3qUfEIQQQvpEqi3lyMhIab48IYQQIlOkGpTXr18vzZcnhBBCZIpMjL4mhBBCCAVlQgghRGZQUCaEEEJkBAVlQgghREZQUP5/+fn5eP7552FjYwMVFRXY2dnho48+otVNCCGEDBupjr6WJenp6eDz+di9ezfs7e2RmpqKTZs2obm5GV999ZW0i0cIIWQMoKD8/8LDwxEeHs4+t7W1RUZGBnbt2kVBmRBCyLCgoNyD+vp66Orq9nhMe3u7UJL1pqamoS4WIYSQUYruKYuRnZ2N//znP9i8eXOPx+3YsQNaWlrsIzAwcJhKSAghZLQZ9UF569at4HA4PT7S09OFzikpKUF4eDiWLl2KTZs29Xj9d999F/X19ewjLi5uKN8OIaQPOjo6cOvWLRqwSUaMUd99/fbbb2PDhg09HmNra8v+/+PHjxEcHAx/f3/s2bOn1+srKSlBSUmJfa6urt7vshJCBld8fDwOHTqErq4uBAQESLs4hPRq1AdlAwMDGBgYSHRsSUkJgoOD4ePjg8jISKFVrAghI0t7ezuio6ORl5eHqKgo+Pr6Cv2AJkQWUdT5fyUlJQgKCoKlpSW++uorVFZWoqysDGVlZdIuGiGkHxISEpCZmQlPT09kZmYiMTFR2kUipFejvqUsqUuXLiE7OxvZ2dkwNzcX2ifFJacJIf0gaCUrKipCU1MTioqK1FomIwK1lP/fhg0bwDCMyAchZGRJSUlBTk4Ompub8fDhQzQ3NyMnJwcpKSnSLhohPaKWMiFk1LGwsMDq1atFbidEllFQJoSMOmZmZjAzMxO7v6OjA0lJSZgwYQIUFRWHsWSE9Iy6rwkhY058fDwiIyORkJAg7aIQIoSCMiFkTHl2qtTTaXIJkTYKyoSQUUtURi+aKkVkGQVlQsio9Ww3tbipUtRaJrKCgjIhZFQS1U1NU6WIrKPR14SQUUlUN7WtrS1NlSIyjYIyIWTUEddN/eGHH2Lu3LnSLh4hYlH3NSFk1KFuajJSUUuZEDLqUEYvMlJRUCaEjDq9ZfQiRFZR9zUhhBAiIygoE0IIITKCgjIhhBAiI+ie8hhQWlqK0tJSaReDEDIGmZiYwMTERNrFGDEoKA8yExMTfPTRRzLzIWxvb8fKlSsRFxcn7aIQQsagwMBAREdHQ0lJSdpFGRE4DMMw0i4EGToNDQ3Q0tJCXFwc1NXVpV2cMaepqQmBgYFU/1JC9S9dgvqvr6+HpqamtIszIlBLeYzw9vamPwopaGhoAED1Ly1U/9IlqH8iORroRQghhMgICsqEEEKIjKCgPMopKSnho48+okEWUkL1L11U/9JF9d93NNCLEEIIkRHUUiaEEEJkBAVlQgghREZQUCaEEEJkBAVlQgghREZQUCZjGofDkehx9erVAb9WS0sLtm3b1qdrffrpp5g3bx6MjIzA4XCwbdu2AZdDlshy/aenp+Odd96Bt7c3NDQ0YGJigjlz5iApKWnAZZEVslz/jx8/xpo1a+Dk5AQNDQ1oa2vD19cXBw4cwGgen0wZvciYdujQIaHnBw8exKVLl7ptd3FxGfBrtbS0YPv27QCAoKAgic55//33YWxsjHHjxiE6OnrAZZA1slz/P/zwA/bt24fFixfjlVdeQX19PXbv3g0/Pz9ERUVhxowZAy6TtMly/VdVVaG4uBhLliyBpaUlOjs7cenSJWzYsAEZGRn47LPPBlwmmcQQQlivvvoqM1R/FpWVlQwA5qOPPpL4nLy8vH6fOxLJUv0nJSUxjY2NQtuqqqoYAwMDZsqUKUNQQumTpfoXJyIiglFTU2N4PN7gFEzGUPc1Ib3g8/nYuXMn3NzcoKysDCMjI2zevBm1tbVCxyUlJSEsLAz6+vpQUVGBjY0NnnvuOQBAfn4+DAwMAADbt29nuwV76462trYeirc0okir/n18fLotYqGnp4eAgAA8evRocN+kDJPm518Ua2trtLS0oKOjY8DvTRZR9zUhvdi8eTP279+PjRs34vXXX0deXh7++9//IiUlBTdv3gSXy0VFRQVCQ0NhYGCArVu3QltbG/n5+fjtt98AAAYGBti1axdefvllLFy4EIsWLQIAeHp6SvOtjQiyVv9lZWXQ19cf1Pcoy6Rd/62trWhubkZTUxPi4uIQGRmJyZMnQ0VFZUjft9RIu6lOiCx5tvvu+vXrDADm559/FjouKipKaPvJkycZAMydO3fEXnsg3XdjtftaVupf4Nq1awyHw2E++OCDfl9Dlsli/e/YsYMBwD6mT5/OFBYW9ukaIwl1XxPSg+PHj0NLSwszZ85EVVUV+xB0bcbGxgIAtLW1AQBnz55FZ2enFEs8ushS/VdUVGDVqlWwsbHBO++8MySvIWtkof5XrlyJS5cu4fDhw1i1ahWAJ63n0YqCMiE9yMrKQn19PQwNDWFgYCD0aGpqQkVFBQAgMDAQixcvxvbt26Gvr4/58+cjMjIS7e3tUn4HI5us1H9zczMiIiLQ2NiI06dPd7vXPFrJQv1bWVlhxowZWLlyJX7++WfY2tpixowZozYw0z1lQnrA5/NhaGiIn3/+WeR+weAVDoeDEydOID4+HmfOnEF0dDSee+45fP3114iPjx8zX+KDTRbqv6OjA4sWLcL9+/cRHR0Nd3f3fl9rpJGF+n/WkiVLsHfvXly7dg1hYWGDdl1ZQUGZkB7Y2dnh8uXLmDJlikQDS/z8/ODn54dPP/0Uhw8fxurVq3HkyBG88MIL4HA4w1Di0UXa9c/n87Fu3TpcuXIFx44dQ2BgYH/exogl7foXRdBCrq+vH5TryRrqviakB8uWLUNXVxc++eSTbvt4PB7q6uoAALW1td2yDHl7ewMA24WnqqoKAOw5pHfSrv8tW7bg6NGj+O6779gRw2OJNOu/srJS5PZ9+/aBw+Fg/PjxEl1npKGWMiE9CAwMxObNm7Fjxw7cu3cPoaGh4HK5yMrKwvHjx/HNN99gyZIlOHDgAL777jssXLgQdnZ2aGxsxN69e6GpqYnZs2cDAFRUVODq6oqjR4/C0dERurq6cHd377E79NChQygoKEBLSwsA4Nq1a/j73/8OAFi7di2srKyGvhKkSJr1v3PnTnz33XeYPHkyVFVV8dNPPwntX7hwIdTU1Ia8DqRJmvX/6aef4ubNmwgPD4elpSVqamrw66+/4s6dO9iyZQvs7e2HsyqGj5RHfxMiU8RlNNqzZw/j4+PDqKioMBoaGoyHhwfzzjvvMI8fP2YYhmGSk5OZlStXMpaWloySkhJjaGjIREREMElJSULXuXXrFuPj48MoKipKND0kMDBQaDrI04/Y2NjBetsyQ5bqf/369WLrHgCbbW00kaX6v3jxIhMREcGYmpoyXC6X0dDQYKZMmcJERkYyfD5/UN+3LOEwzCjO7E0IIYSMIHRPmRBCCJERFJQJIYQQGUFBmRBCCJERFJQJIYQQGUFBmRBCCJERFJQJIYQQGUFBmZAByM/PB4fDwf79+6VdlDGJ6l+6qP4HHwVlQgghREZQ8hBCBoBhGLS3t4PL5UJeXl7axRlzqP6li+p/8FFQJoQQQmQEdV+TMW/btm3gcDjIzMzEmjVroKWlBQMDA3zwwQdgGAZFRUWYP38+NDU1YWxsjK+//po9V9Q9tQ0bNkBdXR0lJSVYsGAB1NXVYWBggD//+c/o6upij7t69So4HA6uXr0qVB5R1ywrK8PGjRthbm4OJSUlmJiYYP78+cjPzx+iWhk+VP/SRfUvWygoE/L/li9fDj6fj3/84x+YNGkS/v73v2Pnzp2YOXMmzMzM8Pnnn8Pe3h5//vOfce3atR6v1dXVhbCwMOjp6eGrr75CYGAgvv76a+zZs6dfZVu8eDFOnjyJjRs34rvvvsPrr7+OxsZGFBYW9ut6sojqX7qo/mWEdNbBIER2fPTRRwwA5sUXX2S38Xg8xtzcnOFwOMw//vEPdnttbS2joqLCrF+/nmEYhsnLy2MAMJGRkewxgtWFPv74Y6HXGTduHOPj48M+j42NFbna07PXrK2tZQAwX3755eC8YRlD9S9dVP+yhVrKhPy/F154gf1/eXl5TJgwAQzD4Pnnn2e3a2trw8nJCbm5ub1e76WXXhJ6HhAQINF5z1JRUYGioiKuXr2K2traPp8/UlD9SxfVv2ygoEzI/7O0tBR6rqWlBWVlZejr63fb3tuXg7KyMgwMDIS26ejo9OtLRUlJCZ9//jkuXLgAIyMjTJs2DV988QXKysr6fC1ZRvUvXVT/soGCMiH/T9SUDnHTPJheJi1IMj2Ew+GI3P70YBiBN954A5mZmdixYweUlZXxwQcfwMXFBSkpKb2+zkhB9S9dVP+ygYIyIVKio6MDAKirqxPaXlBQIPJ4Ozs7vP3227h48SJSU1PR0dEhNBKW9A3Vv3RR/YtGQZkQKbGysoK8vHy3kazfffed0POWlha0tbUJbbOzs4OGhgba29uHvJyjFdW/dFH9i6Yg7QIQMlZpaWlh6dKl+M9//gMOhwM7OzucPXsWFRUVQsdlZmZi+vTpWLZsGVxdXaGgoICTJ0+ivLwcK1askFLpRz6qf+mi+heNgjIhUvSf//wHnZ2d+P7776GkpIRly5bhyy+/hLu7O3uMhYUFVq5ciStXruDQoUNQUFCAs7Mzjh07hsWLF0ux9CMf1b90Uf13R2k2CSGEEBlB95QJIYQQGUFBmRBCCJERFJQJIYQQGUFBmRBCCJERFJQJIYQQGUFBmZARQtQ6s2T4UP1L11ipfwrKZFTKycnB5s2bYWtrC2VlZWhqamLKlCn45ptv0NraOmSvm5aWhm3btkl98fVPP/0U8+bNg5GRETgcDrZt2zasr0/1T/UvTdKu/4Gg5CFk1Dl37hyWLl0KJSUlrFu3Du7u7ujo6MCNGzfwl7/8BQ8fPuz3Yuu9SUtLw/bt2xEUFARra+sheQ1JvP/++zA2Nsa4ceMQHR09rK9N9U/1P5brf6AoKJNRJS8vDytWrICVlRViYmJgYmLC7nv11VeRnZ2Nc+fOSbGE/8MwDNra2qCiojLo187Ly4O1tTWqqqq6LaE3lKj+n6D6791orP/BQN3XZFT54osv0NTUhH379gl9IQnY29vjT3/6E/ucx+Phk08+gZ2dHZSU/q+9uwdpq4/CAP7cBBurRkzAD1o0N6kQdLGIBEEl6CJVQQelU5sO6hhKFdF0KEKXLqKo0OrgR6AOYicRXVRUWnDqINIqMc2mdTBRlEKx5x3kTblvLG9ClVzj8wMHT87937/PcI9JvNEEVVXh8/liPuheVVU0NTVhY2MDLpcL6enpcDgcmJ6ejvZMTk6ira0NAFBbWwtFUaAoClZXVzVrLC0toaKiAnfv3sW7d+8AAHt7e2hra4PVakVGRgYqKyv/6uKZrGcpzP/3fpOB+f/e740lRCnk/v374nA44u73eDwCQFpbW2V0dFSePn0qAKSlpUXTZ7PZxOl0Sn5+vvh8PhkZGZHy8nJRFEW2trZERCQQCIjX6xUA4vP5xO/3i9/vl/39/egaxcXFYrFYpLe3V96+fSsrKyuyv78v+fn5Yjab5eXLlzIwMCBlZWViMBjkw4cP0T0Eg0EBIBMTE3H/fIeHhwJAXr16Ffcxf4P5azH/25X/VeBQppQRiUQEgDQ3N8fV//nzZwEg7e3tmnp3d7cAkOXl5WjNZrMJAFlbW4vWvn//LiaTSbq6uqK12dlZASArKysx5/t3jcXFRU39+fPnAkDW19ejtZOTE7Hb7aKqqpyfn4uI/i9KzD8W85eYNVI1/6vCl68pZRwfHwMAzGZzXP0LCwsAgBcvXmjqXV1dABDz8llpaSlqamqi3+fm5sLpdGJvby/uPdrtdtTX18fsw+Vyobq6OlrLyspCZ2cnvn37hu3t7bjXTybmn1zMPzVwKFPKyM7OBgCcnJzE1R8KhWAwGFBcXKypFxQUICcnB6FQSFMvKiqKWcNiseDo6CjuPdrt9kv34XQ6Y+olJSXRx28C5p9czD81cChTysjOzsa9e/ewtbWV0HGKosTVZzQaL61LAv/99Dr+0lQvmH9yMf/UwKFMKaWpqQmBQACfPn36316bzYZfv35hd3dXUz84OEA4HIbNZkv4/PFe4P67j69fv8bUv3z5En38pmD+ycX8bz4OZUopPT09yMzMRHt7Ow4ODmIeDwQCGBoaAgA0NDQAAAYHBzU9AwMDAIDGxsaEz5+ZmQkACIfDcR/T0NCAzc1NzYX09PQUY2NjUFUVpaWlCe8jWZh/cjH/m48fHkIp5cGDB3j//j0eP36MkpISzScaffz4EbOzs3j27BkAoKysDB6PB2NjYwiHw3C73djc3MTU1BRaWlpQW1ub8PkfPnwIo9GIN2/eIBKJwGQyoa6uDnl5eX88pre3FzMzM3j06BG8Xi+sViumpqYQDAYxNzcHgyHx3539fj9CoRDOzs4AAGtra3j9+jUA4MmTJ9f27IP5X2D+tzP/K5Hkv/4muhY7OzvS0dEhqqrKnTt3xGw2S1VVlQwPD8uPHz+ifT9//pT+/n6x2+2SlpYmhYWF0tfXp+kRubido7GxMeY8brdb3G63pjY+Pi4Oh0OMRqPm9pA/rSFycY9na2ur5OTkSHp6urhcLpmfn9f0JHJLiNvtFgCXfl12u8pVY/7M/zbn/zcUkQTepSciIqJrw/eUiYiIdIJDmYiISCc4lImIiHSCQ5mIiEgnOJSJiIh0gkOZiIhIJziUiYiIdIJDmYiISCc4lImIiHSCQ5mIiEgnOJSJiIh0gkOZiIhIJziUiYiIdOIfkiutJVP+8AoAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 480x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "repeated_measures.mean_diff.plot(contrast_paired_lines=True);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Effect size line kwargs can be utilised via `contrast_paired_lines_kwargs` in the `.plot()` method.\n",
+    "\n",
+    "By default, the following keywords are passed:\n",
+    "\n",
+    "- `'linestyle'` - Linestyle\n",
+    "- `'linewidth'` - Linewidth\n",
+    "- `'zorder'` - Zorder (the layering relative to other plot elements)\n",
+    "- `'color'` - Color. Default is 'dimgray'\n",
+    "- `'alpha'` - Alpha (transparency)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 480x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "repeated_measures.mean_diff.plot(contrast_paired_lines=True, \n",
+    "                                 contrast_paired_lines_kwargs={\"color\": \"red\", \"alpha\": 0.5, \"linestyle\": \"-\"});   "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Baseline error curve\n",
+    "\n",
+    "In DABEST **v2025.03.27**, we introduce a new aspect to the contrast axes: the baseline dot and error curve. \n",
+    "While the baseline dot is always present, the error curve can be turned on by setting `show_baseline_ec=True` in the `.plot()` method."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 480x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "repeated_measures.mean_diff.plot(show_baseline_ec=True);   "
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "python3",
+   "language": "python",
+   "name": "python3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/nbs/tutorials/forest_plot.ipynb b/nbs/tutorials/forest_plot.ipynb
deleted file mode 100644
index f6492b12..00000000
--- a/nbs/tutorials/forest_plot.ipynb
+++ /dev/null
@@ -1,805 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "cf1612f8",
-   "metadata": {},
-   "source": [
-    "# Forest Plot\n",
-    "\n",
-    "> Explanation of how to use forest_plot for contrast objects e.g delta-delta and mini-meta.\n",
-    "\n",
-    "- order: 7"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "cfdb7e31",
-   "metadata": {},
-   "source": [
-    "Since v2024.03.29, DABEST supports the comparison and analysis of different delta-delta analysis through a function called \"forest_plot\". \n",
-    "\n",
-    "Many experimental designs investigate the effects of two interacting independent variables on a dependent variable. The delta-delta effect size enables us distill the net effect of the two variables. \n",
-    "\n",
-    "\n",
-    "Consider 3 experiments where in each of the experiment we test the efficacy of 3 drugs named ``Drug1``, ``Drug2`` , and ``Drug3`` on a disease-causing mutation M based on disease metric Y. The greater the value Y has, the more severe the disease phenotype is. Phenotype Y has been shown to be caused by a gain-of-function mutation M, so we expect a difference between wild type (W) subjects and mutant subjects (M). Now, we want to know whether this effect is ameliorated by the administration of Drug treatment. We also administer a placebo as a control. In theory, we only expect Drug to have an effect on the M group, although in practice, many drugs have non-specific effects on healthy populations too."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7a202204",
-   "metadata": {},
-   "source": [
-    "|       | Wildtype | Mutant   |\n",
-    "|-------|---------|----------|\n",
-    "| Drug1  | X<sub>D, W</sub> | X<sub>D, M</sub> |\n",
-    "| Placebo | X<sub>P, W</sub> | X<sub>P, M</sub> |"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c75e54ab",
-   "metadata": {},
-   "source": [
-    "|       | Wildtype | Mutant   |\n",
-    "|-------|---------|----------|\n",
-    "| Drug2  | X<sub>D, W</sub> | X<sub>D, M</sub> |\n",
-    "| Placebo | X<sub>P, W</sub> | X<sub>P, M</sub> |"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e1b09711",
-   "metadata": {},
-   "source": [
-    "|       | Wildtype | Mutant   |\n",
-    "|-------|---------|----------|\n",
-    "| Drug3  | X<sub>D, W</sub> | X<sub>D, M</sub> |\n",
-    "| Placebo | X<sub>P, W</sub> | X<sub>P, M</sub> |"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "be4d9084",
-   "metadata": {},
-   "source": [
-    "There are two ``Treatment`` conditions, ``Placebo`` (control group) and ``Drug`` (test group). There are two ``Genotype``\\s: ``W`` (wild type population) and ``M`` (mutant population). Additionally, each experiment was conducted twice (``Rep1`` and ``Rep2``). We will perform several analyses to visualise these differences in a simulated dataset. "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9ec30d58",
-   "metadata": {},
-   "source": [
-    "## Load libraries"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "0fdd66d0",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "We're using DABEST v2024.03.29\n"
-     ]
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "import dabest\n",
-    "from dabest.forest_plot import forest_plot\n",
-    "import scipy as sp\n",
-    "import matplotlib as mpl\n",
-    "import matplotlib.pyplot as plt\n",
-    "# %matplotlib inline\n",
-    "import seaborn as sns\n",
-    "import dabest \n",
-    "print(\"We're using DABEST v{}\".format(dabest.__version__))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "96a35aa6",
-   "metadata": {},
-   "source": [
-    "## Simulate datasets for the contrast objects"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "9c6e3f02",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\n",
-    "from scipy.stats import norm\n",
-    "\n",
-    "def create_delta_dataset(N=20, \n",
-    "                        seed=9999, \n",
-    "                        second_quarter_adjustment=3, \n",
-    "                        third_quarter_adjustment=-0.1):\n",
-    "    np.random.seed(seed)  # Set the seed for reproducibility\n",
-    "\n",
-    "    # Create samples\n",
-    "    y = norm.rvs(loc=3, scale=0.4, size=N*4)\n",
-    "    y[N:2*N] += second_quarter_adjustment\n",
-    "    y[2*N:3*N] += third_quarter_adjustment\n",
-    "\n",
-    "    # Treatment, Rep, Genotype, and ID columns\n",
-    "    treatment = np.repeat(['Placebo', 'Drug'], N*2).tolist()\n",
-    "    rep = ['Rep1', 'Rep2'] * (N*2)\n",
-    "    genotype = np.repeat(['W', 'M', 'W', 'M'], N).tolist()\n",
-    "    id_col = list(range(0, N*2)) * 2\n",
-    "\n",
-    "    # Combine all columns into a DataFrame\n",
-    "    df = pd.DataFrame({\n",
-    "        'ID': id_col,\n",
-    "        'Rep': rep,\n",
-    "        'Genotype': genotype,\n",
-    "        'Treatment': treatment,\n",
-    "        'Y': y\n",
-    "    })\n",
-    "\n",
-    "    return df\n",
-    "\n",
-    "# Generate the first dataset with a different seed and adjustments\n",
-    "df_delta2_drug1 = create_delta_dataset(seed=9999, second_quarter_adjustment=1, third_quarter_adjustment=-0.5)\n",
-    "\n",
-    "# Generate the second dataset with a different seed and adjustments\n",
-    "df_delta2_drug2 = create_delta_dataset(seed=9999, second_quarter_adjustment=0.1, third_quarter_adjustment=-1)\n",
-    "\n",
-    "# Generate the third dataset with the same seed as the first but different adjustments\n",
-    "df_delta2_drug3 = create_delta_dataset(seed=9999, second_quarter_adjustment=3, third_quarter_adjustment=-0.1)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "556f9b89",
-   "metadata": {},
-   "source": [
-    "### Creating contrast objects required for forest_plot"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "09c54fb9",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "unpaired_delta_01 = dabest.load(data = df_delta2_drug1, \n",
-    "                                x = [\"Genotype\", \"Genotype\"], \n",
-    "                                y = \"Y\", delta2 = True, \n",
-    "                                experiment = \"Treatment\")\n",
-    "unpaired_delta_02 = dabest.load(data = df_delta2_drug2, \n",
-    "                                x = [\"Genotype\", \"Genotype\"], \n",
-    "                                y = \"Y\", delta2 = True, \n",
-    "                                experiment = \"Treatment\")\n",
-    "unpaired_delta_03 = dabest.load(data = df_delta2_drug3, \n",
-    "                                x = [\"Genotype\", \"Genotype\"], \n",
-    "                                y = \"Y\", \n",
-    "                                delta2 = True, \n",
-    "                                experiment = \"Treatment\")\n",
-    "paired_delta_01 = dabest.load(data = df_delta2_drug1, \n",
-    "                                paired = \"baseline\", id_col=\"ID\",\n",
-    "                                x = [\"Treatment\", \"Rep\"], y = \"Y\", \n",
-    "                                delta2 = True, experiment = \"Genotype\")\n",
-    "paired_delta_02 = dabest.load(data = df_delta2_drug2,\n",
-    "                                paired = \"baseline\", id_col=\"ID\",\n",
-    "                                x = [\"Treatment\", \"Rep\"], y = \"Y\", \n",
-    "                                delta2 = True, experiment = \"Genotype\")\n",
-    "paired_delta_03 = dabest.load(data = df_delta2_drug3,\n",
-    "                                paired = \"baseline\", id_col=\"ID\",\n",
-    "                                x = [\"Treatment\", \"Rep\"], y = \"Y\", \n",
-    "                                delta2 = True, experiment = \"Genotype\")\n",
-    "contrasts = [unpaired_delta_01, unpaired_delta_02, unpaired_delta_03]\n",
-    "paired_contrasts = [paired_delta_01, paired_delta_02, paired_delta_03]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "50d94de3",
-   "metadata": {},
-   "source": [
-    "## Visualize the delta delta plots for each datasets "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f4315e6f",
-   "metadata": {},
-   "source": [
-    "To create a delta-delta plot, you simply need to set ``delta2=True`` in the \n",
-    "``dabest.load()`` function and ``mean_diff.plot()``"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "36a5e3fd",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 500x500 with 3 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 500x500 with 3 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 500x500 with 3 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 675x600 with 3 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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VFaVafqDXoDWYbuv/ls9+9rOkUim++93vbt72yU9+kvn5ec6ePYuiKHz1q1/lT/7kT1hfX6enp4ff+Z3f4X/6n/4noLaK/cILL/D973+fL3zhC0xNTbFr1y7+9E//9KYd5z760Y9SV1fHn/3Zn93zcxUr3cIt1XmdfOzAMKcnFjkxNstyNMG+wQ4spge/YicI90Kj0eLr3Vfb+DhzGnf7LrxdT5CcPUsln8LffwCd4d7DosnpJzj8EpGR19i48FOCwx9Ab7Leh2dw/2g0GgJuBwG3gyd6W4mkamPop1cidzSGXioXiI0fpZKJ4WrbibN5QLxIF4RtSqmWWTnxNw/0Gpr2ffKuXpSPjo5y/PhxWltbAXjllVf4i7/4C/74j/+Y7u5ujhw5wj/9p/+UQCDAoUOHNu/3W7/1W/yn//SfqK+v54tf/CIf//jHmZqawmC4/myDdDpNf3//3T25dxEr3dvAdl/pvtpyJMGJ0TkA9g520FLnfcBXJAj3TlVV0osXSS+O4moZxOSuJz5+FI1OT2DwOYz2+/PvvFrIEBn5OWi01O14Eb3FcV8e970kKwrr8QwL6zGWNhJUJXlzDH1rvW9LyVkxvkp88kRtUmf/Acyu4AO8ckEQbkWulB6a0P3Zz36Wv/iLv8BsNiNJEuVyGa1Wy1/91V/xsY99DK/Xy89+9rMtE8H/+T//5xQKBb71rW9trnR/+9vf5tOf/jQAiUSCpqYmXn31Vf7RP/pH15zzr/7qr/hn/+yfcfbsWQYHB+/5uYqVbmGLiiRh1N/4n0Vz0Iv/gIOTY3McPjdJV2OQJ/tbb3ofQdjuNBoN7radaPVGkrNncUgV6nZ/iNilo2yc/wnenr3Ygm33fB6D1Undzn9AZOTnrF/4KXU7PoDB5rr3J/Ae0mm1NAbcNAbc7B1QWI2lWAjHt4yhbw168FZWqGzMYPE14uvde1/eIRAEQbjaCy+8wNe//nXy+Tx/+Id/iF6v55Of/CRjY2MUCgU++MEPbjm+Uqmwe/fuLbddHcq9Xi+9vb2Mj49fc67XX3+dX/mVX+G//Jf/cl8CN4jQLVwlky/y9ydH2dPTQk9z3Q2Ps5gMPL+7h9nVKG+PL7CeSHNgR5cYqCM89JxN/Wh1BhLTb6PIVYLDL5KcOU1s/BjVXBJX+857rvPWm23U7fzgO6UmO17E6Hg43jHS6bS01HlpuTKGPppkYWmF6eP/A10li7lpiGZHHw5Fx40LUARBEO6OzWajq6sLgD/7sz9j586d/Nf/+l83a7J/8IMf0NjYuOU+JpPpjs/zxhtv8PGPf5w//MM/5DOf+cy9X/hlInQLm2wWE+31fk6OzRFL53imv/2GwzM0Gg1dTUHqvE6Ojczyk7cv0d8WYne3GKgjPNzsoS60eiOxiWOoUhVv3wGMdg+p+fNU8kl8fQfQGe78P/Gr6YwWgsMvER09zMbFnxEYev6hK8Uw6HXUGQropSnUznqKvhdZziicmljg1MSCGEMvCA8BrcFE075PPvBruKv7abV88Ytf5POf/zxTU1OYTCaWlpa21G9fz8mTJ2lpaQEgmUwyNTW1pWb78OHDfOxjH+OrX/0q//P//D/f1bXdiPifUNik02p5ZrAdn8vGW5fmSeWKHNrVjc18428Ih9W8ZaBOOJ7iwI4uvE4xUEd4eFkDLQR0emKXjhAbO0xg8HmMdg+x8aNsnPsx/sHnMNrc93QOncFEcMeLRMfeIDryGv6BQ1i8ofvzBN5jqiKTnDtLdnUKa6AFX88zaPVGerkyhj7BwnqMEyNznNTOizH0grBNaTSah7qz0Kc+9Sl+67d+i2984xv8y3/5L/nN3/xNFEXh4MGDpNNpjh07htPp5Jd/+Zc37/PlL38Zn89HXV0d/9f/9X/h9/v5xCc+AdRKSj72sY/xuc99jk9+8pOsr9dmCxiNRrzee39HUixJCtfoagryoacHKZQq/OD4CBuJzE2Pf/dAnb8/KQbqCA8/i7eBwI4XqeSSRC7+DIPdQ/3uD6PR6dg492MKsaV7PodWbyAw9Dwmdz3RscP35THfa7VBQj8hF57B2/0U/v6DW3qP18bQ1/Ghpwf5h8/vZk9PC8VKlTcvTBNJZh/glQuC8KjR6/X8+q//On/wB3/AF77wBX7nd36HV155hf7+fj784Q/zgx/8gPb2rXMXvvKVr/C5z32OJ554gvX1db73ve9hNNb+D/vmN79JoVDglVdeIRQKbX78w3/4D+/L9YruJdvAdu1eUixXOXJhikgyy5N9rfS11N+y9ZcsK1yYWWFsfo2AxyEG6ggPvUouQWTkdbSXV6a1egOJyZPko0u4WoZwtQ3fc0s8VZGJT56gEF3C27sXe13Hfbr6+ysfWSAx9RY6kwV//8E76uqSLZSwmo3v6bh5QRCEG7nSvSSZTOJ2ux/INYj//YQbspgMvPRkP30t9ZwaX+DYyCySLN/0Pjqdlj29LXzomQEKpQrfO3aR6eUI4rWd8LAy2r3U7XwJVa6yceGnyJUSvv6DuNt3kVkeIzr2BopUuadzaLQ6fH37sdV3EJ84QXZt6j5d/f2hyBLxqbeIjR/D4muifvdH7riNosNqFoFbEITHmvgfULgpnVbLU/1tHBzuYnEjwY/eGiNXLN3yfkGPk48d2EFbyMeJsVlePzdJsXxvwUQQHhSD1UXdzn+ARqNh4/xPqBbSuFoGCQwdopyOsH7uR1QL6Xs6h0ajxdv9DM6mXhLTp0gvjd2nq7831XyajXM/ohCZx9e7F1/ffrT66w+REARBEG5MlJdsA9u1vOTdEpk8h89NUpUUntvVTch3e/2FrwzUUVXYO9RBqxioIzyk5EqJyMhryOU8gaEXMDn9VAsZopeOIJcL+Pr2Y/U13dM5aoN6RkgvjuBqGcTVtvOBTHRUVZX8xhzJmVPozPZaOck9bh4VBEF4nImVbuG2eZ02Xt63A6/Tys9OjXNpYe22ykaag14+fmAnQY+DN85NcmxkhookvQ9XLAj3l85opm7nS+itLiIXf04pGcZgdVK/60OY3fVER98gvThyT+VUtUE9w3g6dpNeGiM5e/p9L89S5CrxyRPEJ09iDbRSv/vDInALgiDcIxG6hTtiNhr4wBP9DLSHOD2xyJsXZqhKN6/zhncG6uwf6mRpI8H3j11kPXFvb8cLwoOg1RsJ7ngRkytAZPQwhdgyWr0B/8CzuNt2klq4SOzSERSpek/ncTYP4Ot5mtzaNImpk6iqcp+ewc1VcgnWz/6IYmwZf99+fL370OpEd1lBEIR7JUK3cMe0Wg1P9Lby3M4elqNJfvTWKNnCreu8rwzU+fiBYaxmEz99e5zTE4vI8vsTJgThftHq9AQGD2H1NRG79Ca5jTk0Gg2u1iECQ4copTZYP/9jqoWbt9u8FXuoG1/ffvIb88TGj6Eqt36Be7dUVSW7Ns36uZ+g0eqo3/MRbHXtt76jIAiCcFtE6L7K17/+dYaHh3E6nTidTvbt28cPf/jDGx7/6quvotFotnyYzY9Pe7y2kI+X9w4iyQo/ODHCajR1W/ezW2oDdfb0tjCxtM4PToyQyOTf24sVhPtMo9Xh6z/wTseR1UkArL4m6nd/CFSV9XM/ophYvafz2IJt+AefoxhfrXVKke9/aZYiVYiPHyUx/Tb2+g7qd38Ig9V5388jCILwOBOh+ypNTU185Stf4cyZM5w+fZoXX3yRX/zFX2Rs7MZdBJxOJ+FwePNjcXHxfbziB8/jsPHy3h0E3A5eOzPByOzqbdWfarUaBttrA3U0Go0YqCO8byRZ5menx5kPx+7539s7HUf6ScycJr04iqqqGKwu6nd/CJMrWKvzXhq9p7psq6+J4NAhyuko0ZHX77l05WrlbJzw2R9STIYJDDyLt/tpNFoxNVIQBOF+E91LbsHr9fK1r32NX/3VX73mc6+++iq/8Ru/QSqVuqdzPCzdS25GUVQuzK4wMrtCa52PfTs6MOpvrw5UDNQR3k+5YonjI3OsJ9LYzCb6WuvpbgpiNNx93bKqqmSWxkgtXMDZ1I+7YzcajeZyJ5KLpBdHa+PSe/ei1d19u71yOkpk9HUMFgeBHS+gM9z994mqqmRXJ0nNn8No8+DvP4De4rjrxxMEQRBuTqx034Asy3z7298mn8+zb9++Gx6Xy+VobW2lubn5lqvijzKtVsPu7mae393LWjzFD0+OkckXb+u+1xuoM7W8IQbqCO8Ju8XMP3h6gI/tH6bO6+Tc9DJ/88ZZTk0s3FYP+uu5Us/t7XqSzMo4iem3UFXlcieSnQQGn6WUWGPj/E+Qinc/Ct3kClC38yWkcoHIhZ8hlQt39ThytUxs7A2Ss2dwNPRQt+uDInALgiC8x8RK97uMjIywb98+SqUSdrudb33rW7z88svXPfbEiRNMT08zPDxMOp3m3//7f8+RI0cYGxujqenGvXrL5TLlcnnzz+fPn+fQoUMP9Ur31VK5AofPTVEsV3h2uJumoOe271uRJE5PLDKzEqEp4GHfUAcWk/E9vFrhcVcoVZhYWmdqeYNqVaal3stAW4iA++5CaG5jjsTkSaz+Znx9+zdLNSr5FLGxIyhSGV/fQSze0F1fc7WQJnLxNTRaHcHhF9Gb7bd933I6SmziKKos4e3dd899xQVBEITbI0L3u1QqFZaWlkin0/z1X/81f/qnf8obb7zBwMDALe9brVbp7+/nl37pl/i3//bf3vC43/u93+NLX/rSNbc/KqEbauH52MVZliMJdnY1MdzZdEcDPsRAHeH9VpVk5taijC+skykUCbgdDLSFaA560WrvbDhNIbZMbPwoZncd/oFnN0tK5GqZ+MQxSsl13O27cDT13/XgG6mUI3LxNVRFJjj8IgbrzYdVqapKZvkS6YULGB0+/P0H0Zttd3Xuu6HI1XsqrREEQXjYidB9Cy+99BKdnZ184xvfuK3jP/WpT6HX6/nLv/zLGx7zqK90X6GqKhdnV7k4s0Jj0M3BHV13VDdbLFd569IcSxsJOhsDPNXXdk91t4JwRSSZIeB2XDfwqqrKSjTJpYUwG4kMdouJvtYQXU2B296nAFBKrhMdewOD3UNw6Hm0euPlx1dIz18gvXwJW7ANb88zd90HWyoXiI68hlwtE9zxAkb79V+cypUS8cnjFBPh2pTL1h3v62bJQnSJxPTb+Aeew+wOvm/nFQRB2E5ETfctKIqyJSDfjCzLjIyMEArd/G1jk8m02ZbQ6XRit9/+W8MPE41Gw86uJl7Y00skkeXvT4ySyt1+DarFZODQrh7276gN1PmeGKgj3AepXIEfvTXG4XNTlCvXtt/TaDQ0B7186OlBXt63g4DHyZnJRf7m8FlOTyySL97e/wdmTz3B4Q8gFdJsXPgZcqV0+fG1uDt24x84SDG+XKvzLuXu6rnoTVaCO19Cb7KyceFnlNPRa44ppTYIn/17KrkkwR0v4G7f9b4FbkWqTbaMXnoTkyuIwSbaEAqC8PgSK91X+cIXvsBHPvIRWlpayGazfOtb3+KrX/0qP/7xj/ngBz/IZz7zGRobG3nllVcA+PKXv8zevXvp6uoilUrxta99je9+97ucOXPmtspRrngUupfcSiZf5PVzU+SLZQ4Md91xuUiuWOLoxVmiySz9bSF2dzej04nXjMLdWdpIcHxkFoNex7M7uwl6bl6/nS+VmVhcZ3o5QlWWaa3zMdAWwu++9QvmSj5FZOQ1tDo9wR0f2FLSUckliV46gipV8fcfxOypv6vno0hVoqOHqeQSBAafw+wJoaoKmaUx0osjmFwBfH0H0Jusd/X4d6OUjhCfPIFSKeHpehJbXcddl9IIgiA8CkRquUokEuEzn/kMvb29fOADH+DUqVObgRtgaWmJcDi8eXwymeRf/It/QX9/Py+//DKZTIbjx4/fUeB+XDhtFl7eO0Sj380b5yY5O7V0Rz2S7RYzH3paDNQR7o+WOi8fO7ADq9nIj98eY3Ru7abdcmxmE0/0tvLJ5/fwZF8rsXSOvz85wo/eGmNxI3HTf8tGm5u6nR9EVVU2LvyEauGdd2uMdk9tEI3dQ2TkNTIrE3fVtUerNxDY8cLmaPpseJrIxddIL47gbBkiOPyB9y1wq4pMav4CkQs/Q2cwE3riZez1nSJwC4Lw2BMr3dvA47DSfYWqqozNr3FuapmQ38Wzw92YjHdWz5rM5jl6cZZ0vsjOziYG2xvueKObIADIisKF6RVG51dp8Ls5sKMLi+nWm/0URWU5mmR8IUwkmcFuMdPfVk9XYxCD/vqlG1K5QGTkNZRqmeDQCxgd77zbo6oKqbnzZFbGsdW14+1++q7qvFVFZu30D0hOv421ro2GJz5216vnd6NayBCfOEYll8TVOoyzZQCNRqztCIIggAjd28LjFLqvWIulOHJhGqNezwt7evA47qyLwtUDdfxuOwd2dOG0iYE6wt1ZjaY4NjKDRqPh4HAXId/NO4FcLZbKcWkhzOJGHINOR09zHb2tddjMpmuOlasloiOvUy1mCQw9j9m1dVNhPrJAfPIkRpsL/8Bzd9RdRFUV0gsXSS+NIZdyaI0W/H37sIe6b/sx7paqquTCM6TmzqAzWfH17sfk9L/n5xUEQXiYiNC9DTyOoRsgWyhx+NwUmUKJ/UMdtIfu/Id0JJnh6MVZSpUqT/a10t0UFG9jC3elUKpw9OIMG4kMw12N7OhouqN3UPLFMuOL60yvbCDJCm0hPwOtIXyurcFZkapExw5TycbxDzyHxduw5fOVbILopTdQFQV//7O31e1DKuWJTRyjkonhatuJo6mf1NxZsquTeDp242x+70re5EqR+NRbFOOrOBq6cXfsFq0BBUEQrkOE7m3gcQ3dAJIsc2J0jvlwjIG2Bvb0tNxxqUhFkjgzscT0yoYYqCPcE0VRGZmrtbkMeh0cHO667or1zVQkiZmVKBOLYXLFMnVeJwNtIZoCns0XhIosER8/SjEZxte3H1ugdctjyJUSsfE3KaejeLqexB7qvuGLyWJ8lfjkcTQ6Pb6+A5ur56qqXl75HsXVOoSrdfi+vyAtxFdITL0FgLfnGTFoRxAE4SZE6N4GHufQDbVwML4Y5szkEvVeJ8/u7MZsvPOVspVIkhNjcyiKKgbqCPdkPZHmzQszKIrKgeFOmgK3P1X1CkVRWY4kuLQQJprK4rRa6G+rp6MhgEGvQ1Vk4pMnKUQX8XY/jT3UteX+tQ2J58isTGIPdeLtempLq7/a58+TWZnA6mvE27sXneHaEqvM8iWSc+dwNvXi7njivgRvRa6SmjtHdm0ai68RX88z6IyWe35cQRCER5kI3dvAdgrdqqo+sPKMcDzNkfPTGPRaDu3qveZt+dtx9UCdjoYAT/eLgTrC3SmWqxwfnWU1mmSwvYFd3c3otHe3KTCSzDK+GGZpPYHBUKv77mupx2IykJw5RXZt+oZlILn1WRLTpzDaPfgHnkVvsiIVs8TGj1HJJ3G378bR2HvT79vs2hTJmdPY6jvwdj99T5sby5kY8cnjyOUC7o4nsIe6REmXIAjCbRChexvYLqG7WK7w01Pj7O5ppjn4YFaJ88Uyh89NkcoV2DvYQWdj4I4fQ1VVZteinBpfwKjXc2C4k3rv7W+MEx5tiqJyYXaFzobALTffqqrKpYUwZ6eW8LnsPLezC7vl7jfsZgulWr/vlQiKqtJW76O/tR5dYob00hiuliFcbdeWgZQzMWKX3kRVFez1nWTXptAaTPj7Dtz2hsX8xjzxyRNYAy34evfd8YCcq/t+G+0efH37bzl6XhAEQXiHCN3bwHYJ3aVKbWVvJZKku6mOJ/tab9j+7L0kyTJvXZpndjVKX2s9T/S23tUKY65Y4tjILJFElr62evZ0t4iBOgKZfJEfvTVGuSLRWu9jsL3hlu+qRFNZ3rwwTaUqs29H5z2XLlWqEtMrESYW18mXyoR8LtrNWQyJaRyNvXg6n7wmeEulHItH/hv5jXk8HU/Q+Mwvbo6Wv12F2BKx8WOYPSH8/Qdvuy1htZglPnGcSjaOs3kQV+vQ+zpGXhAE4VEgQvc2sF1CN9RW9qZXIpyaWMRqMnBwuIuA++bT+t6r65hc2uDUxAIBt4NDu7rvanPklXrxs1PLOK1mDuzouquyFeHRIskys6tRxubD5IolGvxuhjoaqPM4b1gqUalKnBidY3EjTm9LPU/2tt7zizhZUVjaSDC+ECaWzuFREjTJqzR29hPoP7BZBlItZIiNH6WST6E3WZBKeRwNPXg6n7jj8FtMhIldegOjw0dg8Hm0+hvvn1BVlfzGHMmZ02iNZvy9+zG57vzdJ0EQBEGE7m1hO4VuqZhFb3GQyRc5OjJLPJ1juPPO26fdLxuJDEcuTKPRaHh+V89tjd2+nmS2wNGLM2KgjrCFoqgsrscZnV8lmS3gdzsYam+gOei5bvhWVZWp5Q1OTyzitFk4tKsbp+3eNxCqqko0leXSQpj1hUmc6SkCzR0M7v8IanaDxNRb6EwW/P0HMdq95MIzJGZO1YLzwLN3vImxlI4QHT2MweIksOMFdIbr9xRPTL1NIbaMvb4DT+eTNw3ogiAIws2J0L0NbJfQXS1kCJ/+PmZ3Pc6WQQyOAKPzq1ycXcXnsnNwR+d9CRh3Kl8q88b5aRKZPM/0t9PdfOu+xdcjK5cH6syJgTrCVqqqshpLMTa/xkYig9NmYai9gfYG/3VLmxKZPEfOT1MoV9g72E5Hw/1b/c3kS4yPXSA6doR8RcLk9LOjr5v2HQe2hN5yOkp0/E00aPAPPHvHw2gq2QSRkdfQmSwEd7y4JbgXE2vEp06CIuPtfgZroOW+PT9BEITHlQjd28B2Cd2qqlCILpFZHqOSS2FyBXC1DJLVODg2MkuhXOWpBzSARpYV3h5fYHplg57mOp7qb7uHThJioM7jTFVVirFlzJ7QdVduI8ksY/NrLEcSWM0mBtrq6W6qu2Z/Q0WSeOvSAvNrUboagzzV33bf9kBU8imW3/4ByzMjpPVB0sEnCQUDDLSFaPC7Nv+9SuUCsUtvUskl8XY/hb2+847OU82n2Rj5OVqdnuCOD6A1mEjNnye7OonFE8Lbuxe9yXpfnpMgCMLjToTubWC7hO4rVFWlmFglszRGORPDaPdibexnLCYzsxKhKehh32AnFtP7/1bz1PIGb48v4HPaOLSrB6v57obgVCWZ0xOLYqDOY6iUjjL7w/8vOpMFi7cRW30n9rpODDbXljKLVK7A2Pwac2sxDHodfS319LXWb+khf6VTzluXFrBbTDy3sxuP4+5D6js11KfQm+04mgdJzJwmXVJYNHSQKEi47Vb6W0N0NPjR6bSoikxi5hS58GxtE2bH7juq85aKWTZGXkMq5dDqTaiyhKdjF/aGm7chFARBEO6MCN3bwHYL3Veoqko5tU56aYxSagODzUXW2sL5tRIarZb9Q500Be98aMi9iqayHD43BcChXd0EPc67fqwtA3UG22mt992vyxS2qdrY8rcpRBcoxleoFjJotHoMVicmVwCzpwGD1YnB4kBvcVDRmJnayDETTgDQ3RRgsK0Bm2VrQD9yfppsocRT/W139e6JIlVJzLxNfmMBe6izVkOt01MtpIlcfA00GjTNTzOxnmE1ksJk1NPbUkdPcz1mo55ceIbkzClMrgD+/mfRGW+vdEpVFVJz51k79T/Q6Ay0Hvr/YAu23dG1C4IgCLcmQvc2sF1D99XK6Sjp5TGK8VUknYXxkpd41UhvS62l3/vdWrBYrvDG+Wli6RxP9bXR03z3JSJioM7jrZSJkl2ZIBeeoZpPgQb0JjtaoxnQvFPKoTWylDcwn5ZRNAbaGwLs6G7D7w+g1emR5Nq7J1PLG7TV+9k71I5Rf3v/jiq5BLHxo8jlIt7up7HVtW/5vFTKEbn4GooiEdzxIiVMjC+sM7MWBVWloyFAf1s9ZilHbPxNNFot/oHnMDlu/iJSKuWIT56gnI5iq++knI6hVAsEhl644xpxQRAE4eZE6N4GHobQfUUllyCzNEY+usRSXst0wYEnEOLZnb133VnkbsmKwumJRSaX1ulqDPLMQPtdt3BTVZW5tRhvj89j1OvZv6OTkE8M/nicqKpKJRMjH5mnEF1CrpbRm22YnH4MNg+qIiMVs5RyaWbCCaajRYpVlZBDT2/IQZ3fi8HiYC0PZ5cyWK02nn9iAL/nxv+OVFUlF54mOXsWg9WJv/8gBuv137mRK0UiI68hl98JxaVKlenlCBNL6xTLFRoDHnpCbvThs1QLGXw9z1wT4K/Ib8yTmDmFVm/A17sfs7sORaoQGT1MNZckMPQ8ZnfdffnaCoIgCCJ0bwsPU+i+olpIk1m+xPrSLOc2JEoGL0/tHmZHV+v73opvZjXCW2PzeBxWDu3q2fK2/526MlBnI5Ghvy0kBuo8plRFppRaJx9ZoBhbQZFrXURswTasgVa0BhPVcpGZhWVG55ZJpbN4zNDlBp++RLYkcWatTLassqPJRW+TH6PNid7sQG9xYrA60Oj0JKdPkY8u4Wjovq2e23K1THT0MNV8aksolhWFhXCcSwthktk8bruFRk0MT2UdT0s/7o7dmz2/5WqZ5Mxp8pEFbME2vN1PbRmyo8hVYmNvUkpHCAw8i8XX+J59nQVBEB4nInRvAw9j6L5CKuVILY5xZnSC6YREKNTABw7uw+12v6/XEUvneOPcFLKi8Nyu7nsa+35loM65qWXsVjMHxUCdR4qiyCjVCjqD8bY2HCpylWJ8lXxkgVJiDQCzpx5bsB2LrwmNTs9yJMno/BqxVLa20bHZR71Nw9nJBSYWN6i3adgZ1KKVCqiKglQuUIyvoNHqcLUMYqvrwGCxo7c40Vsc6IyWG5ZLXR2K/QMHsfqaNj+nqirriQzjC2GWI0m05RT1yga9bY007niWai5FfPIEilytlbHcoHZbVWRi48coxlfw9R/AFmi98y+0IAiCsIUI3dvAwxy6r5DKBebHz/PmuQnKssLTg10M734Kg/n9C6vFcpUjF6aIJLM82ddKX0v9PXVfEAN1Hk2pRIQT3/4PaLV6NEYzGoMFrdmGzmRDb7SiN5rRG80YTSYMJgsGkwWTyYLRbMGg06EUolQTK1QyMbQ6HRZfE9ZgG2Z3PZF0ntG5NdZiKewWE4PtDRgNet66NI9Bp+PAjg5MqTmSs6fR6o3Y6jtQZYlqMYtcynPlv2OtTo/e4ri8mdOJ3mLHYHGitzrQ6k2gKu+E4t591y0hSeeKjC+GmZydpxCZp8OYpsVvw9vYia93P/pbfG+qqkJ88gSFyCLenmfuuB2hIAiCsJUI3dvAdgrd5Wwco91795sSi3mOHj/J5NwC9XYt+4d7CHQMY7C8P6PkFUXlzNQi4wth2hsC7BtsR6+7+02eYqDOoyeXiTF1+DtIxTxytYQiSyiyjCpLyKqKooKigqyqqKoGVaND1epqv2r0td9rDWi1GvRyGYNSQKdIaAxGtI469K4GKgYXG5kS0WwZo9lCU12AZK5IfG2RDluJp3cN4e/ag1b3zkZLVZGRSjmqxSxSIVP79cpHubB5nM5gRG92oLPYKcZWqOTieLqextO+67p9x7PJKOPHvs/a/CQJrZv6/v3s3rmTeu+NR95vXpOqkpw5RXZtGk/nEzib+u7fX4QgCMJjRoTubWC7hO5qPs3a6e9jC7Tg7XlmS53nnVpY3eDIW6cppyPsqtPR3t6Js2UQo819/y74JubXYhwfm8NlM/P87h7slnsLypFklqMXZyhVqjzR23pP3VKEB0uRqmxc+Blmdx1Gpx+92Y4qlZEqReRyAblSRC7Xfl8t5qgUs1SrFWSpiixVUWQZGS0KmsvhHJRKGbmcRS1nUaUqisaAZLBSwkKurFAol5GrZWS0yHorJqsDr9uN0WxBZzBdXl2v/WowmjGYzRiNVoxmMwajEYNOg04qoZVKaKp5NNUCaiWHXMyS35inko1hdtdj8TdvtjvUmR1UczFy6/MYHV7cXXu5NHaBsZklymYfDS0dDLY30Frvu+mgKVVVSc+fJ718CXfbMM6WIfFvXxAE4S6I0H2Vr3/963z9619nYWEBgMHBQf7Nv/k3fOQjH7nhfb7zne/wO7/zOywsLNDd3c1Xv/pVXn755Ts673YJ3RVJ4vjxYzTJq1isFnx9BzC77m7kOkChVOHYxWkWFuZpNGTodSs4g804W4Zu2crsfkhk8hw+N0VVknluZzch/711I7l6oE5jwMO+wY67Hs4jPDhSuUBq/jylZBi5UkKj1dX6c7vrsXhCGOyeLaFSVRXkSqkWxq8E83IRufLOr1K5gCJVARWplKeSTyIVMqiqiixLlMsVUho3GZ2bQlWhXKliNZtoCboxG7RIlVooV2QZWVGRFeXyR23lXdUaULV6FK3+8u8NqBo9WoMJrc5ANRdHykYx2T2YnT60UolKcgXKWSx2FzaXH6vNhsliR1PJsr4eZgMPBUsTDqeT/rYGupvqMBlv3OIwvTRKav4CzqYrGzNF8BYEQbgTInRf5Xvf+x46nY7u7m5UVeWb3/wmX/va1zh37hyDg4PXHH/8+HGee+45XnnlFT72sY/xrW99i69+9aucPXuWoaGh2z7vdgnd+WKZH789RrWUY5ctiUXJ42wZxNW6Y7PzwZ1SVZXJpQ1OTyxglHIMOTLY1CIWTwhnyxBm992H+ttRrkgcuTDNejzN7p4WBttD9xwWxECdR4OqqlQLaUrJMKXkOuX0BoosozOYMHvqMXtCmN31t6x9vkKRpXdCeaVIJZsgOn6EQnQZjUZTG1ajNZJXDMSKKvFsbchUa2OIrrYW9CYLWr0Bjc6ARqNDo9Oh0epQZJWqVKVaKVEplZAqJaqVIlKljFQpIVXLyLJCOZ+mlE2gagzIsoSEDq3Vg6Q1IKsaFEVFUmolU9VKmXKxQBUdBZ2TnGJCYzAR9LhoDfnxuN1YrHbMNhtGvQGjQYdRr6cSXyC/PIo71EGw7ymMeoMI34IgCLdJhO5b8Hq9fO1rX+NXf/VXr/ncpz/9afL5PN///vc3b9u7dy+7du3ij//4j2/7HNsldGfyJX5wYgRQkSSZPb4qjuIKRocPf/8B9Oa778OdytU2JSYzeXoDBhrVdaR8GpMrgKtlELOn4T374a0oKuenlxmdX6W13sf+oc57HuZTqlR5a2yexY24GKjzkFEUlVyxjEGvRa/Toddp0Wg0qIpMORO7HMLDVHJJVFXFYHXWArinHrOr7rp10+9WSm0QmzgGqoqvdx8mV4BifJXcxjzF2DKyVCFTNTC2UWAlVSbgtPBMTz1eix6lWkKulLY8nlanQ2e0ojNa0Jmu/Hr59wYTGp0eVVHYOP9jEjNnsfoaCQ5/AI1Gi1QtI5WLVMpFquUS1Urto5JPk4+tUq2UUQ02shWVTKFMWQG9TofZaECr0yHrzKg6E6q+tulUkmTKhQxamxeLvwW9yYrRaMJoNGDQ18L5lZBuNOg2bzMY9ATdDiymW3/9BEEQHkUidN+ALMt85zvf4Zd/+Zc5d+4cAwMD1xzT0tLC5z//eX7jN35j87bf/d3f5bvf/S4XLly44WOXy2XK5fLmn8+fP8+hQ4ceeOiWZYXjo7PMrkWxGI2UKlUG6s00VBZRpMpNW4zd1uO/a1PiE81WlMg05UwMo92Lq2UQi7/5PQvfi+txjo3M4rCaOLSr9543RIqBOg+nfLHM37xxdsttV8K3Xqd7J4wjY6ik0JWS6IpxNFIJnU6H3u7F4KzH5ApidPkx6vWb99NpNZTWJsivXsLqqcfXtx+9ybrlXHKlRCG2VGtBmIqwnsxxLlwmIjvo7O7n2V09NPldqFIJ6V015rUSl3fKWhRZBkAq5ynGlgEwOQNIxSwmVxBP1xO1+m6jtRbSjVa0BtPmiwypnCc+cYJ8ZBF7XTsmfyurGxHmF5fJ5dLYdTJBi4pdW6ZazFMtF5FlmWqlTLWUB4MFvc2LqjOg6s2oeguKzlxbXUdHVdVRVbVIqpYqOp7bv5eWhtD78xctCIKwzYjQ/S4jIyPs27ePUqmE3W7nW9/61g1rtI1GI9/85jf5pV/6pc3b/uiP/ogvfelLbGxs3PAcv/d7v8eXvvSla25/0KEbakHy/MwKF2eWsZqNFEtVmv0OBixJSvElbHXteLueuq3VvhvZSGQ4NjJDuSrxVF8bjVaJzPIlSqkNDDYXzuYBbMG2uy5puZlktsAb56YoVao8u7ObxoD7nh8zXyxzbGSW9USa/tYQu3ua76ljivDekmSZaCqHJMtIkkJVlmu/l5Wtf978vYIkSbWWfvkYFGJoiwk0ioSi1SOZ3EgmD5LeiiWzgL6aoeRopWxvRaevrfTqtNqrVtbfCfY6uYQuv4GcXGZ1dYVYrkLZ5MPd2EVXVy8djQHMRv3m/WovDC6vzKsqSrVEcvYs6aVR9CYbjsYeAIqJNdKLF9HqjLXhNld9L2m02tpK+VWr5qXUBoXIAhZfE/6BA+gtLtaTeS4thAnHU1jNJvpa6ulu9EElRyWXILc2TXL2DBqtDqPdi1wpoioyqnL5hQA68lUoVmQKFYlyVWHni5+ko+faBQxBEITHgQjd71KpVFhaWiKdTvPXf/3X/Omf/ilvvPHGdVe67zZ0b9eVbqi1LdNodcysRDgxNofJoKdclfDYLTzTbKK0dAGd0YKvbz8mp/+uz1OpSrw9vsDcWpTWeh97BzqgmCS9PEYxvoreYsfZNIC9vuO2Bpjc6bmPXpxhNZpiV3czQx33XtoiBuo8PFRVZXY1SmPAjcV0dxthFUWmlI6Rj69RSKyRj8xTiq2A3oi5ZRd6Xweq1Yes0VGVFGT5BmH+qj/LxRS59QVK8SW0UglVb6Zo8mPwNOH0BTc7jGjQoNNpMaolbMkJ9NU8qq8Tja8Tg+HyqrtOh7acQl05jc5kw9L6BAadBq1cRiNXLv9aBrkM1RJIJar5JMXYMhqdHlugtTakx2SlrOhYTRZZS5VQdCbq6vy0NDRgtjoo5DLEpk+hGmwQ7CWaSBOJJ4mnsuSKJWSpgh4Zs1bBrNfwCy89R3evCN2CIDyeROi+hZdeeonOzk6+8Y1vXPO5uy0vebftUtMtlQtsnP8xzqZ+7A09rMezHD4/iUYDqgJ6vY7nBhpRV89TySVwtQ3jbB64pxXphXCck5fm0Gm1HNjRSYPfTSWXILM0RiG2jNZorl1PqAut7v7VgqqqyoWZFS7OrtBS52X/jk6M+nuvyU5mCxwbmSGZLbCzq4mh9kYxUGebSeeK/I9jF0CFep+L9pCPljrvXdXkq6pCev4CqcUR9CYrZk+ISi5BtZBBo9FgtHs3N2WanP5bvoBUVbX2TtBbpygllnDJKSrlErLehqexk4b2AXRmK+XILKXVsVqtdWgY2ejYEuarkkS5IlEtpNGtX6Siasm4epA1RhRVRbnSGUVRUFQVWVFBltBLecy5ZbRKGcwutDo9OqWKTimjUyrIlRLVahVVVWsr9Xo9sgpyOY+EkaLZj9HqwGJz4HS58bo9WO1OdHo9WqVCf0cbbtf707NfEARhuxGh+xZefPFFWlpaePXVV6/53Kc//WkKhQLf+973Nm/bv38/w8PDD+VGSkWqkJo/Ty48g8Hmwdv9FAWNldfPTlCsVDHo9FRlmQNDHbhLK2SWL2FyBa9bt3on8qUyx0dmCce3lmdUC2kyy5fIb8yj1RtxNPbhaOy5p/7h77YcSXD04gxWs4nnd/Xgslvu+TFlReHi7Cqjs6tioM42VapUWdpIMB+OEUlk0Wg1NPrdtIV8NAc9t1UeJJXyxCaOUcnEcLXtvPwCVLP5uVJqfbMzilwto9XpMLnqNkO4weraPF5RVCRZpirJVGWZQrHC2xMLLK/HaLBImMtRspElVKmKVq/HYDRhquvBVNeJpGioSvJV91eQL9d6A8jVCvmNOVRVxRJoA50BRQWdVoNGo0F7+QMNaC/XeiuJ+VoJjasRjbMRRVXJlyrkiiXy2RzpTIpqKYdWruA0aQhZFJo06xi0WqrWAJotP1Y0aHR6NAYzwwc/THOrmGwpCMLjSYTuq3zhC1/gIx/5CC0tLWSz2c0WgD/+8Y/54Ac/yGc+8xkaGxt55ZVXgFrLwEOHDvGVr3yFj370o3z729/m93//9x/aloFXlDMxEtOnqOaT2ENdmEL9vDGyQCKTx2Y2kS2W2NPTQqdHR2LyBKoi4e15Bqu/5a7PWSvPWOfc1BIOq5mDw114nbXyDKmUI7N8idz6HBqtFkdDD47GvloLtvsgnSvy+rlJiuUKB4e7aA5678vjRpJZjo3MUChXeVIM1Nm28qUyi+sJFsIxYukcep2O5qCHtpCPBr/7uoNjstFlohMnkNHh7Hgarc29GXglSX4nBMsKlapEKZemkElQyCYo5TJUZQVFq0djdIDRhsZk2zKdEkBVIZ7JsxZLYTbqaXHqKK5PQjGF06Bgt9lwBpuwBdrQuxtqQ3T0us3yEoNeh16vxajXgVwhNXEUnVolNPwCFqfvpv8WK1WJ+UtnWZgeI6tzUbKEUKhtOA24HdR5HQTdDkqVKuOL64TjaazaKi2VOdwOC46u/choqZbytY9yHqlUpGNgDx5f4H7/FQqCIDwUROi+yq/+6q/y85//nHA4jMvlYnh4mN/+7d/mgx/8IADPP/88bW1tW1a9v/Od7/Cv//W/3hyO8wd/8AcP7XCcq6mqQm5tmtTCBTRaHY7WYc6GZZYjSVwOK+lcga7GIE/2hEjPnKYQW8bR0I27Y8814eFOJLO11oLpfJFdXc0MtIU2yzOkcoHsygS58DSgYg914WgauKdV9isqksSxi7MsRxIMdzaxs6vpvgTkqiRzZnKRqWUxUGe7kGWFeCa/ZWVZkhSqkkw6X2Q1lmQtliabL6HVanDbrbjtFiwmA5Ikk42tUkhFMVgdWHzNaK+zKq7RaDDorw6/Ogw6bW1TpQbUcg61mEIpJKGcQ68Fs92J3R3E5q3D6g5iMpnQ63Rk8zlOHf4hpFdo7eqn+8kXmVuNMT1xATm5gsdQpc7rxt/ciT3YjtlTf90yFrlSIjLyOlIpS3DoBUyud8JvqVIlksxe/sgQz+RRVRVttYA5v0LQZaVn97MEg3XXLZeKp/OML4ZZWF7BlRzB77TQd+DjeHx19/cvTxAE4SEmQvc2sB1D9xVSuUBq7hz5yAJGZ4BlTYiJ9Rxuh5V0rkjQ4+C5nd3IiUWSs2fQmWz4+w9gtN/9arEsK5yfWebSfJig18HBHV3YLKZ3Pl8tkV2dJLs6iarI2OracTYPYrDcW62oqqqMzK1yYXqFxoCbg8Nd96339ko0yYlRMVBnO7hey0CtttYVxHhlpVivo1yVSGbzJDJ5ylUZsw5cUoKAqUpzZz+exk4Men0tXOu1m/cz6HRoL5du3A65UqSUXKeUWqeYDCOXi2i0WkzOADqTlUJ0EUVRWdGGmMuZ6GwK8nR/O1qthsX1OGOTsxRji3hJU2fT4na7sAVasQbbMDkDW65DkSpER98gl4qg1O8iIVvZSGZI5QoAWM0m6jwOgh4ndV4HLpsFqZghOnYEuVLE338Ai7fxxl/bUpmJuSXWzv0UtVLE1f8cA319BNyijlsQBEGE7m1gu4TuiiTxo5Nj7N/Rid+1dRBOKRkmMXMaqZglZajjQtKMw26jUK5gMRn4wBN9WKgQmzhGtZDG3b4bR2PvPa0WryfSHLs4S1WSeWagnfaGrd1SFKlKLjxNZmUcpVrGGmjF2TKI0ea+63NCLSAfvTCD2Wjg0O4ePI57X0mHrQN12hsCPCMG6jwQsqKQyZe2BOXrlZBcoaoqK/NTjJx7i7UcaN1NuN1u2ur9tDf4cNut961sSFVVpEKGQmKV5OwZsivjaPUm7KFObIFWwlUbF9YK2O12Du3qxuOwoaoqq9EUo3OrxKNhPGqaRnMJlxEMFhvWQBuKvZ5EWUcklWEjloK1s+jLKTQNu/A1dVJ3OWTbzKbrPhdFqhCbOE4psXZ5A/XgTZ9zuVhg/PgPWA+vknD04qmrvWvVHPSKjcWCIDy2ROjeBrZL6F5PpPmj/98PqGrM7Opu5oneVhr87s1yCFWRySyPk14aJVtWGCu40TnqUKgFmUO7eqj32EnNnyezMoHFG8LXuw+d8e43J1aqEm9dmmc+HKOt3s8zA+2YjFuDqiJL5NdnyaxcQioVsPqbcLYMYXLc/WpyJl/i8LlJcsUyB3Z03reVaTFQ5+GiKnIt/K5NYwu04O56mliuxPxanMWNOJWqhMtupa3eR3vIf182zFaLWeITx6lk4zia+jG76ymnI5enZCbIlGTOxTSUtFaeHuhiqL8PnaH2PRpJZrg4u8rsSgRtOYVPk8VUiqJUy8gGOyZ/C97GTuoCQYyxMaTUGt6eZ7DX33pzo6qqpBcvkl4cxRpowde796YdhRS5SmTkDaLry6yZOtgoG3l+dy8tdfdnz4QgCMLD5qEJ3fv27eO//Jf/ckcbFB8W2yV0l7JJpt78Wy5k7UymTRiNepoDHvxuO/U+Fw0+N3VeB1QKJGZOkwgvMJ2CkqsLg9VBtlBi70AH3c1Biok14pMnAPD17r3pW9K3Y34txluX5tHrdRy4QVBVFZl8ZIHM8hjVQhaLJ4SzZQizO3hX56xKMsdHZ1lcjzPU3siu7ub7tkonBupsf9VChtj4UaqFDJ7OJ7CHuras7sqKQjiWZn49zvJGAkmW8TnttDf4aK33YTObbvLo11JVlfzGHMmZ02iNZvy9+7fUXQPI1TLl1AbZ2CqnJxaY3cjS4NTT1RSgqHORUcwkyrUXjbF0rvaiwGpmZ8hEj1uB3AaKLGNyBbAGWqhk4uQjC3g6n8DZ1Hdb11mILRGfOIHebMM/eOimZV2qIhO79CbFZBh90x7qW3vESrcgCI+thyZ0h0IhEokE/+f/+X/yb/7Nv8FsfnRasG2X0A2QXZsmMf02GWsL5+IGKlWJxoCLfKlKoVRGq9USdDsI+Zz4tAVKKxeYXVohbW7EWt9HLJtnsL2B3d0tqFKJ+ORJiok1nE29uNt339Ogm6uD6kBbA7u7m9Hpri0LUFWFQnSJzPIYlVwKkyuAq2UQs+fOh+CoqsrYfJhzU0uE/C6eHe6+ZqX9bl3dseX53b33ZTqmcH/kN+ZJTL+NzmTB33/wlnsUJFlmJZJiYT3GajSFoqgEPA7aQ35a6rxYTDfvMS9XSySm3qYQW8Ze34Gn88kbTn2tSjKxVI6NZIbRmQXOTy6iSmUabQoNdi1+h5GG+jpCDc1g8zO1nmVmNYpGo6GrwUOHU0FJrVJKrqMCqlShWsrj692Hp2P3bX2PVPIpYmNHUKQyvr6DWLw3Hu2uKjLxyZMUoosEhp7H4m245eMLgiA8ih6a0J3JZPjCF77AN77xDdrb2/n617/OSy+99KAv677YLqG7Wi4yfeRvMBv1KPk4xtYnGEnbSOYK7OluJuR3EY5nWI+nWU9kkGQZk05DUNkgvzZJSTVi63iKWElHU9DDweEu9DotubVJknPnMFhd+PsOYLDdfTmFqqpcWghzbnoZl63WWtDjuP7kR1VVKSZWySyNUc7EMNq9uFoGsfib7zh8h2Np3rgwhVGv5/ndPZvtDO+HQqkiOppsE4pcJTlzmtz6HLa6NrzdT9/xUKZKVWIpkmAhHCccTwMQ8rloD/lprvNcM4Rp810hVblu681yRarVYieyRFJZ4ukcqqpiNOip8zixW0zMrcUoV6sMN7tps0mUU+uUM9HaEBuzDewBlgom5pNVFLR0hPz0NXkwFKKX3x26RDkdwdHUT3D4RSye0C1fIMvVMvGJ45SSYdztu3A09d/w+0pVFbKrk9hD3ffU3UgQBOFh9tCE7itOnTrF//K//C+cP3+ef/JP/gn/8T/+RwKBh7vv63YJ3al0ip/8+IcYSnHMhTDmahr8nayZe4iU9HQ01XFody8umxlFVYmmsoTjGcKxFInoBuWVC2hLSTTeDgqOFhqCfj74VD82s4lKLkFs4jhyKYe749q36u9UMpvnzQszZAol9vQ0098auskPfJVyap300hil1AYGmwtn8wC2YNsdTdPMFUu8fnaKTKHE/sGOazZ2Cg+3Sj5FbPwocimHp+spbHUd97xBsliusrQRZz4cJ5LMoNNqaQy4aQ/5CXnt5JYukl2dwuIN4e3Zi95kpVCqsJHMEElkiaQyJLPv7ixS6y7itls2r09WFM5NLXNpYY2mgKc2YVUL5XSEYjJMKbVONZ9GUmCtYmEhq0fSW2lrbmSoswmPCaLjR0lMnURntGCr77xhB5SrqapCeuEi6aUxbME2vD3PXDdUS7JMvlh7cWnQizIqQRAeTw9d6AZQFIX//J//M7/zO7+DTqejubn5mmM0Gs0djWJ/kLZL6FYUlVyxTKZQJJWIk5k6RnVtlLLWxFrVyWzBAkYb7S3N1NfV47BZcFrNOG1mTHoD2WKB0fOnScycQdboWNE2YXYFOLS7l/7WEG6biczCObJr01j9TXh7nkFnuPsyIVlWODu9xPhCmHqviwPDnbesoy2no6SXxyjGV9Fb7DibBrDXd9x22Ysky5wYm2d+LUp/W4gnelpFjepD7p1a6lPozXb8/c/e07sxN5IvlllYj7MQjhOJRqkkVwiaZdq7+rEEWoll8kSSWbKFEgBOq4Wgtxayr6xo3+pFwEokybGRWXQ6Lc/t7CLocW5+TioXNidk5hNhlmI5ZpIyJY2FUF2QXf3deI0S8bHDaHR69GY7cqWE3myrBfC69ut2BlJVleTaHJHxY8g6C2rjboqynnypTK5YJl+sUK5WAXjpyX4a/Nc+hiAIwuPgoQzdlUqFL3/5y3zta1/D5/PR29t73eNef/319/nK7s52Cd1Q+wF69Q/21MIFUnPnMXkbSBaqHB1bIpGv0uqz4PP5yWidFFQzXL6PxWSkUi6QmruIXY6zqnhI4qEpFKLO5yTkdREwFDDFxjGZTfh792P21N/TNYdjaY6NziJJMs8MttMeuvUKdCWXILM0RiG2jNZoxtnUjz3UdVulBFdqsc9MLlLncfLszu5b1uwK25MiVUlMv00+soA91FmrpX4Pyx8URWZt6jzzl86wkNUwV/aQqajodToa/W56W+voaaqj3ufEYrq7kqN8qcybF2aIprLs7GpiqL3xmheGqqpSzScpJMLMLywyurBOsiDhsZkYCFlxVqPYAm2424fJx1bIhOcplQrIBhtVaz0lk4+CrCNfrJAvlZEVBV01hy1xCR0KamgnVm8Im9mEzWLc/NXrsIk2mYIgPLYeutD9s5/9jF/7tV9jbm6OX/u1X+P//r//bxyOh3vwwnYJ3eWKxE9OjTHY3kB7yI9Go0FV1VoLwOVLeLufxhxo4dS5i1ycmsenL7IzoMNktqDaglTMXvIaO9lihdVYkqmpKfzlJQqyjg3ZgcPXgM1mRZIVzFqJZmUFv6FCfddO2ob2YjLdWbeHd1/7yUtzLK7fWQ/saiFNZvkS+Y15tHojjsY+HI09aPW3DjzriTRHzk+j02o5tLvnmt7mwvZWySVq5STlIt6eZ7AF2+77OWRFIZHJ1+qxIxvk5k5BKUnF0YKtcYCg143ZaCBXKrMaTZIrlrGYjLSFfLTX+/G5bHdV4qIoKhdmVxidXaXe5+TgcNd1Q7yqqlQlmUw2x9zCLKOT86zH4jiqSdo0axgtVrKufipmLxpVwViKY6kmMek1GBwBTP4W7MFW7A4ndrMJs14lN3OScjqCu2PPPffqFwRBeJQ8NKE7Go3ym7/5m/zlX/4lO3bs4E/+5E94+umnH/Rl3RfbJXQXyxXeHptjMZIk6HHydH8bXmdt+EZy9gzZ1Ul8vXux13fW3sa+OI1aKbAnpMdWiSKVCmj1Biy+Rqy+ZiomNz89NUkpOoumEGcpA766Bjx1TURTedK5PPrMEp7SKrLBTjU4RH1diNZ6Ly11Hlx2G2aj/rZ/aKuqyny41lrQqNdzYLiTeu/tlQlIpRyZ5Uvk1ufQaLU4GnpwNPahM968/CVfLHP43BSpXIFnBtvpary79oTC+0dVVXLhaZKzZzFYnfj7D2KwOm99x9sgyTLRVI5IsrbxMZrOIUsS5kqcQGkRu91O3cBBGlrar2kRqaoq0VSOhfUYi+sJiuUKDquZtpCftnrfXQ1pWo2mOHxuknJVYqi9AavZSL5YIVcqky+WyZfKVCV583itVguqSjKVoZhcpak0jcesoT7UjN1uxe6tr7XgVFUquQTl1AYAZm8DtmAbFl8jGq2O1Nx5Mivj2OraL29GFavbgiAID03o9nq9VCoVfvd3f5fPf/7z6B6hnsbbJXTLlSIb539KxdXOhYhMJl+ip7mOXd3NGA06kjOnyIVn8PXtxxZs2/I29q6uJnoCFoqJFYrxFSq5FBqtDr0jwFhMIlrSEdBmmVyOEvS6+QcvvoDO4iCTLxFeXSR66RjZTJoFJciG7ECr02K3mPE6rDT43QQ8tZHUTqsZh61WR/7uLhBX5Ioljl6cJZrMMtAeYlfX9VsLXo9ULpBdmSAXngZU7KEuHE0D6E03DjyyrPDWpXlmViP0ttTzZF/rTSccCg9WNZ8mfOYH2Bu68XTsuac2luWKRDSVZSOZYSO5tbNI0OMg6DBjTU+jzW1gr2/H2/XUbb2LoigqG4kM8+sxltYTVCQJj8NKW72ftpAPh7X2YrAqyeRL5c0yj3ypcrmOukzh8p8rVYnlSIJsoUxjwE1XYxC71VQr+TCbsFveKQGxmAybL3Iz+SKjE1NER18DwNe2gxanBk0+hiJL6AxGjHYvKipyKU+1mEOr02PxN2ELtiFXyiSm38Zoc+EfeK7WRUUQBOEx9tCE7pdffpk/+qM/oq2t7UFfyn23bUJ3tURy5gz5yAImb4i4qZWLS3G0Gi27upvpagyQmnmL/MY8/oGDWP0tKIrK+ZllRudWafC7ObCjC4vJgFTMUoivUIytUExHWIkkiJb1ON11zESSGDQqLz09TFPfbrQ6A4pUJTl7mmx4FskWZN3QwmK01p6wVK6CBox6HSajHrvFjF6nxWIy4ri8kdNptdR+tZlxWMxoNBouLYQ5P7OMy2a53Frw9lcK5WqJ7Ook2dVJVEXGVteBs3nghoNAVFVlanmDUxOL+F21Ed13W5MrvPeqhcxdrW4XShUiyVrIjiQzpLJFVFQsJuPmKPWgx4HbbqWc2iA+eQJFruLtfvqOy1dUVaVUqZLJl1gIx5hdi7EcSVCqSJgMemxm45ZuIBo0WM1GbBYTNvOVOupaoLaajCxuxBmdWyPgdvDscBc2y+2Vc2VSCcaP/g8iyTQ57w7aWlvo8pkwlBOUkutUsnFUVUVrMIIKcrUIKuhNVox2L8XEKlqjhcDAc3c9qEoQBOFR8NCE7kfZdgndVxTiKySm30aVJazNOxhP6Zlbi+F12niqrxXdxgiF2DKBgeew+GqTJlejKY5enEGr1fDczm7qvO8EGrlSohBfYfLSRdYWZ7CYjSzmDJTKFfZ2+uh/6nmsl3tn5yMLJKbfRqs34u87gNbmZSORYS2eIhzPkEjnKFdlzCY9drMJk0EPGg25YmnzbXINGmwWU20ktwbmVmPIisIzA23s7Lz9VW+obbTLrk2RXZ1AqZaxBlpxtgxet4sD1MZwv3F+GoDnd/cQcD/c+w0eZ6pa6+ZzpX3fRjKz2VnEYTVT53HWuou4nTis73QWURWZ1PwFMivjmN11+Hr3XXeVV5YV8qUrK9SXyz2urFhftUHxCp1Oh8VkoHw5iOeKZfQ6LQ1+Nz3NQbqb67De4oVeJJnhzQszVGWZAzs6aQ7e3kh2qVwgfP5nbESiLBjaKWKltd7HYHsDHpuBUmqdUrL2USlkUKtlVFVGkaqg0SAVM2h1Rhqe/gXsoa7b/SsQBEF4pIjQvQ1st9ANtcEXqbmz5NbnsHhCyMEBzsxFSGTydIR8tKqrqNl1AoPPb06j2yw3SWbZ2d3Ejo7Ga+qxZ5bCnDl3Bjc54qks6/EE/Y4Cfe0tBAYPYatrR64UiU0cp5KJ4WrdgbNlcLOfdr5UJhxLE46nWYulKVer6HU6gh4HPpcNu9mEqkK2WCKTL5EplEjnCqxGU8TSOVw2CwNtIXwu+zUr5Gaj4Yb144oskV+fJbNyCalUwOpvwtkyhMnhu+bYQqnCG+eniGfyPNPfTnezWN17GKiqSipX3KzH3khmKJYraNDgdlgIepy1oO1x3HCYUSWXJD55nGo+jbV5BxpvO4VyZbN13pUSkHyxTLFc2XJfi8l4zQr1lRIQm8WIybB1f0O5UisbmQ/HWI9nQAONfjdtIR/NQe8N+2GXKxLHRmdYiSTpbwuxp6fltsqh5GqZ6Ohhyrkked8OJqIVcsUSDX43Qx0N1HmcaDQapGKWUmqdYnKdUjJMJZdEKmao5JK0Pv/PcDb138HfiiAIwqNDhO5tYDuG7iuKiTUS02+hVCs423axrrg4P72CrMi0amM0GjLUD7+I2V0HvLtrgouDw53XlFmsJ9IcPjuF2ajDqqkwPT1NSFqi05zD6mvA3b4Lq7+ZSi5BdmUCo9OPv28/evPW7iCqqpLIFAjHU4TjaSLJLLKiYDWbCPmcNPjc1PtcmAy1nsEzKxGOj85SKFVpCnowGWotz1Rq3wIGvW5LCHdaLdfUj6uKfHmC3xjVQhaLJ4SzZeiat81lReHU+AJTyxt0N9XxdH/bHa2wC++9K51F3ikXyVKpSmg0GnwuO3WX+2MH3A5Mxq37BxRFpViubPaizhXK5MOTlNcuUcZI0t5NRWfZPF6r1V4VqI21OuqrArXNbLqnfx/FcoXF9VoAj6ay6HQ6mgJu2kJ+mvzuax77StvLs1NLeOxWnt3ZXXtn6BYUuUp07AjldBRf30EikpnRuTWS2Tx+t4PB9gZagp53Vv1VhUq2VoZSTKzi6X4ak91z189TEAThYSZC9zawnUM31EosUvO1oTZmdxB725OMrqaYXAyjzazQ76oyuO+DmF3vBM9wLM2bF6fRaDQcHO4i5NvaRSSdK/LzMxNIskxz0MvUUhh3NUK3Oo9WqWCweTBYXehMVsrpCHqTdXMD541IssxGIks4niYcT21O8vM4bDT4XTT43DhtZk5PLLK4EaezMcCenhbKVam2Kp4vkSkUyeRLZAulLSuR764fd1iNmEox5OgMUiGNyRXA1TKI2dOwZTVyejnCW+PzeJ02nt/VI8a9bwP5Ypnjo7NEUjlkWUan0xFw2S/XYzs3Wz9eKfPIXe7ycWWFOl8sUyhXuPJfp0Yu48pOY5Gy6AMdWJuGsFutWwL2zd5Fud9yxRIL4doUzGQ2j0Gvo6XOS3vIT73XtaVndyyd480L05QqVfYNdtIWuvadm3dTFZnY+FGK8VV8ffuxBlpZi6UZnV9lI5HBabMw1N5Ae4NfbCgWBEG4igjd28B2D91XlJLrxKdOolRLuNp2UXU08valeeYnR6gzSzz/wgfx1TVsHl8oVTh6cYaNRIbhrkZ2dDRt+YFfLFd5/dwkyWyB7qYgMysRrHqFnbYkZMPozXb0JhuVXIJifBVZquBs6iM4/BImx61rUYvlymYZSjiepliuoNNqCbgdqKisRFK47RaefdfkvisqUi2MZ68K41eC+Wb9uApOMjiKa5iVPBaXH3frEL7GTuwWM1qthlgqx+HzU6iqyqFd3dc9l/D+qUoSPz8zicNiwmI2YtBpKVaqWwJ2pSptHq/RaLCaLm9Q3LJJ0Yi+EKG0fB693oCvZ99mqdV2kcoVWAjXpmBmCkXMRgOtdT7aG3wE3A40Gg0VSeLk6DwL6zF6mut4sq/1mnaG76aqCvHJkxQiC3i6nsLR0A1AJJllbH6N5UgCq9nEQFs93U11YvS7IAgCInRvCw9L6Iba28up+QtkVycxOf14e55hOVnijcOvUSoVeeaZfewc6N38oa0oKiNzK1ycWaXO6+TZnVuHdEiyzLGLsyxtJOhurmM1mkRRVZ5pMqONjqOqCo6mfrQGE+m5c6QWLqLR6nC1DOBo7Mfib8Jo995yFbFWr1vYDOEbySyFUpn1RAYNGnZ1N3FoV89mK7ZbPdaVrhJXVsUz+QK5eBgpMo2ulEA22Cg7WzD7WnDarJiMemaWIxTKFfYPdTDU0VjriSy87zL5Et9989zmn/U6HTbL5dZ51ykBsZiM10x0VKQqiZlT5DfmsQZa8HY/jc5w98Od3mtXSrHmwzEW1uMUSmVsZhOt9T7aQ348Dgszq1FOjS/gtJl5dmc3bvvNu/1c3b/f3b4LV8vg5udSuQJj82vMrcUw6HX0tdTT11qP2SgmtwqC8PgSoXsbeJhC9xWlVITE9EmkUgF32zAGfytHf/4jptfTBFr7eGa4j+arajvD8TRHL86gqirPDncT8r9TbqKqKmenlhibX6O9wU86VyKdL7K/vwVXcYlceAaDzYO3+ynQaNg49yMKsRX0Zht6qxuDxYbF14TV14TJFbytvsuyrBBJZVmNpjgzucj08gZmk4EdHY10NAYI+VzUeZx3vEKnKCqp6CqxuQtkoytUMFCwNZHWeckWq6zFahs6A247g20NuB1WnDYz7aHAbdXUCvdOVhRWo6nNUG006O6o9KOUjhCfOI4iVfB0PYkt2P5QTV1UVZVIMstCOM7iRpxSpYrTaqEtVBvAc35mhVyxzDMDbbcc9qSqKunFi6QXR3E1D+Bq37Xla5Evlrm0EGZqJQLAC7t7aPC738unJwiCsG2J0H2VV155hb/9279lYmICi8XC/v37+epXv0pvb+8N7/Pqq6/yK7/yK1tuM5lMlEql2z7vdgrdxXLltvtLK7JEeuEi2dUJjHYv7o7dLI29zfnlJHlbC82hOp7qa8Nlt1x+7CpHL86wHk8z1NnIzs6t5SaTSxu8PT5PvdeJTqtlJZpkT08LXT4jyZlTVPMp7KEunC1D5FYnSS+PoTNaMLnrqGSunYhp9obQ6m5vZW0lmuLHb48RSWRw2S3YzEZ0Oh1Bt4OQz0XI78LnvLOR3JVcgszSGIXYMlqjGXuoD8XdyPhilLfGF9DrtLTWealIsig7eQioikx6cYTM8qXa5t7efehv0Lf9YaEoKuF4moX1GEsbCaqSjNNmoVSukiuW6GsN8cxA+y1ffGZWxknOnsXR0I2n66lrvk9KlSqTSxv0t9ZjNIjplIIgPJ5E6L7Khz/8Yf7xP/7HPPXUU0iSxBe/+EVGR0e5dOkSNtv1p6m9+uqrfO5zn2NycnLzNo1GQ11d3W2fd7uE7nypzN8cPovHYaUx4KEp4Mbvclzz1vq7lTMx4lMnkYpZHA09FOIrrKVKLGhbKCta+lvr2dHZiFGvR1VVRufWOD+9TMDj4NmdXdjM77wtvxpN8cb5KRxWEwG3k6nldTobAzwz0EZxfWazvMTdsRudwUJ86gQoCp7upzGY7RTiy1smYpo99Vh9TVh8Tbcc6S7JMmcml5hYXMfrtNEcdJPIFFhPZJBkGZPBQP3lrigNftdtDxepFtJkli+R35hHqzfiaOyj4mjkyMV5JFnhuV3d12w0FbaXaiFNfOI4lVwSV9swzuaBzTaWjwpZVliJpVgIx1iJJImlcySzBUJeFx8/MExDwH3T++fWZ0lMvYU10Iqvd+89TfoUBEF4FInQfRPRaJRgMMgbb7zBc889d91jXn31VX7jN36DVCp11+fZLqG7KsmsRJOsRlOsxVKUKlWMBj0NPjdNQTchnxuL6forx1evAupMVuRqEVVnIu7awfhKAqNBzxO9LbSH/Gg0GjYSGY5cmEZRVA4Od9F41Q/0RCbPa2cn0QCdjX5G58P4XXae392DXq2SmjtLPrKI2R3E1bqT7Oo4hdgKjoZu3B170Or0VItZipcnYpYzUQCMTn8tgPubbzhZEmAlmuTE6ByKorJ3qIOmgJtYKsdavLYhM57KoaLitFkI+Vw0+F3UeZ3vtBVUVVRZQpEqWz6qhTTZ1SkKsSVQVXSOAPMphWy+wJ6DH6K1rf2+/V0K94eqquTC06TmzqIzWfH1Hbhub/ZHTUWSWIkkGV8Ic2JsnnKlyo7ORvYNdtBa77vhanUhukRs4hhmTwh//0G0OrGqLQiCcIUI3TcxMzNDd3c3IyMjDA0NXfeYV199lX/+z/85jY2NKIrCnj17+P3f/30GBwevezxAuVymXC5v/vn8+fMcOnTogYfuq6mqSjyd3wzh8UwODRp8bjuNfjdNAQ9ep/Wat5Er2QTxqROUMzGkUh6zJ4S973kuzEdZ3IgT9Dh5ur8Nr9NGsVzl2MgMa7EUQ+2N7Opu3lxVz5fKvH52kmyhxGBbA+OL6xgNOl7c04fLbqGYCJOcPYVUzOFo7ENnspCav4DeYsffdwDjVb2A5UqJYmKVYmyZYnIdVZEx2lxYfM3X3YipqirFYonjo9MsryforHOxq92HTpVRpArlUol4Mkk8lSaZSlMuFdGqEg6zHodZh8Okw3KDFnFanQ4VDZVsgmoujqrRktb5GDr4URyBpvfob1O4G3KlSHzyJMXE2uUXdLtvu1zpUZIvlvnx25e4MLOMXldrP9hS56Ut5KMp4Lmm9KSYWCN26QhGu4/A0PNo9Y/f10wQBOF6ROi+AUVR+IVf+AVSqRRHjx694XEnTpxgenqa4eFh0uk0//7f/3uOHDnC2NgYTU3XD1G/93u/x5e+9KVrbt9OofvdiuUKq7EUq5EUa/EUVUnGYjLS6HfTGHAT8ru2DJBJL42RmjtLMbGKra6Dxmc+QSRT4e2JBTK5Ij3NdezqbsZo0DE2v8a56WUCbgfPDndtlm1UJZk3L0yzGkuxo6ORxY0ExVKFQ7t7CPlcqIpMZvkS6aUxdEYT9oZeCpF5qoUs7rZhrMF2VLl6eaW5iiJXkMsFSskwxUSYcnoDuVJGq9eht7gwmO1oDEZUWUJVFFRVZSktMbpRwaTXsCdkwmfTozWY0OoNaHVGtHojJVlDqlghma8Qz5apqBr0BhN+r5ug308o4MPpcKDVG7a85S5XS2RXJ8muThIcegGTK/BA/m6FaxViyySm3gKNBl/PXiy+xgd9SQ/c4kaCN85NkiuW8bvslCpVdDodLUEPbSEfDX73Zl/uUjpCdPQwBouDwI4X0BnEJmFBEAQRum/gf/1f/1d++MMfcvTo0RuG5+upVqv09/fzS7/0S/zbf/tvr3vMw7DSfTOyohC93PljJZoinSvU6tg9ThoDtRDuslmo5lNExw6TnD2DxdtIy/P/DK3RyuTSOhdmVtBqtOzuaaarMUgsneXIhRkkWebgjlq5iSpXkcplzk7MMreyRkfQQ66QI5FKM9gcIOS1Xi7byJBbm6acjWMw21CBSjaO3uLA6mtGc9Vb3BqNphZ+9Ua0Wj1ytUS1mKWaT6HKEjqDCZOnHouvEYunAZ3JSr6icmJilUSuxI7OJoa7mm449ENRVGLp3OUBPWmiqSyqqmK3mGnwuwj53YS8zi1vzyty9bFcQd2OFLlKcvYsufAMVl8j3p69t9wL8DjJFUscuTBDPJ2jr6Uek1HP4nqcZLaAUa+npd5Le72fOq8TqZAkMvI6WoOJ4I4X0Ztu3oJQEAThUSdC93X8+q//On/3d3/HkSNHaG+/8zrbT33qU+j1ev7yL//yto7fLjXdqqqQXhjB7A1hcvpve6NYrlhiNZpiNVobxy4rCnazkQa/k5DbjDE+SfT8T9AazdTv+Qgmu4discDU4irrkTgOs46OOjcGnYbTCynC6RKdHh39AQNajQZQiaZyhGNpXHYzOr2BWLZCXcBHa0MdeoMRjc5AtZgmvz4PqoLRFUQqZtDoDPh6nsHqb0arrx13vbIPVVWp5pLXbMS0eOqx+JoweRq5tBLn4uwKPqeNg8NdOG2Wa78Y71KRJDYSmdqAnliaTKG4WabT4HMR8rnwu+1ict82UM7EiE8cR64U8HQ+ga2+66FqBfh+kRWF89PLjM2v0RjwcGBHJ8VylYX1OPNrMXLFEhaTkdZ6L00uE8rSSTQaLcHhD9x0L4UgCMKjToTuq6iqyv/+v//v/Pf//t85fPgw3d3dd/wYsiwzODjIyy+/zH/8j//xtu6zXUJ3tZhl4/xPkCsldAYjJnc9JncQk722cezdGwMVqVa6oW7+vky1UiaTzZLOZMnmC1SqUm2in07CkFrEpFNxNvZgC7aiM1rIVxTmNlLkyjJ1fh9dLQ0spaqMLKfwuWwcHGrDYbej1RtZSeQ4PraEx2Glwe9mZHaVxqCbZ4e7N+tKFblKemGE7OoEWoMZjUaDVC7gbOrD3b7rtjsq3GgjZtHo50y4QlnR8mRvKz3NwTsKZrliiXAsw1q89gKlUpUw6HU8v6t3S+9y4f2jqgqZpTHSiyMY7V58ffsxWEX7xltZiSY5dnEWnU7Ls8Nd1Hmdm3tB5tdjLITjtRakeg3u4iINNg1dT93eNFlBEIRHkQjdV/m1X/s1vvWtb/F3f/d3W3pzu1wuLJbaquZnPvMZGhsbeeWVVwD48pe/zN69e+nq6iKVSvG1r32N7373u5w5c4aBgYHbOu92Cd1SuUDk4s+p5JNUMnGqhRRypQRoaoNoLE4MVgc6gxmt3nj5o1aqodMbayUbl2+7sqqcryhEMkXCySLhSBxp7TxBbQZ/UydNw4doaOlAg4aZ1QjnppZRVIWdXU14nTaOj8xSkWT2D3XSUlf7QR1L5Xjt7AR6nY7+1nrOTS/jsJp58YneLa0HK7kkiZlTlFIRdAYjSrWE0eHH338Ag/XOwu27N2JWJYnJtIGVoonWliaefWIHVvOdTyNUFJVENk84lqazMYDVfHv90YX7p1rM1loBZuM4WwZxtQyJVnd3IF8qc/TiDJFEluGuJnZ0NG5uhlYUlUgqw/xanIW1DVLhBT78wkHaW5of8FULgiA8GCJ0X+VGK5Z//ud/zmc/+1kAnn/+edra2nj11VcB+M3f/E3+9m//lvX1dTweD0888QT/7t/9O3bv3n3b590uoVuRqqQXL24J06osUcnGKWdiVHIJQIPB7rnc+7rxjspQKpLE4sICE6dfo5yKYNTrkF3NeNv30Fjvx++yM7m0wdTyBk67hV2dTcyFYyxHEvS3htjT24JOqyVXLPHzM5MUyxV2dzczOreGoqq8sKcXv8u+eT5VVclvzJGaO0e1lANZQme24e168q5LBxS5SikZphBbYX5xgbPLebR6A/sGO+jq6sHkCojQ9hBQVZX8+izJ2TNojWb8vfvFRta7pCgqI3MrXJxZpc7r5OBw1zUvIGVFIRxP39WUV0EQhEeFCN3bwHYJ3beiSFVKqTDF+CrFxNpmGYrZ24DF14TFE0Krv/VqbSkdYePCz8kWKxRKJTIlhaiplarZg89px2Ezs5HIUCiVaa3z4bSbubSwjtdh5dmd3TisZipVicPnpoiksuzpaWZhPUEyW+Dgjk5a67f2UZarJVLzF8iuTiKXcmh0BhyNvXh7nkFnuPMV6itURSYVXePNMyMsrW3QbFfZ2WTH7m/C6m/C7Ln9iZjC+0eulkhMvUUhtoI91Imn4wnR1u4+CMfTHL04g6rWeu+Lce+CIAhbidC9DTwsoftqqqpSycZrdc+J1drGQ40Gk7sOi7cRi6/xppumSsl1IqOHMdjcaHU6cvEwJUsdCVMzq8k85YpEoVQhWyxhNRvpbw2RzOaRJIV9OzpprfMiKwonx+aYXY0y1N5ItlhicT3O7u4WhjoarlnJLmdiJKbfphBdRK4UsfiaCQw+h9l9+9NDb/S1mFre4K2LExikPMM+CbuSe2cjpr8Zi7dRdMHYJuRKkY2LP6+1lfS3POjLeaS8u/f+zu4bd/oRBEF43IjQvQ08jKH73aRS7vIK+Cql1AaqomCwuTYD+PXKUIqJNaJjb2D2NGDxhkjNn0ej0+PpfJq83slKNMXSRoKJxXVi6RxOmxmvw4ZBr2VXVzNP9rWh1WoYmVvl/PQyHQ1+rGYjo3NrdDYG2DvYcc0PfFVVyK5OkZw9TSG2gsFix9d3AHfb8D2XhWTyRY5enCGeyTPY5KXDKVGOr25uxDQ5A1j8tZH0oovDg6WqquhM8h5RVZWx+TDnppfwu+w8t7N7s/e+IAjC40yE7m1gu4RuRaqwfu5HGO3eyx8eDHbvHZdgXL8MxXS5DKVxSxlKIb5CbOwI1kArrrZhkjOnKSbWsNW14el8Ep3BRL5UZnJxg6MjMyxtxFFV0Gq0NNd5+OBT/XQ1BlmJJDk2OkvAbae1zsfpycXN0fFm47WlA1K5QHL2DMmZ00jlHK7mIYI7X7rnMCwrCiOzq4zMruJz2zm4owubgWsnYtrdWHxNWH3NGOweEQCFR04kmeXNC9NU5a2boQVBEB5XInRvA9sldMuVIumlMSq5BNVcEkWWANBb7Jsh/Eogv91SidspQ6nmksTGj2Kra8fT/QyFyDzJ2TNotDq83U9tlgCoqsrCepzD56ZY3kiQyBbQ6zS01vvpa6nHajIwvRrBbbcy3NnI6YlFDPp3RsdfTzERJjp2mOzKOAa7h9ATH8XR0HPPX8tIMsvRizOUKlWe6mujqymARqPZshGzlFhFrlbQm634eg9gdgfv+byCsJ2UKxLHR2ff2Qzd04JOJ8pNBEF4PInQvQ1sl9CtKjLJ2TPYQ90YbC6kYpZKLkklm6CSq30oUhUAvcm6uRJudFwJ4pZbrtjeqAxFo9VRjK3gahvG2/00cqVIcuYUhdgKtkALnq6nNoO+JMuMzYe5ML3MUiSBJCvUeZ3YzCbKlSpr8TR2i4mDw12sxVKUKxKHdvXcsA+2qsikFi6yceGnSMUsnu6nCO3+8G1tCr2ZiiRxemKRmZUIzUEvewc7sJjeWXVXFZlyOkohvoKzqR+92XZP5xOE7UhVVSaW1jkzucTzu3poCnoe9CUJgiA8ECJ0bwPbJXRXi1miI69TLWaxBVtxte7Y0tNaVVXkUu5yEI/Xfs0lkKu1kfY6o3nLarjR7kFntt8wiL+7DKWYWKOUXMfZ1Id/4FnMnhClZJjkzGkAPF1PYg20bj5erlji9Pgi56aXSOdL9DTXsbu7iWgqx5EL06RyRVrrfaiKiqIqvLCnj51dTTd9/utn/57U/AVMDh+N+z6JLdh2z1/XpY0EJ0bn0Go17BvqoCkgQofw+MkXy6K2WxCEx5oI3dvAdgndUFt9zW/Mk14aQS4XsdW142oZQn+DWmdVVZHLhc2V8M0gXi4CoNUb3gnhjloQ11uc1wTxK2Uo8am3SE6fQm+xY/Y2YHbXYXL4KacjlNIRrP4mPF1PoTdZN+8bjqV5/fwko7NruOwWPnFwJ52NAX5yapxLC2u4HVYiySzxdI72kJ99gx0013nwuxybgzyuvo7s6gRrb/0d1WIGX+9e6vd85J5b/xVKFY6PzrIWS9HbXM8TfS3odaJfsSAIgiA8LkTo3ga2U+i+QlVkcuEZ0kujKNUy9lAXzpahLWH3ZuRKsRbCs8nNQC6V8gBodXoMdg8mh7dWnmL3YrA6N7ubZFYmiE+ewOQKojdZNstQVFWhmk+hN9vxDzyLvb5zM7zLisLY3Co/ODFKMlvg6YE2Pr5/mLGFMKNzq7SH/JSrEm+NzaPRQsjnxmIy0OBz0xR0b/558/qrZcKnvkdy9jRGh4/GvZ/EXt9xb1/Ty60FT08sYrtc/nL1MB9BEARBEB5dInRvA9sxdF+hyBK5tSkyy5dQZAlHQxfO5kF0xutvTLwZuVqimkturoZXcgmqhSwAGq3ucmlKrTylnImRDU/j6diNo6F3swwlH10ivzGPVMxiDbYSGHgOe33HO91QSmV+cHyUt8bn8TpsfPoDT6IoKicvzdHgd9PR4OfE6CygoaPBTzydJ57JoUGDz22n0e+mKeDB67Si0WjIrk2zeuJvqBYzeLufpn73h+7quV8tnSvy5sVpktkCO7uaGGpvvGbFXRAEQRCER4sI3dvAdg7dVyhSlezqBJmVcVAVHA29OJoH7mmi45XHfSeE1+rEpUIGVVUpZ6JU8ymcLUO424Yx2r3orS6kQprU/HkS028hlfJYPCHsTf1YfU1YfY3oLQ6mVyL81WunSWUL7B3sYHdPM29dmsduMbOnp5mTY/Obo+NtZiOr0RSr0RRr8RRVScZiMtLod9MYcBNwmkmc/yHJuXMY7R7qn3gZZ1PfNX3H74SsKFyYWWFsbo2Ax8GBHZ04rGJ4jiAIgiA8qkTo3gYehtB9hVwtk10ZJ7s6CRoNjsY+nE1999zp42qKXKWaS1HOxknNnSW7NnW5HtyHRqPBYHVisHvRmx0UInPk1ufQ6vTorU40Wv3mUB6ts44fX1zj9MQiXqeVZ4d7WIunADgw1Mn52ZVrRsfLikI0lWUlkmI1liKdK6DRaAh6HHjUNNrF45iqGTztOwjseBGTw3eTZ3JrkWSG46NzPLezG69TdC8RBEEQhEeVCN3bwMMUuq+QKyUyy2Nk16bR6nQ4mgZwNPbc84bDd1NVldTcWdLLl3A1DWCwOd9pY5hPoSoyUilHORNFo9VjDbZgsLqQijlUWUJnMLFcdfLaTJaCpKG3NYReqwMN7N/RydJ6goX12A1Hx2cLJdZiKVaiKdbjaSrlIkp8HmdpjQaPja7hZwh0P3FPLzoURRXlJYIgCILwiBOhext4GEP3FVK5QGZplNz6LFq9EWfzII6G7nseqX41VVVJzpwiF57B17sPW1177XZFplrIUMklKKWjpBcuUIgsoDVasPgaa7XXioxcLZEuSpwMa1gqGHE5HdgdDiwWOweGOylVJC7OrtxwdPzmc5Vl1hMZViMJpifGiK7MoVEqNAQD9AzupLNnQJSICIIgCIJwXSJ0bwMPc+i+QirlSC+OkN+YR2e04GwZwl7fcd/Ct6qqJKbfIr8+h6//ALZA63WPKybDxMbeoJJLYfbUozNZqeZTSKU85XyakUiVybiCBrAadeitTp7Ys4dgqJmTlxZuOjr+3dcTWV1g7MxRlsMbpCQTBruX+uZOWhpDNAXdBNyOGwZ4QRAEQRAeLyJ0bwOPQui+olrIkF4coRBdRGe24WoZwlbXfk+bDq9QVYX45AkKkUX8A89i9Tdf9zhFrpJeuEB2dQqjw4e3+xk0Ol2tW0o2wdTCMm9NLFPOZ/DKUbSKRMBjp6m1m4m8BaOnkReeeQK349btEeVKicTUSeKL40RzEnHVRkbvR7X6MBprLQkbA7UPi+n+1b0LgiAIgvBwEaF7G3iUQvcVlXyK9MJFCrFlDFYnrtYdW6ZJ3i1VVYiPH6MQXyEw+BwWb+MNjy2lIySmTiKV8rhad+BsHtgM/6lsgcNnxtiIRCG7Tim2QFBfoNkBiaKKpDPT2dFBY3svFl9TrXOKxXHd61dVlVx4muTMaaRSDrR6CnonBWcnsYqBeCqHiorPad8M4D6nXdRxC4IgCMJjRITubeBRDN1XlLNx0gsXKSbWMNrduNp2YvE23lP4VhWZ2PhRiokwwaFDmD2hGx6ryBLpxRGyK+MY7R68PXsx2mtj2CVZ5vTEIpNLG2g0GqaWN9BW8+xvNlJOrZNNRmmxVfFaDRgsDox2LxZ/0w2H+lTyKeLjRyln42h0elRFxeZvwtKyk42sVGtJGEtRkSRMBsNmAG/wuTEZ9Xf99RAEQRAEYfsToXsbeJRD9xXldJTUwgVKqQ1MDh+utmHMntBdh29VkYmOvUE5HSEw9CJmd/Dm58/ESEydpFrM4moZqq16X643nw/HODk2h6rCeiLNRiJLd3MQv8NCLBahw16lTR/n/8/efYdHVaV/AP/emSQzqZPegFRKCAQSQg1I6KGoICyCigIqsLquC5afotIsi30pKqi7AqJYEAVFEOlIFQgBAoSaTnqvk2Tm/P5gM8uQQtqUJN/P88zzcO899953LgfmnTOnVJUUQGirIFlY3pqiUGkPmYWV3qI+FjYOKMlIQHHaFcgtlRBCCwgtHHxC4NAxCAIyZBcUISUrH6lZecgrKoUECSPCg9DBzbFJz4KIiIjMH5NuM9Aeku5q5XnpyE84C3VhNpQqd6j8et81Ya6LVlOFrNgDqCjKgXuvkVA4uNZbXmg1KEiMRWHyBVjaquDSdRCs7J0BAIUlZTh09ipyC0tQUanBzex8ONgqobKzRmWlBp07uqO/vxOqCtJQlpsKdWEOtJXlkFlZQ26pgCTJoK2qgBACkkwGCIHyvHTIrKyhdPSARl0KSztHOHfuB6Wjhy6mknI1UrPy4ePhfNfBm0RERNR6cWqF2yxfvhz9+vWDvb093N3dMWnSJFy+fPmu523evBlBQUFQKpUICQnBjh07jBBt66R08oRH6Bi4hwyDVlOFjLO7kXluH9SF2Y2+lkxuAbeekbC0c0Lm+X1QF+XUW16SyeHo3xueYWMBSEg/8xvy42MgtBo42Fpj3ICe6ObjCQsLGbxcHSGTSSgpr0BFlQan4hKwO/YmLD27wyt8AjoOmgy3HpGwcekAodVAU6mGXGkLa2dv2Lj7w8bdD7aeAagszkXu1RMozUlGYfJFxO9dh+Qj36M0KwnaqkrYKhXo2smDCTcREVEbx5bu24wdOxbTp09Hv379UFVVhVdeeQWxsbG4ePEibG1rXy3w6NGjGDp0KJYvX457770XmzZtwjvvvIPo6Gj07NmzQfdtTy3dtxNCoCwnGQUJ51BRUgAblw5Q/Xe598bQVlUi8/w+VJUVwr3XKF2f7XrvrdWgMPkSCpLOw8LaHi5dB+payhPTc3Dk/PVbXT8kwNHOBmXlFUjIyIGXiwp/GRYOdyd7vfuX56ehLCcVZbk3oakoh9xSAaWzNxSOHlDnpSM//gwgySCzsEJpZgKE0ELp5AlbNz9Y2bvAoVP3Rr9vIiIiaj2YdNcjKysL7u7uOHjwIIYOHVprmWnTpqGkpATbt2/X7Rs4cCBCQ0Oxdu3aBt2nvSbd1YTQojQrCQUJ51BZVgRbNx+ofHvB0lbV4GtoqyqQcXYvNBWl8Og1qsHnVpTkI/fycVQU58K+QxBUfr0gk1ugqLQch2KuIj4tG1UaDfy9XKG0ssDh89chSRIeuCcUYV19ankvAhVFOSjLSUFZbioqivMhyWSQK2ygLsiC3MIKqoA+KM9PQ1HKZcjkFlA4usO9xzBdVxciIiJqe9i9pB4FBQUAAGfnupOhY8eOYdSoUXr7oqKicOzYMYPG1pZIkgy27n7w6ncvXLoNhLooG2mnf0V23FFUlhU16BoyCyu49xoOuZUSGef3orK0sEHnWdk6wiNsDBz9Q1F08wrSo3egPD8T9jZKjB3QAwOC/aGwtMSFhDQUlarxaNRAONnZ4KvfT2DT7j9RWq6+471IUDi4wtE/FF7hE9BhwEQ4BfSBpbU95FZKlOWlIeXoZpTnpt2a8tC1I7QV5dBUljX6uREREVHrwXnK6qDVajF//nwMHjy43m4i6enp8PDw0Nvn4eGB9PT0Os9Rq9VQq/+XrBUXFzc/4DZAkmSw8wyErbsfitOvoyApFqWZCbD1DITKpycslLV38akmt1TCPWTkf/uJ74VH71GwsLav95zq+zp0Coa1SwfkXD6OzHN7YOfdFY7+vdGvux88nB2w7/RlXIi/icKSckwf1Q+nLyfiUMxVJGXkYsKgEAT7e9W6+qSF0g72HbrBvkM3XTeU/PgY5F07jeK0q7D1CICljQNkllw+noiIqC1jS3cd/va3vyE2Nhbffvtti197+fLlUKlUuldkZGSL36M1k2Ry2Ht3hXe/++EYEIay7GTcPPkzcq+dQpW6tN5z5VZKuPcaCUkmR8b5fagqL2nwfS1tVPAIHQ3HgD4oSb+GtNM7UJ6XDh8PZ/xleBjCuvogMSMH3+45iWA/LzwxYTAAYPOB09h6KAZp2QX1Xl9mYQkbVx9497sfXSe9CKeAPqgszoO6KBtgLy8iIqI2jUl3LZ555hls374d+/fvR8eOHest6+npiYyMDL19GRkZ8PT0rPOchQsXoqCgQPc6ePBgi8Td1sjkFnDo2B3e/SdC5RuCkowbSDv5M/JunIGmsrzO8ywUNnDvNRIQApnn9941Ub+dJMng0DEInuETYKGwQca5vci9+idsLOWYEBGCCYNCUFyuxsZdx1GirsDs8REI8HLF5aR0/HL0HA6euYLisrpjq2ZpbQfvARPhEToGlkp7aCvVdz2HiIiIWi8OpLyNEAJ///vf8dNPP+HAgQPo0qXLXc+ZNm0aSktL8csvv+j2RUREoFevXhxI2cK0VRUoTLmEopQ4AIB9xyA4dOwOmYVVreWryoqQcXYPJAtLePQaBblV47pwVC/vnn/jDGSWVnDuMgDWzt5IysjFlgPRyMgrRGRoVwzqGYADZ67gZlY+lFaWUCgs0dPfGz39vSGX3/17bWVpISxtHBoVGxEREbUuTLpv8/TTT2PTpk3Ytm0bunXrptuvUqlgbW0NAHjsscfQoUMHLF++HMCtKQMjIyPx9ttvY8KECfj222/xz3/+k1MGGpCmshyFyZdQfPPyra4oHbvDvkM3yOQ157quLC1Extndum4n8ib0na4qL0bulT9RlpcGO88AOAb0QXkV8MPBaJy/nooe/t74S2QYTlxKQEpWHpzsbVBUqoaNwgr9uvuio5tTs5a9JyIiotaPSfdt6kqM1q1bh1mzZgEAhg0bBj8/P6xfv153fPPmzXjttdeQkJCALl264N1338X48eMbfF8m3U1TpS5FYfIFFKddg8zCCg6dgmHn1QUyuf744MqSAmSc3Q0LpS3ce42ss2W8PkIIlGTcQN7105DkFnDu0h8KJ2/sOx2H3Scvwc3RDrPHReB6WjYuJ93qB15RqUF6bgG8XR3Rv7sfHGytW+qtExERUSvDpNsMMOlunqryYhQkxaIk/QZkVkqofHrCzjMQkkyuK1NRnIvMc3thYe0A95ARkFk0bQXIKnUpcq/+ibKcVNi6+8Gpcziu3MzDpt1/AgAeGT0AAHDyUgK83VTw93JFzNVklKor0d3XEyGBHWBlwUmDiIiI2hsm3WaASXfLqCwrQkHiOZRmJkKusIHKNwS2Hv6QpFv9qtVFOcg8txdWtk5wCxlWa3eUhhBCoDQzAXnXTwGSDM6d+6HMyhlf/HoEmfnFGDsgGN06eeKPc1dhZ63E0N6dkZCei9gbqbCytEDfbr7w83JhlxMiIqJ2hEm3GWDS3bIqSwqQn3gOpVlJsLSxh8q3F2zcfCFJEtQFWcg8vw9WDq5w6xFZoytKY2gqypB79SRKs5Nh4+YDO99QfHfwPGJv3ERI5w4Y0y8Yx2KvQ6MVGB7WDUqFBU7HJSExIwfuTg7o390Pzg71zz1OREREbQOTbjPApNswKopykZ94DmU5qbCyVUHl1wvWLp2gLshCVuw+KFQecOsxVK8bSmMJIVCanYS8qycBAI6B4TgSX4J9Zy7Dw9kBk+4JRVxSBvKKSjEkJBC+ni5Iyy7AiUvxKCopR1cfD4R27gSFFbucEBERtWVMus0Ak27DUhdmIz/hLMrz0qGwd4bKrxcACVkXDsHa2Quu3Yc0K/EGAE1FOfKun0JJZiJsXDogVd4BPx29BJlMhvEDe6JMXYGE9ByEdfFBzwBvaIVAXGI6zl1PgUySYVhYV3g4c9pAIiKitopJtxlg0m0c5fkZyE84C3VBFhQOrlA6eaIw+SJsXDrCpftgXd/v5ijNTkbu1T8htBqonTpj29ls5BWXYWCwP9yd7BGXlI7ADm4YGBwAuVyG0vIKnLuegt6dO8Fa0bQ+5kRERGT++Js2tRtKRw949B6N8rw0FCScRUFiLGQWlihMiQNkcrh0G9TswY02rp2gULkj/0Y0tOmX8ECgMw6l2+HEpXh07uCO7r6euJqSiaJSNYaFdYWN0goDewS00DskIiIic8Vl4KldkSQJ1s7e8AgbC7ceQ2GhsIG2qgJZ5/cj49wetMQPP3JLBVy6DYJ7yDAotOW4R5WBvp4yJKZn42RcIrxdHVFQXIqdx2NRUFzWAu+KiIiIzB2TbmqXJEmCjWsneIZPgFffCVA6eyPz7F4k7F8PdXFei9zD2rkDvPpOgKNXIEJs8zHEOQ/q0kKcvZYCa4UlNBotdh6PRVp2QYvcj4iIiMwXk25q1yRJgq27H/xGzIRnnyiUpN9Awp7/IPvSEVSWFjb7+jILK7h0HQDv0FHo6uGAgTapUFVm4EZqNiqqqiCXy7Dn9CXczM5v/pshIiIis8U+3UQAJEkG957DoVR5IuPsbuTHn0FpViJsPQOg8ukJC6Vds66vdPKCd78JsL5xBlYXo5GQn4+sXD9Y2zvCwdYarqrmXZ+IiIjMG5Nuots4dOoOQCD32mlY2qhQlpOKkox42Hl1hkOnHrBQ2DT52jK5JVy69Ietmx+Up/ZCkXwBBaIjCrQ+yMgrRCd355Z7I0RERGRWmHQT3cGhUzCE0CI//ixUviGQZHIUpVxEcdp12Ht3hUOnYMitlE2+vtLRHcHDp8Iu9jguxhxDpSYftrLOLfgOiIiIyNywTzdRLVQ+PaHy7YmCxPOQyeXw7j8RDp2CUZx+DTf/3Ib8+LPQVKqbfH2Z3AJ+vYdgwNiHYPPfQZVERETUdrGlm6gOKt9eEFotcq+dhiSTw9GvF+w7dEVR8iUUpV5C0c3LcOgYDPsO3SCzaNrCNi4eHTFy8uOQyfj9l4iIqC1j0k1UB0mS4OgfCqHVIPfqSUCSwc4zEI4BYbDvGITC5AsoSDqPotQ4OHQKhp13V8jkjf8nxYSbiIio7WPSTVQPSZLgFBh+K/G+cgKSTA5bdz/IrazhFNgX9h26ozD5AvLjY1CYcgkqn56w8+oMSSY3dehERERkRph0E92FJElw7tIfEFrkxB2FJJPBxtUHAGChtIVzl/5w6Ngd+YnnkXf9NApTLkLlEwJbD38m30RERASAAymJGkSSJDh3HQAbNx9kXzqCspxUveMW1vZwDYqAV/gEKOxdkXPlBNJO/YqSjHgIwUGSRERE7R2TbqIGkiQZXLoNgrVzB2RdPISy3LQaZSxtVXANvgde4eNgaeOA7LijSDu9A6VZSRBCmCBqIiIiMgdMuokaQZLJ4dp9MJROXsi+eBDl+Rm1lrOyc4Zbz2HwDIuChZUNsi7+gfTo31CWk8rkm4iIqB1i0n2HQ4cO4b777oO3tzckScLWrVvrLX/gwAFIklTjlZ6ebpyAyegkmRxuwfdA4eCOrNgDUBdk1VlW4eAK914j4NF7NGRyC2TGHkBGzO8oz0tj8k1ERNSOMOm+Q0lJCXr37o2PP/64UeddvnwZaWlpupe7u7uBIiRzIMnkcO0xFFb2zsiM3Q91YXa95ZWO7nDvPQruISMAIZBxbh8yz+1BeUGmkSImIiIiU+LsJXcYN24cxo0b1+jz3N3d4ejo2PIBkdmSyS3g1nMYMs/tQ1bsfrj3GgkrO+c6y0uSBGtnLyidPFGWm4qChHPIiNkNa2cvOAWGw9JGZcToiYiIyJjY0t1CQkND4eXlhdGjR+PIkSP1llWr1SgsLNS9iouLjRQltTSZ3BLuIcNhobRD5rl9qCjJv+s5kiTBxqUjPPuMg2vwEGjUpQB7mhAREbVpTLqbycvLC2vXrsWWLVuwZcsWdOrUCcOGDUN0dHSd5yxfvhwqlUr3ioyMNGLE1NJkFlZwCxkBucIGmef2orK0sEHnSZIEWzdfeIZPgKUtW7mJiIjaMklwNFedJEnCTz/9hEmTJjXqvMjISPj4+GDjxo21Hler1VCr1brtmJgYREZG4vTp0+jTp09zQiYT0lSUI+PcHoiqSrj3HgVLa3tTh0RERERmgi3dBtC/f39cu3atzuMKhQIODg66l52dnRGjI0ORWynh0WskJLkcmef2oKqc3YaIiIjoFibdBhATEwMvLy9Th0EmILeyhnuvUYAkQ+a5fahSl5o6JCIiIjIDnL3kDsXFxXqt1PHx8YiJiYGzszN8fHywcOFCpKam4ssvvwQArFixAv7+/ujRowfKy8vx73//G/v27cPvv/9uqrdAJmahsIFHr5HIOLsbmef2wqP3KMitrE0dFhEREZkQk+47nDp1CsOHD9dtP/fccwCAmTNnYv369UhLS0NSUpLueEVFBZ5//nmkpqbCxsYGvXr1wp49e/SuQe2PhdIO7r1G6RJv996jILdUmjosIiIiMhEOpDQD0dHRCA8P50DKNqiytAAZZ/dAbmUD914jILdUmDokIiIiMgH26SYyIEsbFdx7jYRGXYKs8/uhrao0dUhERERkAky6iQzMytYR7iEjUFlWiMzY/dBqmHgTERG1N0y6iYzAyt75VuJdko+s2IPQaqpMHRIREREZEZNuIiNROLjCrecwVBRlI/viIQitxtQhERERkZEw6SYyIqXKHW49hqE8PxPZF/9g4k1ERNROMOkmMjKlkyfcegxFWV4asuOOQgitqUMiIiIiA2PSTWQC1s7ecA2+B2XZyciJO8bEm4iIqI1j0k1kIjYuHeHafQhKsxJRlnvT1OEQERGRAXFFSiITsnHzgZfNBFjaqkwdChERERkQW7qJTIwJNxERUdvHpJuIiIiIyMCYdBMRERERGRiTbiIiIiIiA2PSTURERERkYEy6iYiIiIgMjEk3EREREZGBcZ5uqiEtLQ1paWmmDqNd8fLygpeXl6nDaFdYz42P9ZyI2jMm3WbAy8sLS5YsMYsPI7VajYceeggHDx40dSjtSmRkJHbt2gWFQmHqUNoF1nPTYD0novZMEkIIUwdB5qOwsBAqlQoHDx6EnZ2dqcNpF4qLixEZGYmCggI4ODiYOpx2gfXc+FjPiai9Y0s31So0NJQfjEZSWFho6hDaLdZz42E9J6L2jgMpiYiIiIgMjEk3EREREZGBMekmPQqFAkuWLOFAJyPiMzc+PnPj4zMnovaOAymJiIiIiAyMLd1ERERERAbGpJuIiIiIyMCYdBMRERERGRiTbjNz4MABSJKEAwcOmEUcP/zwg0njoLaLdZ2IiNoTJt1Gsn79ekiSpHsplUp07doVzzzzDDIyMkwdnkn8+eefkCQJ//rXv2ocmzhxIiRJwrp162ocGzp0KDp06NDi8dT2d+Tt7Y2oqCisWrUKRUVFLX7P5vr+++8hSRJ++umnGsd69+4NSZKwf//+Gsd8fHwQERFhkJhY12tXndxLkoSvvvqq1jKDBw+GJEno2bOnQWNpjXUd0I/78OHDNY4LIdCpUydIkoR7773XBBESEdWNSbeRvf7669i4cSM++ugjREREYM2aNRg0aBBKS0tNHZrR9enTBzY2NrV+eB49ehQWFhY4cuSI3v6KigqcPHkSgwcPNlhc1X9Ha9aswd///ncAwPz58xESEoJz584Z7L5NMWTIEACo8QwLCwsRGxtb6zNMTk5GcnKy7lxDYV2vnVKpxKZNm2rsT0hIwNGjR6FUKo0WS2uq67er6xkePHgQKSkpnJaQiMwSl4E3snHjxqFv374AgCeffBIuLi748MMPsW3bNjz00EMmjs64LCwsMGDAgBpJ4eXLl5GdnY2HH364RjJ5+vRplJeXGzRhvP3vCAAWLlyIffv24d5778X999+PS5cuwdraus7zS0pKYGtra7D4buft7Q1/f/8az+nYsWMQQmDq1Kk1jlVvGzrpZl2v3fjx4/Hzzz8jOzsbrq6uuv2bNm2Ch4cHunTpgry8PKPE0prq+u3Gjx+PzZs3Y9WqVbCw+N/H2KZNmxAeHo7s7Gyjx0REdDds6TaxESNGAADi4+PrLPPHH39g6tSp8PHxgUKhQKdOnbBgwQKUlZXVKBsXF4cHH3wQbm5usLa2Rrdu3fDqq6/qlUlNTcXjjz8ODw8PKBQK9OjRA1988UWt99ZoNHjllVfg6ekJW1tb3H///UhOTq5RbvPmzQgPD4e1tTVcXV0xY8YMpKam3vX9DxkyBBkZGbh27Zpu35EjR+Dg4IC5c+fqEvDbj1WfZ0wjRozAokWLkJiYqNc1YNasWbCzs8P169cxfvx42Nvb45FHHgEA+Pn5YdasWTWuNWzYMAwbNkxvX2JiIu6//37Y2trC3d0dCxYswK5duxrU53nIkCE4c+aMXn04cuQIevTogXHjxuH48ePQarV6xyRJMuivBbVp73W92sSJE6FQKLB582a9/Zs2bcKDDz4IuVze4GsZgjnX9WoPPfQQcnJysHv3bt2+iooK/PDDD3j44Ycb/Z6JiIyBSbeJXb9+HQDg4uJSZ5nNmzejtLQUTz31FFavXo2oqCisXr0ajz32mF65c+fOYcCAAdi3bx/mzJmDlStXYtKkSfjll190ZTIyMjBw4EDs2bMHzzzzDFauXInOnTvjiSeewIoVK2rc+6233sKvv/6Kl156Cc8++yx2796NUaNG6SVB69ev1yULy5cvx5w5c/Djjz9iyJAhyM/Pr/f919Y94siRIxg4cCAGDBgAS0tLHD16VO+Yvb09evfuXe91DeHRRx8FAPz+++96+6uqqhAVFQV3d3e8//77mDJlSqOuW1JSghEjRmDPnj149tln8eqrr+Lo0aN46aWXGnT+kCFDUFlZiRMnTuj2HTlyBBEREYiIiEBBQQFiY2P1jgUFBdVb5wyhvdf1ajY2Npg4cSK++eYb3b6zZ8/iwoULZpMwmmtdr+bn54dBgwbpPcOdO3eioKAA06dPb9S1iIiMRpBRrFu3TgAQe/bsEVlZWSI5OVl8++23wsXFRVhbW4uUlBQhhBD79+8XAMT+/ft155aWlta43vLly4UkSSIxMVG3b+jQocLe3l5vnxBCaLVa3Z+feOIJ4eXlJbKzs/XKTJ8+XahUKt29quPo0KGDKCws1JX7/vvvBQCxcuVKIYQQFRUVwt3dXfTs2VOUlZXpym3fvl0AEIsXL673uRQWFgq5XC6eeOIJ3b5u3bqJZcuWCSGE6N+/v3jxxRd1x9zc3MTo0aPrvWZTVf8dnTx5ss4yKpVKhIWF6bZnzpwpAIiXX365RllfX18xc+bMGvsjIyNFZGSkbvuDDz4QAMTWrVt1+8rKykRQUFCNulCbCxcuCADijTfeEEIIUVlZKWxtbcWGDRuEEEJ4eHiIjz/+WAjxv+c9Z86ceq/ZHKzrtau+z+bNm8X27duFJEkiKSlJCCHEiy++KAICAoQQt+pHjx496r1Wc7XWun573B999JGwt7fX/T1OnTpVDB8+XBfPhAkT6r0WEZGxsaXbyEaNGgU3Nzd06tQJ06dPh52dHX766ad6Z+O4vU9lSUkJsrOzERERASEEzpw5AwDIysrCoUOH8Pjjj8PHx0fvfEmSANwa2b9lyxbcd999EEIgOztb94qKikJBQQGio6P1zn3sscdgb2+v2/7LX/4CLy8v7NixAwBw6tQpZGZm4umnn9YbADZhwgQEBQXh119/rfd52Nvbo1evXrqW7uzsbFy+fFk3s8bgwYN1XUquXLmCrKwso3ctuZ2dnV2tMzs89dRTTb7mb7/9hg4dOuD+++/X7VMqlZgzZ06Dzu/evTtcXFx0z/Ds2bMoKSnRPcOIiAjdMzx27Bg0Go1RniHret3GjBkDZ2dnfPvttxBC4NtvvzW7fu7mWNdv9+CDD6KsrAzbt29HUVERtm/fbja/FBAR1YYDKY3s448/RteuXWFhYQEPDw9069YNMln9332SkpKwePFi/PzzzzUGWBUUFAAAbty4AQD1TjWWlZWF/Px8fPbZZ/jss89qLZOZmam33aVLF71tSZLQuXNnJCQkALjVPxMAunXrVuNaQUFBtc5McqchQ4Zg9erVyM7OxtGjRyGXyzFw4EAAtxLGTz75BGq12mT9uW9XXFwMd3d3vX0WFhbo2LFjk6+ZmJiIwMBAXcJYrXPnzg06X5IkRERE4NChQ9BqtThy5Ajc3d1150dEROCjjz4CYNw+8azrdbO0tMTUqVOxadMm9O/fH8nJyWaXMJpjXb+dm5sbRo0ahU2bNqG0tBQajQZ/+ctfmhwbEZGhMek2sv79++vNFnA3Go0Go0ePRm5uLl566SUEBQXB1tYWqampmDVrlt4AubupLjtjxgzMnDmz1jK9evVq8PVaSnXSfeTIERw9ehQhISGws7MDcCthVKvVOHnyJA4fPgwLCwtdQm5sKSkpKCgoqJEgKBSKWpPJOxOLahqNpsUHyw0ZMgS//PILzp8/r+vPXS0iIgIvvvgiUlNTcfjwYXh7eyMgIKBF718b1vX6Pfzww1i7di2WLl2K3r17Izg42KTx3M6c6/rtHn74YcyZMwfp6ekYN24cHB0dDXYvIqLmYtJt5s6fP48rV65gw4YNeoPJbh+1D0CXRN0+YO5Obm5usLe3h0ajwahRoxp0/6tXr+ptCyFw7do1XcLi6+sL4NY0f9WzU1S7fPmy7nh9bh9MeezYMb1ZNby9veHr64sjR47gyJEjCAsLg42NTYNib2kbN24EAERFRTWovJOTU62D6xITE/WSXl9fX1y8eBFCCL3k5fYZXe7m9md45MgRzJ8/X3csPDwcCoUCBw4cwIkTJzB+/PgGX9eY2kNdv92QIUPg4+ODAwcO4J133mnUuYZmznX9dg888ADmzZuH48eP47vvvmvSNYiIjIV9us1cdSuREEK3TwiBlStX6pVzc3PD0KFD8cUXXyApKUnvWPW5crkcU6ZMwZYtW2pNWLKysmrs+/LLL/X6df7www9IS0vDuHHjAAB9+/aFu7s71q5dC7VarSu3c+dOXLp0CRMmTLjre6yea3rv3r04depUjZUSIyIisHXrVly+fNlkXUv27duHN954A/7+/rpp0u4mMDAQx48fR0VFhW7f9u3ba0xDFxUVhdTUVPz888+6feXl5fj8888bHF/fvn2hVCrx9ddfIzU1Ve8ZKhQK9OnTBx9//DFKSkpM2j2nPu2hrt9OkiSsWrUKS5Ys0c0WYg7Mva7fzs7ODmvWrMHSpUtx3333NekaRETGwpZuMxcUFITAwEC88MILSE1NhYODA7Zs2VLr4hmrVq3CkCFD0KdPH8ydOxf+/v5ISEjAr7/+ipiYGADA22+/jf3792PAgAGYM2cOgoODkZubi+joaOzZswe5ubl613R2dsaQIUMwe/ZsZGRkYMWKFejcubNu4JOlpSXeeecdzJ49G5GRkXjooYeQkZGBlStXws/PDwsWLGjQ+xwyZIiude3O+aMjIiJ0U4MZI2HcuXMn4uLiUFVVhYyMDOzbtw+7d++Gr68vfv755wavGPjkk0/ihx9+wNixY/Hggw/i+vXr+OqrrxAYGKhXbt68efjoo4/w0EMP4R//+Ae8vLzw9ddf6+5T10/3t7OyskK/fv3wxx9/QKFQIDw8XO94REQEPvjgAwCm7RNfn/ZS1283ceJETJw4sfEPq4W0xrp+p7q6DxERmR2jz5fSTjVkii4hap9G7eLFi2LUqFHCzs5OuLq6ijlz5oizZ88KAGLdunV658fGxooHHnhAODo6CqVSKbp16yYWLVqkVyYjI0P87W9/E506dRKWlpbC09NTjBw5Unz22Wc14vjmm2/EwoULhbu7u7C2thYTJkyoMU2bEEJ89913IiwsTCgUCuHs7CweeeQR3dRwDfHpp5/qpm27U3R0tAAgAIiMjIwGX7Oxqv+Oql9WVlbC09NTjB49WqxcuVJvOrlqM2fOFLa2tnVe84MPPhAdOnQQCoVCDB48WJw6darGNGpCCHHjxg0xYcIEYW1tLdzc3MTzzz8vtmzZIgCI48ePNyj+hQsXCgAiIiKixrEff/xRABD29vaiqqqqQddrKtb1+t/v5s2b6y1nzCkDW1tdb2jd4pSBRGSOJCFu+y2XiMzGihUrsGDBAqSkpNQ7zR5Ra8e6TkTtAZNuIjNQVlamN0d1eXk5wsLCoNFocOXKFRNGRtSyWNeJqL1in24iMzB58mT4+PggNDQUBQUF+OqrrxAXF4evv/7a1KERtSjWdSJqr5h0E5mBqKgo/Pvf/8bXX38NjUaD4OBgfPvtt5g2bZqpQyNqUazrRNResXsJEREREZGBcZ5uIiIiIiIDY9JNRERERGRgTLpbgfXr10OSJCiVSqSmptY4PmzYMPTs2dOoMe3duxePP/44unbtChsbGwQEBODJJ59EWlpareWPHj2KIUOGwMbGBp6ennj22WdRXFxs1Jgbg8/c+PjMjY/PnIjIeJh0tyJqtRpvv/22qcMAALz00ks4cOAAHnjgAaxatQrTp0/H999/j7CwMKSnp+uVjYmJwciRI1FaWooPP/wQTz75JD777DNMnTrVRNE3HJ+58fGZGx+fORGREZhyZR5qmOpV2EJDQ4VCoRCpqal6x42xgt2dDh48KDQaTY19AMSrr76qt3/cuHHCy8tLFBQU6PZ9/vnnAoDYtWuXUeJtLD5z4+MzNz4+cyIi42FLdyvyyiuvQKPRmEWL1NChQyGTyWrsc3Z2xqVLl3T7CgsLsXv3bsyYMQMODg66/Y899hjs7Ozw/fffGy3mpuAzNz4+c+PjMyciMjzO092K+Pv747HHHsPnn3+Ol19+Gd7e3o06v7S0FKWlpXctJ5fL4eTk1Oj4iouLUVxcDFdXV92+8+fPo6qqCn379tUra2VlhdDQUJw5c6bR9zEmPnPj4zM3Pj5zIiLDY0t3K/Pqq6+iqqoK77zzTqPPfffdd+Hm5nbXV1hYWJNiW7FiBSoqKvQWuage/OTl5VWjvJeXF27evNmkexkTn7nx8ZkbH585EZFhsaW7lQkICMCjjz6Kzz77DC+//HKtHzh1eeyxxzBkyJC7lrO2tm50XIcOHcKyZcvw4IMPYsSIEbr9ZWVlAACFQlHjHKVSqTtuzvjMjY/P3Pj4zImIDItJdyv02muvYePGjXj77bexcuXKBp8XEBCAgICAFo8nLi4ODzzwAHr27Il///vfeseqP2TVanWN88rLy5v0IWwKfObGx2dufHzmRESGw6S7FQoICMCMGTN0LVINVd0v8m7kcjnc3NwadM3k5GSMGTMGKpUKO3bsgL29vd7x6tay2ubYTUtLa3TfUVPhMzc+PnPj4zMnIjIc9ulupV577bVG9798//334eXldddXv379GnS9nJwcjBkzBmq1Grt27ar15+iePXvCwsICp06d0ttfUVGBmJgYhIaGNjh+U+MzNz4+c+PjMyciMgy2dLdSgYGBmDFjBj799FP4+vrCwuLuf5Ut2e+ypKQE48ePR2pqKvbv348uXbrUWk6lUmHUqFH46quvsGjRIl1r1caNG1FcXNyqFrHgMzc+PnPj4zMnIjIMSQghTB0E1W/9+vWYPXs2Tp48qTc91rVr1xAUFASNRoMePXogNjbWaDFNmjQJ27Ztw+OPP47hw4frHbOzs8OkSZN029HR0YiIiEBwcDDmzp2LlJQUfPDBBxg6dCh27dpltJgbg8/c+PjMjY/PnIjIiEy9Og/dXfWqcSdPnqxxbObMmQKA0VeN8/X1FQBqffn6+tYo/8cff4iIiAihVCqFm5ub+Nvf/iYKCwuNGnNj8JkbH5+58fGZExEZD1u6iYiIiIgMjAMpiYiIiIgMjEk3EREREZGBMekmIiIiIjIwJt1ERERERAbGpJuIiIiIyMCYdBMRERERGRiTbiIiIiIiA2PSTURERERkYEy6iYiIiIgMjEk3EREREZGBMekmIiIiIjIwJt1ERERERAbGpJuIiIiIyMCYdBMRERERGRiTbiIiIiIiA2PSbQbS0tKwdOlSpKWlmToUIiIiMkPMFVo/Jt1mIC0tDcuWLeM/JCIiIqoVc4XWj0k3EREREZGBMekmIiIiIjIwJt1ERERERAbGpJuIiIxKCAGtptLUYRARGRWTbiIiMqrCpAtIOfIDNJVqU4dCRGQ0TLqJiMhoNJVq5F77E8Xp11CUGmfqcIiIjIZJNxERGU1RShzK89IhyS2Rd/00W7uJqN1g0k1ERAYnhICmUo28G6chs7CC0skL5XnpbO2mNket5hdJqp2FqQMgIqK2raqsCOkxv8PK3gVlOamwsHZAVXkxtJoq5F0/DfsOQZBbKkwdJlGL0Gq1pg6BzBSTbiIiMqj8xFgUJF2ApY09LO2cIEECACjsXQAhUFVezKSbiNo8Jt1ERGQwlWVFyI8/A0kmh1ajhc/AiVA6e+mOS5IESSY3YYRELYst3VQX9ukmIiKDKUiMRWVxLmxcfaCtLENB0nlIMjlkcgvI5BZMuKnNYdJNdWHSfYdDhw7hvvvug7e3NyRJwtatW+96zoEDB9CnTx8oFAp07twZ69evN3icRETmTtfKLbeEprIccoUtCv87ewlRW6XRaEwdAgDmM+aISfcdSkpK0Lt3b3z88ccNKh8fH48JEyZg+PDhiImJwfz58/Hkk09i165dBo6UiMi83UquBWQWVtCUF0MCIJNboDwv1dShERlMZaV5rLbKfMb8sE/3HcaNG4dx48Y1uPzatWvh7++PDz74AADQvXt3HD58GP/6178QFRVlqDCJiMyenVdnKFXuEBB6+y2t7U0UEZHhmUvSzXzG/LClu5mOHTuGUaNG6e2LiorCsWPH6jxHrVajsLBQ9youLjZ0mERERidJEixtVbCyddR7sR83tWWGnqe7uLhYL4doqfs1JZ+hxmHS3Uzp6enw8PDQ2+fh4YHCwkKUlZXVes7y5cuhUql0r8jISGOESkRERAZWXl5u0OtHRkbq5RDLly9vkes2JZ+hxmHSbQILFy5EQUGB7nXw4EFTh0REREQtoKSkxKDXP3jwoF4OsXDhQoPej1oO+3Q3k6enJzIyMvT2ZWRkwMHBAdbW1rWeo1AooFD8byEIOzs7g8ZIRERExlFQUGDQ69vZ2cHBwaHFr9uUfIYahy3dzTRo0CDs3btXb9/u3bsxaNAgE0VEREREppKTkwMhxN0LmhnmM4bHpPsOxcXFiImJQUxMDIBbU+jExMQgKSkJwK2uIY899piu/F//+lfcuHED//d//4e4uDh88skn+P7777FgwQJThE9EREQmVF5ejpycHFOHwXzGDDHpvsOpU6cQFhaGsLAwAMBzzz2HsLAwLF68GACQlpamq7AA4O/vj19//RW7d+9G79698cEHH+Df//43p9chIiJqp65du2bqEJjPmCFJtMbfQNqY6OhohIeH4/Tp0+jTp4+pwyEiIqIm+vLLLwEAjzzyCOTylpsek7lC68eWbiIiIqIWVF5ebhat3WReOHsJERERUQvo27cvbty4AXt7ezg5OaFr166QJMnUYZGZYEs3ERERUQtIT09HXl4eCgsLkZeXh/j4eFOHRGaESTcRERGRAZw6dQpardbUYZCZYNJNREREZAD5+fm4cOGCqcMgM8Gkm4iIiMhATp06heLiYlOHQWaASTcRERGRgVRWVuKPP/5olatUUsti0k1ERERkQMnJybhy5YqpwyATY9JNREREZGBHjx5FQUGBqcMgEzLLpDstLQ1nz55FSUmJqUMhIiIiarbKykrs3r0bFRUVpg6FTMSsku5t27YhKCgIHTt2RJ8+fXDixAkAQHZ2NsLCwrB161bTBkhERETURLm5udi1axcqKytNHQqZgNkk3b/88gsmT54MV1dXLFmyRG/AgaurKzp06IB169aZMEIiIiKi5klLS8POnTvZ4t0OmU3S/frrr2Po0KE4fPgw/va3v9U4PmjQIJw5c8YEkRERERG1nPT0dGzfvh1lZWWmDoWMyGyS7tjYWDz44IN1Hvfw8EBmZqYRIyIiIiIyjOzsbGzfvp3j19oRs0m6bWxs6q14N27cgIuLixEjIiIiIjKcvLw8/PLLL1w8p50wm6R7+PDh2LBhA6qqqmocS09Px+eff44xY8aYIDIiIiIiwygsLGTi3U6YTdL91ltvISUlBf369cOnn34KSZKwa9cuvPbaawgJCYEQAkuWLDF1mEREREQtqqioiIl3O2A2SXe3bt1w+PBhuLi4YNGiRRBC4L333sM///lPhISE4I8//oCfn5+pwyQiIiJqcUVFRezj3cZZmDqA2/Xo0QN79uxBXl4erl27Bq1Wi4CAALi5uZk6NCIiIiKDKiwsxI4dO3D//fdDoVCYOhxqYWaVdFdzcnJCv379TB0GERERkVHl5eVhz549GDduHGQys+mQQC3AbP42V61ahaioqDqPjxs3DmvWrDFiREREREQNk5SUhNLSUgBARUUFcnNzm3yt1NRUREdHt1RoZCbMJun+z3/+g+Dg4DqPBwcH47PPPjNiRERERET1+/PPP3HffffBz88PeXl5AIDS0lK88sor+Pjjj5GQkNCk6545cwbZ2dktGCmZmtkk3devX0f37t3rPB4UFITr168bMSIiIiKiuv34448YPHgwdu7cCSGE3jEhBGJjY/HOO+80qdVaCIEjR47UuC61XmaTdFtZWSE9Pb3O42lpaUbr2/Txxx/Dz88PSqUSAwYMwJ9//lln2fXr10OSJL2XUqk0SpxERERkGn/++SemTZsGjUYDjUZTaxmtVgutVovPP/+8SS3eGRkZuHz5cpNjZD5jXswm6R44cCDWr1+PoqKiGscKCgqwbt06DBw40OBxfPfdd3juueewZMkSREdHo3fv3oiKiqp3CXoHBwekpaXpXomJiQaPk4iIiEznzTffhBCiwS3RO3bsaNJ9Tpw4oesr3hjMZ8yP2STdS5Yswc2bNxEaGorVq1dj37592LdvH1atWoWwsDCkpaUZZXGcDz/8EHPmzMHs2bMRHByMtWvXwsbGBl988UWd50iSBE9PT93Lw8PD4HESERGRaSQlJWH79u11tnDfSavV4ty5c00aXKlWq3Hw4MFGdzNhPmN+zCbpHjBgAH755RcIIfCPf/wDo0ePxujRozF//nxIkoSff/4ZgwYNMmgMFRUVOH36NEaNGqXbJ5PJMGrUKBw7dqzO84qLi+Hr64tOnTph4sSJuHDhQr33UavVKCws1L24AhUREVHrsXfv3kYnwUIIxMXFNel+ycnJSE5OBnAr57g9h1Cr1TXKGyufocYxm6QbAEaPHo1r167h5MmT+Oabb/DNN9/g5MmTuHbtGsaMGWPw+2dnZ0Oj0dT4Zufh4VFnf/Nu3brhiy++wLZt2/DVV19Bq9UiIiICKSkpdd5n+fLlUKlUuldkZGSLvg8iIiIynKKiokaPM5MkCeXl5U2+58WLFwEAkZGRejnE8uXLa5Q1Vj5DjWN2i+PIZDKEh4cjPDzc1KE0yKBBg/Ra4CMiItC9e3d8+umneOONN2o9Z+HChXjuued02zExMUy8iYiIWgl7e3totdpGnSOEaNbAxOp+3QcPHkRoaKhuf0utXNmUfKYlnDt3DqtXr0Z0dDQKCgpqPFdJktrM7HVml3RfvHgRN27cQF5eXq0/3Tz22GMGu7erqyvkcjkyMjL09mdkZMDT07NB17C0tERYWBiuXbtWZxmFQqH3j8TOzq5pARMREZHRjRw5EpIkNaqLiSRJCAoKava97ezs4ODgUG8ZY+UzzXXgwAGMHTsWTk5O6Nu3L86cOYMRI0agvLwcx44dQ48ePVpNI2xDmE3Sff36dcyYMQN//vlnnZVYkiSDJt1WVlYIDw/H3r17MWnSJAC3Bj/s3bsXzzzzTIOuodFocP78eYwfP95gcRIREZHp+Pj44N5778WOHTsaNJhSJpMhJCQEzs7ORoiu9eQzixcvRkBAAI4fP46Kigq4u7vjlVdewYgRI3DixAmMGzcO77zzjsHub2xmk3TPmzcP58+fx4oVK3DPPffAycnJJHE899xzmDlzJvr27Yv+/ftjxYoVKCkpwezZswHcamnv0KGDrg/V66+/joEDB6Jz587Iz8/He++9h8TERDz55JMmiZ+IiIgMb9GiRdi5c2eDW7ybm7za2to2qnxryGeio6OxbNkyODg46FbzrP4SM2DAAMybNw+LFi3CuHHjDBaDMZlN0n3kyBG88sor+Pvf/27SOKZNm4asrCwsXrwY6enpCA0NxW+//aYbjJCUlKQ3eCIvLw9z5sxBeno6nJycEB4ejqNHj9a7pD0RUXsihEBqdj68nFWQy81q/D5Rk/Xr1w/fffcdpk2bBiFErS3e1fnC3Llz4efn1+R7yeVy9OnTp1HntIZ8xsLCAvb29gAAR0dHWFpa6s0jHhAQoBtA2hZIwkzWF+3UqRNefPFFPPvss6YOxeiio6MRHh6O06dPN/ofFRGRuUtMz8EvR85haGgX9ArsaOpwiFrUyZMn8cYbb2D79u16Ld6SJKFXr14YP358sxJuKysrjBo1CpmZmW0uV+jbty+GDx+O9957DwDQu3dvdOnSBT/88AMAYNKkSTh37hxu3LhhyjBbjNk0Ofz1r3/FV1991eCJ5omIyPwJIXDqciISM3JxKi4RFVVVpg6JqEX169cPP//8MxISEnRdY21sbPDPf/4TTz/9dLMSbicnJ0yaNAkdO7bNL6vjx4/HN998g6r//r/w3HPP4ccff0SXLl3QpUsX/Pzzz5g3b56Jo2w5ZtO9pGvXrtBoNOjduzcef/xxdOrUCXK5vEa5yZMnmyA6IiJqiqSMXFxJzkAnd0ekZOYhLjGdrd3UJvn4+MDGxgZ5eXmwsrJq9qBJX19fjBgxApaWli0UoflZtGgR/vGPf+jyvZkzZ0Iul2PLli2Qy+V49dVXMWvWLNMG2YLMJumeNm2a7s8vvPBCrWUkSWJLOBFRK1Hdyl1ZpYGjnQ1KyipwKi4RQb6esLIwm48fIrMTHByMwYMHQ5IkU4diUJaWlnBxcdHbN2PGDMyYMcNEERmW2fyvt3//flOHQERELSg5Mw9XkjNQWaVBUkYuqjQaJGfk4kpSBnoGdDB1eERmqWfPnhg0aFCbT7iBWwMlV6xYgfvvv7/W49u3b8ezzz7bZvp0m03SzRUZiYjaFqWVBXoHdoIQt60wJwG21i2zgh5RW9OjR492k3ADQEJCAoqLi+s8XlxcjMTERCNGZFhmk3RXU6vViI6ORmZmJgYPHgxXV1dTh0RERE3g7uSA8YN6mjoMolahT58+CA8PbzcJd7X63u/Jkyfh6OhovGAMzGxmLwGAVatWwcvLC0OGDMHkyZNx7tw5AEB2djZcXV3xxRdfmDhCIiIiopYjk8kQGRmJvn37touEe+XKlQgICEBAQAAkScL8+fN127e/XFxcsGLFija1wrfZtHSvW7cO8+fPx/Tp0zFmzBg8/vjjumOurq4YMWIEvv32W739RERERK2VUqnEmDFj4OnpaepQjMbd3R09evQAcKt7SYcOHdChg/4YD0mSYGtri/DwcDz99NOmCNMgzCbp/uCDDzBx4kRs2rQJOTk5NY6Hh4dj1apVJoiMiIiIqGU5Ojpi7NixcHBwMHUoRvXQQw/hoYceAgAMHz4cr732GkaOHGniqIzDbJLua9eu1bsapbOzc63JOBEREVFr4ubmhnHjxkGpVJo6FJNqbzPXmU3S7ejoiOzs7DqPX7x4sV39/EJERERtj5ubGyZMmAArKytTh2J0hw4datJ5Q4cObeFITMNsku7x48fjs88+q7XvzoULF/D555+zPzcRUStUUqbG7lOX0L+bD7zdm7dKH1Fr5uDggLFjx7bLhBsAhg0bpjdYVAhR7+DR6uNtZWFEs0m633zzTQwYMAA9e/bEfffdB0mSsGHDBnzxxRfYsmULvLy8sHjxYlOHSUREjXTuegrOXEmGDAL3M+mmdsrCwgJjxoyBtbW1qUMxmfbWneROZpN0e3t74/Tp03jllVfw3XffQQiBjRs3wt7eHg899BDefvttztlNRNTKlJSpEX0lGYDAxYQ09O8RAE9nlanDIjK6IUOGwNm5fX/pbO8LIZrFPN1qtRo///wz0tPT8e9//xu5ubnIyMhAWloa8vLy8MUXX8Dd3d3UYRIRUSOdu56CrPwi+Hm5oLi0HGeuJJs6JCKj8/f3R5cuXUwdhllLS0vD2bNnUVJSYupQDMYskm4rKytMnToVR48e1e1zc3ODh4cHZDKzCJGIiBqpupXbylKOyioNlFaWuBB/E+m5BaYOjcholEolhgwZ0i4WvmmKbdu2ISgoCB07dkSfPn1w4sQJALcWRgwLC8PWrVtNG2ALMouMVpIkdOnSpd7ZS4jaqqryYgghTB0GUYtLzsxDZVUVZJIMuYWlqKyqAgAkZeSaODIi4+nXr1+77sddn19++QWTJ0+Gq6srlixZovdZ6Orqig4dOmDdunUmjLBlmU2f7ldeeQXPPfccpk6dim7dupk6HCKjKM/PRNqpX+DWIxJ2Xp1NHQ5Ri+rSyR3ODgMA3PogTUpOhk+nTnCytzVtYERG4uzsjKCgIFOHYbZef/11DB06FPv370dOTg6WLl2qd3zQoEH49NNPTROcAZhN0n38+HG4uLigZ8+eGDZsGPz8/Gp8M5QkCStXrjRRhEQtLz/+DIoz4mGhtIOthz8kmdzUIRG1GLlMBncne5RXVOLIueuwl1XB3al9rb5H7YunpydKS0thb28PAOjbty+7ldQjNjYWH374YZ3HPTw8kJmZacSIDMtsku6PPvpI9+e9e/fWWoZJN7Ul5fmZKEy5BCs7Z5RkJqAkI56t3dQmXYxPw7EL1+FpI6F/n96mDofIYE6dOoUvv/wS5eXlcHFxga+vr6lDMms2Njb1Dpy8ceMGXFxcjBiRYTUq6fb392/0NzZJknD9+vW7ltNqtY26LlFrlx9/BlXlJbBx80V5TjLyrp9maze1OeUVlTh1OQFl6krE5RYgp6AELip2L6G2LzQ0lK3cdzF8+HBs2LAB8+fPr3EsPT0dn3/+Oe69917jB2YgjUq6IyMja1SgU6dO4cKFCwgODtb1xb58+TIuXryInj17Ijw8vOWiJWojqlu5AaCiMBuQZCjJjEdJxg3YeXFaKWo7LsanIS2nEAHerjh2Jh1nriZhVN/upg6LyKCsra3h5+dn6jDM3ltvvYWBAweiX79+mDp1KiRJwq5du7Bv3z58+umnEEJgyZIlpg6zxTRq9pL169dj3bp1utfEiRORkpKC3bt3IzY2Flu2bMGWLVsQGxuLXbt2ITk5GZMmTWpUQMePH8fy5cuxYMECXL16FQBQWlqK6OhoFBcXN+paTfXxxx/Dz88PSqUSAwYMwJ9//llv+c2bNyMoKAhKpRIhISHYsWOHUeKk1ktbpYbC3gXWLt6wtFNB4egOpaMXtJoqU4dG1GKqW7kVlnLIZRIsJS1OXriOnIK2Ow8vEQD4+PhALjf9r5bmns9069YNhw8fhouLCxYtWgQhBN577z3885//REhICP7444829eWlWVMGLl68GH//+98xcuTIGsdGjx6NZ555Bq+99lqDrlVRUYHJkydj8ODBePXVV7Fq1SokJ99aREEmk2HMmDFG6c/93Xff4bnnnsOSJUsQHR2N3r17Iyoqqs6O/EePHsVDDz2EJ554AmfOnMGkSZMwadIkxMbGGjxWar1sXDvBd9ij8B8xW/fyGzETDh3ZAkhtR2J6DopK1ais0iI5Mx8VVQK5+QW4frPtDIwiqo23t7epQ2g1+UyPHj2wZ88eZGdn48SJEzh27BgyMjKwb98+dO/etj4Tm5V0X716td4O7i4uLg3qzw0AixYtwvbt27FmzRpcvnxZb65GpVKJqVOnYtu2bc0Jt0E+/PBDzJkzB7Nnz0ZwcDDWrl0LGxsbfPHFF7WWX7lyJcaOHYsXX3wR3bt3xxtvvIE+ffroDQwlImqP/L1cMTkyDDPGDMCMMQPQy8saAQ4CwX5epg6NyKDMYRXt1pbPODk5oV+/fhgwYADc3NyMck9ja9bsJYGBgVi3bh2eeOIJ2NnZ6R0rKirCF198gYCAgAZd65tvvsFTTz2FuXPnIicnp8bx7t27Y/Pmzc0J964qKipw+vRpLFy4ULdPJpNh1KhROHbsWK3nHDt2DM8995zevqioqHpXUFKr1VCr1brt6m4zVVVVqKysbMY7ICIyHxKAjq4q3ba9JaAuL0JJYQEUFqb/6Z3IECRJgrW1dYt/nlf9d3Gp4uJiFBYW6vYrFAooFAq9ssbKZxrryy+/bNJ5jz32WIvFYErNSrrffPNN/OUvf0FQUBBmzZqFzp1vTXd29epVbNiwARkZGQ1OlDMzMxESElLncblcjtLS0uaEe1fZ2dnQaDTw8PDQ2+/h4YG4uLhaz0lPT6+1fHp6ep33Wb58OZYtW1Zj/4ABA5oQNRFR6/L000+bOgQigzJkkhgZGam3vWTJkhqLyhgrn2msWbNm1dhXPUHHnSsz3z5xB5NuAJMmTcKOHTvw0ksv4Z///KfesdDQUPznP/9BVFRUg67VqVOnOisCABw5ckSX1Ld2Cxcu1Ps2GRMTg8jISJw4cQJhYWEmjIyIyDD8/Pxw8+ZNqFQq/Otf/8K0adNgaWlZZ3khBKdbo1bp+PHjGDhwYItf98yZMxgwYAAOHjyI0NBQ3f47W7nNWXx8vN52fn4+Zs6cCZVKhb///e+6WfDi4uKwevVqFBUVYcOGDaYI1SCavTjOmDFjMGbMGKSnpyMxMREA4OvrC09Pz0Zd5+GHH8aHH36IKVOmoGvXrgD+9y3n888/x/fff4+33367ueHWy9XVFXK5HBkZGXr7MzIy6nw/np6ejSoP1PwpqLprjoWFRb0fQkRErVX1/+eSJKGyshIXLlxAv379ai2bXVCMPScvYVS/7nBV2dVahshcOTo6GuSz3MLiVspmZ2cHB4f6V3Y1Vj7TWHcuFrR06VK4ubnh999/1/uSHRISgilTpmDMmDH417/+hXXr1rVYDKbUrIGUt/P09MSAAQMwYMCAJv0Fvfrqq4iIiMDQoUMxfPhwSJKEBQsWwMfHB/PmzcPYsWOxYMGClgq3VlZWVggPD9dbEVOr1WLv3r0YNGhQrecMGjSoxgqau3fvrrM8EVF7I4SA5o4F0GJiYuqcRSHmSjIuxN9EzJVkY4RH1KJsbGxMHUKryWe2bt2KBx54oNZftWQyGSZPnmyUSTSMpdlJd1JSEv7617+iW7ducHZ2xqFDhwDc6k/07LPP4syZMw26jpWVFX777TesW7cOAQEBCAoKglqtRq9evbB+/Xr88ssvRpnz8rnnnsPnn3+ODRs24NKlS3jqqadQUlKC2bNnA7jVr+j2gQn/+Mc/8Ntvv+GDDz5AXFwcli5dilOnTuGZZ54xeKxERK1BUkYuytX6g8qEENi3bx8qKioghBZV6ltjdrLzi3H2egosLeQ4dz0F2fnGWZ+BqKVYW1ubOgQArSOfEULU27X44sWLNfp6t2bN6l5y8eJF3HPPPdBqtRgwYACuXbumG13r6uqKw4cPo6SkBP/5z39qnPvcc8/h0Ucf1fVhTkpKgpubG2bMmIEZM2Y0J6xmmTZtGrKysrB48WKkp6cjNDQUv/32m25wQVJSEmSy/31XiYiIwKZNm/Daa6/hlVdeQZcuXbB161b07NnTVG+BiMhsCCFw6nIiqu5o6QaAwsJCHDhwAOH+jihKuYROgx9EzNVkFJaWw9/LBQlpOYi5moxR/drWXL3UtplLH+vWkM9MmjQJa9asgZ+fH/7617/qfiUoLS3FmjVr8Omnn+KRRx4x2P2NTRLN+Apx77334tKlSzh+/DgkSYK7uzv27NmDESNGALg19/Z3332HK1eu1DhXJpPhq6++wsMPPwzg1uwkGzdu1G23J9HR0QgPD8fp06fRp08fU4dDRNRiEtNzsGnPn3jnH4+hIDcbDipHvPfuO7rjMm0lgq2zoVJKUAaNxtZLxaisqoLK1hoFJWWwsrDAY2MHwdWRfbupdSgoKIBKpbp7wUZqi7lCQUEB7r//fvzxxx+wtLSEl9etOfzT0tJQWVmJwYMH45dffoGjo6NpA20hzWrpPnToEBYvXgw3N7da59b28fFBampqred6eHjgxo0buu229PMBERH9r5W7skoD2X/7bGoEoNEKyGW3tm0qc5BfmAyZmweSL52FDAGwkMtRUl4BC7kccrkMN3PymXRTq3F76zHVT6VS4eDBg9i2bRt27typm5Bj7NixGD9+PO677742NYtRs5JurVZb74CBrKysOn9mmTBhAl5//XX8/vvvum8wH3zwAb799ts6rydJUpvqUE9E1JYlZ+bhSnIGKqs0uoGUQgiUFOejk2URiqw8YVuRCY1kgbTcEniobmDywDA4+vXSu469jdIU4RM1SVtKEo1l4sSJmDhxoqnDMLhmJd19+vTBr7/+WutCB1VVVfj222/rnKty5cqVcHd3x/79+3HhwgVIkoTk5GTk5ubWeT9WZCKi1kNpZYHegZ0ghBZr/rsCpUwCnKqyYCuKIAkNrDSl0EhWsNCWoyC3EDjzOzp06QULhXkMRiNqLOYqVJdmJd0LFy7Evffei6eeegrTp08HcGtOxz179uCf//wnLl26hI8++qjWc21tbfUW1JHJZFixYkW77NNNRNQWuTs5YPygW4OwlFa35i22lAl4yvIhCQFlZR7K5fa31osHUAUFyrLysXf3bxg2erzZDEgjagwm3VSXZnU8GjduHNavX4/vvvtON3hyxowZGDNmDKKjo/Hll19i6NChtZ47efJk/PHHH7rt/fv3Y/To0c0Jh4iIzJxMaCAkOSplNpBBoMDaB1l2PXSvbLtuSLyZhR9//BE3b940dbhEjcY+3VSXZq9I+eijj2Ly5MnYvXs3rl69Cq1Wi8DAQERFRcHe3r7O87Zt24YpU6botkeMGNFuZy8hqpaUkQs3R3tYK7gyKbUdWk0VhOa/83QLAY1kCSFJkGurYFuRiQq5HXBH62BRURG2b9+O7t27o3///mz1plaDSTfVpclJd2lpKTp16oSXX34ZL774IiZNmtSo8zt06IAzZ87o5l8UQvAnGWrXsguKse1wDEICOmJYWFdTh0PUYorTrkJTqb61IUmQi0pAABrJAlaaUshEJbSSVa3nXrp0CQkJCYiIiEBAQAA/J8jssY5SXZqcdNvY2MDCwgK2trZNOn/69Ol4//338f333+tmL3n55ZexfPnyOs+RJAlnz55t0v2IzF3M1WSkZRdAoxHo3bkjnOxNv5QwUXNpNVXIux4N8d/ZS7SSBTLtgnXHhSSDVlZ7wl2trKwMe/fuxbVr1zB06FCzWfGPqDZMupuuvLwc33//PaKionSL+LQlzfoNZMqUKfjhhx+aNMf28uXLsXbtWgwePBhubm6QJAm2trZwcXGp8+Xs7NyccInMVnZBMc5dS4G7kz3yikpx9lqKqUMiahHFaVdx/dI5lFdqAAAVajUyC8pQJbdGldwaGlnDu40kJiZiy5YtSE9PN1S4RM3GpLvpCgoKMHv2bFy4cMHUoRhEs/p0T58+HU8//TSGDx+OOXPmwM/Pr9YWiNpWTpLL5Zg7dy7mzp0L4FYfqNdee419uqldun3pa/HfbbZ2U2t3/NhRvPb8M9h3PEbXOFNSVo5XXnkFISEhmDBhAvz8/Bp1zdLSUvz6668YOnQounTpYoCoiZpHLpebOoRWrS0vltispHvYsGG6P98+E0m16n7aGo3mrteKj4+Hm5tbc8IhanWEVoPkq7E4eyUN6soqJKTnQggBdUUVzl9PxdBQJhXUOv3444+YNm0ahFZb40NUCIHY2FjExsZizpw5jV7SWqPRYP/+/VCr1ejZs2dLhk1EJtaWfyloVtK9bt26looDvr6+LXYtotaiJOMG8i4eQDenrgjtGqR3zN2p7tl/iMzZn3/+iWnTpkGj0dTZaqX9bx/vzz//HC+99FKjW7wB4OjRo5AkCT169GhOuERkRtjSXYeZM2c2+VyZTAaZTIbS0lJYWVlBJpPd9duNJEmoqqpq8j2JzInQapB7/TRkJRkItrWGb69hkFtxgBi1fm+++SaEEA3+8NyxY0etKxs3xNGjR+Hs7AwvL68mnU9E5sPDw0P3hbwtavY83U21ePFiSJIECwsLvW2i9qIk4wZKMxNh4+4HdUEWCpMvwikw3NRhETVLUlIStm/f3uCEW6vV4ty5c8jNzW3SYHkhBA4fPoy//OUv/AwhIrPWqKT78ccfhyRJ+OyzzyCXy/H444/f9RxJkvCf//ynxv6lS5fWu03UllW3ckMIWChsUWVVjLzrp+HQKZit3dSq7d27t9E/DwshEBcXh4iIiCbdMy8vD1lZWXB3d2/S+URExtCopHvfvn2QyWTQarWQy+XYt29fg7qEEJG+0qwklGWnQFtVgdKsRAihhbayDEWpV+Do39vU4RE1WVFRke5zoqEkSUJ5eXmz7tvc84mIDK1RSXdCQkK9243x5ZdfNum8xx57rMn3JDIXlnZOcA2+B4B+i6DSse0tBkDti729faP7ZAohoFQqm3xPmUzG2a+IyOyZrE/3rFmzauyrbhW/86fJ21vLmXRTW2Bl6wiXrgNMHQZRixs5ciQkSWpUFxNJkhAUFHT3gnXo0aMHV6kkIrNnsqQ7Pj5ebzs/Px8zZ86ESqXC3//+d3Tr1g0AEBcXh9WrV6OoqAgbNmwwRahEZicrvwgX49MwpFdnyOXNWliWqEX5+Pjg3nvvxY4dOxq0RoNMJkNISEiTVxx2cHBA3759m3QuEZExNTvp3rlzJz788ENER0ejoKCg1taN2v7jvXNe7qVLl8LNzQ2///67Xst2SEgIpkyZgjFjxuBf//pXi84NTtQaCSFwPPYGzsffhIezA4J8PU0dEpGeRYsWYefOnQ1u8R4/fnyT7iOTyTBixAhYWlo26XwiMg9HjhzR5ZF3dk+TJAmLFi0yUWQtq1lJ95YtW/Dggw+iR48emD59OtasWYOHH34YQghs27YNXbp0waRJkxp0ra1bt+Ktt96qdeClTCbD5MmT8dprrzUnXKJW6/TlRLg72aOTuzNuZhfgUlI6SsrUOBWXiC4d3dnaTWalX79++O67726tSClErQ0vMtmtOjt37twmLYwDAAMGDOCMJUStWG5uLiZMmIA///xTt4p59Rf16j+3paS7WZ/Uy5cvR//+/XHmzBksW7YMwK1pBb/++mvExsYiLS0N/v7+DbpW9ZRRdbl48WKbXqWIqFppdhKy447o6ntmXiEOnLmMA2euoKpKg+grSSgrr4CvpzMS0nNwLTXTxBET1TR58mQcPXoUoyIjcGdbiiRJCAkJwUsvvYSwsLAmXT8gIIBLwBO1ci+++CLOnTuHTZs24caNGxBCYNeuXbhy5Qr++te/IjQ0FDdv3jR1mC2mWUn3xYsXMX36dMjlct0iN5WVlQAAPz8/PP3003jnnXcadK1JkyZhzZo1+PDDD1FaWqrbX1paig8++ACffvopJk6c2JxwicyO0GpQdPMKtFUVuu3sS4eRc/k4ynNv/UcTfSUJBcVlSEzPwbELN3ApMQ3OKltYWVoAEDgZlwiNpu2u4EWtV1ivHvjkpRnYs/ZlqOxsAAB21lZ4543FePrpp5vcwu3h4YHIyEhOSUvUyu3YsQPz5s3DtGnTYG9vD+DWr2CdO3fGxx9/DD8/P8yfP9+0QbagZiXdNjY2sLKyAgA4OjpCoVAgLS1Nd9zDw6PGgMm6rFy5EhEREXjhhRfg5OQEPz8/+Pn5wcnJCS+++CIGDhyIFStWNCfcu8rNzcUjjzwCBwcHODo64oknnkBxcXG95wwbNgySJOm9/vrXvxo0Tmo7SjLikX5mFwqSLgAAitOuoTQrCRp1CfJunEZGbgFib9yEq6MdJEg4cOYyKiqrkFdYiqT0XGi1Atn5RUjNzjftGyGqRVl2MjSVani7OUFpdathRmFliQ4O8iZf09vbG+PGjWM/bqIWZKr8Jz8/Hz169AAA2NnZAYDefceMGYNdu3Y18t2Yr2b16e7WrRsuXryo2w4NDcXGjRsxY8YMVFVVYdOmTfDx8WnQtVQqFQ4ePIht27Zh586dSExMBACMHTsW48ePx3333WfwVo1HHnkEaWlp2L17NyorKzF79mzMnTsXmzZtqve8OXPm4PXXX9dt29jYGDROahuEVoO866dRnpeOvOunYO/dFXk3TgMAFCoPFN28isuFziguU8Pf0QXWVpZIycrHPb27wN/LVXcdmSTBy0VlqrdBVCc7r87oZKMCICBXrABQCK1kgRIr17ucWZMkSejVqxf69eun6w9ORC3DVPmPt7c30tPTAQAKhQLu7u44e/asrmdDampqm/pFq1lJ9+TJk7Fq1Sq8//77UCgUePXVVzFx4kQ4OjpCkiSUlJTgiy++aNQ1J06caJJuJJcuXcJvv/2GkydP6qafWr16NcaPH4/3338f3t7edZ5rY2MDT0/OIEGNU5IRj5LMBNi4+UBdkImMc/tQmpUEKzsXyCwVyMvNwqWbVwCFGxLScwDcSrALS8oR2IELgZD5k2RyKJ08//vnW4mygAQh3Wrprh4kVa1CIyCTAAuZ/oesSqXC0KFD4eXlZaTIidoPU+Y/Q4cOxe7du/Hqq68CAKZNm4Z3330XcrkcWq0WK1asQFRUVJOvb26alHSXl5dj27ZtqKysxGuvvYbc3Fx4eXnh3nvvxYEDB/Djjz9CLpdjwoQJGD58eEvHbBDHjh2Do6Oj3nyvo0aNgkwmw4kTJ/DAAw/Uee7XX3+Nr776Cp6enrjvvvuwaNGier/tqdVqqNVq3fbdfsKhtqe6lVsIAQulHSpLi5Bz+SgslHZQF2UDAKwt5RjoWg6XkCBY2tjrzlXZcREQal2EEBBCf9xBbpkGWaVadHG2gOy/sxRcz6uChQzo7GSh+7m6d+/e6NOnj27cEFF7V1xcjMLCQt22QqGAQqFo8vWMmf/c6bnnnsPu3buhVquhUCiwdOlSXLhwQTdbydChQ7Fq1aomvzdz0+j/xTIzMxEREYH4+HhdK4W1tTW2bt2KUaNG4Z577sE999xjiFgNKj09vcbUUxYWFnB2dtb99FGbhx9+GL6+vvD29sa5c+fw0ksv4fLly/jxxx/rPGf58uW62V6ofSrNTkZpdhK0VWqUZiWgQq2G3MIKzl36wt67m66cv9wC1s7ekCT+nE6tV3nuTWgqynTbWiFws1iDQrUWLmUyuNrIkVeuRX65BjJJQpGtgJ+XC4YPHw4XFxcTRk5kfiIjI/W2lyxZgqVLlzb5esbMf+4UEhKCkJAQ3baTkxP27NmD/Px8yOVy3eDKtqLRSfcbb7yBhIQELFiwACNGjMC1a9fwxhtvYN68ebh+/bohYmyWl19++a4zqFy6dKnJ1587d67uzyEhIfDy8sLIkSNx/fp1BAYG1nrOwoUL8dxzz+m2Y2JiavwjorbN0kYF1+5DdNMCFuTnQ+XoCFt3f1g78yd0ajuEEMi7EQ1x21zduWVaFKlv1f20Yg2clBIySrSQIEEjAK2tCyZNmsTWbaJaHDx4EKGhobrtulq5zTH/udPrr7+OyZMn15j+09HREQBw4cIFbNmyBYsXL25ynOak0f+j/f7773jsscfw/vvv6/Z5eHjg4YcfxuXLl3XLt5uL559/HrNmzaq3TEBAADw9PZGZqT/fcVVVFXJzcxvVX2nAgAEAgGvXrtVZ6e78Kah6xC61H1Z2TnDpNki3XZ6SAteOHU0YEZFhlOfeRNHNK7o+3RK0SC/RQAJgaymhuEIgsUCDgnItrC0BVzcPaK3skJpTCF+Ppi0NT9SW2dnZwcHB4a7lzDH/udPSpUvRuXPnOufcj42NxbJly9pv0p2UlISXXnpJb9+QIbda7DIyMswu6XZzc4Ob290HnQ0aNAj5+fk4ffo0wsPDAQD79u2DVqvVVaSGiImJAQAO+KFGub2PP1FbUd3Kra0s13WRElotCsu1UFjcatXWaLVILNBCJklwdXWFtZ098ovKcDoukUk3UTO0hfwnNzdXNzV1W9DopFutVkOpVOrtq96uqqpqmahMoHv37hg7dizmzJmDtWvXorKyEs888wymT5+uG7mbmpqKkSNH4ssvv0T//v1x/fp1bNq0CePHj4eLiwvOnTuHBQsWYOjQoejVq5eJ3xG1Jjk5OQ1uGSBqLcrzbrVyC60WQnure4kQgLVUgSqhgFYAVnIJVVqBYH9vBPr73jrRC7CzbvrAMCJqOGPnP4cOHcKBAwd02z/++COuXbtWo1x+fj6+++47vT7frV2TOswlJCQgOjpat11QUAAAuHr1qq4fzu369OnToOteunQJ69atw40bN5CXl1dj2XdJkrB3796mhNwgX3/9NZ555hmMHDkSMpkMU6ZM0Rs1W1lZicuXL+tWzLSyssKePXuwYsUKlJSUoFOnTpgyZQpee+01g8VIbVN8fDxnaKA2R5LksPMMhBBaSPJbdVsuA8JcBSos/9d61a9fPwzo27DPCSJqecbMf/bv36+bTEKSJPz44491Dr4MDg7G6tWrW+AdmgdJ3JnZ3oVMJqt1ovI751u9fZ/mtgE0ddm4cSNmz54NS0tLdOvWDU5OTrWW279/f2PCbRWio6MRHh6O06dPN/gLCrUtX375JcLCwtrUN3qi23Xs2BGpqalwdHTUG9w1cOBA/jJI1ABtJVcoKytDaWkphBBwd3fH2rVrMWXKFL0ykiTBxsamRs+K1q7RzWrr1q0zRBxYunQpwsLCsHPnTri6Nn61MqLW7syZM+jSpUub+0+GqC7du3dnwk3UzlhbW8Pa+tZ6E/Hx8XBzc2s3K3k3OumeOXOmIeLAzZs38cILLzDhpnanb9++uHHjBuzt7eHi4oKxY8dCLpebOiwig7K3t8egQYPuXpCI2ixfX19Th2BUZtOBtFevXrh586apwyAyuvT0dN0YhtTUVOzYsQOjRo3StQQQtQWenp4oLS3VLXbh6B2AuKQM9AzoYOLIiMhY/P39a+2iXB9JksxyHZimMJuk+8MPP8TUqVMxbtw4REREmDocIpNJS0vDjz/+iGHDhqFDByYk1DacOnUKX375JcrLy6G0dcDVrBLcyLkKfy9X2HKmEqJ2ITIystFJd1tiNkn3O++8A5VKhXvuuQfBwcHw8fGp8RO7JEnYtm2biSIkMp6SkhLs2LEDvXr1Qr9+/SCTcRl4ar20WoEbaVnQaG+N21e6eCPnZgEkAOdvpGJgjwDTBkhERrF+/XpTh2BSZpN0nzt3DpIkwcfHB8XFxbh48WKNMu352xG1E0JALtTQSAoIAGfPnkVaWhqGDh0KZ2cuFEKtU3xaNn49eh7lJVVwtVUivagSDjZKCCFw+nISQgI6sLWbiNo8s0m6ExISTB0CkckpqgrhWJ6EPGtfVFjcWuY3MzMTW7ZsQdeuXREaGgqVSmXiKIkaTqsVOBWXgNSsfKgLKuDk6oGcwlL4ejpBCCApI4+t3UTtWGFhIT755BPs378fmZmZ+PTTT9G/f3/k5uZi/fr1uP/++9G5c2dTh9kizCbpJmqPkpKSdIsNVFRUoCzjGjxsy2GnzkSu3B747687QghcvnwZV65cga+vL3r16gUPDw/++kNmLz4tG9dTs+Dr6YzTGWlILqiAlZUCqVm3FlWzkMtwIf4mBgQ3foAVEbVuKSkpiIyMRHJyMrp06YK4uDgUFxcDAJydnfHpp58iMTERK1euNHGkLcMsk+6ioiIUFBRAq9XWOObj42OCiIha1p9//ok33ngDv/76q27l1dLSUjz95mfoG+SL6aPC4BzkrmvtriaEQEJCAhISEuDi4oLg4GAEBgbCysqqttsQmVR1K7dGK2Bvo4SVpQUcHewxYVAIFJb/+/ixVloy4SZqh1588UUUFRUhJiYG7u7ucHd31zs+adIkbN++3UTRtTyzSrrXrFmDDz/8EDdu3KizTENWtyQyZz/++COmTZsGIQTuXBBWCOD05SREX07C/JlA1wFjda3dd8rJycEff/yBY8eOISAgAN26dYOnpyeTFzIbiRk5uHEzG+rKKsSn5UBmYYXCknKoKysR5Otp6vCIyMR+//13LFiwAMHBwcjJyalxPCAgAMnJySaIzDDMJuleu3Yt/va3vyEqKgqPP/44Xn31VSxYsABKpRLr16+Hh4cHnn32WVOHSdQsf/75J6ZNmwaNRlMj4a6m/e8MDys2bMMrbj7oGNij3mtWVVXhypUruHLlCpydnREaGorAwEAm32RyDjbWiAgJBP5b1eMTLOHv5w8XlZ1pAyMis1BWVgY3N7c6jxcVFRkxGsMzm3nIVq9ejaioKOzcuRNz584FAEyYMAFvvfUWLl68iKKiolq/BRG1Jm+++WatLdy1EQB+2bmnUdfPzc3Fvn37sH37dqjV6iZGSdQyXFS2iAztisiwW6+IHv6IDOuKjm5Opg6NiMxAcHAwDh06VOfxrVu3IiwszIgRGZbZJN3Xr1/HfffdBwCwtLQEcGtgGQCoVCo8+eST+OSTT0wWH1FzJSUlYfv27Q3uIqXVCpyNvYTc3NxG3ystLQ0HDx5s9HlEhmRjY2PqEIjIjMyfPx/ffvst3nnnHRQU3BpcrdVqce3aNTz66KM4duwYFixYYOIoW47ZdC9RqVSoqqoCADg4OMDGxkavH4+9vT3S09NNFR5Rs+3du7dBLdy3E0IgLi6uSau0JiQkID8/H46Ojo0+l8gQlEqlqUMgIjMyY8YMJCYm4rXXXsOrr74KABg7diyEEJDJZPjnP/+JSZMmmTbIFmQ2SXfPnj1x9uxZ3fbAgQOxZs0ajB8/HlqtFp9++im6du1qwgiJmqeoqAgymazWWXnqIkkSysvLm3zPmJgYDBs2rMnnE7UkhYIL4BCRvldffRWPPvootmzZgmvXrkGr1SIwMBCTJ09GQEDbmr/fbJLuGTNmYO3atVCr1VAoFFi2bBlGjRqlmyLQ0tISW7ZsMXGURE1nb2/fqIQbuNXS3ZzWwatXr6JPnz5wcHC4e2EiA6vuOkhEdDsfH5821Y2kLmaTdM+ePRuzZ8/WbQ8ePBgXLlzAL7/8ArlcjjFjxrClm1q1kSNHQpKkRnUxkSQJQUFBzbovB1SSuZDJzGYYERGR0ZlN0l2bgIAA/OMf/zB1GEQtwsfHB/feey927NjRoMGUMpkMISEhcHZ2btL93N3d0b9//3qnYyIyJk5jSdS+yWSyJv0/0FbWaDG7pPv48ePYv38/MjMz8fTTT6NLly4oLS1FXFwcunbtCjs7zu9KrdeiRYuwc+fOBrd4jx8/vlHXl8vlCAwMRHBwcI2VvYhMjd1LiNq3xYsX10i6f/rpJ1y4cAFRUVHo1q0bACAuLg6///47evbsyYGUhlBRUYHp06dj27ZtEEJAkiTcd9996NKlC2QyGcaMGYMFCxboRrcStUb9+vXDd999p1uRsrZv75IkAyTggRlPwM/Pr0HXVSgU6NmzJ3r06MEZIoiIyCwtXbpUb/uzzz5DZmYmYmNjdQl3tUuXLmHEiBHw9vY2YoSGZTYd7BYtWoTt27djzZo1uHz5sl4roFKpxNSpU7Ft2zYTRkjUMiZPnoyjR49i/PjxtfzMJqFT12BMfGIBnP1DUF5Vf2u4XC5HaGgoHnroIYSHhzPhJiKiVuO9997DM888UyPhBoDu3bvjmWeewbvvvmuCyAzDbFq6v/nmGzz11FOYO3durStPdu/eHZs3bzZBZEQtr1+/fvhpy/c4tnk1JsxdjMKSciitrTH+yf+Dp5sLJACFFQKZJVXwUdX+k7yHhweGDRsGlUpl3OCJiIhaQEpKSr3dziwtLZGSkmLEiAzLbFq6MzMzERISUudxuVyO0tJSg8bw1ltvISIiAjY2Ng1eUEQIgcWLF8PLywvW1tYYNWoUrl69atA4qW0oSrkMF8sK2FhbAwAsLCyhcnRBWSVQWglYSBLyygW0d/T9lsvl6NevH+6//34m3ERE1Gymyn969uyJTz75BKmpqTWOpaSk4JNPPqk3N2xtzKalu1OnToiLi6vz+JEjR9C5c2eDxlBRUYGpU6di0KBB+M9//tOgc959912sWrUKGzZsgL+/PxYtWoSoqChcvHiRP/VTnTSV5ci7fhqSpZVun5WkQS9XLTSy/y0gYiEBsv92QZEkCYGBgejbty/n3SYiohZjqvznX//6F6KiotC1a1c88MADujzv6tWr2Lp1K4QQ+Oqrr5r8vsyN2STdDz/8MD788ENMmTJFNx93dX/Xzz//HN9//z3efvttg8awbNkyAMD69esbVF4IgRUrVuC1117DxIkTAQBffvklPDw8sHXrVkyfPt1QoVIrV5qVhMqyQoiqSgjtrcGUkgR4IA8WVeUoUnihSn6rBdzS0hJdu3ZFSEgIk20iImpxpsp/hgwZghMnTmDRokX46aefUFZWBgCwtrZGVFQUli1bxpZuQ3j11Vdx/PhxDB06FN27d4ckSViwYAFyc3ORkpKC8ePHm91qRfHx8UhPT8eoUaN0+1QqFQYMGIBjx47VWenUarXegiXFxcUGj5XMi42bL7z73w8IAbliFYBCaCULQGhgW5EFrSRHuWNX9OrVC8HBwVw+m4iIANzKGQoLC3XbCoXC6J8RTc1/atOzZ0/89NNP0Gq1yMrKAgC4ubm1ycW0zOYdWVlZ4bfffsO6desQEBCAoKAgqNVq9OrVC+vXr9etTGlO0tPTAdwa0HY7Dw8P3bHaLF++HCqVSveKjIw0aJxkfuSWCth5BMDOMxCS7NZ3XwEJNlV5AAAv6yo8MG4EwsLCmHATEZFOZGSkXg6xfPlyo8fQ1PynPjKZDB4eHvDw8GiTCTdgRkk3cKs7yYwZM7B161ZcuHABly5dwvbt2/HYY481eSWzl19+GZIk1fuqry+5ISxcuBAFBQW618GDB416fzIfWq2Ak4srnJyc4GRvDUtNOdz9e8DNyR6lqRdMHR4REZmZgwcP6uUQCxcurLWcOeY/7Z3ZdC8xlOeffx6zZs2qt0xAQECTru3p6QkAyMjIgJeXl25/RkYGQkND6zzvzp+CuMpm+5WQno05r32Iiowr6Kq+AHc3Jzg7qlBVJkNh8iU4+odB6fi/loSKyiokpOegcwd3yGRcUpuIqL2xs7Nr0Pgec8x/2juzSroPHz6ML774Ajdu3EBeXl6NZbIlScLZs2cbdU03Nze4ubm1ZJg6/v7+8PT0xN69e3WVrLCwECdOnMBTTz1lkHtS26HVCpyMS0RKVh5sC/OgcreHvUqFytJ8AIAkk6M0O1kv6T57LQVHzl/HxCG94e/taqLIiYjI3DH/MT9mk3R/+OGHePHFF6FUKtGtWzc4OzsbPYakpCTk5uYiKSkJGo0GMTExAIDOnTvrWqODgoKwfPlyPPDAA5AkCfPnz8ebb76JLl266KbM8fb2xqRJk4weP7UuCenZuJ6SBR8PZ5zJtEdV5wj49emhV6Y8Lw1luamwdu6A0vIKnIpLxM3sfJyKS4Svpwtbu4mIqNmY/xiH2STd7733HgYPHoxffvnFZAt+LF68GBs2bNBth4WFAQD279+PYcOGAQAuX76MgoICXZn/+7//Q0lJCebOnYv8/HwMGTIEv/32G+fopnpVt3JrtFo42CphIZfjWoGEIUpHKKxu/bOsKM5FZuwBKOxd0GnINFyIv4mM/CL4ejrjamomEtNz2NpNRETNxvzHOCRxZx8OE1GpVHj33Xcxb948U4didNHR0QgPD8fp06fRp08fU4dDRpCQloNNe/5EabkalnI50jOz4O7mismRfdC7c0cAQGbsAWRfOASZhRWc+9yHLTHZKCorh6ezAxLTcxDk44Upw/qwtZtahTJ1JW5m5yPA27XJA+OJ2jPmCq2f2bR0Dx8+HOfPnzd1GERGYWejwKAeARC49Z03M0MFR2cXnLuWDC8XFRwtK1GQeA6Wdk7QqEsRffok0gucAMgQn5aDyioNrt/MQnJWHnw9jN8Vi6ixTl9OxOnLiZg6PBzero6mDoeIyOjMJulevXo1xowZg/fffx+PP/64Sfp0ExmLq8oOw/t0020XFHjhalo+Tly8gT8vxaO/fQ4qSwth4+YLucIWNukZGBzgD2tn7/9dRJLgYMOf8cj8FZaU48yVJKTlFOD05SR4uajY2k1E7Y7ZJN2dOnXCvHnz8MILL+Cll16CUqmssRiOJEl6/YmI2gqZhSVOxSVAoxW4fCMRPnbxsBACZdnJAABXKw0crHPQsTcXUqLW59z1FOQUlqCDqyMuJaYhvJsPW7uJqN0xm6R78eLFeOutt9ChQwf07dvXZIMpiUzhcnIWMvKK4OfpgqT0bKS6dkZEH1+91kALa3sTRkjUNNWt3PY2SjjYKpFTWMLWbiJql8wm6V67di0mTJiArVu3ttnlP4lqU6auxKm4BFjIZf+dzcQGsblahNp0hJcrv3xS63YhPhXpuYWwsJCjuEyNyioN4hLT0b+7Hzyc777ABxFRW2E2SXdFRQUmTJjAhJvalTJ1BTbvj0ZBSRlkkoTs/GIAgFwuQ3x6NpNuavU8nB0wpn8wIAD8t2FbJkmwVliZNC4iImMzm6T73nvvxR9//NEupwyk9utCfBri07LQ068DInoF6h1zcbA1UVRELSfA2w0+ztbIOLsbLkGDoVS5mzokIiKTMJtm5SVLluDixYt4+umncfr0aWRlZSE3N7fGi6itKFNX4FRcAiqrtEjIyIGlhRxeLirdy8rSbL4TEzVLfmIsChLPI+96NMxkaQgiIqMzm0/1bt1uTZ8WExODTz/9tM5yGo3GWCERGdSF+DRk5BUhwNsFiRl5iLmSjFH9ups6LKIWVVVWhIL4M4AkQ1HKJTj5h0Lp5GnqsIiIjM5sku7FixdzJDu1G9Wt3FYWt6bFVNkocfZ6CkK7doKrys7E0RG1nPykC1AX5cDG1Qdl2UnIi4+Bp2MU/78nonbHbJLupUuXmjoEIqNJTM9FqboCWq1AWnYhAMDCQo4bN7NQpdGgsKQcXTt5mDhKouapbuWWyS2hraqAXGHL1m4iarfMJukmak8CO7hh6vBw3Nm91cXBFlv/iEFOYQk8nR3gYGttmgCJWkBZXhqEVgtJboHK0lsLm8nkFijNSWHSTUTtDpNuIhOwtJCjk7szSssrsC86DgN7BMBVZYcryRlISM+BurIKZ6+l4J7eXUwdKlGT2Xl1hsLB9dZ0gbextOVUmETU/pjN7CVE7VFsfCpOX07E6cuJ0Gi1OHkpAUIIONpaI/pKEgpLykwdIlGTSZIMVnbOsLLXf0kyualDIyIyOibdRCZSUq7GqbhEaDRaxN5IxalLiUhIz4G7kz2cHWyRW1SKs9dSTB0mERERtQAm3UQmciH+JrLyiuDv5YqiUjV2n76EMnUF0nMKkZSRC61Wi/M3UlGmrjR1qERERNRM7NNNZALVrdxWlhbQaLWwt1GgsKQcI/oEwdHORlfOylKum1aQiIiIWi8m3UQmkJyRB3VFFQAgK78YAGBnrYSttQJ9uvmYMjQiIiIyACbdRCbQpaM7VKP61ZjVwUVla5qAiIiIyKCYdBOZgFwug5cLp02j9ktTqUZlaQGUKndTh0JEZBQcSElERAYlhEBpTgqEVqPbl3v1BG6e2IbKsiITRkZEZDxMuomIyKBKs5OQdvIXFCZfAgBUlOQjP/4synJTUZB43sTREREZB5NuIiIyGCEE8m9EozQ7GXnXT0NbVYGCxHOoLC2Apa0j8uNj2NpNRO0Ck+7bvPXWW4iIiICNjQ0cHR0bdM6sWbMgSZLea+zYsYYNlIiolSjNTkJx2jVYO3dEWW4q8q5FIz/+LCxtVLCyc0FFcS5bu4lMjPmPcTDpvk1FRQWmTp2Kp556qlHnjR07FmlpabrXN998Y6AIiYhaj+pWbm1VJSxtVZDklsg4twfl+emoKi9Bed5NaKsqkB8fgyq2dhOZDPMf4+DsJbdZtmwZAGD9+vWNOk+hUMDT09MAERERtV5lOSkoTrsGbVUlSrOTIDQaVJTmw6FDNyidvHTlZBZWgCSZMFKi9o35j3Ew6W4BBw4cgLu7O5ycnDBixAi8+eabcHFxqbO8Wq2GWq3WbRcXFxsjTCIio5JbKqHy7Q0I7f92SoB9x+6wdfM1XWBErVhxcTEKCwt12wqFAgqFwiSxNDb/ae+YdDfT2LFjMXnyZPj7++P69et45ZVXMG7cOBw7dgxyee3Ldy9fvlz3rZKIqK1SqNzgGTbG1GEQtSmRkZF620uWLMHSpUuNHkdT8p/2rs336X755ZdrdPS/8xUXF9fk60+fPh33338/QkJCMGnSJGzfvh0nT57EgQMH6jxn4cKFKCgo0L0OHjzY5PsTERFR+3Hw4EG9HGLhwoW1ljPH/Ke9a/Mt3c8//zxmzZpVb5mAgIAWu19AQABcXV1x7do1jBw5stYyd/4UZGdn12L3p9ZFCC1K0q/Dxs0PMgtLU4dDRERmzs7ODg4ODnctZ475T3vX5pNuNzc3uLm5Ge1+KSkpyMnJgZeX190LU7tXkhGPtDO74BY0GI4BYaYOh4iI2gjmP+anzXcvaYykpCTExMQgKSkJGo0GMTExiImJ0RvoGBQUhJ9++gnArcEML774Io4fP46EhATs3bsXEydOROfOnREVFWWqt0GthNBqkHf9NMpzbyLvejQ0FeWmDomIiNoh5j/G0eZbuhtj8eLF2LBhg247LOxWy+P+/fsxbNgwAMDly5dRUFAAAJDL5Th37hw2bNiA/Px8eHt7Y8yYMXjjjTdMNpKYWo+SzASUZMTDxs0X5fnpKEq5xNZuIiIyOuY/xiEJIYSpg2jvoqOjER4ejtOnT6NPnz6mDoeMQGg1SDm2BcXp12Hj6oPyvHRY2TrCJ/IRyK2Upg6PiIjMDHOF1o/dS4hMoDQ7GaVZiRBVFSjNSoSmohRl+WkounnF1KERERGRAbB7CZEJWNo4wCUoArjjdyaFg6tpAiIiIiKDYtJNZAJWds5wDRps6jCIiIjISNi9hIiIiIjIwJh0ExEREREZGJNuIiIiIiIDY9JNRERERGRgHEhJNaSlpSEtLc3UYbQrXl5eXDrXyFjPjY/13PhYz42P9ZzqwqTbDHh5eWHJkiVm8Y9UrVbjoYcewsGDB00dSrsSGRmJXbt2cSUvI2E9Nw3Wc+NiPTcNQ9Vzc8oVqGm4IiXpKSwshEqlwsGDB2FnZ2fqcNqF4uJiREZGoqCgAA4ODqYOp11gPTc+1nPjYz03PtZzqg9buqlWoaGh/A/DSAoLC00dQrvFem48rOemw3puPKznVB8OpCQiIiIiMjAm3UREREREBsakm/QoFAosWbKEA52MiM/c+PjMjY/P3Pj4zI2Pz5zqw4GUREREREQGxpZuIiIiIiIDY9JNRERERGRgTLqJiIiIiAyMSTcRERG1SkuXLoUkSY0+b9asWfDz82v5gIwUw7BhwzBs2LAWjYcMj0k30X+tX78ekiTpXkqlEt7e3oiKisKqVatQVFRk6hBr+P777yFJEn766acax3r37g1JkrB///4ax3x8fBAREWGMEMkMtca6DujHffjw4RrHhRDo1KkTJEnCvffea4IIqbUqLS3F0qVLceDAAVOH0iQ3b97E0qVLERMTY+pQqB5Muonu8Prrr2Pjxo1Ys2YN/v73vwMA5s+fj5CQEJw7d87E0ekbMmQIANRIQAoLCxEbGwsLCwscOXJE71hycjKSk5N151L71Zrq+u2USiU2bdpUY//BgweRkpLC6dqo0UpLS7Fs2bJWnXQvW7aMSbeZ4zLwRHcYN24c+vbtq9teuHAh9u3bh3vvvRf3338/Ll26BGtr6zrPLykpga2trTFChbe3N/z9/Wsk3ceOHYMQAlOnTq1xrHqbSTe1prp+u/Hjx2Pz5s1YtWoVLCz+9zG2adMmhIeHIzs72+gxERHdDVu6iRpgxIgRWLRoERITE/HVV1/p9s+aNQt2dna4fv06xo8fD3t7ezzyyCMAAD8/P8yaNavGtWrri5eYmIj7778ftra2cHd3x4IFC7Br1y5IknTXlpchQ4bgzJkzKCsr0+07cuQIevTogXHjxuH48ePQarV6xyRJwuDBgxv/IKjNM+e6Xu2hhx5CTk4Odu/erdtXUVGBH374AQ8//HCj3zO1DocPH0a/fv2gVCoRGBiITz/9tNZyX331FcLDw2FtbQ1nZ2dMnz4dycnJdV43ISEBbm5uAIBly5bpujAtXboUAHDu3DnMmjULAQEBUCqV8PT0xOOPP46cnJwGx75161b07NkTSqUSPXv2rLVLIABotVqsWLECPXr0gFKphIeHB+bNm4e8vLw6r33gwAH069cPADB79mxd/OvXrwcA/PHHH5g6dSp8fHygUCjQqVMnLFiwQO8zg4yDSTdRAz366KMAgN9//11vf1VVFaKiouDu7o73338fU6ZMadR1S0pKMGLECOzZswfPPvssXn31VRw9ehQvvfRSg84fMmQIKisrceLECd2+I0eOICIiAhERESgoKEBsbKzesaCgILi4uDQqTmo/zLWuV/Pz88OgQYPwzTff6Pbt3LkTBQUFmD59eqOuRa3D+fPnMWbMGGRmZmLp0qWYPXs2lixZUiN5feutt/DYY4+hS5cu+PDDDzF//nzs3bsXQ4cORX5+fq3XdnNzw5o1awAADzzwADZu3IiNGzdi8uTJAIDdu3fjxo0bmD17NlavXo3p06fj22+/xfjx49GQ9QV///13TJkyBZIkYfny5Zg0aRJmz56NU6dO1Sg7b948vPjiixg8eDBWrlyJ2bNn4+uvv0ZUVBQqKytrvX737t3x+uuvAwDmzp2ri3/o0KEAgM2bN6O0tBRPPfUUVq9ejaioKKxevRqPPfbYXWOnFiaISAghxLp16wQAcfLkyTrLqFQqERYWptueOXOmACBefvnlGmV9fX3FzJkza+yPjIwUkZGRuu0PPvhAABBbt27V7SsrKxNBQUECgNi/f3+9cV+4cEEAEG+88YYQQojKykpha2srNmzYIIQQwsPDQ3z88cdCCCEKCwuFXC4Xc+bMqfea1La11rp+e9wfffSRsLe3F6WlpUIIIaZOnSqGDx+ui2fChAn1Xotal0mTJgmlUikSExN1+y5evCjkcrmoTmUSEhKEXC4Xb731lt6558+fFxYWFnr7Z86cKXx9fXXbWVlZAoBYsmRJjXtX17HbffPNNwKAOHTo0F1jDw0NFV5eXiI/P1+37/fffxcA9GL4448/BADx9ddf653/22+/1dh/57+tkydPCgBi3bp1DYp/+fLlQpIkvedJhseWbqJGsLOzq3Vmh6eeeqrJ1/ztt9/QoUMH3H///bp9SqUSc+bMadD53bt3h4uLi66v9tmzZ1FSUqKbnSQiIkI3mPLYsWPQaDTsz013ZY51/XYPPvggysrKsH37dhQVFWH79u3sWtJGaTQa7Nq1C5MmTYKPj49uf/fu3REVFaXb/vHHH6HVavHggw8iOztb9/L09ESXLl1qncmpIW4f11BeXo7s7GwMHDgQABAdHV3vuWlpaYiJicHMmTOhUql0+0ePHo3g4GC9sps3b4ZKpcLo0aP14g8PD4ednV2LxF9SUoLs7GxERERACIEzZ8406ZrUNBxISdQIxcXFcHd319tnYWGBjh07NvmaiYmJCAwMrDHXbOfOnRt0viRJiIiIwKFDh6DVanHkyBG4u7vrzo+IiMBHH30EALrkm0k33Y051vXbubm5YdSoUdi0aRNKS0uh0Wjwl7/8pcmxkfnKyspCWVkZunTpUuNYt27dsGPHDgDA1atXIYSotRwAWFpaNun+ubm5WLZsGb799ltkZmbqHSsoKABwa0xBbm6u3jE3NzckJiYCQJ2x3560X716FQUFBTX+3VW7894NlZSUhMWLF+Pnn3+u0Te8On4yDibdRA2UkpKCgoKCGgmCQqGATFbzR6O6FmzQaDSQy+UtGtuQIUPwyy+/4Pz587r+3NUiIiLw4osvIjU1FYcPH4a3tzcCAgJa9P7UtphzXb/dww8/jDlz5iA9PR3jxo2Do6Ojwe5F5k+r1UKSJOzcubPWemdnZ9ek6z744IM4evQoXnzxRYSGhsLOzg5arRZjx47VDVI/evQohg8frndefHx8o+N3d3fH119/Xevx6sGejaHRaDB69Gjk5ubipZdeQlBQEGxtbZGamopZs2bpDbInw2PSTdRAGzduBAC9nzPr4+TkVOvAncTERL2k19fXFxcvXoQQQi95uXbtWoNju32+7iNHjmD+/Pm6Y+Hh4VAoFDhw4ABOnDiB8ePHN/i61D6Zc12/3QMPPIB58+bh+PHj+O6775p0DTJ/bm5usLa2xtWrV2scu3z5su7PgYGBEELA398fXbt2bdQ96vrimJeXh71792LZsmVYvHixbv+dsfTu3VtvNh0A8PT01M0Zf7fYq+Pfs2cPBg8eXO9UnY2J//z587hy5Qo2bNigN3DyzljJONinm6gB9u3bhzfeeAP+/v66adLuJjAwEMePH0dFRYVu3/bt22tMXRUVFYXU1FT8/PPPun3l5eX4/PPPGxxf3759oVQq8fXXXyM1NVWvpVuhUKBPnz74+OOPUVJSwq4lVC9zr+u3s7Ozw5o1a7B06VLcd999TboGmT+5XI6oqChs3boVSUlJuv2XLl3Crl27dNuTJ0+GXC7HsmXLaswqIoSod4o/GxsbAKjx5bG6xfzO661YsUJv28nJCaNGjdJ7KZVKeHl5ITQ0FBs2bNDryrF7925cvHhR7xoPPvggNBoN3njjjRrxVVVV1Tn7CgDdfPkNiV8IgZUrV9Z5LTIctnQT3WHnzp2Ii4tDVVUVMjIysG/fPuzevRu+vr74+eefoVQqG3SdJ598Ej/88APGjh2LBx98ENevX8dXX32FwMBAvXLz5s3DRx99hIceegj/+Mc/4OXlha+//lp3n7paMG5nZWWFfv364Y8//oBCoUB4eLje8YiICHzwwQcA2J+b/qc11vU7zZw5s9HnUOuzbNky/Pbbb7jnnnvw9NNPo6qqCqtXr0aPHj10q6cGBgbizTffxMKFC5GQkIBJkybB3t4e8fHx+OmnnzB37ly88MILtV7f2toawcHB+O6779C1a1c4OzujZ8+e6NmzJ4YOHYp3330XlZWV6NChA37//fdGdR1Zvnw5JkyYgCFDhuDxxx9Hbm6uLvbi4mJducjISMybNw/Lly9HTEwMxowZA0tLS1y9ehWbN2/GypUr6xy3EBgYCEdHR6xduxb29vawtbXFgAEDEBQUhMDAQLzwwgtITU2Fg4MDtmzZUu+832RAppo2hcjcVE9HVv2ysrISnp6eYvTo0WLlypWisLCwxjkzZ84Utra2dV7zgw8+EB06dBAKhUIMHjxYnDp1qsZUT0IIcePGDTFhwgRhbW0t3NzcxPPPPy+2bNkiAIjjx483KP6FCxcKACIiIqLGsR9//FEAEPb29qKqqqpB16O2q7XW9YZMdSgEpwxsqw4ePCjCw8OFlZWVCAgIEGvXrhVLliwRd6YyW7ZsEUOGDBG2trbC1tZWBAUFib/97W/i8uXLujJ3ThkohBBHjx7VXR+3TR+YkpIiHnjgAeHo6ChUKpWYOnWquHnzZp1TDNZmy5Ytonv37kKhUIjg4GDx448/1hqDEEJ89tlnIjw8XFhbWwt7e3sREhIi/u///k/cvHlTV6a2f1vbtm0TwcHBwsLCQm/6wIsXL4pRo0YJOzs74erqKubMmSPOnj1b5xSDZDiSEA2Y2Z2IjG7FihVYsGABUlJS0KFDB1OHQ2QwrOtE1B4w6SYyA2VlZTXmgg0LC4NGo8GVK1dMGBlRy2JdJ6L2in26iczA5MmT4ePjg9DQUBQUFOCrr75CXFxcnVNHEbVWrOtE1F4x6SYyA1FRUfj3v/+Nr7/+GhqNBsHBwfj2228xbdo0U4dG1KJY14movWL3EiIiIiIiA+M83UREREREBsakm4iIiIjIwJh0ExlYQkICJEnC+vXrTR0KkcGwnhMR1Y9JNxERERGRgXEgJZGBCSGgVqthaWkJuVxu6nCIDIL1nIiofky6iYiIiIgMjN1LiBpg6dKlkCQJV65cwYwZM6BSqeDm5oZFixZBCIHk5GRMnDgRDg4O8PT0xAcffKA7t7a+rrNmzYKdnR1SU1MxadIk2NnZwc3NDS+88AI0Go2u3IEDByBJEg4cOKAXT23XTE9Px+zZs9GxY0coFAp4eXlh4sSJSEhIMNBTobaG9ZyIyHCYdBM1wrRp06DVavH2229jwIABePPNN7FixQqMHj0aHTp0wDvvvIPOnTvjhRdewKFDh+q9lkajQVRUFFxcXPD+++8jMjISH3zwAT777LMmxTZlyhT89NNPmD17Nj755BM8++yzKCoqQlJSUpOuR+0X6zkRkQEIIrqrJUuWCABi7ty5un1VVVWiY8eOQpIk8fbbb+v25+XlCWtrazFz5kwhhBDx8fECgFi3bp2uzMyZMwUA8frrr+vdJywsTISHh+u29+/fLwCI/fv365W785p5eXkCgHjvvfda5g1Tu8R6TkRkOGzpJmqEJ598UvdnuVyOvn37QgiBJ554Qrff0dER3bp1w40bN+56vb/+9a962/fcc0+DzruTtbU1rKyscODAAeTl5TX6fKLbsZ4TEbU8Jt1EjeDj46O3rVKpoFQq4erqWmP/3ZICpVIJNzc3vX1OTk5NSiYUCgXeeecd7Ny5Ex4eHhg6dCjeffddpKenN/paRKznREQtj0k3USPUNhVaXdOjibtMDNSQadUkSap1/+2D0KrNnz8fV65cwfLly6FUKrFo0SJ0794dZ86cuet9iG7Hek5E1PKYdBOZMScnJwBAfn6+3v7ExMRaywcGBuL555/H77//jtjYWFRUVOjNMEFkjljPiag9YNJNZMZ8fX0hl8trzBDxySef6G2XlpaivLxcb19gYCDs7e2hVqsNHidRc7CeE1F7YGHqAIiobiqVClOnTsXq1ashSRICAwOxfft2ZGZm6pW7cuUKRo4ciQcffBDBwcGwsLDATz/9hIyMDEyfPt1E0RM1DOs5EbUHTLqJzNzq1atRWVmJtWvXQqFQ4MEHH8R7772Hnj176sp06tQJDz30EPbu3YuNGzfCwsICQUFB+P777zFlyhQTRk/UMKznRNTWcRl4IiIiIiIDY59uIiIiIiIDY9JNRERERGRgTLqJiIiIiAyMSTcRERERkYEx6SYiIiIiMjAm3dTuHThwAJIk4cCBA2YRxw8//GDSOKjtYl0nIjIdJt3UZq1fvx6SJOleSqUSXbt2xTPPPIOMjAxTh2cSf/75JyRJwr/+9a8axyZOnAhJkrBu3boax4YOHYoOHToYI0RqAtb12lUn95Ik4auvvqq1zODBgyFJkt584EREhsCkm9q8119/HRs3bsRHH32EiIgIrFmzBoMGDUJpaampQzO6Pn36wMbGBocPH65x7OjRo7CwsMCRI0f09ldUVODkyZMYPHiwscKkJmJdr51SqcSmTZtq7E9ISMDRo0ehVCpNEBURtTdckZLavHHjxqFv374AgCeffPL/27v7mBrfPw7g7/OtlkMHq04pq0icJg/zVO3syEYYjWbUEqJabR7+YGMNE/5o8Y9JY2nGsFUcDSn9gWV0silJHqNQaXY6ZyHzkK0+vz/auecofcP36Efv19bWfd2f67qv+9619enuOp/g4eGBAwcO4OLFi1i5cuUAz+73cnZ2RlhYWI/Euq6uDlarFfHx8T0S8jt37uDz588wGAy/c6r0E7jWe7d48WIUFRXBarXC09NTac/Ly4O3tzfGjx+PN2/eDOAMiWgw4JtuGnTmzp0LAHjx4sV3Y27evImYmBj4+/vD1dUVfn5+2LJlCz59+tQj9smTJ4iNjYVWq4VarYZOp8POnTvtYlpaWpCUlARvb2+4uroiJCQEx48f7/XanZ2d2LFjB0aNGoVhw4Zh6dKlaG5u7hFnNBoxY8YMqNVqeHp6YvXq1WhpafnX+zcYDDCbzaivr1faTCYThg8fjtTUVCUB//qcrR/9WQb7WreJjo6Gq6srjEajXXteXh5iY2Ph5OTU77GIiH4W33TToNPQ0AAA8PDw+G6M0WjEx48fsX79enh4eOD27dvIzs7Gq1ev7H5w19bWYvbs2XBxcUFqairGjBmDhoYGXLp0CRkZGQAAs9mM8PBwqFQqbNq0CVqtFqWlpUhOTkZ7ezs2b95sd+2MjAyoVCqkpaWhtbUVBw8eRGRkJGpqaqBWqwF07+FNTEzErFmzkJmZCbPZjKysLJhMJty9excjR4787r3Zkufy8nIEBQUB6E6sw8PDERYWBhcXF1RUVGDp0qXKOY1Gg6lTp/7Yg6YBN9jXus3QoUMRHR2N/Px8rF+/HgBw7949PHz4EMeOHUNtbe2PPFYiop8jRH+pEydOCAC5evWqWCwWaW5uloKCAvHw8BC1Wi2vXr0SEZGysjIBIGVlZUrfjx8/9hgvMzNTVCqVNDY2Km0RERGi0Wjs2kREurq6lO+Tk5PFx8dHrFarXUxcXJyMGDFCuZZtHqNHj5b29nYl7uzZswJAsrKyRETky5cv4uXlJZMmTZJPnz4pccXFxQJA0tPT+3wu7e3t4uTkJMnJyUqbTqeTvXv3iohIaGiobNu2TTmn1Wpl/vz5fY5JA4trvXe26xiNRikuLhaVSiVNTU0iIrJt2zYJDAwUEZE5c+ZISEhIn2MREf0qbi+hv15kZCS0Wi38/PwQFxcHNzc3nD9/vs9qHLa3bADw4cMHWK1W6PV6iAju3r0LALBYLLhx4waSkpLg7+9v11+lUgEARASFhYVYsmQJRARWq1X5WrhwId69e4fq6mq7vgkJCdBoNMrxihUr4OPjg8uXLwMAqqqq0Nraig0bNth9ACwqKgrBwcEoKSnp83loNBpMmTJF2btttVpRV1cHvV4PoLuag21LydOnT2GxWLi15A/Btf59CxYsgLu7OwoKCiAiKCgoGNT73Ino9+P2EvrrHT58GBMmTICzszO8vb2h0+nwzz99/77Z1NSE9PR0FBUVw5rjhgAABCpJREFU9fiA1bt37wAAz58/B4A+S41ZLBa8ffsWubm5yM3N7TWmtbXV7nj8+PF2xyqVCkFBQXj58iUAoLGxEQCg0+l6jBUcHNxrZZJvGQwGZGdnw2q1oqKiAk5OTggPDwcA6PV6HDlyBB0dHdzP/YfhWv8+FxcXxMTEIC8vD6GhoWhubkZ8fHy/+xMR/Som3fTXCw0NVSo69EdnZyfmz5+PtrY2pKWlITg4GMOGDUNLSwvWrVuHrq6ufo9li129ejXWrl3ba8yUKVP6Pd5/xZZ0m0wmVFRUYPLkyXBzcwPQnXR3dHSgsrIS5eXlcHZ2VhJy+v/Gtd63+Ph45OTkYM+ePZg6dSomTpw4oPMhosGFSTfRN+7fv4+nT5/i5MmTSEhIUNqvXLliFxcYGAgAePDgwXfH0mq10Gg06OzsRGRkZL+u/+zZM7tjEUF9fb2SsAQEBADoLvNnq05hU1dXp5zvy9cfprx165ZdDW5fX18EBATAZDLBZDJh2rRpGDp0aL/mTn+WwbDWv2YwGODv74/r169j//79P9SXiOhXcU830Tds5cNERGkTEWRlZdnFabVaRERE4Pjx42hqarI7Z+vr5OSE5cuXo7CwsNeExWKx9Gg7deoU3r9/rxyfO3cOr1+/xqJFiwAAM2fOhJeXF3JyctDR0aHElZaW4vHjx4iKivrXe/T19cXYsWNx7do1VFVVKfu5bfR6PS5cuIC6ujpuLfmLDYa1/jWVSoVDhw5h9+7dWLNmzQ/1JSL6VXzTTfSN4OBgjBs3Dlu3bkVLSwuGDx+OwsLCXv95xqFDh2AwGDB9+nSkpqZi7NixePnyJUpKSlBTUwMA2LdvH8rKyhAWFoaUlBRMnDgRbW1tqK6uxtWrV9HW1mY3pru7OwwGAxITE2E2m3Hw4EEEBQUhJSUFQPfe1P379yMxMRFz5szBypUrlTJqY8aMwZYtW/p1nwaDAadPnwaAHv9tUq/XIz8/X4mjv9NgWetfi46ORnR09I8/LCKiXzUAFVOIfgtbGbXKyso+43oro/bo0SOJjIwUNzc38fT0lJSUFLl3754AkBMnTtj1f/DggSxbtkxGjhwpQ4YMEZ1OJ7t27bKLMZvNsnHjRvHz8xMXFxcZNWqUzJs3T3Jzc3vMIz8/X7Zv3y5eXl6iVqslKiqqR5k2EZEzZ87ItGnTxNXVVdzd3WXVqlVKabj+OHr0qFK27VvV1dUCQACI2Wzu95g0MLjW+75fo9HYZxxLBhLR76AS+ervikRERERE9J/jnm4iIiIiIgdj0k1ERERE5GBMuomIiIiIHIxJNxERERGRgzHpJiIiIiJyMCbdREREREQOxqSbiIiIiMjBmHQTERERETkYk24iIiIiIgdj0k1ERERE5GBMuomIiIiIHIxJNxERERGRgzHpJiIiIiJysP8B4g5QBV58zDgAAAAASUVORK5CYII=",
-      "text/plain": [
-       "<Figure size 675x600 with 3 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 675x600 with 3 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "''' \n",
-    "In this case,``x`` needs to be declared as a list consisting of 2 elements, unlike most cases where it is a single element. \n",
-    "The first element in ``x`` will represent the variable plotted along the horizontal axis, and the second one will determine the \n",
-    "color of dots for scattered plots or the color of lines for slope graphs. We use the ``experiment`` input to specify the grouping of the data.\n",
-    "'''\n",
-    "f1 = unpaired_delta_01.mean_diff.plot(\n",
-    "    contrast_label='Mean Diff',\n",
-    "    fig_size = (5, 5),\n",
-    "    raw_marker_size = 5,\n",
-    "    es_marker_size = 5,\n",
-    "    color_col='Genotype'\n",
-    ");\n",
-    "\n",
-    "\n",
-    "f2 = unpaired_delta_02.mean_diff.plot(                  \n",
-    "            contrast_label='Mean Diff',\n",
-    "            fig_size = (5, 5),\n",
-    "            raw_marker_size = 5,\n",
-    "            es_marker_size = 5,\n",
-    "            color_col='Genotype'\n",
-    ");\n",
-    "\n",
-    "\n",
-    "f3 = unpaired_delta_03.mean_diff.plot(                  \n",
-    "                    contrast_label='Mean Diff',\n",
-    "                    fig_size = (5, 5),\n",
-    "                    raw_marker_size = 5,\n",
-    "                    es_marker_size = 5,\n",
-    "                    color_col='Genotype'\n",
-    ");\n",
-    "\n",
-    "p1 = paired_delta_01.mean_diff.plot();\n",
-    "p2 = paired_delta_02.mean_diff.plot();\n",
-    "p3 = paired_delta_03.mean_diff.plot();\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "bb289b05",
-   "metadata": {},
-   "source": [
-    "# Plot all the delta-delta plots into a forest plot \n",
-    "### For comparisons of differen ``Durg`` effects"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "982afbdb",
-   "metadata": {},
-   "source": [
-    "Important Inputs:\n",
-    "\n",
-    "1. A list of contrast objects \n",
-    "\n",
-    "2. contrast_labels e.g ``['Dug1', 'Drug2', 'Drug3']``\n",
-    "\n",
-    "3. title: default is ``\"ΔΔ Forest\"``\n",
-    "\n",
-    "4. y_label: default as ``\"value\"``, please change it according to your measurement units/ types\n",
-    "\n",
-    "5. contrast_type ``delta-delt`` and ``mini-meta`` are supported\n",
-    "\n",
-    "6. Which effect size to plot (default is ``delta-delta mean-diff``, but you can specify which effect size you want to use)\\n\n",
-    "\n",
-    "7. Axes to put the plot into existing figures \\n\n",
-    "\n",
-    "8. The argument ``horizontal`` is a boolean input (``True``/ ``False``) \\n\n",
-    "\n",
-    "   default is ``vertical``, (``False``) that changes the default orientation, \\n\n",
-    "   \n",
-    "   if ``True`` the delta-delta values will be reflected on the x axis and the delta plots will be plotted horizontally. \\n\n",
-    "9. Plot kwargs are supported such as violin plot kwargs, fontsize, marker_size, ci_line_width\n",
-    "\n",
-    "output:\n",
-    "\n",
-    "- A plot with horizontally/vertically laid out half violin plots of each of the prescribed delta bootstraps. \n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "06b93055",
-   "metadata": {},
-   "source": [
-    "#### Vertical (default) Layout"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "c4a7e5a4",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 450x400 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "forest1_vertical = forest_plot(contrasts, \n",
-    "                               contrast_labels =['Drug1', 'Drug2', 'Drug3']);"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b3eee52e",
-   "metadata": {},
-   "source": [
-    "#### Horizontal Layout"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "d8313860",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 400x450 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "forest1_horizontal = forest_plot(contrasts, \n",
-    "                                 contrast_labels =['Drug1', 'Drug2', 'Drug3'],\n",
-    "                                 horizontal=True);\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "dc49a603",
-   "metadata": {},
-   "source": [
-    "Additiionall, for aesthetics and labels, you can use:\n",
-    "\n",
-    "1. The ``custom_palette`` argument to specify the colors you would like to indicate each experiment in a list \\n\n",
-    "    e.g [\"gray\", \"blue\", \"green\" ].\n",
-    "    \n",
-    "2. Additionally. the argument ``ylabel`` should be specified to specify the unit or \n",
-    "    the exact name of the measurement of experiments, for example \"delta_deltas\", the default is \"value\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4100ba2c",
-   "metadata": {},
-   "source": [
-    "#### Changing ``custom_palette`` and ``effect_size``"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "23c9446e",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 450x400 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "forest2_vertical = forest_plot(paired_contrasts, \n",
-    "                               contrast_labels =['Drug1', 'Drug2', 'Drug3'], \n",
-    "                               custom_palette= ['gray', 'blue', 'green' ], \n",
-    "                               effect_size='delta_g');"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "d5f2a4dd",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 400x450 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "\n",
-    "forest2_horizontal = forest_plot(paired_contrasts, \n",
-    "                                 contrast_labels =['Drug1', 'Drug2', 'Drug3'], \n",
-    "                                 custom_palette= ['gray', 'blue', 'green' ],\n",
-    "                                 horizontal=True, effect_size='delta_g');\n",
-    "\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6787aa97",
-   "metadata": {},
-   "source": [
-    "### Using existing axis \"ax\" as the optional input to plot forest_plot \\n\n",
-    "\n",
-    "\n",
-    "\n",
-    "With other kinds of dabest plots side by side or in other possible orientations, \\n\n",
-    "\n",
-    "We will specify the x_labels that we want to indicate in a list of strings and parse it as the argument contrast_labels, \\n\n",
-    "\n",
-    "for example ['Drug1', 'Drug2', 'Drug3']."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "180cae3a",
-   "metadata": {},
-   "source": [
-    "### Two forest plots plotted together in one axis"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "6e0fbdb1",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Text(0.5, 1.0, 'Paired')"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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EEbxy5UqNGDHC/XvVOLXx48dr7ty5NkWFYJWbm6v169dLOzdJJXnKdH2nxHOvtDsswCt0JsA2xblS9nYptZPdkSDIWMYYY3cQQDD59ttvNWTIEPfvS16dqsHX3iElUSggcH3zzTfVOhOq0JkAf3B3JuxbJ+XtUWa3zkrsP1YKZ5w5fCckeoIB2+3+XoprJkVE2x0J4JHhw4eLPhM0lvXr15/emdB6pdRukI1RIdhwYxzQGEoLpZ2LpYoKuyMBgMB0ZIuUk2V3FAgiFMFAY8nbK2V9X/koZQBAw+36tnL+dcAHKIKBxnRki7T7O3qEAcATZSXStn9JpTxOGd6jCAYa25Gt0vZ/S+WldkcCAIGnOFfa/pVUXmZ3JAhwFMGAHXJ/ljYvqHwSEgCgYY4fknZ8LVWU2x0JAhhFMGCXomPST/+UcvfYHQkABJ7cPdK2r6SyE3ZHggBFEQzYqaxY2vqllLWcr/YAoKHy9kqbP5EKj9odCQIQRTDgBIc2SZs+lnL32h0JAASW4tzKQvjgRmbfQYNQBANOUZIvbf1C2rFIKi2yOxoACBwV5dLPP0hbFnKvBeqNIhhwmqM7pI0fSdnb7Y4EAAJL/n5p03zyJ+qFIhhworKSyifM7VzCWGEAaIjyEyflT6aiRO0oggEfM6eMSfNqiFr2NmnrwsqiGABQf9nbpJ/+wU1zqBVFMOAjOTk5mj17tm644YZqr9/w/72k2X/9Ujn5hZ5t+PihynFuTAMEIET4rDOhOK/yprkjW70PCkHHMqceaQAabOHChRozZowKCwtPS96SZFlSbHSUPph1l0YPONuzncSlSZ1/IYVHeRktADhTTk6O5s2bpyeeeEL79u1zv94yLUl/GHexxl82SEkJsZ5tPP0sqfV5lQkZEEUw4LWFCxfq0ksvlTFGFRUVtS7nsixZlqUFz9zjeSEck1xZCEfGeRgtADhTo3QmpHaWMgZRCEMSRTDglZycHLVu3VpFRUV1FsBVXJalmOhI7VnwtOe9GRGxUqeRUlyKZ+sDgMM0amdC83Ok1n08jBTBhDHBgBfmzZunwsLCehXAklRhjAqLS/TGgu8832lpofSfT6W8fWdeFgAcLicnR2PGjDljASxV5lBjjMZMed7z+ywO/MgUapBEEQx4zBijOXPmeLCmpWf/9q8av+6rt4oyadtXUlGO59sAAAewpTNh93dS/kHP10dQoAgGPJSdna3t27c3uJg1xmj7nsM6mlvgXQAVZdL+dd5tAwBsZFtnQkW5tO1fTJ8W4iiCAQ8dP+7doznzC4u9D+IEjwcFELhs7UwoPyFt/VI64eGwCgQ8imDAQ/Hx8V6tnxAb7X0QsanebwMAbGJ7Z0JpobRnhXfbQMCiCAY8lJKSoo4dO8pq4FQ7lmWpY+s0NU30wTRnaV293wYA2MQRnQm5e7zfBgISRTDgIcuyNHHiRA/WNLr7+pENLp5P07S9FJPk3TYAwEaO6EwIi/B+GwhIFMGAF8aPH6/Y2Fi5XPU7lVwuS7HRUbr50kHe7dhySS3P9W4bAGAz2zsTJCndw/mGEfAoggEvJCUl6YMPPpBlWWcshF2WJUuWPnziLs8flFGleaYU3cS7bQCAA9jXmWBJrftK6T282w4CFkUw4KXRo0drwYIFiomJqbVXwrKkmOhIfTr7Ho3q72WvQ3wzqUUv77YBAA5hS2dCQnOp+xWVHQoIWTw2GfCRnJwcvfHGG5o1a5b27fvv09xapiVpys0Xa/xlg5QY72UPcGxTqfNoKcIHN4MAgIMsXLhQY8aMUWFhYY1TplmWFBsdpQ+fuMvzzoTIOKn1eZX3VCDkUQQDPrZkyRINHTrU/fviV6ZqSO8u3m84pZPUdoAUFu79tgDAgfzamZDWtbIA5kY4/D9cTQEfO3VIhNf3bUQlSG36SUltvNwQADhbUlKS7r77bvXu3btaZ8K7//d2zzsTwiKldoOk5Ha+CRJBgyIYcCrLVTlerfk59P4CCCk+60xIaCG1GyxFeTcfMYITV1bAiWKbSu2HSjHJdkcCAIEnPEpq1VdK7eyDr+MQrCiCAadp2qGy58IVZnckABB4UjpVjv3lBmKcAUUw4CTpZ0ttzrM7CgAIPFEJlR0ICc3tjgQBgiIYcIrW50nNeXIRADRYWlep9fncP4EG4WgB7BYeXTn+N7GV3ZEAQGCxXFLGICm1k92RIABRBAN2SmpTmcAjYuyOBAACS1iE1PFCqUkLuyNBgKIIBuwQFlk59y+9FwDQcGERlU/PjE+zOxIEMIpgoLHFpkgdhkvRTeyOBAACj+Wq7AGmAIaXKIKBxpTURmo/nJs3AMBT7QYzBAI+wZUYaCxNWkkdLpBcLrsjAYDA1Ky7lNLR7igQJLgaA40hIlbqMIwCGAA8FZNUOZUk4CP0BAONoXXfysd4AgDOKDMzU0uWLJH2rZPy9iizcxup3RCepAmfsowxxu4ggGCSm5ur9evXSzuXSCV5yuzeVYkDbqYXGAAaaucSKXublNZNyhhgdzQIMiFzVX7++efVrl07RUdHq1+/fvrhhx/sDglBKjExUYMHD9bgPj00uFcXJbbLpABGUCCPwjbNM+2OAEEoJK7Mf/vb3zR58mRNnz5dq1evVs+ePTV69GgdOnTI7tAQCpp2sDsCwGvkUdgmPl2Kirc7CgShkBgO0a9fP5133nl67rnnJEkVFRVq06aNJk6cqKlTp9ocHYLWpvmSqZDOutruSACvkUdhi51LKudUb9HT7kgQhIK+J/jEiRNatWqVRo4c6X7N5XJp5MiR+v7772tcp6SkRHl5edV+SkpKGitkBJOkDLsjALxGHoWtEpgTGP4R9EXwkSNHVF5ervT09Gqvp6en68CBAzWuM3PmTCUmJlb7mTlzZmOEi2DTpJXdEQBeI4/CNpar8imbgB8wRVoNpk2bpsmTJ1d7LSqK6a3QQFaYFJdqdxSALcij8ImoBKZFg98EfRGcmpqqsLAwHTx4sNrrBw8eVPPmzWtcJyoqimQN70UnkrwRFMijsA03xMGPgn44RGRkpPr06aOvvvrK/VpFRYW++uorDRjAnIPwo+gmdkcA+AR5FLaJiLM7AgSxoO8JlqTJkydr/Pjx6tu3r84//3w988wzKigo0K9+9Su7Q0MwI3kjiJBHYYuwCLsjQBALiSL4+uuv1+HDh/XQQw/pwIED6tWrlz7//PPTbvIAfCoi2u4IAJ8hj8IWrpAoU2CTkJgnGLBFwRFujAMAb5TkV94cB/hB0I8JBmwTFml3BAAQ2CzKFPgPRxfgL4xlAwDvUATDjzi6AH+xmB4NALxCHoUfUQQD/kJPMAB4h55g+BFHF+AvlmV3BAAQ2Mij8COKYAAA4Ez0BMOPOLoAAIAz0RMMP6IIDnAVFUzzDADeKC4ttzsEADagCA5wRwtP2B0CAAQ0HhkFhCaK4ABXUlahcnqDAcBjRuRQIBRRBAeBwhNldocAAAGNoWVA6KEIDgIFJYxnAwBvlFZU2B0CgEZGERwE8otL7Q4BAALaiTKKYCDUUAQHgbxihkMAgDdKKIKBkEMRHAToCQYA7xSdYFgZEGoogoPA8RJ6ggHAG/l8owaEHIrgIHC8uIw7mwHAC9kFJXaHAKCRUQQHgQoj5TEkAgA8tj+3WGXljAsGQglFcJDYm1NkdwgAELDKyo22Hy6wOwwAjSjc7gDgmdzcXK1fv16H80v0n6wc6UQPdW/eRC6XZXdoABCQNuzNVfvUOEWG0z8EhAKK4AC1fv16DRkyxP37Qy/9XVs7t1bX5gk2RgUAgaOqM6G4tEz/2ZKtNp26at2eWJ3XrqndoQFoBBTBQWTdnhy1To5RXBTNCgBnUlNnQmx8E2WkxKpZQrSNkQFoDHznE0TKyo2W7ciWMcwUAQCe+n57Nk+QA0IARXCQOZhXojU/59gdBgAErIKScn1PhwIQ9CiCg9Dm/fnasDfX7jAAIGDtPVak1VnH7A4DgB9RBAepH/fkak3WMXoyAMBD/zlwXJv25dkdBgA/oQgOYj/tz9fS7dlMAA8AHlr7c462Hcq3OwwAfkARHOR2Zxfqq82HVHiizO5QACAg/bDzmDbuY4gZEGwogkNA9vET+mz9AZ4qBwAeWvdzrr7deoRZI4AgQhEcIkrKKrToP4e1fAdT/wCAJ7KOFuqzDft1MK/Y7lAA+ABFcIjZfrhAC9bvU1Z2od2hAEDAKSgp11c/HdKq3Ue53wIIcBTBIajoRIW+3XZE3/znkApKGCsMAA31nwPHtXDjQeUUnrA7FAAeoggOYftyivXp+v3acfi43aEAQMDJLSrVwo0HtOUgs0cAgYgiOMSVlhst23FUy3dkq6KCOYUBoCHKK6SVu45p8ZbDKikrtzscAA1AERygTn0IhpF3Bez2wwVatPWwyimEAYQIX+bRPceK9PmGAzpWwPAIIFBQBAeYnJwczZ49WzfccEO11+c8+Dt9/rfXVZDv+VyW+3OKtYRCGECQ81ceLSgp15ebDjIdJRAgLOOH5+ru2bNHBw4cUGFhoc477zzFxMT4ehchaeHChRozZowKCwtrfByyZVmKjI7RPTNf0jn9h3m8nxZJ0RrSKVXhYfyNBCC4NEYedVnS4M6pap0c6224APzIZ0Vwfn6+nnjiCc2dO1f79u1zv75+/Xr16NHD/fu7776rDz/8UImJiXr11Vd9seuQsHDhQl166aUyxqiiovZpeSzLJcuS7v/zXK8K4ZT4SA3rkqboiDCPtwEATtKYeTTMJY3snq6U+ChPwwXgZz4pgrdu3apLLrlEO3bsqPaXtWVZpxXBu3btUqdOnWSM0aJFizR48GBvdx/0cnJy1Lp1axUVFdWZuKtYlkuR0dGa849liktI9Hi/cVFhGt61mRJjIjzeBgA4gR15NDYyTKPPaq6YSDoTACfy+vvu4uJiXXrppdq+fbtiY2P1hz/8QZ988kmty7dr104jRoyQJP3jH//wdvchYd68eSosLKxX4pYkYyp0orhISz790Kv9FpSU61+bDiq3sNSr7QCA3ezIo4UnyrVoyyGe0gk4lNdF8Isvvqht27YpLi5OS5Ys0Z/+9Cddcsklda5z8cUXyxij77//3tvdBz1jjObMmePRugvf+98ax7w1RElZhRZvPcz0aQAClp159GhBKTccAw7ldRH84YcfyrIsTZo0Sb169arXOj179pRUOYwCdcvOztb27dsbnISNMTq0d7eO5+V4HUN+cZkOHy/xejsAYAe78+jBvBL9sPOoV9sA4HteF8E//fSTJGnUqFH1XiclJUVS5Rgt1O34ce+e5lZcwNPgAIQ2J+TRnUcKdCi/2OvtAPAdr4vgquQSHx9f73VKSip7FSMi/H/D1f/9v/9XAwcOVGxsrJKSkvy+P19ryP/XmkTHebe+JEWFu5TKHc5AyCKPep9HJelQHt+oAU7idRFc1au7a9eueq+zceNGSVLz5s293f0ZnThxQtddd53uuOMOv+/LH1JSUtSxY0dZltWg9SzLUrNWGYpvkuR1DF3SExTmatj+AQQP8miST+KIZZYIwFG8LoLPPfdcSdLixYvrvc4bb7why7I0YMAAb3d/RjNmzNC9996rzMxMv+/LHyzL0sSJEz1ad/Qvf9XgpH+qyHCXujZP8GobAAIbedT7ToDk2Ai1bcrDMwAn8boIvvbaa2WM0SuvvKKsrKwzLv/MM8+4C+Ybb7zR2937RUlJifLy8qr9VA3hsMP48eMVGxsrl6t+zWW5XIqMjtGQS67xet+ZrRIVGc6T4wA0DHn0v1olx2hEt2Y8hRNwGK/PyHHjxumcc85RcXGxhg8frs8+++y0B2YYY7RixQqNHTtW9913nyzL0pAhQ3TxxRd7u3u/mDlzphITE6v9zJw507Z4kpKS9MEHH8iyrDMmcMtyyZJ0z59e9upBGVLlU+M6N/PNWDgAoYU8WvnAocGdUnn6JuBQPnliXFZWlgYPHqw9e/bIsizFxsaqsLBQkpSamqr8/Hx3D4AxRh07dtR3332nZs2aebS/qVOnatasWXUu89NPP6lbt27u3+fOnat77rmnXjNSlJSUnNZjERUVpagoe28Oq/cz7//0ss7pN9SrfcVEujSqR3PFRYV7tR0AzkQe9V8eDXNJPVokqnuLBHp/AQfzSREsSUePHtXEiRP13nvvqby8vOadWZauu+46vfjii0pOTvZ4X4cPH1Z2dnady3To0EGRkZHu3xuSvJ0sJydHb7zxhmbNmqV9+/a5X09KTdfl4+7Q0EvHKDa+iVf7iI8O17AuaTwuGQhi5FH/5NGmcREa0DGV/AkEAJ8VwVV2796tBQsWaOXKlTp06JDKy8uVkpKi3r176/LLL1eXLl18ubt6C5bkXWXJkiUaOvS/vRR/fOl9det1vtfbbZ8apz4ZyYwDBnAa8mjdujZPUO82SXIxmw4QEHz+XXdGRobuvPNOX2/WY1lZWTp69KiysrJUXl6utWvXSpI6derk9dyRdjr1bmVL3iXdJjHhOq9dU6U3ifZqOwCCD3m0brGRYTq/fVO1TIrxRVgAGknQD/h86KGHNG/ePPfvvXv3liR9/fXXGj58uE1ROUeYSzqrZaJ6tGhC7wWAGpFHa9clPV7ntE7i2zMgAAV9ETx37lzNnTvX7jAcqUlMuAZ3SlVSbOSZFwYQssijp2saF6nz2zdV0zjyJxCovC6CG/KQjJqcPB4LjadFYrQGdUql9wIAGiDcZalnmyR1SY/3yUM0ANjH6yJ4+PDhHicCy7JUVlbmbQhooIyUWA3okMLwBwBogOTYCA3qnKom0cz8AAQDnwyH8PEEE/CjHi2bqGfrRHowAKABMlJi1a99U+b9BYKI10Xw119/fcZlCgoKtGXLFr377rv64YcfNGjQIM2YMUNhYTxBp7FEhbvUr0NTtU7m2fUA0BBdmyfo3LZJdB4AQcbrInjYsGH1Wu6SSy7RPffcoyeffFJTpkzR66+/rrfeesvb3aMeWiXH6Px2TRUTyR8dANAQ3Vok6Ny2nj/cCYBzNfr3Ovfff7+uueYa/fWvf9W7777b2LsPKeEuS/07NNWwLmkUwADQQB3S4iiAgSBmy+Cmm2++WcYYvfLKK3bsPiQkRIdr9NnN1SEtcCeyBwC7pDeJ0vntmtodBgA/smWe4LZt20qS1q9fb8fug17TuAgN79pM0RH0/gJAQ0VHuDSoUyoz6ABBzpae4IMHD0qqvGEOvhUXFUYBDABe6NchhRwKhABbeoKff/55Sf/tEYbv9Cd5A0C9ZGZmasmSJSouLdOSLdlq06mr2qfGqVVSjN2hAWgEjVYEHzt2TCtXrtRf/vIXff7557IsS9dcc01j7T7oVCXvw/klWpuVozaduqplUrTSm0TbHRoABITExEQNHjxYhSfKdCh2n8LDLPVum2R3WAAaiddFsKdz/Xbu3FlTpkzxdvchqyp5780pUlHTw5Iq57IEAHimQ2oc36QBIcTrMcHGmAb9hIWF6cYbb9TixYuVmJjoi88ASTGRLjWnFxgAPNY2hYcJAaHE657g6dOnn3EZl8ulhIQEtW/fXgMHDlRaWpq3u8Up2jaN5WlGAOChiDBLqXFRdocBoBE1ShEM/2vTlB4MAPBUakIUU6IBIcaWKdLgW1HhLqXF04MBAJ5KiYu0OwQAjYwiOAikN4lmKAQAeCExJsLuEAA0MorgIJASTw8GAHgjLsqWafMB2KjeZ/0jjzzilwAeeughv2w3lCTF0oMBAN6IYWo0IORYxhhTnwVdLpdfvnIvLy/3+TZDyd6cIiVEh6tJNIUwAHii8ESZwl0uRYbz5SgQShr0/U8962U0MnowAMA7YcwMAYScehfBFRUV/owDHgp3WYoIo/cCALxBCQyEHqqnABdJAQwAXmOOYCD0UEEFOMawAYB3XEwxCYQkKqgAxzg2APAORTAQmiiCAxxFMAB4hzQKhCafzg7+9ddf6+OPP9a6det05MgRFRUV1TmjhGVZ2r59uy9DCDncFAcAANBwPimCDx06pBtuuEGLFi2SVPtUapZlVXuPR/0CAOzGN2pAaPK6CC4tLdXFF1+stWvXyhijXr16qVWrVlqwYIEsy9JNN92ko0ePavXq1dq/f78sy9K5556rs88+2xfxAwDgFTpkgNBU7yfG1ebVV1/VbbfdJsuy9Prrr2v8+PHauHGjMjMzZVlWtSfCffzxx/rd736nY8eO6Y033tCYMWO8/gAAAABAQ3k9oPSDDz6QJF100UUaP358ncteddVVWrRokSIjIzVhwgRt3brV290DAAAADeZ1Ebxu3Tr3sIeanNrR3LFjR02aNEkFBQWaPXu2t7sHAAAAGszrIvjo0aOSpPbt27tfi4yMdP+7sLDwtHUuvPBCSdKXX37p7e4BAACABvO6CK4qeE8ufJs0aeL+9969e09bJzo6utb3AAAAAH/zughu27atJOngwYPu19LT05WQkCBJWr58+WnrbNiwQRJ35AIAAMAeXhfB5557riRpzZo11V4fOnSojDGaPXu2SkpK3K/n5ORo1qxZsixLPXr08Hb3AAAAQIN5XQRfeOGFMsZowYIF1V6//fbbJVUWx+ecc47uv/9+3XnnncrMzNSWLVskSTfffLO3uwcAAAAazOt5gnNyctSrVy8ZY/Tvf/9bHTt2dL93yy236PXXX6/c0f8b+lC1u9GjR2vBggVyuXjsLwAAABqX10XwmfzP//yPXnvtNW3cuFFlZWXq3Lmzbr75Zk2aNEnh4T55ajMAAADQIH4vggEAAACn8XoswksvveSeKxgAAAAIBF73BLtcLkVERGj06NEaO3asrrzySvc8wAAAAIAT+aQIlv5741t8fLyuuuoqjR07ViNHjuTGNwAAADiO1xXq0qVLdddddyktLU3GGOXn5+utt97SxRdfrFatWunee+/VihUrfBErAKCR7dq1S7/5zW/Uvn17xcTEqGPHjpo+fbpOnDhhd2gA4BWvi+D+/ftrzpw52rt3rz777DPddNNNiouLkzFGBw8e1LPPPqv+/furS5cueuSRR7Rt2zZfxF0vJG8A8M7mzZtVUVGhl19+WRs3btRf/vIXvfTSS3rggQfsDg0AvOKX2SGKi4v1j3/8Q2+//bYWLlzoLjqrhkz07dtXN910k66//no1a9bM17t3+/zzz/W3v/1NN954ozp16qQNGzbo1ltv1bhx4/TUU0/5bb8AEMyefPJJvfjii9qxY4fdoQCAx/w+RdqxY8f097//XW+//baWLFnifliGZVkKDw+v9kjlxkDyBgDvPPjgg/r888+1cuXKWpcpKSk5Lb9HRUUpKirK3+EBQL34/a615ORk3Xrrrfrmm2+UlZWlWbNmKSkpScYYlZWV+Xv3p8nNzVXTpk3rXKakpER5eXnVfhq7WAcAJ9q2bZvmzJmj2267rc7lZs6cqcTExGo/M2fObKQoAeDMGm3qhg0bNui5557T888/r9zc3MbabTUkbwCoNHXqVFmWVefP5s2bq62zd+9eXXTRRbruuut066231rn9adOmKTc3t9rPtGnT/PmRAKBB/DocIisrS3/961/1zjvvaMOGDZLkHg4RExOjyy+/XO+++26Dtzt16lTNmjWrzmV++ukndevWzf373r17NWzYMA0fPlyvvfZanevyNR6AYHf48GFlZ2fXuUyHDh0UGRkpSdq3b5+GDx+u/v37a+7cuUx/CSDg+bwIPnbsmN577z29/fbbWrp0qYwx7sI3LCxMF1xwgcaOHatrrrlG8fHxHu2D5A0AjWfv3r0aMWKE+vTpo7feekthYWF2hwQAXvNJEVxUVKT58+frnXfe0RdffKHS0lJJ/+317du3r8aOHasbbrhB6enp3u6uQUjeAOC5vXv3avjw4crIyNC8efOq5dDmzZvbGBkAeMfrInjcuHGaP3++CgoKJP238O3YsaPGjh2rsWPHqnPnzt5H6gGSNwB4Z+7cufrVr35V43t+nlwIAPzKZ49NlqRmzZrp+uuv19ixY3X++ed7HZy3SN4AAACoiddFcEJCgq6++mqNHTtWI0eOZLgBAAAAHM/rIrioqEgxMTG+igcAAADwO78/MQ4AAABwGuYKAwAAQMihCAYAAEDIoQgGAABAyKEIBgAAQMihCAYAAEDIoQgGAABAyKEIBgAAQMihCAYAAEDIoQgGAABAyKEIBgAAQMihCAYAAEDIoQgGAABAyKEIBgAAQMihCAYAAEDIoQgGAABAyKEIBgAAQMihCAYAAEDIoQgGAABAyKEIBgAAQMihCAYAAEDIoQgGAABAyKEIBgAAQMihCAYAAEDIoQgGAABAyKEIBgAAQMihCAYAAEDIoQgGAABAvZWUl9gdgk9QBAMAAKDeKkyF3SH4BEUwgJBy/MRxu0MAgIBmjLE7BJ+gCAYQUo6XUgQDgDeMKIIBIOCUVpSqtKLU7jAAIGCVm/KgGBJBEQwg5BSWFtodAgAEtBPlJ+wOwWsUwQBCDkMiAMA7wTBDBEUwgJDDzXEA4J2isiK7Q/AaRTCAkJN/It/uEAAgoBWUFtgdgtcoggGEnGBI3gBgp6PFR+0OwWsUwQBCDmOCAcA7+wv2B/wMERTBAEJOYVmhSsuZJg0APFVSXqKsvCy7w/AKRTCAkHS46LDdIQBAQNuYvVFlFWV2h+GxcLsDAAA77MrbpZbxLe0OAwACRm5urtavX6+C0gJt3r9Zbbu21eYmm3V26tl2h+YRimAAIaEqeR8sPKjNRzarsGuhzko5S4lRiXaH5nhXXHGF1q5dq0OHDik5OVkjR47UrFmz1LIlf0QAoWT9+vUaMmSI+/dH3n5E8QnxapPQJiBzKcMhAISEquR97ehr9dDYh5T1nyytPrhaxhi7Q3O8ESNG6L333tN//vMfffDBB9q+fbuuvfZau8MC4AAVqtDy/csDclhE0BfBV1xxhdq2bavo6Gi1aNFC48aN0759++wOC4ADHCk+oq05W+0Ow/Huvfde9e/fXxkZGRo4cKCmTp2qZcuWqbSUmwsBSLkncrXm0Bq7w2iwoC+C6cEAUJf1h9cruyjb7jACxtGjR/X2229r4MCBioiIqHW5kpIS5eXlVfspKQn8x6wCqNmuvF3alL3J7jAaJOiLYHowANSlQhVaum+p8k7k2R2Ko02ZMkVxcXFKSUlRVlaW5s+fX+fyM2fOVGJiYrWfmTNnNlK0AOywMXujdubutDuMegv6Ivhk9GAAqElxebEW/bxIuSW5dofSaKZOnSrLsur82bx5s3v5+++/X2vWrNEXX3yhsLAw3XzzzXWOp542bZpyc3Or/UybNq0xPhoAG606uEo/5/9sdxj1EhKzQ0yZMkXPPfecCgsL1b9/f33yySd1Lj9z5kzNmDGj2mvTp0/Xww8/7McoAdipuLxYX//8tQa0GKD0uHS7w/G7++67TxMmTKhzmQ4dOrj/nZqaqtTUVHXp0kXdu3dXmzZttGzZMg0YMKDGdaOiohQVFeXLkAEEACOj5fuXq7SiVB0SO5x5BRsFZE8wPRgA/KG0olTf7v1W23O22x2K36Wlpalbt251/kRGRta4bkVF5aNS+YYMQE2MjFYdXKVVB1c5etaIgOwJpgcDgL9UqEKrD63WseJj6tWsl8JdAZkmfWb58uVasWKFBg8erOTkZG3fvl1//OMf1bFjx1pzKABI0o7cHTpcdFjnNz9fTaOb2h3OaQIyu6elpSktLc2jdenBAFAfO/N26kjxEccm78YSGxurDz/8UNOnT1dBQYFatGihiy66SA8++CCdBQDOKP9Evv6d9W91T+mu7k27y2U5ZxBCQBbB9UUPBgBvVCXvzsmddVbKWSHZK5yZmal///vfdocBIIAZGW3K3qQDBQfUr3k/xUfG2x2SpAAdE1xfVT0YF154obp27arf/OY3Ouecc7Ro0SJ6MADUi5HRlmNbtHDXQu0/vt/ucAAgYB0tPqovd3+prLwsu0ORFOQ9wfRgAPCVwrJCfbvvW7VNaKvezXorMqzmm8YAALUrM2VafmC5DhYeVO9mvW39hi2oe4IBwNey8rP0xe4vdKToiN2hAECjOnVmLaPaZ9o6k115u/TvrH/r+Inj3oblMYpgACHBl8m7qKxIi/cs1p78Pd6GBQCOl5OTo9mzZ+uGG26o9voz9z6jT+d9qoK8Ao+2m3siV19lfWVbp4Jl6powFwACXE5OjubNm6cnnnhC+/btc7+e3CxZV95ypYZdPUxxTeI82rYlS/1a9FObhDa+ChcAHGXhwoUaM2aMCgsLa3zGgmVZioyJ1H3P3qdeQ3p5tI9wK1yDWw1WWqxnM395iiIYQNBqjOTtkkv9WvRT64TWXkYLAM6ycOFCXXrppTLGuKeYrUnVg8qmvjLV41wa4YrQBW0vUJPIJh5G23AUwQCCUmMmb0uWzk0/1/GPCAWA+srJyVHr1q1VVFRUZw6tUtWp8NKilzz+di0hIkEXtL2g0W48ZkwwgKCTk5OjMWPGnLEAlirHChtj9PTdT3s8rq3qEaE/Hv6xzkeyA0CgmDdvngoLC+tVAEuVufRE0Qkt+niRx/vML83X9/u+V3lFucfbaAiKYABBx47kLUn/OfYf/XDgB1WY+u0XAJzIGKM5c+Z4tO5nb37mVWfAoaJDWr5/eaPkUYpgAEHFzuQtVU6h9uPhH73aBgDYKTs7W9u3b29wPjTG6GDWQR3P8W7as70Fexslj1IEAwgqdidvSdqas1WFpYVebwcA7HD8uHd5sKigyOsYtuZsVW5JrtfbqQtFMICg4oTkLUnHS+2bAB4AvBEfH+/V+jFxMT6J41DhIZ9spzYUwQCCihOSt0suJUYler0dALBDSkqKOnbsKMuyGrSeZVlKb5uu+CTv8nCV6LBon2ynNhTBAIKKE5J364TWigqL8no7AGAHy7I0ceJEj9a9eNzFDc6/NUmKSlLL+JZeb6cuFMEAgordyduSpe4p3b3aBgDYbfz48YqNjZXLVb9S0XJVzhM87KphXu+7RVwLDW09VGGuMK+3VReKYABBx87kndEko1GfeAQA/pCUlKQPPvhAlmWdMZdaliVLln4/5/cePyhDkmLCY9S/eX8NbjW4Ub5NowgGEHTsSN6SFOmKVGZqplfbAACnGD16tBYsWKCYmJhavyWrelLctFenqefgnh7txyWXejTtoYvaXaQ2Tdp4E3KD8NhkAEFr4cKFGjNmjAoLC2ucMq0qef9+zu89Tt4nG9RykN/HsAFAY8vJydEbb7yhWbNmad++fe7Xk5sl68pbr9Twq4crNiHWo20nRSXp/Obn23IzMUUwgKDmz+R9sj7N+qhDUgevtwMATrVkyRINHTrU/fuMt2eoe1/P74HonNRZmamZfh/7W5twW/YKAI0kKSlJd999t3r37l0ted/zl3u8St5VosOidV7z89Q8rrnX2wIAJzt1SIQlz24kjg6LVt/0vmoR38IXYXmMIhhASPBV8j5ZuybtdE7aOUyHBgD11CGxgzJTMxUZFml3KBTBANBQiZGJOjf9XKXGpNodCgAEhOSoZPVu1lspMSl2h+JGEQwA9VQ1B3D3pt3lsphcBwDOJMwK01kpZ6lzcmfH5U2KYACoh0hXpAa0HKBmsc3sDgUAAkJiZKL6t+zv2LnTKYIB4AxiwmM0pNUQW6bwAYBA1Cahjfqm91W4y7mlpnMjAwAHaBLZRENaDVFshPfTqAFAKOic1Fk903p6/Rh6f6MIBoBatElooz7pfRThirA7FAAICF2Su6hnmvcPH2oMFMEAcIoIV4R6pfVSu8R2docCAAGjfZP2AVMASxTBAFBNemy6+qb3ZfgDADRAemy6zk0/1+4wGoQiGABUOf1ZZmqmuiR3cfw4NgBwkuiwaPVr0c9xU6CdCUUwgJAXboVrQMsBPPoYADxwXvPzAvLJmRTBAEKaSy4KYACoh8zMTC1ZskQFpQX6Yf8Patu1rdo3aR+w+ZMiGEBIOzv17IBN4ADQmBITEzV48GDlnchT3q48RbgilJmWaXdYHguswRsA4EMJkQnqnNzZ7jAAICC1T2wfkMMgqtATDCAkVH2Nd7DwoDYd2aS2XduqS3KXgLuRAwCcok1CG7tD8ApFMICQUPU13s/5P6tif4XCrfCAT+AAYJeosCglRyXbHYZX6AIBEJKaxzXnSXAA4KHU6NSAn06SIhhASKIXGAA81zSmqd0heI0iGEDICbfCmRECALyQEJlgdwheowgGEHJSYlIU7uKWCADwVHxEvN0heI0iGEDISY1JtTsEAAho0eHRdofgNYpgACEn0O9oBgC7BcONxRTBAEJOXGSc3SEAQMByyRUUc6wH/icAgAaKDY+1O4SAVFJSol69esmyLK1du9bucADYJNCnRqtCEQwgpES4IrgpzkN/+MMf1LJlS7vDAGAzSxTBABBwgmEcmx0+++wzffHFF3rqqafsDgWAzYJhKIQUQkUwX+MBkCof9YmGOXjwoG699Va9+eabio2t31CSkpIS5eXlVfspKSnxc6QAGkOYFWZ3CD4RMkUwX+MBkMRQiAYyxmjChAm6/fbb1bdv33qvN3PmTCUmJlb7mTlzph8jBdBYGBMcQPgaD0CVYOnB8NbUqVNlWVadP5s3b9acOXOUn5+vadOmNWj706ZNU25ubrWfhm4DAPzJMsYYu4Pwp4MHD6pPnz76+OOPlZqaqvbt22vNmjXq1atXreuUlJSc9rVdVFSUoqL4GhUIdMaYoOnF8Mbhw4eVnZ1d5zIdOnTQL3/5S/3zn/+s9v+svLxcYWFhGjt2rObNm+fvUAE4TLDk0aAugo0xuuSSSzRo0CA9+OCD2rVrV72K4IcfflgzZsyo9tr06dP18MMP+zdgAHCYrKws5eXluX/ft2+fRo8erb///e/q16+fWrdubWN0AOC5gBwcN3XqVM2aNavOZX766Sd98cUXHn+NN3ny5Gqv0QsMIBS1bdu22u/x8fGSpI4dO1IAAwhoAdkTzNd4AGCP+n6jBgBOF5BFcH3xNR4AAABqEpDDIeqLr/EAAABQk5CYIg0AAAA4WVAPhwAAAABqQk8wAAAAQg5FMAAAAEIORTAAAABCDkUwAAAAQg5FMAAAAEIORTAAAABCDkVwACspKdHDDz+skpISu0PBKWgbZ6JdcCqOCeeibZwpmNqFeYIDWF5enhITE5Wbm6smTZrYHQ5OQts4E+2CU3FMOBdt40zB1C70BAMAACDkUAQDAAAg5FAEAwAAIORQBAewqKgoTZ8+XVFRUXaHglPQNs5Eu+BUHBPORds4UzC1CzfGAQAAIOTQEwwAAICQQxEMAACAkEMRDAAAgJBDEQwAAICQQxEMAACAkEMRDAAAgJBDEQwAAICQQxEMAACAkEMRDDjUyc+xKS8vtzESAAhM5FHUhSIYkqSKigq7Q8ApLMty//vUBzvyoEf71Pb/njYBedR5yKPO5YRcymOTQ9yePXuUkJCgxMREVVRUyOXi7yInOHTokFauXKkPP/xQe/bs0bFjx3TllVeqb9++GjVqlKTKRHFygkfjqCp0Nm7cKElKTk5W69atJdEmoYo86kzkUWdzQi6lCA5hP/74oy666CINGDBAr7/+OgncIbZs2aI777xTmzZt0oEDB6q916FDB/3617/WAw88IIkE3tiysrL0+uuv64svvtC6detUVFSkrl27atSoUZo9e7Yk2iTUkEediTzqbI7JpQYhaePGjaZJkybGsiyTnJxsbr75ZnPs2DFjjDHl5eX2BhfCNm7caFq2bGksyzJpaWmmY8eOpl+/fiYzM9NYlmVcLpeJjY01TzzxhN2hhpxNmzaZ3r17m4iICGNZ1mn/HTdunN0hopGRR52JPOpsTsqlFMEhaO/evWbgwIHGsiwTGRlpLMsySUlJJHCbbd261bRt29ZYlmVGjBhhPv30U5OTk2OMMaagoMBMmTLFncB79+5tVq5caXPEoWPTpk0mLS3NWJZlunfvbm666Sbz0UcfmQceeMCcc845xuVyGcuyzOOPP253qGgk5FFnIo86m9NyKUVwiCksLDSPP/64SU1NNVFRUWbEiBHGsix6Mmx26NAhc+WVVxrLsszgwYPNhg0b3O+d3A533323u71effVVO0INOdu2bTPdunVzX1QXL15s8vPz3e//9a9/NcnJycayLHPdddeZiooKG6NFYyCPOhN51NmcmEspgkPMjz/+aM4//3xjWZZp06aNyc/PN1999RUJ3CZVJ/k777xjWrRoYVq1amXef/99U1ZWVm25qt+zsrJMjx49jGVZZuTIkaa8vPy0ZeE7Bw4cMJdccomxLMsMHDjQrFu3zv1eaWmp+99VF94+ffpUS+oITuRRZyGPOp9Tcykj90PMn//8Z61YsUJRUVF67bXXFB8frwsuuEDz58+XJOXk5Oif//ynJk2apJycHLlcLqb98aOqQf9z587VgQMH1L17dw0bNkxhYWHVlqv6vVmzZkpNTZUk7d+/XxUVFactC984ceKE3n33XX377bfq3Lmzpk6dqrPOOsv9fnh4uCTp6NGjysrKkiQNHjxY8fHxKi8v19GjR22JG/5HHnUW8qizOTmXUgSHmNGjR6tZs2a6++67NWzYMJWXl6uiokKXX365/vGPf0gigTe2RYsWadmyZYqIiNC0adOUlpZW43IVFRWKiorSiBEj5HK5VFBQoOzs7EaONnRkZ2dr7ty5ys/P17hx4zR8+PBqF8qqife3bdumY8eOKSEhQREREZowYYKGDx+ukSNHauzYsfrkk0+Um5tr18eAH5BHnYc86lyOzqV+72uG4yxfvtysWLHC/XtFRYX766R//vOffKVng0ceecSMGDHC7Nq164zLPvvss8ayLJOYmGi2bdvWCNGFrmnTpplWrVqZ7du3V3u96uu7EydOuMeDVt0clZKS4j6HLMsy3bp1Mw888ID7PEJwII86D3nUuZyaSymCQ0hdg8wbmsC5+cc3Tr4gLl68uF7rzJ0711iWZZo0aWK2bNnir9BgKttn//79xpj/HvMnH/uXXXaZsSzLpKammilTpphPPvnEHD161Hz22WdmxowZJiwszFiWZTp16mReeOGFamPfEJjIo85DHnU+p+ZSimC41TeBn3zw7dmzxxQVFdkRbtA4tWfoTBfGhQsXmsjISNO0aVPz448/nnGb9Dz5x9VXX20syzItW7Y0H374ocnNza32fllZmXnjjTfcyXvo0KGcKyGAPGoP8mjgsjOXMiYYblU3FxhjdNlll9U4tu3o0aPuQew//fST/vjHP2rcuHE6duyYbXEHulOfLHWmJ+SEhYWptLRUxcXFKioqOu398vJyzZ49W7fcckuN24f3iouLdd5556lt27Z68cUXdfXVV6tJkyaS/vso0LCwMF177bW666675HK5tGTJEn3yySd2ho1GQB61B3k0MNmeS31SSsPRTv0L9kx/0dbVkzF+/HiTl5dnfv7552pzLf7v//6vv8IPWg1tlyqffvqpsSzLREdHmx9++KHae2VlZeaVV15xt8u7777rs3hR3fHjx01WVtYZl6v62tWyLPM///M/jRAZ/IE86kzk0cBnZy7lT5sgV15eLpfLpf379+vzzz+XVPkXrTGm1nXq6sn4xz/+oXHjxunRRx/VnDlzJEl33323JkyY4N8PEmQ8aZcqSUlJioyMlGVZ1XowysvL9dprr+m2226TJE2aNEnXX3+9fz5AEDv1Dv7a7uiPi4tTmzZtat1OWVmZJCkzM9M9HVNcXJyPokRjIo86E3nU2QIil/qklIaj7dixw7Rq1cr06NHDzJ8/3/36mcZMndyT8cknn7j/AouLi3P/e9KkSe7luemnYTxtl7Vr15r4+HgTFhZmPvvsM2NM5Z21L730krtd7rnnHvfytEv9VU2Yv2/fPvf/W2O8u4HpqaeecrfLqlWrvI4R9iCPOhN51JkCJZdSBAexqq+FHn/8cWNZlomKijIDBgwwH374oXuZMx2QJz9F54UXXnA/19uyLDNx4sQal0PdvG2X5cuXm8jISBMeHm4WLlxojDHm5ZdfrjFx0y4N5+lFtSY7d+40l112mXG5XGb8+PGmpKSEGQECDHnUmcijzhcIuZQiOAhVnbBV/y0uLja33XabiYuLMy6XywwcOLBBCdwYYzZs2GCmTZvmThC/+93vTtsf6uardqnqwQgPDzfz5883b775JonbB3xR7JyspKTEPPnkk8ayLBMfH8+4wgBDHnUm8qjzBVIupQgOMid/BXH//febfv36mdTUVNO+fXv3CR4WFmYGDx5c7wNyw4YN1W7euPvuu0/bH+rmy3bZsWOHSUlJMWFhYeaCCy4gcXvJVxfVk2/Iyc7ONnPmzHG3zcMPP3zG9eEc5FFnIo86WyDmUorgIFJ14Ozevdv06NHDWJZlWrVqZQYNGmQmT55sBg8ebLp3796gRHHw4EEzZcqUGseukSDqx9ftsnXrVpOcnFztK1USt2f8UewsWrSoWrFz3333ud9jrlHnI486E3nU2QI1l1IEB5n9+/ebXr16GcuyTL9+/cw333xjcnJyjDHG5Ofnm/Xr15tx48YZy7KMy+UygwYNqvOAPHLkiLn33nuNZVnmzjvvdL9OgmgYX7bL5s2bTbt27dzJm69UPePLi2pxcbFZunSpmThxosnIyDAul8tERESYRx991L0MbRM4yKPORB51pkDOpRTBQaLqIHrqqadMRESE6dChg1m0aJH7/VP/avr9739f77/MNm/ebD766CP37ySI+vNXu9x6660kbh/w5UW16iu7qq/9XnvtNfd7tE1gII86E3nU+QI1l1IEB5kxY8a4D8Ls7OzT3j/5AKrqmWjIVxQkCM/4ql1OnDjh/vfJNwfQLg3jy4vqyf/vZ8yYYV5++WWzevXqWrcF5yOPOhN51HkCPZdSBAeJ8vJyk5ub6/5L7Pbbb6912ZMPtOuuu67aX1ye3r2JmvmjXUpKSmpdDw3jq4vqqW1ShXMosJBHnYk86nyBmkt5YlyQcLlcatKkiZo3by5J2rZtm4qKilReXn7asmFhYe7Xb731VjVr1kzGGC1btkxPP/20Pv30U0lnfvY6zswf7RIZGXnaemiYiooK5eXlafv27ZKk3r17q2nTpqctd3Kb/PnPf9a1116riooKLV26VE899ZQ++ugjSZVtUlObcg4FFvKoM5FHnSvQcylFcIAytTwWsipJrF27Vj///LPCwsJqfFRh1Qnfs2dPRUZGKiIiQpGRkfr+++/1wgsvaNmyZf4LPojRLoHBHxdVLqKBh/PVmWiXwBHouZQiOMBUPUO76r9VB1RV0vjtb3+rtm3bKjs7WxMnTtThw4flcrlqfWZ3s2bN1LZtW40aNUoTJ06UMUYLFy7Uxx9/rJKSkno9gx20i9NxUcXJOF+diXZxvqDLpX4ZZAG/qBoTs2XLFtOxY0ezbNkyY0z1weL79u0zV155pbEsy6SmpppJkya5x+fUNKi86qk5Q4YMMcYY89vf/tY9Vudf//qXvz9SUKBdnKu0tNQY898bYarGpVW12dKlS01GRoaxLMuMGjXKHDp0yBhT9w0YgwYNMpdddpm5//77jWVZJjw83EyZMsUUFxcz/jMAcL46E+3ibMGaSymCA0xubq4ZMmSIsSzLJCUlmVWrVhljKg/IqoNm5cqVJiUlxViWZVq3bm3uuusuc/DgQfdyVYqLi83jjz9uXC6Xuffee40xxhw6dMgMGDDAWJZlbrzxxmrbRe1oF+fhooracL46E+3iTMGcSymCA0xOTo555JFHTGZmprEsy8TFxbkTRWlpqftgW7RokYmKijKWZZmEhATTr18/s3z5cvPzzz8bYyoTyaxZs9xPYnn//feNMcYUFRWZq666yliWZXr37l3rnZqojnZxJi6qqAnnqzPRLs4VrLmUIjgAHTt2zDz11FOmW7duNSaKqoNt8eLFpnXr1iYiIsJYlmUSExNNmzZtzKBBg0xqaqo7QTzwwAPVtv/oo4+asLAwk5mZaY4dO9bYHy9g0S7Ow0UVteF8dSbaxZmCNZdSBAeYqr+Mjh49Wq9EsXbtWjNhwgTTqVMn90FX9ZOammoefPDBatvPzs42Q4cONZZlmUsvvbRxP1wAo12ci4sqTsX56ky0i7MFYy6lCA5A9U0UVX+ZHTt2zOzcudM88sgj5p577jE33nijefbZZ81XX33l3mbVsp9//rlp166dSUhIMC+++GK1/aFutIvzcFFFbThfnYl2caZgzaUUwQ506l2XNT3Jpr4HZENO8E2bNplhw4YZy6p86svevXu9/ShBhXYJTFxUQxPnqzPRLoErGHMpRbDDVB0QO3fuNL/73e/MkSNHqr1+sroOyJUrVxpjak4wpyahnJwc88knn5hBgwYZy7JMixYtzJYtW3z/4QIY7eJcXFRxKs5XZ6JdnC0UcylFsAPt27fPtGrVyliWZSZMmGCOHj1qjDlzonj44YdNixYtjGVZJjY21p0o6pqn79NPPzUXX3yx6dChg7Esy5x11llm8+bNfvhUgY92cR4uqqgN56sz0S7OFKq5lCLYIU7+q2n16tWmR48eJjo62sTExJhx48bVK1Hs3bvXPcXIqX+Z1XRAlpaWmscee8w9Rueaa64xO3bs8MfHC1i0i/NxUUUVzldnol0CQyjmUopgB6g6gY8cOWIKCgqMMcb88MMPZvDgwSYsLKxBieL99993n/RViaJqYuvavP322+bDDz90T2yNSrSLc3FRxak4X52JdnG2UM+lFME2qzqwdu3aZbp27WquueYac/z4cWNM5Xx69U0UVQfjl19+aVwul/nlL39pzjnnHGNZlnsyatQf7eJcXFRxKs5XZ6JdnI1cShHsCAcOHDDnnnuusSzL9OnTx7z33nvug23VqlVm0KBB9T4g//d//9eEhYWZt956yzz22GNmxowZjfpZggnt4jxcVFEbzldnol2ciVxaiSLYRhUVFaasrMz88Y9/NJZlmfPOO8989913Jjc3t9pyq1atOu2ArPrL6eSnqhw7dszcfPPNxrIss2HDhmoHa2lpaeN8qCBAuzgbF1WcjPPVmWgX5yOXUgTb4tQxMhdccIFJSkoyX3/9dbXXTx6rc3KiiIqKMldffbU5dOiQ+/2cnBzz3HPPGZfLZXr27Gn27dtX43ZQO9rF2bio4mScr85EuzgfufS/KIIb2clfQfzlL38xixcvNs2bNzcDBgww+fn5da67evVqM2zYMPdzuTMyMsxDDz1kpk2bZsaNG+celP7UU081xkcJKrSLc3FRxak4X52JdnE2cunpKIJtsG/fPvcJ/dBDD5n09HRz4403GmNqvpPyZOvWrTM33HCDadmypbEsy8TExBjLskxYWJixLMvcd9997mUD4QB0EtrFebioojacr85EuzgTubRmFMF+VFFRUeOJmpWVZXr06OGeUy8yMtJceeWV9d7u7t27zZtvvmn69etn0tPTjWVZZvTo0ebpp592L3OmZBPKaJfAwkU1tHG+OhPtEnjIpaejCPahuiaGrlJ1oO3atavavHqRkZHmyy+/bPA+9+3bZ3bs2GEKCwtP2wcq0S6BgYsqjOF8dSraJXCQS+vPMsYYwac+//xz7dmzRxs2bJBlWRowYIBat26tgQMHVltu165duvHGG7V8+XJFRUXppptu0rRp09ShQ4cz7qO8vFxhYWGnvW6MkWVZPvsswYR2cZaKigq5XK46l6n6/7l7927deOONWrZsmSQpIiJCCxYs0MiRIxu0z/3796u4uFjNmzdXTExMtX3AWThfnYl2cR5yqRfsrMCDSXZ2tvn73/9urrjiChMZGen+C9iyLBMeHm7i4+PNHXfcYVatWlXtrsqdO3eagQMHGsuyTFJSknn00UfNnj17bPwkwYV2cb7PPvvMvPrqq2bSpEnmnnvuMX/729/Md999d9pyO3fuNP379zeWZZno6Ghzyy23mO3bt9drH7X1TgTS13ahgPPVmWiXwEAubTiKYB/YsWOHGT9+vGnTpo2xLMu4XC4TGRlpkpOTTUJCQrWEMWDAAPPEE0+4J6U2pvKAHDRokLEsyyQnJ5vHH3+cROEDtItzcVHFqThfnYl2cTZyqXcogr20efNm06tXL2NZlmnWrJkZNWqU+eKLL8zatWvNvn37zOrVq829997rXsayLNOuXTtzzz33mLy8PPd2Tk4UTZs2JVF4iXZxLi6qOBXnqzPRLs5GLvUeRbAXNmzYYFq1amUsyzL9+vUzb7zxhjl48KAx5vSvDBYtWmQmT57sPiBTU1PNXXfdVesBSaLwHO3iXFxUcSrOV2eiXZyNXOobFMEe2rBhg0lOTjaWZZkLLrjArFy50v1klJPHxpx8R+2xY8fME0884T4g27Ztax555BFTVFTkXqamA3Lv3r2N98ECHO3iXFxUcSrOV2eiXZyNXOo7FMEeWL9+vTtB/OIXvzDbtm1r0Pp//vOf3Qfkueee6546purgPfmATEtLM48//rjZvXu3zz9HsKFdnIuLKk7F+epMtIuzkUt9iyK4gdasWWNSUlKMZVlmxIgRHv21lJ+fbx544AH3ATlu3Dj3e1UH8c6dO83QoUPdyyxcuNBnnyEY0S7OxUUVp+J8dSbaxdnIpb5HEdwAR48eNS1atHD/pXTfffeZ7OxsY0zDpwdZs2aNueCCC9wH5Pvvv3/aMjt37jTdunUz119/vU/iD1a0i3NxUcWpOF+diXZxNnKpf1AEN0BJSYmZPXu2+y+xjh07mpkzZ5rDhw97tL3HH3/cfaA9+uijxpjTk01VEjIm8J7E0lhoF2fiooqacL46E+3iXORS/6EIbqCSkhLzyiuvuKcf8SRRVI3Vyc/PNx07djSWZZnBgwebsrKyWhNBfR5ZGcpoF+fhooracL46E+3iTORS/6EI9oAvEkVVQujWrZuxLMsMHz7cjxGHBtrFebioojacr85EuzgTudQ/wu1+bHMgioyM1Pjx4yVJ9913n3bs2KHXXntNknTLLbcoNTX1jNuwLEsul8v9nO2EhAT/BRwiaBfn8UWbuFwulZeXKyYmRhEREZKk8PDwOp9R73K5fBA9/Inz1ZloF2cil/oHRbCHvDkgjTFyuVwqLi5WeXm5JKlPnz6SpIqKiqA/6PyJdnEeLqqoDeerM9EuzkQu9QN7O6IDnzdfUcyePdtYlmUiIyPNmjVr/B9sCKFdnMfTNqkaq1ZUVOT+evXhhx82xgT/V3WhgvPVmWgXZyKX+g5FsA94ckCuX7/ejBgxwrhcLnPfffcZYxp+lyfqRrs4DxdV1Ibz1ZloF2cil/oGRbCPnOmAPDkB5ObmmunTpxvLsky3bt3M119/bVPUwY92cR4uqqgN56sz0S7ORC71HkWwD9V2QB46dMi9TGlpqXn22Wfd05M8++yzNkYcGmgX5+GiitpwvjoT7eJM5FLvUAT72JkOyBdffNGdIB566CH3eqE6Hqex0C7Ow0UVteF8dSbaxZnIpZ6jCPaDmg7Ixx57zDz55JPuA3Dy5Mnu5YN5ImonoV2ch4sqasP56ky0izORSz1DEewnpx6QLVu2JEE4AO3iPFxUURvOV2eiXZyJXNpwFMF+VHVAJiUluQ/ASZMmud/nALQH7eI8XFRRG85XZ6JdnIlc2jAUwX5WUlJiXn75ZWNZlrn11lvdr3MA2ot2cR4uqqgN56sz0S7ORC6tP8sYY+x+YEewKykp0apVqzRw4EBJUnl5eZ2PKUTjoF2c58SJE5o7d65uv/123XLLLXrllVck0TbgfHUq2sWZyKX1QxHcyDgAnYl2cQ4uqjgTjglnol2chVx6ZhTBAByLpA0A3iOX1owiGAAAACHHZXcAAAAAQGOjCAYAAEDIoQgGAABAyKEIBgAAQMihCAYAAEDIoQgGAABAyKEIBgAAQMihCAYAAEDIoQgGAABAyKEIBgAAQMihCAYAAEDI+f8BnZeW2PVOEHAAAAAASUVORK5CYII=",
-      "text/plain": [
-       "<Figure size 800x400 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "f_forest_drug_profiles, axes  = plt.subplots(1, 2, figsize = [8, 4])\n",
-    "['Drug1', 'Drug2', 'Drug3']\n",
-    "forest_plot(contrasts, contrast_labels = ['Drug1', 'Drug2', 'Drug3'], ax = axes[0])\n",
-    "forest_plot(paired_contrasts, contrast_labels = ['Drug1', 'Drug2', 'Drug3'], ax = axes[1])\n",
-    "axes[0].set_title('Unpaired', fontsize = 20)\n",
-    "axes[1].set_ylabel('')\n",
-    "axes[1].set_title('Paired', fontsize = 20)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "829f0d03",
-   "metadata": {},
-   "source": [
-    "### Four different plots, 3 ``delta delta`` and 1 ``forest plot``"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "0e0d544f",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Text(0.0, 1.0, 'Forest plot')"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1800x1800 with 7 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "f_forest_drug_profiles, axes  = plt.subplots(2, 2, figsize=[18, 18])\n",
-    "contrast_labels1 = ['Drug1', 'Drug2', 'Drug3']\n",
-    "unpaired_delta_01.mean_diff.plot(                  \n",
-    "                    contrast_label='Mean Diff',\n",
-    "                    fig_size = (5, 5),\n",
-    "                    raw_marker_size = 5,\n",
-    "                    es_marker_size = 5,\n",
-    "                    color_col='Genotype',\n",
-    "                    ax = axes[0,0]\n",
-    ")\n",
-    "\n",
-    "unpaired_delta_02.mean_diff.plot(                  \n",
-    "            contrast_label='',\n",
-    "            fig_size = (5, 5),\n",
-    "            raw_marker_size = 5,\n",
-    "            es_marker_size = 5,\n",
-    "            color_col='Genotype',\n",
-    "            ax = axes[0,1]\n",
-    ")\n",
-    "\n",
-    "\n",
-    "unpaired_delta_03.mean_diff.plot(                  \n",
-    "                    contrast_label='Mean Diff',\n",
-    "                    fig_size = (5, 5),\n",
-    "                    raw_marker_size = 5,\n",
-    "                    es_marker_size = 5,\n",
-    "                    color_col='Genotype',\n",
-    "                    ax = axes[1,0]\n",
-    ")\n",
-    "forest_plot(contrasts, contrast_labels = contrast_labels1 , ax = axes[1,1])\n",
-    "axes[0,0].set_title('Drug1 delta2', fontsize = 13, loc='left')\n",
-    "axes[0,0].set_ylabel('')\n",
-    "axes[0,1].set_ylabel('')\n",
-    "axes[0,1].set_title('Drug2 delta2', fontsize = 13, loc='left')\n",
-    "axes[1,0].set_title('Drug3 delta2', fontsize = 13, loc='left')\n",
-    "axes[0,1].set_ylabel('')\n",
-    "axes[1,1].set_title('Forest plot', fontsize = 13, loc='left') "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "964471ab",
-   "metadata": {},
-   "source": [
-    "## Forest plot also supports:\n",
-    "\n",
-    "### ``mini-meta`` comparisons and with the contrast type changed to ``\"mini_meta_delta\"``"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "22bd3eab",
-   "metadata": {},
-   "source": [
-    "### Simulate the datasets for unpaired mini_meta "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "f729136b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def create_mini_meta_dataset(N=20, seed=9999, control_locs=[3, 3.5, 3.25], control_scales=[0.4, 0.75, 0.4], \n",
-    "                             test_locs=[3.5, 2.5, 3], test_scales=[0.5, 0.6, 0.75]):\n",
-    "    np.random.seed(seed)  # Set the seed for reproducibility\n",
-    "\n",
-    "    # Create samples for controls and tests\n",
-    "    controls_tests = []\n",
-    "    for loc, scale in zip(control_locs + test_locs, control_scales + test_scales):\n",
-    "        controls_tests.append(norm.rvs(loc=loc, scale=scale, size=N))\n",
-    "\n",
-    "    # Add a `Gender` column for coloring the data\n",
-    "    gender = ['Female'] * (N // 2) + ['Male'] * (N // 2)\n",
-    "\n",
-    "    # Add an `ID` column for paired data plotting\n",
-    "    id_col = list(range(1, N + 1))\n",
-    "\n",
-    "    # Combine samples and gender into a DataFrame\n",
-    "    df_columns = {f'Control {i+1}': controls_tests[i] for i in range(len(control_locs))}\n",
-    "    df_columns.update({f'Test {i+1}': controls_tests[i + len(control_locs)] for i in range(len(test_locs))})\n",
-    "    df_columns['Gender'] = gender\n",
-    "    df_columns['ID'] = id_col\n",
-    "\n",
-    "    df = pd.DataFrame(df_columns)\n",
-    "\n",
-    "    return df\n",
-    "\n",
-    "# Customizable dataset creation with different arguments\n",
-    "df_mini_meta01 = create_mini_meta_dataset(seed=9999, \n",
-    "                                          control_locs=[3, 3.5, 3.25], \n",
-    "                                          control_scales=[0.4, 0.75, 0.4], \n",
-    "                                          test_locs=[3.5, 2.5, 3], \n",
-    "                                          test_scales=[0.5, 0.6, 0.75])\n",
-    "\n",
-    "df_mini_meta02 = create_mini_meta_dataset(seed=9999, \n",
-    "                                          control_locs=[4, 2, 3.25], \n",
-    "                                          control_scales=[0.3, 0.75, 0.45], \n",
-    "                                          test_locs=[2, 1.5, 2.75], \n",
-    "                                          test_scales=[0.5, 0.6, 0.4])\n",
-    "\n",
-    "df_mini_meta03 = create_mini_meta_dataset(seed=9999, \n",
-    "                                          control_locs=[6, 5.5, 4.25], \n",
-    "                                          control_scales=[0.4, 0.75, 0.45], \n",
-    "                                          test_locs=[4.5, 3.5, 3], \n",
-    "                                          test_scales=[0.5, 0.6, 0.9])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "9f68e5fe",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "contrast_mini_meta01 = dabest.load(data = df_mini_meta01,\n",
-    "                                   idx=((\"Control 1\", \"Test 1\"), (\"Control 2\", \"Test 2\"), (\"Control 3\", \"Test 3\")), \n",
-    "                                   mini_meta=True)\n",
-    "contrast_mini_meta02 = dabest.load(data = df_mini_meta02,\n",
-    "                                    idx=((\"Control 1\", \"Test 1\"), (\"Control 2\", \"Test 2\"), (\"Control 3\", \"Test 3\")), \n",
-    "                                    mini_meta=True)\n",
-    "contrast_mini_meta03 = dabest.load(data = df_mini_meta03,\n",
-    "                                   idx=((\"Control 1\", \"Test 1\"), (\"Control 2\", \"Test 2\"), (\"Control 3\", \"Test 3\")),\n",
-    "                                    mini_meta=True)\n",
-    "contrasts_mini_meta = [contrast_mini_meta01, contrast_mini_meta02, contrast_mini_meta03]    \n",
-    "                              "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e04e1ac4",
-   "metadata": {},
-   "source": [
-    "## Use the contrast list and forest_plot() function to generate figures"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c760a179",
-   "metadata": {},
-   "source": [
-    "### Verticle (default) Layout"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "9deb1001",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 450x400 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "forest_plot(contrasts_mini_meta, contrast_type='mini_meta', contrast_labels=['mini_meta1', 'mini_meta2', 'mini_meta3']);"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0eb263d3",
-   "metadata": {},
-   "source": [
-    "### Horizontal Layout"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "89af4a33",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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xtra2YC3NJyktiVP3TuX5VnsP7j9gWIthpCSmPNGzWp1Oh52jHd/v/j5Pu5edbJyoXbw2ZV3KFslfpITI72wsXQFhGhnD9s8//2T//v0sXryY1NRUSpQoQaVKlZg3bx4+Pj4YDAasrApvd2RCagKXYy5zKeYSaSrvV2ravWH3E4ctpP9sUhJT2L1xNy+9+VKe1SMhLYGDdw7ibONMOddylHIuhbejt3Q1C5FPSAu3EMoYtt988w0zZswgOjqalJSUTMdVqlSJP/74g0qVKhW60E1KS+L2g9vcjLtJeEI4ivT/zRPiErh+4XqeXUehmDNmDtHh0U99rmcJT0Z/PRodedsaLV+1PE6uTgBY66zxsPfAy8ELD3sPPOw9cLN3kxAWwgIkcAuZjGH7+eef8//+3/8DoEGDBtSrVw8/Pz+WL1/OtWvXuH//Pi+88AJ79uzB0dExz+uSnJxMcnJyptfs7e2xt7d/5rKVUqQZ0kgxpJCsTyYpLYkHqQ+4n3KfqKQoYpJjsjzvfMh5Jveb/MzXz88+Wf4J1RpUy/Z9a5017vbueNh74GLrgpOtEw7WDthZ22FnbYe9tb0EshAmIF3KhYwxbL/99lstbCdNmkTPnj2pXbs2AO3bt2fs2LEEBQVx5coVTpw4QZMmTfK8LtOnT2fatGmZXpsyZQpTp0596rL0Bj3J+mQMyoBe6TEog9ZqBXCwccDBxgFvR28quFfIthznG3n3zDS/eqHkCzQp//Q/T73Sk5iWSFJaEtZW1tjobHCydTJBDYUomiRwC6FffvmFCRMmAOkBN2bMGNzc3ABITU2lTp06vPfee+zcuZPo6GgiIjIvE5hXo5cnTJjABx98kOm13LZura2scbJ69g9/N3u3Zy4jv3Ozd8PLwcvS1RBCPEQCt5A5f/48ixYt4sGDBwwaNIjBgwdrYQtga5s+bSQ6OhqlFFZWVlSuXFl7Py+f5eZV93FeqlWrFsHBwXlWnlKK3r17c/v27ac+18fHh1WrVuX5iOJatWrlaXlCiLwhgVuIKKXYuXMn27dvp2HDhgwcOJBy5co9cozBYODkyZNYW1vTrl079u/fz5w5cwgPD8fW1pY33niDmjVr8txzz1nmRkzI3d2dF198MU/L/PjjjxkzZsxTLd+o0+kYN24c/v7+eVoXIUT+JYOmCpldu3bRunVrPvroI2bOnJnpPWPrdcuWLbz22mta1/HD/wt4e3vTsWNHxo4dS926dc1Y+4IpJiaGsmXLkpiYiMHw+N2GrKyscHR05ObNm3h4eJi+gkKIfEECtxA6ffo0NWvWBMgUqjqdjuDgYFq3bo1er+fFF18kICCAzp07ExcXx99//82SJUu4ePEibm5u9OrVi//85z+PtJLFo7Zt28bLL7+s9SBkx8rKCp1Ox2+//Ub79u3NWEMhhKVJ4BZiD68idfToURo0aABAjx49mDx5MlWrVtWe66alpXH69Gn69+/P2bNnKVGiBD/99BMvvfRSkV0G8mls27aN7t27k5CQvm1eVmspOzk5sX79eglbIYogmWxXiD28ZGN8fDyenp506tSJOXPmULNmTWxtbbUWmY2NDXXr1mX69OmUKFGC8PBw5s6dS1pamoTtE+jQoQM3b95kzpw5VKxYMdN7FStWZM6cOdy6dUvCVogiSlq4RUhaWhqhoaEUK1aM4sWLZ3vc7du3ad++PWfPnqVRo0bs3LnTJAtjFGZKKaKiooiLi8PV1RUvLy/5pUWIIk5GKRchNjY2VK9ePcdj9Ho9Pj4+VKtWjbNnz+Lo6ChBkQs6nQ5vb2+8vb0tXRUhRD4hXcoiE2tra+7du8eJEycAKFmyJA4ODhaulRBCFHwSuIVAxlGxxicEuX1SkJaWxtatWwkLC6Ns2bK8/fbbz1SeEEKIdBK4BVTGkLWysiI+Pp7Y2FhiY2OBf0fFPm1QXrlyhWXLlhEfH0/dunWpUaNGpvKEEELkjgyaKoAyLr/4119/sWvXLjZs2EBqaire3t68/PLL9O/fn3LlymFlZZXjco3G6T4Gg4ErV64wbtw4NmzYQMmSJfnjjz+oU6eOOW9NCCEKLQncAiZjeP7www+MHz+e+Ph40tIyb6zevn17evfuTb9+/bSpP9mFbmxsLDt27GD+/PkEBQXh4OBAUFAQTZo0kfm3QgiRR2SUcgFi3GwA4H//+x/vv/8+AP7+/tSqVYvatWtz584dpk2bxp9//smNGzcIDw9n9OjR2NnZPRK6t27d4p9//uHzzz/n9OnT3LhxAz8/P5YtW0ajRo0K3ab0QghhSRK4BYixpblq1SotbEePHs1bb72lPWuF9B2BJk+ezLlz51iwYAF6vZ6xY8c+Erq3b99m5MiRHD16FG9vb1577TU+/fRTatasKWErhBB5TLqUC5hjx44xePBgTpw4wYcffsikSZNwd3cH0kcY79u3j7feeovLly9jY2NDWloafn5+DBo0KMvQXbVqFb/++iudOnWidevW+Pj4SDeyEEKYgARuATNnzhwmTpxI69atmTNnDn5+ftp7e/bsYeDAgVy7dg1/f3/ef/99+vfvT0pKCpUrV2bgwIFa6KalpWFjk97BkZqaqq2nLIQQwjSkS7kASUhI4PvvvycpKYkePXrg5+entUaPHj3KoEGDuHbtGk2aNGHbtm04ODgQFxfHkCFDCA0NZdGiRQCPhK6ErRBCmJ60cPOp7J6hrl27lsDAQFavXq2tbxwREYG/vz8XL16kUaNG/PXXXzg7O5OSkoLBYKB3795s3rwZAF9fX4YPH64NpBJCCGEe0sK1MOMWeikpKVoAZuzu3bx5M1ZWVnTu3BlI31avffv2WtgmJSUxatQoLl68SM2aNVm+fDnOzs7o9XqtvPLlywPg6enJP//8w6xZszAYDIwfP97ctyuEEEWWDEO1IKUU1tbWnDp1iilTpnD27FkALWy//vprunTpwqxZs7h8+bJ2npubm/b9zZs3OX78OPb29gwdOpQKFSoA6WsiG+fm1qhRAw8PD0aOHIlOpyMyMpI2bdqY6zaFEEIgLVyL0ul03LhxQ1vNKSUlhWHDhlG5cmXmzZvH2LFjAWjZsiVly5bNsoz9+/dz/vx53Nzc8Pf3x8rKSnuua2NjQ2pqKrt37yYmJoYhQ4ZQv359ihUrRsOGDWU0shBCmJEEroW5u7vTuXNntmzZwpw5c3B0dEQpxfTp0wGYMmUKH330Efb29lme7+XlhZ2dHfb29oSFhQGZ1z0+c+YMJ06coHz58ri4uPDKK68A2T8jFkIIYRryiWthbm5uLFmyhL59+2pBawzbqVOnMm7cOJycnLI938XFhZSUFCIiIti6davW9ayU4uzZs0yZMoXz58/TtGlTbGxstM0MJGyFEMK8ZJSyhWXs1m3UqBHHjx9Hr9fTqFEj5s+fT926dR857mEff/wxs2fPBqBt27ZUr16dyMhILl68SEhICMWLF+fPP/+UjQiEEMKCpEvZwowhOmvWLEJCQrTXDx8+TGBgII6OjlStWjXLsDV2C3/wwQfExMSwcOFCduzYwY4dO7Rj3Nzc2LRpE3Xq1JFntkIIYUHSr5hPxMfHA/Dhhx/Ss2dPlFJ88803fP/991y4cCHLc4zdwqVKleLrr79m2rRp+Pn5aSHdu3dvdu7cSZMmTTAYDBK2QghhQdKlnI/s27ePBg0akJSUxLvvvsvKlSvR6XSMGjWKoUOHUrVq1ceWERYWRkpKCsWKFUMphZOTk7RshRAiH5DAtZCMIZhx0Quj2NjYpwrd5ORkbSSzsatZglYIIfIP6VI2o4y/2+h0Om1hiodHDOv1etzd3fnuu+/o06eP1r28YMGCTN3LBoMBgHv37jFnzhw2btyYqTwJWyGEyD9k0JSZZJz3eujQIU6dOsXmzZtRSuHu7k6nTp0ICAigXLlyWFtbZwpdgJUrV/LNN98AMGTIEJ5//nmsrKy4d+8ew4cPZ926dXTu3JlWrVpp2/UJIYTIP6RL2Qwyhu2iRYuYMmUK4eHhWgvXqGPHjvTs2ZNBgwYB/66zfP/+fYYPH651Lw8aNIiOHTvi4+PDV199xfr16wEIDg6mefPm5r05IYQQT0QC18QyPkedP38+I0aMAKB58+bUqlWLYsWKcf36dQIDAwGoVKkSffv2Zdq0aZnKiYuLY8SIESxbtgwrKytsbW3x8PAgLCwMZ2dn/vzzT5o2bSorSAkhRD4lgWsma9eu5fXXXwdg/PjxDB06FF9fX+39efPm8fHHH5OcnEzz5s1Zs2YNpUqVeqSc0aNHs3TpUqKjo3F3d6dy5cp89913NGjQQMJWCCHyMQlcE1NKER0dTf/+/fnjjz94++23mTZtGqVKldK6jKOjo/H39+fs2bNUr16dn3/+mQYNGmQqJ2OY7t+/nxs3buDj40PFihUpU6aMhK0QQuRzErhmcOrUKRo3boy1tTXr16+nbdu2KKW0QU/+/v5cuHCB2rVrs2jRIl544YUsyzEGtBBCiIJHmkQmZPxd5tChQyQlJdGgQQPatWuHTqfTwjYgICDHsL137x4JCQkAErZCCFGASeCakHGwlDF4S5curb0XERFBQEAA58+fzzJs9Xo9AAsXLuT777/X5twKIYQomGQerhk4ODgAcOTIEZRSxMfH06JFi2zDVimFtbU1MTExzJs3jzt37lC5cmVtL1shhBAFj7RwzaBVq1b4+fkRHR3NjBkzaNCggRa2CxcufKQbWafTkZKSwuLFiwkPD6datWo0bdrUQrUXQgiRFyRwzcDd3Z2qVasSFRXFF198QWhoKM8//zxLly59ZDSy0enTp1m9ejV6vZ5XX30VFxcXZHybEEIUXBK4z+BJnqsaDAZcXV2ZPXs2Hh4ePHjwAGdnZ7p160a5cuW0Y4xlKaW4evUqn3zyCYcPH6ZWrVoMGTIEBwcHWRtZCCEKMJkWlEsZ571u376dkydPcunSJVxdXenTpw8+Pj6ULFkSpRR6vR4bGxvWrVvH4MGDiYuLo1q1anTp0oWBAwdSpUoVAGJiYti7dy/z58/n999/x8nJiZ07d9KwYUPZ+UcIIQo4CdxceHi5xjFjxpCWlqa1UsuVK4e/vz9jxozhhRde0I5PTk5mzZo1vP/++8TGxuLs7Iy9vT0vvfQSdnZ2hISEEB4ezt27d/H09GTLli2yXKMQQhQSErjPYOnSpQwYMACAqlWrYmdnR3h4OGFhYdjY2FCpUiUWLFhAQECAdo5SiuDgYIYMGUJERAT379/PVKaPjw81a9Zk+vTp1KtXT8JWCCEKCQncXLp+/Tpdu3bl2LFjjBs3jjfeeINy5coRFRXFhx9+yO7du7l37x4+Pj4sX76cFi1aAP92Rd+6dYs9e/awfft2wsPDsbe3p1ixYvTv359KlSrh4+Mj3chCCFGISODm0pEjRwgICKB79+7MmzcPT09P7b3Y2FhmzpzJ8uXLuXHjBqVLl2bFihW0aNEi0zNdo9TUVGxtbS1xG0IIIcxE+ipz6ezZsyQnJ9OuXbtMYWswGHB3d2fixIm8+eablCtXjjt37tC3b192796NTqfD2toapZQ2zSdj2MrvP0IIUThJ4D5GdlN/3NzccHBwoFWrVpmOs7KywmAw4OLiwrhx47INXePXw6QLWQghCidZ2jEHGQcsBQUFcfr0aS5cuECpUqUoX748SUlJHD58mPLly2ca2PRw6AIEBgZy48YN+vbtm6l7WQJWCCGKBgncbBi3zwNYsGABY8eO1XbtAfDw8ADSt97r0aPHI6OJcwrdAQMGsHDhQtq2bWu+GxJCCGFRMmjqMQIDAxk4cCAAfn5+eHh4cO7cORITEzEYDDg5ObFt2zaaN2+e5X61xiCOj49n5syZrFy5kitXrlCmTBnOnDmDq6urtHKFEKIIkGe4Obh8+TKzZs0CYNy4cWzatImDBw9y+PBh3nzzTXx9fUlISKBTp06cOHECa2trbVs9o4dbul27duW5555j6dKluLm5SdgKIUQRIS3cDB5uoR44cIDWrVvTtWtXvv32W7y8vLT3wsPD+emnn1iyZAkXL17E1dWVPXv2UKdOnce2dCMjI/H19ZVFLYQQogiRT/sMjCH5448/cujQIa5cuUJycjLt27fPFLZKKUqUKME777zDkCFDqFKlCnFxcQQEBDxRS9fX11d7TQghRNEgn/gPWbRoEUOHDqVXr16cO3eO0qVL07hxYwAtRHU6HUopvLy8GDJkyFOFrhBCiKJJEiCD5ORkEhISKFu2LNevX2f27NncuXOHs2fPAmTqJs5t6AohhCiaJHAzsLe3p3///kyePJny5cuTkpKCjY0Ne/bsIS4u7pHjcwrdNm3acPTo0Uee5QohhCiaZNBUFqKjo1mzZg3Tp0/nn3/+oVSpUixdupQ2bdpkebxxAYuoqCgWLVrEzz//zNmzZ3F3d+fWrVs4OjrKaGQhhCjipIWbBU9PT3r27MnEiROpUKECd+/e5a233uLIkSNZHp+xpfvWW2/Rq1cvqlWrxpo1a3BycpKwFUIIIS3cnERHR7N27VpmzpzJlStX8PX1ZfXq1TRq1CjH86KiorRnwTL1RwghBEjgPtbThq6sjyyEECIr0vR6DE9PT3r06MG4ceOoWLEi//zzD7169eLw4cNZHi9hK4QQIisSuE/gaUNXCCGEeFihCdzs9q3NK1mFbv/+/Tlw4IBJryuEEKJwKBSBGxMTw7hx4zh16pRJr5MxdKtWrcqlS5fo168f8fHxJr2uEEKIgq/A74cbHx9P3bp1uX79OteuXWPKlCnUrFnTZNczThlKSUlh7ty5fPvtt7i4uJjsekIIIQqHAj9KOTY2lnbt2hESEqKtCtWkSROTXzcmJobk5GRKliwpU3+EEEI8VoEOXGPQRUVF8dprrzFs2DD69etn6WoJIYQQjyjQgQv/7mGbmpqKra2t9vrWrVspX748tWrVsmDthBBCiHQFvh/UuDlAxrCdMWMGr7zyCp9++innzp2zVNWEEEIITYEP3IclJyezfv16AIKCgvj000+17fVMpYB3EgghhDCDQhW4Sins7e35448/aN++PVFRUWzdupXPPvvMZKF79epVNm3aRFhYmEnKF0IIUTgUqsDV6XTo9Xq8vLxYsWIFrVu3Ji4uzmShGxoaSr9+/ejWrRu//vqryRffEEIIUXAVqsCF9Ge6xtD95ZdfTBa6oaGhvPvuuxw8eBBbW1tatGghU4OEEEJkq0AmRE7PTJVSJg/d0NBQhg8fzl9//YW3tzcnTpygcuXKz1SmEEKIwq3ATQvKuMhEeHg48fHx3Lt3jxo1auDg4ICNjY02Vcj436ioKF5//XWCgoJwdXXl5Zdf5j//+Q81atR46usbwzYoKAgvLy+Cg4OpXr16Xt+mEEKIQqZABW7GsF26dCmBgYEcPXqU+Ph4GjZsyAsvvMDUqVPx9PTUjs3L0JWwFUIIkVsFJnAzbuz+3Xff8d5772V53PPPP8/atWupWrWq9lpehK6ErRBCiGdRYALXaP369fTo0QOAvn37UrFiRXx8fPj555+5cuUKERERVKhQgdWrV9OgQQPtvOxC99VXX+Xjjz/OcUUqCVshhBDPTOVzBoNBKaVUWlqaSk5OVj179lQ6nU5NmDBB3bt3Tzvu0qVLasaMGapChQpKp9OpihUrqiNHjmQqKy0tTSmlVGRkpGrTpo3S6XRKp9Opvn37qoSEhCyvf/HiRe1Yb29vdfbsWRPdqRBCiMIsX7dwH96FR6/X4+3tTZ06dQgMDMTX1zfTcZGRkaxdu5Yvv/ySq1ev5tjSjY6OplOnThw+fJi9e/fSrFmzR65/7tw5Ro8ezfbt26VlK4QQ4plYfFpQTnlvDNuZM2fStWtXIiIisLW1pXbt2lrYZjzO29ubnj17Mm7cOCpUqMDVq1fp1asXISEh2rHG0cuenp789ttvnDhxgmbNmmW5aMU333zD9u3bKVGiBHv37pWwFUIIkWsW3YA+KiqKWbNmMWjQIKpUqZLlMRs2bGDChAlA+t638fHx1K5dG3i0BQzg5eVFz549gfSgNoZuxpZuxnm6Xl5eKKWyXLRi1qxZXLhwgblz51KtWrU8u28hhBBFj8VauElJSTRs2JCZM2cydepUQkNDszzO09OTtm3b4uTkxL59+0hOTiYkJASDwZBt69jT0zPLlu7ff/+tHWPcZQjQRj9nlJqaiqurK0FBQbLFnxBCiGdmscB98OABr7zyCgCrVq0iKioqy+NatmzJ1KlTCQgI0FqhBw8eJC4uTmupZiWr0O3QoUOm7uWcGLf7yyqMhRBCiKdlsS5lb29vxo8fj62tLfXq1aNx48aPHGPsMm7WrBmTJk1Cp9Oxa9cuTp06xcCBA1m/fn2mFaUeZgxdKysrxo8fT1RUlGylJ4QQwiIsPko5OTkZe3t77c9//PEHFStW1J7pZnxOu2/fPqZPn05QUBBJSUn069ePwMBAbZegrEIXIDIykvXr11O3bl0aNmyYaRENIYQQwhwsHrgZzZw5kwkTJtCnTx8++eQTKlWqBDwaul988QVBQUEkJyfTt29fli5d+tjQNb4nYSuEEMISLD4tyCghIYE7d+4AsHLlSj755BMuX74MpE/7MU7bad68ORMnTqR169bY29uzYsUK3njjjUy7BGXFGMQStkIIISwh3wSuk5MT48ePZ9KkSUD65gR5HbpCCCGEpeSrLmWAsLAw5s6dy/Tp0wHo378/kydPxs/PD8jcvbx//36tezkpKYk+ffqwdOnSTLsECSGEEPmB2QM3u2eoGQPyWUL3tddeY926dVkuZCFEkZEYDamJ4OgJto6Wro0QAjMHbsagNBgMxMXFYWdnh6Pjox8ITxu6X375JZs3bwbS10DOuD2fEEXK9UMQfvbfPzt6gOdzUKIG2Nhnd5YQwsTMFrgZA3LFihVs3bqVPXv2ULJkSerVq8eAAQOoV68ezs7OWiv4aUJ3z549zJo1i/Hjx9O8eXMZjSyKptvH4faxrN+zdYQKLcCttFmrJIRIZ5bAVVlsHq/T6TINcqpduzYdO3bko48+wtvbO1ehGxcXh6ura5ZrLAtR6MXegkvbIad/0jor8G0GxSqbr15CCMDMXcobN26kW7duAHTu3Bl3d3ccHR356aefMBgMuLu707lzZ7766iuKFy+eq9AVokhKjodzv0Ja0pMdX7YhlKpp2joJITIxy9KOSin0ej2rV68GYOLEibz//vuULFkSgEGDBjFixAhOnDjB2rVrSUtLY+7cuVrolixZkpEjRwIwffp0li1bhrW1NRMmTKBKlSoStqJo06fB5aAnD1uAm0fAyhpKyJaTQpiL2Vq44eHhVK9enTp16vDzzz9TtmxZdDodaWlp2NjYcOLECUaNGsXevXuxtbWlS5cumUI3q5buG2+8wYIFC3BwcDDHLQiRP13ZBVFXn/48nQ4qd5BnukKYiVmbhrGxsdSvX59y5cppz3RtbGxQSlGnTh3mzp3Liy++SGpqKhs3bmTkyJFERERoz3tLlizJqFGjGD16NABvvvmmhK0o2iIu5C5sIf1Z79U9kPoULWMhRK7leeAaV4Myfh8XF0dsbCweHh44Ojpqz10zNqyNgVq7du3Hhm6JEiUYP348V69epU2bNpmuJ0SRkhyf3jX8LFIT4MahvKmPECJHefoMN+PgpdWrV/P777+zd+9e7O3tadWqFdbW1hw9elRbevHhTeAzhu7IkSPZu3cvGzduBMjUvVyiRAntPHl+K4qsm0dAn/rs5URdAa+K4FHu2csSQmQrz57hZpz6M3/+fEaMGJFp/WOjKlWqEBwcTPHixbNcftFYzsmTJ7XQdXJyomXLlixatIjixYvnRXWFKNjiwuDCb3lXnp0LPN8FrG3zrkwhRCZ5Pmhq/fr19OjRA4BXXnkFT09PHB0dWbx4MQaDgbS0NNq2bcvmzZtxcHB4bOh+8MEHBAUF4ejoyN9//021atXysrpCFEznt0J8eN6WWaI6lG+St2UKITTP3B9rzGu9Xk9iYiLLli0D0qf+/PjjjyxZsoT58+fz66+/EhAQgLOzMzt27KBv374kJSVlubtPxu7lWbNm0bJlS7Zt20a1atUw47RhIfKn6H/yPmwBIs6bplwhBPCMLdyHF5xITU2lePHi1K9fP9PUH2OL9fDhw0yfPp3t27eTkJBAly5dWLFixWNbugkJCTg5OckCF0IoBWc3QmKMacp3cIcar6XP0RVC5KlnSi9j+E2ePJkOHTqQmJiIra0tNWrUyDT1x6hRo0ZMmjSJdu3a4eTkxMaNGx/b0oX0vXIzXk+IIuveRdOFLUBSbPp6zEKIPPfMCbZt2zY+++wztm/fTqdOnbCysqJOnToAWoBmDN4GDRo8VegKIf5PWjLcOmr664SdhgeRpr+OEEXMMwduzZo16d+/Py4uLhw4cICIiAhtb9rsNoCX0BUiF279/XTLN+aWMsA/+3LeBEEI8dSeOXDLlCnDzJkz6datG3Z2dkD6frS3b98GyDY8swrdV199NcegFqLIirubvqqUuSREQvg5811PiCIgTx6Kli5dmi+++II+ffpgb2/PyZMnteUXra2tSUtLy/I8Y+h26tQJgB07dnD1ai6XqROisNKnwbW95r/u7WOQmmj+6wpRSOXZKCQfHx8+//xz+vTpg4ODA1u2bKF79+5A+nrJOYXuhx9+SNeuXQkODqZ69eoy9UeIjG6FQHKc+a+rT4GbIea/rhCFVJ4vfHHnzh0mTZrEqlWrSEpKomvXrqxbtw5A2xkoKw8ePMDZ2Vmm/giR0f07cPEPy9ah6kvgWtKydRCiEMjzZCtdujSff/45vXv3xsHBgQ0bNjxRS9fZ2Tm9QhK2QqTTp6UPXrK06wdANgkR4pmZJN1Kly7NZ5999tShK4TI4NbflulKflhiNNw9aelaCFHgmaw5aXymK6ErRC7E3oLws5auxb/unID4CEvXQogCzaT9t1l1L3fo0AEg22e5QhR5KQlwLfiZilBKcS8mjmu373EvJu7ZByIqA1zdLZvVC/EM8nzQVFZu377N1KlTWbhwIQBnzpyhevXqpr6sEAVPWkr6IKmE3K30FBOXwM9b9jHvlx1cvvlvi7RS2eK8/3pbBnRujoerU+7r51wcqnQEa/mFWYinZZbABbh16xZTpkxh8ODBNGvWLNP+uUII0pduDN0OD3LXdbvtwGm6j/sfCUnJgC5Tqzb935rCycGedTNH0KFpzdzX06Uk+LUFG7vclyFEEWS2wAVISUnBzs5Opv5YSMa/d/mFJ59JiILLQbkeJLXtwGleHj0HpRSGHP5JW+l06HQ6ts4Z/Wyh6+AOfm3S/yuEeCJmDVyR/0jwWphS6ZsF3D4GhtytIR4Tl0DZl8eSmJSSY9gaWel0ODrYcXPrf5+te9naFso1gWJ+uS9DiCJEHsQUUhmD9MKFC5w/f54NGzYAULZsWVq0aMGLL76Io6Oj9DhYSnw4XD+Y6+e1Rj9v2UdCUvIT7zVgUIqEpGQCt+5jZO92ub+wPjV9cFdkKPjUA9dSuS9LiCJAWriFUMaw/eGHH1i0aBHHjh3LNBXrueeeo1mzZnz33Xe4ublJS9ec4sLSW7Ux1wGIjU/g1KWbuSpKKeg96XtuR8Q89bk+xT1Y9fkwnuXHXsuvLO4u/9dKdnAHD9/04HUuBjb2uS9YiEJIAreQyRic06dPZ9KkSUD6NCw/Pz/i4+O5eTP9w93KyopXX32VwMBAXFxc8rwuycnJJCcnZ3rN3t4ee/si8kFsMEBaIiTHp2/s/iAC4u488px27/GL+L89w0KVfDbBP47nxbpVsn7TzhkcPf/9cvAAZ2+z1k+I/ES6lAsZY9j+97//1cL2448/pnXr1rRv354bN26wf/9+3n77beLj49m9ezcLFixgzJgx6P5vQE1emT59OtOmTcv02pQpU5g6dWqeXSPfMOjTRxkrAyh9+p+VAhRYWYOTV/pX8aqPnht90OzVzTO+zaF6E0vXQogCQVq4hdCGDRt44403SEhIYNq0aYwZM0Zrwer1eqytrdmyZQuvvvoqAJ07d2bz5s15Xo8i38J9Qnv37sXf39/S1ciV4OBgXnzxRUtXQ4gCQQK3kLl79y6jRo1izZo19O7dm5kzZ1KuXDnt/YxdzqNGjWLevHkAHDx4kEaNGlmkzkVdbGwsp06dytW5Sil69+7N7du3n/pcHx8fVq1a9Uy9GrVq1cLdXaYGCfEkpEu5kDl69CgbN24E4OWXX84UtkCmD9dq1app39+/f98s9ROPcnd3f6ZW4scff8yYMWOeavlGnU7HuHHjCmzLWoiCSOaCFCLJycksWrSI1NRU2rdvT79+/QAe+SA2/rldu3aULJm+z2loaGiWx4r8b8CAATg5OT3x1C4rKyucnJx48803TVwzIURGEriFiL29PYMGDaJOnTp07twZSH9m+3CXofHPzs7OPHjwAIDIyMhM74mCw8PDg3Xr1qHT6R4bulZWVuh0OtavX4+Hh4d5KiiEAKRLudAwPpvt3LkzNjY22uYQ1tbWWR5vMBhwd3enWLFixMfHy3aJBVyHDh3YunUr3bt3JyEhASCLtZTB0dGR9evX0759e4vUU4iiTFq4hYRO9+9i9R07dsTX1zfH7mFjt2KxYsUAiI+PB9JbxA8zliOhnL916NCBmzdvMmfOHCpWrJjpvYoVKzJnzhxu3bolYSuEhUjgFiLZdR1nxWAwAGjTdoxdyw93SV6/fp3Ro0cTEREhexgXAB4eHowcOZLQ0FDu3bvH1atXuXfvHqGhoYwcOVJGFAthQfIJWkQZDIZMC10YW7YZQ/rmzZt8/PHH/PLLL/z111+cOHFCewYo8jedToe3tzfe3rKykxD5hQRuEWVsrRYvXhz4t4WblpaGjY0NN2/eZMyYMaxbtw4HBwe+/fbbbJ8HCyGEeDzpUi4EjN3D8O/z1sdN7zG+b+xCNg60sbKy4tatW1rYWltbs2PHDlq2bJnpOkIIIZ6OBG4BlTH8rKysiI+PJzY2ltjYWODfruHsgtc4AMr4X2Pr9fbt24wePZp169ZhY2PD7t27adasmWzhJ4QQz0i6lAugjOH3119/sWvXLjZs2EBqaire3t68/PLL9O/fn3LlymFlZZVlWBoD1jgXMzk5mYiICN5//302bdqEjY0Nu3btkrAVQog8IoFbwGQMvx9++IHx48dnmkcbGhrKwYMHCQ4Opnfv3vTr1w9bW9tHQtP4vaOjIwDXrl3j7bffZvPmzRK2QghhAhK4BYhSSgu///3vf7z//vsA+Pv7U6tWLWrXrs2dO3eYNm0af/75Jzdu3CA8PJzRo0djZ2eXKTyNuwYZu56vXr3KmTNnsLa2lrAVQggTkMAtQIzhuGrVKi1sR48ezVtvvUWNGjW042xtbZk8eTLnzp1jwYIF6PV6xo4dmyl0jUHq5uaGTqcjISEBGxsbgoKCJGyFEMIE5BO1gDl27BgzZ84E4MMPP2TKlCla2KalpbF7924WL16MwWDAxsaGq1evsnjxYv773/+SkpKiPdM1hnfr1q21gVV//fUXL774ooStEEKYgHyqFjC7d+/mwoULvPTSS7zzzjuZVg7av38/gwYN4vLly/j7+7NixQrs7Oy4dOkSS5YsyRS6xme+3bt3Z/Hixdom6BK2QghhGtKlXIAkJCTw/fffk5SURI8ePfDz89M2LTh69CiDBg3i2rVrNGnShG3btuHg4EBcXBxDhgwhNDSURYsWAWjdyykpKdjZ2TFgwAAACVshhDAhnZINUPOl7MJv7dq1BAYGsnr1am2EcUREBP7+/ly8eJFGjRrx119/4ezsTEpKCgaDgd69e7N582YAfH19GT58uDaQSgghhHlIC9fCjKOFja1N+Hd5RYDNmzdjZWWl7W/bo0cP2rdvr4VtUlISo0aN4uLFi9SsWZPly5fj7OyMXq/XyitfvjwAnp6e/PPPP8yaNQuDwcD48ePNfbtCCFFkSf+hBSmlsLa25tSpU0yZMoWzZ88C/65z/PXXX9OlSxdmzZrF5cuXtfPc3Ny072/evMnx48ext7dn6NChVKhQAUhf2ML4nLZGjRraLjI6nY7IyEjatGljrtsUQgiBtHAtSqfTcePGDerUqQNASkoKw4YNo3LlysybN4+xY8cC0LJlS8qWLZtlGfv37+f8+fO4ubnh7++PlZWV9lzXxsaG1NRUdu/eTUxMDEOGDKF+/foUK1aMhg0bascJIYQwPQlcC3N3d6dz585s2bKFOXPm4OjoiFKK6dOnAzBlyhQ++ugj7O3tszzfy8sLOzs77O3tCQsLAzJvsXfmzBlOnDhB+fLlcXFx4ZVXXgFkgJQQQpibfOJamJubG0uWLKFv375a0BrDdurUqYwbNw4nJ6dsz3dxcSElJYWIiAi2bt2qdT0rpTh79ixTpkzh/PnzNG3aFBsbm0d2CRJCCGEeMkrZwjJ26zZq1Ijjx4+j1+tp1KgR8+fPp27duo8c97CPP/6Y2bNnA9C2bVuqV69OZGQkFy9eJCQkhOLFi/Pnn39qXddCCCHMT7qULcwYorNmzSIkJER7/fDhwwQGBuLo6EjVqlWzDFtjt/AHH3xATEwMCxcuZMeOHezYsUM7xs3NjU2bNlGnTh15ZiuEEBYk/Yr5RHx8PJC+XGPPnj1RSvHNN9/w/fffc+HChSzPMXYLlypViq+//ppp06bh5+enhXTv3r3ZuXMnTZo0ybScoxBCCPOTLuV8ZN++fTRo0ICkpCTeffddVq5ciU6nY9SoUQwdOpSqVas+toywsDBSUlIoVqwYSimcnJykZSuEEPmABK6FZAzBjIteGMXGxj5V6CYnJ2sjmY1dzRK0QgiRf0iXshll/N1Gp9NpC1M8PGJYr9fj7u7Od999R58+fbTu5QULFmTqXjYYDADcu3ePOXPmsHHjxkzlSdgKIUT+IYOmzCTjvNdDhw5x6tQpNm/ejFIKd3d3OnXqREBAAOXKlcPa2jpT6AKsXLmSb775BoAhQ4bw/PPPY2Vlxb179xg+fDjr1q2jc+fOtGrVKtMOQkIIIfIH6VI2g4xhu2jRIqZMmUJ4eLjWwjXq2LEjPXv2ZNCgQcC/6yzfv3+f4cOHa93LgwYNomPHjvj4+PDVV1+xfv16AIKDg2nevLl5b04IIcQTkcA1sYzPUefPn8+IESMAaN68ObVq1aJYsWJcv36dwMBAACpVqkTfvn2ZNm1apnLi4uIYMWIEy5Ytw8rKCltbWzw8PAgLC8PZ2Zk///yTpk2bygpSQgiRT0ngmsnatWt5/fXXARg/fjxDhw7F19dXe3/evHl8/PHHJCcn07x5c9asWUOpUqUeKWf06NEsXbqU6Oho3N3dqVy5Mt999x0NGjSQsBVCiHxMAtfElFJER0fTv39//vjjD95++22mTZtGqVKltC7j6Oho/P39OXv2LNWrV+fnn3+mQYMGmcrJGKb79+/nxo0b+Pj4ULFiRcqUKSNhK4QQ+ZwErhmcOnWKxo0bY21tzfr162nbti1KKW3Qk7+/PxcuXKB27dosWrSIF154IctyjAEthBCi4JEmkQkZf5c5dOgQSUlJNGjQgHbt2qHT6bSwDQgIyDFs7927R0JCAoCErRBCFGASuCZkHCxlDN7SpUtr70VERBAQEMD58+ezDFu9Xg/AwoUL+f7777U5t0IIIQommYdrBg4ODgAcOXIEpRTx8fG0aNEi27BVSmFtbU1MTAzz5s3jzp07VK5cWdvLVgghRMEjLVwzaNWqFX5+fkRHRzNjxgwaNGighe3ChQsf6UbW6XSkpKSwePFiwsPDqVatGk2bNrVQ7YUQQuQFCVwzcHd3p2rVqkRFRfHFF18QGhrK888/z9KlSx8ZjWx0+vRpVq9ejV6v59VXX8XFxQUZ3yaEEAWXBO4zeJLnqgaDAVdXV2bPno2HhwcPHjzA2dmZbt26Ua5cOe0YY1lKKa5evconn3zC4cOHqVWrFkOGDMHBwUHWRhZCiAJMpgXlUsZ5r9u3b+fkyZNcunQJV1dX+vTpg4+PDyVLlkQphV6vx8bGhnXr1jF48GDi4uKoVq0aXbp0YeDAgVSpUgWAmJgY9u7dy/z58/n9999xcnJi586dNGzYUHb+EUKIAk4CNxceXq5xzJgxpKWlaa3UcuXK4e/vz5gxY3jhhRe045OTk1mzZg3vv/8+sbGxODs7Y29vz0svvYSdnR0hISGEh4dz9+5dPD092bJliyzXKIQQhYQE7jNYunQpAwYMAKBq1arY2dkRHh5OWFgYNjY2VKpUiQULFhAQEKCdo5QiODiYIUOGEBERwf379zOV6ePjQ82aNZk+fTr16tWTsBVCiEJCAjeXrl+/TteuXTl27Bjjxo3jjTfeoFy5ckRFRfHhhx+ye/du7t27h4+PD8uXL6dFixbAv13Rt27dYs+ePWzfvp3w8HDs7e0pVqwY/fv3p1KlSvj4+Eg3shBCFCISuLl05MgRAgIC6N69O/PmzcPT01N7LzY2lpkzZ7J8+XJu3LhB6dKlWbFiBS1atMj0TNcoNTUVW1tbS9yGEEIIM5G+ylw6e/YsycnJtGvXLlPYGgwG3N3dmThxIm+++SblypXjzp079O3bl927d6PT6bC2tkYppU3zyRi28vuPEEIUThK4j5Hd1B83NzccHBxo1apVpuOsrKwwGAy4uLgwbty4bEPX+PUw6UIWQojCSZZ2zEHGAUtBQUGcPn2aCxcuUKpUKcqXL09SUhKHDx+mfPnymQY2PRy6AIGBgdy4cYO+fftm6l6WgBVCiKJBAjcbxu3zABYsWMDYsWO1XXsAPDw8gPSt93r06PHIaOKcQnfAgAEsXLiQtm3bmu+GhBBCWJQMmnqMwMBABg4cCICfnx8eHh6cO3eOxMREDAYDTk5ObNu2jebNm2e5X60xiOPj45k5cyYrV67kypUrlClThjNnzuDq6iqtXCGEKALkGW4OLl++zKxZswAYN24cmzZt4uDBgxw+fJg333wTX19fEhIS6NSpEydOnMDa2lrbVs/o4ZZu165dee6551i6dClubm4StkIIUURICzeDh1uoBw4coHXr1nTt2pVvv/0WLy8v7b3w8HB++uknlixZwsWLF3F1dWXPnj3UqVPnsS3dyMhIfH19ZVELIYQoQuTTPgNjSP74448cOnSIK1eukJycTPv27TOFrVKKEiVK8M477zBkyBCqVKlCXFwcAQEBT9TS9fX11V4TQghRNMgn/kMWLVrE0KFD6dWrF+fOnaN06dI0btwYQAtRnU6HUgovLy+GDBnyVKErhBCiaJIEyCA5OZmEhATKli3L9evXmT17Nnfu3OHs2bMAmbqJcxu6QgghiiYJ3Azs7e3p378/kydPpnz58qSkpGBjY8OePXuIi4t75PicQrdNmzYcPXr0kWe5QgghiiYZNJWF6Oho1qxZw/Tp0/nnn38oVaoUS5cupU2bNlkeb1zAIioqikWLFvHzzz9z9uxZ3N3duXXrFo6OjjIaWQghijhp4WbB09OTnj17MnHiRCpUqMDdu3d56623OHLkSJbHZ2zpvvXWW/Tq1Ytq1aqxZs0anJycJGyFEEJICzcn0dHRrF27lpkzZ3LlyhV8fX1ZvXo1jRo1yvG8qKgo7VmwTP0RQggBEriP9bShK+sjCyGEyIo0vR7D09OTHj16MG7cOCpWrMg///xDr169OHz4cJbHS9gKIYTIigTuE3ja0BVCCCEeVmgCN7t9a/NKVqHbv39/Dhw4YNLrCiGEKBwKReDGxMQwbtw4Tp06ZdLrZAzdqlWrcunSJfr160d8fLxJryuEEKLgK/D74cbHx1O3bl2uX7/OtWvXmDJlCjVr1jTZ9YxThlJSUpg7dy7ffvstLi4uJrueEEKIwqHAj1KOjY2lXbt2hISEaKtCNWnSxOTXjYmJITk5mZIlS8rUHyGEEI9VoAPXGHRRUVG89tprDBs2jH79+lm6WkIIIcQjCnTgwr972KampmJra6u9vnXrVsqXL0+tWrUsWDshhBAiXYHvBzVuDpAxbGfMmMErr7zCp59+yrlz5yxVNSGEEEJT4AP3YcnJyaxfvx6AoKAgPv30U217PVMp4J0EQgghzKBQBa5SCnt7e/744w/at29PVFQUW7du5bPPPjNZ6F69epVNmzYRFhZmkvKFEEIUDoUqcHU6HXq9Hi8vL1asWEHr1q2Ji4szWeiGhobSr18/unXrxq+//mryxTeEEEIUXIUqcCH9ma4xdH/55ReThW5oaCjvvvsuBw8exNbWlhYtWsjUICGEENkqkAmR0zNTpZTJQzc0NJThw4fz119/4e3tzYkTJ6hcufIzlSmEEKJwK3DTgjIuMhEeHk58fDz37t2jRo0aODg4YGNjo00VMv43KiqK119/naCgIFxdXXn55Zf5z3/+Q40aNZ76+sawDQoKwsvLi+DgYKpXr57XtymEEKKQKVCBmzFsly5dSmBgIEePHiU+Pp6GDRvywgsvMHXqVDw9PbVj8zJ0JWyFEELkVoEJ3Iwbu3/33Xe89957WR73/PPPs3btWqpWraq9lhehK2ErhBDiWRSYwDVav349PXr0AKBv375UrFgRHx8ffv75Z65cuUJERAQVKlRg9erVNGjQQDsvu9B99dVX+fjjj3NckUrCVgghxDNT+ZzBYFBKKZWWlqaSk5NVz549lU6nUxMmTFD37t3Tjrt06ZKaMWOGqlChgtLpdKpixYrqyJEjmcpKS0tTSikVGRmp2rRpo3Q6ndLpdKpv374qISEhy+tfvHhRO9bb21udPXvWRHcqhBCiMMvXLdyHd+HR6/V4e3tTp04dAgMD8fX1zXRcZGQka9eu5csvv+Tq1as5tnSjo6Pp1KkThw8fZu/evTRr1uyR6587d47Ro0ezfft2adkKIYR4JhafFpRT3hvDdubMmXTt2pWIiAhsbW2pXbu2FrYZj/P29qZnz56MGzeOChUqcPXqVXr16kVISIh2rHH0sqenJ7/99hsnTpygWbNmWS5a8c0337B9+3ZKlCjB3r17JWyFEELkmkU3oI+KimLWrFkMGjSIKlWqZHnMhg0bmDBhApC+9218fDy1a9cGHm0BA3h5edGzZ08gPaiNoZuxpZtxnq6XlxdKqSwXrZg1axYXLlxg7ty5VKtWLc/uWwghRNFjsRZuUlISDRs2ZObMmUydOpXQ0NAsj/P09KRt27Y4OTmxb98+kpOTCQkJwWAwZNs69vT0zLKl+/fff2vHGHcZArTRzxmlpqbi6upKUFCQbPEnhBDimVkscB88eMArr7wCwKpVq4iKisryuJYtWzJ16lQCAgK0VujBgweJi4vTWqpZySp0O3TokKl7OSfG7f6yCmMhhBDiaVmsS9nb25vx48dja2tLvXr1aNy48SPHGLuMmzVrxqRJk9DpdOzatYtTp04xcOBA1q9fn2lFqYcZQ9fKyorx48cTFRUlW+kJIYSwCIuPUk5OTsbe3l778x9//EHFihW1Z7oZn9Pu27eP6dOnExQURFJSEv369SMwMFDbJSir0AWIjIxk/fr11K1bl4YNG2ZaREMIIYQwB4sHbkYzZ85kwoQJ9OnTh08++YRKlSoBj4buF198QVBQEMnJyfTt25elS5c+NnSN70nYCiGEsASLTwsySkhI4M6dOwCsXLmSTz75hMuXLwPp036M03aaN2/OxIkTad26Nfb29qxYsYI33ngj0y5BWTEGsYStEEIIS8g3gevk5MT48eOZNGkSkL45QV6HrhBCCGEp+apLGSAsLIy5c+cyffp0APr378/kyZPx8/MDMncv79+/X+teTkpKok+fPixdujTTLkFCCCFEfmD2wM3uGWrGgHyW0H3ttddYt25dlgtZCCGEMK2YhBQSU/U42dng7mhr6erkK2YN3IxBaTAYiIuLw87ODkdHx0eOfdrQ/fLLL9m8eTOQvgZyxu35hBBCmNaViHhO375PfFKa9pqTnTUVijlTpaQrjnbS42i2wM0YkCtWrGDr1q3s2bOHkiVLUq9ePQYMGEC9evVwdnbWWsFPE7p79uxh1qxZjB8/nubNm8toZCGEMIOkVD0HrkRyJyYp22NsrHQ8X8aNGqXdivTnslkCV2WxebxOp8s0yKl27dp07NiRjz76CG9v71yFblxcHK6urlmusSyEECJvxSWlsvNCRKZWbU58PBx40a8YNtZF8/PZrF3KGzdupFu3bgB07twZd3d3HB0d+emnnzAYDLi7u9O5c2e++uorihcvnqvQFUIIYXpxSansOBdGYsqjO63lpJS7PS2rlMDKqui1dM2SUkop0tLSWL16NQATJ07kxx9/ZOnSpfzwww8EBwdTt25d7t+/z9q1axk5ciQRERFaK7hkyZKMHDlS2zVo2bJlfP7551y8eDH9JiRshRDCbJJS9ey8EPHUYQtwNzaZw9eyXju/sDNbCzc8PJzq1atTp04dfv75Z8qWLYtOpyMtLQ0bGxtOnDjBqFGj2Lt3L7a2tnTp0oW5c+fm2NJ94403WLBgAQ4ODua4BSGEKPIMBsXOC+GE3U9+pnIaPOdJlZKueVSrgsGsTcPY2Fjq169PuXLltGe6NjY2KKWoU6cOc+fO5cUXXyQ1NZWNGzdm2dIdNWoUo0ePBuDNN9+UsBVCCDM6eSv2mcMW4Og/0YTHZT/QqjDK88A1rgZl/D4uLo7Y2Fg8PDxwdHTUnrtmbFgbA7V27dqPDd0SJUowfvx4rl69Sps2bTJdTwghhOncjU3i7O37eVKWQcHe0HskpDzZgKvCIE8DN+PgpdWrVzN48GDq1atHs2bN+OCDD7C2tubo0aPo9fpHgvJpQ9fX1zf9BuT5rRBCmFz69J97eVymgV0XIkhJKxoNpzx7hptx6s/8+fMZMWJEpvWPjapUqUJwcDDFixfPcvlFYzknT55k5MiR7N27FycnJ1q2bMmiRYsoXrx4XlRXCCHEUwgOjeBGVKJJyi7pZk/LqiWwLuQjl/N80NT69evp0aMHAK+88gqenp44OjqyePFiDAYDaWlptG3bls2bN+Pg4PDY0P3ggw8ICgrC0dGRv//+m2rVquVldYUQQjzGpfB4Dl817cjisp6O+FcuVqgXxnjm/lhjXuv1ehITE1m2bBnw79SfJUuWMH/+fH799VcCAgJwdnZmx44d9O3bl6SkpCx398nYvTxr1ixatmzJtm3bqFatGmacNiyEEEVebEIqR/+JNvl1bkYnEmKG61jSM7VwH15wIjU1leLFi1O/fv1MU3+MLdbDhw8zffp0tm/fTkJCAl26dGHFihWPbekmJCTg5OQkC1wIIYQZpeoNbDtzl/uJ5hvYVN/Xg2ql3Mx2PXN6pvQyht/kyZPp0KEDiYmJ2NraUqNGjUxTf4waNWrEpEmTaNeuHU5OTmzcuPGxLV1I3ys34/WEEEKY3sErkWYNW4Bj12O4G1s4pws9c4Jt27aNzz77jO3bt9OpUyesrKyoU6cOgBagGYO3QYMGTxW6QgghzO/cnfsmGySVE6Vg36V7PEgufNOFnjlwa9asSf/+/XFxceHAgQNERERoe9NmtwG8hK4QQuRf9+KTOXEjxmLXT04zEBx6D4OhcI3ZeebALVOmDDNnzqRbt27Y2dkB6fvR3r59GyDb8MwqdF999dUcg1oIIYRppekNHLgciaWzLupBCn9fL1yDqPLkoWjp0qX54osv6NOnD/b29pw8eVJbftHa2pq0tKy7Boyh26lTJwB27NjB1atX86JKQgghcuHkrVjinnC7PVMLDYvn2r0Hlq5GnsmzUUg+Pj58/vnn9OnTBwcHB7Zs2UL37t2B9PWScwrdDz/8kK5duxIcHEz16tVl6o8QQlhAZHwyF+7GWboamRy+GkVMQoqlq5En8nzhizt37jBp0iRWrVpFUlISXbt2Zd26dQDazkBZefDgAc7OzjL1RwghLMBgUPxx5i4xCamWrsojXB1s6PB8KexsCnY25HntS5cuzeeff07v3r1xcHBgw4YNT9TSdXZ2Tq+QhK0QQpjdqVux+TJsAeKS0ky+0pU5mCTdSpcuzWefffbUoSuEEML8wu4ncfZO3uwCZCrXoxLyXXf30zJZc9L4TFdCVwgh8q/45DT2ht6jIAydOXY9mnvxz74Xr6Xk+TPchz38TLddu3Zs27bNlJcUQgjxBBJT9Gw/F0Z8Ho5KVkoRHxtNUsIDHJyccXH3zNMNCZzsrOlYsxQOtgVv+qjJAxfg9u3bTJ06lYULFwJw5swZqlevburLCiGEyEZcUio7L0TkWdg+iIsl+Ld1bPtlCeG3/tFeL1HGlw6vD8T/pe44u7rnybW8XexoU60ENtYFa8yPWQIX4NatW0yZMoXBgwfTrFmzTPvnCiGEMJ+w+0nsDb1Hch5t/H7y4G7mTBhGSlL6UpAZY8X4OW/n4Mjo6d9Tu0mLPLlmaXcH/CsXK1Cha7bABUhJScHOzk6m/gghhAUopThz+z6nbsXm2TPbkwd3M+uDgSgFSmUf4DqdFTodfPTVkjwLXW8XO170K4azfdbTTfMbswauMC/pRRBCGEU/SOHwtSgi4/NuEYkHcbG8/2oTUpKScgxbI53OCjsHB+ZtPphn3cs21jpqlHajSknXfD9PN3/XTuRaxrC9efOmhWsjhLCUmIQU9l++xx9n7uZp2AIE/7aOlKTEJwpbSG8BpyQlEvzb+jyrQ5pecfJmLBuP3+LglUjuxCbm200PCkY7XDyVjGE7depUVq5cyZIlS2jatKmFayaEMIeElDRuRidy7d4D7mUI2YT4+9y4dCFPrqFQ/Lr0+6deilcpxa9L5/Nc1efRkXc9cOX8qnJFr7gS8QAbax2l3Bwo6eaAt4sdHo62+eJZr3QpF2JTp07lk08+AaBZs2bMnj2bJk2amO36ycnJJCdnnjNnb2+Pvb292eogRGGlNyhS0gwkpepJSNXzIDmNmIRU7sUnZ7ti1IXjR/hkWA8z19Q8Jn+/lqp1G2b5nk4HzvY2uDrY4GJvg5OdNc52NjjaWad/2Vpja4ZAlsAtpH7++WcGDRqk/dnOzo769evz1VdfmS10p06dyrRp0zK9NmXKFKZOnWqW6wtRmCSl6tEbFAal0BvSv57Wwf376NyhjQlqZ3lbtv1Fk2bNc32+tZUORztr7G1MN79XArcQOn78OGPHjmXnzp1UqFCBhIQEwsLCcHR0pE6dOmYLXWnhCpG/7N27F39/f0tXwySCg4N58cUXLV2NHMkz3ELE+Oz2999/58CBAwAMHjyYd999l2bNmnHhwgWOHz/OBx98YJbQlXAVIn+pVasWwcHBeVKWUorevXtz+/btpz7Xx8eHVatW5eksilq1auVZWaYiLdxC5tSpUzRs2JCUlBQ6duzIli1bsLKy4u7du7Rs2ZKLFy+avaUrhCicvvnmG8aMGfNUA6d0Oh1z5sxh5MiRJqxZ/iSBW8jcvn0bf39/bG1tWbx4MU2bNiUpKQkHBwciIiLw9/eX0BVC5ImYmBjKli1LYmIiBsPjpwZZWVnh6OjIzZs38fDwMH0F8xnLj5MWecZgMODj48O+ffv46KOPeP755wFwcHAgLS2N4sWLExwcTJUqVUhMTNS6lw8ePGjhmgshCiIPDw/WrVuHTqd77OqBVlZW6HQ61q9fXyTDFqSFW+gYl83MapWptLQ0bGxsMrV0HRwcqFu3rrR0hRC5tm3bNrp3705CQgKQ9VrKTk5OrF+/nvbt21ukjvmBtHALGeNvmVkNRjDuQ5yxpZuUlJRlS9fYPZSSkkJsbKx5Ki+EKJA6dOjAzZs3mTNnDhUrVsz0XsWKFZkzZw63bt0q0mEL0sItkrJq6Wb1TDcxMZEtW7awZs0aPvjgA2kBCyEeSylFVFQUcXFxuLq64uXlJWu6/x8J3CIqu+7lOnXq8O233/LCCy+wadMmvvrqK4KDg/Hz8+PYsWM4OztbuupCCFEgSeAWYdm1dGvVqkW3bt347bff2LNnD/b29vz++++0bNnS0lUWQogCSwK3EMi4v7BxsNSTbs1nDN3w8HBatGjBhQsXsLOzw9nZmejoaKytrdm1axfNmzeXfYyFEOIZSOAWUA+HX3x8PHq9HqVUpiH3TxK8qamp2NracuPGDZo2baqtHCNhK4QQeUeWdiyAMobfX3/9xa5du9iwYQOpqal4e3vz8ssv079/f8qVK4eVldVjw9LW1pb4+HiuXLlC2bJluX37NtbW1gQFBUnYCiFEHpEWbgGTMfx++OEHxo8fT3x8PGlpaZmOa9++Pb1796Zfv37Y2trmGJrJycls27aNWbNmsW/fPi1s/f39JWyFECKPSAu3AFFKaeH3v//9j/fffx8Af39/atWqRe3atblz5w7Tpk3jzz//5MaNG4SHhzN69Gjs7OyyDE+DwcC+ffv49NNP+fvvv7G1tZWWrRBCmIAEbgFifBa7atUqLWxHjx7NW2+9RY0aNbTjbG1tmTx5MufOnWPBggXo9XrGjh2bbejGxsZy6dIlAAlbIYQwEelSLmCOHTvG4MGDOXHiBB9++CGTJk3C3d0dSB9xvG/fPt566y0uX76srSzl5+fHoEGDsg3duLg4li1bRpMmTahXr56ErRBCmIB8qhYwu3fv5sKFC7z00ku88847WtgC7N+/n0GDBnH58mX8/f1ZsWIFdnZ2XLp0iSVLlvDf//6XlJQUbSAVpHdTu7q6MmzYMOrVq5ep21oIIUTekS7lAiQhIYHvv/+epKQkevTogZ+fnzbt5+jRowwaNIhr167RpEkTtm3bhoODA3FxcQwZMoTQ0FAWLVoEkGVL19hdLUuwCSGEaUiXcj6VXbfu2rVrCQwMZPXq1Tg6OgJkWimqUaNG/PXXXzg7O5OSkoLBYKB3795s3rwZAF9fX4YPH64NpBJCCGEe0sK1ML1ej7W1NSkpKVoAGld/Ati8eTNWVlZ07twZgB49etC+fXstbJOSkhg1ahQXL16kZs2aLF++HGdnZ/R6vVZe+fLlAfD09OSff/5h1qxZGAwGxo8fb+7bFUKIIkse1lmQUgpra2tOnTrFlClTOHv2LIAWtl9//TVdunRh1qxZXL58WTvPzc1N+/7mzZscP34ce3t7hg4dSoUKFYD0VaKMc3Nr1KiBh4cHI0eORKfTERkZSZs2bcx1m0IIIZAWrkXpdDpu3LhBnTp1gPS9Z4cNG0blypWZN28eY8eOBaBly5aULVs2yzL279/P+fPncXNzw9/fP9Pm8zY2NqSmprJ7925iYmIYMmQI9evXp1ixYjRs2PCJ11sWQgjx7CRwLczd3Z3OnTuzZcsW5syZg6OjI0oppk+fDsCUKVP46KOPsLe3z/J8Ly8v7OzssLe3JywsDMg88OnMmTOcOHGC8uXL4+LiwiuvvAJk/4xYCCGEacgnroW5ubmxZMkS+vbtqwWtMWynTp3KuHHjcHJyyvZ8FxcXUlJSiIiIYOvWrVrXs1KKs2fPMmXKFM6fP0/Tpk2xsbHBOEZOwlYIIcxLRilbWMZu3UaNGnH8+HH0ej2NGjVi/vz51K1b95HjHvbxxx8ze/ZsANq2bUv16tWJjIzk4sWLhISEULx4cf7880+t61oIIYT5SZeyhRlDdNasWYSEhGivHz58mMDAQBwdHalatWqWYWvsFv7ggw+IiYlh4cKF7Nixgx07dmjHuLm5sWnTJurUqSPPbIUQwoKkXzGfiI+PB+DDDz+kZ8+eKKX45ptv+P7777lw4UKW5xi7hUuVKsXXX3/NtGnT8PPz00K6d+/e7Ny5kyZNmmAwGCRshRDCgqRLOR/Zt28fDRo0ICkpiXfffZeVK1ei0+kYNWoUQ4cOpWrVqo8tIywsjJSUFIoVK4ZSCicnJ2nZCiFEPiCBayEZQzDjohdGsbGxTxW6ycnJ2khmY1ezBK0QQuQf0qVsRhl/t9HpdNrCFA+PGNbr9bi7u/Pdd9/Rp08frXt5wYIFmbqXjRsQ3Lt3jzlz5rBx48ZM5UnYCiFE/iGDpswk47zXQ4cOcerUKTZv3oxSCnd3dzp16kRAQADlypXD2to6U+gCrFy5km+++QaAIUOG8Pzzz2NlZcW9e/cYPnw469ato3PnzrRq1SrTDkJCCCHyB+lSNoOMYbto0SKmTJlCeHi41sI16tixIz179mTQoEHAv+ss379/n+HDh2vdy4MGDaJjx474+Pjw1VdfsX79egCCg4Np3ry5eW9OCCHEE5HANbGMz1Hnz5/PiBEjAGjevDm1atWiWLFiXL9+ncDAQAAqVapE3759mTZtWqZy4uLiGDFiBMuWLcPKygpbW1s8PDwICwvD2dmZP//8k6ZNm8oKUkIIkU9J4JrJ2rVref311wEYP348Q4cOxdfXV3t/3rx5fPzxxyQnJ9O8eXPWrFlDqVKlHiln9OjRLF26lOjoaNzd3alcuTLfffcdDRo0kLAVQoh8TALXxJRSREdH079/f/744w/efvttpk2bRqlSpbQu4+joaPz9/Tl79izVq1fn559/pkGDBpnKyRim+/fv58aNG/j4+FCxYkXKlCkjYSuEEPmcBK4ZnDp1isaNG2Ntbc369etp27YtSilt0JO/vz8XLlygdu3aLFq0iBdeeCHLcowBLYQQouCRJpEJGX+XOXToEElJSTRo0IB27dqh0+m0sA0ICMgxbO/du0dCQgKAhK0QQhRgErgmZBwsZQze0qVLa+9FREQQEBDA+fPnswxbvV4PwMKFC/n++++1ObdCCCEKJpmHawYODg4AHDlyBKUU8fHxtGjRItuwVUphbW1NTEwM8+bN486dO1SuXFnby1YIIUTBIy1cM2jVqhV+fn5ER0czY8YMGjRooIXtwoULH+lG1ul0pKSksHjxYsLDw6lWrRpNmza1UO2FEELkBQlcM3B3d6dq1apERUXxxRdfEBoayvPPP8/SpUsfGY1sdPr0aVavXo1er+fVV1/FxcUFGd8mhBAFlwTuM3iS56oGgwFXV1dmz56Nh4cHDx48wNnZmW7dulGuXDntGGNZSimuXr3KJ598wuHDh6lVqxZDhgzBwcFB1kYWQogCTKYF5VLGea/bt2/n5MmTXLp0CVdXV/r06YOPjw8lS5ZEKYVer8fGxoZ169YxePBg4uLiqFatGl26dGHgwIFUqVIFgJiYGPbu3cv8+fP5/fffcXJyYufOnTRs2FB2/hFCiAJOAjcXHl6uccyYMaSlpWmt1HLlyuHv78+YMWN44YUXtOOTk5NZs2YN77//PrGxsTg7O2Nvb89LL72EnZ0dISEhhIeHc/fuXTw9PdmyZYss1yiEEIWEBO4zWLp0KQMGDACgatWq2NnZER4eTlhYGDY2NlSqVIkFCxYQEBCgnaOUIjg4mCFDhhAREcH9+/czlenj40PNmjWZPn069erVk7AVQohCQj7Jc+n69evMmTMHgHHjxrFu3Tr27t3LwYMH6d69Ox4eHly4cIG+ffuye/du7TylFAEBAezatYv58+czcOBAXnrpJbp27crbb7/NypUr+emnn6hXr562GpX4V3JyMlOnTiU5OdnSVTE5udfCp6jcJxSte31S0sLNpSNHjhAQEED37t2ZN28enp6e2nuxsbHMnDmT5cuXc+PGDUqXLs2KFSto0aJFpme6Rqmpqdja2lriNgqc+/fv4+7uTmxsLG5ubpaujknJvRY+ReU+oWjd65OS5lMunT17luTkZNq1a5cpbA0GA+7u7kycOJE333yTcuXKcefOHa2lq9PpsLa2RimlTfPJGLby+48QQhROEriPkd3UHzc3NxwcHGjVqlWm46ysrDAYDLi4uDBu3LhsQ9f49TAZiSyEEIWTLO2Yg4wDloKCgjh9+jQXLlygVKlSlC9fnqSkJA4fPkz58uUzPWt9OHQBAgMDuXHjBn379s3UvSwBK4QQRYMEbjYyDlhasGABY8eO1XbtAfDw8ADSt97r0aPHI6OJcwrdAQMGsHDhQtq2bWu+Gyok7O3tmTJlCvb29pauisnJvRY+ReU+oWjd65OSQVOPERgYyMCBAwHw8/PDw8ODc+fOkZiYiMFgwMnJiW3bttG8efMs96s1BnF8fDwzZ85k5cqVXLlyhTJlynDmzBlcXV2llSuEEEWAPMPNweXLl5k1axaQPvVn06ZNHDx4kMOHD/Pmm2/i6+tLQkICnTp14sSJE1hbW2vb6hk93NLt2rUrzz33HEuXLsXNzU3CVgghighp4WbwcAv1wIEDtG7dmq5du/Ltt9/i5eWlvRceHs5PP/3EkiVLuHjxIq6uruzZs4c6deo8tqUbGRmJr6+vLGohhBBFiHzaZ2AMyR9//JFDhw5x5coVkpOTad++faawVUpRokQJ3nnnHYYMGUKVKlWIi4sjICDgiVq6vr6+2mtCCCGKBvnEf8iiRYsYOnQovXr14ty5c5QuXZrGjRsDaCGq0+lQSuHl5cWQIUOeKnSFEEIUTZIAGSQnJ5OQkEDZsmW5fv06s2fP5s6dO5w9exYgUzdxbkNXCCFE0SSBm4G9vT39+/dn8uTJlC9fnpSUFGxsbNizZw9xcXGPHJ9T6LZp04ajR48+8ixXPJ1r164xZMgQKlSogKOjI5UqVWLKlCmkpKTkeF5SUhIjRozA29sbFxcXunfvTlhYmJlqnTuff/45zZo1w8nJSZt29jgDBw7MtJCKTqejY8eOpq1oHsjNvSqlmDx5MqVLl8bR0ZG2bdsSGhpq2ormgaioKPr164ebmxseHh4MGTKE+Pj4HM9p2bLlIz/XYcOGmanGT+5///sfzz33HA4ODjRu3JjDhw/nePyaNWuoVq0aDg4O1KpVi99++81MNc0fJHAf4unpSffu3Zk4cSK+vr6kpaWxZs2abP9Hyip0a9SoQVRUFG3atCEhIUGWa3wG58+fx2AwsGDBAs6cOcPXX3/N999/z8SJE3M8b8yYMfz666+sWbOG3bt3c/v2bbp162amWudOSkoKPXv2ZPjw4U91XseOHblz5472tXLlShPVMO/k5l6//PJL5s6dy/fff8+hQ4dwdnamQ4cOJCUlmbCmz65fv36cOXOG7du3s2XLFvbs2cM777zz2PPefvvtTD/XL7/80gy1fXKrV6/mgw8+YMqUKRw9epQ6derQoUMHwsPDszx+//799OnThyFDhnDs2DG6dOlCly5dOH36tJlrbkFKZCkqKkr98MMPqmLFikqn06nnnntOHT58ONvjDQaDdt4nn3yiqlevrrZv326u6hYpX375papQoUK278fExChbW1u1Zs0a7bVz584pQB04cMAcVXwmixcvVu7u7k907IABA9Rrr71m0vqY0pPeq8FgUKVKlVKzZs3SXouJiVH29vZq5cqVJqzhszl79qwC1JEjR7TXfv/9d6XT6dStW7eyPa9FixZq1KhRZqhh7jVq1EiNGDFC+7Ner1c+Pj5q+vTpWR7/+uuvq5dffjnTa40bN1ZDhw41aT3zE2nhZsPT05MePXowfvx4KlasyD///MPrr7+eY0vXeN6IESP4888/adu2bbZrMYvci42NzTRq/GF///03qampmVbyqlatGuXLl+fAgQPmqKJZ7dq1ixIlSlC1alWGDx9OZGSkpauU565evcrdu3cz/Uzd3d1p3Lhxvv6ZHjhwAA8PDxo0aKC91rZtW6ysrDh06FCO5y5fvpxixYpRs2ZNJkyYkGmlO0tLSUnh77//zvTzsLKyom3bttn+PA4cOPDI6nodOnTI1z+/vCZLO+bAGLoAM2fO5MqVK/Tq1YvVq1fTqFGjLM9R/9e9bAwEGZmcty5dusS8efOYPXt2tsfcvXsXOzu7R54NlixZkrt375q4hubVsWNHunXrRoUKFbh8+TITJ06kU6dOHDhwoFCNHzD+3EqWLJnp9fz+M7179y4lSpTI9JqNjQ1eXl451rtv3774+vri4+PDyZMnGTduHBcuXGD9+vWmrvITuXfvHnq9Psufx/nz57M85+7duwXu55fXJA0ewxi648aN01q6vXr1emxLV+Rs/PjxjwwKefjr4X+4t27domPHjvTs2ZO3337bQjV/Orm5z6fRu3dvXn31VWrVqkWXLl3YsmULR44cYdeuXXl3E0/I1Pean5j6Xt955x06dOhArVq16NevH4GBgWzYsIHLly/n4V0Ic5MW7hPITUtX5Gzs2LHaGtXZqVixovb97du3adWqFc2aNeOHH37I8bxSpUqRkpJCTExMplZuWFgYpUqVepZqP7Wnvc9nVbFiRYoVK8alS5do06ZNnpX7JEx5r8afW1hYGKVLl9ZeDwsLo27durkq81k86b2WKlXqkUFEaWlpREVFPdX/i8a1AC5dukSlSpWeur55rVixYlhbWz8y8j+nf2OlSpV6quMLJUs/RM4rer3e5NcwDqSqVKmS0ul0qnLlymr//v0mv25Rd/PmTVW5cmXVu3dvlZaW9tjjjYOm1q5dq712/vz5Qjlo6mE3btxQOp1Obdq0KW8rZSJPO2hq9uzZ2muxsbEFZtBUSEiI9tq2bdseO2jqYXv37lWAOnHihCmqmSuNGjVS7733nvZnvV6vypQpk+Ogqc6dO2d6rWnTpkVq0FShCNzo6Gj14YcfqpMnT5r8WsbQrVatmtLpdKpChQoqLi7O5Nctqm7evKn8/PxUmzZt1M2bN9WdO3e0r4zHVK1aVR06dEh7bdiwYap8+fIqKChIhYSEqKZNm6qmTZta4hae2D///KOOHTumpk2bplxcXNSxY8fUsWPHMv3/VbVqVbV+/XqllFJxcXHqww8/VAcOHFBXr15VO3bsUPXr11eVK1dWSUlJlrqNJ/K096qUUjNmzFAeHh5q06ZN6uTJk+q1115TFSpUUImJiZa4hSfWsWNHVa9ePXXo0CG1d+9eVblyZdWnTx/t/Yf//7106ZL65JNPVEhIiLp69aratGmTqlixogoICLDULWRp1apVyt7eXi1ZskSdPXtWvfPOO8rDw0PdvXtXKaXUG2+8ocaPH68dv2/fPmVjY6Nmz56tzp07p6ZMmaJsbW3VqVOnLHULZlfgAzcuLk75+voqnU6nevToYZYfXnR0tPr2229VlSpV1J9//mny6xVlixcvVkCWX0ZXr15VgNq5c6f2WmJionr33XeVp6encnJyUl27ds0U0vnRgAEDsrzPjPcFqMWLFyullEpISFDt27dXxYsXV7a2tsrX11e9/fbb2gdefva096pUeiv3//2//6dKliyp7O3tVZs2bdSFCxfMX/mnFBkZqfr06aNcXFyUm5ubGjRoUKZfLB7+//f69esqICBAeXl5KXt7e+Xn56c++ugjFRsba6E7yN68efNU+fLllZ2dnWrUqJE6ePCg9l6LFi3UgAEDMh3/yy+/qCpVqig7Ozv1/PPPq61bt5q5xpZV4HcLio2NpV27doSEhGirQjVp0sTk142JiSE5OZmSJUvKrj9CCCEeq0AHrjHooqKieO211xg2bBj9+vWzdLWEEEKIRxTowIV/97BNTU3F1tZWe33r1q2UL1+eWrVqWbB2QgghRLoC3w9qnNyfMWxnzJjBK6+8wqeffsq5c+csVTUhhBBCU+AD92HJycnaaixBQUF8+umn2vZ6plLAOwmEEEKYQaEKXKUU9vb2/PHHH7Rv356oqCi2bt3KZ599ZrLQvXr1Kps2bcr3W78JIYSwrEIVuDqdDr1ej5eXFytWrKB169bExcWZLHRDQ0Pp168f3bp149dff5WNCoQQQmSrUAUupD/TNYbuL7/8YrLQDQ0N5d133+XgwYPY2trSokULmRokhBAiWwUyIXJ6ZqqUMnnohoaGMnz4cP766y+8vb05ceIElStXfqYyhRB5w7h5wNSpUy1dFSEyKXCBazAYtB15wsPDuXLlCocPHyY+Pp60tDStW9lUoWsM26CgILy8vNizZw/VqlXLy1sUQghRCBWowM24otPSpUvp168fDRs2xN/fn44dOzJ27Fiio6OxtrbGYDDkeeg+HLbBwcFUr17dFLcqhBCikCkwgauU0sL2u+++Y8CAAfz1119ER0eTmprK/v37mTdvHgEBAVy4cEE7Nq9CV8JWCCHEsygwgWvsRl6/fj3vvfceAH379uU///kP3333HY0bN6Z48eKcOXOGl156iZCQEO3cnEJ3+vTpnDp1KsdrS9gKIYR4ZhbaNOGJGQwGpZRSaWlpKjk5WfXs2VPpdDo1YcIEde/ePe24S5cuqRkzZqgKFSoonU6nKlasqI4cOZKpLONeqpGRkapNmzZKp9MpnU6n+vbtqxISErK8/sWLF7Vjvb291dmzZ010p0KIvMD/7Tw0ZcoUS1dFiEzydQs34wApa2trrK2t+fPPP/H392fo0KF4e3trx1WqVIm33nqLcePGUaFCBa5evUqvXr2ybemuWbOGRo0aATBixAgcHR0fuf65c+d47733pGUrxGMkJCTg6uqKTqd7og1EDhw4oI0m/u6777TXo6OjWbx4Mf3796dGjRq4uLhgZ2dHqVKl6NChAz/88AMpKSm5rufUqVO16+Zk165d2nG7du3K9ji9Xs/PP/9M586d8fHxwd7eHm9vb1588UW++uorEhMTc11XUQhZOvGNLdiczJgxQ3Xp0kXduXNHFStWTL333nvZHhsZGakWLFigKlas+EQtXeOm9Xq9/pGyhg4dqnQ6nSpZsqQ6d+7c09yWEEVO//79FaCcnZ1VfHx8jseOGDFCAcrGxkZFRERor/v6+ma7/7Hxq169ejnubUwOLdwpU6Y8sp9yVnbu3JnlHr0Z/fPPP6pOnTo51tXPz69A7NkrzMOiLdyoqCgmTpzIxYsXsz1mw4YNTJgwgU2bNtG3b1/i4+OpXbs2QJYrO3l5edGzZ88nbunWqlUr04CsjGbNmkWLFi3Yvn27TP0R4jGMLdsHDx6wadOmbI9LS0tjzZo1AHTo0IFixYpp7+n1eho3bsynn37Kli1bOHLkCPv27WPZsmV07NgRgGPHjtG7d28T3snjRUZG8uKLL3LixAns7e157733WLNmDUeOHGHnzp1MmDABJycnLl26RKdOnYiNjbVofUU+YamkT0xM1Fqhffr0URcvXszyuJ07d6p27dopZ2dnZWdnp3Q6nXrnnXeUXq/XWqpZiYqKeqSlGxIS8sT1S0lJUUo9WQtcCKFUamqqKlGihALUyy+/nO1xv//+u9YCXLFiRab3svscMPrpp5+0c3fs2JHlMZihhdu3b18FKF9fX3XlypUsyzh69KhydnZWgJo4cWKO1xNFg8VauA8ePOCVV14BYNWqVURFRWV5XMuWLZk6dSoBAQFaK/TgwYPExcVpLdWseHp6PtLS7dChQ6aWbk6M2/097lmPECKdjY0NvXr1AuDPP/8kMjIyy+OWL18OgIuLC6+99lqm9x63YtugQYOoW7cuABs3bny2CufStWvXWL16NQDffvstFSpUyPK4evXqMWLECACWLFliruqJfMxigevt7c348eMZO3Ysy5Yto3Hjxo8cY+wybtasGZMmTaJ169Y4Ojpy6tQpBg4cmGkZx6wYQ3fChAl4eXkRFRUlW+kJYULGbuXU1FR++eWXR95PTEzUgrJLly44OTllW5ZSirt373Lx4kVOnz6tfZUpUwaAEydO5P0NPIGtW7ei1+txcnKiU6dOOR4bEBAAwO3bt7l+/bo5qifyMRtLXrxUqVJ89tln2Nvba6/98ccfVKxYkSpVqmBlZaWtLtW8eXMmTpyITqcjKCiITZs28eabbxIYGKiFrnEz+ow8PT3p2rUrSinq1q1Lw4YNUUpJy1UIE2jcuDGVKlXi8uXLLF++nOHDh2d6f/PmzcTHxwNkO5p569atzJ8/nz179hAXF5ftte7du5d3FX8Kxl6yhIQEbGye/CP07t27lC9f3lTVEgWAxacFZQzbmTNn8tJLLzFt2jQuX74MoIUuQPPmzZkwYQKtWrXC3t6e5cuX88Ybbzy2pevt7c3gwYMlbIUwA2OQ7t+/n2vXrmV6z9idXKJECdq2bZvpPaUUb731Fp07d2br1q05hi1gsSk34eHhuTovISEhj2siChqLtnAzSkhI4M6dOwCsXLkSGxsbJk+eTKVKlbJs6QIEBQWxYsUKIH1t5ZxausbXJGyFMK1+/frxySefoJRi5cqVTJgwAUiflbBt2zYAevXq9Ujr8KeffmLRokUA1K1bl9GjR9O4cWPKlCmDk5OT9m/4zTffZOnSpRZ7PGT8xb5YsWLs3Lnzic/L7lmvKDryTeA6OTkxfvx4XF1d+fzzz1m6dClAnoauEML0qlSpQoMGDQgJCWHFihVa4K5du1ZbtCKr7uQff/wRAD8/P/bv35/lYjRAtgMsn0TG6X8ZN0N52IMHD7Itw7jgTlxcHNWrV5fPGvHELN6lnFGpUqV47733tH+gS5cuZdq0aVy6dAl4tHt50qRJtGnTBgcHB1asWEH//v0z7RIkhLAMY6CePn2akydPAv92J1eqVCnLQZJnzpwB4NVXX802bJVSHD16NNf1cnV11b6Pjo7O9ric1gaoV68eAMnJyU8860EIsEDgZtcNZAzIkiVLMnLkSC10ly1bxieffJJl6DZr1oyJEyfSunVrHBwcWLlyJd27d9dCVwhhGb1799b+DS5fvpybN28SHBwMZD9YKi0tDci5dblp0ybt0VNuZOzWzSksV61ale17r7zyivZoas6cObmuiyiCzDnpN+PyiXq9XsXExGS7acDdu3fVxIkTtQ0G3njjDRUaGpplWfv27VOvvfaaduz58+dNdxNCiCfSrl07Bahy5cqpmTNnagtJZLfUYa1atRSgfHx8VGRk5CPvX7p0Sfn4+Gjl+Pr6ZlkOOSx8ERYWpmxsbBSgOnTokOXCNl9++WWm5RmzWvji9ddf197/73//m+Pfw5UrVx5Z4EMUTWYL3IwBuXz5ctW3b19VtmxZ9cILL6i33npLBQcHa+uvGv8RPE3o7t69W3Xu3Fnt3bs3UxlCCMtYsmSJFkoeHh4KUA0aNMj2+FmzZmnHV6lSRS1atEgdOnRI7d69W02ZMkW5u7srBwcHVb9+/VwHrlJK9enTRzumc+fO6vfff1dHjx5VGzduVN27d1eAatasWY6BGxkZqSpWrKgdExAQoBYuXKgOHDigjh49qrZv365mz56t2rZtq6ysrFT37t1z8TcoChuzBG7G8Pvf//6ndDqdsrKyUjqdTtnY2CidTqfq1Kmjxo0bp225l5vQvX///iOvCSEs4/79+8rR0TFTa/Hrr7/O9viUlBTVvn37bDcCcHR0VL/88osaMGDAMwXu3bt3VeXKlbO9Tu/evdWOHTseu3nBnTt3lL+//2M3WwDUoEGDnu4vTxRKZu1S3rBhgxacr7zyiurfv796++23lbW1tdLpdMrDw0P1799fhYeHK6VyF7pCiPwjY9ertbV1jrv8KJW+HvPcuXNVgwYNlJOTk3J0dFR+fn5q2LBh2o5dzxq4SqWvtT5u3DhVuXJlZW9vr7y8vFRAQIBatmyZUurJdgsy2rJli+rXr5+qWLGicnJyUra2tqp48eKqWbNmauzYsWr37t05ni+KDrO1cFNTU1Xv3r2VTqdTkyZNUnfv3tXe379/v6pXr56ysrJSDg4Oqnfv3o8N3YEDB8q2V0IIIQoMnVLmmT0eHh5O9erVqVOnDj///DNly5ZFp9ORlpaGjY0NJ06cYNSoUezduxdbW1u6dOnC3LlzKV68uLY6VFhYGHPnzmX69OkAvPHGGyxYsAAHBwdz3IIQQgiRa2adFhQbG0v9+vUpV66cNqzexsYGpRR16tRh7ty5vPjii6SmprJx40ZGjhxJREQEOp0OpRQlS5Zk1KhRjB49GkhfcUbCVgghREGQ5y3cjKu3GAwGHjx4gMFgwNHREW9vb2bNmsWwYcMeWdPY+OeTJ08ycuTIHFu64eHhJCYm4uvrm+NqMUIIIUR+kadLO2YMv9WrV/P777+zd+9e7O3tadWqFdbW1hw9elRb5CLj4hTGVmzt2rWZO3euFrrGrbwyhm6JEiW08yRshRBCFAR51sLN2GKdP38+I0aMyLQqlFGVKlUIDg6mePHiWa55nFVL18nJiZYtW7Jo0SKKFy+eF9UVQgghzCrPu5TXr19Pjx49gPQl0Dw9PXF0dGTx4sUYDAbS0tJo27YtmzdvxsHB4bGh+8EHHxAUFISjoyN///031apVy8vqCiGEEGbxzP2xxrzW6/UkJiaybNkyACZOnMiPP/7IkiVLmD9/Pr/++isBAQE4OzuzY8cO+vbtS1JSUpYbDWTsXp41axYtW7Zk27ZtVKtWzWJbcgkhhBDP4plauA8PWEpNTaV48eLUr18/09QfY4v18OHDTJ8+ne3bt5OQkECXLl1YsWLFY1u6CQkJODk5yQApIYQQBdYzpZcx/CZPnkyHDh1ITEzE1taWGjVqZJr6Y9SoUSMmTZpEu3btcHJyYuPGjY9t6UL6XrkZryeEEEIUNM+cYNu2beOzzz5j+/btdOrUCSsrK+rUqQP8u+VexuBt0KDBU4WuEEIIURg8c+DWrFmT/v374+LiwoEDB4iIiCAoKEgL0KxI6AohhChqnjlwy5Qpw8yZM+nWrRt2dnYAnDt3jtu3bwNkG55Zhe6rr76aY1ALIYQQBVWeTQu6ffs2kyZNYtWqVSQnJ9O5c2c2b94MoK2XnJWQkBBmzpzJunXrADhz5gzVq1fPiyoJIYQQ+UaejULy8fHh888/p0+fPjg4OLBlyxa6d+8OpK+XnJaWluV5DRo04MMPP6Rr164EBwdTvXp1mfojhBCi0MnzhS/u3LmjtXSTkpLo2rWr1nrNqaX74MEDnJ2dZeqPEEKIQskk2/PlNnSFEEKIwsokTcnSpUvz2Wef0bt3bxwcHNiwYcMTdS8LIYQQhZXJ+m6Nz3QldIUQQggTdSln9HD3crt27di2bZspLymEEELkOyYPXEifMjR16lQWLlwIyNQfIYQQRY9ZRi/5+PgwZcoUDAYDgwcP1qb+PLzWshBCCFFYmaWFa5SSkoKdnZ1M/RFCCFHkmDVwhRBCiKJKmplCCCGEGUjgCiGEEGYggSuEEEKYgQSuEEIIYQYSuEIIIYQZSOAKIYQQZiCBK4QQQpiBBK4QQghhBhK4QgghhBlI4AohhBBmIIErhBBCmIEErhBCCGEG/x82n7dnBZd3QgAAAABJRU5ErkJggg==",
-      "text/plain": [
-       "<Figure size 400x450 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "forest_plot(contrasts_mini_meta, contrast_type='mini_meta', contrast_labels=['mini_meta1', 'mini_meta2', 'mini_meta3'], horizontal=True);\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "python3",
-   "language": "python",
-   "name": "python3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/settings.ini b/settings.ini
index 5c22d22d..004552ec 100644
--- a/settings.ini
+++ b/settings.ini
@@ -2,8 +2,8 @@
 ### Python library ###
 repo = DABEST-python
 lib_name = dabest
-version = 2024.03.29
-min_python = 3.8
+version = 2025.03.27
+min_python = 3.10
 license = apache2
 
 ### nbdev ###
@@ -37,8 +37,8 @@ language = English
 status = 3
 user = acclab
 
-requirements = fastcore pandas~=1.5.0 numpy~=1.23.5 matplotlib~=3.6.3 seaborn~=0.12.2 scipy~=1.9.3 datetime statsmodels lqrt
-dev_requirements = pytest~=7.2.1 pytest-mpl~=0.16.1
+requirements = fastcore pandas~=2.2.3 numpy~=2.1.0 matplotlib~=3.10.0 seaborn~=0.13.2 scipy~=1.15.2 numba~=0.61.0 datetime statsmodels lqrt tqdm
+dev_requirements = pytest~=8.3.4 pytest-mpl~=0.17.0
 
 ### Optional ###
 # requirements = fastcore pandas
diff --git a/setup.py b/setup.py
index 90568753..35417447 100644
--- a/setup.py
+++ b/setup.py
@@ -22,7 +22,7 @@
 }
 statuses = [ '1 - Planning', '2 - Pre-Alpha', '3 - Alpha',
     '4 - Beta', '5 - Production/Stable', '6 - Mature', '7 - Inactive' ]
-py_versions = '3.8 3.9 3.10 3.11'.split()
+py_versions = '3.10 3.11 3.12 3.13'.split()
 requirements = shlex.split(cfg.get('requirements', ''))
 if cfg.get('pip_requirements'): requirements += shlex.split(cfg.get('pip_requirements', ''))
 min_python = cfg['min_python']