diff --git a/.github/ISSUE_TEMPLATE/bug_report.md b/.github/ISSUE_TEMPLATE/bug_report.md new file mode 100644 index 00000000..0f4d0257 --- /dev/null +++ b/.github/ISSUE_TEMPLATE/bug_report.md @@ -0,0 +1,34 @@ +--- +name: Bug report +about: Create a report to help us improve +title: '' +labels: bug +assignees: '' + +--- + +**Describe the bug** +A clear and concise description of what the bug is. + +**To Reproduce** +Steps to reproduce the behavior: +1. +2. + +**Expected behavior** +A clear and concise description of what you expected to happen. + +**Screenshots** +If applicable, add screenshots to help explain your problem. + +**Your package version (please complete the following information):** + - dabest: [e.g. 2023.3.29] + - pandas: + - numpy: + - matplotlib: + - seaborn: + - scipy: + + +**Additional context** +Add any other context about the problem here. diff --git a/.github/ISSUE_TEMPLATE/feature_request.md b/.github/ISSUE_TEMPLATE/feature_request.md new file mode 100644 index 00000000..b260e30f --- /dev/null +++ b/.github/ISSUE_TEMPLATE/feature_request.md @@ -0,0 +1,23 @@ +--- +name: Feature request +about: Suggest an idea for this project +title: '' +labels: enhancement +assignees: '' + +--- + +**Is your feature request related to a problem? Please describe.** +A clear and concise description of what the problem is. Ex. I'm always frustrated when [...] + +**Describe the solution you'd like** +A clear and concise description of what you want to happen. + +**Describe alternatives you've considered** +A clear and concise description of any alternative solutions or features you've considered. + +**Is a dataset available for testing out the functionality** +If yes, please leave a Google Drive link + +**Additional context** +Add any other context or screenshots about the feature request here. diff --git a/.github/workflows/test.yaml b/.github/workflows/test-nbdev.yaml similarity index 79% rename from .github/workflows/test.yaml rename to .github/workflows/test-nbdev.yaml index 56085923..948a7b69 100644 --- a/.github/workflows/test.yaml +++ b/.github/workflows/test-nbdev.yaml @@ -1,7 +1,7 @@ -name: CI +name: nbdev_prepare on: [workflow_dispatch, pull_request, push] jobs: - test: + test-nbdev: runs-on: ubuntu-latest steps: [uses: fastai/workflows/nbdev-ci@master] diff --git a/.github/workflows/test-image.yaml b/.github/workflows/test-pytest.yaml similarity index 66% rename from .github/workflows/test-image.yaml rename to .github/workflows/test-pytest.yaml index e55c35a9..599c62a6 100644 --- a/.github/workflows/test-image.yaml +++ b/.github/workflows/test-pytest.yaml @@ -2,17 +2,17 @@ name: Python pytest on: [workflow_dispatch, pull_request, push] jobs: - test: + test-pytest: runs-on: ubuntu-latest steps: - uses: actions/checkout@v3 - uses: actions/setup-python@v4 with: - python-version: 3.9 + python-version: 3.8 cache: "pip" cache-dependency-path: settings.ini - name: Run pytest run: | python -m pip install --upgrade pip - pip install .[dev] - pytest nbs/tests/ \ No newline at end of file + pip install -e '.[dev]' + pytest nbs/tests/ --mpl --mpl-baseline-path=nbs/tests/mpl_image_tests/baseline_images diff --git a/.pre-commit-config.yaml.yaml b/.pre-commit-config.yaml similarity index 100% rename from .pre-commit-config.yaml.yaml rename to .pre-commit-config.yaml diff --git a/CHANGELOG.md b/CHANGELOG.md new file mode 100644 index 00000000..a71ebf15 --- /dev/null +++ b/CHANGELOG.md @@ -0,0 +1,30 @@ +# Release notes + + + +## v2024.03.29 + +### New Features + +- **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. + +- **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. + +- **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. + +### Enhancement + +- **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. + + + +## v2023.03.29 + +### New Features +- **Repeated measures**: Augments the prior function for plotting (independent) multiple test groups versus a shared control; it can now do the same for repeated-measures experimental designs. Thus, together, these two methods can be used to replace both flavors of the 1-way ANOVA with an estimation analysis. + +- **Proportional data**: Generates proportional bar plots, proportional differences, and calculates Cohen’s h. Also enables plotting Sankey diagrams for paired binary data. This is the estimation equivalent to a bar chart with Fischer’s exact test. + +- **The ∆∆ plot**: Calculates the delta-delta (∆∆) for 2 × 2 experimental designs and plots the four groups with their relevant effect sizes. This design can be used as a replacement for the 2 × 2 ANOVA. + +- **Mini-meta**: Calculates and plots a weighted delta (∆) for meta-analysis of experimental replicates. Useful for summarizing data from multiple replicated experiments, for example by different scientists in the same lab, or the same scientist at different times. When the observed values are known (and share a common metric), this makes meta-analysis available as a routinely accessible tool. \ No newline at end of file diff --git a/CODE_OF_CONDUCT.md b/CODE_OF_CONDUCT.md new file mode 100644 index 00000000..191d1ab4 --- /dev/null +++ b/CODE_OF_CONDUCT.md @@ -0,0 +1,76 @@ +# Contributor Covenant Code of Conduct + +## Our Pledge + +In the interest of fostering an open and welcoming environment, we as +contributors and maintainers pledge to making participation in our project and +our community a harassment-free experience for everyone, regardless of age, body +size, disability, ethnicity, sex characteristics, gender identity and expression, +level of experience, education, socio-economic status, nationality, personal +appearance, race, religion, or sexual identity and orientation. + +## Our Standards + +Examples of behavior that contributes to creating a positive environment +include: + +* Using welcoming and inclusive language +* Being respectful of differing viewpoints and experiences +* Gracefully accepting constructive criticism +* Focusing on what is best for the community +* Showing empathy towards other community members + +Examples of unacceptable behavior by participants include: + +* The use of sexualized language or imagery and unwelcome sexual attention or + advances +* Trolling, insulting/derogatory comments, and personal or political attacks +* Public or private harassment +* Publishing others' private information, such as a physical or electronic + address, without explicit permission +* Other conduct which could reasonably be considered inappropriate in a + professional setting + +## Our Responsibilities + +Project maintainers are responsible for clarifying the standards of acceptable +behavior and are expected to take appropriate and fair corrective action in +response to any instances of unacceptable behavior. + +Project maintainers have the right and responsibility to remove, edit, or +reject comments, commits, code, wiki edits, issues, and other contributions +that are not aligned to this Code of Conduct, or to ban temporarily or +permanently any contributor for other behaviors that they deem inappropriate, +threatening, offensive, or harmful. + +## Scope + +This Code of Conduct applies both within project spaces and in public spaces +when an individual is representing the project or its community. Examples of +representing a project or community include using an official project e-mail +address, posting via an official social media account, or acting as an appointed +representative at an online or offline event. Representation of a project may be +further defined and clarified by project maintainers. + +## Enforcement + +Instances of abusive, harassing, or otherwise unacceptable behavior may be +reported by contacting the project team at joseshowh@gmail.com. All +complaints will be reviewed and investigated and will result in a response that +is deemed necessary and appropriate to the circumstances. The project team is +obligated to maintain confidentiality with regard to the reporter of an incident. +Further details of specific enforcement policies may be posted separately. + +Project maintainers who do not follow or enforce the Code of Conduct in good +faith may face temporary or permanent repercussions as determined by other +members of the project's leadership. + +## Attribution + +This Code of Conduct is adapted from the [Contributor Covenant][homepage], version 1.4, +available at https://www.contributor-covenant.org/version/1/4/code-of-conduct.html + +[homepage]: https://www.contributor-covenant.org + +For answers to common questions about this code of conduct, see +https://www.contributor-covenant.org/faq diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md new file mode 100644 index 00000000..52b583c1 --- /dev/null +++ b/CONTRIBUTING.md @@ -0,0 +1,23 @@ +# Contributing to DABEST-Python + + +## Did you find a bug? +- Ensure the bug was not already reported by searching in [Issues](https://github.com/ACCLAB/DABEST-python/issues). Check that the bug hasn't been addressed in a closed issue. + +- If the bug isn't being addressed, open a new issue using the Bug report template. Be sure to fill in the necessary information, and a [minimally reproducible code sample](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) demonstrating the expected behavior that is not occurring. + + +## Did you write a patch that fixes a bug? +- Open a new GitHub [pull request](https://help.github.com/en/articles/about-pull-requests) (PR for short) with the patch. + +- Create the PR into the development branch, which is indicated by `v{latest version number}-dev`. + +- Clearly state the problem and solution in the PR description. Include the relevant [issue number](https://guides.github.com/features/issues/) if applicable. + + +## Do you intend to add a new feature or change an existing one? +- Suggest your change by opening an issue using the Feature request template. +- If the maintainers and the community are in favour, create a fork and start writing code. + + +DABEST is a community tool for estimation statistics and analysis. We look forward to more robust and more elegant data visualizations from you all! diff --git a/README.md b/README.md index 4033e127..8ba4290e 100644 --- a/README.md +++ b/README.md @@ -1,36 +1,49 @@ -DABEST-Python -================ +# DABEST-Python +[![minimal Python +version](https://img.shields.io/badge/Python%3E%3D-3.8-6666ff.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) +[![Free-to-view +citation](https://zenodo.org/badge/DOI/10.1038/s41592-019-0470-3.svg)](https://rdcu.be/bHhJ4) +[![License](https://img.shields.io/badge/License-BSD%203--Clause--Clear-orange.svg)](https://spdx.org/licenses/BSD-3-Clause-Clear.html) + ## Recent Version Update -On 20 March 2023, we officially released **DABEST v2023.02.14 for -Python**. This new version provided the following new features: - -1. **Repeated measures.** Augments the prior function for plotting - (independent) multiple test groups versus a shared control; it can - now do the same for repeated-measures experimental designs. Thus, - together, these two methods can be used to replace both flavors of - the 1-way ANOVA with an estimation analysis. - -2. **Proportional data.** Generates proportional bar plots, - proportional differences, and calculates Cohen’s h. Also enables - plotting Sankey diagrams for paired binary data. This is the - estimation equivalent to a bar chart with Fischer’s exact test. - -3. **The $\Delta\Delta$ plot.** Calculates the delta-delta - ($\Delta\Delta$) for 2 × 2 experimental designs and plots the four - groups with their relevant effect sizes. This design can be used as - a replacement for the 2 × 2 ANOVA. - -4. **Mini-meta.** Calculates and plots a weighted delta ($\Delta$) for - meta-analysis of experimental replicates. Useful for summarizing - data from multiple replicated experiments, for example by different - scientists in the same lab, or the same scientist at different - times. When the observed values are known (and share a common - metric), this makes meta-analysis available as a routinely - accessible tool. +On 22 March 2024, we officially released **DABEST Version Ondeh +(v2024.03.29)**. This new version 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. ## Contents @@ -54,21 +67,21 @@ DABEST is a package for **D**ata **A**nalysis using **B**ootstrap-Coupled **EST**imation. [Estimation -statistics](https://en.wikipedia.org/wiki/Estimation_statistics) is a +statistics](https://en.wikipedia.org/wiki/Estimation_statistics) are a [simple framework](https://thenewstatistics.com/itns/) that avoids the [pitfalls](https://www.nature.com/articles/nmeth.3288) of significance -testing. It uses familiar statistical concepts: means, mean differences, -and error bars. More importantly, it focuses on the effect size of one’s -experiment/intervention, as opposed to a false dichotomy engendered by -*P* values. +testing. It employs familiar statistical concepts such as means, mean +differences, and error bars. More importantly, it focuses on the effect +size of one’s experiment or intervention, rather than succumbing to a +false dichotomy engendered by *P* values. -An estimation plot has two key features. +An estimation plot comprises two key features. -1. It presents all datapoints as a swarmplot, which orders each point - to display the underlying distribution. +1. It presents all data points as a swarm plot, ordering each point to + display the underlying distribution. -2. It presents the effect size as a **bootstrap 95% confidence - interval** on a **separate but aligned axes**. +2. It illustrates the effect size as a **bootstrap 95% confidence + interval** on a **separate but aligned axis**. ![The five kinds of estimation plots](showpiece.png "The five kinds of estimation plots.") @@ -78,21 +91,24 @@ allowing everyone access to high-quality estimation plots. ## Installation -This package is tested on Python 3.6, 3.7, and 3.8. It is highly +This package is tested on Python 3.8 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. You can install this package via `pip`. -To install, at the command line run + +or –\> ``` shell -pip install --upgrade dabest +pip install dabest ``` You can also @@ -111,7 +127,7 @@ pip install . import pandas as pd import dabest -# Load the iris dataset. Requires internet access. +# Load the iris dataset. This step requires internet access. iris = pd.read_csv("https://github.com/mwaskom/seaborn-data/raw/master/iris.csv") # Load the above data into `dabest`. @@ -126,8 +142,8 @@ iris_dabest.mean_diff.plot(); dataset](iris.png) Please refer to the official -[tutorial](https://acclab.github.io/DABEST-python-docs/tutorial.html) -for more useful code snippets. +[tutorial](https://acclab.github.io/DABEST-python/) for more useful code +snippets. ## How to cite @@ -145,56 +161,24 @@ PDF](https://rdcu.be/bHhJ4) ## Bugs -Please report any bugs on the [Github issue -tracker](https://github.com/ACCLAB/DABEST-python/issues/new). +Please report any bugs on the [issue +page](https://github.com/ACCLAB/DABEST-python/issues/new). ## Contributing All contributions are welcome; please read the [Guidelines for -contributing](https://github.com/ACCLAB/DABEST-python/blob/master/CONTRIBUTING.md) -first. +contributing](CONTRIBUTING.md) first. -We also have a [Code of -Conduct](https://github.com/ACCLAB/DABEST-python/blob/master/CODE_OF_CONDUCT.md) -to foster an inclusive and productive space. +We also have a [Code of Conduct](CODE_OF_CONDUCT.md) to foster an +inclusive and productive space. ### A wish list for new features -Currently, DABEST offers functions to handle data traditionally analyzed -with Student’s paired and unpaired t-tests. It also offers plots for -multiplexed versions of these, and the estimation counterpart to a 1-way -analysis of variance (ANOVA), the shared-control design. While these -five functions execute a large fraction of common biomedical data -analyses, there remain three others: 2-way data, time-series group data, -and proportional data. We aim to add these new functions to both the R -and Python libraries. - -- In many experiments, four groups are investigate to isolate an - interaction, for example: a genotype × drug effect. Here, wild-type - and mutant animals are each subjected to drug or sham treatments; the - data are traditionally analysed with a 2×2 ANOVA. We have received - requests by email, Twitter, and GitHub to implement an estimation - counterpart to the 2-way ANOVA. To do this, we will implement - $\Delta\Delta$ plots, in which the difference of means ($\Delta$) of - two groups is subtracted from a second two-group $\Delta$. - **Implemented in v2023.02.14.** - -- Currently, DABEST can analyse multiple paired data in a single plot, - and multiple groups with a common, shared control. However, a common - design in biomedical science is to follow the same group of subjects - over multiple, successive time points. An estimation plot for this - would combine elements of the two other designs, and could be used in - place of a repeated-measures ANOVA. **Implemented in v2023.02.14** - -- We have observed that proportional data are often analyzed in - neuroscience and other areas of biomedical research. However, compared - to other data types, the charts are frequently impoverished: often, - they omit error bars, sample sizes, and even P values—let alone effect - sizes. We would like DABEST to feature proportion charts, with error - bars and a curve for the distribution of the proportional differences. - **Implemented in v2023.02.14** - -We encourage contributions for the above 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 +Group](https://groups.google.com/g/estimationstats). ## Acknowledgements @@ -207,13 +191,20 @@ Stanislav Ott. ## Testing -To test DABEST, you will need to install -[pytest](https://docs.pytest.org/en/latest). +To test DABEST, you need to install +[pytest](https://docs.pytest.org/en/latest) and +[nbdev](https://nbdev.fast.ai/). + +- Run `pytest` in the root directory of the source distribution. This + runs the test suite in the folder `dabest/tests/mpl_image_tests`. +- Run `nbdev_test` in the root directory of the source distribution. + This runs the value assertion tests in the folder `dabest/tests` + +The test suite ensures that the bootstrapping functions and the plotting +functions perform as expected. -Run `pytest` in the root directory of the source distribution. This runs -the test suite in the folder `dabest/tests`. The test suite will ensure -that the bootstrapping functions and the plotting functions perform as -expected. +For detailed information, please refer to the [test +folder](nbs/tests/README.md) ## DABEST in other languages diff --git a/bumpver.toml b/bumpver.toml new file mode 100644 index 00000000..ef5e7c48 --- /dev/null +++ b/bumpver.toml @@ -0,0 +1,23 @@ +# bumpver.toml +# This file is used for BumpVer, don't use nbdev_bump_version to bump version +# since it's only available for increasing one digit. +# After finishing all the setup for this package, run through all the notebooks for version updates in docs. + +[bumpver] +current_version = "2023.03.29" +version_pattern = "YYYY.0M.0D" +commit_message = "bump version {old_version} -> {new_version}" +commit = true +tag = true +push = false + +[bumpver.file_patterns] +"bumpver.toml" = [ + 'current_version = "{version}"', +] +"settings.ini" = [ + 'version = {version}' +] +"dabest/__init__.py" = [ + '__version__ = "{version}"' +] diff --git a/dabest/__init__.py b/dabest/__init__.py index faabafbe..6f7d114e 100644 --- a/dabest/__init__.py +++ b/dabest/__init__.py @@ -1,5 +1,6 @@ -from ._api import load +from ._api import load, prop_dataset from ._stats_tools import effsize as effsize -from ._classes import TwoGroupsEffectSize, PermutationTest +from ._effsize_objects import TwoGroupsEffectSize, PermutationTest +from ._dabest_object import Dabest -__version__ = "2023.2.14" +__version__ = "2024.03.29" diff --git a/dabest/_api.py b/dabest/_api.py index b8af2237..7c8d0eac 100644 --- a/dabest/_api.py +++ b/dabest/_api.py @@ -1,14 +1,27 @@ # AUTOGENERATED! DO NOT EDIT! File to edit: ../nbs/API/load.ipynb. # %% auto 0 -__all__ = ['load'] +__all__ = ['load', 'prop_dataset'] # %% ../nbs/API/load.ipynb 4 -def load(data, idx=None, x=None, y=None, paired=None, id_col=None, - ci=95, resamples=5000, random_seed=12345, proportional=False, - delta2 = False, experiment = None, experiment_label = None, - x1_level = None, mini_meta=False): - ''' +def load( + data, + idx=None, + x=None, + y=None, + paired=None, + id_col=None, + ci=95, + resamples=5000, + random_seed=12345, + proportional=False, + delta2=False, + experiment=None, + experiment_label=None, + x1_level=None, + mini_meta=False, +): + """ Loads data in preparation for estimation statistics. This is designed to work with pandas DataFrames. @@ -22,15 +35,15 @@ def load(data, idx=None, x=None, y=None, paired=None, id_col=None, with each individual tuple producing its own contrast plot x : string or list, default None Column name(s) of the independent variable. This can be expressed as - a list of 2 elements if and only if 'delta2' is True; otherwise it + a list of 2 elements if and only if 'delta2' is True; otherwise it can only be a string. y : string, default None Column names for data to be plotted on the x-axis and y-axis. paired : string, default None - The type of the experiment under which the data are obtained. If 'paired' + The type of the experiment under which the data are obtained. If 'paired' is None then the data will not be treated as paired data in the subsequent - calculations. If 'paired' is 'baseline', then in each tuple of x, other - groups will be paired up with the first group (as control). If 'paired' is + calculations. If 'paired' is 'baseline', then in each tuple of x, other + groups will be paired up with the first group (as control). If 'paired' is 'sequential', then in each tuple of x, each group will be paired up with its previous group (as control). id_col : default None. @@ -45,7 +58,7 @@ def load(data, idx=None, x=None, y=None, paired=None, id_col=None, This integer is used to seed the random number generator during bootstrap resampling, ensuring that the confidence intervals reported are replicable. - proportional : boolean, default False. + proportional : boolean, default False. An indicator of whether the data is binary or not. When set to True, it specifies that the data consists of binary data, where the values are limited to 0 and 1. The code is not suitable for analyzing proportion @@ -55,25 +68,115 @@ def load(data, idx=None, x=None, y=None, paired=None, id_col=None, delta2 : boolean, default False Indicator of delta-delta experiment experiment : String, default None - The name of the column of the dataframe which contains the label of + The name of the column of the dataframe which contains the label of experiments experiment_lab : list, default None A list of String to specify the order of subplots for delta-delta plots. - This can be expressed as a list of 2 elements if and only if 'delta2' - is True; otherwise it can only be a string. + This can be expressed as a list of 2 elements if and only if 'delta2' + is True; otherwise it can only be a string. x1_level : list, default None A list of String to specify the order of subplots for delta-delta plots. - This can be expressed as a list of 2 elements if and only if 'delta2' - is True; otherwise it can only be a string. + This can be expressed as a list of 2 elements if and only if 'delta2' + is True; otherwise it can only be a string. mini_meta : boolean, default False Indicator of weighted delta calculation. Returns ------- A `Dabest` object. - ''' - from ._classes import Dabest + """ + from dabest import Dabest - return Dabest(data, idx, x, y, paired, id_col, ci, resamples, random_seed, proportional, delta2, experiment, experiment_label, x1_level, mini_meta) + return Dabest( + data, + idx, + x, + y, + paired, + id_col, + ci, + resamples, + random_seed, + proportional, + delta2, + experiment, + experiment_label, + x1_level, + mini_meta, + ) +# %% ../nbs/API/load.ipynb 5 +import numpy as np +from typing import Union, Optional +import pandas as pd + +def prop_dataset( + group: Union[ + list, tuple, np.ndarray, dict + ], # Accepts lists, tuples, or numpy ndarrays of numeric types. + group_names: Optional[list] = None, +): + """ + Convenient function to generate a dataframe of binary data. + """ + + if isinstance(group, dict): + # If group_names is not provided, use the keys of the dict as group_names + if group_names is None: + group_names = list(group.keys()) + elif not set(group_names) == set(group.keys()): + # Check if the group_names provided is the same as the keys of the dict + raise ValueError("group_names must be the same as the keys of the dict.") + + # Check if the values in the dict are numeric + if not all( + [isinstance(group[name], (list, tuple, np.ndarray)) for name in group_names] + ): + raise ValueError( + "group must be a dict of lists, tuples, or numpy ndarrays of numeric types." + ) + + # Check if the values in the dict only have two elements under each parent key + if not all([len(group[name]) == 2 for name in group_names]): + raise ValueError("Each parent key should have only two elements.") + group_val = group + + else: + if group_names is None: + raise ValueError("group_names must be provided if group is not a dict.") + + # Check if the length of group is two times of the length of group_names + if not len(group) == 2 * len(group_names): + raise ValueError( + "The length of group must be two times of the length of group_names." + ) + group_val = { + group_names[i]: [group[i * 2], group[i * 2 + 1]] + for i in range(len(group_names)) + } + + # Check if the sum of values in group_val under each key are the same + if not all( + [ + sum(group_val[name]) == sum(group_val[group_names[0]]) + for name in group_val.keys() + ] + ): + raise ValueError("The sum of values under each key must be the same.") + + id_col = pd.Series(range(1, sum(group_val[group_names[0]]) + 1)) + + final_df = pd.DataFrame() + + for name in group_val.keys(): + col = ( + np.repeat(0, group_val[name][0]).tolist() + + np.repeat(1, group_val[name][1]).tolist() + ) + df = pd.DataFrame({name: col}) + final_df = pd.concat([final_df, df], axis=1) + + final_df["ID"] = id_col + + return final_df diff --git a/dabest/_bootstrap_tools.py b/dabest/_bootstrap_tools.py index d04a46c8..0951ffb5 100644 --- a/dabest/_bootstrap_tools.py +++ b/dabest/_bootstrap_tools.py @@ -5,12 +5,18 @@ # %% ../nbs/API/bootstrap.ipynb 3 import numpy as np +import pandas as pd +import seaborn as sns +from scipy.stats import norm +from scipy.stats import ttest_1samp, ttest_ind, ttest_rel +from scipy.stats import mannwhitneyu, wilcoxon, norm +import warnings # %% ../nbs/API/bootstrap.ipynb 4 class bootstrap: - ''' - Computes the summary statistic and a bootstrapped confidence interval. - + """ + Computes the summary statistic and a bootstrapped confidence interval. + Returns ------- An `bootstrap` object reporting the summary statistics, percentile CIs, bias-corrected and accelerated (BCa) CIs, and the settings used: @@ -47,85 +53,84 @@ class bootstrap: `pvalue_mann_whitney`: float Two-sided p-value obtained from scipy.stats.mannwhitneyu. If a single array was given (x1 only), returns 'NIL'. The Mann-Whitney U-test is a nonparametric unpaired test of the null hypothesis that x1 and x2 are from the same distribution. See - ''' - def __init__(self, - x1:np.array, # The data in a one-dimensional array form. Only x1 is required. If x2 is given, the bootstrapped summary difference between the two groups (x2-x1) is computed. NaNs are automatically discarded. - x2:np.array=None, # The data in a one-dimensional array form. Only x1 is required. If x2 is given, the bootstrapped summary difference between the two groups (x2-x1) is computed. NaNs are automatically discarded. - paired:bool=False, # Whether or not x1 and x2 are paired samples. If 'paired' is None then the data will not be treated as paired data in the subsequent calculations. If 'paired' is 'baseline', then in each tuple of x, other groups will be paired up with the first group (as control). If 'paired' is 'sequential', then in each tuple of x, each group will be paired up with the previous group (as control). - statfunction:callable=np.mean,#The summary statistic called on data. - smoothboot:bool=False,#Taken from seaborn.algorithms.bootstrap. If True, performs a smoothed bootstrap (draws samples from a kernel destiny estimate). - alpha_level:float=0.05,#Denotes the likelihood that the confidence interval produced does not include the true summary statistic. When alpha = 0.05, a 95% confidence interval is produced. - reps:int=5000 # Number of bootstrap iterations to perform. - ): - - import numpy as np - import pandas as pd - import seaborn as sns - - from scipy.stats import norm - from numpy.random import randint - from scipy.stats import ttest_1samp, ttest_ind, ttest_rel - from scipy.stats import mannwhitneyu, wilcoxon, norm - import warnings + """ + def __init__( + self, + x1: np.array, # The data in a one-dimensional array form. Only x1 is required. If x2 is given, the bootstrapped summary difference between the two groups (x2-x1) is computed. NaNs are automatically discarded. + x2: np.array = None, # The data in a one-dimensional array form. Only x1 is required. If x2 is given, the bootstrapped summary difference between the two groups (x2-x1) is computed. NaNs are automatically discarded. + paired: bool = False, # Whether or not x1 and x2 are paired samples. If 'paired' is None then the data will not be treated as paired data in the subsequent calculations. If 'paired' is 'baseline', then in each tuple of x, other groups will be paired up with the first group (as control). If 'paired' is 'sequential', then in each tuple of x, each group will be paired up with the previous group (as control). + stat_function: callable = np.mean, # The summary statistic called on data. + smoothboot: bool = False, # Taken from seaborn.algorithms.bootstrap. If True, performs a smoothed bootstrap (draws samples from a kernel destiny estimate). + alpha_level: float = 0.05, # Denotes the likelihood that the confidence interval produced does not include the true summary statistic. When alpha = 0.05, a 95% confidence interval is produced. + reps: int = 5000, # Number of bootstrap iterations to perform. + ): # Turn to pandas series. x1 = pd.Series(x1).dropna() diff = False - # Initialise statfunction - if statfunction == None: - statfunction = np.mean + # Initialise stat_function + if stat_function is None: + stat_function = np.mean # Compute two-sided alphas. - if alpha_level > 1. or alpha_level < 0.: + if alpha_level > 1.0 or alpha_level < 0.0: raise ValueError("alpha_level must be between 0 and 1.") - alphas = np.array([alpha_level/2., 1-alpha_level/2.]) + alphas = np.array([alpha_level / 2.0, 1 - alpha_level / 2.0]) - sns_bootstrap_kwargs = {'func': statfunction, - 'n_boot': reps, - 'smooth': smoothboot} + sns_bootstrap_kwargs = { + "func": stat_function, + "n_boot": reps, + "smooth": smoothboot, + } if paired: # check x2 is not None: if x2 is None: - raise ValueError('Please specify x2.') - else: - x2 = pd.Series(x2).dropna() - if len(x1) != len(x2): - raise ValueError('x1 and x2 are not the same length.') - - if (x2 is None) or (paired is not None) : + raise ValueError("Please specify x2.") + + x2 = pd.Series(x2).dropna() + if len(x1) != len(x2): + raise ValueError("x1 and x2 are not the same length.") + if (x2 is None) or (paired is not None): if x2 is None: tx = x1 paired = False ttest_single = ttest_1samp(x1, 0)[1] - ttest_2_ind = 'NIL' - ttest_2_paired = 'NIL' - wilcoxonresult = 'NIL' + ttest_2_ind = "NIL" + ttest_2_paired = "NIL" + wilcoxonresult = "NIL" - elif paired is not None: + else: # only two options to enter here diff = True tx = x2 - x1 - ttest_single = 'NIL' - ttest_2_ind = 'NIL' + ttest_single = "NIL" + ttest_2_ind = "NIL" ttest_2_paired = ttest_rel(x1, x2)[1] - wilcoxonresult = wilcoxon(x1, x2)[1] - mannwhitneyresult = 'NIL' + + try: + wilcoxonresult = wilcoxon(x1, x2)[1] + except ValueError as e: + warnings.warn("Wilcoxon test could not be performed. This might be due " + "to no variability in the difference of the paired groups. \n" + "Error: {}\n" + "For detailed information, please refer to https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.wilcoxon.html " + .format(e)) + mannwhitneyresult = "NIL" # Turns data into array, then tuple. tdata = (tx,) # The value of the statistic function applied # just to the actual data. - summ_stat = statfunction(*tdata) + summ_stat = stat_function(*tdata) statarray = sns.algorithms.bootstrap(tx, **sns_bootstrap_kwargs) statarray.sort() # Get Percentile indices - pct_low_high = np.round((reps-1) * alphas) - pct_low_high = np.nan_to_num(pct_low_high).astype('int') - + pct_low_high = np.round((reps - 1) * alphas) + pct_low_high = np.nan_to_num(pct_low_high).astype("int") elif x2 is not None and paired is None: diff = True @@ -137,42 +142,45 @@ def __init__(self, tdata = exp_statarray - ref_statarray statarray = tdata.copy() statarray.sort() - tdata = (tdata, ) # Note tuple form. + tdata = (tdata,) # Note tuple form. # The difference as one would calculate it. - summ_stat = statfunction(x2) - statfunction(x1) + summ_stat = stat_function(x2) - stat_function(x1) # Get Percentile indices - pct_low_high = np.round((reps-1) * alphas) - pct_low_high = np.nan_to_num(pct_low_high).astype('int') + pct_low_high = np.round((reps - 1) * alphas) + pct_low_high = np.nan_to_num(pct_low_high).astype("int") # Statistical tests. - ttest_single='NIL' - ttest_2_ind = ttest_ind(x1,x2)[1] - ttest_2_paired='NIL' - mannwhitneyresult = mannwhitneyu(x1, x2, alternative='two-sided')[1] - wilcoxonresult = 'NIL' + ttest_single = "NIL" + ttest_2_ind = ttest_ind(x1, x2)[1] + ttest_2_paired = "NIL" + mannwhitneyresult = mannwhitneyu(x1, x2, alternative="two-sided")[1] + wilcoxonresult = "NIL" # Get Bias-Corrected Accelerated indices convenience function invoked. - bca_low_high = bca(tdata, alphas, statarray, - statfunction, summ_stat, reps) + bca_low_high = bca(tdata, alphas, statarray, stat_function, summ_stat, reps) # Warnings for unstable or extreme indices. for ind in [pct_low_high, bca_low_high]: - if np.any(ind == 0) or np.any(ind == reps-1): - warnings.warn("Some values used extremal samples;" - " results are probably unstable.") - elif np.any(ind<10) or np.any(ind>=reps-10): - warnings.warn("Some values used top 10 low/high samples;" - " results may be unstable.") + if np.any(ind == 0) or np.any(ind == reps - 1): + warnings.warn( + "Some values used extremal samples;" + " results are probably unstable." + ) + elif np.any(ind < 10) or np.any(ind >= reps - 10): + warnings.warn( + "Some values used top 10 low/high samples;" + " results may be unstable." + ) self.summary = summ_stat self.is_paired = paired self.is_difference = diff - self.statistic = str(statfunction) + self.statistic = str(stat_function) self.n_reps = reps - self.ci = (1-alpha_level)*100 + self.ci = (1 - alpha_level) * 100 self.stat_array = np.array(statarray) self.pct_ci_low = statarray[pct_low_high[0]] @@ -189,33 +197,33 @@ def __init__(self, self.pvalue_wilcoxon = wilcoxonresult self.pvalue_mann_whitney = mannwhitneyresult - self.results = {'stat_summary': self.summary, - 'is_difference': diff, - 'is_paired': paired, - 'bca_ci_low': self.bca_ci_low, - 'bca_ci_high': self.bca_ci_high, - 'ci': self.ci - } + self.results = { + "stat_summary": self.summary, + "is_difference": diff, + "is_paired": paired, + "bca_ci_low": self.bca_ci_low, + "bca_ci_high": self.bca_ci_high, + "ci": self.ci, + } def __repr__(self): - import numpy as np - - if 'mean' in self.statistic: - stat = 'mean' - elif 'median' in self.statistic: - stat = 'median' + if "mean" in self.statistic: + stat = "mean" + elif "median" in self.statistic: + stat = "median" else: stat = self.statistic - diff_types = {'sequential': 'paired', 'baseline': 'paired', None: 'unpaired'} + diff_types = {"sequential": "paired", "baseline": "paired", None: "unpaired"} if self.is_difference: - a = 'The {} {} difference is {}.'.format(diff_types[self.is_paired], - stat, self.summary) + a = "The {} {} difference is {}.".format( + diff_types[self.is_paired], stat, self.summary + ) else: - a = 'The {} is {}.'.format(stat, self.summary) + a = "The {} is {}.".format(stat, self.summary) - b = '[{} CI: {}, {}]'.format(self.ci, self.bca_ci_low, self.bca_ci_high) - return '\n'.join([a, b]) + b = "[{} CI: {}, {}]".format(self.ci, self.bca_ci_low, self.bca_ci_high) + return "\n".join([a, b]) # %% ../nbs/API/bootstrap.ipynb 5 def jackknife_indexes(data): @@ -228,48 +236,42 @@ def jackknife_indexes(data): For a given set of data Y, the jackknife sample J[i] is defined as the data set Y with the ith data point deleted. """ - import numpy as np - base = np.arange(0,len(data)) - return (np.delete(base,i) for i in base) + base = np.arange(0, len(data)) + return (np.delete(base, i) for i in base) -def bca(data, alphas, statarray, statfunction, ostat, reps): - ''' + +def bca(data, alphas, stat_array, stat_function, ostat, reps): + """ Subroutine called to calculate the BCa statistics. Borrowed heavily from scikits.bootstrap code. - ''' - import warnings - - import numpy as np - import pandas as pd - import seaborn as sns - - from scipy.stats import norm - from numpy.random import randint + """ # The bias correction value. - z0 = norm.ppf( ( 1.0*np.sum(statarray < ostat, axis = 0) ) / reps ) + z0 = norm.ppf((1.0 * np.sum(stat_array < ostat, axis=0)) / reps) # Statistics of the jackknife distribution - jackindexes = jackknife_indexes(data[0]) - jstat = [statfunction(*(x[indexes] for x in data)) - for indexes in jackindexes] - jmean = np.mean(jstat,axis = 0) + jack_indexes = jackknife_indexes(data[0]) + jstat = [stat_function(*(x[indexes] for x in data)) for indexes in jack_indexes] + jmean = np.mean(jstat, axis=0) # Acceleration value - a = np.divide(np.sum( (jmean - jstat)**3, axis = 0 ), - ( 6.0 * np.sum( (jmean - jstat)**2, axis = 0)**1.5 ) - ) + a = np.divide( + np.sum((jmean - jstat) ** 3, axis=0), + (6.0 * np.sum((jmean - jstat) ** 2, axis=0) ** 1.5), + ) if np.any(np.isnan(a)): nanind = np.nonzero(np.isnan(a)) - warnings.warn("Some acceleration values were undefined." - "This is almost certainly because all values" - "for the statistic were equal. Affected" - "confidence intervals will have zero width and" - "may be inaccurate (indexes: {})".format(nanind)) - zs = z0 + norm.ppf(alphas).reshape(alphas.shape+(1,)*z0.ndim) - avals = norm.cdf(z0 + zs/(1-a*zs)) - nvals = np.round((reps-1)*avals) - nvals = np.nan_to_num(nvals).astype('int') + warnings.warn( + "Some acceleration values were undefined." + "This is almost certainly because all values" + "for the statistic were equal. Affected" + "confidence intervals will have zero width and" + "may be inaccurate (indexes: {})".format(nanind) + ) + zs = z0 + norm.ppf(alphas).reshape(alphas.shape + (1,) * z0.ndim) + avals = norm.cdf(z0 + zs / (1 - a * zs)) + nvals = np.round((reps - 1) * avals) + nvals = np.nan_to_num(nvals).astype("int") return nvals diff --git a/dabest/_classes.py b/dabest/_classes.py deleted file mode 100644 index da8a5e17..00000000 --- a/dabest/_classes.py +++ /dev/null @@ -1,2920 +0,0 @@ -# AUTOGENERATED! DO NOT EDIT! File to edit: ../nbs/API/class.ipynb. - -# %% auto 0 -__all__ = ['Dabest', 'DeltaDelta', 'MiniMetaDelta', 'TwoGroupsEffectSize', 'EffectSizeDataFrame', 'PermutationTest'] - -# %% ../nbs/API/class.ipynb 4 -import numpy as np -from scipy.stats import norm -import pandas as pd -from scipy.stats import randint - -# %% ../nbs/API/class.ipynb 6 -class Dabest(object): - - """ - Class for estimation statistics and plots. - """ - - def __init__(self, data, idx, x, y, paired, id_col, ci, - resamples, random_seed, proportional, delta2, - experiment, experiment_label, x1_level, mini_meta): - - """ - Parses and stores pandas DataFrames in preparation for estimation - statistics. You should not be calling this class directly; instead, - use `dabest.load()` to parse your DataFrame prior to analysis. - """ - - # Import standard data science libraries. - import numpy as np - import pandas as pd - import seaborn as sns - - self.__delta2 = delta2 - self.__experiment = experiment - self.__ci = ci - self.__data = data - self.__id_col = id_col - self.__is_paired = paired - self.__resamples = resamples - self.__random_seed = random_seed - self.__proportional = proportional - self.__mini_meta = mini_meta - - # Make a copy of the data, so we don't make alterations to it. - data_in = data.copy() - # data_in.reset_index(inplace=True) - # data_in_index_name = data_in.index.name - - - # Check if it is a valid mini_meta case - if mini_meta is True: - - # Only mini_meta calculation but not proportional and delta-delta function - if proportional is True: - err0 = '`proportional` and `mini_meta` cannot be True at the same time.' - raise ValueError(err0) - elif delta2 is True: - err0 = '`delta` and `mini_meta` cannot be True at the same time.' - raise ValueError(err0) - - # Check if the columns stated are valid - if all([isinstance(i, str) for i in idx]): - if len(pd.unique([t for t in idx]).tolist())!=2: - err0 = '`mini_meta` is True, but `idx` ({})'.format(idx) - err1 = 'does not contain exactly 2 columns.' - raise ValueError(err0 + err1) - elif all([isinstance(i, (tuple, list)) for i in idx]): - all_idx_lengths = [len(t) for t in idx] - if (np.array(all_idx_lengths) != 2).any(): - err1 = "`mini_meta` is True, but some idx " - err2 = "in {} does not consist only of two groups.".format(idx) - raise ValueError(err1 + err2) - - - - # Check if this is a 2x2 ANOVA case and x & y are valid columns - # Create experiment_label and x1_level - if delta2 is True: - if proportional is True: - err0 = '`proportional` and `delta` cannot be True at the same time.' - raise ValueError(err0) - # idx should not be specified - if idx: - err0 = '`idx` should not be specified when `delta2` is True.'.format(len(x)) - raise ValueError(err0) - - # Check if x is valid - if len(x) != 2: - err0 = '`delta2` is True but the number of variables indicated by `x` is {}.'.format(len(x)) - raise ValueError(err0) - else: - for i in x: - if i not in data_in.columns: - err = '{0} is not a column in `data`. Please check.'.format(i) - raise IndexError(err) - - # Check if y is valid - if not y: - err0 = '`delta2` is True but `y` is not indicated.' - raise ValueError(err0) - elif y not in data_in.columns: - err = '{0} is not a column in `data`. Please check.'.format(y) - raise IndexError(err) - - # Check if experiment is valid - if experiment not in data_in.columns: - err = '{0} is not a column in `data`. Please check.'.format(experiment) - raise IndexError(err) - - # Check if experiment_label is valid and create experiment when needed - if experiment_label: - if len(experiment_label) != 2: - err0 = '`experiment_label` does not have a length of 2.' - raise ValueError(err0) - else: - for i in experiment_label: - if i not in data_in[experiment].unique(): - err = '{0} is not an element in the column `{1}` of `data`. Please check.'.format(i, experiment) - raise IndexError(err) - else: - experiment_label = data_in[experiment].unique() - - # Check if x1_level is valid - if x1_level: - if len(x1_level) != 2: - err0 = '`x1_level` does not have a length of 2.' - raise ValueError(err0) - else: - for i in x1_level: - if i not in data_in[x[0]].unique(): - err = '{0} is not an element in the column `{1}` of `data`. Please check.'.format(i, experiment) - raise IndexError(err) - - else: - x1_level = data_in[x[0]].unique() - elif experiment is not None: - experiment_label = data_in[experiment].unique() - x1_level = data_in[x[0]].unique() - self.__experiment_label = experiment_label - self.__x1_level = x1_level - - - # # Check if idx is specified - # if delta2 is False and not idx: - # err = '`idx` is not a column in `data`. Please check.' - # raise IndexError(err) - - - # 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 new column which is a combination of experiment and the first variable - new_col_name = experiment+x[0] - while new_col_name in data_in.columns: - new_col_name += "_" - data_in[new_col_name] = data_in[x[0]].astype(str) + " " + data_in[experiment].astype(str) - - #create idx and record the first and second x variable - idx = [] - for i in list(map(lambda x: str(x), experiment_label)): - temp = [] - for j in list(map(lambda x: str(x), 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) - - # Having parsed the idx, check if it is a kosher paired plot, - # if so stated. - #if paired is True: - # all_idx_lengths = [len(t) for t in self.__idx] - # if (np.array(all_idx_lengths) != 2).any(): - # err1 = "`is_paired` is True, but some idx " - # err2 = "in {} does not consist only of two groups.".format(idx) - # raise ValueError(err1 + err2) - - # Check if there is a typo on paired - if paired is not None: - if paired not in ("baseline", "sequential"): - err = '{} assigned for `paired` is not valid.'.format(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) - - elif y is None and x is not None: - err = 'You have only specified `x`. Please also specify `y`.' - raise ValueError(err) - - # Identify the type of data that was passed in. - elif 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 data_in.columns: - err = '{0} is not a column in `data`. Please check.'.format(x) - raise IndexError(err) - if y not in data_in.columns: - err = '{0} is not a column in `data`. Please check.'.format(y) - raise IndexError(err) - - # check y is numeric. - if not np.issubdtype(data_in[y].dtype, np.number): - err = '{0} is a column in `data`, but it is not numeric.'.format(y) - raise ValueError(err) - - # check all the idx can be found in data_in[x] - for g in all_plot_groups: - if g not in data_in[x].unique(): - err0 = '"{0}" is not a group in the column `{1}`.'.format(g, x) - err1 = " Please check `idx` and try again." - raise IndexError(err0 + err1) - - # Select only rows where the value in the `x` column - # is found in `idx`. - plot_data = data_in[data_in.loc[:, x].isin(all_plot_groups)].copy() - - # plot_data.drop("index", inplace=True, axis=1) - - # Assign attributes - self.__x = x - self.__y = y - self.__xvar = x - self.__yvar = y - - elif x is None and y is None: - # Assume we have a wide dataset. - # Assign attributes appropriately. - self.__x = None - self.__y = None - self.__xvar = "group" - self.__yvar = "value" - - # First, check we have all columns in the dataset. - for g in all_plot_groups: - if g not in data_in.columns: - err0 = '"{0}" is not a column in `data`.'.format(g) - err1 = " Please check `idx` and try again." - raise IndexError(err0 + err1) - - set_all_columns = set(data_in.columns.tolist()) - set_all_plot_groups = set(all_plot_groups) - id_vars = set_all_columns.difference(set_all_plot_groups) - - plot_data = pd.melt(data_in, - id_vars=id_vars, - value_vars=all_plot_groups, - value_name=self.__yvar, - var_name=self.__xvar) - - # Added in v0.2.7. - # remove any NA rows. - plot_data.dropna(axis=0, how='any', subset=[self.__yvar], inplace=True) - - - # Lines 131 to 140 added in v0.2.3. - # Fixes a bug that jammed up when the xvar column was already - # a pandas Categorical. Now we check for this and act appropriately. - if isinstance(plot_data[self.__xvar].dtype, - pd.CategoricalDtype) is True: - plot_data[self.__xvar].cat.remove_unused_categories(inplace=True) - plot_data[self.__xvar].cat.reorder_categories(all_plot_groups, - ordered=True, - inplace=True) - else: - plot_data.loc[:, self.__xvar] = pd.Categorical(plot_data[self.__xvar], - categories=all_plot_groups, - ordered=True) - - # # The line below was added in v0.2.4, removed in v0.2.5. - # plot_data.dropna(inplace=True) - - self.__plot_data = plot_data - - self.__all_plot_groups = all_plot_groups - - - # Sanity check that all idxs are paired, if so desired. - #if paired is True: - # if id_col is None: - # err = "`id_col` must be specified if `is_paired` is set to True." - # raise IndexError(err) - # elif id_col not in plot_data.columns: - # err = "{} is not a column in `data`. ".format(id_col) - # raise IndexError(err) - - # Check if `id_col` is valid - if paired: - if id_col is None: - err = "`id_col` must be specified if `paired` is assigned with a not NoneType value." - raise IndexError(err) - elif id_col not in plot_data.columns: - err = "{} is not a column in `data`. ".format(id_col) - raise IndexError(err) - - EffectSizeDataFrame_kwargs = dict(ci=ci, is_paired=paired, - random_seed=random_seed, - resamples=resamples, - proportional=proportional, - delta2=delta2, - experiment_label=self.__experiment_label, - x1_level=self.__x1_level, - x2=self.__x2, - mini_meta = mini_meta) - - self.__mean_diff = EffectSizeDataFrame(self, "mean_diff", - **EffectSizeDataFrame_kwargs) - - self.__median_diff = EffectSizeDataFrame(self, "median_diff", - **EffectSizeDataFrame_kwargs) - - self.__cohens_d = EffectSizeDataFrame(self, "cohens_d", - **EffectSizeDataFrame_kwargs) - - self.__cohens_h = EffectSizeDataFrame(self, "cohens_h", - **EffectSizeDataFrame_kwargs) - - self.__hedges_g = EffectSizeDataFrame(self, "hedges_g", - **EffectSizeDataFrame_kwargs) - - if not paired: - self.__cliffs_delta = EffectSizeDataFrame(self, "cliffs_delta", - **EffectSizeDataFrame_kwargs) - else: - self.__cliffs_delta = "The data is paired; Cliff's delta is therefore undefined." - - - def __repr__(self): - from .__init__ import __version__ - import datetime as dt - import numpy as np - - from .misc_tools import print_greeting - - # Removed due to the deprecation of is_paired - #if self.__is_paired: - # es = "Paired e" - #else: - # es = "E" - - greeting_header = print_greeting() - - RM_STATUS = {'baseline' : 'for repeated measures against baseline \n', - 'sequential': 'for the sequential design of repeated-measures experiment \n', - 'None' : '' - } - - PAIRED_STATUS = {'baseline' : 'Paired e', - 'sequential' : 'Paired e', - 'None' : 'E' - } - - first_line = {"rm_status" : RM_STATUS[str(self.__is_paired)], - "paired_status": PAIRED_STATUS[str(self.__is_paired)]} - - s1 = "{paired_status}ffect size(s) {rm_status}".format(**first_line) - s2 = "with {}% confidence intervals will be computed for:".format(self.__ci) - desc_line = s1 + s2 - - out = [greeting_header + "\n\n" + desc_line] - - comparisons = [] - - if self.__is_paired == 'sequential': - for j, current_tuple in enumerate(self.__idx): - for ix, test_name in enumerate(current_tuple[1:]): - control_name = current_tuple[ix] - comparisons.append("{} minus {}".format(test_name, control_name)) - else: - for j, current_tuple in enumerate(self.__idx): - control_name = current_tuple[0] - - for ix, test_name in enumerate(current_tuple[1:]): - comparisons.append("{} minus {}".format(test_name, control_name)) - - if self.__delta2 is True: - comparisons.append("{} minus {} (only for mean difference)".format(self.__experiment_label[1], self.__experiment_label[0])) - - if self.__mini_meta is True: - comparisons.append("weighted delta (only for mean difference)") - - for j, g in enumerate(comparisons): - out.append("{}. {}".format(j+1, g)) - - resamples_line1 = "\n{} resamples ".format(self.__resamples) - resamples_line2 = "will be used to generate the effect size bootstraps." - out.append(resamples_line1 + resamples_line2) - - return "\n".join(out) - - - # def __variable_name(self): - # return [k for k,v in locals().items() if v is self] - # - # @property - # def variable_name(self): - # return self.__variable_name() - - @property - def mean_diff(self): - """ - Returns an :py:class:`EffectSizeDataFrame` for the mean difference, its confidence interval, and relevant statistics, for all comparisons as indicated via the `idx` and `paired` argument in `dabest.load()` - - """ - return self.__mean_diff - - - @property - def median_diff(self): - """ - Returns an :py:class:`EffectSizeDataFrame` for the median difference, its confidence interval, and relevant statistics, for all comparisons as indicated via the `idx` and `paired` argument in `dabest.load()`. - - """ - return self.__median_diff - - - @property - def cohens_d(self): - """ - Returns an :py:class:`EffectSizeDataFrame` for the standardized mean difference Cohen's `d`, its confidence interval, and relevant statistics, for all comparisons as indicated via the `idx` and `paired` argument in `dabest.load()`. - - """ - return self.__cohens_d - - - @property - def cohens_h(self): - """ - Returns an :py:class:`EffectSizeDataFrame` for the standardized mean difference Cohen's `h`, its confidence interval, and relevant statistics, for all comparisons as indicated via the `idx` and `directional` argument in `dabest.load()`. - - """ - return self.__cohens_h - - - @property - def hedges_g(self): - """ - Returns an :py:class:`EffectSizeDataFrame` for the standardized mean difference Hedges' `g`, its confidence interval, and relevant statistics, for all comparisons as indicated via the `idx` and `paired` argument in `dabest.load()`. - - """ - return self.__hedges_g - - - @property - def cliffs_delta(self): - """ - Returns an :py:class:`EffectSizeDataFrame` for Cliff's delta, its confidence interval, and relevant statistics, for all comparisons as indicated via the `idx` and `paired` argument in `dabest.load()`. - - """ - return self.__cliffs_delta - - - @property - def data(self): - """ - Returns the pandas DataFrame that was passed to `dabest.load()`. - When `delta2` is True, a new column is added to support the - function. The name of this new column is indicated by `x`. - """ - return self.__data - - - @property - def idx(self): - """ - Returns the order of categories that was passed to `dabest.load()`. - """ - return self.__idx - - - @property - def x1(self): - """ - Returns the first variable declared in x when it is a delta-delta - case; returns None otherwise. - """ - return self.__x1 - - - @property - def x1_level(self): - """ - Returns the levels of first variable declared in x when it is a - delta-delta case; returns None otherwise. - """ - return self.__x1_level - - - @property - def x2(self): - """ - Returns the second variable declared in x when it is a delta-delta - case; returns None otherwise. - """ - return self.__x2 - - - @property - def experiment(self): - """ - Returns the column name of experiment labels that was passed to - `dabest.load()` when it is a delta-delta case; returns None otherwise. - """ - return self.__experiment - - - @property - def experiment_label(self): - """ - Returns the experiment labels in order that was passed to `dabest.load()` - when it is a delta-delta case; returns None otherwise. - """ - return self.__experiment_label - - - @property - def delta2(self): - """ - Returns the boolean parameter indicating if this is a delta-delta - situation. - """ - return self.__delta2 - - - @property - def is_paired(self): - """ - Returns the type of repeated-measures experiment. - """ - return self.__is_paired - - - @property - def id_col(self): - """ - Returns the id column declared to `dabest.load()`. - """ - return self.__id_col - - - @property - def ci(self): - """ - The width of the desired confidence interval. - """ - return self.__ci - - - @property - def resamples(self): - """ - The number of resamples used to generate the bootstrap. - """ - return self.__resamples - - - @property - def random_seed(self): - """ - The number used to initialise the numpy random seed generator, ie. - `seed_value` from `numpy.random.seed(seed_value)` is returned. - """ - return self.__random_seed - - - @property - def x(self): - """ - Returns the x column that was passed to `dabest.load()`, if any. - When `delta2` is True, `x` returns the name of the new column created - for the delta-delta situation. To retrieve the 2 variables passed into - `x` when `delta2` is True, please call `x1` and `x2` instead. - """ - return self.__x - - - @property - def y(self): - """ - Returns the y column that was passed to `dabest.load()`, if any. - """ - return self.__y - - - @property - def _xvar(self): - """ - Returns the xvar in dabest.plot_data. - """ - return self.__xvar - - - @property - def _yvar(self): - """ - Returns the yvar in dabest.plot_data. - """ - return self.__yvar - - - @property - def _plot_data(self): - """ - Returns the pandas DataFrame used to produce the estimation stats/plots. - """ - return self.__plot_data - - - @property - def proportional(self): - """ - Returns the proportional parameter class. - """ - return self.__proportional - - - @property - def mini_meta(self): - """ - Returns the mini_meta boolean parameter. - """ - return self.__mini_meta - - - @property - def _all_plot_groups(self): - """ - Returns the all plot groups, as indicated via the `idx` keyword. - """ - return self.__all_plot_groups - -# %% ../nbs/API/class.ipynb 25 -class DeltaDelta(object): - """ - 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: - - - $$\Delta_{1} = \overline{X}_{A_{2}, B_{1}} - \overline{X}_{A_{1}, B_{1}}$$ - - $$\Delta_{2} = \overline{X}_{A_{2}, B_{2}} - \overline{X}_{A_{1}, B_{2}}$$ - - - where $\overline{X}_{A_{i}, B_{j}}$ is the mean of the sample with A = i and B = j, $\Delta$ is the mean difference between two samples. - - A delta-delta value is then calculated as the mean difference between the two primary deltas: - - - $$\Delta_{\Delta} = \Delta_{2} - \Delta_{1}$$ - - """ - - def __init__(self, effectsizedataframe, permutation_count, - ci=95): - - import numpy as np - from numpy import sort as npsort - from numpy import sqrt, isinf, isnan - from ._stats_tools import effsize as es - from ._stats_tools import confint_1group as ci1g - from ._stats_tools import confint_2group_diff as ci2g - - - from string import Template - import warnings - - self.__effsizedf = effectsizedataframe.results - self.__dabest_obj = effectsizedataframe.dabest_obj - self.__ci = ci - self.__resamples = effectsizedataframe.resamples - self.__alpha = ci2g._compute_alpha_from_ci(ci) - self.__permutation_count = permutation_count - self.__bootstraps = np.array(self.__effsizedf["bootstraps"]) - self.__control = self.__dabest_obj.experiment_label[0] - self.__test = self.__dabest_obj.experiment_label[1] - - - # Compute the bootstrap delta-delta and the true dela-delta based on - # the raw data - self.__bootstraps_delta_delta = self.__bootstraps[1] - self.__bootstraps[0] - - self.__difference = self.__effsizedf["difference"][1] - self.__effsizedf["difference"][0] - - - - sorted_delta_delta = npsort(self.__bootstraps_delta_delta) - - self.__bias_correction = ci2g.compute_meandiff_bias_correction( - self.__bootstraps_delta_delta, self.__difference) - - self.__jackknives = np.array(ci1g.compute_1group_jackknife( - self.__bootstraps_delta_delta, - np.mean)) - - self.__acceleration_value = ci2g._calc_accel(self.__jackknives) - - # Compute BCa intervals. - bca_idx_low, bca_idx_high = ci2g.compute_interval_limits( - self.__bias_correction, self.__acceleration_value, - self.__resamples, ci) - - self.__bca_interval_idx = (bca_idx_low, bca_idx_high) - - if ~isnan(bca_idx_low) and ~isnan(bca_idx_high): - self.__bca_low = sorted_delta_delta[bca_idx_low] - self.__bca_high = sorted_delta_delta[bca_idx_high] - - err1 = "The $lim_type limit of the interval" - err2 = "was in the $loc 10 values." - err3 = "The result 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 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.__bca_low = self.__difference - warnings.warn(err_temp.substitute(lim_type="lower"), - stacklevel=0) - - if isnan(bca_idx_high): - self.__bca_high = self.__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.__pct_interval_idx = (pct_idx_low, pct_idx_high) - self.__pct_low = sorted_delta_delta[pct_idx_low] - self.__pct_high = sorted_delta_delta[pct_idx_high] - - - - def __permutation_test(self): - """ - Perform a permutation test and obtain the permutation p-value - based on the permutation data. - """ - import numpy as np - self.__permutations = np.array(self.__effsizedf["permutations"]) - - THRESHOLD = np.abs(self.__difference) - - self.__permutations_delta_delta = np.array(self.__permutations[1]-self.__permutations[0]) - - count = sum(np.abs(self.__permutations_delta_delta)>THRESHOLD) - self.__pvalue_permutation = count/self.__permutation_count - - - - def __repr__(self, header=True, sigfig=3): - from .__init__ import __version__ - import datetime as dt - import numpy as np - - from .misc_tools import print_greeting - - first_line = {"control" : self.__control, - "test" : self.__test} - - out1 = "The delta-delta between {control} and {test} ".format(**first_line) - - base_string_fmt = "{:." + str(sigfig) + "}" - if "." in str(self.__ci): - ci_width = base_string_fmt.format(self.__ci) - else: - ci_width = str(self.__ci) - - ci_out = {"es" : base_string_fmt.format(self.__difference), - "ci" : ci_width, - "bca_low" : base_string_fmt.format(self.__bca_low), - "bca_high" : base_string_fmt.format(self.__bca_high)} - - out2 = "is {es} [{ci}%CI {bca_low}, {bca_high}].".format(**ci_out) - out = out1 + out2 - - if header is True: - out = print_greeting() + "\n" + "\n" + out - - - pval_rounded = base_string_fmt.format(self.pvalue_permutation) - - - p1 = "The p-value of the two-sided permutation t-test is {}, ".format(pval_rounded) - p2 = "calculated for legacy purposes only. " - pvalue = p1 + p2 - - - bs1 = "{} bootstrap samples were taken; ".format(self.__resamples) - bs2 = "the confidence interval is bias-corrected and accelerated." - bs = bs1 + bs2 - - pval_def1 = "Any p-value reported is the probability of observing the " + \ - "effect size (or greater),\nassuming the null hypothesis of " + \ - "zero difference is true." - pval_def2 = "\nFor each p-value, 5000 reshuffles of the " + \ - "control and test labels were performed." - pval_def = pval_def1 + pval_def2 - - - return "{}\n{}\n\n{}\n{}".format(out, pvalue, bs, pval_def) - - - def to_dict(self): - """ - Returns the attributes of the `DeltaDelta` object as a - dictionary. - """ - # Only get public (user-facing) attributes. - attrs = [a for a in dir(self) - if not a.startswith(("_", "to_dict"))] - out = {} - for a in attrs: - out[a] = getattr(self, a) - return out - - - @property - def ci(self): - """ - Returns the width of the confidence interval, in percent. - """ - return self.__ci - - - @property - def alpha(self): - """ - Returns the significance level of the statistical test as a float - between 0 and 1. - """ - return self.__alpha - - - @property - def bias_correction(self): - return self.__bias_correction - - - @property - def bootstraps(self): - ''' - Return the bootstrapped deltas from all the experiment groups. - ''' - return self.__bootstraps - - - @property - def jackknives(self): - return self.__jackknives - - - @property - def acceleration_value(self): - return self.__acceleration_value - - - @property - def bca_low(self): - """ - The bias-corrected and accelerated confidence interval lower limit. - """ - return self.__bca_low - - - @property - def bca_high(self): - """ - The bias-corrected and accelerated confidence interval upper limit. - """ - return self.__bca_high - - - @property - def bca_interval_idx(self): - return self.__bca_interval_idx - - - @property - def control(self): - ''' - Return the name of the control experiment group. - ''' - return self.__control - - - @property - def test(self): - ''' - Return the name of the test experiment group. - ''' - return self.__test - - - @property - def bootstraps_delta_delta(self): - ''' - Return the delta-delta values calculated from the bootstrapped - deltas. - ''' - return self.__bootstraps_delta_delta - - - @property - def difference(self): - ''' - Return the delta-delta value calculated based on the raw data. - ''' - return self.__difference - - - @property - def pct_interval_idx (self): - return self.__pct_interval_idx - - - @property - def pct_low(self): - """ - The percentile confidence interval lower limit. - """ - return self.__pct_low - - - @property - def pct_high(self): - """ - The percentile confidence interval lower limit. - """ - return self.__pct_high - - - @property - def pvalue_permutation(self): - try: - return self.__pvalue_permutation - except AttributeError: - self.__permutation_test() - return self.__pvalue_permutation - - - @property - def permutation_count(self): - """ - The number of permuations taken. - """ - return self.__permutation_count - - - @property - def permutations(self): - ''' - Return the mean differences of permutations obtained during - the permutation test for each experiment group. - ''' - try: - return self.__permutations - except AttributeError: - self.__permutation_test() - return self.__permutations - - - @property - def permutations_delta_delta(self): - ''' - Return the delta-delta values of permutations obtained - during the permutation test. - ''' - try: - return self.__permutations_delta_delta - except AttributeError: - self.__permutation_test() - return self.__permutations_delta_delta - - - -# %% ../nbs/API/class.ipynb 29 -class MiniMetaDelta(object): - """ - A class to compute and store the weighted delta. - A weighted delta is calculated if the argument ``mini_meta=True`` is passed during ``dabest.load()``. - - """ - - def __init__(self, effectsizedataframe, permutation_count, - ci=95): - - import numpy as np - from numpy import sort as npsort - from numpy import sqrt, isinf, isnan - from ._stats_tools import effsize as es - from ._stats_tools import confint_1group as ci1g - from ._stats_tools import confint_2group_diff as ci2g - - - from string import Template - import warnings - - self.__effsizedf = effectsizedataframe.results - self.__dabest_obj = effectsizedataframe.dabest_obj - self.__ci = ci - self.__resamples = effectsizedataframe.resamples - self.__alpha = ci2g._compute_alpha_from_ci(ci) - self.__permutation_count = permutation_count - self.__bootstraps = np.array(self.__effsizedf["bootstraps"]) - self.__control = np.array(self.__effsizedf["control"]) - self.__test = np.array(self.__effsizedf["test"]) - self.__control_N = np.array(self.__effsizedf["control_N"]) - self.__test_N = np.array(self.__effsizedf["test_N"]) - - - idx = self.__dabest_obj.idx - dat = self.__dabest_obj._plot_data - xvar = self.__dabest_obj._xvar - yvar = self.__dabest_obj._yvar - - # compute the variances of each control group and each test group - control_var=[] - test_var=[] - for j, current_tuple in enumerate(idx): - cname = current_tuple[0] - control = dat[dat[xvar] == cname][yvar].copy() - control_var.append(np.var(control, ddof=1)) - - tname = current_tuple[1] - test = dat[dat[xvar] == tname][yvar].copy() - test_var.append(np.var(test, ddof=1)) - self.__control_var = np.array(control_var) - self.__test_var = np.array(test_var) - - # Compute pooled group variances for each pair of experiment groups - # based on the raw data - self.__group_var = ci2g.calculate_group_var(self.__control_var, - self.__control_N, - self.__test_var, - self.__test_N) - - # Compute the weighted average mean differences of the bootstrap data - # using the pooled group variances of the raw data as the inverse of - # weights - self.__bootstraps_weighted_delta = ci2g.calculate_weighted_delta( - self.__group_var, - self.__bootstraps, - self.__resamples) - - # Compute the weighted average mean difference based on the raw data - self.__difference = es.weighted_delta(self.__effsizedf["difference"], - self.__group_var) - - sorted_weighted_deltas = npsort(self.__bootstraps_weighted_delta) - - - self.__bias_correction = ci2g.compute_meandiff_bias_correction( - self.__bootstraps_weighted_delta, self.__difference) - - self.__jackknives = np.array(ci1g.compute_1group_jackknife( - self.__bootstraps_weighted_delta, - np.mean)) - - self.__acceleration_value = ci2g._calc_accel(self.__jackknives) - - # Compute BCa intervals. - bca_idx_low, bca_idx_high = ci2g.compute_interval_limits( - self.__bias_correction, self.__acceleration_value, - self.__resamples, ci) - - self.__bca_interval_idx = (bca_idx_low, bca_idx_high) - - if ~isnan(bca_idx_low) and ~isnan(bca_idx_high): - self.__bca_low = sorted_weighted_deltas[bca_idx_low] - self.__bca_high = sorted_weighted_deltas[bca_idx_high] - - err1 = "The $lim_type limit of the interval" - err2 = "was in the $loc 10 values." - err3 = "The result 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 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.__bca_low = self.__difference - warnings.warn(err_temp.substitute(lim_type="lower"), - stacklevel=0) - - if isnan(bca_idx_high): - self.__bca_high = self.__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.__pct_interval_idx = (pct_idx_low, pct_idx_high) - self.__pct_low = sorted_weighted_deltas[pct_idx_low] - self.__pct_high = sorted_weighted_deltas[pct_idx_high] - - - - def __permutation_test(self): - """ - Perform a permutation test and obtain the permutation p-value - based on the permutation data. - """ - import numpy as np - self.__permutations = np.array(self.__effsizedf["permutations"]) - self.__permutations_var = np.array(self.__effsizedf["permutations_var"]) - - THRESHOLD = np.abs(self.__difference) - - all_num = [] - all_denom = [] - - groups = len(self.__permutations) - for i in range(0, len(self.__permutations[0])): - weight = [1/self.__permutations_var[j][i] for j in range(0, groups)] - all_num.append(np.sum([weight[j]*self.__permutations[j][i] for j in range(0, groups)])) - all_denom.append(np.sum(weight)) - - output=[] - for i in range(0, len(all_num)): - output.append(all_num[i]/all_denom[i]) - - self.__permutations_weighted_delta = np.array(output) - - count = sum(np.abs(self.__permutations_weighted_delta)>THRESHOLD) - self.__pvalue_permutation = count/self.__permutation_count - - - - def __repr__(self, header=True, sigfig=3): - from .__init__ import __version__ - import datetime as dt - import numpy as np - - from .misc_tools import print_greeting - - is_paired = self.__dabest_obj.is_paired - - PAIRED_STATUS = {'baseline' : 'paired', - 'sequential' : 'paired', - 'None' : 'unpaired' - } - - first_line = {"paired_status": PAIRED_STATUS[str(is_paired)]} - - - out1 = "The weighted-average {paired_status} mean differences ".format(**first_line) - - base_string_fmt = "{:." + str(sigfig) + "}" - if "." in str(self.__ci): - ci_width = base_string_fmt.format(self.__ci) - else: - ci_width = str(self.__ci) - - ci_out = {"es" : base_string_fmt.format(self.__difference), - "ci" : ci_width, - "bca_low" : base_string_fmt.format(self.__bca_low), - "bca_high" : base_string_fmt.format(self.__bca_high)} - - out2 = "is {es} [{ci}%CI {bca_low}, {bca_high}].".format(**ci_out) - out = out1 + out2 - - if header is True: - out = print_greeting() + "\n" + "\n" + out - - - pval_rounded = base_string_fmt.format(self.pvalue_permutation) - - - p1 = "The p-value of the two-sided permutation t-test is {}, ".format(pval_rounded) - p2 = "calculated for legacy purposes only. " - pvalue = p1 + p2 - - - bs1 = "{} bootstrap samples were taken; ".format(self.__resamples) - bs2 = "the confidence interval is bias-corrected and accelerated." - bs = bs1 + bs2 - - pval_def1 = "Any p-value reported is the probability of observing the" + \ - "effect size (or greater),\nassuming the null hypothesis of" + \ - "zero difference is true." - pval_def2 = "\nFor each p-value, 5000 reshuffles of the " + \ - "control and test labels were performed." - pval_def = pval_def1 + pval_def2 - - - return "{}\n{}\n\n{}\n{}".format(out, pvalue, bs, pval_def) - - - def to_dict(self): - """ - Returns all attributes of the `dabest.MiniMetaDelta` object as a - dictionary. - """ - # Only get public (user-facing) attributes. - attrs = [a for a in dir(self) - if not a.startswith(("_", "to_dict"))] - out = {} - for a in attrs: - out[a] = getattr(self, a) - return out - - - @property - def ci(self): - """ - Returns the width of the confidence interval, in percent. - """ - return self.__ci - - - @property - def alpha(self): - """ - Returns the significance level of the statistical test as a float - between 0 and 1. - """ - return self.__alpha - - - @property - def bias_correction(self): - return self.__bias_correction - - - @property - def bootstraps(self): - ''' - Return the bootstrapped differences from all the experiment groups. - ''' - return self.__bootstraps - - - @property - def jackknives(self): - return self.__jackknives - - - @property - def acceleration_value(self): - return self.__acceleration_value - - - @property - def bca_low(self): - """ - The bias-corrected and accelerated confidence interval lower limit. - """ - return self.__bca_low - - - @property - def bca_high(self): - """ - The bias-corrected and accelerated confidence interval upper limit. - """ - return self.__bca_high - - - @property - def bca_interval_idx(self): - return self.__bca_interval_idx - - - @property - def control(self): - ''' - Return the names of the control groups from all the experiment - groups in order. - ''' - return self.__control - - - @property - def test(self): - ''' - Return the names of the test groups from all the experiment - groups in order. - ''' - return self.__test - - @property - def control_N(self): - ''' - Return the sizes of the control groups from all the experiment - groups in order. - ''' - return self.__control_N - - - @property - def test_N(self): - ''' - Return the sizes of the test groups from all the experiment - groups in order. - ''' - return self.__test_N - - - @property - def control_var(self): - ''' - Return the estimated population variances of the control groups - from all the experiment groups in order. Here the population - variance is estimated from the sample variance. - ''' - return self.__control_var - - - @property - def test_var(self): - ''' - Return the estimated population variances of the control groups - from all the experiment groups in order. Here the population - variance is estimated from the sample variance. - ''' - return self.__test_var - - - @property - def group_var(self): - ''' - Return the pooled group variances of all the experiment groups - in order. - ''' - return self.__group_var - - - @property - def bootstraps_weighted_delta(self): - ''' - Return the weighted-average mean differences calculated from the bootstrapped - deltas and weights across the experiment groups, where the weights are - the inverse of the pooled group variances. - ''' - return self.__bootstraps_weighted_delta - - - @property - def difference(self): - ''' - Return the weighted-average delta calculated from the raw data. - ''' - return self.__difference - - - @property - def pct_interval_idx (self): - return self.__pct_interval_idx - - - @property - def pct_low(self): - """ - The percentile confidence interval lower limit. - """ - return self.__pct_low - - - @property - def pct_high(self): - """ - The percentile confidence interval lower limit. - """ - return self.__pct_high - - - @property - def pvalue_permutation(self): - try: - return self.__pvalue_permutation - except AttributeError: - self.__permutation_test() - return self.__pvalue_permutation - - - @property - def permutation_count(self): - """ - The number of permuations taken. - """ - return self.__permutation_count - - - @property - def permutations(self): - ''' - Return the mean differences of permutations obtained during - the permutation test for each experiment group. - ''' - try: - return self.__permutations - except AttributeError: - self.__permutation_test() - return self.__permutations - - - @property - def permutations_var(self): - ''' - Return the pooled group variances of permutations obtained during - the permutation test for each experiment group. - ''' - try: - return self.__permutations_var - except AttributeError: - self.__permutation_test() - return self.__permutations_var - - - @property - def permutations_weighted_delta(self): - ''' - Return the weighted-average deltas of permutations obtained - during the permutation test. - ''' - try: - return self.__permutations_weighted_delta - except AttributeError: - self.__permutation_test() - return self.__permutations_weighted_delta - - - -# %% ../nbs/API/class.ipynb 34 -class TwoGroupsEffectSize(object): - - """ - A class to compute and store the results of bootstrapped - mean differences between two groups. - - Compute the effect size between two groups. - - Parameters - ---------- - control : array-like - test : array-like - These should be numerical iterables. - effect_size : string. - Any one of the following are accepted inputs: - 'mean_diff', 'median_diff', 'cohens_d', 'hedges_g', or 'cliffs_delta' - is_paired : string, default None - resamples : int, default 5000 - The number of bootstrap resamples to be taken for the calculation - of the confidence interval limits. - permutation_count : int, default 5000 - The number of permutations (reshuffles) to perform for the - computation of the permutation p-value - ci : float, default 95 - The confidence interval width. The default of 95 produces 95% - confidence intervals. - random_seed : int, default 12345 - `random_seed` is used to seed the random number generator during - bootstrap resampling. This ensures that the confidence intervals - reported are replicable. - - Returns - ------- - A :py:class:`TwoGroupEffectSize` object: - `difference` : float - The effect size of the difference between the control and the test. - `effect_size` : string - The type of effect size reported. - `is_paired` : string - The type of repeated-measures experiment. - `ci` : float - Returns the width of the confidence interval, in percent. - `alpha` : float - Returns the significance level of the statistical test as a float between 0 and 1. - `resamples` : int - The number of resamples performed during the bootstrap procedure. - `bootstraps` : numpy ndarray - The generated bootstraps of the effect size. - `random_seed` : int - The number used to initialise the numpy random seed generator, ie.`seed_value` from `numpy.random.seed(seed_value)` is returned. - `bca_low, bca_high` : float - The bias-corrected and accelerated confidence interval lower limit and upper limits, respectively. - `pct_low, pct_high` : float - The percentile confidence interval lower limit and upper limits, respectively. - """ - - def __init__(self, control, test, effect_size, - proportional=False, - is_paired=None, ci=95, - resamples=5000, - permutation_count=5000, - random_seed=12345): - - - import numpy as np - from numpy import array, isnan, isinf - from numpy import sort as npsort - from numpy.random import choice, seed - - import scipy.stats as spstats - - # import statsmodels.stats.power as power - import statsmodels - - from string import Template - import warnings - - from ._stats_tools import effsize as es - from ._stats_tools import confint_2group_diff as ci2g - - - self.__EFFECT_SIZE_DICT = {"mean_diff" : "mean difference", - "median_diff" : "median difference", - "cohens_d" : "Cohen's d", - "cohens_h" : "Cohen's h", - "hedges_g" : "Hedges' g", - "cliffs_delta" : "Cliff's delta"} - - - kosher_es = [a for a in self.__EFFECT_SIZE_DICT.keys()] - if effect_size not in kosher_es: - err1 = "The effect size '{}'".format(effect_size) - err2 = "is not one of {}".format(kosher_es) - raise ValueError(" ".join([err1, err2])) - - if effect_size == "cliffs_delta" and is_paired: - err1 = "`paired` is not None; therefore Cliff's delta is not defined." - raise ValueError(err1) - - if proportional==True and 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." - raise ValueError(err1) - - if proportional==True and (np.isin(control, [0, 1]).all() == False or np.isin(test, [0, 1]).all() == False): - err1 = "`proportional` is True; Only accept binary data consisting of 0 and 1." - raise ValueError(err1) - - # Convert to numpy arrays for speed. - # NaNs are automatically dropped. - control = array(control) - test = array(test) - control = control[~isnan(control)] - test = test[~isnan(test)] - - self.__effect_size = effect_size - self.__control = control - self.__test = test - self.__is_paired = is_paired - self.__resamples = resamples - self.__permutation_count = permutation_count - self.__random_seed = random_seed - self.__ci = ci - self.__alpha = ci2g._compute_alpha_from_ci(ci) - - self.__difference = es.two_group_difference( - control, test, is_paired, effect_size) - - self.__jackknives = ci2g.compute_meandiff_jackknife( - control, test, is_paired, effect_size) - - self.__acceleration_value = ci2g._calc_accel(self.__jackknives) - - bootstraps = ci2g.compute_bootstrapped_diff( - control, test, is_paired, effect_size, - resamples, random_seed) - self.__bootstraps = bootstraps - - sorted_bootstraps = npsort(self.__bootstraps) - # Added in v0.2.6. - # Raises a UserWarning if there are any infiinities in the bootstraps. - num_infinities = len(self.__bootstraps[isinf(self.__bootstraps)]) - - if num_infinities > 0: - warn_msg = "There are {} bootstrap(s) that are not defined. "\ - "This is likely due to smaple sample sizes. "\ - "The values in a bootstrap for a group will be more likely "\ - "to be all equal, with a resulting variance of zero. "\ - "The computation of Cohen's d and Hedges' g thus "\ - "involved a division by zero. " - warnings.warn(warn_msg.format(num_infinities), - category=UserWarning) - - self.__bias_correction = ci2g.compute_meandiff_bias_correction( - self.__bootstraps, self.__difference) - - # Compute BCa intervals. - bca_idx_low, bca_idx_high = ci2g.compute_interval_limits( - self.__bias_correction, self.__acceleration_value, - self.__resamples, ci) - - self.__bca_interval_idx = (bca_idx_low, bca_idx_high) - - if ~isnan(bca_idx_low) and ~isnan(bca_idx_high): - self.__bca_low = sorted_bootstraps[bca_idx_low] - self.__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 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 >= resamples-9: - warnings.warn(err_temp.substitute(lim_type="upper", - loc="top"), - stacklevel=1) - - else: - err1 = "The $lim_type limit of the BCa interval 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.__bca_low = self.__difference - warnings.warn(err_temp.substitute(lim_type="lower"), - stacklevel=0) - - if isnan(bca_idx_high): - self.__bca_high = self.__difference - warnings.warn(err_temp.substitute(lim_type="upper"), - stacklevel=0) - - # Compute percentile intervals. - pct_idx_low = int((self.__alpha/2) * resamples) - pct_idx_high = int((1-(self.__alpha/2)) * resamples) - - 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] - - # Perform statistical tests. - - self.__PermutationTest_result = PermutationTest(control, test, - effect_size, - is_paired, - permutation_count) - - if is_paired and proportional is False: - # Wilcoxon, a non-parametric version of the paired T-test. - wilcoxon = spstats.wilcoxon(control, test) - self.__pvalue_wilcoxon = wilcoxon.pvalue - self.__statistic_wilcoxon = wilcoxon.statistic - - - if effect_size != "median_diff": - # Paired Student's t-test. - paired_t = spstats.ttest_rel(control, test, nan_policy='omit') - self.__pvalue_paired_students_t = paired_t.pvalue - self.__statistic_paired_students_t = paired_t.statistic - - standardized_es = es.cohens_d(control, test, is_paired) - # self.__power = power.tt_solve_power(standardized_es, - # len(control), - # alpha=self.__alpha) - - elif is_paired and proportional is True: - # for binary paired data, use McNemar's test - # References: - # https://en.wikipedia.org/wiki/McNemar%27s_test - from statsmodels.stats.contingency_tables import mcnemar - import pandas as pd - df_temp = pd.DataFrame({'control': control, 'test': 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]] - _mcnemar = mcnemar(table, exact=True, correction=True) - self.__pvalue_mcnemar = _mcnemar.pvalue - self.__statistic_mcnemar = _mcnemar.statistic - - elif effect_size == "cliffs_delta": - # Let's go with Brunner-Munzel! - brunner_munzel = spstats.brunnermunzel(control, test, - nan_policy='omit') - self.__pvalue_brunner_munzel = brunner_munzel.pvalue - self.__statistic_brunner_munzel = brunner_munzel.statistic - - - elif effect_size == "median_diff": - # According to scipy's documentation of the function, - # "The Kruskal-Wallis H-test tests the null hypothesis - # that the population median of all of the groups are equal." - kruskal = spstats.kruskal(control, test, nan_policy='omit') - self.__pvalue_kruskal = kruskal.pvalue - self.__statistic_kruskal = kruskal.statistic - # self.__power = np.nan - - else: # for mean difference, Cohen's d, and Hedges' g. - # Welch's t-test, assumes normality of distributions, - # but does not assume equal variances. - welch = spstats.ttest_ind(control, test, equal_var=False, - nan_policy='omit') - self.__pvalue_welch = welch.pvalue - self.__statistic_welch = welch.statistic - - # Student's t-test, assumes normality of distributions, - # as well as assumption of equal variances. - students_t = spstats.ttest_ind(control, test, equal_var=True, - nan_policy='omit') - self.__pvalue_students_t = students_t.pvalue - self.__statistic_students_t = students_t.statistic - - # Mann-Whitney test: Non parametric, - # does not assume normality of distributions - try: - mann_whitney = spstats.mannwhitneyu(control, test, - alternative='two-sided') - self.__pvalue_mann_whitney = mann_whitney.pvalue - self.__statistic_mann_whitney = mann_whitney.statistic - except ValueError: - # Occurs when the control and test are exactly identical - # in terms of rank (eg. all zeros.) - pass - - - - standardized_es = es.cohens_d(control, test, is_paired = None) - - # The Cohen's h calculation is for binary categorical data - try: - self.__proportional_difference = es.cohens_h(control, test) - except ValueError: - # Occur only when the data consists not only 0's and 1's. - pass - # self.__power = power.tt_ind_solve_power(standardized_es, - # len(control), - # alpha=self.__alpha, - # ratio=len(test)/len(control) - # ) - - - - - - - def __repr__(self, show_resample_count=True, define_pval=True, sigfig=3): - - # # Deprecated in v0.3.0; permutation p-values will be reported by default. - # UNPAIRED_ES_TO_TEST = {"mean_diff" : "Mann-Whitney", - # "median_diff" : "Kruskal", - # "cohens_d" : "Mann-Whitney", - # "hedges_g" : "Mann-Whitney", - # "cliffs_delta" : "Brunner-Munzel"} - # - # TEST_TO_PVAL_ATTR = {"Mann-Whitney" : "pvalue_mann_whitney", - # "Kruskal" : "pvalue_kruskal", - # "Brunner-Munzel" : "pvalue_brunner_munzel", - # "Wilcoxon" : "pvalue_wilcoxon"} - - RM_STATUS = {'baseline' : 'for repeated measures against baseline \n', - 'sequential': 'for the sequential design of repeated-measures experiment \n', - 'None' : '' - } - - PAIRED_STATUS = {'baseline' : 'paired', - 'sequential' : 'paired', - 'None' : 'unpaired' - } - - first_line = {"rm_status" : RM_STATUS[str(self.__is_paired)], - "es" : self.__EFFECT_SIZE_DICT[self.__effect_size], - "paired_status": PAIRED_STATUS[str(self.__is_paired)]} - - - out1 = "The {paired_status} {es} {rm_status}".format(**first_line) - - base_string_fmt = "{:." + str(sigfig) + "}" - if "." in str(self.__ci): - ci_width = base_string_fmt.format(self.__ci) - else: - ci_width = str(self.__ci) - - ci_out = {"es" : base_string_fmt.format(self.__difference), - "ci" : ci_width, - "bca_low" : base_string_fmt.format(self.__bca_low), - "bca_high" : base_string_fmt.format(self.__bca_high)} - - out2 = "is {es} [{ci}%CI {bca_low}, {bca_high}].".format(**ci_out) - out = out1 + out2 - - # # Deprecated in v0.3.0; permutation p-values will be reported by default. - # if self.__is_paired: - # stats_test = "Wilcoxon" - # else: - # stats_test = UNPAIRED_ES_TO_TEST[self.__effect_size] - - - # pval_rounded = base_string_fmt.format(getattr(self, - # TEST_TO_PVAL_ATTR[stats_test]) - # ) - - pval_rounded = base_string_fmt.format(self.pvalue_permutation) - - # # Deprecated in v0.3.0; permutation p-values will be reported by default. - # pvalue = "The two-sided p-value of the {} test is {}.".format(stats_test, - # pval_rounded) - - # pvalue = "The two-sided p-value of the {} test is {}.".format(stats_test, - # pval_rounded) - - - p1 = "The p-value of the two-sided permutation t-test is {}, ".format(pval_rounded) - p2 = "calculated for legacy purposes only. " - pvalue = p1 + p2 - - bs1 = "{} bootstrap samples were taken; ".format(self.__resamples) - bs2 = "the confidence interval is bias-corrected and accelerated." - bs = bs1 + bs2 - - pval_def1 = "Any p-value reported is the probability of observing the" + \ - "effect size (or greater),\nassuming the null hypothesis of" + \ - "zero difference is true." - pval_def2 = "\nFor each p-value, 5000 reshuffles of the " + \ - "control and test labels were performed." - pval_def = pval_def1 + pval_def2 - - if show_resample_count and define_pval: - return "{}\n{}\n\n{}\n{}".format(out, pvalue, bs, pval_def) - elif show_resample_count is False and define_pval is True: - return "{}\n{}\n\n{}".format(out, pvalue, pval_def) - elif show_resample_count is True and define_pval is False: - return "{}\n{}\n\n{}".format(out, pvalue, bs) - else: - return "{}\n{}".format(out, pvalue) - - - - def to_dict(self): - """ - Returns the attributes of the `dabest.TwoGroupEffectSize` object as a - dictionary. - """ - # Only get public (user-facing) attributes. - attrs = [a for a in dir(self) - if not a.startswith(("_", "to_dict"))] - out = {} - for a in attrs: - out[a] = getattr(self, a) - return out - - - @property - def difference(self): - """ - Returns the difference between the control and the test. - """ - return self.__difference - - @property - def effect_size(self): - """ - Returns the type of effect size reported. - """ - return self.__EFFECT_SIZE_DICT[self.__effect_size] - - @property - def is_paired(self): - return self.__is_paired - - @property - def ci(self): - """ - Returns the width of the confidence interval, in percent. - """ - return self.__ci - - @property - def alpha(self): - """ - Returns the significance level of the statistical test as a float - between 0 and 1. - """ - return self.__alpha - - @property - def resamples(self): - """ - The number of resamples performed during the bootstrap procedure. - """ - return self.__resamples - - @property - def bootstraps(self): - """ - The generated bootstraps of the effect size. - """ - return self.__bootstraps - - @property - def random_seed(self): - """ - The number used to initialise the numpy random seed generator, ie. - `seed_value` from `numpy.random.seed(seed_value)` is returned. - """ - return self.__random_seed - - @property - def bca_interval_idx(self): - return self.__bca_interval_idx - - @property - def bca_low(self): - """ - The bias-corrected and accelerated confidence interval lower limit. - """ - return self.__bca_low - - @property - def bca_high(self): - """ - The bias-corrected and accelerated confidence interval upper limit. - """ - return self.__bca_high - - @property - def pct_interval_idx(self): - return self.__pct_interval_idx - - @property - def pct_low(self): - """ - The percentile confidence interval lower limit. - """ - return self.__pct_low - - @property - def pct_high(self): - """ - The percentile confidence interval lower limit. - """ - return self.__pct_high - - - - @property - def pvalue_brunner_munzel(self): - from numpy import nan as npnan - try: - return self.__pvalue_brunner_munzel - except AttributeError: - return npnan - - @property - def statistic_brunner_munzel(self): - from numpy import nan as npnan - try: - return self.__statistic_brunner_munzel - except AttributeError: - return npnan - - - - @property - def pvalue_wilcoxon(self): - from numpy import nan as npnan - try: - return self.__pvalue_wilcoxon - except AttributeError: - return npnan - - @property - def statistic_wilcoxon(self): - from numpy import nan as npnan - try: - return self.__statistic_wilcoxon - except AttributeError: - return npnan - - @property - def pvalue_mcnemar(self): - from numpy import nan as npnan - try: - return self.__pvalue_mcnemar - except AttributeError: - return npnan - - @property - def statistic_mcnemar(self): - from numpy import nan as npnan - try: - return self.__statistic_mcnemar - except AttributeError: - return npnan - - - - @property - def pvalue_paired_students_t(self): - from numpy import nan as npnan - try: - return self.__pvalue_paired_students_t - except AttributeError: - return npnan - - @property - def statistic_paired_students_t(self): - from numpy import nan as npnan - try: - return self.__statistic_paired_students_t - except AttributeError: - return npnan - - - - @property - def pvalue_kruskal(self): - from numpy import nan as npnan - try: - return self.__pvalue_kruskal - except AttributeError: - return npnan - - @property - def statistic_kruskal(self): - from numpy import nan as npnan - try: - return self.__statistic_kruskal - except AttributeError: - return npnan - - - - @property - def pvalue_welch(self): - from numpy import nan as npnan - try: - return self.__pvalue_welch - except AttributeError: - return npnan - - @property - def statistic_welch(self): - from numpy import nan as npnan - try: - return self.__statistic_welch - except AttributeError: - return npnan - - - - @property - def pvalue_students_t(self): - from numpy import nan as npnan - try: - return self.__pvalue_students_t - except AttributeError: - return npnan - - @property - def statistic_students_t(self): - from numpy import nan as npnan - try: - return self.__statistic_students_t - except AttributeError: - return npnan - - - - @property - def pvalue_mann_whitney(self): - from numpy import nan as npnan - try: - return self.__pvalue_mann_whitney - except AttributeError: - return npnan - - - - @property - def statistic_mann_whitney(self): - from numpy import nan as npnan - try: - return self.__statistic_mann_whitney - except AttributeError: - return npnan - - # Introduced in v0.3.0. - @property - def pvalue_permutation(self): - return self.__PermutationTest_result.pvalue - - # - # - @property - def permutation_count(self): - """ - The number of permuations taken. - """ - return self.__PermutationTest_result.permutation_count - - - @property - def permutations(self): - return self.__PermutationTest_result.permutations - - - @property - def permutations_var(self): - return self.__PermutationTest_result.permutations_var - - - @property - def proportional_difference(self): - from numpy import nan as npnan - try: - return self.__proportional_difference - except AttributeError: - return npnan - - -# %% ../nbs/API/class.ipynb 38 -class EffectSizeDataFrame(object): - """A class that generates and stores the results of bootstrapped effect - sizes for several comparisons.""" - - def __init__(self, dabest, effect_size, - is_paired, ci=95, proportional=False, - resamples=5000, - permutation_count=5000, - random_seed=12345, - x1_level=None, x2=None, - delta2=False, experiment_label=None, - mini_meta=False): - """ - Parses the data from a Dabest object, enabling plotting and printing - capability for the effect size of interest. - """ - - self.__dabest_obj = dabest - self.__effect_size = effect_size - self.__is_paired = is_paired - self.__ci = ci - self.__resamples = resamples - self.__permutation_count = permutation_count - self.__random_seed = random_seed - self.__proportional = proportional - self.__x1_level = x1_level - self.__experiment_label = experiment_label - self.__x2 = x2 - self.__delta2 = delta2 - self.__mini_meta = mini_meta - - - def __pre_calc(self): - import pandas as pd - from .misc_tools import print_greeting, get_varname - - idx = self.__dabest_obj.idx - dat = self.__dabest_obj._plot_data - xvar = self.__dabest_obj._xvar - yvar = self.__dabest_obj._yvar - - out = [] - reprs = [] - - for j, current_tuple in enumerate(idx): - if self.__is_paired!="sequential": - cname = current_tuple[0] - control = dat[dat[xvar] == cname][yvar].copy() - - 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() - - result = TwoGroupsEffectSize(control, test, - self.__effect_size, - self.__proportional, - self.__is_paired, - self.__ci, - self.__resamples, - self.__permutation_count, - self.__random_seed) - r_dict = result.to_dict() - r_dict["control"] = cname - r_dict["test"] = tname - r_dict["control_N"] = int(len(control)) - 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 == "mean_diff": - resamp_count = False - def_pval = False - elif self.__mini_meta and self.__effect_size == "mean_diff": - resamp_count = False - def_pval = False - else: - resamp_count = True - def_pval = True - else: - resamp_count = False - def_pval = False - - text_repr = result.__repr__(show_resample_count=resamp_count, - define_pval=def_pval) - - to_replace = "between {} and {} is".format(cname, tname) - text_repr = text_repr.replace("is", to_replace, 1) - - reprs.append(text_repr) - - - self.__for_print = "\n\n".join(reprs) - - out_ = pd.DataFrame(out) - - columns_in_order = ['control', 'test', 'control_N', 'test_N', - 'effect_size', 'is_paired', - 'difference', 'ci', - - 'bca_low', 'bca_high', 'bca_interval_idx', - 'pct_low', 'pct_high', 'pct_interval_idx', - - 'bootstraps', 'resamples', 'random_seed', - - 'permutations', 'pvalue_permutation', 'permutation_count', 'permutations_var', - - 'pvalue_welch', - 'statistic_welch', - - 'pvalue_students_t', - 'statistic_students_t', - - 'pvalue_mann_whitney', - 'statistic_mann_whitney', - - 'pvalue_brunner_munzel', - 'statistic_brunner_munzel', - - 'pvalue_wilcoxon', - 'statistic_wilcoxon', - - 'pvalue_mcnemar', - 'statistic_mcnemar', - - 'pvalue_paired_students_t', - 'statistic_paired_students_t', - - 'pvalue_kruskal', - 'statistic_kruskal', - 'proportional_difference' - ] - self.__results = out_.reindex(columns=columns_in_order) - self.__results.dropna(axis="columns", how="all", inplace=True) - - # Add the is_paired column back when is_paired is None - if self.is_paired is None: - self.__results.insert(5, 'is_paired', self.__results.apply(lambda _: None, axis=1)) - - # Create and compute the delta-delta statistics - if self.__delta2 is True and self.__effect_size == "mean_diff": - self.__delta_delta = DeltaDelta(self, - self.__permutation_count, - self.__ci) - reprs.append(self.__delta_delta.__repr__(header=False)) - elif self.__delta2 is True and self.__effect_size != "mean_diff": - 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 is True and self.__effect_size == "mean_diff": - self.__mini_meta_delta = MiniMetaDelta(self, - self.__permutation_count, - self.__ci) - reprs.append(self.__mini_meta_delta.__repr__(header=False)) - elif self.__mini_meta is True and self.__effect_size != "mean_diff": - self.__mini_meta_delta = "Weighted delta is not supported for {}.".format(self.__effect_size) - else: - self.__mini_meta_delta = "`mini_meta` is False; weighted delta is therefore not calculated." - - - varname = get_varname(self.__dabest_obj) - lastline = "To get the results of all valid statistical tests, " +\ - "use `{}.{}.statistical_tests`".format(varname, self.__effect_size) - reprs.append(lastline) - - reprs.insert(0, print_greeting()) - - self.__for_print = "\n\n".join(reprs) - - - def __repr__(self): - try: - return self.__for_print - except AttributeError: - self.__pre_calc() - return self.__for_print - - - - def __calc_lqrt(self): - import lqrt - import pandas as pd - - rnd_seed = self.__random_seed - db_obj = self.__dabest_obj - dat = db_obj._plot_data - xvar = db_obj._xvar - yvar = db_obj._yvar - delta2 = self.__delta2 - - - out = [] - - 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() - - 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() - - if self.__is_paired: - # Refactored here in v0.3.0 for performance issues. - lqrt_result = lqrt.lqrtest_rel(control, test, - random_state=rnd_seed) - - out.append({"control": cname, "test": tname, - "control_N": int(len(control)), - "test_N": int(len(test)), - "pvalue_paired_lqrt": lqrt_result.pvalue, - "statistic_paired_lqrt": lqrt_result.statistic - }) - - else: - # Likelihood Q-Ratio test: - lqrt_equal_var_result = lqrt.lqrtest_ind(control, test, - random_state=rnd_seed, - equal_var=True) - - - lqrt_unequal_var_result = lqrt.lqrtest_ind(control, test, - random_state=rnd_seed, - equal_var=False) - - out.append({"control": cname, "test": tname, - "control_N": int(len(control)), - "test_N": int(len(test)), - - "pvalue_lqrt_equal_var" : lqrt_equal_var_result.pvalue, - "statistic_lqrt_equal_var" : lqrt_equal_var_result.statistic, - "pvalue_lqrt_unequal_var" : lqrt_unequal_var_result.pvalue, - "statistic_lqrt_unequal_var" : lqrt_unequal_var_result.statistic, - }) - self.__lqrt_results = pd.DataFrame(out) - - - def plot(self, color_col=None, - - raw_marker_size=6, es_marker_size=9, - - swarm_label=None, barchart_label=None, contrast_label=None, delta2_label=None, - swarm_ylim=None, barchart_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 plot - bar_label=None, bar_desat=0.5, bar_width = 0.5,bar_ylim = None, - # error bar of proportion plot - ci=None, ci_type='bca', err_color=None, - - float_contrast=True, - show_pairs=True, - show_delta2=True, - show_mini_meta=True, - group_summaries=None, - group_summaries_offset=0.1, - - fig_size=None, - dpi=100, - ax=None, - - swarmplot_kwargs=None, - barplot_kwargs=None, - violinplot_kwargs=None, - slopegraph_kwargs=None, - sankey_kwargs=None, - reflines_kwargs=None, - group_summary_kwargs=None, - legend_kwargs=None): - - """ - Creates an estimation plot for the effect size of interest. - - - Parameters - ---------- - color_col : string, default None - Column to be used for colors. - raw_marker_size : float, default 6 - The diameter (in points) of the marker dots plotted in the - swarmplot. - es_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 - 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 - limits for y. When `show_delta2` is True, if both of the `contrast_ylim` - and `delta2_ylim` are not None, then they must be specified with the - same values; when `show_delta2` is True and only one of them is specified, - then the other will automatically be assigned with the same value. - Specifying `delta2_ylim` does not have any effect when `show_delta2` is - False. - custom_palette : dict, list, or matplotlib color palette, default None - This keyword accepts a dictionary with {'group':'color'} pairings, - a list of RGB colors, or a specified matplotlib palette. This - palette will be used to color the swarmplot. If `color_col` is not - specified, then each group will be colored in sequence according - to the default palette currently used by matplotlib. - Please take a look at the seaborn commands `color_palette` - and `cubehelix_palette` to generate a custom palette. Both - these functions generate a list of RGB colors. - See: - https://seaborn.pydata.org/generated/seaborn.color_palette.html - 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 - desired proportion. Uses `seaborn.desaturate()` to acheive this. - halfviolin_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 - The alpha (transparency) level of the half-violin bootstrap curves. - float_contrast : boolean, default True - Whether or not to display the halfviolin bootstrapped difference - distribution alongside the raw data. - show_pairs : boolean, default True - 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_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. - 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 - The dots per inch of the resulting figure. - 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. - 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}. - 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`. - 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} - 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 - 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}. - 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}. - - - Returns - ------- - A :class:`matplotlib.figure.Figure` with 2 Axes, if ``ax = None``. - - The first axes (accessible with ``FigName.axes[0]``) contains the rawdata swarmplot; the second axes (accessible with ``FigName.axes[1]``) has the bootstrap distributions and effect sizes (with confidence intervals) plotted on it. - - If ``ax`` is specified, the rawdata swarmplot is accessed at ``ax`` - itself, while the effect size axes is accessed at ``ax.contrast_axes``. - See the last example below. - - - - """ - - from .plotter import EffectSizeDataFramePlotter - - if hasattr(self, "results") is False: - self.__pre_calc() - - if self.__delta2: - color_col = self.__x2 - - # if self.__proportional: - # raw_marker_size = 0.01 - - all_kwargs = locals() - del all_kwargs["self"] - - out = EffectSizeDataFramePlotter(self, **all_kwargs) - - return out - - - @property - def proportional(self): - """ - Returns the proportional parameter - class. - """ - return self.__proportional - - @property - def results(self): - """Prints all pairwise comparisons nicely.""" - try: - return self.__results - except AttributeError: - self.__pre_calc() - return self.__results - - - - @property - def statistical_tests(self): - results_df = self.results - - # Select only the statistics and p-values. - stats_columns = [c for c in results_df.columns - if c.startswith("statistic") or c.startswith("pvalue")] - - default_cols = ['control', 'test', 'control_N', 'test_N', - 'effect_size', 'is_paired', - 'difference', 'ci', 'bca_low', 'bca_high'] - - cols_of_interest = default_cols + stats_columns - - return results_df[cols_of_interest] - - - @property - def _for_print(self): - return self.__for_print - - @property - def _plot_data(self): - return self.__dabest_obj._plot_data - - @property - def idx(self): - return self.__dabest_obj.idx - - @property - def xvar(self): - return self.__dabest_obj._xvar - - @property - def yvar(self): - return self.__dabest_obj._yvar - - @property - def is_paired(self): - return self.__is_paired - - @property - def ci(self): - """ - The width of the confidence interval being produced, in percent. - """ - return self.__ci - - @property - def x1_level(self): - return self.__x1_level - - - @property - def x2(self): - return self.__x2 - - - @property - def experiment_label(self): - return self.__experiment_label - - - @property - def delta2(self): - return self.__delta2 - - - @property - def resamples(self): - """ - The number of resamples (with replacement) during bootstrap resampling." - """ - return self.__resamples - - @property - def random_seed(self): - """ - The seed used by `numpy.seed()` for bootstrap resampling. - """ - return self.__random_seed - - @property - def effect_size(self): - """The type of effect size being computed.""" - return self.__effect_size - - @property - def dabest_obj(self): - """ - Returns the `dabest` object that invoked the current EffectSizeDataFrame - class. - """ - return self.__dabest_obj - - @property - def proportional(self): - """ - Returns the proportional parameter - class. - """ - return self.__proportional - - @property - def lqrt(self): - """Returns all pairwise Lq-Likelihood Ratio Type test results - as a pandas DataFrame. - - For more information on LqRT tests, see https://arxiv.org/abs/1911.11922 - """ - try: - return self.__lqrt_results - except AttributeError: - self.__calc_lqrt() - return self.__lqrt_results - - - @property - def mini_meta(self): - """ - Returns the mini_meta boolean parameter. - """ - return self.__mini_meta - - - @property - def mini_meta_delta(self): - """ - Returns the mini_meta results. - """ - try: - return self.__mini_meta_delta - except AttributeError: - self.__pre_calc() - return self.__mini_meta_delta - - - @property - def delta_delta(self): - """ - Returns the mini_meta results. - """ - try: - return self.__delta_delta - except AttributeError: - self.__pre_calc() - return self.__delta_delta - - - -# %% ../nbs/API/class.ipynb 56 -class PermutationTest: - """ - A class to compute and report permutation tests. - - Parameters - ---------- - control : array-like - test : array-like - These should be numerical iterables. - effect_size : string. - Any one of the following are accepted inputs: - '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. - random_seed : int, default 12345 - `random_seed` is used to seed the random number generator during - bootstrap resampling. This ensures that the generated permutations - are replicable. - - Returns - ------- - A :py:class:`PermutationTest` object: - `difference`:float - The effect size of the difference between the control and the test. - `effect_size`:string - The type of effect size reported. - - - """ - - def __init__(self, control:np.array, - test:np.array, # These should be numerical iterables. - effect_size:str, # Any one of the following are accepted inputs: 'mean_diff', 'median_diff', 'cohens_d', 'hedges_g', or 'cliffs_delta' - 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. - **kwargs): - - import numpy as np - from numpy.random import PCG64, RandomState - from ._stats_tools.effsize import two_group_difference - from ._stats_tools.confint_2group_diff import calculate_group_var - - - self.__permutation_count = permutation_count - - # Run Sanity Check. - if is_paired and len(control) != len(test): - raise ValueError("The two arrays do not have the same length.") - - # Initialise random number generator. - # rng = np.random.default_rng(seed=random_seed) - rng = RandomState(PCG64(random_seed)) - - # Set required constants and variables - control = np.array(control) - test = np.array(test) - - control_sample = control.copy() - test_sample = test.copy() - - BAG = np.array([*control, *test]) - CONTROL_LEN = int(len(control)) - EXTREME_COUNT = 0. - THRESHOLD = np.abs(two_group_difference(control, test, - is_paired, effect_size)) - self.__permutations = [] - self.__permutations_var = [] - - for i in range(int(permutation_count)): - - if is_paired: - # Select which control-test pairs to swap. - random_idx = rng.choice(CONTROL_LEN, - rng.randint(0, CONTROL_LEN+1), - replace=False) - - # Perform swap. - for i in random_idx: - _placeholder = control_sample[i] - control_sample[i] = test_sample[i] - test_sample[i] = _placeholder - - else: - # Shuffle the bag and assign to control and test groups. - # NB. rng.shuffle didn't produce replicable results... - shuffled = rng.permutation(BAG) - control_sample = shuffled[:CONTROL_LEN] - test_sample = shuffled[CONTROL_LEN:] - - - es = two_group_difference(control_sample, test_sample, - False, effect_size) - - var = calculate_group_var(np.var(control_sample, ddof=1), - CONTROL_LEN, - np.var(test_sample, ddof=1), - len(test_sample)) - self.__permutations.append(es) - self.__permutations_var.append(var) - - if np.abs(es) > THRESHOLD: - EXTREME_COUNT += 1. - - self.__permutations = np.array(self.__permutations) - self.__permutations_var = np.array(self.__permutations_var) - - self.pvalue = EXTREME_COUNT / permutation_count - - - def __repr__(self): - return("{} permutations were taken. The p-value is {}.".format(self.permutation_count, - self.pvalue)) - - - @property - def permutation_count(self): - """ - The number of permuations taken. - """ - return self.__permutation_count - - - @property - def permutations(self): - """ - The effect sizes of all the permutations in a list. - """ - return self.__permutations - - - @property - def permutations_var(self): - """ - The experiment group variance of all the permutations in a list. - """ - return self.__permutations_var - diff --git a/dabest/_dabest_object.py b/dabest/_dabest_object.py new file mode 100644 index 00000000..3f618a2a --- /dev/null +++ b/dabest/_dabest_object.py @@ -0,0 +1,717 @@ +# AUTOGENERATED! DO NOT EDIT! File to edit: ../nbs/API/dabest_object.ipynb. + +# %% auto 0 +__all__ = ['Dabest'] + +# %% ../nbs/API/dabest_object.ipynb 4 +# Import standard data science libraries +from numpy import array, repeat, random, issubdtype, number +import pandas as pd +from scipy.stats import norm +from scipy.stats import randint + +# %% ../nbs/API/dabest_object.ipynb 6 +class Dabest(object): + + """ + Class for estimation statistics and plots. + """ + + def __init__( + self, + data, + idx, + x, + y, + paired, + id_col, + ci, + resamples, + random_seed, + proportional, + delta2, + experiment, + experiment_label, + x1_level, + mini_meta, + ): + """ + Parses and stores pandas DataFrames in preparation for estimation + statistics. You should not be calling this class directly; instead, + use `dabest.load()` to parse your DataFrame prior to analysis. + """ + + self.__delta2 = delta2 + self.__experiment = experiment + self.__ci = ci + self.__input_data = data + self.__output_data = data.copy() + self.__id_col = id_col + self.__is_paired = paired + self.__resamples = resamples + self.__random_seed = random_seed + self.__proportional = proportional + self.__mini_meta = mini_meta + + # 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) + + 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): + from .__init__ import __version__ + from .misc_tools import print_greeting + + greeting_header = print_greeting() + + RM_STATUS = { + "baseline": "for repeated measures against baseline \n", + "sequential": "for the sequential design of repeated-measures experiment \n", + "None": "", + } + + PAIRED_STATUS = {"baseline": "Paired e", "sequential": "Paired e", "None": "E"} + + first_line = { + "rm_status": RM_STATUS[str(self.__is_paired)], + "paired_status": PAIRED_STATUS[str(self.__is_paired)], + } + + s1 = "{paired_status}ffect size(s) {rm_status}".format(**first_line) + s2 = "with {}% confidence intervals will be computed for:".format(self.__ci) + desc_line = s1 + s2 + + out = [greeting_header + "\n\n" + desc_line] + + comparisons = [] + + if self.__is_paired == "sequential": + for j, current_tuple in enumerate(self.__idx): + for ix, test_name in enumerate(current_tuple[1:]): + control_name = current_tuple[ix] + comparisons.append("{} minus {}".format(test_name, control_name)) + else: + for j, current_tuple in enumerate(self.__idx): + control_name = current_tuple[0] + + for ix, test_name in enumerate(current_tuple[1:]): + comparisons.append("{} minus {}".format(test_name, control_name)) + + if self.__delta2: + comparisons.append( + "{} minus {} (only for mean difference)".format( + self.__experiment_label[1], self.__experiment_label[0] + ) + ) + + if self.__mini_meta: + comparisons.append("weighted delta (only for mean difference)") + + for j, g in enumerate(comparisons): + out.append("{}. {}".format(j + 1, g)) + + resamples_line1 = "\n{} resamples ".format(self.__resamples) + resamples_line2 = "will be used to generate the effect size bootstraps." + out.append(resamples_line1 + resamples_line2) + + return "\n".join(out) + + @property + def mean_diff(self): + """ + Returns an :py:class:`EffectSizeDataFrame` for the mean difference, its confidence interval, and relevant statistics, for all comparisons as indicated via the `idx` and `paired` argument in `dabest.load()` + + """ + return self.__mean_diff + + @property + def median_diff(self): + """ + Returns an :py:class:`EffectSizeDataFrame` for the median difference, its confidence interval, and relevant statistics, for all comparisons as indicated via the `idx` and `paired` argument in `dabest.load()`. + + """ + return self.__median_diff + + @property + def cohens_d(self): + """ + Returns an :py:class:`EffectSizeDataFrame` for the standardized mean difference Cohen's `d`, its confidence interval, and relevant statistics, for all comparisons as indicated via the `idx` and `paired` argument in `dabest.load()`. + + """ + return self.__cohens_d + + @property + def cohens_h(self): + """ + Returns an :py:class:`EffectSizeDataFrame` for the standardized mean difference Cohen's `h`, its confidence interval, and relevant statistics, for all comparisons as indicated via the `idx` and `directional` argument in `dabest.load()`. + + """ + return self.__cohens_h + + @property + def hedges_g(self): + """ + Returns an :py:class:`EffectSizeDataFrame` for the standardized mean difference Hedges' `g`, its confidence interval, and relevant statistics, for all comparisons as indicated via the `idx` and `paired` argument in `dabest.load()`. + + """ + return self.__hedges_g + + @property + def cliffs_delta(self): + """ + Returns an :py:class:`EffectSizeDataFrame` for Cliff's delta, its confidence interval, and relevant statistics, for all comparisons as indicated via the `idx` and `paired` argument in `dabest.load()`. + + """ + 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()`. + """ + return self.__delta_g + + @property + def input_data(self): + """ + Returns the pandas DataFrame that was passed to `dabest.load()`. + When `delta2` is True, a new column is added to support the + function. The name of this new column is indicated by `x`. + """ + return self.__input_data + + @property + def idx(self): + """ + Returns the order of categories that was passed to `dabest.load()`. + """ + return self.__idx + + @property + def x1(self): + """ + Returns the first variable declared in x when it is a delta-delta + case; returns None otherwise. + """ + return self.__x1 + + @property + def x1_level(self): + """ + Returns the levels of first variable declared in x when it is a + delta-delta case; returns None otherwise. + """ + return self.__x1_level + + @property + def x2(self): + """ + Returns the second variable declared in x when it is a delta-delta + case; returns None otherwise. + """ + return self.__x2 + + @property + def experiment(self): + """ + Returns the column name of experiment labels that was passed to + `dabest.load()` when it is a delta-delta case; returns None otherwise. + """ + return self.__experiment + + @property + def experiment_label(self): + """ + Returns the experiment labels in order that was passed to `dabest.load()` + when it is a delta-delta case; returns None otherwise. + """ + return self.__experiment_label + + @property + def delta2(self): + """ + Returns the boolean parameter indicating if this is a delta-delta + situation. + """ + return self.__delta2 + + @property + def is_paired(self): + """ + Returns the type of repeated-measures experiment. + """ + return self.__is_paired + + @property + def id_col(self): + """ + Returns the id column declared to `dabest.load()`. + """ + return self.__id_col + + @property + def ci(self): + """ + The width of the desired confidence interval. + """ + return self.__ci + + @property + def resamples(self): + """ + The number of resamples used to generate the bootstrap. + """ + return self.__resamples + + @property + def random_seed(self): + """ + The number used to initialise the numpy random seed generator, ie. + `seed_value` from `numpy.random.seed(seed_value)` is returned. + """ + return self.__random_seed + + @property + def x(self): + """ + Returns the x column that was passed to `dabest.load()`, if any. + When `delta2` is True, `x` returns the name of the new column created + for the delta-delta situation. To retrieve the 2 variables passed into + `x` when `delta2` is True, please call `x1` and `x2` instead. + """ + return self.__x + + @property + def y(self): + """ + Returns the y column that was passed to `dabest.load()`, if any. + """ + return self.__y + + @property + def _xvar(self): + """ + Returns the xvar in dabest.plot_data. + """ + return self.__xvar + + @property + def _yvar(self): + """ + Returns the yvar in dabest.plot_data. + """ + return self.__yvar + + @property + def _plot_data(self): + """ + Returns the pandas DataFrame used to produce the estimation stats/plots. + """ + return self.__plot_data + + @property + def proportional(self): + """ + Returns the proportional parameter class. + """ + return self.__proportional + + @property + def mini_meta(self): + """ + Returns the mini_meta boolean parameter. + """ + return self.__mini_meta + + @property + def _all_plot_groups(self): + """ + Returns the all plot groups, as indicated via the `idx` keyword. + """ + return self.__all_plot_groups + + def _check_errors(self, x, y, idx, experiment, experiment_label, x1_level): + ''' + Function to check some input parameters and combinations between them. + At the end of this function these two class attributes are updated + self.__experiment_label and self.__x1_level + ''' + # Check if it is a valid mini_meta case + if self.__mini_meta: + # Only mini_meta calculation but not proportional and delta-delta function + if self.__proportional: + err0 = "`proportional` and `mini_meta` cannot be True at the same time." + raise ValueError(err0) + if self.__delta2: + err0 = "`delta2` and `mini_meta` cannot be True at the same time." + raise ValueError(err0) + + # Check if the columns stated are valid + # Initialize a flag to track if any element in idx is neither str nor (tuple, list) + valid_types = True + + # Initialize variables to track the conditions for str and (tuple, list) + is_str_condition_met, is_tuple_list_condition_met = False, False + + # Single traversal for optimization + for item in idx: + if isinstance(item, str): + is_str_condition_met = True + elif isinstance(item, (tuple, list)) and len(item) == 2: + is_tuple_list_condition_met = True + else: + valid_types = False + break # Exit the loop if an invalid type is found + + # Check if all types are valid + if not valid_types: + err0 = "`mini_meta` is True, but `idx` ({})".format(idx) + err1 = "does not contain exactly 2 unique columns." + raise ValueError(err0 + err1) + + # Handling str type condition + if is_str_condition_met: + if len(pd.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) + + # Handling (tuple, list) type condition + if is_tuple_list_condition_met: + all_idx_lengths = [len(t) for t in idx] + if (array(all_idx_lengths) != 2).any(): + err1 = "`mini_meta` is True, but some elements in idx " + err2 = "in {} do not consist only of two groups.".format(idx) + raise ValueError(err1 + err2) + + + # Check if this is a 2x2 ANOVA case and x & y are valid columns + # Create experiment_label and x1_level + elif self.__delta2: + 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: + err0 = "`idx` should not be specified when `delta2` is True.".format( + len(x) + ) + raise ValueError(err0) + + # Check if x is valid + if len(x) != 2: + err0 = "`delta2` is True but the number of variables indicated by `x` is {}.".format( + len(x) + ) + raise ValueError(err0) + + for i in x: + if i not in self.__output_data.columns: + err = "{0} is not a column in `data`. Please check.".format(i) + raise IndexError(err) + + # Check if y is valid + if not y: + err0 = "`delta2` is True but `y` is not indicated." + raise ValueError(err0) + + if y not in self.__output_data.columns: + err = "{0} is not a column in `data`. Please check.".format(y) + raise IndexError(err) + + # Check if experiment is valid + if experiment not in self.__output_data.columns: + err = "{0} is not a column in `data`. Please check.".format(experiment) + raise IndexError(err) + + # Check if experiment_label is valid and create experiment when needed + if experiment_label: + if len(experiment_label) != 2: + err0 = "`experiment_label` does not have a length of 2." + raise ValueError(err0) + + for i in experiment_label: + if i not in self.__output_data[experiment].unique(): + err = "{0} is not an element in the column `{1}` of `data`. Please check.".format( + i, experiment + ) + raise IndexError(err) + else: + experiment_label = self.__output_data[experiment].unique() + + # Check if x1_level is valid + if x1_level: + if len(x1_level) != 2: + err0 = "`x1_level` does not have a length of 2." + raise ValueError(err0) + + for i in x1_level: + if i not in self.__output_data[x[0]].unique(): + err = "{0} is not an element in the column `{1}` of `data`. Please check.".format( + i, experiment + ) + raise IndexError(err) + + else: + x1_level = self.__output_data[x[0]].unique() + + elif experiment: + experiment_label = self.__output_data[experiment].unique() + x1_level = self.__output_data[x[0]].unique() + 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 + 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) + 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 y 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) + raise ValueError(err) + + # 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(): + err0 = '"{0}" is not a group in the column `{1}`.'.format(g, x) + err1 = " Please check `idx` and try again." + raise IndexError(err0 + err1) + + # Select only rows where the value in the `x` column + # is found in `idx`. + plot_data = self.__output_data[ + self.__output_data.loc[:, x].isin(all_plot_groups) + ].copy() + + # Assign attributes + self.__x = x + self.__y = y + self.__xvar = x + self.__yvar = y + + elif x is None and y is None: + # Assume we have a wide dataset. + # Assign attributes appropriately. + 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) + + # First, check we have all columns in the dataset. + for g in all_plot_groups: + if g not in self.__output_data.columns: + err0 = '"{0}" is not a column in `data`.'.format(g) + err1 = " Please check `idx` and try again." + raise IndexError(err0 + err1) + + set_all_columns = set(self.__output_data.columns.tolist()) + set_all_plot_groups = set(all_plot_groups) + id_vars = set_all_columns.difference(set_all_plot_groups) + + plot_data = pd.melt( + self.__output_data, + id_vars=id_vars, + value_vars=all_plot_groups, + value_name=self.__yvar, + var_name=self.__xvar, + ) + + # Added in v0.2.7. + plot_data.dropna(axis=0, how="any", subset=[self.__yvar], inplace=True) + + + if isinstance(plot_data[self.__xvar].dtype, pd.CategoricalDtype): + plot_data[self.__xvar].cat.remove_unused_categories(inplace=True) + plot_data[self.__xvar].cat.reorder_categories( + all_plot_groups, ordered=True, inplace=True + ) + else: + plot_data.loc[:, self.__xvar] = pd.Categorical( + plot_data[self.__xvar], categories=all_plot_groups, ordered=True + ) + + return plot_data + + def _compute_effectsize_dfs(self): + ''' + Function to compute all attributes based on EffectSizeDataFrame. + It returns nothing. + ''' + from ._effsize_objects import EffectSizeDataFrame + + effectsize_df_kwargs = dict( + ci=self.__ci, + is_paired=self.__is_paired, + random_seed=self.__random_seed, + resamples=self.__resamples, + proportional=self.__proportional, + delta2=self.__delta2, + experiment_label=self.__experiment_label, + x1_level=self.__x1_level, + x2=self.__x2, + mini_meta=self.__mini_meta, + ) + + self.__mean_diff = EffectSizeDataFrame( + self, "mean_diff", **effectsize_df_kwargs + ) + + self.__median_diff = EffectSizeDataFrame( + self, "median_diff", **effectsize_df_kwargs + ) + + self.__cohens_d = EffectSizeDataFrame(self, "cohens_d", **effectsize_df_kwargs) + + self.__cohens_h = EffectSizeDataFrame(self, "cohens_h", **effectsize_df_kwargs) + + 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 + ) + else: + self.__cliffs_delta = ( + "The data is paired; Cliff's delta is therefore undefined." + ) diff --git a/dabest/_delta_objects.py b/dabest/_delta_objects.py new file mode 100644 index 00000000..30c44895 --- /dev/null +++ b/dabest/_delta_objects.py @@ -0,0 +1,801 @@ +# AUTOGENERATED! DO NOT EDIT! File to edit: ../nbs/API/delta_objects.ipynb. + +# %% auto 0 +__all__ = ['DeltaDelta', 'MiniMetaDelta'] + +# %% ../nbs/API/delta_objects.ipynb 5 +from scipy.stats import norm +import pandas as pd +import numpy as np +from numpy import sort as npsort +from numpy import isnan +from string import Template +import warnings +import datetime as dt + +# %% ../nbs/API/delta_objects.ipynb 6 +class DeltaDelta(object): + """ + 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: + + + $$\Delta_{1} = \overline{X}_{A_{2}, B_{1}} - \overline{X}_{A_{1}, B_{1}}$$ + + $$\Delta_{2} = \overline{X}_{A_{2}, B_{2}} - \overline{X}_{A_{1}, B_{2}}$$ + + + where $\overline{X}_{A_{i}, B_{j}}$ is the mean of the sample with A = i and B = j, $\Delta$ is the mean difference between two samples. + + A delta-delta value is then calculated as the mean difference between the two primary deltas: + + + $$\Delta_{\Delta} = \Delta_{2} - \Delta_{1}$$ + + and a deltas' 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: + + $$\Delta_{g} = \frac{\Delta_{\Delta}}{s_{\Delta_{\Delta}}}$$ + + $$s_{\Delta_{\Delta}} = \sqrt{\frac{(n_{A_{2}, B_{1}}-1)s_{A_{2}, B_{1}}^2+(n_{A_{1}, B_{1}}-1)s_{A_{1}, B_{1}}^2+(n_{A_{2}, B_{2}}-1)s_{A_{2}, B_{2}}^2+(n_{A_{1}, B_{2}}-1)s_{A_{1}, B_{2}}^2}{(n_{A_{2}, B_{1}} - 1) + (n_{A_{1}, B_{1}} - 1) + (n_{A_{2}, B_{2}} - 1) + (n_{A_{1}, B_{2}} - 1)}}$$ + + where $s$ is the standard deviation and $n$ is the sample size. + + + """ + + def __init__( + self, effectsizedataframe, permutation_count, bootstraps_delta_delta, ci=95 + ): + from ._stats_tools import effsize as es + from ._stats_tools import confint_1group as ci1g + from ._stats_tools import confint_2group_diff as ci2g + + self.__effsizedf = effectsizedataframe.results + self.__dabest_obj = effectsizedataframe.dabest_obj + self.__ci = ci + self.__resamples = effectsizedataframe.resamples + self.__effect_size = effectsizedataframe.effect_size + self.__alpha = ci2g._compute_alpha_from_ci(ci) + self.__permutation_count = permutation_count + self.__bootstraps = np.array(self.__effsizedf["bootstraps"]) + 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 + if self.__effect_size == "mean_diff": + self.__bootstraps_delta_delta = bootstraps_delta_delta[2] + self.__difference = ( + self.__effsizedf["difference"][1] - self.__effsizedf["difference"][0] + ) + else: + self.__bootstraps_delta_delta = bootstraps_delta_delta[0] + self.__difference = bootstraps_delta_delta[1] + + sorted_delta_delta = npsort(self.__bootstraps_delta_delta) + + self.__bias_correction = ci2g.compute_meandiff_bias_correction( + self.__bootstraps_delta_delta, self.__difference + ) + + self.__jackknives = np.array( + ci1g.compute_1group_jackknife(self.__bootstraps_delta_delta, np.mean) + ) + + self.__acceleration_value = ci2g._calc_accel(self.__jackknives) + + # Compute BCa intervals. + bca_idx_low, bca_idx_high = ci2g.compute_interval_limits( + self.__bias_correction, self.__acceleration_value, self.__resamples, ci + ) + + self.__bca_interval_idx = (bca_idx_low, bca_idx_high) + + if ~isnan(bca_idx_low) and ~isnan(bca_idx_high): + self.__bca_low = sorted_delta_delta[bca_idx_low] + self.__bca_high = sorted_delta_delta[bca_idx_high] + + err1 = "The $lim_type limit of the interval" + err2 = "was in the $loc 10 values." + err3 = "The result 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 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.__bca_low = self.__difference + warnings.warn(err_temp.substitute(lim_type="lower"), stacklevel=0) + + if isnan(bca_idx_high): + self.__bca_high = self.__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.__pct_interval_idx = (pct_idx_low, pct_idx_high) + self.__pct_low = sorted_delta_delta[pct_idx_low] + self.__pct_high = sorted_delta_delta[pct_idx_high] + + def __permutation_test(self): + """ + Perform a permutation test and obtain the permutation p-value + based on the permutation data. + """ + self.__permutations = np.array(self.__effsizedf["permutations"]) + + THRESHOLD = np.abs(self.__difference) + + self.__permutations_delta_delta = np.array( + self.__permutations[1] - self.__permutations[0] + ) + + count = sum(np.abs(self.__permutations_delta_delta) > THRESHOLD) + self.__pvalue_permutation = count / self.__permutation_count + + def __repr__(self, header=True, sigfig=3): + from .misc_tools import print_greeting + + first_line = {"control": self.__control, "test": self.__test} + + 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) + + base_string_fmt = "{:." + str(sigfig) + "}" + if "." in str(self.__ci): + ci_width = base_string_fmt.format(self.__ci) + else: + ci_width = str(self.__ci) + + ci_out = { + "es": base_string_fmt.format(self.__difference), + "ci": ci_width, + "bca_low": base_string_fmt.format(self.__bca_low), + "bca_high": base_string_fmt.format(self.__bca_high), + } + + out2 = "is {es} [{ci}%CI {bca_low}, {bca_high}].".format(**ci_out) + out = out1 + out2 + + if header is True: + out = print_greeting() + "\n" + "\n" + out + + pval_rounded = base_string_fmt.format(self.pvalue_permutation) + + p1 = "The p-value of the two-sided permutation t-test is {}, ".format( + pval_rounded + ) + p2 = "calculated for legacy purposes only. " + pvalue = p1 + p2 + + bs1 = "{} bootstrap samples were taken; ".format(self.__resamples) + bs2 = "the confidence interval is bias-corrected and accelerated." + bs = bs1 + bs2 + + pval_def1 = ( + "Any p-value reported is the probability of observing the " + + "effect size (or greater),\nassuming the null hypothesis of " + + "zero difference is true." + ) + pval_def2 = ( + "\nFor each p-value, 5000 reshuffles of the " + + "control and test labels were performed." + ) + pval_def = pval_def1 + pval_def2 + + return "{}\n{}\n\n{}\n{}".format(out, pvalue, bs, pval_def) + + def to_dict(self): + """ + Returns the attributes of the `DeltaDelta` object as a + dictionary. + """ + # Only get public (user-facing) attributes. + attrs = [a for a in dir(self) if not a.startswith(("_", "to_dict"))] + out = {} + for a in attrs: + out[a] = getattr(self, a) + return out + + @property + def ci(self): + """ + Returns the width of the confidence interval, in percent. + """ + return self.__ci + + @property + def alpha(self): + """ + Returns the significance level of the statistical test as a float + between 0 and 1. + """ + return self.__alpha + + @property + def bias_correction(self): + return self.__bias_correction + + @property + def bootstraps(self): + """ + Return the bootstrapped deltas from all the experiment groups. + """ + return self.__bootstraps + + @property + def jackknives(self): + return self.__jackknives + + @property + def acceleration_value(self): + return self.__acceleration_value + + @property + def bca_low(self): + """ + The bias-corrected and accelerated confidence interval lower limit. + """ + return self.__bca_low + + @property + def bca_high(self): + """ + The bias-corrected and accelerated confidence interval upper limit. + """ + return self.__bca_high + + @property + def bca_interval_idx(self): + return self.__bca_interval_idx + + @property + def control(self): + """ + Return the name of the control experiment group. + """ + return self.__control + + @property + def test(self): + """ + Return the name of the test experiment group. + """ + return self.__test + + @property + def bootstraps_delta_delta(self): + """ + Return the delta-delta values calculated from the bootstrapped + deltas. + """ + return self.__bootstraps_delta_delta + + @property + def difference(self): + """ + Return the delta-delta value calculated based on the raw data. + """ + return self.__difference + + @property + def pct_interval_idx(self): + return self.__pct_interval_idx + + @property + def pct_low(self): + """ + The percentile confidence interval lower limit. + """ + return self.__pct_low + + @property + def pct_high(self): + """ + The percentile confidence interval lower limit. + """ + return self.__pct_high + + @property + def pvalue_permutation(self): + try: + return self.__pvalue_permutation + except AttributeError: + self.__permutation_test() + return self.__pvalue_permutation + + @property + def permutation_count(self): + """ + The number of permuations taken. + """ + return self.__permutation_count + + @property + def permutations(self): + """ + Return the mean differences of permutations obtained during + the permutation test for each experiment group. + """ + try: + return self.__permutations + except AttributeError: + self.__permutation_test() + return self.__permutations + + @property + def permutations_delta_delta(self): + """ + Return the delta-delta values of permutations obtained + during the permutation test. + """ + try: + return self.__permutations_delta_delta + except AttributeError: + self.__permutation_test() + return self.__permutations_delta_delta + +# %% ../nbs/API/delta_objects.ipynb 10 +class MiniMetaDelta(object): + """ + A class to compute and store the weighted delta. + A weighted delta is calculated if the argument ``mini_meta=True`` is passed during ``dabest.load()``. + + """ + + def __init__(self, effectsizedataframe, permutation_count, + ci=95): + from ._stats_tools import effsize as es + from ._stats_tools import confint_1group as ci1g + from ._stats_tools import confint_2group_diff as ci2g + + self.__effsizedf = effectsizedataframe.results + self.__dabest_obj = effectsizedataframe.dabest_obj + self.__ci = ci + self.__resamples = effectsizedataframe.resamples + self.__alpha = ci2g._compute_alpha_from_ci(ci) + self.__permutation_count = permutation_count + self.__bootstraps = np.array(self.__effsizedf["bootstraps"]) + self.__control = np.array(self.__effsizedf["control"]) + self.__test = np.array(self.__effsizedf["test"]) + self.__control_N = np.array(self.__effsizedf["control_N"]) + self.__test_N = np.array(self.__effsizedf["test_N"]) + + + idx = self.__dabest_obj.idx + dat = self.__dabest_obj._plot_data + xvar = self.__dabest_obj._xvar + yvar = self.__dabest_obj._yvar + + # compute the variances of each control group and each test group + control_var=[] + test_var=[] + for j, current_tuple in enumerate(idx): + cname = current_tuple[0] + control = dat[dat[xvar] == cname][yvar].copy() + control_var.append(np.var(control, ddof=1)) + + tname = current_tuple[1] + test = dat[dat[xvar] == tname][yvar].copy() + test_var.append(np.var(test, ddof=1)) + self.__control_var = np.array(control_var) + self.__test_var = np.array(test_var) + + # Compute pooled group variances for each pair of experiment groups + # based on the raw data + self.__group_var = ci2g.calculate_group_var(self.__control_var, + self.__control_N, + self.__test_var, + self.__test_N) + + # Compute the weighted average mean differences of the bootstrap data + # using the pooled group variances of the raw data as the inverse of + # weights + self.__bootstraps_weighted_delta = ci2g.calculate_weighted_delta( + self.__group_var, + self.__bootstraps) + + # Compute the weighted average mean difference based on the raw data + self.__difference = es.weighted_delta(self.__effsizedf["difference"], + self.__group_var) + + sorted_weighted_deltas = npsort(self.__bootstraps_weighted_delta) + + + self.__bias_correction = ci2g.compute_meandiff_bias_correction( + self.__bootstraps_weighted_delta, self.__difference) + + self.__jackknives = np.array(ci1g.compute_1group_jackknife( + self.__bootstraps_weighted_delta, + np.mean)) + + self.__acceleration_value = ci2g._calc_accel(self.__jackknives) + + # Compute BCa intervals. + bca_idx_low, bca_idx_high = ci2g.compute_interval_limits( + self.__bias_correction, self.__acceleration_value, + self.__resamples, ci) + + self.__bca_interval_idx = (bca_idx_low, bca_idx_high) + + if ~isnan(bca_idx_low) and ~isnan(bca_idx_high): + self.__bca_low = sorted_weighted_deltas[bca_idx_low] + self.__bca_high = sorted_weighted_deltas[bca_idx_high] + + err1 = "The $lim_type limit of the interval" + err2 = "was in the $loc 10 values." + err3 = "The result 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 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.__bca_low = self.__difference + warnings.warn(err_temp.substitute(lim_type="lower"), + stacklevel=0) + + if isnan(bca_idx_high): + self.__bca_high = self.__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.__pct_interval_idx = (pct_idx_low, pct_idx_high) + self.__pct_low = sorted_weighted_deltas[pct_idx_low] + self.__pct_high = sorted_weighted_deltas[pct_idx_high] + + + + def __permutation_test(self): + """ + Perform a permutation test and obtain the permutation p-value + based on the permutation data. + """ + self.__permutations = np.array(self.__effsizedf["permutations"]) + self.__permutations_var = np.array(self.__effsizedf["permutations_var"]) + + THRESHOLD = np.abs(self.__difference) + + all_num = [] + all_denom = [] + + groups = len(self.__permutations) + for i in range(0, len(self.__permutations[0])): + weight = [1/self.__permutations_var[j][i] for j in range(0, groups)] + all_num.append(np.sum([weight[j]*self.__permutations[j][i] for j in range(0, groups)])) + all_denom.append(np.sum(weight)) + + output=[] + for i in range(0, len(all_num)): + output.append(all_num[i]/all_denom[i]) + + self.__permutations_weighted_delta = np.array(output) + + count = sum(np.abs(self.__permutations_weighted_delta)>THRESHOLD) + self.__pvalue_permutation = count/self.__permutation_count + + + + def __repr__(self, header=True, sigfig=3): + from .misc_tools import print_greeting + + is_paired = self.__dabest_obj.is_paired + + PAIRED_STATUS = {'baseline' : 'paired', + 'sequential' : 'paired', + 'None' : 'unpaired' + } + + first_line = {"paired_status": PAIRED_STATUS[str(is_paired)]} + + + out1 = "The weighted-average {paired_status} mean differences ".format(**first_line) + + base_string_fmt = "{:." + str(sigfig) + "}" + if "." in str(self.__ci): + ci_width = base_string_fmt.format(self.__ci) + else: + ci_width = str(self.__ci) + + ci_out = {"es" : base_string_fmt.format(self.__difference), + "ci" : ci_width, + "bca_low" : base_string_fmt.format(self.__bca_low), + "bca_high" : base_string_fmt.format(self.__bca_high)} + + out2 = "is {es} [{ci}%CI {bca_low}, {bca_high}].".format(**ci_out) + out = out1 + out2 + + if header is True: + out = print_greeting() + "\n" + "\n" + out + + + pval_rounded = base_string_fmt.format(self.pvalue_permutation) + + + p1 = "The p-value of the two-sided permutation t-test is {}, ".format(pval_rounded) + p2 = "calculated for legacy purposes only. " + pvalue = p1 + p2 + + + bs1 = "{} bootstrap samples were taken; ".format(self.__resamples) + bs2 = "the confidence interval is bias-corrected and accelerated." + bs = bs1 + bs2 + + pval_def1 = "Any p-value reported is the probability of observing the" + \ + "effect size (or greater),\nassuming the null hypothesis of " + \ + "zero difference is true." + pval_def2 = "\nFor each p-value, 5000 reshuffles of the " + \ + "control and test labels were performed." + pval_def = pval_def1 + pval_def2 + + + return "{}\n{}\n\n{}\n{}".format(out, pvalue, bs, pval_def) + + + def to_dict(self): + """ + Returns all attributes of the `dabest.MiniMetaDelta` object as a + dictionary. + """ + # Only get public (user-facing) attributes. + attrs = [a for a in dir(self) + if not a.startswith(("_", "to_dict"))] + out = {} + for a in attrs: + out[a] = getattr(self, a) + return out + + + @property + def ci(self): + """ + Returns the width of the confidence interval, in percent. + """ + return self.__ci + + + @property + def alpha(self): + """ + Returns the significance level of the statistical test as a float + between 0 and 1. + """ + return self.__alpha + + + @property + def bias_correction(self): + return self.__bias_correction + + + @property + def bootstraps(self): + ''' + Return the bootstrapped differences from all the experiment groups. + ''' + return self.__bootstraps + + + @property + def jackknives(self): + return self.__jackknives + + + @property + def acceleration_value(self): + return self.__acceleration_value + + + @property + def bca_low(self): + """ + The bias-corrected and accelerated confidence interval lower limit. + """ + return self.__bca_low + + + @property + def bca_high(self): + """ + The bias-corrected and accelerated confidence interval upper limit. + """ + return self.__bca_high + + + @property + def bca_interval_idx(self): + return self.__bca_interval_idx + + + @property + def control(self): + ''' + Return the names of the control groups from all the experiment + groups in order. + ''' + return self.__control + + + @property + def test(self): + ''' + Return the names of the test groups from all the experiment + groups in order. + ''' + return self.__test + + @property + def control_N(self): + ''' + Return the sizes of the control groups from all the experiment + groups in order. + ''' + return self.__control_N + + + @property + def test_N(self): + ''' + Return the sizes of the test groups from all the experiment + groups in order. + ''' + return self.__test_N + + + @property + def control_var(self): + ''' + Return the estimated population variances of the control groups + from all the experiment groups in order. Here the population + variance is estimated from the sample variance. + ''' + return self.__control_var + + + @property + def test_var(self): + ''' + Return the estimated population variances of the control groups + from all the experiment groups in order. Here the population + variance is estimated from the sample variance. + ''' + return self.__test_var + + + @property + def group_var(self): + ''' + Return the pooled group variances of all the experiment groups + in order. + ''' + return self.__group_var + + + @property + def bootstraps_weighted_delta(self): + ''' + Return the weighted-average mean differences calculated from the bootstrapped + deltas and weights across the experiment groups, where the weights are + the inverse of the pooled group variances. + ''' + return self.__bootstraps_weighted_delta + + + @property + def difference(self): + ''' + Return the weighted-average delta calculated from the raw data. + ''' + return self.__difference + + + @property + def pct_interval_idx (self): + return self.__pct_interval_idx + + + @property + def pct_low(self): + """ + The percentile confidence interval lower limit. + """ + return self.__pct_low + + + @property + def pct_high(self): + """ + The percentile confidence interval lower limit. + """ + return self.__pct_high + + + @property + def pvalue_permutation(self): + try: + return self.__pvalue_permutation + except AttributeError: + self.__permutation_test() + return self.__pvalue_permutation + + + @property + def permutation_count(self): + """ + The number of permuations taken. + """ + return self.__permutation_count + + + @property + def permutations(self): + ''' + Return the mean differences of permutations obtained during + the permutation test for each experiment group. + ''' + try: + return self.__permutations + except AttributeError: + self.__permutation_test() + return self.__permutations + + + @property + def permutations_var(self): + ''' + Return the pooled group variances of permutations obtained during + the permutation test for each experiment group. + ''' + try: + return self.__permutations_var + except AttributeError: + self.__permutation_test() + return self.__permutations_var + + + @property + def permutations_weighted_delta(self): + ''' + Return the weighted-average deltas of permutations obtained + during the permutation test. + ''' + try: + return self.__permutations_weighted_delta + except AttributeError: + self.__permutation_test() + return self.__permutations_weighted_delta + + diff --git a/dabest/_effsize_objects.py b/dabest/_effsize_objects.py new file mode 100644 index 00000000..f8bf3846 --- /dev/null +++ b/dabest/_effsize_objects.py @@ -0,0 +1,1503 @@ +# AUTOGENERATED! DO NOT EDIT! File to edit: ../nbs/API/effsize_objects.ipynb. + +# %% auto 0 +__all__ = ['TwoGroupsEffectSize', 'EffectSizeDataFrame', 'PermutationTest'] + +# %% ../nbs/API/effsize_objects.ipynb 5 +import pandas as pd +import lqrt +from scipy.stats import norm +from numpy import array, isnan, isinf, repeat, random, isin, abs, var +from numpy import sort as npsort +from numpy import nan as npnan +from numpy.random import PCG64, RandomState +from statsmodels.stats.contingency_tables import mcnemar +import warnings +from string import Template +import scipy.stats as spstats + +# %% ../nbs/API/effsize_objects.ipynb 6 +class TwoGroupsEffectSize(object): + + """ + A class to compute and store the results of bootstrapped + mean differences between two groups. + + Compute the effect size between two groups. + + Parameters + ---------- + control : array-like + test : array-like + These should be numerical iterables. + effect_size : string. + Any one of the following are accepted inputs: + 'mean_diff', 'median_diff', 'cohens_d', 'hedges_g', or 'cliffs_delta' + is_paired : string, default None + resamples : int, default 5000 + The number of bootstrap resamples to be taken for the calculation + of the confidence interval limits. + permutation_count : int, default 5000 + The number of permutations (reshuffles) to perform for the + computation of the permutation p-value + ci : float, default 95 + The confidence interval width. The default of 95 produces 95% + confidence intervals. + random_seed : int, default 12345 + `random_seed` is used to seed the random number generator during + bootstrap resampling. This ensures that the confidence intervals + reported are replicable. + + Returns + ------- + A :py:class:`TwoGroupEffectSize` object: + `difference` : float + The effect size of the difference between the control and the test. + `effect_size` : string + The type of effect size reported. + `is_paired` : string + The type of repeated-measures experiment. + `ci` : float + Returns the width of the confidence interval, in percent. + `alpha` : float + Returns the significance level of the statistical test as a float between 0 and 1. + `resamples` : int + The number of resamples performed during the bootstrap procedure. + `bootstraps` : numpy ndarray + The generated bootstraps of the effect size. + `random_seed` : int + The number used to initialise the numpy random seed generator, ie.`seed_value` from `numpy.random.seed(seed_value)` is returned. + `bca_low, bca_high` : float + The bias-corrected and accelerated confidence interval lower limit and upper limits, respectively. + `pct_low, pct_high` : float + The percentile confidence interval lower limit and upper limits, respectively. + """ + + def __init__( + self, + control, + test, + effect_size, + proportional=False, + is_paired=None, + ci=95, + resamples=5000, + permutation_count=5000, + random_seed=12345, + ): + from ._stats_tools import confint_2group_diff as ci2g + from ._stats_tools import effsize as es + + self.__EFFECT_SIZE_DICT = { + "mean_diff": "mean difference", + "median_diff": "median difference", + "cohens_d": "Cohen's d", + "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._check_errors(control, test) + + # Convert to numpy arrays for speed. + # NaNs are automatically dropped. + control = array(control) + test = array(test) + self.__control = control[~isnan(control)] + self.__test = test[~isnan(test)] + self.__permutation_count = permutation_count + + self.__alpha = ci2g._compute_alpha_from_ci(self.__ci) + + self.__difference = es.two_group_difference( + self.__control, self.__test, self.__is_paired, self.__effect_size + ) + + self.__jackknives = ci2g.compute_meandiff_jackknife( + self.__control, self.__test, self.__is_paired, self.__effect_size + ) + + self.__acceleration_value = ci2g._calc_accel(self.__jackknives) + + bootstraps = ci2g.compute_bootstrapped_diff( + self.__control, + self.__test, + self.__is_paired, + self.__effect_size, + self.__resamples, + self.__random_seed, + ) + self.__bootstraps = bootstraps + + sorted_bootstraps = npsort(self.__bootstraps) + # Added in v0.2.6. + # Raises a UserWarning if there are any infiinities in the bootstraps. + num_infinities = len(self.__bootstraps[isinf(self.__bootstraps)]) + + if num_infinities > 0: + warn_msg = ( + "There are {} bootstrap(s) that are not defined. " + "This is likely due to smaple sample sizes. " + "The values in a bootstrap for a group will be more likely " + "to be all equal, with a resulting variance of zero. " + "The computation of Cohen's d and Hedges' g thus " + "involved a division by zero. " + ) + warnings.warn(warn_msg.format(num_infinities), category=UserWarning) + + self.__bias_correction = ci2g.compute_meandiff_bias_correction( + self.__bootstraps, self.__difference + ) + + self._compute_bca_intervals(sorted_bootstraps) + + # Compute percentile intervals. + pct_idx_low = int((self.__alpha / 2) * self.__resamples) + pct_idx_high = int((1 - (self.__alpha / 2)) * self.__resamples) + + 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._perform_statistical_test() + + def __repr__(self, show_resample_count=True, define_pval=True, sigfig=3): + RM_STATUS = { + "baseline": "for repeated measures against baseline \n", + "sequential": "for the sequential design of repeated-measures experiment \n", + "None": "", + } + + PAIRED_STATUS = { + "baseline": "paired", + "sequential": "paired", + "None": "unpaired", + } + + first_line = { + "rm_status": RM_STATUS[str(self.__is_paired)], + "es": self.__EFFECT_SIZE_DICT[self.__effect_size], + "paired_status": PAIRED_STATUS[str(self.__is_paired)], + } + + out1 = "The {paired_status} {es} {rm_status}".format(**first_line) + + base_string_fmt = "{:." + str(sigfig) + "}" + if "." in str(self.__ci): + ci_width = base_string_fmt.format(self.__ci) + else: + ci_width = str(self.__ci) + + ci_out = { + "es": base_string_fmt.format(self.__difference), + "ci": ci_width, + "bca_low": base_string_fmt.format(self.__bca_low), + "bca_high": base_string_fmt.format(self.__bca_high), + } + + out2 = "is {es} [{ci}%CI {bca_low}, {bca_high}].".format(**ci_out) + out = out1 + out2 + + pval_rounded = base_string_fmt.format(self.pvalue_permutation) + + p1 = "The p-value of the two-sided permutation t-test is {}, ".format( + pval_rounded + ) + p2 = "calculated for legacy purposes only. " + pvalue = p1 + p2 + + bs1 = "{} bootstrap samples were taken; ".format(self.__resamples) + bs2 = "the confidence interval is bias-corrected and accelerated." + bs = bs1 + bs2 + + pval_def1 = ( + "Any p-value reported is the probability of observing the" + + "effect size (or greater),\nassuming the null hypothesis of " + + "zero difference is true." + ) + pval_def2 = ( + "\nFor each p-value, 5000 reshuffles of the " + + "control and test labels were performed." + ) + pval_def = pval_def1 + pval_def2 + + if show_resample_count and define_pval: + 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: + return "{}\n{}\n\n{}".format(out, pvalue, bs) + else: + return "{}\n{}".format(out, pvalue) + + def _check_errors(self, control, test): + ''' + Function to check configuration errors for the given control and test data. + ''' + kosher_es = [a for a in self.__EFFECT_SIZE_DICT.keys()] + if self.__effect_size not in kosher_es: + err1 = "The effect size '{}'".format(self.__effect_size) + err2 = "is not one of {}".format(kosher_es) + raise ValueError(" ".join([err1, err2])) + + if self.__effect_size == "cliffs_delta" and self.__is_paired: + 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." + raise ValueError(err1) + + if self.__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." + ) + raise ValueError(err1) + + def _compute_bca_intervals(self, sorted_bootstraps): + ''' + Function to compute the bca intervals given the sorted bootstraps. + ''' + from ._stats_tools import confint_2group_diff as ci2g + + # Compute BCa intervals. + bca_idx_low, bca_idx_high = ci2g.compute_interval_limits( + self.__bias_correction, + self.__acceleration_value, + self.__resamples, + self.__ci, + ) + + self.__bca_interval_idx = (bca_idx_low, bca_idx_high) + + if ~isnan(bca_idx_low) and ~isnan(bca_idx_high): + self.__bca_low = sorted_bootstraps[bca_idx_low] + self.__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 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 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.__bca_low = self.__difference + warnings.warn(err_temp.substitute(lim_type="lower"), stacklevel=0) + + if isnan(bca_idx_high): + self.__bca_high = self.__difference + warnings.warn(err_temp.substitute(lim_type="upper"), stacklevel=0) + + def _perform_statistical_test(self): + ''' + Function to complete the statistical tests + ''' + from ._stats_tools import effsize as es + + # Perform statistical tests. + self.__PermutationTest_result = PermutationTest( + self.__control, + self.__test, + self.__effect_size, + self.__is_paired, + self.__permutation_count, + ) + + if self.__is_paired and not self.__proportional: + # Wilcoxon, a non-parametric version of the paired T-test. + try: + wilcoxon = spstats.wilcoxon(self.__control, self.__test) + self.__pvalue_wilcoxon = wilcoxon.pvalue + self.__statistic_wilcoxon = wilcoxon.statistic + except ValueError as e: + warnings.warn("Wilcoxon test could not be performed. This might be due " + "to no variability in the difference of the paired groups. \n" + "Error: {}\n" + "For detailed information, please refer to https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.wilcoxon.html " + .format(e)) + + if self.__effect_size != "median_diff": + # Paired Student's t-test. + paired_t = spstats.ttest_rel( + self.__control, self.__test, nan_policy="omit" + ) + self.__pvalue_paired_students_t = paired_t.pvalue + self.__statistic_paired_students_t = paired_t.statistic + + elif self.__is_paired and self.__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]] + _mcnemar = mcnemar(table, exact=True, correction=True) + self.__pvalue_mcnemar = _mcnemar.pvalue + self.__statistic_mcnemar = _mcnemar.statistic + + elif self.__proportional: + # The Cohen's h calculation is for binary categorical data + try: + self.__proportional_difference = es.cohens_h( + self.__control, self.__test + ) + except ValueError as e: + warnings.warn(f"Calculation of Cohen's h failed. This method is applicable " + f"only for binary data (0's and 1's). Details: {e}") + + elif self.__effect_size == "cliffs_delta": + # Let's go with Brunner-Munzel! + brunner_munzel = spstats.brunnermunzel( + self.__control, self.__test, nan_policy="omit" + ) + self.__pvalue_brunner_munzel = brunner_munzel.pvalue + self.__statistic_brunner_munzel = brunner_munzel.statistic + + elif self.__effect_size == "median_diff": + # According to scipy's documentation of the function, + # "The Kruskal-Wallis H-test tests the null hypothesis + # that the population median of all of the groups are equal." + kruskal = spstats.kruskal(self.__control, self.__test, nan_policy="omit") + self.__pvalue_kruskal = kruskal.pvalue + self.__statistic_kruskal = kruskal.statistic + + else: # for mean difference, Cohen's d, and Hedges' g. + # Welch's t-test, assumes normality of distributions, + # but does not assume equal variances. + welch = spstats.ttest_ind( + self.__control, self.__test, equal_var=False, nan_policy="omit" + ) + self.__pvalue_welch = welch.pvalue + self.__statistic_welch = welch.statistic + + # Student's t-test, assumes normality of distributions, + # as well as assumption of equal variances. + students_t = spstats.ttest_ind( + self.__control, self.__test, equal_var=True, nan_policy="omit" + ) + self.__pvalue_students_t = students_t.pvalue + self.__statistic_students_t = students_t.statistic + + # Mann-Whitney test: Non parametric, + # does not assume normality of distributions + try: + mann_whitney = spstats.mannwhitneyu( + self.__control, self.__test, alternative="two-sided" + ) + self.__pvalue_mann_whitney = mann_whitney.pvalue + self.__statistic_mann_whitney = mann_whitney.statistic + except ValueError as e: + warnings.warn("Mann-Whitney test could not be performed. This might be due " + "to identical rank values in both control and test groups. " + "Details: {}".format(e)) + + standardized_es = es.cohens_d(self.__control, self.__test, is_paired=None) + + + def to_dict(self): + """ + Returns the attributes of the `dabest.TwoGroupEffectSize` object as a + dictionary. + """ + # Only get public (user-facing) attributes. + attrs = [a for a in dir(self) if not a.startswith(("_", "to_dict"))] + out = {} + for a in attrs: + out[a] = getattr(self, a) + return out + + @property + def difference(self): + """ + Returns the difference between the control and the test. + """ + return self.__difference + + @property + def effect_size(self): + """ + Returns the type of effect size reported. + """ + return self.__EFFECT_SIZE_DICT[self.__effect_size] + + @property + def is_paired(self): + return self.__is_paired + + @property + def proportional(self): + return self.__proportional + + @property + def ci(self): + """ + Returns the width of the confidence interval, in percent. + """ + return self.__ci + + @property + def alpha(self): + """ + Returns the significance level of the statistical test as a float + between 0 and 1. + """ + return self.__alpha + + @property + def resamples(self): + """ + The number of resamples performed during the bootstrap procedure. + """ + return self.__resamples + + @property + def bootstraps(self): + """ + The generated bootstraps of the effect size. + """ + return self.__bootstraps + + @property + def random_seed(self): + """ + The number used to initialise the numpy random seed generator, ie. + `seed_value` from `numpy.random.seed(seed_value)` is returned. + """ + return self.__random_seed + + @property + def bca_interval_idx(self): + return self.__bca_interval_idx + + @property + def bca_low(self): + """ + The bias-corrected and accelerated confidence interval lower limit. + """ + return self.__bca_low + + @property + def bca_high(self): + """ + The bias-corrected and accelerated confidence interval upper limit. + """ + return self.__bca_high + + @property + def pct_interval_idx(self): + return self.__pct_interval_idx + + @property + def pct_low(self): + """ + The percentile confidence interval lower limit. + """ + return self.__pct_low + + @property + def pct_high(self): + """ + The percentile confidence interval lower limit. + """ + return self.__pct_high + + @property + def pvalue_brunner_munzel(self): + try: + return self.__pvalue_brunner_munzel + except AttributeError: + return npnan + + @property + def statistic_brunner_munzel(self): + try: + return self.__statistic_brunner_munzel + except AttributeError: + return npnan + + @property + def pvalue_wilcoxon(self): + try: + return self.__pvalue_wilcoxon + except AttributeError: + return npnan + + @property + def statistic_wilcoxon(self): + try: + return self.__statistic_wilcoxon + except AttributeError: + return npnan + + @property + def pvalue_mcnemar(self): + try: + return self.__pvalue_mcnemar + except AttributeError: + return npnan + + @property + def statistic_mcnemar(self): + try: + return self.__statistic_mcnemar + except AttributeError: + return npnan + + @property + def pvalue_paired_students_t(self): + try: + return self.__pvalue_paired_students_t + except AttributeError: + return npnan + + @property + def statistic_paired_students_t(self): + try: + return self.__statistic_paired_students_t + except AttributeError: + return npnan + + @property + def pvalue_kruskal(self): + try: + return self.__pvalue_kruskal + except AttributeError: + return npnan + + @property + def statistic_kruskal(self): + try: + return self.__statistic_kruskal + except AttributeError: + return npnan + + @property + def pvalue_welch(self): + try: + return self.__pvalue_welch + except AttributeError: + return npnan + + @property + def statistic_welch(self): + try: + return self.__statistic_welch + except AttributeError: + return npnan + + @property + def pvalue_students_t(self): + try: + return self.__pvalue_students_t + except AttributeError: + return npnan + + @property + def statistic_students_t(self): + try: + return self.__statistic_students_t + except AttributeError: + return npnan + + @property + def pvalue_mann_whitney(self): + try: + return self.__pvalue_mann_whitney + except AttributeError: + return npnan + + @property + def statistic_mann_whitney(self): + try: + return self.__statistic_mann_whitney + except AttributeError: + return npnan + + @property + def pvalue_permutation(self): + """ + p value of permutation test + """ + return self.__PermutationTest_result.pvalue + + @property + def permutation_count(self): + """ + The number of permutations taken. + """ + return self.__PermutationTest_result.permutation_count + + @property + def permutations(self): + return self.__PermutationTest_result.permutations + + @property + def permutations_var(self): + return self.__PermutationTest_result.permutations_var + + @property + def proportional_difference(self): + try: + return self.__proportional_difference + except AttributeError: + return npnan + +# %% ../nbs/API/effsize_objects.ipynb 10 +class EffectSizeDataFrame(object): + """A class that generates and stores the results of bootstrapped effect + sizes for several comparisons.""" + + def __init__( + self, + dabest, + effect_size, + is_paired, + ci=95, + proportional=False, + resamples=5000, + permutation_count=5000, + random_seed=12345, + x1_level=None, + x2=None, + delta2=False, + experiment_label=None, + mini_meta=False, + ): + """ + Parses the data from a Dabest object, enabling plotting and printing + capability for the effect size of interest. + """ + + self.__dabest_obj = dabest + self.__effect_size = effect_size + self.__is_paired = is_paired + self.__ci = ci + self.__resamples = resamples + self.__permutation_count = permutation_count + self.__random_seed = random_seed + self.__proportional = proportional + self.__x1_level = x1_level + self.__experiment_label = experiment_label + self.__x2 = x2 + self.__delta2 = delta2 + self.__mini_meta = mini_meta + + def __pre_calc(self): + from .misc_tools import print_greeting, get_varname + from ._stats_tools import confint_2group_diff as ci2g + from ._delta_objects import MiniMetaDelta, DeltaDelta + + idx = self.__dabest_obj.idx + dat = self.__dabest_obj._plot_data + xvar = self.__dabest_obj._xvar + yvar = self.__dabest_obj._yvar + + out = [] + reprs = [] + + 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() + + 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() + mixed_data.append(control) + mixed_data.append(test) + bootstraps_delta_delta = ci2g.compute_delta2_bootstrapped_diff( + mixed_data[0], + mixed_data[1], + mixed_data[2], + mixed_data[3], + self.__is_paired, + self.__resamples, + self.__random_seed, + ) + + for j, current_tuple in enumerate(idx): + if self.__is_paired != "sequential": + cname = current_tuple[0] + control = dat[dat[xvar] == cname][yvar].copy() + + 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() + + result = TwoGroupsEffectSize( + control, + test, + self.__effect_size, + self.__proportional, + self.__is_paired, + self.__ci, + self.__resamples, + self.__permutation_count, + self.__random_seed, + ) + r_dict = result.to_dict() + r_dict["control"] = cname + r_dict["test"] = tname + r_dict["control_N"] = int(len(control)) + 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"]: + resamp_count = False + def_pval = False + elif self.__mini_meta and self.__effect_size == "mean_diff": + resamp_count = False + def_pval = False + else: + resamp_count = True + def_pval = True + else: + resamp_count = False + def_pval = False + + text_repr = result.__repr__( + show_resample_count=resamp_count, define_pval=def_pval + ) + + to_replace = "between {} and {} is".format(cname, tname) + text_repr = text_repr.replace("is", to_replace, 1) + + reprs.append(text_repr) + + self.__for_print = "\n\n".join(reprs) + + out_ = pd.DataFrame(out) + + columns_in_order = [ + "control", + "test", + "control_N", + "test_N", + "effect_size", + "is_paired", + "difference", + "ci", + "bca_low", + "bca_high", + "bca_interval_idx", + "pct_low", + "pct_high", + "pct_interval_idx", + "bootstraps", + "resamples", + "random_seed", + "permutations", + "pvalue_permutation", + "permutation_count", + "permutations_var", + "pvalue_welch", + "statistic_welch", + "pvalue_students_t", + "statistic_students_t", + "pvalue_mann_whitney", + "statistic_mann_whitney", + "pvalue_brunner_munzel", + "statistic_brunner_munzel", + "pvalue_wilcoxon", + "statistic_wilcoxon", + "pvalue_mcnemar", + "statistic_mcnemar", + "pvalue_paired_students_t", + "statistic_paired_students_t", + "pvalue_kruskal", + "statistic_kruskal", + "proportional_difference", + ] + self.__results = out_.reindex(columns=columns_in_order) + self.__results.dropna(axis="columns", how="all", inplace=True) + + # Add the is_paired column back when is_paired is None + if self.is_paired is None: + self.__results.insert( + 5, "is_paired", self.__results.apply(lambda _: None, axis=1) + ) + + # Create and compute the delta-delta statistics + if 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( + 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( + self.__effect_size + ) + else: + self.__mini_meta_delta = ( + "`mini_meta` is False; weighted delta is therefore not calculated." + ) + + varname = get_varname(self.__dabest_obj) + lastline = ( + "To get the results of all valid statistical tests, " + + "use `{}.{}.statistical_tests`".format(varname, self.__effect_size) + ) + reprs.append(lastline) + + reprs.insert(0, print_greeting()) + + self.__for_print = "\n\n".join(reprs) + + def __repr__(self): + try: + return self.__for_print + except AttributeError: + self.__pre_calc() + return self.__for_print + + def __calc_lqrt(self): + rnd_seed = self.__random_seed + db_obj = self.__dabest_obj + dat = db_obj._plot_data + xvar = db_obj._xvar + yvar = db_obj._yvar + delta2 = self.__delta2 + + out = [] + + 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() + + 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() + + if self.__is_paired: + # Refactored here in v0.3.0 for performance issues. + lqrt_result = lqrt.lqrtest_rel(control, test, random_state=rnd_seed) + + out.append( + { + "control": cname, + "test": tname, + "control_N": int(len(control)), + "test_N": int(len(test)), + "pvalue_paired_lqrt": lqrt_result.pvalue, + "statistic_paired_lqrt": lqrt_result.statistic, + } + ) + + else: + # Likelihood Q-Ratio test: + lqrt_equal_var_result = lqrt.lqrtest_ind( + control, test, random_state=rnd_seed, equal_var=True + ) + + lqrt_unequal_var_result = lqrt.lqrtest_ind( + control, test, random_state=rnd_seed, equal_var=False + ) + + out.append( + { + "control": cname, + "test": tname, + "control_N": int(len(control)), + "test_N": int(len(test)), + "pvalue_lqrt_equal_var": lqrt_equal_var_result.pvalue, + "statistic_lqrt_equal_var": lqrt_equal_var_result.statistic, + "pvalue_lqrt_unequal_var": lqrt_unequal_var_result.pvalue, + "statistic_lqrt_unequal_var": lqrt_unequal_var_result.statistic, + } + ) + self.__lqrt_results = pd.DataFrame(out) + + def plot( + self, + 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, + swarm_side=None, + custom_palette=None, + swarm_desat=0.5, + halfviolin_desat=1, + halfviolin_alpha=0.8, + face_color=None, + # bar plot + bar_label=None, + bar_desat=0.5, + bar_width=0.5, + bar_ylim=None, + # error bar of proportion plot + ci=None, + ci_type="bca", + err_color=None, + float_contrast=True, + show_pairs=True, + show_delta2=True, + show_mini_meta=True, + group_summaries=None, + group_summaries_offset=0.1, + 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, + sankey_kwargs=None, + reflines_kwargs=None, + group_summary_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, + ): + """ + Creates an estimation plot for the effect size of interest. + + + Parameters + ---------- + color_col : string, default None + Column to be used for colors. + raw_marker_size : float, default 6 + The diameter (in points) of the marker dots plotted in the + swarmplot. + es_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 + 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 + limits for y. When `show_delta2` is True, if both of the `contrast_ylim` + and `delta2_ylim` are not None, then they must be specified with the + same values; when `show_delta2` is True and only one of them is specified, + then the other will automatically be assigned with the same value. + Specifying `delta2_ylim` does not have any effect when `show_delta2` is + False. + custom_palette : dict, list, or matplotlib color palette, default None + This keyword accepts a dictionary with {'group':'color'} pairings, + a list of RGB colors, or a specified matplotlib palette. This + palette will be used to color the swarmplot. If `color_col` is not + specified, then each group will be colored in sequence according + to the default palette currently used by matplotlib. + Please take a look at the seaborn commands `color_palette` + and `cubehelix_palette` to generate a custom palette. Both + these functions generate a list of RGB colors. + See: + https://seaborn.pydata.org/generated/seaborn.color_palette.html + 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 + desired proportion. Uses `seaborn.desaturate()` to acheive this. + halfviolin_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 + The alpha (transparency) level of the half-violin bootstrap curves. + float_contrast : boolean, default True + Whether or not to display the halfviolin bootstrapped difference + distribution alongside the raw data. + show_pairs : boolean, default True + 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_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. + 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 + The dots per inch of the resulting figure. + 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}. + 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`. + 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} + 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 + 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}. + 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}. + 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' + 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. + fontsize_rawxlabel : float, default 12 + Font size for the raw axes xlabel. + fontsize_rawylabel : float, default 12 + Font size for the raw axes ylabel. + fontsize_contrastxlabel : float, default 12 + Font size for the contrast axes xlabel. + fontsize_contrastylabel : float, default 12 + Font size for the contrast axes ylabel. + fontsize_delta2label : float, default 12 + Font size for the delta-delta axes ylabel. + + + Returns + ------- + A :class:`matplotlib.figure.Figure` with 2 Axes, if ``ax = None``. + + The first axes (accessible with ``FigName.axes[0]``) contains the rawdata swarmplot; the second axes (accessible with ``FigName.axes[1]``) has the bootstrap distributions and effect sizes (with confidence intervals) plotted on it. + + If ``ax`` is specified, the rawdata swarmplot is accessed at ``ax`` + itself, while the effect size axes is accessed at ``ax.contrast_axes``. + See the last example below. + + + + """ + + from .plotter import effectsize_df_plotter + + if hasattr(self, "results") is False: + self.__pre_calc() + + if self.__delta2: + color_col = self.__x2 + + # if self.__proportional: + # raw_marker_size = 0.01 + + # Modification incurred due to update of Seaborn + ci = ("ci", ci) if ci is not None else None + + all_kwargs = locals() + del all_kwargs["self"] + + out = effectsize_df_plotter(self, **all_kwargs) + + return out + + @property + def proportional(self): + """ + Returns the proportional parameter + class. + """ + return self.__proportional + + @property + def results(self): + """Prints all pairwise comparisons nicely.""" + try: + return self.__results + except AttributeError: + self.__pre_calc() + return self.__results + + @property + def statistical_tests(self): + results_df = self.results + + # Select only the statistics and p-values. + stats_columns = [ + c + for c in results_df.columns + if c.startswith("statistic") or c.startswith("pvalue") + ] + + default_cols = [ + "control", + "test", + "control_N", + "test_N", + "effect_size", + "is_paired", + "difference", + "ci", + "bca_low", + "bca_high", + ] + + cols_of_interest = default_cols + stats_columns + + return results_df[cols_of_interest] + + @property + def _for_print(self): + return self.__for_print + + @property + def _plot_data(self): + return self.__dabest_obj._plot_data + + @property + def idx(self): + return self.__dabest_obj.idx + + @property + def xvar(self): + return self.__dabest_obj._xvar + + @property + def yvar(self): + return self.__dabest_obj._yvar + + @property + def is_paired(self): + return self.__is_paired + + @property + def ci(self): + """ + The width of the confidence interval being produced, in percent. + """ + return self.__ci + + @property + def x1_level(self): + return self.__x1_level + + @property + def x2(self): + return self.__x2 + + @property + def experiment_label(self): + return self.__experiment_label + + @property + def delta2(self): + return self.__delta2 + + @property + def resamples(self): + """ + The number of resamples (with replacement) during bootstrap resampling." + """ + return self.__resamples + + @property + def random_seed(self): + """ + The seed used by `numpy.seed()` for bootstrap resampling. + """ + return self.__random_seed + + @property + def effect_size(self): + """The type of effect size being computed.""" + return self.__effect_size + + @property + def dabest_obj(self): + """ + Returns the `dabest` object that invoked the current EffectSizeDataFrame + class. + """ + return self.__dabest_obj + + @property + def proportional(self): + """ + Returns the proportional parameter + class. + """ + return self.__proportional + + @property + def lqrt(self): + """Returns all pairwise Lq-Likelihood Ratio Type test results + as a pandas DataFrame. + + For more information on LqRT tests, see https://arxiv.org/abs/1911.11922 + """ + try: + return self.__lqrt_results + except AttributeError: + self.__calc_lqrt() + return self.__lqrt_results + + @property + def mini_meta(self): + """ + Returns the mini_meta boolean parameter. + """ + return self.__mini_meta + + @property + def mini_meta_delta(self): + """ + Returns the mini_meta results. + """ + try: + return self.__mini_meta_delta + except AttributeError: + self.__pre_calc() + return self.__mini_meta_delta + + @property + def delta_delta(self): + """ + Returns the mini_meta results. + """ + try: + return self.__delta_delta + except AttributeError: + self.__pre_calc() + return self.__delta_delta + +# %% ../nbs/API/effsize_objects.ipynb 29 +class PermutationTest: + """ + A class to compute and report permutation tests. + + Parameters + ---------- + control : array-like + test : array-like + 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' + is_paired : string, default None + permutation_count : int, default 10000 + The number of permutations (reshuffles) to perform. + random_seed : int, default 12345 + `random_seed` is used to seed the random number generator during + bootstrap resampling. This ensures that the generated permutations + are replicable. + + Returns + ------- + A :py:class:`PermutationTest` object: + `difference`:float + The effect size of the difference between the control and the test. + `effect_size`:string + The type of effect size reported. + + + """ + + def __init__(self, control: array, + test: array, # These should be numerical iterables. + effect_size:str, # Any one of the following are accepted inputs: 'mean_diff', 'median_diff', 'cohens_d', 'hedges_g', or 'cliffs_delta' + 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. + **kwargs): + from ._stats_tools.effsize import two_group_difference + from ._stats_tools.confint_2group_diff import calculate_group_var + + + self.__permutation_count = permutation_count + + # Run Sanity Check. + if is_paired and len(control) != len(test): + raise ValueError("The two arrays do not have the same length.") + + # Initialise random number generator. + # rng = random.default_rng(seed=random_seed) + rng = RandomState(PCG64(random_seed)) + + # Set required constants and variables + control = array(control) + test = array(test) + + control_sample = control.copy() + test_sample = test.copy() + + BAG = array([*control, *test]) + CONTROL_LEN = int(len(control)) + EXTREME_COUNT = 0. + THRESHOLD = abs(two_group_difference(control, test, + is_paired, effect_size)) + self.__permutations = [] + self.__permutations_var = [] + + for i in range(int(self.__permutation_count)): + if is_paired: + # Select which control-test pairs to swap. + random_idx = rng.choice(CONTROL_LEN, + rng.randint(0, CONTROL_LEN+1), + replace=False) + + # Perform swap. + for i in random_idx: + _placeholder = control_sample[i] + control_sample[i] = test_sample[i] + test_sample[i] = _placeholder + + else: + # Shuffle the bag and assign to control and test groups. + # NB. rng.shuffle didn't produce replicable results... + shuffled = rng.permutation(BAG) + control_sample = shuffled[:CONTROL_LEN] + test_sample = shuffled[CONTROL_LEN:] + + + es = two_group_difference(control_sample, test_sample, + False, effect_size) + + group_var = calculate_group_var(var(control_sample, ddof=1), + CONTROL_LEN, + var(test_sample, ddof=1), + len(test_sample)) + self.__permutations.append(es) + self.__permutations_var.append(group_var) + + if abs(es) > THRESHOLD: + EXTREME_COUNT += 1. + + 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)) + + + @property + def permutation_count(self): + """ + The number of permuations taken. + """ + return self.__permutation_count + + + @property + def permutations(self): + """ + The effect sizes of all the permutations in a list. + """ + return self.__permutations + + + @property + def permutations_var(self): + """ + The experiment group variance of all the permutations in a list. + """ + return self.__permutations_var + diff --git a/dabest/_modidx.py b/dabest/_modidx.py index 88b66c28..14bfa3da 100644 --- a/dabest/_modidx.py +++ b/dabest/_modidx.py @@ -2,8 +2,8 @@ d = { 'settings': { 'branch': 'master', 'doc_baseurl': '/DABEST-python', - 'doc_host': 'https://ZHANGROU-99.github.io', - 'git_url': 'https://github.com/ZHANGROU-99/DABEST-python', + 'doc_host': 'https://acclab.github.io', + 'git_url': 'https://github.com/acclab/DABEST-python', 'lib_path': 'dabest'}, 'syms': { 'dabest._stats_tools.confint_1group': { 'dabest._stats_tools.confint_1group.compute_1group_acceleration': ( 'API/confint_1group.html#compute_1group_acceleration', 'dabest/_stats_tools/confint_1group.py'), @@ -31,6 +31,8 @@ 'dabest/_stats_tools/confint_2group_diff.py'), 'dabest._stats_tools.confint_2group_diff.compute_bootstrapped_diff': ( 'API/confint_2group_diff.html#compute_bootstrapped_diff', 'dabest/_stats_tools/confint_2group_diff.py'), + 'dabest._stats_tools.confint_2group_diff.compute_delta2_bootstrapped_diff': ( 'API/confint_2group_diff.html#compute_delta2_bootstrapped_diff', + 'dabest/_stats_tools/confint_2group_diff.py'), 'dabest._stats_tools.confint_2group_diff.compute_interval_limits': ( 'API/confint_2group_diff.html#compute_interval_limits', 'dabest/_stats_tools/confint_2group_diff.py'), 'dabest._stats_tools.confint_2group_diff.compute_meandiff_bias_correction': ( 'API/confint_2group_diff.html#compute_meandiff_bias_correction', @@ -59,17 +61,35 @@ '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.forest_plot.forest_plot': ('API/forest_plot.html#forest_plot', '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.merge_two_dicts': ('API/misc_tools.html#merge_two_dicts', 'dabest/misc_tools.py'), 'dabest.misc_tools.print_greeting': ('API/misc_tools.html#print_greeting', '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.check_data_matches_labels': ( 'API/plot_tools.html#check_data_matches_labels', + '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__', + 'dabest/plot_tools.py'), + 'dabest.plot_tools.SwarmPlot._adjust_gutter_points': ( 'API/plot_tools.html#swarmplot._adjust_gutter_points', + 'dabest/plot_tools.py'), + 'dabest.plot_tools.SwarmPlot._check_errors': ( 'API/plot_tools.html#swarmplot._check_errors', + 'dabest/plot_tools.py'), + 'dabest.plot_tools.SwarmPlot._format_palette': ( 'API/plot_tools.html#swarmplot._format_palette', + 'dabest/plot_tools.py'), + 'dabest.plot_tools.SwarmPlot._generate_order': ( 'API/plot_tools.html#swarmplot._generate_order', + '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.check_data_matches_labels': ( 'API/plot_tools.html#check_data_matches_labels', '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.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.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.plotter': { 'dabest.plotter.EffectSizeDataFramePlotter': ( 'API/plotter.html#effectsizedataframeplotter', - 'dabest/plotter.py')}}} + 'dabest.plot_tools.single_sankey': ('API/plot_tools.html#single_sankey', 'dabest/plot_tools.py'), + 'dabest.plot_tools.swarmplot': ('API/plot_tools.html#swarmplot', '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 29d82f74..a9b0beb1 100644 --- a/dabest/_stats_tools/confint_1group.py +++ b/dabest/_stats_tools/confint_1group.py @@ -6,25 +6,23 @@ # %% ../../nbs/API/confint_1group.ipynb 4 import numpy as np +from numpy.random import PCG64, RandomState +from scipy.stats import norm +from numpy import sort as npsort # %% ../../nbs/API/confint_1group.ipynb 5 def create_bootstrap_indexes(array, resamples=5000, random_seed=12345): """Given an array-like, returns a generator of bootstrap indexes to be used for resampling. """ - import numpy as np - from numpy.random import PCG64, RandomState + rng = RandomState(PCG64(random_seed)) - + indexes = range(0, len(array)) - out = (rng.choice(indexes, len(indexes), replace=True) - for i in range(0, resamples)) - - # Reset RNG - # rng = RandomState(MT19937()) - return out + out = (rng.choice(indexes, len(indexes), replace=True) for i in range(0, resamples)) + return out def compute_1group_jackknife(x, func, *args, **kwargs): @@ -32,53 +30,56 @@ def compute_1group_jackknife(x, func, *args, **kwargs): Returns the jackknife bootstraps for func(x). """ from . import confint_2group_diff as ci_2g + jackknives = [i for i in ci_2g.create_jackknife_indexes(x)] out = [func(x[j], *args, **kwargs) for j in jackknives] - del jackknives # memory management. + del jackknives # memory management. return out - def compute_1group_acceleration(jack_dist): + """ + Returns the accaleration value based on the jackknife distribution. + """ from . import confint_2group_diff as ci_2g - return ci_2g._calc_accel(jack_dist) + return ci_2g._calc_accel(jack_dist) -def compute_1group_bootstraps(x, func, resamples=5000, random_seed=12345, - *args, **kwargs): +def compute_1group_bootstraps( + x, func, resamples=5000, random_seed=12345, *args, **kwargs +): """Bootstraps func(x), with the number of specified resamples.""" - import numpy as np - # Create bootstrap indexes. - boot_indexes = create_bootstrap_indexes(x, resamples=resamples, - random_seed=random_seed) + boot_indexes = create_bootstrap_indexes( + x, resamples=resamples, random_seed=random_seed + ) out = [func(x[b], *args, **kwargs) for b in boot_indexes] - + del boot_indexes - - return out + return out def compute_1group_bias_correction(x, bootstraps, func, *args, **kwargs): - from scipy.stats import norm metric = func(x, *args, **kwargs) prop_boots_less_than_metric = sum(bootstraps < metric) / len(bootstraps) return norm.ppf(prop_boots_less_than_metric) - -def summary_ci_1group(x:np.array,# An numerical iterable. - func, #The function to be applied to x. - resamples:int=5000, #The number of bootstrap resamples to be taken of func(x). - alpha:float=0.05, #Denotes the likelihood that the confidence interval produced _does not_ include the true summary statistic. When alpha = 0.05, a 95% confidence interval is produced. - 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. - sort_bootstraps:bool=True, - *args, **kwargs): +def summary_ci_1group( + x: np.array, # An numerical iterable. + func, # The function to be applied to x. + resamples: int = 5000, # The number of bootstrap resamples to be taken of func(x). + alpha: float = 0.05, # Denotes the likelihood that the confidence interval produced _does not_ include the true summary statistic. When alpha = 0.05, a 95% confidence interval is produced. + 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. + sort_bootstraps: bool = True, + *args, + **kwargs +): """ Given an array-like x, returns func(x), and a bootstrap confidence interval of func(x). @@ -101,11 +102,10 @@ def summary_ci_1group(x:np.array,# An numerical iterable. """ from . import confint_2group_diff as ci2g - from numpy import sort as npsort - boots = compute_1group_bootstraps(x, func, resamples=resamples, - random_seed=random_seed, - *args, **kwargs) + boots = compute_1group_bootstraps( + x, func, resamples=resamples, random_seed=random_seed, *args, **kwargs + ) bias = compute_1group_bias_correction(x, boots, func) jk = compute_1group_jackknife(x, func, *args, **kwargs) @@ -126,10 +126,13 @@ def summary_ci_1group(x:np.array,# An numerical iterable. del boots del boots_sorted - out = {'summary': func(x), 'func': func, - 'bca_ci_low': low, 'bca_ci_high': high, - 'bootstraps': B} + out = { + "summary": func(x), + "func": func, + "bca_ci_low": low, + "bca_ci_high": high, + "bootstraps": B, + } del B return out - diff --git a/dabest/_stats_tools/confint_2group_diff.py b/dabest/_stats_tools/confint_2group_diff.py index cc8c0de8..3b07eb96 100644 --- a/dabest/_stats_tools/confint_2group_diff.py +++ b/dabest/_stats_tools/confint_2group_diff.py @@ -2,11 +2,18 @@ # %% auto 0 __all__ = ['create_jackknife_indexes', 'create_repeated_indexes', 'compute_meandiff_jackknife', 'compute_bootstrapped_diff', - 'compute_meandiff_bias_correction', 'compute_interval_limits', 'calculate_group_var', - 'calculate_weighted_delta'] + '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 +from numpy import arange, delete, errstate +from numpy import mean as npmean +from numpy import sum as npsum +from numpy.random import PCG64, RandomState +import pandas as pd +from scipy.stats import norm +from numpy import isnan # %% ../../nbs/API/confint_2group_diff.ipynb 5 def create_jackknife_indexes(data): @@ -24,43 +31,45 @@ def create_jackknife_indexes(data): ------- Generator that yields all jackknife bootstrap samples. """ - from numpy import arange, delete index_range = arange(0, len(data)) return (delete(index_range, i) for i in index_range) - def create_repeated_indexes(data): """ Convenience function. Given an array-like with length N, returns a generator that yields N indexes [0, 1, ..., N]. """ - from numpy import arange index_range = arange(0, len(data)) return (index_range for i in index_range) - def _create_two_group_jackknife_indexes(x0, x1, is_paired): """Creates the jackknife bootstrap for 2 groups.""" if is_paired and len(x0) == len(x1): - out = list(zip([j for j in create_jackknife_indexes(x0)], - [i for i in create_jackknife_indexes(x1)] - ) - ) + out = list( + zip( + [j for j in create_jackknife_indexes(x0)], + [i for i in create_jackknife_indexes(x1)], + ) + ) else: - jackknife_c = list(zip([j for j in create_jackknife_indexes(x0)], - [i for i in create_repeated_indexes(x1)] - ) - ) - - jackknife_t = list(zip([i for i in create_repeated_indexes(x0)], - [j for j in create_jackknife_indexes(x1)] - ) - ) + jackknife_c = list( + zip( + [j for j in create_jackknife_indexes(x0)], + [i for i in create_repeated_indexes(x1)], + ) + ) + + jackknife_t = list( + zip( + [i for i in create_repeated_indexes(x0)], + [j for j in create_jackknife_indexes(x1)], + ) + ) out = jackknife_c + jackknife_t del jackknife_c del jackknife_t @@ -68,7 +77,6 @@ def _create_two_group_jackknife_indexes(x0, x1, is_paired): return out - def compute_meandiff_jackknife(x0, x1, is_paired, effect_size): """ Given two arrays, returns the jackknife for their effect size. @@ -83,46 +91,40 @@ def compute_meandiff_jackknife(x0, x1, is_paired, effect_size): x0_shuffled = x0[j[0]] x1_shuffled = x1[j[1]] - es = __es.two_group_difference(x0_shuffled, x1_shuffled, - is_paired, effect_size) + es = __es.two_group_difference(x0_shuffled, x1_shuffled, is_paired, effect_size) out.append(es) return out - def _calc_accel(jack_dist): - from numpy import mean as npmean - from numpy import sum as npsum - from numpy import errstate - + """ + Given the Jackknife distribution, calculates the acceleration factor. + """ jack_mean = npmean(jack_dist) - numer = npsum((jack_mean - jack_dist)**3) - denom = 6.0 * (npsum((jack_mean - jack_dist)**2) ** 1.5) + numer = npsum((jack_mean - jack_dist) ** 3) + denom = 6.0 * (npsum((jack_mean - jack_dist) ** 2) ** 1.5) - with errstate(invalid='ignore'): + with errstate(invalid="ignore"): # does not raise warning if invalid division encountered. return numer / denom -def compute_bootstrapped_diff(x0, x1, is_paired, effect_size, - resamples=5000, random_seed=12345): +def compute_bootstrapped_diff( + x0, x1, is_paired, effect_size, resamples=5000, random_seed=12345 +): """Bootstraps the effect_size for 2 groups.""" - + from . import effsize as __es - import numpy as np - from numpy.random import PCG64, RandomState - - # rng = RandomState(default_rng(random_seed)) + rng = RandomState(PCG64(random_seed)) out = np.repeat(np.nan, resamples) x0_len = len(x0) x1_len = len(x1) - + for i in range(int(resamples)): - if is_paired: if x0_len != x1_len: raise ValueError("The two arrays do not have the same length.") @@ -132,35 +134,87 @@ def compute_bootstrapped_diff(x0, x1, is_paired, effect_size, else: x0_sample = rng.choice(x0, x0_len, replace=True) x1_sample = rng.choice(x1, x1_len, replace=True) - - out[i] = __es.two_group_difference(x0_sample, x1_sample, - is_paired, effect_size) - - # check whether there are any infinities in the bootstrap, - # which likely indicates the sample sizes are too small as - # the computation of Cohen's d and Hedges' g necessitated - # a division by zero. - # Added in v0.2.6. - - # num_infinities = len(out[np.isinf(out)]) - # print(num_infinities) - # if num_infinities > 0: - # warn_msg = "There are {} bootstraps that are not defined. "\ - # "This is likely due to smaple sample sizes. "\ - # "The values in a bootstrap for a group will be more likely "\ - # "to be all equal, with a resulting variance of zero. "\ - # "The computation of Cohen's d and Hedges' g will therefore "\ - # "involved a division by zero. " - # warnings.warn(warn_msg.format(num_infinities), category="UserWarning") - + + 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 + """ + Bootstraps the effect size deltas' g. + + """ + + 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]] -def compute_meandiff_bias_correction(bootstraps, #An numerical iterable, comprising bootstrap resamples of the effect size. - effsize # The effect size for the original sample. - ): #The bias correction value for the given bootstraps and effect size. + 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) + deltadelta = np.empty(resamples) + + # 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) + + 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 + 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 + + return out_delta_g, delta_g, deltadelta + + +def compute_meandiff_bias_correction( + bootstraps, # An numerical iterable, comprising bootstrap resamples of the effect size. + effsize, # The effect size for the original sample. +): # The bias correction value for the given bootstraps and effect size. """ Computes the bias correction required for the BCa method of confidence interval construction. @@ -172,22 +226,18 @@ def compute_meandiff_bias_correction(bootstraps, #An numerical iterable, compris and effect size. """ - from scipy.stats import norm - from numpy import array - B = array(bootstraps) + B = np.array(bootstraps) prop_less_than_es = sum(B < effsize) / len(B) return norm.ppf(prop_less_than_es) - def _compute_alpha_from_ci(ci): if ci < 0 or ci > 100: raise ValueError("`ci` must be a number between 0 and 100.") - return (100. - ci) / 100. - + return (100.0 - ci) / 100.0 def _compute_quantile(z, bias, acceleration): @@ -197,15 +247,12 @@ def _compute_quantile(z, bias, acceleration): return bias + (numer / denom) - def compute_interval_limits(bias, acceleration, n_boots, ci=95): """ Returns the indexes of the interval limits for a given bootstrap. Supply the bias, acceleration factor, and number of bootstraps. """ - from scipy.stats import norm - from numpy import isnan, nan alpha = _compute_alpha_from_ci(ci) @@ -215,31 +262,30 @@ def compute_interval_limits(bias, acceleration, n_boots, ci=95): z_low = norm.ppf(alpha_low) z_high = norm.ppf(alpha_high) - kws = {'bias': bias, 'acceleration': acceleration} + kws = {"bias": bias, "acceleration": acceleration} low = _compute_quantile(z_low, **kws) high = _compute_quantile(z_high, **kws) if isnan(low) or isnan(high): return low, high - else: - low = int(norm.cdf(low) * n_boots) - high = int(norm.cdf(high) * n_boots) - return low, high + + low = int(norm.cdf(low) * n_boots) + high = int(norm.cdf(high) * n_boots) + return low, high -def calculate_group_var(control_var, control_N,test_var, test_N): - return control_var/control_N + test_var/test_N +def calculate_group_var(control_var, control_N, test_var, test_N): + return control_var / control_N + test_var / test_N -def calculate_weighted_delta(group_var, differences, resamples): - ''' +def calculate_weighted_delta(group_var, differences): + """ Compute the weighted deltas. - ''' - import numpy as np + """ - weight = 1/group_var + weight = 1 / group_var denom = np.sum(weight) num = np.sum(weight[i] * differences[i] for i in range(0, len(weight))) - return num/denom + return num / denom diff --git a/dabest/_stats_tools/effsize.py b/dabest/_stats_tools/effsize.py index ddc8681f..32f965b1 100644 --- a/dabest/_stats_tools/effsize.py +++ b/dabest/_stats_tools/effsize.py @@ -3,6 +3,9 @@ # %% ../../nbs/API/effsize.ipynb 4 from __future__ import annotations import numpy as np +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'] @@ -56,13 +59,12 @@ def two_group_difference(control:list|tuple|np.ndarray, #Accepts lists, tuples, median of `test`. """ - import numpy as np - import warnings + if effect_size == "mean_diff": return func_difference(control, test, np.mean, is_paired) - elif effect_size == "median_diff": + if effect_size == "median_diff": mes1 = "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" @@ -72,21 +74,21 @@ def two_group_difference(control:list|tuple|np.ndarray, #Accepts lists, tuples, warnings.warn(message=mes1+mes2, category=UserWarning) return func_difference(control, test, np.median, is_paired) - elif effect_size == "cohens_d": + if effect_size == "cohens_d": return cohens_d(control, test, is_paired) - elif effect_size == "cohens_h": + if effect_size == "cohens_h": return cohens_h(control, test) - elif effect_size == "hedges_g": + if effect_size == "hedges_g" or effect_size == "delta_g": return hedges_g(control, test, is_paired) - elif effect_size == "cliffs_delta": + if effect_size == "cliffs_delta": if is_paired: err1 = "`is_paired` is not None; therefore Cliff's delta is not defined." raise ValueError(err1) - else: - return cliffs_delta(control, test) + + return cliffs_delta(control, test) # %% ../../nbs/API/effsize.ipynb 6 @@ -100,13 +102,12 @@ def func_difference(control:list|tuple|np.ndarray, # NaNs are automatically disc Applies func to `control` and `test`, and then returns the difference. """ - import numpy as np # Convert to numpy arrays for speed. # NaNs are automatically dropped. - if control.__class__ != np.ndarray: + if ~isinstance(control, np.ndarray): control = np.array(control) - if test.__class__ != np.ndarray: + if ~isinstance(test, np.ndarray): test = np.array(test) if is_paired: @@ -128,10 +129,10 @@ def func_difference(control:list|tuple|np.ndarray, # NaNs are automatically disc return func(test - control) - else: - control = control[~np.isnan(control)] - test = test[~np.isnan(test)] - return func(test) - func(control) + + control = control[~np.isnan(control)] + test = test[~np.isnan(test)] + return func(test) - func(control) # %% ../../nbs/API/effsize.ipynb 7 @@ -178,13 +179,12 @@ def cohens_d(control:list|tuple|np.ndarray, - https://en.wikipedia.org/wiki/Bessel%27s_correction - https://en.wikipedia.org/wiki/Standard_deviation#Corrected_sample_standard_deviation """ - import numpy as np # Convert to numpy arrays for speed. # NaNs are automatically dropped. - if control.__class__ != np.ndarray: + if ~isinstance(control, np.ndarray): control = np.array(control) - if test.__class__ != np.ndarray: + if ~isinstance(test, np.ndarray): test = np.array(test) control = control[~np.isnan(control)] test = test[~np.isnan(test)] @@ -209,7 +209,10 @@ def cohens_d(control:list|tuple|np.ndarray, else: M = np.mean(test) - np.mean(control) divisor = pooled_sd - + + if divisor == 0: + raise ValueError("The divisor is zero, indicating no variability in the data.") + return M / divisor # %% ../../nbs/API/effsize.ipynb 8 @@ -226,9 +229,7 @@ def cohens_h(control:list|tuple|np.ndarray, and a dict for mapping the 0s and 1s to the actual labels, e.g.{1: "Smoker", 0: "Non-smoker"} ''' - import numpy as np np.seterr(divide='ignore', invalid='ignore') - import pandas as pd # Check whether dataframe contains only 0s and 1s. if np.isin(control, [0, 1]).all() == False or np.isin(test, [0, 1]).all() == False: @@ -237,10 +238,10 @@ def cohens_h(control:list|tuple|np.ndarray, # Convert to numpy arrays for speed. # NaNs are automatically dropped. # Aligned with cohens_d calculation. - if control.__class__ != np.ndarray: + if ~isinstance(control, np.ndarray): control = np.array(control) - if test.__class__ != np.ndarray: - test = np.array(test) + if ~isinstance(test, np.ndarray): + test = np.array(test) control = control[~np.isnan(control)] test = test[~np.isnan(test)] @@ -266,13 +267,12 @@ def hedges_g(control:list|tuple|np.ndarray, See [here](https://en.wikipedia.org/wiki/Effect_size#Hedges'_g) """ - import numpy as np # Convert to numpy arrays for speed. # NaNs are automatically dropped. - if control.__class__ != np.ndarray: + if ~isinstance(control, np.ndarray): control = np.array(control) - if test.__class__ != np.ndarray: + if ~isinstance(test, np.ndarray): test = np.array(test) control = control[~np.isnan(control)] test = test[~np.isnan(test)] @@ -291,14 +291,12 @@ 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) """ - import numpy as np - from scipy.stats import mannwhitneyu # Convert to numpy arrays for speed. # NaNs are automatically dropped. - if control.__class__ != np.ndarray: + if ~isinstance(control, np.ndarray): control = np.array(control) - if test.__class__ != np.ndarray: + if ~isinstance(test, np.ndarray): test = np.array(test) c = control[~np.isnan(control)] @@ -311,55 +309,56 @@ def cliffs_delta(control:list|tuple|np.ndarray, U, _ = mannwhitneyu(t, c, alternative='two-sided') cliffs_delta = ((2 * U) / (control_n * test_n)) - 1 - # more = 0 - # less = 0 - # - # for i, c in enumerate(control): - # for j, t in enumerate(test): - # if t > c: - # more += 1 - # elif t < c: - # less += 1 - # - # cliffs_delta = (more - less) / (control_n * test_n) - return cliffs_delta # %% ../../nbs/API/effsize.ipynb 11 def _compute_standardizers(control, test): - from numpy import mean, var, sqrt, nan - # For calculation of correlation; not currently used. + """ + Computes the pooled and average standard deviations for two datasets. + + This function is useful in the context of statistical analysis, particularly + when calculating standardized mean differences between two groups. It supports + both unpaired and paired data scenarios. + + Parameters: + control (array-like): A numeric array representing the control group data. + test (array-like): A numeric array representing the test group data. + + Returns: + tuple: A tuple containing two elements: + - pooled (float): The pooled standard deviation, calculated for unpaired two-group + scenarios. It is computed using the sample variances of the + control and test groups, weighted by their sample sizes. + - average (float): The average standard deviation, calculated for paired data + scenarios. It is the average of the sample standard deviations + of the control and test groups. + + Note: + The function assumes that the input arrays are independent samples and calculates + the sample variances using N-1 degrees of freedom. + + For calculation of correlation; not currently used. + + """ # from scipy.stats import pearsonr control_n = len(control) test_n = len(test) - control_mean = mean(control) - test_mean = mean(test) + control_var = np.var(control, ddof=1) # use N-1 to compute the variance. + test_var = np.var(test, ddof=1) - control_var = var(control, ddof=1) # use N-1 to compute the variance. - test_var = var(test, ddof=1) - - control_std = sqrt(control_var) - test_std = sqrt(test_var) # For unpaired 2-groups standardized mean difference. - pooled = sqrt(((control_n - 1) * control_var + (test_n - 1) * test_var) / + pooled = np.sqrt(((control_n - 1) * control_var + (test_n - 1) * test_var) / (control_n + test_n - 2) ) # For paired standardized mean difference. - average = sqrt((control_var + test_var) / 2) + average = np.sqrt((control_var + test_var) / 2) - # if len(control) == len(test): - # corr = pearsonr(control, test)[0] - # std_diff = sqrt(control_var + test_var - (2 * corr * control_std * test_std)) - # std_diff_corrected = std_diff / (sqrt(2 * (1 - corr))) - # return pooled, average, std_diff_corrected - # - # else: - return pooled, average # indent if you implement above code chunk. + return pooled, average # %% ../../nbs/API/effsize.ipynb 12 def _compute_hedges_correction_factor(n1, @@ -377,16 +376,12 @@ def _compute_hedges_correction_factor(n1, ISBN 0-12-336380-2. """ - from scipy.special import gamma - from numpy import sqrt, isinf - import warnings - df = n1 + n2 - 2 numer = gamma(df / 2) denom0 = gamma((df - 1) / 2) - denom = sqrt(df / 2) * denom0 + denom = np.sqrt(df / 2) * denom0 - if isinf(numer) or isinf(denom): + if np.isinf(numer) or np.isinf(denom): # occurs when df is too large. # Apply Hedges and Olkin's approximation. df_sum = n1 + n2 @@ -404,7 +399,6 @@ def weighted_delta(difference, group_var): Compute the weighted deltas where the weight is the inverse of the pooled group difference. ''' - import numpy as np weight = np.true_divide(1, group_var) return np.sum(difference*weight)/np.sum(weight) diff --git a/dabest/forest_plot.py b/dabest/forest_plot.py new file mode 100644 index 00000000..7d29464f --- /dev/null +++ b/dabest/forest_plot.py @@ -0,0 +1,300 @@ +# AUTOGENERATED! DO NOT EDIT! File to edit: ../nbs/API/forest_plot.ipynb. + +# %% auto 0 +__all__ = ['load_plot_data', 'extract_plot_data', '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 + + +# %% ../nbs/API/forest_plot.ipynb 6 +def load_plot_data( + contrasts: List, effect_size: str = "mean_diff", contrast_type: str = "delta2" +) -> List: + """ + Loads plot data based on specified effect size and contrast type. + + Parameters + ---------- + contrasts : List + List of contrast objects. + effect_size: str + Type of effect size ('mean_diff', 'median_diff', etc.). + contrast_type: str + Type of contrast ('delta2', 'mini_meta'). + + 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" + } + + contrast_attr_map = {"delta2": "delta_delta", "mini_meta": "mini_meta_delta"} + + effect_attr = effect_attr_map.get(effect_size) + contrast_attr = contrast_attr_map.get(contrast_type) + + 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 [ + getattr(getattr(contrast, effect_attr), contrast_attr) for contrast in contrasts + ] + + +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" + else: + attribute_suffix = "delta_delta" + + 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] + + return bootstraps, differences, bcalows, bcahighs + + +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. + + 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. + + Returns + ------- + plt.Figure + The matplotlib figure object with the generated forest plot. + """ + from .plot_tools import halfviolin + + # 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.") + + 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_type, str): + raise TypeError("The `contrast_type` argument must be a string.") + + 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 not isinstance(effect_size, str): + raise TypeError("The `effect_size` argument must be a string.") + + 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 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(ylabel, str): + raise TypeError("The `ylabel` argument must be a string.") + + 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.") + + 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.") + + 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 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.") + + 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.") + + # Load plot data + contrast_plot_data = load_plot_data(contrasts, effect_size, contrast_type) + + # 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) + + if ax is None: + fig, ax = plt.subplots(figsize=fig_size) + else: + fig = ax.figure + + # Adjust violin plot orientation based on the 'horizontal' argument + violin_kwargs = violin_kwargs or { + "widths": 0.5, + "showextrema": False, + "showmedians": False, + } + violin_kwargs["vert"] = not horizontal + v = ax.violinplot(bootstraps, **violin_kwargs) + + # Adjust the halfviolin function call based on 'horizontal' + if horizontal: + half = "top" + else: + half = "right" # Assuming "right" is the default or another appropriate value + + # 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: + if isinstance(custom_palette, dict): + violin_colors = [ + custom_palette.get(c, sns.color_palette()[0]) for c in contrasts + ] + elif isinstance(custom_palette, list): + violin_colors = custom_palette[: len(contrasts)] + elif isinstance(custom_palette, str): + if custom_palette in plt.colormaps(): + violin_colors = sns.color_palette(custom_palette, len(contrasts)) + else: + raise ValueError( + f"The specified `custom_palette` {custom_palette} is not a recognized Matplotlib palette." + ) + else: + violin_colors = sns.color_palette()[: len(contrasts)] + + for patch, color in zip(v["bodies"], violin_colors): + patch.set_facecolor(color) + patch.set_alpha(alpha_violin_plot) + + # Flipping the axes for plotting based on 'horizontal' + for k in range(1, len(contrasts) + 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 + 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) + + # Adjusting labels, ticks, and limits based on 'horizontal' + 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) + 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) + + # Setting the title and adjusting spines as before + ax.set_title(title, fontsize=fontsize) + if remove_spines: + for spine in ax.spines.values(): + spine.set_visible(False) + + # Apply additional customizations if provided + if additional_plotting_kwargs: + ax.set(**additional_plotting_kwargs) + + return fig diff --git a/dabest/misc_tools.py b/dabest/misc_tools.py index 4b2617ef..7c5b2020 100644 --- a/dabest/misc_tools.py +++ b/dabest/misc_tools.py @@ -4,9 +4,13 @@ __all__ = ['merge_two_dicts', 'unpack_and_add', 'print_greeting', 'get_varname'] # %% ../nbs/API/misc_tools.ipynb 4 -def merge_two_dicts(x:dict, - y:dict - )->dict:#A dictionary containing a union of all keys in both original dicts. +import datetime as dt +from numpy import repeat + +# %% ../nbs/API/misc_tools.ipynb 5 +def merge_two_dicts( + x: dict, y: dict +) -> dict: # A dictionary containing a union of all keys in both original dicts. """ Given two dicts, merge them into a new dict as a shallow copy. Any overlapping keys in `y` will override the values in `x`. @@ -20,24 +24,31 @@ def merge_two_dicts(x:dict, return z - def unpack_and_add(l, c): """Convenience function to allow me to add to an existing list without altering that list.""" t = [a for a in l] t.append(c) - return(t) - + return t def print_greeting(): + """ + Generates a greeting message based on the current time, along with the version information of DABEST. + + This function dynamically generates a greeting ('Good morning', 'Good afternoon', 'Good evening') + based on the current system time. It also retrieves and displays the version of DABEST (Data Analysis + using Bootstrap-Coupled ESTimation). The message includes a header with the DABEST version and the + current time formatted in a user-friendly manner. + + Returns: + str: A formatted string containing the greeting message, DABEST version, and current time. + """ from .__init__ import __version__ - import datetime as dt - import numpy as np line1 = "DABEST v{}".format(__version__) - header = "".join(np.repeat("=", len(line1))) - spacer = "".join(np.repeat(" ", len(line1))) + header = "".join(repeat("=", len(line1))) + spacer = "".join(repeat(" ", len(line1))) now = dt.datetime.now() if 0 < now.hour < 12: @@ -53,9 +64,7 @@ def print_greeting(): def get_varname(obj): - matching_vars = [k for k,v in globals().items() if v is obj] + matching_vars = [k for k, v in globals().items() if v is obj] if len(matching_vars) > 0: return matching_vars[0] - else: - return "" - + return "" diff --git a/dabest/plot_tools.py b/dabest/plot_tools.py index a349afdc..65fea009 100644 --- a/dabest/plot_tools.py +++ b/dabest/plot_tools.py @@ -4,35 +4,39 @@ from __future__ import annotations # %% auto 0 -__all__ = ['halfviolin', 'get_swarm_spans', 'error_bar', 'check_data_matches_labels', 'normalize_dict', 'single_sankey', - 'sankeydiag'] +__all__ = ['halfviolin', 'get_swarm_spans', 'error_bar', 'check_data_matches_labels', 'normalize_dict', 'width_determine', + 'single_sankey', 'sankeydiag', 'swarmplot', 'SwarmPlot'] # %% ../nbs/API/plot_tools.ipynb 4 +import math +import warnings +import itertools +import numpy as np import pandas as pd -from collections import defaultdict -import matplotlib.pyplot as plt import seaborn as sns -import numpy as np -import itertools +import matplotlib.pyplot as plt +import matplotlib.lines as mlines +import matplotlib.axes as axes +from collections import defaultdict +from typing import List, Tuple, Dict, Iterable, Union +from pandas.api.types import CategoricalDtype +from matplotlib.colors import ListedColormap # %% ../nbs/API/plot_tools.ipynb 5 -def halfviolin(v, half='right', fill_color='k', alpha=1, - line_color='k', line_width=0): - import numpy as np - - for b in v['bodies']: +def halfviolin(v, half="right", fill_color="k", alpha=1, line_color="k", line_width=0): + for b in v["bodies"]: V = b.get_paths()[0].vertices mean_vertical = np.mean(V[:, 0]) mean_horizontal = np.mean(V[:, 1]) - if half == 'right': + if half == "right": V[:, 0] = np.clip(V[:, 0], mean_vertical, np.inf) - elif half == 'left': + elif half == "left": V[:, 0] = np.clip(V[:, 0], -np.inf, mean_vertical) - elif half == 'bottom': + elif half == "bottom": V[:, 1] = np.clip(V[:, 1], -np.inf, mean_horizontal) - elif half == 'top': + elif half == "top": V[:, 1] = np.clip(V[:, 1], mean_horizontal, np.inf) b.set_color(fill_color) @@ -46,41 +50,49 @@ def get_swarm_spans(coll): Given a matplotlib Collection, will obtain the x and y spans for the collection. Will return None if this fails. """ - import numpy as np + if coll is None: + raise ValueError("The collection `coll` parameter cannot be None") + x, y = np.array(coll.get_offsets()).T try: return x.min(), x.max(), y.min(), y.max() - except ValueError: + except ValueError as e: + warnings.warn(f"Failed to calculate spans for the collection. Details: {e}") return None -def error_bar(data:pd.DataFrame, # This DataFrame should be in 'long' format. - x:str, #x column to be plotted. - y:str, # y column to be plotted. - type:str='mean_sd', # Choose from ['mean_sd', 'median_quartiles']. Plots the summary statistics for each group. If 'mean_sd', then the mean and standard deviation of each group is plotted as a gapped line. If 'median_quantiles', then the median and 25th and 75th percentiles of each group is plotted instead. - offset:float=0.2, #Give a single float (that will be used as the x-offset of all gapped lines), or an iterable containing the list of x-offsets. - ax=None, #If a matplotlib Axes object is specified, the gapped lines will be plotted in order on this axes. If None, the current axes (plt.gca()) is used. - line_color="black", gap_width_percent=1, - pos:list=[0, 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'. - **kwargs:dict - ): - ''' + +def error_bar( + data: pd.DataFrame, # This DataFrame should be in 'long' format. + x: str, # x column to be plotted. + y: str, # y column to be plotted. + type: str = "mean_sd", # Choose from ['mean_sd', 'median_quartiles']. Plots the summary statistics for each group. If 'mean_sd', then the mean and standard deviation of each group is plotted as a gapped line. If 'median_quantiles', then the median and 25th and 75th percentiles of each group is plotted instead. + offset: float = 0.2, # Give a single float (that will be used as the x-offset of all gapped lines), or an iterable containing the list of x-offsets. + ax=None, # If a matplotlib Axes object is specified, the gapped lines will be plotted in order on this axes. If None, the current axes (plt.gca()) is used. + line_color="black", # The color of the gapped lines. + gap_width_percent=1, # The width of the gap in the gapped lines, as a percentage of the y-axis span. + pos: list = [ + 0, + 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'. + **kwargs: dict, +): + """ Function to plot the standard deviations as vertical errorbars. The mean is a gap defined by negative space. This function combines the functionality of gapped_lines(), proportional_error_bar(), and sankey_error_bar(). - ''' - import numpy as np - import pandas as pd - import matplotlib.pyplot as plt - import matplotlib.lines as mlines + """ if gap_width_percent < 0 or gap_width_percent > 100: raise ValueError("`gap_width_percent` must be between 0 and 100.") - if method not in ['gapped_lines', 'proportional_error_bar', 'sankey_error_bar']: - raise ValueError("Invalid `method`. Must be one of 'gapped_lines', 'proportional_error_bar', or 'sankey_error_bar'.") + if method not in ["gapped_lines", "proportional_error_bar", "sankey_error_bar"]: + raise ValueError( + "Invalid `method`. Must be one of 'gapped_lines', \ + 'proportional_error_bar', or 'sankey_error_bar'." + ) if ax is None: ax = plt.gca() @@ -89,14 +101,14 @@ def error_bar(data:pd.DataFrame, # This DataFrame should be in 'long' format. gap_width = ax_yspan * gap_width_percent / 100 keys = kwargs.keys() - if 'clip_on' not in keys: - kwargs['clip_on'] = False + if "clip_on" not in keys: + kwargs["clip_on"] = False - if 'zorder' not in keys: - kwargs['zorder'] = 5 + if "zorder" not in keys: + kwargs["zorder"] = 5 - if 'lw' not in keys: - kwargs['lw'] = 2. + if "lw" not in keys: + kwargs["lw"] = 2.0 if isinstance(data[x].dtype, pd.CategoricalDtype): group_order = pd.unique(data[x]).categories @@ -105,8 +117,10 @@ def error_bar(data:pd.DataFrame, # This DataFrame should be in 'long' format. means = data.groupby(x)[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))) + 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) else: sd = data.groupby(x)[y].std().reindex(index=group_order) @@ -115,23 +129,25 @@ def error_bar(data:pd.DataFrame, # This DataFrame should be in 'long' format. upper_sd = means + sd if (lower_sd < ax_ylims[0]).any() or (upper_sd > ax_ylims[1]).any(): - kwargs['clip_on'] = True + kwargs["clip_on"] = True medians = data.groupby(x)[y].median().reindex(index=group_order) - quantiles = data.groupby(x)[y].quantile([0.25, 0.75]) \ - .unstack() \ - .reindex(index=group_order) + quantiles = ( + data.groupby(x)[y].quantile([0.25, 0.75]).unstack().reindex(index=group_order) + ) lower_quartiles = quantiles[0.25] upper_quartiles = quantiles[0.75] - if type == 'mean_sd': + if type == "mean_sd": central_measures = means lows = lower_sd highs = upper_sd - elif type == 'median_quartiles': + elif type == "median_quartiles": central_measures = medians lows = lower_quartiles highs = upper_quartiles + else: + raise ValueError("Only accepted values for type are ['mean_sd', 'median_quartiles']") n_groups = len(central_measures) @@ -155,38 +171,51 @@ def error_bar(data:pd.DataFrame, # This DataFrame should be in 'long' format. err2 = "{} offset(s) were supplied in `offset`.".format(len_offset) raise ValueError(err1 + err2) - kwargs['zorder'] = kwargs['zorder'] + kwargs["zorder"] = kwargs["zorder"] for xpos, central_measure in enumerate(central_measures): - kwargs['color'] = custom_palette[xpos] + kwargs["color"] = custom_palette[xpos] - if method == 'sankey_error_bar': + if method == "sankey_error_bar": _xpos = pos[xpos] + offset[xpos] else: _xpos = xpos + offset[xpos] low = lows[xpos] - low_to_mean = mlines.Line2D([_xpos, _xpos], - [low, central_measure - gap_width], - **kwargs) - ax.add_line(low_to_mean) - high = highs[xpos] - mean_to_high = mlines.Line2D([_xpos, _xpos], - [central_measure + gap_width, high], - **kwargs) - ax.add_line(mean_to_high) - -def check_data_matches_labels(labels,#list of input labels - data, #Pandas Series of input data - side:str # 'left' or 'right' on the sankey diagram - ): - ''' - Function to check that the labels and data match in the sankey diagram. + if low == high == central_measure: + low_to_mean = mlines.Line2D( + [_xpos, _xpos], [low, central_measure], **kwargs + ) + ax.add_line(low_to_mean) + + mean_to_high = mlines.Line2D( + [_xpos, _xpos], [central_measure, high], **kwargs + ) + ax.add_line(mean_to_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) + + +def check_data_matches_labels( + labels, # list of input labels + data, # Pandas Series of input data + side: str, # 'left' or 'right' on the sankey diagram +): + """ + Function to check that the labels and data match in the sankey diagram. And enforce labels and data to be lists. Raises an exception if the labels and data do not match. - ''' - if len(labels > 0): + """ + if len(labels) > 0: if isinstance(data, list): data = set(data) if isinstance(data, pd.Series): @@ -199,85 +228,192 @@ def check_data_matches_labels(labels,#list of input labels msg = "Labels: " + ",".join(labels) + "\n" if len(data) < 20: msg += "Data: " + ",".join(data) - raise Exception('{0} labels and data do not match.{1}'.format(side, msg)) - + raise Exception(f"{side} labels and data do not match.{msg}") + + def normalize_dict(nested_dict, target): + """ + Normalizes the values in a nested dictionary based on a target dictionary. + + This function iterates through a nested dictionary, calculates the sum of values for each key + across all sub-dictionaries, and then normalizes these values according to a target dictionary. + The normalization is performed such that the values in each sub-dictionary are proportionally + scaled to match the corresponding 'right' values in the target dictionary. + + Parameters: + nested_dict (dict of dict): A nested dictionary where each key maps to another dictionary. + The values in these inner dictionaries are subject to normalization. + target (dict): A dictionary with the target values for normalization. Each key in nested_dict + should have a corresponding key in target, and each target[key] should be a + dictionary with a 'right' key containing the target normalization value. + + Returns: + dict: The normalized nested dictionary. The original nested_dict is modified in place. + + Note: + - If the sum of values for a particular key in nested_dict is zero, the normalized value is set to 0. + - If a key in a sub-dictionary of nested_dict does not exist in the target dictionary, the + corresponding 'right' value from the target dictionary is directly assigned. + - The function modifies the input nested_dict in place and also returns it. + """ val = {} for key in nested_dict.keys(): - val[key] = np.sum([nested_dict[sub_key][key] for sub_key in nested_dict.keys()]) - + val[key] = np.sum( + [ + nested_dict[sub_key][key] + for sub_key in nested_dict.keys() + if key in nested_dict[sub_key] + ] + ) + for key, value in nested_dict.items(): if isinstance(value, dict): for subkey in value.keys(): - value[subkey] = value[subkey] * target[subkey]['right']/val[subkey] + if subkey in val.keys(): + if val[subkey] != 0: + # Address the problem when one of the labels has zero value + value[subkey] = ( + value[subkey] * target[subkey]["right"] / val[subkey] + ) + else: + value[subkey] = 0 + else: + value[subkey] = target[subkey]["right"] return nested_dict -def single_sankey(left:np.array,# data on the left of the diagram - right:np.array, # data on the right of the diagram, len(left) == len(right) - xpos:float=0, # the starting point on the x-axis - leftWeight:np.array=None, #weights for the left labels, if None, all weights are 1 - rightWeight:np.array=None, #weights for the right labels, if None, all weights are corresponding leftWeight - colorDict:dict=None, #input format: {'label': 'color'} - leftLabels:list=None, #labels for the left side of the diagram. The diagram will be sorted by these labels. - rightLabels:list=None, #labels for the right side of the diagram. The diagram will be sorted by these labels. - ax=None, #matplotlib axes to be drawn on - width=0.5, - alpha=0.65, - bar_width=0.2, - rightColor: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 - ): - - ''' + +def width_determine(labels, data, pos="left"): + """ + Calculates normalized width positions for a set of labels based on their associated data. + + This function is designed to determine width positions for plotting or graphical representation. + It takes into account the cumulative weight of each label in the data and adjusts their positions + accordingly. The function allows for adjusting the position of labels to either the 'left' or 'right'. + + Parameters: + labels (list): A list of labels whose width positions are to be calculated. + data (DataFrame): A pandas DataFrame containing the data used for calculating width positions. + The DataFrame should have columns corresponding to the 'pos' and 'posWeight'. + pos (str, optional): The position of labels. It can be either 'left' or 'right'. Defaults to 'left'. + + Returns: + defaultdict: A dictionary where each key is a label and the value is another dictionary with keys + 'bottom', 'top', and 'pos', representing the calculated width positions. + + Note: + The function assumes that the data DataFrame contains columns named after the value of 'pos' and + an additional column named 'posWeight' which represents the weight of each label. + """ + if labels is None: + raise ValueError("The `labels` parameter cannot be None") + + if data is None: + raise ValueError("The `data` parameter cannot be None") + + widths_norm = defaultdict() + for i, label in enumerate(labels): + myD = {} + myD[pos] = data[data[pos] == label][pos + "Weight"].sum() + if len(labels) != 1: + if i == 0: + myD["bottom"] = 0 + myD[pos] -= 0.01 + myD["top"] = myD[pos] + elif i == len(labels) - 1: + myD[pos] -= 0.01 + myD["bottom"] = 1 - myD[pos] + myD["top"] = 1 + else: + myD[pos] -= 0.02 + myD["bottom"] = widths_norm[labels[i - 1]]["top"] + 0.02 + myD["top"] = myD["bottom"] + myD[pos] + else: + myD["bottom"] = 0 + myD["top"] = 1 + widths_norm[label] = myD + return widths_norm + + +def single_sankey( + left: np.array, # data on the left of the diagram + right: np.array, # data on the right of the diagram, len(left) == len(right) + xpos: float = 0, # the starting point on the x-axis + left_weight: np.array = None, # weights for the left labels, if None, all weights are 1 + right_weight: np.array = None, # weights for the right labels, if None, all weights are corresponding left_weight + colorDict: dict = None, # input format: {'label': 'color'} + 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. + ax=None, # matplotlib axes to be drawn on + flow: bool = True, # if True, draw the sankey in a flow, else draw 1 vs 1 Sankey diagram for each group comparison + sankey: bool = True, # if True, draw the sankey diagram, else draw barplot + width=0.5, + alpha=0.65, + bar_width=0.2, + error_bar_on: bool = True, # if True, draw error bar for each group comparison + 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 +): + """ 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 - ''' + """ # Initiating values if ax is None: ax = plt.gca() - if leftWeight is None: - leftWeight = [] - if rightWeight is None: - rightWeight = [] - if leftLabels is None: - leftLabels = [] - if rightLabels is None: - rightLabels = [] + if left_weight is None: + left_weight = [] + if right_weight is None: + right_weight = [] + if left_labels is None: + left_labels = [] + if right_labels is None: + right_labels = [] # Check weights - if len(leftWeight) == 0: - leftWeight = np.ones(len(left)) - if len(rightWeight) == 0: - rightWeight = leftWeight + if len(left_weight) == 0: + left_weight = np.ones(len(left)) + if len(right_weight) == 0: + right_weight = np.ones(len(right)) # Create Dataframe if isinstance(left, pd.Series): left.reset_index(drop=True, inplace=True) if isinstance(right, pd.Series): right.reset_index(drop=True, inplace=True) - dataFrame = pd.DataFrame({'left': left, 'right': right, 'leftWeight': leftWeight, - 'rightWeight': rightWeight}, index=range(len(left))) - - if dataFrame[['left', 'right']].isnull().any(axis=None): - raise Exception('Sankey graph does not support null values.') + dataFrame = pd.DataFrame( + { + "left": left, + "right": right, + "left_weight": left_weight, + "right_weight": right_weight, + }, + index=range(len(left)), + ) + + if dataFrame[["left", "right"]].isnull().any(axis=None): + raise Exception("Sankey graph does not support null values.") # Identify all labels that appear 'left' or 'right' - allLabels = pd.Series(np.sort(np.r_[dataFrame.left.unique(), dataFrame.right.unique()])[::-1]).unique() + allLabels = pd.Series( + np.sort(np.r_[dataFrame.left.unique(), dataFrame.right.unique()])[::-1] + ).unique() # Identify left labels - if len(leftLabels) == 0: - leftLabels = pd.Series(np.sort(dataFrame.left.unique())[::-1]).unique() + if len(left_labels) == 0: + left_labels = pd.Series(np.sort(dataFrame.left.unique())[::-1]).unique() else: - check_data_matches_labels(leftLabels, dataFrame['left'], 'left') + check_data_matches_labels(left_labels, dataFrame["left"], "left") # Identify right labels - if len(rightLabels) == 0: - rightLabels = pd.Series(np.sort(dataFrame.right.unique())[::-1]).unique() + if len(right_labels) == 0: + right_labels = pd.Series(np.sort(dataFrame.right.unique())[::-1]).unique() else: - check_data_matches_labels(leftLabels, dataFrame['right'], 'right') + check_data_matches_labels(left_labels, dataFrame["right"], "right") # If no colorDict given, make one if colorDict is None: @@ -286,190 +422,253 @@ def single_sankey(left:np.array,# data on the left of the diagram colorPalette = sns.color_palette(palette, len(allLabels)) for i, label in enumerate(allLabels): colorDict[label] = colorPalette[i] - fail_color = {0:"grey"} + fail_color = {0: "grey"} colorDict.update(fail_color) else: missing = [label for label in allLabels if label not in colorDict.keys()] if missing: msg = "The palette parameter is missing values for the following labels : " - msg += '{}'.format(', '.join(missing)) + msg += "{}".format(", ".join(missing)) raise ValueError(msg) if align not in ("center", "edge"): - err = '{} assigned for `align` is not valid.'.format(align) + err = "{} assigned for `align` is not valid.".format(align) raise ValueError(err) if align == "center": try: leftpos = xpos - width / 2 except TypeError as e: - raise TypeError(f'the dtypes of parameters x ({xpos.dtype}) ' - f'and width ({width.dtype}) ' - f'are incompatible') from e - else: + raise TypeError( + f"the dtypes of parameters x ({xpos.dtype}) " + f"and width ({width.dtype}) " + f"are incompatible" + ) from e + else: leftpos = xpos # Combine left and right arrays to have a pandas.DataFrame in the 'long' format - left_series = pd.Series(left, name='values').to_frame().assign(groups='left') - right_series = pd.Series(right, name='values').to_frame().assign(groups='right') + left_series = pd.Series(left, name="values").to_frame().assign(groups="left") + right_series = pd.Series(right, name="values").to_frame().assign(groups="right") concatenated_df = pd.concat([left_series, right_series], ignore_index=True) # Determine positions of left label patches and total widths # We also want the height of the graph to be 1 leftWidths_norm = defaultdict() - for i, leftLabel in enumerate(leftLabels): + for i, left_label in enumerate(left_labels): myD = {} - myD['left'] = (dataFrame[dataFrame.left == leftLabel].leftWeight.sum()/ \ - dataFrame.leftWeight.sum())*(1-(len(leftLabels)-1)*0.02) - if i == 0: - myD['bottom'] = 0 - myD['top'] = myD['left'] + myD["left"] = ( + dataFrame[dataFrame.left == left_label].left_weight.sum() + / dataFrame.left_weight.sum() + ) + if len(left_labels) != 1: + if i == 0: + myD["bottom"] = 0 + myD["left"] -= 0.01 + myD["top"] = myD["left"] + elif i == len(left_labels) - 1: + myD["left"] -= 0.01 + myD["bottom"] = 1 - myD["left"] + myD["top"] = 1 + else: + myD["left"] -= 0.02 + myD["bottom"] = leftWidths_norm[left_labels[i - 1]]["top"] + 0.02 + myD["top"] = myD["bottom"] + myD["left"] + topEdge = myD["top"] else: - myD['bottom'] = leftWidths_norm[leftLabels[i - 1]]['top'] + 0.02 - myD['top'] = myD['bottom'] + myD['left'] - topEdge = myD['top'] - leftWidths_norm[leftLabel] = myD + myD["bottom"] = 0 + myD["top"] = 1 + myD["left"] = 1 + leftWidths_norm[left_label] = myD # Determine positions of right label patches and total widths rightWidths_norm = defaultdict() - for i, rightLabel in enumerate(rightLabels): + for i, right_label in enumerate(right_labels): myD = {} - myD['right'] = (dataFrame[dataFrame.right == rightLabel].rightWeight.sum()/ \ - dataFrame.rightWeight.sum())*(1-(len(leftLabels)-1)*0.02) - if i == 0: - myD['bottom'] = 0 - myD['top'] = myD['right'] + myD["right"] = ( + dataFrame[dataFrame.right == right_label].right_weight.sum() + / dataFrame.right_weight.sum() + ) + if len(right_labels) != 1: + if i == 0: + myD["bottom"] = 0 + myD["right"] -= 0.01 + myD["top"] = myD["right"] + elif i == len(right_labels) - 1: + myD["right"] -= 0.01 + myD["bottom"] = 1 - myD["right"] + myD["top"] = 1 + else: + myD["right"] -= 0.02 + myD["bottom"] = rightWidths_norm[right_labels[i - 1]]["top"] + 0.02 + myD["top"] = myD["bottom"] + myD["right"] + topEdge = myD["top"] else: - myD['bottom'] = rightWidths_norm[rightLabels[i - 1]]['top'] + 0.02 - myD['top'] = myD['bottom'] + myD['right'] - topEdge = myD['top'] - rightWidths_norm[rightLabel] = myD + myD["bottom"] = 0 + myD["top"] = 1 + myD["right"] = 1 + rightWidths_norm[right_label] = myD # Total width of the graph xMax = width + # 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 (not flow and sankey) or one_sankey: + for right_label in right_labels: + ax.fill_between( + [ + xMax + leftpos + (-bar_width * xMax * 0.5), + leftpos + xMax + (bar_width * xMax * 0.5), + ], + 2 * [rightWidths_norm[right_label]["bottom"]], + 2 * [rightWidths_norm[right_label]["top"]], + color=colorDict[right_label], + alpha=0.99, + ) + + # 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], + ) + # Determine widths of individual strips, all widths are normalized to 1 ns_l = defaultdict() ns_r = defaultdict() ns_l_norm = defaultdict() ns_r_norm = defaultdict() - for leftLabel in leftLabels: + for left_label in left_labels: leftDict = {} rightDict = {} - for rightLabel in rightLabels: - leftDict[rightLabel] = dataFrame[ - (dataFrame.left == leftLabel) & (dataFrame.right == rightLabel) - ].leftWeight.sum() - - rightDict[rightLabel] = dataFrame[ - (dataFrame.left == leftLabel) & (dataFrame.right == rightLabel) - ].rightWeight.sum() - factorleft = leftWidths_norm[leftLabel]['left']/sum(leftDict.values()) - leftDict_norm = {k: v*factorleft for k, v in leftDict.items()} - ns_l_norm[leftLabel] = leftDict_norm - ns_r[leftLabel] = rightDict - + for right_label in right_labels: + leftDict[right_label] = dataFrame[ + (dataFrame.left == left_label) & (dataFrame.right == right_label) + ].left_weight.sum() + + rightDict[right_label] = dataFrame[ + (dataFrame.left == left_label) & (dataFrame.right == right_label) + ].right_weight.sum() + factorleft = leftWidths_norm[left_label]["left"] / sum(leftDict.values()) + leftDict_norm = {k: v * factorleft for k, v in leftDict.items()} + ns_l_norm[left_label] = leftDict_norm + ns_r[left_label] = rightDict + # ns_r should be using a different way of normalization to fit the right side # It is normalized using the value with the same key in each sub-dictionary - ns_r_norm = normalize_dict(ns_r, rightWidths_norm) - # Plot vertical bars for each label - for leftLabel in leftLabels: - ax.fill_between( - [leftpos + (-(bar_width) * xMax), leftpos], - 2 * [leftWidths_norm[leftLabel]["bottom"]], - 2 * [leftWidths_norm[leftLabel]["bottom"] + leftWidths_norm[leftLabel]["left"]], - color=colorDict[leftLabel], - alpha=0.99, - ) - for rightLabel in rightLabels: - ax.fill_between( - [xMax + leftpos, leftpos + ((1 + bar_width) * xMax)], - 2 * [rightWidths_norm[rightLabel]['bottom']], - 2 * [rightWidths_norm[rightLabel]['bottom'] + rightWidths_norm[rightLabel]['right']], - color=colorDict[rightLabel], - alpha=0.99 - ) - - # Plot error bars - error_bar(concatenated_df, x='groups', y='values', ax=ax, offset=0, gap_width_percent=2, - method="sankey_error_bar", - pos=[(leftpos + (-(bar_width) * xMax) + leftpos)/2, \ - (xMax + leftpos + leftpos + ((1 + bar_width) * xMax))/2]) - # Plot strips - for leftLabel, rightLabel in itertools.product(leftLabels, rightLabels): - labelColor = leftLabel - if rightColor: - labelColor = rightLabel - if len(dataFrame[(dataFrame.left == leftLabel) & (dataFrame.right == rightLabel)]) > 0: - # Create array of y values for each strip, half at left value, - # half at right, convolve - ys_d = np.array(50 * [leftWidths_norm[leftLabel]['bottom']] + \ - 50 * [rightWidths_norm[rightLabel]['bottom']]) - ys_d = np.convolve(ys_d, 0.05 * np.ones(20), mode='valid') - ys_d = np.convolve(ys_d, 0.05 * np.ones(20), mode='valid') - ys_u = np.array(50 * [leftWidths_norm[leftLabel]['bottom'] + ns_l_norm[leftLabel][rightLabel]] + \ - 50 * [rightWidths_norm[rightLabel]['bottom'] + ns_r_norm[leftLabel][rightLabel]]) - ys_u = np.convolve(ys_u, 0.05 * np.ones(20), mode='valid') - ys_u = np.convolve(ys_u, 0.05 * np.ones(20), mode='valid') - - # Update bottom edges at each label so next strip starts at the right place - leftWidths_norm[leftLabel]['bottom'] += ns_l_norm[leftLabel][rightLabel] - rightWidths_norm[rightLabel]['bottom'] += ns_r_norm[leftLabel][rightLabel] - ax.fill_between( - np.linspace(leftpos, leftpos + xMax, len(ys_d)), ys_d, ys_u, alpha=alpha, - color=colorDict[labelColor], 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 - leftLabels:list=None, #labels for the left side of the diagram. The diagram will be sorted by these labels. - rightLabels:list=None, #labels for the right side of the diagram. The diagram will be sorted by these labels. - palette:str|dict=None, - ax=None, #matplotlib axes to be drawn on - one_sankey:bool=False,# determined by the driver function on plotter.py, if True, draw the sankey diagram across the whole raw data axes - width:float=0.4, # the width of each sankey diagram - rightColor: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 - **kwargs): - ''' + if sankey and strip_on: + for left_label, right_label in itertools.product(left_labels, right_labels): + labelColor = left_label + + if right_color: + labelColor = right_label + + if len(dataFrame[(dataFrame.left == left_label) & + (dataFrame.right == right_label)]) > 0: + # Create array of y values for each strip, half at left value, + # half at right, convolve + ys_d = np.array( + 50 * [leftWidths_norm[left_label]["bottom"]] + + 50 * [rightWidths_norm[right_label]["bottom"]] + ) + ys_d = np.convolve(ys_d, 0.05 * np.ones(20), mode="valid") + ys_d = np.convolve(ys_d, 0.05 * np.ones(20), mode="valid") + # to remove the array wrapping behaviour of black + # fmt: off + ys_u = np.array(50 * [leftWidths_norm[left_label]['bottom'] + ns_l_norm[left_label][right_label]] + \ + 50 * [rightWidths_norm[right_label]['bottom'] + ns_r_norm[left_label][right_label]]) + # fmt: on + ys_u = np.convolve(ys_u, 0.05 * np.ones(20), mode="valid") + ys_u = np.convolve(ys_u, 0.05 * np.ones(20), mode="valid") + + # Update bottom edges at each label so next strip starts at the right place + leftWidths_norm[left_label]["bottom"] += ns_l_norm[left_label][right_label] + rightWidths_norm[right_label]["bottom"] += ns_r_norm[left_label][ + right_label + ] + ax.fill_between( + np.linspace( + leftpos + (bar_width * xMax * 0.5), + leftpos + xMax - (bar_width * xMax * 0.5), + len(ys_d), + ), + ys_d, + ys_u, + alpha=alpha, + color=colorDict[labelColor], + 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 + 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, + ax=None, # matplotlib axes to be drawn on + flow: bool = True, # if True, draw the sankey in a flow, else draw 1 vs 1 Sankey diagram for each group comparison + sankey: bool = True, # if True, draw the sankey diagram, else draw barplot + one_sankey: bool = False, # determined by the driver function on plotter.py, if True, draw the sankey diagram across the whole raw data axes + width: float = 0.4, # the width of each sankey diagram + 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 + **kwargs, +): + """ Read in melted pd.DataFrame, and draw multiple sankey diagram on a single axes using the value in column yvar according to the value in column xvar left_idx in the column xvar is on the left side of each sankey diagram right_idx in the column xvar is on the right side of each sankey diagram - ''' - - import numpy as np - import pandas as pd - import seaborn as sns - import matplotlib.pyplot as plt + """ if "width" in kwargs: width = kwargs["width"] if "align" in kwargs: align = kwargs["align"] - + if "alpha" in kwargs: alpha = kwargs["alpha"] - - if "rightColor" in kwargs: - rightColor = kwargs["rightColor"] - + + if "right_color" in kwargs: + right_color = kwargs["right_color"] + if "bar_width" in kwargs: bar_width = kwargs["bar_width"] + if "sankey" in kwargs: + sankey = kwargs["sankey"] + + if "flow" in kwargs: + flow = kwargs["flow"] + if ax is None: ax = plt.gca() 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 unique_xvar = data[xvar].unique() if not all(elem in unique_xvar for elem in left_idx): @@ -481,7 +680,7 @@ def sankeydiag(data:pd.DataFrame, # For baseline comparison, broadcast left_idx to the same length as right_idx # so that the left of sankey diagram will be the same - # For sequential comparison, left_idx and right_idx can have anything different + # For sequential comparison, left_idx and right_idx can have anything different # but should have the same length if len(left_idx) == 1: broadcasted_left = np.broadcast_to(left_idx, len(right_idx)) @@ -493,8 +692,7 @@ def sankeydiag(data:pd.DataFrame, if isinstance(palette, dict): if not all(key in allLabels for key in palette.keys()): raise ValueError(f"keys in palette should be in {yvar} column") - else: - plot_palette = palette + plot_palette = palette elif isinstance(palette, str): plot_palette = {} colorPalette = sns.color_palette(palette, len(allLabels)) @@ -503,25 +701,76 @@ def sankeydiag(data:pd.DataFrame, else: plot_palette = None - for left, right in zip(broadcasted_left, right_idx): - if one_sankey == False: - single_sankey(data[data[xvar]==left][yvar], data[data[xvar]==right][yvar], - xpos=xpos, ax=ax, colorDict=plot_palette, width=width, - leftLabels=leftLabels, rightLabels=rightLabels, - rightColor=rightColor, bar_width=bar_width, - align=align, alpha=alpha) + # Create a strip_on list to determine whether to draw the strip during repeated measures + strip_on = [ + int(right not in broadcasted_left[:i]) for i, right in enumerate(right_idx) + ] + + draw_idx = list(zip(broadcasted_left, right_idx)) + for i, (left, right) in enumerate(draw_idx): + if not one_sankey: + if flow: + width = 1 + align = "edge" + sankey = ( + False if i == len(draw_idx) - 1 else sankey + ) # Remove last strip in flow + error_bar_on = ( + False if i == len(draw_idx) - 1 and flow else True + ) # Remove last error_bar in flow + bar_width = 0.4 if sankey == False and flow == False else bar_width + single_sankey( + data[data[xvar] == left][yvar], + data[data[xvar] == right][yvar], + xpos=xpos, + ax=ax, + colorDict=plot_palette, + width=width, + left_labels=left_labels, + right_labels=right_labels, + strip_on=strip_on[i], + right_color=right_color, + bar_width=bar_width, + sankey=sankey, + error_bar_on=error_bar_on, + flow=flow, + align=align, + alpha=alpha, + ) xpos += 1 else: - xpos = 0 + bar_width/2 - width = 1 - bar_width - single_sankey(data[data[xvar]==left][yvar], data[data[xvar]==right][yvar], - xpos=xpos, ax=ax, colorDict=plot_palette, width=width, - leftLabels=leftLabels, rightLabels=rightLabels, - rightColor=rightColor, bar_width=bar_width, - align='edge', alpha=alpha) - - if one_sankey == False: - sankey_ticks = [f"{left}\n v.s.\n{right}" for left, right in zip(broadcasted_left, right_idx)] + xpos = 0 + width = 1 + if not sankey: + bar_width = 0.5 + single_sankey( + data[data[xvar] == left][yvar], + data[data[xvar] == right][yvar], + xpos=xpos, + ax=ax, + colorDict=plot_palette, + width=width, + left_labels=left_labels, + right_labels=right_labels, + right_color=right_color, + bar_width=bar_width, + sankey=sankey, + one_sankey=one_sankey, + flow=False, + align="edge", + alpha=alpha, + ) + + # Now only draw vs xticks for two-column sankey diagram + if not one_sankey or (sankey and not flow): + sankey_ticks = ( + [f"{left}" for left in broadcasted_left] + if flow + 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) else: @@ -529,3 +778,560 @@ def sankeydiag(data:pd.DataFrame, ax.set_xticks([0, 1]) ax.set_xticklabels(sankey_ticks) +# %% ../nbs/API/plot_tools.ipynb 6 +def swarmplot( + data: pd.DataFrame, + x: str, + y: str, + ax: axes.Subplot, + order: List = None, + hue: str = None, + palette: Union[Iterable, str] = "black", + zorder: float = 1, + size: float = 5, + side: str = "center", + jitter: float = 1, + is_drop_gutter: bool = True, + gutter_limit: float = 0.5, + **kwargs, +): + """ + API to plot a swarm plot. + + Parameters + ---------- + data : pd.DataFrame + The input data as a pandas DataFrame. + x : str + 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. + order : List + The order in which x-axis categories should be displayed. Default is None. + hue : str + The column in the DataFrame that determines the grouping for color. + If None (by default), it assumes that it is being grouped by x. + palette : Union[Iterable, str] + The color palette to be used for plotting. Default is "black". + zorder : int | float + The z-order for drawing the swarm plot wrt other matplotlib drawings. Default is 1. + dot_size : int | float + The size of the markers in the swarm plot. Default is 20. + side : str + 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. + 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. + **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. + """ + s = SwarmPlot(data, x, y, ax, order, hue, palette, zorder, size, side, jitter) + ax = s.plot(is_drop_gutter, gutter_limit, ax, **kwargs) + return ax + + +class SwarmPlot: + def __init__( + self, + data: pd.DataFrame, + x: str, + y: str, + ax: axes.Subplot, + order: List = None, + hue: str = None, + palette: Union[Iterable, str] = "black", + zorder: float = 1, + size: float = 5, + side: str = "center", + jitter: float = 1, + ): + """ + Initialize a SwarmPlot instance. + + Parameters + ---------- + data : pd.DataFrame + The input data as a pandas DataFrame. + x : str + 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. + order : List + The order in which x-axis categories should be displayed. Default is None. + hue : str + The column in the DataFrame that determines the grouping for color. + If None (by default), it assumes that it is being grouped by x. + palette : Union[Iterable, str] + The color palette to be used for plotting. Default is "black". + zorder : int | float + The z-order for drawing the swarm plot wrt other matplotlib drawings. Default is 1. + dot_size : int | float + The size of the markers in the swarm plot. Default is 20. + side : str + 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. + + Returns + ------- + None + """ + self.__x = x + self.__y = y + self.__order = order + self.__hue = hue + self.__zorder = zorder + self.__palette = palette + self.__jitter = jitter + + # Input validation + self._check_errors(data, ax, size, side) + + self.__size = size * 4 + self.__side = side.lower() + self.__data = data + self.__color_col = self.__x if self.__hue is None else self.__hue + + # Generate default values + if order is None: + self.__order = self._generate_order() + + # Reformatting + if not isinstance(self.__palette, dict): + self.__palette = self._format_palette(self.__palette) + data_copy = data.copy(deep=True) + if not isinstance(self.__data[self.__x].dtype, pd.CategoricalDtype): + # make x column into CategoricalDType to sort by + data_copy[self.__x] = data_copy[self.__x].astype( + CategoricalDtype(categories=self.__order, ordered=True) + ) + data_copy.sort_values(by=[self.__x, self.__y], inplace=True) + self.__data_copy = data_copy + + x_vals = range(len(self.__order)) + y_vals = self.__data_copy[self.__y] + + 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) + + 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] + + # increases jitter distance based on number of swarms that is going to be drawn + jitter = jitter * (1 + 0.05 * (math.log(ax_xspan))) + + gsize = ( + math.sqrt(self.__size) * 1.0 / (70 / jitter) * ax_xspan * 1.0 / (w * 0.8) + ) + dsize = ( + math.sqrt(self.__size) * 1.0 / (70 / jitter) * ax_yspan * 1.0 / (h * 0.8) + ) + self.__gsize = gsize + self.__dsize = dsize + + def _check_errors( + self, data: pd.DataFrame, ax: axes.Subplot, size: float, side: str + ) -> None: + """ + Check the validity of input parameters. Raises exceptions if detected. + + Parameters + ---------- + 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. + size : int | float + scalar value determining size of dots of the swarmplot. + side: str + The side on which points are swarmed ("center", "left", or "right"). Default is "center". + + Returns + ------- + None + """ + # 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)): + raise ValueError( + f"`ax` must be a Matplotlib AxesSubplot. The current `ax` is a {type(ax)}" + ) + if not isinstance(size, (int, float)): + raise ValueError("`size` must be a scalar or float.") + if not isinstance(side, str): + raise ValueError( + "Invalid `side`. Must be one of 'center', 'right', or 'left'." + ) + if not isinstance(self.__x, str): + raise ValueError("`x` must be a string.") + if not isinstance(self.__y, str): + raise ValueError("`y` must be a string.") + if not isinstance(self.__zorder, (int, float)): + raise ValueError("`zorder` must be a scalar or float.") + 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.") + 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): + raise ValueError("`order` must be either an Iterable or None.") + + # More thorough input validation checks + if self.__x not in data.columns: + err = "{0} is not a column in `data`.".format(self.__x) + raise IndexError(err) + if self.__y not in data.columns: + err = "{0} is not a column in `data`.".format(self.__y) + raise IndexError(err) + if self.__hue is not None and self.__hue not in data.columns: + err = "{0} is not a column in `data`.".format(self.__hue) + raise IndexError(err) + + color_col = self.__x if self.__hue is None else self.__hue + if self.__order is not None: + for group_i in self.__order: + if group_i not in pd.unique(data[self.__x]): + err = "{0} in `order` is not in the '{1}' column of `data`.".format( + group_i, self.__x + ) + raise IndexError(err) + + if isinstance(self.__palette, str) and self.__palette.strip() == "": + 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 = ( + "{0} in `palette` is not in the '{1}' column of `data`.".format( + group_i, color_col + ) + ) + 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) + + if side.lower() not in ["center", "right", "left"]: + raise ValueError( + "Invalid `side`. Must be one of 'center', 'right', or 'left'." + ) + + return None + + def _generate_order(self) -> List: + """ + Generates order value that determines the order in which x-axis categories should be displayed. + + Parameters + ---------- + None + + Returns + ------- + List: + contains the order in which the x-axis categories should be displayed. + """ + if isinstance(self.__data[self.__x].dtype, pd.CategoricalDtype): + order = pd.unique(self.__data[self.__x]).categories.tolist() + else: + order = pd.unique(self.__data[self.__x]).tolist() + + return order + + def _format_palette(self, palette: Union[str, List, Tuple]) -> Dict: + """ + Reformats palette into appropriate Dictionary form for swarm plot + + Parameters + ---------- + palette: str | List | Tuple + The color palette used for the swarm plot. Conventions are based on Matplotlib color + specifications. + + Could be a singular string value - in which case, would be a singular color name. + In the case of a List or Tuple - it could be a Sequence of color names or RGB(A) values. + + Returns + ------- + Dict: + Dictionary mapping unique groupings in the color column (of the data used for the swarm plot) + to a color name (str) or a RGB(A) value (Tuple[float, float, float] | List[float, float, float]). + """ + reformatted_palette = dict() + groups = pd.unique(self.__data[self.__color_col]).tolist() + + if isinstance(palette, str): + for group_i in groups: + reformatted_palette[group_i] = palette + if isinstance(palette, (list, tuple)): + if len(groups) != len(palette): + err = ( + "unique values in '{0}' column in `data` " + "and `palette` do not have the same length. Number of unique values is {1} " + "while length of palette is {2}. The assignment of the colors in the " + "palette will be cycled." + ).format(self.__color_col, len(groups), len(palette)) + warnings.warn(err) + for i, group_i in enumerate(groups): + reformatted_palette[group_i] = palette[i % len(palette)] + + return reformatted_palette + + def _swarm( + self, values: Iterable[float], gsize: float, dsize: float, side: str + ) -> pd.Series: + """ + Perform the swarm algorithm to position points without overlap. + + Parameters + ---------- + values : Iterable[int | float] + The values to be plotted. + gsize : int | float + The size of the gap between points. + dsize : int | float + The size of the markers. + side : str + The side on which points are swarmed ("center", "left", or "right"). + + Returns + ------- + pd.Series: + The x-offset values for the swarm plot. + """ + # Input validation + if not isinstance(values, Iterable): + raise ValueError("`values` must be an Iterable") + if not isinstance(gsize, (int, float)): + raise ValueError("`gsize` must be a scalar or float.") + if not isinstance(dsize, (int, float)): + 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)} + ) + for i in range(1, points_data.shape[0]): + y_i = points_data["y"].values[i] + points_placed = points_data[0:i] + is_points_overlap = ( + abs(y_i - points_placed["y"]) < 1 + ) # Checks if y_i is overlapping with any points already placed + if any(is_points_overlap): + points_placed = points_placed[is_points_overlap] + x_offsets = points_placed["y"].apply( + lambda y_j: math.sqrt(1 - (y_i - y_j) ** 2) + ) + if side == "center": + potential_x_offsets = pd.Series( + [0] + + (points_placed["x"] + x_offsets).tolist() + + (points_placed["x"] - x_offsets).tolist() + ) + if side == "right": + potential_x_offsets = pd.Series( + [0] + (points_placed["x"] + x_offsets).tolist() + ) + if side == "left": + potential_x_offsets = pd.Series( + [0] + (points_placed["x"] - x_offsets).tolist() + ) + bad_x_offsets = [] + for x_i in potential_x_offsets: + dists = (y_i - points_placed["y"]) ** 2 + ( + x_i - points_placed["x"] + ) ** 2 + if any([item < 0.999 for item in dists]): + bad_x_offsets.append(True) + else: + bad_x_offsets.append(False) + potential_x_offsets[bad_x_offsets] = np.infty + 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)) + ] + points_data.loc[i, "x"] = valid_x_offset + else: + points_data.loc[i, "x"] = 0 + + points_data.loc[np.isnan(points_data["y"]), "x"] = np.nan + + return points_data["x"] * gsize + + def _adjust_gutter_points( + self, + points_data: pd.DataFrame, + x_position: float, + is_drop_gutter: bool, + gutter_limit: float, + value_column: str, + ) -> pd.DataFrame: + """ + Adjust points that hit the gutters or drop them based on the provided conditions. + + Parameters + ---------- + points_data: pd.DataFrame + Data containing coordinates of points for the swarm plot. + x_position: int | float + X-coordinate of the center of a singular swarm group of the swarm plot + 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. + value_column : str + column in points_data that contains the coordinates for the points in the axis against the gutter + + Returns + ------- + pd.DataFrame: + DataFrame with adjusted points based on the gutter limit. + """ + if self.__side == "center": + gutter_limit = gutter_limit / 2 + + hit_gutter = abs(points_data[value_column] - x_position) >= gutter_limit + total_num_of_points = points_data.shape[0] + num_of_points_hit_gutter = points_data[hit_gutter].shape[0] + if any(hit_gutter): + if is_drop_gutter: + # Drop points that hit gutter + points_data.drop(points_data[hit_gutter].index.to_list(), inplace=True) + err = ( + "{0:.1%} of the points cannot be placed. " + "You might want to decrease the size of the markers." + ).format(num_of_points_hit_gutter / total_num_of_points) + warnings.warn(err) + else: + for i in points_data[hit_gutter].index: + points_data.loc[i, value_column] = np.sign( + points_data.loc[i, value_column] + ) * (x_position + gutter_limit) + + return points_data + + def plot( + self, is_drop_gutter: bool, gutter_limit: float, ax: axes.Subplot, **kwargs + ) -> axes.Subplot: + """ + Generate a swarm plot. + + Parameters + ---------- + 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. + ax : axes.Subplot + The matplotlib figure object to which the swarm plot will be added. + **kwargs: + Additional keyword arguments to be passed to the scatter plot. + + Returns + ------- + axes.Subplot: + The matplotlib figure 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.") + + # 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): + x_new = [] + values_i_y = values_i[self.__y] + x_offset = self._swarm( + values=values_i_y, + gsize=self.__gsize, + dsize=self.__dsize, + side=self.__side, + ) + x_new = [ + x_position + offset for offset in x_offset + ] # apply x-offsets based on _swarm algo + values_i["x_new"] = x_new + values_i = self._adjust_gutter_points( + values_i, x_position, is_drop_gutter, gutter_limit, "x_new" + ) + x_tick_tabels.extend([group_i]) + x_position = x_position + 1 + + if values_i.empty: + ax.scatter( + values_i["x_new"], + values_i[self.__y], + s=self.__size, + zorder=self.__zorder, + **kwargs, + ) + continue + + if self.__hue is not None: + # color swarms based on `hue` column + cmap_values, index = np.unique( + values_i[self.__hue], return_inverse=True + ) + 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], + s=self.__size, + c=index, + cmap=cmap, + zorder=self.__zorder, + edgecolor="face", + **kwargs, + ) + else: + # color swarms based on `x` column + ax.scatter( + values_i["x_new"], + values_i[self.__y], + s=self.__size, + c=self.__palette[group_i], + zorder=self.__zorder, + edgecolor="face", + **kwargs, + ) + + ax.get_xaxis().set_ticks(np.arange(x_position)) + ax.get_xaxis().set_ticklabels(x_tick_tabels) + + return ax diff --git a/dabest/plotter.py b/dabest/plotter.py index e8fe7018..fcd65ee5 100644 --- a/dabest/plotter.py +++ b/dabest/plotter.py @@ -1,16 +1,27 @@ # AUTOGENERATED! DO NOT EDIT! File to edit: ../nbs/API/plotter.ipynb. # %% auto 0 -__all__ = ['EffectSizeDataFramePlotter'] +__all__ = ['effectsize_df_plotter'] # %% ../nbs/API/plotter.ipynb 4 -def EffectSizeDataFramePlotter(EffectSizeDataFrame, **plot_kwargs): +import numpy as np +import seaborn as sns +import matplotlib +import matplotlib.pyplot as plt +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): """ Custom function that creates an estimation plot from an EffectSizeDataFrame. - + Keywords + -------- Parameters ---------- - EffectSizeDataFrame + effectsize_df A `dabest` EffectSizeDataFrame object. plot_kwargs color_col=None @@ -30,6 +41,7 @@ def EffectSizeDataFramePlotter(EffectSizeDataFrame, **plot_kwargs): fig_size=None, dpi=100, ax=None, + gridkey_rows=None, swarmplot_kwargs=None, violinplot_kwargs=None, slopegraph_kwargs=None, @@ -37,51 +49,60 @@ def EffectSizeDataFramePlotter(EffectSizeDataFrame, **plot_kwargs): reflines_kwargs=None, group_summary_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 """ - - import numpy as np - import seaborn as sns - import matplotlib.pyplot as plt - import pandas as pd - import warnings - warnings.filterwarnings('ignore', 'This figure includes Axes that are not compatible with tight_layout') - from .misc_tools import merge_two_dicts - from .plot_tools import halfviolin, get_swarm_spans, error_bar, sankeydiag - from ._stats_tools.effsize import _compute_standardizers, _compute_hedges_correction_factor + from .plot_tools import ( + halfviolin, + get_swarm_spans, + error_bar, + sankeydiag, + swarmplot, + ) + from ._stats_tools.effsize import ( + _compute_standardizers, + _compute_hedges_correction_factor, + ) + + warnings.filterwarnings( + "ignore", "This figure includes Axes that are not compatible with tight_layout" + ) - import logging # Have to disable logging of warning when get_legend_handles_labels() # tries to get from slopegraph. logging.disable(logging.WARNING) # Save rcParams that I will alter, so I can reset back. original_rcParams = {} - _changed_rcParams = ['axes.grid'] + _changed_rcParams = ["axes.grid"] for parameter in _changed_rcParams: original_rcParams[parameter] = plt.rcParams[parameter] - plt.rcParams['axes.grid'] = False + 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 = EffectSizeDataFrame.dabest_obj - plot_data = EffectSizeDataFrame._plot_data - xvar = EffectSizeDataFrame.xvar - yvar = EffectSizeDataFrame.yvar - is_paired = EffectSizeDataFrame.is_paired - delta2 = EffectSizeDataFrame.delta2 - mini_meta = EffectSizeDataFrame.mini_meta - effect_size = EffectSizeDataFrame.effect_size - proportional = EffectSizeDataFrame.proportional + 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 + idx = dabest_obj.idx - if effect_size != "mean_diff" or not delta2: + if effect_size not in ["mean_diff", "delta_g"] or not delta2: show_delta2 = False else: show_delta2 = plot_kwargs["show_delta2"] @@ -97,16 +118,16 @@ def EffectSizeDataFramePlotter(EffectSizeDataFrame, **plot_kwargs): # 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 = EffectSizeDataFrame.effect_size + 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']: + if effect_size_type in ["cliffs_delta"]: float_contrast = False if show_delta2 or show_mini_meta: - float_contrast = False + float_contrast = False if not is_paired: show_pairs = False @@ -114,81 +135,123 @@ def EffectSizeDataFramePlotter(EffectSizeDataFrame, **plot_kwargs): show_pairs = plot_kwargs["show_pairs"] # Set default kwargs first, then merge with user-dictated ones. - default_swarmplot_kwargs = {'size': plot_kwargs["raw_marker_size"]} + # 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"]) + 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. + ) # Barplot kwargs - default_barplot_kwargs = {"estimator": np.mean, "ci": plot_kwargs["ci"]} + 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"]) + barplot_kwargs = merge_two_dicts( + default_barplot_kwargs, plot_kwargs["barplot_kwargs"] + ) # Sankey Diagram kwargs - default_sankey_kwargs = {"width": 0.4, "align": "center", - "alpha": 0.4, "rightColor": False, - "bar_width":0.2} + 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"]) - + 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} + 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"]) + violinplot_kwargs = merge_two_dicts( + default_violinplot_kwargs, plot_kwargs["violinplot_kwargs"] + ) - # slopegraph kwargs. - default_slopegraph_kwargs = {'lw':1, 'alpha':0.5} + # 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"]) + 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} + 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"]) + reflines_kwargs = merge_two_dicts( + default_reflines_kwargs, plot_kwargs["reflines_kwargs"] + ) # Legend kwargs. - default_legend_kwargs = {'loc': 'upper left', 'frameon': False} + 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"]) + 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} + 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) ) + raise ValueError( + "group_summaries must be one of" " these: {}.".format(gs_default) + ) - default_group_summary_kwargs = {'zorder': 3, 'lw': 2, - 'alpha': 1} + 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"]) + 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"] @@ -214,35 +277,24 @@ def EffectSizeDataFramePlotter(EffectSizeDataFrame, **plot_kwargs): 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} - - # # check that all the keys in custom_pal are found in the - # # color column. - # col_grps = {k for k in color_groups} - # pal_grps = {k for k in custom_pal.keys()} - # not_in_pal = pal_grps.difference(col_grps) - # if len(not_in_pal) > 0: - # err1 = 'The custom palette keys {} '.format(not_in_pal) - # err2 = 'are not found in `{}`. Please check.'.format(color_col) - # errstring = (err1 + err2) - # raise IndexError(errstring) + 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] + 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.' + 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: @@ -272,144 +324,165 @@ def EffectSizeDataFramePlotter(EffectSizeDataFrame, **plot_kwargs): plot_palette_sankey = custom_pal # Infer the figsize. - fig_size = plot_kwargs["fig_size"] + 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 is True and proportional is False: + if is_paired and show_pairs and proportional is False: frac = 0.75 else: frac = 1 - if float_contrast is True: + 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) + width_inches = each_group_width_inches * all_groups_count fig_size = (width_inches, height_inches) # Initialise the figure. - # sns.set(context="talk", style='ticks') - init_fig_kwargs = dict(figsize=fig_size, dpi=plot_kwargs["dpi"] - ,tight_layout=True) + init_fig_kwargs = dict(figsize=fig_size, dpi=plot_kwargs["dpi"], tight_layout=True) width_ratios_ga = [2.5, 1] - h_space_cummings = 0.3 + + ###################### 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) - inset_contrast = True 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 is True: + + if float_contrast: axins = rawdata_axes.inset_axes( - [1, 0, - width_ratios_ga[1]/width_ratios_ga[0], 1]) + [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)]) + [ + 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)) + 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]) - - # If the contrast axes are NOT floating, create lists to store - # raw ylims and raw tick intervals, so that I can normalize - # their ylims later. - contrast_ax_ylim_low = list() - contrast_ax_ylim_high = list() - contrast_ax_ylim_tickintervals = list() + [ + ax_position.x0, + ax_position.y0 + (1 + h_space_cummings) * plot_height, + (ax_position.x1 - ax_position.x0), + plot_height, + ] + ) + contrast_axes = axins rawdata_axes.contrast_axes = axins else: - inset_contrast = False # Here, we hardcode some figure parameters. - if float_contrast is True: + if float_contrast: fig, axx = plt.subplots( - ncols=2, - gridspec_kw={"width_ratios": width_ratios_ga, - "wspace": 0}, - **init_fig_kwargs) + ncols=2, + gridspec_kw={"width_ratios": width_ratios_ga, "wspace": 0}, + **init_fig_kwargs + ) fig.patch.set_facecolor(face_color) else: - fig, axx = plt.subplots(nrows=2, - gridspec_kw={"hspace": 0.3}, - **init_fig_kwargs) + fig, axx = plt.subplots( + nrows=2, gridspec_kw={"hspace": h_space_cummings}, **init_fig_kwargs + ) fig.patch.set_facecolor(face_color) - # If the contrast axes are NOT floating, create lists to store - # raw ylims and raw tick intervals, so that I can normalize - # their ylims later. - contrast_ax_ylim_low = list() - contrast_ax_ylim_high = list() - contrast_ax_ylim_tickintervals = list() - - rawdata_axes = axx[0] + + # 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} + 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 = None - if is_paired is not None: - one_sankey = False # Flag to indicate if only one sankey is plotted. + 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 is True: + 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:])] + 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:])] + 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 proportional==False: - # Plot the raw data as a slopegraph. - # Pivot the long (melted) data. + + 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) + 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() + 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(): @@ -417,76 +490,225 @@ def EffectSizeDataFramePlotter(EffectSizeDataFrame, **plot_kwargs): y_points = observation[yvar].tolist() if color_col is None: - slopegraph_kwargs['color'] = ytick_color + slopegraph_kwargs["color"] = ytick_color else: color_key = observation[color_col][0] - if isinstance(color_key, str) == True: - slopegraph_kwargs['color'] = plot_palette_raw[color_key] - slopegraph_kwargs['label'] = color_key + 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 == None: + if err_color is None: err_color = "black" - if show_pairs is True: + if show_pairs: sankey_control_group = [] sankey_test_group = [] - for i in temp_idx: + # 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]) + sankey_test_group.append(i[1]) if len(temp_all_plot_groups) == 2: - one_sankey = True - + 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 - sankey = 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) - + 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 proportional==False: + if not proportional: # Plot the raw data as a swarmplot. - rawdata_plot = sns.swarmplot(data=plot_data, x=xvar, y=yvar, - ax=rawdata_axes, - order=all_plot_groups, hue=color_col, - palette=plot_palette_raw, zorder=1, - **swarmplot_kwargs) + 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) + 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. - bar.set_x(centre - bar_width / 2.) + 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. @@ -495,54 +717,73 @@ def EffectSizeDataFramePlotter(EffectSizeDataFrame, **plot_kwargs): if group_summaries is None: group_summaries = "mean_sd" - if group_summaries is not None and proportional==False: + 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: - _, x_max, _, _ = get_swarm_spans(c) - x_max_span = x_max - jj + 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 is True: + 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) - - if group_summaries is not None and proportional == True: - + 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 + ) + + if group_summaries is not None and proportional: err_color = plot_kwargs["err_color"] - if err_color == None: + if err_color is None: err_color = "black" - 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) + 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 + ) # 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"):] + te = t[t.rfind("\n") + len("\n") :] N = str(counts.loc[te]) te = t else: @@ -551,11 +792,13 @@ def EffectSizeDataFramePlotter(EffectSizeDataFrame, **plot_kwargs): ticks_with_counts.append("{}\nN = {}".format(te, N)) - rawdata_axes.set_xticklabels(ticks_with_counts) + 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_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) @@ -566,73 +809,76 @@ def EffectSizeDataFramePlotter(EffectSizeDataFrame, **plot_kwargs): # Plot effect sizes and bootstraps. # Take note of where the `control` groups are. - if is_paired == "baseline" and show_pairs == True: - if proportional == True and one_sankey == False: + 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_sankey = np.cumsum([len(i)-1 for i in idx]).tolist() - ticks_to_start_sankey.pop() - ticks_to_start_sankey.insert(0, 0) + 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_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 proportional == True and one_sankey == False: + 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_plot = [ + t for t in range(0, len(temp_idx)) if t not in ticks_to_skip + ] ticks_to_skip = [] - ticks_to_start_sankey = np.cumsum([len(i)-1 for i in idx]).tolist() - ticks_to_start_sankey.pop() - ticks_to_start_sankey.insert(0, 0) + 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] + 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"] + es_marker_size = plot_kwargs["es_marker_size"] halfviolin_alpha = plot_kwargs["halfviolin_alpha"] ci_type = plot_kwargs["ci_type"] - results = EffectSizeDataFrame.results + 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_group = results.test[j] + current_control = results.control[j] current_bootstrap = results.bootstraps[j] - current_effsize = results.difference[j] + current_effsize = results.difference[j] if ci_type == "bca": - current_ci_low = results.bca_low[j] - current_ci_high = results.bca_high[j] + 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] - + 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) + v = contrast_axes.violinplot( + current_bootstrap[~np.isinf(current_bootstrap)], + positions=[tick], + **violinplot_kwargs + ) # 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 is True: + if bootstraps_color_by_group: fc = plot_palette_contrast[current_group] else: fc = "grey" @@ -640,66 +886,114 @@ def EffectSizeDataFramePlotter(EffectSizeDataFrame, **plot_kwargs): 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) - # Plot the confidence interval. - contrast_axes.plot([tick, tick], - [current_ci_low, current_ci_high], - linestyle="-", - color=ytick_color, - linewidth=group_summary_kwargs['lw']) + 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 + ) + 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 - contrast_xtick_labels.append("{}\nminus\n{}".format(current_group, - current_control)) + # Plot the confidence interval. + contrast_axes.plot( + [tick, tick], + [current_ci_low, current_ci_high], + linestyle="-", + color=ytick_color, + linewidth=group_summary_kwargs["lw"], + ) + + contrast_xtick_labels.append( + "{}\nminus\n{}".format(current_group, current_control) + ) # Plot mini-meta violin if show_mini_meta or show_delta2: if show_mini_meta: - mini_meta_delta = EffectSizeDataFrame.mini_meta_delta - data = mini_meta_delta.bootstraps_weighted_delta - difference = mini_meta_delta.difference + 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 + 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 = EffectSizeDataFrame.delta_delta - data = delta_delta.bootstraps_delta_delta - difference = delta_delta.difference + 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 + 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) + 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 + ) 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) + 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_axes.plot( + [position, position], + [ci_low, ci_high], + linestyle="-", + color=ytick_color, + linewidth=group_summary_kwargs["lw"], + ) if show_mini_meta: - contrast_xtick_labels.extend(["","Weighted delta"]) + contrast_xtick_labels.extend(["", "Weighted delta"]) + elif effect_size == "delta_g": + contrast_xtick_labels.extend(["", "deltas' g"]) else: - contrast_xtick_labels.extend(["","delta-delta"]) + 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. @@ -707,22 +1001,22 @@ def EffectSizeDataFramePlotter(EffectSizeDataFrame, **plot_kwargs): contrast_axes.set_xticks(rawdata_axes.get_xticks()) else: temp = rawdata_axes.get_xticks() - temp = np.append(temp, [max(temp)+1, max(temp)+2]) + temp = np.append(temp, [max(temp) + 1, max(temp) + 2]) contrast_axes.set_xticks(temp) - if show_pairs is True: + if show_pairs: max_x = contrast_axes.get_xlim()[1] rawdata_axes.set_xlim(-0.375, max_x) - if float_contrast is True: + 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) + rawdata_axes.set_xlim(temp[0], temp[1] + 0.25) else: - rawdata_axes.set_xlim(temp[0], temp[1]+2) + 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()) @@ -730,53 +1024,68 @@ def EffectSizeDataFramePlotter(EffectSizeDataFrame, **plot_kwargs): # Properly label the contrast ticks. for t in ticks_to_skip: contrast_xtick_labels.insert(t, "") - - contrast_axes.set_xticklabels(contrast_xtick_labels) + + if plot_kwargs["fontsize_contrastxlabel"] is not None: + fontsize_contrastxlabel = plot_kwargs["fontsize_contrastxlabel"] + + contrast_axes.set_xticklabels( + contrast_xtick_labels, fontsize=fontsize_contrastxlabel + ) 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() + legend_handles_unique = ( + pd.Series(legend_handles, dtype="object").loc[unique_idx] + ).tolist() if len(legend_handles_unique) > 0: - if float_contrast is True: + if float_contrast: axes_with_legend = contrast_axes - if show_pairs is True: + if show_pairs: bta = (1.75, 1.02) else: bta = (1.5, 1.02) else: axes_with_legend = rawdata_axes - if show_pairs is True: - bta = (1.02, 1.) + if show_pairs: + bta = (1.02, 1.0) else: - bta = (1.,1.) - leg = axes_with_legend.legend(legend_handles_unique, - legend_labels_unique, - bbox_to_anchor=bta, - **legend_kwargs) - if show_pairs is True: + 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 is True: + 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] + 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] + 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() @@ -784,7 +1093,7 @@ def EffectSizeDataFramePlotter(EffectSizeDataFrame, **plot_kwargs): _, 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. @@ -802,48 +1111,53 @@ def EffectSizeDataFramePlotter(EffectSizeDataFrame, **plot_kwargs): 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) + contrast_axes.set_xlim(contrast_xlim_max - 1, contrast_xlim_max) - elif effect_size_type in ["cohens_d", "hedges_g","cohens_h"]: + 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] - + 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() + + 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) - + 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 + 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() + + 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) + 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 @@ -853,66 +1167,74 @@ def EffectSizeDataFramePlotter(EffectSizeDataFrame, **plot_kwargs): elif jj == 1: ref = 0 diff = ref + difference - effsize_line_start = contrast_xlim_max-1.1 + 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) - + 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: + 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) - + 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 + 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=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.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) - + contrast_axes.hlines( + contrast_axes.get_ylim()[0], + contrast_xlim_max - 0.8, + contrast_xlim_max, + **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: + 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_delta2_ylim = plot_kwargs["delta2_ylim"] custom_contrast_ylim = custom_delta2_ylim if len(custom_contrast_ylim) != 2: @@ -922,8 +1244,8 @@ def EffectSizeDataFramePlotter(EffectSizeDataFrame, **plot_kwargs): 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] + 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) @@ -940,185 +1262,340 @@ def EffectSizeDataFramePlotter(EffectSizeDataFrame, **plot_kwargs): if contrast_ylim_low < 0 < contrast_ylim_high: contrast_axes.axhline(y=0, **reflines_kwargs) - if is_paired == "baseline" and show_pairs == True: - if proportional == True and one_sankey == False: - rightend_ticks_raw = np.array([len(i)-2 for i in idx]) + np.array(ticks_to_start_sankey) - else: - rightend_ticks_raw = np.array([len(i)-1 for i in temp_idx]) + np.array(ticks_to_skip) + 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 proportional == True and one_sankey == False: - for k, start_tick in enumerate(ticks_to_start_sankey): + 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: + 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.hlines(xmin=start_tick, xmax=end_tick, **redraw_axes_kwargs) ax.set_ylim(ylim) - del redraw_axes_kwargs['y'] - - if proportional == False: - temp_length = [(len(i)-1) for i in idx] + 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: - temp_length = [(len(i)-1)*2-1 for i in idx] - if proportional == True and one_sankey == False: - rightend_ticks_contrast = np.array([len(i)-2 for i in idx]) + np.array(ticks_to_start_sankey) - else: - rightend_ticks_contrast = np.array(temp_length) + np.array(ticks_to_skip_contrast) + 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 proportional == True and one_sankey == False: - for k, start_tick in enumerate(ticks_to_start_sankey): + 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) + 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.hlines(xmin=start_tick, xmax=end_tick, **redraw_axes_kwargs) + ax.set_ylim(ylim) - del redraw_axes_kwargs['y'] + del redraw_axes_kwargs["y"] else: # Compute the end of each x-axes line. - if proportional == True and one_sankey == False: - rightend_ticks = np.array([len(i)-2 for i in idx]) + np.array(ticks_to_start_sankey) + 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) - + 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 proportional == True and one_sankey == False: - for k, start_tick in enumerate(ticks_to_start_sankey): + 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) + 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.hlines(xmin=start_tick, xmax=end_tick, **redraw_axes_kwargs) + ax.set_ylim(ylim) - del redraw_axes_kwargs['y'] + del redraw_axes_kwargs["y"] - if show_delta2 is True or show_mini_meta is True: + if show_delta2 or show_mini_meta: ylim = contrast_axes.get_ylim() - redraw_axes_kwargs['y'] = ylim[0] + 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'] + 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'] + 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'] + 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"} - - if proportional == True and effect_size_type != "cohens_h": + 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[EffectSizeDataFrame.effect_size] - + default_contrast_label = contrast_label_dict[effectsize_df.effect_size] - if plot_kwargs['contrast_label'] is None: + 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'] + contrast_label = plot_kwargs["contrast_label"] - contrast_axes.set_ylabel(contrast_label) - if float_contrast is True: + 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 proportional == False: - rawdata_axes.set_ylabel(swarm_label) + if not proportional: + rawdata_axes.set_ylabel(swarm_label, fontsize=fontsize_rawylabel) else: - rawdata_axes.set_ylabel(bar_label) + 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 float_contrast==False: + 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) + 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 is True: + 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) - - - if show_delta2 is True: - if plot_kwargs['delta2_label'] is None: + contrast_axes.vlines( + xpos, og_ylim_contrast[0], og_ylim_contrast[1], **redraw_axes_kwargs + ) + + 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 = plot_kwargs['delta2_label'] + else: + delta2_label = "deltas' g" delta2_axes = contrast_axes.twinx() delta2_axes.set_frame_on(False) - delta2_axes.set_ylabel(delta2_label) + 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) + delta2_axes.vlines( + og_xlim_delta[1], og_ylim_delta[0], og_ylim_delta[1], **redraw_axes_kwargs + ) + + ################################################### 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} + ) + + # 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) + + ####################################################### END GRIDKEY MAIN CODE WIP # Make sure no stray ticks appear! - rawdata_axes.xaxis.set_ticks_position('bottom') - rawdata_axes.yaxis.set_ticks_position('left') - contrast_axes.xaxis.set_ticks_position('bottom') + rawdata_axes.xaxis.set_ticks_position("bottom") + rawdata_axes.yaxis.set_ticks_position("left") + contrast_axes.xaxis.set_ticks_position("bottom") if float_contrast is False: - contrast_axes.yaxis.set_ticks_position('left') + contrast_axes.yaxis.set_ticks_position("left") # Reset rcParams. for parameter in _changed_rcParams: @@ -1126,3 +1603,4 @@ def EffectSizeDataFramePlotter(EffectSizeDataFrame, **plot_kwargs): # Return the figure. return fig + diff --git a/nbs/01-getting_started.ipynb b/nbs/01-getting_started.ipynb index 680bbfc5..f624e06d 100644 --- a/nbs/01-getting_started.ipynb +++ b/nbs/01-getting_started.ipynb @@ -12,6 +12,43 @@ "- order: 1" ] }, + { + "cell_type": "markdown", + "id": "5b3dcdd6", + "metadata": {}, + "source": [ + "## Introduction" + ] + }, + { + "cell_type": "markdown", + "id": "2aebebc2", + "metadata": {}, + "source": [ + "DABEST is a package for **D**ata **A**nalysis with **B**ootstrapped **EST**imation\n", + "\n", + "[Estimation statistics](https://en.wikipedia.org/wiki/Estimation_statistics) is a simple framework that avoids the [pitfalls](https://www.nature.com/articles/nmeth.3288) of significance testing. It uses familiar statistical concepts: means, mean differences, and error bars. More importantly, it focuses on the effect size of one’s experiment/intervention, as opposed to a false dichotomy engendered by *P* values." + ] + }, + { + "cell_type": "markdown", + "id": "0fc075f5", + "metadata": {}, + "source": [ + "An estimation plot has two key features.\n", + "\n", + "1. It **presents all datapoints** as a swarmplot, which orders each point to display the underlying distribution.\n", + "2. It presents the **effect size** as a **bootstrap 95% confidence interval** on a **separate but aligned axes**." + ] + }, + { + "cell_type": "markdown", + "id": "e4c2e459", + "metadata": {}, + "source": [ + "DABEST powers [estimationstats.com](estimationstats.com), allowing everyone access to high-quality estimation plots." + ] + }, { "cell_type": "markdown", "id": "d1d5cb1a", @@ -27,16 +64,16 @@ "source": [ "\n", "\n", - "Python 3.8 is strongly recommended. DABEST has also been tested with Python 3.6 and 3.7.\n", + "Python 3.10 is strongly recommended. DABEST has also been tested with Python 3.6, 3.7 and 3.8.\n", "\n", "In addition, the following packages are also required (listed with their minimal versions):\n", "\n", - "* [numpy 1.22.3](https://www.numpy.org)\n", + "* [numpy 1.22.4](https://www.numpy.org)\n", "* [scipy 1.9.3](https://www.scipy.org)\n", - "* [matplotlib 3.5.1](https://www.matplotlib.org)\n", - "* [pandas 1.5.0](https://pandas.pydata.org)\n", - "* [seaborn 0.11.2](https://seaborn.pydata.org)\n", - "* [lqrt 0.3](https://github.com/alyakin314/lqrt)\n", + "* [matplotlib 3.6.3](https://www.matplotlib.org)\n", + "* [pandas 1.5.3](https://pandas.pydata.org)\n", + "* [seaborn 0.12.2](https://seaborn.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" ] @@ -58,8 +95,10 @@ "\n", "At the command line, run\n", "\n", + "``` shell\n", + "$ pip install dabest\n", + "```\n", "\n", - "**$ pip install dabest**\n", "\n" ] }, @@ -70,11 +109,12 @@ "source": [ "2. Using Github\n", "\n", - "Clone the [DABEST-python repo](https://github.com/ACCLAB/DABEST-python) locally (see instructions [here] (https://help.github.com/articles/cloning-a-repository/).\n", + "Clone the [DABEST-python repo](https://github.com/ACCLAB/DABEST-python) locally (see instructions [here](https://help.github.com/articles/cloning-a-repository/)).\n", "\n", "Then, navigate to the cloned repo in the command line and run\n", - "\n", - "**$ pip install**" + "``` shell\n", + "$ pip install .\n", + "```" ] }, { @@ -90,9 +130,13 @@ "id": "a9f8cb3e", "metadata": {}, "source": [ - "To test DABEST, you will need to install [pytest](https://docs.pytest.org/en/latest/).\n", + "To test DABEST, you will need to install [pytest](https://docs.pytest.org/en/latest/) and [nbdev](https://nbdev.fast.ai/). \n", + "\n", + "Run ``nbdev_export && nbdev_test`` in the root directory of the source distribution. This runs the value assertion tests in ``dabest/tests`` folder\n", "\n", - "Run ``pytest`` in the root directory of the source distribution. This runs the test suite in ``dabest/tests`` folder. The test suite will ensure that the bootstrapping functions and the plotting functions perform as expected.\n", + "Run ``pytest`` in the root directory of the source distribution. This runs the image-based tests in ``dabest/tests/mpl_image_tests`` sub folder.\n", + "\n", + "The test suite will ensure that the bootstrapping functions and the plotting functions perform as expected.\n", "\n" ] }, @@ -127,14 +171,6 @@ "source": [ "All contributions are welcome. Please fork the [Github repo](https://github.com/ACCLAB/DABEST-python/) and open a pull request.\n" ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "23a7b823", - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { diff --git a/nbs/02-about.ipynb b/nbs/02-about.ipynb index 71ed2501..2dec09aa 100644 --- a/nbs/02-about.ipynb +++ b/nbs/02-about.ipynb @@ -17,7 +17,9 @@ "\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", - "Additional 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", + "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", "\n", "To find out more about the authors' research, please visit the [Claridge-Chang lab webpage](http://www.claridgechang.net/).\n", "\n", @@ -66,6 +68,7 @@ "\n", " * Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission.\n", "\n", + "
\n", "NO EXPRESS OR IMPLIED LICENSES TO ANY PARTY'S PATENT RIGHTS ARE GRANTED BY\n", "THIS LICENSE. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND\n", "CONTRIBUTORS \"AS IS\" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT\n", @@ -77,16 +80,9 @@ "BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER\n", "IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)\n", "ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE\n", - "POSSIBILITY OF SUCH DAMAGE.\n" + "POSSIBILITY OF SUCH DAMAGE.\n", + "
\n" ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "de35a697", - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { diff --git a/nbs/API/bootstrap.ipynb b/nbs/API/bootstrap.ipynb index 2a1a0970..eb33a083 100644 --- a/nbs/API/bootstrap.ipynb +++ b/nbs/API/bootstrap.ipynb @@ -29,10 +29,11 @@ "outputs": [], "source": [ "#| hide\n", + "from __future__ import division\n", "from nbdev.showdoc import *\n", "import nbdev\n", - "nbdev.nbdev_export()\n", - "from __future__ import division" + "\n", + "nbdev.nbdev_export()" ] }, { @@ -42,8 +43,14 @@ "metadata": {}, "outputs": [], "source": [ - "#|export\n", - "import numpy as np" + "#| export\n", + "import numpy as np\n", + "import pandas as pd\n", + "import seaborn as sns\n", + "from scipy.stats import norm\n", + "from scipy.stats import ttest_1samp, ttest_ind, ttest_rel\n", + "from scipy.stats import mannwhitneyu, wilcoxon, norm\n", + "import warnings" ] }, { @@ -53,11 +60,11 @@ "metadata": {}, "outputs": [], "source": [ - "#|export\n", + "#| export\n", "class bootstrap:\n", - " '''\n", - " Computes the summary statistic and a bootstrapped confidence interval. \n", - " \n", + " \"\"\"\n", + " Computes the summary statistic and a bootstrapped confidence interval.\n", + "\n", " Returns\n", " -------\n", " An `bootstrap` object reporting the summary statistics, percentile CIs, bias-corrected and accelerated (BCa) CIs, and the settings used:\n", @@ -94,85 +101,84 @@ " `pvalue_mann_whitney`: float\n", " Two-sided p-value obtained from scipy.stats.mannwhitneyu. If a single array was given (x1 only), returns 'NIL'. The Mann-Whitney U-test is a nonparametric unpaired test of the null hypothesis that x1 and x2 are from the same distribution. See \n", "\n", - " '''\n", - " def __init__(self, \n", - " x1:np.array, # The data in a one-dimensional array form. Only x1 is required. If x2 is given, the bootstrapped summary difference between the two groups (x2-x1) is computed. NaNs are automatically discarded.\n", - " x2:np.array=None, # The data in a one-dimensional array form. Only x1 is required. If x2 is given, the bootstrapped summary difference between the two groups (x2-x1) is computed. NaNs are automatically discarded.\n", - " paired:bool=False, # Whether or not x1 and x2 are paired samples. If 'paired' is None then the data will not be treated as paired data in the subsequent calculations. If 'paired' is 'baseline', then in each tuple of x, other groups will be paired up with the first group (as control). If 'paired' is 'sequential', then in each tuple of x, each group will be paired up with the previous group (as control).\n", - " statfunction:callable=np.mean,#The summary statistic called on data.\n", - " smoothboot:bool=False,#Taken from seaborn.algorithms.bootstrap. If True, performs a smoothed bootstrap (draws samples from a kernel destiny estimate).\n", - " alpha_level:float=0.05,#Denotes the likelihood that the confidence interval produced does not include the true summary statistic. When alpha = 0.05, a 95% confidence interval is produced.\n", - " reps:int=5000 # Number of bootstrap iterations to perform.\n", - " ):\n", - "\n", - " import numpy as np\n", - " import pandas as pd\n", - " import seaborn as sns\n", - "\n", - " from scipy.stats import norm\n", - " from numpy.random import randint\n", - " from scipy.stats import ttest_1samp, ttest_ind, ttest_rel\n", - " from scipy.stats import mannwhitneyu, wilcoxon, norm\n", - " import warnings\n", + " \"\"\"\n", "\n", + " def __init__(\n", + " self,\n", + " x1: np.array, # The data in a one-dimensional array form. Only x1 is required. If x2 is given, the bootstrapped summary difference between the two groups (x2-x1) is computed. NaNs are automatically discarded.\n", + " x2: np.array = None, # The data in a one-dimensional array form. Only x1 is required. If x2 is given, the bootstrapped summary difference between the two groups (x2-x1) is computed. NaNs are automatically discarded.\n", + " paired: bool = False, # Whether or not x1 and x2 are paired samples. If 'paired' is None then the data will not be treated as paired data in the subsequent calculations. If 'paired' is 'baseline', then in each tuple of x, other groups will be paired up with the first group (as control). If 'paired' is 'sequential', then in each tuple of x, each group will be paired up with the previous group (as control).\n", + " stat_function: callable = np.mean, # The summary statistic called on data.\n", + " smoothboot: bool = False, # Taken from seaborn.algorithms.bootstrap. If True, performs a smoothed bootstrap (draws samples from a kernel destiny estimate).\n", + " alpha_level: float = 0.05, # Denotes the likelihood that the confidence interval produced does not include the true summary statistic. When alpha = 0.05, a 95% confidence interval is produced.\n", + " reps: int = 5000, # Number of bootstrap iterations to perform.\n", + " ):\n", " # Turn to pandas series.\n", " x1 = pd.Series(x1).dropna()\n", " diff = False\n", "\n", - " # Initialise statfunction\n", - " if statfunction == None:\n", - " statfunction = np.mean\n", + " # Initialise stat_function\n", + " if stat_function is None:\n", + " stat_function = np.mean\n", "\n", " # Compute two-sided alphas.\n", - " if alpha_level > 1. or alpha_level < 0.:\n", + " if alpha_level > 1.0 or alpha_level < 0.0:\n", " raise ValueError(\"alpha_level must be between 0 and 1.\")\n", - " alphas = np.array([alpha_level/2., 1-alpha_level/2.])\n", + " alphas = np.array([alpha_level / 2.0, 1 - alpha_level / 2.0])\n", "\n", - " sns_bootstrap_kwargs = {'func': statfunction,\n", - " 'n_boot': reps,\n", - " 'smooth': smoothboot}\n", + " sns_bootstrap_kwargs = {\n", + " \"func\": stat_function,\n", + " \"n_boot\": reps,\n", + " \"smooth\": smoothboot,\n", + " }\n", "\n", " if paired:\n", " # check x2 is not None:\n", " if x2 is None:\n", - " raise ValueError('Please specify x2.')\n", - " else:\n", - " x2 = pd.Series(x2).dropna()\n", - " if len(x1) != len(x2):\n", - " raise ValueError('x1 and x2 are not the same length.')\n", - "\n", - " if (x2 is None) or (paired is not None) :\n", + " raise ValueError(\"Please specify x2.\")\n", + " \n", + " x2 = pd.Series(x2).dropna()\n", + " if len(x1) != len(x2):\n", + " raise ValueError(\"x1 and x2 are not the same length.\")\n", "\n", + " if (x2 is None) or (paired is not None):\n", " if x2 is None:\n", " tx = x1\n", " paired = False\n", " ttest_single = ttest_1samp(x1, 0)[1]\n", - " ttest_2_ind = 'NIL'\n", - " ttest_2_paired = 'NIL'\n", - " wilcoxonresult = 'NIL'\n", + " ttest_2_ind = \"NIL\"\n", + " ttest_2_paired = \"NIL\"\n", + " wilcoxonresult = \"NIL\"\n", "\n", - " elif paired is not None:\n", + " else: # only two options to enter here\n", " diff = True\n", " tx = x2 - x1\n", - " ttest_single = 'NIL'\n", - " ttest_2_ind = 'NIL'\n", + " ttest_single = \"NIL\"\n", + " ttest_2_ind = \"NIL\"\n", " ttest_2_paired = ttest_rel(x1, x2)[1]\n", - " wilcoxonresult = wilcoxon(x1, x2)[1]\n", - " mannwhitneyresult = 'NIL'\n", + "\n", + " try:\n", + " wilcoxonresult = wilcoxon(x1, x2)[1]\n", + " except ValueError as e:\n", + " warnings.warn(\"Wilcoxon test could not be performed. This might be due \"\n", + " \"to no variability in the difference of the paired groups. \\n\"\n", + " \"Error: {}\\n\"\n", + " \"For detailed information, please refer to https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.wilcoxon.html \"\n", + " .format(e))\n", + " mannwhitneyresult = \"NIL\"\n", "\n", " # Turns data into array, then tuple.\n", " tdata = (tx,)\n", "\n", " # The value of the statistic function applied\n", " # just to the actual data.\n", - " summ_stat = statfunction(*tdata)\n", + " summ_stat = stat_function(*tdata)\n", " statarray = sns.algorithms.bootstrap(tx, **sns_bootstrap_kwargs)\n", " statarray.sort()\n", "\n", " # Get Percentile indices\n", - " pct_low_high = np.round((reps-1) * alphas)\n", - " pct_low_high = np.nan_to_num(pct_low_high).astype('int')\n", - "\n", + " pct_low_high = np.round((reps - 1) * alphas)\n", + " pct_low_high = np.nan_to_num(pct_low_high).astype(\"int\")\n", "\n", " elif x2 is not None and paired is None:\n", " diff = True\n", @@ -184,42 +190,45 @@ " tdata = exp_statarray - ref_statarray\n", " statarray = tdata.copy()\n", " statarray.sort()\n", - " tdata = (tdata, ) # Note tuple form.\n", + " tdata = (tdata,) # Note tuple form.\n", "\n", " # The difference as one would calculate it.\n", - " summ_stat = statfunction(x2) - statfunction(x1)\n", + " summ_stat = stat_function(x2) - stat_function(x1)\n", "\n", " # Get Percentile indices\n", - " pct_low_high = np.round((reps-1) * alphas)\n", - " pct_low_high = np.nan_to_num(pct_low_high).astype('int')\n", + " pct_low_high = np.round((reps - 1) * alphas)\n", + " pct_low_high = np.nan_to_num(pct_low_high).astype(\"int\")\n", "\n", " # Statistical tests.\n", - " ttest_single='NIL'\n", - " ttest_2_ind = ttest_ind(x1,x2)[1]\n", - " ttest_2_paired='NIL'\n", - " mannwhitneyresult = mannwhitneyu(x1, x2, alternative='two-sided')[1]\n", - " wilcoxonresult = 'NIL'\n", + " ttest_single = \"NIL\"\n", + " ttest_2_ind = ttest_ind(x1, x2)[1]\n", + " ttest_2_paired = \"NIL\"\n", + " mannwhitneyresult = mannwhitneyu(x1, x2, alternative=\"two-sided\")[1]\n", + " wilcoxonresult = \"NIL\"\n", "\n", " # Get Bias-Corrected Accelerated indices convenience function invoked.\n", - " bca_low_high = bca(tdata, alphas, statarray,\n", - " statfunction, summ_stat, reps)\n", + " bca_low_high = bca(tdata, alphas, statarray, stat_function, summ_stat, reps)\n", "\n", " # Warnings for unstable or extreme indices.\n", " for ind in [pct_low_high, bca_low_high]:\n", - " if np.any(ind == 0) or np.any(ind == reps-1):\n", - " warnings.warn(\"Some values used extremal samples;\"\n", - " \" results are probably unstable.\")\n", - " elif np.any(ind<10) or np.any(ind>=reps-10):\n", - " warnings.warn(\"Some values used top 10 low/high samples;\"\n", - " \" results may be unstable.\")\n", + " if np.any(ind == 0) or np.any(ind == reps - 1):\n", + " warnings.warn(\n", + " \"Some values used extremal samples;\"\n", + " \" results are probably unstable.\"\n", + " )\n", + " elif np.any(ind < 10) or np.any(ind >= reps - 10):\n", + " warnings.warn(\n", + " \"Some values used top 10 low/high samples;\"\n", + " \" results may be unstable.\"\n", + " )\n", "\n", " self.summary = summ_stat\n", " self.is_paired = paired\n", " self.is_difference = diff\n", - " self.statistic = str(statfunction)\n", + " self.statistic = str(stat_function)\n", " self.n_reps = reps\n", "\n", - " self.ci = (1-alpha_level)*100\n", + " self.ci = (1 - alpha_level) * 100\n", " self.stat_array = np.array(statarray)\n", "\n", " self.pct_ci_low = statarray[pct_low_high[0]]\n", @@ -236,33 +245,33 @@ " self.pvalue_wilcoxon = wilcoxonresult\n", " self.pvalue_mann_whitney = mannwhitneyresult\n", "\n", - " self.results = {'stat_summary': self.summary,\n", - " 'is_difference': diff,\n", - " 'is_paired': paired,\n", - " 'bca_ci_low': self.bca_ci_low,\n", - " 'bca_ci_high': self.bca_ci_high,\n", - " 'ci': self.ci\n", - " }\n", + " self.results = {\n", + " \"stat_summary\": self.summary,\n", + " \"is_difference\": diff,\n", + " \"is_paired\": paired,\n", + " \"bca_ci_low\": self.bca_ci_low,\n", + " \"bca_ci_high\": self.bca_ci_high,\n", + " \"ci\": self.ci,\n", + " }\n", "\n", " def __repr__(self):\n", - " import numpy as np\n", - "\n", - " if 'mean' in self.statistic:\n", - " stat = 'mean'\n", - " elif 'median' in self.statistic:\n", - " stat = 'median'\n", + " if \"mean\" in self.statistic:\n", + " stat = \"mean\"\n", + " elif \"median\" in self.statistic:\n", + " stat = \"median\"\n", " else:\n", " stat = self.statistic\n", "\n", - " diff_types = {'sequential': 'paired', 'baseline': 'paired', None: 'unpaired'}\n", + " diff_types = {\"sequential\": \"paired\", \"baseline\": \"paired\", None: \"unpaired\"}\n", " if self.is_difference:\n", - " a = 'The {} {} difference is {}.'.format(diff_types[self.is_paired],\n", - " stat, self.summary)\n", + " a = \"The {} {} difference is {}.\".format(\n", + " diff_types[self.is_paired], stat, self.summary\n", + " )\n", " else:\n", - " a = 'The {} is {}.'.format(stat, self.summary)\n", + " a = \"The {} is {}.\".format(stat, self.summary)\n", "\n", - " b = '[{} CI: {}, {}]'.format(self.ci, self.bca_ci_low, self.bca_ci_high)\n", - " return '\\n'.join([a, b])" + " b = \"[{} CI: {}, {}]\".format(self.ci, self.bca_ci_low, self.bca_ci_high)\n", + " return \"\\n\".join([a, b])" ] }, { @@ -272,7 +281,7 @@ "metadata": {}, "outputs": [], "source": [ - "#|export\n", + "#| export\n", "def jackknife_indexes(data):\n", " # Taken without modification from scikits.bootstrap package.\n", " \"\"\"\n", @@ -283,49 +292,43 @@ " For a given set of data Y, the jackknife sample J[i] is defined as the\n", " data set Y with the ith data point deleted.\n", " \"\"\"\n", - " import numpy as np\n", "\n", - " base = np.arange(0,len(data))\n", - " return (np.delete(base,i) for i in base)\n", + " base = np.arange(0, len(data))\n", + " return (np.delete(base, i) for i in base)\n", + "\n", "\n", - "def bca(data, alphas, statarray, statfunction, ostat, reps):\n", - " '''\n", + "def bca(data, alphas, stat_array, stat_function, ostat, reps):\n", + " \"\"\"\n", " Subroutine called to calculate the BCa statistics.\n", " Borrowed heavily from scikits.bootstrap code.\n", - " '''\n", - " import warnings\n", - "\n", - " import numpy as np\n", - " import pandas as pd\n", - " import seaborn as sns\n", - "\n", - " from scipy.stats import norm\n", - " from numpy.random import randint\n", + " \"\"\"\n", "\n", " # The bias correction value.\n", - " z0 = norm.ppf( ( 1.0*np.sum(statarray < ostat, axis = 0) ) / reps )\n", + " z0 = norm.ppf((1.0 * np.sum(stat_array < ostat, axis=0)) / reps)\n", "\n", " # Statistics of the jackknife distribution\n", - " jackindexes = jackknife_indexes(data[0])\n", - " jstat = [statfunction(*(x[indexes] for x in data))\n", - " for indexes in jackindexes]\n", - " jmean = np.mean(jstat,axis = 0)\n", + " jack_indexes = jackknife_indexes(data[0])\n", + " jstat = [stat_function(*(x[indexes] for x in data)) for indexes in jack_indexes]\n", + " jmean = np.mean(jstat, axis=0)\n", "\n", " # Acceleration value\n", - " a = np.divide(np.sum( (jmean - jstat)**3, axis = 0 ),\n", - " ( 6.0 * np.sum( (jmean - jstat)**2, axis = 0)**1.5 )\n", - " )\n", + " a = np.divide(\n", + " np.sum((jmean - jstat) ** 3, axis=0),\n", + " (6.0 * np.sum((jmean - jstat) ** 2, axis=0) ** 1.5),\n", + " )\n", " if np.any(np.isnan(a)):\n", " nanind = np.nonzero(np.isnan(a))\n", - " warnings.warn(\"Some acceleration values were undefined.\"\n", - " \"This is almost certainly because all values\"\n", - " \"for the statistic were equal. Affected\"\n", - " \"confidence intervals will have zero width and\"\n", - " \"may be inaccurate (indexes: {})\".format(nanind))\n", - " zs = z0 + norm.ppf(alphas).reshape(alphas.shape+(1,)*z0.ndim)\n", - " avals = norm.cdf(z0 + zs/(1-a*zs))\n", - " nvals = np.round((reps-1)*avals)\n", - " nvals = np.nan_to_num(nvals).astype('int')\n", + " warnings.warn(\n", + " \"Some acceleration values were undefined.\"\n", + " \"This is almost certainly because all values\"\n", + " \"for the statistic were equal. Affected\"\n", + " \"confidence intervals will have zero width and\"\n", + " \"may be inaccurate (indexes: {})\".format(nanind)\n", + " )\n", + " zs = z0 + norm.ppf(alphas).reshape(alphas.shape + (1,) * z0.ndim)\n", + " avals = norm.cdf(z0 + zs / (1 - a * zs))\n", + " nvals = np.round((reps - 1) * avals)\n", + " nvals = np.nan_to_num(nvals).astype(\"int\")\n", "\n", " return nvals" ] diff --git a/nbs/API/class.ipynb b/nbs/API/class.ipynb deleted file mode 100644 index 59994e49..00000000 --- a/nbs/API/class.ipynb +++ /dev/null @@ -1,4103 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "ed122c74", - "metadata": {}, - "source": [ - "# Class\n", - "\n", - "> Several classes for estimating statistics and generating plots.\n", - "\n", - "- order: 2" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "fb97d9b1", - "metadata": {}, - "outputs": [], - "source": [ - "#| default_exp _classes" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1d5d586f", - "metadata": {}, - "outputs": [], - "source": [ - "#| hide\n", - "from __future__ import annotations" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "dcd32470", - "metadata": {}, - "outputs": [], - "source": [ - "#| hide\n", - "from nbdev.showdoc import *\n", - "import nbdev\n", - "nbdev.nbdev_export()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d3c6f47a", - "metadata": {}, - "outputs": [], - "source": [ - "#| export\n", - "import numpy as np\n", - "from scipy.stats import norm\n", - "import pandas as pd\n", - "from scipy.stats import randint" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "204a64b4", - "metadata": {}, - "outputs": [], - "source": [ - "#| hide\n", - "import dabest" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "350b12c1", - "metadata": {}, - "outputs": [], - "source": [ - "#| export\n", - "class Dabest(object):\n", - "\n", - " \"\"\"\n", - " Class for estimation statistics and plots.\n", - " \"\"\"\n", - "\n", - " def __init__(self, data, idx, x, y, paired, id_col, ci, \n", - " resamples, random_seed, proportional, delta2, \n", - " experiment, experiment_label, x1_level, mini_meta):\n", - "\n", - " \"\"\"\n", - " Parses and stores pandas DataFrames in preparation for estimation\n", - " statistics. You should not be calling this class directly; instead,\n", - " use `dabest.load()` to parse your DataFrame prior to analysis.\n", - " \"\"\"\n", - "\n", - " # Import standard data science libraries.\n", - " import numpy as np\n", - " import pandas as pd\n", - " import seaborn as sns\n", - "\n", - " self.__delta2 = delta2\n", - " self.__experiment = experiment\n", - " self.__ci = ci\n", - " self.__data = data\n", - " self.__id_col = id_col\n", - " 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", - "\n", - " # Make a copy of the data, so we don't make alterations to it.\n", - " data_in = data.copy()\n", - " # data_in.reset_index(inplace=True)\n", - " # data_in_index_name = data_in.index.name\n", - "\n", - "\n", - " # Check if it is a valid mini_meta case\n", - " if mini_meta is True:\n", - "\n", - " # Only mini_meta calculation but not proportional and delta-delta function\n", - " if proportional is True:\n", - " err0 = '`proportional` and `mini_meta` cannot be True at the same time.'\n", - " raise ValueError(err0)\n", - " elif delta2 is True:\n", - " err0 = '`delta` and `mini_meta` cannot be True at the same time.'\n", - " raise ValueError(err0)\n", - " \n", - " # Check if the columns stated are valid\n", - " if all([isinstance(i, str) for i in idx]):\n", - " if len(pd.unique([t for t in idx]).tolist())!=2:\n", - " err0 = '`mini_meta` is True, but `idx` ({})'.format(idx) \n", - " err1 = 'does not contain exactly 2 columns.'\n", - " raise ValueError(err0 + err1)\n", - " elif all([isinstance(i, (tuple, list)) for i in idx]):\n", - " all_idx_lengths = [len(t) for t in idx]\n", - " if (np.array(all_idx_lengths) != 2).any():\n", - " err1 = \"`mini_meta` is True, but some idx \"\n", - " err2 = \"in {} does not consist only of two groups.\".format(idx)\n", - " raise ValueError(err1 + err2)\n", - " \n", - "\n", - "\n", - " # Check if this is a 2x2 ANOVA case and x & y are valid columns\n", - " # Create experiment_label and x1_level\n", - " if delta2 is True:\n", - " if proportional is True:\n", - " err0 = '`proportional` and `delta` cannot be True at the same time.'\n", - " raise ValueError(err0)\n", - " # idx should not be specified\n", - " if idx:\n", - " err0 = '`idx` should not be specified when `delta2` is True.'.format(len(x))\n", - " raise ValueError(err0)\n", - "\n", - " # Check if x is valid\n", - " if len(x) != 2:\n", - " err0 = '`delta2` is True but the number of variables indicated by `x` is {}.'.format(len(x))\n", - " raise ValueError(err0)\n", - " else:\n", - " for i in x:\n", - " if i not in data_in.columns:\n", - " err = '{0} is not a column in `data`. Please check.'.format(i)\n", - " raise IndexError(err)\n", - "\n", - " # Check if y is valid\n", - " if not y:\n", - " err0 = '`delta2` is True but `y` is not indicated.'\n", - " raise ValueError(err0)\n", - " elif y not in data_in.columns:\n", - " err = '{0} is not a column in `data`. Please check.'.format(y)\n", - " raise IndexError(err)\n", - "\n", - " # Check if experiment is valid\n", - " if experiment not in data_in.columns:\n", - " err = '{0} is not a column in `data`. Please check.'.format(experiment)\n", - " raise IndexError(err)\n", - "\n", - " # Check if experiment_label is valid and create experiment when needed\n", - " if experiment_label:\n", - " if len(experiment_label) != 2:\n", - " err0 = '`experiment_label` does not have a length of 2.'\n", - " raise ValueError(err0)\n", - " else: \n", - " for i in experiment_label:\n", - " if i not in data_in[experiment].unique():\n", - " err = '{0} is not an element in the column `{1}` of `data`. Please check.'.format(i, experiment)\n", - " raise IndexError(err)\n", - " else:\n", - " experiment_label = data_in[experiment].unique()\n", - "\n", - " # Check if x1_level is valid\n", - " if x1_level:\n", - " if len(x1_level) != 2:\n", - " err0 = '`x1_level` does not have a length of 2.'\n", - " raise ValueError(err0)\n", - " else: \n", - " for i in x1_level:\n", - " if i not in data_in[x[0]].unique():\n", - " err = '{0} is not an element in the column `{1}` of `data`. Please check.'.format(i, experiment)\n", - " raise IndexError(err)\n", - "\n", - " else:\n", - " x1_level = data_in[x[0]].unique() \n", - " elif experiment is not None:\n", - " experiment_label = data_in[experiment].unique()\n", - " x1_level = data_in[x[0]].unique() \n", - " self.__experiment_label = experiment_label\n", - " self.__x1_level = x1_level\n", - "\n", - "\n", - " # # Check if idx is specified\n", - " # if delta2 is False and not idx:\n", - " # err = '`idx` is not a column in `data`. Please check.'\n", - " # raise IndexError(err)\n", - "\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 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 data_in.columns:\n", - " new_col_name += \"_\"\n", - " data_in[new_col_name] = data_in[x[0]].astype(str) + \" \" + data_in[experiment].astype(str)\n", - "\n", - " #create idx and record the first and second x variable \n", - " idx = []\n", - " for i in list(map(lambda x: str(x), experiment_label)):\n", - " temp = []\n", - " for j in list(map(lambda x: str(x), 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", - "\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 '\\\n", - " 'entered--{}.'.format(idx)\n", - " raise ValueError(err)\n", - "\n", - " # Having parsed the idx, check if it is a kosher paired plot,\n", - " # if so stated.\n", - " #if paired is True:\n", - " # all_idx_lengths = [len(t) for t in self.__idx]\n", - " # if (np.array(all_idx_lengths) != 2).any():\n", - " # err1 = \"`is_paired` is True, but some idx \"\n", - " # err2 = \"in {} does not consist only of two groups.\".format(idx)\n", - " # raise ValueError(err1 + err2)\n", - "\n", - " # Check if there is a typo on paired\n", - " if paired is not None:\n", - " if paired not in (\"baseline\", \"sequential\"):\n", - " err = '{} assigned for `paired` is not valid.'.format(paired)\n", - " raise ValueError(err)\n", - "\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", - " elif y is None and x is not None:\n", - " err = 'You have only specified `x`. Please also specify `y`.'\n", - " raise ValueError(err)\n", - "\n", - " # Identify the type of data that was passed in.\n", - " elif 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 data_in.columns:\n", - " err = '{0} is not a column in `data`. Please check.'.format(x)\n", - " raise IndexError(err)\n", - " if y not in data_in.columns:\n", - " err = '{0} is not a column in `data`. Please check.'.format(y)\n", - " raise IndexError(err)\n", - "\n", - " # check y is numeric.\n", - " if not np.issubdtype(data_in[y].dtype, np.number):\n", - " err = '{0} is a column in `data`, but it is not numeric.'.format(y)\n", - " raise ValueError(err)\n", - "\n", - " # check all the idx can be found in data_in[x]\n", - " for g in all_plot_groups:\n", - " if g not in data_in[x].unique():\n", - " err0 = '\"{0}\" is not a group in the column `{1}`.'.format(g, x)\n", - " err1 = \" Please check `idx` and try again.\"\n", - " raise IndexError(err0 + err1)\n", - "\n", - " # Select only rows where the value in the `x` column \n", - " # is found in `idx`.\n", - " plot_data = data_in[data_in.loc[:, x].isin(all_plot_groups)].copy()\n", - " \n", - " # plot_data.drop(\"index\", inplace=True, axis=1)\n", - "\n", - " # Assign attributes\n", - " self.__x = x\n", - " self.__y = y\n", - " self.__xvar = x\n", - " self.__yvar = y\n", - "\n", - " elif x is None and y is None:\n", - " # Assume we have a wide dataset.\n", - " # Assign attributes appropriately.\n", - " self.__x = None\n", - " self.__y = None\n", - " self.__xvar = \"group\"\n", - " self.__yvar = \"value\"\n", - "\n", - " # First, check we have all columns in the dataset.\n", - " for g in all_plot_groups:\n", - " if g not in data_in.columns:\n", - " err0 = '\"{0}\" is not a column in `data`.'.format(g)\n", - " err1 = \" Please check `idx` and try again.\"\n", - " raise IndexError(err0 + err1)\n", - " \n", - " set_all_columns = set(data_in.columns.tolist())\n", - " set_all_plot_groups = set(all_plot_groups)\n", - " id_vars = set_all_columns.difference(set_all_plot_groups)\n", - "\n", - " plot_data = pd.melt(data_in,\n", - " id_vars=id_vars,\n", - " value_vars=all_plot_groups,\n", - " value_name=self.__yvar,\n", - " var_name=self.__xvar)\n", - " \n", - " # Added in v0.2.7.\n", - " # remove any NA rows.\n", - " plot_data.dropna(axis=0, how='any', subset=[self.__yvar], inplace=True)\n", - "\n", - " \n", - " # Lines 131 to 140 added in v0.2.3.\n", - " # Fixes a bug that jammed up when the xvar column was already \n", - " # a pandas Categorical. Now we check for this and act appropriately.\n", - " if isinstance(plot_data[self.__xvar].dtype, \n", - " pd.CategoricalDtype) is True:\n", - " plot_data[self.__xvar].cat.remove_unused_categories(inplace=True)\n", - " plot_data[self.__xvar].cat.reorder_categories(all_plot_groups, \n", - " ordered=True, \n", - " inplace=True)\n", - " else:\n", - " plot_data.loc[:, self.__xvar] = pd.Categorical(plot_data[self.__xvar],\n", - " categories=all_plot_groups,\n", - " ordered=True)\n", - " \n", - " # # The line below was added in v0.2.4, removed in v0.2.5.\n", - " # plot_data.dropna(inplace=True)\n", - " \n", - " self.__plot_data = plot_data\n", - " \n", - " self.__all_plot_groups = all_plot_groups\n", - "\n", - "\n", - " # Sanity check that all idxs are paired, if so desired.\n", - " #if paired is True:\n", - " # if id_col is None:\n", - " # err = \"`id_col` must be specified if `is_paired` is set to True.\"\n", - " # raise IndexError(err)\n", - " # elif id_col not in plot_data.columns:\n", - " # err = \"{} is not a column in `data`. \".format(id_col)\n", - " # raise IndexError(err)\n", - "\n", - " # Check if `id_col` is valid\n", - " if 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", - " elif id_col not in plot_data.columns:\n", - " err = \"{} is not a column in `data`. \".format(id_col)\n", - " raise IndexError(err)\n", - "\n", - " EffectSizeDataFrame_kwargs = dict(ci=ci, is_paired=paired,\n", - " random_seed=random_seed,\n", - " resamples=resamples,\n", - " proportional=proportional, \n", - " delta2=delta2, \n", - " experiment_label=self.__experiment_label,\n", - " x1_level=self.__x1_level,\n", - " x2=self.__x2,\n", - " mini_meta = mini_meta)\n", - "\n", - " self.__mean_diff = EffectSizeDataFrame(self, \"mean_diff\",\n", - " **EffectSizeDataFrame_kwargs)\n", - "\n", - " self.__median_diff = EffectSizeDataFrame(self, \"median_diff\",\n", - " **EffectSizeDataFrame_kwargs)\n", - "\n", - " self.__cohens_d = EffectSizeDataFrame(self, \"cohens_d\",\n", - " **EffectSizeDataFrame_kwargs)\n", - "\n", - " self.__cohens_h = EffectSizeDataFrame(self, \"cohens_h\",\n", - " **EffectSizeDataFrame_kwargs) \n", - "\n", - " self.__hedges_g = EffectSizeDataFrame(self, \"hedges_g\",\n", - " **EffectSizeDataFrame_kwargs)\n", - "\n", - " if not paired:\n", - " self.__cliffs_delta = EffectSizeDataFrame(self, \"cliffs_delta\",\n", - " **EffectSizeDataFrame_kwargs)\n", - " else:\n", - " self.__cliffs_delta = \"The data is paired; Cliff's delta is therefore undefined.\"\n", - "\n", - "\n", - " def __repr__(self):\n", - " from .__init__ import __version__\n", - " import datetime as dt\n", - " import numpy as np\n", - "\n", - " from .misc_tools import print_greeting\n", - "\n", - " # Removed due to the deprecation of is_paired\n", - " #if self.__is_paired:\n", - " # es = \"Paired e\"\n", - " #else:\n", - " # es = \"E\"\n", - "\n", - " greeting_header = print_greeting()\n", - "\n", - " RM_STATUS = {'baseline' : 'for repeated measures against baseline \\n', \n", - " 'sequential': 'for the sequential design of repeated-measures experiment \\n',\n", - " 'None' : ''\n", - " }\n", - "\n", - " PAIRED_STATUS = {'baseline' : 'Paired e', \n", - " 'sequential' : 'Paired e',\n", - " 'None' : 'E'\n", - " }\n", - "\n", - " first_line = {\"rm_status\" : RM_STATUS[str(self.__is_paired)],\n", - " \"paired_status\": PAIRED_STATUS[str(self.__is_paired)]}\n", - "\n", - " s1 = \"{paired_status}ffect size(s) {rm_status}\".format(**first_line)\n", - " s2 = \"with {}% confidence intervals will be computed for:\".format(self.__ci)\n", - " desc_line = s1 + s2\n", - "\n", - " out = [greeting_header + \"\\n\\n\" + desc_line]\n", - "\n", - " comparisons = []\n", - "\n", - " if self.__is_paired == 'sequential':\n", - " for j, current_tuple in enumerate(self.__idx):\n", - " for ix, test_name in enumerate(current_tuple[1:]):\n", - " control_name = current_tuple[ix]\n", - " comparisons.append(\"{} minus {}\".format(test_name, control_name))\n", - " else:\n", - " for j, current_tuple in enumerate(self.__idx):\n", - " control_name = current_tuple[0]\n", - "\n", - " for ix, test_name in enumerate(current_tuple[1:]):\n", - " comparisons.append(\"{} minus {}\".format(test_name, control_name))\n", - "\n", - " if self.__delta2 is True:\n", - " comparisons.append(\"{} minus {} (only for mean difference)\".format(self.__experiment_label[1], self.__experiment_label[0]))\n", - " \n", - " if self.__mini_meta is True:\n", - " comparisons.append(\"weighted delta (only for mean difference)\")\n", - "\n", - " for j, g in enumerate(comparisons):\n", - " out.append(\"{}. {}\".format(j+1, g))\n", - "\n", - " resamples_line1 = \"\\n{} resamples \".format(self.__resamples)\n", - " resamples_line2 = \"will be used to generate the effect size bootstraps.\"\n", - " out.append(resamples_line1 + resamples_line2)\n", - "\n", - " return \"\\n\".join(out)\n", - "\n", - "\n", - " # def __variable_name(self):\n", - " # return [k for k,v in locals().items() if v is self]\n", - " #\n", - " # @property\n", - " # def variable_name(self):\n", - " # return self.__variable_name()\n", - " \n", - " @property\n", - " def mean_diff(self):\n", - " \"\"\"\n", - " Returns an :py:class:`EffectSizeDataFrame` for the mean difference, its confidence interval, and relevant statistics, for all comparisons as indicated via the `idx` and `paired` argument in `dabest.load()`\n", - "\n", - " \"\"\"\n", - " return self.__mean_diff\n", - " \n", - " \n", - " @property \n", - " def median_diff(self):\n", - " \"\"\"\n", - " Returns an :py:class:`EffectSizeDataFrame` for the median difference, its confidence interval, and relevant statistics, for all comparisons as indicated via the `idx` and `paired` argument in `dabest.load()`.\n", - "\n", - " \"\"\"\n", - " return self.__median_diff\n", - " \n", - " \n", - " @property\n", - " def cohens_d(self):\n", - " \"\"\"\n", - " Returns an :py:class:`EffectSizeDataFrame` for the standardized mean difference Cohen's `d`, its confidence interval, and relevant statistics, for all comparisons as indicated via the `idx` and `paired` argument in `dabest.load()`.\n", - "\n", - " \"\"\"\n", - " return self.__cohens_d\n", - " \n", - " \n", - " @property\n", - " def cohens_h(self):\n", - " \"\"\"\n", - " Returns an :py:class:`EffectSizeDataFrame` for the standardized mean difference Cohen's `h`, its confidence interval, and relevant statistics, for all comparisons as indicated via the `idx` and `directional` argument in `dabest.load()`.\n", - "\n", - " \"\"\"\n", - " return self.__cohens_h\n", - "\n", - "\n", - " @property \n", - " def hedges_g(self):\n", - " \"\"\"\n", - " Returns an :py:class:`EffectSizeDataFrame` for the standardized mean difference Hedges' `g`, its confidence interval, and relevant statistics, for all comparisons as indicated via the `idx` and `paired` argument in `dabest.load()`.\n", - "\n", - " \"\"\"\n", - " return self.__hedges_g\n", - " \n", - " \n", - " @property \n", - " def cliffs_delta(self):\n", - " \"\"\"\n", - " Returns an :py:class:`EffectSizeDataFrame` for Cliff's delta, its confidence interval, and relevant statistics, for all comparisons as indicated via the `idx` and `paired` argument in `dabest.load()`.\n", - "\n", - " \"\"\"\n", - " return self.__cliffs_delta\n", - "\n", - "\n", - " @property\n", - " def data(self):\n", - " \"\"\"\n", - " Returns the pandas DataFrame that was passed to `dabest.load()`.\n", - " When `delta2` is True, a new column is added to support the \n", - " function. The name of this new column is indicated by `x`.\n", - " \"\"\"\n", - " return self.__data\n", - "\n", - "\n", - " @property\n", - " def idx(self):\n", - " \"\"\"\n", - " Returns the order of categories that was passed to `dabest.load()`.\n", - " \"\"\"\n", - " return self.__idx\n", - " \n", - "\n", - " @property\n", - " def x1(self):\n", - " \"\"\"\n", - " Returns the first variable declared in x when it is a delta-delta\n", - " case; returns None otherwise.\n", - " \"\"\"\n", - " return self.__x1\n", - "\n", - "\n", - " @property\n", - " def x1_level(self):\n", - " \"\"\"\n", - " Returns the levels of first variable declared in x when it is a \n", - " delta-delta case; returns None otherwise.\n", - " \"\"\"\n", - " return self.__x1_level\n", - "\n", - "\n", - " @property\n", - " def x2(self):\n", - " \"\"\"\n", - " Returns the second variable declared in x when it is a delta-delta\n", - " case; returns None otherwise.\n", - " \"\"\"\n", - " return self.__x2\n", - "\n", - "\n", - " @property\n", - " def experiment(self):\n", - " \"\"\"\n", - " Returns the column name of experiment labels that was passed to \n", - " `dabest.load()` when it is a delta-delta case; returns None otherwise.\n", - " \"\"\"\n", - " return self.__experiment\n", - " \n", - "\n", - " @property\n", - " def experiment_label(self):\n", - " \"\"\"\n", - " Returns the experiment labels in order that was passed to `dabest.load()`\n", - " when it is a delta-delta case; returns None otherwise.\n", - " \"\"\"\n", - " return self.__experiment_label\n", - "\n", - "\n", - " @property\n", - " def delta2(self):\n", - " \"\"\"\n", - " Returns the boolean parameter indicating if this is a delta-delta \n", - " situation.\n", - " \"\"\"\n", - " return self.__delta2\n", - "\n", - "\n", - " @property\n", - " def is_paired(self):\n", - " \"\"\"\n", - " Returns the type of repeated-measures experiment.\n", - " \"\"\"\n", - " return self.__is_paired\n", - "\n", - "\n", - " @property\n", - " def id_col(self):\n", - " \"\"\"\n", - " Returns the id column declared to `dabest.load()`.\n", - " \"\"\"\n", - " return self.__id_col\n", - "\n", - "\n", - " @property\n", - " def ci(self):\n", - " \"\"\"\n", - " The width of the desired confidence interval.\n", - " \"\"\"\n", - " return self.__ci\n", - "\n", - "\n", - " @property\n", - " def resamples(self):\n", - " \"\"\"\n", - " The number of resamples used to generate the bootstrap.\n", - " \"\"\"\n", - " return self.__resamples\n", - "\n", - "\n", - " @property\n", - " def random_seed(self):\n", - " \"\"\"\n", - " The number used to initialise the numpy random seed generator, ie.\n", - " `seed_value` from `numpy.random.seed(seed_value)` is returned.\n", - " \"\"\"\n", - " return self.__random_seed\n", - "\n", - "\n", - " @property\n", - " def x(self):\n", - " \"\"\"\n", - " Returns the x column that was passed to `dabest.load()`, if any.\n", - " When `delta2` is True, `x` returns the name of the new column created \n", - " for the delta-delta situation. To retrieve the 2 variables passed into \n", - " `x` when `delta2` is True, please call `x1` and `x2` instead.\n", - " \"\"\"\n", - " return self.__x\n", - "\n", - "\n", - " @property\n", - " def y(self):\n", - " \"\"\"\n", - " Returns the y column that was passed to `dabest.load()`, if any.\n", - " \"\"\"\n", - " return self.__y\n", - "\n", - "\n", - " @property\n", - " def _xvar(self):\n", - " \"\"\"\n", - " Returns the xvar in dabest.plot_data.\n", - " \"\"\"\n", - " return self.__xvar\n", - "\n", - "\n", - " @property\n", - " def _yvar(self):\n", - " \"\"\"\n", - " Returns the yvar in dabest.plot_data.\n", - " \"\"\"\n", - " return self.__yvar\n", - "\n", - "\n", - " @property\n", - " def _plot_data(self):\n", - " \"\"\"\n", - " Returns the pandas DataFrame used to produce the estimation stats/plots.\n", - " \"\"\"\n", - " return self.__plot_data\n", - "\n", - " \n", - " @property\n", - " def proportional(self):\n", - " \"\"\"\n", - " Returns the proportional parameter class.\n", - " \"\"\"\n", - " return self.__proportional\n", - "\n", - " \n", - " @property\n", - " def mini_meta(self):\n", - " \"\"\"\n", - " Returns the mini_meta boolean parameter.\n", - " \"\"\"\n", - " return self.__mini_meta\n", - "\n", - "\n", - " @property\n", - " def _all_plot_groups(self):\n", - " \"\"\"\n", - " Returns the all plot groups, as indicated via the `idx` keyword.\n", - " \"\"\"\n", - " return self.__all_plot_groups" - ] - }, - { - "cell_type": "markdown", - "id": "c86c0487", - "metadata": {}, - "source": [ - "#### Example: mean_diff" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6d07d58b", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "DABEST v2023.2.14\n", - "=================\n", - " \n", - "Good evening!\n", - "The current time is Fri Mar 31 19:41:17 2023.\n", - "\n", - "The unpaired mean difference between control and test is 0.5 [95%CI -0.0412, 1.0].\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", - "Any p-value reported is the probability of observing theeffect size (or greater),\n", - "assuming the null hypothesis ofzero 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": [ - "control = norm.rvs(loc=0, size=30, random_state=12345)\n", - "test = norm.rvs(loc=0.5, size=30, random_state=12345)\n", - "my_df = pd.DataFrame({\"control\": control,\n", - " \"test\": test})\n", - "my_dabest_object = dabest.load(my_df, idx=(\"control\", \"test\"))\n", - "my_dabest_object.mean_diff" - ] - }, - { - "cell_type": "markdown", - "id": "cf5ca0a0", - "metadata": {}, - "source": [ - "This is simply the mean of the control group subtracted from\n", - "the mean of the test group.\n", - "\n", - "$$\\text{Mean difference} = \\overline{x}_{Test} - \\overline{x}_{Control}$$\n", - "\n", - "where $\\overline{x}$ is the mean for the group $x$." - ] - }, - { - "cell_type": "markdown", - "id": "8b3b146c", - "metadata": {}, - "source": [ - "#### Example: median_diff" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8e9b8635", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "c:\\users\\zhang\\desktop\\vnbdev-dabest\\dabest-python\\dabest\\effsize.py:72: 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='percentile'`. For detailed information, refer to https://github.com/ACCLAB/DABEST-python/issues/129 \n", - "\n", - " return func_difference(control, test, np.median, is_paired)\n" - ] - }, - { - "data": { - "text/plain": [ - "DABEST v2023.2.14\n", - "=================\n", - " \n", - "Good afternoon!\n", - "The current time is Thu Mar 30 17:07:33 2023.\n", - "\n", - "The unpaired median difference between control and test is 0.5 [95%CI -0.0758, 0.991].\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", - "Any p-value reported is the probability of observing theeffect size (or greater),\n", - "assuming the null hypothesis ofzero 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 `.median_diff.statistical_tests`" - ] - }, - "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", - "my_df = pd.DataFrame({\"control\": control,\n", - " \"test\": test})\n", - "my_dabest_object = dabest.load(my_df, idx=(\"control\", \"test\"))\n", - "my_dabest_object.median_diff" - ] - }, - { - "cell_type": "markdown", - "id": "838b2978", - "metadata": {}, - "source": [ - "\n", - "This is the median difference between the control group and the test group.\n", - "\n", - "If the comparison(s) are unpaired, median_diff is computed with the following equation:\n", - "\n", - "\n", - "$$\\text{Median difference} = \\widetilde{x}_{Test} - \\widetilde{x}_{Control}$$\n", - "\n", - "where $\\widetilde{x}$ is the median for the group $x$.\n", - "\n", - "If the comparison(s) are paired, median_diff is computed with the following equation:\n", - "\n", - "$$\\text{Median difference} = \\widetilde{x}_{Test - Control}$$\n", - " \n", - "\n", - "##### Things to note\n", - "\n", - "Using median difference as the statistic in bootstrapping may result in a biased estimate and cause problems with BCa confidence intervals. Consider using mean difference instead. \n", - "\n", - "When plotting, consider using percentile confidence intervals instead of BCa confidence intervals by specifying `ci_type = 'percentile'` in .plot(). \n", - "\n", - "For detailed information, please refer to [Issue 129](https://github.com/ACCLAB/DABEST-python/issues/129). \n" - ] - }, - { - "cell_type": "markdown", - "id": "a5324d21", - "metadata": {}, - "source": [ - "#### Example: cohens_d" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "748b5c60", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "DABEST v2023.2.14\n", - "=================\n", - " \n", - "Good afternoon!\n", - "The current time is Thu Mar 30 17:07:39 2023.\n", - "\n", - "The unpaired Cohen's d between control and test is 0.471 [95%CI -0.0843, 0.976].\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", - "Any p-value reported is the probability of observing theeffect size (or greater),\n", - "assuming the null hypothesis ofzero 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_d.statistical_tests`" - ] - }, - "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", - "my_df = pd.DataFrame({\"control\": control,\n", - " \"test\": test})\n", - "my_dabest_object = dabest.load(my_df, idx=(\"control\", \"test\"))\n", - "my_dabest_object.cohens_d" - ] - }, - { - "cell_type": "markdown", - "id": "6f66579c", - "metadata": {}, - "source": [ - "\n", - "Cohen's `d` is simply the mean of the control group subtracted from\n", - "the mean of the test group.\n", - "\n", - "If `paired` is None, then the comparison(s) are unpaired; \n", - "otherwise the comparison(s) are paired.\n", - "\n", - "If the comparison(s) are unpaired, Cohen's `d` is computed with the following equation:\n", - "\n", - "\n", - "$$d = \\frac{\\overline{x}_{Test} - \\overline{x}_{Control}} {\\text{pooled standard deviation}}$$\n", - "\n", - "\n", - "For paired comparisons, Cohen's d is given by\n", - "\n", - "$$d = \\frac{\\overline{x}_{Test} - \\overline{x}_{Control}} {\\text{average standard deviation}}$$\n", - "\n", - "where $\\overline{x}$ is the mean of the respective group of observations, ${Var}_{x}$ denotes the variance of that group,\n", - "\n", - "\n", - "$$\\text{pooled standard deviation} = \\sqrt{ \\frac{(n_{control} - 1) * {Var}_{control} + (n_{test} - 1) * {Var}_{test} } {n_{control} + n_{test} - 2} }$$\n", - "\n", - "and\n", - "\n", - "\n", - "$$\\text{average standard deviation} = \\sqrt{ \\frac{{Var}_{control} + {Var}_{test}} {2}}$$\n", - "\n", - "The sample variance (and standard deviation) uses N-1 degrees of freedoms.\n", - "This is an application of [Bessel's correction](https://en.wikipedia.org/wiki/Bessel%27s_correction), and yields the unbiased sample variance.\n", - "\n", - "References:\n", - "\n", - "\n", - " \n", - "\n", - " \n", - "" - ] - }, - { - "cell_type": "markdown", - "id": "40f4eff9", - "metadata": {}, - "source": [ - "#### Example: cohens_h" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f713781c", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "DABEST v2023.2.14\n", - "=================\n", - " \n", - "Good evening!\n", - "The current time is Mon Mar 27 00:48:59 2023.\n", - "\n", - "The unpaired Cohen's h between control and test is 0.0 [95%CI -0.613, 0.429].\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", - "Any p-value reported is the probability of observing theeffect size (or greater),\n", - "assuming the null hypothesis ofzero 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": [ - "control = randint.rvs(0, 2, size=30, random_state=12345)\n", - "test = randint.rvs(0, 2, size=30, random_state=12345)\n", - "my_df = pd.DataFrame({\"control\": control,\n", - " \"test\": test})\n", - "my_dabest_object = dabest.load(my_df, idx=(\"control\", \"test\"))\n", - "my_dabest_object.cohens_h" - ] - }, - { - "cell_type": "markdown", - "id": "9e3e57bd", - "metadata": {}, - "source": [ - "Cohen's *h* uses the information of proportion in the control and test groups to calculate the distance between two proportions.\n", - "\n", - "It can be used to describe the difference between two proportions as \"small\", \"medium\", or \"large\".\n", - "\n", - "It can be used to determine if the difference between two proportions is \"meaningful\".\n", - "\n", - "A directional Cohen's *h* is computed with the following equation:\n", - "\n", - "\n", - "$$h = 2 * \\arcsin{\\sqrt{proportion_{Test}}} - 2 * \\arcsin{\\sqrt{proportion_{Control}}}$$\n", - "\n", - "For a non-directional Cohen's *h*, the equation is:\n", - "\n", - "$$h = |2 * \\arcsin{\\sqrt{proportion_{Test}}} - 2 * \\arcsin{\\sqrt{proportion_{Control}}}|$$\n", - "\n", - "References:\n", - "\n", - "" - ] - }, - { - "cell_type": "markdown", - "id": "970fb3b2", - "metadata": {}, - "source": [ - "#### Example: hedges_g" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "26960f9e", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "DABEST v2023.2.14\n", - "=================\n", - " \n", - "Good evening!\n", - "The current time is Mon Mar 27 00:50:18 2023.\n", - "\n", - "The unpaired Hedges' g between control and test is 0.465 [95%CI -0.0832, 0.963].\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", - "Any p-value reported is the probability of observing theeffect size (or greater),\n", - "assuming the null hypothesis ofzero 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": [ - "control = norm.rvs(loc=0, size=30, random_state=12345)\n", - "test = norm.rvs(loc=0.5, size=30, random_state=12345)\n", - "my_df = pd.DataFrame({\"control\": control,\n", - " \"test\": test})\n", - "my_dabest_object = dabest.load(my_df, idx=(\"control\", \"test\"))\n", - "my_dabest_object.hedges_g" - ] - }, - { - "cell_type": "markdown", - "id": "66c8a83a", - "metadata": {}, - "source": [ - "Hedges' `g` is `cohens_d` corrected for bias via multiplication with the following correction factor:\n", - " \n", - "$$\\frac{ \\Gamma( \\frac{a} {2} )} {\\sqrt{ \\frac{a} {2} } \\times \\Gamma( \\frac{a - 1} {2} )}$$\n", - "\n", - "where\n", - "\n", - "$$a = {n}_{control} + {n}_{test} - 2$$\n", - "\n", - "and $\\Gamma(x)$ is the [Gamma function](https://en.wikipedia.org/wiki/Gamma_function).\n", - "\n", - "\n", - "\n", - "References:\n", - "\n", - "\n", - " \n", - "" - ] - }, - { - "cell_type": "markdown", - "id": "b1cf0080", - "metadata": {}, - "source": [ - "#### Example: cliffs_delta" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "dce86c76", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "DABEST v2023.2.14\n", - "=================\n", - " \n", - "Good evening!\n", - "The current time is Mon Mar 27 00:53:30 2023.\n", - "\n", - "The unpaired Cliff's delta between control and test is 0.28 [95%CI -0.0244, 0.533].\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", - "Any p-value reported is the probability of observing theeffect size (or greater),\n", - "assuming the null hypothesis ofzero 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 `.cliffs_delta.statistical_tests`" - ] - }, - "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", - "my_df = pd.DataFrame({\"control\": control,\n", - " \"test\": test})\n", - "my_dabest_object = dabest.load(my_df, idx=(\"control\", \"test\"))\n", - "my_dabest_object.cliffs_delta" - ] - }, - { - "cell_type": "markdown", - "id": "9661ab37", - "metadata": {}, - "source": [ - "Cliff's delta is a measure of ordinal dominance, ie. how often the values from the test sample are larger than values from the control sample.\n", - "\n", - "$$\\text{Cliff's delta} = \\frac{\\#({x}_{test} > {x}_{control}) - \\#({x}_{test} < {x}_{control})} {{n}_{Test} \\times {n}_{Control}}$$\n", - " \n", - " \n", - "where $\\#$ denotes the number of times a value from the test sample exceeds (or is lesser than) values in the control sample. \n", - " \n", - "Cliff's delta ranges from -1 to 1; it can also be thought of as a measure of the degree of overlap between the two samples. An attractive aspect of this effect size is that it does not make an assumptions about the underlying distributions that the samples were drawn from. \n", - "\n", - "References:\n", - "\n", - "\n", - " \n", - "" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "87f50106", - "metadata": {}, - "outputs": [], - "source": [ - "#| export\n", - "class DeltaDelta(object):\n", - " \"\"\"\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", - " $$\\Delta_{1} = \\overline{X}_{A_{2}, B_{1}} - \\overline{X}_{A_{1}, B_{1}}$$\n", - "\n", - " $$\\Delta_{2} = \\overline{X}_{A_{2}, B_{2}} - \\overline{X}_{A_{1}, B_{2}}$$\n", - "\n", - "\n", - " where $\\overline{X}_{A_{i}, B_{j}}$ is the mean of the sample with A = i and B = j, $\\Delta$ is the mean difference between two samples. \n", - "\n", - " A delta-delta value is then calculated as the mean difference between the two primary deltas:\n", - "\n", - "\n", - " $$\\Delta_{\\Delta} = \\Delta_{2} - \\Delta_{1}$$\n", - "\n", - " \"\"\"\n", - " \n", - " def __init__(self, effectsizedataframe, permutation_count,\n", - " ci=95):\n", - "\n", - " import numpy as np\n", - " from numpy import sort as npsort\n", - " from numpy import sqrt, isinf, isnan\n", - " from ._stats_tools import effsize as es\n", - " from ._stats_tools import confint_1group as ci1g\n", - " from ._stats_tools import confint_2group_diff as ci2g\n", - "\n", - "\n", - " from string import Template\n", - " import warnings\n", - " \n", - " self.__effsizedf = effectsizedataframe.results\n", - " self.__dabest_obj = effectsizedataframe.dabest_obj\n", - " self.__ci = ci\n", - " self.__resamples = effectsizedataframe.resamples\n", - " self.__alpha = ci2g._compute_alpha_from_ci(ci)\n", - " self.__permutation_count = permutation_count\n", - " self.__bootstraps = np.array(self.__effsizedf[\"bootstraps\"])\n", - " self.__control = self.__dabest_obj.experiment_label[0]\n", - " self.__test = self.__dabest_obj.experiment_label[1]\n", - "\n", - "\n", - " # Compute the bootstrap delta-delta and the true dela-delta based on \n", - " # the raw data \n", - " self.__bootstraps_delta_delta = self.__bootstraps[1] - self.__bootstraps[0]\n", - "\n", - " self.__difference = self.__effsizedf[\"difference\"][1] - self.__effsizedf[\"difference\"][0]\n", - "\n", - "\n", - "\n", - " sorted_delta_delta = npsort(self.__bootstraps_delta_delta)\n", - "\n", - " self.__bias_correction = ci2g.compute_meandiff_bias_correction(\n", - " self.__bootstraps_delta_delta, self.__difference)\n", - " \n", - " self.__jackknives = np.array(ci1g.compute_1group_jackknife(\n", - " self.__bootstraps_delta_delta, \n", - " np.mean))\n", - "\n", - " self.__acceleration_value = ci2g._calc_accel(self.__jackknives)\n", - "\n", - " # Compute BCa intervals.\n", - " bca_idx_low, bca_idx_high = ci2g.compute_interval_limits(\n", - " self.__bias_correction, self.__acceleration_value,\n", - " self.__resamples, ci)\n", - " \n", - " self.__bca_interval_idx = (bca_idx_low, bca_idx_high)\n", - "\n", - " if ~isnan(bca_idx_low) and ~isnan(bca_idx_high):\n", - " self.__bca_low = sorted_delta_delta[bca_idx_low]\n", - " self.__bca_high = sorted_delta_delta[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 should be considered unstable.\"\n", - " err_temp = Template(\" \".join([err1, err2, err3]))\n", - "\n", - " if bca_idx_low <= 10:\n", - " warnings.warn(err_temp.substitute(lim_type=\"lower\",\n", - " loc=\"bottom\"),\n", - " stacklevel=1)\n", - "\n", - " if bca_idx_high >= self.__resamples-9:\n", - " warnings.warn(err_temp.substitute(lim_type=\"upper\",\n", - " loc=\"top\"),\n", - " stacklevel=1)\n", - "\n", - " else:\n", - " err1 = \"The $lim_type limit of the BCa interval 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.__bca_low = self.__difference\n", - " warnings.warn(err_temp.substitute(lim_type=\"lower\"),\n", - " stacklevel=0)\n", - "\n", - " if isnan(bca_idx_high):\n", - " self.__bca_high = self.__difference\n", - " warnings.warn(err_temp.substitute(lim_type=\"upper\"),\n", - " 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.__pct_interval_idx = (pct_idx_low, pct_idx_high)\n", - " self.__pct_low = sorted_delta_delta[pct_idx_low]\n", - " self.__pct_high = sorted_delta_delta[pct_idx_high]\n", - " \n", - " \n", - "\n", - " def __permutation_test(self):\n", - " \"\"\"\n", - " Perform a permutation test and obtain the permutation p-value\n", - " based on the permutation data.\n", - " \"\"\"\n", - " import numpy as np\n", - " self.__permutations = np.array(self.__effsizedf[\"permutations\"])\n", - "\n", - " THRESHOLD = np.abs(self.__difference)\n", - "\n", - " self.__permutations_delta_delta = np.array(self.__permutations[1]-self.__permutations[0])\n", - "\n", - " count = sum(np.abs(self.__permutations_delta_delta)>THRESHOLD)\n", - " self.__pvalue_permutation = count/self.__permutation_count\n", - "\n", - "\n", - "\n", - " def __repr__(self, header=True, sigfig=3):\n", - " from .__init__ import __version__\n", - " import datetime as dt\n", - " import numpy as np\n", - "\n", - " from .misc_tools import print_greeting\n", - "\n", - " first_line = {\"control\" : self.__control,\n", - " \"test\" : self.__test}\n", - " \n", - " out1 = \"The delta-delta between {control} and {test} \".format(**first_line)\n", - " \n", - " base_string_fmt = \"{:.\" + str(sigfig) + \"}\"\n", - " if \".\" in str(self.__ci):\n", - " ci_width = base_string_fmt.format(self.__ci)\n", - " else:\n", - " ci_width = str(self.__ci)\n", - " \n", - " ci_out = {\"es\" : base_string_fmt.format(self.__difference),\n", - " \"ci\" : ci_width,\n", - " \"bca_low\" : base_string_fmt.format(self.__bca_low),\n", - " \"bca_high\" : base_string_fmt.format(self.__bca_high)}\n", - " \n", - " out2 = \"is {es} [{ci}%CI {bca_low}, {bca_high}].\".format(**ci_out)\n", - " out = out1 + out2\n", - "\n", - " if header is True:\n", - " out = print_greeting() + \"\\n\" + \"\\n\" + out\n", - "\n", - "\n", - " pval_rounded = base_string_fmt.format(self.pvalue_permutation)\n", - "\n", - " \n", - " p1 = \"The p-value of the two-sided permutation t-test is {}, \".format(pval_rounded)\n", - " p2 = \"calculated for legacy purposes only. \"\n", - " pvalue = p1 + p2\n", - "\n", - "\n", - " bs1 = \"{} bootstrap samples were taken; \".format(self.__resamples)\n", - " bs2 = \"the confidence interval is bias-corrected and accelerated.\"\n", - " bs = bs1 + bs2\n", - "\n", - " pval_def1 = \"Any p-value reported is the probability of observing the \" + \\\n", - " \"effect size (or greater),\\nassuming the null hypothesis of \" + \\\n", - " \"zero difference is true.\"\n", - " pval_def2 = \"\\nFor each p-value, 5000 reshuffles of the \" + \\\n", - " \"control and test labels were performed.\"\n", - " pval_def = pval_def1 + pval_def2\n", - "\n", - "\n", - " return \"{}\\n{}\\n\\n{}\\n{}\".format(out, pvalue, bs, pval_def)\n", - "\n", - "\n", - " def to_dict(self):\n", - " \"\"\"\n", - " Returns the attributes of the `DeltaDelta` object as a\n", - " dictionary.\n", - " \"\"\"\n", - " # Only get public (user-facing) attributes.\n", - " attrs = [a for a in dir(self)\n", - " if not a.startswith((\"_\", \"to_dict\"))]\n", - " out = {}\n", - " for a in attrs:\n", - " out[a] = getattr(self, a)\n", - " return out\n", - "\n", - "\n", - " @property\n", - " def ci(self):\n", - " \"\"\"\n", - " Returns the width of the confidence interval, in percent.\n", - " \"\"\"\n", - " return self.__ci\n", - "\n", - "\n", - " @property\n", - " def alpha(self):\n", - " \"\"\"\n", - " Returns the significance level of the statistical test as a float\n", - " between 0 and 1.\n", - " \"\"\"\n", - " return self.__alpha\n", - "\n", - "\n", - " @property\n", - " def bias_correction(self):\n", - " return self.__bias_correction\n", - "\n", - "\n", - " @property\n", - " def bootstraps(self):\n", - " '''\n", - " Return the bootstrapped deltas from all the experiment groups.\n", - " '''\n", - " return self.__bootstraps\n", - "\n", - "\n", - " @property\n", - " def jackknives(self):\n", - " return self.__jackknives\n", - "\n", - "\n", - " @property\n", - " def acceleration_value(self):\n", - " return self.__acceleration_value\n", - "\n", - "\n", - " @property\n", - " def bca_low(self):\n", - " \"\"\"\n", - " The bias-corrected and accelerated confidence interval lower limit.\n", - " \"\"\"\n", - " return self.__bca_low\n", - "\n", - "\n", - " @property\n", - " def bca_high(self):\n", - " \"\"\"\n", - " The bias-corrected and accelerated confidence interval upper limit.\n", - " \"\"\"\n", - " return self.__bca_high\n", - "\n", - "\n", - " @property\n", - " def bca_interval_idx(self):\n", - " return self.__bca_interval_idx\n", - "\n", - "\n", - " @property\n", - " def control(self):\n", - " '''\n", - " Return the name of the control experiment group.\n", - " '''\n", - " return self.__control\n", - "\n", - "\n", - " @property\n", - " def test(self):\n", - " '''\n", - " Return the name of the test experiment group.\n", - " '''\n", - " return self.__test\n", - "\n", - "\n", - " @property\n", - " def bootstraps_delta_delta(self):\n", - " '''\n", - " Return the delta-delta values calculated from the bootstrapped \n", - " deltas.\n", - " '''\n", - " return self.__bootstraps_delta_delta\n", - "\n", - "\n", - " @property\n", - " def difference(self):\n", - " '''\n", - " Return the delta-delta value calculated based on the raw data.\n", - " '''\n", - " return self.__difference\n", - "\n", - "\n", - " @property\n", - " def pct_interval_idx (self):\n", - " return self.__pct_interval_idx \n", - "\n", - "\n", - " @property\n", - " def pct_low(self):\n", - " \"\"\"\n", - " The percentile confidence interval lower limit.\n", - " \"\"\"\n", - " return self.__pct_low\n", - "\n", - "\n", - " @property\n", - " def pct_high(self):\n", - " \"\"\"\n", - " The percentile confidence interval lower limit.\n", - " \"\"\"\n", - " return self.__pct_high\n", - "\n", - "\n", - " @property\n", - " def pvalue_permutation(self):\n", - " try:\n", - " return self.__pvalue_permutation\n", - " except AttributeError:\n", - " self.__permutation_test()\n", - " return self.__pvalue_permutation\n", - " \n", - "\n", - " @property\n", - " def permutation_count(self):\n", - " \"\"\"\n", - " The number of permuations taken.\n", - " \"\"\"\n", - " return self.__permutation_count\n", - "\n", - " \n", - " @property\n", - " def permutations(self):\n", - " '''\n", - " Return the mean differences of permutations obtained during\n", - " the permutation test for each experiment group.\n", - " '''\n", - " try:\n", - " return self.__permutations\n", - " except AttributeError:\n", - " self.__permutation_test()\n", - " return self.__permutations\n", - "\n", - " \n", - " @property\n", - " def permutations_delta_delta(self):\n", - " '''\n", - " Return the delta-delta values of permutations obtained \n", - " during the permutation test.\n", - " '''\n", - " try:\n", - " return self.__permutations_delta_delta\n", - " except AttributeError:\n", - " self.__permutation_test()\n", - " return self.__permutations_delta_delta\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "id": "c6a7192f", - "metadata": {}, - "source": [ - "\n", - "\n", - "and the standard deviation of the delta-delta value is calculated from a pooled variance of the 4 samples:\n", - "\n", - "\n", - "$$s_{\\Delta_{\\Delta}} = \\sqrt{\\frac{(n_{A_{2}, B_{1}}-1)s_{A_{2}, B_{1}}^2+(n_{A_{1}, B_{1}}-1)s_{A_{1}, B_{1}}^2+(n_{A_{2}, B_{2}}-1)s_{A_{2}, B_{2}}^2+(n_{A_{1}, B_{2}}-1)s_{A_{1}, B_{2}}^2}{(n_{A_{2}, B_{1}} - 1) + (n_{A_{1}, B_{1}} - 1) + (n_{A_{2}, B_{2}} - 1) + (n_{A_{1}, B_{2}} - 1)}}$$\n", - "\n", - "where $s$ is the standard deviation and $n$ is the sample size." - ] - }, - { - "cell_type": "markdown", - "id": "a5905b79", - "metadata": {}, - "source": [ - "#### Example: delta-delta" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "088f734b", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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RXTh8jqiVcgqKRuj9T8AlvGuD7aruMfECAIiiqK9YRERERCbHaIuiJUuWQBAEPP/881JHIbqnmsoyXDu0CWd/XYaLf3ypufenRlmO7NStuLjjK1w9sBHV5UVa21WXFUGQNdyha+PmD1v3wAbXN9RLFBsbC39/f8TGxurwjoiIiIhMh1EOnzt06BC+/PJLdOzYUeooRPdUmn0OJ354EzXlxZpl1w7+Crd2fXAzI11rCu5LCd+gzegX4eDTBhf/+AIF5w//714hAUDdvT0+XYcDEHD65yV1tgns/wgEQag3X05ODq5evdrEd0dERERk/Iyup6i0tBSPPvooVq5cCRcXF6njEDVIVKtwav27WgXRbfmnU2o9k0hdU4WzGz/A0dVzUHDu4B2TJ9RdEPl2HwPHwA6ovJkDp5AYWNg4atZZu/ii7di58IiK09v7ISIiIjJFRtdT9NRTT2HEiBG47777sGjRIqnjEDWo8EIqlEW5Om0jqlWoLiusd71r2x6wdvGBZ4d4KItv4PAn06Guqfq7gcwCwYOmw6/7mAZ7iIiIiIjoFqMqitauXYu0tDQcOnSoUe2VSiWUSqXmdWlpqaGiEdWpsjBH7/t08I1AQN+JUJbk4+jqORBV1doN1DW4tHMV3CN6w9rZU+/HJyIiIjI1RjN87vLly3juuefw3//+F9bW1o3aZsmSJXByctL8xMVxGBG1LIUBipKy3ExcP/onru7/pXZB9D+iugbX07fr/dhEREREpkgQjWSu3o0bN+KBBx6AXC7XLFOpVBAEATKZDEqlUmsdULunKD09HXFxcUhNTUWXLl1aLLuhqdRqHPgrA6ezcmBnrUB8l7bwdHG894ZkcKJahUOfzGjUtNn65h7ZD+3GvXLPdv7+/rh69Sr8/Pxw5cqVFkhGRERE1LoYzfC5QYMG4fjx41rLpk+fjnbt2mHevHm1CiIAUCgUUCgUmtf29vYGz9nSrhcU47UvNyLreoFm2ddbUzBlSE88en8PCZMRAAgyOdqNfxV/rX0TqkrtSRUcA6JQfPkk7p5Ewa/HOOSdTtb5XqS7KRzdm7U9ERERkbkwmqLIwcEBHTp00FpmZ2cHNze3WsvNydurf9MqiABArRax+vd9CPFxR+/oMImS0W2O/u0QO3slrqfvQGnOeVhY28MzeiAcAyJRmnMBOalbUZpzAdXlxaipLMWNk0lwDolBdVkRbmYcgahWAYLsjpnoGkOAV6f7DfaeiMydsugG8s8dgKiqhnNwDOy8QqSOREREzWA0RRHVdjLzGs5evl7v+o170lkUtRKWtk7w7/1greX23mFwj+qP3OO7NDPIqZRlyD22E1b2rug0/V+QWSmQ9vn/6XA0ASGD/wFbj/of6EpETZfx539w9cBGrS8qXNv2RMQDcyG3bNw9r0RE1LoYdVGUmJgodQRJZeYU3GN9fgsloea4uG2F9pTa/1NVWoCrBzYgYuxcyCytoa6urHcfbu36QF2jhLWTF7w6D4G9N4thIkPIPrwFV/dvqLW84Ox+XNz+JdqMfFaCVERE1FxGXRSZO1cH24bXO9q1UBJqqtLrF1Gel1Xv+rxTyfDsNBiOgVG4eSG1zjbWLr5oN/5VPpOIqAVcPfBrvetyj+9C8MCpsLR1asFERESkD0YzJTfV1q1dMNyc6i98hnSPbME01BQqZXmD60VVDf5a83q9BZEgt0TY0CdZEBG1gBplOSoLr9W7XlRVo/zG5RZMRERE+sKiyIjJ5TK8/MgQWFvV7vDr1i4Io/p0lCAV6cLOMwQyS8W9G95BbmUDSztnuEf2R6dpH8IlzHSmlydqzeSWinuerxa2Di2UhoiI9IlFkZHr0jYQX8ydjPEDuiAi0At+7s7w93BGVY0KW/YeR4Wy7od7UutgYW0H7y7DdNpGVKvQZdYKtBs3D/Y+4QZKRkR3E2RyeETV/xBwe59w2HkEtWAiIiLSFxZFJsDX3RlDu0fhekExrubdxJUbN3H0/BV8tiERz//7R5SU13+DPkkveOB0eHceCkFW+1lbdVHXVKHkyikDpyKiugQNeAzWLr61lssVdggb9pQEiYiISB840YKJ+Ne6P3GztKLW8ovX8vDN7/vw9Ph4CVJRY8jkFggf8QwC+k1C0aVjEOSWOLvpXxBrlPVvY6HbkDsi0g8re1d0mrEM2Yd/Q/6ZvaipKIXCyRNu7fvAzjNY6nhERNRE7CkyAZdzC3AyM7ve9TsOn4JKrcuDP0kKCkd3eEYPhEdkP3hE9q23naW9CxwDo1owGRHdydLGAZ7RAwEAyqLrKM46joztK3Dw46nIP7tf4nRERNQULIpMQEFxwzOYlVdWQVlV00JpSB8C+02qZ1pfASEDZ0AmZycvkVREUY2TPy5EWc4FreU1FcU4/fMSlN24JFEyIiJqKhZFJiDA0wVyWf0fpZeLA2wUli2YiJrL2sUHHacthUf0QMgsrAAIcAyIQuTDC+HZcaDU8YjMWuGFVJTXU/iIqhpkH9rcwomIiKi5+HWzCXB1tEP/mDZISDtT5/ox/WL4HBsjZOPqg4gxLwFjXoIoqiEI/A6DqDUovXau4fXZDa8nIqLWh0WRiXjuwYHILyrFsQtXtZYP69kB4+P4HBtjJwgyqFXVyDuZjJsZRyDILOAW0RMu4bEslohamIW1fbPWExFR68OiyETY2Siw9OkJOHbhCtLOXoalXIa+HcMR5O0mdTTSg6rSApz47+soz8vSLLuevh1OwZ0QOXEB5Do+AJaIms49sh8ydv4HoqruezU9ojnElYjI2LAoMjEdw/zRMcxf6hikZ+e3fqZVEN1WlHkUWbvXIGTQDAlSEZknK3sXhAyaiYt/fFFrnUtYLDw7DGj5UERE1CwsiohaOWVxHgrOHax3/fX0HQiOn9roh78SUfP5dh8NW49AXDu0CWXXM2Fp5wSvTvfBK2YIz0UiIiPEooiolVMW5QJi/c+ZqqkoRo2yHJY2Di2YioicQ2LgHBIjdQwiItIDFkVErUBR1l+4fnQHqkryYeseAO8uw2DrHgAAUDh5AoKs3sLIwsYBFgrbloxLREREZFJYFBFJLHPXN7iyd53m9c2Lacg+vAVtRr0Aj6j+qLx5HfY+bVB6re4p1706DW7WcB1vb2+t/xIRERGZGxZFRBIqunRcqyC6TVSrcG7zv5Dx59eoLiv431IBgKjVzjEwGoFxjzYrw+HDh5u1PREREZGxM1hRdOXKFfj7cxY0oobkpP9R7zpRrbqjIAJuF0Q2bv5w8IuAW0QvuLbpzpu6iSRSnpeF7MO/oSz3EqzsnODZ6T64hneTOhYRETWBwYqiDh064JNPPsFjjz1mqEMQGb2qknydt1EW3UDHaR9yYgUiCeWd3IMzGz+EqP77WUV5p5LhFXM/2ox8TsJkRETUFDJD7fjdd9/FU089hfHjxyM/X/cLPyJzYOPqp/M26holSq6cMkAaImqMmsoynN38kVZBdNv19D+Qf2afBKmIiKg5DFYUzZ49G0ePHkVhYSGioqKwadMmQx2KyGj5dB1+a2Y5HcksFAZIQ0SNkXdyN9TVlfWub2hYLBERtU4GnWghJCQEu3btwqefforx48ejffv2sLDQPmRaWpohIxC1anZeIQgf/jQu/P4ZRLVKs1yQW0BU1f4WGgAs7V3gGBjVUhGJ6C5VpYUNr2/CsFgiIpKWwWefu3TpEn7++We4urpizJgxtYoiInPn3XkIXMJjkXtsp+Y5RQ7+kTjx/euoKS++q7WAkIEzIJPzPCKSis3/niFWH1v3wBZKQkRE+mLQK6uVK1fipZdewn333YcTJ07Aw8PDkIcjMloKBzcE9HlIa1mnaUuRtft75J9OgbqmCo4BUfDvM4GzWxFJzC2iF6wc3FFVklfHWgE+sSNbPBMRETWPwYqioUOH4uDBg/j0008xZcoUQx2GyGTZuPoiYuwciOJLgKjm1NsmoLKqGnuOnkdBcRkCvVzRPTIYcpnBbu0kA5HJLRA58U2cXLsQVaV/T5svyOQIHfJ/cPRvJ2E6IiJqCoMVRSqVCseOHeOzioiaSRAEQGBBZOz2nbiI97/fjtIKpWaZt6sj3v7HaIT4uEuYjJrC3jsMsU//B3knk1GWmwFLO2d4doiHlYOr1NGIiKgJBFEURalDtJS0tDR07doVqamp6NKli9RxmiXtbBY2pxzDtbyb8HRxxPCeHdCrQ6jUsUjPRFFE2fWLUNdUwc4rBHJLa6kjURNcuVGIJ97/L6prVLXWeTjbY/Xr02DF+y2JiIgkw9/CRmjNHwew+ve/n4Nx8Voe9v91EeP6d8aTD8RJmIz0qeDcQWTs+AoVBVcBAHJrO/h2G4PA/o/c6j0io7E5+VidBREA3LhZit3p53BfbPsWTkVERES3cTC7kcm6XqBVEN1pw+4jOHHxmsGOHRsbC39/f8TGxhrsGHRLUdZfOPXTIk1BBACqyjJc3vM9LiV+K2EyaooL1240vP5qw+uJiIjIsNhTZGR2HDrZ4PrvdxxEmJ87LORy9OsUjlBf/c34l5OTg6tXr967ITXblZQftZ5bdKdrBzfBv9eDsLC2a+FU1FSOdjYNrne6x3qihsTGxiInJwfe3t44fPiw1HGIiIwSiyIjc7O0osH1h05n4tDpTADAf/84gMHd2mPOw/dDJuNwK2NyMyO93nXq6koUXznJqbmNyP3d2mPP0XN1rpPJBAzsytnKqOn4hRURUfNx+JyRCdOx52fHoVP4KSHVQGnIUARZw99XyO6xnlqXHpEhGFzPPUNPjOoHTxeHFk5EREREd2JRZGQGd2sPB1vdZiD7NfkozGiSQZPgFtGr3nWWtk5wDOzQgmmouQRBwNxH7sdrjw1Dl7aBCPJyRb+O4fjwqfEYP8C4Z8IkIiIyBfy62cjY2Siw+IkxWPj1FhQUlzVqmxs3S1ChrIattZWB05G+BPR7GAUXDkFVWfszDhrwGGQWlhKkouYQBAHxXSIQ3yVC6ihERER0FxZFRqh9kA/+O38GUo6fx9W8IljKZFi5Jbne9nbWVlBY8aM2JrbuAeg49QNcSvwOBWcPAKIadt5hCOg9Ae6R/aSOR0RERGRSeKVspCwt5BjQ+e9vnPf+dRF/ZdQ9HffgbpGQyzhS0tjYeQQhcsIbUNdUQa2qgYXCVupIRERERCaJV8om4sWJ98HVofZFc7ifB6YO6ylBItIXmYUVCyIiIiIiA2JPkYkI9HLFFy9Pxm/7TuDI2SxYyGXo17EN7ottz6FzREREREQN4NWyCXG2t8Wjg7vj0cHda62rUamQkHYWu9JOo7yyCu2DvDG6byf4uju3fFAiE3P0/GWcvZwLB1tr9OsYDjsbhdSRiIiISAcsioyMSq3GwZOZyM6/CW9XJ/SIDIFc3vAoyKqaGsxfuQlpZ7M0y05mZuO3fcfx9j/GoHObAEPHJjJJeTdL8eZ/NuHclVzNss82JOCpcfEY2iNKwmRERESkCxZFRuRM1nW8vXoLcgtLNMvcnewxf9pwRAb71rvdpuRjWgXRbZVVNXh/zXb8d/6MexZWRFTbwlWbtQoi4NZ59a8f/4S/hzM6hPo1aj+iKEJZXQNrK+2p1jOu5aGsUolgHzfY2+j2fDIiIiJqPBZFRqKsQonXvvwFxWWVWsvzikrx2he/Yky/jth7/CJKK5RoF+SN8XFd0CH0VqG0/eBf9e43r6gUqWcvoXv7EIPmJzI1Jy5ew5ms63WuU4siNiQduWdRVFapxHfbD+CPg3+hpFwJTxcHjOrTEe0CvbH8lyRkZOcBAKytLDC0Rwc8MbofLC3ken8v1HqIooiizGNQFufC2sUHTnxQMxFRizCaoujzzz/H559/jszMTABAVFQU3nzzTQwbNkzaYC3kj0MnaxVEt5VVKvH9jkOa18nHzmPv8QuY+8j9uC+2PQqLyxvcd8E91hNRbeeu1F0Q3ZZ6NguPvfM1BEFAj8gQPDigC7xcHTXrq2pq8MrnG3D6jsIqt7AE/9mSApkgQC2KmuWVVTXYuCcd5coqzJ10v/7fjJkovnIahRdSIchkcG3bA/ZeoZLmURbnofzGJVjaOsLepw1Ks8/h9Ib3UVn49+MVbD2C0G78q7B15zBnIiJDMpqiyN/fH//85z8RHh4OAPjmm28wZswYHDlyBFFRpj92/9zl3Hs3uoNaFPHZhkT07RiOIG9XHLtwtd62IT5uzY1HZHac7GwaXF9eWYXyyioAwMY96UhIO4OlTz+IIO9b59uu1DNaBdGd7iyI7vTnoVOYMqSnVnFF96aqrsTp9UtQeOGwZllW0n/hERWHNqNfwM2Moyi9dhZyazt4RPaDlb0rAKC6vAiVN3NhZe8ChaO73vLUKMtx/rdPkHcqGRDVAAAbN39UlRZApdT+kqr8xiWcWPMGus7+AnJLDqEkIjIUoymKRo0apfV68eLF+Pzzz7F//36zKIocbHX/ZVhaocTG3elo4+9Zb1HUPsgbEYHezY1HZHZ6dQiDrbWVpvC5l6KyCnzx6268+38PAABSjp3X+ZhqUcSRc5c5iYOOMnb8R6sguu3GX0kozEhHTXmRZlnmn18joN/DqMi/irxTeyCqagAIcAnrirBhT8Ha2bPZeU7/vAQ3L6ZpLavIv1Jv+6qSPNw4kQTvzkOafWwiIqqb0RRFd1KpVPjpp59QVlaGXr161dtOqVRCqVRqXpeWlrZEPIO4L7Y9Nuw+ovN2//ktpd51wT5umD9tRHNiEZktG4UlnpswEO+t2Q61uu6enbsdPn0Jb6z8FSXllbheWNyk4/KeIt3UVJYh9/jO+tffURABgKiuQVbSf+9qJaLwwmEc/+4VdH7iU62HKRddOo6y3ExY2jnDtU13yC1rT8euLMlH/qkUqKoqYGHjWKsgaoySq6dZFBERGZBRFUXHjx9Hr169UFlZCXt7e/zyyy+IjIyst/2SJUvw1ltvtWBCw2kT4InxA7rg50Tdf5neycbKEiN6RyMmPADd2gdDJhP0lJDI/Azs0g5+7s74ZXc6zl6+DitLC1y4eqPe9iKAAyczmnw8haUFurULrnd9bGwscnJy4O3tjcOHa/eMmKPKmzlQVyvv3bARlEXXkXv0T/h2H43Km7k49dM7KLt+UbPewsYBbUY+B7eIv7+sy9rzAy7v+QGiWtWsY8vvKMSIiEj/jGoe5oiICKSnp2P//v148sknMXXqVJw8ebLe9q+++iqKioo0P0lJSS2YVv9mjemPN6ePRJe2gfBxc0JMmwBMGdITMqHxhU1FVTU8nB3QIyqEBRGRHkQEeuOVyUPx9atT8e/nJ97zXqPGsKhnivxJ93WDo139Q2lzcnJw9epV5OTkNDuDqbC0cwagv3/rCi+mQhTVOLl2gVZBBAA1FSU4veGfKLtxCQBw4+QeZCX9t9kFEQB4dohv9j6IiKh+RtVTZGVlpZloITY2FocOHcLHH3+ML774os72CoUCCsXfQxns7e1bJKch9esYjn4dw7WWebs54rMNiShr5L0Nf2Vcw7i4zoaIR2TWrCwsMKZfJ3y7bX+TtpfJBPTuEIZHB3fH+sQ07D56DtU1KgR6uWJCfFfeS9QECgc3OId2btKQtboIggyFF1JRnlf72W8AIKpqkH1oM8KHP41rBzbq5ZjeXYfD3if83g2JiKjJjKooupsoilr3DBm76hoVEtLOIDH9LCqrqtEx1A8j+3SEu1PDxdzgbpHo27ENdh89i4vX8pBxLQ9Hzl2ut721wrLedUTUPI8O7oGi0gps3nus0fca3d89EoNj28PPwxkezg4AgFcmD8WcSYNRVa2CrbWVISObvLChs3H8u3moKsnXXiHINLO/NZalvStunEhssE1p9jkAQNl13YZK2rgFwLvLMOQe3wll8Q3YuPjCu+tweHUcpNN+iIhId0ZTFL322msYNmwYAgICUFJSgrVr1yIxMRHbtm2TOppeVFZV47UvN+L4HbPEHb9wFZtSjmLJ/42DXCbg8JlLkMtk6NUhFP4eLpp2oijix12H8UvSEZQr791bNKBz2yZl9Pb21vovEdUmkwl4enw8Jg6KxeHTlyAIAo5fuIo/DtU/1Dc61A8xbWo/h8ZCLoeFnBMrNJeNqw86P/4JctK2ofBCKiDI4BbRA1YO7jj761KIqmqt9ha2jqgpr3sijOtH7v07x8L61hdZlraOUBbXf4+ZwskTyqJcyK3t4Bk9CIH9JsHS1hF+Pcbo8O74bzMRkT4YTVF0/fp1PPbYY8jOzoaTkxM6duyIbdu2YfDgwVJH04ufdqVqFUS3lZQrMXf5elQo//6lvXLzHgzvGY1nHxwImUzAd9v3Y80fBxp1nH4dwxEbEdSkjLxxm6jxPJwd0DbAC/v+ughrKwvIZEKdPUfO9raI7xwhQULzYmnrhIC+ExHQd6LWcjvPIGQf2oKSa2dhYW0Hjw4D4Na+L66krEPOkW23iiMde5TcI/sh/8w+2HmH1VsUKZy9EPvUV4AoQpA1r/Dlv81ERM1nNEXRf/7zH6kjGNS2g3/Vu+7OgggARBH4bd9x+Lg5YXTfTvg5qf6puhWWFhAEwNPFESN6RWNMv04QhFsXZ4fPZCI7vxg+bo6IjeBMdET6olKp8d7325GQdkZruSAIEO94MKuLgy3e+cdoKKyM5p9ik2PrHoiwYbNrLQ+On4qguMm4mXkMf33/RuP35xmCjJ2roKqs/xEQMksF2ox8HoIg0+ccEERE1Az8TdxKFBSX6bzNr8npaBfo3eDDI5XVNejXKRyZ2fnYffQcrK0sEOLrjne//R05BX8PD/F2dcT8aSPQNsCrSfmJ6G/f/XGgVkEE3BrqGh3qh6hQXwR6uiIupg2sLPnPcGslyOSoLKj7wde3ySwUsLRzhqWdExz82yP74Cbcmnxdm4WNAxSOHnAMiIRv99GwcfUzUGoiImoK/jZuJQK9XHHxWp5O29y4WYpq1b2net1z9Pz//lSIvzKuwUIuQ41KeyhITkExXvtiI1a/PhX2NnVP+ctnoBDdW3WNCltSjtW7/tyVXCx6fAwnTzAScquGnw8kyP43tE5UoygjHXUVRABQU1GKTtOXGqQY4r/NRETNZ1TPKTJlY/vF6LyNlaUcHUJ84eXioNN2dxdEtxWVVeCPg/XfDM5noBDdW2FJOYrKKupdX1lVjZyCohZMRM3h2rYHZJaKeterqiqgLL6B0uzzKP/f84nqJqLkSu3eQ33gv81ERM3HoqiVGNazAx7oH4O7n8NqbVX/9Nl21grMXb4ewT5uEHR4gGtDzmRd18t+iMyVg60Clg3MGCcTBDjbN9z7QK2HhbUdggfN0Mu+5IrmP9iXiIgMg8PnWpHZDwzAqD4dkZR+DpXKakSH+cHd2R7zlm+o85vnwpJyFJaUA7jVaxTg4YIL1/JgIZfBz8MFl3Lya21zL/a2dQ+dI6LGsVFYoV+ncOyq454iAJDLZZi08CsEeLpgTL8YjOwdrbcvNcgwfGNHwtrZC1f3/4LS7PMQBBlqKkt02oeFjSNcwroaKCERETUXi6JWJsDTFZPv76G1bMWcR/FrylGknr6EwpJy5BXVntWoqlqFgpJytPX3wNkrN5DVhIIIAAZ1bdek7Yjob0+M7o/TWddxLe9mrXXVNbfuA7x0vQD/Xr8LGdl5ePbBgS2ckHTlGt4NruHdAABZu79H1u41jd9YkCH0/icgs+B9ZERErRWHzxkBd2d7zBzRB8tfegRWlvUPyyksKcfZK7eeiVH3rb4NG9WnIyKDfZqYkohuc3Oyw/IXJ+GJ0f0QHeYHPw/nettuTjmGzOymfYlB0rD3bfgB2HbeYVA4e93qHWrTHdGT34Vr2x7IPZ6A7NStKLue0UJJiYiosdhTZGRuD5drKluFFZ6bMBAQgC17jyMnvxjebo4Y2TsaA7uwl4hIX+xsFJgQ3xUT4rvijZW/4uqNm/W2TUo/i2CfXi0XjppEWZKPivyrUDh7wtYjqO6JFQQZqspuQlVRCluPALi374vyvCz8tXYh1NWVmmbOoV3Qbtw8WFjbt+A7ICKi+rAoMjLB3m44dUm3GYYEADNG9oGrgx36dgzXTAXMIoioZSira5q1nqRVU1GC81s/Rd7pvbem3wZg790Gth6BKL+RpWknyOQQ1SpUl9zq+SvNPo9zm5bVuc+bF9Nw9teliJy4wPBvgIiI7onD54zMA/0767yNiFtTft/fPZLPRiGSQMfQhp9N0zGMD/JsrURRxF9rFyLvVLKmIAKA0pxzqK4oQeTEBQgbNhveXUdCVN/7uXF3Kjh3CBX5DT8cloiIWgaLIiMT3yUCj97fAzKZ9mxVd7++U1SIb4NTexORYY3oHQ1Hu7pndgzz80D39iG1lpdVKHHgZAYOnc6Esoo9SVK5eTENJVdP17muurQQZbmZ8Ok6AsqbTXlGkIiS7HPNC0hERHrB4XNGaNqwXhjWIwq7j55DhbIKUSG+OJt1HV9v3VurrUwQ8Oj93SVISUS3uTra4b0nx+H9NX8gIztPs7xrRCDmPTpE60sNURTxzbb9+DkxDZVV1QBuPftoypBeGNs/pqWjm72bmUcbXp+RDu+YIahRljVp/xbWdk3ajoiI9ItFkZHycnXEhPi/n3nRNSIIMpkM6xIOo7js1s283q6OeHxUP3RrFyxRSiK6LdzPE1++PBlnsq4jv7gUgZ6u8Pd0qdXuhz8PYc0fB7SWlZQr8dkvibCzUWBwt/YtFZkAyOQN97IXXz6JA/96BBB0H3hhaecM5xDdh0QTEZH+sSgyIRMHxSK2XRB2HD4FhYUco/p0grszZzYiMiRlVQ3Sz19GjUqFDiF+cLK3abB9RKAXAK8611VV1+DnpLR6t/1x5yEWRS3MrV1vXE5eW+96UVX9vz+o620jWFhBrKnSXiaTI2zYbMjk/DVMRNQa8F9jE1FVXYP31mzH7qN/j09fl5CKhwbGYvrw3hImIzJdm1OOYdXWvSgpv9U7a2khx6jeHfHEmH6Qy3TvObiUU6Dp6a1z/fUC3Cwth7O9bZ3rvb29tf5LzWfvHQaP6IG4cXxXk7Z3Cu6EsKGzkX86GTf+2g2VshwO/u3h1/MBONzjeUdERNRyWBSZiOW/JGkVRABQo1Lj+x0H4ensgBG9oyVKRmSaktLP4t/rtS+Uq2tU2LD7CCwt5PjHqL6N2s+VG4X4aVcqDp7KhEpdf28DcOseQSuL+v/ZPnz4cKOOSbppO+p52HkEIfvwFiiLb0BmYQX1XT0/d7LxCEJgv0mw9QiEnUcQAMC278MI6PtwS0UmIiIdsSgyAcVlFfjj0Ml61/+clMaiiEjP1v55qN51vyYfhZWFHNfyi+DqaIf7u0Ui2MetVrvzV3Ix57P1KKus/wL7TrHtgjitvgQEmRz+vR+EX6/xUFdX4uqBjchK+m+97eWWCnhE9mvBhERE1FwsikxARnY+qmvqfz7G5dxCVCirYKPgxRSRPlQoq3H+6o1611dWVeO7OyZL+CkhFTNG9MaoPh2RkHYWBcVlCPRyxeaUY40uiGytrTBjRJ9mZ6emEwQBcisbuIR1bbAocgnt0oKpiIhIH1gUmQBH27qff3KbwtJCa8hNcVkFEo6cRWFJOUK83dCnYxgs5HJDxyQyGZYWMljIZahRNTzc7U5f/7YX/91+AFUNfIFxNwu5DHKZDL2jw/Do4O4I8q7d23Sn2NhY5OTkwNvbm0PpDMjBty1cwruh8Hzt3kJLO2f4xI6QIBURETUHiyITEOLrjjA/D1yo55vrvh3DIUIEAOw4dBIf/bQTVdV/X5h5ODtg8eNjEOLr3iJ5iYydhVyOvh3DkXjkrE7b6VIQAcDXr06Fj5tTo9vn5OTg6tWrOh2DdFORfxXl+Zfh33sCFA5uyD2+S3N/kbWrL+RWtjjx39dh5x0G3+5j4ODbRuLERETUGCyKTMSzDw7Eqyt+QblSeyiOTBCwM/U09p64gG7tgpF8/DzUalGrzY2bJXh95a/49o1p7DEiaqSpw3rhyNnLKCqrMMj+3Zzs4OnsYJB9k+6qSgtw9tdluJlxRLPMzisEkY+8A0EQkLlzNUqu/H1vZ3leFm78lYSIMS/Bo8MACRITEZEudJ8zllqlyGAffPbSJIzq0xF+Hs6ws1EAANTirQKoQlmN3UfP1SqIbrtxswQpxy+0WF4iY+fv4YJPnn8YQ3tEwc7aCgpLC4T66K+3dXxcF8jl/Ce6NRBFNU58/6ZWQQQAZdczcHr9YpRcPqVVEP29oRrnf/8MqirDFM63eXt7w8/Pj1OxExE1A3uKTIi/hwuefXAgLl67gf/7YI3O22dk5yMupv71fAYKkTYfdye89PBgvPTwYABAWaUSjyz8T60e23uRCYLmCwxLuRwPxMXgwQG8Wb+1KDh3EOW5GXWuqykvxrXDW+rdVqUsR/6ZffCMHmioeLx/jIhID1gUmaCUY03r8XFxqPuBkLfxFy9Rw+ysFXh96jC8veo3KKtrGrWNXCbDpy8+jIxr+RCEW9Nu1/dwVpJGcdaJBtfXlBc3uL76HuuJiEh6LIqM1KlL2diVegZllUq0D/LGfbHtNVNu19zjAZB1sbKUI74zn65O1Fzd24dg9evTsO3AX7iUkw8XB1sM6R6FjXvSse3AX1ptBQF4atwAhPt5ItzPU6LEdC8yC0WD6y2s7VBVqqx3vZ1XqL4jERGRnrEoMkIfrduJ3/Yd17zecegU/vvHQfxz1gMI8XFHl7YB+H7HwUbvTyYT8NyDg+BoZ2OIuERmx93JHpPv74EalQo/7krFGyt/RV5RKeysreDiYAsbhRWCvd0wqm9HtA/ykTou3YN7ZD9cTl5b73oH//bIP7MXEGvfs2nnHQbn4I6GjEdERHrAosjI7Dh0Uqsguq2guAzvrP4NDw/qhvTzl+HqYIuCkvJa7VzsbTBzVF+kHL+AwuIyBPu4Y0zfTgj357fURPq26JutWhOYlFVWoayyCjFtAvDSpMGQyziRgjGw8wyGd5dhyEn7vc71+adT/vcnAcDfhZGdVwgiJ8w3fEAiImo2FkVGZnPKsXrXXc4txAc//KG17M5f0dFhfnh+wiAEerliSPcow4UkIhw9f6XeGR3Tz13GvhMX0bdjeAunoqYKG/YU7LxCkH1oC8rzr0CQySCq7r5vTIQgt4Rf9zFwDu0Mp+BOEARBkrxERKQbFkVGJju/SKf2IoDB3dpj8v094OvubJBMRFTbnqPn7rmeRZHxEAQBPl1HwKfrCNzMPIoT/32tznaiqhpqdQ2cQ2JaNiARETULiyIj4+XqiJuluj3z4tCpS5jz8P0GSkRkvqpqarAr9QySj51HdY0KndsEYHivDnC0s0F1jeoe2za8nlqv4qy/mrWeiIhaHxZFRmZEr2icybqu0zY3S8tRWVUNW2srA6UiMj9llUq88vkGnL7jfEw7m4Vf9qTjw6fGI6ZNALbur38q585tAjR/VqtFnLmcg6rqGrTx9+K52srJrKwbXm/Z8HoiImp9WBQZmaE9onD8wlXsOHyq0ds429vA2srSgKmIzM932w9oFUS3FRSXYenaP/HhU+MR9IcrLl0vqNXG29UR98W2BwDsOXYeX/66GzkFt55lY6uwwph+nTBtWG/IZLwfpTVyb98XmTtXAWLdjz/wiOrfwomIiKi5WBQZGUEQ8PKjQzCkRyR2pp5BWYUS7YK8cfBUBtLPXalzm2E9O/DiikiP1GoRfxysf4jUXxnX8OZXmwABcLKzQVHZ30NenexsYG+jwOcbk9A2wBOfbkiEWv33jGXlyir88OchiKKImSP7GvR9UNNYO3kioPcEXE75sdY6B78IeHYcJEEqIiJqDhZFRqpTeAA6hf89/GZA57aYu/xnXL1xU6td5zYBmHx/jxZOR2TaqmpqUFJe/8M6AeDQ6Utar71cHHC9sARFZRUoKqvA+as3aj3M9U4b9xzFw4O6wc6m4QeHkjSC4qfAxj0A1w7+irLcDFjaOsOr033w7/0g5Jb8zIiIjA2LIhPh4eyAL+ZMRsKRMzhyNgsWFnL0jQ5Hj8gQ9hIR6Zm1lSU8XRyQW1jS6G2u69AWACqrqnHyUja6tQvWMR21FM/oeHhGx0sdg4iI9IBFkQlRWFlgaI8oDO3BZxARGdroPp3w1ZZkgx7DykJu0P0TERHRLXycOhFREzwY3wWDurYz2P5dHWwRFeJrsP0TERHR39hTRETUBHKZDK9MHorxcV2w59g5VNeoUKNSY+Oe9GbvWxCAmSP7wkLOniIiIqKWwKKIiKgZ2gR4ok2AJwCgQlmFxCNndHrAsoOtNWLC/bHvr4uoUanRPsgbk+7rjl4dQg0VmYiIiO7CooiISE9sFFZY9PhYLPh6E/KLyjTL7aytMLxXNDYlH4Wyukaz3MXBFu/8YzQiAr2hUqmhUqthZdn0f5a9vb21/ktERESNI4iiKN67mWlIS0tD165dkZqaii5dukgdh4hMVHWNCnuPX8CVG4Vwd7ZH/05tYaOwRHFZJXalnUZ+cRkCPV0RF9OmWUUQERER6Qd/GxMR6ZmlhRxxndvWWu5oZ43RfTpBWV0DG4WlBMmIiIioLkZTFC1ZsgQbNmzA6dOnYWNjg969e+O9995DRESE1NGIiO6prEKJb7ftxx+HTqK0QglPFweM7tMJD8Z3gVzGiUCJiIikZDS/iZOSkvDUU09h//792LFjB2pqanD//fejrKzs3hsTEUmoqroGL3++ARt2H0FphRIAkFtYgq+2JOPDH3ZInI6IiIiMpqdo27ZtWq9XrVoFT09PpKamon///hKlIiK6t52pp3H28vU61/15+BTGD+iMcD/PFk5FREREtxlNT9HdioqKAACurq4SJyEiatieY+cbXn+04fVERERkWEbTU3QnURTx4osvom/fvujQoUO97ZRKJZRKpeZ1aWlpS8QjItJSU6Nq1noiIiIyLKPsKXr66adx7Ngx/PDDDw22W7JkCZycnDQ/cXFxLZSQiOhvndsG3GN9YAslISIioroYXVH0zDPPYNOmTUhISIC/v3+DbV999VUUFRVpfpKSklooJRHR34b3jIarg22d69oHeaNrBIsiIiIiKRlNUSSKIp5++mls2LABu3btQkhIyD23USgUcHR01PzY29u3QFIiIm1O9jb44KkHERXiq1kmkwno16kNFj0+FoIgSJiOiIiIjOaeoqeeegrff/89fv31Vzg4OCAnJwcA4OTkBBsbG4nTERE1LNDLFR89+xCu5BYiv7gUfh4ucHfiFzVEREStgSCKoih1iMao75vUVatWYdq0aY3aR1paGrp27YrU1FR06dJFj+mIiIiIiMhYGU1PkZHUbkREREREZGSM5p4iIiIiIiIiQ2BRREREREREZs1ohs8RERm7otIK7Ew9jYLiMgR6uSIupi0UVvxnmIiISGr8bUxE1AJ2pZ7G0h93oKpapVm2cnMy3vnHaLQL8pYwGREREXH4HBGRgWVm5+P97//QKogA4GZpOeZ/9Ssqq6olSkZEREQAiyIiIoPblHIUKrW6znU3SyuQeORsCyciIiKiO7EoIiIysKzrBQ2uz8zJb6EkREREVBcWRUREBubiYNvgejdHuxZKQkRERHVhUUREZGBDe0TVu85CLsOgru1aMA0RERHdjUUREZGBdY0Iwth+MbWWywQBz00YBFf2FBEREUmKU3ITEbWAp8YNQPfIYGzb/xfy//ecotF9OiLc31PqaERERGaPRRERUQvp1i4Y3doFSx2DiIiI7sLhc0REREREZNZYFBERERERkVljUURERERERGaN9xSZqOzsbGRnZ0sdg/TEx8cHPj4+UscgPeH5aXp4jhIRGTezKop8fHywYMECk//FpVQqMWnSJCQlJUkdhfQkLi4O27dvh0KhkDoKNRPPT9PEc5SIyLgJoiiKUocg/SouLoaTkxOSkpJgb28vdRxqptLSUsTFxaGoqAiOjo5Sx6Fm4vlpeniOEhEZP7PqKTI3MTEx/AVtAoqLi6WOQAbA89N08BwlIjJ+nGiBiIiIiIjMGosiIiIiIiIyayyKTJBCocCCBQt4w6+J4OdpWvh5mh5+pkRExo8TLRARERERkVljTxEREREREZk1FkVERERERGTWWBQREREREZFZY1FkhDIzMyEIAtLT01vsmAsXLkRMTEyLHc+cBAcH46OPPmqx4yUmJkIQBNy8ebPFjklERETUmrEo0pMVK1bAwcEBNTU1mmWlpaWwtLREv379tNru2bMHgiDg7Nmzde5r4cKFEAQBgiBALpcjICAA//jHP3Djxg2Dvgeqbdq0aRAEAbNmzaq1bvbs2RAEAdOmTat3+9sFyO0fDw8PDBs2DEePHjVgamoqQ53HFhYWcHd3R//+/fHRRx9BqVQa9H2QNn2exzKZDE5OTujcuTNefvllZGdnGzA5ERG1FBZFehIfH4/S0lIcPnxYs2zPnj3w9vbGoUOHUF5erlmemJgIX19ftG3btt79RUVFITs7G1lZWfj888+xefNmTJkyxaDvgeoWEBCAtWvXoqKiQrOssrISP/zwAwIDAxu1jzNnziA7Oxu//fYbCgsLMXToUBQVFRkqMjWRIc/jhIQETJgwAUuWLEHv3r1RUlJS73ZVVVX6eUOkoa/z+Nq1azh06BDmzZuHP//8Ex06dMDx48fr3YafJRGRcWBRpCcRERHw9fVFYmKiZlliYiLGjBmDsLAw7N27V2t5fHx8g/uzsLCAt7c3/Pz8MHLkSDz77LP4448/tH6h36ZSqTBz5kyEhITAxsYGERER+Pjjj2u1+/rrrxEVFQWFQgEfHx88/fTTmnVFRUV44okn4OnpCUdHRwwcOLDO3owvvvgCAQEBsLW1xYQJE7SGYKnVarz99tvw9/eHQqFATEwMtm3b1uD7NAZdunRBYGAgNmzYoFm2YcMGBAQEoHPnzo3ah6enJ7y9vdG9e3csXboUOTk52L9/f51tly1bhujoaNjZ2SEgIACzZ89GaWmpVpuUlBTExcXB1tYWLi4uGDJkCAoLCwEAoiji/fffR2hoKGxsbNCpUyesX7++1nFSUlLQqVMnWFtbo0ePHrUu7H7++WfN35fg4GAsXbq0Ue/VmBnqPPb19UV0dDSeeeYZJCUl4cSJE3jvvfc07YKDg7Fo0SJMmzYNTk5OePzxx+sc5pieng5BEJCZmalZtnLlSs05+cADD2DZsmVwdnZu7v8Kk6PP87ht27Z4+OGHkZKSAg8PDzz55JOaNtOmTcPYsWOxZMkSraJZEARs3LhRa3/Ozs5YvXq15vXevXsRExMDa2trxMbGYuPGjS0+VJqIyFyxKNKjAQMGICEhQfM6ISEBAwYMQFxcnGZ5VVUV9u3bd8+LqbvZ2NhArVZrDeu5Ta1Ww9/fH+vWrcPJkyfx5ptv4rXXXsO6des0bT7//HM89dRTeOKJJ3D8+HFs2rQJ4eHhAG5dRI8YMQI5OTnYunUrUlNT0aVLFwwaNAgFBQWafZw/fx7r1q3D5s2bsW3bNqSnp+Opp57SrP/444+xdOlSfPjhhzh27BiGDBmC0aNH49y5czq919Zo+vTpWLVqleb1119/jRkzZjRpXzY2NgCA6urqOtfLZDL8+9//xokTJ/DNN99g165dePnllzXr09PTMWjQIERFRWHfvn1ITk7GqFGjoFKpAABvvPEGVq1ahc8//xx//fUXXnjhBUyePBlJSUlax5k7dy4+/PBDHDp0CJ6enhg9erQmU2pqKh566CE8/PDDOH78OBYuXIj58+drXcCZKkOexwDQrl07DBs2TOviHAA++OADdOjQAampqZg/f36j9pWSkoJZs2bhueeeQ3p6OgYPHozFixfrnMlc6PM8Bm6dy7NmzUJKSgpyc3M1y3fu3IlTp05hx44d2LJlS6P2VVJSglGjRiE6OhppaWl45513MG/evCZnIyIiHYmkN19++aVoZ2cnVldXi8XFxaKFhYV4/fp1ce3atWLv3r1FURTFpKQkEYB44cKFevezYMECsVOnTprXp06dEsPDw8Xu3buLoiiKGRkZIgDxyJEj9e5j9uzZ4vjx4zWvfX19xddff73Otjt37hQdHR3FyspKreVhYWHiF198ockkl8vFy5cva9b//vvvokwmE7OzszXHWLx4sdY+unXrJs6ePbvenK3d1KlTxTFjxog3btwQFQqFmJGRIWZmZorW1tbijRs3xDFjxohTp06td/uEhAQRgFhYWCiKoijm5eWJo0ePFh0cHMTr16+LoiiKQUFB4r/+9a9697Fu3TrRzc1N83rSpElinz596mxbWloqWltbi3v37tVaPnPmTHHSpElamdauXatZn5+fL9rY2Ig//vijKIqi+Mgjj4iDBw/W2sfcuXPFyMjIenOaCkOdx3eaN2+eaGNjo3kdFBQkjh07VqvN3X93RFEUjxw5IgIQMzIyRFEUxYkTJ4ojRozQ2u7RRx8VnZycGv+GzYC+z+M7/f777yIA8cCBA5pjeXl5iUqlUqsdAPGXX37RWubk5CSuWrVKFEVR/Pzzz0U3NzexoqJCs37lypX3/LeeiIj0w0KiWswkxcfHo6ysDIcOHUJhYSHatm0LT09PxMXF4bHHHkNZWRkSExMRGBiI0NDQBvd1/Phx2NvbQ6VSQalUYsCAAfjyyy/rbb9ixQp89dVXuHTpEioqKlBVVaWZLS43NxfXrl3DoEGD6tw2NTUVpaWlcHNz01peUVGBCxcuaF4HBgbC399f87pXr15Qq9U4c+YMbG1tce3aNfTp00drH3369DGJSQXc3d0xYsQIfPPNN5qeNXd390Zvf/v/W1lZGdq0aYOffvoJnp6edbZNSEjAu+++i5MnT6K4uBg1NTWorKxEWVkZ7OzskJ6ejgkTJtS57cmTJ1FZWYnBgwdrLa+qqqo1RKhXr16aP7u6uiIiIgKnTp0CAJw6dQpjxozRat+nTx989NFHUKlUkMvljX7vxkaf53F9RFGEIAhay2JjY3Xez5kzZ/DAAw9oLevevXujeyfMTXPP47qIoggAWp9ndHQ0rKysdNrPmTNn0LFjR1hbW2uWde/evVnZiIio8VgU6VF4eDj8/f2RkJCAwsJCxMXFAQC8vb0REhKClJQUJCQkYODAgffcV0REBDZt2gS5XA5fX18oFIp6265btw4vvPACli5dil69esHBwQEffPABDhw4AODv4Vr1UavV8PHx0bqP4raG7k24fRFw58XA3Rd6dV38GasZM2Zo7sP67LPPdNp2z549cHR0hIeHBxwdHettd+nSJQwfPhyzZs3CO++8A1dXVyQnJ2PmzJmaoW0NfZ5qtRoA8Ntvv8HPz09rXUN/h267/VnV9bndvvgzdfo8j+tz6tQphISEaC2zs7PTei2T3RrdfOf/97uHXJrz59RUzTmP63L7i4Tg4GDNsrs/S+DWuXX3Z3Pn58nPkohIWrynSM/i4+ORmJiIxMREDBgwQLM8Li4O27dvx/79+xt1H4KVlRXCw8MREhJyz4vZPXv2oHfv3pg9ezY6d+6M8PBwrR4eBwcHBAcHY+fOnXVu36VLF+Tk5MDCwgLh4eFaP3d+i5qVlYVr165pXu/btw8ymQxt27aFo6MjfH19kZycrLXvvXv3on379vd8v8Zg6NChqKqqQlVVFYYMGaLTtiEhIQgLC2uwIAKAw4cPo6amBkuXLkXPnj3Rtm1brf/nANCxY8d6P8vIyEgoFApkZWXV+iwDAgK02t450UNhYSHOnj2Ldu3aafZT12fZtm1bk+4luk1f53FdTp8+jW3btmH8+PENtvPw8AAArSmf777hvl27djh48KDWsjtnzqPamnMe362iogJffvkl+vfvr/m86uPh4aH1WZ47d05rNsN27drh2LFjWtO187MkImo57CnSs/j4eDz11FOorq7WfMMM3LqYevLJJ1FZWdnki6n6hIeH49tvv8X27dsREhKC7777DocOHdL6JnrhwoWYNWsWPD09MWzYMJSUlCAlJQXPPPMM7rvvPvTq1Qtjx47Fe++9h4iICFy7dg1bt27F2LFjNcN6rK2tMXXqVHz44YcoLi7Gs88+i4ceegje3t4Abt24v2DBAoSFhSEmJgarVq1Ceno61qxZo9f3KxW5XK75VthQhUFYWBhqamrwySefYNSoUUhJScGKFSu02rz66quIjo7G7NmzMWvWLFhZWWmme3Z3d8ecOXPwwgsvQK1Wo2/fviguLsbevXthb2+PqVOnavbz9ttvw83NDV5eXnj99dfh7u6OsWPHAgBeeukldOvWDe+88w4mTpyIffv24dNPP8Xy5csN8r5bG32dxzU1NcjJyYFarUZ+fj4SExOxaNEixMTEYO7cuQ1ue7uQXbhwIRYtWoRz587VmgHwmWeeQf/+/bFs2TKMGjUKu3btwu+//24yvbOG0JzzODc3F5WVlSgpKUFqairef/995OXl1Zo0oy4DBw7Ep59+ip49e0KtVmPevHmwtLTUrH/kkUfw+uuv44knnsArr7yCrKwsfPjhhwBq98ATEZEBSHInkwm7PQlCu3bttJZfvnxZBCCGhYXdcx8N3aB95zFu33xbWVkpTps2TXRychKdnZ3FJ598UnzllVdq7WPFihViRESEaGlpKfr4+IjPPPOMZl1xcbH4zDPPiL6+vqKlpaUYEBAgPvroo2JWVpZWpuXLl4u+vr6itbW1OG7cOLGgoECzD5VKJb711luin5+faGlpKXbq1En8/fff7/l+W7PbN2jXpzk3aN9290QLy5YtE318fEQbGxtxyJAh4rfffltrH4mJiWLv3r1FhUIhOjs7i0OGDNGsV6vV4scff6z5rD08PMQhQ4aISUlJWpk2b94sRkVFiVZWVmK3bt3E9PR0rVzr168XIyMjRUtLSzEwMFD84IMP6n0PpkZf5zEAEYAol8tFV1dXsW/fvuK//vWvWpOa1DfZRnJyshgdHS1aW1uL/fr1E3/66SetiRZE8dbEEH5+fqKNjY04duxYcdGiRaK3t3eT3rep0td5DEAUBEF0cHAQO3XqJM6dO1cz0cy9jnX16lXx/vvvF+3s7MQ2bdqIW7du1ZpoQRRFMSUlRezYsaNoZWUldu3aVfz+++9FAOLp06d1fMdERKQrQRQ5aJmIyFQ8/vjjOH36NPbs2SN1FGqmNWvWYPr06SgqKrrnvaFERNQ8HD5HRGTEPvzwQwwePBh2dnb4/fff8c0335jNMEdT8+233yI0NBR+fn44evQo5s2bh4ceeogFERFRC2BRRERkxA4ePIj3338fJSUlCA0Nxb///W/84x//kDoWNUFOTg7efPNN5OTkwMfHBxMmTODDeImIWgiHzxERERERkVnjlNxERERERGTWWBQREREREZFZY1EkoWnTpkEQBPzzn//UWr5x40aDPpeiuroa8+bNQ3R0NOzs7ODr64spU6bUekioUqnEM888A3d3d9jZ2WH06NG4cuWKwXIZO36epoWfp2nh50lERA1hUSQxa2trvPfeeygsLGyxY5aXlyMtLQ3z589HWloaNmzYgLNnz2L06NFa7Z5//nn88ssvWLt2LZKTk1FaWoqRI0dCpVK1WFZjw8/TtPDzNC38PImIqF7SPibJvE2dOlUcOXKk2K5dO3Hu3Lma5b/88ovY0h/NwYMHRQDipUuXRFEUxZs3b4qWlpbi2rVrNW2uXr0qymQycdu2bS2azVjw8zQt/DxNCz9PIiJqCHuKJCaXy/Huu+/ik08+0WmoxLBhw2Bvb9/gjy6KioogCAKcnZ0BAKmpqaiursb999+vaePr64sOHTpg7969Ou3bnPDzNC38PE0LP08iIqoPn1PUCjzwwAOIiYnBggUL8J///KdR23z11VeoqKjQy/ErKyvxyiuv4JFHHoGjoyOAW8/LsLKygouLi1ZbLy8v5OTk6OW4poqfp2nh52la+HkSEVFdWBS1Eu+99x4GDhyIl156qVHt/fz89HLc6upqPPzww1Cr1Vi+fPk924uiaNCbkk0FP0/Tws/TtPDzJCKiu3H4XCvRv39/DBkyBK+99lqj2utjOEd1dTUeeughZGRkYMeOHZpvLQHA29sbVVVVtW5Izs3NhZeXl25vzgzx8zQt/DxNCz9PIiK6G3uKWpF//vOfiImJQdu2be/ZtrnDOW7/gj537hwSEhLg5uamtb5r166wtLTEjh078NBDDwEAsrOzceLECbz//vtNPq454edpWvh5mhZ+nkREdCcWRa1IdHQ0Hn30UXzyySf3bNuc4Rw1NTV48MEHkZaWhi1btkClUmnGrbu6usLKygpOTk6YOXMmXnrpJbi5ucHV1RVz5sxBdHQ07rvvviYf25zw8zQt/DxNCz9PIiLSIu3kd+Zt6tSp4pgxY7SWZWZmigqFwqBTxGZkZIgA6vxJSEjQtKuoqBCffvpp0dXVVbSxsRFHjhwpZmVlGSyXsePnaVr4eZoWfp5ERNQQQRRFsWXKLyIiIiIiotaHEy0QEREREZFZY1FERERERERmjUURERERERGZNRZFRERERERk1lgUERERERGRWWNRREREREREZo1FERERERERmTUWRUREREREZNZYFBERERERkVljUURERERERGaNRREREREREZk1FkVERERERGTWWBQREREREZFZY1FERERERERmjUURERERERGZNRZFRERERERk1lgUERERERGRWWNRREREREREZo1FERERERERmTUWRUREREREZNZYFBERERERkVljUURERERERGbNrIqi7OxsLFy4ENnZ2VJHISIiIiLSC17jNp/ZFUVvvfUW/8IQERERkcngNW7zmVVRREREREREdDcWRUREREREZNZYFBERERERkVljUURERERERGaNRREREREREZk1FkVERERERGTWWBQREREREZFZY1FEZAQqKyuljkBERERkslgUERmBGzduSB2BiIiIyGSxKCIyAlVVVaiqqpI6BhEREZFJYlFEZCSKi4uljkBERERkklgUERmJwsJCqSMQERERmSQWRURGgvcVERERERkGiyIiI3Hp0iWIoih1DCIiIiKTw6KIyEiUlJQgKytL6hhEREREJodFEZEROXz4MHuLiIiIiPSMRRGREcnPz8eZM2ekjkFERERkUlgUEbVysbGx6NOnDxYvXgwAOHDgAEpKSiRORURERGQ6WBQRtXI5OTm4fv265jlFSqUSf/zxB5RKpcTJiIiIiEwDiyIiI5Sfn4+tW7eioqJC6ihERERERo9FEZGRunHjBn799Vfk5+dLHYWIiIjIqLEoIjJixcXF2LhxI44dOwa1Wi11HCIiIiKjxKKIyMipVCrs378fv/zyC65evSp1HCIiIiKjw6KIyETk5+fjt99+w2+//Ybc3Fyp4xAREREZDQupAxCRfl29ehVXr15FUFAQYmNj4ebmJnUkIiIiolaNRRGRibp06RIuXbqEkJAQdO7cGe7u7lJHIiIiImqVWBQRmbiMjAxkZGTAx8cH7du3R3BwMCwseOoTERER3cYrI6JWLCsrC+Xl5QCAqqoqFBQUwNXVtUn7ys7ORnZ2NiwtLREcHIzw8HD4+flBJuOthURERGTeWBQRtUIHDx7EO++8g99++w2iKAIAysvL8dprryE6OhojRoxAcHBwk/ZdXV2Nc+fO4dy5c7CxsUFERASioqJgZ2enx3dAREREZDxYFBG1Mhs2bMDEiRMhiqKmILpNFEWcOHECJ06cwOOPP44uXbo061gVFRVIT0/HiRMn0KNHD0RFRTVrf0RERETGiONmiFqRgwcPYuLEiVCpVFCpVHW2UavVUKvVWLlyJTIzM/Vy3JqaGqSkpODChQt62R8RERGRMWFRRNSKLFq0qM4eovps3bpVr8dPTk5GSUmJXvdJRERE1NqxKCJqJbKysrBly5Z6e4juplarcezYMRQUFOgtg1KpxObNm1FYWKi3fRIRERG1diyKiFqJnTt3NrqH6DZRFHH69Gm95igtLcWvv/6KGzdu6HW/RERERK0ViyKiVqKkpETn6bEFQUBlZaXes1RVVSE9PV3v+yUiIiJqjVgUEbUSDg4OUKvVOm0jiiKsra31nsXS0hLR0dF63y8RERFRa8QpuYlaiUGDBkEQBJ2G0AmCgHbt2uk1h6enJ+Lj4+Hk5KTX/RIRERG1VuwpImolAgMDMXLkSMjl8ka1l8lk6NixI1xdXfVyfLlcju7du2P06NEsiIiIiMissCgiakXmz58PQRAgCEKj2g8fPlwvx/X29sa4ceMQExOj831NRERERMaOVz9ErUi3bt3w448/Qi6X19tjJJPJIJPJ8MQTTyA4OLhZx3N2dsZ9992HUaNGwcXFpVn7IiIiIjJWvKeIqJUZN24c9u7di3feeQdbtmzRusdIEARER0dj+PDhzSqIXF1d0blzZ4SGhja6V4qIiIjIVLEoImqFunXrhk2bNiErKwsxMTEoLCyEra0t5s+f36x7iDw8PNClSxcEBgayGCIiIiL6H6MaPrd7926MGjUKvr6+EAQBGzdulDoSkUEFBgbC1tYWAGBlZdXkgsjJyQmDBw/G2LFjERQUxIKIiIjIzPG6WptRFUVlZWXo1KkTPv30U6mjEBkFS0tL9OzZEw8++CBCQkJYDBEREREAXlffrUnD5y5cuIBVq1bhwoUL+Pjjj+Hp6Ylt27YhICAAUVFR+s6oMWzYMAwbNsxg+ycyFYIgoG3btujWrZump4mIiIjoNl5Xa9O5pygpKQnR0dE4cOAANmzYgNLSUgDAsWPHsGDBAr0HbA6lUoni4mLNz+2sRKYsJCQEDz74IOLi4lgQERERmZHS0lKta1+lUil1JKOhc1H0yiuvYNGiRdixYwesrKw0y+Pj47Fv3z69hmuuJUuWwMnJSfMTFxcndSQigxAEAeHh4ZgwYQIGDx7M6bWJiIjMUFxcnNa175IlS6SOZDR0Hj53/PhxfP/997WWe3h4ID8/Xy+h9OXVV1/Fiy++qHmdnp7OwohMTlBQELp3785CiIiIyMwlJSUhJiZG81qhUEgXxsjoXBQ5OzsjOzsbISEhWsuPHDkCPz8/vQXTB4VCofWXwd7eXsI0RPplbW2Nfv361ToXiYiIyDzZ29vD0dFR6hhGSefhc4888gjmzZuHnJwcCIIAtVqNlJQUzJkzB1OmTDFERiK6i5ubG8aNG8eCiIiIiEgPdO4pWrx4MaZNmwY/Pz+IoojIyEioVCo88sgjeOONNwyRUaO0tBTnz5/XvM7IyEB6ejpcXV0RGBho0GMTScXb2xs1NTWaXk8PDw+MGDFC654+IiIiIl3wulqbIIqi2JQNL168iLS0NKjVanTu3Blt2rTRd7ZaEhMTER8fX2v51KlTsXr16ntun5aWhq5duyI1NRVdunQxQEIiw7hw4QJ27twJS0tLTJgwgUNBiYiISKMp17jNva42NU16ThEAhIaGIjQ0VJ9Z7mnAgAFoYg1HZBIiIiJYEBEREVGz8bpam873FD344IP45z//WWv5Bx98gAkTJuglFBHVjfcQEREREelfkx7eOmLEiFrLhw4dit27d+slFBHVZmVlBS8vL6ljEBEREZkcnYui0tLSOm/wtrS0RHFxsV5CEVFt/v7+kMl0PmWJiIiI6B50vsLq0KEDfvzxx1rL165di8jISL2EIqLagoKCpI5AREREZJJ0nmhh/vz5GD9+PC5cuICBAwcCAHbu3IkffvgBP/30k94DEtEtHDpHREREZBg6F0WjR4/Gxo0b8e6772L9+vWwsbFBx44d8eeffyIuLs4QGYnMniAInHWOiIiIjFpFRQWqq6u1ljk6OkqURluTpuQeMWJEnZMtEJFhWFtb834iIiIiMjrl5eV4+eWXsW7dOuTn59dar1KpJEhVW5OvsqqqqnDlyhVkZWVp/RCR/llbW0sdgYgaUFNTI3UEIqJWae7cudi1axeWL18OhUKBr776Cm+99RZ8fX3x7bffSh1PQ+eeonPnzmHGjBnYu3ev1nJRFCEIQqup9ohMSWvpWiaiupWXl/M8JSKqw+bNm/Htt99iwIABmDFjBvr164fw8HAEBQVhzZo1ePTRR6WOCKAJRdG0adNgYWGBLVu2wMfHB4IgGCIXEd3BwqJJI12JqIWUlpayKCIiqkNBQYHm4fOOjo4oKCgAAPTt2xdPPvmklNG06HyllZ6ejtTUVLRr184QeYiIiIxOXl4efH19pY5BRNTqhIaGIjMzE0FBQYiMjMS6devQvXt3bN68Gc7OzlLH09D5nqLIyEjk5eUZIgsREZFRunz5MkRRlDoGEVGrM336dBw9ehQA8Oqrr2ruLXrhhRcwd+5cidP9Teeeovfeew8vv/wy3n33XURHR8PS0lJrPYcPEBGRuSktLUVmZqZmiAgREd3ywgsvaP4cHx+P06dP4/DhwwgLC0OnTp0kTKZN56LovvvuAwAMGjRIazknWiAiInN25MgRBAcH815bIqI7fPvtt5g4cSIUCgUAIDAwEIGBgaiqqsK3336LKVOmSJzwFp2LooSEBEPkICIiMmp5eXm4du0a/Pz8pI5CRNRqTJ8+HUOHDoWnp6fW8pKSEkyfPt14i6K4uDhD5CAiIjJKsbGxyMjIgL29Pdzc3DB27FjI5XKpYxERtQq3R5Pd7cqVK3BycpIgUd2aNM/vnj178MUXX+DixYv46aef4Ofnh++++w4hISHo27evvjMSERG1Wjk5OSgoKIBarUZ+fj5SUlLQr18/DqMjIrPWuXNnCIIAQRAwaNAgrceLqFQqZGRkYOjQoRIm1KZzUfTzzz/jsccew6OPPoq0tDQolUoAt7rA3n33XWzdulXvIYmIiIzF6dOnUVFRgT59+sDe3l7qOEREkhg7diyAW4/zGTJkiNa/h1ZWVggODsb48eMlSlebzkXRokWLsGLFCkyZMgVr167VLO/duzfefvttvYYjIiIyRpcuXcKVK1fQpk0bdOjQAa6urlJHIiJqUQsWLAAABAcHY+LEibC2tpY4UcN0LorOnDmD/v3711ru6OiImzdv6iMTERGR0VOpVDh9+jROnz4NHx8fxMTEwN/fn8PqiMisTJ06VeoIjaJzUeTj44Pz588jODhYa3lycjJCQ0P1lYuIiMhkZGdnIzs7G35+fujfvz8cHBykjkREZDAuLi6N/gKooKDAwGkaR+ei6P/+7//w3HPP4euvv4YgCLh27Rr27duHOXPm4M033zRERiIiIpNw9epV/Pzzz+jVqxfatm3LXiMiMkkfffSR1BF0pnNR9PLLL6OoqAjx8fGorKxE//79oVAoMGfOHDz99NOGyEhERGQyqqqqkJSUhOPHj6NDhw4ICwuDpaWl1LGIiPTGWIbM3UmnokilUiE5ORkvvfQSXn/9dZw8eRJqtRqRkZGcYYeIiMxOVlYWysvLAdwqdgoKCho9qUJBQQF2796NvXv3IjAwECEhIfD399c89Z2IyFRcuHABq1atwoULF/Dxxx/D09MT27ZtQ0BAAKKioqSOBwCQ6dJYLpdjyJAhKCoqgq2tLWJjY9G9e3cWREREZFYOHjyIUaNGITg4GIWFhQCA8vJyvPbaa/jss8+QmZnZ6H3V1NTg4sWL2LlzJ7777jts3rwZ6enpKCgogCiKBnoHREQtIykpCdHR0Thw4AA2bNiA0tJSAMCxY8c0M9S1BjoPn4uOjsbFixcREhJiiDxERESt2oYNGzBx4kSIoliraBFFESdOnMCJEyfw+OOPo0uXLjrtW61WayZlOHjwIOzt7REcHIyIiAi4ubnp820QEbWIV155BYsWLcKLL76oNclMfHw8Pv74YwmTadOppwgAFi9ejDlz5mDLli3Izs5GcXGx1g8REZGpOnjwICZOnAiVSgWVSlVnG7VaDbVajZUrV+rUY1SX0tJSnDhxAj///DO2bdumeWA6EZGxOH78OB544IFayz08PJCfny9BorrpXBQNHToUR48exejRo+Hv7w8XFxe4uLjA2dkZLi4uhshIRETUKixatKjOHqL6bN26VW/HzsrKwp49e/S2PyKiluDs7Izs7Oxay48cOQI/Pz8JEtVN5+FzCQkJhshBRETUqmVlZWHLli2NLojUajWOHTum0+QL93Lx4kWcOXMGERERetkfEZGhPfLII5g3bx5++uknCIIAtVqNlJQUzJkzB1OmTJE6nobORVFcXJwhchAREbVqO3fu1HniA1EUcfr0afTu3VtvOXbv3g2ZTIY2bdrobZ9ERIayePFiTJs2DX5+fhBFEZGRkVCpVHjkkUfwxhtvSB1PQ+fhcwCwZ88eTJ48Gb1798bVq1cBAN999x2Sk5P1Go6IiKi1KCkpgUym269NQRBQWVmp1xyiKGL37t2aqcCJiFozS0tLrFmzBmfPnsW6devw3//+F6dPn8Z3330HuVwudTwNnYuin3/+GUOGDIGNjQ3S0tI0N32WlJTg3Xff1XtAIiKi1sDBwQFqtVqnbURRhLW1tYESEREZj7CwMDz44IN46KGHWmVPt87D5xYtWoQVK1ZgypQpWLt2rWZ579698fbbb+s1HBERUWsxaNAgCIKg0xA6QRDQrl07veawtLTEwIEDYWtrq9f9EhHpy4svvtjotsuWLTNgksbTuSg6c+YM+vfvX2u5o6Mjbt68qY9MRERErU5gYCBGjhyJrVu31jsd951kMhmio6P1NskCcGsK2/j4eDg7O+ttn0RE+nbkyBGt16mpqVCpVJpJYs6ePQu5XI6uXbtKEa9OOhdFPj4+OH/+PIKDg7WWJycnIzQ0VF+5iIiIWp358+fj999/b3SP0fDhw/VyXAsLC3Tp0gUdO3bU+b4mIqKWduds1cuWLYODgwO++eYbzeN7CgsLMX36dPTr10+qiLXo/C/r//3f/+G5557DgQMHIAgCrl27hjVr1mDOnDmYPXu2ITISERG1Ct26dcOPP/4IuVxe7w3CMpkMMpkMTzzxRK0vEHUlCAIiIiIwceJExMTEsCAiIqOzdOlSLFmyROt5pi4uLli0aBGWLl0qYTJtOvcUvfzyyygqKkJ8fDwqKyvRv39/KBQKzJkzB08//bQhMhIREbUa48aNw969e/HOO+/Uem6RIAiIjo7G8OHDm1UQWVlZoW3btoiOjoaDg4MeUhMRSaO4uBjXr19HVFSU1vLc3FyUlJRIlKq2RhVFx44dQ4cOHTTfUC1evBivv/46Tp48CbVajcjISNjb2xs0KBERUWvRrVs3bNq0CVlZWYiJiUFhYSFsbW0xf/78Jt9DJAgCfH190aZNG4SEhMDS0lLPqYmIWt4DDzyA6dOnY+nSpejZsycAYP/+/Zg7dy7GjRsncbq/Naoo6ty5M7Kzs+Hp6YnQ0FAcOnQIbm5uiI2NNXQ+IiKiViswMBC2trYoLCyElZVVkwoiV1dXtG3bFmFhYbCzszNASiIi6axYsQJz5szB5MmTUV1dDeDWfZIzZ87EBx98IHG6vzWqKHJ2dkZGRgY8PT2RmZmp83MaiIiI6G8WFhYICwtDZGQk3N3dIQiC1JGIiAzC1tYWy5cvxwcffIALFy5AFEWEh4e3ui+BGlUUjR8/HnFxcfDx8YEgCIiNja33BtOLFy/qNSAREZGpsLe3R2RkJNq3bw+FQiF1HCKiFmNnZ4eOHTtKHaNejSqKvvzyS4wbNw7nz5/Hs88+i8cff5w3fhIRETWSp6cnoqOjERISwhnkiIhaoUZPtHD//fdj6NChSE1NxXPPPceiiIiI6B48PT3RvXt3zUgLIiJqnXSeaCEpKQlVVVWGzkVERGS05HI5evXqhfbt27MYIiIyAo3qw7890QIAySdaWL58OUJCQmBtbY2uXbtiz549kmUhIiLy9vaGq6srHB0dAdwaNz9mzBhERkayICKiVo3X1X8zqokWfvzxRzz//PNYvnw5+vTpgy+++ALDhg3DyZMnERgYaLDjEhER1efw4cP48ccfUVRUBHt7e4waNYpDzImo1ZPiutrR0RHp6ekIDQ01yP6bw6gmWli2bBlmzpyJf/zjHwCAjz76CNu3b8fnn3+OJUuWtHgeIiKi2+RyOYYOHcqCiIiMghTX1aIoGmS/+tCooggAhg4dCgCSTbRQVVWF1NRUvPLKK1rL77//fuzdu7fObZRKJZRKpeZ1aWkpAKCmpkbz8CgiIqLmqqmpQYcOHeDg4MDfL0TU4mpqagDcutYtLi7WLFcoFHVO/9+U62pT1+ii6LZVq1YZIsc95eXlQaVSwcvLS2u5l5cXcnJy6txmyZIleOutt2ot79Gjh0EyEhERERFJJS4uTuv1ggULsHDhwlrtmnJdrQ+TJ0/W3H/Z2jSqKBo3bhxWr14NR0dHjBs3rsG2GzZs0Euw+tx906ooivXeyPrqq6/ixRdf1LxOT09HXFwcDhw4gM6dOxs0JxERmY/9+/ejZ8+eUscgIjN15MgR9OjRA0lJSYiJidEsv9dDonW5rtaHzz//3GD7bq5GFUVOTk6a/0FOTk4GDVQfd3d3yOXyWtVrbm5urSr3tru7DO3t7QEAFhYWsLS0NFxYIiIyK97e3vy9QkSSsbC4dUlvb2/fqJ6YplxXm7pGFUV3DpmTaviclZUVunbtih07duCBBx7QLN+xYwfGjBkjSSYiIiLg7y/diIiMAa+ra9P5niIpvfjii3jssccQGxuLXr164csvv0RWVhZmzZoldTQiIjJj1tbWUkcgItIJr6u1Naoo6ty5c6PHF6alpTUrUEMmTpyI/Px8vP3228jOzkaHDh2wdetWBAUFGeyYRERE98KiiIiMDa+rtTWqKBo7dqzmz5WVlVi+fDkiIyPRq1cvALduMP3rr78we/Zsg4S80+zZs1vkOERERI1lZWUldQQiIp219HV1RUVFrccWtJbZ6BpVFC1YsEDz53/84x949tln8c4779Rqc/nyZf2mIyIiMgIymUzqCERErVJ5eTlefvllrFu3Dvn5+bXWq1QqCVLVpvO/4j/99BOmTJlSa/nkyZPx888/6yUUEREREREZv7lz52LXrl1Yvnw5FAoFvvrqK7z11lvw9fXFt99+K3U8DZ2LIhsbGyQnJ9danpyczDHVRERERESksXnzZixfvhwPPvggLCws0K9fP7zxxht49913sWbNGqnjaeg8+9zzzz+PJ598EqmpqZoH1e3fvx9ff/013nzzTb0HJCIiau0M/cBDIiJjVVBQgJCQEAC37h8qKCgAAPTt2xdPPvmklNG06FwUvfLKKwgNDcXHH3+M77//HgDQvn17rF69Gg899JDeAxIREbV2oihKHYGIqFUKDQ1FZmYmgoKCEBkZiXXr1qF79+7YvHkznJ2dpY6n0aTnFD300EMsgIiIiP5HLpdLHYGIqFWaPn06jh49iri4OLz66qsYMWIEPvnkE9TU1GDZsmVSx9Mwqoe3EhERERGR8XjhhRc0f46Pj8fp06dx+PBhhIWFoVOnThIm08Y5RImIiIjIZN39XBxqWd9++y2USqXmdWBgIMaNG4f27dsb9+xzRERERETGoqqqSuoIZm369OkoKiqqtbykpATTp0+XIFHdWBQRERERkcniRCjSqm92zitXrsDJyUmCRHXjPUVEREREZLJYFEmjc+fOEAQBgiBg0KBBsLD4u+xQqVTIyMjA0KFDJUyoTeeiSKVSYfXq1di5cydyc3OhVqu11u/atUtv4YiIiIiImoP3FElj7NixAID09HQMGTIE9vb2mnVWVlYIDg7G+PHjJUpXm85F0XPPPYfVq1djxIgR6NChAx9WR0RERESt1p03+VPLWbBgAQAgODgYEydOhLW1tcSJGqZzUbR27VqsW7cOw4cPN0QeIiIiIiK9qaysrPe+FjK8qVOnSh2hUXQuiqysrBAeHm6ILEREREREeqVSqaBUKlt9T4UpcXFxaXQRWlBQYOA0jaNzUfTSSy/h448/xqeffsqKm4iIiIhaveLiYhZFLeijjz6SOoLOdC6KkpOTkZCQgN9//x1RUVGwtLTUWr9hwwa9hSMiIiIiaq68vDx4enpKHcNsGMuQuTvpXBQ5OzvjgQceMEQWIiIiIiK9y8rKQmRkpNQxzNaFCxewatUqXLhwAR9//DE8PT2xbds2BAQEICoqSup4AJpQFK1atcoQOYiIiIiIDOLy5csoKipqVQ8LNRdJSUkYNmwY+vTpg927d2Px4sXw9PTEsWPH8NVXX2H9+vVSRwQAyKQOQERERERkSKIo4sCBA1LHMEuvvPIKFi1ahB07dsDKykqzPD4+Hvv27ZMwmTade4oAYP369Vi3bh2ysrJQVVWltS4tLU0vwYiIiIiI9CUzMxMXL15EaGio1FHMyvHjx/H999/XWu7h4YH8/HwJEtVN556if//735g+fTo8PT1x5MgRdO/eHW5ubrh48SKGDRtmiIxERERERDqLjY1F3759sXjxYgDAnj17UFZWJnEq8+Ls7Izs7Oxay48cOQI/Pz8JEtVN56Jo+fLl+PLLL/Hpp5/CysoKL7/8Mnbs2IFnn30WRUVFhshIRERERKSznJwcXL9+HcXFxQAApVKJnTt3Qq1WS5zMfDzyyCOYN28ecnJyIAgC1Go1UlJSMGfOHEyZMkXqeBo6F0VZWVno3bs3AMDGxgYlJSUAgMceeww//PCDftMREREREelRTk4OkpOTIYqi1FHMwuLFixEYGAg/Pz+UlpYiMjIS/fv3R+/evfHGG29IHU9D53uKvL29kZ+fj6CgIAQFBWH//v3o1KkTMjIy+JeLiIiIiFq906dPw8bGBrGxsRAEQeo4Js3S0hJr1qzB22+/jSNHjkCtVqNz585o06aN1NG06FwUDRw4EJs3b0aXLl0wc+ZMvPDCC1i/fj0OHz6McePGGSIjEREREZFeHTlyBIIgoGvXriyMWkBYWBjCwsKkjlEvnYuiL7/8UjMOc9asWXB1dUVycjJGjRqFWbNm6T0gEREREZEhpKWlQaVSoXv37iyM9OjFF19sdNtly5YZMEnj6VwUyWQyyGR/34r00EMP4aGHHtJrKCIiIiKilnD06FFUVlaib9++kMvlUscxCUeOHNF6nZqaCpVKhYiICADA2bNnIZfL0bVrVyni1alJzynas2cPvvjiC1y4cAHr16+Hn58fvvvuO4SEhKBv3776zkhEREREZDBnzpxBYWEhBg4cCEdHR6njGL2EhATNn5ctWwYHBwd88803cHFxAQAUFhZi+vTp6Nevn1QRa9F59rmff/4ZQ4YMgY2NDY4cOQKlUgkAKCkpwbvvvqv3gEREREREhpabm4v169fj2LFjnLJbj5YuXYolS5ZoCiIAcHFxwaJFi7B06VIJk2nTuShatGgRVqxYgZUrV8LS0lKzvHfv3khLS9NrOCIiIiKillJTU4P9+/fjl19+wfXr16WOYxKKi4vr/H+Zm5urebRPa6BzUXTmzBn079+/1nJHR0fcvHlTH5mIiIiIiCSTn5+PX3/9Fbt379aMiqKmeeCBBzB9+nSsX78eV65cwZUrV7B+/XrMnDmzVc1crfM9RT4+Pjh//jyCg4O1licnJyM0NFRfuYiIiIiIJHX69GlcunQJvXv3RmhoKGeoa4IVK1Zgzpw5mDx5MqqrqwEAFhYWmDlzJj744AOJ0/1N556i//u//8Nzzz2HAwcOQBAEXLt2DWvWrMGcOXMwe/ZsQ2QkIiIiIpJERUUFdu7ciT/++ANlZWVSxzE6tra2WL58OfLz83HkyBGkpaWhoKAAy5cvh52dndTxNHTuKXr55ZdRVFSE+Ph4VFZWon///lAoFJgzZw6efvppQ2QkIiIiIpLUpUuXkJOTg969eyM8PJy9Rjqys7NDx44dpY5RryZNyb148WK8/vrrOHnyJNRqNSIjI2Fvb6/vbERERERETZKVlYXy8nIAQFVVFQoKCuDq6tqsfSqVSiQkJCAzM1PTMUCmQefhc7fZ2toiNjYW3bt3Z0FERERERK3CwYMHMWrUKAQHB6OwsBAAUF5ejtdeew2fffYZMjMzm32MjIwM/PLLL5r9k/FrdE/RjBkzGtXu66+/bnIYIiIiIqKm2rBhAyZOnAhRFCGKotY6URRx4sQJnDhxAo8//ji6dOnSrGMVFxdj06ZNGD58ODw8PJq1L5Jeo3uKVq9ejYSEBNy8eROFhYX1/hARERERtbSDBw9i4sSJUKlUUKlUdbZRq9VQq9VYuXKlXnqMlEoltm7dymtgE9DonqJZs2Zh7dq1uHjxImbMmIHJkyc3e1wmEREREZE+LFq0qM4eovps3bpVLzMn3y6MRo8eDQcHh2bvj6TR6J6i5cuXIzs7G/PmzcPmzZsREBCAhx56CNu3b2/0Xz4iIiIiIn3LysrCli1b6u0huptarcaxY8dQUFCgl+OXlZVhy5YtKC4u1sv+qOXpNNGCQqHApEmTsGPHDpw8eRJRUVGYPXs2goKCUFpaaqiMRERERET12rlzp85f0ouiiNOnT+stQ0lJCTZv3oybN2/qbZ/Ucpo8+5wgCBAEAaIoQq1W6zMTEREREVGjlZSUQCbT7bJWEARUVlbqNUdZWRm2bt0KpVKp1/2S4en0t0epVOKHH37A4MGDERERgePHj+PTTz9FVlaWwaflXrx4MXr37g1bW1s4Ozsb9FhEREREZDwcHBx0/pJeFEVYW1vrPUtpaSmOHTum9/1KxVyuwRtdFM2ePRs+Pj547733MHLkSFy5cgU//fQThg8frnNl3hRVVVWYMGECnnzySYMfi4iIiIiMx6BBgyAIgk7bCIKAdu3aGSSPKc1GZy7X4I2efW7FihUIDAxESEgIkpKSkJSUVGe7DRs26C3cnd566y0At6YGJyIiIiK6LTAwECNHjsTWrVsbNdmCTCZDdHS0wWZS9vLyMsh+pWAu1+CNLoqmTJmicwVORERERNQS5s+fj99//11zz/u9DB8+3CA5goKC0KFDB4Psmwyn0UWRMVaHSqVS60Y3zpBHREREZJq6deuGH3/8ERMnToQoinX2GN2+5eOJJ55AcHCwXo+vUCgQGxuLyMhIyToSSktLtaYFVygUUCgUkmQxNoa/GagBCxcu1MxiV9/P4cOHm7z/JUuWwMnJSfMTFxenx/RERERE1JqMGzcOe/fuxfDhw2sVJoIgIDo6GvPmzUPnzp31dkxLS0t06dIFkyZNQlRUlKQjq+Li4rSufZcsWVJnO0NfgxujRvcUGcLTTz+Nhx9+uME2zaniX331Vbz44oua1+np6SyMiIiIiExYt27dsGnTJmRlZSEmJgaFhYWwtbXF/Pnz9XoPkUwmQ1RUFDp37myQWeyaIikpCTExMZrX9fUSGfoa3BhJWhS5u7vD3d3dYPu/u8vQ0NOGExEREVHrEBgYCFtbWxQWFsLKykqvBZGXlxf69+8PFxcXve1TH+zt7eHo6HjPdoa+BjdGkhZFusjKykJBQQGysrKgUqmQnp4OAAgPD2exQ0REREQGZ2lpiW7dukk+TK4lmcs1uNEURW+++Sa++eYbzevbY0ETEhIwYMAAiVIRERERkTkIDQ1Fz549TaoQaAxzuQY3mqJo9erVRjkDHhEREREZLy8vL/To0QPe3t5SR5GEuVyDG01RRERERETUUpycnNC9e3cEBwebzVA5c8aiiIiIiIjofywtLREbG4uoqCjNc43I9LEoIiIiIiLCrRnr+vXrBzs7O6mjUAtjUUREREREZk0mk6Fnz55mNascaWNRRERERERmS6FQ4P7774ePj4/UUUhCLIqIiIiIyCzZ2dlh+PDhre4hrNTyWBQRERERkdmxt7fHqFGj4ODgIHUUagU4pQYRERERmRUbGxuMGDGCBRFpsCgiIiIiIrMhk8kwePBgODk5SR2FWhEOnyMiIiIik+Tt7Y2amhooFArNsl69esHb21vCVNQasSgiIiIiIpN0+PBhnD9/Hrt27QIAhISEIDIyUuJU1Bpx+BwRERERmTwrKyv07duXzyGiOrEoIiIiIiKTFxkZCRsbG6ljUCvFooiIiIiITF7btm2ljkCtGIsiIiIiIjJpzs7OcHZ2ljoGtWIsioiIiIjIpPn7+0sdgVo5FkVEREREZNK8vLykjkCtHIsiIiIiIjJprq6uUkegVo5FERERERGZLEEQ4OjoKHUMauVYFBERERGRybK2toZcLpc6BrVyLIqIiIiIyGRZW1tLHYGMAIsiIiIiIjJZ7CWixmBRREREREQmSxAEqSOQEWBRREREREQmSybj5S7dG/+WEBEREZHJ4vA5agwWRURERERkslgUUWOwKCIiIiIik8Xhc9QY/FtCRERERCaLEy1QY7AoIiIiIiKTxeFz1BgsioiIiIjIZLGniBqDRREREREREZk1FkVERERERGTWWBQREREREZFZY1FERERERERmjUURERERERGZNRZFRERERERk1iykDkCGkZ2djezsbKljkJ74+PjAx8dH6hikJzw/TQ/PUdPCc9S08PykxjCrosjHxwcLFiww+RNDqVRi0qRJSEpKkjoK6UlcXBy2b98OhUIhdRRqJp6fponnqOngOWp6zOH8NJdrXEMSRFEUpQ5B+lVcXAwnJyckJSXB3t5e6jjUTKWlpYiLi0NRUREcHR2ljkPNxPPT9PAcNS08R00Lz09qLLPqKTI3MTEx/AfABBQXF0sdgQyA56fp4DlqmniOmgaen9RYnGiBiIiIiIjMGosiIiIiIiIyayyKTJBCocCCBQtM+oZCc8LP07Tw8zQ9/ExNCz9P08LPkxqLEy0QEREREZFZY08RERERERGZNRZFRERERERk1lgUERERERGRWWNRRCSx4OBgfPTRRy12vMTERAiCgJs3b7bYMYmIyLwNGDAAzz//fKParl69Gs7OzgbNc6eFCxciJiam0e0zMzMhCALS09MNlolaHosiogZMmzYNgiBg1qxZtdbNnj0bgiBg2rRp9W5/uwC5/ePh4YFhw4bh6NGjBkxNRHfS53ksk8ng5OSEzp074+WXX0Z2drYBkxMRoHvR0tL4ZaNpYFFEdA8BAQFYu3YtKioqNMsqKyvxww8/IDAwsFH7OHPmDLKzs/Hbb7+hsLAQQ4cORVFRkaEiE9Fd9HUeX7t2DYcOHcK8efPw559/okOHDjh+/Hi921RVVTU7OxERGR6LIqJ76NKlCwIDA7FhwwbNsg0bNiAgIACdO3du1D48PT3h7e2N7t27Y+nSpcjJycH+/fvrbLts2TJER0fDzs4OAQEBmD17NkpLS7XapKSkIC4uDra2tnBxccGQIUNQWFgIABBFEe+//z5CQ0NhY2ODTp06Yf369bWOk5KSgk6dOsHa2ho9evSodWH3888/IyoqCgqFAsHBwVi6dGmj3itRa6TP87ht27Z4+OGHkZKSAg8PDzz55JOaNtOmTcPYsWOxZMkS+Pr6om3btgAAQRCwceNGrf05Oztj9erVmtd79+5FTEwMrK2tERsbi40bN3KIDhmlsrIyTJkyBfb29vDx8an1+6Oqqgovv/wy/Pz8YGdnhx49eiAxMbHOfa1evRpvvfUWjh49qumxvX3eNOb3ZV3++c9/wsvLCw4ODpg5cyYqKytrtVm1ahXat28Pa2trtGvXDsuXL69zX5mZmYiPjwcAuLi4aPU8b9u2DX379oWzszPc3NwwcuRIXLhw4Z75SBosiogaYfr06Vi1apXm9ddff40ZM2Y0aV82NjYAgOrq6jrXy2Qy/Pvf/8aJEyfwzTffYNeuXXj55Zc169PT0zFo0CBERUVh3759SE5OxqhRo6BSqQAAb7zxBlatWoXPP/8cf/31F1544QVMnjwZSUlJWseZO3cuPvzwQxw6dAienp4YPXq0JlNqaioeeughPPzwwzh+/DgWLlyI+fPna13AERkbfZ7HwK1zedasWUhJSUFubq5m+c6dO3Hq1Cns2LEDW7ZsadS+SkpKMGrUKERHRyMtLQ3vvPMO5s2b1+RsRFKaO3cuEhIS8Msvv+CPP/5AYmIiUlNTNeunT5+OlJQUrF27FseOHcOECRMwdOhQnDt3rta+Jk6ciJdeeglRUVHIzs5GdnY2Jk6cCODevy/rsm7dOixYsACLFy/G4cOH4ePjU6vgWblyJV5//XUsXrwYp06dwrvvvov58+fjm2++qbW/gIAA/PzzzwD+HhXy8ccfA7hVHL744os4dOgQdu7cCZlMhgceeABqtVq3/6HUMkQiqtfUqVPFMWPGiDdu3BAVCoWYkZEhZmZmitbW1uKNGzfEMWPGiFOnTq13+4SEBBGAWFhYKIqiKObl5YmjR48WHRwcxOvXr4uiKIpBQUHiv/71r3r3sW7dOtHNzU3zetKkSWKfPn3qbFtaWipaW1uLe/fu1Vo+c+ZMcdKkSVqZ1q5dq1mfn58v2tjYiD/++KMoiqL4yCOPiIMHD9bax9y5c8XIyMh6cxK1Vvo+j+/0+++/iwDEAwcOaI7l5eUlKpVKrXYAxF9++UVrmZOTk7hq1SpRFEXx888/F93c3MSKigrN+pUrV4oAxCNHjjTlbRNJoqSkRLSysqrzd8xzzz0nnj9/XhQEQbx69arWdoMGDRJfffVVURRFcdWqVaKTk5Nm3YIFC8ROnTrd89h3/76sS69evcRZs2ZpLevRo4fW/gMCAsTvv/9eq80777wj9urVSxRFUczIyNA6Nxv6N+JOubm5IgDx+PHj93wv1PIsJKvGiIyIu7s7RowYgW+++QaiKGLEiBFwd3dv9Pb+/v4Abn1r1KZNG/z000/w9PSss21CQgLeffddnDx5EsXFxaipqUFlZSXKyspgZ2eH9PR0TJgwoc5tT548icrKSgwePFhreVVVVa0hQr169dL82dXVFRERETh16hQA4NSpUxgzZoxW+z59+uCjjz6CSqWCXC5v9Hsnai2aex7XRRRFALeGx90WHR0NKysrnfZz5swZdOzYEdbW1ppl3bt3b1Y2IilcuHABVVVVdf6OAYC0tDSIoqgZWnqbUqmEm5ubTse61+9Le3t7TdvJkydjxYoVOHXqVK1JV3r16oWEhAQAwI0bN3D58mXMnDkTjz/+uKZNTU0NnJycdMp34cIFzJ8/H/v370deXp6mhygrKwsdOnTQaV9keCyKiBppxowZePrppwEAn332mU7b7tmzB46OjvDw8ICjo2O97S5duoThw4dj1qxZeOedd+Dq6ork5GTMnDlTM7Tt9vC7utz+B/e3336Dn5+f1jqFQnHPnLcv7ERR1LrIu72MyNg15zyuy+0vEoKDgzXL7OzsarUTBKHWOXTnEFqec2Qq7vX3Vq1WQy6XIzU1tdYXbHcWMffSmN+Xd96P19Dv3rvzAbeG0PXo0UNrna5fCI4aNQoBAQFYuXIlfH19oVar0aFDB07A0kqxKCJqpKFDh2r+IRsyZIhO24aEhDTqmQuHDx9GTU0Nli5dCpns1i1/69at02rTsWNH7Ny5E2+99Vat7SMjI6FQKJCVlYW4uLgGj7V//37NrFuFhYU4e/Ys2rVrp9lPcnKyVvu9e/eibdu27CUio9ac8/huFRUV+PLLL9G/f394eHg02NbDw0Nr+u5z586hvLxc87pdu3ZYs2YNlEql5guMw4cPNysfkRTCw8NhaWlZ5++YuLg4dO7cGSqVCrm5uejXr1+j9mllZaW5b/a2xvy+DA8Pr7Wv9u3bY//+/ZgyZYpm2Z0TH3l5ecHPzw8XL17Eo48+2uh8ALQy5ufn49SpU/jiiy807/Pu36vUurAoImokuVyu+VbYUIVBWFgYampq8Mknn2DUqFFISUnBihUrtNq8+uqriI6OxuzZszFr1ixYWVkhISEBEyZMgLu7O+bMmYMXXngBarUaffv2RXFxMfbu3Qt7e3tMnTpVs5+3334bbm5u8PLywuuvvw53d3eMHTsWAPDSSy+hW7dueOeddzBx4kTs27cPn376ab2z7xAZi+acx7m5uaisrERJSQlSU1Px/vvvIy8vT2tGu/oMHDgQn376KXr27Am1Wo158+bB0tJSs/6RRx7B66+/jieeeAKvvPIKsrKy8OGHHwJArR4kotbM3t4eM2fOxNy5c7V+x9wuXNq2bYtHH30UU6ZMwdKlS9G5c2fk5eVh165diI6OxvDhw2vtMzg4GBkZGUhPT4e/vz8cHBwa9fuyLs899xymTp2K2NhY9O3bF2vWrMFff/2F0NBQTZuFCxfi2WefhaOjI4YNGwalUonDhw+jsLAQL774Yq19BgUFQRAEbNmyBcOHD4eNjQ1cXFzg5uaGL7/8Ej4+PsjKysIrr7zSjP+zZHBS3cxEZAxu36Bdn+bcoH3b3RMtLFu2TPTx8RFtbGzEIUOGiN9++22tfSQmJoq9e/cWFQqF6OzsLA4ZMkSzXq1Wix9//LEYEREhWlpaih4eHuKQIUPEpKQkrUybN28Wo6KiRCsrK7Fbt25ienq6Vq7169eLkZGRoqWlpRgYGCh+8MEH9b4HotZMX+cxAFEQBNHBwUHs1KmTOHfuXDE7O7tRx7p69ap4//+3d+9BUVb/H8DfCwvITXEEYVcuAgtIyEUiG2BiAW+VTqEENlNeS4dQQDIzS4lxZBpUTHMGQWaarCGLP/gDk0sN7dp6KUHkYgGSwdAMkI1XLhLont8f/XzG1YW0LyvYvl8zzHDOc855zmHmo89nn+c5u3ChsLe3F35+fqK8vNxgowUhhDh16pQICQkR1tbW4umnnxZffvmlACBaWloeccVE46u3t1e8/vrrws7OTri6uordu3cLtVotMjIyhBBCDA0NiaysLDFz5kxhZWUl3NzcxNKlS0VjY6MQ4sGNFgYHB0ViYqJwcnISAKS4eZj/L43JyckRzs7OwsHBQaxatUq8++67D2zkUFxcLMLCwoS1tbWYOnWqiImJEaWlpUKIBzdaEEKInTt3Cjc3NyGTyaR/T7777jsRGBgobGxsREhIiNBqtUY3XaGJQSYEH1omIiKaaIqLi7FmzRrcuHFj1HcJiYjof8fH54iIiCaAzz//HD4+PpgxYwYaGhqwdetWJCcnMyEiInoMmBQRERFNAD09PcjKykJPTw8UCgWSkpKQk5Mz3tMiIjILfHyOiIiIiIjMmsV4T4CIiIiIiGg8MSkimgC0Wi1kMhmuX78+3lMhIiMYo0RE/218fI5oAhgaGsLVq1fh6urK7yQhmoAYo0RE/21MioiIiIiIyKzx8TkiE4iNjUVaWho2bdqEqVOnwtXVFYcPH0Z/fz/WrFkjfRt3RUUFgAcfzfnss8/g5OSEqqoqBAYGwsHBAc8//zy6u7sNzrFp0yaD8yYkJGD16tVSOT8/H35+fpg0aRJcXV3xyiuvmHrpRE8ExigREd2LSRGRiRw5cgTOzs44e/Ys0tLS8NZbbyEpKQlRUVGoq6vDokWLsGLFCgwMDBjtPzAwgL179+KLL77ADz/8gM7OTrzzzjsPff7a2lqkp6dj586daG1tRWVlJWJiYsZqeURPPMYoERHdxaSIyERCQ0Oxfft2+Pn5Ydu2bbC1tYWzszPWrVsHPz8/ZGVl4cqVK2hsbDTaf3h4GAUFBYiIiEB4eDg2btyI6urqhz5/Z2cn7O3tsWTJEnh5eWHOnDlIT08fq+URPfEYo0REdBeTIiITCQkJkX63tLTEtGnTEBwcLNW5uroCAC5fvmy0v52dHXx9faWyQqEYsa0xCxYsgJeXF3x8fLBixQoUFxeP+Ik3kTlijBIR0V1MiohMxMrKyqAsk8kM6u7uYKXX6x+6/737olhYWOD+fVKGh4el3x0dHVFXV4ejR49CoVAgKysLoaGh3FKY6P8xRomI6C4mRURPKBcXF4OXuu/cuYMLFy4YtJHL5Zg/fz52796NxsZGdHR04Pvvv3/cUyUyS4xRIqInh3y8J0BE/058fDzefvttHD9+HL6+vvj4448NPmH+5ptv8NtvvyEmJgZTp05FeXk59Ho9AgICxm/SRGaEMUpE9ORgUkT0hFq7di0aGhqwcuVKyOVyZGZmIi4uTjru5OSE0tJSZGdnY3BwEH5+fjh69CiCgoLGcdZE5oMxSkT05OCXtxIRERERkVnjO0VERERERGTWmBQREREREZFZY1JERERERERmjUkRERERERGZNSZFROOso6MDMpkM9fX1j+2c2dnZCAsLe2znIyIiIprImBQRGVFQUABHR0fcvn1bquvr64OVlRWee+45g7Y6nQ4ymQwXL140OlZ2djZkMhlkMhksLS3h4eGBN998E3/++adJ10BEpotluVwOZ2dnxMTEYP/+/fjrr79Mug4iIjItJkVERsTFxaGvrw+1tbVSnU6ng5ubG2pqajAwMCDVa7VaKJVK+Pv7jzheUFAQuru70dnZiUOHDuHYsWNYuXKlSddARKaNZY1Gg6SkJHz00UeIiopCb2/viP2GhobGZkFERGQSTIqIjAgICIBSqYRWq5XqtFotXn75Zfj6+uL06dMG9fd+IaMxcrkcbm5umDFjBpYsWYL09HR8++23uHXr1gNt79y5gzfeeAPe3t6wtbVFQEAADhw48EC7Tz/9FEFBQbCxsYFCocDGjRulYzdu3MD69esxffp0TJ48GfHx8WhoaHhgjMLCQnh4eMDOzg5JSUm4fv26dEyv12Pnzp1wd3eHjY0NwsLCUFlZOeo6iSYaU8WyUqlEcHAw0tLScOLECVy4cAG5ublSu5kzZ2LXrl1YvXo1pkyZgnXr1kGr1UImkxnEWX19PWQyGTo6OqS6oqIiKS6XLl2Kffv2wcnJ6X/9UxAR0SiYFBGNIDY2FhqNRiprNBrExsZCrVZL9UNDQzhz5sw/Xkjdz9bWFnq93uCRnrv0ej3c3d1RUlKCX375BVlZWXj//fdRUlIitTl06BA2bNiA9evXo6mpCWVlZVCpVAAAIQQWL16Mnp4elJeX49y5cwgPD8e8efNw9epVaYxff/0VJSUlOHbsGCorK1FfX48NGzZIxw8cOIC8vDzs3bsXjY2NWLRoEV566SW0tbU90lqJxpspYxkAZs2ahRdeeAGlpaUG9Xv27MHs2bNx7tw57Nix46HGOnXqFFJSUpCRkYH6+nosWLAAOTk5jzwnIiJ6RIKIjDp8+LCwt7cXw8PD4ubNm0Iul4s//vhDfPXVVyIqKkoIIcSJEycEAHHp0qURx/nwww9FaGioVG5ubhYqlUrMnTtXCCFEe3u7ACDOnz8/4hipqakiMTFRKiuVSvHBBx8YbVtdXS0mT54sBgcHDep9fX1FYWGhNCdLS0vx+++/S8crKiqEhYWF6O7uls6Rk5NjMMYzzzwjUlNTR5wn0URkqli+19atW4Wtra1U9vLyEgkJCQZtNBqNACCuXbsm1Z0/f14AEO3t7UIIIZYvXy4WL15s0O+1114TU6ZMefgFExHRI+OdIqIRxMXFob+/HzU1NdDpdPD398f06dOhVqtRU1OD/v5+aLVaeHp6wsfHZ9Sxmpqa4ODgAFtbWzz11FPw8PBAcXHxiO0LCgoQEREBFxcXODg4oKioCJ2dnQCAy5cvo6urC/PmzTPa99y5c+jr68O0adPg4OAg/bS3t+PSpUtSO09PT7i7u0vlyMhI6PV6tLa24ubNm+jq6kJ0dLTB2NHR0Whubv7Hvx3RRDKWsTwSIQRkMplBXURExCOP09rairlz5xrU3V8mIqKxJx/vCRBNVCqVCu7u7tBoNLh27RrUajUAwM3NDd7e3jh16hQ0Gg3i4+P/cayAgACUlZXB0tISSqUSNjY2I7YtKSlBZmYm8vLyEBkZCUdHR+zZswc//fQTgL8fvRuNXq+HQqEweIfirtHeS7h7QXfvhd39F3nGLvyIJrqxjOWRNDc3w9vb26DO3t7eoGxh8ffnkEIIqW54eNigjbEYu7c9ERGZBu8UEY0iLi4OWq0WWq0WsbGxUr1arUZVVRV+/PHHh3oHwdraGiqVCt7e3qMmRMDfO2NFRUUhNTUVc+bMgUqlMrjD4+joiJkzZ6K6utpo//DwcPT09EAul0OlUhn8ODs7S+06OzvR1dUllc+cOQMLCwv4+/tj8uTJUCqVOHnypMHYp0+fRmBg4D+ul2iiGatYNqalpQWVlZVITEwctZ2LiwsAoLu7W6q7//vJZs2ahbNnzxrU3btzHhERmQaTIqJRxMXF4eTJk6ivr5c+XQb+vpAqKirC4ODgv76QGolKpUJtbS2qqqpw8eJF7NixAzU1NQZtsrOzkZeXh08++QRtbW2oq6vDwYMHAQDz589HZGQkEhISUFVVhY6ODpw+fRrbt283uLiaNGkSVq1ahYaGBuh0OqSnpyM5ORlubm4AgC1btiA3Nxdff/01Wltb8d5776G+vh4ZGRljul6ix2GsYvn27dvo6elBV1cXmpqacPDgQajVaoSFhWHLli2j9lWpVPDw8EB2djYuXryI48ePIy8vz6BNWloaysvLsW/fPrS1taGwsBAVFRW8Q0tEZGJMiohGERcXh1u3bkGlUsHV1VWqV6vV6O3tha+vLzw8PMb0nCkpKVi2bBmWL1+OZ599FleuXEFqaqpBm1WrVmH//v3Iz89HUFAQlixZIu0KJ5PJUF5ejpiYGKxduxb+/v549dVX0dHRYbAGlUqFZcuW4cUXX8TChQsxe/Zs5OfnS8fT09OxefNmbN68GcHBwaisrERZWRn8/PzGdL1Ej8NYxfLPP/8MhUIBT09PxMbGoqSkBNu2bYNOp4ODg8Oofa2srHD06FG0tLQgNDQUubm52LVrl0Gb6OhoFBQUYN++fQgNDUVlZSUyMzMxadKkf7dwIiJ6KDLBh5WJiIgmrHXr1qGlpQU6nW68p0JE9J/FjRaIiIgmkL1792LBggWwt7dHRUUFjhw5YnAXl4iIxh7vFBEREU0gycnJ0Gq16O3thY+PD9LS0pCSkjLe0yIi+k9jUkRERERERGaNGy0QEREREZFZY1JERERERERmjUkRERERERGZNSZFRERERERk1pgUERERERGRWWNSREREREREZo1JERERERERmTUmRUREREREZNaYFBERERERkVn7Pw2xMU9O3TTcAAAAAElFTkSuQmCC", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "np.random.seed(9999) # Fix the seed so the results are replicable.\n", - "N = 20\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", - "# 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", - "# 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", - "# 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", - "genotype = wt + mt + wt2 + mt2\n", - "# Add an `id` column for paired data plotting.\n", - "id = list(range(0, N*2))\n", - "id_col = id + id \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", - "unpaired_delta2 = dabest.load(data = df_delta2, x = [\"Genotype\", \"Genotype\"], y = \"Y\", delta2 = True, experiment = \"Treatment\")\n", - "unpaired_delta2.mean_diff.plot();" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "24c4b036", - "metadata": {}, - "outputs": [], - "source": [ - "#| export\n", - "class MiniMetaDelta(object):\n", - " \"\"\"\n", - " A class to compute and store the weighted delta.\n", - " A weighted delta is calculated if the argument ``mini_meta=True`` is passed during ``dabest.load()``.\n", - " \n", - " \"\"\"\n", - "\n", - " def __init__(self, effectsizedataframe, permutation_count,\n", - " ci=95):\n", - "\n", - " import numpy as np\n", - " from numpy import sort as npsort\n", - " from numpy import sqrt, isinf, isnan\n", - " from ._stats_tools import effsize as es\n", - " from ._stats_tools import confint_1group as ci1g\n", - " from ._stats_tools import confint_2group_diff as ci2g\n", - "\n", - "\n", - " from string import Template\n", - " import warnings\n", - " \n", - " self.__effsizedf = effectsizedataframe.results\n", - " self.__dabest_obj = effectsizedataframe.dabest_obj\n", - " self.__ci = ci\n", - " self.__resamples = effectsizedataframe.resamples\n", - " self.__alpha = ci2g._compute_alpha_from_ci(ci)\n", - " self.__permutation_count = permutation_count\n", - " self.__bootstraps = np.array(self.__effsizedf[\"bootstraps\"])\n", - " self.__control = np.array(self.__effsizedf[\"control\"])\n", - " self.__test = np.array(self.__effsizedf[\"test\"])\n", - " self.__control_N = np.array(self.__effsizedf[\"control_N\"])\n", - " self.__test_N = np.array(self.__effsizedf[\"test_N\"])\n", - "\n", - "\n", - " idx = self.__dabest_obj.idx\n", - " dat = self.__dabest_obj._plot_data\n", - " xvar = self.__dabest_obj._xvar\n", - " yvar = self.__dabest_obj._yvar\n", - "\n", - " # compute the variances of each control group and each test group\n", - " control_var=[]\n", - " test_var=[]\n", - " for j, current_tuple in enumerate(idx):\n", - " cname = current_tuple[0]\n", - " control = dat[dat[xvar] == cname][yvar].copy()\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_var.append(np.var(test, ddof=1))\n", - " self.__control_var = np.array(control_var)\n", - " self.__test_var = np.array(test_var)\n", - "\n", - " # Compute pooled group variances for each pair of experiment groups\n", - " # based on the raw data\n", - " self.__group_var = ci2g.calculate_group_var(self.__control_var, \n", - " self.__control_N,\n", - " self.__test_var, \n", - " self.__test_N)\n", - "\n", - " # Compute the weighted average mean differences of the bootstrap data\n", - " # using the pooled group variances of the raw data as the inverse of \n", - " # weights\n", - " self.__bootstraps_weighted_delta = ci2g.calculate_weighted_delta(\n", - " self.__group_var, \n", - " self.__bootstraps, \n", - " self.__resamples)\n", - "\n", - " # Compute the weighted average mean difference based on the raw data\n", - " self.__difference = es.weighted_delta(self.__effsizedf[\"difference\"],\n", - " self.__group_var)\n", - "\n", - " sorted_weighted_deltas = npsort(self.__bootstraps_weighted_delta)\n", - "\n", - "\n", - " self.__bias_correction = ci2g.compute_meandiff_bias_correction(\n", - " self.__bootstraps_weighted_delta, self.__difference)\n", - " \n", - " self.__jackknives = np.array(ci1g.compute_1group_jackknife(\n", - " self.__bootstraps_weighted_delta, \n", - " np.mean))\n", - "\n", - " self.__acceleration_value = ci2g._calc_accel(self.__jackknives)\n", - "\n", - " # Compute BCa intervals.\n", - " bca_idx_low, bca_idx_high = ci2g.compute_interval_limits(\n", - " self.__bias_correction, self.__acceleration_value,\n", - " self.__resamples, ci)\n", - " \n", - " self.__bca_interval_idx = (bca_idx_low, bca_idx_high)\n", - "\n", - " if ~isnan(bca_idx_low) and ~isnan(bca_idx_high):\n", - " self.__bca_low = sorted_weighted_deltas[bca_idx_low]\n", - " self.__bca_high = sorted_weighted_deltas[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 should be considered unstable.\"\n", - " err_temp = Template(\" \".join([err1, err2, err3]))\n", - "\n", - " if bca_idx_low <= 10:\n", - " warnings.warn(err_temp.substitute(lim_type=\"lower\",\n", - " loc=\"bottom\"),\n", - " stacklevel=1)\n", - "\n", - " if bca_idx_high >= self.__resamples-9:\n", - " warnings.warn(err_temp.substitute(lim_type=\"upper\",\n", - " loc=\"top\"),\n", - " stacklevel=1)\n", - "\n", - " else:\n", - " err1 = \"The $lim_type limit of the BCa interval 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.__bca_low = self.__difference\n", - " warnings.warn(err_temp.substitute(lim_type=\"lower\"),\n", - " stacklevel=0)\n", - "\n", - " if isnan(bca_idx_high):\n", - " self.__bca_high = self.__difference\n", - " warnings.warn(err_temp.substitute(lim_type=\"upper\"),\n", - " 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.__pct_interval_idx = (pct_idx_low, pct_idx_high)\n", - " self.__pct_low = sorted_weighted_deltas[pct_idx_low]\n", - " self.__pct_high = sorted_weighted_deltas[pct_idx_high]\n", - " \n", - " \n", - "\n", - " def __permutation_test(self):\n", - " \"\"\"\n", - " Perform a permutation test and obtain the permutation p-value\n", - " based on the permutation data.\n", - " \"\"\"\n", - " import numpy as np\n", - " self.__permutations = np.array(self.__effsizedf[\"permutations\"])\n", - " self.__permutations_var = np.array(self.__effsizedf[\"permutations_var\"])\n", - "\n", - " THRESHOLD = np.abs(self.__difference)\n", - "\n", - " all_num = []\n", - " all_denom = []\n", - "\n", - " groups = len(self.__permutations)\n", - " for i in range(0, len(self.__permutations[0])):\n", - " weight = [1/self.__permutations_var[j][i] for j in range(0, groups)]\n", - " all_num.append(np.sum([weight[j]*self.__permutations[j][i] for j in range(0, groups)]))\n", - " all_denom.append(np.sum(weight))\n", - " \n", - " output=[]\n", - " for i in range(0, len(all_num)):\n", - " output.append(all_num[i]/all_denom[i])\n", - " \n", - " self.__permutations_weighted_delta = np.array(output)\n", - "\n", - " count = sum(np.abs(self.__permutations_weighted_delta)>THRESHOLD)\n", - " self.__pvalue_permutation = count/self.__permutation_count\n", - "\n", - "\n", - "\n", - " def __repr__(self, header=True, sigfig=3):\n", - " from .__init__ import __version__\n", - " import datetime as dt\n", - " import numpy as np\n", - "\n", - " from .misc_tools import print_greeting\n", - " \n", - " is_paired = self.__dabest_obj.is_paired\n", - "\n", - " PAIRED_STATUS = {'baseline' : 'paired', \n", - " 'sequential' : 'paired',\n", - " 'None' : 'unpaired'\n", - " }\n", - "\n", - " first_line = {\"paired_status\": PAIRED_STATUS[str(is_paired)]}\n", - " \n", - "\n", - " out1 = \"The weighted-average {paired_status} mean differences \".format(**first_line)\n", - " \n", - " base_string_fmt = \"{:.\" + str(sigfig) + \"}\"\n", - " if \".\" in str(self.__ci):\n", - " ci_width = base_string_fmt.format(self.__ci)\n", - " else:\n", - " ci_width = str(self.__ci)\n", - " \n", - " ci_out = {\"es\" : base_string_fmt.format(self.__difference),\n", - " \"ci\" : ci_width,\n", - " \"bca_low\" : base_string_fmt.format(self.__bca_low),\n", - " \"bca_high\" : base_string_fmt.format(self.__bca_high)}\n", - " \n", - " out2 = \"is {es} [{ci}%CI {bca_low}, {bca_high}].\".format(**ci_out)\n", - " out = out1 + out2\n", - "\n", - " if header is True:\n", - " out = print_greeting() + \"\\n\" + \"\\n\" + out\n", - "\n", - "\n", - " pval_rounded = base_string_fmt.format(self.pvalue_permutation)\n", - "\n", - " \n", - " p1 = \"The p-value of the two-sided permutation t-test is {}, \".format(pval_rounded)\n", - " p2 = \"calculated for legacy purposes only. \"\n", - " pvalue = p1 + p2\n", - "\n", - "\n", - " bs1 = \"{} bootstrap samples were taken; \".format(self.__resamples)\n", - " bs2 = \"the confidence interval is bias-corrected and accelerated.\"\n", - " bs = bs1 + bs2\n", - "\n", - " pval_def1 = \"Any p-value reported is the probability of observing the\" + \\\n", - " \"effect size (or greater),\\nassuming the null hypothesis of\" + \\\n", - " \"zero difference is true.\"\n", - " pval_def2 = \"\\nFor each p-value, 5000 reshuffles of the \" + \\\n", - " \"control and test labels were performed.\"\n", - " pval_def = pval_def1 + pval_def2\n", - "\n", - "\n", - " return \"{}\\n{}\\n\\n{}\\n{}\".format(out, pvalue, bs, pval_def)\n", - "\n", - "\n", - " def to_dict(self):\n", - " \"\"\"\n", - " Returns all attributes of the `dabest.MiniMetaDelta` object as a\n", - " dictionary.\n", - " \"\"\"\n", - " # Only get public (user-facing) attributes.\n", - " attrs = [a for a in dir(self)\n", - " if not a.startswith((\"_\", \"to_dict\"))]\n", - " out = {}\n", - " for a in attrs:\n", - " out[a] = getattr(self, a)\n", - " return out\n", - "\n", - "\n", - " @property\n", - " def ci(self):\n", - " \"\"\"\n", - " Returns the width of the confidence interval, in percent.\n", - " \"\"\"\n", - " return self.__ci\n", - "\n", - "\n", - " @property\n", - " def alpha(self):\n", - " \"\"\"\n", - " Returns the significance level of the statistical test as a float\n", - " between 0 and 1.\n", - " \"\"\"\n", - " return self.__alpha\n", - "\n", - "\n", - " @property\n", - " def bias_correction(self):\n", - " return self.__bias_correction\n", - "\n", - "\n", - " @property\n", - " def bootstraps(self):\n", - " '''\n", - " Return the bootstrapped differences from all the experiment groups.\n", - " '''\n", - " return self.__bootstraps\n", - "\n", - "\n", - " @property\n", - " def jackknives(self):\n", - " return self.__jackknives\n", - "\n", - "\n", - " @property\n", - " def acceleration_value(self):\n", - " return self.__acceleration_value\n", - "\n", - "\n", - " @property\n", - " def bca_low(self):\n", - " \"\"\"\n", - " The bias-corrected and accelerated confidence interval lower limit.\n", - " \"\"\"\n", - " return self.__bca_low\n", - "\n", - "\n", - " @property\n", - " def bca_high(self):\n", - " \"\"\"\n", - " The bias-corrected and accelerated confidence interval upper limit.\n", - " \"\"\"\n", - " return self.__bca_high\n", - "\n", - "\n", - " @property\n", - " def bca_interval_idx(self):\n", - " return self.__bca_interval_idx\n", - "\n", - "\n", - " @property\n", - " def control(self):\n", - " '''\n", - " Return the names of the control groups from all the experiment \n", - " groups in order.\n", - " '''\n", - " return self.__control\n", - "\n", - "\n", - " @property\n", - " def test(self):\n", - " '''\n", - " Return the names of the test groups from all the experiment \n", - " groups in order.\n", - " '''\n", - " return self.__test\n", - " \n", - " @property\n", - " def control_N(self):\n", - " '''\n", - " Return the sizes of the control groups from all the experiment \n", - " groups in order.\n", - " '''\n", - " return self.__control_N\n", - "\n", - "\n", - " @property\n", - " def test_N(self):\n", - " '''\n", - " Return the sizes of the test groups from all the experiment \n", - " groups in order.\n", - " '''\n", - " return self.__test_N\n", - "\n", - "\n", - " @property\n", - " def control_var(self):\n", - " '''\n", - " Return the estimated population variances of the control groups \n", - " from all the experiment groups in order. Here the population \n", - " variance is estimated from the sample variance. \n", - " '''\n", - " return self.__control_var\n", - "\n", - "\n", - " @property\n", - " def test_var(self):\n", - " '''\n", - " Return the estimated population variances of the control groups \n", - " from all the experiment groups in order. Here the population \n", - " variance is estimated from the sample variance. \n", - " '''\n", - " return self.__test_var\n", - "\n", - " \n", - " @property\n", - " def group_var(self):\n", - " '''\n", - " Return the pooled group variances of all the experiment groups \n", - " in order. \n", - " '''\n", - " return self.__group_var\n", - "\n", - "\n", - " @property\n", - " def bootstraps_weighted_delta(self):\n", - " '''\n", - " Return the weighted-average mean differences calculated from the bootstrapped \n", - " deltas and weights across the experiment groups, where the weights are \n", - " the inverse of the pooled group variances.\n", - " '''\n", - " return self.__bootstraps_weighted_delta\n", - "\n", - "\n", - " @property\n", - " def difference(self):\n", - " '''\n", - " Return the weighted-average delta calculated from the raw data.\n", - " '''\n", - " return self.__difference\n", - "\n", - "\n", - " @property\n", - " def pct_interval_idx (self):\n", - " return self.__pct_interval_idx \n", - "\n", - "\n", - " @property\n", - " def pct_low(self):\n", - " \"\"\"\n", - " The percentile confidence interval lower limit.\n", - " \"\"\"\n", - " return self.__pct_low\n", - "\n", - "\n", - " @property\n", - " def pct_high(self):\n", - " \"\"\"\n", - " The percentile confidence interval lower limit.\n", - " \"\"\"\n", - " return self.__pct_high\n", - "\n", - "\n", - " @property\n", - " def pvalue_permutation(self):\n", - " try:\n", - " return self.__pvalue_permutation\n", - " except AttributeError:\n", - " self.__permutation_test()\n", - " return self.__pvalue_permutation\n", - " \n", - "\n", - " @property\n", - " def permutation_count(self):\n", - " \"\"\"\n", - " The number of permuations taken.\n", - " \"\"\"\n", - " return self.__permutation_count\n", - "\n", - " \n", - " @property\n", - " def permutations(self):\n", - " '''\n", - " Return the mean differences of permutations obtained during\n", - " the permutation test for each experiment group.\n", - " '''\n", - " try:\n", - " return self.__permutations\n", - " except AttributeError:\n", - " self.__permutation_test()\n", - " return self.__permutations\n", - "\n", - "\n", - " @property\n", - " def permutations_var(self):\n", - " '''\n", - " Return the pooled group variances of permutations obtained during\n", - " the permutation test for each experiment group.\n", - " '''\n", - " try:\n", - " return self.__permutations_var\n", - " except AttributeError:\n", - " self.__permutation_test()\n", - " return self.__permutations_var\n", - "\n", - " \n", - " @property\n", - " def permutations_weighted_delta(self):\n", - " '''\n", - " Return the weighted-average deltas of permutations obtained \n", - " during the permutation test.\n", - " '''\n", - " try:\n", - " return self.__permutations_weighted_delta\n", - " except AttributeError:\n", - " self.__permutation_test()\n", - " return self.__permutations_weighted_delta\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "id": "ae5bac56", - "metadata": {}, - "source": [ - "The weighted delta is calcuated as follows:\n", - "\n", - "$$\\theta_{\\text{weighted}} = \\frac{\\Sigma\\hat{\\theta_{i}}w_{i}}{{\\Sigma}w_{i}}$$\n", - "\n", - "where:\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", - "$$s_{i}^2 = \\text{Pooled variance for replicate }i = \\frac{(n_{test}-1)s_{test}^2+(n_{control}-1)s_{control}^2}{n_{test}+n_{control}-2}$$\n", - "\n", - "$$n = \\text{sample size and }s^2 = \\text{variance for control/test.}$$\n" - ] - }, - { - "cell_type": "markdown", - "id": "dc1239ee", - "metadata": {}, - "source": [ - "#### Example: mini-meta-delta" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e144ed50", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "DABEST v2023.2.14\n", - "=================\n", - " \n", - "Good morning!\n", - "The current time is Mon Mar 27 01:01:11 2023.\n", - "\n", - "The weighted-average unpaired mean differences is 0.0336 [95%CI -0.137, 0.228].\n", - "The p-value of the two-sided permutation t-test is 0.736, 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", - "For each p-value, 5000 reshuffles of the control and test labels were performed." - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Ns = 20\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", - "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", - "my_df = pd.DataFrame({'Control 1' : c1, 'Test 1' : t1,\n", - " '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" - ] - }, - { - "cell_type": "markdown", - "id": "669285cb", - "metadata": {}, - "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", - "\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": "code", - "execution_count": null, - "id": "6017e0d4", - "metadata": {}, - "outputs": [], - "source": [ - "#| export\n", - "class TwoGroupsEffectSize(object):\n", - "\n", - " \"\"\"\n", - " A class to compute and store the results of bootstrapped\n", - " mean differences between two groups.\n", - " \n", - " Compute the effect size between two groups.\n", - "\n", - " Parameters\n", - " ----------\n", - " control : array-like\n", - " test : array-like\n", - " 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', or 'cliffs_delta'\n", - " is_paired : string, default None\n", - " resamples : int, default 5000\n", - " The number of bootstrap resamples to be taken for the calculation\n", - " of the confidence interval limits.\n", - " permutation_count : int, default 5000\n", - " The number of permutations (reshuffles) to perform for the \n", - " computation of the permutation p-value\n", - " ci : float, default 95\n", - " The confidence interval width. The default of 95 produces 95%\n", - " confidence intervals.\n", - " random_seed : int, default 12345\n", - " `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", - "\n", - " Returns\n", - " -------\n", - " A :py:class:`TwoGroupEffectSize` object:\n", - " `difference` : float\n", - " The effect size of the difference between the control and the test.\n", - " `effect_size` : string\n", - " The type of effect size reported.\n", - " `is_paired` : string\n", - " The type of repeated-measures experiment.\n", - " `ci` : float\n", - " Returns the width of the confidence interval, in percent.\n", - " `alpha` : float\n", - " Returns the significance level of the statistical test as a float between 0 and 1.\n", - " `resamples` : int\n", - " The number of resamples performed during the bootstrap procedure.\n", - " `bootstraps` : numpy ndarray\n", - " The generated bootstraps of the effect size.\n", - " `random_seed` : int\n", - " The number used to initialise the numpy random seed generator, ie.`seed_value` from `numpy.random.seed(seed_value)` is returned.\n", - " `bca_low, bca_high` : float\n", - " The bias-corrected and accelerated confidence interval lower limit and upper limits, respectively.\n", - " `pct_low, pct_high` : float\n", - " The percentile confidence interval lower limit and upper limits, respectively.\n", - " \"\"\"\n", - "\n", - " def __init__(self, control, test, effect_size,\n", - " proportional=False,\n", - " is_paired=None, ci=95,\n", - " resamples=5000, \n", - " permutation_count=5000, \n", - " random_seed=12345):\n", - "\n", - " \n", - " import numpy as np\n", - " from numpy import array, isnan, isinf\n", - " from numpy import sort as npsort\n", - " from numpy.random import choice, seed\n", - "\n", - " import scipy.stats as spstats\n", - "\n", - " # import statsmodels.stats.power as power\n", - " import statsmodels\n", - "\n", - " from string import Template\n", - " import warnings\n", - " \n", - " from ._stats_tools import effsize as es\n", - " from ._stats_tools import confint_2group_diff as ci2g\n", - "\n", - "\n", - " self.__EFFECT_SIZE_DICT = {\"mean_diff\" : \"mean difference\",\n", - " \"median_diff\" : \"median difference\",\n", - " \"cohens_d\" : \"Cohen's d\",\n", - " \"cohens_h\" : \"Cohen's h\",\n", - " \"hedges_g\" : \"Hedges' g\",\n", - " \"cliffs_delta\" : \"Cliff's delta\"}\n", - "\n", - "\n", - " kosher_es = [a for a in self.__EFFECT_SIZE_DICT.keys()]\n", - " if effect_size not in kosher_es:\n", - " err1 = \"The effect size '{}'\".format(effect_size)\n", - " err2 = \"is not one of {}\".format(kosher_es)\n", - " raise ValueError(\" \".join([err1, err2]))\n", - "\n", - " if effect_size == \"cliffs_delta\" and is_paired:\n", - " err1 = \"`paired` is not None; therefore Cliff's delta is not defined.\"\n", - " raise ValueError(err1)\n", - "\n", - " if proportional==True and 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", - " raise ValueError(err1)\n", - "\n", - " if proportional==True and (np.isin(control, [0, 1]).all() == False or np.isin(test, [0, 1]).all() == False):\n", - " err1 = \"`proportional` is True; Only accept binary data consisting of 0 and 1.\"\n", - " raise ValueError(err1)\n", - "\n", - " # Convert to numpy arrays for speed.\n", - " # NaNs are automatically dropped.\n", - " control = array(control)\n", - " test = array(test)\n", - " control = control[~isnan(control)]\n", - " test = test[~isnan(test)]\n", - "\n", - " self.__effect_size = effect_size\n", - " self.__control = control\n", - " self.__test = test\n", - " self.__is_paired = is_paired\n", - " self.__resamples = resamples\n", - " self.__permutation_count = permutation_count\n", - " self.__random_seed = random_seed\n", - " self.__ci = ci\n", - " self.__alpha = ci2g._compute_alpha_from_ci(ci)\n", - "\n", - " self.__difference = es.two_group_difference(\n", - " control, test, is_paired, effect_size)\n", - " \n", - " self.__jackknives = ci2g.compute_meandiff_jackknife(\n", - " control, test, is_paired, effect_size)\n", - "\n", - " self.__acceleration_value = ci2g._calc_accel(self.__jackknives)\n", - "\n", - " bootstraps = ci2g.compute_bootstrapped_diff(\n", - " control, test, is_paired, effect_size,\n", - " resamples, random_seed)\n", - " self.__bootstraps = bootstraps\n", - " \n", - " sorted_bootstraps = npsort(self.__bootstraps)\n", - " # Added in v0.2.6.\n", - " # Raises a UserWarning if there are any infiinities in the bootstraps.\n", - " num_infinities = len(self.__bootstraps[isinf(self.__bootstraps)])\n", - " \n", - " if num_infinities > 0:\n", - " warn_msg = \"There are {} bootstrap(s) that are not defined. \"\\\n", - " \"This is likely due to smaple sample sizes. \"\\\n", - " \"The values in a bootstrap for a group will be more likely \"\\\n", - " \"to be all equal, with a resulting variance of zero. \"\\\n", - " \"The computation of Cohen's d and Hedges' g thus \"\\\n", - " \"involved a division by zero. \"\n", - " warnings.warn(warn_msg.format(num_infinities), \n", - " category=UserWarning)\n", - "\n", - " self.__bias_correction = ci2g.compute_meandiff_bias_correction(\n", - " self.__bootstraps, self.__difference)\n", - "\n", - " # Compute BCa intervals.\n", - " bca_idx_low, bca_idx_high = ci2g.compute_interval_limits(\n", - " self.__bias_correction, self.__acceleration_value,\n", - " self.__resamples, ci)\n", - "\n", - " self.__bca_interval_idx = (bca_idx_low, bca_idx_high)\n", - "\n", - " if ~isnan(bca_idx_low) and ~isnan(bca_idx_high):\n", - " self.__bca_low = sorted_bootstraps[bca_idx_low]\n", - " self.__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 should be considered unstable.\"\n", - " err_temp = Template(\" \".join([err1, err2, err3]))\n", - "\n", - " if bca_idx_low <= 10:\n", - " warnings.warn(err_temp.substitute(lim_type=\"lower\",\n", - " loc=\"bottom\"),\n", - " stacklevel=1)\n", - "\n", - " if bca_idx_high >= resamples-9:\n", - " warnings.warn(err_temp.substitute(lim_type=\"upper\",\n", - " loc=\"top\"),\n", - " stacklevel=1)\n", - "\n", - " else:\n", - " err1 = \"The $lim_type limit of the BCa interval 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.__bca_low = self.__difference\n", - " warnings.warn(err_temp.substitute(lim_type=\"lower\"),\n", - " stacklevel=0)\n", - "\n", - " if isnan(bca_idx_high):\n", - " self.__bca_high = self.__difference\n", - " warnings.warn(err_temp.substitute(lim_type=\"upper\"),\n", - " stacklevel=0)\n", - "\n", - " # Compute percentile intervals.\n", - " pct_idx_low = int((self.__alpha/2) * resamples)\n", - " pct_idx_high = int((1-(self.__alpha/2)) * resamples)\n", - "\n", - " 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", - " # Perform statistical tests.\n", - " \n", - " self.__PermutationTest_result = PermutationTest(control, test, \n", - " effect_size, \n", - " is_paired,\n", - " permutation_count)\n", - " \n", - " if is_paired and proportional is False:\n", - " # Wilcoxon, a non-parametric version of the paired T-test.\n", - " wilcoxon = spstats.wilcoxon(control, test)\n", - " self.__pvalue_wilcoxon = wilcoxon.pvalue\n", - " self.__statistic_wilcoxon = wilcoxon.statistic\n", - " \n", - " \n", - " if effect_size != \"median_diff\":\n", - " # Paired Student's t-test.\n", - " paired_t = spstats.ttest_rel(control, test, nan_policy='omit')\n", - " self.__pvalue_paired_students_t = paired_t.pvalue\n", - " self.__statistic_paired_students_t = paired_t.statistic\n", - "\n", - " standardized_es = es.cohens_d(control, test, is_paired)\n", - " # self.__power = power.tt_solve_power(standardized_es,\n", - " # len(control),\n", - " # alpha=self.__alpha)\n", - "\n", - " elif is_paired and proportional is True:\n", - " # for binary paired data, use McNemar's test\n", - " # References:\n", - " # https://en.wikipedia.org/wiki/McNemar%27s_test\n", - " from statsmodels.stats.contingency_tables import mcnemar\n", - " import pandas as pd\n", - " df_temp = pd.DataFrame({'control': control, 'test': 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", - " _mcnemar = mcnemar(table, exact=True, correction=True)\n", - " self.__pvalue_mcnemar = _mcnemar.pvalue\n", - " self.__statistic_mcnemar = _mcnemar.statistic\n", - "\n", - " elif effect_size == \"cliffs_delta\":\n", - " # Let's go with Brunner-Munzel!\n", - " brunner_munzel = spstats.brunnermunzel(control, test,\n", - " nan_policy='omit')\n", - " self.__pvalue_brunner_munzel = brunner_munzel.pvalue\n", - " self.__statistic_brunner_munzel = brunner_munzel.statistic\n", - "\n", - "\n", - " elif effect_size == \"median_diff\":\n", - " # According to scipy's documentation of the function,\n", - " # \"The Kruskal-Wallis H-test tests the null hypothesis\n", - " # that the population median of all of the groups are equal.\"\n", - " kruskal = spstats.kruskal(control, test, nan_policy='omit')\n", - " self.__pvalue_kruskal = kruskal.pvalue\n", - " self.__statistic_kruskal = kruskal.statistic\n", - " # self.__power = np.nan\n", - "\n", - " else: # for mean difference, Cohen's d, and Hedges' g.\n", - " # Welch's t-test, assumes normality of distributions,\n", - " # but does not assume equal variances.\n", - " welch = spstats.ttest_ind(control, test, equal_var=False,\n", - " nan_policy='omit')\n", - " self.__pvalue_welch = welch.pvalue\n", - " self.__statistic_welch = welch.statistic\n", - "\n", - " # Student's t-test, assumes normality of distributions,\n", - " # as well as assumption of equal variances.\n", - " students_t = spstats.ttest_ind(control, test, equal_var=True,\n", - " nan_policy='omit')\n", - " self.__pvalue_students_t = students_t.pvalue\n", - " self.__statistic_students_t = students_t.statistic\n", - "\n", - " # Mann-Whitney test: Non parametric,\n", - " # does not assume normality of distributions\n", - " try:\n", - " mann_whitney = spstats.mannwhitneyu(control, test, \n", - " alternative='two-sided')\n", - " self.__pvalue_mann_whitney = mann_whitney.pvalue\n", - " self.__statistic_mann_whitney = mann_whitney.statistic\n", - " except ValueError:\n", - " # Occurs when the control and test are exactly identical\n", - " # in terms of rank (eg. all zeros.)\n", - " pass\n", - " \n", - " \n", - "\n", - " standardized_es = es.cohens_d(control, test, is_paired = None)\n", - " \n", - " # The Cohen's h calculation is for binary categorical data\n", - " try:\n", - " self.__proportional_difference = es.cohens_h(control, test)\n", - " except ValueError:\n", - " # Occur only when the data consists not only 0's and 1's.\n", - " pass\n", - " # self.__power = power.tt_ind_solve_power(standardized_es,\n", - " # len(control),\n", - " # alpha=self.__alpha,\n", - " # ratio=len(test)/len(control)\n", - " # )\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - " def __repr__(self, show_resample_count=True, define_pval=True, sigfig=3):\n", - " \n", - " # # Deprecated in v0.3.0; permutation p-values will be reported by default.\n", - " # UNPAIRED_ES_TO_TEST = {\"mean_diff\" : \"Mann-Whitney\",\n", - " # \"median_diff\" : \"Kruskal\",\n", - " # \"cohens_d\" : \"Mann-Whitney\",\n", - " # \"hedges_g\" : \"Mann-Whitney\",\n", - " # \"cliffs_delta\" : \"Brunner-Munzel\"}\n", - " # \n", - " # TEST_TO_PVAL_ATTR = {\"Mann-Whitney\" : \"pvalue_mann_whitney\",\n", - " # \"Kruskal\" : \"pvalue_kruskal\",\n", - " # \"Brunner-Munzel\" : \"pvalue_brunner_munzel\",\n", - " # \"Wilcoxon\" : \"pvalue_wilcoxon\"}\n", - " \n", - " RM_STATUS = {'baseline' : 'for repeated measures against baseline \\n', \n", - " 'sequential': 'for the sequential design of repeated-measures experiment \\n',\n", - " 'None' : ''\n", - " }\n", - "\n", - " PAIRED_STATUS = {'baseline' : 'paired', \n", - " 'sequential' : 'paired',\n", - " 'None' : 'unpaired'\n", - " }\n", - "\n", - " first_line = {\"rm_status\" : RM_STATUS[str(self.__is_paired)],\n", - " \"es\" : self.__EFFECT_SIZE_DICT[self.__effect_size],\n", - " \"paired_status\": PAIRED_STATUS[str(self.__is_paired)]}\n", - " \n", - "\n", - " out1 = \"The {paired_status} {es} {rm_status}\".format(**first_line)\n", - " \n", - " base_string_fmt = \"{:.\" + str(sigfig) + \"}\"\n", - " if \".\" in str(self.__ci):\n", - " ci_width = base_string_fmt.format(self.__ci)\n", - " else:\n", - " ci_width = str(self.__ci)\n", - " \n", - " ci_out = {\"es\" : base_string_fmt.format(self.__difference),\n", - " \"ci\" : ci_width,\n", - " \"bca_low\" : base_string_fmt.format(self.__bca_low),\n", - " \"bca_high\" : base_string_fmt.format(self.__bca_high)}\n", - " \n", - " out2 = \"is {es} [{ci}%CI {bca_low}, {bca_high}].\".format(**ci_out)\n", - " out = out1 + out2\n", - " \n", - " # # Deprecated in v0.3.0; permutation p-values will be reported by default.\n", - " # if self.__is_paired:\n", - " # stats_test = \"Wilcoxon\"\n", - " # else:\n", - " # stats_test = UNPAIRED_ES_TO_TEST[self.__effect_size]\n", - " \n", - " \n", - " # pval_rounded = base_string_fmt.format(getattr(self,\n", - " # TEST_TO_PVAL_ATTR[stats_test])\n", - " # )\n", - " \n", - " pval_rounded = base_string_fmt.format(self.pvalue_permutation)\n", - " \n", - " # # Deprecated in v0.3.0; permutation p-values will be reported by default.\n", - " # pvalue = \"The two-sided p-value of the {} test is {}.\".format(stats_test,\n", - " # pval_rounded)\n", - " \n", - " # pvalue = \"The two-sided p-value of the {} test is {}.\".format(stats_test,\n", - " # pval_rounded)\n", - " \n", - " \n", - " p1 = \"The p-value of the two-sided permutation t-test is {}, \".format(pval_rounded)\n", - " p2 = \"calculated for legacy purposes only. \"\n", - " pvalue = p1 + p2\n", - " \n", - " bs1 = \"{} bootstrap samples were taken; \".format(self.__resamples)\n", - " bs2 = \"the confidence interval is bias-corrected and accelerated.\"\n", - " bs = bs1 + bs2\n", - "\n", - " pval_def1 = \"Any p-value reported is the probability of observing the\" + \\\n", - " \"effect size (or greater),\\nassuming the null hypothesis of\" + \\\n", - " \"zero difference is true.\"\n", - " pval_def2 = \"\\nFor each p-value, 5000 reshuffles of the \" + \\\n", - " \"control and test labels were performed.\"\n", - " pval_def = pval_def1 + pval_def2\n", - "\n", - " if show_resample_count and define_pval:\n", - " return \"{}\\n{}\\n\\n{}\\n{}\".format(out, pvalue, bs, pval_def)\n", - " elif show_resample_count is False and define_pval is True:\n", - " return \"{}\\n{}\\n\\n{}\".format(out, pvalue, pval_def)\n", - " elif show_resample_count is True and define_pval is False:\n", - " return \"{}\\n{}\\n\\n{}\".format(out, pvalue, bs)\n", - " else:\n", - " return \"{}\\n{}\".format(out, pvalue)\n", - "\n", - "\n", - "\n", - " def to_dict(self):\n", - " \"\"\"\n", - " Returns the attributes of the `dabest.TwoGroupEffectSize` object as a\n", - " dictionary.\n", - " \"\"\"\n", - " # Only get public (user-facing) attributes.\n", - " attrs = [a for a in dir(self)\n", - " if not a.startswith((\"_\", \"to_dict\"))]\n", - " out = {}\n", - " for a in attrs:\n", - " out[a] = getattr(self, a)\n", - " return out\n", - "\n", - "\n", - " @property\n", - " def difference(self):\n", - " \"\"\"\n", - " Returns the difference between the control and the test.\n", - " \"\"\"\n", - " return self.__difference\n", - "\n", - " @property\n", - " def effect_size(self):\n", - " \"\"\"\n", - " Returns the type of effect size reported.\n", - " \"\"\"\n", - " return self.__EFFECT_SIZE_DICT[self.__effect_size]\n", - "\n", - " @property\n", - " def is_paired(self):\n", - " return self.__is_paired\n", - "\n", - " @property\n", - " def ci(self):\n", - " \"\"\"\n", - " Returns the width of the confidence interval, in percent.\n", - " \"\"\"\n", - " return self.__ci\n", - "\n", - " @property\n", - " def alpha(self):\n", - " \"\"\"\n", - " Returns the significance level of the statistical test as a float\n", - " between 0 and 1.\n", - " \"\"\"\n", - " return self.__alpha\n", - "\n", - " @property\n", - " def resamples(self):\n", - " \"\"\"\n", - " The number of resamples performed during the bootstrap procedure.\n", - " \"\"\"\n", - " return self.__resamples\n", - "\n", - " @property\n", - " def bootstraps(self):\n", - " \"\"\"\n", - " The generated bootstraps of the effect size.\n", - " \"\"\"\n", - " return self.__bootstraps\n", - "\n", - " @property\n", - " def random_seed(self):\n", - " \"\"\"\n", - " The number used to initialise the numpy random seed generator, ie.\n", - " `seed_value` from `numpy.random.seed(seed_value)` is returned.\n", - " \"\"\"\n", - " return self.__random_seed\n", - "\n", - " @property\n", - " def bca_interval_idx(self):\n", - " return self.__bca_interval_idx\n", - "\n", - " @property\n", - " def bca_low(self):\n", - " \"\"\"\n", - " The bias-corrected and accelerated confidence interval lower limit.\n", - " \"\"\"\n", - " return self.__bca_low\n", - "\n", - " @property\n", - " def bca_high(self):\n", - " \"\"\"\n", - " The bias-corrected and accelerated confidence interval upper limit.\n", - " \"\"\"\n", - " return self.__bca_high\n", - "\n", - " @property\n", - " def pct_interval_idx(self):\n", - " return self.__pct_interval_idx\n", - "\n", - " @property\n", - " def pct_low(self):\n", - " \"\"\"\n", - " The percentile confidence interval lower limit.\n", - " \"\"\"\n", - " return self.__pct_low\n", - "\n", - " @property\n", - " def pct_high(self):\n", - " \"\"\"\n", - " The percentile confidence interval lower limit.\n", - " \"\"\"\n", - " return self.__pct_high\n", - "\n", - "\n", - "\n", - " @property\n", - " def pvalue_brunner_munzel(self):\n", - " from numpy import nan as npnan\n", - " try:\n", - " return self.__pvalue_brunner_munzel\n", - " except AttributeError:\n", - " return npnan\n", - "\n", - " @property\n", - " def statistic_brunner_munzel(self):\n", - " from numpy import nan as npnan\n", - " try:\n", - " return self.__statistic_brunner_munzel\n", - " except AttributeError:\n", - " return npnan\n", - "\n", - "\n", - "\n", - " @property\n", - " def pvalue_wilcoxon(self):\n", - " from numpy import nan as npnan\n", - " try:\n", - " return self.__pvalue_wilcoxon\n", - " except AttributeError:\n", - " return npnan\n", - "\n", - " @property\n", - " def statistic_wilcoxon(self):\n", - " from numpy import nan as npnan\n", - " try:\n", - " return self.__statistic_wilcoxon\n", - " except AttributeError:\n", - " return npnan\n", - "\n", - " @property\n", - " def pvalue_mcnemar(self):\n", - " from numpy import nan as npnan\n", - " try:\n", - " return self.__pvalue_mcnemar\n", - " except AttributeError:\n", - " return npnan\n", - "\n", - " @property\n", - " def statistic_mcnemar(self):\n", - " from numpy import nan as npnan\n", - " try:\n", - " return self.__statistic_mcnemar\n", - " except AttributeError:\n", - " return npnan\n", - "\n", - "\n", - "\n", - " @property\n", - " def pvalue_paired_students_t(self):\n", - " from numpy import nan as npnan\n", - " try:\n", - " return self.__pvalue_paired_students_t\n", - " except AttributeError:\n", - " return npnan\n", - "\n", - " @property\n", - " def statistic_paired_students_t(self):\n", - " from numpy import nan as npnan\n", - " try:\n", - " return self.__statistic_paired_students_t\n", - " except AttributeError:\n", - " return npnan\n", - "\n", - "\n", - "\n", - " @property\n", - " def pvalue_kruskal(self):\n", - " from numpy import nan as npnan\n", - " try:\n", - " return self.__pvalue_kruskal\n", - " except AttributeError:\n", - " return npnan\n", - "\n", - " @property\n", - " def statistic_kruskal(self):\n", - " from numpy import nan as npnan\n", - " try:\n", - " return self.__statistic_kruskal\n", - " except AttributeError:\n", - " return npnan\n", - "\n", - "\n", - "\n", - " @property\n", - " def pvalue_welch(self):\n", - " from numpy import nan as npnan\n", - " try:\n", - " return self.__pvalue_welch\n", - " except AttributeError:\n", - " return npnan\n", - "\n", - " @property\n", - " def statistic_welch(self):\n", - " from numpy import nan as npnan\n", - " try:\n", - " return self.__statistic_welch\n", - " except AttributeError:\n", - " return npnan\n", - "\n", - "\n", - "\n", - " @property\n", - " def pvalue_students_t(self):\n", - " from numpy import nan as npnan\n", - " try:\n", - " return self.__pvalue_students_t\n", - " except AttributeError:\n", - " return npnan\n", - "\n", - " @property\n", - " def statistic_students_t(self):\n", - " from numpy import nan as npnan\n", - " try:\n", - " return self.__statistic_students_t\n", - " except AttributeError:\n", - " return npnan\n", - "\n", - "\n", - "\n", - " @property\n", - " def pvalue_mann_whitney(self):\n", - " from numpy import nan as npnan\n", - " try:\n", - " return self.__pvalue_mann_whitney\n", - " except AttributeError:\n", - " return npnan\n", - "\n", - "\n", - "\n", - " @property\n", - " def statistic_mann_whitney(self):\n", - " from numpy import nan as npnan\n", - " try:\n", - " return self.__statistic_mann_whitney\n", - " except AttributeError:\n", - " return npnan\n", - " \n", - " # Introduced in v0.3.0.\n", - " @property\n", - " def pvalue_permutation(self):\n", - " return self.__PermutationTest_result.pvalue\n", - " \n", - " # \n", - " # \n", - " @property\n", - " def permutation_count(self):\n", - " \"\"\"\n", - " The number of permuations taken.\n", - " \"\"\"\n", - " return self.__PermutationTest_result.permutation_count\n", - "\n", - " \n", - " @property\n", - " def permutations(self):\n", - " return self.__PermutationTest_result.permutations\n", - "\n", - " \n", - " @property\n", - " def permutations_var(self):\n", - " return self.__PermutationTest_result.permutations_var\n", - "\n", - "\n", - " @property\n", - " def proportional_difference(self):\n", - " from numpy import nan as npnan\n", - " try:\n", - " return self.__proportional_difference\n", - " except AttributeError:\n", - " return npnan\n" - ] - }, - { - "cell_type": "markdown", - "id": "d72ccb04", - "metadata": {}, - "source": [ - "#### Example" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5d8a7a87", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "The unpaired mean difference is -0.253 [95%CI -0.78, 0.25].\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", - "For each p-value, 5000 reshuffles of the control and test labels were performed." - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.random.seed(12345)\n", - "control = norm.rvs(loc=0, size=30)\n", - "test = norm.rvs(loc=0.5, size=30)\n", - "effsize = dabest.TwoGroupsEffectSize(control, test, \"mean_diff\")\n", - "effsize" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "72a4c93e", - "metadata": {}, - "outputs": [ - { - "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", - " 'ci': 95,\n", - " 'difference': -0.25315417702752846,\n", - " 'effect_size': 'mean difference',\n", - " 'is_paired': None,\n", - " 'pct_high': 0.24951887238295106,\n", - " 'pct_interval_idx': (125, 4875),\n", - " 'pct_low': -0.7801782111071534,\n", - " 'permutation_count': 5000,\n", - " 'permutations': array([ 0.17221029, 0.03112419, -0.13911387, ..., -0.38007941,\n", - " 0.30261507, -0.09073054]),\n", - " 'permutations_var': array([0.07201642, 0.07251104, 0.07219407, ..., 0.07003705, 0.07094885,\n", - " 0.07238581]),\n", - " 'proportional_difference': nan,\n", - " 'pvalue_brunner_munzel': nan,\n", - " 'pvalue_kruskal': nan,\n", - " 'pvalue_mann_whitney': 0.5201446121616038,\n", - " 'pvalue_mcnemar': nan,\n", - " 'pvalue_paired_students_t': nan,\n", - " 'pvalue_permutation': 0.3484,\n", - " 'pvalue_students_t': 0.34743913903372836,\n", - " 'pvalue_welch': 0.3474493875548964,\n", - " 'pvalue_wilcoxon': nan,\n", - " 'random_seed': 12345,\n", - " 'resamples': 5000,\n", - " 'statistic_brunner_munzel': nan,\n", - " 'statistic_kruskal': nan,\n", - " 'statistic_mann_whitney': 494.0,\n", - " 'statistic_mcnemar': nan,\n", - " 'statistic_paired_students_t': nan,\n", - " 'statistic_students_t': 0.9472545159069105,\n", - " 'statistic_welch': 0.9472545159069105,\n", - " 'statistic_wilcoxon': nan}" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "effsize.to_dict() " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "eb366b18", - "metadata": {}, - "outputs": [], - "source": [ - "#| export\n", - "class EffectSizeDataFrame(object):\n", - " \"\"\"A class that generates and stores the results of bootstrapped effect\n", - " sizes for several comparisons.\"\"\"\n", - "\n", - " def __init__(self, dabest, effect_size,\n", - " is_paired, ci=95, proportional=False,\n", - " resamples=5000, \n", - " permutation_count=5000,\n", - " random_seed=12345, \n", - " x1_level=None, x2=None, \n", - " delta2=False, experiment_label=None,\n", - " mini_meta=False):\n", - " \"\"\"\n", - " Parses the data from a Dabest object, enabling plotting and printing\n", - " capability for the effect size of interest.\n", - " \"\"\"\n", - "\n", - " self.__dabest_obj = dabest\n", - " self.__effect_size = effect_size\n", - " self.__is_paired = is_paired\n", - " self.__ci = ci\n", - " self.__resamples = resamples\n", - " self.__permutation_count = permutation_count\n", - " self.__random_seed = random_seed\n", - " self.__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", - "\n", - "\n", - " def __pre_calc(self):\n", - " import pandas as pd\n", - " from .misc_tools import print_greeting, get_varname\n", - "\n", - " idx = self.__dabest_obj.idx\n", - " dat = self.__dabest_obj._plot_data\n", - " xvar = self.__dabest_obj._xvar\n", - " yvar = self.__dabest_obj._yvar\n", - "\n", - " out = []\n", - " reprs = []\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", - "\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", - " result = TwoGroupsEffectSize(control, test,\n", - " self.__effect_size,\n", - " self.__proportional,\n", - " self.__is_paired,\n", - " self.__ci,\n", - " self.__resamples,\n", - " self.__permutation_count,\n", - " self.__random_seed)\n", - " r_dict = result.to_dict()\n", - " r_dict[\"control\"] = cname\n", - " r_dict[\"test\"] = tname\n", - " r_dict[\"control_N\"] = int(len(control))\n", - " 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 == \"mean_diff\":\n", - " resamp_count = False\n", - " def_pval = False\n", - " elif self.__mini_meta and self.__effect_size == \"mean_diff\":\n", - " resamp_count = False\n", - " def_pval = False\n", - " else:\n", - " resamp_count = True\n", - " def_pval = True\n", - " else:\n", - " resamp_count = False\n", - " def_pval = False\n", - "\n", - " text_repr = result.__repr__(show_resample_count=resamp_count,\n", - " define_pval=def_pval)\n", - "\n", - " to_replace = \"between {} and {} is\".format(cname, tname)\n", - " text_repr = text_repr.replace(\"is\", to_replace, 1)\n", - "\n", - " reprs.append(text_repr)\n", - "\n", - "\n", - " self.__for_print = \"\\n\\n\".join(reprs)\n", - "\n", - " out_ = pd.DataFrame(out)\n", - "\n", - " columns_in_order = ['control', 'test', 'control_N', 'test_N',\n", - " 'effect_size', 'is_paired',\n", - " 'difference', 'ci',\n", - "\n", - " 'bca_low', 'bca_high', 'bca_interval_idx',\n", - " 'pct_low', 'pct_high', 'pct_interval_idx',\n", - " \n", - " 'bootstraps', 'resamples', 'random_seed',\n", - " \n", - " 'permutations', 'pvalue_permutation', 'permutation_count', 'permutations_var',\n", - " \n", - " 'pvalue_welch',\n", - " 'statistic_welch',\n", - "\n", - " 'pvalue_students_t',\n", - " 'statistic_students_t',\n", - "\n", - " 'pvalue_mann_whitney',\n", - " 'statistic_mann_whitney',\n", - "\n", - " 'pvalue_brunner_munzel',\n", - " 'statistic_brunner_munzel',\n", - "\n", - " 'pvalue_wilcoxon',\n", - " 'statistic_wilcoxon',\n", - "\n", - " 'pvalue_mcnemar',\n", - " 'statistic_mcnemar',\n", - "\n", - " 'pvalue_paired_students_t',\n", - " 'statistic_paired_students_t',\n", - "\n", - " 'pvalue_kruskal',\n", - " 'statistic_kruskal',\n", - " 'proportional_difference'\n", - " ]\n", - " self.__results = out_.reindex(columns=columns_in_order)\n", - " self.__results.dropna(axis=\"columns\", how=\"all\", inplace=True)\n", - " \n", - " # Add the is_paired column back when is_paired is None\n", - " if self.is_paired is None:\n", - " self.__results.insert(5, 'is_paired', self.__results.apply(lambda _: None, axis=1))\n", - " \n", - " # Create and compute the delta-delta statistics\n", - " if self.__delta2 is True and self.__effect_size == \"mean_diff\":\n", - " self.__delta_delta = DeltaDelta(self,\n", - " self.__permutation_count,\n", - " self.__ci)\n", - " reprs.append(self.__delta_delta.__repr__(header=False))\n", - " elif self.__delta2 is True and self.__effect_size != \"mean_diff\":\n", - " self.__delta_delta = \"Delta-delta is not supported for {}.\".format(self.__effect_size)\n", - " else:\n", - " self.__delta_delta = \"`delta2` is False; delta-delta is therefore not calculated.\"\n", - "\n", - " # Create and compute the weighted average statistics\n", - " if self.__mini_meta is True and self.__effect_size == \"mean_diff\":\n", - " self.__mini_meta_delta = MiniMetaDelta(self,\n", - " self.__permutation_count,\n", - " self.__ci)\n", - " reprs.append(self.__mini_meta_delta.__repr__(header=False))\n", - " elif self.__mini_meta is True and self.__effect_size != \"mean_diff\":\n", - " self.__mini_meta_delta = \"Weighted delta is not supported for {}.\".format(self.__effect_size)\n", - " else:\n", - " self.__mini_meta_delta = \"`mini_meta` is False; weighted delta is therefore not calculated.\"\n", - " \n", - " \n", - " varname = get_varname(self.__dabest_obj)\n", - " lastline = \"To get the results of all valid statistical tests, \" +\\\n", - " \"use `{}.{}.statistical_tests`\".format(varname, self.__effect_size)\n", - " reprs.append(lastline)\n", - "\n", - " reprs.insert(0, print_greeting())\n", - "\n", - " self.__for_print = \"\\n\\n\".join(reprs)\n", - "\n", - "\n", - " def __repr__(self):\n", - " try:\n", - " return self.__for_print\n", - " except AttributeError:\n", - " self.__pre_calc()\n", - " return self.__for_print\n", - " \n", - " \n", - " \n", - " def __calc_lqrt(self):\n", - " import lqrt\n", - " import pandas as pd\n", - " \n", - " rnd_seed = self.__random_seed\n", - " db_obj = self.__dabest_obj\n", - " dat = db_obj._plot_data\n", - " xvar = db_obj._xvar\n", - " yvar = db_obj._yvar\n", - " delta2 = self.__delta2\n", - " \n", - "\n", - " out = []\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", - "\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", - " if self.__is_paired: \n", - " # Refactored here in v0.3.0 for performance issues.\n", - " lqrt_result = lqrt.lqrtest_rel(control, test, \n", - " random_state=rnd_seed)\n", - " \n", - " out.append({\"control\": cname, \"test\": tname, \n", - " \"control_N\": int(len(control)), \n", - " \"test_N\": int(len(test)),\n", - " \"pvalue_paired_lqrt\": lqrt_result.pvalue,\n", - " \"statistic_paired_lqrt\": lqrt_result.statistic\n", - " })\n", - "\n", - " else:\n", - " # Likelihood Q-Ratio test:\n", - " lqrt_equal_var_result = lqrt.lqrtest_ind(control, test, \n", - " random_state=rnd_seed,\n", - " equal_var=True)\n", - " \n", - " \n", - " lqrt_unequal_var_result = lqrt.lqrtest_ind(control, test, \n", - " random_state=rnd_seed,\n", - " equal_var=False)\n", - " \n", - " out.append({\"control\": cname, \"test\": tname, \n", - " \"control_N\": int(len(control)), \n", - " \"test_N\": int(len(test)),\n", - " \n", - " \"pvalue_lqrt_equal_var\" : lqrt_equal_var_result.pvalue,\n", - " \"statistic_lqrt_equal_var\" : lqrt_equal_var_result.statistic,\n", - " \"pvalue_lqrt_unequal_var\" : lqrt_unequal_var_result.pvalue,\n", - " \"statistic_lqrt_unequal_var\" : lqrt_unequal_var_result.statistic,\n", - " }) \n", - " self.__lqrt_results = pd.DataFrame(out)\n", - "\n", - "\n", - " def plot(self, color_col=None,\n", - "\n", - " raw_marker_size=6, es_marker_size=9,\n", - "\n", - " swarm_label=None, barchart_label=None, contrast_label=None, delta2_label=None,\n", - " swarm_ylim=None, barchart_ylim=None, contrast_ylim=None, delta2_ylim=None,\n", - "\n", - " custom_palette=None, swarm_desat=0.5, halfviolin_desat=1,\n", - " halfviolin_alpha=0.8, \n", - "\n", - " face_color = None,\n", - " #bar plot\n", - " bar_label=None, bar_desat=0.5, bar_width = 0.5,bar_ylim = None,\n", - " # error bar of proportion plot\n", - " ci=None, ci_type='bca', err_color=None,\n", - "\n", - " float_contrast=True,\n", - " show_pairs=True,\n", - " show_delta2=True,\n", - " show_mini_meta=True,\n", - " group_summaries=None,\n", - " group_summaries_offset=0.1,\n", - "\n", - " fig_size=None,\n", - " dpi=100,\n", - " ax=None,\n", - "\n", - " swarmplot_kwargs=None,\n", - " barplot_kwargs=None,\n", - " violinplot_kwargs=None,\n", - " slopegraph_kwargs=None,\n", - " sankey_kwargs=None,\n", - " reflines_kwargs=None,\n", - " group_summary_kwargs=None,\n", - " legend_kwargs=None):\n", - "\n", - " \"\"\"\n", - " Creates an estimation plot for the effect size of interest.\n", - " \n", - "\n", - " Parameters\n", - " ----------\n", - " color_col : string, default None\n", - " Column to be used for colors.\n", - " 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", - " 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", - " 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", - " limits for y. When `show_delta2` is True, if both of the `contrast_ylim`\n", - " and `delta2_ylim` are not None, then they must be specified with the \n", - " same values; when `show_delta2` is True and only one of them is specified,\n", - " then the other will automatically be assigned with the same value.\n", - " Specifying `delta2_ylim` does not have any effect when `show_delta2` is\n", - " False. \n", - " custom_palette : dict, list, or matplotlib color palette, default None\n", - " This keyword accepts a dictionary with {'group':'color'} pairings,\n", - " a list of RGB colors, or a specified matplotlib palette. This\n", - " palette will be used to color the swarmplot. If `color_col` is not\n", - " specified, then each group will be colored in sequence according\n", - " to the default palette currently used by matplotlib.\n", - " Please take a look at the seaborn commands `color_palette`\n", - " and `cubehelix_palette` to generate a custom palette. Both\n", - " these functions generate a list of RGB colors.\n", - " See:\n", - " https://seaborn.pydata.org/generated/seaborn.color_palette.html\n", - " 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", - " desired proportion. Uses `seaborn.desaturate()` to acheive this.\n", - " halfviolin_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", - " The alpha (transparency) level of the half-violin bootstrap curves. \n", - " float_contrast : boolean, default True\n", - " Whether or not to display the halfviolin bootstrapped difference\n", - " distribution alongside the raw data.\n", - " show_pairs : boolean, default True\n", - " 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_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", - " 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", - " The dots per inch of the resulting figure.\n", - " 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", - " 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", - " 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", - " 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", - " 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", - " 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", - " 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", - "\n", - "\n", - " Returns\n", - " -------\n", - " A :class:`matplotlib.figure.Figure` with 2 Axes, if ``ax = None``.\n", - " \n", - " The first axes (accessible with ``FigName.axes[0]``) contains the rawdata swarmplot; the second axes (accessible with ``FigName.axes[1]``) has the bootstrap distributions and effect sizes (with confidence intervals) plotted on it.\n", - " \n", - " If ``ax`` is specified, the rawdata swarmplot is accessed at ``ax`` \n", - " itself, while the effect size axes is accessed at ``ax.contrast_axes``.\n", - " See the last example below.\n", - " \n", - "\n", - "\n", - " \"\"\"\n", - "\n", - " from .plotter import EffectSizeDataFramePlotter\n", - "\n", - " 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", - " \n", - " all_kwargs = locals()\n", - " del all_kwargs[\"self\"]\n", - "\n", - " out = EffectSizeDataFramePlotter(self, **all_kwargs)\n", - "\n", - " return out\n", - "\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 results(self):\n", - " \"\"\"Prints all pairwise comparisons nicely.\"\"\"\n", - " try:\n", - " return self.__results\n", - " except AttributeError:\n", - " self.__pre_calc()\n", - " return self.__results\n", - "\n", - "\n", - "\n", - " @property\n", - " def statistical_tests(self):\n", - " results_df = self.results\n", - "\n", - " # Select only the statistics and p-values.\n", - " stats_columns = [c for c in results_df.columns\n", - " if c.startswith(\"statistic\") or c.startswith(\"pvalue\")]\n", - "\n", - " default_cols = ['control', 'test', 'control_N', 'test_N',\n", - " 'effect_size', 'is_paired',\n", - " 'difference', 'ci', 'bca_low', 'bca_high']\n", - "\n", - " cols_of_interest = default_cols + stats_columns\n", - "\n", - " return results_df[cols_of_interest]\n", - "\n", - "\n", - " @property\n", - " def _for_print(self):\n", - " return self.__for_print\n", - "\n", - " @property\n", - " def _plot_data(self):\n", - " return self.__dabest_obj._plot_data\n", - "\n", - " @property\n", - " def idx(self):\n", - " return self.__dabest_obj.idx\n", - "\n", - " @property\n", - " def xvar(self):\n", - " return self.__dabest_obj._xvar\n", - "\n", - " @property\n", - " def yvar(self):\n", - " return self.__dabest_obj._yvar\n", - "\n", - " @property\n", - " def is_paired(self):\n", - " return self.__is_paired\n", - "\n", - " @property\n", - " def ci(self):\n", - " \"\"\"\n", - " The width of the confidence interval being produced, in percent.\n", - " \"\"\"\n", - " return self.__ci\n", - "\n", - " @property\n", - " def x1_level(self):\n", - " return self.__x1_level\n", - "\n", - "\n", - " @property\n", - " def x2(self):\n", - " return self.__x2\n", - "\n", - "\n", - " @property\n", - " def experiment_label(self):\n", - " return self.__experiment_label\n", - " \n", - "\n", - " @property\n", - " def delta2(self):\n", - " return self.__delta2\n", - " \n", - "\n", - " @property\n", - " def resamples(self):\n", - " \"\"\"\n", - " The number of resamples (with replacement) during bootstrap resampling.\"\n", - " \"\"\"\n", - " return self.__resamples\n", - "\n", - " @property\n", - " def random_seed(self):\n", - " \"\"\"\n", - " The seed used by `numpy.seed()` for bootstrap resampling.\n", - " \"\"\"\n", - " return self.__random_seed\n", - "\n", - " @property\n", - " def effect_size(self):\n", - " \"\"\"The type of effect size being computed.\"\"\"\n", - " return self.__effect_size\n", - "\n", - " @property\n", - " def dabest_obj(self):\n", - " \"\"\"\n", - " Returns the `dabest` object that invoked the current EffectSizeDataFrame\n", - " class.\n", - " \"\"\"\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", - " \"\"\"Returns all pairwise Lq-Likelihood Ratio Type test results \n", - " as a pandas DataFrame.\n", - " \n", - " For more information on LqRT tests, see https://arxiv.org/abs/1911.11922\n", - " \"\"\"\n", - " try:\n", - " return self.__lqrt_results\n", - " except AttributeError:\n", - " self.__calc_lqrt()\n", - " return self.__lqrt_results\n", - " \n", - " \n", - " @property\n", - " def mini_meta(self):\n", - " \"\"\"\n", - " Returns the mini_meta boolean parameter.\n", - " \"\"\"\n", - " return self.__mini_meta\n", - "\n", - " \n", - " @property\n", - " def mini_meta_delta(self):\n", - " \"\"\"\n", - " Returns the mini_meta results.\n", - " \"\"\"\n", - " try:\n", - " return self.__mini_meta_delta\n", - " except AttributeError:\n", - " self.__pre_calc()\n", - " return self.__mini_meta_delta\n", - "\n", - " \n", - " @property\n", - " def delta_delta(self):\n", - " \"\"\"\n", - " Returns the mini_meta results.\n", - " \"\"\"\n", - " try:\n", - " return self.__delta_delta\n", - " except AttributeError:\n", - " self.__pre_calc()\n", - " return self.__delta_delta\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "id": "0e1b8353", - "metadata": {}, - "source": [ - "#### Example: plot\n", - "\n", - "Create a Gardner-Altman estimation plot for the mean difference." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6a151b86", - "metadata": {}, - "outputs": [], - "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", - "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", - "my_data = dabest.load(df, idx=(\"Control 1\", \"Test 1\"))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "91d15864", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig1 = my_data.mean_diff.plot();" - ] - }, - { - "cell_type": "markdown", - "id": "a37d4519", - "metadata": {}, - "source": [ - " Create a Gardner-Altman plot for the Hedges' g effect size." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5e9cac0b", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig2 = my_data.hedges_g.plot();" - ] - }, - { - "cell_type": "markdown", - "id": "f40f8fe0", - "metadata": {}, - "source": [ - "Create a Cumming estimation plot for the mean difference." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f0e6a68e", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig3 = my_data.mean_diff.plot(float_contrast=True);" - ] - }, - { - "cell_type": "markdown", - "id": "1ee59074", - "metadata": {}, - "source": [ - " Create a paired Gardner-Altman plot." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "89a19ee0", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "my_data_paired = dabest.load(df, idx=(\"Control 1\", \"Test 1\"),\n", - " id_col = \"ID\", paired='baseline')\n", - "fig4 = my_data_paired.mean_diff.plot();" - ] - }, - { - "cell_type": "markdown", - "id": "3c37066a", - "metadata": {}, - "source": [ - "Create a multi-group Cumming plot." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "896cac2a", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "my_multi_groups = dabest.load(df, id_col = \"ID\", \n", - " idx=((\"Control 1\", \"Test 1\"),\n", - " (\"Control 2\", \"Test 2\")))\n", - "fig5 = my_multi_groups.mean_diff.plot();" - ] - }, - { - "cell_type": "markdown", - "id": "de81e2e4", - "metadata": {}, - "source": [ - "Create a shared control Cumming plot." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f7d518b5", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "my_shared_control = dabest.load(df, id_col = \"ID\",\n", - " idx=(\"Control 1\", \"Test 1\",\n", - " \"Test 2\", \"Test 3\"))\n", - "fig6 = my_shared_control.mean_diff.plot();" - ] - }, - { - "cell_type": "markdown", - "id": "c80ba34f", - "metadata": {}, - "source": [ - "Create a repeated meausures (against baseline) Slopeplot." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8d46fd3a", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "my_rm_baseline = dabest.load(df, id_col = \"ID\", paired = \"baseline\",\n", - " idx=(\"Control 1\", \"Test 1\",\n", - " \"Test 2\", \"Test 3\"))\n", - "fig7 = my_rm_baseline.mean_diff.plot();" - ] - }, - { - "cell_type": "markdown", - "id": "4eaf4362", - "metadata": {}, - "source": [ - "Create a repeated meausures (sequential) Slopeplot." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4b6a3727", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "my_rm_sequential = dabest.load(df, id_col = \"ID\", paired = \"sequential\",\n", - " idx=(\"Control 1\", \"Test 1\",\n", - " \"Test 2\", \"Test 3\"))\n", - "fig8 = my_rm_sequential.mean_diff.plot();" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d22bdc4c", - "metadata": {}, - "outputs": [], - "source": [ - "#| export\n", - "class PermutationTest:\n", - " \"\"\"\n", - " A class to compute and report permutation tests.\n", - " \n", - " Parameters\n", - " ----------\n", - " control : array-like\n", - " test : array-like\n", - " 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', or 'cliffs_delta'\n", - " is_paired : string, default None\n", - " permutation_count : int, default 10000\n", - " The number of permutations (reshuffles) to perform.\n", - " random_seed : int, default 12345\n", - " `random_seed` is used to seed the random number generator during\n", - " bootstrap resampling. This ensures that the generated permutations\n", - " are replicable.\n", - " \n", - " Returns\n", - " -------\n", - " A :py:class:`PermutationTest` object:\n", - " `difference`:float\n", - " The effect size of the difference between the control and the test.\n", - " `effect_size`:string\n", - " The type of effect size reported.\n", - " \n", - " \n", - " \"\"\"\n", - " \n", - " def __init__(self, control:np.array,\n", - " test:np.array, # These should be numerical iterables.\n", - " effect_size:str, # Any one of the following are accepted inputs: 'mean_diff', 'median_diff', 'cohens_d', 'hedges_g', or 'cliffs_delta'\n", - " 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", - " **kwargs):\n", - " \n", - " import numpy as np\n", - " from numpy.random import PCG64, RandomState\n", - " from ._stats_tools.effsize import two_group_difference\n", - " from ._stats_tools.confint_2group_diff import calculate_group_var\n", - " \n", - "\n", - " self.__permutation_count = permutation_count\n", - "\n", - " # Run Sanity Check.\n", - " if is_paired and len(control) != len(test):\n", - " raise ValueError(\"The two arrays do not have the same length.\")\n", - "\n", - " # Initialise random number generator.\n", - " # rng = np.random.default_rng(seed=random_seed)\n", - " rng = RandomState(PCG64(random_seed))\n", - "\n", - " # Set required constants and variables\n", - " control = np.array(control)\n", - " test = np.array(test)\n", - "\n", - " control_sample = control.copy()\n", - " test_sample = test.copy()\n", - "\n", - " BAG = np.array([*control, *test])\n", - " CONTROL_LEN = int(len(control))\n", - " EXTREME_COUNT = 0.\n", - " THRESHOLD = np.abs(two_group_difference(control, test, \n", - " is_paired, effect_size))\n", - " self.__permutations = []\n", - " self.__permutations_var = []\n", - "\n", - " for i in range(int(permutation_count)):\n", - " \n", - " if is_paired:\n", - " # Select which control-test pairs to swap.\n", - " random_idx = rng.choice(CONTROL_LEN,\n", - " rng.randint(0, CONTROL_LEN+1),\n", - " replace=False)\n", - "\n", - " # Perform swap.\n", - " for i in random_idx:\n", - " _placeholder = control_sample[i]\n", - " control_sample[i] = test_sample[i]\n", - " test_sample[i] = _placeholder\n", - " \n", - " else:\n", - " # Shuffle the bag and assign to control and test groups.\n", - " # NB. rng.shuffle didn't produce replicable results...\n", - " shuffled = rng.permutation(BAG) \n", - " control_sample = shuffled[:CONTROL_LEN]\n", - " test_sample = shuffled[CONTROL_LEN:]\n", - "\n", - "\n", - " es = two_group_difference(control_sample, test_sample, \n", - " False, effect_size)\n", - " \n", - " var = calculate_group_var(np.var(control_sample, ddof=1), \n", - " CONTROL_LEN, \n", - " np.var(test_sample, ddof=1), \n", - " len(test_sample))\n", - " self.__permutations.append(es)\n", - " self.__permutations_var.append(var)\n", - "\n", - " if np.abs(es) > THRESHOLD:\n", - " EXTREME_COUNT += 1.\n", - "\n", - " self.__permutations = np.array(self.__permutations)\n", - " self.__permutations_var = np.array(self.__permutations_var)\n", - "\n", - " self.pvalue = EXTREME_COUNT / 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", - "\n", - "\n", - " @property\n", - " def permutation_count(self):\n", - " \"\"\"\n", - " The number of permuations taken.\n", - " \"\"\"\n", - " return self.__permutation_count\n", - "\n", - "\n", - " @property\n", - " def permutations(self):\n", - " \"\"\"\n", - " The effect sizes of all the permutations in a list.\n", - " \"\"\"\n", - " return self.__permutations\n", - "\n", - " \n", - " @property\n", - " def permutations_var(self):\n", - " \"\"\"\n", - " The experiment group variance of all the permutations in a list.\n", - " \"\"\"\n", - " return self.__permutations_var\n" - ] - }, - { - "cell_type": "markdown", - "id": "3214e42a", - "metadata": {}, - "source": [ - "**Notes**:\n", - " \n", - "The basic concept of permutation tests is the same as that behind bootstrapping.\n", - "In an \"exact\" permutation test, all possible resuffles of the control and test \n", - "labels are performed, and the proportion of effect sizes that equal or exceed \n", - "the observed effect size is computed. This is the probability, under the null \n", - "hypothesis of zero difference between test and control groups, of observing the\n", - "effect size: the p-value of the Student's t-test.\n", - "\n", - "Exact permutation tests are impractical: computing the effect sizes for all reshuffles quickly exceeds trivial computational loads. A control group and a test group both with 10 observations each would have a total of $20!$ or $2.43 \\times {10}^{18}$ reshuffles.\n", - "Therefore, in practice, \"approximate\" permutation tests are performed, where a sufficient number of reshuffles are performed (5,000 or 10,000), from which the p-value is computed.\n", - "\n", - "More information can be found [here](https://en.wikipedia.org/wiki/Resampling_(statistics)#Permutation_tests).\n" - ] - }, - { - "cell_type": "markdown", - "id": "cc181ae2", - "metadata": {}, - "source": [ - "#### Example: permutation test" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3fc2c6b7", - "metadata": {}, - "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", - "perm_test = dabest.PermutationTest(control, test, \n", - " effect_size=\"mean_diff\", \n", - " is_paired=None)\n", - "perm_test" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "07a84d5f", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "python3", - "language": "python", - "name": "python3" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/nbs/API/confint_1group.ipynb b/nbs/API/confint_1group.ipynb index 1e547098..d15c1499 100644 --- a/nbs/API/confint_1group.ipynb +++ b/nbs/API/confint_1group.ipynb @@ -53,8 +53,11 @@ "metadata": {}, "outputs": [], "source": [ - "#|export\n", - "import numpy as np" + "#| export\n", + "import numpy as np\n", + "from numpy.random import PCG64, RandomState\n", + "from scipy.stats import norm\n", + "from numpy import sort as npsort" ] }, { @@ -64,24 +67,19 @@ "metadata": {}, "outputs": [], "source": [ - "#|export\n", + "#| export\n", "def create_bootstrap_indexes(array, resamples=5000, random_seed=12345):\n", " \"\"\"Given an array-like, returns a generator of bootstrap indexes\n", " to be used for resampling.\n", " \"\"\"\n", - " import numpy as np\n", - " from numpy.random import PCG64, RandomState\n", + "\n", " rng = RandomState(PCG64(random_seed))\n", - " \n", + "\n", " indexes = range(0, len(array))\n", "\n", - " out = (rng.choice(indexes, len(indexes), replace=True)\n", - " for i in range(0, resamples))\n", - " \n", - " # Reset RNG\n", - " # rng = RandomState(MT19937())\n", - " return out\n", + " out = (rng.choice(indexes, len(indexes), replace=True) for i in range(0, resamples))\n", "\n", + " return out\n", "\n", "\n", "def compute_1group_jackknife(x, func, *args, **kwargs):\n", @@ -89,53 +87,56 @@ " Returns the jackknife bootstraps for func(x).\n", " \"\"\"\n", " from . import confint_2group_diff as ci_2g\n", + "\n", " jackknives = [i for i in ci_2g.create_jackknife_indexes(x)]\n", " out = [func(x[j], *args, **kwargs) for j in jackknives]\n", - " del jackknives # memory management.\n", + " del jackknives # memory management.\n", " return out\n", "\n", "\n", - "\n", "def compute_1group_acceleration(jack_dist):\n", + " \"\"\"\n", + " Returns the accaleration value based on the jackknife distribution.\n", + " \"\"\"\n", " from . import confint_2group_diff as ci_2g\n", - " return ci_2g._calc_accel(jack_dist)\n", "\n", + " return ci_2g._calc_accel(jack_dist)\n", "\n", "\n", - "def compute_1group_bootstraps(x, func, resamples=5000, random_seed=12345,\n", - " *args, **kwargs):\n", + "def compute_1group_bootstraps(\n", + " x, func, resamples=5000, random_seed=12345, *args, **kwargs\n", + "):\n", " \"\"\"Bootstraps func(x), with the number of specified resamples.\"\"\"\n", "\n", - " import numpy as np\n", - " \n", " # Create bootstrap indexes.\n", - " boot_indexes = create_bootstrap_indexes(x, resamples=resamples,\n", - " random_seed=random_seed)\n", + " boot_indexes = create_bootstrap_indexes(\n", + " x, resamples=resamples, random_seed=random_seed\n", + " )\n", "\n", " out = [func(x[b], *args, **kwargs) for b in boot_indexes]\n", - " \n", + "\n", " del boot_indexes\n", - " \n", - " return out\n", "\n", + " return out\n", "\n", "\n", "def compute_1group_bias_correction(x, bootstraps, func, *args, **kwargs):\n", - " from scipy.stats import norm\n", " metric = func(x, *args, **kwargs)\n", " prop_boots_less_than_metric = sum(bootstraps < metric) / len(bootstraps)\n", "\n", " return norm.ppf(prop_boots_less_than_metric)\n", "\n", "\n", - "\n", - "def summary_ci_1group(x:np.array,# An numerical iterable.\n", - " func, #The function to be applied to x.\n", - " resamples:int=5000, #The number of bootstrap resamples to be taken of func(x).\n", - " alpha:float=0.05, #Denotes the likelihood that the confidence interval produced _does not_ include the true summary statistic. When alpha = 0.05, a 95% confidence interval is produced.\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", - " sort_bootstraps:bool=True, \n", - " *args, **kwargs):\n", + "def summary_ci_1group(\n", + " x: np.array, # An numerical iterable.\n", + " func, # The function to be applied to x.\n", + " resamples: int = 5000, # The number of bootstrap resamples to be taken of func(x).\n", + " alpha: float = 0.05, # Denotes the likelihood that the confidence interval produced _does not_ include the true summary statistic. When alpha = 0.05, a 95% confidence interval is produced.\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", + " sort_bootstraps: bool = True,\n", + " *args,\n", + " **kwargs\n", + "):\n", " \"\"\"\n", " Given an array-like x, returns func(x), and a bootstrap confidence\n", " interval of func(x).\n", @@ -158,11 +159,10 @@ "\n", " \"\"\"\n", " from . import confint_2group_diff as ci2g\n", - " from numpy import sort as npsort\n", "\n", - " boots = compute_1group_bootstraps(x, func, resamples=resamples,\n", - " random_seed=random_seed,\n", - " *args, **kwargs)\n", + " boots = compute_1group_bootstraps(\n", + " x, func, resamples=resamples, random_seed=random_seed, *args, **kwargs\n", + " )\n", " bias = compute_1group_bias_correction(x, boots, func)\n", "\n", " jk = compute_1group_jackknife(x, func, *args, **kwargs)\n", @@ -183,21 +183,17 @@ " del boots\n", " del boots_sorted\n", "\n", - " out = {'summary': func(x), 'func': func,\n", - " 'bca_ci_low': low, 'bca_ci_high': high,\n", - " 'bootstraps': B}\n", + " out = {\n", + " \"summary\": func(x),\n", + " \"func\": func,\n", + " \"bca_ci_low\": low,\n", + " \"bca_ci_high\": high,\n", + " \"bootstraps\": B,\n", + " }\n", "\n", " del B\n", - " return out\n" + " return out" ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d1bdd2b6", - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { diff --git a/nbs/API/confint_2group_diff.ipynb b/nbs/API/confint_2group_diff.ipynb index 2d482f3b..dd6477aa 100644 --- a/nbs/API/confint_2group_diff.ipynb +++ b/nbs/API/confint_2group_diff.ipynb @@ -54,8 +54,15 @@ "metadata": {}, "outputs": [], "source": [ - "#|export\n", - "import numpy as np" + "#| export\n", + "import numpy as np\n", + "from numpy import arange, delete, errstate\n", + "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 scipy.stats import norm\n", + "from numpy import isnan" ] }, { @@ -65,7 +72,7 @@ "metadata": {}, "outputs": [], "source": [ - "#|export\n", + "#| export\n", "def create_jackknife_indexes(data):\n", " \"\"\"\n", " Given an array-like, creates a jackknife bootstrap.\n", @@ -81,43 +88,45 @@ " -------\n", " Generator that yields all jackknife bootstrap samples.\n", " \"\"\"\n", - " from numpy import arange, delete\n", "\n", " index_range = arange(0, len(data))\n", " return (delete(index_range, i) for i in index_range)\n", "\n", "\n", - "\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", - " from numpy import arange\n", "\n", " index_range = arange(0, len(data))\n", " return (index_range for i in index_range)\n", "\n", "\n", - "\n", "def _create_two_group_jackknife_indexes(x0, x1, is_paired):\n", " \"\"\"Creates the jackknife bootstrap for 2 groups.\"\"\"\n", "\n", " if is_paired and len(x0) == len(x1):\n", - " out = list(zip([j for j in create_jackknife_indexes(x0)],\n", - " [i for i in create_jackknife_indexes(x1)]\n", - " )\n", - " )\n", + " out = list(\n", + " zip(\n", + " [j for j in create_jackknife_indexes(x0)],\n", + " [i for i in create_jackknife_indexes(x1)],\n", + " )\n", + " )\n", " else:\n", - " jackknife_c = list(zip([j for j in create_jackknife_indexes(x0)],\n", - " [i for i in create_repeated_indexes(x1)]\n", - " )\n", - " )\n", - "\n", - " jackknife_t = list(zip([i for i in create_repeated_indexes(x0)],\n", - " [j for j in create_jackknife_indexes(x1)]\n", - " )\n", - " )\n", + " jackknife_c = list(\n", + " zip(\n", + " [j for j in create_jackknife_indexes(x0)],\n", + " [i for i in create_repeated_indexes(x1)],\n", + " )\n", + " )\n", + "\n", + " jackknife_t = list(\n", + " zip(\n", + " [i for i in create_repeated_indexes(x0)],\n", + " [j for j in create_jackknife_indexes(x1)],\n", + " )\n", + " )\n", " out = jackknife_c + jackknife_t\n", " del jackknife_c\n", " del jackknife_t\n", @@ -125,7 +134,6 @@ " return out\n", "\n", "\n", - "\n", "def compute_meandiff_jackknife(x0, x1, is_paired, effect_size):\n", " \"\"\"\n", " Given two arrays, returns the jackknife for their effect size.\n", @@ -140,46 +148,40 @@ " x0_shuffled = x0[j[0]]\n", " x1_shuffled = x1[j[1]]\n", "\n", - " es = __es.two_group_difference(x0_shuffled, x1_shuffled,\n", - " is_paired, effect_size)\n", + " es = __es.two_group_difference(x0_shuffled, x1_shuffled, is_paired, effect_size)\n", " out.append(es)\n", "\n", " return out\n", "\n", "\n", - "\n", "def _calc_accel(jack_dist):\n", - " from numpy import mean as npmean\n", - " from numpy import sum as npsum\n", - " from numpy import errstate\n", - "\n", + " \"\"\"\n", + " Given the Jackknife distribution, calculates the acceleration factor.\n", + " \"\"\"\n", " jack_mean = npmean(jack_dist)\n", "\n", - " numer = npsum((jack_mean - jack_dist)**3)\n", - " denom = 6.0 * (npsum((jack_mean - jack_dist)**2) ** 1.5)\n", + " numer = npsum((jack_mean - jack_dist) ** 3)\n", + " denom = 6.0 * (npsum((jack_mean - jack_dist) ** 2) ** 1.5)\n", "\n", - " with errstate(invalid='ignore'):\n", + " with errstate(invalid=\"ignore\"):\n", " # does not raise warning if invalid division encountered.\n", " return numer / denom\n", "\n", "\n", - "def compute_bootstrapped_diff(x0, x1, is_paired, effect_size,\n", - " resamples=5000, random_seed=12345):\n", + "def compute_bootstrapped_diff(\n", + " x0, x1, is_paired, effect_size, resamples=5000, random_seed=12345\n", + "):\n", " \"\"\"Bootstraps the effect_size for 2 groups.\"\"\"\n", - " \n", + "\n", " from . import effsize as __es\n", - " import numpy as np\n", - " from numpy.random import PCG64, RandomState\n", - " \n", - " # rng = RandomState(default_rng(random_seed))\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", - " \n", + "\n", " for i in range(int(resamples)):\n", - " \n", " if is_paired:\n", " if x0_len != x1_len:\n", " raise ValueError(\"The two arrays do not have the same length.\")\n", @@ -189,35 +191,87 @@ " else:\n", " x0_sample = rng.choice(x0, x0_len, replace=True)\n", " x1_sample = rng.choice(x1, x1_len, replace=True)\n", - " \n", - " out[i] = __es.two_group_difference(x0_sample, x1_sample,\n", - " is_paired, effect_size)\n", - " \n", - " # check whether there are any infinities in the bootstrap,\n", - " # which likely indicates the sample sizes are too small as\n", - " # the computation of Cohen's d and Hedges' g necessitated \n", - " # a division by zero.\n", - " # Added in v0.2.6.\n", - " \n", - " # num_infinities = len(out[np.isinf(out)])\n", - " # print(num_infinities)\n", - " # if num_infinities > 0:\n", - " # warn_msg = \"There are {} bootstraps that are not defined. \"\\\n", - " # \"This is likely due to smaple sample sizes. \"\\\n", - " # \"The values in a bootstrap for a group will be more likely \"\\\n", - " # \"to be all equal, with a resulting variance of zero. \"\\\n", - " # \"The computation of Cohen's d and Hedges' g will therefore \"\\\n", - " # \"involved a division by zero. \"\n", - " # warnings.warn(warn_msg.format(num_infinities), category=\"UserWarning\")\n", - " \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", + " \"\"\"\n", + " Bootstraps the effect size deltas' g.\n", + "\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", - "def compute_meandiff_bias_correction(bootstraps, #An numerical iterable, comprising bootstrap resamples of the effect size.\n", - " effsize # The effect size for the original sample.\n", - " ): #The bias correction value for the given bootstraps and effect size.\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", + " deltadelta = np.empty(resamples)\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", + " 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", + " 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", + " 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", + "\n", + " return out_delta_g, delta_g, deltadelta\n", + "\n", + "\n", + "def compute_meandiff_bias_correction(\n", + " bootstraps, # An numerical iterable, comprising bootstrap resamples of the effect size.\n", + " effsize, # The effect size for the original sample.\n", + "): # The bias correction value for the given bootstraps and effect size.\n", " \"\"\"\n", " Computes the bias correction required for the BCa method\n", " of confidence interval construction.\n", @@ -229,22 +283,18 @@ " and effect size.\n", "\n", " \"\"\"\n", - " from scipy.stats import norm\n", - " from numpy import array\n", "\n", - " B = array(bootstraps)\n", + " B = np.array(bootstraps)\n", " prop_less_than_es = sum(B < effsize) / len(B)\n", "\n", " return norm.ppf(prop_less_than_es)\n", "\n", "\n", - "\n", "def _compute_alpha_from_ci(ci):\n", " if ci < 0 or ci > 100:\n", " raise ValueError(\"`ci` must be a number between 0 and 100.\")\n", "\n", - " return (100. - ci) / 100.\n", - "\n", + " return (100.0 - ci) / 100.0\n", "\n", "\n", "def _compute_quantile(z, bias, acceleration):\n", @@ -254,15 +304,12 @@ " return bias + (numer / denom)\n", "\n", "\n", - "\n", "def compute_interval_limits(bias, acceleration, n_boots, ci=95):\n", " \"\"\"\n", " Returns the indexes of the interval limits for a given bootstrap.\n", "\n", " Supply the bias, acceleration factor, and number of bootstraps.\n", " \"\"\"\n", - " from scipy.stats import norm\n", - " from numpy import isnan, nan\n", "\n", " alpha = _compute_alpha_from_ci(ci)\n", "\n", @@ -272,34 +319,33 @@ " z_low = norm.ppf(alpha_low)\n", " z_high = norm.ppf(alpha_high)\n", "\n", - " kws = {'bias': bias, 'acceleration': acceleration}\n", + " kws = {\"bias\": bias, \"acceleration\": acceleration}\n", " low = _compute_quantile(z_low, **kws)\n", " high = _compute_quantile(z_high, **kws)\n", "\n", " if isnan(low) or isnan(high):\n", " return low, high\n", "\n", - " else:\n", - " low = int(norm.cdf(low) * n_boots)\n", - " high = int(norm.cdf(high) * n_boots)\n", - " return low, high\n", + " \n", + " low = int(norm.cdf(low) * n_boots)\n", + " high = int(norm.cdf(high) * n_boots)\n", + " return low, high\n", "\n", "\n", - "def calculate_group_var(control_var, control_N,test_var, test_N):\n", - " return control_var/control_N + test_var/test_N\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", "\n", - "def calculate_weighted_delta(group_var, differences, resamples):\n", - " '''\n", + "def calculate_weighted_delta(group_var, differences):\n", + " \"\"\"\n", " Compute the weighted deltas.\n", - " '''\n", - " import numpy as np\n", + " \"\"\"\n", "\n", - " weight = 1/group_var\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", - " return num/denom" + " return num / denom" ] }, { diff --git a/nbs/API/dabest_object.ipynb b/nbs/API/dabest_object.ipynb new file mode 100644 index 00000000..776b4fb1 --- /dev/null +++ b/nbs/API/dabest_object.ipynb @@ -0,0 +1,1364 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "id": "ed122c74", + "metadata": {}, + "source": [ + "# Dabest object\n", + "\n", + "> Main class for estimating statistics and generating plots.\n", + "\n", + "- order: 2" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fb97d9b1", + "metadata": {}, + "outputs": [], + "source": [ + "#| default_exp _dabest_object" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1d5d586f", + "metadata": {}, + "outputs": [], + "source": [ + "#| hide\n", + "from __future__ import annotations" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "dcd32470", + "metadata": {}, + "outputs": [], + "source": [ + "#| hide\n", + "from nbdev.showdoc import *\n", + "import nbdev\n", + "nbdev.nbdev_export()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d3c6f47a", + "metadata": {}, + "outputs": [], + "source": [ + "#| export\n", + "# Import standard data science libraries\n", + "from numpy import array, repeat, random, issubdtype, number\n", + "import pandas as pd\n", + "from scipy.stats import norm\n", + "from scipy.stats import randint" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "204a64b4", + "metadata": {}, + "outputs": [], + "source": [ + "#| hide\n", + "import dabest" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "350b12c1", + "metadata": {}, + "outputs": [], + "source": [ + "#| export\n", + "class Dabest(object):\n", + "\n", + " \"\"\"\n", + " Class for estimation statistics and plots.\n", + " \"\"\"\n", + "\n", + " def __init__(\n", + " self,\n", + " data,\n", + " idx,\n", + " x,\n", + " y,\n", + " paired,\n", + " id_col,\n", + " ci,\n", + " resamples,\n", + " random_seed,\n", + " proportional,\n", + " delta2,\n", + " experiment,\n", + " experiment_label,\n", + " x1_level,\n", + " mini_meta,\n", + " ):\n", + " \"\"\"\n", + " Parses and stores pandas DataFrames in preparation for estimation\n", + " statistics. You should not be calling this class directly; instead,\n", + " use `dabest.load()` to parse your DataFrame prior to analysis.\n", + " \"\"\"\n", + "\n", + " self.__delta2 = delta2\n", + " self.__experiment = experiment\n", + " self.__ci = ci\n", + " self.__input_data = data\n", + " self.__output_data = data.copy()\n", + " self.__id_col = id_col\n", + " 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", + "\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", + "\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", + " from .__init__ import __version__\n", + " from .misc_tools import print_greeting\n", + "\n", + " greeting_header = print_greeting()\n", + "\n", + " RM_STATUS = {\n", + " \"baseline\": \"for repeated measures against baseline \\n\",\n", + " \"sequential\": \"for the sequential design of repeated-measures experiment \\n\",\n", + " \"None\": \"\",\n", + " }\n", + "\n", + " PAIRED_STATUS = {\"baseline\": \"Paired e\", \"sequential\": \"Paired e\", \"None\": \"E\"}\n", + "\n", + " first_line = {\n", + " \"rm_status\": RM_STATUS[str(self.__is_paired)],\n", + " \"paired_status\": PAIRED_STATUS[str(self.__is_paired)],\n", + " }\n", + "\n", + " s1 = \"{paired_status}ffect size(s) {rm_status}\".format(**first_line)\n", + " s2 = \"with {}% confidence intervals will be computed for:\".format(self.__ci)\n", + " desc_line = s1 + s2\n", + "\n", + " out = [greeting_header + \"\\n\\n\" + desc_line]\n", + "\n", + " comparisons = []\n", + "\n", + " if self.__is_paired == \"sequential\":\n", + " for j, current_tuple in enumerate(self.__idx):\n", + " for ix, test_name in enumerate(current_tuple[1:]):\n", + " control_name = current_tuple[ix]\n", + " comparisons.append(\"{} minus {}\".format(test_name, control_name))\n", + " else:\n", + " for j, current_tuple in enumerate(self.__idx):\n", + " control_name = current_tuple[0]\n", + "\n", + " for ix, test_name in enumerate(current_tuple[1:]):\n", + " comparisons.append(\"{} minus {}\".format(test_name, control_name))\n", + "\n", + " if self.__delta2:\n", + " comparisons.append(\n", + " \"{} minus {} (only for mean difference)\".format(\n", + " self.__experiment_label[1], self.__experiment_label[0]\n", + " )\n", + " )\n", + "\n", + " if self.__mini_meta:\n", + " comparisons.append(\"weighted delta (only for mean difference)\")\n", + "\n", + " for j, g in enumerate(comparisons):\n", + " out.append(\"{}. {}\".format(j + 1, g))\n", + "\n", + " resamples_line1 = \"\\n{} resamples \".format(self.__resamples)\n", + " resamples_line2 = \"will be used to generate the effect size bootstraps.\"\n", + " out.append(resamples_line1 + resamples_line2)\n", + "\n", + " return \"\\n\".join(out)\n", + "\n", + " @property\n", + " def mean_diff(self):\n", + " \"\"\"\n", + " Returns an :py:class:`EffectSizeDataFrame` for the mean difference, its confidence interval, and relevant statistics, for all comparisons as indicated via the `idx` and `paired` argument in `dabest.load()`\n", + "\n", + " \"\"\"\n", + " return self.__mean_diff\n", + "\n", + " @property\n", + " def median_diff(self):\n", + " \"\"\"\n", + " Returns an :py:class:`EffectSizeDataFrame` for the median difference, its confidence interval, and relevant statistics, for all comparisons as indicated via the `idx` and `paired` argument in `dabest.load()`.\n", + "\n", + " \"\"\"\n", + " return self.__median_diff\n", + "\n", + " @property\n", + " def cohens_d(self):\n", + " \"\"\"\n", + " Returns an :py:class:`EffectSizeDataFrame` for the standardized mean difference Cohen's `d`, its confidence interval, and relevant statistics, for all comparisons as indicated via the `idx` and `paired` argument in `dabest.load()`.\n", + "\n", + " \"\"\"\n", + " return self.__cohens_d\n", + "\n", + " @property\n", + " def cohens_h(self):\n", + " \"\"\"\n", + " Returns an :py:class:`EffectSizeDataFrame` for the standardized mean difference Cohen's `h`, its confidence interval, and relevant statistics, for all comparisons as indicated via the `idx` and `directional` argument in `dabest.load()`.\n", + "\n", + " \"\"\"\n", + " return self.__cohens_h\n", + "\n", + " @property\n", + " def hedges_g(self):\n", + " \"\"\"\n", + " Returns an :py:class:`EffectSizeDataFrame` for the standardized mean difference Hedges' `g`, its confidence interval, and relevant statistics, for all comparisons as indicated via the `idx` and `paired` argument in `dabest.load()`.\n", + "\n", + " \"\"\"\n", + " return self.__hedges_g\n", + "\n", + " @property\n", + " def cliffs_delta(self):\n", + " \"\"\"\n", + " Returns an :py:class:`EffectSizeDataFrame` for Cliff's delta, its confidence interval, and relevant statistics, for all comparisons as indicated via the `idx` and `paired` argument in `dabest.load()`.\n", + "\n", + " \"\"\"\n", + " return self.__cliffs_delta\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", + " \"\"\"\n", + " return self.__delta_g\n", + "\n", + " @property\n", + " def input_data(self):\n", + " \"\"\"\n", + " Returns the pandas DataFrame that was passed to `dabest.load()`.\n", + " When `delta2` is True, a new column is added to support the\n", + " function. The name of this new column is indicated by `x`.\n", + " \"\"\"\n", + " return self.__input_data\n", + "\n", + " @property\n", + " def idx(self):\n", + " \"\"\"\n", + " Returns the order of categories that was passed to `dabest.load()`.\n", + " \"\"\"\n", + " return self.__idx\n", + "\n", + " @property\n", + " def x1(self):\n", + " \"\"\"\n", + " Returns the first variable declared in x when it is a delta-delta\n", + " case; returns None otherwise.\n", + " \"\"\"\n", + " return self.__x1\n", + "\n", + " @property\n", + " def x1_level(self):\n", + " \"\"\"\n", + " Returns the levels of first variable declared in x when it is a\n", + " delta-delta case; returns None otherwise.\n", + " \"\"\"\n", + " return self.__x1_level\n", + "\n", + " @property\n", + " def x2(self):\n", + " \"\"\"\n", + " Returns the second variable declared in x when it is a delta-delta\n", + " case; returns None otherwise.\n", + " \"\"\"\n", + " return self.__x2\n", + "\n", + " @property\n", + " def experiment(self):\n", + " \"\"\"\n", + " Returns the column name of experiment labels that was passed to\n", + " `dabest.load()` when it is a delta-delta case; returns None otherwise.\n", + " \"\"\"\n", + " return self.__experiment\n", + "\n", + " @property\n", + " def experiment_label(self):\n", + " \"\"\"\n", + " Returns the experiment labels in order that was passed to `dabest.load()`\n", + " when it is a delta-delta case; returns None otherwise.\n", + " \"\"\"\n", + " return self.__experiment_label\n", + "\n", + " @property\n", + " def delta2(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", + " \"\"\"\n", + " Returns the type of repeated-measures experiment.\n", + " \"\"\"\n", + " return self.__is_paired\n", + "\n", + " @property\n", + " def id_col(self):\n", + " \"\"\"\n", + " Returns the id column declared to `dabest.load()`.\n", + " \"\"\"\n", + " return self.__id_col\n", + "\n", + " @property\n", + " def ci(self):\n", + " \"\"\"\n", + " The width of the desired confidence interval.\n", + " \"\"\"\n", + " return self.__ci\n", + "\n", + " @property\n", + " def resamples(self):\n", + " \"\"\"\n", + " The number of resamples used to generate the bootstrap.\n", + " \"\"\"\n", + " return self.__resamples\n", + "\n", + " @property\n", + " def random_seed(self):\n", + " \"\"\"\n", + " The number used to initialise the numpy random seed generator, ie.\n", + " `seed_value` from `numpy.random.seed(seed_value)` is returned.\n", + " \"\"\"\n", + " return self.__random_seed\n", + "\n", + " @property\n", + " def x(self):\n", + " \"\"\"\n", + " Returns the x column that was passed to `dabest.load()`, if any.\n", + " When `delta2` is True, `x` returns the name of the new column created\n", + " for the delta-delta situation. To retrieve the 2 variables passed into\n", + " `x` when `delta2` is True, please call `x1` and `x2` instead.\n", + " \"\"\"\n", + " return self.__x\n", + "\n", + " @property\n", + " def y(self):\n", + " \"\"\"\n", + " Returns the y column that was passed to `dabest.load()`, if any.\n", + " \"\"\"\n", + " return self.__y\n", + "\n", + " @property\n", + " def _xvar(self):\n", + " \"\"\"\n", + " Returns the xvar in dabest.plot_data.\n", + " \"\"\"\n", + " return self.__xvar\n", + "\n", + " @property\n", + " def _yvar(self):\n", + " \"\"\"\n", + " Returns the yvar in dabest.plot_data.\n", + " \"\"\"\n", + " return self.__yvar\n", + "\n", + " @property\n", + " def _plot_data(self):\n", + " \"\"\"\n", + " Returns the pandas DataFrame used to produce the estimation stats/plots.\n", + " \"\"\"\n", + " return self.__plot_data\n", + "\n", + " @property\n", + " def proportional(self):\n", + " \"\"\"\n", + " Returns the proportional parameter class.\n", + " \"\"\"\n", + " return self.__proportional\n", + "\n", + " @property\n", + " def mini_meta(self):\n", + " \"\"\"\n", + " Returns the mini_meta boolean parameter.\n", + " \"\"\"\n", + " return self.__mini_meta\n", + "\n", + " @property\n", + " def _all_plot_groups(self):\n", + " \"\"\"\n", + " Returns the all plot groups, as indicated via the `idx` keyword.\n", + " \"\"\"\n", + " return self.__all_plot_groups\n", + "\n", + " def _check_errors(self, x, y, idx, experiment, experiment_label, x1_level):\n", + " '''\n", + " Function to check some input parameters and combinations between them.\n", + " At the end of this function these two class attributes are updated\n", + " self.__experiment_label and self.__x1_level\n", + " '''\n", + " # Check if it is a valid mini_meta case\n", + " if self.__mini_meta:\n", + " # Only mini_meta calculation but not proportional and delta-delta function\n", + " if self.__proportional:\n", + " err0 = \"`proportional` and `mini_meta` cannot be True at the same time.\"\n", + " raise ValueError(err0)\n", + " if self.__delta2:\n", + " err0 = \"`delta2` and `mini_meta` cannot be True at the same time.\"\n", + " raise ValueError(err0)\n", + "\n", + " # Check if the columns stated are valid\n", + " # Initialize a flag to track if any element in idx is neither str nor (tuple, list)\n", + " valid_types = True\n", + "\n", + " # Initialize variables to track the conditions for str and (tuple, list)\n", + " is_str_condition_met, is_tuple_list_condition_met = False, False\n", + "\n", + " # Single traversal for optimization\n", + " for item in idx:\n", + " if isinstance(item, str):\n", + " is_str_condition_met = True\n", + " elif isinstance(item, (tuple, list)) and len(item) == 2:\n", + " is_tuple_list_condition_met = True\n", + " else:\n", + " valid_types = False\n", + " break # Exit the loop if an invalid type is found\n", + "\n", + " # Check if all types are valid\n", + " if not valid_types:\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", + "\n", + " # Handling str type condition\n", + " if is_str_condition_met:\n", + " if len(pd.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", + "\n", + " # Handling (tuple, list) type condition\n", + " if is_tuple_list_condition_met:\n", + " all_idx_lengths = [len(t) for t in idx]\n", + " if (array(all_idx_lengths) != 2).any():\n", + " err1 = \"`mini_meta` is True, but some elements in idx \"\n", + " err2 = \"in {} do not consist only of two groups.\".format(idx)\n", + " raise ValueError(err1 + err2)\n", + "\n", + "\n", + " # Check if this is a 2x2 ANOVA case and x & y are valid columns\n", + " # Create experiment_label and x1_level\n", + " elif self.__delta2:\n", + " 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", + " err0 = \"`idx` should not be specified when `delta2` is True.\".format(\n", + " len(x)\n", + " )\n", + " raise ValueError(err0)\n", + "\n", + " # Check if x is valid\n", + " if len(x) != 2:\n", + " err0 = \"`delta2` is True but the number of variables indicated by `x` is {}.\".format(\n", + " len(x)\n", + " )\n", + " raise ValueError(err0)\n", + "\n", + " for i in x:\n", + " if i not in self.__output_data.columns:\n", + " err = \"{0} is not a column in `data`. Please check.\".format(i)\n", + " raise IndexError(err)\n", + "\n", + " # Check if y is valid\n", + " if not y:\n", + " err0 = \"`delta2` is True but `y` is not indicated.\"\n", + " raise ValueError(err0)\n", + "\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", + "\n", + " # Check if experiment is valid\n", + " if experiment not in self.__output_data.columns:\n", + " err = \"{0} is not a column in `data`. Please check.\".format(experiment)\n", + " raise IndexError(err)\n", + "\n", + " # Check if experiment_label is valid and create experiment when needed\n", + " if experiment_label:\n", + " if len(experiment_label) != 2:\n", + " err0 = \"`experiment_label` does not have a length of 2.\"\n", + " raise ValueError(err0)\n", + "\n", + " for i in experiment_label:\n", + " if i not in self.__output_data[experiment].unique():\n", + " err = \"{0} is not an element in the column `{1}` of `data`. Please check.\".format(\n", + " i, experiment\n", + " )\n", + " raise IndexError(err)\n", + " else:\n", + " experiment_label = self.__output_data[experiment].unique()\n", + "\n", + " # Check if x1_level is valid\n", + " if x1_level:\n", + " if len(x1_level) != 2:\n", + " err0 = \"`x1_level` does not have a length of 2.\"\n", + " raise ValueError(err0)\n", + "\n", + " for i in x1_level:\n", + " if i not in self.__output_data[x[0]].unique():\n", + " err = \"{0} is not an element in the column `{1}` of `data`. Please check.\".format(\n", + " i, experiment\n", + " )\n", + " raise IndexError(err)\n", + "\n", + " else:\n", + " x1_level = self.__output_data[x[0]].unique()\n", + "\n", + " elif experiment:\n", + " experiment_label = self.__output_data[experiment].unique()\n", + " x1_level = self.__output_data[x[0]].unique()\n", + " 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", + " 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", + " 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", + "\n", + " # check y 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", + " raise ValueError(err)\n", + "\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", + " err0 = '\"{0}\" is not a group in the column `{1}`.'.format(g, x)\n", + " err1 = \" Please check `idx` and try again.\"\n", + " raise IndexError(err0 + err1)\n", + "\n", + " # Select only rows where the value in the `x` column\n", + " # is found in `idx`.\n", + " plot_data = self.__output_data[\n", + " self.__output_data.loc[:, x].isin(all_plot_groups)\n", + " ].copy()\n", + "\n", + " # Assign attributes\n", + " self.__x = x\n", + " self.__y = y\n", + " self.__xvar = x\n", + " self.__yvar = y\n", + "\n", + " elif x is None and y is None:\n", + " # Assume we have a wide dataset.\n", + " # Assign attributes appropriately.\n", + " 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", + "\n", + " # First, check we have all columns in the dataset.\n", + " for g in all_plot_groups:\n", + " if g not in self.__output_data.columns:\n", + " err0 = '\"{0}\" is not a column in `data`.'.format(g)\n", + " err1 = \" Please check `idx` and try again.\"\n", + " raise IndexError(err0 + err1)\n", + "\n", + " set_all_columns = set(self.__output_data.columns.tolist())\n", + " set_all_plot_groups = set(all_plot_groups)\n", + " id_vars = set_all_columns.difference(set_all_plot_groups)\n", + "\n", + " plot_data = pd.melt(\n", + " self.__output_data,\n", + " id_vars=id_vars,\n", + " value_vars=all_plot_groups,\n", + " value_name=self.__yvar,\n", + " var_name=self.__xvar,\n", + " )\n", + "\n", + " # Added in v0.2.7.\n", + " plot_data.dropna(axis=0, how=\"any\", subset=[self.__yvar], inplace=True)\n", + "\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.reorder_categories(\n", + " all_plot_groups, ordered=True, inplace=True\n", + " )\n", + " else:\n", + " plot_data.loc[:, self.__xvar] = pd.Categorical(\n", + " plot_data[self.__xvar], categories=all_plot_groups, ordered=True\n", + " )\n", + "\n", + " return plot_data\n", + "\n", + " def _compute_effectsize_dfs(self):\n", + " '''\n", + " Function to compute all attributes based on EffectSizeDataFrame.\n", + " It returns nothing.\n", + " '''\n", + " from ._effsize_objects import EffectSizeDataFrame\n", + "\n", + " effectsize_df_kwargs = dict(\n", + " ci=self.__ci,\n", + " is_paired=self.__is_paired,\n", + " random_seed=self.__random_seed,\n", + " resamples=self.__resamples,\n", + " proportional=self.__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", + " )\n", + "\n", + " self.__mean_diff = EffectSizeDataFrame(\n", + " self, \"mean_diff\", **effectsize_df_kwargs\n", + " )\n", + "\n", + " self.__median_diff = EffectSizeDataFrame(\n", + " self, \"median_diff\", **effectsize_df_kwargs\n", + " )\n", + "\n", + " self.__cohens_d = EffectSizeDataFrame(self, \"cohens_d\", **effectsize_df_kwargs)\n", + "\n", + " self.__cohens_h = EffectSizeDataFrame(self, \"cohens_h\", **effectsize_df_kwargs)\n", + "\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", + " )\n", + " else:\n", + " self.__cliffs_delta = (\n", + " \"The data is paired; Cliff's delta is therefore undefined.\"\n", + " )" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "c86c0487", + "metadata": {}, + "source": [ + "#### Example: mean_diff" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6d07d58b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "DABEST v2024.03.29\n", + "==================\n", + " \n", + "Good afternoon!\n", + "The current time is Tue Mar 19 15:33:25 2024.\n", + "\n", + "The unpaired mean difference between control and test is 0.5 [95%CI -0.0412, 1.0].\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", + "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": [ + "control = norm.rvs(loc=0, size=30, random_state=12345)\n", + "test = norm.rvs(loc=0.5, size=30, random_state=12345)\n", + "my_df = pd.DataFrame({\"control\": control,\n", + " \"test\": test})\n", + "my_dabest_object = dabest.load(my_df, idx=(\"control\", \"test\"))\n", + "my_dabest_object.mean_diff" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "cf5ca0a0", + "metadata": {}, + "source": [ + "This is simply the mean of the control group subtracted from\n", + "the mean of the test group.\n", + "\n", + "$$\\text{Mean difference} = \\overline{x}_{Test} - \\overline{x}_{Control}$$\n", + "\n", + "where $\\overline{x}$ is the mean for the group $x$." + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "8b3b146c", + "metadata": {}, + "source": [ + "#### Example: median_diff" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8e9b8635", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "DABEST v2024.03.29\n", + "==================\n", + " \n", + "Good afternoon!\n", + "The current time is Tue Mar 19 15:33:26 2024.\n", + "\n", + "The unpaired median difference between control and test is 0.5 [95%CI -0.0758, 0.991].\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", + "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 `.median_diff.statistical_tests`" + ] + }, + "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", + "my_df = pd.DataFrame({\"control\": control,\n", + " \"test\": test})\n", + "my_dabest_object = dabest.load(my_df, idx=(\"control\", \"test\"))\n", + "my_dabest_object.median_diff" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "838b2978", + "metadata": {}, + "source": [ + "\n", + "This is the median difference between the control group and the test group.\n", + "\n", + "If the comparison(s) are unpaired, median_diff is computed with the following equation:\n", + "\n", + "\n", + "$$\\text{Median difference} = \\widetilde{x}_{Test} - \\widetilde{x}_{Control}$$\n", + "\n", + "where $\\widetilde{x}$ is the median for the group $x$.\n", + "\n", + "If the comparison(s) are paired, median_diff is computed with the following equation:\n", + "\n", + "$$\\text{Median difference} = \\widetilde{x}_{Test - Control}$$\n", + " \n", + "\n", + "##### Things to note\n", + "\n", + "Using median difference as the statistic in bootstrapping may result in a biased estimate and cause problems with BCa confidence intervals. Consider using mean difference instead. \n", + "\n", + "When plotting, consider using percentile confidence intervals instead of BCa confidence intervals by specifying `ci_type = 'percentile'` in .plot(). \n", + "\n", + "For detailed information, please refer to [Issue 129](https://github.com/ACCLAB/DABEST-python/issues/129). \n" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "a5324d21", + "metadata": {}, + "source": [ + "#### Example: cohens_d" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "748b5c60", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "DABEST v2024.03.29\n", + "==================\n", + " \n", + "Good afternoon!\n", + "The current time is Tue Mar 19 15:33:27 2024.\n", + "\n", + "The unpaired Cohen's d between control and test is 0.471 [95%CI -0.0843, 0.976].\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", + "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_d.statistical_tests`" + ] + }, + "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", + "my_df = pd.DataFrame({\"control\": control,\n", + " \"test\": test})\n", + "my_dabest_object = dabest.load(my_df, idx=(\"control\", \"test\"))\n", + "my_dabest_object.cohens_d" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "6f66579c", + "metadata": {}, + "source": [ + "\n", + "Cohen's `d` is simply the mean of the control group subtracted from\n", + "the mean of the test group.\n", + "\n", + "If `paired` is None, then the comparison(s) are unpaired; \n", + "otherwise the comparison(s) are paired.\n", + "\n", + "If the comparison(s) are unpaired, Cohen's `d` is computed with the following equation:\n", + "\n", + "\n", + "$$d = \\frac{\\overline{x}_{Test} - \\overline{x}_{Control}} {\\text{pooled standard deviation}}$$\n", + "\n", + "\n", + "For paired comparisons, Cohen's d is given by\n", + "\n", + "$$d = \\frac{\\overline{x}_{Test} - \\overline{x}_{Control}} {\\text{average standard deviation}}$$\n", + "\n", + "where $\\overline{x}$ is the mean of the respective group of observations, ${Var}_{x}$ denotes the variance of that group,\n", + "\n", + "\n", + "$$\\text{pooled standard deviation} = \\sqrt{ \\frac{(n_{control} - 1) * {Var}_{control} + (n_{test} - 1) * {Var}_{test} } {n_{control} + n_{test} - 2} }$$\n", + "\n", + "and\n", + "\n", + "\n", + "$$\\text{average standard deviation} = \\sqrt{ \\frac{{Var}_{control} + {Var}_{test}} {2}}$$\n", + "\n", + "The sample variance (and standard deviation) uses N-1 degrees of freedoms.\n", + "This is an application of [Bessel's correction](https://en.wikipedia.org/wiki/Bessel%27s_correction), and yields the unbiased sample variance.\n", + "\n", + "References:\n", + "\n", + "\n", + " \n", + "\n", + " \n", + "" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "40f4eff9", + "metadata": {}, + "source": [ + "#### Example: cohens_h" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f713781c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "DABEST v2024.03.29\n", + "==================\n", + " \n", + "Good afternoon!\n", + "The current time is Tue Mar 19 15:33:29 2024.\n", + "\n", + "The unpaired Cohen's h between control and test is 0.0 [95%CI -0.613, 0.429].\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", + "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": [ + "control = randint.rvs(0, 2, size=30, random_state=12345)\n", + "test = randint.rvs(0, 2, size=30, random_state=12345)\n", + "my_df = pd.DataFrame({\"control\": control,\n", + " \"test\": test})\n", + "my_dabest_object = dabest.load(my_df, idx=(\"control\", \"test\"))\n", + "my_dabest_object.cohens_h" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "9e3e57bd", + "metadata": {}, + "source": [ + "Cohen's *h* uses the information of proportion in the control and test groups to calculate the distance between two proportions.\n", + "\n", + "It can be used to describe the difference between two proportions as \"small\", \"medium\", or \"large\".\n", + "\n", + "It can be used to determine if the difference between two proportions is \"meaningful\".\n", + "\n", + "A directional Cohen's *h* is computed with the following equation:\n", + "\n", + "\n", + "$$h = 2 * \\arcsin{\\sqrt{proportion_{Test}}} - 2 * \\arcsin{\\sqrt{proportion_{Control}}}$$\n", + "\n", + "For a non-directional Cohen's *h*, the equation is:\n", + "\n", + "$$h = |2 * \\arcsin{\\sqrt{proportion_{Test}}} - 2 * \\arcsin{\\sqrt{proportion_{Control}}}|$$\n", + "\n", + "References:\n", + "\n", + "" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "970fb3b2", + "metadata": {}, + "source": [ + "#### Example: hedges_g" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "26960f9e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "DABEST v2024.03.29\n", + "==================\n", + " \n", + "Good afternoon!\n", + "The current time is Tue Mar 19 15:33:30 2024.\n", + "\n", + "The unpaired Hedges' g between control and test is 0.465 [95%CI -0.0832, 0.963].\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", + "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 `.hedges_g.statistical_tests`" + ] + }, + "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", + "my_df = pd.DataFrame({\"control\": control,\n", + " \"test\": test})\n", + "my_dabest_object = dabest.load(my_df, idx=(\"control\", \"test\"))\n", + "my_dabest_object.hedges_g" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "66c8a83a", + "metadata": {}, + "source": [ + "Hedges' `g` is `cohens_d` corrected for bias via multiplication with the following correction factor:\n", + " \n", + "$$\\frac{ \\Gamma( \\frac{a} {2} )} {\\sqrt{ \\frac{a} {2} } \\times \\Gamma( \\frac{a - 1} {2} )}$$\n", + "\n", + "where\n", + "\n", + "$$a = {n}_{control} + {n}_{test} - 2$$\n", + "\n", + "and $\\Gamma(x)$ is the [Gamma function](https://en.wikipedia.org/wiki/Gamma_function).\n", + "\n", + "\n", + "\n", + "References:\n", + "\n", + "\n", + " \n", + "" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "b1cf0080", + "metadata": {}, + "source": [ + "#### Example: cliffs_delta" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "dce86c76", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "DABEST v2024.03.29\n", + "==================\n", + " \n", + "Good afternoon!\n", + "The current time is Tue Mar 19 15:33:41 2024.\n", + "\n", + "The unpaired Cliff's delta between control and test is 0.28 [95%CI -0.0244, 0.533].\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", + "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 `.cliffs_delta.statistical_tests`" + ] + }, + "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", + "my_df = pd.DataFrame({\"control\": control,\n", + " \"test\": test})\n", + "my_dabest_object = dabest.load(my_df, idx=(\"control\", \"test\"))\n", + "my_dabest_object.cliffs_delta" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "9661ab37", + "metadata": {}, + "source": [ + "Cliff's delta is a measure of ordinal dominance, ie. how often the values from the test sample are larger than values from the control sample.\n", + "\n", + "$$\\text{Cliff's delta} = \\frac{\\#({x}_{test} > {x}_{control}) - \\#({x}_{test} < {x}_{control})} {{n}_{Test} \\times {n}_{Control}}$$\n", + " \n", + " \n", + "where $\\#$ denotes the number of times a value from the test sample exceeds (or is lesser than) values in the control sample. \n", + " \n", + "Cliff's delta ranges from -1 to 1; it can also be thought of as a measure of the degree of overlap between the two samples. An attractive aspect of this effect size is that it does not make an assumptions about the underlying distributions that the samples were drawn from. \n", + "\n", + "References:\n", + "\n", + "\n", + " \n", + "" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "bd341f7c", + "metadata": {}, + "source": [ + "#### Example: delta_g" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9abb53c1", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "DABEST v2024.03.29\n", + "==================\n", + " \n", + "Good afternoon!\n", + "The current time is Tue Mar 19 15:33:45 2024.\n", + "\n", + "The unpaired deltas' g between W Placebo and M Placebo is 1.74 [95%CI 1.1, 2.31].\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 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 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", + "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": [ + "random.seed(12345) # Fix the seed so the results are replicable.\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", + "t1 = repeat('Placebo', N*2).tolist()\n", + "t2 = repeat('Drug', N*2).tolist()\n", + "treatment = t1 + t2\n", + "rep = []\n", + "for i in range(N*2):\n", + " rep.append('Rep1')\n", + " rep.append('Rep2')\n", + "wt = repeat('W', N).tolist()\n", + "mt = repeat('M', N).tolist()\n", + "wt2 = repeat('W', N).tolist()\n", + "mt2 = repeat('M', N).tolist()\n", + "genotype = wt + mt + wt2 + mt2\n", + "id = list(range(0, N*2))\n", + "id_col = id + id\n", + "df_delta2 = pd.DataFrame({'ID' : id_col,\n", + " 'Rep' : rep,\n", + " 'Genotype' : genotype,\n", + " '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" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "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", + "\n", + "\n", + " $$\\Delta_{1} = \\overline{X}_{A_{2}, B_{1}} - \\overline{X}_{A_{1}, B_{1}}$$\n", + "\n", + " $$\\Delta_{2} = \\overline{X}_{A_{2}, B_{2}} - \\overline{X}_{A_{1}, B_{2}}$$\n", + "\n", + "\n", + "where $\\overline{X}_{A_{i}, B_{j}}$ is the mean of the sample with A = i and B = j, $\\Delta$ is the mean difference between two samples.\n", + "\n", + "A delta-delta value is then calculated as the mean difference between the two primary deltas:\n", + "\n", + "$$\\Delta_{\\Delta} = \\Delta_{2} - \\Delta_{1}$$\n", + "\n", + "and the standard deviation of the delta-delta value is calculated from a pooled variance of the 4 samples:\n", + "\n", + "\n", + "$$s_{\\Delta_{\\Delta}} = \\sqrt{\\frac{(n_{A_{2}, B_{1}}-1)s_{A_{2}, B_{1}}^2+(n_{A_{1}, B_{1}}-1)s_{A_{1}, B_{1}}^2+(n_{A_{2}, B_{2}}-1)s_{A_{2}, B_{2}}^2+(n_{A_{1}, B_{2}}-1)s_{A_{1}, B_{2}}^2}{(n_{A_{2}, B_{1}} - 1) + (n_{A_{1}, B_{1}} - 1) + (n_{A_{2}, B_{2}} - 1) + (n_{A_{1}, B_{2}} - 1)}}$$\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", + "$\\Delta_{g} = \\frac{\\Delta_{\\Delta}}{s_{\\Delta_{\\Delta}}}$" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "python3", + "language": "python", + "name": "python3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/nbs/API/delta_objects.ipynb b/nbs/API/delta_objects.ipynb new file mode 100644 index 00000000..358e45ad --- /dev/null +++ b/nbs/API/delta_objects.ipynb @@ -0,0 +1,1038 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Delta objects\n", + "\n", + "> Auxiliary delta classes for estimating statistics and generating plots.\n", + "\n", + "- order: 9" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#| default_exp _delta_objects" + ] + }, + { + "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": [ + "#| hide\n", + "import dabest" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#| export\n", + "from scipy.stats import norm\n", + "import pandas as pd\n", + "import numpy as np\n", + "from numpy import sort as npsort\n", + "from numpy import isnan\n", + "from string import Template\n", + "import warnings\n", + "import datetime as dt" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#| export\n", + "class DeltaDelta(object):\n", + " \"\"\"\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", + " $$\\Delta_{1} = \\overline{X}_{A_{2}, B_{1}} - \\overline{X}_{A_{1}, B_{1}}$$\n", + "\n", + " $$\\Delta_{2} = \\overline{X}_{A_{2}, B_{2}} - \\overline{X}_{A_{1}, B_{2}}$$\n", + "\n", + "\n", + " where $\\overline{X}_{A_{i}, B_{j}}$ is the mean of the sample with A = i and B = j, $\\Delta$ is the mean difference between two samples.\n", + "\n", + " A delta-delta value is then calculated as the mean difference between the two primary deltas:\n", + "\n", + "\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", + " 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", + "\n", + " $$s_{\\Delta_{\\Delta}} = \\sqrt{\\frac{(n_{A_{2}, B_{1}}-1)s_{A_{2}, B_{1}}^2+(n_{A_{1}, B_{1}}-1)s_{A_{1}, B_{1}}^2+(n_{A_{2}, B_{2}}-1)s_{A_{2}, B_{2}}^2+(n_{A_{1}, B_{2}}-1)s_{A_{1}, B_{2}}^2}{(n_{A_{2}, B_{1}} - 1) + (n_{A_{1}, B_{1}} - 1) + (n_{A_{2}, B_{2}} - 1) + (n_{A_{1}, B_{2}} - 1)}}$$\n", + "\n", + " where $s$ is the standard deviation and $n$ is the sample size.\n", + "\n", + "\n", + " \"\"\"\n", + "\n", + " def __init__(\n", + " self, effectsizedataframe, permutation_count, bootstraps_delta_delta, ci=95\n", + " ):\n", + " from ._stats_tools import effsize as es\n", + " from ._stats_tools import confint_1group as ci1g\n", + " from ._stats_tools import confint_2group_diff as ci2g\n", + "\n", + " self.__effsizedf = effectsizedataframe.results\n", + " self.__dabest_obj = effectsizedataframe.dabest_obj\n", + " self.__ci = ci\n", + " self.__resamples = effectsizedataframe.resamples\n", + " self.__effect_size = effectsizedataframe.effect_size\n", + " self.__alpha = ci2g._compute_alpha_from_ci(ci)\n", + " self.__permutation_count = permutation_count\n", + " self.__bootstraps = np.array(self.__effsizedf[\"bootstraps\"])\n", + " 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", + " if self.__effect_size == \"mean_diff\":\n", + " self.__bootstraps_delta_delta = bootstraps_delta_delta[2]\n", + " self.__difference = (\n", + " self.__effsizedf[\"difference\"][1] - self.__effsizedf[\"difference\"][0]\n", + " )\n", + " else:\n", + " self.__bootstraps_delta_delta = bootstraps_delta_delta[0]\n", + " self.__difference = bootstraps_delta_delta[1]\n", + "\n", + " sorted_delta_delta = npsort(self.__bootstraps_delta_delta)\n", + "\n", + " self.__bias_correction = ci2g.compute_meandiff_bias_correction(\n", + " self.__bootstraps_delta_delta, self.__difference\n", + " )\n", + "\n", + " self.__jackknives = np.array(\n", + " ci1g.compute_1group_jackknife(self.__bootstraps_delta_delta, np.mean)\n", + " )\n", + "\n", + " self.__acceleration_value = ci2g._calc_accel(self.__jackknives)\n", + "\n", + " # Compute BCa intervals.\n", + " bca_idx_low, bca_idx_high = ci2g.compute_interval_limits(\n", + " self.__bias_correction, self.__acceleration_value, self.__resamples, ci\n", + " )\n", + "\n", + " self.__bca_interval_idx = (bca_idx_low, bca_idx_high)\n", + "\n", + " if ~isnan(bca_idx_low) and ~isnan(bca_idx_high):\n", + " self.__bca_low = sorted_delta_delta[bca_idx_low]\n", + " self.__bca_high = sorted_delta_delta[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 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 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.__bca_low = self.__difference\n", + " warnings.warn(err_temp.substitute(lim_type=\"lower\"), stacklevel=0)\n", + "\n", + " if isnan(bca_idx_high):\n", + " self.__bca_high = self.__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.__pct_interval_idx = (pct_idx_low, pct_idx_high)\n", + " self.__pct_low = sorted_delta_delta[pct_idx_low]\n", + " self.__pct_high = sorted_delta_delta[pct_idx_high]\n", + "\n", + " def __permutation_test(self):\n", + " \"\"\"\n", + " Perform a permutation test and obtain the permutation p-value\n", + " based on the permutation data.\n", + " \"\"\"\n", + " self.__permutations = np.array(self.__effsizedf[\"permutations\"])\n", + "\n", + " THRESHOLD = np.abs(self.__difference)\n", + "\n", + " self.__permutations_delta_delta = np.array(\n", + " self.__permutations[1] - self.__permutations[0]\n", + " )\n", + "\n", + " count = sum(np.abs(self.__permutations_delta_delta) > THRESHOLD)\n", + " self.__pvalue_permutation = count / self.__permutation_count\n", + "\n", + " def __repr__(self, header=True, sigfig=3):\n", + " from .misc_tools import print_greeting\n", + "\n", + " first_line = {\"control\": self.__control, \"test\": self.__test}\n", + "\n", + " 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", + "\n", + " base_string_fmt = \"{:.\" + str(sigfig) + \"}\"\n", + " if \".\" in str(self.__ci):\n", + " ci_width = base_string_fmt.format(self.__ci)\n", + " else:\n", + " ci_width = str(self.__ci)\n", + "\n", + " ci_out = {\n", + " \"es\": base_string_fmt.format(self.__difference),\n", + " \"ci\": ci_width,\n", + " \"bca_low\": base_string_fmt.format(self.__bca_low),\n", + " \"bca_high\": base_string_fmt.format(self.__bca_high),\n", + " }\n", + "\n", + " out2 = \"is {es} [{ci}%CI {bca_low}, {bca_high}].\".format(**ci_out)\n", + " out = out1 + out2\n", + "\n", + " if header is True:\n", + " out = print_greeting() + \"\\n\" + \"\\n\" + out\n", + "\n", + " pval_rounded = base_string_fmt.format(self.pvalue_permutation)\n", + "\n", + " p1 = \"The p-value of the two-sided permutation t-test is {}, \".format(\n", + " pval_rounded\n", + " )\n", + " p2 = \"calculated for legacy purposes only. \"\n", + " pvalue = p1 + p2\n", + "\n", + " bs1 = \"{} bootstrap samples were taken; \".format(self.__resamples)\n", + " bs2 = \"the confidence interval is bias-corrected and accelerated.\"\n", + " bs = bs1 + bs2\n", + "\n", + " pval_def1 = (\n", + " \"Any p-value reported is the probability of observing the \"\n", + " + \"effect size (or greater),\\nassuming the null hypothesis of \"\n", + " + \"zero difference is true.\"\n", + " )\n", + " pval_def2 = (\n", + " \"\\nFor each p-value, 5000 reshuffles of the \"\n", + " + \"control and test labels were performed.\"\n", + " )\n", + " pval_def = pval_def1 + pval_def2\n", + "\n", + " return \"{}\\n{}\\n\\n{}\\n{}\".format(out, pvalue, bs, pval_def)\n", + "\n", + " def to_dict(self):\n", + " \"\"\"\n", + " Returns the attributes of the `DeltaDelta` object as a\n", + " dictionary.\n", + " \"\"\"\n", + " # Only get public (user-facing) attributes.\n", + " attrs = [a for a in dir(self) if not a.startswith((\"_\", \"to_dict\"))]\n", + " out = {}\n", + " for a in attrs:\n", + " out[a] = getattr(self, a)\n", + " return out\n", + "\n", + " @property\n", + " def ci(self):\n", + " \"\"\"\n", + " Returns the width of the confidence interval, in percent.\n", + " \"\"\"\n", + " return self.__ci\n", + "\n", + " @property\n", + " def alpha(self):\n", + " \"\"\"\n", + " Returns the significance level of the statistical test as a float\n", + " between 0 and 1.\n", + " \"\"\"\n", + " return self.__alpha\n", + "\n", + " @property\n", + " def bias_correction(self):\n", + " return self.__bias_correction\n", + "\n", + " @property\n", + " def bootstraps(self):\n", + " \"\"\"\n", + " Return the bootstrapped deltas from all the experiment groups.\n", + " \"\"\"\n", + " return self.__bootstraps\n", + "\n", + " @property\n", + " def jackknives(self):\n", + " return self.__jackknives\n", + "\n", + " @property\n", + " def acceleration_value(self):\n", + " return self.__acceleration_value\n", + "\n", + " @property\n", + " def bca_low(self):\n", + " \"\"\"\n", + " The bias-corrected and accelerated confidence interval lower limit.\n", + " \"\"\"\n", + " return self.__bca_low\n", + "\n", + " @property\n", + " def bca_high(self):\n", + " \"\"\"\n", + " The bias-corrected and accelerated confidence interval upper limit.\n", + " \"\"\"\n", + " return self.__bca_high\n", + "\n", + " @property\n", + " def bca_interval_idx(self):\n", + " return self.__bca_interval_idx\n", + "\n", + " @property\n", + " def control(self):\n", + " \"\"\"\n", + " Return the name of the control experiment group.\n", + " \"\"\"\n", + " return self.__control\n", + "\n", + " @property\n", + " def test(self):\n", + " \"\"\"\n", + " Return the name of the test experiment group.\n", + " \"\"\"\n", + " return self.__test\n", + "\n", + " @property\n", + " def bootstraps_delta_delta(self):\n", + " \"\"\"\n", + " Return the delta-delta values calculated from the bootstrapped\n", + " deltas.\n", + " \"\"\"\n", + " return self.__bootstraps_delta_delta\n", + "\n", + " @property\n", + " def difference(self):\n", + " \"\"\"\n", + " Return the delta-delta value calculated based on the raw data.\n", + " \"\"\"\n", + " return self.__difference\n", + "\n", + " @property\n", + " def pct_interval_idx(self):\n", + " return self.__pct_interval_idx\n", + "\n", + " @property\n", + " def pct_low(self):\n", + " \"\"\"\n", + " The percentile confidence interval lower limit.\n", + " \"\"\"\n", + " return self.__pct_low\n", + "\n", + " @property\n", + " def pct_high(self):\n", + " \"\"\"\n", + " The percentile confidence interval lower limit.\n", + " \"\"\"\n", + " return self.__pct_high\n", + "\n", + " @property\n", + " def pvalue_permutation(self):\n", + " try:\n", + " return self.__pvalue_permutation\n", + " except AttributeError:\n", + " self.__permutation_test()\n", + " return self.__pvalue_permutation\n", + "\n", + " @property\n", + " def permutation_count(self):\n", + " \"\"\"\n", + " The number of permuations taken.\n", + " \"\"\"\n", + " return self.__permutation_count\n", + "\n", + " @property\n", + " def permutations(self):\n", + " \"\"\"\n", + " Return the mean differences of permutations obtained during\n", + " the permutation test for each experiment group.\n", + " \"\"\"\n", + " try:\n", + " return self.__permutations\n", + " except AttributeError:\n", + " self.__permutation_test()\n", + " return self.__permutations\n", + "\n", + " @property\n", + " def permutations_delta_delta(self):\n", + " \"\"\"\n", + " Return the delta-delta values of permutations obtained\n", + " during the permutation test.\n", + " \"\"\"\n", + " try:\n", + " return self.__permutations_delta_delta\n", + " except AttributeError:\n", + " self.__permutation_test()\n", + " return self.__permutations_delta_delta" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "and the standard deviation of the delta-delta value is calculated from a pooled variance of the 4 samples:\n", + "\n", + "\n", + "$$s_{\\Delta_{\\Delta}} = \\sqrt{\\frac{(n_{A_{2}, B_{1}}-1)s_{A_{2}, B_{1}}^2+(n_{A_{1}, B_{1}}-1)s_{A_{1}, B_{1}}^2+(n_{A_{2}, B_{2}}-1)s_{A_{2}, B_{2}}^2+(n_{A_{1}, B_{2}}-1)s_{A_{1}, B_{2}}^2}{(n_{A_{2}, B_{1}} - 1) + (n_{A_{1}, B_{1}} - 1) + (n_{A_{2}, B_{2}} - 1) + (n_{A_{1}, B_{2}} - 1)}}$$\n", + "\n", + "where $s$ is the standard deviation and $n$ is the sample size." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Example: delta-delta" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "np.random.seed(9999) # Fix the seed so the results are replicable.\n", + "N = 20\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", + "# 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", + "# 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", + "# 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", + "genotype = wt + mt + wt2 + mt2\n", + "# Add an `id` column for paired data plotting.\n", + "id = list(range(0, N*2))\n", + "id_col = id + id \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", + "unpaired_delta2 = dabest.load(data = df_delta2, x = [\"Genotype\", \"Genotype\"], y = \"Y\", delta2 = True, experiment = \"Treatment\")\n", + "unpaired_delta2.mean_diff.plot();" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#| export\n", + "class MiniMetaDelta(object):\n", + " \"\"\"\n", + " A class to compute and store the weighted delta.\n", + " A weighted delta is calculated if the argument ``mini_meta=True`` is passed during ``dabest.load()``.\n", + " \n", + " \"\"\"\n", + "\n", + " def __init__(self, effectsizedataframe, permutation_count,\n", + " ci=95):\n", + " from ._stats_tools import effsize as es\n", + " from ._stats_tools import confint_1group as ci1g\n", + " from ._stats_tools import confint_2group_diff as ci2g\n", + " \n", + " self.__effsizedf = effectsizedataframe.results\n", + " self.__dabest_obj = effectsizedataframe.dabest_obj\n", + " self.__ci = ci\n", + " self.__resamples = effectsizedataframe.resamples\n", + " self.__alpha = ci2g._compute_alpha_from_ci(ci)\n", + " self.__permutation_count = permutation_count\n", + " self.__bootstraps = np.array(self.__effsizedf[\"bootstraps\"])\n", + " self.__control = np.array(self.__effsizedf[\"control\"])\n", + " self.__test = np.array(self.__effsizedf[\"test\"])\n", + " self.__control_N = np.array(self.__effsizedf[\"control_N\"])\n", + " self.__test_N = np.array(self.__effsizedf[\"test_N\"])\n", + "\n", + "\n", + " idx = self.__dabest_obj.idx\n", + " dat = self.__dabest_obj._plot_data\n", + " xvar = self.__dabest_obj._xvar\n", + " yvar = self.__dabest_obj._yvar\n", + "\n", + " # compute the variances of each control group and each test group\n", + " control_var=[]\n", + " test_var=[]\n", + " for j, current_tuple in enumerate(idx):\n", + " cname = current_tuple[0]\n", + " control = dat[dat[xvar] == cname][yvar].copy()\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_var.append(np.var(test, ddof=1))\n", + " self.__control_var = np.array(control_var)\n", + " self.__test_var = np.array(test_var)\n", + "\n", + " # Compute pooled group variances for each pair of experiment groups\n", + " # based on the raw data\n", + " self.__group_var = ci2g.calculate_group_var(self.__control_var, \n", + " self.__control_N,\n", + " self.__test_var, \n", + " self.__test_N)\n", + "\n", + " # Compute the weighted average mean differences of the bootstrap data\n", + " # using the pooled group variances of the raw data as the inverse of \n", + " # weights\n", + " self.__bootstraps_weighted_delta = ci2g.calculate_weighted_delta(\n", + " self.__group_var, \n", + " 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.__group_var)\n", + "\n", + " sorted_weighted_deltas = npsort(self.__bootstraps_weighted_delta)\n", + "\n", + "\n", + " self.__bias_correction = ci2g.compute_meandiff_bias_correction(\n", + " self.__bootstraps_weighted_delta, self.__difference)\n", + " \n", + " self.__jackknives = np.array(ci1g.compute_1group_jackknife(\n", + " self.__bootstraps_weighted_delta, \n", + " np.mean))\n", + "\n", + " self.__acceleration_value = ci2g._calc_accel(self.__jackknives)\n", + "\n", + " # Compute BCa intervals.\n", + " bca_idx_low, bca_idx_high = ci2g.compute_interval_limits(\n", + " self.__bias_correction, self.__acceleration_value,\n", + " self.__resamples, ci)\n", + " \n", + " self.__bca_interval_idx = (bca_idx_low, bca_idx_high)\n", + "\n", + " if ~isnan(bca_idx_low) and ~isnan(bca_idx_high):\n", + " self.__bca_low = sorted_weighted_deltas[bca_idx_low]\n", + " self.__bca_high = sorted_weighted_deltas[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 should be considered unstable.\"\n", + " err_temp = Template(\" \".join([err1, err2, err3]))\n", + "\n", + " if bca_idx_low <= 10:\n", + " warnings.warn(err_temp.substitute(lim_type=\"lower\",\n", + " loc=\"bottom\"),\n", + " stacklevel=1)\n", + "\n", + " if bca_idx_high >= self.__resamples-9:\n", + " warnings.warn(err_temp.substitute(lim_type=\"upper\",\n", + " loc=\"top\"),\n", + " stacklevel=1)\n", + "\n", + " else:\n", + " err1 = \"The $lim_type limit of the BCa interval 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.__bca_low = self.__difference\n", + " warnings.warn(err_temp.substitute(lim_type=\"lower\"),\n", + " stacklevel=0)\n", + "\n", + " if isnan(bca_idx_high):\n", + " self.__bca_high = self.__difference\n", + " warnings.warn(err_temp.substitute(lim_type=\"upper\"),\n", + " 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.__pct_interval_idx = (pct_idx_low, pct_idx_high)\n", + " self.__pct_low = sorted_weighted_deltas[pct_idx_low]\n", + " self.__pct_high = sorted_weighted_deltas[pct_idx_high]\n", + " \n", + " \n", + "\n", + " def __permutation_test(self):\n", + " \"\"\"\n", + " Perform a permutation test and obtain the permutation p-value\n", + " based on the permutation data.\n", + " \"\"\"\n", + " self.__permutations = np.array(self.__effsizedf[\"permutations\"])\n", + " self.__permutations_var = np.array(self.__effsizedf[\"permutations_var\"])\n", + "\n", + " THRESHOLD = np.abs(self.__difference)\n", + "\n", + " all_num = []\n", + " all_denom = []\n", + "\n", + " groups = len(self.__permutations)\n", + " for i in range(0, len(self.__permutations[0])):\n", + " weight = [1/self.__permutations_var[j][i] for j in range(0, groups)]\n", + " all_num.append(np.sum([weight[j]*self.__permutations[j][i] for j in range(0, groups)]))\n", + " all_denom.append(np.sum(weight))\n", + " \n", + " output=[]\n", + " for i in range(0, len(all_num)):\n", + " output.append(all_num[i]/all_denom[i])\n", + " \n", + " self.__permutations_weighted_delta = np.array(output)\n", + "\n", + " count = sum(np.abs(self.__permutations_weighted_delta)>THRESHOLD)\n", + " self.__pvalue_permutation = count/self.__permutation_count\n", + "\n", + "\n", + "\n", + " def __repr__(self, header=True, sigfig=3):\n", + " from .misc_tools import print_greeting\n", + " \n", + " is_paired = self.__dabest_obj.is_paired\n", + "\n", + " PAIRED_STATUS = {'baseline' : 'paired', \n", + " 'sequential' : 'paired',\n", + " 'None' : 'unpaired'\n", + " }\n", + "\n", + " first_line = {\"paired_status\": PAIRED_STATUS[str(is_paired)]}\n", + " \n", + "\n", + " out1 = \"The weighted-average {paired_status} mean differences \".format(**first_line)\n", + " \n", + " base_string_fmt = \"{:.\" + str(sigfig) + \"}\"\n", + " if \".\" in str(self.__ci):\n", + " ci_width = base_string_fmt.format(self.__ci)\n", + " else:\n", + " ci_width = str(self.__ci)\n", + " \n", + " ci_out = {\"es\" : base_string_fmt.format(self.__difference),\n", + " \"ci\" : ci_width,\n", + " \"bca_low\" : base_string_fmt.format(self.__bca_low),\n", + " \"bca_high\" : base_string_fmt.format(self.__bca_high)}\n", + " \n", + " out2 = \"is {es} [{ci}%CI {bca_low}, {bca_high}].\".format(**ci_out)\n", + " out = out1 + out2\n", + "\n", + " if header is True:\n", + " out = print_greeting() + \"\\n\" + \"\\n\" + out\n", + "\n", + "\n", + " pval_rounded = base_string_fmt.format(self.pvalue_permutation)\n", + "\n", + " \n", + " p1 = \"The p-value of the two-sided permutation t-test is {}, \".format(pval_rounded)\n", + " p2 = \"calculated for legacy purposes only. \"\n", + " pvalue = p1 + p2\n", + "\n", + "\n", + " bs1 = \"{} bootstrap samples were taken; \".format(self.__resamples)\n", + " bs2 = \"the confidence interval is bias-corrected and accelerated.\"\n", + " bs = bs1 + bs2\n", + "\n", + " pval_def1 = \"Any p-value reported is the probability of observing the\" + \\\n", + " \"effect size (or greater),\\nassuming the null hypothesis of \" + \\\n", + " \"zero difference is true.\"\n", + " pval_def2 = \"\\nFor each p-value, 5000 reshuffles of the \" + \\\n", + " \"control and test labels were performed.\"\n", + " pval_def = pval_def1 + pval_def2\n", + "\n", + "\n", + " return \"{}\\n{}\\n\\n{}\\n{}\".format(out, pvalue, bs, pval_def)\n", + "\n", + "\n", + " def to_dict(self):\n", + " \"\"\"\n", + " Returns all attributes of the `dabest.MiniMetaDelta` object as a\n", + " dictionary.\n", + " \"\"\"\n", + " # Only get public (user-facing) attributes.\n", + " attrs = [a for a in dir(self)\n", + " if not a.startswith((\"_\", \"to_dict\"))]\n", + " out = {}\n", + " for a in attrs:\n", + " out[a] = getattr(self, a)\n", + " return out\n", + "\n", + "\n", + " @property\n", + " def ci(self):\n", + " \"\"\"\n", + " Returns the width of the confidence interval, in percent.\n", + " \"\"\"\n", + " return self.__ci\n", + "\n", + "\n", + " @property\n", + " def alpha(self):\n", + " \"\"\"\n", + " Returns the significance level of the statistical test as a float\n", + " between 0 and 1.\n", + " \"\"\"\n", + " return self.__alpha\n", + "\n", + "\n", + " @property\n", + " def bias_correction(self):\n", + " return self.__bias_correction\n", + "\n", + "\n", + " @property\n", + " def bootstraps(self):\n", + " '''\n", + " Return the bootstrapped differences from all the experiment groups.\n", + " '''\n", + " return self.__bootstraps\n", + "\n", + "\n", + " @property\n", + " def jackknives(self):\n", + " return self.__jackknives\n", + "\n", + "\n", + " @property\n", + " def acceleration_value(self):\n", + " return self.__acceleration_value\n", + "\n", + "\n", + " @property\n", + " def bca_low(self):\n", + " \"\"\"\n", + " The bias-corrected and accelerated confidence interval lower limit.\n", + " \"\"\"\n", + " return self.__bca_low\n", + "\n", + "\n", + " @property\n", + " def bca_high(self):\n", + " \"\"\"\n", + " The bias-corrected and accelerated confidence interval upper limit.\n", + " \"\"\"\n", + " return self.__bca_high\n", + "\n", + "\n", + " @property\n", + " def bca_interval_idx(self):\n", + " return self.__bca_interval_idx\n", + "\n", + "\n", + " @property\n", + " def control(self):\n", + " '''\n", + " Return the names of the control groups from all the experiment \n", + " groups in order.\n", + " '''\n", + " return self.__control\n", + "\n", + "\n", + " @property\n", + " def test(self):\n", + " '''\n", + " Return the names of the test groups from all the experiment \n", + " groups in order.\n", + " '''\n", + " return self.__test\n", + " \n", + " @property\n", + " def control_N(self):\n", + " '''\n", + " Return the sizes of the control groups from all the experiment \n", + " groups in order.\n", + " '''\n", + " return self.__control_N\n", + "\n", + "\n", + " @property\n", + " def test_N(self):\n", + " '''\n", + " Return the sizes of the test groups from all the experiment \n", + " groups in order.\n", + " '''\n", + " return self.__test_N\n", + "\n", + "\n", + " @property\n", + " def control_var(self):\n", + " '''\n", + " Return the estimated population variances of the control groups \n", + " from all the experiment groups in order. Here the population \n", + " variance is estimated from the sample variance. \n", + " '''\n", + " return self.__control_var\n", + "\n", + "\n", + " @property\n", + " def test_var(self):\n", + " '''\n", + " Return the estimated population variances of the control groups \n", + " from all the experiment groups in order. Here the population \n", + " variance is estimated from the sample variance. \n", + " '''\n", + " return self.__test_var\n", + "\n", + " \n", + " @property\n", + " def group_var(self):\n", + " '''\n", + " Return the pooled group variances of all the experiment groups \n", + " in order. \n", + " '''\n", + " return self.__group_var\n", + "\n", + "\n", + " @property\n", + " def bootstraps_weighted_delta(self):\n", + " '''\n", + " Return the weighted-average mean differences calculated from the bootstrapped \n", + " deltas and weights across the experiment groups, where the weights are \n", + " the inverse of the pooled group variances.\n", + " '''\n", + " return self.__bootstraps_weighted_delta\n", + "\n", + "\n", + " @property\n", + " def difference(self):\n", + " '''\n", + " Return the weighted-average delta calculated from the raw data.\n", + " '''\n", + " return self.__difference\n", + "\n", + "\n", + " @property\n", + " def pct_interval_idx (self):\n", + " return self.__pct_interval_idx \n", + "\n", + "\n", + " @property\n", + " def pct_low(self):\n", + " \"\"\"\n", + " The percentile confidence interval lower limit.\n", + " \"\"\"\n", + " return self.__pct_low\n", + "\n", + "\n", + " @property\n", + " def pct_high(self):\n", + " \"\"\"\n", + " The percentile confidence interval lower limit.\n", + " \"\"\"\n", + " return self.__pct_high\n", + "\n", + "\n", + " @property\n", + " def pvalue_permutation(self):\n", + " try:\n", + " return self.__pvalue_permutation\n", + " except AttributeError:\n", + " self.__permutation_test()\n", + " return self.__pvalue_permutation\n", + " \n", + "\n", + " @property\n", + " def permutation_count(self):\n", + " \"\"\"\n", + " The number of permuations taken.\n", + " \"\"\"\n", + " return self.__permutation_count\n", + "\n", + " \n", + " @property\n", + " def permutations(self):\n", + " '''\n", + " Return the mean differences of permutations obtained during\n", + " the permutation test for each experiment group.\n", + " '''\n", + " try:\n", + " return self.__permutations\n", + " except AttributeError:\n", + " self.__permutation_test()\n", + " return self.__permutations\n", + "\n", + "\n", + " @property\n", + " def permutations_var(self):\n", + " '''\n", + " Return the pooled group variances of permutations obtained during\n", + " the permutation test for each experiment group.\n", + " '''\n", + " try:\n", + " return self.__permutations_var\n", + " except AttributeError:\n", + " self.__permutation_test()\n", + " return self.__permutations_var\n", + "\n", + " \n", + " @property\n", + " def permutations_weighted_delta(self):\n", + " '''\n", + " Return the weighted-average deltas of permutations obtained \n", + " during the permutation test.\n", + " '''\n", + " try:\n", + " return self.__permutations_weighted_delta\n", + " except AttributeError:\n", + " self.__permutation_test()\n", + " return self.__permutations_weighted_delta\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The weighted delta is calcuated as follows:\n", + "\n", + "$$\\theta_{\\text{weighted}} = \\frac{\\Sigma\\hat{\\theta_{i}}w_{i}}{{\\Sigma}w_{i}}$$\n", + "\n", + "where:\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", + "$$s_{i}^2 = \\text{Pooled variance for replicate }i = \\frac{(n_{test}-1)s_{test}^2+(n_{control}-1)s_{control}^2}{n_{test}+n_{control}-2}$$\n", + "\n", + "$$n = \\text{sample size and }s^2 = \\text{variance for control/test.}$$\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Example: mini-meta-delta" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "DABEST v2024.03.29\n", + "==================\n", + " \n", + "Good afternoon!\n", + "The current time is Tue Mar 19 15:34:33 2024.\n", + "\n", + "The weighted-average unpaired mean differences is 0.0336 [95%CI -0.137, 0.228].\n", + "The p-value of the two-sided permutation t-test is 0.736, 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." + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Ns = 20\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", + "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", + "my_df = pd.DataFrame({'Control 1' : c1, 'Test 1' : t1,\n", + " '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" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "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", + "\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" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "python3", + "language": "python", + "name": "python3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/nbs/API/effsize.ipynb b/nbs/API/effsize.ipynb index bbce4c26..b2232515 100644 --- a/nbs/API/effsize.ipynb +++ b/nbs/API/effsize.ipynb @@ -53,9 +53,12 @@ "metadata": {}, "outputs": [], "source": [ - "#|export\n", + "#| export\n", "from __future__ import annotations\n", - "import numpy as np" + "import numpy as np\n", + "import warnings\n", + "from scipy.special import gamma\n", + "from scipy.stats import mannwhitneyu" ] }, { @@ -65,7 +68,7 @@ "metadata": {}, "outputs": [], "source": [ - "#|export\n", + "#| export\n", "def two_group_difference(control:list|tuple|np.ndarray, #Accepts lists, tuples, or numpy ndarrays of numeric types.\n", " test:list|tuple|np.ndarray, #Accepts lists, tuples, or numpy ndarrays of numeric types.\n", " is_paired=None, #If not None, returns the paired Cohen's d\n", @@ -114,13 +117,12 @@ " median of `test`.\n", "\n", " \"\"\"\n", - " import numpy as np\n", - " import warnings\n", + "\n", "\n", " if effect_size == \"mean_diff\":\n", " return func_difference(control, test, np.mean, is_paired)\n", "\n", - " elif effect_size == \"median_diff\":\n", + " if effect_size == \"median_diff\":\n", " mes1 = \"Using median as the statistic in bootstrapping may \" + \\\n", " \"result in a biased estimate and cause problems with \" + \\\n", " \"BCa confidence intervals. Consider using a different statistic, such as the mean.\\n\"\n", @@ -130,21 +132,21 @@ " warnings.warn(message=mes1+mes2, category=UserWarning)\n", " return func_difference(control, test, np.median, is_paired)\n", "\n", - " elif effect_size == \"cohens_d\":\n", + " if effect_size == \"cohens_d\":\n", " return cohens_d(control, test, is_paired)\n", "\n", - " elif effect_size == \"cohens_h\":\n", + " if effect_size == \"cohens_h\":\n", " return cohens_h(control, test)\n", "\n", - " elif effect_size == \"hedges_g\":\n", + " if effect_size == \"hedges_g\" or effect_size == \"delta_g\":\n", " return hedges_g(control, test, is_paired)\n", "\n", - " elif effect_size == \"cliffs_delta\":\n", + " if effect_size == \"cliffs_delta\":\n", " if is_paired:\n", " err1 = \"`is_paired` is not None; therefore Cliff's delta is not defined.\"\n", " raise ValueError(err1)\n", - " else:\n", - " return cliffs_delta(control, test)\n" + " \n", + " return cliffs_delta(control, test)\n" ] }, { @@ -154,7 +156,7 @@ "metadata": {}, "outputs": [], "source": [ - "#|export\n", + "#| export\n", "def func_difference(control:list|tuple|np.ndarray, # NaNs are automatically discarded.\n", " test:list|tuple|np.ndarray, # NaNs are automatically discarded.\n", " func, # Summary function to apply.\n", @@ -165,13 +167,12 @@ " Applies func to `control` and `test`, and then returns the difference.\n", " \n", " \"\"\"\n", - " import numpy as np\n", "\n", " # Convert to numpy arrays for speed.\n", " # NaNs are automatically dropped.\n", - " if control.__class__ != np.ndarray:\n", + " if ~isinstance(control, np.ndarray):\n", " control = np.array(control)\n", - " if test.__class__ != np.ndarray:\n", + " if ~isinstance(test, np.ndarray):\n", " test = np.array(test)\n", "\n", " if is_paired:\n", @@ -193,10 +194,10 @@ "\n", " return func(test - control)\n", "\n", - " else:\n", - " control = control[~np.isnan(control)]\n", - " test = test[~np.isnan(test)]\n", - " return func(test) - func(control)\n" + " \n", + " control = control[~np.isnan(control)]\n", + " test = test[~np.isnan(test)]\n", + " return func(test) - func(control)\n" ] }, { @@ -206,7 +207,7 @@ "metadata": {}, "outputs": [], "source": [ - "#|export\n", + "#| export\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", @@ -250,13 +251,12 @@ " - https://en.wikipedia.org/wiki/Bessel%27s_correction\n", " - https://en.wikipedia.org/wiki/Standard_deviation#Corrected_sample_standard_deviation\n", " \"\"\"\n", - " import numpy as np\n", "\n", " # Convert to numpy arrays for speed.\n", " # NaNs are automatically dropped.\n", - " if control.__class__ != np.ndarray:\n", + " if ~isinstance(control, np.ndarray):\n", " control = np.array(control)\n", - " if test.__class__ != np.ndarray:\n", + " if ~isinstance(test, np.ndarray):\n", " test = np.array(test)\n", " control = control[~np.isnan(control)]\n", " test = test[~np.isnan(test)]\n", @@ -281,7 +281,10 @@ " else:\n", " M = np.mean(test) - np.mean(control)\n", " divisor = pooled_sd\n", - " \n", + " \n", + " if divisor == 0:\n", + " raise ValueError(\"The divisor is zero, indicating no variability in the data.\")\n", + "\n", " return M / divisor" ] }, @@ -292,7 +295,7 @@ "metadata": {}, "outputs": [], "source": [ - "#|export\n", + "#| export\n", "def cohens_h(control:list|tuple|np.ndarray, \n", " test:list|tuple|np.ndarray\n", " )->float:\n", @@ -306,9 +309,7 @@ " and a dict for mapping the 0s and 1s to the actual labels, e.g.{1: \"Smoker\", 0: \"Non-smoker\"}\n", " '''\n", "\n", - " import numpy as np\n", " np.seterr(divide='ignore', invalid='ignore')\n", - " import pandas as pd\n", "\n", " # Check whether dataframe contains only 0s and 1s.\n", " if np.isin(control, [0, 1]).all() == False or np.isin(test, [0, 1]).all() == False:\n", @@ -317,10 +318,10 @@ " # Convert to numpy arrays for speed.\n", " # NaNs are automatically dropped.\n", " # Aligned with cohens_d calculation.\n", - " if control.__class__ != np.ndarray:\n", + " if ~isinstance(control, np.ndarray):\n", " control = np.array(control)\n", - " if test.__class__ != np.ndarray:\n", - " test = np.array(test)\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", @@ -341,7 +342,7 @@ "metadata": {}, "outputs": [], "source": [ - "#|export\n", + "#| export\n", "def hedges_g(control:list|tuple|np.ndarray, \n", " test:list|tuple|np.ndarray, \n", " is_paired:str=None)->float:\n", @@ -353,13 +354,12 @@ " See [here](https://en.wikipedia.org/wiki/Effect_size#Hedges'_g)\n", "\n", " \"\"\"\n", - " import numpy as np\n", "\n", " # Convert to numpy arrays for speed.\n", " # NaNs are automatically dropped.\n", - " if control.__class__ != np.ndarray:\n", + " if ~isinstance(control, np.ndarray):\n", " control = np.array(control)\n", - " if test.__class__ != np.ndarray:\n", + " if ~isinstance(test, np.ndarray):\n", " test = np.array(test)\n", " control = control[~np.isnan(control)]\n", " test = test[~np.isnan(test)]\n", @@ -378,7 +378,7 @@ "metadata": {}, "outputs": [], "source": [ - "#|export\n", + "#| export\n", "def cliffs_delta(control:list|tuple|np.ndarray, \n", " test:list|tuple|np.ndarray\n", " )->float:\n", @@ -386,14 +386,12 @@ " Computes Cliff's delta for 2 samples.\n", " See [here](https://en.wikipedia.org/wiki/Effect_size#Effect_size_for_ordinal_data)\n", " \"\"\"\n", - " import numpy as np\n", - " from scipy.stats import mannwhitneyu\n", "\n", " # Convert to numpy arrays for speed.\n", " # NaNs are automatically dropped.\n", - " if control.__class__ != np.ndarray:\n", + " if ~isinstance(control, np.ndarray):\n", " control = np.array(control)\n", - " if test.__class__ != np.ndarray:\n", + " if ~isinstance(test, np.ndarray):\n", " test = np.array(test)\n", "\n", " c = control[~np.isnan(control)]\n", @@ -406,18 +404,6 @@ " U, _ = mannwhitneyu(t, c, alternative='two-sided')\n", " cliffs_delta = ((2 * U) / (control_n * test_n)) - 1\n", "\n", - " # more = 0\n", - " # less = 0\n", - " #\n", - " # for i, c in enumerate(control):\n", - " # for j, t in enumerate(test):\n", - " # if t > c:\n", - " # more += 1\n", - " # elif t < c:\n", - " # less += 1\n", - " #\n", - " # cliffs_delta = (more - less) / (control_n * test_n)\n", - "\n", " return cliffs_delta\n" ] }, @@ -428,40 +414,53 @@ "metadata": {}, "outputs": [], "source": [ - "#|export\n", + "#| export\n", "def _compute_standardizers(control, test):\n", - " from numpy import mean, var, sqrt, nan\n", - " # For calculation of correlation; not currently used.\n", + " \"\"\"\n", + " Computes the pooled and average standard deviations for two datasets.\n", + "\n", + " This function is useful in the context of statistical analysis, particularly\n", + " when calculating standardized mean differences between two groups. It supports\n", + " both unpaired and paired data scenarios.\n", + "\n", + " Parameters:\n", + " control (array-like): A numeric array representing the control group data.\n", + " test (array-like): A numeric array representing the test group data.\n", + "\n", + " Returns:\n", + " tuple: A tuple containing two elements:\n", + " - pooled (float): The pooled standard deviation, calculated for unpaired two-group \n", + " scenarios. It is computed using the sample variances of the \n", + " control and test groups, weighted by their sample sizes.\n", + " - average (float): The average standard deviation, calculated for paired data \n", + " scenarios. It is the average of the sample standard deviations \n", + " of the control and test groups.\n", + "\n", + " Note:\n", + " The function assumes that the input arrays are independent samples and calculates\n", + " the sample variances using N-1 degrees of freedom.\n", + "\n", + " For calculation of correlation; not currently used.\n", + "\n", + " \"\"\"\n", " # from scipy.stats import pearsonr\n", "\n", " control_n = len(control)\n", " test_n = len(test)\n", "\n", - " control_mean = mean(control)\n", - " test_mean = mean(test)\n", - "\n", - " control_var = var(control, ddof=1) # use N-1 to compute the variance.\n", - " test_var = var(test, ddof=1)\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", - " control_std = sqrt(control_var)\n", - " test_std = sqrt(test_var)\n", "\n", " # For unpaired 2-groups standardized mean difference.\n", - " pooled = sqrt(((control_n - 1) * control_var + (test_n - 1) * test_var) /\n", + " pooled = np.sqrt(((control_n - 1) * control_var + (test_n - 1) * test_var) /\n", " (control_n + test_n - 2)\n", " )\n", "\n", " # For paired standardized mean difference.\n", - " average = sqrt((control_var + test_var) / 2)\n", - "\n", - " # if len(control) == len(test):\n", - " # corr = pearsonr(control, test)[0]\n", - " # std_diff = sqrt(control_var + test_var - (2 * corr * control_std * test_std))\n", - " # std_diff_corrected = std_diff / (sqrt(2 * (1 - corr)))\n", - " # return pooled, average, std_diff_corrected\n", - " #\n", - " # else:\n", - " return pooled, average # indent if you implement above code chunk." + " average = np.sqrt((control_var + test_var) / 2)\n", + "\n", + " return pooled, average " ] }, { @@ -471,7 +470,7 @@ "metadata": {}, "outputs": [], "source": [ - "#|export\n", + "#| export\n", "def _compute_hedges_correction_factor(n1, \n", " n2\n", " )->float:\n", @@ -487,16 +486,12 @@ " ISBN 0-12-336380-2.\n", " \"\"\"\n", "\n", - " from scipy.special import gamma\n", - " from numpy import sqrt, isinf\n", - " import warnings\n", - "\n", " df = n1 + n2 - 2\n", " numer = gamma(df / 2)\n", " denom0 = gamma((df - 1) / 2)\n", - " denom = sqrt(df / 2) * denom0\n", + " denom = np.sqrt(df / 2) * denom0\n", "\n", - " if isinf(numer) or isinf(denom):\n", + " if np.isinf(numer) or np.isinf(denom):\n", " # occurs when df is too large.\n", " # Apply Hedges and Olkin's approximation.\n", " df_sum = n1 + n2\n", @@ -516,25 +511,16 @@ "metadata": {}, "outputs": [], "source": [ - "#|export\n", + "#| export\n", "def weighted_delta(difference, group_var):\n", " '''\n", " Compute the weighted deltas where the weight is the inverse of the\n", " pooled group difference.\n", " '''\n", - " import numpy as np\n", "\n", " weight = np.true_divide(1, group_var)\n", " return np.sum(difference*weight)/np.sum(weight)" ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "37ca3f32", - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { diff --git a/nbs/API/effsize_objects.ipynb b/nbs/API/effsize_objects.ipynb new file mode 100644 index 00000000..fd8496c1 --- /dev/null +++ b/nbs/API/effsize_objects.ipynb @@ -0,0 +1,1927 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Effectsize objects\n", + "\n", + "> The auxiliary classes involved in the computations of bootstrapped effect sizes.\n", + "\n", + "- order: 10" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#| default_exp _effsize_objects" + ] + }, + { + "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": [ + "#| hide\n", + "import dabest" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#| export\n", + "import pandas as pd\n", + "import lqrt\n", + "from scipy.stats import norm\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", + "from numpy.random import PCG64, RandomState\n", + "from statsmodels.stats.contingency_tables import mcnemar\n", + "import warnings\n", + "from string import Template\n", + "import scipy.stats as spstats" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#| export\n", + "class TwoGroupsEffectSize(object):\n", + "\n", + " \"\"\"\n", + " A class to compute and store the results of bootstrapped\n", + " mean differences between two groups.\n", + "\n", + " Compute the effect size between two groups.\n", + "\n", + " Parameters\n", + " ----------\n", + " control : array-like\n", + " test : array-like\n", + " 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', or 'cliffs_delta'\n", + " is_paired : string, default None\n", + " resamples : int, default 5000\n", + " The number of bootstrap resamples to be taken for the calculation\n", + " of the confidence interval limits.\n", + " permutation_count : int, default 5000\n", + " The number of permutations (reshuffles) to perform for the\n", + " computation of the permutation p-value\n", + " ci : float, default 95\n", + " The confidence interval width. The default of 95 produces 95%\n", + " confidence intervals.\n", + " random_seed : int, default 12345\n", + " `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", + "\n", + " Returns\n", + " -------\n", + " A :py:class:`TwoGroupEffectSize` object:\n", + " `difference` : float\n", + " The effect size of the difference between the control and the test.\n", + " `effect_size` : string\n", + " The type of effect size reported.\n", + " `is_paired` : string\n", + " The type of repeated-measures experiment.\n", + " `ci` : float\n", + " Returns the width of the confidence interval, in percent.\n", + " `alpha` : float\n", + " Returns the significance level of the statistical test as a float between 0 and 1.\n", + " `resamples` : int\n", + " The number of resamples performed during the bootstrap procedure.\n", + " `bootstraps` : numpy ndarray\n", + " The generated bootstraps of the effect size.\n", + " `random_seed` : int\n", + " The number used to initialise the numpy random seed generator, ie.`seed_value` from `numpy.random.seed(seed_value)` is returned.\n", + " `bca_low, bca_high` : float\n", + " The bias-corrected and accelerated confidence interval lower limit and upper limits, respectively.\n", + " `pct_low, pct_high` : float\n", + " The percentile confidence interval lower limit and upper limits, respectively.\n", + " \"\"\"\n", + "\n", + " def __init__(\n", + " self,\n", + " control,\n", + " test,\n", + " effect_size,\n", + " proportional=False,\n", + " is_paired=None,\n", + " ci=95,\n", + " resamples=5000,\n", + " permutation_count=5000,\n", + " random_seed=12345,\n", + " ):\n", + " from ._stats_tools import confint_2group_diff as ci2g\n", + " from ._stats_tools import effsize as es\n", + "\n", + " self.__EFFECT_SIZE_DICT = {\n", + " \"mean_diff\": \"mean difference\",\n", + " \"median_diff\": \"median difference\",\n", + " \"cohens_d\": \"Cohen's d\",\n", + " \"cohens_h\": \"Cohen's h\",\n", + " \"hedges_g\": \"Hedges' g\",\n", + " \"cliffs_delta\": \"Cliff's delta\",\n", + " \"delta_g\": \"deltas' g\",\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._check_errors(control, test)\n", + "\n", + " # Convert to numpy arrays for speed.\n", + " # NaNs are automatically dropped.\n", + " control = array(control)\n", + " test = array(test)\n", + " self.__control = control[~isnan(control)]\n", + " self.__test = test[~isnan(test)]\n", + " self.__permutation_count = permutation_count\n", + "\n", + " self.__alpha = ci2g._compute_alpha_from_ci(self.__ci)\n", + "\n", + " self.__difference = es.two_group_difference(\n", + " self.__control, self.__test, self.__is_paired, self.__effect_size\n", + " )\n", + "\n", + " self.__jackknives = ci2g.compute_meandiff_jackknife(\n", + " self.__control, self.__test, self.__is_paired, self.__effect_size\n", + " )\n", + "\n", + " self.__acceleration_value = ci2g._calc_accel(self.__jackknives)\n", + "\n", + " bootstraps = ci2g.compute_bootstrapped_diff(\n", + " self.__control,\n", + " self.__test,\n", + " self.__is_paired,\n", + " self.__effect_size,\n", + " self.__resamples,\n", + " self.__random_seed,\n", + " )\n", + " self.__bootstraps = bootstraps\n", + "\n", + " sorted_bootstraps = npsort(self.__bootstraps)\n", + " # Added in v0.2.6.\n", + " # Raises a UserWarning if there are any infiinities in the bootstraps.\n", + " num_infinities = len(self.__bootstraps[isinf(self.__bootstraps)])\n", + "\n", + " if num_infinities > 0:\n", + " warn_msg = (\n", + " \"There are {} bootstrap(s) that are not defined. \"\n", + " \"This is likely due to smaple sample sizes. \"\n", + " \"The values in a bootstrap for a group will be more likely \"\n", + " \"to be all equal, with a resulting variance of zero. \"\n", + " \"The computation of Cohen's d and Hedges' g thus \"\n", + " \"involved a division by zero. \"\n", + " )\n", + " warnings.warn(warn_msg.format(num_infinities), category=UserWarning)\n", + "\n", + " self.__bias_correction = ci2g.compute_meandiff_bias_correction(\n", + " self.__bootstraps, self.__difference\n", + " )\n", + "\n", + " self._compute_bca_intervals(sorted_bootstraps)\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.__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._perform_statistical_test()\n", + "\n", + " def __repr__(self, show_resample_count=True, define_pval=True, sigfig=3):\n", + " RM_STATUS = {\n", + " \"baseline\": \"for repeated measures against baseline \\n\",\n", + " \"sequential\": \"for the sequential design of repeated-measures experiment \\n\",\n", + " \"None\": \"\",\n", + " }\n", + "\n", + " PAIRED_STATUS = {\n", + " \"baseline\": \"paired\",\n", + " \"sequential\": \"paired\",\n", + " \"None\": \"unpaired\",\n", + " }\n", + "\n", + " first_line = {\n", + " \"rm_status\": RM_STATUS[str(self.__is_paired)],\n", + " \"es\": self.__EFFECT_SIZE_DICT[self.__effect_size],\n", + " \"paired_status\": PAIRED_STATUS[str(self.__is_paired)],\n", + " }\n", + "\n", + " out1 = \"The {paired_status} {es} {rm_status}\".format(**first_line)\n", + "\n", + " base_string_fmt = \"{:.\" + str(sigfig) + \"}\"\n", + " if \".\" in str(self.__ci):\n", + " ci_width = base_string_fmt.format(self.__ci)\n", + " else:\n", + " ci_width = str(self.__ci)\n", + "\n", + " ci_out = {\n", + " \"es\": base_string_fmt.format(self.__difference),\n", + " \"ci\": ci_width,\n", + " \"bca_low\": base_string_fmt.format(self.__bca_low),\n", + " \"bca_high\": base_string_fmt.format(self.__bca_high),\n", + " }\n", + "\n", + " out2 = \"is {es} [{ci}%CI {bca_low}, {bca_high}].\".format(**ci_out)\n", + " out = out1 + out2\n", + "\n", + " pval_rounded = base_string_fmt.format(self.pvalue_permutation)\n", + "\n", + " p1 = \"The p-value of the two-sided permutation t-test is {}, \".format(\n", + " pval_rounded\n", + " )\n", + " p2 = \"calculated for legacy purposes only. \"\n", + " pvalue = p1 + p2\n", + "\n", + " bs1 = \"{} bootstrap samples were taken; \".format(self.__resamples)\n", + " bs2 = \"the confidence interval is bias-corrected and accelerated.\"\n", + " bs = bs1 + bs2\n", + "\n", + " pval_def1 = (\n", + " \"Any p-value reported is the probability of observing the\"\n", + " + \"effect size (or greater),\\nassuming the null hypothesis of \"\n", + " + \"zero difference is true.\"\n", + " )\n", + " pval_def2 = (\n", + " \"\\nFor each p-value, 5000 reshuffles of the \"\n", + " + \"control and test labels were performed.\"\n", + " )\n", + " pval_def = pval_def1 + pval_def2\n", + "\n", + " if show_resample_count and define_pval:\n", + " 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", + " return \"{}\\n{}\\n\\n{}\".format(out, pvalue, bs)\n", + " else:\n", + " return \"{}\\n{}\".format(out, pvalue)\n", + "\n", + " def _check_errors(self, control, test):\n", + " '''\n", + " Function to check configuration errors for the given control and test data.\n", + " '''\n", + " kosher_es = [a for a in self.__EFFECT_SIZE_DICT.keys()]\n", + " if self.__effect_size not in kosher_es:\n", + " err1 = \"The effect size '{}'\".format(self.__effect_size)\n", + " err2 = \"is not one of {}\".format(kosher_es)\n", + " raise ValueError(\" \".join([err1, err2]))\n", + "\n", + " if self.__effect_size == \"cliffs_delta\" and self.__is_paired:\n", + " 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", + " raise ValueError(err1)\n", + "\n", + " if self.__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", + " )\n", + " raise ValueError(err1)\n", + "\n", + " def _compute_bca_intervals(self, sorted_bootstraps):\n", + " '''\n", + " Function to compute the bca intervals given the sorted bootstraps.\n", + " '''\n", + " from ._stats_tools import confint_2group_diff as ci2g\n", + "\n", + " # Compute BCa intervals.\n", + " bca_idx_low, bca_idx_high = ci2g.compute_interval_limits(\n", + " self.__bias_correction,\n", + " self.__acceleration_value,\n", + " self.__resamples,\n", + " self.__ci,\n", + " )\n", + "\n", + " self.__bca_interval_idx = (bca_idx_low, bca_idx_high)\n", + "\n", + " if ~isnan(bca_idx_low) and ~isnan(bca_idx_high):\n", + " self.__bca_low = sorted_bootstraps[bca_idx_low]\n", + " self.__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 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 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.__bca_low = self.__difference\n", + " warnings.warn(err_temp.substitute(lim_type=\"lower\"), stacklevel=0)\n", + "\n", + " if isnan(bca_idx_high):\n", + " self.__bca_high = self.__difference\n", + " warnings.warn(err_temp.substitute(lim_type=\"upper\"), stacklevel=0)\n", + "\n", + " def _perform_statistical_test(self):\n", + " '''\n", + " Function to complete the statistical tests\n", + " '''\n", + " from ._stats_tools import effsize as es\n", + "\n", + " # Perform statistical tests.\n", + " self.__PermutationTest_result = PermutationTest(\n", + " self.__control,\n", + " self.__test,\n", + " self.__effect_size,\n", + " self.__is_paired,\n", + " self.__permutation_count,\n", + " )\n", + "\n", + " if self.__is_paired and not self.__proportional:\n", + " # Wilcoxon, a non-parametric version of the paired T-test.\n", + " try:\n", + " wilcoxon = spstats.wilcoxon(self.__control, self.__test)\n", + " self.__pvalue_wilcoxon = wilcoxon.pvalue\n", + " self.__statistic_wilcoxon = wilcoxon.statistic\n", + " except ValueError as e:\n", + " warnings.warn(\"Wilcoxon test could not be performed. This might be due \"\n", + " \"to no variability in the difference of the paired groups. \\n\"\n", + " \"Error: {}\\n\"\n", + " \"For detailed information, please refer to https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.wilcoxon.html \"\n", + " .format(e))\n", + "\n", + " if self.__effect_size != \"median_diff\":\n", + " # Paired Student's t-test.\n", + " paired_t = spstats.ttest_rel(\n", + " self.__control, self.__test, nan_policy=\"omit\"\n", + " )\n", + " 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", + " # 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", + " _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", + " # The Cohen's h calculation is for binary categorical data\n", + " try:\n", + " self.__proportional_difference = es.cohens_h(\n", + " self.__control, self.__test\n", + " )\n", + " except ValueError as e:\n", + " warnings.warn(f\"Calculation of Cohen's h failed. This method is applicable \"\n", + " f\"only for binary data (0's and 1's). Details: {e}\")\n", + "\n", + " elif self.__effect_size == \"cliffs_delta\":\n", + " # Let's go with Brunner-Munzel!\n", + " brunner_munzel = spstats.brunnermunzel(\n", + " self.__control, self.__test, nan_policy=\"omit\"\n", + " )\n", + " self.__pvalue_brunner_munzel = brunner_munzel.pvalue\n", + " self.__statistic_brunner_munzel = brunner_munzel.statistic\n", + "\n", + " elif self.__effect_size == \"median_diff\":\n", + " # According to scipy's documentation of the function,\n", + " # \"The Kruskal-Wallis H-test tests the null hypothesis\n", + " # that the population median of all of the groups are equal.\"\n", + " kruskal = spstats.kruskal(self.__control, self.__test, nan_policy=\"omit\")\n", + " self.__pvalue_kruskal = kruskal.pvalue\n", + " self.__statistic_kruskal = kruskal.statistic\n", + "\n", + " else: # for mean difference, Cohen's d, and Hedges' g.\n", + " # Welch's t-test, assumes normality of distributions,\n", + " # but does not assume equal variances.\n", + " welch = spstats.ttest_ind(\n", + " self.__control, self.__test, equal_var=False, nan_policy=\"omit\"\n", + " )\n", + " self.__pvalue_welch = welch.pvalue\n", + " self.__statistic_welch = welch.statistic\n", + "\n", + " # Student's t-test, assumes normality of distributions,\n", + " # as well as assumption of equal variances.\n", + " students_t = spstats.ttest_ind(\n", + " self.__control, self.__test, equal_var=True, nan_policy=\"omit\"\n", + " )\n", + " self.__pvalue_students_t = students_t.pvalue\n", + " self.__statistic_students_t = students_t.statistic\n", + "\n", + " # Mann-Whitney test: Non parametric,\n", + " # does not assume normality of distributions\n", + " try:\n", + " mann_whitney = spstats.mannwhitneyu(\n", + " self.__control, self.__test, alternative=\"two-sided\"\n", + " )\n", + " self.__pvalue_mann_whitney = mann_whitney.pvalue\n", + " self.__statistic_mann_whitney = mann_whitney.statistic\n", + " except ValueError as e:\n", + " warnings.warn(\"Mann-Whitney test could not be performed. This might be due \"\n", + " \"to identical rank values in both control and test groups. \"\n", + " \"Details: {}\".format(e))\n", + "\n", + " standardized_es = es.cohens_d(self.__control, self.__test, is_paired=None)\n", + "\n", + "\n", + " def to_dict(self):\n", + " \"\"\"\n", + " Returns the attributes of the `dabest.TwoGroupEffectSize` object as a\n", + " dictionary.\n", + " \"\"\"\n", + " # Only get public (user-facing) attributes.\n", + " attrs = [a for a in dir(self) if not a.startswith((\"_\", \"to_dict\"))]\n", + " out = {}\n", + " for a in attrs:\n", + " out[a] = getattr(self, a)\n", + " return out\n", + "\n", + " @property\n", + " def difference(self):\n", + " \"\"\"\n", + " Returns the difference between the control and the test.\n", + " \"\"\"\n", + " return self.__difference\n", + "\n", + " @property\n", + " def effect_size(self):\n", + " \"\"\"\n", + " Returns the type of effect size reported.\n", + " \"\"\"\n", + " return self.__EFFECT_SIZE_DICT[self.__effect_size]\n", + "\n", + " @property\n", + " def is_paired(self):\n", + " return self.__is_paired\n", + "\n", + " @property\n", + " def proportional(self):\n", + " return self.__proportional\n", + "\n", + " @property\n", + " def ci(self):\n", + " \"\"\"\n", + " Returns the width of the confidence interval, in percent.\n", + " \"\"\"\n", + " return self.__ci\n", + "\n", + " @property\n", + " def alpha(self):\n", + " \"\"\"\n", + " Returns the significance level of the statistical test as a float\n", + " between 0 and 1.\n", + " \"\"\"\n", + " return self.__alpha\n", + "\n", + " @property\n", + " def resamples(self):\n", + " \"\"\"\n", + " The number of resamples performed during the bootstrap procedure.\n", + " \"\"\"\n", + " return self.__resamples\n", + "\n", + " @property\n", + " def bootstraps(self):\n", + " \"\"\"\n", + " The generated bootstraps of the effect size.\n", + " \"\"\"\n", + " return self.__bootstraps\n", + "\n", + " @property\n", + " def random_seed(self):\n", + " \"\"\"\n", + " The number used to initialise the numpy random seed generator, ie.\n", + " `seed_value` from `numpy.random.seed(seed_value)` is returned.\n", + " \"\"\"\n", + " return self.__random_seed\n", + "\n", + " @property\n", + " def bca_interval_idx(self):\n", + " return self.__bca_interval_idx\n", + "\n", + " @property\n", + " def bca_low(self):\n", + " \"\"\"\n", + " The bias-corrected and accelerated confidence interval lower limit.\n", + " \"\"\"\n", + " return self.__bca_low\n", + "\n", + " @property\n", + " def bca_high(self):\n", + " \"\"\"\n", + " The bias-corrected and accelerated confidence interval upper limit.\n", + " \"\"\"\n", + " return self.__bca_high\n", + "\n", + " @property\n", + " def pct_interval_idx(self):\n", + " return self.__pct_interval_idx\n", + "\n", + " @property\n", + " def pct_low(self):\n", + " \"\"\"\n", + " The percentile confidence interval lower limit.\n", + " \"\"\"\n", + " return self.__pct_low\n", + "\n", + " @property\n", + " def pct_high(self):\n", + " \"\"\"\n", + " The percentile confidence interval lower limit.\n", + " \"\"\"\n", + " return self.__pct_high\n", + "\n", + " @property\n", + " def pvalue_brunner_munzel(self):\n", + " try:\n", + " return self.__pvalue_brunner_munzel\n", + " except AttributeError:\n", + " return npnan\n", + "\n", + " @property\n", + " def statistic_brunner_munzel(self):\n", + " try:\n", + " return self.__statistic_brunner_munzel\n", + " except AttributeError:\n", + " return npnan\n", + "\n", + " @property\n", + " def pvalue_wilcoxon(self):\n", + " try:\n", + " return self.__pvalue_wilcoxon\n", + " except AttributeError:\n", + " return npnan\n", + "\n", + " @property\n", + " def statistic_wilcoxon(self):\n", + " try:\n", + " return self.__statistic_wilcoxon\n", + " except AttributeError:\n", + " return npnan\n", + "\n", + " @property\n", + " def pvalue_mcnemar(self):\n", + " try:\n", + " return self.__pvalue_mcnemar\n", + " except AttributeError:\n", + " return npnan\n", + "\n", + " @property\n", + " def statistic_mcnemar(self):\n", + " try:\n", + " return self.__statistic_mcnemar\n", + " except AttributeError:\n", + " return npnan\n", + "\n", + " @property\n", + " def pvalue_paired_students_t(self):\n", + " try:\n", + " return self.__pvalue_paired_students_t\n", + " except AttributeError:\n", + " return npnan\n", + "\n", + " @property\n", + " def statistic_paired_students_t(self):\n", + " try:\n", + " return self.__statistic_paired_students_t\n", + " except AttributeError:\n", + " return npnan\n", + "\n", + " @property\n", + " def pvalue_kruskal(self):\n", + " try:\n", + " return self.__pvalue_kruskal\n", + " except AttributeError:\n", + " return npnan\n", + "\n", + " @property\n", + " def statistic_kruskal(self):\n", + " try:\n", + " return self.__statistic_kruskal\n", + " except AttributeError:\n", + " return npnan\n", + "\n", + " @property\n", + " def pvalue_welch(self):\n", + " try:\n", + " return self.__pvalue_welch\n", + " except AttributeError:\n", + " return npnan\n", + "\n", + " @property\n", + " def statistic_welch(self):\n", + " try:\n", + " return self.__statistic_welch\n", + " except AttributeError:\n", + " return npnan\n", + "\n", + " @property\n", + " def pvalue_students_t(self):\n", + " try:\n", + " return self.__pvalue_students_t\n", + " except AttributeError:\n", + " return npnan\n", + "\n", + " @property\n", + " def statistic_students_t(self):\n", + " try:\n", + " return self.__statistic_students_t\n", + " except AttributeError:\n", + " return npnan\n", + "\n", + " @property\n", + " def pvalue_mann_whitney(self):\n", + " try:\n", + " return self.__pvalue_mann_whitney\n", + " except AttributeError:\n", + " return npnan\n", + "\n", + " @property\n", + " def statistic_mann_whitney(self):\n", + " try:\n", + " return self.__statistic_mann_whitney\n", + " except AttributeError:\n", + " return npnan\n", + "\n", + " @property\n", + " def pvalue_permutation(self):\n", + " \"\"\"\n", + " p value of permutation test\n", + " \"\"\"\n", + " return self.__PermutationTest_result.pvalue\n", + "\n", + " @property\n", + " def permutation_count(self):\n", + " \"\"\"\n", + " The number of permutations taken.\n", + " \"\"\"\n", + " return self.__PermutationTest_result.permutation_count\n", + "\n", + " @property\n", + " def permutations(self):\n", + " return self.__PermutationTest_result.permutations\n", + "\n", + " @property\n", + " def permutations_var(self):\n", + " return self.__PermutationTest_result.permutations_var\n", + "\n", + " @property\n", + " def proportional_difference(self):\n", + " try:\n", + " return self.__proportional_difference\n", + " except AttributeError:\n", + " return npnan" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Example" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "The unpaired mean difference is -0.253 [95%CI -0.78, 0.25].\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", + "For each p-value, 5000 reshuffles of the control and test labels were performed." + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "random.seed(12345)\n", + "control = norm.rvs(loc=0, size=30)\n", + "test = norm.rvs(loc=0.5, size=30)\n", + "effsize = dabest.TwoGroupsEffectSize(control, test, \"mean_diff\")\n", + "effsize" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "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", + " 'ci': 95,\n", + " 'difference': -0.25315417702752846,\n", + " 'effect_size': 'mean difference',\n", + " 'is_paired': None,\n", + " 'pct_high': 0.24951887238295106,\n", + " 'pct_interval_idx': (125, 4875),\n", + " 'pct_low': -0.7801782111071534,\n", + " 'permutation_count': 5000,\n", + " 'permutations': array([ 0.17221029, 0.03112419, -0.13911387, ..., -0.38007941,\n", + " 0.30261507, -0.09073054]),\n", + " 'permutations_var': array([0.07201642, 0.07251104, 0.07219407, ..., 0.07003705, 0.07094885,\n", + " 0.07238581]),\n", + " 'proportional_difference': nan,\n", + " 'pvalue_brunner_munzel': nan,\n", + " 'pvalue_kruskal': nan,\n", + " 'pvalue_mann_whitney': 0.5201446121616038,\n", + " 'pvalue_mcnemar': nan,\n", + " 'pvalue_paired_students_t': nan,\n", + " 'pvalue_permutation': 0.3484,\n", + " 'pvalue_students_t': 0.34743913903372836,\n", + " 'pvalue_welch': 0.3474493875548964,\n", + " 'pvalue_wilcoxon': nan,\n", + " 'random_seed': 12345,\n", + " 'resamples': 5000,\n", + " 'statistic_brunner_munzel': nan,\n", + " 'statistic_kruskal': nan,\n", + " 'statistic_mann_whitney': 494.0,\n", + " 'statistic_mcnemar': nan,\n", + " 'statistic_paired_students_t': nan,\n", + " 'statistic_students_t': 0.9472545159069105,\n", + " 'statistic_welch': 0.9472545159069105,\n", + " 'statistic_wilcoxon': nan}" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "effsize.to_dict() " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# | export\n", + "class EffectSizeDataFrame(object):\n", + " \"\"\"A class that generates and stores the results of bootstrapped effect\n", + " sizes for several comparisons.\"\"\"\n", + "\n", + " def __init__(\n", + " self,\n", + " dabest,\n", + " effect_size,\n", + " is_paired,\n", + " ci=95,\n", + " proportional=False,\n", + " resamples=5000,\n", + " permutation_count=5000,\n", + " random_seed=12345,\n", + " x1_level=None,\n", + " x2=None,\n", + " delta2=False,\n", + " experiment_label=None,\n", + " mini_meta=False,\n", + " ):\n", + " \"\"\"\n", + " Parses the data from a Dabest object, enabling plotting and printing\n", + " capability for the effect size of interest.\n", + " \"\"\"\n", + "\n", + " self.__dabest_obj = dabest\n", + " self.__effect_size = effect_size\n", + " self.__is_paired = is_paired\n", + " self.__ci = ci\n", + " self.__resamples = resamples\n", + " self.__permutation_count = permutation_count\n", + " self.__random_seed = random_seed\n", + " self.__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", + "\n", + " def __pre_calc(self):\n", + " from .misc_tools import print_greeting, get_varname\n", + " from ._stats_tools import confint_2group_diff as ci2g\n", + " from ._delta_objects import MiniMetaDelta, DeltaDelta\n", + "\n", + " idx = self.__dabest_obj.idx\n", + " dat = self.__dabest_obj._plot_data\n", + " xvar = self.__dabest_obj._xvar\n", + " yvar = self.__dabest_obj._yvar\n", + "\n", + " out = []\n", + " reprs = []\n", + "\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", + "\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", + " mixed_data.append(control)\n", + " mixed_data.append(test)\n", + " bootstraps_delta_delta = ci2g.compute_delta2_bootstrapped_diff(\n", + " mixed_data[0],\n", + " mixed_data[1],\n", + " mixed_data[2],\n", + " mixed_data[3],\n", + " self.__is_paired,\n", + " self.__resamples,\n", + " self.__random_seed,\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", + "\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", + " result = TwoGroupsEffectSize(\n", + " control,\n", + " test,\n", + " self.__effect_size,\n", + " self.__proportional,\n", + " self.__is_paired,\n", + " self.__ci,\n", + " self.__resamples,\n", + " self.__permutation_count,\n", + " self.__random_seed,\n", + " )\n", + " r_dict = result.to_dict()\n", + " r_dict[\"control\"] = cname\n", + " r_dict[\"test\"] = tname\n", + " r_dict[\"control_N\"] = int(len(control))\n", + " 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", + " resamp_count = False\n", + " def_pval = False\n", + " elif self.__mini_meta and self.__effect_size == \"mean_diff\":\n", + " resamp_count = False\n", + " def_pval = False\n", + " else:\n", + " resamp_count = True\n", + " def_pval = True\n", + " else:\n", + " resamp_count = False\n", + " def_pval = False\n", + "\n", + " text_repr = result.__repr__(\n", + " show_resample_count=resamp_count, define_pval=def_pval\n", + " )\n", + "\n", + " to_replace = \"between {} and {} is\".format(cname, tname)\n", + " text_repr = text_repr.replace(\"is\", to_replace, 1)\n", + "\n", + " reprs.append(text_repr)\n", + "\n", + " self.__for_print = \"\\n\\n\".join(reprs)\n", + "\n", + " out_ = pd.DataFrame(out)\n", + "\n", + " columns_in_order = [\n", + " \"control\",\n", + " \"test\",\n", + " \"control_N\",\n", + " \"test_N\",\n", + " \"effect_size\",\n", + " \"is_paired\",\n", + " \"difference\",\n", + " \"ci\",\n", + " \"bca_low\",\n", + " \"bca_high\",\n", + " \"bca_interval_idx\",\n", + " \"pct_low\",\n", + " \"pct_high\",\n", + " \"pct_interval_idx\",\n", + " \"bootstraps\",\n", + " \"resamples\",\n", + " \"random_seed\",\n", + " \"permutations\",\n", + " \"pvalue_permutation\",\n", + " \"permutation_count\",\n", + " \"permutations_var\",\n", + " \"pvalue_welch\",\n", + " \"statistic_welch\",\n", + " \"pvalue_students_t\",\n", + " \"statistic_students_t\",\n", + " \"pvalue_mann_whitney\",\n", + " \"statistic_mann_whitney\",\n", + " \"pvalue_brunner_munzel\",\n", + " \"statistic_brunner_munzel\",\n", + " \"pvalue_wilcoxon\",\n", + " \"statistic_wilcoxon\",\n", + " \"pvalue_mcnemar\",\n", + " \"statistic_mcnemar\",\n", + " \"pvalue_paired_students_t\",\n", + " \"statistic_paired_students_t\",\n", + " \"pvalue_kruskal\",\n", + " \"statistic_kruskal\",\n", + " \"proportional_difference\",\n", + " ]\n", + " self.__results = out_.reindex(columns=columns_in_order)\n", + " self.__results.dropna(axis=\"columns\", how=\"all\", inplace=True)\n", + "\n", + " # Add the is_paired column back when is_paired is None\n", + " if self.is_paired is None:\n", + " self.__results.insert(\n", + " 5, \"is_paired\", self.__results.apply(lambda _: None, axis=1)\n", + " )\n", + "\n", + " # Create and compute the delta-delta statistics\n", + " if 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", + " 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", + " 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", + " self.__effect_size\n", + " )\n", + " else:\n", + " self.__mini_meta_delta = (\n", + " \"`mini_meta` is False; weighted delta is therefore not calculated.\"\n", + " )\n", + "\n", + " varname = get_varname(self.__dabest_obj)\n", + " lastline = (\n", + " \"To get the results of all valid statistical tests, \"\n", + " + \"use `{}.{}.statistical_tests`\".format(varname, self.__effect_size)\n", + " )\n", + " reprs.append(lastline)\n", + "\n", + " reprs.insert(0, print_greeting())\n", + "\n", + " self.__for_print = \"\\n\\n\".join(reprs)\n", + "\n", + " def __repr__(self):\n", + " try:\n", + " return self.__for_print\n", + " except AttributeError:\n", + " self.__pre_calc()\n", + " return self.__for_print\n", + "\n", + " def __calc_lqrt(self):\n", + " rnd_seed = self.__random_seed\n", + " db_obj = self.__dabest_obj\n", + " dat = db_obj._plot_data\n", + " xvar = db_obj._xvar\n", + " yvar = db_obj._yvar\n", + " delta2 = self.__delta2\n", + "\n", + " out = []\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", + "\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", + " if self.__is_paired:\n", + " # Refactored here in v0.3.0 for performance issues.\n", + " lqrt_result = lqrt.lqrtest_rel(control, test, random_state=rnd_seed)\n", + "\n", + " out.append(\n", + " {\n", + " \"control\": cname,\n", + " \"test\": tname,\n", + " \"control_N\": int(len(control)),\n", + " \"test_N\": int(len(test)),\n", + " \"pvalue_paired_lqrt\": lqrt_result.pvalue,\n", + " \"statistic_paired_lqrt\": lqrt_result.statistic,\n", + " }\n", + " )\n", + "\n", + " else:\n", + " # Likelihood Q-Ratio test:\n", + " lqrt_equal_var_result = lqrt.lqrtest_ind(\n", + " control, test, random_state=rnd_seed, equal_var=True\n", + " )\n", + "\n", + " lqrt_unequal_var_result = lqrt.lqrtest_ind(\n", + " control, test, random_state=rnd_seed, equal_var=False\n", + " )\n", + "\n", + " out.append(\n", + " {\n", + " \"control\": cname,\n", + " \"test\": tname,\n", + " \"control_N\": int(len(control)),\n", + " \"test_N\": int(len(test)),\n", + " \"pvalue_lqrt_equal_var\": lqrt_equal_var_result.pvalue,\n", + " \"statistic_lqrt_equal_var\": lqrt_equal_var_result.statistic,\n", + " \"pvalue_lqrt_unequal_var\": lqrt_unequal_var_result.pvalue,\n", + " \"statistic_lqrt_unequal_var\": lqrt_unequal_var_result.statistic,\n", + " }\n", + " )\n", + " self.__lqrt_results = pd.DataFrame(out)\n", + "\n", + " def plot(\n", + " self,\n", + " color_col=None,\n", + " raw_marker_size=6,\n", + " es_marker_size=9,\n", + " swarm_label=None,\n", + " contrast_label=None,\n", + " delta2_label=None,\n", + " swarm_ylim=None,\n", + " contrast_ylim=None,\n", + " delta2_ylim=None,\n", + " swarm_side=None,\n", + " custom_palette=None,\n", + " swarm_desat=0.5,\n", + " halfviolin_desat=1,\n", + " halfviolin_alpha=0.8,\n", + " face_color=None,\n", + " # bar plot\n", + " bar_label=None,\n", + " bar_desat=0.5,\n", + " bar_width=0.5,\n", + " bar_ylim=None,\n", + " # error bar of proportion plot\n", + " ci=None,\n", + " ci_type=\"bca\",\n", + " err_color=None,\n", + " float_contrast=True,\n", + " show_pairs=True,\n", + " show_delta2=True,\n", + " show_mini_meta=True,\n", + " group_summaries=None,\n", + " group_summaries_offset=0.1,\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", + " swarmplot_kwargs=None,\n", + " barplot_kwargs=None,\n", + " violinplot_kwargs=None,\n", + " slopegraph_kwargs=None,\n", + " sankey_kwargs=None,\n", + " reflines_kwargs=None,\n", + " group_summary_kwargs=None,\n", + " legend_kwargs=None,\n", + " title=None,\n", + " fontsize_title=16,\n", + " fontsize_rawxlabel=12,\n", + " fontsize_rawylabel=12,\n", + " fontsize_contrastxlabel=12,\n", + " fontsize_contrastylabel=12,\n", + " fontsize_delta2label=12,\n", + " ):\n", + " \"\"\"\n", + " Creates an estimation plot for the effect size of interest.\n", + "\n", + "\n", + " Parameters\n", + " ----------\n", + " color_col : string, default None\n", + " Column to be used for colors.\n", + " 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", + " 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", + " 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", + " limits for y. When `show_delta2` is True, if both of the `contrast_ylim`\n", + " and `delta2_ylim` are not None, then they must be specified with the\n", + " same values; when `show_delta2` is True and only one of them is specified,\n", + " then the other will automatically be assigned with the same value.\n", + " Specifying `delta2_ylim` does not have any effect when `show_delta2` is\n", + " False.\n", + " custom_palette : dict, list, or matplotlib color palette, default None\n", + " This keyword accepts a dictionary with {'group':'color'} pairings,\n", + " a list of RGB colors, or a specified matplotlib palette. This\n", + " palette will be used to color the swarmplot. If `color_col` is not\n", + " specified, then each group will be colored in sequence according\n", + " to the default palette currently used by matplotlib.\n", + " Please take a look at the seaborn commands `color_palette`\n", + " and `cubehelix_palette` to generate a custom palette. Both\n", + " these functions generate a list of RGB colors.\n", + " See:\n", + " https://seaborn.pydata.org/generated/seaborn.color_palette.html\n", + " 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", + " desired proportion. Uses `seaborn.desaturate()` to acheive this.\n", + " halfviolin_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", + " The alpha (transparency) level of the half-violin bootstrap curves.\n", + " float_contrast : boolean, default True\n", + " Whether or not to display the halfviolin bootstrapped difference\n", + " distribution alongside the raw data.\n", + " show_pairs : boolean, default True\n", + " 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_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", + " 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", + " The dots per inch of the resulting figure.\n", + " 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", + " 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", + " 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", + " 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", + " 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", + " 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", + " 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", + " 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", + " fontsize_rawxlabel : float, default 12\n", + " Font size for the raw axes xlabel.\n", + " fontsize_rawylabel : float, default 12\n", + " Font size for the raw axes ylabel.\n", + " fontsize_contrastxlabel : float, default 12\n", + " Font size for the contrast axes xlabel.\n", + " fontsize_contrastylabel : float, default 12\n", + " 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", + " Returns\n", + " -------\n", + " A :class:`matplotlib.figure.Figure` with 2 Axes, if ``ax = None``.\n", + "\n", + " The first axes (accessible with ``FigName.axes[0]``) contains the rawdata swarmplot; the second axes (accessible with ``FigName.axes[1]``) has the bootstrap distributions and effect sizes (with confidence intervals) plotted on it.\n", + "\n", + " If ``ax`` is specified, the rawdata swarmplot is accessed at ``ax``\n", + " itself, while the effect size axes is accessed at ``ax.contrast_axes``.\n", + " See the last example below.\n", + "\n", + "\n", + "\n", + " \"\"\"\n", + "\n", + " from .plotter import effectsize_df_plotter\n", + "\n", + " 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", + "\n", + " # Modification incurred due to update of Seaborn\n", + " ci = (\"ci\", ci) if ci is not None else None\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", + " \"\"\"\n", + " Returns the proportional parameter\n", + " class.\n", + " \"\"\"\n", + " return self.__proportional\n", + "\n", + " @property\n", + " def results(self):\n", + " \"\"\"Prints all pairwise comparisons nicely.\"\"\"\n", + " try:\n", + " return self.__results\n", + " except AttributeError:\n", + " self.__pre_calc()\n", + " return self.__results\n", + "\n", + " @property\n", + " def statistical_tests(self):\n", + " results_df = self.results\n", + "\n", + " # Select only the statistics and p-values.\n", + " stats_columns = [\n", + " c\n", + " for c in results_df.columns\n", + " if c.startswith(\"statistic\") or c.startswith(\"pvalue\")\n", + " ]\n", + "\n", + " default_cols = [\n", + " \"control\",\n", + " \"test\",\n", + " \"control_N\",\n", + " \"test_N\",\n", + " \"effect_size\",\n", + " \"is_paired\",\n", + " \"difference\",\n", + " \"ci\",\n", + " \"bca_low\",\n", + " \"bca_high\",\n", + " ]\n", + "\n", + " cols_of_interest = default_cols + stats_columns\n", + "\n", + " return results_df[cols_of_interest]\n", + "\n", + " @property\n", + " def _for_print(self):\n", + " return self.__for_print\n", + "\n", + " @property\n", + " def _plot_data(self):\n", + " return self.__dabest_obj._plot_data\n", + "\n", + " @property\n", + " def idx(self):\n", + " return self.__dabest_obj.idx\n", + "\n", + " @property\n", + " def xvar(self):\n", + " return self.__dabest_obj._xvar\n", + "\n", + " @property\n", + " def yvar(self):\n", + " return self.__dabest_obj._yvar\n", + "\n", + " @property\n", + " def is_paired(self):\n", + " return self.__is_paired\n", + "\n", + " @property\n", + " def ci(self):\n", + " \"\"\"\n", + " The width of the confidence interval being produced, in percent.\n", + " \"\"\"\n", + " return self.__ci\n", + "\n", + " @property\n", + " def x1_level(self):\n", + " return self.__x1_level\n", + "\n", + " @property\n", + " def x2(self):\n", + " return self.__x2\n", + "\n", + " @property\n", + " def experiment_label(self):\n", + " 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", + " \"\"\"\n", + " return self.__resamples\n", + "\n", + " @property\n", + " def random_seed(self):\n", + " \"\"\"\n", + " The seed used by `numpy.seed()` for bootstrap resampling.\n", + " \"\"\"\n", + " return self.__random_seed\n", + "\n", + " @property\n", + " def effect_size(self):\n", + " \"\"\"The type of effect size being computed.\"\"\"\n", + " return self.__effect_size\n", + "\n", + " @property\n", + " def dabest_obj(self):\n", + " \"\"\"\n", + " Returns the `dabest` object that invoked the current EffectSizeDataFrame\n", + " class.\n", + " \"\"\"\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", + " \"\"\"Returns all pairwise Lq-Likelihood Ratio Type test results\n", + " as a pandas DataFrame.\n", + "\n", + " For more information on LqRT tests, see https://arxiv.org/abs/1911.11922\n", + " \"\"\"\n", + " try:\n", + " return self.__lqrt_results\n", + " except AttributeError:\n", + " self.__calc_lqrt()\n", + " return self.__lqrt_results\n", + "\n", + " @property\n", + " def mini_meta(self):\n", + " \"\"\"\n", + " Returns the mini_meta boolean parameter.\n", + " \"\"\"\n", + " return self.__mini_meta\n", + "\n", + " @property\n", + " def mini_meta_delta(self):\n", + " \"\"\"\n", + " Returns the mini_meta results.\n", + " \"\"\"\n", + " try:\n", + " return self.__mini_meta_delta\n", + " except AttributeError:\n", + " self.__pre_calc()\n", + " return self.__mini_meta_delta\n", + "\n", + " @property\n", + " def delta_delta(self):\n", + " \"\"\"\n", + " Returns the mini_meta results.\n", + " \"\"\"\n", + " try:\n", + " return self.__delta_delta\n", + " except AttributeError:\n", + " self.__pre_calc()\n", + " return self.__delta_delta" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Example: plot\n", + "\n", + "Create a Gardner-Altman estimation plot for the mean difference." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "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", + "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 = repeat('Female', Ns/2).tolist()\n", + "males = 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", + "my_data = dabest.load(df, idx=(\"Control 1\", \"Test 1\"))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig1 = my_data.mean_diff.plot();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " Create a Gardner-Altman plot for the Hedges' g effect size." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig2 = my_data.hedges_g.plot();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Create a Cumming estimation plot for the mean difference." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig3 = my_data.mean_diff.plot(float_contrast=True);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " Create a paired Gardner-Altman plot." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "my_data_paired = dabest.load(df, idx=(\"Control 1\", \"Test 1\"),\n", + " id_col = \"ID\", paired='baseline')\n", + "fig4 = my_data_paired.mean_diff.plot();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Create a multi-group Cumming plot." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "my_multi_groups = dabest.load(df, id_col = \"ID\", \n", + " idx=((\"Control 1\", \"Test 1\"),\n", + " (\"Control 2\", \"Test 2\")))\n", + "fig5 = my_multi_groups.mean_diff.plot();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Create a shared control Cumming plot." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "my_shared_control = dabest.load(df, id_col = \"ID\",\n", + " idx=(\"Control 1\", \"Test 1\",\n", + " \"Test 2\", \"Test 3\"))\n", + "fig6 = my_shared_control.mean_diff.plot();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Create a repeated meausures (against baseline) Slopeplot." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "my_rm_baseline = dabest.load(df, id_col = \"ID\", paired = \"baseline\",\n", + " idx=(\"Control 1\", \"Test 1\",\n", + " \"Test 2\", \"Test 3\"))\n", + "fig7 = my_rm_baseline.mean_diff.plot();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Create a repeated meausures (sequential) Slopeplot." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "my_rm_sequential = dabest.load(df, id_col = \"ID\", paired = \"sequential\",\n", + " idx=(\"Control 1\", \"Test 1\",\n", + " \"Test 2\", \"Test 3\"))\n", + "fig8 = my_rm_sequential.mean_diff.plot();" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#| export\n", + "class PermutationTest:\n", + " \"\"\"\n", + " A class to compute and report permutation tests.\n", + " \n", + " Parameters\n", + " ----------\n", + " control : array-like\n", + " test : array-like\n", + " 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", + " is_paired : string, default None\n", + " permutation_count : int, default 10000\n", + " The number of permutations (reshuffles) to perform.\n", + " random_seed : int, default 12345\n", + " `random_seed` is used to seed the random number generator during\n", + " bootstrap resampling. This ensures that the generated permutations\n", + " are replicable.\n", + " \n", + " Returns\n", + " -------\n", + " A :py:class:`PermutationTest` object:\n", + " `difference`:float\n", + " The effect size of the difference between the control and the test.\n", + " `effect_size`:string\n", + " The type of effect size reported.\n", + " \n", + " \n", + " \"\"\"\n", + " \n", + " def __init__(self, control: array,\n", + " test: array, # These should be numerical iterables.\n", + " effect_size:str, # Any one of the following are accepted inputs: 'mean_diff', 'median_diff', 'cohens_d', 'hedges_g', or 'cliffs_delta'\n", + " 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", + " **kwargs):\n", + " from ._stats_tools.effsize import two_group_difference\n", + " from ._stats_tools.confint_2group_diff import calculate_group_var\n", + " \n", + "\n", + " self.__permutation_count = permutation_count\n", + "\n", + " # Run Sanity Check.\n", + " if is_paired and len(control) != len(test):\n", + " raise ValueError(\"The two arrays do not have the same length.\")\n", + "\n", + " # Initialise random number generator.\n", + " # rng = random.default_rng(seed=random_seed)\n", + " rng = RandomState(PCG64(random_seed))\n", + "\n", + " # Set required constants and variables\n", + " control = array(control)\n", + " test = array(test)\n", + "\n", + " control_sample = control.copy()\n", + " test_sample = test.copy()\n", + "\n", + " BAG = array([*control, *test])\n", + " CONTROL_LEN = int(len(control))\n", + " EXTREME_COUNT = 0.\n", + " THRESHOLD = abs(two_group_difference(control, test, \n", + " is_paired, effect_size))\n", + " self.__permutations = []\n", + " self.__permutations_var = []\n", + "\n", + " for i in range(int(self.__permutation_count)):\n", + " if is_paired:\n", + " # Select which control-test pairs to swap.\n", + " random_idx = rng.choice(CONTROL_LEN,\n", + " rng.randint(0, CONTROL_LEN+1),\n", + " replace=False)\n", + "\n", + " # Perform swap.\n", + " for i in random_idx:\n", + " _placeholder = control_sample[i]\n", + " control_sample[i] = test_sample[i]\n", + " test_sample[i] = _placeholder\n", + " \n", + " else:\n", + " # Shuffle the bag and assign to control and test groups.\n", + " # NB. rng.shuffle didn't produce replicable results...\n", + " shuffled = rng.permutation(BAG) \n", + " control_sample = shuffled[:CONTROL_LEN]\n", + " test_sample = shuffled[CONTROL_LEN:]\n", + "\n", + "\n", + " es = two_group_difference(control_sample, test_sample, \n", + " False, effect_size)\n", + " \n", + " group_var = calculate_group_var(var(control_sample, ddof=1), \n", + " CONTROL_LEN, \n", + " var(test_sample, ddof=1), \n", + " len(test_sample))\n", + " self.__permutations.append(es)\n", + " self.__permutations_var.append(group_var)\n", + "\n", + " if abs(es) > THRESHOLD:\n", + " EXTREME_COUNT += 1.\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", + "\n", + "\n", + " @property\n", + " def permutation_count(self):\n", + " \"\"\"\n", + " The number of permuations taken.\n", + " \"\"\"\n", + " return self.__permutation_count\n", + "\n", + "\n", + " @property\n", + " def permutations(self):\n", + " \"\"\"\n", + " The effect sizes of all the permutations in a list.\n", + " \"\"\"\n", + " return self.__permutations\n", + "\n", + " \n", + " @property\n", + " def permutations_var(self):\n", + " \"\"\"\n", + " The experiment group variance of all the permutations in a list.\n", + " \"\"\"\n", + " return self.__permutations_var\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Notes**:\n", + " \n", + "The basic concept of permutation tests is the same as that behind bootstrapping.\n", + "In an \"exact\" permutation test, all possible resuffles of the control and test \n", + "labels are performed, and the proportion of effect sizes that equal or exceed \n", + "the observed effect size is computed. This is the probability, under the null \n", + "hypothesis of zero difference between test and control groups, of observing the\n", + "effect size: the p-value of the Student's t-test.\n", + "\n", + "Exact permutation tests are impractical: computing the effect sizes for all reshuffles quickly exceeds trivial computational loads. A control group and a test group both with 10 observations each would have a total of $20!$ or $2.43 \\times {10}^{18}$ reshuffles.\n", + "Therefore, in practice, \"approximate\" permutation tests are performed, where a sufficient number of reshuffles are performed (5,000 or 10,000), from which the p-value is computed.\n", + "\n", + "More information can be found [here](https://en.wikipedia.org/wiki/Resampling_(statistics)#Permutation_tests).\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Example: permutation test" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "control = norm.rvs(loc=0, size=30, random_state=12345)\n", + "test = norm.rvs(loc=0.5, size=30, random_state=12345)\n", + "perm_test = dabest.PermutationTest(control, test, \n", + " effect_size=\"mean_diff\", \n", + " is_paired=None)\n", + "perm_test" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": {}, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/nbs/API/forest_plot.ipynb b/nbs/API/forest_plot.ipynb new file mode 100644 index 00000000..9725a619 --- /dev/null +++ b/nbs/API/forest_plot.ipynb @@ -0,0 +1,374 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Forest plot\n", + "\n", + "> Creating forest plots from contrast objects.\n", + "\n", + "- order: 4" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#| default_exp forest_plot" + ] + }, + { + "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": [ + "#| hide\n", + "import dabest" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#| export\n", + "import matplotlib.pyplot as plt\n", + "# %matplotlib inline\n", + "import seaborn as sns\n", + "from typing import List, Optional, Union\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#| export\n", + "def load_plot_data(\n", + " contrasts: List, effect_size: str = \"mean_diff\", contrast_type: str = \"delta2\"\n", + ") -> List:\n", + " \"\"\"\n", + " Loads plot data based on specified effect size and contrast type.\n", + "\n", + " Parameters\n", + " ----------\n", + " contrasts : List\n", + " List of contrast objects.\n", + " 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", + "\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", + "\n", + " contrast_attr_map = {\"delta2\": \"delta_delta\", \"mini_meta\": \"mini_meta_delta\"}\n", + "\n", + " effect_attr = effect_attr_map.get(effect_size)\n", + " contrast_attr = contrast_attr_map.get(contrast_type)\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", + "\n", + " return [\n", + " getattr(getattr(contrast, effect_attr), contrast_attr) for contrast in contrasts\n", + " ]\n", + "\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", + " else:\n", + " attribute_suffix = \"delta_delta\"\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", + " \n", + " return bootstraps, differences, bcalows, bcahighs\n", + "\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", + "\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", + "\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", + " # Validate inputs\n", + " if contrasts is None:\n", + " raise ValueError(\"The `contrasts` parameter cannot be None\")\n", + " \n", + " if not isinstance(contrasts, list) or not contrasts:\n", + " raise ValueError(\"The `contrasts` argument must be a non-empty list.\")\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", + " \n", + " if not isinstance(contrast_type, str):\n", + " raise TypeError(\"The `contrast_type` argument must be a string.\")\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", + " \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", + " \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", + " \n", + " if not isinstance(ylabel, str):\n", + " raise TypeError(\"The `ylabel` argument must be a string.\")\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", + " \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", + " \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", + " \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", + " \n", + " if not isinstance(horizontal, bool):\n", + " raise TypeError(\"`horizontal` must be a boolean value.\")\n", + "\n", + " # Load plot data\n", + " contrast_plot_data = load_plot_data(contrasts, effect_size, contrast_type)\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", + "\n", + " if ax is None:\n", + " fig, ax = plt.subplots(figsize=fig_size)\n", + " else:\n", + " fig = ax.figure\n", + "\n", + " # Adjust violin plot orientation based on the 'horizontal' argument\n", + " violin_kwargs = violin_kwargs or {\n", + " \"widths\": 0.5,\n", + " \"showextrema\": False,\n", + " \"showmedians\": False,\n", + " }\n", + " violin_kwargs[\"vert\"] = not horizontal\n", + " v = ax.violinplot(bootstraps, **violin_kwargs)\n", + "\n", + " # Adjust the halfviolin function call based on 'horizontal'\n", + " if horizontal:\n", + " half = \"top\"\n", + " else:\n", + " half = \"right\" # Assuming \"right\" is the default or another appropriate value\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", + " if isinstance(custom_palette, dict):\n", + " violin_colors = [\n", + " custom_palette.get(c, sns.color_palette()[0]) for c in contrasts\n", + " ]\n", + " elif isinstance(custom_palette, list):\n", + " violin_colors = custom_palette[: len(contrasts)]\n", + " elif isinstance(custom_palette, str):\n", + " if custom_palette in plt.colormaps():\n", + " violin_colors = sns.color_palette(custom_palette, len(contrasts))\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", + "\n", + " for patch, color in zip(v[\"bodies\"], violin_colors):\n", + " patch.set_facecolor(color)\n", + " patch.set_alpha(alpha_violin_plot)\n", + "\n", + " # Flipping the axes for plotting based on 'horizontal'\n", + " for k in range(1, len(contrasts) + 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", + " 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", + "\n", + " # Adjusting labels, ticks, and limits based on 'horizontal'\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", + " 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", + "\n", + " # Setting the title and adjusting spines as before\n", + " ax.set_title(title, fontsize=fontsize)\n", + " if remove_spines:\n", + " for spine in ax.spines.values():\n", + " spine.set_visible(False)\n", + "\n", + " # Apply additional customizations if provided\n", + " if additional_plotting_kwargs:\n", + " ax.set(**additional_plotting_kwargs)\n", + "\n", + " return fig" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "python3", + "language": "python", + "name": "python3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/nbs/API/load.ipynb b/nbs/API/load.ipynb index 24f65512..c628b30a 100644 --- a/nbs/API/load.ipynb +++ b/nbs/API/load.ipynb @@ -39,6 +39,7 @@ "#| hide\n", "from nbdev.showdoc import *\n", "import nbdev\n", + "\n", "nbdev.nbdev_export()" ] }, @@ -49,11 +50,24 @@ "outputs": [], "source": [ "#| export\n", - "def load(data, idx=None, x=None, y=None, paired=None, id_col=None,\n", - " ci=95, resamples=5000, random_seed=12345, proportional=False, \n", - " delta2 = False, experiment = None, experiment_label = None,\n", - " x1_level = None, mini_meta=False):\n", - " '''\n", + "def load(\n", + " data,\n", + " idx=None,\n", + " x=None,\n", + " y=None,\n", + " paired=None,\n", + " id_col=None,\n", + " ci=95,\n", + " resamples=5000,\n", + " random_seed=12345,\n", + " proportional=False,\n", + " delta2=False,\n", + " experiment=None,\n", + " experiment_label=None,\n", + " x1_level=None,\n", + " mini_meta=False,\n", + "):\n", + " \"\"\"\n", " Loads data in preparation for estimation statistics.\n", "\n", " This is designed to work with pandas DataFrames.\n", @@ -67,15 +81,15 @@ " with each individual tuple producing its own contrast plot\n", " x : string or list, default None\n", " Column name(s) of the independent variable. This can be expressed as\n", - " a list of 2 elements if and only if 'delta2' is True; otherwise it \n", + " a list of 2 elements if and only if 'delta2' is True; otherwise it\n", " can only be a string.\n", " y : string, default None\n", " Column names for data to be plotted on the x-axis and y-axis.\n", " paired : string, default None\n", - " The type of the experiment under which the data are obtained. If 'paired' \n", + " The type of the experiment under which the data are obtained. If 'paired'\n", " is None then the data will not be treated as paired data in the subsequent\n", - " calculations. If 'paired' is 'baseline', then in each tuple of x, other \n", - " groups will be paired up with the first group (as control). If 'paired' is \n", + " calculations. If 'paired' is 'baseline', then in each tuple of x, other\n", + " groups will be paired up with the first group (as control). If 'paired' is\n", " 'sequential', then in each tuple of x, each group will be paired up with\n", " its previous group (as control).\n", " id_col : default None.\n", @@ -90,7 +104,7 @@ " This integer is used to seed the random number generator during\n", " bootstrap resampling, ensuring that the confidence intervals\n", " reported are replicable.\n", - " proportional : boolean, default False. \n", + " proportional : boolean, default False.\n", " An indicator of whether the data is binary or not. When set to True, it\n", " specifies that the data consists of binary data, where the values are\n", " limited to 0 and 1. The code is not suitable for analyzing proportion\n", @@ -100,27 +114,125 @@ " delta2 : boolean, default False\n", " Indicator of delta-delta experiment\n", " experiment : String, default None\n", - " The name of the column of the dataframe which contains the label of \n", + " The name of the column of the dataframe which contains the label of\n", " experiments\n", " experiment_lab : list, default None\n", " A list of String to specify the order of subplots for delta-delta plots.\n", - " This can be expressed as a list of 2 elements if and only if 'delta2' \n", - " is True; otherwise it can only be a string. \n", + " This can be expressed as a list of 2 elements if and only if 'delta2'\n", + " is True; otherwise it can only be a string.\n", " x1_level : list, default None\n", " A list of String to specify the order of subplots for delta-delta plots.\n", - " This can be expressed as a list of 2 elements if and only if 'delta2' \n", - " is True; otherwise it can only be a string. \n", + " This can be expressed as a list of 2 elements if and only if 'delta2'\n", + " is True; otherwise it can only be a string.\n", " mini_meta : boolean, default False\n", " Indicator of weighted delta calculation.\n", "\n", " Returns\n", " -------\n", " A `Dabest` object.\n", - " '''\n", - " from ._classes import Dabest\n", + " \"\"\"\n", + " from dabest import Dabest\n", + "\n", + " return Dabest(\n", + " data,\n", + " idx,\n", + " x,\n", + " y,\n", + " paired,\n", + " id_col,\n", + " ci,\n", + " resamples,\n", + " random_seed,\n", + " proportional,\n", + " delta2,\n", + " experiment,\n", + " experiment_label,\n", + " x1_level,\n", + " mini_meta,\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#| export\n", + "import numpy as np\n", + "from typing import Union, Optional\n", + "import pandas as pd\n", + "\n", "\n", - " return Dabest(data, idx, x, y, paired, id_col, ci, resamples, random_seed, proportional, delta2, experiment, experiment_label, x1_level, mini_meta)\n", - "\n" + "def prop_dataset(\n", + " group: Union[\n", + " list, tuple, np.ndarray, dict\n", + " ], # Accepts lists, tuples, or numpy ndarrays of numeric types.\n", + " group_names: Optional[list] = None,\n", + "):\n", + " \"\"\"\n", + " Convenient function to generate a dataframe of binary data.\n", + " \"\"\"\n", + "\n", + " if isinstance(group, dict):\n", + " # If group_names is not provided, use the keys of the dict as group_names\n", + " if group_names is None:\n", + " group_names = list(group.keys())\n", + " elif not set(group_names) == set(group.keys()):\n", + " # Check if the group_names provided is the same as the keys of the dict\n", + " raise ValueError(\"group_names must be the same as the keys of the dict.\")\n", + " \n", + " # Check if the values in the dict are numeric\n", + " if not all(\n", + " [isinstance(group[name], (list, tuple, np.ndarray)) for name in group_names]\n", + " ):\n", + " raise ValueError(\n", + " \"group must be a dict of lists, tuples, or numpy ndarrays of numeric types.\"\n", + " )\n", + " \n", + " # Check if the values in the dict only have two elements under each parent key\n", + " if not all([len(group[name]) == 2 for name in group_names]):\n", + " raise ValueError(\"Each parent key should have only two elements.\")\n", + " group_val = group\n", + "\n", + " else:\n", + " if group_names is None:\n", + " raise ValueError(\"group_names must be provided if group is not a dict.\")\n", + " \n", + " # Check if the length of group is two times of the length of group_names\n", + " if not len(group) == 2 * len(group_names):\n", + " raise ValueError(\n", + " \"The length of group must be two times of the length of group_names.\"\n", + " )\n", + " group_val = {\n", + " group_names[i]: [group[i * 2], group[i * 2 + 1]]\n", + " for i in range(len(group_names))\n", + " }\n", + "\n", + " # Check if the sum of values in group_val under each key are the same\n", + " if not all(\n", + " [\n", + " sum(group_val[name]) == sum(group_val[group_names[0]])\n", + " for name in group_val.keys()\n", + " ]\n", + " ):\n", + " raise ValueError(\"The sum of values under each key must be the same.\")\n", + "\n", + " id_col = pd.Series(range(1, sum(group_val[group_names[0]]) + 1))\n", + "\n", + " final_df = pd.DataFrame()\n", + "\n", + " for name in group_val.keys():\n", + " col = (\n", + " np.repeat(0, group_val[name][0]).tolist()\n", + " + np.repeat(1, group_val[name][1]).tolist()\n", + " )\n", + " df = pd.DataFrame({name: col})\n", + " final_df = pd.concat([final_df, df], axis=1)\n", + "\n", + " final_df[\"ID\"] = id_col\n", + "\n", + " return final_df" ] }, { @@ -159,7 +271,7 @@ "N = 10\n", "c1 = sp.stats.norm.rvs(loc=100, scale=5, size=N)\n", "t1 = sp.stats.norm.rvs(loc=115, scale=5, size=N)\n", - "df = pd.DataFrame({'Control 1' : c1, 'Test 1': t1})" + "df = pd.DataFrame({\"Control 1\": c1, \"Test 1\": t1})" ] }, { @@ -177,11 +289,11 @@ { "data": { "text/plain": [ - "DABEST v2023.2.14\n", - "=================\n", - " \n", - "Good evening!\n", - "The current time is Thu Mar 30 12:22:55 2023.\n", + "DABEST v2024.03.29\n", + "==================\n", + " \n", + "Good afternoon!\n", + "The current time is Tue Mar 19 15:34:58 2024.\n", "\n", "Effect size(s) with 95% confidence intervals will be computed for:\n", "1. Test 1 minus Control 1\n", @@ -216,16 +328,9 @@ "N = 10\n", "c1 = np.random.binomial(1, 0.2, size=N)\n", "t1 = np.random.binomial(1, 0.5, size=N)\n", - "df = pd.DataFrame({'Control 1' : c1, 'Test 1': t1})\n", - "my_data = dabest.load(df, idx=(\"Control 1\", \"Test 1\"),proportional=True)" + "df = pd.DataFrame({\"Control 1\": c1, \"Test 1\": t1})\n", + "my_data = dabest.load(df, idx=(\"Control 1\", \"Test 1\"), proportional=True)" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { diff --git a/nbs/API/misc_tools.ipynb b/nbs/API/misc_tools.ipynb index da49407b..0395a57c 100644 --- a/nbs/API/misc_tools.ipynb +++ b/nbs/API/misc_tools.ipynb @@ -46,6 +46,18 @@ "nbdev.nbdev_export()" ] }, + { + "cell_type": "code", + "execution_count": null, + "id": "5f54be1c", + "metadata": {}, + "outputs": [], + "source": [ + "#| export\n", + "import datetime as dt\n", + "from numpy import repeat" + ] + }, { "cell_type": "code", "execution_count": null, @@ -54,9 +66,9 @@ "outputs": [], "source": [ "#| export\n", - "def merge_two_dicts(x:dict,\n", - " y:dict\n", - " )->dict:#A dictionary containing a union of all keys in both original dicts.\n", + "def merge_two_dicts(\n", + " x: dict, y: dict\n", + ") -> dict: # A dictionary containing a union of all keys in both original dicts.\n", " \"\"\"\n", " Given two dicts, merge them into a new dict as a shallow copy.\n", " Any overlapping keys in `y` will override the values in `x`.\n", @@ -70,24 +82,31 @@ " return z\n", "\n", "\n", - "\n", "def unpack_and_add(l, c):\n", " \"\"\"Convenience function to allow me to add to an existing list\n", " without altering that list.\"\"\"\n", " t = [a for a in l]\n", " t.append(c)\n", - " return(t)\n", - "\n", + " return t\n", "\n", "\n", "def print_greeting():\n", + " \"\"\"\n", + " Generates a greeting message based on the current time, along with the version information of DABEST.\n", + "\n", + " This function dynamically generates a greeting ('Good morning', 'Good afternoon', 'Good evening')\n", + " based on the current system time. It also retrieves and displays the version of DABEST (Data Analysis\n", + " using Bootstrap-Coupled ESTimation). The message includes a header with the DABEST version and the\n", + " current time formatted in a user-friendly manner.\n", + "\n", + " Returns:\n", + " str: A formatted string containing the greeting message, DABEST version, and current time.\n", + " \"\"\"\n", " from .__init__ import __version__\n", - " import datetime as dt\n", - " import numpy as np\n", "\n", " line1 = \"DABEST v{}\".format(__version__)\n", - " header = \"\".join(np.repeat(\"=\", len(line1)))\n", - " spacer = \"\".join(np.repeat(\" \", len(line1)))\n", + " header = \"\".join(repeat(\"=\", len(line1)))\n", + " spacer = \"\".join(repeat(\" \", len(line1)))\n", "\n", " now = dt.datetime.now()\n", " if 0 < now.hour < 12:\n", @@ -103,20 +122,11 @@ "\n", "\n", "def get_varname(obj):\n", - " matching_vars = [k for k,v in globals().items() if v is obj]\n", + " 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", - " else:\n", - " return \"\"\n" + " return \"\"" ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4f6841f9", - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { diff --git a/nbs/API/plot_tools.ipynb b/nbs/API/plot_tools.ipynb index 675d1cef..7187025b 100644 --- a/nbs/API/plot_tools.ipynb +++ b/nbs/API/plot_tools.ipynb @@ -1,6 +1,7 @@ { "cells": [ { + "attachments": {}, "cell_type": "markdown", "id": "5d5d1b29", "metadata": {}, @@ -54,12 +55,19 @@ "outputs": [], "source": [ "#| export\n", + "import math\n", + "import warnings\n", + "import itertools\n", + "import numpy as np\n", "import pandas as pd\n", - "from collections import defaultdict\n", - "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", - "import numpy as np\n", - "import itertools" + "import matplotlib.pyplot as plt\n", + "import matplotlib.lines as mlines\n", + "import matplotlib.axes as axes\n", + "from collections import defaultdict\n", + "from typing import List, Tuple, Dict, Iterable, Union\n", + "from pandas.api.types import CategoricalDtype\n", + "from matplotlib.colors import ListedColormap" ] }, { @@ -69,24 +77,21 @@ "metadata": {}, "outputs": [], "source": [ - "#| export \n", - "def halfviolin(v, half='right', fill_color='k', alpha=1,\n", - " line_color='k', line_width=0):\n", - " import numpy as np\n", - "\n", - " for b in v['bodies']:\n", + "#| export\n", + "def halfviolin(v, half=\"right\", fill_color=\"k\", alpha=1, line_color=\"k\", line_width=0):\n", + " for b in v[\"bodies\"]:\n", " V = b.get_paths()[0].vertices\n", "\n", " mean_vertical = np.mean(V[:, 0])\n", " mean_horizontal = np.mean(V[:, 1])\n", "\n", - " if half == 'right':\n", + " if half == \"right\":\n", " V[:, 0] = np.clip(V[:, 0], mean_vertical, np.inf)\n", - " elif half == 'left':\n", + " elif half == \"left\":\n", " V[:, 0] = np.clip(V[:, 0], -np.inf, mean_vertical)\n", - " elif half == 'bottom':\n", + " elif half == \"bottom\":\n", " V[:, 1] = np.clip(V[:, 1], -np.inf, mean_horizontal)\n", - " elif half == 'top':\n", + " elif half == \"top\":\n", " V[:, 1] = np.clip(V[:, 1], mean_horizontal, np.inf)\n", "\n", " b.set_color(fill_color)\n", @@ -100,41 +105,49 @@ " Given a matplotlib Collection, will obtain the x and y spans\n", " for the collection. Will return None if this fails.\n", " \"\"\"\n", - " import numpy as np\n", + " if coll is None:\n", + " raise ValueError(\"The collection `coll` parameter cannot be None\")\n", + "\n", " x, y = np.array(coll.get_offsets()).T\n", " try:\n", " return x.min(), x.max(), y.min(), y.max()\n", - " except ValueError:\n", + " except ValueError as e:\n", + " warnings.warn(f\"Failed to calculate spans for the collection. Details: {e}\")\n", " return None\n", "\n", - "def error_bar(data:pd.DataFrame, # This DataFrame should be in 'long' format.\n", - " x:str, #x column to be plotted.\n", - " y:str, # y column to be plotted.\n", - " type:str='mean_sd', # Choose from ['mean_sd', 'median_quartiles']. Plots the summary statistics for each group. If 'mean_sd', then the mean and standard deviation of each group is plotted as a gapped line. If 'median_quantiles', then the median and 25th and 75th percentiles of each group is plotted instead.\n", - " offset:float=0.2, #Give a single float (that will be used as the x-offset of all gapped lines), or an iterable containing the list of x-offsets.\n", - " ax=None, #If a matplotlib Axes object is specified, the gapped lines will be plotted in order on this axes. If None, the current axes (plt.gca()) is used.\n", - " line_color=\"black\", gap_width_percent=1, \n", - " pos:list=[0, 1],#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", - " **kwargs:dict\n", - " ):\n", - " '''\n", + "\n", + "def error_bar(\n", + " data: pd.DataFrame, # This DataFrame should be in 'long' format.\n", + " x: str, # x column to be plotted.\n", + " y: str, # y column to be plotted.\n", + " type: str = \"mean_sd\", # Choose from ['mean_sd', 'median_quartiles']. Plots the summary statistics for each group. If 'mean_sd', then the mean and standard deviation of each group is plotted as a gapped line. If 'median_quantiles', then the median and 25th and 75th percentiles of each group is plotted instead.\n", + " offset: float = 0.2, # Give a single float (that will be used as the x-offset of all gapped lines), or an iterable containing the list of x-offsets.\n", + " ax=None, # If a matplotlib Axes object is specified, the gapped lines will be plotted in order on this axes. If None, the current axes (plt.gca()) is used.\n", + " line_color=\"black\", # The color of the gapped lines.\n", + " gap_width_percent=1, # The width of the gap in the gapped lines, as a percentage of the y-axis span.\n", + " pos: list = [\n", + " 0,\n", + " 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", + " **kwargs: dict,\n", + "):\n", + " \"\"\"\n", " Function to plot the standard deviations as vertical errorbars.\n", " The mean is a gap defined by negative space.\n", "\n", " This function combines the functionality of gapped_lines(),\n", " proportional_error_bar(), and sankey_error_bar().\n", "\n", - " '''\n", - " import numpy as np\n", - " import pandas as pd\n", - " import matplotlib.pyplot as plt\n", - " import matplotlib.lines as mlines\n", + " \"\"\"\n", "\n", " if gap_width_percent < 0 or gap_width_percent > 100:\n", " raise ValueError(\"`gap_width_percent` must be between 0 and 100.\")\n", - " if method not in ['gapped_lines', 'proportional_error_bar', 'sankey_error_bar']:\n", - " raise ValueError(\"Invalid `method`. Must be one of 'gapped_lines', 'proportional_error_bar', or 'sankey_error_bar'.\")\n", + " if method not in [\"gapped_lines\", \"proportional_error_bar\", \"sankey_error_bar\"]:\n", + " raise ValueError(\n", + " \"Invalid `method`. Must be one of 'gapped_lines', \\\n", + " 'proportional_error_bar', or 'sankey_error_bar'.\"\n", + " )\n", "\n", " if ax is None:\n", " ax = plt.gca()\n", @@ -143,14 +156,14 @@ " gap_width = ax_yspan * gap_width_percent / 100\n", "\n", " keys = kwargs.keys()\n", - " if 'clip_on' not in keys:\n", - " kwargs['clip_on'] = False\n", + " if \"clip_on\" not in keys:\n", + " kwargs[\"clip_on\"] = False\n", "\n", - " if 'zorder' not in keys:\n", - " kwargs['zorder'] = 5\n", + " if \"zorder\" not in keys:\n", + " kwargs[\"zorder\"] = 5\n", "\n", - " if 'lw' not in keys:\n", - " kwargs['lw'] = 2.\n", + " if \"lw\" not in keys:\n", + " kwargs[\"lw\"] = 2.0\n", "\n", " if isinstance(data[x].dtype, pd.CategoricalDtype):\n", " group_order = pd.unique(data[x]).categories\n", @@ -159,8 +172,10 @@ "\n", " means = data.groupby(x)[y].mean().reindex(index=group_order)\n", "\n", - " if method in ['proportional_error_bar', 'sankey_error_bar']:\n", - " g = lambda x: np.sqrt((np.sum(x) * (len(x) - np.sum(x))) / (len(x) * len(x) * len(x)))\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", " else:\n", " sd = data.groupby(x)[y].std().reindex(index=group_order)\n", @@ -169,23 +184,25 @@ " upper_sd = means + sd\n", "\n", " if (lower_sd < ax_ylims[0]).any() or (upper_sd > ax_ylims[1]).any():\n", - " kwargs['clip_on'] = True\n", + " kwargs[\"clip_on\"] = True\n", "\n", " medians = data.groupby(x)[y].median().reindex(index=group_order)\n", - " quantiles = data.groupby(x)[y].quantile([0.25, 0.75]) \\\n", - " .unstack() \\\n", - " .reindex(index=group_order)\n", + " quantiles = (\n", + " data.groupby(x)[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", "\n", - " if type == 'mean_sd':\n", + " if type == \"mean_sd\":\n", " central_measures = means\n", " lows = lower_sd\n", " highs = upper_sd\n", - " elif type == 'median_quartiles':\n", + " elif type == \"median_quartiles\":\n", " central_measures = medians\n", " lows = lower_quartiles\n", " highs = upper_quartiles\n", + " else:\n", + " raise ValueError(\"Only accepted values for type are ['mean_sd', 'median_quartiles']\")\n", "\n", " n_groups = len(central_measures)\n", "\n", @@ -209,38 +226,51 @@ " err2 = \"{} offset(s) were supplied in `offset`.\".format(len_offset)\n", " raise ValueError(err1 + err2)\n", "\n", - " kwargs['zorder'] = kwargs['zorder']\n", + " kwargs[\"zorder\"] = kwargs[\"zorder\"]\n", "\n", " for xpos, central_measure in enumerate(central_measures):\n", - " kwargs['color'] = custom_palette[xpos]\n", + " kwargs[\"color\"] = custom_palette[xpos]\n", "\n", - " if method == 'sankey_error_bar':\n", + " if method == \"sankey_error_bar\":\n", " _xpos = pos[xpos] + offset[xpos]\n", " else:\n", " _xpos = xpos + offset[xpos]\n", "\n", " low = lows[xpos]\n", - " low_to_mean = mlines.Line2D([_xpos, _xpos],\n", - " [low, central_measure - gap_width],\n", - " **kwargs)\n", - " ax.add_line(low_to_mean)\n", - "\n", " high = highs[xpos]\n", - " mean_to_high = mlines.Line2D([_xpos, _xpos],\n", - " [central_measure + gap_width, high],\n", - " **kwargs)\n", - " ax.add_line(mean_to_high)\n", - "\n", - "def check_data_matches_labels(labels,#list of input labels \n", - " data, #Pandas Series of input data\n", - " side:str # 'left' or 'right' on the sankey diagram\n", - " ):\n", - " '''\n", - " Function to check that the labels and data match in the sankey diagram. \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", + "\n", + " mean_to_high = mlines.Line2D(\n", + " [_xpos, _xpos], [central_measure, high], **kwargs\n", + " )\n", + " ax.add_line(mean_to_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", + "def check_data_matches_labels(\n", + " labels, # list of input labels\n", + " data, # Pandas Series of input data\n", + " side: str, # 'left' or 'right' on the sankey diagram\n", + "):\n", + " \"\"\"\n", + " Function to check that the labels and data match in the sankey diagram.\n", " And enforce labels and data to be lists.\n", " Raises an exception if the labels and data do not match.\n", - " '''\n", - " if len(labels > 0):\n", + " \"\"\"\n", + " if len(labels) > 0:\n", " if isinstance(data, list):\n", " data = set(data)\n", " if isinstance(data, pd.Series):\n", @@ -253,85 +283,192 @@ " msg = \"Labels: \" + \",\".join(labels) + \"\\n\"\n", " if len(data) < 20:\n", " msg += \"Data: \" + \",\".join(data)\n", - " raise Exception('{0} labels and data do not match.{1}'.format(side, msg))\n", - " \n", + " raise Exception(f\"{side} labels and data do not match.{msg}\")\n", + "\n", + "\n", "def normalize_dict(nested_dict, target):\n", + " \"\"\"\n", + " Normalizes the values in a nested dictionary based on a target dictionary.\n", + "\n", + " This function iterates through a nested dictionary, calculates the sum of values for each key\n", + " across all sub-dictionaries, and then normalizes these values according to a target dictionary.\n", + " The normalization is performed such that the values in each sub-dictionary are proportionally\n", + " scaled to match the corresponding 'right' values in the target dictionary.\n", + "\n", + " Parameters:\n", + " nested_dict (dict of dict): A nested dictionary where each key maps to another dictionary.\n", + " The values in these inner dictionaries are subject to normalization.\n", + " target (dict): A dictionary with the target values for normalization. Each key in nested_dict\n", + " should have a corresponding key in target, and each target[key] should be a\n", + " dictionary with a 'right' key containing the target normalization value.\n", + "\n", + " Returns:\n", + " dict: The normalized nested dictionary. The original nested_dict is modified in place.\n", + "\n", + " Note:\n", + " - If the sum of values for a particular key in nested_dict is zero, the normalized value is set to 0.\n", + " - If a key in a sub-dictionary of nested_dict does not exist in the target dictionary, the\n", + " corresponding 'right' value from the target dictionary is directly assigned.\n", + " - The function modifies the input nested_dict in place and also returns it.\n", + " \"\"\"\n", " val = {}\n", " for key in nested_dict.keys():\n", - " val[key] = np.sum([nested_dict[sub_key][key] for sub_key in nested_dict.keys()])\n", - " \n", + " val[key] = np.sum(\n", + " [\n", + " nested_dict[sub_key][key]\n", + " for sub_key in nested_dict.keys()\n", + " if key in nested_dict[sub_key]\n", + " ]\n", + " )\n", + "\n", " for key, value in nested_dict.items():\n", " if isinstance(value, dict):\n", " for subkey in value.keys():\n", - " value[subkey] = value[subkey] * target[subkey]['right']/val[subkey]\n", + " if subkey in val.keys():\n", + " if val[subkey] != 0:\n", + " # Address the problem when one of the labels has zero value\n", + " value[subkey] = (\n", + " value[subkey] * target[subkey][\"right\"] / val[subkey]\n", + " )\n", + " else:\n", + " value[subkey] = 0\n", + " else:\n", + " value[subkey] = target[subkey][\"right\"]\n", " return nested_dict\n", "\n", - "def single_sankey(left:np.array,# data on the left of the diagram\n", - " right:np.array, # data on the right of the diagram, len(left) == len(right)\n", - " xpos:float=0, # the starting point on the x-axis\n", - " leftWeight:np.array=None, #weights for the left labels, if None, all weights are 1\n", - " rightWeight:np.array=None, #weights for the right labels, if None, all weights are corresponding leftWeight\n", - " colorDict:dict=None, #input format: {'label': 'color'}\n", - " leftLabels:list=None, #labels for the left side of the diagram. The diagram will be sorted by these labels.\n", - " rightLabels:list=None, #labels for the right side of the diagram. The diagram will be sorted by these labels.\n", - " ax=None, #matplotlib axes to be drawn on\n", - " width=0.5, \n", - " alpha=0.65, \n", - " bar_width=0.2, \n", - " rightColor: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", - " ):\n", - "\n", - " '''\n", + "\n", + "def width_determine(labels, data, pos=\"left\"):\n", + " \"\"\"\n", + " Calculates normalized width positions for a set of labels based on their associated data.\n", + "\n", + " This function is designed to determine width positions for plotting or graphical representation.\n", + " It takes into account the cumulative weight of each label in the data and adjusts their positions\n", + " accordingly. The function allows for adjusting the position of labels to either the 'left' or 'right'.\n", + "\n", + " Parameters:\n", + " labels (list): A list of labels whose width positions are to be calculated.\n", + " data (DataFrame): A pandas DataFrame containing the data used for calculating width positions.\n", + " The DataFrame should have columns corresponding to the 'pos' and 'posWeight'.\n", + " pos (str, optional): The position of labels. It can be either 'left' or 'right'. Defaults to 'left'.\n", + "\n", + " Returns:\n", + " defaultdict: A dictionary where each key is a label and the value is another dictionary with keys\n", + " 'bottom', 'top', and 'pos', representing the calculated width positions.\n", + "\n", + " Note:\n", + " The function assumes that the data DataFrame contains columns named after the value of 'pos' and\n", + " an additional column named 'posWeight' which represents the weight of each label.\n", + " \"\"\"\n", + " if labels is None:\n", + " raise ValueError(\"The `labels` parameter cannot be None\")\n", + "\n", + " if data is None:\n", + " raise ValueError(\"The `data` parameter cannot be None\")\n", + " \n", + " widths_norm = defaultdict()\n", + " for i, label in enumerate(labels):\n", + " myD = {}\n", + " myD[pos] = data[data[pos] == label][pos + \"Weight\"].sum()\n", + " if len(labels) != 1:\n", + " if i == 0:\n", + " myD[\"bottom\"] = 0\n", + " myD[pos] -= 0.01\n", + " myD[\"top\"] = myD[pos]\n", + " elif i == len(labels) - 1:\n", + " myD[pos] -= 0.01\n", + " myD[\"bottom\"] = 1 - myD[pos]\n", + " myD[\"top\"] = 1\n", + " else:\n", + " myD[pos] -= 0.02\n", + " myD[\"bottom\"] = widths_norm[labels[i - 1]][\"top\"] + 0.02\n", + " myD[\"top\"] = myD[\"bottom\"] + myD[pos]\n", + " else:\n", + " myD[\"bottom\"] = 0\n", + " myD[\"top\"] = 1\n", + " widths_norm[label] = myD\n", + " return widths_norm\n", + "\n", + "\n", + "def single_sankey(\n", + " left: np.array, # data on the left of the diagram\n", + " right: np.array, # data on the right of the diagram, len(left) == len(right)\n", + " xpos: float = 0, # the starting point on the x-axis\n", + " left_weight: np.array = None, # weights for the left labels, if None, all weights are 1\n", + " right_weight: np.array = None, # weights for the right labels, if None, all weights are corresponding left_weight\n", + " colorDict: dict = None, # input format: {'label': 'color'}\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", + " ax=None, # matplotlib axes to be drawn on\n", + " flow: bool = True, # if True, draw the sankey in a flow, else draw 1 vs 1 Sankey diagram for each group comparison\n", + " sankey: bool = True, # if True, draw the sankey diagram, else draw barplot\n", + " width=0.5,\n", + " alpha=0.65,\n", + " bar_width=0.2,\n", + " error_bar_on: bool = True, # if True, draw error bar for each group comparison\n", + " 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", + "):\n", + " \"\"\"\n", " Make a single Sankey diagram showing proportion flow from left to right\n", " Original code from: https://github.com/anazalea/pySankey\n", " Changes are added to normalize each diagram's height to be 1\n", "\n", - " '''\n", + " \"\"\"\n", "\n", " # Initiating values\n", " if ax is None:\n", " ax = plt.gca()\n", "\n", - " if leftWeight is None:\n", - " leftWeight = []\n", - " if rightWeight is None:\n", - " rightWeight = []\n", - " if leftLabels is None:\n", - " leftLabels = []\n", - " if rightLabels is None:\n", - " rightLabels = []\n", + " if left_weight is None:\n", + " left_weight = []\n", + " if right_weight is None:\n", + " right_weight = []\n", + " if left_labels is None:\n", + " left_labels = []\n", + " if right_labels is None:\n", + " right_labels = []\n", " # Check weights\n", - " if len(leftWeight) == 0:\n", - " leftWeight = np.ones(len(left))\n", - " if len(rightWeight) == 0:\n", - " rightWeight = leftWeight\n", + " if len(left_weight) == 0:\n", + " left_weight = np.ones(len(left))\n", + " if len(right_weight) == 0:\n", + " right_weight = np.ones(len(right))\n", "\n", " # Create Dataframe\n", " if isinstance(left, pd.Series):\n", " left.reset_index(drop=True, inplace=True)\n", " if isinstance(right, pd.Series):\n", " right.reset_index(drop=True, inplace=True)\n", - " dataFrame = pd.DataFrame({'left': left, 'right': right, 'leftWeight': leftWeight,\n", - " 'rightWeight': rightWeight}, index=range(len(left)))\n", - " \n", - " if dataFrame[['left', 'right']].isnull().any(axis=None):\n", - " raise Exception('Sankey graph does not support null values.')\n", + " dataFrame = pd.DataFrame(\n", + " {\n", + " \"left\": left,\n", + " \"right\": right,\n", + " \"left_weight\": left_weight,\n", + " \"right_weight\": right_weight,\n", + " },\n", + " index=range(len(left)),\n", + " )\n", + "\n", + " if dataFrame[[\"left\", \"right\"]].isnull().any(axis=None):\n", + " raise Exception(\"Sankey graph does not support null values.\")\n", "\n", " # Identify all labels that appear 'left' or 'right'\n", - " allLabels = pd.Series(np.sort(np.r_[dataFrame.left.unique(), dataFrame.right.unique()])[::-1]).unique()\n", + " allLabels = pd.Series(\n", + " np.sort(np.r_[dataFrame.left.unique(), dataFrame.right.unique()])[::-1]\n", + " ).unique()\n", "\n", " # Identify left labels\n", - " if len(leftLabels) == 0:\n", - " leftLabels = pd.Series(np.sort(dataFrame.left.unique())[::-1]).unique()\n", + " if len(left_labels) == 0:\n", + " left_labels = pd.Series(np.sort(dataFrame.left.unique())[::-1]).unique()\n", " else:\n", - " check_data_matches_labels(leftLabels, dataFrame['left'], 'left')\n", + " check_data_matches_labels(left_labels, dataFrame[\"left\"], \"left\")\n", "\n", " # Identify right labels\n", - " if len(rightLabels) == 0:\n", - " rightLabels = pd.Series(np.sort(dataFrame.right.unique())[::-1]).unique()\n", + " if len(right_labels) == 0:\n", + " right_labels = pd.Series(np.sort(dataFrame.right.unique())[::-1]).unique()\n", " else:\n", - " check_data_matches_labels(leftLabels, dataFrame['right'], 'right')\n", + " check_data_matches_labels(left_labels, dataFrame[\"right\"], \"right\")\n", "\n", " # If no colorDict given, make one\n", " if colorDict is None:\n", @@ -340,190 +477,253 @@ " colorPalette = sns.color_palette(palette, len(allLabels))\n", " for i, label in enumerate(allLabels):\n", " colorDict[label] = colorPalette[i]\n", - " fail_color = {0:\"grey\"}\n", + " fail_color = {0: \"grey\"}\n", " colorDict.update(fail_color)\n", " else:\n", " missing = [label for label in allLabels if label not in colorDict.keys()]\n", " if missing:\n", " msg = \"The palette parameter is missing values for the following labels : \"\n", - " msg += '{}'.format(', '.join(missing))\n", + " msg += \"{}\".format(\", \".join(missing))\n", " raise ValueError(msg)\n", "\n", " if align not in (\"center\", \"edge\"):\n", - " err = '{} assigned for `align` is not valid.'.format(align)\n", + " err = \"{} assigned for `align` is not valid.\".format(align)\n", " raise ValueError(err)\n", " if align == \"center\":\n", " try:\n", " leftpos = xpos - width / 2\n", " except TypeError as e:\n", - " raise TypeError(f'the dtypes of parameters x ({xpos.dtype}) '\n", - " f'and width ({width.dtype}) '\n", - " f'are incompatible') from e\n", - " else: \n", + " raise TypeError(\n", + " f\"the dtypes of parameters x ({xpos.dtype}) \"\n", + " f\"and width ({width.dtype}) \"\n", + " f\"are incompatible\"\n", + " ) from e\n", + " else:\n", " leftpos = xpos\n", "\n", " # Combine left and right arrays to have a pandas.DataFrame in the 'long' format\n", - " left_series = pd.Series(left, name='values').to_frame().assign(groups='left')\n", - " right_series = pd.Series(right, name='values').to_frame().assign(groups='right')\n", + " left_series = pd.Series(left, name=\"values\").to_frame().assign(groups=\"left\")\n", + " right_series = pd.Series(right, name=\"values\").to_frame().assign(groups=\"right\")\n", " concatenated_df = pd.concat([left_series, right_series], ignore_index=True)\n", "\n", " # Determine positions of left label patches and total widths\n", " # We also want the height of the graph to be 1\n", " leftWidths_norm = defaultdict()\n", - " for i, leftLabel in enumerate(leftLabels):\n", + " for i, left_label in enumerate(left_labels):\n", " myD = {}\n", - " myD['left'] = (dataFrame[dataFrame.left == leftLabel].leftWeight.sum()/ \\\n", - " dataFrame.leftWeight.sum())*(1-(len(leftLabels)-1)*0.02)\n", - " if i == 0:\n", - " myD['bottom'] = 0\n", - " myD['top'] = myD['left']\n", + " myD[\"left\"] = (\n", + " dataFrame[dataFrame.left == left_label].left_weight.sum()\n", + " / dataFrame.left_weight.sum()\n", + " )\n", + " if len(left_labels) != 1:\n", + " if i == 0:\n", + " myD[\"bottom\"] = 0\n", + " myD[\"left\"] -= 0.01\n", + " myD[\"top\"] = myD[\"left\"]\n", + " elif i == len(left_labels) - 1:\n", + " myD[\"left\"] -= 0.01\n", + " myD[\"bottom\"] = 1 - myD[\"left\"]\n", + " myD[\"top\"] = 1\n", + " else:\n", + " myD[\"left\"] -= 0.02\n", + " myD[\"bottom\"] = leftWidths_norm[left_labels[i - 1]][\"top\"] + 0.02\n", + " myD[\"top\"] = myD[\"bottom\"] + myD[\"left\"]\n", + " topEdge = myD[\"top\"]\n", " else:\n", - " myD['bottom'] = leftWidths_norm[leftLabels[i - 1]]['top'] + 0.02\n", - " myD['top'] = myD['bottom'] + myD['left']\n", - " topEdge = myD['top']\n", - " leftWidths_norm[leftLabel] = myD\n", + " myD[\"bottom\"] = 0\n", + " myD[\"top\"] = 1\n", + " myD[\"left\"] = 1\n", + " leftWidths_norm[left_label] = myD\n", "\n", " # Determine positions of right label patches and total widths\n", " rightWidths_norm = defaultdict()\n", - " for i, rightLabel in enumerate(rightLabels):\n", + " for i, right_label in enumerate(right_labels):\n", " myD = {}\n", - " myD['right'] = (dataFrame[dataFrame.right == rightLabel].rightWeight.sum()/ \\\n", - " dataFrame.rightWeight.sum())*(1-(len(leftLabels)-1)*0.02)\n", - " if i == 0:\n", - " myD['bottom'] = 0\n", - " myD['top'] = myD['right']\n", + " myD[\"right\"] = (\n", + " dataFrame[dataFrame.right == right_label].right_weight.sum()\n", + " / dataFrame.right_weight.sum()\n", + " )\n", + " if len(right_labels) != 1:\n", + " if i == 0:\n", + " myD[\"bottom\"] = 0\n", + " myD[\"right\"] -= 0.01\n", + " myD[\"top\"] = myD[\"right\"]\n", + " elif i == len(right_labels) - 1:\n", + " myD[\"right\"] -= 0.01\n", + " myD[\"bottom\"] = 1 - myD[\"right\"]\n", + " myD[\"top\"] = 1\n", + " else:\n", + " myD[\"right\"] -= 0.02\n", + " myD[\"bottom\"] = rightWidths_norm[right_labels[i - 1]][\"top\"] + 0.02\n", + " myD[\"top\"] = myD[\"bottom\"] + myD[\"right\"]\n", + " topEdge = myD[\"top\"]\n", " else:\n", - " myD['bottom'] = rightWidths_norm[rightLabels[i - 1]]['top'] + 0.02\n", - " myD['top'] = myD['bottom'] + myD['right']\n", - " topEdge = myD['top']\n", - " rightWidths_norm[rightLabel] = myD \n", + " myD[\"bottom\"] = 0\n", + " myD[\"top\"] = 1\n", + " myD[\"right\"] = 1\n", + " rightWidths_norm[right_label] = myD\n", "\n", " # Total width of the graph\n", " xMax = width\n", "\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 (not flow and sankey) or one_sankey:\n", + " for right_label in right_labels:\n", + " ax.fill_between(\n", + " [\n", + " xMax + leftpos + (-bar_width * xMax * 0.5),\n", + " leftpos + xMax + (bar_width * xMax * 0.5),\n", + " ],\n", + " 2 * [rightWidths_norm[right_label][\"bottom\"]],\n", + " 2 * [rightWidths_norm[right_label][\"top\"]],\n", + " color=colorDict[right_label],\n", + " alpha=0.99,\n", + " )\n", + "\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", + "\n", " # Determine widths of individual strips, all widths are normalized to 1\n", " ns_l = defaultdict()\n", " ns_r = defaultdict()\n", " ns_l_norm = defaultdict()\n", " ns_r_norm = defaultdict()\n", - " for leftLabel in leftLabels:\n", + " for left_label in left_labels:\n", " leftDict = {}\n", " rightDict = {}\n", - " for rightLabel in rightLabels:\n", - " leftDict[rightLabel] = dataFrame[\n", - " (dataFrame.left == leftLabel) & (dataFrame.right == rightLabel)\n", - " ].leftWeight.sum()\n", - " \n", - " rightDict[rightLabel] = dataFrame[\n", - " (dataFrame.left == leftLabel) & (dataFrame.right == rightLabel)\n", - " ].rightWeight.sum()\n", - " factorleft = leftWidths_norm[leftLabel]['left']/sum(leftDict.values())\n", - " leftDict_norm = {k: v*factorleft for k, v in leftDict.items()}\n", - " ns_l_norm[leftLabel] = leftDict_norm\n", - " ns_r[leftLabel] = rightDict\n", - " \n", + " for right_label in right_labels:\n", + " leftDict[right_label] = dataFrame[\n", + " (dataFrame.left == left_label) & (dataFrame.right == right_label)\n", + " ].left_weight.sum()\n", + "\n", + " rightDict[right_label] = dataFrame[\n", + " (dataFrame.left == left_label) & (dataFrame.right == right_label)\n", + " ].right_weight.sum()\n", + " factorleft = leftWidths_norm[left_label][\"left\"] / sum(leftDict.values())\n", + " leftDict_norm = {k: v * factorleft for k, v in leftDict.items()}\n", + " ns_l_norm[left_label] = leftDict_norm\n", + " ns_r[left_label] = rightDict\n", + "\n", " # ns_r should be using a different way of normalization to fit the right side\n", " # It is normalized using the value with the same key in each sub-dictionary\n", - "\n", " ns_r_norm = normalize_dict(ns_r, rightWidths_norm)\n", "\n", - " # Plot vertical bars for each label\n", - " for leftLabel in leftLabels:\n", - " ax.fill_between(\n", - " [leftpos + (-(bar_width) * xMax), leftpos],\n", - " 2 * [leftWidths_norm[leftLabel][\"bottom\"]],\n", - " 2 * [leftWidths_norm[leftLabel][\"bottom\"] + leftWidths_norm[leftLabel][\"left\"]],\n", - " color=colorDict[leftLabel],\n", - " alpha=0.99,\n", - " )\n", - " for rightLabel in rightLabels:\n", - " ax.fill_between(\n", - " [xMax + leftpos, leftpos + ((1 + bar_width) * xMax)], \n", - " 2 * [rightWidths_norm[rightLabel]['bottom']],\n", - " 2 * [rightWidths_norm[rightLabel]['bottom'] + rightWidths_norm[rightLabel]['right']],\n", - " color=colorDict[rightLabel],\n", - " alpha=0.99\n", - " )\n", - "\n", - " # Plot error bars\n", - " error_bar(concatenated_df, x='groups', y='values', ax=ax, offset=0, gap_width_percent=2,\n", - " method=\"sankey_error_bar\",\n", - " pos=[(leftpos + (-(bar_width) * xMax) + leftpos)/2, \\\n", - " (xMax + leftpos + leftpos + ((1 + bar_width) * xMax))/2])\n", - " \n", " # Plot strips\n", - " for leftLabel, rightLabel in itertools.product(leftLabels, rightLabels):\n", - " labelColor = leftLabel\n", - " if rightColor:\n", - " labelColor = rightLabel\n", - " if len(dataFrame[(dataFrame.left == leftLabel) & (dataFrame.right == rightLabel)]) > 0:\n", - " # Create array of y values for each strip, half at left value,\n", - " # half at right, convolve\n", - " ys_d = np.array(50 * [leftWidths_norm[leftLabel]['bottom']] + \\\n", - " 50 * [rightWidths_norm[rightLabel]['bottom']])\n", - " ys_d = np.convolve(ys_d, 0.05 * np.ones(20), mode='valid')\n", - " ys_d = np.convolve(ys_d, 0.05 * np.ones(20), mode='valid')\n", - " ys_u = np.array(50 * [leftWidths_norm[leftLabel]['bottom'] + ns_l_norm[leftLabel][rightLabel]] + \\\n", - " 50 * [rightWidths_norm[rightLabel]['bottom'] + ns_r_norm[leftLabel][rightLabel]])\n", - " ys_u = np.convolve(ys_u, 0.05 * np.ones(20), mode='valid')\n", - " ys_u = np.convolve(ys_u, 0.05 * np.ones(20), mode='valid')\n", - "\n", - " # Update bottom edges at each label so next strip starts at the right place\n", - " leftWidths_norm[leftLabel]['bottom'] += ns_l_norm[leftLabel][rightLabel]\n", - " rightWidths_norm[rightLabel]['bottom'] += ns_r_norm[leftLabel][rightLabel]\n", - " ax.fill_between(\n", - " np.linspace(leftpos, leftpos + xMax, len(ys_d)), ys_d, ys_u, alpha=alpha,\n", - " color=colorDict[labelColor], edgecolor='none'\n", - " )\n", - " \n", - "def sankeydiag(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", - " leftLabels:list=None, #labels for the left side of the diagram. The diagram will be sorted by these labels.\n", - " rightLabels:list=None, #labels for the right side of the diagram. The diagram will be sorted by these labels.\n", - " palette:str|dict=None, \n", - " ax=None, #matplotlib axes to be drawn on\n", - " one_sankey:bool=False,# determined by the driver function on plotter.py, if True, draw the sankey diagram across the whole raw data axes\n", - " width:float=0.4, # the width of each sankey diagram\n", - " rightColor: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", - " **kwargs):\n", - " '''\n", + " if sankey and strip_on:\n", + " for left_label, right_label in itertools.product(left_labels, right_labels):\n", + " labelColor = left_label\n", + " \n", + " if right_color:\n", + " labelColor = right_label\n", + " \n", + " if len(dataFrame[(dataFrame.left == left_label) & \n", + " (dataFrame.right == right_label)]) > 0:\n", + " # Create array of y values for each strip, half at left value,\n", + " # half at right, convolve\n", + " ys_d = np.array(\n", + " 50 * [leftWidths_norm[left_label][\"bottom\"]]\n", + " + 50 * [rightWidths_norm[right_label][\"bottom\"]]\n", + " )\n", + " ys_d = np.convolve(ys_d, 0.05 * np.ones(20), mode=\"valid\")\n", + " ys_d = np.convolve(ys_d, 0.05 * np.ones(20), mode=\"valid\")\n", + " # to remove the array wrapping behaviour of black\n", + " # fmt: off\n", + " ys_u = np.array(50 * [leftWidths_norm[left_label]['bottom'] + ns_l_norm[left_label][right_label]] + \\\n", + " 50 * [rightWidths_norm[right_label]['bottom'] + ns_r_norm[left_label][right_label]])\n", + " # fmt: on\n", + " ys_u = np.convolve(ys_u, 0.05 * np.ones(20), mode=\"valid\")\n", + " ys_u = np.convolve(ys_u, 0.05 * np.ones(20), mode=\"valid\")\n", + "\n", + " # Update bottom edges at each label so next strip starts at the right place\n", + " leftWidths_norm[left_label][\"bottom\"] += ns_l_norm[left_label][right_label]\n", + " rightWidths_norm[right_label][\"bottom\"] += ns_r_norm[left_label][\n", + " right_label\n", + " ]\n", + " ax.fill_between(\n", + " np.linspace(\n", + " leftpos + (bar_width * xMax * 0.5),\n", + " leftpos + xMax - (bar_width * xMax * 0.5),\n", + " len(ys_d),\n", + " ),\n", + " ys_d,\n", + " ys_u,\n", + " alpha=alpha,\n", + " color=colorDict[labelColor],\n", + " 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", + " 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", + " ax=None, # matplotlib axes to be drawn on\n", + " flow: bool = True, # if True, draw the sankey in a flow, else draw 1 vs 1 Sankey diagram for each group comparison\n", + " sankey: bool = True, # if True, draw the sankey diagram, else draw barplot\n", + " one_sankey: bool = False, # determined by the driver function on plotter.py, if True, draw the sankey diagram across the whole raw data axes\n", + " width: float = 0.4, # the width of each 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: str = \"center\", # the alignment of each sankey diagram, can be 'center' or 'left'\n", + " alpha: float = 0.65, # the transparency of each strip\n", + " **kwargs,\n", + "):\n", + " \"\"\"\n", " Read in melted pd.DataFrame, and draw multiple sankey diagram on a single axes\n", " using the value in column yvar according to the value in column xvar\n", " left_idx in the column xvar is on the left side of each sankey diagram\n", " right_idx in the column xvar is on the right side of each sankey diagram\n", "\n", - " '''\n", - "\n", - " import numpy as np\n", - " import pandas as pd\n", - " import seaborn as sns\n", - " import matplotlib.pyplot as plt\n", + " \"\"\"\n", "\n", " if \"width\" in kwargs:\n", " width = kwargs[\"width\"]\n", "\n", " if \"align\" in kwargs:\n", " align = kwargs[\"align\"]\n", - " \n", + "\n", " if \"alpha\" in kwargs:\n", " alpha = kwargs[\"alpha\"]\n", - " \n", - " if \"rightColor\" in kwargs:\n", - " rightColor = kwargs[\"rightColor\"]\n", - " \n", + "\n", + " if \"right_color\" in kwargs:\n", + " right_color = kwargs[\"right_color\"]\n", + "\n", " if \"bar_width\" in kwargs:\n", " bar_width = kwargs[\"bar_width\"]\n", "\n", + " if \"sankey\" in kwargs:\n", + " sankey = kwargs[\"sankey\"]\n", + "\n", + " if \"flow\" in kwargs:\n", + " flow = kwargs[\"flow\"]\n", + "\n", " if ax is None:\n", " ax = plt.gca()\n", "\n", " allLabels = pd.Series(np.sort(data[yvar].unique())[::-1]).unique()\n", - " \n", + "\n", " # Check if all the elements in left_idx and right_idx are in xvar column\n", " unique_xvar = data[xvar].unique()\n", " if not all(elem in unique_xvar for elem in left_idx):\n", @@ -535,7 +735,7 @@ "\n", " # For baseline comparison, broadcast left_idx to the same length as right_idx\n", " # so that the left of sankey diagram will be the same\n", - " # For sequential comparison, left_idx and right_idx can have anything different \n", + " # For sequential comparison, left_idx and right_idx can have anything different\n", " # but should have the same length\n", " if len(left_idx) == 1:\n", " broadcasted_left = np.broadcast_to(left_idx, len(right_idx))\n", @@ -547,8 +747,7 @@ " if isinstance(palette, dict):\n", " if not all(key in allLabels for key in palette.keys()):\n", " raise ValueError(f\"keys in palette should be in {yvar} column\")\n", - " else: \n", - " plot_palette = palette\n", + " plot_palette = palette\n", " elif isinstance(palette, str):\n", " plot_palette = {}\n", " colorPalette = sns.color_palette(palette, len(allLabels))\n", @@ -557,40 +756,649 @@ " else:\n", " plot_palette = None\n", "\n", - " for left, right in zip(broadcasted_left, right_idx):\n", - " if one_sankey == False:\n", - " single_sankey(data[data[xvar]==left][yvar], data[data[xvar]==right][yvar], \n", - " xpos=xpos, ax=ax, colorDict=plot_palette, width=width, \n", - " leftLabels=leftLabels, rightLabels=rightLabels, \n", - " rightColor=rightColor, bar_width=bar_width,\n", - " align=align, alpha=alpha)\n", + " # Create a strip_on list to determine whether to draw the strip during repeated measures\n", + " strip_on = [\n", + " int(right not in broadcasted_left[:i]) for i, right in enumerate(right_idx)\n", + " ]\n", + "\n", + " draw_idx = list(zip(broadcasted_left, right_idx))\n", + " for i, (left, right) in enumerate(draw_idx):\n", + " if not one_sankey:\n", + " if flow:\n", + " width = 1\n", + " align = \"edge\"\n", + " sankey = (\n", + " False if i == len(draw_idx) - 1 else sankey\n", + " ) # Remove last strip in flow\n", + " error_bar_on = (\n", + " False if i == len(draw_idx) - 1 and flow else True\n", + " ) # Remove last error_bar in flow\n", + " bar_width = 0.4 if sankey == False and flow == False else bar_width\n", + " single_sankey(\n", + " data[data[xvar] == left][yvar],\n", + " data[data[xvar] == right][yvar],\n", + " xpos=xpos,\n", + " ax=ax,\n", + " colorDict=plot_palette,\n", + " width=width,\n", + " left_labels=left_labels,\n", + " right_labels=right_labels,\n", + " strip_on=strip_on[i],\n", + " right_color=right_color,\n", + " bar_width=bar_width,\n", + " sankey=sankey,\n", + " error_bar_on=error_bar_on,\n", + " flow=flow,\n", + " align=align,\n", + " alpha=alpha,\n", + " )\n", " xpos += 1\n", " else:\n", - " xpos = 0 + bar_width/2\n", - " width = 1 - bar_width\n", - " single_sankey(data[data[xvar]==left][yvar], data[data[xvar]==right][yvar], \n", - " xpos=xpos, ax=ax, colorDict=plot_palette, width=width, \n", - " leftLabels=leftLabels, rightLabels=rightLabels, \n", - " rightColor=rightColor, bar_width=bar_width,\n", - " align='edge', alpha=alpha)\n", - "\n", - " if one_sankey == False:\n", - " sankey_ticks = [f\"{left}\\n v.s.\\n{right}\" for left, right in zip(broadcasted_left, right_idx)]\n", + " xpos = 0\n", + " width = 1\n", + " if not sankey:\n", + " bar_width = 0.5\n", + " single_sankey(\n", + " data[data[xvar] == left][yvar],\n", + " data[data[xvar] == right][yvar],\n", + " xpos=xpos,\n", + " ax=ax,\n", + " colorDict=plot_palette,\n", + " width=width,\n", + " left_labels=left_labels,\n", + " right_labels=right_labels,\n", + " right_color=right_color,\n", + " bar_width=bar_width,\n", + " sankey=sankey,\n", + " one_sankey=one_sankey,\n", + " flow=False,\n", + " align=\"edge\",\n", + " alpha=alpha,\n", + " )\n", + "\n", + " # Now only draw vs xticks for two-column sankey diagram\n", + " if not one_sankey or (sankey and not flow):\n", + " sankey_ticks = (\n", + " [f\"{left}\" for left in broadcasted_left]\n", + " if flow\n", + " else [\n", + " 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", " else:\n", " sankey_ticks = [broadcasted_left[0], right_idx[0]]\n", " ax.set_xticks([0, 1])\n", - " ax.set_xticklabels(sankey_ticks)\n" + " ax.set_xticklabels(sankey_ticks)" ] }, { "cell_type": "code", "execution_count": null, - "id": "43844ece", + "id": "24823471", "metadata": {}, "outputs": [], - "source": [] + "source": [ + "# | export\n", + "def swarmplot(\n", + " data: pd.DataFrame,\n", + " x: str,\n", + " y: str,\n", + " ax: axes.Subplot,\n", + " order: List = None,\n", + " hue: str = None,\n", + " palette: Union[Iterable, str] = \"black\",\n", + " zorder: float = 1,\n", + " size: float = 5,\n", + " side: str = \"center\",\n", + " jitter: float = 1,\n", + " is_drop_gutter: bool = True,\n", + " gutter_limit: float = 0.5,\n", + " **kwargs,\n", + "):\n", + " \"\"\"\n", + " API to plot a swarm plot.\n", + "\n", + " Parameters\n", + " ----------\n", + " data : pd.DataFrame\n", + " The input data as a pandas DataFrame.\n", + " x : str\n", + " 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", + " order : List\n", + " The order in which x-axis categories should be displayed. Default is None.\n", + " hue : str\n", + " The column in the DataFrame that determines the grouping for color.\n", + " If None (by default), it assumes that it is being grouped by x.\n", + " palette : Union[Iterable, str]\n", + " The color palette to be used for plotting. Default is \"black\".\n", + " zorder : int | float\n", + " The z-order for drawing the swarm plot wrt other matplotlib drawings. Default is 1.\n", + " dot_size : int | float\n", + " The size of the markers in the swarm plot. Default is 20.\n", + " side : str\n", + " 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", + " 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", + " **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", + " \"\"\"\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", + " return ax\n", + "\n", + "\n", + "class SwarmPlot:\n", + " def __init__(\n", + " self,\n", + " data: pd.DataFrame,\n", + " x: str,\n", + " y: str,\n", + " ax: axes.Subplot,\n", + " order: List = None,\n", + " hue: str = None,\n", + " palette: Union[Iterable, str] = \"black\",\n", + " zorder: float = 1,\n", + " size: float = 5,\n", + " side: str = \"center\",\n", + " jitter: float = 1,\n", + " ):\n", + " \"\"\"\n", + " Initialize a SwarmPlot instance.\n", + "\n", + " Parameters\n", + " ----------\n", + " data : pd.DataFrame\n", + " The input data as a pandas DataFrame.\n", + " x : str\n", + " 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", + " order : List\n", + " The order in which x-axis categories should be displayed. Default is None.\n", + " hue : str\n", + " The column in the DataFrame that determines the grouping for color.\n", + " If None (by default), it assumes that it is being grouped by x.\n", + " palette : Union[Iterable, str]\n", + " The color palette to be used for plotting. Default is \"black\".\n", + " zorder : int | float\n", + " The z-order for drawing the swarm plot wrt other matplotlib drawings. Default is 1.\n", + " dot_size : int | float\n", + " The size of the markers in the swarm plot. Default is 20.\n", + " side : str\n", + " 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", + "\n", + " Returns\n", + " -------\n", + " None\n", + " \"\"\"\n", + " self.__x = x\n", + " self.__y = y\n", + " self.__order = order\n", + " self.__hue = hue\n", + " self.__zorder = zorder\n", + " self.__palette = palette\n", + " self.__jitter = jitter\n", + "\n", + " # Input validation\n", + " self._check_errors(data, ax, size, side)\n", + "\n", + " self.__size = size * 4\n", + " self.__side = side.lower()\n", + " self.__data = data\n", + " self.__color_col = self.__x if self.__hue is None else self.__hue\n", + "\n", + " # Generate default values\n", + " if order is None:\n", + " self.__order = self._generate_order()\n", + "\n", + " # Reformatting\n", + " if not isinstance(self.__palette, dict):\n", + " self.__palette = self._format_palette(self.__palette)\n", + " data_copy = data.copy(deep=True)\n", + " if not isinstance(self.__data[self.__x].dtype, pd.CategoricalDtype):\n", + " # make x column into CategoricalDType to sort by\n", + " data_copy[self.__x] = data_copy[self.__x].astype(\n", + " CategoricalDtype(categories=self.__order, ordered=True)\n", + " )\n", + " data_copy.sort_values(by=[self.__x, self.__y], inplace=True)\n", + " self.__data_copy = data_copy\n", + "\n", + " x_vals = range(len(self.__order))\n", + " y_vals = self.__data_copy[self.__y]\n", + "\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", + "\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", + "\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", + "\n", + " gsize = (\n", + " math.sqrt(self.__size) * 1.0 / (70 / jitter) * ax_xspan * 1.0 / (w * 0.8)\n", + " )\n", + " dsize = (\n", + " math.sqrt(self.__size) * 1.0 / (70 / jitter) * ax_yspan * 1.0 / (h * 0.8)\n", + " )\n", + " self.__gsize = gsize\n", + " self.__dsize = dsize\n", + "\n", + " def _check_errors(\n", + " self, data: pd.DataFrame, ax: axes.Subplot, size: float, side: str\n", + " ) -> None:\n", + " \"\"\"\n", + " Check the validity of input parameters. Raises exceptions if detected.\n", + "\n", + " Parameters\n", + " ----------\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", + " size : int | float\n", + " scalar value determining size of dots of the swarmplot.\n", + " side: str\n", + " The side on which points are swarmed (\"center\", \"left\", or \"right\"). Default is \"center\".\n", + "\n", + " Returns\n", + " -------\n", + " None\n", + " \"\"\"\n", + " # 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", + " raise ValueError(\n", + " f\"`ax` must be a Matplotlib AxesSubplot. 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", + " if not isinstance(side, str):\n", + " raise ValueError(\n", + " \"Invalid `side`. Must be one of 'center', 'right', or 'left'.\"\n", + " )\n", + " if not isinstance(self.__x, str):\n", + " raise ValueError(\"`x` must be a string.\")\n", + " if not isinstance(self.__y, str):\n", + " raise ValueError(\"`y` must be a string.\")\n", + " if not isinstance(self.__zorder, (int, float)):\n", + " raise ValueError(\"`zorder` must be a scalar or float.\")\n", + " 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", + " 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", + " raise ValueError(\"`order` must be either an Iterable or None.\")\n", + "\n", + " # More thorough input validation checks\n", + " if self.__x not in data.columns:\n", + " err = \"{0} is not a column in `data`.\".format(self.__x)\n", + " raise IndexError(err)\n", + " if self.__y not in data.columns:\n", + " err = \"{0} is not a column in `data`.\".format(self.__y)\n", + " raise IndexError(err)\n", + " if self.__hue is not None and self.__hue not in data.columns:\n", + " err = \"{0} is not a column in `data`.\".format(self.__hue)\n", + " raise IndexError(err)\n", + "\n", + " color_col = self.__x if self.__hue is None else self.__hue\n", + " if self.__order is not None:\n", + " for group_i in self.__order:\n", + " if group_i not in pd.unique(data[self.__x]):\n", + " err = \"{0} in `order` is not in the '{1}' column of `data`.\".format(\n", + " group_i, self.__x\n", + " )\n", + " raise IndexError(err)\n", + "\n", + " if isinstance(self.__palette, str) and self.__palette.strip() == \"\":\n", + " 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", + " \"{0} in `palette` is not in the '{1}' column of `data`.\".format(\n", + " group_i, color_col\n", + " )\n", + " )\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", + "\n", + " if side.lower() not in [\"center\", \"right\", \"left\"]:\n", + " raise ValueError(\n", + " \"Invalid `side`. Must be one of 'center', 'right', or 'left'.\"\n", + " )\n", + "\n", + " return None\n", + "\n", + " def _generate_order(self) -> List:\n", + " \"\"\"\n", + " Generates order value that determines the order in which x-axis categories should be displayed.\n", + "\n", + " Parameters\n", + " ----------\n", + " None\n", + "\n", + " Returns\n", + " -------\n", + " List:\n", + " contains the order in which the x-axis categories should be displayed.\n", + " \"\"\"\n", + " if isinstance(self.__data[self.__x].dtype, pd.CategoricalDtype):\n", + " order = pd.unique(self.__data[self.__x]).categories.tolist()\n", + " else:\n", + " order = pd.unique(self.__data[self.__x]).tolist()\n", + "\n", + " return order\n", + "\n", + " def _format_palette(self, palette: Union[str, List, Tuple]) -> Dict:\n", + " \"\"\"\n", + " Reformats palette into appropriate Dictionary form for swarm plot\n", + "\n", + " Parameters\n", + " ----------\n", + " palette: str | List | Tuple\n", + " The color palette used for the swarm plot. Conventions are based on Matplotlib color\n", + " specifications.\n", + "\n", + " Could be a singular string value - in which case, would be a singular color name.\n", + " In the case of a List or Tuple - it could be a Sequence of color names or RGB(A) values.\n", + "\n", + " Returns\n", + " -------\n", + " Dict:\n", + " Dictionary mapping unique groupings in the color column (of the data used for the swarm plot)\n", + " to a color name (str) or a RGB(A) value (Tuple[float, float, float] | List[float, float, float]).\n", + " \"\"\"\n", + " reformatted_palette = dict()\n", + " groups = pd.unique(self.__data[self.__color_col]).tolist()\n", + "\n", + " if isinstance(palette, str):\n", + " for group_i in groups:\n", + " reformatted_palette[group_i] = palette\n", + " if isinstance(palette, (list, tuple)):\n", + " if len(groups) != len(palette):\n", + " err = (\n", + " \"unique values in '{0}' column in `data` \"\n", + " \"and `palette` do not have the same length. Number of unique values is {1} \"\n", + " \"while length of palette is {2}. The assignment of the colors in the \"\n", + " \"palette will be cycled.\"\n", + " ).format(self.__color_col, len(groups), len(palette))\n", + " warnings.warn(err)\n", + " for i, group_i in enumerate(groups):\n", + " reformatted_palette[group_i] = palette[i % len(palette)]\n", + "\n", + " return reformatted_palette\n", + "\n", + " def _swarm(\n", + " self, values: Iterable[float], gsize: float, dsize: float, side: str\n", + " ) -> pd.Series:\n", + " \"\"\"\n", + " Perform the swarm algorithm to position points without overlap.\n", + "\n", + " Parameters\n", + " ----------\n", + " values : Iterable[int | float]\n", + " The values to be plotted.\n", + " gsize : int | float\n", + " The size of the gap between points.\n", + " dsize : int | float\n", + " The size of the markers.\n", + " side : str\n", + " The side on which points are swarmed (\"center\", \"left\", or \"right\").\n", + "\n", + " Returns\n", + " -------\n", + " pd.Series:\n", + " The x-offset values for the swarm plot.\n", + " \"\"\"\n", + " # Input validation\n", + " if not isinstance(values, Iterable):\n", + " raise ValueError(\"`values` must be an Iterable\")\n", + " if not isinstance(gsize, (int, float)):\n", + " raise ValueError(\"`gsize` must be a scalar or float.\")\n", + " if not isinstance(dsize, (int, float)):\n", + " 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", + " 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", + " is_points_overlap = (\n", + " abs(y_i - points_placed[\"y\"]) < 1\n", + " ) # Checks if y_i is overlapping with any points already placed\n", + " if any(is_points_overlap):\n", + " points_placed = points_placed[is_points_overlap]\n", + " x_offsets = points_placed[\"y\"].apply(\n", + " lambda y_j: math.sqrt(1 - (y_i - y_j) ** 2)\n", + " )\n", + " if side == \"center\":\n", + " potential_x_offsets = pd.Series(\n", + " [0]\n", + " + (points_placed[\"x\"] + x_offsets).tolist()\n", + " + (points_placed[\"x\"] - x_offsets).tolist()\n", + " )\n", + " if side == \"right\":\n", + " potential_x_offsets = pd.Series(\n", + " [0] + (points_placed[\"x\"] + x_offsets).tolist()\n", + " )\n", + " if side == \"left\":\n", + " potential_x_offsets = pd.Series(\n", + " [0] + (points_placed[\"x\"] - x_offsets).tolist()\n", + " )\n", + " bad_x_offsets = []\n", + " for x_i in potential_x_offsets:\n", + " dists = (y_i - points_placed[\"y\"]) ** 2 + (\n", + " x_i - points_placed[\"x\"]\n", + " ) ** 2\n", + " if any([item < 0.999 for item in dists]):\n", + " bad_x_offsets.append(True)\n", + " else:\n", + " bad_x_offsets.append(False)\n", + " potential_x_offsets[bad_x_offsets] = np.infty\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", + " ]\n", + " points_data.loc[i, \"x\"] = valid_x_offset\n", + " else:\n", + " points_data.loc[i, \"x\"] = 0\n", + "\n", + " points_data.loc[np.isnan(points_data[\"y\"]), \"x\"] = np.nan\n", + "\n", + " return points_data[\"x\"] * gsize\n", + "\n", + " def _adjust_gutter_points(\n", + " self,\n", + " points_data: pd.DataFrame,\n", + " x_position: float,\n", + " is_drop_gutter: bool,\n", + " gutter_limit: float,\n", + " value_column: str,\n", + " ) -> pd.DataFrame:\n", + " \"\"\"\n", + " Adjust points that hit the gutters or drop them based on the provided conditions.\n", + "\n", + " Parameters\n", + " ----------\n", + " points_data: pd.DataFrame\n", + " Data containing coordinates of points for the swarm plot.\n", + " x_position: int | float\n", + " X-coordinate of the center of a singular swarm group of the swarm plot\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", + " value_column : str\n", + " column in points_data that contains the coordinates for the points in the axis against the gutter\n", + "\n", + " Returns\n", + " -------\n", + " pd.DataFrame:\n", + " DataFrame with adjusted points based on the gutter limit.\n", + " \"\"\"\n", + " if self.__side == \"center\":\n", + " gutter_limit = gutter_limit / 2\n", + "\n", + " hit_gutter = abs(points_data[value_column] - x_position) >= gutter_limit\n", + " total_num_of_points = points_data.shape[0]\n", + " num_of_points_hit_gutter = points_data[hit_gutter].shape[0]\n", + " if any(hit_gutter):\n", + " if is_drop_gutter:\n", + " # Drop points that hit gutter\n", + " points_data.drop(points_data[hit_gutter].index.to_list(), inplace=True)\n", + " err = (\n", + " \"{0:.1%} of the points cannot be placed. \"\n", + " \"You might want to decrease the size of the markers.\"\n", + " ).format(num_of_points_hit_gutter / total_num_of_points)\n", + " warnings.warn(err)\n", + " else:\n", + " for i in points_data[hit_gutter].index:\n", + " points_data.loc[i, value_column] = np.sign(\n", + " points_data.loc[i, value_column]\n", + " ) * (x_position + gutter_limit)\n", + "\n", + " return points_data\n", + "\n", + " def plot(\n", + " self, is_drop_gutter: bool, gutter_limit: float, ax: axes.Subplot, **kwargs\n", + " ) -> axes.Subplot:\n", + " \"\"\"\n", + " Generate a swarm plot.\n", + "\n", + " Parameters\n", + " ----------\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", + " ax : axes.Subplot\n", + " The matplotlib figure object to which the swarm plot will be added.\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", + " \"\"\"\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", + "\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", + " x_new = []\n", + " values_i_y = values_i[self.__y]\n", + " x_offset = self._swarm(\n", + " values=values_i_y,\n", + " gsize=self.__gsize,\n", + " dsize=self.__dsize,\n", + " side=self.__side,\n", + " )\n", + " x_new = [\n", + " x_position + offset for offset in x_offset\n", + " ] # apply x-offsets based on _swarm algo\n", + " values_i[\"x_new\"] = x_new\n", + " values_i = self._adjust_gutter_points(\n", + " values_i, x_position, is_drop_gutter, gutter_limit, \"x_new\"\n", + " )\n", + " x_tick_tabels.extend([group_i])\n", + " x_position = x_position + 1\n", + "\n", + " if values_i.empty:\n", + " ax.scatter(\n", + " values_i[\"x_new\"],\n", + " values_i[self.__y],\n", + " s=self.__size,\n", + " zorder=self.__zorder,\n", + " **kwargs,\n", + " )\n", + " continue\n", + "\n", + " if self.__hue is not None:\n", + " # color swarms based on `hue` column\n", + " cmap_values, index = np.unique(\n", + " values_i[self.__hue], return_inverse=True\n", + " )\n", + " cmap = []\n", + " for cmap_group_i in cmap_values:\n", + " cmap.append(self.__palette[cmap_group_i])\n", + " cmap = ListedColormap(cmap)\n", + " ax.scatter(\n", + " values_i[\"x_new\"],\n", + " values_i[self.__y],\n", + " s=self.__size,\n", + " c=index,\n", + " cmap=cmap,\n", + " zorder=self.__zorder,\n", + " edgecolor=\"face\",\n", + " **kwargs,\n", + " )\n", + " else:\n", + " # color swarms based on `x` column\n", + " ax.scatter(\n", + " values_i[\"x_new\"],\n", + " values_i[self.__y],\n", + " s=self.__size,\n", + " c=self.__palette[group_i],\n", + " zorder=self.__zorder,\n", + " edgecolor=\"face\",\n", + " **kwargs,\n", + " )\n", + "\n", + " ax.get_xaxis().set_ticks(np.arange(x_position))\n", + " ax.get_xaxis().set_ticklabels(x_tick_tabels)\n", + "\n", + " return ax" + ] } ], "metadata": { diff --git a/nbs/API/plotter.ipynb b/nbs/API/plotter.ipynb index 687a31f9..7e054ea4 100644 --- a/nbs/API/plotter.ipynb +++ b/nbs/API/plotter.ipynb @@ -1,6 +1,7 @@ { "cells": [ { + "attachments": {}, "cell_type": "markdown", "id": "984371b3", "metadata": {}, @@ -49,18 +50,37 @@ { "cell_type": "code", "execution_count": null, - "id": "36a42b1c", + "id": "7562c1a1", "metadata": {}, "outputs": [], "source": [ "#| export\n", - "def EffectSizeDataFramePlotter(EffectSizeDataFrame, **plot_kwargs):\n", + "import numpy as np\n", + "import seaborn as sns\n", + "import matplotlib\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "import warnings\n", + "import logging" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "36a42b1c", + "metadata": {}, + "outputs": [], + "source": [ + "# | export\n", + "# TODO refactor function name\n", + "def effectsize_df_plotter(effectsize_df, **plot_kwargs):\n", " \"\"\"\n", " Custom function that creates an estimation plot from an EffectSizeDataFrame.\n", - " \n", + " Keywords\n", + " --------\n", " Parameters\n", " ----------\n", - " EffectSizeDataFrame\n", + " effectsize_df\n", " A `dabest` EffectSizeDataFrame object.\n", " plot_kwargs\n", " color_col=None\n", @@ -80,6 +100,7 @@ " 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", @@ -87,51 +108,60 @@ " reflines_kwargs=None,\n", " group_summary_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", " \"\"\"\n", - "\n", - " import numpy as np\n", - " import seaborn as sns\n", - " import matplotlib.pyplot as plt\n", - " import pandas as pd\n", - " import warnings\n", - " warnings.filterwarnings('ignore', 'This figure includes Axes that are not compatible with tight_layout')\n", - "\n", " from .misc_tools import merge_two_dicts\n", - " from .plot_tools import halfviolin, get_swarm_spans, error_bar, sankeydiag\n", - " from ._stats_tools.effsize import _compute_standardizers, _compute_hedges_correction_factor\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", + " )\n", + "\n", + " warnings.filterwarnings(\n", + " \"ignore\", \"This figure includes Axes that are not compatible with tight_layout\"\n", + " )\n", "\n", - " import logging\n", " # Have to disable logging of warning when get_legend_handles_labels()\n", " # tries to get from slopegraph.\n", " logging.disable(logging.WARNING)\n", "\n", " # Save rcParams that I will alter, so I can reset back.\n", " original_rcParams = {}\n", - " _changed_rcParams = ['axes.grid']\n", + " _changed_rcParams = [\"axes.grid\"]\n", " for parameter in _changed_rcParams:\n", " original_rcParams[parameter] = plt.rcParams[parameter]\n", "\n", - " plt.rcParams['axes.grid'] = False\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 = EffectSizeDataFrame.dabest_obj\n", - " plot_data = EffectSizeDataFrame._plot_data\n", - " xvar = EffectSizeDataFrame.xvar\n", - " yvar = EffectSizeDataFrame.yvar\n", - " is_paired = EffectSizeDataFrame.is_paired\n", - " delta2 = EffectSizeDataFrame.delta2\n", - " mini_meta = EffectSizeDataFrame.mini_meta\n", - " effect_size = EffectSizeDataFrame.effect_size\n", - " proportional = EffectSizeDataFrame.proportional\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", + " idx = dabest_obj.idx\n", "\n", - " if effect_size != \"mean_diff\" or not delta2:\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", @@ -147,16 +177,16 @@ "\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 = EffectSizeDataFrame.effect_size\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", + " 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", + " float_contrast = False\n", "\n", " if not is_paired:\n", " show_pairs = False\n", @@ -164,81 +194,123 @@ " show_pairs = plot_kwargs[\"show_pairs\"]\n", "\n", " # Set default kwargs first, then merge with user-dictated ones.\n", - " default_swarmplot_kwargs = {'size': plot_kwargs[\"raw_marker_size\"]}\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(default_swarmplot_kwargs,\n", - " plot_kwargs[\"swarmplot_kwargs\"])\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", + " )\n", "\n", " # Barplot kwargs\n", - " default_barplot_kwargs = {\"estimator\": np.mean, \"ci\": plot_kwargs[\"ci\"]}\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(default_barplot_kwargs,\n", - " plot_kwargs[\"barplot_kwargs\"])\n", + " barplot_kwargs = merge_two_dicts(\n", + " default_barplot_kwargs, plot_kwargs[\"barplot_kwargs\"]\n", + " )\n", "\n", " # Sankey Diagram kwargs\n", - " default_sankey_kwargs = {\"width\": 0.4, \"align\": \"center\",\n", - " \"alpha\": 0.4, \"rightColor\": False,\n", - " \"bar_width\":0.2}\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(default_sankey_kwargs,\n", - " plot_kwargs[\"sankey_kwargs\"])\n", - " \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 = {'widths':0.5, 'vert':True,\n", - " 'showextrema':False, 'showmedians':False}\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(default_violinplot_kwargs,\n", - " plot_kwargs[\"violinplot_kwargs\"])\n", + " violinplot_kwargs = merge_two_dicts(\n", + " default_violinplot_kwargs, plot_kwargs[\"violinplot_kwargs\"]\n", + " )\n", "\n", - " # slopegraph kwargs.\n", - " default_slopegraph_kwargs = {'lw':1, 'alpha':0.5}\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(default_slopegraph_kwargs,\n", - " plot_kwargs[\"slopegraph_kwargs\"])\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 = {'linestyle':'solid', 'linewidth':0.75,\n", - " 'zorder': 2,\n", - " 'color': ytick_color}\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(default_reflines_kwargs,\n", - " plot_kwargs[\"reflines_kwargs\"])\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", + " 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(default_legend_kwargs,\n", - " plot_kwargs[\"legend_kwargs\"])\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", + " gs_default = {\"mean_sd\", \"median_quartiles\", None}\n", " if plot_kwargs[\"group_summaries\"] not in gs_default:\n", - " raise ValueError('group_summaries must be one of'\n", - " ' these: {}.'.format(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,\n", - " 'alpha': 1}\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(default_group_summary_kwargs,\n", - " plot_kwargs[\"group_summary_kwargs\"])\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", @@ -264,35 +336,24 @@ " if custom_pal is None:\n", " unsat_colors = sns.color_palette(n_colors=n_groups)\n", " else:\n", - "\n", " if isinstance(custom_pal, dict):\n", - " groups_in_palette = {k: v for k,v in custom_pal.items()\n", - " if k in color_groups}\n", - "\n", - " # # check that all the keys in custom_pal are found in the\n", - " # # color column.\n", - " # col_grps = {k for k in color_groups}\n", - " # pal_grps = {k for k in custom_pal.keys()}\n", - " # not_in_pal = pal_grps.difference(col_grps)\n", - " # if len(not_in_pal) > 0:\n", - " # err1 = 'The custom palette keys {} '.format(not_in_pal)\n", - " # err2 = 'are not found in `{}`. Please check.'.format(color_col)\n", - " # errstring = (err1 + err2)\n", - " # raise IndexError(errstring)\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", + " 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", + " 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", @@ -322,144 +383,165 @@ " plot_palette_sankey = custom_pal\n", "\n", " # Infer the figsize.\n", - " fig_size = plot_kwargs[\"fig_size\"]\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 is True and proportional is False:\n", + " if is_paired and show_pairs and proportional is False:\n", " frac = 0.75\n", " else:\n", " frac = 1\n", - " if float_contrast is True:\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", + " width_inches = each_group_width_inches * all_groups_count\n", " fig_size = (width_inches, height_inches)\n", "\n", " # Initialise the figure.\n", - " # sns.set(context=\"talk\", style='ticks')\n", - " init_fig_kwargs = dict(figsize=fig_size, dpi=plot_kwargs[\"dpi\"]\n", - " ,tight_layout=True)\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 = 0.3\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", - " inset_contrast = True\n", " rawdata_axes = plot_kwargs[\"ax\"]\n", " ax_position = rawdata_axes.get_position() # [[x0, y0], [x1, y1]]\n", - " \n", + "\n", " fig = rawdata_axes.get_figure()\n", " fig.patch.set_facecolor(face_color)\n", - " \n", - " if float_contrast is True:\n", + "\n", + " if float_contrast:\n", " axins = rawdata_axes.inset_axes(\n", - " [1, 0,\n", - " width_ratios_ga[1]/width_ratios_ga[0], 1])\n", + " [1, 0, width_ratios_ga[1] / width_ratios_ga[0], 1]\n", + " )\n", " rawdata_axes.set_position( # [l, b, w, h]\n", - " [ax_position.x0,\n", - " ax_position.y0,\n", - " (ax_position.x1 - ax_position.x0) * (width_ratios_ga[0] /\n", - " sum(width_ratios_ga)),\n", - " (ax_position.y1 - ax_position.y0)])\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", "\n", " else:\n", " axins = rawdata_axes.inset_axes([0, -1 - h_space_cummings, 1, 1])\n", - " plot_height = ((ax_position.y1 - ax_position.y0) /\n", - " (2 + h_space_cummings))\n", + " plot_height = (ax_position.y1 - ax_position.y0) / (2 + h_space_cummings)\n", " rawdata_axes.set_position(\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", - " # If the contrast axes are NOT floating, create lists to store\n", - " # raw ylims and raw tick intervals, so that I can normalize\n", - " # their ylims later.\n", - " contrast_ax_ylim_low = list()\n", - " contrast_ax_ylim_high = list()\n", - " contrast_ax_ylim_tickintervals = list()\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", " contrast_axes = axins\n", " rawdata_axes.contrast_axes = axins\n", "\n", " else:\n", - " inset_contrast = False\n", " # Here, we hardcode some figure parameters.\n", - " if float_contrast is True:\n", + " if float_contrast:\n", " fig, axx = plt.subplots(\n", - " ncols=2,\n", - " gridspec_kw={\"width_ratios\": width_ratios_ga,\n", - " \"wspace\": 0},\n", - " **init_fig_kwargs)\n", + " ncols=2,\n", + " gridspec_kw={\"width_ratios\": width_ratios_ga, \"wspace\": 0},\n", + " **init_fig_kwargs\n", + " )\n", " fig.patch.set_facecolor(face_color)\n", "\n", " else:\n", - " fig, axx = plt.subplots(nrows=2,\n", - " gridspec_kw={\"hspace\": 0.3},\n", - " **init_fig_kwargs)\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", - " # If the contrast axes are NOT floating, create lists to store\n", - " # raw ylims and raw tick intervals, so that I can normalize\n", - " # their ylims later.\n", - " contrast_ax_ylim_low = list()\n", - " contrast_ax_ylim_high = list()\n", - " contrast_ax_ylim_tickintervals = list()\n", - "\n", - " rawdata_axes = axx[0]\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 = {'colors' : ytick_color,\n", - " 'facecolors' : ytick_color,\n", - " 'lw' : 1,\n", - " 'zorder' : 10,\n", - " 'clip_on' : False}\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 = None\n", - " if is_paired is not None:\n", - " one_sankey = False # Flag to indicate if only one sankey is plotted.\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 is True:\n", + " if show_pairs:\n", " # Determine temp_idx based on is_paired and proportional conditions\n", " if is_paired == \"baseline\":\n", - " idx_pairs = [(control, test) for i in idx for control, test in zip([i[0]] * (len(i) - 1), i[1:])]\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 = [(control, test) for i in idx for control, test in zip(i[:-1], i[1:])]\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 proportional==False:\n", - " # Plot the raw data as a slopegraph.\n", - " # Pivot the long (melted) data.\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(data=plot_data, index=dabest_obj.id_col,\n", - " columns=xvar, values=pivot_values)\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[:, pd.MultiIndex.from_product([pivot_values, current_tuple])].dropna()\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", @@ -467,76 +549,225 @@ " y_points = observation[yvar].tolist()\n", "\n", " if color_col is None:\n", - " slopegraph_kwargs['color'] = ytick_color\n", + " slopegraph_kwargs[\"color\"] = ytick_color\n", " else:\n", " color_key = observation[color_col][0]\n", - " if isinstance(color_key, str) == True:\n", - " slopegraph_kwargs['color'] = plot_palette_raw[color_key]\n", - " slopegraph_kwargs['label'] = color_key\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", + "\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 == None:\n", + " if err_color is None:\n", " err_color = \"black\"\n", "\n", - " if show_pairs is True:\n", + " if show_pairs:\n", " sankey_control_group = []\n", " sankey_test_group = []\n", - " for i in temp_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(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", + " sankey_test_group.append(i[1])\n", "\n", " if len(temp_all_plot_groups) == 2:\n", - " one_sankey = True \n", - " \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", - " sankey = sankeydiag(plot_data, xvar=xvar, 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", + " 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 proportional==False:\n", + " if not proportional:\n", " # Plot the raw data as a swarmplot.\n", - " rawdata_plot = sns.swarmplot(data=plot_data, x=xvar, y=yvar,\n", - " ax=rawdata_axes,\n", - " order=all_plot_groups, hue=color_col,\n", - " palette=plot_palette_raw, zorder=1,\n", - " **swarmplot_kwargs)\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({xvar: all_plot_groups, 'proportion': np.ones(len(all_plot_groups))})\n", - " bar1 = sns.barplot(data=bar1_df, x=xvar, y=\"proportion\",\n", - " ax=rawdata_axes,\n", - " order=all_plot_groups,\n", - " linewidth=2, facecolor=(1, 1, 1, 0), edgecolor=bar_color,\n", - " zorder=1)\n", - " bar2 = sns.barplot(data=plot_data, x=xvar, y=yvar,\n", - " ax=rawdata_axes,\n", - " order=all_plot_groups,\n", - " palette=plot_palette_bar,\n", - " zorder=1,\n", - " **barplot_kwargs)\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", " # 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.\n", - " bar.set_x(centre - bar_width / 2.)\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", @@ -545,54 +776,73 @@ " if group_summaries is None:\n", " group_summaries = \"mean_sd\"\n", "\n", - " if group_summaries is not None and proportional==False:\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", - " _, x_max, _, _ = get_swarm_spans(c)\n", - " x_max_span = x_max - jj\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 is True:\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", - " error_bar(plot_data, x=xvar, 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, ax=rawdata_axes,\n", - " method=\"gapped_lines\",\n", - " **group_summary_kwargs)\n", - "\n", - " if group_summaries is not None and proportional == True:\n", - "\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 == None:\n", + " if err_color is None:\n", " err_color = \"black\"\n", - " error_bar(plot_data, x=xvar, y=yvar,\n", - " offset=0,\n", - " line_color=err_color,\n", - " gap_width_percent=1.5,\n", - " type=group_summaries, ax=rawdata_axes,\n", - " method=\"proportional_error_bar\",\n", - " **group_summary_kwargs)\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", + " )\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", + " te = t[t.rfind(\"\\n\") + len(\"\\n\") :]\n", " N = str(counts.loc[te])\n", " te = t\n", " else:\n", @@ -601,11 +851,13 @@ "\n", " ticks_with_counts.append(\"{}\\nN = {}\".format(te, N))\n", "\n", - " rawdata_axes.set_xticklabels(ticks_with_counts)\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_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", @@ -616,73 +868,76 @@ "\n", " # Plot effect sizes and bootstraps.\n", " # Take note of where the `control` groups are.\n", - " if is_paired == \"baseline\" and show_pairs == True:\n", - " if proportional == True and one_sankey == False:\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_sankey = np.cumsum([len(i)-1 for i in idx]).tolist()\n", - " ticks_to_start_sankey.pop()\n", - " ticks_to_start_sankey.insert(0, 0)\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 = [t for t in range(0, len(all_plot_groups))\n", - " if t not in ticks_to_skip]\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 proportional == True and one_sankey == False:\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 = [t for t in range(0, len(temp_idx))\n", - " if t not in ticks_to_skip]\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_sankey = np.cumsum([len(i)-1 for i in idx]).tolist()\n", - " ticks_to_start_sankey.pop()\n", - " ticks_to_start_sankey.insert(0, 0)\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 = [t for t in range(0, len(all_plot_groups))\n", - " if t not in ticks_to_skip]\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", + " 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 = EffectSizeDataFrame.results\n", + " results = effectsize_df.results\n", " contrast_xtick_labels = []\n", "\n", - "\n", " for j, tick in enumerate(ticks_to_plot):\n", - " current_group = results.test[j]\n", - " current_control = results.control[j]\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", + " 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", + " 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", + " 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(current_bootstrap[~np.isinf(current_bootstrap)],\n", - " positions=[tick],\n", - " **violinplot_kwargs)\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 is True:\n", + " if bootstraps_color_by_group:\n", " fc = plot_palette_contrast[current_group]\n", " else:\n", " fc = \"grey\"\n", @@ -690,66 +945,114 @@ " halfviolin(v, fill_color=fc, alpha=halfviolin_alpha)\n", "\n", " # Plot the effect size.\n", - " contrast_axes.plot([tick], current_effsize, marker='o',\n", - " color=ytick_color,\n", - " markersize=es_marker_size)\n", - " # Plot the confidence interval.\n", - " contrast_axes.plot([tick, tick],\n", - " [current_ci_low, current_ci_high],\n", - " linestyle=\"-\",\n", - " color=ytick_color,\n", - " linewidth=group_summary_kwargs['lw'])\n", + " contrast_axes.plot(\n", + " [tick],\n", + " current_effsize,\n", + " marker=\"o\",\n", + " color=ytick_color,\n", + " markersize=es_marker_size,\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", + " )\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", - " contrast_xtick_labels.append(\"{}\\nminus\\n{}\".format(current_group,\n", - " current_control))\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", + "\n", + " contrast_xtick_labels.append(\n", + " \"{}\\nminus\\n{}\".format(current_group, current_control)\n", + " )\n", "\n", " # Plot mini-meta violin\n", " if show_mini_meta or show_delta2:\n", " if show_mini_meta:\n", - " mini_meta_delta = EffectSizeDataFrame.mini_meta_delta\n", - " data = mini_meta_delta.bootstraps_weighted_delta\n", - " difference = mini_meta_delta.difference\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", + " 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 = EffectSizeDataFrame.delta_delta\n", - " data = delta_delta.bootstraps_delta_delta\n", - " difference = delta_delta.difference\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", + " 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(data[~np.isinf(data)],\n", - " positions=[position],\n", - " **violinplot_kwargs)\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([position], difference, marker='o',\n", - " color=ytick_color,\n", - " markersize=es_marker_size)\n", + " contrast_axes.plot(\n", + " [position],\n", + " difference,\n", + " marker=\"o\",\n", + " color=ytick_color,\n", + " markersize=es_marker_size,\n", + " )\n", " # Plot the confidence interval.\n", - " contrast_axes.plot([position, position],\n", - " [ci_low, ci_high],\n", - " linestyle=\"-\",\n", - " color=ytick_color,\n", - " linewidth=group_summary_kwargs['lw'])\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", + " )\n", " if show_mini_meta:\n", - " contrast_xtick_labels.extend([\"\",\"Weighted delta\"])\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", + " 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", @@ -757,22 +1060,22 @@ " 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", + " temp = np.append(temp, [max(temp) + 1, max(temp) + 2])\n", " contrast_axes.set_xticks(temp)\n", "\n", - " if show_pairs is True:\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 is True:\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", + " rawdata_axes.set_xlim(temp[0], temp[1] + 0.25)\n", " else:\n", - " rawdata_axes.set_xlim(temp[0], temp[1]+2)\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", @@ -780,53 +1083,68 @@ " # Properly label the contrast ticks.\n", " for t in ticks_to_skip:\n", " contrast_xtick_labels.insert(t, \"\")\n", - " \n", - " contrast_axes.set_xticklabels(contrast_xtick_labels)\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", + " )\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 = (pd.Series(legend_handles, dtype=\"object\").loc[unique_idx]).tolist()\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 is True:\n", + " if float_contrast:\n", " axes_with_legend = contrast_axes\n", - " if show_pairs is True:\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 is True:\n", - " bta = (1.02, 1.)\n", + " if show_pairs:\n", + " bta = (1.02, 1.0)\n", " else:\n", - " bta = (1.,1.)\n", - " leg = axes_with_legend.legend(legend_handles_unique,\n", - " legend_labels_unique,\n", - " bbox_to_anchor=bta,\n", - " **legend_kwargs)\n", - " if show_pairs is True:\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 is True:\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 = plot_data.groupby(xvar)\\\n", - " .mean(numeric_only=True).loc[current_control, yvar]\n", - " test_group_summary = plot_data.groupby(xvar)\\\n", - " .mean(numeric_only=True).loc[current_group, yvar]\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 = plot_data.groupby(xvar)\\\n", - " .median().loc[current_control, yvar]\n", - " test_group_summary = plot_data.groupby(xvar)\\\n", - " .median().loc[current_group, yvar]\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", @@ -834,7 +1152,7 @@ " _, contrast_xlim_max = contrast_axes.get_xlim()\n", "\n", " difference = float(results.difference[0])\n", - " \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", @@ -852,48 +1170,53 @@ " 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", + " 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", + " 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", + " 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", + "\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(len_control, len_test)\n", - " \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 = (np.mean(temp_test)-np.mean(temp_control)) / difference\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 = ((rawdata_axes.get_ylim() - control_group_summary) / ylim_scale_factor).tolist()\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", + " 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", - "\n", " # Draw effect size line.\n", " if jj == 0:\n", " ref = control_group_summary\n", @@ -903,66 +1226,74 @@ " elif jj == 1:\n", " ref = 0\n", " diff = ref + difference\n", - " effsize_line_start = contrast_xlim_max-1.1\n", + " effsize_line_start = contrast_xlim_max - 1.1\n", "\n", " xlimlow, xlimhigh = axx.get_xlim()\n", "\n", " # Draw reference line.\n", - " axx.hlines(ref, # y-coordinates\n", - " 0, xlimhigh, # x-coordinates, start and end.\n", - " **reflines_kwargs)\n", - " \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,\n", - " effsize_line_start, xlimhigh,\n", - " **reflines_kwargs)\n", - " else: \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(ref, # y-coordinates\n", - " effsize_line_start, xlimhigh, # x-coordinates, start and end.\n", - " **reflines_kwargs)\n", - " \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,\n", - " effsize_line_start, xlimhigh,\n", - " **reflines_kwargs) \n", - " rawdata_axes.set_xlim(og_xlim_raw) # to align the axis\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=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(og_ylim_raw[0], # yindex\n", - " rawdata_axes.get_xlim()[0], 1.3, # xmin, xmax\n", - " **redraw_axes_kwargs)\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(contrast_axes.get_ylim()[0],\n", - " contrast_xlim_max-0.8, contrast_xlim_max,\n", - " **redraw_axes_kwargs)\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", " 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 (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", + " 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_delta2_ylim = plot_kwargs[\"delta2_ylim\"]\n", " custom_contrast_ylim = custom_delta2_ylim\n", "\n", " if len(custom_contrast_ylim) != 2:\n", @@ -972,8 +1303,8 @@ "\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", + " 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", @@ -990,198 +1321,353 @@ " 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 == True:\n", - " if proportional == True and one_sankey == False:\n", - " rightend_ticks_raw = np.array([len(i)-2 for i in idx]) + np.array(ticks_to_start_sankey)\n", - " else: \n", - " rightend_ticks_raw = np.array([len(i)-1 for i in temp_idx]) + np.array(ticks_to_skip)\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", + "\n", " ylim = ax.get_ylim()\n", " xlim = ax.get_xlim()\n", - " redraw_axes_kwargs['y'] = ylim[0]\n", - " \n", - " if proportional == True and one_sankey == False:\n", - " for k, start_tick in enumerate(ticks_to_start_sankey):\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,\n", - " **redraw_axes_kwargs)\n", - " else: \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,\n", - " **redraw_axes_kwargs)\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 proportional == False:\n", - " temp_length = [(len(i)-1) for i in idx]\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 proportional == True and one_sankey == False:\n", - " rightend_ticks_contrast = np.array([len(i)-2 for i in idx]) + np.array(ticks_to_start_sankey)\n", - " else: \n", - " rightend_ticks_contrast = np.array(temp_length) + np.array(ticks_to_skip_contrast)\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", + "\n", " ylim = ax.get_ylim()\n", " xlim = ax.get_xlim()\n", - " redraw_axes_kwargs['y'] = ylim[0]\n", - " \n", - " if proportional == True and one_sankey == False:\n", - " for k, start_tick in enumerate(ticks_to_start_sankey):\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,\n", - " **redraw_axes_kwargs)\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,\n", - " **redraw_axes_kwargs) \n", - " \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", + " del redraw_axes_kwargs[\"y\"]\n", " else:\n", " # Compute the end of each x-axes line.\n", - " if proportional == True and one_sankey == False:\n", - " rightend_ticks = np.array([len(i)-2 for i in idx]) + np.array(ticks_to_start_sankey)\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(ticks_to_skip)\n", - " \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", + "\n", " ylim = ax.get_ylim()\n", " xlim = ax.get_xlim()\n", - " redraw_axes_kwargs['y'] = ylim[0]\n", - " \n", - " if proportional == True and one_sankey == False:\n", - " for k, start_tick in enumerate(ticks_to_start_sankey):\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,\n", - " **redraw_axes_kwargs)\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,\n", - " **redraw_axes_kwargs)\n", - " \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", + " del redraw_axes_kwargs[\"y\"]\n", "\n", - " if show_delta2 is True or show_mini_meta is True:\n", + " if show_delta2 or show_mini_meta:\n", " ylim = contrast_axes.get_ylim()\n", - " redraw_axes_kwargs['y'] = ylim[0]\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],\n", - " **redraw_axes_kwargs)\n", - " del redraw_axes_kwargs['y']\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", + " 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", + " 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 = {'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", - " if proportional == True and effect_size_type != \"cohens_h\":\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[EffectSizeDataFrame.effect_size]\n", - "\n", + " default_contrast_label = contrast_label_dict[effectsize_df.effect_size]\n", "\n", - " if plot_kwargs['contrast_label'] is None:\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", + " 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)\n", - " if float_contrast is True:\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 proportional == False:\n", - " rawdata_axes.set_ylabel(swarm_label)\n", + " if not proportional:\n", + " rawdata_axes.set_ylabel(swarm_label, fontsize=fontsize_rawylabel)\n", " else:\n", - " rawdata_axes.set_ylabel(bar_label)\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 float_contrast==False:\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(og_xlim_raw[0],\n", - " og_ylim_raw[0], og_ylim_raw[1],\n", - " **redraw_axes_kwargs)\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 is True:\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(xpos,\n", - " og_ylim_contrast[0], og_ylim_contrast[1],\n", - " **redraw_axes_kwargs)\n", - "\n", - "\n", - " if show_delta2 is True:\n", - " if plot_kwargs['delta2_label'] is None:\n", + " contrast_axes.vlines(\n", + " xpos, og_ylim_contrast[0], og_ylim_contrast[1], **redraw_axes_kwargs\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 = plot_kwargs['delta2_label']\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)\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(og_xlim_delta[1],\n", - " og_ylim_delta[0], og_ylim_delta[1],\n", - " **redraw_axes_kwargs)\n", + " delta2_axes.vlines(\n", + " og_xlim_delta[1], og_ylim_delta[0], og_ylim_delta[1], **redraw_axes_kwargs\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", + " )\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", + "\n", + " ####################################################### END GRIDKEY MAIN CODE WIP\n", "\n", " # Make sure no stray ticks appear!\n", - " rawdata_axes.xaxis.set_ticks_position('bottom')\n", - " rawdata_axes.yaxis.set_ticks_position('left')\n", - " contrast_axes.xaxis.set_ticks_position('bottom')\n", + " rawdata_axes.xaxis.set_ticks_position(\"bottom\")\n", + " rawdata_axes.yaxis.set_ticks_position(\"left\")\n", + " contrast_axes.xaxis.set_ticks_position(\"bottom\")\n", " if float_contrast is False:\n", - " contrast_axes.yaxis.set_ticks_position('left')\n", + " contrast_axes.yaxis.set_ticks_position(\"left\")\n", "\n", " # Reset rcParams.\n", " for parameter in _changed_rcParams:\n", " plt.rcParams[parameter] = original_rcParams[parameter]\n", "\n", " # Return the figure.\n", - " return fig" + " return fig\n" ] }, { "cell_type": "code", "execution_count": null, - "id": "d02b55f2", + "id": "7355251f", "metadata": {}, "outputs": [], "source": [] diff --git a/nbs/_quarto.yml b/nbs/_quarto.yml index 6fcc349d..bf368b27 100644 --- a/nbs/_quarto.yml +++ b/nbs/_quarto.yml @@ -15,11 +15,11 @@ website: sidebar: style: floating contents: - - auto: "/*.ipynb" - - section: Tutorials - contents: tutorials/* + - auto: "/0*.ipynb" + - auto: "tutorials/0*.ipynb" # Autogenerate a section of tutorial notebooks - section: API contents: API/* + favicon: images/Favicon-3-outline.svg navbar: background: primary search: true diff --git a/nbs/blog/posts/bootstraps/bootstraps.ipynb b/nbs/blog/posts/bootstraps/bootstraps.ipynb index ae4ff5ad..8a5f73c9 100644 --- a/nbs/blog/posts/bootstraps/bootstraps.ipynb +++ b/nbs/blog/posts/bootstraps/bootstraps.ipynb @@ -7,7 +7,7 @@ "source": [ "# Bootstrap Confidence Intervals\n", "\n", - "> Explaination of the bootstrap method and its application in hypothesis testing using DABEST.\n", + "> Explanation of the bootstrap method and its application in hypothesis testing using **DABEST**.\n", "\n", "- order: 3" ] @@ -17,7 +17,7 @@ "id": "6321ea6f", "metadata": {}, "source": [ - "## Sampling from Populations" + "## Sampling from populations" ] }, { @@ -27,7 +27,7 @@ "source": [ "In a typical scientific experiment, we are interested in two populations\n", "(Control and Test), and whether there is a difference between their means\n", - "$(\\mu_{Test}-\\mu_{Control})$\n" + "$(\\mu_{Test}-\\mu_{Control})$.\n" ] }, { @@ -43,7 +43,7 @@ "id": "5573045c", "metadata": {}, "source": [ - "We go about this by collecting observations from the control population, and from the test population." + "We go about this by collecting observations from the control population and from the test population." ] }, { @@ -62,7 +62,7 @@ "We can easily compute the mean difference in our observed samples. This is our\n", "estimate of the population effect size that we are interested in.\n", "\n", - "**But how do we obtain a measure of precision and confidence about our estimate?\n", + "**But how do we obtain a measure of the precision and confidence about our estimate?\n", "Can we get a sense of how it relates to the population mean difference?**\n" ] }, @@ -79,11 +79,11 @@ "id": "fe977cc6", "metadata": {}, "source": [ - "We want to obtain a 95% confidence interval (95% CI) around the our estimate of the mean difference. The 95% indicates that any such confidence interval will capture the population mean difference 95% of the time.\n", + "We want to obtain a 95% confidence interval (95% CI) around our estimate of the mean difference. The 95% indicates that any such confidence interval will capture the population mean difference 95% of the time.\n", "\n", - "In other words, if we repeated our experiment 100 times, gathering 100 independent sets of observations, and computing a 95% confidence interval for the mean difference each time, 95 of these intervals would capture the population mean difference. That is to say, we can be 95% confident the interval contains the true mean of the population.\n", + "In other words, if we were to repeat our experiment 100 times, gathering 100 independent sets of observations and computing a 95% confidence interval for the mean difference each time, 95 of these intervals would capture the population mean difference. That is to say, we can be 95% confident the interval contains the true mean of the population.\n", "\n", - "We can calculate the 95% CI of the mean difference with [bootstrap resampling](https://en.wikipedia.org/wiki/Bootstrapping_(statistics))\n" + "We can calculate the 95% CI of the mean difference with [bootstrap resampling](https://en.wikipedia.org/wiki/Bootstrapping_(statistics)).\n" ] }, { @@ -99,7 +99,7 @@ "id": "0685adaf", "metadata": {}, "source": [ - "The [`bootstrap`](#1)[1] is a simple but powerful technique. It was [first described] (https://projecteuclid.org/euclid.aos/1176344552) by [Bradley Efron](https://statistics.stanford.edu/people/bradley-efron).\n", + "The [`bootstrap`](#1)[1] is a simple but powerful technique. It was [first described](https://projecteuclid.org/euclid.aos/1176344552) by [Bradley Efron](https://statistics.stanford.edu/people/bradley-efron).\n", "\n", "It creates multiple *resamples* (with replacement) from a single set of\n", "observations, and computes the effect size of interest on each of these\n", @@ -134,11 +134,7 @@ "the Central Limit Theorem, the resampling distribution of the effect size will\n", "approach a normality.\n", "\n", - "2. *Easy construction of the 95% CI from the resampling distribution.* For 1000\n", - "bootstrap resamples of the mean difference, one can use the 25th value and the\n", - "975th value of the ranked differences as boundaries of the 95% confidence\n", - "interval. (This captures the central 95% of the distribution.) Such an interval\n", - "construction is known as a *percentile interval*." + "2. *Easy construction of the 95% CI from the resampling distribution.* In the context of bootstrap resampling or other non-parametric methods, the 2.5th and 97.5th percentiles are often used to define the lower and upper limits, respectively. The use of these percentiles ensures that the resulting interval contains the central 95% of the resampled distribution. Such an interval construction is known as a *percentile interval*." ] }, { @@ -156,12 +152,10 @@ "source": [ "While resampling distributions of the difference in means often have a normal\n", "distribution, it is not uncommon to encounter a skewed distribution. Thus, Efron\n", - "developed the [bias-corrected and accelerated bootstrap]\n", - "(https://en.wikipedia.org/wiki/Bootstrapping_(statistics)#History) (BCa\n", - "bootstrap) to account for the skew, and still obtain the central 95% of the\n", + "developed the [bias-corrected and accelerated bootstrap](https://en.wikipedia.org/wiki/Bootstrapping_(statistics)#History) (BCa bootstrap) to account for the skew, and still obtain the central 95% of the\n", "distribution.\n", "\n", - "DABEST applies the BCa correction to the resampling bootstrap distributions of\n", + "**DABEST** applies the BCa correction to the resampling bootstrap distributions of\n", "the effect size." ] }, @@ -186,7 +180,7 @@ "id": "fb1a8fa6", "metadata": {}, "source": [ - "The estimation plot produced by DABEST presents the rawdata and the bootstrap\n", + "The estimation plot produced by DABEST presents the raw data and the bootstrap\n", "confidence interval of the effect size (the difference in means) side-by-side as\n", "a single integrated plot." ] @@ -204,7 +198,7 @@ "id": "eaad7dd5", "metadata": {}, "source": [ - "It thus tightly couples visual presentation of the raw data with an indication of the population mean difference, and its confidence interval." + "Thus, it tightly couples a visual presentation of the raw data with an indication of the population mean difference plus its confidence interval." ] }, { @@ -215,14 +209,6 @@ "\n", "`[1]`: The name is derived from the saying \"[pull oneself by one's bootstraps](https://en.wiktionary.org/wiki/pull_oneself_up_by_one%27s_bootstraps)\", often used as an exhortation to achieve success without external help.\n" ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "87e5611b", - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { diff --git a/nbs/blog/posts/robust-beautiful/four_samples.csv b/nbs/blog/posts/robust-beautiful/four_samples.csv new file mode 100644 index 00000000..547296b5 --- /dev/null +++ b/nbs/blog/posts/robust-beautiful/four_samples.csv @@ -0,0 +1,16 @@ +A,B,C,D +8.109188439895592,9.33184689521894,9.354787823122058,12.672612419124242 +10.131749781263766,9.33184689521894,9.455474658800501,7.66146433397944 +7.178065881431648,14.342995181076896,9.577728760474645,7.66146410459298 +7.070698283373329,6.826272752289961,9.580692407448097,7.66146410459298 +14.530513949101707,11.837421038147918,8.976055745893488,7.66146410459298 +12.837160139759924,9.33184689521894,13.280120639175362,12.672612419124242 +8.98967523846707,9.33184689521894,5.284223374586355,7.66146410459298 +9.548347716611921,14.342995181076896,9.580597428100837,12.672612419124242 +10.98994063849879,9.33184689521894,9.547969576803277,7.66146410459298 +12.402350479094743,6.826272752289961,9.435510391485826,7.66146410459298 +11.694550072150143,6.826272752289961,9.277034488877351,12.672612419124242 +4.8799780463809785,9.33184689521894,9.389691597155812,12.672612419124242 +9.528364669906528,9.33184689521894,9.586309213728654,12.672612419124242 +9.392042031837274,9.33184689521894,17.540147276068026,7.66146410459298 +12.717374632226587,14.342995181076896,10.13365661827971,12.672612419124242 diff --git a/nbs/blog/posts/robust-beautiful/robust-beautiful.ipynb b/nbs/blog/posts/robust-beautiful/robust-beautiful.ipynb index 200260d1..703526fb 100644 --- a/nbs/blog/posts/robust-beautiful/robust-beautiful.ipynb +++ b/nbs/blog/posts/robust-beautiful/robust-beautiful.ipynb @@ -69,13 +69,13 @@ "In the above figure, four different samples with wildly different\n", "distributions--as seen in the swarmplot on the left panel--look exactly\n", "the same when visualized with a barplot on the right panel. (You can\n", - "download the [dataset](_static/four_samples.csv) to see for yourself.)\n", + "download the [dataset](four_samples.csv) to see for yourself.)\n", "\n", - "We're not the first ones (see\n", - "[this](https://www.nature.com/articles/nmeth.2837),\n", - "[this](http://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.1002128),\n", + "We're not the first ones (see these articles:\n", + "[article 1](https://www.nature.com/articles/nmeth.2837),\n", + "[article 2](http://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.1002128),\n", "or\n", - "[that](https://onlinelibrary.wiley.com/doi/full/10.1111/ejn.13400))\n", + "[article 3](https://onlinelibrary.wiley.com/doi/full/10.1111/ejn.13400))\n", "to point out the barplot's fatal flaws. Indeed, it is both sobering and\n", "fascinating to realise that the barplot is a [17th century\n", "invention](https://en.wikipedia.org/wiki/Bar_chart#History) initially\n", @@ -118,7 +118,7 @@ "The figure above visualizes the same four samples as a swarmplot (left\n", "panel) and as a boxplot. If we did not label the x-axis with the sample\n", "size, it would be impossible to definitively distinguish the sample with\n", - "5 obesrvations from the sample with 50.\n", + "5 observations from the sample with 50.\n", "\n", "Even if the world gets rid of barplots and boxplots, the problems\n", "plaguing statistical practices will remain unsolved. Null-hypothesis\n", @@ -148,24 +148,21 @@ "id": "a7e3b1ad", "metadata": {}, "source": [ - "hown above is a Gardner-Altman estimation plot. (The plot draws its name from\n", - "[Martin J. Gardner]\n", - "(https://www.independent.co.uk/news/people/obituary-professor-martin-gardner-1470261.html)\n", + "This is a *Gardner-Altman* estimation plot. The plot draws its name from\n", + "[Martin J. Gardner](https://www.independent.co.uk/news/people/obituary-professor-martin-gardner-1470261.html)\n", "and [Douglas Altman](https://www.bmj.com/content/361/bmj.k2588), who are\n", - "credited with [creating the design]\n", - "(https://www.bmj.com/content/bmj/292/6522/746.full.pdf) in 1986).\n", + "credited with [creating the design](https://www.bmj.com/content/bmj/292/6522/746.full.pdf) in 1986.\n", "\n", "This plot has two key features:\n", "\n", - " 1. It presents all datapoints as a *swarmplot*, which orders each point to\n", - " display the underlying distribution.\n", + " 1. It presents all data points as a swarmplot, ordering each point to display the underlying distribution.\n", "\n", " 2. It presents the effect size as a *bootstrap 95% confidence interval* (95% CI)\n", - " on a separate but aligned axes. where the effect size is displayed to the right\n", - " of the war data, and the mean of the test group is aligned with the effect size.\n", + " on a separate but aligned axis. The effect size is displayed to the right of the raw data, and the mean of the test group is aligned with the effect size.\"\n", + "\n", + "
Thus, estimation plots are robust, beautiful, and convey important statistical\n", + "information elegantly and efficiently.
\n", "\n", - "*Thus, estimation plots are robust, beautiful, and convey important statistical\n", - "information elegantly and efficiently.*\n", "\n", "An estimation plot obtains and displays the 95% CI through nonparametric\n", "bootstrap resampling. This enables visualization of the confidence interval as\n", @@ -283,13 +280,11 @@ "id": "b7b643f8", "metadata": {}, "source": [ - "For comparisons between 3 or more groups that typically employ analysis\n", + "For comparisons between three or more groups that typically employ analysis\n", "of variance (ANOVA) methods, one can use the [Cumming estimation\n", "plot](https://en.wikipedia.org/wiki/Estimation_statistics#Cumming_plot),\n", - "named after [Geoff\n", - "Cumming](https://www.youtube.com/watch?v=nDN-hcKR7j8), and draws its\n", - "design heavily from his 2012 textbook [Understanding the New\n", - "Statistics](https://www.routledge.com/Understanding-The-New-Statistics-Effect-Sizes-Confidence-Intervals-and/Cumming/p/book/9780415879682).\n", + "named after [Geoff Cumming](https://www.youtube.com/watch?v=nDN-hcKR7j8), and draws its\n", + "design heavily from his 2012 textbook [\"Understanding the New Statistics\"](https://www.routledge.com/Understanding-The-New-Statistics-Effect-Sizes-Confidence-Intervals-and/Cumming/p/book/9780415879682).\n", "This estimation plot design can be considered a variant of the\n", "Gardner-Altman plot.\n" ] @@ -307,8 +302,8 @@ "id": "b443b0a8", "metadata": {}, "source": [ - "The effect size and 95% CIs are still plotted a separate axes, but\n", - "unlike the Gardner-Altman plot, this axes is positioned beneath the raw\n", + "The effect size and 95% CIs are still plotted on a separate axis, but\n", + "unlike the Gardner-Altman plot, this axis is positioned beneath the raw\n", "data.\n", "\n", "Such a design frees up visual space in the upper panel, allowing the\n", diff --git a/nbs/images/DABEST-square-outline.svg b/nbs/images/DABEST-square-outline.svg new file mode 100644 index 00000000..4290401c --- /dev/null +++ b/nbs/images/DABEST-square-outline.svg @@ -0,0 +1,45 @@ + + + + + + + + + + + + + + + + + + + + + diff --git a/nbs/images/Favicon-3-outline.svg b/nbs/images/Favicon-3-outline.svg new file mode 100644 index 00000000..7ee0f769 --- /dev/null +++ b/nbs/images/Favicon-3-outline.svg @@ -0,0 +1,29 @@ + + + + + + + + + + + diff --git a/nbs/images/customizable.svg b/nbs/images/customizable.svg new file mode 100644 index 00000000..877df670 --- /dev/null +++ b/nbs/images/customizable.svg @@ -0,0 +1,70 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/nbs/images/estimations.svg b/nbs/images/estimations.svg new file mode 100644 index 00000000..0d8d9afd --- /dev/null +++ b/nbs/images/estimations.svg @@ -0,0 +1,50 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/nbs/images/favicon.svg b/nbs/images/favicon.svg new file mode 100644 index 00000000..0468d12c --- /dev/null +++ b/nbs/images/favicon.svg @@ -0,0 +1,62 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/nbs/images/gaussian.svg b/nbs/images/gaussian.svg new file mode 100644 index 00000000..aad230ed --- /dev/null +++ b/nbs/images/gaussian.svg @@ -0,0 +1,24 @@ + + + + + + + + diff --git a/nbs/images/python.svg b/nbs/images/python.svg new file mode 100644 index 00000000..91d19040 --- /dev/null +++ b/nbs/images/python.svg @@ -0,0 +1,35 @@ + + + + + + + + + + + + + + + + + + + + + + diff --git a/nbs/images/splash-propplot.png b/nbs/images/splash-propplot.png new file mode 100644 index 00000000..352e37c6 Binary files /dev/null and b/nbs/images/splash-propplot.png differ diff --git a/nbs/index.qmd.py b/nbs/index.qmd.py index 7f8cf559..f6dbe449 100644 --- a/nbs/index.qmd.py +++ b/nbs/index.qmd.py @@ -15,12 +15,14 @@ def btn(txt, link): return qmd.btn(txt, link=link, classes=['btn-action-primary' def banner(txt, classes=None, style=None): return qmd.div(txt, L('hero-banner')+classes, style=style) features = L( - ('docs', 'Beautiful technical documentation and scientific articles with Quarto'), - ('testing', 'Out-of-the-box continuous integration with GitHub Actions'), - ('packaging', 'Publish code to PyPI and conda, and prose to GitHub Pages'), - ('visualization', 'Estimation plots are robust, beautiful, and convey important statistical information elegantly and efficiently.'), - ('jupyter', 'Write prose, code, and tests in notebooks'), - ('git', 'Git-friendly notebooks: human-readable merge conflicts') + ('estimations', 'Shift from p-values to effect size and confidence intervals for richer data insights'), + # ('User-Friendly Interface', 'Accessible to both novices and experts, ensuring ease of use'), + ('gaussian', 'Robust and elegant statistical visualizations for efficient information conveyance'), + ('python', 'Seamless integration with the scientific Python libraries for comprehensive data analysis'), + ('customizable', 'Flexible plot customization to meet diverse presentation needs'), + ('jupyter', 'Promotes reproducibility with easy sharing of data and analysis code'), + # ('Educational Resources', 'Extensive documentation and tutorials to enhance statistical literacy'), + ('git', 'Ongoing support and development through an engaged user community') ) def industry(im, **kwargs): return qmd.div(img(im, **kwargs), ["g-col-12", "g-col-sm-6", "g-col-md-3"]) @@ -33,7 +35,7 @@ def testm(im, nm, detl, txt): ## {detl} ### {txt}""", ["testimonial", "g-col-12", "g-col-md-6"]) - + def feature(im, desc): return qmd.div(f"{img(im+'.svg')}\n\n{desc}\n", ['feature', 'g-col-12', 'g-col-sm-6', 'g-col-md-4']) @@ -52,7 +54,7 @@ def d(*args, **kwargs): print(qmd.div(*args, **kwargs)) {btn('Get started', '/01-getting_started.ipynb')} -{img('showpiece.png', style={"margin-top": "20px", "margin-bottom": "20px"}, link=True)}""", "content-block") +{img('splash-propplot.png', style={"margin-top": "100px", "margin-bottom": "20px"}, link=True)}""", "content-block") feature_h = banner(f"""## Robust and Beautiful
Statistical Visualization @@ -62,10 +64,4 @@ def d(*args, **kwargs): print(qmd.div(*args, **kwargs)) b(f"""## Get started in seconds -{btn('Install dabest', '/01-getting_started.ipynb')}""", 'content-block', style={"margin-top": "40px"}) - - - - - - +{btn('Install dabest', '/01-getting_started.ipynb')}""", 'content-block', style={"margin-top": "40px"}) \ No newline at end of file diff --git a/nbs/nbdev.yml b/nbs/nbdev.yml index d5ab7123..34cfc3c6 100644 --- a/nbs/nbdev.yml +++ b/nbs/nbdev.yml @@ -3,7 +3,7 @@ project: website: title: "dabest" - site-url: "https://ZHANGROU-99.github.io/DABEST-python" + site-url: "https://acclab.github.io/DABEST-python" description: "Data Analysis and Visualization using Bootstrap-Coupled Estimation." repo-branch: master - repo-url: "https://github.com/ZHANGROU-99/DABEST-python" + repo-url: "https://github.com/acclab/DABEST-python" diff --git a/nbs/pytest.ini b/nbs/pytest.ini index 60beac8c..5856c886 100644 --- a/nbs/pytest.ini +++ b/nbs/pytest.ini @@ -3,7 +3,7 @@ filterwarnings = ignore::UserWarning ignore::DeprecationWarning -addopts = --mpl --mpl-baseline-path=nbs/tests/baseline_images +addopts = --mpl --mpl-baseline-path=nbs/tests/mpl_image_tests/baseline_images markers = mpl_image_compare: mark a test as implementing mpl image comparison. \ No newline at end of file diff --git a/nbs/read_me.ipynb b/nbs/read_me.ipynb index c89729cc..dc1460e7 100644 --- a/nbs/read_me.ipynb +++ b/nbs/read_me.ipynb @@ -5,31 +5,39 @@ "id": "205a828a", "metadata": {}, "source": [ - "# DABEST-Python\n", - "\n", - "[![minimal Python version](https://img.shields.io/badge/Python%3E%3D-3.6-6666ff.svg)](https://www.anaconda.com/distribution/)\n", + "# DABEST-Python" + ] + }, + { + "cell_type": "markdown", + "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", "[![PyPI version](https://badge.fury.io/py/dabest.svg)](https://badge.fury.io/py/dabest)\n", - "[![Downloads](https://pepy.tech/badge/dabest/month)](https://pepy.tech/project/dabest/month)\n", + "[![Downloads](https://img.shields.io/pepy/dt/dabest.svg\n", + ")](https://pepy.tech/project/dabest)\n", "[![Free-to-view citation](https://zenodo.org/badge/DOI/10.1038/s41592-019-0470-3.svg)](https://rdcu.be/bHhJ4)\n", "[![License](https://img.shields.io/badge/License-BSD%203--Clause--Clear-orange.svg)](https://spdx.org/licenses/BSD-3-Clause-Clear.html)" ] }, { + "attachments": {}, "cell_type": "markdown", "id": "8fcb9b6e", "metadata": {}, "source": [ "## Recent Version Update\n", "\n", - "On 20 March 2023, we officially released **DABEST v2023.02.14 for Python**. This new version provided the following new features:\n", + "On 22 March 2024, we officially released **DABEST Version Ondeh (v2024.03.29)**. This new version provides several new features and includes performance improvements.\n", "\n", - "1. **Repeated measures.** Augments the prior function for plotting (independent) multiple test groups versus a shared control; it can now do the same for repeated-measures experimental designs. Thus, together, these two methods can be used to replace both flavors of the 1-way ANOVA with an estimation analysis.\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", "\n", - "2. **Proportional data.** Generates proportional bar plots, proportional differences, and calculates Cohen's h. Also enables plotting Sankey diagrams for paired binary data. This is the estimation equivalent to a bar chart with Fischer's exact test.\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", "\n", - "3. **The $\\Delta\\Delta$ plot.** Calculates the delta-delta ($\\Delta\\Delta$) for 2 × 2 experimental designs and plots the four groups with their relevant effect sizes. This design can be used as a replacement for the 2 × 2 ANOVA.\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", "\n", - "4. **Mini-meta.** Calculates and plots a weighted delta ($\\Delta$) for meta-analysis of experimental replicates. Useful for summarizing data from multiple replicated experiments, for example by different scientists in the same lab, or the same scientist at different times. When the observed values are known (and share a common metric), this makes meta-analysis available as a routinely accessible tool." + "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" ] }, { @@ -61,13 +69,13 @@ "\n", "DABEST is a package for **D**ata **A**nalysis using **B**ootstrap-Coupled **EST**imation.\n", "\n", - "[Estimation statistics](https://en.wikipedia.org/wiki/Estimation_statistics) is a [simple framework](https://thenewstatistics.com/itns/) that avoids the [pitfalls](https://www.nature.com/articles/nmeth.3288) of significance testing. It uses familiar statistical concepts: means, mean differences, and error bars. More importantly, it focuses on the effect size of one's experiment/intervention, as opposed to a false dichotomy engendered by *P* values.\n", + "[Estimation statistics](https://en.wikipedia.org/wiki/Estimation_statistics) are a [simple framework](https://thenewstatistics.com/itns/) that avoids the [pitfalls](https://www.nature.com/articles/nmeth.3288) of significance testing. It employs familiar statistical concepts such as means, mean differences, and error bars. More importantly, it focuses on the effect size of one's experiment or intervention, rather than succumbing to a false dichotomy engendered by *P* values.\n", "\n", - "An estimation plot has two key features.\n", + "An estimation plot comprises two key features.\n", "\n", - "1. It presents all datapoints as a swarmplot, which orders each point to display the underlying distribution.\n", + "1. It presents all data points as a swarm plot, ordering each point to display the underlying distribution.\n", "\n", - "2. It presents the effect size as a **bootstrap 95% confidence interval** on a **separate but aligned axes**.\n", + "2. It illustrates the effect size as a **bootstrap 95% confidence interval** on a **separate but aligned axis**.\n", "\n", "![The five kinds of estimation plots](showpiece.png \"The five kinds of estimation plots.\")\n", "\n", @@ -81,19 +89,20 @@ "source": [ "## Installation\n", "\n", - "This package is tested on Python 3.6, 3.7, and 3.8.\n", + "This package is tested on Python 3.8 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", "\n", "To install, at the command line run\n", - "\n", "```shell\n", - "pip install --upgrade dabest\n", + "pip install dabest\n", "```\n", "You can also [clone](https://help.github.com/articles/cloning-a-repository) this repo locally.\n", "\n", @@ -115,7 +124,7 @@ "import pandas as pd\n", "import dabest\n", "\n", - "# Load the iris dataset. Requires internet access.\n", + "# Load the iris dataset. This step requires internet access.\n", "iris = pd.read_csv(\"https://github.com/mwaskom/seaborn-data/raw/master/iris.csv\")\n", "\n", "# Load the above data into `dabest`.\n", @@ -127,7 +136,7 @@ "```\n", "![A Cumming estimation plot of petal width from the iris dataset](iris.png)\n", "\n", - "Please refer to the official [tutorial](https://acclab.github.io/DABEST-python-docs/tutorial.html) for more useful code snippets.\n", + "Please refer to the official [tutorial](https://acclab.github.io/DABEST-python/) for more useful code snippets.\n", "\n" ] }, @@ -149,7 +158,7 @@ "\n", "## Bugs\n", "\n", - "Please report any bugs on the [Github issue tracker](https://github.com/ACCLAB/DABEST-python/issues/new).\n", + "Please report any bugs on the [issue page](https://github.com/ACCLAB/DABEST-python/issues/new).\n", "\n" ] }, @@ -160,9 +169,9 @@ "source": [ "## Contributing\n", "\n", - "All contributions are welcome; please read the [Guidelines for contributing](https://github.com/ACCLAB/DABEST-python/blob/master/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](https://github.com/ACCLAB/DABEST-python/blob/master/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" ] }, { @@ -171,15 +180,7 @@ "metadata": {}, "source": [ "### A wish list for new features\n", - "Currently, DABEST offers functions to handle data traditionally analyzed with Student's paired and unpaired t-tests. It also offers plots for multiplexed versions of these, and the estimation counterpart to a 1-way analysis of variance (ANOVA), the shared-control design. While these five functions execute a large fraction of common biomedical data analyses, there remain three others: 2-way data, time-series group data, and proportional data. We aim to add these new functions to both the R and Python libraries.\n", - "\n", - "- In many experiments, four groups are investigate to isolate an interaction, for example: a genotype × drug effect. Here, wild-type and mutant animals are each subjected to drug or sham treatments; the data are traditionally analysed with a 2×2 ANOVA. We have received requests by email, Twitter, and GitHub to implement an estimation counterpart to the 2-way ANOVA. To do this, we will implement $\\Delta\\Delta$ plots, in which the difference of means ($\\Delta$) of two groups is subtracted from a second two-group $\\Delta$. **Implemented in v2023.02.14.**\n", - "\n", - "- Currently, DABEST can analyse multiple paired data in a single plot, and multiple groups with a common, shared control. However, a common design in biomedical science is to follow the same group of subjects over multiple, successive time points. An estimation plot for this would combine elements of the two other designs, and could be used in place of a repeated-measures ANOVA. **Implemented in v2023.02.14**\n", - "\n", - "- We have observed that proportional data are often analyzed in neuroscience and other areas of biomedical research. However, compared to other data types, the charts are frequently impoverished: often, they omit error bars, sample sizes, and even P values—let alone effect sizes. We would like DABEST to feature proportion charts, with error bars and a curve for the distribution of the proportional differences. **Implemented in v2023.02.14**\n", - "\n", - "We encourage contributions for the above 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 Group](https://groups.google.com/g/estimationstats)." ] }, { @@ -194,10 +195,14 @@ "\n", "## Testing\n", "\n", - "To test DABEST, you will need to install [pytest](https://docs.pytest.org/en/latest).\n", + "To test DABEST, you need to install [pytest](https://docs.pytest.org/en/latest) and [nbdev](https://nbdev.fast.ai/).\n", + "\n", + "- Run `pytest` in the root directory of the source distribution. This runs the test suite in the folder `dabest/tests/mpl_image_tests`. \n", + "- Run `nbdev_test` in the root directory of the source distribution. This runs the value assertion tests in the folder `dabest/tests`\n", "\n", - "Run `pytest` in the root directory of the source distribution. This runs the test suite in the folder `dabest/tests`. The test suite will ensure that the bootstrapping functions and the plotting functions perform as expected.\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", "\n", "## DABEST in other languages\n", "\n", @@ -205,11 +210,9 @@ ] }, { - "cell_type": "code", - "execution_count": null, - "id": "d2b56748", + "cell_type": "markdown", + "id": "7106313a", "metadata": {}, - "outputs": [], "source": [] } ], diff --git a/nbs/tests/README.md b/nbs/tests/README.md new file mode 100644 index 00000000..8372b68c --- /dev/null +++ b/nbs/tests/README.md @@ -0,0 +1,14 @@ +# Testing + +We use [pytest](https://docs.pytest.org/en/latest) to execute the tests. For testing of plot generation, we use the [mpl plugin](https://github.com/matplotlib/pytest-mpl) for pytest. A range of different plots are created, and compared against the baseline images in the `baseline_images` subfolder. + +If you have developed a new feature for the package and it is related to modifying original plots or generating new plots, you will need to generate new baseline images. To do so, run +```shell +pip install -e '.[dev]' +pytest --mpl-generate-path=nbs/tests/mpl_image_tests/baseline_images +``` + +To run the tests, go to the root of this repo directory and run +```shell +pytest dabest +``` \ No newline at end of file diff --git a/nbs/tests/baseline_images/test_01_gardner_altman_unpaired_meandiff.png b/nbs/tests/baseline_images/test_01_gardner_altman_unpaired_meandiff.png deleted file mode 100644 index 0ff1dbdd..00000000 Binary files a/nbs/tests/baseline_images/test_01_gardner_altman_unpaired_meandiff.png and /dev/null differ diff --git a/nbs/tests/baseline_images/test_02_gardner_altman_unpaired_mediandiff.png b/nbs/tests/baseline_images/test_02_gardner_altman_unpaired_mediandiff.png deleted file mode 100644 index 040604a0..00000000 Binary files a/nbs/tests/baseline_images/test_02_gardner_altman_unpaired_mediandiff.png and /dev/null differ diff --git a/nbs/tests/baseline_images/test_03_gardner_altman_unpaired_hedges_g.png 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Understanding the New Statistics: +# Effect Sizes, Confidence Intervals, and Meta-Analysis. Routledge, 2012, +# from Cumming 2012 Table 11.1 Pg 287. +wb = { + "control": [34, 54, 33, 44, 45, 53, 37, 26, 38, 58], + "expt": [66, 38, 35, 55, 48, 39, 65, 32, 57, 41], +} +wellbeing = pd.DataFrame(wb) + + +# from Cumming 2012 Table 11.2 Page 291 +paired_wb = { + "pre": [43, 28, 54, 36, 31, 48, 50, 69, 29, 40], + "post": [51, 33, 58, 42, 39, 45, 54, 68, 35, 44], + "ID": np.arange(10), +} +paired_wellbeing = pd.DataFrame(paired_wb) + + +# Data for testing Cohen's calculation. +# Only work with binary data. +# See Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S. Fourth edition. Springer. +# Make two groups of `smoke` by choosing `low` as a standard, and the data is trimed from the back. + +# to remove the array wrapping behaviour of black +# fmt: off +sk = { "low": [0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, + 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0], + "high": [1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, + 0, 0, 1, 1, 0, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1]} +# fmt: on +smoke = pd.DataFrame(sk) + + +# Data from Hogarty and Kromrey (1999) +# Kromrey, Jeffrey D., and Kristine Y. Hogarty. 1998. +# "Analysis Options for Testing Group Differences on Ordered Categorical +# Variables: An Empirical Investigation of Type I Error Control +# Statistical Power." +# Multiple Linear Regression Viewpoints 25 (1): 70 - 82. +likert_control = [1, 1, 2, 2, 2, 3, 3, 3, 4, 5] +likert_treatment = [1, 2, 3, 4, 4, 5] + + +# Data from Cliff (1993) +# Cliff, Norman. 1993. "Dominance Statistics: Ordinal Analyses to Answer +# Ordinal Questions." +# Psychological Bulletin 114 (3): 494-509. +a_scores = [6, 7, 9, 10] +b_scores = [1, 3, 4, 7, 8] + + +# kwargs for Dabest class init. +dabest_default_kwargs = dict( + x=None, + y=None, + ci=95, + resamples=5000, + random_seed=12345, + proportional=False, + delta2=False, + experiment=None, + experiment_label=None, + x1_level=None, + mini_meta=False, +) diff --git a/nbs/tests/data/mocked_data_test_04.py b/nbs/tests/data/mocked_data_test_04.py new file mode 100644 index 00000000..b1f74e17 --- /dev/null +++ b/nbs/tests/data/mocked_data_test_04.py @@ -0,0 +1,34 @@ +import pandas as pd +import numpy as np + +# Data for tests +# See Der, G., & Everitt, B. S. (2009). A handbook +# of statistical analyses using SAS, from Display 11.1 + +# to remove the array wrapping behaviour of black +# fmt: off +group = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, + 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2] +first = [20, 14, 7, 6, 9, 9, 7, 18, 6, 10, 5, 11, 10, 17, 16, 7, 5, 16, 2, 7, 9, 2, 7, 19, + 7, 9, 6, 13, 9, 6, 11, 7, 8, 3, 4, 11, 1, 6, 0, 18, 15, 10, 6, 9, 4, 4, 10] +second = [15, 12, 5, 10, 7, 9, 3, 17, 9, 15, 9, 11, 2, 12, 15, 10, 0, 7, 1, 11, 16, + 5, 3, 13, 5, 12, 7, 18, 10, 7, 11, 10, 18, 3, 10, 10, 3, 7, 3, 18, 15, 14, 6, 9, 3, 13, 11] +third = [14, 12, 5, 9, 9, 9, 7, 16, 9, 12, 7, 8, 9, 14, 12, 4, 5, 7, 1, 7, 14, 6, 5, 14, 8, 16, 10, + 14, 12, 8, 12, 11, 19, 3, 11, 10, 2, 7, 3, 19, 15, 16, 7, 13, 4, 13, 13] +fourth = [13, 10, 6, 8, 5, 11, 6, 14, 9, 12, 3, 8, 3, 10, 7, 7, 0, 6, 2, 5, 10, 7, 5, 12, 8, 17, 15, + 21, 14, 9, 14, 12, 19, 7, 17, 15, 4, 9, 4, 22, 18, 17, 9, 16, 7, 16, 17] +fifth = [13, 10, 5, 7, 4, 8, 5, 12, 9, 11, 5, 9, 5, 9, 9, 5, 0, 4, 2, 8, 6, 6, 5, 10, 6, 18, 16, 21, + 15, 12, 16, 14, 22, 8, 18, 16, 5, 10, 6, 22, 19, 19, 10, 20, 9, 19, 21] +# fmt: on + +df = pd.DataFrame( + { + "Group": group, + "First": first, + "Second": second, + "Third": third, + "Fourth": fourth, + "Fifth": fifth, + "ID": np.arange(0, 47), + } +) diff --git a/nbs/tests/data/mocked_data_test_06.py b/nbs/tests/data/mocked_data_test_06.py new file mode 100644 index 00000000..5a43b75e --- /dev/null +++ b/nbs/tests/data/mocked_data_test_06.py @@ -0,0 +1,65 @@ +import pandas as pd +import numpy as np + + +# Data for tests. +# See: Asheber Abebe. Introduction to Design and Analysis of Experiments +# with the SAS, from Example: Two-way RM Design Pg 137. +# to remove the array wrapping behaviour of black +# fmt: off +hr = [72, 78, 71, 72, 66, 74, 62, 69, 69, 66, 84, 80, 72, 65, 75, 71, + 86, 83, 82, 83, 79, 83, 73, 75, 73, 62, 90, 81, 72, 62, 69, 70] +# fmt: on + +# Add experiment column +e1 = np.repeat("Treatment1", 8).tolist() +e2 = np.repeat("Control", 8).tolist() +experiment = e1 + e2 + e1 + e2 + +# Add a `Drug` column as the first variable +d1 = np.repeat("AX23", 8).tolist() +d2 = np.repeat("CONTROL", 8).tolist() +drug = d1 + d2 + d1 + d2 + +# Add a `Time` column as the second variable +t1 = np.repeat("T1", 16).tolist() +t2 = np.repeat("T2", 16).tolist() +time = t1 + t2 + +# Add an `id` column for paired data plotting. +id_col = [] +for i in range(1, 9): + id_col.append(str(i) + "a") +for i in range(1, 9): + id_col.append(str(i) + "c") +id_col.extend(id_col) + +# Combine samples and gender into a DataFrame. +df_test = pd.DataFrame( + { + "ID": id_col, + "Drug": drug, + "Time": time, + "Experiment": experiment, + "Heart Rate": hr, + } +) + + +df_test_control = df_test[df_test["Experiment"] == "Control"] +df_test_control = df_test_control.pivot(index="ID", columns="Time", values="Heart Rate") + + +df_test_treatment1 = df_test[df_test["Experiment"] == "Treatment1"] +df_test_treatment1 = df_test_treatment1.pivot( + index="ID", columns="Time", values="Heart Rate" +) + +dabest_default_kwargs = dict( + ci=95, + resamples=5000, + random_seed=12345, + idx=None, + proportional=False, + mini_meta=False, +) diff --git a/nbs/tests/data/mocked_data_test_08.py b/nbs/tests/data/mocked_data_test_08.py new file mode 100644 index 00000000..450b1665 --- /dev/null +++ b/nbs/tests/data/mocked_data_test_08.py @@ -0,0 +1,31 @@ +import pandas as pd + +# Data for tests. +# See Oehlert, G. W. (2000). A First Course in Design +# and Analysis of Experiments (1st ed.). W. H. Freeman. +# from Problem 16.3 Pg 444. + +rep1_yes = [53.4, 54.3, 55.9, 53.8, 56.3, 58.6] +rep1_no = [58.2, 60.4, 62.4, 59.5, 64.5, 64.5] +rep2_yes = [46.5, 57.2, 57.4, 51.1, 56.9, 60.2] +rep2_no = [49.2, 61.6, 57.2, 51.3, 66.8, 62.7] +df_mini_meta = pd.DataFrame( + {"Rep1_Yes": rep1_yes, "Rep1_No": rep1_no, "Rep2_Yes": rep2_yes, "Rep2_No": rep2_no} +) +N = 6 # Size of each group + +# kwargs for Dabest class init. +dabest_default_kwargs = dict( + x=None, + y=None, + ci=95, + resamples=5000, + random_seed=12345, + proportional=False, + delta2=False, + experiment=None, + experiment_label=None, + x1_level=None, + paired=None, + id_col=None, +) diff --git a/nbs/tests/data/mocked_data_test_forestplot.py b/nbs/tests/data/mocked_data_test_forestplot.py new file mode 100644 index 00000000..3509c64d --- /dev/null +++ b/nbs/tests/data/mocked_data_test_forestplot.py @@ -0,0 +1,60 @@ +import pandas as pd +import scipy as sp +import numpy as np +import matplotlib.pyplot as plt +from numpy import random +from scipy.stats import norm +import dabest + +np.random.seed(9999) # Set the seed for reproducibility +N=20 +# Create samples +y = norm.rvs(loc=3, scale=0.4, size=N*4) +y[N:2*N] += 1 +y[2*N:3*N] -= 0.5 + +# Treatment, Rep, Genotype, and ID columns +treatment = np.repeat(['Placebo', 'Drug'], N*2).tolist() +rep = ['Rep1', 'Rep2'] * (N*2) +genotype = np.repeat(['W', 'M', 'W', 'M'], N).tolist() +id_col = list(range(0, N*2)) * 2 + + # Combine all columns into a DataFrame +dummy_df = pd.DataFrame({ + 'ID': id_col, + 'Rep': rep, + 'Genotype': genotype, + 'Treatment': treatment, + 'Y': y +}) + +unpaired_delta_01 = dabest.load(data = dummy_df, + x = ["Genotype", "Genotype"], + y = "Y", delta2 = True, + experiment = "Treatment") + +dummy_contrasts = [unpaired_delta_01] + +# 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. + "effect_size": "mean_diff", # Ensure it's a string. + "contrast_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. + "horizontal": False, # Ensure it's a boolean. +} diff --git a/nbs/tests/data/mocked_data_test_load_errors.py b/nbs/tests/data/mocked_data_test_load_errors.py new file mode 100644 index 00000000..d83d08fa --- /dev/null +++ b/nbs/tests/data/mocked_data_test_load_errors.py @@ -0,0 +1,17 @@ +import pandas as pd +import scipy as sp +from numpy import random + +random.seed(88888) +N = 10 +c1 = sp.stats.norm.rvs(loc=100, scale=5, size=N) +c2 = sp.stats.norm.rvs(loc=115, scale=5, size=N) +c3 = sp.stats.norm.rvs(loc=3.25, scale=0.4, size=N) + +t1 = sp.stats.norm.rvs(loc=3.5, scale=0.5, size=N) +t2 = sp.stats.norm.rvs(loc=2.5, scale=0.6, size=N) +id_col = pd.Series(range(1, N+1)) +dummy_df = pd.DataFrame({'Control 1' : c1, 'Test 1' : t1, + 'Control 2' : c2, 'Test 2' : t2, + 'Control 3' : c3, 'ID' : id_col + }) diff --git a/nbs/tests/data/mocked_data_test_swarmplot.py b/nbs/tests/data/mocked_data_test_swarmplot.py new file mode 100644 index 00000000..26bd7338 --- /dev/null +++ b/nbs/tests/data/mocked_data_test_swarmplot.py @@ -0,0 +1,32 @@ +import pandas as pd +import scipy as sp +import numpy as np +import matplotlib.pyplot as plt +from numpy import random + +# Dummy Pandas DataFrame used for swarmplots unit testing +random.seed(88888) +N = 10 +c1 = sp.stats.norm.rvs(loc=100, scale=5, size=N) +t1 = sp.stats.norm.rvs(loc=115, scale=5, size=N) + +females = np.repeat("Female", N / 2).tolist() +males = np.repeat("Male", N / 2).tolist() +gender = females + males + +dummy_df = pd.DataFrame({"Control 1": c1, "Test 1": t1, "gender": gender}) +dummy_df = pd.melt( + dummy_df, + id_vars=["gender"], + value_vars=["Control 1", "Test 1"], + var_name="group", + value_name="value", +) + +# Default swarmplot params for unit testing +default_swarmplot_kwargs = { + "data": dummy_df, + "x": "group", + "y": "value", + 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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 new file mode 100644 index 00000000..dd9a202a Binary files /dev/null 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 new file mode 100644 index 00000000..7cc9b3ab --- /dev/null +++ b/nbs/tests/mpl_image_tests/test_03_plotting.py @@ -0,0 +1,423 @@ +import pytest +import numpy as np +from scipy.stats import norm +import pandas as pd +import matplotlib as mpl +import os +from pathlib import Path + +mpl.use("Agg") +import matplotlib.ticker as Ticker +import matplotlib.pyplot as plt + +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 + + +df = create_demo_dataset() + +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_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") +) + +multi_groups = load( + df, + idx=( + ( + "Control 1", + "Test 1", + ), + ("Control 2", "Test 2", "Test 3"), + ("Control 3", "Test 4", "Test 5", "Test 6"), + ), +) + +multi_groups_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_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", +) + + +@pytest.mark.mpl_image_compare(tolerance=8) +def test_01_gardner_altman_unpaired_meandiff(): + return two_groups_unpaired.mean_diff.plot() + + +@pytest.mark.mpl_image_compare(tolerance=8) +def test_02_gardner_altman_unpaired_mediandiff(): + return two_groups_unpaired.median_diff.plot() + + +@pytest.mark.mpl_image_compare(tolerance=8) +def test_03_gardner_altman_unpaired_hedges_g(): + return two_groups_unpaired.hedges_g.plot() + + +@pytest.mark.mpl_image_compare(tolerance=8) +def test_04_gardner_altman_paired_meandiff(): + return two_groups_paired.mean_diff.plot() + + +@pytest.mark.mpl_image_compare(tolerance=8) +def test_04_gardner_altman_paired_hedges_g(): + return two_groups_paired.hedges_g.plot() + + +@pytest.mark.mpl_image_compare(tolerance=8) +def test_05_cummings_two_group_unpaired_meandiff(): + 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(): + 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(): + return multi_2group.mean_diff.plot() + + +@pytest.mark.mpl_image_compare(tolerance=8) +def test_08_cummings_multi_group_paired(): + return multi_2group_paired.mean_diff.plot(fig_size=(6, 6)) + + +@pytest.mark.mpl_image_compare(tolerance=8) +def test_09_cummings_shared_control(): + return shared_control.mean_diff.plot() + + +@pytest.mark.mpl_image_compare(tolerance=8) +def test_10_cummings_multi_groups(): + return multi_groups.mean_diff.plot() + + +@pytest.mark.mpl_image_compare(tolerance=8) +def test_11_inset_plots(): + # Load the iris dataset. + try: + # parent directory of the current working directory + parent = Path(__file__).parent.parent.absolute() + # print(f"parent={parent}") + iris_path = os.path.join(str(parent), "data", "iris.csv") + # print(f"iris_path={iris_path}") + iris = pd.read_csv(iris_path) + print(iris.head()) + except Exception as e: + print(f"Error while loading the iris dataset. Reason {e}") + + iris_melt = pd.melt( + iris.reset_index(), id_vars=["species", "index"], var_name="metric" + ) + + # Load the above data into `dabest`. + iris_dabest1 = load( + data=iris, + x="species", + y="petal_width", + idx=("setosa", "versicolor", "virginica"), + ) + + iris_dabest2 = load( + data=iris, x="species", y="sepal_width", idx=("setosa", "versicolor") + ) + + iris_dabest3 = load( + data=iris_melt[iris_melt.species == "setosa"], + x="metric", + y="value", + idx=("sepal_length", "sepal_width"), + paired="baseline", + id_col="index", + ) + + # Create Figure. + fig, ax = plt.subplots( + nrows=2, ncols=2, figsize=(15, 15), gridspec_kw={"wspace": 0.5} + ) + + iris_dabest1.mean_diff.plot(ax=ax.flat[0]) + + iris_dabest2.mean_diff.plot(ax=ax.flat[1]) + + iris_dabest3.mean_diff.plot(ax=ax.flat[2]) + + iris_dabest3.mean_diff.plot(ax=ax.flat[3], float_contrast=False) + + return fig + + +@pytest.mark.mpl_image_compare(tolerance=8) +def test_12_gardner_altman_ylabel(): + return two_groups_unpaired.mean_diff.plot( + swarm_label="This is my\nrawdata", contrast_label="The bootstrap\ndistribtions!" + ) + + +@pytest.mark.mpl_image_compare(tolerance=8) +def test_13_multi_2group_color(): + return multi_2group.mean_diff.plot(color_col="Gender") + + +@pytest.mark.mpl_image_compare(tolerance=8) +def test_14_gardner_altman_paired_color(): + 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(): + return multi_2group.mean_diff.plot( + fig_size=(8, 6), color_col="Gender", custom_palette="Dark2" + ) + + +@pytest.mark.mpl_image_compare(tolerance=8) +def test_16_change_palette_b(): + return multi_2group.mean_diff.plot(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_17_change_palette_c(): + return multi_2group.mean_diff.plot(custom_palette=my_color_palette) + + +@pytest.mark.mpl_image_compare(tolerance=8) +def test_18_desat(): + return multi_2group.mean_diff.plot( + custom_palette=my_color_palette, swarm_desat=0.75, halfviolin_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) + + +@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)) + + +@pytest.mark.mpl_image_compare(tolerance=8) +def test_21_invert_ylim(): + return multi_2group.mean_diff.plot( + contrast_ylim=(2, -2), contrast_label="More negative is better!" + ) + + +@pytest.mark.mpl_image_compare(tolerance=8) +def test_22_ticker_gardner_altman(): + f = two_groups_unpaired.mean_diff.plot() + + rawswarm_axes = f.axes[0] + contrast_axes = f.axes[1] + + rawswarm_axes.yaxis.set_major_locator(Ticker.MultipleLocator(1)) + rawswarm_axes.yaxis.set_minor_locator(Ticker.MultipleLocator(0.5)) + + contrast_axes.yaxis.set_major_locator(Ticker.MultipleLocator(0.5)) + contrast_axes.yaxis.set_minor_locator(Ticker.MultipleLocator(0.25)) + + return f + + +@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)) + + rawswarm_axes = f.axes[0] + contrast_axes = f.axes[1] + + rawswarm_axes.yaxis.set_major_locator(Ticker.MultipleLocator(2)) + rawswarm_axes.yaxis.set_minor_locator(Ticker.MultipleLocator(1)) + + contrast_axes.yaxis.set_major_locator(Ticker.MultipleLocator(0.5)) + contrast_axes.yaxis.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_24_wide_df_nan(): + wide_df_dabest = load(wide_df, idx=("Control", "Test 1", "Test 2", "Test 3")) + + return wide_df_dabest.mean_diff.plot() + + +@pytest.mark.mpl_image_compare(tolerance=8) +def test_25_long_df_nan(): + 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() + + +@pytest.mark.mpl_image_compare(tolerance=8) +def test_26_slopegraph_kwargs(): + 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(): + 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(): + return two_groups_unpaired.mean_diff.plot( + 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_29_paired_cumming_slopegraph_reflines_kwargs(): + return two_groups_paired.mean_diff.plot( + float_contrast=False, + color_col="Gender", + slopegraph_kwargs=dict(linestyle="dotted"), + reflines_kwargs=dict(linestyle="dashed", linewidth=2), + contrast_ylim=(-1, 1), + ) + + +@pytest.mark.mpl_image_compare(tolerance=8) +def test_30_sequential_cumming_slopegraph(): + return multi_groups_sequential.mean_diff.plot() + + +@pytest.mark.mpl_image_compare(tolerance=8) +def test_31_baseline_cumming_slopegraph(): + 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.style.use("dark_background") + + return multi_2group.mean_diff.plot() diff --git a/nbs/tests/mpl_image_tests/test_05_forest_plot.py b/nbs/tests/mpl_image_tests/test_05_forest_plot.py new file mode 100644 index 00000000..430a4eb3 --- /dev/null +++ b/nbs/tests/mpl_image_tests/test_05_forest_plot.py @@ -0,0 +1,198 @@ +import pytest +import numpy as np +from scipy.stats import norm +import pandas as pd +import matplotlib as mpl +import os +from pathlib import Path + +mpl.use("Agg") +import matplotlib.ticker as Ticker +import matplotlib.pyplot as plt + +from dabest._api import load + +import numpy as np +import pandas as pd +from scipy.stats import norm + +def create_delta_dataset(N=20, + seed=9999, + second_quarter_adjustment=3, + third_quarter_adjustment=-0.1): + np.random.seed(seed) # Set the seed for reproducibility + + # Create samples + y = norm.rvs(loc=3, scale=0.4, size=N*4) + y[N:2*N] += second_quarter_adjustment + y[2*N:3*N] += third_quarter_adjustment + + # Treatment, Rep, Genotype, and ID columns + treatment = np.repeat(['Placebo', 'Drug'], N*2).tolist() + rep = ['Rep1', 'Rep2'] * (N*2) + genotype = np.repeat(['W', 'M', 'W', 'M'], N).tolist() + id_col = list(range(0, N*2)) * 2 + + # Combine all columns into a DataFrame + df = pd.DataFrame({ + 'ID': id_col, + 'Rep': rep, + 'Genotype': genotype, + 'Treatment': treatment, + 'Y': y + }) + + return df + +def create_mini_meta_dataset(N=20, seed=9999, control_locs=[3, 3.5, 3.25], control_scales=[0.4, 0.75, 0.4], + test_locs=[3.5, 2.5, 3], test_scales=[0.5, 0.6, 0.75]): + np.random.seed(seed) # Set the seed for reproducibility + + # Create samples for controls and tests + controls_tests = [] + for loc, scale in zip(control_locs + test_locs, control_scales + test_scales): + controls_tests.append(norm.rvs(loc=loc, scale=scale, size=N)) + + # Add a `Gender` column for coloring the data + gender = ['Female'] * (N // 2) + ['Male'] * (N // 2) + + # Add an `ID` column for paired data plotting + id_col = list(range(1, N + 1)) + + # Combine samples and gender into a DataFrame + df_columns = {f'Control {i+1}': controls_tests[i] for i in range(len(control_locs))} + df_columns.update({f'Test {i+1}': controls_tests[i + len(control_locs)] for i in range(len(test_locs))}) + df_columns['Gender'] = gender + df_columns['ID'] = id_col + + df = pd.DataFrame(df_columns) + + return df + +# Generate the first dataset with a different seed and adjustments +df_delta2_drug1 = create_delta_dataset(seed=9999, + second_quarter_adjustment=1, + third_quarter_adjustment=-0.5) + +# Generate the second dataset with a different seed and adjustments +df_delta2_drug2 = create_delta_dataset(seed=9999, + second_quarter_adjustment=0.1, + third_quarter_adjustment=-1) + +# Generate the third dataset with the same seed as the first but different adjustments +df_delta2_drug3 = create_delta_dataset(seed=9999, + second_quarter_adjustment=3, + third_quarter_adjustment=-0.1) + + +unpaired_delta_01 = load(data = df_delta2_drug1, + x = ["Genotype", "Genotype"], + y = "Y", delta2 = True, + experiment = "Treatment") + +unpaired_delta_02 = load(data = df_delta2_drug2, + x = ["Genotype", "Genotype"], + y = "Y", delta2 = True, + experiment = "Treatment") + +unpaired_delta_03 = load(data = df_delta2_drug3, + x = ["Genotype", "Genotype"], + y = "Y", + delta2 = True, + experiment = "Treatment") + +paired_delta_01 = load(data = df_delta2_drug1, + paired = "baseline", id_col="ID", + x = ["Treatment", "Rep"], y = "Y", + delta2 = True, experiment = "Genotype") + +paired_delta_02 = load(data = df_delta2_drug2, + paired = "baseline", id_col="ID", + x = ["Treatment", "Rep"], y = "Y", + delta2 = True, experiment = "Genotype") +paired_delta_03 = load(data = df_delta2_drug3, + paired = "baseline", id_col="ID", + x = ["Treatment", "Rep"], y = "Y", + delta2 = True, experiment = "Genotype") + +contrasts = [unpaired_delta_01, unpaired_delta_02, unpaired_delta_03] + +paired_contrasts = [paired_delta_01, paired_delta_02, paired_delta_03] + +# Customizable dataset creation with different arguments +df_mini_meta01 = create_mini_meta_dataset(seed=9999, + control_locs=[3, 3.5, 3.25], + control_scales=[0.4, 0.75, 0.4], + test_locs=[3.5, 2.5, 3], + test_scales=[0.5, 0.6, 0.75]) + +df_mini_meta02 = create_mini_meta_dataset(seed=9999, + control_locs=[4, 2, 3.25], + control_scales=[0.3, 0.75, 0.45], + test_locs=[2, 1.5, 2.75], + test_scales=[0.5, 0.6, 0.4]) + +df_mini_meta03 = create_mini_meta_dataset(seed=9999, + control_locs=[6, 5.5, 4.25], + control_scales=[0.4, 0.75, 0.45], + test_locs=[4.5, 3.5, 3], + test_scales=[0.5, 0.6, 0.9]) + +contrast_mini_meta01 = load(data = df_mini_meta01, + idx=(("Control 1", "Test 1"), ("Control 2", "Test 2"), ("Control 3", "Test 3")), + mini_meta=True) + +contrast_mini_meta02 = load(data = df_mini_meta02, + idx=(("Control 1", "Test 1"), ("Control 2", "Test 2"), ("Control 3", "Test 3")), + mini_meta=True) + +contrast_mini_meta03 = load(data = df_mini_meta03, + idx=(("Control 1", "Test 1"), ("Control 2", "Test 2"), ("Control 3", "Test 3")), + mini_meta=True) + +contrasts_mini_meta = [contrast_mini_meta01, contrast_mini_meta02, contrast_mini_meta03] + + +# 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']) + +@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']) + +@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) + +@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) + +@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']) + +@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) + + + + diff --git a/nbs/tests/test_07_delta-delta_plots.py b/nbs/tests/mpl_image_tests/test_07_delta-delta_plots.py similarity index 81% rename from nbs/tests/test_07_delta-delta_plots.py rename to nbs/tests/mpl_image_tests/test_07_delta-delta_plots.py index 754c0689..9dd63e1e 100644 --- a/nbs/tests/test_07_delta-delta_plots.py +++ b/nbs/tests/mpl_image_tests/test_07_delta-delta_plots.py @@ -82,74 +82,86 @@ def create_demo_dataset_delta(seed=9999, N=20): paired="sequential", id_col="ID") -@pytest.mark.mpl_image_compare(tolerance=10) +@pytest.mark.mpl_image_compare(tolerance=8) def test_47_cummings_unpaired_delta_delta_meandiff(): return unpaired.mean_diff.plot(); -@pytest.mark.mpl_image_compare(tolerance=10) +@pytest.mark.mpl_image_compare(tolerance=8) def test_48_cummings_sequential_delta_delta_meandiff(): return sequential.mean_diff.plot(); -@pytest.mark.mpl_image_compare(tolerance=10) +@pytest.mark.mpl_image_compare(tolerance=8) def test_49_cummings_baseline_delta_delta_meandiff(): return baseline.mean_diff.plot(); -@pytest.mark.mpl_image_compare(tolerance=10) +@pytest.mark.mpl_image_compare(tolerance=8) def test_50_delta_plot_ylabel(): return baseline.mean_diff.plot(swarm_label="This is my\nrawdata", contrast_label="The bootstrap\ndistribtions!", delta2_label="This is delta!"); -@pytest.mark.mpl_image_compare(tolerance=10) +@pytest.mark.mpl_image_compare(tolerance=8) def test_51_delta_plot_change_palette_a(): return sequential.mean_diff.plot(custom_palette="Dark2"); -@pytest.mark.mpl_image_compare(tolerance=10) +@pytest.mark.mpl_image_compare(tolerance=8) def test_52_delta_specified(): return unpaired_specified.mean_diff.plot(); -@pytest.mark.mpl_image_compare(tolerance=10) +@pytest.mark.mpl_image_compare(tolerance=8) def test_53_delta_change_ylims(): return sequential.mean_diff.plot(swarm_ylim=(0, 9), contrast_ylim=(-2, 2), fig_size=(15,6)); -@pytest.mark.mpl_image_compare(tolerance=10) +@pytest.mark.mpl_image_compare(tolerance=8) def test_54_delta_invert_ylim(): return sequential.mean_diff.plot(contrast_ylim=(2, -2), contrast_label="More negative is better!"); -@pytest.mark.mpl_image_compare(tolerance=10) +@pytest.mark.mpl_image_compare(tolerance=8) def test_55_delta_median_diff(): return sequential.median_diff.plot(); -@pytest.mark.mpl_image_compare(tolerance=10) +@pytest.mark.mpl_image_compare(tolerance=8) def test_56_delta_cohens_d(): return unpaired.cohens_d.plot(); -@pytest.mark.mpl_image_compare(tolerance=10) +@pytest.mark.mpl_image_compare(tolerance=8) def test_57_delta_show_delta2(): return unpaired.mean_diff.plot(show_delta2=False); -@pytest.mark.mpl_image_compare(tolerance=10) +@pytest.mark.mpl_image_compare(tolerance=8) def test_58_delta_axes_invert_ylim(): return unpaired.mean_diff.plot(delta2_ylim=(2, -2), delta2_label="More negative is better!"); -@pytest.mark.mpl_image_compare(tolerance=10) +@pytest.mark.mpl_image_compare(tolerance=8) def test_59_delta_axes_invert_ylim_not_showing_delta2(): return unpaired.mean_diff.plot(delta2_ylim=(2, -2), delta2_label="More negative is better!", - show_delta2=False); \ No newline at end of file + show_delta2=False); + +@pytest.mark.mpl_image_compare(tolerance=8) +def test_71_unpaired_delta_g(): + return unpaired.delta_g.plot(); + +@pytest.mark.mpl_image_compare(tolerance=8) +def test_72_sequential_delta_g(): + return sequential.mean_diff.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 diff --git a/nbs/tests/test_09_mini_meta_plots.py b/nbs/tests/mpl_image_tests/test_09_mini_meta_plots.py similarity index 84% rename from nbs/tests/test_09_mini_meta_plots.py rename to nbs/tests/mpl_image_tests/test_09_mini_meta_plots.py index a09a2dcb..2ba4ad5e 100644 --- a/nbs/tests/test_09_mini_meta_plots.py +++ b/nbs/tests/mpl_image_tests/test_09_mini_meta_plots.py @@ -55,9 +55,8 @@ def create_demo_dataset(seed=9999, N=20): df = create_demo_dataset() -unpaired = load(df, - idx=(("Control 1", "Test 1"), ("Control 2", "Test 2"), ("Control 3", "Test 3")), - mini_meta=True) +unpaired = load(df, idx=(("Control 1", "Test 1"), ("Control 2", "Test 2"), ("Control 3", "Test 3")), + mini_meta=True) baseline = load(df, id_col = "ID", @@ -72,61 +71,61 @@ def create_demo_dataset(seed=9999, N=20): -@pytest.mark.mpl_image_compare(tolerance=10) +@pytest.mark.mpl_image_compare(tolerance=8) def test_60_cummings_unpaired_mini_meta_meandiff(): return unpaired.mean_diff.plot(); -@pytest.mark.mpl_image_compare(tolerance=10) +@pytest.mark.mpl_image_compare(tolerance=8) def test_61_cummings_sequential_mini_meta_meandiff(): return sequential.mean_diff.plot(); -@pytest.mark.mpl_image_compare(tolerance=10) +@pytest.mark.mpl_image_compare(tolerance=8) def test_62_cummings_baseline_mini_meta_meandiff(): return baseline.mean_diff.plot(); -@pytest.mark.mpl_image_compare(tolerance=10) +@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", contrast_label="The bootstrap\ndistribtions!"); -@pytest.mark.mpl_image_compare(tolerance=10) +@pytest.mark.mpl_image_compare(tolerance=8) def test_64_mini_meta_plot_change_palette_a(): return unpaired.mean_diff.plot(custom_palette="Dark2"); -@pytest.mark.mpl_image_compare(tolerance=10) +@pytest.mark.mpl_image_compare(tolerance=8) def test_65_mini_meta_dot_sizes(): return sequential.mean_diff.plot(show_pairs=False,raw_marker_size=3, es_marker_size=12); -@pytest.mark.mpl_image_compare(tolerance=10) +@pytest.mark.mpl_image_compare(tolerance=8) def test_66_mini_meta_change_ylims(): return sequential.mean_diff.plot(swarm_ylim=(0, 5), contrast_ylim=(-2, 2), fig_size=(15,6)); -@pytest.mark.mpl_image_compare(tolerance=10) +@pytest.mark.mpl_image_compare(tolerance=8) def test_67_mini_meta_invert_ylim(): return sequential.mean_diff.plot(contrast_ylim=(2, -2), contrast_label="More negative is better!"); -@pytest.mark.mpl_image_compare(tolerance=10) +@pytest.mark.mpl_image_compare(tolerance=8) def test_68_mini_meta_median_diff(): return sequential.median_diff.plot(); -@pytest.mark.mpl_image_compare(tolerance=10) +@pytest.mark.mpl_image_compare(tolerance=8) def test_69_mini_meta_cohens_d(): return unpaired.cohens_d.plot(); -@pytest.mark.mpl_image_compare(tolerance=10) +@pytest.mark.mpl_image_compare(tolerance=8) def test_70_mini_meta_not_show(): 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 new file mode 100644 index 00000000..5a75aa86 --- /dev/null +++ b/nbs/tests/mpl_image_tests/test_10_proportion_plot.py @@ -0,0 +1,397 @@ +import pytest +import numpy as np +import pandas as pd +import matplotlib as mpl + +mpl.use("Agg") +import matplotlib.ticker as Ticker +import matplotlib.pyplot as plt +from dabest._api import load + + +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_prop_dataset() + +two_groups_unpaired = load(df, idx=("Control 1", "Test 1"), proportional=True) + +multi_2group = load( + df, + idx=( + ( + "Control 1", + "Test 1", + ), + ("Control 2", "Test 2"), + ), + proportional=True, +) + +shared_control = load( + df, + idx=("Control 1", "Test 1", "Test 2", "Test 3", "Test 4", "Test 5", "Test 6"), + proportional=True, +) + +multi_groups = load( + df, + idx=( + ( + "Control 1", + "Test 1", + ), + ("Control 2", "Test 2", "Test 3"), + ("Control 3", "Test 4", "Test 5", "Test 6"), + ), + proportional=True, +) + +two_groups_paired = load( + df, idx=("Control 1", "Test 1"), paired="baseline", id_col="ID", proportional=True +) + +multi_2group_paired = load( + df, + idx=(("Control 1", "Test 1"), ("Control 2", "Test 2")), + paired="baseline", + id_col="ID", + proportional=True, +) + +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", + proportional=True, +) + +two_groups_sequential = load( + df, idx=("Control 1", "Test 1"), paired="sequential", id_col="ID", proportional=True +) + +multi_2group_sequential = load( + df, + idx=(("Control 1", "Test 1"), ("Control 2", "Test 2")), + paired="sequential", + id_col="ID", + proportional=True, +) + +multi_groups_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", + proportional=True, +) +shared_control_paired = load( + df, + idx=("Control 1", "Test 1", "Test 2", "Test 3", "Test 4", "Test 5", "Test 6"), + paired="sequential", + id_col="ID", + proportional=True, +) + +zero_to_zero = load( + df, idx=("Test 7", "Test 9"), proportional=True, paired="sequential", id_col="ID" +) +zero_to_one = load( + df, idx=("Test 7", "Test 8"), proportional=True, paired="sequential", id_col="ID" +) +one_to_zero = load( + df, idx=("Test 8", "Test 7"), proportional=True, paired="sequential", id_col="ID" +) + +one_in_separate_control = load( + df, + idx=( + (("Control 1", "Test 1"), ("Test 2", "Test 3"), ("Test 4", "Test 8", "Test 6")) + ), + proportional=True, + paired="sequential", + id_col="ID", +) + + +@pytest.mark.mpl_image_compare(tolerance=8) +def test_101_gardner_altman_unpaired_propdiff(): + return two_groups_unpaired.mean_diff.plot() + + +@pytest.mark.mpl_image_compare(tolerance=8) +def test_103_cummings_two_group_unpaired_propdiff(): + 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(): + return multi_2group.mean_diff.plot() + + +@pytest.mark.mpl_image_compare(tolerance=8) +def test_106_cummings_shared_control_propdiff(): + return shared_control.mean_diff.plot() + + +@pytest.mark.mpl_image_compare(tolerance=8) +def test_107_cummings_multi_groups_propdiff(): + return multi_groups.mean_diff.plot() + + +@pytest.mark.mpl_image_compare(tolerance=8) +def test_109_gardner_altman_ylabel(): + return two_groups_unpaired.mean_diff.plot( + bar_label="This is my\nrawdata", contrast_label="The bootstrap\ndistribtions!" + ) + + +@pytest.mark.mpl_image_compare(tolerance=8) +def test_110_change_fig_size(): + 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(): + return multi_2group.mean_diff.plot(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_112_change_palette_c(): + return multi_2group.mean_diff.plot(custom_palette=my_color_palette) + + +@pytest.mark.mpl_image_compare(tolerance=8) +def test_113_desat(): + return multi_2group.mean_diff.plot( + custom_palette=my_color_palette, bar_desat=0.1, halfviolin_desat=0.25 + ) + + +@pytest.mark.mpl_image_compare(tolerance=8) +def test_114_change_ylims(): + return multi_2group.mean_diff.plot(contrast_ylim=(-2, 2)) + + +@pytest.mark.mpl_image_compare(tolerance=8) +def test_115_invert_ylim(): + return multi_2group.mean_diff.plot( + contrast_ylim=(2, -2), contrast_label="More negative is better!" + ) + + +@pytest.mark.mpl_image_compare(tolerance=8) +def test_116_ticker_gardner_altman(): + fig = two_groups_unpaired.mean_diff.plot() + + rawswarm_axes = fig.axes[0] + contrast_axes = fig.axes[1] + + rawswarm_axes.yaxis.set_major_locator(Ticker.MultipleLocator(1)) + rawswarm_axes.yaxis.set_minor_locator(Ticker.MultipleLocator(0.5)) + + contrast_axes.yaxis.set_major_locator(Ticker.MultipleLocator(0.5)) + contrast_axes.yaxis.set_minor_locator(Ticker.MultipleLocator(0.25)) + return fig + + +@pytest.mark.mpl_image_compare(tolerance=8) +def test_117_err_color(): + return two_groups_unpaired.mean_diff.plot(err_color="purple") + + +@pytest.mark.mpl_image_compare(tolerance=8) +def test_118_cummings_two_group_unpaired_meandiff_bar_width(): + return two_groups_unpaired.mean_diff.plot(bar_width=0.4, float_contrast=False) + + +np.random.seed(9999) +Ns = [20, 10, 21, 20] +n = 1 +c1 = pd.DataFrame({"Control": np.random.binomial(n, 0.2, size=Ns[0])}) +t1 = pd.DataFrame({"Test 1": np.random.binomial(n, 0.5, size=Ns[1])}) +t2 = pd.DataFrame({"Test 2": np.random.binomial(n, 0.4, size=Ns[2])}) +t3 = pd.DataFrame({"Test 3": np.random.binomial(n, 0.7, 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_119_wide_df_nan(): + wide_df_dabest = load( + wide_df, idx=("Control", "Test 1", "Test 2", "Test 3"), proportional=True + ) + + return wide_df_dabest.mean_diff.plot() + + +@pytest.mark.mpl_image_compare(tolerance=8) +def test_120_long_df_nan(): + long_df_dabest = load( + long_df, + x="group", + y="value", + idx=("Control", "Test 1", "Test 2", "Test 3"), + proportional=True, + ) + + return long_df_dabest.mean_diff.plot() + + +@pytest.mark.mpl_image_compare(tolerance=8) +def test_121_cohens_h_gardner_altman(): + return two_groups_unpaired.cohens_h.plot() + + +@pytest.mark.mpl_image_compare(tolerance=8) +def test_122_cohens_h_cummings(): + return two_groups_unpaired.cohens_h.plot(float_contrast=False) + + +@pytest.mark.mpl_image_compare(tolerance=8) +def test_123_sankey_gardner_altman(): + return two_groups_paired.mean_diff.plot() + + +@pytest.mark.mpl_image_compare(tolerance=8) +def test_124_sankey_cummings(): + return two_groups_paired.mean_diff.plot(float_contrast=False) + + +@pytest.mark.mpl_image_compare(tolerance=8) +def test_125_sankey_2paired_groups(): + return multi_2group_paired.mean_diff.plot() + + +@pytest.mark.mpl_image_compare(tolerance=8) +def test_126_sankey_2sequential_groups(): + return multi_2group_sequential.mean_diff.plot() + + +@pytest.mark.mpl_image_compare(tolerance=8) +def test_127_sankey_multi_group_paired(): + return multi_groups_paired.mean_diff.plot() + + +@pytest.mark.mpl_image_compare(tolerance=8) +def test_128_sankey_transparency(): + 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(): + return zero_to_zero.mean_diff.plot() + + +@pytest.mark.mpl_image_compare(tolerance=8) +def test_130_zero_to_one(): + return zero_to_one.mean_diff.plot() + + +@pytest.mark.mpl_image_compare(tolerance=8) +def test_131_one_to_zero(): + return one_to_zero.mean_diff.plot() + + +@pytest.mark.mpl_image_compare(tolerance=8) +def test_132_shared_control_sankey_off(): + 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(): + 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(): + 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(): + return multi_groups_sequential.mean_diff.plot(sankey_kwargs={"flow": False}) + + +@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.style.use("dark_background") + return multi_2group.mean_diff.plot(face_color="black") diff --git a/nbs/tests/test_01_effsizes_pvals.ipynb b/nbs/tests/test_01_effsizes_pvals.ipynb index fa848f90..f2997a42 100644 --- a/nbs/tests/test_01_effsizes_pvals.ipynb +++ b/nbs/tests/test_01_effsizes_pvals.ipynb @@ -10,8 +10,6 @@ "import pytest\n", "import lqrt\n", "import numpy as np\n", - "from numpy import median as npmedian\n", - "from numpy import mean as npmean\n", "import scipy as sp\n", "import pandas as pd" ] @@ -24,7 +22,7 @@ "outputs": [], "source": [ "from dabest._stats_tools import effsize\n", - "from dabest._classes import TwoGroupsEffectSize, PermutationTest, Dabest" + "from dabest import Dabest, TwoGroupsEffectSize, PermutationTest" ] }, { @@ -34,62 +32,7 @@ "metadata": {}, "outputs": [], "source": [ - "# Data for tests.\n", - "# See Cumming, G. Understanding the New Statistics:\n", - "# Effect Sizes, Confidence Intervals, and Meta-Analysis. Routledge, 2012,\n", - "# from Cumming 2012 Table 11.1 Pg 287.\n", - "wb = {\"control\": [34, 54, 33, 44, 45, 53, 37, 26, 38, 58],\n", - " \"expt\": [66, 38, 35, 55, 48, 39, 65, 32, 57, 41]}\n", - "wellbeing = pd.DataFrame(wb)\n", - "\n", - "\n", - "\n", - "# from Cumming 2012 Table 11.2 Page 291\n", - "paired_wb = {\"pre\": [43, 28, 54, 36, 31, 48, 50, 69, 29, 40],\n", - " \"post\": [51, 33, 58, 42, 39, 45, 54, 68, 35, 44],\n", - " \"ID\": np.arange(10)}\n", - "paired_wellbeing = pd.DataFrame(paired_wb)\n", - "\n", - "\n", - "\n", - "# Data for testing Cohen's calculation.\n", - "# Only work with binary data.\n", - "# See Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S. Fourth edition. Springer.\n", - "# Make two groups of `smoke` by choosing `low` as a standard, and the data is trimed from the back.\n", - "sk = { \"low\": [0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, \n", - " 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0],\n", - " \"high\": [1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, \n", - " 0, 0, 1, 1, 0, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1]}\n", - "smoke = pd.DataFrame(sk)\n", - "\n", - "\n", - "\n", - "# Data from Hogarty and Kromrey (1999)\n", - "# Kromrey, Jeffrey D., and Kristine Y. Hogarty. 1998.\n", - "# \"Analysis Options for Testing Group Differences on Ordered Categorical\n", - "# Variables: An Empirical Investigation of Type I Error Control\n", - "# Statistical Power.\"\n", - "# Multiple Linear Regression Viewpoints 25 (1): 70 - 82.\n", - "likert_control = [1, 1, 2, 2, 2, 3, 3, 3, 4, 5]\n", - "likert_treatment = [1, 2, 3, 4, 4, 5]\n", - "\n", - "\n", - "\n", - "# Data from Cliff (1993)\n", - "# Cliff, Norman. 1993. \"Dominance Statistics: Ordinal Analyses to Answer\n", - "# Ordinal Questions.\"\n", - "# Psychological Bulletin 114 (3): 494-509.\n", - "a_scores = [6, 7, 9, 10]\n", - "b_scores = [1, 3, 4, 7, 8]\n", - "\n", - "\n", - "\n", - "# kwargs for Dabest class init.\n", - "dabest_default_kwargs = dict(x=None, y=None, ci=95, \n", - " resamples=5000, random_seed=12345,\n", - " proportional=False, delta2=False, experiment=None, \n", - " experiment_label=None, x1_level=None, mini_meta=False\n", - " )" + "from data.mocked_data_test_01 import wellbeing, paired_wellbeing, smoke, likert_control, likert_treatment, a_scores, b_scores, dabest_default_kwargs" ] }, { @@ -128,7 +71,7 @@ "outputs": [], "source": [ "median_diff = effsize.func_difference(wellbeing.control, wellbeing.expt,\n", - " npmedian, is_paired=False)\n", + " np.median, is_paired=False)\n", "assert median_diff == pytest.approx(3.5)" ] }, @@ -149,7 +92,7 @@ "source": [ "mean_diff = effsize.func_difference(paired_wellbeing.pre,\n", " paired_wellbeing.post,\n", - " npmean, is_paired=\"baseline\")\n", + " np.mean, is_paired=\"baseline\")\n", "assert mean_diff == pytest.approx(4.10)" ] }, @@ -170,7 +113,7 @@ "source": [ "median_diff = effsize.func_difference(paired_wellbeing.pre,\n", " paired_wellbeing.post,\n", - " npmedian, is_paired=\"baseline\")\n", + " np.median, is_paired=\"baseline\")\n", "assert median_diff == pytest.approx(4.5)" ] }, @@ -365,18 +308,7 @@ "execution_count": null, "id": "371d7182", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\zhang\\Desktop\\vnbdev-dabest\\DABEST-python\\dabest\\effsize.py:77: 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='percentile'`. For detailed information, refer to https://github.com/ACCLAB/DABEST-python/issues/129 \n", - "\n", - " warnings.warn(message=mes1+mes2, category=UserWarning)\n" - ] - } - ], + "outputs": [], "source": [ "c = wellbeing.control\n", "t = wellbeing.expt\n", diff --git a/nbs/tests/test_03_plotting.py b/nbs/tests/test_03_plotting.py deleted file mode 100644 index 40a753a9..00000000 --- a/nbs/tests/test_03_plotting.py +++ /dev/null @@ -1,408 +0,0 @@ -import pytest -import numpy as np -from scipy.stats import norm -import pandas as pd -import matplotlib as mpl -mpl.use('Agg') -import matplotlib.ticker as Ticker -import matplotlib.pyplot as plt - -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 -df = create_demo_dataset() - -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_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") - ) - -multi_groups = load(df, idx=(("Control 1", "Test 1",), - ("Control 2", "Test 2","Test 3"), - ("Control 3", "Test 4","Test 5", "Test 6") - ) - ) - -multi_groups_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_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" - ) - - - -@pytest.mark.mpl_image_compare(tolerance=10) -def test_01_gardner_altman_unpaired_meandiff(): - return two_groups_unpaired.mean_diff.plot(); - - - -@pytest.mark.mpl_image_compare(tolerance=10) -def test_02_gardner_altman_unpaired_mediandiff(): - return two_groups_unpaired.median_diff.plot(); - - - -@pytest.mark.mpl_image_compare(tolerance=10) -def test_03_gardner_altman_unpaired_hedges_g(): - return two_groups_unpaired.hedges_g.plot(); - - - -@pytest.mark.mpl_image_compare(tolerance=10) -def test_04_gardner_altman_paired_meandiff(): - return two_groups_paired.mean_diff.plot(); - - - -@pytest.mark.mpl_image_compare(tolerance=10) -def test_04_gardner_altman_paired_hedges_g(): - return two_groups_paired.hedges_g.plot(); - - - -@pytest.mark.mpl_image_compare(tolerance=10) -def test_05_cummings_two_group_unpaired_meandiff(): - return two_groups_unpaired.mean_diff.plot(fig_size=(4, 6), - float_contrast=False); - - - -@pytest.mark.mpl_image_compare(tolerance=10) -def test_06_cummings_two_group_paired_meandiff(): - return two_groups_paired.mean_diff.plot(fig_size=(6, 6), - float_contrast=False); - - - -@pytest.mark.mpl_image_compare(tolerance=10) -def test_07_cummings_multi_group_unpaired(): - return multi_2group.mean_diff.plot(); - - - -@pytest.mark.mpl_image_compare(tolerance=10) -def test_08_cummings_multi_group_paired(): - return multi_2group_paired.mean_diff.plot(fig_size=(6, 6)); - - - -@pytest.mark.mpl_image_compare(tolerance=10) -def test_09_cummings_shared_control(): - return shared_control.mean_diff.plot(); - - - -@pytest.mark.mpl_image_compare(tolerance=10) -def test_10_cummings_multi_groups(): - return multi_groups.mean_diff.plot(); - - - -@pytest.mark.mpl_image_compare(tolerance=10) -def test_11_inset_plots(): - - # Load the iris dataset. Requires internet access. - iris = pd.read_csv("https://github.com/mwaskom/seaborn-data/raw/master/iris.csv") - iris_melt = pd.melt(iris.reset_index(), - id_vars=["species", "index"], var_name="metric") - - - - # Load the above data into `dabest`. - iris_dabest1 = load(data=iris, x="species", y="petal_width", - idx=("setosa", "versicolor", "virginica")) - - iris_dabest2 = load(data=iris, x="species", y="sepal_width", - idx=("setosa", "versicolor")) - - iris_dabest3 = load(data=iris_melt[iris_melt.species=="setosa"], - x="metric", y="value", - idx=("sepal_length", "sepal_width"), - paired="baseline", id_col="index") - - - - # Create Figure. - fig, ax = plt.subplots(nrows=2, ncols=2, - figsize=(15, 15), - gridspec_kw={"wspace":0.5}) - - iris_dabest1.mean_diff.plot(ax=ax.flat[0]); - - iris_dabest2.mean_diff.plot(ax=ax.flat[1]); - - iris_dabest3.mean_diff.plot(ax=ax.flat[2]); - - iris_dabest3.mean_diff.plot(ax=ax.flat[3], float_contrast=False); - - return fig - - - -@pytest.mark.mpl_image_compare(tolerance=10) -def test_12_gardner_altman_ylabel(): - return two_groups_unpaired.mean_diff.plot(swarm_label="This is my\nrawdata", - contrast_label="The bootstrap\ndistribtions!"); - - - -@pytest.mark.mpl_image_compare(tolerance=10) -def test_13_multi_2group_color(): - return multi_2group.mean_diff.plot(color_col="Gender"); - - - -@pytest.mark.mpl_image_compare(tolerance=10) -def test_14_gardner_altman_paired_color(): - return two_groups_paired.mean_diff.plot(fig_size=(6, 6), - color_col="Gender"); - - - -@pytest.mark.mpl_image_compare(tolerance=10) -def test_15_change_palette_a(): - return multi_2group.mean_diff.plot(fig_size=(8, 6), - color_col="Gender", - custom_palette="Dark2"); - - - -@pytest.mark.mpl_image_compare(tolerance=10) -def test_16_change_palette_b(): - return multi_2group.mean_diff.plot(custom_palette="Paired"); - - - -my_color_palette = {"Control 1" : "blue", - "Test 1" : "purple", - "Control 2" : "#cb4b16", # This is a hex string. - "Test 2" : (0., 0.7, 0.2) # This is a RGB tuple. - } - -@pytest.mark.mpl_image_compare(tolerance=10) -def test_17_change_palette_c(): - return multi_2group.mean_diff.plot(custom_palette=my_color_palette); - - - -@pytest.mark.mpl_image_compare(tolerance=10) -def test_18_desat(): - return multi_2group.mean_diff.plot(custom_palette=my_color_palette, - swarm_desat=0.75, - halfviolin_desat=0.25); - - - -@pytest.mark.mpl_image_compare(tolerance=10) -def test_19_dot_sizes(): - return multi_2group.mean_diff.plot(raw_marker_size=3, - es_marker_size=12); - - - -@pytest.mark.mpl_image_compare(tolerance=10) -def test_20_change_ylims(): - return multi_2group.mean_diff.plot(swarm_ylim=(0, 5), - contrast_ylim=(-2, 2)); - - - -@pytest.mark.mpl_image_compare(tolerance=10) -def test_21_invert_ylim(): - return multi_2group.mean_diff.plot(contrast_ylim=(2, -2), - contrast_label="More negative is better!"); - - - -@pytest.mark.mpl_image_compare(tolerance=10) -def test_22_ticker_gardner_altman(): - - f = two_groups_unpaired.mean_diff.plot() - - rawswarm_axes = f.axes[0] - contrast_axes = f.axes[1] - - rawswarm_axes.yaxis.set_major_locator(Ticker.MultipleLocator(1)) - rawswarm_axes.yaxis.set_minor_locator(Ticker.MultipleLocator(0.5)) - - contrast_axes.yaxis.set_major_locator(Ticker.MultipleLocator(0.5)) - contrast_axes.yaxis.set_minor_locator(Ticker.MultipleLocator(0.25)) - - return f - - - -@pytest.mark.mpl_image_compare(tolerance=10) -def test_23_ticker_cumming(): - f = multi_2group.mean_diff.plot(swarm_ylim=(0,6), - contrast_ylim=(-3, 1)) - - rawswarm_axes = f.axes[0] - contrast_axes = f.axes[1] - - rawswarm_axes.yaxis.set_major_locator(Ticker.MultipleLocator(2)) - rawswarm_axes.yaxis.set_minor_locator(Ticker.MultipleLocator(1)) - - contrast_axes.yaxis.set_major_locator(Ticker.MultipleLocator(0.5)) - contrast_axes.yaxis.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=10) -def test_24_wide_df_nan(): - - wide_df_dabest = load(wide_df, - idx=("Control", "Test 1", "Test 2", "Test 3") - ) - - return wide_df_dabest.mean_diff.plot(); - - - -@pytest.mark.mpl_image_compare(tolerance=10) -def test_25_long_df_nan(): - - 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(); - - - -@pytest.mark.mpl_image_compare(tolerance=10) -def test_26_slopegraph_kwargs(): - - return two_groups_paired.mean_diff.plot( - slopegraph_kwargs=dict(linestyle='dotted') - ); - - - -@pytest.mark.mpl_image_compare(tolerance=10) -def test_27_gardner_altman_reflines_kwargs(): - - return two_groups_unpaired.mean_diff.plot( - reflines_kwargs=dict(linestyle='dotted') - ); - - - -@pytest.mark.mpl_image_compare(tolerance=10) -def test_28_unpaired_cumming_reflines_kwargs(): - - return two_groups_unpaired.mean_diff.plot( - fig_size=(12,10), - float_contrast=False, - reflines_kwargs=dict(linestyle='dotted', - linewidth=2), - contrast_ylim=(-1, 1) - ); - - - -@pytest.mark.mpl_image_compare(tolerance=10) -def test_29_paired_cumming_slopegraph_reflines_kwargs(): - - return two_groups_paired.mean_diff.plot(float_contrast=False, - color_col="Gender", - slopegraph_kwargs=dict(linestyle='dotted'), - reflines_kwargs=dict(linestyle='dashed', - linewidth=2), - contrast_ylim=(-1, 1) - ); - -@pytest.mark.mpl_image_compare(tolerance=10) -def test_30_sequential_cumming_slopegraph(): - return multi_groups_sequential.mean_diff.plot(); - -@pytest.mark.mpl_image_compare(tolerance=10) -def test_31_baseline_cumming_slopegraph(): - return multi_groups_baseline.mean_diff.plot(); - - -@pytest.mark.mpl_image_compare(tolerance=10) -def test_99_style_sheets(): - # Perform this test last so we don't have to reset the plot style. - plt.style.use("dark_background") - - return multi_2group.mean_diff.plot(); \ No newline at end of file diff --git a/nbs/tests/test_04_repeated_measures_effsizes_pvals.ipynb b/nbs/tests/test_04_repeated_measures_effsizes_pvals.ipynb index b3f77f83..ac53339e 100644 --- a/nbs/tests/test_04_repeated_measures_effsizes_pvals.ipynb +++ b/nbs/tests/test_04_repeated_measures_effsizes_pvals.ipynb @@ -21,8 +21,9 @@ "metadata": {}, "outputs": [], "source": [ - "from dabest._stats_tools import effsize\n", - "from dabest._classes import TwoGroupsEffectSize, PermutationTest, Dabest, EffectSizeDataFrame" + "from dabest import Dabest\n", + "from data.mocked_data_test_01 import dabest_default_kwargs\n", + "from data.mocked_data_test_04 import df" ] }, { @@ -32,37 +33,6 @@ "metadata": {}, "outputs": [], "source": [ - "# Data for tests\n", - "# See Der, G., & Everitt, B. S. (2009). A handbook\n", - "# of statistical analyses using SAS, from Display 11.1\n", - "group = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", - " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]\n", - "first = [20, 14, 7, 6, 9, 9, 7, 18, 6, 10, 5, 11, 10, 17, 16, 7, 5, 16, 2, 7, 9, 2, 7, 19,\n", - " 7, 9, 6, 13, 9, 6, 11, 7, 8, 3, 4, 11, 1, 6, 0, 18, 15, 10, 6, 9, 4, 4, 10]\n", - "second = [15, 12, 5, 10, 7, 9, 3, 17, 9, 15, 9, 11, 2, 12, 15, 10, 0, 7, 1, 11, 16,\n", - " 5, 3, 13, 5, 12, 7, 18, 10, 7, 11, 10, 18, 3, 10, 10, 3, 7, 3, 18, 15, 14, 6, 9, 3, 13, 11]\n", - "third = [14, 12, 5, 9, 9, 9, 7, 16, 9, 12, 7, 8, 9, 14, 12, 4, 5, 7, 1, 7, 14, 6, 5, 14, 8, 16, 10,\n", - " 14, 12, 8, 12, 11, 19, 3, 11, 10, 2, 7, 3, 19, 15, 16, 7, 13, 4, 13, 13]\n", - "fourth = [13, 10, 6, 8, 5, 11, 6, 14, 9, 12, 3, 8, 3, 10, 7, 7, 0, 6, 2, 5, 10, 7, 5, 12, 8, 17, 15,\n", - " 21, 14, 9, 14, 12, 19, 7, 17, 15, 4, 9, 4, 22, 18, 17, 9, 16, 7, 16, 17]\n", - "fifth = [13, 10, 5, 7, 4, 8, 5, 12, 9, 11, 5, 9, 5, 9, 9, 5, 0, 4, 2, 8, 6, 6, 5, 10, 6, 18, 16, 21,\n", - " 15, 12, 16, 14, 22, 8, 18, 16, 5, 10, 6, 22, 19, 19, 10, 20, 9, 19, 21] \n", - "\n", - "df = pd.DataFrame({'Group' : group,\n", - " 'First' : first,\n", - " 'Second': second,\n", - " 'Third' : third,\n", - " 'Fourth': fourth,\n", - " 'Fifth' : fifth,\n", - " 'ID': np.arange(0, 47)\n", - " })\n", - "\n", - "# kwargs for Dabest class init.\n", - "dabest_default_kwargs = dict(x=None, y=None, ci=95, \n", - " resamples=5000, random_seed=12345, proportional=False,\n", - " delta2 = False, experiment=None, \n", - " experiment_label=None, x1_level=None, mini_meta=False)\n", - "\n", "# example of sequential repeated measures\n", "sequential = Dabest(df, id_col = \"ID\",\n", " idx=(\"First\", \"Second\", \"Third\", \"Fourth\", \"Fifth\"),\n", @@ -73,7 +43,7 @@ "baseline = Dabest(df, id_col = \"ID\",\n", " idx=(\"First\", \"Second\", \"Third\", \"Fourth\", \"Fifth\"),\n", " paired = \"baseline\",\n", - " **dabest_default_kwargs)\n" + " **dabest_default_kwargs)" ] }, { diff --git a/nbs/tests/test_06_delta-delta_effsize_pvals.ipynb b/nbs/tests/test_06_delta-delta_effsize_pvals.ipynb index 521117dc..43a0295f 100644 --- a/nbs/tests/test_06_delta-delta_effsize_pvals.ipynb +++ b/nbs/tests/test_06_delta-delta_effsize_pvals.ipynb @@ -8,10 +8,8 @@ "outputs": [], "source": [ "import pytest\n", - "import lqrt\n", "import numpy as np\n", - "import scipy as sp\n", - "import pandas as pd" + "from math import gamma" ] }, { @@ -21,8 +19,8 @@ "metadata": {}, "outputs": [], "source": [ - "from dabest._stats_tools import effsize\n", - "from dabest._classes import TwoGroupsEffectSize, PermutationTest, Dabest" + "from dabest import Dabest, PermutationTest\n", + "from data.mocked_data_test_06 import df_test, df_test_control, df_test_treatment1, dabest_default_kwargs" ] }, { @@ -32,58 +30,6 @@ "metadata": {}, "outputs": [], "source": [ - "# Data for tests.\n", - "# See: Asheber Abebe. Introduction to Design and Analysis of Experiments \n", - "# with the SAS, from Example: Two-way RM Design Pg 137.\n", - "hr = [72, 78, 71, 72, 66, 74, 62, 69, 69, 66, 84, 80, 72, 65, 75, 71, \n", - " 86, 83, 82, 83, 79, 83, 73, 75, 73, 62, 90, 81, 72, 62, 69, 70]\n", - "\n", - "# Add experiment column\n", - "e1 = np.repeat('Treatment1', 8).tolist()\n", - "e2 = np.repeat('Control', 8).tolist()\n", - "experiment = e1 + e2 + e1 + e2\n", - "\n", - "# Add a `Drug` column as the first variable\n", - "d1 = np.repeat('AX23', 8).tolist()\n", - "d2 = np.repeat('CONTROL', 8).tolist()\n", - "drug = d1 + d2 + d1 + d2\n", - "\n", - "# Add a `Time` column as the second variable\n", - "t1 = np.repeat('T1', 16).tolist()\n", - "t2 = np.repeat('T2', 16).tolist()\n", - "time = t1 + t2\n", - "\n", - "# Add an `id` column for paired data plotting.\n", - "id_col = []\n", - "for i in range(1, 9):\n", - " id_col.append(str(i)+\"a\")\n", - "for i in range(1, 9):\n", - " id_col.append(str(i)+\"c\")\n", - "id_col.extend(id_col)\n", - "\n", - "# Combine samples and gender into a DataFrame.\n", - "df_test = pd.DataFrame({'ID' : id_col,\n", - " 'Drug' : drug,\n", - " 'Time' : time, \n", - " 'Experiment': experiment,\n", - " 'Heart Rate': hr\n", - " })\n", - "\n", - "\n", - "df_test_control = df_test[df_test[\"Experiment\"]==\"Control\"]\n", - "df_test_control = df_test_control.pivot(index=\"ID\", columns=\"Time\", values=\"Heart Rate\")\n", - "\n", - "\n", - "df_test_treatment1 = df_test[df_test[\"Experiment\"]==\"Treatment1\"]\n", - "df_test_treatment1 = df_test_treatment1.pivot(index=\"ID\", columns=\"Time\", values=\"Heart Rate\")\n", - "\n", - "\n", - "# kwargs for Dabest class init.\n", - "dabest_default_kwargs = dict(ci=95, \n", - " resamples=5000, random_seed=12345,\n", - " idx=None, proportional=False, mini_meta=False\n", - " )\n", - "\n", "# example of unpaired delta-delta calculation\n", "unpaired = Dabest(data = df_test, x = [\"Time\", \"Drug\"], y = \"Heart Rate\", \n", " delta2 = True, experiment = \"Experiment\",\n", @@ -333,7 +279,6 @@ "metadata": {}, "outputs": [], "source": [ - "from math import gamma\n", "hedges_g = unpaired.hedges_g.results['difference'].to_list()\n", "a = 8*2-2\n", "fac = gamma(a/2)/(np.sqrt(a/2)*gamma((a-1)/2))\n", @@ -360,7 +305,6 @@ "metadata": {}, "outputs": [], "source": [ - "from math import gamma\n", "hedges_g = paired.hedges_g.results['difference'].to_list()\n", "a = 8*2-2\n", "fac = gamma(a/2)/(np.sqrt(a/2)*gamma((a-1)/2))\n", @@ -387,7 +331,6 @@ "metadata": {}, "outputs": [], "source": [ - "from math import gamma\n", "hedges_g = paired_specified_level.hedges_g.results['difference'].to_list()\n", "a = 8*2-2\n", "fac = gamma(a/2)/(np.sqrt(a/2)*gamma((a-1)/2))\n", diff --git a/nbs/tests/test_08_mini_meta_pvals.ipynb b/nbs/tests/test_08_mini_meta_pvals.ipynb index c5d58184..464d3524 100644 --- a/nbs/tests/test_08_mini_meta_pvals.ipynb +++ b/nbs/tests/test_08_mini_meta_pvals.ipynb @@ -7,7 +7,6 @@ "metadata": {}, "outputs": [], "source": [ - "import pandas as pd\n", "import numpy as np\n", "import pytest" ] @@ -21,7 +20,8 @@ "source": [ "from dabest._stats_tools import effsize\n", "from dabest._stats_tools import confint_2group_diff as ci2g\n", - "from dabest._classes import PermutationTest, Dabest" + "from dabest import Dabest, PermutationTest\n", + "from data.mocked_data_test_08 import df_mini_meta, rep1_yes, rep1_no, rep2_yes, rep2_no, N, dabest_default_kwargs" ] }, { @@ -31,33 +31,6 @@ "metadata": {}, "outputs": [], "source": [ - "# Data for tests.\n", - "# See Oehlert, G. W. (2000). A First Course in Design \n", - "# and Analysis of Experiments (1st ed.). W. H. Freeman.\n", - "# from Problem 16.3 Pg 444.\n", - "\n", - "rep1_yes = [53.4,54.3,55.9,53.8,56.3,58.6]\n", - "rep1_no = [58.2,60.4,62.4,59.5,64.5,64.5]\n", - "rep2_yes = [46.5,57.2,57.4,51.1,56.9,60.2]\n", - "rep2_no = [49.2,61.6,57.2,51.3,66.8,62.7]\n", - "df_mini_meta = pd.DataFrame({\n", - " \"Rep1_Yes\":rep1_yes,\n", - " \"Rep1_No\" :rep1_no,\n", - " \"Rep2_Yes\":rep2_yes,\n", - " \"Rep2_No\" :rep2_no\n", - "})\n", - "N=6 # Size of each group\n", - "\n", - "\n", - "# kwargs for Dabest class init.\n", - "dabest_default_kwargs = dict(x=None, y=None, ci=95, \n", - " resamples=5000, random_seed=12345,\n", - " proportional=False, delta2=False, experiment=None, \n", - " experiment_label=None, x1_level=None, paired=None,\n", - " id_col=None\n", - " )\n", - "\n", - "\n", "unpaired = Dabest(data = df_mini_meta, idx =((\"Rep1_No\", \"Rep1_Yes\"), \n", " (\"Rep2_No\", \"Rep2_Yes\")), \n", " mini_meta=True,\n", diff --git a/nbs/tests/test_10_proportion_plot.py b/nbs/tests/test_10_proportion_plot.py deleted file mode 100644 index a7113c60..00000000 --- a/nbs/tests/test_10_proportion_plot.py +++ /dev/null @@ -1,249 +0,0 @@ -import pytest -import numpy as np -from scipy.stats import norm -import pandas as pd -import matplotlib as mpl -mpl.use('Agg') -import matplotlib.ticker as Ticker -import matplotlib.pyplot as plt - -from dabest._api import load - -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) - - # 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 - - -df = create_demo_prop_dataset() - -two_groups_unpaired = load(df, idx=("Control 1", "Test 1"), proportional=True) - -multi_2group = load(df, idx=(("Control 1", "Test 1",), - ("Control 2", "Test 2")), - proportional=True) - -shared_control = load(df, idx=("Control 1", "Test 1", - "Test 2", "Test 3", - "Test 4", "Test 5", "Test 6"), - proportional=True) - -multi_groups = load(df, idx=(("Control 1", "Test 1",), - ("Control 2", "Test 2","Test 3"), - ("Control 3", "Test 4","Test 5", "Test 6") - ),proportional=True) - -two_groups_paired = load(df, idx=("Control 1", "Test 1"), - paired="baseline", id_col="ID",proportional=True) - -multi_2group_paired = load(df, idx=(("Control 1", "Test 1"), - ("Control 2", "Test 2")), - paired="baseline", id_col="ID", proportional=True) - -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", proportional=True) - -two_groups_sequential = load(df, idx=("Control 1", "Test 1"), - paired="sequential", id_col="ID",proportional=True) - -multi_2group_sequential = load(df, idx=(("Control 1", "Test 1"), - ("Control 2", "Test 2")), - paired="sequential", id_col="ID", proportional=True) - -multi_groups_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", proportional=True) - - -@pytest.mark.mpl_image_compare -def test_101_gardner_altman_unpaired_propdiff(): - return two_groups_unpaired.mean_diff.plot(); - -@pytest.mark.mpl_image_compare -def test_103_cummings_two_group_unpaired_propdiff(): - return two_groups_unpaired.mean_diff.plot(fig_size=(4, 6), - float_contrast=False); - -@pytest.mark.mpl_image_compare -def test_105_cummings_multi_group_unpaired_propdiff(): - return multi_2group.mean_diff.plot(); - -@pytest.mark.mpl_image_compare -def test_106_cummings_shared_control_propdiff(): - return shared_control.mean_diff.plot(); - -@pytest.mark.mpl_image_compare -def test_107_cummings_multi_groups_propdiff(): - return multi_groups.mean_diff.plot(); - -@pytest.mark.mpl_image_compare -def test_109_gardner_altman_ylabel(): - return two_groups_unpaired.mean_diff.plot(bar_label="This is my\nrawdata", - contrast_label="The bootstrap\ndistribtions!"); - -@pytest.mark.mpl_image_compare -def test_110_change_fig_size(): - return two_groups_unpaired.mean_diff.plot(fig_size=(6, 6), - custom_palette="Dark2"); - -@pytest.mark.mpl_image_compare -def test_111_change_palette_b(): - return multi_2group.mean_diff.plot(custom_palette="Paired"); - - -my_color_palette = {"Control 1" : "blue", - "Test 1" : "purple", - "Control 2" : "#cb4b16", # This is a hex string. - "Test 2" : (0., 0.7, 0.2) # This is a RGB tuple. - } - -@pytest.mark.mpl_image_compare -def test_112_change_palette_c(): - return multi_2group.mean_diff.plot(custom_palette=my_color_palette); - -@pytest.mark.mpl_image_compare -def test_113_desat(): - return multi_2group.mean_diff.plot(custom_palette=my_color_palette, - bar_desat=0.1, - halfviolin_desat=0.25); - -@pytest.mark.mpl_image_compare -def test_114_change_ylims(): - return multi_2group.mean_diff.plot(contrast_ylim=(-2, 2)); - -@pytest.mark.mpl_image_compare -def test_115_invert_ylim(): - return multi_2group.mean_diff.plot(contrast_ylim=(2, -2), - contrast_label="More negative is better!"); - -@pytest.mark.mpl_image_compare -def test_116_ticker_gardner_altman(): - - fig = two_groups_unpaired.mean_diff.plot() - - rawswarm_axes = fig.axes[0] - contrast_axes = fig.axes[1] - - rawswarm_axes.yaxis.set_major_locator(Ticker.MultipleLocator(1)) - rawswarm_axes.yaxis.set_minor_locator(Ticker.MultipleLocator(0.5)) - - contrast_axes.yaxis.set_major_locator(Ticker.MultipleLocator(0.5)) - contrast_axes.yaxis.set_minor_locator(Ticker.MultipleLocator(0.25)) - return fig - -@pytest.mark.mpl_image_compare -def test_117_err_color(): - return two_groups_unpaired.mean_diff.plot(err_color="purple"); - -@pytest.mark.mpl_image_compare -def test_118_cummings_two_group_unpaired_meandiff_bar_width(): - return two_groups_unpaired.mean_diff.plot(bar_width=0.4,float_contrast=False); - -np.random.seed(9999) -Ns = [20, 10, 21, 20] -n=1 -c1 = pd.DataFrame({'Control':np.random.binomial(n, 0.2, size=Ns[0])}) -t1 = pd.DataFrame({'Test 1': np.random.binomial(n, 0.5, size=Ns[1])}) -t2 = pd.DataFrame({'Test 2': np.random.binomial(n, 0.4, size=Ns[2])}) -t3 = pd.DataFrame({'Test 3': np.random.binomial(n, 0.7, 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 -def test_119_wide_df_nan(): - - wide_df_dabest = load(wide_df, - idx=("Control", "Test 1", "Test 2", "Test 3"), - proportional=True - ) - - return wide_df_dabest.mean_diff.plot(); - -@pytest.mark.mpl_image_compare -def test_120_long_df_nan(): - - long_df_dabest = load(long_df, x="group", y="value", - idx=("Control", "Test 1", "Test 2", "Test 3"), - proportional=True - ) - - return long_df_dabest.mean_diff.plot(); - -@pytest.mark.mpl_image_compare -def test_121_cohens_h_gardner_altman(): - return two_groups_unpaired.cohens_h.plot(); - -@pytest.mark.mpl_image_compare -def test_122_cohens_h_cummings(): - return two_groups_unpaired.cohens_h.plot(float_contrast=False); - -@pytest.mark.mpl_image_compare -def test_123_sankey_gardner_altman(): - return two_groups_paired.mean_diff.plot(); - -@pytest.mark.mpl_image_compare -def test_124_sankey_cummings(): - return two_groups_paired.mean_diff.plot(float_contrast=False); - -@pytest.mark.mpl_image_compare -def test_125_sankey_2paired_groups(): - return multi_2group_paired.mean_diff.plot(); - -@pytest.mark.mpl_image_compare -def test_126_sankey_2sequential_groups(): - return multi_2group_sequential.mean_diff.plot(); - -@pytest.mark.mpl_image_compare -def test_127_sankey_multi_group_paired(): - return multi_groups_paired.mean_diff.plot(); - -@pytest.mark.mpl_image_compare -def test_128_sankey_transparency(): - return two_groups_paired.mean_diff.plot(sankey_kwargs = {"alpha": 0.2}); - - -@pytest.mark.mpl_image_compare -def test_129_style_sheets(): - # Perform this test last so we don't have to reset the plot style. - plt.style.use("dark_background") - return multi_2group.mean_diff.plot(face_color="black"); - - diff --git a/nbs/tests/test_99_confidence_intervals.ipynb b/nbs/tests/test_99_confidence_intervals.ipynb new file mode 100644 index 00000000..2475793b --- /dev/null +++ b/nbs/tests/test_99_confidence_intervals.ipynb @@ -0,0 +1,220 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "a3d966b3", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "from scipy.stats import norm\n", + "from scipy.stats import skewnorm\n", + "import pandas as pd\n", + "import pytest" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9920ab6c", + "metadata": {}, + "outputs": [], + "source": [ + "from dabest._api import load" + ] + }, + { + "cell_type": "markdown", + "id": "fa5cc9c1", + "metadata": {}, + "source": [ + "test_paired_mean_diff_ci" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c39d5daa", + "metadata": {}, + "outputs": [], + "source": [ + "# See Altman et al., Statistics with Confidence: \n", + "# Confidence Intervals and Statistical Guidelines (Second Edition). Wiley, 2000.\n", + "# Pg 31.\n", + "# Added in v0.2.5.\n", + "blood_pressure = {\"before\": [148, 142, 136, 134, 138, 140, 132, 144,\n", + " 128, 170, 162, 150, 138, 154, 126, 116],\n", + " \"after\" : [152, 152, 134, 148, 144, 136, 144, 150, \n", + " 146, 174, 162, 162, 146, 156, 132, 126],\n", + " \"subject_id\" : np.arange(1, 17)}\n", + "exercise_bp = pd.DataFrame(blood_pressure)\n", + "\n", + "\n", + "ex_bp = load(data=exercise_bp, idx=(\"before\", \"after\"), \n", + " 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]" + ] + }, + { + "cell_type": "markdown", + "id": "de5c07cc", + "metadata": {}, + "source": [ + "test_unpaired_ci" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "11e82b97", + "metadata": {}, + "outputs": [], + "source": [ + "# Dropped to 30 reps to save time. v0.2.5.\n", + "reps=30\n", + "ci=95\n", + "POPULATION_N = 10000\n", + "SAMPLE_N = 10\n", + "\n", + "# Create data for hedges g and cohens d.\n", + "CONTROL_MEAN = np.random.randint(1, 1000)\n", + "POP_SD = np.random.randint(1, 15)\n", + "POP_D = np.round(np.random.uniform(-2, 2, 1)[0], 2)\n", + "\n", + "TRUE_STD_DIFFERENCE = CONTROL_MEAN + (POP_D * POP_SD)\n", + "norm_sample_kwargs = dict(scale=POP_SD, size=SAMPLE_N)\n", + "c1 = norm.rvs(loc=CONTROL_MEAN, **norm_sample_kwargs)\n", + "t1 = norm.rvs(loc=CONTROL_MEAN+TRUE_STD_DIFFERENCE, **norm_sample_kwargs)\n", + "\n", + "std_diff_df = pd.DataFrame({'Control' : c1, 'Test': t1})\n", + "\n", + "\n", + "\n", + "# Create mean_diff data\n", + "CONTROL_MEAN = np.random.randint(1, 1000)\n", + "POP_SD = np.random.randint(1, 15)\n", + "TRUE_DIFFERENCE = np.random.randint(-POP_SD*5, POP_SD*5)\n", + "\n", + "c1 = norm.rvs(loc=CONTROL_MEAN, **norm_sample_kwargs)\n", + "t1 = norm.rvs(loc=CONTROL_MEAN+TRUE_DIFFERENCE, **norm_sample_kwargs)\n", + "\n", + "mean_df = pd.DataFrame({'Control' : c1, 'Test': t1})\n", + "\n", + "\n", + "\n", + "# Create median_diff data\n", + "MEDIAN_DIFFERENCE = np.random.randint(-5, 5)\n", + "A = np.random.randint(-7, 7)\n", + "\n", + "skew_kwargs = dict(a=A, scale=5, size=POPULATION_N)\n", + "skewpop1 = skewnorm.rvs(**skew_kwargs, loc=100)\n", + "skewpop2 = skewnorm.rvs(**skew_kwargs, loc=100+MEDIAN_DIFFERENCE)\n", + "\n", + "sample_kwargs = dict(replace=False, size=SAMPLE_N)\n", + "skewsample1 = np.random.choice(skewpop1, **sample_kwargs)\n", + "skewsample2 = np.random.choice(skewpop2, **sample_kwargs)\n", + "\n", + "median_df = pd.DataFrame({'Control' : skewsample1, 'Test': skewsample2})\n", + "\n", + "\n", + "\n", + "# Create two populations with a 50% overlap.\n", + "CD_DIFFERENCE = np.random.randint(1, 10)\n", + "SD = np.abs(CD_DIFFERENCE)\n", + "\n", + "pop_kwargs = dict(scale=SD, size=POPULATION_N)\n", + "pop1 = norm.rvs(loc=100, **pop_kwargs)\n", + "pop2 = norm.rvs(loc=100+CD_DIFFERENCE, **pop_kwargs)\n", + "\n", + "sample_kwargs = dict(replace=False, size=SAMPLE_N)\n", + "sample1 = np.random.choice(pop1, **sample_kwargs)\n", + "sample2 = np.random.choice(pop2, **sample_kwargs)\n", + "\n", + "cd_df = pd.DataFrame({'Control' : sample1, 'Test': sample2})\n", + "\n", + "\n", + "\n", + "# Create several CIs and see if the true population difference lies within.\n", + "error_count_cohens_d = 0\n", + "error_count_hedges_g = 0\n", + "error_count_mean_diff = 0\n", + "error_count_median_diff = 0\n", + "error_count_cliffs_delta = 0\n", + "\n", + "for i in range(0, reps):\n", + " print(i) # for debug.\n", + " # pick a random seed\n", + " rnd_sd = np.random.randint(0, 999999)\n", + " load_kwargs = dict(ci=ci, random_seed=rnd_sd)\n", + "\n", + " std_diff_data = load(data=std_diff_df, idx=(\"Control\", \"Test\"), **load_kwargs)\n", + " cd = std_diff_data.cohens_d.results\n", + " # print(\"cohen's d\") # for debug.\n", + " cd_low, cd_high = float(cd.bca_low), float(cd.bca_high)\n", + " if cd_low < POP_D < cd_high is False:\n", + " error_count_cohens_d += 1\n", + "\n", + " hg = std_diff_data.hedges_g.results\n", + " # print(\"hedges' g\") # for debug.\n", + " hg_low, hg_high = float(hg.bca_low), float(hg.bca_high)\n", + " if hg_low < POP_D < hg_high is False:\n", + " error_count_hedges_g += 1\n", + "\n", + "\n", + " mean_diff_data = load(data=mean_df, idx=(\"Control\", \"Test\"), **load_kwargs)\n", + " mean_d = mean_diff_data.mean_diff.results\n", + " # print(\"mean diff\") # for debug.\n", + " mean_d_low, mean_d_high = float(mean_d.bca_low), float(mean_d.bca_high)\n", + " if mean_d_low < TRUE_DIFFERENCE < mean_d_high is False:\n", + " error_count_mean_diff += 1\n", + "\n", + "\n", + " median_diff_data = load(data=median_df, idx=(\"Control\", \"Test\"),\n", + " **load_kwargs)\n", + " median_d = median_diff_data.median_diff.results\n", + " # print(\"median diff\") # for debug.\n", + " median_d_low, median_d_high = float(median_d.bca_low), float(median_d.bca_high)\n", + " if median_d_low < MEDIAN_DIFFERENCE < median_d_high is False:\n", + " error_count_median_diff += 1\n", + "\n", + "\n", + " cd_data = load(data=cd_df, idx=(\"Control\", \"Test\"), **load_kwargs)\n", + " cliffs = cd_data.cliffs_delta.results\n", + " # print(\"cliff's delta\") # for debug.\n", + " low, high = float(cliffs.bca_low), float(cliffs.bca_high)\n", + " if low < 0.5 < high is False:\n", + " error_count_cliffs_delta += 1\n", + "\n", + "\n", + "max_errors = int(np.ceil(reps * (100 - ci) / 100))\n", + "\n", + "assert error_count_cohens_d <= max_errors\n", + "assert error_count_hedges_g <= max_errors\n", + "assert error_count_mean_diff <= max_errors\n", + "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": { + "kernelspec": { + "display_name": "python3", + "language": "python", + "name": "python3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/nbs/tests/test_99_confint.ipynb b/nbs/tests/test_99_confint.ipynb deleted file mode 100644 index 90557586..00000000 --- a/nbs/tests/test_99_confint.ipynb +++ /dev/null @@ -1,257 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "id": "a3d966b3", - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "from scipy.stats import norm\n", - "from scipy.stats import skewnorm\n", - "import pandas as pd\n", - "import pytest" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9920ab6c", - "metadata": {}, - "outputs": [], - "source": [ - "from dabest._api import load" - ] - }, - { - "cell_type": "markdown", - "id": "fa5cc9c1", - "metadata": {}, - "source": [ - "test_paired_mean_diff_ci" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c39d5daa", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\zhang\\anaconda3\\lib\\site-packages\\scipy\\stats\\_morestats.py:3337: UserWarning: Exact p-value calculation does not work if there are zeros. Switching to normal approximation.\n", - " warnings.warn(\"Exact p-value calculation does not work if there are \"\n" - ] - } - ], - "source": [ - "# See Altman et al., Statistics with Confidence: \n", - "# Confidence Intervals and Statistical Guidelines (Second Edition). Wiley, 2000.\n", - "# Pg 31.\n", - "# Added in v0.2.5.\n", - "blood_pressure = {\"before\": [148, 142, 136, 134, 138, 140, 132, 144,\n", - " 128, 170, 162, 150, 138, 154, 126, 116],\n", - " \"after\" : [152, 152, 134, 148, 144, 136, 144, 150, \n", - " 146, 174, 162, 162, 146, 156, 132, 126],\n", - " \"subject_id\" : np.arange(1, 17)}\n", - "exercise_bp = pd.DataFrame(blood_pressure)\n", - "\n", - "\n", - "ex_bp = load(data=exercise_bp, idx=(\"before\", \"after\"), \n", - " 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]" - ] - }, - { - "cell_type": "markdown", - "id": "de5c07cc", - "metadata": {}, - "source": [ - "test_unpaired_ci" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "11e82b97", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "c:\\users\\zhang\\desktop\\vnbdev-dabest\\dabest-python\\dabest\\_classes.py:1663: UserWarning: The lower limit of the interval was in the bottom 10 values. The result should be considered unstable.\n", - " warnings.warn(err_temp.substitute(lim_type=\"lower\",\n" - ] - }, - { - "ename": "ModuleNotFoundError", - "evalue": "No module named 'dabest.effsize'", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)", - "File \u001b[1;32mc:\\users\\zhang\\desktop\\vnbdev-dabest\\dabest-python\\dabest\\_classes.py:2621\u001b[0m, in \u001b[0;36mEffectSizeDataFrame.results\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 2620\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m-> 2621\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m__results\u001b[49m\n\u001b[0;32m 2622\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mAttributeError\u001b[39;00m:\n", - "\u001b[1;31mAttributeError\u001b[0m: 'EffectSizeDataFrame' object has no attribute '_EffectSizeDataFrame__results'", - "\nDuring handling of the above exception, another exception occurred:\n", - "\u001b[1;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[1;32mIn[5], line 79\u001b[0m\n\u001b[0;32m 76\u001b[0m load_kwargs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mdict\u001b[39m(ci\u001b[38;5;241m=\u001b[39mci, random_seed\u001b[38;5;241m=\u001b[39mrnd_sd)\n\u001b[0;32m 78\u001b[0m std_diff_data \u001b[38;5;241m=\u001b[39m load(data\u001b[38;5;241m=\u001b[39mstd_diff_df, idx\u001b[38;5;241m=\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mControl\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mTest\u001b[39m\u001b[38;5;124m\"\u001b[39m), \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mload_kwargs)\n\u001b[1;32m---> 79\u001b[0m cd \u001b[38;5;241m=\u001b[39m \u001b[43mstd_diff_data\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcohens_d\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mresults\u001b[49m\n\u001b[0;32m 80\u001b[0m \u001b[38;5;66;03m# print(\"cohen's d\") # for debug.\u001b[39;00m\n\u001b[0;32m 81\u001b[0m cd_low, cd_high \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mfloat\u001b[39m(cd\u001b[38;5;241m.\u001b[39mbca_low), \u001b[38;5;28mfloat\u001b[39m(cd\u001b[38;5;241m.\u001b[39mbca_high)\n", - "File \u001b[1;32mc:\\users\\zhang\\desktop\\vnbdev-dabest\\dabest-python\\dabest\\_classes.py:2623\u001b[0m, in \u001b[0;36mEffectSizeDataFrame.results\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 2621\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m__results\n\u001b[0;32m 2622\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mAttributeError\u001b[39;00m:\n\u001b[1;32m-> 2623\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m__pre_calc\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 2624\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m__results\n", - "File \u001b[1;32mc:\\users\\zhang\\desktop\\vnbdev-dabest\\dabest-python\\dabest\\_classes.py:2233\u001b[0m, in \u001b[0;36mEffectSizeDataFrame.__pre_calc\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 2230\u001b[0m control \u001b[38;5;241m=\u001b[39m dat[dat[xvar] \u001b[38;5;241m==\u001b[39m cname][yvar]\u001b[38;5;241m.\u001b[39mcopy()\n\u001b[0;32m 2231\u001b[0m test \u001b[38;5;241m=\u001b[39m dat[dat[xvar] \u001b[38;5;241m==\u001b[39m tname][yvar]\u001b[38;5;241m.\u001b[39mcopy()\n\u001b[1;32m-> 2233\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mTwoGroupsEffectSize\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcontrol\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtest\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 2234\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m__effect_size\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 2235\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m__proportional\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 2236\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m__is_paired\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 2237\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m__ci\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 2238\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m__resamples\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 2239\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m__permutation_count\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 2240\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m__random_seed\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 2241\u001b[0m r_dict \u001b[38;5;241m=\u001b[39m result\u001b[38;5;241m.\u001b[39mto_dict()\n\u001b[0;32m 2242\u001b[0m r_dict[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcontrol\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m cname\n", - "File \u001b[1;32mc:\\users\\zhang\\desktop\\vnbdev-dabest\\dabest-python\\dabest\\_classes.py:1698\u001b[0m, in \u001b[0;36mTwoGroupsEffectSize.__init__\u001b[1;34m(self, control, test, effect_size, proportional, is_paired, ci, resamples, permutation_count, random_seed)\u001b[0m\n\u001b[0;32m 1694\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m__pct_high \u001b[38;5;241m=\u001b[39m sorted_bootstraps[pct_idx_high]\n\u001b[0;32m 1696\u001b[0m \u001b[38;5;66;03m# Perform statistical tests.\u001b[39;00m\n\u001b[1;32m-> 1698\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m__PermutationTest_result \u001b[38;5;241m=\u001b[39m \u001b[43mPermutationTest\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcontrol\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtest\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\n\u001b[0;32m 1699\u001b[0m \u001b[43m \u001b[49m\u001b[43meffect_size\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\n\u001b[0;32m 1700\u001b[0m \u001b[43m \u001b[49m\u001b[43mis_paired\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1701\u001b[0m \u001b[43m \u001b[49m\u001b[43mpermutation_count\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 1703\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_paired \u001b[38;5;129;01mand\u001b[39;00m proportional \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mFalse\u001b[39;00m:\n\u001b[0;32m 1704\u001b[0m \u001b[38;5;66;03m# Wilcoxon, a non-parametric version of the paired T-test.\u001b[39;00m\n\u001b[0;32m 1705\u001b[0m wilcoxon \u001b[38;5;241m=\u001b[39m spstats\u001b[38;5;241m.\u001b[39mwilcoxon(control, test)\n", - "File \u001b[1;32mc:\\users\\zhang\\desktop\\vnbdev-dabest\\dabest-python\\dabest\\_classes.py:2820\u001b[0m, in \u001b[0;36mPermutationTest.__init__\u001b[1;34m(self, control, test, effect_size, is_paired, permutation_count, random_seed, **kwargs)\u001b[0m\n\u001b[0;32m 2818\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m\n\u001b[0;32m 2819\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mrandom\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m PCG64, RandomState\n\u001b[1;32m-> 2820\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01meffsize\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m two_group_difference\n\u001b[0;32m 2821\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mconfint_2group_diff\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m calculate_group_var\n\u001b[0;32m 2824\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m__permutation_count \u001b[38;5;241m=\u001b[39m permutation_count\n", - "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'dabest.effsize'" - ] - } - ], - "source": [ - "# Dropped to 30 reps to save time. v0.2.5.\n", - "reps=30\n", - "ci=95\n", - "POPULATION_N = 10000\n", - "SAMPLE_N = 10\n", - "\n", - "# Create data for hedges g and cohens d.\n", - "CONTROL_MEAN = np.random.randint(1, 1000)\n", - "POP_SD = np.random.randint(1, 15)\n", - "POP_D = np.round(np.random.uniform(-2, 2, 1)[0], 2)\n", - "\n", - "TRUE_STD_DIFFERENCE = CONTROL_MEAN + (POP_D * POP_SD)\n", - "norm_sample_kwargs = dict(scale=POP_SD, size=SAMPLE_N)\n", - "c1 = norm.rvs(loc=CONTROL_MEAN, **norm_sample_kwargs)\n", - "t1 = norm.rvs(loc=CONTROL_MEAN+TRUE_STD_DIFFERENCE, **norm_sample_kwargs)\n", - "\n", - "std_diff_df = pd.DataFrame({'Control' : c1, 'Test': t1})\n", - "\n", - "\n", - "\n", - "# Create mean_diff data\n", - "CONTROL_MEAN = np.random.randint(1, 1000)\n", - "POP_SD = np.random.randint(1, 15)\n", - "TRUE_DIFFERENCE = np.random.randint(-POP_SD*5, POP_SD*5)\n", - "\n", - "c1 = norm.rvs(loc=CONTROL_MEAN, **norm_sample_kwargs)\n", - "t1 = norm.rvs(loc=CONTROL_MEAN+TRUE_DIFFERENCE, **norm_sample_kwargs)\n", - "\n", - "mean_df = pd.DataFrame({'Control' : c1, 'Test': t1})\n", - "\n", - "\n", - "\n", - "# Create median_diff data\n", - "MEDIAN_DIFFERENCE = np.random.randint(-5, 5)\n", - "A = np.random.randint(-7, 7)\n", - "\n", - "skew_kwargs = dict(a=A, scale=5, size=POPULATION_N)\n", - "skewpop1 = skewnorm.rvs(**skew_kwargs, loc=100)\n", - "skewpop2 = skewnorm.rvs(**skew_kwargs, loc=100+MEDIAN_DIFFERENCE)\n", - "\n", - "sample_kwargs = dict(replace=False, size=SAMPLE_N)\n", - "skewsample1 = np.random.choice(skewpop1, **sample_kwargs)\n", - "skewsample2 = np.random.choice(skewpop2, **sample_kwargs)\n", - "\n", - "median_df = pd.DataFrame({'Control' : skewsample1, 'Test': skewsample2})\n", - "\n", - "\n", - "\n", - "# Create two populations with a 50% overlap.\n", - "CD_DIFFERENCE = np.random.randint(1, 10)\n", - "SD = np.abs(CD_DIFFERENCE)\n", - "\n", - "pop_kwargs = dict(scale=SD, size=POPULATION_N)\n", - "pop1 = norm.rvs(loc=100, **pop_kwargs)\n", - "pop2 = norm.rvs(loc=100+CD_DIFFERENCE, **pop_kwargs)\n", - "\n", - "sample_kwargs = dict(replace=False, size=SAMPLE_N)\n", - "sample1 = np.random.choice(pop1, **sample_kwargs)\n", - "sample2 = np.random.choice(pop2, **sample_kwargs)\n", - "\n", - "cd_df = pd.DataFrame({'Control' : sample1, 'Test': sample2})\n", - "\n", - "\n", - "\n", - "# Create several CIs and see if the true population difference lies within.\n", - "error_count_cohens_d = 0\n", - "error_count_hedges_g = 0\n", - "error_count_mean_diff = 0\n", - "error_count_median_diff = 0\n", - "error_count_cliffs_delta = 0\n", - "\n", - "for i in range(0, reps):\n", - " # print(i) # for debug.\n", - " # pick a random seed\n", - " rnd_sd = np.random.randint(0, 999999)\n", - " load_kwargs = dict(ci=ci, random_seed=rnd_sd)\n", - "\n", - " std_diff_data = load(data=std_diff_df, idx=(\"Control\", \"Test\"), **load_kwargs)\n", - " cd = std_diff_data.cohens_d.results\n", - " # print(\"cohen's d\") # for debug.\n", - " cd_low, cd_high = float(cd.bca_low), float(cd.bca_high)\n", - " if cd_low < POP_D < cd_high is False:\n", - " error_count_cohens_d += 1\n", - "\n", - " hg = std_diff_data.hedges_g.results\n", - " # print(\"hedges' g\") # for debug.\n", - " hg_low, hg_high = float(hg.bca_low), float(hg.bca_high)\n", - " if hg_low < POP_D < hg_high is False:\n", - " error_count_hedges_g += 1\n", - "\n", - "\n", - " mean_diff_data = load(data=mean_df, idx=(\"Control\", \"Test\"), **load_kwargs)\n", - " mean_d = mean_diff_data.mean_diff.results\n", - " # print(\"mean diff\") # for debug.\n", - " mean_d_low, mean_d_high = float(mean_d.bca_low), float(mean_d.bca_high)\n", - " if mean_d_low < TRUE_DIFFERENCE < mean_d_high is False:\n", - " error_count_mean_diff += 1\n", - "\n", - "\n", - " median_diff_data = load(data=median_df, idx=(\"Control\", \"Test\"),\n", - " **load_kwargs)\n", - " median_d = median_diff_data.median_diff.results\n", - " # print(\"median diff\") # for debug.\n", - " median_d_low, median_d_high = float(median_d.bca_low), float(median_d.bca_high)\n", - " if median_d_low < MEDIAN_DIFFERENCE < median_d_high is False:\n", - " error_count_median_diff += 1\n", - "\n", - "\n", - " cd_data = load(data=cd_df, idx=(\"Control\", \"Test\"), **load_kwargs)\n", - " cliffs = cd_data.cliffs_delta.results\n", - " # print(\"cliff's delta\") # for debug.\n", - " low, high = float(cliffs.bca_low), float(cliffs.bca_high)\n", - " if low < 0.5 < high is False:\n", - " error_count_cliffs_delta += 1\n", - "\n", - "\n", - "max_errors = int(np.ceil(reps * (100 - ci) / 100))\n", - "\n", - "assert error_count_cohens_d <= max_errors\n", - "assert error_count_hedges_g <= max_errors\n", - "assert error_count_mean_diff <= max_errors\n", - "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": { - "kernelspec": { - "display_name": "python3", - "language": "python", - "name": "python3" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/nbs/tests/test_forest_plot.py b/nbs/tests/test_forest_plot.py new file mode 100644 index 00000000..6b57c6e6 --- /dev/null +++ b/nbs/tests/test_forest_plot.py @@ -0,0 +1,47 @@ +import pytest +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 + +def test_forest_plot_no_input_parameters(): + error_msg = "The `contrasts` parameter cannot be None" + with pytest.raises(ValueError) as excinfo: + forest_plot(contrasts = None) + + assert error_msg in str(excinfo.value) + +@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), + ("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), + ("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), +]) +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() + + # Replace the tested parameter with the invalid value + valid_inputs[param_name] = param_value + + # Perform the test + with pytest.raises(error_type) as excinfo: + forest_plot(**valid_inputs) + + # Check the error message + assert error_msg in str(excinfo.value) diff --git a/nbs/tests/test_load_errors.py b/nbs/tests/test_load_errors.py new file mode 100644 index 00000000..eb598796 --- /dev/null +++ b/nbs/tests/test_load_errors.py @@ -0,0 +1,216 @@ +import pytest +from dabest._api import load +from data.mocked_data_test_load_errors import dummy_df, N + + +def test_wrong_params_combinations(): + error_msg = "`proportional` and `mini_meta` cannot be True at the same time." + with pytest.raises(ValueError) as excinfo: + my_data = load( + dummy_df, idx=("Control 1", "Test 1"), proportional=True, mini_meta=True + ) + + assert error_msg in str(excinfo.value) + + error_msg = ( + "If `delta2` is True. `x` parameter cannot be None. String or list expected" + ) + with pytest.raises(ValueError) as excinfo: + my_data = load( + dummy_df, + idx=("Control 1", "Test 1"), + delta2=True + ) + assert error_msg in str(excinfo.value) + + error_msg = "`delta2` and `mini_meta` 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, + mini_meta=True + ) + + 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( + dummy_df, + x=["Control 1", "Control 1"], + idx=("Control 1", "Test 1"), + delta2=True + ) + + assert error_msg in str(excinfo.value) + + error_msg = "`id_col` must be specified if `paired` is assigned with a not NoneType value." + with pytest.raises(IndexError) as excinfo: + my_data = load( + dummy_df, idx=("Control 1", "Test 1"), paired="baseline" + ) + + assert error_msg in str(excinfo.value) + + error_msg = "`delta2` is True but `y` is not indicated." + with pytest.raises(ValueError) as excinfo: + my_data = load( + dummy_df, + x=["Control 1", "Control 1"], + delta2=True + ) + + +def test_param_validations(): + error_msg = "`idx` contains duplicated groups. Please remove any duplicates and try again.".format(N) + with pytest.raises(ValueError) as excinfo: + my_data = load( + dummy_df, idx=("Control 1", "Control 1") + ) + + assert error_msg in str(excinfo.value) + + err0 = "Groups are repeated across tuples," + err1 = " or a tuple has repeated groups in it." + err2 = " Please remove any duplicates and try again." + error_msg = err0 + err1 + err2 + with pytest.raises(ValueError) as excinfo: + my_data = load( + dummy_df, idx=(("Control 1", "Control 1", "Test 1"), ("Control 2", "Test 2")) + ) + + assert error_msg in str(excinfo.value) + + wrong_idx = ("Control 1", ("Control 1", "Test 1")) + error_msg = "There seems to be a problem with the idx you " "entered--{}.".format(wrong_idx) + with pytest.raises(ValueError) as excinfo: + my_data = load( + dummy_df, idx=wrong_idx + ) + + assert error_msg in str(excinfo.value) + + wrong_paired = 'not_valid' + error_msg = "{} assigned for `paired` is not valid.".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" + ) + + assert error_msg in str(excinfo.value) + + + wrong_id_col = 'not_valid' + error_msg = "{} is not a column in `data`. ".format(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 + ) + + assert error_msg in str(excinfo.value) + + wrong_idx_mmeta = ("Control 1", "Test 1", "Test 2") + err0 = "`mini_meta` is True, but `idx` ({})".format(wrong_idx_mmeta) + err1 = "does not contain exactly 2 unique columns." + error_msg = err0 + err1 + with pytest.raises(ValueError) as excinfo: + my_data = load( + dummy_df, idx=wrong_idx_mmeta, mini_meta=True + ) + + assert error_msg in str(excinfo.value) + + wrong_idx_mmeta = (("Control 1", "Test 1", "Test 2"), ("Control 1", "Control 2", "Test 3")) + err0 = "`mini_meta` is True, but `idx` ({})".format(wrong_idx_mmeta) + err1 = "does not contain exactly 2 unique columns." + error_msg = err0 + err1 + with pytest.raises(ValueError) as excinfo: + my_data = load( + dummy_df, idx=wrong_idx_mmeta, mini_meta=True + ) + + assert error_msg in str(excinfo.value) + + wrong_x = ["Control 1", "Control 1", "Control 2"] + error_msg = "`delta2` is True but the number of variables indicated by `x` is {}.".format(len(wrong_x)) + with pytest.raises(ValueError) as excinfo: + my_data = load( + dummy_df, x=wrong_x, y="Test 1", delta2=True + ) + + assert error_msg in str(excinfo.value) + + wrong_x = ["Control 4", "Control 5"] + error_msg = "is not a column in `data`. Please check." + with pytest.raises(IndexError) as excinfo: + my_data = load( + dummy_df, x=wrong_x, y="Test 1", delta2=True + ) + + assert error_msg in str(excinfo.value) + + wrong_y = "Test 3" + error_msg = "is not a column in `data`. Please check." + with pytest.raises(IndexError) as excinfo: + my_data = load( + dummy_df, x=["Control 1", "Control 2"], y=wrong_y, delta2=True + ) + + assert error_msg in str(excinfo.value) + + wrong_experiment = "not_valid" + error_msg = "is not a column in `data`. Please check." + with pytest.raises(IndexError) as excinfo: + my_data = load( + dummy_df, + x=["Control 1", "Control 1"], + y="Test 1", + delta2=True, + experiment=wrong_experiment + ) + + assert error_msg in str(excinfo.value) + + #TODO experiment and experiment_label are different + + wrong_experiment_label = ["A", "B", "C"] + error_msg = "`experiment_label` does not have a length of 2." + with pytest.raises(ValueError) as excinfo: + my_data = load( + dummy_df, + x=["Control 1", "Control 1"], + y="Test 1", + delta2=True, + experiment="Control 1", + experiment_label=wrong_experiment_label + ) + + assert error_msg in str(excinfo.value) + + wrong_x1_level = "not_valid" + error_msg = "`x1_level` does not have a length of 2." + with pytest.raises(ValueError) as excinfo: + my_data = load( + dummy_df, + x=["Control 1", "Control 1"], + y="Test 1", + delta2=True, + experiment="Control 1", + x1_level=wrong_x1_level + ) + + assert error_msg in str(excinfo.value) \ No newline at end of file diff --git a/nbs/tests/test_plot_tools.py b/nbs/tests/test_plot_tools.py new file mode 100644 index 00000000..b47dba7f --- /dev/null +++ b/nbs/tests/test_plot_tools.py @@ -0,0 +1,189 @@ +import pytest +import pandas as pd +import numpy as np +import matplotlib.pyplot as plt +from dabest.plot_tools import get_swarm_spans, width_determine, error_bar, check_data_matches_labels, swarmplot +from data.mocked_data_test_swarmplot import dummy_df, default_swarmplot_kwargs + + +def test_get_swarm_spans_wrong_parameters(): + error_msg = "The collection `coll` parameter cannot be None" + with pytest.raises(ValueError) as excinfo: + get_swarm_spans(None) + + assert error_msg in str(excinfo.value) + + +def test_width_determine(): + error_msg = "The `labels` parameter cannot be None" + with pytest.raises(ValueError) as excinfo: + width_determine(None, []) + + assert error_msg in str(excinfo.value) + + error_msg = "The `data` parameter cannot be None" + with pytest.raises(ValueError) as excinfo: + width_determine("some_labels", None) + + assert error_msg in str(excinfo.value) + + +def test_error_bar(): + data = pd.DataFrame({ + 'group': ['A', 'A', 'B', 'B'], + 'value': [1, 2, 3, 4] + }) + error_msg = "`gap_width_percent` must be between 0 and 100." + with pytest.raises(ValueError) as excinfo: + error_bar( + data=data, + x='group', + y='value', + type='mean_sd', + gap_width_percent=-10 # Invalid as it's less than 0 + ) + + assert error_msg in str(excinfo.value) + + error_msg = "Invalid `method`. Must be one of 'gapped_lines', \ + 'proportional_error_bar', or 'sankey_error_bar'." + with pytest.raises(ValueError) as excinfo: + error_bar( + data=data, + x='group', + y='value', + type='mean_sd', + method='invalid_method' # Invalid as it's not one of the accepted values + ) + + assert error_msg in str(excinfo.value) + + error_msg = "Only accepted values for type are ['mean_sd', 'median_quartiles']" + with pytest.raises(ValueError) as excinfo: + error_bar( + data=data, + x='group', + y='value', + type='invalid_type' + ) + assert error_msg in str(excinfo.value) + +def test_check_data_matches_labels(): + wrong_labels = ['A', 'B', 'C'] + wrong_data = pd.Series(['A', 'B', 'D']) + error_msg = "labels and data do not match." + with pytest.raises(Exception) as excinfo: + check_data_matches_labels(wrong_labels, wrong_data, side='left') + + assert error_msg in str(excinfo.value) + +# swarmplot() UNIT TESTS +# fmt: off +@pytest.mark.parametrize("param_name, param_value, error_msg, error_type", [ + # Basic input validation checks + ("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 ", 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), + ("zorder", None, "`zorder` must be a scalar or float.", ValueError), + ("size", None, "`size` must be a scalar or float.", ValueError), + ("side", None, "Invalid `side`. Must be one of 'center', 'right', or 'left'.", ValueError), + ("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), + + # More thorough input validation checks + ("x", "a", "a is not a column in `data`.", IndexError), + ("y", "b", "b is not a column in `data`.", IndexError), + ("hue", "c", "c is not a column in `data`.", IndexError), + ("order", ["Control 1", "Test 2"], "Test 2 in `order` is not in the 'group' column of `data`.", IndexError), + ("palette", " ", "`palette` cannot be an empty string. It must be either a string indicating a color name or an Iterable.", ValueError), + ("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) +]) +def test_swarmplot_input_error_handling(param_name, param_value, error_msg, error_type): + with pytest.raises(error_type) as excinfo: + my_data = swarmplot( + data=dummy_df if param_name != "data" else param_value, + x="group" if param_name != "x" else param_value, + y="value" if param_name != "y" else param_value, + ax=plt.gca() if param_name != "ax" else param_value, + order=["Control 1", "Test 1"] if param_name != "order" else param_value, + hue=None if param_name != "hue" else param_value, + palette="black" if param_name != "palette" else param_value, + zorder=1 if param_name != "zorder" else param_value, + 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, + 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, + ) + + assert error_msg in str(excinfo.value) + +def test_swarmplot_warnings(): + warning_msg = ( + "{0:.1f}% of the points cannot be placed. " + "You might want to decrease the size of the markers." + ) + with pytest.warns(UserWarning) as warn_rec: + my_data = swarmplot(size=100, **default_swarmplot_kwargs) + + assert warning_msg.format(10) in str(warn_rec[0].message) + assert warning_msg.format(20) in str(warn_rec[1].message) + + warning_msg = ( + "unique values in '{0}' column in `data` " + "and `palette` do not have the same length. Number of unique values is {1} " + "while length of palette is {2}. The assignment of the colors in the " + "palette will be cycled." + ) + with pytest.warns(UserWarning) as warn_rec: + my_data = swarmplot(palette=["black"], **default_swarmplot_kwargs) + + assert warning_msg.format("group", 2, 1) in str(warn_rec[0].message) + + +def test_swarmplot_order_params(): + # `order` should be able to handle customised order -> swapping of params in `order` list + swarmplot(order=["Control 1", "Test 1"], **default_swarmplot_kwargs) + swarmplot(order=["Test 1", "Control 1"], **default_swarmplot_kwargs) + + # `order` should be able to handle None, where it will then be autogenerated + swarmplot(order=None, **default_swarmplot_kwargs) + + +def test_swarmplot_hue_params(): + swarmplot(hue="gender", **default_swarmplot_kwargs) + + +@pytest.mark.parametrize("hue, palette", [ + # `palette` can be a string, list, tuple or a dict + # Testing `palette` when color of swarms is based on `x` value + (None, "black"), + (None, ("black", "red")), + (None, {"Control 1": "black", "Test 1": "red"}), + + # Testing `palette` when color of swarms is based on `hue` value + ("gender", "black"), + ("gender", ["black", "red"]), + ("gender", ("black", "red")), + ("gender", {"Female": "black", "Male": "red"}), + + # Testing auto assignment of `palette` when `palette` is: + # (list | tuple) and len(palette) != len(unique_color_groups) + (None, ["black"]), +]) +def test_swarmplot_palette_params(hue, palette): + swarmplot(hue=hue, palette=palette, **default_swarmplot_kwargs) + + +def test_swarmplot_side_params(): + swarmplot(side="center", **default_swarmplot_kwargs) + swarmplot(side="right", **default_swarmplot_kwargs) + swarmplot(side="left", **default_swarmplot_kwargs) diff --git a/nbs/tutorials/01-basics.ipynb b/nbs/tutorials/01-basics.ipynb index 5816c471..23c16177 100644 --- a/nbs/tutorials/01-basics.ipynb +++ b/nbs/tutorials/01-basics.ipynb @@ -7,7 +7,7 @@ "source": [ "# Basics\n", "\n", - "> An end-to-end tutorial on how to use the dabest.\n", + "> An end-to-end tutorial on how to use the dabest library.\n", "\n", "- order: 1" ] @@ -17,7 +17,7 @@ "id": "c964abcb", "metadata": {}, "source": [ - "## Load Libraries" + "## Load libraries" ] }, { @@ -30,7 +30,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "We're using DABEST v2023.02.14\n" + "We're using DABEST v2024.03.29\n" ] } ], @@ -42,6 +42,18 @@ "print(\"We're using DABEST v{}\".format(dabest.__version__))" ] }, + { + "cell_type": "code", + "execution_count": null, + "id": "11eb9759", + "metadata": {}, + "outputs": [], + "source": [ + "#| hide\n", + "import warnings\n", + "warnings.filterwarnings(\"ignore\", category=UserWarning)" + ] + }, { "cell_type": "markdown", "id": "61f4ab6b", @@ -55,7 +67,7 @@ "id": "c45f63cd", "metadata": {}, "source": [ - "Here, we create a dataset to illustrate how ``dabest`` functions. In\n", + "Here, we create a dataset to illustrate how ``dabest`` works. In\n", "this dataset, each column corresponds to a group of observations." ] }, @@ -68,8 +80,8 @@ "source": [ "from scipy.stats import norm # Used in generation of populations.\n", "\n", - "np.random.seed(9999) # Fix the seed so the results are replicable.\n", - "# pop_size = 10000 # Size of each population.\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", @@ -102,13 +114,21 @@ " })" ] }, + { + "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 also has a non\\-numerical column indicating gender, and another\n", + "dataset has also a non\\-numerical column indicating gender, and another\n", "column indicating the identity of each observation." ] }, @@ -117,7 +137,7 @@ "id": "e975d14a", "metadata": {}, "source": [ - "This is known as a 'wide' dataset. See this \n", + "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." ] @@ -267,7 +287,7 @@ "id": "7dd2c3f4", "metadata": {}, "source": [ - "## Loading Data" + "## Loading data" ] }, { @@ -275,12 +295,9 @@ "id": "eda4a39f", "metadata": {}, "source": [ - "Before we create estimation plots and obtain confidence intervals for\n", - "our effect sizes, we need to load the data and the relevant groups.\n", + "Before creating estimation plots and obtaining confidence intervals for our effect sizes, we need to load the data and specify the relevant groups.\n", "\n", - "We simply supply the DataFrame to ``dabest.load()``. We also must supply\n", - "the two groups you want to compare in the ``idx`` argument as a tuple or\n", - "list." + "We can achieve this by supplying the dataframe to ``dabest.load()``. Additionally, we must provide the two groups to be compared in the ``idx`` argument as a tuple or list." ] }, { @@ -311,11 +328,11 @@ { "data": { "text/plain": [ - "DABEST v2023.02.14\n", + "DABEST v2024.03.29\n", "==================\n", " \n", - "Good evening!\n", - "The current time is Sun Mar 19 22:36:20 2023.\n", + "Good afternoon!\n", + "The current time is Tue Mar 19 15:35:21 2024.\n", "\n", "Effect size(s) with 95% confidence intervals will be computed for:\n", "1. Test 1 minus Control 1\n", @@ -345,8 +362,7 @@ "id": "f71a2c3d", "metadata": {}, "source": [ - "You can change the width of the confidence interval that will be\n", - "produced by manipulating the ``ci`` argument." + "You can change the width of the confidence interval by manipulating the ``ci`` argument." ] }, { @@ -368,11 +384,11 @@ { "data": { "text/plain": [ - "DABEST v2023.02.14\n", + "DABEST v2024.03.29\n", "==================\n", " \n", - "Good evening!\n", - "The current time is Sun Mar 19 22:36:23 2023.\n", + "Good afternoon!\n", + "The current time is Tue Mar 19 15:35:21 2024.\n", "\n", "Effect size(s) with 90% confidence intervals will be computed for:\n", "1. Test 1 minus Control 1\n", @@ -402,13 +418,16 @@ "id": "837ffe5c", "metadata": {}, "source": [ - "``dabest`` now features a range of effect sizes:\n", - " - the mean difference (``mean_diff``)\n", - " - the 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", - " - [Cliff's delta](https://en.wikipedia.org/wiki/Effect_size#Effect_size_for_ordinal_data)(``cliffs_delta``)\n", + "The **dabest** library now features a range of effect sizes:\n", + "\n", + " - the mean difference (`mean＿diff`)\n", + " - the 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", + " - [Cliff's delta](https://en.wikipedia.org/wiki/Effect_size#Effect_size_for_ordinal_data) (`cliffs＿delta`)\n", " \n", + "[comment]: <> (Please copy this underline for the above ＿)\n", " \n", "Each of these are attributes of the ``Dabest`` object." ] @@ -422,18 +441,18 @@ { "data": { "text/plain": [ - "DABEST v2023.02.14\n", + "DABEST v2024.03.29\n", "==================\n", " \n", - "Good evening!\n", - "The current time is Sun Mar 19 22:36:25 2023.\n", + "Good afternoon!\n", + "The current time is Tue Mar 19 15:35:22 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", "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.\n", "\n", "To get the results of all valid statistical tests, use `.mean_diff.statistical_tests`" @@ -457,14 +476,13 @@ "\"unpaired mean difference\"). The confidence interval is reported as:\n", "[*confidenceIntervalWidth* *LowerBound*, *UpperBound*]\n", "\n", - "This confidence interval is generated through bootstrap resampling. See\n", - ":doc:`bootstraps` for more details.\n", + "This confidence interval is generated through bootstrap resampling. See [`bootstraps`](/blog/posts/bootstraps/bootstraps.ipynb) for more details.\n", "\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", + "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", "[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 in addition. For paired comparisons, the p-values and test statistics of the \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", "and [Wilcoxon](https://en.wikipedia.org/wiki/Wilcoxon_signed-rank_test) tests are presented.\n" ] @@ -695,7 +713,7 @@ "id": "2548d82c", "metadata": {}, "source": [ - "Let's compute the Hedges' *g* for our comparison." + "Let's compute the *Hedges'g* for our comparison." ] }, { @@ -707,18 +725,18 @@ { "data": { "text/plain": [ - "DABEST v2023.02.14\n", + "DABEST v2024.03.29\n", "==================\n", " \n", - "Good evening!\n", - "The current time is Sun Mar 19 22:36:30 2023.\n", + "Good afternoon!\n", + "The current time is Tue Mar 19 15:35:23 2024.\n", "\n", "The unpaired Hedges' g between Control 1 and Test 1 is 1.03 [95%CI 0.349, 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", "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.\n", "\n", "To get the results of all valid statistical tests, use `.hedges_g.statistical_tests`" @@ -869,12 +887,11 @@ "id": "b451ab38", "metadata": {}, "source": [ - "To produce a **Gardner-Altman estimation plot**, simply use the\n", - "``.plot()`` method. You can read more about its genesis and design\n", - "inspiration at :doc:`robust-beautiful`.\n", + "To generate a **Gardner-Altman estimation plot**, simply use the\n", + "``.plot()`` method. You can learn more about its genesis and design\n", + "inspiration at [`robust-beautiful`](/blog/posts/robust-beautiful/robust-beautiful.ipynb).\n", "\n", - "Every effect size instance has access to the ``.plot()`` method. This\n", - "means you can quickly create plots for different effect sizes easily." + "Each instance of an effect size has access to the ``.plot()`` method. This allows you to quickly create plots for different effect sizes with ease." ] }, { @@ -885,7 +902,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -906,7 +923,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -924,7 +941,7 @@ "id": "5b566185", "metadata": {}, "source": [ - "Instead of a Gardner-Altman plot, you can produce a **Cumming estimation\n", + "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, and also\n", "displays the the mean (gap) and ± standard deviation of each group\n", @@ -940,7 +957,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -966,8 +983,8 @@ "``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), used in\n", - "meta-analyses to aggregate and compare data from different experiments." + "plot](https://en.wikipedia.org/wiki/Forest_plot), commonly used in\n", + "meta-analyses to aggregate and to compare data from different experiments." ] }, { @@ -978,7 +995,7 @@ "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", "text/plain": [ "
" ] @@ -1030,11 +1047,11 @@ { "data": { "text/plain": [ - "DABEST v2023.02.14\n", + "DABEST v2024.03.29\n", "==================\n", " \n", - "Good evening!\n", - "The current time is Sun Mar 19 22:36:43 2023.\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", @@ -1065,11 +1082,11 @@ { "data": { "text/plain": [ - "DABEST v2023.02.14\n", + "DABEST v2024.03.29\n", "==================\n", " \n", - "Good evening!\n", - "The current time is Sun Mar 19 22:36:48 2023.\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", @@ -1091,7 +1108,7 @@ "\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.\n", "\n", "To get the results of all valid statistical tests, use `.mean_diff.statistical_tests`" @@ -1114,7 +1131,7 @@ "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -1132,8 +1149,7 @@ "id": "0848f20b", "metadata": {}, "source": [ - "``dabest`` thus empowers you to robustly perform and elegantly present\n", - "complex visualizations and statistics." + "Thus ``dabest`` empowers you to perform robust analyses and present complex visualizations of your statistics elegantly." ] }, { @@ -1158,11 +1174,11 @@ { "data": { "text/plain": [ - "DABEST v2023.02.14\n", + "DABEST v2024.03.29\n", "==================\n", " \n", - "Good evening!\n", - "The current time is Sun Mar 19 22:36:56 2023.\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", @@ -1193,11 +1209,11 @@ { "data": { "text/plain": [ - "DABEST v2023.02.14\n", + "DABEST v2024.03.29\n", "==================\n", " \n", - "Good evening!\n", - "The current time is Sun Mar 19 22:37:01 2023.\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", @@ -1219,7 +1235,7 @@ "\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.\n", "\n", "To get the results of all valid statistical tests, use `.mean_diff.statistical_tests`" @@ -1242,7 +1258,7 @@ "outputs": [ { "data": { - "image/png": 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56bbuKN3PrEgA3Od2AaasrEyLFy/Wli1blJ2dXeViaFmWpk2bVu8AgcYqJSVFI0eO1AcffFCrgXidDumCLvFKiffgDGRRSdIZU6Q2g8urOwAAAA3s+MQEtf1RqsxVpg+/W6mdh/eoVXxzj8ZSvLtAxhhZfC4C4Aa3CjBr167VJZdcop07d9Z4IaQAA9TftGnT9O9///uUPWEsSZYs3T0i1TMnDo6Quk+Quo6XgrjPGQAA2Me9iQmMPtm0RpedcZFHYynLLVLpwaMKTojw6HEBNA5uzYJ0ww036OjRo3r33Xd1+PBhuVyuKo/aTp0LoGZ9+vTRggUL5HQ65XRWP7aL0yE5HZZev66T+qRF1++EEfFSr6ukiQuknpMovgAAANsdn5igLhyWpfxjBV6J59iGbK8cF0Dgc6sA88033+iPf/yjLrzwQsXFxXk4JAAnGjNmjFatWqURI0ZU6e5qWeW3Ha2a2l2je7g5FpPlkFL7SefdL018Q+p9tRQW44HIASAwlBTzoxJgJ/cmJjCKCov0SjzF23O9clwAgc+tW5BatWpV526AANzXp08fvf/++8rKylL37t2VnZ2tJhFBWnd3T/fHfGmSJnUYIbU7t7znCwCgWiXHyhQc4oUZ5gDUinsTE1g6u10fr8RTll/ileOiXFlZWY09vwF/51YPmD/+8Y+aPXu28vLyPB0PgJNISUlRRET5PccRIY66F1+Cw6WOF0gXPyONmyt1G0/xBQBOoay0br+8A/Cs4xMT1PZLudPh1HmnDfD4ALzHOSKCvXJcAIHPrR4wR44cUVRUlNq2bavLLrtMrVu3rnJBtCxLv/vd7zwSJIB6atFdane+lDFQCmHQOACoi7ISCjCA3Wo/MYElS9It51/rtVhCWkd57dgQM0wFmKKifQoNTbI7DJ/hVgHm97//fcX/P/3009VuQwEGsFlkM6njSKn9MCnGO78AAUBjwBgwgP2OT0wwfvx4GWOqnfDD6XDKkjTr6ofVI/U0r8US3rmp144NyeVy1XnQZfiuwsLtFGBO4FYBZuvWrZ6OA4CnxLSQel0ttRksOd1q4gCAE5QW0wMG8AXHJya4//77tXDhwko9YSzL0tDOZ+uW86/1avElND1WwUneGdwX5UpLSxUUxGfYQHH06HY1adLX7jB8hlvv7NTUVE/HAaC+LEvqeln5LEZMHw0AHlNSVGp3CAD+p7qJCWLDo7X0j695bcyX45xRwYoZ3Nqr54B07NgxhYW5OckEfE5x8QGVlh5RUFC03aH4hHr17dq1a5f+8Y9/aObMmdq5c6ek8lGrDx8+XG23QABeEhotDXtYOvPXFF8AwMNKivhMA/iayhMThHu/+BITorjR7RiAtwEUFhbaHQI8rKBgs90h+Ay3CjDGGN16661KT0/X5ZdfrltvvVU//vijJCk/P19paWl66qmnPBoogHLJyclqmdRMyTEh5QsimkoXPSWlnGFvYAAQoIoK6AEDNGahqTGKH99BQXH8yNUQDh8+bHcI8LDcvK/sDsFnuFWAefTRRzVz5kz9/ve/15IlSyrdfxkbG6sxY8borbfe8liQAH62du1a7fzkH1p7Z8/y3i4jHpXi0+0OCwAC1tH8YpWVMQ4M0NhYwQ5FZ7ZS7IUZcoQxJklD2bJli0pLKXwHkoMH/6OyMno2SW4WYGbPnq1JkybpoYceUvfu3aus79q1a0WPGABe1HOS1LSN3VEAQEAzRjpy6JjdYQBoQKGpMWo6sZMiuiYwLXIDKyws5LtkgCktzdP2rBdPOoV8Y+FWAWbHjh3q169fjesjIyOVl5fndlAAaiE4XDpttN1RAECjkL2nwO4QADQAR3iQYs9PU+yFGXIev90bDW7dunX0ggkwBw9+pJ27Xmn0RRi3CjCJiYnasWNHjeu/+OILpaSkuB0UgFpIO0cKYRpEAGgI+7cfsTsEAF4W1q6Jml7eSWHtm9DrxWZHjhzRZ5991ui/rPu73r1766yz7tLkSSskSXv2vKlNPz2k4uJDNkdmH7cKMGPGjNHf/vY3bdmypWLZ8YvUhx9+qLlz52rcuHGeifAUnnnmGaWlpSksLExnnHGGVq9e3SDnBWzXZrDdEQBAo7Fva66Miy8CQCByhDoVOyxNscPS5AhnrBc79e7dWzfeeKMeeughff/991q2bJmKi4vtDgtu2rt3r/buzdGhw0UVy3JyVuubb2/Qjp1/V3Fx4xtw2a0CzPTp09W8eXN1795dkyZNkmVZevjhh3X22Wdr+PDh6tq1q+68805Px1rFggULdOutt+qee+7Rl19+qW7duun888/X/v37vX5uwFaWU2rRw+4oAKDROFZQqtwDR+0OA4CHBSdHKn5CR4W1a2J3KFD5F/bDhw9XDGexadMmvf7669q4caNcLgZDDxQu1zHt2fOWvv7mOv20+VHl5n0tYxpHft0qwMTGxurzzz/X1KlTtWvXLoWFhWnFihXKycnRPffco48//lgRERGejrWKJ554Qtddd52uvvpqde7cWX/7298UERGhl156yevnBmwVnSwFh9kdBQA0Kvu3M74dEEgiuiaoyZi2ckYz1osvKygo0PLlyynEBCBjSnX48CfauPFP+vbbG7R37/sqKwvsQe/r3Mfu2LFjmjVrlrp37667775bd999tzfiOqXi4mJ98cUXuuOOOyqWORwODR06VJ999pktMQENJiTK7ggAoNE5uCNf7fvaHQWA+rKclqIHpyi8Y7zdoaAOcnNztXz5cn311Vfq06ePMjIyGKsngBwr2qOsHS9q95431KL5WCUmDpPDEWp3WB5X5wJMWFiY/vjHP+qvf/2rBgwY4I2YauXgwYMqKytTUlJSpeVJSUnasGFDtfsUFRWpqOjn+8/y8/MlSaWlpSopKfFesICnGafEexYAGkRpaYlKy0q1LytbxcXFfOAHfMTxAVqNMSopq93nImdksGKHpykoKZLP/z7oxJyWlZVVu83hw4e1ePFiJSYmqmfPnmrVqhXXZR91Yj5LS2vXc6m0NEdbtr6grB1vKTnpAiUknKugoGhvhukRwcHBtdrOrVGmunTpom3btrmzq61mzJih6dOnV1l+xhln2BANAADwO7faHQCAX9qbd0Ctf8fn+UCSm5urG2+80e4w4CEHDxSpf7+Fbuw5z+OxeEttZ+xyqwDz4IMPauLEiRo0aJCGDh3qziHqrVmzZnI6ndq3b1+l5fv27VNycnK1+9xxxx269dafPzmtW7dOmZmZ+u9//6sePRjQFH6kuFAK8f44SwAA6YtF27Rl3UFJUpfMlup0VnObIwIgSWlpadq9e7eSYxK0ZvrJv9yFd2qq6MxWspxuDYGJBpCVlaXevXsrJydH4eHhuvPOOxUfX/vbxCzLUlJSktLT05Wenq7IyEgvRouarFmzRg8++KA++OCDKuv690/UVVe3U+fO7g16HRKcqISEoUpIGKrg4Lh6RmoPtwowTz/9tOLj43X++edXvMHDw8MrbWNZlt577z2PBFmdkJAQ9erVS0uXLtWoUaMkSS6XS0uXLtVvfvObavcJDQ1VaOjP95FFRZWPoxEUFFTrLkOAT7DCpSDeswDQEIKCghXkLP/IlPVNtk7r30pOvsQBtjt+24llWQp21vy5KKpfC0X0TOQ2FR+1evVq3X///frXv/5V0Yvg6NGj+tOf/qTTTz9dI0aMUFpaWq2OdeDAAR04cEBr1qxRcnKy2rRpo4yMjCrfVeEdb7/9tsaPH19jb5DPPj+gzz8/oAcf6qVBg1rU+fguc1D79r+m/QfeUtOmmWqePErh4a3rG3aDcqsA880338iyLKWkpKisrEw//fRTlW0a4gJ36623avLkyerdu7f69u2rJ598UgUFBbr66qu9fm7AVg63mi4AoJ4K84r143/3qlO/un9wBNDALClmaCqD7fqwE7+w//JLuzFG69ev1/r163XdddfV6Y4FY4z27NmjPXv2aNWqVUpJSVGHDh2UkpIih4MCujesXr1a48ePV1lZWY0FGFeZkWVJd935hV54MdztnjDGlOjgwY908OBHahJ3hpo3v0RRUR3qE36DcetbnK+M/zJ+/HgdOHBAf/rTn7R37151795dixYtqjIwLxBw+AUHABpE7969tWPbLkWFxmnGjX+XJH338W4lZ8SqSTLd2wGfZUmx56UprL17X/DgfbX6wv6/Kadnz56tqVOn1ronzC+PsW3bNm3btk0RERFq166dOnbsqLi4uHpEj1964IEHqi2k/ZIxkpH00ks/6rHH6j92U3bOf5Wd81/FxHRTq1ZXKiqyXb2P6U1+X/77zW9+o+3bt6uoqEj//e9/GVAXjUMtB3kCANTP3r17tf/QXuXkH65Y5iozWvX2Zh0rYAYVwFdFn9OK4ouPq+0X9uOqG1OkrgoLC/X1119rwYIF+uc//6nt27fX+vyoWVZWlhYuXFjjzFW/5Coz+uTjfdq7t9BjMeTlfa3vv/+Ddu6a79M5dbsAU1ZWptdee01TpkzR6NGj9e2330oqH7H67bffrjI4LgAPMrWbxg0A4B0FOUVa9fZPKqvltJoAPC85OVktW7RUQkzTSsvDOsYrvGszm6JCbdT5C7vLpW+//VaHDx8+9ca1tHv3bi1atEhLly716S/s/sCdf0NjpLVrD3o4EqPduxcoL+9rDx/Xc9wqwOTk5Kh///6aOHGi/vGPf+j999/XgQMHJJUPbHvzzTdr5syZHg0UwAkowACA7Q7uyNfaD7bxwR2wydq1a7X9x6368A+vVixzRoeUz3bE7do+zb0v7EYbNmzweCybN2/WsWPHPH7cxuTIkSN1HlvHsqSCglKvxHP06HavHNcT3CrA3H777fruu++0ePFibdmypVLjcTqdGjt2rEe6iAGowUlG+gcANJzt6w/ph0/32B0GgP+JzmwlR4jT7jBwCu59Ybe8VigpLPTcrTCNUXR0dMV4PbVljBQZ6fmJRYKDm6hJk7M8flxPcasA8+677+qmm27SueeeW211uX379j4zUC8QkPhVBwB8xvqVu7Tje891iwfgnpAWUQpJi7E7DNSCe1/YjcLCwrwSS2Qkg6rXx5AhQ+rc68yypN69PXeroGUFKylxhLqc9leFhiZ67Lie5lbJKTc3V+np6TWuLykpUWmpd7oTAQAA+JrVC7coPCZYzVpF2x0K0GhF9Ejg1iM/cfwLe11uQ7IsSx07dvRYDDExMerUqZM6d+6skJAQjx23MUpJSdHIkSP1wQcf1GpcH4fTUv/+iUpOjqj3uYOCYpSYcL4SE4crJKTpqXewmVsFmDZt2ujLL7+scf2HH36ozp07ux0UAACAPykrNfrkjU0adEVHxSbU/wMlgLpxhDkVkhprdxiopTp/YXc41KVLF8XHx9frvE6nUxkZGerYsaOSk5PrfBsUajZt2jT9+9//PmVhzbIkS9I117Sv1/nCw1OVnHyxmsafI4fDfwpobr3jrr32Wr300ktasGBBxT+uZVkqKirSXXfdpUWLFmnKlCkeDRQAAMCXFR8t04r5G5V36KjdoQCNTkhqjCwnvV/8ybRp02RZVq17LY0YMcLtc0VGRuqMM87QFVdcocGDB6tFixYUXzysT58+WrBggZxOp5zO6sdhcjgtORyWHnqotzp3dm+a+PCw1mrb5o/qctpMJTQb4lfFF8nNHjC//e1v9d1332nChAmKi4uTJE2cOFGHDh1SaWmppkyZol/96leejBMAAMDnHSso1fJXNihzYkfFJoTbHQ7QaIRw+5/fOf6Fffz48TLGVNsT5niR5LrrrlNaWlqdzxEREaEePXqoY8eOCgry/ICvqGzMmDFatWqV7r//fi1cuLBSTxjLkvr3T9Q117Svc/HFkkPRMacrMWGYmjQ5U5blv8Uzt96FlmVp9uzZmjx5st58801t2rRJLpdLbdq00aWXXqoBAwZ4Ok4AAAC/cKygVMtf3aDMCR0Ul8TtSEBDCKat+aWTf2G31KVLF40YMaLOxZfw8HB1795dnTp1UnAws4c2pD59+uj9999XVlaWunfvruzsbEVHB+uVVzPrNOaLwxGqmJhuiovtrSZNzlBwcJz3gm5AtSrAjBkzRr/73e90zjnnSJJWrlypTp066eyzz9bZZ5/t1QABAAD8TVFhqZbP36AB4zsovgWzawDeZDktOZt4fnYcNIzqvrBHRETo7rvvrvOYL1FRUerWrRs9XnxASkqKIiIilJ2drbBwZ62KL0HOKDVpcpaaNDlTMTFd/e72otqo1bvyvffe0yWXXFLxfNCgQZo3b54mTpzotcAAAAD8WfHRMq34x0adM769mrWKsjscIGA5Y0NlORj/xd+d+IU9JCSkTsWX5ORkde7cWRkZGTWOPwLfFRbWSi2aj1V8fP+ALLqcqFYFmJYtW+qrr77S5ZdfLql8DnameAMAADi5kqIyrXxto84e106JqTF2hwMEJGdsqN0hwAYJCQlKT09XRkaGYmOZAcsfWVaQWrWcqOTkUbKsxlE4q1UB5rLLLtNjjz2m119/vWLQ3dtvv10zZsyocR/LsvT11197JEgAAAB/VVrs0scLftRZo9uqRbs4u8MBAo4zjgJMY5GQkKC2bdsqPT1d0dEMvOzPIiIylJF+syIi0u0OpUHVqgAzY8YMtW3bVsuWLdP+/ftlWZYiIyPVtGlTb8cHAADg98pKjT596yf1vTBdqafx+QnwJHrABL7U1FT16tVLCQkJdoeCerKsYLVseZmSk0bJ4Wh84/TU6hU7nU5df/31uv766yWVTwd29913MwYMAABALRmX0er3t6i0qExteibaHQ4QMJwxgT1mRGNmWZYGDBigjh072h0KPCA4uInatbtLUZHt7A7FNrWaQLtnz55atGhRxfM5c+aoR48eXgsKAAAgEBkjfbFouzat3Wd3KEDAcEQ0vl/RA1VycrLi4+MVE1M+Zlbfvn0pvvix5ORkJSfHqWl8qByOMHXscF+jLr5ItewB88033+jgwYMVz6+55hrNmzdPnTp18lpgAAAAgeqrD7MUFOxQeje60wP15QijABMo1q5dq9dee025ubnq0KGDunXrZndIqIe1a9dq27Zntf/AYrVqdYXCw1PsDsl2teoBk5qaqo8++khlZWWSmAUJAACgvtZ+sE27f8qxOwzA71nBtfpKAz/SvHlznXPOOXznDBBhYS2VlDjC7jB8Qq2uVr/+9a/197//XWFhYYqJiZFlWfrVr36lmJiYGh9MBQYAAFAzY6TP392s7L0FdocC+C3LYckKogATaPr37y+ns3FMS9wYtGg+rtFMM30qteqv94c//EHdunXTsmXLtG/fPr388svq06ePMjIyvB0fAABAwDo+RfWgKzspOj7M7nAAv+MI5/ajQNO0aVNm2w0gliNE8fH97Q7DZ9T6inXeeefpvPPOkyTNnTtXU6ZMYRYkAACqUZZfIGdUpN1hoJ6ysrJUWFgoSSoqPqqDOXvVLC7Z4+c5VlCqFfM3atAVHRUZx3S6ABq31q1b2x0CPCgsrKUcDmYqO86t/noul4viCwAANTAlxXaHgHpYvXq1LrzwQqWlpSk7O1uSVHDsiH7z2MV6ZN5t+mnn9x4/Z2FesZbP36jCPN47ABq3+Ph4u0OAB4UEN7E7BJ9Sqx4wWVlZkqSUlJRKz0/l+PYAADQmppgv0f7q7bff1vjx42WMkTGm0jpjjL7auErrflyl3172kM44bZBHz12QU6SVr/2oIVd1UnAI98oDaJxCQugtEUiCKcBUUqsCTFpamizL0tGjRxUSElLx/FSOz5oEAEBj4iootDsEuGH16tUaP368ysrKqhRfjnOZMslYmvnanbpvyotq26qzR2PIO3hU65Zkqc8F6R49LgD4i5iYGLtDgAcFB9Oj6US1KsC89NJLsixLwcHBlZ4DAICqXHm5docANzzwwAPV9nypysgY6Z1lL+kPVz7m8Ti2fXNQnfq1UFQTxoMB0Pg4HMxqFUiCghgT70S1KsBcddVVJ30OAAB+VnrokN0hoI6ysrK0cOHCWhRfyrlMmb7Y+LFXBuY1Rtq/PU9RTRI8elwAABqaZQXbHYJPobwIAICHlezZa3cIqKOlS5fWuvhynDFG67es9Uo8xcdKvXJcAAAaksNBAeZEteoBc99999X5wJZladq0aXXeDwAAf1eya5fdIaCOjhw5IofDIZfLVet9LMvS0WMFXoknLJIPrAAABJpaFWDuvffeKsuOjwHzy1+LLMuSMYYCDACg0SrZs0empERWMF+i/UV0dHSdii9S+Weg8DDv3Nue0DraK8cFAAD2qdUtSC6Xq9Jjx44dOv300zVhwgStXr1aubm5ys3N1X//+19ddtll6tatm3bs2OHt2AEA8E1lpSrZvdvuKFAHQ4YMqfMEA5ZlqUtGb4/HktwmVpFxDMALAECgcWsMmBtvvFHt2rXTK6+8ot69eys6OlrR0dHq06ePXn31VbVp00Y33nijp2MFAMBvFG/bZncIqIOUlBSNHDlSTqezVts7LKd6dTjH4wPwOpyWug5q5dFjAgAA3+BWAeY///mPBg8eXOP6IUOGaOnSpW4HBQCAvzv2ww92h4A6mjZtmizLqkVPGEuWJY0edI3HYzh9YCvFJUZ4/LgAAMB+bhVgwsLC9Nlnn9W4ftWqVQoLC3M7KAAA/F3h6jUypcxk40/69OmjBQsWyOl01tgTxmE55XQ4dMtlD6ltq84ePX/rTvFq3zfJo8cEAAC+w60CzOWXX65XX31VN998szZt2lQxNsymTZt00003af78+br88ss9HSsAAH6jLDdXBatW2R0G6mjMmDFatWqVRowYUaUnjGVZ6tmhv+6b8qL6njbIo+eNS4pQnwvS6jwODQAA8B+1mgXplx5++GEdPHhQTz/9tJ555hk5HOV1HJfLJWOMJkyYoIcfftijgQIA4G9y3nhDkf36yQpy688tbNKnTx+9//77ysrKUvfu3ZWdna3I8Gg98ptXPT7miySFRgSp/yVtFRRSu/FnAACAf3LrE2FISIjmzZunP/zhD/rggw+0fft2SVJqaqqGDx+ubt26eTRIAAD8UcnuPTqyZIlihg+3OxS4ISUlRREREcrOzlZocLhXii+Ww9JZo9sw6xEAAI1AvX6S69q1q7p27eqpWAAACDjZry1QZP/+csbE2B0KfFCPc1OUmMp7AwCAxsCtMWAAAEDtuPLzdfjlv9sdBnxQ+75Jatsr0e4wAABAA6EAAwCAB/Xu3Vt95szRRf9ZWrEsf/lyBuRFJaldmqrb4NZ2hwEAABoQBRgAADxo79692ltQoIPHiiotP/DMsyratMmmqOBLWnVoUj7jkYMZjwAAaEz8tgDz4IMPql+/foqIiFBcXJzd4QAAcFLm2DHtfeBBFW3ebHcosFHLDk10xqgMOZx++xEMAAC4yW//+hcXF2vcuHH6v//7P7tDAQCgVlz5+dp7z706uv47u0OBDVq2j9OZozLkpPgCAECj5LefAKZPn67f/e53Ov300+0OBQCAWnMdPap9Dzyggs8/tzsUNKAW7eJ05ug2FF8AAGjE3J6GevHixXrxxRe1ZcsWZWdnyxhTab1lWdrsY92si4qKVFT08z35+fn5NkYDAGisTEmJ9j/+hJr9368VPXiw3eHAy5LSYnQWxRcAABo9twowjz76qG6//XYlJSWpb9++ftMLZcaMGZo+fbrdYQAAILlcOvjMszIlpYo5/zy7o4GXxLeIVL+xbeUMovgCAEBj51YBZubMmRo8eLA++OADBQcHeyyY22+/XQ8//PBJt/nhhx/UsWNHt45/xx136NZbb614vm7dOmVmZrp1LAAAPOHQrFmSRBEmAMUlhmvA+PYKDnHaHQoAAPABbhVgsrOzNXbsWI8WXyTptttu01VXXXXSbTIyMtw+fmhoqEJDQyueR0VFuX0sAAA85dCsWZKrTDHDh9sdCjwkLilCAy5rr5Bwt+/2BgAAAcatTwV9+/bVxo0bPR2LEhISlJCQ4PHjAgDg6w698KLKjhxR3LhxsizL7nBQD81aR+nsse0ovgAAgErcuiH52Wef1dtvv6358+d7Op5ay8rK0rp165SVlaWysjKtW7dO69atY2BdAIDfylnwug785Um5jh61OxS4qVXHJsqc0IHiCwAAqMKtTwfjx49XaWmprrzySv3f//2fWrVqJaez8v3NlmXp66+/9kiQ1fnTn/6kl19+ueJ5jx49JEnLli3TwIEDvXZeAAC8qeDTT1W8dYua3XSTwtq3tzsc1EG7PknqPqS1LAc9mAAAQFVuFWDi4+PVtGlTtWvXztPx1NrcuXM1d+5c284PAIC3lOzeoz133a3YkRcobvx4OcLC7A4Jp9D57BY67ZwW3D4GAABq5FYBZvny5R4OAwAAVOJyKff9f6pg1Wdqeu2vFNGnj90RoQZteyVSfAEAAKfk1hgwAACgYZQePKh9f35Y+x59VKWHDtkdDn4hoXWUepybQvEFAACcUr1GiCspKdGGDRuUm5srl8tVZf2AAQPqc3gAAPA/hZ//V8e+Xa+mv7pGUZmZdocDSc4gh/pemMGYLwAAoFbcKsC4XC7dcccdevbZZ1VYWFjjdmVlZW4HBgAAKnMVFOjAX5/S0W/Xq9n118kKCbE7pEat41nJiowLtTsMAADgJ9y6Bemhhx7So48+qiuuuEJ///vfZYzRn//8Z/3tb39T165d1a1bNy1evNjTsQIAAEn5y5Zp7/33M121jUIjgtT+jGS7wwAAAH7ErQLM3Llzdemll+q5557TsGHDJEm9evXSddddp//+97+yLEv/+c9/PBooAAC+Lisrq6JnaGFZqXadpJdofR37/gftf/RRmWpuAYb3teudpOAQp91hAAAAP+JWAWbnzp0aPHiwJCk0tLzr7bFjxyRJISEhuuKKKzRv3jwPhQgAgG9bvXq1LrzwQqWlpSk7O1uSlFdSogGL/q3rVq3S14cPe+W8R7/+Rnn/+sArx0bNHE5LGT0S7A4DAAD4GbcKME2bNlV+fr4kKSoqSjExMdqyZUulbY5/AAUAIJC9/fbb6t+/v/7973/LGFNpnZG0fN9ejVuxXIt27fLK+XPeekumuNgrx0b1WrZvorDIYLvDAAAAfsatAkyPHj20Zs2aiueDBg3Sk08+qU8//VQff/yx/vrXv6pbt24eCxIAAF+0evVqjR8/XmVlZTUOPF9mjMqM0c2r/+uVnjCuI0dU+OWXHj8uapZ2elO7QwAAAH7IrQLM9ddfr6KiIhUVFUmSHnzwQeXk5GjAgAHKzMxUXl6eHn/8cY8GCgCAr3nggQdkjKnS8+WXzP8ez2zc4JU4Cr/4wivHhZScnKzEpsmKi4qXJIVFBikpI9bmqAAAgD9yaxrqiy66SBdddFHF886dO2vz5s1avny5nE6n+vXrp/j4eI8FCQCAr8nKytLChQtPWXw5rswYLd2zR7sKC9UyIsKjsRz95huPHg8/W7t2rdb+e5u2fHVAkpRyWlM5HJbNUQEAAH/kVgGmOrGxsbr44os9dTgAAHza0qVLa118Oc5I+uzAfo1NTfNoLGUHD6ksJ0fOuDiPHhdVpXbh9iMAAOAet25BkqSysjK99tprmjJlikaPHq1vv/1WkpSbm6u3335b+/bt81iQAAD4miNHjsjhqNufUYek/JJSr8RTepjB770tOj5McUme7b0EAAAaD7cKMDk5Oerfv78mTpyof/zjH3r//fd14EB519yoqCjdfPPNmjlzpkcDBQDAl0RHR8vlctVpH5ekqGCPdT6txBFJYcDbWnVsIsvi9iMAAOAetwowt99+u7777jstXrxYW7ZsqdQF2+l0auzYsfrggw88FiQAAL5myJAhdf4ybkk6KyHR47EEJSUpKNHzx0VlLdrF2R0CAADwY24VYN59913ddNNNOvfcc6v98Nm+fXtt27atvrEBAOCzUlJSNHLkSDmdzlpt77QsDWne3OMD8EpS3CVj6JnhZaERQYpvHml3GAAAwI+5VYDJzc1Venp6jetLSkpUWuqde9wBAPAV06ZNk2VZpyx+WP973Niho8djCO/VU1GDB3v8uKgsKS1GFrMfAQCAenCrANOmTRt9+eWXNa7/8MMP1blzZ7eDAgDAH/Tp00cLFiyQ0+mssSeM07LktCw91fcMdYuP9+j5Q1JTlPjb39L7pQEkpEbbHQIAAPBzbhVgrr32Wr300ktasGBBxfgvlmWpqKhId911lxYtWqQpU6Z4NFAAAHzRmDFjtGrVKo0YMaJKIcSSNCg5WW9kDtT5LVt69LxBzZop6a675IjktpiG0LRFlN0hAAAAP+fWVAy//e1v9d1332nChAmKi4uTJE2cOFGHDh1SaWmppkyZol/96leejBMAAJ/Vp08fvf/++8rKylL37t2VnZ2t2OBgLRwy1CtjvjgiI5U07W4FNW3q8WOjKsuSopuF2R0GAADwc24VYCzL0uzZszV58mS9+eab2rRpk1wul9q0aaNLL71UAwYM8HScAAD4vJSUFEVERCg7O1vhziCvFF/kcCjxtlsV0qqV54+NaoVHh8jpdKvTMAAAQAW3CjDHnX322Tr77LM9FQsAADiF+CuvUHi3bnaH0aiERQbbHQIAAAgA/JwDAICfiBk+TDEXXmh3GI1OSFjtphoHAAA4mVr3gLnooovqdGDLsvTee+/VOSAAAFBVzMiRip88iRmPbBAUQgEGAADUX60LMAsXLlRYWJiSk5MrZj46GT4gAgBQf1ZwsOJ/dY1izj3X7lAaraAQOgwDAID6q3UBpmXLltq1a5eaNWumiRMn6rLLLlNycrI3YwMAoFELTmmtxN/+ViFpaXaH0qg5gyjAAACA+qv1J4odO3Zo2bJl6tGjh+6//361bt1aQ4cO1Zw5c3TkyBFvxggAQONiWYq9+CK1fPhhii8+gAIMAADwhDp9osjMzNTzzz+vvXv36s0331TTpk31m9/8RomJiRozZozefPNNFRUVeStWAAACXlBioprfN13xkybJCgmxOxyIAgwAAPAMtz5RBAcH6+KLL9aCBQu0b9++iqLM+PHj9cgjj3g6RgAAGoWozEy1fPwxhXXubHcoOIHDybh2AACg/mo9Bkx1ioqKtHjxYr333nv66quvFBYWpjS6SgMAUCdWWJiaTbleUQMG2B0KqkEBBgAAeEKdCzAul0tLlizRP/7xD7377rsqLCzU0KFDNXv2bI0ePVqRkZHeiBMAgIAUlJyspNv/qJDWre0OBTWwHBRgAABA/dW6ALNq1SrNnz9fb7zxhg4dOqQzzzxTDz30kC699FI1a9bMmzECABCQQtu2VdJdd8oZE2N3KDgJCjAAAMATal2AOfvssxUeHq4RI0ZowoQJFbcaZWVlKSsrq9p9evbs6ZEgAQAINKFt2yr5nj/JERFhdyg4BYv6CwAA8IA63YJ09OhRvfXWW3r77bdPup0xRpZlqaysrF7BAQAQiIKSk5V05x0UX/yERQUGAAB4QK0LMHPmzPFmHAAANAqO8HAl3XGHnLGxdoeCWqL+AgAAPKHWBZjJkyd7Mw4AAAKfZSnhlt8qpFVLuyNBHdADBgAAeILD7gAAAGgsmlw2XhG9e9sdBgAAAGxAAQYAgAYQ3qunYseMsTsMuIMOMAAAwAMowAAA4GXO2Bgl3HijLAd/dgEAABorv/wkuG3bNv3qV79Senq6wsPD1aZNG91zzz0qLi62OzQAAKpocsUVDLrrz4zdAQAAgEBQp2mofcWGDRvkcrn0/PPPq23btlq/fr2uu+46FRQU6LHHHrM7PABAI5acnKyynBw1Cw6WJAW3aKGogQPtDQr1YsRdSAAAoP78sgAzbNgwDRs2rOJ5RkaGNm7cqOeee44CDADAVmvXrtXOm25Sye49kqSYC0dy65G/M5RgAABA/fllAaY6ubm5io+PP+k2RUVFKioqqnien5/v7bAAAI2YFR6mqHPOsTsM1BN3IAEAAE8IiJ/kfvrpJz311FOaMmXKSbebMWOGYmNjKx6ZmZkNFCEAoDGK7NdPjvBwu8NAfVGBAQAAHuBTBZjbb79dlmWd9LFhw4ZK++zatUvDhg3TuHHjdN111530+HfccYdyc3MrHitWrPDmywEANHLRjP0SECwHtx8BAID686lbkG677TZdddVVJ90mIyOj4v93796tQYMGqV+/fpo1a9Ypjx8aGqrQ0NCK51FRUW7HCgDAyTibNVVox452hwEPcFCAAQAAHuBTBZiEhAQlJCTUattdu3Zp0KBB6tWrl+bMmSMHAxwCAHxI5BlnMvguAAAAKvhUAaa2du3apYEDByo1NVWPPfaYDhw4ULEuOTnZxsgAACgX0aun3SEAAADAh/hlAWbJkiX66aef9NNPP6lVq1aV1hnDSHkAAHtZwcEK7dTJ7jAAAADgQ/yyb/RVV10lY0y1DwAA7BaSli5HSIjdYQAAAMCH+GUBBgAAXxac0truEAAAAOBjKMAAAOBhQbUcUB4AAACNBwUYAAA8zBkTY3cIAAAA8DEUYAAA8DBHWJjdIQAAAMDHUIABAMDTnH45ySAAAAC8iAIMAAAeZjksu0MAAACAj6EAAwCApzmddkcAAAAAH0MBBgAAT7P48woAAIDK+IQIAICHWcHBdocAAAAAH0MBBgAADwtKaGZ3CAAAAPAxFGAAAPAwy8GfVwAAAFTGJ0QAAAAAAAAvowADAAAAAADgZRRgAAAAAAAAvIwCDAAAAAAAgJdRgAEAAAAAAPAyCjAAAAAAAABeFmR3AGgYe/bs0Z49e+wOAx7SvHlzNW/e3O4w4CG0T8C3cc0NLFxzAaDh8De0skZdgGnevLnuueeegH9DFBUVacKECVqxYoXdocBDMjMztXjxYoWGhtodCuqJ9gn4Pq65gYNrLgA0LP6GVmYZY4zdQcC78vLyFBsbqxUrVigqKsrucFBP+fn5yszMVG5urmJiYuwOB/VE+ww8x9soOQ0MXHMDC9fcwML1NvCQ08DC39CqGnUPmMame/fuvPEDQF5ent0hwAton4HjeBs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"text/plain": [ "
" ] @@ -1268,15 +1284,13 @@ "id": "1f532032", "metadata": {}, "source": [ - "``dabest`` can also work with 'melted' or 'long' data. This term is so\n", - "used because each row will now correspond to a single datapoint, with\n", - "one column carrying the value and other columns carrying 'metadata'\n", - "describing that datapoint.\n", + "``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", + "describing that data point.\n", "\n", - "More details on wide vs long or 'melted' data can be found in this\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", - "gives recipes for melting dataframes.\n" + "provides recipes for melting dataframes.\n" ] }, { @@ -1386,7 +1400,7 @@ "id": "1ffb38fa", "metadata": {}, "source": [ - "When your data is in this format, you will need to specify the ``x`` and\n", + "When your data is in this format, you need to specify the ``x`` and\n", "``y`` columns in ``dabest.load()``.\n" ] }, @@ -1399,11 +1413,11 @@ { "data": { "text/plain": [ - "DABEST v2023.02.14\n", + "DABEST v2024.03.29\n", "==================\n", " \n", - "Good evening!\n", - "The current time is Sun Mar 19 22:37:07 2023.\n", + "Good afternoon!\n", + "The current time is Tue Mar 19 15:35:38 2024.\n", "\n", "Effect size(s) with 95% confidence intervals will be computed for:\n", "1. Test 1 minus Control 1\n", @@ -1431,7 +1445,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -1443,14 +1457,6 @@ "source": [ "analysis_of_long_df.mean_diff.plot();" ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ec5c9c8b", - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { diff --git a/nbs/tutorials/02-repeated_measures.ipynb b/nbs/tutorials/02-repeated_measures.ipynb index dca9afcd..67ca28f9 100644 --- a/nbs/tutorials/02-repeated_measures.ipynb +++ b/nbs/tutorials/02-repeated_measures.ipynb @@ -5,7 +5,7 @@ "id": "5a4db386", "metadata": {}, "source": [ - "# Repeated Measures\n", + "# Repeated measures\n", "\n", "> Explanation of how to use dabest for repeated measures analysis.\n", "\n", @@ -27,7 +27,10 @@ "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", - "**(Please note that** ``paired = True`` **and** ``paired = False`` **are no longer valid in v2023.02.14)**" + "
\n", + " **(Please note that** ``paired = True`` **and** ``paired = False`` **are no longer valid since v2023.02.14)**\n", + "
\n", + "\n" ] }, { @@ -48,7 +51,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "We're using DABEST v2023.02.14\n" + "We're using DABEST v2024.03.29\n" ] } ], @@ -60,12 +63,24 @@ "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": [ - "## Create dataset for demo" + "## Creating a demo dataset" ] }, { @@ -77,8 +92,7 @@ "source": [ "from scipy.stats import norm # Used in generation of populations.\n", "\n", - "np.random.seed(9999) # Fix the seed so the results are replicable.\n", - "# pop_size = 10000 # Size of each population.\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", @@ -131,11 +145,11 @@ { "data": { "text/plain": [ - "DABEST v2023.02.14\n", + "DABEST v2024.03.29\n", "==================\n", " \n", - "Good evening!\n", - "The current time is Sun Mar 19 22:39:03 2023.\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", @@ -173,11 +187,11 @@ { "data": { "text/plain": [ - "DABEST v2023.02.14\n", + "DABEST v2024.03.29\n", "==================\n", " \n", - "Good evening!\n", - "The current time is Sun Mar 19 22:39:04 2023.\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", @@ -200,8 +214,7 @@ "id": "17eae308", "metadata": {}, "source": [ - "When only 2 paired data groups are involved, assigning either ``baseline``\n", - "or ``sequential`` to ``paired`` will give you the same numerical results." + "When dealing with only 2 paired data groups, assigning either ``baseline`` or ``sequential`` to the ``paired`` parameter will yield the same numerical results" ] }, { @@ -213,11 +226,11 @@ { "data": { "text/plain": [ - "DABEST v2023.02.14\n", + "DABEST v2024.03.29\n", "==================\n", " \n", - "Good evening!\n", - "The current time is Sun Mar 19 22:39:08 2023.\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", @@ -225,7 +238,7 @@ "\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.\n", "\n", "To get the results of all valid statistical tests, use `.mean_diff.statistical_tests`" @@ -249,11 +262,11 @@ { "data": { "text/plain": [ - "DABEST v2023.02.14\n", + "DABEST v2024.03.29\n", "==================\n", " \n", - "Good evening!\n", - "The current time is Sun Mar 19 22:39:09 2023.\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", @@ -261,7 +274,7 @@ "\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.\n", "\n", "To get the results of all valid statistical tests, use `.mean_diff.statistical_tests`" @@ -295,7 +308,7 @@ "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -316,7 +329,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -337,7 +350,7 @@ "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -358,7 +371,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -376,9 +389,7 @@ "id": "e86d261e", "metadata": {}, "source": [ - "You can also create repeated-measures plots with multiple test groups.In\n", - "this case, declaring ``paired`` to be ``sequential`` or ``baseline`` will\n", - "generate different results." + "When creating repeated-measures plots with multiple test groups, declaring ``paired`` as ``sequential`` or ``baseline`` will generate different results." ] }, { @@ -401,11 +412,11 @@ { "data": { "text/plain": [ - "DABEST v2023.02.14\n", + "DABEST v2024.03.29\n", "==================\n", " \n", - "Good evening!\n", - "The current time is Sun Mar 19 22:39:18 2023.\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", @@ -421,7 +432,7 @@ "\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.\n", "\n", "To get the results of all valid statistical tests, use `.mean_diff.statistical_tests`" @@ -444,7 +455,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -477,11 +488,11 @@ { "data": { "text/plain": [ - "DABEST v2023.02.14\n", + "DABEST v2024.03.29\n", "==================\n", " \n", - "Good evening!\n", - "The current time is Sun Mar 19 22:39:26 2023.\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", @@ -497,7 +508,7 @@ "\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.\n", "\n", "To get the results of all valid statistical tests, use `.mean_diff.statistical_tests`" @@ -520,7 +531,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -538,8 +549,8 @@ "id": "139563f1", "metadata": {}, "source": [ - "Same as that for unpaired data, DABEST empowers you to perform complex \n", - "visualizations and statistics for paired data as well." + "Similar to unpaired data, DABEST empowers you to perform complex \n", + "visualizations and statistics for paired data." ] }, { @@ -550,7 +561,7 @@ "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -565,14 +576,6 @@ " paired=\"baseline\", id_col=\"ID\")\n", "multi_baseline_repeated_measures.mean_diff.plot();" ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f44b0ecf", - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { diff --git a/nbs/tutorials/03-proportion_plot.ipynb b/nbs/tutorials/03-proportion_plot.ipynb index 5b1d9099..3ec2f34e 100644 --- a/nbs/tutorials/03-proportion_plot.ipynb +++ b/nbs/tutorials/03-proportion_plot.ipynb @@ -1,28 +1,31 @@ { "cells": [ { + "attachments": {}, "cell_type": "markdown", "id": "29d885e4", "metadata": {}, "source": [ - "# Proportion Plots\n", + "# Proportion plots\n", "\n", - "> A guide to plot proportion plot with binary data.\n", + "> A guide to plot proportion plots with binary data.\n", "\n", "- order: 3" ] }, { + "attachments": {}, "cell_type": "markdown", "id": "098387ff", "metadata": {}, "source": [ - "As of v2023.02.14, DABEST can be used to produce 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", + "
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'.\n" + "analyzing proportion data that contains non-numeric values, such as strings like 'yes' and 'no'.
\n" ] }, { + "attachments": {}, "cell_type": "markdown", "id": "325366c2", "metadata": {}, @@ -40,7 +43,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "We're using DABEST v2023.02.14\n" + "We're using DABEST v2024.03.29\n" ] } ], @@ -53,11 +56,12 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "id": "7942a214", "metadata": {}, "source": [ - "## Create dataset for demo" + "## Creating a demo dataset" ] }, { @@ -96,6 +100,9 @@ " Test 4\n", " Test 5\n", " Test 6\n", + " Test 7\n", + " Test 8\n", + " Test 9\n", " Gender\n", " ID\n", " \n", @@ -104,7 +111,7 @@ " \n", " 0\n", " 1\n", - " 1\n", + " 0\n", " 0\n", " 0\n", " 1\n", @@ -112,13 +119,16 @@ " 0\n", " 1\n", " 0\n", + " 1.0\n", + " 0.0\n", + " 0.0\n", " Female\n", " 1\n", " \n", " \n", " 1\n", " 0\n", - " 0\n", + " 1\n", " 0\n", " 1\n", " 1\n", @@ -126,13 +136,16 @@ " 0\n", " 0\n", " 0\n", + " 1.0\n", + " 0.0\n", + " 0.0\n", " Female\n", " 2\n", " \n", " \n", " 2\n", " 0\n", - " 0\n", + " 1\n", " 0\n", " 0\n", " 1\n", @@ -140,13 +153,16 @@ " 1\n", " 1\n", " 0\n", + " 1.0\n", + " 0.0\n", + " 0.0\n", " Female\n", " 3\n", " \n", " \n", " 3\n", " 0\n", - " 0\n", + " 1\n", " 0\n", " 0\n", " 1\n", @@ -154,13 +170,16 @@ " 0\n", " 1\n", " 0\n", + " 1.0\n", + " 0.0\n", + " 0.0\n", " Female\n", " 4\n", " \n", " \n", " 4\n", " 0\n", - " 1\n", + " 0\n", " 0\n", " 0\n", " 1\n", @@ -168,6 +187,9 @@ " 0\n", " 0\n", " 1\n", + " 1.0\n", + " 0.0\n", + " 0.0\n", " Female\n", " 5\n", " \n", @@ -177,18 +199,18 @@ ], "text/plain": [ " Control 1 Test 1 Control 2 Test 2 Control 3 Test 3 Test 4 Test 5 \\\n", - "0 1 1 0 0 1 0 0 1 \n", - "1 0 0 0 1 1 1 0 0 \n", - "2 0 0 0 0 1 0 1 1 \n", - "3 0 0 0 0 1 0 0 1 \n", - "4 0 1 0 0 1 0 0 0 \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 Gender ID \n", - "0 0 Female 1 \n", - "1 0 Female 2 \n", - "2 0 Female 3 \n", - "3 0 Female 4 \n", - "4 1 Female 5 " + " 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, @@ -197,55 +219,179 @@ } ], "source": [ - "np.random.seed(9999) # Fix the seed so the results are replicable.\n", - "Ns = 40 # The number of samples taken from each population\n", + "def create_demo_prop_dataset(seed=9999, N=40):\n", + " import numpy as np\n", + " import pandas as pd\n", "\n", - "# Create samples\n", - "n = 1\n", - "c1 = np.random.binomial(n, 0.2, size=Ns)\n", - "c2 = np.random.binomial(n, 0.2, size=Ns)\n", - "c3 = np.random.binomial(n, 0.8, size=Ns)\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, 0.5, size=Ns)\n", - "t2 = np.random.binomial(n, 0.2, size=Ns)\n", - "t3 = np.random.binomial(n, 0.3, size=Ns)\n", - "t4 = np.random.binomial(n, 0.4, size=Ns)\n", - "t5 = np.random.binomial(n, 0.5, size=Ns)\n", - "t6 = np.random.binomial(n, 0.6, size=Ns)\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 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", + " # Add an `id` column for paired data plotting.\n", + " id_col = pd.Series(range(1, N + 1))\n", "\n", - "# Add an `id` column for paired data plotting.\n", - "id_col = pd.Series(range(1, Ns + 1))\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", - "# 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", + " 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": [ + "
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" + ], + "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" + "## Loading data" ] }, { + "attachments": {}, "cell_type": "markdown", "id": "a10dd2b3", "metadata": {}, "source": [ - "When loading data, specify ``proportional=True``." + "When loading data, you need to set the parameter ``proportional=True``." ] }, { @@ -267,11 +413,11 @@ { "data": { "text/plain": [ - "DABEST v2023.02.14\n", + "DABEST v2024.03.29\n", "==================\n", " \n", - "Good evening!\n", - "The current time is Sun Mar 19 22:41:40 2023.\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", @@ -289,6 +435,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "id": "217bf60d", "metadata": {}, @@ -297,15 +444,17 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "id": "32a8ce9b", "metadata": {}, "source": [ - "For proportion plot, dabest features two effect sizes:\n", + "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", - "Each of these are attributes of the ``Dabest`` object." + "These are attributes of the ``Dabest`` object." ] }, { @@ -317,18 +466,18 @@ { "data": { "text/plain": [ - "DABEST v2023.02.14\n", + "DABEST v2024.03.29\n", "==================\n", " \n", - "Good evening!\n", - "The current time is Sun Mar 19 22:42:28 2023.\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 0.375 [95%CI 0.15, 0.525].\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 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.\n", "\n", "To get the results of all valid statistical tests, use `.mean_diff.statistical_tests`" @@ -344,11 +493,12 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "id": "103bfed3", "metadata": {}, "source": [ - "Let's compute the Cohen's h for our comparison." + "Let's compute the *Cohen's h* for our comparison." ] }, { @@ -360,18 +510,18 @@ { "data": { "text/plain": [ - "DABEST v2023.02.14\n", + "DABEST v2024.03.29\n", "==================\n", " \n", - "Good evening!\n", - "The current time is Sun Mar 19 22:42:45 2023.\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 0.825 [95%CI 0.33, 1.22].\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 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.\n", "\n", "To get the results of all valid statistical tests, use `.cohens_h.statistical_tests`" @@ -387,23 +537,24 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "id": "f8e4b193", "metadata": {}, "source": [ - "## Producing Proportional Plots" + "## Generating proportion plots" ] }, { + "attachments": {}, "cell_type": "markdown", "id": "66e29a7e", "metadata": {}, "source": [ - "To produce a **Gardner-Altman estimation plot**, simply use the\n", + "To generate a **Gardner-Altman estimation plot**, simply use the\n", "``.plot()`` method. \n", "\n", - "Every effect size instance has access to the ``.plot()`` method. This\n", - "means you can quickly create plots for different effect sizes easily.\n" + "Each effect size instance has access to the ``.plot()`` method, allowing you to quickly create plots for different effect sizes with ease." ] }, { @@ -414,7 +565,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -435,7 +586,7 @@ "outputs": [ { "data": { - "image/png": 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+ "image/png": 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", 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" ] @@ -449,21 +600,14 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "id": "36f8b4c0", "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.\n", + "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 produce 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." + "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." ] }, { @@ -474,7 +618,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -488,11 +632,12 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "id": "78740e4c", "metadata": {}, "source": [ - "You can also modify the width of bars as you expect by setting ``bar_width`` in the ``plot()`` method. \n" + "You can also modify the width of bars by setting the parameter ``bar_width`` in the ``plot()`` method. \n" ] }, { @@ -503,7 +648,7 @@ "outputs": [ { "data": { - "image/png": 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", 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8LSIiUuRODGDTCqjXXtORaJ2AgAAYGRnBx8dH4fGjR4/iwIED2Ldvn+g2RCXquXPnwtPTE66urhAEAf3794eb2/+eYURFRaF169aiAvL19S30VndsbKzctp6eHoKCghAUFCSqLSIiKqaTiwBrZ8CouqYj0SqXLl2Su9P7rrZt2yI0NFSpNkQlaldXV9y6dQunT5+Gqamp3GCslJQU+Pj4cIAWEVF58joVOL0U+CRQ05FolZcvX8om/FJER0dH1KNguTrEnmhubo5evXoVSMimpqaYMGGCqClEiYhIi907AiRc03QUWuXDDz/EwYMHCz0eHR2t9Mhv0Yk6Ly8P27Ztw+jRo9GnTx9cu5Z/8VJTU7Fr164Cr1kREVE5cHYlF+14y/Dhw7F//374+fnJBlkD+XeXJ02ahOjoaAwfPlypNkQl6pSUFNkc21u3bsXevXuRnJwMIP91p/Hjxyu1pBcREWmpxL+BxOuajqJQJV2BMSUlBePGjYO1tTUMDAzQoEEDHDhwoNjtjR8/HkOHDkVYWBjMzMxQt25d1K1bF2ZmZggPD8fgwYOVfm1LVKKeNm0a/v77b8TExODBgwdy7yzr6uqif//+JfqiRERUhlzfpekIFCrpCozZ2dn49NNPERcXh507d+L27dtYs2YNateuXew2JRIJNmzYgCNHjmDMmDFwcnKCk5MTxo4di6NHjyIyMlLhqlolIWow2e7du/HNN9/g008/xX///VfgeIMGDbBx40alAiMiIi0VfxbIzQb09DUdiZySrsC4fv16PH/+HKdPn0alSpUAQG4yr5Lo0KEDOnToIDr29xHVo05NTYWdnV2hx3NycpCbmys6KCIi0mI5mUCidg0qE7MC4969e9GyZUuMGzcOlpaWcHJywrx585CXl1daYReLqERtb2+Py5cvF3r84MGDcHR0FB0UERFpuRePSq2p9PR0pKWlyT5vr4D4xvtWYExISFBY74MHD7Bz507k5eXhwIEDmDlzJhYuXIg5c+YUOzZBELB69Wq4ubnJ1qx49/O+17eKQ9TZI0aMwHfffYf27dvjk08+AZB/nz4rKwuzZ89GdHQ0IiIilAqMiIiKLz4+HpmZmQCAzGwp4p+/Rt0ahuprMCNZfXW/493XgIOCgjBr1iyl65VKpbCwsEBERAR0dXXRvHlzPHnyBD/88EOxJ9KaOnUqFi1aBBcXFwwePBjVq6t+QhhRiXrChAn4+++/MWjQIJiamgLIXxv6v//+Q25uLkaPHq30cHQiIira+fPnERISgv3798sG9r7IzIVtwHl0b1wDM7va4GPbqqpvWEe5XmJJHD9+XG5ujrcXVnpDzAqM1tbWqFSpktzaEI0aNUJCQgKys7Ohr1/0M/jIyEj069cPv/zySzG/TcmJuvUtkUiwZs0anDhxAt7e3ujSpQtcXFwwatQoxMbGYuXKlaqOk4iI3rFr1y60bt0av//+e4EVAwUBOHD9OVp9fxW7rjxTfeP6pbealrGxMUxMTGQfRYn67RUY33izAmPLli0V1tu6dWvZspRv3LlzB9bW1sVK0gDw6tUruefi6qDUn0Rt2rRBmzZtVBULEREV0/nz5+Hl5YW8vLxCl/XNkwISCPBacxOnp7qotmdd7QPV1aUifn5+GDp0KFxdXeHm5oawsLACKzDWrl1bNvf22LFjsWzZMkyYMAHffPMN7t69i3nz5mH8+PHFbvOTTz7BhQsXMGrUKLV8J0Bkj/rhw4f47bffCj3+22+/IS4uTmxMRFSKrKysULt27UJvD5J2mjNnDgRBKDRJvyEAECBgzgEVD/6qbqPa+lTAy8sLP/74IwIDA+Hi4oKrV68WWIHx6dOnsvJ16tRBTEwMLly4gCZNmmD8+PGYMGGCwle5CrNixQqcPXsW8+bNU/i6siqI6lFPmTIFaWlpcktbvm358uUwNTXFtm3blAqOiNTv4sWLmg6BSig+Ph779u0rMkm/kScFfrv2XHUDzCpVBqrWUr4eNSjJCowA0LJlS5w9e1Z0ew4ODpBKpZg5cyZmzpwJQ0NDuWfeQP7jYmUW5hCVqM+cOYOJEycWevyTTz5BWFiYyJCIqLTl5eXJPacTKzdPitw8KXTypMjJyVFBZKRITExMsZP0G4IAHLzxAkNbWhZduChmtkBeXv5HjcrCfBz9+vVTeuaxoohK1C9evEDVqoU/6zA2NlbbLQAiypcqqQak52L/3C+UruvnI9ew9ZiK52+e/LNq6yOljfzpLkb+dFcFNZ0CsEoF9ZR9pTELp6hEXbduXfzxxx8YO3aswuMnT57EBx9o30ADIlJsYIeP4NX+I5XUVU1IhX7Vmvj4mw0qqY8K2rhxo6jBS2sGf6iaHnXriYBjT+XrKcKVK1fg7u6u9na0nahEPWjQIISEhMDNzQ2+vr7Q0ckfk5aXl4dly5Zh+/btCAgIUGmgRKQ+ujqiV7wtQE/QgZ6ujmzuZFI9T09PSCSSEt3+lkiAzo7VUUlXBdfa3B4oheur7IxepSU+Ph7z5s3DsWPHkJSUhD179qBdu3Z49uwZZs+ejWHDhqFp06ai6xf1X8Hf3x+nTp3CxIkTMXfuXDg4OAAAbt++jeTkZLRv356JmohITerWrYvu3bvjwIEDxZqXWlcH6OZUQ3UzlVWro5p6yoEbN26gbdu2kEqlcHd3x71792TP1s3MzHDq1ClkZGRg3bp1otsQlagNDAxw8OBBREZGYteuXbh//z4AwM3NDf369YO3t7esl01E2m3iihi8SH+F6sZGCPPx1HQ4VEwzZ87E77//XmTPWgJAAglmdFXR61S6+kDlGqqpqxyYOnUqTE1NcfbsWUgkElhYWMgd79atG7Zv365UG6LvK+jo6GDYsGGyF8mJqGx6kf4K/6W90nQYVEIff/wxtm/fDi8vLwiCoLBnrauTn6R/GdlIdZOdVLXKv49OAIATJ04gMDAQ5ubmCgdR161bF0+ePFGqDVHd3ufPn+Ovv/4q9Pi1a9fw4sUL0UEREVHR+vbti9OnT6Nr164FXhGSSPJvd5+e6oI+Tc1U16hJbdXVVQ5IpVJUrly50OPJyckKpzwtCVGJetKkSe8dcTh69GhMmTJFdFBERFQ8H3/8Mfbu3Yu4uDjZyk3VK+shbq4b9vg4qX5BDtO6qq2vjGvWrBn279+v8Fhubi62bduGFi1aKNWGqER99OhR9OxZ+ND8Hj164PDhw6KDIiKikqlbt66sZ1dZX0d9S1yafaieessof39/REdHY+zYsbh+PX8ugsTERBw+fBidO3fGzZs3SzQlqSKinlEnJyfDzKzwWyk1a9ZEUlKS6KCIiEhLWarmffvyokuXLti4cSMmTJiAiIgIAMDgwYMhCAJMTEywadMmtGvXTqk2RCVqa2trXLlypdDjly5dgrm5ueigiIhIC1W1Bky0c45vTRoyZAj69u2LQ4cO4e7du5BKpbC3t4enp+d7Z/EsLlGJunfv3li+fDm6dOlS4Bb4nj17sGHDhkJnLSMiojLKlssavy0zMxN16tTBtGnT8O2336J3795qaUdUop41axYOHz6MPn36wNnZGU5OTgCA69ev488//0SjRo0QHBys0kCJiEjD6nfSdARapXLlytDT00OVKlXU2o6owWTVqlXD2bNnMWPGDOTk5GDnzp3YuXMncnJyMHPmTJw7dw6mpqYqDpWIiDTGvCFg7qDpKLROv379sHPnzhKvZlYSoic8qVKlCoKDg9lzJiKqCFwGcaITBQYOHAgfHx906NABI0eOhK2tLYyMjAqUa9asmeg2ysaM50REpDmWHwG2yo1cLq/at28v+/eTJ08WOC4IAiQSSbHmZC+MqET99ddfF1lGIpEoNQk5ERFpAYkEaDUe4PoNCm3YoP7lXEUl6qNHjxaYri4vLw9Pnz5FXl4ezM3N1f5wnYiISoFTf8Cioaaj0FpDhw5VexuiEnVcXJzC/Tk5OVi9ejXCwsJw6NAhZeIiIiJNq/YB8PEITUdRZjx9+hRJSUmoX7++SjurKr2XUalSJfj6+qJz587w9fVVZdVERFTaPKYCldQ0FWk5smfPHjRs2BAffPABmjVrhnPnzgEAnj17hqZNmyIqKkqp+tXy0MHZ2RknTpxQR9VERFQaPuoDWDtrOgqt99tvv6Fv374wMzNDUFCQ3GtaZmZmqF27NjZu3KhUG2pJ1IcOHXrvsl9EpD2qGxuhpokRqhsXfKWEKigjU+Dj4ZqOokyYPXs22rVrh1OnTmHcuHEFjrds2fK9U24Xh6hn1LNnz1a4PyUlBSdOnMDly5eVXi2EiEpHmI+npkMgbfPxSMBAxctjllPXr1/HokWLCj1uaWmp9CJVoqcQVaR69eqwt7fHqlWrMHLkSGXiIiIiTaj2AeDQRdNRlBmVK1dGRkZGoccfPHiAmjVrKtWGqEQtlUqVapSIiLRU868AHV1NR1FmdOjQAZGRkZg4cWKBYwkJCVizZg26d++uVBt8g52IiPIZWwL2HTUdRZkyd+5cPH78GB9//DFWr14NiUSCmJgYzJgxA40bN4YgCAgKClKqDVGJOj4+HqdOnZLb9+eff8Lb2xteXl7YvXu3UkEREZEGOPVjb7qEHBwccOrUKdSsWRMzZ86EIAj44YcfMG/ePDRu3BgnT56Era2tUm2IuvU9fvx4pKen4/DhwwCAxMREdOjQAdnZ2ahatSp27tyJHTt2oG/fvkoFR0REpUTPgM+mi+Gvv/6CjY0NqlWrJtv30Ucf4fDhw3jx4gXu3bsHqVSKevXqwdzcXCVtiupRnz9/Hp9++qlse9OmTXj16hX+/PNPPHnyBJ988gl+/PFHlQRIRESloH4nwNBE01FovaZNm2L//v2y7Y4dO+LIkSMA8gdUf/zxx3B3d1dZkgZEJurnz5/DwsJCtr1v3z54eHjA3t4eOjo66Nu3L27duqWyIImISM2c+mk6gjLByMgImZmZsu3Y2FgkJiaqtU1Rt77Nzc3x6NEjAPnvTp89exbz58+XHc/NzUVubq5qIiQiIvWq1RSoaa/pKMoEZ2dnLFq0CLq6urLb3xcuXICh4funWlXmUbCoRN2pUycsWbIEJiYmiI2NhVQqRe/evWXHb9y4gTp16ogKaPny5fjhhx+QkJAAZ2dnLF26FG5uboWWT0lJQUBAAHbt2oXnz5/DxsYGYWFh6Nq1q6j2iYgqHJcvNB1BmREWFoYBAwZg+PD8mdskEgnCw8MRHh5e6DkaWY96/vz5uHPnDqZMmQJ9fX38+OOPsLOzAwBkZWXhl19+wRdflPzCb9++HX5+fli1ahXc3d0RFhYGT09P3L59W+5W+xvZ2dn49NNPYWFhgZ07d6J27dp49OgRTE1NxXwtIqKKx9wB+OBjTUdRZnz88ce4d+8e7t+/j8TERLRv3x7Tp0+XG7elaqIStaWlJf744w+kpqbCyMgI+vr6smNSqRRHjhwR1aNetGgRRo4ciWHDhgEAVq1ahf3792P9+vUKpyRdv349nj9/jtOnT6NSpUoAoPQweCKiCqX5MEAi0XQUZcbevXvh6uoKBwcHODg4YOjQoejRowfc3d3V1qZSE55Uq1ZNLkkD+Q/anZ2dUaNGjRLVlZ2djUuXLqFTp07/C05HB506dcKZM2cUnrN37160bNkS48aNg6WlJZycnDBv3jylbjEQEVUYlh8BdVtoOooypU+fPoiNjZVtHz9+XDsHk6nDs2fPkJeXB0tLS7n9lpaWhY4gf/DgAY4ePYovv/wSBw4cwL179+Dj44OcnJxCZ4LJyspCVlaWbDs9PV11X4KIqCxxH8PedAlVrVoVKSkpsu24uDi15xGtSdRiSKVSWFhYICIiArq6umjevDmePHmCH374odBEHRoaiuDg4FKOlIhIy9i1BaybaDqKMsfNzQ1z585FYmKibNT3gQMHkJCQUOg5EokEkyZNEt2m1iRqMzMz6OrqFriFkJiYCCsrK4XnWFtbo1KlStDV/d+Ud40aNUJCQgKys7ML3JYHAH9/f/j5+cm2r169Cg8PDxV9CyKiMkBXH2hRcO1kKtqKFSvg7e2NkJAQAPlJeMuWLdiyZUuh55SbRK2vr4/mzZvjyJEjsle93gxM8/X1VXhO69atsWXLFkilUujo5D9uv3PnDqytrRUmaQAwMDCAgYGBbNvY2Fi1X4SISNs5DwRMrDUdRZlUv359nD59Gq9fv0ZSUhJsbW0RFhaGXr16qa3NYg0mq1GjBnbu3Cnbnj17Nq5fv67yYPz8/LBmzRpERkbi5s2bGDt2LDIyMmSjwL29veHv7y8rP3bsWDx//hwTJkzAnTt3sH//fsybNw/jxvEvRSIihYwtAZcvNR1FmWdoaIi6desiKCgIHTt2hI2NzXs/yihWjzo9PV1uyrRZs2ahfv36cHJyUqrxd3l5eSE5ORmBgYFISEiAi4sLoqOjZQPM4uPjZT1nAKhTpw5iYmIwadIkNGnSBLVr18aECRPw3XffqTQuIqJyw300UOn9s2hR8Sm7hGVxFCtR29vbY+fOnWjbti1MTPInbc/IyMDz58/fe15JX9ECAF9f30Jvdb89JP6Nli1b4uzZsyVuh4iowjF3AOp10HQUZdrXX38NiUQiG8T89ddfF3mORCLBunXrRLdZrEQ9ffp0DBs2TLZiiEQiwZgxYzBmzJj3nsf3mYmItEjzYYCOUtNnVHhHjx6Fjo4OpFIpdHV1cfToUUiKeMWtqONFKVaiHjJkCNzc3GSrhMyaNQt9+vRBkyYc2k9EVCaY1gHqqG/2rIoiLi7uvdvqUOxR32+mSwOADRs2YOjQoejZs6faAiMiIhVq1JO96TJK1OtZDx8+VHUcRESkTvXaazoCEkn0n1d5eXmIjIzE559/Dnd3d7i7u+Pzzz/Hpk2b+GyaiEibmH0IGBdcgbA8Wr58OWxtbWFoaAh3d3ecP3++WOdt27YNEolEbslmRXR0dKCrq1vijzJE9ahTU1Ph6emJCxcuoGrVqqhXrx4A4NChQ/j111+xcuVKxMTEyEaIExGRBtVurukISkVJl0p+Iy4uDlOmTEHbtm2LbCMwMLDA4LCoqCj8/fff8PT0lD0ivnXrFg4ePAgnJ6cik39RRCXqgIAAXLp0CUuXLsXIkSNlS0zm5ORg7dq1GD9+PAICArB06VKlgiMiIhWwqhgDf0u6VDKQf3f4yy+/RHBwME6ePCm34IYis2bNktuOiIhAUlISrl+/LkvSb9y8eRMdO3ZErVq1RH8nQOSt76ioKPj4+MDHx0eWpAGgUqVKGDt2LMaOHYtff/1VqcCIiEhFLBppOgKlpKenIy0tTfZ5ewXEN8QslQzkz7RpYWGB4cOHi4rthx9+gK+vb4EkDeSvPeHr64vvv/9eVN1viErU//33n8Kg3mjYsGGRk6EQEVEpqGoFVC755FPaxMPDA9WqVZN9QkNDC5R531LJha1sderUKaxbtw5r1qwRHdvjx4/lOqzvqlSpEh4/fiy6fkBkoq5fvz727t1b6PG9e/fC3t5edFBERKQi5oV3qsqK48ePIzU1VfZ5e80HsV6+fIkhQ4ZgzZo1MDMzE12Pk5MTVqxYgSdPnhQ49vjxY6xYsQKNGzdWJlRxz6h9fHzg6+uLrl27YuLEiWjQoAEA4Pbt21iyZAkOHTqEZcuWKRUYERGpgHnZvu0N5K9yWNTg5JIulXz//n3ExcWhR48esn1SqRQAoKenh9u3bxerw7l48WJ4enqiQYMG6NOnD+rXrw8AuHv3Lnbv3g1BEPDTTz8VWc/7iE7USUlJmD9/PmJiYuSOVapUCYGBgRg7dqxSgRERkQpYNNR0BKWipEslN2zYENeuXZPbN2PGDLx8+RLh4eGoU6dOsdpt06YNzp07h5kzZyIqKgqvXr0CABgZGcHT0xPBwcGa6VED+SPffH19cfjwYTx69AgAYGNjg06dOil1G4GIiFREIgHMyv6t7+Ly8/PD0KFD4erqCjc3N4SFhRVYKrl27doIDQ2FoaFhgRUgTU1NAaDEK0M6OTkhKioKUqkUycnJAABzc3O51R6VITpRA/m3GgYOHKiSQIiISMVM6wL6lTUdRakp6VLJqqajo1NgMJsqKJWoiYhIi1Wg3vQbJV0q+W0bN25UfUAqwBnaiYjKK7MPNR0BqQATNRFReVWDr8mWB0zURETlVQ07TUdAKsBETURUHhmalPkZySgfB5MREZVHpnU1HUGFcuPGDTx48AAvXryAIAgFjnt7e4uuW1SiFgQBERERWLdunSywd0kkEuTm5ooOjIiIlFCNibo03L9/H4MHD8b58+cVJmggPx+WeqKeOnUqFi1aBBcXFwwePBjVq1cXHQAREamBiXJLK1LxjB49GteuXUNYWBjatm2rlnwoKlFHRkaiX79++OWXX1QdDxERqUJVa01HUCH88ccfmD59Or755hu1tSEqUb969UpuzU8iItI8KysrQJDCSu9l/vKWpHZmZmaoVq2aWtsQNer7k08+wYULF1QdCxERKeHixYt4fPMSLk5vBhirfipLKmjMmDH46aefkJeXp7Y2RPWoV6xYAU9PT8ybNw+jR49GzZo1VR0XERGJJdHhq1mlpEGDBsjLy4OzszO+/vpr1KlTB7q6ugXK9e3bV3QbohK1g4MDpFIpZs6ciZkzZ8LQ0LBAYBKJBKmpqaIDIyIikYyqAzoFkwWpnpeXl+zfp0yZorCMRCJRqsctKlH369cPEolEdKNERKRG7E2XmmPHjqm9DVGJWltXGCEiIuT3qKlUeHh4qL0NzkxGRFTeGKp3FDIpduPGDTx69AgAYGNjA0dHR5XUK3qu77S0NAQHB8PNzQ2WlpawtLSEm5sbZs+ejbS0NJUER0REIugbazqCCmXPnj2wt7dH48aN0b17d3Tv3h2NGzdG/fr1sXfvXqXrF5Wo//33XzRt2hTBwcFIT09H69at0bp1a2RkZGDWrFlo1qwZnj59qnRwREQkgn4VTUdQYRw4cAD9+vUDAMybNw9RUVGIiorCvHnzIAgC+vbti+joaKXaEHXr+7vvvkNCQgL27duHrl27yh37/fffMWDAAEybNg2RkZFKBUdERCIwUZeakJAQNGnSBCdPnkSVKv/7796zZ0/4+vqiTZs2CA4OxmeffSa6DVE96ujoaEycOLFAkgaALl26YPz48Thw4IDooIiISAl6hpqOoML466+/MHToULkk/UaVKlXw1Vdf4a+//lKqDVGJOiMjA5aWhc96Y2VlhYyMDNFBERGREpioS42hoSGeP39e6PHnz5/D0FC56yEqUTs6OmLr1q3Izs4ucCwnJwdbt25V2Wg3IiIqIT19TUdQYXTs2BHh4eE4c+ZMgWPnzp3DkiVLlF4bQ/Qzai8vL7i5ucHHxwcNGjQAANy+fRurVq3CX3/9he3btysVGBERiaRTSdMRVBjff/89WrZsiTZt2sDNzQ0ODg4A8vPh+fPnYWFhgQULFijVhqhEPWDAAGRkZGDatGkYM2aMbJYyQRBgYWGB9evXo3///koFRkREIumyR11a7Ozs8NdffyE0NBS///67rJNqY2ODCRMmYNq0abCwsFCqDdETnnz11VcYPHgwLl68KPeCt6urK/T0OI8KEZHGMFGXKgsLCyxevBiLFy9WS/1KZVQ9PT20aNECLVq0UFU8RESkLF3e+i5PipWoT5w4AQBo166d3HZR3pQnIqJSxB612nz99deQSCSIiIiArq4uvv766yLPkUgkWLduneg2i5Wo27dvD4lEglevXkFfX1+2XRhBEJRe1ouIiERij1ptjh49Ch0dHUilUujq6uLo0aNFriap7GqTxUrUb5bx0tfXl9smIiItxEStNnFxce/dVodiJep3l/EqjWW9iIhIJL6eVWri4+Nhbm4OIyMjhcdfvXqF5ORk1K1bV3QboiY86dixI44cOVLo8WPHjqFjx46igyIiIiXoGWg6ggrDzs4OUVFRhR7fu3cv7OzslGpDVKKOjY1FYmJioceTkpJw/Phx0UEREZESdPiKbGkRBOG9x3NycqCjI3pFaQBKvJ71vofj9+7dQ9WqVcVWTUREyuAzarVKS0tDSkqKbPu///5DfHx8gXIpKSnYtm0brK2tlWqv2Ik6MjJSbtnKOXPmYM2aNQoD++uvvxSurFVcy5cvxw8//ICEhAQ4Oztj6dKlcHNzK/K8bdu2YdCgQejVqxd2794tun0iojKNr2ep1eLFizF79mwA+Z3WiRMnYuLEiQrLCoKAOXPmKNVesRN1ZmYmkpOTZdsvX74s0J2XSCSoUqUKxowZg8DAQFEBbd++HX5+fli1ahXc3d0RFhYGT09P3L59+73TsMXFxWHKlClo27atqHaJiMoFHd38D6lN586dYWxsDEEQMHXqVAwaNAjNmjWTK/MmHzZv3hyurq5KtVfsRD127FiMHTsWQP7D8/DwcPTs2VOpxhVZtGgRRo4ciWHDhgEAVq1ahf3792P9+vWYNm2awnPy8vLw5ZdfIjg4GCdPnpS7JUFEVKHw+bTatWzZEi1btgSQv+xzv3794OTkpLb2SvyE+9WrV+jdu7fSL3Arkp2djUuXLsktCaajo4NOnTopXELsjdmzZ8PCwgLDhw8vso2srCykpaXJPunp6SqJnYhIK7A3XWoyMzOxZMkS/P7772ptp8SJ2sjICBEREe8d9S3Ws2fPkJeXB0tLS7n9lpaWSEhIUHjOqVOnsG7dOoXPyxUJDQ1FtWrVZB++E05E5Qp71KWmcuXK0NPTQ5UqVdTajqgx482bN8f169dVHUuJvXz5EkOGDMGaNWtgZmZWrHP8/f2Rmpoq+/A1MiIqVyTKvQpEJdOvXz/s3LmzyNe0lCHqT6+wsDB07doVTk5O+Oqrr1S2rKWZmRl0dXUL9NYTExNhZWVVoPz9+/cRFxeHHj16yPZJpVIA+St73b59G/b29nLnGBgYwMDgf5MBGBsbqyR2IiKtwERdqgYOHAgfHx906NABI0eOhK2trcJZyt4dbFYSojLsV199BR0dHYwePRrjx49H7dq1CwQmkUjw559/lqhefX19NG/eHEeOHEHv3r0B5CfeI0eOwNfXt0D5hg0b4tq1a3L7ZsyYgZcvXyI8PBx16tQp2RcjIirzVD9+iArXvn172b+fPHmywHFVLFIlKlHXqFEDNWvWhIODg+iGC+Pn54ehQ4fC1dUVbm5uCAsLQ0ZGhmwUuLe3N2rXro3Q0FAYGhoWGGlnamoKAGodgUdERAQAGzZsUHsbohJ1bGysisP4Hy8vLyQnJyMwMBAJCQlwcXFBdHS0bIBZfHy80tOxERERqcLQoUPV3oZWDg/09fVVeKsbKPqPhI0bN6o+ICIioiKkp6fjn3/+AQDUqVNHZWOgRHdN8/LyEBkZic8//xzu7u5wd3fH559/jk2bNil1L56IiJSghjku6P0uXLiADh06oHr16nBycoKTkxOqV6+Ojh074uLFi0rXL6pHnZqaCk9PT1y4cAFVq1ZFvXr1AACHDh3Cr7/+ipUrVyImJgYmJiZKB0hERCXABTlK1blz59C+fXvo6+tjxIgRaNSoEQDg5s2b2Lp1K9q1a4fY2NhirVdRGFGJOiAgAJcuXcLSpUsxcuRIVKqU/4ORk5ODtWvXYvz48QgICMDSpUtFB0ZERKTtAgICULt2bZw6darAa8SzZs1C69atERAQgEOHDoluQ9St76ioKPj4+MDHx0eWpAGgUqVKsjnBf/31V9FBERERibF8+XLY2trC0NAQ7u7uOH/+fKFl16xZg7Zt26J69eqoXr06OnXq9N7yipw7dw6jR49WONeHpaUlRo0ahbNnz5b4e7xNVKL+77//3vtqVsOGDfH8+XPRQREREZXUm9UXg4KCcPnyZTg7O8PT0xNJSUkKy8fGxmLQoEE4duwYzpw5gzp16qBz58548uRJsdvU0dFBbm5uocfz8vKUflNJ1Nn169fH3r17Cz2+d+/eAjOCERERqdPbqy86Ojpi1apVqFy5MtavX6+w/M8//wwfHx+4uLigYcOGWLt2rWySreJq1aoVli9fjkePHhU4Fh8fjxUrVqB169aivxMg8hm1j48PfH190bVrV0ycOBENGjQAANy+fRtLlizBoUOHsGzZMqUCIyIiAvJfe0pLS5NtvzsVNPC/1Rf9/f1l+4qz+uLbMjMzkZOTgxo1ahQ7tnnz5qFdu3Zo2LAh+vTpI5cP9+zZAz09PYSGhha7PkVEJ+qkpCTMnz8fMTExcscqVaqEwMBA2drVREREynh3lcOgoCDMmjVLbt/7Vl+8detWsdr57rvvUKtWLbmllovStGlTnDt3DgEBAdi7dy8yMzMB5K+s9dlnn2HOnDlwdHQsdn2KiJ7wZNasWfD19cWhQ4cQHx8PALCxsUGnTp2KvZIVERFRUY4fPw4XFxfZ9ru9aVWYP38+tm3bhtjYWBgaGpboXEdHR0RFRUEqlSI5ORkAYG5urrJZNJWamczMzAyDBg1SSSBERESKGBsbFzkvR0lXX3zbjz/+iPnz5+Pw4cNo0qSJ6DglEgkk/z/hjESFE88ole737dsHHx8fdO3aFV27doWPjw/27dunqtiIiIiK5e3VF994MzCsZcuWhZ73/fffIyQkBNHR0XB1dRXV9o0bN9C/f3+YmJjA2toa1tbWMDExQf/+/XH9+nVRdb5NVI86JSUFffr0wYkTJ6Crqwtra2sAwOHDh7F69Wq0bdsWu3fvlq1kRUREpG4lWX0RABYsWIDAwEBs2bIFtra2SEhIAJDfgy/uPN0nT55Ely5dIJVK0atXL7nBZHv37sXvv/+O6OhotG3bVvT3EpWoJ0yYgJMnT2LBggUYO3YsqlSpAgDIyMjAihUr4O/vjwkTJiAyMlJ0YERERCVR0tUXV65ciezsbPTv31+uHkWD1QozadIkWFhY4Pjx46hTp47csX/++Qft2rWDn58fLly4IPp7iUrUu3fvho+PD6ZMmSK3v0qVKvj2228RHx+PTZs2iQ6KiIhIjJKsvhgXF6d0e3///TdCQkIKJGkgfwWtsWPHFjvpF0bUM+pKlSoVOTPZ21OLEhERlUc2NjbIysoq9Hh2drbCJF4SohJ1v379sGPHDoXLWebm5uKXX37BgAEDlAqMiIhI2wUGBmLJkiW4evVqgWNXrlzB0qVLle5Ri7r1PXjwYPj6+qJVq1YYNWoU6tevDwC4e/cuIiIikJ2djS+//BKXL1+WO69Zs2ZKBUtERKRNzp49C0tLSzRv3hytWrWSy4dnzpyBk5MTzpw5Izc7mkQiQXh4eLHbEJWo354l5sKFC7L3xQRBUFhGEARIJBKFPXAiIqKy6u3psv/44w/88ccfcsevXbuGa9euye0rlUS9YcMGMacRERGVK1KpVO1tiErUQ4cOVXUcREREpIBSU4gC+aua/PPPPwDyh6IX9yVxIiKi8uLhw4f4/fffZctd2tjYoEuXLrCzs1O6btGJ+sKFC5g6dSpOnTol6/rr6Oigbdu2+P7770VPxUZERFSWTJ48GeHh4QVug+vo6GDixIn48ccflapfVKI+d+4c2rdvD319fYwYMQKNGjUCANy8eRNbt25Fu3btEBsbCzc3N6WCIyIi0mYLFy7E4sWL0b9/f0yePFkuHy5evBiLFy9G7dq1MWnSJNFtiErUAQEBqF27Nk6dOlVgVZJZs2ahdevWCAgIwKFDh0QHRkREpO3WrFmDnj174pdffpHb7+7ujm3btuH169dYvXq1Uola1IQn586dw+jRoxUuHWZpaYlRo0bh7NmzooMiIiIqC+Li4uDp6VnocU9PT6WnKhWVqHV0dJCbm1vo8by8PJUtmE1ERKStLCws8OeffxZ6/M8//4S5ublSbYjKpq1atcLy5ctlo9veFh8fjxUrVqB169ZKBUZERKTtBgwYgLVr12L+/PnIyMiQ7c/IyMCCBQuwdu1aeHl5KdWGqGfU8+bNQ9u2bdGwYUP06dNHbv3NPXv2QE9PT7beJxERUXkVEhKCq1evYvr06QgMDEStWrUAAP/++y9yc3PRoUMHzJ49W6k2RCXqpk2b4vz58wgICMDevXuRmZkJAKhcuTI+++wzzJkzB46OjkoFRkREpO0qV66MI0eOYM+ePXLvUX/22Wfo2rUrevToIZtmW6wSJ+qsrCzExMTA1tYWUVFRkEqlSE5OBgCYm5vz2TQREVUImZmZGDx4MPr164cvv/wSvXr1Uks7Jc6q+vr6GDBgAE6fPp1fgY4OLC0tYWlpySRNREQVRuXKlXH48GHZXWV1KXFmlUgk+PDDD/Hs2TN1xENERFRmtGnTRm4JS3UQ1QWePn06li1bhtu3b6s6HiIiojJj2bJlOHnyJGbMmIHHjx+rpQ1Rg8nOnj2LmjVrwsnJCe3bt4etrS2MjIzkypR0vU0iIqKyxtnZGbm5uQgNDUVoaCj09PRgYGAgV0YikSA1NVV0G6IS9dsLZR85ckRhGSZqIiIq7/r166f0qO6iiErUpbFQNhERkbbbuHGj2ttQej1qIiKiiub169fYs2cPHj58CDMzM3Tr1g3W1tZqaUupRH39+nUcOHBANuG4ra0tunTpgsaNG6siNiIiIq2TlJSEVq1a4eHDhxAEAUD+q1q7d+9Gp06dVN6eqESdlZWF0aNHY/PmzRAEQfb+tFQqhb+/P7788kusXbsW+vr6Kg2WiIhI00JCQhAXF4dJkyahY8eOuHfvHkJCQjB69Gjcv39f5e2JStTfffcdNm3aBB8fH3zzzTewt7eHRCLBvXv3sGTJEqxcuRI1atRAWFiYisMlIiLSrIMHD8Lb2xs//vijbJ+lpSW++OIL3L59Gw4ODiptT9R71D/99BOGDBmCZcuWwcHBAXp6etDV1YWDgwOWL1+OL7/8Ej/99JNKAyUiItIG8fHxaNOmjdy+Nm3aQBAEJCYmqrw9UYk6JycHLVq0KPR4q1at3rteNRERUVmVlZUFQ0NDuX1vttWR+0Td+vb09ERMTAzGjh2r8Hh0dDQ6d+6sVGBERETaKi4uDpcvX5Ztv5nQ5O7duzA1NS1QvlmzZqLbEpWoQ0JC8Pnnn6Nv374YN24c6tevLwtw+fLlePToEbZv347nz5/LnVejRg3RgRIREWmLmTNnYubMmQX2+/j4yG0LggCJRIK8vDzRbYlK1I0aNQIAXLt2DXv27CkQFACF61ErEygREZE22LBhQ6m2JypRBwYGqn3KNCIiIm00dOjQUm1PVKKeNWuWisOQt3z5cvzwww9ISEiAs7Mzli5dCjc3N4Vl16xZg02bNuH69esAgObNm2PevHmFliciIipLRI36Vqft27fDz88PQUFBuHz5MpydneHp6YmkpCSF5WNjYzFo0CAcO3YMZ86cQZ06ddC5c2c8efKklCMnIiJSPa1L1IsWLcLIkSMxbNgwODo6YtWqVahcuTLWr1+vsPzPP/8MHx8fuLi4oGHDhli7di2kUmmhq3qRPFdXV3zwwQdwdXXVdChERKSAVi3KkZ2djUuXLsHf31+2T0dHB506dcKZM2eKVUdmZiZycnIKHWGelZWFrKws2XZ6erpyQZdxCQkJvPtARKTFtKpH/ezZM+Tl5cHS0lJuv6WlJRISEopVx3fffYdatWoVOjF6aGgoqlWrJvt4eHgoHTcREZG6aFWiVtb8+fOxbds2REVFFZg15g1/f3+kpqbKPsePHy/lKImIiIpPq259m5mZQVdXt8BcqYmJibCysnrvuT/++CPmz5+Pw4cPo0mTJoWWMzAwgIGBgWzb2NhYuaCJiIjUSKt61Pr6+mjevLncQLA3A8NatmxZ6Hnff/89QkJCEB0dzUFRRERUrmhVjxoA/Pz8MHToULi6usLNzQ1hYWHIyMjAsGHDAADe3t6oXbs2QkNDAQALFixAYGAgtmzZAltbW9mzbGNjY/aWiYiozNO6RO3l5YXk5GQEBgYiISEBLi4uiI6Olg0wi4+Ph47O/24ErFy5EtnZ2ejfv79cPUFBQWqfmIWIiEjdtC5RA4Cvry98fX0VHouNjZXbjouLU39AREREGqJVz6iJiIhIHhM1ERGRFmOiJiIi0mJM1EREVG4sX74ctra2MDQ0hLu7O86fP//e8jt27EDDhg1haGiIxo0b48CBA6UUafExUVdwVlZWqF27dpETyhARabuSrr54+vRpDBo0CMOHD8eVK1fQu3dv9O7dW7ZssrZgoq7gLl68iMePH+PixYuaDoWISCklXX0xPDwcn332Gb799ls0atQIISEhaNasGZYtW1bKkb8fEzUREZV5b1ZffHtBpqJWXzxz5kyBBZw8PT2LvVpjadHK96ipdOXl5UEqlaqkLmleLqR5eZDm5SInJ0cldVJBuXlS5Oap5pqpWq4ghU6elNeflJabmwsgfznitLQ02f5312wA3r/64q1btxTWn5CQoNRqjaWFibqMqixkIjMlE70mzlO6rltnj+DOuaMqiEreL3PGqrxOKkMm/6zpCKiceHc54oo28yQTNcHBrQMafNxeZfVlSirDrFoV/BQ4XGV1krwLS4chOT1X02EoVE1IhX7Vmvj4mw2aDoXKuCtXrsDd3R3Hjx+Hi4uLbP+7vWlA3OqLVlZWolZrLG1M1ASJjg4kKqxPR6ILHV09VKpUSYW10tv0dHWgp6udQ0z0hPzYeP1JWXp6+SnK2NgYJiYm7y379uqLvXv3BvC/1RcLm5K6ZcuWOHLkCCZOnCjbd+jQofeu1qgJTNQV3PGty5GVmQ6DysbwGDRO0+EQEYlW0tUXJ0yYAA8PDyxcuBDdunXDtm3bcPHiRURERGjyaxTARF3BZWWm43V6WtEFiYi0XElXX2zVqhW2bNmCGTNmYPr06fjwww+xe/duODk5aeorKMRETURE5UZJVl8EgAEDBmDAgAFqjko52vmQi4iIiAAwURMREWk1JmoiIiItxkRNRESkxZioiYiItBgTNRERkRZjoiYiItJiTNRERERajImaiIhIizFRExERaTEmaiIiIi3Gub4rOIPKxnL/JCIi7cJEXcFxaUsiIu3GW99ERERajImaiIhIizFRExERaTEmaiIiIi3GRE1ERKTFmKiJiIi0GBM1ERGRFmOiJiIi0mJM1ERERFqMiZqIiEiLMVETERFpMSZqIiIiLcZETUREpMWYqImIiLQYEzUREZEWY6ImIiLSYkzUREREWoyJmoiISIsxURMREWkxrUzUy5cvh62tLQwNDeHu7o7z58+/t/yOHTvQsGFDGBoaonHjxjhw4EApRUpERKReWpeot2/fDj8/PwQFBeHy5ctwdnaGp6cnkpKSFJY/ffo0Bg0ahOHDh+PKlSvo3bs3evfujevXr5dy5ERERKqndYl60aJFGDlyJIYNGwZHR0esWrUKlStXxvr16xWWDw8Px2effYZvv/0WjRo1QkhICJo1a4Zly5aVcuRERESqp1WJOjs7G5cuXUKnTp1k+3R0dNCpUyecOXNG4TlnzpyRKw8Anp6ehZYnIiIqS/Q0HcDbnj17hry8PFhaWsrtt7S0xK1btxSek5CQoLB8QkKCwvJZWVnIysqSbaenpwMAbt68qUzoper500d4/fK5psMo1GuJIXQyK+Py5cuaDqXcuvXPf3jxKk/TYShkLKSjUmVATwXX39raGtbW1iqIqvQ9ffoUT58+1XQYZVpZ+r2sTlqVqEtDaGgogoOD5fbZ2Nhg8ODBGoqo/DoYMUfTIZAmLVR+UGdQUBBmzZqlfCwasHr16gK/a6jkPDw8yuwfa6qiVYnazMwMurq6SExMlNufmJgIKysrhedYWVmVqLy/vz/8/Pzk9j1//hzPn2tvD1Wd0tPT4eHhgePHj8PY2FjT4ZAGaPPPQFn+BT169Gj07NmzVNvU5mspVlm+q6IqEkEQBE0H8TZ3d3e4ublh6dKlAACpVIq6devC19cX06ZNK1Dey8sLmZmZ+O2332T7WrVqhSZNmmDVqlWlFndZlZaWhmrVqiE1NRUmJiaaDoc0gD8D5QevZfmkVT1qAPDz88PQoUPh6uoKNzc3hIWFISMjA8OGDQMAeHt7o3bt2ggNDQUATJgwAR4eHli4cCG6deuGbdu24eLFi4iIiNDk1yAiIlIJrUvUXl5eSE5ORmBgIBISEuDi4oLo6GjZgLH4+Hjo6PxvsHqrVq2wZcsWzJgxA9OnT8eHH36I3bt3w8nJSVNfgYiISGW07tY3la6srCyEhobC398fBgYGmg6HNIA/A+UHr2X5xERNRESkxbRqwhMiIiKSx0RNRESkxZioSaXi4uIgkUiwceNGTYdCRFQuMFFr0P379zF69GjUq1cPhoaGMDExQevWrREeHo5Xr16prd0bN25g1qxZiIuLU1sbxTF37lz07NkTlpaWkEgkZXYGKnWTSCTF+sTGxirdVmZmJmbNmlWiungdS4bXk0pK617Pqij279+PAQMGwMDAAN7e3nByckJ2djZOnTqFb7/9Fn///bfa3gW/ceMGgoOD0b59e9ja2qqljeKYMWMGrKys0LRpU8TExGgsDm23efNmue1Nmzbh0KFDBfY3atRI6bYyMzNl0162b9++WOfwOpYMryeVFBO1Bjx8+BADBw6EjY0Njh49Kjc93rhx43Dv3j3s379fgxH+jyAIeP36NYyMjFRe98OHD2Fra4tnz57B3Nxc5fWXF+/OQ3/27FkcOnRIa+an53UsGV5PKine+taA77//Hunp6Vi3bp3COWzr16+PCRMmyLZzc3MREhICe3t7GBgYwNbWFtOnT5dbBQwAbG1t0b17d5w6dQpubm4wNDREvXr1sGnTJlmZjRs3YsCAAQCADh06FLjN9qaOmJgYuLq6wsjICKtXrwYAPHjwAAMGDECNGjVQuXJltGjRQqk/KDTZmy9vpFIpwsLC8NFHH8HQ0BCWlpYYPXo0Xrx4IVfu4sWL8PT0hJmZGYyMjGBnZ4evv/4aQP74gje/mIODg2U/G0Xd+uR1VD1eT3obe9Qa8Ntvv6FevXpo1apVscqPGDECkZGR6N+/PyZPnoxz584hNDQUN2/eRFRUlFzZe/fuoX///hg+fDiGDh2K9evX46uvvkLz5s3x0UcfoV27dhg/fjyWLFmC6dOny26vvX2b7fbt2xg0aBBGjx6NkSNHwsHBAYmJiWjVqhUyMzMxfvx41KxZE5GRkejZsyd27tyJPn36qO4/EJXY6NGjsXHjRgwbNgzjx4/Hw4cPsWzZMly5cgV//PEHKlWqhKSkJHTu3Bnm5uaYNm0aTE1NERcXh127dgEAzM3NsXLlSowdOxZ9+vRB3759AQBNmjTR5FerkHg9SY5ApSo1NVUAIPTq1atY5a9evSoAEEaMGCG3f8qUKQIA4ejRo7J9NjY2AgDhxIkTsn1JSUmCgYGBMHnyZNm+HTt2CACEY8eOFWjvTR3R0dFy+ydOnCgAEE6ePCnb9/LlS8HOzk6wtbUV8vLyBEEQhIcPHwoAhA0bNhTr+wmCICQnJwsAhKCgoGKfU5GNGzdOePt/3ZMnTwoAhJ9//lmuXHR0tNz+qKgoAYBw4cKFQutW5lrwOorD60lF4a3vUpaWlgYAqFq1arHKHziQv6bvu0tzTp48GQAK3Hp2dHRE27ZtZdvm5uZwcHDAgwcPih2jnZ0dPD09C8Th5uaGNm3ayPYZGxtj1KhRiIuLw40bN4pdP6nWjh07UK1aNXz66ad49uyZ7NO8eXMYGxvj2LFjAABTU1MAwL59+5CTk6PBiOl9eD3pXUzUpezN0nMvX74sVvlHjx5BR0cH9evXl9tvZWUFU1NTPHr0SG5/3bp1C9RRvXr1As+23sfOzk5hHA4ODgX2v7ll/m4cVHru3r2L1NRUWFhYwNzcXO6Tnp6OpKQkAICHhwf69euH4OBgmJmZoVevXtiwYUOBsQ6kWbye9C4+oy5lJiYmqFWrFq5fv16i8yQSSbHK6erqKtwvlGBKd3WM8Cb1kUqlsLCwwM8//6zw+JsBRRKJBDt37sTZs2fx22+/ISYmBl9//TUWLlyIs2fPwtjYuDTDpkLwetK7mKg1oHv37oiIiMCZM2fQsmXL95a1sbGBVCrF3bt35QZ8JSYmIiUlBTY2NiVuv7hJ/904bt++XWD/rVu3ZMdJM+zt7XH48GG0bt26WH9ktWjRAi1atMDcuXOxZcsWfPnll9i2bRtGjBgh6meDVIvXk97FW98aMHXqVFSpUgUjRoxAYmJigeP3799HeHg4AKBr164AgLCwMLkyixYtAgB069atxO1XqVIFAJCSklLsc7p27Yrz58/jzJkzsn0ZGRmIiIiAra0tHB0dSxwHqcbnn3+OvLw8hISEFDiWm5sru84vXrwocGfFxcUFAGS3SytXrgygZD8bpFq8nvQu9qg1wN7eHlu2bIGXlxcaNWokNzPZ6dOnsWPHDnz11VcAAGdnZwwdOhQRERFISUmBh4cHzp8/j8jISPTu3RsdOnQocfsuLi7Q1dXFggULkJqaCgMDA3Ts2BEWFhaFnjNt2jRs3boVXbp0wfjx41GjRg1ERkbi4cOH+PXXX6GjU/K/+TZv3oxHjx4hMzMTAHDixAnMmTMHADBkyBD20ovJw8MDo0ePRmhoKK5evYrOnTujUqVKuHv3Lnbs2IHw8HD0798fkZGRWLFiBfr06QN7e3u8fPkSa9asgYmJiewPQiMjIzg6OmL79u1o0KABatSoAScnJzg5ORXaPq+javF6UgEaHnVeod25c0cYOXKkYGtrK+jr6wtVq1YVWrduLSxdulR4/fq1rFxOTo4QHBws2NnZCZUqVRLq1Kkj+Pv7y5URhPxXq7p161agHQ8PD8HDw0Nu35o1a4R69eoJurq6cq9qFVaHIAjC/fv3hf79+wumpqaCoaGh4ObmJuzbt0+uTElez/Lw8BAAKPwoenWM8r37Os8bERERQvPmzQUjIyOhatWqQuPGjYWpU6cK//77ryAIgnD58mVh0KBBQt26dQUDAwPBwsJC6N69u3Dx4kW5ek6fPi00b95c0NfXL9brObyOyuH1pKJIBKEEo4yIiIioVPEZNRERkRZjoiYiItJiTNRERERajImaiIhIizFRExERaTEmaiIiIi3GRE1EVIbExcVBIpFg48aNmg6FSgkTtZbauHEjJBIJDA0N8eTJkwLH27dv/97ZhUrDyJEjIZFI0L17d4XH9+7di2bNmsHQ0BB169ZFUFAQcnNzSznKsonXn4jeYKLWcllZWZg/f76mwyjg4sWL2LhxIwwNDRUe//3339G7d2+Ymppi6dKl6N27N+bMmYNvvvmmlCMt23j96V02NjZ49eoVhgwZoulQqJRwrm8t5+LigjVr1sDf3x+1atXSdDgA8pfMHD9+PLy9vXHkyBGFZaZMmYImTZrg4MGD0NPL/zEzMTHBvHnzMGHCBDRs2LA0Qy6zeP3pXW/utFDFwR61lps+fTry8vK0qle1efNmXL9+HXPnzlV4/MaNG7hx4wZGjRol+yUNAD4+PhAEATt37iytUMs8Xv/yadasWZBIJLhz5w4GDx6MatWqwdzcHDNnzoQgCPjnn3/Qq1cvmJiYwMrKCgsXLpSdq+gZ9VdffQVjY2M8efIEvXv3hrGxMczNzTFlyhTk5eXJysXGxkIikSA2NlYuHkV1JiQkYNiwYfjggw9gYGAAa2tr9OrVC3FxcWr6r0KFYaLWcnZ2dvD29saaNWvw77//lvj8zMxMPHv2rMjPixcvilXfy5cv8d1332H69OmwsrJSWObKlSsAAFdXV7n9tWrVwgcffCA7TkXj9S/fvLy8IJVKMX/+fLi7u2POnDkICwvDp59+itq1a2PBggWoX78+pkyZghMnTry3rry8PHh6eqJmzZr48ccf4eHhgYULFyIiIkJUbP369UNUVBSGDRuGFStWYPz48Xj58iXi4+NF1UfiMVGXAQEBAcjNzcWCBQtKfO73338Pc3PzIj9NmzYtVn2zZ8+GkZERJk2aVGiZp0+fAgCsra0LHLO2thaVcCoyXv/yy83NDVu2bMHYsWOxZ88efPDBB5g8ebIsOY4dOxb79u2DkZER1q9f/966Xr9+DS8vL6xbtw5jxozBzp070bRpU6xbt67EcaWkpOD06dOYMWMGQkJCMHz4cPj7++Po0aNo166d2K9LIvEZdRlQr149DBkyBBEREZg2bZrCX4CF8fb2Rps2bYosZ2RkVGSZO3fuIDw8HFu3boWBgUGh5V69egUACssYGhoiLS2tyLbof3j9y68RI0bI/l1XVxeurq54/Pgxhg8fLttvamoKBwcHPHjwoMj6xowZI7fdtm1bbN68ucRxGRkZQV9fH7GxsRg+fDiqV69e4jpIdZioy4gZM2Zg8+bNmD9/PsLDw4t9Xr169VCvXj2VxDBhwgS0atUK/fr1e2+5N7/0s7KyChx7/fp1sZICyeP1L5/q1q0rt12tWjUYGhrCzMyswP7//vvvvXUZGhrC3Nxcbl/16tWL/VjjbQYGBliwYAEmT54MS0tLtGjRAt27d4e3t3ehjzxIfZioy4h69eph8ODBsl5VcaWnpyM9Pb3Icrq6ugX+J3/b0aNHER0djV27dskNJsnNzcWrV68QFxeHGjVqwMTERNbje/r0KerUqSNXz9OnT+Hm5lbs+Ckfr3/5pKurW6x9QP5o+5LW9S6JRKJw/9sDzt6YOHEievTogd27dyMmJgYzZ85EaGgojh49WuxHJaQafEZdhsyYMaPEzyp//PFHWFtbF/n5+OOP31vPmwEkffv2hZ2dnezz5MkTHD16FHZ2drJnaC4uLgDy37V927///ovHjx/LjlPJ8PqTst7cwk5JSZHb/+jRI4Xl7e3tMXnyZBw8eBDXr19Hdna23Ah0Kh3sUZch9vb2GDx4MFavXg0bGxu5V18Ko6pnlB07dkRUVFSB/aNGjYKNjQ0CAgLQuHFjAMBHH32Ehg0bIiIiAqNHj5b9pb9y5UpIJBL079+/yHioIF5/UpaNjQ10dXVx4sQJ9O7dW7Z/xYoVcuUyMzOho6Mj9762vb09qlatqvCRBqkXE3UZExAQgM2bN+P27dv46KOPiiyvqmeUdevWLfA8Dci/PWZpaSn3Pz0A/PDDD+jZsyc6d+6MgQMH4vr161i2bBlGjBiBRo0aKR1PRcXrT8qoVq0aBgwYgKVLl0IikcDe3h779u1DUlKSXLk7d+7gk08+weeffw5HR0fo6ekhKioKiYmJGDhwoIair7h467uMqV+/PgYPHqzpMIrUvXt37Nq1C8+fP8c333yDXbt2Yfr06Vi+fLmmQyvTeP1JWUuXLkWvXr2watUqzJgxA3Xr1kVkZKRcmTp16mDQoEGIjY2Fv78//P39kZaWhl9++aXIwYSkehKhqBEKREREpDHsURMREWkxJmoiIiItxkRNRESkxZioiYiItBgTNRERkRZjoiYiItJiTNRERFRAXFwcJBIJNm7cqOlQKjwmaiIiJd2/fx+jR49GvXr1YGhoCBMTE7Ru3Rrh4eGyZT/V4caNG5g1a5bcQimaMHfuXPTs2ROWlpaQSCSYNWuWRuMpbziFKBGREvbv348BAwbAwMAA3t7ecHJyQnZ2Nk6dOoVvv/0Wf//9NyIiItTS9o0bNxAcHIz27dvD1tZWLW0Ux4wZM2BlZYWmTZsiJiZGY3GUV0zUREQiPXz4EAMHDoSNjQ2OHj0qW+ITAMaNG4d79+5h//79GozwfwRBUNt64A8fPoStrS2ePXv23uVSSRze+iYiEun7779Heno61q1bJ5ek36hfvz4mTJgg287NzUVISAjs7e1hYGAAW1tbTJ8+vcCKVLa2tujevTtOnToFNzc3GBoaol69eti0aZOszMaNGzFgwAAAQIcOHSCRSCCRSBAbGytXR0xMDFxdXWFkZITVq1cDAB48eIABAwagRo0aqFy5Mlq0aKHUHxSa7M1XBEzUREQi/fbbb6hXrx5atWpVrPIjRoxAYGAgmjVrhsWLF8PDwwOhoaEKV6S6d+8e+vfvj08//RQLFy5E9erV8dVXX+Hvv/8GALRr1w7jx48HAEyfPh2bN2/G5s2b5VYnu337NgYNGoRPP/0U4eHhcHFxQWJiIlq1aoWYmBj4+Phg7ty5eP36NXr27KlwKVPSAgIREZVYamqqAEDo1atXscpfvXpVACCMGDFCbv+UKVMEAMLRo0dl+2xsbAQAwokTJ2T7kpKSBAMDA2Hy5MmyfTt27BAACMeOHSvQ3ps6oqOj5fZPnDhRACCcPHlStu/ly5eCnZ2dYGtrK+Tl5QmCIAgPHz4UAAgbNmwo1vcTBEFITk4WAAhBQUHFPoeKxh41EZEIaWlpAICqVasWq/yBAwcAAH5+fnL7J0+eDAAFbj07Ojqibdu2sm1zc3M4ODjgwYMHxY7Rzs4Onp6eBeJwc3NDmzZtZPuMjY0xatQoxMXF4caNG8Wun0oHEzURkQgmJiYAgJcvXxar/KNHj6Cjo4P69evL7beysoKpqSkePXokt79u3boF6qhevTpevHhR7Bjt7OwUxuHg4FBg/5tb5u/GQZrHRE1EJIKJiQlq1aqF69evl+g8iURSrHK6uroK9wuCUOy21DHCm0ofEzURkUjdu3fH/fv3cebMmSLL2tjYQCqV4u7du3L7ExMTkZKSAhsbmxK3X9yk/24ct2/fLrD/1q1bsuOkXZioiYhEmjp1KqpUqYIRI0YgMTGxwPH79+8jPDwcANC1a1cAQFhYmFyZRYsWAQC6detW4varVKkCAEhJSSn2OV27dsX58+fl/rjIyMhAREQEbG1t4ejoWOI4SL044QkRkUj29vbYsmULvLy80KhRI7mZyU6fPo0dO3bgq6++AgA4Oztj6NChiIiIQEpKCjw8PHD+/HlERkaid+/e6NChQ4nbd3Fxga6uLhYsWIDU1FQYGBigY8eOsLCwKPScadOmYevWrejSpQvGjx+PGjVqIDIyEg8fPsSvv/4KHZ2S9982b96MR48eITMzEwBw4sQJzJkzBwAwZMgQ9tKVpelh50REZd2dO3eEkSNHCra2toK+vr5QtWpVoXXr1sLSpUuF169fy8rl5OQIwcHBgp2dnVCpUiWhTp06gr+/v1wZQch/tapbt24F2vHw8BA8PDzk9q1Zs0aoV6+eoKurK/eqVmF1CIIg3L9/X+jfv79gamoqGBoaCm5ubsK+ffvkypTk9SwPDw8BgMKPolfHqGQkglCCkQlERERUqviMmoiISIsxURMREWkxJmoiIiItxkRNRESkxZioiYiItBgTNRERkRZjoiYiItJiTNRERERajImaiIhIizFRExERaTEmaiIiIi3GRE1ERKTFmKiJiIi02P8Bv/herI6lzA8AAAAASUVORK5CYII=", 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" ] @@ -517,12 +662,13 @@ ] }, { + "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). Default is 0.8.\n" + "while a value of 1.0 means no desaturation (i.e., full color saturation). The default one is 0.8.\n" ] }, { @@ -533,7 +679,7 @@ "outputs": [ { "data": { - "image/png": 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", + "image/png": 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sWgH12ms6Eq0TGBgIY2Nj+Pr6Kjx+5MgR7N+/H3v37hXdhqhEPW/ePHh7e8Pd3R2CIKB///7w8PjvGUZ0dDRat24tKiA/P78ib3XHxcXJbevp6SE4OBjBwcGi2iIiIiWdWAzYuALGNTQdiVa5ePGi3J3et7Vt2xZhYWEqtSEqUbu7u+PmzZs4deoUzMzM5AZjvXjxAr6+vhygRURUkbxKBU4tAz4K0nQkWiU9PV024ZciOjo6oh4Fy9Uh9kQLCwv06tWrUEI2MzPDxIkTRU0hSkREWuzuYSDxqqaj0Crvv/8+Dhw4UOTxmJgYlUd+i07U+fn52Lp1K3x8fNCnTx9cvVpw8VJTU7Fz585Cr1kREVEFcGYVF+14w8iRI7Fv3z74+/vLBlkDBXeXJ0+ejJiYGIwcOVKlNkQl6hcvXsjm2P7pp5+wZ88epKSkACh43WnChAkqLelFRERaKukvIOmapqMoUklXYHzx4gXGjx8PGxsbGBoaokGDBti/f7/S7U2YMAHDhw9HeHg4zM3NYWtrC1tbW5ibmyMiIgJDhgxR+bUtUYl6+vTp+OuvvxAbG4v79+/LvbOsq6uL/v37l+iLEhFROXJtp6YjUKikKzDm5OTg448/Rnx8PHbs2IFbt25h7dq1qFOnjtJtSiQSbNiwAYcPH8bYsWPh4uICFxcXjBs3DkeOHEFUVJTCVbVKQtRgsl27duGrr77Cxx9/jH///bfQ8QYNGmDjxo0qBUZERFoq4QyQlwPoGWg6EjklXYFx/fr1ePbsGU6dOgV9fX0AkJvMqyQ6dOiADh06iI79XUT1qFNTU+Hg4FDk8dzcXOTl5YkOioiItFhuFpCkXYPKxKzAuGfPHrRs2RLjx4+HlZUVXFxcMH/+fOTn55dV2EoRlagdHR1x6dKlIo8fOHAAzs7OooMiIiIt9/xhmTWVkZGBtLQ02efNFRBfe9cKjImJiQrrvX//Pnbs2IH8/Hzs378fs2bNwqJFizB37lylYxMEAWvWrIGHh4dszYq3P+96fUsZos4eNWoUvvnmG7Rv3x4fffQRgIL79NnZ2ZgzZw5iYmIQGRmpUmBERKS8hIQEZGVlAQCycqRIePYKtjWNSq/BzJTSq/stb78GHBwcjNmzZ6tcr1QqhaWlJSIjI6Grq4vmzZvj8ePH+O6775SeSGvatGlYvHgx3NzcMGTIENSoof4JYUQl6okTJ+Kvv/7C4MGDYWZmBqBgbeh///0XeXl58PHxUXk4OhERFe/cuXMIDQ3Fvn37ZAN7n2flwT7wHLo3rolZXe3woX019Teso1ovsSSOHTsmNzfHmwsrvSZmBUYbGxvo6+vLrQ3RqFEjJCYmIicnBwYGxT+Dj4qKQr9+/fDzzz8r+W1KTtStb4lEgrVr1+L48eMYNmwYunTpAjc3N4wZMwZxcXFYtWqVuuMkIqK37Ny5E61bt8Zvv/1WaMVAQQD2X3uGVt9ewc7LT9XfuEHZraZlYmICU1NT2UdRon5zBcbXXq/A2LJlS4X1tm7dWrYs5Wu3b9+GjY2NUkkaAF6+fCn3XLw0qPQrUZs2bdCmTRt1xUJEREo6d+4cBg4ciPz8/CKX9c2XAhIIGLj2Bk5Nc1Nvz7r6e+qrS038/f0xfPhwuLu7w8PDA+Hh4YVWYKxTp45s7u1x48Zh+fLlmDhxIr766ivcuXMH8+fPx4QJE5Ru86OPPsL58+cxZsyYUvlOgMge9YMHD/Drr78WefzXX39FfHy82JiIqAxZW1ujTp06Rd4eJO00d+5cCIJQZJJ+TQAgQMDc/Woe/FXDTr31qcHAgQPx/fffIygoCG5ubrhy5UqhFRifPHkiK1+3bl3Exsbi/PnzaNKkCSZMmICJEycqfJWrKCtXrsSZM2cwf/58ha8rq4OoHvXUqVORlpYmt7Tlm1asWAEzMzNs3bpVpeCIqPRduHBB0yFQCSUkJGDv3r3FJunX8qXAr1efqW+AmX4VoFpt1espBSVZgREAWrZsiTNnzohuz8nJCVKpFLNmzcKsWbNgZGQk98wbKHhcrMrCHKIS9enTpzFp0qQij3/00UcIDw8XGRIRlbX8/Hy553Rl27jw3yc3VzMxlDOxsbFKJ+nXBAE4cP05hre0Kr5wccztgfz8gk8pKg/zcfTr10/lmceKIypRP3/+HNWqFf2sw8TEpNRuARBVRAk6tkAmgDmNNdL+kqNJCI8ru9dtFLsFfKNdM11VNKN/vIPRP95RQ00nAaxWQz3lX1nMwikqUdva2uL333/HuHHjFB4/ceIE3ntP+wYaEJFiE7ws4dfOUmPt20oTCm6lTtau2a601caNG0UNXlo75H319KhbTwKce6peTzEuX74MT0/PUm9H24lK1IMHD0ZoaCg8PDzg5+cHHZ2CMWn5+flYvnw5tm3bhsDAQLUGSkSlR1dHAt3ii5UafYkE0JUA/z/fMr2bt7c3JBJJiW5/SyRAZ+ca0NcVvbrxfywcy+RaqTqjV1lJSEjA/PnzcfToUSQnJ2P37t1o164dnj59ijlz5mDEiBFo2rSp6PpF/V8ICAjAyZMnMWnSJMybNw9OTk4AgFu3biElJQXt27dnoiYiKiW2trbo3r079u/fr9S81Lo6QDeXmuqbqax6XfXUUwFcv34dbdu2hVQqhaenJ+7evSt7tm5ubo6TJ08iMzMT69atE92GqF+tDA0NceDAAaxbtw4eHh54+vQpnj59Cg8PD6xfvx6HDh1S+EI6EWmf7mvuwnPRTXRfc1fToVAJzJo1CxKJpNiBTBIAEkgws6uaXqfSNQCq1FRPXRXAtGnTYGZmhtu3b+PHH38sdJejW7duOHHihEptiL6voKOjgxEjRsheJCei8iklIw+Jado/upbkffjhh9i2bRsGDhwIQRAU9qx1dQqS9M+jG6lvspNq1gX30QkAcPz4cQQFBcHCwkLhIGpbW1s8fvxYpTZE9aifPXuGP//8s8jjV69exfPnz0UHRURExevbty9OnTqFrl27FupZSyQFt7tPTXNDn6bm6mvUtI766qoApFIpqlSpUuTxlJQUle8wi0rUkydPfueIQx8fH0ydOlV0UEREpJwPP/wQe/bsQXx8vGzlphpV9BA/zwO7fV3UvyCHma166yvnmjVrhn379ik8lpeXh61bt6JFixYqtSEqUR85cgQ9exY9NL9Hjx44dOiQ6KCIiKhkbG1tZT27KgY6pbfEpfn7pVNvORUQEICYmBiMGzcO165dA1CwYtehQ4fQuXNn3Lhxo0RTkioi6hl1SkoKzM2LvpVSq1YtJCcniw6KiIi0lNUHmo5Aq3Tp0gUbN27ExIkTERkZCQAYMmQIBEGAqakpNm3ahHbt2qnUhqhEbWNjg8uXLxd5/OLFi7CwsBAdFBERaaFqNoCpds7xrUlDhw5F3759cfDgQdy5cwdSqRSOjo7w9vZ+5yyeyhKVqHv37o0VK1agS5cuhW6B7969Gxs2bChy1jIiIiqn7Lms8ZuysrJQt25dTJ8+HV9//TV69+5dKu2IStSzZ8/GoUOH0KdPH7i6usLFxQUAcO3aNfzxxx9o1KgRQkJC1BooERFpWP1Omo5Aq1SpUgV6enqoWrVqqbYjajBZ9erVcebMGcycORO5ubnYsWMHduzYgdzcXMyaNQtnz56FmZmZmkMlIiKNsWgIWDhpOgqt069fP+zYsaPEq5mVhOgJT6pWrYqQkBD2nImIKgO3wZzoRIFBgwbB19cXHTp0wOjRo2Fvbw9jY+NC5Zo1aya6jfIx4zkREWmO1QeAvWojlyuq9u3by/6saKpQQRAgkUiUmpO9KKIS9ZdffllsGYlEotIk5EREpAUkEqDVBEBHDatuVUAbNmwo9TZEJeojR44Umq4uPz8fT548QX5+PiwsLEr94ToREZUBl/6AZUNNR6G1hg8fXuptiErU8fHxCvfn5uZizZo1CA8Px8GDB1WJi4iINK36e8CHozQdRbnx5MkTJCcno379+mrtrKr1Xoa+vj78/PzQuXNn+Pn5qbNqIiIqa17TAP1Smoq0Atm9ezcaNmyI9957D82aNcPZs2cBAE+fPkXTpk0RHR2tUv2l8tDB1dUVx48fL42qiYioLHzQB7Bx1XQUWu/XX39F3759YW5ujuDgYLnXtMzNzVGnTh1s3LhRpTZKJVEfPHjwnct+EZH2sDDRg7WpHixM+BII/T9jM+DDkZqOolyYM2cO2rVrh5MnT2L8+PGFjrds2fKdU24rQ9S/zDlz5ijc/+LFCxw/fhyXLl1SebUQIiobe33qazoE0jYfjgYM1bw8ZgV17do1LF68uMjjVlZWKi9SJXoKUUVq1KgBR0dHrF69GqNHj1YlLiIi0oTq7wFOXTQdRblRpUoVZGZmFnn8/v37qFWrlkptiErUUqlUpUaJiEhLNf8C0NHVdBTlRocOHRAVFYVJkyYVOpaYmIi1a9eie/fuKrXBN9iJiKiAiRXg2FHTUZQr8+bNw6NHj/Dhhx9izZo1kEgkiI2NxcyZM9G4cWMIgoDg4GCV2hCVqBMSEnDy5Em5fX/88QeGDRuGgQMHYteuXSoFRUREGuDSj73pEnJycsLJkydRq1YtzJo1C4Ig4LvvvsP8+fPRuHFjnDhxAvb29iq1IerW94QJE5CRkYFDhw4BAJKSktChQwfk5OSgWrVq2LFjB7Zv346+ffuqFBwREZURPUM+m1bCn3/+CTs7O1SvXl2274MPPsChQ4fw/Plz3L17F1KpFPXq1YOFhYVa2hTVoz537hw+/vhj2famTZvw8uVL/PHHH3j8+DE++ugjfP/992oJkIiIykD9ToCRqaaj0HpNmzbFvn37ZNsdO3bE4cOHARQMqP7www/h6emptiQNiEzUz549g6WlpWx779698PLygqOjI3R0dNC3b1/cvHlTbUESEVEpc+mn6QjKBWNjY2RlZcm24+LikJSUVKptirr1bWFhgYcPHwIoeHf6zJkzWLBggex4Xl4e8vLy1BMhERGVrtpNgVqOmo6iXHB1dcXixYuhq6sru/19/vx5GBm9e6pVVR4Fi0rUnTp1wtKlS2Fqaoq4uDhIpVL07t1bdvz69euoW7euqIBWrFiB7777DomJiXB1dcWyZcvg4eFRZPkXL14gMDAQO3fuxLNnz2BnZ4fw8HB07dpVVPtERJWO22eajqDcCA8Px4ABAzByZMHMbRKJBBEREYiIiCjyHI2sR71gwQLcvn0bU6dOhYGBAb7//ns4ODgAALKzs/Hzzz/js89KfuG3bdsGf39/rF69Gp6enggPD4e3tzdu3bold6v9tZycHHz88cewtLTEjh07UKdOHTx8+BBmZmZivhYRUeVj4QS896Gmoyg3PvzwQ9y9exf37t1DUlIS2rdvjxkzZsiN21I3UYnaysoKv//+O1JTU2FsbAwDAwPZMalUisOHD4vqUS9evBijR4/GiBEjAACrV6/Gvn37sH79eoVTkq5fvx7Pnj3DqVOnoK+vDwAqD4MnIqpUmo8AJBJNR1Fu7NmzB+7u7nBycoKTkxOGDx+OHj16wNPTs9TaVGnCk+rVq8slaaDgQburqytq1qxZorpycnJw8eJFdOrU6b/gdHTQqVMnnD59WuE5e/bsQcuWLTF+/HhYWVnBxcUF8+fPV+kWAxFRpWH1AWDbQtNRlCt9+vRBXFycbPvYsWPaOZisNDx9+hT5+fmwsrKS229lZVXkCPL79+/jyJEj+Pzzz7F//37cvXsXvr6+yM3NLXImmOzsbGRnZ8u2MzIy1PcliIjKE8+x7E2XULVq1fDixQvZdnx8fKnnEa1J1GJIpVJYWloiMjISurq6aN68OR4/fozvvvuuyEQdFhaGkJCQMo6UiEjLOLQFbJpoOopyx8PDA/PmzUNSUpJs1Pf+/fuRmJhY5DkSiQSTJ08W3abWJGpzc3Po6uoWuoWQlJQEa2trhefY2NhAX18furr/TXnXqFEjJCYmIicnp9BteQAICAiAv7+/bPvKlSvw8vJS07cgIioHdA2AFoXXTqbirVy5EsOGDUNoaCiAgiS8ZcsWbNmypchzKkyiNjAwQPPmzXH48GHZq16vB6b5+fkpPKd169bYsmULpFIpdHQKHrffvn0bNjY2CpM0ABgaGsLQ0FC2bWJiot4vQkSk7VwHAaY2mo6iXKpfvz5OnTqFV69eITk5Gfb29ggPD0evXr1KrU2lBpPVrFkTO3bskG3PmTMH165dU3sw/v7+WLt2LaKionDjxg2MGzcOmZmZslHgw4YNQ0BAgKz8uHHj8OzZM0ycOBG3b9/Gvn37MH/+fIwfz98UiYgUMrEC3D7XdBTlnpGREWxtbREcHIyOHTvCzs7unR9VKNWjzsjIkJsybfbs2ahfvz5cXFxUavxtAwcOREpKCoKCgpCYmAg3NzfExMTIBpglJCTIes4AULduXcTGxmLy5Mlo0qQJ6tSpg4kTJ+Kbb75Ra1xERBWGpw+g/+5ZtEh5qi5hqQylErWjoyN27NiBtm3bwtS0YNL2zMxMPHv27J3nlfQVLQDw8/Mr8lb3m0PiX2vZsiXOnDlT4naIiCodCyegXgdNR1Guffnll5BIJLJBzF9++WWx50gkEqxbt050m0ol6hkzZmDEiBGyFUMkEgnGjh2LsWPHvvM8vs9MRKRFmo8AdFSaPqPSO3LkCHR0dCCVSqGrq4sjR45AUswrbsUdL45SiXro0KHw8PCQrRIye/Zs9OnTB02acGg/EVG5YFYXqFt6s2dVFvHx8e/cLg1Kj/p+PV0aAGzYsAHDhw9Hz549Sy0wIiJSo0Y92Zsup0S9nvXgwQN1x0FERKWpXntNR0Aiif71Kj8/H1FRUfj000/h6ekJT09PfPrpp9i0aROfTRMRaRPz9wGTwisQVkQrVqyAvb09jIyM4OnpiXPnzil13tatWyGRSOSWbFZER0cHurq6Jf6oQlSPOjU1Fd7e3jh//jyqVauGevXqAQAOHjyIX375BatWrUJsbKxshDgREWlQneaajqBMlHSp5Nfi4+MxdepUtG3bttg2goKCCg0Oi46Oxl9//QVvb2/ZI+KbN2/iwIEDcHFxKTb5F0dUog4MDMTFixexbNkyjB49WrbEZG5uLn744QdMmDABgYGBWLZsmUrBERGRGlhXjoG/JV0qGSi4O/z5558jJCQEJ06ckFtwQ5HZs2fLbUdGRiI5ORnXrl2TJenXbty4gY4dO6J27dqivxMg8tZ3dHQ0fH194evrK0vSAKCvr49x48Zh3Lhx+OWXX1QKjIiI1MSykaYjUElGRgbS0tJknzdXQHxNzFLJQMFMm5aWlhg5cqSo2L777jv4+fkVStJAwdoTfn5++Pbbb0XV/ZqoRP3vv/8qDOq1hg0bFjsZChERlYFq1kCVkk8+pU28vLxQvXp12ScsLKxQmXctlVzUylYnT57EunXrsHbtWtGxPXr0SK7D+jZ9fX08evRIdP2AyERdv3597Nmzp8jje/bsgaOjo+igiIhITSyK7lSVF8eOHUNqaqrs8+aaD2Klp6dj6NChWLt2LczNzUXX4+LigpUrV+Lx48eFjj169AgrV65E48aNVQlV3DNqX19f+Pn5oWvXrpg0aRIaNGgAALh16xaWLl2KgwcPYvny5SoFRkREamBRvm97AwWrHBY3OLmkSyXfu3cP8fHx6NGjh2yfVCoFAOjp6eHWrVtKdTiXLFkCb29vNGjQAH369EH9+vUBAHfu3MGuXbsgCAJ+/PHHYut5F9GJOjk5GQsWLEBsbKzcMX19fQQFBWHcuHEqBUZERGpg2VDTEZSJki6V3LBhQ1y9elVu38yZM5Geno6IiAjUrVtXqXbbtGmDs2fPYtasWYiOjsbLly8BAMbGxvD29kZISIhmetRAwcg3Pz8/HDp0CA8fPgQA2NnZoVOnTirdRiAiIjWRSADz8n/rW1n+/v4YPnw43N3d4eHhgfDw8EJLJdepUwdhYWEwMjIqtAKkmZkZAJR4ZUgXFxdER0dDKpUiJSUFAGBhYSG32qMqRCdqoOBWw6BBg9QSCBERqZmZLWBQRdNRlJmSLpWsbjo6OoUGs6mDSomaiIi0WCXqTb9W0qWS37Rx40b1B6QGnKGdiKiiMn9f0xGQGjBRExFVVDX5mmxFwERNRFRR1XTQdASkBkzUREQVkZFpuZ+RjApwMBkRUUVkZqvpCCqV69ev4/79+3j+/DkEQSh0fNiwYaLrFpWoBUFAZGQk1q1bJwvsbRKJBHl5eaIDIyIiFVRnoi4L9+7dw5AhQ3Du3DmFCRooyIdlnqinTZuGxYsXw83NDUOGDEGNGjVEB0BERKXAVLWlFUk5Pj4+uHr1KsLDw9G2bdtSyYeiEnVUVBT69euHn3/+Wd3xEBGROlSz0XQElcLvv/+OGTNm4Kuvviq1NkQl6pcvX8qt+UlERJpnbW0NCFJY66UXLG9Jpc7c3BzVq1cv1TZEjfr+6KOPcP78eXXHQkREKrhw4QIe3biICzOaASbqn8qSChs7dix+/PFH5Ofnl1obonrUK1euhLe3N+bPnw8fHx/UqlVL3XEREZFYEh2+mlVGGjRogPz8fLi6uuLLL79E3bp1oaurW6hc3759RbchKlE7OTlBKpVi1qxZmDVrFoyMjAoFJpFIkJqaKjowIiISybgGoFM4WZD6DRw4UPbnqVOnKiwjkUhU6nGLStT9+vWDRCIR3SgREZUi9qbLzNGjR0u9DVGJWltXGCEiIhT0qKlMeHl5lXobnJmMiKiiMSrdUcik2PXr1/Hw4UMAgJ2dHZydndVSr+i5vtPS0hASEgIPDw9YWVnBysoKHh4emDNnDtLS0tQSHBERiWBgoukIKpXdu3fD0dERjRs3Rvfu3dG9e3c0btwY9evXx549e1SuX1Si/ueff9C0aVOEhIQgIyMDrVu3RuvWrZGZmYnZs2ejWbNmePLkicrBERGRCAZVNR1BpbF//37069cPADB//nxER0cjOjoa8+fPhyAI6Nu3L2JiYlRqQ9St72+++QaJiYnYu3cvunbtKnfst99+w4ABAzB9+nRERUWpFBwREYnARF1mQkND0aRJE5w4cQJVq/73/71nz57w8/NDmzZtEBISgk8++UR0G6J61DExMZg0aVKhJA0AXbp0wYQJE7B//37RQRERkQr0jDQdQaXx559/Yvjw4XJJ+rWqVaviiy++wJ9//qlSG6ISdWZmJqysip71xtraGpmZmaKDIiIiFTBRlxkjIyM8e/asyOPPnj2DkZFq10NUonZ2dsZPP/2EnJycQsdyc3Px008/qW20GxERlZCegaYjqDQ6duyIiIgInD59utCxs2fPYunSpSqvjSH6GfXAgQPh4eEBX19fNGjQAABw69YtrF69Gn/++Se2bdumUmBERCSSjr6mI6g0vv32W7Rs2RJt2rSBh4cHnJycABTkw3PnzsHS0hILFy5UqQ1RiXrAgAHIzMzE9OnTMXbsWNksZYIgwNLSEuvXr0f//v1VCoyIiETSZY+6rDg4OODPP/9EWFgYfvvtN1kn1c7ODhMnTsT06dNhaWmpUhuiJzz54osvMGTIEFy4cEHuBW93d3fo6XEeFSIijWGiLlOWlpZYsmQJlixZUir1q5RR9fT00KJFC7Ro0UJd8RARkap0eeu7IlEqUR8/fhwA0K5dO7nt4rwuT0REZYg96lLz5ZdfQiKRIDIyErq6uvjyyy+LPUcikWDdunWi21QqUbdv3x4SiQQvX76EgYGBbLsogiCovKwXERGJxB51qTly5Ah0dHQglUqhq6uLI0eOFLuapKqrTSqVqF8v42VgYCC3TUREWoiJutTEx8e/c7s0KJWo317GqyyW9SIiIpH4elaZSUhIgIWFBYyNjRUef/nyJVJSUmBrayu6DVETnnTs2BGHDx8u8vjRo0fRsWNH0UEREZEK9Aw1HUGl4eDggOjo6CKP79mzBw4ODiq1ISpRx8XFISkpqcjjycnJOHbsmOigiIhIBTp8RbasCILwzuO5ubnQ0RG9ojQAFV7PetfD8bt376JatWpiqyYiIlXwGXWpSktLw4sXL2Tb//77LxISEgqVe/HiBbZu3QobGxuV2lM6UUdFRcktWzl37lysXbtWYWB//vmnwpW1lLVixQp89913SExMhKurK5YtWwYPD49iz9u6dSsGDx6MXr16YdeuXaLbJyIq1/h6VqlasmQJ5syZA6Cg0zpp0iRMmjRJYVlBEDB37lyV2lM6UWdlZSElJUW2nZ6eXqg7L5FIULVqVYwdOxZBQUGiAtq2bRv8/f2xevVqeHp6Ijw8HN7e3rh169Y7p2GLj4/H1KlT0bZtW1HtEhFVCDq6BR8qNZ07d4aJiQkEQcC0adMwePBgNGvWTK7M63zYvHlzuLu7q9Se0ol63LhxGDduHICCh+cRERHo2bOnSo0rsnjxYowePRojRowAAKxevRr79u3D+vXrMX36dIXn5Ofn4/PPP0dISAhOnDghd0uCiKhS4fPpUteyZUu0bNkSQMGyz/369YOLi0uptVfiJ9wvX75E7969VX6BW5GcnBxcvHhRbkkwHR0ddOrUSeESYq/NmTMHlpaWGDlyZLFtZGdnIy0tTfbJyMhQS+xERFqBvekyk5WVhaVLl+K3334r1XZKnKiNjY0RGRn5zlHfYj19+hT5+fmwsrKS229lZYXExESF55w8eRLr1q1T+LxckbCwMFSvXl324TvhRFShsEddZqpUqQI9PT1UrVq1VNsRNWa8efPmuHbtmrpjKbH09HQMHToUa9euhbm5uVLnBAQEIDU1Vfbha2REVKFIVHsViEqmX79+2LFjR7GvaalC1K9e4eHh6Nq1K1xcXPDFF1+obVlLc3Nz6OrqFuqtJyUlwdraulD5e/fuIT4+Hj169JDtk0qlAApW9rp16xYcHR3lzjE0NISh4X+TAZiYmKgldiIircBEXaYGDRoEX19fdOjQAaNHj4a9vb3CWcreHmxWEqIy7BdffAEdHR34+PhgwoQJqFOnTqHAJBIJ/vjjjxLVa2BggObNm+Pw4cPo3bs3gILEe/jwYfj5+RUq37BhQ1y9elVu38yZM5Geno6IiAjUrVu3ZF+MiKjcU//4ISpa+/btZX8+ceJEoePqWKRKVKKuWbMmatWqBScnJ9ENF8Xf3x/Dhw+Hu7s7PDw8EB4ejszMTNko8GHDhqFOnToICwuDkZFRoZF2ZmZmAFCqI/CIiIgAYMOGDaXehqhEHRcXp+Yw/jNw4ECkpKQgKCgIiYmJcHNzQ0xMjGyAWUJCgsrTsREREanD8OHDS70NrRwe6Ofnp/BWN1D8LwkbN25Uf0BERETFyMjIwN9//w0AqFu3rtrGQInumubn5yMqKgqffvopPD094enpiU8//RSbNm1S6V48ERGpoBTmuKB3O3/+PDp06IAaNWrAxcUFLi4uqFGjBjp27IgLFy6oXL+oHnVqaiq8vb1x/vx5VKtWDfXq1QMAHDx4EL/88gtWrVqF2NhYmJqaqhwgERGVABfkKFNnz55F+/btYWBggFGjRqFRo0YAgBs3buCnn35Cu3btEBcXp9R6FUURlagDAwNx8eJFLFu2DKNHj4a+fsFfjNzcXPzwww+YMGECAgMDsWzZMtGBERERabvAwEDUqVMHJ0+eLPQa8ezZs9G6dWsEBgbi4MGDotsQdes7Ojoavr6+8PX1lSVpANDX15fNCf7LL7+IDoqIiEiMFStWwN7eHkZGRvD09MS5c+eKLLt27Vq0bdsWNWrUQI0aNdCpU6d3llfk7Nmz8PHxUTjXh5WVFcaMGYMzZ86U+Hu8SVSi/vfff9/5albDhg3x7Nkz0UERERGV1OvVF4ODg3Hp0iW4urrC29sbycnJCsvHxcVh8ODBOHr0KE6fPo26deuic+fOePz4sdJt6ujoIC8vr8jj+fn5Kr+pJOrs+vXrY8+ePUUe37NnT6EZwYiIiErTm6svOjs7Y/Xq1ahSpQrWr1+vsPz//vc/+Pr6ws3NDQ0bNsQPP/wgm2RLWa1atcKKFSvw8OHDQscSEhKwcuVKtG7dWvR3AkQ+o/b19YWfnx+6du2KSZMmoUGDBgCAW7duYenSpTh48CCWL1+uUmBERERAwWtPaWlpsu23p4IG/lt9MSAgQLZPmdUX35SVlYXc3FzUrFlT6djmz5+Pdu3aoWHDhujTp49cPty9ezf09PQQFhamdH2KiE7UycnJWLBgAWJjY+WO6evrIygoSLZ2NRERkSreXuUwODgYs2fPltv3rtUXb968qVQ733zzDWrXri231HJxmjZtirNnzyIwMBB79uxBVlYWgIKVtT755BPMnTsXzs7OSteniOgJT2bPng0/Pz8cPHgQCQkJAAA7Ozt06tRJ6ZWsiIiIinPs2DG4ubnJtt/uTavDggULsHXrVsTFxcHIyKhE5zo7OyM6OhpSqRQpKSkAAAsLC7XNoqnSzGTm5uYYPHiwWgIhIiJSxMTEpNh5OUq6+uKbvv/+eyxYsACHDh1CkyZNRMcpkUgg+f8JZyRqnHhGpXS/d+9e+Pr6omvXrujatSt8fX2xd+9edcVGRESklDdXX3zt9cCwli1bFnnet99+i9DQUMTExMDd3V1U29evX0f//v1hamoKGxsb2NjYwNTUFP3798e1a9dE1fkmUT3qFy9eoE+fPjh+/Dh0dXVhY2MDADh06BDWrFmDtm3bYteuXbKVrIiIiEpbSVZfBICFCxciKCgIW7Zsgb29PRITEwEU9OCVnaf7xIkT6NKlC6RSKXr16iU3mGzPnj347bffEBMTg7Zt24r+XqIS9cSJE3HixAksXLgQ48aNQ9WqVQEAmZmZWLlyJQICAjBx4kRERUWJDoyIiKgkSrr64qpVq5CTk4P+/fvL1aNosFpRJk+eDEtLSxw7dgx169aVO/b333+jXbt28Pf3x/nz50V/L1GJeteuXfD19cXUqVPl9letWhVff/01EhISsGnTJtFBERERiVGS1Rfj4+NVbu+vv/5CaGhooSQNFKygNW7cOKWTflFEPaPW19cvdmayN6cWJSIiqojs7OyQnZ1d5PGcnByFSbwkRCXqfv36Yfv27QqXs8zLy8PPP/+MAQMGqBQYERGRtgsKCsLSpUtx5cqVQscuX76MZcuWqdyjFnXre8iQIfDz80OrVq0wZswY1K9fHwBw584dREZGIicnB59//jkuXbokd16zZs1UCpaIiEibnDlzBlZWVmjevDlatWollw9Pnz4NFxcXnD59Wm52NIlEgoiICKXbEJWo35wl5vz587L3xQRBUFhGEARIJBKFPXAiIqLy6s3psn///Xf8/vvvcsevXr2Kq1evyu0rk0S9YcMGMacRERFVKFKptNTbEJWohw8fru44iIiISAGVphAFClY1+fvvvwEUDEVX9iVxIiKiiuLBgwf47bffZMtd2tnZoUuXLnBwcFC5btGJ+vz585g2bRpOnjwp6/rr6Oigbdu2+Pbbb0VPxUZERFSeTJkyBREREYVug+vo6GDSpEn4/vvvVapfVKI+e/Ys2rdvDwMDA4waNQqNGjUCANy4cQM//fQT2rVrh7i4OHh4eKgUHBERkTZbtGgRlixZgv79+2PKlCly+XDJkiVYsmQJ6tSpg8mTJ4tuQ1SiDgwMRJ06dXDy5MlCq5LMnj0brVu3RmBgIA4ePCg6MCIiIm23du1a9OzZEz///LPcfk9PT2zduhWvXr3CmjVrVErUoiY8OXv2LHx8fBQuHWZlZYUxY8bgzJkzooMiIiIqD+Lj4+Ht7V3kcW9vb5WnKhWVqHV0dJCXl1fk8fz8fLUtmE1ERKStLC0t8ccffxR5/I8//oCFhYVKbYjKpq1atcKKFStko9velJCQgJUrV6J169YqBUZERKTtBgwYgB9++AELFixAZmambH9mZiYWLlyIH374AQMHDlSpDVHPqOfPn4+2bduiYcOG6NOnj9z6m7t374aenp5svU8iIqKKKjQ0FFeuXMGMGTMQFBSE2rVrAwD++ecf5OXloUOHDpgzZ45KbYhK1E2bNsW5c+cQGBiIPXv2ICsrCwBQpUoVfPLJJ5g7dy6cnZ1VCoyIiEjbValSBYcPH8bu3bvl3qP+5JNP0LVrV/To0UM2zbZYJU7U2dnZiI2Nhb29PaKjoyGVSpGSkgIAsLCw4LNpIiKqFLKysjBkyBD069cPn3/+OXr16lUq7ZQ4qxoYGGDAgAE4depUQQU6OrCysoKVlRWTNBERVRpVqlTBoUOHZHeVS0uJM6tEIsH777+Pp0+flkY8RERE5UabNm3klrAsDaK6wDNmzMDy5ctx69YtdcdDRERUbixfvhwnTpzAzJkz8ejRo1JpQ9RgsjNnzqBWrVpwcXFB+/btYW9vD2NjY7kyJV1vk4iIqLxxdXVFXl4ewsLCEBYWBj09PRgaGsqVkUgkSE1NFd2GqET95kLZhw8fVliGiZqIiCq6fv36qTyquziiEnVZLJRNRESk7TZu3Fjqbai8HjUREVFl8+rVK+zevRsPHjyAubk5unXrBhsbm1JpS6VEfe3aNezfv1824bi9vT26dOmCxo0bqyM2IiIirZOcnIxWrVrhwYMHEAQBQMGrWrt27UKnTp3U3p6oRJ2dnQ0fHx9s3rwZgiDI3p+WSqUICAjA559/jh9++AEGBgZqDZaIiEjTQkNDER8fj8mTJ6Njx464e/cuQkND4ePjg3v37qm9PVGJ+ptvvsGmTZvg6+uLr776Co6OjpBIJLh79y6WLl2KVatWoWbNmggPD1dzuERERJp14MABDBs2DN9//71sn5WVFT777DPcunULTk5Oam1P1HvUP/74I4YOHYrly5fDyckJenp60NXVhZOTE1asWIHPP/8cP/74o1oDJSIi0gYJCQlo06aN3L42bdpAEAQkJSWpvT1RiTo3NxctWrQo8nirVq3euV41ERFReZWdnQ0jIyO5fa+3SyP3ibr17e3tjdjYWIwbN07h8ZiYGHTu3FmlwIiIiLRVfHw8Ll26JNt+PaHJnTt3YGZmVqh8s2bNRLclKlGHhobi008/Rd++fTF+/HjUr19fFuCKFSvw8OFDbNu2Dc+ePZM7r2bNmqIDJSIi0hazZs3CrFmzCu339fWV2xYEARKJBPn5+aLbEpWoGzVqBAC4evUqdu/eXSgoAArXo1YlUCIiIm2wYcOGMm1PVKIOCgoq9SnTiIiItNHw4cPLtD1RiXr27NlqDkPeihUr8N133yExMRGurq5YtmwZPDw8FJZdu3YtNm3ahGvXrgEAmjdvjvnz5xdZnoiIqDwRNeq7NG3btg3+/v4IDg7GpUuX4OrqCm9vbyQnJyssHxcXh8GDB+Po0aM4ffo06tati86dO+Px48dlHDkREZH6aV2iXrx4MUaPHo0RI0bA2dkZq1evRpUqVbB+/XqF5f/3v//B19cXbm5uaNiwIX744QdIpdIiV/Uiee7u7njvvffg7u6u6VCIiEgBrVqUIycnBxcvXkRAQIBsn46ODjp16oTTp08rVUdWVhZyc3OLHGGenZ2N7Oxs2XZGRoZqQZdziYmJvPtARKTFtKpH/fTpU+Tn58PKykpuv5WVFRITE5Wq45tvvkHt2rWLnBg9LCwM1atXl328vLxUjpuIiKi0aFWiVtWCBQuwdetWREdHF5o15rWAgACkpqbKPseOHSvjKImIiJSnVbe+zc3NoaurW2iu1KSkJFhbW7/z3O+//x4LFizAoUOH0KRJkyLLGRoawtDQULZtYmKiWtBERESlSKt61AYGBmjevLncQLDXA8NatmxZ5HnffvstQkNDERMTw0FRRERUoWhVjxoA/P39MXz4cLi7u8PDwwPh4eHIzMzEiBEjAADDhg1DnTp1EBYWBgBYuHAhgoKCsGXLFtjb28ueZZuYmLC3TERE5Z7WJeqBAwciJSUFQUFBSExMhJubG2JiYmQDzBISEqCj89+NgFWrViEnJwf9+/eXqyc4OLjUJ2YhIiIqbVqXqAHAz88Pfn5+Co/FxcXJbcfHx5d+QERERBqiVc+oiYiISB4TNRERkRZjoiYiItJiTNRERFRhrFixAvb29jAyMoKnpyfOnTv3zvLbt29Hw4YNYWRkhMaNG2P//v1lFKnymKgrOWtra9SpU6fYCWWIiLRdSVdfPHXqFAYPHoyRI0fi8uXL6N27N3r37i1bNllbMFFXchcuXMCjR49w4cIFTYdCRKSSkq6+GBERgU8++QRff/01GjVqhNDQUDRr1gzLly8v48jfjYmaiIjKvderL765IFNxqy+ePn260AJO3t7eSq/WWFa08j1qKlv5+fmQSqUaa1+anwchPw/S/Dzk5uZqLA6NyReQmy9oOgqNypUKQL4AVMbrT0XKy8sDULAccVpammz/22s2AO9effHmzZsK609MTFRptcaywkStBWrl/4vc50DzrzdppP1/fo9G4uldGmn7TQkADII0HQVpzi3gGwNNB0Fa6O3liCvbzJNM1ASblr1g06KHRmP4V7cWrEyN8Pv0jhqNQyOWNEZCZuX+p2grTQCq1QYmX9V0KKRFLl++DE9PTxw7dgxubm6y/W/3pgFxqy9aW1uLWq2xrFXunw4EAJDo6EDTwxUkunrQ0dWDvr6+RuPQCF0J9HUlmo5Co/QlEkBXAlTG609F0tMrSFEmJiYwNTV9Z9k3V1/s3bs3gP9WXyxqSuqWLVvi8OHDmDRpkmzfwYMH37laoyYwUVdyNzcHIzczFfpVq6Ph0BBNh0NEJFpJV1+cOHEivLy8sGjRInTr1g1bt27FhQsXEBkZqcmvUQgTdSWXm5mK3Iznmg6DiEhlJV19sVWrVtiyZQtmzpyJGTNm4P3338euXbvg4uKiqa+gEBM1ERFVGCVZfREABgwYgAEDBpRyVKrhe9RERERajImaiIhIizFRExERaTEmaiIiIi3GRE1ERKTFmKiJiIi0GBM1ERGRFmOiJiIi0mJM1ERERFqMiZqIiEiLMVETERFpMc71XcnpV60u918iItIuTNSVHJe2JCLSbrz1TUREpMWYqImIiLQYEzUREZEWY6ImIiLSYkzUREREWoyJmoiISIsxURMREWkxJmoiIiItxkRNRESkxZioiYiItBgTNRERkRZjoiYiItJiTNRERERajImaiIhIizFRExERaTEmaiIiIi3GRE1ERKTFmKiJiIi0GBM1ERGRFtPKRL1ixQrY29vDyMgInp6eOHfu3DvLb9++HQ0bNoSRkREaN26M/fv3l1GkREREpUvrEvW2bdvg7++P4OBgXLp0Ca6urvD29kZycrLC8qdOncLgwYMxcuRIXL58Gb1790bv3r1x7dq1Mo6ciIhI/bQuUS9evBijR4/GiBEj4OzsjNWrV6NKlSpYv369wvIRERH45JNP8PXXX6NRo0YIDQ1Fs2bNsHz58jKOnIiISP20KlHn5OTg4sWL6NSpk2yfjo4OOnXqhNOnTys85/Tp03LlAcDb27vI8kREROWJnqYDeNPTp0+Rn58PKysruf1WVla4efOmwnMSExMVlk9MTFRYPjs7G9nZ2bLtjIwMAMCNGzdUCV20zMd3kJeu+LZ+ZZKt8xyZ6Ya4dKmGpkMpe49e4clLXU1HoVFPhXwg9RVw6ZLcfhsbG9jY2GgoKtU8efIET5480XQY5Zqmfi5rG61K1GUhLCwMISEhcvvs7OwwZMgQDUVEryUCaL5S01GQ5mQC4c3l9gQHB2P27NmaCUdFa9asKfSzhkrOy8ur3P6ypi5alajNzc2hq6uLpKQkuf1JSUmwtrZWeI61tXWJygcEBMDf319u37Nnz/Ds2TMVIi+/MjIy4OXlhWPHjsHExETT4ZAGaPPfgfL8A9rHxwc9e/Ys0za1+VqKVZ7vqqiLRBAEQdNBvMnT0xMeHh5YtmwZAEAqlcLW1hZ+fn6YPn16ofIDBw5EVlYWfv31V9m+Vq1aoUmTJli9enWZxV1epaWloXr16khNTYWpqammwyEN4N+BioPXsmLSqh41APj7+2P48OFwd3eHh4cHwsPDkZmZiREjRgAAhg0bhjp16iAsLAwAMHHiRHh5eWHRokXo1q0btm7digsXLiAyMlKTX4OIiEgttC5RDxw4ECkpKQgKCkJiYiLc3NwQExMjGzCWkJAAHZ3/Bqu3atUKW7ZswcyZMzFjxgy8//772LVrF1xcXDT1FYiIiNRG6259U9nKzs5GWFgYAgICYGhoqOlwSAP4d6Di4LWsmJioiYiItJhWTXhCRERE8pioiYiItBgTNalVfHw8JBIJNm7cqOlQiIgqBCZqDbp37x58fHxQr149GBkZwdTUFK1bt0ZERARevnxZau1ev34ds2fPRnx8fKm1oYx58+ahZ8+esLKygkQiKbczUJU2iUSi1CcuLk7ltrKysjB79uwS1cXrWDK8nlRSWvd6VmWxb98+DBgwAIaGhhg2bBhcXFyQk5ODkydP4uuvv8Zff/1Vau+CX79+HSEhIWjfvj3s7e1LpQ1lzJw5E9bW1mjatCliY2M1Foe227x5s9z2pk2bcPDgwUL7GzVqpHJbWVlZsmkv27dvr9Q5vI4lw+tJJcVErQEPHjzAoEGDYGdnhyNHjshNjzd+/HjcvXsX+/bt02CE/xEEAa9evYKxsbHa637w4AHs7e3x9OlTWFhYqL3+iuLteejPnDmDgwcPas389LyOJcPrSSXFW98a8O233yIjIwPr1q1TOIdt/fr1MXHiRNl2Xl4eQkND4ejoCENDQ9jb22PGjBlyq4ABgL29Pbp3746TJ0/Cw8MDRkZGqFevHjZt2iQrs3HjRgwYMAAA0KFDh0K32V7XERsbC3d3dxgbG2PNmjUAgPv372PAgAGoWbMmqlSpghYtWqj0C4Ume/MVjVQqRXh4OD744AMYGRnBysoKPj4+eP78uVy5CxcuwNvbG+bm5jA2NoaDgwO+/PJLAAXjC17/YA4JCZH93Sju1ievo/rxetKb2KPWgF9//RX16tVDq1atlCo/atQoREVFoX///pgyZQrOnj2LsLAw3LhxA9HR0XJl7969i/79+2PkyJEYPnw41q9fjy+++ALNmzfHBx98gHbt2mHChAlYunQpZsyYIbu99uZttlu3bmHw4MHw8fHB6NGj4eTkhKSkJLRq1QpZWVmYMGECatWqhaioKPTs2RM7duxAnz591Pc/iErMx8cHGzduxIgRIzBhwgQ8ePAAy5cvx+XLl/H7779DX18fycnJ6Ny5MywsLDB9+nSYmZkhPj4eO3fuBABYWFhg1apVGDduHPr06YO+ffsCAJo0aaLJr1Yp8XqSHIHKVGpqqgBA6NWrl1Llr1y5IgAQRo0aJbd/6tSpAgDhyJEjsn12dnYCAOH48eOyfcnJyYKhoaEwZcoU2b7t27cLAISjR48Wau91HTExMXL7J02aJAAQTpw4IduXnp4uODg4CPb29kJ+fr4gCILw4MEDAYCwYcMGpb6fIAhCSkqKAEAIDg5W+pzKbPz48cKb/3RPnDghABD+97//yZWLiYmR2x8dHS0AEM6fP19k3apcC15HcXg9qTi89V3G0tLSAADVqlVTqvz+/fsBoNDSnFOmTAGAQreenZ2d0bZtW9m2hYUFnJyccP/+faVjdHBwgLe3d6E4PDw80KZNG9k+ExMTjBkzBvHx8bh+/brS9ZN6bd++HdWrV8fHH3+Mp0+fyj7NmzeHiYkJjh49CgAwMzMDAOzduxe5ubkajJjehdeT3sZEXcZeLz2Xnp6uVPmHDx9CR0cH9evXl9tvbW0NMzMzPHz4UG6/ra1toTpq1KhR6NnWuzg4OCiMw8nJqdD+17fM346Dys6dO3eQmpoKS0tLWFhYyH0yMjKQnJwMAPDy8kK/fv0QEhICc3Nz9OrVCxs2bCg01oE0i9eT3sZn1GXM1NQUtWvXxrVr10p0nkQiUaqcrq6uwv1CCaZ0L40R3lR6pFIpLC0t8b///U/h8dcDiiQSCXbs2IEzZ87g119/RWxsLL788kssWrQIZ86cgYmJSVmGTUXg9aS3MVFrQPfu3REZGYnTp0+jZcuW7yxrZ2cHqVSKO3fuyA34SkpKwosXL2BnZ1fi9pVN+m/HcevWrUL7b968KTtOmuHo6IhDhw6hdevWSv2S1aJFC7Ro0QLz5s3Dli1b8Pnnn2Pr1q0YNWqUqL8bpF68nvQ23vrWgGnTpqFq1aoYNWoUkpKSCh2/d+8eIiIiAABdu3YFAISHh8uVWbx4MQCgW7duJW6/atWqAIAXL14ofU7Xrl1x7tw5nD59WrYvMzMTkZGRsLe3h7Ozc4njIPX49NNPkZ+fj9DQ0ELH8vLyZNf5+fPnhe6suLm5AYDsdmmVKlUAlOzvBqkXrye9jT1qDXB0dMSWLVswcOBANGrUSG5mslOnTmH79u344osvAACurq4YPnw4IiMj8eLFC3h5eeHcuXOIiopC79690aFDhxK37+bmBl1dXSxcuBCpqakwNDREx44dYWlpWeQ506dPx08//YQuXbpgwoQJqFmzJqKiovDgwQP88ssv0NEp+e98mzdvxsOHD5GVlQUAOH78OObOnQsAGDp0KHvpSvLy8oKPjw/CwsJw5coVdO7cGfr6+rhz5w62b9+OiIgI9O/fH1FRUVi5ciX69OkDR0dHpKenY+3atTA1NZX9QmhsbAxnZ2ds27YNDRo0QM2aNeHi4gIXF5ci2+d1VC9eTypEw6POK7Xbt28Lo0ePFuzt7QUDAwOhWrVqQuvWrYVly5YJr169kpXLzc0VQkJCBAcHB0FfX1+oW7euEBAQIFdGEApererWrVuhdry8vAQvLy+5fWvXrhXq1asn6Orqyr2qVVQdgiAI9+7dE/r37y+YmZkJRkZGgoeHh7B37165MiV5PcvLy0sAoPCj6NUxKvD26zyvRUZGCs2bNxeMjY2FatWqCY0bNxamTZsm/PPPP4IgCMKlS5eEwYMHC7a2toKhoaFgaWkpdO/eXbhw4YJcPadOnRKaN28uGBgYKPV6Dq+jang9qTgSQSjBKCMiIiIqU3xGTUREpMWYqImIiLQYEzUREZEWY6ImIiLSYkzUREREWoyJmoiISIsxURMRlSPx8fGQSCTYuHGjpkOhMsJEraU2btwIiUQCIyMjPH78uNDx9u3bv3N2obIwevRoSCQSdO/eXeHxPXv2oFmzZjAyMoKtrS2Cg4ORl5dXxlGWT7z+RPQaE7WWy87OxoIFCzQdRiEXLlzAxo0bYWRkpPD4b7/9ht69e8PMzAzLli1D7969MXfuXHz11VdlHGn5xutPb7Ozs8PLly8xdOhQTYdCZYRzfWs5Nzc3rF27FgEBAahdu7amwwFQsGTmhAkTMGzYMBw+fFhhmalTp6JJkyY4cOAA9PQK/pqZmppi/vz5mDhxIho2bFiWIZdbvP70ttd3WqjyYI9ay82YMQP5+fla1avavHkzrl27hnnz5ik8fv36dVy/fh1jxoyR/ZAGAF9fXwiCgB07dpRVqOUer3/FNHv2bEgkEty+fRtDhgxB9erVYWFhgVmzZkEQBPz999/o1asXTE1NYW1tjUWLFsnOVfSM+osvvoCJiQkeP36M3r17w8TEBBYWFpg6dSry8/Nl5eLi4iCRSBAXFycXj6I6ExMTMWLECLz33nswNDSEjY0NevXqhfj4+FL6v0JFYaLWcg4ODhg2bBjWrl2Lf/75p8TnZ2Vl4enTp8V+nj9/rlR96enp+OabbzBjxgxYW1srLHP58mUAgLu7u9z+2rVr47333pMdp+Lx+ldsAwcOhFQqxYIFC+Dp6Ym5c+ciPDwcH3/8MerUqYOFCxeifv36mDp1Ko4fP/7OuvLz8+Ht7Y1atWrh+++/h5eXFxYtWoTIyEhRsfXr1w/R0dEYMWIEVq5ciQkTJiA9PR0JCQmi6iPxmKjLgcDAQOTl5WHhwoUlPvfbb7+FhYVFsZ+mTZsqVd+cOXNgbGyMyZMnF1nmyZMnAAAbG5tCx2xsbEQlnMqM17/i8vDwwJYtWzBu3Djs3r0b7733HqZMmSJLjuPGjcPevXthbGyM9evXv7OuV69eYeDAgVi3bh3Gjh2LHTt2oGnTpli3bl2J43rx4gVOnTqFmTNnIjQ0FCNHjkRAQACOHDmCdu3aif26JBKfUZcD9erVw9ChQxEZGYnp06cr/AFYlGHDhqFNmzbFljM2Ni62zO3btxEREYGffvoJhoaGRZZ7+fIlACgsY2RkhLS0tGLbov/w+ldco0aNkv1ZV1cX7u7uePToEUaOHCnbb2ZmBicnJ9y/f7/Y+saOHSu33bZtW2zevLnEcRkbG8PAwABxcXEYOXIkatSoUeI6SH2YqMuJmTNnYvPmzViwYAEiIiKUPq9evXqoV6+eWmKYOHEiWrVqhX79+r2z3Osf+tnZ2YWOvXr1SqmkQPJ4/SsmW1tbue3q1avDyMgI5ubmhfb/+++/76zLyMgIFhYWcvtq1Kih9GONNxkaGmLhwoWYMmUKrKys0KJFC3Tv3h3Dhg0r8pEHlR4m6nKiXr16GDJkiKxXpayMjAxkZGQUW05XV7fQP/I3HTlyBDExMdi5c6fcYJK8vDy8fPkS8fHxqFmzJkxNTWU9vidPnqBu3bpy9Tx58gQeHh5Kx08FeP0rJl1dXaX2AQWj7Uta19skEonC/W8OOHtt0qRJ6NGjB3bt2oXY2FjMmjULYWFhOHLkiNKPSkg9+Iy6HJk5c2aJn1V+//33sLGxKfbz4YcfvrOe1wNI+vbtCwcHB9nn8ePHOHLkCBwcHGTP0Nzc3AAUvGv7pn/++QePHj2SHaeS4fUnVb2+hf3ixQu5/Q8fPlRY3tHREVOmTMGBAwdw7do15OTkyI1Ap7LBHnU54ujoiCFDhmDNmjWws7OTe/WlKOp6RtmxY0dER0cX2j9mzBjY2dkhMDAQjRs3BgB88MEHaNiwISIjI+Hj4yP7TX/VqlWQSCTo379/sfFQYbz+pCo7Ozvo6uri+PHj6N27t2z/ypUr5cplZWVBR0dH7n1tR0dHVKtWTeEjDSpdTNTlTGBgIDZv3oxbt27hgw8+KLa8up5R2traFnqeBhTcHrOyspL7Rw8A3333HXr27InOnTtj0KBBuHbtGpYvX45Ro0ahUaNGKsdTWfH6kyqqV6+OAQMGYNmyZZBIJHB0dMTevXuRnJwsV+727dv46KOP8Omnn8LZ2Rl6enqIjo5GUlISBg0apKHoKy/e+i5n6tevjyFDhmg6jGJ1794dO3fuxLNnz/DVV19h586dmDFjBlasWKHp0Mo1Xn9S1bJly9CrVy+sXr0aM2fOhK2tLaKiouTK1K1bF4MHD0ZcXBwCAgIQEBCAtLQ0/Pzzz8UOJiT1kwjFjVAgIiIijWGPmoiISIsxURMREWkxJmoiIiItxkRNRESkxZioiYiItBgTNRERkRZjoiYiokLi4+MhkUiwceNGTYdS6TFRExGp6N69e/Dx8UG9evVgZGQEU1NTtG7dGhEREbJlP0vD9evXMXv2bLmFUjRh3rx56NmzJ6ysrCCRSDB79myNxlPRcApRIiIV7Nu3DwMGDIChoSGGDRsGFxcX5OTk4OTJk/j666/x119/ITIyslTavn79OkJCQtC+fXvY29uXShvKmDlzJqytrdG0aVPExsZqLI6KiomaiEikBw8eYNCgQbCzs8ORI0dkS3wCwPjx43H37l3s27dPgxH+RxCEUlsP/MGDB7C3t8fTp0/fuVwqicNb30REIn377bfIyMjAunXr5JL0a/Xr18fEiRNl23l5eQgNDYWjoyMMDQ1hb2+PGTNmFFqRyt7eHt27d8fJkyfh4eEBIyMj1KtXD5s2bZKV2bhxIwYMGAAA6NChAyQSCSQSCeLi4uTqiI2Nhbu7O4yNjbFmzRoAwP379zFgwADUrFkTVapUQYsWLVT6hUKTvfnKgImaiEikX3/9FfXq1UOrVq2UKj9q1CgEBQWhWbNmWLJkCby8vBAWFqZwRaq7d++if//++Pjjj7Fo0SLUqFEDX3zxBf766y8AQLt27TBhwgQAwIwZM7B582Zs3rxZbnWyW7duYfDgwfj4448REREBNzc3JCUloVWrVoiNjYWvry/mzZuHV69eoWfPngqXMiUtIBARUYmlpqYKAIRevXopVf7KlSsCAGHUqFFy+6dOnSoAEI4cOSLbZ2dnJwAQjh8/LtuXnJwsGBoaClOmTJHt2759uwBAOHr0aKH2XtcRExMjt3/SpEkCAOHEiROyfenp6YKDg4Ngb28v5OfnC4IgCA8ePBAACBs2bFDq+wmCIKSkpAgAhODgYKXPoeKxR01EJEJaWhoAoFq1akqV379/PwDA399fbv+UKVMAoNCtZ2dnZ7Rt21a2bWFhAScnJ9y/f1/pGB0cHODt7V0oDg8PD7Rp00a2z8TEBGPGjEF8fDyuX7+udP1UNpioiYhEMDU1BQCkp6crVf7hw4fQ0dFB/fr15fZbW1vDzMwMDx8+lNtva2tbqI4aNWrg+fPnSsfo4OCgMA4nJ6dC+1/fMn87DtI8JmoiIhFMTU1Ru3ZtXLt2rUTnSSQSpcrp6uoq3C8IgtJtlcYIbyp7TNRERCJ1794d9+7dw+nTp4sta2dnB6lUijt37sjtT0pKwosXL2BnZ1fi9pVN+m/HcevWrUL7b968KTtO2oWJmohIpGnTpqFq1aoYNWoUkpKSCh2/d+8eIiIiAABdu3YFAISHh8uVWbx4MQCgW7duJW6/atWqAIAXL14ofU7Xrl1x7tw5uV8uMjMzERkZCXt7ezg7O5c4DipdnPCEiEgkR0dHbNmyBQMHDkSjRo3kZiY7deoUtm/fji+++AIA4OrqiuHDhyMyMhIvXryAl5cXzp07h6ioKPTu3RsdOnQocftubm7Q1dXFwoULkZqaCkNDQ3Ts2BGWlpZFnjN9+nT89NNP6NKlCyZMmICaNWsiKioKDx48wC+//AIdnZL33zZv3oyHDx8iKysLAHD8+HHMnTsXADB06FD20lWl6WHnRETl3e3bt4XRo0cL9vb2goGBgVCtWjWhdevWwrJly4RXr17JyuXm5gohISGCg4ODoK+vL9StW1cICAiQKyMIBa9WdevWrVA7Xl5egpeXl9y+tWvXCvXq1RN0dXXlXtUqqg5BEIR79+4J/fv3F8zMzAQjIyPBw8ND2Lt3r1yZkrye5eXlJQBQ+FH06hiVjEQQSjAygYiIiMoUn1ETERFpMSZqIiIiLcZETUREpMWYqImIiLQYEzUREZEWY6ImIiLSYkzUREREWoyJmoiISIsxURMREWkxJmoiIiItxkRNRESkxZioiYiItBgTNRERkRb7P3FxUDRW6LW1AAAAAElFTkSuQmCC", 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" ] @@ -547,11 +693,12 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "id": "2bd92f6a", "metadata": {}, "source": [ - "``bar_label`` and ``contrast_label`` can be used to set labels for the y-axis of the bar plot and the contrast plot.\n" + "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." ] }, { @@ -562,7 +709,7 @@ "outputs": [ { "data": { - "image/png": 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t27C2tlaOjxs3Dl9//bVW2+SMmoiIVAkCcGItMG4TYGIqdjV65eTJkzh16hQsLCxUxuVyOW7cuKHVNjmjJiIidaW/AOf3il2F3qmvr4dCoX49+vXr12Fvb6/VNhnURESkWdY2oFy7WaCxGjFiBBITE5XPZTIZKisrsXTpUowePVqrbYoe1ElJSfDx8YGVlRX8/Pxw4sSJRy5fXV2NxYsXQy6Xw9LSEk8++SS2b9/eTtUSERmRumogcxVQXy92JXpj/fr1yMzMRM+ePXH//n1MnDgR3t7euHHjBlatWqXVNkX9jjo1NRUxMTFISkpCUFAQNm/ejLCwMFy4cAFdunTRuM6ECRNw8+ZNbNu2Dd26dUNJSQnq6urauXIiIiNR9APw8yGg5ytiV6IXPD09kZeXh3/961/Izs5GfX09IiIi8Oabb6qcXNYSOglqhUKB8+fPQy6Xw9nZudnrrVu3DhEREZgxYwYAIDExEUeOHEFycjISEhLUlk9PT0dmZiauXLkCFxcXAIC3t7cu3gKR0fL390dxcTHc3d2RlZUldjkkRf/ZBHQJAOw6iV2JXrC2tsa0adMwbdo0nWxPq0PfMTEx2LZtG4CGkA4JCcFzzz0HLy8vfPvtt83aRk1NDbKzsxEaGqoyHhoaitOnT2tc5/PPP4e/vz9Wr16Nzp07o0ePHliwYAHu3bunzdsgIgDFxcW4ceMGiouLxS6FpKq2CjiZKHYVeiEhIUHj17Hbt2/X+tC3VkG9d+9e9OnTBwBw6NAhXL16FT///DNiYmKwePHiZm2jtLQUCoUCbm5uKuNubm5N/oNx5coVnDx5Ej/++CMOHDiAxMRE7N27F3PmzGlyP9XV1aioqFA+tL0zDBGRUSs4BVw7KXYVkrd582Y8/fTTauPPPPOMVjc7AbQM6tLSUri7uwMA0tLSMH78ePTo0QMRERE4f/58i7Ylk8lUnguCoDbWqL6+HjKZDLt378aAAQMwevRorFu3DikpKU3OqhMSEuDo6Kh8hISEtKg+IiL6P6c/BOpqxK5C0oqLi+Hh4aE23rFjRxQVFWm1Ta2C2s3NDRcuXIBCoUB6ejqGDx8OAKiqqoKpafMujnd1dYWpqana7LmkpERtlt3Iw8MDnTt3hqOjo3LM19cXgiDg+vXrGteJi4tDeXm58pGZmdms+oiI6CF3ioEf94ldhaR5eXnh1KlTauOnTp2Cp6enVtvUKqinTZuGCRMmoFevXpDJZBgxYgQA4LvvvtM45dfEwsICfn5+yMjIUBnPyMhAYGCgxnWCgoLUbmz+yy+/wMTEBE888YTGdSwtLeHg4KB82NnZNas+IiLSIPdToJpfITZlxowZiImJwY4dO1BQUICCggJs374d8+bNw8yZM7XaplZnfS9btgy9evXCb7/9hvHjx8PS0hIAYGpqinfeeafZ24mNjcWkSZPg7++PgIAAbNmyBYWFhZg9ezaAhtnwjRs38MknnwAAJk6ciPfeew/Tpk3D8uXLUVpaioULF2L69Olan/ZOREQtUFMJ/LQfeG6y2JVI0qJFi1BWVobIyEjU1DR8TWBlZYX/+Z//QVxcnFbb1PryrNdff13l+R9//IEpU6a0aBvh4eG4desW4uPjUVRUhF69eiEtLQ1yuRwAUFRUhMLCQuXydnZ2yMjIQFRUFPz9/dGhQwdMmDABK1as0PZtEBFRS53fC/R+AzCzePyyRkYmk2HVqlX4+9//jvz8fFhbW6N79+7KCa02tArqVatWwdvbG+Hh4QAabkKyb98+eHh4IC0tDb179272tiIjIxEZGanxtZSUFLWxp59+Wu1wORERtaP75cC140C34WJXIll2dnbo37+/Tral1XfUmzdvhpeXF4CG75QzMjLw5ZdfYtSoUViwYIFOCiMiIgn75YjYFUjS3bt38fe//x2BgYHo1q0bunbtqvLQhlYz6qKiImVQHz58GBMmTEBoaCi8vb0xcOBArQohIiI9ciMHqLkLWNiKXYmkzJgxA5mZmZg0aRI8PDyavNy4JbQKamdnZ/z222/w8vJCenq68jtiQRA0tvciIiIDU18H/J4LeD8vdiWS8uWXX+KLL75AUFCQzrapVVC/+uqrmDhxIrp3745bt24hLCwMAJCXl4du3brprDgiIpKw4h8Z1A9xdnZW9qLQFa2+o16/fj3mzp2Lnj17IiMjQ3ltclFRUZMnhhERkYEp+UnsCh6rpa2UG506dQpmZmbo27dvi/b33nvvYcmSJaiqqtKiWs20mlGbm5trPGksJiamtfUQEZG+KP21oVe1iVZzvjanTStlACgvL8fkyZPxwgsv4ObNmy3a59q1a3H58mW4ubnB29sb5ubmKq/n5OS0+H1ofR31rl27sHnzZly5cgVnzpyBXC5HYmIifHx88Mor7FtKRGTwaquAiuuAU9OhJ6aWtlJuNGvWLEycOBGmpqY4ePBgi/Y5duzYVlSsmVZBnZycjCVLliAmJgbvv/++8gQyJycnJCYmMqiJiIxFyc/tHtSVlZWoqKhQPre0tFS7oUhjK+WH75b5qFbKALBjxw5cvnwZn376qVY301q6dGmL13kcrY5XfPjhh/jHP/6BxYsXqzTh8Pf3b3H3LCIi0mMlF9p9lyEhISpdETXNjrVppXzp0iW888472L17N8zMtD7gjD/++ANbt25FXFwcysrKADQc8r5x44ZW29OqkqtXr6Jfv35q45aWlrh7965WhRARkW4UFhYqT2aqqqlHYdl9dHGxapudFbf/5CwzM1PlJK9H3Z6zua2UFQoFJk6ciOXLl6NHjx5a13bu3DkMHz4cjo6OuHbtGmbOnAkXFxccOHAABQUFyt4VLaHVjNrHxwd5eXlq419++SV69uypzSaJiKiVzp49izFjxsDb2xu3b98GANyuqoP34rN4OelHfH/tju53WnYZqG6D7T6CnZ2dSldETUHd0lbKd+7cQVZWFubOnQszMzOYmZkhPj4eP/zwA8zMzHDs2LFm1RYbG4upU6fi0qVLsLL685ejsLAwHD9+vIXvtIFWM+qFCxdizpw5uH//PgRBwNmzZ7Fnzx4kJCRg69atWhVCRETa279/P8LDwyEIAgRBUHlNEIC0H8vw5Y+3kTrTF6/2c9XdjgUBuHkB6CKtu1I+2Ep53LhxyvGMjAyN51E5ODiofXWblJSEY8eOYe/evfDx8WnWfr///nts3rxZbbxz585NHnJ/HK2Cetq0aairq8OiRYtQVVWFiRMnonPnztiwYQPeeOMNrQohInG4u7ur/Jf0z9mzZxEeHg6FQqEW0o0U9YAMAsL/kY/Ti/qiv7e97gq4eV5yQQ20rJWyiYkJevXqpbJ+p06dYGVlpTb+KFZWVionujW6ePEiOnbsqNX70Prb8pkzZ2LmzJkoLS1FfX09OnXqpO2miEhEWVlZYpdArbRixQqNM+mHCQAECFiRVoB/RzY/fB6r7KrutqVDLW2lrAuvvPIK4uPj8b//+78AGr4jLywsxDvvvIPXXntNq21q9R311atXcenSJQAN3wM0hvSlS5dw7do1rQohIqKWKywsxOHDh5vdZ0FRDxw6X4bCsvu6K6JCu7OZ20NkZCSuXbuG6upqZGdnY/DgwcrXUlJS8O233za57rJlyzSej/Uoa9aswX//+1906tQJ9+7dQ0hICLp16wZ7e3u8//77Wr0HrWbUU6dOxfTp09G9e3eV8e+++w5bt2595BsnIulRKBSor68Xbf91inrUKephoqhHbW2taHXooyNHjjx2Jv0wQQCOXriNKQHqJ1VppboaaIfPra6urs330VoODg44efIkjh07hpycHNTX1+O5557D8OHa9+7WKqhzc3M1dgYZNGgQ5s6dq3UxRMaoXOYIVNbhi/cnilbD7q/PY883P4q2fxXzd4tdgVGY+eklzPz0ku42OOlfutuWnqqrq4OVlRXy8vIwbNgwDBs2TCfb1SqoZTIZ7txRPx2/vLycbS6J9NAbQ59B+JBnRK3BUSiHhX0H9I/aIWod+iYlJQV//etfW7zeP/7SXXczau/ngRHxutnWI+Tm5mLgQOmdtNbIzMwMcrlc5zmoVVAHBwcjISEBe/bsUd6ZTKFQICEhAc8/z5ZnRPrGVAJNFcwEE5iZmqg1MaBHGzlyJGQyWYsOf8tkQGhPZ5ib6uhzd/cF2uFza83dwtrL3/72N8TFxeHTTz/VWbtLrd716tWrMXjwYDz11FMIDg4GAJw4cQIVFRXNviiciIhar0uXLnjppZeQlpbWrJmcqQnwYi8X3d6pjD2plTZu3Ihff/0Vnp6ekMvlsLW1VXm93bpn9ezZE+fOncNHH32EH374AdbW1pg8eTLmzp2r84bZRNS2YpKO4HblPTjbWSMxcqTY5ZAW/v73v+PLL7987MxaBkAGGf42Wq67nTt1AZybdzMQYyCZ7lkA4OnpiQ8++ECXtRCRCG5X3sOtintil0Gt0L9/f6SmpirvTKZpZm1q0hDS/zvTV7c3O3n6xYZj6QRAQt2zduzYgc8++0xt/LPPPsPOnTtbXRQREbXMq6++itOnT2P06NFqTSdksobD3acX9cU4Xd4+1MQM6MGjMA/TdfcsrYJ65cqVcHVV/7A7derEWTYRkUj69++Pzz//HNeuXYOzszMAwNnGDNfeH4B/R/bS7UwaAOSBgLWzbrep586dO4cePXpg1apVWLNmDf744w8AwIEDBxAXF6fVNrUK6oKCAo03KJfL5Tq/HRsREbVMly5dYGNjAwCwsTBpuxaXT41um+3qsbbonqVVUHfq1Annzp1TG//hhx/QoUMHrQohIiI9YmkPPNFf7Cok5/vvv8esWbPUxlvTPUuroH7jjTcQHR2Nb775BgqFAgqFAseOHcPbb7/N7llERMbAOxgwlf51ze2tLbpnaRXUK1aswMCBA/HCCy/A2toa1tbWCA0NxbBhw/gdNRGRMZBgW0spaOye1XjPel10z9Lq1yELCwukpqbivffeU15H/eyzzypbhxERkQGTmQCez4ldhSStWbMGo0ePVumeVVxcjICAgPbtntWoR48e6NGjR2s2QURE+qZDN8DKQewqJEky3bOmT5/+yNe3b9+uVTFERKQHPPqIXYGkuLi44JdffoGrqyumT5+ODRs26LR7llbfUd++fVvlUVJSgmPHjmH//v3Ka8aIiMhAefYTuwJJqampUZ5AtnPnTty/f1+n29dqRn3gwAG1sfr6ekRGRqJr166tLoqIiCRKZgJ49Ba7CkkJCAjA2LFj4efnB0EQEB0dDWtra43LanPEWWe97UxMTDBv3jysX79eV5skIiKp6dSz4RpqUvr0008xevRoVFZWAgDKy8vVjjw3PrSh04vgLl++jLq6Ol1ukoiIpKTLILErkBw3NzesXLkSAODj44Ndu3bp9OZfWgV1bGysynNBEFBUVIQvvvgCU6ZM0UlhREQkQU/q5gQpQ/LgyWRDhw6FhYWFTrevVVDn5uaqPDcxMUHHjh2xdu3ax54RTkREesqtF+DYWewqJKfxZDJXV1fs3LkTq1atgr297r4e0Cqov/jiCwiCAFtbWwDAtWvXcPDgQcjlcpiZ8ZZyREQG6dnXxa5AkiR5MtnYsWOxa9cuAA19NwcNGoS1a9di7NixSE5O1maTREQkZY5PAD4hYlchSQ+eTCaTyaRxMllOTo7y7O69e/fCzc0Nubm52LdvH5YsWYK33npLq2KIiEiiBr0FmOjsQiGDIsmTyaqqqpTH348ePYpXX30VJiYmGDRoEAoKCnRWHBERSYDXAEAeJHYVeuHq1as636ZWQd2tWzccPHgQ48aNw5EjRzBv3jwAQElJCRwceP9XIn3ibGet8l8iFeY2wPOxgEwmdiWStXHjRvz1r3+FlZUVNm7c+Mhlo6OjW7x9rYJ6yZIlmDhxIubNm4cXXngBAQEBABpm1/368dZyRPokMXKk2CWQlAXMARw8xK5C0tavX48333wTVlZWj7zpl0wma7+gfv311/H888+jqKgIffr8eXP2F154AePGjdNmk0REJDXyIODpF8WuQvIePNwtmUPfAODu7g53d3eVsQEDBrS6ICIikgBrZyBkIQ95N8PDNwFrikwmw9q1a1u8fV70TERE6gYvbAhreqyHbwKWnZ0NhUKBp556CgDwyy+/wNTUFH5+flptn0FNRESqngoDvHmWd3N98803yj+vW7cO9vb22LlzJ5ydG37RuX37NqZNm4bg4GCtts+L4oiI6E/WzsCgSLGr0Ftr165FQkKCMqQBwNnZGStWrNDqsDfAoCYiogcFzAWseJmttioqKnDz5k218ZKSEty5c0erbYoe1ElJSfDx8YGVlRX8/Pxw4sSJZq136tQpmJmZoW/fvm1bIBGRsXDrBXR7Qewq9Nq4ceMwbdo07N27F9evX8f169exd+9eRERE4NVXX9Vqm6IGdWpqKmJiYrB48WLk5uYiODgYYWFhKCwsfOR65eXlmDx5Ml54gT9QREQ6M2g2z/JupU2bNuHFF1/EX/7yF8jlcsjlcrz55psICwtDUlKSVtsUNajXrVuHiIgIzJgxA76+vkhMTISXl9djG3vMmjULEydOVN5ohYiIWqmzH+D+rNhV6D0bGxskJSXh1q1byM3NRU5ODsrKypCUlKTsONlSogV1TU0NsrOzERoaqjIeGhqK06dPN7nejh07cPnyZSxdurRZ+6murkZFRYXyUVlZ2aq6iYgMUp83xK7AoNja2qJ3797o06eP1gHdSLTLs0pLS6FQKODm5qYy7ubmhuLiYo3rXLp0Ce+88w5OnDjR7L7XCQkJWL58eavrJSIyWI5PAE/0F7sKaoLoJ5PJHvo+RBAEtTEAUCgUmDhxIpYvX44ePXo0e/txcXEoLy9XPjIzM1tdMxGRQekeyu+mJUy0GbWrqytMTU3VZs8lJSVqs2wAuHPnDrKyspCbm4u5c+cCAOrr6yEIAszMzHD06FEMGzZMbT1LS0tYWloqn9vZ2en4nRAR6Tme6S1pos2oLSws4Ofnh4yMDJXxjIwMBAYGqi3v4OCA8+fPIy8vT/mYPXs2nnrqKeTl5WHgwIHtVToRkeFw6dpw6JskS9RbiMbGxmLSpEnw9/dHQEAAtmzZgsLCQsyePRtAw2HrGzdu4JNPPoGJiQl69eqlsn6nTp1gZWWlNk5ERM0kV58YkbSIGtTh4eG4desW4uPjUVRUhF69eiEtLQ1yuRwAUFRU9NhrqomIqBW68DJXqRO9KUdkZCQiIzXfVzYlJeWR6y5btgzLli3TfVFERMbAygHo1FPsKugxRD/rm4iIRNIlADBhDEgdPyEiImMlZytLfcCgJiIyRubWQJdBYldBzcCgJiIyRvJAwMzy8cuR6BjURETGqPtIsStoFy1ppbx//36MGDECHTt2hIODAwICAnDkyJF2rFYzBjURkbGxdmrolmXgWtpK+fjx4xgxYgTS0tKQnZ2NoUOHYsyYMcjNzW3nylUxqImIjM2TLwCmol+d2+Za2ko5MTERixYtQv/+/dG9e3d88MEH6N69Ow4dOtTOlatiUBMRGZvuI8SuoFUqKytV2hdXV1erLaNtK+UH1dfX486dO3BxcdFJ3dpiUBMRGRMHT6Dj02JX0SohISFwdHRUPhISEtSW0aaV8sPWrl2Lu3fvYsKECTqpW1uGf+yDiIj+1HWI3re0zMzMRN++fZXPH+yQ+LDmtlJ+2J49e7Bs2TL8+9//RqdOnbSuVRcY1ERExsT7ebEraDU7Ozs4ODg8cpmWtlJ+UGpqKiIiIvDZZ59h+PDhra63tXjom4jIWFg7Ax19xa6iXbS0lXKjPXv2YOrUqfjnP/+JF198sa3LbBbOqImIjEVnP6O6t3dLWikDDSE9efJkbNiwAYMGDVLOxq2treHo6Cja+2BQExEZCyO4dvpBLW2lvHnzZtTV1WHOnDmYM2eOcnzKlCmP7ebYlhjURETGwv1ZsStody1ppfztt9+2fUFaMJ5jIERExszSHnB8QuwqSAsMaiIiY9DxKb2/LMtYMaiJiIxBh+5iV0BaYlATERkDFx+xKyAtMaiJiIyBUxexKyAtMaiJiIwBTyTTWwxqIiJDZ+XYcNY36SUGNRGRoXPoLHYF1AoMaiIiQ2fvLnYF1Aq8MxkRkQFyd3cH6qrhbnm/oQc16S0GNRGRAcrKygJ+OQp88z5n1HqOh76JiAydPWfU+oxBTURk6Ow6iV0BtQKDmojI0Nm5iV0BtQKDmojIkFk7A2YWYldBrcCgJiIyZJxN6z0GNRGRIbN1FbsCaiUGNRGRIbPpIHYF1EoMaiIiQ2bjInYF1EoMaiIiQ2bNoNZ3DGoiIkNm5Sh2BdRKDGoiIkNm5SB2BdRKDGoiIkNmYSd2BdRKDGoiIkNmYSt2BdRKDGoiIkNmZiV2BdRKDGoiIkPGoNZ7DGoiIkNmZil2BdRKDGoiIkMlMwFMTMWuglqJQU1EZKhMzcWugHSAQU1EZKhMzMSugHSAQU1EZKgY1AaBQU1EZKgY1AaBQU1EZKh4IplBYFATERkqzqgNguhBnZSUBB8fH1hZWcHPzw8nTpxoctn9+/djxIgR6NixIxwcHBAQEIAjR460Y7VERHqEQW0QRA3q1NRUxMTEYPHixcjNzUVwcDDCwsJQWFiocfnjx49jxIgRSEtLQ3Z2NoYOHYoxY8YgNze3nSvXf/7+/njiiSfg7+8vdilE1FYY1AZB1E9x3bp1iIiIwIwZMwAAiYmJOHLkCJKTk5GQkKC2fGJiosrzDz74AP/+979x6NAh9OvXrz1KNhjFxcW4ceOG2GUQUVvid9QGQbQZdU1NDbKzsxEaGqoyHhoaitOnTzdrG/X19bhz5w5cXFyaXKa6uhoVFRXKR2VlZavqJiLSGwxqgyBaUJeWlkKhUMDNzU1l3M3NDcXFxc3axtq1a3H37l1MmDChyWUSEhLg6OiofISEhLSqbiIivcFD3wZB9JPJZDKZynNBENTGNNmzZw+WLVuG1NRUdOrUqcnl4uLiUF5ernxkZma2umYiIr0gE/2feNIB0X7dcnV1hampqdrsuaSkRG2W/bDU1FRERETgs88+w/Dhwx+5rKWlJSwt/+weY2dnp33RRET6RMZD34ZAtF+3LCws4Ofnh4yMDJXxjIwMBAYGNrnenj17MHXqVPzzn//Eiy++2NZlEhHpL86oDYKoX2DExsZi0qRJ8Pf3R0BAALZs2YLCwkLMnj0bQMNh6xs3buCTTz4B0BDSkydPxoYNGzBo0CDlbNza2hqOjo6ivQ8iIklqxteIJH2iBnV4eDhu3bqF+Ph4FBUVoVevXkhLS4NcLgcAFBUVqVxTvXnzZtTV1WHOnDmYM2eOcnzKlClISUlp7/KJiKSNM2qDIPopgZGRkYiMjNT42sPh++2337Z9QUREhoJBbRD4KRIREUkYg5qIyFDxO2qDwKAmIjJYDGpDwKAmIjJUnFEbBAa1kXJ3d0fnzp3h7u4udilE1GYY1C1ppQwAmZmZ8PPzg5WVFbp27YpNmza1U6VNY1AbqaysLFy/fh1ZWVlil0JE1CZa2kr56tWrGD16NIKDg5Gbm4t3330X0dHR2LdvXztXropBTUREBunBVsq+vr5ITEyEl5cXkpOTNS6/adMmdOnSBYmJifD19cWMGTMwffp0rFmzpp0rVyX6ddQkHoVCgfr6etH2X6+oQ71CgXpFHWpra0WrQ0x1inrUKcT7DKSkTqiHiaLeaH8W2kRdHSAznL/Puro6AEBlZSUqKiqU4w/3dAD+bKX8zjvvqIw/qpXymTNn1Fovjxw5Etu2bUNtbS3Mzc118TZajEEtEhuhClV/VOGVmA9Eq+Hn/3yNX747Jtr+H/S/K94SuwSSivm7xa6AJO7hdsVLly7FsmXLVMa0aaVcXFyscfm6ujqUlpbCw8Oj9cVrgUFtxJ4aMBQ9+g8RtYYqmQ1cHW3x6ZIIUesQy/cfTsN/K+vELkMSHIVyWNh3QP+oHWKXQhKVm5uLgQMHIjMzE3379lWOPzybflBLWylrWl7TeHtiUBsxmYmJ6OeEmshMYWJqJtohJbGZmZrAzJSnigCAmdDwd2GsPwv0eGZmDZFlZ2cHBweHRy6rTStld3d3jcubmZmhQ4cOrai8dfgvBBERGRxtWikHBASoLX/06FH4+/uL+gskZ9RGKnPPx6iuqoSljR1C/t+cx69ARKRnWtpKefbs2fjoo48QGxuLmTNn4syZM9i2bRv27Nkj5ttgUBur6qpK3K+sePyCRER6qqWtlH18fJCWloZ58+bh448/hqenJzZu3IjXXntNrLcAgEFNREQGrCWtlIGGM8pzcnLauKqW4XfUREREEsagJiIikjAGNRERkYQxqImIiCSMQU1ERCRhDGoiIiIJY1ATERFJGIOaiIhIwhjUREREEsagJiIikjAGNRERkYTxXt9GytLGTuW/REQkTQxqI8XWlkRE+oGHvomIiCSMQU1ERCRhDGoiIiIJY1ATERFJGIOaiIhIwhjUREREEsagJiIikjAGNRERkYQxqImIiCSMQU1ERCRhDGoiIiIJY1ATERFJGIOaiIhIwhjUREREEsagJiIikjAGNRERkYQxqImIiCSMQU1ERCRhDGoiIiIJY1ATERFJmOhBnZSUBB8fH1hZWcHPzw8nTpx45PKZmZnw8/ODlZUVunbtik2bNrVTpURERO1P1KBOTU1FTEwMFi9ejNzcXAQHByMsLAyFhYUal7969SpGjx6N4OBg5Obm4t1330V0dDT27dvXzpUTERG1D1GDet26dYiIiMCMGTPg6+uLxMREeHl5ITk5WePymzZtQpcuXZCYmAhfX1/MmDED06dPx5o1a9q5ciIiovYhWlDX1NQgOzsboaGhKuOhoaE4ffq0xnXOnDmjtvzIkSORlZWF2traNquViIhILGZi7bi0tBQKhQJubm4q425ubiguLta4TnFxscbl6+rqUFpaCg8PD7V1qqurUV1drXxeWVkJAMjPz2/tW9BaWVEB7t8pE23/UnJfZgWTKhvk5OSIXYoofv7tFm7fU4hdhiTYCZUwtwHMHvpZ8PDw0Pj/tr4oKipCUVGR2GUYBDH/3RaTaEHdSCaTqTwXBEFt7HHLaxpvlJCQgOXLl6uMyeVy/OUvf9GmXGojR7esELsEkoq1aSpPly5dimXLlolTiw5s3rxZ7d8g0l5ISIhe/+KmDdGC2tXVFaampmqz55KSErVZcyN3d3eNy5uZmaFDhw4a14mLi0NsbKzKWFlZGcrKjHtGW1lZiZCQEGRmZsLOzk7sckhEUv9Z0Pd/lGfNmoWXX3653fcr9c9VW/p+hEUbogW1hYUF/Pz8kJGRgXHjxinHMzIy8Morr2hcJyAgAIcOHVIZO3r0KPz9/WFubq5xHUtLS1haWqqMOTg4wNvbu3VvQM9VVFQAAPr27QsHBweRqyEx8WehbYkVLPxcDYeoZ33HxsZi69at2L59O/Lz8zFv3jwUFhZi9uzZABpmw5MnT1YuP3v2bBQUFCA2Nhb5+fnYvn07tm3bhgULFoj1FoiIiNqUqN9Rh4eH49atW4iPj0dRURF69eqFtLQ0yOVyAA0nYTx4TbWPjw/S0tIwb948fPzxx/D09MTGjRvx2muvifUWiIiI2pRMaDwbi4xKdXU1EhISEBcXp/bVABkX/iwYJn6uhoNBTUREJGGi3+ubiIiImsagJiIikjAGNRERkYQxqEkr3377LWQyGf744w+xSyEiMmgMagkoLi5GVFQUunbtCktLS3h5eWHMmDH4+uuvdbqfIUOGICYmRqfbfJQtW7ZgyJAhcHBwYKjrmEwme+Rj6tSpWm/b29sbiYmJj12On6/u8XMlTUS/17exu3btGoKCguDk5ITVq1ejd+/eqK2txZEjRzBnzhz8/PPP7VqPIAhQKBQwM2v9j0ZVVRVGjRqFUaNGIS4uTgfVUaMHmzykpqZiyZIluHjxonLM2tq6zWvg56t7/FxJI4FEFRYWJnTu3FmorKxUe+327dvKPxcUFAgvv/yyYGtrK9jb2wvjx48XiouLla8vXbpU6NOnj/DJJ58IcrlccHBwEMLDw4WKigpBEARhypQpAgCVx9WrV4VvvvlGACCkp6cLfn5+grm5uXDs2DHh/v37QlRUlNCxY0fB0tJSCAoKEs6ePavcX+N6D9bYlJYsSy23Y8cOwdHRUWXs888/F5577jnB0tJS8PHxEZYtWybU1tYqX1+6dKng5eUlWFhYCB4eHkJUVJQgCIIQEhKi9nPyOPx82wY/V2rEQ98iKisrQ3p6OubMmQNbW1u1152cnAA0zHLHjh2LsrIyZGZmIiMjA5cvX0Z4eLjK8pcvX8bBgwdx+PBhHD58GJmZmVi5ciUAYMOGDQgICMDMmTOVbfe8vLyU6y5atAgJCQnIz89H7969sWjRIuzbtw87d+5ETk4OunXrhpEjRxp9MxN9cOTIEfzlL39BdHQ0Lly4gM2bNyMlJQXvv/8+AGDv3r1Yv349Nm/ejEuXLuHgwYN49tlnAQD79+/HE088obxbINszSgc/VyMm9m8Kxuy7774TAAj79+9/5HJHjx4VTE1NhcLCQuXYTz/9JABQznKXLl0q2NjYKGfQgiAICxcuFAYOHKh8HhISIrz99tsq2278rfngwYPKscrKSsHc3FzYvXu3cqympkbw9PQUVq9erbIeZ9Tie3jmFRwcLHzwwQcqy+zatUvw8PAQBEEQ1q5dK/To0UOoqanRuD25XC6sX7++2fvn59s2+LlSI86oRSQ8ppd2o/z8fHh5eanMgHv27AknJyeVRure3t6wt7dXPvfw8EBJSUmzavH391f++fLly6itrUVQUJByzNzcHAMGDDDaxu36JDs7G/Hx8bCzs1M+Go+kVFVVYfz48bh37x66du2KmTNn4sCBA6irqxO7bHoMfq7Gi0Etou7du0Mmkz02/ARB0BjmD48/3OpTJpOhvr6+WbU8eOi9qV8gmqqDpKW+vh7Lly9HXl6e8nH+/HlcunQJVlZW8PLywsWLF/Hxxx/D2toakZGRGDx4MGpra8UunR6Bn6vxYlCLyMXFBSNHjsTHH3+Mu3fvqr3eeFlEz549UVhYiN9++0352oULF1BeXg5fX99m78/CwgIKheKxy3Xr1g0WFhY4efKkcqy2thZZWVkt2h+J47nnnsPFixfRrVs3tYeJScP/8tbW1nj55ZexceNGfPvttzhz5gzOnz8PoPk/J9S++LkaL16eJbKkpCQEBgZiwIABiI+PR+/evVFXV4eMjAwkJycjPz8fw4cPR+/evfHmm28iMTERdXV1iIyMREhIiMoh68fx9vbGd999h2vXrsHOzg4uLi4al7O1tcVbb72FhQsXwsXFBV26dMHq1atRVVWFiIiIZu+vuLgYxcXF+PXXXwEA58+fh729Pbp06dLkvqn1lixZgpdeegleXl4YP348TExMcO7cOZw/fx4rVqxASkoKFAoFBg4cCBsbG+zatQvW1tbK9rLe3t44fvw43njjDVhaWsLV1VXjfvj5ti9+rkZM1G/ISRAEQfj999+FOXPmCHK5XLCwsBA6d+4svPzyy8I333yjXKa5l2c9aP369YJcLlc+v3jxojBo0CDB2tpa7fKsh08YuXfvnhAVFSW4urpqfXnW0qVL1S4JASDs2LFDi78laoqmy3jS09OFwMBAwdraWnBwcBAGDBggbNmyRRAEQThw4IAwcOBAwcHBQbC1tRUGDRokfPXVV8p1z5w5I/Tu3VuwtLR85GU8/HzbFj9XasQ2l0RERBLG76iJiIgkjEFNREQkYQxqIiIiCWNQExERSRiDmohIT7EvvHFgUEvc1KlTIZPJlM01Gh08eLBd7xI2a9YsyGQytX621dXViIqKgqurK2xtbfHyyy/j+vXr7VaXMeHPAj0sMDAQRUVFcHR0FLsUakMMaj1gZWWFVatW4fbt26Ls/+DBg/juu+/g6emp9lpMTAwOHDiAf/3rXzh58iQqKyvx0ksv8Q5IbYQ/C/QgCwsLuLu789a+Bo5BrQeGDx8Od3d3JCQktPu+b9y4gblz52L37t1q9xIvLy/Htm3bsHbtWgwfPhz9+vXDp59+ivPnz+Orr75q91qNAX8WDNuQIUMQFRWFmJgYODs7w83NDVu2bMHdu3cxbdo02Nvb48knn8SXX34JQP3Qd0pKCpycnHDkyBH4+vrCzs4Oo0aNUmlrOWTIEMTExKjsd+zYsZg6daryeVJSErp37w4rKyu4ubnh9ddfb+u3To/AoNYDpqam+OCDD/Dhhx+26FBiWFiYSqcdTY9Hqa+vx6RJk7Bw4UI888wzaq9nZ2ejtrYWoaGhyjFPT0/06tULp0+fbv4bpGbjz4Lh27lzJ1xdXXH27FlERUXhrbfewvjx4xEYGIicnByMHDkSkyZNQlVVlcb1q6qqsGbNGuzatQvHjx9HYWEhFixY0Oz9Z2VlITo6GvHx8bh48SLS09MxePBgXb090gLv9a0nxo0bh759+2Lp0qXYtm1bs9bZunUr7t27p/U+V61aBTMzM0RHR2t8vbi4GBYWFnB2dlYZd3NzQ3Fxsdb7pUfjz4Jh69OnD/72t78BAOLi4rBy5Uq4urpi5syZABru+Z2cnIxz585pXL+2thabNm3Ck08+CQCYO3cu4uPjm73/wsJC2Nra4qWXXoK9vT3kcjn69evXyndFrcGg1iOrVq3CsGHDMH/+/GYt37lzZ633lZ2djQ0bNiAnJ6fF338JbIfZ5vizYLh69+6t/LOpqSk6dOiAZ599Vjnm5uYGACgpKYGDg4Pa+jY2NsqQBlrWlx4ARowYAblcjq5du2LUqFEYNWoUxo0bBxsbG23eDukAD33rkcGDB2PkyJF49913m7V8aw53njhxAiUlJejSpQvMzMxgZmaGgoICzJ8/H97e3gAAd3d31NTUqJ3YVFJSovzHhNoGfxYMl6a+8g+ONf7i01SveU3rP9jSwcTEBA+3eHiwZ7W9vT1ycnKwZ88eeHh4YMmSJejTpw8vARMRZ9R6ZuXKlejbty969Ojx2GVbc7hz0qRJGD58uMpY43dj06ZNAwD4+fnB3NwcGRkZmDBhAgCgqKgIP/74I1avXq3Vfqn5+LNA2ujYsaPKyWUKhQI//vgjhg4dqhwzMzPD8OHDMXz4cCxduhROTk44duwYXn31VTFKNnoMaj3z7LPP4s0338SHH3742GVbc7izQ4cO6NChg8qYubk53N3d8dRTTwEAHB0dERERgfnz56NDhw5wcXHBggUL8Oyzz6r9w066x58F0sawYcMQGxuLL774Ak8++STWr1+vMls+fPgwrly5gsGDB8PZ2RlpaWmor69XftbU/njoWw+99957aoeuxLJ+/XqMHTsWEyZMQFBQEGxsbHDo0CGYmpqKXZpR4M8CtdT06dMxZcoUTJ48GSEhIfDx8VGZTTs5OWH//v0YNmwYfH19sWnTJuzZs0fj2f7UPtiPmoiISMI4oyYiIpIwBjUREZGEMaiJiIgkjEFNREQkYQxqIiJSw17X0sGgJiJqY8XFxYiKikLXrl1haWkJLy8vjBkzBl9//bVO96OpM1Zb2rJlC4YMGQIHBweGehtiUBMRtaFr167Bz88Px44dw+rVq3H+/Hmkp6dj6NChmDNnTrvXIwgC6urqdLKtqqoqjBo1qtm3siUtCURE1GbCwsKEzp07C5WVlWqv3b59W/nngoIC4eWXXxZsbW0Fe3t7Yfz48UJxcbHy9aVLlwp9+vQRPvnkE0EulwsODg5CeHi4UFFRIQiCIEyZMkUAoPK4evWq8M033wgAhPT0dMHPz08wNzcXjh07Jty/f1+IiooSOnbsKFhaWgpBQUHC2bNnlftrXO/BGpvSkmWp5TijJiJqI2VlZUhPT8ecOXNga2ur9rqTkxOAhlnu2LFjUVZWhszMTGRkZODy5csIDw9XWf7y5cs4ePAgDh8+jMOHDyMzMxMrV64EAGzYsAEBAQGYOXMmioqKUFRUBC8vL+W6ixYtQkJCAvLz89G7d28sWrQI+/btw86dO5GTk4Nu3bph5MiRKCsra7u/ENIK7/VNRNRGfv31VwiCgKeffvqRy3311Vc4d+4crl69qgzXXbt24ZlnnsH333+P/v37A2jomJWSkgJ7e3sADQ1Tvv76a7z//vtwdHSEhYUFbGxs4O7urraP+Ph4jBgxAgBw9+5dJCcnIyUlBWFhYQCAf/zjH8jIyMC2bduwcOFCnf0dUOtxRk1E1EaE/7tD8+N6cufn58PLy0tlBtyzZ084OTkhPz9fOebt7a0MaaBlvab9/f2Vf758+TJqa2sRFBSkHDM3N8eAAQNU9kfSwKAmImoj3bt3h0wme2z4CYKgMcwfHtfUa7qpvtQPe/DQe1O/QDRVB4mLQU1E1EZcXFwwcuRIfPzxx7h7967a642XM/Xs2ROFhYX47bfflK9duHAB5eXl8PX1bfb+LCwsoFAoHrtct27dYGFhgZMnTyrHamtrkZWV1aL9UftgUBMRtaGkpCQoFAoMGDAA+/btw6VLl5Cfn4+NGzciICAAADB8+HD07t0bb775JnJycnD27FllG8oHD1k/jre3N7777jtcu3YNpaWlTc62bW1t8dZbb2HhwoVIT0/HhQsXMHPmTFRVVSEiIqLZ+ysuLkZeXh5+/fVXAMD58+eRl5fHE9J0jEFNRNSGfHx8kJOTg6FDh2L+/Pno1asXRowYga+//hrJyckAGg5BHzx4EM7Ozhg8eDCGDx+Orl27IjU1tUX7WrBgAUxNTdGzZ0907NgRhYWFTS67cuVKvPbaa5g0aRKee+45/Prrrzhy5AicnZ2bvb9NmzahX79+mDlzJgBg8ODB6NevHz7//PMW1U2Pxn7UREREEsYZNRERkYQxqImIiCSMQU1ERCRhDGoiIiIJY1ATERFJGIOaiIhIwhjUREREEsagJiIikjAGNRERkYQxqImIiCSMQU1ERCRhDGoiIiIJ+/8/+cxjWVH/hwAAAABJRU5ErkJggg==", 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", 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" ] @@ -576,11 +723,12 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "id": "411d9947", "metadata": {}, "source": [ - "The color of error bar can be modified by setting 'err_color'.\n" + "The color of the error bar can be modified by setting ``err_color``." ] }, { @@ -591,7 +739,7 @@ "outputs": [ { "data": { - "image/png": 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", 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EityNAmxaAfXaqzsSjePn5wdDQ0N4eXkpPB4dHY1Dhw7hwIEDotsQlajnz58PT09PuLq6QhAE9O/fH25u/3uGERERgdatW4sKyNvbu9Bb3TExMXLbOjo6CAgIQEBAgKi2iIiomE4tAaycAcPq6o5Eo1y+fFnuTu+72rZti+DgYKXaEJWoXV1dcfv2bZw5cwampqZyg7GSk5Ph5eXFAVpERBXJ6xTgzHLgE391R6JR0tLSZBN+KaKlpSXqUbBcHWJPNDc3R69evQokZFNTU0yaNEnUFKJERKTB7h8D4q+rOwqN8uGHH+Lw4cOFHo+MjFR65LfoRJ2Xl4cdO3Zg3Lhx6NOnD65fz794KSkp2LNnT4HXrIiIqAI4t5qLdrxl1KhROHjwIHx8fGSDrIH8u8tTpkxBZGQkRo0apVQbohJ1cnKybI7t7du3Y//+/UhKSgKQ/7rTxIkTlVrSi4iINFTC30DCDXVHUaiSrsCYnJyMr7/+GlZWVtDX10eDBg1w6NChYrc3ceJEDB8+HCEhITAzM4O1tTWsra1hZmaG0NBQDBkyROnXtkQl6hkzZuDvv/9GVFQUHj58KPfOsra2Nvr371+iL0pEROXIjT3qjkChkq7AmJ2djU8//RSxsbHYvXs37ty5g3Xr1qFOnTrFblMikWDTpk04duwYxo8fDycnJzg5OWHChAmIjo5GeHi4wlW1SkLUYLK9e/fim2++waeffor//vuvwPEGDRpg8+bNSgVGREQaKu4ckJsN6OipOxI5JV2BcePGjXjx4gXOnDkDXV1dAJCbzKskOnTogA4dOoiO/X1E9ahTUlJgZ2dX6PGcnBzk5uaKDoqIiDRYTiaQoFmDysSswLh//360bNkSX3/9NSwsLODk5IQFCxYgLy+vrMIuFlGJ2t7eHleuXCn0+OHDh+Ho6Cg6KCIi0nAvH5dZU+np6UhNTZV93l4B8Y33rcAYHx+vsN6HDx9i9+7dyMvLw6FDhzB79mwsXrwY8+bNK3ZsgiBg7dq1cHNzk61Z8e7nfa9vFYeos0ePHo3vvvsO7du3xyeffAIg/z59VlYW5s6di8jISISFhSkVGBERFV9cXBwyMzMBAJnZUsS9eA3rGgal12BGUunV/Y53XwMOCAjAnDlzlK5XKpWiVq1aCAsLg7a2Npo3b46nT5/ihx9+KPZEWtOnT8eSJUvg4uKCIUOGoHp11U8IIypRT5o0CX///TcGDx4MU1NTAPlrQ//333/Izc3FuHHjlB6OTkRERbtw4QKCgoJw8OBB2cDel5m5sPW7gO6Na2B2Vxt8bFtV9Q1rKddLLIkTJ07Izc3x9sJKb4hZgdHKygq6urpya0M0atQI8fHxyM7Ohp5e0c/gw8PD0a9fP/zyyy/F/DYlJ+rWt0Qiwbp163Dy5EkMGzYMXbp0gYuLC8aOHYuYmBisXr1a1XESEdE79uzZg9atW+P3338vsGKgIACHbrxAq++vYc/V56pvXK/sVtMyNjaGiYmJ7KMoUb+9AuMbb1ZgbNmypcJ6W7duLVuW8o27d+/CysqqWEkaAF69eiX3XLw0KPUnUZs2bdCmTRtVxUJERMV04cIFDBw4EHl5eYUu65snBSQQMHDdLZyZ7qLannW1D1RXl4r4+Phg+PDhcHV1hZubG0JCQgqswFinTh3Z3NsTJkzAihUrMGnSJHzzzTe4d+8eFixYgIkTJxa7zU8++QQXL17E2LFjS+U7ASJ71I8ePcJvv/1W6PHffvsNsbGxYmMiojJkbGmMqnWqwtiS6w2XJ/PmzYMgCIUm6TcEAAIEzDuk4sFf1W1UW58KDBw4ED/++CP8/f3h4uKCa9euFViB8dmzZ7LydevWRVRUFC5evIgmTZpg4sSJmDRpksJXuQqzatUqnDt3DgsWLFD4urIqiOpRT5s2DampqXJLW75t5cqVMDU1xY4dO5QKjohK39hLpdcToNIRFxeHAwcOFJmk38iTAr9df6G6AWa6RkDV2srXUwpKsgIjALRs2RLnzp0T3Z6DgwOkUilmz56N2bNnw8DAQO6ZN5D/uFiZhTlEJeqzZ89i8uTJhR7/5JNPEBISIjIkIipreXl5cs/pylJunhS5eVJo5UmRk5OjlhjKm6ioqGIn6TcEATh88yWGt7QounBRzGyBvLz8TykqD/Nx9OvXT+mZx4oiKlG/fPkSVasW/qzD2Ni41G4BEFVEKZJqQHouDs7/Qi3t/3zsOrYf14D5m6f+rO4IKrQxP93DmJ/uqaCm0wDWqKCe8q8sZuEUlaitra3xxx9/YMKECQqPnzp1Ch98oHkDDYhIsUEdPsLA9h+prf1qQgr0qtbEx99sUlsM5cnmzZtFDV5aN+RD1fSoW08GHHsqX08Rrl69Cnd391JvR9OJStSDBw9GUFAQ3Nzc4O3tDS2t/DFpeXl5WLFiBXbu3Ak/Pz+VBkpEpUdbS/SKtyqhI2hBR1tLNt8yvZ+npyckEkmJbn9LJEBnx+rQ1VbBtTa3B8rgWik7o1dZiYuLw4IFC3D8+HEkJiZi3759aNeuHZ4/f465c+di5MiRaNq0qej6Rf0r+Pr64vTp05g8eTLmz58PBwcHAMCdO3eQlJSE9u3bM1ETEZUSa2trdO/eHYcOHSrWvNTaWkA3pxqqm6msWl3V1FMB3Lx5E23btoVUKoW7uzvu378ve7ZuZmaG06dPIyMjAxs2bBDdhqg/rfT19XH48GFs2LABbm5ueP78OZ4/fw43Nzds3LgRR48eVfhCOhFpnpurbPDn9/Vwc5XmvW5DhZs9ezYkEkmRA5kkACSQYFZXFV1fbT3AqIZq6qoApk+fDlNTU9y9exc//fRTgbsc3bp1w6lTp5RqQ/R9BS0tLYwcOVL2IjkRlU856drISeUt5/Lm448/xs6dOzFw4EAIgqCwZ62tlZ+kfxnTSHWTnVS1zL+PTgCAkydPwt/fH+bm5goHUVtbW+Pp06dKtSGqR/3ixQv89ddfhR6/fv06Xr58KTooIiIqWt++fXHmzBl07dq1QM9aIsm/3X1mugv6NDVTXaMmdVRXVwUglUphZGRU6PGkpCSl7zCLStRTpkx574jDcePGYdq0aaKDIiKi4vn444+xf/9+xMbGylZuqm6kg9j5btjn5aT6BTlMrVVbXznXrFkzHDx4UOGx3Nxc7NixAy1atFCqDVGJOjo6Gj17Fj40v0ePHjh69KjooIiIqGSsra1lPTsjPa3SW+LS7MPSqbec8vX1RWRkJCZMmIAbN/LnIkhISMDRo0fRuXNn3Lp1q0RTkioi6hl1UlISzMwKv5VSs2ZNJCYmig6KiIg0lIX63rfXRF26dMHmzZsxadIkhIWFAQCGDBkCQRBgYmKCLVu2oF27dkq1ISpRW1lZ4erVq4Uev3z5MszNzUUHRUREGqiqFWCimXN8q9PQoUPRt29fHDlyBPfu3YNUKoW9vT08PT3fO4tncYlK1L1798bKlSvRpUuXArfA9+3bh02bNhU6axkREZVTtlzW+G2ZmZmoW7cuZsyYgW+//Ra9e/culXZEJeo5c+bg6NGj6NOnD5ydneHk5AQAuHHjBv788080atQIgYGBKg2UiIjUrH4ndUegUYyMjKCjo4MqVaqUajuiBpNVq1YN586dw6xZs5CTk4Pdu3dj9+7dyMnJwezZs3H+/HmYmpqqOFQiIlIb84aAuYO6o9A4/fr1w+7du0u8mllJiJ7wpEqVKggMDGTPmYioMnAZzIlOFBg0aBC8vLzQoUMHjBkzBra2tjA0NCxQrlmzZqLbKB8znhMRkfpYfATYKjdyuaJq37697L8VTRUqCAIkEkmx5mQvjKhE/dVXXxVZRiKRKDUJORERaQCJBGg1EVDzCmuaatOm0l+aVVSijo6OLjBdXV5eHp49e4a8vDyYm5uX+sN1IiIqA079gVoN1R2Fxho+fHiptyEqUcfGxircn5OTg7Vr1yIkJARHjhxRJi4iIlK3ah8AH49WdxTlxrNnz5CYmIj69eurtLOq0nsZurq68Pb2RufOneHt7a3KqomIqKx5TAd0S2kq0gpk3759aNiwIT744AM0a9YM58+fBwA8f/4cTZs2RUREhFL1l8pDB2dnZ5w8ebI0qiYiorLwUR/AylndUWi83377DX379oWZmRkCAgLkXtMyMzNDnTp1sHnzZqXaKJVEfeTIkfcu+0VEmkPXOA+6JjnQNRY/KpUqGENT4ONR6o6iXJg7dy7atWuH06dP4+uvvy5wvGXLlu+dcrs4RD2jnjt3rsL9ycnJOHnyJK5cuaL0aiFEVDYcvR6rOwTSNB+PAfRVvDxmBXXjxg0sWbKk0OMWFhZKL1IlegpRRapXrw57e3usWbMGY8aMUSYuIiJSh2ofAA5d1B1FuWFkZISMjIxCjz98+BA1a9ZUqg1RiVoqlSrVKBERaajmIwAtbXVHUW506NAB4eHhmDx5coFj8fHxWLduHbp3765UG3yDnYiI8hlbAPYd1R1FuTJ//nw8efIEH3/8MdauXQuJRIKoqCjMmjULjRs3hiAICAgIUKoNUYk6Li4Op0+fltv3559/YtiwYRg4cCD27t2rVFBERKQGTv3Ymy4hBwcHnD59GjVr1sTs2bMhCAJ++OEHLFiwAI0bN8apU6dga2urVBuibn1PnDgR6enpOHr0KAAgISEBHTp0QHZ2NqpWrYrdu3dj165d6Nu3r1LBERFRGdHR57PpYvjrr79gY2ODatWqyfZ99NFHOHr0KF6+fIn79+9DKpWiXr16MDc3V0mbonrUFy5cwKeffirb3rJlC169eoU///wTT58+xSeffIIff/xRJQESEVEZqN8JMDBRdxQar2nTpjh48KBsu2PHjjh27BiA/AHVH3/8Mdzd3VWWpAGRifrFixeoVauWbPvAgQPw8PCAvb09tLS00LdvX9y+fVtlQRIRUSlz6qfuCMoFQ0NDZGZmyrZjYmKQkJBQqm2KuvVtbm6Ox4/z371MTk7GuXPnsHDhQtnx3Nxc5ObmqiZCIiIqXbWbAjXt1R1FueDs7IwlS5ZAW1tbdvv74sWLMDB4/1SryjwKFpWoO3XqhGXLlsHExAQxMTGQSqXo3bu37PjNmzdRt25dUQGtXLkSP/zwA+Lj4+Hs7Izly5fDzc2t0PLJycnw8/PDnj178OLFC9jY2CAkJARdu3YV1T4RUaXj8oW6Iyg3QkJCMGDAAIwalT9zm0QiQWhoKEJDQws9Ry3rUS9cuBB3797FtGnToKenhx9//BF2dnYAgKysLPzyyy/44ouSX/idO3fCx8cHa9asgbu7O0JCQuDp6Yk7d+7I3Wp/Izs7G59++ilq1aqF3bt3o06dOnj8+DFMTU3FfC0iosrH3AH44GN1R1FufPzxx7h//z4ePHiAhIQEtG/fHjNnzpQbt6VqohK1hYUF/vjjD6SkpMDQ0BB6enqyY1KpFMeOHRPVo16yZAnGjBmDkSNHAgDWrFmDgwcPYuPGjQqnJN24cSNevHiBM2fOQFdXFwCUHgZPRFSpNB8JSCTqjqLc2L9/P1xdXeHg4AAHBwcMHz4cPXr0gLu7e6m1qdSEJ9WqVZNL0kD+g3ZnZ2fUqFGjRHVlZ2fj8uXL6NSp0/+C09JCp06dcPbsWYXn7N+/Hy1btsTXX38NCwsLODk5YcGCBUrdYiAiqjQsPgKsW6g7inKlT58+iImJkW2fOHFCMweTlYbnz58jLy8PFhYWcvstLCwKHUH+8OFDREdH48svv8ShQ4dw//59eHl5IScnp9CZYLKyspCVlSXbTk9PV92XICIqT9zHszddQlWrVkVycrJsOzY2ttTziMYkajGkUilq1aqFsLAwaGtro3nz5nj69Cl++OGHQhN1cHAwAgMDyzhSIiINY9cWsGqi7ijKHTc3N8yfPx8JCQmyUd+HDh1CfHx8oedIJBJMmTJFdJsak6jNzMygra1d4BZCQkICLC0tFZ5jZWUFXV1daGv/b8q7Ro0aIT4+HtnZ2QVuywOAr68vfHx8ZNvXrl2Dh4eHir4FEVE5oK0HtCi4djIVbdWqVRg2bBiCgoIA5Cfhbdu2Ydu2bYWeU2EStZ6eHpo3b45jx47JXvV6MzDN29tb4TmtW7fGtm3bIJVKoaWV/7j97t27sLKyUpikAUBfXx/6+vqybWNjY9V+ESIiTec8CDCxUncU5VL9+vVx5swZvH79GomJibC1tUVISAh69epVam0WazBZjRo1sHv3btn23LlzcePGDZUH4+Pjg3Xr1iE8PBy3bt3ChAkTkJGRIRsFPmzYMPj6+srKT5gwAS9evMCkSZNw9+5dHDx4EAsWLMDXX/MvRSIihYwtAJcv1R1FuWdgYABra2sEBASgY8eOsLGxee9HGcXqUaenp8tNmTZnzhzUr18fTk5OSjX+roEDByIpKQn+/v6Ij4+Hi4sLIiMjZQPM4uLiZD1nAKhbty6ioqIwZcoUNGnSBHXq1MGkSZPw3XffqTQuIqIKw30coPv+WbSo+JRdwrI4ipWo7e3tsXv3brRt2xYmJvmTtmdkZODFixfvPa+kr2gBgLe3d6G3ut8eEv9Gy5Ytce7cuRK3Q0RU6Zg7APU6qDuKcu2rr76CRCKRDWL+6quvijxHIpFgw4YNotssVqKeOXMmRo4cKVsxRCKRYPz48Rg/fvx7z+P7zEREGqT5SEBLqekzKr3o6GhoaWlBKpVCW1sb0dHRkBTxiltRx4tSrEQ9dOhQuLm5yVYJmTNnDvr06YMmTTi0n4ioXDCtC9QtvdmzKovY2Nj3bpeGYo/6fjNdGgBs2rQJw4cPR8+ePUstMCIiUqFGPdmbLqdEvZ716NEjVcdBRESlqV57dUdAIon+8yovLw/h4eH4/PPP4e7uDnd3d3z++efYsmULn00TEWkSsw8B44IrEFZEK1euhK2tLQwMDODu7o4LFy4U67wdO3ZAIpHILdmsiJaWFrS1tUv8UYaoHnVKSgo8PT1x8eJFVK1aFfXq1QMAHDlyBL/++itWr16NqKgo2QhxIiJSozrN1R1BmSjpUslvxMbGYtq0aWjbtm2Rbfj7+xcYHBYREYG///4bnp6eskfEt2/fxuHDh+Hk5FRk8i+KqETt5+eHy5cvY/ny5RgzZoxsicmcnBysX78eEydOhJ+fH5YvX65UcEREpAKWlWPgb0mXSgby7w5/+eWXCAwMxKlTp+QW3FBkzpw5ctthYWFITEzEjRs3ZEn6jVu3bqFjx46oXbu26O8EiLz1HRERAS8vL3h5ecmSNADo6upiwoQJmDBhAn799VelAiMiIhWp1UjdESglPT0dqampss/bKyC+IWapZCB/ps1atWph1KhRomL74Ycf4O3tXSBJA/lrT3h7e+P7778XVfcbohL1f//9pzCoNxo2bFjkZChERFQGqloCRiWffEqTeHh4oFq1arJPcHBwgTLvWyq5sJWtTp8+jQ0bNmDdunWiY3vy5Ilch/Vdurq6ePLkiej6AZGJun79+ti/f3+hx/fv3w97e3vRQRERkYqYF96pKi9OnDiBlJQU2eftNR/ESktLw9ChQ7Fu3TqYmZmJrsfJyQmrVq3C06dPCxx78uQJVq1ahcaNGysTqrhn1F5eXvD29kbXrl0xefJkNGjQAABw584dLFu2DEeOHMGKFSuUCoyIiFTAvHzf9gbyVzksanBySZdKfvDgAWJjY9GjRw/ZPqlUCgDQ0dHBnTt3itXhXLp0KTw9PdGgQQP06dMH9evXBwDcu3cPe/fuhSAI+Omnn4qs531EJ+rExEQsXLgQUVFRcsd0dXXh7++PCRMmKBUYERGpQK2G6o6gTJR0qeSGDRvi+vXrcvtmzZqFtLQ0hIaGom7dusVqt02bNjh//jxmz56NiIgIvHr1CgBgaGgIT09PBAYGqqdHDeSPfPP29sbRo0fx+PFjAICNjQ06deqk1G0EIiJSEYkEMCv/t76Ly8fHB8OHD4erqyvc3NwQEhJSYKnkOnXqIDg4GAYGBgVWgDQ1NQWAEq8M6eTkhIiICEilUiQlJQEAzM3N5VZ7VIboRA3k32oYNGiQSgIhIiIVM7UG9IzUHUWZKelSyaqmpaVVYDCbKiiVqImISINVot70GyVdKvltmzdvVn1AKsAZ2omIKiqzD9UdAakAEzURUUVVg6/JVgRM1EREFVUNO3VHQCrARE1EVBEZmJT7GckoHweTERFVRKbW6o6gUrl58yYePnyIly9fQhCEAseHDRsmum5RiVoQBISFhWHDhg2ywN4lkUiQm5srOjAiIlJCNSbqsvDgwQMMGTIEFy5cUJiggfx8WOaJevr06ViyZAlcXFwwZMgQVK9eXXQARERUCkyUW1qRimfcuHG4fv06QkJC0LZt21LJh6ISdXh4OPr164dffvlF1fEQEZEqVLVSdwSVwh9//IGZM2fim2++KbU2RCXqV69eya35SURE6mdpaQkIUljqpOUvb0mlzszMDNWqVSvVNkSN+v7kk09w8eJFVcdCRERKuHTpEp7cuoxLM5sBxqqfypIKGj9+PH766Sfk5eWVWhuietSrVq2Cp6cnFixYgHHjxqFmzZqqjouIiMSSaPHVrDLSoEED5OXlwdnZGV999RXq1q0LbW3tAuX69u0rug1RidrBwQFSqRSzZ8/G7NmzYWBgUCAwiUSClJQU0YEREZFIhtUBrYLJglRv4MCBsv+eNm2awjISiUSpHreoRN2vXz9IJBLRjRIRUSlib7rMHD9+vNTbEJWoNXWFESIiQn6PmsqEh4dHqbfBmcmIiCoag9IdhUyK3bx5E48fPwYA2NjYwNHRUSX1ip7rOzU1FYGBgXBzc4OFhQUsLCzg5uaGuXPnIjU1VSXBERGRCHrG6o6gUtm3bx/s7e3RuHFjdO/eHd27d0fjxo1Rv3597N+/X+n6RSXqf//9F02bNkVgYCDS09PRunVrtG7dGhkZGZgzZw6aNWuGZ8+eKR0cERGJoFdF3RFUGocOHUK/fv0AAAsWLEBERAQiIiKwYMECCIKAvn37IjIyUqk2RN36/u677xAfH48DBw6ga9eucsd+//13DBgwADNmzEB4eLhSwRERkQhM1GUmKCgITZo0walTp1Clyv/+3Xv27Alvb2+0adMGgYGB+Oyzz0S3IapHHRkZicmTJxdI0gDQpUsXTJw4EYcOHRIdFBERKUHHQN0RVBp//fUXhg8fLpek36hSpQpGjBiBv/76S6k2RCXqjIwMWFgUPuuNpaUlMjIyRAdFRERKYKIuMwYGBnjx4kWhx1+8eAEDA+Wuh6hE7ejoiO3btyM7O7vAsZycHGzfvl1lo92IiKiEdPTUHUGl0bFjR4SGhuLs2bMFjp0/fx7Lli1Tem0M0c+oBw4cCDc3N3h5eaFBgwYAgDt37mDNmjX466+/sHPnTqUCIyIikbR01R1BpfH999+jZcuWaNOmDdzc3ODg4AAgPx9euHABtWrVwqJFi5RqQ1SiHjBgADIyMjBjxgyMHz9eNkuZIAioVasWNm7ciP79+ysVGBERiaTNHnVZsbOzw19//YXg4GD8/vvvsk6qjY0NJk2ahBkzZqBWrVpKtSF6wpMRI0ZgyJAhuHTpktwL3q6urtDR4TwqRERqw0RdpmrVqoWlS5di6dKlpVK/UhlVR0cHLVq0QIsWLVQVDxERKUubt74rkmIl6pMnTwIA2rVrJ7ddlDfliYioDLFHXWq++uorSCQShIWFQVtbG1999VWR50gkEmzYsEF0m8VK1O3bt4dEIsGrV6+gp6cn2y6MIAhKL+tFREQisUddaqKjo6GlpQWpVAptbW1ER0cXuZqksqtNFitRv1nGS09PT26biIg0EBN1qYmNjX3vdmkoVqJ+dxmvsljWi4iIROLrWWUmLi4O5ubmMDQ0VHj81atXSEpKgrW1teg2RE140rFjRxw7dqzQ48ePH0fHjh1FB0VERErQ0Vd3BJWGnZ0dIiIiCj2+f/9+2NnZKdWGqEQdExODhISEQo8nJibixIkTooMiIiIlaPEV2bIiCMJ7j+fk5EBLS/SK0gCUeD3rfQ/H79+/j6pVq4qtmoiIlMFn1KUqNTUVycnJsu3//vsPcXFxBcolJydjx44dsLKyUqq9Yifq8PBwuWUr582bh3Xr1ikM7K+//lK4slZxrVy5Ej/88APi4+Ph7OyM5cuXw83NrcjzduzYgcGDB6NXr17Yu3ev6PaJiMo1vp5VqpYuXYq5c+cCyO+0Tp48GZMnT1ZYVhAEzJs3T6n2ip2oMzMzkZSUJNtOS0sr0J2XSCSoUqUKxo8fD39/f1EB7dy5Ez4+PlizZg3c3d0REhICT09P3Llz573TsMXGxmLatGlo27atqHaJiCoELe38D5Wazp07w9jYGIIgYPr06Rg8eDCaNWsmV+ZNPmzevDlcXV2Vaq/YiXrChAmYMGECgPyH56GhoejZs6dSjSuyZMkSjBkzBiNHjgQArFmzBgcPHsTGjRsxY8YMhefk5eXhyy+/RGBgIE6dOiV3S4KIqFLh8+lS17JlS7Rs2RJA/rLP/fr1g5OTU6m1V+In3K9evULv3r2VfoFbkezsbFy+fFluSTAtLS106tRJ4RJib8ydOxe1atXCqFGjimwjKysLqampsk96erpKYici0gjsTZeZzMxMLFu2DL///nuptlPiRG1oaIiwsLD3jvoW6/nz58jLy4OFhYXcfgsLC8THxys85/Tp09iwYYPC5+WKBAcHo1q1arIP3wknogqFPeoyY2RkBB0dHVSpUqVU2xE1Zrx58+a4ceOGqmMpsbS0NAwdOhTr1q2DmZlZsc7x9fVFSkqK7MPXyIioQpEo9yoQlUy/fv2we/fuIl/TUoaoP71CQkLQtWtXODk5YcSIESpb1tLMzAza2toFeusJCQmwtLQsUP7BgweIjY1Fjx49ZPukUimA/JW97ty5A3t7e7lz9PX1oa//v8kAjI2NVRI7EZFGYKIuU4MGDYKXlxc6dOiAMWPGwNbWVuEsZe8ONisJURl2xIgR0NLSwrhx4zBx4kTUqVOnQGASiQR//vlnierV09ND8+bNcezYMfTu3RtAfuI9duwYvL29C5Rv2LAhrl+/Lrdv1qxZSEtLQ2hoKOrWrVuyL0ZEVO6pfvwQFa59+/ay/z516lSB46pYpEpUoq5RowZq1qwJBwcH0Q0XxsfHB8OHD4erqyvc3NwQEhKCjIwM2SjwYcOGoU6dOggODoaBgUGBkXampqYAUKoj8IiIiABg06ZNpd6GqEQdExOj4jD+Z+DAgUhKSoK/vz/i4+Ph4uKCyMhI2QCzuLg4padjIyIiUoXhw4eXehsaOTzQ29tb4a1uoOg/EjZv3qz6gIiIiIqQnp6Of/75BwBQt25dlY2BEt01zcvLQ3h4OD7//HO4u7vD3d0dn3/+ObZs2aLUvXgiIlJCKcxxQe938eJFdOjQAdWrV4eTkxOcnJxQvXp1dOzYEZcuXVK6flE96pSUFHh6euLixYuoWrUq6tWrBwA4cuQIfv31V6xevRpRUVEwMTFROkAiIioBLshRps6fP4/27dtDT08Po0ePRqNGjQAAt27dwvbt29GuXTvExMQUa72KwohK1H5+frh8+TKWL1+OMWPGQFc3/wcjJycH69evx8SJE+Hn54fly5eLDoyIiEjT+fn5oU6dOjh9+nSB14jnzJmD1q1bw8/PD0eOHBHdhqhb3xEREfDy8oKXl5csSQOArq6ubE7wX3/9VXRQREREYqxcuRK2trYwMDCAu7s7Lly4UGjZdevWoW3btqhevTqqV6+OTp06vbe8IufPn8e4ceMUzvVhYWGBsWPH4ty5cyX+Hm8Tlaj/+++/976a1bBhQ7x48UJ0UERERCX1ZvXFgIAAXLlyBc7OzvD09ERiYqLC8jExMRg8eDCOHz+Os2fPom7duujcuTOePn1a7Da1tLSQm5tb6PG8vDyl31QSdXb9+vWxf//+Qo/v37+/wIxgREREpent1RcdHR2xZs0aGBkZYePGjQrL//zzz/Dy8oKLiwsaNmyI9evXyybZKq5WrVph5cqVePz4cYFjcXFxWLVqFVq3bi36OwEin1F7eXnB29sbXbt2xeTJk9GgQQMAwJ07d7Bs2TIcOXIEK1asUCowIiIiIP+1p9TUVNn2u1NBA/9bfdHX11e2rzirL74tMzMTOTk5qFGjRrFjW7BgAdq1a4eGDRuiT58+cvlw37590NHRQXBwcLHrU0R0ok5MTMTChQsRFRUld0xXVxf+/v6ytauJiIiU8e4qhwEBAZgzZ47cvvetvnj79u1itfPdd9+hdu3ackstF6Vp06Y4f/48/Pz8sH//fmRmZgLIX1nrs88+w7x58+Do6Fjs+hQRPeHJnDlz4O3tjSNHjiAuLg4AYGNjg06dOhV7JSsiIqKinDhxAi4uLrLtd3vTqrBw4ULs2LEDMTExMDAwKNG5jo6OiIiIgFQqRVJSEgDA3NxcZbNoKjUzmZmZGQYPHqySQIiIiBQxNjYucl6Okq6++LYff/wRCxcuxNGjR9GkSRPRcUokEkj+f8IZiQonnlEq3R84cABeXl7o2rUrunbtCi8vLxw4cEBVsRERERXL26svvvFmYFjLli0LPe/7779HUFAQIiMj4erqKqrtmzdvon///jAxMYGVlRWsrKxgYmKC/v3748aNG6LqfJuoHnVycjL69OmDkydPQltbG1ZWVgCAo0ePYu3atWjbti327t0rW8mKiIiotJVk9UUAWLRoEfz9/bFt2zbY2toiPj4eQH4PvrjzdJ86dQpdunSBVCpFr1695AaT7d+/H7///jsiIyPRtm1b0d9LVKKeNGkSTp06hUWLFmHChAmoUqUKACAjIwOrVq2Cr68vJk2ahPDwcNGBERERlURJV19cvXo1srOz0b9/f7l6FA1WK8yUKVNQq1YtnDhxAnXr1pU79s8//6Bdu3bw8fHBxYsXRX8vUYl679698PLywrRp0+T2V6lSBd9++y3i4uKwZcsW0UERERGJUZLVF2NjY5Vu7++//0ZQUFCBJA3kr6A1YcKEYif9woh6Rq2rq1vkzGRvTy1KRERUEdnY2CArK6vQ49nZ2QqTeEmIStT9+vXDrl27FC5nmZubi19++QUDBgxQKjAiIiJN5+/vj2XLluHatWsFjl29ehXLly9Xukct6tb3kCFD4O3tjVatWmHs2LGoX78+AODevXsICwtDdnY2vvzyS1y5ckXuvGbNmikVLBERkSY5d+4cLCws0Lx5c7Rq1UouH549exZOTk44e/as3OxoEokEoaGhxW5DVKJ+e5aYixcvyt4XEwRBYRlBECCRSBT2wImIiMqrt6fL/uOPP/DHH3/IHb9+/TquX78ut69MEvWmTZvEnEZERFShSKXSUm9DVKIePny4quMgIiIiBZSaQhTIX9Xkn3/+AZA/FL24L4kTERFVFI8ePcLvv/8uW+7SxsYGXbp0gZ2dndJ1i07UFy9exPTp03H69GlZ119LSwtt27bF999/L3oqNiIiovJk6tSpCA0NLXAbXEtLC5MnT8aPP/6oVP2iEvX58+fRvn176OnpYfTo0WjUqBEA4NatW9i+fTvatWuHmJgYuLm5KRUcERGRJlu8eDGWLl2K/v37Y+rUqXL5cOnSpVi6dCnq1KmDKVOmiG5DVKL28/NDnTp1cPr06QKrksyZMwetW7eGn58fjhw5IjowIiIiTbdu3Tr07NkTv/zyi9x+d3d37NixA69fv8batWuVStSiJjw5f/48xo0bp3DpMAsLC4wdOxbnzp0THRQREVF5EBsbC09Pz0KPe3p6Kj1VqahEraWlhdzc3EKP5+XlqWzBbCIiIk1Vq1Yt/Pnnn4Ue//PPP2Fubq5UG6KyaatWrbBy5UrZ6La3xcXFYdWqVWjdurVSgREREWm6AQMGYP369Vi4cCEyMjJk+zMyMrBo0SKsX78eAwcOVKoNUc+oFyxYgLZt26Jhw4bo06eP3Pqb+/btg46Ojmy9TyIioooqKCgI165dw8yZM+Hv74/atWsDAP7991/k5uaiQ4cOmDt3rlJtiErUTZs2xYULF+Dn54f9+/cjMzMTAGBkZITPPvsM8+bNg6Ojo1KBERERaTojIyMcO3YM+/btk3uP+rPPPkPXrl3Ro0cP2TTbYpU4UWdlZSEqKgq2traIiIiAVCpFUlISAMDc3JzPpomIqFLIzMzEkCFD0K9fP3z55Zfo1atXqbRT4qyqp6eHAQMG4MyZM/kVaGnBwsICFhYWTNJERFRpGBkZ4ejRo7K7yqWlxJlVIpHgww8/xPPnz0sjHiIionKjTZs2cktYlgZRXeCZM2dixYoVuHPnjqrjISIiKjdWrFiBU6dOYdasWXjy5EmptCFqMNm5c+dQs2ZNODk5oX379rC1tYWhoaFcmZKut0lERFTeODs7Izc3F8HBwQgODoaOjg709fXlykgkEqSkpIhuQ1Sifnuh7GPHjiksw0RNREQVXb9+/ZQe1V0UUYm6LBbKJiIi0nSbN28u9TaUXo+aiIiosnn9+jX27duHR48ewczMDN26dYOVlVWptKVUor5x4wYOHTokm3Dc1tYWXbp0QePGjVURGxERkcZJTExEq1at8OjRIwiCACD/Va29e/eiU6dOKm9PVKLOysrCuHHjsHXrVgiCIHt/WiqVwtfXF19++SXWr18PPT09lQZLRESkbkFBQYiNjcWUKVPQsWNH3L9/H0FBQRg3bhwePHig8vZEJervvvsOW7ZsgZeXF7755hvY29tDIpHg/v37WLZsGVavXo0aNWogJCRExeESERGp1+HDhzFs2DD8+OOPsn0WFhb44osvcOfOHTg4OKi0PVHvUf/0008YOnQoVqxYAQcHB+jo6EBbWxsODg5YuXIlvvzyS/z0008qDZSIiEgTxMXFoU2bNnL72rRpA0EQkJCQoPL2RCXqnJwctGjRotDjrVq1eu961UREROVVVlYWDAwM5Pa92S6N3Cfq1renpyeioqIwYcIEhccjIyPRuXNnpQIjIiLSVLGxsbhy5Yps+82EJvfu3YOpqWmB8s2aNRPdlqhEHRQUhM8//xx9+/bF119/jfr168sCXLlyJR4/foydO3fixYsXcufVqFFDdKBERESaYvbs2Zg9e3aB/V5eXnLbgiBAIpEgLy9PdFuiEnWjRo0AANevX8e+ffsKBAVA4XrUygRKRESkCTZt2lSm7YlK1P7+/qU+ZRoREZEmGj58eJm2JypRz5kzR8VhyFu5ciV++OEHxMfHw9nZGcuXL4ebm5vCsuvWrcOWLVtw48YNAEDz5s2xYMGCQssTERGVJ6JGfZemnTt3wsfHBwEBAbhy5QqcnZ3h6emJxMREheVjYmIwePBgHD9+HGfPnkXdunXRuXNnPH36tIwjJyIiUj2NS9RLlizBmDFjMHLkSDg6OmLNmjUwMjLCxo0bFZb/+eef4eXlBRcXFzRs2BDr16+HVCotdFUvkhfmGoYlHyxBmGuYukMhIiIFNCpRZ2dn4/Lly3JzpWppaaFTp044e/ZsserIzMxETk5OoSPMs7KykJqaKvukp6erJPbyKj0+HWlP05AeX7n/HYiINJVGJernz58jLy8PFhYWcvstLCwQHx9frDq+++471K5du9CJ0YODg1GtWjXZx8PDQ+m4iYiISotGJWplLVy4EDt27EBERESBWWPe8PX1RUpKiuxz4sSJMo6SiIio+DRqPWozMzNoa2sXmCs1ISEBlpaW7z33xx9/xMKFC3H06FE0adKk0HL6+vrQ19eXbRsbGysXNBERUSnSqB61np4emjdvLjcQ7M3AsJYtWxZ63vfff4+goCBERkbC1dW1LEIlIiIqExrVowYAHx8fDB8+HK6urnBzc0NISAgyMjIwcuRIAMCwYcNQp04dBAcHAwAWLVoEf39/bNu2Dba2trJn2cbGxuwtExFRuadxiXrgwIFISkqCv78/4uPj4eLigsjISNkAs7i4OGhp/e9GwOrVq5GdnY3+/fvL1RMQEFDqE7MQERGVNo1L1ADg7e0Nb29vhcdiYmLktmNjY0s/ICIiIjXRqGfUREREJI+JmoiISIMxURMREWkwJmoiIqowVq5cCVtbWxgYGMDd3R0XLlx4b/ldu3ahYcOGMDAwQOPGjXHo0KEyirT4mKgrOWNLY1StUxXGlnyVjYjKt5KuvnjmzBkMHjwYo0aNwtWrV9G7d2/07t1btmyypmCiruTGXhoLnyc+GHtprLpDISJSSklXXwwNDcVnn32Gb7/9Fo0aNUJQUBCaNWuGFStWlHHk78dETURE5Z6Y1RfPnj1bYAEnT0/PYq/WWFY08j1qKlt5eXmQSqVqa1+alwtpXh6kebnIyclRWxzqkpsnRW6e+v79NUGuIIVWnrRSXn8qXG5uLgAgPT0dqampsv3vrtkAvH/1xdu3byusPz4+XqnVGssKE7UGMBIykZmciV6TF6il/dvnjuHu+Wi1tP2uX+ZNUHcIpE5Tf1Z3BKSB3l2OuLLNPMlETXBw64AGH7dXawyZEiOYVauCn/xHqTUOdbi4fCSS0nPVHYZaVRNSoFe1Jj7+ZpO6QyENcvXqVbi7u+PEiRNwcXGR7X+3Nw2IW33R0tJS1GqNZY2JmiDR0oJEzTFoSbShpa0DXV1dNUdS9nS0taCjXbmHi+gI+f8GlfH6U+F0dPJTlLGxMUxMTN5b9u3VF3v37g3gf6svFjYldcuWLXHs2DFMnjxZtu/IkSPvXa1RHZioKzmH7bnQzRSQYyTBncH8cSCi8qukqy9OmjQJHh4eWLx4Mbp164YdO3bg0qVLCAsLU+fXKIC/mSs53UwBeukAIKg7FCIipZR09cVWrVph27ZtmDVrFmbOnIkPP/wQe/fuhZOTk7q+gkJM1EREVGGUZPVFABgwYAAGDBhQylEpp3I/GCMiItJwTNREREQajImaiIhIgzFRExERaTAmaiIiIg3GRE1ERKTBmKiJiIg0GBM1ERGRBmOiJiIi0mBM1ERERBqMiZqIiEiDca7vSi7HSAJA+P//JSIiTcNEXclxaUsiIs3GW99EREQajImaiIhIgzFRExERaTAmaiIiIg3GRE1ERKTBmKiJiIg0GBM1ERGRBmOiJiIi0mBM1ERERBqMiZqIiEiDMVETERFpMCZqIiIiDcZETUREpMGYqImIiDQYEzUREZEGY6ImIiLSYEzUREREGoyJmoiISIMxURMREWkwjUzUK1euhK2tLQwMDODu7o4LFy68t/yuXbvQsGFDGBgYoHHjxjh06FAZRUpERFS6NC5R79y5Ez4+PggICMCVK1fg7OwMT09PJCYmKix/5swZDB48GKNGjcLVq1fRu3dv9O7dGzdu3CjjyImIiFRP4xL1kiVLMGbMGIwcORKOjo5Ys2YNjIyMsHHjRoXlQ0ND8dlnn+Hbb79Fo0aNEBQUhGbNmmHFihVlHDkREZHqaVSizs7OxuXLl9GpUyfZPi0tLXTq1Alnz55VeM7Zs2flygOAp6dnoeWJiIjKEx11B/C258+fIy8vDxYWFnL7LSwscPv2bYXnxMfHKywfHx+vsHxWVhaysrJk2+np6QCAW7duKRO6aC+ePcbrtBdqaVuTvJYYQCvTCFeuXFF3KGXu9j//4eWrPHWHoVbGQjp0jQCdd66/lZUVrKys1BSVcp49e4Znz56pO4xyTV2/lzWNRiXqshAcHIzAwEC5fTY2NhgyZIiaIqK3HQ6bp+4QSJ0Wyw8EDQgIwJw5c9QTi5LWrl1b4HcNlZyHh0e5/WNNVTQqUZuZmUFbWxsJCQly+xMSEmBpaanwHEtLyxKV9/X1hY+Pj9y+Fy9e4MWLytmrTU9Ph4eHB06cOAFjY2N1h0NqoMk/A+X5F/S4cePQs2fPMm1Tk6+lWOX5roqqSARBENQdxNvc3d3h5uaG5cuXAwCkUimsra3h7e2NGTNmFCg/cOBAZGZm4rfffpPta9WqFZo0aYI1a9aUWdzlVWpqKqpVq4aUlBSYmJioOxxSA/4MVBy8lhWTRvWoAcDHxwfDhw+Hq6sr3NzcEBISgoyMDIwcORIAMGzYMNSpUwfBwcEAgEmTJsHDwwOLFy9Gt27dsGPHDly6dAlhYWHq/BpEREQqoXGJeuDAgUhKSoK/vz/i4+Ph4uKCyMhI2YCxuLg4aGn9b7B6q1atsG3bNsyaNQszZ87Ehx9+iL1798LJyUldX4GIiEhlNO7WN5WtrKwsBAcHw9fXF/r6+uoOh9SAPwMVB69lxcRETUREpME0asITIiIiksdETUREpMGYqEmlYmNjIZFIsHnzZnWHQkRUITBRq9GDBw8wbtw41KtXDwYGBjAxMUHr1q0RGhqKV69elVq7N2/exJw5cxAbG1tqbRTH/Pnz0bNnT1hYWEAikZTbGahKm0QiKdYnJiZG6bYyMzMxZ86cEtXF61gyvJ5UUhr3elZlcfDgQQwYMAD6+voYNmwYnJyckJ2djdOnT+Pbb7/F33//XWrvgt+8eROBgYFo3749bG1tS6WN4pg1axYsLS3RtGlTREVFqS0OTbd161a57S1btuDIkSMF9jdq1EjptjIzM2XTXrZv375Y5/A6lgyvJ5UUE7UaPHr0CIMGDYKNjQ2io6Plpsf7+uuvcf/+fRw8eFCNEf6PIAh4/fo1DA0NVV73o0ePYGtri+fPn8Pc3Fzl9VcU785Df+7cORw5ckRj5qfndSwZXk8qKd76VoPvv/8e6enp2LBhg8I5bOvXr49JkybJtnNzcxEUFAR7e3vo6+vD1tYWM2fOlFsFDABsbW3RvXt3nD59Gm5ubjAwMEC9evWwZcsWWZnNmzdjwIABAIAOHToUuM32po6oqCi4urrC0NAQa9euBQA8fPgQAwYMQI0aNWBkZIQWLVoo9QeFOnvzFY1UKkVISAg++ugjGBgYwMLCAuPGjcPLly/lyl26dAmenp4wMzODoaEh7Ozs8NVXXwHIH1/w5hdzYGCg7GejqFufvI6qx+tJb2OPWg1+++031KtXD61atSpW+dGjRyM8PBz9+/fH1KlTcf78eQQHB+PWrVuIiIiQK3v//n30798fo0aNwvDhw7Fx40aMGDECzZs3x0cffYR27dph4sSJWLZsGWbOnCm7vfb2bbY7d+5g8ODBGDduHMaMGQMHBwckJCSgVatWyMzMxMSJE1GzZk2Eh4ejZ8+e2L17N/r06aO6fyAqsXHjxmHz5s0YOXIkJk6ciEePHmHFihW4evUq/vjjD+jq6iIxMRGdO3eGubk5ZsyYAVNTU8TGxmLPnj0AAHNzc6xevRoTJkxAnz590LdvXwBAkyZN1PnVKiVeT5IjUJlKSUkRAAi9evUqVvlr164JAITRo0fL7Z82bZoAQIiOjpbts7GxEQAIJ0+elO1LTEwU9PX1halTp8r27dq1SwAgHD9+vEB7b+qIjIyU2z958mQBgHDq1CnZvrS0NMHOzk6wtbUV8vLyBEEQhEePHgkAhE2bNhXr+wmCICQlJQkAhICAgGKfU5l9/fXXwtv/1z116pQAQPj555/lykVGRsrtj4iIEAAIFy9eLLRuZa4Fr6M4vJ5UFN76LmOpqakAgKpVqxar/KFD+evzvrs059SpUwGgwK1nR0dHtG3bVrZtbm4OBwcHPHz4sNgx2tnZwdPTs0Acbm5uaNOmjWyfsbExxo4di9jYWNy8ebPY9ZNq7dq1C9WqVcOnn36K58+fyz7NmzeHsbExjh8/DgAwNTUFABw4cAA5OTlqjJjeh9eT3sVEXcbeLD2XlpZWrPKPHz+GlpYW6tevL7ff0tISpqamePz4sdx+a2vrAnVUr169wLOt97Gzs1MYh4ODQ4H9b26ZvxsHlZ179+4hJSUFtWrVgrm5udwnPT0diYmJAAAPDw/069cPgYGBMDMzQ69evbBp06YCYx1IvXg96V18Rl3GTExMULt2bdy4caNE50kkkmKV09bWVrhfKMGU7qUxwptKj1QqRa1atfDzzz8rPP5mQJFEIsHu3btx7tw5/Pbbb4iKisJXX32FxYsX49y5czA2Ni7LsKkQvJ70LiZqNejevTvCwsJw9uxZtGzZ8r1lbWxsIJVKce/ePbkBXwkJCUhOToaNjU2J2y9u0n83jjt37hTYf/v2bdlxUg97e3scPXoUrVu3LtYfWS1atECLFi0wf/58bNu2DV9++SV27NiB0aNHi/rZINXi9aR38da3GkyfPh1VqlTB6NGjkZCQUOD4gwcPEBoaCgDo2rUrACAkJESuzJIlSwAA3bp1K3H7VapUAQAkJycX+5yuXbviwoULOHv2rGxfRkYGwsLCYGtrC0dHxxLHQarx+eefIy8vD0FBQQWO5ebmyq7zy5cvC9xZcXFxAQDZ7VIjIyMAJfvZINXi9aR3sUetBvb29ti2bRsGDhyIRo0ayc1MdubMGezatQsjRowAADg7O2P48OEICwtDcnIyPDw8cOHCBYSHh6N3797o0KFDidt3cXGBtrY2Fi1ahJSUFOjr66Njx46oVatWoefMmDED27dvR5cuXTBx4kTUqFED4eHhePToEX799VdoaZX8b76tW7fi8ePHyMzMBACcPHkS8+bNAwAMHTqUvfRi8vDwwLhx4xAcHIxr166hc+fO0NXVxb1797Br1y6Ehoaif//+CA8Px6pVq9CnTx/Y29sjLS0N69atg4mJiewPQkNDQzg6OmLnzp1o0KABatSoAScnJzg5ORXaPq+javF6UgFqHnVeqd29e1cYM2aMYGtrK+jp6QlVq1YVWrduLSxfvlx4/fq1rFxOTo4QGBgo2NnZCbq6ukLdunUFX19fuTKCkP9qVbdu3Qq04+HhIXh4eMjtW7dunVCvXj1BW1tb7lWtwuoQBEF48OCB0L9/f8HU1FQwMDAQ3NzchAMHDsiVKcnrWR4eHgIAhR9Fr45Rvndf53kjLCxMaN68uWBoaChUrVpVaNy4sTB9+nTh33//FQRBEK5cuSIMHjxYsLa2FvT19YVatWoJ3bt3Fy5duiRXz5kzZ4TmzZsLenp6xXo9h9dRObyeVBSJIJRglBERERGVKT6jJiIi0mBM1ERERBqMiZqIiEiDMVETERFpMCZqIiIiDcZETUREpMGYqImIypHY2FhIJBJs3rxZ3aFQGWGi1lCbN2+GRCKBgYEBnj59WuB4+/bt3zu7UFkYM2YMJBIJunfvrvD4/v370axZMxgYGMDa2hoBAQHIzc0t4yjLJ15/InqDiVrDZWVlYeHCheoOo4BLly5h8+bNMDAwUHj8999/R+/evWFqaorly5ejd+/emDdvHr755psyjrR84/Wnd9nY2ODVq1cYOnSoukOhMsK5vjWci4sL1q1bB19fX9SuXVvd4QDIXzJz4sSJGDZsGI4dO6awzLRp09CkSRMcPnwYOjr5P2YmJiZYsGABJk2ahIYNG5ZlyOUWrz+9682dFqo82KPWcDNnzkReXp5G9aq2bt2KGzduYP78+QqP37x5Ezdv3sTYsWNlv6QBwMvLC4IgYPfu3WUVarnH618xzZkzBxKJBHfv3sWQIUNQrVo1mJubY/bs2RAEAf/88w969eoFExMTWFpaYvHixbJzFT2jHjFiBIyNjfH06VP07t0bxsbGMDc3x7Rp05CXlycrFxMTA4lEgpiYGLl4FNUZHx+PkSNH4oMPPoC+vj6srKzQq1cvxMbGltK/ChWGiVrD2dnZYdiwYVi3bh3+/fffEp+fmZmJ58+fF/l5+fJlsepLS0vDd999h5kzZ8LS0lJhmatXrwIAXF1d5fbXrl0bH3zwgew4FY3Xv2IbOHAgpFIpFi5cCHd3d8ybNw8hISH49NNPUadOHSxatAj169fHtGnTcPLkyffWlZeXB09PT9SsWRM//vgjPDw8sHjxYoSFhYmKrV+/foiIiMDIkSOxatUqTJw4EWlpaYiLixNVH4nHRF0O+Pn5ITc3F4sWLSrxud9//z3Mzc2L/DRt2rRY9c2dOxeGhoaYMmVKoWWePXsGALCysipwzMrKSlTCqcx4/SsuNzc3bNu2DRMmTMC+ffvwwQcfYOrUqbLkOGHCBBw4cACGhobYuHHje+t6/fo1Bg4ciA0bNmD8+PHYvXs3mjZtig0bNpQ4ruTkZJw5cwazZs1CUFAQRo0aBV9fX0RHR6Ndu3Zivy6JxGfU5UC9evUwdOhQhIWFYcaMGQp/ARZm2LBhaNOmTZHlDA0Niyxz9+5dhIaGYvv27dDX1y+03KtXrwBAYRkDAwOkpqYW2Rb9D69/xTV69GjZf2tra8PV1RVPnjzBqFGjZPtNTU3h4OCAhw8fFlnf+PHj5bbbtm2LrVu3ljguQ0ND6OnpISYmBqNGjUL16tVLXAepDhN1OTFr1ixs3boVCxcuRGhoaLHPq1evHurVq6eSGCZNmoRWrVqhX79+7y335pd+VlZWgWOvX78uVlIgebz+FZO1tbXcdrVq1WBgYAAzM7MC+//777/31mVgYABzc3O5fdWrVy/2Y4236evrY9GiRZg6dSosLCzQokULdO/eHcOGDSv0kQeVHibqcqJevXoYMmSIrFdVXOnp6UhPTy+ynLa2doH/k78tOjoakZGR2LNnj9xgktzcXLx69QqxsbGoUaMGTExMZD2+Z8+eoW7dunL1PHv2DG5ubsWOn/Lx+ldM2traxdoH5I+2L2ld75JIJAr3vz3g7I3JkyejR48e2Lt3L6KiojB79mwEBwcjOjq62I9KSDX4jLocmTVrVomfVf7444+wsrIq8vPxxx+/t543A0j69u0LOzs72efp06eIjo6GnZ2d7Bmai4sLgPx3bd/277//4smTJ7LjVDK8/qSsN7ewk5OT5fY/fvxYYXl7e3tMnToVhw8fxo0bN5CdnS03Ap3KBnvU5Yi9vT2GDBmCtWvXwsbGRu7Vl8Ko6hllx44dERERUWD/2LFjYWNjAz8/PzRu3BgA8NFHH6Fhw4YICwvDuHHjZH/pr169GhKJBP379y8yHiqI15+UZWNjA21tbZw8eRK9e/eW7V+1apVcuczMTGhpacm9r21vb4+qVasqfKRBpYuJupzx8/PD1q1bcefOHXz00UdFllfVM0pra+sCz9OA/NtjFhYWcv+nB4AffvgBPXv2ROfOnTFo0CDcuHEDK1aswOjRo9GoUSOl46mseP1JGdWqVcOAAQOwfPlySCQS2Nvb48CBA0hMTJQrd/fuXXzyySf4/PPP4ejoCB0dHURERCAhIQGDBg1SU/SVF299lzP169fHkCFD1B1Gkbp37449e/bgxYsX+Oabb7Bnzx7MnDkTK1euVHdo5RqvPylr+fLl6NWrF9asWYNZs2bB2toa4eHhcmXq1q2LwYMHIyYmBr6+vvD19UVqaip++eWXIgcTkupJhKJGKBAREZHasEdNRESkwZioiYiINBgTNRERkQZjoiYiItJgTNREREQajImaiIhIgzFRExFRAbGxsZBIJNi8ebO6Q6n0mKiJiJT04MEDjBs3DvXq1YOBgQFMTEzQunVrhIaGypb9LA03b97EnDlz5BZKUYf58+ejZ8+esLCwgEQiwZw5c9QaT0XDKUSJiJRw8OBBDBgwAPr6+hg2bBicnJyQnZ2N06dP49tvv8Xff/+NsLCwUmn75s2bCAwMRPv27WFra1sqbRTHrFmzYGlpiaZNmyIqKkptcVRUTNRERCI9evQIgwYNgo2NDaKjo2VLfALA119/jfv37+PgwYNqjPB/BEEotfXAHz16BFtbWzx//vy9y6WSOLz1TUQk0vfff4/09HRs2LBBLkm/Ub9+fUyaNEm2nZubi6CgINjb20NfXx+2traYOXNmgRWpbG1t0b17d5w+fRpubm4wMDBAvXr1sGXLFlmZzZs3Y8CAAQCADh06QCKRQCKRICYmRq6OqKgouLq6wtDQEGvXrgUAPHz4EAMGDECNGjVgZGSEFi1aKPUHhTp785UBEzURkUi//fYb6tWrh1atWhWr/OjRo+Hv749mzZph6dKl8PDwQHBwsMIVqe7fv4/+/fvj008/xeLFi1G9enWMGDECf//9NwCgXbt2mDhxIgBg5syZ2Lp1K7Zu3Sq3OtmdO3cwePBgfPrppwgNDYWLiwsSEhLQqlUrREVFwcvLC/Pnz8fr16/Rs2dPhUuZkgYQiIioxFJSUgQAQq9evYpV/tq1awIAYfTo0XL7p02bJgAQoqOjZftsbGwEAMLJkydl+xITEwV9fX1h6tSpsn27du0SAAjHjx8v0N6bOiIjI+X2T548WQAgnDp1SrYvLS1NsLOzE2xtbYW8vDxBEATh0aNHAgBh06ZNxfp+giAISUlJAgAhICCg2OdQ0dijJiISITU1FQBQtWrVYpU/dOgQAMDHx0du/9SpUwGgwK1nR0dHtG3bVrZtbm4OBwcHPHz4sNgx2tnZwdPTs0Acbm5uaNOmjWyfsbExxo4di9jYWNy8ebPY9VPZYKImIhLBxMQEAJCWllas8o8fP4aWlhbq168vt9/S0hKmpqZ4/Pix3H5ra+sCdVSvXh0vX74sdox2dnYK43BwcCiw/80t83fjIPVjoiYiEsHExAS1a9fGjRs3SnSeRCIpVjltbW2F+wVBKHZbpTHCm8oeEzURkUjdu3fHgwcPcPbs2SLL2tjYQCqV4t69e3L7ExISkJycDBsbmxK3X9yk/24cd+7cKbD/9u3bsuOkWZioiYhEmj59OqpUqYLRo0cjISGhwPEHDx4gNDQUANC1a1cAQEhIiFyZJUuWAAC6detW4varVKkCAEhOTi72OV27dsWFCxfk/rjIyMhAWFgYbG1t4ejoWOI4qHRxwhMiIpHs7e2xbds2DBw4EI0aNZKbmezMmTPYtWsXRowYAQBwdnbG8OHDERYWhuTkZHh4eODChQsIDw9H79690aFDhxK37+LiAm1tbSxatAgpKSnQ19dHx44dUatWrULPmTFjBrZv344uXbpg4sSJqFGjBsLDw/Ho0SP8+uuv0NIqef9t69atePz4MTIzMwEAJ0+exLx58wAAQ4cOZS9dWeoedk5EVN7dvXtXGDNmjGBrayvo6ekJVatWFVq3bi0sX75ceP36taxcTk6OEBgYKNjZ2Qm6urpC3bp1BV9fX7kygpD/alW3bt0KtOPh4SF4eHjI7Vu3bp1Qr149QVtbW+5VrcLqEARBePDggdC/f3/B1NRUMDAwENzc3IQDBw7IlSnJ61keHh4CAIUfRa+OUclIBKEEIxOIiIioTPEZNRERkQZjoiYiItJgTNREREQajImaiIhIgzFRExERaTAmaiIiIg3GRE1ERKTBmKiJiIg0GBM1ERGRBmOiJiIi0mBM1ERERBqMiZqIiEiDMVETERFpsP8Dyap11Heo/0AAAAAASUVORK5CYII=", "text/plain": [ "
" ] @@ -605,11 +753,12 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "id": "67dbf66e", "metadata": {}, "source": [ - "## Producing Paired Proportion Plots" + "## Generating results" ] }, { @@ -650,15 +799,15 @@ " bca_low\n", " bca_high\n", " ...\n", + " pct_high\n", + " pct_interval_idx\n", + " bootstraps\n", + " resamples\n", + " random_seed\n", + " permutations\n", " pvalue_permutation\n", " permutation_count\n", " permutations_var\n", - " pvalue_welch\n", - " statistic_welch\n", - " pvalue_students_t\n", - " statistic_students_t\n", - " pvalue_mann_whitney\n", - " statistic_mann_whitney\n", " proportional_difference\n", " \n", " \n", @@ -671,44 +820,47 @@ " 40\n", " Cohen's h\n", " None\n", - " 0.825418\n", + " 1.242163\n", " 95\n", - " 0.329684\n", - " 1.219937\n", + " 0.769088\n", + " 1.659486\n", " ...\n", + " 1.72357\n", + " (125, 4875)\n", + " [1.4827506328621212, 1.0122770907407532, 1.491...\n", + " 5000\n", + " 12345\n", + " [-0.25268025514207904, 0.050400851615126196, -...\n", " 0.0\n", " 5000\n", - " [0.011266025641025641, 0.011266025641025641, 0...\n", - " 0.000289\n", - " -3.81474\n", - " 0.000271\n", - " -3.81474\n", - " 0.000434\n", - " 500.0\n", - " 0.825418\n", + " [0.012419871794871796, 0.012612179487179487, 0...\n", + " 1.242163\n", " \n", " \n", "\n", - "

1 rows × 28 columns

\n", + "

1 rows × 22 columns

\n", "" ], "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 0.825418 95 \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", - " bca_low bca_high ... pvalue_permutation permutation_count \\\n", - "0 0.329684 1.219937 ... 0.0 5000 \n", + " bootstraps resamples random_seed \\\n", + "0 [1.4827506328621212, 1.0122770907407532, 1.491... 5000 12345 \n", "\n", - " permutations_var pvalue_welch \\\n", - "0 [0.011266025641025641, 0.011266025641025641, 0... 0.000289 \n", + " permutations pvalue_permutation \\\n", + "0 [-0.25268025514207904, 0.050400851615126196, -... 0.0 \n", "\n", - " statistic_welch pvalue_students_t statistic_students_t \\\n", - "0 -3.81474 0.000271 -3.81474 \n", + " permutation_count permutations_var \\\n", + "0 5000 [0.012419871794871796, 0.012612179487179487, 0... \n", "\n", - " pvalue_mann_whitney statistic_mann_whitney proportional_difference \n", - "0 0.000434 500.0 0.825418 \n", + " proportional_difference \n", + "0 1.242163 \n", "\n", - "[1 rows x 28 columns]" + "[1 rows x 22 columns]" ] }, "execution_count": null, @@ -721,6 +873,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "id": "845b7224", "metadata": {}, @@ -736,7 +889,7 @@ "outputs": [ { "data": { - "image/png": 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", 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+FhERKXInBrBpDdTroOlItI6fnx+MjIzg5eWl8PjRo0dx4MAB7Nu3T3QbohL1vHnz4OHhARcXFwiCgAEDBsDV9X/PMKKiotCmTRtRAXl7exd6qzs2NlZuW09PDwEBAQgICBDVFhERKenkYsDaCTCqrulItMqlS5fk7vS+q127dggJCVGpDVGJ2sXFBbdu3cLp06dhamoqNxgrJSUFXl5eHKBFRFSevE4FTi8DPvHXdCRa5eXLl7IJvxTR0dER9ShYrg6xJ5qbm6N3794FErKpqSkmTZokagpRIiLSYveOAAnXNB2FVvnwww9x8ODBQo9HR0erPPJbdKLOy8vDtm3bMHbsWPTt2xfXruVfvNTUVOzatavAa1ZERFQOnF3FRTveMnLkSOzfvx8+Pj6yQdZA/t3lKVOmIDo6GiNHjlSpDVGJOiUlRTbH9tatW7F3714kJycDyH/daeLEiSot6UVERFoq8W8g8bqmoyhUcVdgTElJwYQJE2BtbQ0DAwM0aNAABw4cULq9iRMnYvjw4QgNDYWZmRnq1q2LunXrwszMDGFhYRg6dKjKr22JStQzZszA33//jZiYGDx48EDunWVdXV0MGDCgWF+UiIjKkOu7NB2BQsVdgTE7Oxuffvop4uLisHPnTty+fRsRERGoXbu20m1KJBJs2LABR44cwbhx4+Do6AhHR0eMHz8eR48eRWRkpMJVtYpD1GCy3bt345tvvsGnn36K//77r8DxBg0aYOPGjSoFRkREWir+LJCbDejpazoSOcVdgXH9+vV4/vw5Tp8+jUqVKgGA3GRexdGxY0d07NhRdOzvI6pHnZqaCjs7u0KP5+TkIDc3V3RQRESkxXIygUTtGlQmZgXGvXv3olWrVpgwYQIsLS3h6OiI+fPnIy8vr7TCVoqoRG1vb4/Lly8XevzgwYNo3Lix6KCIiEjLvXhUak2lp6cjLS1N9nl7BcQ33rcCY0JCgsJ6Hzx4gJ07dyIvLw8HDhzA7NmzsWjRIsydO1fp2ARBwJo1a+Dq6ipbs+Ldz/te31KGqLNHjRqF7777Dh06dMAnn3wCIP8+fVZWFubMmYPo6GiEh4erFBgRESkvPj4emZmZAIDMbCnin79G3RqGJddgRnLJ1f2Od18DDggIQGBgoMr1SqVSWFhYIDw8HLq6umjRogWePHmCH374QemJtKZPn47FixfD2dkZQ4cORfXq6p8QRlSinjRpEv7++28MGTIEpqamAPLXhv7vv/+Qm5uLsWPHqjwcnYiIinb+/HkEBwdj//79soG9LzJzYet3Hj2a1MDsbjb42Laq+hvWUa2XWBzHjx+Xm5vj7YWV3hCzAqO1tTUqVaoktzZEo0aNkJCQgOzsbOjrF/0MPjIyEv3798cvv/yi5LcpPlG3viUSCSIiInDixAl4enqia9eucHZ2xpgxYxAbG4tVq1apO04iInrHrl270KZNG/z+++8FVgwUBODA9edo/f1V7LryTP2N65fealrGxsYwMTGRfRQl6rdXYHzjzQqMrVq1UlhvmzZtZMtSvnHnzh1YW1srlaQB4NWrV3LPxUuCSn8StW3bFm3btlVXLEREpKTz589j0KBByMvLK3RZ3zwpIIGAQRE3cXq6s3p71tU+UF9dauLj44Phw4fDxcUFrq6uCA0NLbACY+3atWVzb48fPx7Lly/HpEmT8M033+Du3buYP38+Jk6cqHSbn3zyCS5cuIAxY8aUyHcCRPaoHz58iN9++63Q47/99hvi4uLExkREpcjKygq1a9cu9PYgaae5c+dCEIRCk/QbAgABAuYeUPPgr+o26q1PDQYNGoQff/wR/v7+cHZ2xtWrVwuswPj06VNZ+Tp16iAmJgYXLlxA06ZNMXHiREyaNEnhq1yFWblyJc6ePYv58+crfF1ZHUT1qKdNm4a0tDS5pS3ftmLFCpiammLbtm0qBUdEJe/ixYuaDoGKKT4+Hvv27SsySb+RJwV+u/ZcfQPMKlUGqtZSvZ4SUJwVGAGgVatWOHv2rOj2HBwcIJVKMXv2bMyePRuGhoZyz7yB/MfFqizMISpRnzlzBpMnTy70+CeffILQ0FCRIRFRacvLy5N7TleacvOkyM2TQidPipycHI3EUNbExMQonaTfEATg4I0XGN7KsujCRTGzBfLy8j8lqCzMx9G/f3+VZx4riqhE/eLFC1StWvizDmNj4xK7BUBUHqVKqgHpudg/7wuNtP/zkWvYekwL5m+e+rOmIyjXRv90F6N/uquGmk4BWK2Gesq+0piFU1Sirlu3Lv744w+MHz9e4fGTJ0/igw+0b6ABESk2uONHGNThI421X01IhX7Vmvj4mw0ai6Es2bhxo6jBSxFDP1RPj7rNZKBxL9XrKcKVK1fg5uZW4u1oO1GJesiQIQgODoarqyu8vb2ho5M/Ji0vLw/Lly/H9u3b4efnp9ZAiajk6OqIXvFWLfQEHejp6sjmW6b38/DwgEQiKdbtb4kE6NK4OirpquFam9sDpXCtVJ3Rq7TEx8dj/vz5OHbsGJKSkrBnzx60b98ez549w5w5czBixAg0a9ZMdP2i/hV8fX1x6tQpTJ48GfPmzYODgwMA4Pbt20hOTkaHDh2YqImISkjdunXRo0cPHDhwQKl5qXV1gO6ONdQ3U1m1Ouqppxy4ceMG2rVrB6lUCjc3N9y7d0/2bN3MzAynTp1CRkYG1q1bJ7oNUYnawMAABw8eRGRkJHbt2oX79+8DAFxdXdG/f394enrKetlEpN0mr4zBi/RXqG5shFAvD02HQ0qaPXs2fv/99yJ71hIAEkgwq5uaXqfS1Qcq11BPXeXA9OnTYWpqirNnz0IikcDCwkLuePfu3bF9+3aV2hB9X0FHRwcjRoyQvUhORGXTi/RX+C/tlabDoGL6+OOPsX37dgwaNAiCICjsWevq5CfpX0Y3Ut9kJ1Wt8u+jEwDgxIkT8Pf3h7m5ucJB1HXr1sWTJ09UakNUt/f58+f466+/Cj1+7do1vHjxQnRQRERUtH79+uH06dPo1q1bgVeEJJL8292npzujbzMz9TVqUlt9dZUDUqkUlStXLvR4cnKywilPi0NUop4yZcp7RxyOHTsW06ZNEx0UEREp5+OPP8bevXsRFxcnW7mpemU9xM1zxR4vR/UvyGFaV731lXHNmzfH/v37FR7Lzc3Ftm3b0LJlS5XaEJWojx49il69Ch+a37NnTxw+fFh0UEREVDx169aV9ewq6+uU3BKXZh+WTL1llK+vL6KjozF+/Hhcv54/F0FiYiIOHz6MLl264ObNm8WaklQRUc+ok5OTYWZW+K2UmjVrIikpSXRQRESkpSw19769NuratSs2btyISZMmITw8HAAwdOhQCIIAExMTbNq0Ce3bt1epDVGJ2traGleuXCn0+KVLl2Bubi46KCIi0kJVrQET7ZzjW5OGDRuGfv364dChQ7h79y6kUins7e3h4eHx3lk8lSUqUffp0wcrVqxA165dC9wC37NnDzZs2FDorGVERFRG2XJZ47dlZmaiTp06mDFjBr799lv06dOnRNoRlagDAwNx+PBh9O3bF05OTnB0dAQAXL9+HX/++ScaNWqEoKAgtQZKREQaVr+zpiPQKpUrV4aenh6qVKlSou2IGkxWrVo1nD17FrNmzUJOTg527tyJnTt3IicnB7Nnz8a5c+dgamqq5lCJiEhjzBsC5g6ajkLr9O/fHzt37iz2ambFIXrCkypVqiAoKIg9ZyKiisB5CCc6UWDw4MHw8vJCx44dMXr0aNja2sLIyKhAuebNm4tuo2zMeE5ERJpj+RFgq9rI5fKqQ4cOsv8+efJkgeOCIEAikSg1J3thRCXqr7/+usgyEolEpUnIiYhIC0gkQOuJANdvUGjDhpJfmlVUoj569GiB6ery8vLw9OlT5OXlwdzcvMQfrhMRUSlwHABYNNR0FFpr+PDhJd6GqEQdFxencH9OTg7WrFmD0NBQHDp0SJW4iIhI06p9AHw8StNRlBlPnz5FUlIS6tevr9bOqlrvZVSqVAne3t7o0qULvL291Vk1ERGVNvfpQKUSmoq0HNmzZw8aNmyIDz74AM2bN8e5c+cAAM+ePUOzZs0QFRWlUv0l8tDByckJJ06cKImqiYioNHzUF7B20nQUWu+3335Dv379YGZmhoCAALnXtMzMzFC7dm1s3LhRpTZKJFEfOnTovct+EZH2qG5shJomRqhuXPCVEqqgjEyBj0dqOooyYc6cOWjfvj1OnTqFCRMmFDjeqlWr9065rQxRz6jnzJmjcH9KSgpOnDiBy5cvq7xaCBGVjlAvD02HQNrm49GAgZqXxyynrl+/jsWLFxd63NLSUuVFqkRPIapI9erVYW9vj9WrV2P06NGqxEVERJpQ7QPAoaumoygzKleujIyMjEKPP3jwADVr1lSpDVGJWiqVqtQoERFpqRZfATq6mo6izOjYsSMiIyMxefLkAscSEhIQERGBHj16qNQG32AnIqJ8xpaAfSdNR1GmzJs3D48fP8bHH3+MNWvWQCKRICYmBrNmzUKTJk0gCAICAgJUakNUoo6Pj8epU6fk9v3555/w9PTEoEGDsHv3bpWCIiIiDXDsz950MTk4OODUqVOoWbMmZs+eDUEQ8MMPP2D+/Plo0qQJTp48CVtbW5XaEHXre+LEiUhPT8fhw4cBAImJiejYsSOys7NRtWpV7Ny5Ezt27EC/fv1UCo6IiEqJngGfTSvhr7/+go2NDapVqybb99FHH+Hw4cN48eIF7t27B6lUinr16sHc3FwtbYrqUZ8/fx6ffvqpbHvTpk149eoV/vzzTzx58gSffPIJfvzxR7UESEREpaB+Z8DQRNNRaL1mzZph//79su1OnTrhyJEjAPIHVH/88cdwc3NTW5IGRCbq58+fw8LCQra9b98+uLu7w97eHjo6OujXrx9u3bqltiCJiKiEOfbXdARlgpGRETIzM2XbsbGxSExMLNE2Rd36Njc3x6NHjwDkvzt99uxZLFiwQHY8NzcXubm56omQiIhKVq1mQE17TUdRJjg5OWHx4sXQ1dWV3f6+cOECDA3fP9WqKo+CRSXqzp07Y+nSpTAxMUFsbCykUin69OkjO37jxg3UqVNHVEArVqzADz/8gISEBDg5OWHZsmVwdXUttHxKSgr8/Pywa9cuPH/+HDY2NggNDUW3bt1EtU9EVOE4f6HpCMqM0NBQDBw4ECNH5s/cJpFIEBYWhrCwsELP0ch61AsWLMCdO3cwbdo06Ovr48cff4SdnR0AICsrC7/88gu++KL4F3779u3w8fHB6tWr4ebmhtDQUHh4eOD27dtyt9rfyM7OxqeffgoLCwvs3LkTtWvXxqNHj2BqairmaxERVTzmDsAHH2s6ijLj448/xr1793D//n0kJiaiQ4cOmDlzpty4LXUTlagtLS3xxx9/IDU1FUZGRtDX15cdk0qlOHLkiKge9eLFizF69GiMGDECALB69Wrs378f69evVzgl6fr16/H8+XOcPn0alSpVAgCVh8ETEVUoLUYAEommoygz9u7dCxcXFzg4OMDBwQHDhw9Hz5494ebmVmJtqjThSbVq1eSSNJD/oN3JyQk1atQoVl3Z2dm4dOkSOnfu/L/gdHTQuXNnnDlzRuE5e/fuRatWrTBhwgRYWlrC0dER8+fPV+kWAxFRhWH5EVC3paajKFP69u2L2NhY2fbx48e1czBZSXj27Bny8vJgaWkpt9/S0rLQEeQPHjzA0aNH8eWXX+LAgQO4d+8evLy8kJOTU+hMMFlZWcjKypJtp6enq+9LEBGVJW7j2JsupqpVqyIlJUW2HRcXV+J5RGsStRhSqRQWFhYIDw+Hrq4uWrRogSdPnuCHH34oNFGHhIQgKCiolCMlItIydu0A66aajqLMcXV1xbx585CYmCgb9X3gwAEkJCQUeo5EIsGUKVNEt6k1idrMzAy6uroFbiEkJibCyspK4TnW1taoVKkSdHX/N+Vdo0aNkJCQgOzs7AK35QHA19cXPj4+su2rV6/C3d1dTd+CiKgM0NUHWhZcO5mKtnLlSnh6eiI4OBhAfhLesmULtmzZUug55SZR6+vro0WLFjhy5IjsVa83A9O8vb0VntOmTRts2bIFUqkUOjr5j9vv3LkDa2trhUkaAAwMDGBgYCDbNjY2Vu8XISLSdk6DARNrTUdRJtWvXx+nT5/G69evkZSUBFtbW4SGhqJ3794l1qZSg8lq1KiBnTt3yrbnzJmD69evqz0YHx8fREREIDIyEjdv3sT48eORkZEhGwXu6ekJX19fWfnx48fj+fPnmDRpEu7cuYP9+/dj/vz5mDCBfykSESlkbAk4f6npKMo8Q0ND1K1bFwEBAejUqRNsbGze+1GFUj3q9PR0uSnTAgMDUb9+fTg6OqrU+LsGDRqE5ORk+Pv7IyEhAc7OzoiOjpYNMIuPj5f1nAGgTp06iImJwZQpU9C0aVPUrl0bkyZNwnfffafWuIiIyg23sUCl98+iRcpTdQlLZSiVqO3t7bFz5060a9cOJib5k7ZnZGTg+fPn7z2vuK9oAYC3t3eht7rfHhL/RqtWrXD27Nlit0NEVOGYOwD1Omo6ijLt66+/hkQikQ1i/vrrr4s8RyKRYN26daLbVCpRz5w5EyNGjJCtGCKRSDBu3DiMGzfuvefxfWYiIi3SYgSgo9L0GRXe0aNHoaOjA6lUCl1dXRw9ehSSIl5xK+p4UZRK1MOGDYOrq6tslZDAwED07dsXTZtyaD8RUZlgWgeoU3KzZ1UUcXFx790uCUqP+n4zXRoAbNiwAcOHD0evXr1KLDAiIlKjRr3Ymy6jRL2e9fDhQ3XHQUREJaleB01HQCKJ/vMqLy8PkZGR+Pzzz+Hm5gY3Nzd8/vnn2LRpE59NExFpE7MPAeOCKxCWRytWrICtrS0MDQ3h5uaG8+fPK3Xetm3bIJFI5JZsVkRHRwe6urrF/qhCVI86NTUVHh4euHDhAqpWrYp69eoBAA4dOoRff/0Vq1atQkxMjGyEOBERaVDtFpqOoFQUd6nkN+Li4jBt2jS0a9euyDb8/f0LDA6LiorC33//DQ8PD9kj4lu3buHgwYNwdHQsMvkXRVSi9vPzw6VLl7Bs2TKMHj1atsRkTk4O1q5di4kTJ8LPzw/Lli1TKTgiIlIDq4ox8Le4SyUD+XeHv/zySwQFBeHkyZNyC24oEhgYKLcdHh6OpKQkXL9+XZak37h58yY6deqEWrVqif5OgMhb31FRUfDy8oKXl5csSQNApUqVMH78eIwfPx6//vqrSoEREZGaWDTSdAQqSU9PR1pamuzz9gqIb4hZKhnIn2nTwsICI0eOFBXbDz/8AG9v7wJJGshfe8Lb2xvff/+9qLrfEJWo//vvP4VBvdGwYcMiJ0MhIqJSUNUKqFz8yae0ibu7O6pVqyb7hISEFCjzvqWSC1vZ6tSpU1i3bh0iIiJEx/b48WO5Duu7KlWqhMePH4uuHxCZqOvXr4+9e/cWenzv3r2wt7cXHRQREamJeeGdqrLi+PHjSE1NlX3eXvNBrJcvX2LYsGGIiIiAmZmZ6HocHR2xcuVKPHnypMCxx48fY+XKlWjSpIkqoYp7Ru3l5QVvb29069YNkydPRoMGDQAAt2/fxtKlS3Ho0CEsX75cpcCIiEgNzMv2bW8gf5XDogYnF3ep5Pv37yMuLg49e/aU7ZNKpQAAPT093L59W6kO55IlS+Dh4YEGDRqgb9++qF+/PgDg7t272L17NwRBwE8//VRkPe8jOlEnJSVhwYIFiImJkTtWqVIl+Pv7Y/z48SoFRkREamDRUNMRlIriLpXcsGFDXLt2TW7frFmz8PLlS4SFhaFOnTpKtdu2bVucO3cOs2fPRlRUFF69egUAMDIygoeHB4KCgjTTowbyR755e3vj8OHDePToEQDAxsYGnTt3Vuk2AhERqYlEApiV/VvfyvLx8cHw4cPh4uICV1dXhIaGFlgquXbt2ggJCYGhoWGBFSBNTU0BoNgrQzo6OiIqKgpSqRTJyckAAHNzc7nVHlUhOlED+bcaBg8erJZAiIhIzUzrAvqVNR1FqSnuUsnqpqOjU2AwmzqolKiJiEiLVaDe9BvFXSr5bRs3blR/QGrAGdqJiMorsw81HQGpARM1EVF5VYOvyZYHTNREROVVDTtNR0BqwERNRFQeGZqU+RnJKB8HkxERlUemdTUdQYVy48YNPHjwAC9evIAgCAWOe3p6iq5bVKIWBAHh4eFYt26dLLB3SSQS5Obmig6MiIhUUI2JujTcv38fQ4cOxfnz5xUmaCA/H5Z6op4+fToWL14MZ2dnDB06FNWrVxcdABERlQAT1ZZWJOWMHTsW165dQ2hoKNq1a1ci+VBUoo6MjET//v3xyy+/qDseIiJSh6rWmo6gQvjjjz8wc+ZMfPPNNyXWhqhE/erVK7k1P4mISPOsrKwAQQorvZf5y1tSiTMzM0O1atVKtA1Ro74/+eQTXLhwQd2xEBGRCi5evIjHNy/h4szmgLH6p7KkgsaNG4effvoJeXl5JdaGqB71ypUr4eHhgfnz52Ps2LGoWbOmuuMiIiKxJDp8NauUNGjQAHl5eXBycsLXX3+NOnXqQFdXt0C5fv36iW5DVKJ2cHCAVCrF7NmzMXv2bBgaGhYITCKRIDU1VXRgREQkklF1QKdgsiD1GzRokOy/p02bprCMRCJRqcctKlH3798fEolEdKNERFSC2JsuNceOHSvxNkQlam1dYYSIiJDfo6ZS4e7uXuJtcGYyIqLyxrBkRyGTYjdu3MCjR48AADY2NmjcuLFa6hU913daWhqCgoLg6uoKS0tLWFpawtXVFXPmzEFaWppagiMiIhH0jTUdQYWyZ88e2Nvbo0mTJujRowd69OiBJk2aoH79+ti7d6/K9YtK1P/++y+aNWuGoKAgpKeno02bNmjTpg0yMjIQGBiI5s2b4+nTpyoHR0REIuhX0XQEFcaBAwfQv39/AMD8+fMRFRWFqKgozJ8/H4IgoF+/foiOjlapDVG3vr/77jskJCRg37596Natm9yx33//HQMHDsSMGTMQGRmpUnBERCQCE3WpCQ4ORtOmTXHy5ElUqfK/f/devXrB29sbbdu2RVBQED777DPRbYjqUUdHR2Py5MkFkjQAdO3aFRMnTsSBAwdEB0VERCrQM9R0BBXGX3/9heHDh8sl6TeqVKmCr776Cn/99ZdKbYhK1BkZGbC0LHzWGysrK2RkZIgOioiIVMBEXWoMDQ3x/PnzQo8/f/4choaqXQ9Ribpx48bYunUrsrOzCxzLycnB1q1b1TbajYiIiklPX9MRVBidOnVCWFgYzpw5U+DYuXPnsHTpUpXXxhD9jHrQoEFwdXWFl5cXGjRoAAC4ffs2Vq9ejb/++gvbt29XKTAiIhJJp5KmI6gwvv/+e7Rq1Qpt27aFq6srHBwcAOTnw/Pnz8PCwgILFy5UqQ1RiXrgwIHIyMjAjBkzMG7cONksZYIgwMLCAuvXr8eAAQNUCoyIiETSZY+6tNjZ2eGvv/5CSEgIfv/9d1kn1cbGBpMmTcKMGTNgYWGhUhuiJzz56quvMHToUFy8eFHuBW8XFxfo6XEeFSIijWGiLlUWFhZYsmQJlixZUiL1q5RR9fT00LJlS7Rs2VJd8RARkap0eeu7PFEqUZ84cQIA0L59e7ntorwpT0REpYg96hLz9ddfQyKRIDw8HLq6uvj666+LPEcikWDdunWi21QqUXfo0AESiQSvXr2Cvr6+bLswgiCovKwXERGJxB51iTl69Ch0dHQglUqhq6uLo0ePFrmapKqrTSqVqN8s46Wvry+3TUREWoiJusTExcW9d7skKJWo313GqzSW9SIiIpH4elapiY+Ph7m5OYyMjBQef/XqFZKTk1G3bl3RbYia8KRTp044cuRIocePHTuGTp06iQ6KiIhUoGeg6QgqDDs7O0RFRRV6fO/evbCzs1OpDVGJOjY2FomJiYUeT0pKwvHjx0UHRUREKtDhK7KlRRCE9x7PycmBjo7oFaUBqPB61vsejt+7dw9Vq1YVWzUREamCz6hLVFpaGlJSUmTb//33H+Lj4wuUS0lJwbZt22Btba1Se0on6sjISLllK+fOnYuIiAiFgf31118KV9ZS1ooVK/DDDz8gISEBTk5OWLZsGVxdXYs8b9u2bRgyZAh69+6N3bt3i26fiKhM4+tZJWrJkiWYM2cOgPxO6+TJkzF58mSFZQVBwNy5c1VqT+lEnZmZieTkZNn2y5cvC3TnJRIJqlSpgnHjxsHf319UQNu3b4ePjw9Wr14NNzc3hIaGwsPDA7dv337vNGxxcXGYNm0a2rVrJ6pdIqJyQUc3/0MlpkuXLjA2NoYgCJg+fTqGDBmC5s2by5V5kw9btGgBFxcXldpTOlGPHz8e48ePB5D/8DwsLAy9evVSqXFFFi9ejNGjR2PEiBEAgNWrV2P//v1Yv349ZsyYofCcvLw8fPnllwgKCsLJkyflbkkQEVUofD5d4lq1aoVWrVoByF/2uX///nB0dCyx9or9hPvVq1fo06ePyi9wK5KdnY1Lly7JLQmmo6ODzp07K1xC7I05c+bAwsICI0eOLLKNrKwspKWlyT7p6elqiZ2ISCuwN11qMjMzsXTpUvz+++8l2k6xE7WRkRHCw8PfO+pbrGfPniEvLw+WlpZy+y0tLZGQkKDwnFOnTmHdunUKn5crEhISgmrVqsk+fCeciMoV9qhLTeXKlaGnp4cqVaqUaDuixoy3aNEC169fV3csxfby5UsMGzYMERERMDMzU+ocX19fpKamyj58jYyIyhWJaq8CUfH0798fO3fuLPI1LVWI+tMrNDQU3bp1g6OjI7766iu1LWtpZmYGXV3dAr31xMREWFlZFSh///59xMXFoWfPnrJ9UqkUQP7KXrdv34a9vb3cOQYGBjAw+N9kAMbGxmqJnYhIKzBRl6rBgwfDy8sLHTt2xOjRo2Fra6twlrJ3B5sVh6gM+9VXX0FHRwdjx47FxIkTUbt27QKBSSQS/Pnnn8WqV19fHy1atMCRI0fQp08fAPmJ98iRI/D29i5QvmHDhrh27ZrcvlmzZuHly5cICwtDnTp1ivfFiIjKPPWPH6LCdejQQfbfJ0+eLHBcHYtUiUrUNWrUQM2aNeHg4CC64cL4+Phg+PDhcHFxgaurK0JDQ5GRkSEbBe7p6YnatWsjJCQEhoaGBUbamZqaAkCJjsAjIiICgA0bNpR4G6ISdWxsrJrD+J9BgwYhOTkZ/v7+SEhIgLOzM6Kjo2UDzOLj41Wejo2IiEgdhg8fXuJtaOXwQG9vb4W3uoGi/0jYuHGj+gMiIiIqQnp6Ov755x8AQJ06ddQ2Bkp01zQvLw+RkZH4/PPP4ebmBjc3N3z++efYtGmTSvfiiYhIBSUwxwW934ULF9CxY0dUr14djo6OcHR0RPXq1dGpUydcvHhR5fpF9ahTU1Ph4eGBCxcuoGrVqqhXrx4A4NChQ/j111+xatUqxMTEwMTEROUAiYioGLggR6k6d+4cOnToAH19fYwaNQqNGjUCANy8eRNbt25F+/btERsbq9R6FYURlaj9/Pxw6dIlLFu2DKNHj0alSvk/GDk5OVi7di0mTpwIPz8/LFu2THRgRERE2s7Pzw+1a9fGqVOnCrxGHBgYiDZt2sDPzw+HDh0S3YaoW99RUVHw8vKCl5eXLEkDQKVKlWRzgv/666+igyIiIhJjxYoVsLW1haGhIdzc3HD+/PlCy0ZERKBdu3aoXr06qlevjs6dO7+3vCLnzp3D2LFjFc71YWlpiTFjxuDs2bPF/h5vE5Wo//vvv/e+mtWwYUM8f/5cdFBERETF9Wb1xYCAAFy+fBlOTk7w8PBAUlKSwvKxsbEYMmQIjh07hjNnzqBOnTro0qULnjx5onSbOjo6yM3NLfR4Xl6eym8qiTq7fv362Lt3b6HH9+7dW2BGMCIiopL09uqLjRs3xurVq1G5cmWsX79eYfmff/4ZXl5ecHZ2RsOGDbF27VrZJFvKat26NVasWIFHjx4VOBYfH4+VK1eiTZs2or8TIPIZtZeXF7y9vdGtWzdMnjwZDRo0AADcvn0bS5cuxaFDh7B8+XKVAiMiIgLyX3tKS0uTbb87FTTwv9UXfX19ZfuUWX3xbZmZmcjJyUGNGjWUjm3+/Plo3749GjZsiL59+8rlwz179kBPTw8hISFK16eI6ESdlJSEBQsWICYmRu5YpUqV4O/vL1u7moiISBXvrnIYEBCAwMBAuX3vW33x1q1bSrXz3XffoVatWnJLLRelWbNmOHfuHPz8/LB3715kZmYCyF9Z67PPPsPcuXPRuHFjpetTRPSEJ4GBgfD29sahQ4cQHx8PALCxsUHnzp2VXsmKiIioKMePH4ezs7Ns+93etDosWLAA27ZtQ2xsLAwNDYt1buPGjREVFQWpVIrk5GQAgLm5udpm0VRpZjIzMzMMGTJELYEQEREpYmxsXOS8HMVdffFtP/74IxYsWIDDhw+jadOmouOUSCSQ/P+EMxI1TjyjUrrft28fvLy80K1bN3Tr1g1eXl7Yt2+fumIjIiJSyturL77xZmBYq1atCj3v+++/R3BwMKKjo+Hi4iKq7Rs3bmDAgAEwMTGBtbU1rK2tYWJiggEDBuD69eui6nybqB51SkoK+vbtixMnTkBXVxfW1tYAgMOHD2PNmjVo164ddu/eLVvJioiIqKQVZ/VFAFi4cCH8/f2xZcsW2NraIiEhAUB+D17ZebpPnjyJrl27QiqVonfv3nKDyfbu3Yvff/8d0dHRaNeunejvJSpRT5o0CSdPnsTChQsxfvx4VKlSBQCQkZGBlStXwtfXF5MmTUJkZKTowIiIiIqjuKsvrlq1CtnZ2RgwYIBcPYoGqxVmypQpsLCwwPHjx1GnTh25Y//88w/at28PHx8fXLhwQfT3EpWod+/eDS8vL0ybNk1uf5UqVfDtt98iPj4emzZtEh0UERGRGMVZfTEuLk7l9v7++28EBwcXSNJA/gpa48ePVzrpF0bUM+pKlSoVOTPZ21OLEhERlUc2NjbIysoq9Hh2drbCJF4cohJ1//79sWPHDoXLWebm5uKXX37BwIEDVQqMiIhI2/n7+2Pp0qW4evVqgWNXrlzBsmXLVO5Ri7r1PXToUHh7e6N169YYM2YM6tevDwC4e/cuwsPDkZ2djS+//BKXL1+WO6958+YqBUtERKRNzp49C0tLS7Ro0QKtW7eWy4dnzpyBo6Mjzpw5Izc7mkQiQVhYmNJtiErUb88Sc+HCBdn7YoIgKCwjCAIkEonCHjgREVFZ9fZ02X/88Qf++OMPuePXrl3DtWvX5PaVSqLesGGDmNOIiIjKFalUWuJtiErUw4cPV3ccREREpIBKU4gC+aua/PPPPwDyh6Ir+5I4ERFRefHw4UP8/vvvsuUubWxs0LVrV9jZ2alct+hEfeHCBUyfPh2nTp2Sdf11dHTQrl07fP/996KnYiMiIipLpk6dirCwsAK3wXV0dDB58mT8+OOPKtUvKlGfO3cOHTp0gL6+PkaNGoVGjRoBAG7evImtW7eiffv2iI2Nhaurq0rBERERabNFixZhyZIlGDBgAKZOnSqXD5csWYIlS5agdu3amDJliug2RCVqPz8/1K5dG6dOnSqwKklgYCDatGkDPz8/HDp0SHRgRERE2i4iIgK9evXCL7/8Irffzc0N27Ztw+vXr7FmzRqVErWoCU/OnTuHsWPHKlw6zNLSEmPGjMHZs2dFB0VERFQWxMXFwcPDo9DjHh4eKk9VKipR6+joIDc3t9DjeXl5alswm4iISFtZWFjgzz//LPT4n3/+CXNzc5XaEJVNW7dujRUrVshGt70tPj4eK1euRJs2bVQKjIiISNsNHDgQa9euxYIFC5CRkSHbn5GRgYULF2Lt2rUYNGiQSm2IekY9f/58tGvXDg0bNkTfvn3l1t/cs2cP9PT0ZOt9EhERlVfBwcG4evUqZs6cCX9/f9SqVQsA8O+//yI3NxcdO3bEnDlzVGpDVKJu1qwZzp8/Dz8/P+zduxeZmZkAgMqVK+Ozzz7D3Llz0bhxY5UCIyIi0naVK1fGkSNHsGfPHrn3qD/77DN069YNPXv2lE2zLVaxE3VWVhZiYmJga2uLqKgoSKVSJCcnAwDMzc35bJqIiCqEzMxMDB06FP3798eXX36J3r17l0g7xc6q+vr6GDhwIE6fPp1fgY4OLC0tYWlpySRNREQVRuXKlXH48GHZXeWSUuzMKpFI8OGHH+LZs2clEQ8REVGZ0bZtW7klLEuCqC7wzJkzsXz5cty+fVvd8RAREZUZy5cvx8mTJzFr1iw8fvy4RNoQNZjs7NmzqFmzJhwdHdGhQwfY2trCyMhIrkxx19skIiIqa5ycnJCbm4uQkBCEhIRAT08PBgYGcmUkEglSU1NFtyEqUb+9UPaRI0cUlmGiJiKi8q5///4qj+ouiqhEXRoLZRMREWm7jRs3lngbKq9HTUREVNG8fv0ae/bswcOHD2FmZobu3bvD2tq6RNpSKVFfv34dBw4ckE04bmtri65du6JJkybqiI2IiEjrJCUloXXr1nj48CEEQQCQ/6rW7t270blzZ7W3JypRZ2VlYezYsdi8eTMEQZC9Py2VSuHr64svv/wSa9euhb6+vlqDJSIi0rTg4GDExcVhypQp6NSpE+7du4fg4GCMHTsW9+/fV3t7ohL1d999h02bNsHLywvffPMN7O3tIZFIcO/ePSxduhSrVq1CjRo1EBoaquZwiYiINOvgwYPw9PTEjz/+KNtnaWmJL774Ardv34aDg4Na2xP1HvVPP/2EYcOGYfny5XBwcICenh50dXXh4OCAFStW4Msvv8RPP/2k1kCJiIi0QXx8PNq2bSu3r23bthAEAYmJiWpvT1SizsnJQcuWLQs93rp16/euV01ERFRWZWVlwdDQUG7fm+2SyH2ibn17eHggJiYG48ePV3g8OjoaXbp0USkwIiIibRUXF4fLly/Ltt9MaHL37l2YmpoWKN+8eXPRbYlK1MHBwfj888/Rr18/TJgwAfXr15cFuGLFCjx69Ajbt2/H8+fP5c6rUaOG6ECJiIi0xezZszF79uwC+728vOS2BUGARCJBXl6e6LZEJepGjRoBAK5du4Y9e/YUCAqAwvWoVQmUiIhIG2zYsKFU2xOVqP39/Ut8yjQiIiJtNHz48FJtT1SiDgwMVHMY8lasWIEffvgBCQkJcHJywrJly+Dq6qqwbEREBDZt2oTr168DAFq0aIH58+cXWp6IiKgsETXquyRt374dPj4+CAgIwOXLl+Hk5AQPDw8kJSUpLB8bG4shQ4bg2LFjOHPmDOrUqYMuXbrgyZMnpRw5ERGR+mldol68eDFGjx6NESNGoHHjxli9ejUqV66M9evXKyz/888/w8vLC87OzmjYsCHWrl0LqVRa6KpeJM/FxQUffPABXFxcNB0KEREpoFWLcmRnZ+PSpUvw9fWV7dPR0UHnzp1x5swZperIzMxETk5OoSPMs7KykJWVJdtOT09XLegyLiEhgXcfiIi0mFb1qJ89e4a8vDxYWlrK7be0tERCQoJSdXz33XeoVatWoROjh4SEoFq1arKPu7u7ynETERGVFK1K1KpasGABtm3bhqioqAKzxrzh6+uL1NRU2ef48eOlHCUREZHytOrWt5mZGXR1dQvMlZqYmAgrK6v3nvvjjz9iwYIFOHz4MJo2bVpoOQMDAxgYGMi2jY2NVQuaiIioBGlVj1pfXx8tWrSQGwj2ZmBYq1atCj3v+++/R3BwMKKjozkoioiIyhWt6lEDgI+PD4YPHw4XFxe4uroiNDQUGRkZGDFiBADA09MTtWvXRkhICABg4cKF8Pf3x5YtW2Brayt7lm1sbMzeMhERlXlal6gHDRqE5ORk+Pv7IyEhAc7OzoiOjpYNMIuPj4eOzv9uBKxatQrZ2dkYMGCAXD0BAQElPjELERFRSdO6RA0A3t7e8Pb2VngsNjZWbjsuLq7kAyIiItIQrXpGTURERPKYqImIiLQYEzUREZEWY6ImIqJyY8WKFbC1tYWhoSHc3Nxw/vz595bfsWMHGjZsCENDQzRp0gQHDhwopUiVx0RdwVlZWaF27dpFTihDRKTtirv64unTpzFkyBCMHDkSV65cQZ8+fdCnTx/Zssnagom6grt48SIeP36MixcvajoUIiKVFHf1xbCwMHz22Wf49ttv0ahRIwQHB6N58+ZYvnx5KUf+fkzURERU5r1ZffHtBZmKWn3xzJkzBRZw8vDwUHq1xtKile9RU+nKy8uDVCrVWPvSvFxI8/IgzctFTk6OxuLQlNw8KXLzNPfvrw1yBSl08qQV8vpT4XJzcwHkL0eclpYm2//umg3A+1dfvHXrlsL6ExISVFqtsbQwUWuBykImMlMy0XvyfI20f+vsEdw5d1Qjbb/rl7njNR0CadLUnzUdAWmhd5cjrmgzTzJRExxcO6LBxx00GkOmpDLMqlXBT/4jNRqHJlxYNgLJ6bmaDkOjqgmp0K9aEx9/s0HToZAWuXLlCtzc3HD8+HE4OzvL9r/bmwbErb5oZWUlarXG0sZETZDo6ECi4Rh0JLrQ0dVDpUqVNBxJ6dPT1YGebsUeLqIn5P8bVMTrT4XT08tPUcbGxjAxMXlv2bdXX+zTpw+A/62+WNiU1K1atcKRI0cwefJk2b5Dhw69d7VGTWCiruCOb12BrMx0GFQ2hvuQCZoOh4hItOKuvjhp0iS4u7tj0aJF6N69O7Zt24aLFy8iPDxck1+jACbqCi4rMx2v09OKLkhEpOWKu/pi69atsWXLFsyaNQszZ87Ehx9+iN27d8PR0VFTX0EhJmoiIio3irP6IgAMHDgQAwcOLOGoVFOxH4wRERFpOSZqIiIiLcZETUREpMWYqImIiLQYEzUREZEWY6ImIiLSYkzUREREWoyJmoiISIsxURMREWkxJmoiIiItxkRNRESkxTjXdwVnUNlY7n+JiEi7MFFXcFzakohIu/HWNxERkRZjoiYiItJiTNRERERajImaiIhIizFRExERaTEmaiIiIi3GRE1ERKTFmKiJiIi0GBM1ERGRFmOiJiIi0mJM1ERERFqMiZqIiEiLMVETERFpMSZqIiIiLcZETUREpMWYqImIiLQYEzUREZEWY6ImIiLSYkzUREREWkwrE/WKFStga2sLQ0NDuLm54fz58+8tv2PHDjRs2BCGhoZo0qQJDhw4UEqREhERlSytS9Tbt2+Hj48PAgICcPnyZTg5OcHDwwNJSUkKy58+fRpDhgzByJEjceXKFfTp0wd9+vTB9evXSzlyIiIi9dO6RL148WKMHj0aI0aMQOPGjbF69WpUrlwZ69evV1g+LCwMn332Gb799ls0atQIwcHBaN68OZYvX17KkRMREamfViXq7OxsXLp0CZ07d5bt09HRQefOnXHmzBmF55w5c0auPAB4eHgUWp6IiKgs0dN0AG979uwZ8vLyYGlpKbff0tISt27dUnhOQkKCwvIJCQkKy2dlZSErK0u2nZ6eDgC4efOmKqGL9vzpI7x++VwjbWuT1xJD6GRWxuXLlzUdSqm79c9/ePEqT9NhaJSxkI5KlQG9d66/tbU1rK2tNRSVap4+fYqnT59qOowyTVO/l7WNViXq0hASEoKgoCC5fTY2Nhg6dKiGIqK3HQyfq+kQSJMWyQ8EDQgIQGBgoGZiUdGaNWsK/K6h4nN3dy+zf6ypi1YlajMzM+jq6iIxMVFuf2JiIqysrBSeY2VlVazyvr6+8PHxkdv3/PlzPH9eMXu16enpcHd3x/Hjx2FsbKzpcEgDtPlnoCz/gh47dix69epVqm1q87UUqyzfVVEXiSAIgqaDeJubmxtcXV2xbNkyAIBUKkXdunXh7e2NGTNmFCg/aNAgZGZm4rfffpPta926NZo2bYrVq1eXWtxlVVpaGqpVq4bU1FSYmJhoOhzSAP4MlB+8luWTVvWoAcDHxwfDhw+Hi4sLXF1dERoaioyMDIwYMQIA4Onpidq1ayMkJAQAMGnSJLi7u2PRokXo3r07tm3bhosXLyI8PFyTX4OIiEgttC5RDxo0CMnJyfD390dCQgKcnZ0RHR0tGzAWHx8PHZ3/DVZv3bo1tmzZglmzZmHmzJn48MMPsXv3bjg6OmrqKxAREamN1t36ptKVlZWFkJAQ+Pr6wsDAQNPhkAbwZ6D84LUsn5ioiYiItJhWTXhCRERE8pioiYiItBgTNalVXFwcJBIJNm7cqOlQiIjKBSZqDbp//z7Gjh2LevXqwdDQECYmJmjTpg3CwsLw6tWrEmv3xo0bCAwMRFxcXIm1oYx58+ahV69esLS0hEQiKbMzUJU0iUSi1Cc2NlbltjIzMxEYGFisungdi4fXk4pL617Pqij279+PgQMHwsDAAJ6ennB0dER2djZOnTqFb7/9Fn///XeJvQt+48YNBAUFoUOHDrC1tS2RNpQxa9YsWFlZoVmzZoiJidFYHNpu8+bNctubNm3CoUOHCuxv1KiRym1lZmbKpr3s0KGDUufwOhYPrycVFxO1Bjx8+BCDBw+GjY0Njh49Kjc93oQJE3Dv3j3s379fgxH+jyAIeP36NYyMjNRe98OHD2Fra4tnz57B3Nxc7fWXF+/OQ3/27FkcOnRIa+an53UsHl5PKi7e+taA77//Hunp6Vi3bp3COWzr16+PSZMmybZzc3MRHBwMe3t7GBgYwNbWFjNnzpRbBQwAbG1t0aNHD5w6dQqurq4wNDREvXr1sGnTJlmZjRs3YuDAgQCAjh07FrjN9qaOmJgYuLi4wMjICGvWrAEAPHjwAAMHDkSNGjVQuXJltGzZUqU/KDTZmy9vpFIpQkND8dFHH8HQ0BCWlpYYO3YsXrx4IVfu4sWL8PDwgJmZGYyMjGBnZ4evv/4aQP74gje/mIOCgmQ/G0Xd+uR1VD9eT3obe9Qa8Ntvv6FevXpo3bq1UuVHjRqFyMhIDBgwAFOnTsW5c+cQEhKCmzdvIioqSq7svXv3MGDAAIwcORLDhw/H+vXr8dVXX6FFixb46KOP0L59e0ycOBFLly7FzJkzZbfX3r7Ndvv2bQwZMgRjx47F6NGj4eDggMTERLRu3RqZmZmYOHEiatasicjISPTq1Qs7d+5E37591fcPRMU2duxYbNy4ESNGjMDEiRPx8OFDLF++HFeuXMEff/yBSpUqISkpCV26dIG5uTlmzJgBU1NTxMXFYdeuXQAAc3NzrFq1CuPHj0ffvn3Rr18/AEDTpk01+dUqJF5PkiNQqUpNTRUACL1791aq/NWrVwUAwqhRo+T2T5s2TQAgHD16VLbPxsZGACCcOHFCti8pKUkwMDAQpk6dKtu3Y8cOAYBw7NixAu29qSM6Olpu/+TJkwUAwsmTJ2X7Xr58KdjZ2Qm2trZCXl6eIAiC8PDhQwGAsGHDBqW+nyAIQnJysgBACAgIUPqcimzChAnC2//XPXnypABA+Pnnn+XKRUdHy+2PiooSAAgXLlwotG5VrgWvozi8nlQU3vouZWlpaQCAqlWrKlX+wIH89XnfXZpz6tSpAFDg1nPjxo3Rrl072ba5uTkcHBzw4MEDpWO0s7ODh4dHgThcXV3Rtm1b2T5jY2OMGTMGcXFxuHHjhtL1k3rt2LED1apVw6effopnz57JPi1atICxsTGOHTsGADA1NQUA7Nu3Dzk5ORqMmN6H15PexURdyt4sPffy5Uulyj969Ag6OjqoX7++3H4rKyuYmpri0aNHcvvr1q1boI7q1asXeLb1PnZ2dgrjcHBwKLD/zS3zd+Og0nP37l2kpqbCwsIC5ubmcp/09HQkJSUBANzd3dG/f38EBQXBzMwMvXv3xoYNGwqMdSDN4vWkd/EZdSkzMTFBrVq1cP369WKdJ5FIlCqnq6urcL9QjCndS2KEN5UcqVQKCwsL/PzzzwqPvxlQJJFIsHPnTpw9exa//fYbYmJi8PXXX2PRokU4e/YsjI2NSzNsKgSvJ72LiVoDevTogfDwcJw5cwatWrV6b1kbGxtIpVLcvXtXbsBXYmIiUlJSYGNjU+z2lU3678Zx+/btAvtv3bolO06aYW9vj8OHD6NNmzZK/ZHVsmVLtGzZEvPmzcOWLVvw5ZdfYtu2bRg1apSonw1SL15PehdvfWvA9OnTUaVKFYwaNQqJiYkFjt+/fx9hYWEAgG7dugEAQkND5cosXrwYANC9e/dit1+lShUAQEpKitLndOvWDefPn8eZM2dk+zIyMhAeHg5bW1s0bty42HGQenz++efIy8tDcHBwgWO5ubmy6/zixYsCd1acnZ0BQHa7tHLlygCK97NB6sXrSe9ij1oD7O3tsWXLFgwaNAiNGjWSm5ns9OnT2LFjB7766isAgJOTE4YPH47w8HCkpKTA3d0d58+fR2RkJPr06YOOHTsWu31nZ2fo6upi4cKFSE1NhYGBATp16gQLC4tCz5kxYwa2bt2Krl27YuLEiahRowYiIyPx8OFD/Prrr9DRKf7ffJs3b8ajR4+QmZkJADhx4gTmzp0LABg2bBh76Upyd3fH2LFjERISgqtXr6JLly6oVKkS7t69ix07diAsLAwDBgxAZGQkVq5cib59+8Le3h4vX75EREQETExMZH8QGhkZoXHjxti+fTsaNGiAGjVqwNHREY6OjoW2z+uoXryeVICGR51XaHfu3BFGjx4t2NraCvr6+kLVqlWFNm3aCMuWLRNev34tK5eTkyMEBQUJdnZ2QqVKlYQ6deoIvr6+cmUEIf/Vqu7duxdox93dXXB3d5fbFxERIdSrV0/Q1dWVe1WrsDoEQRDu378vDBgwQDA1NRUMDQ0FV1dXYd++fXJlivN6lru7uwBA4UfRq2OU793Xed4IDw8XWrRoIRgZGQlVq1YVmjRpIkyfPl34999/BUEQhMuXLwtDhgwR6tatKxgYGAgWFhZCjx49hIsXL8rVc/r0aaFFixaCvr6+Uq/n8DqqhteTiiIRhGKMMiIiIqJSxWfUREREWoyJmoiISIsxURMREWkxJmoiIiItxkRNRESkxZioiYiItBgTNRFRGRIXFweJRIKNGzdqOhQqJUzUWmrjxo2QSCQwNDTEkydPChzv0KHDe2cXKg2jR4+GRCJBjx49FB7fu3cvmjdvDkNDQ9StWxcBAQHIzc0t5SjLJl5/InqDiVrLZWVlYcGCBZoOo4CLFy9i48aNMDQ0VHj8999/R58+fWBqaoply5ahT58+mDt3Lr755ptSjrRs4/Wnd9nY2ODVq1cYNmyYpkOhUsK5vrWcs7MzIiIi4Ovri1q1amk6HAD5S2ZOnDgRnp6eOHLkiMIy06ZNQ9OmTXHw4EHo6eX/mJmYmGD+/PmYNGkSGjZsWJohl1m8/vSuN3daqOJgj1rLzZw5E3l5eVrVq9q8eTOuX7+OefPmKTx+48YN3LhxA2PGjJH9kgYALy8vCIKAnTt3llaoZR6vf/kUGBgIiUSCO3fuYOjQoahWrRrMzc0xe/ZsCIKAf/75B71794aJiQmsrKywaNEi2bmKnlF/9dVXMDY2xpMnT9CnTx8YGxvD3Nwc06ZNQ15enqxcbGwsJBIJYmNj5eJRVGdCQgJGjBiBDz74AAYGBrC2tkbv3r0RFxdXQv8qVBgmai1nZ2cHT09PRERE4N9//y32+ZmZmXj27FmRnxcvXihV38uXL/Hdd99h5syZsLKyUljmypUrAAAXFxe5/bVq1cIHH3wgO05F4/Uv3wYNGgSpVIoFCxbAzc0Nc+fORWhoKD799FPUrl0bCxcuRP369TFt2jScOHHivXXl5eXBw8MDNWvWxI8//gh3d3csWrQI4eHhomLr378/oqKiMGLECKxcuRITJ07Ey5cvER8fL6o+Eo+Jugzw8/NDbm4uFi5cWOxzv//+e5ibmxf5adasmVL1zZkzB0ZGRpgyZUqhZZ4+fQoAsLa2LnDM2tpaVMKpyHj9yy9XV1ds2bIF48ePx549e/DBBx9g6tSpsuQ4fvx47Nu3D0ZGRli/fv1763r9+jUGDRqEdevWYdy4cdi5cyeaNWuGdevWFTuulJQUnD59GrNmzUJwcDBGjhwJX19fHD16FO3btxf7dUkkPqMuA+rVq4dhw4YhPDwcM2bMUPgLsDCenp5o27ZtkeWMjIyKLHPnzh2EhYVh69atMDAwKLTcq1evAEBhGUNDQ6SlpRXZFv0Pr3/5NWrUKNl/6+rqwsXFBY8fP8bIkSNl+01NTeHg4IAHDx4UWd+4cePkttu1a4fNmzcXOy4jIyPo6+sjNjYWI0eORPXq1YtdB6kPE3UZMWvWLGzevBkLFixAWFiY0ufVq1cP9erVU0sMkyZNQuvWrdG/f//3lnvzSz8rK6vAsdevXyuVFEger3/5VLduXbntatWqwdDQEGZmZgX2//fff++ty9DQEObm5nL7qlevrvRjjbcZGBhg4cKFmDp1KiwtLdGyZUv06NEDnp6ehT7yoJLDRF1G1KtXD0OHDpX1qpSVnp6O9PT0Isvp6uoW+D/5244ePYro6Gjs2rVLbjBJbm4uXr16hbi4ONSoUQMmJiayHt/Tp09Rp04duXqePn0KV1dXpeOnfLz+5ZOurq5S+4D80fbFretdEolE4f63B5y9MXnyZPTs2RO7d+9GTEwMZs+ejZCQEBw9elTpRyWkHnxGXYbMmjWr2M8qf/zxR1hbWxf5+fjjj99bz5sBJP369YOdnZ3s8+TJExw9ehR2dnayZ2jOzs4A8t+1fdu///6Lx48fy45T8fD6k6re3MJOSUmR2//o0SOF5e3t7TF16lQcPHgQ169fR3Z2ttwIdCod7FGXIfb29hg6dCjWrFkDGxsbuVdfCqOuZ5SdOnVCVFRUgf1jxoyBjY0N/Pz80KRJEwDARx99hIYNGyI8PBxjx46V/aW/atUqSCQSDBgwoMh4qCBef1KVjY0NdHV1ceLECfTp00e2f+XKlXLlMjMzoaOjI/e+tr29PapWrarwkQaVLCbqMsbPzw+bN2/G7du38dFHHxVZXl3PKOvWrVvgeRqQf3vM0tJS7v/0APDDDz+gV69e6NKlCwYPHozr169j+fLlGDVqFBo1aqRyPBUVrz+polq1ahg4cCCWLVsGiUQCe3t77Nu3D0lJSXLl7ty5g08++QSff/45GjduDD09PURFRSExMRGDBw/WUPQVF299lzH169fH0KFDNR1GkXr06IFdu3bh+fPn+Oabb7Br1y7MnDkTK1as0HRoZRqvP6lq2bJl6N27N1avXo1Zs2ahbt26iIyMlCtTp04dDBkyBLGxsfD19YWvry/S0tLwyy+/FDmYkNRPIhQ1QoGIiIg0hj1qIiIiLcZETUREpMWYqImIiLQYEzUREZEWY6ImIiLSYkzUREREWoyJmoiICoiLi4NEIsHGjRs1HUqFx0RNRKSi+/fvY+zYsahXrx4MDQ1hYmKCNm3aICwsTLbsZ0m4ceMGAgMD5RZK0YR58+ahV69esLS0hEQiQWBgoEbjKW84hSgRkQr279+PgQMHwsDAAJ6ennB0dER2djZOnTqFb7/9Fn///TfCw8NLpO0bN24gKCgIHTp0gK2tbYm0oYxZs2bBysoKzZo1Q0xMjMbiKK+YqImIRHr48CEGDx4MGxsbHD16VLbEJwBMmDAB9+7dw/79+zUY4f8IglBi64E/fPgQtra2ePbs2XuXSyVxeOubiEik77//Hunp6Vi3bp1ckn6jfv36mDRpkmw7NzcXwcHBsLe3h4GBAWxtbTFz5swCK1LZ2tqiR48eOHXqFFxdXWFoaIh69eph06ZNsjIbN27EwIEDAQAdO3aERCKBRCJBbGysXB0xMTFwcXGBkZER1qxZAwB48OABBg4ciBo1aqBy5cpo2bKlSn9QaLI3XxEwURMRifTbb7+hXr16aN26tVLlR40aBX9/fzRv3hxLliyBu7s7QkJCFK5Ide/ePQwYMACffvopFi1ahOrVq+Orr77C33//DQBo3749Jk6cCACYOXMmNm/ejM2bN8utTnb79m0MGTIEn376KcLCwuDs7IzExES0bt0aMTEx8PLywrx58/D69Wv06tVL4VKmpAUEIiIqttTUVAGA0Lt3b6XKX716VQAgjBo1Sm7/tGnTBADC0aNHZftsbGwEAMKJEydk+5KSkgQDAwNh6tSpsn07duwQAAjHjh0r0N6bOqKjo+X2T548WQAgnDx5Urbv5cuXgp2dnWBrayvk5eUJgiAIDx8+FAAIGzZsUOr7CYIgJCcnCwCEgIAApc+horFHTUQkQlpaGgCgatWqSpU/cOAAAMDHx0du/9SpUwGgwK3nxo0bo127drJtc3NzODg44MGDB0rHaGdnBw8PjwJxuLq6om3btrJ9xsbGGDNmDOLi4nDjxg2l66fSwURNRCSCiYkJAODly5dKlX/06BF0dHRQv359uf1WVlYwNTXFo0eP5PbXrVu3QB3Vq1fHixcvlI7Rzs5OYRwODg4F9r+5Zf5uHKR5TNRERCKYmJigVq1auH79erHOk0gkSpXT1dVVuF8QBKXbKokR3lT6mKiJiETq0aMH7t+/jzNnzhRZ1sbGBlKpFHfv3pXbn5iYiJSUFNjY2BS7fWWT/rtx3L59u8D+W7duyY6TdmGiJiISafr06ahSpQpGjRqFxMTEAsfv37+PsLAwAEC3bt0AAKGhoXJlFi9eDADo3r17sduvUqUKACAlJUXpc7p164bz58/L/XGRkZGB8PBw2NraonHjxsWOg0oWJzwhIhLJ3t4eW7ZswaBBg9CoUSO5mclOnz6NHTt24KuvvgIAODk5Yfjw4QgPD0dKSgrc3d1x/vx5REZGok+fPujYsWOx23d2doauri4WLlyI1NRUGBgYoFOnTrCwsCj0nBkzZmDr1q3o2rUrJk6ciBo1aiAyMhIPHz7Er7/+Ch2d4vffNm/ejEePHiEzMxMAcOLECcydOxcAMGzYMPbSVaXpYedERGXdnTt3hNGjRwu2traCvr6+ULVqVaFNmzbCsmXLhNevX8vK5eTkCEFBQYKdnZ1QqVIloU6dOoKvr69cGUHIf7Wqe/fuBdpxd3cX3N3d5fZFREQI9erVE3R1deVe1SqsDkEQhPv37wsDBgwQTE1NBUNDQ8HV1VXYt2+fXJnivJ7l7u4uAFD4UfTqGBWPRBCKMTKBiIiIShWfURMREWkxJmoiIiItxkRNRESkxZioiYiItBgTNRERkRZjoiYiItJiTNRERERajImaiIhIizFRExERaTEmaiIiIi3GRE1ERKTFmKiJiIi0GBM1ERGRFvs//llerBm7u0MAAAAASUVORK5CYII=", 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" ] @@ -757,7 +910,7 @@ "outputs": [ { "data": { - "image/png": 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+ "image/png": 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", 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" ] @@ -771,6 +924,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "id": "5f33004b", "metadata": {}, @@ -785,11 +939,12 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "id": "f3865a7a", "metadata": {}, "source": [ - "Instead of a Gardner-Altman plot, you can produce a **Cumming estimation\n", + "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." ] @@ -802,7 +957,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -816,20 +971,23 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "id": "3e649272", "metadata": {}, "source": [ - "## Producing Paired Proportion Plots" + "## Generating Sankey plots for paired proportions and repeated-measures proportions" ] }, { + "attachments": {}, "cell_type": "markdown", "id": "e6c37cd5", "metadata": {}, "source": [ - "For paired version of proportional plot, we adapt the style of Sankey Diagram. The width of each bar in each xticks represent \n", - "the proportion of corresponding label in the group, and the strip denotes the paired relationship for each observation.\n", + "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" @@ -843,7 +1001,7 @@ "outputs": [ { "data": { - "image/png": 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", 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XLF++HI0bN5bf+p49eza6du1aaVteRD2WZZGdnY3s7GyYmZmhRo0acHFxUbhDYmpKUlNQnPCY7zBIFfPs2TOUlJTA3Nyc71CqPXt7e1y5cgXLli3Dvn37IBaLce7cOfj5+WHu3Ln48ssvYWlpqVcdOiXqs2fPYuDAgfLtnTt34tatW/jxxx/RqFEjfPrpp4iKisKhQ4e0KrdPnz5ITU3FnDlzkJSUhKCgIBw/flzewezp06cKLehZs2aBYRjMmjULL168gIuLC7p27YqFCxfq8mORSqS0tBTJyclITk6Gvb093NzcYGtry3dYCmSFBSi8d4fvMEgVJJPJ8OLFC3h7e/MdCgFgaWmJWbNmYdasWUYpX6dEnZSUpPAGOXToEIKDg9GvXz8AwMiRI/HNN9/oFNCECRMwYcIElcfi4uIUts3MzDB37lzMnTtXp7pI1VDWyra2toa7u7vWQwONgWVZFN67C1ZqvJ7zpHp7+fIlJWoTUFpaioKCAtjZ2ak8npOTAysrK72Gu+r0jNra2hpZWVnyIOPi4hSGZ9na2ir05CWEC/n5+Xj06BHu3r3L+/tP8vwZSrMzeY2BVG3lO94SfkycOLHC4VmtWrXC1KlT9apDp0TdpEkTbNy4EX/++ScWLlyI3NxcdO3aVX780aNHSuOhCeFKQUEBHj58iPj4eOTn53Nev6y4CMVPHnFeL6le0tPT9Z5Dmujv+PHj6NWrl9rjvXr1wrFjx/SqQ6e2+MKFCxEWFobg4GCwLItevXohJCREfvzgwYNo1aqVXoERoq+8vDzcu3cPNWrUgKenJ2cdb4oePQQrow9QYlwymQwZGRlwcXHhO5Rq7eXLlxVO61qzZk28eKHfqA+dWtTBwcG4d+8eDhw4gLNnz+Knn36SH8vKysK4ceMwbdo0vQIjxFDS09Nx+/ZtpKSkGH1YV2lWFkpStJ/nnhBdqJqykm/aroCYlZWF8ePHw8PDAyKRCHXr1tW7BcqlGjVqID4+Xu3xu3fvqn1+rSmdn267uLgoLMRRxsHBAZMmTdIrKEIMTSqV4tmzZ0hPT4eXlxesrKyMUk/RI1o3mHDH1BK1tisgSiQSdOzYEa6urti3bx88PT2RmJhoEh1CNfXhhx9iw4YNGDBggNKQ5Bs3biAmJga9e/fWqw6dE7VUKsXevXtx9uxZpKSkYP78+WjQoAGys7Nx+vRptGrVip5TE5NTUFCAe/fuwdXVFTVr1jToNIwl6WmQ5lInSsKdzEzT6rCo7QqIW7ZsQUZGBi5duiR/NFXZerJHR0fj+PHjCAkJQbdu3fDee+8BAG7duoVffvkFrq6uiI6O1qsOnT6lsrKy5MtL7tq1C4cPH0Zq6uuVgGxsbDBx4kSsXLlSr8AIMRaWZZGcnIzbt28jIyPDYOUWP6GJTQi3uEjUZVPLlr3eXH3wTWUrIHbo0EG+720rIB4+fBgtWrTA+PHj4ebmhvr162PRokWVqpNczZo1ce3aNfTv3x+nT5/GggULsGDBApw5cwYDBgzAH3/8gXfeeUevOnRK1DNmzMDt27cRGxuLx48fKzz3EwqFBunlRoixSSQSPHnyxCC9w0tSUyDNo3myiaKnT5+ioKAAwOv3myG/GAJAYWEhJBLjLmsaGhoKe3t7+atsZsjyKloBMSlJdb+Nx48fY9++fZBKpTh27Bhmz56NZcuWYcGCBQb/OYzJw8MD27ZtQ2ZmJpKSkpCUlITMzExs3boVNWvW1Lt8nRL1oUOH8MUXX6Bjx44qVzaqW7cuEhIS9I2NEE6U9Q5//PgxioqKdCqj+Kn289uTquvq1avo2rUrvL295a3egoICzJw5E2vXrjXo56OxF1I5d+6cfFKh7OxsREREGKxsmUwGV1dXxMTEoGnTpujTpw8iIyOxfv16g9XBJYZh4OrqCldXV4Ou+qfTM+rs7OwKV24pKSkx6lrGhBhDZmYmMjMzUaNGDbi7u0MsFmt0XWlWVpV4Nl22khytKKefAwcOoE+fPmBZVmmUAcuyuHXrFm7duoWRI0eiSZMmeteXl5cHZ2dnvctRx8bGRqNey7qsgOjh4QFzc3OFtRkCAgKQlJQEiUQCCwsL/YLnSGZmJnbt2oXHjx+rXDSIYRhs3rxZ5/J1StR+fn64ceOG2uMnTpxAYGCgzkERwqf09HSkp6fD0dERbm5usLa2rvB8yXPTbU1rMxxt1KhROl1H/nP16lX06dMHUqlU7e9QJpMBADZu3Ijp06fr3XmKj0l9VHlzBcTu3bsD+G8FRHXTQrdq1Qo7d+6ETCaTd+y8f/8+PDw8Kk2Sjo2NRa9evZCfnw87Ozs4OjoqnaNv61qnRD1ixAhMnz4d7dq1wwcffCAPpLi4GPPnz8fx48cRExOjV2CE8K2shW1jYwMXFxc4Ojoq/Q8nKyxASVqa1mUzjADm7h4wc6oBoa0dYMDbZCWZmWB+v8ZpsmUYptJ8sBrTggULVLak1Tl27BjGjRunV53GfkatDW1XQBw7dizWrFmDSZMm4YsvvsCDBw+waNEiTJw4kc8fQytTp06Fu7s7Dhw4gAYNGhilDp0S9aRJk3D79m3069dPPt6tf//+SE9PR2lpKUaPHo3hw4cbMk5CeJOXl4e8vDw8e/YMTk5OqFGjhnwcdvGL5wC0S4jmbh4Q+9aBwEjLczq7u2P8+PGcfoBbWFigRo0anNVnip4+fYojR45onKRlMhn+/vtvZGRkwMnJSed6TSlRa7sCYq1atRAbG4spU6agYcOG8PT0xKRJkzB9+nS+fgStPXz4EN98843RkjSgY6JmGAYbN27EkCFDsG/fPjx48AAymQx+fn747LPP0LZtW0PHSQjvSktLkZKSgpSUFIjFYjjY2cH82VOINGwMM0IzWPrXg7mr6ud1hlRdk6ZUKpXfWuZabGys1ncxWJbFnTt30KJFC53rLSkpQUlJic7Xq6NrPyNtVkAEgBYtWuDKlSs61WUK3n33XeTm5hq1Dt3X3QLQunVrtG7d2lCxEFJpFBUV4XlyEkqSkmAuFMJWZAEbkQjWFuYwe6NjTBlGaAarRo1hZmfPQ7TVR3R0NKKiovgOQys7duzAjh07+A6D6GjBggUYP348+vfvb7TJWnRK1E+ePMGtW7cUVsx60y+//IIGDRpUuhlmCNGG9N+lXkukUmQUFCKjoBAAYGEmhJW5OcTm5rA0N4NYJIZDoyYQ6jnfL3m72bNnIzIykpe6t27dqtAhT1ODBg3Sq0XdpEkTg/QeL+/PP/9Es2bNDF5uVXP69Gm4uLggICAAHTt2RK1atRR6sQOv70LrMwmYTol62rRpyMnJUZuo165dCwcHB+zevVvnwAgxZbLiIsiKClUek5RKISmVAoVFABhYeL4D80ePYGFhAQsLC/lwFKFQCIFAYNBpTIHXkw7ZlkjAFus2JlwXjEgMC3cPzupTp+z3yoewsDAwDKPV7W+GYRAYGKhXzHZ2dkZZGc7MTK8brtXGmjVr5P8+cuSIynN4SdSXL1/G5MmT1R7/4IMPsGLFCh1DIsT0lWZrNm7a3NkFQhsbyGQyFBUV6TyhijbMigphvWmt0espr/bi5SaRrPlSu3ZtdOnSBceOHdNoCkyBQIAGDRro1ZEMAGxtbfW6nuiHiz4ROn2Vz8zMrPDNYWNjY3KruhBiKCzLQqrBbFACa2uY8dCpizVCxyKN6uWwBW+qZs+eDYZhNB4327lzZ73rrEwrTRHd6JSoa9eujd9++03t8QsXLug9CTkhpkqWnw9I39IjViAwjdYlw2j86vHLCbT+6Wf0+OWEVteR//zvf//Dnj17KrwFX/a4Y9SoUXr347G0tHzrhDyEG1euXMHixYsxZcoUPHjwernbgoIC3LhxA3l5eXqVrVOi7tevH3bt2oVVq1YpNPulUilWrlyJPXv2oH///noFRoipkua8/ba3uasbGDPDPzc0ptTCQiQXFCK1UPWzd6KZnj174tKlS+jcubNSy5phGDRo0ADTp09XWrtYF25ubgadU5poTyKRoGfPnmjVqhUiIyOxatUqPHv2DMDrL2WdOnXSezVJnRJ1REQE2rdvj8mTJ8PDwwNt27ZF27ZtUbNmTUyZMgWhoaG89bwkxJhYqRTSvIrHTAosrWBm78BNQMQk/e9//8Phw4eRkJAgn1LSysoKixYtwrhx4ww2IqZWrVoGKYfobvbs2Thy5AjWrVuH+Ph4hc6EYrEYvXv3xs8//6xXHTolapFIhBMnTmDz5s0ICQlBWloa0tLSEBISgi1btuDUqVMQGWnWJUL4JM3LBSrs1cvA3NWVs3iIaatdu7Z8FjsLCwu9O46VR4maf7t27cLYsWMxatQolX/fgIAAPH6s31r1Ove/FwgEGDZsmHwOV0KqA2luxZ3IhPb2EIgtOYqGVGdubm600pkJSElJqXD6UKFQKF+TXFc6tagzMjLw999/qz3+zz//yNdgJaSqYEtLIavofziGgbmzC3cBkWrNz8+P7xAIXt/VuHfvntrjv/32G+rUqaNXHTol6ilTplQ4A8/o0aMxbdo0nYMixBRJ8/IqvO0tdHAEQ5NEEA4IBAK9P/yJYfTv3x8bNmzA5cuX5fvKOvht3LgRP/30EwYPHqxXHTp9qpw5cwZjx45Ve7xr165Yv369zkERYooqvO0tEMDcqXouhEG4V7t2bYjFYr7DIAAiIyNx5coVtG3bFgEBAWAYBlOmTEFGRgaeP3+Ozp07Y8qUKXrVoVOLOjU1Fc7OzmqP16hRAykpKToHRYipYaXSCm97m1FrmnDI39+f7xDIvywsLHD8+HF8//338PX1Rb169VBcXIyGDRti69at+OWXX/Se1lanTxYPDw/8+eefao9fv34dLi70rI5UHa+HZKm57c0wMHM0bG9eQtSxsrKi3t4morCwEJGRkWjfvj0GDhyIgQMHGqUenVrU3bt3x+bNm3H48GGlYz///DO+//579OjRQ+/gCDEVsgpmFhLa2lFrmnCmbt26Bl/IhejG0tISGzZsQHJyslHr0enTZd68eTh16hR69OiBRo0aoX79+gCAW7du4a+//kJAQEClWxOWEHVYmQzSgny1x83o2TThCMMwCAgI4DsM8oamTZvi1q1bRq1Dp69l9vb2uHLlCmbNmoWSkhLs27cP+/btQ0lJCWbPno3ff/+dJoonVYYsPx9Qs0KOwNoaAprch3CkVq1atFqWiVmxYgV2796NTZs2obT0LWsA6Ejn+3XW1taIioqiljOp8iqaMtTM3pHDSEh1V9HEGoQfQ4cOhUAgwOjRozFx4kR4enrC0lJx0iOGYfDXX3/pXAc9WCOkAizLQpqv+rY3Y24OAc0MRTji7OyMmjVr8h0GKcfJyQk1atQwak98nRL1559//tZzGIbB5s2bdSmeEJMhKypSu6Sl0N6hSq1c5PJvK8DFkqZANUXBwcFV6v1WVcTFxRm9Dp0nPCn/hpFKpXj16hWkUilcXFxojVRSJcjy1fT2Zpgqt0LWwW5hfIdA1HBzc6MhWdWYTp3JEhIS8OTJE4XX06dPUVBQgFWrVsHW1hanT582dKyEcE5dohZa29CQLMKZli1bUmvahOXk5GDJkiUICwtD48aNcfXqVQCv18VYvnw5Hj58qFf5Bh2MZ25ujgkTJqBTp06YMGGCIYsmhHNsaenrW98qCO3sOY6GVFf+/v40gZQJe/78ORo3bow5c+bg+fPn+Pvvv5H377wLTk5O2LBhA1avXq1XHUYZNd+oUSOcP3/eGEUTwhmputveQjPqREY4IRKJEBISwncYpAJffvklcnNzcfPmTZw7dw5suYV7unfvjlOnTulVh1ES9cmTJ+WLpRNSWcnU9PYW2tnSbUjCiWbNmikN9SGm5cSJE5g4cSICAwNVfi74+vri2bNnetWh00O2+fPnq9yflZWF8+fP48aNG5gxY4ZegRHCJ5Zl1c5GZmZLt72J8bm5udHiG5VAYWFhhY8mcnPVz8OgKZ2nEFXF0dERfn5+WL9+PUaOHKlPXITwii0qAqRSpf2MhQgCauEQI2MYBq1bt6Y7N5VAYGAgzp8/j9GjR6s8fujQITRu3FivOnRK1DI10ykSUlXI1LSmhTR9I+FAQEAAatSgOeQrg8mTJ2PIkCFo2LAhevfuDeB1jnz48CGioqJw+fJl7N+/X686aHwJISqom41MaGvHcSSkuhGJRAgODuY7DKKhgQMHIjExEbNmzUJkZCQA4MMPPwTLshAIBFi0aBG6d++uVx06dSZ7+vQpLl68qLDvr7/+wuDBg9GnTx8cOnRI54DWrl0Lb29viMViNGvWTD4eTZ2srCyMHz8eHh4eEIlEqFu3Lo4dO6Zz/YSwMhlkRYVK+wUiES3AQYwuKCgIYrGY7zCIFiIjI/Ho0SN88803GDt2LEaOHImlS5ciPj4e06dP17t8nVrUEydORF5enrzLeXJyMtq3bw+JRAJbW1vs27cPe/fuRc+ePbUqd8+ePQgPD8f69evRrFkzrFixAmFhYYiPj4erq6vS+RKJBB07doSrqyv27dsHT09PJCYm0spdRC+yggKg3BALgFrTxPisrKzw3nvv8R0GqUCTJk2waNEifPjhhwCA7du3o23btvD29saUKVOMUqdOLeqrV6+iY8eO8u3t27ejsLAQf/31F168eIEPPvgA3377rdblLl++HCNHjsSwYcMQGBiI9evXw8rKClu2bFF5/pYtW5CRkYFDhw6hVatW8Pb2RmhoKBo1aqTLj0UIAKjt7S2gRE2MrFGjRjCjGe9M2t9//420tDT59rBhw3Dp0iWj1qlTos7IyFBo4R45cgShoaHw8/ODQCBAz549ce/ePa3KlEgkuH79Ojp06PBfcAIBOnTogMuXL6u85vDhw2jRogXGjx8PNzc31K9fH4sWLYJURW/dMsXFxcjJyZG/ymaQIaQMW1CgtE8gEkFgYcFDNKS6EIvFCAgI4DsM8hZeXl44deqUPM+wLGv03vk6JWoXFxckJiYCeP2M+MqVKwgL+29C/9LSUq0X0E5LS4NUKoWbm5vCfjc3NyQlJam85vHjx9i3bx+kUimOHTuG2bNnY9myZViwYIHaehYvXgx7e3v5KzQ0VKs4SdXGlpZCVqw8bajAhnp7E+N67733qDVdCYwZMwbbt2+HWCyGnZ0dGIbB8OHDYWdnp/Zlb6/f3As6vSs6dOiAVatWwc7ODnFxcZDJZAq92u7cucPJSi8ymQyurq6IiYmBUChE06ZN8eLFC3zzzTeYO3euymsiIiIQHh4u37558yYlayInK1TuRAYAQpoylBiRQCBAYGAg32EQDXz55Zdo1KgRzp49i+TkZGzduhX/+9//4Ovra7Q6dUrUS5Yswf379zFt2jRYWFjg22+/hY+PD4DXt5Z/+ukn9O/fX6synZ2dIRQKkZycrLA/OTkZ7u7uKq/x8PCAubk5hEKhfF9AQACSkpIgkUhgoeJWpUgkguiNnrs29AFM3iAtVH4+zZibQyCmSU6I8fj6+tJUoZVIp06d0KlTJwDA1q1bMXr0aK1znjZ0uvXt5uaG3377DZmZmcjJycGkSZPkx2QyGU6fPq129jJ1LCws0LRpU4XlMcvKatGihcprWrVqhYcPHypMwHL//n14eHioTNKEvA2br+L5NH2ZI0ZGz6YrDycnJ+zbt0++PXfuXDRs2NCodeq1KIe9vb1SQrS0tESjRo3g5OSkdXnh4eHYuHEjtm3bhrt372Ls2LHIz8/HsGHDAACDBw9GRESE/PyxY8ciIyMDkyZNwv3793H06FEsWrQI48eP1+fHItUUW1oKmaRYab+Qnk8TI7K3t1d715CYnry8PBS80eF0/vz5+Pvvv41ap0n1XOjTpw9SU1MxZ84cJCUlISgoCMePH5d3MHv69CkEgv++W9SqVQuxsbGYMmUKGjZsCE9PT0yaNMkgA8xJ9SMrVG5NQyCAwJJWgiPGU7duXZrTuxLx8/PDvn370KZNG9jZ2YFlWeTn5yMjI6PC63RpvJYxqUQNABMmTMCECRNUHouLi1Pa16JFC1y5csXIUZHqQKoiUQutbehDlBgNwzB49913+Q6jyvPx8dH6/2OGYfDo0SOl/TNnzsSwYcNw9OhR+XljxozBmDFjKiyvomHDb2NyiZoQvqgcP21tzUMkpLrw8PCgDq0cCA0NVUrU165dw+3btxEYGChfTjQ+Ph537txB/fr10bRpU5VlDRo0CCEhIYiLi0NycjLmzZuHHj16GPU5NSVqQgCwUilkxRKl/UJr+hAlxuPn58d3CNXC1q1bFbYPHTqEQ4cO4eTJk/jggw8Ujp08eRKfffYZoqOj1Zbn7+8vT+7ff/89hgwZgm7duhk87jIadSYr38tt/vz5uHXrltGCIoRrr8dPK87vLRCLwdAEFMRIBAKBUcfe8kXbhZXK7N69GwzD6L3SlCbmzJmDL774QilJA0DHjh0xYcIEzJo1S6Oynjx5YtQkDWjYoi7fy23evHmoU6cO6tevb7TACOGSqo5kNCyLGJOnp6fCnA5VgbYLK5VJSEjAtGnT0KZNG07ifPDgQYXrfdeoUUPl82kAOH/+PACgbdu2CttvU3a+LjRK1OV7uQEwei83QrikakYygRU9nybG4+3tzXcIBvfmwkoAsH79ehw9ehRbtmzBjBkzVF4jlUoxYMAAREVF4cKFC8jKyjJ6nH5+fvj+++8xfPhwpT4Cubm52LJli9q7He3atQPDMCgsLISFhYV8W52yucCN3pmMj15uhHBF5frTAgHNRkaMysvLi+8QDKpsYaU357p428JKwOtHqa6urhg+fDguXLjARahYsGABevXqhXr16mHo0KGoU6cOgNct7W3btiE5ORl79+5Vee3Zs2cBQD6HSNm2MWmUqPno5UYIV2RFRUrrTwutrGlYFjEaFxcXWFlVjvH5eXl5yMnJkW+Xn4a5TEULK6lbTfHixYvYvHkzbt68adCY36Z79+44duwYpk+fjkWLFikcCwoKwubNmxUWmnpT+bUhuFgrQuOeMlz3ciOEK6yq59M0LIsY0TvvvMN3CBorn4jmzp2r9RTRquTm5mLQoEHYuHEjnJ2d9S5PW2XzdSclJclXg/Ty8jLJWeJ06tL65MkTQ8dBCG+kKp5PC+n5NDEiT09PvkPQ2Llz5xAUFCTfVtcBTtuFlR49eoSEhAR07dpVvq9s3QYzMzPEx8dzMnzN3d1dq+T8+eefa10HwzDYvHmz1teV0XnsiVQqxQ8//ICjR48qfBvp0qULBgwYoLCiFSGmTFakuP40Y24BhhZ1IUYiFAor7AFtamxsbOSdiCvy5sJKZUOsyhZWUjXbZL169fDPP/8o7Js1axZyc3OxcuVKoy+V/PTpUyxatAhnz55FamoqDh06hLZt2yItLQ3z58/HsGHD0LhxY6Xrzpw5o/RYrKCgAKmpqQAAR0dHAEBmZiaA1485rPW8Q6dTos7OzkZYWBj++OMP2NraynvHnTx5Evv378e6desQGxur0R+XED7JiosBaanCPrrtTYzJ2dkZZlV0fH54eDiGDBmC4OBghISEYMWKFUoLK3l6emLx4sUQi8VKQ3wdHBwAwOhDf+/cuYM2bdpAJpOhWbNmePjwIUpLX38OODs74+LFi8jPz1fZCk5ISFAqq1OnTpg5cyYmT54sv42flpaG//u//8P27dvlHbF1pdO7JTIyEtevX8fq1asxcuRImJubAwBKSkqwadMmTJw4EZGRkVi9erVewRFibEq9vQEIK0knH1I5ubi48B2C0Wi7sBJfvvrqKzg4OODKlStgGEbpDsfHH3+MPXv2aFTWF198gY8++ggLFixQ2O/s7IyFCxciJSUFX3zxBU6dOqVzvDol6oMHD2LcuHEYN26cwn5zc3OMHTsWd+/exb59+yhRE5Oncvw0rZZFjKgqJ2pA+4WV3lR+qk9jOX/+PObMmQMXFxekp6crHa9duzZevHihUVlXrlxBr1691B5v3Lgxdu3apXOsgI7rUaenp8t7gKtSr169t06GQogpKJ+oBSIRTRtKjKqiGbEIN2QyWYXD41JTUzWeNc7JyQm//vqr2uPHjh2T39LXlU6Juk6dOjh8+LDa44cPH6bJ5onJY6VSsJJihX0M3fYmRiQQCGBvb893GNVekyZN1D43Li0txe7du9G8eXONyho9ejSOHDmCTz75BKdOnUJCQgISEhJw8uRJdOvWDb/++utbJwd7G52aDuPGjcOECRPQuXNnTJ48GXXr1gXweomwVatW4eTJk1izZo1egRFibCqfT1tSRzJiPPb29jQixgRERESgS5cuGDt2LPr27Qvg9TCyU6dOYdGiRbh7967GOWzWrFkoLi7GN998gyNHjigcMzMzw4wZMzRe4EMdnRN1SkoKlixZgtjYWIVj5ubmmDNnDsaOHatXYIQYW/lhWQADAbWoiRFRa9o0fPTRR9i6dSsmTZqEmJgYAMDAgQPBsizs7Oywfft2rRbRiI6OxqRJk3Dq1CmF4codOnQwyGQuOj+MmzdvHiZMmGC0wAgxNqXn02IRGGrtECOiIaumY9CgQejZsydOnjyJBw8eQCaTwc/PD2FhYbC1tdW6PGdnZ3nr3ND06jVjzMAIMbbyLWoB3fYmRqZLAiCGVVBQgFq1amHGjBn48ssvOVn/Wl/8D2gjhAesRKI80YklrZZFjIsSNf+srKxgZmam92xhXKJETaolWbGq8dOUqIlxVabkUJV9+umn2LdvH9hyq+aZKhowSqolaWG5294WNH6aGF9lWdqyquvbty/GjRuH9u3bY+TIkfD29oalii/qTZo04SE6ZfTJRKoltvxCHPQBSjggFov5DoEAaNeunfzfFy5cUDrOsiwYhoFUKuUwKvUoUZNqh2VZyIrLdySj297EuBiGUVp1ifDj+++/5zsErVCiJtUOKykG/l33tgwtxEGMjZK06RgyZIjBymJZFjExMdi8eTMeP34sX97yTQzDyFfn0oVOiZqLwAgxFllh+fWnzcGYmfMUDakuKFFXTV999RWWL1+OoKAgDBw4UL4etSHplKi5CIwQYyk/dSjd9iakavv888/BMAxiYmIgFArx+eefv/UahmFUrkdd3rZt2/Dpp5/ip59+MkSoKumUqLkIjBBjYZWeT9Ntb2J81KLmz5kzZyAQCCCTySAUCnHmzJm3/j00/XsVFhaiQ4cOhghTLZ0SNReBEWIMrzuSKa6YRS1qwgVK1PxJSEiocFsfH3zwAf744w+MGjXKYGWWp9OEJ2WBEVLZsEVFwJuTHAgEYCw0W3eWEELK++6773DlyhUsWrQI6enpRqlDp0TNRWCEGIPSsCyxJbV0CCfofVY1+fv74/Hjx5g9ezZcXV1hbW0NOzs7hZe+q6bpdOvb398fMpkMs2fPxuzZsyEWi5XWWGUYBtnZ2XoFR4ihKS/EQbe9CamOfv31Vyxfvhw3btxAdna2yulENZnw5NNPPzX6lzCdEjUXgRFiDCz1+CY8oc9M07F//3589tlneO+999C3b1+sW7cO/fv3B8uy+Pnnn/Huu+9qvKrW1q1bjRoroGOi5iIwQgyNlUohK5a8sYeBQEyJmnCDErXpWLx4MUJCQnDx4kVkZmZi3bp1+Pzzz/H+++8jISEBzZs3h4+PD99hytHqWaTaeN3b+7/bWwILCzDlHtkQQqq+O3fuoG/fvhAKhTD7dzGekpISAIC3tzfGjRuHpUuXalxeTk4OoqKiEBISAjc3N7i5uSEkJATz589HTk6O3vHqnKiNHRghhlb+tjdDt70JB9zd3eHk5AQ3Nze+QyH/srKygoWFBQDAwcEBIpEIr169kh93c3PDkydPNCrr5cuXaNy4MaKiopCXl4dWrVqhVatWyM/Px7x589CkSROFsnWhU6LmIjBCDE1WWO75NN32Jhy4du0aYmJicOnSJb5DIf/y9/fHnTt35NtBQUHYsWMHSktLUVRUhJ07d6J27doalTV9+nQkJSXhyJEjuHPnDg4cOIADBw7g9u3bOHr0KJKSkjBjxgy94tUpUXMRGCGGptzjm5YcJNxgGIaWuDQhPXv2xOHDh1H87+RHkZGRiIuLg4ODA1xcXHDhwgWNc9jx48cxefJkdO7cWenYRx99hIkTJ+LYsWN6xatTZzJNAtu4caNegRFiSGxpCdjSkv920EQnhEMikQgCAXUJ4ltRURF+/vlnlJSUYNasWcjIyICHhwe6dOmCuLg4HDhwAEKhEB9//DHat2+vUZn5+fkVPtZwd3dHfn6+XnHrlKi5CIwQQyq/YpZAJKZeuIQzltQfgncpKSlo2bIlnjx5ApZlwTAMLC0tcejQIXTo0AFt2rRBmzZttC43MDAQu3btwpgxY+TPvcuUlJRg165dCAwM1Ct2nb7ilQUmkUiUjhkqMEIMiVbMInwSiejuDd+io6ORkJCAKVOm4MiRI/i///s/WFpaYvTo0XqVO336dPz+++8ICQlBTEwM4uLiEBcXhw0bNiAkJARXr17V+1GwTi3q6dOno0+fPggJCcG4ceNQt25dAEB8fDzWr1+Pv//+G3v27NErMEIMiTqSET7R82n+nThxAoMHD8a3334r3+fm5ob+/fsjPj4e/v7+OpXbu3dv5OfnY8aMGRgzZoz8Th3LsnB1dcWWLVvQq1cvvWLXKVFzERghhvJ6xazyc3zTByfhTvlbooR7T58+xfTp0xX2tW7dGizLIjk5WedEDQBDhw7FwIEDce3aNSQmJgIAvLy8EBwcLB+nrQ+dezcMHToUz58/x6VLl7Bz507s3LkTly5dwvPnzzFkyBC9glq7di28vb0hFovRrFkzXL16VaPrdu/eDYZhNJ76jVQPbFERIJPJtxkzMzDm5jxGRKobStT8Ky4uVrqzUbZdWlqqd/lmZmZo3rw5+vTpgz59+qB58+YGSdKAji3q8oE1b97cIMEAwJ49exAeHo7169ejWbNmWLFiBcLCwhAfHw9XV1e11yUkJGDatGk6dQYgVVv559OMiFrThFuG+sAm+klISMCNGzfk22ULRz148AAODg5K5zdp0kRp3/nz5wEAbdu2Vdh+m7LzdaHRu4fLwJYvX46RI0di2LBhAID169fj6NGj2LJli9oH8lKpFAMGDEBUVBQuXLiArKwsreslVZfS82kaP004RonaNJSt+FjeuHHjFLbLeoWrWj2rXbt2YBgGhYWFsLCwkG+rU1FZmtLo3cNVYBKJBNevX0dERIR8n0AgQIcOHXD58mW1182fPx+urq4YPnw4Lly4oFWdpOpTStQi6khGuFV+GWDCve+//94g5Zw9exbAf48zyraNSaNEzVVgaWlpkEqlSmO03dzccO/ePZXXXLx4EZs3b8bNmzc1qqO4uFg+Gw0A5OXl6RwvMX1sSbmJTkAdyQj3KFHzT9++U2VCQ0Mr3DYGjRI1H4FpIjc3F4MGDcLGjRvh7Oys0TWLFy9GVFSUkSMjpkLp+bS5ORi6DUk4RrOSVV3vv/8+IiMj8cEHH6g8fvbsWURHR+PMmTM616HTu+f999/H6dOn1R4/e/Ys3n//fa3LdXZ2hlAoRHJyssL+5ORkuLu7K53/6NEjJCQkoGvXrjAzM4OZmRm2b9+Ow4cPw8zMDI8ePVK6JiIiAtnZ2fLXuXPntI6TVB7SwgKFbQF1JCM8oBZ11RUXF6eUs96UkpKid57RKVEbKzALCws0bdpU4UuATCbD6dOn0aJFC6Xz69Wrh3/++Qc3b96Uv7p164b27dvj5s2bqFWrltI1IpEIdnZ28peNjY3WcZLKgy0oP9EJJWrCPWpRV20V9dl6+PAhbG1t9Spf53uAxgosPDwcQ4YMQXBwMEJCQrBixQrk5+fLe4EPHjwYnp6eWLx4McRiMerXr69wfVkX+/L7SfXDSqWQvdEfAQAYStSEB5Soq5Zt27Zh27Zt8u0FCxaoXIgqKysLf//9t8oFrLShcaLmKrA+ffogNTUVc+bMQVJSEoKCgnD8+HF5B7OnT5/Sm55o5HVvb1ZhH00dSvhAn1lVS0FBAVJTU+Xbubm5Sn9jhmFgbW2NMWPGYM6cOXrVp3Gi5jKwCRMmYMKECSqPxcXFVXjt1q1bda6XVC2ycs+nGQsRGHpWSHhAibpqGTt2LMaOHQsA8PHxwcqVK9GtWzej1adxouY6MEL0pbwQB932JvygJVWrpsLCQnTv3t3of1+tv+ZxFRgh+mBlMlrakpgM+rysmiwtLRETE1Nh52pD0DpRcxUYIfqQFRUCbPnn09SiJvygRF11NW3aFLdu3TJqHTo9OOEiMEL0Uf75NAQCWoyDEA5os/rhxo0b0aZNGzg6OsLR0REdOnTQeLVEU7FixQrs3r0bmzZtMsgqXKrolKi5CIwQfcjylSc6oVYN4Ut1ee+VrX44d+5c3LhxA40aNUJYWBhSUlJUnh8XF4d+/frh7NmzuHz5MmrVqoVOnTrhxYsXHEeuu6FDh0IgEGD06NGws7PDu+++i4YNGyq8GjVqpFcdOo2jfjOwiRMnwtPTE5blnv8xDIO//vpLr+AI0YXK59M0LIvwqLokam1XP/zxxx8Vtjdt2oT9+/fj9OnTGDx4MCcx68vJyQk1atSAv7+/0erQKVFzERghulL5fJqWtiREJ3l5ecjJyZFvi0QiiEQipfN0Xf3wTQUFBSgpKYGTk5P+gXPkbUOGDUGnRM1FYIToqvxtb4B6fBN+VeYWdflFmObOnYt58+YpnafL6oflTZ8+HTVr1kSHDh10jrcqomWESJWjNNGJuTkYM3OeoiHkvyWCK6Nz584hKChIvq2qNW0IS5Yswe7duxEXFwdxJRuhIZVK8cMPP+Do0aNITEwEAHh5eaFLly4YMGCA3ouy6JyojR0YIbpgpVIaP02IAdnY2MDOzu6t52m7+uGbvv32WyxZsgSnTp1Cw4YN9YqXa9nZ2QgLC8Mff/wBW1tb+Pr6AgBOnjyJ/fv3Y926dYiNjdXod6iOTr2+s7Oz0apVK3z++ec4ceIESkpKUFJSgpMnT2LYsGFo3bq1wjMNQrgiK1Q1fpoSNSHGpu3qh2W+/vprREdH4/jx4wgODuYiVIOKjIzE9evXsXr1aqSmpuLGjRu4ceMGUlJSsGbNGly7dg2RkZF61aFTouYiMEJ0IS3IU9pHLWpCuBEeHo6NGzdi27ZtuHv3LsaOHau0+uGbnc2WLl2K2bNnY8uWLfD29kZSUhKSkpKQl6f8/7GpOnjwIMaNG4dx48bB3Py/R2zm5ubyqbf379+vVx063fp+M7A3lQV29+5d7Nu3D6tXr9YrOEK0pdSRjCY6IYQz2q5+uG7dOkgkEvTq1UuhHHUd1kxRenp6hSOg6tWrh4yMDL3q0ClRcxEYIdpiS0vAShTXnxaIaaITQrikzeqHCQkJxg/IyOrUqYPDhw8rNVzLHD58GH5+fnrVodOt77LA1DFEYIRoS5qfr7RPILbiIRJCSHUxbtw4nDhxAp07d8aJEyeQkJCAhIQExMbG4uOPP8bJkyfVfnHRlE4t6nHjxmHChAno3LkzJk+ejLp16wIA4uPjsWrVKpw8eRJr1qzRKzBCtCUrUJGorej5NCHEeMaNG4eUlBQsWbIEsbGxCsfMzc0xZ84c+RLRutI5URs7MEK0JVWa6IShHt+EEKObN28eJkyYgJMnT+Lp06cAXg9X7tChA5ydnfUuX+dx1MYOjBBtyAoLAaniAjECsQgMjecnhHDA2dkZ/fr1M0rZes1MZszACNGG6mFZ1jxEQgipjo4cOYJjx47JO8h5e3ujc+fO6NKli95l65WojRkYIdqQqRh3SeOnCSHGlpWVhR49euD8+fMQCoXw8PAAAJw6dQobNmxAmzZtcOjQITg4OOhch069vrOystC+fXt88skn2LRpE27fvo3bt29j06ZN+OSTT9CuXTtkZWXpHBQh2mBLSyErKlLaT4maEGJskyZNwoULF7B06VJkZmYiMTERiYmJyMzMxJIlS3Dx4kVMmjRJrzp0StRcBEaIpqT5KlrTFiIwZrTmDCHEuA4dOoRx48Zh2rRpsLb+73GbtbU1vvzyS4wdOxaHDh3Sqw6dPsneDOxNZYE9ffoU27dv1yswQjQlUzF+mrGi8dOEEOMzNzd/6wRgb04tqgudWtRcBEaIJliWVdmiFlKiJoRw4NNPP8XevXshlUqVjpWWluKnn35C79699apDpxZ1WWBjxoxRWs7SUIERoglZQT4gkyntF1hSoiaEGN/AgQMxYcIEtGzZEqNGjUKdOnUAAA8ePEBMTAwkEgkGDBiAGzduKFzXpEkTjevQKVFzERghmpCq6u0toufThBBuhIaGyv/9xx9/yNcWYN9YbvfNc1iWBcMwKlvg6uj0acZFYIRoQtWwLHo+TQjhyvfff2/0OnRK1FwERsjbSAsLwZaWKO0X0kQnhBCODBkyxOh16JSouQiMkLeR5eWq2MtAQC1qQggP8vLy8OzZMwBArVq1YGNjY5Byder1/aa8vDzcvXsXd+/eRZ6K25CEGIvK59M0vzchhGN//PEH2rdvD0dHR9SvXx/169eHo6Mj3n//fVy7dk3v8nXucfPHH3/gq6++wsWLFyH7t9etQCBAmzZt8PXXXyM4OFjv4AhRR1ZcBFZSrLRfYEW3vQkh3Pn999/Rrl07WFhYYMSIEQgICAAA3L17F7t27ULbtm0RFxeHkJAQnevQKVFzERghFZHm5KjcL7CmRE0I4U5kZCQ8PT1x8eJFuLu7KxybN28eWrVqhcjISJw8eVLnOnRK1FwERkhFpLkqnk8LBDR+mhDCqd9//x1z5sxRyoUA4ObmhlGjRiE6OlqvOnR6Rv37779j9OjRFQZ25coVvQIjRB1ZUSHYEonSfoGllXyoICGEcEEgEKC0tFTtcalUCoFAv+5gOl3NRWCEqKOyNQ1ASLe9CSEca9myJdauXYvExESlY0+fPsV3332HVq1a6VWHTre+ywLr378/vLy8jBIYIaqwLAtpTrbKY9SRjBDCtUWLFqFNmzaoV68eevTogbp16wIA4uPj8fPPP8PMzAyLFy/Wqw6dEjUXgRGiiqwgH6yKuzmMuTkEIhEPERFCqrPGjRvj6tWriIyMxOHDh1FQUAAAsLKywocffogFCxYgMDBQrzp0StRcBEaIKmpb09aGmViAEEI0VVxcjNjYWHh7e+PgwYOQyWRITU0FALi4uBjsEbDWiZqrwAgpj5VKVU5yAgBCStSEEI5ZWFigd+/eWLlyJRo2bAiBQAA3NzeD16N1Vi0L7NKlS68L+DcwNzc3StLEqKS5uSqXtARD04YSQrjHMAzeffddpKWlGbUerTMrV4ERUp40O1PlfoGVFRj6kkgI4cHMmTOxZs0axMfHG60OnZ5Rz5w5E+Hh4ejduzf8/f0NHRMhSmRFhZAVFak8JjTQxPeEEKKtK1euoEaNGqhfvz7atWsHb29vWFpaKpzDMAxWrlypcx06JWouAiPkTaVZWWqPCa0oURNC+LFmzRr5v0+fPq3yHF4SNReBEVKGlUohzVUzt7dIBMbCguOICCHkNZmqfjMGptODPZlM9taXVCrVOai1a9fC29sbYrEYzZo1w9WrV9Weu3HjRrRp0waOjo5wdHREhw4dKjyfVD7SnGzVncgACGxtOY6GEEK4pfMyl8ayZ88ehIeHY/369WjWrBlWrFiBsLAwxMfHw9XVVen8uLg49OvXDy1btoRYLMbSpUvRqVMn3L59G56enjz8BMSQWJZFaWaG2uNCG0rUhBD+3bp1C8eOHUNCQgIAwNvbGx999BEaNGigd9l6JWpjBLZ8+XKMHDkSw4YNAwCsX78eR48exZYtWzBjxgyl83/88UeF7U2bNmH//v04ffo0Bg8erHMcldmGDRuQl5cHGxsbjB49mu9w9CLLywFbUqLy2OvZyMQcR0QIIf8pLi7G6NGjsWPHDrAsKx+mLJPJEBERgQEDBmDTpk2w0OMRnU6J2liBSSQSXL9+HREREfJ9AoEAHTp0wOXLlzUqo6CgACUlJXByclIbe3FxsXw7T80EGpVZXl4ectSs11zZlGaob00LqDVNCOHZ9OnTsX37dowbNw5ffPEF/Pz8wDAMHj58iFWrVmHdunVwcnLCihUrdK5Dp2fUZYGNHTsWd+/eRVFREYqLi3H37l2MGTMGP/zwA7766iuty01LS4NUKlWa2cXNzQ1JSUkax1azZk106NBB5fHFixfD3t5e/goNDdU6TsINaUG+2iFZAN32JoTw74cffsCgQYOwZs0a+Pv7w8zMDEKhEP7+/li7di0GDBiAH374Qa86dErUXASmiyVLlmD37t04ePAgxGLVt0QjIiKQnZ0tf507d47jKImmSiuYVIcxM4eQZiMjhPCspKQEzZs3V3u8ZcuWFS4LrQmdErWxAnN2doZQKERycrLC/uTkZLi7u1d47bfffoslS5bgxIkTaNiwodrzRCIR7Ozs5C8bmizDJEnz8yArLFB7nHp7E0JMQVhYGGJjY9UeP378ODp16qRXHTolamMFZmFhgaZNmyqMzZbJZDh9+jRatGih9rqvv/4a0dHROH78OIKDg7Wul5ie0vT0Co8Lbe04ioQQQtSLjo7GkydP0LNnT5w+fRqJiYlITEzEqVOn0KNHDyQmJiI6OhoZGRkKL23o1JksOjoan332GXr27Inx48ejTp06AIAHDx5g7dq1SExMxJ49e5SCUdfB603h4eEYMmQIgoODERISghUrViA/P1/eC3zw4MHw9PSUr3e9dOlSzJkzBzt37oS3t7f8WbaNjQ21lispaW5uha1pxtwcwnIz4RFCCB8CAgIAAP/88w9+/vlnhWMsywKAymWftZlrRKdEbczA+vTpg9TUVMyZMwdJSUkICgrC8ePH5R3Mnj59qrBK17p16yCRSNCrVy+FcubOnYt58+Zp9XMR/rEsi5LU5ArPodY0IcRUzJkzBwzDGLUOnRK1sQObMGECJkyYoPJYXFycwnbZGG5SNZRmpKsdN11GaGfPUTSEEFIxLhqEOiVqaqkSY5BJJCjNqPjZtEAshkAk4igiQgjhHy3iS0wCy7IoSXqldk7vMtSaJoRUNyY31zfRX1knusrUmU6amVFhBzIAAMPQ82lCSLVDLepKgmVZjV+jRo1CeHg4Ro0apdV1ZR0BuSYtKEBJWupbzxNY24Axo++WhJgybVY/BIC9e/eiXr16EIvFaNCgAY4dO8ZRpJUHfeqZMAsLCzAMw0sC9fPzg6Ojo0HKYllWvvxpaWkpSktLUVJSAolEgsK8PBS+fAFo8DOa2TsYJB5CiHFou/rhpUuX0K9fPyxevBhdunTBzp070b17d9y4cQP169fn4ScwTQzLVzPKRNy4cQNNmzbF9evX0aRJE77DUZKeng6JRMJpnRYWFqhRo4bR65FJJCi4eQMl+bkoLi1FYUkJCiUlKCgpQVFpKfDGO5MxN4fIx8/owyCqAmFuDmy2xbze4OL39e9HSK15iyDy8jF+fYQTunw2NmvWDP/73/+wZs0aAK8nrKpVqxa++OILlasf9unTB/n5+Thy5Ih8X/PmzREUFIT169cb5gepAqhFbeK4SJh8kBUXI/+vG5AV5EPAMLA0N4eluTnw7/TdUpkM+RIJ8oolyC0uBmvvQEmavJVUKoXsLR0SiWbKpoEuvxqfSCSCSMXIC11WP7x8+TLCw8MV9oWFheHQoUMG+AmqDkrUhHOlWVkovHsLsuIKVsYSCGAnFsNOLAYYAcyCmiKnoAAZGRkoqmBFLVK9RUdHIyoqiu8wqpTyKwyqm0yqotUP7927p7LspKQkvVZLrC4oURPOyCQSSJ4movj5Uyjc134LcxcXWNnbw9reHh4eHigoKEB6ejrS09O1moaPVH2zZ89GZGQk32FUCX/++SeaNWuGc+fOISgoSL5fVWuaGBclahMnSXoFtoKWp6HJiovBArBwUe74oXOZhYUozc5CaWoKWJn2iVX0jpfCtpWVFaysrODp6YnMzEykpKSgoOAtQ7tItSAUCiEUCvkOo0ow+3eEhY2NDezs3j4sUpfVD93d3XVaLbG6oURtwiRJr/A0IvztJxrSvx2DnHr0htCe/8lFzOwdIVTzISEQCFCjRg3UqFEDeXl5SEpKQnZ2NscREkIAxdUPu3fvDuC/1Q/VTQndokULnD59GpMnT5bvO3nyZIWrJVZHlKhNGJctaaW69Vzo3FAsanu9/SS8/tZfp04dFBYW4tWrV8jMzDRyZISQ8rRd/XDSpEkIDQ3FsmXL8PHHH2P37t24du0aYmJi+PwxTA4l6spCix7PPQ7HIrWwEC6WljjYLUy7ekxotJ7QygbmNZy1usbS0hK+vr4oLCzEixcvqIVNCIe0Xf2wZcuW2LlzJ2bNmoWZM2fi3XffxaFDh2gMdTmUqKug1MJCJBcU8h2G3kTeuo/JtbS0RJ06dZCbm4vnz5/TM2xCOKLN6ocA0Lt3b/Tu3dvIUVVuNIUoMUlCa1uYu7q9/cS3sLW1Rb169eDl5SXvHEMIIZUJJWpikvRpTZfHMAycnZ3x3nvvwdlZu1vphBDCN0rUxOQI7RxgbsDhYWXMzMzg5eUFf39/iMVig5dPCCHGQImamBgGlu/WNWoNNjY2CAwMxDvvvENjbgkhJo8SNTEpFh41OVlzmmEYuLm50e1wQojJo0RNTAZjbgGRjx+ndZqbm8PLywuBgYFwcHDgtG5CCNEEdYMlJsOybj0ILCz4qdvSEn5+fsjPz8erV69o/DUhxGRQoiYmwdzV3SgdyLRlbW2NOnXqoKCgAElJScjKykI1X7KdEMIzStSEdwIra1jWrcd3GAqsrKzg6+sLiUSC1NRUpKWlydfnJYQQLlGiroJcLC0V/mvKGKEZrOo3AmOik5FYWFjA09MTHh4eyMrKQnp6OnJycvgOixBSjZjmpyPRi9bze/OEEQhhVb8hhFZWfIfyVgKBAE5OTnByckJJSQkyMjKQlZWFvLw8vkMjhFRxlKgJLxiBEFYNGsHM0YnvULRmbm4ONzc3uLm5oaSkBDk5OcjOzkZubi7dHieEGBwlasI5gdgKVoH11a4zXZmYm5vL18QGgKKiIuTl5SE/Px8FBQUoKiqCTCbjOUpCSGVGiZpwhxHAwqMmxL51TPaZ9NuwMtnrpUD/fbFl/wYLsIAFK4OjlRUcLS1f7wNQXCxBcXERJJISlJRIUFJSgtJSKWQy2euXgXuVM6Be6oRUJZXz05IYn4ABwxhgPhyGASMWw8zOAeZu7hCIRWBLJK9fLN5Y/5r9N6+VJUGABQvI2Df2vZEYWRZgZWBlbx6TvT5fJvv3PNnrf5cl17J/v/FfeRn/nsP+u09eV9n5ZclYRwIA4n9fxibLz0c6B/UQQrhBiZqoJvs38emLBdiCfEgK8iFJeqF/eYQQUs3QFKKEEEKICaNETQghhJgwStSEEEKICaNETQghhJgwStSEEEKICaNETQghhJgwStSEEEKICaNETQghhJgwStSEEEKICaNETQghhJgwStSEEEKICaNETQghhJgwStSEEEKICaNETQghhJgwk0zUa9euhbe3N8RiMZo1a4arV69WeP7evXtRr149iMViNGjQAMeOHeMoUkIIIcS4TC5R79mzB+Hh4Zg7dy5u3LiBRo0aISwsDCkpKSrPv3TpEvr164fhw4fjzz//RPfu3dG9e3fcunWL48gJIYQQwzO5RL18+XKMHDkSw4YNQ2BgINavXw8rKyts2bJF5fkrV67Ehx9+iC+//BIBAQGIjo5GkyZNsGbNGo4jJ4QQQgzPpBK1RCLB9evX0aFDB/k+gUCADh064PLlyyqvuXz5ssL5ABAWFqb2/OLiYuTk5MhfeXl5hvsBjIlluXmRqoXeM4RUemZ8B/CmtLQ0SKVSuLm5Kex3c3PDvXv3VF6TlJSk8vykpCSV5y9evBhRUVGGCdjIGJGYh0oZgGVh07INLNw9uK+f6E2S9Arp+3ZzXi8v71dCqgGTStRciIiIQHh4uHz75s2bCA0N5TEi9SzcPVB78XKwxUWc1suIxJSkKzGRR03O3zf0niHEeEwqUTs7O0MoFCI5OVlhf3JyMtzd3VVe4+7urtX5IpEIIpFIvm1jY6Nn1MZFH35EF/S+IaTqMKln1BYWFmjatClOnz4t3yeTyXD69Gm0aNFC5TUtWrRQOB8ATp48qfZ8QgghpDIxqRY1AISHh2PIkCEIDg5GSEgIVqxYgfz8fAwbNgwAMHjwYHh6emLx4sUAgEmTJiE0NBTLli3Dxx9/jN27d+PatWuIiYnh88cghBBCDMLkEnWfPn2QmpqKOXPmICkpCUFBQTh+/Li8w9jTp08hEPx3I6Bly5bYuXMnZs2ahZkzZ+Ldd9/FoUOHUL9+fb5+BEIIIcRgGJat3uMrbty4gaZNm+L69eto0qQJ3+EQQohJoM9G02FSz6gJIYQQoogSNSGEEGLCKFETQgjhXEZGBgYMGAA7Ozs4ODhg+PDhFc4UmZGRgS+++AL+/v6wtLRE7dq1MXHiRGRnZ3MYNT8oURNCCOHcgAEDcPv2bZw8eRJHjhzB+fPnMWrUKLXnv3z5Ei9fvsS3336LW7duYevWrTh+/DiGDx/OYdT8MLle34QQQqq2u3fv4vjx4/jjjz8QHBwMAFi9ejU6d+6Mb7/9FjVr1lS6pn79+ti/f79828/PDwsXLsTAgQNRWloKM7Oqm86oRU0IIYRTly9fhoODgzxJA0CHDh0gEAjw+++/a1xOdnY27OzsqnSSBqhFLXf37l2+QyCEaMjDwwMeHrpPk/rq1Su8evXKgBFVPWWfiXl5ecjJyZHvLz8Nsy6SkpLg6uqqsM/MzAxOTk5qF1QqLy0tDdHR0RXeLq8y2Gru5cuXbGhoKAuAXvSiVyV5zZ07V6//7+fOncv7z1AZXl5eXlr97qdPn/7WMu/evcsuXLiQrVu3rtL1Li4u7HfffffWv192djYbEhLCfvjhh6xEItHnrVApVPsJT4Cq9+06Ly8PoaGhOHfunMkvOkJMR2V631SmFnVl+r2W5+TkBCcnJ4V9FbWoU1NTkZ6eXmGZvr6++OGHHzB16lRkZmbK95eWlkIsFmPv3r3o0aOH2utzc3MRFhYGKysrHDlyBGJx1V9elRJ1FZSTkwN7e3v58xtCNEHvG+Og36uyu3fvIjAwENeuXUPTpk0BACdOnMCHH36I58+fq+xMBrz+XYaFhUEkEuHYsWOwsrLiMmzeUGcyQgghnAoICMCHH36IkSNH4urVq/jtt98wYcIE9O3bV56kX7x4gXr16uHq1asAXifpTp06IT8/H5s3b0ZOTg6SkpKQlJQEqVTK549jdNSZjBBCCOd+/PFHTJgwAR988AEEAgE+/fRTrFq1Sn68pKQE8fHxKCgoAPB67vGyHuF16tRRKOvJkyfw9vbmLHauUaKugkQiEebOnat3z0xSvdD7xjjo96qak5MTdu7cqfa4t7c33nwy265dO1TXJ7X0jJoQQggxYfSMmhBCCDFhlKgJIYQQE0aJmrxVQkICGIbB1q1b+Q6FEEKqHUrUBvbo0SOMHj0avr6+EIvFsLOzQ6tWrbBy5UoUFhYard47d+5g3rx5SEhIMFodmli4cCG6desGNzc3MAyDefPm8RpPVcMwjEavuLg4vesqKCjAvHnztCqrMv796XdKTB31+jago0ePonfv3hCJRBg8eDDq168PiUSCixcv4ssvv8Tt27cRExNjlLrv3LmDqKgotGvXjtdhCrNmzYK7uzsaN26M2NhY3uKoqnbs2KGwvX37dpw8eVJpf0BAgN51FRQUICoqCsDrHreaqIx/f/qdElNHidpAnjx5gr59+8LLywtnzpxRmN5w/PjxePjwIY4ePcpjhP9hWRZFRUWwtLQ0eNll4xnT0tLg4uJi8PKru4EDBypsX7lyBSdPnlTaz5fK+Pen3ykxdXTr20C+/vpr5OXlYfPmzSrnIK5Tpw4mTZok3y4tLUV0dDT8/PwgEong7e2NmTNnori4WOE6b29vdOnSBRcvXkRISAjEYjF8fX2xfft2+Tlbt25F7969AQDt27dXulVXVkZsbCyCg4NhaWmJDRs2AAAeP36M3r17w8nJCVZWVmjevLleXyiq8qQDlYVMJsOKFSvw3nvvQSwWw83NDaNHj1aYVxkArl27hrCwMDg7O8PS0hI+Pj74/PPPAbzul1CWFKKiouTvqbfddq2qf3/6nRI+UYvaQH755Rf4+vqiZcuWGp0/YsQIbNu2Db169cLUqVPx+++/Y/Hixbh79y4OHjyocO7Dhw/Rq1cvDB8+HEOGDMGWLVswdOhQNG3aFO+99x7atm2LiRMnYtWqVZg5c6b8Ft2bt+ri4+PRr18/jB49GiNHjoS/vz+Sk5PRsmVLFBQUYOLEiahRowa2bduGbt26Yd++fRVOjE9M1+jRo7F161YMGzYMEydOxJMnT7BmzRr8+eef+O2332Bubo6UlBR06tQJLi4umDFjBhwcHJCQkIADBw4AAFxcXLBu3TqMHTsWPXr0QM+ePQEADRs25PNH4w39TgmveFu3qwrJzs5mAbCffPKJRuffvHmTBcCOGDFCYf+0adNYAOyZM2fk+8qWmTt//rx8X0pKCisSidipU6fK9+3du5cFwJ49e1apvrIyjh8/rrB/8uTJLAD2woUL8n25ubmsj48P6+3tzUqlUpZlWfbJkycsAPb777/X6OdjWZZNTU01yHKEpGLjx49n3/zf+MKFCywA9scff1Q47/jx4wr7Dx48yAJg//jjD7Vl6/M3rMx/f/qdElNDt74NoGxRdVtbW43OP3bsGAAgPDxcYf/UqVMBQOnWc2BgINq0aSPfdnFxgb+/Px4/fqxxjD4+PggLC1OKIyQkBK1bt5bvs7GxwahRo5CQkIA7d+5oXD4xDXv37oW9vT06duyItLQ0+atp06awsbHB2bNnAQAODg4AgCNHjqCkpITHiE0f/U4J3yhRG0DZ0nW5ubkanZ+YmAiBQKA0sby7uzscHByQmJiosL927dpKZTg6Oio9H6uIj4+Pyjj8/f2V9pfdMi8fBzF9Dx48QHZ2NlxdXeHi4qLwysvLQ0pKCgAgNDQUn376KaKiouDs7IxPPvkE33//vVIfCUK/U8I/ekZtAHZ2dqhZsyZu3bql1XUMw2h0nlAoVLmf1WKadmP08CamRyaTwdXVFT/++KPK42WdmRiGwb59+3DlyhX88ssviI2Nxeeff45ly5bhypUrsLGx4TJsk0a/U8I3StQG0qVLF8TExODy5cto0aJFhed6eXlBJpPhwYMHCh2+kpOTkZWVBS8vL63r1zTpl48jPj5eaf+9e/fkx0nl4ufnh1OnTqFVq1YafTlr3rw5mjdvjoULF2Lnzp0YMGAAdu/ejREjRuj0nqqK6HdK+Ea3vg3kq6++grW1NUaMGIHk5GSl448ePcLKlSsBAJ07dwYArFixQuGc5cuXAwA+/vhjreu3trYGAGRlZWl8TefOnXH16lVcvnxZvi8/Px8xMTHw9vZGYGCg1nEQfn322WeQSqWIjo5WOlZaWip/f2RmZirdkQkKCgIA+a1aKysrANq9p6oi+p0SvlGL2kD8/Pywc+dO9OnTBwEBAQozk126dAl79+7F0KFDAQCNGjXCkCFDEBMTg6ysLISGhuLq1avYtm0bunfvjvbt22tdf1BQEIRCIZYuXYrs7GyIRCK8//77cHV1VXvNjBkzsGvXLnz00UeYOHEinJycsG3bNjx58gT79++HQKD997gdO3YgMTFRvtj7+fPnsWDBAgDAoEGDqJVuZKGhoRg9ejQWL16MmzdvolOnTjA3N8eDBw+wd+9erFy5Er169cK2bdvw3XffoUePHvDz80Nubi42btwIOzs7+RdJS0tLBAYGYs+ePahbty6cnJxQv3591K9fX239VfHvT79Twjuee51XOffv32dHjhzJent7sxYWFqytrS3bqlUrdvXq1WxRUZH8vJKSEjYqKor18fFhzc3N2Vq1arEREREK57Ds66FVH3/8sVI9oaGhbGhoqMK+jRs3sr6+vqxQKFQYqqWuDJZl2UePHrG9evViHRwcWLFYzIaEhLBHjhxROEeb4VmhoaEsAJUvVUPHiH7KDyUqExMTwzZt2pS1tLRkbW1t2QYNGrBfffUV+/LlS5ZlWfbGjRtsv3792Nq1a7MikYh1dXVlu3Tpwl67dk2hnEuXLrFNmzZlLSwsNBoaVBX+/vQ7JaaGYVkteiQRQgghhFP0jJoQQggxYZSoCSGEEBNGiZoQQggxYZSoCSGEEBNGiZoQQggxYZSoCSGEEBNGiZoQQvSQkJAAhmGwdetWvkMhVRQlao5s3boVDMNALBbjxYsXSsfbtWtX4exEXBg5ciQYhkGXLl1UHj98+DCaNGkCsViM2rVrY+7cuSgtLeU4yuqF3jeEEErUHCsuLsaSJUv4DkPJtWvXsHXrVojFYpXHf/31V3Tv3h0ODg5YvXo1unfvjgULFuCLL77gONLqid43psvLywuFhYUYNGgQ36GQKorm+uZYUFAQNm7ciIiICNSsWZPvcAC8Xi5z4sSJGDx4ME6fPq3ynGnTpqFhw4Y4ceIEzMxev23s7OywaNEiTJo0CfXq1eMy5GqH3jemq+yOByHGQi1qjs2cORNSqdSkWkc7duzArVu3sHDhQpXH79y5gzt37mDUqFHyD1sAGDduHFiWxb59+7gKtdqi941xzZs3DwzD4P79+xg4cCDs7e3h4uKC2bNng2VZPHv2DJ988gns7Ozg7u6OZcuWya9V9Yx66NChsLGxwYsXL9C9e3fY2NjAxcUF06ZNg1QqlZ8XFxcHhmEQFxenEI+qMpOSkjBs2DC88847EIlE8PDwwCeffIKEhAQj/VaIqaBEzTEfHx8MHjwYGzduxMuXL7W+vqCgAGlpaW99ZWZmalRebm4upk+fjpkzZ8Ld3V3lOX/++ScAIDg4WGF/zZo18c4778iPE+Oh9w03+vTpA5lMhiVLlqBZs2ZYsGABVqxYgY4dO8LT0xNLly5FnTp1MG3aNJw/f77CsqRSKcLCwlCjRg18++23CA0NxbJlyxATE6NTbJ9++ikOHjyIYcOG4bvvvsPEiRORm5uLp0+f6lQeqTwoUfMgMjISpaWlWLp0qdbXfv3113BxcXnrq3HjxhqVN3/+fFhaWmLKlClqz3n16hUAwMPDQ+mYh4eHTomDaI/eN8YXEhKCnTt3YuzYsfj555/xzjvvYOrUqfLkOHbsWBw5cgSWlpbYsmVLhWUVFRWhT58+2Lx5M8aMGYN9+/ahcePG2Lx5s9ZxZWVl4dKlS5g1axaio6MxfPhwRERE4MyZM2jbtq2uPy6pJOgZNQ98fX0xaNAgxMTEYMaMGSo/yNQZPHgwWrdu/dbzLC0t33rO/fv3sXLlSuzatQsikUjteYWFhQCg8hyxWIycnJy31kX0R+8b4xsxYoT830KhEMHBwXj+/DmGDx8u3+/g4AB/f388fvz4reWNGTNGYbtNmzbYsWOH1nFZWlrCwsICcXFxGD58OBwdHbUug1RelKh5MmvWLOzYsQNLlizBypUrNb7O19cXvr6+Bolh0qRJaNmyJT799NMKzyv78C4uLlY6VlRUpNGHOzEMet8YV+3atRW27e3tIRaL4ezsrLQ/PT29wrLEYjFcXFwU9jk6Omr8eOFNIpEIS5cuxdSpU+Hm5obmzZujS5cuGDx4sNpHD6TqoETNE19fXwwcOFDeOtJUXl4e8vLy3nqeUChU+pB405kzZ3D8+HEcOHBAoTNKaWkpCgsLkZCQACcnJ9jZ2clbbq9evUKtWrUUynn16hVCQkI0jp/oh943xiUUCjXaB7zu9a5tWeUxDKNy/5sdzspMnjwZXbt2xaFDhxAbG4vZs2dj8eLFOHPmjMaPLEjlRM+oeTRr1iytnzl+++238PDweOvrf//7X4XllHVA6dmzJ3x8fOSvFy9e4MyZM/Dx8ZE/gwsKCgLweszsm16+fInnz5/LjxNu0Pum6ii7hZ2VlaWwPzExUeX5fn5+mDp1Kk6cOIFbt25BIpEo9EAnVRO1qHnk5+eHgQMHYsOGDfDy8lIYwqKOoZ41vv/++zh48KDS/lGjRsHLywuRkZFo0KABAOC9995DvXr1EBMTg9GjR8tbCuvWrQPDMOjVq9db4yGGQ++bqsPLywtCoRDnz59H9+7d5fu/++47hfMKCgogEAgUxmv7+fnB1tZW5aMFUrVQouZZZGQkduzYgfj4eLz33ntvPd9Qzxpr166t9DwOeH17zc3NTeFDAwC++eYbdOvWDZ06dULfvn1x69YtrFmzBiNGjEBAQIDe8RDt0PumarC3t0fv3r2xevVqMAwDPz8/HDlyBCkpKQrn3b9/Hx988AE+++wzBAYGwszMDAcPHkRycjL69u3LU/SEK3Trm2d16tTBwIED+Q7jrbp06YIDBw4gIyMDX3zxBQ4cOICZM2di7dq1fIdWLdH7pupYvXo1PvnkE6xfvx6zZs1C7dq1sW3bNoVzatWqhX79+iEuLg4RERGIiIhATk4Ofvrpp7d26iOVH8O+rUcEIYQQQnhDLWpCCCHEhFGiJoQQQkwYJWpCCCHEhFGiJoQQQkwYJWpCCCHEhFGiJoQQQkwYJWpCCKkkEhISwDAMtm7dyncohEOUqAkhVdKjR48wevRo+Pr6QiwWw87ODq1atcLKlSvlS3Aaw507dzBv3jyFRUv4sHDhQnTr1g1ubm5gGAbz5s3jNR6iO5pClBBS5Rw9ehS9e/eGSCTC4MGDUb9+fUgkEly8eBFffvklbt++jZiYGKPUfefOHURFRaFdu3bw9vY2Sh2amDVrFtzd3dG4cWPExsbyFgfRHyVqQkiV8uTJE/Tt2xdeXl44c+aMfLlNABg/fjwePnyIo0eP8hjhf1iWNdra3E+ePIG3tzfS0tIqXLqUmD669U0IqVK+/vpr5OXlYfPmzQpJukydOnUwadIk+XZpaSmio6Ph5+cHkUgEb29vzJw5U2lVKm9vb3Tp0gUXL15ESEgIxGIxfH19sX37dvk5W7duRe/evQEA7du3B8MwYBgGcXFxCmXExsYiODgYlpaW2LBhAwDg8ePH6N27N5ycnGBlZYXmzZvr9YWCz9Y8MSxK1ISQKuWXX36Br68vWrZsqdH5I0aMwJw5c9CkSRP83//9H0JDQ7F48WKVq1I9fPgQvXr1QseOHbFs2TI4Ojpi6NChuH37NgCgbdu2mDhxIgBg5syZ2LFjB3bs2KGwUlh8fDz69euHjh07YuXKlQgKCkJycjJatmyJ2NhYjBs3DgsXLkRRURG6deumcllRUs2whBBSRWRnZ7MA2E8++USj82/evMkCYEeMGKGwf9q0aSwA9syZM/J9Xl5eLAD2/Pnz8n0pKSmsSCRip06dKt+3d+9eFgB79uxZpfrKyjh+/LjC/smTJ7MA2AsXLsj35ebmsj4+Pqy3tzcrlUpZlmXZJ0+esADY77//XqOfj2VZNjU1lQXAzp07V+NriGmhFjUhpMrIyckBANja2mp0/rFjxwAA4eHhCvunTp0KAEq3ngMDA9GmTRv5touLC/z9/fH48WONY/Tx8UFYWJhSHCEhIWjdurV8n42NDUaNGoWEhATcuXNH4/JJ1UOJmhBSZdjZ2QEAcnNzNTo/MTERAoEAderUUdjv7u4OBwcHJCYmKuyvXbu2UhmOjo7IzMzUOEYfHx+Vcfj7+yvtL7tlXj4OUr1QoiaEVBl2dnaoWbMmbt26pdV1DMNodJ5QKFS5n2VZjesyRg9vUrVRoiaEVCldunTBo0ePcPny5bee6+XlBZlMhgcPHijsT05ORlZWFry8vLSuX9OkXz6O+Ph4pf337t2THyfVFyVqQkiV8tVXX8Ha2hojRoxAcnKy0vFHjx5h5cqVAIDOnTsDAFasWKFwzvLlywEAH3/8sdb1W1tbAwCysrI0vqZz5864evWqwpeL/Px8xMTEwNvbG4GBgVrHQaoOmvCEEFKl+Pn5YefOnejTpw8CAgIUZia7dOkS9u7di6FDhwIAGjVqhCFDhiAmJgZZWVkIDQ3F1atXsW3bNnTv3h3t27fXuv6goCAIhUIsXboU2dnZEIlEeP/99+Hq6qr2mhkzZmDXrl346KOPMHHiRDg5OWHbtm148uQJ9u/fD4FA+zbVjh07kJiYiIKCAgDA+fPnsWDBAgDAoEGDqJVemfDd7ZwQQozh/v377MiRI1lvb2/WwsKCtbW1ZVu1asWuXr2aLSoqkp9XUlLCRkVFsT4+Pqy5uTlbq1YtNiIiQuEcln09tOrjjz9Wqic0NJQNDQ1V2Ldx40bW19eXFQqFCkO11JXBsiz76NEjtlevXqyDgwMrFovZkJAQ9siRIwrnaDM8KzQ0lAWg8qVq6BgxXQzLatELghBCCCGcomfUhBBCiAmjRE0IIYSYMErUhBBCiAmjRE0IIYSYMErUhBBCiAmjRE0IIYSYMErUhBBCiAmjRE0IIYSYMErUhBBCiAmjRE0IIYSYMErUhBBCiAmjRE0IIYSYMErUhBBCiAn7f3ocKLNgCsDGAAAAAElFTkSuQmCC", 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" ] @@ -860,11 +1018,12 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "id": "6984eaf5", "metadata": {}, "source": [ - "The paired proportional plot also supports the ``float_contrast`` parameter, which can be set to ``False`` to produce a **Cumming estimation plot**.\n" + "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" ] }, { @@ -875,7 +1034,7 @@ "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -889,17 +1048,16 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "id": "c9cea250", "metadata": {}, "source": [ - "The upper part (grey part) of 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 lower part, on the other hand, represents the proportion of observations that belong to the category, which is\n", - "or **success**, which is equivalent to the proportion of 1 in the data. \n", + "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", - "Repeated measures is also supported in paired proportional plot, by changing the ``is_paired`` parameter, two types of plot can be produced.\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) ." ] }, { @@ -910,7 +1068,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -935,7 +1093,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -953,15 +1111,14 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "id": "539a0a22", "metadata": {}, "source": [ - "From the above two images, we can see that the on both the observed value plot and delta plot, the pairs compared are different in terms of the paired settings. And for detailed information about repeated measures, please refer to :doc:`repeatedmeasures` .\n", - "\n", "If you want to specify the order of the groups, you can use the ``idx`` parameter in the ``.load()`` method.\n", "\n", - "For all the groups to be compared together, you can put all the groups in the ``idx`` parameter in the ``.load()`` method without subbrackets.\n" + "To compare all groups together, you can include all the groups in the ``idx`` parameter of the ``load()`` method without using subbrackets.\"" ] }, { @@ -972,7 +1129,7 @@ "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -990,12 +1147,70 @@ ] }, { + "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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", 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", 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", + "text/plain": [ + "
" + ] + }, + "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 supplied to the ``plot()`` method to customize the paired proportional plot.\n", - "By updating the sankey_kwargs parameter, you can customize the Sankey plot. The following parameters are supported:\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", "- **width**: The width of each Sankey bar. Default is 0.5.\n", "- **align**: The alignment of each Sankey bar. Default is \"center\".\n", @@ -1011,7 +1226,7 @@ "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -1023,14 +1238,6 @@ "source": [ "two_groups_baseline.mean_diff.plot(sankey_kwargs = {\"alpha\": 0.2});" ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e225358c", - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { diff --git a/nbs/tutorials/04-mini_meta_delta.ipynb b/nbs/tutorials/04-mini_meta_delta.ipynb index 8dca8cca..595432d9 100644 --- a/nbs/tutorials/04-mini_meta_delta.ipynb +++ b/nbs/tutorials/04-mini_meta_delta.ipynb @@ -17,9 +17,9 @@ "id": "a9ca4dd5", "metadata": {}, "source": [ - "When scientists perform replicates of the same experiment, the effect size of each replicate often varies, which complicates interpretation of the results. As of 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", + "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 uses the generic *inverse-variance* method to calculate the effect size, as follows:\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", @@ -43,9 +43,9 @@ "id": "5fb1dc0f", "metadata": {}, "source": [ - "Note that this uses the *fixed-effects* model of meta-analysis, as opposed to the random-effects model; that is to say, all variation between the results of each replicate is assumed to be due solely to sampling error. We thus recommend that this function only be used for replications of the same experiment, i.e. situations where it can be safely assumed that each replicate estimates the same population mean $\\mu$. \n", + "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", - "Also note that as of v2023.02.14, DABEST can only compute weighted effect size *for mean difference only*, and not standardized measures such as Cohen's *d*.\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" ] @@ -55,7 +55,7 @@ "id": "12c4d226", "metadata": {}, "source": [ - "## Load Libraries" + "## Load libraries" ] }, { @@ -68,7 +68,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "We're using DABEST v2023.02.14\n" + "We're using DABEST v2024.03.29\n" ] } ], @@ -80,6 +80,18 @@ "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", @@ -93,7 +105,7 @@ "id": "09a9b692", "metadata": {}, "source": [ - "We will now create a dataset to demonstrate the mini-meta function." + "Let´s create a dataset to demonstrate the mini-meta function." ] }, { @@ -105,8 +117,7 @@ "source": [ "from scipy.stats import norm # Used in generation of populations.\n", "\n", - "np.random.seed(9999) # Fix the seed so the results are replicable.\n", - "# pop_size = 10000 # Size of each population.\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", @@ -140,8 +151,8 @@ "id": "e5b9dbbd", "metadata": {}, "source": [ - "We now have 3 Control and 3 Test groups, simulating 3 replicates of the same experiment. Our\n", - "dataset also has a non-numerical column indicating gender, and another\n", + "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." ] }, @@ -275,7 +286,7 @@ "id": "21171074", "metadata": {}, "source": [ - "## Loading Data" + "## Loading data" ] }, { @@ -283,9 +294,7 @@ "id": "adc6d626", "metadata": {}, "source": [ - "Next, we load data as we would normally using ``dabest.load()``. This time, however,\n", - "we also specify the argument ``mini_meta=True``. As we are loading three experiments' worth of data,\n", - "``idx`` is passed as a tuple of tuples, as follows:" + "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:" ] }, { @@ -303,7 +312,7 @@ "id": "1a3bcd5c", "metadata": {}, "source": [ - "When this ``Dabest`` object is called, it should show that effect sizes will be calculated for each group, as well as the weighted delta. Note once again that weighted delta will only be calcuated for mean difference.\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" ] }, { @@ -315,11 +324,11 @@ { "data": { "text/plain": [ - "DABEST v2023.02.14\n", + "DABEST v2024.03.29\n", "==================\n", " \n", - "Good evening!\n", - "The current time is Sun Mar 19 22:59:33 2023.\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", @@ -356,11 +365,11 @@ { "data": { "text/plain": [ - "DABEST v2023.02.14\n", + "DABEST v2024.03.29\n", "==================\n", " \n", - "Good evening!\n", - "The current time is Sun Mar 19 22:59:27 2023.\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", @@ -376,7 +385,7 @@ "\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.\n", "\n", "To get the results of all valid statistical tests, use `.mean_diff.statistical_tests`" @@ -396,7 +405,7 @@ "id": "0de6f65c", "metadata": {}, "source": [ - "You can view the details of each experiment by accessing `.mean_diff.results`, as follows." + "You can view the details of each experiment by accessing the property `mean_diff.results` as follows." ] }, { @@ -607,17 +616,17 @@ "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, for example:\n", + "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", + " - ``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 on their own:" + "You can call each of the above attributes individually:" ] }, { @@ -629,7 +638,7 @@ { "data": { "text/plain": [ - "-0.010352287701068538" + "-0.01035228770106855" ] }, "execution_count": null, @@ -646,7 +655,7 @@ "id": "5eafcc8e", "metadata": {}, "source": [ - "Attributes of the weighted delta can also be written to a `dict` by using the ``.to_dict()`` function. Below, we do this and subsequently convert the dict into a dataframe for better readability:\n" + "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" ] }, { @@ -710,7 +719,7 @@ " \n", " \n", " bootstraps_weighted_delta\n", - " [0.1771640316740503, 0.055052653330973, 0.1635...\n", + " [0.1771640316740503, 0.05505265333097302, 0.16...\n", " \n", " \n", " ci\n", @@ -726,7 +735,7 @@ " \n", " \n", " control_var\n", - " [0.17628013404546256, 0.9584767911266554, 0.16...\n", + " [0.17628013404546258, 0.9584767911266554, 0.16...\n", " \n", " \n", " difference\n", @@ -738,7 +747,7 @@ " \n", " \n", " jackknives\n", - " [-0.008668330406027464, -0.008643903244926629,...\n", + " [-0.008668330406027476, -0.00864390324492664, ...\n", " \n", " \n", " pct_high\n", @@ -782,7 +791,7 @@ " \n", " \n", " test_var\n", - " [0.245120718701526, 0.4860998992516514, 0.9667...\n", + " [0.24512071870152594, 0.4860998992516514, 0.96...\n", " \n", " \n", "\n", @@ -797,14 +806,14 @@ "bca_low -0.221666\n", "bias_correction 0.005013\n", "bootstraps [[0.6686169333655454, 0.4382051534234943, 0.66...\n", - "bootstraps_weighted_delta [0.1771640316740503, 0.055052653330973, 0.1635...\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 [0.17628013404546256, 0.9584767911266554, 0.16...\n", + "control_var [0.17628013404546258, 0.9584767911266554, 0.16...\n", "difference -0.010352\n", "group_var [0.021070042637349427, 0.07222883451891535, 0....\n", - "jackknives [-0.008668330406027464, -0.008643903244926629,...\n", + "jackknives [-0.008668330406027476, -0.00864390324492664, ...\n", "pct_high 0.213769\n", "pct_interval_idx (125, 4875)\n", "pct_low -0.222307\n", @@ -815,7 +824,7 @@ "pvalue_permutation 0.9374\n", "test [Test 1, Test 2, Test 3]\n", "test_N [20, 20, 20]\n", - "test_var [0.245120718701526, 0.4860998992516514, 0.9667..." + "test_var [0.24512071870152594, 0.4860998992516514, 0.96..." ] }, "execution_count": null, @@ -834,7 +843,7 @@ "id": "7797244d", "metadata": {}, "source": [ - "## Producing estimation plots - unpaired data" + "## Generating estimation plots - unpaired data" ] }, { @@ -842,7 +851,7 @@ "id": "d51a505d", "metadata": {}, "source": [ - "Simply passing the ``.plot()`` method will produce a **Cumming estimation plot** showing the data for each experimental replicate as well as the calculated weighted delta.\n" + "Calling the ``plot()`` method produces a **Cumming estimation plot** showing the data for each experimental replicate as well as the calculated weighted delta.\n" ] }, { @@ -853,7 +862,7 @@ "outputs": [ { "data": { - "image/png": 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", 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6Ajk7lwGSDEmScH7b13APjUe3Wa/Bwdnd2iHSDSirLjObvAOASlahtLrUwhER0ZX0jXps/HEfTh0+Z9wmyxL6jUxEn5QEK0ZGra2kIB8bli8DAAhFMfm9Y80qJPUbgE5R0VaLj4joapyFnsgGFKVtNiTvACAUiIvJX9WFU8hY9U8rRkY3I8wvzOws84ChS32YX5iFIyKiK+1adwSnjpwz2aYoArvXHzVJ6sn2Hd2727A8qxmyLOPInp0WjoiIqGVM4IlswIW9PwLmusoLBSUndkJXxRZbWzK+53jIsgwJpnUqSzL83P0woMsAK0VGRDptA47uzQSEmUIJOLjthMVjorZTX1fb4lC0+to6C0ZDRHRtdpPAv/3225AkCU899ZS1QyFqdfUlFwBh7tskAAjUleVZNB66NYFegXhl+ivQOGoAGLrNA4CPmw/evOtNOKg4uonIWirLaqBvND/EBQIoLay0bEDUpjpFRkPRm69vRVEQGs3u80TUvtjFt8R9+/bh008/Rffu3a0dClGbULv7oq4kp9lyjbuvBaOh1tAvth++fOpL7Di5AyVVJQj3C0e/2H5M3omszMlF3WK5xplLObY3H897FdWV5XDz8MLvX3uzxX1rqqrQ2KCDu5c3ZFlGfK8+8PYPQEVJMZSLY98BQJJluLq5o/uAwW0dPhHRDbH5b4rV1dW4++678dlnn+H//u//rB0OUZsI6n0bzq5d2LRAkuER1g1O3kGWD4pumYvGBWN6jLF2GER0BTcPF3SKCkDuuSIIxbTnkyRJSOjNFtn2prqyHJVlZS3uk3vuLFb97yucSz8JAPD09cPIO6ah15BhePBPL+GbjxbgQtZZ4/7+QSGY/egT0Dhx5QEial9sPoF/9NFHMXHiRIwePfqaCbxWq4VWqzU+rq6ubuvwiFpFSJ9JqDh3FKWndgGyytCdXihQu/ugy+1/tHZ4RER2ZcQdffD9ZxtQX6s1jF6SAAjAL9iLs9DboMLcC/j3/DfR2Nho3FZRUozl/1mIxgYd+o0Yjd/9+U3knstCaWEBvHz90Ckqmsu0ElG7ZNMJ/DfffIODBw9i375917X//PnzMW/evDaOynbsOHoaX6/bh9M5hXDWOGJM3wTcM7Y/PN2crR0aXUWSVYif/hLKzxxC8YntUBq08AhPREDSCKjUrC8iotbk5euOux6fgGP7M5F9Oh8qBxmdu4UhrkcEHBxt+qtTh7T1l5+gb2w0Lg93pfU/LEOvISlwcHRESEQkQiIiLR8gEdENsNlPofPnz+PJJ5/EunXr4HSd3ZtefPFFPP3008bHqampSElJaasQ27WVO47gg+82QpYkKEKgpl6Hn3Ycxt4TWfjw6dlwc2aXsfZGkmR4x/SGd0xva4dC7VhpdSlW7F2B3ad2AwAGxA3AHX3vgI+7j5UjI7Itzq4a9ElJYIu7HUg/kmoyvv1KdTU1yMs+h7CYzhaOiojo5thsAn/gwAEUFhaiV69exm16vR5bt27Fv/71L2i1WqhUKpNjNBoNNBqN8bGbm5vF4m1P6rQ6LPxpGwBAuWJmc0URyCupwModRzBndD9rhUdENym/LB9PL34albWVUIThy+qFXRewNnUt3nvgPQRxrgQiu6at10FRBJyc1ez+fYVr/S0kmX8rIrIdNpvAjxo1CkePHjXZ9sADD6Br1654/vnnmyTvdFlqRg7qdQ1my4QQ2JKawQSeyAYt2rDIJHkHAEUoqKqrwqINi/Dy9JetGB0RtZX888XYseYw8s4VAwB8Az0xcEx3RMaFWDmy9iGhV18c3L7FbCu8q4cngsMjLR8UEdFNsmgCf+HCBWzduhWFhYW48847ERoaCr1ej4qKCnh6et5Q0u3u7o7ExESTba6urvD19W2ynUw1NrPe6SUNza1/S20mddGT0FWXQe3mjeSHFlg7HLJB9Q312HlqJ4QQTcoUoWDnqZ2o19XDSc3hMUQ3QlffgNxzRQCAkAh/qJ0sv4xcbXU9zhzPgU7XiKAwXwSH+xlblQsulOKHRZtMxneXFFbg5y+34ba7hiA6vpPF421vhk2cjLT9e6Ctrzf+nSRJghACE2bdxUYfIrIpFknghRB45pln8K9//QuNjY2QJAlJSUkIDQ1FdXU1IiMj8cYbb+Cpp56yRDgdSk29FqfOFcDBQUZ8RDAcHVRIjA6BSpagV5p+0ZdlCX27Rlgh0o5NV10GXVWJtcMgG6Zt0JpN3i8RQkDboGUCT3SdhBA4tOMU9m5MQ2OD4ca2g6MK/UYmoufguFbroq6t0yHzeA7qa7XwC/ZGWHSgSZfuw7vTsePXVCiKMCadQWG+mHTPUDi5aLB3w1EIRcDk8r/47x1rUhHVNaTDd6f39g/AI6/Mw7rvl+LEoQMQioLAsHCMvGMa4ntyXhkisi0WSeD/9re/YcGCBXj++ecxatQojBlzed1jT09PTJs2Dd9///0tJ/CbN2++tUDtiBACX67Zg2827IPuYou6h6sTHp06HCN7d8WUocn4fsshk2NkSYKz2hHTUnpaIWIiuhUezh4I8AxAYUWh2XJ/D3+4u7hbOCoi23X8wBnsXHPYZFtjgx471xyGxskR3frE3PJznDp8Dht/3At9o2JMzr39PXD7fSlw93TB+cx8bPvl8mf1pZt0BTmlWPf9Hky6eyiyT+ejuXt3FSXVqCqvgYd3x5zz50p+QcGY8+iT0Dc2QlEUOKrV1g6JiOimyJZ4ks8++wxz587FX/7yFyQnJzcp7969O9LT0y0RSofx7Yb9+O+a3cbkHQAqa+ox/8vV2HcyC7+5fSjuHdcfLprLH2BdI4Lw3uMzEeDtYY2Qieg6VdVV4ZcDv+CLTV9gw5ENqG+ohyRJmD1kdrPHzBkyB7Jkkbd8IpsnhMD+LcebLd+/5XiLPV6upK3XoaSgAnW1WpPtRXllWPf9bugbFeNzAkB5SRV++WobhBBI3ZFudoI1IQTOpeehoqz6OiLo2K3vV1M5ODB5JyKbZpEW+PPnz2PQoEHNlru6uqKystISoXQIuoZGfLNhv9kyWZLw9dq96Ns1EnPHD8SskX2RW1IOVycNArzZOkfUGrzdvE1+Xy8hBDanbcZP+39Cflk+gryCMKnPJIxMGmnsArsnfQ/m/zAfDY0NkGUZekWPhesW4s05b2Jc8jhU1VXhq61fQdeoAwCoHdS4a+hdGNdzXOu+SCI7Vl+rQ1V5bbPlVeW1qK/VwdlV0+w+Om0Dtv1yCKcOZxm7v8ckhCJlci84uzrh6J4MY6v7lYQiUJxXjvzzJSguKIcwM9ztkrLiKoTHBiM7I9/sDQUvP3e4e7lcxysmIiJbYZEEPiAgAOfPn2+2/MCBAwgPD7dEKB3ChaJy1NRrzZYpQuDEuTzjY43aAVHBfpYKjahD+OChD27quIVrF2LFvhXGL/WVdZV496d3cerCKfxhwh9QVFGEt757C41KIwBArxh62NTU1+DP//szvnjiC8wYNAMTe0/E0WzDKh1J4Ulw0fALPNEl3368FrXV9XBxc8Ks3481u4+DowqShGa7pkuSYZ/mCCGwcslW5GeXGBNrIQQyj+egtKgCs34/FsX5FS0m56WFFXB1d0ZNZV2z+7i4OaH/qETkZBZAUS634kMCIIDB43p0+PHvRET2xiL9KadNm4ZPPvkEZ86cMW679IGydu1aLF68GDNmzLBEKB2C8zVmyNU4Wn4GXSJqWWZ+JlbsWwEAJl/4AeDnAz8jIy8Da1LXmCwRd4kiFFTWVWL7ie0AABeNC/rH9kf/2P5M3omuUltdj5rKOtRW1ze7j6PaAVFdO5lNfiVJQlTXTnBUN98GknOmEHnnipu2rguB0sJKZB6/ADcP5xaTaxd3Z3TrE222TJIkePt7ICDEGwEhPpj28EiERPoby/2DvDD53mGI6soZ6ImI7I1FWuDnzZuHTZs2ITk5GUOHDoUkSfjrX/+KV199Fbt27ULPnj3x0ksvWSKUDiHIxxOxoQHIvFAE5aovD7IsYWTvOCtFRkTN2XZ8G2RZNrtOsUpWYevxrSiuKG72eJWsQk5JTluGSNdQlZuLzDVrUHb2LJy9vBA5ciQCe7AF1FYNHp+MvOxi1NfqjIm4JElwclFj8PjkFo89n1kASZbMtrBLsoScM/lI6B2NMycuNC2XACcXDcI7B0GSgOzT+cg8lmPsmSNJgFrjgHEzBhj/bwWG+mLqgyOg0zZAKAIaZ47xJiKyVxZJ4D09PbF79268++67+O677+Dk5IQtW7YgJiYGr732Gv70pz/B2dnZEqF0GE9MH4lnP/wOjXq9cbk4WZLg6+GKe8cNsHJ0RPbtiUVPoKy6DN5u3tfdnb6+oR5SC5NN1evq4efR/HAXvaKHv4d/s+XUtnL378fOv/0NEAJCUSDJMs7v3ImY8ePR86GHmMTbIE8fN8x+dBwO70rHmeOGRDs6oRN6DOwCV3fDd5bKsmqkH85GXa0W/iHe6NwtDA6OKqhULde3LMuI6BKMpP6dcXTPaWOyL0kSVA4yxs8aBJXK0Ely/MxByErPRfqRbDRoGxAU7oeE3tFwcWu6JKRawx52RET2ziIJPAA4OzvjlVdewSuvvGKpp+zQukYE4aNn7sKyTQew/2QWHFQqDO/ZBdNSesHbnV1qidpSWXUZSqpKbuiY+NB4/LTvJ7NlekWPhLAExATF4Pvd3zcplyBB7aDGsG7DbipeujWN9fXYs2ABhGEQMgAY/g0gc/VqhPTpgyAzK7BQ++fq7oxBY3tg0NgeTcpSd57C9tWpkCBBkgBFEdi19gimPDAcUfGdsG+z+VnshSIQnRAKSZIwbGIvxCSE4mRqFuprDDcBuvWJgZvn5c9pSTZ02Wd3eCIiAiyYwJPlhQf64JnZY6wdBtkpIRTotbWQHZ0gq/hWcqsGdR2EEJ8Q5Jflm4xzlyUZgV6BGNJ1CBwdHPHohEfx4a8fmiwJJ8syXrjzBbg5ca1na8jdvx+NdeYnGpNkGVmbNjGBtzO554qw/ddUAICAME52V1tTj1++2o67n5yArj0jcfJQlumBEhARG4yw6EDDQ0lCaHQgQi8+JiIiuhaLfOt+8MEHr7mPJElYtGiRBaIholshhILcvStwYdcP0FWXQlI5IrD7KESMuA+OLh7WDs9mOaoc8fY9b+PvK/6OI+eOGLd3C+uGZ+94Fo4Ohq6xE3tPRPeI7lh7eC2KK4vRyacTxiWPg7+n+e7zOSU5yCvLQ5BXEML8wkzKhBDYcXIHfj34KworChHmF4bb+96O5Khk4z4NjQ34ce+P+PXgr6iorUC4fzjuHHAnhsQPaf0/go3SVlSguSnLhaKgvrzc8kFRm7qy2/uVhCJQXlKF3KwijJzSF35BXji8Kx1V5bVwdXdCUr9Y9BwSZ3Ztd7Id2vp6HN27C8V5eXD38kKPAYPh5ulp7bCIqIOwSAK/cePGJuP/9Ho98vLyoNfr4e/vD1dXV0uEQkTXQQgBva4OssoBsoPpZEhn1v0beXtXXN5X34D81LWoyE5D8kMLoFI3HZdJ18fPww9v3/s2cktzkV+ej0CvQHTyadptNswvDA+NeqjFcxVXFuOdH99BWnaacVtCaAKem/ocAjwDAACfrPkEK/evhCzJUISCvPI87E7fjYdHP4xpA6ZBr+jx+revI/VsKgQMiUp6bjr+8v1fcN/w+zBryKxWfPW2yzMiotn1xiRZhldUlIUjotYghEBBTglyzhRCVsmIju8EL193AEB5SVWLS8BVlFajU1QAkgfFIXlQ3MXJ55i024OcM5n44h/voL62BrJKBaEoWPvdt5j+m98jqR/nGCKitmeRBD4rK8vs9oaGBnz66ad4//33sW7dOkuEQkTXUHxiB7K3fonaomxAkuAT2w+RIx+Ai18Y6ssLkbfXzDhtoaCuJAeFRzcguPdEywdtw+p0daiqq4K3q7exlT3EJwQhPiE3fc4GfQNeWPIC8svzTbafvHASzy95Hgt/txAZeRlYuX8lABi77F+aAX/RhkUYGj8UJy6cwKGzh0zOcWk27iVblmBMjzHwcfe56TjthX+3bvAMD0dlTo5x7DsAQ6u8JCFm3DjrBUc3pUHXiF//twPZp/MvJt4CO9ccRo+BXTBkQjI8vF1RnF/ebBLv7mXaKMHk3T40NOiw5P2/QVtXCwBQ9HoAgNDrsWzhRwiJiIRvYJA1QySiDsAi68A3x9HREY899hjGjh2Lxx57zJqhENkFbWUx8g+uRt6BVagrzTO7T03ROeTs+h45u39AbXG2SVnB4fU4+f1fUFt03rBBCJRm7MPhz59GfVkeyjL3A2iu1UlCyak9rfdi7FxFbQX+vuLvmPn3mbj/n/djzj/mYPHGxWjQN9zyuXee3Incstwma8YrQkFBeQG2n9iOTWmboJJVzZ5j24lt2H5iu8lY+ysJIbArfdctx2oPJEnCkJdegkdY2KUNAABHZ2cMfu45uAcHWzE6uhnbfz2E85kFAAz/1y91sDi8Kx3H9p9BUr/O5peIkyR4eLsiNCrAkuGShRw/sB+11dXGG5lXO7B1s2UDIqIOqV3MPNWjRw8sWbLE2mEQ2SwhBM5t+gI5u74z6cobmDwWnW97DJKsglD0SF/5HoqObrqYYEjIWr8IAT3GIHbi4xBC4OyGS/NQXPHlRCjQ6+pxfucyuAXFtBQFmk/u6UraBi2e/+/zyCnJMSbZtbpaLNu1DHlleXjxzhdv6fwnck5AJaugV/RNylSyCsdzjqOmvqbZL6GyJKOqrgraBm2TmwBGkuF1kIGLnx/G/P3vKD5+HOXnzsHJywshvXtDpdFYOzS6Qdp6HU4cymr2+kjdeQr3PHkb+o9KxJ4Nacbx7Ib11x1x211DOMbdTpUWFkCWVVDMvLcKIVBSWGCFqIioo2kXCfy6devg4sKlzYhuVv7BX5Gzc1mT7QWp66B280HE8HuRve1/KDq62VAgLifbhYfXwdknBF6R3dFYW2n+CYSCkpO7EDZoRgtRSPCJ7XsrL6PD2HJsC7Kv6v0AGL4AbjuxDbMKZiE6MPqmz++sdjaOWW+uvJNvJ2w5tsVsuV7RI65THFydXLH/9H6z5xJCICki6aZjtEeSJMG/Wzf4d+tm7VDoFlSV10LRN3PjCkBFSRUAoO/wboiOD8Wpw1mor9XBP8Qbcd0joHbiWuz2ysvX12zyDgCyJMHL19fCERFRR2SRBP6NN94wu728vBxbt27FwYMH8cILL1giFCK7I4QwtLybL0Xuvp/QaeB05O77Cc21kOfu+RGeEYnXeiY4eQcjqNcE5B/81bRIkuHkFYiA7qNvNPwO6cCZA8aJ464mSzIOZB64pQR+WLdh+HbHt2bL9IoewxKGIcg7CP/b9j/U1Nc0WbYuxCcEfTv3Rbewbvhh9w+orK1ssk9yVDJig2NvOkai9srFreWJOJ1dL5f7BnqaXSPeGirLapB++Bxqa+rhF+SF2KRwOKrbRTuNTWpsaEDuubMAgJCIKDg4OiKhdz/88tUS6LT1TXpoKIqC3kNHWCNUIupgLPLO/vrrr5vd7u3tjZiYGHzyySf4zW9+Y4lQiNoltZu3ye8bIfQN0JY3321Pr61FbdE56Otrmt2nobYCGs8gODi7o7GuqukOkgyf2P4AgJjxv4fa3Q+5e39EY10VJFkFv4ShiBr9EBw0TXvSNGprUXhkAyqz0yA7aOCXMATenftAamZsdUcgX2P6keud8KpR34idp3Zi16ld0Ct69IrqheFJwxEVEIWp/adi+Z7lxhsFkiRBCIHb+96OzsGdAQBv3/s2/vLdX3Ch9ILxnLHBsXjpzpegklVwd3bHO3PfwXsr38OJnBOG2CUZwxOH49EJj97kqydq31zcnBAZF4xz6flNkjRJAhL7tjSUyDqO7MnA1l8OQoIESQIURWDXuiO44/7h8AvysnZ4Nmf/lk1Y+903qKsxfG46u7hizPSZ6Dt8FO56/Cl8ueBdNDY0GN5XAUAITL73AQR0arpqCBFRa7NIAq8ozXdFI8s5m1uMdftPoKyqFtEhfhjbNwGebs7WDosAJD+04KaPlVQOkB2doDTUN7ODBCcPf0gqBwh9o9ldZAc1HF08EDniPpxe9a+rjpchO6oRNnjmxYcqhA+djbDBM6CrKYeDxrXZpePqy/Jx5Is/QVddCsAwI3fh0Q3wie2HrtNfhqzqmK1D/WL7Yctx893XFaGgX2y/a56jXlePV75+BcdzjkOWZAgIbD+xHd/t+g7v3PcOHh79MLqEdMHK/SuRV5qHYO9gTOozCSndUozniAqIwsLfL8SJnBMoqixCqG8oYq6a5yDUNxTv3v8ucktzUVZThk4+neDl6nVLr99eVZw/j8zVq1F25gycvL0RNXIkgnv35gzkNmj47X3ww783orKsxvDWBcMNsE5RAeg1NN7i8QghkH++BOUlVXD3dEGnyADjOPuCnBJs/fmgYT9cnnCvvlaHn7/chrlPT4Qsd9wbpjfqyO6dWPHFIpNtdbU1+Om/n0OtcUKPgYPx9Dvv4dCObSjOy4W7lzd6DRkGn4BAK0VMRB1Nx/z23AF9s2EfFv28AyrZcLd4wwGBJWt2Y/4jU9Et6uaXqyLLadTWIm//zyhK2wylQQuvqGSEDJgKF99QBPYYjbwDq4Cru2RLMny69Ifawxf+iSNQeGSD2X0CeoyGrHJAUK8JkB00OLf1S2Orvmdkd0SPfhjOvp2uOkwFjXvL4/3SV74HXU35xUfCOMFeacY+5O3/GZ36T7nJv4ZtG5IwBD/u/RGZ+ZlNutGPTR6LcL/wa57jf9v/h5MXTgKAyTnyy/Px8eqP8fL0l5HSLcUkYTdHkiQkhCVc8/ludWk7e3dhzx7sevddAIBQFEiyjNy9exE9ejR6PfIIk/h25FIX+Za6yrt5uGDOY+Nx6vA55GQWQFbJiOkWiqi4EMiq1k+Gy4oqUVJQAWc3DULC/U0mwasorcaqr7ejpKDCuM3D2xUT5gyGf7A30vZlQpKlJrPiCyFQXVGL85kFiIjlSggNOh0O7diKo3t2Q6fTIjq+GwaMGgNPn8ufY0IIbFzxQ7Pn2LjiB3QfMAhuHp4YOmGSJcImImqiTRL47OymkzNdj/Dwa39ppRt3PCsXi37eAQDQX/EBr9U14rX/rMT/XnsYjg7NLyd1o0oqalBQVokAb3f4ebq12nk7ssb6ahxZ/CfUlpw3JsH5qWtReHQjEu9+CxEp96Di3FHUFp0DYFizGJCgdvdFzLjfAQCiRj2I6tx0wz6Xkgkh4BoQicgR9xmfK6D7SPgnjUBDTRlkBzUcnG6uDuvL8lCZndZMqUD+gVUdNoF3VDli/j3z8eWWL7E6dTXqdfXwdvXGlP5TMG3ANJN9s4uykV+ej0CvQET4RwAwfMn89eCvZsfQK0LBzlM7UVVXBXdnd4u8no6usa4Oez74wGQN+Ev/PrN+PUL69UNwr17WCo+uMuv3Y69rP0e1AxL7xtxyl/nivDIU5pZB7eSIiNhgk3HpdbVarF22C+dPXx4G5e7lgrEzBiI43A96vYIfP9+M6spak3NWldfix883494/TkR5cVWz69EDhhsAHZ1OW4//vPMXXDh7xrgtL/sc9m3agAeffxkhEZEAgLqaGpQU5Dd7ntLCAtRWVcHVw6OtQyYialabJPCRkZE31dqg15uf2ZNM/eHdr1FWVQtvdxd89Mxd19x/1a40qGTJJHkHAEUIVFTXYc/xsxjSvfMtx1VeXYv3vt2AXccyjV34BnSLwtOzxsDbnasMtCR10ZPQVZdB7eZttjt9zq7vUVuSY7JEHIQCRd+IjF8+QK9HPkaPB/+BwiMbUXJqN4TSCJ/OfRGYPMaYgDu6eCD5ofdRdGwLSk/vgwQJ3rH94J8wDLKD6azJkiRB7eZzXbE31FSgMG0jtBVFcPIORkDicDg4u0NbVdricYZu9R2Xi8YFvx37Wzw0+iFoG7RwVjubvG8WVhTir8v/ahx7DgBdO3XF81Ofh5+HH6rrm/9SLoRARW0FE3gLyd2/H3qt+SX1JFlG1ubNTODtkKJXkHUqF7nniuCodkDnxDD4BnoZy7V1Oqz+dqdxPXnAcFNg1NR+6JwYBiEEVn21HfnnS0zOW11RixWLN+OeJ29D/vkSVJU3nb9ECAFtnQ6nUrPg6eOG/JySZpN4Dy/X1nnBNmzHml+Rm3XWZJtQFOh0Wiz//DM8+vpbAAAHR0fjfCHNcVCr2zRWIqJraZME/j//+Q+7C7ahsqpaFFdc/x31/NLKJsn7JZIkobDMzKRlN0ivV/DcRz/gXEGJSY6590QW/vTR9/jk2bvgoGq9Vn57o6sug66qpNlys13fAUAoqCs+j9qic3ANiERw79sQ3Pu2Zs8jO6gR2GMMAnuMaY2wUXJqF07+8FcIpRGSZFhrPmvjYiTM+jNc/MIMLf3mvghJEpx9Q1slBlunklVwuWryv4bGBryw5AUUVhSabE/PTccLX76Ahb9biADPgCbll6gd1PBz92uzmMmUtrKy2f/rQlGgq2xmeUayWbXV9fjx800oLayELEsQAti3+Th6DIzFkAk9IUkS1i7bhZwzptdog64Ra5buhIfPGCh6gbzs4ibnFgLQNypI25cJRVEgyxIUM5/hkiSh8EIpEvt1xsnULLPlLu5OCO8c1Gqv21Yd3LbFbFIuFAX52edQmHsBASGdoNZo0KV7MtKPHjbpUQMYbsZ17pYEjVPLqxQQEbW1Nkng77///rY4Ld2ksABvpJ25YDaJF0Kgk7/XLT/HrmNncDav6RcRRRE4l1+CnUfPYFgyl5y6WXpdXcvl2toWy9uCtrIYJ79/G0IxTIwnhOG30qjF8aVvoN8T/4Vf/FAUn9je9OaDEB22+/z12H5yO/LLm3bjVISCgvICbD+5HdMGTMMnaz5pso8kSZjQawKcmplYkFqfV1SU+RtVMHzp94qKsnBE1JJvP16L2up6uLg5XXd3+qut/34PyooNN7+vTK4P78pAQIgPAjr54FxGM12xJQmHd6YjoFPzvZwMk9YVI7xzUPOtwRLg5KJGcLgfBo/vgR2rDxvHzgtFQO3kiIl3D2mTMfu2pq6m5UaPS7PNA8DYGbORlX4SDVqtcRJmWZbhqNZg/Mxr93okImprfFfvACYN6m42eZclCYHe7ujTNeKWn+Pw6RyompnlViXLOJyZc8vP0ZG5d+oKNLPsmqRyhIv/rdfhjSo4vB7CbK8AAUVXj6Jjm9H5tsfgcWmCNEk2jr0PGzIbfteYXK0jO5lzEirZfI8VlazC8fPHManPJEzuMxmAIWmXL/7/6B/bHw+MfMBisRLgFx8Pr8hISFe/B0oSJFlGzLhx1gmMzKqtrkdNZR1qq5tZueMaKstqkH0632yXdUkCDu/JQHF+ebPHC0Wg4EIpNE6Oze4jSRI0TmrEdm/+vV0oAnE9IgEAPQd3xd1P3obeQ+OR0CsKKZN6Ye7TExEQcn1DoexdSERUs0uXyioV/IMvT9AZENIJf3jtLfQeNhyuHh5w9fBAryEp+MPr/8dl4oioXbDoLPQ7duzAwYMHUVFR0WRpOUmS8Oqrr1oynA4jppM/npk9Bu8vXQ9FACpZQqNegZebM/7vN1OaTbxvhNpRBcPEaeYIqFtxkryOKHTQDJSfOWimREJI38lwcLL8GMf6sjwYJsxrSpJVqC/Lg4OTK5LufRuV548Z14H37ToITl5cbueS7OJslFaVopNPJ/h7+gMAnNXNL+8oIOCicYEsyfj9+N/j9n63Y0/6HjTqG9ErupdxjXeyHEmSMOSll7Dj7bdRdubyJFlqV1f0f+opuAWxC7M9qSxraf4JoKKkGk4umuZPIAHOrhpEde0ElYMMfWPTG6FCCMT1iIC7pwuGTOiJbasOGWeavzRGu9fQeJNWfG8/dwwYnXRLr81eDZkwCWdPnWiyXZIk9Bk2HC5uppO1+gQE4Pa5D+L2uQ+2eiwNDToc27cX5zNPw8nFGUn9ByEoNKzVn4eI7JdFEvjS0lJMnDgRe/fuhRDCZIKQS/9mAt+2xvfvhn7xkdh48BTKq2oRFeKHod07Q+3YOv8FhvWIxdKNB8yW6RWBoT3Yff5WeEV2R9zU55D560dovDR5mSQjuPdtiBx5/y2dW9+gRWNdFRxdPSGrmrYI6WrKUXxsK3Q1ZXDxj4Bf10GQHdQXk3DzN22EokDjaUjSJUmCZ3giPMMTbylOe5NTkoO/r/g70nPTjdsGxg3EU5OeQkq3FCzdudTscYqimCwN18mnU5OZ68nynH18MOqvf0VpejoqsrOh8fREUM+eUDk238pKtqFB1whZlqC6eCPa/RqTwrl7uaBTpD9c3J1QV13fdHSFAOJ7RUHjrMbwyX2wYfneK5Jzw02Azt3CENXV0NrbY2AX+Ad74+jeDJQWVsLD2w2JfWMQ0YVLw12vLt174Pa5D+LXb75Eg05n3J7UfyAmzL7HYnGUFhbiP++8hYrSEsgqFSAEtv6yEkNvm4wxd87k/FFEdF0sksD/6U9/wpEjR/D111+jf//+iI6Oxpo1axAVFYX33nsPu3btwq+//mqJUDo0Hw9XTB9+azMh19RpsWp3GnYfM7QyDUyMwYT+3RAXHoTx/bph9d5jTY4Z3Sce8RFsgbpV/t1S4Bs3CBXZaVAa6uEeGg+1q1ez+yv6BpRm7EV9eQGcPAPhE9vPZLb5xvpqnF3/HxQe3Qihb4BK7YzgPhMRPuwe436FRzchY+X7EEIPSZIhFD3Ouvkg8a43EdB9NM5v/8bM0F8JsoMj/BOHt/rfwF5U11fjuf8+h8pa08nN9qTvwevfvo6/3/d33DngTny/+3vIkgxFKMbfU/tPRXRgtJUip5ZIkgTfuDj4xsVZOxRqBecy8rBnQxoKL5QCEhARG4yBY7rDL8gLnSL9kZtdbLYbfff+sZBVMsZOH4CV/90KRQiT5Dyqawi6Xuz6Ht8rCl5+7ji8Mx1FeWVwcXdCQu9oxPWIMFkLPiTSHyGR/pZ66Xap7/CRSOo/AKfTjqJBp0VEbBx8Alq/N1hZcRFOpx2FJEmITepuXGdeCIFvP/knqsrLAADKFSsvbVu1EqHRMUjo1afV4yEi+2ORBH7VqlV45JFHMGvWLJSUGGbalmUZnTt3xocffohp06bhqaeewv/+9z9LhENXKCitxE/bD+NwZg5cnTQY1bsrRvaOMztjfElFDZ764FsUlFUak7ajZy7gp+2H8f4TM/HHWaPRJTwQP25LRUFpJQK9PXDH0B6YNKg77yq3EtnBEd7RPa+5X3VeBo59Mw8NNWWGsedCgaOLJxJm/RnunbpC0Tfg6JcvoabgrHGCOb2uzrBcXXEOEma+itqibKT/9K5xci4hDF82GmrKcfyb19HnsUXocsczSF/x7hU9axRIsgO6Tn8JjlzCrFnrDq9DRU0FxFU9GBSh4ETOCaRlp+HBUQ+iS0gXrNy/EnlleQj2DsakPpMwNH6olaKmW1WakYGTy5ej6PhxODg5IXzYMMTdfjvUV3XfJes7czwHq/634/IoIQFkZ+TjwtlCzPzdGIyePgDLF21EZVmNycRx8b2iEN/TMGlhaHQg5jw+Hkf3ZCAvuwROLmrE9YhEbFIY5CuGrgWH+yE4nKtGWIKTswsS+/Zvtvx85mns3rAWRbkX4Onrh74pIxCb1OO6vsMoioKfv/oC+zZtMG6TJAmDxk7AuJlzkJd9rslSdsb9ZBl7N65nAk9E18UiCXx5eTm6desGAHC7+EWluvryGLKxY8fipZdeskQoHYquoRFbUjNwKCMbjioVBnePQZ+4SMgXv2ycOJeH5z76AbrGRigXx9UdTM/Guv0n8NZv74DawfS/x8KftqKwvMp0KXIBFJRV4rOV2/H83eMweXB3TB7c3ZIvk67SqK1F2levoPHSzPQXE/SGuiqkff0q+j7+OcoyD6ImP7PpwUKgNH03qnLTUXh048XkX3/VPgq0lUUoyzwA/24p8AhNQMGR9dBWFMLZJwQB3Udd9xryHdXx88ebLZMlGcfPH0dSRBKGJgzF0AQm7PYg7+BB7Hj7bQAXl5arrsapH39Ezq5dGDV/PpP4dkQoAttXp158cMV2IaDXK9i76RjGzxqEux6fgNPHzhvWgXc0rAMfFOZrkux5+bpj6G231vONrs3Nw8vk983Yt3kDfvrv55BlGYqiID/nPE4eOoAh4ydi3Mw51zx+6y8rTZJ3wPB/ZseaVfD08TG2xJsjFAUlhQU3HTsRdSwWSeBDQkKQn29YTkWj0SAgIACHDx/GHXfcAQC4cOHCDbfQfvzxx/j444+RlZUFAOjWrRv+/Oc/Y8KECa0au60qq6rBM//6DucLy6C6mLCv2p2GfvGReP3ByXBQyfjrl2uga2iEYmxhNfw+nHEeP+84imkpl1t6tTrDzQBza9EqisDmQ6fwx5mjWm1MPd284mNbLo+Tv5JQoNfWofDoJlTnZRhb5puQZZSd3o+6khxA0TctBwBJRm1JDnxi+0Hj6Y/wodf+ckOXOTk6mcwFciUBAY26hQmwyOYIvR4HPv3UUN9X1LlQFNQUFCB95UokzuE11F6Ul1ShsqzGbJlQBM6evAAAcHBUoWtyJLomR1owOoOcMwU4tOMUCi+UwtlVg4Te0Ujq19k4Tr+j+f1rb97S8VUV5fj5yy8AwDjJ8qV14Lev/gUJvfsiLKb5CUL1ej12rm1+KOj2X3/B3U883Wy5JMvwbYPu/ERknyySbQ0dOhTr1q3Dyy+/DACYNWsW3nnnHahUKiiKgvfffx/jbnCZndDQULz99tuIjY2FEAJffPEF7rjjDhw6dMjY2t+RLVi2EReKywHAZAm5fSezsGzTAfSOCzeWX00AWL3nmEkCX6vVQa+YSfYuatQrqNc1MIFvQ0IIVJ4/huIT26E06OAZngi/hCGQHdQm+9UUnYMkqyDMJN+SLKO26BwAydA11NwcdBe3aTwCAEnVtAUeAIQCjQfHY96soQlDseHoBvOFAhjSdYhlA6IWrX/uOdSXl8PJywuj33nnho8vzcxE3cXhY1cTioJz27YxgW9Hmlt2/XrLb1V5SRWO7M5AblYR1E6O6NI9AvE9I43J+fEDZ7Dxx33Gm4B1NVps/zUVZ0/m4vb7UqDiuu83LG3vbrM3VAHDkM/Du3a0mMDXVFa2uNZ8ZXkZvP0DEBIZhbzsc8abA5cIRUG/kaNvLngi6nAskm0988wzWLduHbRaLTQaDV5//XUcO3bMOOv8sGHD8M9//vOGzjl58mSTx2+99RY+/vhj7N69u8Mn8OXVtdiZlmn2S4YQwModRxAbGtDiOSpqak0ee7g6wdvdBWVVtWb39/FwhZuz003HTC0Tih7pP72HorRNwMX1wQtS1yB729dIuvdtaDwuj59Uu3o1+0VECAFHV2+4Bkai8Mj65p4MPl36Q+gbUJC6pmm5JMFB4wrfLs2PI6SW9encB4O6DsLOkzuN2y5NUnfv8Hvh58HxsO1JfXk56kpLr2tfoShoqKmBg5MT5Isz0Ou12haP0dff3Hrk1Da8/Nzg5uGM6sq6JmWSJCEi1vzs7zptA04dPofsjDxIsoyoriHokhRu0ipeV6vF8f1nkHOmACoHFWK6hZrsk5tVhBVfbIGiKIYJ8iTDtvQj53D73BQoegVbfzEsKXr1+/yFs4VIP3wO8b2iWutPYVcadDoc3LYFR/buQoNWh+iEBAwYNRZevn6ora6GJMsQ+qY3rIUQqK2uavHcTi7Oxq735jg4OkKt0WDW7x7H53/7C8pLio2z0CuKgmETJyO+Z+9WeZ1EZP8sksCrVCo8/fTlrkPe3t5Yv349ysvLoVKp4O5+a5Nd6fV6LFu2DDU1NRg4cGCz+2m1Wmiv+CJ15Th8e1JaWdNiC0FpZQ2igv2MM+JeTZYldAkz7cqlkmXMGNEbC3/aZvacM0f0No6tp9aXf2i1IXkHTLq115cXIGPl+0i8+/+M2/yTRuLcli/Nn0gIBCSNhJNXANxD41F14dRV3egl+HUbBregGABA5Mj7kbVxseGmgRAABGQHNeJnvNKk5Z+unyzJeHHai/j14K9YdXAVSqpKEO4Xjqn9p2JQ10HWDo9uglAUpP/8M9J/+gn15eWQHRwQkZKCpLvvhld0NGQHByiNjU2Ok2QZ/h38pnN7I8syBo7tgXXf7TbZLkkSZFlCvxFN66umqg4//HsjKkovLfNpmAjvyO4MTH1gBNROjqgorcb3n21AbU29oaeTBGSdysWxfZm44/7hcHBQYd33e6Do9Zc/my/+zs0qQtq+03Bxc0Zjg/mhTZIEpB/tmAn8x/NeRXVlOdw8vMx2p9fW1+Pzd97ChSsmkcvPyca+zRvx0PMvIzgi0mRW+KuFRES2+PxqjRO69emHY/v3NkniZVlGj4GDoXJwgE9AAJ74yzs4tn8vcjIzoXF2RvcBgxDYKfTGXjARdWgWSeATExORlJSEWbNmYebMmejc2dANycvL65bOe/ToUQwcOBD19fVwc3PD8uXLkZCQ0Oz+8+fPx7x5827pOW2Bv5c7VLLcbJf3IF8P+Hm5YXjPOGw5lG4cA3+JoghMH970TvCdKb1QUlGNH7amGu/8S5KE6cN7mXS3p9aXt+9n8wVCQfnZQ6gvL4STl6FXhZNnADpPeAynV/3TOM79Upf6mPG/g7OPofUo8a43cW7zEuSnroGiq4eDiwdC+t6OsMEzjacPHTQD3jF9UHBkAxqqy+ASEIHAHmOgdvNu89ds71SyCpP6TMKkPpOsHQq1gtTPP8fpK5ZDVRobkbVpE4pPnMDov/4VsZMm4dSPP5oeJEmAJKHr1KmWDZauKa5HBGRZwu4NR1FRYkjKA0N9MGRCMvxDmr7/bfvlECrLrxg3f/FjtTivHHs2pWHohJ7Y+OM+1NVoLw9duvi7IKcEB7edQHhsMKrKzY+9B4ATB8+ix8AuzZYLAejqm94k6giqK8tRWVbWbPmONauQey7LZJtQFDRotVi+aCEe+fOb8PEPQHlJsUkCLkkS1E5O6Dkk5ZoxTJh9N3LOZKKspNikdcQnIBBj7pxlfOzoqEbywCFIHsihUkR0cyySwH/88cdYunQp/vznP+PVV19FcnIyZs+ejZkzZyIiIuKmzxsXF4fU1FRUVFTgu+++w3333YctW7Y0m8S/+OKLJj0BUlNTkZJy7TdlW+Pu4oRRvbti/f4TTZJzAJiWYpgR948zR6Fe14BdaWeMZRq1Ax6/cwR6dG56N1iWJfxuSgqmD++N/afOAQD6xEXAz6t1Z0+uqK5Dek4BnNWOiI8I5ng+ANrKomuWX0rgASCo13i4deqCgoOrUVeWByevIMO2iy3rAKBSOyN67G8RNfoh6HV1UGlcIElN/9augVGIHvNw670YIjtTU1hokrxfIhQFVbm5yNqyBUlz5kCSJKT//DOUhgYAgIufH3o/8gi8o6MtHTJdh9ikcHRODENNVT1UKhnOruYnl9TW65B5PMf8pJRC4Pj+M0ge0AUXzhaaPV4I4Nj+MwgMbX6WcgCor9MhKKz5fSRJQieuFW/Wwe1bmq2f/JzzKC0swP3PvoCv/vkeCnLOG8vdvbwx57En4XodPUXdvbzxh3lv4dD2rTh1+BAgSYhP7o3kwUOhceIQQyJqPRZJ4B955BE88sgjKCgowLJly7B06VK88MILeOGFF9CvXz/Mnj0bM2bMQEhIyA2dV61WG1vze/fujX379mHBggX49NNPze6v0Wig0Vz+AHaz0WV7vN1dTH6b8+i04cgrqcDRMxegkmUICCiKwMSBSZg8yLDMm7NGjTceuh3n8ktwPCsPLho1+iVEwlnTctdoPy83jO9/410+a+q0WLP3GA5lnIejgwOGdu+MoT06G9ecb9Tr8emKbVi544ix94CPhyv+OHMUBnTr2F9wnbyDDWu2m511DnDyajp7rVtgNNwm/OGa55ZkFRycbPNaIGoP8g8dar5QkpC7fz86jx+PpLvvRtcpU1CelQUHJyd4RUVBkm/9BmVDXR3Kz56FSq2Gd1QUJFXHnIm8LUiSBDcP5xb3qa/VNTvvCAA06BpR3cz8MZfPoYVfkFfzccgSAjv5wCfAE1FdQ5B1Ks/kOSVJgoOjCkn9m59orSOrr2m+ZwMA1NXUICC2Cx6d9xdkn85ASX4ePLx9EJ3QDXIz16gQArlZZ1FfW4ugsHC4enjAydkFA8eMx8Ax49viZRARAbBQAn9JYGAgHnvsMTz22GO4cOGCMZl/5pln8Oyzz6LhYqvEzVIUxWSMu7366Jm7rrmPi5Ma7z42HUcyc3Aw/TwcHVQYktQZkcFN795HBPkiIqjlO//Xo7KmDkXl1fD3coOHq+kXnvzSCvzxg2UoqayGEIAsSdh2OAPdd3bCX347FRq1Axau2IYV21JNUtSyyhq8/p+VWPDkLMSFB91yjLYqpO/tyPj5/aYFkgyfzn1NJrEjonbkqqXjHF1dW23Mu1AUHF+2DKdWrIBepwMAOPv6otdvfoOQPn1a5Tno2lzdneGodkCDznz3dTdPF3j7e0ClkqHXmxnaJgE+AZ5w83RBl+7hyDh6vskNAaEI9BzSFQAwdsZAbFqxHxlHzxn/a3n6umHMnf3h7uXaqq/NXoRERCEr/RSEmaVTZZUK/sGGBiTDJIVdEBHb/FAFAMhKP4nlixaitMjQq0KWZfQamoKJd82Fw8XJK4mI2orV1vwKDg5Gt27dEB8fj7S0NNRc4+7o1V588UVMmDAB4eHhqKqqwtdff43NmzdjzRozs2Z3UJIkoUfnMPToHHZL5zmVXYCVOw4ju6AUAd4euG1gInp1CTeWV9bU4YPvNmHb4QwoQkCWJKQkd8Hj00fA3cXQbewf36xHadXlyfUude0/eiYXyzYfwO2Du2PlziNN2pcvPV668QBevX/iLb0OWxbQYzSqCzKRt28lIMmG5YMUPVwDIhE7+UnjfvUVhSjN2Auhb4RXVDJcAyJNzlNXcgHndyxFabphcibv2H4IGzILLr6cQIfoZgX26NFieXDvm5tdurakBBk//4zcffsASUKnfv0QO2kSnL0NY7BPfP89ji9bZnJMXWkpdr7zDoa/8Qb8una9qeelG2No+Y7FwW0nzJb3HBwHJ2cNEvpEI23v6aaTxwoYk/MRd/SFXq8g81iOsVitccDw2/sgONxwo9ZR7YCxMwZg0LjuKC2shJOzGv4h3pAkTiTbnKG3TcLZU03rR5Ik9EkZAZcb6JFZlJeLL979K/RXTEqpKAoObN2MxsZG3PnQI60SMxFRcyyawAshsHnzZnz77bdYvnw5iouL4e3tjdmzZ2PWrFnXPsEVCgsLMXfuXOTl5cHT0xPdu3fHmjVrMGbMmDaKvmNatTsN7327HipZgl4RSD9fgC2p6bhnbH/cN2Eg9HoFz338A87mFRuTckUIbDmcjpyiMvzzj7NRWlmDQxnnzZ5fCIFfdh5FQkQwGs21TMCwjv3RMxfa7DXaAkmSEDPudwjqOf7iOvBaeEYkwTumt2GCOiGQtfFzXNj1w8UDAAgB3/ghiLvjWcgOjqgpzMKRxc9C36A1zjxflLYZJad2osf97zZJ9ql9E0Jg/ZH1WLF3BS6UXoCfux8m9pmIyX0mQyWzC7UluQUFIXrsWJxZu9ZkuyTLcA0IQOSIETd8zqq8PGx86SU01NQY14xOX7kSWZs3Y9T8+dB4eODk1ZPiAYbWfknCyeXLMeTFF2/m5dg9Fzcnk983QlEUnEvPQ152MRzVDuicGA5vP3f0H9kNVeU1yDiabVjhBQAEkNS/M7r3jwUADB6XjNrqepPkXJIMs9p36W64Ke6odsCE2YNRXlKF/PMlUGscEd45EA6OTb+uuXm4wM2j+aF0dFlsUg/ccd9DWPW/JWi42FsFALoPGIwJs+6+oXPtWrfm4koBV/WSEAKpO7dj9NTp8PS59V6NRETNsUgCv23bNixduhTfffcdCgsL4eHhgSlTpmDWrFkYPXo0HBxuPIxFixa1QaS24Q/vfo2yqlp4u7tcV3f6liiKwP5TWTielQ8XjSNSkrsg0McDAFBSUYMPlm0AYEiir/z95do9GJQYg8LySmReaDrBmqIIZOQUYu/xLPh6ttylr7y6Fs5OLXc5u9a4fFt3aVb3a83u7hoQaTbRLji0Ghd2fX95w8XvFSUnd+Ccpz+iRj+MrA2fmyTvhv0UKA06nF2/CIl3NV16h9qvz9Z/hh/3/AgJEgQELpRewMK1C3Es+xheuvMltsZZWK+HHoKLry/Sf/4ZuqoqSCoVwgYPRo+5c+Ho3HQMtRAChUeP4vyOHWisq4NvXBwihw+Ho6vh/fLw4sUmyTtg6DKvq6rCkSVL0GXy5GbXlxeKgqJjx9rmhdqBWb8fe1PH1VbX48fPN6O0sAKyLEEIYM+GNPQeFo8Bo5MwbuZA9ElJQPbpfMiyhMi4EHj6XG7ZdXBUYcLswSgpqMCFs4VQOciIjAuBq3vT/x9evu7w8r21JXbJVJ+UEUjqNwCnjx1Fg06HiNgu8PYPuPaBVzl78niz671DCJzPPM0EnojalEUS+JSUFLi5uWHy5MmYNWsWxo8fD7XavhOytlRWVYviiutbw75e14DMC0VwUKnQOdQfqismYymrqsGLnyxHZm6xYaI7IfDvn7fj/gmDcNeYfth86BSUZublUckS1u0/DkURzS5Zp5JlHMrIxtzxA+Cgks22sEsSEB7og7iwIAR4u6OovKpJ90JZkjC6j313BU1+aMEtHZ9zqeX9akIg78AqdBowHWWZ+5vZR0H5mYNo1NbCQcPWHFuQXZyNH/f8CAAQVw082XFyBw5nHUZyVLLlA+vAJJUK8XfeibgpU6CrrISDiwscNOZnLReKgr3//Ceyt22DdPG99/yuXTjxww8Y8cYbcPLxQd7Bg2ja19pw7IU9exB3xx0txqNq5rnp5q37fjfKiisBGG5SX3Jg6wn4BXkhNikcvoGe8A30bPE817MPtQ2NszO69el3zf2EEMg5k4mTqQchFAWdE7sjqms8JEmCxswNuSupNZxxnojalkUS+GXLlmHixIlw4jIaFiOEwLJNB/DV2r2o1Rq6i/l5uuLRaSMwpLthltr5S1YjK78EAEwS8M9X7UR0iB8qauogSxL0ZpdeASpr6uHVwkz4AOCoUsHN2Qlj+yVg9e5jTZa1EwKYPrw3ZFnCH2eOxiufrQAkYWzplyQJ4YE+XGe+BUIoqC/LbbZcadCiviLv2ufR39okkmQ5O07sgCzJUMxMyKSSVdh2YhsTeCuRVSo4ebfckyZr0yZkb9sGACYt7LqqKuxesABDX3zRbPJ+iVAUuAYFwS04GNX5+U32lWQZ4UOH3sKroKtVlFbj/OkCs2WSJOHI7gzEJoWbLSfbotfr8d3Cj5C2b8/FGeglbPv1Z0R1TcA9Tz6NHgMG4ULWWbPXqJOLC6LjzS9lTETUWiyywPadd97J5N3Clm89hM9Wbjcm7wBQXFGDNxb/jEMZ55FTWIZDGeeNifKVZEnC8q2piOnkb7ZlHTC0+kWH+GFIUkyz++gVBYMv3iz4/ZQU9EuINJ5fgqH1/e4x/Yyt6326RuBff5yNlOQu8PV0Rai/N+aOH4D3n5wJVye2JjVHkuRrLAMnwckrGM5+YTAMjm9a7uzbCQ7OHm0UIbU2XaOu2S7yQgjoGnRmy6h9yFy71vAGeBWhKCg/cwb15eVw9m2+C65bUBDUrq7o/dvfQpJlk6XoJFmGs68vuk6d2iaxd1QVpc33ehNCoLykyoLRUFva/uvPSNu/F4BhzgNF0QMAsk6dwNpl36L3sBHoFBFp8h4syTIgSbh97oOchZ6I2pzVZqGnttPQqMeXa/eaLZMg4au1ezB9eK9mj1eEwLmCEgxKjEGAtzuKK6pNugvKkgQntSPG9esGD1cnpCR3wdbU9CYzyI/oFYf4CMPSb05qR7z58B04nVN4cR14FQYnxcDfy3SMX+fQALx474Sbe+F2TlddivM7lqHo2GaIxgZ4RCQhbPBMeITGI7DnOFzYvdx0fDtwcZm5PlC7eiEi5R6c/H6+mTMLhA+7h2OmbUhSRBK+3fGt2TJFKEiKSLJwRGRO5fnzKDp+HCq1GsF9+kDjbni/qyspabGFva6sDPF33omDCxeaLY+fPh2SJCEgKQmj5s/HyeXLUXj0KFRqNcKHDkXc7bdD48ku2q3J3bOF3maSYak4sn1CCOxav9b88BUhcGDbZoydMQsPPv8ydq1fi0Pbt6K+tgZhMbEYMn4iIrrEWT5oIupwmMDboZyiMlTV1pstU4RA2pkL+N2UYc0eL0kSArzc4eigwl9/Pw2vfvYTcorKjOVe7i54/cFJ8HQzjAN78Z7xiAsPxIrtqSgqq0aAtzumDE3GlGHJTZLCzqEB6Bx645PGdHS66jKkLvojdNWlxiS97PR+lJ3ej4RZryF86BxUnDuK6tx0Q8ueEAAkaNx9ETP+DwAAv/ghiJ38R2Rt+A8aaisAAI4unogc+QD8uzX//4FunPfFyQi9rzEp4fUqrSpFXUMdAj0D4aByQHJUMuJC4pCRl2HSjV6WZPh7+iOlW0qrPC/dnEatFnsWLEDu3ss3UiWVCt3vvRddJk2Ce2go6isrgWZ6L7l36oSgnj3RUFOD48uWGdd4V2k0SJw9G5HDhxv39Y6OxsBnnmnT10OAt78HgsJ8UXChFOLqnmsCSOrX2TqBUatqbGhATWVFi+XVFRXwCQhEysTbkTLxdgtGR0RkwATeDmnMLDdzJbWDA2JC/BHTyd+w/Jty9bh0gcmDuwMAQv29seiFuTiSmYOcojL4e7qjT9cIqFSXu2yqVDJmjOiNGSNubq1juracnctMkncAF/8tIfPXD9HnsUXoPvcdFB/fhuJTOyH0jfCO7oWAHqNNJqYL7DEa/onDUVNwFgDgGhgFWcW3gdb2wUMftMp5zhacxUerP8Kx84YZxT2cPTBz8ExM7T8Vb8x5A/9c9U/sOLnDuJxRj8ge+OPkP0LjyCEn1pS6aJFh7fYrCL0ehxcvhltgILpMnIiitLQmx0myjMDu3eEWGAgA6Dp1KmLGjUPxyZMAAP/4eDhcYwItajtjZwzAD4s2obqiFpJsuDktFIH4nlGI7xll5ejoZlSWlUHR6+Hh4wNZluHg6AhnVzfU1ZgfMiGrVHB153AzIrIufnO3Q8G+nogO8cPZvJIm65TKsoThveIgSRJeuncCnv3wO5RV1RrGpUuGZeLG9k3AqN7xJsckx4YhOTbM0i+FLio6tqVp93gAgIC2ohA1BWfhFhSDgO4jEdB9ZIvnklUOcA+JbZtA6YZV1laivLYc/h7+cFZfTs7yyvLw7BfPQttweamwyrpK/Hv9v1HfUI+7ht6Fl+58CaXVpSgoL4Cvuy8CPNm7xdq0lZXI2rLFfBd5Wcapn37CiDffROKcOUj75hsAhl5PQlHgGRmJfo8/bnKIo4sLgns1P+SJLMfD2w13PzEBGUezkXuuCI5qB8QmhSM43I9DkGxMVvpJ/Pq/L5F7LgsA4O3nj9HTZqD7gEHoN2IUtv7yU5PvT5Iso8eAwdechZ6IqK0xgbdDkiTh8TtH4LmPf4BeUYwt7LIswdPVGfeM7Q/AsHzb5y/eh3X7T+B4Vh5cNI4Y0SsO3WNCW/3LyKnsfCzbdABpZ3Lh4qTGmL7xmDI02e7Xd28tSmPLk5IpDebXg6b2q7SqFB+t/gi70ndBCAG1gxrje47HAyMfgMZRgx92/wBtg9bsTPNLdyzFlH5T4KJxgY+bD3zcfKzwCsicqtxcCL3efKGioDwrCwAQf+edCB82DDm7dqGxvh5+cXEISEoymZCuNZVlZhqWpgMQ3Ls3vKOj2+R57J2j2gEJvaOR0Jt/P1uVczYTn/9tvskKEGXFRVi28CMoQkHK5DuQczYTmcfSLs5Cb5jMLjgsHBPm3G2tsImIjJjA26nE6E741x9n43/r92H/yXNwUKkwvGcXzBrVB36el2csd3XWGMarD01us1h2pmVi3n9+Nrbwl1TW4PNVu7AlNQPvPT6DSfx18IzsgdL0PWZb4WVHJ7gG8sukLanX1eO5/z6H/PJ8YyuPrlGHlftXoqC8AK/Neg37Tu8zm7xf2vdEzgn0juGwlfZG49Fy99ory139/RF3+62PoRVCoLa4GIpOB9egIMgqlbFMaWjAngULkLN7t/HmwLFvv0XowIHo/+STkB34NaA5il5B1qlc5JwthKySERPfCUFsbbd5m1YshxCiSQs7AKz7fim69x+EuX98DpnHjuJk6kEoegWdk7qja4+eUPF6IaJ2gO9Ediw6xB8vz73NqjE06vV479v1EELgyqH2QgicyS3Ciu2HMXtUX+sFaCPCBs9EWcZeCEhNuuaGDZ4BlZrLNNqSTWmbkFuW22S7EAJ7MvYgPTcdDnLLb88qWdViOVmHe0gIvGNiUH72rEkLHwBAkhA1alSrPl/R8eM4tGgRKs6dAwA4eXuj26xZiB49GgBw/LvvkLNnDwDTNedzdu+GR2gous2a1arx2Iv6Wi1WLN6CorwyyLIEASB1xynEJoZhzPQBkFUWWYWXWpkQAqePHW16bV5UWVqKkoJ8+AeHIDapB2KTelg4QiKia+MnELUKIQROXyhEasZ5VFTXGbcfO5uL8uq6JkvMGY4BNh44abkgbZh7SBckzJ4HJ68g4zaV2hkRw+cidDC/gNuaQ2cPNduKJ0syUs+mYnD8YMiS+bdoV40rEsIS2jJEugV9H3sMDi4ul9d6v/jbr2tXdJk4sdWepywzE1vmzUNFdrZxW31ZGQ588gky166Fotfj9K+/mh+PLwQyVq2C0lx3/w5uy88HUZxfDgBQFGGceT4j7TwO7063YmR0q67Vg0Juo2EsRESthS3wdF2Ky6tRXl2LYF9PuDqbznB9PCsXf/t6nXGpOZUsY8KAbvj91BTU1je0eN7a+pbHdtNl3tE90fsPn6G26ByUhnq4BERC5ciWd1ukklWQIEGYubUlIOCgcsDU/lOx8ehGlFWXGbvSS5IEIQQeGv0Q1A4cetJeeYaFYfz77+PMunUoTEuDSq1G2ODBCB88GLKjY6s9z/HvvjMk52YS9GPffIPg3r3RUFvb7PENNTVoqKm5Zrf/jqauVovTaefNdrEGgCO7M9BzcFcLR0WtQZIkxPfsjWMH9pntIeMbEAifgEDrBEdEdJ2YwFOL8koq8N6363Eo4zwAwNFBhQn9u+G3tw+DRu2AC0XleO6jH6BrvNyKo1cU/LIrDboGPR6aNBiyJEEx80VIJUvoHhNqsddiDyRJgmtApLXDoFs0MG4gthzbYrZMCIH+XfrDy9UL7z3wHr7c8iU2HduEhsYGxATGYPbQ2RgUN8jCEdONcvLyQsKMGUiYMeOWzlNTUIAz69ejKi8Pzj4+iBo5El6RkQCAgsOHm+0KrK2sRH1ZGWS1GorO/I1SlVoNR86o3URNZV2zyTsAVFc0f1OE2r8Rd0xD+pHDaGjQGa+fS63yE2bfzTkOiKjdYwJPzaquq8dTHyxFefXlLysNjXr8vPMoSipr8PqDk/HD1kNo0OubfNkRQmDd/uO4b8IAjB/QDb/uTjNpJJIkwwfm9BFcHok6nkFxg9AtrBtO5JxoMlHd5D6T0cmnEwDAz8MPT01+Ck9OehKKUDju3Y4IIXBuyxacWrEClTk5ULu5IXr0aMRPm2Zc6/3C3r3Y9e67wMUJtyRJwulVq5D8wAOInTjxmjPWO2g0iBoxAmfWrWuS6EuyjMiRI1u1R4C9cPVwNvZ2McfN08XCEVFrCgjphN++8jo2LP8OJw8dgBACYZ1jMWrKnYiO72bt8IiIrokJPDVr9Z7jKKuqadI7UxECO45m4mxuMQ6fPm9cpu5qQgDHs/Lw2LQRkCUJv+5Og/7ivn6ebnhm9hhEh/i39csganccVA5486438e32b/HrwV9RWVeJIK8gTB0wFZN6T2qyvyRJUElM3m1BTVERKrOzofH0hHdMTLOteceXLsXxZcsMdzOFgK6qCid//BEFR45gxJtvQq/TYc/775ssSXcpoUz9/HMEJCUhdOBAnNuyxWxXYNeAALh36oSku+5C6enTKMvMNCb8QlHgFRWFpLvuaps/go1zdtGgc2JYs93ouw+ItUJU1JoCO4Xirseegv5iA4QDZ5cnIhvCdywb5O3uYvL7VhzJzME3G/bheFY+XDRqjO0bj+kjesHN2QmH0rPNzn0EGL5zHso4Dyd1y603TmpHODqo8OSMUZg7fiBOXyiEq5MaXcODIcvspkYdl5OjE+4bcR/uG3Ef9Iqeres2rqGmBns//BC5e/cat7mHhKD/k0/COybGZN+60lKc+P57wwNhsjwHyjIzkb19O5SGBugbzM8hIskyzm7ciPjp03Fh3z401tZe7gosyxBCIPmBByDJMhxdXTHyrbdwYe9e5B04AAAI6dMHIX37cgm5FqRM6oXy4iqTWeiFIhCbGIYeA7pYOzxqJSoV33eJyPbw09sGffTM9beaZF4owqGM83BQyRiUGI0A78uTFW05lI63lqyCJElQFIGaOi3+t34fth/NxIInZ8LRQdVsN0IhDOPhR/bqivTsArOzzLto1OgZG2587O3ugr5dI2/kpRJ1CEzebd/Ov/0NRcePm2yrzs/H5tdfx/gFC+Ds42PcnnfgQLNj1yFJyNm9G95RUYZk3Mws8UII1JeWwi0wEKPffhvHli5Fzq5dUBob4Rcfj4QZMxCQmGjcX3ZwQNigQQgbxLkTrpeTiwYzHhmNrPQ85JwpMKwDnxCKoDBfjpEmIiKrYgJvp7S6RvxlySrsTDtj+LIhBD5avhmzRvXFg7cNQqNewYLvNl6cwPhy+q0IgeyCUqzcfgRDe8Rix9FMs+eXJAkDE6Ph5qzBhgMnkJFTZDyPLBtuCDwxYyQ0av4XIyLb5uTlZfL7aqWnT6MwLa3JdqEo0Gu1OLNuncl660pjY/NPJgSUxkZ4hIaaTd4Bw/uveyfDPAluQUHo/8QT6Pf444AQ1xwXT9dPVsmIju+E6PhO1g6FWkFdTQ12rluNI7t3okGnRXR8NwyZMAlBoWHWDo2I6IYwu7JTC3/ait3HzgK4IkEXwDfr96GTnxcCvN1RVVtv9lghBDYcPImPnr4LK3ccwfGsPOM5Lt4LwN1j+sHP0w0A8PdHp+OHralYu/cYqmq1iI8IwsyRfdCjM2eYJyLbN/qdd1osL0lPv/zmeBWhKCg+ccJkW0BSUvMnkyQEdu+OTv37Q+PpCV1VlWlrvSRBkmVEjR591WHS5XXnichEbXU1Fr71OkoLCyEuThx6dM8upO3bg/uefg5RXROsHCER0fXjrXo7VFOvxa97jplduk0CsGzTAWh1LbQAAajXNsDRQYW3fzcVd4/pBx8PF6hkGZHBfnjurnGYO36AcV9njRp3j+mHL15+AD+89Tu89dspTN6JqMNwdHY2m7wDACTJOKv8JR6hoQgbPLhJwi3JMpy8vBA9ahRUajWGvfIK1BfXaJcujtVVqdUY9NxzcPH1bf0XQmSntv36M0qLLifvAKAoChS9Hiu++E+LywYSEbU3bIG3QwWllWhoNN/1UgDIKSxD14ggY1f3q6lkCT27GMauO6kdcd+EgbhvwsC2DJmIyGaF9OkD2cHBfNd4IRA+dGiTzX0fewxO3t7IXLMGysXJ6gISE9H7kUegdncHAHhFRWHixx8jd+9eVOXmwtnHB6EDB8LRhcuYEd2Iw7t2mJ13QgiBkoJ8FOScR1BYuJkjiYjaHybwdsjLreUvd55uzvB2d8HkQd3x0/bDJhPQSZIEWZZx53Cuz05EBADrn3sO9eXlcPLyMtudXu3ujuQHH8TBhQsNE88pirFLfXDv3gjt37/JMSpHRyTffz8SZ81CTWEh1B4ecPb2Nrtf2ODBbfK6iDoKndb8kMHrLSciak+YwNshHw9X9OkagYPp2U1a2GVJwm0DDLMT/35KChwdVFix/bCxxb6TvxeemTUaEYE+Tc5LRK1Hr+jx68FfsergKpRWlSLcPxxT+k/BoDjOFN7e1JeXo660tMV9YsaOhXtwME6tXInyrCxjV/ioUaOM3d/NcXB2hmdERGuHTERXiOjcBaePHYViphXeQa1GYCdOZEdEtoMJvJ16asYoPPXPpSgpr4aAIXFXhEB8ZDBmj+4LAFCpZDxyxzDcPbYfsvJK4OKkRlSwH5fIIWpjilAw/4f52Hlyp3Hb8fPHkZadhvuG34dZQ2a1cDS1VwFJSS1PUHeRrqYGUBRjV3kialvDJt6OjLQjZssGjRkPzVXzVBARtWdM4O1UoI8H/v3cvViz9zgOZWTDUaXCkO6dMSw5Fg5XtQa5OTshMZrL5BBZyoHMAybJO2BI6gHgv5v/i1HdR8HPw88aoVEbKj55EkeWLEHJqVMADGPck+6+G0HJydYNjMjORXSJw+w/PIGflixGTWUFAEClcsCAMWMxaup0K0dHRHRjmMDbMVdnDaal9MS0lJ7WDoWIrrD12FbIkmxM2k1IwPaT2zGl3xSLx0VtpyQ9HZtfe81ktuvyrCxse+stDHnxRQT34rwjRG0poXdfxCX3wvnMDDTqGhASGQUXNzdrh0VEdMOYwBMRWVh9Q32zyxZJkKDVaS0cEbW1tK+/NtT5lWNwhQAkCUeWLEFQz54cvkTUxlQqFSK7dLV2GEREt4TrwBMRWVhCWEKzZYpQWiwn26M0NKAwLc00eb9ECFSeP4/6sjLLB0ZEREQ2x2YT+Pnz56Nv375wd3dHQEAApkyZglMXxxUSEbVno7uPhqerJ2TJ9C1YlmTEh8YjMTzRSpHRzRJCIGvzZqz54x+xbMYM/PTggzj61VdorKuzdmhERERkR2w2gd+yZQseffRR7N69G+vWrUNDQwPGjh2Lmpoaa4dGRNQid2d3vDP3HcQExZhs7x/bH6/Pep1dqW3Q8WXLsO9f/0JlTg4gBLSVlTj544/YMm8ehBAISEqCJJv5yJUkeIaHw8nMGvBEREREV7PZMfCrV682ebx48WIEBATgwIEDGDZsmJWiIiK6PqG+oVjw0AJkF2WjpKoEob6h8Pf0t3ZYdBPqSktx4rvvDA+unNtACJSePo3s7duROGcONr36KiBJl/e5eKOm+7338qYN0S1w8/Ay+U1EZM9sNoG/WkWFYVkQHx8fK0dCRHT9wv3DEe4fbu0w6BbkHTgAYW58OwBIEnJ278bQl17CiDfewNGvvkLR8eMAAO+YGCTNmYPAHj0sGC2R/fn9a2+2+XPo9Xqk7tyOg9u3oKayEp0iozFw7DiERsVc+2AiolZkFwm8oih46qmnMHjwYCQmNj92VKvVQqu9PLtzdXW1JcIjIiI7pjQ2Nl8ohLHcNy4Ow994Aw11dYCiwNHV1UIREtGtUBQF33y4ACdTD0KSJAghUFZUiKN7d2HGI48iqd8Aa4dIRB2IzY6Bv9Kjjz6KtLQ0fPPNNy3uN3/+fHh6ehp/UlJSLBQhERHZq4CkpOYLJQmB3bubbHJ0dmbyTmRD0vbuxsnUgwBgXAJUURQIIfDj5/+GTltvzfCIqIOx+QT+sccew88//4xNmzYhNDS0xX1ffPFFVFRUGH+2bNlioSiJiMheeYSGImzQIOOY9kskWYaTlxeiR42yUmRE1BpSd+1odp4KnbYe6UcOWzgiIurIbLYLvRACjz/+OJYvX47NmzcjKirqmsdoNBpoNBrjYzc3t7YMkYiIOoi+jz8OJ29vZK5dC6WhAQAQkJiIXo88ArW7u5WjI6JbUVdbY2x5N6e+ttaC0RBRR2ezCfyjjz6Kr7/+GitWrIC7uzvy8/MBAJ6ennB2drZydERE1JGoHB2R/MADSJw9GzWFhVB7eMCZS8MR2RRtfT1OHjqAmqpKBHYKQ1R8AmRZRnhMZ+SePQOlmckqO0VFWzhSIurIbDaB//jjjwEAw4cPN9n++eef4/7777d8QERE1OE5ODvDMyLC2mEQ0Q06cegAvlv4MXTaeuNEdf4hnTD3qT+h/8gx2LtpA4QQJi3xkiwjKi4eweG85onIcmx2DPylN9Grf5i8ExEREdH1KsrLxTcffWCcjO5Skl6Sn4clC/4Ob/8AzH36Obh7epkcF5vYHbMffcLS4RJRB2ezLfBERERERLdq38XW9aspioLCCzk4e/IEouMT8MzfFyDr1EnUVlchKCwcfkHBVoiWiDo6JvBERERWpK2owMkVK5C9bRv0Wi38u3VD16lT4duli7VDI+oQ8s5nQzQzvh2ShIKc84i+OB4+Oj7BssEREV2FCTwREZGVaCsrseHFF1FbXGxMIPIOHEDegQMY/PzzCO7d28oREtkHRVFw6vAhpO3bgwadDlFxXdFz8DA4ubjA3csLkiybT+KFgJunp+UDJiJqBhN4IiIiKzm1YoVJ8g7A8G9JwoGFCzHx448hyTY7XQ1Ru9DY2Ij//et9pB9JNSTqQuDEwf3YtupnPPTCK+g1ZBiO7tnV9EBJgsbJCV2Te1k+aCKiZvBbARERURtrrK9HRXY26kpLTbZnb9/ebKtfXUkJys6csVCERPZrz/q1SD96GMDFG2QXx7vXVFXih/8sRExCIgaOGQcAkC/eMJNkGSqVCjMfeQyOarV1AiciMoMt8ERERG1EaWxE2jff4PSvv0Kv1QIA/BMT0fuRR+AeHAy9Ttfi8dcqJ6Jr27dlozFpv5KiKMjOSEdZUSEmzL4HcT164tCObaiuqEBQWDj6jRgFn4BAK0RMRNQ8JvBERERt5ODChTi7aZNJ8lB8/Dg2vfwyxr73HgISE3Fhzx6zrfAqtRpeUVGWDJfILlVXlLdYXlVeDp+AQMQkJCImIdEyQRER3SR2oSciImoDNYWFOLuxacufUBRoq6pwZu1adJ0yBZAkw89Vutx+OxydnS0ULZH98g/pBMnMNQYAkiTBNzDIwhEREd08JvBEREQtcPLygrOPD5y8vG7ouMK0tOYLhUB+aiq8Y2Iw9OWX4RoQYCxSaTRImDED3WbOvMmIiehKg8ZOMLvOuyTLSOw3gLPME5FNYRd6IiKiFox+552bOk52aOEjVpKM5YHdu2PCv/6FiqwsNGq18IqIgANb3olaTWLf/ijKy8Xmn5YbNkgShKIgIjYOt899wLrBERHdICbwREREbSCoZ09IKhWEXt+0UAiEDhxofChJEse7E7URSZIw8o5p6D00BccP7EODTofIuK4Ii4lttms9EVF7xQSeiIioDWjc3ZE4ezaOfvWVYYz7xS68kizDIzQUkcOHWzdAog7G08cXA8eMt3YYRES3hAk8ERFRG+k6dSpc/Pxw8scfUZGdDUcXF0SNHIn46dPh4ORk7fCIiIjIxjCBJyIiagVCCJSdPo2K7GxoPD0R1KMHZEdHhA8divChQyGEYHddIiIiuiVM4ImIiG5RXVkZdr7zDkozMozb1B4eGPDUUwjs3h0AmLwTERHRLWMCT0REdAuEENg+fz4qsrJMtuuqqrD9L3/BuAUL4BYYaJ3giAgAUFVRjq0//4Qje3aisaEBkXHxSJl0B8I7x1o7NCKiG8J14ImIiG5B8YkTKD9zBkJRTAuEgFAUZK5ZY53AiAiAIXn/5I0/Y++m9aitroZOq0VG2hH8++03kX4k1drhERHdECbwREREt6D87FnDLPNmCEVB+ZkzFo6IiK607ZeVqKooh3LFTTahKBBCYOWSxSbbiYjaOybwREREt0Dt7m5cIu5qkixD7eFh4YiI6EpH9+5u2kMGAIRAeUkx8s9nWz4oIqKbxASeiIjoFoT07QuVRmO2TCgKIlNSLBwREV2poUHXYnljQ4OFIiEiunVM4ImIiG6Bo7Mz+j32GCRZhiQbPlYv/Y4cMQJBvXpZMzyiDi86vhtk2fxXXrVGg6CwMAtHRER08zgLPRER0S0KHTgQo4ODcfrXX1GelQUnLy9EjRyJkH79uHwckZWlTLwdpw4fgiRJEFcNdxl62+1Qa5ysFBkR0Y2TxNXvZB3IwYMH0bt3bxw4cAC92EJCREREZJcyj6Vh5ZeLUVKQDwDQODlj6MTJGHbbZN5kIyKbwhZ4IiIiIrJrMd0S8eRf/obCCzloaNAhsFMYHNVqa4dFRHTDmMATERFZgBACOTt3InPdOtQWFsI9NBSxt92GoORka4dG1CFIkoTAUI53JyLbxgSeiIiojQkhcOjf/0bmmjWQZBlCUVBbXIz8gweRdM896DplirVDJCIiIhvABJ6IiKgV1JWV4ez69ag4dw4aLy9EDh8On86dAQAlp04hc80aADCuR33p99GvvkLY4MFw9fe3TuBERERkM5jAExER3aKiY8ew7a23oL+4nrQky8hcvRoJM2ag26xZyN62DZJKBaHXmz0+Z+dOxN1xhyVDJiIiIhvEdeCJiIhugV6nw86//c2QvAsBCGFM1I8vW4ai48fRUFvbZPmqSyRZRkNtrSVDJiIiIhtl0wn81q1bMXnyZISEhECSJPz444/WDomIiDqYvAMHoKuuNiTvV5FkGWc3bIBPbKzZcgAQer2hnIiIiOgabDqBr6mpQY8ePfDhhx9aOxQiIuqg6kpLgWbWkb40WV1kSgo07u6QZNOPXUmW4d6pE4J79rREqERERGTjbHoM/IQJEzBhwgRrh0FERB2Ye6dOzbauS7IMj9BQOLq6Yvi8edj17ruozMkxlvvExmLg009DUqksFS4RERHZMJtO4G+UVquFVqs1Pq6urrZiNEREZA8Ck5LgGhiI2qIi48zylwghEDNuHADAIywMY997D2WnT6O2pATuISHwDA+3RshERERko2y6C/2Nmj9/Pjw9PY0/KSkp1g6JiIhsnKRSYejLL8PZ19f4GJIE2cEB/Z94wiRJlyQJPrGxCB0wgMk7ERER3TBJNDctro2RJAnLly/HlClTmt3n6hb41NRUpKSk4MCBA+jVq5cFoiQiInulNDYid/9+VJw7BycvL4QOGgSNu7u1wyIiIiI70qG60Gs0Gmg0GuNjNzc3K0ZDRET2RHZwQOiAAQgdMMDaoRAREZGd6lBd6ImIiIiIiIhslU23wFdXV+P06dPGx2fPnkVqaip8fHwQzrGFREREREREZEdsOoHfv38/RowYYXz89NNPAwDuu+8+LF682EpREREREREREbU+m07ghw8fDjuZg6/N5eXlIS8vz9phUCsJDg5GcHCwtcOgVsLr0/7wGrUvvEbtC69PIrJlNp3A36rg4GC89tprdv8mrtVqMWfOHGzZssXaoVArSUlJwZo1a0wmZSTbxOvTPvEatR+8Ru0Pr08ismV2s4wcNa+yshKenp7YsmULZ963A9XV1UhJSUFFRQU8PDysHQ7dIl6f9ofXqH3hNWpfeH0Ska3r0C3wHU1ycjI/rOxAZWWltUOgNsDr037wGrVPvEbtA69PIrJ1XEaOiIiIiIiIyAYwgSciIiIiIiKyAUzgOwCNRoPXXnuNk7XYCdanfWF92h/WqX1hfdoX1icR2TpOYkdERERERERkA9gCT0RERERERGQDmMATERERERER2QAm8EREREREREQ2gAk83ZCsrCxIkoTFixdbOxQiMoPXKFH7xeuTiIhuFRP4NpSZmYlHHnkE0dHRcHJygoeHBwYPHowFCxagrq6uzZ73+PHjeP3115GVldVmz3E93nrrLdx+++0IDAyEJEl4/fXXrRqPJUmSdF0/mzdvvuXnqq2txeuvv35D5+rIdXOljnyNnjx5Es899xySk5Ph7u6O4OBgTJw4Efv377daTJbSnq/PjlwvV+vI12dubi7uuecexMXFwd3dHV5eXujXrx+++OILdIS5h9vzNdrR64aIrM/B2gHYq19++QUzZsyARqPB3LlzkZiYCJ1Oh+3bt+NPf/oTjh07hoULF7bJcx8/fhzz5s3D8OHDERkZ2SbPcT1eeeUVBAUFoWfPnlizZo3V4rCGJUuWmDz+73//i3Xr1jXZHh8ff8vPVVtbi3nz5gEAhg8ffl3HdOS6uaSjX6P//ve/sWjRItx55534wx/+gIqKCnz66acYMGAAVq9ejdGjR1slLktoz9dnR66XK3X067O4uBg5OTmYPn06wsPD0dDQgHXr1uH+++/HqVOn8Je//MUqcVlKe75GO3rdEFE7IKjVnTlzRri5uYmuXbuK3NzcJuUZGRni/fffb7PnX7ZsmQAgNm3adM19FUURtbW1133us2fPCgDi888/v659hRCiqKhIABCvvfbadT+PvXn00UdFW11uN/P37eh1w2tUiP3794uqqiqTbcXFxcLf318MHjz4up/PHrSn65P1wuuzJZMmTRKurq6isbHxpo63Ve3pGm1OR60bIrI8dqFvA++88w6qq6uxaNEiBAcHNynv3LkznnzySePjxsZGvPnmm4iJiYFGo0FkZCReeuklaLVak+MiIyMxadIkbN++Hf369YOTkxOio6Px3//+17jP4sWLMWPGDADAiBEjmnQzu3SONWvWoE+fPnB2dsann34KADhz5gxmzJgBHx8fuLi4YMCAAfjll19u+u9gzdZ/W6AoCt5//31069YNTk5OCAwMxCOPPIKysjKT/fbv349x48bBz88Pzs7OiIqKwoMPPgjAMJ7S398fADBv3jxjfV+rS3xHrxteo0Dv3r3h5uZmss3X1xdDhw7FiRMnbuqc9sRa1yfrhddnSyIjI1FbWwudTteq57VF1vwMNYd1Q0QWY+07CPaoU6dOIjo6+rr3v++++wQAMX36dPHhhx+KuXPnCgBiypQpJvtFRESIuLg4ERgYKF566SXxr3/9S/Tq1UtIkiTS0tKEEEJkZmaKJ554QgAQL730kliyZIlYsmSJyM/PN56jc+fOwtvbW7zwwgvik08+EZs2bRL5+fkiMDBQuLu7i5dffln84x//ED169BCyLIsffvjBGMPNtB501FbeK5lrPXj44YeFg4OD+M1vfiM++eQT8fzzzwtXV1fRt29fodPphBBCFBQUCG9vb9GlSxfxt7/9TXz22Wfi5ZdfFvHx8UIIIaqrq8XHH38sAIipU6ca6/vw4cPXFVdHrRteo80bNGiQ6NKly00da6va6/V5pY5UL7w+L6utrRVFRUXi7NmzYvHixcLV1VUMGjTouv829qI9XqOsGyKyFibwrayiokIAEHfcccd17Z+amioAiIcffthk+7PPPisAiI0bNxq3RURECABi69atxm2FhYVCo9GIZ555xritpe5/l86xevVqk+1PPfWUACC2bdtm3FZVVSWioqJEZGSk0Ov1Qggm8Dfr6i8f27ZtEwDEV199ZbLf6tWrTbYvX75cABD79u1r9ty38vftiHXDa7R5W7duFZIkiVdfffWGj7Vl7fX6vKQj1QuvT1Pz588XAIw/o0aNEtnZ2dd1rD1pj9co64aIrIVd6FtZZWUlAMDd3f269l+1ahUA4OmnnzbZ/swzzwBAk+53CQkJGDp0qPGxv78/4uLicObMmeuOMSoqCuPGjWsSR79+/TBkyBDjNjc3N/z2t79FVlYWjh8/ft3np2tbtmwZPD09MWbMGBQXFxt/LnWf3bRpEwDAy8sLAPDzzz+joaHBihHbD16j5hUWFuKuu+5CVFQUnnvuuVs6l61rT9dnR6sXXp+m5syZg3Xr1uHrr7/GXXfdBQBtOgO/rWgP1yjrhoishQl8K/Pw8AAAVFVVXdf+586dgyzL6Ny5s8n2oKAgeHl54dy5cybbw8PDm5zD29u7yZivlkRFRZmNIy4ursn2SzO8Xh0H3ZqMjAxUVFQgICAA/v7+Jj/V1dUoLCwEAKSkpODOO+/EvHnz4OfnhzvuuAOff/55k7GddP14jTZVU1ODSZMmoaqqCitWrGgyBrujaS/XZ0esF16fpiIiIjB69GjMmTMHX331FaKjozF69OgOnyi2h2uUdUNE1sJl5FqZh4cHQkJCkJaWdkPHSZJ0XfupVCqz28UNrD3q7Ox83ftS21AUBQEBAfjqq6/Mll+aVEeSJHz33XfYvXs3Vq5ciTVr1uDBBx/Eu+++i927d3eIL/StjdeoKZ1Oh2nTpuHIkSNYs2YNEhMTLfbc7VV7uD47ar3w+mzZ9OnT8dlnn2Hr1q1NegF0JO3hGr0a64aILIUJfBuYNGkSFi5ciF27dmHgwIEt7hsREQFFUZCRkWGynmlBQQHKy8sRERFxw89/vV9kro7j1KlTTbafPHnSWE6tJyYmBuvXr8fgwYOv68vggAEDMGDAALz11lv4+uuvcffdd+Obb77Bww8/fFP13dHxGjVQFAVz587Fhg0bsHTpUqSkpNzwOeyRta/Pjl4vvD6bd6l1t6KiolXOZ6usfY2aw7ohIkthF/o28Nxzz8HV1RUPP/wwCgoKmpRnZmZiwYIFAIDbbrsNAPD++++b7POPf/wDADBx4sQbfn5XV1cAQHl5+XUfc9ttt2Hv3r3YtWuXcVtNTQ0WLlyIyMhIJCQk3HAc1LyZM2dCr9fjzTffbFLW2NhorLuysrImLUPJyckAYOwC6OLiAuDG6ruj4zVq8Pjjj+Pbb7/FRx99hGnTpt3w8fbK2tdnR68XXp9AUVGR2e2LFi2CJEno1avXDZ3P3ljzGmXdEJG1sQW+DcTExODrr7/GrFmzEB8fj7lz5yIxMRE6nQ47d+7EsmXLcP/99wMAevTogfvuuw8LFy5EeXk5UlJSsHfvXnzxxReYMmUKRowYccPPn5ycDJVKhb/+9a+oqKiARqPByJEjERAQ0OwxL7zwAv73v/9hwoQJeOKJJ+Dj44MvvvgCZ8+exffffw9ZvvF7PUuWLMG5c+dQW1sLANi6dSv+7//+DwBw7733duhW/ZSUFDzyyCOYP38+UlNTMXbsWDg6OiIjIwPLli3DggULMH36dHzxxRf46KOPMHXqVMTExKCqqgqfffYZPDw8jF9cnZ2dkZCQgG+//RZdunSBj48PEhMTW+xy29HrhteoIeH56KOPMHDgQLi4uODLL780KZ86daoxkelorHl9sl54fQLAW2+9hR07dmD8+PEIDw9HaWkpvv/+e+zbtw+PP/54kzH/HY01r1HWDRFZnTWnwLd36enp4je/+Y2IjIwUarVauLu7i8GDB4t//vOfor6+3rhfQ0ODmDdvnoiKihKOjo4iLCxMvPjiiyb7CGFYvmbixIlNniclJUWkpKSYbPvss89EdHS0UKlUJsvhNHcOIQzr306fPl14eXkJJycn0a9fP/Hzzz+b7HMjS+CkpKSYLLFy5Y+55Xnsmbk1bIUQYuHChaJ3797C2dlZuLu7i6SkJPHcc8+J3NxcIYQQBw8eFHPmzBHh4eFCo9GIgIAAMWnSJLF//36T8+zcuVP07t1bqNXq61oOh3Vj0JGv0UtrZzf3c/bs2RaPtyft6fpkvVzWka/PtWvXikmTJomQkBDh6OhofO2ff/65UBSlxWPtUXu6Rlk3RGRtkhA3MHMLEREREREREVkFx8ATERERERER2QAm8EREREREREQ2gAk8ERERERERkQ1gAk9ERERERERkA5jAExEREREREdkAJvBERERERERENoAJvJUsXrwYkiTByckJFy5caFI+fPhwJCYmWjSmDRs24MEHH0SXLl3g4uKC6OhoPPzww8jLyzO7/86dOzFkyBC4uLggKCgITzzxBKqrqy0ac3vB+rQvrE/7wzq1L6xP+8L6JCK6fkzgrUyr1eLtt9+2dhgAgOeffx6bN2/G1KlT8cEHH2D27NlYunQpevbsifz8fJN9U1NTMWrUKNTW1uIf//gHHn74YSxcuBAzZsywUvTtA+vTvrA+7Q/r1L6wPu0L65OI6DoIsorPP/9cABDJyclCo9GICxcumJSnpKSIbt26WTSmLVu2CL1e32QbAPHyyy+bbJ8wYYIIDg4WFRUVxm2fffaZACDWrFljkXjbE9anfWF92h/WqX1hfdoX1icR0fVjC7yVvfTSS9Dr9e3ijvOwYcMgy3KTbT4+Pjhx4oRxW2VlJdatW4d77rkHHh4exu1z586Fm5sbli5darGY2xvWp31hfdof1ql9YX3aF9YnEdG1OVg7gI4uKioKc+fOxWeffYYXXngBISEhN3R8bW0tamtrr7mfSqWCt7f3DcdXXV2N6upq+Pn5GbcdPXoUjY2N6NOnj8m+arUaycnJOHTo0A0/j71gfdoX1qf9YZ3aF9anfWF9EhFdG1vg24GXX34ZjY2N+Otf/3rDx77zzjvw9/e/5k/Pnj1vKrb3338fOp0Os2bNMm67NIFLcHBwk/2Dg4ORm5t7U89lL1if9oX1aX9Yp/aF9WlfWJ9ERC1jC3w7EB0djXvvvRcLFy7ECy+8YPZDoDlz587FkCFDrrmfs7PzDce1detWzJs3DzNnzsTIkSON2+vq6gAAGo2myTFOTk7G8o6K9WlfWJ/2h3VqX1if9oX1SUTUMibw7cQrr7yCJUuW4O2338aCBQuu+7jo6GhER0e3ejwnT57E1KlTkZiYiH//+98mZZc++LRabZPj6uvrb+qD0d6wPu0L69P+sE7tC+vTvrA+iYiaxwS+nYiOjsY999xjvON8vS6Nx7oWlUoFf3//6zrn+fPnMXbsWHh6emLVqlVwd3c3Kb90N9zcWqh5eXk3PGbNHrE+7Qvr0/6wTu0L69O+sD6JiJrHMfDtyCuvvHLD477+/ve/Izg4+Jo/ffv2va7zlZSUYOzYsdBqtVizZo3ZrmuJiYlwcHDA/v37TbbrdDqkpqYiOTn5uuO3Z6xP+8L6tD+sU/vC+rQvrE8iIvPYAt+OxMTE4J577sGnn36KiIgIODhcu3pac7xXTU0NbrvtNly4cAGbNm1CbGys2f08PT0xevRofPnll3j11VeNd6OXLFmC6upqzJgx45rP1RGwPu0L69P+sE7tC+vTvrA+iYjMk4QQwtpBdESLFy/GAw88gH379pksPXL69Gl07doVer0e3bp1Q1pamsVimjJlClasWIEHH3wQI0aMMClzc3PDlClTjI8PHjyIQYMGISEhAb/97W+Rk5ODd999F8OGDcOaNWssFnN7wfq0L6xP+8M6tS+sT/vC+iQiugGCrOLzzz8XAMS+ffualN13330CgOjWrZtFY4qIiBAAzP5EREQ02X/btm1i0KBBwsnJSfj7+4tHH31UVFZWWjTm9oL1aV9Yn/aHdWpfWJ/2hfVJRHT92AJPREREREREZAM4iR0RERERERGRDWACT0RERERERGQDmMATERERERER2QAm8EREREREREQ2gAk8ERERERERkQ1gAk9ERERERERkA5jAExEREREREdkAJvBERERERERENoAJPBEREREREZENYAJPREREREREZAOYwBMRERERERHZACbwRERERERERDaACTwRERERERGRDWACT0RERERERGQDmMATERERERER2YAOncDn5eXh9ddfR15enrVDISIiIiIiImpRh0/g582bxwSeiIiIiIiI2r0OncATERERERER2Qom8EREREREREQ2wKYT+K1bt2Ly5MkICQmBJEn48ccfrR0SERERERERUZuw6QS+pqYGPXr0wIcffmjtUIiIiIiIiIjalIO1A7gVEyZMwIQJE6wdBhEREREREVGbs+kE/kZptVpotVrj4+rqaitGQ0RERERERHT9bLoL/Y2aP38+PD09jT8pKSnWDomIiIiIiIjounSoBP7FF19ERUWF8WfLli3WDono5jRqr70PERERERHZlQ7VhV6j0UCj0Rgfu7m5WTEaolvQqAUcNNfej4iIiIiI7EaHaoEnshtCsXYERERERERkYTbdAl9dXY3Tp08bH589exapqanw8fFBeHi4FSMjamMNtYCzl7WjICIiIiIiC7LpBH7//v0YMWKE8fHTTz8NALjvvvuwePFiK0VFZAGVeYBHiLWjICIiIiIiC7LpBH748OEQQlg7DCLLqy8DqgoA90BrR0JERERERBbCMfBEturCfmtHQEREREREFsQEnshWndls7QiIiIiIiMiCmMAT2aqcfUDFBWtHQUREREREFsIEnshWCQEc+tLaURARERER/X97dx4XVb3/cfx9GFYBQQXFJUDRXHLDLcUFl0wzNTP3Fq1cbovVLa/ZvZmamrdb/W62eFMzNdOy1LLFNDPNUsvMzLI0TRHD3BFRlGXm/P7wwg0BhWHozAyv5+PB49Gc5XveA32Rz5zv+X7xJ6GABzzZLx9Lv39vdQoAAAAAfwIKeMDDtGrVSrU6DFWrp7ZfvAu/bqqUccrqWAAAAADKGAU84GGOHDmilKMndORM1sUN545La/4hZWVYGwwAAABAmaKAB7zBsZ+kjx6Rzp20OgkAAACAMkIBD3iLYz9Jy+6U9n16cWg9AAAAAK9CAQ94kwtpF5+J//Ah6cQ+q9MAAAAAcCEKeMAbHd4hrRglbXxWOn/a6jQAAAAAXIACHvBWpkP6+QNp6W3SD8uknCyrEwEAAAAoBQp4wNtlpkubX5Tevl3a9Z6Uk2l1IgAAAABOoIAHyov0I9KX/5YWD5S2vcba8QAAAICHoYAHypsLadK3C6Ulg6WNz0hpv1mdCACAP0125gWrIwCA0yjgAQ+SnJysjIwMSVJGlkPJp0rxR4g9S/r5Q2np7dJn06XTyS5KCQCA+3LY7VZHAACnUcADHmDr1q3q06ePYmNjlZqaKklKzchR7D+2qu+sH/VNUrrzjZsOae8n0tvDpc+mSakHXZQaAAD343A4rI4AAE7ztToAgMtbsWKFBg8eLNM0ZZpmvn2mKa368ZQ+/jFVS0c1VP/4COcvZDqkvWulfeukutdJre+WQqNKmR4AAACAq3AHHnBjW7du1eDBg2W322UvYsif3SHZHaYGz/25dHfic+XekV96+8Xl5y750AAAAI/Gv2sAPBgFPODGpk2bVuid90uZkkyZmrbKhcPf7VkXl5/budR1bQIAYDGTIfQAPFipCvjMzExt2bJFK1eu1IkTJ1yVCYAuTlj34YcfFnnn/VJ2h/TBD6dKN7FdYb5dyNrxAACvYbfnWB0BAJzmdAH/wgsvqHr16urQoYP69++vnTt3SpJOnDihiIgIvfbaay4LCZRH69atu+Kd90uZpvTZ7tOuDZKdIe3/3LVtAgBgkZysLKsjAIDTnCrg58+fr4ceekg9e/bUvHnz8hUZERER6tq1q9566y2XhQTKo/T0dPn4lKyL+hjSmQtlsDzO1tkX148HAMDDZV9gHXgAnsupAv65557TTTfdpCVLlqhPnz4F9rds2VK7du0qdTigPAsNDS3xUjcOU6oYaHN9mHMnLg6lBwDAw2VlZFgdAQCc5lQBv2/fPt1www1F7q9cubJOnjzpdCgAUrdu3WQYRonOMQypa4PwsgkUWr1s2gUA4E+Uk5Upe0621TEAwClOFfDh4eGXnbTup59+UlQU60cDpREdHa3evXvLZiveHXWbj9SnSWVFVw50bRC/IKnDX6UmA1zbLgAAFsk8d87qCADgFKcK+F69emnOnDk6ffp0gX27du3S3Llz1bdv39JmA8q9iRMnyjCMK96JNyQZMvR4rxjXBohpLw16Xbqm38Xb+wAAeIHz6elWRwAApzhVwE+bNk12u12NGzfW448/LsMwtHDhQt12221q1aqVqlatqieeeMLVWYFyp3Xr1lq6dKlsNluRd+JtPpLNx9DboxqqdWyoay4cUlXq/qTUY/rF/wYAwIucO51qdQQAcIpTBXyNGjX07bffqmfPnlq6dKlM09SiRYv0wQcfaOjQofrqq68UERHh6qxAudS/f39t3rxZvXr1KnAn3jCkGxtX1ubxzXVzvAv6XHCk1O5+afBiqU4id90BAF4p7fgRqyMAKEI6I2Quy9fZE6tWrapXX31Vr776qo4fPy6Hw6HIyMgSL3sF4Mpat26t999/X8nJyWrevLlSU1NVqYKvdjzewjXPvFdrLDW+WaqdKNn8St8eAABu7ETyQasjACjCqVOnFBrqolGlXsjpAv6PIiMjXdEMgCuIjo5WhQoVlJqaqgr+PqUr3n0DpXrdpUb9pIi6LssIAIC7O7p/r+w52bL58qE14G5OnDihmBgXz+vkRZy6Xf7444+refPmRe6Pj4/XlClTnM0EoCyFVpfa3iPd+o7UaRzFOwCg3MnJylLK7p+sjgGgEEePHrU6gltzqoBftmzZZdeB79Wrl5YuXep0KABloGZL6fpp0pAlUrMhUmBFqxMBAGCZX7d9bXUEAIVIT0/XmTNnrI7htpwaQp+cnKy4uLgi99euXVsHD/JsEWC5gFCp/g1Sw75S+FVWpwEAwG0c/uVnnTudquDwSlZHAXCJffv2qUWLFlbHcEtO3YEPCQm5bIF+4MABBQa6YGItAM4JjpTaPyjdukxqdx/FOwAAlzBNU7989aXVMQAUYteuXcrOzrY6hltyqoDv3LmzZs+erZSUlAL7Dh06pDlz5qhLly6lDgeghAwfqcXt0pDFUuP+kh8fpAEAUJS9X29W1oXzVscAcInz589r27ZtVsdwS04NoZ86daratGmja665RnfffbeuueYaSdKPP/6o1157TaZpaurUqS4NCuAKgipJ3Z+Uqje1OgkAAB4h68J5/bj+U7W4oY/VUQBIatWqlQ4cOKCQkBD94x//ULVq1VSnTh2rY7kVpwr4+vXr64svvtDYsWP173//O9++Tp066YUXXlDDhg1dEhBAflFRUVJOpqICLvxvY1gtqdezUsXq1gUDAMDNtWrVSimHDinIR3pyxFBJ0s9frldM0+aqUpPHzQCrHTlyRKdOnZLD4ZAkrVu3TqmpqWrevLlsNpvF6dyD0+vAN23aVJ9//rlOnDih/fv3S5Lq1KmjiIgIl4UDUNC2bdukfZ9K6/47yqViTanvi1KFytYGAwDAzR05ckRHjh1TpdCQvG2mw6Ev33xdve5/RH7M4QS4FdM09e2332rPnj1q0qSJ6tevL39/f6tjWcrpAj5XREQERTtgFZu/1GM6xTsAAKWQfvK4tix/Ux2HjZBhGFbHAXCJs2fPasuWLfrmm29Up04dNWzYUFWrVi2X/dXpAt5ut2vNmjXav3+/UlNTZZpmvv2GYWjixImlDgjgMpoOkirXtjoFAAAeL/nH77VnyxdqkNDJ6igAipCTk6NffvlFv/zyiypVqqT69evr6quvLlcroDlVwG/btk233HKLfvvttwKFey4KeKCMGT7SNf2tTgEAgNfYvmqlqsbGqXKNmlZHAXAFqamp+uqrr/TNN98oNjZWDRo0UI0aNbz+rrxTy8jde++9On/+vN577728SQYu/bLb7a7OCuCPasRLwVWsTgEAgNdw2O36Ysl8ZWZkWB0FKHeSk5OV8d++l5WVpVOnThXrPLvdrl9//VUfffSRli5dqu+//14XLly48okeyqkCfufOnXr00UfVp08fhYeHuzgSgGKp3dHqBAAAeJ30kye0fuEcZWd6bwEAuJOtW7eqT58+io2NVWpqqiQpIyNDf//73/Xyyy8rKSmp2G2dOXNGX3/9tRYvXqyNGzcqLS2tjFJbx6kCvlatWkUOnf+zvfzyy4qNjVVgYKCuvfZabd261epIwJ/jqmutTgAAgFc6kZykNa+8oLOpxbsDCMA5K1asUPv27fXxxx8XqC9N09SPP/6op59+Wtu3by9Ru3a7Xbt379bbb7+ttWvX6tixY66MbSmnCvhHH31Uc+fO1ZkzZ1ydp0SWLl2qhx9+WJMmTdL27dvVrFkz9ejRw6t+QECh/EOlUNZ8BwCgrJw+clirXnhGB3fusDoK4JW2bt2qwYMHy263F/n4de7j2XPnzi3RnfhcpmnqwIEDeu+99/Tuu+9qz549ysnJKWVyazk1iV16erpCQkJUt25dDRkyRFdddZVsNlu+YwzD0F//+leXhCzK//3f/2nUqFG68847JUmvvPKKPvroI7322muaMGFCmV4bsFRwhOTlE3QAAGC1rAvn9cWbC3TopxZq3fcWBVQItjoS4DWmTZsm0zSLPbJ71apVuvfee52+3vHjx/X5559ry5YtatCggZo2baoKFSo43Z5VDNOJsfA+Ple+cW8YRplOZJeVlaUKFSpo2bJl6tevX9724cOH6/Tp01q5cuUV29i+fbtatmypb7/9Vi1atCizrIDLnTksVaxhdQoAADxKrVq1lJKSokqhIXrhvrtLdG5gSKja3jxYtRo1LqN0QPmRnJys2NjYEj2WbRiGnnrqKVWuXNklGfz8/NS2bVs1bNjQJe39WZy6A3/gwAFX5yixEydOyG63q1q1avm2V6tWTbt37y70nMzMTGVmZua9Pnv2rKSL6wlmZ2eXXVjA1RyGxP+zAACUSG6xYJqmckp4o+ls2ml9umC24lq2Ucsb+8kvoPysOw242po1a0o8p5ppmvrpp5/Url07l2Sw2+3asGGDzp8/ryZNmrikzZLw8/Nz6jynCviYmBinLma1GTNmaMqUKQW2X3stk4EBAACUF6fPntOdz7xkdQwAJbRo0SItWrTI6hgu4eyk8E4V8LlSUlK0ceNGHTt2TLfccotq1aolu92utLQ0hYWFFXgu3pUiIiJks9l09OjRfNuPHj2qqKioQs957LHH9PDDD+e93rFjhxITE/X1118rPj6+zLICLpeVIfl73jM7AABYKTY2VocPH1Z4SLD+fc+dpWrLx2ZTq979Vbd1WxnMSwOUyIIFCzR69OgSn3f77be77A58Lh8fHw0ZMsRjnod3qoA3TVOPPPKIXnrpJeXk5MgwDDVp0kS1atXS2bNnFRsbqyeffFIPPfSQi+P+j7+/v1q2bKl169blPQPvcDi0bt063X///YWeExAQoICAgLzXISEhkiRfX1+nhzAAljCCJF/+nwUAoCRyC23DMOTrghtN2z9cobTfU9Sm3wDZ+HcZKLYePXrIMIwSPwPfqFEjl94k9vf3V9euXRUWFuayNsuaU8vIPfPMM5o5c6bGjRuntWvX5vvGh4WFqX///lq+fLnLQhbl4Ycf1ty5c7Vw4UL9/PPPuueee3Tu3Lm8WekBr+VTdqNbAABA8f367ddaO/dlXfjv3EoAriw6Olq9e/cudjHu4+Ojpk2bumwCO0mqU6eOBgwYoOjoaJe1+Wdw6g783Llzdccdd+ipp57SyZMnC+xv2rSpPv7441KHu5LBgwfr+PHjeuKJJ3TkyBE1b95cq1evLjCxHeB1DKc+ewMAAGXgRHKSVv/n3+p6519UMSLS6jiAR5g4caI+/vjjYt+J79WrV6mvabPZFBcX5/IPA/5MThXwhw4dUkJCQpH7g4ODdebMGadDlcT9999f5JB5AAAA4M9w9tRJfTL7BV13970Kj6pudRzA7bVu3VpLly7V4MGDZZpmoUuQ5y5fPnr0aMXGxjp9rcjISF199dWqW7duvkeqPZFTBXzVqlV16NChIvd/++23HjcUAfAopikxYQ4AAG7lwtl0rZ37oq4beZ8qVa9pdRzA7fXv31+bN2/W1KlT9eGHH+a7E587z1qvXr2cKt5DQkJUr1491atXT+Hh4a4LbTGnCvj+/fvrlVde0YgRI/Ie+M+dFOSTTz7RggULNH78eNelBJCfaZeTU1gAAFAuJScnKyMjQ5KUlZ2tE2lnFBFW0eXXyczI0KevzlL30WMVXq3wlZEA/E/r1q31/vvvKzk5Wc2bN1dqaqoqVKigiRMnlniYu4+Pj2JiYtSgQQPVqlXLK1eIMEwnFqBLS0tTp06ddODAAXXs2FGrV69W9+7ddfbsWW3ZskXx8fHauHGj20/Fv337drVs2VLffvutWrRoYXUcoPhysiRff6tTAADg9rZu3aqpU6fqo48+yn93T1LzurXVr30b1anu+kI7MCRU148Zq4oRVV3eNuCtatWqpZSUFIWHh+vpp58u9nkRERGKi4tTvXr13L4GLS2nbuGFhYXpq6++0vjx45WSkqLAwEB9/vnnOn36tCZNmqQvvvjC679xgKUo3gEAuKIVK1aoffv2+vjjjwtMkmVK+v7XJD256B19s2efy6994Wy61s37jzLOpLm8bQAXi/Y2bdpoyJAh6t+/v5o1a1YuatASD6G/cOGC5syZo+bNm+vxxx/X448/Xha5AAAAAKdt3bpVgwcPlt1uL3KGa4dpSqapl1d+rCduH+jyO/HnTqdq4+L5un7MWPmwBCxQan5+fmrQoIEaNGigSpUqWR3HEiW+Ax8YGKhHH31Ue/bsKYs8AAAAQKlNmzZNpmkWa3kq0zS1ctM3ZZLjRHKSkr7fXiZtA+WFzWZTfHy8hg0bpnbt2pXb4l1ychK7xo0bKykpycVRAAAAgNJLTk4uMKP15ThMU9/t219mE9sd3vOz6sS3dnm7QHlQrVo1de7cOW/y9PLOqWfgp0+frtmzZ+vTTz91dR4AAACgVNatW1fs4j2XKemng7+VSR57Tk6ZtAt4m6ioKFWuXFkVK178IK1Jkybq06cPxfsfOHUH/qWXXlLlypXVo0cP1a5dW7Vr11ZQUFC+YwzD0MqVK10SEgAAACiu9PR0+fj4yOFwFPscwzB0PiurTPIEh5ff4b5ASWzbtk1Lly5VWlqa4uPj1bo1I1cu5VQBv3PnThmGoejoaNntdu3bV3DmTm9ccw8AAADuLzQ0tETFu3TxOfgg/7JZ5SWkcpUyaRfwVjVq1FCrVq2sjuGWnCrgef4dAAAA7qpbt24yDKNEw+gNSY1iapVJnqBQ1z9XD3grwzDUoUMHbggXwaln4AEAAAB3FR0drd69e8tmK97SbT6Gofi6dcpkAjtJqhgRWSbtAt6oevXqCg8PtzqG23K6gLfb7Xrrrbc0ZswY3Xzzzfrhhx8kSWlpaVqxYoWOHj3qspAAAABASUycOFGGYRTrLp5hGLqpfdk8axtWNUrhUTXKpG3AG0VFRVkdwa05VcCfPn1a7du317Bhw/Tmm2/q/fff1/HjxyVJISEheuCBBzRz5kyXBgUAAACKq3Xr1lq6dKlsNluRd+J9DEM2Hx/df9MNqlPd9UWDr7+/EgYOYygwUALcfb88pwr4CRMmaNeuXVqzZo3279+f7/kim82mAQMGaNWqVS4LCQAAAJRU//79tXnzZvXq1atAEW1Iah5XW0/cPlCt6td1+bUDgkPU9c6/qEqtaJe3DXizwMBAqyO4NacmsXvvvfc0duxYde/eXSdPniyw/+qrr9aCBQtKmw0AAAAoldatW+v9999XcnKymjdvrtTUVAUHBmjancPK7Jn3qLirlTBwmCqEhZdJ+4A3o4C/PKcK+LS0NNWuXbvI/dnZ2crJyXE6FAAAAOBK0dHRqlChglJTU+Xv51cmxbtfYKBa9Oyrum3aMWwecJKfn5/VEdyaUwV8XFyctm/fXuT+Tz75RI0aNXI6FAAAAOBJohs3U6s+/VWhYpjVUQB4MacK+JEjR+rRRx9V586d1a1bN0kXZ+/MzMzUk08+qdWrV2vOnDkuDQoAAAC4m8CQULW5aaCiGze1OgqAcsCpAv7BBx/Url27NHTo0LxZAocNG6aTJ08qJydHY8aM0d133+3KnAAAAIBbqXF1QyUMvFWBISFWRwFQTjhVwBuGoblz52r48OFatmyZ9u7dK4fDobi4OA0aNEidOnVydU4AAADAbTTt1lNNul4vw8epRZ0AwCnFKuD79++vv/71r+rYsaMkaePGjWrYsKE6dOigDh06lGlAAAAAwF34+PoqYeCtim0ab3UUAOVQsT4yXLlypZKTk/Ned+nSRWvXri2zUAAAeDKTlVgAr1ShYph6jHmA4h2AZYpVwNesWVPfffdd3mvTNFkaAwCAIjgyMqyOAMDFouLq6Yax41SlVrTVUQCUY8UaQj9kyBA9++yzevvtt/MmrZswYYJmzJhR5DmGYej77793SUgAADyJmZ1tdQQALmOoabcePO8OwC0Uq4CfMWOG6tatq/Xr1+vYsWMyDEPBwcGqUqVKWecDAMDjmFlZVkcA4AIBFYLVYegdql63vtVRAEBSMQt4m82m0aNHa/To0ZIkHx8fPf744xo2bFiZhgMAwBM5zp+3OgKAUgqPqqHOd4xUSKXKVkcBgDzFGgfUokULrV69Ou/1/PnzFR/P5B0AABTGkZ5udQQApVC1dpx6jHmA4h2A2ylWAb9z506dOHEi7/Vdd92Vb1I7AADwPzmpqVZHAOCkqrXj1HXEGPkFBlodBQAKKFYBHxMTo08//VR2u10Ss9ADAHA5OcePWx0BgBPCqkap8+13y9ff3+ooAFCoYhXwf/nLX/T6668rMDBQFStWlGEYuvvuu1WxYsUiv8LCwso6OwAAbinn9yNWRwBQQr7+AUq8/S75B1WwOgoAFKlYk9j97W9/U7NmzbR+/XodPXpUCxcuVOvWrVWnTp2yzgcAgMfJ/v13RqsBHqZVn/6qGFHV6hgAcFnFKuAl6frrr9f1118vSVqwYIHGjBnDLPQAABTCcTZdjjNnZGM0GuARqtdroLiWbayOAQBXVOwC/o8cDoercwAA4FWyfvtNQRTwgNvz8fVVm34DGDEDwCMUq4BPTk6WJEVHR+d7fSW5xwMAUN5kJycr6JprrI4B4A+ioqJkz85W0B9mgWrUoYtCK0dYFwoASqBYBXxsbKwMw9D58+fl7++f9/pKcmetBwCgvLnw825VvOEGq2MA+INt27Ypace3+nLpIkmSX2CgGiV2tTgVABRfsQr41157TYZhyM/PL99rAABQuPPffSczK0sGy1EBbqte63byDwyyOgYAFFuxCvgRI0Zc9jUAAMjPkZGhc19/rZCOHa2OAqAIddskWB0BAEqkWOvAAwCAkkt7b6VM07Q6BoBCVKtdVxUjIq2OAQAlUqw78E8++WSJGzYMQxMnTizxeQAAeIuspCRlbP1GwdeyPBXgbuJaX2t1BAAosWIV8JMnTy6wLfcZ+EvvLBiGIdM0KeABAJCU+tabqtC6lQwfBr0B7sLXP0DR1zSzOgYAlFix/ppwOBz5vg4dOqQmTZpo6NCh2rp1q9LS0pSWlqavv/5aQ4YMUbNmzXTo0KGyzg4AgNvLTj6ksxs2WB0DwB/UbNBIvkwwCcADOXU74L777lO9evX0xhtvqFWrVgoNDVVoaKhat26txYsXKy4uTvfdd5+rswIA4PZatWql1vPnq+9n6/K2pb7xhuxnz1qYCsAfVY2NszoCADjFqQL+s88+U9euRa+Z2a1bN61bt67I/QAAeKsjR47oyLlzOnEhM2+bPe2MTr7yChPaAW4iLLKq1REAwClOFfCBgYHasmVLkfs3b96swMBAp0MBAOBtzm35Smnvvmd1DACSAoKDrY4AAE5xqoC/9dZbtXjxYj3wwAPau3dv3rPxe/fu1dixY7VkyRLdeuutrs6az/Tp05WQkKAKFSooPDy8TK8FAIArpC5erPTPPrM6BlDu2fx4/h2AZyrWLPSXevrpp3XixAm99NJLevnll+Xz35l1HQ6HTNPU0KFD9fTTT7s06KWysrI0cOBAtWvXTvPmzSvTawEA4ConZv1Hhs2mkMREq6MA5ZbN16k/gQHAck799vL399eiRYv0t7/9TatWrdLBgwclSTExMbrhhhvUrFnZL8sxZcoUSdKCBQvK/FoAALiMaer4Sy9LhqGQTp2sTgOUSzZfP6sjAIBTSvXxY9OmTdW0aVNXZSlzmZmZysz836RCZ5kRGABgBYdDx198SbLZFNK+vdVpgHLHLzDA6ggA4BSnnoH3VDNmzFBYWFjeVyLDFwEAVnE4dHzmC8rY/p3VSYByxebnxx14AB7LrQr4CRMmyDCMy37t3r3b6fYfe+wxpaWl5X19/vnnLkwPAEAJ2e069uyzuvDLL1YnAcoN/wrMQA/Ac7nVDB6PPPKIRowYcdlj6tSp43T7AQEBCgj435CpkJAQp9sCAMAVzMxMHX1qhqpPmyb/WjWtjgN4vQAKeAAezK0K+MjISEVGRlodAwCAP5UjPV1Hnpyi6lOnyq9aNavjAF7NL4Dn3wF4LrcaQl8SycnJ2rFjh5KTk2W327Vjxw7t2LGDiekAAB7JfvKUfp84UVmHDlkdBfBq/kFBVkcAAKd5bAH/xBNPKD4+XpMmTdLZs2cVHx+v+Ph4bdu2zepoAAA4xX7ylH7/+z+U8R0T2wFlxS8g0OoIAOA0p4fQr1mzRvPmzdP+/fuVmpoq0zTz7TcMQ7/++mupAxZlwYIFrAEPAPA6jowMHZ3+lMIHDlT4wAEyfDz2s3YAAOBiThXwzzzzjCZMmKBq1aqpTZs2atKkiatzAQBQfpmmTr/9ts7/sFORYx+QX7WqVicCAABuwKkCfubMmeratatWrVolPz/W0QQAoCxk/rxbKY88rMp33KHQ7t1lGIbVkQAAgIWcGpeXmpqqAQMGULwDAFDGzPMXdHL2HB19aoZyUlOtjgMAACzkVAHfpk0b7dmzx9VZAABAEc5v367Dj4zT+Z07rY4CAAAs4lQBP2vWLK1YsUJLlixxdR4AAFAEe1qajkydprQPP7I6CgAAsIBTz8APHjxYOTk5uv3223XPPfeoVq1astls+Y4xDEPff/+9S0ICAID/cjh0av582c+kqfKwYVanAQAAfyKnCvjKlSurSpUqqlevnqvzAACAYkhbvkK+lSqp4g03WB0FAAD8SZwq4Dds2ODiGAAAeL7k5GRlZGRIkjLsOUrJyFDNChXK7HonFyxQ4DXXyD86usyuAQAA3IdTz8ADAID/2bp1q/r06aPY2Fil/nem+DPZ2eq0+mON2rxZ3586VTYXzrErdcmbZdM2AABwO07dgc+VnZ2t3bt3Ky0tTQ6Ho8D+Tp06laZ5AADc3ooVKzR48GCZpinTNPPtMyVtOHpEnx89ohfaXKueNWu6/PoZ27Yp59Qp+Vau7PK2AQCAe3GqgHc4HHrsscc0a9asvKGChbHb7U4HAwDA3W3dulWDBw+W3W4vULznspumDEkPbP1a7yR2VjNXF9qmqYxt21Tx+utd2y4AAHA7Tg2hf+qpp/TMM8/otttu0+uvvy7TNPXPf/5Tr7zyipo2bapmzZppzZo1rs4KAIBbmTZtWqF33i9l/vfr5T27yyTHhR9+LJN2AQCAe3GqgF+wYIEGDRqk//znP+rZs6ckqWXLlho1apS+/vprGYahzz77zKVBAQBwJ8nJyfrwww+LPdrMbppa9/vvSrnMyDVnXfj5Z5e3CQAA3I9TBfxvv/2mrl27SpICAgIkSRcuXJAk+fv767bbbtOiRYtcFBEAAPezbt26K955v5QpacvxYy7PYk9Nlf30aZe3CwAA3ItTBXyVKlV09uxZSVJISIgqVqyo/fv35zsmdxZeAAC8UXp6unx8SvbPqI+ks9k5ZZIn5xT/7gIA4O2cmsQuPj5e33zzTd7rLl266Pnnn1d8fLwcDodeeOEFNWvWzGUhAQBwN6GhoYWuwHI5DkkhfqVaAKZIRhm1CwAA3IdTd+BHjx6tzMxMZWZmSpKmT5+u06dPq1OnTkpMTNSZM2f03HPPuTQoAADupFu3bjIMo0TnGJLaRVZ1eRYjMFB+UVEubxcAALgXpz6u79u3r/r27Zv3ulGjRvr111+1YcMG2Ww2JSQkqDLr0QIAvFh0dLR69+6tVatWFWsiO5thqEtUlGpWqODyLMHtE2T4+bm8XQAA4F5cNt4uLCxMN910k6uaAwDA7U2cOFEff/yxDMO47IR2xn+/7qvfwOUZjIAAVRo40OXtAgAA9+PUEHpJstvteuuttzRmzBjdfPPN+uGHHyRJaWlpWrFihY4ePeqykAAAuKPWrVtr6dKlstlsstlshR5jMwzZDEMvtrlWzcpgdFqVu++Sb2Sky9sFAADux6kC/vTp02rfvr2GDRumN998U++//76OHz8u6eKs9A888IBmzpzp0qAAALij/v37a/PmzerVq1eBZ+INSV2iovROYmf1qFnT5dcOva6bQv67rCsAAPB+ThXwEyZM0K5du7RmzRrt378/37BBm82mAQMGaNWqVS4LCQCAO2vdurXef/99JSUlqVKlSpKkMD8/bex5g+a0SyiTO+8BDRuoysiRJZ5IDwAAeC6nCvj33ntPY8eOVffu3Qv9w+Hqq69WUlJSabMBAOBRoqOjVeG/k9QF2XzLZMI6SbJVqqSqjzzCxHUAAJQzThXwaWlpql27dpH7s7OzlZOT43QoAABQBMNQ5EMPyfe/d/oBAED54VQBHxcXp+3btxe5/5NPPlGjRo2cDgUAAAoXPuAWBTW+xuoYAADAAk4V8CNHjtRrr72mpUuX5j3/bhiGMjMz9Y9//EOrV6/WmDFjXBoUAIDyLqhZM4UPGmR1DAAAYBGn1oF/8MEHtWvXLg0dOlTh4eGSpGHDhunkyZPKycnRmDFjdPfdd7syJwAA5VpA3ThVHfeIDB+nV4AFAAAezqkC3jAMzZ07V8OHD9eyZcu0d+9eORwOxcXFadCgQerUqZOrcwIAUG4FXnONqj06Xj5lNCkeAADwDE4V8Lk6dOigDh06uCoLAAC4RGj361Tlrrtk+PtbHQUAAFisVAU8AAAoG4afn6qMHqXQrl2tjgIAANxEsQv4vn37lqhhwzC0cuXKEgcCAKC886tRXZEPP6yAyyzZCgAAyp9iF/AffvihAgMDFRUVlTfz/OUYhlGqYAAAlEfB7doq4t57ed4dAAAUUOwCvmbNmkpJSVFERISGDRumIUOGKCoqqiyzAQBQfvj4qPLtt6linz58CA4AAApV7LVoDh06pPXr1ys+Pl5Tp07VVVddpeuuu07z589Xenp6WWYEAMCr+QQHK+rxfyisb1+KdwAAUKQSLSabmJio2bNn68iRI1q2bJmqVKmi+++/X1WrVlX//v21bNkyZWZmllVWAAC8ji2iiqpPn6agZs2sjgIAANxciQr4XH5+frrpppu0dOlSHT16NK+oHzx4sP71r3+5OiMAAF7Jt1o11Zg2Tf5XXWV1FAAA4AGcKuBzZWZmas2aNVq5cqW+++47BQYGKjY21kXRAADwXrawMEVNekK+kZFWRwEAAB6ixAW8w+HQmjVrNGLECFWrVk1Dhw7V+fPnNXfuXB07dky33357WeQEAMB7+NpU9dHx8qtWzeokAADAgxR7FvrNmzdryZIleuedd3Ty5Em1bdtWTz31lAYNGqSIiIiyzAgAgFepMmKEAuvXtzoGAADwMMUu4Dt06KCgoCD16tVLQ4cOzRsqn5ycrOTk5ELPadGihUtCAgDgLSpce61Ce/a0OgYAAPBAxS7gJen8+fNavny5VqxYcdnjTNOUYRiy2+2lCgcAgDexRVRRxL33sFQcAABwSrEL+Pnz55dlDgAAvJthqOqDD8oWEmJ1EgAA4KGKXcAPHz68LHMAAODVwvr2VWCjRlbHAAAAHqxUy8gBAIAr842MVPiQwVbHAAAAHs4jC/ikpCTdfffdql27toKCghQXF6dJkyYpKyvL6mgAABRQadhQ+fj7Wx0DAAB4uBJNYucudu/eLYfDodmzZ6tu3br68ccfNWrUKJ07d07PPvus1fEAAOVYVFSU7KdPK8LPT5LkW7Wqgjt0sDgVAADwBh5ZwPfs2VM9/7AET506dbRnzx795z//oYAHAFhq27Zt+m3sWGUf/l2SFNq9uwwfjxzwBgAA3IzX/EWRlpamypUrWx0DAID/MQyFdE60OgUAAPASHnkH/lL79u3Tiy++eMW775mZmcrMzMx7ffbs2bKOBgAoxwIbXyNfPlwGAAAu4lZ34CdMmCDDMC77tXv37nznpKSkqGfPnho4cKBGjRp12fZnzJihsLCwvK/ERO6KAADKTnBCgtURAACAFzFM0zStDpHr+PHjOnny5GWPqVOnjvz/O5Pv4cOH1blzZ7Vt21YLFiyQzxWeMbz0DvyOHTuUmJiob7/9Vi1atCj9GwAAQLr4DPyRo4p+da5sYWFWxwEAAF7CrYbQR0ZGKjIysljHpqSkqEuXLmrZsqXmz59/xeJdkgICAhQQEJD3OiQkxOmsAABcTkD9qyneAQCAS7lVAV9cKSkp6ty5s2JiYvTss8/q+PHjefuioqIsTAYAwEUV4uOtjgAAALyMRxbwa9eu1b59+7Rv3z7VqlUr3z43eiIAAFCOBTZuYnUEAADgZdxqErviGjFihEzTLPQLAACrGf4BCoirY3UMAADgZTyygAcAwJ35x8TI8PXIQW4AAMCNUcADAOBifjVrWh0BAAB4IQp4AABczDcywuoIAADAC1HAAwDgYj6hoVZHAAAAXogCHgAAF/OpUMHqCAAAwAtRwAMA4GJMYAcAAMoCBTwAAK7mwz+vAADA9fgLAwAAFzMMw+oIAADAC1HAAwDgajab1QkAAIAXooAHAMDFDIbQAwCAMsBfGAAAuJqvn9UJAACAF6KABwDAxXwjI6yOAAAAvBAFPAAALsYQegAAUBb4CwMAAAAAAA9AAQ8AAAAAgAeggAcAAAAAwANQwAMAAAAA4AEo4AEAAAAA8AAU8AAAAAAAeABfqwPgz/H777/r999/tzoGXKR69eqqXr261THgIvRP70Mf9S70Ue9C//Qu9E/vQx+9vHJdwFevXl2TJk3y+v9BMjMzNXToUH3++edWR4GLJCYmas2aNQoICLA6CkqJ/umd6KPegz7qfeif3oP+6Z3oo5dnmKZpWh0CZevMmTMKCwvT559/rpCQEKvjoJTOnj2rxMREpaWlqWLFilbHQSnRP70PfdS70Ee9C/3Tu9A/vQ999MrK9R348qZ58+Z0BC9w5swZqyOgDNA/vQd91DvRR70D/dM70T+9B330ypjEDgAAAAAAD0ABDwAAAACAB6CALwcCAgI0adIkJoLwEvw8vQs/T+/Dz9S78PP0Lvw8vQs/T+/Dz/TKmMQOAAAAAAAPwB14AAAAAAA8AAU8AAAAAAAegAIeAAAAAAAPQAEPAAAAAOXUiBEjFBsb6/S5ISEhrg1UQgsWLJBhGEpKSnLq/NK8/86dO6tz585OnessCnigDBiGUayvDRs2lPpaGRkZmjx5conamj59uvr27atq1arJMAxNnjy51DkAT+HO/XP37t0aP368mjdvrtDQUFWvXl033nijtm3bVuosgKdw5z56+PBh3Xbbbapfv75CQ0MVHh6uNm3aaOHChWJeaLjS22+/LcMw9O677xbY16xZMxmGofXr1xfYFx0drYSEhD8jYok48/eqJzp8+LAmT56sHTt2lNk1fMusZaAcW7RoUb7Xr7/+utauXVtge8OGDUt9rYyMDE2ZMkWSiv0J4OOPP66oqCjFx8drzZo1pc4AeBJ37p+vvvqq5s2bp1tuuUX33nuv0tLSNHv2bLVt21arV6/WddddV+pMgLtz5z564sQJ/fbbbxowYICio6OVnZ2ttWvXasSIEdqzZ4+eeuqpUmcCJKlDhw6SpC+//FI333xz3vYzZ87oxx9/lK+vrzZt2qQuXbrk7Tt06JAOHTqkIUOGlOhac+fOlcPhcE3wIjjz96onOnz4sKZMmaLY2Fg1b968TK5BAQ+Ugdtuuy3f66+++kpr164tsN0qBw4cUGxsrE6cOKHIyEir4wB/Knfun0OHDtXkyZPzDUe866671LBhQ02ePJkCHuWCO/fRpk2bFriDeP/996tPnz564YUXNHXqVNlsNmvCwavUqFFDtWvX1pdffplv+5YtW2SapgYOHFhgX+7r3OK/uPz8/EoXFn8qhtADFnE4HHr++ed1zTXXKDAwUNWqVdOYMWOUmpqa77ht27apR48eioiIUFBQkGrXrq277rpLkpSUlJRXgE+ZMiVvWOGVhsQ7+5wPUF5Y1T9btmxZ4FnCKlWqqGPHjvr5559d+yYBD2blv6GFiY2NVUZGhrKyskr93oBcHTp00Hfffafz58/nbdu0aZOuueYa3XDDDfrqq6/y3TnftGmTDMNQ+/bt87a98cYbatmypYKCglS5cmUNGTJEhw4dynedwp4BP3nypG6//XZVrFhR4eHhGj58uL7//nsZhqEFCxYUyJqSkqJ+/fopJCREkZGRGjdunOx2u6Ti9bXdu3drwIABqly5sgIDA9WqVSu9//77Ba6za9cude3aVUFBQapVq5amTZtWotED7733nho3bqzAwEA1bty40EcUpOL/jvmjDRs2qHXr1pKkO++8M+995n6/vvjiCw0cOFDR0dEKCAjQVVddpb/+9a/5fr7FwR14wCJjxozRggULdOedd+qBBx7QgQMH9NJLL+m7777Tpk2b5Ofnp2PHjun6669XZGSkJkyYoPDwcCUlJWnFihWSpMjISP3nP//RPffco5tvvln9+/eXdPEOAQDnuVv/PHLkiCIiIlz6HgFPZnUfPX/+vM6dO6ezZ8/q888/1/z589WuXTsFBQWV6ftG+dKhQwctWrRIX3/9dd6w802bNikhIUEJCQlKS0vTjz/+mPf/7KZNm9SgQQNVqVJF0sU5jyZOnKhBgwZp5MiROn78uF588UV16tRJ3333ncLDwwu9rsPhUJ8+fbR161bdc889atCggVauXKnhw4cXerzdblePHj107bXX6tlnn9Wnn36q5557TnFxcbrnnnuu2Nd27dql9u3bq2bNmpowYYKCg4P19ttvq1+/flq+fHneIwRHjhxRly5dlJOTk3fcnDlzit3vPvnkE91yyy1q1KiRZsyYoZMnT+rOO+9UrVq1ChxbnN8xl2rYsKGefPJJPfHEExo9erQ6duwoSXlzErzzzjvKyMjQPffcoypVqmjr1q168cUX9dtvv+mdd94p1nuQJJkAytx9991n/rG7ffHFF6Ykc/HixfmOW716db7t7777rinJ/Oabb4ps+/jx46Ykc9KkSSXOVZpzAW/hrv0z18aNG03DMMyJEyc63Qbgydyxj86YMcOUlPfVrVs3Mzk5uURtAFeya9cuU5I5depU0zRNMzs72wwODjYXLlxomqZpVqtWzXz55ZdN0zTNM2fOmDabzRw1apRpmqaZlJRk2mw2c/r06fna/OGHH0xfX99824cPH27GxMTkvV6+fLkpyXz++efzttntdrNr166mJHP+/Pn5zpVkPvnkk/muEx8fb7Zs2TLv9eX6Wrdu3cwmTZqYFy5cyNvmcDjMhIQEs169ennbHnroIVOS+fXXX+dtO3bsmBkWFmZKMg8cOFDo9zFX8+bNzerVq5unT5/O2/bJJ5+YkvK9/+L+jjFN00xMTDQTExPzXn/zzTcFvke5MjIyCmybMWOGaRiGefDgwctm/yOG0AMWeOeddxQWFqbu3bvrxIkTeV+5w2dzZxXN/WT0ww8/VHZ2toWJgfLDnfrnsWPHNGzYMNWuXVvjx48vk2sAnsYd+ujQoUO1du1aLVmyRMOGDZOkEg+DBa6kYcOGqlKlSt6z7d9//73OnTuXd0c3ISFBmzZtknTx2Xi73Z73/PuKFSvkcDg0aNCgfP0kKipK9erVK3QG+1yrV6+Wn5+fRo0albfNx8dH9913X5Hn/OUvf8n3umPHjtq/f/8V3+OpU6f02WefadCgQUpPT8/LefLkSfXo0UN79+5VSkqKJGnVqlVq27at2rRpk3d+ZGSkbr311ite5/fff9eOHTs0fPhwhYWF5W3v3r27GjVqlO/Y4v6OKak/jhQ4d+6cTpw4oYSEBJmmqe+++67Y7TCEHrDA3r17lZaWpqpVqxa6/9ixY5KkxMRE3XLLLZoyZYr+/e9/q3PnzurXr5+GDRumgICAPzMyUG64S/88d+6cevfurfT0dH355ZeWr7MLuAt36KMxMTGKiYmRdLGYHz16tK677jrt2bOHYfRwGcMwlJCQoI0bN8rhcGjTpk2qWrWq6tatK+liAf/SSy9JUl4hn1vA7927V6Zpql69eoW2fbmJ6w4ePKjq1aurQoUK+bbnXvdSgYGBBSZFrlSp0mWfF8+1b98+maapiRMnauLEiYUec+zYMdWsWVMHDx7UtddeW2B//fr1r3idgwcPSlKh34/69etr+/btea+L+zumpJKTk/XEE0/o/fffL/C9SUtLK3Y7FPCABRwOh6pWrarFixcXuj/3l6BhGFq2bJm++uorffDBB1qzZo3uuusuPffcc/rqq6/4gx4oA+7QP7OystS/f3/t3LlTa9asUePGjZ1uC/A27tBHLzVgwADNnTtXGzduVI8ePVzWLtChQwd98MEH+uGHH/Kef8+VkJCgv/3tb0pJSdGXX36pGjVqqE6dOpIu9hPDMPTxxx8XujKCK///L83KC7kT0I0bN67IvlPUBwdlpbi/Y0rCbrere/fuOnXqlB599FE1aNBAwcHBSklJ0YgRI0o0ER8FPGCBuLg4ffrpp2rfvn2xPqlv27at2rZtq+nTp2vJkiW69dZb9dZbb2nkyJEyDONPSAyUH1b3T4fDoTvuuEPr1q3T22+/rcTERGfeBuC1rO6jhckdPl+Su2hAcfxxPfhNmzbpoYceytvXsmVLBQQEaMOGDfr666/Vq1evvH1xcXEyTVO1a9fW1VdfXaJrxsTEaP369crIyMh3F37fvn1Ov4+i+lruBw5+fn5XXCo1JiZGe/fuLbB9z549V7x+7oiZ4pxf0t8xf1TU+/zhhx/0yy+/aOHChbrjjjvytq9du7ZE7UssIwdYYtCgQbLb7Zo6dWqBfTk5OTp9+rQkKTU1VaZp5tvfvHlzSVJmZqYk5f1izT0HQOlY3T/Hjh2rpUuXatasWXkz9QL4Hyv76PHjxwvdPm/ePBmGoRYtWhSrHaC4WrVqpcDAQC1evFgpKSn57sAHBASoRYsWevnll3Xu3Ll867/3799fNptNU6ZMKdAPTNPUyZMni7xmjx49lJ2drblz5+Ztczgcevnll51+H0X1tapVq6pz586aPXu2fv/99wLn/bHP9erVS1999ZW2bt2ab39Rd8r/qHr16mrevLkWLlyY74O2tWvX6qeffsp3bHF/xxQmODhYUsH3mTtK4Y8/C9M0NXPmzCtmvxR34AELJCYmasyYMZoxY4Z27Nih66+/Xn5+ftq7d6/eeecdzZw5UwMGDNDChQs1a9Ys3XzzzYqLi1N6errmzp2rihUr5n3KGhQUpEaNGmnp0qW6+uqrVblyZTVu3PiyQ24XLVqkgwcPKiMjQ5K0ceNGTZs2TZJ0++23531KCZRHVvbP559/XrNmzVK7du1UoUIFvfHGG/n233zzzXl/HADllZV9dPr06dq0aZN69uyp6OhonTp1SsuXL9c333yjsWPH/ulDfeH9/P391bp1a33xxRcKCAhQy5Yt8+1PSEjQc889J0n5Cvi4uDhNmzZNjz32mJKSktSvXz+FhobqwIEDevfddzV69GiNGzeu0Gv269dPbdq00SOPPKJ9+/apQYMGev/993Xq1ClJRd9lvpzL9bWXX35ZHTp0UJMmTTRq1CjVqVNHR48e1ZYtW/Tbb7/p+++/lySNHz9eixYtUs+ePfXggw/mLSMXExOjnTt3XjHDjBkzdOONN6pDhw666667dOrUKb344ou65pprdPbs2bzjivs7pjBxcXEKDw/XK6+8otDQUAUHB+vaa69VgwYNFBcXp3HjxiklJUUVK1bU8uXLizVPQAHFnq8egNMuXQIn15w5c8yWLVuaQUFBZmhoqNmkSRNz/Pjx5uHDh03TNM3t27ebQ4cONaOjo82AgACzatWqZu/evc1t27bla2fz5s1my5YtTX9//2Ith5OYmJhv+Zs/fq1fv95VbxvwCO7UP3OX4ynq60pL5ADeyJ366CeffGL27t3brFGjhunn52eGhoaa7du3N+fPn286HA6Xvm8g12OPPWZKMhMSEgrsW7FihSnJDA0NNXNycgrsX758udmhQwczODjYDA4ONhs0aGDed9995p49e/KOuXQZOdO8uOzbsGHDzNDQUDMsLMwcMWKEuWnTJlOS+dZbb+U7Nzg4uMB1J02aVKDfXq6v/frrr+Ydd9xhRkVFmX5+fmbNmjXN3r17m8uWLcvXxs6dO83ExEQzMDDQrFmzpjl16lRz3rx5xf43cvny5WbDhg3NgIAAs1GjRuaKFSsKff+meeXfMaZZcBk50zTNlStXmo0aNTJ9fX3zLSn3008/mdddd50ZEhJiRkREmKNGjTK///77IpedK4phmpeMqQAAAAAA4A/ee+893Xzzzfryyy/Vvn17q+OUWxTwAAAAAIA858+fzzeBm91u1/XXX69t27bpyJEjLJdoIZ6BBwAAAADkGTt2rM6fP6927dopMzNTK1as0ObNm/XUU09RvFuMO/AAAAAAgDxLlizRc889p3379unChQuqW7eu7rnnHt1///1WRyv3KOABAAAAAPAArAMPAAAAAIAHoIAHAAAAAMADUMADbiYpKUmGYWjBggVWRwFQCPoo4L7onwC8HQU8AAAAAAAegEnsADdjmqYyMzPl5+cnm81mdRwAl6CPAu6L/gnA21HAAwAAAADgARhCD5SByZMnyzAM/fLLL7rtttsUFhamyMhITZw4UaZp6tChQ7rppptUsWJFRUVF6bnnnss7t7Dn90aMGKGQkBClpKSoX79+CgkJUWRkpMaNGye73Z533IYNG2QYhjZs2JAvT2FtHjlyRHfeeadq1aqlgIAAVa9eXTfddJOSkpLK6LsCuA/6KOC+6J8AUDQKeKAMDR48WA6HQ//85z917bXXatq0aXr++efVvXt31axZU08//bTq1q2rcePGaePGjZdty263q0ePHqpSpYqeffZZJSYm6rnnntOcOXOcynbLLbfo3Xff1Z133qlZs2bpgQceUHp6upKTk51qD/BE9FHAfdE/AaAQJgCXmzRpkinJHD16dN62nJwcs1atWqZhGOY///nPvO2pqalmUFCQOXz4cNM0TfPAgQOmJHP+/Pl5xwwfPtyUZD755JP5rhMfH2+2bNky7/X69etNSeb69evzHXdpm6mpqaYk85lnnnHNGwY8DH0UcF/0TwAoGnfggTI0cuTIvP+22Wxq1aqVTNPU3Xffnbc9PDxc9evX1/79+6/Y3l/+8pd8rzt27Fis8y4VFBQkf39/bdiwQampqSU+H/AW9FHAfdE/AaAgCnigDEVHR+d7HRYWpsDAQEVERBTYfqU/AgIDAxUZGZlvW6VKlZz64yEgIEBPP/20Pv74Y1WrVk2dOnXSv/71Lx05cqTEbQGejD4KuC/6JwAURAEPlKHClrApalkb8woLQhRnORzDMArd/sdJenI99NBD+uWXXzRjxgwFBgZq4sSJatiwob777rsrXgfwFvRRwH3RPwGgIAp4wItUqlRJknT69Ol82w8ePFjo8XFxcXrkkUf0ySef6Mcff1RWVla+2XwBuBZ9FHBf9E8AnoACHvAiMTExstlsBWbjnTVrVr7XGRkZunDhQr5tcXFxCg0NVWZmZpnnBMor+ijgvuifADyBr9UBALhOWFiYBg4cqBdffFGGYSguLk4ffvihjh07lu+4X375Rd26ddOgQYPUqFEj+fr66t1339XRo0c1ZMgQi9ID3o8+Crgv+icAT0ABD3iZF198UdnZ2XrllVcUEBCgQYMG6ZlnnlHjxo3zjrnqqqs0dOhQrVu3TosWLZKvr68aNGigt99+W7fccouF6QHvRx8F3Bf9E4C7M8wrzfoBAAAAAAAsxzPwAAAAAAB4AAp4AAAAAAA8AAU8AAAAAAAegAIeAAAAAAAPQAEPAAAAAIAHoIAHyrGkpCQZhqEFCxZYHQVAIeijgPuifwKwAgU8UEy//vqrxowZozp16igwMFAVK1ZU+/btNXPmTJ0/f77MrvvTTz9p8uTJSkpKKrNrFMf06dPVt29fVatWTYZhaPLkyZbmAS5Vnvvo7t27NX78eDVv3lyhoaGqXr26brzxRm3bts2yTMAflef+efjwYd12222qX7++QkNDFR4erjZt2mjhwoViNWcAJeVrdQDAE3z00UcaOHCgAgICdMcdd6hx48bKysrSl19+qb/97W/atWuX5syZUybX/umnnzRlyhR17txZsbGxZXKN4nj88ccVFRWl+Ph4rVmzxrIcQGHKex999dVXNW/ePN1yyy269957lZaWptmzZ6tt27ZavXq1rrvuOktyARL988SJE/rtt980YMAARUdHKzs7W2vXrtWIESO0Z88ePfXUU5bkAuCZKOCBKzhw4ICGDBmimJgYffbZZ6pevXrevvvuu0/79u3TRx99ZGHC/zFNUxcuXFBQUJDL2z5w4IBiY2N14sQJRUZGurx9wFn0UWno0KGaPHmyQkJC8rbdddddatiwoSZPnkwBD8vQP6WmTZtqw4YN+bbdf//96tOnj1544QVNnTpVNpvNpdcE4L0YQg9cwb/+9S+dPXtW8+bNy/eHR666devqwQcfzHudk5OjqVOnKi4uTgEBAYqNjdXf//53ZWZm5jsvNjZWvXv31pdffqk2bdooMDBQderU0euvv553zIIFCzRw4EBJUpcuXWQYhgzDyPtDILeNNWvWqFWrVgoKCtLs2bMlSfv379fAgQNVuXJlVahQQW3bti3VH0lW3v0HLoc+KrVs2TJf8S5JVapUUceOHfXzzz871SbgCvTPosXGxiojI0NZWVkubReAd6OAB67ggw8+UJ06dZSQkFCs40eOHKknnnhCLVq00L///W8lJiZqxowZGjJkSIFj9+3bpwEDBqh79+567rnnVKlSJY0YMUK7du2SJHXq1EkPPPCAJOnvf/+7Fi1apEWLFqlhw4Z5bezZs0dDhw5V9+7dNXPmTDVv3lxHjx5VQkKC1qxZo3vvvVfTp0/XhQsX1LdvX7377rsu+K4A7oM+WrQjR44oIiLCZe0BJUX//J/z58/rxIkTSkpK0sKFCzV//ny1a9euTEbNAfBiJoAipaWlmZLMm266qVjH79ixw5Rkjhw5Mt/2cePGmZLMzz77LG9bTEyMKcncuHFj3rZjx46ZAQEB5iOPPJK37Z133jElmevXry9wvdw2Vq9enW/7Qw89ZEoyv/jii7xt6enpZu3atc3Y2FjTbrebpmmaBw4cMCWZ8+fPL9b7M03TPH78uCnJnDRpUrHPAcoKfbRoGzduNA3DMCdOnFjicwFXoH/mN2PGDFNS3le3bt3M5OTkYp0LALm4Aw9cxpkzZyRJoaGhxTp+1apVkqSHH3443/ZHHnlEkgoMv2vUqJE6duyY9zoyMlL169fX/v37i52xdu3a6tGjR4Ecbdq0UYcOHfK2hYSEaPTo0UpKStJPP/1U7PYBd0YfLdyxY8c0bNgw1a5dW+PHjy9VW4Cz6J/5DR06VGvXrtWSJUs0bNgwSSrTGfgBeCcKeOAyKlasKElKT08v1vEHDx6Uj4+P6tatm297VFSUwsPDdfDgwXzbo6OjC7RRqVIlpaamFjtj7dq1C81Rv379Attzhw1emgPwVPTRgs6dO6fevXsrPT1dK1euLPBsPPBnoX/mFxMTo+uuu05Dhw7V4sWLVadOHV133XUU8QBKhAIeuIyKFSuqRo0a+vHHH0t0nmEYxTquqFlnzRKsC8uzcyjP6KP5ZWVlqX///tq5c6dWrlypxo0b/2nXBi5F/7y8AQMG6NChQ9q4caNlGQB4Hgp44Ap69+6tX3/9VVu2bLnisTExMXI4HNq7d2++7UePHtXp06cVExNT4usX9w+ZS3Ps2bOnwPbdu3fn7Qe8BX30IofDoTvuuEPr1q3TkiVLlJiYWOI2AFejfxYt9857WlqaS9oDUD5QwANXMH78eAUHB2vkyJE6evRogf2//vqrZs6cKUnq1auXJOn555/Pd8z//d//SZJuvPHGEl8/ODhYknT69Olin9OrVy9t3bo13x9M586d05w5cxQbG6tGjRqVOAfgruijF40dO1ZLly7VrFmz1L9//xKfD5QF+qd0/PjxQrfPmzdPhmGoRYsWJWoPQPnma3UAwN3FxcVpyZIlGjx4sBo2bKg77rhDjRs3VlZWljZv3qx33nlHI0aMkCQ1a9ZMw4cP15w5c3T69GklJiZq69atWrhwofr166cuXbqU+PrNmzeXzWbT008/rbS0NAUEBKhr166qWrVqkedMmDBBb775pm644QY98MADqly5shYuXKgDBw5o+fLl8vEp+Wd3ixYt0sGDB5WRkSFJ2rhxo6ZNmyZJuv3227mrD8vQRy8WPLNmzVK7du1UoUIFvfHGG/n233zzzXmFDPBnon9K06dP16ZNm9SzZ09FR0fr1KlTWr58ub755huNHTu2wDP/AHBZFs+CD3iMX375xRw1apQZGxtr+vv7m6GhoWb79u3NF1980bxw4ULecdnZ2eaUKVPM2rVrm35+fuZVV11lPvbYY/mOMc2Ly9fceOONBa6TmJhoJiYm5ts2d+5cs06dOqbNZsu3HE5RbZimaf7666/mgAEDzPDwcDMwMNBs06aN+eGHH+Y7piRL4CQmJuZb/uaPX4UtzwP82cpzHx0+fHiR/VOSeeDAgcueD5S18tw/P/nkE7N3795mjRo1TD8/v7z3Pn/+fNPhcFz2XAC4lGGaJZjpAwAAAAAAWIJn4AEAAAAA8AAU8AAAAAAAeAAKeAAAAAAAPAAFPAAAAAAAHoACHgAAAAAAD0ABDwAAAACAB6CABwAAAADAA1DAAwAAAADgASjgAQAAAADwABTwAAAAAAB4AAp4AAAAAAA8AAU8AAAAAAAegAIeAAAAAAAP8P/9n2MXFq9EQgAAAABJRU5ErkJggg==", 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", 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", 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" ] @@ -893,7 +902,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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", 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", 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" ] @@ -930,8 +939,7 @@ "id": "9103409b", "metadata": {}, "source": [ - "The tutorial up to this point has dealt with unpaired data. If your data is paired data, the process for loading, plotting and accessing the data is the same as for unpaired data, except the argument ``paired = \"sequential\" or \"baseline\"`` and an appropriate ``id_col`` are passed during the ``dabest.load()`` step, as follows:\n", - " " + "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:" ] }, { @@ -942,7 +950,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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", 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", 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" ] diff --git a/nbs/tutorials/05-delta_delta.ipynb b/nbs/tutorials/05-delta_delta.ipynb new file mode 100644 index 00000000..9dfe1c32 --- /dev/null +++ b/nbs/tutorials/05-delta_delta.ipynb @@ -0,0 +1,842 @@ +{ + "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 | XD, W | XD, M |\n", + "| Placebo | XP, W | XP, M |" + ] + }, + { + "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": [ + "
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IDRepGenotypeTreatmentY
00Rep1WPlacebo2.793984
11Rep2WPlacebo3.236759
22Rep1WPlacebo3.019149
33Rep2WPlacebo2.804638
44Rep1WPlacebo2.858019
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" + ], + "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": [ + "
" + ] + }, + "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": [ + "
" + ] + }, + "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": [ + "
" + ] + }, + "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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OsRdSYaWywKCuoegW6gtJkgwdGjURWVOOs2vfxdW4fZWJDIDkXT/BteMAhN7xAsoLcwEJ2qkz15MkBcqKcuDeaTAuR/9dPQmSJFi7+cHOpz3cOg5E5ul/qx8jBNr0mgDnkJ5QOXpAnZepe4ykgMLcAh5dhjfq+yYiIqLWxWgTnKFDh+LkyZM622bOnIn27dvjpZdeqpbcAIBKpYJKpdI+trWtqayuaUvNzMXsz35Ddn4RFJIESQI2HIjF4G7t8NK0kVAqTKZ6eKskZA00ZSVQqqwhSf+dy+TdK3D17P6qg7Tbs07vgbVrW1i7+wGxNS8WauPmD6fgCGSfO4jSvAydSm0KpRmCRz8FSZIQNPpJlBVmIy/p5LVESgAC8B0wBa4dB0CSJHS69y2cWvU6SrKSta9hbu2ADpPmwsLWqdF/J0RERNR6GG2CY2dnh/Bw3cnUNjY2cHFxqbaddC3+aQNyC4sBALIQ2jv2O46eRefgNri9L8ttG6MKdTGSdv6IK8c2Qy4vhbm1A7x73gGfvpMghEDa4XU1LAgqcDn6L3R/9Ask/7sCcrla9zhJAXNre7h27A+FmQW6PPgRLh/6E5mndkGuKINjQHf49JkIa1dfAICZyhrh0xahIDUOeUknoTBXwbVdX6gc3LRNWjp5ofujnyM/ORbFWSlQ2bvAMbA7FEqj/ZNEVCcVpUXIiN2B4owkWNg6wb3TEFg6eRo6LCIik8KriVYm6Uo24pKv6N0nAVi37yQTHCMka8oR+9NcFKaf1/aslBfnIWnnjyi5mgL/oTOhURfX+PyKkgJICiXCp7yB07++gYqSfO1cGgs7F4TdswAKMwsAlYUO/KKmwS9qWo3tSZIEe58OsPfpUOsxDn6d4ODXqZ7vmsi4FKSeReyKedCoiyEpFBBCIHn3CgTd9nijrlVFRNTamVSCs3PnTkOH0OJdza25cpwAkFXLfmq5rsbtQ2FavJ49Ahknt8OrxzhISnMITbne5yvMVVCqrGHv2xE9n/kB2fEHoc7PhKWTN5yDIyEpqg/5JKK6kzXlOP3r69CUlQAQOoU2zm/4HHZt2sPWM8hwARIRmRCTSnDo5rzdHGrcp5Ak+Hpw/kNLps7PQs75GEAIOAZ2g6WjBwAg+9yhGiugQVIg9+JReHQZjvSjG/UUCFDAo+sI7fAwhZk5XDv0b+q3QtSqZJ87dG0BXD0UCqQf2Yjg0bOaNSYiIlPFBKeV8XR2QK+OAYiOS4Qs687HkIXAxIHdDBQZ1UYIgcTty5B6YI3O/BjPiNEIGvkY9JY+020A/kPuR8HlcyhKT9BWUYOQYesVAr9B9zVd8ESE0rwrNd+EkGWU5qY3f1BERCaKCU4r9L+pIzBn6R84d+kKlAoJQlQmN/eN7I0BXUIMHR7pkRb9N1L3/15te3rMeqjsXOAUFInMU7v0P1nIcAqOhJmlDbrMfB9Xz+5HTnzl4rjOoT3hHNqbk/uJmpilo2eNi+RCUrDQABFRI+JVjQk7dfEyVm07jFMXL8Pa0gLDe3TAnYO6w97GCp89dw+OnruE2IuV6+AM6BIMT+eah6+R4QghkKInuamSevAPRD65DNbu/ijOTL5hbRkJLu37asf2K5RmcOs4AG4dBzR12ER0HeeQHjC3cUR5cb6eNaJkeHYbZZjAiIhMEBMcE/XviQS8sfwfSBIgywL5xaX4efMh/Hs8AR8/MxnWlhbo3q4turdra+hQ6SY0ZSUoK8iqcX9FSQE06iJ0mr4YF7d+h8zY7RCaCihV1vCKvB1tB05txmiJSB+F0hwdJy/AqRWvoqK0EJJCCSEEAIHg22axwAARUSNigmOCyis0+PjXbRBC6CxnIguBpCvZ+OPfY5g6vKfhAqRbojRX1VoBDZICSpU1zFTWCB37DIJGPYqKkkKY2zhAoTRv3mCJCHJFOa7G7UVBWjzMLG3hFhYFK2dv2HmHIPKpZciM3YXizESY21xbB8fR3dAhExGZFCY4Jij24mXkFZXo3SeEwI4jZ5ngGBFJoYR7pyG4cnyL3gporu37wUxlrd2kNLeE0tyymaMkIgAozUnDyZ/mQp2Xoe2lSd71E/wGz4Bvv7thprKGV8Rthg6TiMikKQwdADU+dVkNd/qvKVGXNVMk1Fj8h8yAlbP3DVslqOzdEDDiYYPERES6hBA4s/otqPMrh5QKWaO9KZG043vkXDhqyPCIiFoN9uCYoHZtPaFUSNDI1UsHKxUSuob4GiAqaghzawd0ffAjXDmxDdln90MIAeeQHvDoMgJmljaGDo+IABRePoeiKxf175QUSDu8Dk6BLMVPRNTUmOCYICc7a4zp0xl/7z2uszqKJAEKhQKTBkUYLDaqP6WFFbwjb4d35O2GDoWI9CjNSat5p5BRcjWl+YIhImrFmOCYqMcnDIRSKeHvvSdQoakcIuHp7IDn7xkGfy8XA0dHLUleUizSYv5BaU4arFzawLP7aDi0DTN0WERGR+VQS7EASVG5Fg4RETU5JjgmykypxBMTBmH6yN64cDkT1ioVgn3cIEmSoUOjFuTS3l+RtON7QKEEZA0Kr1xAZuxOBAx7CG16TzB0eERGxc6nA6xcfVFyNVXvWjdekWMMExgRUSvDIgMmzs7aEl2CfRHi687khnQUZ12qTG4AQNbo/Htx6zcoya5luA0RVSNJEjpMmgtza/vKDQolJIUSAODT9244BfcwYHRERK0He3CIWqnMkzsAhQKQ5eo7JQUyY3dwkVCiW2Tt2haRs75BZuxOFFyOh5mVLdzCB8HWI9DQoRERtRpMcIhaqfKSfEiQUL3WXuWd6PKSgmaPicgUKC2s4Nn9Nnh253o3RESGwASHyIQJISPjxDakH92EsvwsWLv7o03PO+AY2A02HoGV63Toe56s4R1nIiIiMkpMcIgMzNPTU+ffWyGEQOHlcyhMT4CZpS2cQ3pCaWGl3Xfuz/eRGbuzska4EFAXXEVOQjQCRzwK9y7DkLTrR1SUFOpOiJYUMLe2h2vYgMZ4e0RERETNqskSnD59+uDrr79GeHh4U70EkUk4fPhwvZ5XXpSH07+9gYKUM9ptCgtLhN7+LFw7DkDuhSOVyQ0AiGsD0a4lMhe2fg3Xjv3R6d63cfrXN6DOu6Jtw9LRAx3vfg1Kc8t6xUVERERkSE2W4CQmJiIiIgLPP/88XnvtNVha8mKJqDGd+X0RClLP6myTy0oRt/ZddHX2RmbsrpqLCMgCWXH74B15OyKf/AZ5F4+jNPcKLJ284ODfCZLEAotEt0IIgawze3D50J8ozb4MSydPeEXcDrdOg1nBkoiomTXZVczZs2fx0EMP4d1330WnTp2wdevWpnopolan8MoF5CefrL7WBgBIEi5H/42KsmL9yc21YzTq4mv/qYBjYDd4dh8Fx4AuTG6I6iFx+zKcXbMYBalnUV6ch4LL53Dur/dxfsMSQ4dGRNTqNNmVjL29PZYsWYL9+/fD3t4eI0eOxPTp05GZmdlUL0nUahRfSax5p6xBYVoC7Nu0q5x7o4+QYdemXZPERtTaFGcmI3X/75UPqm46XBsWmn5kAwpS4wwUGRFR69TkRQZ69OiB6OhofPrpp5g3bx7WrVsHX1/fasdJkoTjx483dThEJsHcxr7mnZICFjaO8Og6Ain7VqNCXVytiICtZxAc/Do3faBErUDm6d2ApNDfo6pQIvP0v7Br0775AyMiaqWaZSxKRUUFMjMzoVarYWVlBRcXl2o/zs7OzREKkUlw8O8KcxtH/T00QoZH1+Ewt3ZA+PRFsHL21tnt6N8FYfcs5LwAokYil5fW+nnSlJU0YzRERNTkPThbt27FE088gQsXLuCJJ57AW2+9BTs7u6Z+WSKTplCaod34/+HUyvmVa9kIWXsH2S0sCq4dK0s823oEovtjX6Lw8jmUFWbDytUX1i4+Bo6eyLTYtekAIa/Vv1PWwN6nQ/MGRETUyjVZgpOZmYnnnnsOv/zyCzp16oR9+/ahZ8+eTfVyRK2OY0AXRDz2BdJi/kFB6lmYWdnDvfNQuLTrrVMoQJIkzrchakLOob1g5eKDkuzL1YaDquxd4dpxoOGCIyJqhZoswWnXrh3KysqwePFizJ49G0qlsqleiqjVsnTyQsCwhwwdBtXTxbQsbI+JQ0GxGqG+7hjcvR2sVBaGDotukUJphvBpb+Pcn/+HvMQT2u12bdqh3fgXoTRXGTA6IqLWp8kSnN69e+Pzzz+Hv79/U70EEZHR+mHjfvy46SCUCgmAhH/2n8TyDfvx3hN3ws/TxdDh0S1S2bmg07RFKMlORWlOOlQObrB2bWvosIiIWqUmS3DWr1/fVE1TI8kvKsWqbdHYHH0GJeoydPDzxJRhPdG9Hb+UW6ry4jzkXjwOCBkO/l1gYetk6JCoHqLPJOLHTQcBABpZAKgsKZxXVIIFy9bhu5fvYxEII2Xl3AZWzm0MHQYRUavW5EUGqGUqKlHjmY9X4nJWHuRr6zWcOJ+KYwlr8PK0URgawZKmLYkQApf+/QWX9qyCkCsqN0oKtOk9Af5D7ufinEbm770noFBIkGWhs12WBVIycnDq4mWEB/IimYiIqD54VWSEIiMj4ePjg8jIyHq38ce/x5B6XXIDQPvfn/2+A2UVFQ2OkxrPlaMbkbz75/+SGwAQMlL3/47UAzVUb6IWK+1qXrXk5nrp2fnNGA0REZFpYYJjhNLT05Gamor09PRaj8svKsVvO2Lw+vJ/8P7KLThyNhniWhKz4+hZ7X/fqLBEjZPnUxs9bqofIQQu7f21xv2p+3+vLBVNRqONmyMUipqHoHm5ODRjNHQzjXFTiYiImg+HqJmoi2lZeGHJahQUlwIAFJKEjQdPYVhkB7w4ZQTKymvvoSkr5wVzS6EpK4E6L6PG/eXFeSgrzIHK3rUZo6KGGNe/C/aePF9tu0IhwdfdGR39vQwQFdWk6qYSEREZB/bgmCAhBN76YQMKi9UQAhCiaiIzsPXwGWyLOYPuoX7XqjdVp1Qo0MHfszlDploozCwgKWu5FyFJUKqsmy8garDuoW3xwJi+ACqTGqWy8k+xs50NFjxwOwsMEBERNQB7cEzQuUsZSEq/qnefJElYt+8kXpwyAlsPn4EQGp15OBKAOwZ0gaMtL5hbCoXSDG5hUcg4uUN3EUEAkBRwDu0FMyY4RmfKsJ4Y0CUEO2LOoqCkFCE+HojqGgILc/5ZJiIiagh+k5qgq/mFNe4TQiAztxA+7k54b9ad+HDVNlxMywIAqMzNMCGqG+6/rU9zhUp15D/kfuQlxUKdn1HZJQcAkgLmNo4IHP6IYYOjevNxc8L0Ub0NHQYREZFJYYJjgnzda14bRaGQ4O/pDADo4OeFpS/ei5TMHBSVlMHP05mrqLdQFrbO6PbQx0g7sgFXz+4HhIBzSE94RYyBuQ0npBMRERFVYYJjgnzdndEtxBcnzqdo595UkWWBCVHdtI8lqXJSM7V8ZlZ28O13N3z73W3oUIiIiIhaLBYZMFFzpt+GAK/KqlpKhQRJqqyk9si4AejR3t+wwRERERERNRH24JgoJztrLJk9FUfjk3EmKR3WKgsM7BICV0dbQ4dGRERERNRkmOCYMIVCQkQ7P0S08zN0KFoXLmdi5dbDOHIuGeZmSkR1C8XkIRFwsrMxdGhEpEdWXiG2Rp9BRm4B2rg6YlhkBzjYWhk6LCIiohoxwaF6yysswdbDZ5CenQ8vF3sMjaj9wufk+VS89OUaaGQZ8rW5QWt3H8WuY+fw2bNT4OLAJIeoJdl59CwW/7QJQghIkgRZCCxbvw+vPzgO3du1NXR4REREenEODtWqvEKjTUaud/DURUxd+A2W/vUv1u07gS//3I2pr3+LQ2cu6m1HCIGPV29HhUbWaU+WBbLzi/DzloNN9h6IWishBC5n5SIp/SoqNBq9x6Rl5WFL9GnsOnYORaVq7fYr2flY/NPGyhsSQkAjyxBCoKyiAvO/+xtFJWq97RERERkae3BIr32x5/HjxgNISM2EUqFAVNcQzBzTF57ODsgpKMbry9ehvEIDAaBCU5mwlJdXYOGyf7DitQer9eSkZubWuPioLAvsOHIWT08a0tRvi6jFEdeSBzOlslHbPRZ/CZ+t2an93DnaWuG+UX1we99OkCQJZRUV+GDlVmyLidM+R2VuhsfHR2FM307YePAURPV7GxACKC0rx/YjZzG2X+dGjZkahzo/C1ln/kVFSSFsvUPhHBwJSdG4/38REbVkRpvgfPHFF/jiiy+QmJgIAAgLC8Nrr72G2267zbCBmYAt0Wfw7opNkCQJAKCRZew8dg4xZ5PxxQtTsfPoOZRrZNx47SNQ2eOz5fAZTBrUXWdfaVl5ra+pLq9oxHdA1PLlFBTjh437sSX6DNTlFfDzcMY9w3pgWGSHBrcdl5SOl79cC1nI2m25hSX4ZPV2CCEwrn8XfLl2N7YfOavzPHV5BT76bRvcnexwJTsfkgRU+6ADMFMqkJ6d1+A4qfFdjv4LFzZ/BQCQJAWErIGVSxuET30LKgc3A0dHRNQ8jHaImo+PDxYvXoyYmBgcPnwYQ4YMwR133IFTp04ZOjSjVqHRYOmfuwFU3lmuIssCBSWl+HV7DNKu5kFxLfm5kUIhIe1q9Qufth7OsLHUv4ioQiEhPMC7EaInMg6FJaV49uNVWH8gVpvcJ1/Jxjs/b8Kv2w83uP2fNx+EgNDbA/P9xv3Izi/ChoOxOp/xKgpJwqrth+HpYq/3+QBQoZHh6cwFZg2lODMZmad2IzfxOIT839DDvKSTuLBpaWU3mxDafSXZaTiz+i1DhUtE1OyMNsEZO3YsRo8ejZCQEISGhuKtt96Cra0tDhw4YOjQjEKJugxbok9jxZZD+PdEgnZ8/tnkK8grKtH7HFkW+Pd4PNyd7CDXcOUjywKezvbVtluYm+GeYT30PkcIgXtH9KznOyEyPuv2nkRadr7OfLSq/1q+YT8KS0ob1P6Rc8l6584BQH5RKQ6duYgKjax3vywE4lMyMLJnGPTdx5AkwNLCHEO6t2tQjHTryovzcPLnV3Bk6eM4u/YdxP40F9GfPoC85Mobe5ej/wIUer7WhYzCtHgUpiU0c8RERIZhtEPUrqfRaPDbb7+hqKgIffr0qfE4tVoNtfq/ibGFhYXNEV6LEx2XiDeW/4MSdTmUCgkaWcDVwRZvPzIeGln/RU8VjSxjeGRHfL9hv3buzfWUCqnGITaTh0RCo5Gxctth7ZA1F3sbPHnnYHQJ9m34GyMyEntOJujtPQEqh3keOXsJA7uG1NpGQmoGVm07jKPnLsHCXInB3dvhrsERcLS1hplSibIK/UUFAMDBxrrWtu2sVPBwtsec6bdh0Y8bK6uoKSTIsgwLMzMsfHAsbKxUN3+jpKMk+zIyY3eiQl0EW89guHboD4WZuXa/kDXITjiMnIRoAIBTcA/t/BkhBE7/9iYKUuJ02iwrvIpTv8xD98e+RHHmJaCWv+ElV1Ng6xXcNG+OiKgFMeoE5+TJk+jTpw9KS0tha2uLtWvXomPHjjUev2jRIixcuLAZI2x5MnMLMP/bv1Fx7eJHc+0ub3ZBEV5euhZfvzQN1ioLFKvLqj1XoZDQq2MAXBxs8OqM0Xjz+/WQ5f/KxyoVEubdPwZOdvovniRJwr0jemFiVDfEp2TA3EyJUF8PKPXdcSQyYpGRkUhPT4enpycOH64+5Kym3pMqN7vRcDzhEl7+cu21AgWVn+HVO45g19Fz+PS5KRjQJRhbD5/R7qsiAfBycUCvjv4I9HZFYtrVar2xkiRhZK8wAEBU11CEB7TBlsOnkZFTAB+3ynVw7G24Ds6turT3VyTt+B6QFJAkCULWIGnH9wiftghWzl7QlJXi1C/zkH/ptLYgQPqRDbD37YiwKW+gODMRBZdOV29YCMgV5UiPWQ+VgxtKslMBof//Hws716Z8i0RELYZRX1m2a9cOx44dw8GDB/H4449jxowZOH1azxfANXPmzEFeXp72Z9euXc0Ybcuw4UAsNHoKBFSVaz5yNhn3jepd7XkKSYKFmRkmD40EAPTrFIyf5j2ImaP7YlSvjnhgTF/8/NqD6BsedNMYrFQW6Bzkgw5+XkxuyCSlp6cjNTUV6enpevf37OBf8zw2SUKXYJ8a2xZC4KNft0EjyzoJjCwEMvMKsXJrNKaN7AVrSxUUiv9eQyFJgCThiYmDoFAo8OKUEVBZmGmPqToy1NcdkwZFaJ/n4mCDe4b2wNOThmBiVHcmN/WQe+FoZXIDAELWzo1RF1zFmdVvQgiBpJ0/IP9a74yQNdpj8lPikLTzBxRcjsd/Z+kGQkZB6ll4dr9Nf3IjKWDp5AX7tjXfACQiMiVG3YNjYWGB4ODK7vaIiAhER0fj448/xtKlS/Uer1KpoFL9N6zC1ta2WeJsSZKvZOsrigQAUCoUSErPxn2jesPCXImfNh9Cdn4RAKCjvxdmTRwEX3dn7fEuDjY1zqshoprd0b8r1u+PRUFJabW5MhMGdoWzfc2L3ialZyMlM1fvPlkW2H4kDo9PiMKS2VPw06YD2HUsHhUaDToH+WD6yN7oFNQGABDs445vXpqOP/89jqPxl2BpYY7B3UMxokcYVBZG/dXQ4lw+vA6QFNWTDyGjOCMR+ZdOIf3oJv3JiZCRfnQTgkY9Br0l7QBAUsDMyhYu7frAq8dYpEX/rR3WBgiYqazR/s45kCTeUCKi1sGkvsVkWdaZY0PVOdvbQCEBeqbPQJZlONvbQJIkjO3XBaP7dEJGTgEsLczgZFfzBVdjEELgxPlUJF/JhquDLSLb+8Hc7L91G8oqKvDXnhPYeDAWeYUlCPFxx6TBEegeytXUyfi4ONjgo2fuxpLfd+Lw2SQAgK2VCpMGR2DK0NpvGtys5HppWWVVNi8XB7w4dSRenDqyxmPdnezx8LgBtxh96+Pp6anz762qbdgYABRlJEIur7mwhFxeCjvfMCjMLCBXVB8+DCHDLXwwJElC0MjH4NZxIDJid6CipBB23qFw7zIM5lZ2etvWlJUi68weqPMzYeXsDZd2faAw01/xkojIWBhtgjNnzhzcdtttaNu2LQoKCrBixQrs3LkTmzZtMnRoTa4hX7Yje4Zh7e5jeveZmykxqFuo9rFSoYCXS8NLwZZVVGDP8QQcS0iBhZkSA7oEo3OQj3adnbSsPMz75k8kXcnWPsfR1gqv3X87OgW1QYVGg1e++hPH4y9p71/GnEtGdFwSnps8DKN7hzc4RqLm5uPmhEWPTUBeYQkKS9Rwd7LTSepr4ufpAisLc5ToSXQUCgmdglhyvbHpm0d1KyydvFByteYkx8bND0qVNTTqYr37lRbWsHRwR/CYp3Duzw8gKSrXt4EkAULAtcMAuLT7b2ixvW9H2PvefDha7sVjOLP6LWjUxZU9PrIG5tYO6HjPQth5117kgoioJTPaBCcjIwP33Xcf0tLS4ODggM6dO2PTpk0YPny4oUNrcg35sg1q44ZHxg3AV3/9q62gplBIkAC8PP022FlbNl6gALLzi/DCktW4lJFzbb6NwJ97jmNgl2DMnT4aAPDyl2uQnpOv87y8ohLM+Wotvp87EzFnk3As/pLO/qphPUvW7EBU1xDYWLKiExknB1srONhWn9dS2auZgh1HzqG0rBwd/b0wLLIDrC0tcNeQCPywsXpJfCGAqcNYct1QNOWlyDq9B6U5aVA5uMO14wCYqazhFTEaOfGHqj9BUsDK2Rv2bcPhFTEGKftXo9riQ5IEr8jRUCjN4N5pCKycvZF66C8UpSXA3NYJnl1Hwi086paHn5UV5uD0qtchayp7hKrm/JSXFODUL/PQ46nlUFo07vcBEVFzMdoE59tvvzV0CEbrrsER6Brsiw0HY5GVVwhfdyeM6dMJ3q6O9WrvclYudhw9h6ISNdr5eqBvpyDtnegPf92K1KxcALqVof49noC1/sfg7eqAy3oWBhUCKC/XYMPBWJy6eLmy6pCesrpl5RocPH0RQ7q3r1fsRC2RLAu898tmbD18BkqFBCGAbTFx+HnLIXzw5F24d3gvyLLAbztitAuFujrY4Kk7hyA8sI2Bo2+d8i+dxulVC1FRWqjtDbm45Wt0uOtVOAf3gG+/ybi0d9W1dWokQNbA3NoeHSa9AkmS0HbgvSjKSKwsEX2tihpkDZyCItF24DTt69i1aY/2Exr+9+7K8S2Vyc2Nf1eFjIqSAmSe3g3PriMa/DpERIZgtAkO3dyhMxexYks04pLSYaUyx7DIDrh3RE842lojxNcdIb5DGvwaK7dG49t/9kIhSZAkCRpZhqezPd6bdSfMlWY4cOqi3ucJAH/tOY4RPTtqe5KqHSMELqRmori0rMY1QwCgVF37nAQiY7Pp0ClsPXwGAHQ+G7mFxVj00wZ89twUzLitDyYN7o6ElExYmLPkelO6adlvdTFOrZwPTVnlIslVvSGaslKcXvU6Ip/6Dn6D74NrxwHXzY0JgVv4ICgtKnvvFGbm6Dh5PgpSziD7Wm+Pc0hP2Pl00A7nrS8hBCBkbflpoHJNnKohbjeSFMrK/URERooJjhG62ZctAGw+dBrv/bIZimtr1BSWqPHX3uM4cOoClsyeCnubhg89OByXhG//2QugskRt1RdlRm4BXl/2D56eNLjW52flFcLBxrLGFdcVCgn2NlZo4+aIuKT0aut1VAkL4JwDMi1/7z0BCdVrZsmywNnkK0hMuwp/LxfYWKpqLSlNjaOq7HdNMmN31TB/RkDWlCHjxDb49LkTNh4BCPAIqLEdSZLqPH+mLsoKs5G862dknNwBuUINa3d/+PabDLewgbCwda6xKJuQ5cr9RERGirf7jNDN1tgoK6/A539UrvFzfVIgywIZOQX449+jjRLHn3uO6ayzcf3rxKdkoKi0rNY7j57O9hjYJRRKpf7/DTWywPAeHTC2X5fK9TpuaEshSegTFgg/T5eGvRGiFiYzt6DGcu4AkJlX0Gyx0M2VZKdAUui/XyhJCoP0hpSXFOD4sheQfmwz5IrK6qLFGUk4u/YdXI7+Cx5dhtdY9EBSKOEWPqgZoyUialxMcEzQyQupKCrRXy5bFgI7j56rc1vxlzLw+rJ1mDD3C0x+7St8+ccu5BRUro1zKSOnxt4XACgoLsXALiF6kyCgcr0PB1srzJ48HJIEKK8dV3X83UMiEBbgDXcnO7z3xCS0cXPUPleSJAzqFoo500fV+b0QtUQFxaW4kp2PCo1Gu83X3bnGhUABwMfVqTlCozqysHOBEBq9+4QQsLBr/pswadF/Q52feUMSU/n3OnH7cljYOSPoticqNyuUAKTKtXokBULHvwALG8fmDpmIqNFwiJoJqqioeb0FACiv+O+LWAiBy1l5KFGXwcfdCZYW5tp9R+MvYc6XayEgtInM2n+PYdfxeCx5bgq8XByQlpVX49Axdyc7PHPXEKRdzcO5S1e0VdQ0ssDoPuEY06czAGB4jw4I8HbBX3tOIDEtC+5Odritdzgi2vlp22rX1gPfvnwfzqdmIq+oBH6eLnB1aH0LtZLpSMvKw5K1O3HozEUIAdjbWOLuwZG4a3AEJkR1w8kL1YdEKRQSuoe2hZdrw8u3U+NxDx+MpO3fQ4gKPXsFPDoPa/aYsuL21thDI5erkZt4Al4RY2DvG4YrxzZDnZcBS2dveHYbBStnDvslIuPGBMcEdfD3gplSgQpN9S83pUJCZPvKxCEuKR0f/boV5y9nAQCsVOa4a3AE7h3eC5IEfPzrNshC1pmDKssC2flF+GVrNMb27YzDcUnVXkOhkODj5oQOfl6QJAmfPnsPDp9NxPH4FJibm2FA52AEtXHTeU5wG3fMnlz7RYAkSQj2cb/VXwdRi5NTUISnP16J/OJS7ecrv6gU36zbg/yiEjw8bgDuG9kbP2w6oFPAI8DTBf+rZeFOMgwLWyeEjn8BZ/9491oniQBQOYE/eMzTsHSq3wKhDVFV6OBm+23c/RE44pHmCImIqNkwwTFB9jaWmDSoO1Zu0y1AoJAkmJuZYdKgCFzKyMYLS1br9OaUqMvxw8YDKKvQYGj39tryzjeSZYHtR+LwxIQo3D04Ar/uiNHpnbG3tsRrM2/Xzr9RKCT07BCAnh1qnlxL1Jr8uecE8otK9fZ+/r7rCCYNjsD0Ub0xrEcH7D4WjxJ1GcICvBHRzq/GIZ9kWG4dB8DOKxjpxzZr18Hx7DoSVi4NK9staypQUZwPpaUNlObV1/tS52Ui89ROlBfnwcYjEK4d+kNhZgHn4Eik1rC4qKRQwqFtWIPiIiJqyZjgmKiZo/tBZWGO37bHoFhduZBbsI87nrlrCNq4OeKDVVtRodHov8DaeQTdQnxrbb+svAKSJOHhcQMQ1S0U24+cRVGJGqG+Hhga0R7WlhZN8r6ITMHB0xdrHNqpkQWOJ1zCoG7t4OXigMlDI5s5OqovSycv+A+eUefjS3PSkZd8EgozCzgFRcDM8r9ht7KmApf+/QWXo/+GRl0ESWkO906DETD0AZhZ2QEA0g6vw/lNXwKo7OUTsgaJ25YhfNrb8O45HleObUGFurhaktOm90SYW3OYIxGZLiY4JkqhkDBtRC/cNSgCKZk5sLa0gJfLf19oMXFJeteeASrn6BSr1bBSmaNEzxozCoWEzkH/laYN9fVAqK9H478JIhNVWwEBAA1e94RaNllTjoR/PkXGiW3abZLSHAHDHoB3j3EAgHN/fYCsU7tRVRhAaMpx5fhWFFyOR9cHPkTRlfM4v/GLa88W2qGOZUW5OL1qISKe+Aqd738P5zd+ibzE4wAAMys7+PS5E2363Nlcb5WIyCCY4Jg4lYVZtfkuAGBurtRz9H9sLS1xz9AeWLZ+n852CZXL3UwZ1rMxwyRqVfp3DkJ8SobeBWzNzZToHtrWAFFRc7m4bRkyTm7X2SY05biwaSlU9m5QOXog69Su6k8UMoozLiLrzL/IvXgUkkJZfa6NkFGak4a8xJNwDOiCTtPeRllRLjTqIqgc3KFQmldvl4jIxLBMdCs1qFu7Gu8i21lbIizAG1OG9cD9o/vqVFZzc7LD6w+ORXggq+wQ1dftfTvDw8lO73ya6SN7wc664Qvx3qqE1Ays3X0U6/ef1JaCp8ZXUVqE9Jj1gL4hipKElH2rkZNwuLJksz6SAtnx0SjJSq21kEBJ9mXtf1vYOMLKuQ2TGyJqNdiD00pNHNgV2w6fQUZugbYEtEKSIAuBWRMHwdyssofn3uE9MXFgN5xPzYSFuRLBbdw5yZmogeysLfHJs5Px3T/7sD0mDmUVGrRxdcSU4T0wokfjrGJfVyXqcrz5/T84dCYRUmXhL3yyejseGNMPdw/h/J9bVVaQjdK8K5U9Mfau1faXZKdCaKoP/QUACIGiKxfg0q53ra8hSRJUTp4oSIuvsRS0pWP1nnsiotaCCU4rZW9jhU+fuwc/bz6ErYfPoLSsHB38vDB1eE9tGekqVipz9tgQNTInOxs8f89wPHv3UJRXaKAyNzPI3JtPf9+uLfde1amgkQW+/nsPvF0d0b9zcLPHZIzKi/MR/88nyD57AFXzZpyCIhFy+zOwsHPWHmd+rUBATZSWNnAK7oHE7cv1HyBkOIX0hMrORf8wNkkBC1tnOAZ0q+c7ISIyfkxwWoESdRl2HD2Hi5ez4GRnjWGR7eHuZA9HW2vMmjgIsyYOMnSIRK2WUqGA0qJpRwsXlaqx98R55BQUI9DbVVtuOqegGNsOx+mt6KaQJPy2I4YJTh0IWYPYFa+i6MpFVCU3AJBz4QhO/vgyuj2yBAqzyuFhlk5esPVuh0J9vS+SAp5dRsDG3R/unYYg4+QOnfYgKa6Vgu4HhdIcfoOmI2nnj4BCWXmcLGCmskbHu1+FpKh9niURkSljgmPiElIy8NKXa5BfVAqlQgEhBL7fsB9PTRqM2/t2NnR4RCbJ09NT59+mJoRAaVk5LMzMoFTqJkv/nkjAOz9vhLqsQjsM1cfdCYseHY+s3MIay1XLQiAx7WpzhG/0cs7HoCj9fPUdQkZJdiqy4vbCPXyQdnPI7U/j5A8vXVfCWQIgYOMRgDZ9J1UeM/ZZWLm0QeqhP1FRnA+FmQoeXYfDb9B92rk0vv3vgXNob2Sc3I7y4nzYegTCvfMQnXLTREStERMcE6bRyJj3zV8oLFZXPpb/u1v48W/b0b6tJ4J93A0VHpHJOnz48M0PagRCCPy55zh+2xGDjJwCWJibYXhkBzwwpi/sbayQfCUbb33/j7YkfFUyczkrF6989Sfmz7y91vYd7aya/D2YgrykE/ormqFyUc38pJM6CY6Nuz+6Pfo50g//g5yLR6EwU8EtbADcOw/TLuYpKZTw7X8PfPrehQp1MZQWVlAoq39l27j7I2DoA0323oiIjBETHBN26EwisvIK9e5TKiSs23cSz949tJmjIqK6yi8qxfr9J3Hg9EVIAPp2CsLo3uGwsaq8CP5m3R78uj1Ge3xZeQU2HIzFifMpWDJ7Cv7ee0Jvu7IskHwlG1fzi9C+rQfOpWRoi41UkSTgtt7hTfbeTInCTAX9/WCVJLPqCx+r7FzgN/g++A2+r9a2JYXypvN2iIhIFxMcE5Z2NbdydWs9Q1A0skBqVm7zB0VEWnFJ6dgWE4eC4lKE+rpjeI+O2hLRGTkFeOaTVbiaV6T9DJ9KvIx1e0/go2fuhkYj8NuOI9XalGWBSxk52BJ9BhcuZ9W4oK8kAUlpV/Hi1JGY/elvyC8uhRBCO4ytS7AvJkZxonpduLTvi0t7VurdJ2QNXDv0b+aIiIhaNyY4JszD2V5vcgNU9uB4uTg0yuucTb6CrYfPIL+oBME+7hjZsyPsbTi0hVqvyMhIpKenw9PTU+9wNSEqq5T9tiMGSoUEIYDtR+Lw0+aDePeJOxHcxh1f/rEL2flFOp9hIYD0nHx8u24vwgK8a/x8SwD2njwPFwcbKBRStd6Zqrac7K3R1sMZ3758H9YfOIkTCSlQWZgjqmsoBnQJhpmSE9XrwtYzCJ7dRyP9yHpUzaep4hYWBXvf5i39TUTU2jHBMWE9OwTAyc4aeYUl1SYSa2SBMX3qNvyksKQUl7Py4GRnDTdH3aES3/6zFyu3Rmsv0nYcPYuftxzCO49NRLu2Ho32XoiMSXp6OlJTU2vcf+DURfy2o3Jo2fU9LEUlZXh92T/4fPZU7D15Xm8BAFkW2BYTh9BaPl8ClfNtRvUKw44jZ6vtlyTA2lKFPmGBAAAHWytMGdYTU4b1rOtbpBsE3fY4bD2DcDn6L5TmpkNl7wavyDHwihijLf9doS7G1bP7UVFSAFvPYNi3DdMpDV6clYzk3b8gO/4gIAScgnug7cCpsHH3N9C7IiIyTkxwTJi5mRKvPzgOLy9di+ISNRQKBQQEZFngiQlRaNe29gpPpWXl+GLtLmyOPo0KTWWBgm4hvnhu8jB4uTgg+kwiVm6NBqB7kVZSWoaFy/7Gj/MegFLRtOVviYzRuv0n9PasyEIg7WoejpxLrrG6GQBUaGSEB9S8NpUkAb06BqBbiC/uGhyh7SnSyAIKhQSlQoFX7xsNC3N+BTQWSVLAs/soeHYfpXd/5qndiP/7I8gValStqGrjGYSwexbAwtYZRRmJOL7secgVZdry0VfP7kdOQjQ63fcu7LxDmvPtEBEZNX67mbj2fp74ad5MbD0ch8S0LDja2WB4ZAe0cXO86XPf+mE9Dp1O1LnQOn4+Bc998iu+ffk+/LP/ZI0XaZm5hTgWfwkR7fxubJao1cvILtA7bKxKaVkZHGyskFdUone/u5MdArxcMaZPJ6zff1JngrtCIcHNwRajenWEJEl4eGx/9A4LwOZDp7Xr4Izp2wmezo0zRLU1aGjZ78L08zj7x7v/raR67d+iKxdx5re30GXm+0jctkwnuak8ToasqUDitm/RafriBr0HIqLWhAmOEbrVL1tbK0uMH9D1ll4jISUDB05drLZdlgWyC4qw6dApXMnJr/UiLTNXfwU3otaurYczLmVk11gAwNfDGZMGd8e36/bq3T95SCQkScJTkwbDxcEGa3YdRWGJGgpJQv9OwXh8fBRsrSqLFUiShM5BPugc5NNk78fUNbTs9+XovwFJAYgbykgLGQWpcci/dAo552t4DSEjL+kkKkoKYMZqakREdcIExwg1xxobR+Mvaasp3UiIyv1+Hi64WEuVJh83p6YOk8go3TGgC3Yfj6+2XaGQEODlivZtPdHO1xM5BcVYu/uYtpiAUiFh8tAeGNuv87XHCkwf2RtThvVAdn4xbKwsYGOpatb3QjdXlH4e0LNGTpXCK9VvJt1I1lQ0ZkhERCaNCU4rJoRAzNlkbDgQi4zcAgR4umBc/y4I9nGHhblZjRWaFJIECzMzjOvfBdti4qrtVyok+Lo7IyzAq6nfApFR6hzkgycmROHLP3ZDoPIzpZFleDrZY8EDt0OSJEgS8Pj4KNw1KAJHziVDkiREtm8LJzubau2ZKZVwd2qau/v5RaXIKSiCq4Otdv0dujVVc2x0hp9dx9LREzYeAdeOufHvrgQrF2+Y2zg2cZRERKaDCU4rVrVIYNU8mvhLV7Dx0Cm8OGUk+oQFYsmaHdC3ep0sBAZ0CUZHfy88PWkIPvt9h85FmpujHV5/cJxOdSAi0jVhYDf07xyMnUfPIb+4FKG+HugTFlCtNLOroy1G9Gz+MsO5hcX47Ped+Pd4PGQhYKZUYHhkBzw2PgrWltUXriRA1pQj+9whlOakQeXgDpd2vaEws4BHtxH6h6BJEsytHeAY0BVCno4zv76up1UBv6jp/HtKRHQLmOCYsLSrefhrz3GcvJAKWysVhkZ0wODuoTBTKnHq4mXtCuhV82iqhpp9sGoLVi18GFOH9cTPWw5VFfwBUDmev1OgN/p3DgYAjO3XGX3CA7Hz6DkUFJciuI0b+oQHcv0Mojpwc7TDXYMjDB1GNeUVGryw5HdcysjWDlOt0MjYdOg0UjJz8f6Tk3jBfYPCtAScWrkA5UU51+bbyDCzskfHu+fBpV1fuHcZjozjW7T7ICmgUJqh3YSXoFCawSW0F9pPfBkXtn6DsvwsAJU9P/5DZ8K14wADvzsiIuPCBMdExV64jJe/XINyjQayXLk6eczZZGyLicMbD43Dlugz2rKxN9JoZOw6dg4zbuuDNm5OWL0zBslXsuFoa43RfcJx9+BInQTG1cEWkwZ1b863R0RN6N8T8UhKv1ptuywETl5IxbGEFHQL8TVAZC2TpqwEsSteRUVpUeWGa0PRKkoLcOqX1xD55HcIuf0ZuHboj4yT21FRnAdbrxB4dh8NS0d3bTuuHQfApUM/FGcmAwCsXX0hKXiziIjoVjHBMUGyLLD4540oq9Bo59FU3YWNOZuEDQdikVdUUmMFNIVCQl5RKSRJwvAeHTC8R4dmi52IDO/ouUs13gBRKhQ4ci6ZCc51Mk/tRkVJQfUdQkBTVoqMk9vQptcEOAdHwjk4sta2JEnBhT2JiBqICY4JOpOUhivZ+Xr3SQA2HjyFvp2CsO/keb2FBDSyQJC3WxNHSUSGVKIuwx//HsPWw3EoLi1Dp8A2uHtIBIJ93GtdoFdAwIwL+OoozkqGpDCDkKtXOpMUCm2PDBERNQ9+S5mgmhYHBCprBuQVleC2XuEwN1fixmH0CoUEb1cH9Ozo36QxElHTkmWBY/GXsH7/SRyOS4JG/q+CV4m6DM998huWrd+P5CvZyMorxK7j5/DkhysRfSYR/ToH1Vj+XZYF+nUOaq63YRQsbJwgaqiQJoSAhQ1L5hMRNScmOCYo0Nu1xn0KhYRQXw+4ONhg8aMT4GBjpbPf190Zix+bWOsdXCJq2ZKvZOOBRd/jxc9/x4e/bsOcpWtx35vLkJCSAQBYu/sYLqRl6fTgyrKALMv4YNVWdA32Rc8O/noLCYzs2RHBbdyrbW/N3MIH1bxTyHDvMrTZYiEiIg5RM0mezg7o3zkY+06er7ZQpxBCWxAgPLANflnwEA7HJSErrxBtPZzRKbANqyMRGVBOQTF+2xGDHUfPorxcg+6hbXHPsEgEXjds9HRiGn7ZcgjHz6fAwswMg7u3w5RhPeBsb4Oy8gr87/PfkVNYrNNuVl4h/vfF7/jx1Qew9fAZvcNTxbXjziZfwfwHbsdv22Pw194TyM4vgoezPSYO7Io7BnRt4t+A8VHZuyLk9qcR//fHgEIByBpICiWErEHgyMdg5dzG0CESEbUqTHBM1ItTRuCt8vU4dCZRu83SwhzP3j0UYQHe2m1mSiV6hwUaIEIiulF2fhGe/PAXXM0v0hYB2XX8HP49EY93Hp+IzkE+iD6TiFe//hOQKntdStTl+Gvvcew5kYAls6cg5mwyruYXVWtblgUKitXYcvgMitVltcZRrFbDwswM947ohXtH9IIQgjc+bsKjy3DYeoUg/ehGlGanQeXoDs9uo2DryeF8RETNjQmOibK2tMBbj4xHYtpVnElKg7WlBXp2CICVytzQoRFRDX7ZGq2T3ACViYmQBD75bTuWvjgNn6zeDiGEzoL3siyQXVCEVdsOQ1JIMFMqUKGpPidEqVDgfGomOgW2wb/H42uskhbq66GzjclN3di4+yNo5GOGDoOIqNXjRAsT5+/lgtt6hyOqayiTG6IWbltMnN7y7UIASVeyceD0BaRn50Pf9H9ZFth+5CwcbCxrLAEPCNjbWOLuwZWlim9MWyQJGN07HI621g16H0RERIbEBIeIqIVQl5XXur+wWF3r/rKKCgzp3h5CbwpUWQJ+WGQHhPi64/WHxsHZ3ka7T6mQcHufznh8QtStB05ERNSCcIgaEVELER7YBsfiL1UrDgIA1ioL9A4LhJXKHCXq6omQQiGhW4gvPJzt8fj4KHy+dpd2sc6qf2fc1gcBXpVVFnt2CMDP8x/E6YtpKFaXIdTXA0527LkhIiLjxwSHiKiFmDq8J47G618UcvLQSDjYWuGeoT2wbP0+nX2SBEiQcM/QHgCACQO7IdTXA3/vPYGUjBx4utjj9r6d0TXEV+d5SoUCnYJY4YuIiEwLExwiokbm6emp829ddQn2wWv3347Pft+hrYSmMjfD5KGRmDKsMnmZMqwHJAlYufWwthqal7MDnr5rCNr7/fd6YQHeOhUTiYiIWgtJ6FsMoZU4cuQIIiIiEBMTg+7duxs6HCIiAIBGlnHu0hWUlWsQ6usOK5VFtWNKy8qRmHYVKnMz+Hu5sNIZERHRNezBISIyoIycAhSVqOHl6gBLi8pKh0qFAh38vGp9nqWFuU6PDREREVUy2gRn0aJFWLNmDeLi4mBlZYW+ffvinXfeQbt27QwdGhHRTSWlX8VHv21D7IXLACoTlolR3XDfyN5QKlngkoiIqL6M9lt0165dmDVrFg4cOIAtW7agvLwcI0aMQFFR9RW8iYhakqzcQjz7ya84fTFNu620rBy/bDmEJWt3Gi4wIiIiE2C0PTgbN27Uebx8+XK4u7sjJiYGAwcONFBUREQ398e/x1BcWlatHLQAsG7fSUwd1hOujraGCY6IiMjIGW0Pzo3y8vIAAM7OzgaOhIiodofjkvSudQMAQgicuJDSzBERERGZDqPtwbmeLMt49tln0a9fP4SHh9d4nFqthlr930rghYWFzREeEZEOc7Pa7y2ZK5XNFAkREZHpMYkenFmzZiE2NhYrV66s9bhFixbBwcFB+xMVFdVMERIR/Wdg11DUVNXZwlyJ7u3aNm9AREREJsToE5wnn3wS69atw44dO+Dj41PrsXPmzEFeXp72Z9euXc0UJRHRf8b06QRfd2corstyqtaxefj2AbCxVBkqNCIiIqNntEPUhBB46qmnsHbtWuzcuRMBAQE3fY5KpYJK9d+Fg60tJ/ESUfOztrTAh0/djRVbDmLTodMoLi1DkI8bpgztgQFdQgwdHhERkVEz2gRn1qxZWLFiBf7880/Y2dkhPT0dAODg4AArKysDR0dEVDt7G0s8Nj4Kj43nUFkiIqLGZLRD1L744gvk5eVh0KBB8PLy0v6sWrXK0KEREREREZGBGG0PjqihxCoREREREbVeRtuDQ0REREREdCMmOEREREREZDKMdogaEZExE0Lg1MXL2Ho4DgXFpQj19cCoXmFwsGWRFCIiooZggkNE1MyEEFiyZif+3HMcSoUEWQj8eyIev2w9hHefuBOhvh6GDpGIiMhocYgaEVEz2xd7Hn/uOQ4A0MgCQgBCACXqcry+bB1kmUVUiIiI6osJDhFRM/tn/0koJKnadlkIXMkpwInzKQaIioiIyDQwwSEiamaZOYWQayl1n5VX2IzREBERmRYmOEREzczP0wVKRfUenCq+7k7NGA0REZFpYYJDRNTMxg/oCo2eeTYKhYRQXw8WGSAiImoAJjhERM0sPNAbT08aDKVCgiRB25vTxtUR82feDknP/BwiIiKqG5aJJiIygLH9uqBfp2DsOnYOhcVqhPp6ILKDH5QK3nciIiJqCCY4REQG4mxvgwkDuxk6DCIiIpPCW4VERERERGQymOAQEREREZHJYIJDREREREQmg3NwWom0tDSkpaUZOgxqJF5eXvDy8jJ0GNRI+Pk0PfyMEhEZTqtOcLy8vDB//nyT/xJSq9WYMmUKdu3aZehQqJFERUVh06ZNUKlUhg6FGoifT9PEzygRkeFIQojqq82RScnPz4eDgwN27doFW1tbQ4dDDVRYWIioqCjk5eXB3t7e0OFQA/HzaXr4GSUiMqxW3YPT2nTt2pVftiYgPz/f0CFQE+Dn03TwM0pEZFgsMkBERERERCaDCQ4REREREZkMJjitgEqlwvz58znZ1UTwfJoWnk/Tw3NKRGRYLDJAREREREQmgz04RERERERkMpjgEBERERGRyWCCQ0REREREJoMJjhFbsGABJEkydBjaOLKysgwdilG7//774e/vb+gwcP/993PBSSIiIjJaTHDq6ddff4UkSVi7dm21fV26dIEkSdixY0e1fW3btkXfvn1rbfv++++HJEnaH3t7e3Tp0gXvv/8+1Gp1o70H0m/58uXa3/2ePXuq7RdCwNfXF5Ik4fbbb79pe4MGDdI5n87OzujRowe+++47yLLcFG+B6qE5P9O2trYIDAzEpEmT8Pvvv/P/gybUlJ9nhUIBe3t7tGvXDtOnT8eWLVua4i0QEdEtYoJTT/379weAal+Y+fn5iI2NhZmZGfbu3auz79KlS7h06ZL2ubVRqVT48ccf8eOPP+Ltt9+Gs7MzXnjhBcyYMaPx3gTVytLSEitWrKi2fdeuXUhJSbmlErA+Pj7a8zlv3jxUVFTgwQcfxNy5cxszZGqA5vxMf/jhh5g6dSri4+MxadIkDB06FPn5+Y33Zqiapvg8//DDD3jvvfcwbtw47Nu3DyNGjMDkyZNRXl7emKETEdEtMjN0AMbK29sbAQEB1S6G9u/fDyEE7rrrrmr7qh7X5WLIzMwM06ZN0z5+4okn0KtXL6xatQoffPABvL29G+FdUG1Gjx6N3377DZ988gnMzP77qKxYsQIRERG3NCTPwcFB53w++uijaNeuHT777DO88cYbMDc3b9TY6dY192caAN58800sXrwYc+bMwcMPP4xVq1bV+HwhBEpLS2FlZVXXt0TXacrPMwAsXrwYTz/9ND7//HP4+/vjnXfeqfH5siyjrKwMlpaWt/5GiIjoptiD0wD9+/fH0aNHUVJSot22d+9ehIWF4bbbbsOBAwd0hp7s3bsXkiShX79+t/xaCoUCgwYNAgAkJibWeNyyZcswZMgQuLu7Q6VSoWPHjvjiiy/0HrthwwZERUXBzs4O9vb26NGjR7U7nAcPHsSoUaPg4OAAa2trREVFVbuLXSUrKwt333037O3t4eLigmeeeQalpaU6x1RUVOCNN95AUFAQVCoV/P39MXfu3BY59G7KlCm4evWqzrCTsrIyrF69GlOnTm1Q29bW1ujduzeKioqQmZlZ43H/93//h759+8LFxQVWVlaIiIjA6tWr9R77008/oWfPnrC2toaTkxMGDhyIzZs36xyzYcMGDBgwADY2NrCzs8OYMWNw6tQpve1duHABI0eOhI2NDby9vfH666/jxmWzioqK8Pzzz8PX1xcqlQrt2rXD//3f/1U7zlg052e6yssvv4wRI0bgt99+w7lz57Tb/f39cfvtt2PTpk2IjIyElZUVli5disTEREiShOXLl1drS5IkLFiwQGfbzp07ERkZCUtLSwQFBWHp0qUtZv5ec2rKzzMAKJVKfPLJJ+jYsSM+++wz5OXlafdJkoQnn3wSP//8M8LCwqBSqbBx40bs3LkTkiRh586dOm3VdI5/++03dOzYEZaWlggPD8fatWtbzNw9IqKWhAlOA/Tv3x/l5eU4ePCgdtvevXvRt29f9O3bF3l5eYiNjdXZ1759e7i4uNTr9c6fPw8AtT7/iy++gJ+fH+bOnYv3338fvr6+eOKJJ7BkyRKd45YvX44xY8YgOzsbc+bMweLFi9G1a1ds3LhRe8z27dsxcOBA5OfnY/78+Xj77beRm5uLIUOG4NChQ9Ve++6770ZpaSkWLVqE0aNH45NPPsEjjzyic8xDDz2E1157Dd27d8eHH36IqKgoLFq0CPfcc0+9fidNyd/fH3369MEvv/yi3bZhwwbk5eU1SrwXLlyAUqmEo6Njjcd8/PHH6NatG15//XW8/fbbMDMzw1133YV//vlH57iFCxdi+vTpMDc3x+uvv46FCxfC19cX27dv1x7z448/YsyYMbC1tcU777yDefPm4fTp0+jfv3+1pFmj0WDUqFHw8PDAu+++i4iICMyfPx/z58/XHiOEwLhx4/Dhhx9i1KhR+OCDD9CuXTu8+OKLmD17doN/P4bQ3J/pKtOnT4cQotocjrNnz2LKlCkYPnw4Pv74Y3Tt2vWW2j169ChGjRqFq1evYuHChXjwwQfx+uuv448//mhQvMaoqT/PQGWSM2XKFBQXF1fr7du+fTuee+45TJ48GR9//PEtJyX//PMPJk+eDHNzcyxatAgTJ07Egw8+iJiYmEaJnYjIpAiqt1OnTgkA4o033hBCCFFeXi5sbGzE999/L4QQwsPDQyxZskQIIUR+fr5QKpXi4Ycfvmm7M2bMEDY2NiIzM1NkZmaKhIQE8fbbbwtJkkTnzp21x82fP1/ceAqLi4urtTdy5EgRGBiofZybmyvs7OxEr169RElJic6xsixr/w0JCREjR47UbqtqPyAgQAwfPrxaHOPGjdNp64knnhAAxPHjx4UQQhw7dkwAEA899JDOcS+88IIAILZv337T301zWLZsmQAgoqOjxWeffSbs7Oy0v9e77rpLDB48WAghhJ+fnxgzZsxN24uKihLt27fXns8zZ86Ip59+WgAQY8eO1R43Y8YM4efnp/PcG89nWVmZCA8PF0OGDNFui4+PFwqFQkyYMEFoNBqd46vOXUFBgXB0dKz2/196erpwcHDQ2T5jxgwBQDz11FM67YwZM0ZYWFiIzMxMIYQQf/zxhwAg3nzzTZ02J02aJCRJEgkJCTf93bQ0Tf2ZrsnRo0cFAPHcc89pt/n5+QkAYuPGjTrHXrx4UQAQy5Ytq9YOADF//nzt47Fjxwpra2uRmpqq3RYfHy/MzMyq/e0wVU3xeQ4LC6tx/9q1awUA8fHHH2u3ARAKhUKcOnVK59gdO3YIAGLHjh062/Wd406dOgkfHx9RUFCg3bZz504BoNrfDSKi1o49OA3QoUMHuLi4aO/UHT9+HEVFRdqKSn379tUO59q/fz80Gk2dxuoDlUN/3Nzc4ObmhuDgYMydOxd9+vTRW+HpetePz8/Ly0NWVhaioqJw4cIF7ZCJLVu2oKCgAC+//HK1MeBVw1aOHTuG+Ph4TJ06FVevXkVWVhaysrJQVFSEoUOHYvfu3dUqP82aNUvn8VNPPQUAWL9+vc6/N97df/755wGgWq9ES3D33XejpKQE69atQ0FBAdatW1ev4SxxcXHa89mhQwd8+umnGDNmDL777rtan3f9+czJyUFeXh4GDBiAI0eOaLf/8ccfkGUZr732GhQK3Y901fncsmULcnNzMWXKFO25zMrKglKpRK9evfRWB3vyySd12nnyySdRVlaGrVu3Aqg8n0qlEk8//bTO855//nkIIbBhw4Y6/nZajqb8TNemqix3QUGBzvaAgACMHDmyXm1qNBps3boV48eP15mzFxwcjNtuu63+wRqxxvo816amcxkVFYWOHTvWq83Lly/j5MmTuO+++3RKuEdFRaFTp071D5aIyESxyEADSJKEvn37ai/29+7dC3d3dwQHBwOovBj67LPPAEB7UVTXiyFLS0v8/fffACqrLwUEBMDHx+emz9u7dy/mz5+P/fv3o7i4WGdfXl4eHBwctEPdwsPDa2wnPj4eAGqt2paXlwcnJyft45CQEJ39QUFBUCgU2uFPSUlJUCgU2t9PFU9PTzg6OiIpKemm76+5ubm5YdiwYVixYgWKi4uh0WgwadKkW27H398fX3/9NSRJgqWlJUJCQuDu7n7T561btw5vvvkmjh07pjNP6fr5E+fPn4dCoaj14qnqfA4ZMkTvfnt7e53HCoUCgYGBOttCQ0MBQOd8ent7w87OTue4Dh06aPcbm6b8TNemsLAQAKr9LgMCAurdZkZGBkpKSqp93gDo3dYaNNbnuTZNcS6rPks1ncvrb3gQERETnAbr378//v77b5w8eVI7Vr9K37598eKLLyI1NRV79uyBt7d3tYvGmiiVSgwbNuyWYjl//jyGDh2K9u3b44MPPoCvry8sLCywfv16fPjhh7e01kbVse+9916N4/5vthhkTZOYjW1y89SpU/Hwww8jPT0dt912W61zZmpiY2Nzy+fz33//xbhx4zBw4EB8/vnn8PLygrm5OZYtW6a33G1tqs7njz/+CE9Pz2r7r68q1do11We6NlXzem68gNVXMa2mz49Go2lwHK1BY3yea8NzSURkeLyqaaDr187Yu3cvnn32We2+iIgIqFQq7Ny5EwcPHsTo0aObNJa///4barUaf/31F9q2bavdfuPwo6CgIACVX8Q13cmtOsbe3r7OF+bx8fE6dykTEhIgy7J2Mq2fnx9kWUZ8fLz2Lj8AXLlyBbm5ufDz86vT6zS3CRMm4NFHH8WBAwdqLePb2H7//XdYWlpi06ZNOmt0LFu2TOe4oKAgyLKM06dP15iMVp1Pd3f3Op1PWZZx4cIFba8NAG2Fr+vP59atW1FQUKBztzouLk673xgZ4jP9448/QpIkDB8+/KbHVvWa5ubm6my/scfM3d0dlpaWSEhIqNaGvm2tRVN+njUaDVasWAFra+s69ezV9VxWfZZ4LomI6oZzcBqoqvzqzz//jNTUVJ27vSqVCt27d8eSJUtQVFTUKENZaqNUKgFAp0RvXl5etQviESNGwM7ODosWLapWxrnquREREQgKCsL//d//aYdcXE9faeMbK7V9+umnAKAd7191MfjRRx/pHPfBBx8AAMaMGVP7GzQQW1tbfPHFF1iwYAHGjh3bbK+rVCohSZLO3dzExMRqFbDGjx8PhUKB119/vVovXdX5HDlyJOzt7fH222/rXYRQ3/msGopV1c5nn30Gc3NzDB06FEDl+dRoNDrHAcCHH34ISZKMdp5Hc3+mFy9ejM2bN2Py5MnVhnnqY29vD1dXV+zevVtn++eff67zuKoX+I8//sDly5e12xMSEoxyflRjaarPs0ajwdNPP40zZ87g6aefrjbsUx8/Pz8olcqbnktvb2+Eh4fjhx9+0Pl7vGvXLpw8ebJx3gARkQlhD04DWVhYoEePHvj333+hUqkQERGhs79v3754//33ATTOWP3ajBgxAhYWFhg7diweffRRFBYW4uuvv4a7uzvS0tK0x9nb2+PDDz/EQw89hB49emDq1KlwcnLC8ePHUVxcjO+//x4KhQLffPMNbrvtNoSFhWHmzJlo06YNUlNTsWPHDtjb22vnCFW5ePEixo0bh1GjRmH//v346aefMHXqVHTp0gUA0KVLF8yYMQNfffUVcnNzERUVhUOHDuH777/H+PHjMXjw4Cb9/TREbXORmsqYMWPwwQcfYNSoUZg6dSoyMjKwZMkSBAcH48SJE9rjgoOD8corr+CNN97AgAEDMHHiRKhUKkRHR8Pb2xuLFi2Cvb09vvjiC0yfPh3du3fHPffcAzc3NyQnJ+Off/5Bv379dBIVS0tLbNy4ETNmzECvXr2wYcMG/PPPP5g7dy7c3NwAAGPHjsXgwYPxyiuvIDExEV26dMHmzZvx559/4tlnn9X2GhmbpvpMV1RU4KeffgIAlJaWIikpCX/99RdOnDiBwYMH46uvvqpzWw899BAWL16Mhx56CJGRkdi9e7fOGjpVFixYgM2bN6Nfv354/PHHtQlpeHg4jh07VufXMzUN/Tzn5eVpz2VxcTESEhKwZs0anD9/Hvfccw/eeOONOrXj4OCAu+66C59++ikkSUJQUBDWrVuHjIyMase+/fbbuOOOO9CvXz/MnDkTOTk52nOp7yYUEVGrZtAabiZizpw5AoDo27dvtX1r1qwRAISdnZ2oqKioU3s3KylbRV+Z6L/++kt07txZWFpaCn9/f/HOO++I7777TgAQFy9erHZs3759hZWVlbC3txc9e/YUv/zyi84xR48eFRMnThQuLi5CpVIJPz8/cffdd4tt27ZVi+P06dNi0qRJws7OTjg5OYknn3yyWhnq8vJysXDhQhEQECDMzc2Fr6+vmDNnjigtLa3T76Y5XF9WtjaNVVa2ir4y0d9++60ICQkRKpVKtG/fXixbtkzveRdCiO+++05069ZNqFQq4eTkJKKiosSWLVt0jtmxY4cYOXKkcHBwEJaWliIoKEjcf//94vDhwzpx2NjYiPPnz4sRI0YIa2tr4eHhIebPn1+tDHVBQYF47rnnhLe3tzA3NxchISHivffe0yktboya4jMNQPtjbW0t/P39xZ133ilWr15d7fcqRO3/fxUXF4sHH3xQODg4CDs7O3H33XeLjIyMamWihRBi27Ztolu3bsLCwkIEBQWJb775Rjz//PPC0tKyTrEbu6b4PF9/Lm1tbUVISIiYNm2a2Lx5s97nABCzZs3Suy8zM1PceeedwtraWjg5OYlHH31UxMbG6i0FvnLlStG+fXuhUqlEeHi4+Ouvv8Sdd94p2rdvf9O4iYhaE0kII11ynIiI6mX8+PE4deqUtroeGa+uXbvCzc2t2iKxREStGefgEBGZsJKSEp3H8fHxWL9+PQYNGmSYgKheysvLUVFRobNt586dOH78OM8lEdEN2INDRGTCvLy8cP/99yMwMBBJSUn44osvoFarcfTo0ToVNaCWITExEcOGDcO0adPg7e2NuLg4fPnll3BwcEBsbCxcXFwMHSIRUYvBIgNERCZs1KhR+OWXX5Ceng6VSoU+ffrg7bffZnJjZJycnBAREYFvvvkGmZmZsLGxwZgxY7B48WImN0REN2APDhERERERmQzOwSEiIiIiIpPBBIeIiIiIiEwGExwDWb58OSRJgqWlJVJTU6vtHzRoEMLDw5s1pm3btuGBBx5AaGgorK2tERgYiIceekhnkdDr7du3D/3794e1tTU8PT3x9NNPt9oF53g+TQvPp+nhOSUiaj2Y4BiYWq3G4sWLDR0GAOCll17Czp07MWHCBHzyySe455578Ouvv6Jbt25IT0/XOfbYsWMYOnQoiouL8cEHH+Chhx7CV199hbvuustA0bcMPJ+mhefT9PCcEhG1AoZcZbQ1q1pdu2vXrkKlUonU1FSd/VFRUSIsLKxZY9q1a1e1FdV37dolAIhXXnlFZ/ttt90mvLy8RF5ennbb119/LQCITZs2NUu8LQnPp2nh+TQ9PKdERK0He3AMbO7cudBoNC3ijuLAgQOhUCiqbXN2dsaZM2e02/Lz87FlyxZMmzYN9vb22u333XcfbG1t8euvvzZbzC0Nz6dp4fk0PTynRESmj+vgGFhAQADuu+8+fP3113j55Zfh7e19S88vLi5GcXHxTY9TKpVwcnK65fgKCwtRWFgIV1dX7baTJ0+ioqICkZGROsdaWFiga9euOHr06C2/jqng+TQtPJ+mh+eUiMj0sQenBXjllVdQUVGBd95555af++6778LNze2mP926datXbB999BHKysowefJk7baqCbBeXl7Vjvfy8sLly5fr9VqmgufTtPB8mh6eUyIi08YenBYgMDAQ06dPx1dffYWXX35Z75dYTe677z7079//psdZWVndcly7d+/GwoULcffdd2PIkCHa7SUlJQAAlUpV7TmWlpba/a0Vz6dp4fk0PTynRESmjQlOC/Hqq6/ixx9/xOLFi/Hxxx/X+XmBgYEIDAxs9Hji4uIwYcIEhIeH45tvvtHZV/XFrVarqz2vtLS0Xl/spobn07TwfJoenlMiItPFBKeFCAwMxLRp07R3FOuqarz2zSiVSri5udWpzUuXLmHEiBFwcHDA+vXrYWdnp7O/6m6nvrUa0tLSbnlMuyni+TQtPJ+mh+eUiMh0cQ5OC/Lqq6/e8rjw//u//4OXl9dNf3r06FGn9q5evYoRI0ZArVZj06ZNeoduhIeHw8zMDIcPH9bZXlZWhmPHjqFr1651jt+U8XyaFp5P08NzSkRkmtiD04IEBQVh2rRpWLp0Kfz8/GBmdvPT05jjwYuKijB69GikpqZix44dCAkJ0Xucg4MDhg0bhp9++gnz5s3T3m388ccfUVhYyIXnruH5NC08n6aH55SIyDRJQghh6CBao+XLl2PmzJmIjo7WKf2ZkJCA9u3bQ6PRICwsDLGxsc0W0/jx4/Hnn3/igQcewODBg3X22draYvz48drHR44cQd++fdGxY0c88sgjSElJwfvvv4+BAwdi06ZNzRZzS8HzaVp4Pk0PzykRUSti6JVGW6uqVbWjo6Or7ZsxY4YA0Oyravv5+QkAen/8/PyqHf/vv/+Kvn37CktLS+Hm5iZmzZol8vPzmzXmloLn07TwfJoenlMiotaDPThERERERGQyWGSAiIiIiIhMBhMcIiIiIiIyGUxwiIiIiIjIZDDBISIiIiIik8EEh4iIiIiITAYTHCIiIiIiMhlMcIiIiIiIyGQwwSEiIiIiIpPBBIeIiIiIiEwGExwiIiIiIjIZTHCIiIiIiMhkMMEhIiIiIiKTwQSHiIiIiIhMBhMcIiIiIiIyGUxwiIiIiIjIZLTqBCctLQ0LFixAWlqaoUMhIiIiImoUrf0at9UnOAsXLmy1J5+IiIiITE9rv8Zt1QkOERERERGZFiY4RERERERkMpjgEBERERGRyWCCQ0REREREJoMJDhERERERmQwmOEREREREZDKY4BARERERkclggkNkhDQajaFDICIiImqRmOAQGSEhhKFDICIiImqRmOAQEREREZHJYIJDZITYg0NERESkHxMcIiPEOThERERE+jHBITJCFRUVhg6BiIiIqEVigkNkhNiDQ0RERKQfExwiIyTLsqFDICIiImqRmOAQGSEmOERERET6McEhIiIiIiKTwQSHyAixTDQRERGRfkxwiIyQJEmGDoGIiIioRWKCQ0REREREJoMJDpERYpEBIiIiIv1MJsFZvHgxJEnCs88+a+hQiJpceXm5oUMgIiIiE2Xs19UmkeBER0dj6dKl6Ny5s6FDIWoWJSUlhg6BiIiITJApXFcbfYJTWFiIe++9F19//TWcnJwMHQ5RsygqKmIlNSIiImpUpnJdbfQJzqxZszBmzBgMGzbspseq1Wrk5+drfwoLC5shQqLGp9FoUFRUZOgwiIiIqAUrLCzUufZVq9W1Hn8r19UtmZmhA2iIlStX4siRI4iOjq7T8YsWLcLChQubOCqi5pGfnw9bW1tDh0FEREQtVFRUlM7j+fPnY8GCBXqPvdXr6pbMaBOcS5cu4ZlnnsGWLVtgaWlZp+fMmTMHs2fP1j4+duxYtRNPZCzy8vLg7e1t6DCIiIiohdq1axe6du2qfaxSqfQeV5/r6pbMaBOcmJgYZGRkoHv37tptGo0Gu3fvxmeffQa1Wg2lUqnzHJVKpXNiefebjFlOTo6hQyAiIqIWzNbWFvb29jc9rj7X1S2Z0SY4Q4cOxcmTJ3W2zZw5E+3bt8dLL71kVCeBqD4yMzMNHQIRERGZAFO7rjbaBMfOzg7h4eE622xsbODi4lJtO5EpysrKQnl5OczNzQ0dChERERkxU7uuNvoqakStlUajwaVLlwwdBhEREVGLYrQ9OPrs3LnT0CEQNav4+HgEBgYaOgwiIiIyMcZ8Xc0eHCIjlpyczPVwiIiIiK7DBIfIiAkhcPz4cUOHQURERNRiMMEhMnJnzpxBfn6+ocMgIiIiahGY4BAZOY1Gg/379xs6DCIiIqIWgQkOkQlISkpiRTUiIiIiMMEhMhl79uxBWVmZocMgIiIiMigmOEQmoqCgADt37oQQwtChEBERERkMExwiIxMZGYlRo0bhrbfeqrYvMTERO3bsgEajMUBkRERERIbHBIfIyKSnpyMjI6PGymkJCQn4559/uD4OERERtUpMcIhMUHp6OlavXo3z589zyBoRERG1KkxwiEyUWq3Gtm3bsHXrVpSUlBg6HCIiIqJmYWboAIioaV28eBGXL19Gnz59EBISAkmSDB0SERERUZNhDw5RK6BWq7Fz5078888/yMnJMXQ4RERERE2GCQ5RK3L58mWsXr0au3fvRkFBgaHDISIiImp0HKJG1MoIIRAXF4dz584hKCgInTt3houLi6HDIiIiImoUTHCIWilZlhEfH4/4+Hh4e3sjPDwcfn5+nKNDRERERo0JDhHh8uXLuHz5MhwcHNC5c2eEhoZCqVQaOiwiIiKiW8Y5OERGJDk5GcXFxQCAsrIyZGdnN2r7eXl5+Pfff/Hrr78iOTm5UdsmIiIiag5McIiMwKFDhzB27Fj4+/trq6AVFxdj7ty5WLJkCRITExv19QoKCrBx40acOHGiUdslIiIiampMcIhauDVr1qBfv37YsGEDhBA6+4QQiI2NxTvvvIMjR440+msfOnQIpaWljd4uERERUVNhgkPUgh06dAiTJ0+GRqOBRqPRe4wsy5BlGV9//XWj9+TIsoy8vLxGbZOIiIioKTHBIWrB3nzzTQghqvXc1GT9+vWNHkN6enqjt0lERETUVJjgELVQycnJWLduXY09NzeSZRknTpxo9MIDR44cQW5ubqO2SURERNRUmOAQtVDbtm2rc89NlapFPBtTeXk5NmzYwLk4REREZBSY4BC1UAUFBVAobu0jKklSkyQiBQUFOHXqVKO3S0RERNTYmOAQtVB2dnaQZfmWniOEgKWlZZPEc6u9SURERESGwASHqIUaOnQoJEm6pedIkoT27ds3eiy2trYICwtr9HaJiIiIGptZfZ70wAMP1LpfkiRYWlrCx8cHgwYNQp8+feoVHFFr1rZtW9x+++1Yv359nQoNKBQKdOrUCc7Ozo0ah5eXF4YMGQIrK6tGbZeIiIioKdQrwdm+fTtKSkqQmZkJAHBycgIA7Qrrbm5ukGUZV69ehSRJGDlyJFavXg1ra+tGCpuodZg3bx42bNgASZLqNERs9OjRjfbaDg4OiIiIQFBQ0C33JBEREREZSr2GqG3YsAEqlQoLFizA1atXtT9ZWVmYP38+rKyssHfvXuTk5GDevHnYuHEj5s2b19ixE5m8Hj16YNWqVVAqlVAqlXqPUSgUUCgUeOSRR+Dv79/g13RycsLgwYNx1113ITg4mMkNERERGRVJ1GPm8NChQxESEoIvv/xS7/7HHnsMFy5cwObNmwEAU6dOxd69e5GUlNSwaBvZkSNHEBERgZiYGHTv3t3Q4RDVKDo6Gm+88QbWrVun05MjSRI6d+6M0aNHNzi5cXBwQGRkJAIDA5nUEBERGbHWfo1bryFqBw4cwKRJk2rc36VLF/z000/axwMGDMCaNWvq81JEhMqenL/++gvJycno2rUrcnJyYG1tjXnz5jV4zo2lpSUiIiLQoUOHWy5LTURERNTS1OtqxtHRUds7o8/GjRvh4OCgfVxYWAh7e/v6vFSNFi1ahB49esDOzg7u7u4YP348zp4926ivQdTStG3bVjuXzcLCokHJjSRJCAsLw+TJkxEWFsbkhoiIqJUytevqevXgPPzww3j99dcxadIkPP744wgODgYAJCQk4IsvvsC6det05tysX78eXbt2bZSAq+zatQuzZs1Cjx49UFFRgblz52LEiBE4ffo0bGxsGvW1iEyJJEkIDAxEZGSkzo0IIiIiap2a+7q6qSsy1yvBmT9/PkpKSvDhhx9i7dq1OvuUSiVmz56N+fPnAwBKS0tx//33o3PnzvV5qRpt3LhR5/Hy5cvh7u6OmJgYDBw4sFFfi8gUmJmZISQkBJ06dYKjo6OhwyEyaWVlZbCwsDB0GEREddLc19VNXZG5XgmOJEl455138Pzzz2Pbtm3a4gF+fn4YOnQo3N3dtcdaWlpixowZ9XmZW5KXlwcAtQ7ZUavVUKvV2seFhYVNHheRoTk4OKBjx44IDQ2FSqUydDhErQILdRBRS1BYWIj8/HztY5VKVadrgbpcVzfEhg0bMHLkSCxYsABPPfWUNsHJzs7Gp59+imXLlmHz5s3w8PDAhx9+iNdffx3z5s3D+++/X6f261VFraWRZRnjxo1Dbm4u9uzZU+NxCxYswMKFC6ttb60VJsg4+fj4IDU1FY6OjnjnnXdqPM7X1xfh4eHw8fHhxRZRMysvL4e5ubmhwyCiVqqqitqN5s+fjwULFtT63LpeVzdEU1dkrlcPTksza9YsxMbG3vQkzJkzB7Nnz9Y+PnbsGKKiopo6PKJmFRgYiO7duzfZXRciujkTuHdIRCZg165dOvPg69J7U9fr6oZo6orMRp/gPPnkk1i3bh12794NHx+fWo+9sVvO1ta2qcMjajYeHh7o168fXF1dDR0KUatXUVHBOThEZHC2tra3VMn4Vq6rG6KqIvPjjz+ud39DKzIbbV1YIQSefPJJrF27Ftu3b0dAQIChQyIyCIVCgT59+mDcuHFMbohaiPLyckOHQERUZ819Xf3www/jzz//xKRJk7Tz+ZOSkrBt2zZMmjQJ69atw8MPP6w9/lYrMhttD86sWbOwYsUK/Pnnn7Czs0N6ejqAygnVVlZWBo6OqHmYm5tj1KhR8PLyMnQoRHSd0tJSlmEnIqPR3NfVTV2R2WiLDNQ0aXrZsmW4//7769RG1QQsFhkgY3J9kYGYmBgEBgYaOiQiukFCQoJ2jTgiouZ2q9e4jXFdXR8ZGRk3rchcH0bbg2OkeRlRo1EqlUxuiFqo7OxsQ4dARFRnhrqudnd3x5QpUxq93TonOEOGDKm2bfv27Y0aDBHVnZmZ0d6fIDJ5WVlZhg6BiKjVqvMVkp+fX1PGQUR15OnpifLycs67IWrBrl69ClmWoVAYbS0fIiKjVecEZ9myZU0ZBxHV0eHDh3H8+HF06tTJ0KEQUQ3KysqQmpoKX19fQ4dCRNTq8NYSkRGys7PjnWGiFu7AgQMsF01EZAD1ukI6duwYfvnlF51tmzZtwsCBA9GrVy98/PHHjRIcEelnbW1t6BCI6CZycnKwefNmaDQaQ4dCRNSq1CvB+d///odVq1ZpH1+8eBETJkzAxYsXAQCzZ8/GV1991TgRElE1XOuJyDikpqZi/fr1UKvVhg6FiKjVqFcZpuPHj+PFF1/UPv7hhx+gVCpx9OhRuLq6YvLkyfjyyy/xyCOPNFqgRPQfW1tbQ4dARDWIjIzEhQsXYGdnh1deeQVpaWn4/fffERUVhTZt2hg6PCIig2jOisz1SnDy8vLg4uKifbx+/XoMHz4crq6uAIDhw4djw4YNjRMhEVWjVCoNHQIR1SA9PR05OTk660oUFhbin3/+QWhoKHr16sVeWCJqdZqzInO9EhwvLy+cOXMGAJCWloaYmBjMnDlTu7+wsJAToImIiG5w7tw5JCYmokuXLggPD4e5ubmhQyIiahbNWZG5XgnOHXfcgU8//RSlpaU4ePAgVCoVJkyYoN1//PhxrrBORESkR1lZGaKjoxEbG4tOnTqhY8eOsLCwMHRYREQmo17dLG+++SYmTpyIH3/8ERkZGVi+fDk8PDwAAPn5+Vi9ejVGjBjRqIESERGZkpKSEhw6dAgrVqzAwYMHUVBQYOiQiIiaRVNXZK5XD46trS1+/vnnGvelpKSwjC0REVEdlJWV4fjx4zhx4gTatGmD4OBg+Pn5QaVSGTo0IqIm8b///Q/W1taYMmUKgP8qMru4uMDb2xuzZ8+GlZVVvQuW1SvBuVFeXh5sbW2hVCqhUCjg4ODQGM0SEREZleTkZBQXFwOoTFyys7Ph7Oxcp+cKIZCSkoKUlBRIkgRPT0+0bdsWbdu2haOjIyRJasrQiYiaTVNXZK53JYDDhw9j1KhRsLa2houLC3bt2gUAyMrKwh133IGdO3fWt2kiIiKjcujQIYwdOxb+/v7IyckBABQXF2Pu3LlYsmQJEhMTb6k9IQTS0tJw8OBB/Pbbb1i1ahX279+PK1eu6FRnIyIyRnWpyJyQkFDv9uuV4Ozbtw/9+/dHfHw8pk2bBlmWtftcXV2Rl5eHpUuX1jsoIiIiY7FmzRr069cPGzZsqJZ8CCEQGxuLd955B0eOHKn3a+Tn5+PkyZP4888/sXLlSsTExGh7ioiIjI2+iszXz99vaEXmej1z7ty56NChA06fPo2333672v7Bgwfj4MGD9Q6KiIjIGBw6dAiTJ0+GRqOBRqPRe4wsy5BlGV9//fUt9+ToU1BQgJiYGKxYsQL79u1DeXl5g9skImpOVRWZn376aYwfP77RKzLXK8GJjo7GzJkzoVKp9I4JbtOmDdLT0+sdFBERkTF48803IYSo87Cx9evXN9pry7KM2NhY/Pnnn1Cr1Y3WLhFRU2vqisz1KjJgbm6uMyztRqmpqbC1ta13UERERC1dcnIy1q1bV+fkRpZlnDhx4pYKD9RFdnY29uzZg6FDhzZam0RETampKzLXqwend+/eWL16td59RUVFWLZsGaKiouodFBERUUu3bdu2W57wL4RAXFxco8dy4cKFGofIEREZk6qKzObm5vVuo149OAsXLkRUVBTGjBmjrV99/PhxXLhwAf/3f/+HzMxMzJs3r95BERERtXQFBQVQKBS1jmi4kSRJKC0tbdQ4zM3N0bdvXyiVykZtl4ioqe3duxdHjhxBXl5etb+lkiTVO5+oV4LTq1cvrF+/Ho8//jjuu+8+AMDzzz8PAAgKCsL69evRuXPnegVERERkDOzs7G4puQEqe3AsLS0b5fXNzMwQFhaGLl26NFqbRETNITs7G2PGjMGhQ4cghIAkSdoe8ar/bvYEBwCGDBmCs2fP4tixY4iPj4csywgKCkJERAQXIyMiIpM3dOhQnS/lupAkCe3bt2/wa4eGhqJnz54NGqNORGQoL774Ik6cOIEVK1agV69eCAwMxKZNmxAQEIAPP/wQ+/fvx4YNG+rdfr0TnCpdu3ZF165dG9oMERGRUWnbti1uv/12rF+/vk7zXxQKBTp16tSgAgM2NjaIioqCj49PvdsgIjK09evX49FHH8XkyZNx9epVAJV/I4ODg7FkyRJMnDgRzz77LH755Zd6tV+vBGf37t217pckCZaWlvDx8YGXl1e9AiMiImrp5s2bhw0bNtS5J2f06NH1eh1JktChQwf06NEDKpWqXm0QEbUUubm5CAsLAwBt5eXCwkLt/hEjRmDu3Ln1br9eCc6gQYPqPAwtJCQECxcuxOTJk+vzUkRERC1Wjx49sGrVKkyePBlCCL09OVWrcT/yyCPw9/e/pfatrKwQGhqKsLAwLr9ARCbD29tbu2amSqWCu7s7jh8/jjvuuANA5ZIzDZnyUq8EZ+PGjXjppZegVqvx8MMPIzg4GAAQHx+Pb775BlZWVnj11VeRlJSEpUuXYurUqVAqlZg0aVK9AyUiImqJJk6ciH379uGNN96oti6OJEno1KkTRo8eXefkxtzcHP7+/ggODkabNm20CRIRkakYOHAgtmzZgldeeQUAMHnyZLz77rtQKpWQZRkfffQRRo4cWe/2JXGrRfwBzJ49G/v378euXbtgYWGhs6+0tBSDBg1CVFQU3nnnHZSWliIyMhJWVlaIjo6ud6BN4ciRI4iIiEBMTAy6d+9u6HCIiMjIJScno2vXrsjJyYG1tTXmzZtXpzk3SqUSfn5+CA4Ohq+vL0s+E1GDtPRr3JMnT2LLli2YNWsWVCoVcnJycNddd2H79u0AKhOgFStWwNvbu17t16sH5+eff8arr75aLbkBAEtLS9x7771466238M4778DS0hLTpk3DG2+8Ua8AiYiIjEXbtm1hbW2NnJwcWFhY3DS58fLyQmhoKAICAvR+pxIRmaJOnTqhU6dO2sdOTk7YunUrcnNzoVQqYWdn16D265XgFBUV4cqVKzXuT0tL05ko5OjoyLtRREREqFy/pl27dggLC4Ojo6OhwyEianavv/46Jk6ciPDwcJ3tVX8TT506hd9//x2vvfZavdqv18DeIUOG4KOPPsK6deuq7fv777/x8ccfY8iQIdptx44du+WJlURERKamffv2uOeee9CvXz8mN0TUai1YsAAnTpyocX9sbCwWLlxY7/br1YPz2WefYfDgwbjjjjvQpk0bBAUFAQDOnz+P1NRU+Pn54dNPPwVQOScnOTkZDz30UL2DJCIiMmY2NjYYMmQIl04gIqqD7OzsBg3brVeC07ZtW5w8eRJffvklNm3ahKSkJABAhw4d8Oyzz+LRRx+FjY0NgMo5OevXr693gDezZMkSvPfee0hPT0eXLl3w6aefomfPnk32ekRERLfCzc0No0aNgpWVlaFDISKqVVNeV+/evRs7d+7UPl6zZg0SEhKqHZebm4tVq1bpzNG5VfVKcADA2toas2fPxuzZs+v94g21atUqzJ49G19++SV69eqlLSl39uxZuLu7GywuIiIiAHBxccGYMWNYQICIWrymvq7esWOHdtiZJElYs2YN1qxZo/fYjh07akeD1YdRF9f/4IMP8PDDD2PmzJno2LEjvvzyS1hbW+O7774zdGhERNRKeXp6wsnJCY6Ojhg1ahSTGyIyCk19Xf2///0PmZmZyMjIgBACX375JTIzM3V+srKyUFxcjNjYWPTq1aver1WnHpzrCwbUlSRJ2LZt2y0/r67KysoQExODOXPmaLcpFAoMGzYM+/fvb7LXJSIiqs3hw4fx7bffon///trh2kRELVlzXFdbWVlph+pevHgRbm5usLa2bpS2b1SnBEeWZUiSpLPt0qVLuHDhAhwcHBAYGAigMtjc3FwEBQXB19e38aO9TlZWFjQaDTw8PHS2e3h4IC4uTu9z1Go11Gq19nFVKeuKigqUl5c3XbBERNSqmJmZwd/fn98tRGQQFRUVACqvdfPz87XbVSoVVCpVtePrc13dEH5+fo3e5vXqlOBcPyEIAPbs2YNx48bh66+/xowZM2BmVtlMRUUFli1bhpdeegnLly9v7FgbbNGiRXpLzjWkC4yIiEif+++/39AhEFErFxUVpfN4/vz5WLBgQbPHERAQUK2z5GYkScL58+fr9Xr1KjLwwgsvYObMmXjwwQd1GzMzw8MPP4y4uDjMnj0bBw8erFdQdeHq6gqlUlltwdErV67A09NT73PmzJmjUxTh2LFjiIqKwsGDB9GtW7cmi5WIiFqX+Ph4hISEGDoMImqljh49il69emHXrl3o2rWrdru+3hugftfVtyIqKuqWE5yGqFeCc+LECUyfPr3G/QEBAfjiiy/qHVRdWFhYICIiAtu2bcP48eMBVA6l27ZtG5588km9z7mxW87W1hZAZWJmbm7epPESEVHr4ejoyO8VIjKYqtFVtra2sLe3v+nx9bmuvhXNPbKrXlXUvL29sWrVKu34vutVVFRg1apV8Pb2bnBwNzN79mx8/fXX+P7773HmzBk8/vjjKCoqwsyZM5v8tYmIiGrC5IaIjI0pXVfXqwfnf//7Hx577DH07t0bjz32GIKDgwFUdsl/+eWXOHbsGD7//PNGDVSfyZMnIzMzE6+99hrS09PRtWtXbNy4sdoEKSIioubE0tBEZGya+7o6Pz8fn3/+OXbs2IGMjAwsXboUPXv2RHZ2NpYvX45x48Zpc4xbJQkhRH2e+O233+KVV15BRkaGdkydEAJubm5488038fDDD9croOZ05MgRREREICYmBt27dzd0OEREZCJKS0thaWlp6DCIqJVq6de4KSkpiIqKwqVLlxASEoK4uDhs2bJFuzRNu3btMGrUKHz88cf1ar9ePTgA8OCDD2LGjBmIjo5GcnIygMqSb5GRkdpxf0RERK0Rh6gREdXsxRdfREFBAY4dOwZ3d3e4u7vr7B8/fjzWrVtX7/YblImYmZmhT58+6NOnT0OaISIiMikKRb2muBIRtQqbN2/Gc889h44dO+Lq1avV9gcGBuLSpUv1br/ef4Hz8/OxePFijBw5Et26dcOhQ4cAANnZ2fjggw+QkJBQ76CIiIiMWXOWQyUiMjYlJSVwc3OrcX9BQUGD2q9XgpOSkoJu3brhtddeQ0pKCk6cOIHCwkIAgLOzM5YuXYpPP/20QYEREREREZHp6dixI3bv3l3j/j/++KNBa1TWa4haU4+bIyIiMmZCCPbiEBHV4Nlnn8WMGTPQuXNn3HXXXQAq191JSEjAwoULsX//fvz+++/1br9eCU5Tj5sjIiIyZkxwiIhqNm3aNCQlJeHVV1/FK6+8AgAYNWoUhBBQKBR4++23tQuO1ke9EpymHjdHRERkzOq5AgMRUavxyiuvYPr06fj999+RkJAAWZYRFBSEiRMnIjAwsEFt1yvBqRo39+ijj+rd39Bxc0RERMaMVdSIWq7i4mJYW1sbOgwC0LZtWzz33HON3m69/gI/++yzWLlyJd555x3k5eUB+G/c3PTp07F///4mCZaIiMgYcHgaUcuVlZVl6BCoidWrB6epx80RERERETWFgoICzpNrZgqFol6/b41GU6/Xq/dCn005bo6IiIiIqCmUlZWhuLgYNjY2hg6l1XjttdeqJThr167FqVOnMHLkSLRr1w4AEBcXh82bNyM8PLz5iwxUaapxc0RERERETSU7O5sJTjNasGCBzuOvvvoKGRkZiI2N1SY3Vc6cOYMhQ4bA29u73q/HWZBERERE1KroW+aEms97772HJ598slpyAwAdOnTAk08+iXfffbfe7de5B6dz58631LAkSTh+/PgtB0RERERE1JSys7MNHUKrlpKSAnNz8xr3m5ubIyUlpd7t1znBcXZ21hk7V15ejn379qFz585wcnKqdwBERERERM2JCY5hhYeH4/PPP8fUqVPRpk0bnX0pKSn4/PPP0alTp3q3X+cEZ+fOnTqPs7Ky4O7ujg8++ABDhgypdwBERERERM0pNzcXGo0GSqXS0KG0Sh9++CFGjhyJ0NBQTJgwAcHBwQCA+Ph4/PHHHxBC4Keffqp3+/UuMsDSekRERERkjGRZRnp6erXeA2oe/fv3x8GDBzFv3jysXbsWJSUlAAArKyuMHDkSCxcubJ4eHCIiIiIiU3H69GkmOAYUHh6OtWvXQpZlZGZmAgDc3NygUDS8BhqrqBERERFRq3Px4kWkpqYaOoxWT6FQwMPDAx4eHo2S3ABMcIiIiIiolYiMjMSoUaPw1ltvAQB27NiB4uJiA0dFja3OQ9SOHDmi8zgvLw9A5WQgR0dHvc/p3r17/SMjIiIiImpE6enpyMjI0F67FhcXY/PmzRgzZkytZYvJuNQ5wYmMjNRbWOCJJ56otk0IAUmSoNFoGhYdEREREVETysjIwObNmzFixAgmOSaizgnOsmXLmjIOIiIiIiKDSE1Nxbp16zBixAjY2NgYOhxqoDonODNmzGjKOIiIiIiIDCYzMxNr1qzBoEGD4Ovra+hwqAFYZICIiIiICEBJSQk2bNiAPXv2oLy83NDhUD0xwSEiIiIius7p06exevVqpKSkGDoUqgcmOERERERENygoKMD69euxZcsW5OfnGzocugV1noNDRERERNTaXLx4EUlJSWjfvj26devGIgRGgAkOEREREVEtZFnG6dOncfbsWXTo0AFdu3aFtbW1ocOiGjDBISIiIiKqA41Gg9jYWJw5cwZhYWHo1q0bVCqVocOiG3AODhERERHRLdBoNDhx4gRWrlyJuLg4CCEMHRJdhwkOEREREVE9qNVq7N69Gxs3bkRpaamhw6FrjDLBSUxMxIMPPoiAgABYWVkhKCgI8+fPR1lZmaFDIyIiIqIWKDk5GcXFxQCAsrIyZGdnN1rbly5dwtq1axu1zZbIWK7BjTLBiYuLgyzLWLp0KU6dOoUPP/wQX375JebOnWvo0IiIiIioBTl06BDGjh0Lf3///2/v3uOiKvM/gH/ODDDDbRAJRQQZGFlRKVA0ilS8pKCkkRe8/Cxw1WytXFvt9wv7GWAmKl5q8dLtF7a6mmXqmopiJrpSW22peU1Q0RYz0QVEUZKZ5/dHy7w8DJcBZxgYPu/Xa155nvOc53zP2OM833N5DkpKSgAAFRUVmDdvHlavXo3CwkKL7Ke8vBx/+9vfcO7cOYu01xK1ljG4JOzkpsGMjAysXbsW58+fN3ub77//HhEREfjuu+/Qu3dvK0ZHRERERM1t69atGD9+PIQQ0Ov1JusVit/O9U+fPt2iY8Hg4GBERUXZbAKC5hzjNmUMbm2t8gpObcrKytC+fft661RWVuLGjRvGz82bN5spOiIiIiJqTt988w3Gjx8PvV5fa3ID/Db9s8FgwHvvvWexKzkAkJ+fjy1btuDy5csWa7Mpbt68KRv7VlZWWnwf5ozBm5tdJDgFBQXIzMzEjBkz6q2Xnp4ODw8P4yc6OrqZIiQiIiKi5rRw4UIIIcye4Wz37t0W3f+tW7ewa9cunDx50qLtNkZ0dLRs7Juenm7R9s0dgze3FpXgvPLKK5Akqd7PmTNnZNsUFRUhNjYW48aNw/Tp0+ttPzk5GWVlZcbPwYMHrXk4RERERGQDly5dws6dO+u8clOTwWDADz/8YPFJAoQQyMvLQ1FRkUXbNdfBgwdlY9/k5ORa61l7DN7cWtSLPufMmYOkpKR66wQFBRn/fPnyZQwaNAhRUVF49913G2xfpVLJ7oV0c3NrcqxERERE1DLt37+/0e+mEULgzJkziIqKsng8ly5dQufOnS3ebkPc3Nyg0WgarGftMXhza1EJjre3N7y9vc2qW1RUhEGDBiEiIgJZWVnGh8SIiIiIqG0rLy+HQqGAwWAwextJkqz2LpsHHnjAKu1air2NwVtUgmOuoqIiDBw4EAEBAVi2bBmKi4uN63x8fGwYGRERERHZmru7e6OSG+C3KzhqtdrisbRr1w46nc7i7dpCaxmDt8oEZ9++fSgoKEBBQQH8/Pxk6+xk1msiIiIiaqIhQ4ZAkqRGjQslSUJISIjFY+nXr1+LvMrRFK1lDN4qv+2kpCTjrBg1P0RERETUtnXp0gVPPPEElEqlWfUVCgUeeughi093HBoaCl9fX4u2aUutZQzeKhMcIiIiIqL6zJ8/3zgDmDlGjBhh0f37+PggMjLSom2SeZjgEBEREZHd6du3LzZv3gylUlnnlRyFQgGFQoFnn30WWq3WYvvu2LEjYmNjzb6CRJbFBIeIiIiI7NLo0aPx5ZdfYsSIESZXciRJwoMPPoj/+Z//Qa9evSyyP0mSEBYWhieeeAJOTk4WaZMar1VOMkBEREREZI6+fftix44duHTpEsLDw1FSUgIXFxfMnz/fos/caLVa9O3bF56enhZrk5qGCQ4RERER2b0uXbrAxcUFJSUlcHJyskhyI0kSgoKCEB4eDi8vLwtESZbABIeIiIiIqJECAgLw8MMP84pNC8QEh4iIiIjITG5ubujfvz/8/f1tHQrVgQkOEREREZEZAgMDMWDAAKhUKluHQvVggkNEREREVA9HR0c88sgjCAkJMfu9OmQ7THCIiIiIiOoQFBSERx55BG5ubrYOhczEBIeIiIiIqIaOHTsiMjISPj4+tg6FGokJDhERERHRf2g0GkRGRkKr1fJ2tFaKCQ4RERERtXmSJCEsLAwRERFQKpW2DofuAxMcIiIiImrTVCoVhg4dCl9fX1uHQhbABIeIiIiI2iyVSoWRI0eiffv2tg6FLIQJDhERERG1CT4+Prh79y7UarWxbNCgQUxu7AwTHCIiIiJqE/75z3/iyJEj+PbbbwEAWq0WXbp0sXFUZGkKWwdARERERGQLvXr1snUIZAVMcIiIiIiozfHx8YG3t7etwyArYIJDRERERG1Oz549bR0CWQkTHCIiIiJqU1QqFbRara3DICthgkNEREREbYpWq+XLPO0YExwiIiIialM6d+5s6xDIipjgEBEREVGbwskF7BsTHCIiIiJqMxQKBTQaja3DICtigkNEREREbYarqyskSbJ1GGRFTHCIiIiIqM1wcXGxdQhkZUxwiIiIiKjNcHZ2tnUIZGVMcIiIiIiozXBycrJ1CGRlTHCIiIiIqM1wcHCwdQhkZUxwiIiIiKjN4BUc+8cEh4iIiIjaDM6gZv9afYJTWVmJ8PBwSJKEo0eP2jocIiIiIiK715LH4K0+wfnv//5v+Pr62joMIiIiIqI2oyWPwVt1gpOdnY2cnBwsW7bM1qEQEREREbUJLX0M3mqnkfjll18wffp0bN++3ewXNlVWVqKystK4fPPmTWuFR0RERERkUzdv3sSNGzeMyyqVCiqV6r7abMoYvLm1yis4QggkJSXhueeeQ58+fczeLj09HR4eHsZPdHS0FaMkIiIiIrKd6Oho2dg3PT39vtpr6hi8ubWoBOeVV16BJEn1fs6cOYPMzEyUl5cjOTm5Ue0nJyejrKzM+Dl48KCVjoSIiIiIyLYOHjwoG/vWNXa29hi8uUlCCGHrIKoVFxfj+vXr9dYJCgpCQkICPvvsM9k0f3q9HkqlEv/1X/+FDz/80Kz9ff/994iIiMB3332H3r1731fsLd3PP/+Mn3/+2dZhkIV06tQJnTp1snUYZCHsn/aHfdS+sI/al7bQPxs7xm3uMbi1tagEx1yXLl2S3U94+fJlxMTEYMuWLYiMjISfn59Z7fz888945513MGPGDLv+H72yshIxMTG8YmVHoqOjsXfv3vu+j5Zsj/3TPrGP2g/2UfvTFvqntca4lhqDW1urTHBqKiwsRGBgII4cOYLw8HBbh9Pi3LhxAx4eHjh48CDc3NxsHQ7dp5s3byI6OhplZWXQaDS2DofuE/un/WEftS/so/aF/dOyWuoYvNXOokaNFx4ezs5sB+49c0L2g/3TfrCP2if2UfvA/tk22EWCo9VqYQcXooiIiIiIWo2WOgZvUbOoERERERER3Q8mOG2ASqVCSkqKXT9M15bw79O+8O/T/vDv1L7w79O+8O+zbbCLSQaIiIiIiIgAXsEhIiIiIiI7wgSHiIiIiIjsBhMcIiIiIiKyG0xwiFqIpKQkaLVaW4eBpKQkvsyOiIiaRWpqKiRJatK2LeV3k1oeJjhENaxbtw6SJEGSJBw+fNhkvRAC/v7+kCQJTzzxRIPtDRw40NieJElo3749+vbtiw8++AAGg8Eah0BE/2HN/qxQKKDRaNCtWzc8/fTT2LdvnzUOgYjMVFFRgdTUVOTm5lp1P0lJSRg4cKBV90H3hwkOUR3UajU2btxoUn7w4EH861//atQUk35+fli/fj3Wr1+P+fPno6qqClOnTsW8efMsGTIR1cEa/fkvf/kLMjIyMGrUKHz55ZcYNmwYxo8fj7t371oydCIyU0VFBdLS0qye4FDLxwSHqA4jRozAJ598gqqqKln5xo0bERERAR8fH7Pb8vDwwOTJkzF58mS89NJLyMvLg5+fH1atWsXBEFEzsFZ/njFjBjIyMnD27FnMnDkTH3/8Mf73f/+33u0NBgPu3LnTpOMgIqKGMcEhqsPEiRNx/fp12W0nv/76K7Zs2YJJkybdV9suLi545JFHcOvWLRQXF9dZb9myZYiKioKXlxecnZ0RERGBLVu21Fp3w4YNePjhh+Hi4gJPT08MGDAAOTk5sjrZ2dno378/XF1d4e7ujri4OJw8ebLW9s6fP4+YmBi4urrC19cXCxYsQM3XZt26dQtz5syBv78/VCoVunXrhmXLlpnUI7I1a/ZnAFAqlfjzn/+MHj16YNWqVSgrKzOukyQJL7zwAv7617+iZ8+eUKlU2LNnD3JzcyFJksnZ5sLCQkiShHXr1snKP/nkE/To0QNqtRqhoaHYtm0bn0GgVuXw4cPo27cv1Go1dDod3nnnnTrrbtiwAREREXB2dkb79u0xYcIE/PTTT3XWLywshLe3NwAgLS3NeCtpamoqAOCHH35AUlISgoKCoFar4ePjg9///ve4fv26rJ3y8nLMnj0bWq0WKpUKHTp0wNChQ/H99983+ngNBgNSU1Ph6+sLFxcXDBo0CKdOnYJWq0VSUlKj2yPzMcEhqoNWq8Wjjz6KTZs2Gcuys7NRVlaGCRMm3Hf758+fh1KpRLt27eqs89Zbb6FXr15YsGABFi1aBAcHB4wbNw67du2S1UtLS8PTTz8NR0dHLFiwAGlpafD398cXX3xhrLN+/XrExcXBzc0NS5Yswfz583Hq1Cn069cPhYWFsvb0ej1iY2PRsWNHLF26FBEREUhJSUFKSoqxjhACo0aNwsqVKxEbG4sVK1agW7duePnll/GnP/3pvr8fIkuydn8GfktyJk6ciIqKCpPnfb744gu89NJLGD9+PN56661GJyW7du3C+PHj4ejoiPT0dIwePRpTp07Fd999Z5HYiazt+PHjGDZsGK5evYrU1FRMmTIFKSkp2LZtm0ndN954A8888wyCg4OxYsUKzJ49G/v378eAAQNQWlpaa/ve3t5Yu3YtAOCpp54y3hY+evRoAMC+fftw/vx5TJkyBZmZmZgwYQI++ugjjBgxQnZS7rnnnsPatWsxZswYrFmzBnPnzoWzszNOnz7d6GNOTk5GWloa+vTpg4yMDAQHByMmJga3bt1qdFvUSIKIZLKysgQA8e2334pVq1YJd3d3UVFRIYQQYty4cWLQoEFCCCECAgJEXFxcg+1FR0eLkJAQUVxcLIqLi8Xp06fFrFmzBAAxcuRIY73ExEQREBAg27Z6v9V+/fVXERoaKgYPHmwsy8/PFwqFQjz11FNCr9fL6hsMBiGEEOXl5aJdu3Zi+vTpsvVXrlwRHh4esvLExEQBQLz44ouyduLi4oSTk5MoLi4WQgixfft2AUAsXLhQ1ubYsWOFJEmioKCgwe+GyNqs0Z979uxZ5/pt27YJAOKtt94ylgEQCoVCnDx5Ulb3wIEDAoA4cOCArPzChQsCgMjKyjKWPfjgg8LPz0+Ul5cby3JzcwUAk383iFqi+Ph4oVarxcWLF41lp06dEkqlUtw7HC0sLBRKpVK88cYbsu2PHz8uHBwcZOU1fzeLi4sFAJGSkmKy/5q/p0IIsWnTJgFAHDp0yFjm4eEhnn/++aYcosyVK1eEg4ODiI+Pl5WnpqYKACIxMfG+90F14xUconokJCTg9u3b2LlzJ8rLy7Fz584m3c5y5swZeHt7w9vbG927d0dmZibi4uLwwQcf1Luds7Oz8c8lJSUoKytD//79ZZfKt2/fDoPBgNdeew0KhbxLV0+9uW/fPpSWlmLixIm4du2a8aNUKhEZGYkDBw6Y7PuFF16QtfPCCy/g119/xeeffw4A2L17N5RKJWbNmiXbbs6cORBCIDs728xvh6h5WKo/16d6ivXy8nJZeXR0NHr06NGkNi9fvozjx4/jmWeekU3hHh0djQcffLDpwRI1E71ej7179yI+Ph5dunQxlnfv3h0xMTGyulu3boXBYEBCQoLs98rHxwfBwcG1/l6Z497f0zt37uDatWt45JFHAED2m9quXTt8/fXXuHz5cpP2U23//v2oqqrCzJkzZeUvvvjifbVL5nGwdQBELZm3tzcef/xxbNy4ERUVFdDr9Rg7dmyj29FqtXjvvfcgSRLUajWCg4PRoUOHBrfbuXMnFi5ciKNHj6KystJYfu87A86dOweFQlHv4Ck/Px8AMHjw4FrXazQa2bJCoUBQUJCs7He/+x0AGG9nu3jxInx9feHu7i6r1717d+N6opbEUv25Pjdv3gQAk34RGBjY5Dar+1LXrl1N1nXt2rVJzwYQNafi4mLcvn0bwcHBJuu6deuG3bt3G5fz8/MhhKi1LgA4Ojo2KYZ///vfSEtLw0cffYSrV6/K1t37zNzSpUuRmJgIf39/REREYMSIEXjmmWdMfhMbUle/bd++PTw9PZt0DGQ+JjhEDZg0aRKmT5+OK1euYPjw4fU+M1MXV1dXPP74443a5u9//ztGjRqFAQMGYM2aNejUqRMcHR2RlZVV63S39al+38769etrnS3KwYH/FFDbYIn+XJ8TJ04AMB3U3Hv2uFpdLzfU6/UWjYmoNTEYDJAkCdnZ2VAqlSbrm/oi6oSEBHz55Zd4+eWXER4eDjc3NxgMBsTGxsreSZeQkID+/ftj27ZtyMnJQUZGBpYsWYKtW7di+PDhTT4ual4c1RA14KmnnsKMGTPwj3/8A5s3b262/X766adQq9XYu3ev7B0dWVlZsno6nQ4GgwGnTp1CeHh4rW3pdDoAQIcOHcxKtAwGA86fP2+8agMAZ8+eBQDjw9EBAQH4/PPPUV5eLjtbfebMGeN6opbGmv1Zr9dj48aNcHFxQb9+/RqsX30Wt+ZD0zWvflb3pYKCApM2aisjamm8vb3h7OxsvJvgXj/++KNsWafTQQiBwMBA2W+QOeo6aVBSUoL9+/cjLS0Nr732mrG8tngAoFOnTpg5cyZmzpyJq1evonfv3njjjTcaleDc22/vvYJ7/fp1lJSUmN0ONQ2fwSFqgJubG9auXYvU1FSMHDmy2farVCohSZLsbG5hYSG2b98uqxcfHw+FQoEFCxbIzkIBMM4MExMTA41Gg0WLFtX63p3apqpetWqVrJ1Vq1bB0dERQ4YMAfDbe0X0er2sHgCsXLkSkiTxTBe1SNbqz3q9HrNmzcLp06cxa9Ysk9s+axMQEAClUolDhw7JytesWSNb9vX1RWhoKP7yl78Yb4EDfntJ6fHjxy1zAERWpFQqERMTg+3bt+PSpUvG8tOnT2Pv3r2yuqNHj4ZSqURaWprJKweEECbTOt/LxcUFgOlJg+orQTXbe/PNN2XLer1edrsa8NuJQV9fX9lt4uYYMmQIHBwcjDO7Vav5m0nWwSs4RGZITExs9n3GxcVhxYoViI2NxaRJk3D16lWsXr0aXbt2xQ8//GCs17VrV7z66qt4/fXX0b9/f4wePRoqlQrffvstfH19kZ6eDo1Gg7Vr1+Lpp59G7969MWHCBHh7e+PSpUvYtWsXHnvsMdk/umq1Gnv27EFiYiIiIyORnZ2NXbt2Yd68ecb3DIwcORKDBg3Cq6++isLCQoSFhSEnJwd/+9vfMHv2bONVI6KW5n77c1lZGTZs2ADgtzenFxQUYOvWrTh37hwmTJiA119/3ax2PDw8MG7cOGRmZkKSJOh0OuzcudPk+QAAWLRoEZ588kk89thjmDJlCkpKSrBq1SqEhobKkh6iliotLQ179uxB//79MXPmTFRVVSEzMxM9e/aU/abpdDosXLgQycnJKCwsRHx8PNzd3XHhwgVs27YNzz77LObOnVvrPpydndGjRw9s3rwZv/vd79C+fXuEhoYiNDQUAwYMwNKlS3H37l107twZOTk5uHDhgmz78vJy+Pn5YezYsQgLC4Obmxs+//xzfPvtt1i+fHmjjrdjx4744x//iOXLl2PUqFGIjY3FsWPHkJ2djQceeKDOq01kIbabwI2oZbp3Wtn6WGpa2Wq1TRP9f//3fyI4OFioVCoREhIisrKyREpKiqit637wwQeiV69eQqVSCU9PTxEdHS327dsnq3PgwAERExMjPDw8hFqtFjqdTiQlJYl//vOfsjhcXV3FuXPnxLBhw4SLi4vo2LGjSElJMZmGury8XLz00kvC19dXODo6iuDgYJGRkWGcnprI1qzRnwEYP25ubiI4OFhMnjxZ5OTk1LoNgDqnnS0uLhZjxowRLi4uwtPTU8yYMUOcOHHCZJpoIYT46KOPREhIiFCpVCI0NFTs2LFDjBkzRoSEhDQYN1FLcPDgQRERESGcnJxEUFCQePvtt+v8Tfv0009Fv379hKurq3B1dRUhISHi+eefFz/++KOxTm2/m19++aVxH7hnyuh//etf4qmnnhLt2rUTHh4eYty4ceLy5cuyOpWVleLll18WYWFhwt3dXbi6uoqwsDCxZs2aJh1vVVWVmD9/vvDx8RHOzs5i8ODB4vTp08LLy0s899xzTWqTzCMJwVeOExERtUbh4eHw9vbGvn37bB0KEZmhtLQUnp6eWLhwIV599VVbh2O3+AwOERFRC3f37l1UVVXJynJzc3Hs2DEMHDjQNkERUb1u375tUlb93A/7rXXxCg4REVELV1hYiMcffxyTJ0+Gr68vzpw5g7fffhseHh44ceIEvLy8bB0iEdWwbt06rFu3DiNGjICbmxsOHz6MTZs2YdiwYSaTK5BlcZIBIiKiFs7T0xMRERF4//33UVxcDFdXV8TFxWHx4sVMbohaqIceeggODg5YunQpbty4YZx4YOHChbYOze7xCg4REREREdkNPoNDRERERER2gwkOERERERHZDSY4RC1MYWEhJEnCunXrbB0KEdWCfZSIqGVjgkNERERERHaDkwwQtTBCCFRWVsLR0RFKpdLW4RBRDeyjREQtGxMcIiIiIiKyG7xFjcgKUlNTIUkSzp49i8mTJ8PDwwPe3t6YP38+hBD46aef8OSTT0Kj0cDHxwfLly83blvb/f1JSUlwc3NDUVER4uPj4ebmBm9vb8ydOxd6vd5YLzc3F5IkITc3VxZPbW1euXIFU6ZMgZ+fH1QqFTp16oQnn3wShYWFVvpWiFoO9lEiIvvFBIfIisaPHw+DwYDFixcjMjISCxcuxJtvvomhQ4eic+fOWLJkCbp27Yq5c+fi0KFD9bal1+sRExMDLy8vLFu2DNHR0Vi+fDnefffdJsU2ZswYbNu2DVOmTMGaNWswa9YslJeX49KlS01qj6g1Yh8lIrJDgogsLiUlRQAQzz77rLGsqqpK+Pn5CUmSxOLFi43lJSUlwtnZWSQmJgohhLhw4YIAILKysox1EhMTBQCxYMEC2X569eolIiIijMsHDhwQAMSBAwdk9Wq2WVJSIgCIjIwMyxwwUSvDPkpEZL94BYfIiqZNm2b8s1KpRJ8+fSCEwNSpU43l7dq1Q7du3XD+/PkG23vuuedky/379zdru5qcnZ3h5OSE3NxclJSUNHp7InvBPkpEZH+Y4BBZUZcuXWTLHh4eUKvVeOCBB0zKGxrEqNVqeHt7y8o8PT2bNPhRqVRYsmQJsrOz0bFjRwwYMABLly7FlStXGt0WUWvGPkpEZH+Y4BBZUW1TyNY1raxoYEJDc6ajlSSp1vJ7H3KuNnv2bJw9exbp6elQq9WYP38+unfvjiNHjjS4HyJ7wT5KRGR/mOAQ2RFPT08AQGlpqaz84sWLtdbX6XSYM2cOcnJycOLECfz666+y2aKIyLLYR4mIrI8JDpEdCQgIgFKpNJntac2aNbLliooK3LlzR1am0+ng7u6OyspKq8dJ1FaxjxIRWZ+DrQMgIsvx8PDAuHHjkJmZCUmSoNPpsHPnTly9elVW7+zZsxgyZAgSEhLQo0cPODg4YNu2bfjll18wYcIEG0VPZP/YR4mIrI8JDpGdyczMxN27d/H2229DpVIhISEBGRkZCA0NNdbx9/fHxIkTsX//fqxfvx4ODg4ICQnBxx9/jDFjxtgweiL7xz5KRGRdkmjoqUkiIiIiIqJWgs/gEBERERGR3WCCQ0REREREdoMJDhERERER2Q0mOEREREREZDeY4BARERERkd1ggkPUgqSmpkKSJFuHYYzj2rVrtg6FiIiIqFGY4BD9x8cffwxJkrBt2zaTdWFhYZAkCQcOHDBZ16VLF0RFRdXbdlJSEiRJMn40Gg3CwsKwfPlyvpWcyAqasz+7ubkhKCgIY8eOxaeffgqDwWCx4yAiosZjgkP0H/369QMAHD58WFZ+48YNnDhxAg4ODsjLy5Ot++mnn/DTTz8Zt62PSqXC+vXrsX79eixatAjt27fH3LlzkZiYaLmDICIAzdufV65ciUmTJiE/Px9jx47FkCFDcOPGDcsdDBERNYqDrQMgail8fX0RGBhoMiD66quvIITAuHHjTNZVL5szIHJwcMDkyZONyzNnzkRkZCQ2b96MFStWwNfX1wJHQURA8/dnAFi4cCEWL16M5ORkTJ8+HZs3b65zeyEE7ty5A2dnZ3MPiYiIzMQrOET36NevH44cOYLbt28by/Ly8tCzZ08MHz4c//jHP2S3n+Tl5UGSJDz22GON3pdCocDAgQMBAIWFhXXWy8rKwuDBg9GhQweoVCr06NEDa9eurbVudnY2oqOj4e7uDo1Gg759+2Ljxo2yOl9//TViY2Ph4eEBFxcXREdHm5zJrnbt2jUkJCRAo9HAy8sLf/zjH3Hnzh1ZnaqqKrz++uvQ6XRQqVTQarWYN28eb70jm2vO/lztlVdewbBhw/DJJ5/g7NmzxnKtVosnnngCe/fuRZ8+feDs7Ix33nkHhYWFkCQJ69atM2lLkiSkpqbKynJzc9GnTx+o1WrodDq88847LebZPSKiloIJDtE9+vXrh7t37+Lrr782luXl5SEqKgpRUVEoKyvDiRMnZOtCQkLg5eXVpP2dO3cOAOrdfu3atQgICMC8efOwfPly+Pv7Y+bMmVi9erWs3rp16xAXF4d///vfSE5OxuLFixEeHo49e/YY63zxxRcYMGAAbty4gZSUFCxatAilpaUYPHgwvvnmG5N9JyQk4M6dO0hPT8eIESPw5z//Gc8++6yszrRp0/Daa6+hd+/eWLlyJaKjo5Geno4JEyY06TshspTm7s/Vnn76aQghsG/fPln5jz/+iIkTJ2Lo0KF46623EB4e3qh2jxw5gtjYWFy/fh1paWmYOnUqFixYgO3bt99XvEREdkcQkdHJkycFAPH6668LIYS4e/eucHV1FR9++KEQQoiOHTuK1atXCyGEuHHjhlAqlWL69OkNtpuYmChcXV1FcXGxKC4uFgUFBWLRokVCkiTx0EMPGeulpKSImt2yoqLCpL2YmBgRFBRkXC4tLRXu7u4iMjJS3L59W1bXYDAY/xscHCxiYmKMZdXtBwYGiqFDh5rEMWrUKFlbM2fOFADEsWPHhBBCHD16VAAQ06ZNk9WbO3euACC++OKLBr8bImuxdn+uy5EjRwQA8dJLLxnLAgICBACxZ88eWd0LFy4IACIrK8ukHQAiJSXFuDxy5Ejh4uIiioqKjGX5+fnCwcHB5N8NIqK2jFdwiO7RvXt3eHl5Ge/FP3bsGG7dumWcVSkqKsp4O9dXX30FvV5v1v36AHDr1i14e3vD29sbXbt2xbx58/Doo4/WOsvTve69R7+srAzXrl1DdHQ0zp8/j7KyMgDAvn37UF5ejldeeQVqtVq2ffWtK0ePHkV+fj4mTZqE69ev49q1a7h27Rpu3bqFIUOG4NChQyazPz3//POy5RdffBEAsHv3btl///SnP8nqzZkzBwCwa9euhr8YIiuxZn+uj5ubGwCgvLxcVh4YGIiYmJgmtanX6/H5558jPj5e9rxe165dMXz48KYHS0RkhzjJANE9JElCVFSUcbCfl5eHDh06oGvXrgB+GxCtWrUKAIwDI3MHRGq1Gp999hmA32ZgCgwMhJ+fX4Pb5eXlISUlBV999RUqKipk68rKyuDh4WG81S00NLTOdvLz8wGg3lnbysrK4OnpaVwODg6WrdfpdFAoFMZnhi5evAiFQmH8fqr5+PigXbt2uHjxYoPHR2Qt1uzP9bl58yYAwN3dXVYeGBjY5DavXr2K27dvm/Q1ALWWERG1ZUxwiGro168fPvvsMxw/ftx4v361qKgovPzyyygqKsLhw4fh6+uLoKAgs9pVKpV4/PHHGxXLuXPnMGTIEISEhGDFihXw9/eHk5MTdu/ejZUrVzbqfRvVdTMyMuq897/6zHNd6nqQmQ84U0tlrf5cn+rnemomHrXNmFZX39Hr9fcdBxFRW8UEh6iGe9+fkZeXh9mzZxvXRUREQKVSITc3F19//TVGjBhh1Vg+++wzVFZWYseOHejSpYuxvOYLCnU6HYDfBlZ1nc2trqPRaMxOtPLz82VnnQsKCmAwGKDVagEAAQEBMBgMyM/PR/fu3Y31fvnlF5SWliIgIMCs/RBZiy368/r16yFJEoYOHdpg3eorpqWlpbLymlc/O3ToALVajYKCApM2aisjImrL+AwOUQ3VU7D+9a9/RVFRkeyMr0qlQu/evbF69WrcunXLIrez1EepVAL47Z0Z1crKypCVlSWrN2zYMLi7uyM9Pd1kGufqbSMiIqDT6bBs2TLjLTT3Ki4uNimrOVNbZmYmABjv+a8eEL755puyeitWrAAAxMXF1X+ARFbW3P158eLFyMnJwfjx401u8ayNRqPBAw88gEOHDsnK16xZI1uuvgK8fft2XL582VheUFCA7Ozs+46biMie8AoOUQ1OTk7o27cv/v73v0OlUiEiIkK2PioqCsuXLwdgmfv16zNs2DA4OTlh5MiRmDFjBm7evIn33nsPHTp0wM8//2ysp9FosHLlSkybNg19+/bFpEmT4OnpiWPHjqGiogIffvghFAoF3n//fQwfPhw9e/bElClT0LlzZxQVFeHAgQPQaDTGZ4SqXbhwAaNGjUJsbCy++uorbNiwAZMmTUJYWBgAICwsDImJiXj33XdRWlqK6OhofPPNN/jwww8RHx+PQYMGWfX7IWqItfpzVVUVNmzYAAC4c+cOLl68iB07duCHH37AoEGD8O6775rd1rRp07B48WJMmzYNffr0waFDh2Tv0KmWmpqKnJwcPPbYY/jDH/4AvV6PVatWITQ0FEePHjV7f0REds/W07gRtUTJyckCgIiKijJZt3XrVgFAuLu7i6qqKrPaa2ha2Wq1TRO9Y8cO8dBDDwm1Wi20Wq1YsmSJ+OCDDwQAceHCBZO6UVFRwtnZWWg0GvHwww+LTZs2yeocOXJEjB49Wnh5eQmVSiUCAgJEQkKC2L9/v0kcp06dEmPHjhXu7u7C09NTvPDCCybTUN+9e1ekpaWJwMBA4ejoKPz9/UVycrK4c+eOWd8NkbVZoz8DMH5cXFyEVqsVY8aMEVu2bBF6vd5km4CAABEXF1drexUVFWLq1KnCw8NDuLu7i4SEBHH16lWTaaKFEGL//v2iV69ewsnJSeh0OvH++++LOXPmCLVabVbsRERtgSTEPfe+EBERUasSHx+PkydPGmdKJCJq6/gMDhERUStx+/Zt2XJ+fj52796NgQMH2iYgIqIWiFdwiIiIWolOnTohKSkJQUFBuHjxItauXYvKykocOXLErEkNiIjaAk4yQERE1ErExsZi06ZNuHLlClQqFR599FEsWrSIyQ0R0T14BYeIiIiIiOwGn8EhIiIiIiK7wQSHiIiIiIjsBhMcIiIiIiKyG0xwiIiIiIjIbjDBISIiIiIiu8EEh4iIiIiI7AYTHCIiIiIishtMcIiIiIiIyG4wwSEiIiIiIrvx/3HOzY7f7kHIAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "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": [ + "
" + ] + }, + "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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4ODR4nJZm29HzmPvDekDSfj7IJOC3bUfwyrihGNo93NzhUQ3svAIgNMabFISmEnaeAbrHmspyaNSVsLqlJiHrzC5t4qA9SJcjFKVfwsXV8xF63yvIOrMLxRmX/0kgbiQZIXf/B5JMDrlMjo4T3sHpn2aiojgXkkwOoVFDrrBFh/unw8bRDQAQ0H8SKkoKkHFik+75JZkcbfo9DK9OA0z0WyGipsBikgdHR0dERETobbO3t4e7u7vBdtLKzi/GBz9ugEYI3ZdB9Y1///vzJnQO8UMrNyfzBUg1atVpIJJ3/AB1eZnBkEwrWwd4doxFSVYykrYsQU7CIUAI2HkFIiB2ItxDewIArh38E5Akw5oFoUHOhf2oLC1C5OQPce3Q38g4tRVqVQmcAzqh9V1j9EZzOHgHo9vzS5Bzfj9Kc65B4eQJ97BekN80tbVMboX2o15Amz7jkJd0AjK5NVxDomFtxxkrqeXQqCuQfXYPClLOQGajhGd4Xzj41L9fUlNlMckD1d2WI2e1iUM1Nh46g0eG3dWIEVFdWNk6ouP4d3D217e1U03fSAJs7F0QPm4WyotycGLJyzeSixvDIjOu4Oxv76L9va/Aq9MAlGZfNbpqZpXS3Gtwce4M/94Pwb/3QzXGI5NbwyO8723jVrp6w9u1fgt+ETVlqoIsnPphGspyrmlr6ACk7vsD3l1HInjEsy2q349FJw/bt283dwgWLTOvCHKZhEq14YeHJEnIyuPoE0vn5BeGbv/5DjkXD0KVnwGlqw9cg2Mgk1vhwt//M6yVuFHFdHnLN/Ds2A82Th6oLKv+OiucPBr4FRC1HBf+moey3HQA0OtknH50LRxbh6JV58HmCq3RWcxoC6o7fy9XqNXG28w1QsC/lVsjR0R1JTRq5CedQHlRLmzdWsMtpBtkcm1On3N+X7XDLCuKclF8PRE+0SONn1iSwdE/HLZurRsqdKIWpTQnDflJJ6q5JyVcO/x3o8dkThZd80A1GxQdhq9X70ZZeeWNiYC0JAmwsZZjSEwHM0ZHt1OckYQzv7wNVf513TYbBzd0eOgtOPq207umxggh4N1lOPKvxCPrzE5tNaoQgNDAxsENoTVMHkVEdaPKz6hhr4Aq73oN+5sf1jw0Yfa2Crz/1GjYKW0AAHKZ9nIqbazx7hP3wtnB1pzhUQ3UFWWI//ENqAoy9baXF+ch/sc3UFlWBLf23audB8Lazhn2rYIgyeQIHf1/iJjwLrw6D4ZneD8Ej5iC6Ge+gNLVpzFeClGLUDXlu1GSBKVLy+oHxJqHJqRUVYGVO49hw8EzKCwpQ1ibVnhoYAx+mvkEdp24iGvZ+fB2c0JsVDvYKmzMHS7VIOvMLuPzOAgN1KoSZJzaCv/eDyH73F5oKisMqkoDBkzWNW9IkgSXoC5wCerSCJETtUxKVx84B3Y2PmurEPDpNso8gZkJk4cmQlVeiVc//x0XUjJ01dlHLiTj0Lkr+L8JnNOhqSnOSNLNp3ArSSZD8fUk+Ha7B5GTP8Llzd9o21qhfQPjvApE5tH+3pcR/8M0lGan6kZbQKOGT8zd8Oo00NzhNSomD03EugPxuJB8XW9uP41G++izP7ahT2Q72CqszRMc1Zm1nXO1fRqEELC2087P4eAdjE4T30dlaSE0lRWwdnBtUcPBiMxJCIHCq2eRc/EAhBBwDY5G1JMLkXthP/JTTkNubQuP8D4GK+C2BEwemohtR88bTApcpVRVgWMXktGrU8v7A26qPCP6a6eNNkZo4BWp/y3GytaxEaIioioadQXO/TEXORf2Q5JpZzhO3fcHnNtGIfyhGbWaE6U5Y4fJJqKsvOKO9pNlUTp7IXj4M9oHN96Yqt6gAgc+BjuPNuYKjYgApOz6GTkXDwDQDqmuamLMTzqJK9uqSfxbENY8NBGdQ/yQlJ6ta6q4VXhb9qxvanyi4+Dg0x5pR9aiLOcqlK4+8O4yHE7+7L9CZE5CaJB2eLXx2VuFBunH1iNw4KOQWbXcjulMHpqI0X27YO3+eFRUqPWmpJYkCYO6hsLbjesLNEWOvu3g6Psfc4dBRDdRq0prnLlVU6FCRUlBi57BlclDE+Hj4YyPnr0fHy3fiJSMXADaeR2G9QjHlNH9zRobmVdpTiquHVqNwtRzsLZ1glfkQHh06KNrBiGiupHbKCFX2EGtKjG6X5Jbt/h+SEwempAOAT745vVJSLyWhcKSMrT18eBEUC1cXuIxnP5lFoTQABoNIMmQe+kwMs/sQof7pzGBIKoHSSaHd9cRSN2/wrDpQpKhVechkFsrzBOchWCHySZGkiQEt/ZEVDt/Jg4tnEZdifN/fqztyKW5MWnNjclrcs7vQ2b8DjNGR9S0ten3MJwDIgFok4mqRNzRtz0CBz1mztAsAmseiJqo/KQTxmepBABJwvUTmwyGfBJR7citFYh4+F3kJR5DzoUDEBBwDY6BW0gMa/TA5IGoyaooLax+pxDVJxZEVCuSJINrcDRcg6PNHYrFYfJAZOGK0i8hdf8KFKScgVxhj1aRg+AdPRIOrYKqP0gmh4Nv+8YLkohaFCYPRA3A29tb79/qCI0aeZePoTQ3HUpnL7gGR+tVieYkHMKZX98GhAQI7SQ1lzd/g8wzu9DpkTlwCeqCvMsnjC7U49v9HtO+KCKiG2qdPPTs2ROLFy9GREREQ8ZD1CwcPnz4tmWKM5Jw5pe3ocq/rttm4+iODg/OgKNvOwiNGhf//h+gEQBuTg4Eiq5dQPqRtQgb/RrO/vE+8pNO6vbKbezQbtQLLXK+fSJqHLVOHpKSkhAdHY2XX34Zb731FpRKZUPGRdSsqSvKEP/jG6goKdDbXl6Ui9PL30TM1G9QlH6phn4LAhmntqL1XaPRaeIcFKVfQlFaAqxsHeAaHNPih5ERmYKqIAup+1cg+9xeCCHg1r47/O66H0rXmmsUW4JaD9U8f/48nnjiCXz44Yfo1KkTNm/e3JBxETVrWWd2axMDg+YGDSrLipFxamu1E9RUqSwr1v3fwTsY3l2GwSOsNxMHIhMoy03Dsa+fw7VDf0NVkInywiykH12PY18/h5LMZHOHZ3a1Th6cnJywcOFC7Nu3D05OThg2bBgeeeQRZGZmNmR8RM1Sccblaod7STIZiq8nwcGnHVDd8tsyGZzadGzACIlatstbl6KytEg/wRcaqMvLkLjpK/MFZiHq3GGyW7duOHToED777DPMmDEDq1evhr+/v0E5SZJw4sQJkwRJ1NxY2zlDGFt0B4AQAtZ2TlA4ecCr00BknNp6yyx3EiRIaH3X6MYJlqiF0agrkH1ur2HNIAAIDfISj6GyrAhWSofGD85C1Gu0RWVlJTIzM6FSqeDu7g53d3dTx0XUrHlG9MeV7dUs6ys0usmdQkZOBQC9BMLa3kXbIbKmoZpEVG9CXWk8cbiJukLF5KEuNm/ejGeffRaJiYl49tln8d5778HRsWUvEEJUV0pnLwQPfwaX1n0OyOSARg1JJofQqBE48DHYebQBAMisbND+npcQ0H8SitITYKWwh5N/OGe4I2pAMmslbN39UJqdCsCwhtDGyQM2Dq6NH5gFqXXykJmZiRdffBE//fQTOnXqhL1796J79+4NGRtRs+YTHQcHn3ZIO7IWpdlXoXT1gU/XEXDyDzcoq3DyaNHL/xI1JkmS0KbfBJxf+aHR/W36jIckteyloWqdPISGhqK8vBxz587FSy+9BLmc33yI7pSjb3s4cibIZkNVXontx8/jbFI67JU2GNA1FCF+XuYOi+rBs2MsKstKkLR1CdQq7cgmmbUSAbET0arLMDNHZ361Th7uuusufP755wgMDGzAcIiImqa0rHy8vPA3ZOYVQS6TARD4ddsR3B/bBU/f2w9SdSNnyGL5RI9Aq86DUHD1HCAEHP1CIbfmHEdAHZKHtWvXNmQcZCKpmXlYvukA9py6BCGAbh0C8fDQ7mjrwypvSySEQEHKGZTlpkHh7AXngIgWXx3aVL3z/RrkFGi/oao1/3S2+2PHMXQI9EFsFGuYmiKZlQ1cAiPNHYbF4doWzUjy9Rw8/7+fUVZeAbVG28ln18mL2Hf6EuZNfRChbTgrmiUpzUnFmV/fRWnWPxPOKF190OHBN2HvFWi+wKjOLqVm4mJKhtF9MknC33tOMnmgZoVfcSxETEwM/Pz8EBMTU+9zfLtmD0pvShwAQKMRqKzU4ItVO00RJpmIprIcp5ZNR2n2Vb3tZXnXceqHaai8zeySZFmu5xZUu08jBK5l5TdiNEQNjzUPFiI9PR2pqak1lhFC4FRiKrYcPofCUhXa+3lh+F0d4eJgh0q1GvviE6ExMvGQRgjEX76GvKISuDjYNdRLoDrIOrsH5YVZhjuEBpUlBciM3waf6LjGD4zqxcfdudp9MklCa0+XxguGbismJgbp6enw9vau1SJ2ZIjJQxMhhMDCFdvx5+4TkMskaDQCu08m4Kcth/DhM/cj0NvdaOJws4pKdSNFS7dTlJ6gm9fhVpJMjqK0S2aIiuqrrY8HOgR440LKdb2aP0CbvN/bp7OZIiNjavNljWrGZosmYs+pS/hzt3a6b7VGQECbUJSpKvDO0tWwtpKjnZ9XtT26W7k5wd2p5c6GZmmslI7VT08NwMqW16qpeXPySHi5OgEA5HIZZDLtvTh+cDf07sTl0al5Yc1DE7F2/ynIJMmgdkEjBK7nFuLEpauYPLwn3vz6T6PHTx7eU/dmRubnFRGL5B3LjO/UqOEVMaBxA6I75uXqhG9en4TdJxNw9koa7JQKDOjSHgHenL6fmh/WPDQRmblFNTZLZOcXoUfHtnhz0kh4OP/zrdXZwRYvjh2MId06NEaYVEtKVx8EDvqX9oHsxm14Y4imf9/xsG/V1kyR0Z2wtpJjQNdQPDu6Px4d0ZOJAzVbrHloIgJ93JGSkWPQnlrF38sNABDbpT36dA7B5bQsCA3Q1tcdVpwN1CL59bz/xvTUa1CanQqlqzd8uo6Aa3C0uUMjIqoRk4cm4r6+Udh+7ILBdrlMQoifF9r7e920TYaQ1pwStylwCYzkBDRE1OSw2aKJ6NjWF/95cCDkMgmSpE0aAKC1pytmPnY3p74lIqJGw5qHJuTuXpHo3SkYO45fRFGpCu38vBATFnBjHn0iIqLGweShiXF1tMd9faMa9DlSM/Pwy5ZD2Hc6EZIkoXenYIwdFANvt+onwiEiy3Q1MxdbDp9DfnEpgn09MTA6FLYKG3OHRU0ck4cm5mpGLrYdPY+CkjK092+F2Kh2sLE23WVMSsvGf+b/AlXFP9Ncr90fj+3HLuDTF8bqOmYSkeX7fftRfPnnTshkEiRIUGs0+HbtHnz4zP0Ibu1p7vCoCWN9dxPy0+aDeGzOd/hh0wH8veckPly+AZPfW4qrGbl1Ppdao0Gl2nB2wy//3ImyCsP1MUpU5Vj89+47ip+Iaq9SrcaV9Gxcy8qrdkKxsvIK7D6ZgI0Hz+DK9Ry9ffGJ1/Dln9o1bTQaoVvps6hEhZnf/KW38idRXbHmoYk4eiEZ367ZC0D7RqCdhxDIKSzGrCWrsfj/Jtaq02RSWjaWrN2D/acvQyMEItr6YvKInohq54/iMhUOn79i9DiNRuDA6ctQlVdCYcM/G6I7VVGphpVcZnDfCiGweu9JfL9+P/KKSgEAAd7umDqmP6La+evK7Th2AfN+2YwSVblu210d22LaxBGwU9pg9d6TkMsko9NlX88txNELyegWFthwL5CaNYuqeVi0aBEiIyPh5OQEJycn9OzZE+vWrTN3WBbhr90njM4QqdEIXEnPxpmktNue48qNJbv3n7msm3DqTFIa/m/RChw8exnlFZU1Hq8RAhXqmssQUfU0GoEVO47h4dnfYOSrn2HMG19g8d+7UKqq0JX5a89JfPr7Nl3iAADJ17Px+hcrcT45HQBw7ko63lu2Ti9xAICDZ5Lw8c8bAQBp2fnVzgsDAOnZ1a8ESndGXaFC1tndSDu8GgUpZ6qtOWrKLOorpJ+fH+bOnYt27dpBCIHvvvsO9957L44dO4aOHTuaOzyzSs3Mu1HjYFxadj46tvWt8Rzfr9sHVWWl3nk0QkACsGjlTnzz+iPwcXdGWrbh8sESAP9WbrBXKur7EohavAUrtuHvPSd1j4tKVfh921HEJ17Df6c+ACGA79fvMzhOCEBIAj9sPIB3nrgXv28/CpkEqG95S9AIgV0nEpCWnQ9fDxecT06vNoHwdncy6WtrrirLipB/5RSE0MC5TSdY29X8e8u5eBDnV30MtapYt83eOxjhY2dC4dh4M46q8jNx/cQmlOamQenshVadB0Pp6mOy81tU8jBq1Ci9x++99x4WLVqE/fv3t/jkwcfdGckZOdUmEN5u2j/oUlUFdp+8iMy8Ivh7ueKujkGwtpJDCIG98ZeMHi+g7ZGdnlOAR4b1wIfLNxotM2nYXZxPgqieUjJy9BKHKhohcCYpDbtPJsDPyxUFxWVGj9doBI5dSAGAGpMCAEi8lom7e3XC5sNnDfbJJAmeLg7o2r5NPV9JyyCEQOq+33Flx48Qam3NkCSzgl+vB9Am1ngzcWl2Ks7+9p7BarnFGZdx5pfZiHp8fqO8h2af34dzf8yFEP/0a0nZ8wvaj3oJXpEDTfIcFpU83EytVuO3335DcXExevbsabSMSqWCSqXSPS4qKmqs8BrdqN6R2Hc60WC7TCbB18MFHdv64uj5ZMxeuholZeW6tk43J3u8/9R9CPL1uG3VmVqjwZBu4Sgrr8C3a/aiqFT7u3WyU+KJUX0Q26V9g7w2oubi9OVr+GXrYZy+fA12ChsM6dYB9/fvCnulAvtPXza6uB2g/UDfG5+ICYO713h+uVzb0uxsb4vrOQWo7o52tFOiY1tf/Pvefvjiz52QSRIkSTvawtFOibefuIfzw9xGxonNSNq6VG+b0FQiZffPsLJzQuvu9xock3ZkzY0P7FurhDQoTr+EgpQzcG5T/RdhIQQKr55FzsUDEELANagrnAM7GyQcpTmpyDixBaqiHNh5+KNV5GBY22uH0leU5OPcig8gNIZNzBf+/gRObTpC6dKqdr+EGlhc8nDq1Cn07NkTZWVlcHBwwMqVKxEeHm607Jw5czB79uxGjtA8unUIxCPDemDZhgM3ZpfUvhE429ti1r9GIbugGDO+/hMVN0ZQVH0rySsswbQvV+KHGf9Cl/ZtcPR8stE3L08XB/h6uAAARvXujGHdO+JCynUAEkLbtIK1FdfHoJYrJiYG6enp8Pb2xuHDh42W2XUyAe8sXQNJ0tYSFBSX4ceNB7HrRALm/2cs1OrqRzcICKjVGgR4u8HH3Rnp2fkGiYFMJiG2czsAwNDu4Tifct3gPDJJgruzva4J8/7+XdEzIgibD59FfnEZglt7YkCXUNgqrOv3i2ghhBBI2fNLtfuv7vkNvjF3Q5Lpvy8WpSUAovrrXJJxudrkQaOuxPmVHyD73F7deVP3/QHngEiEj50JuY0SAJB2ZC0urfsckCRte7IQSN75I8LHzoJLYCQy4rcbTRyqXD+xGQGxD1e7v7YsLvUMDQ3F8ePHceDAATzzzDOYPHkyzpw5Y7TstGnTkJ+fr/vZsWNHI0fbuCYN74nF//cIHhwYg+E9OuLFsYPx3RuPIaCVG9YfOI1KtQa35gUaIZBbWILdJxMwabi22cFYtdm/4nrrfROxsbZCRFBrRAT5MnGgFi89PR2pqalIT083ur9Srcb8X7dACGHQp+jK9Rz8ufs4okMDql0ZVwggOiwAkiRhypj+gCRBdtN9KpdJsFcqMGGotmZixF0RiL7R7FBVTCaTIJfL8NrDw/XuZV8PF0wa3hPP3T8AI++KYOJQC+ryUpTlVt8JvaI4F+VFhkPkre1ddKvjGmNlV/1Ee1f3/obsc9r+LkKj1jV95CfHI2nrEgBAcUYSLq1bCEBokxSNBhACmspynP3tXagryqDKz4RUTQySJEFVkFltDHVhcTUPNjY2CAkJAQBER0fj0KFDmD9/Pr788kuDsgqFAgrFPx34HBwcDMo0N4E+7ng8rrfB9uTrOdVWYcrlMly5noOB0WH48Nkx+GLVTly8mgFA21fisbheGNg1rAGjJmre4hOvIb+41Og+IQS2HjmP8YO7o1dEMPadTtRrQpTJJPi4O2Ng11AAQI/wtvh4yv34YcMBnLx0FVZyOWKj2mHisLt0s7xaW8nx7lP3YvPhc9hy5ByKSsoQEdQa9/btDD9P14Z/wc2czMoGkkxu0HfhHxLkNrYGW1t1Hozsc3uMl1fYwq1dN6NnE0Ig7dDfMGjuAAChwfXjGxE46DGkH9tgPC4hoFYVI/vsXti6+VYbtxAa2LrV3LG+tiwuebiVRqPR69fQkqnKK7Fq13FsOHgaRSUqhAa0wkMDYtApuDXcnOyN9r4GtL9DNyd7AEBksB8+f3kCsvKLoFZr4OniaHQIaEMSQuB88nUkpGbAyc4W3cMDobThtyFqusrKK2rcX3pjSOUbk0bg2zV7sHrvKagqKiGTJPSNbIcpY2L17oHIYD98+Kxfjee0kssxvEdHDO/RsjuT14e3t7fev7eSya3gEd4Xmad3GjZDSDK4hcTASmlvcJxrSDe06jIc14+t19ZACA0gaefyCL3vVUgyK2Sd24OSzGTYOLjBo0NvWCkdoKlUoaLEcJRbFU1lOSqK8qDKz6g2MZBkcqgKMuATMwpJW76FuqIM+lXREiSZFVp1HlzzL6eWLCp5mDZtGkaMGIE2bdqgsLAQy5cvx/bt27FhwwZzh9bgbvfHXF5Rif9b9AfOXknT/T0cPJuE/acv4/WJwzGsezj+2H7U6LFWcjkG3NLZ0cO5YWpprmbkYuOhM8gpKEaAtzuGdguHs8M/GXpeUQlmffM3Tt80L4Wdwgb/9/BQ9O4U0iAxETW00DatjE7IBGibHKomd7KxtsK/74vFoyN7ISu/CC4OtnCwVTZ2uC1edf1WbhY48DHkX4lHeVHOPwmEJIO1rSPaDnnK6DGSJCFk5FS4t++B6yc2o7w4Fw7eIfCJiQOEBocXPoHygkxd7UHihi/Q/r5X4R7aE1ZKB1SWGe/0L8mtYW3vAls3n3+SklsIjRpKVx9YKe0RPm4WzvwyG2pVCSSZFYRGDZmVDcIemA4bB9MsMWBRyUNGRgYmTZqEtLQ0ODs7IzIyEhs2bMCQIUPMHVqDu90f8/oDpw0mgqpqW/30t6345e0n8fS9/fDlnzt1b2La+eyB1x8eDid7wyo2U1u16zgWrtiue16NAL5fvx/vP3UfOgW3BgC8vWQNzibrtxuXqMrxztI1WPTyw2jr69HgcRKZmqujPUb27ITVe0/qfdmTJEAmk+GB/tEAgIspGdh0+Azyi0oR5OuJYT2MdwYn81M4eaDLk58i7fAaZJ3bCwgN3Nr3gG+3UUY/gCtVJcg6uxuqvOtQuvqg/T0v6To5Co1amzgUZuseA9oahXMr5iD631/AJ+ZubSfNW/vFSDJ4dRoIuY0SrboMR+qBVYbBSjJY2TrAPVQ7MtG5TQS6Pf8dsk7vRGnuNSidveDZMRZWto4m+/1YVPLwzTffmDsEi7X16Hltx1oj+0pU5Th6IRkP9O+KqBA/rDtwGpl5hfD3dMXInp3Q2tPFJDFcy8rDtmMXUFyqQnt/L/TuFKLrTJlwNQMLV2wHAL0OY+UVlZj57V/4aeaTSMnIwanE1GrPv2rXcbw41jRVakSN7dnRsbCSyfD33pOovDGywtvNGS+PG4xAH3d8v36fbrSUEMC2Yxfw46YDmPvv0QgPNE07NJmWtZ0z2vSbgDb9JtRYLu/yCZz97R2oy0t1tQqXNy1G+NiZcPIPR07CYajyM4wfLID0o+sQ0H8SCq9dQF7iUd1oC6FRw8EnBG2HPAEAsHP3Q/t7X8HFv+ZBCA0kSQahUcNKYYeO42ZDZvXPaqlWCjt4dx1uml+EERaVPFD1SsvKq+0QCQBlN6a3DfHzwnN+XnU697kr6Vi2cT+OXUiBTJLQJzIEjwy7Sy/p+HnzIXyzZo/eeHFvNyd8+Oz98HF3xtr98dXOo19YosKe+ASUV1TX+Ug7tPSCkaFnRE2FlVyOZ8f0x8RhdyHxWibslDZo5+cFSZJw/GIKlm04AAB694iqvBKzvl2N5TMfh5Wco5oaS22G3t5MCO2ohluHZgJARXE+zvw6G5pKbb+WqlqFSlUxTv88E92e/w6lWSnVNjdAaFCSlQKZlQ06jp+NvMsnkHNhP4RGA9eQaLiFdNN7Xq+I/rohmRVFObB194dnx1hdLUdjYfJgIW73xxwZ4oek69nVzjDZIaB+046eSEjBa4tWQuCfIWbbj53H/jOXseDFcfDzdMWR81fwzRptD2KNELpqtYy8QryzdA0WvjQeGbkF1c54J5NJyMgtRKB39VOzyiQJLo529XoNRJbEyV6pt4AVAKzdV31ynVtYgkNnr6BnRFBjhtmiVQ29vZ2i9Eu4suMH5CYcBiDg0jYKAbET4dj6n9Fp109t0SYOtzY3CAG1qgSZ8dtg4+hW/fwPkgw2N6atliQZXIO6wDWoS41x2Ti4we+uMbeNvyFZ3DwPLdXtxpHf1zcK1nK53thvQPuhO6BrKHw8qh8/XB0hBBb8sR0aodFLStQagVJVOb5ftx8A8Oeu6hflung1AxevZsDXw+XG5FWGNBoBX3cXRIcGwMleCWOzs2qEwPDu7DVOzVNGXvXJNQBk5Rc2YjRUG0VpCTi59BVt4nCj5iHv8gmc/O415Cef1pUrzbpa/bwKMjlKs1PhHtoLcoUdtLM63UJo0Krz0AZ6FQ2HyUMT0drTBXP/PUZvMRuZJGFItw54eWz1HUoz8wqx4I9teHDGVxjzxhd4//t1uHwtCwBwPbcASenZBgkzoP3A33XyonamtRrW1AC0i3bF9exU7bS7ro526BmhXWNj2sQRkMtkumSkKhka2DUUfW/MnkfUlKk1GlzPKUDBTfM++Hu5VZtcA0Brzs1gcZK2LYVGXalfYyA0EEKNpC3/9M9TOFU/9b8QGtg4ukNuo0TYmNchya1uTCIlATeaIgL6PwInv6Y3zw6bLZqQiCBfLJ3+KC6kZKCotAxtfTzgaKdE8vUcWFvJ4e/lqjd75PWcAkz95CcUlJTpPvx3nriA3acS8PGUB+BkV3MbmVqjnbHSx90Z17Lyq50dr5WbIwK83fHq+GH4788bIYS2qaJSrYGDnQLvPXmfrmNlTFgAFr/2CP7afQLnk6/DxcEWg7uFo3dEcKPPN0FkSkII/LnrBJZvPojcwhIAQHT7NnhmTH/c07sz1h84bXCMTCbBx80ZUSH+BvvIfDSV5chLPGZ8pxAoTD2PipJ8WNs5wytyEJJ3LTdaVJJk8Oo0AADgGhyNmGcXI/3YepRkJcPG3hVenYfA0bdpfmli8tDESJJ2rYmqN6rvN+xHYYl2Fb7WHi6Yev8AxIQFANAu7Vt4U+IAaJskhNBg4Yrt+PSFsXB3skd2QbHB88gkCZ2CW0Mmk3B370gcOnfFsIxMgp+nq66/xZBuHdC1fRtsPXpON89DbFR7g+lw/Txd8ezo/ib6jRBZhuWbDmLpOv3ltI9dTMF/5v+Cr159GC+PG4L//boZGqG9v9QaDTycHfDuk/cycbYwoob1KXRlbnSMVLq0Qru7/4OLf8+/MVe4QFXzRPt7X9Eb1qlw9kRA/0caIuRGx+Shifpz1wksXLldb9u17Dy8uXgV5j33IDoE+GD78QtG21k1QjuyITu/GI+O7IX//rxJb3/Vn/8jw+4CAPTsGISHBkbj161HbsyZL6DWCDjZKfHWo3F6tR3uzvZ4cEC0iV8tkWUrLlVh+aaDBts1Qtt/6I/tx/DM6Fj0CA/EtqMXkF9ciiBfD/SKCObaMRZIbq2Eg28oitIuGHaEhARbDz9Y2//T1NSq8xA4+YUj/fgGqPIzoHTxgXeXoVC6/tORXWjUqCjJh9zG1ujU1gBQlHYR2ef2QQg1XNp2MbqipqVg8tAEVVSq8f2G/QbbhQCEBPyw8QDefeI+VFRWPzQSAFQVFRjeoyM0QmDp2r26qlZfDxc8OyYWnUO00+NKkoQnR/VFbFR7bD16XjfPw6DoDrBT2tT0FEQtwumkayiv5n7TaAT2nU7EM6Nj4epojzGxNfekJ8sQ2P8RxC+fARjMsCMQOGCy7kNdaNTITTyG8sJsuLe/C45+HfQ+8KvWrUjZ+ysqinIBSYJ7aE+0HfwklC5eunNc+PsTZJ7aphuWeXXv73BqE4HwsTNhpbC8kWhMHpqg5Os5uqaKW2k0AscvXoVMJiE80Bdnk9KM9lVwcbCFj7t2hMbIuyIwrFs4kjNyYCWXwc/T1Wi2296/Fdr73/k68ETNze2+HbJZoulxCeqC8HGzcHnz19p5GgAoXX0QOOgx3UyOBSlncPaPOagoytEdZ98qCB0emgGlszYxSN75I1J2/fTPiYVA9vn9KEg5iy5PLYCNvQtSD6xC5qnt2t03rV1RkHIGlzd9jXZ3P9+wL7YemDw0Qber5rS+MdnMxKE9MO3LlUbLPDy0h96kNHK5DG19ODU0UX10CmoNWxtrlBpZIEsmSRxJ1ES5hcTANTgaqvxMAAIKZy9dolhelIP4n2ZAU1Gud0xxRhJOL38TXZ9ehMqyYlzd85vhiYUGFSX5SDu8BgGxD+Pawb9Q3YqaGae2oO2QJyyu9oFDNZsgfy9XtPZwMTpfgkwmITZK+0YVExaAGZPj4O70z+pvdkobPH1vP9zbp3NjhUvU7CltrPH4qD4A9Efyy2Tayc/G9IsyS1wAUF5ZiZ3HL+KP7Udx4PRlqNW37wxI/5AkCUoXLyhdWunVMKUf26hNHG7tXCk0KM1ORW7iUeRfOQmhqTR+YqG5MZOkGuWFWdU+v1BX1rjfXFjz0ARJkoSp9w/Am4tXQUj/rCUhk0lwtFXi4aE9dGX7RbVD707BSEjNQKVag5DWXlDY8LITmdq9fTrDyU6JHzcdxJX0bFjJZYiNao9/xfWGq6Ph8s2NIT4xFTO//RsFxWWQSRI0QsDL1RHvPXkfAn2qn/GV/iE0ahRnJAFCAzuvtpDJte+fxdcvGelMeYNMhqK0S7DzqHlZdUA7kZS1vQsqivOqPZepVsI0JX6KNFExYQGY99yD+GHjARy/eBXWcjliu7THw0O6o5Wbk15ZuVyG0DbGl/omItMZ0DUUA7qGQlVeCSu5DHK5+Sp3cwtLMO3LVVBVaJtSqvo+ZeUX4bVFf2DZjH/BxpofATXJjN+OxM1fazs6QrtQVsCAyfDuMky7QqVMBmiMdJTVCFjbOWpHS8itINRGah8kma7vhE/M3Uje8SMMmi4kGTw79IWV0sHEr+zO8S+nibmamYttR86jsLQM7f1bYdZjo/gGQGRhGqt2T63W4MDZy0i5ngtPFwf06hQMpY12XpUNB09DVVFp8OVYoxHIKSzBrpMJGBTd9GY2bCzZF/bj/KqP9LZVlOQjYc2nkFnZwKvTQFw/tsHosZJMDo8OfWBt6wj/PuORvGPZrQVg4+AGn5g4AIBfrwdQePUcci8d1ltR084zAEHDnzH9izMBfuo0IcZWtvxmzR589Oz98OP0tkQNxtvbW+/fxqJWa1BeWQmljbXBiI6ktGxM/2olMvOKdE0S9kobvPXo3ega2gZJadkGgwyryOUyJKVZXju6JUne8YN20icjTRPJO35A12cXw6f7vUg7+Oc/K2bK5IBGjZC4qbC2045m8+8zFjb2zkjZ8ytU+RnaxCK8LwIHPqYrI5NbI3zcTORdPoHs83sh1Gq4BHWBe2hPXTOJpbHMqMjA0QvJRle2zCkoxqxvV2Px/0202MlEiJq62izbbEoFxaX4ds1ebDp8BuUVani5OuKhgTG4p3ckJElCeWUlXv9ihW5ulqomiRJVOWZ88ye+m/4YXBzt/pnw8BYajYArV7Gtlrq8FMXXL1e7vywvHZXFeQga8iRcg7og/eh6qAqyYO8VCJ+Yu/WmnJYkCd5dR6BVl+FQlxVBZq2EzMra4Jy1XVHTUjB5aCL+2q1d2fLWBao0GoEr6dk4k5SGjm19zRQdEdVWqaoc6/afxu6TCVBrNOjeIRB394qEs4Otbv8Ln/6K1Kw83f2ekatd4C4ztxBPjOqDfacSjU4rLwRQWanBugPxGNYtHH9sP2o0hqrVeMk4SWZVba2DroyVtjbILaQb3EK63f6ckqTtJ9FMcKhmE5GamVfjypZp2fmNGA0RGVNcpsKfu09g7g/r8envWxGfmKq34mJBcRmmfvIzvli1A6cSU3EmKQ3frd+PJz9chrQs7T284eAZpGTkGr3ff9t2BFn5RUhKz74xVbwhAeBKejba+nrg8bt7A4BuRU+5TIIkAS+PG2y2ESBNgczKGm7tut9YAfMWkgzOAZ1g3YwSgfpgzUMT4ePujOQalsb2vmWExZ0qr6jEtmPncfziVVjJZegTGYJuYYGcKY9apJiYGKSnp8Pb27vaJozk6zl4ZeHvyC0sgUwmQQLw956TGNkzAi88OAiSJOH79ftwNTNXf7JjIZBfXIoFK7bhvafuw774S9XGoRECh84mwc3JDhqN8fkaZBJ0icG4Qd0QGeyHdfvjkZFbCD8vV9zdqxMnhKuFwIGPIj/pFNQVZf/M5SDJIJNbo+3gJ8wbnAVg8tBEjOodiX2nEw22y2QSfD1c6txkUVyqQmpWHlwc7ODlqp9B5xQU46UFvyE1M0/3Jrj+wGl07xCIWf8axYV8qMVJT09HampqtfuFEHjv+7XILy4FAL0kf+2+eHQO9sOArqHYcPCM0S8AGo3AwbNJyC8qNbqY3a3PFRvVHotW7TS6fo1aIzCsR7jucXigD8IDfQzKUc3sPNog6on/IWX3r8i+sA8QAm7tusO/91jYebYBAFSUFiL7/D6oy0vh1DoUDr6hButapB9dj2sHV6EsNx02jm7wiY6Db4/7LLYjZG017ehbkG4dAvHIsB5YtuHAjSpI7WgLZ3tbzHrs7lp3liyvqMRXf+3C2v3xujeeyODWeHHsYN2Ijf/9ukXXDHLzG92hc0n4ffsRjB/c3bQvjqiJS7iaicRrxkcvyCQJf+89iX5R7VBmZPrqmxWWlqFHeFucvHS12ub26NAAONnb4rWHh+H979cBNyaKk8skqDUCj8f1Rkhrrzt9SQTA1q012t/zIoAXDfalHVmHxI1f3JjDQdsz1alNBMIffFPXtyFxwxdIO7xad4wqPwNJW5eiIOU0Ojw0A5KxZpEmgslDEzJpeE/07dwOW4+cQ2GJdp6HAV1DYauo/cqWH/y4AbtOJui1w8ZfvoYXP/0V37w+CWqNBvvPJBp94xJCWw3L5IFIX2Z+YbX7NELgek4hrORy+Hm6IvWWZosq9kobeLk6YsRdHbFq13Fk5RcZ1FLc3auTbhK42Kj2CPb1xOp9p5ByPQeeLg4Y3iMCYQGcEO527nTobV7SCVxat+CmLdrrVJByBhf+mofwsTNRkpmslzjcXDbn4kHkJR6Da3B0vZ7fEjB5sBC1/WNu6+OBx+/uU6/nuJKejZ0nLhps12gE8ovLsHZfPGI6BNTUwRjZ+YY9vIlautY1zLMikyS08dLuHzc4Bh//tMmgjARgTGwX2FhZwcbKCvOfH4vPV27HnlOXoBECjrYKjOnfFeMH6/fq9/Nyxb/v7WfS19IS3OnQ22sHVv0zt8PNhAY5Fw+iLDcNWef2Gi8DADI5ss7tYfJAd64xxpEfT7ha7aQxQggcvZCMuF6dIJfJoDbSGUsC4Ovh0sBREjU9Aa3cEBncGqcvXzPos6ARAqP7acfuD+0Wjqy8IizbcEB3j0kScHfPSL01aTxcHPDWY3ejuEyF4tJyuDnZ6a2CS+ZVdD3ReFJwQ3FmMoS6ApIkGf8yJgQ0lTU3YVk6Jg/N0JHzV7B2Xzwy8goR6O2Oe3p3Rjt/L9hYyY0mDoB2DLKNlRyOdkoMig7D5sNndRPPVBEAxsRGNXT4RE3StEdG4PVFK3Dleg7kMhmEENAIgcdG9kKPjm0BaO+zh4f2QFyvTjh87grUGg26tPOHl6vx0VL2SgXslYo7ikut0SAtKx9ymQze7k6cTM4EbOxdUV6QDeNfxQAbexc4B0YiZffPxk8gNHAJjGy4ABsBk4dm5pvVu/HzlsO6CaUuplzHhoOn8fK4IegR3lbXqepWQgj0i2oPAJgypj/Sc/Jx8lIq5DIJAtqmjVG9IxHXs2n/wRM1FA9nB3z5fxNx8EwSzl5Jg53SBv27tIe3m7NBWRcHOwyO6dDgMW08eAZL1u5B1o3mxjat3PDs6FhEhwY0+HM3F6U515CbcBhCaOAa3BV2Hm3g3WU4EtI+MywsyWDr6gMHX+17qZN/RxRcPatfSyHJoHTxhkd4025ukoSoqYW7aTl69Ciio6Nx5MgRdO3a1dzhmJxao8GuExex8dBZFBSXokOAD+7t0xl+N9pTzySl4T/zfzF6rJVchp9mPYE1++KxdO1evcnTZJKE0IBW+HjKA7Cx0uaTQgicvHQVxy5ehfWNeR4CvLmEL7VMfn5+SE1NRevWrXH16lVzh1Mrmw6dwYfLN+ptkyRAgoT/Tn0AEUGtzRRZ0yCEBpfWf4H0I2u0vzjtRnh1GojgkVNxftWHyDm//59+DZIMcmslOj3yPhx8tNNTV6pKcGn958iM36FLINzadUfIyOdg42h5y2zXBWsemgi1RoN3v1uL3ScTdIvgJFzNwOq9J/Huk/ciOjQAmw+frbZmQa3RYMfxi3h4SHf4ujvjt21HcDktC872thhxVwQeGhijSxwAbfVq5xB/dA7xb8yXSUQmoNZo8O2avQbbhdB+Dn6/fj8+fPZ+M0TWdKTuX6lNHAC9aaozTm2DwqUVOtw/HdkX9iMzfgfUqmI4+XeEd5fhekmBlcIOofe+gqAhT0GVnwEbRzfYODTtpKEKk4cmYuuR89h9MgHAP4vgqDUCkiQw54f1+HnWE8gvKjXop1BFJslQUKSdwGZA11DOa0/UjKVl5SMrv8joPo0QOJFwFUII9n+ohhACqftXVrcX1w7+Bf8+Y+ER1hseYb1vez5rOydY25l2FmBzY/LQRGw8dEZX43AzIYD8olIcu5CCoNae2HUyAcY68ag1GgS15pS0RM3JoXNJ+GP7MSSlZcHD2QEje3XCsO7hkMtrnnyI08zXTF1eiori3Or3q4pRUZwPhVPLfU9l8tBE5BWWVFurAAAFJWUYeVdH/LLlEMrKK/UmgZLJJLRydcJd4UGNESoRmUBFpRpHzl9BTmEJAr3d0CHAR6+m4I/tR/HFnzt1naNzCopx/pfrOHr+CqZNHIEAb3ckX882GCook0no3SmEtQ41kFsrILNSQFOpMrpfkslhpXRo5KgsS9OdG7OFCW3TSrcynjHBvp5wdbTHnKdHw+XG0r5V/D1d8cG/x9z22wgRWYYTCSkYP/trzPj6L3zyy2b8Z/6vmDLvJ2TmaWeyzCkoxuK/dwH4Zwr5qhxhx/GLOHz+Cp69LxYySYLspiRBJpOgtLbG5BF3NerraWokmRytOg+udlVNj/C+kNsoGz8wC8KahyZidL8u2HTorMF2mUxCVIg/An20IyE6tvXF8pmP4/C5K8jKL4K/l3byGn7LIGp4py9fwy9bDuP05WuwU9pgSLcOuL9/V725GrLyi/Dz5kPYduw8KirViGrnjwmDu+umlc7ILcD0r1aholJ/EqLEa5mY/uUqfPnqROw+mVB9/yaZhG1Hz+P1icPx36kPYtmG/Th2IQUymYQ+kSGYNPwu+Hs1j057DSlgwCTkp5xBScZlaKfIAwABW1cftB3ypDlDswhMHpqI4NaemPFoHD7+aSOKy8p12yODW+ONSSP1ylrJ5birI5soiBrTrpMJeGfpGkg3FqoqKCnDjxsPYteJBMz/z1jYKW2QlV+EKfN+Ql5Ria7G4MCZyzh45jLee+o+RIcGYPXeU6is1ODWUfRqjUBSejaOJ6SgRFUOCRKEkf5NGo1AqUr7HtGxrS/m/nuM7lz8ElF7VkoHRP1rHjLjdyD74gFACLgGx8Cr04AWX+sAMHloUvpEhqBbWCAOnku6sTCWF1fPI7IAlWo15v+6BUIIvT4GGiFw5XoO/tx9HOMHd8dPmw/pJQ6A9sNekoDPft+GJdMn41JqZg2jpiRcSs1EeKBPtWUkCQgP9L1lG5OG+pBZ2aBV1BC0ihpi7lAsDhvBmxiFjRX6RoZg5F0RTByILER84jXkF5ca3SeEwNYj5wEA24+eN1gpU1sGSM3KQ1J6NpzsbasdDaERAo52SnQKao0OAd4G5WQyCQ62Sgzv0fEOXxFRzZg8EBHdobLymhc5qmpGKK+srLFceYUaQ2I6GE0wAMDGWvvlQZIkvPvkvYi5ZZrpNl5u+O/UB+B8S6dpIlNjswUR0R2qGg1lbHZXuUxCVDvtTK2dQ/xx6FyS0eTAXmmDQG932FjLcU/vSPy156RuGKZcJkEjgFfGDYG9rbbzpZO9Ld576j5cy8pDyvVcuDnZI8TPk00U1CiYPBAR3SFXR3uM7NkJq/ee1OvzIEmATCbDA/2jAQAThnTH4XNJkGA4ldv4wd2hsNG+JU+9fwBiwgKx/sBpZOcXIcjXA/f07Wy0qdLXwwW+Hi4N88KIqsHkgYjoNry9vfX+NebZ0bGwksvw956TqFRrh1l6uznj5XGDdUOpwwN98M4T9+KzP7YhLTsfAGCntMGEwd3x0MBo3bkkSULPiCD0jOCoKbJMXFWTiMiECorLkHgtE3ZKG7Tz8zLajCCEQFJaNlQVlWjr46GrcSBqKvgXS0RkQkobKzjb28JOaVNt/wNJktDWt+Wui0BNn0UlD3PmzMGKFStw7tw52NraolevXvjggw8QGsoVIInIsmk0Aj9tOYRftx5GyY2J3MLaeOP5BwainT+HVVPzYlFDNXfs2IEpU6Zg//792LRpEyoqKjB06FAUFxebOzQiohotWbcXS9fu1SUOAHAh5TpeWvAbUjPzzBcYUQOwqJqH9evX6z1eunQpvLy8cOTIEfTr189MURER1ayguAy/bztisF0jBMorK/HHjqN4/oGBZoiMqGFYVM3DrfLztb2R3dy4iAsRWa6zSWm6ERa30mgEDp1NatyAiBqYRdU83Eyj0eCFF15A7969ERERYbSMSqWCSvXPeutFRUWNFR4RkY6VVc3fw6yt5I0UCVHjsNjkYcqUKYiPj8fu3burLTNnzhzMnj27EaMiIjLUKag17JU2eiveVpFJEmKj2pshKqKGY5HNFlOnTsXq1auxbds2+Pn5VVtu2rRpyM/P1/3s2LGjEaMkItKysbbCM6NjAWiThSoymQRPV0eM7hdlpsiIGoZF1TwIIfDcc89h5cqV2L59O9q2bVtjeYVCAYVCoXvs4ODQ0CESERk1rHtHuNjbYfnmgzh3JR1KhTUGx3TAxKE94GTPhaqoebGo5GHKlClYvnw5/vzzTzg6OiI9PR0A4OzsDFtb3nxEZNl6dGyLHh1r/tJD1BxYVLPFokWLkJ+fj/79+8PHx0f388svv5g7NCIiIrrBomoemtEyG0RERM2WRdU8EBERkeVj8kBERER1YlHNFkRETVlhSRk2HjyDs1fSYKdUYEDXUESF+FW7uiZRU8XkgYjIBJLSsvHKwt9RUFIKQDvfw7r98RjaLRwvjxsCmYwJBDUfbLYgIrpDQgi8+/1aFJaWQQhACECt0XYA33joDLYcOWvmCIlMi8kDEdEdupCSgSvp2dBoDEeMSZKENXtPmSEqoobD5IGI6A5lF1S/KJ8QAhl5XLSPmhcmD0REd8jP07XafTKZhABvt0aMhqjhMXkgIrpDbVq5Iaqdv9FOkRqNwJh+XcwQFVHDYfJARGQC0x8Zjrbe7gAAuUyCJGl/nrqnL7p1CDRvcEQmxqGaREQm4Opoj89ffhhHLyTjbFIa7JQ2iI1qDw8XrvZLzQ+TByIiE5HJJMSEBSAmLMDcoRA1KDZbEBERUZ0weSAiIqI6YfJAREREdcI+D01UWloa0tLSzB0GmZCPjw98fHzMHQaZCO/R5oX3p75mlTz4+Phg5syZzf4Cq1QqjB8/Hjt27DB3KGRCsbGx2LBhAxQKhblDoTvEe7T54f2pTxJCGE7GThatoKAAzs7O2LFjBxwcOAysOSgqKkJsbCzy8/Ph5ORk7nDoDvEebV54fxpqVjUPLU1UVBT/kJuJgoICc4dADYD3aPPA+9MQO0wSERFRnTB5ICIiojph8tAEKRQKzJw5kx13mhFe0+aF17N54fU0xA6TREREVCeseSAiIqI6YfJAREREdcLkgYiIiOqEyYOFmTVrFiRJMncYujiysrLMHUqT9eijjyIwMNDcYeDRRx/lREVEZFJMHm7y66+/QpIkrFy50mBf586dIUkStm3bZrCvTZs26NWrV43nfvTRRyFJku7HyckJnTt3xn//+1+oVCqTvQYytHTpUt3vfffu3Qb7hRDw9/eHJEm4++67b3u+/v37611LNzc3dOvWDd9++y00Gk1DvASqp8a8px0cHBAUFIQHHngAf/zxB/8WGlBD3tMymQxOTk4IDQ3FI488gk2bNjXES2jymDzcpE+fPgBg8MdYUFCA+Ph4WFlZYc+ePXr7UlJSkJKSoju2JgqFAsuWLcOyZcvw/vvvw83NDa+88gomT55suhdB1VIqlVi+fLnB9h07duDq1at1Gobl5+enu5YzZsxAZWUlHn/8cUyfPt2UIdMdasx7+pNPPsGECRNw8eJFPPDAAxg0aBBnJmxgDXFPf//99/joo49wzz33YO/evRg6dCjGjh2LiooKU4be5HF66pv4+vqibdu2Bm80+/btgxACDz74oMG+qse1eaOxsrLCxIkTdY+fffZZ9OjRA7/88gvmzZsHX19fE7wKqs7IkSPx22+/4dNPP4WV1T9/+suXL0d0dHSdmmicnZ31ruXTTz+N0NBQLFiwAO+88w6sra1NGjvVT2Pf0wDw7rvvYu7cuZg2bRqefPJJ/PLLL9UeL4RAWVkZbG1ta/uS6CYNeU8DwNy5c/H888/j888/R2BgID744INqj9doNCgvL4dSqaz7C2mCWPNwiz59+uDYsWMoLS3VbduzZw86duyIESNGYP/+/XrVkXv27IEkSejdu3edn0smk6F///4AgKSkpGrLLVmyBAMHDoSXlxcUCgXCw8OxaNEio2XXrVuH2NhYODo6wsnJCd26dTPIzA8cOIDhw4fD2dkZdnZ2iI2NNfj2VSUrKwsPPfQQnJyc4O7ujv/85z8oKyvTK1NZWYl33nkHwcHBUCgUCAwMxPTp0y2uOWb8+PHIzs7Wq4YsLy/H77//jgkTJtzRue3s7HDXXXehuLgYmZmZ1Zb7+OOP0atXL7i7u8PW1hbR0dH4/fffjZb94Ycf0L17d9jZ2cHV1RX9+vXDxo0b9cqsW7cOffv2hb29PRwdHREXF4fTp08bPV9iYiKGDRsGe3t7+Pr64u2338at07wUFxfj5Zdfhr+/PxQKBUJDQ/Hxxx8blGtKGvOervL6669j6NCh+O2333DhwgXd9sDAQNx9993YsGEDYmJiYGtriy+//BJJSUmQJAlLly41OJckSZg1a5betu3btyMmJgZKpRLBwcH48ssvLaa/VGNqyHsaAORyOT799FOEh4djwYIFyM/P1+2TJAlTp07Fjz/+iI4dO0KhUGD9+vXYvn07JEnC9u3b9c5V3TX+7bffEB4eDqVSiYiICKxcudJi+kvVhMnDLfr06YOKigocOHBAt23Pnj3o1asXevXqhfz8fMTHx+vtCwsLg7u7e72e79KlSwBQ4/GLFi1CQEAApk+fjv/+97/w9/fHs88+i4ULF+qVW7p0KeLi4pCTk4Np06Zh7ty5iIqKwvr163Vltm7din79+qGgoAAzZ87E+++/j7y8PAwcOBAHDx40eO6HHnoIZWVlmDNnDkaOHIlPP/0UTz31lF6ZJ554Am+99Ra6du2KTz75BLGxsZgzZw7GjRtXr99JQwkMDETPnj3x008/6batW7cO+fn5Jok1MTERcrkcLi4u1ZaZP38+unTpgrfffhvvv/8+rKys8OCDD2LNmjV65WbPno1HHnkE1tbWePvttzF79mz4+/tj69atujLLli1DXFwcHBwc8MEHH2DGjBk4c+YM+vTpY5CMqtVqDB8+HK1atcKHH36I6OhozJw5EzNnztSVEULgnnvuwSeffILhw4dj3rx5CA0NxauvvoqXXnrpjn8/5tLY93SVRx55BEIIgzbz8+fPY/z48RgyZAjmz5+PqKioOp332LFjGD58OLKzszF79mw8/vjjePvtt7Fq1ao7ircpauh7GtAmEOPHj0dJSYlBLdXWrVvx4osvYuzYsZg/f36dP/DXrFmDsWPHwtraGnPmzMGYMWPw+OOP48iRIyaJvUEJ0nP69GkBQLzzzjtCCCEqKiqEvb29+O6774QQQrRq1UosXLhQCCFEQUGBkMvl4sknn7zteSdPnizs7e1FZmamyMzMFAkJCeL9998XkiSJyMhIXbmZM2eKWy9LSUmJwfmGDRsmgoKCdI/z8vKEo6Oj6NGjhygtLdUrq9FodP+2a9dODBs2TLet6vxt27YVQ4YMMYjjnnvu0TvXs88+KwCIEydOCCGEOH78uAAgnnjiCb1yr7zyigAgtm7detvfTUNbsmSJACAOHTokFixYIBwdHXW/0wcffFAMGDBACCFEQECAiIuLu+35YmNjRVhYmO5anj17Vjz//PMCgBg1apSu3OTJk0VAQIDesbdey/LychERESEGDhyo23bx4kUhk8nE6NGjhVqt1itfdd0KCwuFi4uLwd9eenq6cHZ21ts+efJkAUA899xzeueJi4sTNjY2IjMzUwghxKpVqwQA8e677+qd84EHHhCSJImEhITb/m4sUUPf09U5duyYACBefPFF3baAgAABQKxfv16v7OXLlwUAsWTJEoPzABAzZ87UPR41apSws7MTqampum0XL14UVlZWBu8dzVVD3NMdO3asdv/KlSsFADF//nzdNgBCJpOJ06dP65Xdtm2bACC2bdumt93YNe7UqZPw8/MThYWFum3bt28XAAzeOywNax5u0aFDB7i7u+syzBMnTqC4uFjX87pXr166Kv59+/ZBrVbXqm0U0FYJe3p6wtPTEyEhIZg+fTp69uxptCf4zW5uD83Pz0dWVhZiY2ORmJioq0bbtGkTCgsL8frrrxu0uVVVZR4/fhwXL17EhAkTkJ2djaysLGRlZaG4uBiDBg3Czp07DXqIT5kyRe/xc889BwBYu3at3r+3fjN9+eWXAcDgG7W5PfTQQygtLcXq1atRWFiI1atX16t689y5c7pr2aFDB3z22WeIi4vDt99+W+NxN1/L3Nxc5Ofno2/fvjh69Khu+6pVq6DRaPDWW29BJtO/Rauu5aZNm5CXl4fx48frrmNWVhbkcjl69OhhdATB1KlT9c4zdepUlJeXY/PmzQC011Iul+P555/XO+7ll1+GEALr1q2r5W/HsjTkPV2TquGxhYWFetvbtm2LYcOG1eucarUamzdvxn333afXRyokJAQjRoyof7BNmKnu6ZpUdy1jY2MRHh5er3Neu3YNp06dwqRJk/SGUsfGxqJTp071D7aRsMPkLSRJQq9evXQfpHv27IGXlxdCQkIAaN9oFixYAAC6N5zavtEolUr8/fffALS9tNu2bQs/P7/bHrdnzx7MnDkT+/btQ0lJid6+/Px8ODs765o/IiIiqj3PxYsXAaDG0R35+flwdXXVPW7Xrp3e/uDgYMhkMl21+JUrVyCTyXS/nyre3t5wcXHBlStXbvv6GpOnpycGDx6M5cuXo6SkBGq1Gg888ECdzxMYGIjFixdDkiQolUq0a9cOXl5etz1u9erVePfdd3H8+HG9PiE3t1VfunQJMpmsxjelqms5cOBAo/udnJz0HstkMgQFBelta9++PQDoXUtfX184OjrqlevQoYNuf1PUkPd0TYqKigDA4PfZtm3bep8zIyMDpaWlBvcbAKPbWgJT3dM1aYhrWXU/VXctb/5CYYmYPBjRp08f/P333zh16pSubbRKr1698OqrryI1NRW7d++Gr6+vwZtydeRyOQYPHlynWC5duoRBgwYhLCwM8+bNg7+/P2xsbLB27Vp88skndRpLXlX2o48+qrad9XaTCVXXIaspddSaMGECnnzySaSnp2PEiBE19lGojr29fZ2v5a5du3DPPfegX79++Pzzz+Hj4wNra2ssWbLE6HCzmlRdy2XLlsHb29tg/809z6nh7umaVPWjuPXDwdjIiuruH7VafcdxtASmuKdrwmtpiO8wRtw8NnzPnj144YUXdPuio6OhUCiwfft2HDhwACNHjmzQWP7++2+oVCr89ddfaNOmjW77rdXSwcHBALR/5NV9A6kq4+TkVOsPvosXL+pl1wkJCdBoNLqOQQEBAdBoNLh48aLuGyoAXL9+HXl5eQgICKjV8zSm0aNH4+mnn8b+/ftrHEZnan/88QeUSiU2bNigN/58yZIleuWCg4Oh0Whw5syZapO8qmvp5eVVq2up0WiQmJioq20AoBsFcPO13Lx5MwoLC/W+YZ07d063v6kyxz29bNkySJKEIUOG3LZsVW1fXl6e3vZba3u8vLygVCqRkJBgcA5j21qKhryn1Wo1li9fDjs7u1rVSNX2WlbdT031WrLPgxFVQ6B+/PFHpKam6n1LUSgU6Nq1KxYuXIji4mKTVG/WRC6XA4DeULn8/HyDD5yhQ4fC0dERc+bMMRhKWXVsdHQ0goOD8fHHH+uq4W5mbIjhrSM6PvvsMwDQta9WvdH+73//0ys3b948AEBcXFzNL9AMHBwcsGjRIsyaNQujRo1qtOeVy+WQJEnvG0hSUpJBL/n77rsPMpkMb7/9tkHNUtW1HDZsGJycnPD+++8bnbzG2LWsqpqvOs+CBQtgbW2NQYMGAdBeS7VarVcOAD755BNIktSk29Qb+56eO3cuNm7ciLFjxxo0/Rnj5OQEDw8P7Ny5U2/7559/rve4qvZy1apVuHbtmm57QkJCk+2TYgoNdU+r1Wo8//zzOHv2LJ5//nmD5kBjAgICIJfLb3stfX19ERERge+//17v/XjHjh04deqUaV5AA2LNgxE2Njbo1q0bdu3aBYVCgejoaL39vXr1wn//+18ApmkbrcnQoUNhY2ODUaNG4emnn0ZRUREWL14MLy8vpKWl6co5OTnhk08+wRNPPIFu3bphwoQJcHV1xYkTJ1BSUoLvvvsOMpkMX3/9NUaMGIGOHTviscceQ+vWrZGamopt27bByclJ1yejyuXLl3HPPfdg+PDh2LdvH3744QdMmDABnTt3BqCd4nfy5Mn46quvkJeXh9jYWBw8eBDfffcd7rvvPgwYMKBBfz/1ZY5ZPePi4jBv3jwMHz4cEyZMQEZGBhYuXIiQkBCcPHlSVy4kJARvvPEG3nnnHfTt2xdjxoyBQqHAoUOH4Ovrizlz5sDJyQmLFi3CI488gq5du2LcuHHw9PREcnIy1qxZg969e+slAUqlEuvXr8fkyZPRo0cPrFu3DmvWrMH06dPh6ekJABg1ahQGDBiAN954A0lJSejcuTM2btyIP//8Ey+88IKutqMpaqh7urKyEj/88AMAoKysDFeuXMFff/2FkydPYsCAAfjqq69qfa4nnngCc+fOxRNPPIGYmBjs3LlTb46IKrNmzcLGjRvRu3dvPPPMM7qELyIiAsePH6/18zU3d3pP5+fn665lSUkJEhISsGLFCly6dAnjxo3DO++8U6vzODs748EHH8Rnn30GSZIQHByM1atXIyMjw6Ds+++/j3vvvRe9e/fGY489htzcXN21NPYFz6KYdayHBZs2bZoAIHr16mWwb8WKFQKAcHR0FJWVlbU63+2GdVUxNlTzr7/+EpGRkUKpVIrAwEDxwQcfiG+//VYAEJcvXzYo26tXL2FrayucnJxE9+7dxU8//aRX5tixY2LMmDHC3d1dKBQKERAQIB566CGxZcsWgzjOnDkjHnjgAeHo6ChcXV3F1KlTDYaCVlRUiNmzZ4u2bdsKa2tr4e/vL6ZNmybKyspq9btpaDcP66qJqYZ1VTE2VPObb74R7dq1EwqFQoSFhYklS5YYveZCCPHtt9+KLl26CIVCIVxdXUVsbKzYtGmTXplt27aJYcOGCWdnZ6FUKkVwcLB49NFHxeHDh/XisLe3F5cuXRJDhw4VdnZ2olWrVmLmzJkGQ0ELCwvFiy++KHx9fYW1tbVo166d+Oijj/SG9jZVDXFPA9D92NnZicDAQHH//feL33//3eB3K0TNf2MlJSXi8ccfF87OzsLR0VE89NBDIiMjw2CophBCbNmyRXTp0kXY2NiI4OBg8fXXX4uXX35ZKJXKWsXe1DXEPX3ztXRwcBDt2rUTEydOFBs3bjR6DAAxZcoUo/syMzPF/fffL+zs7ISrq6t4+umnRXx8vNHhuD///LMICwsTCoVCREREiL/++kvcf//9Iiws7LZxm5MkRBOeOo6IiABom7tOnz6tG4lDTVdUVBQ8PT0telEu9nkgImpibp5qG9B2bF67dq1uuntqGioqKlBZWam3bfv27Thx4oTFX0vWPBARNTE+Pj549NFHERQUhCtXrmDRokVQqVQ4duxYrTpokmVISkrC4MGDMXHiRPj6+uLcuXP44osv4OzsjPj4+DueIr0hscMkEVETM3z4cPz0009IT0+HQqFAz5498f777zNxaGJcXV0RHR2Nr7/+GpmZmbC3t0dcXBzmzp1r0YkDwJoHIiIiqiP2eSAiIqI6YfJAREREdcLkwUSWLl2qWyQpNTXVYH///v1rXLSqIWzZsgX/+te/0L59e9jZ2SEoKAhPPPGE3uRSN9u7dy/69OkDOzs7eHt74/nnn7f8iUoaEK9p88Lr2bzwepoXkwcTU6lUmDt3rrnDAAC89tpr2L59O0aPHo1PP/0U48aNw6+//oouXbogPT1dr+zx48cxaNAglJSUYN68eXjiiSfw1Vdf4cEHHzRT9JaD17R54fVsXng9zcScM1Q1J1UznkVFRQmFQiFSU1P19td2VkJT2rFjh8Esdzt27BAAxBtvvKG3fcSIEcLHx0fk5+frti1evFgAEBs2bGiUeC0Nr2nzwuvZvPB6mhdrHkxs+vTpUKvVFpEJ9+vXDzKZzGCbm5sbzp49q9tWUFCATZs2YeLEiXoLv0yaNAkODg749ddfGy1mS8Rr2rzwejYvvJ7mwXkeTKxt27aYNGkSFi9ejNdffx2+vr51Or6kpAQlJSW3LSeXy3VLv9ZFUVERioqK4OHhodt26tQpVFZWIiYmRq+sjY0NoqKicOzYsTo/T3PCa9q88Ho2L7ye5sGahwbwxhtvoLKyEh988EGdj/3www/h6el5258uXbrUK7b//e9/KC8vx9ixY3Xbqjrz+Pj4GJT38fHRW/q3peI1bV54PZsXXs/Gx5qHBhAUFIRHHnkEX331FV5//XWjfyDVmTRpUq2WBLa1ta1zXDt37sTs2bPx0EMPYeDAgbrtVfPkKxQKg2OUSqXBPPotEa9p88Lr2bzwejY+Jg8N5M0338SyZcswd+5czJ8/v9bHBQUFISgoyOTxnDt3DqNHj0ZERAS+/vprvX1VN4VKpTI4rqysrF43TXPEa9q88Ho2L7yejYvJQwMJCgrCxIkTdZlwbVW1j92OXC6Hp6dnrc6ZkpKCoUOHwtnZGWvXroWjo6Pe/qos3dhY5LS0tDq3ITZXvKbNC69n88Lr2bjY56EBvfnmm3Vuh/v444/h4+Nz259u3brV6nzZ2dkYOnQoVCoVNmzYYLQ6LyIiAlZWVjh8+LDe9vLychw/fhxRUVG1jr+54zVtXng9mxdez8bDmocGFBwcjIkTJ+LLL79EQEAArKxu/+s2ZftbcXExRo4cidTUVGzbtq3aFfecnZ0xePBg/PDDD5gxY4YuS162bBmKioqazqQljYDXtHnh9WxeeD0bD1fVNJGlS5fisccew6FDh/SG3yQkJCAsLAxqtRodO3ZEfHx8o8V033334c8//8S//vUvDBgwQG+fg4MD7rvvPt3jo0ePolevXggPD8dTTz2Fq1ev4r///S/69euHDRs2NFrMloTXtHnh9WxeeD3NzNyzVDUXVbOdHTp0yGDf5MmTBYBGn+0sICBAADD6ExAQYFB+165dolevXkKpVApPT08xZcoUUVBQ0KgxWxJe0+aF17N54fU0L9Y8EBERUZ2wwyQRERHVCZMHIiIiqhMmD0RERFQnTB6IiIioTpg8EBERUZ0weSAiIqI6YfJAREREdcLkgYiIiOqEyQMRERHVCZMHIiIiqhMmD0RERFQnTB6IiIioTpg8EBERUZ0weSAiIqI6YfJAREREddKskoe0tDTMmjULaWlp5g6FiIio2Wp2ycPs2bOZPBARETWgZpU8EBERUcOzqORh1qxZkCRJ7ycsLMzcYREREdFNrMwdwK06duyIzZs36x5bWVlciERERC2axX0yW1lZwdvb29xhEBERUTUsqtkCAC5evAhfX18EBQXh4YcfRnJycrVlVSoVCgoKdD9FRUWNGCkREVHLZFHJQ48ePbB06VKsX78eixYtwuXLl9G3b18UFhYaLT9nzhw4OzvrfmJjYxs5YiIiopZHEkIIcwdRnby8PAQEBGDevHl4/PHHDfarVCqoVCrd4+PHjyM2NhZHjhxB165dGzNUIiKiFsPi+jzczMXFBe3bt0dCQoLR/QqFAgqFQvfYwcGhsUIjMjm1Wg25XG7uMIiIbsuimi1uVVRUhEuXLsHHx8fcoRA1OAuuBCQi0mNRycMrr7yCHTt2ICkpCXv37sXo0aMhl8sxfvx4c4dG1OAkSTJ3CEREtWJRzRZXr17F+PHjkZ2dDU9PT/Tp0wf79++Hp6enuUMjanCseSCipsKikoeff/7Z3CEQERHRbVhUswVRS8aaByJqKpg8EFkIjUZj7hCIiGqFyQORhVCr1eYOgYioVpg8EFkI1jwQUVPB5IHIQrDmgYiaCiYPRBaCHSaJqKlg8kBkIZg8EFFTweSByEIweSCipoLJA5GFYPJARE0FkwciIiKqEyYPRBaCNQ9E1FQweSCyEFxVk4iaCiYPREREVCdMHoiIiKhOmDwQERFRnTB5ILIQ7DBJRE0FkwciC8G1LYioqWDyQGQhKisrzR0CEVGtMHkgshBMHoioqWDyQGQhVCqVuUMgIqoVJg9EFqKsrMzcIRAR1QqTByILUVpaau4QiIhqxWKTh7lz50KSJLzwwgvmDoWoUTB5IKKmwiKTh0OHDuHLL79EZGSkuUMhajRMHoioqbC45KGoqAgPP/wwFi9eDFdXV3OHQ9Ro2OeBiJoKi0sepkyZgri4OAwePPi2ZVUqFQoKCnQ/RUVFjRAhUcMoKSkxdwhERLViZe4Abvbzzz/j6NGjOHToUK3Kz5kzB7Nnz27gqIgaR1lZGdRqNeRyublDISKqkcXUPKSkpOA///kPfvzxRyiVylodM23aNOTn5+t+duzY0cBREjWs4uJic4dARHRbFlPzcOTIEWRkZKBr1666bWq1Gjt37sSCBQugUqkMvpEpFAooFArdYwcHh0aLl6ghFBQUwMnJydxhEBHVyGKSh0GDBuHUqVN62x577DGEhYXhtddeY1UutQh5eXnw8/MzdxhERDWymOTB0dERERERetvs7e3h7u5usJ2oucrOzjZ3CEREt2UxfR6ICMjKyjJ3CEREt2UxNQ/GbN++3dwhEDWqnJwclJeXw8bGxtyhEBFVizUPRBZECIHr16+bOwwiohoxeSCyMNeuXTN3CERENWLyQGRhUlJSzB0CEVGNmDwQWZicnBzk5+ebOwwiomoxeSCyQAkJCeYOgYioWkweiCzQxYsXIYQwdxhEREYxeSCyQAUFBRx1QUQWi8kDkYW6cOGCuUMgIjKKyQORhbp06RIqKirMHQYRkQEmD0QWqqKigrUPRGSRmDwQWbBTp05BrVabOwwiIj1MHogsWEFBAc6ePWvuMIiI9DB5ILJwhw4dQlFRkbnDICLSYfJAZAFiYmLw6quv4r333jPYV1FRgS1btrD5gogsBpMHIguQnp6O3NxcFBQUGN1//fp17N69mxNHEZFFsKpNobfffrvOJ5YkCTNmzKjzcURk3Pnz52Fvb4+YmBhzh0JELVytkodZs2YZbJMkCQAMvglJkgQhBJMHogZw9OhRWFtbo3PnzuYOhYhasFo1W2g0Gr2flJQUdOrUCePHj8fBgweRn5+P/Px8HDhwAOPGjUPnzp25rDBRAzlw4ABOnz5t7jCIqAWrV5+HKVOmoF27dvjhhx8QExMDR0dHODo6olu3bvjxxx8RHByMKVOmmDpWIrphz549HMJJRGZTr+Rh69atGDhwYLX7Bw0ahC1bttQ7KCK6vV27duHcuXPmDoOIWqB6JQ9KpRL79u2rdv/evXuhVCrrHRQR1c7OnTsRHx9v7jCIqIWpV/Lw8MMP48cff8Tzzz+Pixcv6vpCXLx4Ec899xyWL1+Ohx9+2NSxEpERe/fuxf79+zmMk4gaTb2Shw8++AATJkzAggULEBYWBoVCAYVCgbCwMCxcuBDjxo3DBx98UOfzLlq0CJGRkXBycoKTkxN69uyJdevW1SdEohbl5MmTWL9+PcrKyswdChG1ALUaqnkrGxsbLFu2DK+++irWrl2LK1euAAACAgIwYsSIeg8j8/Pzw9y5c9GuXTsIIfDdd9/h3nvvxbFjx9CxY8d6nZOopUhJScEff/yBAQMGwNfX19zhEFEzVq/koUpkZCQiIyNNFQtGjRql9/i9997DokWLsH//fiYPRLVQXFyMNWvWoFOnTujWrRvkcrm5QyKiZuiOkoeGpFar8dtvv6G4uBg9e/Y0WkalUkGlUukec/EgIu3EbSdPnkRKSgr69+8PT09Pc4dERM2Mxa1tcerUKTg4OEChUODf//43Vq5cifDwcKNl58yZA2dnZ91PbGxsI0dLZLlyc3Px559/4tixY+xMSUQmZXHJQ2hoKI4fP44DBw7gmWeeweTJk3HmzBmjZadNm6ab3TI/Px87duxo5GiJLJtGo8GhQ4ewYcMGlJeXmzscImomLC55sLGxQUhICKKjozFnzhx07twZ8+fPN1pWoVDoRmY4OTnBwcGhkaMlahqSk5OxZs0avWY+IqL6srjk4VYajYZveEQmkJmZia1bt7IJg4jumMk6TAohsG3bNqhUKvTp0weOjo51Pse0adMwYsQItGnTBoWFhVi+fDm2b9+ODRs2mCpMohYtJSUFV69ehb+/v7lDIaImrF7JwxtvvIG9e/di27ZtALSJw9ChQ3Xfatq0aYMtW7YgODi4TufNyMjApEmTkJaWBmdnZ0RGRmLDhg0YMmRIfcIkIiOuXLnC5IGI7ki9mi3++OMPdO/eXff4999/x5YtW/Duu+9i9erVUKvVmDVrVp3P+8033yApKQkqlQoZGRnYvHkzEwdq9pKTk1FSUgIAKC8vR05OToM+X3FxcYOen4iav3olD6mpqQgJCdE9XrFiBcLDwzFt2jSMHDkSzzzzDLZv326qGImapYMHD2LUqFEIDAxEbm4uAKCkpATTp0/HwoULkZSU1CDPy1EXRHSn6pU8WFlZ6ToxCiGwZcsWDB8+XLe/VatWyMrKMk2ERM3QihUr0Lt3b6xbt86gA6MQAvHx8fjggw9w9OhRkz93Tk4OO00S0R2pV/IQERGBH374Abm5uViyZAmys7MRFxen23/lyhV4eHiYLEii5uTgwYMYO3Ys1Go11Gq10TJVK9UuXrzY5DUQKpUKV69eNek5iahlqVfy8NZbb+H48ePw8PDAk08+id69e2PAgAG6/WvWrEG3bt1MFiRRc/Luu+9CCFHrb/9r1641eQxHjhxh7QMR1Vu9RlsMGTIER48exaZNm+Di4oKxY8fq9uXm5qJfv3649957TRYkUXORnJyM1atX1/qDW6PR4OTJk8jJyYGbm5vJ4sjIyEBiYmKdR0QREQF3MM9DeHi40TUnXF1d8cknn9xRUETN1ZYtW+r8jV8IgXPnzqFXr14mjeXEiRNMHoioXix+hkmi5qSwsBAyWd1uO0mSUFZWZvJYsrKyOPKCiOql1jUPbdu2hSRJetsSExNNHhBRc+bo6AiNRlOnY4QQUCqVDRIP+z0QUX3UOnmYPHmyQfJARHUzaNAgSJJUpw9tSZIQFhZm8licnZ2hUChMfl4iav5qnTzUZ8ZIItLXpk0b3H333Vi7dm21wzRvJpPJ0KlTJ5N2lqwSERFh8nMSUcvAPg9EjWzGjBmQJKnWNXkjR440eQy+vr7o0KGDyc9LRC1DvUdbqNVqbNiwAYmJicjNzTWohpUkCTNmzLjjAImam27duuGXX37B2LFjIYQwWgNR1anyqaeeQmBgoEmf39PTE0OGDKlzx00ioiqSqEePqcOHD+P+++/H1atXq227lSSpVtWypnT06FFER0fjyJEj6Nq1a6M+N1FdHTp0CO+8847BvA+SJCEyMhIjR440eeIQEBCAgQMHwtra2qTnJaKWpV41D88++yxKS0uxatUq9O3bFy4uLiYOi6j569atG/766y8kJycjKioKubm5sLOzw4wZM0zex0GSJERHR6NLly7s+ExEd6xeycPJkyfx3nvvYdSoUaaOh6jFadOmDezs7JCbmwsbGxuTJw62trYYOHAgWrdubdLzElHLVa/kwc/Pj+PDiZoALy8vDBkyBPb29uYOhYiakXr1mHrttdewePFiFBQUmDoeIjKR4OBgjBo1iokDEZlcvWoeCgsL4eDggJCQEIwbNw7+/v6Qy+V6ZSRJwosvvmiSIImobjp37ozu3buzfwMRNYh6JQ+vvPKK7v8LFiwwWobJA5F53HXXXYiMjDR3GETUjNUrebh8+bKp4yCiOySXy9GvXz+0a9fO3KEQUTNXr+QhICDA1HEQ0R2ws7PD4MGD4e3tbe5QiKgFuKMp5lJTU/HTTz9h/vz5uHr1KgDtzJM5OTn1miBqzpw56NatGxwdHeHl5YX77rsP58+fv5MQiZo9Pz8/3H///UwciBpQRUWFuUOwKPVKHoQQeOmll9C2bVs8/PDDeOmll3DhwgUAQFFREQIDA/HZZ5/V+bw7duzAlClTsH//fmzatAkVFRUYOnQoiouL6xMmUbNmbW2NPn36YMSIEbC1tTV3OETNGqcn0FevZouPPvoI8+fPx2uvvYZBgwZhyJAhun3Ozs4YM2YM/vjjD7zwwgt1Ou/69ev1Hi9duhReXl44cuQI+vXrV59QiZolPz8/9O3bF46OjuYOhYhaoHolD4sXL8akSZPw/vvvIzs722B/ZGQk1q1bd8fB5efnA0CDLEdM1BTZ2NigZ8+eaN++PYdhEjUi3m/66pU8pKSkoFevXtXut7e3v+MJpDQaDV544QX07t0bERERRsuoVCqoVCrd46Kiojt6TiJL1rp1a/Tv35+TPhGR2dUrefDy8kJKSkq1+48cOYI2bdrUOygAmDJlCuLj47F79+5qy8yZMwezZ8++o+chsnSSJCEmJgZRUVH89kNkJuzzoK9eHSbHjBmDL774AomJibptVW9qGzduxNKlS/Hggw/WO6ipU6di9erV2LZtG/z8/KotN23aNOTn5+t+duzYUe/nJLJE1tbWGD58OFfDJDIzjUZj7hAsSr2Sh9mzZ8PHxwdRUVGYNGkSJEnCBx98oOv5HRkZienTp9f5vEIITJ06FStXrsTWrVvRtm3bGssrFAo4OTnpfhwcHOrzcogsklKpxKhRo+Dv72/uUIhavPpMP9Cc1St5cHZ2xv79+/F///d/SE1NhVKpxI4dO5CXl4eZM2di165dsLOzq/N5p0yZgh9++AHLly+Ho6Mj0tPTkZ6ejtLS0vqESdRk2djYYOTIkfDw8DB3KEQEJg+3qlefBwCwtbXFm2++iTfffNNkwSxatAgA0L9/f73tS5YswaOPPmqy5yGyZJIkYeDAgUwciCxIZWWluUOwKPVOHhoCO6QQAeHh4Xfc4ZiITKu8vNzcIViUeicPZ8+exZIlS5CYmIjc3FyDD35JkrBly5Y7DpCoJakaWUFElqWsrMzcIViUeiUPy5Ytw2OPPQZra2uEhobC1dXVoAxrEYjqzsrKCgqFwtxhENEtuEyCvnolD7NmzUKXLl2wbt06tssSmZCVlUW1JBLRDeXl5VCpVEzub6jXaItr167hX//6FxMHIhOTye5ooVsiakCcxfgf9XqnioyMxLVr10wdC1GL5e3tDXd3dy6rTWTBCgsLzR2CxahX8jBv3jx888032Lt3r6njIWqRDh8+jF9//RWHDx82dyhEVA0mD/+oVQPrPffcY7DN2dkZffv21Q0rk8vlevslScKff/5pmiiJWgAueEVk2XJycswdgsWoVfJw8uRJo/Pqt2nTBkVFRThz5ozBPs7DT1Q39ZmVlYgaT2pqKoQQ/HxDLZOHpKSkBg6DiGxtbc0dAhHVoKioCKmpqTUu2NhSsGs3kYXgEDAiy3f48GHOY4Q7nJ56x44dWLNmDa5cuQIACAgIQFxcHGJjY00SHFFLYmNjY+4QiOg2MjIycP78eYSFhZk7FLOqV/JQXl6O8ePHY9WqVRBCwMXFBQCQl5eH//73vxg9ejR++uknWFtbmzJWomaNyQNR07B//360bt0ajo6O5g7FbOrVbDF79mysXLkSL7/8MtLS0pCTk4OcnBykp6fjlVdewYoVK/D222+bOlaiZo3JNlHTUF5ejs2bN7folTbrlTwsX74ckydPxocffohWrVrptnt5eeGDDz7ApEmTsGzZMpMFSdQScKgmUdORmZmJzZs3Q61WmzsUs6hX8pCWloYePXpUu79Hjx5IT0+vd1BELRGHfxFZppiYGAwfPhzvvfee3vbk5OQWm0DUK3nw8/PD9u3bq92/Y8cODmUhIqJmIT09HRkZGSgoKDDYd+XKFWzYsAEVFRVmiMx86pU8TJ48Gb/++iv+/e9/4/z581Cr1dBoNDh//jyeeeYZ/Pbbb3j00UdNHCoREZHluXr1KlavXt2ilu2u12iL6dOn49KlS/jqq6+wePFi3UqAGo0GQghMnjwZ06dPN2mgREREliozMxMrV67E4MGDW8QCd/VKHuRyOZYuXYqXXnoJa9eu1ZvnYeTIkYiMjDRpkERERJaupKQEf//9N7p164bOnTs3635MdzRJVGRkJBMFIiKiG4QQOHjwIFJTU9G/f/9mO4qq1n0eysrK8O9//xufffZZjeU+/fRTPPPMMy2u8wgREVGV1NRU/Pbbbzh79myznM661snDV199haVLlyIuLq7GcnFxcViyZAm+/vrrOw6OiIioqSovL8euXbuwYsUKJCUlNaskotbJw6+//or7778fQUFBNZYLDg7Ggw8+iJ9++qnOwezcuROjRo2Cr68vJEnCqlWr6nwOIiIiS5KdnY2NGzdixYoVuHjxIjQajblDumO1Th5OnTqFPn361Kpsr169cPLkyToHU1xcjM6dO2PhwoV1PpaIiMiSZWdnY9u2bfj5558RHx/fpJv3a91hsry8vNYL99jY2EClUtU5mBEjRmDEiBF1Po6IiKipKCoqwt69e3H06FF07twZHTt2hJXVHY1faHS1jtbX1xfx8fG1KhsfHw9fX996B1VbKpVKL0kpKipq8OckIiIyhbKyMhw4cADx8fHo3r07QkJCmszwzlo3WwwePBjff/89MjIyaiyXkZGB77//HkOGDLnj4G5nzpw5cHZ21v3ExsY2+HMSEVHLkZycjJKSEgDaGvicnByTP0dxcTG2bduGtWvXNplZKmudPLz22msoKyvDwIEDceDAAaNlDhw4gEGDBqGsrAyvvvqqyYKszrRp05Cfn6/72bFjR4M/JxERNX8HDx7EqFGjEBgYiNzcXADaSaCmT5+OhQsXIikpyeTPmZqaihUrViAzM9Pk5za1WjdbBAUF4ddff8X48ePRq1cvBAUFoVOnTnB0dERhYSHi4+Nx6dIl2NnZ4eeff0ZwcHBDxg0AUCgUUCgUuscODg4N/pxERNS8rVixAmPHjoUQwmB4pRAC8fHxiI+Px5NPPomuXbua9LlLS0uxbt06jBkzxqI/0+q0MFZcXBxOnjyJp556CmVlZVi1ahWWLVuGVatWoaSkBE8++SROnDiBUaNGNVS8REREDebgwYMYO3Ys1Gp1tUttazQaaDQaLF68uEFqIKr6QliyOq+qGRgYiEWLFiElJQX5+fm6f69evYovvvjitvNA1KSoqAjHjx/H8ePHAQCXL1/G8ePHkZycXO9zEhER1da7775rtMahOmvXrm2QOBITE1FYWNgg5zaFei3JXcXR0RGtW7eGo6OjSYI5fPgwunTpgi5dugAAXnrpJXTp0gVvvfWWSc5PRERUneTkZKxevbraGodbaTQanDx5skE6UQohkJiYaPLzmopFDSzt379/s5q+k4iImo4tW7bU+TNICIFz586hV69eJo+nvLzc5Oc0lTuqeSAiImouCgsLIZPV7WNRkiSUlZWZPBaFQoHQ0FCTn9dULKrmgYiIyFwcHR3rvO6EEAJKpdKkcdjb22Po0KFwcnIy6XlNickDERERgEGDBkGSpDo1XUiShLCwMJPF4O3tjcGDB8POzs5k52wIbLYgIiIC0KZNG9x9992Qy+W1Ki+TyRAZGQk3NzeTPH/79u0RFxdn8YkDwOSBiIhIZ8aMGZAkqdZrTIwcOdIkz9uxY0fExsbWOnExNyYPREREN3Tr1g2//PIL5HJ5tR/kMpkMMpkMTz31FAIDA+/4Odu3b49evXo1mUWxACYPREREesaMGYO9e/di5MiRBh/okiShU6dOeO2113RzEt2JwMBA9OvXr0klDgA7TBIRERno1q0b/vrrLyQnJyMqKgq5ubmws7PDjBkzTNbHITw8HL169arz8FBLwOSBiIioGm3atIGdnR1yc3NhY2NjksTBzs4OvXv3Rtu2bU0QoXkweSAiImoEcrkcERER6NKlC2xsbMwdzh1h8kBERNSAJElCSEgIYmJiTLYWlLkxeSAiImog/v7+6NGjh8n6SVgKJg9EREQm5uHhgbvuugu+vr7mDqVBMHkgIiIyEaVSie7duyM0NLTJDb+sCyYPREREJtCuXTv07NnT5AtlWSImD0RERHfA2toa/fr1Q3BwsLlDaTRMHoiIiOrJzs4OI0aMgLu7u7lDaVRMHoiIiOpBqVQiLi4Orq6u5g6l0TW9OTGJiIjMTC6XY9iwYS0ycQCYPBAREdVZz5490apVK3OHYTZMHoiIiOqgdevW6NChg7nDMCsmD0RERLUkSRJ69erVrOdwqA2LTB4WLlyIwMBAKJVK9OjRAwcPHjR3SERE1EJ5e3vDy8sLTk5OaN++fYvt53Azixtt8csvv+Cll17CF198gR49euB///sfhg0bhvPnz8PLy8vc4RERUQtz+PBhHDt2DIcOHULnzp3NHY5FsLiah3nz5uHJJ5/EY489hvDwcHzxxRews7PDt99+a+7QiIioBfPy8oKLi4u5w7AIFlXzUF5ejiNHjmDatGm6bTKZDIMHD8a+ffsMyqtUKqhUKt3joqIiAEBlZSUqKioaPmAiImoRKioq4O3t3ew/W6ytrWtVzqKSh6ysLKjVaoPhL61atcK5c+cMys+ZMwezZ8822N6jR48Gi5GIiKi5EkLUqpxFJQ91NW3aNLz00ku6x8ePH0dsbCwOHDiALl26mDEyIiJqTo4dO4awsDDY2tqaOxSLYFHJg4eHB+RyOa5fv663/fr16/D29jYor1AooFAodI8dHBwAAFZWVrWueiEiIrodBwcHODo6tvghmlUsqsOkjY0NoqOjsWXLFt02jUaDLVu2oGfPnmaMjIiIWjI7OzsmDjexqJoHAHjppZcwefJkxMTEoHv37vjf//6H4uJiPPbYY+YOjYiIWigbGxtzh2BRLC55GDt2LDIzM/HWW28hPT0dUVFRWL9+fYueQ5yIiMxLLpebOwSLYnHJAwBMnToVU6dONXcYREREALR96egfFtXngYiIyBIxedDH5IGIiOg2mDzoY/JARER0G0we9DF5ICIiug12mNTH5IGIiOg2ZDJ+XN6Mvw0iIqLb4ARR+pg8EBER3QaTB31MHoiIiKhOmDwQERFRnTB5ICIiojph8kBERHQbQghzh2BRmDwQERHdBpMHfUweiIiIboPJgz4mD0RERFQnTB6IiIhugzNM6uNvg4iI6DY4SZQ+Jg9ERERUJ0weiIiIqE6YPBAREVGdMHkgIiKiOmHyQERERHXC5IGIiIjqxMrcAVD9pKWlIS0tzdxhkAn5+PjAx8fH3GGQifAebV54f+prVsmDj48PZs6c2ewvsEqlwvjx47Fjxw5zh0ImFBsbiw0bNkChUJg7FLpDvEebH96f+iTBCbubnIKCAjg7O2PHjh1wcHAwdzhkAkVFRYiNjUV+fj6cnJzMHQ7dId6jzQvvT0PNquahpYmKiuIfcjNRUFBg7hCoAfAebR54fxpih0kiIiKqEyYPREREVCdMHpoghUKBmTNnsuNOM8Jr2rzwejYvvJ6G2GGSiIiI6oQ1D0RERFQnTB6IiIioTpg8EBERUZ0weSBqII8++igCAwPNHQYeffRRTlRERCbF5IGavaVLl0KSJEiShN27dxvsF0LA398fkiTh7rvvvu35+vfvrzufJElwc3NDt27d8O2330Kj0TTESyCimzTkPS2TyeDk5ITQ0FA88sgj2LRpU0O8hCaPyQO1GEqlEsuXLzfYvmPHDly9erVOw7D8/PywbNkyLFu2DDNmzEBlZSUef/xxTJ8+3ZQhE1ENGuKe/v777/HRRx/hnnvuwd69ezF06FCMHTsWFRUVpgy9yWPyQC3GyJEj8dtvv6GyslJv+/LlyxEdHQ1vb+9an8vZ2RkTJ07ExIkT8eKLL2LPnj3w8/PDggUL+CZD1Ega6p5++umn8dFHH+HChQt49tln8euvv+LNN9+s8XiNRoOysrJ6vY6miMkDtRjjx49Hdna2XjVkeXk5fv/9d0yYMOGOzm1nZ4e77roLxcXFyMzMrLbcxx9/jF69esHd3R22traIjo7G77//brTsDz/8gO7du8POzg6urq7o168fNm7cqFdm3bp16Nu3L+zt7eHo6Ii4uDicPn3a6PkSExMxbNgw2Nvbw9fXF2+//TZunealuLgYL7/8Mvz9/aFQKBAaGoqPP/7YoByRJWjIexoA5HI5Pv30U4SHh2PBggXIz8/X7ZMkCVOnTsWPP/6Ijh07QqFQYP369di+fTskScL27dv1zpWUlARJkrB06VK97b/99hvCw8OhVCoRERGBlStXWkx/qZoweaAWIzAwED179sRPP/2k27Zu3Trk5+dj3Lhxd3z+xMREyOVyuLi4VFtm/vz56NKlC95++228//77sLKywoMPPog1a9bolZs9ezYeeeQRWFtb4+2338bs2bPh7++PrVu36sosW7YMcXFxcHBwwAcffIAZM2bgzJkz6NOnD5KSkvTOp1arMXz4cLRq1QoffvghoqOjMXPmTMycOVNXRgiBe+65B5988gmGDx+OefPmITQ0FK+++ipeeumlO/79EJlaQ9/TgDaBGD9+PEpKSgz6V2zduhUvvvgixo4di/nz59f5A3/NmjUYO3YsrK2tMWfOHIwZMwaPP/44jhw5YpLYG5QgauaWLFkiAIhDhw6JBQsWCEdHR1FSUiKEEOLBBx8UAwYMEEIIERAQIOLi4m57vtjYWBEWFiYyMzNFZmamOHv2rHj++ecFADFq1ChducmTJ4uAgAC9Y6uet0p5ebmIiIgQAwcO1G27ePGikMlkYvTo0UKtVuuV12g0QgghCgsLhYuLi3jyySf19qenpwtnZ2e97ZMnTxYAxHPPPad3nri4OGFjYyMyMzOFEEKsWrVKABDvvvuu3jkfeOABIUmSSEhIuO3vhqgxNMQ93bFjx2r3r1y5UgAQ8+fP120DIGQymTh9+rRe2W3btgkAYtu2bXrbL1++LACIJUuW6LZ16tRJ+Pn5icLCQt227du3CwAG7x2WhjUP1KI89NBDKC0txerVq1FYWIjVq1fXq3rz3Llz8PT0hKenJzp06IDPPvsMcXFx+Pbbb2s8ztbWVvf/3Nxc5Ofno2/fvjh69Khu+6pVq6DRaPDWW29BJtO/RSVJAgBs2rQJeXl5GD9+PLKysnQ/crkcPXr0wLZt2wyee+rUqXrnmTp1KsrLy7F582YAwNq1ayGXy/H888/rHffyyy9DCIF169bV8rdD1HhMdU/XpGqoc2Fhod722NhYhIeH1+uc165dw6lTpzBp0iS9odSxsbHo1KlT/YNtJFbmDoCoMXl6emLw4MFYvnw5SkpKoFar8cADD9T5PIGBgVi8eDEkSYJSqUS7du3g5eV12+NWr16Nd999F8ePH4dKpdJtr0oKAODSpUuQyWQ1vildvHgRADBw4ECj+52cnPQey2QyBAUF6W1r3749AOiaOK5cuQJfX184OjrqlevQoYNuP5GlMdU9XZOioiIAMLg32rZtW+9zVt1PISEhBvtCQkL0vlBYIiYP1OJMmDABTz75JNLT0zFixIga+yhUx97eHoMHD67TMbt27cI999yDfv364fPPP4ePjw+sra2xZMkSo8PNalI1n8SyZcuM9ii3suKtTS2HKe7pmsTHxwMw/KC/uSaxys1fBG6mVqtNGpO58R2GWpzRo0fj6aefxv79+/HLL7802vP+8ccfUCqV2LBhg9748yVLluiVCw4OhkajwZkzZxAVFWX0XMHBwQAALy+vWiUxGo0GiYmJutoGALhw4QIA6Dp5BQQEYPPmzSgsLNT7hnXu3DndfiJL1JD3tFqtxvLly2FnZ4c+ffrctryrqysAIC8vT2/7rTV3VfdTQkKCwTmMbbM07PNALY6DgwMWLVqEWbNmYdSoUY32vHK5HJIk6X0DSUpKwqpVq/TK3XfffZDJZHj77bcNZqwUN4ZMDhs2DE5OTnj//feNzithbLjoggUL9M6zYMECWFtbY9CgQQC0Y+bVarVeOQD45JNPIEkSRowYUbcXTNRIGuqeVqvVeP7553H27Fk8//zzBs2BxgQEBEAul2Pnzp162z///HO9x76+voiIiMD333+vaxYBtBNcnTp1yjQvoAGx5oFapMmTJzf6c8bFxWHevHkYPnw4JkyYgIyMDCxcuBAhISE4efKkrlxISAjeeOMNvPPOO+jbty/GjBkDhUKBQ4cOwdfXF3PmzIGTkxMWLVqERx55BF27dsW4cePg6emJ5ORkrFmzBr1799ZLApRKJdavX4/JkyejR48eWLduHdasWYPp06fD09MTADBq1CgMGDAAb7zxBpKSktC5c2ds3LgRf/75J1544QVdbQeRJbrTezo/Px8//PADAKCkpAQJCQlYsWIFLl26hHHjxuGdd96p1XmcnZ3x4IMP4rPPPoMkSQgODsbq1auRkZFhUPb999/Hvffei969e+Oxxx5Dbm4uFixYgIiICL2EwiKZebQHUYO7eVhXTUw1rKuKsaGa33zzjWjXrp1QKBQiLCxMLFmyRMycOVMYuxW//fZb0aVLF6FQKISrq6uIjY0VmzZt0iuzbds2MWzYMOHs7CyUSqUIDg4Wjz76qDh8+LBeHPb29uLSpUti6NChws7OTrRq1UrMnDnTYChoYWGhePHFF4Wvr6+wtrYW7dq1Ex999JFuiCiRJWiIexqA7sfBwUG0a9dOTJw4UWzcuNHoMQDElClTjO7LzMwU999/v7CzsxOurq7i6aefFvHx8QZDNYUQ4ueffxZhYWFCoVCIiIgI8ddff4n7779fhIWF3TZuc5KE4NRxREREliIqKgqenp4WvSgX+zwQERGZQUVFhcG6HNu3b8eJEyfQv39/8wRVS6x5ICIiMoOkpCQMHjwYEydOhK+vL86dO4cvvvj/9u41JoqrjQP4fwDZRdldKYJXXGAx9RZvYI0rlSpFUKqSqliJDRrRtvZmK03ExIKXwPoqYqtgtaZqsDXai0ZbtVqFWDdUv6AtNimgQA2tClZAQSiX834wO3FYbmtlgfX/S4zOmTNnz2zyjM+eOWfmM+h0OuTl5cHT07Oru9gqTpgkIiLqAh4eHggMDMTevXtRVlaGPn36IDIyEiaTqVsnDgBHHoiIiMhGnPNARERENmHyQERERDZh8kBkB8XFxZAkCfv37+/qrhBRM4xP2zF5ICIiIptwwiSRHQghUFdXh169esHZ2bmru0NEj2F82o7JAxEREdmEty2IOigpKQmSJCE/Px+LFy+GTqeDl5cX1q1bByEEbt68iblz50Kr1WLAgAFITU2Vj23pnuqSJUvg7u6O0tJSREVFwd3dHV5eXoiPj1e8eTM7OxuSJCE7O1vRn5bavHXrFpYuXYohQ4ZApVJh4MCBmDt3LoqLizvpWyHqHhif9sXkgchGCxcuRFNTE0wmEyZNmoRNmzZh+/btCAsLw+DBg7F582YEBAQgPj7e6rW8zTU2NiI8PByenp7YunUrQkJCkJqaij179jxR3+bNm4ejR49i6dKlyMjIwHvvvYf79+/jzz//fKL2iHoaxqeddMXbuIh6IsvbL1esWCGXNTQ0iCFDhghJkoTJZJLL7927J9zc3ERsbKwQQoiioiKrN+rFxsYKAGLDhg2Kzxk/frwIDAyUt7OysgQAkZWVpajXvM179+4JAGLLli1P54SJehDGp31x5IHIRnFxcfK/nZ2dERQUBCEEli1bJpf37dsXzz//PG7cuNFue2+++aZi+8UXX+zQcc25ubnB1dUV2dnZuHfvns3HEzkCxqd9MHkgstHQoUMV2zqdDmq1Gv369bMqb+8ioVar4eXlpSjz8PB4oouLSqXC5s2bcerUKfTv3x9Tp07F//73P9y6dcvmtoh6KsanfTB5ILJRS0u5WlveJdpZzNSRZWGSJLVY/vikLYtVq1YhPz8fKSkpUKvVWLduHUaMGIHc3Nx2P4fIETA+7YPJA1E35+HhAQCoqKhQlJeUlLRY32AwYPXq1Thz5gzy8vLw77//KmaWE9HT86zGJ5MHom5Or9fD2dnZamZ4RkaGYrumpga1tbWKMoPBAI1Gg7q6uk7vJ9Gz6FmNT5eu7gARtU2n02HBggXYsWMHJEmCwWDA999/jzt37ijq5efnIzQ0FNHR0Rg5ciRcXFxw9OhR3L59G6+99loX9Z7IsT2r8cnkgagH2LFjB+rr6/HZZ59BpVIhOjoaW7ZswejRo+U6Pj4+WLRoEc6dO4fMzEy4uLhg+PDhOHLkCObNm9eFvSdybM9ifPLx1ERERGQTznkgIiIimzB5ICIiIpsweSAiIiKbMHkgIiIimzB5ICIiIpsweSDqRElJSa0+vrYr+lFeXt7VXSEiB8DkgRzWkSNHIEkSjh49arVv7NixkCQJWVlZVvuGDh0Ko9HYZttLliyBJEnyH61Wi7FjxyI1NbVHPi2OqCewZ0y7u7vD398f8+fPx7fffoumpqandh6OgMkDOazg4GAAwMWLFxXlVVVVyMvLg4uLC8xms2LfzZs3cfPmTfnYtqhUKmRmZiIzMxPJycl47rnnEB8fj9jY2Kd3EkQks2dMp6WlISYmBgUFBZg/fz5CQ0NRVVX19E6mh+MTJslhDRo0CH5+flYXmpycHAghsGDBAqt9lu2OXGhcXFywePFieXvlypWYNGkSDh8+jG3btmHQoEFP4SyIyMLeMQ0AmzZtgslkQkJCApYvX47Dhw+3erwQArW1tXBzc+voKfVYHHkghxYcHIzc3Fw8fPhQLjObzRg1ahRmzpyJX375RTEcaTabIUkSpkyZYvNnOTk54aWXXgIAFBcXt1pv3759mD59Ory9vaFSqTBy5Ejs2rWrxbqnTp1CSEgINBoNtFotJk6ciK+++kpR59KlS4iIiIBOp0Pv3r0REhJi9evLory8HNHR0dBqtfD09MT7779v9bKehoYGbNy4EQaDASqVCr6+vli7di1vx1C3YM+YtlizZg1mzJiBr7/+Gvn5+XK5r68vXnnlFfz4448ICgqCm5sbdu/ejeLiYkiShP3791u1JUkSkpKSFGXZ2dkICgqCWq2GwWDA7t27u818qdYweSCHFhwcjPr6ely6dEkuM5vNMBqNMBqNqKysRF5enmLf8OHD4enp+USfd/36dQBo8/hdu3ZBr9dj7dq1SE1NhY+PD1auXIn09HRFvf379yMyMhL//PMPEhISYDKZMG7cOJw+fVquc/78eUydOhVVVVVITExEcnIyKioqMH36dFy+fNnqs6Ojo1FbW4uUlBTMmjULn376KVasWKGoExcXh48//hgTJkxAWloaQkJCkJKS0iNf3kOOx94xbfH6669DCIGzZ88qyv/44w8sWrQIYWFh+OSTTzBu3Dib2s3NzUVERATu3r2L9evXY9myZdiwYQOOHTv2n/rb6QSRA7t27ZoAIDZu3CiEEKK+vl706dNHHDhwQAghRP/+/UV6eroQQoiqqirh7Owsli9f3m67sbGxok+fPqKsrEyUlZWJwsJCkZycLCRJEmPGjJHrJSYmiuZhVlNTY9VeeHi48Pf3l7crKiqERqMRkyZNEg8fPlTUbWpqkv8eNmyYCA8Pl8ss7fv5+YmwsDCrfsyZM0fR1sqVKwUAcfXqVSGEEFeuXBEARFxcnKJefHy8ACDOnz/f7ndD1Jk6O6Zbk5ubKwCIDz74QC7T6/UCgDh9+rSiblFRkQAg9u3bZ9UOAJGYmChvz549W/Tu3VuUlpbKZQUFBcLFxcXq2tGdcOSBHNqIESPg6ekp3/e8evUqqqur5ZnXRqNRHuLPyclBY2Njh+6NAkB1dTW8vLzg5eWFgIAArF27FpMnT25xJvjjHr8fWllZifLycoSEhODGjRuorKwEAJw9exb379/HmjVroFarFcdbhjKvXLmCgoICxMTE4O7duygvL0d5eTmqq6sRGhqKCxcuWM0Qf/vttxXb7777LgDg5MmTir8//PBDRb3Vq1cDAH744Yf2vxiiTtS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+ "text/plain": [ + "
" + ] + }, + "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-deltadelta.ipynb b/nbs/tutorials/05-deltadelta.ipynb deleted file mode 100644 index 6bd5d03f..00000000 --- a/nbs/tutorials/05-deltadelta.ipynb +++ /dev/null @@ -1,697 +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 allows 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 lets us distill the net effect of the two variables. To illustrate this, let's delve into 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 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 4 groups of subjects for comparison. \n" - ] - }, - { - "cell_type": "markdown", - "id": "7a202204", - "metadata": {}, - "source": [ - "| | Wildtype | Mutant |\n", - "|-------|---------|----------|\n", - "| Drug | XD, W | XD, M |\n", - "| Placebo | XP, W | XP, M |" - ] - }, - { - "cell_type": "markdown", - "id": "be4d9084", - "metadata": {}, - "source": [ - "There are 2 ``Treatment`` conditions, ``Placebo`` (control group) and ``Drug`` (test group). There are 2 ``Genotype``\\s: ``W`` (wild type population) and ``M`` (mutant population). In addition, each experiment was done twice (``Rep1`` and ``Rep2``). We shall do a few 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 v2023.02.14\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": "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 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", - " })" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0c00f10e", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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44Rep1WPlacebo2.858019
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" - ], - "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 make a delta-delta plot, you need to simply set ``delta2 = True`` in the \n", - "``dabest.load()`` function. However, here ``x`` needs to be declared as a list\n", - "consisting of 2 elements rather than 1 in most of the cases. The first element\n", - "in ``x`` will be the variable plotted along the horizontal axis, and the second\n", - "one will determine the colour of dots for scattered plots or the colour of lines\n", - "for slopegraphs. We use the ``experiment`` input to specify grouping of the data.\n" - ] - }, - { - "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 v2023.02.14\n", - "==================\n", - " \n", - "Good evening!\n", - "The current time is Sun Mar 19 23:07:31 2023.\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 v2023.02.14\n", - "==================\n", - " \n", - "Good evening!\n", - "The current time is Sun Mar 19 23:07:42 2023.\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 -1.26, -0.535].\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": [ - "
" - ] - }, - "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 -1.26, -0.535], 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", - "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", - "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 experiment groups as well as the horizontal axis variable by setting ``experiment_label`` and ``x1_level``.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c9398a01", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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rqi5vdL0oqg2Wi4iIiG4xq56pFStWQBAEPPfcc1JHISIzVJxxBpd2rML5LR8iO/kPzb1NJZlnkbZnLa7s/go3rxzT2kZUq1CRnwmlvVuj+3YK7tboeufQ6AbXRUdHw8/PD9HRDbchIiKiusymZ+rIkSP44osv0KVLF6mjEJGZEUURF3//N66n7tQsyz2+G9f2fg9r90AUXTmqWZ554Gc4BESi49Q3kHv8T2Ts/xnVJY1PhW7jHgj//lNRcP4QynIu1V3vEQy3Dv0b3D4nJweZmZl6vDMiIiLzZhY9U6WlpXj44YexevVqODs7Sx2HiMzM9dSdWoXUbdWlBVqF1G3F6Sdw4ttXcHnH53ctpCztXRE+cSGKr56AvU8YrFx8IcjkAABBpoB7pxhETlsGmdzCMG+GiIiINMyiZ2ru3LkYO3Yshg0bhqVLl0odh4jMTE7KHzpvU5ZzscF1SicvOAZ0hr1vBFwj+uLcL++iKE17eKCtVyg63PcqrJw8dT42ERERNY3JF1Pr169HSkoKjhw50qT2VVVVqKqq0rwuLS01VjQiMhOVhdkG3V91yQ2EjX8eAHBpx6o6hRRwqxjLSNyA0LFPG/TYRERE9D8mPczv2rVrePbZZ/Hdd9/BysqqSdusWLECjo6Omp+YmBgjpyQiU6c0dO+QKOJ66i7cOLsfucf+bLBZ7sk41FY1PssfERER6U8QRVGUOoSxbN68GRMnToRcLtcsU6lUEAQBMpkMVVVVWuuAuj1TqampiImJQXJyMqKiolosOxGZjuzkbbj0x6e6bSTIAVHV7GNHzf4MNm4Bjbbx8/NDZmYmfH19kZGR0exjUl0qtRqHTl3B2fQc2FopERsVBg9nB6ljERFRM5n0ML+hQ4fixIkTWstmzZqFiIgILFy4sE4hBQBKpRJKpVLz2s7Ozug5ici0eUWNQnH6KeSditdarrBxgEJpW2cYoNLBHW6dByNz/0/NOq4gU8DClpPuSO16QTFe/WIz0q8XaJZ9tS0R00f2wcMjekuYjIiImsukiyl7e3t07txZa5mtrS1cXV3rLDdHolqFwkvJKL9xDZZ2LnCN6Ae5hfLuGxKRTgRBhvCJC+DZfQTyTiZAVV0Oe98IeHYdBkGuQO7xOOSf24/Km9ehqipHbXUFStJPwbPbcNy8chxVRdf1Oq5rRD9YWNsb+N2Qrt76+netQgoA1GoRX/9xAMHebugX2U6iZERE1FwmXUxRw8ryruLMhre1vhFX7LBD2IQX4dK+l4TJiEyXU1BXOAV1rbPcq/sIFFw4iMqC/z3rqTjjNIozTsO372R4R41B1uFfkXVkS5OPZe3qh5ARTxokN+nvdFoWzl9ruBjevDeVxRQRURtmdsVUfHy81BEkp66twanv36jz/JraylKc/XkFuj/5KaxdfCRKR2R+8s/uR+HFpHrXZR7YBK/uI2Hp4NboPqzdAmDt6guIIlxCe8I9cjDkFk2beIeMJy2n4C7r81soCRERGYPZFVME3Dizt8EHgaprq5GdvA0hwx9v4VRE5iv3VEIja0VkJ22DrVcwIMgAUV1vK/9+U+DRZYhxApLeXOxtGl/vYNtCSYiIyBhYTJmh0pzLja4vy7nUQkmICABUd5m+POvw5kbXOwV3h1unQQZMRIbSMyIIro62yC8qq3f9yF4dWzgREREZkkk/Z4rqZ2HT+HS8ChvesE7Ukhx8I3TbQJBBYW0PG48gBA9/HB0fWAyZnN+NtUZyuQwvPTQSVpZ1P5+eEYEY17+LBKmIiMhQWEyZIffOg28NF2qAR+TQlgtDRPDqMQZyZePDwbSIanh2G4GoJz+Fb++JkMktjBeOmi0qLACfL5iGyYOjEB7gCV83J/i5O6G6VoXf9p9ARVWN1BGJiEhPLKbMkJWjB4KGzKx3nXvnwZzNj6iFKR3c0OmBN6F09GzyNkVpx42YiAzNx80Jo3p1wvWCYmTeuImMvJs4djEDn26Kx3P//hEl5ZVSRyQiIj1wXIiZ8us7GXZe7ZCd9BvK8q5CEOSw9w2DT59JEARB6nhEZsfBvyOi532Jm1eOobokH6XZF5GdtLXB9jKFZQumI0P414Y/cbO0os7yy1k3sPaPA5g3OVaCVERE1BzsmTJjTsHdYO8bgeriG6jIv4bc47uR+sVcnN7wNlTV/JaUqKUJggzOId3h2XUYfHtPaHQ4rlvHAS2YjJrrWm4BTqdlN7h+V9IZqNT1z9RIREStF4spM5Z36i+k7VkDdW211vKC8wdxcdt/JEpFRABg5ewNn14T6l1n6xkCz64jWjgRNUdBceMzNpZXVqOquraF0hARkaGwmDJjmYd+aXBd3qm/UFVc/7OoiKhlhAx/HKFj5sHGLQAAoLC2h0/viYh8ZAXklnwgb1vi7+EMuazhf3I9ne1hreREIkREbQ3vmTJToiiiNOtCIw3UKLt+GUoHt5YLRUR1eEWNhlfUaIhqFQSZXOo4pCcXB1sM6tYecSnn6l0/YWA33q9KRNQGsZgyU4IgQGFli9rK0gbbKKzsWjARETXmdiFVkZ+J68d2oqo4H9aufvDsNhxKe1eJ01FTPHvfEOQXleL4pUyt5aP7dMbkmCiJUhERUXOwmDJj7pFDkH1kS73rrJy9Ye/XoYUTEVFjsg5vweWdXwAQNcsyEn9E+KSFcA3rI10wahJbayU+mDcFxy9lIOX8NVjIZRjQJRSBXiyGiYjaKhZTZixg4AO4eTkFFfkZWstlCkuEjp7LISdErUhp9sU6hRQAqGurce6Xd9Hz6a9hYeMgTTjSSZd2fujSzk/qGEREZAAspsyYhY0jus58H1lHtuLGmX1Q1VTCMbALfHvfC1uPIKnjEdEdco5ux98LqdvUNVXIPbEHvr3vbdFMRERE5o7FlJlTWNsjYNBDCBj0kNRRiKgRlYU5ja+/2fh6IiIiMjwWU0RErUBtZSmuH9uFm5dTIcgVcI3oB/eOgyBT3Jou28rJs9HtrRwbX09ERESGx2KKiEhiFQXZOPHty6gu+d+z3QrOH0R20m/o/PAyqCrLYOMe2OD2MoUlPLoM0fv4Xl5eWv8lIiKipmExRUQksYu/f6xVSN1WmnUeqf99BpWF1wFRXe+2glyBsHvnw8LGUe/jJyUl6b0tERGROWtVxVRGRgb8/DjDUUtS19Yg71Q8bpzZB3VNNRwCOsO7x2hY2rlIHY3ILFQUZKPo6okG11cWZNdZJsgVcAqJgp1XO3h1Gwmlo7sxIxIREVEDZFIHuFPnzp3x7bffSh3DbKiqK3Hyu1dxYetHKLyYhKKrx3Ft7/dI+XwuSnMuSR2PyCxUl+brvI2oqoWNmz8CY6axkCIiIpJQqyqmli9fjrlz52Ly5MnIz9f9AoN0k7H/JxRnnK6zvLaiGBe2/kuCRETmx8rZGxB0/1VclHbcCGmIiIhIF62qmJozZw6OHTuGwsJCdOrUCVu2bJE6kkm7nrqzwXVl16+gNPtCC6YhMk9Ke1e4hvfVeTuZwtIIaYiIiEgXreqeKQAIDg7Gnj178Mknn2Dy5Mno0KEDFArtmCkpKRKlMx2iKKK6tLDRNlUlBbDzbqFARGYsdMw8VBXnoTTrfJO3ces4wIiJiIiIqClaXTEFAFevXsXPP/8MFxcXTJgwoU4xRc0nCAKs3fxQceNaQy1g4+bfopmIzJWFjQO6zvoAhReTcPPKredMuUX0Q97pvcg6tLlOe1vPYHh2HdHyQYmIiEhLq6tSVq9ejRdffBHDhg3DyZMn4e7Om6uNxafnOFz6Y2W965zb9YC1i08LJyIyX4Igg0v7XnBp30uzzN43AtYufsg68isqblyDwtoeHl2GImDgg5BbWkmYloiIiIBWVkyNGjUKhw8fxieffILp06dLHcfkeUWNQVluGnKSt2ktt/Vqh/bjn5coFTVXZXUN9h67iILiMgR4uqBXxyDIZa3q9kjSgXeP0fDuMRqiWgVBJpc6DhEREd2hVRVTKpUKx48f57OmWoggCAgdPRc+0eNw48w+qGoq4RgYCed2PSDoMbsYSe/Ayct49/sdKK2o0izzcnHAW4+PR7C3m4TJqLlYSBEREbU+giiKotQhWrOUlBT06NEDycnJiIqKkjoOUYMy8grx5LvfoaZWVWedu5Mdvn5tJix5/2GbVFNehIr8TFjYOsLaxVfqONREKefTsTXxOLJu3ISHswPG9OmMvp1DpI5FREQGxCsrIhOxdd/xegspAMi7WYq/Ui9gWHSHFk5FzaGqrsCl7auQdyoeoqoWAGDv1wGhY+bB1iNI2nDUqHU7D+HrPw5oXl/OuoGDpy5j0qDueGpijITJiIjIkDiWi8hEXMrKa3x9ZuPrqfU5s3E5co//qSmkAKAk4wxOfPcKqkr4YPPWKv16gVYhdadNfx3FyctZRjt2dHQ0/Pz8EB0dbbRjEBHR/7BnilqN6Oho5OTkwMvLC0lJSVLHaXMcbK0bXe94l/XUupRknsXNy/U/U6+2vBg5Sb8jMJYT9bRGu46cbnT997sOo52vGxRyOQZ2DUWIj+Fmrc3JyUFmZqbB9kdERI1jzxS1GrcvAnJycqSO0iaN6NnwED6ZTMCQHhEtmIaaq/ByauPrrxxtmSCks5ulFY2uP3I2Det3J+G7nYfwj/fW4d3vd0Ct5u3LRERtEYspIhPRu2MwhjdwT9ST4wbCw9m+hRNRc8jkjc/eJ5NxYEFr1U7HnqZdR87gp7hkI6UhIiJjYjFFZCIEQcCCh0bg1UdGIyosAIGeLhjYJRTvz52MyYM5E2Vb4xreD4DQ8PqIfi0XhnQyvGcH2Nvo9lDlX/cdAyfXJSJqe/jVJpEJEQQBsVHhiI0KlzoKNZO1qy+8okYhJ+WPetb5wbP7SAlSUVPYWiux7MkJWPLVbygoLmvSNnk3S1BRVQMbK0sjpyMiIkNiMUVE1Eq1Gz0HVi4+yD68BVXFeZBZWMG9cwyCBs+AQmkjdTxqRIdAb3y36FEknriIzBtFsJDJsPq3fQ22t7WyhNKS/yQTEbU1/M1NRNRKCYIMfn0mwbf3RKiqyiCzsIJMzl/bbYWFQo7B3f/XS7z/1GWculL/tOjDe3aEXMaR90REbQ1/cxMRtXKCIEBhZcdCqo17YeowuNjX7VEM9XXHjNF9JEhERETNxX+ZiYiIWkCApws+f2kafj9wEkfPp0Mhl2Fgl/YYFt2BQ/yIiNoo/vYmaiOOXbyG89dyYW9jhYFdQmFrrZQ6EhHpyMnOBg8P74WHh/eqs65WpUJcynnsSTmL8spqdAj0wvgBXeHj5tTyQYmIqElYTBG1cjduluKN/27BhYxczbJPN8Vh7qRYjOrdScJkRFQflVqNw6fTkJ1/E14ujujdMRhyeeOj6qtra7Fo9RaknE/XLDudlo3fD5zAW49PQPf2/saOTUREemAxRdTKLVmzVauQAoDK6lr868c/4efuhM4hvk3ajyiKqKqphZWlhdbyK1k3UFZZhSBvV9hZ6/ZsHCLSdi79Ot76+jfkFpZolrk52mHRzDHoGOTT4HZb9h3XKqRuq6yuxbvrduC7RY/etSAjIqKWx2KKjKK6tAA3r6QCggzOIVGwsHGQOlKbdPJyFs6lX693nVoUsSnh6F2LqbLKKny74xB2Hj6FkvIqeDjbY1z/LogI8MLKXxJwJfsGAMDKUoFRvTvjyfEDYaGQG/y9EJm6sooqvPrFLyguq9RafqOoFK9+/ismDOyC/Scuo7SiChGBXpgcE4XOIbcKrB2HTzW43xtFpUg+fxW9OgQbNT8REenOpIupzz77DJ999hnS0tIAAJ06dcIbb7yB0aNHSxusGWory5B36i9UF+fBysUbbh0HQm4hXW+CKKpRknEWqupK2Pm0h8LKFpd3rkZO8jaI6loAgExhCd8+kxA4+BHJcrZVFzLqL6RuSz6fjkfe/gqCIKB3x2DcNzgKni7/K1yra2vx8mebcPaOgiy3sAT//S0RMkGAWhQ1yyura7F5byrKq6qx4MERhn8zRCZu55HTdQqp28oqq/D9riOa1/uOX8T+E5ew4KERGBbdAYXF5Y3uu+Au64mISBomXUz5+fnhn//8J0JDQwEAa9euxYQJE3D06FF06tT27jXJP38I5ze/B1V1hWbZlT+/Qof7XoO1iw/yzuyFqrIM9r5hcArpAUEQIIoiyvOuQlSrYOMeaNCplfPPHcDlnV+gqujWEDSZwhI2HkEozTqv1U5dW41r+9bDwsYRPr3GG+z45sDR1rrR9eWV1SivrAYAbN6biriUc/hg3n0I9HIFAOxJPqdVSN3pzkLqTn8eOYPpI/toFWVEdHcXruXevdEd1KKITzfFY0CXUAR6ueD4pcwG2wZ7uzY3HhERGYFJF1Pjxo3Ter1s2TJ89tlnOHjwYJsrpipv5uLszysgqmq0ltdWFOPkD4sgqmoBUa1ZbuMRBO/occg8+DMqC249JNLSzgV+/e+HT0/t/y/6KEo/iTMbl2sdU11bXaeQulPmwU3w7nkPBIHj/puqb+d2sLGy1BRMd1NUVoHPf/0Ly/8xEQCQePyizsdUiyKOXrjGyS2IdGRvo/sogdKKKmz+KxXt/TwaLKY6BHohPMCrufGIiMgITLqYupNKpcJPP/2EsrIy9O3bt8F2VVVVqKqq0rwuLS1tiXh3lXP0jzqF1G1ibd0L7fLcNFza9h+tZdWlBbi8YxUAET49/9dDVFNejILzh6CurYJDQGfYegTVPYaoRuGlZJTlXILC2hH5ZxO1CqmmqCrOQ3VJAZQObjptZ86slRZ4dsoQvLNuB9Tq+nuS/i7p7FW8vvpXlJRX4nphsV7H5T1TRLobFt0Bm/46qvN2//09scF1Qd6uWDRzbHNiERGREZl8MXXixAn07dsXlZWVsLOzwy+//IKOHTs22H7FihV48803WzBh05Tn1Z3lSV/X9v0Ir6gxkMkVyNj/E9L/+h7qOwoyl7DeCL93AeSWt4aYVRZm49SPb6LixrXmHViQQW7J2eJ0NSQqAr5uTvjlr1Scv3YdlhYKXMrMa7C9CODQ6St6H09poUDPiKAG10dHRyMnJwdeXl5ISkrS+zhEpqa9vwcmD47Cz/EpzdqPtaUFxvaLRLdQf/TsEASZTDBQQiIiMjSTH28VHh6O1NRUHDx4EE899RRmzJiB06dPN9j+lVdeQVFRkeYnISGhBdM2zNLWyWD7qim7ibLrl5F36i+k7flaq5ACgILzh3Dx908A3JpO+/SPbzW/kALgHBoNhZVds/djjsIDvPDytFH46pUZ+PdzU+96L1VTKBqYZvnBYT3hYNtw0ZuTk4PMzEzk5OQ0OwORqZk9YRDemHUPosIC4O3qiG7t/TF9ZB/IhKYXRBXVNXB3skfvTsEspIiIWjmT75mytLTUTEARHR2NI0eO4OOPP8bnn39eb3ulUgmlUql5bWfXOi7+PboOR87R7QbbnyDIkHnolwbX553+C0FDZ6E87yrKbzS/V0xh7YDgobOavR8CLBUKTBjYFd9sP6jX9jKZgH6d2+Hh4b2wMT4Ffx27gJpaFQI8XTAltgfvlSJqpoFdQjGwS6jWMi9XB3y6KR5lTbz/8dSVLEyK6W6MeEREZEAmX0z9nSiKWvdEtRUOfhHw63cfMvZvbPa+LGydUJaXjtKsCw03EtUou35Zr+GFAYMeRmVRLgovJUEQZHAJ6w2/PpNh5cwbqA3l4eG9UVRaga37jzf5XqoRvTpieHQH+Lo7wd3JHgDw8rRRmP/gcFTXqGBjZWnMyEQmoaZWhbiUc4hPPY/K6hp0CfHFPf27wM2x8S/ehvfsiAFd2uOvY+dxOesGrmTdwNELDff4WyktGlxHRESth0kXU6+++ipGjx4Nf39/lJSUYP369YiPj8f27Ybr4WlJQUNmwSmoG3JSd6CqKA9Wzt7w7DYC1/atR1HaMa22guzWR3v7WU93qim7iQtbPrjr8RRWdrCwcWy0jYWtE1RVFVDXVsHeLwJ+fe+Da3jDE3w0xsvLS+u/1DCZTMC8ybGYOjQaSWevQhAEnLiUiZ1HGh7CGhnii27t/essV8jlUMg54QTR3VRW1+DVLzbjxB2z7p24lIkticew4h+TIJcJSDp3FXKZDH07h8DP3VnTThRF/LgnCb8kHEV51d17pwZ3D9MrI3+PEhG1LJMupq5fv45HHnkE2dnZcHR0RJcuXbB9+3YMHz5c6mh6cwrpDqcQ7aEfDv4dkHcyHnkn41FbWQZ7nzB49xyH2ooSXI3/FkVXj+t8HCtnb8iUNlDVVkOmsKxzX9VtQbEz4dltOES1CoKseRfknMxAd+5O9gjz98SBU5dhZamATCbU21PlZGeD2O7hEiQkMh0/7UnWKqRuKymvwoKVG1FR9b8ZV1dv3YsxfSLxzH1DIJMJ+HbHQazbeahJxxnYJRTR4YF6ZeTvUSKilmXSxdR///tfqSO0CJncAp5dh8Oza90iMfKRFaitLMPZX97BzUvJTdqfoLCAXGmL1C/mNtrOJaw3PLoMubVNMwsp0p1KpcY73+9AXMo5reW3H9Z8m7O9Dd5+fDyUliZ9uhMZ3fbDpxpcd2chBQCiCPx+4AS8XR0xfkBX/JzQ8JTpSgsFBAHwcHbA2L6RmDCwKwTh1hcjSefSkJ1fDG9XB0SHc2Y/IqLWhldXZkBhZYuy7MYf3mpp7wqZ3AIOgZEoz7vawMN3BVi5eEPp4A6PLkPh0XkwiygJfbvzUJ1CCrg1nCgyxBedQnwQ4OGCmG7tYWnBU52ouQqKy3Te5td9qYgI8Gr0wdtVNbUY2DUUadn5+OvYBVhZKhDs44bl3/yBnIL/PSvOy8UBi2aORZi/p175iYjI8HiFZSbkltaoKS9quIF468G81WWFDRRStxrZ+4Qh/N4FRsnI5xc1XU2tCr8lNjx880JGLpY+MYGTShAZUICnCy5n3dBpm7ybpahRqe7abu+x2194FeLUlSwo5DLUqrQfjJ5TUIxXP9+Mr1+bATvr+h9fwN+jREQty+SfM0W3uHUa1Oj66tJ8VBXl4ubFxv/xLck4a8hYWvj8oqYrLClHUVlFg+srq2uQU9BI8UxEOrt3YDedt7G0kKNzsA88ne112u7vhdRtRWUV2Hm44Ylm+HuUiKhlsZgyE759JsHa1a/Z+5ErbQyQhprL3kYJi0Zm4JMJApzs+FkRGdLoPp0xcVA3/P35u1aWDU9jbmulxIKVGxHk7QpBhwf3NuZc+nWD7IeIiJqPw/zMhIW1PbrMeA+ZB39G3skE1FaVQ11TCVF99+End3LvPNg4AUkn1kpLDOwaij313DMFAHK5DA8u+RL+Hs6YMLAb7ukXabALOSJzNmfiYIzr3wUJqRdQWVWDyHa+cHOyw8KVm+rtLS4sKUdhSTmAW71U/u7OuJR1Awq5DL7uzriak69zBjub+of4ERFRy2MxZUYsbBwQNGQWgobMgiiKSFw2Tqft7bxD4d1jjJHSka6eHD8IZ9OvI+vGzTrrampvFclXrxfg3xv34Er2DTxz35AWTkhkmvw9XDBtRG+tZavmP4xfE48h+exVFJaU40ZRaZ3tqmtUKCgpR5ifO85n5CFdj0IKAIb2iNBrOyIiMjwO8zNTgiDAzqd9Iw1kcAiIhMLaAVbOPgiImYbO01agPC8d2cm/I+/UX1DVVLZcYKrD1dEWK194EE+OH4jIdr7wdXdqsO3WxONIy9bvwo2I7s7NyQ6Pje2PlS8+BEuLhofgFpaU43xGHgCg7hPh7m5c/y7oGOStZ0oiIjI09kyZGVGtQmn2BahVtfDpOQ7nf/2g3naWds4ozb4AmcICTkFd4BwSjdM/LEZxxv9ufJZb2SJ09Fy4d4ppqfj0N7bWSkyJ7YEpsT3w+upfkZl3s8G2CannEeTdt+XCEZmp28P69GWjtMSzU4YAAvDb/hPIyS+Gl6sD7ukXiSFR7JUiImpNWEyZkbxTfyFt91eoKr71rajcyhZOIVEoTj8FdW2VVtvqklu9GOqaSuQc3Y7rx3bVub9KVVmGc5vfh5WTF+x9w1vmTVCDqmpqm7WeiAwjyMsVZ67qNpueAODRe/rDxd4WA7qEah5rwOKJiKh14zA/M1Fw8QjO/fKuppACbhVDNy+nwKf3BISNfxHtRs+Fpb1bvds3OFGFqEbW4S3GiEw66hLi2/j6do2vJyLDmDiou87biLg19fqIXh35fDgiojaExZSZuLZ3PRoaoZ+d/DtcO/SDnVc7VJfo9kBKACjJbughv9SSxvaLhINt/bN8tfN1R68OwXWWl1VU4dDpKzhyNg1V1ey5IjKE2KhwPDyiN2Qy7Rk0//76Tp2CfRqdYp2IiFonDvMzA6qaKpRkNvywXVVlGYqvnYaqWr8JJRRWdvpGIwNycbDFO09NwrvrduJK9v+K4h7hAVj48EitCzlRFLF2+0H8HJ+CyuoaALeeXTV9ZF/cO6hbS0cnMjkzR/fF6N6d8NexC6ioqkanYB+cT7+Or7btr9NWJgh4eEQvCVISEVFzsZgyA4JMBggyQFQ32ObU92/gVs+VAF3nmPLoHNusfGQ4ob4e+OKlaTiXfh35xaUI8HCBn4dznXY//HkE63Ye0lpWUl6FT3+Jh621EsN7dmipyEQmy9PFAVNie2he9wgPhEwmw4a4JBSX3fryysvFAU+MG4ieEUESpSQiouZgMWUGZHILuLTviYLzhxppJf7tv03j4NcRnt1H6p2NmqaquhapF6+hVqVC52BfONpZN9o+PMATgGe966pravFzQkqD2/64+wiLKSIjmTo0GtERgdiVdAZKhRzj+neFmxN794mI2ioWU2YiIGYaitKOQ1VdofO2Cis7BMbOgI2bPzIP/4qy7ItQWNvDI3IIvHqMgdxCaYTEdNvWxONYs20/SspvfZNtoZBjXL8ueHLCQMhlut/2eDWnQPOteL3rrxfgZmk5nOxs6l3v5eWl9V8iaprqmlq8s24H/jp2QbNsQ1wy7h8SjVlj+kmYjIiI9MViykzYeYYgcsa7SE9Yh4ILh/9/yF/jQ/r8BjwAB99wOAZ11RRMjoGRLROYANx6NtS/N+7RWlZTq8Kmv47CQiHH4+MGNGk/GXmF+GlPMg6fSYNK3fBwT+DW/RuWioZ/NSQlJTXpmESkbeUvCVqFFADUqtT4ftdheDjZY2w//n4lImprWEyZETvPEHS8fxHUqhqIajUOvjel4SnPATj4dYBLaHQLJqS/W//nkQbX/brvGCwVcmTlF8HFwRYjenZEkLdrnXYXM3Ix/9ONKKusbtIxoyMCOTUzkYEVl1Vg55HTDa7/OSGFxRQRURvEYsoMyeQWgBxwCumOwov19zLILa3h4N+xhZPRnSqqanAxM6/B9ZXVNfj2jkkkfopLxqNj+2Fc/y6ISzmPguIyBHi6YGvi8SYXUjZWlnh0bP9mZycibVey81FT2/CXV9dyC1FRVQ1rJb/IICJqS1hMmTH//lNx83IqRHXd5wv59JoAhbL+e2aoZVgoZFDIZahVNT4s705f/b4f3+04hOpGLtr+TiGXQS6ToV9kOzw8vBcCver2bt0pOjoaOTk58PLy4pA/oiZysKn/GXC3KS0UWsNri8sqEHf0PApLyhHs5Yr+XdpBIZcbOyYREemIxZQZUtfWoCj9JERVDcInLkD6X9+jPO8qAEBuZQsrZ2/cOLsfN6+kwq3TIHh1Hwm5ReMXAmR4CrkcA7qEIv6obg9F1qWQAoCvXpkBb1fHJrfPyclBZmamTscgMnfBPm5o5+uOSw30Ng/oEgrx/+9h3XXkND76aTeqa/53Lrs72WPZExMQ7OPWInmJiKhpWEyZmZyUP5AW/w1qy4sBADILJXx6jkP4xJdQlHYMl//8CmXZFzXtSzLPIu9kPCKnLYfcsvHpuMnwZozui6Pnr6GoTPdZGJvC1dEWHk72Rtk3EWl75r4heGXVLyiv0h52KxME7E4+i/0nL6FnRBD2nbgItVp7cqC8myV4bfWv+Ob1meyhIiJqRXSfV5narBtn9uHitk80hRQAqGuqkLF/I/JOJSDjwM9APUP+SrPOI/PgL0bP5+XlBV9fX065fQc/d2f857kHMKp3J9haWUJpoUCIt+G+mZ4cEwW5nL8GiFpCxyBvfPrigxjXvwt83Z1ga31rllS1eKtwqqiqwV/HLtQppG7Lu1mCxBOXWiwvERHdHXumzMi1xA0Nrss6/CvUNVUNrs89sQcBgx4yRiwN3n9TP283R7z4wHC8+MBwAEBZZRUeWvLfOt9u341MEDQXbRZyOSbGdMN9g6MMnpeIGubn7oxn7huCy1l5+Md763Te/kp2PmK6Nbyez4EjImpZLKbMhKq6AmU5DX+j2VghBQA1d/RmkbRsrZR4bcZovLXmd1TV1O1JrI9cJsMnLzyAK1n5EIRb05839FBeIjK+xOP69TA52zd+3vJLKSKilsViykwIMgUEmbzR50o1xtYzxMCJqDl6dQjG16/NxPZDp3A1Jx/O9jYY2asTNu9NxfZDp7TaCgIwd9JghPp6INTXQ6LERObnzNVs7Ek+h7LKKnQI9MKw6A6aqc9r7/Lw7PpYWsgR2z3M0DGJiKgZWEyZCZnCAi5hvZF/dn+96y3tXaGwdkB57pV61/v2nmDMeKQHN0c7TBvRG7UqFX7ck4zXV/+KG0WlsLWyhLO9DayVlgjycsW4AV3QIdBb6rhEZuWjDbvx+4ETmte7jpzBdzsP45+zJyLY2w1RYf74ftfhJu9PJhPw7H1D4WDLiYCIiFoTFlNmJHDwIyhKO47aytI666pL8lFdkl9nuSBXIHDwdLiG922JiKSHpWu3ad2UXlZZjbLKanRr748XHxwOuYwTTBC1pF1HTmsVUrcVFJfh7a9/xwNDeyL14jW42NugoKS8TjtnO2s8Nm4AEk9cQmFxGYK83TBhQFeE+rFnmYiotWExZUZs3ALQZdYHuLbvR+SfTYS6tgYQ6x9qYu8bAY8uQ+EW0R8Wtk1/BhG1rGMXMxqc3Sv1wjUcOHkZA7qEtnAqIvO2NfF4g+uu5RbivR92ai0TANyevy+ynS+emzIUAZ4uGNmrk/FCEhGRQfArazNj4+qH8Akvot/CTXDw79hgu9KcSyyk2oC9xy40az0RGV52fpFO7UUAw3t2wNrXZuLDeVMQ4OlinGBERGRw7JkyU6Ioovja6YbXq2pQknUOLu17tWAqqk91bS32JJ/DvuMXUVOrQvf2/hjTtzMcbK1RU9v4hCLVd1lPRIbn6eKAm6W6PWj7yJmrmP/ACCMlIiIiY2ExZaYEQYDcQglVdcP/4MssrFowEdWnrLIKL3+2CWfTr2uWpZxPxy97U/H+3Mno1t4f2w6ebHD77u39NX9Wq0Wcu5aD6ppatPfzhI2VpVGzE5mrsX0jce6Oc7YpbpaWo7K6huclEVEbw2LKjLl1HITrqTvqXWdp7wrHAI7Xl9q3Ow5pFVK3FRSX4YP1f+L9uZMRuNMFV68X1Gnj5eKAYdEdAAB7j1/EF7/+hZyCW88Ls1FaYsLArpg5uh9kMsG4b4LIzIzq3QknLmViV9KZJm/jZGcNK0sLI6YiIiJjYDFlxvwHPoDCS0l1Z/ETZAgZ/gQEmVyaYATgVk/SzsOnGlx/6koW3vhyCyAAjrbWKCr7Xy+jo6017KyV+GxzAsL8PfDJpnio1aJmfXlVNX748whEUcRj9www6vsgMjeCIOClh0diZO+O2J18DmUVVYgI9MLhM1eQeiGj3m1G9+nMLzaIiNogFlNmzMrRA11nfoBriRtw48xeqGuq4BgYCd++98EpqIvU8cxedW0tSsqrGm1z5OxVrdeezva4XliCorIKFJVV4GJmXp2H+N5p895jeGBoT9haKw2SmYj+p2uoP7qG/m+o7eDuYViw8mdk5t3Uate9vT+mjejdwumIiMgQWEyZOaWjO0LHzEXomLlSR6G/sbK0gIezPXILS5q8zXUd2gJAZXUNTl/NRs+IIB3TEZGu3J3s8fn8aYg7eg5Hz6dDoZBjQGQoencMZq8UEVEbxWKKqBUb378rvvxtn1GPYangcE6ilqK0VGBU704Y1Zv3pBIRmQI+Z4qoFbsvNgpDe0QYbf8u9jboFOxjtP0TERERmTL2TBG1YnKZDC9PG4XJMVHYe/wCampVqFWpsXlvarP3LQjAY/cMgELOnikiIiIifbCYImoD2vt7oL2/BwCgoqoa8UfP6fRQUHsbK3QL9cOBU5dRq1KjQ6AXHhzWC307hxgrMhEREZHJYzFF1MZYKy2x9Il7sfirLcgvKtMst7WyxJi+kdiy7xiqamo1y53tbfD24+MRHuAFlUoNlVoNSwv9T30vLy+t/xIRERGZK0EURfHuzcxXSkoKevTogeTkZERFRUkdh0ijplaF/ScuISOvEG5OdhjUNQzWSgsUl1ViT8pZ5BeXIcDDBTHd2jereCIiIiKi+vEKi6iNslDIEdM9rM5yB1srjO/fFVU1tbBWWkiQjIiIiMg8mHQxtWLFCmzatAlnz56FtbU1+vXrh3feeQfh4eFSRyMyirKKKnyz/SB2HjmN0ooqeDjbY3z/rrgvNgpyGSfvJCIiIjIkk766SkhIwNy5c3Hw4EHs2rULtbW1GDFiBMrKyu6+MVEbU11Ti5c+24RNfx1FaUUVACC3sARf/rYP7/+wS+J0RERERKbHpHumtm/frvV6zZo18PDwQHJyMgYNGiRRKiLj2J18FuevXa933Z9JZzB5cHeE+nq0cCoiIiIi02XSPVN/V1RUBABwcXGROAmR4e09frHx9ccaX09EREREujHpnqk7iaKIF154AQMGDEDnzp0bbFdVVYWqqirN69LS0paIR9RstbWqZq0nIiIiIt2YTc/UvHnzcPz4cfzwww+NtluxYgUcHR01PzExMS2UkKh5uof532V9QAslISIiIjIPZlFMPf3009iyZQvi4uLg5+fXaNtXXnkFRUVFmp+EhIQWSknUPGP6RMLF3qbedR0CvdAjnMUUERERkSGZdDEliiLmzZuHTZs2Yc+ePQgODr7rNkqlEg4ODpofOzu7FkhK1HyOdtZ4b+596BTso1kmkwkY2LU9lj5xLwRBkDAdERERkekx6Xum5s6di++//x6//vor7O3tkZOTAwBwdHSEtbW1xOmIDC/A0wUfPXM/MnILkV9cCl93Z7g58gsBIiIiImMQRFEUpQ5hLA19E79mzRrMnDmzSftISUlBjx49kJycjKioKAOmIyIiIiKitsyke6ZMuE4kIiIiIiKJmfQ9U0RERERERMbCYoqIiIiIiEgPJj3Mj8gcFZVWYHfyWRQUlyHA0wUx3cKgtOSpTkRERGRovMIiMiF7ks/igx93obpGpVm2eus+vP34eEQEekmYjIiIiMj0cJgfkYlIy87Hu9/v1CqkAOBmaTkWffkrKqtrJEpGREREZJpYTBGZiC2Jx6BSq+tdd7O0AvFHz7dwIiIiIiLTxmKKyESkXy9odH1aTn4LJSEiIiIyDyymiEyEs71No+tdHWxbKAkRERGReWAxRWQiRvXu1OA6hVyGoT0iWjANERERkeljMUVkInqEB+Legd3qLJcJAp6dMhQu7JkiIiIiMihOjU5kQuZOGoxeHYOw/eAp5P//c6bG9++CUD8PqaMRERERmRwWU0QmpmdEEHpGBEkdg4iIiMjkcZgfERERERGRHlhMERERERER6YHFFBERERERkR54zxRpZGdnIzs7W+oYZCDe3t7w9vaWOgYZEM9R08JzlIio7WMxdRfe3t5YvHixyf+DV1VVhQcffBAJCQlSRyEDiYmJwY4dO6BUKqWOQgbAc9T08BwlImr7BFEURalDkPSKi4vh6OiIhIQE2NnZSR2Hmqm0tBQxMTEoKiqCg4OD1HHIAHiOmhaeo0REpoE9U6SlW7du/IfdBBQXF0sdgYyE56hp4DlKRGQaOAEFERERERGRHlhMERERERER6YHFFAEAlEolFi9ezBuhTQQ/T9PDz9S08PMkIjINnICCiIiIiIhID+yZIiIiIiIi0gOLKSIiIiIiIj2wmCIiIiIiItIDiykiIokFBQXho48+arHjxcfHQxAE3Lx5s8WOaW7S0tIgCAJSU1Nb7JhLlixBt27dWux4RETEYqpNmTlzJgRBwOzZs+usmzNnDgRBwMyZMxvc/vYFlCAIkMlkcHR0RPfu3fHSSy8hOzvbiMnpTqtWrYK9vT1qa2s1y0pLS2FhYYGBAwdqtd27dy8EQcD58+fr3deSJUs0n6lCoYCbmxsGDRqEjz76CFVVVUZ9H+bCkOedIAhwd3fH6NGjcezYMSOmpvoY69yTy+Xw9/fH448/jry8PKO+ByIial1YTLUx/v7+WL9+PSoqKjTLKisr8cMPPyAgIKBJ+zh37hyysrJw5MgRLFy4EH/++Sc6d+6MEydONLhNdXV1s7PTLbGxsSgtLUVSUpJm2d69e+Hl5YUjR46gvLxcszw+Ph4+Pj4ICwtrcH+dOnVCdnY20tPTERcXhylTpmDFihXo168fSkpKGtyOn2nTGeq8y87Oxu+//47CwkKMGjUKRUVFxopM9TDmuffZZ59h69atmD59ulHfAxERtS4sptqYqKgoBAQEYNOmTZplmzZtgr+/P7p3796kfXh4eMDLywthYWF44IEHkJiYCHd3dzz11FOaNjNnzsS9996LFStWaF1QCIKAzZs3a+3PyckJX3/9teb1/v370a1bN1hZWSE6OhqbN29u8eEurVl4eDh8fHwQHx+vWRYfH48JEyagXbt22L9/v9by2NjYRvenUCjg5eUFHx8fREZG4umnn0ZCQgJOnjyJd955R9MuKCgIS5cuxcyZM+Ho6Ignnnii3uFeqampEAQBaWlpmmWrV6+Gv78/bGxsMHHiRHz44YdwcnJq7v+KNsOQ512vXr3wwQcfICcnBwcPHqy37YcffojIyEjY2trC398fc+bMQWlpqVabxMRExMTEwMbGBs7Ozhg5ciQKCwsBAKIo4t1330VISAisra3RtWtXbNy4sc5xEhMT0bVrV1hZWaF37951vlD5+eef0alTJyiVSgQFBeGDDz5o0nttrYx17vn6+uKee+7BM888g507d2oV3bepVCo89thjCA4OhrW1NcLDw/Hxxx/XaffVV19p/p97e3tj3rx5mnVFRUV48skn4eHhAQcHBwwZMqTeHs7PP/9cc75OmTJF6/xWq9V466234OfnB6VSiW7dumH79u2Nvk8iImoYi6k2aNasWVizZo3m9VdffYVHH31U7/1ZW1tj9uzZSExMRG5urmb57t27cebMGezatQu//fZbk/ZVUlKCcePGITIyEikpKXj77bexcOFCvbOZqsGDByMuLk7zOi4uDoMHD0ZMTIxmeXV1NQ4cOHDXC7r6REREYPTo0VoX/wDw3nvvoXPnzkhOTsaiRYuatK/ExETMnj0bzz77LFJTUzF8+HAsW7ZM50xtnSHPO2trawBATU1NvetlMhn+/e9/4+TJk1i7di327NmDl156SbM+NTUVQ4cORadOnXDgwAHs27cP48aNg0qlAgC8/vrrWLNmDT777DOcOnUKzz//PKZNm4aEhASt4yxYsADvv/8+jhw5Ag8PD4wfP16TKTk5Gffffz8eeOABnDhxAkuWLMGiRYu0vjhpi4x57llbW0OtVmsNI7xNrVbDz88PGzZswOnTp/HGG2/g1VdfxYYNGzRtPvvsM8ydOxdPPvkkTpw4gS1btiA0NBTArQJ57NixyMnJwbZt25CcnIyoqCgMHToUBQUFmn1cvHgRGzZswNatW7F9+3akpqZi7ty5mvUff/wxPvjgA7z//vs4fvw4Ro4cifHjx+PChQs6vVciIvp/IrUZM2bMECdMmCDm5eWJSqVSvHLlipiWliZaWVmJeXl54oQJE8QZM2Y0uH1cXJwIQCwsLKyz7o8//hABiIcOHdIcy9PTU6yqqtJqB0D85ZdftJY5OjqKa9asEUVRFD/77DPR1dVVrKio0KxfvXq1CEA8evSoPm/bJH3xxReira2tWFNTIxYXF4sKhUK8fv26uH79erFfv36iKIpiQkKCCEC8dOlSg/tZvHix2LVr13rXLVy4ULS2tta8DgwMFO+9916tNvX9nTh69KgIQLxy5YooiqI4depUcezYsVrbPfzww6Kjo2PT33AbZujz7saNG+L48eNFe3t78fr166Io3vps/vWvfzW4jw0bNoiurq6a1w8++KDYv3//etuWlpaKVlZW4v79+7WWP/bYY+KDDz6olWn9+vWa9fn5+aK1tbX4448/iqIoig899JA4fPhwrX0sWLBA7NixY4M52wJjnXtnzpwRQ0NDxV69eomiKIpXrly56++9OXPmiJMnT9a89vHxEV977bV62+7evVt0cHAQKysrtZa3a9dO/PzzzzWZ5HK5eO3aNc36P/74Q5TJZGJ2drbmGMuWLdPaR8+ePcU5c+Y0mJOIiBqmkKiGo2Zwc3PD2LFjsXbtWs23lW5ubs3apyiKAG4N47stMjISlpaWOu3n3Llz6NKlC6ysrDTLevXq1axspig2NhZlZWU4cuQICgsLERYWBg8PD8TExOCRRx5BWVkZ4uPjERAQgJCQEL2OIYqi1ucJANHR0Trv59y5c5g4caLWsl69ejW5t9JUNPe88/PzAwCUlZWhffv2+Omnn+Dh4VFv27i4OCxfvhynT59GcXExamtrUVlZibKyMtja2iI1NRVTpkypd9vTp0+jsrISw4cP11peXV1dZ0hi3759NX92cXFBeHg4zpw5AwA4c+YMJkyYoNW+f//++Oijj6BSqSCXy5v83lsTQ557J06cgJ2dHVQqFaqqqjB48GB88cUXDbZftWoVvvzyS1y9ehUVFRWorq7WzL6Xm5uLrKwsDB06tN5tk5OTUVpaCldXV63lFRUVuHTpkuZ1QECA5u8acOszVqvVOHfuHGxsbJCVlYX+/ftr7aN///6cEIWISE8sptqoRx99VDOW/tNPP232/m5fQAUFBWmW2dra1mknCIKm8LrtzqFK9V3A/709AaGhofDz80NcXBwKCwsRExMDAPDy8kJwcDASExMRFxeHIUOG6H2MM2fOIDg4WGvZ3z9TmezWSN87P6O/Dz3jZ/o/zTnv9u7dCwcHB7i7u8PBwaHBdlevXsWYMWMwe/ZsvP3223BxccG+ffvw2GOPaT6b28ME66NWqwEAv//+O3x9fbXWKZXKu+a8/Vmb6uduyHMvPDwcW7ZsgVwuh4+PT6P/fzds2IDnn38eH3zwAfr27Qt7e3u89957OHToEIDGP1Pg1ufq7e2tdb/XbY3dv3j7M7zzs6zvc/37MiIiahreM9VGjRo1CtXV1aiursbIkSObta+Kigp88cUXGDRoENzd3Rtt6+7urjWN+oULF7RmwIqIiMDx48e1puW+c+Ys+p/Y2FjEx8cjPj4egwcP1iyPiYnBjh07cPDgQb3ulwKAs2fPYvv27Zg8eXKj7W5/3nd+pn+fKCQiIgKHDx/WWmaun2lzzrvg4GC0a9eu0UIKuPX/tra2Fh988AH69OmDsLAwZGVlabXp0qULdu/eXe/2HTt2hFKpRHp6OkJDQ7V+/P39tdreOQFGYWEhzp8/j4iICM1+9u3bp9V+//79CAsLa7O9UrcZ6tyztLREaGgogoOD71qo7t27F/369cOcOXPQvXt3hIaGavUo2dvbIygoqMHPNSoqCjk5OVAoFHU+1zt7SNPT07X+vhw4cAAymQxhYWFwcHCAj49PvZ9rhw4d7vp+iYioLvZMtVFyuVzTm6TrhU1ubi4qKytRUlKC5ORkvPvuu7hx40adyQrqM2TIEHzyySfo06cP1Go1Fi5cCAsLC836hx56CK+99hqefPJJvPzyy0hPT8f7778PoO63oeYuNjYWc+fORU1NjebbceDWBd1TTz2FysrKJl3Q1dbWIicnB2q1Gvn5+YiPj8fSpUvRrVs3LFiwoNFtb19gL1myBEuXLsWFCxfqzNj29NNPY9CgQfjwww8xbtw47NmzB3/88YdZfp7NOe+aql27dqitrcV//vMfjBs3DomJiVi1apVWm1deeQWRkZGYM2cOZs+eDUtLS820+G5ubpg/fz6ef/55qNVqDBgwAMXFxdi/fz/s7OwwY8YMzX7eeustuLq6wtPTE6+99hrc3Nxw7733AgBefPFF9OzZE2+//TamTp2KAwcO4JNPPsHKlSuN8r5bkqHOPV2Ehobim2++wY4dOxAcHIxvv/0WR44c0eo9XrJkCWbPng0PDw+MHj0aJSUlSExMxNNPP41hw4ahb9++uPfee/HOO+8gPDwcWVlZ2LZtG+69917NEF4rKyvMmDED77//PoqLi/HMM8/g/vvvh5eXF4Bbk44sXrwY7dq1Q7du3bBmzRqkpqZi3bp1Bn2/RERmQ5I7tUgvt2+Eb0hTb4QHIAqCINrb24tdu3YVFyxYoLk5+W7HyszMFEeMGCHa2tqK7du3F7dt26Y1AYUoimJiYqLYpUsX0dLSUuzRo4f4/fffiwDEs2fP6viOTdvtG9QjIiK0ll+7dk0EILZr1+6u+1i8eLHmM5XL5aKLi4s4YMAA8V//+ledG9UbmuRg3759YmRkpGhlZSUOHDhQ/Omnn7QmoBDFWzft+/r6itbW1uK9994rLl26VPTy8tLrfbc1hjrv6pv45ba/fzYffvih6O3tLVpbW4sjR44Uv/nmmzr7iI+PF/v16ycqlUrRyclJHDlypGa9Wq0WP/74YzE8PFy0sLAQ3d3dxZEjR4oJCQlambZu3Sp26tRJtLS0FHv27CmmpqZq5dq4caPYsWNH0cLCQgwICBDfe++9Bt9DW2Koc6+hyV/uPMbtCSgqKyvFmTNnio6OjqKTk5P41FNPiS+//HKdfaxatUrzuXl7e4tPP/20Zl1xcbH49NNPiz4+PqKFhYXo7+8vPvzww2J6erpWppUrV4o+Pj6ilZWVOGnSJLGgoECzD5VKJb755puir6+vaGFhIXbt2lX8448/7vp+iYiofoIomsAgeGrV1q1bh1mzZqGoqOiu9wVQ2/DEE0/g7Nmz2Lt3r9RRiIiIiCTDYX5kcN988w1CQkLg6+uLY8eOYeHChbj//vtZSLVh77//PoYPHw5bW1v88ccfWLt2rUkM9yIiIiJqDhZTZHA5OTl44403kJOTA29vb0yZMsUsH/JqSg4fPox3330XJSUlCAkJwb///W88/vjjUsciIiIikhSH+REREREREemBU6MTERERERHpgcUUERERERGRHlhMtTEzZ86EIAj45z//qbV88+bNRn3uT01NDRYuXIjIyEjY2trCx8cH06dPr/Mw0aqqKjz99NNwc3ODra0txo8fj4yMDKPlauv4eZoWfp6mh58pERE1hsVUG2RlZYV33nkHhYWFLXbM8vJypKSkYNGiRUhJScGmTZtw/vx5jB8/Xqvdc889h19++QXr16/Hvn37UFpainvuuQcqlarFsrY1/DxNCz9P08PPlIiIGiTtY65IVzNmzBDvueceMSIiQlywYIFm+S+//CK29Md5+PBhEYB49epVURRF8ebNm6KFhYW4fv16TZvMzExRJpOJ27dvb9FsbQU/T9PCz9P08DMlIqLGsGeqDZLL5Vi+fDn+85//6DScY/To0bCzs2v0RxdFRUUQBAFOTk4AgOTkZNTU1GDEiBGaNj4+PujcuTP279+v077NCT9P08LP0/TwMyUioobwOVNt1MSJE9GtWzcsXrwY//3vf5u0zZdffomKigqDHL+yshIvv/wyHnroITg4OAC49XwpS0tLODs7a7X19PRETk6OQY5rqvh5mhZ+nqaHnykREdWHxVQb9s4772DIkCF48cUXm9Te19fXIMetqanBAw88ALVajZUrV961vSiKRr1R21Tw8zQt/DxNDz9TIiL6Ow7za8MGDRqEkSNH4tVXX21Se0MMOampqcH999+PK1euYNeuXZpvSAHAy8sL1dXVdW7Szs3Nhaenp25vzgzx8zQt/DxNDz9TIiL6O/ZMtXH//Oc/0a1bN4SFhd21bXOHnNz+R/3ChQuIi4uDq6ur1voePXrAwsICu3btwv333w8AyM7OxsmTJ/Huu+/qfVxzws/TtPDzND38TImI6E4sptq4yMhIPPzww/jPf/5z17bNGXJSW1uL++67DykpKfjtt9+gUqk0Y/JdXFxgaWkJR0dHPPbYY3jxxRfh6uoKFxcXzJ8/H5GRkRg2bJjexzYn/DxNCz9P08PPlIiItEg7mSDpasaMGeKECRO0lqWlpYlKpdKo0/ReuXJFBFDvT1xcnKZdRUWFOG/ePNHFxUW0trYW77nnHjE9Pd1oudo6fp6mhZ+n6eFnSkREjRFEURRbpmwjIiIiIiIyHZyAgoiIiIiISA8spoiIiIiIiPTAYoqIiIiIiEgPLKaIiIiIiIj0wGKKiIiIiIhIDyymiIiIiIiI9MBiioiIiIiISA8spoiIiIiIiPTAYoqIiIiIiEgPLKaIiIiIiIj0wGKKiIiIiIhIDyymiIiIiIiI9MBiioiIiIiISA8spoiIiIiIiPTAYoqIiIiIiEgPLKaIiIiIiIj0wGKKiIiIiIhIDyymiIiIiIiI9MBiioiIiIiISA8spoiIiIiIiPTAYoqIiIiIiEgPLKaIiIiIiIj0wGLqLrKzs7FkyRJkZ2dLHYWIiIiIyGB4ndt8LKbuIjs7G2+++Sb/khERERGRSeF1bvOxmCIiIiIiItIDiykiIiIiIiI9sJgiIiIiIiLSA4spIiIiIiIiPbCYIiIiIiIi0gOLKSIiIiIiIj2wmCIiIiIiItIDiykiE1VbWwtRFKWOQURERGSyWEwRmajq6mqoVCqpYxARERGZLBZTRCaspqZG6ghEREREJovFFJEJq6qqkjoCERERkcliMUVkwioqKqSOQERERGSy2lwxtXLlSgQHB8PKygo9evTA3r17G22fkJCAHj16wMrKCiEhIVi1alULJSWSXmVlpdQRiIiIyITwWlxbmyqmfvzxRzz33HN47bXXcPToUQwcOBCjR49Genp6ve2vXLmCMWPGYODAgTh69CheffVVPPPMM/j5559bODmRNDjMj4iIiAyF1+J1CWIbmju5d+/eiIqKwmeffaZZ1qFDB9x7771YsWJFnfYLFy7Eli1bcObMGc2y2bNn49ixYzhw4ECTjpmSkoIePXogOTkZUVFRzX8TRC2kvLwcFy5cQNeuXaWOQkRERK2Qrte5UlyLt3YKqQM0VXV1NZKTk/Hyyy9rLR8xYgT2799f7zYHDhzAiBEjtJaNHDkS//3vf1FTUwMLC4s621RVVWl9m19aWgrg1jN7ODMatSU1NTUoKSnh31siIiKqV21tLYBb17vFxcWa5UqlEkqlUqttS12LtzVtppi6ceMGVCoVPD09tZZ7enoiJyen3m1ycnLqbV9bW4sbN27A29u7zjYrVqzAm2++WWd57969m5GeiIiIiKh1iomJ0Xq9ePFiLFmyRGtZS12LtzVtppi6TRAErdeiKNZZdrf29S2/7ZVXXsELL7ygeZ2amoqYmBgcOnQI3bt31zc2UYsrLy/H5s2b8eCDDzZ6jhAREZF5Onr0KHr37o2EhAR069ZNs/zvvVJ3Mva1eFvTZoopNzc3yOXyOpVvbm5unYr3Ni8vr3rbKxQKuLq61rvN37s17ezsAAAKhcIkuiLJfFhYWKCqqgrFxcVwc3OTOg4RERG1MgrFrVLAzs4ODg4OjbZtqWvxtqbNzOZnaWmJHj16YNeuXVrLd+3ahX79+tW7Td++feu037lzJ6Kjo1kYkdk4deqU1BGIiIiojeO1eP3aTDEFAC+88AK+/PJLfPXVVzhz5gyef/55pKenY/bs2QBuDdGbPn26pv3s2bNx9epVvPDCCzhz5gy++uor/Pe//8X8+fOlegtELe7ChQtaN5USERER6YPX4nW1mWF+ADB16lTk5+fjrbfeQnZ2Njp37oxt27YhMDAQAJCdna01z31wcDC2bduG559/Hp9++il8fHzw73//G5MnT5bqLRC1OLVajQMHDmDkyJFSRyEiIqI2jNfidbWp50xJgc+ZoraqvLwc3333neb1sGHDEBISImEiIiIiak14ndt8bWqYHxHpb9++fSgvL5c6BhEREZHJYDFFZCYqKyuxe/duqNVqqaMQERERmQQWU0QmKDo6Gu3bt8eyZcu0lmdnZ2Pv3r3g6F4iIiKi5mMxRWSCcnJykJWVVe8sfufOncORI0ckSEVERERkWlhMEZmh1NRUnDx5UuoYRERERG0aiykiM3XgwAGt6UuJiIiISDcspojMlCiKiIuLQ0lJidRRiIiIiNokFlNEZqyqqgq7d++GSqWSOgoRERFRm8NiisjM5ebmIiEhgTP8ERER/T/+m0hNxWKKiHDx4kXEx8ezh4qIiAjgMxmpyVhMEREA4MKFC9i2bRvKysqkjkJERETUJrCYIiKN7Oxs/Pzzz7h48SKHOBARERHdBYspItJSWVmJPXv2YPv27Zzpj4iIzJIgCFJHoDaCxRQR1evatWvYsGEDjh49ynupiIjIrLCYoqZiMUVEDVKpVDhy5Ag2bdqE3NxcqeMQERERtSospojorgoLC/Hrr78iKSmJMxwRERER/T8WU0TUJKIoIiUlBb///jsqKiqkjkNEREQkORZTRKST7OxsbNq0CXl5eVJHISIiIpIUiykiE5Oeno7y8nIAQHV1NQoKCgx+jLKyMmzZsgUXLlww+L6JiIikxseDUFOxmCIyEYcPH8a4ceMQFBSEwsJCAEB5eTleffVVfPrpp0hLSzPo8VQqFeLi4nDgwAHeR0VERCaFxRQ1lULqAETUfJs2bcLUqVMhimKdfwBEUcTJkydx8uRJPPHEE4iKijLosU+cOIHCwkIMGzYMlpaWBt03ERGRFNRqNeRyudQxqA3Qq2fq0qVLeP311/Hggw9qpkvevn07Tp06ZdBwRHR3hw8fxtSpU6FSqRp8HpRarYZarcbq1asN3kMFABkZGdi2bRtqa2sNvm8iIqKWxhEX1FQ6F1MJCQmIjIzEoUOHsGnTJpSWlgIAjh8/jsWLFxs8IBE1bunSpfX2SDVk27ZtRsmRm5uLQ4cOGWXfRERELUmlUnGoHzWJzsXUyy+/jKVLl2LXrl1aQ3piY2Nx4MABg4Yjosalp6fjt99+a7BH6u/UajWOHz9ulEkpgFu91kRERKaAoy2oKXQupk6cOIGJEyfWWe7u7o78/HyDhCKiptm9e7fO35yJooizZ88aJY+NjY1R9ktERNTSWExRU+hcTDk5OSE7O7vO8qNHj8LX19cgoYioaUpKSiCT6XYaC4KAyspKo+Tp3r27UfZLRETU0mpqaqSOQG2AzsXUQw89hIULFyInJweCIECtViMxMRHz58/H9OnTjZGRiBpgb2+v802yoijCysrK4FlCQkIQEhJi8P0SERFJgT1T1BQ6F1PLli1DQEAAfH19UVpaio4dO2LQoEHo168fXn/9dWNkJKIGDB06FIIg6LSNIAiIiIgwaA4bGxsMHDhQ5yxEREStFXumqCl0fs6UhYUF1q1bh7fffhspKSlQq9Xo3r072rdvb4x8RNSIgIAA3HPPPdi2bVuTJqGQyWSIjIyEi4uLQXP07NkTSqXSoPskIiKSUlMndyLzpvdDezmkh6h1WLRoEf744w8IgtCkySjGjBlj0OPb29vzyxQiIjI5HOZHTaHzML/77rsP//znP+ssf++99zBlyhSDhCKipuvZsyd+/PFHyOXyBp/WLpPJIJPJ8OSTTyIoKMjgx9d1EgwiIqLWjsP8qCn0emjv2LFj6ywfNWoU/vrrL4OEIiLdTJo0Cfv378eYMWPq3LckCAIiIyOxcOFCg8+2Fxoainbt2hl0n0RERK1BdXW11BGoDdB5mF9paanWw3pvs7CwQHFxsUFCEZHuevbsiS1btiA9PR3dunVDYWEhbGxssGjRIoPfIwUAgYGBiImJ4aQTRERkkoz1GBEyLTr3THXu3Bk//vhjneXr169Hx44dDRKKiPQXEBCgeXiupaWlUQqpDh06YPjw4Q0OKyQiImrrKioqpI5AbYDOPVOLFi3C5MmTcenSJQwZMgQAsHv3bvzwww/46aefDB6QiFoPQRDQt29fdOrUiT1SRERk0kpLS6WOQG2AzsXU+PHjsXnzZixfvhwbN26EtbU1unTpgj///BMxMTHGyEhErYCVlRWGDRsGHx8fqaMQEREZHW9fadsqKirqTCLi4OBg8OPoNTX62LFj652EgohMk7u7O4YPHw47OzupoxAREbWIoqIiqFQqDmlvQ8rLy/HSSy9hw4YNyM/Pr7PeGM8O0/s5U9XV1cjNzYVardZaHhAQ0OxQRNR6hIeHY8CAAfzHhIiIzIpKpUJBQQHc3d2ljkJNtGDBAsTFxWHlypWYPn06Pv30U2RmZuLzzz+v99FOhqDzBBQXLlzAwIEDYW1tjcDAQAQHByM4OBhBQUEIDg42RkYAQGFhIR555BE4OjrC0dERjzzyCG7evNnoNjNnzoQgCFo/ffr0MVpGIlMik8kwYMAADBo0iIUUERGZpezsbKkjtFlSXLtv3boVK1euxH333QeFQoGBAwfi9ddfx/Lly7Fu3bpmvqP66dwzNXPmTCgUCvz222/w9vZusZvQH3roIWRkZGD79u0AgCeffBKPPPIItm7d2uh2o0aNwpo1azSv65vWnYi02djYYNiwYfDy8pI6ChERkWQyMjLQpUsXqWO0SVJcuxcUFGg6dxwcHFBQUAAAGDBgAJ566ild30KT6FxMpaamIjk5GREREcbIU68zZ85g+/btOHjwIHr37g0AWL16Nfr27Ytz584hPDy8wW2VSiUvCIl04O3tjaFDh2qmVyciIjJX2dnZqKmpgYWFhdRR2hSprt1DQkKQlpaGwMBAdOzYERs2bECvXr2wdetWODk56bXPu9F5mF/Hjh1x48YNY2Rp0IEDB+Do6Kj5MACgT58+cHR0xP79+xvdNj4+Hh4eHggLC8MTTzyB3NzcRttXVVWhuLhY88NpMclcCIKAqKgojB07loUUERERbt03de3aNaljGF1paanW9W9VVVWz9teS1+53mjVrFo4dOwYAeOWVV7By5UoolUo8//zzWLBggX5v5i507pl655138NJLL2H58uWIjIysU6kbY8rBnJwceHh41Fnu4eGBnJycBrcbPXo0pkyZgsDAQFy5cgWLFi3CkCFDkJycDKVSWe82K1aswJtvvmmw7ERtgb29PWJjY9mLS0RE9DeXL19GSEiI1DGM6u+PN1q8eDGWLFmi9/5a8tr9Ts8//7zmz7GxsTh79iySkpLQrl07dO3aVb83cxc6F1PDhg0DAAwdOlRruSiKEARBpykHlyxZctfC5ciRIwBQ771Zt4/ZkKlTp2r+3LlzZ0RHRyMwMBC///47Jk2aVO82r7zyCl544QXN69TUVD4/i0xacHAwBg0a1KRfUkREROYmPT3d5If6JSQkoFu3bprXDV0TtMZr9zt98803mDp1qiZ/QEAAAgICUF1djW+++QbTp0+/6z50pXMxFRcXZ7CDz5s3Dw888ECjbYKCgnD8+HFcv369zrq8vDx4eno2+Xje3t4IDAzEhQsXGmyjVCq1/gLxuTpkqgRBQN++fdGpU6cWm0iGiIiotYuOjkZ2djYUCgVee+011NbW4vLly43e59PW2dnZNWl0WWu8dr/TrFmzMGrUqDq9YiUlJZg1a1brKKYM2Uvj5uYGNze3u7br27cvioqKcPjwYfTq1QsAcOjQIRQVFaFfv35NPl5+fj6uXbsGb29vvTMTmQKFQoHhw4fD399f6ihEREStSk5ODrKysrQmLDh79qxJF1NN1dqv3Rvq+crIyICjo2OTj6sLnSegAIC9e/di2rRp6NevHzIzMwEA3377Lfbt22fQcLd16NABo0aNwhNPPIGDBw/i4MGDeOKJJ3DPPfdo/cWOiIjAL7/8AuDWjXTz58/HgQMHkJaWhvj4eIwbNw5ubm6YOHGiUXIStQVyuRyjRo1iIUVERNRE169f12kiBHPX0tfu3bt3R1RUFARBwNChQxEVFaX56dq1KwYOHKi5VcnQdO6Z+vnnn/HII4/g4YcfRkpKima2j5KSEixfvhzbtm0zeEgAWLduHZ555hmMGDECADB+/Hh88sknWm3OnTuHoqIiALcuGE+cOIFvvvkGN2/ehLe3N2JjY/Hjjz/C3t7eKBmJ2oLBgwfDx8dH6hhERERtSmpqquY6lO6uJa/d7733XgC3PqORI0dq3aZjaWmJoKAgTJ482YDv7n90LqaWLl2KVatWYfr06Vi/fr1meb9+/fDWW28ZNNydXFxc8N133zXaRhRFzZ+tra2xY8cOo+Uhaos6duyIdu3aSR2DiIiozUlLS8ONGzeaNMyNWvbaffHixQBu3a81depUWFlZ6bUffeg8zO/cuXMYNGhQneUODg64efOmITIRkRHY2dlpPe+BiIiIdHPo0CGtAoBalxkzZrRoIQXo0TPl7e2NixcvIigoSGv5vn37TH4OfqK2LDo62qSndSUiIjK2zMxMpKenIzAwUOoo9P+cnZ2bPCtxQUGBwY+vczH1j3/8A88++yy++uorCIKArKwsHDhwAPPnz8cbb7xh8IBEpDsvLy+IogiF4tYpbmdnh9DQUIlTERERtX2JiYnw9vaGpaWl1FEIwEcffSTp8XUupl566SUUFRUhNjYWlZWVmod9zp8/H/PmzTNGRiLSUVJSEsrLyzVjlcPCwiCT6TV5JxEZyd0eXklErVNpaSkOHDhg0McFkf5mzJgh6fF1urpSqVRISEjAiy++iBs3buDw4cM4ePAg8vLy8PbbbxsrIxE1U3BwsNQRiOhv1Gq11BGISE/nzp3D+fPnpY5B9bh06RJef/11PPjgg5rp7Ldv345Tp04Z5Xg6FVNyuRwjR45EUVERbGxsEB0djV69emlNP0hErYudnR1cXFykjkFERGRS9u7di7y8PKlj0B0SEhIQGRmJQ4cOYdOmTSgtLQUAHD9+XDPjn6HpPO4nMjISly9fNkYWIjICf39/DiUiIiIyMJVKhZ07d6K8vFzqKPT/Xn75ZSxduhS7du3SuqctNjYWBw4cMMoxdS6mli1bhvnz5+O3335DdnY2iouLtX6IqHXhjENErRO/5CBq+8rKyrBz506oVCqpoxCAEydOYOLEiXWWu7u7Iz8/3yjH1HkCilGjRgG49RTjO/8huH0jLf8yEbUeCoUCPj4+UscgonqwmCIyDbm5uThw4AAGDBggdRSz5+TkhOzs7Dr3ih89ehS+vr5GOabOxVRcXJwxchCREXh6emqmRyciIiLjOH36NHx9fTnhk8QeeughLFy4ED/99BMEQYBarUZiYiLmz5+P6dOnG+WYOl9lcRpIorbDw8ND6ghE1ABOjU5kWhISEuDm5gZ7e3upo5itZcuWYebMmfD19YUoiujYsSNUKhUeeughvP7660Y5pl4Pntm7dy+mTZuGfv36ITMzEwDw7bffYt++fQYNR0TN4+TkJHUEImqAKIpSRyAiA6qursbu3bt5y4uELCwssG7dOpw/fx4bNmzAd999h7Nnz+Lbb7+FXC43yjF1LqZ+/vlnjBw5EtbW1khJSUFVVRUAoKSkBMuXLzd4QCLSHx9bQERE1HJyc3ORmJjIL0sk1q5dO9x33324//770b59e6MeS+dhfkuXLsWqVaswffp0rF+/XrO8X79+eOuttwwajoiax9raWuoIRNQAXmwRmaazZ8/C0dERXbt2lTqKWXjhhRea3PbDDz80+PF1LqbOnTuHQYMG1Vnu4OCAmzdvGiITERmIUqmUOgIRNYDFFJHpOnToEGxsbIzeK0K3Zuq7U3JyMlQqFcLDwwEA58+fh1wuR48ePYxyfJ2LKW9vb1y8eBFBQUFay/ft24eQkBBD5SIiA7CwsJA6AhE1gMUUkWmLj4+HhYVFnWtmMqw7Zxr/8MMPYW9vj7Vr18LZ2RkAUFhYiFmzZmHgwIFGOb7O90z94x//wLPPPotDhw5BEARkZWVh3bp1mD9/PubMmWOMjESkJ2PdbElEzcdiisi0iaKI3bt3IyMjQ+ooZuODDz7AihUrNIUUADg7O2Pp0qX44IMPjHJMnXumXnrpJRQVFSE2NhaVlZUYNGgQlEol5s+fj3nz5hkjIxHpQRAETrtM1Ipxxi8i06dSqbBz506MHTsWnp6eUscxecXFxbh+/To6deqktTw3NxclJSVGOWaTeqaOHz8OtVqteb1s2TLcuHEDhw8fxsGDB5GXl4e3337bKAGJSD/81puodaupqZE6AhG1gNraWmzfvh35+flSRzF5EydOxKxZs7Bx40ZkZGQgIyMDGzduxGOPPYZJkyYZ5ZhNKqa6d++OGzduAABCQkKQn58PGxsbREdHo1evXpx+mYiISEfV1dVSRyCiFlJVVYVt27ahqKhI6igmbdWqVRg7diymTZuGwMBABAYG4uGHH8bo0aOxcuVKoxyzScWUk5MTrly5AgBIS0vT6qUiotbJ0tJS6ghE1IjKykqpIxBRC6qoqMC2bdtQVlYmdRSTZWNjg5UrVyI/Px9Hjx5FSkoKCgoKsHLlStja2hrlmE26Z2ry5MmIiYmBt7c3BEFAdHR0gze2X7582aABiUg/CoXOt0QSUQuqqKiQOgIRtbCSkhL8/vvvGDduHJ8FaUS2trbo0qVLixyrSVdbX3zxBSZNmoSLFy/imWeewRNPPAF7e3tjZyMiIjJZ/HaayDzdvHkT27Ztw7hx4ziKxAQ0qZg6fvw4RowYgVGjRiE5ORnPPvssiykiIqJmKCsrg0ql4iMMiMxQfn4+du3ahdGjR0Mm0/lJRdSK6DwBRUJCAm+aJSIiaiZRFI02VS8RtX6ZmZlISkqSOgY1EyegICIikkhhYaHUEYjoDunp6SgvLwdwa8bNgoICox7v2LFjyMvLM+oxyLiaVEzdnoAiODhYMwFFSEhIvT9ERETUNHzuDFHrcPjwYYwbNw5BQUGaLznKy8vx6quv4tNPP0VaWppRjiuKInunjMTBwaFFJsbjBBREREQS4TfSRNLbtGkTpk6dClEU6zzwXhRFnDx5EidPnsQTTzyBqKgogx8/IyMDFRUVnN3PwP7+WRpLk+dOHjVqFABwAgoiIiIDycvLgyiKEARB6ihEZunw4cOYOnUqVCpVgxfft29vWb16NRYuXIigoCCDZhBFEVlZWWjXrp1B90stQ+fpQ9asWcNCioiIyAAqKyt53xSRhJYuXVpvj1RDtm3bZpQc169fN8p+zdm0adPg4OBg9OM0qWdq0qRJ+Prrr+Hg4IBJkyY12nbTpk0GCUZERGQOMjIy4OLiInUMIrOTnp6O3377rcmFlFqtxvHjx1FQUGDwc5ZDfg3vs88+a5HjNKlnytHRUTMEwdHRsdEfIiIiarr09HSpIxCZpd27d+t8X40oijh79qzBs9y4cQMqlcrg+yXja1LP1Jo1a+r9MxERETVPdnY2KisrYWVlJXUUIrNSUlICmUym0yN/BEFAZWWlwbOoVCrk5OTA19fX4Psm4+Ijl4mIiCQkiqLRpl0moobZ29vr/OxUURSN9sVHRkaGUfZLxtWknqnu3bs3eaahlJSUZgUiIiIyddHR0UhPT4e1tTVee+01XLx4EREREVLHIjIrQ4cOhSAIOg31EwTBaOcqJ6Fom5pUTN17772aP1dWVmLlypXo2LEj+vbtCwA4ePAgTp06hTlz5hglJBERkSnJyclBXl4enJycAABZWVm4efOm5jURGV9AQADuuecebNu2rUn3K8lkMkRGRhptwpji4mKj7NdcVVRUoKamRmuZMWb3a1IxtXjxYs2fH3/8cTzzzDN4++2367S5du2aYdMRERGZidTUVAwePFjqGERmZdGiRfjjjz+a3EM1ZswYo2XhBBTNV15ejpdeegkbNmxAfn5+nfXG+H+s8z1TP/30E6ZPn15n+bRp0/Dzzz8bJBQREZG5uXDhAof5ELWwnj174scff4RcLodcLq+3jUwmg0wmw5NPPmnwB/beycbGxmj7NhcLFizAnj17sHLlSiiVSnz55Zd488034ePjg2+++cYox9S5mLK2tsa+ffvqLN+3b59RZyJatmwZ+vXrBxsbmyYPgxBFEUuWLIGPjw+sra0xePBgnDp1ymgZiYiI9CWKIuLi4lBVVSV1FCKzMmnSJOzfvx9jxoypM0eAIAiIjIzEwoUL0b17d6PmMLWZ/KS4dt+6dStWrlyJ++67DwqFAgMHDsTrr7+O5cuXY926dXq+k8bpXEw999xzeOqppzBv3jx89913+O677zBv3jzMnTsXzz//vDEyAgCqq6sxZcoUPPXUU03e5t1338WHH36ITz75BEeOHIGXlxeGDx+OkpISo+UkIiLSV3FxMf7880+dZxgjoubp2bMntmzZgrS0NDg7OwO41VO0fPlyzJkzx6g9Urd16NDB6MdoSVJcuxcUFCA4OBjArfujCgoKAAADBgzAX3/9pfubaAKdi6mXX34Z33zzDY4ePYpnnnkGzzzzDI4ePYqvv/4aL7/8sjEyAgDefPNNPP/884iMjGxSe1EU8dFHH+G1117DpEmT0LlzZ6xduxbl5eX4/vvvjZaTiIioOTIzM5GQkKDzw0SJqPkCAgI0w+0sLS2NNtnE37Vr105TxJkKKa7dQ0JCNI+a6NixIzZs2ADgVo+VsSb40es5U/fffz8SExNRUFCAgoICJCYm4v777zd0tma5cuUKcnJyMGLECM0ypVKJmJgY7N+/v8HtqqqqUFxcrPkpLS1tibhEREQaFy5cwP79+1lQEZkBCwsL9OnTR9IMpaWlWte/Ugw31vfa/U6zZs3CsWPHAACvvPKK5t6p559/HgsWLDBK7ibN5tcW5eTkAAA8PT21lnt6euLq1asNbrdixQq8+eabRs1GRER0N6dOnYJCoUCvXr2a/KxHImp7evfuDVtbW0kzxMTEaL1evHgxlixZ0qIZ9L12v9OdtxzFxsbi7NmzSEpKQrt27dC1a1fDhb2DXj1ThrJkyRIIgtDoT1JSUrOO8fd/gERRbPQfpVdeeQVFRUWan4SEhGYdn4iISF/Hjh1DUlISe6iITJSvr2+ruFcqISFB6/r3lVdeqbdda7x2v9M333yj1asWEBCASZMmoUOHDkabzU/Snql58+bhgQceaLSNvjf8eXl5AbhV5Xp7e2uW5+bm1ql476RUKqFUKjWv7ezs9Do+ERGRIRw9ehRqtZo9VEQmxtLSEoMGDWoV57WdnV2THmjbGq/d7zRr1iyMGjUKHh4eWstLSkowa9aseh/v1FySFlNubm5wc3Mzyr6Dg4Ph5eWFXbt2aaayrK6uRkJCAt555x2jHJOIiMgYjh07hpqaGvTv379VXHgRUfMNGjQI9vb2UsfQSWu/dm+oFysjIwOOjo4GzXtbm7lnKj09HQUFBUhPT4dKpUJqaioAIDQ0VNN7FBERgRUrVmDixIkQBAHPPfccli9fjvbt26N9+/ZYvnw5bGxs8NBDD0n4ToiIiHR3+vRpVFZWIjY2tsGHixJR29C9e3eEhIRIHcOoWvLavXv37pphhkOHDoVC8b8SR6VS4cqVKxg1apRR3qfOxZRKpcLXX3+N3bt3Izc3t86zMPbs2WOwcHd64403sHbtWs3r2xVrXFwcBg8eDAA4d+4cioqKNG1eeuklVFRUYM6cOSgsLETv3r2xc+fONvctABEREQBcvnwZ5eXlGDFiBKysrKSOQ0R6CAsLQ3R0tNQxjK4lr93vvfdeAEBqaipGjhypdZuOpaUlgoKCMHnyZAO9M22CqONdrfPmzcPXX3+NsWPHwtvbu05X2r/+9S+DBpRaSkoKevTogeTkZERFRUkdh4iITICfnx8yMzPh5OSk19Bze3t7jBgxAq6urkZIR2Temnt+NqZ9+/YYPHhwqxmua2rXuWvXrsXUqVNb9MsmnXum1q9fjw0bNmDMmDHGyENERER3UVJSgs2bN6N///4IDw9vNRdmRNSwyMhI9OnTh+erEc2YMaPFj6lzMWVpaYnQ0FBjZCEiIqImUqlU+Ouvv5CVlYWBAwfCwsJC6khEVA9BENCnTx907tyZhZQRODs7N/n/a0FBgcGPr3Mx9eKLL+Ljjz/GJ598wr8QREREErt48SLy8vIwbNgwDvsjamUUCgWGDBmi93ThdHcfffSRpMfXuZjat28f4uLi8Mcff6BTp051vgnbtGmTwcIRERHR3RUVFWHz5s0YMGAAwsPDpY5DRABsbW0xcuRIo00lTrdIMbTvTjoXU05OTpg4caIxshAREZGeVCoVEhIScOPGDfTt2xcymUzqSERmy9XVFaNGjYKtra3UUczOpUuXsGbNGly6dAkff/wxPDw8sH37dvj7+6NTp04GP57OxdSaNWsMHoKIiIgM49SpUygsLMTw4cOhVCqljkNkdnx9fTFixAjexyiBhIQEjB49Gv3798dff/2FZcuWwcPDA8ePH8eXX36JjRs3GvyY/NqKiIjIxGRlZeGXX35BYWGh1FGIzEpQUBBGjRrFQkoiL7/8MpYuXYpdu3bB0tJSszw2NhYHDhwwyjF17pkCgI0bN2LDhg1IT09HdXW11rqUlBSDBCMiIiL9FRcXY/PmzYiNjeXN70QtIDg4GEOHDuUQWwmdOHEC33//fZ3l7u7uyM/PN8oxdf60//3vf2PWrFnw8PDA0aNH0atXL7i6uuLy5csYPXq0MTISERGZjPT0dJSXlwMAqqurjTJV7201NTXYuXMnTpw4YbRjEBHg7++PIUOGsJCSmJOTE7Kzs+ssP3r0KHx9fY1yTJ0/8ZUrV+KLL77AJ598AktLS7z00kvYtWsXnnnmGRQVFRkjIxERUZt3+PBhjBs3DkFBQZrhd+Xl5Xj11Vfx6aefIi0tzWjHPnDgAE6fPm20/ROZM3d3dwwbNgxyuVzqKGbvoYcewsKFC5GTkwNBEKBWq5GYmIj58+dj+vTpRjmmzsVUeno6+vXrBwCwtrZGSUkJAOCRRx7BDz/8YNh0REREJmDTpk3o378//vjjD4iiqLVOFEWcPHkS77zzjlGHyu/fv1/zbzYRGYadnR3vkWpFli1bhoCAAPj6+qK0tBQdO3bEoEGD0K9fP7z++utGOabOxZSXl5dmzGFgYCAOHjwIALhy5UqdfyCIiIjM3eHDhzF16lSoVCqoVKp626jVaqjVaqxevdpoPVRqtRrp6elG2TeROZLL5RgxYgSsra2ljkL/z8LCAuvWrcP58+exYcMGfPfddzh79iy+/fZbo/Uc6jwBxZAhQ7B161ZERUXhsccew/PPP4+NGzciKSkJkyZNMkZGIiKiNmvp0qUQRbHJXzhu27YNc+bMMUoWtVptlP0SmaMBAwbwgbytVLt27dCuXbsWOZbOxdQXX3yh+WU8e/ZsuLi4YN++fRg3bhxmz55t8IBERERtVXp6On777bcmF1JqtRrHjx9HQUEBXFxcDJ7H2dnZ4PskMkehoaEICwuTOgYBeOGFF5rc9sMPPzT48XUupmQymdZMJffffz/uv/9+g4YiIiIyBbt379Z5CLwoijh79qzm/mRD4rfoRM1nZ2eH/v37QxAEqaMQbs3Ud6fk5GSoVCqEh4cDAM6fPw+5XI4ePXoY5fh6PWdq7969+Pzzz3Hp0iVs3LgRvr6++PbbbxEcHIwBAwYYOiMREVGbVFJSAplMptPwOkEQUFlZafAsVlZWsLKyMvh+icxNTEwMlEql1DHo/8XFxWn+/OGHH8Le3h5r167V9MQXFhZi1qxZGDhwoFGOr/MEFD///DNGjhwJa2trHD16FFVVVQBu/YOxfPlygwckIiJqq+zt7XW+T0kURaMUPSykiJovIiLCaM8roub74IMPsGLFCq0hzc7Ozli6dCk++OADoxxT52Jq6dKlWLVqFVavXq01DWS/fv2MOqUrERFRWzN06FCdhwIJgoCIiAiDZ+HUzURN4+XlBR8fHzg4OGgtt7a2Ru/evSVKRU1RXFyM69ev11mem5trtEdD6FxMnTt3DoMGDaqz3MHBATdv3jREJiIiIpMQEBCAe+65p8lT8spkMnTp0sUok09YWloafJ9EpigpKQkXLlzAa6+9prW8V69eHN7Xyk2cOBGzZs3Cxo0bkZGRgYyMDGzcuBGPPfaY0WYd17mY8vb2xsWLF+ss37dvH0JCQgwSioiIyFQsWrQIgiA0uYdqzJgxRsnx92/ZiajpnJ2dOXtfG7Bq1SqMHTsW06ZNQ2BgIAIDA/Hwww9j9OjRWLlypVGOqXMx9Y9//APPPvssDh06BEEQkJWVhXXr1mH+/PlGey4GERFRW9WzZ0/8+OOPkMvlDfZQ3Z4p98knn0RQUJBRcvj4+Bhlv0TmoGvXrpy9rw2wsbHBypUrkZ+fj6NHjyIlJQUFBQVYuXIlbG1tjXJMnWfze+mll1BUVITY2FhUVlZi0KBBUCqVmD9/PubNm2eMjERERG3apEmTsH//frz99tt1njslCAIiIyMxZswYoxVSVlZWCAwMNMq+iUydUqlssQfAkmHY2tqiS5cuLXIsvaZGX7ZsGV577TWcPn0aarUaHTt2hJ2dnaGzERERmYyePXtiy5YtSE9PR7du3VBYWAgbGxssWrTIKPdI3Sk6OhoKhV7/5BOZvcDAwCbf90jmR+/frDY2NoiOjjZkFiIiIpMXEBAAGxsbFBYWwtLS0uiFVGBgIDp06GDUYxCZsoCAAKkjUCvW5GLq0UcfbVK7r776Su8wREREZDgeHh6IjY3lvR5EzcD7DakxTS6mvv76awQGBqJ79+5aY72JiIio9fHw8MDo0aM5JTpRMzg6OvKB19SoJhdTs2fPxvr163H58mU8+uijmDZtmtGHJhAREZHufH19MWLECD6ol6iZ3N3dpY5ArVyTp0ZfuXIlsrOzsXDhQmzduhX+/v64//77sWPHDvZUERERtRLt27fHqFGjWEgRGYCrq6vUEaiV0+k5U0qlEg8++CB27dqF06dPo1OnTpgzZw4CAwNRWlpqrIxERER0F4IgoGfPnhg8eDBnHiMyEI7CorvReza/209zF0URarXakJmIiIhIB0qlErGxsZx1jMjAnJ2dpY5ArZxOPVNVVVX44YcfMHz4cISHh+PEiRP45JNPkJ6ezudMERERScDd3R2TJk1iIUVkYAqFAra2tlLHoFauyT1Tc+bMwfr16xEQEIBZs2Zh/fr1HEdKREQkoQ4dOqBfv34c1kdkBI6OjnysAN1Vk4upVatWISAgAMHBwUhISEBCQkK97TZt2mSwcERERFSXTCbDgAEDEBERIXUUIpNlb28vdQRqA5pcTE2fPp3VORERkcQsLS0xYsQIPkiUyMgcHBykjkBtgE4P7SUiIiLp2NjYYPTo0RxmT9QCOB8ANYXes/kRERFRy7G3t8eYMWPg6OgodRQis8DJJ6gpWEwRERG1ck5OThg7diwv7ohakLW1tdQRqA3QaWp0KS1btgz9+vWDjY0NnJycmrTNzJkzNc/Duv3Tp08f4wYlIiIyIA8PD4wfP56FFFELs7KykjpCm2Yu1+5tppiqrq7GlClT8NRTT+m03ahRo5Cdna352bZtm5ESEhERGZafnx/Gjh3LizoiCVhaWkodoU0zl2v3NjPM78033wSg+0QYSqUSXl5eRkhERERkPCEhIYiNjeUzpIgkolC0mcvkVslcrt3bTM+UvuLj4+Hh4YGwsDA88cQTyM3NlToSERFRo9q1a4chQ4awkCKSEM8/abS1a3eTLrlHjx6NKVOmIDAwEFeuXMGiRYswZMgQJCcnQ6lU1rtNVVUVqqqqNK9LS0tbKi4RERF8fX0xePBgyGQm/30nUatmTudgaWkpiouLNa+VSmWD18rGpM+1u9Qk/VuyZMmSOjeZ/f0nKSlJ7/1PnToVY8eORefOnTFu3Dj88ccfOH/+PH7//fcGt1mxYgUcHR01PzExMXofn4iISBe2trYYOnQovxEnktjt61BzERMTo3X9u2LFinrbtcZrd6lJ2jM1b948PPDAA422CQoKMtjxvL29ERgYiAsXLjTY5pVXXsELL7ygeZ2amsqCioiIWsSAAQM42QQRtbiEhAR069ZN87qhXqDWeO0uNUmLKTc3N7i5ubXY8fLz83Ht2jV4e3s32Obv3Zp8+jUREbUELy8vBAQESB2DiACz6pUCbl3vOjg43LVda7x2l1qbGQyanp6O1NRUpKenQ6VSITU1FampqVr3NEVEROCXX34BcGvs5/z583HgwAGkpaUhPj4e48aNg5ubGyZOnCjV2yAiIoKXlxfc3d21Ll66d+9udhdwRK0Vz8XmM5dr9zYzAcUbb7yBtWvXal53794dABAXF4fBgwcDAM6dO4eioiIAt2ZgOXHiBL755hvcvHkT3t7eiI2NxY8//gh7e/sWz09ERHRbUlIS9u7dizNnzgAAnJyc4OfnJ3EqIrqttU520JaYy7V7mymmvv7667vOUy+KoubP1tbW2LFjh5FTERERNV+nTp34TTgRmRRzuXZvM8P8iIiITJGlpSXCwsKkjkFERHpgMUVERCShsLAwWFhYSB2DiIj0wGKKiIhIQuHh4VJHICIiPbGYIiIikoijoyNcXFykjkFERHpiMUVERCQRPz8/TjxBRNSGsZgiIiKSiKenp9QRiIioGVhMERERSYRD/IiI2jYWU0RERBJxdHSUOgIRETUDiykiIiIJWFtbQy6XSx2DiIiagcUUERGRBKytraWOQEREzcRiioiISAJWVlZSRyAiomZiMUVERCQBS0tLqSMQEVEzsZgiIiKSgIWFhdQRiIiomVhMERERSYDFFBFR28diioiISAIspoiI2j4WU0RERBJQKBRSRyAiomZiMUVERCQBPmOKiKjtYzFFREQkAZmM/wQTEbV1/E1OREQkAUEQpI5ARETNxGKKiIiIiIhIDyymiIiIiIiI9MBiioiISAKcgIKIqO1jMUVERCQB3jNFRNT2sZgiIiIiIiLSA4spIiIiIiIiPbCYIiIiIiIi0gOLKSIiIiIiIj2wmCIiIiIiItIDiykiIiIiIiI9KKQOQK1HdnY2srOzpY5BBuLt7Q1vb2+pY5AB8Rw1LTxHTQvPT9PC85OaisXUXXh7e2Px4sUmf0JVVVXhwQcfREJCgtRRyEBiYmKwY8cOKJVKqaOQAfAcNT08R00Hz0/TYy7np7lc5xqTIIqiKHUIkl5xcTEcHR2RkJAAOzs7qeNQM5WWliImJgZFRUVwcHCQOg4ZAM9R08Jz1LTw/DQtPD9JF+yZIi3dunXjLw4TUFxcLHUEMhKeo6aB56hp4vlpGnh+ki44AQUREREREZEeWEwRERERERHpgcUUAQCUSiUWL15s8jdamgt+nqaHn6lp4edpWvh5mhZ+nqQLTkBBRERERESkB/ZMERERERER6YHFFBERERERkR5YTBEREREREemBxRQREZGBpaWlQRAEpKamttgxlyxZgm7durXY8YiMbfDgwXjuueea1Pbrr7+Gk5OTUfPcSdfzTYrfCdQyWEwRtbBVq1bB3t4etbW1mmWlpaWwsLDAwIEDtdru3bsXgiDg/Pnz9e5ryZIlEAQBgiBAoVDAzc0NgwYNwkcffYSqqiqjvg+itsZY555cLoe/vz8ef/xx5OXlGfU9EFHTtPYvF+Lj4yEIAm7evCl1FGomFlNELSw2NhalpaVISkrSLNu7dy+8vLxw5MgRlJeXa5bHx8fDx8cHYWFhDe6vU6dOyM7ORnp6OuLi4jBlyhSsWLEC/fr1Q0lJSYPbVVdXG+YNEbURxjz3PvvsM2zduhXTp0836nsgIqLWhcUUUQsLDw+Hj48P4uPjNcvi4+MxYcIEtGvXDvv379daHhsb2+j+FAoFvLy84OPjg8jISDz99NNISEjAyZMn8c4772jaBQUFYenSpZg5cyYcHR3xxBNP1PvNWGpqKgRBQFpammbZ6tWr4e/vDxsbG0ycOBEffvhhiw6nIDIEY517vr6+uOeee/DMM89g586dqKioqNNWpVLhscceQ3BwMKytrREeHo6PP/64TruvvvoKnTp1glKphLe3N+bNm6dZV1RUhCeffBIeHh5wcHDAkCFDcOzYsTr7+PzzzzXn65QpU7TOb7Vajbfeegt+fn5QKpXo1q0btm/f3uj7JGoJZWVlmD59Ouzs7ODt7Y0PPvhAa311dTVeeukl+Pr6wtbWFr1799Y6l+/09ddf480338SxY8c0Pchff/01AODDDz9EZGQkbG1t4e/vjzlz5qC0tPSu+f75z3/C09MT9vb2eOyxx1BZWVmnzZo1a9ChQwdYWVkhIiICK1eurHdfaWlpmt8vzs7OEAQBM2fOBABs374dAwYMgJOTE1xdXXHPPffg0qVLd81H0mExRSSBwYMHIy4uTvM6Li4OgwcPRkxMjGZ5dXU1Dhw4cNcLuvpERERg9OjR2LRpk9by9957D507d0ZycjIWLVrUpH0lJiZi9uzZePbZZ5Gamorhw4dj2bJlOmciag2Mee5ZW1tDrVZrDSO8Ta1Ww8/PDxs2bMDp06fxxhtv4NVXX8WGDRs0bT777DPMnTsXTz75JE6cOIEtW7YgNDQUACCKIsaOHYucnBxs27YNycnJiIqKwtChQ1FQUKDZx8WLF7FhwwZs3boV27dvR2pqKubOnatZ//HHH+ODDz7A+++/j+PHj2PkyJEYP348Lly4oNN7JTK0BQsWIC4uDr/88gt27tyJ+Ph4JCcna9bPmjULiYmJWL9+PY4fP44pU6Zg1KhR9f7dnTp1Kl588UVN73F2djamTp0KAJDJZPj3v/+NkydPYu3atdizZw9eeumlRrNt2LABixcvxrJly5CUlARvb+86hdLq1avx2muvYdmyZThz5gyWL1+ORYsWYe3atXX25+/vj59//r/27j0oquuOA/h3ea885P1YWN4siiJgCAa1UF+Dj9KSppKMRkEdO0CDr6rIkCARjQkBzDSpRe2UYIbKmMakiYbYlIeVVRrU4CMSkhoNToDSEBoIZsNjT/9wuPXK0/UBxO9nxpm955577rk7/tj93T3n3LcAAPX19WhqapJurnR2dmLTpk2oqalBWVkZjIyM8Pjjj0Ov19/ZG0oPjiCiB27//v3C0tJSdHd3i/b2dmFiYiL+/e9/i5KSEjFz5kwhhBAnTpwQAMSVK1cGbWf79u0iJCRkwH1paWlCqVRK215eXiIuLk5Wp6KiQgAQbW1tUtnHH38sAIirV68KIYR48sknxZIlS2THLV++XEycOHHkF0w0Rtyv2KurqxP+/v4iIiJCCCHE1atXBQDx8ccfD9pGSkqKeOKJJ6RtlUolMjIyBqxbVlYmbGxshE6nk5X7+fmJffv2SX0yNjYW169fl/aXlpYKIyMj0dTUJJ1j165dsjYeffRRkZKSMmg/ie63jo4OYWZmJkpKSqSy1tZWoVQqxfr168W//vUvoVAoxFdffSU7bt68eSI9PV0IIURhYaHsc2moz8dbHT58WDg4OAxZJzIyUiQlJcnKZsyYIWtfrVaLP//5z7I62dnZIjIyUgjR/2/CQJ+/A2lpaREAxMWLF4e9Fhod/GWKaBTMmTMHnZ2dqKmpwcmTJ6HRaODs7Izo6GjU1NSgs7MTlZWV8PT0hK+vr0HnEEJAoVDIysLDw++4nfr6ekRERMjKbt8mGi/uZexdvHgRVlZWUCqVCAoKglqtRnFx8aD1CwoKEB4eDicnJ1hZWeHAgQNoaGgAALS0tKCxsRHz5s0b8NizZ8/iu+++g4ODA6ysrKR/V69elQ0B8vT0hIeHh7QdGRkJvV6P+vp6tLe3o7GxEbNmzZK1PWvWLNTV1Q373hHdL1euXEFXVxciIyOlMnt7ewQGBgIAzp07ByEENBqN7P//iRMn7ngIXEVFBRYsWAB3d3dYW1tj5cqVaG1tRWdnJwDI2k9KSgIA1NXVyfoGQLb9n//8B9evX8eaNWtkx+/cufOO+3flyhUsW7YMvr6+sLGxgY+PDwBIfyto7DEZ7Q4QPYz8/f3h4eGBiooKtLW1ITo6GgDg6uoKHx8faLVaVFRUYO7cuQafo66uTvoj3MfS0lK2bWR0836KEEIq6+7ultUZKCm7tT7ReHIvYy8wMBDvvvsujI2NoVKpYG5uPmjdw4cPY+PGjcjLy0NkZCSsra3x8ssv45///CeAm0MEh6LX6+Hm5jbgHJGh5i/2xe6tMTxQPN9eRvQgDfeZotfrYWxsjLNnz8LY2Fi2z8rKasTn+fLLL7F48WIkJSUhOzsb9vb2qKqqwpo1a6TPvluXLrexsRlRu31D8A4cOIAZM2bI9t3e3+HExsZCrVbjwIEDUKlU0Ov1mDp1KheNGsOYTBGNkjlz5qCyshJtbW3YsmWLVB4dHY3jx4+juroaq1atMqjtTz/9FB988AHS09OHrOfk5AQAaGpqgp2dHQD0ewbGpEmT8NFHH8nKbl0NjWi8uVexZ2ZmJs1pGs7Jkycxc+ZMpKSkSGW33rG2traGt7c3ysrKBpyrNX36dDQ3N8PExATe3t6DnqehoQGNjY1QqVQAgNOnT8PIyAgajQY2NjZQqVSoqqpCVFSUdMypU6f4azONKn9/f5iamqK6uhqenp4AgLa2Nnz22WeIjo5GWFgYent70dLS0u8xBoMxMzNDb2+vrOzMmTPo6elBXl6edDPx1nmLfX253eTJk1FdXS1brbO6ulp67eLiAnd3d3zxxRdYvnz5iPsHQNbH1tZW1NXVYd++fdJ1VlVVjag9Gj1MpohGyZw5c/Cb3/wG3d3d0t1x4OYXuuTkZOh0uhFNgO/p6UFzczP0ej1aW1tRWVmJnTt3IjQ0VPZFcSD+/v5Qq9XIysrCzp078fnnn/dbQSk1NRVRUVHIz89HbGwsysvLUVpayjvZNG7dq9i7E/7+/jh48CCOHz8OHx8fvPHGG6ipqZH9epyVlYWkpCQ4Oztj0aJF6OjogFarRWpqKubPn4/IyEjExcXhpZdeQmBgIBobG/H+++8jLi5OGsJrYWGBhIQE5Obmor29HevWrUN8fDxcXV0B3Jzkv337dvj5+SE0NBSFhYWora0dcngi0f1mZWWFNWvWYMuWLXBwcICLiwsyMjKkhEej0WD58uVYuXIl8vLyEBYWhq+//hrl5eUIDg7G4sWL+7Xp7e2Nq1evora2Fh4eHrC2toafnx96enrw6quvIjY2FlqtFgUFBcP2b/369UhISEB4eDhmz56N4uJifPLJJ7KhwFlZWVi3bh1sbGywaNEi/PDDDzhz5gza2tqwadOmfm16eXlBoVDg6NGjWLx4MZRKJezs7ODg4ID9+/fDzc0NDQ0N2LZt2128s/RAjOJ8LaKHWt9k1EmTJsnKr1+/LgAIPz+/YdvYvn27ACAACGNjY2Fvby9mz54t9uzZ02+iupeXl9izZ0+/NqqqqkRwcLCwsLAQP/nJT8Sbb74pW4BCiJuT9t3d3YVSqRRxcXFi586dwtXV1aDrJhpt9yr2hprcfvtkc51OJxITE8XEiROFra2tSE5OFtu2bevXRkFBgQgMDBSmpqbCzc1NpKamSvva29tFamqqUKlUwtTUVKjVarF8+XLR0NAg69PevXuFSqUSFhYW4pe//KX45ptvpDZ6e3vF888/L9zd3YWpqakICQkRpaWlw14v0f3W0dEhnn76aTFhwgTh4uIicnJyRHR0tFi/fr0QQoiuri6RmZkpvL29hampqXB1dRWPP/64uHDhghCi/wIUOp1OPPHEE8LW1lYAEIWFhUIIIfLz84Wbm5tQKpUiJiZGHDx4cEQLQezatUs4OjoKKysrkZCQILZu3dovfouLi0VoaKgwMzMTdnZ2IioqShw5ckQIMfCiNDt27BCurq5CoVCIhIQEIYQQH374oZg8ebIwNzcX06ZNE5WVlQKAePvttw18Z+l+UwjByQ9EdGfWrl2LTz/9FCdPnhztrhARERGNGg7zI6Jh5ebmYsGCBbC0tERpaSmKiooGfRghERER0cOCv0wR0bDi4+NRWVmJjo4O+Pr6IjU1VVoyloiIiOhhxWSKiIiIiIjIAHxoLxERERERkQGYTBGNY5WVlVAoFPjvf/872l0hotswPomIfvw4zI9oHOvq6sI333wDFxcXPveJaIxhfBIR/fgxmSIiIiIiIjIAh/kRjSE//elPkZqaig0bNsDOzg4uLi7Yv38/Ojs7sWrVKukJ7qWlpQD6DyN6/fXXYWtri+PHj2Py5MmwsrLCwoUL0dTUJDvHhg0bZOeNi4tDYmKitL13714EBATAwsICLi4u+NWvfnW/L51ozGN8EhHR7ZhMEY0xRUVFcHR0xEcffYTU1FQkJydj6dKlmDlzJs6dO4eYmBisWLECN27cGPD4GzduIDc3F2+88Qb+8Y9/oKGhAZs3bx7x+c+cOYN169Zhx44dqK+vxwcffICoqKh7dXlE4xrjk4iIbsVkimiMCQkJwbPPPouAgACkp6dDqVTC0dERa9euRUBAADIzM9Ha2ooLFy4MeHx3dzcKCgoQHh6O6dOn45lnnkFZWdmIz9/Q0ABLS0v87Gc/g5eXF8LCwrBu3bp7dXlE4xrjk4iIbsVkimiMmTZtmvTa2NgYDg4OCA4OlspcXFwAAC0tLQMeP2HCBPj5+Unbbm5ug9YdyIIFC+Dl5QVfX1+sWLECxcXFg95lJ3rYMD6JiOhWTKaIxhhTU1PZtkKhkJX1rQqm1+tHfPyt68wYGRnh9nVnuru7pdfW1tY4d+4cDh06BDc3N2RmZiIkJITLOxOB8UlERHJMpogeMk5OTrIJ7729vbh06ZKsjomJCebPn4+cnBxcuHAB165dQ3l5+YPuKtFDh/FJRDS+mIx2B4jowZo7dy42bdqEY8eOwc/PD3v27JHd1T569Ci++OILREVFwc7ODu+//z70ej0CAwNHr9NEDwnGJxHR+MJkiughs3r1apw/fx4rV66EiYkJNm7ciDlz5kj7bW1tceTIEWRlZUGn0yEgIACHDh3ClClTRrHXRA8HxicR0fjCh/YSEREREREZgHOmiIiIiIiIDMBkioiIiIiIyABMpoiIiIiIiAzAZIqIiIiIiMgATKaIiIjuIW9vb7zyyisP7HyVlZVQKBR8cC8R0ShgMkU0ShITE6FQKJCUlNRvX0pKChQKBRITEwc9vu8LlEKhgJGRESZOnIiwsDBs3bpV9tBPIvq/exl3CoUCTk5OWLRoEc6fP38fe01ERGMVkymiUaRWq1FSUoLvv/9eKtPpdDh06BA8PT1H1EZ9fT0aGxtRU1ODtLQ0/P3vf8fUqVNx8eLFQY/p6uq6674TjVf3Ku6amppw7NgxtLW1YeHChfj222/vV5eJiGiMYjJFNIqmT58OT09PHDlyRCo7cuQI1Go1wsLCRtSGs7MzXF1dodFo8NRTT0Gr1cLJyQnJyclSncTERMTFxWH37t1QqVTQaDQAAIVCgXfeeUfWnq2tLV5//XVp+9SpUwgNDYWFhQXCw8PxzjvvQKFQoLa21uDrJhpN9zLuIiIikJeXh+bmZlRXVw9YNz8/H8HBwbC0tIRarUZKSgq+++47WR2tVovo6GhMmDABdnZ2iImJQVtbGwBACIGcnBz4+vpCqVQiJCQEf/nLX/qdR6vVIiQkBBYWFpgxY0a/GypvvfUWpkyZAnNzc3h7eyMvL29E10pERINjMkU0ylatWoXCwkJp+09/+hNWr15tcHtKpRJJSUnQarVoaWmRysvKylBXV4cPP/wQR48eHVFbHR0diI2NRXBwMM6dO4fs7GykpaUZ3DeiseJexp1SqQQAdHd3D7jfyMgIv/vd73Dp0iUUFRWhvLwcW7dulfbX1tZi3rx5mDJlCk6fPo2qqirExsait7cXAPDss8+isLAQf/jDH/DJJ59g48aNePrpp3HixAnZebZs2YLc3FzU1NTA2dkZP//5z6U+nT17FvHx8Xjqqadw8eJFZGVl4bnnnpPdOCEiojtnMtodIHrYrVixAunp6bh27RoUCgW0Wi1KSkpQWVlpcJuTJk0CAFy7dg3Ozs4AAEtLS/zxj3+EmZnZiNspLi6GQqHAgQMHYGFhgaCgIHz11VdYu3atwX0jGgvuVdy1trbi+eefh7W1NSIiIgass2HDBum1j48PsrOzkZycjL179wIAcnJyEB4eLm0DwJQpUwAAnZ2dyM/PR3l5OSIjIwEAvr6+qKqqwr59+xAdHS0ds337dixYsAAAUFRUBA8PD7z99tuIj49Hfn4+5s2bh+eeew4AoNFocPnyZbz88stDzhEjIqKhMZkiGmWOjo5YsmQJioqKIITAkiVL4OjoeFdtCiEA3BzG1yc4OPiOEing5ryQadOmwcLCQiob7Asj0Xhyt3Hn4eEB4GayExAQgDfffFO6cXG7iooKvPDCC7h8+TLa29vR09MDnU6Hzs5OWFpaora2FkuXLh3w2MuXL0On00lJUp+urq5+QxL7ki0AsLe3R2BgIOrq6gAAdXV1+MUvfiGrP2vWLLzyyivo7e2FsbHxiK+diIj+j8kU0RiwevVqPPPMMwCA3//+93fdXt8XKG9vb6nM0tKyXz2FQiElXn1uHaokhJAlZH1lRD8GdxN3J0+ehI2NDZycnGBjYzNovS+//BKLFy9GUlISsrOzYW9vj6qqKqxZs0aKtb5hggPR6/UAgGPHjsHd3V22z9zcfNh+9sUvY5mI6P7gnCmiMWDhwoXo6upCV1cXYmJi7qqt77//Hvv370dUVBScnJyGrOvk5CRbRv3zzz/HjRs3pO1JkybhwoUL+OGHH6SyM2fO3FX/iMaKu4k7Hx8f+Pn5DZlIATfjpaenB3l5eXjssceg0WjQ2NgoqzNt2jSUlZUNeHxQUBDMzc3R0NAAf39/2T+1Wi2re+sCGG1tbfjss8+kIb9BQUGoqqqS1T916hQ0Gg1/lSIiugv8ZYpoDDA2NpZ+TbrTLzYtLS3Q6XTo6OjA2bNnkZOTg6+//lq2Utlg5s6di9deew2PPfYY9Ho90tLSYGpqKu1ftmwZMjIy8Otf/xrbtm1DQ0MDcnNzAaDfXW6i8eZu4m6k/Pz80NPTg1dffRWxsbHQarUoKCiQ1UlPT0dwcDBSUlKQlJQEMzMzVFRUYOnSpXB0dMTmzZuxceNG6PV6zJ49G+3t7Th16hSsrKyQkJAgtbNjxw44ODjAxcUFGRkZcHR0RFxcHADgt7/9LR599FFkZ2fjySefxOnTp/Haa6/J5mkREdGd4y9TRGOEjY3NsHe5BxIYGAiVSoVHHnkEL774IubPn49Lly4hKCho2GPz8vKgVqsRFRWFZcuWYfPmzZgwYYKsT++99x5qa2sRGhqKjIwMZGZmAoBsHhXReGVo3I1UaGgo8vPz8dJLL2Hq1KkoLi7G7t27ZXU0Gg3+9re/4fz584iIiEBkZCT++te/wsTk5v3O7OxsZGZmYvfu3Zg8eTJiYmLw3nvvwcfHR9bOiy++iPXr1+ORRx5BU1MT3n33XWme5PTp03H48GGUlJRg6tSpyMzMxI4dO7j4BBHRXVIIDpomojtQXFyMVatW4dtvvx1yrgcRERHRjx2H+RHRkA4ePAhfX1+4u7vj/PnzSEtLQ3x8PBMpIiIieugxmSKiITU3NyMzMxPNzc1wc3PD0qVLsWvXrtHuFhEREdGo4zA/IiIiIiIiA3ABCiIiIiIiIgMwmSIiIiIiIjIAkykiIiIiIiIDMJkiIiIiIiIyAJMpIiIiIiIiAzCZIiIiIiIiMgCTKSIiIiIiIgMwmSIiIiIiIjIAkykiIiIiIiID/A/ousgRs2ZYygAAAABJRU5ErkJggg==", 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" - ] - }, - "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, which is useful for us to visualise the data in an alternate way. Assuming that the placebo and drug treatment were done on 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 done two replicates of the experiments, we can also colour the slope lines according to ``Rep``. \n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0949bfea", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "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": [ - "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", - "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": "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": [ - "
" - ] - }, - "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": [ - "
" - ] - }, - "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 \n", - "effect size object." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "205b0b55", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "DABEST v0.0.1\n", - "=============\n", - " \n", - "Good evening!\n", - "The current time is Sun Mar 12 00:55:42 2023.\n", - "\n", - "The delta-delta between Placebo and Drug is -0.903 [95%CI -1.26, -0.535].\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 ofzero 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": "3ba800cc", - "metadata": {}, - "source": [ - "``delta_delta`` 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 to you all the attributes in a dictionary format." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ddefa6e4", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "python3", - "language": "python", - "name": "python3" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/nbs/tutorials/06-plot_aesthetics.ipynb b/nbs/tutorials/06-plot_aesthetics.ipynb new file mode 100644 index 00000000..fc4141ce --- /dev/null +++ b/nbs/tutorials/06-plot_aesthetics.ipynb @@ -0,0 +1,850 @@ +{ + "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": [ + "
" + ] + }, + "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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", + "text/plain": [ + "
" + ] + }, + "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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QHxnuv6/LZMVss5MzhadbympbOHSigX0VdRe8W44gBIpI6MKwmg1GEmP0KM5qoVjdMrVL/W0OF/uP9yXyirpW2rrEKF2YHERCF4bk8njo6Ontt1xxOpT6l9W20GG0kJkYg9Xh4uCJejFKFyYFkdCFIbV1mZGk/ssVWwxGnG43OSlTc7rl9OhcpZDj8XoJ12nEKF2YNERCF4bUYjCi12kJ153phV3TYiAqXEdU+NQs9W/q7MHl9qKQy+ky23B7vICMurauQIcmCOcl2ucKg+pbrmgkNT7Kf9/pUv/imWkBjGx8zUiJ54tXLeLcGZYovW7wJwhCEBEJXRiU2erAYneSHBPpv6+urWvKl/rL5TLioyZmWzhBGGtiykUYVLPBiFwuJyH6THKraTGIUn9BCGIioQuDau0ykRAV7i8cOl3qP1UvhgrCVCASujCAx+ulrdvcb3VLTYsBpUJBWrwo9ReEYCUSujBAR08vXq/Xn9CnQ6m/IEwFIqELAzQbjOg0aiLDtMCZUn/RWVEQgptI6MIAfd0VI5DJ+ipBp3qpvyBMFSKhC/1Y7A5MFpu/3H86lPoLwlQhErrQT4vBhEwmIykm4tTtqV3qLwhTiUjoQj8tBiNxkeGEqPpqzmqaDUSFh07ZUn9BmEpEQhf8vD4frV198+dwqtS/s0dcDBWESUIkdMHPYLTg9pxZrjgdSv0FYSoRCV3wazYYUatUxOj7pldEqb8gTC4ioQt+LQYjyXGRyGQyem2i1F8QJhuR0AUA7E4X3WYrKafmz0WpvyBMPiKhC0DfckWApJhIJEmitkWU+gvCZCMSugD0TbdE60PRqlUYTBbMNjs5yXEjfr7bZqKn+hA+j3scoxQEYTgioQundicy+Ve31LQY0KpD+vVCPx9TQym2znpkcvEjJQiBIn77BLrMVpxuNymxkf5S/+zkkZf6u+292Drq0afNQiYXUzSCECgioQu0GIyolApiI8No7uwr9b+QYiJzQylylZrQxJxxjFIQhPMRCV2gxWAiKSYChVxObcuFlfp7HBas7bXo02YhV4gtagUhkERCn+acLg+dPb0kx0bidHlo7Oy5oLXn5sZy5Co1YUkzxzFKQRBGImgT+s9+9jNkMhkPPvjgsMe9/vrr5Ofno9FomD17Nu+9997EBDhFtHWbkJBIiY2kvr0LySeRmTiyhO5xWLG0VaNPLRCjc0EIAkGZ0Pfv388LL7zAnDlzhj3u008/5Qtf+AJf/epXOXz4MDfeeCM33ngjpaWlExTp5NdsMBIRpiNUq6amxUBS7MhL/c1N5cgVSjE6F4QgEXQJ3WKx8KUvfYkXX3yRqKioYY999tlnWbduHd/5zncoKCjgqaeeYv78+fzud7+boGgntzPLFSP8pf4jvRjqcdqwtFYTnlKAXKka50gFQRiJoEvo9913H9deey2rV68+77G7d+8ecNzatWvZvXv3eIU3pRgtdmwOJymxkRdc6t/bVIFMLic8JXecoxQEYaSCauLz1Vdf5dChQ+zfv39Ex7e1tZGQkNDvvoSEBNra2oZ8jtPpxOl0+m9bLJbRBTsFtBiMKBQK4iPD2VteO+JSf6/LgaX1JOGps5ArRSdGQQgWQTNCb2xs5IEHHuBvf/sbGo1m3F5n48aNRERE+L9Wrlw5bq8V7FoMRhKj9fRYbPTaHCMu9Tc3VYBMTnhK3jhHKAjChQiahH7w4EE6OjqYP38+SqUSpVLJtm3b+M1vfoNSqcTr9Q54TmJiIu3t7f3ua29vJzExccjX2bBhAyaTyf+1bdu2MX8vk4Hb46W9p5fk2Ahqmg3oNOoRlfp73Q4sLScIT85FoVJPQKSB4fO46CzfgdtqCnQoAtDa2sqRI0ewWq2BDiWoBU1Cv/LKKzl27BglJSX+r4ULF/KlL32JkpISFIqBUwHLli3jo48+6nffli1bWLZs2ZCvo1ar0ev1/q+wsLAxfy+TQXu3GZ/PR2K0nto2A1lJMSMq9e9tqgQgPDV/vEMMqN7mSrqOf0pPzcFAhzKtvfXWW+Tn55Oamsr8+fPZu3cvAAaDgXnz5vHmm28GNsAgEzQJPTw8nKKion5foaGhxMTEUFRUBMD69evZsGGD/zkPPPAAmzdv5le/+hXHjx/niSee4MCBA9x///2BehuTRrPBSJhWg9nqwOX2kD2C6Rav20lvSyVhybkoVOM3LRZoPo+LnuqDeJw2TPWluHq7Ax3StPT2229z0003ERsby+OPP44kSf7HYmNjSUlJ4eWXXw5ghMEnaBL6SDQ0NNDa2uq/vXz5cl555RX+8Ic/MHfuXN544w3efPNN/x8AYWgtBiPJsRHUtXadKvXXnfc5vc2VIPnQT4PRub27hdD4TNw2M8a6kkCHNC396Ec/YsWKFezcuZP77rtvwOPLli3j8OHDAYgseAXVKpdzbd26ddjbALfccgu33HLLxAQ0RZitDnptDmZnp7KnvIb5uWnnfY7P46a3+ThhSTNQhGgnIMrAOD06lylUyBQqVKGRmOpLicwsJiRc7N40kUpLS/n1r3895OMJCQl0dHRMYETBb1KN0IWx0WIwIpPJcLjcIy71722pRPL5CE+dNQERBo7N0IjLakLyurAb6vHYTXjdTixtVYEObdrR6XTDXgStqakhJiZmAiMKfkE9QhfGR0uXkfiocJo6e0ZU6u/zuultOk5YUg5K9fmnZiYzbUwqyYuuG3C/JiI+ANFMb5dffjl//vOfB+3n1NbWxosvvsh11w38t5rOxAh9mvF6fbR1mYkK09HRYx7R2nNLy0l8Xjf6KT46B1Co1IQl5hAan4nHYUUTlURYYg5KbXjAYtq+fTvXX389ycnJyGSyEa3s2Lp1K/Pnz0etVjNjxgw2bdo07nGOtZ/85Cc0NTWxaNEiXnjhBWQyGR988AGPPPIIs2fPRpIkHn/88UCHGVREQp9mOoy9eLxenG5vX6l/wvD9cnxeD+amCkITslFqRtYjfSqwtNXQcexjjHVHAx0KVquVuXPn8vvf/35Ex9fW1nLttddy+eWXU1JSwoMPPshdd93FBx98MM6Rjq28vDx27txJTEwMjz76KJIk8Ytf/IKf/vSnzJ49mx07dpCZmRnoMIOKmHKZZloMRjQhKjqNZjISo1EOsr7/bJbWKnxuJxFpU390fprk89JTfRCXuRNj7WEi0wsDOkK/+uqrufrqq0d8/PPPP09WVha/+tWvACgoKGDnzp08/fTTrF27drzCHBeFhYX85z//oaenh6qqKnw+H9nZ2cTFjXwD8+lEjNCnmRaDkTCdGovdSXbS8L8Uks+Luamc0ISsgCa0iWZpq8HWWYcuPgtXrwFjQ9n4vI7Fgtls9n+d3WPoYkzFpnVRUVEsWrSIJUuWiGQ+DJHQx4nX5+NQZQNOlyfQofhZHU56em143L4RlfpbWqvwuRzo0wonKMLAOz06RwJFiBalJhxj7WE89t4xf62VK1f26yu0cePGMTnvUE3rzGYzdrt9TF5jIvzmN78Z9hPF1VdfzXPPPTeBEQU/kdDHSa/NwYmmdt7bcwyjxRbocABoNZiQJDDb7WQnxQ5b6i/5vJgby9HFZ6DSnb/Hy1RhMzRi727B63HSU7Ufl6UHt6WH3pYTY/5a27Zt69dX6OwqaAH+9Kc/MWvW0FN9s2bN4g9/+MMERhT8REIfJ5FhOq5dNhuFXM77e0pp7Ah8+XizwYhCIcfnk8g6z0YW1vYavC47EWnTq+pWHR5D/OzL0acVIlOq0cWlkzhvHdrY8xdfXaiwsLB+fYXU6rFpdjZU0zq9Xo9WO3mKwqqrqykoKBjy8fz8fKqrqycwouAnEvo4CtdpWLe0kMToCLYeOsGx6uZ+/Sgmks8n0dplwuv1Eq0fvtRf8nkxNZShi01DFRoxgVEGnlIbjj5tFh67GWWIFo/dgi4ufVKtQx9N07pgFBISMuzeBq2trcjlIoWdTXw3xlmIUsmqebnMzknh8MkGdhypwjNIK+DxZjBZsDlcOFye824zZ+2ow+Owok+fXqPz03qbj+PoaUMXn4nH0Yux7khA47FYLP4OpNC3LLGkpISGhgagryX0+vXr/cd/7Wtfo6amhu9+97scP36c//f//h9///vfeeihhwIR/qgtXbqUTZs20ds78PqFyWTi5ZdfZunSpQGILHiJZYsTQCaTUTwzjcgwHbtKq9m8t4zL5+URqp24fuItBiM2hwu9TkNW0tAJXZJ8mE+NzkPChl+jPhV53c5TvVyUyORyVDr9qV4uc1HrR7bf6lg7cOAAl19+uf/2ww8/DMDtt9/Opk2baG1t9Sd3gKysLN59910eeughnn32WVJTU/njH/846ZYsPv7446xcuZLi4mIefPBBCgv7Ls6XlpbyzDPP0NrayiuvvBLgKIOLSOgTKDMpBn2ohk8OVfLenlJWFucSHzUxywFbDEa8Ph/JcZFo1UOX+ts66nHbe4ktuHRC4go2dkMjHrsFyefF3t0MkoRcGYK1vSZgCX3VqlXDTtUNVgW6atWqSd+JcMmSJbz99tvce++9PPDAA8hkfRfxJUkiKyuLf//735NuGmm8iYQ+waL1oVyzbDbbSk7w4f5yls7KYkbq+M7POlxuWgwmJEkattRfknyYGkrRxqRM286CurgMkpfcAKcSqM8nIZfLUE+iOfSp5KqrrqKqqorDhw/7L4Dm5OQwf/58f4IXzhAJPQC0ahVXLSpgX3kdn5ZW091rZWFe5oh2DBqNVoOJ7l4rCVH6YUv9bZ0NuG1mYvKm76hHrlQRGpfhv+3z+cSFtwCTy+UsWLCABQsWBDqUoCcSeoAo5HKWFmYRrdexr6IOk8XOirm5qEPG/p+kqbMHp8vDjNS4IUv9JUnC3FCKNjopYFMLwShQq5KEM8rLy6mpqaGnp2fQf4+zLwhPdyKhB5BMJiMvPRF9qJbtJSd5b88xVs3LG9HuQSMlSRJVTZ2olIpht5mzd/X1AU+cuWTMXlsQLkZ1dTW33XYb+/btG/IPq0wmEwn9LCKhB4GkmAiuWVbEJ4cq2by3lEvnzCAtfmzmsHt6bbR2mUiM0ZM4RKm/JEmY6kvRRCagjhB9Ms4mRuiBc++993Ls2DGeeeYZLrvsMqKipt+qqwslEnqQOF2EtOtoNVsPnaB4ZhpF2ckXfeGnsaMHs83OmkWzhjyXvbsZl6WHhLmrB318OhMJPXB27drFD37wA/77v/870KFMGuJqTxAZjyKkstoWtGoVOamDj7wlScJcX4omIh5NZMKgx0xnPp8v0CFMW7GxsURETK9K5YslEnqQOV2EtGJuLo2dPWzeW4bVPrq2qi6Ph+rmDlLiooacl3f0tOLs7SIiY3pWhZ6PGKEHzte+9jX+93//F28AKqsnKzHlEqTGogipoa0Hk9XB1UsHbyzVN3d+DLU+FnVk4liEPeWIhB44ubm5eL1e5s6dy5133klaWhqKQVZp3XTTTQGILjiJhB7ELrYIqaSqkRCVksKspEEfdxrbcJoNxM9eJYo0hiCmXALn1ltv9f//t7/97UGPkclkYgR/FpHQx5HD5UYTorqoc4y2CEmSJCrr28hMjBmy1N/UUIo6PBpNVPJFxTiViYQeOJ988kmgQ5h0REIfJz29NjbvLWV+bgZ56Rd3sXE0RUjNnUa6e61cuTB/0Mcdxg4cxg7iilaK0fkwxOgvcFauXBnoECYdcVF0nESEaslOjmNveQ37j9fh813cXOzpIqTVCwvoNtt4b88xenqH3gnp0Il6lAo5c2ekDvq4qeEYIWFRaKNTLiquqc7jCZ4tBKcrp9PJ7t27eeuttzAYDIEOJ6iJhD5O5HIZS2Zlsaggk+N1bWwrOYHbc/GjvdNFSMPthCRJEuV1bWQMMd3iNHXi6GkjIr1IjM7Pw+12BzqEae03v/kNSUlJXHrppdx0000cPXoUAIPBQGxsLC+99FKAIwwuIqGPs4KMJC6fn0drl4kP9pVhdVz8zu6ni5CSYgbfCam1y4zBZKEoe/DRt6mhFFVoxLhsqzbVuFyuQIcwbb388ss8+OCDrFu3jj/96U/9fsZjY2O54oorePXVVwMYYfARCX0CpMZHsW5JIQ6Xh/f3lNFttl70OYcrQjp8arqlKGvgxU6n2YC9u0WMzkdIJPTA+dWvfsUNN9zAK6+8wvXXXz/g8QULFlBWVhaAyIKXSOgTJFofyjVLi9CGqNi8t4ymjp6LPufpIqSVxWeKkMwWOxX1bSRGRwxaTGRuKEWlC0cXl37Rrz8diIQeOFVVVVx99dVDPh4dHU1XV9cERhT8REKfQDpNCGsWzyIpJoJPDlVSXtc6JoUrGYkxXL2kEKfLw6sfH6C1y0RhVtKAEbjL0o2tqxl9ehEymfinH47P66b75D5cVnOgQ5m2IiMjh70IWl5eTmKiKIg7m/itnmAqpYJV83KZlZXEgeN17Cu/+BUwcKYIyWy10222Drr+3dRQhlIbRmh85kW/3lRnaTlJZ9l2nG3HAx3KtHXNNdfwhz/8AaPROOCxsrIyXnzxRf7rv/5r4gMLYiKhB4BMJmNBXgZLC7M50dTOx4eO4xqD5XEyGSgUcuKj9VQ1dbCvotb/x8JlNWLrbCAirVCMzs/D53XTU30Qt82Mx1CNy2oMdEjT0o9//GO8Xi9FRUU88sgjyGQy/vznP3PbbbexcOFC4uPjeeyxxwIdZlARv9kBlJuWwOoFBRiMFjbvKcNid1zU+erbuui1OVhemM3y2TlUNrTz0cEKnC4P5oZSlJpQQhOyxij6qcvSchJbVxO6+Ex8jl5M9ccCHdK0lJyczMGDB1m3bh2vvfYakiTx17/+lbfffpsvfOEL7Nmzh9hYsbvW2URCD7Ck2AjWLS3E4/Xx/p4yDEbLqM9V1dSJXCYjKzm2XxHS+zv20NVUjT6tEJl88C3ohD6nR+cyuQKFSo1HHoKp7ogYpU8wp9PJv//9b9ra2vjjH/9Id3c37e3ttLa20tPTw0svvUR8vNi4+1wioQeByDAdVy8tIkyr5oN9ZdS3XfiVe7PVQX17F+E6DcmxkcCZIiS1uYETrUZ65JFjG/gUZOtswGk2IHnc2Drr8TmseBwWLK0nAx3atBISEsItt9zCp59+6r8vLi6OhIQEsWn3MMR3Jkho1SrWLJpFWnw020pOUFrTfEErYGpbO7E7XSRE6YnRh/rv18jc5Ef5CE8tYFtJ9YAiJKE/bXQyifPXkbL0M6QsuZGesJnEzl1HWOKMQIc2rchkMmbOnClK/S+QSOhBRKGQc9ncGczJSeXQiQZ2l9bgHUG3P0mSqGkxoFQoSImP6rdc0dxQikqt5bIVl4/pTkhTlSJEiz61AH3aLMKSZuDUxOHUxBESJvaznGg/+MEP+N3vfkdlZWWgQ5k0RLfFIHO6WChcp2F3WQ0Wu5OVxcN3Vuw0Wug2W/sSeuyZLbs8DgvW9lois+ehUKoonplGVLiOnceq2by3jMvn5RGqVU/E25p0XJZuFi5YQHNnDwmJyZSXlwc6pGlnz549xMTEUFRUxKpVq8jMzESr1fY7RiaT8eyzzwYowuAjEnqQykmJI0yr5pPDlby/t5Qr5uejD9UMemxNSycer48wrZqkmEj//ebGcuQqNWFJZ6YLMhJjCNf17YT07u5jrJqXd8E7IU0HxrqjtBu66O7pRSYXvyaB8Lvf/c7//x999NGgx4iE3p+YcgliCdF6rllaBBK8v6eUjp6BVYter4+6ti40ISpiI8LQqvsKijwOK5a2asJT8pEr+hcZnS5C0odq+XB/OVVNHRPyfiYLl6UbU/1RZKd+PXxej2ijGwA+n++8X6JffX9i6BHk9KFarl5axNaSSj7cX8Hyomyyk+P8jzcZjDhdfS1ek+Mi/febm8qRK5SEJ+cOet7TOyHtr7iwnZCmA2PdUdw2M7JTqylkkpfm5iYyMjIDG1iQycrKuuAGbzKZjOrq6nGKSLiohN7c3Mz27dvp6Ojg5ptvJjU1Fa/Xi8lkIiIiYtANXYULpw5RsnphAXvKath5tIpem4M5OanIZDJqmjvRhKhwuj3++XOP04altZqI9CLkyqG3wOvbCSmbqPCR74Q01flH5zI5ktR3QVqGREP5AZHQz7Fy5cDdrg4cOEBZWRmzZs0iLy8PgMrKSsrLyykqKmLBggUX/Dp79uzhk08+oaOjg2984xvMnDkTm83G8ePHyc3NJSwsbEzez1Qwqt9cSZL41re+xe9+9zs8Hg8ymYzZs2eTmpqKxWIhMzOTH/3oRzz44INjHO70pZDLWV6Ug16n5fDJBsxWB/Nz02k2GNHrNEgSxEb0zYX3NlUgk8sJTxl8dH6uvPREIsK0bDt8kvf29M2rD9apcTrw2C0oVNpT8+ZnklV7Ux2SJImWw2fZtGlTv9tvvvkmb775Jlu2bOHKK6/s99iWLVv43Oc+x1NPPTXi87tcLj7/+c/z1ltv+b/3119/PTNnzkQul7NmzRoeeughfvjDH47F25kSRjWH/otf/IJnn32Wb3/722zZsqXfuuaIiAhuuukm/vGPf4xZkEIfmUzG7JwUVszNpaG9m9c/OYjL7UECEmP0yOUyvC4HltaThKfkIVcOvjn0YBKj+4qQlIqhd0KaDnRx6WRddRc5676OUtO3nt8rU9HtC6e1tTXA0QW3xx57jP/+7/8ekMwBrrrqKu6//34eeeSREZ/v0Ucf5Z133uG5556jsrKyX57RaDTccsstvPXWW2MS+1QxqoT+4osvsn79en76059SXFw84PE5c+Zw4sSJi41NGEJmUgxrFs+ioaObti4zHd1mf3WouakCkBGeMvjm0MMJ12lYt6SI5NjBd0KaLmQy2cCRuExGVVVVYAKaJE6ePElMTMyQj8fExFzQ/Pn//d//8fWvf5177rmH6OjoAY8XFBRQU1MzqlinqlEl9MbGRpYvXz7k46GhoZjNoo/0eFKrlCTHRKBVqzjZ1IFSLsfrdmBpOUF4Sh4K1ejWl6uUClYW5zJnhihCOldNTY1YVTGMnJwcXn75ZSyWgf2Ient7eemll8jOzh7x+To6Opg9e/aQjysUCmy2oTdKn45GNYceHx9PY2PjkI8fPHiQ9HSxI854qmkxEKpVk5Mch8vjZVdpNfYoB3qkUY3OzyaTyZg7I43IMFGEdDaXy0V9ff0FJaXp5Mc//jGf/exnyc/P5ytf+QozZvTVP5w8eZI///nPtLe38/rrr4/4fGlpaRw/PnQ/+l27dvlfQ+gzqhH6TTfdxPPPP9/v487pj6gffvghmzZt4pZbbhmbCIUBTpf6p8dHYzBbWTUvlxlJkVSXHaDNG4l8lKPzc529E9K7u48Nug5+uvD4JIwOHydPiiZdQ7nxxht57733iIuL46c//Sl33nknd955Jxs3biQ+Pp533nmHG2+8ccTn++IXv8gLL7zA7t27/fedzjMvvvgif//731m/fv1Yv41JbVQj9CeffJJPPvmE4uJiLrvsMmQyGT//+c959NFH2b17N/PmzeMHP/jBWMcqnNJp7MVid1CYnUR1Sydp8VHoLPWExOqpsOiwlZzkkjk5KMdg2ejpIqRtJSf4cH8FSwqymJk2/dqWtlm8tFu9hFQ3sMrpRK2e3p9WhrJmzRrWrFlDW1sb9fX1AGRkZIxqq7gf/vCH7NmzhxUrVlBQUIBMJuOhhx6iu7ubpqYmrrnmGh566KGxfguT2qhG6BEREezZs4fvfve7NDc3o9Fo2LZtG0ajkccff5wdO3ag0134srfnnnuOOXPmoNfr0ev1LFu2jPfff3/I4zdt2uS/gHX6S6MZvDx+KqlpMRCqUeN2e1EoFMSFa+ltriR71nxWLCyiyWDkw33l2Bxjs8Hx6SKkGSlx7C6r7rcT0lRld7pwnCrYkoB2qw+rS6LF7BIX4kYgMTGRJUuWsGTJklHv+xkSEsLmzZt5+eWXyc7OJj8/H6fTyZw5c9i0aRNvv/22qHU5x6hL/7VaLY888gglJSVYrVbsdjulpaU89thjAxrojFRqaio/+9nPOHjwIAcOHOCKK67ghhtuoKysbMjn6PV6Wltb/V+nRwVTldfro661i6zkWFq7TCRG67G1n0TyeQlPnUV6QjTrFhdidbh4f08pPb3WMXnd00VIS2Zl9dsJaaoqrWnB5em7AOqTwOHxERYio9vuY1/J0D+P011DQwNf+9rXyMvLIzo6mu3btwNgMBj45je/yeHDh4d87sMPP9zv8YaGBhwOB7fddhtvvvkmZWVlVFRU8M4777B+/XpREzCIoOrlcv3113PNNdcwc+ZMcnNz+clPfkJYWBh79uwZ8jkymYzExET/V0JCwgRGPPGaOntweTykxUfR3tNLUlQovU3HCUvMQanu+1QUExHKNcuKCFEp2by3jKbOnjF7/bz0RK5a1LcT0nt7jtHT27fKwGnqpKN0K163c8xeK1BsDhcHjp8ZGHh9EmqFjBAFeHxQVt9OT8/YfU+nivLycubNm8drr71GVlYWJpPJ3wMnNjaWnTt39mu4da5nnnmGiooK/+2srCz+9a9/jXvcU8mo5tDvvPPO8x4jk8n405/+NJrTA+D1enn99dexWq0sW7ZsyOMsFgsZGRn4fD7mz5/PT3/6UwoLC4c83ul04nQ6+z1/MqlpMRCjD8Pp8uDz+YjyGHB73ejT+r/nUI2adUsK2XH0JJ8crGRRQSb5GaP76Huu00VIWw9X8v6eUi6bO4OQtqP4XPYLKmYKVmW1LbQbe1Ge6uXik8DplXD5ZPgkiR6Hj90Hj3DN6lWBDTTIfPe73yUyMpI9e/Ygk8kGbBF37bXX8tprrw35/ISEhH7TWdOxBuJijSqhf/zxxwM+7ni9XlpbW/F6vcTFxREaGjrEs4d37Ngxli1bhsPhICwsjH/961/MmjVr0GPz8vJ46aWXmDNnDiaTiV/+8pcsX76csrIyUlNTB33Oxo0befLJJ0cVW6A5XG6aDUYW5PWV/IepVXgM1YQmZPurGs+mUipYVZzHwcp69lXUYrbZx6wB1+kipF3Hqti5ex853ipmzBn6D+9kcXp0rlLIOZ1OZDJICFWgUfZ932RAU30tHs+lKJXTt+/NubZv385jjz1GXFwcXV0Dt1FMT0+nubl5yOdfe+21/OhHP+LDDz8kMjISgF/96le8+uqrQz5HJpOJatGzjOqnsa6ubtD73W43L7zwAs888wxbtmwZVUB5eXmUlJRgMpl44403uP3229m2bdugSX3ZsmX9Ru/Lly+noKCAF154YcieERs2bODhhx/23y4pKWHlypWjinWi1bd1IUkSmYmxbN5bSpLSjORxEZE2+B88ALlcxqKCTPShGvZV1GGxObl07gxCxiARqZQKVsydyaHOEtrqunAc2sOS1ALUmsnbB6apswen24NcLsPnO92cC3QqGcnhZ33PvG7q6urEOuiz+Hy+YRdDdHZ2Drs66NlnnyU+Pp5PPvmEsrIyZDIZjY2NdHcP3YZCzKP3N6bDC5VKxf333095eTn3338/77777gWfIyQkxP9LsmDBAvbv38+zzz7LCy+8MKLXnzdv3rAl2mq1ut8P1WTq1FbdYiAlNhK3x0uv1UauqoXQtByU2vNvUJGXnkiYVsP2Iyf4YG85VyzII1Rz8UvvXKYOYlUulAkxdCgSkE3yKZeclDi+sHoREhK//a4aM6BSyEgIHbia4uTJkyKhn2X+/Pm8++67fOMb3xjwmMfj4dVXX2Xp0qVDPj80NJSf/vSn/ttyuZxnnnmGL37xi+MS71Q0LhdF586d67+6fbF8Pl+/Oe/heL1ejh07RlJS0pi8djAxW+0YjL1kJcfSYjCisbejUzJg7nw4KXGRrFtSiMvt4b3dpRhMF3f9QJIkjHVHQPISFqYnNXfemIz8A0khl5MQrScxOsI/NSUDFINMUzU3N+NwOCY4wuC1YcMGNm/ezNe//nVKS0sBaG9v5z//+Q9r1qyhoqKC73//+0M+/6abbmLHjh3+25988glXXXXVuMc9lYxLQt+yZcuo1qFv2LCB7du3U1dXx7Fjx9iwYQNbt27lS1/6EgDr169nw4YN/uNPz7fV1NRw6NAhbrvtNurr67nrrrvG7L0Ei5oWAyqlgrT4KFo6u4n1dhCemIVKp7+g80SF962ACdWE8MG+chraR99V0dHTirGzhab2bkobuzhxdA9259isfZ8MfD7fkNOP09HVV1/Npk2beO2117jiiisAuO2221izZg2HDh3iL3/5CytWrBjy+W+99RYNDQ3+21dcccWop26nq1ENp370ox8Ner/RaGT79u0cOnRo2L/EQ+no6GD9+vW0trYSERHBnDlz+OCDD/x/pRsaGpDLz/wN6unp4e6776atrY2oqCgWLFjAp59+OuRF1MnqdKl/RkIMMmR0NZ0gKwQi0opGdT6tOoQ1i2ex62g12w6fYH5eOrMyky5oPrLN0MXx//wdh6ERlc9BSvJMkjKjCJFPr5UJ1dXV5OdfXO+cqeTLX/4yN910E1u2bOHkyZP4fD5ycnJYu3Yt4eHDTw2mpKRw+PBh/wBO9J+/cDJpFGuDzk6qZ4uKiiInJ4e77rqLu+++e1L8Yxw6dIgFCxZw8OBB5s+fH+hwBtXRY2bz3jLWLJ6Fz+Nj3/t/prioiMwFqy/qvJIkcfhEI6W1zcxMTWDxrEwUQ/zbAnjdTuqrj1NTeQxnexU6RyehEVHEpeeStuwmlJrJcz1iJFJTU2lubiYyMpKf//zngx4jk8n40pe+dEGfSCfDz9yFstlspKWl8f3vf5/vfOc7ozrHd7/7XX75y1+SmppKZGQkpaWlpKWlERERMeRzZDIZR44cGW3YU86oRuinr/4LE6O6ua/UPyFKz/69u1DjJin34hOBTCZjfl46+lANu8tqsNgdrCzOJUR15sfC63ZiMzTRUFVOS0MVDocTTUQcqQlxREb2XRBMmHPllEvmIyVJEtXV1cO2eZ0OdDodSqVy1MuVoW9J8YwZM/zbzclkMkJDQ4ftsS70N7mvYE0DXq+P+rYuctMTAImeumOEJ2SgDh/Y8H+0ZqTGE6pVs+3wCd7fW8aq2Zko7AZ622tpbaiho6cXu1yHPrGABUVziVC4MZTvQKHWoVBpUEeOTcFSMGloaPD32na5XHR3dw+6yQL0rXaZiIT++9//nl/84he0tbUxd+5cfvvb37J48eJBj920aRN33HFHv/vUavW4XsS9+eabeeONN/j6178+qk/nCoWCe+65h3vuuQfomwl45JFHxCqXCzCihH72hYoLIXqiX7zTpf45yXF0NZ3EZTWRs2jsr/zH69WsytJy6NA+dp78kPBQDSavBpsqhqRZy1g0M5uYiFAkSaLt4LsotWF47Baisuchk8loa2ulvGQfy1auRaudvA3S9u3bx1NPPcW7777rr1S02Wz84Ac/YPbs2Vx77bVkZmb2e47BYKCnp4eoqKhxi+u1117j4Ycf5vnnn2fJkiU888wzrF27lsrKygEVmafp9XoqKyv9t8d7CvTzn/883/jGN7j88su5++67yczMHLSv00inmWpra4mLixvrMKe0ESX0zMzMUf0wiN1dLt7pUn99qJqjuw/i1sSQmpYxJuf2uhzYDI3YDA04je24PV4iQjXs7Y6m0xbK0jn5XD0/H33omQRt66zHZTUREhaFUhuGLi4dm8PBwa3voJZ7USqC/7rJUP75z39y6623IknSgLJzSZIoLS2ltLSUu+++e0BSqqmpGdWO9iP161//mrvvvts/6n7++ed59913eemll4ZcgHC6z9FEWbVqlf//z15+eNrpi5wjzQsZGWPzcz6djCihv/TSS5PiAudU43C5aersYWF+BjZDI6ZuA9qUBWhCVKM+p9dl70vinQ04TR0ASNoommRJ1DmVyEPUXLYiHrPNTnOnkbo2A7OzU5DJZEiSD1P9MULConBbjUTNWIgkydi17SNkTiML1n0eVcjk7BO+b98+br31Vrxe75A9RE5fO3rxxRf53ve+12+k3tDQMG4J3eVycfDgwX5LduVyOatXr+63+cO5LrTP0cV6+eWXL+r5crkcuVyOzWYjJCQEuVx+3rwjk8n8DcCEESb0r3zlK+MchjCYuta+fhgZCTH0HPsQo09HRuqFT2N5nDbsp0fipk4ANJGJyJOKqDbKaOiyolGrmJuXRG5aPCEqJZIkcbS6mZKTjZitDpYVZmM31OG2mdFEJiBXqQlNyObA0WM4WsoonL+cqIS0MX3/E+nHP/7xoCPzobz33nv9KiINBgNer/eC+nNbLJZ+e++eW8V87rnP7SSakJAw5BZto+lzdLFuv/32i3r+Y489hkwm8/fHOX1bGDlxUTSI1bT2lfpjacPU3UGvLpXk2MgRPbcviTdg7WzAZTYAoIlKJGrmYszoOdJkoLXFRLhOw5LCLHKS41AozixZ7NtXNBW9TsOu0mosNjsFUhWaiDicZgMR6YXUtXXRfHQ7GSmpZM2evI25GhoaeOedd0aczH0+H0ePHu13oVSSJEwm05AXTgdzbg+hxx9/nCeeeGLEzx/OaPocBdq5732svhfTyUUl9F27dnHo0CFMJtOApYwymYxHH330ooKbzk6X+l82Zyam+v1YZWEowmKI0Q+9PNDjsJ6aTqnHaTYgk8vRRCURnbsEdVQKLT1W9te00GXuJFofyoq5uaQnRA/bfTErOZZQbQg7d2zjuLGagjnzkcnAo0/l8EebiddC4bJ1yOTBtXOM1+sd8fLaDz744IJbtUqSRHl5eb+kaTabz1s8A/inCLZt20ZxcbH//qEaV8XGxqJQKGhvb+93f3t7+4jnyEfS5+hC3XnnnchkMv7whz+gUCgmpK22MLxRJfTu7m6uvfZa9u3b57/QcfoX4vT/i4R+cU6X+scqbXRbujEoU0mOjhyQfD0OC7bOBmyGRn8S10YlEZO/DF1MKpJMSXVLJ+V7j2O22UmMjmD1wgKSYiJG/HE2LiKUuVFOKl2xHDt6hJmz5nFo/2H0jlYKL7lqTJdQjpWnnnpq3Nsk//Wvf+Wvf/3rqJ8fFhaGXn/+1g0hISEsWLCAjz76yL/Jss/n46OPPuL+++8f0Wud7nN0zTXXjDrec3388cfI5XJ8Ph8KhWLQttrnGu7xv/zlL6OKQ2wUfcaoEvp3vvMdjh49yiuvvMKSJUvIzs7mgw8+ICsri6effprdu3cPuxeoMLy+Uv9O0hOisTaVIw+NxtAZwsy4voo5j733zIXN3i5kcgXa6CRi85ejjUlFrlTh8ngob+igor4Vh9NNekI0l86ZQWzkhRcAWdpqUEkuFhTmU1ayhzdKOsiRGpg7p4CozPG7yHYxHn30UX74wx+O6NhNmzb51z5fiC9/+cv9RuhXXXXViFZmHD58mCVLllzQaz388MPcfvvtLFy4kMWLF/PMM89gtVr9q17Wr19PSkoKGzduBPracyxdupQZM2ZgNBr5xS9+MeZ9js7tY3OxfW0Gu1Z3+g/AuZ+gzv7DIBL6GaNK6O+99x733nsvt956q7+RvVwuZ8aMGfz+97/npptu4sEHH+T//u//xjTY6aKjpxeL3cnCMC/Ohi5cCcXI2trR21toO3QQZ2/3qSSeTGxqPtroFOTKvpUvdqeLipoGTjS04/H5yE6OpTAzmYiw0e3zKvm8mBtK0cam4jK3Ep6QTfSJRmQyN91hOWQQnBetFArFiC9Qrl27tt+nzJGQyWTMmjWr32uEhISgUp1/BdJoNsW49dZb6ezs5LHHHqOtrY3i4mI2b97sv1A6Ffoc1dbW9rttNBq5/fbbiYiI4L//+7/Jy8sD4Pjx4/z2t7+lt7eXP//5z4EINWiNKqEbjUb/8qfT/cTP3sptzZo1/OAHPxiD8KanmhYDoeoQFB0VuDxO2iv3k9DbjbMtGU10CrFps9BGJyNXnEkevTYH5bWtVDX3lUznpiVQkJl40T3PLa1VeF12QkKjaKs/QY1Fz7wEGeq0VRxt7MHirmL57GyUk3j39fT0dK677jree++9Ea2RlsvlzJ49e8AF0JEk84tx//33DznFsnXr1n63n376aZ5++ulxjWesnfvp5oknniAuLo4PP/yw34h89uzZ3HzzzaxZs4ann376opdLTiWjap+bnJxMW1sb0HchJz4+vl+DnObmZrHcaJQcvT20njhIqmk/XZW7kbweelxyovIuIWXZzcTNuozQuAx/Mu82W9lx5CRvbi+hvr2L2Tkp3LxyPgvzMy46mfu8HkyNZWjjMuhurqamx0sCXWTNnMXcBUtZOS+Pxs4ePtxfMenb5j766KPIZLIR/9wONhc9mpbRU83777/PVVddRUxMDEql0v9J6eyvkXrzzTf5zGc+M+i/iVwu56abbhLbz51jVCP0yy67jC1btvjnKG+99Vb+53/+B4VCgc/n85clCyPjtpqwdtZjNzTQ2d6K0mQkPEZDWOYctIXX0rO/goXZ+f4kLkkS7T1mSmtaaDEYCdOqWVSQyYzUuDEdKVtaT+JzOVCGxlB1YDchSh1ZSbHE5C5BJpORkRBN6KJZfHJqs+jL5+cTFT45k9qiRYt47bXX+ipFfT68g6yQOT2lcc899wwo/1coFCNa4TKV/eMf/+Bzn/schYWFfP7zn+e5557ji1/8IpIk8dZbbzFz5kz/Rd2RkCRpyHX2AOXl5WIj6XOMKqF/61vfYsuWLTidTtRqNU888QRlZWX+VS0rVqzgt7/97ZgGOpVIkoTbasRm6Fud4raakCtVaGNSMNjCUWkkQpXNxM5aQbXRilKhID4qHEmSaOzoobS2BYOxl6hwHZfNmUlGYsyYbPx8Np/XjbmxHF1CNqVHD+B0e5mRoCUufymKkDPz8bGRYVyztIiPDx3ng71l3HDZXLTqybkN3U033cSuXbv40ROP8d7mD/slC5lMxuzZs7nmmmsGJHOAuLi4IdtKTxcbN25k8eLF7Ny5k56eHp577jnuvPNOrrjiCurq6li6dClZWVkjPt+NN97Ic889R2ZmJl/72tf8n4BsNhvPPfccL7zwgr93utBnVAldoVD022g5KiqK//znPxiNRjFSGUJfEu/B1tlXsem2mU8l8VSisuahiUrE6fHRXH+Q2apmQsIi0cak0lxdTnxUOLWtBkprWjBb7cRH6bliQT4psZHjNrXV23wCn8dFi12JqbWOlFg9cRn56GIHVqqGatWsXVJIU0fPpE3mpy1evJh33ttMQ0MDxcXF9PT0oNPpePTRR4ctGkpLm7xVsmOlvLycjRs3olAo/Bd+3W430NcP6hvf+AY///nPR7wq5dlnn6W2tpZvf/vbbNiwwb+1ZGtrK263m0suuYRnnnlmXN7LZDWqhF5UVMTs2bO59dZb+dznPuffKDcyMnIsY5v0JEnCbek5NRJvwG3rRaEK6UviOfPRRCb2K8ipa+1E4TQRrrISkb4Im8NFeV0rmhAVLQYjqfFRLC/KIT5qfP9g+jxuepvKcYcmUVVWQmKIh7ikVKJyhu5VEqJUkp08dTrjpaeno9Pp6OnpISQk5LwVoBcy8pyqdDodISF9f9AjIyNRq9W0trb6H09ISBiwkmU4ERERbNu2jbfeeov333+f+vp6ANatW8c111zD9ddfL67VnWNUCf25557j73//O4899hiPPvooxcXFfP7zn+dzn/uc6JB2ittmpqP0Ezx2S18Sj00jKmchmsiEIasqa1o6ScaAShdBZQ/s3bWH5s4e1i0pZGF+5oTNT/c2V+J0Oigxuol2NREXF0Ns/iXIlZN79D1eYmJixGCGvv4x5eXl/tvFxcX89a9/5bbbbsPj8fDKK6+MqqX2DTfcwA033DCWoU5Zo5r0u/fee/noo49obm7m2WefJTQ0lO9///tkZ2ezbNkynn32WVpaWsY61klFqQlFG51M/OwrSFl6EzG5S9FGJw+ZzE0WOx2tjbjNbexpl1Ne34ZOE8LigiyuWjRrwpK5z+PC1FhGtVWLureRaJWHmPylaCITzv/kaer0J9Tp7qabbuLf//43TqcTgB/+8Ids3bqVyMhI4uLi2LFjx6j2GhZG7qKu4iQkJHD//fezfft2Ghoa+NWvfoVMJuNb3/rWtB+py+QKomcsQhuddN4+Jz29Nv698wjGhjLsUgi5s+Zw04piwrRqspIndvstc1MFTe3dGCQ9Ke5awuMziMo+/4YEVoeTstqp80dckiQ83vP3gpHJZNM+oTscDl577TXcbjePPPII3d3dAFx33XVs3bqVu+++2z8IFJ1bx9eYdVtMSkqisLCQgoICSktLsVqtY3XqKaujx0xpbQuN7d00NTYwLxKWX3E1kSkZmCx2LHbniLsrjgWv20FdxUGaPeEUaJtRynwkL75+2D9IkiRR1dTJgco6lAoF2clxaNXjW2AzEeraunC43Oc9Li0t7aL20ZzsOjo6WL58ObW1tf4eTlqtljfffJPVq1dz2WWXcdlllwU6zGnjohK6JEls3bqV1157jX/9618YDAaioqL4/Oc/z6233jpWMU4pkiTR3GmktLaFjh4zEWE68jOSCO0uJzs5nYjkbACaDUYUcjmJ0edv3jRWmo8fprG9m6QZi1HWbT415z90Nz+L3cHu0hpau0zMSIlnYX5Gvw2mJyufT+LA8fpB16Kfazw3jJgMnnrqKerq6njooYe44oorqKqq4qmnnuLee++luro60OFNO6P67duxYwd///vfeeONN+jo6ECv13PjjTdy6623snr16lH1qpjqfD6JujYDZbUt9PTaiI0M5/L5eaTGRbH70FHCvUZS8lYik/XNgrUYjMRH6SespN5q6aW8ZA+q6AwSLRW4QrQkzFsz6LGSJFHZ0M6hEw2oVUpWLyyY0E8S462+vYuqpg6Up9aV+4aoXYmJiRm3zSImiw8//JD169fzy1/+0n9fQkICX/ziF6msrPT3XxEmxqgy78qVKwkLC+P666/n1ltvZd26df7lSkJ/Hq+XqqZOyutasNidpMRFsbggi/io8FP7K/rorCohKTqGsMRs/3Pau83My52Ytc0+n8SeHf/BK0nMTYvBePwY8UWrUGkGdmY0Wx3sLqumvdtMbloC8/PSCZlCf8BPj87dHq9/SZxXAo9PQnlO8VZxcfG0XzbX0NDA9773vX73XXrppX3VzO3tIqFPsFH9Jr7++utce+21aDSTd3f38eZyezje0EZFXRsut4fMpBhWzcsjWt9/vrWuoQGZtY20Bdf456rbu3vx+nwTNuo9XH4SW2sVeXkF2FvKUIfHEpXdf825zydxvKGVwyeb0IaouGrRLJJiIiYkvonU1NlDdXMnVocLp7tvIwpJkui2+4gPPfNpKSoqiuzs7ECFGTScTueAPHD69ljs9VlRUcHLL79MTU0NPT09g7bR/eijjy76daaKUSX0m2++eazjmHLcHi9ltS1kJ8UxKyuJcN3gf/waKvajCw0nMftMW9MWg5FQjZqI0NG1vL0Q9e3d1JTtJTNGj1ayYvO4iMqejyr0TLI2Wex8WlqNwWghLyOBeTPTUSknb3fF4YRp1SwrzKaivo3Tg2+FXIZG2X8kPm/evGk/Oj+trq6OQ4cO+W+bTCYATp48Oej6/Pnzz79qCvo2ELnjjjtQqVTk5eURFRU14BjRy6W/qfNZOciEatV8dtWCYRNfr7EbS1stmXMu7beSpNlgJHkcy/pPM1nsfHqojBSMxEdG4Xb0EhIWQ0R6EdA3Ki+va+FIVROhGjVrl8wiPmrwi7R2p4v6tm7yM0a2JVqwitaHMi83naPVTShOzaHLAb36zArfsLAwMTo/y6OPPjro7mRnb6IN+FfBjKRFMfS1z503bx7vv/8+sbGxYxLrVCcS+jg63yi2pnQfPrmKnMJ5/vssdgdmq515uRdeUXchXB4PnxyuJMLVQmKECsnnQxGiQ62PRR0RR0+vjU+PVdNttlKQmUTxzNQhL9C2GIzsOlaNJElkJMZM+mWLR6ubMJit/oTulc4kI4D8/Pxp34jrtPHsRd7S0sK3v/1tkcwvgEjoAeJxWOisr0Cflo9Oe2ZqpbnThEwmI2kclytKksSnx6pxWM3ka3qReTyo9bG4LD2EpeRzpKqJYzXNhOs0rFtaSFzk4L1jvD4fR042UVbbQmJMBJfOyZn0ydxsdXD4RAMqhdz/cd4nSfS6JPTqvoQ+c+bMQIYYVG6//fZxO/ecOXOmfcX5hRIJPUBaTxzG6vIxJ6//fGKLwUhcZPi4rucuq22hob2bRVFOaDagTZqBXBmCS67hk8oejFY7RdkpzMlOQaEYfCRqsTvYfqSKLpOFebnpFGYlTYk55U5jLzKZDJ0mhNOzszLA6pbQqyE+Pl50E50gv/71r7nlllu4+uqrWb58eaDDmRREQg8Aj9NGS00Z3ogM0pPOdCj0+ny0dZsozEoet9duMRg5fKKRotQolHX7kVQawtNmc3z/R9SRjC5exjVLZxMTMXT1Y11rF3vKaghRKVi3ZOgR/GSUkxLHnddegoTErx5WYwJUChmJoX1/2HJycgIb4DTy85//nIiICC677DJmzZpFenr6gB2PZDKZ2LXoLCKhB4C5oYwei4PE2bP7jYA7jb24Pd5xW65osTvYcaSKpNgIkjwNdJk7Cc2/kk/3H8JtdpG3bD6zZ6T6547P5fF6OXC8nhON7WQkxrCsMHtKVIaeS6fpq6k4+wOHTCbzb4QuTIyjR48ik8lIT0/HYrH06+R42lT4VDiWpt5vY5DzOG2015ZhVicyLy2p32MtnSY0ISpi9GPfG8Tj9bL18AlUSgVLsuOo//A1LMooSlp8JNrambN0BSnDXIjt6bWx48hJem0OlhZmMzM1ftr9MmVkZKDVjv9SUqFPXV1doEOYdERCn2C9TRV0WxwoYwsGbFTRbDCSNA7LFSVJYm95LSaLnXVLimjc8xrtXT10pi+nMMpDTGQySTmzh3zuyaYO9lfUEa7TcM2y2ZN239CLlZ+f7///E43tmCx2FhVkBi4gQTiHSOgTyOtyYG4+QYcUTVZqcr/EbXe66Om1UpiVNMwZRudEYwfVzZ0smZVFVdlhzMcPIKUsYM1li7GWfkB4Sh5y5cDVKS63h91lNdS3dZGblsDC/IwJ6y0TbEJDQ/19WxwuN1sPV2KxO8lOjhv2eoMwNnp7ezGZTPgGaZg2mk0zpiqR0CdQb1MFJqsDizaHnOT+a2tbDCZkyEiKiRzT1+zo6WX/8Trio/SUVTeirXyfmKg45t/wZaytJ5AkifCUgf02Oo297DhyEpfby8riXDISJ7Yve6BJPi+S90z73KysLI5UNZGeEE1dWxetXWZ8PomSqkauXJA/zJmEi/Hcc8/x61//mpqamiGPGWmh0nQgqiMmiNftoLflBF3yGGKiotCfU9bfYjASrQ8d03XcdqeLjw8ex2y109ZtQm88TqLWQ+6Km1EoFPQ2VxKakIUi5EwskiRRWtPM5r1laNUhXHfJ7GmXzAEsbdV43U7/bU1EDB8drGDroUr2V9ShCVESFa7lSFUjXSbR+388PP/889x3333MmDGDH//4x0iSxIMPPsj3v/99EhMTmTt3Ln/6058CHWZQEQl9gvQ2VeL2eGn2Rg7YTNnnk2gxmEiOixyz1/P6fLy14whlda2EatQsSg0l2dNARPJMItILsbbX4HM70acW+J9jd7r4z4HjHD7RSGFWMmsWzyJMO/0asEk+Lz3VB5FOfbyXAQ1dNrrNNvYer6O21UCMPpSIMC0mi52SqsbABjxF/fa3v2Xt2rW8//773HPPPQBce+21/OQnP6G8vJze3l66uroCHGVwEQl9Avg8LnpbKulVxyMpQshM6j/i7TZbcbrdpMSOTfdCl9vD/36wl/3H6yiekcJnLplFWPcxkCTiZl8OMhnmpgq0sWmodH0Vqc2dRt7edRSjxcbqhQXMz00fcvniVGdpq8bWWY/cf71A4kRjB8mxERh6ejFZ7LR0mWjqMKJUKDjZ2D6i3Y2EC1NdXc31118PgErV98nV5XIBEBERwV133cX/+3//L2DxBSMxhz4BepsrkXw+mn2RpMSFoQnpP63SbDASolQSG3HxBTpNHT28t/sYJ5s7WLu4kKsWFdB1fBd2QxP6tALCErKwG5pw23qJyVuO1+ej5GQjZbUtJMdGcsnsGZO+fP9inB6dA8RFR+F2u9GGhmKz20mIDic/IxGzzcGaRYUknFqlFKJSop6C6/EDLSIiwt+CV6/Xo9PpaGw882koPDyctra2QIUXlMRP4TjzedyYmyqQR6ZhaHazcmbcgGNaDEaSYiKQy0e/XNHp8rDveB3ltS209/SydnEhVy7Ix9ZZj7mxHIU2nKicBYAMc2M5msh4XMowPtpbRpfZyoK8DGZlTo3y/Yth62zA3t2Kz+PmtZ/cRVlNMwcdybhsZurblSCBXCaj12ZnaWFWoMOd0oqKijhy5Ij/9tKlS3nuuee45ppr8Pl8vPDCC+Tm5gYwwuAjEvo4622pRPJ56VTEEaI0kRrXv6ez0+XBYLSwtHD07Vjr27vZV16L0+VBhoziGamsmpeL12Wn++Q+fD4vYfEZ6OIycJo6cPZ24Uos5uNPj6JWqbh6SRGxkQN3J7pQLo9n0u9epI6II2HOFXCqMdeRro+4JD+LhPQ8QsLOTIlNpXYHweq2227j+eefx+l0olarefLJJ1m9erV/maJKpeIf//hHgKMMLpP7ty/I+bxuepuOE5qQw4FWCxmJMQOaXbV2mZCQSB7F/Lnd6WZfRS31bV2kxkfhcHlQKuVcPj8PhVxOZ+UevC4HihAdERlzkMlkGOtLaTK7qbaZyEyKY2lh1kWX75utdo5UN9PSaeQzK4sndVJXasKIzCr23/aElrF0+UrRwjUA7rjjDu644w7/7UsuuYSysjLefvttFAoFa9asESP0c0ze37xJwNJyEp/XjSM8DUtNHdnJA5NCi8FIZJiOUK16xOeVJIm6ti72ldcBcNncmRh7bZTWtHDFgnzCdRp6myux97SgCNGi1ISijU2js62Zo0dLMOpzWT57BjkpcRc1xWK22jla3Uxti4FQmYN8jQk8hTCJE/ppkiRh72oEyYdeP36tjIULk52dzQMPPBDoMILW5P/NC1I+rwdzUwWhCVlUdtkI06oHlPpLkkSzwThg1ctwbA4Xe8traezoJiMxhsUFWXSZLByraaZ4ZhopcZG4bSaMtYdR6+Nwmg3oM5ZyorGdsk+3EKbScNXlq4jSj37KwGx1cKymiZoWA5oQFQvz0tEbDiOXh6BSj/wPUzCzdzXScuBd9D632AA9wPbs2cMnn3xCR0cH3/jGN5g5cyY2m43jx4+Tm5tLWNjFTxdOFSKhjxOvy45SE0ZocgH1eyopyBh4wdFosWF3ukgZQXdFSZKoaTGw/3gdcpnMX71pttrZebSKtPhoZmenIPm8dB3fjTxEh+TzotBFsb/BSlNLK9kqGwWLLh91Mu+1OThW3Ux1S2dfIs/PIDc1AWvLcVp6OmgJm0WMx0eIanIvd5QkiZ7qw9gNTYS55XjdDhSq6bceP9BcLhef//zneeutt/w7Rl1//fXMnDkTuVzOmjVreOihh/jhD38Y6FCDxuT+zQtiKm04ifPW0tbrwe3xDjrd0txpQqFQkDDEPp2nWR1OPj5Uya5jVaTERfFfl84lIzEGt6evg6ImRMUls3OQyWSYGspwWboJS8ymx9DGAYOKtu5eFibKSE+KIyLlwnfbsdgdfFpazZs7Smg2GFmQl8FnVhRTkJGEz2XFUH2YE1YdDpluSqxdt3c1Ymk9iTYmhRCPhd6mykCHNC09+uijvPPOOzz33HNUVlb22xBao9Fwyy23iF7o5xAj9HHi87rpPrmPGqOO2MjwAaX+0Dd/nhitH3JXIEmSqGrq5EBlHUqFgivm55MaH+V/bHdpDRa7k2uWFRGiUuI0GzA3lBKWOouqimM0dDrRzkjiillpmI6+T1hqAXLFyNeYW+wOjlW3UNXcgVqlZEFeOrlpCf4GXZIk0XViL7UdvThj5nHlvNwh38tkcXp07vO40EQlIQ/R0lN9kPDUPDFKn2D/93//x9e//nXuueeeQStCCwoKeP311wMQWfASCX2ceF0OLN3tWE7UkbNozYDH3R4vHcZeFuQN3inOYnewu7SG1i4TM1LiWZif0W81SkV9K3VtBlbMzSUyTIfP66Grcjeo9Rys68bR1ED6/KuYN7eQ3sZSAMKTBzbhGozV7uRYTTNVzZ2EKBXMz00nLz1hQKdFa0ctNSeP06nJ4Yr5BYRqJv/8uaO7BUvrSXweF7bOBhSSG3t3M73NJ4jMnBPo8KaVjo4OZs8evK0zgEKhwGazTWBEwS+ohlPPPfccc+bMQa/Xo9frWbZsGe+///6wz3n99dfJz89Ho9Ewe/Zs3nvvvQmKdngqbTi2hPl4VaFoO0r6qkXP+sjY1t3XCvTc+XNJkjhe38a/dx7FbHWwemEBy2fn9Evmbd0mDlY2UJiV7L+gaqwtobvbwL6eMFxtJ8jNL2B+8TyQvH1NuBJzUIQMP8K0OpzsLavlXztKqG/rpnhGGp9ZMY/CrOQBydzrclB1aDutbh3z580n/jzTRpOFPERDZHYxsQWXEj1zEZq0ucTkLfO3SBAmTlpaGsePHx/y8V27dokdpM4RVCP01NRUfvaznzFz5kwkSeLPf/4zN9xwA4cPH6awsHDA8Z9++ilf+MIX2LhxI9dddx2vvPIKN954I4cOHaKoqCgA76C/2vZeIvIvIyrSRnfVAVzWHqJnLEImV9DSaSJMqyFcdybJmq0OdpdV095tJi8tkXl5aQPWdFsdTraXnCQhSs+8mX2je2tXC5VH9tIgxRMXCxmJOlIKlyKTybC01+DzuNCnDN3i1epwUlrTwsmmDlQKBcUzUslLT0SlHLr3ed3RXTS0d5M872py0xIu8jsVPNThMSTMWe2/ba+rIyEzM3ABTWNf/OIX+fWvf83NN9/sX29+emHBiy++yN///nd+9rOfBTLEoBNUCf10I57TfvKTn/Dcc8+xZ8+eQRP6s88+y7p16/jOd74DwFNPPcWWLVv43e9+x/PPPz8hMQ/FZLHTZbawsjiXqMQYVKFRdJ/ch9tqIq5wBS1dRlJO7U7k80kcb2jl8MkmtCEqrlo0i6SYgYVGXq+PbYdPoJDLuWzuTORyGUaTmf1b3sTiU1G4dBnRXSUo1CloIuORJB/mpgp0cekotQNXttgcLkprmznZ2IFCIWduTip5GQnnLQzqbq3nRNkhNKnFLJ49smmcyUo5BdbUT1Y//OEP2bNnDytWrKCgoACZTMZDDz1Ed3c3TU1NXHPNNTz00EOBDjOoBO1Pq9fr5fXXX8dqtbJs2bJBj9m9ezcPP/xwv/vWrl3Lm2++OQERDi9cp+GKBfkkRfcl5rDEHFS6CDrLt1O3521sxkiS8zMwWex8WlqNwWghLyOBeTPThxwZ76uoo6fXxtolhWjVKmpbDBzZ+S4aj4vlaz5LGDYMjSZicpcAfX1JPHYLcQWX9TuPzeGirLaFE43tKBRyZuekkJ+ROKIKT7fLyaEd7+HVRLPy0hVTYlXLcM7dZV6YOCEhIWzevJm//e1vvPHGG3i9XpxOJ3PmzOHHP/4xX/7yl6d976FzBV1CP3bsGMuWLcPhcBAWFsa//vUvZs2aNeixbW1tJCT0/7ifkJAwbAc2p9OJ03lm4wKLxTI2gZ9DLpcN6Nui1seSOG8dpTveQd91hI76MLb3KAjVqFm7ZNaw89AnGzs42dTOssIcIkK1fHqsmvqqMlIxMfuK64iIi6X1wLtoo5NRR8QhSRLmxgq0UUmEhEcDff3OS2tbONHQjkIupyg7hYKMxBGX/kuSxL6dW3BYLSy6+svopsBF0PMRI/TAkslk3Hbbbdx2222BDmVSCLqf1ry8PEpKSjCZTLzxxhvcfvvtbNu2bcikfqE2btzIk08+OSbnGg2lWkejegbtni44uoOZM+Ywb/lqVIPs6XmawWhhX0UtM1MTiIkI5d3dx7BZe5ml6SIlt5iIpBysHbW4bWZi8pcD4DS24bJ0kzDnCuxON2W1LVQ2tiOXQVF2MgUZSRfcw6Wi8jhddWXMKL6EhMTEi/o+TBZihC5MJkGX0ENCQvxXrhcsWMD+/ft59tlneeGFFwYcm5iYSHt7e7/72tvbSRwm2WzYsKHfNE1JSQkrV64co+iH5/X5OFrVxI5jtWQnFTC3QA8dx+mp2EFM/iUoVANHvHanm20lJ4gM0xIRquH9PaXodRqWx9pQuvVE5y4GyYe5vhRdTArq8L5VL6bGcmSaCI61OTnRdBi5DAozkyjISEIdcuH/7G1dRk7s/5iEhCQKigefApuKxAg9sHbu3MlLL71ETU0NPT09/VaKQd8I/uwWu9Nd0P+0+ny+flMkZ1u2bBkfffQRDz74oP++LVu2DDnnDqBWq1Gf1W9kovpAdJmsfFpaTVNHDzERoXz28gXERobjSEjCULGT9sMfEFe4ElXomYuhPp/EjiMncbrdhGpDOFBZT35GInnhLozVBmJmr0Kh0mBpq8Zt7yV21qUAmLvbqa06ToMiA29vB7Myk5g1ykQOfevSd+/4mAilm7mXXo1MPvioVfJ5h3xsshIj9MD59a9/zXe+8x00Gg15eXlER0cHOqSgF1QJfcOGDVx99dWkp6fT29vLK6+8wtatW/nggw8AWL9+PSkpKWzcuBGABx54gJUrV/KrX/2Ka6+9lldffZUDBw7whz/8IZBvox+v18fRmmZKa5qJDNORl55Il9lCTETfHxJNVBIJ89ZhKNtGW8kHxOQvRxeTCsDhkw3+vilGi51V8/JIClfSdug9wpNnoo3u691iqj+GLi4dX0g4hyobqD/8MUq3l5zFRRRmpQzYIelCeLxetu4rQdtbx6xFl6CLGLhBh9tmwtxYjqOnjaRF1yNXBNWP1UURCT1wfvGLX3DJJZfw9ttvExExNtszTnVB9ZvX0dHB+vXraW1tJSIigjlz5vDBBx9w1VVXAdDQ0ID8rFUVy5cv55VXXuGRRx7hBz/4ATNnzuTNN98MijXo0Df3/WlpNWabgzk5qRRlJ/Pup8f8yxVPU2nDSSheS1flbgxl24nInINBkcDHB48jl8vJSorlsjkz0GlUtJdsQRGiIzJ7HgCWthqcNgtt2hlUbjuMzGMnO8TGjGWricnIvKj4T7cXcDcfJTc9lfiZ8/s97jQbMDeWY+9qQh6iGXat+2Qln+KreIKZzWbjS1/6kkjmFyCoEvqf/vSnYR/funXrgPtuueUWbrnllnGKaPR6eq28v6eUaH0o1y4rIio8FKvDidFiY3ZOyoDj5UoVsbMuw1R/jMayveyoteDUZnHdpfOYk5OKXC7DVF+Kq7eLhLlXIVeocDicVBzaSYtVhtVjIz89gRRvCx5TAlGpF9/4v6K+jebqMgr0kDr7EuQKFZIk4ehpxdxYhsPYgUoXTvTMxYQmZE256RZALIsLoMsvv5xjx44FOoxJJagS+lQSGaZjRfFM0uKj/XuFtnSakCEbtGgI+pJHT0gS79YrSXAbuWmWk9y0KORyGa7ebkz1R9GnzUKmi+JIVSNVx/ajNnaSMv9aivJzCZF5ad67m4j0woue9mg1mDhUXsUMRScpOQVoopKwdtRhbizHZelBHR5N3KzL0MamIpNN3VGsSOiB89vf/pY1a9bwy1/+kjvvvFPMoY+ASOjjRCaTkZHYf+OKFoORmMiwQee0vV4fByvreX9vGUptHGuvvxxf82HaDm0mJn85PTWHkGvCqXNHc3zbYbweN9nybjIWLiV5dl8VrbGuHJkMwpIvbnTea3Ow/chJknytJEXrUeoiadn/Nh67BW1UEglzrkAdmTgtkt10eI/BKi0tjXvvvZdvf/vbfO9730Oj0Qy4piGTyTCZTAGKMPiIhD5BfD6J1i4TBZkDl1SarXa2HzlJZUM7YVo1n7t8AUkJ0Xjj4zFU7KJu298wO7zURyzBbWwlNy2BTLUZe5OOuBl9c+k+rxtLywnCkmYMuvxxpPp6rFeicRpI9jTisYdjrj+KLjaN2IJL/csihYn3+9//nl/84he0tbUxd+5cfvvb37J48eIhj3/99dd59NFHqaurY+bMmfz85z/nmmuumcCIL85jjz3GT37yE1JSUli4cKGYSx8BkdAniMHUi8vjIfmc7oo1LZ3sKavF5fYSplWzZFYWaQl9Hy29MiWtvmja27rwyeSkx5uZvWwpWrWSln0HCU3MRnWqR4ultRqfx014SsGoY5QkiU9LynC3lJLtPI6kVhORcQkRabNEt8EAe+2113j44Yd5/vnnWbJkCc888wxr166lsrKS+Pj4AccHe+O6kXj++ee59tprefPNN8XF6RES36UJ0mwwEaJSEqPvW67o9njZdayKnUeriIsMR6WUMyM1nrkzUnF7vJTWNPOvT/bTcHQ7+sy5LLj6yySH2Oit3I6p/hg+j4uI9L5fTMnnpbe5Al18BkpN6Kjic9vMlOx4D9PRzWT6mtDpo8i66m5i85ZO62QeLFMuv/71r7n77ru54447mDVrFs8//zw6nY6XXnpp0OPPblxXUFDAU089xfz58/nd7343wZGPnsvl4tprrxXJ/AKIEfoEaek0khwTiVwuo8tkZceRk9icLpYWZlHZ0IFWHcKSgizKalspq23B7fWSI2snPiOOjCXXo9SE4YyKo6P0E7oqdxM76zKUmr4/DrbOBjwOG3GFF94ewdnbhbmxnPb6EzS0m0nOKiBGZiQyex7ayMnbFtfr9eLz+S76PD6fb8wTisfjAfr6CJnNZv/95xa9neZyuTh48CAbNmzw3yeXy1m9ejW7d+8e9DWCuXHdSF133XXs2LGDe++9N9ChTBoioU8Au9NNt9lKbno8FfWtHKxsIDJMy7XzizhW00KPxUZuajzv7TmGy+NlRkocMyMkLNVVxOQt9SdutT6W0PgsepuOY+9qxtpeiy4+E3NTOdroZELCos4TSR9JknAa2zCdKgbyKtRUOmOJmrWYVEUTEI0+dfRTN8HgqaeeCmjPnpE4t+XE448/zhNPPDHgOIPBgNfrHbQR3VAbQIymcV2wefzxx7n11lv5xje+wVe/+lXS09MHLfQSq1/OEAl9ArR1mXB7PVQ3GegwminISGJ+bjrH69vYW15DqEbNyaYOZqTEMzsnGa1CovXAu+hiUwlNyPafx+dxYW2vIXHeWiSfF8PxT1G3VuGy9JAwd+F545AkHzZDI+aGclyWbkLCoonIXca2KhOKGBlz42VYG3pIKF4z6deUP/roo2OyG/zp3ebH0uHDh1myZAnbtm2juLjYf/9go/PpLC+vr9d+SUnJoL2cTvN6vRMVUtATCX0ClNW10NhhJFSj4Yr5+STG6NlTXsPbu44SGaZjzoxU5uSkEKbVIEkSnWXbQCYjeuaSfsnE3FSB5POhTy9CEaJFFRpF06evo9SGo9QNvQJA8nmxttdgbqzAbe9FE5lA/OwrUEcmsK3kJDanm7XFWViPf0RYci5qfexEfFvGlUKhCNqy/dMNv8LCwtDrz399IjY2FoVCcUGN6EbTuC7YPPbYY0FzDWOyEAl9nHjdTjpKt9Js8nDsaCPpicmsmZdFq7lvieLR6iZS46K4be0SIsN0/udZ26qwdzUTV7Sy3x6gXreD3uZKwpNzUar7jlfrY1FHxCGTK+go+XBgcy+Pi96Wk/Q2H8fndqKNTSMmf7k/YR+paqKxo5tV83LxtB5BrlITmTl3gr5DwkiFhISwYMECPvroI2688Uagb27/o48+4v777x/0OaNpXBdsBpt+EoYnEvo4sdnslDb20GNoJ11uJh0Xe96uxO2TgVfFoshIVsybgdrVg8fhQ6EOxeOw0FN9iLCkHH+DrtN6GytAktCnnbnwaW6qQBudTGzhSrrKd/ibe6nDY+htOk5v60kkn5fQhCz0qQWozhrFN3Z0c6Sqkbkz0oiR9WLobiW+aBXyYfqyC4Hz8MMPc/vtt7Nw4UIWL17MM888g9Vq5Y477gAmZ+M6YeyJhD5OlJpQnLGz8GizKatuxqeNICtVjcdmxmPooChVj6OlHFtj3/yfTK7A0d0McgVhKfnYuppQ6SJQakLxuV30tlQSnpznH7W7bWbshkaiZy4iRBdBQvFaOo59ROP2V5Cr1KgjEwlPziU8Jc8/oj/NaLGx82gV6QnRFKbH0HbwPULj0tHGDOwxIwSHW2+9lc7OTh577DHa2tooLi5m8+bN/gufk61xnTA+REIfJy63B7vTzfGGNtISo7nhisV0Gi3sOlbFkuXzyU1PQJIkvE4rbpsJY00JNq+H0Nh0LC2VmBvcQF+id1uNuO29hCXPxGZoQKWLwNRYgVylJjQhG1dvN+bGMpzmLuQhOiSvG01EHBEZRcgVqgFxbT10Ap1GzfLZOZiq9yP5vETNOP9FVSGw7r///iGnWCZT4zph/IiEPk7CdGpSYiMxWmysmJuL2+NjT1kNOSlx5Kb1VfbJZDKUmjB8Hhduu5mE4jVEZc/rS/QuO26rCYepnfaSLajDo7G21WBuPI7kddPbUok6IpGaD/+Ax2knJDyKiIw5RKQX4TS103V8N+0lW4grXOFf9ujzSew4WoXD5eaaZbPx9XZiaashJncJihBtIL9dQcfrstNx7BOiZyxCPUgPeEEIRiKhjxOFXE5KfBRVzR3E6kPZVlJJRJiWJbOy+l25l3xeuo5/ikqnJzJzDnAq0at1KNU67N3NhMZnkLLkRmQKFV6XjdYD7yF5fXhddiSfB5VOj1yhorfpOJbmyr5VL1o91o5a7F1NxBWuIjQxmyM1rbR0GrliQT5hGiWtZfvQRMYTmpgTqG9T0DI3ltNTcxhkchLnrRWrLYRJQST0cdTSaSRcq+FwVSNuj481i3NRnrOUzlh3FLe9ty9pnLP22+O0YWk9SUT6bGRyBda2akwNxzDWHiYseSZJC65BE5WETCbD63bgtpnx2Ey4bSbcVjNqfSy9zZXUfvQSXm0M9bYQCtPSCe1V097QitPcReySy0SyOofXZaen+lBfS4WmCqKyitFETZ7lfsL0JRL6OJEkiRaDEZfHi8Vg4sqF+YRpNf2OcRg76G2qICJzLiFhA6vdzA2lIJMjST6a972F12lHJpMRlphD2mVf6HexU6HSoIjQQET/Rk1et4P6w59QefhToqPiSYkJx9xUgbHuKJrIBNoOvY9SE4pKF9H3FXrqvzo9cmXI+Hxzgpy5sRynqQNdXAaOrkZ6aktIjBSjdCH4iYQ+TkxWOy0GIzKZjFXzcgd0WfR53HRVfkqIPhZ92sAye6fZgKFiJzKFCrPkIzQ+k/DkfDrLthKakDVg5cpQPJKSEnsimoxLSNd24fN5UWrCiM1fRnTeMjwOCx5r36je3tVEb/Nx/87qSrXuVJLX9624OZX0B2vP63XZsXbUEZ6SP6kT3+nRuUyhRPJ5UWjCxShdmDREQh8vkgytWkVWciyFWckDHu6pPoDP7SRhzpX9dvzx2HsxN1XQUboNj91M0sLriMiYjVKtw9JWjcdpQ586siZcPp/E9iMncXu8rFm5CpXbQuPuN3B0NZOxaj2aiPgBI3qf14PHbsZtM+M+nei7W+ltPuFP9IoQTb/RvEyhxFh9GEkGofGZk/oCq72rCa/bgUwux23pAkAmV2LtrBcJXQh6IqGPkzBdCPNzMyjKSR4wYrUZGv2rS5Sn+pm7LN2YG8uxdTYgSRJypYrU5bf4L5RKkoS5qQJdTEq/atDhHDrRQFu3idULCgjTavDIPKg0YciiU+ipPohcoSQ0Iavfc+QKJSFh0QOmgCSfF7e999T8vAmPzYTT2I65vpTetmpkMjn6lDxkF7n1XaCFJmSTdsnn4NQfr9NCwkUDKCH4Te7fviCmVCiYn5c+4H6vy0H3yX3oYlLQJWTjMLZjbizD3t2KUhNKVM4CHKYOFCGaflWh9u5m3FYTMTOXjOj1a1sMlNe1sDA/k6TYCCRJovvkfpTqUFKXfRZj7WEMxz/FZekhMrv4vPuCyuQKQkIjCQmNhFOr+JxmAx3HPiIqu5iIzLlIXs+Ade+TjUyuQDOJ2wYL05tI6BOoL6nuRZJ8aGKS6TiyBafZQEhYJLH5y9HFpZ8q/z9IVM78fhs9mxvLUUfEjWhNdJfJyqel1WQnx1GQ0TdNYOusx97d4u8RE527FFVoFMaaQ7htRmLyL7mgrevs3a0YyrejCos61TJgel5AFYRgIrYCmUCW1pMY647iddrpPrEfmUxO/OxVJM6/htCELGRyBab6UhQhWsKSZvqf5zB14DR19huxD8XudLP1cCWRYTqWFmadWtLopKf6ILq4dH+PGJlMhj41n/jZl+Pq7aL98Ae4rSPbbNfW2UBn2VbUEfHEz75CJHNBCBIioU8An8dNT/UhGrb9DY/DSmhcOonFa0govgptdIp/jt1lNWLrrEefXtRvTXpvYzmq0Ai00cP3WvH6fGw/cgKvz8eqeWfWvBtr+tZUR+UsGPAcTVQSCfPWIZPLaSv5AFtX07CvYWk9iaFiJ7rYdOIKV/T7FCEIQmCJ38Zx5HX1tbw1Nx/H0nICVVgU6Su+2Le6ZBCm+mMo1DrCEs9sauG2mrB1NROTt/S8ywEPVtbT0dPLmkWzCNX2TZ84etpOXYBdPORSR5U2nITitXRVfoqhbDsRmXPQpxX2r2iVJMyN5RhrSwhPySUqZ+GkXp4oCFORSOjjxGU10n54MwAKZQi62DQS5189ZDJ3WbqxdTb0Je6zRufmpnKUah2h8ZnDvl5VUwfH69tYMiuLhOi+TRN8Xg/dJ/ehiYgnNHHGsM+XK1XEzlqBqf4YxtojuC09ROctRa5QIUkSxprDmJsqiMiYTUTGbJHMBSEIiSmXcaLSRRCZVUxs0Sq8bicRGbOHXT1hqjuKShvebxmhx2k7VayTN+yWcJ3GXvaU1zIjNZ7ctDOvYW4oxeO0Ep27eEQJWCaTEZk5h7jCy7B3t9BesgW33Uz3iT2YmyqInrGAyMw5IpkLQpASCX2cyGQywpJmYqw+hEobTmRW8ZDHOnu7sHU1o88o6rd8sLfpODK5ot8F0nPZnS62Hj5BtD6UJQVnGn+5LD2YG8uJSC/qt7HFSOhi00koXoPX7aRm8/OY6kuJzV9OeEr+BZ1HEISJJRL6ODLVHcVtMxGTt3zYEbap7igqnb7ftIrP48LSVkV4cu6Quwh5fT62Hj4BwKriXBSKvn9OSfLRfWIvSp1+RCtjBqPUhKEI0eDzuJF8Xnxet79SVBCE4CQS+jhxWXr8c87DVRk6TZ3Yu1tOzUufNTpvOYnk8xGekjfkc/dX1NFltrJqXi46zZmlg5aWE7gs3cTMXDLsH5KheF0OOo7+B4+9l8yrvkr0zIV0n9xP98l9SD6xw7ogBCtxUXScqEIjiSm4BF1s2rDHGeuPEhIagS4uw3+f5PPS23yc0ISsIfuiVDa0c6KxnWWFOcRFhvvv9zisGGuPEJY0Y1QbM3gcVjqOfYzP4yJh7mpCwqLRRiaiCo2i++Q+3DYTcbMum9T9WgRhqhIj9HEik8kIjcsYtqTeYezA0dNGxDkXGq3tNfjcTvSpA7swAnT0mNl/vI689ERmpp1ZNSNJEt1V+5ErQ4jMmnfBMbttJtqPfIjk85Iw96p+/VzCEnNImHsVHoeFtkObcfZ2XfD5BUEYXyKhB4gkSZjqjhASFoU2Ju2s+32YmyrQxqah0ukHPM/qcLKt5CSxEWEszM/o95jN0IC9q5moGQuHnHcfirO3i/aSLcgVKhKK1wz62mp9LInz1qEI0dJesgVre+0FvYYgCONLJPQAcRrbcJg6BozO7V1NuG29g47OvV4f2w6fQCaTsbI4F8VZu7x73U56qg6gi0077zTPuRzGdjqOfoRSG0783KuG7bWuVOtIKL6K0PgMDMc/pafmMJLku6DXEwRhfIg59ACQJAlj3VHU4TH9yvklScLcUI4mMh61PnbAc/aU19DTa2PtkkK06v4jcGPt4b7y/hkLLygWm6ERQ8UuNBHxxBZeNqJuiTK5on9zL2vPBTf3EgRh7IkRegA4elpxmg0DRudOUwfO3q5Blxoeb2ijurmTpYXZxEaE9T+fsR1LazWRWfNGvJMRgKWtGkP5DnQxKcQVrbyg1rcDmnuVjLy5lyAI40Mk9Al2eu5crY9FE5XU7zFzYzkhYZFoovrvcNTWbeLA8XoKMpPISem/ckXyeek+uRd1RBxhScOX9/d7raYKuir3EJqYQ0zBJaNa3ghnNfeS9TX38rrsozqPIAgXTyT0CWbvbsbZ2z2ghN5l6cHe3YI+dVa/+y12B9sOnyQhSs+C3IwB5zPVl+JxWPvWnI+gJF+SJIy1JfRUHyIivZDomYvPu7nF+Zxu7hU9c7FYzigIASTm0CdQ3+j8aN8ceWT//SnNTeUoNTp0cWd2OfJ4vWw9fAKVUs6K4pnI5f0Tdl95fxn69KIRbUsnST66T+7H0lpFVPa8UVeRDkauVJ23gZggCONLjNAnkN3QiMvSQ0TG3H6jaY/Dgq2jHn1qgX/qQ5IkdpfWYLI6WDUvD01I//ntvuS8D6VOT0R64XlfW/J56arYhbWtmpi8pWOazAVBCA4ioU8QSZIw1R9FE5WIJrJ/C93e5uN9I9zEHP995XWt1LYauKQoh2h96IDzWVpO4jQb+qZMzjP/7fO66Szdhq2ridhZlxJ21usIgjB1iIQ+QWyd9bisJiIz5vS73+t2YGmtIiw517/KpMVg5FBlA0VZKWQmxQw4V195fwnhyTOH7K9+5vxOOo5+jNPcSXzR5ehiB25cLQjC1CDm0CeAJPkw1R9DG508oL+KpeUkAOHJfU24zFYH24+cJCk2guKZAwuEJEmip2o/MqVq2Ja80NdPvfPYx3hdDuLnrkYdPvCPgyAIU4cYoU8Aa0cdbpuZiMz+o3Of10NvcyWhiTkoQjS4PV62Hq5ErVJy2ZyBF0Ghbx7e1tVM9IyFw27O7LaZaS/5EJ/XTULxVSKZC8I0IBL6OJN8Xsz1pehiUwckVWt7DT6PC31KPpIk8emxaix2J6vm5aEOGfjhyet20l21H11s6rBTJy5LN+1HtiCTK0iYu+aCN7gQBGFyEgl9nFnba3Hbe4nImN3v/tNNuHRx6Si14ZTWtFDf3sWlc2YQFT54taextuRUef+iIV/PYeyg/ch/+nquzL0KpWbgBVVBEKYmkdDHkeTzYmooRReX3q8VLYCtswGP3YI+dRZNHT2UnGxk7oxU0hMG3wyjr7y/isis4iHL++1dzXQc+5iQsGji56xGEaIZ8/ckCELwEgl9HFnaavA6bYOMziXMjRVoo5Kwy7TsOHqSlPhI5uSkDnqevvL+faj1sUPuL2ptr6WzbBva6CTiZ19+we1zBUGY/ERCHyeSz4u5oRRdXAYhoZH9HnMa23BZutEk5/LJ4Uq06hAunTNjyNJ9U0MpHoeFmNylgx7T23wcw/FPCU3IInbWZaPuyyIIwuQmli2OE4/DikypIiKjaMBj5sYKQsKiOFDfi93p4tplswlRDv5P4bIaMTeWo0+bNaC8v69Y6Rim+mPoUwuIzJ43on4ugiBMTUE1Qt+4cSOLFi0iPDyc+Ph4brzxRiorK4d9zqZNm5DJZP2+NJrAzx2rdHqSFlw7YIWJq7cbe08rLb5omjtNXDZnJvrQwRtaSZJE94m9KDVhRKQXDXisp/oApvpjRGYVi2QuCEJwJfRt27Zx3333sWfPHrZs2YLb7WbNmjVYrdZhn6fX62ltbfV/1dfXT1DEwxsswZqbyun1yCkz+CiemUZqfNSQz7e0ni7vX9JvGkXyeek6/imWlpPE5C4mIr1QJPMJJkk+fB53oMMQhH6Caspl8+bN/W5v2rSJ+Ph4Dh48yIoVK4Z8nkwmIzExccjHg4XH3ktXUzXHLXoyMmMpyk4e+linDWNtCWFJM/r1fvF5PRjKd+AwthFTcAmhcQNb6gpjr7SmGa9PYu6MvgvXXSf2YTc0krrsJnHNQggaQTVCP5fJ1LcDTnT04Ev5TrNYLGRkZJCWlsYNN9xAWVnZkMc6nU7MZrP/y2KxjGnMw+mqK6O2w4Q6NoPls3OGHFX7y/vlCqKy5/nv93lcdBz7GKepnbjCVSKZTxCL3cHWkpNsLzmB2erAbTNjrDmIpa0KS2tVoMMTBL+gTeg+n48HH3yQSy65hKKigRcWT8vLy+Oll17irbfe4n//93/x+XwsX76cpqamQY/fuHEjERER/q+VK1eO11vox+2wU350PzZtEqsWzEKlHHpUZ+9qxGZoInrGIn95v9dlp/3IFjw2E/FzVqONThry+cLFc3k8bC85gcli51h1MwZjL11mK0ermzDVH8Nl6QGgp+Ygks8b4GgFoU/QJvT77ruP0tJSXn311WGPW7ZsGevXr6e4uJiVK1fyz3/+k7i4OF544YVBj9+wYQMmk8n/tW3btvEIf4CS/TvptTpYvHwl4bqhL9r6PC66qw6gi0lBG9vXnMtj76W9ZAtet5P4uVcN2EBaGHvH69vYebSanceqOHiikTCtmnCdhqMVlXScPIhSE446Ih5bZ4MYpQtBI6jm0E+7//77eeedd9i+fTupqYMX2wxFpVIxb948qqoG/yVTq9Wo1Wd2pw8LCxv0uLFU29ROS9VRMvLmkpo4fLtbY20JktdD1MzFyGQyXJYeOko/QS5Xkjj3KpTa8HGPd7pzeTwcOF6Pxe5ge8lJ5HIZM07t5WqvK6c7pImE5DS8Tjtel52emoOEJc0Qc+lCwAXVCF2SJO6//37+9a9/8fHHH5OVlXXB5/B6vRw7doykpOCYkug2WzmwfxfRYSEUzl8+7LEOYwe9LSeJzJyLUq3Daeqk/cgWFCoNCcUimU+U4/VtNHcayUqKob3bTK/NQYvBRIvBhAYn3R41yBXI5HLUEQn4PB68LkegwxaE4Bqh33fffbzyyiu89dZbhIeH09bWBkBERARabd9a7fXr15OSksLGjRsB+NGPfsTSpUuZMWMGRqORX/ziF9TX13PXXXcF7H2c5nC52XqogkhnG3lzilFph/400Ffev7evvD95JvbuFgzl21GFRRNftGrYVrnC2Dk9Olcq5KhDVOSmx+P2+Lh++RwiwrVI0mI0IUoSo/VnXdSWiWWjQlAIqoT+3HPPAbBq1ap+97/88st85StfAaChoQG5/MwHi56eHu6++27a2tqIiopiwYIFfPrpp8yaFdg9M30+ie0lJ5HMbWTGhRKZPvSFXQBTQxkeh4XE+Vdj62ygq3I3mqgkYgsuRa4Iqn+mKa2hrZueXisut4fa1i6QJFRKJU63h6wkce1CCG5BlSkkSTrvMVu3bu13++mnn+bpp58ep4hGz2yzY+y1MjfSQURUOiFhQxcQua0mzI1l6FNn4TR10lO1H118Rl/vFjEvO6HS4qP5r0uKkej/s5gYLXrKC8EvqBL6VBIZpuPqOYl0l59Anzb0pwVJkug6sQeFuq9veffJfYSn5BGVs0B8jA8AdYiSmWnDX7gWhGAVVBdFpxpby3HU4TGoh9nI2dJahdPciVKtw9RQSmTmHJHMhVHr7u7mS1/6Enq9nsjISL761a+et3hu1apVA/ohfe1rX5ugiIWxJEbo48RlNeIwdhBXeNmQydnjtGGsOYTk8+IwthM9YyHhKXkTHKkwlXzpS1+itbXV3wvpjjvu4J577uGVV14Z9nl33303P/rRj/y3dbrBN1ERgptI6OMkJDSSxHlrCQkfum1B94m92DrrUUcmEltwCaHxmRMXoDDlVFRUsHnzZvbv38/ChQsB+O1vf8s111zDL3/5S5KTh+4dpNPpJkU/JGF4YsplHKn1schkg3+LLW3VGCp2oNTpSZh7pUjmwkXbvXs3kZGR/mQOsHr1auRyOXv37h32uX/729+IjY2lqKiIDRs2YLPZxjtcYRyIEXoAuG1mGne+hlwZQvKSz6CNTAh0SEIAWCwWzGaz//a5VcwXqq2tjfj4/tdrlEol0dHR/pqOwXzxi18kIyOD5ORkjh49yve+9z0qKyv55z//OepYhMAQCX2CeRwW6rf+Ga/bQfaae0Qyn8bObQz3+OOP88QTTww47vvf/z4///nPhz1XRUXFqOO45557/P8/e/ZskpKSuPLKK6muriYnJ2fU5xUmnkjoE8htNdGy/984jB2kLP0MupgL61MjTC3btm2juLjYf3uo0fm3vvUtf2HdULKzs0lMTKSjo6Pf/R6Ph+7u7guaH1+yZAkAVVVVIqFPMiKhTxCn2UDH0Y+xdzUTPXMxkVnFgQ5JCLCwsDD0ev15j4uLiyMuLu68xy1btgyj0cjBgwdZsGABAB9//DE+n8+fpEeipKQEIGj6IQkjJy6KTgBHTysdRz/C4+hFE5NCbMGlQ14sFYTRKigoYN26ddx9993s27ePXbt2cf/99/P5z3/ev8KlubmZ/Px89u3bB0B1dTVPPfUUBw8epK6ujn//+9+sX7+eFStWMGfOnEC+HWEUxAh9nNkMDRgqdqHUhuPzuolILxq2DYAgXIy//e1v3H///Vx55ZXI5XJuvvlmfvOb3/gfd7vdVFZW+lexhISE8J///IdnnnkGq9VKWloaN998M4888kig3oJwEURCH0eW1iq6T+5DG5uG12lFpdMTkTE70GEJU1h0dPSwRUSZmZn9eialpaVN2CYvwvgTn/vHicvSQ9eJvac2eU7Aae4iesZi0WxLEIRxI0bo4yQkLIqEuatRaMJoO/guYYnZaKJEJZ4gCONHjNDHkSYyAWPNIWRyBZHZ8wMdjiAIU5xI6OPIZmjE1tlAVM4CFKrRVwAKgiCMhEjo48TncdNTdQBtdDK6uIxAhyMIwjQgEvo48XmcKLXhRM9cJHqbC4IwIcRF0XGi1ISRMHd1oMMQBGEaESN0QRCEKUIkdEEQhClCJHRBEIQpQiR0QRCEKUIkdEEQhClCJHRBEIQpQiR0QRCEKUIkdEEQhClCJHRBEIQpQiR0QRCEKUIkdEEQhClC9HI5paKiItAhCKckJSWN+Y7zra2ttLa2juk5R0v8rAnjZdon9KSkJFauXMltt90W6FCEUx5//HGeeOKJMT3nCy+8wJNPPjmm57wYK1euHPM/WoIgk87eMXaaGq/Rm8ViYeXKlWzbto2wsLAxP38wGcv3Giwj9PH89xuP9ygIIqGPI7PZTEREBCaTCb1eH+hwxtVUfK9T8T0JU5u4KCoIgjBFiIQuCIIwRYiEPo7UajWPP/44avXU3yB6Kr7XqfiehKlNzKELgiBMEWKELgiCMEWIhC4IgjBFiIQ+SdTV1SGTydi0aVOgQxEEIUhNyYReXV3NvffeS3Z2NhqNBr1ezyWXXMKzzz6L3W4ft9ctLy/niSeeoK6ubtxeYyR+8pOf8F//9V8kJCQgk8n8VZcymWxEX1u3br3oGGw2G0888cQFnWuouIcy1d6PIFysKVf6/+6773LLLbegVqtZv349RUVFuFwudu7cyXe+8x3Kysr4wx/+MC6vXV5ezpNPPsmqVavIzMwcl9cYiUceeYTExETmzZvHBx984L//r3/9a7/j/vKXv7Bly5YB9xcUFFx0DDabzV9qv2rVqhE9Z6i4hzLV3o8gXKwpldBra2v5/Oc/T0ZGBh9//HG/0ur77ruPqqoq3n333QBGeIYkSTgcDrRa7Zifu7a2lszMTAwGA3Fxcf77z+1Xs2fPHrZs2RI0fWyGinsoU+39CMLFmlJTLv/zP/+DxWLhT3/606B9MmbMmMEDDzzgv+3xeHjqqafIyclBrVaTmZnJD37wA5xOZ7/nZWZmct1117Fz504WL16MRqMhOzubv/zlL/5jNm3axC233ALA5ZdfPuDj/ulzfPDBByxcuBCtVssLL7wAQE1NDbfccgvR0dHodDqWLl16UX94LubTgc/n45lnnqGwsBCNRkNCQgL33nsvPT09/Y47cOAAa9euJTY2Fq1WS1ZWFnfeeSfQN99/OoE9+eST/u/F+aYcxuNTzVR7P4IwnCk1Qn/77bfJzs5m+fLlIzr+rrvu4s9//jOf/exn+da3vsXevXvZuHEjFRUV/Otf/+p3bFVVFZ/97Gf56le/yu23385LL73EV77yFRYsWEBhYSErVqzgm9/8Jr/5zW/4wQ9+4P+Yf/bH/crKSr7whS9w7733cvfdd5OXl0d7ezvLly/HZrPxzW9+k5iYGP785z/zX//1X7zxxht85jOfGbtv0Ajce++9bNq0iTvuuINvfvOb1NbW8rvf/Y7Dhw+za9cuVCoVHR0drFmzhri4OL7//e8TGRlJXV0d//znPwGIi4vjueee4+tf/zqf+cxnuOmmmwCYM2fOhL6Xqfh+BGFY0hRhMpkkQLrhhhtGdHxJSYkESHfddVe/+7/97W9LgPTxxx/778vIyJAAafv27f77Ojo6JLVaLX3rW9/y3/f6669LgPTJJ58MeL3T59i8eXO/+x988EEJkHbs2OG/r7e3V8rKypIy/3979x4T1fH2Afy7LLDL/VJBsOreUAvSiMUiBXW9FKxABSN4iQqoiE1JqBFrvVapEqEVK6ExcWmjdKu2UkMT0UpTL0WDBk1LUkQBEamiRbRyE1GB+f3Rd8/LYRfZRRB6eD7JJt05c+bMbOyzh9k588jlrL29nTHGWFVVFQPADhw4YNT4GGOsrq6OAWDbtm0zeDwhIYF1/idw/vx5BoAdOnSIV+/UqVO88tzcXAaAXb58udfXfpl+d0do4yHEVIKZcmlsbAQA2NnZGVX/5MmTAIC1a9fyypOSkgBAb8rDy8sLU6dO5d67uLhg3LhxuHnzptF9VCgUmD17tl4//Pz8MGXKFK7M1tYW8fHxuHXrFkpLS41u/2Xl5OTAwcEBQUFBePDgAffy9fWFra0tzp49CwBwdHQEAOTl5eH58+evrH+mEtp4COmJYAK6bnvTpqYmo+pXV1fDzMwMHh4evHI3Nzc4OjqiurqaVz569Gi9NpycnPTmYl9EoVAY7Me4ceP0ynVTNV370Z8qKirQ0NAAV1dXuLi48F7Nzc24f/8+gH+TM8yfPx/JyckYNmwYwsPDceDAAb3fHgaa0MZDSE8EM4dub2+PESNGoKSkxKTzRCKRUfXEYrHBcmbCVjj9saKlL3V0dMDV1RWHDh0yeFz3w6BIJMKPP/6IS5cu4fjx48jPz8eKFSuQnp6OS5cuDZpkHkIbDyE9EUxAB4CwsDBoNBpcvHgR77zzzgvrymQydHR0oKKigvfDZW1tLerr6yGTyUy+vrFfDl37UVZWpld+/fp17virolKp8OuvvyIwMNCoLx9/f3/4+/sjJSUFhw8fxpIlS/D9998jLi6uV59FXxPaeAjpiWCmXABg/fr1sLGxQVxcHGpra/WOV1ZWIiMjAwAQEhICANi7dy+vzp49ewAAoaGhJl/fxsYGAFBfX2/0OSEhISgqKsLFixe5ssePH0Oj0UAul8PLy8vkfvTWggUL0N7ejh07dugda2tr48b16NEjvb9MfHx8AICbprC2tgZg2mfR14Q2HkJ6Iqg7dJVKhcOHD2PhwoXw9PTkPSlaWFiInJwcxMbGAgAmTJiAmJgYaDQa1NfXQ61Wo6ioCNnZ2YiIiMCMGTNMvr6Pjw/EYjHS0tLQ0NAAiUSCmTNnwtXVtdtzNmzYgCNHjmDOnDlITEyEs7MzsrOzUVVVhWPHjsHMzPTvXK1Wi+rqarS0tAAACgoKsHPnTgDAsmXLur3rV6vVWL16NXbt2oXi4mIEBwfDwsICFRUVyMnJQUZGBiIjI5GdnY19+/Zh3rx5UKlUaGpqQlZWFuzt7bkvSisrK3h5eeGHH37A2LFj4ezsDG9vb3h7e/d5v7sjtPEQ0qMBXmXTL8rLy9mqVauYXC5nlpaWzM7OjgUGBrLMzEzW2trK1Xv+/DlLTk5mCoWCWVhYsFGjRrGNGzfy6jD275LD0NBQveuo1WqmVqt5ZVlZWUypVDKxWMxbwthdG4wxVllZySIjI5mjoyOTSqXMz8+P5eXl8eqYsmxRrVYzAAZfnZdUdl3mp6PRaJivry+zsrJidnZ27M0332Tr169nd+/eZYwx9vvvv7PFixez0aNHM4lEwlxdXVlYWBi7cuUKr53CwkLm6+vLLC0tjVq2Z2y/uyO08RBiKkpwQQghAiGoOXRCCBnKKKATQohAUEAnhBCBoIBOCCECQQGdEEIEggI6IYQIBAV0MmRR4m0iNEMuoB88eBAikQhSqRQ1NTV6x6dPn/7Cp//6w+nTp7FixQqMHTsW1tbWUCqViIuLw7179wzWLywsxJQpU2BtbQ03NzckJiaiublZr95QGishRGCP/pvi6dOnSE1NRWZm5kB3BZ988gn++ecfREVFYcyYMbh58ya++uor5OXlobi4GG5ublzd4uJizJo1C56entizZw/u3LmD3bt3o6KiAj///LPB9ofSWE0hk8nw5MkTWFhYvHRbhAwKA/2o6qt24MABBoD5+PgwiUTCampqeMfVajUbP378K+3Tb7/9xmUm6lwGgG3evJlXPmfOHObu7s4aGhq4sqysLAaA5efn8+oOpbESQgSUschUmzZtQnt7O1JTUwe6K5g2bZreJlzTpk2Ds7Mzrl27xpU1NjZyWe11CT0AIDo6Gra2tjh69KjB9oU81u3bt0MkEqG8vBxLly6Fg4MDXFxcsHXrVjDGcPv2bYSHh8Pe3h5ubm5IT0/n2jI0hx4bGwtbW1vU1NQgIiICtra2cHFxwbp169De3s7VO3fuHC8J+Iva/Pvvv7F8+XKMHDkSEokE7u7uCA8Px61bt17ikyRE35AN6AqFAtHR0cjKysLdu3dNPr+lpYWX1qy7lykZjTprbm5Gc3Mzhg0bxpX9+eefaGtrw6RJk3h1LS0t4ePjgz/++MNgW0NhrAsXLkRHRwdSU1MxefJk7Ny5E3v37kVQUBBef/11pKWlwcPDA+vWrUNBQcEL+9Pe3o7Zs2fjtddew+7du6FWq5Geng6NRtOr8c2fPx+5ublYvnw59u3bh8TERDQ1NeGvv/7qVXuEdGug/0R41XTTEJcvX2aVlZXM3NycJSYmcseNnYbYtm1btzvpdX7JZLJe9XPHjh0MADt9+jRXpktC3TlZtU5UVBRzc3MbcmPV9S0+Pp473tbWxkaOHMlEIhFLTU3lyh89esSsrKxYTEwMY8zwDpYxMTEMAPvss89415w4cSLz9fXl3p89e9bgrold23z06BEDwL744guTPhNCemPI/igKAEqlEsuWLYNGo8GGDRvg7u5u9LnR0dG8xM7d6U3auYKCAiQnJ2PBggWYOXMmV/7kyRMAgEQi0TtHKpVyxw0R+ljj4uK4/xaLxZg0aRLu3LmDlStXcuWOjo5GJ/b+4IMPeO+nTp0KrVZr3KA6sbKygqWlJc6dO4eVK1fCycnJ5DYIMdaQDugAsGXLFmi1WqSmpnLZjIyhVCqhVCr7vD/Xr1/HvHnz4O3tja+//pp3TBcwDSUvbm1t7TGgCnmsXZN4Ozg4QCqV8qZxdOUPHz58Yb+kUimXb1TH1ITgOhKJBGlpaUhKSsLw4cPh7++PsLAwREdH81b0ENIXhnxAVyqVWLp0KXfnaizdvG9PxGKxXnDozu3btxEcHAwHBwecPHkSdnZ2vOO6u2pDa7bv3buHESNGvLB9IY/VUBLv3ib27u68zrrLMdr5h1OdNWvW4P3338dPP/2E/Px8bN26Fbt27cKZM2cwceLEHq9FiLGG7I+inW3ZsgVtbW1IS0sz+pzdu3fD3d29x9fbb79tVHsPHz5EcHAwnj59ivz8fINTIt7e3jA3N8eVK1d45c+ePUNxcTGXB/NFhtJY+5Nu6qRrjtHq6mqD9VUqFZKSkvDLL7+gpKQEz5494624IaQvDPk7dODf/9mWLl2K/fv3QyaTwdy854+lL+eVHz9+jJCQENTU1ODs2bMYM2aMwXoODg5499138d1332Hr1q3cXa1Wq0VzczOioqJ6vNZQGmt/kslkEIvFKCgoQEREBFe+b98+Xr2WlhaYmZlBKpVyZSqVCnZ2dgankwh5GRTQ/8/mzZuh1WpRVlaG8ePH91i/L+eVlyxZgqKiIqxYsQLXrl3jrce2tbXlBYyUlBQEBARArVYjPj4ed+7cQXp6OoKDg/Hee+8Zdb2hNNb+4uDggKioKGRmZkIkEkGlUiEvLw/379/n1SsvL8esWbOwYMECeHl5wdzcHLm5uaitrcWiRYsGqPdEsAZ6mc2r1nkpX1e6JWuv+ulJmUxm0lLA8+fPs4CAACaVSpmLiwtLSEhgjY2NevWGwlh1yxbr6up458XExDAbGxu99jov1exu2aKh83TX6ayuro7Nnz+fWVtbMycnJ7Z69WpWUlLCa/PBgwcsISGBvfHGG8zGxoY5ODiwyZMns6NHjxr7kRFiNEoSTQghAkE/ihJCiEBQQCeEEIGggE4IIQJBAZ0QQgSCAjohhAgEBXRCCBEICuiEvAKUkJq8ChTQyaBTWVmJ1atXQ6lUQiqVwt7eHoGBgcjIyHjhFsEvq7S0FNu3bx/wTEIpKSmYO3cuhg8fDpFIhO3btw9of8h/Bz36TwaVEydOICoqChKJBNHR0fD29sazZ89w4cIFfPzxx7h69WqvMwf1pLS0FMnJyZg+fTrkcnm/XMMYW7ZsgZubGyZOnIj8/PwB6wf576GATgaNqqoqLFq0CDKZDGfOnOHtwpiQkIAbN27gxIkTA9jD/8cYM2oP+t6oqqqCXC7HgwcPjN6OmBCAplzIIPL555+jubkZ33zzjcEtdT08PPDRRx9x79va2rBjxw6oVCpIJBLI5XJs2rRJbxdDuVyOsLAwXLhwAX5+fpBKpVAqlfj222+5OgcPHuR2cJwxYwZEIhEvCbSujfz8fEyaNAlWVlbYv38/AODmzZuIioqCs7MzrK2t4e/v/1JfPAP51wH5b6OATgaN48ePQ6lUIiAgwKj6cXFx+PTTT/HWW2/hyy+/hFqtxq5duwzuYnjjxg1ERkYiKCgI6enpcHJyQmxsLK5evQoAmDZtGhITEwEAmzZtglarhVarhaenJ9dGWVkZFi9ejKCgIGRkZMDHxwe1tbUICAhAfn4+PvzwQ6SkpKC1tRVz585Fbm5uH3wqhJhggDcHI4QxxlhDQwMDwMLDw42qX1xczACwuLg4Xvm6desYAHbmzBmuTLfDY+eE0/fv32cSiYQlJSVxZbrE1F0TP3du49SpU7zyNWvWMADs/PnzXFlTUxNTKBRMLpez9vZ2xpjhnR17UldXxwCwbdu2GX0OGdroDp0MCo2NjQCgl4quOydPngQArF27lleelJQEAHpTHl5eXpg6dSr33sXFxeiE0ToKhQKzZ8/W64efnx8vAYitrS3i4+Nx69YtlJaWGt0+IS+LAjoZFOzt7QEATU1NRtWvrq6GmZkZPDw8eOVubm5wdHTUSwXXNYk0YHriZ4VCYbAf48aN0yvXTdV0l5KOkP5AAZ0MCvb29hgxYgRKSkpMOq+7ZM1d9TZhdGf9saKFkL5EAZ0MGmFhYaisrMTFixd7rCuTydDR0YGKigpeeW1tLerr6yGTyUy+vrFfDl37UVZWpld+/fp17jghrwoFdDJorF+/HjY2NoiLi0Ntba3e8crKSmRkZAAAQkJCAAB79+7l1dmzZw8AIDQ01OTr29jYAADq6+uNPickJARFRUW8L6HHjx9Do9FALpfDy8vL5H4Q0lv0YBEZNFQqFQ4fPoyFCxfC09OT96RoYWEhcnJyEBsbCwCYMGECYmJioNFoUF9fD7VajaKiImRnZyMiIgIzZsww+fo+Pj4Qi8VIS0tDQ0MDJBIJZs6cCVdX127P2bBhA44cOYI5c+YgMTERzs7OyM7ORlVVFY4dOwYzM9PvmbRaLaqrq9HS0gIAKCgowM6dOwEAy5Yto7t+0r2BXmZDSFfl5eVs1apVTC6XM0tLS2ZnZ8cCAwNZZmYma21t5eo9f/6cJScnM4VCwSwsLNioUaPYxo0beXUY+3fJYWhoqN511Go1U6vVvLKsrCymVCqZWCzmLWHsrg3GGKusrGSRkZHM0dGRSaVS5ufnx/Ly8nh1TFm2qFaru02kbWhJJSE6lCSaEEIEgubQCSFEICigE0KIQFBAJ4QQgaCATgghAkEBnRBCBIICOiGECAQFdEIIEQgK6IQQIhAU0AkhRCAooBNCiEBQQCeEEIGggE4IIQJBAZ0QQgTif0eS8RLyJJrAAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "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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", 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", 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" + ] + }, + "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": [ + "
" + ] + }, + "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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", + "text/plain": [ + "
" + ] + }, + "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": [ + "
" + ] + }, + "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": [ + "
" + ] + }, + "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": [ + "
" + ] + }, + "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": [ + "
" + ] + }, + "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": [ + "
" + ] + }, + "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": [ + "
" + ] + }, + "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": [ + "
" + ] + }, + "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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", 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", 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" + ] + }, + "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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lUpGenk5zczNvvvnmSyfUJJEmIfEIuru7OXDgAN3d3bz66qtMnz79RS9J4hHcb3Y7ZcoUTE1N2bBhA9bW1pw8eRKlUklKSsqoX+rm5uZs2LCBnTt3kp6e/sC6M5lMxsKFC9Hr9Zw+fRoTE5OHij8JCYl7GAwGqquryczM5PTp07S2thIdHc1PfvIT4uLiHnhiNPT+ubm5XLlyBY1GQ0pKCgaD4aU7eZZEmoTEQzDObrS1teX999/Hw8PjRS9JYgyMZnbr4eGBTCZj+fLl2NjYcOnSJRQKBUuXLh1VqPn6+pKSkkJaWhphYWF4e3s/8LmWLFmCXq/n5MmTmJqaEhcX96xfooTEpKS3t5f8/Hxyc3OpqKigtbUVb29v/vIv/5Lk5OQxRcKqq6s5d+4cbW1txMbGsmjRIhwcHJ7D6p8/UnfnOCN1d74cGAwGrl69yrVr14iIiGDdunVjcq+XmFioVCq++OILlEol3/ve94Z9kWdnZ3PmzBliY2NZu3btqGfuer2eP/3pT+j1ej744INH1sScOnWKnJwcNmzYQExMzDN5TRISkw2dTkdZWRl5eXlUV1ej1+uRy+UAzJ49mxUrVmBnZ/fIx+nq6uL8+fPcvXsXf39/li9fjq+v77Ne/gtFEmnjjCTSJj8KhYIjR45QXV3N4sWLmTNnzqhndy9roerLRn9/Pzt37sTKyop33nlnmNguLCzk2LFjhIaGPnDeZ3t7O5999hnJycksWbLkoc8lCALHjx8nPz+fTZs2PbRBQULiZaetrY28vDwKCgpQKBT4+flhaWlJTU0NdnZ2rFq1imnTpj3ycZRKJdeuXePmzZs4ODiwZMkSIiMjvxPfv1K6U0JiCE1NTRw8eBCdTsebb75JYGDgiNsoFAqxo2/FihUvYJUSj4ODgwM7duxg586dHDhwQDS7BcY079PDw4OFCxdy8eJFpk2bRkBAwAOfSyaTsWbNGvR6vTiUPSIi4pm+PgmJiYRaraaoqIjc3FyampqwtbVlxowZ+Pv7k5GRQVVVFYmJiSxatOiR2QmDwcDt27e5evUqOp2O1NRUkpOTRz2ZelmRImnjjBRJm5wIgkBOTg5nzpzB29ub1157bUSNgyAI5ObmcvHiRQRBYNGiRSQmJr6gFb/8GAwG1Go11tbW4/J4o5ndGqmvr2fv3r04OzuzY8cObG1tR6zlyy+/ZGBggO9///tYWFg8cu1Hjx6ltLSULVu2EBYWNi6vQUJiIiIIAg0NDeTm5lJcXIxOpyMkJIT4+HiCg4PJzMzk2rVrODs7s2bNGqZMmfLIx6ysrOTcuXN0dnYyY8YMFi5ciL29/XN4NRMLSaSNM5JIm3xotVpOnTpFfn4+iYmJLFu2bETtUXNzM6dOnaKpqYkZM2awePHiMdVQSDw5R48epa+vjzfffHPcOrZKSko4dOgQs2fPZunSpcOua2tr4+uvv8bS0pI33nhjROt/d3c3n376KbGxsaxevfqRz6XX6zl8+DDl5eVs27aNkJCQcXkNEhITBblczp07d8jLy6OzsxNnZ2fi4uKYMWMGDg4ONDY2cvz4cTo7O0lJSWHevHkPreuEe2Ogzp07R2VlJVOnTmXZsmUPbNr5LiCJtHFGEmmTi56eHg4cOEBXVxerV68mNjZ22PVKpZJLly6Rk5ODh4cHq1atemi6S2L8qK+v58svvyQ5OXmEoHoajLM3ly9fTnJy8rDrhs773LFjx4hu3lu3bnHq1Cl27NgxJtGl1+s5ePAgVVVVvP766wQFBY3b65CQeBEYDAYqKyvJzc2lvLxcTOnHxcURGBiITCZDo9Fw6dIlsrOz8fHxYe3atXh6ej70cRUKBVevXuX27ds4OjqydOlSwsPDvxN1Zw9DEmnjjCTSJg8VFRUcOXIEGxsbtmzZMuxLRBAE8vLyuHjxInq9noULF5KYmPhI7x6J8SUzM5Nz586xZcuWca3tunDhAhkZGaMW9z9s3qcgCOzevZv29nY++uijMaVidTod+/fvp66ujh07dowp1SMhMdHo7u4mLy+P/Px8BgYG8PLyIj4+nunTpw/bDyorKzlx4gQKhYKFCxeSlJT00O9NvV7PrVu3uHr1KoIgMG/ePJKSkh4ZcfuuIIm0cUYSaRMfg8FAWloaaWlpTJs2jQ0bNgwrYG1paeHUqVM0NjYSGxvLkiVLpNTmC0IQBA4ePEh1dTUffvjhuA20FwSBo0ePUlJSwhtvvMHUqVOHXa9Sqdi7dy+tra1s2bKF4OBg8br+/n4++eQTwsLCxjwaTKvVsm/fPhobG3njjTfw9/cfl9chIfEs0Wq1lJaWkpeXR01NDZaWlsTExBAXF4e3t/ewKJdCoeDs2bMUFBQQFBTEmjVrcHZ2fuBjC4JAeXk558+fp7u7m4SEBFJTU0fUg37XkUTaOCOJtImNQqHg6NGjVFVVsXDhwmGO80qlksuXL3P79m08PDxYuXKlFPWYAKhUKv7whz9gYWHBe++9N26dXTqdjj179tDS0iKa3Q5Fq9WKAvH+eZ8FBQUcPXqU1157jcjIyDE9n1arFZ/vzTfffOn9nSQmLy0tLaJ1hkqlYurUqcTFxREZGTli/xMEgaKiIs6cOYMgCCxbtozY2NiHpinb2to4d+4c1dXVBAUFsWzZskemQ7+rSCJtnJFE2sSlubmZgwcPotFoePXVV8XoiCAI5Ofnc+HCBfR6PampqcyaNUtKbU4g2tra+OMf/yjO9BsvHmZ2C/dSMd9++y2FhYWsWrVKHPlkjPDV1dXx0UcfjTnSqtFo+Prrr+no6OCtt976ThdES0wsVCoVhYWF5Obm0tLSgp2dHTNmzCAuLg5XV9dR79PX18fJkyepqKggOjqa5cuXP3RfGBwc5MqVK+Tk5ODi4sKyZcsIDQ39ztedPQxJpI0zkkibmOTm5nL69Gk8PT157bXXcHR0BO6dMZ4+fZqGhgZiYmJYsmTJd7LNeyLS3t6OwWDAy8sLgPz8fL755hvWrVs3rmOXHmZ2C/cE2dmzZ7l58yaLFi0So6+Dg4N88skn+Pn5sXXr1jEfaFQqFV9//TXd3d28/fbbUgRB4oUhCAJ1dXXk5uZSUlKCwWAgNDSU+Ph4QkNDH3iiKggCt27d4uLFi1hZWT3SlFan03Hz5k2uXbuGTCZjwYIFJCYmvnRzNp8FkkgbZySRNrHQ6XScPn2a3NxcZs6cyfLlyzEzM0OpVHLlyhVu3bqFu7s7q1atklKbE4yDBw9SUVHBunXriI6OBuD48eMUFBTwve99TxRv40FHRwc7d+7E29t7mNmtEUEQuHbtGleuXBHtO2QyGWVlZezfv5/169czY8aMMT+fUqnkq6++or+/n7fffht3d/dxey0SEo9iYGCA/Px88vLy6O7uxsXFhfj4eGJjYx95ktrR0cHx48dpaGh4pCmtIAiUlZVx/vx5+vr6mDlzJgsWLMDGxuZZvKyXEkmkjTOSSJs49Pb2cvDgQdrb21m9ejUzZsxAEATu3LnDhQsX0Ol0LFiwgFmzZklndBMQrVbL8ePHKSws5JVXXmHRokXo9Xp27tyJRqPhgw8+GNd5qg8zuzUy2rzPY8eOUVZWxkcffSRGaMeCQqFg165dDA4O8vbbb+Pm5jZur0VC4n70ej0VFRXiYHMzMzMiIyOJj48nICDgkZFgvV7P9evXuXbtGk5OTqxdu/ahJ7YtLS2cO3eO2tpaQkNDWbp06TM7GWltbaWpqYmEhIRn8vgvEkmkjTOSSJsYVFZWcuTIEaysrNiyZQteXl60trZy6tQpGhoamD59OkuXLn3i1KZWq2VwcHCE4anE+CIIApmZmVy4cIGgoCA2bdqEUqnkD3/4A1OnTmXLli3jWs/yMLNbI/fP+9Tr9XzyySe4ubnxxhtvPNZ6BgcH+fLLL1GpVLzzzjvj1r0qMXHQaDQIgoClpeULef6uri5yc3O5c+cOcrkcHx8f4uPjiY6OHvNJzuOY0srlci5dukR+fj5ubm4sXbqU0NDQ8XxJIh0dHVy9epXi4mI8PDz48MMPX7oTbkmkPYR/+qd/4sc//jE/+MEP+N3vfjem+0gi7cViTEtdvXqVkJAQNm7ciEwm48qVK2RnZ+Pm5saqVatGWC6MlYGBAW7dusWtW7fw9vbmzTffHN8XIDEq1dXVHDp0CCsrK7Zu3UpPTw/79+9n6dKlzJkzZ1yf62Fmt0YqKio4ePAgPj4+bNu2jebmZr766itWrlzJrFmzHuv55HI5X3zxBTqdjnfeeUcS/i8ZZ8+epaysjLVr1z43M2ONRkNJSQl5eXnU1dVhbW0tWmc8TpnA45jS6nQ6MjMzSU9Px9TUlNTUVBISEp6JaOrp6eHq1asUFBTg4ODAggULiI2NfSmbvSSR9gBu3bolzm9MTU2VRNokQKlUcuzYMSoqKliwYAFz586lsLCQ8+fPo9Vqxa7NJ/nSaGtrIzMzk8LCQkxNTYmPjycpKemhPkAS44txOkR3dzfr1q2jubmZzMxM3nrrrXGvJ3yY2a2RhoYG9uzZI877TEtLIy8vjz/7sz97YDfcg+jv7+fLL79EEATefvvtx0qbSkxsenp6OH78ODU1NcycOZMlS5Y8k6iaIAg0NzeTm5tLUVERarWaoKAg4uLiiIiIeGxz2LGa0gqCQElJCRcuXKC/v5+kpCTmzZs3bjN3h9Lf38+1a9fIzc3FxsaGefPmER8f/1Ib30oibRTkcjnx8fF88skn/PKXv2TGjBmSSJvgtLS0cPDgQVQqFa+++ip2dnacPn2a+vr6J05tCoJAZWUlmZmZVFdX4+DgQHJyMvHx8eNaCyUxdjQaDcePH6eoqIg5c+bQ0NBAb28vH3744bgaDj/K7NZIW1sbu3fvxsLCgi1btrB//35sbGx49913H/usvq+vjy+++AITExPeeecdqcv4JUIQBG7fvs2FCxewtrZm3bp14xZVUygUFBQUkJeXR1tbGw4ODqJ1xpOcRD6OKW1zczNnz56lvr6eadOmsXTp0sc+QRkLg4ODpKenc/v2bSwsLEhJSSExMXHcPBMnMpJIG4W33noLFxcX/u3f/o0FCxY8VKSp1WrUarX4d35+PvPnz5dE2nMkPz+fkydP4u7uzrp168jLyxNTmytXriQwMPCxHk+n01FQUEBmZiYdHR34+Pgwe/ZsIiMjX7p6h8mIIAhkZGRw8eJFfHx86OzsxMfHhzfeeGNc0x2PMrs10tPTw9dff41Wq2XhwoUcP36chQsXMnfu3Md+zp6eHr744gssLCx4++23pUkXLxlDo2oJCQksXbr0iaJqgiBQXV1NXl4epaWlCIJAeHg4cXFxBAcHP9F+8DimtP39/Vy6dIk7d+7g4eHB8uXLn0kqV6lUkpGRwc2bN5HJZMyZM4fk5OQXVt/3IpBE2n3s37+f//t//y+3bt3CysrqkSLt5z//Ob/4xS9GXC6JtGePTqfj7Nmz3L59m7i4OPz8/Lhy5QoajYYFCxaQlJT0WKJqcHBQrDdTKBRMmzaN2bNnj6nzSeL5U1VVxeHDh1EoFCgUCpYvX86iRYvG9TkeZXZrRC6X8/XXX9Pf309AQACVlZW8//77T2QT0t3dzRdffIG1tTVvvfWWNCbnJeP+qNratWuHjR17GH19faJ1Rm9vL+7u7sTFxREbG/tUn5OxmtJqtVoyMjK4fv06FhYWpKamEh8fP+61YGq1mps3b5KRkYFerycpKYlXXnnlmaRQJzqSSBtCQ0MDM2fO5MKFC8TExABIkbQJSl9fHwcPHqStrY2kpCQaGhqor68nOjqapUuXPvBgOhodHR1kZmZSUFCATCYjLi6OpKSkZxK2lxhfjA0EeXl5mJub84Mf/ICwsLBxfY6BgQH+9Kc/YWlpybvvvvvAVLdx3mdjYyMA7u7uvP/++09UL9PZ2cmXX36JnZ0db7311nfy4PSy09vby7fffvvIqJper+fu3bvk5uZSVVWFubk5UVFRxMfH4+fn91QnkGM1pRUEgcLCQi5evMjg4CDJycnMnTt33Ms+tFott27d4vr166jVahITE0lJScHW1halUsng4OADN3Nz8zHP0p1MSCJtCN988w0bNmwYFn3R6/XIZDJMTExQq9WPjMxINWnPnurqag4fPoxMJsPHx4eqqipcXFxYuXLlmEPugiBQU1NDRkYGlZWV2NvbM2vWLGbOnCkdECcZGo2Gb775hgMHDuDi4sI//dM/jbvANprdenl5sWPHjgcKL61Wy6FDh8jPz0epVLJ+/fonju61t7fz5Zdf4uTkxJtvvinVQb6ECIJATk4O58+fHxFV6+joEK0zFAoFfn5+xMfHExUVNS7pvrGa0jY2NnL27FkaGxuJiIhgyZIl42IVo9FoRIHV399PTk4OWVlZ9Pf34+fnx5QpUxAEgcHBQRQKBQaDYdj9TU1NsbW1FTfjMeBlQxJpQxgYGKCurm7YZe+88w7h4eH86Ec/El3PH8ZEEGlZWVkoFAo8PDzw9PTE1dX1pWhNFgSB69evc+nSJSwsLMTL58+fT3Jy8phSmzqdjqKiIjIzM2lra8PLy4vZs2cTHR0t1ZtNYgRB4PLly/zmN7/Bx8eHf//3fx/3eq6xmN3C/5v3efLkSczNzfm7v/s7/P39n+g5W1tb2bVrF66urrzxxhvfqVqc7xK9vb0cP36c8vJy3NzcsLS0pK2tDRsbG2JjY4mLi3tgTeTjMlZT2r6+Pi5evEhhYSFeXl4sX778odZFer0ehULx0GjX0E2r1SIIAm1tbdTW1qJWq/H39yc6OhoPD49hAmy0zdLS8jtRhjLpRVpTUxPXrl2jvb2dV199FT8/P/R6PX19fTg6Oj71gfdR6c77mQgi7fTp05SWljIwMACAmZkZ7u7ueHp6DtsmU62LSqXi2LFj5ObmIpPJsLa2Jjo6mmXLlo0ptalQKLh9+zbZ2dnI5XLCwsKYPXs2U6dO/U7s6JORmpoaNBrNQ2cC3k9GRgY///nPCQwM5Je//OW4O5yPxewW7onG06dP89lnnxEYGMivf/3rJxZYRg82Dw8PduzYMewERWLyIwgCjY2N5OTkcOHCBcrKyvDy8uKNN95gyZIl43ryOBZTWo1Gw/Xr17lx4wZmZmYkJSURGBj4yHSjUqkc8XwWFhajCiwbGxtaWlooKChALpcTExPDsmXLpDm2ozBpRZogCPzN3/wN//Vf/4VOp0Mmk3HhwgUWLlxIX18f/v7+/MM//AMff/zxUz3PZBRpRhQKBW1tbbS3t9PW1ib+rtVqAbCzsxMFmzHq5u7uPuE8Z9ra2tizZw9FRUXY2dkRFhY25tRmV1cXWVlZ5OfnIwgCsbGxzJ49WxrBMwk4fvw4eXl5LFy4UBxqPhYuX77Mb3/7W4KDg/n+979PRETEuK5rLGa3cO876sSJE/zud78jNTWVv/u7v3viE4LGxka+/vprcbbod8F64GVncHCQO3fukJeXR0dHB05OTsTFxTF16lTS0tKorq4mPj6epUuXPnWqW6PRcPnyZTIyMnBxcWH+/PnY2toOE1lyuZzS0lLy8/MZHBzE09MTPz+/YccDExOTR0a4hm73f04FQaC8vJzLly/T1tZGaGgoCxcuxNvb+6le38vMpBVpv/71r/nxj3/Mj370IxYtWsSSJUu4ePEiCxcuBODtt9+mqqqK9PT057quiSTSRsNgMNDT0yOKNuPW09MD3NsJXV1dR0TdHBwcXkjEKT8/n507d9Lc3ExERATLly9n9uzZDz27FASBuro6MjMzuXv3Lra2tmK92WSKHn7XEQSBtLQ0rl69SnR0NOvWrRuTOBEEgUOHDnH06FH8/f1ZuXIlCxYsGNfP71jMbo18/vnnfPXVV2zZsoUPPvjgiSMj9fX17N69G39/f7Zt2zbhTqYkRqegoICamhpWr16NTCajqqqKvLw8ysrKkMlkREREEBcXR1BQkPgZFQSB3Nxczp07h5WVFWvXriUkJGTEYxsMhmERLrlcPiLCVVNTw61bt5DL5QQEBIxoNrC2tkatVlNVVYVCoSAkJIRXXnlFzLYM3aysrJ5oPzJahly+fJmmpiamTp3KwoULCQgIePI39jvCpBVpoaGhpKSk8MUXX9DV1YW7u/swkfav//qv/PM//zNtbW3PdV0TXaQ9CLVaTUdHxwjxplKpALCyshoRdfPw8HhmNTJ6vZ79+/eLo4DWrl3LqlWrHurErtfrKS4uJjMzk5aWFjw8PJg9ezbTp0+XDmiTmJKSEo4dO4abmxtbt24dkxu/Vqvlj3/8I+Xl5djb2xMREcHGjRvHrfh+rGa3xtv+3//7f7l+/Trbtm3j9ddff+JIWG1tLXv27BHnlkqf64nPzZs3OXz4MBYWFjg6OqJQKPD09CQuLo6YmBhsbGzE2wqCMKygvqWlhTNnzlBbW0tAQADTpk0T5wYbC+rvP4SbmZlhZ2eHmZkZVVVVtLS0MGXKFDFiNVR0qdVqLl++TElJCT4+PixfvnzchVN9fT2XL1+mtrYWPz8/Fi5cSGBgoFRmMkYmrUizsrLiP//zP3n//fdHFWmfffYZH3/88ah58mfJRBBpgiCMyw4gCAL9/f0jhFtXV5fYaePs7Dwi6ubs7PxUjQodHR386le/4s6dOyQkJPDhhx+OehZpRKlUkpOTQ3Z2Nv39/QQHBzNnzpxhZ6YSk5vW1lb279+PVqtly5YtYzqQdHV18Yc//EFM69jZ2bF169Zxq1PT6/Xs3r37kWa3cK8I+//8n/9DS0sLixcvZtu2bU8sGKurq9m7dy/BwcG89tprUsPLBOeXv/wlJ06cQKVSERERweuvv46rq+sDi+x1Ot2Ix+jt7aWmpgZbW1vmzJlDSEjIA1OMZmZmFBcXP9SUVq1Wk56eTmZmJjY2NixevJiYmJhx/b5sbm7m8uXLVFZW4uXlRWpqKmFhYdJ38mMyaUVaQEAAb7/9Nv/wD/8wqkj74IMPSEtL4+7du891XRNBpB0+fJjOzk68vLyGbeMVRdDpdHR2do4Qb3K5HABzc3M8PDzEiJtxG3rGOBqCIHDu3Dn+67/+C4PBwPe+9z3Wrl37wGhBd3c3N2/eJC8vD71eT0xMDLNnzx63LiiJicXg4CAHDx6ksbGRVatWjWn/Kikp4eDBg8yZM4eKigr6+/vZuHHjYzUjPIyxmt3CvdT9rl27kMlkhIWFsWPHjifuQK2oqGD//v1MmzaNTZs2vRTd2y8rn3zyCSdPnqS3t5f29nacnJxYuHAhfn5+orCys7N7oOiysrLCxMSEvr4+jh8/TlVV1QNr1Yaa0kZFRbFixYphnzGDwUB+fj6XLl1Co9HwyiuvMGfOnHFtRmlvb+fKlSuUlpbi5uZGamoqkZGRkjh7QiatSPv444/Zu3cvWVlZODo64u7uzqVLl0hNTeX8+fOsXr2av/3bv+WXv/zlc13XRBBpBQUF1NbW0traSnt7u3hm5uTkhJeXF56enqJwc3JyGredZ3BwcIRw6+joEJ/f3t5+RNTNzc0NU1NT2tvb+fd//3du3LhBVFQUf/d3fzdqMakgCDQ0NJCZmUlZWRnW1tYkJiaSmJgojdB5yVCr1SiVSpycnMTL9Ho9Z86c4fbt2yQlJbFs2bJHCpSzZ8+SnZ3N66+/Tk5ODqWlpSxYsID58+ePy2d/rGa3giCwf/9+SktLsbKyws7OjjfffHPY63sc7t69y4EDB4iKimLDhg2SUJugtLW10d/fj4mJCbdu3WLPnj2o1Wq2bt3KmjVrHiuya6xVO3/+PJaWlmKt2lhMaWtqajh37hytra3ExMSwaNGiMZUOjJXu7m6uXr1KYWEhjo6OLFiwgJiYGOlz+ZRMWpHW19fHvHnzqKmpYe7cuZw9e5YlS5Ygl8vJzMwkLi6Oa9euPTJ6M95MBJE2FIPBQGdnJ62trbS1tdHa2kprayuDg4MAWFpajoi4jWeHp8FgoLu7e4R46+3tHba+/Px8DAYDr776Ku+//z6Ojo7DDqAGg4GSkhIyMzNpamrCzc2N2bNnExMTI3W6vaT8x3/8By0tLfz0pz8dYTB869Ytzpw5w9SpU9m0adND93O9Xs+XX35JX18fH3zwAbm5uVy5coWwsDA2btw4bsagYzG7lcvlfPLJJ7i6uiKXy9HpdLzxxhtPHP0tKSnh8OHDTJ8+nfXr10vRiglIVlYW5eXl4verhYUFX3zxBYWFhYSGhjJnzhxSUlLw9fUd82MOjaoFBQWhUChobW0d1ZS2u7ub8+fPU1ZWhp+fH8uXL8fPz2/cXl9fXx9paWnk5+dja2vL/PnziYuLk9Lw48SkFWlwrxbpt7/9LYcPH6aiogKDwSDWafzP//k/X4hz/EQTaaMhCAJyuVwUbMatu7sbQRAwMTHBzc1thHgbT8GrVCq5fv06hw4dIi8vDxsbG+Li4sSogrW1tVjf1tPTQ21tLXq9npCQEGbPnk1oaKh0QHrJ2bdvH59//jmLFy/mhz/84Ygv/draWg4ePIiVlRVbt259qNDp7+/ns88+w8vLi+3bt1NZWcmRI0ewt7dn69at42LJMlazW2MKdvny5eTn59PX18f27duf+MBZVFTEkSNHiIuLY82aNdJ+McEoLi6msLCQ1tZW8eQUELMd7u7uuLi4EBUVxaJFi8ZcS6vT6fjiiy84ePAgdnZ2/OAHP2DBggXi9SqVimvXrnHz5k3s7OxYsmQJUVFR4/b5kMvlpKenc/v2baysrEhJSWHmzJnSSfM4M6lF2kRkMoi0B6HRaGhvbx8m3Nra2kRfNQcHhxHpUhcXl8fe6Ts6Ojh9+jRZWVn09PSQmJjIO++8g7OzM319fbS1tVFZWUlGRgaFhYXI5XLc3d3x9/dnypQpI2rdnJ2dpQPTS4jBYOA///M/OXHiBK+//jrvvPPOiP+zcXZnT08Pr7766kNrzaqrq/n666+ZP38+CxYsoKuri/37949rndpYzW6PHj1KeXk57777LidPnqSlpYWtW7eOedD2/dy5c4dvvvmGmTNnsnLlSml/mKCoVCoxm9Dc3Mz58+cpLS3F2dkZjUaDVqvFz8+PV155hcTERLy9vXFxcRmRMhxqSjtjxgy6u7upra0lLi6OJUuWUFxczJUrV9DpdKSkpDB79uxxE08KhYIbN26QnZ2Nqakpc+bMITk5WTJZfkZIIm2cmcwibTSM6cr706XGaQYWFhbDRJtROI32haDRaEhLS+PGjRu0trZibm5Oamoqa9asEW/f2NhIZmYmJSUlWFlZMXPmTGbMmIFarR6RMlUoFOIa7hduHh4e0gzOlwCNRsMvf/lLsrKy+MEPfsCqVatGvc2xY8coKyt7pPHttWvXuHLlCtu3byckJAS1Wi3eNzU1lXnz5j21wBmL2a1SqeSTTz7Bw8ODLVu2cPjwYaqqqti4ceMjfdceRG5uLsePHyc5OZlly5aJr0Ov14sCQKPRjPjd0tLyod3TEs8O4zizs2fPEhgYiK2tLZmZmVRXV2Nqair6mg09Ka6qqqKiogJ/f3/Wrl2Ll5cXgiCQl5fH3r17qampwcfHh0WLFrFo0SLs7e3HZa1qtZrMzEwyMzMRBIHk5GRmz54tfc8+YyatSHv33XcfeRuZTMbOnTufw2r+Hy+bSHsQg4ODIyJunZ2dGAwGZDIZrq6uw4Rbb28v6enp9PT0oNFosLGxYeXKlcycORNBELh79y4ZGRk0NDTg4uJCcnIyM2bMeODZmXHw7miNCnq9HrgX+bu/UcHV1VWqlZhkyOVyfvzjH1NVVcUvfvELEhMTR9xmrMa3giCwd+9empqa+PDDD3F0dEQQBFG8hYeHs2HDhqeuUxuL2W1lZSW7d+9m1apVzJgxg6NHj3Lnzh0WLlxIdHT0CEH1IJE19PqKigry8vIICAhg6tSpaLXaEYOp78fHx4cPPvjgqV6vxIPp6+tDJpM9tPP39u3bnDp1SvTza2tr4+LFi+Tl5WEwGPDx8UGhUHDr1i3UajWBgYFER0fj4+ODp6cnlpaWFBUVUVVVRXd3N/b29sydO5fly5c/dVe/VqslOzub69evo9VqSUxMJCUlRTIGf05MWpE22sxFvV5PS0sLer0ed3d3bG1tqa6ufq7r+q6ItNHQarV0dHQME2/V1dUUFxfT09ODk5MTpqamuLm5sWnTJiIiImhoaCA7O5uenh6mTJnC7NmzCQsLe+KOIL1eT1dX1zDh1t7eTl9fHwCmpqYPnGMqpYgmLh0dHfz1X/81crmc3/72tw8cCTYW41uFQsFnn32Gvb0977zzjija7969y9GjR3FwcGDr1q24uroOu58gCCME04OEk3EMT3V1NXPnzsXZ2XnU2965c4fGxkYSEhKwsrKisrKSpqYmAgMDCQgIGPaZlMlkWFhYYGFhgbm5+bCfQ3+vrq4mJyeHhIQEMQ31oNsaf0onLs+OM2fOcPPmTZycnAgICBA3d3f3Yf/fsrIyDh8+jJ+fH1u3bsXKyoqOjg6uXLnC0aNH6ezsJCEhga1bt2JiYkJraysNDQ1kZGRQU1ODpaUl4eHhREdHo1AoKC8vx8XFha1btxIZGfnY69bpdOTk5JCeno5CoSA+Pp558+aNaVayxPgxaUXag9BqtXz22Wf87ne/48KFCwQGBj7X5/8ui7ShaDQarl27RkZGBmZmZlhZWVFYWIiVlRVeXl5UVlbS0tKCIAiEhoaSnJxMVFSUKJrGe5KBUqkcNsPUKN40Gg0ANjY2I4Sbu7u7VAQ7gaipqeFv/uZvsLW15Xe/+90IEQX3hFRTUxN79+5FpVKxZs0aPDw8RgirxsZGvvnmG0JCQoiLixMv7+rqEg9K0dHRuLi4DBNWY8EofszMzMjLy2NwcJDU1FRcXV1HiCSAEydO4ODgwKuvvoqlpSV5eXlkZ2cza9Ysli1bhqWlpSikxnoikZGRwfnz50lNTWX+/Pljf5Mlxp3BwUHq6+vFraWlBYPBgJWVFf7+/qJo8/X1pbm5mX379mFvb8/27dupr6/nzJkzKJVK3Nzc6OnpQSaTMWPGDKysrLh9+zZ6vZ6ZM2fi7+8v+le2trbS1NTE3bt36e7uJjQ0lNTUVPz9/cXshr29/aifJ71ez507d0hLS6O/v5/Y2Fjmz5+Ps7PzC3j3JF46kWbko48+oq6ujlOnTj3X5/2uizRBECgtLeXcuXMMDg6SlJREZ2cnd+/eJTw8HFNTU0pLS4F70VBvb2/kcrkomoypGRcXlxHdpQ/6Unmatd4/x7S9vV3scjWmbe8XbzKZjNu3b2Nqajqsm0pifDl58iRNTU34+vpibm6OVqulrKyMPXv24OrqKnZQ3h+dMo7WKS4upr+/n7CwsBGee2ZmZrS1tVFRUUFCQgJTp04dJpxycnJoa2sjISGB+Ph4LC0tHxqJMv5ubm4+7DM6FrPburo6vvzySxYvXswrr7wC3LMYOX36NDExMaxdu/aJIl3p6elcunSJxYsXk5KS8tj3l3g2aDQampqaqK+vp6GhgYaGBtRqNaampvj4+ODg4EBGRgYtLS34+/sza9Ys0ZR2cHCQo0ePcvjwYfr7+5kzZw7vv//+qGPJtFotbW1tXLt2jfPnz6NUKvHz8xNr1IwnpkbR5uHhQXt7O9euXaO7u5uoqChSU1PHpfP5WaPT6ejq6sLT0/NFL2XceWlF2meffcYPf/hDscD9efFdFmmdnZ2cOXOGqqoqpk2bxsyZMzl79iw1NTW4ubmh0WhwcnIiOTmZuLi4EdEy4ySD+61BjPNDh36pGDejGe54otFoRswxNdbdNTQ00NLSgkajISYm5rnXPH6XeP3118nLyxO9/AIDA4mIiKCpqYkzZ84QHh7OG2+8gZWV1aiCydTUlBs3blBcXCxGpaysrDA3N8fExEScv3n37l3ef//9YaaiQ2vcIiIiWL9+/RNHd8didnv+/Hlu3rzJBx98IB5oCgsLOXbsGKGhoWzatOmJorpXr17l6tWrLFu2jNmzZz/R+iWeDrVajVqtfmCa0GAw0N7eTn19PXV1ddy4cYO8vDza29vx9vZm+/btJCQkYG1tTXZ2NlVVVfj7++Pp6cndu3eRy+WEh4c/1Gutv7+f48ePU1FRQWhoKNHR0fT29tLW1kZLSwsVFRXU1taiUCgIDAwkJSWF8PBwUcBN1PqzwcFBbt++za1bt5DJZPzVX/3VS2ee+9KKtE2bNpGeni4NWH8OaDQa0tPTycjIwMHBgRUrVqBQKPjDH/5AZ2cnU6dOZdq0acyePZvw8PDH2omM80PvF249PT3AvRozDw+PEdYgT1MsazAY6OnpEevr8vLyyM3NpaKigs7OTvR6PWZmZkRGRvLtt98+8fNIPJyysjJKSkrIz8+npKSErq4uAHx9fdFqtdy9e5d169bxox/96KEefkONbzdv3jysG02j0fDHP/4RgPfff39Eo0pZWRnHjh3DwcGBbdu24eLi8kSv5VFmtzqdjj/84Q+YmJjw/vvviycelZWVHDhwAB8fnyea9ykIApcuXeL69eusXLmSWbNmPdH6JZ6cU6dOUVpaymuvvfbQmbMdHR0cP36c+vp6IiMj8fPz4/Dhw9y9excTExMGBwdxdHRk/vz5JCUlMWXKFNzc3CguLubGjRt0dXURGBjI3LlzRx1gLggC+fn5nD17FgsLC1avXo2pqSmXL1+mvr4eV1dXQkNDEQRBPDk1pviN02KGfs+6urq+MEHU1tZGVlYWhYWFYvo3KSlpUkT9HpdJK9L+4R/+YdTLe3t7uXbtGrm5ufyv//W/+NWvfvVc1/VdEmmCIFBWVsbZs2cZHBwkJSWFqKgodu7cycWLF3F1dWX9+vWkpKTg7+8/rs9t9Bsaag1y/wgsYzenk5MT9vb2mJmZMTAwQFdXF52dnfT09NDb20tvby99fX0MDAwwMDDA4OAgGo1G/NsYybO0tMTDw4OgoCB8fX2ZMmUKf/EXfzGur0vi/3H9+nX6+/tJTU3FwsKC+vp6MjMzycnJobKykpKSEnp7e5kxYwZLly4lICAAb29vcRs6JuxhxrcdHR388Y9/FDs77z+4dXR0sH//fgYHB9m0adMT21U8yuy2paWFP/7xj8ydO5fU1FTx8oaGBvbs2YOTk9MTzfsUBIHz58+TmZnJmjVrSEhIeKL1SzwZg4ODHDp0iPr6epYvX05iYuKw/71er+f69etcu3YNJycn1q5dy5QpU9Dr9WRkZPD73/+elpYWFi1aRGJiIk1NTTQ1NaHT6TA3N8fPzw8/Pz+0Wi1VVVV0dHTg4+MjRsPuF1L9/f188cUXXL58GSsrKxYsWMCKFStGpEyHTosZ+j3b398P3CsZuP8E2dPTc9xmRN+PIAiUl5eTlZVFTU0NDg4OzJo1S4wyvqxMWpH2IAXv7OxMcHAw3/ve93j//fefe8fed0WkdXV1cebMGSorKwkLCyM+Pp68vDwOHjzIwMAAq1ev5s0333ziyMNQjF11SqVS3BQKxbC/+/v76e7uprGxkebmZtrb2+nq6mJgYACNRoNOp0MQBMzMzLC0tMTGxgZbW1scHR2HbYIg0N3dTVdXF2q1GnNzczw9PYmPjycmJoaamhqysrIoKioiJCSE//zP/xyHd1NiNLKzs7l06RKmpqYsXLiQ+Ph4cb+Xy+XcuXOHn/3sZ5SVlREdHc3UqVOxtLQUBbm9vf0w0WZtbc2pU6fo7e0dYXxbVFTE4cOHWbVq1agWHyqViqNHj1JRUfFIL7aH8Siz27S0NNLS0njvvfeGpa7a2trYvXs35ubmTzTvUxAEzpw5w61bt1i3bh0zZsx47LVLPDl6vZ4LFy6QlZXFjBkzWLVqFebm5sNMaVNSUpg3bx6mpqbcvXuX8+fP09PTQ0JCAjqdjvz8fObOncvChQsxGAy0tLQMa0gw+kaamprS09ODSqUiKCiIRYsWERMTg6mpKU1NTVy+fJnKykp0Oh1qtRovLy/Wrl1LWFjYmF6LQqEYUQbS3t4uWh8ZT5CHWjA9jeG4Wq0mPz+fmzdv0t3djZ+fH8nJyURERHwnupInrUibqLzsIk2r1ZKens6NGzews7MjMjKStrY28vLyqKqqEqNLD9rhdTrdqCLrQX8PDg7S39/P4OAgarUajUYj/jQWiRsMBgRBEDvqzM3NsbKyEoWXnZ0dMpkMg8EgPr9KpRILvY0dfMaZpjKZDBMTE+zs7HBxcUGn01FeXk59fT16vR5vb28SEhJYsGCBFJV4xsjlci5dukReXh5eXl6sWLGCKVOmiNcrFAq+//3vU1FRwcqVK9Fqteh0OlxcXHB0dMTMzIyOjg5xVq2FhQVNTU0MDAywePFiVq1aJU7NOH36NDk5Obz77ruj1vYIgsCVK1e4du0akZGRrF+//olc1h9mdqvX69m5cycajYYPP/xwWB1aT08PX3/9NVqt9onmfQqCwMmTJ8nNzWXjxo1Mnz79sdcu8XQUFBRw/PhxXFxc8PT0pKioCG9vb9GUtrW1lXPnzlFTU0NwcDDLli3Dw8MDQRDIzMzk/PnzxMXFialKI4Ig0NXVJTYj1NfXU1NTQ319PXK5HEdHR5ycnLCxsSE4OJhFixYRHh7OwMAAJ06coKKigtjYWJYvX/5EUSm9Xj+ss9T4c+h+d3+61MPD46H7T09PD9nZ2eTm5qLVaomMjCQ5OXlc545OBiSRNs68rCJtaGqzv78fDw8PsStzcHCQvr4+QkNDmTt3LsAwwTVUdBlrHIZ6ThnFlvFyQRDQ6/XodDoxpG8UX5aWljg5OeHi4oKLiwsODg7Y29uP2CwtLR965qbRaKisrCQtLY3s7Gza2tqQy+UoFAq0Wi0mJibIZDIEQcDOzo6wsDDmz5/P/PnzR/hXSTx7jM0CjY2NREdHs2TJEtEDrbOzkz//8z9Hp9Pxq1/9iq6uLoqKimhubsbc3JzQ0FACAwOxtramq6uL5uZmrl+/TnFxMR4eHsTExODn54eHhwc3b97E3Nycjz/++IFpxdLSUo4dO4aTkxNbt259omjxw8xuOzo6+Oyzz5g5cybLly8fdp1cLmf37t309fXx+uuvP3YZgSAIfPvttxQUFPDqq68+8XQDibFjPNk01ktlZGTwL//yLyiVSt5//302bNiAQqHgypUr5Obm4urqytKlS0edT1xQUMA333xDcHAwmzdvfqjIkcvlFBQUcOjQIa5fv05fXx/Ozs7ExMSI84+NJQIlJSWcPXsWc3NzVq9ePS4j0oxruH9aTVdXl2h6bhSrxqibh4cHvb293Lx5k7KyMqysrEhISGDWrFmP9GczduS/bEwakVZfX/9E93tYoeazYLKJNIPB8EBBZdza2trIzs6mrq4OnU6HwWBAr9fj4uKCWq1GLpcTEBBAUFCQ6Olk3FmMUS69Xo9er0er1aLVasWWc6MAMzExwdbWVhRZDxJfNjY2T7UjCoIgGuiWlJSgUCjo7++noaFBnFYgCAI2Nja4uLiIw4+tra1HjMDy9fXFy8trvP4VEvfR1taGTCYTI0aCIHDnzh0uXryIWq0mJSWFOXPmYG5uTnV1NT/4wQ9wcXHhk08+wdbWlu7uboqLiykuLqa1tRVLS0umTZtGdHQ0wcHB4hgdU1NToqOj6evro7m5mdu3b+Pi4sKSJUvw8fER06UeHh5i9KKjo4N9+/ahVCp59dVXH7tOTRAEjh07RnFxMW+88caIeqDMzEzOnTvH22+/PeI6lUrF3r17aWlpYcuWLY/93AaDgW+++YaioiJee+01wsPDH+v+Eo/HyZMnKSoqYv369ZSWlnLnzh18fX3R6XS0tbXh5eVFd3e3aOkzc+bMh6bxqqqqOHDgAG5ubmzfvn3Uzsve3l7S0tLIz8/H3t6e+fPn4+vry9mzZ7lx4wa9vb3Y2dmJnZs+Pj64uLhQWVlJb2+veILwLGq9dDqd6FlpFHBNTU00NDTQ2NiISqXCy8uLWbNmkZycjL+/P+7u7iOabQwGA62trdTU1FBTU4NGoxnTJKLJxqQRacbIxuNizJM/LyaCSDM67D8qlWhM+42G0cqgqamJ8vJyVCoV1tbWODg4EBQUhIeHB3l5ecjlcmJiYnB3d0elUjE4ODjC9NPa2vqR4svOzu6ZdgrpdDqKiorIyMjg7t27DAwMiIaPRmuQkJAQEhISmDNnDpGRkWIkxRgxHNpd2tnZSWhoKK+//vozW/N3nYMHD1JeXs7SpUuHFVur1WquXbtGVlYW9vb2LFu2jPDwcHJycvjJT35CZGQkv/nNb4Z9qXd2dlJcXExRUREdHR1YWVkRERGBh4cHGRkZGAwGtm7diru7O1lZWezevZupU6fi7OxMZ2cngiCIncRG0ebs7MyNGzeora1l0aJFvPLKK4/1HaXX69mzZw/Nzc28++67w9KXgiCwa9cuent7+f73vz/C/kOr1XLo0CGqqqrYsGED0dHRj/XeGgwGjhw5QllZGVu2bBlzPZLE46NUKvntb3/LtWvXiI6O5s033yQmJoaSkhI++eQTSktLmTNnDj/60Y/G7Obf0tLCnj17sLCwYMeOHWI0d2BgQGycs7KyYt68eSQkJAzbFxQKBTdv3hRrvNzd3XF1dRUbqIzD311dXVm3bh3z5s17qpqyh2G00MjOzqarqwtXV1d8fX0xNTWlra2Nnp4eBEHAxMQEV1dXLC0t0ev1KJVK+vr6xDIXY5Bgzpw5L100bdKItC+//PKJ3vy33nrrGazmwUwEkfb1119TVVUF3OvAsbGxwdraWtzu/9vS0lKMjul0OlQqFXfv3uXMmTPU1dVhYmKCi4uLeHDq7e2lrKwMW1tbZs+ejbe390MF2P1nQM+Tvr4+Ll++zOXLl2lqaqK/v5/e3l7kcjlWVlaEhYWxYsUKZs6cSWho6EM7k4x2IN3d3bS3t2NiYjJqkbnE+KDVarlw4QLZ2dmEhYWxbt26YVGDrq4uzp49S0VFBUFBQSxfvpzbt2/zL//yLyxevJif/OQno9oQtLe3i4Ktu7tbrFszNTUVPamMtWdvvPEGfn5+op+UcTMaL8tkMnEMWUxMjGizMFZPtYeZ3fb09PDpp58SHR3N2rVrR9xXr9fz7bffUlhYyMqVKx/7s6jX60VT1O9973sv3cFtonDmzBlu3LiBSqXCwsKC5ORkurq6qKurIywsjICAANLS0nB1dWXLli1jdvbv6elh9+7dqFQqNm7cSFVVFdnZ2Zibm/PKK68wa9ash6ZD1Wo1ubm5ZGRkiF5rMTEx6HQ6ysrKOHPmDNXV1Xh6ehIbG0twcLA4HcHLy+upivbvt9CIjY0lKSlphFdhc3Mzubm5FBUVUV5eTldXF0qlEltbW5ydnfHx8SEsLEyMeD/uycpkYNKItMnCRBBpxoG+lpaWaLVa0Uqiv79f/H3oZizuhHtnNnl5eTQ3N2NlZUVUVBRz5swhKioKBwcHCgoKKCoqYsaMGeIYm4mGSqUiIyODCxcukJeXJ87t1Gg0GAwGPD09Wbp0KStWrCA0NHSYiBQEgYGBAbq7u8UuT+PPnp4eMUook8mIiopi06ZNL+Q1fhcw1piUl5fzzTffYGJiwvr160ek98rLyzl37hw9PT3MmjWLuro69u3bx5tvvvnQ9IcgCLS2tlJUVERhYSG3b98Wu+y2b9/O9evXaW9v58MPPxwR4TCmbIyiLScnh+vXr2NhYUF0dDR+fn7DOku9vLwe6OX2MLPb3Nxcjh8/zuuvvz5qtEsQBM6ePcvNmzdZuHAhc+fOfexonkajeaktDF40+/bto7S0lE2bNnHs2DGuXLlCVFQUH3/8MaGhocA90XLgwAGUSiWbNm0iODh4TI/d3d3Nr371KwoKCpg+fTqrV69m9uzZj2WDodPpKCgoEL3WgoKCSElJYerUqdy+fVvs2J86dap4Im+0/ggICMDf3x9/f/9HHgseZaFh7Kyvra2lpqaG2tpa5HI5pqam+Pr6EhgYSGBgIL6+vmImZGi9m7m5Of/jf/yPMb/uyYIk0saZiSDSjh8/TmVlJXK5XByzBPeEhZ2d3aipR0EQOHXqFNeuXcPExIQlS5awceNG0RRRLpdz+PBh6uvrWbJkCcnJyRPmzFsQBFpaWigrKyM9PZ3s7Gw6OjowMTHBysoKQRDE4cNr1qxhwYIFqNXqYQJs6M+hQszR0RFXV1dcXFzEn87Ozpibm2MwGMbFYkRidE6dOkVDQwNBQUF4enqSl5dHbW0tycnJLF68eJi41ul03Lx5k7S0NExNTamrq6OkpIQf/ehHrFix4pHPZZz5efToUU6fPo21tTXx8fE0NDQQFhbGxx9//MiIcEtLCzt37qSjo4PY2FgAWltbxfmwTk5Ow4TbUC+3B5ndCoLAvn37aG5u5qOPPhpV6AmCwLVr17hy5QrJycksW7ZswuybErB7924+++wz2tvbcXd3JzY2FpVKRXR0NJs2bcLDwwNLS0uUSiVHjx6lsrLykelzjUbDzZs3ycjIQK1Wo1QqMTU1ZfPmzeJn73ExGAzid2hLS4votebn58fJkycpLy8nOjqa2NhYOjo6hll/yGQyPD09hw2QN57YPMxCY2BgQBRkNTU19Pf3Y2Jigo+PD1OnTiUwMBB/f/8xdVHrdLoXmrV5Vkx6kXbjxg1yc3Pp6+sbJkjg3kH27//+75/reiaCSLt16xZyuXxE2tHW1nZE3VdnZyeHDx/mxIkTqFQqFi5cyLvvvjts1mFDQwMHDx5EEAQ2b948zALhRTEwMEBVVRVVVVUUFxdz9+5damtr0el02Nraij5SJiYmBAYGMn36dKysrMQImfHAKZPJcHBwEAXYUDHm6OiIXC6ns7OTjo6OYT9VKhWhoaFs3779Bb4LLzd3796lpKSE6upqBgYGMDExQavV0tzcTEhICO++++6IWX0DAwNcvHhRTONoNBp+97vfPZZVSk1NDX/4wx/o7u7GxsaG/Px8pk2bJnZDent7P/DgqVQqOXLkCFVVVeLJTHd397BUaUtLi1gLOtTLzWAwcOXKFWJjY9m0aZP4HAMDA3zyyScEBQWxefPmB657POZ9Sow/u3bt4saNG1hYWNDZ2YlCocDCwoKuri4cHByIjo4WLTIcHR2pr6+noqKC6OhoXnvtNdzd3cX/pU6n4/bt26Snp6NSqUhISGDu3LnY2tpy4sQJ8vLyxBmwTyrUBUGgurqa69eviyP95syZA9wbX2ZmZsbq1asJDw8XrT+Mth/19fXiZBALCwuUSiXd3d3Y2tqSmJjI9OnT0Wg0ojAzDow3jn2bOnUqU6ZMmZAZmhfFpBVp3d3drFq1iuzsbDEtYnwpxt9lMtl3snHgUQiCQG1tLRcuXODcuXMMDAyQmJjIn/3Znw1r6RcEgezsbM6dO4efnx+bN28Wh/M+b3Q6HfX19VRVVVFZWSkW7zc0NNDa2opWq8XR0RFbW1uUSiU6nQ53d3emTJmCg4MDDg4OwwTY0KiYsa7ofjHW1dUlTjCwsLDAzc0Nd3d38aeHh4cUSXsOCIJAZ2cnVVVVVFdXU1hYyJ07d9DpdCxYsIClS5cSHBw8rJansbGRo0ePsnPnTszNzfniiy8eyxesp6eH/fv309XVhaOjI5mZmfj4+IjeeVFRUURHR+Ph4THiYGgwGLh8+TLXr19n+vTprF27dpjfmSAI9Pb20traKoq25uZmBgcH6ejooLy8nLi4OJYuXSoKuKamJo4cOcKmTZseWndTVFTE0aNHn2rep8T40tfXh06nw9XVFbVaTVpaGpmZmWI038XFhZSUFPR6PT09PfT09FBVVUVJSQlWVlbi56y/v5/6+npxDFJqaipTp04VfSAFQeDq1aukpaUxa9Ysli9f/tTNWI2NjVy/fp2ysjIcHByIiYmhubmZ6upqYmJiWL58+bDoriAIlJaWcvbsWfLy8sTpBObm5mg0GszMzHB0dCQ4OJjY2FhCQ0OZMmWKlG5/CJNWpL333nvs37+fzz//nKSkJIKCgjh37hyBgYH827/9G5mZmZw5c2bEmfazZiKLNL1eT3FxMdevXxe7acLCwnj77beJjo4edrDRaDScOHGCwsJCkpOTWbJkyXM9MzeeoRm/rEpLSxkYGBDdrpubm+nv78fc3BwvLy/s7OwwGAzY2NgQHh7OnDlzCAkJEaNj5ubmqNXqERGxjo4OsYMIwNbWdoQYc3Nzw8HBQUohTRD0ej01NTUcOnSIzMxMLC0tCQsLE0d2BQcHExgYiJWVFZcvX+YHP/gBMpmMf/qnf2LZsmVjToloNBqOHTtGaWkpJiYmmJiYsHz5cpqamigtLUWlUuHu7i4KtvvnBhYXF/PNN9+MqSBcEATkcjktLS1cunSJS5cu4evrK97H0tKSxsZGscEgJCQENze3UQ/CTzvvU+LZ09nZyZkzZygsLKSlpYWQkBDef/99fHx8gHuf8erqanbv3k15eTkmJiaiSfP9xzQzMzOcnZ1xcnLC2dmZ1tZWcnNziYmJ4fXXX3/sMWKj0dHRwY0bNygoKBBPWJubm7GxsWH16tWEhoZSVFTEtWvXKCsrw2Aw4ODgIKYpjZNe4F7609gt7e3tLaZH/f39J+wg9xfJpBVp3t7ebNu2jX/913+lq6sLd3d3Lly4wKJFiwDYuHEjlpaW7Nu377muayKKNKVSSU5ODjdv3qS2tpbe3l7c3NxYtWoV8+bNG5Hv7+rq4sCBA/T29rJ27drn0jGjUqlobm6msLCQkpIScZi5UqnExMQEvV5Pd3c3AwMDmJmZERgYyKxZs3Bzc6OpqQkrKytmzpzJ7NmzsbCwGFWMDQwMiM/n5OQ0qhh72KBuiYlHWVkZR44coa+vj2nTpqFQKOjq6kImk+Ht7U1wcDAajYa///u/RxAEtm3bxpo1awgLCxuT6BYEgbS0NC5evEhzczPx8fF8+OGHmJiYiKn2srIy1Go1np6eREdHExUVJUZY29ra2L9/PyqVis2bNxMUFDSm12U0u125ciUuLi60tLRQW1vL4cOHMTExYfr06aJv39AaN6OX29PO+5R49giCwN27d/n222/JyMjA1dWVv/mbvyEqKkqMSJ0/f5709HRkMhnbt29n/fr1yGQyNBoNvb29YuTt/t+bm5spKSnB3t6exMREPDw8hgk54+9OTk6PdfLd19dHRkYGubm5qNVq+vr6qK6uRqlUYm5ujrW1NX5+fgQFBREUFCSmMIdmYAwGA+3t7eJ0hLq6OjHi5ubmJgq2gIAAcRrIgzDacahUKvR6/XMPyjwPJq1Is7a25r/+67947733UKvVWFtbc+zYMdatWwfA73//e37yk5/Q3d39XNc1kURad3c3WVlZ5OXliUPDZTIZMTExrFixYsSZP9w76B07dgx7e3tee+21xx498zDUavWwAv2Ojg5qamqorKykpaWF/v5+BEEQi/WtrKwYGBigvb0dpVKJr68vCxYsYO7cuVRWVpKVlYVarSYgIABPT08xXWSs9zF669wvxlxdXZ9onI8RQRBQKBTo9fox+xpJPD5nzpyhpqZGPJgYa3aMvxuNjQcGBjh27BjV1dXMmTOHhIQEMTVeU1PD4OAgLS0tXL16FVdXV5KTk5kxYwYrVqwY1vL/MEpKSvj6668pKSnhtdde4/XXXxcPHjqdjsrKStEmQKPR4OPjQ1RUFFFRUVhaWnL48GGqq6tZunTpmJpuHmR2W1FRwa5du0hKSsLNzU1Ml47m5WZhYcGNGzdwcXHhnXfeeex5nxLPB61WS1paGp999hl9fX2sWLECa2trWltbCQkJYcGCBVRVVXH16lXCwsLYsGHDI62CFAoFxcXF7Nu3D5lMxqxZs9BoNPT09Ayr3zbW5N4v3oy/G1OpRjQaDfX19WRmZnLkyBEKCwtRqVTY2NgQFRXFW2+9RWpq6pg/a4IgoNFoaGtro7q6murqaurq6mhra0Or1YpRQkdHR+zt7bGyskKtVqNSqVCpVMM8OZ2cnPj444+f6H8wkZm0Ii04OJh3332X//2//zcAXl5efPTRR/z0pz8F4O///u/55JNPxCLGsfDpp5/y6aefUltbC0BUVBQ//elPx9QdZmQiiDTjTlRWVoalpSVWVlZ0dXXh7OzM8uXLiYiIeGgdTUREBOvXr3+i4k2NRjNqx2R3dzdyuRy1Wk1PTw+Dg4NiR5KDgwPBwcG4urpibm5Oa2srtbW1dHV1YWZmho+PD/Hx8ZiZmZGdnc3du3fR6XR4e3vj6+uLtbW1KMCGijFnZ+fHTtEKgiDOCx1t6+vro6GhgaamJvz9/fn1r3/92O+RxNgoKSkRI7/GzdjwAffqXIzCzdHRkcbGRoqLi/Hz82Pr1q1ig4vxALB//36OHz+On58frq6uWFtbM3fuXDZv3jymk5HW1lZ+85vfUFBQwMcff8zKlStH3Ear1VJeXk5xcTHl5eXodDr8/PyIjIykvb2d/Px8YmJiWLNmzSPrxR5kdnv8+HGKior4/ve/L6ZDjQe6+73cBgcHKSwsxMrKirVr1xIeHi5agkjF2c+H/Px8KioqkMlkwzZg2N9VVVXs3r2bhoYGAgIC2LFjB+Hh4eJtm5qauHHjBtbW1qSmpor1tA97zN7eXs6fPw/A8uXLcXV1FUVcf38/crl8mB2TXC5HqVSKj2P8qdVqUSqVtLe309/fj06nw8fHh4SEBNzc3CgtLaWkpASDwUB8fDwLFixAJpMNm7d8/+8qlUq0RRr6XHDvs69SqcSRWoODg2K3voeHB35+fmK0zdHREWtra2xtbYc1vL0sTFqR9vbbb1NbW8vVq1cB+MEPfsDOnTv58Y9/jMFg4Ne//jXLli3j8OHDY37MEydOYGpqSmhoqOj4/S//8i/k5eWNecbdRBBpv/nNb+jr6yMsLIz29nYUCgWzZ88eNbUJ97zRDh8+TG1tLYsXL36ka7NWq32gEBuaUrS2tsbR0RGDwcDg4CC9vb0olUpsbGyYMmUK/v7+mJiY0N3dTXl5Oe3t7XR1dYkRNWNxv0wmo76+noGBAdzc3Jg5cyazZs3C29sbNzc3HB0dx5S6Mq7jYQJsYGBgWLOJqakp9vb2GAwGmpqaqKmpobe3F71ez8yZM/ntb3/7mP8diSdFEARUKtUw0WZ0STf+3tHRIdaLTZs2jYiIiGGpndOnT3PmzBlSU1MxNTUlLy8PExMT4uPjmTdvHiEhIUydOvWBAmZwcJCf/OQnFBUV8aMf/YilS5c+cL1qtZry8nKKioqorKzEYDBgYmJCc3Mz4eHhvPXWW4+MOKjVar744gsUCoVodqtWq/n0009xdHTk7bfffuBn3+jlVlVVxf79+2ltbSUoKEis+3F1dcXb25spU6ZIpszPkNu3b1NSUgL8v9nEQ7euri5KS0vp7OzEwcEBrVZLcXEx1tbWJCQkEB4ejrm5uVi3mJOTg1KpZPr06Xh6eo76mEM3lUpFXl4earWa6dOn4+joOOI2BoMBnU6HRqOhq6tLLBPp6+sbIaiMaU0zMzNkMhkmJiaYmpqi0+no7+9HqVRiYWGBj48PHh4emJmZibOXjb8P3R50+dB6S4PBgFwup6+vT9yMUTQ7OzscHR3x8/Pjn/7pn1662uFJK9IKCwu5cOECf/7nf46lpSU9PT1s3ryZy5cvAzBv3jz27dv31MraxcWFf/mXf+G9994b0+0ngkj7j//4D9LS0sRavVdeeYWQkBA8PT3x8PDA09NTDJc3NjZy8OBB9Ho9mzdvFtMqWq2Wnp6eYQLM+LuxfgDuFTQP7Zh0dnYWhVdTUxO1tbVotVrs7e0JCgrCycmJzs5OSktLqaioYGBgAK1Wi0KhQKPRYGVlRVBQEDNmzMDKykqsVwgICGDRokVER0ePGh0z7sSPEmBDbVrMzMzEzs/Rtr6+PjIzM8nKyqK+vl6seYiNjSUxMVF0upaYOKhUKtrb2zl16hQ5OTl4eHgQHh6OUqmkt7eXwcFBrl69SkNDA6mpqQQFBVFaWkpDQ4Nomunh4UFgYCBRUVGEhISIY2qMqNVq/vqv/5qqqiq+//3vs2bNmkd20alUKsrKyiguLubOnTsUFBTg6OjI9u3bWbx48UNrIUczu62treXLL79k2bJlzJ49e0zvy969e2lqamLJkiVYWVmJ3aXW1tZs3bp17G+yxLjQ0tLClStXKC8vx9PTk9TUVKZNm4ZMJiM3N5dPP/2Uvr4+YmNjWbp0KQkJCZiYmKDRaPjmm28oKSlh3rx5w6JWxvos42b8u7+/n3PnztHa2kpcXBwuLi7i9W1tbXR2doonPAaDAVNTU2xsbMQJNJaWlgQEBDBt2jRcXFxEQafVasXHMY4c7OjooLi4mL6+Pry8vJg/fz6RkZE4OzsP+341niyMJiwfdPlQUdnb2yt2Rre0tGBqasovf/nLF/kvfSZMWpFWUlJCZGTkiMt7e3vF6MfToNfrOXToEG+99RZ5eXmjPhcghm6N5OfnM3/+/Bcq0nbu3ElbWxszZszA3t6ejo4OcUc0RokcHBwYGBigtLQUd3d35s6di6WlJb29vaIQM340LCwsRlhXGH/a2NigVquprq4W7TF6e3vRarViKkqv19PQ0CA2AxhNYo0dk4Ig4O3tTUpKCnPnzhXbvltbW/Hz82POnDn4+PiIUxPuF1/GsP1QAWZubv5A8WV8bmtr62FnXXq9nqKiIq5cucKtW7doamrCzMyMoKAgkpKSmDNnjjhEXmLiU1payvHjxzEzMxONmdVqNZ2dnfzwhz+kpqaGd999FycnJ2pqasjOzqa1tRUbGxtsbW0ZHBzE1NQUOzs7pkyZQnBwMOHh4QQGBgLw61//ms7OTlavXs1rr702ZhsBhUJBXl4eu3btoqKigpCQEObNm0d0dDQRERGj1ht1dHTw+eef4+npKZrdnj17ltu3b/Phhx+Oqbbuaed9SjwZVVVVdHV1ER4ejoODAx0dHVy5coWSkhJcXV1JTU0lKioKg8EwTFiVl5dz6NAhOjo6sLa2xtnZmdjYWOzt7cWas6KiIhwcHEZMThmKseTFwsKC4uJi6uvrmTp1Kra2tmLNtrW1tTj/0s7OTswa2NrakpCQQGJiIo6Ojo98rQaDgf7+frq6ujh16hRHjx6lp6cHf39/QkNDsbe3F09ojHYco9XCOTk5PbYth1arfSktZyatSDN2OG3ZsoXXXnttxKiYJ6WwsJDZs2ejUqmws7Nj7969o9aeGPn5z3/OL37xixGXv0iR1t3djZ2dHRYWFqL3jnHeZG1tLdXV1WRkZFBXVycOPzczM8PW1lbM9xsPSiEhIXh5eY0IPTc3N1NeXk5BQQGVlZXiAc0YBjeuo6enB4PBgL29PaGhoUybNk10hFcqlQQFBREVFYWTkxO3b98mKyuLjo4OnJyc8PX1xdzcnMHBQYZ+TM3NzUWh9SABZmVlNabi7O7ubu7cuUN6ejp5eXn09vaK3kSvvPIK8+bNw9XVddh9lEolAwMDyGSycW2skBh/+vv7OXbsGLW1tcyZM4eFCxdiampKf38/f/Znf4ZCoeC//uu/8PPzw2AwkJ2dzenTp5HL5UybNg07OzuxoLm5uRm1Wo2lpaVYD1RfX4+lpSWBgYGsWrWKqVOnDmtyeFhNpMFg4OTJk5w4cQIrKytxkkVISAhRUVFMmzZt2AlBfX09X331FeHh4bz66qvodDo+++wzLCwseO+998ZUf/m08z4lHp9Dhw5x4cIFcSyfSqXCxcWFyMhI3N3dxXTi0CJ4I3K5nMLCQgwGA3Z2duh0OgIDA0lMTMTZ2Znu7m4yMzOxs7Nj9erV+Pn5YW1tjZWVFVZWVlhaWtLZ2Sk6+tfU1FBcXExzczOJiYmsWrWKoKAgPDw8KCsrIysri+bmZtzc3EhOTiYmJuapmqzkcjm7du3iypUrmJiYEBsbS3x8PP7+/mIJzNCu1KEBD+M+MVpTg5OT00s5XWA0Jq1I++yzzzh48CBpaWkIgsCMGTPYunUrr7322lM54hu7V/r6+jh8+DB/+tOfSEtLm1SRtBs3blBdXU13dze9vb2iwDE3N8fS0pLS0lIMBgPLly8nKSkJGxsbFAoF7e3ttLW1iT+NRdoWFhbIZDL6+vrEegWVSoVOp8PJyUkcvm5ra4tCoRBrtpycnAgJCcHHxwetVktubi53795Fq9WKBf46nY7m5mYaGhrQaDTikFzjWJHRBJilpeUT1x0olUqqq6spLi4mMzOT6upq+vv7cXZ2JioqSqwBUavVo845HVqzJk0cmBwYDAYyMjK4fPkyXl5evPrqq7i6utLY2Mif//mfY2dnx6effip26iqVStLS0sjOzsbJyYnly5cTFhaGRqMRzXRLS0tpbGykpqaGjo4ObGxsMDc3JyoqShTuMpkMe3v7EV2pQ0WcmZkZhYWFHD9+HDs7O6Kjo6mtraWhoQEzMzNCQ0OJjo4mNDQUCwsLSkpKOHToELNnz2bp0qU0NTWxc+dOMe01Fp523qfE47Fz506OHj1Ka2srer1eHA7u5uZGcHAw06ZNw9/fXxRXQ0WWtbU1CoWCvXv30t/fz/Tp0ykpKUGr1TJ//nySk5Pp7+/nwIEDdHV1sW7dOry8vIaNWlIoFJiamuLv7y+OWqqrq+Py5cuEh4fj6elJTk4OcrmckJAQkpOTCQ4OHrfPhCAIFBUVcfDgQerr63FwcMDPz4+kpCRmzZolpvqNJ8CjWYoYfw7Nltjb2w8Tb66ursTExIzLmicSk1akGWlra+PQoUMcPHiQGzduADBr1iy2bt3K5s2bn7pmaPHixQQHB/PZZ5+N6fYToSbNOGz6/vRkc3Mz33zzDTY2NmzZsmWEp8zg4KDoKWY0RDSGx/v7+9FoNFhYWODs7CwWHVtYWIiePTqdTrzeGJ3r7OykqamJgYEB7O3tCQ8PJyIiAnt7e/EgJ5PJSEhIYNGiRfj6+o7re2E0hSwuLqawsJCCggLa2tpQqVRYWVnh7e2Nv78/Dg4OI8aKWVlZDRurNXTWqb29vdgWLjE5aG5u5siRI/T397NixQri4uIoKirihz/8IcHBwfzud78bFjVob2/n7NmzVFdXExoayrJly4bZ1sjlciorK/n888+pqqrC3Nyc3t5e4uLiSEpKEs/2BwYGxMaGoWUEcK/o2cnJCUEQyM3NxdzcnA0bNhAQEEBTUxNlZWU0Nzdjbm7OtGnTiIqKoru7mwsXLrB8+XKSk5O5cuUK6enpfO973xvz95007/P58Ytf/ILr168TEBCAj48PMpmMnp4eOjs76ezsRKfTYWNjg4+PD76+vvj4+GBtbY2FhQWWlpbiZ/L69et0dXWRmppKX18fpaWlODs7k5ycjJmZGSdOnKC4uBgvLy9CQkLw8/MTfcr8/f2HpQKNA92//fZbXFxceP3110lJSRmzJc2TIJfLOXnyJPn5+ZiamooGtwkJCcyePfuRdkYGg4GBgYEHesNZWFjwF3/xF89s/S+KSS/ShtLU1CQKtuzsbLF1+GlYuHAhAQEBfPnll2O6/UQQafdjMBi4evUq165dY9q0aaSmpiKXy4cZvRp/N355KBQKTExMsLOzE1v2PTw8GBwcpLS0lJqaGtrb20VPMmPNhKurK05OTmIhv7W1NTNmzGDx4sVERUXR399PZmYmOTk5AOIOOpZ6h/vR6/UjWsj7+/vFsSV1dXU0NTXR3d0tulxbWVnh4+PDtGnTRNf2+2ecGreXsb7hu45Go+HMmTNinemaNWu4ceMGv/rVr0hJSeHnP//5sNS+IAiUlZWJ49OSkpKYP3/+sDSkXC7n97//Pebm5piamnLlyhWxAcbOzo7AwEDR3NPR0ZH+/v4RXam9vb20t7eTlZVFT08PwcHB+Pr6Ymdnh7m5Of39/aK5s7GWU6FQ8Gd/9mdERUXxpz/9CZ1Ox4cffvhYaaBbt27R3t7OypUrJZH2jBgcHESr1Q6zpDBaUahUKrFet7q6mt7eXkxMTPDw8BBthAwGAxqNBoVCwZ07d2hsbMTd3R2DwSDOtbWxscHf31+03fDz82PWrFnY2dmJQs84O7S6upqOjg4cHBzw8fGhvr5ejDA7OTkNu72lpeW4TpoxRtVOnz4t2ii1t7ej1WqJiYnhlVdeGdW/cyzo9fqXcl7tSyXSDAYDly5dYv/+/Rw6dIjBwcHHmt354x//mBUrVhAQEMDAwAB79+7ln//5nzl37hxLliwZ02NMBJHW0tKCUqlELpfT3t7O6dOnqa2tZcqUKdjb26NUKtFoNKhUKjGt19/fj1qtxsTEBEdHR9zc3HB2dsbW1hZzc3MUCoVYpG/0LouOjiYuLo7AwEA0Gg0FBQWkpaVRWFgoHky8vLxE01HjWY+TkxNz585l6dKlo549Gb3KHpRuNNZ2KBQKBEEQO1F7e3tRKBTodDp0Op3YIu7s7ExERARxcXGir490QPpuU1JSwvHjx7GwsGDjxo3cuHGDP/7xj2zevJmPPvpoxOdDq9WSmZlJeno6lpaWLF68mNjYWPF2dXV17Nq1S2xyOXLkCGZmZkyfPp329nYaGxsxGAw4OzsPc2O/v6tTp9Nx/Phxrl27xpQpU4iNjWVgYEAUdEM90Orq6tBoNCQlJREeHs7du3dJTExk2bJlYnpVGgk1eRAEgfb2dkpLSykrK6O1tRUzMzO8vb1FU9mWlhZu3bpFXV0dkZGRzJ8/H41GQ0lJCWq1mujoaJycnLh8+TIymYxXXnkFCwsLKioqKC0tpa+vD3t7e/z8/HB2dkan09Hb20tBQQGmpqbExMSMKNg3NTUVhdv9Au5Rl412nampqRhVKysrIywsDE9PT/Lz85HL5URERJCSkiJ1zv//THqRZhwqe+DAAY4dO0ZnZyfOzs5s3LiRLVu2iGOixsJ7773HpUuXaGlpwdHRkZiYGH70ox+NWaDBxBBpb7/9NkVFRSiVSrq6uhAEQTxDMhgM6PV69Hq9KGTs7Oxwd3fH09MTHx8fseBZLpfT3d1NX18fJiYmeHt7iwcYo/EsQG1tLYWFhXR2duLi4kJiYiLx8fHY2NhQUVHBhQsXKCoqQqfT4ejoKDY0aLVasfbCwsJC9MbR6XTDUkIymQw7OzsxwmVjYyPWLnR0dIgzPI1eQoODg9ja2uLn5yeO6ZHc1iXup6+vj2PHjlFXV8crr7xCbm4uZ86c4S/+4i/YuHHjA+9j/Dz7+vqycuVKMUV/48YNLly4wLZt23B0dGTfvn3o9Xq2bNmCh4eH2LRTVVUldjkb96ng4GD8/f3FKFhBQQHHjx/Hw8ODLVu2iJFmY4S6t7eXiooKPv/8cyoqKnBzcxPF3MyZM8WoipWV1QMnNhhFnHTCMrFQKBTid2p2djaVlZX09fVha2tLWFgYiYmJODg4kJOTQ2RkJBs2bMBgMHD9+nVu3LiBnZ0ds2fPJjMzkzt37ogTXCIjI0lOTsbPz2/Y8+n1etra2vj6669RKBSsWbMGZ2dnMdo39OdYLntUYMTMzAwLCwvMzc3p6uqirKwMc3Nz4uPjMTExoaKiAoVCQUBAAAkJCQQGBj5U/L2M0bOhTFqRlp6ezsGDBzl8+DDt7e04ODiwfv16tmzZwuLFi19Y58dEEGk/+9nPuHXrFt3d3Tg7OxMSEiLWjRkMBqytrUW3Zh8fHywtLUUTQ2PRsnFSg4uLC+7u7ri4uGBiYoJWq0Wn06FQKGhubqa5uRmtVoujoyPu7u7Y2tqiVqvp6uqiqalJtEQxtlQPHaVjTEUbfW9kMhmWlpbY2Njg7u6Ol5cXPj4++Pn5YWFhQXd3txhJEAQBGxsb0eJDLpeL3ZaxsbFERUU9cdhc4ruDwWDgxo0bXLlyBU9PT4qKiigrK+NnP/sZKSkpD7xfXV0dZ86cobW1VUzn29racuDAAWpra/nwww+xsLDg4MGDNDY2snr1auLi4sT79/X1UVNTQ1VVFdXV1QwODmJubi7aIAQHB2MwGDhw4AA6ne6BDVFqtZrPP/9ctNzZv38/nZ2dJCYm4u/vj4eHB5aWlmIkbqgJKCB2pko+aS8OpVJJXV2dWOjf1tYG3DMbNhb6u7u709zcTFlZGVVVVeh0OtFge/r06XzwwQfY2NjQ1dXF3r17uXbtGhqNRrTfWLduHevXr3+ooDE2KLS3tz+VY4Jer38sUdfX1yd6URrr6drb26moqKCnpwc7OzsCAgIemAExij5XV9cx+5lOJiatSDPWS61Zs4YtW7awfPnyp2oVHi9etEgTBIFPPvmEGzduiMLJaNJptNTw9fXFxMQEQRBobGykrKyM0tJSuru7sbS0JDQ0lPDwcEJDQzE1NR2WZqyurub27dtil6aLiwtOTk5iBKyrq4vGxkYUCgVOTk5ihMDa2hpLS0vMzc3FsygzMzMxXanRaMTInTFC1tzcTGdnJ3K5HL1eL1pvGFOwxoONtbU1Hh4eeHh4YGtri6mpqfj4o/18msuM0xaMNR1JSUnP/X8sMf40NTVx5MgR0ZJFrVbz29/+loiIiAfex2AwkJuby+XLl9Hr9cyfP5/Y2Fh27tyJpaUl7733HjKZjDNnznD79m2SkpJYtmzZCONbY5rLKNjq6urQarXY2tri4+NDZWUlarWa9evXk5iYOOJANdTsdt26dfzHf/wHNjY22Nvb09/fj4ODA5GRkURHR+Pt7S3uo83NzbS2tmJlZcWOHTueyfsqMRK1Wk19fb1oidHa2ipmO4yF/oGBgQ8spNdoNFRWVlJWVkZ2djY5OTnY29vzyiuviGUsxk5Ja2trXF1d6erqIigoiM2bN4smsg967MOHD1NZWcm6deuIjY19Vm/DMARBoLi4mNOnTwOwatUqIiMjqa6uJi0tjaqqKhwdHYmPjyckJGSYEDT+NDU1Zc6cOc9lvc+TSSvSjhw5wqpVqyZczcWLFmlwr5uov7+fefPmERwcTFBQkFj7otfrqa2tFbsdu7q6MDU1xcvLC3d3d+zs7FAoFKIwUygUGAwGOjo6aGxsZHBwECcnJyIjI4mMjMTV1RUbGxva2tooLS1lYGCA0NBQFixYQEhIyJhTKTqdThyKXVVVRWtrK4A4EF2j0Yjr7ujowGAw4OrqiqurK76+vqJYNHZcDk2pGqN2xp+Pukyr1SIIAjqdbkRjgnGunampKXFxcfzmN795Nv9ECQoKCsRROUO7a21tbZ9Jik6tVnPmzBlu3LjBjRs38PT05JNPPnnk1BKlUikaILu4uJCQkMDly5eJjY1lzZo1wL0C/TNnzjB16lQ2b978UKNOnU5HQ0OD6M/W2NgoGqLOnDmTLVu2EBISMuy7r6Ojg507d4r+gmfPnmX+/PlotVpKSkrElJlMJsPNzQ0PDw/s7OwwNTVl6tSpvPnmm+PzJkqMQKvVUl9fL0bKmpubRe/IwMBAUZgZ57A+Dv39/ezfv5/PP/+c7u5uQkJCiI6OZs6cOYSGhtLR0UFGRgZyuRyFQoG/vz9bt259aBe90b8vNzeXRYsWkZKS8txS4nK5nFOnTlFaWkpUVBQrV67E1tZWNDgvKyvDwcGBOXPmEB8fPyECM8+aSSvSJioTQaQZuxmNAsM4G7OiooK6ujrkcjkmJia4ubmJsy+NUxqGbqampqJw0uv1hIeHk5KSQlhYmNg5m5eXR0ZGBr29vYSFhZGSkkJAQMAj1zg0elBVVUVdXR06nQ47OzuCg4MJDg7G3t6e2tpaioqK6OrqwsbGRowIuLu709nZSVtbm7i1t7eL3m7W1tZ4enqKmzHS9qCdWqPRiCNGmpqaaGpqEsWgiYmJ6OtmFIZ2dnbY2tqO6bVKPBkXLlygsLBwxDSJoZ/V+wXc0J9PWvJQVFTEF198wdmzZ4mMjGTnzp2PtAeAe7YGZ8+epaamRuzI3LZtGzNmzACgpqaGQ4cOYWVlxbZt28Zsd6BUKqmsrOT06dOcO3cOg8HAlClTcHFxEUWrqakpzc3N5Ofn4+rqKo7zWbRokfh5NY7MamlpwWAw4OXlRXx8vDgDUuLZcPLkSW7fvo2tra0YJQsMDBTnEj8JbW1t3Lx5k4KCAmQyGWFhYdTV1dHZ2UlkZKTYDWxhYYGvry9dXV20tLTQ1taGp6cnr7/++rD0+/0IgkBaWhpXr15l1qxZLF++/JGjz8aL0aJqxtnZ7e3t3Lhxg8LCQiwtLUd4rb2MSCJtnJkIIm3Xrl2Ul5fT1dUlpgvNzMzw8PAgODiYiIgI0SzWeLAbGp1obGzk5s2bFBcXY2ZmxowZM5g1a5ZY46VSqbh16xZZWVkoFAqio6NJSUl55Bf9wMCAWDhdXV2NXC7H3NxcnG4QFBSEubm5OO6kra0NS0tLIiIiiI6OJjAw8KE1FYIg0NvbO0y0tbW1ic0Txk5PV1dXMdWq0WjEMSaCIIjdVN7e3vj4+ODj44Obm9tz+4KSGInBYBC7fY2dvcaxYEMvG2oqDfeEutEI+UFC7v7RYEZ6e3v59a9/zYEDB4iPj2fXrl1jOhAIgkBpaSnnzp3j1q1bWFpa8otf/EIU8z09Pezbt4++vj42btzItGnT0Gq1o448G7oZp24MDAyQl5eHSqXCz88PmUyGTCbD1tZW7BatqqoiJSWF2tpawsLCePXVV0e8n0bn+dLSUpycnPjwww+f4j8k8TC6urrQ6/W4u7s/VURKEAQqKirIysqiuroaBwcHZs2aJTZpDU1Vrl27Fj8/P7FT1OhV2dnZSUtLC/b29mzbto0NGzY89Ds1JyeHkydPEhERwcaNG59rrffg4CCnTp0SR0CuWrVKTNX29vaSmZlJbm6u6LM5Fq+1yYgk0saZiSDSPv/8c0pKSrCwsBAHRYeHh+Pi4vLA++h0OkpKSrh58yZNTU04OzuTlJQkDjqHe6HorKwsbt26hU6nIy4ujjlz5jzwcbVaLXV1daIoMxbEent7i6IsICBg2By6pqYm0bgzOjqakJCQp/pi0Gq1NDY2UlJSQnl5OVVVVTQ1NSGXy8XInbOzMwEBAYSGhhIaGoq3t7dY3yYxeRhqJ/MgQSeXy4d1DpuZmT1QwNna2rJz5052797NjBkz+OMf/zjmZhStVktaWhq/+93vMBgMvP/++0yZMoWBgQG6urq4fPkytbW1+Pn54enpOezgPZq4HLqZmpry7bff0tjYyLJly/Dz8xObEOrr66mvr6euro6IiAg0Gg3vvvvuA8c/6fV6ceKGxMREo9GQn59PVlYW3d3d+Pr6Mnv2bCIiIkYILIPBwKlTp8jJySE1NZV58+Yhk8no7+/n7t274qSVvLw85HI5M2fO5Gc/+xnBwcEPfP67d+9y6NAhfH192bp162PP1HwaBEGgpKSEU6dOAcOjanBPyN28eZPs7GzMzc35q7/6q5fuhFoSaePMRBBpubm5GAwGwsPDsbOze+htBwYGyMnJ4fbt28jlcoKDg0lKSiIkJET8sPf29nLjxg3y8vIwMTEhMTGR5OTkEW77giDQ2toqRsvq6+vR6XQ4ODiIoiwoKEgcXl1aWkpRURF1dXWYmJiII3DCwsKeqNZAp9PR3t4udp02NzfT3t6OwWDA1NRUtBgxRsmM3VBDI2/t7e3odDrgnhv80HSpp6cn7u7u35mZcS8jRguLhwm5/v7+YZ3HN27cID8/Hz8/P7Zs2UJ0dLRY92iMZBkLme+PiLW0tHDlyhX0ej0hISHExMTg7++Pvb09dXV1lJeXExUVxdq1a3F1dRUf91Ho9XrOnTtHdnY28fHxrFy5EjMzM7H+6eDBg1y/fl2sr1y5ciWRkZEEBQUxZcqU70Qtz2Snt7eX7OxscnNz0Wg0D7TQuB9BEEhPT+fy5cskJCSwatWqYcJFqVRSUFDArl27uHDhAmZmZqxYsYKVK1cSEREhTkUYSkNDA3v37sXe3p7t27c/kfn40/CwqBrcO0Frb2/H39//ua7reSCJtHFmIoi0sWBMaZaUlGBqakpsbCyzZs0aVifT3t7O9evXKSoqwsrKiuTkZBITE4edSRk7Pu+3Epg6dapYW2ZsnVapVJSVlVFUVER1dTUAQUFBREdHEx4e/lhNIHq9flRBptfrRcduY7rSx8cHDw+PMYkrg8FAd3f3sHRpW1sbPT09wL2uYldXVzF1PJH/xxJPhvEzYOx+bGlpYc+ePeTn5+Pg4ICnpyc2NjYjppkYLWGMKXVXV1fc3d1RqVRcuXJFvDw+Pp5FixZha2tLSUkJx44dw83Nja1btz72wS8/P5+TJ0/i5eXFli1bxBMnQRA4duwYN2/epLOzEycnJ3x8fBgYGBDnOBpPmnx8fF666MNkRRAEGhoayMrKorS0FCsrKxISEkhMTHyiz8bx48cJDg5m8+bNowrz0tJS/vf//t9UVFTg6+tLbGwsnp6eTJs2jfDwcKZOnSpG6zo7O9m9ezcGg4EdO3aIM2qfJ8XFxWJUbeXKlURFRb30Pn+SSBtnJrJIG0tKE+4JuPT0dO7evYujo6PYSWNubo5GoxFTmFVVVXR0dIimnEZR5ufnJwoijUZDeXk5RUVFVFRUYDAYCAgIYPr06URERIwppWjsLh0qyIzDio3eaEMjZJ6enuM+0kmtVtPR0TGsUcHd3Z3Vq1eP6/NIPFuM8/+GRrtG+3uoIafRcf3ixYvU19cTFBREWFiYaGRrjKQNDg6OWiun0+moqqqioaEBb29v+vv7sbCwIC4ujhkzZohGpObm5mzZsoWIiIhhY6ceRVNTEwcOHMBgMLBlyxYxmqDX69mzZw937tzBxMSEbdu2ERAQIHaN1tTUoFar8fHx4YMPPhj391pi7Oj1eoqLi8nKyqK5uRk3NzeSkpKIjY19qqhnVVUVBw4cwM3Njddff33UzIpOp+Ozzz7jm2++wdbWlqSkJCwsLBgYGMDKyoqwsDDCw8MJCQlBrVazZ88eent72bZt26jefc+a+6NqK1eufGTGaDIjibRxZiKKtLGkNAVBoLq6mvT0dGpra3FzcyMlJYXo6OhhXZgNDQ3o9XocHR1FUXb/eBudTkdlZSVFRUWin5qvr6/o/v+w4k6DwUBnZ+cIQabT6UT7gKERMi8vL2nG5ktKWloa3d3dJCcnP9IGA+597h5VgH9/TZq5ufmImq/7a8GMTTVdXV388Ic/pL29neTkZAwGA/Pnz2fevHkPjEQZ/ap6e3vZtWsXHR0dzJ07l7y8PIqLi8XGGVNTU4qLi+nv7ycsLIwpU6Y8sunB1tZWfF65XM7Bgwdpampi5cqVJCQkAPdOLr744gtu3rxJQEAAf/VXfyVO3zCaoSqVSsLCwp7yvyXxJAwODpKTk0N2drb43ZycnPxY9kWPorW1lT179mBmZsaOHTtwdXUd9XbZ2dl88skn9PT0kJycTEpKClqtlrt379LW1oaZmZn4fV9QUEB7ezsbN24kMjJyXNb5uHxXomqSSBtnJpJIa2xsJDs7m+Li4gemNA0GA2VlZaSnp9PS0oKPjw8zZszA1NRUPNtWKBRiE4KxtszV1XXYDqHX66mpqREd21UqFZ6enkRHRxMdHT1qYbIgCHR1dQ0TZC0tLWIaySjIjBEyb29vqZbmO0ROTg7p6en09vbi6+tLVFQUHh4eYk3Z/VGwwcHBYfe3tLQcVYAN3R53LFJtbS1/+7d/i5mZGRs3bqS4uBg/Pz9xOPXDGBgY4Pe//z2enp7s2LGD9vZ2zpw5Q11dHaGhoSQlJZGWlkZOTg7BwcFERkYOq5+7P8JnNPQe6h9XUlJCTU0NCQkJ4ngftVrNp59+SnZ2NsuWLeN73/veS3kwm0wMtdAAiI2NJSkp6ZmlEHt7e9mzZw+Dg4Ns27btgbVbzc3NfP755xQXF+Pp6UlCQgIrVqzAxMSEu3fvUlpaSkNDg3gyrVar2bZt22ONXxxPBgcHOX36NMXFxURERLBp06aXbkyUJNLGmYkg0oqKisjMzBRTmrNmzSIuLm5YSlOv11NQUMD169dpa2vD3t4ed3d3BgcH6erqQiaT4evrK46o8fPzG/HhFwSB+vp6CgsLKSkpQaFQ4OrqKgqzoWJQEASxzmeoIDP6mrm4uIyIkE00o2KJ58vVq1fF+sWqqir6+vqwsrISP5eurq4PjYI9TsrwccjLy+OnP/0pfn5+fPTRR1y6dAmlUsnq1auZPn36Q+9bU1PDV199xbx580hNTRU9oc6fP49CoSA5ORkbGxsuXrw4wvhWEAQUCsUjmx5qamooLy/H3t6eqKgoHB0dkclkpKen09HRwfbt25kzZ86IaKHEs+V+Cw17e3tmzZpFQkLCc/H5UiqV7N+/n6amJl599dUHTtMYHBzk0KFD5ObmiiP9Zs+ezfz587G0tGRwcFAUbOfOnaO+vp4ZM2awbt06IiMj8fDweO4nAcXFxdTX17NixYrn+rzPA0mkjTMTQaQdOnQIlUo1IqUJ92rEbt++zdmzZ2loaMDS0hI7Ozvs7OzEMU7GkPZordaCINDc3ExRURFFRUUMDAzg6OgoCjMvLy/g3pnb/YJMpVIB4OzsLIoxoyfZ82zrlpgc5OTk0NbWJgoJpVLJ3bt3qa6uFmu6kpKSHmot86w4d+4c//Zv/0ZcXBx/+7d/y+XLlyksLCQmJoZVq1Y9VCCmp6dz6dIltm/fTmhoKHBvvzROOrCxsSEiIoKCggKsra0fy/gW7tl/lJWVsX//flQqFa+88grW1tZUV1eze/duVCoVS5YsEYWBt7e35JP2DDFaaNy8eZOuri58fX1JTk4mMjLyuUd9dDodx44do6SkhBUrVjBr1qxRb2cwGLh48SLXr1/HwsICg8GAjY0NS5YsISYmRhRharWaQ4cOcfz4cSwsLMSTp/DwcMLDw/H395eaUp4SSaSNMxNBpOn1+hE7f0tLCydOnCAtLY329nZcXFwICQlh+vTpojBzdnYe9QzIOB3AKMyMQ2+joqKIiorC3t5edOs3CjLj+CRHR8dhETJvb++X2h1a4tkzMDDA7du3uXXrllhPlZyczNSpU5/bGbwgCOzevZuvv/6axYsX89d//dein5ONjQ2vvvrqA60SBEFg3759NDQ08OGHHw5Lk/b09HD+/HlKS0txdXVFLpcDiMa3j4NcLufAgQM0NzezatUq4uLiyMrK4sc//rHYDdrS0iLOGZV4NhhHLEVERIgWGi8y3SwIAhcuXCAjI4NXXnmFxYsXP3A9hYWFHD9+HFtbW5ycnKitrcXf35+VK1cOqxMtLCzk6NGj2NnZERQURFVVFXK5HFtbW7FTNCgoSLIvegIkkTbOTASRBvemAtTW1lJYWMiVK1e4e/cuANOnT2fx4sXExsaKg9YfRFdXlyjMOjo6sLKyIjAwUCw8bW1tpbm5GYVCAYCDg8MwMebj4yOlUSSeGVqtlsLCQrKysmhvb8fLy4vk5GSio6Ofy8FAr9fz7//+75w9e5atW7fyzjvv0Nvby5EjR2hubmb+/PnMnTt31H1MqVTy2WefYWtryzvvvDNivdXV1Zw9e1aMQFtYWLB8+fIxzVFUqVR0d3fT2dlJe3s7ly5dorCwEBcXF6ZOnUplZSUZGRkkJiayfv16/P39mTdv3ri+NxL/j97eXmQy2XP3FnsUWVlZnDt3jqioKNavX//Afaa1tZUDBw6gVqtJSkoS5ycnJCSwcOFC8aS7urqaAwcO4OrqyrZt2+jr6xMnHnR1dWFhYUFoaCjh4eGEhoZK5SxjRBJp48xEEGlGf6S6ujr6+/txd3dnwYIFrFmz5oGdPUZ6e3tF9//a2lpUKhVOTk7Y2tqi1+vFCJmdnd2wCJmPj89L3QYtMXERBIGamhqysrIoLy/H1taWxMREZs6c+cw/k0qlkl/96lfk5OTw4Ycfsm7dOvR6PdeuXePatWsEBASwcePGUQ/Qzc3N7Ny5k4SEBFauXDnier1ez+3bt7l8+TLV1dUIgsDSpUvZsGEDMpmMnp4eurq6RmzG6BuAvb09rq6u9PT0UFJSQmBgIK+//jr79u3j/Pnz/PznP2f+/PnP9D2SmLiUlJRw9OhR/Pz82Lp16wOFk1Kp5PDhw1RXV7Nw4ULMzMxIS0tDJpOxcOFCEhISMDExobW1ld27d2NhYcGOHTtwcXFBEAQ6OzspKyujtLSU5uZmTE1NCQwMJDw8nGnTpo0wRpf4f0gibZyZCCLt3//93ykpKcHX15eFCxeSmJj40BoZuVzOrVu3yMzMpLy8HIVCIXbGubi4DIuQGaNk9vb2UoeYxISjq6uLmzdvkp+fj16vZ/r06SQlJY3JwuNJ6e7u5mc/+xn19fX8z//5P0lJSQGgrq6Oo0ePolarWb16NdHR0SPue/v2bU6ePMmrr746oulAEAT6+vpoaGjg/PnzXL16laamJry8vIiNjRX3aUtLS9E89/5t6H7f0NDAwYMHkclkbNiwgX/8x3+kpaWFf/u3fyMkJOSZvT8SE5v6+nr27dv3yGkCBoOBK1eukJ6eTnR0NIsWLeLatWvk5eXh5eXFihUrmDJlCr29vezevRulUsn27dvx8fEZ9jh9fX3cvXuXsrIyamtrMRgM+Pn5ERERQXh4+CMDCd81JJE2zkwEkXbz5k1MTU2ZMWPGqCFshUJBdXU1WVlZ5ObmUl1djVqtxsXFBX9/f2JiYpgyZYooyhwcHCRBJjGpUKlU5Obmkp2dTW9vL1OnTiU5OZmwsLBnUsjc0NDA3//936NUKvnpT38qzhdUqVScOHGC4uJiZsyYwYoVK4YJJ4PBwIEDB8jLy2P58uWiLY1xM44oMzU1xdTUlNLSUkpLS/Hw8ODjjz9mzpw5oo/bWBgYGODgwYM0Nzcza9Ys/vCHP+Dn58fvfvc7aR//DmOcJqDX/3/t3XdUVNf6N/DvgPSOFBGlqthFQYKiYq8YFXuJGktMTLlpN9fEGDXN5KZpcqNRY9AYTdTYYkVjVFQERSQqWBABKwIBkSYDzH7/8J35MVJkgGEOw/ez1qwVzpzycLJlntln72eXYurUqaoJYBVJSEjArl27YGdnh4kTJ6KwsBD79+/HnTt30KlTJwwaNAhNmjTB5s2bkZ6ejvHjx6smyDypsLAQ165dw5UrV3D9+nUUFxfD0dERbdu2Rbt27eDi4tLo2yWTtDomhSStrMLCQtWg/tTUVPz9999ISkpCVlYWDA0N4eXlpZol5+npCVtb20b/j4L0h7IOYFRUFG7evKlaZaNr1651XqLj4sWL+Oijj2BlZYXFixfDzc0NwOMesZiYGOzYsQMA4O/vDwMDA7XHk7GxsRBCoF+/fnB2dlbrDXNwcICNjQ0MDAwghMCZM2fwzTffICMjAxMnTsTzzz+vUUHnkpISHDhwAOfOnYOxsTFcXV0xffp0/rtv5PLy8rBp0yZkZWVh4sSJ8PLyqnTf9PR0bNmyBfn5+Rg3bhy8vb0RFxeHP//8E8XFxejTpw/8/Pywa9cuJCYm4tlnn4Wvr2+V1y8uLkZSUhKuXLmCq1evorCwENbW1qqEzc3NTe9qoFUHk7Q6JoUkLTY2FklJSbh79y4yMzORlZWFrKwsFBUVwcLCQjUbrqJF0on01d27dxEVFYVLly7ByMgIXbt2RUBAQJ2V8FAoFNizZw++++47NG3aFKNGjUJxcTH++ecfPHz4EIWFhUhISEBRUZHqi5Gjo6Pq8c727dvRrl07jB079qkJU2FhIb766iscOXIErVu3xiuvvIJOnTpplGjFxMRg//79cHd3Z5JGAB6XC9m6dStu3LiBUaNGoUuXLpXu++jRI+zYsQOJiYno378/evXqhaKiIhw7dgxnzpyBnZ0dBg8ejGvXruHcuXMYMGBAtSa+AI//LaWmpuLKlSu4cuUKcnJyYGZmprZEVWNZaYZJWh2TQpL2+++/IzExEQUFBaoCoN7e3ujUqZOquCVRY5Wbm4uzZ88iJiYGhYWF8PHxQWBgINzd3Z/6ASKEQF5eXoUD9rOyslRrMF68eBHe3t6YNGkSXF1d4eDggKZNm8LGxgZnzpzByZMn4e7ujjFjxqj+PcbHx2Pbtm0YPnx4pfWrnozlyJEjWL16NUpKSjB06FCMGjWqykdVT7p58yaysrKe2stBjUdpaSn27duH2NjYpyZWQggcP34cx44dQ7t27TB69GiYmJioVtNITk5G69atYWVlhdjYWHTv3l21gkF1CSGQlpammimanp4OIyMjeHt7o23btmjTpo1el3ViklbHpJCkrV+/HikpKXB0dFQlZhyMSaSuqhIeJSUlFSZi//zzj2qVDJlMBjs7u3KD9W1tbfHbb79h3759GDBgAF555ZVyy5mlpKRgx44dKC4uxsiRI1XrHx44cAAxMTGYNWsWXF1dq/V7JCcn44cffkBSUhK8vLwQHByMfv366fUHF2mXEAIRERE4evQo/P39MXz48CoTq6tXr2LHjh2wtrbGxIkT4eDgACGEalWCvLw8ODg44O7du+jYsSPGjh1b4zI5//zzj2rFg9u3b0Mmk8Hd3R1t27ZFQECA3vUIM0mrY1JI0lJTU2FqaqqT5TmIGoqSkhJkZ2cjMzMTFy9eRHR0NK5fv47S0lLVurHGxsawtLRUjQ0rm4zZ2dlVOkamsLAQ33zzDSIjIxEaGoqZM2eW+5ArLCzEnj17kJCQgG7dumHo0KEwNDREWFgYcnNzMW/evGonWtnZ2di0aRPi4+NhZmYGZ2dn9OvXTzX+jagmzp8/jz179qB169YYO3ZslWsnZ2Zm4rfffkNubq5a8eXi4mKcPHkSp06dQl5eHgoKCuDr64vJkyfXeqWZvLw81UzRgoICzJ07t1bnkyImaXVMCkkaET2mLGNRUY/YgwcPoPzzpyxjYWxsjLS0NNy5cwempqYICAhA7969NXqEqJSdnY3PPvsM165dw4wZMzBq1KhyX5qEEDh//jwOHDgAa2trjB07FhYWFli9ejWaN2+OqVOnVvuLVlFREXbu3ImLFy/C0tISRUVFcHZ2xrBhw+Dh4aFx/EQAcP36dWzduhWOjo6YMmVKlQXKi4qKsGvXLly+fBnBwcHo27evqv1mZ2cjPDwc0dHRuH37Nrp164b58+fX2fAbhUKhl19ImKTVMSZpRPVLCIHCwsJySZhy0kzZMhb29vYV1hN7soxFYWEhzp8/j+joaOTk5NS4hMetW7fw2WefITMzE/Pnz6+0cOw///yD7du3Iy0tDQMGDICzszM2bdqEvn37alRsVgiBY8eO4fjx42jevDmEELh37x46dOiAQYMGqS1BRVRd9+7dw6ZNm2BsbIypU6dWOXxGCIGTJ0/ir7/+QuvWrREaGqpWJPf69evYtm0bIiIi0KJFC7z//vtwd3evj1+jQWKSVseYpBFph1wuR1ZWVoW9YsqVMIDH68VWlIjZ2tpq/E27Lkp4XLp0CV9++SUA4I033qh0xlxpaSn++usvREZGwsPDAw4ODoiJicFzzz1XZTmEiiQkJGDnzp1o2rQpOnbsiOjoaBQWFqJXr14ICgpqNDPjqO4oi9QWFBRgypQpla5Nq5SYmIjt27fDwsICEydOhJOTk+o9ZVv/3//+B7lcjpdffhkjRozg8JwKMEmrY0zSiOpGQkICbty4oUrEHj58qHrP3Ny8wkTM3t5eawnInTt3EB0drVbC45lnnoGdnd1Tj42IiMCqVatgZ2eHt956C97e3pXum5ycjB07dkAul8PAwAAymQwvvvgirK2tNYo3LS0Nv/76K0pLSzF69GikpKTg9OnTsLKywuDBg9GuXTt+KJJGCgsL8euvv+LevXsYO3Ys2rZtW+X+WVlZ2LJlC7KzszF69GjVBBmlzMxMfPjhh7h48SKCg4MxZ86cpyZ/jQ2TtDrGJI2obhw4cAApKSkVJmO6nLn48OFDnD17FufOnat2CQ8hBHbu3InNmzfDy8sLb775ZpXj3AoKCrBnzx5cuHABaWlpCAgIwJw5czQu5pmfn4+tW7fi9u3bCAkJgZubG8LDw3Ht2jV4enpi6NChcHZ21uic1LiVlJRgx44duHz5MoYPH47u3btXub9cLscff/yBS5cuoVevXujfv79aj3ZpaSnWrl2LvXv3wsXFBSNGjMDAgQO5FvT/xyStjjFJI2ociouLceHCBURFRSEjI0OthEdF5QVKS0uxfv167Nu3D76+vvjXv/5V5aBpIQRiY2OxZcsWJCQkYMqUKZgyZYrGcZaWlmL//v04d+4cAgMDMXjwYCQlJeHgwYPIyspCQEAAhg4dyl41qjYhBMLDwxEVFYVevXphwIABVbYfIQROnz6Nw4cPw8vLC+PGjVOb2ak8344dO6BQKODl5YV+/fohICCgUa4yUBaTtDKWLVuGHTt24MqVKzAzM0PPnj3x+eefq6YSVweTNKLGRQihWgs3MTERFhYW6N69O/z9/cv1BhQWFmLlypU4deoU+vTpgxdffFFtUHVFMjMz8eWXXyI6OhqzZ8/WaMZn2RhjYmJw4MABeHh4YPz48TA2NkZ0dDRyc3MxZMgQjX9votOnT+PQoUPo2LEjRo0a9dTaZzdu3MDvv/8OExMTTJw4sVxv8unTp7F3714AQJMmTeDk5IRhw4ZpPCZTnzBJK2Po0KGYNGkSunfvjpKSErz33nu4dOkSEhISqpx2XBaTNKLGKzMzE9HR0YiLi4NCoUCnTp0QGBio9mGUnZ2NFStW4NKlSxg2bBhmzJjx1A+34uJifPjhh4iMjMTYsWPx3HPP1WhJt+TkZGzbtg2mpqaYPHkyHB0dNT4HUVnx8fHYuXMnWrZsiYkTJz71S8eDBw+wZcsWZGZm4tlnn0WnTp3U3r906RJ27twJW1tbGBsb4969e2jXrh2GDBnSKGcnM0mrQkZGBpycnHD8+HH06dOnWscwSSOiwsJCxMbG4syZM8jJyYGnpycCAwPRunVrGBgY4NatW1ixYgXu3LmDsWPHVmu9zqKiInz66ae4ePEi/P39MXbsWI16+ZWys7Px66+/IicnR63oKFFNpaam4rfffoOVlRWmTp361NpnxcXF2Lt3L/7++2/06NEDgwYNUhunlpycjN9++w329vbw9fXFyZMnG+3sZCZpVbh+/Tpat26NixcvomPHjtU6hkkaESkpFApcvnwZUVFRuHXrFuzt7fHMM8/A19cXiYmJWLlyJQoKCjBt2jQMHDjwqedLT0/HypUr8fDhQ1hZWSEgIACDBw/W+ENLWfj26tWrqsWxOSaNaiMjIwObNm2CQqHA1KlTnzohRQiBM2fOIDw8HO7u7hg3bpzaE6u0tDRs2rQJTZo0wYQJExAfH6+anTxkyBC0bdu2UbRZJmmVUCgUePbZZ/HgwQOcPHmy0v2KiopQVFSk+jkuLg7BwcFM0ohIzZ07dxAVFYX4+HgYGRmhW7duKC0txfbt22FsbIxZs2Y9daYcAFy4cAHbt29Hq1atkJqaCjs7O4wdO1bjVRGUhW//+eefavXkET1Nbm4uNm/ejKysLEyaNAmenp5PPSYlJQXbtm1DkyZNMHHiRDRv3lz1nrI2W2FhIaZMmQJTU1McPHgQiYmJ8Pb2xrBhw+Dg4KDNX0nnmKRV4qWXXsKBAwdw8uTJKuu2LFmyBEuXLi23nUkaEVVEWcIjJiYGhYWFyM/PR1JSEtzc3PDiiy8+tfYUAOzbtw+xsbEYPXo0Tp06hYyMDAwcOBCBgYE1mlTABI3qSlFREbZt24bk5GSMGjUKnTt3fuoxDx8+xJYtW3D//n2EhITA19dX9V5BQQE2b96M+/fvY8KECWjVqhWuXbuGgwcPIicnB4GBgQgODq52cemGhklaBV555RXs3r0bERERT/0mwJ40IqoJZQmPyMhIHDlyBHfv3kXnzp3x/vvvP3WtzZKSEvz0008oKCjA7NmzERkZidOnT6NVq1YYPXo0a0yRTpWWlmLv3r04f/48Bg4ciKCgoKd+ESgpKcH+/fsRGxuLgIAADBkyRFV+o7i4GL///jsSExPx7LPPwtfXFyUlJYiMjMSJEydgYmKCQYMGoXPnznr3hYNJWhlCCLz66qvYuXMnjh07htatW2t8Do5JIyJNCCGQkJCAzz77DHFxcXB1dcXLL7+M/v37Vzmr/MGDB1i9ejVatmyJyZMnIykpCbt27YIQAqNGjUKbNm3q8bcgUieEwPHjx3Hs2DF0794dw4YNe+qybEIInDt3DgcOHECLFi0wfvx41RcOhUKBffv24dy5c+jfvz969+4NmUyGnJwcHDp0CBkZGZg3b57e1VVjklbG/PnzsXnzZuzevVttxpONjY1a4b2qMEkjoprIzs7GF198gcjISJibm8PPzw/+/v545plnKh1vdu3aNWzevBkDBw5Er169kJ+fj927d+PatWsICAjAoEGDGtVMOJKe2NhY7N27F23atMHYsWOr1R5v3bqFrVu3AgAmTpyoGnIkhEBERASOHj1aLvErKirSy0eeTNLKqKybNCwsDDNnzqzWOZikEVFN3bp1Cz/88AOSk5PRsmVLODg4ID8/v1wJj7L++usvnDhxAjNmzICHhweEEDh79iwOHToEe3t7jB07lks/kU4lJiZi27ZtcHJywuTJk6tVdzQ3Nxdbt27F3bt3MXz4cPj5+anei42NxZ49e9C2bVuEhobq9RcRJml1jEkaEdVGfHw8fvrpJzx48AADBgxAp06dEB0djdu3b6uV8FD2GigUCmzcuFH1uEdZ5DY9PR2///47srKyMGjQIAQEBOjdeB1qOO7evYvNmzfD2NgY06ZNg729/VOPKS0txcGDB3H27Fn4+flh2LBhqsLPV69exe+//w4XFxdMnjy52k+7GhomaXWMSRoR1daJEyewdetWlJaWYsSIERg+fLiqhEdCQoKqhEdAQADs7OyQn5+PH374Afb29pgxY4aqt62kpASHDx9GdHQ0WrdujdGjR1d79RSiupadnY1NmzapSmq4urpW67jz58+rFmCfMGECrK2tAQC3b9/G5s2bYWFhgWnTpj21iG5DxCStjjFJI6LaEkLgjz/+wMGDB2FiYoLx48cjKCgIwONyBWfOnMG5c+fw6NEjtG3bFoGBgQCADRs2qCq4l5WYmIhdu3YBAEaPHl2jSVFEdaGgoAC//vor0tLSMG7cuGqveHHnzh1s2bIFCoUCEyZMgJubG4DHS7H98ssvkMlkePnll5+6xFpDwyStjjFJI6K6UFpail9++QUnT56EnZ0dnnvuObV1DouLi/H3338jKioKmZmZcHFxgZmZGa5fv44pU6aUq7eWl5eH3bt3IzExEYGBgRg4cKDefaBRw1BcXIwdO3bgypUrGDFiBPz9/at1XH5+PrZt24abN29i6NCh6N69O2QyGXJzc3Hv3j29nNHMJK2OMUkjorpSWFiIH3/8EX///TdcXFwwa9ascrUbhRBISkpCVFQUEhMTkZSUBHNzcyxatAgtW7Yst++ZM2dw6NAhODg4YOzYsXBycqrPX4kIwOOxlOHh4YiOjkbv3r3Rv3//ao2ZLC0txeHDhxEVFQVfX1+MGDGCEweo+pikEVFdys7Oxpo1a5CYmAhPT0/MnTu30sQqIyMDJ06cwE8//QQDAwPMmDEDvXr1Kje78/79+/j999+RnZ2NwYMHq3okiOqTEAKnT5/GoUOH0KVLFzz77LPVrnN24cIF/PHHH3BycsLEiRP1cjwawCStzjFJI6K6duvWLaxbtw53795F27ZtMWfOHNXg6YokJyfj008/hUKhQMuWLVUlPNq0aaNKxoqLi3H48GHk5ORg0qRJTNJIZy5duoSdO3fC3d0dEyZMgKmpabWOu3fvHrZs2QK5XI7x48dXa63QhoZJWh1jkkZE2hAfH49ffvkFOTk56NSpE2bNmlVl8c64uDjs2LEDnTp1Qk5OTpUlPJ5WCZ5I21JSUvDbb7/BxsYGU6dOrfJLSFkFBQX4/fff8c8//+DVV1/Vu3GWTNLqGJM0ItKWEydO4I8//oBcLoefnx+mTp1a5eOhP/74AxcuXMCcOXNQUlJSroTHM888A1tb2/r7BYiqkJ6ejk2bNkEIgWnTplV7vKRCoUBOTg7s7Oy0HGH9Y5JWx5ikEZG2KEtzHD9+HADQu3dvjB49utJHlcXFxVi3bh3kcjleeOEFmJqaIicnB2fPnlWV8OjUqRPGjBnDx50kCbm5udi0aRMePHiASZMmwcPDQ9ch6RT7uImIGgiZTIaQkBB06dJFNej66NGjle5vZGSECRMmoKCgALt374YQAjY2Nhg4cCDeeOMNjBgxAo6OjkzQSDKsrKzw/PPPo3nz5ti4cSMuXryo65B0ikkaEVEDYmhoiIkTJ6JVq1aQy+U4cuQIzp07V+n+9vb2GD16NC5fvoyoqCjVdmNjY/j7+6N37971ETZRtZmYmGDq1Kno2LEjtm/fjlOnTqGxPvRjkkZE1MCYmppiypQpaN68OfLy8vDHH38gMTGx0v3btm2LoKAgHD58GDdv3qzHSIlqxtDQEKNHj0afPn1w+PBhHDhwAAqFQtdh1TsmaUREDZCdnR2mTJkCBwcHPHz4EFu2bMHdu3cr3X/AgAFo2bIltm3bhry8vHqMlKhmZDIZ+vfvj5EjR+Ls2bPYtm0biouLdR1WvWKSRkTUQLVo0QKhoaGwtLTEgwcPsGnTJmRnZ1e4r4GBAcaNGwchBLZv394oeyWoYfLz88PkyZNx/fp1/PzzzygoKNB1SPWGSRoRUQPWoUMHDBkyBMbGxqrFpiv7ELOyssK4ceOQkpKCY8eO1W+gRLXQpk0bzJw5E1lZWVi3bl2lX0b0DZM0IqIGLigoCIGBgTA0NMSdO3fw66+/VvpYyMPDAwMGDEBERASuXbtWz5ES1Zyrqytmz54NAPjxxx+rfLyvL5ikERE1cDKZDCNGjEDbtm1hYGCApKQk7Ny5s9JHmkFBQfDx8cHOnTvx4MGD+g2WqBbs7e0xe/Zs2NnZISwsrMoJM/qASRoRkR4wNDTEhAkT4OrqCplMhr///huHDh2qcF+ZTIbRo0fD1NQU27Zta7TlDahhMjc3x4wZM+Dt7Y1ff/21yhI0DR2TNCIiPaEszWFrawsDAwNERkbi9OnTFe5rZmaGCRMmoH///ixmSw2OslCzv78/9uzZg6NHj+rllw0maUREesTOzg6TJ0+GiYkJZDIZwsPDER8fX+G+Li4u8Pb2rucIieqGgYEBhg0bhkGDBiE1NVUvZywzSSMi0jMtWrTAmDFjADx+tLlz506kpqbqOCqiuieTyRAUFITp06fD0NBQ1+HUOSZpRER6qEOHDhg0aBBKS0uhUCjw22+/ISMjQ9dhEWmFgYF+pjP6+VsRERGCgoLg7++P0tJSyOVybNq0iasNEDUgTNKIiPSUsjSHt7c3FAoFHj58iE2bNkEul+s6NCKqBiZpRER6TFmaw8HBAQYGBkhLS8PWrVtRWlqq69CI6CmYpBER6TllaQ4TExOYmJggMTER+/bt08uSBUT6hEkaEVEjoCzNUVJSAmtra5w7dw4RERG6DouIqsAkjYiokWjRogVCQ0ORm5sLOzs7HD16FHFxcboOi4gqwSSNiKgRad++PQYOHIjs7GzY29vj/PnzfOxJJFFM0p4QERGBkSNHonnz5pDJZNi1a5euQyIiqlNBQUHw8/PDgwcP0KtXLy4LRSRRTNKekJ+fjy5duuD777/XdShERFqhLM3h4eGBP//8kz1pRBLVRNcBSM2wYcMwbNgwXYdBRKRVytIcxcXF7EkjkigmabVUVFSEoqIi1c+s5k1EDYWpqSlMTU11HQYRVYKPO2tp2bJlsLGxUb2Cg4N1HRIRERHpASZptfTuu+8iJydH9Tp+/LiuQyIiIiI9wMedtaSs4K1kaWmpw2iIiIhIX7AnjYiIiEiC2JP2hLy8PFy/fl31c3JyMuLi4mBvbw83NzcdRkZERESNCZO0J8TExKBfv36qn998800AwIwZM7B+/XodRUVERESNDZO0J/Tt21cvCjveu3cP9+7d03UYes3FxQUuLi66DkOvsR1rH9ux9rEda5++tmMmaXXMxcUFixcv1mljKSoqwuTJkznTVMuCg4MRHh6uNnGE6g7bcf1gO9YutuP6oa/tWCb0oduI1Dx8+BA2NjY4fvw4Z5tqSV5eHoKDg5GTkwNra2tdh6OX2I61j+1Y+9iOtU+f2zF70vSYr6+v3jVYqXj48KGuQ2g02I61h+24/rAda48+t2OW4CAiIiKSICZpRERERBLEJE0PmZiYYPHixXo3gFJKeI+1j/dY+3iPtY/3WPv0+R5z4gARERGRBLEnjYiIiEiCmKQRERERSRCTNCIiIiIJYpJGVUpJSYFMJuO6pdSgsR2TPmA7bnyYpNWhpKQkzJs3D15eXjA1NYW1tTWCgoKwYsUKFBYWau26CQkJWLJkCVJSUrR2jer45JNP8Oyzz8LZ2RkymQxLlizRaTwymaxar2PHjtX6WgUFBViyZIlG55La/VJqzO34ypUreOedd+Dr6wsrKyu4uLhgxIgRiImJ0VlMUm7HUrxfSo25Hd+9exfTpk2Dj48PrKysYGtri4CAAGzYsEFna1NLuR1L8X4pccWBOrJv3z6MHz8eJiYmmD59Ojp27Ai5XI6TJ0/i3//+N+Lj47FmzRqtXDshIQFLly5F37594eHhoZVrVMf777+PZs2aoWvXrggPD9dZHEobN25U+/nnn3/G4cOHy21v165dra9VUFCApUuXAgD69u1brWOkdr8AtuMff/wR69atw9ixYzF//nzk5ORg9erVCAwMxMGDBzFw4MB6j0nK7ViK9wtgO87MzMTt27cxbtw4uLm5obi4GIcPH8bMmTNx9epVfPrpp/Uek5TbsRTvl4qgWrtx44awtLQUbdu2FXfv3i33fmJioli+fLnWrr9t2zYBQBw9evSp+yoUClFQUFDtcycnJwsAIiwsrFr7CiFERkaGACAWL15c7evUh5dfflloq8nX5HeW2v1iOxYiJiZG5Obmqm3LzMwUjo6OIigoqNrX0yYptWMp3i+248qFhIQICwsLUVJSUqPj65KU2nFlpHC/mKTVgRdffFEAEKdOnarW/sXFxeLDDz8UXl5ewtjYWLi7u4t3331XPHr0SG0/d3d3MWLECHHixAnRvXt3YWJiIjw9PcWGDRtU+4SFhQkA5V7KPxDKcxw8eFD4+fkJExMT8c033wghhEhKShLjxo0TdnZ2wszMTDzzzDNi7969ajHU5I+CVJKOJ1X0R6G0tFR88803on379sLExEQ4OTmJF154QWRlZantd/bsWTF48GDRtGlTYWpqKjw8PMTzzz8vhPi/e/Tkq7q/v1TuF9tx5UJDQ4W9vX2Njq1rUm3HZenyfrEdV+6VV14RMplMo8RQWxpCO5bC/WKSVgdcXV2Fl5dXtfefMWOGACDGjRsnvv/+ezF9+nQBQIwePVptP3d3d+Hj4yOcnZ3Fe++9J/73v/+Jbt26CZlMJi5duiSEePwP+7XXXhMAxHvvvSc2btwoNm7cKNLS0lTnaNWqlbCzsxMLFiwQP/zwgzh69KhIS0sTzs7OwsrKSixcuFB8/fXXokuXLsLAwEDs2LFDFYO+J2lz5swRTZo0EXPnzhU//PCD+M9//iMsLCxE9+7dhVwuF0IIcf/+fWFnZyfatGkjvvjiC7F27VqxcOFC0a5dOyGEEHl5eWLVqlUCgBgzZozq/8Hff/9drbikcr/YjivXs2dP0aZNmxodW9ek2o7L0uX9Yjv+PwUFBSIjI0MkJyeL9evXCwsLC9GzZ89q3xttkmI7luL9YpJWSzk5OQKAGDVqVLX2j4uLEwDEnDlz1La//fbbAoD466+/VNvc3d0FABEREaHalp6eLkxMTMRbb72l2lZV97ryHAcPHlTb/vrrrwsA4sSJE6ptubm5wtPTU3h4eIjS0lIhhH4naSdOnBAAxKZNm9T2O3jwoNr2nTt3CgDi7NmzlZ67Nr+zFO4X23HlIiIihEwmE4sWLdL4WG2QajtW0uX9YjtWt2zZMrXepAEDBoibN29W61htk2I7luL94uzOWnr48CEAwMrKqlr779+/HwDw5ptvqm1/6623ADwe8FpW+/bt0bt3b9XPjo6O8PHxwY0bN6odo6enJ4YMGVIujoCAAPTq1Uu1zdLSEi+88AJSUlKQkJBQ7fM3VNu2bYONjQ0GDRqEzMxM1cvPzw+WlpY4evQoAMDW1hYAsHfvXhQXF+swYu1hO65Yeno6pkyZAk9PT7zzzju1Ope2SKkd6/p+sR2rmzx5Mg4fPozNmzdjypQpAKDVma21IYV2LMX7xSStlqytrQEAubm51do/NTUVBgYGaNWqldr2Zs2awdbWFqmpqWrb3dzcyp3Dzs4O2dnZ1Y7R09Ozwjh8fHzKbVfOrHkyDn2UmJiInJwcODk5wdHRUe2Vl5eH9PR0AEBwcDDGjh2LpUuXwsHBAaNGjUJYWBiKiop0/BvUHbbj8vLz8xESEoLc3Fzs3r0blpaWNT6XNkmlHUvhfrEdq3N3d8fAgQMxefJkbNq0CV5eXhg4cKDOE4+KSKEdS/F+sQRHLVlbW6N58+a4dOmSRsfJZLJq7WdoaFjhdqFB7RYzM7Nq79uYKBQKODk5YdOmTRW+7+joCODx/6vff/8dUVFR2LNnD8LDwzFr1ix89dVXiIqKkuyHtybYjtXJ5XKEhobiwoULCA8PR8eOHevt2pqSQjuWyv1iO67auHHjsHbtWkRERJTrzdM1KbTjJ0nhfjFJqwMhISFYs2YNTp8+jR49elS5r7u7OxQKBRITE9Xqwdy/fx8PHjyAu7u7xtev7h+YJ+O4evVque1XrlxRva/vvL298eeffyIoKKhafzgDAwMRGBiITz75BJs3b8bUqVPx22+/Yc6cOTX6fyA1bMePKRQKTJ8+HUeOHMHWrVsRHBys8Tnqk67bsdTuF9tx5ZQ9Qjk5OXVyvrqk63ZcESncLz7urAPvvPMOLCwsMGfOHNy/f7/c+0lJSVixYgUAYPjw4QCA5cuXq+3z9ddfAwBGjBih8fUtLCwAAA8ePKj2McOHD8eZM2dw+vRp1bb8/HysWbMGHh4eaN++vcZxNDQTJkxAaWkpPvroo3LvlZSUqO5ndnZ2uW/Kvr6+AKDqYjc3Nweg2f8DqWE7fuzVV1/Fli1bsHLlSoSGhmp8fH3TdTuW2v1iOwYyMjIq3L5u3TrIZDJ069ZNo/PVB122YynfL/ak1QFvb29s3rwZEydORLt27dQqXEdGRmLbtm2YOXMmAKBLly6YMWMG1qxZgwcPHiA4OBhnzpzBhg0bMHr0aPTr10/j6/v6+sLQ0BCff/45cnJyYGJigv79+8PJyanSYxYsWIBff/0Vw4YNw2uvvQZ7e3ts2LABycnJ2L59OwwMNM/fN27ciNTUVBQUFAAAIiIi8PHHHwMAnnvuOcn1zgUHB2PevHlYtmwZ4uLiMHjwYBgZGSExMRHbtm3DihUrMG7cOGzYsAErV67EmDFj4O3tjdzcXKxduxbW1taqP/JmZmZo3749tmzZgjZt2sDe3h4dO3as8rGP1O4X2/HjD+uVK1eiR48eMDc3xy+//KL2/pgxY1QfwlKhy3YsxfvFdvx4yblTp05h6NChcHNzQ1ZWFrZv346zZ8/i1VdfLTcGTwp02Y4lfb90ObVU31y7dk3MnTtXeHh4CGNjY2FlZSWCgoLEd999p1YYsbi4WCxdulR4enoKIyMj0bJlyyqLJz4pODhYBAcHq21bu3at8PLyEoaGhhUWT6yIsniira2tMDU1FQEBAbUqnhgcHFxhEUFUMh29vlVW4XrNmjXCz89PmJmZCSsrK9GpUyfxzjvvqKqVx8bGismTJws3NzdVgcWQkBARExOjdp7IyEjh5+cnjI2NqzX9W6r3qzG3Y2XNrMpeylUidElK7VjK96sxt+NDhw6JkJAQ0bx5c2FkZKT63cPCwoRCoajy2PoipXYs5fslE0LHq4cSERERUTkck0ZEREQkQUzSiIiIiCSISRoRERGRBDFJIyIiIpIgJmlEREREEsQkjYiIiEiCmKTVk/Xr10Mmk8HU1BR37twp937fvn3rfb27I0eOYNasWWjTpg3Mzc3h5eWFOXPm4N69exXuHxkZiV69esHc3BzNmjXDa6+9hry8vHqNuSq8x9rHe6x9vMfax3usfbzHdYNJWj0rKirCZ599puswAAD/+c9/cOzYMYwZMwbffvstJk2ahK1bt6Jr165IS0tT2zcuLg4DBgxAQUEBvv76a8yZMwdr1qzB+PHjdRR95XiPtY/3WPt4j7WP91j7eI9rSaeldBuRsLAwAUD4+voKExMTcefOHbX3g4ODRYcOHeo1puPHj4vS0tJy2wCIhQsXqm0fNmyYcHFxETk5Oapta9euFQBEeHh4vcT7NLzH2sd7rH28x9rHe6x9vMd1gz1p9ey9995DaWmpJL5Z9OnTp9yacH369IG9vT0uX76s2vbw4UMcPnwY06ZNg7W1tWr79OnTYWlpia1bt9ZbzNXBe6x9vMfax3usfbzH2sd7XDtcYL2eeXp6Yvr06Vi7di0WLFiA5s2ba3R8QUGBakHuqhgaGsLOzk7j+PLy8pCXlwcHBwfVtosXL6KkpAT+/v5q+xobG8PX1xfnz5/X+DraxHusfbzH2sd7rH28x9rHe1w77EnTgYULF6KkpASff/65xsf+97//haOj41NfXbt2rVFsy5cvh1wux8SJE1XblIMqXVxcyu3v4uKCu3fv1uha2sR7rH28x9rHe6x9vMfax3tcc+xJ0wEvLy8899xzWLNmDRYsWFBhQ6jM9OnT0atXr6fuZ2ZmpnFcERERWLp0KSZMmID+/furthcWFgIATExMyh1jamqqel9KeI+1j/dY+3iPtY/3WPt4j2uOSZqOvP/++9i4cSM+++wzrFixotrHeXl5wcvLq87juXLlCsaMGYOOHTvixx9/VHtP2fiLiorKHffo0aMa/eOoD7zH2sd7rH28x9rHe6x9vMc1wyRNR7y8vDBt2jTVN4vqUj4/fxpDQ0M4OjpW65y3bt3C4MGDYWNjg/3798PKykrtfeW3nopqydy7d0/jMQb1hfdY+3iPtY/3WPt4j7WP97hmOCZNh95//32Nn9N/+eWXcHFxeeqre/fu1TrfP//8g8GDB6OoqAjh4eEVdkN37NgRTZo0QUxMjNp2uVyOuLg4+Pr6Vjv++sZ7rH28x9rHe6x9vMfax3usOfak6ZC3tzemTZuG1atXw93dHU2aPP1/R10+n8/Pz8fw4cNx584dHD16FK1bt65wPxsbGwwcOBC//PILFi1apPrWsXHjRuTl5UmygKIS77H28R5rH++x9vEeax/vcQ3UW0W2Rk5Z2O/s2bNq2xMTE4WhoaEAUO+F/UaNGiUAiFmzZomNGzeqvXbu3Km277lz54SJiYno2rWrWLVqlVi4cKEwNTUVgwcPrteYq8J7rH28x9rHe6x9vMfax3tcN5ik1ZPKGqwQQsyYMUMnDdbd3V0AqPDl7u5ebv8TJ06Inj17ClNTU+Ho6Chefvll8fDhw3qNuSq8x9rHe6x9vMfax3usfbzHdUMmhBC17Y0jIiIiorrFiQNEREREEsQkjYiIiEiCmKQRERERSRCTNCIiIiIJYpJGREREJEFM0oiIiIgkiEkaERERkQQxSSMiIiKSICZpRERERBLEJI2IiIhIgpikEREREUkQk7Qyli1bhu7du8PKygpOTk4YPXo0rl69quuwiIiIqBFiklbG8ePH8fLLLyMqKgqHDx9GcXExBg8ejPz8fF2HRkRERI2MTAghdB2EVGVkZMDJyQnHjx9Hnz59dB0OERERNSJNdB2AlOXk5AAA7O3tK92nqKgIRUVFattMTExgYmKi1diIiIhIv/FxZyUUCgVef/11BAUFoWPHjpXut2zZMtjY2Ki9hgwZgnv37tVjtERERKRv+LizEi+99BIOHDiAkydPokWLFpXu92RPWlxcHIKDg3Hu3Dl069atPkIlIiIiPcTHnRV45ZVXsHfvXkRERFSZoAHlH21aWlpqOzwiIiJqBJiklSGEwKuvvoqdO3fi2LFj8PT01HVIRERE1EgxSSvj5ZdfxubNm7F7925YWVkhLS0NAGBjYwMzMzMdR0dERESNCScOlLFq1Srk5OSgb9++cHFxUb22bNmi69CIiIiokWFPWhmcQ0HVJZfLERMTA39/fxgbG+s6HCIi0kPsSSOqgaioKISFhSE6OlrXoRARkZ5ikkakoaKiIoSHhyM5ORkHDx4sV8yYiIioLjBJI9JQdHQ0rl27hs6dO+PatWs4c+aMrkMiIiI9xCSNSAPKXjRjY2NYW1vD2NiYvWlERKQVTNKINHD+/HkkJSUhPz8f8fHxyM/PR1JSEs6fP6/r0IiISM9wdieRBlq2bImpU6dWuJ2IiKguMUkj0oCrqytcXV11HQYRETUCfNxJREREJEFM0oiIiIgkiEkaERERkQQxSSMiIqIGSy6XIzIyEnK5XNeh1DkmaURERNRg6fMyfUzSiIiIqEHS92X6mKQR1YA+d68TETUU+r5MH5M0ohrQ5+51IqKGoDEs08ckjUhD+t69TkTUEDSGZfq44gCRhirqXu/du7euwyIialSetkyfXC5HTEwM/P39YWxsXN/h1QkmaUQaqKx7PSAgACYmJroOj4io0XjaMn1RUVHYuHEjSktLG+wXaT7uJNJAY+heJyJq6PRlWAp70og08LTudSIi0j19GZbCJI1IA0/rXiciovr15NgzfRqWwsedRERE1GA9WRJJn4alsCeNiIiIGqQnx54FBATo1bAUJmlERETUIFU29kxfhqXwcSdRPeFSUkREdYcrDjRCERERGDlyJJo3bw6ZTIZdu3bpOiTSE1xKioio7ujT2LPK8HHnE/Lz89GlSxfMmjULoaGhug6HGqCKqlxXNG6ioc0yIiKSEn0ae1YZSSZp9+7dQ3p6Olq1agULC4t6vfawYcMwbNiwer0mNTxVLTdSUZVrfanZQ0QkFU8riaQPy0JJ6nHn7t270bZtW7Ro0QLdunVTPRbKzMxE165dJfnosaioCA8fPlS98vLydB0S1YPKHl1WVOW6MYybICKSGn0YYiKZJG3Pnj0IDQ2Fg4MDFi9eDCGE6j0HBwe4uroiLCxMhxFWbNmyZbCxsVG9goODdR0SaVlVy41U1GPWGMZNEBFJib4sCyWZJO3DDz9Enz59cPLkSbz88svl3u/Ro4ckP9Teffdd5OTkqF7Hjx/XdUikZRUlYkDlM42cnJwwdepUzJ49G9OnT8fs2bMxdepUvRo3QUQkJZX9nW5oJDMm7dKlS/j6668rfd/Z2Rnp6en1GFH1mJiYqA0At7S01GE0pG1lEzFLS0u15UaUPWaPHj1CfHw8iouLkZSUhPT0dIwcOVLXoRMR6SV9XhZKMkmaubk58vPzK33/xo0baNq0aT1GRFRe2UTs0qVLUCgUqkeXjWGmERGR1Dw5WauyL8znz59HYGCgrsPViGSStH79+mHDhg14/fXXy72XlpaGtWvXIiQkROtx5OXl4fr166qfk5OTERcXB3t7e7i5uWn9+iRtZROxwsJCmJmZqbZz8XUiovrFZaHqySeffILAwEB0794d48ePh0wmQ3h4OP766y+sXr0aQggsXrxY63HExMSgX79+qp/ffPNNAMCMGTOwfv16rV+fpK1sIpaeng4nJydVV7ujo2ODneZNRNQQcVmoeuLj44OTJ0+iadOmWLRoEYQQ+OKLL/Dpp5+iU6dOOHHiBDw8PLQeR9++fSGEKPdigkZPysjIgEKh0Itp3kREDU1jKG8kmZ40AOjQoQP+/PNPZGdn4/r161AoFPDy8oKjo6OuQyMqp6ioCImJiVxJgIhIB/Rp7FllJJWkKdnZ2aF79+66DoPoqfbt26fxSgL6UAWbiEjX9GnsWWUkk6R9++232LdvH8LDwyt8f9iwYXj22Wfx0ksv1XNkROX5+/vj5s2bKCoqQt++fTWa5l3RslFERKSZxjBZSzJj0tatW4f27dtX+n779u2xZs2aeoyIqHJpaWnIyMhAYWEhUlNTcezYMTx8+PCpKwnoSxVsIiIpUSgUug5BKySTpCUlJaFdu3aVvt+2bVskJSXVY0RET2dsbIygoCB06NABXl5e8Pf3h4ODA4DHjzUjIyMhl8tV++tLFWwiItI+yTzuNDY2RlpaWqXv37t3DwYGkskpqRG7efMmcnJyAAAlJSVo2bIl7O3tVe8fO3YMqampePjwIfbv3696rKlPVbCJiKREJpPpOgStkEzWExgYiPXr1yM3N7fcezk5OQgLC9Ob2RrUMJ05cwYjR46Eh4cH8vLyADx+fPnee+/h+++/R0pKCoDH3e5XrlzB2rVrER0djU2bNiE/P58LrRMRaUFFTy30hWR60hYvXozg4GD4+vri9ddfR4cOHQA8XtNz+fLluHfvHjZv3qzjKKmx2rFjByZOnKiqm1eWEAKXLl3CpUuXMHfuXHTr1g3Jycm4f/8+nJyccPbsWXzyySfw9/fHiBEjYGNjo/atT59mIhER1TflZCyFQqF3k7Ekk6Q988wz2LNnD+bNm4d//etfqg8xIQQ8PT3xxx9/oEePHjqOkhqjM2fOYOLEiSgtLS2XoCkpB62uXbsWb731FhISEmBoaAgzMzM8fPgQcXFxcHV1hZGREUxMTNCuXTv4+PiwBAcRUS0oh5HcuHFDL4ePSCZJA4BBgwbh+vXrqsdCAODt7Y1u3brp7fNmkr6PP/64wh60yuzcuRO2trYoLi7G3bt3UVpaioyMDNy6dQteXl548OABTp8+jXPnzsHHxwcdOnSAtbW1ln8LIiL9o5yM1aFDh2rXqmxIJJWkAYCBgQH8/Pzg5+en61CIcPPmTezdu7faCZpCocD169cxdepU2NjYqL1nZ2en9rNcLsfFixdx6dIltGjRAh06dEDLli35hYSIqBrKTsaysLDQy8lYkkvSEhIScOPGDWRnZ1f4wTh9+nQdREWN1ZEjR6qdoJX1zz//oE+fPtXaVwiBW7du4datW7C3t0f//v3VZosSEVF5ZZeFunTpEgwNDbkslLYkJSVh2rRpOHPmTKUfijKZjEka1avc3FwYGBhoXCgxNTUVxcXFMDIy0ui4rKwsnD59GiNGjNDoOCKixqbsslDp6elwcHCAgYGBXk3GkkySNm/ePFy8eBHLly9H7969yz0aItIFKyurGlWyLigoQEpKClq3bq3xsU2bNtX4GCKixqbsslDJycmwt7cvN8ykoZNMknbq1Cm89957ePXVV3UdCpHKgAEDIJPJNH7kaWNjg/j4eHh4eFS7N83a2hpdu3ZFmzZtahIqkdbI5XLExMTA39+fM5JJsh48eKB3SZpkitk6ODjo3c2lhs/NzQ0hISEwNDSs9jHKOmjKGZ1P4+rqikGDBmHixInw8fFRmzigz0UaqeGIiopCWFgYoqOjdR0KUaUePHig6xDqnGR60l588UX88ssvePnllzX6QCTStkWLFuHAgQPV6lEzMDDAwIED4ezsDKD8jE7g8dhKR0dHeHp6wtvbG5aWlpWeT1mkUbm0FFF9U86gS05O1ruZc6Rf/vnnH12HUOckk6S1adMGpaWl6NKlC2bNmoWWLVtWmKyFhobqIDpqzLp3744tW7aoVhwoLS0tt49yXdkXXngBXbt2rfD9Fi1awMPDA25ubjA3N1d7v6LHSfxwJClQ1qHq3LmzXtahIv1x7949CCH0qoyRZJK0iRMnqv777bffrnAfmUxW4QckkbaFhoYiMjISb775Jk6ePFnufS8vL4wfPx4tWrRAUlIS3N3d0aRJE1haWqJDhw7w8fGBgYEBYmJi4OXlVe74inrM+OFI9e3JLwtl61BZW1vrZR0q0h/5+fm4c+cOWrRooetQ6oxkkrSjR4/qOgSiKnXv3h2//fYbwsPD8a9//Qt5eXkwMTHBhAkT0KFDB9jZ2eHatWuIjo6Gk5MTxo4dCzc3N1UvW0RERIWPLivqMQPAD0eqd09+WShbhyo+Ph7FxcV6V4eK9Mv58+fh6uqq6k1r6JNeJJOkBQcH6zoEoqdydXXFrFmz8MEHHyAvLw9mZmbo1asXgMePNLOystCkSRNkZWXBxcVFlaBV9eiyoh4zIyMjfjhSvaqojZatQ1WWPtWhoobP398fd+7cgbGxMRYuXIhbt27Bzc0NQMMf1yuZJE2pqKgIsbGxSE9PR1BQEBwcHHQdEtFTubi4wNTUFOHh4ejatWu5x5OVPbqs7HHS7Nmz+eFI9aqyNqqsQ0UkVWlpaUhLS4OtrS2AxyW9mjVrBiFEgx/XK5kSHADw7bffwsXFBb169UJoaCguXLgAAMjMzISDgwN++uknHUdI9JilpSVMTU1hYmICZ2dn9O/fH8eOHSuXbBUVFVWaiBUVFakeJ+Xn5yM+Ph75+flISkpCeno6Ro4cWe7FD0zShqraKJHUKUsUlZSUAHi8Uszx48cRFRVV7otHQyOZnrSwsDC8/vrrmDRpEgYPHoxZs2ap3nNwcED//v3x22+/qW0n0oWioiKMHTsWf/75J1xcXNCvXz9cvHix0seTACp9j4+TSJeU43VKS0v5eJ0apKKiIjx69AgAUFxcrFqOr+zQkYY8rlcySdpXX32FUaNGYfPmzRXWOvHz88O3336rg8iI1CkfC7Vu3RpZWVn4+++/4eXlVWWyVdl7ZZc1IapvyvE6w4YN45cFapCio6NVPWgKhUK1HN+tW7eQkJAAc3NzyOVylJSUNMgvHpJJ0q5fv47XXnut0vft7e3rrVDd999/jy+++AJpaWno0qULvvvuO9WMO2rcyj4WsrS0RGlpKQ4ePIgPPvgAI0eOrPQ4JmIkNWUnCpw7dw4ffPBBg+phIFK24bKUy/HZ2dmpPrdbt24Nb29vAA3vi4dkxqTZ2toiMzOz0vcTEhLQrFkzrcexZcsWvPnmm1i8eDFiY2PRpUsXDBkyBOnp6Vq/Nklf2TFkt27dQmFhodpjTaKGoqKJAkQNifLvcdmVYJTL8dnZ2aFLly7o0qULzM3N4e7u3iDH9UomSRs+fDjWrFlT4dpb8fHxWLt2LZ599lmtx/H1119j7ty5eP7559G+fXv88MMPMDc356QFAgDVGLLZs2dj5syZqlmYDe3bGTVunChA+kD599jCwgIAYGxsjICAgHLL8ZWUlGDTpk04efIkFAqFLkKtMck87vz444/xzDPPoGPHjhg5ciRkMhk2bNiAn376Cdu3b4eLiws++OADrcYgl8tx7tw5vPvuu6ptyrUYT58+XeExytl7Snl5eQAeN4ri4mKtxkv1z8nJCUOHDgXwuL2ULY7I/9/UUJw9exaJiYl49OgRLl68iOLiYiQmJuLs2bN45plndB0eUbUo/x4rH9M3adIEHTt2BAC11YkSExNx5swZFBcX4/79++jVqxfs7e11EnNZRkZGT99JSMj9+/fF7NmzhZ2dnZDJZEImkwlra2vx/PPPi/v372v9+nfu3BEARGRkpNr2f//73yIgIKDCYxYvXiwA8MUXX3zxxRdffFX7VR2S6ElTdr17eHjgxx9/xI8//oiMjAwoFAo4OjqqqrZL0bvvvos333xT9XNcXByCg4MRHR1d4ULbpB9OnDiBTZs2Ydq0aaoVB4iIqP55eHjg7t27sLGxwZw5c1RrJwPAtWvXcPz4cdja2uLBgwfo27cvWrduDeDx49Hu3bujbdu2kl2UXRJJmrGxMcaPH48VK1agc+fOAABHR8d6j8PBwQGGhoa4f/++2vb79+9XOmnBxMREbUaUpaUlgMfdrtXqyqQGp6ioCEeOHEFqair+/PNP9OzZk7PiiIh0RJlglZaWIjo6GjKZDK1bt0ZxcTGuXr2KJk2awMLCAnl5ebhy5Qq8vb1hZGSE0tJSREVF4datW+jXrx/Mzc11/JuUJ4kuKuUNrWp2Z30wNjaGn58fjhw5otqmUChw5MgR9OjRQ4eRkZRwVhwRkTTcvHkTBQUFAIBHjx7h3r17qoLMt27dQkZGBuRyOe7evQu5XK6a/VnWnTt3sHPnTklWcZBETxoAvPfee3jzzTcxfvx4+Pj46CyON998EzNmzIC/vz8CAgKwfPly5Ofn4/nnn9dZTCQdlc2Ka2hVrImIGrIzZ87go48+wr59+1QlOJQFazMyMmBvb49OnTpVWOP0ydmfAJCfn489e/YgMDAQ7du3l8zjT8kkaVFRUWjatCk6duyIvn37wsPDA2ZmZmr7yGQyrFixQqtxTJw4ERkZGfjggw+QlpYGX19fHDx4EM7Ozlq9LjUMyro8XD6HiEg3duzYgYkTJ0IIoVYjTenhw4fYvHkzZs+eje7du1f7vKWlpTh16hTS0tLQp08fSQxZkomKfkMdqM7kAJlMpjatVopiY2Ph5+eHc+fOoVu3broOh+rYnTt3EBsbW257t27dGlyRRKLqUq7x6e/vr1Z2hqi+nTlzBkFBQSgtLa0wQSvLwMAA//nPf+Dh4aHxdRwcHDBixAidPyHRqCfN09NT4y5AmUyGpKSkp+7X0ArMUePEtTZJn1WWjCnX+CwtLUXv3r11GCE1dh9//HGlPWgV2b9/P+bPn6/xdTIzM3H48GGMGDFCp48+NUrSgoODywUbExOD+Ph4tG/fXjWW7OrVq0hISEDHjh3h5+dXd9ESEZHWVJSMlV3jk+MvSZdu3ryJvXv3VjtBUygUuHDhArKysmpUvPbu3bu4fv26qmSHLmiUpK1fv17t5127dmHXrl04fPgwBgwYoPbe4cOHMWHCBHz00UcaBRQVFYWjR48iPT0d8+fPR+vWrVFQUIArV66gTZs2qhIXRERUdypLxiqazczeNNKFI0eOVDtBUxJC4MqVK+jZs2eNrpmQkKDTJK1WJTg++OADvPrqq+USNAAYNGgQXnnlFbz//vvVOpdcLkdoaCiCgoKwcOFCfPvtt6ppsgYGBhg8eLDWJw0QETVWFSVjXOOTpCQ3N1fj4vYymQyPHj2q8TUfPnxY42PrQq2StMTERDRt2rTS95s2bVqt8WgAsGjRIuzduxerVq3C1atX1bJlU1NTjB8/Hrt3765NuEREVIHKkrHo6GgkJSUhPz8f8fHxyM/PV81mJqpvVlZWGo9fF0LA1NS0xte0tbWt8bF1oVYlOLy9vREWFobZs2eXewyZm5uLn376CV5eXtU616+//oqXXnoJL7zwAv75559y77dr1w7btm2rTbhE9aKqmXCcJUdSVFlpmQcPHmDq1Knl9m/ZsqUOoqTGbsCAAZDJZBo98pTJZDAzM0NJSYlqqShNKFdB0pVaJWkff/wxxo0bh7Zt22LmzJlo1aoVgMc9bBs2bMD9+/ernVilp6ejU6dOlb5vaGioqipMpGtVJVtVzYTjLDmSEmU7dnFxqTAZY2kZkhI3NzeEhIRg//791SrHZWBgAE9PT1y6dAmWlpYajy3r0KED3N3daxpunahVkjZ69Gjs378f//nPf/Dpp5+qvefr64t169ZhyJAh1TpXy5YtceXKlUrfP3XqlCoJJNK1ypKtqmbCcZYcSY2yHU+fPh0jR47UdThET7Vo0SIcOHCg2j1qTk5OyMzMRHx8PDw8PKpdoLZjx46SWA6y1mt3Dh48GOfPn8fdu3dx+vRpnD59Gnfv3kVsbGy1EzQAmDJlClavXo3Tp0+rtinLfaxduxZbt27F9OnTaxsuUa09mWyVHURd1bqeXPOTpKSqdkwkVd27d8eWLVtgaGgIQ0PDCvcxMDCAgYEBQkJCUFRUBFdXV9y/fx8pKSlPPb+BgQGCgoLQs2dPSSwNVWcLrDdr1gzPPPMMnnnmGTRr1kzj4xcuXIiePXuiT58+6NevH2QyGd544w24ublh3rx5GDp0KN544426CpeoxipLtqqaCcdZciQ1/NJADVVoaCgiIyMxfPjwcomUTCZDp06d8NZbb6G4uBiGhoYwMzODoaGharxlZczMzDBixAh06NBB279CtdU6Sbt58yZefPFF+Pj4wN7eHhEREQAeV+t97bXXqj0LSPmhFRYWBi8vL7Rt2xZFRUXo3Lkz1q9fjz179lSaNRPVl6qSLeXg64pmwlX1HlF945cGaui6d++OP/74AykpKaoF05s0aYK5c+di/vz5MDAwQEZGBuRyOe7evQu5XI6MjAxVaa8nOTs7IzQ0FC4uLvX5azxVrcakJSQkoHfv3lAoFHjmmWdw/fp1lJSUAHi87tXJkyeRn5+PdevWlTv2zTffxHPPPYeuXbsCeJzsOTo6Ytq0aZg2bVptwiLSmqoWWG/ZsmWVM+E4S46koqp2HBgYqOvwiKrNzc0N5ubmyM7OBgDcunULnTt3hp2dHQICAsrtr0zolAwMDNC1a1d07dpV4xps9aFWSdo777wDW1tbREVFQSaTwcnJSe39ESNGYMuWLRUeu3z5cvj7+6uSNE9PT2zcuBFTpkypTUhEWlVVIva0dT05S46korJ27OzsjMjISJaIoQZFLpcDeFwFQjn2rHXr1uUSsie5uLggKCioRktG1ZdaJWkRERH44IMP4OjoWGFtMzc3N9y5c6fCY52dnXHjxg3Vz5ou9UCkC1xgnfRB2XasUChUPQgREREsEUMNSlFRkWpFAZlMphp7VtVMTgsLCwQGBsLLy0sSkwOqUqskTaFQwNzcvNL3MzIyKi0xMGLECHz44Yc4dOiQqqLvV199hd9++63S88lkMq46QERUh5RJGkvEUEN0/vx51TCr0tJStbFnTxbTl8lk6NChA7p3717tUhy6VqskrVu3bti3bx/mz59f7r2SkhL89ttvlY5vWLFiBZycnHD06FHEx8dDJpPh1q1byMrKqvR6Us94iYgamtLSUjRp0oQLqVOD1LJlS5ibm6OwsBDGxsYICgoCUH7smZmZGfr379/gnoTUKkl79913ERISgpdeegmTJk0CANy/fx9//vknPv30U1y+fBn/+9//KjzWwsJCrQCugYEBli9fzjFpRET1SDmep6LZnuxNI6lzdXVVrc1pZGSELl26lNunWbNmGDhwYJVP/qSqVlMZhg0bhvXr12PLli3o378/AGDatGkYPHgwYmNj8fPPP6NPnz4VHhsaGooTJ06ofj569CgGDRpUm3CIiEhDBQUFLBFDDZryi4bysWdZHTt2REhISINM0IBa9qQBwHPPPYfQ0FAcPnwYiYmJUCgU8Pb2xpAhQ2BlZVXpcbt378bYsWNVP/fv35+zO4mI6pFcLsexY8fQvXt3loihBqnsxIHi4mIUFxfDyMgIMpkMQUFBaN++vY4jrJ0aJ2kFBQVo2bIlFixYgH//+98YPXq0Rse7urri/Pnzqj8MQgiOOSMiqkdRUVH4/fffYWVlxbU7qUGKjo5W9aApFApV+Y3evXujbdu2Oo6u9mqcpJmbm6NJkyawsLCo0fGTJk3Cl19+ia1bt6pmdy5YsADLli2r9BiZTIa///67RtcjIqL/o5zNefv2bezatQsDBgzg+DNqUJRt2MzMDAYGBhBCID4+HkOHDtWLBA2o5ePOsWPH4vfff8dLL72kcS/YsmXL0KpVKxw9ehTp6emQyWSwsLBA06ZNaxMSERFVg3I2Z5s2bZCQkMDZnNTgKMdS9u7dW7X8U1ZWll4VYq5VkjZp0iTMnz8f/fr1w9y5c+Hh4QEzM7Ny+3Xr1q3cNkNDQ7zwwgt44YUXADye3fn+++9zTBoRkZaVXbvT0tIScrkcf/zxB2dzUoNSduWM+/fv4/z582jdunW5+mgNWa2StL59+6r+u+xMTSXlOLPS0tKnnis5ORmOjo61CYeIiKqh7NqdmZmZkMvliIuL49qd1KCUXTkjOTkZQghMnTq1wc7krEitkrSwsLC6igPu7u51di4iIqpc2R6IW7duIT4+Hk2aNOFwE2rQmjVrplcJGlDLJG3GjBk1PtbAwAAGBgYoKCiAsbExDAwMnjquTSaTVVgHpa588skn2LdvH+Li4mBsbIwHDx5o7VpERLqi7IHw9/fH7du3YWJigoULF+LGjRto1aoVZ9pTg9SiRQtdh1Dnal0nraY++OADyGQyNGnSRO1nXZLL5Rg/fjx69OiBdevW6TQWIiJtS0tLw/3791Uz7FNTU3Ht2jX4+PjoNjCiGnBwcNB1CHVOoyRt1qxZkMlkWLNmDQwNDTFr1qynHiOTySpMeJYsWVLlz7qwdOlSAMD69et1GwgRUT2oqFJ7ZGQknJ2dVYkbUUNhY2Oj6xDqnEZJ2l9//QUDAwMoFAoYGhrir7/+qtYjSn1WVFSEoqIi1c95eXk6jIaIqHoqq9ReXFyM8PBwjBw5Uu/G95B+q6i6REOnUZKWkpJS5c+a+Pnnn2t03PTp02t8TW1YtmyZqgeOiKihqKxSOwDk5ORgz549GDp0qF72TpB+kcvliI2NRfPmzXUdSp3T2Zi0mTNnltum7HUTQlS4HdA8SVuwYAE+//zzKve5fPlyjasTv/vuu3jzzTdVP8fFxSE4OLhG5yIiqg/KOmllxcfHw8PDA0ZGRgAeJ2o7d+5E37594eHhoYMoiaonKioK27dvh5OTk94VZNZZkpacnKz284MHDzBjxgzY2Njg1VdfVQ1cvXLlCr777jvk5uZiw4YNGl/nrbfeqjAhLKs2he9MTEzUij9aWlrW+FxERPVBWSet7BfijIwM3Lp1S+3voVwux6FDh9CpUycEBATA0NBQF+ESVars8mYHDx7Uu4LMtU7SDhw4gK+//hqxsbHIyckp1wsGoMJitk/WRVuyZAkcHR1x6NAhtZ6zTp06YezYsRg8eDC++eYbjWuzOTo6skguEVEZyjppf/75Jx49egRjY2MEBATAzs6uwv0vXryIe/fuoX///pxQQJKiXN6sY8eOuHbtmt4tb2ZQm4O3b9+OkJAQ3L9/H5MmTYJCocDkyZMxadIkmJmZoXPnzvjggw+qda5du3ZhzJgxFU40MDAwQGhoKHbv3l2bcJ/q5s2biIuLw82bN1FaWoq4uDjExcVxMgAR6RVXV1eMHDkSpqamAAAjIyN06dKl0iQNADIzM7Fjxw7Ex8dX+GWcqL6VXd7M1tYWxsbGOHjwoNpkvoauVj1py5YtQ0BAAE6ePIns7GysWrUKs2bNQv/+/ZGSkoLAwEB4enpW61xCCFy5cqXS9xMSErT+h+GDDz5Qe6TatWtXAMDRo0fVlsAiImqMSkpKcOrUKVy7dg09evRAs2bNdB0SNWJllze7cuUKFAoFkpKS9Gp5s1olaQkJCVi2bBkMDQ1VRWmLi4sBAB4eHpg/fz4+//zzag32Hz16NFatWgUPDw+8+OKLqqnfBQUFWLVqFVavXq1axkRb1q9fzxppRERPkZGRgT/++APu7u5VPiYl0qayy5s9uV1f1CpJMzc3h7GxMQDA1tYWJiYmuHfvnup9Z2fnchMEKrNixQokJyfj7bffxrvvvgsXFxcAwL1791BcXIygoCAsX768NuESEdH/d/PmTRQUFAB4PEEgKysL9vb2Gp0jNTUVN2/eRLt27RAQEKD6PCCqD2UXWNdXtRqT5uPjg4SEBNXPvr6+2LhxI0pKSvDo0SNs3rwZbm5u1TqXjY0Njh8/jp07d+L5559Hu3bt0K5dOzz//PPYtWsXIiIiOGCViKiWzpw5g5EjR8LDwwPZ2dkAHj+xeO+99/D9999rXP9SCIGEhARs375ddT4iqhsyUYuBXl999RW+/fZbXLt2DSYmJti7dy9GjRoFMzMzyGQy5Ofn46effnpqCQx9EhsbCz8/P5w7dw7dunXTdThERCo7duzAxIkTIYSocNa9gcHj7+1z586t0d8vCwsLhIaG6mXld2oY5HI5YmJi4O/vrxc9uzXqSXv06BG2bNmC4uJivP/++8jKygIAhISE4NixY5g7dy7mzZuHI0eONKoEjYhIqs6cOYOJEyeitLS0wgQNeLzygEKhwNq1a2u0okx+fj5OnDhRy0iJai4qKgphYWGIjo7WdSh1QuMxaenp6ejZsyeSk5MhhIBMJoOZmRl27dqFgQMHonfv3npVo4SISB98/PHHEEJUe5b8/v37MX/+fI2vk5KSgtTU1HK1MIm0TVmSIzk5WW8K22rck/bRRx8hJSUFb7zxBvbu3YtvvvkGZmZmmDdvnjbiIyKiWrp58yb27t1baQ/akxQKBS5cuKB6SqKpU6dOqWb6E9UXZWHbzp07qwrbNnQa96QdOnQI06dPx5dffqna5uzsjClTpuDq1auq5ZyIiEgajhw5onGdSWXtyp49e2p8vby8PFy4cAF+fn4aH0tUE2UL21pbW6sK2zb03jSNe9Ju3ryJXr16qW3r1asXhBC4f/9+nQVGRER1Izc3VzUpoLpkMhkePXpU42vWZEwbUU0pC9vm5+cjPj4e+fn5qsK2DZnGPWlFRUWqpUSUlD+XlJTUTVRERFRnrKysoFAoNDpGCFHub70m9GFmHTUc+lrYtkbFbFNSUhAbG6v6OScnBwCQmJhYYS2z6k7lvnz5MsLCwnDjxg1kZ2eX656XyWQ4cuRITUImImq0BgwYAJlMptEjT5lMhrZt29b4mm3atKnxsUSakMvlSE1NxZAhQ/Tuy0GNkrRFixZh0aJF5bY/ORNIOfuzOoNVN27ciOeffx5GRkbw8fGpcJkRLupLRKQ5Nzc3hISEYP/+/dX6e2xgYIBOnTppvAKBkrW1NVq1alWjY4k0FRUVhY0bN6K0tFTvqktonKSFhYVpIw4sWbIEXbt2xYEDB+Dg4KCVaxARNVaLFi3CgQMHqt2jNnz48BpdRyaTITg4GIaGhjU6nkgT+lh2oyyNk7QZM2ZoIw7cvXsXb7/9NhM0IiIt6N69O7Zs2VKtFQdeeOEFeHh4aHwNZYKmXHuZSNsqKruhT71ptVq7sy517twZd+/e1XUYRER6KzQ0FJGRkRg+fDhkMpnaezKZDJ06dcJ//vMfdO3aVeNzGxgYoH///hyLRvWmsrIbRUVFug6tzkgmSfv666+xbt06REZG6joUIiK91b17d/zxxx9ISUmBlZUVAMDExASffvop5s+fX6MeNBMTEwwbNgze3t51HC1R5fS17EZZNZo4oA2ff/45bGxs0Lt3b7Rv3x5ubm7lxjTIZDLs3r1bRxESEekPZ2dntZ+VCZummjdvjr59+8LS0rIuwiKqNn0tu1GWZJK0CxcuQCaTwc3NDXl5eUhISCi3z5Pd80REVDPR0dGq2pYKhQIpKSlo3bp1tY83NzdHQEAAWrduzb/NpBOurq5wdXXVdRhaJZkkjdWpiYjqh3IsT1nx8fHw8PCAkZFRlccaGhqic+fO8PX1feq+RFQ7kknSiIiofijH8pQtxZGRkYFbt27By8ur0uM8PDzQo0ePGj8aJSLNSDJJy83NRU5OToXLmLi5uekgIiIi/aEcy/Pnn3/i0aNHMDY2RkBAQIVFxAHAzMwMvXr1gqenZz1HSvR0crkcMTEx8Pf354oD2rRq1Sp8/fXXuHHjRqX7VKdaNhERVU45lke5NqeRkRG6dOmitk9JSQlSU1MRFBSEQYMGwczMTBehEj2VPq84IJkSHD/88ANefvlltGrVCh9//DGEEHj99dexYMECNGvWDF26dMG6det0HSYRUaOQnJysWo+ZCRpJ1ZMrDuhTjTRAQknad999hyFDhuDAgQN44YUXAAAjRozAJ598goSEBOTm5uKff/7RcZRERPpDLpcDgGqWZ1mFhYV4+PAhwsPD9e6Dj/RHRSsO6BPJJGlJSUkYOXIkAKhmDCn/gNjY2GDOnDlYuXKlzuIjItInRUVFePToEQCguLgYxcXFAABTU1M0a9YM9+/f19sPPtIPXHGgHtnY2Ki+zVlbW8Pc3By3bt1SvW9lZYW0tDRdhUdEpFcqqpPWpEkT9O/fH9HR0Xr9wUf6gSsO1KOOHTvi77//Vv0cGBiIVatWYfjw4VAoFFi9erVW14RLSUnBRx99hL/++gtpaWlo3rw5pk2bhoULF+rdbBEiatyUPRBmZmYwMDCAEALx8fF47rnncPv2bSQlJeHRo0eIj49HcXGx6oMvMDBQ16ETqXDFgXo0bdo0/PDDDygqKoKJiQmWLl2KgQMHqkpuGBkZYfv27Vq7/pUrV1TJYKtWrXDp0iXMnTsX+fn5+PLLL7V2XSKi+qbsgejduzcKCwtx//595OTkID8/H25ubnr/wUf6oTorDjT08hwyUbaaocTcuHEDe/bsgaGhIQYPHqzVnrSKfPHFF1i1alWVJUGeFBsbCz8/P5w7dw7dunXTYnRERDVz584dxMbGAgBu3bqF+Ph4tG7dGuPHj9f7ZXZI/1SViEVERGDjxo2YPn16gyzPIZmetIp4eXnhX//6l86un5OTA3t7e51dn4hIG8r2QFy+fBlNmjTBmDFj4OjoqOPIiDRXWZ20J8tzBAQEwMTERIeRak4yEweUoqKisGzZMrzxxhtITEwEABQUFCA2NhZ5eXn1Fsf169fx3XffYd68eVXuV1RUhIcPH6pe9RkjEVFdMDU1hYODg67DINJYVXXS9KE8h2SSNLlcjtDQUAQFBWHhwoX49ttvVbM7DQwMMHjwYKxYsULj8y5YsAAymazK15UrV9SOuXPnDoYOHYrx48dj7ty5VZ5/2bJlsLGxUb2Cg4M1jpGISBfkcjnOnz+PZs2aQSaT6TocIo1VlojpS3kOySRpixYtwt69e7Fq1SpcvXpVbeFfU1NTjB8/Hrt379b4vG+99RYuX75c5avsgsJ3795Fv3790LNnT6xZs+ap53/33XeRk5Ojeh0/flzjGImIdCEqKgo7d+5EVlaWrkMh0lhViZi+lOeQzJi0X3/9FS+99BJeeOGFClcWaNeuHbZt26bxeR0dHas9zuLOnTvo168f/Pz8EBYWBgODp+ewJiYmas+4LS0tNY6RiKi+KT/gbt++jXPnzmHMmDENbrwONW7KRKyicjH6Up5DMklaeno6OnXqVOn7hoaGKCgo0Nr179y5g759+8Ld3R1ffvklMjIyVO81a9ZMa9clItIF5WOitm3bIjk5GWfOnGmQs9+o8aoqEatOeY6GQDJJWsuWLcuNDSvr1KlTaNWqldauf/jwYVy/fh3Xr19HixYt1N6TcJUSIiKNlX1MZG9vD7lc3mBnv1HjpS+JWFUkMyZtypQpWL16NU6fPq3aphzIunbtWmzduhXTp0/X2vVnzpwJIUSFLyIifVJ2vE5iYmKDHa9DpO8k05O2cOFCREVFoU+fPmjXrh1kMhneeOMNZGVl4fbt2xg+fDjeeOMNXYdJRNTglX1MVFhYCDMzM9V2IpIOySRpylkZmzZtwu+//47S0lIUFRWhc+fO+Pjjj/Hcc89xijgRUR0o+5iobJJGRNIimSQNePx4c9q0aZg2bZquQyEi0ntyuRxnzpxBjx49GuS6hkT6TjJj0oiIqH5FRUXh559/RnR0tK5DIaIKSKon7eTJk/jpp59w48YNZGdnlxu0L5PJ8Pfff+soOiIi/aGc4ZmSksKZnUQSJZkk7euvv8a///1vmJqawsfHhwubExFpkbJOWqdOnVTL6bBOGpG0SCZJ++KLLxAUFIQ9e/bAxsZG1+EQEemtsnXSbGxskJGRwd40IgmSzJi0goICTJ06lQkaEZGWla2TlpCQwDppRBIlmZ60fv364eLFi7oOg4hI7+nLuoZE+k4ySdp3332HwYMH48svv8SsWbM4Jo2ISEsaw3I6RPpAMo87W7ZsiXnz5mHBggVwdHSEhYUFrK2t1V58FEpEpFtyuRyRkZGQy+W6DoVI70mmJ+2DDz7AJ598AldXV/j7+zMhIyLSEblcjpiYGPj7+5crchsVFYWNGzeitLSUs0GJtEwySdoPP/yAESNGYNeuXTAwkEwHHxFRo1NZIqacFZqcnMzZoET1QDLZkFwux4gRI5igERHVk4oeXT6ZiBUVFaneU9ZW69y5s6q2GhFpj2QyopCQEJw4cULXYRARNRpRUVEICwtTWxaqskSsbG01a2trGBsbl0viiKhuSSZJW7x4MRISEjB//nycO3cOGRkZyMrKKvciIqLaq6jHrKpErGxttfj4eNZWI6oHkhmT5uPjAwCIi4vD6tWrK92vtLS0vkIiItJbFfWYGRkZISkpCY8ePUJ8fDyKi4tViRhrqxHVP8kkaR988AFkMpmuwyAi0nuV9ZjNnj270kSMtdWI6p9kkrQlS5boOgQiokZB+ejyyR6z9PR0jBw5UtfhEdH/J5kkjYiI6gcfXRI1DEzSiIgaGT66JGoYJDO7k4iIiIj+D5M0IiIiIglikkZEREQkQUzSiIgaqYqWhSIi6WCSVsazzz4LNzc3mJqawsXFBc899xzu3r2r67CIiLSiomWhiEg6mKSV0a9fP2zduhVXr17F9u3bkZSUhHHjxuk6LCKiOlfVQupEJA0swVHGG2+8ofpvd3d3LFiwAKNHj0ZxcTGMjIx0GBkRUd2qaFmo3r176zosIiqDPWmVyMrKwqZNm9CzZ08maESkV6paSJ2IpINJ2hP+85//wMLCAk2bNsXNmzexe/fuKvcvKirCw4cPVa+8vLx6ipSIqGaUy0Ll5+cjPj4e+fn5qoXUiUg6ZEIIoesgtGnBggX4/PPPq9zn8uXLaNu2LQAgMzMTWVlZSE1NxdKlS2FjY4O9e/dWuvj7kiVLsHTp0nLbz507h27dutX+FyAiqmN37txBbGxsue3dunXjSgREEqL3SVpGRgb++eefKvfx8vKCsbFxue23b99Gy5YtERkZiR49elR4bFFRkdojgri4OAQHBzNJIyIiolrR+4kDjo6OcHR0rNGxCoUCAKocp2FiYgITExPVz5aWljW6FhEREVFZep+kVVd0dDTOnj2LXr16wc7ODklJSVi0aBG8vb0r7UUjIiIi0hZOHPj/zM3NsWPHDgwYMAA+Pj6YPXs2OnfujOPHj6v1lBERERHVB/ak/X+dOnXCX3/9peswiIiIiACwJ42IiIhIkpikEREREUkQkzQiokZKLpcjMjIScrlc16EQUQWYpBERNVJRUVEICwtDdHS0rkMhogowSSMiaoSU63cmJydz3U4iiWKSRkTUCEVHR+PatWvo3Lkzrl27hjNnzug6JCJ6ApM0IqJGRtmLZmxsDGtraxgbG7M3jUiCmKQRETUy58+fR1JSEvLz8xEfH4/8/HwkJSXh/Pnzug6NiMpgMVsiokamZcuWmDp1aoXbiUg6mKQRETUyrq6ucHV11XUYRPQUfNxJREREJEFM0oiIiIgkiEkaERERkQRxTJqeunfvHu7du6frMPSai4sLXFxcdB2GXmM71j62Y+1jO9Y+fW3HTNLqmIuLCxYvXqzTxlJUVITJkyfj+PHjOouhMQgODkZ4eDhMTEx0HYpeYjuuH2zH2sV2XD/0tR3LhBBC10FQ3Xr48CFsbGxw/PhxWFpa6jocvZSXl4fg4GDk5OTA2tpa1+HoJbZj7WM71j62Y+3T53bMnjQ95uvrq3cNVioePnyo6xAaDbZj7WE7rj9sx9qjz+2YEweIiIiIJIhJGhEREZEEMUnTQyYmJli8eLHeDaCUEt5j7eM91j7eY+3jPdY+fb7HnDhAREREJEHsSSMiIiKSICZpRERERBLEJI2IiIhIgpikEREREUkQkzTSWzKZrFqvY8eO1fpaBQUFWLJkiUbn+uSTT/Dss8/C2dkZMpkMS5YsqXUcpH+k3I6vXLmCd955B76+vrCysoKLiwtGjBiBmJiYWsdC+kXK7fju3buYNm0afHx8YGVlBVtbWwQEBGDDhg3Q9dxKrjhAemvjxo1qP//88884fPhwue3t2rWr9bUKCgqwdOlSAEDfvn2rdcz777+PZs2aoWvXrggPD691DKSfpNyOf/zxR6xbtw5jx47F/PnzkZOTg9WrVyMwMBAHDx7EwIEDax0T6Qcpt+PMzEzcvn0b48aNg5ubG4qLi3H48GHMnDkTV69exaefflrrmGpMEDUSL7/8stBWk8/IyBAAxOLFi6t9THJyco2PpcZLSu04JiZG5Obmqm3LzMwUjo6OIigoSAsRkr6QUjuuTEhIiLCwsBAlJSV1E1gN8HEnNWoKhQLLly9Hhw4dYGpqCmdnZ8ybNw/Z2dlq+8XExGDIkCFwcHCAmZkZPD09MWvWLABASkoKHB0dAQBLly5Vdds/7fGlh4eHNn4laoR01Y79/PzKLRretGlT9O7dG5cvX67bX5L0ni7/HlfEw8MDBQUFkMvltf7daoqPO6lRmzdvHtavX4/nn38er732GpKTk/G///0P58+fx6lTp2BkZIT09HQMHjwYjo6OWLBgAWxtbZGSkoIdO3YAABwdHbFq1Sq89NJLGDNmDEJDQwEAnTt31uWvRo2I1NpxWloaHBwc6vR3JP2n63ZcWFiI/Px85OXl4fjx4wgLC0OPHj1gZmam1d+7SjrrwyOqZ092r584cUIAEJs2bVLb7+DBg2rbd+7cKQCIs2fPVnru2nSv83EnaUKq7VgpIiJCyGQysWjRohqfg/SfFNvxsmXLBADVa8CAAeLmzZsanaOu8XEnNVrbtm2DjY0NBg0ahMzMTNVL+Qjn6NGjAABbW1sAwN69e1FcXKzDiInKk1I7Tk9Px5QpU+Dp6Yl33nlHK9cg/SSFdjx58mQcPnwYmzdvxpQpUwA87l3TJSZp1GglJiYiJycHTk5OcHR0VHvl5eUhPT0dABAcHIyxY8di6dKlcHBwwKhRoxAWFoaioiId/wZE0mnH+fn5CAkJQW5uLnbv3l1urBpRVaTQjt3d3TFw4EBMnjwZmzZtgpeXFwYOHKjTRI1j0qjRUigUcHJywqZNmyp8Xzn4VCaT4ffff0dUVBT27NmD8PBwzJo1C1999RWioqL4YUQ6JYV2LJfLERoaigsXLiA8PBwdO3as8bmocZJCO37SuHHjsHbtWkRERGDIkCF1dl5NMEmjRsvb2xt//vkngoKCqjUwNDAwEIGBgfjkk0+wefNmTJ06Fb/99hvmzJkDmUxWDxETlafrdqxQKDB9+nQcOXIEW7duRXBwcE1+DWrkdN2OK6LsQcvJyamT89UEH3dSozVhwgSUlpbio48+KvdeSUkJHjx4AADIzs4uV3Xa19cXAFRd7Obm5gCgOoaovui6Hb/66qvYsmULVq5cqZpJR6QpXbbjjIyMCrevW7cOMpkM3bp1q9Z5tIE9adRoBQcHY968eVi2bBni4uIwePBgGBkZITExEdu2bcOKFSswbtw4bNiwAStXrsSYMWPg7e2N3NxcrF27FtbW1hg+fDgAwMzMDO3bt8eWLVvQpk0b2Nvbo2PHjlU+9tm4cSNSU1NRUFAAAIiIiMDHH38MAHjuuefg7u6u/ZtADZ4u2/Hy5cuxcuVK9OjRA+bm5vjll1/U3h8zZgwsLCy0fg+o4dNlO/7kk09w6tQpDB06FG5ubsjKysL27dtx9uxZvPrqq2jVqlV93gp1Op1bSlSPKqtwvWbNGuHn5yfMzMyElZWV6NSpk3jnnXfE3bt3hRBCxMbGismTJws3NzdhYmIinJycREhIiIiJiVE7T2RkpPDz8xPGxsbVmv4dHBysNt277Ovo0aN19WuTnpFSO54xY0albRiAalUNoidJqR0fOnRIhISEiObNmwsjIyNhZWUlgoKCRFhYmFAoFHX6e2tKJoSOVw8lIiIionI4Jo2IiIhIgpikEREREUkQkzQiIiIiCWKSRkRERCRBTNKIiIiIJIhJGhEREZEEMUkjqkBKSgpkMhnWr1+v61CIaoztmPRBY27HTNKIiIiIJIjFbIkqIIRAUVERjIyMYGhoqOtwiGqE7Zj0QWNux0zSiIiIiCSIjztJby1ZsgQymQzXrl3DtGnTYGNjA0dHRyxatAhCCNy6dQujRo2CtbU1mjVrhq+++kp1bEVjIGbOnAlLS0vcuXMHo0ePhqWlJRwdHfH222+jtLRUtd+xY8cgk8lw7NgxtXgqOmdaWhqef/55tGjRAiYmJnBxccGoUaOQkpKipbtCDQ3bMekDtuOaYZJGem/ixIlQKBT47LPP8Mwzz+Djjz/G8uXLMWjQILi6uuLzzz9Hq1at8PbbbyMiIqLKc5WWlmLIkCFo2rQpvvzySwQHB+Orr77CmjVrahTb2LFjsXPnTjz//PNYuXIlXnvtNeTm5uLmzZs1Oh/pL7Zj0gdsxxrSzbruRNq3ePFiAUC88MILqm0lJSWiRYsWQiaTic8++0y1PTs7W5iZmYkZM2YIIYRITk4WAERYWJhqnxkzZggA4sMPP1S7TteuXYWfn5/q56NHjwoA4ujRo2r7PXnO7OxsAUB88cUXdfMLk15iOyZ9wHZcM+xJI703Z84c1X8bGhrC398fQgjMnj1btd3W1hY+Pj64cePGU8/34osvqv3cu3fvah33JDMzMxgbG+PYsWPIzs7W+HhqXNiOSR+wHWuGSRrpPTc3N7WfbWxsYGpqCgcHh3Lbn/aP09TUFI6Ojmrb7OzsavSP2sTEBJ9//jkOHDgAZ2dn9OnTB//973+Rlpam8blI/7Edkz5gO9YMkzTSexVN2a5sGrd4ymTn6kz/lslkFW4vO5hV6fXXX8e1a9ewbNkymJqaYtGiRWjXrh3Onz//1OtQ48J2TPqA7VgzTNKI6pidnR0A4MGDB2rbU1NTK9zf29sbb731Fg4dOoRLly5BLperzWwi0gW2Y9IHDb0dM0kjqmPu7u4wNDQsNzNp5cqVaj8XFBTg0aNHatu8vb1hZWWFoqIircdJVBW2Y9IHDb0dN9HZlYn0lI2NDcaPH4/vvvsOMpkM3t7e2Lt3L9LT09X2u3btGgYMGIAJEyagffv2aNKkCXbu3In79+9j0qRJOoqe6DG2Y9IHDb0dM0kj0oLvvvsOxcXF+OGHH2BiYoIJEybgiy++QMeOHVX7tGzZEpMnT8aRI0ewceNGNGnSBG3btsXWrVsxduxYHUZP9BjbMemDhtyOuSwUERERkQRxTBoRERGRBDFJIyIiIpIgJmlEREREEsQkjYiIiEiCmKQRERERSRCTNCIdS0lJgUwmw/r163UdClGNsR2TPpBaO2aSRg1KUlIS5s2bBy8vL5iamsLa2hpBQUFYsWIFCgsLtXbdhIQELFmyBCkpKVq7RnV88sknePbZZ+Hs7AyZTIYlS5boNB6qmcbcjq9cuYJ33nkHvr6+sLKygouLC0aMGIGYmBidxUQ105jb8d27dzFt2jT4+PjAysoKtra2CAgIwIYNG5665qgmWMyWGox9+/Zh/PjxMDExwfTp09GxY0fI5XKcPHkS//73vxEfH481a9Zo5doJCQlYunQp+vbtCw8PD61cozref/99NGvWDF27dkV4eLjO4qCaa+zt+Mcff8S6deswduxYzJ8/Hzk5OVi9ejUCAwNx8OBBDBw4UCdxkWYaezvOzMzE7du3MW7cOLi5uaG4uBiHDx/GzJkzcfXqVXz66ad1ch0madQgJCcnY9KkSXB3d8dff/0FFxcX1Xsvv/wyrl+/jn379ukwwv8jhMCjR49gZmZW5+dOTk6Gh4cHMjMz4ejoWOfnJ+1iOwYmT56MJUuWwNLSUrVt1qxZaNeuHZYsWcIkrQFgOwY6d+6MY8eOqW175ZVXMHLkSHz77bf46KOPYGhoWOvr8HEnNQj//e9/kZeXh3Xr1qn9QVBq1aoV/vWvf6l+LikpwUcffQRvb2+YmJjAw8MD7733XrmFcj08PBASEoKTJ08iICAApqam8PLyws8//6zaZ/369Rg/fjwAoF+/fpDJZJDJZKp/oMpzhIeHw9/fH2ZmZli9ejUA4MaNGxg/fjzs7e1hbm6OwMDAWv3x0mUvHtUe2zHg5+enlqABQNOmTdG7d29cvny5Ruek+sV2XDkPDw8UFBRALpfXzQkFUQPg6uoqvLy8qr3/jBkzBAAxbtw48f3334vp06cLAGL06NFq+7m7uwsfHx/h7Ows3nvvPfG///1PdOvWTchkMnHp0iUhhBBJSUnitddeEwDEe++9JzZu3Cg2btwo0tLSVOdo1aqVsLOzEwsWLBA//PCDOHr0qEhLSxPOzs7CyspKLFy4UHz99deiS5cuwsDAQOzYsUMVQ3JysgAgwsLCqv37ZWRkCABi8eLF1T6GdI/tuHI9e/YUbdq0qdGxVL/Yjv9PQUGByMjIEMnJyWL9+vXCwsJC9OzZs9r35mmYpJHk5eTkCABi1KhR1do/Li5OABBz5sxR2/72228LAOKvv/5SbXN3dxcAREREhGpbenq6MDExEW+99ZZq27Zt2wQAcfTo0XLXU57j4MGDattff/11AUCcOHFCtS03N1d4enoKDw8PUVpaKoRgktZYsB1XLiIiQshkMrFo0SKNj6X6xXasbtmyZQKA6jVgwABx8+bNah1bHXzcSZL38OFDAICVlVW19t+/fz8A4M0331Tb/tZbbwFAue7t9u3bo3fv3qqfHR0d4ePjgxs3blQ7Rk9PTwwZMqRcHAEBAejVq5dqm6WlJV544QWkpKQgISGh2uenho/tuGLp6emYMmUKPD098c4779TqXKR9bMfqJk+ejMOHD2Pz5s2YMmUKANTpzFYmaSR51tbWAIDc3Nxq7Z+amgoDAwO0atVKbXuzZs1ga2uL1NRUte1ubm7lzmFnZ4fs7Oxqx+jp6VlhHD4+PuW2t2vXTvU+NR5sx+Xl5+cjJCQEubm52L17d7mxaiQ9bMfq3N3dMXDgQEyePBmbNm2Cl5cXBg4cWGeJGpM0kjxra2s0b94cly5d0ug4mUxWrf0qm4EjNKh1o42ZnKRf2I7VyeVyhIaG4sKFC9i9ezc6duxYb9emmmM7rtq4ceNw69YtRERE1Mn5mKRRgxASEoKkpCScPn36qfu6u7tDoVAgMTFRbfv9+/fx4MEDuLu7a3z96v6BeTKOq1evltt+5coV1fvUuLAdP6ZQKDB9+nQcOXIEmzdvRnBwsMbnIN1hO66csgctJyenTs7HJI0ahHfeeQcWFhaYM2cO7t+/X+79pKQkrFixAgAwfPhwAMDy5cvV9vn6668BACNGjND4+hYWFgCABw8eVPuY4cOH48yZM2p/yPLz87FmzRp4eHigffv2GsdBDRvb8WOvvvoqtmzZgpUrVyI0NFTj40m32I6BjIyMCrevW7cOMpkM3bp10+h8lWExW2oQvL29sXnzZkycOBHt2rVTq3AdGRmJbdu2YebMmQCALl26YMaMGVizZg0ePHiA4OBgnDlzBhs2bMDo0aPRr18/ja/v6+sLQ0NDfP7558jJyYGJiQn69+8PJyenSo9ZsGABfv31VwwbNgyvvfYa7O3tsWHDBiQnJ2P79u0wMND8O9LGjRuRmpqKgoICAEBERAQ+/vhjAMBzzz3H3jmJYzt+/GG9cuVK9OjRA+bm5vjll1/U3h8zZozqQ5ikie348RJ9p06dwtChQ+Hm5oasrCxs374dZ8+exauvvlpuDF6N1dk8UaJ6cO3aNTF37lzh4eEhjI2NhZWVlQgKChLfffedePTokWq/4uJisXTpUuHp6SmMjIxEy5Ytxbvvvqu2jxCPp2uPGDGi3HWCg4NFcHCw2ra1a9cKLy8vYWhoqDb9u7JzCPG4ps+4ceOEra2tMDU1FQEBAWLv3r1q+2gy5Ts4OFhtunfZV0XT0UmaGnM7VtbMquyVnJxc5fEkHY25HR86dEiEhISI5s2bCyMjI9XvHhYWJhQKRZXHakImRB2uBEpEREREdYJj0oiIiIgkiEkaERERkQQxSSMiIiKSICZpRERERBLEJI2IiIhIgpikEREREUkQkzQiIiIiCWKSRkRERCRBTNKIiIiIJIhJGhEREZEEMUkjIiIikiAmaUREREQSxCSNiIiISIL+H3T9NkF2RByaAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "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": [ + "
" + ] + }, + "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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DkJCIiB6FBU4iIiIiIiIa1y5fvoyqqipER0c/8ejNjo4OxMTEwMbGBjNmzBimhERE9CgscBIREREREdG4JZPJkJCQADc3N9jZ2T1xe7Gxsbh//z5WrFgBNTV+5CYiUgX+tCUiIiIiIqJxKycnB3V1dZg9e/YTt1VcXIz09HTMmTMHEydOHIZ0REQ0ECxwEhERERER0bjU2dmJpKQkeHt7w9LS8onaam1txdGjRzF58mQEBQUNU0IiIhoIFjiJiIiIiIhoXJJKpWhpaUFkZOQTt/X999+jo6MDy5YtG5Zd2ImIaOBY4CQiIiIiIqJxp7W1FSkpKQgMDISJickTtXXlyhXk5eVhwYIFMDIyGqaEREQ0UCxwEhERERER0bhz4cIFyGQyzJo164naaWpqwokTJ+Dh4QEfH59hSkdERIPBAicRERERERGNK42NjZBKpQgJCYG+vv6Q21EoFDh+/DhEIhEWL17MqelERAJhgZOIiIiIiIjGleTkZGhoaGD69OlP1E5ubi6uX7+OpUuXQk9Pb5jSERHRYLHASUREREREROPGvXv3kJWVhRkzZkBHR2fI7dTX1+P06dPw9/eHm5vbMCYkIqLBYoGTiIiIiIiIxo2EhATo6ekhODh4yG0oFAocOXIEEyZMwPz584cxHRERDQULnERERERERDQuVFVV4dKlS4iIiICmpuaQ25FIJLhx4waWL18ObW3tYUxIRERDwQInERERERERjQvx8fEwNTWFn5/fkNu4c+cO4uLiEBISAkdHx2HLRkREQ8cCJxEREREREY15ZWVlKCgowOzZs6Gurj6kNmQyGWJiYmBsbIyoqKhhTkhEREPFAicRERERERGNaQqFArGxsbCysoKXl9eQ2zl//jyqqqqwcuXKJ5riTjRadHR0IDU1FR0dHUJHIXokFjiJiIiIiIhoTCssLERZWRmio6MhEomG1MatW7eQnJyMWbNmYdKkScOckGhkkkgk2L59O6RSqdBRiB6JBU4iIiIiIiIasxQKBeLi4uDg4ABnZ+chtdHZ2YmYmBhYWVlh5syZw5yQaGRqb2/HmTNnUFpaitOnT6O9vV3oSET9YoGTiIiIiIiIxqzLly+jurr6iUZvxsXFob6+HitWrBjy+p1Eo41UKkVBQQF8fHxQUFCA9PR0oSMR9YsFTiIiIiIiIhqTZDIZ4uPj4ebmBjs7uyG1UVpaColEgujoaJibmw9zQqKR6eHoTS0tLRgaGkJLS4ujOGlEY4GTiIiIiIiIxqTs7GzU19cPecfztrY2HDlyBI6OjhCLxcOcjmjkysnJQXFxMZqbm5Gfn4/m5mYUFxcjJydH6GhEfdIQOgARERERERHRcOvo6EBSUhJ8fHxgYWExpDbOnDmDtrY2LF++fMjT24lGIzs7O6xbt67P94lGIhY4iYiIiIiIaMxJT09Ha2srIiIihnT9tWvXkJOTg2XLlsHY2HhYsxGNdDY2NrCxsRE6BtGAcYo6ERERERERjSmtra1ISUlBYGAgTExMBn19c3Mzjh8/Djc3N/j5+Q1/QCIiGlYscBIREREREdGYcuHCBcjlcsyaNWvQ1yoUCpw4cQIKhQJLlizh1HQiolGABU4iIiIiIiIaMxobGyGVShESEgJ9ff1BX5+Xl4erV69i8eLFQ7qeiIhUjwVOIiIiIiIiGjOSkpKgoaGBsLCwQV/b0NCAU6dOwdfXF56enkpIR0REysACJxEREREREY0J9+7dQ3Z2NmbMmAEdHZ1BXatQKHD06FFoa2tjwYIFSkpIRETKwAInERERERERjQkJCQnQ09NDcHDwoK9NT09HSUkJli1bNujiKBERCYsFTiIiIiIiIhr1qqqqcOnSJUREREBTU3NQ19bW1iI2NhbBwcFwdnZWUkIiIlIWFjiJiIiIiIho1IuLi8PEiRPh5+c3qOvkcjliYmJgaGiIOXPmKCccEREpFQucRERERERENKrdvHkThYWFmD17NtTV1Qd1bUpKCm7fvo0VK1YMeuQnERGNDCxwEhERERER0ailUCgQFxcHa2vrQe98XllZicTERMycORO2trZKSkhERMrGAicRERERERGNWoWFhSgrK0NUVBREItGAr+vq6kJMTAwsLCwQHh6uxIRERKRsLHASERERERHRqPRw9Kajo+OgNweKj4/H3bt3sXLlykFPayciopGFBU4iIiIiIiIalS5duoTq6upBj968efMm0tLSMHv2bFhYWCgxIRERqQILnERERERERDTqyGQyJCQkwN3dHXZ2dgO+rr29HUeOHIGdnR1CQ0OVmJCIiFSFBU4iIiIiIiIadbKzs1FfX4/Zs2cP6rqzZ8+iubkZK1asgJoaPxITEY0F/GlOREREREREo0pHRweSkpLg4+MzqCnmBQUFyMrKwrx582BiYqLEhEREpEoscBIREREREdGoIpVK0draisjIyAFf09LSgmPHjsHV1RUBAQFKTEdERKrGAicRERERERGNGq2trbhw4QKmTZsGY2PjAV2jUChw8uRJyGQyLF26dFAbEhER0cjHAicRERERERGNGhcuXIBcLsfMmTMHfM3ly5eRn5+PxYsXw8DAQInpiIhICCxwEhERERER0ahw//59SCQShISEQF9ff8DXnDx5ElOnToWXl5eSExIRkRBY4CQiIiIiIqJRITk5GZqamggLCxvQ+QqFAseOHYOmpiYWLVqk5HRERCQUFjiJiIiIiIhoxLt79y6ys7Mxc+ZM6OjoDOiarKwsFBUVYdmyZZgwYYKSExIRkVBY4CQiIiIiIqIRLyEhAfr6+ggKChrQ+ffu3cOZM2cwbdo0uLi4KDkdEREJiQVOIiIiIiIiGtGqqqpw+fJlhIeHQ1NT87Hny+VyxMTEQF9fH3PnzlVBQiIiEhILnERERERERDSixcXFYeLEifD39x/Q+ampqaioqMCKFSugpaWl5HRERCQ0FjiJiIiIiIhoxLp58yYKCwsxe/ZsqKk9/iNsdXU1EhISMH36dNjb26sgIRERCY0FTiIiIiIiIhqRFAoFYmNjYW1tDU9Pz8ee39XVhcOHD8PMzAwRERHKD0hERCMCC5xERAJQKBSoL72IW5IYVOfFoau9RehIRERERCNOQUEBysvLER0dDZFI9NjzExMTUVtbixUrVkBDQ0MFCYmIaCTgT3wiIhVrv1+L/P3voaXmBiASAQoFijW04bp0M8w9Zwodj4iIiGhEkMvliIuLg6OjIyZPnvzY88vLy3HhwgXMnj0bVlZWKkhIREQjBUdwEhGpkEKhwJVvf4eWO2UP3wAAyLvacT3m/6G5ulTAdEREREQjx+XLl1FTUzOg0ZsdHR2IiYmBra0tpk+frqKEREQ0UrDASUSkQo23rqO5sghQyPs8fjvzhIoTEREREY08MpkMCQkJcHd3h62t7WPPP3fuHBobG7F8+fIBbUQkpPr6eqEjEBGNOSP7Jz8R0RjTUnOj/4MKOZqrS1SWhYiIiGikysrKQn19PWbPnv3Yc4uKipCRkYG5c+di4sSJKkg3dKmpqfjHP/7BIicR0TBjgZOISIU09U36PyhSg5a+qerCEBEREY1AHR0dSE5Ohq+vLywsLB55bmtrK44ePQpnZ2dMmzZNRQmHJicnB2fPnkVoaCiMjY2FjkNENKawwElEpEImzgHQ0DV8sLnQf1PIYek3V/WhiIiIiEYQqVSK1tZWREREPPbcU6dOobOzE8uWLRvQLutCuXr1Ko4dO4Zp06YhMjJS6DhERGMOC5xERCqkpq4J9xW/gkhNAxD9+0fwv//XKmABTF2DBUxHREREJKzW1lZcuHAB06ZNe+wox/z8fFy6dAkLFy6EoaGhagIOQWlpKQ4dOgQvLy8sXLhwRBdiiYhGKw2hAxARjTfGTr4I/MkXqMr+Hk1VxdDUNYSFdxSMJ/uzw0tERETjWkpKCuRyOWbNmvXI8xobG3Hy5El4enrC29tbRekG7/bt29i3bx+cnJywYsWKEb8BEhHRaMUCJxGRAHSMLeE4+3mhYxARERGNGPfv34dUKsX06dOhp6fX73kKhQLHjx+HmpoaFi9ePGIfENfW1mL37t2wsLDAM888A3V1daEjERGNWXx8RERERERERIJLSkqClpYWQkNDH3leTk4OCgoKsHTpUujq6qoo3eA0NDRg586d0NfXx7p166ClpSV0JCKiMY0FTiIiIiIiIhLU3bt3kZOTgxkzZkBHR6ff8+rq6nD69GkEBARgypQpKkw4cM3Nzdi1axfU1dWxYcMGTJgwQehIRERjHgucREREREREJKiEhATo6+sjKCio33PkcjmOHDkCXV1dzJs3T4XpBq69vR179uxBW1sbNmzYAAMDA6EjERGNCyxwEhERERERkWAqKytx+fJlREREQFNTs9/zJBIJysrKsHz5cmhra6sw4cB0dXVh//79uHfvHtavXw9TU1OhIxERjRsscBIREREREZFg4uLiMHHiRPj5+fV7Tk1NDeLi4hASEgJHR0eVZRsouVyOQ4cOoaKiAmvXroWVlZXQkYiIxhUWOImIiIiIiEgQN27cQFFREWbPng01tb4/nspkMsTExMDU1BRRUVEqTvh4CoUCx44dQ0FBAZ555hnY29sLHYmIaNxhgZOIiIiIiIhUTqFQIC4uDpMmTYKnp2e/5yUnJ6O6uhorVqyAhoaGChM+nkKhwLlz55Cbm4vly5fD1dVV6EhEROMSC5xERERERESkcgUFBSgvL0dUVBREIlGf51RUVOD8+fMIDw/HpEmTVJzw8VJSUpCamoqFCxfCx8dH6DhEROPWmClw/vGPf4RIJMIbb7zxyPO+/fZbuLu7Q0dHB97e3jh16pRqAhIRERGRSrBfSDTyyeVyxMXFwcnJCZMnT+7znM7OTsTExMDa2hozZ85UccLHy8zMRFxcHCIiIhAcHCx0HCKicW1MFDgzMjLwxRdfPPaJWWpqKtasWYMXX3wROTk5WL58OZYvX47Lly+rKCkRERERKRP7hUSjw6VLl1BTU/PI0ZuxsbFoaGjAihUr+l2fUyj5+fk4efIkxGIxwsPDhY5DRDTujazfEkPQ1NSEdevWYevWrTAxMXnkuZ988gnmz5+PX/ziF/Dw8MDvfvc7BAQE4LPPPlNRWiIiIiJSFvYLiUYHmUyGhIQEuLu7w9bWts9zSkpKIJVKMWfOHJiZmak44aMVFxfj8OHD8Pb2xvz58/st0BIRkeqM+gLnT3/6UyxatAjR0dGPPTctLa3XefPmzUNaWlq/17S3t+P+/fvdX01NTU+cmYiIiIiGH/uFRKNDVlYWGhoaMHv27D6Pt7W14ciRI3BychpxU78rKiqwf/9+ODs7Y9myZSxuEhGNECNrC7pB2r9/P7Kzs5GRkTGg86uqqmBpadnjPUtLS1RVVfV7zUcffYT333//iXISERERkXKxX0g0OnR0dCApKQm+vr6wsLDo85zvv/8e7e3tWL58+YgqINbU1GDPnj2wtrbG008/DXV1daEjERHRv43aEZzl5eV4/fXXsWfPHujo6CjtPm+//TYaGhq6v5KSkpR2LyIiIiIaPPYLiUYPiUSCtrY2RERE9Hn86tWruHjxIhYsWAAjIyPVhnuE+vp67Nq1C0ZGRli7di00NTWFjkRERD8wakdwZmVloaamBgEBAd3vyWQyJCcn47PPPkN7e3uvJ2pWVlaorq7u8V51dTWsrKz6vY+2tja0tbW7X+vr6w/Td0BEREREw4H9QqLRoaWlBRcuXEBQUBCMjY17HW9ubsaJEyfg7u4OX19f1QfsR1NTE3bu3AlNTU2sX79eqQ9SiIhoaEbtCM6oqChcunQJubm53V/Tpk3DunXrkJub2+d0gdDQUMTFxfV479y5cwgNDVVVbCIiIiIaZuwXEo0OFy5cgEKhwMyZM3sdUygUOH78OABgyZIlI2ZqeltbG3bv3o3Ozk5s2LCBDzaIiEaoUTuC08DAAFOnTu3xnp6eHiZOnNj9/saNG2FjY4OPPvoIAPD6668jPDwcf/3rX7Fo0SLs378fmZmZ+PLLL1Wen4iIiIiGB/uFRCPf/fv3IZVKMX36dOjp6fU6fvHiRVy7dg2rV6/u87gQOjs7sW/fPjQ0NOCFF16AiYmJ0JGIVK6jowOZmZmYNm0atLS0hI5D1K9RO4JzIMrKylBZWdn9OiwsDHv37sWXX34JX19fHDp0CEeOHOnVISYiIiKisYX9QiJhJSUlQUtLC2FhYb2O1dfX4/vvv4efnx88PDwESNebTCbDt99+i9u3b2Pt2rX9bohENNZJJBJs374dUqlU6ChEjzRqR3D2JTEx8ZGvAeDpp5/G008/rZpARERERCQI9guJRo67d+8iJycHc+bM6bGOLfBgavrRo0eho6OD+fPnC5Swp4eZiouLsXbtWtjZ2QkdiUgQ7e3tOHPmDEpLS3H69GkEBwf3+jdMNFKM6RGcREREREREJKz4+Hjo6+sjKCio1zGpVIrS0lIsX758RGzeo1AocPr0aVy6dAkrV66Es7Oz0JGIBCOVSlFQUAAfHx8UFBQgPT1d6EhE/WKBk4iIiIiIiJSisrIS+fn5iIiIgIZGzwmEtbW1iI2NhVgshpOTk0AJe0pOToZUKsWiRYvg5eUldBwiwTwcvamlpQVDQ0NoaWnh9OnTaG9vFzoaUZ9Y4CQiIiIiIiKliIuLg5mZGfz8/Hq8L5PJcPjwYRgbGyM6OlqYcP8lPT0dCQkJiIqKwrRp04SOQySonJwcFBcXo7m5Gfn5+WhubkZxcTFycnKEjkbUpzG1BicRERERERGNDDdu3EBRURGeeeYZqKn1HFuTkpKCqqoqvPjii9DU1BQo4X9cunQJp06dQmhoKGbMmCF0HCLB2dnZYd26dX2+TzQSscBJREREREREw0qhUCA2NhaTJk3qtTP67du3kZSUhJkzZ8LGxkaghP9RWFiImJgY+Pn5Ye7cuRCJREJHIhKcjY3NiPj3STRQnKJOREREREREw+r69euoqKhAVFRUj4JhZ2cnYmJiYGlpiVmzZgmY8IGysjIcPHgQU6ZMwdKlS1ncJCIapVjgJCIiIiIiomEjl8sRHx8PJycnTJ48ucex+Ph41NXVYcWKFVBXVxco4QNVVVXYu3cvbGxssGrVql7T6ImIaPTgT3AiIiIiIiIaNpcuXUJNTQ2io6N7jIi8ceMGJBIJZs+eDQsLCwETAvfu3cPu3bthYmKCNWvW9NrhnYiIRhcWOImIiIiIiGhYdHV1ISEhAR4eHj3W72tvb8eRI0dgb2+PkJAQARMCjY2N2LVrF7S1tbF+/Xpoa2sLmoeIiJ4cC5xEREREREQ0LLKystDQ0IDZs2f3eP/MmTNoaWnB8uXLBZ0K3trail27dkEmk2Hjxo3Q09MTLAsREQ0fFjiJiIiIVCA5ORlLlizBpEmTIBKJcOTIkcdek5iYiICAAGhra8PFxQU7duxQek4ioqHq6OhAcnIyfH19YW5u3v3+9evXkZ2djfnz58PExETQfHv37kVTUxM2bNgAIyMjwbIQjRYdHR1ITU1FR0eH0FHGFPYLhx8LnDSiVd5twI3Ku+jskgkdhYiI6Ik0NzfD19cX//jHPwZ0fmlpKRYtWoTIyEjk5ubijTfewEsvvYQzZ84oOSkR0dBIJBK0tbUhIiKi+72WlhYcP34cU6ZMgb+/v2DZZDIZDh48iOrqaqxbt65HAZaI+ieRSLB9+3ZIpVKho4wp7BcOP66kTCNSfult/P27BBTfugMAMNDVwbo5wVgZ7t9joXIiIqLRYsGCBViwYMGAz//888/h5OSEv/71rwAADw8PpKSkYMuWLZg3b56yYhIRDUlLSwsuXLiAoKAgGBsbAwAUCgVOnDgBuVyOpUuXCtaPl8vliImJQWlpKdatW9djbVAi6l97ezvOnDmD0tJSnD59GsHBwVyzdpiwXzj8OIKTRpzS27X4xT+/Q8nt2u73Glva8PnRZByMzxIwGRERkeqkpaUhOjq6x3vz5s1DWlqaQImIiPqXkpIChUKBmTNndr936dIlXLlyBYsXL4a+vr4guRQKBU6dOoX8/HysWrUKkydPFiQH0WgklUpRUFAAHx8fFBQUID09XehI4xb7hY/HAieNOPtiMyCXy6FQKHod23NOiraOTgFSERER9a2pqQn379/v/mpvbx+WdquqqmBpadnjPUtLS9y/fx+tra3Dcg8iVeirT0djy/3795Geno6wsLDuTXvu37+PU6dOwdvbG56enoJlS0hIQGZmJpYsWQIPDw/BchCNNg9Hb2ppacHQ0BBaWlo4ffr0sPVzxiJl9QkB9gsHggVOGnEyr9+ETN53R7i1vRNFFTUqTkRERNS/8PBwGBkZdX999NFHQkciGjFaWlrwr3/9q3ttRhqbkpKSoKWlhdDQUAAPitpHjx6FpqYmFi5cKFguiUSC5ORkzJkzBwEBAYLlIBqNcnJyUFxcjObmZuTn56O5uRnFxcXIyckROtqIxT6hsLgGJ404muqPrrtrqKurKAkREdHjJSUlwc/Pr/v1cK1NZWVlherq6h7vVVdXw9DQEBMmTBiWexApW2dnJ6ysrHD27FnEx8fD398fwcHBmDhxotDRaJjU1tYiJycHc+bM6f75l5mZieLiYmzYsEGwn1cXL17E6dOnMWPGDEyfPl2QDESjmZ2dHdatW9fn+9Q3ZfUJAfYLB4IFThpxZvlNwbELFyHvYxTnREM9uNpZCJCKiIiob/r6+jA0NBz2dkNDQ3Hq1Kke7507d657hBTRaGBkZISVK1dizpw5yMjIQFZWFqRSKVxdXRESEoLJkydzA8lRLiEhAQYGBggKCgIA3L17F2fPnkVQUBCcnZ0FyXT9+nUcPXoUAQEBiIqKEiQD0WhnY2PDDbkGSVl9QoD9woHgFHUacVZHTYOx3gSoqf2ns6v2747vqysioK7Gv7ZERErXyamkw62pqQm5ubnIzc0FAJSWliI3NxdlZWUAgLfffhsbN27sPv/HP/4xSkpK8Mtf/hLXrl3DP//5Txw8eBCbN28WIj7REzEwMMDs2bOxefNmLFu2DI2Njdi1axf++c9/IjMzE52dXGN9NLp9+zby8/MREREBDQ2N7t3KDQwMMGfOHEEy3bhxA99++y3c3d2xePFiFtCJnlBHRwdSU1PR0dEhdJQxhf3C4ccRnDTimBnp47M312DXGSkSsq+jo6sLXo6TsH6eGAFT7IWOR4Tcr19HR1MdtPRN4PfiJ0LHIVIOWTugqSN0ijElMzMTkZGR3a/ffPNNAMBzzz2HHTt2oLKysrtTCwBOTk44efIkNm/ejE8++QS2trb46quvMG/ePJVnJxouGhoa8Pf3h5+fH8rKyiCRSHDy5EnExcUhICAAwcHBMDIyEjomDVBcXBzMzMzg6+sLALhw4QJu3bqFTZs2QUtLS+V5KisrsW/fPtjb22PlypVQ48AIoicmkUiwa9cuyGQyzJw5U+g4Ywb7hcOPBU4akcyNDfDm6mi8uTpa6ChEvXQ01aGj8a7QMYiUi7seD7uIiIhH7ia9Y8eOPq/hYv40FolEIjg4OMDBwQH19fVIT09HVlYWUlNT4eHhgZCQENjZ2XH03QhWWlqK4uJiPPPMM1BTU0NVVRUSExMxY8YMQdbou3v3Lnbv3g0zMzM8++yz0NDgR12iJ/VwJ/XS0lKcPn0awcHBw7qu5HjGfuHw4099IiIiIiISjLGxMebOnYuIiAhcvHgRUqkU27Ztg7W1NUJCQuDl5cVi1QijUCgQFxeHSZMmwcPDA11dXTh8+DDMzc0RERGh8jz379/Hzp07oauri3Xr1gkyepRoLJJKpSgoKICPjw8KCgqQnp7OUZw0YnHMPhERERERCU5LSwtBQUH46U9/ivXr10NPTw8xMTH4+OOPkZiYiKamJqEj0r9dv34dFRUViI6OhkgkQkJCAu7evYsVK1ZAXV1dpVlaWlqwa9cuAMCGDRugq6ur0vsTjVUPR29qaWnB0NAQWlpaOH36NNrb24WORtQnPgolIiIiIqIRQyQSwcXFBS4uLqitrYVUKkVqairOnz+PqVOnQiwWY9KkSULHHLfkcjni4uIwefJkTJ48GWVlZUhNTUVUVBQsLS1VmqW9vR179uxBS0sLNm3apLTdi4nGo5ycHBQXF6OtrQ2XLl2CTCZDcXExcnJyEBISInQ8ol5Y4CQiIqI+cA1OIhKemZkZFi1ahKioKGRnZyM9PR0XL16Evb09xGIxPDw8uJGMiuXl5eHOnTtYvnw5Ojo6EBMTA1tbW4SFhak0R1dXFw4cOIDa2lo8//zzmDhxokrvTzTW2dnZYd26dQAe/Ht7uFSIEGvsEg0EC5xERETUGzcZIqIRREdHB2FhYQgJCcH169chlUrx7bffwsjICEFBQQgMDMSECROEjjnmdXV1ITExER4eHrCxscGJEyfQ1NSEDRs2qLTQLJfLcfjwYZSVlWH9+vWwtrZW2b2JxgsbGxvY2NgAADo6Ori2LY14LHASEREREdGooKamBg8PD3h4eKCqqgpSqRSJiYlISkqCj48PxGIxLCwshI45ZmVlZaGhoQHr1q1DYWEhMjMzsXjxYpiamqosg0KhwIkTJ3Dt2jWsXr0ajo6OKrs3ERGNXCxwEhERUW8KudAJiIgeycrKCsuWLUN0dDSysrKQkZGBrKwsTJ48GSEhIXB1dYVIJBI65pjR3t6O5ORk+Pn5QV9fHzt37oSLiwsCAwNVmiMuLg7Z2dlYsWIF3NzcVHpvovFKwZk9NAqwwElERES9yWVCJyAiGhA9PT3MmjUL06dPx5UrVyCRSLB3716YmppCLBbDz88P2traQscc9SQSCdra2hAREYGTJ0+iq6sLS5cuVWkR+cKFC0hJScH8+fPh6+ursvsSEdHIxwInERER9SbvFDoBEdGgqKurw9vbG97e3qioqIBEIsGZM2cQHx8Pf39/BAcHq3Qq9VjS0tKC1NRUBAUFoby8HJcvX8ZTTz2l0l3Ls7Ozce7cOcyaNYs7OBOpGEdw0mjAAicRERH1JmOBk4hGL1tbW6xatQr379/vnroulUoxZcoUiMViODk5cfr6IKSkpEChUMDPzw/ffPMNvLy8MHXqVJXd/+rVqzh+/DiCgoIQGRmpsvsS0QMscNJowAInERER9dbVLnQCIqInZmhoiKioKMyaNQuXLl2CVCrFzp07YWFhAbFYDB8fH2hqagodc0RraGhAeno6pk+fjri4OKirq2PRokUqKxCXlJTg0KFD8PLywoIFC1iYJhIAC5w0GrDASURERL11tQqdgIho2GhqaiIgIAD+/v64ceMGJBIJTpw4gdjYWAQGBiI4OFil061Hk6SkJGhpaUFHRweFhYVYt24ddHV1VXLvW7duYf/+/XBycsKKFSugpqamkvsSUU8scNJowAInERER9dbRInQCIqJhJxKJ4OTkBCcnJ9y7dw8ZGRnIyMhAamoqPDw8EBISAltbW44S/Lfa2lrk5OQgJCQECQkJCAwMhKurq0rufefOHezZsweWlpZ45plnoK6urpL7ElFvcrlc6AhEj8UCJxEREfXW0Sx0AiIipTI1NcW8efMQERGBixcvQiqV4uuvv8akSZMQEhICLy+vcV9US0hIgIGBAcrKyqCnp4e5c+eq5L719fXYtWsX9PX1sXbtWmhpaankvkTUNxY4aTRggZOIiIh662gSOgERkUpoa2sjODgYQUFBKCwshFQqxeHDh3H27FkEBQVh2rRp0NPTEzqmyt2+fRv5+flwcHBAWVkZnn/+eWhrayv9vs3Nzdi1axfU1dWxYcMGTJgwQen3JKJHY4GTRgMWOImIiKi39kahExARqZRIJMKUKVMwZcoU3LlzB1KpFCkpKUhOToa3tzfEYjGsra2FjqkycXFx0NbWRllZGUJDQ+Hg4KD0e7a3t2P37t1ob2/Hpk2bYGBgoPR7EtHjscBJowELnERERNRb+32hExARCcbc3ByLFy9GVFQUsrOzkZ6ejtzcXDg4OEAsFsPd3X1Mb3hTWlqKwsJCiEQiWFpaYvbs2Uq/Z2dnJ/bt24e6ujq88MILMDU1Vfo9iWhgZDKZ0BGIHosFTiIiIuqttV7oBEREgpswYQKmT5+O0NBQXLt2DRKJBAcPHoSxsTGCgoIQEBAw5qZQKxQKxMbGorm5GYaGhlixYgU0NJT7sVEul+PQoUO4desWNmzYAEtLS6Xej4gGhwVOGg1Y4CQiIqLe2uoBuRwYwyOUiIgGSk1NDZ6envD09ERlZSUkEgni4+ORmJgIX19fiMVimJubCx1zWFy/fh1Xr16FTCZDZGSk0qflKxQKHDt2DIWFhVizZg3s7e2Vej8iGjxOUafRgAVOIiIi6k0hB9obgAkmQichIhpRrK2tsWLFCsyZMweZmZndXy4uLhCLxXBxcYFIJBI65pDI5XKcOXMGd+7cQXh4OGbMmKHU+ykUCpw9exYXL17EypUr4erqqtT7EdHQcAQnjQYscBIREVHfWu6xwElE1A99fX1ERERgxowZyM/Ph1QqxZ49ezBx4kSIxWL4+flBS0tL6JiDkpeXh/T0dNja2mL58uVKX2c0JSUFaWlpWLhwIby9vZV6LyIaOhY4aTRggZNGJIVCgas3q5CcW4C2ji54O9tgpq8LtJS8/g8REf1Acy0w0VnoFEREI5qGhgZ8fX3h4+OD8vJySKVSnD59GnFxcQgICEBwcDBMTEb+w6Kuri58++236OjowFNPPQUzMzOl3i8zMxNxcXGIjIxEcHCwUu9FRE+mq6tL6AhEj8VqEY04crkCWw7E4nR6PtT//dT4ZNol7DxtjL/+dBXMjPUFTkhENE401widgIho1BCJRLC3t4e9vT0aGhqQkZGBrKwsSCQSuLm5ISQkBA4ODiN2+vqFCxeQmZmJpUuXIigoSKn3unz5Mk6ePAmxWIxZs2Yp9V5E9ORY4KTRgDsH0IhzOj0fp9PzAQAyuRyyfy9oXHWvAf9v7xkhoxERjS9NLHASEQ2FkZERoqOj8eabb2Lx4sW4e/cuduzYgc8//xw5OTno7OwUOmIP7e3t+Prrr2FpaYn169crtQhbVFSEmJgYeHt7Y/78+SO24EtE/8ECJ40GHMFJI87R87kQAVD81/tyuQI5heWovNsA64lGQkQjIhpfWOAkomFQWloKR0fHcVnI0tTURGBgIAICAlBaWgqJRIJjx47h3LlzmDZtGoKCgmBgYCB0TOzfvx8VFRX44IMPYGSkvH52eXk5Dhw4AGdnZyxbtmxc/p0gGo06OjqEjkD0WCxw0ohTXdfYq7j5QzV1jSxwEhGpQmOl0AmIaJSrqqrCN998AzMzM4SGhsLHxweamppCx1I5kUiEyZMnY/Lkybh37x6kUikkEglSUlLg5eUFsVgMW1tbQbLV1NRg//79CAkJwfTp05V6n71792LSpEl4+umnoa6urrR7EdHwam9vFzoCjTGVlZWoqamBi4sL9PT0hqVNTlGnEWfSRCM86mGulamh6sIQEY1n928LnYAE0N7ejrS0NBw9ehS1tbVCx6FRzsrKCi+++CLMzc1x4sQJfPzxx0hMTERzc7PQ0QRjamqKBQsW4Oc//znmzp2LW7du4auvvsJXX32FS5cuqXS3YoVCgb/97W8AgDfeeENpIyrr6uqwa9cuGBkZYc2aNeOyyE00mrW1tQkdgcaIo0ePwt3dHba2tggICIBUKgUA1NbWwt/fH0eOHBly2yxw0oizfKYfFH0M4VRTEyHIwxGWLHASEalG8x2gi0/sx5NPP/0U1tbWmDFjBlauXIm8vDwADzqdZmZm2LZtm8AJaTSys7PD6tWr8dprr8HLywsXLlzAli1bcOLECdy9e1foeILR1tZGSEgIfvazn2HNmjXQ0tLCd999h48//hjJyckqKQKfP38e6enpWLNmDSwsLJRyj6amJuzatQuamppYv349dHR0lHIfIlKe1tZWoSPQGHD8+HGsXLkSZmZmeO+996D4QeHHzMwMNjY22L59+5DbZ4GTRpw5QR5YNsMXAKCuJureSd3OwhS/WDNHyGhEROPP/VtCJyAV2b59O9544w3Mnz8fX3/9da9O5+zZs7F//34BE9JoZ2pqioULF2Lz5s2YNWsWrl27hs8++wz79+9HWVlZj79z44mamhrc3NywceNGvPrqq5gyZQqSk5OxZcsWHD16FNXV1Uq5b319Pb766qvuArQytLW1Yffu3ejs7MTGjRuhr6+vlPsQkXK1tLQIHYHGgA8++ACzZs1CSkoKfvrTn/Y6HhoaipycnCG3zzU4acQRiUT42VORWBAyFckXC9He0Ympk20Q6jUZ6uqsyRMRqVR9OWA6WegUpAJ//etfsWzZMuzdu7fPUXWBgYH49NNPBUhGY42uri5mzZqFsLAw5OXlIS0tDdu2bYONjQ3CwsLg4eEBNbXx2eezsLDAkiVLEBUVhaysLGRkZCAnJweOjo4ICQnBlClThuXPRqFQYNeuXaipqcE777wDbW3tYUjfU2dnJ/bu3YuGhga88MILMDY2HvZ7EJFqtLW1oaurCxoaLCHR0F2+fLl7WZS+WFpaoqZm6Juc8m8njVjONuZwtjEXOgYR0fhWdwNAuNApSAWKiorwP//zP/0eNzU1HdfTiWn4aWhoICAgAP7+/igqKkJqaiq+/fZbmJiYICQkBP7+/tDS0hI6piB0dXUxc+ZMhIWF4erVq5BKpdi/fz9MTEwQHBwMf3//J5rqLZFIkJycjGnTpiEsLGwYkz8gk8lw8OBBVFVVYePGjUqb/k5EqtPc3AwjI272S0Onq6v7yOVXSkpKMHHixCG3zwInERER9a/uhtAJSEWMjY0fuanQlStXYGVlpcJENF6IRCK4urrC1dUVlZWVSE1NxZkzZ5CQkICgoCAEBwfDwMBA6JiCUFdXx9SpUzF16lTcunULUqkUsbGxSEhIgJ+fH8Ri8aA/DN65cwcxMTHQ1tbGqlWrhn1ElkKhwJEjR1BSUoK1a9cKtjs8EQ0vFjjpSUVGRuKbb77BG2+80etYVVUVtm7disWLFw+5fRY4iYiIqH93C4VOQCqycOFCfPnll3j11Vd7HcvPz8fWrVuxadMmAZLReGJtbY2nnnoK0dHRkEgkSE9PR2pqKry9vREWFjauRwLa2Nhg5cqVmDNnDjIzM5GZmYn09HS4urpCLBbD2dn5sbugy2QyxMTEoKqqCsHBwfDx8RnWjAqFAt9//z0uX76MVatWwdnZeVjbJyLhqGLjMxrb/vCHPyAkJARBQUF4+umnIRKJcObMGcTHx+OLL76AQqHAe++9N+T2WeAkIiKi/jVUAB0tgJau0ElIyX7/+99DLBZj6tSpWLJkCUQiEb755hts27YN3333HaytrfHb3/5W6Jg0ThgZGWHevHkIDw9HdnY2JBIJcnNz4eLigrCwMDg5OT22mDdWGRgYIDIyEjNnzsTly5chkUiwe/dumJmZQSwWw9fXt9+p/efPn8eVK1dgbm6OOXPmDPtap0lJSUhPT8eSJUvg5eU1rG0TkbBY4KQn5ebmhpSUFLz++uv4zW9+A4VCgT//+c8AgIiICPzjH/+Ao6PjkNtngZOIiIj6p1A8GMVp7St0ElKySZMmISsrC++88w4OHDjQvQmJgYEB1qxZgz/+8Y8wMzMTOiaNMzo6OggLC4NYLEZ+fj5SU1Oxc+dOWFlZISwsDF5eXlBXVxc6piA0NDTg5+cHX19flJWVQSKR4NSpU4iLi0NAQACCg4N7bOxz69YtJCUlQUNDA25ubnB3dx/WPFKpFImJiYiKikJgYOCwtk1EwuNO6jQcvLy8EBsbi7q6OhQVFUEul2Py5MkwN3/y/VdY4CQiIqJHq77CAuc4YWFhga+++gpfffUV7ty5A7lcDnNz83G7ozWNHOrq6vDx8YG3tzdKS0uRlpaGw4cPIzY2FmKxGIGBgU+06c5oJhKJ4ODgAAcHB9TX1yM9PR3Z2dlIS0uDu7s7QkJCYG1tjZiYGAAPNnmIjo4e1hGweXl5+P777xEWFoYZM2YMW7tENHK0trYKHYHGEBMTEwQFBQ1rmyxwEhER0aNVXxY6AQlgOJ6kEw03kUiEyZMnY/LkyaipqUFaWhri4+ORlJSEwMBAiMXiHqMWxxtjY2PMnTsXERERuHjxIqRSKbZv34579+6hs7MTtra2mDx5MpycnIbtngUFBThy5Aj8/PwwZ86ccbt0ANFYxxGc9KQ+/fRTnDx5EmfOnOnz+IIFC7B06VL85Cc/GVL7fBxPREREj1Z5EZDLhU5BSvbuu+/Cz8+v3+P+/v54//33VReI6DEsLCywbNkyvPHGGxCLxcjNzcWnn36K7777Drdv3xY6nqC0tLQQFBSEn/70pwgPD0dFRQUqKyuRmJgIHR0dNDY2Dst9bt68iYMHD2LKlClYunQpi5tEYxhHcNKT+vrrr+Hp6dnvcU9PT3z55ZdDbp8FTiIiInq09kbgXonQKUjJDh06hAULFvR7fOHChThw4IAKExENjIGBAaKiorB582bMmzcPFRUV+PLLL7Fjxw4UFBRAoVAIHVEw7e3tyM3NxaJFixAQEIBp06ahsLAQH3/8MQ4fPoxbt24Nue2qqirs27cPdnZ2WLVqFZeyIBqjOjo6UFhYiPv37wsdhUa54uJieHh49Hvc3d0dxcXFQ26fU9SJiIjo8W5nA2YuQqcgJSorK4Ozs3O/x52cnHDz5k0VJiIaHC0tLYjFYgQFBeHatWtITU3F3r17YWZmhrCwMPj4+EBDY3x9/Dlz5gza2towdepUVFRU4Oc//zn09fWRk5MDqVSKvLw82NnZQSwWw8PDY8AbNt27dw+7d++Gqakpnn322XH350o0nkgkEiQnJyMoKAidnZ3Q1NQUOhKNUlpaWqiqqur3eGVl5RM9LONvIiIiInq8ikzA5xmhU5AS6evrP7KAWVpaOm43caHRRU1NDZ6envDw8EB5eTlSU1Nx/PhxxMXFITg4GEFBQdDV1RU6ptJdu3YNOTk5WLBgAZKSkuDv7w8zMzMAQGhoKMRiMQoKCiCRSHDo0CEYGhoiKCgIgYGBj/zzuX//Pnbu3AkdHR2sW7cO2traqvqWiEjF2tvbcebMGdy5cwf5+fm4e/curKyshI5Fo1RISAh27NiBzZs3w8DAoMexhoYGbN++HSEhIUNunwVOIiIierzKXKCrHdDgB9mxKiIiAl988QV+/OMfw8bGpsex8vJyfPnll4iMjBQoHdHgiUQi2Nvbw97eHnfv3oVEIsH58+eRkpICPz8/hIaGwtTUVOiYStHc3Izjx4/Dzc0NLS0t6OjoQHh4eI9z1NTU4O7uDnd3d1RVVUEqlSIpKQlJSUnw8fFBSEgILCwselzT2tqK3bt3Qy6XY8OGDdDT01Plt0VEKiaVSlFQUAA7OztUV1cjOTkZzzzDB940NO+99x7Cw8Ph5+eHN954A15eXgCAy5cv4+OPP0ZlZSX27t075PZZ4CQiIqIepk2bhqob12GlB2S+E/Dgza524HYOYD/0p6o0sv3ud79DcHAwvLy88OKLL/bodG7btg0KhQK/+93vBE5JNDQTJ07EokWLEBERgczMTKSnpyMzMxPu7u4ICwuDnZ2d0BGHjUKhwIkTJ6BQKDB79mxs27YNQUFBMDIy6vcaKysrLFu2DNHR0cjKykJGRgays7Ph5OSEkJAQuLq6oqurC3v27EFTUxM2bdr0yPaIaPR7OHpTS0sLEyZMgLq6Os6cOYNly5Zx5DYNiVgsxvHjx/HKK6/g9ddf796YTqFQwMnJCceOHUNoaOiQ22eBk4iIiHqoqqrCrbtNgEyr54GbqSxwjmFubm44f/48XnvtNWzZsqXHsVmzZuHTTz995MLwRKOBnp4ewsPDERYWhry8PKSlpeHrr7+GnZ0dQkND4e7uPuo3y8nLy8PVq1fxzDPPIDc3FwAwc+bMAV2rp6eHWbNmYfr06bhy5QqkUin27dsHQ0NDNDQ0QF1dHS+99FL3VHciGrtycnJQXFyMtrY23L59Gx0dHSguLkZOTs4TTSOm8W3OnDkoKirq/vsFAM7OzggICOgueA4VC5xEREQ0MDfOA9PfAEb5h3/qn4+PD5KSklBbW4uSkhIAwOTJk1nMoDFHU1MTgYGBCAgIQGFhIVJTU3Hw4EGYmJggNDQUfn5+0NLSenxDI0xDQwNOnToFHx8f2NjY4PDhw5g5c+ag1xxVV1eHt7c3vL29UVZWhk8++QQ5OTkICAjAxYsXoaOjM2an9xPRA3Z2dli3bh0AICEhAe3t7dDU1IStra3AyWi0U1NTQ2BgIAIDA4e1XRY4iYiIaGBa7gFVecAkP6GTkJKZmZmxqEnjgkgkwpQpUzBlyhTcvn0bqampOH36NBISEhAUFITg4GDo6+sLHXNAFAoFjh49Cm1tbSxcuBBnzpyBtrb2E420UigUyMvLg6GhIf70pz+hsbGxe4q/q6srQkJC4OTk9MSjboho5LGxselek7u+vh6tra0AMCof/tDIcuXKFZSUlKCurg4KhaLX8Y0bNw6pXRY4iYiIaOAKz7HAOYbJZDKcOXOm306nSCTCb37zG4HSESnXpEmTsGrVKtTX10MikUAikeDChQvw8fFBWFgYzM3NhY74SOnp6SgpKcGGDRvQ2NiI3NxczJ8//4nWyouPj0dmZiaWLVsGf39/AA+mu1++fBkSiQQ7d+6EhYUFxGIxfHx8oKmpOVzfDhGNUMXFxb02ICMaiOLiYqxfvx7p6el9FjaBB31NFjiJiIhI+YrjgJCfANqjY0QTDVxmZiaeeuopVFRUPLLTyQInjXXGxsaYP38+IiIikJWVBYlEgpycHLi6uiIsLAyOjo4jbsRibW0tYmNjERwcDGdnZxw8eBBGRkZPNP0vLS0N58+fx9y5c7uLm8CD6f3+/v7w8/PDjRs3IJVKceLECcTGxiIwMPCxGxoR0ehWXFwMsVg86tcrJtV75ZVXcOnSJXz88ceYOXMmTExMhrV9FjiJiIho4DpbgcKzwNSVQiehYfbqq6+itbUVR44cwcyZM2FsbCx0JCJB6ejoYPr06QgJCcHly5eRmpqKb775BtbW1ggLC4OnpyfU1dWFjgm5XI6YmBgYGhpizpw5uHXrFq5cuYLly5dDQ2NoH/dyc3Nx5swZzJgxA2FhYX2eIxKJ4OTkBCcnJ9TV1SE9PR0ZGRlITU2Fh4cHxGIx7OzsRlwxmIieTEtLC0pLS+Hs7Cx0FBplLly4gHfeeQevvfaaUtpngZOIiIgGJ+8A4LEUUGc3YizJy8vDH/7wByxZskToKEQjirq6Onx9feHj44OSkhKkpqbiu+++Q2xsLMRiMQIDA59oGviTSklJwe3bt/Hiiy9CU1MTcXFxMDc3h4+Pz5Dau3btGo4dO4bAwEBERUUN6BoTExPMmzcPERERuHjxIqRSKbZt24ZJkyZBLBbDy8tryMVWIhp5Ll++jMmTJ/MBBg2KmZmZUkf487cMERERDU5j1YNRnO4LhU5Cw8jW1rbfqelE9GDEorOzM5ydnVFdXY20tDTExcUhKSkJgYGBEIvFKp+aXVlZicTERMycORO2trYoKSlBSUkJnn322SFNH71x4wYOHToEd3d3LFq0aNDFC21tbQQHByMoKAhFRUWQSCSIiYnBuXPnMG3aNEybNm3UbNpERA9MmzYNJSUlMDAwwK9//WsAQHV1NSorKzFp0iSB09Fo8uMf/xi7d+/GT3/6U6XMgGCBk4iIiAYvawfgEg1ocCfNseKtt97CX/7yF/zoRz+CoaGh0HGIRjRLS0ssX74cUVFRkEqlyMzMhEQiwdSpUxEaGgpra2ulZ+jq6kJMTAwsLCwQHh4OhUKBuLg42Nraws3NbdDt3b59G/v27YODgwNWrlz5ROvriUQiuLq6wtXVFXfu3IFUKsWFCxdw/vx5TJ06FSEhISr5MyKiJ1dVVdXnxoPZ2dkscNKgTJkyBTKZDL6+vti0aRPs7Oz6LHSuXDm0pbBY4CQiIqLBa6oGLn8H+K0ROgkNk8bGRujr68PFxQXPPvtsn51OkUiEzZs3C5SQaOQxMDBAdHQ0Zs6ciZycHEgkEuTl5cHJyQlhYWFwcXFR2hTO+Ph43L17F6+88grU1dVx9epV3Lp1C88999yg71lbW4vdu3fDzMwMq1evHtbp5Obm5li8eDGioqKQnZ2N9PR0XLx4Efb29ggJCYG7uzs3KyEawTo6OgA8eKjyQ7dv38atW7dgY2MjRCwahVavXt39///3f/+3z3NEIhFkMtmQ2meBk4iIiIYmZxcwZR6gayp0EhoGP+xofvbZZ32ewwInUd+0tbUREhKC4OBgXL16FampqdizZw8sLCwQGhoKb2/vYS0a3rx5E2lpaYiOjoaFhQXkcjni4uLg7OwMJyenQbXV0NCAXbt2QU9PD+vWrYOWlnJG5k+YMAHTp09HaGgorl27BqlU2r3be3BwMAICAjBhwgSl3JuIhqa9vR1tbW0AgM7OTnR2dkJTU7P7eEZGBiZNmsS1OGlAEhISlNo+C5xEREQ0NB3NQObXwKxfCJ2EhkFpaanQEYhGPTU1NXh5ecHT0xNlZWVITU3F0aNHERcXB7FYjGnTpj1xEa+9vR1HjhyBnZ0dQkNDAQAXL15EbW3toKf1tbS0YNeuXRCJRNiwYQN0dXWfKNtAqKmpwdPTE56enqisrIRUKkV8fDwSExPh6+sLsVgMc3NzpecgoseTSqXdIzflcjlu3LgBV1fX7uM1NTUoKSnhjuo0IOHh4UptnwVOIiIiGrprJwGPZYD5FKGT0BNycHAQOgLRmCESieDg4AAHBwfU1tZCIpEgKSkJycnJ8Pf3R0hICExNhzb6/ezZs2hubsbGjRuhpqaGrq4uJCYmwsvLa1Dr4bW3t2P37t1obW3Fpk2bBFl719raGsuXL0d0dDSysrKQkZGBzMxMODs7QywWw9XVlSPDiATS3t6OM2fO9HgvPz8fjo6OPUZxpqenw9HRUSmbxtDY1N7ejuzsbNTU1GD69OkwMzMblna52AkRERENnUIBpP39wf/SmHDr1i3s27cPn3zyCSoqKgAAMpkM9+7dG/KaSETjmZmZGRYvXozNmzdj+vTpuHz5Mv7+97/j4MGD3f/GBqqgoABZWVmYN28eTExMAACZmZlobGxEZGTkgNvp6urC/v37cffuXaxfvx4TJ04cVI7hpq+vj/DwcGzevBkrV65Ea2sr9u7di88++wxSqRTt7e2C5iMaj3JyclBcXNxjc6E7d+6gvLy8x3mNjY24ePGiquPRKPXpp5/C2toaM2bMwMqVK5GXlwfgwVrQZmZm2LZt25DbZoGTiIiInkxlHlCi3DV1SPkUCgXefPNNODk5Yd26dXjzzTdRUFAAAGhqaoKjoyP+/ve/C5ySaPTS09NDREQENm/ejEWLFqG6uhpfffUVtm3bhqtXr0Iulz/y+paWFhw7dgyurq4ICAgA8GAUTHJyMvz8/AY8AkYul+O7775DeXk51q5dO6J2M1dXV4ePjw9efvllvPjii7CyssKZM2fwt7/9DadPn0ZdXZ3QEYnGDTs7O6xbtw56enoAAC0tLQQHB3c/XPmh3NxcNDY2qjoijTLbt2/HG2+8gfnz5+Prr7/uUTw3MzPD7NmzsX///iG3zynqRERE9OQknwMO0wENbaGT0BD9+c9/xieffIK33noLUVFRmDNnTvcxIyMjrFy5Et999x3eeOMN4UISjQGampqYNm0aAgMDcf36daSlpeHAgQMwNTVFaGgo/Pz8ekz/BB48gDh58iRkMhmWLl3aPW07LS0NHR0diIiIGNC9FQoFjh8/juvXr2P16tUjdmkKkUgEOzs72NnZoaGhARkZGcjKyoJUKsWUKVMQEhICR0dHTl8nUiIbGxvY2NhAR0cHwIOfXb6+vn2e29XVhZSUFMyfP5//Lqlff/3rX7Fs2TLs3bsXd+/e7XU8MDAQn3766ZDbZ4GTiIiInlxTNZB3EAjYIHQSGqKtW7di48aN+PDDD/vsdPr4+OD7778XIBnR2CQSieDu7g53d3dUVFQgLS0Np06dQkJCAoKCghAUFAR9fX0AwOXLl5Gfn49Vq1bBwMAAANDc3IzU1FQEBwcPeP3M2NhY5OTkYMWKFXBzc1Pa9zacjIyMEB0djfDwcOTl5UEqleKbb76BpaUlxGIxvL29exWEiUj1ysvLUVRU1GMTIqIfKioqwv/8z//0e9zU1LTPPuhAscBJREREwyN3L+C+CNAd2sYZJKzy8nKEhYX1e1xPTw/3799XYSKi8cPW1hZPP/006urqIJFIkJaWhgsXLsDHxwdTp07FyZMnMXXqVEydOrX7mvPnz0MkEmHGjBkDukdKSgouXLiA+fPn9zsKayTT1NREYGAgAgICUFpaCqlUiuPHjyM2NhaBgYEICgoSZKMkIvqP1NRU2NjYQFdXV+goNAIZGxujtra23+NXrlyBlZXVkNvnGpxEREQ0PDpbgPQvhU5BQ2RhYdFr44AfysrKgr29vQoTEY0/JiYmWLBgATZv3oyIiAgUFBTgf//3f3Hp0iVMnTq1e72yh9O2w8LCBlRIyMrKQmxsLMLDwxESEqLsb0OpRCIRJk+ejDVr1uC1116Dj48P0tPT8fHHH+PQoUOD3riJiIZPe3s7zp8/32NtRaKHFi5ciC+//BL19fW9juXn52Pr1q1YunTpkNtngZOIiIiGz/XvgeorQqegIVi5ciU+//xzlJSUdL/3cB2ts2fPYseOHXj66aeFikc0rkyYMAEzZszAjBkzYGFhgSlTpmD//v3YunUrLl++jPj4eOjo6CA0NPSxbV25cgUnTpxAUFDQgNfqHC1MTU0xf/58vPnmm5g7dy5u376Nr776Clu3bkVeXh5kMpnQEYnGnZs3b3ZvUkj0Q7///e8hk8kwdepUvPvuuxCJRPjmm2+wfv16TJs2DRYWFvjtb3875PZZ4CQiIqLhlbIFkPND5Wjz/vvvw9raGn5+fti4cSNEIhH+9Kc/YcaMGViwYAF8fHzwzjvvCB2TaNy4d+8e4uLisHjxYvz2t7/F+vXroaOjg2+++QZffPEFTExMHjtKqqSkBN999x28vLywcOHCMbv5h7a2NkJCQvCzn/0Ma9asgba2Ng4fPoyPP/4YSUlJaG5uFjoi0biSmprKXdWpl0mTJiErKwvz58/HgQMHoFAosGvXLhw/fhxr1qyBRCKBmZnZkNtngZOIiIiGV23Bgw2HaFQxMjKCRCLBL3/5S9y6dQs6OjpISkpCfX093nvvPZw/f55rahGpiFwuR0xMDPT19TF37lyIRCK4uLhg48aNcHFxwaRJk1BRUYEtW7bg3Llzfa6PW1FRgf3798PJyQkrVqwYs8XNH1JTU4Obmxs2btyIV199FVOmTMH58+exZcsWHD16FFVVVUJHJBoXOjs7kZSUxKnq1K29vR3Hjh1DVVUVvvrqK9y7dw/V1dWorKxEXV0dtm3bBgsLiye6BzcZIiIiouGXuQ1wnAEY2wmdhAagra0NX375Jfz8/PDuu+/i3XffFToS0biWmpqKiooKvPDCC9DS0up+/9atW6isrMTrr78OJycnSKVSZGZmIi0tDd7e3ggNDYWVlRXu3LmDPXv2wNLSEqtXr4a6urqA340wLCwssGTJEkRFRSE7Oxvp6enIycmBo6MjxGIx3NzcoKbG8T5EynL79m1cuXIFXl5eQkehEUBLSwtPP/00PvnkE/j4+AAAzM3Nh/UeLHASERHR8JN1APG/B5b9A1Bnd2Ok09HRwVtvvYVPP/0Us2bNEjoO0bhWXV2NhIQEhIWF9drYKy4uDhYWFvD29oaamhrmzJmDWbNmITs7GxKJBBcvXoSlpSXKy8tha2uLtWvXQlNTU6DvZGTQ1dXFjBkzEBoaimvXrkEikeDAgQMwNjZGcHAwAgICoKOjI3RMojFJKpXCzs4OhoaGQkchgYlEIri6uj5yF/UnxUdWREREpBx3rgFZ24VOQQM0depU3LhxQ+gYRONaV1cXDh8+jIkTJyIyMrLHsZKSEpSUlGD27Nk9Rh5qa2sjNDQUr7/+OhYuXIikpCRkZ2ejra0N169fR1dXl6q/jRFJXV0dXl5eePHFF/GjH/0I9vb2iIuLw9/+9jecPHlSqR+6icarrq4uTlWnbu+88w4+++wzXL9+XSntc0gFERERKU/uHmBSAGAbKHQSeow//OEPWLt2LSIjIxEdHS10HKJxKTExEbW1tXj55ZehofGfj2oKhQKxsbGwtbWFm5tbn9d2dHQgJycHgYGBmDt3LvLz83HkyBHExcVBLBYjMDAQEyZMUNW3MqJNmjQJK1euxJw5c5CZmYnMzExkZGTAxcUFISEhcHZ2HhdrlhKpQmVlJaeqEwBAIpFg4sSJmDp1KiIiIuDo6Njr95JIJMInn3wypPZZ4CQiIiLlUSiA+N8BT30N6E0UOg09wmeffQZTU1PMmzcPTk5OcHJy6rPTefToUYESEo1t5eXluHDhAmbPng0rK6sex65evYrbt2/j+eef77Pw1tnZiX379qGurg4vvPACLC0t4evrizt37iAtLQ2JiYlITk6Gv78/QkJCYGJioqpva0QzMDBAZGQkZs6cicuXL0MqlWL37t0wMzODWCyGr69vjzVQica7rq4u3Lx5Ew4ODj0ewjwOp6oT8KCv+VBcXFyf57DASUQ0CnW2NqK1thyaukaYMNFG6DhEytNaB8S9DyzeAqiNv40uRou8vDyIRCLY29tDJpOhqKio1zkc0USkHB0dHYiJiYGNjQ2mT5/e45hcLkd8fDxcXFzg6OjY61qZTIZDhw7h9u3b2LBhAywtLbuPmZubY+nSpZg9ezYyMjKQnp6O9PR0eHp6IiwsDDY27H8AgIaGBvz8/ODr64uysjJIpVKcOnUKcXFxCAgIQHBwMIyNjYWOSSS4kpISSKVSyOVyuLq6Dvi6rq4uJCYmYsmSJexLjHBOTk6D/m8kEolQXFz82PPkcvlQYw2ISguct27dQnJyMmpqavDUU0/B1tYWMpkMDQ0NMDIyGpe7+xHR+CPv6kDJ2S9RnXsWCrkMAKBn5YwpSzZDz9JJ4HRESlJ5Ecj+Bpi2Segk1A9Vr7/JfiHRf5w7dw6NjY1Yt25dr529L168iNraWjz11FO9rlMoFDh27BgKCwuxdu3aXpsSPaSvr4/IyEjMmDEDubm5SEtLw9atW+Hg4ICwsDBMmTKFRQc8+JDu4OAABwcH1NfXIyMjA1lZWUhLS4O7uzvEYjEcHBz4Z0Xj1pUrV1BbW4v8/Hw4OjoOahOzqqoq5Obmwt/fX4kJ6UmFh4f3+hmXmZmJ/Px8eHp6di+Tcv36dVy5cgVTp05FYODIWIpKJQVOhUKBn//85/jss8/Q1dUFkUgEb29v2NraoqmpCY6Ojvjggw/wxhtvqCIOEZGgCo5tQe3V8w+m7v5bc3Up8na+hYAf/wvaBpzGS2NU9k7AypfrcY5z7BcS9VRUVISMjAwsWrQIEyf27AN0dXUhISEBXl5esLa27nFMoVDgzJkzyMvLw8qVK+Hi4vLYe2lqaiIoKAiBgYG4fv060tLSsG/fPkycOBGhoaHw9fUd97uuP2RsbIw5c+YgPDwceXl5kEql2LFjB6ysrCAWi+Ht7T2oKbpEo11XVxeqq6thY2OD6upq3LhxY1CjOAEgKysLNjY2sLCwUFJKelI7duzo8frIkSM4cuQIzp07h6ioqB7Hzp07h2eeeQa/+93vBnUPiUSChIQE1NTU4NVXX4WrqytaWlpw7do1TJkyBfr6+kPKrpJd1P/85z/jk08+wf/+7//i3LlzPXbQMjIywsqVK/Hdd98Nqs1//etf8PHxgaGhIQwNDREaGorvv/++3/N37NgBkUjU40tHR2fI3xMR0VC03ruF2ivJPYqbAACFHLKOVlRlnhQmGJEqKBRAwu+B1nqhk1A/ZDIZ9u/fj1deeQUrVqzApUuXAAANDQ04fPgwqqurn/ge7BcS/UdrayuOHj0KZ2dnTJs2rdfxjIwMNDU1Yfbs2b2OnT9/HhKJBAsXLoS3t/eg7qumpgYPDw9s2rQJL774IiwtLXHy5Els2bIFiYmJaG5uHvL3NNZoaWlh2rRpePXVV7FhwwYYGBjg6NGj2LJlC+Lj49HY2DjoNmUymRKSEilHR0cHAKC9vR3q6uqYMGEC1NXVkZ+fj87OzkG1JZfLERcXh/b2dmVEJSX47W9/i9dee61XcRMA5syZg5/97Gd49913B9RWR0cHVq5cienTp+PXv/41Pv30U5SXlwN48Htp7ty5Q15/E1BRgXPr1q3YuHEjPvzwQ/j5+fU67uPjg4KCgkG1aWtriz/+8Y/IyspCZmYmZs+ejWXLliE/P7/fawwNDVFZWdn9dfPmzcF+K0RET6Th5uX+DyrkqL9xUan3VygUPYoJRCrXcg84/5feRX4SXH19PaZPn461a9di3759OHbsGO7cuQPgwfTW//mf/3miTudD7BcS/cepU6fQ2dmJZcuW9ZoS2N7ejvPnz8Pf37/XyM6MjAzEx8cjMjISQUFBT5TBzs4OzzzzDF577TVMnToVFy5cwJYtW3DixAnU1tY+UdtjiUgkgrOzM9atW4fXXnsNXl5ekEgk2LJlC7777jvcunVrQO0UFhbin//8J+7fv6/kxERPrr29HW1tbQAeFObb2tpw+/ZtdHR04M6dO93FqcFobGxEYmIiP5OMEoWFhb1+B/3QxIkTB7T+JgD85je/wYkTJ/Cvf/0L169f7/F3QEdHB08//fQTbWapkjH15eXlCAsL6/e4np7eoH/AL1mypMfrP/zhD/jXv/4FiUQCLy+vPq8RiUS9diQkIlIlNU3tRxwVPeb40DXeLkTZ+b2oL84CRGqYOCUE9rPWQte877W6iJSq9Dxw/XvAfaHQSegHfvWrXyE/Px9nzpyBv79/j+lj6urqWLVqFU6dOoUPP/zwie7DfiHRA/n5+bh06RJWrlzZ587Cqamp6OjoQHh4eI/3L1++jFOnTiEkJASzZs0atjympqZYuHAhIiIikJmZifT0dGRmZsLNzQ1hYWGwt7fn2pP/NnHiRCxcuBCzZ89GTk4O0tPTsXXrVtja2iIkJAQeHh59riNcWlqKAwcOwNnZGXp6egIkJxocqVSKrq4uAA9+b7q4uPRYLsPExGRI7d68eRN5eXnw9fUdlpykPM7Ozti+fTtefPHFXlPHGxsbsW3bNkyePHlAbe3btw8/+clP8KMf/Qh3797tddzDwwPffvvtkLOqpMBpYWHxyMp+VlZWvwtiD4RMJsO3336L5uZmhIaG9nteU1MTHBwcIJfLERAQgA8//LDfTu9D7e3tPYZPNzU1DTknEZGpyzSI1DWhkPU1nUMBc8/Bf1DpaKpDZeYJ3C2QQiQSYaJbKKynLYKmrhEA4H75FVza9TYUCjmgkAOQofbaBdwryoDvC3+FnoXjE31PREOS9hlgOw3Q5xpMI8WRI0fw2muvYc6cOX12OqdMmdJrXaahYL+Q6MGHwpMnT8LT07PP6eXNzc1IS0tDcHBwj+JnUVERDh8+DG9vb8ybN08pBUddXV3MmjULYWFhuHTpElJTU7F9+3bY2NggLCwMHh4evTZCGq90dHQQGhoKsViMgoICSKVSHDp0CIaGht1rnerq6gIAysrKsG/fPjg4OODpp5/mRmo04rW3t+PMmTPdr9XU1NDW1gZPT89hWas3PT0dFhYWvdYXppHl97//PVatWgV3d3c8//zz3es9FxYW4ptvvkF1dfWAi5I1NTWPXFJFXV0dLS0tQ86qkgLnypUr8fnnn+P555+HkdGDD9wPfxmfPXsWO3bswC9/+ctBt3vp0iWEhoaira0N+vr6iImJgaenZ5/nurm5Ydu2bfDx8UFDQwP+8pe/ICwsDPn5+bC1te33Hh999BHef//9QWcjIuqLho4+nKJfRMmZzwGR2r8LjgBEIhhMmgILn95rbD1KW301Lm5/E50t97vbaq65gaqc0/B94W/QNjRDybmvoFDIek4JVsgh7+rAzcSd8Hzmt8P17RENXEczkPQnYOFfAI4IGhEaGhrg5OTU7/HOzs7uURxPgv1CGu8UCgWOHz8ONTU1LF68uM8i5fnz5yESiTBjxozu98rLy3HgwAG4urr2OaV9uGloaMDf3x9+fn4oKipCamoqvv32WxgbGyMkJAQBAQHQ0tJSaobRQk1NDe7u7nB3d0d1dTWkUimSkpKQlJQEHx8fODg44NSpU5g0aRKeffZZbk5Eo0JOTg6Ki4t7TCN+OC19oCP2HkWhUCA+Ph4rV67EhAkTnrg9Uo7ly5fj1KlTeOutt3rN4vHz88PXX3+NefPmDagtOzs7XLt2rd/jFy5cGNCGef1RyaO3999/H9bW1vDz88PGjRshEonwpz/9CTNmzMCCBQvg4+ODd955Z9Dturm5ITc3F1KpFD/5yU/w3HPP4cqVK32eGxoaio0bN8LPzw/h4eE4fPgwzM3N8cUXXzzyHm+//TYaGhq6v5KSkgadk4johyYFLYHn6vdgaOcJNa0J0DaygP2sdZi67kOoaQzug0LJua09ipsAAIUcHU11uBG/HR1NdWi6fb3v9Q4VctwrTIdc9uQFC6IhqcgErp0QOgX9m7OzM7Kzs/s9fvbs2X4LhoPBfiGNdzk5OSgoKMCSJUu6R/f9UH19PTIyMjB9+vTu49XV1dizZw8mTZqEVatWqXT0n0gkgqurK5577jm88sorsLe3x9mzZ/G3v/0NsbGxQ9pkZyyztLTE0qVL8eabbyI8PBzZ2dn4+c9/jsLCQgQEBOD+/fuIiYnp3riFaKSys7PDunXrupdT0NLSQnBw8JCnpfelubkZSUlJXI9zhJs7dy5ycnJw+/ZtpKWlIS0tDbdv30Z2dvaAi5sAsHbtWnzxxRdIS0vrfu/hw7qtW7fi4MGD2Lhx45BzquTRkZGRESQSCf7617/i0KFD0NHRQVJSEpydnfHee+/hF7/4xZAq9lpaWt3V3cDAQGRkZOCTTz55bOcUADQ1NeHv74+ioqJHnqetrQ1t7f+siTfU7eqJiH7I1DUYpq7BT9RGV3sL7hVI+i1e1l45D/tZ6x/diELRszhKpGpp/wRsgwEDS6GTjHsvvfQS3nrrLURERHTvlCkSidDe3o4PPvgAp0+fxpdffvnE92G/kMazuro6nD59GgEBAXBzc+vznMTEROjo6CAkJKT7ml27dsHY2Bhr1qwZlqmhQ2VtbY2VK1ciKioKUqkUGRkZSEtLg7e3N0JDQ2FpyZ/lD+nq6sLd3R2pqakICwuDtbU1Dhw4gOvXr8POzg6RkZEcAUsjmo2NDWxsbKCjowPgwe9KZayZWVZWhmvXrsHDw2PY26bhZWVl9UTrl//617+GRCLBrFmz4OHhAZFIhM2bN+PevXuoqKjAwoULsXnz5iG3r7Kx8RMmTMC777474O3jh0Iul/dYF+lRZDIZLl26hIULucEBEY1s9SU5KL9wAPcrrkFdSwcW3rNhN301FHLZI3eiVshl0JigDx0Ta7TVVfY+QaQGg0lTBj1qlGhYdbY8mKq+6K+cqi6w119/Hfn5+VizZg2MjY0BPHjSfvfuXXR1deGVV17Biy++OCz3Yr+QxiO5XI4jR45AV1e33xEvd+7cwcWLFzF//nxoaWmhsbERO3fuhJaWFtavX99daBCakZER5s6di1mzZiE7OxsSiQS5ublwcXFBaGgoJk+ePO43JLp37x527twJAwMDvPbaa9DS0sInn3yCCRMmwMDAoMfDEqLxTiKRwMbGps8N10h4ZWVl+PDDD5GQkIA7d+7gyJEjmDVrFmpra/HBBx/ghRdegL+//2Pb0dLSwunTp7Fnzx4cOnQIMpkM7e3t8PHxwe9//3ts2LDhiX53jNrFP95++20sWLAA9vb2aGxsxN69e5GYmNi9CO7GjRthY2ODjz76CADwwQcfICQkBC4uLqivr8ef//xn3Lx5Ey+99JKQ3wYRjXMKhRz1pRdx/+YlqGlqYaL7dOia2XUfv5OfhOsx/697vc6u1k7czjiOuwUS+D7/N2jqm6Czqa7PtrWNLaGhow+HiI24HvOn/zoqAhQK2Ic/ZoQnkSrcygKuHgM8lwmdZFwTiUTYunUrnnvuORw6dAiFhYWQy+VwdnbGM888M6y7NQ839gtpNJBIJCgrK8Nzzz3Xb3ErPj4eRkZGCAwMRGtrK3bv3g2ZTIZNmzaNyBHDOjo6CAsLg1gsxpUrV5Camopdu3bB0tISYWFhmDp16rjcTKe+vh7ffPMNtLS0sHHjRkyYMAHfffcdWltb8eGHH8LS0pKjN4l+oLOzE+fPn8fChQvH/cORkebKlSuYOXMm5HI5xGIxioqKutdkNzMzQ0pKCpqbm/H111/3uvbNN9/Ehg0buoufZWVlMDc3x/r167F+/fB/DlVJgXPTpk2PPUckEvX5B9KfmpoabNy4EZWVlTAyMoKPjw/OnDmDOXPmAHjwB/fD3f3q6urw8ssvo6qqCiYmJggMDERqauqwrCVFRDQUXa2NuLzvPTTdvg6RmjoUCgVuJu7CJPEKOEW/CIW8C8VnPn9w8n+tsdnecAeVmcdhF/YMSs72Pf3SLuwZiEQimHvNgkIuw42EHei4XwsA0DG1hlP0SzCZ/PgnbUQqIfkXYBsEGE4SOsm4sXLlSmzevBkzZ84EACQnJ8PDwwMzZszosbHJcGO/kMajmpoaxMXFISQkBI6Ojn2eU1FRgatXr2LFihVQKBTYu3cv7t+/j02bNnWPqh6p1NXV4e3tjalTp+LGjRtITU1FTEwMYmNjERISgsDAwBEz+lTZHo66VVNTw3PPPQd9fX3ExsYiPz8fTz/9NOzs7B7fCNE4dOvWLRQWFmLKlClCR6Ef+OUvfwljY2NIJBKIRCJYWFj0OL5o0SIcOHCgz2s//vhjTJs2rbvA6eTkhF27dmHt2rVKyaqSAmd8fHyvKrxMJkNlZSVkMhnMzc27F64dqMd1ehMTE3u83rJlC7Zs2TKoexARKVPhqb+jqbIQwIPp5A/dlsZAz8IBOsZW6Gq53/fFCjnu5Ccj8NUv0dl6HxWp30Lx782CROqasJ+5Bpb+/5n+ZuEdCfOp4Wi9ewsiNTXomEzi01EaWTpbgaT/ByzewqnqKnL06FE89dRT3a8jIyOV2ul8iP1CGm9kMhliYmJgamravb7tf1MoFIiLi4OFhQU8PT1x8OBBVFdXY+PGjTA3N1dx4qETiURwcnKCk5MTampqkJaWhvj4eCQlJSEgIAAhISEjvlj7JJqbm/HNN9+gq6sLmzZtgqGhITIzM5GSkoK5c+fCysoKOTk5qKiowOLFi9kXI/ovEokE9vb24+aByGiQnJyM3/72tzA3N8fdu3d7Hbe3t8etW7f6vNbS0hIlJSXdr5W9mZRKCpw3btzo8/3Ozk588cUX+Pjjj3Hu3DlVRCEiGhE6mu7h7tVUAH39kBfhdvoxOM5+/pFtyLvaIRKJ4BC+HpOClqLhZh4AEYydfKGh03sam0ik1mP6O9GIczsHuHoc8FwqdJJxwcbGBjk5OVi3bh2AB51OVXzYZr+Qxpvk5GRUV1fjpZdegoZG3x+/SkpKUFpaimeffRbHjh1DSUkJ1q5dC1tbWxWnHT4WFhZYtmwZoqKikJ6ejoyMDKSnp8PT0xNhYWGYNGlsjdhvbW3Fzp070dbWhhdeeAFGRkaQSqXYvn07zMzMkJaWhrNnz0KhUMDAwACRkZEjctkBIiG1tbUhLS0NkZGRQkehf5PL5dDV1e33+J07d/pddmXRokX44IMPcPbs2e6HW3/961+xf//+ftsTiUQ4evTokLIKuganpqYmfvazn+HKlSv42c9+hpMnTwoZh4hIZdrqqtB3cRMAFGi9dxsGNm4QqWtCIevsfYqaGownB3S/1NQ1hJmH8qaUEqmM5F+A4wxA11ToJGPes88+i7/85S84ePBgd6fzV7/6Vfc6lX0RiUS4ePGiUvKwX0hjUUVFBc6fP4/w8PB+C3oPR2/a2tqiqKgIly9fxtNPPw1nZ2cVp1UOfX19zJ49GzNmzEBubi7S0tLw5ZdfwtHREWFhYXB1dR31Ixnb2tqwc+dOVFVVYcaMGUhMTERubi6SkpIwYcIE6OnpQSQSdS+V0dTUNC7XJiUaiMLCQri6uo7qBzxjSUBAAE6ePIlXX32117Guri7s378fISEhfV77ySefwMLCAgkJCcjPz4dIJEJ5eTnu3bvX7/1G/SZDvr6+2LVrl9AxiIhURsvQ7JHHtQ3NoKGjDxvxClSkHux5UCSCmpoGbENXKjEhkUA6Wx4UOWf/WugkY95HH30EFxcXJCQkoKamBiKRCHp6epg4caKgudgvpLGis7MTMTExsLa2fuS6tlevXsXt27fh5uaGzMxMLFmyZEyuB6ulpYXg4GBMmzYN165dQ2pqKvbu3QszMzOEhobC19e33xGuI5FCoUBtbS2Kioqwbds2FBYWYtKkSSgqKoJcLkdxcTH09fUREBAAIyMjWFhYwMLCApaWlrCwsOAmQ0SPcP78eaxatQqamppCRxn33n77bSxevBg/+clP8OyzzwIAqqurERsbiw8//BBXr17FZ5991ue1enp6+PDDD7tfq6mp4eOPPx7da3A+zrlz5x455JWIaKzRMbKAkZMfGm7k9dxA6N+sAhcCABwiN0BNQxO3JIch62gFAOiaO8Jl4c+ga2av0sxEKlN49sGO6lZThU4ypqmrq+NHP/oRfvSjHwF40Ol89913lb4G5+OwX0hjRWxsLBoaGrBmzZp+R+vJ5XLEx8cDAK5fv47o6GgEBgaqMqbKqampwdPTE56enigvL0dqaipOnDiB+Ph4BAcHIygoaET+DFAoFLh9+zYuXryI/Px8FBQUoKamBiUlJejs7ISXlxcsLCxgb2+Pq1evIjQ0FJs2bYKjoyMMDQ1H/ShVIlVqbGyEVCpV6qaHNDALFizAjh078Prrr+PLL78EAKxfvx4KhQKGhobYuXMnZs2a1ee1/72hZUJCglIf4KmkwPnBBx/0+X59fT2Sk5ORnZ2NX/3qV6qIQkQ0YkxZ/Abydr2F9vpqQKQGkUgEhVyGie7TMWnaYgAP1s20n7UWNqEr0VpbDnVtXW4QROND9k5g4f8TOsWYFhAQgA8//BDz588HAGzfvr17l0tlYr+QxoOSkhJIpVLMnz8fZmb9z9rIzc3F5cuXoaenh3nz5mH69OkqTCk8Ozs7rF69Gnfv3oVEIkFKSgpSUlLg5+eHkJAQwUaUy+Vy3Lt3D1euXOkuZpaWlqK+vh4ikQgGBgawsbGBgYEBAgICsG7dOvj7+8PQ0BD79u2Di4sLXnzxxUf+tyeiR7ty5QqcnJxgY2MjdJRxb8OGDVi5ciXOnTuHwsJCyOVyODs7Y968eTAwMOj3uv/e0HL27Nmjfxf1//u//+vzfRMTEzg7O+Pzzz/Hyy+/rIooREQjhraROQJe+Sdq88+j4eYlqGlqwcxjBowcfXsUMBVyGdQ0tKFv7SpgWiIVK5cCdwoA8ylCJxmz8vLyUFtb2/1606ZN2LVrFzw8PJR6X/YLaaxra2vDkSNH4OTkBLFY3O95XV1dOHjwIOrq6rBgwQLMmTNn3D7AnDhxIhYtWoTIyMjuzYgyMzPh5uaGsLAw2NnZKeXPRqFQoKmpCTU1NaiqqkJBQUF3MfPu3bvo7OyEtrY2bG1tERISAg8PD3h7e8Pa2hrHjh1DYWEh1q5di8mTJ0OhUCAmJgYVFRXYuHEji5tEwyAxMRGrVq3qdxMbUq6WlhbY2dnhV7/6FX7xi19g+fLlg7pe1RtaqqTAKZf3nn5JRDSetd6rRFX2STRWFkFT1wiWPlEwcQnq8QP/zuVElF84iJY7N6GmoQ0Ln9mwD18PLT1j4YITqdLVY4D5/wqdYsxycHBAbGxs9/RZVe2izn4hjXXff/892tvbsXz58kf+mzp27BgyMjLw7LPPYsmSJeO2uPlDurq6CA8Px/Tp05GXl4fU1FRs27YNtra2CAsLg7u7e/dGPYPV3t6Ompoa1NTUoLq6GlVVVSgpKUFlZSUaGhrQ2NgILS0tGBgYwNnZGUuWLIGPjw/c3d17rAMol8sRExOD69ev49lnn8XkyZMBPJh6mZeXh6effhr29lxGiGg4NDc348KFC5g9e7bQUcYlXV1daGhoQE9Pb0jXq3pDyxGxBicR0XhSV5KDKwfeh0Iue7D+pkgNd6+mwNJvLlwW/Q9EIhFuSWNQeu4rAA8+7Mi72lGVcwb1JTnwe/FjaEzofyoA0ZhRmgzM2AyocadZZfjxj3+Mt956C3v27MGECRMgEonw4osv4pVXXun3GpFIhIaGBhWmJBpdrl69iosXL2L58uUwMjLq97wbN25g69at8Pb2xgsvvDDkot1YpaGhgYCAAPj7+6OwsBCpqak4ePAgTExMEBoaCj8/v3436ZHJZLh79y6qq6u7i5k1NTWoq6tDU1MTGhoa0NXVhc7OTmhqasLQ0BD+/v7w8vKCo6MjbG1t+93YRKFQ4Pjx49073U+Z8mCWQVZWFpKTkzF37lx4eXkp7c+FaDwqKiqCo6Nj98MEUq2nnnoKhw4dwk9+8pNBP4hT9YaWSilwlpWVDek6PukiorFO3tWJ6zF/gkLWBUDx4M1/bzJUnXsWpq7BMHL0xc2Enf++QvGfixVytDVUozL7e9hNf0aluYkE0dYAVOUBk5S/LuR49Itf/AK+vr5ISEhAdXU1vvnmGwQFBQ37Bwj2C2m8aG5uxokTJ+Du7g5fX99+z7t79y4++ugjaGlp4de//vWo2jlc1UQiEaZMmYIpU6bg9u3bSE1NxenTp5GQkIDAwEC4ubmhtbW1RzHz7t27kMlkUCge9KEUCgU6OzvR3NwMTU1N2NnZwdHREY6OjnBwcICtre2A/hsoFAqcOnUKubm5WLFiRfdGGUVFRTh58iSCgoIQGhqq1D8PovEqJSUFVlZWI3IDsrHu2WefxauvvorIyEi8/PLLcHR0xIQJE3qdFxAQ0Os9VW9oqZTfpo6OjkOaYiGTyZSQhoho5KgrzkRXa2PfB0VqqL54DgqFAvKujr7PUShQezWFBU4aP2qussCpRHPnzsXcuXMBADt27MArr7wy7J1O9gtpPHg4sg/AI6eb379/H19//TVu376Nl19+Gebm5qqMOWq1traio6MD9vb26OzsRHp6Ov785z+jo6MDlpaWcHZ2hpOTE3R1daGpqYmWlhY0NDRALpdDS0sLLi4u3QVNGxubQReVFQoFzp07h4yMDCxduhQ+Pj4AgKqqKhw8eBCurq5YsGDBgH7WlZaW4tChQ3jzzTehrs4ZCkQD0dbWhtTUVERHRwsdZdyJiIjo/v/nz5/vdfzhEkcD6beVlpYq9feeUgqc27Zt4xoyRER96Gx+xNROhRwdTXWA/NG/HBSPOU40ptRcFTrBuKGstTHZL6Tx4OLFi7h27RpWr17d71plLS0t2LVrF4qKihAQEMAP6n3o7OxEbW1tr+nljY0PHg6rq6vDzMwMM2fOxMKFC3H9+nVcvHgRV65cQWFhIaytrWFubg4HBwcEBAR0FzSftJCYmJiI1NRULFiwoHuUUkNDA/bs2QMzMzM89dRTj11moKurC8eOHcO2bdugoaGBZ599FnZ2dk+Ui2g8KSkpwc2bN+Hg4CB0lHFl+/btw9aWsv/bKaXA+fzzzyujWSKiUU/P0rH/gyI16Fu5wNDBG1BTA/oqNojUYOoyTWn5iEachgqhEwyrf/zjH/jzn/+Mqqoq+Pr64u9//zuCg4P7PHfHjh144YUXerynra2Ntra2YcnycOr4w6ngA51KPtip4+wX0lhXX1+P77//Hn5+fvDw8OjznI6ODuzduxe1tbUwNTVFVFTUuJ5qKZfLUVdX16OIWV1djXv37nVPLTcxMYGFhQX8/f1hYWEBMzMztLa2ory8HDdv3sTVq1fR2dkJBwcHODo6oq6uDjKZDKampvD19YWXl9ewjJA8f/48kpKSMGfOHIjFYgAPRpPt2bMH6urqWLt2bb/rgT5UWVmJf/7zn0hLS8PUqVPxq1/9ClZWVk+cjWi8kUgksLOzGzPrFo+kfmF/nnvuuSFfq6amBjU1NbS0tEBLSwtqamqPfegtEonQ1dU1pPtxwRciokHS0jfp8b+DoT/JDfqTpqCpsqh77c1uIsB62iJo6RnDJng5bkkO/9dxNWjo6ME6aOlQoxM9VllZGVpaWgAALR1ylN1rg72pjnCBmqoBhQIYAyMADxw4gDfffBOff/45xGIxPv74Y8ybNw/Xr1+HhYVFn9cYGhri+vXr3a+HcyTkw6njra2t0NLSGvBUck4dJ/oPhUKBo0ePQkdHB/Pnz+/znK6uLhw4cAA1NTWws7PDvXv3EBISouKkwlAoFGhqauqxe3lNTQ3u3LmDzs5OAA926bW0tISLiwssLS1hYWEBCwsLqKmp4datW7hx4ways7NRXl6Ozs5OaGtrw97eHhEREXBwcIC1tTXU1dWhUChQWlqK1NRUHD58GLGxsQgJCUFAQAB0dIb2e0wikSAuLg4RERGYPn06gAc/Aw8ePIjGxkZs2rQJ+vr6/V4vk8mQmJiIHTt2oK6uDs8++yw2btz42IIo0Ujxw35hR0cH7t27B1NTU8HyNDQ0oKCgAO7u7oJlGC4jrV+oDL/97W8hEom6lwV5+FpZVFrgvHDhArKzs7vXQ/khkUiE3/zmN6qMQ0Q0JH4vfjLka0UiETyf/g3yD/wfmquK8WCXdAXUtHQwZemb0LNwBAA4Rr0ADR193JIcRldbEwDAyNEHzvN/Am0D5ew6R+Nbeno6fve73+HkyZPdo2fqWrrg+Ot0LPY2xW8WOiDI0UD1wTqagfZGQMdQ9fceZn/729/w8ssvdz99//zzz3Hy5Els27YNv/rVr/q8RiQSKW2Uz8Op4w93C1b1VHL2C2kskEqlKC0txcaNG/ssosnlcsTExODmzZuYN28eTp06hQULFozJAld7e3t3IfOHxcyHxRFNTU2Ym5vD0tIS3t7e3cVMPT297hE7FRUVKCoqQlxcHMrLy9HV1QVtbW04ODggMjKyu6DZ1+gtkUiEyZMnY/LkyaiurkZaWhri4uKQlJSEwMBAiMXiR+5s/98yMzNx+vRpTJ8+HeHh4QAeFGyPHTuGmzdvYuPGjY9cS666uhp79+5FYmIiJk6ciF//+tcICQkZ8QUJIqDvfmFLSwveeecdeHt7Y9GiRXB0dBQk2+XLl+Hm5jbq/y2NtH7hQ5s2bYJIJMKXX34JdXV1bNq06bHXiEQifP31173e/7//+79Hvh5uKilw3rt3D4sWLUJ6enr3AqQP/5E8/P/syBLReKFlYAq/Fz/B/fIraK4uhuYEQ5hOCYG61n8+GIlEarCbsRo2oSvR3lADdW09aOkZCxeaxrTDhw9j9erVUCgU3b+fH1IogFOX7+H7y3U48LIHVvqbqT5g850RXeBsamrC/fv3u19ra2tDW1u7xzkdHR3IysrC22+/3f2empoaoqOjkZaW9si2HRwcIJfLERAQgA8//BBeXl7Dkvu/p46raio5+4U0VtTW1iI2NhZisRiTJ0/udVyhUODkyZO4cuUKVq9ejdzcXBgZGSEwMFCAtMNHJpPh7t273QXMh8XM+vp6AA/+HU+cOBEWFhYQi8WwsLCApaUljI2NexQmOzs7UVFRgYyMDNy8eRMVFRXo6uqCjo4OHBwcEBUVBQcHB1hZWQ16OqqlpSWWL1+OqKgopKenIyMjAxKJBF5eXggLC4O1tfUjr7948SJOnjwJsViM6Ojo7kJKYmIiLl68iKeeeqrfteTkcjlSUlJw+PBh3Lx5EwEBAXjppZdgY2MzqO+BSCiP7hcqcPnyZVy+fBkvv/xynztnK9u9e/dw7949TJw48gZ9DKRPCIzMfuFD8fHxUFNTg1wuh7q6OuLj4wc0rXwkUEmB8xe/+AXy8vKwd+/e7g7AmTNn4OTkhC1btiAtLQ3ff/+9KqIQEY0IIpEIRvZeMLJ/9C8kNXVNTDBlh5iUJz09HatXr4ZMJuvViX1IJgdEUGD11qtI/aWf6kdyNt8BJjqr9p6D8HBkz0PvvfderyfUtbW1kMlksLS07PG+paUlrl271me7bm5u2LZtG3x8fNDQ0IC//OUvCAsLQ35+PmxtbYf1e1Al9gtpLJDJZDh8+DCMjIz63SwoPj4eWVlZWLZsGfT19XHt2jWsWLFi1OycrVAo0NDQ0GM0Zk1NTffPM+DBdEkLCwt4enp2j8g0Nzfvc5fyzs5O3LhxAzdu3OguaMpkMkyYMAEODg6Ijo6Gg4MDLC0th219PQMDA0RFRWHmzJnIyclBWloavvjiCzg5OSEsLAwuLi69Ppjn5+fjyJEj8Pf3x/z587uP5+TkICkpCdHR0fD29u7zfjU1NTh8+DAkEgkUCgVWrFiBVatWjev1Vml0GUi/8OGsi61bt+Ktt94SZCRnWVnZiCxwDqRPCIzsfuGNGzce+Xowdu7cOaTrNm7cOKTrVFLgPHXqFF555RWsXr0ad+/eBfCgOu3i4oJ//OMfWLlyJd544w3s27dPFXGI6P+zd99hUZ7p4se/Q++9isDQRJqCCIhd7F1j19gSN9nNluxm+9lkT3Kyu/nl7Nk92c2emOxuihp7AzWWKPaGhUEHxEaX3jvDlPf3h+tsiA0QGMDnc117ZZl35n3vARyeued+7lsQBOFffve73z3yE/pvkwAJid8dzCPptfCeCe6BmgLw6b396k6dOkVkZKT+60d9Ut8Z8fHxxMfH678eOXIkISEhfPLJJ7z77rvPfP7/+q//6vBjuqKyUqwLhf7g7NmzlJSU8PLLL+vbPHzT+fPnOXPmDFOmTCEyMpINGzbg5ub22MSYoTU3Nz9UkVlWVoZKpQLuv665u7vj7e1NdHS0PplpaWn52HO2trZSUFCgT2gWFhai1WqxsrLC19eXKVOmIJfLcXNz6/bqHzMzM+Li4oiJiSEzM5Pz58+zefNmXF1diY+PZ8iQIZiYmHDr1i12795NREQEs2bN0seVlZXF/v37GT58uL4X5zfpdDrOnz/PkSNHyM/Px8XFhblz5zJmzJheU9kkCO3R3nXhAwcPHuS1117r5qgeVlFR0ePXbI/uWhNC968Lu8Ojdgc9eE389u/YN18re3WCs6amRl82+6AJc0NDg/74lClT+I//+I+eCEUQBEEQhH/Jz8/nwIED7V7EanWwX1nV84OHqnN67lqdYGNjg53dk7fQu7i4YGxsTGlpaZvbS0tL291LydTUlKioKO7evdvpWL/pURUFT1p0dtXWcbEuFPq6oqIiTp06xZgxYx657VihUPD1118zZswYRo4cSVZWFrm5uSxbtszgk3/VajUVFRUPJTPr6+sBMDY2xsXFBXd3d4KDg/Xby+3s7J6aqGttbSU/P79NQlOn02FlZYVcLmfq1KnI5XJcXV0NlvQzMjIiLCyM0NBQCgoKOH/+PPv37+f48eMMGDCAmzdvEh4ezrx58/Q/q9LSUnbs2EFAQAAzZsx4KPaKigoSExO5ceMGjY2NhISEsHjx4ke2LRCE3qyj60KdTsf169cNMnjoQUuM3qY9a0LonevC7pCT03YNX1NTw+rVq7G3t+eHP/whwcHBANy8eZMPP/yQ+vp6NmzY0Onr9UiCc8CAAZSUlAD3M9hubm5cu3aNuXPnAlBYWCg+2RIE4bml1WofGrAhCD3hyJEj7V7EPiBJ8PWNalbHuz/9zl2l5Bb8a9pub6LRaNp9XzMzM6Kjo0lOTmbevHnA/TcGycnJ/OAHP2jXObRaLUqlkhkzZnQm3Id8+3WnsLCQmTNnEh4ezo9//OM2i84PPviAGzdu8NVXXz3zdcW6UOjL1Go1e/fuxd3dnbFjxz50PDMzk3379hEdHU1CQgKSJJGcnIy3tzeDBg3qsTh1Oh3V1dUPbS+vrKzUv+47Ojri5uZGVFSUfnK5s7Nzu7fQq1SqNgnNoqIidDod1tbWyOVyhgwZglwux8XFpdf9m5bJZPj4+ODj40NlZSWJiYl89tlnODk5ERMTQ01NDU5OTtTV1bF582acnJxYtGhRmwS1TqfTT1mvqalBJpMRGxvLokWL2pXgEPq+/raG79y6UOLGjRttKgt7QkNDA+petDbsyJoQeue68EkOHTrEn//8Z/1wyEf9njxoXfJN3+5V/Pbbb+Pq6srXX3/d5u9CREQECxYsYMqUKfzv//4vn3/+eafi7JEE55gxYzh69Ci/+c1vAFiyZAn//d//jbGxMTqdjg8++ICpU6f2RCiCIAi9zrvvvss777xj6DAEod2+8+UdvvPlnR684lngsx68Xvd44403WL16NcOHDyc2NpYPPviAxsZG/fTMVatW4eXlxXvvvQfc30I+YsQIAgMDqamp4Y9//CN5eXmsW7euW+L7/ve/T1BQEF9++WWb22NiYti8eTMLFy7k+9//Pnv37n2m64h1odCXHT9+nOrqal555ZWHEoE5OTns2rWL0NBQZs6ciUwmIyMjg6KiItauXdstST5JkmhsbNQnMR/8t7y8XP/m38rKCnd3dwICAhg5cqS+T2ZHt062tLQ8lNCUJAkbGxvkcjmRkZHI5XKcnZ17XULzSZqamigtLWXJkiX4+/uTmppKamoqgYGB5OXlYWlpyfLlyzEzM9M/prKykqSkJHJycvRVqqNHj2bSpEl9pseq8OzEGv6+TZs2sWnTph6/bnume/dmvX1d+MDu3btZvHgxYWFhLF26lPXr17N8+XIkSSIpKYmgoCB9kvZpEhMT+f3vf//IvxFGRka88MILvPnmm52OtUcSnD/96U85evQoKpUKc3Nz3n77bTIyMvRbnMaOHcuHH37YE6EIgiD0Om+99Zb+jb4g9KQvvviCV155pcOP+8eLQT1bwQkw/Y8wsHdNHlYoFMTFxbX7/kuWLKG8vJzf/va3lJSUEBkZyeHDh/UN5vPz89tUB1VXV/Od73yHkpISHB0diY6O5vz584SGhnb5c4H7iZv333//sccnTpzIL3/5y2e+jlgXCn1Vbm4uFy9eZPLkybi5ubU5VlRUxNatW5HL5cyfP18/gfb48eMEBgY+duJ2R6hUKsrLyx9KZjY1NQH3tyu6urri7u5ORESEvk+mtbV1pxKOLS0t5OXl6ROaxcXFSJKEra0tcrmcYcOGIZfLcXJy6lMJzW8qKiriyy+/xNPTkxUrVmBmZsbYsWNRKBR8+OGHFBcXM3PmTO7du0dwcDAymYxLly5x7NgxAExMTDAzM2Pu3Lnd9tos9F79bQ3f2XXhypUre7yCE+j2xF5HdHRNCL1/XfjAe++9R2xsLGfPnqW6upr169fz0ksvkZCQQG5uLiNGjMDPz69d55Ik6bFDlABu3LjR4Srib+qRBKexsTFvvPGG/mtHR0eOHTtGTU0NxsbG2Nr28DRWQRCEXsTY2Fh82i8YxNSpU/W9FdtLJoMpoY6YGvdwH7mKDPDrXYOGHjUl+Gl+8IMfPHbr0cmTJ9t8/b//+7/87//+b2dC6xQLCwsuXLjA9773vUceP3/+PBYWz957VawLhb5IpVKRmJiIj48PI0a0fS2qqKjgyy+/xM3NjSVLluhfG9LS0qisrGThwoUdupZWq6WysrJNErO0tFTfc04mk+Hs7IybmxtxcXH67eWOjo7P1OOzubm5TUKzpKQESZKws7NDLpczfPhw5HI5jo6OfTah+U1lZWVs2rQJV1fXNhWaJiYmFBcXExQUxJo1a8jPz2f79u1YWFjQ1NSEJEm4u7tTVVWFm5sbixcv7pXTnIXu19/W8J1bF8oIDQ3t8e+DTCZ75IA3Q+nMmhB697rwgRs3bvDee+9hbGysf54PdgjI5XJee+013n///XYNBpo3bx7r169HLpfz3e9+FysrK+B+Jf369ev55JNPWLFiRadj7ZEEZ3h4OBERESxZsoTFixcTGBgIgIODQ09cXhAEQRCER/Dx8WHWrFkcPHjwkX1zvs3YCGaGO/XsgKEHSq73/DWfMytWrOCvf/0rDg4O/PCHPyQgIAC4Pz34r3/9K1u2bOFHP/rRM19HrAuFvujIkSM0NTWxevXqNknE2tpaNm7ciLW1dZskmVqt5uTJk4SHh+Pp6fnIc0qSRF1d3UMVmRUVFfrXZFtbW9zd3QkNDdVXZLq4uHTJG/umpqY2Cc3S0lIkScLe3h65XE5sbCxyuRwHB4d+kdD8poqKCjZs2IC9vT0rVqxos13/9OnTKBQKXnjhBYYMGYIkSRw8eJAvv/ySyspK7OzsyM/PZ9q0aSxcuLBXJVkE4Vl0dF1oZGREREREjw8Ygs4nFIWOs7Ky0v9tc3BwwNzcnOLiYv1xd3f3h4YJPc5f/vIXcnJy+NnPfsavf/1r/d/H4uJi1Go1o0aN4oMPPuh0rD3yW7F+/Xp27NjBb3/7W9566y0iIyNZunQpixcv7pLtGoIgCIIgdM5bb73FoUOHnvqJvQyQIePNGQb6u12ZZZjrPkfef/99Kioq+Nvf/sb//d//6ZM4Op0OSZJYtmzZE7ewt5dYFwp9za1bt0hNTWXOnDk4Ojrqb29sbGTTpk0YGRmxcuVKfSUKwOXLl2loaGDChAnA/erIb1dklpWVoVKpgH8P3PL29iY6OlqfzLS0tOyy59HY2PhQQhPuv2GVy+WMGDFCn9Dsz6qrq9m4cSNWVlasXLmyzff42rVrnDhxgoSEBIYMGUJNTQ379u0jOzubuXPnkp+fT0ZGBjY2Nty9e5dDhw4RHx+Pq6urAZ+RIHSd9q4LH+iJATePIj5Y6DnBwcHcuHFD/3VkZCSbNm3ixRdfRKPRsGXLFnx8fNp1Lnt7e06dOkVSUhKHDh0iLy8PgGnTpjFjxgxmz579TB+oyaRn2eDeQaWlpezcuZMdO3Zw7tw5AGJjY1m6dCmLFi1iwIABPRVKp6WmphIdHc3Vq1cZNmyYocMRBEEQhGe2Z88elixZgiRJj/zE3tjofnJzx3dCmB/lYoAI/2XtITCzevr9ekh/XRNcv36dgwcP6hedvr6+TJ8+naFDh3bpdcS6UOgLmpqa+OijjxgwYADLli3Tv/FSqVRs2LCB2tpaXnrpJf0WZY1GQ0FBAR988AHOzs74+/tTWlpKfX09cH9Lq4uLC25ubvokpru7O3Z2dl1eJdnQ0NAmoVlWVgbcbwshl8uRy+X4+vr2+4TmN9XW1vL5559jZGTE2rVr27TEyM7O5ssvvyQyMpJZs2ahUCg4cuQIlpaWhIeHc/XqVSwtLVm8eDEODg5cvXqVlJQU6uvrGTRoECNHjsTX17ffVbsKz5+nrQsffAD6yiuvEBUV1dPhAeDq6sr8+fMNcu1H6c/rgT/96U/89a9/5fbt25ibm3PgwAHmzp2LpaUlMpmMxsZGPvvsM9asWWPoUHs2wflNhYWF+kXtpUuXkMlk+n38vVl//sUVBEEQnl+XL1/m3Xff5cCBA20+sZfJYHaEE2/O8CVGbuDeiEu3gL2XYWP4BrEm6DpiXSj0RpIksXPnTnJzc3nttdewsbEB7icxN23aRHZ2NpMnT0aSJH1FZmVlJdnZ2RQUFDB16lR8fHzaJDOdnZ27rVddfX19m4RmeXk5AE5OTm0Smvb29t1y/d6uvr6ezz//HJ1Ox9q1a9t8H8rKyvj000/x9vZmxowZfPXVV2RlZREZGYmZmRmXLl1i8ODBzJs3r00vYq1Wi1Kp5MKFC5SWluLp6cnIkSMN0pNQELrS5cuXeeedd/jqq68eOhYREcGsWbOQy+U9H9i/BAQEMHHiRINd/9v643qgpaWFpKQkcnJycHZ2ZtasWfot5WfOnGHPnj0YGxszc+ZM/W4FQzNY4wJPT0/CwsIICQkhPT2dxsZGQ4UiCILQIWmfvk5rQzVmNo5EvvyXTp2jtbGG0rSvaSzNxtTSDrchCdh6De7iSAWh/WJiYti3bx/5+flERkZSXV2No5UJaW8OM0zPzUexsDN0BEI3EetCoTdSKpVkZGQwa9YsSktLUSqVlJSUcPDgQXJycggPD+f48eNYWVnh7u5OQEAAQ4cOpampicWLFzNz5sxuja+urk6f0MzNzaWyshIAZ2dn5HI5Y8eOxdfXFzs78drZ2NjIxo0b0Wg0DyU36+vr2bx5Mw4ODgQFBfHJJ59gZmbGvHnzSEtLIz8/n8mTJzNy5MiHqjONjY2JjIxk6NChZGdnc/78eXbv3s2xY8cYMWIEw4YNa9PfUxD6ipiYGN58801kMhlHjhxBrVZjbGxMaGgoM2bMMGhyE8DDw8Og1+/vysrKGDlyJDk5OUiShEwmw9LSksTERCZNmsSYMWMYM2aMocN8SI8mOCVJ4uTJk2zfvp29e/dSUVGBo6MjS5cuZcmSJT0ZiiAIQqe1NlTTWl/Z6cfX3btJxpY30apb7t8gM6L46lcMHLkIecKarglSEDrJx8cHKysrqqursTIz6j3JTWMzMLMxdBRCFxLrQqG3aW1t1ffHzM3NZdu2bVhYWOgrmExMTCgqKqKxsZHvfOc7DB8+HHd3d6ytrfWJr0OHDuHo6EhCQkKXx1dXV6dPZubm5lJVVQWAi4sLfn5+TJgwAV9f3zbbroX7/U83bdpEU1MTa9eubdNHVaVSsXnzZlpaWrCxseHQoUNERkYSEhLC/v37AVi9evVT+wPLZDICAgIICAigpKSECxcucPToUU6ePMnw4cOJi4sTiWahz/H29uaVV17hwoULVFZWYmFhwZgxY9r8GzKUgQMHGjqEfu3dd98lNzeXn/zkJyQkJHD37l3effddXn31VbKyem9f/B5JcJ45c4YdO3awa9cuysrKsLOzY968eSxZsoRJkyaJCViCIDw3JJ2WzF2/R6tWwYNtwNL93jb3zu/E3ncIjgH9Y1uDIHSpAZH398sLfZ5YFwqGptVqqaysbDPsp6ysjOrqav19srOzsbCw4MUXX8THxwdXV1euXLnCxYsX+e53v8uQIUMeOm9NTQ1Xrlxh/PjxXTIcqLa2tk1C80F8rq6u+u2Zvr6++q3zwsNUKhVffvkltbW1rFmzBheXf/eR1mq17Nixg8zMTGxsbDA1NWXp0qVUVlayfft2fHx8WLhwYYe/vx4eHsyfP5+JEyeSkpLC1atXuXDhAuHh4YwcOVJUngl9hpeXF15eXvq2DKampl3ej7szPDw8nttWGz3l66+/ZtWqVfzP//yP/jZ3d3eWL1/OrVu3CA4ONmB0j9cjK8hx48ZhY2PD7NmzWbJkCdOmTdOPmRcEQXieVGenom6oevRBmRElisMiwSkIj+LfO3r7CM9OrAuFniJJEnV1dQ9NLq+oqNAPzrC1tcXd3Z2QkBB9r8zc3FyOHDnCypUrCQgIAODs2bNcvHiR6dOnPzK5CXDixAksLS2Ji4vrVLw1NTVtEpo1NTUAuLm5ERQUpO+haW1t3anzP29aW1vZsmULlZWVrFq1Cnd3d/0xSZLYtWsX+/btY8CAAURHRzNhwgQOHz7MrVu3GDNmDBMmTNAPU+kMOzs7Jk+ezNixY1EoFFy4cIHr16/j7+/PyJEjCQgIEAOJBKETwsLCDB1Cv5efn88vf/nLNreNHj1a33P6uU5w7ty5k5kzZ7ZpyCwIgvA8aq17wtZ2SYeqtqznghGEvsLUCuSjDR2F0EXEulDoDs3NzW2SmA/+q1KpADA3N8fNzQ1vb2+io6P1ycxvV1pWVlZy/PhxYmJi9MnNq1evcuzYMcaNG/fY5GVZWRnXr19n+vTp7UrYS5LUJqGZl5enT2i6u7sTHBysT2haWVk9w3fm+aTRaNi2bRvFxcWsXLmSAQMG6I9JksTGjRvZtGkTERERvPLKKzg4OLBx40aam5tZvnw5gwYN6rJYzM3NGTFiBLGxsdy4cYPz58+zZcsWfvzjH4tt64LQQc7Ozvj7+xs6jH5PpVI9tE578LVGozFESO3SIwnOBQsW9MRlhF7ktT9tobq+CUdbKz766fJOn6elVY1ao8XG0lx8win0C5bOT+gXY2SEleuTezwJwnMp/AUxYKgfEetC4VloNBrKy8sf2l5eV1cH3B/64uLigpubG8HBwbi5ueHm5oa9vf1T15I6nY69e/dia2vL5MmTAcjIyODAgQPExsYyfvz4xz72+PHjODg4EB0d/cjjkiRRXV3dJqFZW1uLTCbD3d2dwYMHI5fL9X2Qhc57sPU8Pz+fF198EW9vb/2xxsZGPvroI7766ivGjx/Pz372M27dusXu3btxdXVl1apV3dZf0MjIiPDwcMLCwqisrBTJTUHohNjYWJEX6CG5ubmkpqbqv66trQXgzp07ODg4PHT/9k6Pz8zM5PPPPyc7O5vq6mqkB23b/kUmk5GcnNypmEWTI6FbVNc3UVHb0OnH55VW8c/9Z0i5kYMkgbebI6umjWB8VO8shRaE9rLzCcPK1Yeminsg6doe1OnwHD7LMIEJQm9lagkRCw0dxXPjyJEjfPrpp09cdPbm5vJC//EgIfjtiszKykr976WDgwPu7u4MHToUd3d33NzccHZ2xtjYuFPXPHfuHIWFhbz00kuYmZmRlZXFnj17CA8PZ/r06Y99U33v3j1u3rzJCy+8oL+2JElUVVW12XJeX1+PTCbDw8OD0NBQfUKzK/p1CvfpdDp2795NVlYWy5YtazPp+caNG2zatImrV6/ywgsv8N3vfpeDBw+SlpbG8OHDmTZtWo/0AJbJZG16gQqC0D5yubzNBxZC93rrrbd46623Hrr9tddea/P1gynrD1q/PMmmTZtYu3YtpqamBAcHP/IDpW+vPTtCJDiFXqe4opbXP9hGc6taP4PlXlk1v994iGaVmukjwg0boCA8A5lMRuji35K++U1aakqQGRkjSTpkMiMCZvwA2wFBhg5REHqXiIVgafhpnc+DP/7xj/zqV7/C3d2d2NhYIiIiDB2S8ByQJInGxsaHtpeXl5ejVqsBsLKyws3NjYCAAOLj43F3d8fV1RVzc/Mui6OkpISTJ08yevRovL29uXfvHtu2bcPf35958+Y9NrkpSRLHjh3Dzc0NDw8Prly5ok9oNjQ0IJPJ8PT0JCIiQp/QFO0ZuodOpyMxMZGbN2+yZMkSAgMDAWhqauLgwYOkpKRQWFjIwoULmTdvHp9++ilVVVXMnz+/VwxOEQTh8UxNTRk5cqShw3hufP75591y3rfffpuoqCgOHTrULR/0iASn0OtsS75Mc6sane7fmfsH/++f+88yaXgIpiad+2ReEHoDC0dPol/7O1V3LtFQkoWplR0uoWMxs3YwdGiC0LtY2MHQZYaO4rnxl7/8hYSEBA4ePIipqamhwxH6odbWVv2W8m8mM5uamoD7b2BdXV1xc3MjIiJC3yfT2tq6W7ckajQa9uzZg4uLC+PGjaOsrIzNmzfj6enJ4sWLH1kRKkkSFRUVnDlzhq+++go/Pz8++ugjjIyM8PT0ZOjQofqEZlcmYoVHkySJAwcOoFQqWbhwoX4Axs2bNzlw4ABNTU3IZDJGjx5NTEwMn376Kba2tnznO9/Bzc3NwNELgvA0sbGx2NjYGDqM58bq1au75bxFRUX87Gc/67YqdpHgFHqdc+lZbZKb31TX1MKde6WEygc88rgg9BUyI2Ocg+NxDo43dCiC0HtFrQQzMS24p1RXV7Nw4UKR3BS6VE1NDYcOHaKsrIzq6mrg/m4GJycnfbXwg+3ljo6OzzS1urNOnDhBZWUlr7zyCg0NDWzatAk7OzuWL1+u//cgSRLl5eVtemg2NDSQmpqKk5MTkydPxs/PD29vb5HQ7GGSJHH48GFSU1OZP38+YWFhNDc3c/jwYa5du0ZAQABVVVVYWVnh4eFBUlISYWFhzJkzR/ysBKEPcHd3JzQ01NBhCF1gyJAhFBUVddv5RYJT6H0633JBEPqMmtxrFF7YQ0PJXUyt7HGPnIxn9CyMTERiQRAAsHaF0HmGjuK5Ehsby61btwwdhtDPmJmZodFoCAkJ0Vdkuri49JpEen5+PufPn2fixIlYW1vz2WefYWJiwooVK6itreXatWv6hGZTUxPGxsYMGDCAYcOG0draik6nY926dfj6iiGBhvCgRUBKSgqzZs1i6NCh3L59m/3796NWq5k7dy43btygqqoKe3t7MjIymD59uhhUIgh9hImJCePHjxf/XvuJP//5zyxatIjp06d3S8sBkeAUep34cH+OXr6B9hFVnLZWFgQOFNtIhL6tJO1r7h74C8iMQNKhbqwh5+inVN2+RNjydzEyFi/NgkD0ajAxM3QUz5WPPvqI6dOnM3z4cJYvX27ocIR+wsrKipUrVxo6jEdqbW1l7969DBw4kKioKP72t79RWFjIkCFDWL9+Pc3NzRgbG+Pl5cXw4cORy+UMHDgQMzMztFotH330EYMHDxbJTQM6deoU586dY9q0aYSHh5OUlIRCoSAoKIhZs2Zx5swZLl++jLm5OQ4ODqxdu5aBAwcaOmxBENopNjYWe3t7Q4chdJH3338fe3t7xowZQ2hoKD4+Pg+1gZHJZCQlJXXq/OJdtNDrLJ0Yw+m0O7So1Q9tVX955ijMemC6oSB0F42qiezDH9//os0UdYnavOuUp5/Afehkg8QmCL2GgzcEzzB0FM+dJUuWoNFoWLlyJd/73vcYOHDgIxed165dM1CEgtB1dDod27Zt48aNG0RGRrJ69WpqamqIjo7GxMSE2NhYfULzUdWmaWlpVFZWsmjRIgNEL8D9qfcnT55k4sSJuLi48NFHH6FSqZg7dy6RkZGcO3eOXbt2YWFhQXR0NAsWLMDKysrQYQuC0E7e3t6EhYUZOgyhC12/fh2ZTIaPjw8NDQ3cuHHjofs8S7WuyBQJvY6XqwN/eX0Jn+w7zZWbeQAMcLFn1bR4JkYPNnB0gvBsqu9eQadRPfqgTEZ5ximR4BSE2FfASAyT62lOTk44OzsTFBRk6FAEocvpdDpKSkr0PTRTU1O5cuUKwcHBpKWl4eDgwBtvvEFMTMxTt8+r1WpOnjxJeHg4Hh4ePfQMhG9KSUnh6NGjxMfHU1NTQ3JyMv7+/sydOxd7e3suX77Mn//8Z+zs7Fi2bBljxowxSH/Xb5Mkifz8fO7evUtCQoLYdisIj2FpaSm2pvdDubm53Xp+keAUeiW5pzPvvTqfppZWWjUa7K0txYub0C9oW5sff1CS0Kqaei4YQeiNBkSBfIyho3gunTx50tAhCEKX0el0FBcX6xOa+fn5qFQqTE1NcXNzo6mpiblz5+Ll5cWtW7dYtmwZgYGB7Tr35cuXaWxsJCEhoZufhfAoqampHDp0CD8/PzIyMmhpaWHWrFlER0cjk8m4ePEi77zzDq6urrz55pvt/rl2F0mSKCkpQalUkp6eTl1dHfb29sTGxmJra2vQ2AShN5LJZEyaNAlLS0tDhyL0MSLBKfRqVhZmWCF6sAn9h513yOMPyoyw8wnvuWAEobcxMoHRPwbxgZYgCB2k1WofSmi2trZiamqKt7c3o0aNQi6XM2DAABITE/H29sbHx4f09HQWLFjQ7iRYS0sLZ86cYdiwYTg5OXXzsxK+7fr16yQmJmJkZER2djb+/v7MmTMHR0dHJEniyJEj/OlPf2LgwIH8z//8j0F/RpWVlSiVSpRKJZWVlVhbWxMaGkpERATe3t6ieEMQHiMuLg5PT09DhyF0s/r6empra9HpdA8d8/Hx6dQ5RYJTEAShB1m5+OA0KI6qO5fb9uCUGWFkaobn8FmGC04QDC1yGTjKDR3Fc0+tVnPz5s3HLjrHjh1rgKgEoS2tVktRUZE+oVlQUEBraytmZmZ4e3szZswYfULzm71k09PTSU9Px9fXF6VSycyZMwkPb/+Hi+fPn0ej0TBu3LjueFrCE9y4cYMvvviCuro65HI5U6ZMISYmBplMhkqlYufOnXz55ZcEBQXx3//931hbW/d4jHV1daSnp6NUKikuLsbc3JzBgwczffp0/P39e8U2eUHoqNbWVgA0Gk23X2vw4MFERER0+3UEw1m/fj1//vOfyc7Ofux9tFptp84tEpyCIAg9LHjeL7h78EPKM06BdH+QlqWTJ4Pm/BQLezcDRycIBuIaDMNWGzqK55pOp+PXv/41H330EU1Nj2+X0dlFpyA8C41G81BCU61WY2Zmho+PD2PHjkUul+Pp6fnQcKwH6uvr+eqrrzA3Nyc3N5eJEycSExPT7hgaGhq4ePEicXFxYmtxD0tPT+f9999HpVIxZcoUXnjhBX11ZllZGVu2bOHUqVNERETw5ptv9mhys6mpiRs3bqBUKsnPz8fY2JigoCDGjBlDUFDQU3u6CkJvplKpaGlpAe5/AKpWq7vtd9rLy4vRo0eL6uZ+7OOPP+b73/8+U6dO5aWXXuI3v/kNP/nJT7CwsOCLL77A3d2dH/3oR50+v0hwCr1aZW0jKrUadyc7jMUnnkI/YWxmQfC8nyNPWEtTeT6mVrZYewSKP+bC88vEHBLeBGPxJtCQ/vCHP/DHP/6RV199ldGjR7Ny5Uref/99HBwc+Oijj5DJZPz3f/+3ocMUnhMajYbCwsI2CU2NRoO5uTm+vr6MHz9en9BsT1WcJEns27ePsrIyLC0tGT16NGPGdKzf75kzZzAyMmLUqFGdfVpCJ5w9e5b33nsPGxsbXn/9dUaOHKlfM12/fp19+/aRnZ1NaGgoP/jBD7C3t+/2mFpbW7l58ybp6encvXsXSZL0Q44GDx6MhYVFt8cgCD0hJSVFX7mp0+nIzc3tlmGErq6uTJkyRVQ593MffvghU6dO5dChQ1RWVvKb3/yGmTNnkpCQwC9+8QuGDx9OZWVlp88vEpxCr3Qrv5T/23uCzNwSAJzsrHlxShyzRkaIJJDQb5jbuWBu52LoMATB8Mb8DBw612tH6DpffPEFixcvZv369frFZXR0NAkJCaxevZr4+HiOHz/OpEmTDByp0B+p1eo2Cc179+6h0WiwsLDA19eXhIQE5HI5Hh4enXoDnJqaysWLFzEyMiI6OpqpU6d2aE1ZXV3NlStXGD9+vBh80UPUajXbtm3jiy++wN/fn9/97ne4u7sD9xPghw8f5vLly2i1Wry9vXnxxRe7tW+fRqPh7t27pKenc+vWLdRqNd7e3kybNo3Q0FBsbGy67dqCYAgqlYojR460uS0jIwO5XN6lVZyOjo5Mnz5dVDs/B7Kysvj+978PoP95P2iBYG9vz7p16/joo4/46U9/2qnziwSn0OvklVbx07/tRK359xa4qrpG/rrrOGqNhhfGDTNgdIIgCEKXCpsPg6YYOgoBuHfvHr/4xS8AMDc3B9BvSzMzM+PFF1/kz3/+M3/4wx8MFqPQf6jVau7du9cmoanVarG0tMTX15eJEycil8txd3d/5oqeqqoqtm/fTk1NDTNmzGDOnDkd/sD85MmTWFpaEhcX90yxCO1z7949Pv/8c06fPk18fDxvvfWW/nWppqaGHTt2UFZWhq+vL3l5ecyZM6dbqsoeVKylp6dz48YNWlpacHd3Z9y4cYSHh+Pg4NDl1xSE3kKhUJCVlYX0r5ZaAOXl5RQUFODv798l17C1tWXGjBmi6vk5YW9vr68ItrOzw8rKioKCAv1xW1tbSkpKOn1+keAUep1txy6j0WrRfeOF9IENhy8yM34I5mbiV1cQBKHPcw+H+O8bOgrhX5ydnWloaADAxsYGOzu7hxrAV1dXGyI0oR9obW1tk9AsLCzUJzTlcjmTJ0/WJzS7creOTqdjw4YNZGZmMnv2bBYuXPjYHp2PU1ZWxvXr15kxYwZmZmZdFpvwMI1Gw8mTJzl8+DDZ2dnMmjWL733ve/rv++3bt9m7dy8WFhaMGjWKU6dOMXbsWKKjo7ssBkmSKCws1A+kamhowNHRkdjYWMLDw3FzE/3SheeDt7c3K1as4NixY7S0tGBmZkZsbCyOjo5dcn4rKytmzpxpkIFggmGEh4dz7do1/dcjRoxg/fr1zJgxA51OxyeffMKgQYM6fX6RJRJ6nUuZOWh1Dyc3AZpaWrlzr4xw/wE9HJUgdC1NSwNl14/TUJqNqZUdbhEJWLvJDR2WIPQca1eY8q7ou9mLREVFcfnyZf3XEyZM4IMPPiAqKgqdTsdf//pXhg4dasAIhb6oqqqKvXv3UlhYiE6nw8rKSj8BWy6X4+bm1q3th44cOcKhQ4cYP348q1at6tQWyOTkZBwcHBg2TOwi6k5FRUXs3buX/Px8GhsbmTJlCmvWrMHMzAydTseJEyc4c+YMwcHBREdHs2PHDiIiIkhISOiS65eVleknoFdXV2NjY0N4eDjh4eF4eXmJNlnCc8fLywsvLy99daWpqWmXrQPMzMyYMWMGdnZ2XXI+oW948cUX+fjjj1GpVJibm/POO+8wadIkfHzut6oyNTVl9+7dnT6/SHAKvc7ThgkZG4vFhdC3NRTfJX3zb9C0NILR/d/nwgu78Rn3Ij5jlhk4OkHoAcZmMOV3YOVk6EiEb3jllVf44osv9IvO3//+94wdO5axY8ciSRKOjo5s3brV0GEKfYy1tTV2dnZEREQgl8txdXXtsURRVlYWH374IUFBQbz++uud2gJZUFDArVu3eOGFFzpc+Sm0j0aj4fTp05w9exZra2t9Ve/q1auxsLCgoaGB3bt3k5uby+TJkwkODubTTz9l4MCBzJ0795l+n2pqavRJzdLSUiwsLAgNDWX27NnI5XIx8EQQuoGxsTFTp07FyUmsA583a9euZe3atfqvR40aRUZGBvv378fY2JgpU6aICk6hfxkzNIgD56+je0QVp6OtFYMGuhsgKkHoGpJOy42dv0OjagIk+Mbvef6pL7HzDsVBLiqkhH5u7M/AbbChoxC+Zc6cOcyZM0f/dWhoKFlZWZw8eRJjY2NGjhwp3owIHWZubs6iRYt6/LoNDQ28+eabWFpa8s4773RqC6QkSRw7dgx3d3ciIiK6IUqhuLiYxMREysvLGTZsGDdv3sTFxYVVq1ZhaWlJXl4eu3btQpIkVq9ejaurK//85z+xsbFhyZIlmJh0/O1sY2MjGRkZKJVKCgoKMDU1JTg4mAkTJhAYGNipcwpCf/ZgCMyD3onPauzYsd06EEzoW/z9/Xn99de75Fzi1VvodZYkDOeU4jb1zS36JKdMBpIE3503FmNj8Umq0HfV5KTRWlf+6IMyI0pSD4kEp9C/hS+AQVMNHYXQTvb29sydO9fQYQhCh7S2tvL2229TUVHB//7v/+Li4tKp82RlZZGXl8fy5cvF9uQuptVqOXPmDKdPn8bNzY1ly5Zx8OBBzMzMWL16NVZWVpw/f55jx47h7e3NwoULsbCwYMOGDajValavXt2hafYqlYrMzEyUSiU5OTkABAYG8sILLzB48OAe7a2q0WgoKCjAz8+vx64pCJ2lUqn0AwfVajVqtfqZpp1HRER0y0AwoW+5ePEiJ06coKysjNdee42goCCampq4efMmgwYNwsbGplPnFQlOoddxc7Tlw58sZcOhC5xKu41Gq2OQtzsrp44gLlQsBIS+TVVb9viDko6W6s5PjROEXs9zCIx4zdBRCE+g1WrZuXOnftH5X//1X0RERFBbW0tycjKjRo3C3V3spBB6L61Wy8cff4xCoeC1114jPDy8U+eRJInk5GR8fHzEm/EuVlpaSmJiIqWlpYwZM4Zhw4axceNGdDoda9euxdTUlO3bt3Pz5k1Gjx6t77G5c+dOSktLWbt2bbuml6vVau7cuYNSqeTOnTtotVp8fX2ZMWMGoaGhWFlZdfMzbausrIzU1FSuX79OU1MTb7zxhug/KPR6KSkp+spNnU5Hbm5up18TXV1diYuL68rwhD6mtbWVpUuXkpSUhCRJyGQyZs+eTVBQEEZGRkyZMoWf/OQn/OY3v+nU+UWCU+iVPJ3t+dWL0/jF8qlIkiSqNoV+w8Lp8QOyZEbGWLp492A0gtCDrJxg4ttgLJYevVVNTQ3Tpk3j0qVL2NjY0NjYyA9/+EPg/lT1H/3oR6xatYo//OEPBo5UEB5Np9Oxc+dOjhw5wsSJE5k/f36nz5WRkUFxcTEvvfSSqN7sIjqdjrNnz3Lq1CmcnZ1Zt24dDg4OfPHFF7S2trJ27VpaWlrYuHEjTU1NLFu2jODgYAAOHz7MzZs3WbZsGQMGPH4tpdPpyM7ORqlUcvPmTVQqFQMGDCAhIYHw8PAeTyiqVCrS09NRKBTcu3cPa2trIiMjiYqKEslNoddTqVQcOXKkzW0ZGRnI5fIOV3GamJgwYcIE0df2OffWW29x4MAB1q9fz4QJE/Sv8QAWFhYsWrSIpKQkkeAU+icjIxkgFpVC/2HvG4GlkxfN1cUg6dock3RaPKNnGCgyQehGMiOY9DZYOxs6EuEJfvWrX5GRkcGRI0eIiorCzc1Nf8zY2JiFCxdy8OBBkeAUeiVJkjh06BAHDhwgMDCQV155pdNvpLVaLSdOnCAoKEg/2VV4NuXl5ezdu5fi4mJGjx7NuHHj0Gg0bNiwgcbGRtasWUNubi4HDx7E1dWVlStX4ujoCNzfynjx4kVmzpz5yOETkiRRUFCAUqnkxo0bNDY24uLiQnx8PBERETg79+zfHkmSuHfvHqmpqWRkZKBWqwkMDGTJkiUMGjRIDKsS+gyFQkFWVhaS9O+ZAeXl5RQUFODv79+hc8XExLSr8lro37Zu3cr3vvc9XnnlFSorKx86HhISws6dOzt9fpHgFARB6EEymREhi98i/cv/oLWhCpmR8b8WDRL+U17FbmCIoUMUhK4X913wFL1le7vExER++MMfMnny5EcuOgcNGsQXX3zR84EJQjucOHGCI0eOYG1tzYoVKzrddxMgLS2NyspKgwxH6m90Oh0XLlzg+PHjODo6sm7dOry8vFCpVHz55ZfU1NSwfPlyzp8/j0KhIDo6munTp+sH/WRmZnLkyBFGjRpFTEyM/rySJFFaWopSqSQ9PZ3a2lrs7OwYOnQoEREReHh49HjlbWNjI9euXSM1NZWKigocHBwYPXo0kZGRolpT6JO8vb1ZsWIFx44do6WlBTMzM2JjY/UfPrSXq6trp9uFCP1LWVnZE4f2GRsb09TU1OnziwSnIAhCD7Ny8Wb4Dz6lIvMcjaXZmFja4ho+Hgt7t6c/WBD6Gr+xMGSxoaMQ2qG2tvaJQy/UanWXTVAVhK508eJFkpOT0el0DyXCOkqtVnPy5El9kkzovIqKChITEyksLCQ+Pp4JEyZgamqKWq1m69atlJeXM3v2bA4cOEBVVRXz589n6NB/fxhWUFDA7t27CQsLY9KkSQBUVVWRnp6OUqmkvLwcKysrQkNDiYiIwMfHp8eTmjqdjqysLBQKBTdv3kQmkxESEsKMGTPw8/MT7Q2EPs3LywsvLy8sLCwAMDU1bfNvtD2MjIwYO3as+LcgAPeT5jdv3nzs8XPnzhEYGNjp84sEpyAIggEYmZjhFjEBIiYYOhRB6D4O3jD+VyAWtX1CQEAAqampjz3+9ddfExoa2oMRCcLTXbt2jcOHD2NiYsLAgQOZO3fuM72RvnTpEo2NjUyYIP4+d5ZOpyMlJYXk5GTs7e156aWX8Pa+32Nco9Gwbds2CgsLiYuLY//+/djY2LBu3bo2A8yqqqrYunUrAwYMYOLEiaSkpKBUKiksLMTMzIzBgwczZcoU/P39DbLlu7q6mrS0NBQKBXV1dbi7uzN16lQiIiJ6fHiRIPRmUVFRPd4mQui9li9fzp///GcWLFigbzny4G/2P/7xD3bs2MH/+3//r9PnFwlOQRAEQRC6nqkVTPk9mFkbOhKhndatW8cvf/lLxo8fz8SJE4H7i06VSsV//dd/cfjwYf7+978bOEpB+Ldbt26RlJSEi4sL5eXlTJ8+HXt7+06fr6WlhbNnzzJs2DCcnJy6MNLnR1VVFYmJiRQUFBAXF8fEiRP1w0i0Wi07d+4kJycHX19fzp49S2hoKHPnzsXc3Fx/jqamJj7//HMqKytxdHTkr3/9K0ZGRgQFBbFo0SIGDRrU4QEnXUGj0XDz5k1SU1PJzs7G3NyciIgIoqKiGDBggKhQE4RvcXZ2JjIy0tBhCL3Ib37zGy5evMjYsWMJCQlBJpPxk5/8hKqqKu7du8eMGTP4yU9+0unziwSnIAiCIAhdL+E34Ohr6CiEDnj99dfJyMhg2bJl+kEAy5cvp7KyEo1Gw6uvvsrLL79s2CAF4V9yc3PZuXMnvr6+FBcXExoaypAhQ57pnOfPn0ej0TBu3LguivL5IUkSly5d4tixY9jY2LBmzRp8ff/9N0Cn07Fnzx7S09OxtrYmLy+PadOmERcXp08Mtra2kpGRwfr168nLyyMqKgpzc3PmzJlDSEiIfptsTystLSU1NZXr16/T3NyMr68v8+bNIzQ0FDMzM4PEJAi9nYmJCRMnThRDtYQ2zMzMOHz4MJs3b2bXrl1otVpUKhVDhgzhd7/7HStXrnymD4tEglMQBEEQhK4Vsw7kow0dhdBBMpmMf/zjH6xevZpdu3Zx584ddDodAQEBLF68mLFjxxo6REEAoLi4mK1btzJw4ECMjY0xNjZm1qxZz/SmqKGhgQsXLhAXF4etrW0XRtv/VVdXk5SURG5uLrGxsUyaNKlN4k+SJJKSkjh37hwmJiY4OzuzaNEivL290Wq1ZGVloVQquXnzJmlpaWg0Gn7wgx8wYcIEbGxsDPKcVCoVSqUShUJBYWEh1tbWDBs2jKioqGcaYCUIz4v4+HgxNV14JJlMxosvvsiLL77Y5ecWCU5BEARBELpOQAJEdf2CReg5o0ePZvRokaAWeqfKykq+/PJLXFxcCAkJ4dChQyxduhRr62drh3H69GmMjY0ZNWpUF0Xa/0mSxJUrVzh69ChWVlasXr36oUFlkiRx4MAB9u3bh5WVFdHR0cyfP5+Kigr279/PjRs3aG5uxs3NDQsLCwICAlizZg2DBw82yPPJz89HoVCQkZGBRqMhKCiIpUuXEhQUJCrRBKGdAgMDDfJvWBBEglMQBEEQhK7hHAjjfimGCgmC0C3q6urYuHEjlpaWzJw5kw0bNhAVFfXMb6Srq6u5evUqEyZMwNLSsoui7d9qamrYt28f2dnZDB8+nMmTJ7fpown/rtzcuHEjbm5uTJgwAWtraz755BPq6+txcHBg+PDhhIeHk5uby6FDh3jhhRd6PDHS0NDAtWvXSE1N1ff9HDt2LEOHDsXOzq5HYxGEvs7BwYExY8aInrTCY509e5bPPvuM7OxsqqurkSSpzXGZTMa1a9c6dW6R4BQEQRAE4dmZ28KU34GpYXqkCZ0zZ86cDt1fJpORlJTUTdEIwuM1NTWxadMmAF588UUSExOxsLBg2rRpz3zuEydOYGlpSVxc3DOfq7+TJAmFQsGRI0ewsLBg5cqVBAQEPPK+O3bs4JNPPsHe3h43Nzd9/83w8HDCw8MZOHAgMpmMmzdvcvjwYUaOHElsbGyPPA+dTsfdu3dJTU3l9u3bGBkZERoayqxZs5DL5SI5IwidYGxs3GawmCB825///Gd+/vOfY2FhQXBwcJcP9BMJTkEQBEEQnl3CW2DnaegohA46cOAAFhYWeHh4PPQJ+qOIN/2CIahUKjZv3kxTUxNr164lMzOT3NxcVq9e/VDVYEeVlpaiVCqZMWOGeFP+FHV1dezbt4+7d+8SFRXF1KlTHzn4p6amht///vfs27cPV1dXhgwZQlRUFBEREfj5+WFkZKS/b2FhIbt37yYkJITJkyd3+3OoqqpCoVCQlpZGfX09Hh4eTJs2jYiICFG9KwjPKDY2FmdnZ0OHIfRif/zjHxk1ahT79+/H3t6+y88vEpyCIAiCIDybiEXgIyqf+iIvLy8KCwtxcXFh+fLlLF26FA8PD0OHJQh6Go2G7du3U1FRwerVq5EkieTkZEaMGPFQv8fOOH78OA4ODgwbNqwLou2fJEni2rVrHD58GFNTU1asWEFQUFCb+zQ1NZGRkUFaWhrbtm0jOzubkSNH8utf/5rBgwdjYvLw287q6mq2bNmCh4cH8+fP77YPUNRqNZmZmSgUCnJycrCwsCAiIoJhw4bh6Sk+mBOEruDh4UF4eLihwxB6uaamJlasWNEtyU0QCU5BEARBEJ6Fkz/EvmLoKIROKigo4NSpU2zZsoV3332Xn//854wbN44VK1awcOFCMU1aMCidTseePXvIz8/nxRdfxN3dnU8//RQHBwcmTpz4zOcvKCjg1q1bLFiwQAyQeYz6+nr279/P7du3GTp0KNOmTdNXOqpUKm7evEl6ejpZWVk0NjaSnp5OfX09v/zlL/nud7/72PM2NTWxefNmLCwsWLZsWbdUz5aUlJCamsr169dpaWlBLpczf/58QkNDRbWuIHRAa2srcP8Dp0cxNjZm3LhxYpeH8FQTJkxAqVR22/lFglMQBEEQhM4b81MwMTN0FMIzGDduHOPGjeNvf/sbBw8eZMuWLfzgBz/gtddeY/r06SxfvpzZs2c/81ZgQeiIB9O3b968yeLFi5HL5Zw8eZKSkhLWrVv3zAkqSZI4duwY7u7uouroESRJQqlUcujQIYyNjVm2bBnBwcFoNBoyMzNJT0/n1q1baDQafHx8CAoK4uTJkwD89Kc/ZfHixY89t0ajYdu2bTQ1NbFu3TqsrKy6LO6WlhaUSiWpqakUFxdjY2PD8OHDiYqKEltnBaETVCoVLS0twP1qaLVa/dDrb2hoaLdV5An9y4cffsiUKVP4n//5H1566SXRg1MQBEEQhF5i0DTwEImB/sLU1JS5c+cyd+5cGhoa2LNnDx9//DFLlizh7bff5q233jJ0iMJzJDk5mdTUVObNm8fgwYMpLCzk9OnTjB07lgEDBjzz+bOyssjLy2P58uWi6uhbGhoa9MnliIgIpk6dSmlpKUlJSWRmZtLS0oKHhwcTJkxg8ODBXLx4kQMHDlBTU8O6deuYN2/eY7+nkiSxd+9eioqKWLNmTZe8uZUkiby8PBQKBRkZGeh0OoKCghg/fjxBQUFten4KgtAxKSkp+spNnU5Hbm5umxYVZmZmosWH0G7e3t68+uqr/OxnP+OXv/wlFhYWD+2gkMlk1NbWdur8IsEpCIIgCELHGZuJren9lEql4siRIyQlJaFQKLCwsEAulxs6LOE5cu7cOc6ePcvUqVOJjIxErVazd+9ePDw8GDNmzDOf/0H15oPKQ+Hf0tPTOXjwIHC/urulpYWPP/6YhoYGnJyciIuLIzw8HFdXV2pqatixYwfp6enodDqWLFnyxOQmwLFjx7hx4waLFy9m4MCBzxRrfX09165dQ6FQUFlZiZOTE+PHj2fo0KGivYYgdIEH64FvysjIQC6XI5PJyMvLY9asWWKHh9Buv/3tb/n973+Pl5cXw4cP7/LKX5HgFARBEASh40JmgbXY7tdf6HQ6jh49ytatW0lMTKSpqYlJkybxj3/8g/nz52NtbW3oEIXnRGpqKkePHmXMmDHEx8cD96s5a2pqePXVV7ukV2ZGRgYlJSW89NJLonrzXxobGzl48CCXLl3C0tISGxsbTp06ha2tLREREYSHhzNgwAD99+vOnTvs2bOHuro6zM3NGTNmzFOTm5cvX+bcuXNMmzaNkJCQTsWp0+m4c+cOqamp3LlzByMjI8LCwpg9eza+vr7i5ykIXUihUJCVlYVWqwXu//srLy+noKAAjUZDSkoKCQkJBo5S6Es+/vhjZs6cSWJiYrdU14sEpyAIgiAIHWNkAkOXGzoKoQucP3+eLVu2sHPnTiorKxkxYgR/+MMfWLx4MS4uLoYOT3jOZGZmsn//foYPH65/05yTk8PFixeZOnUqrq6uz3wNrVbL8ePHGTRoED4+Ps98vv7g4sWLbNy4keLiYjw9PXFxcSEkJISIiAh8fHzavAnV6XScPHmS06dP4+DggImJCREREbzwwgtPfLN6+/ZtDh48yIgRIxgxYkSHY6ysrEShUJCWlkZDQwMDBgxg+vTpREREYGFh0annLQjCk3l7e7N48WL27dsHgJGREdHR0dja2nLu3Dnq6+s5ffo048ePF1WcQru0trYyc+bMbmsdIhKcgiAIgiB0jP94sHn2RINgeKNHj8bS0pIZM2awbNky/Vb0/Px88vPzH/kY0WtL6A7Z2dns2rWL0NBQZsyYgUwmo6WlhcTERORyeaeSYo+iUCiorq5myZIlXXK+vqqhoYGrV6+ybds2MjMzcXd3Z+HChQwfPpzAwMBHVso2Njaya9cucnNzCQ8P5+bNmwQGBrJw4cInVtYWFRWxc+dOBg8ezJQpU9odo1qt5saNGygUCnJzc7GwsGDIkCEMGzYMDw+PTj1vQRDaz8vLCxcXFywsLNDpdBgbG2Nra0t1dTWlpaVERkZy+/ZtLl261CXtQ4T+b9asWZw5c4ZXX321W84vEpyCIAiCIHRM+AuGjkDoQs3NzezevZs9e/Y88X6SJCGTyfRb1QShqxQWFrJt2zbkcnmbSsAjR47Q0tLy1K3P7aVWqzl16hTh4eG4u7s/8/n6mpaWFjIzM1EqlVy+fJnbt2/j5OTED3/4w6f20cvPz2fnzp3odDqmTJnCyZMnGThwIEuXLsXE5PFvKWtqatiyZQvu7u5PrfJ8oLi4mNTUVJRKJS0tLfj5+bFgwQIGDx780PRmQRC6z4MenNOnT6epqYnCwkKuX78OgLGxMV5eXuTm5nL48GFiY2NFFafwVP/5n//JkiVLeO2113j55Zfx8fF55AdknR1AJxKcgiAIgiC0n3MguIUaOgqhi3z++eeGDkF4zpWXl7N582bc3NxYsmSJ/o3OzZs3USgUzJ07FwcHhy651qVLl2hsbGTChAldcr6+QK1Wc/v2bZRKJXfu3EGlUlFXV4dKpWLZsmUsWLDgiQN5JEniwoULHDt2DG9vb8aMGcPu3btxc3Nj2bJlT0w4Njc38+WXX2JmZtau+yqVSlJTUykpKcHW1paYmBiioqK6ZNK6IAgd96AHZ0tLC0VFRbS2tup3d9jZ2XH79m3UajVZWVkoFIouq7QX+q/g4GAA0tLS+OSTTx57v85+mC4SnIIgCH2UtrWZMuVJ6u9lYmxmgUvoWOx8wkSDfeGZeXh4QEstHo+aKxMyC8TvWL+xevVqQ4cgPMdqa2vZtGkTNjY2rFixAjMzM+D+Vuj9+/cTHBxMZGRkl1yrpaWFs2fPEh0d3e8TZlqtluzsbNLT08nMzKS1tRUvLy+Cg4O5c+cONjY2TJs2jaFDhz5xzdDS0kJSUhKZmZmMGjWKIUOGsHHjRhwcHFixYsUTq7U0Gg3bt2+nsbGRdevWPXJQmSRJ5ObmkpqaSmZmJjqdjkGDBpGQkEBgYGC39WgTBKF9vL29WbFiBQAnTpxApVLR0NAAwNChQwkKCmpzX0F4mt/+9rfd+l5VJDgFQRD6oOaqYpSbfklrfSXIjJDJZBRf/Qq3IZMImv06Mpl4UyB03pUrV+DAT6Awte0BYzMInGSYoARB6FcaGxvZuHEjRkZGrFy5EktLS+B+0uvAgQNIksTs2bO77I3QuXPn0Gg0jB07tkvO19tIkkR+fj7p6elkZGTQ1NSEi4sLo0aNIjAwkCtXrqBQKAgMDGTOnDnY2dk98XwlJSXs2LGDpqYmli5dipubG59//jnW1tasXLnyiYN9JEkiKSmJe/fusWrVKpydndscr6urIy0tTd8P1dnZmQkTJjB06FBsbGy65PshCMKz8/LywsvLC7jfbqK5uVl/bPbs2Xh6ehoqNKGPevvtt7v1/CLBKQiC0Eup6iupyUpFknQ4+EVh4eCmP3Yr8b9pbai+/4WkQ5Lu/9+y68ew9wnHPXKyASIW+j3fkWD++K2MgiAI7aFSqfjyyy9RqVS89NJLbbZIX79+nczMTBYvXtxlya6GhgYuXrxIXFzcE7dj9zWSJFFSUkJ6ejrp6enU1tZib29PVFQUERERuLu7k52dzfbt21GpVMyZM4eoqKinJo0VCgVfffUVLi4uvPjiixgZGfH5559jZmbGqlWrsLKyeuLjjx8/Tnp6OosWLdJPqtdqtdy5c4fU1FTu3LmDiYkJYWFhzJ8/H29vb7H7RBD6EGNjY9zc3J5+R0HoYSLBKQiC0MtIkkTeiQ3cu7AbJN2/bpXhET2DgKmv0lxZSEPR7cc8+n4lp0hwCt0iqP3TbwVBEB5Fo9GwdetWqqurWbNmTZvt4rW1tRw8eJAhQ4YQGtp1vX5Pnz6NsbExo0aN6rJzGlJlZSXp6ekolUoqKiqwsrIiLCyMiIgIfbJQpVJx4MABrl69ir+/P3PmzHlqL1O1Ws3BgwdRKBQMGzaM6dOn09zczBdffIGRkRGrV69+atL56tWrnDlzhilTphAaGkpFRQUKhYJr167R0NCAl5cXM2fOJDw8/IlVoP1ZS0sLhYWFBAQEGDoUQegUDw+PRw6GEQRDEwlOod/JLirnpOI2zSo1IXIPRg8JxOwJ0x0FobcpvvoV987v/NatEiVXv8LMxgnbAUGPfNyD+6nqyrszPOF5ZWYD3rGGjkIQhD5Mp9Oxc+dO7t27x8qVK+/3+/2XB9uazc3NmTFjRpdds7q6mitXrpCQkKDfBt8X1dXVkZGRgVKppKioCDMzM0JCQpg2bRp+fn5tkg05OTkkJSXR1NTErFmziI6OfmqFZFVVFTt27KCiooJ58+YRGRmpbyOg0Wh46aWXnrqt/c6dO3z11VdERUVhaWnJZ599Rn5+PpaWlgwZMoRhw4Y9l9PrtVot9+7dIzs7m+zsbAoLC9HpdPz0pz/tVxXFwvNjwIABhg5BEB5JZH2EfkOSJP6x/yw7T1zF2EgGyEg8k4ansz3/8/2FuDmKBYTQ+0mSROH5XY89XpSylyFr/ufxJ5AZYens1Q2RCc8935Fg/PgJuIIgCE8iSRL79u3jzp07LF26FF9f3zbHL126RHZ29lP7O3bUiRMnsLa2Ji4ursvO2VOamprIzMxEqVSSl5eHsbExQUFBjB49mqCgoIemkre2tnLs2DEuXbqEXC5n9erVODo6PvU6mZmZJCYmYm1tzXe+8x3c3d1pbm5m48aNtLS0sHbt2qdWfxYVFfHpp5+i1WpJT09HoVDg7+/PwoULGTx4MCbPUbGBJEmUl5eTnZ1NVlYWeXl5tLa2Ymlpib+/P5GRkbi6uorkptBniQSn0Fs9P39phH7vdNoddp64CoBWJwH3mxKWVtfxh02H+OBHiw0YnSC0VZ2toPDCbhpKsjC1tMU9aioDYmYj6bRPrMDUtDRgbGqBg38UNTnXvrGF/V8kHQNi5nZz9MJzyW+MoSMQBKGPkiSJr7/+mrS0NF544QUGDRrU5nhFRQXHjh0jNja2S7ftlpaWolQqmTlz5kPJwN6qtbWVW7duoVQquXv3LpIk4e/vz9y5cxk8ePBjk795eXkkJibS0NDAjBkziImJeWrVplarJTk5mfPnzxMaGsqcOXOwsLCgpaWFTZs2UV9fz9q1ax8aEvRNTU1NnD9/no8++gi1Ws2YMWOIiYkhMjKyXcnV/qKurk5foZmdnU1DQwMmJib4+PgwduxYAgIC9BXLJ0+e5MiRI7z22mtPTRwLQm9jamqKq6urocMQhEcSCU6h30g6ew0jmQzdg2kr/6LTSWTkFJFXWoWvu9NjHi0IPack9TB3D34IMiOQdGia68hN/pyqO5cIW/YOMhNTJI360Q+WGWFsYc2gOT8lfcubNJXlIjMyRpIkkHR4j16K8+CRPfuEhP7PyAS8hhs6CkEQ+qizZ89y4cIFpk+fzpAhQ9oc0+l07N27Fzs7OyZNmtSl101OTsbR0ZGoqKguPW9X02q13L17F6VSya1bt1Cr1Xh7ezNt2jRCQ0Of2PdSrVaTnJxMSkoK3t7erFy5sk1f08epr6/XtwuYOnUqI0aMQCaT0drayubNm6mqqmL16tWPTGRIkkROTg6pqalcv36dq1ev4urqyo9//GOGDBmCkZHRM30/+gKVSkVubq4+oVleXo5MJsPDw4PIyEj8/f3x9vZuk1hvbW0lMTGRzMxMJk6ciL29vQGfgSB0joeHx3Pxb1zom0SCU+g3iipqHkpuflNJZa1IcAoGp1E1kf31J/e/aFN9KVGXn055xmncIyZSkvb1w9WZMiOcg+MxMbcCcyui1v2V6qyr1BVkYmxmgUvoGCydxJYRoRt4hIPZk6fmCoIgPEpxcTHJycmMHz/+kdvEz549S1FRES+//DJmZmZddt38/Hxu377NggULeuUwDJ1OR15eHkqlkhs3btDS0oK7uzvjxo0jPDy8XZV9+fn5JCUlUVtby5QpU4iLi2tX4iEnJ4ddu3ZhZGTEmjVr9JPO1Wo1W7dupaysjJUrV+Lp6dnmcbW1taSlpaFQKKipqcHJyQmdTseIESP43ve+h4uLS6e+F32BVqulsLBQv+38QR9NBwcHAgICGD9+PH5+fo+dMF9XV8fWrVuprKxkyZIlDB48uIefgSB0XmtrK3fu3MHDw6NN72RB6G1EglPokxqbVdzKL8XM1JgQX0+MjY3wdLGnur7psUlOD6cnN0YXhJ5QffcyOk3row/KZFRknCZ4/s+pzU+nubKQB60WkMkwt3XGf8or/767kTFOQbE4BYnBL0I3G9C7q58EQei9PD09Wbt2rT6J9k3FxcWcPHmSMWPGMHDgwC67piRJJCcn4+HhQXh4eJed91lJkkRRURFKpZKMjAzq6+txdHQkNjaW8PBw3Nzc2nUetVrNiRMnuHDhAl5eXixbtqxdyUVJkjh79izHjx/Hz8+PBQsWYG1tDdyfbr99+3bu3bvHiy++qP95aLVabt26hUKh4O7du5iYmBAeHk5UVBRXrlyhtraWVatW9bvkpiRJVFRUkJWVRXZ2Nrm5ufo+mn5+fsyYMQN/f/92VcsWFhaybds2jIyMeOmllzA3N+fo0aNMmjTpqW0EBKE3uHjxIqdPnyYmJob58+cbOhxBeCyR4BT6FJ1OYsPhC+w8cRW1RguAo60VP3hhAnNGDSU9u+ihxxgZyRjs44Gvx+P7BwlCT9G2tjz+oCShbW3C1MqeyJc/oPTaMSpvXUCSdDgFxuARNRUTi8dvUxOEbuMRYegIBEHow749UAjuJ9T27t2Lm5sb48aN69Lr3b17l7y8PFasWNErEkjl5eUolUrS09OpqqrCxsaG8PBwwsPD8fLy6lCM9+7dIzExkZqaGiZNmkR8fHy7qjabm5vZs2cPd+7cYdy4cYwbN07/OK1Wy+7du8nNzWX58uX4+vpSXl6OQqHg2rVrNDY2MnDgQGbPnk1YWBjm5uacOHGC69evs3Dhwkf+fPui+vr6Nn006+vrMTY21vfR9Pf37/D23PT0dBITE/H09GTx4sVkZmZy7NgxLC0tiY2NFdvUhV5PpVJx5MgRysvLycjIEMOxhF5NJDiFPmXz1ylsOXqpzW3V9U38buNX/Pf3XmDBuCh2n1Lop6hrdTpcHWz5j5XTDROwIHyLnXfo4w/KjLD3vd+bzNjMkgExsxkQM7uHIhOEx5DJwDXE0FEIgtDPHD9+nMrKSl555ZUu3UL+oHrT19eXwMDALjtvR9XU1JCenk56ejolJSVYWFgQGhrKrFmzkMvlHe5hp9FoOHnyJOfOnWPAgAG8+uqr7R70UVRUxI4dO1CpVKxYsYKgoCD9sQc9UG/fvs38+fOpra3l008/paCgACsrK4YMGcKwYcPaVJcqFApOnTrF5MmTe1WFbEepVCry8vL0Cc2ysjLgftXxkCFD8Pf3x8fHp1MDqiRJ4uTJk5w6dYqhQ4cycuRIdu3aRV5eHjExMUyaNAlzc/OufkqC0OVSUlK4ffs23t7eVFVVoVAoGDNGDJ4UeieR4BT6jGaVmh3/mpL+bTJkbD12hfe/9wKTYkI4mXqbJlUrYX4DGDM0EDMT8asu9A5WLt44B4+k8vbFtj02ZUYYm5rjGT3TcMEJwqPYDxT9NwVBeCY6na5NQi8vL48LFy4wadIk3N3du/RaDxKKL730Uo9XbzY2NpKRkUF6ejr5+fmYmpoSHBzM+PHjCQwMxKST69GioiISExOprKwkISGBUaNGtStBKkkSV65c4fDhw3h4eLBmzZo2vT0lSSIpKYmLFy8il8vZt28farUaf39/Fi1aRHBw8EMx3717l/379zN8+HBGjuxbQw21Wi1FRUX6bef37t3T99H09/dn7Nix+Pn56bftd5ZarWbv3r3cuHGDhIQEjI2N+cc//oGtrS1r1qxBLpd3zRMShG72oHrTzMwMS0tLWltbOXz4MLGxsSJBL/RKIusj9Bn5pVW0tD56srROuj8pHSDQy41Ar/b1MBIEQxg076dkHfqIMuUJfZLTymUgg+a8gbl9+6oxBKHHOAc9/T6CIAiPUVdXxyeffEJkZCQxMTFYWlqSmJiIt7c38fHxXXotrVbLiRMnGDRo0CN7fnYHlUpFZmYm6enpZGdnAxAQEMALL7xAcHDwMyUBtFotp0+f5syZM7i7u/PKK6+0OyHc2trKgQMHuH79OrGxsUyZMqVNsrKxsZH169dz4sQJfHx8UKvVjBw5ksjIyMcOOCopKWHHjh0EBgYyY8aMXrH9/0ke9NF8UKGZm5uLSqXCwsICPz8/pk+fTkBAAI6Ojl32XOrq6ti2bRvl5eVMnjyZzMxMCgsLiYuLIyEhoUsHaQlCd1MoFGRlZdHS0kJRURGWlpZkZWWhUCgYMWKEocMThIeIBKfQZ1iYP3l7iIVZx7ePCIIhGJtaMGjOG8gT1tBUXoCJlS3Wbn69/o2C8Jxy7B+91QRBMAxjY2MiIyO5evUq58+fp7GxEUmS+NWvftXhbdpPo1AoqK6uZsmSJV163m/TaDTcvn2b9PR0bt++jUajwdfXlxkzZhAaGvrYSdodUVJSwt69eykvL2fcuHGMHj263Vv5y8vL2bFjB7W1tSxYsICIiPt9lCVJIjs7m6tXr3LgwAEKCgqYNm0aCxcuxM/P74k/j9raWjZv3oyzszMLFy7s8p9dV2loaGjTR7Ourg5jY2O8vb0ZPXo0/v7+eHp6dkv8D4YJyWQywsLCOH78OI6Ojrz00kt4e3t3+fUEobt5e3uzYsUKAE6cOEFAQAA+Pj7i91notUSCU+i1JEmisLyGllY1Pu5O+Lg54uvuRH5ZNdK3JqUbGcmYGD3YQJEKQueY2ThhZvP06ZuCYFD2YhErCELnWVtbM3nyZMaNG8f+/fv59NNPGThwIDt27CAuLo6IiIhO9Tj8NrVazalTp4iIiOjybe9wf5t9Tk4OSqWSzMxMVCoVnp6eJCQkEB4ejp2dXZdcR6vVcvbsWU6dOoWrqyuvvPIKHh4e7X58eno6+/btw97enu985zu4urpSW1uLQqFAoVBQW1tLTU0NpqamvP322+0a8NTS0sKWLVswNjZmxYoVvaoKsbW1Vd9HMysrS99H08PDg/DwcH0fze6O+cEwISsrK4yNjbl27RojR45k/PjxXfL7LQiG4OXlhZeXF3C/r/DYsWP7zVAxoX8SCU6hV7qedY+/7jxOXmkVANYW5iybFMPrixL45cd70el0aHX3k5xGMhluDrYsmxxjyJAFQRD6JzsvQ0cgCEI/oNFoyMnJYfny5cTHx3Pp0iX279/PsWPHiI6OZvjw4c80UTolJYXGxkYmTJjQZTFLksS9e/dQKpVkZGTQ2NiIs7Mz8fHxhIeH4+Li0mXXAigrK2Pv3r2UlpYyevRoxo0b1+6qTY1Gw9dff82lS5eIiIhg+vTp5OTkcPjwYbKzszE1NSU8PBy1Ws3169dZtGgRo0aNeup5tVqtvhr05ZdfxsbG5lmf5jPR6XQUFhbqKzTv3buHVqvF3t4ef39/xowZg7+//zP30WwvSZI4deoUx48fx8TEhNraWtzd3Vm3bp0+MSQI/UVXVKcLQncSCU6h17lbWMYv1+9Bq/v3AJbGFhX/PHCWtTNG8tEby9lx/ApXb+djZmJMQvRgFoyLws7a0oBRC4Ig9FN2noaOQBCEPk6SJL766iu0Wi1z587F1taWgIAAqqqquHz5MpcuXeLcuXOEhIQQFxeHt7d3h9q2NDc3c/bsWaKjo3F0dHzmWMvKylAqlaSnp1NTU4OdnR1Dhw4lIiICDw+PLm8po9PpOHfuHCdPnsTJyYl169YxYMCAdj++pqaGnTt3UlJSQnx8PJIk8be//Y2mpia8vb2ZM2cOYWFhpKamcvjwYSZMmNCu5KYkSezfv5+8vDxWrlzZ7qntXUmSJCorK/UJzZycHH0fTblczrRp0/D398fJyanHW/2o1WoSExO5ePEiAHZ2dowfP54xY8Z0eqCUIPRmFhYWhg5BEJ5IvPIKvc62Y1eQJIlv7UIHYOuxy8wfG8UvVkzt+cAEQRCeN6ZWYN412y4FQXh+paenk5GRwcKFC7G1tdXf7uTkxNSpUxk/fjzXrl0jJSWFzz77DE9PT+Li4ggPD29Xouj8+fNotVrGjh3b6Rirq6tRKpUolUrKy8uxtLQkLCyMiIgIfHx8ui15Vl5eTmJiIkVFRYwaNYrx48d3KDl29+5dtm/fTlVVFR4eHly4cAErKysiIyOJiorSJyUfTFN/UBnaHqdOnSItLY0FCxb06OTvhoYGcnJy9EnN2tpafR/NUaNG4e/vz4ABAwzaB7Suro7Nmzdz+fJlrK2tCQsLY968eR1qJyAIfY1IcAq9nUhwCr1O6u18/fbzb2tpVZNVWEa4v9jyIQiC0O1s3EAMvxIE4RnU1dXx1VdfER4eTnh4+CPvY25uTmxsLDExMWRlZZGSkkJiYiJHjx5l+PDhDB8+vE1i9Jvq6+u5ePEiI0aMeOx9Hqe+vp6MjAyUSiWFhYWYmZkxePBgJk+eTEBAQLu3h3eGTqfjwoULnDhxAgcHB15++WUGDhzY7sdrtVp27dpFYmIiWq2WoKAgBgwYQFRUFMHBwW1iT0tL48CBA8TFxTFx4sR2JWvT0tI4efIkEydO1A8p6i6tra3k5+eTlZVFdnY2paWlALi7uxMaGoq/vz++vr69pvdnUVER69evJyMjg8DAQGbNmsWoUaO69fdFEAzNyMhIVCYLvV6f/Q1dv34969evJzc3F4CwsDB++9vfMn369Mc+ZufOnbz11lvk5uYSFBTE+++/z4wZM3ooYqG9TE2evDh42nFBEAShi9i4GToCQWgXsS7svYyNjQkJCWHKlClPva9MJiMwMJDAwEAqKyu5dOkSFy5c4MyZM4SFhREXF/dQEvD06dOYmJi0a8s13N/OnpmZiVKpJDc3FyMjI4KCgli4cCHBwcE9MhCmoqKCpKQk7t27R3x8PBMmTGj3dRsbG7l48SIbNmwgPz+fiIgIFixYQFRU1CN7mKanp5OUlMSwYcOYNm1au5Kb2dnZ7Nu3j+joaEaPHt3h5/c0Op2OoqIifYVmQUEBWq0WOzs7/P39GT16NH5+fgbv9/koaWlpfPDBB1RVVTF58mSWLFmCm5v4Wyn0f+bm5j3eBkIQOqrPJjgHDhzI//t//4+goCAkSWLDhg3MnTsXhUJBWFjYQ/c/f/48y5Yt47333mPWrFls2bKFefPmkZqa+thPkwXDmBA1iL1n0tA9oorTxd6GwIFiESEIgtAjrLp2gIYgdBexLuy9rK2tmTt3bocf5+zszPTp05kwYQJpaWlcunSJf/7zn3h5eREXF0dYWBi1tbVcvXqViRMnPnHrpFqt5tatW6Snp3Pnzh10Oh1+fn7Mnj2bkJAQLC17po+7JElcvHiR5ORk7OzsWLt2LT4+Pk99nE6nIysrC4VCwcWLF7lx4waurq688847jB079rFJh5s3b7Jnzx4iIiKYNWtWu5ITpaWlbN++HX9/f2bOnNklCQ1JkqiqqmrTR7OlpQVzc3P8/PyYOnUq/v7+ODs799oEiiRJbN++nQ0bNuDg4MBPf/pTxowZY9Bt8oLQk3pLBbUgPEmfTXDOnj27zde///3vWb9+PRcvXnzkQvYvf/kL06ZN4+c//zkA7777LkePHuVvf/sbH3/8cY/ELLTP4oThnEq7Q1V9oz7JaSSTISHxgwXjMRYLCUEQhJ5h0/MDJQShM8S6sP+ysLBgxIgRxMXFcefOHVJSUtizZw9ff/01TU1NmJiYEBsb+9DjtFotWVlZpKenc/PmTVpbWxk4cCCTJ08mLCysw9vZn1VVVRVJSUnk5eUxYsQIJk6c+NSqzZqaGhQKBQqFgtraWpqammhoaGDu3LmsWLECO7vH90i+e/cuO3fuZPDgwcybN69dibgHfSUdHR1ZtGjRMyXvGhsbycnJ0W87r62txcjICG9vb+Lj4/H398fLy6tPJAgbGxv53e9+x4ULF4iLi+NnP/uZQQYuCYIhmZubGzoEQXiqPpvg/CatVsvOnTtpbGwkPj7+kfe5cOECb7zxRpvbpk6dSmJi4hPPrVKpUKlU+q8bGhqeOV7hyZzsrPnwJ0vZcvQSJ1JvoVJriPD3YvnkWIYGtr83kSAIgvCMRAWn0AeJdWH/JJPJGDRoEIMGDaK8vJwjR47w+eefExQUpO8v6enpSV5enn6oUXNzM66urowePZrw8HCcnJx6PG5Jkrh8+TJHjx7FxsaGNWvWPHFgj0aj4ebNm6SmppKTk4OZmRnBwcGUlZVRXFzM5MmTmThx4hP7Pebm5rJt2zYCAgJYsGBBu5KIKpWKLVu2IJPJWLFiRYeTGWq1mry8PH2VZklJCQBubm6EhITg7++PXC7vc1Vg165d4/e//z3V1dV897vfZfHixX0iKSsIXa2v/dsVnk99OsGpVCqJj4+npaUFGxsb9u7dS2ho6CPvW1JSgru7e5vb3N3d9X98H+e9997jnXfe6bKYhfZxsbfhRwsT+NHCBEOHIgiC8PyyFhUqQt8h1oXPD1dXV4yMjJg+fTqxsbGcOHGCPXv20NrairOzM4GBgURHRxMREYGbm5vBtj1XV1eTlJREbm4uMTExTJ48+bFJgtLSUlJTU7l+/TrNzc34+Pgwd+5cnJ2dSUxMpKGhgaVLlxISEvLEaxYUFLBlyxZ8fHxYvHhxuwbfPPhQoLq6mpdffrld1a06nY7i4mJ9QjM/Px+tVoutrS0BAQGMHDkSPz+/Hq+U7SoqlYqtW7eyZcsWnJ2d+ctf/vLY1xNBeB6ICk6hL+jTCc7g4GDS0tKora1l165drF69mlOnTnXpH59f//rXbT7hT0tLY9y4cV12fkEQBEHotUSCU+hDxLrw+ZGfn09aWhohISFcu3YNnU6Hh4cHWq0WSZLQ6XSYmZlhY2NjkOSmJElcvXqVr7/+GktLS1atWoW/v/9D91OpVCiVShQKBYWFhVhbWzNs2DCioqJwcXEhLS2NDRs24OLiwquvvvrUCtSioiK+/PJLPD09Wbp0absmHkuSxFdffUVOTg4vvvjiYwfmSJJEdXU12dnZZGVltemjKZfLmTJlCv7+/ri4uPTaPprtdffuXf7+97+jUCiIi4vjF7/4xRPbAQjC80AkOIW+oE8nOM3MzAgMDAQgOjqay5cv85e//IVPPvnkoft6eHhQWlra5rbS0lI8PDyeeA1zc/M2/5h74zQ/QRD6psayXBpLszGxtMPBLxIj4z79kiz0R1Y9v51TEDpLrAv7v9raWtLT0/nkk0+oqqrCwcGB0NBQZsyYgZ+fH0ZGRpSWlnLp0iVOnz7N6dOniYiIIC4u7qk/266MMSkpiezsbKKjo5kyZUqb3xlJkigoKCA1NZWMjAw0Gg1BQUEsWbKEQYMGYWxsjFqtZt++faSmphIVFcWMGTOe2q+ztLSUTZs24erqyvLly9u9nfTMmTOkpqYyf/58/Pz82hxramrSV2hmZ2dTU1ODkZERAwcOZMSIEfo+mu2pEu0LmpubOXz4MElJSdTW1rJy5UqWLVvW7gn3gtCfiQSn0Bf0q3fTOp2uTV+kb4qPjyc5OZkf//jH+tuOHj362N5MgiAI3UXdXM/N3e9Rm3tNf5uplT2D5v0cR/8oA0YmCN8gMwYLB0NHIQidJtaF/UNTUxM3btxAqVSSl5dHbW0tzc3NfP/732fy5MkPVSm6u7sze/ZsJk6cSGpqKpcvX0ahUODr60tcXByDBw/ulh6KkiShUCg4cuQI5ubmrFy5koCAAP3xhoYGrl27hkKhoKKiAkdHR8aMGUNkZGSb6sCqqip27NhBRUUFc+fOJSrq6euCiooKNm7ciL29fYf6Z16/fp3jx48zYcIEhg4dilqtJj8/X5/QLC4uBu63BAgODiYgIABfX99+mei4desWSUlJXLt2DRsbG3784x8/cUK9IDxvRA9OoS/oswnOX//610yfPh0fHx/q6+vZsmULJ0+e5MiRIwCsWrUKLy8v3nvvPQBef/11xo0bx5/+9CdmzpzJtm3buHLlCn//+98N+TQEQXgO3dz1B2rz09vcpm6u48b2txn26kdYOnkZKDJB+AZLBxCDFIQ+QqwL+xeVSsWtW7dQKpVkZWUB4O/vz7x58zhz5gxDhgxh2rRpT0w+WVlZMXr0aEaOHMnNmzdJSUlhx44d2NvbExMTQ3R0NJaWll0Sb11dHfv27ePu3btERUUxdepULCws0Ol03L17l9TUVG7fvo2RkREhISHMnDkTuVz+UPw3b94kMTERKysr1q1b166q0+rqajZu3IiVlRWrVq1q93PKyckhMTERX19fjIyM2LhxI/n5+Wg0GmxtbfH399dXafbVPprt0dTUxKFDh7hy5Qrl5eX4+/uzbNky0W9TEL5FVDILfUGfTXCWlZWxatUqiouLsbe3Z8iQIRw5coTJkycD93vzfPPT2ZEjR7JlyxbefPNN/uM//oOgoCASExMJDw831FMQnkKSJEoq62hRqxno6oipSf/Y/iI83xpKsqjNu/7wAUlC0ukounyAgKmv9nxggvBtonpT6EPEurDv02g03L17F6VSye3bt1Gr1fj4+DB9+nRCQ0OxtrZGqVRSWVnJvHnz2l1ZZ2RkRGhoKKGhoRQXF3Pp0iVOnjzJqVOnGDJkCLGxsQ8NnGovSZK4du0ahw8fxtTUlBUrVhAUFER1dTXnz59HoVBQX1+Ph4cH06ZNIyIi4pEJSJ1OR3JyMufOnSMkJIS5c+diYWHx1OvX1tayYcMGTE1NWbVqFVZWVk99THV1NZcvX+aLL75Aq9Wi0WgoLi5GLpczadIk/P39cXV1fS4qFzMyMjh48CA1NTW0trYSEhLC8uXL8fT07PZrq9VqkTAS+hTx+yr0BX02wfnpp58+8fjJkycfum3RokUsWrSomyISulJ6dhEf7j5OdlEFALZWFiyfHMuCcVHPxYJL6L8aiu8+/qCko77wZs8FIwhPYiEGKgh9h1gX9k06nY7c3FyUSiWZmZm0tLTg4eHB+PHjCQsLw8HBQX9frVbL8ePHCQ4Oxtvbu1PX8/T0ZO7cuUyaNInU1FQuXbrE1atX8fPzIy4ujkGDBrV7+3p9fT0HDhzg1q1bDB06lEmTJpGbm8uGDRvIycnB3NyciIgIhg0bhqen52PXr/X19ezatYuCggKmTJlCfHx8u9a69fX1bNiwAbhfofy4KsumpiZycnL0285LSkpQKBS4uLiwevVqQkJC+lUfzfZoaGjg4MGD3LhxA1tbW0xMTBg8eDBLly7ttmpVlUpFfn4+ubm55ObmUlpays9//vN+ud1f6J9EglPoC/psglPov3KKKvjF+t1otDr9bfVNLXySdBqNVsvSiTEGjE4Qno2JxRMGUsiMMLUSSSWhlzATw1MEQeh6kiRRWFiIUqkkIyODhoYGnJyciIuLIzw8HFdX10c+LjU1lZqaGpYuXfrMMVhbWzNmzBhGjhxJZmYmKSkpbNu2DQcHB2JjY4mKinrsVm9JkkhPT+fgwYMYGxszceJE6uvr+b//+z9aWlrw9fVl/vz5hIaGPjUhkJOTw+7du5HJZKxZswYfH592xd/Y2MjGjRvRaDSsXbsWe3t7/TGNRvNQH01JknBxccHPz4+amhomTJjA9773veduMrgkSSiVSg4dOoRMJkMul5OTk0NERARz587t0gROS0tLm4Tmg5+Dra0tcrmcqChRtCH0Lc/ThyBC3yUSnEKvs/XYZXQ6HZIkPXRsy9FLzBsTiYWZ+ARJ6JscA4djbG6FVtX08EFJh1vExJ4PShAeRSQ4BUHoQmVlZSiVStLT06mursbW1paIiAjCw8MZMGDAE5M9arWaU6dOERER0ent5I9ibGxMeHg44eHhFBYWcunSJZKTkzlx4gSRkZHExsa2Sbg2NDTw1VdfoVQqsbW1xcLCguTkZGxsbBg+fDhRUVE4Ozs/9bqSJHH27FmOHz+OXC5nwYIF2Ni07zW3ubmZTZs20dzczJo1a3BwcKC4uJjs7GyysrL0fTRtbGzw9/cnNjYWf39/bGxs2Lp1K2ZmZqxZs+a5S27W1dXx1VdfcevWLUJCQlCr1dy9e5eJEycyZsyYZ042Njc3t0lolpSUIEkSdnZ2yOVyhg8fjq+vL05OTiKxKfRJIsEp9AUiwSn0Oldu5aHVPZzcBGhWqbl7r4xwfzGEReibjE3NCZr9E27ufg9kMtBp7/9XknAePAqXkFGGDlEQ7jPtmuEbgiA83yoqKti5cyelpaVYWloSGhpKeHi4frhNe6SkpNDc3MyECRO6LU4vLy/mz5/P5MmTuXr1KpcvX+by5csEBAQQFxeHSqVi8+bNFBcX4+DggJmZGT4+PkyePJnAwMB2v/lvbm5m79693L59m7FjxzJ+/Ph2fx9UKhVffvklpaWljBw5khMnTpCTk0NTUxNmZmb4+voyceJE/P39cXNz0yfSJEniwIEDZGVlsWLFii5NEvd2kiSRlpbGkSNHMDExYfbs2aSmplJWVsbixYs7PUyoqamJvLw88vLy9FvOJUnC3t4euVxObGwsvr6+ODo6ioSm0C+093VKEAxJJDiFXsfU+Mkvnibi0yOhj3MZPJLIdX+h6NJ+GopvY2plj/vQybiGjUVmJH6/hV7CRPQFEwTh2dnb2+Ph4UFCQkKHEoEPNDc3c/bsWaKjo3F0dOymKP/NxsaGcePGMXr0aDIyMjh69CivvvoqJSUleHp6MnnyZEaPHk1kZGSH+zUWFRWxY8cOVCoVy5cvZ9CgQe16XHNzM7du3eKLL74gKyuLQYMGcf78eby8vIiJicHf35+BAwc+9nt77tw5rl69yty5cwkICOhQzH1ZbW0t+/btIysri8jISIYMGUJiYiIAL730UoeGCTU2Nj6U0ARwcHBALpczYsQI5HJ5m76xgtCfiASn0BeIBKfQ64yLGkTS2WvoHlHF6WxnTZC3mwGiEoSuZePuz6DZrxs6DEF4PGMzQ0cgCEI/YGpqyvz58zv9+HPnzqHVahk7dmwXRvVkOp2Ou3fv8vXXX/P111+j1WoZMWIEDg4O6HQ6GhoaaG1tbff5JEni6tWrHDp0CHd3d/3W8sfRaDQUFBTo+2gWFBSgVCrRaDQsWrSI2NhY5HJ5uyatK5VKjh07xrhx44iKimp3zH3Zg+/3119/jYWFBStWrECtVrN161bc3NzaNUyooaFBn8zMy8ujrKwMACcnJ3x9fRk5ciRyubxN/1NB6M9EglPoC0SCU+h1FicM55TiNjWNzfokp5FMhoTE918Yj7F4cRUEQeh+RmKJIAiCYdXX15OSksKIESPa3aPyWVRWVqJQKLh8+TIKhYKmpibGjRvHq6++iouLC/X19Vy5coUrV66QkpJCUFAQcXFxBAQEPHYbcmtrKwcOHOD69evExMQwdepUTEzavr5KkkRpaSlZWVlkZ2eTn5+PWq3G2toaX19fysvLCQsLY926dcjl8nY/n9zcXBITExk6dCjjx49/hu9M31FdXc2+ffvIyckhOjqaSZMmcfnyZY4fP054ePhjhwnV19e3SWiWl5cD4OzsjFwuZ/To0cjl8ueud6kgPCBaLQh9gXj3IvQ6LvY2/O2NZXx5JIUTqbdo1WgI8xvAiilxDBvUvumSgiAIwjMSCU5BEAzs9OnTmJiYMGpU9/WnVqvVZGZmkpqaSm5uLvX19dTU1BAYGMjSpUuJiIjQv7G3tbVlwoQJjBkzhvT0dFJSUvjyyy9xcXEhNjaWoUOHYm7+7/YeFRUV7Nixg+rqahYsWEBERIT+WE1Njb5CMzs7m6amJkxNTfH19SUhIQF/f39cXFzYvXs3Wq2WtWvXdii5WV5ezrZt2/Dx8WHOnDn9PjkhSRIpKSkkJydjbW3NqlWr8Pb2Zt++fSiVSiZMmMDYsWP134e6ujp9QjM3N5fKykoAXFxckMvljB07Frlc3uE2BILQX/X31xChfxDvXoReydXBlp8smcRPlkwydCiCIAjPJ5HgFATBgKqqqrh69SoTJ05s11bsjiouLiY1NRWlUklLSwsDBw7EwcEBrVZLdHQ0s2fPfmxyy8TEhMjISIYOHUpBQQEpKSkcPnyY5ORkhg0bRkxMDEVFRezbtw87OzteeeUVbGxsyMzM1Cc0KysrkclkeHl5MXz4cH0fzQfVnTqdjsTERG7evMmSJUs61DuzoaGBzZs3Y2dnx5IlS/r99OPKykqSkpLIz88nNjaWSZMmoVKp+OKLL/TDhLy8vFAqlfqEZlVVFQCurq74+/uTkJCAr69vj1QKC4IgCN1DvHsRuoWjrVWb/wqCIAh9jGgHIgiCAZ04cQJra2tiY2O77JzNzc0olUpSU1MpKSnB1taWmJgY7OzsOH36NK2trSxYsIChQ4e2q1pJJpPh4+ODj48PtbW1XLlyhcuXL7Np0yaampqIjIwkICCAxMREioqKkCQJZ2dn/P39mTRpEnK5HEtLy4fO+2DquVKpZOHChQQHB7f7Oba2trJlyxa0Wi0rVqzoluRwb6HT6bhw4QInTpzAzs6OtWvX4uvrS3FxMZ999hlVVVWEh4dz9OhRqqurAXBzcyMwMBC5XI6vry/W1tYGfhaC0DeICk6hLxAJTqFbfPTT5YYOQRAEQXgWMpHgFATBMEpKSlAqlcyePfuR/RI7QpIkcnNzSU1NJTMzE51Ox6BBg0hISGDgwIEcO3aMM2fOEBgYyJw5czrdY9HOzg5vb2+OHDlCcXExra2tJCcnk5qaSnx8PNOmTSM4OPipU7YlSeLQoUMoFArmzZtHWFhYu2PQ6XTs2rWLiooKXnrppX49AKesrIykpCSKioqIi4tj2LBhFBYWsm/fPg4fPoyRkRHh4eG0tLQwaNAgfULTykoUXwhCZ4gEp9AXiASnIAiCIAgPk/XvLY2CIPRex48fx8nJicjIyE6fo66ujmvXrpGamkp1dTXOzs5MmDCBoUOHYmNjQ1ZWFp988gktLS3MmTOHqKioDr+Br62t1W85v3TpEqmpqZiamjJnzhyGDx+OmZkZWVlZ3Lp1ixMnTlBbW0tsbOxjk5ySJHHs2DEuXbrE7NmzGTp0aLtjeZAYvXv3LsuXL8fDw6NDz6Wv0Gq1nD17lsOHD6PT6QgMDCQzM5MLFy6Qn59PeXk5kZGRLF26lMDAwEdWyAqCIAj9k0hwCoIgCILwMPFJfbf4v//7P/74xz9SUlLC0KFD+fDDD5+4BXfnzp289dZb5ObmEhQUxPvvv8+MGTN6MGJB6Fl5eXncvn2bhQsXdrh3pFar5c6dO6SmpnLnzh1MTEwIDQ1l3rx5+Pj4IJPJUKlUHDhwgCtXruDv78+cOXOeWlX5QEtLC7m5uWRnZ5OVlUVlZSWSJNHQ0EBFRQUzZszg5ZdfblMFGhMTQ01NDZcvX+bq1atcuHCBwYMHExcXh6+vb5uk6qlTpzh37hzTpk0jOjq6Q8/9/PnzXL58mTlz5hAYGNihx/Z2kiRRVVXFlStX2LNnD7m5ubi7u+Pn54ckSQQHB3P37l00Gg1r1qxpM0xIEAShNxPrwq4lEpyCIAiCIDxMVHB2ue3bt/PGG2/w8ccfExcXxwcffMDUqVO5desWbm5uD93//PnzLFu2jPfee49Zs2axZcsW5s2bR2pqKuHh4QZ4BoLQvSRJIjk5GQ8Pjw5tza6srCQ1NZVr167R0NDAgAEDmDlzJuHh4W16UObk5JCUlERTUxMzZ85k+PDhT0yEabVaCgoK9FWahYWFSJKEk5MT/v7+xMfHc+3aNe7du8ecOXMem1hzcHBg8uTJjBs3juvXr5OSksIXX3yBu7s7cXFxREREkJKSwsmTJ5k0aRIjRozo0PctIyODo0ePMnbsWIYNG9ahx/ZGkiRRUVGhn3KenZ1NRkYGBQUFeHp6snbtWoYNG4aPjw8ajYZt27ZRX1/P8uXLO/R7IwiCYEhiXdj1ZJIkSYYOoi9JTU0lOjqaq1ev9osFhCAIHXfpL6tora/EzNaZ2Nc3GjocQege9SVg2z+3OHaVjq4J4uLiiImJ4W9/+xtwv1+et7c3P/zhD/nVr3710P2XLFlCY2MjBw4c0N82YsQIIiMj+fjjj7vuiQidJtaFXev27dts2bKFF1988alViK2trdy4cQOFQkFeXh6WlpYMGTKEYcOG4e7u/tB9H2z9lsvlzJ07F0dHx4fOKUkSZWVl+oRmbm4uarUaKysr/Pz8CAgIwM/PD0dHRwoKCti5cycajYaFCxfi7+/f7ucpSRI5OTmkpKRw+/ZtysrKqK+vZ9GiRcyePbvd5wHIz89nw4YNhIWFMX/+/D5ZuShJEuXl5eTm5uqTmo2NjRgZGWFpaUlBQQEymYyZM2cyceJEfWVvcXExW7duRZIkli1bxoABAwz8TASh/2ptbcXMzMzQYfRanVkPiHVh1xMVnEKvp9Vq0el0hg5DEPQefCwkSaBWqw0bjCB0F40WxO/3E2k0GgAaGhqoq6vT325ubo65uXmb+7a2tnL16lV+/etf628zMjJi0qRJXLhw4ZHnv3DhAm+88Uab26ZOnUpiYmIXPQNB6D0eVG/K5XICAgIee5+ioiIUCgVKpRKVSoW/vz8LFy5k8ODBmJg8/NYmLy+PxMREGhoamD59OrGxsW2SgHV1dfqEZnZ2Ng0NDZiYmODr68v48ePx9/fHw8ND/xhJkrh48SJff/01Xl5eLFq0qMODiWQyGf7+/vj7+3P8+HE+++wzzMzMSE1NpaWlhbi4OLy9vZ+arKyoqGDr1q14e3szZ86cPpPcfJBI/mZCs6mpCWNjYwYMGMCwYcMYOHCgvrdpWFgYc+fObdNXNDMzkz179uDq6sqyZcuwtbU14DMSnlfP0/tUtVrdZ15jDKEja0IQ68LuIhKcQq/37rvv8s477xg6DEHQ2/2zCbjZW1JYeI8R4pNMQXjujRs3rs3X//mf/8nbb7/d5raKigq0Wu1DlWXu7u7cvHnzkectKSl55P1LSkqePWhB6GXS09MpLS3l5ZdffuhNdHNzM9evXyc1NZXS0lLs7OyIi4sjKirqkZWYcP/NeHJyMikpKXh7e7Ny5UqcnJxoaWkhLy+PrKwssrOzqaioQCaT4enpSWRkJP7+/vj4+DwyWapSqUhKSuLGjRvEx8czadKkDvcJ/abr169z5swZli1bxqRJk7h27RqXLl3is88+w9PTk7i4OMLDwx8ZS0NDA5s3b8bGxoYlS5Y88j69hSRJlJaWkpubq09qNjc3Y2xszMCBAxk+fDhyuZyBAwdiZmZGQUEBSUlJVFdXk5CQwMiRI/XfZ0mSOHPmDMePHycsLIx58+Zhampq4GcoPK/E+1Th29qzJgSxLuwuvfcvoSD8y1tvvcVvfvObTj++VaOhrrEFO2sLzHrx4k/oO1L/72XUDZV4eQ2ktbXV0OEIQvdorARrZ0NH0aspFAri4uI4depUm2nPj/qkXhCEx9NqtRw/fpzg4GC8vb2Bf2/jTk1N5ebNm+h0OoKDg5k0aRIBAQEYGRk99nwFBQUkJiZSW1vLxIkT8fLy4tq1a/o+mjqdDkdHR/z9/UlISEAul2NlZfXEGEtLS9mxYwcNDQ0sXryY0NDQZ3rON27cYO/evURGRjJjxgxkMhmxsbHExMSQlZVFSkoKiYmJHD16lOjoaGJiYvRVimq1mq1bt6JWq1m9enWvmxSu0+naJDTz8/Npbm7GxMSEgQMHEhsbq09ofjM5qVarOXLkCBcvXsTLy4vvfve7uLq66o9rNBr27dvH9evXGT9+POPGjRMVZYJBPev71L5Ep9M98XX3eSfWhL2DyPYIvZ6xsXGnPh1vaVXz+Vfn+eqCEpVag7mpCdNHhPPSzFFYmotPeoXOe7CWlskQVQNC/2VmBuL3+4keVEzZ2Ng8dYuqi4sLxsbGlJaWtrm9tLS0zbbLb/Lw8OjQ/QWhr0pNTaWmpoZly5ZRV1eHQqFAoVBQU1ODi4sLCQkJDBkyBBsbmyeeR6PRcPz4cY4ePYqJiQlyuZzTp0/T2tqKpaUl/v7++irNx1V+Psq1a9c4cOAATk5OvPLKKzg7P9uHP7dv32bXrl2Eh4cze/bsNkk6mUxGYGAggYGBVFZWcunSJS5evMjZs2cJCwsjJiaG8+fPU15ezpo1a9o9Ab476XQ6iouL9dvN8/PzaWlpwcTEBG9vb0aMGIGvry8DBw58bKVpbm4u+/bto66ujsmTJzNixIg2yZSGhga2bdtGSUkJixYtEsOEhF6hs+9Thf6nI2tCEOvC7iISnEKf1qxqpaSyDjtrS5ztrfW363QSb/1zH9fv3kP3r4aJKrWGfWevkVVYzv98fyFGRuITX0EQhMeSiU/pu5KZmRnR0dEkJyczb9484H5SIDk5mR/84AePfEx8fDzJycn8+Mc/1t929OhR4uPjeyBiQegZra2tnDhxAicnJ44ePcrdu3cxMTEhPDycqKiodvWirKurIyUlhe3bt1NQUICXlxd+fn5YWloyduxYfR/NjlYfaTQaDh06xNWrV4mMjGTmzJnP/MFmVlYW27dvJzg4mHnz5j0xJmdnZ6ZPn05CQgJpaWlcvHiRPXv2UF9fz3e+852Htir2FK1W+1BCU6VSYWpqire3NyNHjsTX1xcvL6+nbp1XqVQcO3aMy5cv4+vry4oVKx5KIJeUlLB161Z0Oh1r167Fy8urO5+eIAhCtxPrwu4hEpxCn6TWaPn0q3McOHcdlfp+Q9+oIG9+vHgiA1wcSLtTQNqdgocep5MklNmFXL2VR0yIvIejFvoLMxvHNv8VhH5JbPvrcm+88QarV69m+PDhxMbG8sEHH9DY2MjatWsBWLVqFV5eXrz33nsAvP7664wbN44//elPzJw5k23btnHlyhX+/ve/G/JpCEKXqaioYMOGDSQnJxMZGYmlpSWzZs0iPDz8idv6VCoVubm5ZGdnc+fOHa5cuaJPbK5bt45hw4bh4+PzTMnI6upqduzYQXl5OXPmzCEqKuqZt0Pn5eWxbds2/WCk9lZ+mZubExcXh1ar5e7duwwaNIirV69y69Ythg8fzvDhw59a3fostFotRUVF+v6Z+fn5tLa2Ympqio+PD6NHj0YulzNgwIAOVbNlZWWxf/9+GhsbHzkACtoOE1q6dGmHBzoJgiD0VmJd2PVEglPok/607SjHU2/qp1kDXMu6x4//uoN//nIllzJzMDYyQvuIqXbGRkZcyswVCU6h0yJf/ouhQxCEHiASnF1tyZIllJeX89vf/paSkhIiIyM5fPiwvgorPz+/TTXXyJEj2bJlC2+++Sb/8R//QVBQEImJiYSHhxvqKQhChzU0NLB3715sbGywtrbGzMyMkpISsrOzKSkp4caNG4waNYrvf//7uLm5PfIcWq2WwsJC/aTze/fuodPpkMlklJSUYG9vz8qVK5k4cWKX9Ii7desWe/fuxdLSkpdffhlPT89nPue9e/fYvHkzAwcOZPHixR3e1nrjxg2OHj3K3LlzmTRpEuXl5Vy6dIlz585x5swZwsPDiYuLY8CAAc8c64Pv9zcTmmq1GjMzM3x8fBg7dixyuRxPT8/OtZFqaeHrr78mNTUVPz8/Vq9e/VDLAEmSOHv2LMnJyWKYkCAI/ZJYF3Y9mSR9M0UkPE1qairR0dFcvXqVYcOGGTqcfq9VrUGt0WJlYab/RPdeWTVr39vwyPvLZDLWzointrGFxNNpj01wzh41hO+/ML47QxcEQejbVPVgbmvoKHo1sSYQxO/A09XW1nL48GEKCgq4ffs2eXl5qFQqHB0d0Wq11NfXEx8fj52dHdbW1lhbW2NlZYVWq6W2tpbKykoqKysBsLOzY9CgQQQGBlJaWkpaWhoeHh7MmzevS7Zr63Q6jh8/ztmzZxk8eDDz5s3DwsLimc9bXFzMhg0bcHNz48UXX8TMzKxDjy8oKGDDhg0MHjyYBQsWtKlybGlpQaFQkJKSQk1NDd7e3sTFxRESEtLu5KNGo9EnNHNzc7l37x5qtRpzc3N8fX3x9fXVJzSfNYF8+/ZtDhw4gEqlYsqUKQwbNuyhqk0xTEgQhL5GrAd6B1HBKXSL1/60her6Jhxtrfjop8s7/PiCsir+uf8sFzNy0EkSvu5OrJw2gnGRg1A8Yuv5A5IkcfVWPiunxrH7ZOoj76PV6YgP8+9wTIIgCM8VmWiaLwjCs1Or1VRWVtLQ0EBISAjLly8nPDwclUrFhx9+SFhYGEOGDKGsrIy7d+9y69YtCgoKqKurQ6PRYGVlhb29PY6OjshkMtLS0ti+fTutra2EhYUhl8s5d+6cvkL0Uf97Wh9IgPr6enbtO77adwABAABJREFU2kVBQQGTJ09m5MiRXZJUKysrY9OmTTg7O7NixYoOJzcrKyvZunUrXl5ezJs376GYLCwsiI+PJy4ujtu3b5OSksKuXbuwtbUlJiaG6OhorK2t2zxGo9Fw7969NglNjUaDhYUFvr6+TJgwAblc3qm+pY/T3NzM4cOHuXbtGoGBgcyePRt7e/uH7vfNYUILFy4UlUmCIAhCu4kEp9AtquubqKht6NRjiytr+eEH22lWteoHBOWXVvG7DQdpVqkxNXn8m26ZDExNjBkSMJCYwb5cuZXPN4uUZTIZUUHeRAZ5dyo2QRCE54aolhEEoQvY29vj4eHBlClT8Pf31yfM9u7di0ajwd7ennPnzlFWVgaAn58fEydOJCAgAB8fH0xMTFCpVNTW1nLy5EnOnDlDaGgosbGxWFhY0NjYSG1tLYWFhTQ2NtLS0vJQDObm/5+9+w6L6szbB37P0HtRmo1qAaUoCBYQG/beEEXEnk2ybt5NtiRv9k2y5ZdkN9lNNvsm0STGBtgVFXsDlK6iAopKk46AIB1m5vz+8JWEAEoZmBm5P9fFteGcM+d8cQ/Mw81znq/WCwPQJ0+e4OLFi9DU1ERQUBBsbGzk8rWXlZVh9+7dMDQ0RGBg4AvXFW1LTU0NQkJCoKuri5UrV74wqBWLxRgxYgRGjBiB4uJiJCQkIDo6GlFRUXB0dMTgwYNRU1PTHGhKpVLo6OjA2toa06dPh7W1NSwsLOQWaP7cvXv3cPLkSUgkEixatAiurq5thsdsJkRERN3BgJOUzv6Lic/CTdlPweTz//ruRDS+/u0qqIlFkMpar64gCICP61CIRCJ8uH4+9pyLx4mrt1FT3wA9bU3Mm+CCoFnj2EGdiOil+HOSiLpPQ0MDS5YsgUwma15H8/bt2zh69ChsbGyQkZEBe3t7TJo0Cba2tq1mGwLPuqSHh4ejqKgIS5Ysga+vb7uPX0ulUtTU1LT6qK6ubv7v52FodXV1c7MiY2NjODk5Yffu3W2GoO0FpO2Fjk+ePMGuXbugo6ODNWvWQEdHp1P/bk1NTQgLC0NDQwM2btzYqdebmJjAyckJampqiI6Oxvfff4+6ujqYm5vDy8sL06dPh62tLSwsLHr00e+amhqcPn0aKSkpGD58OObNmwcDg7aXPrl37x4OHz6M/v37IyAggM2EiIio0xhwktK5diejRbj5c09r6lH2tBqBM8dh1+lYiERobjQkFolgP9AM091HAAA0NdSxYe5EBM8ej9r6RuhqaUJNTf5/lSYieiWJ+POSiLrvyZMnOHPmDLKzs9HQ0ABtbW2UlJTA2dkZ77777gtDNplMhpiYGFy+fBmmpqbYsGHDS2f1qampwdDQ8KUBWV1dHY4ePYrGxkbMnz8fbm5uqKuraxWMVlZWoqCgANXV1e3ODv1lAAoAly5dgpaWFlavXo3a2lqIxWJoa2t3KFCUyWQ4cuQIiouLERwc3KoBzy81Njbi0aNHzU2B8vPzIZPJoKenh9GjR2PhwoVoamrCgwcP8OjRI8TFxUEqlcLQ0BC6urovraezBEFAamoqTp06BQBYunQpRo0a1ebX/vNmQk5OTli8eDGbCRERUZcw4CSl87K2V4IArPbzhKWpIQ5cvo6cwjLo62phzrhRCJjuCU2Nlre1mlgMA93uLxBPRNSn8BF1IpIDHR0dNDY2YuLEibC3t4dIJML27dsxf/58WFpatvu6x48f49ixYygoKMDEiRMxefLkDq2l2RGFhYU4cOAA6urqEBQUhGHDhnXode3NDv35DNHMzExcvnwZ9fX1cHV1xaFDh5pfLxaL231M/ucBaVxcHFJTU7F69eo2A92GhoYWgWZBQQFkMhn09fVhbW0NFxcX2NjYoH///i1CRR8fHxQWFiIhIQFXrlzBlStX4OLiAi8vL7k0aQKeraEZERGBu3fvwsnJCXPmzIG+vn6bx/68mZCvry8mT57MZkJERNRlDDhJ6YwbaYsLSXfbfARdX0cLQwebQyQSYbqHI6Z7OCqgQiKivoC/ZBJR92lra2Pt2rXNn4eEhKBfv35wc3Nr83iZTIa4uDhcunQJxsbG2LBhAwYNGiSXWgRBwI0bN3D69GmYmZkhKCjopbMjf+5ls0Nra2uxc+dOeHt7Izg4GHp6em0+Iv/L2aE1NTWoq6sDAOTl5eHhw4cYOnQojhw5Aj09PWhqaqK+vh5Pnz5FZWUlnj59Cg0NDZiYmMDe3h5Tp07F8OHDWwWabbGyssLChQsxffp03LhxA4mJibhx4wZsbW3h5eWFYcOGdWkdTkEQcPv2bZw5cwZisRgrVqyAk5NTu8ezmRAREckbA05SOiunj0XUrQdoaJQ0Nxl6bv3cidCU01/viYjoBTiLhojkLCcnBw8ePMDy5cvbXEOzrKwMx44dQ15eHsaNG4epU6fK7XHlxsZGRERE4NatW/Dw8MCsWbPkNiMUAOrr67Fnzx7U1NQgODgY/fr1A/As4H3+3y8ilUpx8+ZN7Nu3DwEBAbC0tERmZiYyMjJQXFyMxsZGqKmpQU9PDzo6OtDX14e6ujoePXqER48e4dKlS+3ODm1vDVEfHx9MmDABd+/eRXx8PPbt2wdjY2N4enpi9OjRHV738+nTpzhx4gQePHgAFxcXzJo164WPvrOZEBER9QQmRaR0BpmZ4Mut/th2PArX0x8BAKz6GWHNzHHwG8sZm0REvYMBJxHJjyAIuHDhAqysrFrN7BMEAfHx8bh48SIMDAywbt06DBkyRG7XLi0txYEDB/DkyRMsXrwYrq6ucjs38OyR8b1796KiogLBwcEwMzPr1Otra2sRHx+PnTt3QlNTE4WFhSgsLISRkREmT54MGxsbWFtbw8TEBCKRCIIgoKGhod0mSu3NDv2552uHPv+wsLCAoaEhsrKyEBYWhiNHjmD06NHw9vbGkCFD2l0/8+bNmzh79iw0NTUREBCA4cOHv/BrvXfvHo4cOYJ+/fqxmRCRimhsbERSUhI8PDygqamp6HKI2sWAk5SS7YD++OS1Jaipa0CjRAJjfd1WA6uc4nIcuJiIhLvZUBOL4eM6FP5TPdDfuO11foiIiIhIMR48eIDc3FwEBga2GNOVl5cjPDwcOTk58PLywrRp0+T6C3RqairCw8NhaGiITZs2wdzcXG7nBn7qdv748WOsXbu2Q2tZ1tTUICcnBzk5OcjOzkZ2djZu3LiB/v37Y8GCBbC3t4eNjQ2MjY3bfL1IJIK2tnanZoe2t3boLzvLNzQ0wMDAAAUFBdi1axe+++479OvXD0OHDoW1tXXzTNDn4ebjx4/h6uqKWbNmoV+/fpBIJG3OjBUEAdeuXcPFixfh6OiIRYsWMSghUhFxcXHYs2cPpFIpfHx8FF0OUbsYcJJS09PRgh60Wm1Pf1SMt/9zEBKptHmtzuPXbuHKzXT8578CYGHKvwaT6qsry0fJ7YtorKmAnrkNzJ2nQF3HQNFlUV/BR9SJSE4EQcDFixdhY2MDe3v75m2JiYk4f/489PX1ERwcDBsbG7ldUyqV4vz584iLi8PIkSOxYMECaGm1HlN2h0Qiwb59+1BQUIA1a9ZgwIABbR5XXV3dHGbm5OSgpKQEAGBqagpLS0vk5OTAz88Pb775Zo90Ne9oZ3kALWaHPn36FLdu3UJiYiLy8/Px5MkTDB48GDU1NUhJSYEgCLCzs0NJSQl2797dfI5fzg7V1tbG7du3kZubi/Hjx2Ps2LGorKxsftyejYWIlFdDQwPOnj2LrKwsnDlzBp6ennL/WUokLww4SSkJgoDr6Y8QmXwfDU0SONsNwDR3R+hqP/tL7/8evYwmibTFGp0ymYCq2nrsOhOL36+aqajSieQiPyEcWee2AyLxs8fRZDLkRO7BqFV/hcHAFz/+RSQX/IWTiOTkzp07KC4uxoYNGyASiVBRUYHw8HBkZWVh7Nix8PPzk+tsvsrKShw8eBCFhYWYPXs2PD095R6iSaVSHDx4EDk5OVi9ejUGDx7cvK+qqqpFoPn48WMAQL9+/WBtbQ1vb2/Y2NhAV1cXu3fvRr9+/bBhw4YeCTc765ezQ21tbbFo0SLk5eXhwoULCA8Px9OnT+Hj44PNmzfDwsICtbW1bTZRqqmpQVlZGU6dOoXHjx/D3t4ejx49wq5du5qvJxaLoaur2+YaoW2tISrPdVOJ6OXi4+Nx//59uLi44P79+0hISOAsTlJafIcgpSOVyvD/9pxC1K2HUBOLIAjA5RvpCD2fiH/+ejk01NVwN7uo7dfKBFy5eR+/C5jBvwaTShAEodW9WlVw/1m4CQCCDM9zfGljPdIO/Bljt+6EWE0+TReIiIh6klQqxeXLlzFixAgMGjQI169fx9mzZ6Gjo4OgoCDY2dnJ9XoZGRk4fPgwNDQ0sG7dOrl1YP85mUyGI0eO4OHDhwgICEC/fv1w586d5sfNy8rKAAD9+/eHjY0NJk2aBBsbGxgY/PQUhiAIOHToEAoLC7F27VqYmprKvU55kclkyM3NRX5+PsaNG4fBgwcjPz8f27dvx9ChQ+Hl5QV7e/tW45nnzYRcXFwQEBCAAQMGtFo79JdriHZ27dAXNVHi7FCi7nk+e1NTUxOGhobQ1NTkLE5Sagw4SemcjL2DqFsPAaD58XMAKK+qwd9DzuLtAL8Xvv75zE41DmhIiZWkXEFezEHUlmRDXVsfFm5+GOwdAHVtPRTdOAOIxYBM1vJFggxNNRUof5CI/iMmKKZwIiKiTigrK4NUKoWHhwf27t2LjIwMuLu7Y8aMGXL9BVkmkyEqKgqRkZGws7PD0qVLe2RGpCAICA0NRUxMDEaNGoVTp06hvLwcAGBmZgY7OztMmTIFNjY20Ndvf1348+fPIy0tDf7+/j0SwspLaWkpwsPDkZeXB09Pz+Y1UiUSCVJTUxEXF4e9e/eif//+8PT0hKurK7S0tNptJtTZtUNra2vbbKL0/KOwsLB5v1QqbfH657ND2wtAf/mhodE7fzyWyWR49OiRXJdkIOoJN2/eREZGBurr65GamoqmpiZkZGTg5s2bGDdunKLLI2qFAScpnRPXbrW5XSYTkJJVAJlMgLG+DiqqW/9VVyQSYfhgC6iJxT1dJlGX5cUcRPalnXjepVpSX438+HA8ybwJ1+DPUV9R1DrcfE4kQkNlSa/VSkRE1B1mZmaYNGkSDh48CC0tLQQGBsLBwUGu16itrcXhw4eRmZmJyZMnw8fHB2I5jgUrKiqaZ2dGRETgwYMHzZ3gHRwcmruc6+npdeh8CQkJiImJwezZszFixAi51SlPMpkMMTExuHLlCoyMjFp1tldXV4erqytcXFyQm5uL+Ph4nDlzBhcuXICmpiZKS0sxZsyYbjUTUlNTg4GBQYuZr+1pq7P8Lz+6Oju0rYC0K7NDpVIpbt++jejoaDx58gRvvfUWjIyMOnUOot40ePBgrF69us3tRMqIAScpndLKmhfuf1JVi9UzvPC/R6602icIAgJnePVQZUTd11T7FDlX9v7fZz/NUIYgQ21JNkpuX4CO6QBUPkoBZNLWJxAEaJtY9kqt1McJAtfhJKJuKyoqwsmTJzF69GjMnDkT2tracj1/Xl4eDhw4AIlEgsDAwOYmRl0lCEJzoPl8Hc2KigoIgoCysjLU1tZi69atmDVrVpdmiKanp+P06dMYP348vLyUc8xaXFyM8PBwFBYWYvz48ZgyZUq7sxtFIhGGDBmCIUOGoKysDP/5z39w+fJlDBgwABKJBHl5ebC1te3xR8W70ln++ezQXz4m39nZoS+bIaqlpYXU1FRcvXoVFRUVcHR0xPLlyxluktIbOHAgBg4cqOgyiDqMAScpncHmJrj/qLhFA6HnRCJgYH9juNgPRJNEij1n41DX0AQAMNLXwa8W+cJrpG1vl0zUYU8ykiDIJO3sFaH07lXY+m1C0Y3TbewWQ1PfBKYOY3u0RiIiInmxsrLC66+/DnNzc7meVxAEJCQk4Ny5c7CysupyYCQIAp48edI8QzMnJweVlZUQiUSwtLTEiBEjYG1tjYyMDCQlJWHt2rUYO7Zr78P5+fk4dOgQRowYgRkzZnTpHD1JKpXi6tWriIqKgqmpKTZs2NDhx+drampw7NgxqKmp4dNPP4VIJEJcXBx2794NMzMzeHl5wcXFRa7NpLqjs7NDGxsb231M/nnH+V/ODpXJZCgsLMSjR48glUoxZMgQODs7QxAEJCUlyX2ZBiKivo4BJymdpb6j8bfdrcMdsViE8SPt0N/42XpGy6e4Y/5EF9zPLYaaWIzhQyygrqbW2+USdYpM2l64CQACZFIJ9C3t4TDnTTw8/b8ARP/XRV0KdR19OPl/AJGY9zn1As7gJCI5kXe42dDQgOPHjyM1NRXjxo2Dn58f1Do4BhQEAeXl5c2BZnZ2NqqqqiASiWBlZQUnJyfY2NhgyJAh0NHRAQBERkYiKSkJM2fO7HK4+eTJE4SGhsLCwgJLlixRuuY3hYWFCA8PR0lJCby9vTFp0qQOdywvLi5GaGgopFIpgoODm0NRNzc35OTkID4+HhEREbhw4QLGjBkDT09PGBsb9+BXI18ikQhaWlrQ0tLq0OzQ+vp6XL16FdHR0dDV1cXMmTMxYsQIaGhotJgdKs9lFIiIiAEnKSFft2HIKixD6PkEiEUiiETPmg0NG2SB3/q3bDCkrakBF3vlXZid6JeMbVzb3ykSw8RuDADAcsxsmNh7oCTlMppqKqBrbgMzp0lQ05Tvo31ERESqpKSkBAcOHMDTp0+xfPlyjBw58oXHC4KA0tLS5sfNs7OzUV1dDbFYDCsrK7i4uMDa2hpDhgxp8/H5mJgYXL58GVOnTsX48eO7VHNdXR1CQkKgpaWFgICAXmtm0xESiQRRUVG4evUqzM3NsWnTJlhZWXX49enp6Th8+DBMTU0REBDQYhatSCSCjY0NbGxsUFFRgcTERFy/fh2xsbEYMWIEvLy8YG1trXRhb1c1NjYiMTERMTExqKurw5gxY+Dt7d2hUJSIiLqPAScpHZFIhHVzJmCmpxOibz1EQ1MTRtkNxOihg1+ZARD1XdrGFrBwm4Hi5PNosQanSAwNHQNYus9p3qRlZIbBE1f0fpFERERK6NatWzh58iRMTEywefNm9O/fv9UxgiDg8ePHLQLNmpoaiMViDBgwAG5ubs2B5sseD05MTMS5c+fg4+ODSZMmdalmiUSCffv2oba2Fhs2bOhwI6LekJ+fj2PHjqG8vBy+vr7w9vbu1EzYmJgYXLhwASNGjMDixYtf+Pi5sbEx/Pz84Ovrizt37iA+Ph47d+6EhYUFvLy84OzsrFTBb2fU19cjISEBcXFxaGhogJubG7y9vWFiYqLo0oiI+hQGnKS0BvQ3hv80D0WXQSR39rPfgIauIQoST0LWVA8AMLJ2hsOcN6CpZ6zY4oieE2QA+PgcESmeRCLBmTNnkJSUBFdXV8ybN685DBMEASUlJS2aAtXW1kIsFmPgwIEYM2YMbGxsMHjw4E6t/3jz5k1ERERg/PjxmDp1apfqFgQBx44dQ35+PtauXas0M/mamppw5coVxMTEwMrKCps3b4aFhUWHXy+RSHDixAncunULkyZNwpQpUzo8CUFTUxPu7u4YM2YMsrKyEB8fjxMnTuD8+fNwd3fH2LFjVab5Tl1dHeLj4xEXF4empia4u7tj4sSJKlM/EdGrhgEnEVEvE6upw2bqOgz2DkB9ZTE0tA2gaWCq6LKIfqF1ozciot725MkTHDx4ECUlJZg/fz5Gjx7dHGg+DzXr6uqgpqaGgQMHwsPDAzY2Nhg0aFCXG9rcuXMHx48fh4eHB2bMmNHlJ4guXryI1NRULF++HIMHD+7SOeTt0aNHCA8PR2VlJaZNm4YJEyZ0ai3Impoa7N+/HwUFBVi6dCmcnZ27VIdIJIKdnR3s7OxQXl6OxMTE5se7HR0d4eXlhcGDlfPprdraWsTGxiIhIQFSqRQeHh6YOHFihxoWERFRz2HASUSkIGqa2tAzs1Z0GURtExhwEpFipaen48iRI5BIJHB1dcX9+/dx4cIF1NXVQV1dHYMGDYKnp2dzoCmPR5zv3r2Lo0ePwtXVFXPnzu1ywJaUlISrV69i5syZcHJy6nZd3dXY2IiLFy8iISEBAwcOxMqVK2FmZtapcxQXFyMsLAwSiaRFM6HuMjU1xcyZMzFlyhTcunUL8fHx2LFjB6ysrODl5YVRo0Z1uOFRT6qurkZsbCwSExMBAGPHjsX48eOhr6+v4MqIiAhgwEkqRhAE3MnMR/Sth2iSSOHqMAjeLg7QUGdXaSIiIiJVJ5PJUFBQgMOHDyMqKgoaGhqwt7fH7du3MXjwYHh5eTUHmvIOvR48eIBDhw7ByckJCxYs6HK4ef/+fURERMDLywvjxo2Ta41dkZWVhePHj6O6uhozZsyAl5dXpzt4v6iZkLxoampi7Nix8PDwQEZGBuLj43Hs2LEWj68rYpZkVVUVYmJikJSUBLFYDC8vL4wfPx66urq9XgsREbWPASepDKlUhk9CzuDKzftQ+79BWUTsHQyxMMVnbyyDiQEHGURE8sMZnETU86RSKQoLC5vXz3zw4AGSk5NRVVWF8ePHw8/PDzY2Nhg4cGCPzuLLzMzE/v37MXToUCxevLjTAeBzBQUFOHToEIYPH46ZM2cq9BHrhoYGnD9/HklJSbC2tsaaNWtgatq5JXE620xIHkQiERwcHODg4ICysrLmBj5Xr16Fk5MTxo0bh4EDB/b4v21lZSWuXbuGGzduQF1dHRMnToSXlxd0dHR69LpERNQ1DDhJZYRfvYUrN+8DAKQyWfP2vMdP8MWBC/howwJFlUZEREREHSCVSlFQUNAcaD569AiNjY3Q0NCArq4uSkpKMGrUKKxbtw729va9UtOjR48QFhYGGxsbLFu2rMOdxH+poqICoaGhMDMzw9KlS7scksrDw4cPceLECdTV1WHu3Lnw8PDodCAokUhw8uRJJCcnd7qZkLz069cPs2fPxtSpU5GcnIz4+Hh8//33GDhwILy8vODk5CT34LuiogLR0dFITk6GpqYmJk2aBE9PT2hra8v1OkREJF8MOEllhF+91eZ2mUxAbGomyp/WwNRQr5erIiIiIqL2SKVS5OfnNzcEevToEZqamqCpqYkhQ4Zg0qRJsLa2RlZWFq5cuYKxY8di2bJlvfYocn5+PkJCQjBw4ED4+/t3OSyrq6tDSEgINDQ0sGrVKrmsB9oV9fX1OHv2LG7evAk7OzssWLAAxsbGnT7Pz5sJLVmyBC4uLvIvthO0tLTg5eUFT09PPHjwAPHx8Thy5AjOnTsHDw8PeHh4dHstzPLyckRHR+PWrVvQ0dHB1KlT4eHhAS0tLTl9FURE1JMYcJLKeFxR1e4+QXi2nwEnEZGciDlEIKLuKysrw44dO6ClpYUhQ4Zg8uTJsLGxgZWVFcRiMerr63Hs2DHcu3cP3t7emDp1aq/NfCwqKsLevXthbm7erVBSIpFg//79qK6uxoYNG6Cnp5jx6P3793HixAk0NjZiwYIFGD16dJdmXJaUlCA0NFTuzYTkQSQSYdiwYRg2bBgeP36MhIQEXLt2DdHR0Rg5ciTGjRuHAQMGdOqcpaWliIqKwp07d6Cnpwc/Pz+4u7v3+KP4REQkX/zthVSGpakh8kqetLkqnEgkgrmJYa/XRET0yhKzeRsRdZ+ZmRk2b94MS0vLVsFlYWEhDhw4gLq6OgQEBGD48OG9Vtfjx4+xe/dumJiYYPXq1V0OswRBwPHjx5GXl4egoCD0799fzpW+XG1tLc6cOYPbt29j6NChmDdvXpebAN2/fx+HDh3q0WZC8mJmZoa5c+di2rRpuHnzJhISElo0o3J0dHzhcgMlJSWIiopCamoqDAwMMHv2bIwePVphs2+JiKh7GHCSylg8yQ3/PnS51XaxWAQfFwc2GSIiIiJSMiKRqNWMOkEQcPPmTZw6dQpmZmYICgqCiYlJr9VUXl6O3bt3w8DAAIGBgd1aW/Hy5cu4ffs2li9fjiFDhsixyo5JS0tDREQEpFIpFi9eDBcXly7N2hQEAbGxsTh//jyGDx+OJUuWqMwMRm1tbYwfPx5eXl64f/8+4uPjcejQIRgYGGDs2LFwd3dvMau2qKgIkZGRuHv3LoyNjTF37ly4ubn1aBMrIiLqefwpTkqpsUmCswlpuHwjHXWNjRg9dAjmT3TB7HGjcDouBWpiEQARpDIZ7AeYYeuyaYoumYiIiIheoqmpCREREUhOToa7uztmz57dq8FSRUUFdu3aBS0tLaxZswa6ul3/A/n169cRFRUFPz8/jBw5Uo5VvlxNTQ0iIiKQlpaGESNGYO7cuV1et1QikSAiIgI3b96Ej48Ppk6dqtDu710lFosxYsQIjBgxAiUlJYiPj0d0dDSioqIwatQoDBkyBOnp6UhPT4eJiQkWLlwIFxeXLjeVIiIi5cKAk5ROXUMTfv/1Ydx7VAQRAAFAZkEpTl67jX+8sQzzJ7gg+vYDNEmkcHUYhLGONlBTYJdKIiIiInq5srIyHDhwAOXl5Vi8eDFcXV179fpPnz7Frl27IBaLERQU1K2mNA8fPkRERATGjh2LCRMmyLHKFxMEASkpKTh9+jQAYNmyZRg5cmSXA0llayYkL+bm5pg/fz6mTZuGM2fO4ODBg8jPz8egQYOwZMkSLFiwgDM2iYheMfypTkrnSNRNpOcWA0DzepsymYCGJgk+CzuH7b8PxNDB5oorkIiIiIg6JS0tDeHh4dDX18fGjRthYWHRq9evqanB7t27IZPJsG7dOhgadn3t9qKiIhw4cAAODg6YPXt2r812rKqqwsmTJ5Geno5Ro0Zh9uzZ3Wpo9LyZUFNTE9auXYvBgwfLsVrFy87ORlRUFDIzMzF+/HgMGTIEjx8/xu3bt5GTk4OxY8dizJgx3ZrFS0REyoMBJymdM3EpEITWrYRkgoDsojJkF5XB1qr3F3AnIiIios6RSqW4cOECYmNj4eTkhIULF0JLS6tXa6irq8Pu3btRX1+PdevWwdjYuMvnqqysREhICPr3749ly5b1Ssd3QRBw69YtnDlzBurq6vD394ejo2O3zvm8mZCJiQmCg4O79W+iTARBQFZWFiIjI5GTkwNLS0usWLECjo6OzUF0UVER4uPjceXKFVy5cgUuLi7w8vLq9dCdiIjkiwEnKZ2quoYX76+t76VKiIiIiKg7SktLcf36dcyaNQteXl69vrZjfX099uzZg6qqKqxbtw79+vXr1rlCQkKgpqaGVatW9UoTnsrKSpw4cQIPHz6Eq6srZs2aBR0dnS6fT5WbCb2IIAjIyMhAZGQkcnNzMWDAAAQEBGDYsGGt7jlLS0ssXLgQ06dPx40bN5CYmIgbN27A1tYWXl5eGDZsWK8E10REJF8MOEnpDB9sgeSHuZDJWs/iVBOLYW3R9YEpEREREfUeCwsLvPXWWwp5DLixsREhISEoLy9HcHAwzMzMunwuqVSKAwcO4OnTp9iwYUO31u/sCEEQcOPGDZw7dw6amppYtWoVhg0b1q1zSqVSnDx5Ejdv3oS3tzemTZumks2Efk4QBNy/fx+RkZEoKCjAoEGDsHr1ajg4OLz0a9PT04OPjw8mTJiAe/fuIS4uDvv27YOxsTE8PT0xevToboXJRETUuxhwktJZMdUdN+4/arVdJBJhltdIGOlzoEFERESkKhQRbjY1NSEsLAwlJSUICgqCpaVll88lCAKOHz+OnJwcrFmzpltBaUc8efIEx48fR1ZWFsaMGYMZM2ZAW1u7W+esqanBgQMHkJeXp5AGT/ImCALu3r2LqKgoFBUVwdraGkFBQbC1te10aKumpoaRI0di5MiRKCgoQHx8PC5evIjLly/D1dUVXl5ePf7/ORERdR8DTlI67sOt8Vv/6fjfo1fQ0Chp3j5l9DC8vthXgZURERERkbKTSCTYv38/8vLysGbNGgwcOLBb57ty5Qpu3bqFpUuXwsbGRj5FtkEQBCQkJODChQvQ1dXFmjVrYG9v3+3z/ryZUHBwsEo3E5LJZEhLS0NUVBRKSkpga2uL4OBguf3/MmDAACxevBh+fn64fv06EhMTkZSUBHt7e3h5eWHo0KEqP+uViOhVxYCTlNLscaMwefQwJN3LQX2jBKNsB8Cqv5GiyyIiIiIiJSaVSnHo0CFkZ2dj1apVGDJkSLfOd/PmTURGRmL69OlwdnaWU5WtlZWVITw8HI8ePcLYsWMxffp0uTRjun//Pg4fPgxjY2OVbiYkk8mQkpKCqKgolJaWwsHBAfPnz++xsFZfXx++vr7w9vZGWloa4uLiEBoaClNTU3h6esLNza3bs2qJiEi+GHCS0tLR0oSP61BFl0FEREREKkAmk+Ho0aN48OAB/P39YWdn163zZWRk4MSJE3B3d8fEiRPlVGVLMpkMcXFxuHTpEgwMDOQ2G1EQBMTFxeHcuXMYNmwYli5dqpLNhKRSKW7fvo3o6GiUl5dj+PDhWLx4cbdn5XaUmpoanJ2d4ezsjLy8PMTHx+PcuXO4dOkS3Nzc4Onpif79+/dKLURE9GIMOImIiIiISKU9XyczLS0Ny5Yt63ZDnuLiYhw4cAD29vaYO3dujzyW/PjxY4SHhyM/Px9eXl6YOnWqXELIV6GZkEQiQXJyMq5evYqKigo4Ojpi+fLlsLKyUlhNgwYNwqBBgzBjxgwkJSUhKSkJCQkJcHBwwLhx42Bvb69y/85ERK8SBpxERERERKSyBEHAqVOncOvWLSxZsgROTk7dOt/Tp08REhICU1NTLF++HGKxWE6VPiOTyXDt2jVcuXIFJiYmWL9+vdweta6trW1ef1QVmwlJJBLcuHEDV69eRVVVFUaOHImAgABYWFgourRmBgYGmDJlCnx8fJCamoq4uDjs3bsX/fr1g5eXF1xdXeWyvAAREXUOA04iIgUQZFKUP0xETVEm1HUNYeboAw09rjNLRETUGYIg4Ny5c0hMTMTChQu7vU5mQ0MDQkJCIBKJsGrVKrk/1l1cXIxjx46hqKgIEyZMwOTJk6GhoSGXc5eUlCAsLAyNjY0q10yoqakJSUlJiImJQXV1NZydneHj46PU3cvV1dXh6uoKFxcX5ObmIj4+HmfOnMHFixcxevRoeHp6wtTUVNFlEhH1GQw4iYh6Wf2TIqSEvo/6J4UQidUgyGTIOvcdHOa+CQtXP0WXR0REpDIuX76M2NhYzJkzB6NHj+7WuaRSKQ4cOIDKykqsX78eBgYGcqry2bmjo6MRFRWF/v37Y+PGjXJdR/LBgwc4dOgQjI2NsXbtWpVpJtTY2IjExETExMSgrq4Orq6u8Pb2Rr9+/RRdWoeJRCIMGTIEQ4YMwdOnT5GYmIjr168jPj4eQ4cOxbhx42Bra8vH14mIehgDTiKiXiQIAtIO/Bn1FcXPPpdJ/+9/JXhw4gvomdtA34rNtYiIiF7meWDo5+cHT0/Pbp1LEAScPHkS2dnZCAwMhLm5uZyqBAoKChAeHo7Hjx/Dx8cHPj4+UFeXz69hv2wmtGTJEpV4PLqhoQEJCQmIjY1FQ0MD3Nzc4O3tDRMTE0WX1i2GhoaYNm0aJk2ahJSUFMTFxWH37t0wMzODl5cXXFxcVLLZExGRKmDASUTUi57mpqL2cU7bO0ViFCRFYNj8t3q1JiIiIlUTGxuLixcvYsqUKXLpcB4VFYWbN29iyZIlsLW1lUOFz9aTjIyMxLVr12Bubo7NmzfD0tJSLucGns0KjYiIwI0bNzBx4kRMmzZN7uuFyltdXR3i4+MRFxeHpqam5g71Rkav1jI9GhoaGD16NNzc3JCTk4P4+HhERETgwoULGDNmDDw9PVVmli0RkapgwElE1IvqSvPa3ynI2g8/iYiICACQlJSEs2fPwtvbG5MmTer2+W7duoXLly9j6tSpcHFxkUOFQF5eHsLDw1FeXo7Jkydj4sSJUFNTk8u5gZbNhBYtWgQ3Nze5nbsn1NbWIjY2FgkJCZBKpfDw8MDEiRPlugyAMhKJRLCxsYGNjQ0qKiqaH1+PjY3FW2+99coFu0REisSAk4ioF2kavmBNKZEYWob9e68YIiIiFZOcnIyTJ0/Cy8sL06ZN6/a6hpmZmQgPD8eYMWPg4+PT7fqamppw6dIlxMXFYcCAAdiyZYtcH3cHgMePHyM0NBSNjY1Yu3YthgwZItfzy1N1dTViY2ORmJgIABg7dizGjx8PfX19BVfW+4yNjeHn5wdfX19kZmYy3CQikjMGnEREvcjEbgw09EzQVFsJCLKWOwUZLEfPUkxhRERESi4lJQXh4eFwd3fHrFmzuh1ulpSUYP/+/bCzs8PcuXO7fb6cnByEh4fj6dOnmD59OsaPHy/3R8ZVpZlQVVUVYmJikJSUBLFYDC8vL4wfPx66urqKLk3hNDU1MWLECEWXQUT0ymHASUTUi0RiNTguew8pYX+CrKkBEACRWAxBJsXA8UthbDdG0SUSEREpnXv37uHIkSNwdnaWSxhZVVWFkJAQmJiYYPny5d16fLyxsREXLlxAQkICBg8ejFWrVqF/f/k+kaEqzYQqKytx7do13LhxA+rq6pg4cSK8vLygo6Oj6NKIiOgVx4CTiKiXGQ52gsfr36M4+RyqizOgoWMEc5epMBzkqOjSiIiIlM7Dhw9x8OBBjBgxAosWLer2rMiGhgaEhIRAEASsWrWqW0FhZmYmjh8/jpqaGsyaNQuenp5yn7WpCs2EKioqEB0djeTkZGhqamLSpEnw9PSEtra2oksjIqI+ggEnEZECaOqbYLC3v6LLICIiUmrZ2dnYt28fHBwcsHTp0m4He1KpFAcPHsSTJ0+wfv16GBoaduk89fX1OH/+PK5fvw4bGxsEBQXB1NS0W7W1pba2FgcOHEBubq5SNhMqLy9HdHQ0bt26BR0dHUydOhUeHh5KObuUiIhebQw4iYiIiIhI6eTm5iI0NBTW1tbdfowcePaYd0REBDIzMxEYGAgLC4sunefBgwc4ceIE6uvrMW/ePLi7u3f7kfm2PG8m1NDQoHTNhEpLSxEVFYU7d+5AT08Pfn5+cHd3h6ampqJLIyKiPooBJxERERERKZWCggLs3bsXVlZWWLlyJdTVu/9ry9WrV3Hjxg0sWrQIdnZ2nX59XV0dzp49i+TkZNjb22P+/Pk91uTneTMhIyMjpWomVFJSgqioKKSmpsLAwACzZ8/G6NGjoaGhoejSiIioj2PASURERERESqO4uBh79uyBmZkZVq1aJZfw7Pbt27h48SImT57cpce87927h5MnT0IikWDhwoVwc3PrkVmbgiAgPj4eZ8+exdChQ7F06VKleNy7qKgIkZGRuHv3LoyNjTF37ly4ubnJJXgmIiKSB74jERERERGRUigtLcXu3bthbGyMwMBAuYR7WVlZCA8Ph5ubG3x9fTv12traWpw6dQopKSkYNmwY5s2b1+V1O19GKpXi1KlTuH79utI0E8rPz0dUVBTS09NhYmKChQsXwsXFpdvLBRAREckbA04iIiIiIlK48vJy7Nq1C3p6elizZo1cOnA/fvwY+/fvh7W1NebPn9+pWZepqak4deoUZDIZlixZAmdn5x6ZtQkoXzOh3NxcREZG4uHDh+jfvz8WL14MZ2dnhQeuRERE7WHASUREREREClVZWYndu3dDU1MTQUFB0NXV7fY5q6qqEBISAiMjI6xYsaLDsw6rq6tx6tQppKWlwdHREXPnzoW+vn6362nP48ePERYWhvr6eoU3E8rOzkZUVBQyMzNhZmaGZcuWwcnJicEmEREpPQacRERERESkMFVVVdi1axcAICgoSC5hYmNjI0JDQyGVSrFq1aoOzQYVBAF37tzB6dOnIRaLsXz5cjg5OfXYrE0AePjwIQ4ePAgjIyNs2rQJJiYmPXat9giCgKysLERGRiInJweWlpZYsWIFHB0de/RrJyIikicGnEREREREpBA1NTXYvXs3JBIJ1q1bByMjo26fUyaT4dChQygvL+/wOZ8+fYqTJ0/i/v37cHZ2xqxZs6Cnp9ftWtqjDM2EBEFARkYGIiMjkZubiwEDBiAgIADDhg1jsElERCqHAScREREREfW6uro67NmzB3V1dVi3bp1cZi8KgoBTp07h4cOHWLVqFSwtLV96fHJyMs6ePQt1dXWsXLkSI0aM6HYdL/LzZkITJkzA9OnTe/URcEEQcP/+fURGRqKgoACDBg3C6tWr4eDgwGCTiIhUFgNOIiIiIiLqVQ0NDdi7dy+ePn2K4OBg9OvXTy7njYmJQVJSEhYsWAAHB4cXHltRUYETJ04gIyMDbm5umDlzJnR0dORSR3t+3kxo4cKFGD16dI9e7+cEQcDdu3cRFRWFoqIiWFtbIygoCLa2tgw2iYhI5THgJCIiIiKiXtPY2IiQkBCUlZVh7dq1MDc3l8t5U1JScP78eUyaNAljxoxp9zhBEJCUlITz589DW1sbq1evxtChQ+VSw4v8vJlQUFAQrK2te/yawLNH9tPS0hAVFYWSkhLY2toiODgYNjY2vXJ9IiKi3sCAk4iIiIiIeoVEIsG+fftQVFSEoKAgWFlZyeW8OTk5OHr0KFxdXTFlypR2j3vy5AmOHz+OrKwsuLu7w8/Pr0MNiLpLEc2EZDIZUlJSEBUVhdLSUjg4OGD+/PkYPHhwj1+biIiotzHgJKUkkUpx5eZ9XLl5H/WNTXC1H4R5E51hYtBzi70TERERUc+RSqXNj2evXr0agwYNkst5S0tLsW/fPgwZMgQLFixo83Hr5019Ll68CD09PQQFBcHOzk4u138RQRCQkJCAM2fO9FozIalUitu3byM6Ohrl5eUYPnw4Fi9ejIEDB/bodYmIiBSJAScpncYmCd7bfgy3HuZBJAIEAbiTkY+j0Tfx+RvLYTugv6JLJCIiIqJOeN7ZPCMjA6tWrZLb49HV1dXYu3cv9PX14e/vDzU1tVbHlJaW4vjx43j06BE8PT0xffp0aGpqyuX6L9LbzYQkEglu3bqF6OhoVFRUwNHREcuXL5fbLFkiIiJlxoCTlM7RqGTczsgD8CzcBACZIKCmrhF/Dz2Lb95ZrcDqiIiIiKgzZDIZjh49ivT0dPj7+8Pe3l4u521sbERYWBikUinWrVvX6lFzmUyG2NhYXL58GYaGhli3bl2vrXtZW1uLgwcP4tGjRz3eTEgikeDGjRu4evUqqqqqMHLkSAQEBMDCwqLHrklERKRsGHCS0jkdl9IcbP6cTBDwMP8xcorLYW1h2vuFEREREVGnCIKAkydPIiUlBcuWLcPw4cPlcl6ZTIbDhw/j8ePHWLduHYyMjFrsLykpQXh4OAoKCjBu3DhMnToVGhoacrn2y5SWliI0NLTHmwk1NTUhKSkJMTExqK6uhrOzM3x8fGBmZtYj1yMiIlJmDDhJ6VTU1L1wf2V1LcCAk4iIiEjpFRUV4fbt21i0aBFGjhwpl3MKgoAzZ87gwYMHCAgIaPEItlQqxbVr1xAZGQkTExNs2LBBbmt9dkRGRgYOHjwIAwODHmsm1NjYiMTERMTExKCurg6urq7w9vZGv3795H4tIiIiVcGAk5SO/YD+SMksgKyNaZxikQiDzXu+6yQRERERdZ+VlRW2bt0KQ0NDuZ0zNjYWCQkJmD9/PoYOHdq8vaioCMeOHUNJSQkmTpwIX19fqKv3zq87P28m5ODggGXLlsm9mVBDQwMSEhIQGxuLhoYGuLm5wdvbu1c6shMRESk7BpykdFZM8cDtjPBW28UiEaZ5jGAndSIiIiIVIs9wMzU1FefOnYOPjw/c3d0BPJu1GRUVhejoaJiZmWHjxo0YMGCA3K75MlKpFKdPn0ZSUhLGjx8PPz8/uTYTqqurQ3x8POLi4tDU1AR3d3dMnDix1WP5REREfRkDTlI6XiNt8fpiX2w/Hg2JVAYRAAGAp5MNfr10qqLLIyIiIiIFePToEY4ePQpnZ2dMnfpsTJifn4/w8HCUlpZi0qRJ8PHxabOTek+pq6vDgQMHkJOTgwULFmDMmDFyO3dtbW3zbFWpVAoPDw9MnDgRBgYGcrsGERHRq4IBJymlxZNGY5r7CMSmZqKhUQJnu4GwHdBf0WURERERkQKUlZUhLCwMgwYNwsKFCyGVSnHlyhVcu3YNlpaW2Lx5MywtLXu1pl82E7KxsZHLeaurqxEbG4vExEQAwNixYzF+/Hjo6+vL5fxERESvIpUNOD/++GMcOXIE9+7dg46ODiZMmIBPP/30hZ0Zd+7ciXXr1rXYpqWlhfr6+p4ul7rAUE8HMz3lsxg9ERERvbo4Lny11dTUYO/evdDX14e/vz8KCwsRHh6OJ0+eYOrUqZgwYUKvztoEeqaZUFVVFWJiYpCUlASxWAwvLy+MHz8eurq6cqiYiIjo1aayAWdkZCTeeOMNjB07FhKJBO+99x5mzJiBtLQ06Om1v0ajoaEh0tPTmz8XiUS9US4RERER9RCOC19dTU1NCAsLQ1NTE1atWoXIyEjEx8dj4MCBeO2112BmZtbrNT1vJmRvb4+lS5dCW1u7W+errKzEtWvXcOPGDairq2PixInw8vKCjo6OnComIiJ69alswHnmzJkWn+/cuRPm5ua4fv06Jk2a1O7rRCJRrz++QkREREQ9h+PCV5NMJsORI0dQXFyM6dOnIywsDE+fPoWfnx/GjRsn10Y+HSGVSnHmzBkkJibKpZlQRUUFoqOjkZycDE1NTUyaNAmenp7dDkyJiHpDY2MjkpKS4OHhAU1NTUWXQ6S6AecvVVZWAgBMTU1feFx1dTWsra0hk8kwZswY/L//9/8wcmT7j0E3NDSgoaGhxeuJiIiISHlxXPhqOHfuHFJSUjB48GCcPn0a1tbWWL16Nfr169frtdTV1eHgwYPIzs7udjOh8vJyREdH49atW9DR0cHUqVPh4eEBLS0tOVZMRCQ/bYWZcXFx2LNnD6RSKXx8fBRcIdErEnDKZDK89dZbmDhxIkaNGtXuccOHD8eOHTvg4uKCyspKfPbZZ5gwYQJSU1MxaNCgNl/z8ccf46OPPuqp0omIiIhIjjgufDXExcXh1KlTUFdXx+PHjzF79mx4enoqZBmB0tJShIWFoba2tlvNhEpLSxEVFYU7d+5AT08Pfn5+cHd358wnIlJ6vwwzGxoacPbsWWRlZeHMmTPw9PTkH2lI4V6JgPONN95ASkoKrl69+sLjxo8fj/Hjxzd/PmHCBDg6OmLbtm34y1/+0uZr3n33Xfz2t79t/jw5ORm+vr7yKZyIiIiI5IrjQtWXnJyMf//731BTU8P06dOxYMECuTTx6YpfNhN62azgtpSUlCAqKgqpqakwMDDA7NmzMXr0aGhoaPRAxURE8tVWmBkfH4/79+/DxcUF9+/fR0JCAmdxksKpfMD55ptv4uTJk4iKimr3r+3t0dDQwOjRo/Hw4cN2j9HS0mrxlwh9ff0u10pEREREPYfjQtUXGRmJTz75BCYmJvjNb34Dd3d3hTV/et5MyM7ODsuWLev02phFRUWIjIzE3bt3YWxsjLlz58LNzQ3q6ir/KxgR9SG/DDOvXr2KS5cuQVNTE4aGhtDU1OQsTlIKKvvuKggCfv3rX+Po0aO4cuUKbG1tO30OqVSKO3fuYM6cOT1QIRERERH1Bo4LVd/zNS737NkDGxsbfPLJJwpZaxNo2Uxo3LhxmDFjRqeaCeXn5yMqKgrp6ekwNTXFwoUL4eLiAjU1tR6smohI/hoaGnDq1ClUVlZixIgR0NTUxJ49e1BbW4vGxkakpqaiqakJGRkZuHnzJsaNG6fokqkPU9mA84033kBoaCjCw8NhYGCAoqIiAICRkRF0dHQAAEFBQRg4cCA+/vhjAMCf//xnjBs3Dg4ODqioqMA//vEP5OTkYOPGjQr7OoiIiIioezguVG13797F0aNHERsbizFjxuB//ud/oKenp5Baft5MaP78+XB3d+/wa3NzcxEZGYmHDx+if//+WLx4MZydnXu92zsRkbzcvHkTiYmJePjwIRobG2FsbAyZTAYfHx+MGDGixbGDBw9WUJVEz6hswPnNN98AACZPntxi+48//ojg4GAAwKNHj1oMKJ48eYJNmzahqKgIJiYmcHd3R0xMDJycnHqrbCIiIiKSM44LVVNNTQ1OnTqF27dvo7i4GF5eXnjjjTcUFm6WlZUhNDS0082EcnJyEBkZiczMTJiZmWHZsmVwcnJisElEKs/c3Bz9+vVDaWkpzM3NsXLlSmhoaGDMmDEYOHCgossjakFlA05BEF56zJUrV1p8/q9//Qv/+te/eqgi+jkTA90W/ytvjRIJbj/MR31jE0YMsUR/Y66BRURE1FdxXKhaBEFAamoqTp06BUEQYGRkBC0tLaxbt05hzYQyMzNx4MAB6Ovrd6iZkCAIyMrKQmRkJHJycmBpaYkVK1bA0dFRYWuGEhHJW15eHgRBwLRp05CbmwszMzM2EyKlpbIBJym3r99e1WPnjkp+gC8OXkRVbT0AQCQSYaanE369bAo0uWg7ERERkdKqqqpCREQE7t27BycnJ2hqauLWrVvw9/dX2GygxMREnD59ukPNhARBQEZGBiIjI5Gbm4sBAwYgICAAw4YNY7BJRK+U593T2UyIVAXTIFIpqVkF+OvuUy1magiCgLMJqVBXE+M3y6cpsDoiIiIiaosgCLh9+zbOnDkDsViMFStWoKqqCqdPn8bs2bNbreXWG2QyGc6cOYOEhISXNhMSBAH3799HZGQkCgoKMGjQIKxevRoODg4MNonolXTz5k1kZGSgvr6ezYRIJTDgJJVy8PJ1iEWA9BdPogkCcDouFWtnj4exfs88Fk9EREREnff06VOcOHECDx48gIuLC2bNmoVHjx7hzJkzGD9+PLy8vHq9po42ExIEAffu3UNkZCSKiopgbW2NoKAg2NraMtgkolfa4MGDsXr16ja3EykjBpykUlKzCiCVtb3OllQmQ0b+Y7gPt+7lqoiIiIioLcXFxdixYwc0NTUREBCA4cOHIz8/H4cPH4ajoyNmzJjR6zX9vJnQmjVrYGtr2+oYmUyGtLQ0REVFoaSkBHZ2dggODu5w4yEiIlU3cOBANhIilcKAk1SKno4WKqrr2t2vr8O1QIiIiIiUhZmZGSZMmAAvLy9oa2vjyZMnCA0NhaWlJRYvXtzrsyBf1kxIJpMhJSUFUVFRKC0thYODA+bPn88ZS0REREqOAScpJUEQkJpViMjk+6hvbIKL/UD4ug3DjLFO2Hk6tlW3VJEIsDAxxNBBFgqqmIiIiIh+SSwWw9fXFwBQW1uLkJAQaGlpISAgABoaGr1ay4uaCUmlUty+fRvR0dEoLy/H8OHDsXjxYs5eIiIiUhEMOEnpyGQCPtt3DucT70JNLAYg4Ex8KvacjcffNi/E1dsP8TC/BM8zTjWxCGKxGO8EzIBYzLWQiIiIiJSNRCLBvn37UFtbi40bN0JXt/fWTP95MyEvLy/MnDmzuZmQRCLBrVu3EB0djYqKCjg6OmL58uWwsrLqtfqIiIio+xhwktI5HZeC84l3ATxbV/O5kidP8eXBS/j8zeWIiL2Di0l3UdfQBFeHQVgyeQysLUzbOyURERERKYggCDh69CgKCgqwdu3aVo+F96S6ujocOnQIWVlZmDdvHjw8PAA8CzZv3LiBq1evoqqqCiNHjkRAQAAsLPg0EBERkSpiwElKJ/zaLYgA/LKVkFQm4NbDPFRU1WLZ5DFYNnmMIsojIiIiok64cOEC0tLSsGLFil5dy7KtZkJNTU1ISkpCTEwMqqur4ezsDB8fH5iZmfVaXURERCR/DDhJ6ZSUP20VbrbYX1EFq/5GvVYPEREREXVNYWEhrl27hlmzZsHR0bHXrpuVlYUDBw5AT08PGzduhIGBAa5du4aYmBjU1dXB1dUV3t7e6NevX6/VRERERD2HAScpnQH9jVussflLVv0Me7cgIiIiIuoSKysrbN68GQMGDOi1ayYlJeHUqVOwtbXFggULcPv2bcTGxqKhoQFubm7w9vaGiYlJr9VDREREPU+s6AKIfmnxpNFthptisQheTrYwN2HASUREr7by8nKsXr0ahoaGMDY2xoYNG1BdXf3C10yePBkikajFx2uvvdZLFRO1r7fCTZlMhlOnTuHkyZNwcXHBwIED8c033yAyMhKjRo3C1q1bMX/+fIabRESkUjgu7BjO4CSlM91jBB7ml+BI5E2IxSKIIIJUJoONZT+8E+Cn6PKIiIh63OrVq1FYWIjz58+jqakJ69atw+bNmxEaGvrC123atAl//vOfmz/vzU7VRIpUX1+PgwcP4v79+7CyssLdu3chk8ng7u6OiRMnwsDAQNElEhERdQnHhR3DgJOUjkgkwq8W+WK21yhE3bqPhkYJRtkNhKeTDdTEnHRMRESvtrt37+LMmTNITExs7vj81VdfYc6cOfjss89eOBtOV1cXlpaWvVUqkVIoKyvDzp07cffuXRgbG6OiogJjx47F+PHjoa+vr+jyiIiIuozjwo5jWkRKy8aqH4JmjcemBT4YP8qO4SYRESml6upqPH36tPmjoaGhW+eLjY2FsbFx8yAWAKZPnw6xWIz4+PgXvjYkJAT9+/fHqFGj8O6776K2trZbtRApuzt37uD3v/89rly5AisrK8yePRtvvfUW/Pz8GG4SEVGvkveYEOC4sDM4g5OIiIioG3x9fVt8/sEHH+DDDz/s8vmKiopgbm7eYpu6ujpMTU1RVFTU7utWrVoFa2trDBgwALdv38Yf/vAHpKen48iRI12uhUhZVVZWYufOnTh+/Dj69euHX//61/Dx8YGOjo6iSyMioj5K3mNCgOPCzmDASURERNQNkZGRcHNza/5cS0urzeP++Mc/4tNPP33hue7evdvlOjZv3tz8387OzrCyssK0adOQkZEBe3v7Lp+XSJlUVFQgMjIShw4dQmFhIWbOnIk333zzlV9XjIiIlF9Hx4QAx4U9gQEnERERUTfo6+vD0NDwpce9/fbbCA4OfuExdnZ2sLS0RElJSYvtEokE5eXlnVpHycvLCwDw8OHDV3YgS31HeXk5oqOjkZSUhAcPHkBfXx9//vOfMWHCBEWXRkREBKDjY0KA48KewICTiIiIqBeYmZnBzMzspceNHz8eFRUVuH79Otzd3QEAly5dgkwmax6cdkRycjIAwMrKqkv1EimD0tJSREVF4c6dOxCJRKitrYWLiwtWrlwJOzs7RZdHRETUJRwXyh+7thAREREpEUdHR8yaNQubNm1CQkICrl27hjfffBMrV65s7pSZn5+PESNGICEhAQCQkZGBv/zlL7h+/Tqys7Nx/PhxBAUFYdKkSXBxcVHkl0PUJSUlJTh06BD+93//F9nZ2XB1dYW6ujpsbGywZcsWhptERNQncFzYcZzBSURERKRkQkJC8Oabb2LatGkQi8VYunQp/v3vfzfvb2pqQnp6enM3TE1NTVy4cAFffPEFampqMHjwYCxduhTvv/++or4Eoi4pKipCZGQk7t69C2NjY8ydOxcSiQTnzp2DjY0Nli9fzkZCRETUp3Bc2DEMOImIiIiUjKmpKUJDQ9vdb2NjA0EQmj8fPHgwIiMje6M0oh6Rn5+PqKgopKenw9TUFAsXLsSoUaNw4cIFxMfHw9PTEzNnzoSampqiSyUiIupVHBd2DANOUilNEinKn9ZAX0cLejrtdyQjIiIiIuWXm5uLyMhIPHz4EP3798fixYvh7OyMxsZG7N+/H5mZmZg7dy7Gjh2r6FKJiIhIiTHgJJUglcoQej4BhyNvoqa+AWKRCBOc7fHG4snob6yv6PKIiIiIqBNycnIQGRmJzMxMmJubY9myZXBycoJYLEZ5eTlCQ0NRXV2NwMBArrdJREREL8WAk1TCV4cv41TsHTyfdC0TBMSkZOB+bjG2/W419HW0FVofEREREb2YIAjIyspCZGQkcnJyYGlpiRUrVsDR0REikQgAkJWVhQMHDkBXVxebNm1Cv379FFw1ERERqQIGnKT0CssqERF7p9V2mUzA44oqnIlPw7LJYxRQGRERERG9jCAIyMjIQGRkJHJzczFgwAAEBARg2LBhzcEmAFy/fh0RERFsJkRERESdxoCTlN6N9Eft7hMEID4tiwEnERERkRIqKyvD4cOHUVBQgEGDBmH16tVwcHBoEWzKZDKcO3cOcXFxGDt2LGbNmsVmQkRERNQpDDhJ6YnEovb3ARCL2t9PRERERIpjYGAAAwMDBAUFwdbWtkWwCQD19fU4dOgQMjMzMWfOHHh6eiqoUiIiIlJlDDhJ6XmOsIFIJIIgCK32CQAmOtv3flFERERE9FKampoICAhoc195eTnCwsJQVVWF1atXw96eYzoiIiLqGrGiCyB6mf7G+lgxxb3VdrFYhCEWpvAb66SAqoiIiIioq7Kzs/Hdd99BJpNh48aNDDeJiIioWziDk5SWVCZD+qNi1Dc2wX+aO8xNDLD/UhJKnlRBS0MdMzydEDx7AnS0NBRdKhERERF1EJsJERERkbwx4CSlFJeaiS8PXkRpZQ0AQENdDUsmjcau/w6GRCqDhroa1MScgExERESkKthMiIiIiHoKA05SOmnZhfjghxMt1txskkix/1ISRGIRNsydqMDqiIiIiKiz6uvrcfjwYWRkZLCZEBEREckdp8CR0tl/MREQPWsg9EtHrtxATV1Dr9dERERERF1TXl6OH374Abm5uVi9ejXDTSIiIpI7BpykdO5k5kMmayveBBolUmQWlPZyRURERETUFSUlJfj+++8hlUrZTIiIiIh6DB9RJ6WjramBqtr2Z2lqa7KpEBEREZEqMDU1hZubG3x8fNhMiIiIiHoMZ3CS0pnmPgJikajVdpEIsDQ1hP1AMwVURURERESdpa6ujhkzZjDcJCIioh7FgJOUzvIpHrDqZ9Qi5FQTi6AmFuO/VkyHWNw6/CQiIiIiIiIior6Jj6iT0jHU08a/31qJI5E3cPH6PTQ0SeDqMAj+Uz3gMMhc0eUREREREREREZESYcBJSslQTxvBcyYgeM4ERZdCRERERERERERKjI+oExERERERERERkcpiwElEREREREREREQqiwEnERERERERERERqSwGnERERERERERERKSyGHASERERERERERGRymLASURERERERERERCqLAScRERERERERERGpLAacREREREREREREpLIYcBIREREREREREZHKYsBJREREREREREREKosBJxEREREREREREaksBpxERERERERERESkshhwEhERERERERERkcpiwElEREREREREREQqiwEnERERERERERERqSwGnERERERERERERKSyGHASERERERERERGRymLASURERERERERERCpLXdEFqKq7d+8qugQiIqVnZWUFKysrRZehcIWFhSgsLFR0GSRnHAvQc7wXiIjaxrFgaxwXvno4DlAODDg7ycrKCr6+vggMDFR0KURESu+DDz7Ahx9+qOgyFG7btm346KOPFF0G9QBfX1/+4taHcVxIRPRiHAu2xnHhq4ljQsUTCYIgKLoIVcO/uPSe6upq+Pr6IjIyEvr6+oouh0juXvV7nH+1f0bV3jde9ftSnniPk6p9fysCf6YQ/aSvfT/wfbI1VXvf6Gv3bFfxXlc8Bpyk1J4+fQojIyNUVlbC0NBQ0eUQyR3vcVJGvC+JSJ74M4XoJ/x+IFXDe5ZUBZsMERERERERERERkcpiwElEREREREREREQqiwEnKTUtLS188MEH0NLSUnQpRD2C9zgpI96XRCRP/JlC9BN+P5Cq4T1LqoJrcBIREREREREREZHK4gxOIiIiIiIiIiIiUlkMOImIiIiIiIiIiEhlMeAkIiIiIiIiIiIilcWAk/qM7OxsiEQi7Ny5U9GlEBEREVEXcDxHREREbWHASW3KyMjAli1bYGdnB21tbRgaGmLixIn48ssvUVdX12PXTUtLw4cffojs7Oweu0ZH/O1vf8OCBQtgYWEBkUiEDz/8UKH1kOKIRKIOfVy5cqXb16qtrcWHH37YqXPxXu2beF8SUUdwPMefRdQ1fJ8lVcL7legZdUUXQMonIiICy5cvh5aWFoKCgjBq1Cg0Njbi6tWr+N3vfofU1FRs3769R66dlpaGjz76CJMnT4aNjU2PXKMj3n//fVhaWmL06NE4e/aswuogxduzZ0+Lz3fv3o3z58+32u7o6Njta9XW1uKjjz4CAEyePLlDr+G92jfxviSil+F4jj+LqOv4PkuqhPcr0TMMOKmFrKwsrFy5EtbW1rh06RKsrKya973xxht4+PAhIiIiFFjhTwRBQH19PXR0dOR+7qysLNjY2KC0tBRmZmZyPz+pjsDAwBafx8XF4fz58622Kwrv1b6J9yURvQjHc8/wZxF1Fd9nSZXwfiV6ho+oUwt///vfUV1djR9++KHFYPg5BwcH/OY3v2n+XCKR4C9/+Qvs7e2hpaUFGxsbvPfee2hoaGjxOhsbG8ybNw9Xr16Fp6cntLW1YWdnh927dzcfs3PnTixfvhwAMGXKlFZT6Z+f4+zZs/Dw8ICOjg62bdsGAMjMzMTy5cthamoKXV1djBs3rlsDd0XONiDVI5PJ8MUXX2DkyJHQ1taGhYUFtmzZgidPnrQ4LikpCTNnzkT//v2ho6MDW1tbrF+/HsCzNcWev+F/9NFHzff/yx7h4L1K7eF9SdR3cTz3U71EPYXvs6RKeL9SX8AZnNTCiRMnYGdnhwkTJnTo+I0bN2LXrl1YtmwZ3n77bcTHx+Pjjz/G3bt3cfTo0RbHPnz4EMuWLcOGDRuwdu1a7NixA8HBwXB3d8fIkSMxadIkbN26Ff/+97/x3nvvNU+h//lU+vT0dAQEBGDLli3YtGkThg8fjuLiYkyYMAG1tbXYunUr+vXrh127dmHBggU4dOgQFi9eLL9/IKI2bNmyBTt37sS6deuwdetWZGVl4T//+Q9u3ryJa9euQUNDAyUlJZgxYwbMzMzwxz/+EcbGxsjOzsaRI0cAAGZmZvjmm2/wq1/9CosXL8aSJUsAAC4uLor80kiF8b4k6rs4niPqeXyfJVXC+5X6BIHo/1RWVgoAhIULF3bo+OTkZAGAsHHjxhbb33nnHQGAcOnSpeZt1tbWAgAhKiqqeVtJSYmgpaUlvP32283bDh48KAAQLl++3Op6z89x5syZFtvfeustAYAQHR3dvK2qqkqwtbUVbGxsBKlUKgiCIGRlZQkAhB9//LFDX58gCMLjx48FAMIHH3zQ4dfQq+2NN94Qfv6jMzo6WgAghISEtDjuzJkzLbYfPXpUACAkJia2e+7u3G+8V/s23pdE9BzHc63xZxF1F99nSZXwfqW+io+oU7OnT58CAAwMDDp0/KlTpwAAv/3tb1tsf/vttwGg1SNFTk5O8PHxaf7czMwMw4cPR2ZmZodrtLW1xcyZM1vV4enpCW9v7+Zt+vr62Lx5M7Kzs5GWltbh8xN11sGDB2FkZAQ/Pz+UlpY2f7i7u0NfXx+XL18GABgbGwMATp48iaamJgVWTH0B70uivovjOaKex/dZUiW8X6mvYMBJzQwNDQEAVVVVHTo+JycHYrEYDg4OLbZbWlrC2NgYOTk5LbYPGTKk1TlMTExarfvxIra2tm3WMXz48Fbbnz8K9cs6iOTpwYMHqKyshLm5OczMzFp8VFdXo6SkBADg6+uLpUuX4qOPPkL//v2xcOFC/Pjjj63WNyOSB96XRH0Xx3NEPY/vs6RKeL9SX8E1OKmZoaEhBgwYgJSUlE69TiQSdeg4NTW1NrcLgtDha/VEh02i7pDJZDA3N0dISEib+58vxC0SiXDo0CHExcXhxIkTOHv2LNavX4/PP/8ccXFx0NfX782y6RXH+5Ko7+J4jqjn8X2WVAnvV+orGHBSC/PmzcP27dsRGxuL8ePHv/BYa2tryGQyPHjwoMXC8cXFxaioqIC1tXWnr9/RwfUv60hPT2+1/d69e837iXqKvb09Lly4gIkTJ3boF7Zx48Zh3Lhx+Nvf/obQ0FCsXr0a+/btw8aNG7t0/xO1hfclUd/G8RxRz+L7LKkS3q/UV/ARdWrh97//PfT09LBx40YUFxe32p+RkYEvv/wSADBnzhwAwBdffNHimH/+858AgLlz53b6+np6egCAioqKDr9mzpw5SEhIQGxsbPO2mpoabN++HTY2NnBycup0HUQdtWLFCkilUvzlL39ptU8ikTTfy0+ePGk1u8XNzQ0Amh/70NXVBdC5+5+oLbwvifo2jueIehbfZ0mV8H6lvoIzOKkFe3t7hIaGwt/fH46OjggKCsKoUaPQ2NiImJgYHDx4EMHBwQAAV1dXrF27Ftu3b0dFRQV8fX2RkJCAXbt2YdGiRZgyZUqnr+/m5gY1NTV8+umnqKyshJaWFqZOnQpzc/N2X/PHP/4RYWFhmD17NrZu3QpTU1Ps2rULWVlZOHz4MMTizuf4e/bsQU5ODmprawEAUVFR+Otf/woAWLNmDWcRUDNfX19s2bIFH3/8MZKTkzFjxgxoaGjgwYMHOHjwIL788kssW7YMu3btwtdff43FixfD3t4eVVVV+O6772BoaNj8y6WOjg6cnJywf/9+DBs2DKamphg1ahRGjRrV7vV5r1JbeF8S9W0czz3Dn0XUU/g+S6qE9yv1GYps4U7K6/79+8KmTZsEGxsbQVNTUzAwMBAmTpwofPXVV0J9fX3zcU1NTcJHH30k2NraChoaGsLgwYOFd999t8UxgiAI1tbWwty5c1tdx9fXV/D19W2x7bvvvhPs7OwENTU1AYBw+fLlF55DEAQhIyNDWLZsmWBsbCxoa2sLnp6ewsmTJ1sck5WVJQAQfvzxx5d+/b6+vgKANj+e10N90xtvvCG09aNz+/btgru7u6CjoyMYGBgIzs7Owu9//3uhoKBAEARBuHHjhhAQECAMGTJE0NLSEszNzYV58+YJSUlJLc4TExMjuLu7C5qamgIA4YMPPnhhPbxXSRB4XxJR2zie488ikg++z5Iq4f1KfZVIEDqxIjgRERERERERERGREuEanERERERERERERKSyGHASERERERERERGRymLASURERERERERERCqLAScRERERERERERGpLAacREREREREREREpLIYcBIREREREREREZHKYsBJRETUx2RnZ0MkEmHnzp2KLoWIiIiIFIjjQnpVMOCkTtu5cydEIhG0tbWRn5/fav/kyZMxatSoXq3p4sWLWL9+PYYNGwZdXV3Y2dlh48aNKCwsbPP4mJgYeHt7Q1dXF5aWlti6dSuqq6t7tWZSXrzHiYjoVcf3OqKf8PuBiEj1qSu6AFJdDQ0N+OSTT/DVV18puhT84Q9/QHl5OZYvX46hQ4ciMzMT//nPf3Dy5EkkJyfD0tKy+djk5GRMmzYNjo6O+Oc//4m8vDx89tlnePDgAU6fPq3Ar4KUDe9xelVZW1ujrq4OGhoaii6FiBSM73VEP+H3A/VFHBfSK0Mg6qQff/xRACC4ubkJWlpaQn5+fov9vr6+wsiRI3u1psjISEEqlbbaBkD47//+7xbbZ8+eLVhZWQmVlZXN27777jsBgHD27NleqZeUG+9xIiJ61fG9jugn/H4gIlJ9fESduuy9996DVCrFJ598ouhSMGnSJIjF4lbbTE1Ncffu3eZtT58+xfnz5xEYGAhDQ8Pm7UFBQdDX18eBAwd6rWZSfrzHSZl9+OGHEIlEuH//PgIDA2FkZAQzMzP86U9/giAIyM3NxcKFC2FoaAhLS0t8/vnnza9ta62l4OBg6OvrIz8/H4sWLYK+vj7MzMzwzjvvQCqVNh935coViEQiXLlypUU9bZ2zqKgI69atw6BBg6ClpQUrKyssXLgQ2dnZPfSvQkSdxfc6op/w+4FUFceFRFyDk7rB1tYWQUFB+O6771BQUNDp19fW1qK0tPSlH0+ePOlSfdXV1aiurkb//v2bt925cwcSiQQeHh4tjtXU1ISbmxtu3rzZpWvRq4n3OKkCf39/yGQyfPLJJ/Dy8sJf//pXfPHFF/Dz88PAgQPx6aefwsHBAe+88w6ioqJeeC6pVIqZM2eiX79++Oyzz+Dr64vPP/8c27dv71JtS5cuxdGjR7Fu3Tp8/fXX2Lp1K6qqqvDo0aMunY+I5I/vdUQ/4fcDqTqOC6kvY8BJ3fLf//3fkEgk+PTTTzv92r///e8wMzN76cfo0aO7VNsXX3yBxsZG+Pv7N297vii3lZVVq+OtrKy6NJChVxvvcVJ2np6eCA0Nxa9+9SuEh4dj0KBBePvtt5sHj7/61a9w8uRJ6OjoYMeOHS88V319Pfz9/fHDDz/gtddew6FDhzB69Gj88MMPna6roqICMTExeP/99/GXv/wFGzZswLvvvotLly5h0qRJXf1yiagH8L2O6Cf8fiBVxnEh9WVsMkTdYmdnhzVr1mD79u344x//2OYba3uCgoLg7e390uN0dHQ6XVdUVBQ++ugjrFixAlOnTm3eXldXBwDQ0tJq9Rptbe3m/UTP8R4nZbdx48bm/1ZTU4OHhwfy8vKwYcOG5u3GxsYYPnw4MjMzX3q+1157rcXnPj4+2LNnT6fr0tHRgaamJq5cuYINGzbAxMSk0+cgot7B9zqin/D7gVQZx4XUlzHgpG57//33sWfPHnzyySf48ssvO/w6Ozs72NnZyb2ee/fuYfHixRg1ahS+//77FvueDyYaGhpava6+vr5Lgw169fEeJ2U2ZMiQFp8bGRlBW1u7xeNrz7eXlZW98Fza2towMzNrsc3ExKRLj9JpaWnh008/xdtvvw0LCwuMGzcO8+bNQ1BQUIvOr0SkHPheR/QTfj+QquK4kPoyBpzUbXZ2dggMDGz+K2dHPV9D5mXU1NRa/WBtT25uLmbMmAEjIyOcOnUKBgYGLfY//wvs80c5fq6wsBADBgzo0HWob+E9TspMTU2tQ9sAQBCETp/rl0QiUZvbf77g/HNvvfUW5s+fj2PHjuHs2bP405/+hI8//hiXLl3q8uN5RNQz+F5H9BN+P5Cq4riQ+jKuwUly8f7773d6rZrPPvsMVlZWL/0YO3Zsh85XVlaGGTNmoKGhAWfPnm3zcZJRo0ZBXV0dSUlJLbY3NjYiOTkZbm5uHa6f+hbe40TPPH+kqKKiosX2nJycNo+3t7fH22+/jXPnziElJQWNjY0tOncSkfLgex3RT/j9QPRyHBeSMuEMTpILe3t7BAYGYtu2bbC2toa6+stvLXmuUVNTU4M5c+YgPz8fly9fxtChQ9s8zsjICNOnT8fevXvxpz/9qfkvoHv27EF1dTWWL1/+0mtR38R7nOgZa2trqKmpISoqCosWLWre/vXXX7c4rra2FmKxGNra2s3b7O3tYWBg0OZjdESkeHyvI/oJvx+IXo7jQlImDDhJbv77v/8be/bsQXp6OkaOHPnS4+W5Rs3q1auRkJCA9evX4+7du7h7927zPn19/RY/bP/2t79hwoQJ8PX1xebNm5GXl4fPP/8cM2bMwKxZs+RSD72aeI8TPftFavny5fjqq68gEolgb2+PkydPoqSkpMVx9+/fx7Rp07BixQo4OTlBXV0dR48eRXFxMVauXKmg6onoZfheR/QTfj8QvRjHhaRMGHCS3Dg4OCAwMBC7du3q9WsnJycDAHbs2IEdO3a02Gdtbd1iADBmzBhcuHABf/jDH/Bf//VfMDAwwIYNG/Dxxx/3YsWkiniPEz3z1VdfoampCd9++y20tLSwYsUK/OMf/8CoUaOajxk8eDACAgJw8eJF7NmzB+rq6hgxYgQOHDiApUuXKrB6InoRvtcR/YTfD0Qvx3EhKQuR8LKVZYmIiIiIiIiIiIiUFJsMERERERERERERkcpiwElEREREREREREQqiwEnERERERERERERqSwGnERERERERERERKSyGHASERERERERERGRymLASURERD0mOzsbIpEIO3fuVHQpRERERKRAHBdST2LASUREpCQyMjKwZcsW2NnZQVtbG4aGhpg4cSK+/PJL1NXV9dh109LS8OGHHyI7O7vHrtERf/vb37BgwQJYWFhAJBLhww8/VGg9RERERIrCcSHHhdQ56oougIiIiICIiAgsX74cWlpaCAoKwqhRo9DY2IirV6/id7/7HVJTU7F9+/YeuXZaWho++ugjTJ48GTY2Nj1yjY54//33YWlpidGjR+Ps2bMKq4OIiIhIkTgu5LiQOo8BJxERkYJlZWVh5cqVsLa2xqVLl2BlZdW874033sDDhw8RERGhwAp/IggC6uvroaOjI/dzZ2VlwcbGBqWlpTAzM5P7+YmIiIiUHceFz3BcSJ3FR9SJiIgU7O9//zuqq6vxww8/tBjEPufg4IDf/OY3zZ9LJBL85S9/gb29PbS0tGBjY4P33nsPDQ0NLV5nY2ODefPm4erVq/D09IS2tjbs7Oywe/fu5mN27tyJ5cuXAwCmTJkCkUgEkUiEK1eutDjH2bNn4eHhAR0dHWzbtg0AkJmZieXLl8PU1BS6uroYN25ctwbcipwlQERERKQMOC78qV6izmDASUREpGAnTpyAnZ0dJkyY0KHjN27ciP/5n//BmDFj8K9//Qu+vr74+OOPsXLlylbHPnz4EMuWLYOfnx8+//xzmJiYIDg4GKmpqQCASZMmYevWrQCA9957D3v27MGePXvg6OjYfI709HQEBATAz88PX375Jdzc3FBcXIwJEybg7NmzeP311/G3v/0N9fX1WLBgAY4ePSqHfxUiIiKivofjQqIuEoiIiEhhKisrBQDCwoULO3R8cnKyAEDYuHFji+3vvPOOAEC4dOlS8zZra2sBgBAVFdW8raSkRNDS0hLefvvt5m0HDx4UAAiXL19udb3n5zhz5kyL7W+99ZYAQIiOjm7eVlVVJdja2go2NjaCVCoVBEEQsrKyBADCjz/+2KGvTxAE4fHjxwIA4YMPPujwa4iIiIhUHceFrXFcSB3FGZxEREQK9PTpUwCAgYFBh44/deoUAOC3v/1ti+1vv/02ALR6FMjJyQk+Pj7Nn5uZmWH48OHIzMzscI22traYOXNmqzo8PT3h7e3dvE1fXx+bN29GdnY20tLSOnx+IiIiIuK4kKg7GHASEREpkKGhIQCgqqqqQ8fn5ORALBbDwcGhxXZLS0sYGxsjJyenxfYhQ4a0OoeJiQmePHnS4RptbW3brGP48OGttj9/hOmXdRARERHRi3FcSNR1DDiJiIgUyNDQEAMGDEBKSkqnXicSiTp0nJqaWpvbBUHo8LV6ojMmEREREbXEcSFR1zHgJCIiUrB58+YhIyMDsbGxLz3W2toaMpkMDx48aLG9uLgYFRUVsLa27vT1Ozoo/mUd6enprbbfu3eveT8RERERdQ7HhURdw4CTiIhIwX7/+99DT08PGzduRHFxcav9GRkZ+PLLLwEAc+bMAQB88cUXLY755z//CQCYO3dup6+vp6cHAKioqOjwa+bMmYOEhIQWg++amhps374dNjY2cHJy6nQdRERERH0dx4VEXaOu6AKIiIj6Ont7e4SGhsLf3x+Ojo4ICgrCqFGj0NjYiJiYGBw8eBDBwcEAAFdXV6xduxbbt29HRUUFfH19kZCQgF27dmHRokWYMmVKp6/v5uYGNTU1fPrpp6isrISWlhamTp0Kc3Pzdl/zxz/+EWFhYZg9eza2bt0KU1NT7Nq1C1lZWTh8+DDE4s7/DXXPnj3IyclBbW0tACAqKgp//etfAQBr1qzhX/+JiIjolcdx4TMcF1JnMeAkIiJSAgsWLMDt27fxj3/8A+Hh4fjmm2+gpaUFFxcXfP7559i0aVPzsd9//z3s7Oywc+dOHD16FJaWlnj33XfxwQcfdOnalpaW+Pbbb/Hxxx9jw4YNkEqluHz58gsHshYWFoiJicEf/vAHfPXVV6ivr4eLiwtOnDjRpdkCAPDDDz8gMjKy+fPLly/j8uXLAABvb28OZImIiKhP4LiQ40LqPJHQmdVkiYiIiIiIiIiIiJQI1+AkIiIiIiIiIiIilcWAk4iIiIiIiIiIiFQWA04iIiIiIiIiIiJSWQw4iYiIiIiIiIiISGUx4CQiIiIiIiIiIiKVxYCTiIiIiIiIiIiIVBYDTiIiIiIiIiIiIlJZDDiJiIiIiIiIiIhIZTHgJCIiIiIiIiIiIpXFgJOIiIiIiIiIiIhUFgNOIiIiIiIiIiIiUlkMOImIiIiIiIiIiEhlMeAkIiIiIiIiIiIilcWAk4iIiIiIiIiIiFQWA04iIiIiIiIiIiJSWQw4iYiIiIiIiIiISGUx4CQiIiIiIiIiIiKVxYCTiIiIiIiIiIiIVBYDTiIiIiIiIiIiIlJZDDiJiIiIiIiIiIhIZTHgJCIiIiIiIiIiIpXFgJOIiIiIiIiIiIhUFgNOIiIiIiIiIiIiUlkMOImIiIiIiIiIiEhlMeAkIiIiIiIiIiIilcWAk4iIiIiIiIiIiFQWA04iIiIiIiIiIiJSWQw4iYiIiIiIiIiISGUx4CQiIiIiIiIiIiKVxYCTiIiIiIiIiIiIVBYDTiIiIiIiIiIiIlJZDDiJiIiIiIiIiIhIZTHgJCIiIiIiIiIiIpXFgJOIiIiIiIiIiIhUFgNOIiIiIiIiIiIiUlkMOImIiIiIiIiIiEhlMeAkIiIiIiIiIiIilcWAk4iIiIiIiIiIiFQWA04iIiIiIiIiIiJSWQw4O6mwsBAffvghCgsLFV0KERERESkQx4VEREREyoEBZycVFhbio48+4kCWiIiIqI/juJCIiIhIOTDgJCIiIiIiIiIiIpXFgJOIiIiIiIiIiIhUFgNOIiIiIiIiIiIiUlkMOImISO4EQUCjpBGCICi6FCIiIiIiInrFqSu6ACIienXUN9UjLDoMp66fQk1DDUz1TbHYazEWeS2CmlhN0eURERERERHRK4gBJxERyYVUJsWfQv+EtLy05pmb5dXl+OHiD8gtzcVb899SbIFERERERET0SuIj6kREJBdx9+OQmpva5mPp526dQ87jHAVURURERERERK86BpxERNQpRU+KcPnOZcSkx6C+qb55e1x6HMSitt9WxCIx4tLjeqtEIiIiIpKj0tJSHDhwAI2NjYouhYioTXxEnYiIOqRR0ogvTnyBK6lXmrfpaOrg9VmvY5rLNMgE2QtfL5VJe7hCIiIiIuoJEokEGRkZOHDgAAICAqCmxrXViUi5cAYnERF1yDdnvkFkWmSLbXWNdfj8+Oe4k3MHY+zGtBtyygQZ3O3de6NMIiIiIpIzS0tLrFy5EllZWQgPD29zSSIiIkViwElERC9VUVOB87fOtzmYFYvEOBR7CD5OPhjSf0irx9RFIhE8HTwxbMCw3iqXiIiIiOTM0tISixcvxu3bt3HhwgVFl0NE1AIDTiIieqnskuwXzs5ML0iHpromPg36FL4jfaEmfvbYkpaGFhaOXYj3lr0HkUjUmyUTERERkZw8ePAAX375JdTV1TFr1ixcu3YNsbGxii6LiKgZ1+AkIqKXMtAxePF+7Wf7jXSN8LtFv8Mbs99AZU0lTA1MoaWh1RslEhEREVEPGTJkCGxtbbFv3z54e3tjwoQJOHv2LPT19eHs7Kzo8oiIOIOTiIhezs7CDgNNB7Y5C1MkEsHP1a/FNl0tXViZWjHcJCIiInoFaGlpYcWKFfDz88O1a9dQUFCA4cOH49ixY8jMzFR0eUREDDiJiOjlRCIR3l7wNjTVNZvX2BThWdg5fMBwLPBcoMjyiIiIiKiHiUQiTJw4EWvXrsXjx4+Rn58PIyMj7Nu3D4WFhYouj4j6OAacRETUISMGjcDXm7/GgrELYGtuC8dBjnh91uv4ZM0n0NbQVnR5RERERNQLbGxssGXLFpiYmKCsrAzV1dXYs2cPysvLFV0aEfVhXIOTiIg6zMrECptnbFZ0GURERESkQIaGhggODsa5c+cQHR2N+/fvY+fOndiyZQv09PQUXR4R9UGcwUlEREREREREnaKmpobZs2cjICAAlpaWuHr1KrZt24aGhgZFl0ZEfRBncBIRERERERFRlzg7O8PCwgLfffcdzp49i6amJrzzzjtQU1NTdGlE1Icw4CQiIiIiIiKiLjM3N8c777wDQ0NDHDlyBLW1tfif//kfhpxE1Gv4iDoRERERERERdYuWlhbefPNNrF+/HlFRUfjjH/+ImpoaRZdFRH0EA04iIiIiIiIi6jaRSIQ1a9Zg69atSE5Oxrvvvou8vDxFl0VEfQADTiIiIiIiIiKSm8WLF+PNN99EdnY2PvnkEyQkJEAQBEWXRUSvMK7BSURERERERERytWDBAkilUhw9ehR79+5FXl4e5s2bB01NTUWXRkSvIM7gJCIiIiIiIiK5EolEWLhwIWbNmgUAiI+Pxw8//ICysjIFV0ZEryIGnEREREREREQkd2pqali+fDlcXFygra2NyspKbN++Hffu3VN0aUT0imHASUREREREREQ9QlNTE6tWrUL//v2hoaGBAQMGYN++fbhw4QJkMpmiyyOiVwQDTiIiIiIiIiLqMbq6ulizZg0AoK6uDr6+vrh27Rr27NmDmpoaBVdHRK8CBpxERERERERE1KOMjY2xevVqPHnyBLm5uVi9ejVKSkqwbds25OXlKbo8IlJxDDiJiIiIiIiIqMdZWloiICAAOTk5uHXrFjZv3gwjIyP8+OOPSExMhCAIii6RiFQUA04iIiIiIiIi6hU2NjZYsmQJUlJSEBcXh+DgYHh4eCAiIgJHjx5FU1OTokskIhWkrugCiIiIiIiIiKjvGDlyJGpqanDq1CkYGBhg9uzZGDRoEI4fP47i4mL4+/vD1NRU0WUSkQrhDE4iIiIiIiIi6lWenp7w8fHBuXPncPv2bTg7O2PTpk2QSCTYvn070tPTFV0iEakQBpxERERERERE1OumTp2K0aNH49ixY8jIyIC5uTk2bdoEW1tbhIWF4eLFi5DJZIouk4hUAANOIiKSu8eVjxFzLwbJWcmQyqSKLoeIiIiIlJBIJML8+fPh4OCA/fv3o6CgANra2lixYgX8/Pxw9epV7N27FzU1NYoulYiUXJ8KOD/88EOIRKIWHyNGjFB0WUREr4yGpgb849g/EPxVMP566K94L+Q9rPlyDRIfJiq6NCKiFjguJCJSDmKxGMuWLYO5uTlCQkJQVlYGkUiEiRMnIigoCMXFxdi2bRvy8vIUXSoRKbE+FXACzxYzLiwsbP64evWqoksiInpl/Dvi37iSegUChOZtlTWV+POBPyOzOFOBlRERtcZxIRGRctDU1MSqVaugo6ODvXv3orq6GgBga2uLLVu2wNDQED/++CMSExMhCMJLzkZEfVGfCzjV1dVhaWnZ/NG/f39Fl0REpDKapE2ISovCt2e/xY+XfkRGUUbzvseVj3El5UqrQacAAYIg4Gj80d4ul4johTguJCLquMbGxh49v66uLgIDAyGRSBASEoKGhgYAgKGhIdatWwcPDw9ERETg6NGjaGpq6tFaiEj19LmA88GDBxgwYADs7OywevVqPHr06IXHNzQ04OnTp80fz/+SRETU15RVleGN7W/gkyOfIOJ6BA7HHcavv/81vj37LQRBwIPCBy1mbv6cTJAh9VFqL1dMRPRiHBcSEXVMZmYmvvjiC9y9e7dHr2NsbIzAwEA8efIE+/fvh1T6bC13NTU1zJ49G0uXLsXdu3fx/fffo7y8vEdrISLV0qcCTi8vL+zcuRNnzpzBN998g6ysLPj4+KCqqqrd13z88ccwMjJq/vD19e3FiomIlMfn4Z+j4EkBAEAqkzZ3tDyeeByXUy5DV0v3ha/X19bv8RqJiDqK40Iioo6ztLSEtbU19u/fj/Dw8ObZlT3BwsICK1euRE5ODo4dO9bi6SBnZ2ds2rQJEokE27dvR3p6eo/VQUSqRST04QUsKioqYG1tjX/+85/YsGFDm8c0NDS0+OGdnJwMX19fXL9+HWPGjOmtUomIFKqwvBAbvm7756RIJMJQq6H4PPhzrPlyDSprKtucybl5xmYs8lzUw5USEXUNx4VERO0TBAG5ubkoKyvD6dOnoaenhyVLlmDw4ME9ds20tDQcPHgQXl5emDlzJkQiUfO++vp6hIeH4+7du/Dx8cGUKVMgFvep+VtE9At9+ieAsbExhg0bhocPH7Z7jJaWFgwNDZs/9PU5A4mI+p6iiqJ29wmCgKInRVATq+G/5v8XxGIx1MRqzftFEMFpkBPmjJnTG6USEXUJx4VERO1LS0vDjh07UFNTg9deew36+vrYsWMHLl261PwYubw5OTlhzpw5iIuLQ0xMTIt92traWLFiBfz8/HD16lXs3bsXNTU1PVIHEamGPh1wVldXIyMjA1ZWVoouhYhIqVkYW7S7TyQSwdLYEgAw1mEsvtzwJSaPmgxLY0s4WDpg84zN+H+B/w+a6pq9VS4RUadxXEhE1D4nJyf4+vriwoULiI6ORlBQEKZMmYKrV6/ihx9+QGlpaY9cd+zYsZg0aRLOnz+PW7dutdgnEokwceJEBAUFobi4GNu2bUNeXl6P1EFEyk9d0QX0pnfeeQfz58+HtbU1CgoK8MEHH0BNTQ0BAQGKLo2ISKkNMB0AF2sXpDxKgUyQtdgnCALmj53f/LmdhR3eXvB2b5dIRNQpHBcSEXWcSCTClClTYGpqiuPHj6OiogIrVqyAg4MDjhw5gm3btmHGjBnw8PBo8Si5PEyZMgVVVVUIDw+Hnp4eHBwcWuy3tbXFli1bcODAAfz444+YNWtWj9RBRMqtT83gzMvLQ0BAAIYPH44VK1agX79+iIuLg5mZmaJLIyJSeu8sfKd5pqaaWK35MfS57nMx1XmqIksjIuo0jguJiDrP1dUVQUFBKCoqwg8//ABtbW1s2bIFbm5uiIiIQGhoKKqrq+V6TZFIhPnz58PBwQEHDhxAfn5+q2MMDQ2xbt06uLu7IyIiAseOHUNTU5Nc6yAi5danmwx1xY0bN+Du7s7F5ImoT2qSNiHmXgxSHqVAW1Mbk5wmYajVUEWXRUSkEBwXElFfVVZWhtDQUNTV1WHlypUYMmQIHjx4gPDwcMhkMixYsAAjRoyQ6zWbmpqwe/dulJWVYcOGDejXr1+bx92+fRsnTpyAqakp/P39YWpqKtc6iEg5MeDsJA5kiYiIiAjguJCI+rba2lrs378feXl5WLRoEZydnVFTU4MTJ07g3r17GDNmDGbOnAktLS25XnPHjh2QSqXYsGFDu83eiouLsX//ftTW1mLx4sUYPny43GogIuXUpx5RJyIiIiIiIqLu09XVxZo1azBq1CgcPnwYV65cga6uLvz9/bFw4UKkpKTg22+/RW5urtyvKZFIsHfvXjQ0NLR5nIWFBTZv3gwbGxuEhYXh4sWLkMlkbR5LRK8GBpxERERERERE1Gnq6upYtGgRpk6diitXruDo0aOQSqUYPXo0XnvtNejr62PHjh24fPkypFKpXK5pZGSEwMBAVFRUYN++fZBIJG0ep62tDX9/f0yfPh1Xr17F3r17UVNTI5caiEj5MOAkIiIiIiIioi4RiUSYNGkSli1bhrS0NOzevRu1tbUwNTXFunXrMGXKFERHR2PHjh0oLS2VyzUtLCwQEBCA3NxcHDt2DO2tvCcSieDt7Y2goCAUFxdj+/btbTYpIiLVx4CTiIiIiIiIiLpl1KhRWLt2LcrKyvD999+jtLQUYrEYkyZNwoYNG1BfX49t27YhMTGx3UCyM6ytrbF06VKkpqbi7NmzLzynra0ttmzZAgMDA+zYsUNuNRCR8mDASUTUx239YSvWfLkGW3/YquhSiIiIiEiFDR48GBs3boSamhp++OEHZGdnAwAGDhyILVu2wM3NDREREQgLC0N1dXW3r+fo6Ig5c+YgLi4OMTExLzzW0NAQ69atg7u7OyIiInDs2DE0NTV1uwYiUg4MOImI+rgn1U9QVlWGJ9VPFF0KEREREak4ExMTbNiwAVZWVtizZw+Sk5MBAJqampg7dy5Wr16NgoICfP3117h37163rzd27Fj4+vri/PnzuHXr1guPVVNTw5w5c7BkyRKkpaXh+++/R3l5ebdrICLFY8BJRERERERERHKjra2N1atXw9XVFceOHcPFixebHwkfOnQofvWrX2HIkCHYt28fjh8/jsbGxm5db/LkyRgzZgzCw8Px4MGDlx7v4uKCjRs3oqmpCdu3b0d6enq3rk9EiseAk4i6RdJQi6e5aah5nMN1bIiIiIiICMCz2ZLz58+Hn58foqOjcejQoeZHwvX09ODv748FCxYgJSUF3377LXJzc7t8LZFIhHnz5mHo0KE4cOBAhxoJWVhYYPPmzbCxsUFYWBguXrwImUzW5RqISLEYcBJRlwgyKbIu/ID4f67C7V2/w81tr+Pm9tfxNK/7j5kQEREREZHqE4lEmDhxIvz9/XH//n3s2rWree1NkUiEMWPG4LXXXoOuri527NiBy5cvQyqVdulaYrEYy5Ytg6WlJUJCQlBWVvbS12hra8Pf3x/Tp0/H1atXsXfvXtTU1HTp+kSkWAw4iahLMs9tR37cUQjSnxbmri3NRcred1FX9vK/mBIRERERUd/g6OiI4OBgVFRU4Pvvv8fjx4+b95mammL9+vWYPHkyoqOjsWPHjg6Fk23R0NDAqlWroKenhz179qCqquqlrxGJRPD29kZQUBCKi4uxffv2Ds0AJSLlwoCTiDqtsbochddPAfjFI+mCAJlMivz4Y4ooi4iIiIjkhI/qkrwNHDgQmzZtgpaWFr7//ntkZGQ07xOLxfD19cWGDRtQX1+Pb7/9FklJSV1aAktHRweBgYGQyWQICQlBfX19h15na2uLLVu2wMDAADt27Ojy9YlIMRhwElGnVeWnA0I7g16ZFBVZN3u3ICIiIiKSi+rqahw7dgyHDx9WdCn0CjIyMsL69esxePBghISE4Pr16y32Dxw4EFu2bIGrqytOnjyJsLCw5kfaO3udwMBAVFRUYP/+/ZBIJB16naGhIdatWwd3d3ecPHkSx44da143lIiUGwNOIuo0sbrmi/draPVSJUREREQkD1KpFLGxsfjqq6+Qnp4OW1tbzl6jHqGlpYVVq1bB3d0dJ06cwLlz51rca5qampg3bx5WrVqF/Px8fPPNN13qcm5ubo6AgADk5ubi6NGjHb6f1dTUMGfOHCxZsgRpaWn4/vvvUV5e3unrE1HvYsBJRJ1mZO0MNS29tneKRDAfNblX6yEiIiKirsvKysK3336Lc+fOwcXFBb/+9a/h4eEBkUik6NLoFSUWizFnzhzMmjULsbGx2L9/PxobG1scM2zYMLz++usYNGgQwsLCcOLEiVbHvIy1tTWWLl2KtLQ0nDlzplOhvYuLCzZu3IimpiZs3769SyErEfUeBpxE1GlidU3Yz/rVs09EP/sxIhJDt/8QWLrPVUxhRERERNRhlZWVOHjwIHbt2gUdHR1s3rwZc+fOha6urqJLoz5AJBJh3LhxWLlyJTIzM7Fz585WTYH09PSwcuVKLFiwAHfu3MG3336LvLy8Tl3H0dERc+fORXx8PK5du9ap11pYWGDz5s2wsbFBWFgYLl26xPVpiZSUuqILICLVZO48BZr6psiLOYiq/HtQ09KFucs0DBq/FOpaHBQTERERKSuJRIKYmBhER0dDS0sLS5YsgbOzM2dskkIMHz4c69atQ2hoKL777jusWrUKlpaWzftFIhHGjBkDGxsbHDlyBDt27MCkSZPg4+MDNTW1Dl3Dw8MDVVVVuHDhAvT19eHm5tbh+rS1teHv749r167h4sWLyM/Px9KlS/mHACIlw4CTiDrsad49FCSEo6YoAxr6JrB0m4mRAR9BJO7YwIKIiIiIFOv+/fs4ffo0KisrMW7cOPj6+kJLi+unk2JZWVlh06ZNCAsLw44dO7B8+XIMHTq0xTGmpqZYv349oqOjERkZiQcPHmDJkiXo169fh64xefJkVFdX4/jx49DT02t1/hcRiUTw9vbGgAEDcPjwYWzbtg0rVqzAwIEDO/V1ElHP4SPqRNQhJbcv4fbOt1F69yrqyvPxNDcV949/jvSj/4DQXkd1IiIiIlIKZWVlCAkJQWhoKExNTfH6669jxowZDDdJaTzvYG5ra4vQ0FAkJCS0OkYsFsPX1xfr169HfX09vv32WyQlJXVobU2RSIS5c+di6NChOHDgQKcfdQcAOzs7bNmyBQYGBtixY0eHr01EPY8BJxG9lKS+Bg9PffXsk+dh5v+9kZfejUb5/XgFVUbKQCqT4vKdy3g/9H1s/X4rvj7zNfLKOj9gJCIiIvlrbGzExYsX8fXXX+Px48fw9/dHYGAg+vfvr+jSiFrR1NSEv78/xo0bh1OnTuH06dNtrnk5aNAgbNmyBa6urjh58iTCwsJQXV390vOLxWIsW7YMVlZWCA0NRWlpaadrNDQ0RHBwMMaMGYOTJ08iPDwcTU1NnT4PEckXA04ieqny+/GQSdrpWCgSo+TO5d4tiJSGVCbFXw/+Ff8I/wduZt3Ew6KHOHX9FF7f/jpuZN5QdHlERER9liAISElJwX/+8x/ExsbCx8cHb7zxBhwdHV+41qYgCMjOzkZKSkovVkv0E7FYjJkzZ2Lu3LlITEzEvn370NDQ0Oo4TU1NzJs3DwEBAcjPz8c333zToU7nGhoaCAgIgJ6eHvbu3duqsVFHqKurY+7cuViyZAlSU1Pxww8/oLy8vNPnISL5YcBJRC8laagG0M5AWJBBUv/yv5bSq+nSnUuIf/BsBu/zx3NkggxSqRSfhX8GiVSiyPKIiIj6pOLiYuzatQuHDh3CgAED8MYbb2Dy5MnQ0NBo9zUymQwpKSn47rvvsHPnTty4wT9UkmKNHTsWq1atQk5ODn788Uc8ffq0zeOGDx+O119/HYMGDUJYWBhOnDiBxsZ2Jmf8Hx0dHQQGBkImk2Hv3r2or6/vUo0uLi7YuHEjGhsbsX379g4FrETUMxhwEtFL6VsNA9DO2jIiMQwGDO/Vekh5XLh1oc1ZIAIEVNRU4Fb2LQVURURE1DfV19fj9OnT2LZtG6qqqhAYGIiVK1fCxMSk3dc0NjYiPj4eX331FQ4dOgRtbW0EBgZizZo1vVg5KbumpibEx8f3+nqTDg4OWL9+Perq6vDdd9+hoKCgzeP09PSwcuVKzJ8/H7dv38a333770jU2jYyMEBgYiMrKSuzbtw8SSdf+MG9hYYHNmzfDxsYGYWFhuHTpUpuP1RNRz2LASUQvZTBwOAwGOQKiX/zIEIkgVteApftsxRRGCldZW/nCgW5VXecf+SEiIqLOEQQBN2/exFdffYWbN29i2rRpeP311+Hg4NDua6qrq3Hp0iX861//wtmzZ5vXNAwKCoKDg8MLH2OnvicrKwtnzpxBREREr4ecFhYW2LRpEwwNDfHjjz/i3r17bR4nEong7u6O1157Dbq6utixYweuXLnywrDR3Nwcq1atQl5eHo4cOdLlYFJbWxv+/v6YPn06oqOjERIS8v/ZO+/wKM5zb9+zRX3Vey8gIQSqFFGMqMYgmsGm2YANLsmxE6eck+LE34l9nMRJjo/LcWxzbAjYmGaKMb333kFCQgIV1HtbrbR1vj9kjRGSQIAKZe7rmmulnXdmnllpZ9/9zfM8P3Q63T3tS0ZG5t6QBU4ZGZk7IggCfWe+hVNwdIvnrRzc6Df3XWycPHsoMpmeJtw3HMWtwvdNhHqHdmM0MjIyMjIyjx8FBQV8+eWXbNq0ibCwMH72s58xbNgwlEplm+PLy8vZvHkzH374ISdOnCAmJoaf//znzJgxAx8fn26OXuZhITw8nClTpnD27Fm2bNnS7SKng4MDL7zwAr1792bNmjUcP3683Rjc3NxYuHAhI0aM4NChQyxZsoSKiop29x0YGMgzzzxDWloaO3bsuOdzEwSB4cOHM2/ePIqLi1m8eDEFBQX3tC8ZGZm7R9XTAcjIyDwcqO2c6P/cn9GV3aC+NAe1vRNOgf0QFG1PnmUeD6YOmsq+y/taPa8QFMSFxhHoHtgDUcnIyMjIyDz61NfXs3fvXs6dO4e3tzcLFy4kMLD9z90bN25w7Ngxrl69ir29PUlJSQwYMABbW9tujFrmYSYuLg5BENi0aROiKDJ58uRuzfRVq9U8++yz7N27l507d1JRUcGECRPaFPMVCgUjR46kV69ebNiwgc8//5ynnnqK+Pj4NmPu06cPkyZNYvPmzWg0Gp544ol7jjM0NJRXX32VtWvXsnTpUiZMmEBCQoKcFS0j08XIAqeMjMxdYecRiJ2HLFo9zhhNRgoqC7BR2xDmHcabM97kg80fUK+vl8bEhsTy26d/24NRysjIyMjIPJpYLBZOnz7N/v37EQSB5ORkEhISUChaV1RYLBauXr3KsWPHyMvLw93dncmTJxMdHY1KJX8VlLl7YmNjEQSB7777DovFwpQpU9r83+sqBEFg7NixuLq6smXLFqqqqnj22WexsbFpc7y/vz8/+clP2LlzJ5s3byYjI4MpU6Zgb2/famxCQgJ1dXXs3bsXBwcH4uLi7jlOR0dHXnjhBXbu3MmWLVvIz88nOTn5tkZfMjIy94f8qSYjIyMj0yFEUWT9ifWsPboWbaMWgFCvUF6f8Drf/PIbzlw/g7ZBS2/f3oR4hvRwtDIyMjIyMo8eOTk5bN++ndLSUuLj4xkzZgx2dnatxhmNRi5evMjx48epqKggKCiIOXPmEB4eLmeRydw3MTExKBQKNmzYgCiKTJ06tVtFToD4+HicnZ2lLMm5c+fi7Ozc5lgrKysmT55MeHg433//PZ9++ilTp04lPDy81dikpCTq6urYvHkz9vb2bY7pKCqViuTkZAICAti8eTPFxcXMnDkTV1fXe96njIxM+whidzfPeMg5d+4cCQkJnD17lvj4+J4OR0ZGRua+mffRPCrqKnDTuPH1G1+3O271kdV8deCrFs8JgoBaqebjRR8TKGf2ysjIPGbI80KZ7qK2tpZdu3aRkpKCv78/EydOxNfXt9U4nU7HqVOnOHXqFA0NDURGRjJ06FD8/f17IGqZR52UlBQ2bNhAv379mDZtWreLnABlZWWsXLkSg8HAnDlz7vi/rtVq+f7778nIyCAhIYHx48djZWXVYozFYmHt2rVcv36dBQsWdMr7p6SkhDVr1qDT6Zg+ffp9CacyMjJtI5sMycjIyMjckQZDA2uOrmn1vCiKmMwmvj3+bQ9EJSMjIyMj82hjMpk4fPgw//u//0tOTg7Tpk1j0aJFrcTNyspKtm7dygcffMDRo0eJioriZz/7GTNnzpTFTZlO49bcqH79+jFjxgxJ6LxXB/L7wcPDg5deeglXV1eWLVvGlStXbjvewcGBOXPmMHnyZC5dutSmEZBCoZBMt7755hvKy8vvO04vLy9eeeUVgoODWblyJfv27euR10tG5lFGLlGXkZHpMGZDA2Wph6gvzcHK3gmPfqOxcZYd1B8HMgsz0Rv1ba6ziBbOXj/bzRHJyMjIyMg82mRmZrJ9+3aqq6sZPHgwSUlJrfoM5ufnc+zYMdLS0rC1tWX48OEMHDiwzbJ1GZn7obi4mI0bNzJhwgSCg4Ol56OiohAEgXXr1iGKItOnT2/T9Kcrsbe3Z8GCBXz33XesXbuWsWPHMmzYsHbbMQiCQEJCAsHBwWzYsIElS5YwYsQIRowYIWWhqtVq5syZw7/+9S++/vprXnrpJTQazX3FaWNjw6xZszhy5Aj79u2joKCAGTNmyO9XGZlOQhY4ZWRkOoS2+DopK/+ISVeLoFAiiiK5B1YQNuGn+CQk93R4Ml2MSnn7j4s7rZeRkZGRkZHpGJWVlezcuZOrV68SGhrKnDlz8PDwkNaLokhGRgbHjh0jNzcXV1dXkpOTiYmJkQ1MZLoMpVKJlZUVy5YtIzo6mieffBIHBwcA+vbty7PPPsu3337L+vXrmTFjRreLnCqVihkzZuDm5saePXuoqKhg0qRJt43Dzc2NhQsXcvjwYQ4dOsS1a9eYPn261CPT1taW559/ni+//JIVK1bw4osvtmtm1FEEQeCJJ57Az8+PdevWsXjxYmbOnImfn9997VdGRkYuUZeRkekAosXMlTVvY2rQSr8jWgCR69s/RVuU2bMBynQ54b7hONs5t7lOIShI6pvUvQHJyMjIyMg8YhiNRvbt28enn34qmZHMmzdPEjdNJhPnzp3jn//8J6tWrcJisTBr1ixef/11BgwYIIubMl2Kh4cHCxcuZOrUqVy7do1PPvmEU6dOSWXWkZGRzJo1i6tXr7Ju3TrMZnO3xygIAqNGjeLpp5/m0qVLrFixgoaGhttuo1QqGTlyJAsXLkSn0/H5559z9uxZqRzf0dGRefPmUVtby6pVqzCZTJ0Sa2hoKK+++ioajYalS5dy5syZVi0AZGRk7g5Z4JSRkbkjVdfPYqir+EHUvAWFkqJz27s/KJluRaVU8er4V4EmQbMZhaDAxcGF6YnTeyo0GRkZGRmZhxpRFElNTeWTTz7h2LFjDBs2jNdff52+ffsiCAINDQ0cPnyYDz/8kM2bN+Pu7s7ChQtZtGgRkZGRXWLs0tjYSFlZWafvV+bhRhAE4uLieP3114mKimLbtm188cUXUg/LiIgIZs2aRUZGBt9++22PiJzQ5PI+f/58iouLWbJkCZWVlXfcxt/fn5/85Cf079+fzZs3s3r1aurr64EmcXfOnDkUFBR0aq9RJycnXnjhBeLj49myZQubNm3CaDR2yr5lZB5HHmuB87333kMQBH7xi1/0dCgyMg80jVXF0E4PGyxmGiuLujcgmR4hKSqJd+e+S9+AvigVSmytbBkfO54PF36Ii4NLT4cnIyMjc1/I80KZnqC0tJSvvvqKb7/9Fm9vb1577TVGjRqFWq2murqaHTt28MEHH3Dw4EEiIiJ47bXXmD17NoGBgZ0ei16v5/Lly6xevZp//OMfbNy4sdOPIfNoYGdnx+TJk3nppZcQRZEvv/ySLVu20NDQQHh4OLNnz+batWusWbOm0zIe75agoKAW8d24ceOO21hZWTF58mTmzJlDfn4+n376KRkZGQAEBgby7LPPkpaWxvbt2zst21KlUpGcnMzTTz9NamoqS5YsoaqqqlP2LSPzuPHYNk07ffo0ixcvJjo6uqdDkZF54LF28oD2PsQFBTYu3t0bkEyPER8aT3xofE+HISMjI9OpyPNCme6msbGRAwcOcOrUKVxcXHjuuefo3bs3AEVFRRw9epQrV65gbW1NYmIigwYNkvoddiYGg4HMzExSU1PJyMjAZDLh7+/PuHHj6Nu3b6cfT+bRwt/fn1deeYXTp0+zb98+rly5wpNPPklMTAyzZ89m9erVrFmzhlmzZqFSdb/04ObmxqJFi1izZg3Lly9n2rRp9O/f/47bRURE8NOf/pTvv/+elStXMmDAAJ588kkiIiKYNGkSmzdvRqPRMGLEiE6LNSYmBm9vb9asWcPixYuZPn064eHhnbZ/GZnHgcdS4NRqtTz33HN88cUXvPvuuz0djozMA49Lr4Go7V0w6mpal6mLFrzjnuqZwGRkZGRkZO4TeV4o052IosjFixfZvXs3RqOR0aNHk5iYiFKp5Nq1axw9epTs7GycnZ0ZP348cXFxWFlZdWoMJpNJEjWvXr2K0WjE19eXUaNGERUVhbOzc6ceT+bRRqFQMHjwYPr27cuuXbv47rvvOHfuHMnJycyZM4dVq1axevVqZs+e3SMip52dHfPmzWPz5s2sX7+eiooKkpKS2nVYb8bBwYE5c+Zw9uxZdu7cSXZ2NtOnTychIQGtVsu+fftwcHAgPr7zbvx7eXnxyiuvsHHjRlauXMmIESMYOXJkl7ShkJF5FHksBc7XXnuN5ORkxo4de8eJrF6vR6/XS79rtdquDk9G5oFDoVTRd9b/I3XlW5ga6xEUiqayDNFCyLiX0fhF9HSIMjIyMjIy94Q8L5TpLgoLC9m2bRv5+fn069ePJ598Ent7e1JSUjh27BglJSX4+vry7LPPdnpvTZPJxPXr1yVRU6/X4+3tzYgRI4iKipJco2Vk7hWNRsOMGTOIj49n69atLF68mMGDBzNjxgw2bNjAqlWrmD17do+YYalUKqZNm4abmxv79u2jsrKSKVOm3FFwFQSBAQMGEBISwoYNG1iyZAlJSUkMHz6curo6Nm/ejL29PRERnfddyMbGhtmzZ3PkyBH27dtHQUEBM2bMwM7OrtOOISPzqPLYCZyrV6/m3LlznD59ukPj//rXv/L22293cVSPBhaLiEW0oFIqezoUmS5A4xvOgNeXUnp5H7rSHNT2znhGj8bW1a+nQ5ORkZGRkbkn5HmhTHeg0+nYu3cv586dw9PTkxdeeAFvb2/Onj3LyZMnqa2tpXfv3kyYMIGgoKA7ZpZ1FLPZTFZWFqmpqaSnp9PY2IinpydDhw4lKioKd3f3TjmOjMzNhISE8NOf/pTjx49z8OBBUlJSGDBgAKdPn2bVqlXMmTOnR0ROQRAYMWIErq6ufPfdd1RXVzN79uwOCYdubm4sXLiQQ4cOceDAATIzM5k2bRr19fWsW7eO+fPnExAQ0KmxPvHEE/j5+bFu3ToWL17MzJkz8fOTv3fJyNwOQeys7rgPAXl5eQwYMIDdu3dLPZZGjhxJbGwsH374YZvb3Hqn/sKFCyQlJXH27NlOTUd/mCmv1rJs+zH2n7uKwWQm1Ned58YNZkRs754OTUZGpgPM+2geFXUVuGnc+PqNr3s6HBkZGZluQZ4XynQ1FouFM2fOsH//fkRRZNSoUURERHDq1CnOnj2LyWQiOjqaIUOG4Onp2WnHzM7OJjU1lbS0NBoaGnB3dycqKoqoqKhOO46MTEdoNspKT0/H0dGRyspKevfuzZw5czq99cLdkJeXx+rVq7G2tmbu3Ll3Jfbn5eWxceNGtFotY8eOJSUlhbKyMhYuXIiHh0enx1pTU8PatWspLi5mwoQJJCQkdNpNEBmZR43HSuD87rvvePrpp1HelGFoNpsRBAGFQoFer2+xri3OnTtHQkKCPJH9gWqtjp/+90oq6+qxWJr+lQShyY/m58+MYvKwmB6OUEZG5k50l8BpMps4nHaYI2lHMJgMRAdFMz52PI52jl12TBkZGZn2kOeFMl1Jbm4u27dvp6SkhLi4OPr168fFixe5fPkyVlZWDBgwgMGDB6PRaO77WBaLhdzcXEnUrK+vx9XVVRI1vby8ZEFEpkfJyMhg27Zt5OXlUVNTQ2JiIvPnz+9RkbOqqoqVK1ei1WqZNWsWwcHBHd7WYDCwY8cOzp07R2hoKBUVFQAsWrQIR8fOn9eaTCZ27tzJ6dOniY2NJTk5uUeyYGVkHnQeK4Gzrq6O3NzcFs+9+OKL9OnTh9/+9rf069fvjvuQJ7ItWbr1KGv2nsHSxr+RrbWate+8go2VfPGVkXmQ6Q6BU2/U89aqt0i5kYIgCIiiiCAIONk58Y8F/8BPbnUgIyPTzcjzQpmuoK6ujl27dnH58mV8fX3p168fWVlZXLt2DScnJxITE4mPj8fa2vq+jiOKInl5eaSkpHDlyhW0Wi3Ozs6SqOnj4yOLmjIPFEajkcOHD7N161auXr3KsGHD+NWvfnXf74X7obGxkbVr15Kbm8vkyZOJjY29q+3T09P5/vvvMRgM6HQ6/P39efHFF7G1te2SeC9evMiWLVtwc3Nj1qxZuLi4dMlxZGQeVh6rHpwajabVZNXe3h43N7cOTWJlWnP40rU2xU2ABr2Ry1kFDOwT3L1BycjIPHBsOLGB1LxUoOlLWfNjra6WDzd/yD8W/KMnw5ORkXkMkeeFP/L9999jY2ODq6srLi4uuLq64uTkJDv33gVms5kTJ05w8OBBVCoVffv2pbKykl27duHt7c306dOJioq6Y1bw7RBFkYKCAknUrK2txdHRkf79+xMVFYWfn58saso8sKjVakaPHk1MTAxfffUVW7ZsIScnh//6r//qsdYJNjY2PPfcc2zdupXvvvuOiooKRo8e3eH3UZ8+ffD392fTpk1cuHCBU6dOoVarefHFF7vEMT4mJgZvb2/WrFnD4sWLmT59OuHh4Z1+HBmZh5XHSuCU6XzMZstt11vMj02C8EPLhSVvYNBWYeXgQuyij3o6HJlHlB3nd9BWwYBFtJCal0pJdQlezl49EJmMjIzM443FYqGmpobs7GxqamqwWJrmdgqFAicnpxaip4uLi/RzT5aWPmhcu3aN7du3U15ejrOzM0ajkStXrhAWFsa8efMIDQ29Z+FRFEWKiopISUkhNTWVmpoaHBwcpEzNgIAAWdSU6RZMJhOpqalER0ff1/+cm5sbv/jFL4iOjub999/n1Vdf5fXXX2fkyJH3dQPgXlEqlUyePBk3Nzd2795NZWUl06ZN63AJuIODA3PnziUiIoI1a9awadMmTCYTr776apfcJPLy8uKVV15h48aNrFy5khEjRjBy5Ej5hpSMDLLAyYEDB3o6hIeawX1D+P7oRan/5s2oVUqiQn16ICqZu8GgrcJQV9HTYcg84tToam67vrq+WhY4ZWRkepzHcV6oUCiYN28e8KPYWVlZSVVVlfSYn5/PpUuXMBgM0nb29vatRM/mnx0cHB4L0a2qqoqdO3dy8eJFzGYzarWauro6+vXrx9ChQ/H29r6n/YqiSElJiSRqVlVVYW9vT9++fYmKiiIwMFAWM2S6nczMTDZu3MiZM2eYNGkSXl73Pm8TBIExY8YQEhLCu+++y8cff8zly5eZOnUqISEhnRh1x+MZNmwYrq6ubNiwgeXLlzN79mwcHBw6vP2AAQMIDg7m008/Ze3atVRXV/Ob3/ymS0RbGxsbZs+ezZEjR9i3bx8FBQXMmDGjQ47wMjKPMo+9wCnTcep0jew/d5WSqlq8XZ0YHR/BMyPj2X36Cg16Y6tS9dljBuBga9ND0crIyDxIBHkEca34WptZnEqFEl9X3x6ISkZGRkbmZhQKhSRS3oooiuh0Okn0vFkAzc7Opq6uThqrVqvbzPp0cXHB2dm5R7K0OhOj0ciRI0fYtWsXJSUl2NnZ4ePjw4ABA0hMTMTJyeme9ltaWiqJmhUVFdja2kqiZnBwsCxqyvQokZGRvPjii2zZsoXFixczZMgQkpKS7iubOzQ0lHfffZdPP/2U8+fPU15eTnx8PE8++WSHxcXOJDIykhdeeIFVq1bx5ZdfMnfu3Lsqn3d3d+cPf/gDXl5erFq1iqqqKn73u9/h6ura6bEKgsATTzyBn58f69atY/HixcycORM/P7mvvczjywNvMlRQUMChQ4coLS1lxowZ+Pv7YzabqampwcnJqdsnSI9rM/nT6Tm8868t6A0mlEoFJrMFW2s17yyagrODHZ9s2M/Fa/kAONnbMnvsQGYkxT0Wd+8fdk59NB9DXQVWGjcGvfFVT4cj0wN0h8nQwdSD/G3j31o9rxAUPBn7JD9P/nmXHFdGRubRQp4XPrgYDAaqq6vbFECrq6sxm81A05dyJyendgVQG5sH9+a4KIqkpaWxatUq0tPTsbW1JSoqimHDhjFgwIB7ir28vJzU1FRSUlIoKyvDxsaGyMhIoqKiCAkJeejFYJlHD7PZzNGjRzl06BAODg5MnDjxvvtAFhUVsXz5crRaLba2tigUCsaMGcOAAQN6RNivqalh5cqVVFdXM3PmTMLCwu56H+vWrePLL78kLCyMRYsWERfXdd+Na2pqWLt2LcXFxUycOJH4+Hj5e7jMY8kDm8EpiiK//vWv+eSTTzCZTAiCQP/+/fH390er1RIcHMw777zDL37xi54O9ZGnWqvjT0u3YDSaEAHTD303Gw1G3vrye1b+5yL++7VnqKrT0aA34OmiQSVPxmRkHmtulN/gSNoRDEYD/YP680TfJyiqKmLFwRVYRIvkpD6w10BeefKVng5XRkbmAUeeFz74WFlZ4enp2Wa2k8Vioba2toXoWVlZSWFhIampqTQ2Nkpj7ezs2ix7d3V1RaPR9NiX9tLSUpYsWcKxY8ewsrIiMTGRcePG0b9//7sWISsrKyVRs6SkBGtra/r06cO4ceMICwuTRU2ZBxqlUsmIESPo168fW7duZeXKlURGRjJhwgQcHR3vaZ8+Pj688MILfPXVV9ja2uLj48P27ds5f/48ycnJ+Pv7d/JZ3B4nJycWLlzIt99+yzfffENycjIJCQl3tY8ZM2agUqlYu3YtS5cuZfjw4UyePBl7e/suiffFF19k586dbN68mby8PJKTkzvcR1RG5lHhgRU4//GPf/DRRx/x29/+ljFjxjBu3DhpnZOTE9OnT2f9+vXyRLYb2H06DaPJzK2pvqIIeqORvWfSmTYiFheNHS4aue+HjMzDhouDS4vH25Ffkc+mU5u4lHMJWytbRvYbyVPxT2GjbspaEUWRL/Z8wXcnv0MhKBAEgbXH1tLbpzfvzn2XJ2Oe5GTmSfQmPdFB0YR6hXbpucnIyDwayPPCrqW8vByNRoO1tXWX7F+hUODs7Iyzs3Or/nqiKNLQ0NAq67OyspIbN25QW1srjVWpVJLgebMA6urqirOzc5e4Fmu1WpYuXcr27dsBSEpKYsaMGfTq1euuxNaqqiquXLlCSkoKRUVFWFlZERERwahRo+jVq1eXxC4j05W4urry/PPPk5qayo4dO/jkk08YPXo0gwYNuqesS29vbxYsWMDy5cspLy9n7ty57Nu3jyVLlhAfH8+YMWO6tcektbU1c+fOZfv27WzevJmKigrGjRvX4fe9IAhMmTIFg8HA0aNHuXTpEvn5+UydOpXevXt3erwqlUoSg7ds2UJxcTGzZs1qs+WIjMyjygP7SfrFF18wf/58/vKXv1BR0doAJTo6WppoyHQtBWVVKAQBc5u98xQUlFd3f1AyMjKdxseLPu7QuNS8VP7wzR8wW8yYLU2lhplFmexP2c/f5v0NGysb9l7ay3cnvwOaHNKb74xcL77Ox1s/5g/P/IEJ8RO64jRkZGQeYeR5YdchiiKLFy/GaDTi4OCAq6trm0tXlY4LgoCdnR12dnZt9o4zmUytxM+qqiquX7/O2bNnMZlM0n40Gk27ru+2trZ3FZdWq2X16tVs3LiRxsZGRo4cyYIFCwgMDOzwPmpqaiRRs6CgALVaTe/evXniiSfo3bu3nF0l89AjCAL9+vWjV69e7N27VzLdmjx5Mr6+d99f3cvLixdeeIHly5ezZ88enn/+edLS0ti7dy9paWmMGzeO2NjYbsvkVigUTJw4ETc3N3bu3EllZSXTp0/vcN9RhULB9OnTqa+vJy8vD3t7e7755hsGDhzIk08+2SXXgJiYGLy8vFi7di2LFy9m+vTp991CQEbmYeGBFTjz8vIYOnRou+vt7e1b3NGV6To8XBzbNAYBMFtEPF003RyRjIxMdyOKIv/z/f9gNBtbXA9ERK4VX2PTqU3MGj6LTac2ISAg3pLzbREtHEs/RmVdJa6azm+0LiMj82gjzwu7lhdeeIGKigoqKyuprKykvLycjIwMdDqdNMbOzg43N7c2xc+7FQ/vBpVKhYeHBx4eHq3WiaIolb7fLICWlpaSnp5OQ0ODNNbGxqZV1mezAOro6ChlnFVUVLB161Y2btxITU0NiYmJvPrqqwQFBXUo3rq6OknUzMvLQ6VS0atXL5555hnCw8Pvy5BFRqYnEUWRkpISvL29W62zsbEhOTmZmJgYtmzZwhdffMGgQYMYPXr0XWeGe3p6SiLn119/zYIFC+jbty+7du1i06ZNnDt37r5d3O8GQRBITEzExcWF9evXs2zZMubMmYNG07HvwCqVijlz5vCvf/0LnU5HUlISx44dIzs7m+nTp9+TEHwnvL29eeWVV9i4cSMrV64kKSmJpKQk2ahM5pHngRU4PT09ycvLa3f92bNn7+oOqsy98+TASL7ecZxWNeqAUiEwdkCf7g9KRkamW7lWfI2iqqI214miyO6Lu5k1fBYFlQWtxE1pHCJF1UWywPkIYDEaqcrOBsAlNBSFXFop08XI88KuQxAE/Pz82syebC4dr6ysbCGAXrt2jfr6emmcra0trq6ubQqgtra2XZZt1WxY5OTkRHBwcKv1jY2NbZoe5efnU1tbK92wUyqViKJIYWEh6enp6HQ6IiMj+dWvfkViYuIds6y0Wi1paWmkpKRw48YNFAoFYWFhTJ8+nYiIiC4r/ZeR6U4uXLjAJ598wiuvvMLgwYPbHOPv788rr7zCiRMnOHDgAFeuXGHChAlERkbe1XXAw8NDKldftmwZCxYsYPr06cTFxbFt2zYWL17M4MGDGTlyZLe9vyIiInjxxRdZuXIlX3zxBXPnzm1T7G0LGxsbnnvuOZYsWUJaWhrz589n+/btfPnll4wcOZLhw4d3uvhoY2PD7NmzOXLkCPv27SM/P58ZM2Z0a5m/jEx388B+I5k+fTqff/45L7zwAk5OTgDSRXHXrl0sW7aM3/zmNz0Z4mODh7OG3zw3nr+t2Nn0hACITX+PN+dNwEXT+Y2SZWRkuo+fL/k5VdoqXBxc2i1Xr2+sb/P5ZrR6LQAejh7kVbQvQrhr3O89UJkHgqzdu7n8zTcYtE1/cyuNhpj58wkeNaqHI5N5lJHnhT2Dra0ttra2bWYYNTY2SqJh81JRUUFWVhbaH64P0PQl+2bB82YR1M7OrktLTW1sbPD19W0zfpPJRHV1NWfOnOHQoUOcP3+e0tJSHBwciI2Nxdvbm71797J37140Gk2rrE9bW1tKSkq4fv06OTk5CIJAaGgoU6ZMoU+fPl2a1Soj0xPY2dlhMBj4+9//zssvv8z48ePbfP8qFAqGDh1KVFQU27dvZ+3atfTu3Zvk5GScnZ07fDwPDw8pk3P58uUsWLCAkJAQfvKTn3D8+HEOHjxISkoK48ePJyoqqlvK1n18fHj55ZdZtWoVS5cu5dlnn+1wP01HR0fmzZvHkiVL2L17N/Pnz+fYsWPs37+fzMxMpk+f3un9MgVB4IknnsDPz49169axePFiZs6c2eYNLRmZRwFBbK/2uIepqalhxIgRZGdn88QTT7Bjxw7GjRuHVqvl+PHjxMXFcejQoW6/A3Hu3DkSEhI4e/Ys8fHx3Xrsnqa4soadJ69QUlmLj5sT4wf3xdPl3pzyZB4cTn00H0NdBVYaNwa98VVPhyPTA8z7aB4VdRW4adz4+o2v2xxTU1/D8x89L/XevBmFoGBgr4H856z/5PvT3/P5zs/bHBMdHM1fnvtLp8cv033kHjrEqY/bFsGH/PrX+A8Z0s0RyTwuyPPChwuDwdBC+LxZAK2rq5PGWVtbt8r4bBZA7e3tu0ywMJlMXLx4kWPHjpGVlUVFRQWOjo6MHTuWsWPHYm9vj1arbWV6VFJSQkZGBjdu3KCqqgpRFPHw8KBXr15ERkbi4+PTou+nk5OTXBIq80iRmZnJ+++/T35+PnPmzGH27NkolcrbbpOens62bdtoaGggKSmJIUOG3HGbm6moqGDZsmVYW1uzYMECqTS8pqaGHTt2kJaWRmhoKMnJybi5ud3X+XUUg8HA+vXrycjIYMKECQwaNKjD2+bl5fHVV18RFhbGzJkzKSgoYMOGDdTX1zNhwoQu6zFaU1PD2rVrKS4uZuLEicTHx3dbL1MZme7igRU4oaks5v3332fdunVkZmZisVikC8F//Md/9MidUXkiK/OoIQucMh0ROAE+2fYJ289vb9WTV0Dgb/P/Rr/AfpgtZv7x3T84dOUQSkVTyZ9FtODl7MU/5v8Dd0c5g/NhRRRFtr/+OvUlJa1XCgKOfn48+cEH8mRZpsuQ54WPBgaDoVXmZ/NSU1MjjbOysmrX8Eij0dzTtUan03H69GlOnTpFVVUVer0ei8VCZGQkEydOxN/fv9U2jY2NXL16ldTUVK5fv47FYsHX15eAgADc3d3R6/UthNCamhosFgvwo3t8W67vLi4ucj9OmYeSkpIS/vu//5uUlBQmTJjAq6++escycYPBwP79+zl58iTu7u5MmjTprtqKVFZWsmzZMtRqNQsWLMDR8cckm4yMDLZv305tbS3Dhg3jiSee6BYDL4vFwu7duzl+/DiDBw9m/PjxHb6hkZGRwerVq4mPjyc5ORmDwcCOHTs4f/48kZGRTJ48uUtu2JlMJnbs2MGZM2eIjY0lOTlZNjuTeaR4oAXOBxF5IivzqCELnDIdFTiNJiOfbP+EPRf3SH027a3teX3i6yRFJUnjRFHkUu4ljqQdQW/U0y+wH0lRSVir5R5kDzONNTVsXrTotmOmLV+O2l5uWyLz+CDPCzsXo9F4W/Gz+WuLWq1uV/x0dHRsJX5WVVVx/Phxzp8/j9lsxtbWltraWjQaDWPHjiUuLq7FNgaDQRI1r127hslkIjAwkKioKPr27XtbcxGz2UxNTU2r7M/mPqAGg0Eaa29v38rtvfmxKzNYZWTul7q6Oj7++GMOHTrE0KFD+dWvftUh053i4mI2b95MQUEBCQkJjB07tsM3pyorK1m+fDlKpZIXXnihhchpNBo5cuQIR44cQaPRMHHixG5zDj99+jTbt2+nV69ezJgxo8M9Qc+fP8+mTZsYNWoUSUlN8+i0tDQ2b96MQqFg6tSpHS5/bw+LxYLJZMJoNGIymaTl4sWL7Ny5EycnJ8aPH4+dnZ207taxHfl9xIgRxMTE3FesMjKdgSxw3iXyRFbmUUMWOJsQLWYaqopQqKywcfLs6XC6lY4KnM2U1ZSRXpCOjZUNMcExWKlaZ6AYzUYKKwuxUlnh7ezd7pc0URQ5nnGcned3Ul5XTrBnMFMGTCHCL6LFuEs5l1h3fB3pBenYW9szNmYsTw9+GjtruVF6d2Gsr+e7BQvaHyAITF+xAqVspiHzGCHPC7uP5p6ZN5sdNS/V1dWS+KlSqXBxccHNzQ1RFMnNzaWoqAhXV1dCQ0MpLS1Fq9UycOBARo0aJYkrRqORjIwMUlNTycjIwGQy4e/vT1RUFFFRUS3ElHtFFEXq6+tbmR41P97cu1StVrfr+u7s7HxXJb4yMl2BwWBg6dKlfPfdd0RGRvLmm2/i4eFxx+0sFgtnz55lz549qFQqxo8fT//+/Tsk6FdVVbF8+XIEQWjRk7mZiooKtm3bxvXr1+nTpw9PPfXUXfX9vFeuXbvGt99+i4uLC3Pnzm11vWhPaDx8+DBHjhxh5MiR9OnTB6PRSE1NDfv27SM3N5fw8HDi4uIA7kp4bP65OZu8LbRaLampqRiNRiIjI3Fzc0OpVKJSqVCpVKjVaunnO/3ep08f2ehP5oHggRU4Fy5ceMcxgiCwZMmSbojmR+SJrMyjRmcLnKLFTPH5HRSd3YahthxbN398B03Fve8TPZaJYDGbMDXUorJxQHGLGCeKIkVnt5J3aCVGXVNpnL13GGFP/RRH/8ieCLfbuVuB83aIosim05tYfXg1tQ21AIR6hfLTp35KVEBUq7Gf7fiMLWe3oBAUWEQLSoUSs8XMLyb9gidjnwRgf8p+/vHdP6Qx0HT9D/EM4b8X/Dc2Vjb3FbNMxzn0zjuUpqQg3jJhFhQKfOLjGfa73/VQZDKPOvK8UOZ2mM1mqqurpT6fly9f5vTp0+Tl5SEIAh4eHtTV1VFVVYWfnx9Dhw4lLCwMR0dHamtrKSoqIj8/H5PJhK+vryRqdocwcjPN5fu3Zn02i7jNYkWze3x7AqiNjfy5KNM9WCwWNm7cyNKlS/H29uatt94iODi4Q9vW1dWxc+dOUlJSCAkJYdKkSR3qoVldXc2yZcsQBIEFCxa0ep+KosiVK1fYuXNnq76f7QmN9yIe3vp7ZWUlx48fx2QyERMTg729vbS+PaFRFEWuXbtGQUEB/fr1w929qZWTQqGQTMzs7OxISEjAzc3troXHO401m82SIDxy5EhGjRol9w2Weah5YAXO4ODgVmKI2WymqKgIs9mMh4cH9vb2ZGVldWtc8kRW5lHjbgRObfF1Ck9vpr74OlYOrnjFjsOtz1AEoemDUBRFrm78G+VXDv+4kSCAKBIwbBZBo+Z35am0wmIycuPQCorObMNs0KFQWeEZPZbgMS+i+iHzr+Dkd2Tv/qLlhoKAoFARu/AD7L1CujXmnqAzBc71x9ezZG9LgUEQBFQKFR8s/IBQr1Dp+Ys5F/n9it+3uR+VUsWKN1ZgY2XDcx88R72+tYu7gMCisYuYnjj9vmKW6Tg1ubns+8MfMBsMksgpKBSobGwY/Ze/4NhG/zoZmc5AnhfK3AmTycTly5c5duwYZWVl+Pv7M3DgQMrKyti3bx+iKNK/f38cHR1JS0vj6tWr5OTkYDKZcHBwwMvLi969exMQENDK8MjJyanHMyYtFgu1tbVtlr1XVlai1+ulsXZ2dq1Ez+af77V/qYzM7Th06BAffPABarWaN998k9jY2NuObxYaTSYTV69eZfv27dTU1DBgwAApY/F2YmKzwZDJZGLEiBHY2Ni0GtvQ0MDVq1fJysrCxsaGsLCwVhmfd0KhUNyVYNiclVlbW8vo0aMJCwu747YKhYKtW7eSlZXF/PnzCQ0NlUTG8vJyNmzYQHFxMSNHjmT48OGdLkCKosiRI0fYt28foaGhzJgxo9sN+2RkOosHVuBsD6PRyOLFi/nwww/ZvXs3ISHdKz7IE1mZR42OCpxlVw5zdePfQFCAxdz0KFrwjB5D78m/RBAEqrPOk7Lyj+3uI+HfvsTW1acrTqMVoiiS9u27VGaehJsvc4ICB59eRC/4B4gWTn7wPOY2xDMEBe6Rw+kz/bfdEm9PcjcCZ42uhh3nd5Cal4qtlS1PRD7BkIghKBVK9EY9z33wHDqDrtV2CkHB8Mjh/G76jxl+H2z+gL2X9kpZmbfy+oTX8XDy4D9X/2e78fTy7sXHL7Xt6i3TNdQVFpK+cSOFp0+DIOA3eDB9pk3Dwdu7p0OTeQyR54UyjY2NnDlzhpMnT1JXV0dERARDhgyhoaGBnTt3UldXx6BBg/D39yczM5P09HQaGxvx8PCgb9++BAQEIAhCm30/zWYz8KNZUFs9P11cXHpc/BRFkYaGhlaiZ/Pjzc71zSX8bQmgzaXvRUVFpKWlIQgCo0eP7sEzk3mQyM3NZcuWLXh6ekqZljcLj9nZ2Xz77bfodDrGjRtHeHh4uyLlrRmNZrOZ3Nxc8vLysLGxITw8HBcXF2n9rUJjs5h46tQpAEaOHImzs3ObYmJtbS2nTp2irKyM8PBwnnjiiXbH3vr7vYiJRqORjRs3kpaWxpNPPkliYuIdbyqYTCZWrFhBcXExCxcuxNPzx3ZZZrOZgwcPcvjwYQICAnj66adbvDadRVZWFuvWrUOtVjNr1ix8fX07/RgyMl2NqqcDuFvUajWvv/46V65c4fXXX2fr1q09HZKMzCOPSa8jc/MHTUKh2DTZ5wdRqvTSXtwjh+PaexBlaYcRFEpEi7n1TgQFFelH8B/67B2Pp68tJ//Yt5SnHUE0m3AOSyBg6LMtsiktZiMFx9c3lcJrq7Bx9sJ38DR8EiYiCAq0hVepzDjReueiBW1hBpUZJ7HSuLUtbv4wrur62TvG+jiRW5bLb776DdpGLaIoohAUHL5ymIG9BvLWs29xvfh6m+ImgEW0cC7rXIvn6hrq2hU3FYKCusY6HO1u3/Os0dh4bycjc89ofH0Z+NprPR2GjAwgzwsfZ2pqajhx4gRnz57FbDYTExPDkCFDEASBHTt2kJmZiaOjIyEhIVy4cIHjx4/j5ubG4MGDiYqKaiEgAISFhbX4/eaMyZuXnJwczp07h8lkApqqFG4nfqpUXf91SxAE7OzssLOza9MJ3mg0SiX8N4uf165do6qqCpPJRG1tLeXl5dTW1qLT6TCZTPTu3VsWOGUkjh8/zqeffopCocDGxgZ3d3d8fX0JDAzEx8cHZ2dnZs2axaZNm9i1axcWi4WRI0diZWXV4QzI5v6TBQUFRERE8NRTT+Hk5NSu0FhbW8vy5cupr6/n2WefxdXVtc1xU6dO5eLFi+zatYv9+/czevRoBg4c2CXl2Gq1mmeffZa9e/eyc+dOKioqmDBhwm1vhKhUKmbPns2//vUvVqxYwaJFi6RsU6VSyejRo+nVqxcbN27ks88+Y8KECcTGxnZqNnZoaCivvvoqa9euZcmSJUycOJH4+Hg541vmoeKhEzibiYmJ4euv76+UUkZGpmNUZpzEYtS3vVJQUHJpL669B2Ex6GkvKVwQBMyGO4tRjTWlXFz6S4y6WklELb9ymIr0Y/R//i84BvRFFC2krX33BwGy6XiNVUVk7fgMXWkOvSa+TuW1M6BQNmWb3opCQeW10/gOSL5tLILcwF9CFEX+8d0/qG+sl/7GzeLkmWtn2HZuG338+tx2Hyply4+cXt69OJl5ss3/GYtooZd3L4I8ghAEoc0xSoWSmGDZsVFGRkaeFz5OFBcXc+zYMVJSUrCysmLw4MEMHjwYtVrNgQMH2LlzJ1qtVurLp1KpGDBgAFFRUXh5eXX4y3pz1qazszOhoaEt1omi2Kb4eePGDS5cuIDRaAR+7JXZnvipVqs79bVpD7VajYeHh2QAI4oier2eq1evcvbsWc6dO0d2djaVlZXU19djNptRKpV3Xc4r82iTlJSEtbU1ubm5XL9+naKiIulnW1tbAgICCAsL46WXXmLPnj2cOnWKwMBA/u3f/q3DQr+/vz99+/blwoUL7Nq1i//7v/9j7Nix7Qptjo6OvPDCCyxfvpxly5axYMGCNvt4CoJAbGwsERER7N27lx07dnD+/HkmTZrU5k2B+0UQBMaOHYurqytbtmyhqqqKZ5999rb9cW1sbHj++edZsmQJK1asYOHChS0c5gMDA/nJT37Cjh072LRpExkZGUyePLlTy8mdnJx48cUX2bFjB5s3byY/P5+JEyd227VKRuZ+eWgFzt27d8u9IWRkuglTQx0g0CwmtkC0YNI1mck4BkZRlnqgzX2IFjOOgf3ueKwbh1a2EDebjyFaRK7v/Jy4lz6m6vo5qq6faXP74nPb8RkwqXnDdo4iIAD2XqFYObhi0Fa2MUSBR+QTd4z3cSG3LJeskrZ724mIbD+3neSEZFwdXKls4/VUCApG9B3R4rmn4p5i3Yl1GIyGFpmcSoUSfzd/4kLjUAgKnox5kl0Xd7UQORWCAqVCybTB0zrnBGVkZB5q5Hnho40oimRlZXH06FGysrJwdnbmySefJD4+HpVKxZ49e1i7di35+fl4e3vTv39/oqOjiYqKwsfHp9MzkJqFSycnp1ZtEURRRKvVSoZHzeJnfn4+ly5dwmAwSGMdHR1b9Pq8Wfy0srK69bASFosFg8GAXq9Hr9ej1WqpqamRlrq6OmnRarXU19dLj2VlZVKmpl6vx2w2N1VlKBRYW1vj5OSEp6cnGo2GXr16derrJvNw4+bmRq9evZgyZQpKpZKGhgbKysrIyMggLS2NzMxMjh07xq5dTXO2qqoq3nvvPTZt2sRzzz2Hr68vbm5u0uLo6Njme1MQBOLi4ggPD2f37t1s3ryZixcvMmnSpFaZ1wAajYYFCxa0EDmbzXpuxdbWlkmTJhEXF8eWLVv48ssviY+PZ+zYsV3yGRIfH4+zszNr165l6dKlzJ0797bmZRqNhueff56lS5eyatUq5s2b10JctLa2ZurUqYSHh7N582Y+/fRTpk2b1qnvVZVKxaRJkwgICGDz5s0UFxczc+bMLimLl5HpbB5YgfOdd95p8/nq6moOHTrEuXPn+J3s1ioj0y3Ye4fRrlgoKHDw7Q2AZ/9R5B1ZjUFb1VKgFBQ4eIfhHHL7bDtRFClPPdRy2x9XUl98ncbqUirSj7WfnSkoqLh6HNfeg8g7vKrtA1nMuPQehKBQEjr+J6Sv/4vUU7R5H2pbTYfK6R8XqrRVd1yvVCj56VM/5S/r/oIgCJJoqRAUONo58syQZ1ps46px5c9z/8xf1v+FiroK6flQr1DeevYtFD+YV/3bhH/DSmXF9vPbMZmbSgJ9XX35xaRf4Ofq15mnKSMj84AizwsfT8xmMykpKRw7doySkhJ8fHx45plniIyMpKioiHXr1vH9999TXFxMYGAgL7/8MoMHD8bPz6/HyioFQUCj0aDRaAgKCmqxzmQyUVVVRXFxMaWlpZSWllJWVkZWVhaVlZU0NDRgNBrR6/UIgoBSqUShUEiVDBaLBbPZjF6vx2g0Ssut/QyVSiVqtRo7OztsbGwwm800NDSg1Ta1mHF2dsbLywuVSoWbmxuRkZHExcXRt29fRFGUBCtra+vufOlkHnCuX7/O+vXr2b17N4MGDSIhIYHAwEACAwMZO3Ys0PSeLSgo4MqVK1y6dIlt27aRmprKe++9x6BBg3BwcMDJyQkbGxvUarUk8N+62NnZYW9vz7Rp04iNjWXLli18/vnnDB06lKSkpFYZhRqNplUmZ3PGclv4+fnx8ssvc+bMGfbt20d6ejpjx44lLi6u068doaGhLFq0iJUrV/LFF18wZ86c22aNuru7M3fuXJYvX8769euZOXNmq1L6yMhI/P392bRpEytWrGDQoEGMGzeuUzMtY2Ji8PLyYs2aNSxevJgZM2bQu3fvTtu/jExX8MCaDLXXD8PFxUVKfX/55Ze7ffIiN5OXedS4sOQNDNoqrBxciF30UZtjRFHk4tJfoi25DjdPon9wG0/4yefYuDQZjDRWFXP1+/epy7siDXMNT6T3pDdQ/9BP0dhQR8nF3WgLM1Ba2+ERlYRTUDQAR/8ypW2B8wfif/IZ+UfXUZqyv81xgkKJ/9BnCRo5j7R1f2kSQ2lpMqTxiyB6/t8QFE0l6FVZ58k7vJLavCsIKjUefUcQOOI5bJy9OvQaPux0xGSovLacBR8vQGxD6FYICvoF9uO9ee8BcCH7AqsOryI1LxUrlRVJUUnMfWIuHk4e1Ohq2HVhFxmFGdjb2DOq3yiiAqK4lHOJSm0lgR6B9Pbp3ea1va6hjpzSHOxt7AnxDJF7AsnIPEbI88LHC71ez9mzZzlx4gS1tbX06tWLoUOHYm1tTWpqKhcuXODixYuUlZXRq1cv5s6dy7Bhw7rk7y+KoiQq3u3SLCo2L80CZnvLzQYsZrMZs9mMyWTCbDajUCiwsrLCysoKBwcHSRjy9PTEx8cHf39/AgMD8fT0lMqI09LSyMjIoKGhAYvFgkKhQK/XY2trS2hoKJGRkWg0GtLT00lJSSEjI4PCwkJMJhNWVlbExcXxt7/9rdNfU5mHl5KSEk6cOMGlS5dQKBTExcUxePDgNsvCAQwGA1999RWLFy9GrVYzZMgQVCqV1ALBzs4OKysrdDodNTU10nY2NjYtBE8nJyeysrK4dOkSzs7OJCcntym21dfXs3z5cnQ63R1Fzma0Wi27d+/m4sWLBAQEkJycjHcXGCfW19ezevVqioqKmD59On379r3t+IyMDFavXk1cXByTJk1q8/omiiKnT59m165dODs7M2PGDHx8OtfQtaGhgY0bN5KRkUFSUhJJSUld0rtURqYzeGAFzgcVeSJ7exr0Bg5fvEZZjZYADxeG9AtFrZL7GD4KGLRVpK37M3X5adJzantnIp7+Dc5t9EFsqCxAX1uOrYsv1k4/Ti7qS3O4/PXvMDVqARAEBaLFjFfseHol/4zLX/+O2rwrbYqXajsnBr7xFeWph8j4/v12Y+33/F9xDo7GYjaSd2QNRac3Y2rUolDb4B03nqCR81Ba2bbaThQtgPDYCWcddVH/y/q/cCz9WJvGQH+a9ScG9R502+NcL77O777+XZMRkdgkWJgtZsbHjufnyT9/7F73hxWL2Uzh6dMUnz8PgE9CAj4JCSjknrUyjyHyvLBzqa2t5eTJk5w5cwaj0Ui/fv3o3bs3JSUlpKamUllZSXV1NbW1tXh4eDB16lQSExPbNO8QRVHKhrzfpbmMuy1RstlpXepPbbFIAqUoiiiVSknQaTZVsbe3l7I8HR0dpcXe3l4yC7p5UavVNDY2tun0XlFRQUNDAyaTicrKSmpra6mvr0etVkv7bDYhanZM1+l05OTkkJOTQ3V1NdB0syA0NJS+ffsSHx9PZGTkbcvkZR5vtFotZ86c4fTp0+h0OiIiIkhMTCQoKKjN+dzp06d59913EQSBl156CbVaTXZ2NsXFxUBT+XtAQAAuLi7Y29vT0NBARUWFtOh0TSaWOp2OvLw8GhoaiIiIYNy4cQQEBODm5oaLiwtKpZL6+nq++uortFotCxYsaLOsvS1ycnLYunUr5eXlDB48mFGjRnV6FrPJZOK7774jJSWFsWPH3vHGzPnz59m0aRMjR45k5MiR7Y4rKytjw4YNlJSUMGrUKIYNG9apIqQoihw+fJj9+/cTFhbG9OnT5bYwMg8kssB5l8gT2fY5k57LO8u20KA3olQImC0iro72/OWVaYT53fnumczDgbYok/rSHNT2zjiHxKG4xTjG1FhPacp+dKU5qO1d8Ow/GlvXpjuJoihy7vNXaagsalPAjJj2H6jtnEhZ+cc2jx06/lV8B07BYjJw/ouf0VBZ2KoU3jGgL/3nvddisiBazJj0OpRWtq3ilem4wFnfWM+7697lYs5FqVxOqVDy4ugXmZ44/bbHsIgWXv70ZUqqS9oUSH/79G9Jikq673OR6VqMOh2H3nmHymvXJBMu0WzGLSKCEW+9heo2zfNlZB5F5Hlh51BaWsqxY8e4fPkySqWSwMBA1Go1169fp6ysDJVKhaOjI8XFxTQ0NBASEkL//v2ljMT2lva+5jQ/Lwgtb2qKotiiFLx5aRYybxUrVSoV9vb2bQqTtra2bYqVNjY2nXZDr6GhgYyMDC5cuEBqaip1dXUIgkBjYyOlpaVUVFRIr0NzZqjFYsHe3h53d3fCw8OJj49n6NChhIWFdYvju8yjhclk4tKlSxw/fpyysjJ8fHwYMmQIUVFRrW48ZGVl8Yc//IGamhp++9vfkpSUJInt2dnZZGdnU15eDoCXlxchISGEhIQQFBSEKIqSoF9eXs758+c5duwYWq2WgIAA/Pz8UCgUuLi44Orqir29PSdPNhlZLlq0iN69264OuhWz2cyJEyc4cOAANjY2jB8/nqioqE69CS+KIgcOHODgwYNSdubtHNYPHz7M3r17mTRpEgMGDLht7AcOHODIkSMEBATw9NNPd3rfzOY2BVZWVsycORNfX99O3b+MzP3ywAicN27cuKftAgMDOzmS2yNPZNumvFrLgj//C6PJ3KKAVSEIONrb8M3/W4SVWp40PerUFVwlZeVbmPU6BIWi6QuEKBL61E/wHTCJ2vx0Li37ddsbCwqcAqPoP+89ytOOkrXzc8n8R2ltR8ATc/Ab/LQ0wTBoq7i2/Z9UXj0BiAgKJR79RxP65CuorOU7indDRwVOaJqUZRRmkJqXio3ahiERQ3BxuPPkKTUvlf9Y/h9trlMICvoH9eevz//1nuKX6T7OffEFWbt3I97S7w2Fgt4TJhD74os9E5jMI4c8L3z4MJvNLYxv7rQ0NjZKvfoKCwuxWCxYWVlhMBhobGxEpVLh7u6Os7MzlZWVlJaW4uDgQFRUlFSGbW1tjZWVVYtMJYvFIpWVWywWTCaTtBiNRgwGA0ajsU3BwsbGpk1Rsr3Fxsam20s16+vruXr1KleuXCErKwuLxYKTkxP19fUUFRWRl5dHVVUVCoUCpVKJUqmUzIO8vb1xd3fH1tYWo9FIfX19i3O/1ezIw8NDFjBkJEwmE5mZmfTp06fV+6fZCOz48eNcu3YNjUbDoEGDGDBgQAsn8PLyct58802ysrJ49dVXeeaZZ1rsq66uThI7s7Ozqa6uRhAEfH19JcGz+QZIQ0MDe/bs4dixY2g0GhISEhAEQcr6LC0t5dy5c+j1euLj4wkKCmq33+et51NTU8OOHTtIS0sjNDSUiRMntmtcdK9cvHiR77//nsDAQGbOnNnidbr1td2+fTunT59m1qxZ9OnT57b7vXHjBhs2bKChoYEJEyYQExPTqQJtTU0Na9eupbi4mOTk5Mf+s0/mweKBETibG2jfLc1lId3FozaR/bf3V1JVp8NFY8env55727FpuUVsO55CYXk1fh4uJA/pT0RgU4/Cr3eeYMXOk1ja+Xf6/fNPMTrh9hdjmZ6hIz04O4LFbOT0xy9g1NVAG/8HsYs+orG6pMnQpx2snb0Z+PoSoCnrUluchWg2Yu8dhlLddomIsb4Gg7YSa0d3VLaae47/ceZuBM7bYTAZ2HlhJ/su76O+sZ5+gf2YNmgagR6BHLpyiPc2vNfutn6ufnzxb1/c87Fluh6zwcB3L7yA5SYH4JtR2dgwdflyuVRdplOQ54XdS319/X2XcRuNxnb3LwgCVlZWkiBZUVFBbm4uFRUVCIKAra2tZIoTHBxMcHAwnp6epKenc+7cOURRJDo6msDAQAwGAzqdTlqa+0veirW19V2Jlba2tg9sX7m6ujrS09O5cuUK169fR6vVYmVlRX19PXl5eeTl5aHX67G2tsbFxQVfX1+CgoLo378/4eHhBAUF4ezs3Oo9pdfrqaqqauH23rzU1dURFBTEi/KNK5kfuHLlCmvXrsXPz49x48YRHBzc5rjS0lKpT6cgCMTGxpKYmCj16dTpdLzzzjucPHmS6dOn89prr7X73quqqmoheGq1WpRKJf7+/pLg2SwAlpaWtigtt1gsFBUVsXTpUoqLi0lMTEQURSoqKm7b77N5cXV15caNG2zbto3a2lqGDh3KiBEjOtXIJzc3l9WrV2Nvb8/cuXNxdXVtc5zFYmH9+vVcvXqVefPmtTIwuxW9Xs/27du5cOECffv2ZdKkSZ1aUm4ymdixYwdnzpwhLi6OiRMndurrIiNzrzwwKXVLly6V+6/1AFV1OsprtHcct+HgOT777pBUep6aXcj2Eyn8/JlRTB4Ww42Syna3VSoUt10v07MYtFUYbnKwbg9RFCm9tJeCE+vRleehstXgHTuegOGzUFrZUpl5CmN9ddsbCwqKz+/AZ+Dk9g8gKLD3CLzpVyUa3zs79antnVDbO91xnEzXYjAZePObN0nLa+rRKiJSVFXEnkt7eGf2OwS4B7S7rUJQEORx+4maTM9jqK9vV9wEMDU2YtLpsNLINxpk7h95Xti9fPDBB5hMplbPKxQKKVPy5sXe3h5XV9c2192aWdlslFNTU8O5c+c4evQoOTk5GI1GSdR0cHDAxcUFOzs7GhsbOX78OJmZmeh0Onx9fQkPD0cURcrKyrCzs8PJyQkfH5/bloLfruTzYaCmpobU1FROnjzJ1atXqaurQ6lUUltbS3V1NRUVFVgsFhwdHQkNDSUhIYHo6GiCgoIICgrCwcHhjsewtrbG29u7laGK0WikvLxc6nsoIwPQt29fFixYwO7du1m2bBnh4eGMHTu2VY9LT09PpkyZwpgxYzhz5gynTp3i9OnThIeHM2TIEIKDg/nzn//MRx99xIYNGygrK+OPf/xjmz1fm/vGxsfHI4oi5eXlkth58uRJDhw4gFqtxt/fH09PT/bv309KSgrJycn06dMHPz8//uM//oOvv/6agoIC5s+fj6+vL0ajURL3b16uX7/eIrNZo9Hg7OyMVqtl1apV7NmzhylTpjBw4MBOaekQFBTESy+9xMqVK/nyyy+ZPXt2m5UICoWCp59+mhUrVrBq1SoWLlx4296i1tbWTJs2jfDwcDZv3sxnn33G1KlT6dWr133HDKBSqZg0aRL+/v5s2bIFNzc3hg8f3in7lpG5Hx4YgfOFF17o6RBk2iG/rIrPvjsEgNkitnj83/X7GRQZgpujPYIAbRgsY7FYcHW0765wZbqIG4dWknd4JdD0hzbpask/vp7q7AtEL/g7+upSEBRtO6CLFhqrirD3CMIxMIravLTW40QLvoOmdsepyHQBW89uJS0vrYXLutliRhAE3v/+fZb9bBl9A/qSnp/eqgenRbQwZeCU7g5Z5i6xdnBAZWODqbGxzfVqe3vUcsN5mU5Cnhd2L7NmzUKlUrUSKVUqlSQ0G41GKWPy5gxKnU5HZWVlq+d0Oh0mkwmDwUB2djbXrl2jtrYWtVqNp6cnYWFhhISEEBoaiqOjI3Z2dlgsFs6fP09VVRXjxo0jOTmZkJCQxyIzSBRFcnJyOHLkCGfPnpWy1aysrKReoM2GQK6urkyYMIFhw4YRGRlJYGAgNh3sgazX66mtrb3t0tDQAEBAQABhYWFddcoyDyEhISG8/PLLpKamsnfvXj777DNiY2MZNWoUjo6OLcba29uTlJTEsGHDuHz5MsePH2f58uV4e3szZMgQfv7zn+Pt7c2SJUv45S9/yV//+tdW+7gZQRDw8PDAw8ODQYMGYbFYKCkpkQTPqqoqBEHg7NmzHD9+nOjoaObMmUPv3r2ZN28eK1as4KuvvmLevHn4+fnh6enZpkjY0NAg9ftsXkwmEw0NDVy5coXf//73uLu7M2DAAPz9/Vtlfjo5Od3VDTo3NzcWLVrEmjVrWL58OdOmTaN///6txqlUKmbPns2yZctYsWIFixYtwsnp9kkeffv2JSAggE2bNrFixQoGDx7M2LFjO+2aGhsbi6+vr5SdKyPT0zwwAqfMg8ueM+koFAIWS2v1UkBgz9l0xg+KYv3B821ur1YpGRkX0dVhynQh+roK8o6s/uG3m/4PRAvaokzKUg5i4+LdtrhJUzamjUtTD6c+T/+OlJV/RFeWi6BQ/uBcDiFjF+EcEtuFZyHTley9tLeFuNmMKIpU1FWQlp/Gm9Pf5I+r/khOaQ5KhRKLaEEhKHhl3CtEB0f3QNQyd4NCrSZs/Hiufv996zYUgkCvCRMk4yEZGZmHB1EUMRgMVFdXtylSNi9tlaArlcpWmZPNPe2qq6s5fPgwKSkp6HQ6wsLCGD16NEOGDCEiIqJFtpbJZOLYsWMcPXoUa2trFi1aRP/+/R/pLF6dTkdhYSGpqamcOXOG1NRUysvLMRqNkvu5jY0N9fX1CIKAt7c3EyZMYMSIEcTGxrbKdhNFkcbGxjuKl3q9vsV2Dg4Okot7UFBQC1f3O4knMo8XtbW1pKWl4evrS0REBJGRkZw5c4aDBw9y+fJlEhMTGT58eCuxXaVSERcXR2xsLNnZ2Rw/fpyNGzeye/duBg0axK9+9Ss+/vhjfvrTn/L3v/8dPz+/DsWjUCjw8fHBx8eHoUOHYjabKSwsJCsri2PHjnHo0CEOHjxIZGQkI0aMICoqilOnTrF8+XLmz5+Pv79/m/u1tbXFz8+vVRyiKFJXV8fJkyfZtm0b6enpktt7bW2t1CJFpVLh6ura4X6fAHZ2dsybN4/Nmzezfv16KioqSEpKajXWxsaG5557jiVLlrBixQoWLlzYbu/OZjQaDc899xynT59m165dZGVlMX36dHx8fDr0Ot+JjrrUy8h0Bw+8wHn06FHOnTtHTU1Nq/46giDw1ltv9VBkjw/VdfUItJ2eqVAIVNfpCPF15ydTR/D5ph/L2BWKpq1+N28Cjvays+7DTFXm6XbFSwSB8vSjRD77R9T2Lj/04Gw5VrSY8Y5/CgArjStxr3xC1fVzaAszUFrb4R45HGvHzm3cLdO9aBtu3+qivrEeV40rn7z8CeezznO18CoaGw3DIofh6tB2vyGZB4+o2bOpLSig6MyZJjFTFBEtFvwGDaLvjBk9HZ7MY4A8L+x8BEFg06ZNmEymVmJlc9l4e87gVlZWLb6Aa7Va9u/fz5o1a0hPT8fa2prExESSk5OJiYlpM8swIyOD7du3U1NTQ2JiIklJSVhbt913+2HFaDRSXFxMQUEB+fn5pKenk5mZSXFxMXV1dajVapycnPDz80OlUkml4QEBAQwYMIARI0bg4+ODVqultraWixcvtile3ixCC4KARqORxMqwsLAW4qWjoyMajUYq5dfr9VRXV1NTU0N1dTUlJSXY2dkxbNiwHnnNZB48SkpK2L17NyaTCUEQ8PT0xNfXl2HDhpGfn8+xY8c4e/YsI0aMaLOEWxAEQkNDCQ0NpaysjBMnTnDoUFOV4KRJk9i6dSuvvfYaf/7zn4mKirrr+JRKJQEBAQQEBJCUlMTPfvYz1q1bx/79+9m8eTNeXl7Y2tpy7do1fv/73/P8888zZMiQ22aN3hq/o6Mj48aNY8SIERw8eJATJ06gVCqZM2cObm5urUreU1JSqKmpodn25OZ+n7eKoM1l5W5ubuzbt4/KykqmTJnS6nXUaDQ8//zzLF26lJUrVzJ//vw7ZmQKgsCgQYMICQlhw4YNfPHFF4waNYphw4Y9sL2HZWTuhQfGZOhWKisrSU5O5tSpU4iiiCAI0oWh+WdBEORm8vfJnD99SXmNFncnB1b96aU2x2w6cpF/rt/fVvU5AL+cNZaJif0AuFZQyo4TqZRV1xHg6crEIf3wdXfumuBlOoVTH83HUFeBlcaNQW981eaYwjNbyNrxOW32IACcQ2Lp99yf0RZlkvLNHzE1an/IzhQBkbCnXsMnYULXnYTMfXG3JkMW0UK1thortRUONk09vt7b8B5H049itrS+JgsILP/5ctxlEfuRQBRFKjMyKDp3DgQB34QEXHvfuV+ujMz9IM8Luxa9Xt9KrOwoOp2O1NRU9uzZw/Hjx6mrqyMwMJCnnnqKSZMmoWmnL29FRQU7duwgMzOTsLAwJkyY0OkuxT2BxWKhrKyMgoICaSkpKaGmpoaSkhKqqqpoaGhAEAScnJzw8vKSRM3mkvSAgAC8vLywt7dHp9O1yA6Dpsy1W8XKWxcHBwdJuBBFEZ1OJ4mXtz5WV1fTeFP7EaVSiaOjIyEhIUyZIreQkfkRs9lMaWkphYWF0lJSUoLFYsFkMlFVVUV1dTXe3t5MnDiRpKSk2/aq1Ol0Up/OgoICTpw4gVqt5r/+679ISkrqlJiLiorYvHkzeXl5BAQE4O7uznfffUdBQQHR0dGEhoZKhkXBwcHY23e8tVpJSQlbt27lxo0b9OvXj/Hjx7e65rXX77OioqJFv08HBwdJ7KypqeHMmTOEhoby4osvtinC5ufns3z5ckJDQ5k1a1aHhUqz2cyBAwc4cuQIAQEBPP3007i4uHT4nGVkHmQeWIFz0aJFrF69mqVLlzJ48GBCQ0PZuXMnISEhfPDBBxw/fpzt27fj5eXVrXE9KhPZZjoicNY36Hn+v5aiazS0cElXCAIOdtaseGsRttaPfm+kR5WOCJy68huc+/yn7exBIHj0C/gPfQYAk15HeepB6ktzsHJwwaP/aGyc7q10QV9TRkNFPlYaN+w8Wjfclukc7kbg3HVhFysPr6S0phSA+NB4Xh73Mnqjnl/961et+msqBAUj+43k36f+e5fFLyMj8+gjzwsfLBoaGkhPT+fixYscP36cGzduYGNjQ3R0NNOnTyc6OrpdsdRgMHD48GGOHTuGRqNh/Pjx9OnT56EsRxdFkZqamhZiZlFREQaDQSr9LyoqoqCgQBIpHRwccHV1xc7OjtraWsnNuVns9PHxwd3d/bbipb29fYvXy2KxoNVq2xQvmx9vzu5Uq9U4Ozvj5OSEs7Nzi5+dnJxaiKMyMnfCZDJRUlIiCZ4ZGRmcOnWKsrIyHB0dGTx4sNSr0dfXFw8Pj1b/XyaTiZSUFPbv388333xDXV0dCxcu5Ne//nWnmPlYLBbOnDnD3r17UalUjBkzhlOnTpGenk5MTAz19fWUl5cD4OXlJQmeQUFBd+xvK4oiFy9elLJbR40axaBBgzr0HmpsbGxT+KyoqKCsrIyUlBRUKhXDhw8nKCioVcl7SUkJa9asITY2lsmTJ9/VdTQ3N5eNGzfS0NDAhAkTiImJeSivwzIyN/PACpw+Pj7MmTOH//mf/6GiogIPDw92797NmDFjAJg+fTrW1tasWrWqW+N61CayHRE4ATLySnjry++prK1HEJrar7k7O/DuS1MJ8/Poxoh/xGy2cCY9l7KaOvw8XIgJ80ehkC/Kd0tHBE6A9A1/ozztcMvee4ICtb0z8a9+itr2x7uVZqMeQVCgULUWvkWLmdLL+ym9vA9TQx2OAX3xGTgZO7cf++CYGurI2PwhlRknpOccfHoTPu3fW4yT6Rw6KnB+f/p7Pt/5eYvnFIICG7UN//vS/5Jdms1HWz+irqFOWp8UlcQbk97ARi23qZCRkbl35Hlhz6PX60lPTyc1NZX09HTy8vKor6/H2dmZ4cOHM2bMmNv2zhNFkdTUVHbt2oVOp2P48OEMGzbsoTIQamhokITMGzdukJWVRVVVFXq9XhJhysvLKSoqoqSkhIaGBpRKJa6urvj4+EjCTl1d0+ekh4cH/fr1Iy4ujsDAQBwdHbG1tW0lMpjNZsk9vS3x8tYMT1tb2xaC5a0iZlvHkJHpTIxGI2fPnmXz5s1cu3YNGxsbPDw8cHBwQK1W4+3tjZ+fnyR6urm5Sdn4GRkZ/OY3v+HixYvExsbyxhtvMGjQoLvKrGyPuro6duzYQWpqKoGBgTQ0NFBTU8Nzzz2Hq6urZFiUnZ1NdXU1giDg6+srCZ4BAQFtur1D0/Vh3759nDlzBi8vL5KTkwkICLinOEVRRKvVkp2dzcqVKykvLycmJgaFQkFlZWWLfp86nY6MjAwSExMZPXq0JH7eeiOkLfR6Pdu3b+fChQv07duXSZMmYScbRso8xDywPTirq6ul3hsODk0lkFrtjz3ennzySd58880eie1xJDzAi2/+30JOpeVQUlmLr5szA/oEoVR2zd1ds9lCWm4ReqOJ8AAvNHYtxZG03CL+tHQLlbU/pvX7e7rwXy9Nwd9DTrHvCsKn/AorjSvFZ7dhMRkAcA6JodeE1yVxszr7ArkHvqauIB0Al7ABBI2aj4N3kwOnxWwibe1/UXX9DM1KeX1ZLsXndxA16084h8Y1fQFa8zZ1BVdbHF9bfJ3LX/2WhJ8uRvVDWbRM96E36vnqQGsB3CJaaDQ1sv7Eel6f+DoDew/kYs5FdHodEb4ReDl3bzaVTM9jMZspuXCBuqIi7Nzd8UlIQPkQCRgyDybyvLBnMBgMXL16ldTUVK5du0ZdXR2NjY3o9XoCAgJITEwkMTERV9fb91IuKSlh+/bt5OTk0KdPH8aPH/9Al0QaDAYqKyu5fv06WVlZ5OTkkJ+fT3l5OXq9HovFIrnMN4uPzU7yCoUCV1dXhg0bxqBBg4iKiqK8vFxyeXZwcKBfv35ER0fj6+uLIAgYjUaqq6spLCxsU8Ssq6vj5pwUBwcHSbD09fVtJWI+aj1MZR4+1Go1iYmJDB48mPT0dPbu3UtxcTHe3t6EhoZSV1dHZmYmJ040JTNYW1vj4+MjCZ5Llixh8eLFrF69mrfffpuRI0eSkJBAYmIiHh73nlyj0Wh49tlniY2NZevWrdTU1KDX6yXjoejoaKKjm4wvq6qqJLHzwoULHDlyBKVSib+/vyR4+vv7S31sbW1tSU5Olva9ZMkS4uPjGTt27F2Lhs09dKOjowkPD2ft2rXk5uYyefJkoqOjqampaZHtqVAo2LNnDxkZGfj6+kqvaVtGR839PpvHTJs2jfDwcDZv3sxnn33GtGnTCAsLu+fXWEamJ3lgBU5fX1+Ki4uBpjeep6cnFy9eZOrUqQAUFBTIdx67GZVSydB+XX+xO3r5Gh99u4+quqYG62qVkqdHxLIweRhKhYIabQO/+2wjjYaWbp6F5dX89rMNLHvzBdQq2cm3s1Go1ISOe5mgpOdprC5BbeuIlebHLzSV185wZfWfmoTLH6jKOkdN7mViXnwfe68QSi/tbRI34cdMUIsZURC4uul9Br2xnNq8K9Tlp7UOQLRgrK+h5OIe/AZP67oTlWmTjMIMdHpdm+ssFgvHrx7n9Ymvo1aqGRA2oJujk3lQqMnL48hf/oKurEy6iWHl6Miw3/wG9z59ejo8mYcYeV7YfRiNRjIyMkhNTSUjI0MyH4Km3o/+/v4MGjSIgQMH3vFLe2NjI/v37+f06dO4uLjw/PPP06tXr+44jXbR6/WtzHmqq6slA6DCwkLKy8upr69HFEVJsPT09KRXr15YW1uj1+vJz8+XnOc1Gg2RkZEMGjSI4cOH4+XlxZUrV7h06RK7du1CEAQCAgKIjIzE0dGR2tpajhw5IomYzaZC8GN/TScnJ1xdXQkJCWlVQt4ZJbsyMndLYWEh+/fvx8HBod3l1l6+giAQGRlJREQE586d48CBA5w8eZJBgwaxaNEiFAqF1MahsLCQK1eucOzYMaDJkGfEiBEcPnyYQ4cOYTQaOXPmDL1792bIkCGEhobe83W/d+/evPbaaxw8eJDDhw9z7do1PvnkE/7t3/6N0NBQAFxcXHBxcSE+Ph5RFKUbFdnZ2Zw8eZIDBw6gVqsJDAyUBE8fHx/8/Px46aWXOHv2LHv37iUtLY1x48YRFxd3T/E2O6dv3bqV7777joqKCkaPHo2Li4t0PZ0wYQI7duzg+PHjjB07tpXhUVZWVrv9PpuXmTNncvDgQb7++msGDx7M2LFjH6oMexkZeIAFzieeeILdu3fzhz/8AYBZs2bx97//HaVSicVi4cMPP2T8+PE9HKXM3ZKSVcBXO05w6Xo+KqWSJ2J6M/+pRHzcnKT1b/9ra4u71EaTmbX7zqJUKFiYPIydp1JpMBi4tbmCxSJSWlXHsZTrJMWGd+dpPVYorWyx9wxu8ZwoimTv/uKHX27qwShasJiN5B5cQd+Zb1FycTcg0MqsSBQx1ldRk3u5KXNTULTr2l6bd0UWOHuAO03IZGFBxmwwcOjtt9HX1jY98cNF2lBXx+F332Xip59i3UGnUhmZW5HnhV2LyWQiMzNTEjUNBgPe3t6EhYVRUVFBeXk5bm5uPPXUU8TExNzxS68oily4cIE9e/ZgNBoZM2YMiYmJUqZTVyCKIg0NDW26i9+86PV6SeSsq6vDYDBIJebW1tZ4eXkRFxeHn58f9vb2iKJIUVEReXl5lJWVkZ+fj8lkwt7eniFDhkg9YRUKBZcvX+arr77i2rVrkvDZLFbm5eWRl5eHSqWSxEpvb2/69OnTQrx0dHTs0v6Xza9TfX29tOh0uhY/19TUkJ+fj6OjI7/61a+6LBaZhw+lUklZWRnZ2dlotVpMJlOL9Wq1ul3xU6PRMGPGDK5cucKpU6c4f/48w4cPZ/DgwYSEhEj70Ol0LUyMAHbu3ElpaSlDhgwhLy+P3bt3ExQUxNixYxkyZMg9CXFqtZqxY8cSHR3Npk2b2L59O3/84x/5/e9/T//+/VuMFQQBDw8PPDw8GDRoEBaLhZKSEknwPHToEHv27MHGxoagoCBJ8HzttdfYs2cP33//PefOnWPSpEl4e3vf0+s+efJk3Nzc2L17N5WVlUybNk06b0EQeOqpp9BqtRw4cIB58+YRGRnZYh/N/T4rKysl4bO4uJjU1FT0er00rra2liVLlrB582YmTZpEREQErq6uODk5yb15ZR54HliB89e//jW7d+9Gr9djbW3Nn/70J1JTU3nrrbcAGDFiBP/7v/97V/v87LPP+Oyzz8jJyQEgKiqK//f//h8TJsjuzt3BqbRs3vriewAsoojZYmLfuXROXsnmn7+ag4+bE6v2nJZ6fN7KhoPnmT1mIFmF5QgIiG04eisVCq4XlMkCZzejrymloSK/7ZWihcrMU4iiBaOulvac2AGMDXUorGzaHyMIKOVejj1ChG8EDjYOaBu1rdYpBAXDI4f3QFQyDxL5J07QWF3deoUoYtLrydm/n4gfsu1kZO4WeV7YdRiNRv7nf/6HhoYGvLy8GDp0qNQv8+rVqwQEBDBr1iwiIiI69OW2oKCAbdu2SQ7F48aNa9dJvaOIokh9ff0dxcubxZbmEk8bGxtMJhONjY0YDAa0Wi0WiwVHR0fCwsIICAjAz88PZ2dnjEYjRUVF5ObmkpqaitFopKGhAZ1OR11dHQqFAi8vLzw8PHB0dMRsNrNjxw4KCwspKyvDZDJJWVWRkZF4e3u36oXZkb54d/vaNAuWtwqVbYmYOp2OtiwYTCaTZLRSVlaGwWC45/6BMo8mvr6+zJ49W/pdFEX0ej1arbbdJS8vD61WK2VEN2MwGMjKyuLw4cM4OjqSkJBAVFQUjo6OkiAaERFBQkICs2bN4syZM/z+97/nypUrTJ48mZqaGlJTUzl06BAODg7ExsYybNgwwsLC8PX1lVqZdARPT09eeuklYmJi+J//+R9+/etf89prrzFlypR236sKhQIfHx98fHwYOnQoZrOZwsJCSfDcs2ePdCMkODiYhIQE0tLS+Pzzzxk8eDCjRo26o3nRrQiCwLBhw3B1dWXDhg0sX76c2bNnS+cqCAJPP/0033zzDatWreLFF19sYbxnY2ODn59fq17JzdfXmzM+s7Oz2bdvH++//z5BQUEEBASgUqlwdXVts+S9s69rMjL3ygMrcCqVyhZ3DF1cXNizZw/V1dUolcp7mij5+/vz3nvv0bt3b0RRZPny5UydOpXz589LfZ1k7h+LpenD62bDH1EU+ef6A4hiS1nSYhHRNer5ZtdJ/n3Ok6RmF0rb34reaCK7qBxHe5umKug2hllEC072tp14NjI3o68po/j8DupLs1HbO+MVPRbHgL7tZlv+SNMfS+MXQWN1MVjMbY5y8A5FoexD9q4v2tmNBfe+spDWE6hVahaOWcjHWz9ucYNBISiwt7FneuL0Ho5Q5n7Y85vf0FhdjY2zM2P//vd72kfNjRsISiWiufX7WxAEanJz7zdMmccYeV7YdajVaiorK3FxcaG0tJTz588jCAJRUVE888wz9O3bt0PCZn19PXv37uXcuXN4e3uzcOFCAgMD77hdswP47YTLurq6FiY6zX/zZldxX19fyaBHr9dTV1dHZWUlRUVFlJaWAk1f7sPCwiRjE3t7eyorKyWRpbCwEL1ejyAIUoZnTU0NJpMJjUYjZW+5ublJZbWlpaVYLBZCQkJ45plnGDRoEP7+/vf1RV8URRobG2+bYXnzuoaGBiyWlvMwhUKBnZ0d9vb22Nvbo9Fo8PLykn63tbWlvr6e3Nxc0tPTJbOkhoYGHBwcCAgIICQkhLi4uHs+D5lHH0EQsLGxwcbGBnd399uOtVgs6HS6VgJoYWEhJ0+e5MiRI5w6dYrAwMBWYplCocDe3p5x48axadMm1qxZw3PPPcdrr71GcXExFy5c4PLly5w8eRI3NzcCAgKkXp7Ni4+Pz21bagiCwKBBg1i8eDF/+tOf+Oijj0hLS2PhwoV4enre8bVQKpUEBAQQEBDAiBEjMBqN5OfnS4JnWloaJpOJ6upqVqxYwe7du3nmmWcYMmTIXV8vIiMjeeGFF1i1ahVffvklc+fOlWJUqVTMnj2bf/3rX6xYsYKXXnoJJyen2+5PEARJVA4KCpKeX7RoEXv37mXfvn3Y2toyYMAADAYDFRUVpKamUlNTI4nW48ePZ8iQIXd1HjIyXcEDK3D269eP/v37M2vWLGbOnCn1l3B2dr7nfU6ePLnF73/+85/57LPPOHHixGM1ke0qruWXsnz7cU6l5QAQHxHICxOGEBHozY3SKgoratrczmwROXQxk3+f8yQ2VmrqGw3tHsPWWs24AX3ZeOhCm+sVgsCo+Ij7PRWZNqjOvkDq6j8hWsxNgqZCScn5nfgPnUnQqHlYO3uhry5pvaGgwDkkFkFQ4DdoKmUpB9oc49prILauTXcUg0bOI/fAVzeVqjcp2m59huLSS+7v2FM8FfcUdtZ2rDy0khvlN1AIChLDE1k4ZiGeTnee/Mk8uDRWV9NQWXlf+7BxckK0tH+zw/oOE2wZmdshzwu7jsbGRtzd3bl06RI6nQ4XFxfc3NyorKxk3bp1KJVKnJ2dpX50rq6u0s8uLi6oVCpOnz7N/v37EQSB5ORkEhISUCgUmM1m6urqbiteNmdUNqNSqSTh0tnZWXIXv3lpLh0vLy+XXM0vXrxISUkJFosFlUqFj48P4eHhuLu7Y29vT1VVFRkZGRw4cIDc3FyqqqpobGxErVZjb2+PxWJBr9djNBqxtrbGz8+PkSNH0r9/f/z9/VEoFNy4cYO0tDRKSkrQaDQMGTKE6Ojo24qaNwuWHc2wvJ1g2fzo6enZQsS8eZ2NjU2LeERRJC8vj9OnT7N3714yMjIoLy/HbDbj5OSEn58fAwYMoG/fvpI4ZGsrJwzIdB4KhUIS0W5l5syZFBQUsHv3bnJycvD392fo0KE4Ojq2EkQ9PDz46quvWLx4MXFxcfTt2xc7Ozv69etHYWEh+fn55Ofno9FocHV1RaVSIQgCVlZWeHh4EBgYSHBwMGFhYfj7+7fKonRycuIf//gH//znP9m1axeFhYVMnTqVESNG3FUZvFqtlsrUoan3740bN8jOzubKlSscPXqUN998E39/fyZMmEBsbCzBwcEddor38/Pj5ZdfZuXKlSxZsoSZM2dKxkDW1tY899xzLFmyhK+//pqFCxfekzO6UqnkySefJCIigo0bN3L69GkmTpzIhAkTEAQBk8lEZWUllZWV92X8JCPTmQhiWzUKDwCLFy9m7dq1HDx4EFEUiY2NZfbs2cycObPFnYV7xWw28+2337JgwQLOnz9P37592xzX3KenmQsXLpCUlMTZs2eJj4+/7zh6mjl/+pLyGi3uTg6s+tNL97yfjLwSfvnxWkwWy48ZnIKAQiHw3689g42Vmp/89zftbq9WKdn2j5/xxebDrNt/Dsst/5aCAL5uzvzrzQUIgsDy7cdZseskSoWA2SJKj7+cNZaJif3u+TweR059NB9DXQVWGjcGvdHaJRvAYjJw6sN5mPT1bfYP6Pf8XzHpakjf8B4temwKAoKgIHrB39H4NRmMlKcfJXPzR5j1Pza6duk1gIinf4vK+scP34qrxyk4+R26shtYaVzxSZiId9xTCArZQKqzmffRPCrqKnDTuPH1G1/fcbwoijQYGlAr1ahVcvPxR4Etr7xCQ2Ultq6uTPq//7vt2LqCAtI2bKDo7FkAfAcOJHL6dJQ2Nmx99dV2Rc4n338fp074/JZ5PJHnhV2HKIr861//IiQkhIEDB+Lg4IDZbKa6upqqqioqKyupqqqSlsrKSoxGIxaLhdLSUnJycmhsbCQgIIBevXphY2ODKIqYTKZW/fmsrKxaiZW3Lra2tq3EQlEUqa2tlcTMZkMSvV4vlYE6OjpiZ2eHtbU1oiiSn5/PjRs3KC0tpbq6GpPJhEKhwNPTk4CAALy8vFCpVJIDuo2NDREREfTr148+ffqg0WhobGwkLS2NS5cukZOTg1KpJDw8nD59+uDt7d1h4fJWwVIQhHbFyVt/bkuwvBONjY1kZGRw7tw5UlNTyczMpLq6GkEQcHNzIzw8nP79+xMdHU1gYOB9txCQkekMRFEkMzOTPXv2UFpaSlRUFGPGjMHV1bXFOKPRyDvvvMPhw4dJTk5mwYIFUnZoTU0NV65c4fz58xQXF6NWq6UbYfX19dTV1aHVajGbzahUKlxcXPDy8sLX1xd/f38CAwNxcXHB1taWvXv3cuzYMVxcXAgLCyM5ObnTTNJ0Oh2HDh1i48aNFBQU4OrqSlBQEL6+vpIwGhQUdMcydr1ez7fffktWVpZ0c6mZiooKlixZgpubG/Pnz78vw6DGxka2b9/OxYsXiYqKYtKkSfJNEJkHkgdW4GympKSEb7/9lrVr13L06FEABg0axOzZs3n22Wfx9fW9q/1dvnyZIUOG0NjYiIODAytXrmTixIntjv/Tn/7E22+/3er5h3kiezOdJXD+5tP1XLyW30qYVAgCkcE+/PdrM5j9n19SU9/QaluFQmBgn2DefXkqtfUN/OzDNRRX1Ej7UioEBEHgvZ88TUyvH3sBnc/MY+uxy5RW1RLo5crkYTFEBHq12r/M7emIwFl+5fAP4mUbCAo8opKImPbvlKcfJXffchoqCwBw8I0gZOxCnAJbis4Wk4GqrHOYGrRo/MKxc79zCZtM13G3AqfMo0dHBc7qnBz2//GPmA0GScgUFAqU1taM+ctfqLx2jdOffoogCIgWC4JCgWix0P+55+jz9NPddToyjzDyvLBrEEWxhYBmMBjazbhsNqC5ePEixcXFWFlZ4e7ujpWVlSTkWVtbY21tLWUaNgsIXl5eUgaos7Nzu27gDQ0NUjbWtWvXyM7OpqKigsbGRpRKJba2tqjVahQKhST+NQsXzX0zVSoV9vb2BAUFER4eTkREBF5eXmRlZZGWlkZeXh4KhYKQkBB69eqFn58fgiBQW1tLeno6KSkpZGVl0djYiJOTEx4eHmg0GoxGY4tyeWgpWN5OqGz+vS0R915p7hualZXFpUuXuHLlCjdu3ECn06FUKvHz8yMyMpLo6GgSEhLw8vJq99gmkwmdTictKpWqQ20GZGQ6E4vFwsWLF9m/fz9arZYBAwaQlJTUIrtRFEX++c9/sn79egYPHszbb7+NtbV1i/U3btzg+PHjXL16FWtra6Kjo4mIiMBsNks3QPLy8igsLKSkpETq02tlZYVGo8HBwYHi4mKqq6txdnZGEARCQ0MZOnQoHh4eHXKRvxMmk4kjR46wd+9e9Ho9wcHBGAwG6YbEzYJnQEAAVlZWbb5e27dv5/Tp0wwdOpRx48ZJMRQUFLBs2TJCQkKYPXv2fZsEpaamsmXLFlQqFdOmTZOyRmVkHhQeeIHzZgoKCqRJ7alTpxAEAaPReFf7MBgM3Lhxg5qaGtatW8eXX37JwYMHH6s79TdztwJnbX3DD+M1ONo33VFqNBiZ/Nt/3na79e/+hIMXrvLxuv0tnheEph5+H/58Jn2CvH84RiMbDp1n/9l0Go0m4noHMHN0AqG+cup7V9ARgbPw1Pdk7f6/tt2fAKegaPrP+yvQNKEwaCsRFEqs7J27KmyZTkQWOGU6KnAefOcdylJSWmVpCgoF3vHxDP/d76jOySFr927qCgqw8/QkdOxY3MJl4zeZzkeeF3YOoiiyefPmFiJmY2NjizF2dnZSaXhhYSGZmZloNBrGjBnD4MGDcXJykr54G43GFtmeN2d/VlVVSeJgc983tVotmfg0j6mrq5OyRB0cHHB0dESj0eDp6SmVZhsMBnQ6nSS6WllZ4eDgQGBgIL6+vri7u6PRaCgqKuLKlSukp6dTVFSExWLB2dkZV1dXHBwcMBqNmEwmamtrKSkpobS0FJPJhKurqyR+uru73zHDsjvchc1mMyUlJRQWFkpGSNevX6eyspL6+nocHBzw9fWlb9++xMTEEB4ejkKhkIyS7rQYDC3bRAUEBLBo0aIuPy8ZmbYwGo1Sf06LxcKwYcMYMmRIC5Fv3bp1fPbZZ4SFhfG3v/0NFxeXVvuprKzk5MmTnD9/HrPZTP/+/RkyZEgLAx6LxUJZWRmFhYXcuHGDnJwc8vLy0Ol0ZGZmUldXR0BAAHV1dQiCQHh4OG5ubq2ys291kW8WSm9d7O3tUSqVLWLctm0b165dIzw8nCFDhlBVVSX18NRqtSiVSvz9/SXB08/PT7pJJIoiJ0+eZOfOnURERDB9+nTpdbp27RorV64kJibmtsZJHaW2tpZNmzZx/fp1Bg8ezNixY+8rO1RGpjN5YHtwtoWPjw9RUVFERkaSkpJCfX39nTe6BSsrKym1PCEhgdOnT/PRRx+xePHiNsc334Fu5m4c2R4GXDR2LR7bo7a+gY/X7efwxUwsYlNJeFJcOD+bMQqFcOcJnUW0MHlYDCDw1Y7jVGubMjkDvdx4bfpISdwEcLS34YUJQ3hhwv01KtY2NNKoN+HqaN/C8KiZKzmFfH/kEvmlVXi7OTFpaH9ie8tukW1h6x7QrriJoMDO48e7+4IgYK1x66bIZGRkugtjfT2lly61uU60WCg6exazXo9zcDDxL7/czdHJPI7I88LOQRAEqqursbKyIjg4uFXJuEajQa1Wk5mZyfbt29Hr9Tz77LMkJSW1WT6pVqslIdJoNFJdXU1NTQ3V1dVUVFSQmZlJZmYmubm5lJSUSO7nJpMJa2trNBoNTk5OeHp64u/vj7e3NxqNBrPZTG1tLTk5OVRVVWEwGFCpVDg5OeHg4ICdnR0KhYLr169z4cIFysrKKCsro76+HqVSibe3N8HBwQQFBUlu5iaTiYKCAm7cuIEoivTp04e5c+eSkJCAj49PD/w1fsRisVBeXk5hYSF5eXlkZ2eTnp4uldzfnGkWEBCAj48Pjo6OQFMp7v79+9m/v2ViQXMvTzs7O2xtbbGzs5PMV9pbZGR6CrVazfDhw4mPj+fw4cMcOnSI06dPM3LkSOLj41EoFDzzzDN4eHjw3nvv8dOf/pT33nuP4ODgFvtxdXVlwoQJjBo1irNnz3Ly5EkuXLhAaGgoQ4YMoVevXigUCry8vPDy8pLMtcxmM6WlpeTn57N+/XpSU1MJDg6mpqaGS5cuSX16Q0JC0Gg0WFlZ0dDQ0KJnaG5uLlqtFp1Ox615ZXZ2di1ETw8PDxQKBefOnePMmTMkJSUxbtw4HBwcJGfz7OxsTp48yYEDB1Cr1QQGBkqC56BBg3BxcWH9+vUsW7aMOXPmoNFo6NWrF1OnTmXjxo1oNBpGjx59X38XR0dHnn/+eU6ePMmePXvIyspixowZeHt733ljGZku5oEXOEVR5MCBA6xZs4aNGzdSXl6Oi4sLs2fPZtasWfe9/+aG4o8rn/567h3HmM0WfvPpBrKLy6WycbNF5MD5DArKqvnojVmEB3iRmV/SSgMTgEBvN8nZfPKwaCYkRlFQVo1apcTHzanTSnSayS2p5PONBzlztcmx193JgeeeHETykP7Ssb47fIF/bjgg9e68VlDKwQsZLJgwhOefHNyp8TwKOIfEYOvqR0NVUZuO6T4JyT0QlYyMTHdivqWXXitEEYvJhPIm8ed+qc7J4caRIxh1Otx69yZg6NBO3b/Mw4c8L+wa5s+f3+66yspKdu7cydWrVwkNDWXOnDl4eHg09WNuaJDEy1sfb87GbDYTMhgMKJVKrK2t8fLyon///pKo4ObmRn19PaWlpZKgd/bsWcrKytBqtZhMJlQqVQvXZg8PD0nYtFgs1NfXU1tbi16vx8PDgxEjRhATE0O/fv0kYVqr1ZKSksKlS5coLCzExsaGoUOHEh0dTVBQUKfPS2/FYrHQ2NjYInOyvr6ekpIS8vPzKSgooLi4WBJ/6+rqMBgMWCwW1Go1dnZ2eHp64ubmho+PDx4eHlJW6Z0Wa2vrLj8/GZnOxs7OjvHjxzN48GD27dvHli1bOHHiBGPGjKFPnz4kJSXh7u7OW2+9xc9//nPefvttSaS8GRsbG4YNG0ZiYiJpaWkcP36cb775Bnd3dxITE4mJiWmRiahUKvHx8cHHx4eEhAQ2btzIpUuXeOaZZ9DpdGzdupWNGzfi6upKcHAw1tbWeHt7S2XlzZnkN1+fbjVNal5qamooKChAq9Via2tLTk4OH374IZ9//jl9+vQhICBAEkIjIiIwGo3U1NRQVFTE5cuXEQQBjUZDWFgY0dHRnD17lv/7v//jueeew9vbm5iYGLRaLbt370aj0TBw4MD7+psIgkBiYiKhoaFs2LCB8vJyWeCUeSB4YAXOw4cPs3btWtatW0dpaSmOjo5MmzaNWbNmMXbs2HZ79tyO3//+90yYMIHAwEDq6upYuXIlBw4cYOfOnV1wBo8Ox1KzuF5Y1up5i0Xk6o0SzqTl8uLEobz5fxtvtpeBH35emDy0xWRKpVQS5H3vGX5VdTrOZdwAID48ABfNj/1YiitreOOjNTTc5MReXqPlo2/30aA38uyoBEqravls40GgSai9+XH59uMM6xdGiK/7Pcf3KCIICvrOfpvUVW/RWFUECgVYLChUVoRP+VWLDM47IYoiRl0NgqBAbefYhVHLyMh0JtaOjjh4e6MtLm69UhBw9PNDdZfZPqIogigi3FLaKYoil1es4OqmTU3rBIGsXbtIWb2akW+/jYM8iX7skOeF3Y/BYGD37t0cOHAAhUJB//79cXFxYffu3ZKI2SwGm81myYG8WbRrFjSb+1O6u7tLvTNtbW2lv1lZWRm5ubk0NjZK2U8mkwkrKyuioqKkDKXg4GBsbW0xGAw0NjZSVVXF9evXSU9P59SpU2i1WlQqFe7u7gQGBhIWFoaTk5OUbVVRUUFeXh4FBQUolUp69+7N8OHDCQ8Pv6f/H/jRIb0j5d86nY6GhgYaGhpobGyUhN/m3qHNpa5KpRKVSoUoijg5OREcHExISAihoaGEh4cTHByMg4PDXRsPycg87Dg7OzN9+nSGDBnCnj17WLNmDQEBAYwbN46oqCg++eQTfvvb3/L73/+eX/7yl4wfP77N/SiVSvr160dUVBR5eXmcOHGCrVu3snfvXgYOHMjAgQNbGW8pFAqefvppFAoFR48eZfr06Xz66aecOHGCPXv2YDKZ6NWrF1ZWVmRlZXH69GlEUUStVuPj44Ovr6+09OrV67bvXaPRiFarJScnh23btpGTk0NtbS3+/v6YzWbppk+zWZKNjQ11dXVS9npDQwOiKFJRUcH333/P2LFjGTZsGL6+vvTu3ZsNGzZgY2ND//797/tv4unpySuvvNItLTpkZDrCAytwJiUl4eDgwOTJk5k1axZPPfVUm01174bS0lLmz59PUVERTk5OREdHs3PnTsaNG9dJUT+anM+4gVKhwNyGM65SoeB85g1+Mi2JdxZNYfH3h8kvrQLA192Jlyc/wdB+HWs+fCY9l3X7z5JZUIazgy0TEvsxZXg0Vjf1Flm2/Thr9p6WBEmFQuDZkQksmjQMQRBYt/8cDXpDK7MjgK92nGDS0Gj2nb3abgxKhcCeM2m8POWJDsX8OGHr6kPCTxdTdf0s9SXZqO2dcY8cjsqmSWA2GxooSz2ErjwPK40rnv1GYuXQ0vWwIuMkufuXoytryq7V+EUQPGYRToFR3X4+MjIyd4cgCETNmsXJjz5qvVIUiZo9u8Nftquzs0lZs4bic+cQRRHv2FiiZs3C9YdS4cJTp7i6aVPTrm/67GmsquL4++8z9u9/l7/YP2bI88KuQxRF3nnnHXQ6HY2NjTQ2Nko9Hg0GA87Ozjg5OZGSkoJarUatVkv9TptFyWZxU6FQoFKpcHBwwM3NjcDAQHx8fHB3d2/hdN5sYlRZWUlpaSm2trZoNBp8fHwICgoiKCiIgICAViXSFotFEinT0tKora0lICCAsWPHEhISgqOjo7TfiooKMjIySE9P58aNG1gsFpycnPD19SU8PByLxUJubi51dXWS6ZGtrW0LkfZ2QmXzz23ZGdjY2EhZk0qlEoPBgF6vlzJMjUYjarUaf39/qX+nTqeTtvX19SU0NFQyFrlXAVZG5lHEx8eHefPmcf36dXbv3s3SpUvp06cPY8aM4ZNPPuGPf/wjf//73ykpKWHevHntzhcEQSAwMJDAwECqqqo4efIkJ06c4OjRo/Tr148hQ4a0yEpUKBRMnToVQRDYsGEDoigybNgwoqKi2LZtG+np6URERDBv3jxsbGwoKiqisLCQwsJCMjIyOHHiBNDU6uRW0dPFxUWKU61W4+LigouLC7GxsVy6dIldu3Zx/fp1Ro4cyeDBg1EoFNINlluzQaurq8nNzeX69escP36cZcuW8d133+Hu7o6zszOVlZUcOXKEYcOGERgY2GaP0JuXO5miyeKmzIPEA/tp+e2335KcnNxmb597ZcmSJZ22r8cJlfJ2Fy0R1Q8NkhOjQhncN4SSqlpEEbxdHTv8BXTz0Yt8vG4/CoWAxSJSW9/A/206xMkr2fz11WmolEo2HbnIyt2nWmxnsYis2XcGF40dM0bGczw1C4ul7V6RjQYjaTlF1NQ3NB3H3Pa4Wl1jm88/qlg5uLR4vB2CQolr70G49h7U4vm6wkxSV72FqaEOQaFEFC3k7ltG78m/xLP/KAAqrh4n7dt3aWpc0LxdBikr3qT//L/h6N+n805Kpsu4mHOR1UdWcyXvClZqK0b1G8Xs4bNxvUXMlnk0CXziCUx6PZe/+QZDXR0A1k5ORM+bh39iYof2UXX9OvveegvRZJLEy5KLFym9fJmRb7+NW0QE13bskDLFb0a0WKjOzqY6OxuX0NDOPTmZBxp5Xth1iKLI3r17JbOd5v6WzQ7o1tbWkphpMpnQ6/WSGY1KpcLOzg5XV1ecnZ1xdnbG0dFRcjgH0Ol0pKSkSFmftbW1mM1mFAoFTk5OuLq64ubmhqurK/X19Vy9epXr169LmYzNPUKLioooLi5Gr9dL7uihoaH4+PhgZWVFTU0NWq2WyspKsrKyyMzMpL6+HmdnZ/r164ezszN6vZ6qqioKCwtJS0ujpqaG2tpaDAYDph/acDSXwNva2mJra4ujo6MUo7OzMy4uLvj5+bUq/7a1tUWpVFJdXU1xcbEkbFRVNd34t7W1JTAwEIVCgdFolDI4LRYLHh4eDBgwQMpUtZZbccjI3JGwsDBCQ0O5fPky+/bt49NPPyU+Pp7//M//5H//939ZtmwZpaWlvPHGG3c0wXFxceGpp55i5MiRnDt3jpMnT3Lx4kVCQkJITEwkPDwcQRBaiJwbN27EYrEQGxvLnDlzSEtLY/v27fzzn/9k5MiRJCYmtugH2tDQ0EL0vHLlCseOHQOarg83C56+vr44OjZ9l242DNu3bx+7du3iwoULJCcnExgYKF2nPDzaNuO1WCxs3bqVnTt34urqip2dHfn5+aSmpnLo0CGGDh2Kj48POp1OKpG/tU2LUqnE3t7+tiJosxGbjExP88AKnDNmzOjpEB4L/u39lVTV6XDR2LXbj3N4dC82HrrQ5jqzRWR4dC/pd0EQ8HZ1anNsbnEFWYXlODnYEhPmj/IH4bRO18hn3x0CaCFOisCFzDz2n8tgTEIf1uw90+55rNl3hmkjYu8sqArQy98Tk7l1Nmrz8cMeM7f22EVtZGPdBRaTkSur/xNTY5O5g2hpckcVRTMZ37+Pg29vbF39yNn7L7i1iYEoImLhxsEV9Hvu3fuKQ6brOZJ2hL+u/yuCIGARLRjNRrad3cbJjJN8tOgjnO2dezpEmW4gdOxYgpOSqM7JAUHAOTgYxS3ZRVXXr1N07hwAPvHxuIT9mMl/6ZtvWoib0CRcisDFr79m9Lvvoi0paSVu3kx9aakscD5myPPCrkOhUPDqq6+SmZlJdnY2Dg4OREVFYW9vT3V1NZWVlYiiiFKpxN3dHT8/P2nx8vJCoVBgNpsxmUyYzWbq6+slF+IbN25QWFgIgJubG1FRUVLPzWa345u3bX7U6/WSW/uNGzdoaGhArVbj6uqKr68varWa+vp6Tp8+jV6vp6amhtLSUsrLy6VMyGaHcysrKy5cuAA0fVFvzkJtXlxdm27QWSwWzGYzFotF6pNZV1dHUVERCoUChUIhObVrNBrs7e2bPg8tFgwGg5TVKQgCVlZWeHh44ObmhpOTE0ajkdraWnJzcxEEAWdnZwIDA+nfvz9BQUE4OTlJgq5er8dsNku/KxQKOWNdRqYdBEEgOjqavn37cvr0aQ4dOsSlS5cYPnw4Li4ufP/995SWlvLWW2+1Kjtvi+aevDf36Vy1ahVubm5Sn04rKyumTJmCQqFg06ZNiKJIXFwcffv2JSwsjH379rF7924uXbrEpEmT8Pf3B5pEzNDQUEJvmr/U19dLgmdhYSEXLlzg8OHDQNM17GbBc8SIEcTFxbFlyxaWLl1KXFwc4z/A9d4AANSuSURBVMaNu62wqFAomDx5Mt7e3mzfvp3AwEBeffVVbty4wRdffEFWVhYqlYq6ujo8PT2JjY3F398fd3d3TCZTm/1CS0pKuH79ulQiDzBhwgQGD5Z9LGR6ngdW4JTpHqrqdJTXaG87pn+oH09E9+LIpWvcmvM4OiGCiECv225fW9/In7/aJvXNBHBztOf38yYQ08ufE6nZGE3mNrcVBDhw/iqD+4bcNs6qOh219Q0/iLHn28zitLOxIirYF0GAL50cqKyrbzFOIQjY21oxdqCcSXg3VFw9jlFX085agZLzO/AZOIWGyoK2h4gWqrPPI1rMCApll8Upc3+YLWY+3fEpImKLcjyLaKGiroINJzawcMzCHoxQpjtRqNW49u7d6nmL0cjJjz8m//hxqa9m6po1+CcmMviNN7BYLO06sWOxUJGejkGrRePtTUN5eQsR9GYcvG7/uSMjI9NxdDodx44do7y8HHd3d9zc3KQ+p35+fiQkJODn5/f/2bvzuKrq/H/gr3Pv5V7We9n3XUQFBGQTcAGXNLc0S9M0NbOaaZvGmampaXOaftb0baZ9yjQ1zTTLLM0kczcRRUQUVARBZd932e49vz+MkwgoIHBZXs/Hgwdy1vfhnvvh+L7vz+cDR0fHVisLmxKaly9fxuXLl5GXlwdRFGFqagpXV1eMGjUKbm5usLW1laoXW+vyXVFRgfT0dKSlpSEzM1NKapqbm8Pe3h6mpqbNEn2GhobSRBv19fVwcHDA6NGjMXToUHh4eMDIyAiGhoZQKpXSlyAILZKpt/teU1OD0tJSZGVlISsrC/n5+bh69SpKS0ulSielUglDQ0OYmZlJv6PLly+jpqYGMpkMKpUKVlZWUld4AwMD5ObmIjc3V6rguhWFQiElPDvzvT3biKKIqqoqVFRUoLy8HOXl5dBoNPxwgfoEhUKBiIgIjBgxAkeOHMGxY8dgYGCAiRMnYv/+/fjLX/6C119/HXbtfH6QyWTw9fVtNk7nrl27sG/fPgQHByMsLAzTp0+HIAhSkjMoKAgqlQpTpkxBQEAAdu7ciTVr1iAkJAQTJkxotQeCiYkJBg8ejME3PFNVVlY2S3rGx8ejuvp6IYmZmRns7e3h4OCAw4cPIykpCVOnTkVQUNAtPwgJDQ2FhYUFtm7dii1btuDBBx/Em2++iTVr1qChoQFjx45FXl4eLly4gLi4OAiCIE2W5OHhAV9fX6ntrq6ulsZZLisrQ3FxMUxMTNo8N1FPYoKTbksQBPxj0VR8ezAB3x8+jcLySthZqDFzTCDuGe2PXbFnseNoEorKquBia4HZUSMwJuD3RnrF2h04m5HT7JillTV4cdV3WP38ItTWN7R5blEErtU1wFClaHMcUOB6ctJQqcT9UUH4Jf4cKmtqpeRlU83gsumjoFJev+Xf+uNsvLL6B2QXlUnHsNKYYMXSe2Bq1HXd3waC2tKc693Sda0kqUUdrpXkQsDtKmtZmdDbXci+gLLqslbX6UQdDiYfZIKTkPLtt8j6bYypG5OTWXFxMPvmGwyZNeu2x9BptfCaMgX5rSRCBZkM5h4eMPfw6LKYiQY6IyMjmJqaIiAgAF5eXnB0dISTk1Ob1U7l5eVSMvPSpUvIy8tDQ0MDjI2NYWVlBU9PT2g0GigUCly7dg2pqalITEyUkpkNDb8/92m1WhQXF6O4uBiVlZWQyWSwsrKCn58fBg0aBEdHx2YzhBsYGCA7OxtpaWnIyMiAhYUFQkNDMXz4cAwZMuSOx2UFIE3OkZ2dLSUY8vLy0NjYCGtra/j4+Ei/Izs7O1RWVuLcuXM4d+4cLl26hMrKSjQ2NsLMzAympqawsLCQurBrNBqo1Wqo1WpoNBqpGtTIyAg6na5FcrUjidgbq19rampaXd/Y2Iiamhqpe/yNY4nW1v4+RFPT0ANDhgxhgpMkhYWFOHHihPR+vPG9aWJiIg3ToE+GhoaYOHEiQkNDceDAASQmJmLo0KE4ffo0nn32WaxYsQLe3t4dOqaLiwtcXFxQVlaGuLg4nDhxAkePHoWfnx/Cw8MhCAJ++OEH6HQ6hISEAAAcHR2xbNkynDhxAvv27cO5c+dw9913w9fX97YV2WZmZhgyZAiGDBkC4HqbVFFR0SzpWVZWBqVSieTkZBw5cgTu7u6YMmUKhg8fDkdHRxgZGbU4rpeXF5YuXYpNmzbhk08+wYwZMzBhwgRs2LABP/74I8aPHw8/Pz/k5+cjMzMTCQkJ2LlzpzQJmlqtlj6kUavVUKlU0j3AKnPqLZjgpHaRy2WYOz4Ec8eHSMtEUcTKjbuxP+GClESszKzF2YwcPDR5JBbdHYELV/KRlN6yck8nimjU6rDj1yTcFTqszfPKBAH+Xs5QKhSIHuGN/acutKjOlMkEjPEfDCOVAYxUBvjg2XlYs/MIjiSlQasT4WxrgYWTR2J80O+Vma52lvj8hcVISs9CTlEZbCzMEOTtCjkHSe4wlca29eQmAMjkUGlsoFRbw8jaFdeKrgI31wELMlh4BrF6s5dr0Lb9QUR71lP/p9NqkfbTT9c/mbqZKCJt9274zJ0LC09PlGZktNxOEKB2doZKrYZjaCiG3Xcfzn37rTSLuqjVwsjKChF/+UvPXBDRACEIAt58803odDop4dU043h1dTVyc3Nx+fJlaXKfpklymrprazQamJubQyaTobS0FBUVFc3GpTQ2NpaSfE2T7uTl5SErKwvZ2dmwsrKCv78/fH19MWzYMFhbWzeLT6fTITMzE0lJSTh37hzq6urg7OwsJQvupHJIFEWUl5dLyczs7Gzk5uZKlZlWVlZwdHSEn58fHB0dYW9vj+rqamRkZODSpUvYv38/qquroVAo4OLigrFjx8LT0xOOjo4QBAHXrl1DaWkpSktLUVJSIv07LS0NFRUVUo8IuVwuTSpiYWEBS0tLWFhYwNbWFhYWFrcdP/BmDQ0NUuK4qKgIRUVF0r+B6wmUG5MVTefVaDTQaDRQKpXQarWc3IiaqampkSqTa2pqpO7JN2qaYOvm5OeN49Xe+HNTZXVX02g0mDlzpjTjemNjI44fP44nn3wSK1asQGRkZIePaW5ujsmTJyM6OhqnTp3CsWPHkJSUBFdXVzg6OmLHjh0QRRGhoaEArleBjhw5EsOGDcPu3bvxzTff4NSpU5g2bZo0NEZ7CIIgvTeHDbv+/2ZRFFFWVoacnBycOnUKu3fvxn//+19YWlrC3t4eZmZm0lfT77mhoQHV1dUoKytDQkICdu/eDR8fHyiVSsTHx+P8+fMIDw+HqakpPDw84OfnJ028VlpaiqKiIhQWFkptg6urq1Th6eDg0OHfJ1F34F8t6rRTF69if8L1Gcmb/pvaNHv5hpg43BXqg9SreW3ur9OJOJeZi8fuGYNwXw8cT8lsNvu5TBBgpDLA9MjhAIBHZ4zB2UvZKCirlP5fLAgCLNUmeHzm77OeO1hp8NLiaWho1KKhUQsjlUGrfzhlMgGBg10QONjlTn4NA05tWQFqii7DwNgcpg5esBoaCfnuT6Ctr2mZsNBpYR84GYIgwGPiI0jZ/Nr1ak3pBZRBkMnhFr2wx6+DOmaww2AoFUrUN9a3WCeXyTHCc4QeoqLepKGqCg2/daFqdX11NeqrquD34IM4/MYbzdsCABBF+M2fL7XXfvPnw3XMGFw5cgQN1dWw8vaGU3g45B38jz4R3Zooinj77belGcGrqqqaTQgkiiJUKhWsra2l2c1dXV2lCStu/motYVFTU4Pz589LVY5arRbOzs6YOnUqhg0bJo3HeWNM+fn5SEpKwpkzZ1BZWQlLS0tERERg+PDhsLKy6tS1VlVVNUtm5uTkSGN2Ns2wPmbMGGnMO0NDQ1RVVSEjIwOJiYm4dOkSysrKpO6bQUFB0kznrSUhm34nTk5OLdY1NjairKysRfIzMzMTp06dalbpamZm1iL5aW5uLlXJlpSUNEtklpeXS8lTY2NjWFtbw87ODr6+vtIwBBYWFnqvtqO+xc3NDX/84x8BXH+PNlUL39ht+eafCwsLkZmZiZqamhaT1wC/Vwu3VRV68zojI6MOzdpta2uLBx98EJGRkdi8eTM2b96MP/7xj1i+fDkWLVrUqeSqSqVCeHg4wsLCcP78ecTGxiI7OxsFBQVYtWoV6uvrMWrUKGl7tVqNuXPnIjU1Fbt27cLHH3+MsWPHYtSoUa2+B0VRRH19vfQ7vPF7a8tqamrg6ekJpVKJtLQ05OXlwdLSEiqVCvX19ZDJZDAwMIC1tTXs7e3h7OyM0NBQpKamIicnB5MnT8ajjz6KrVu3wtPTE/fcc0+bvxdRFJGXl4eMjAxkZGTg0KFD+OWXX3DXXXc1u2YifWGCkzrt4KlUyGUCtK2MdykTBBw+fRE25m0P5iwTBKhNrncHf/GhKXhv6z7sS7ggPZC52lni+QWTYa0xBXC9C/n//roAP8aewdEz6QCACD9PTIvwl45zIwOFHAYKPrh1lcbaaqTu+C9KLsRKy4ysnDBk1nPwmfsykje/Cl1jPSDIrictRB0GTXkCJnbXu5JaeoXA78HXkXlgA6pyrifGNW7+cB+/BKYOLcfyo55jYWrR7HtrjFXGmBs5FxsPbWy2XBAEyAQZ5kTM6dYYqffQ1tXhypEjyD11CgIAh+BguIwaBQNjY8iVSmjrWybBAUCuVEJpbAz7wECMev55nF6/HlW5uQAAEzs7+D/0EJzCwprto3Z2ht+8ed19SUQDmk6nkybCaZpQyNbWFiNHjoSnpyfc3d3h4uLS4e7fVVVVUtftzMxMiKIIV1dXTJo0CcOGDYNarW6xT3l5Oc6cOYOkpCQUFBTA2NgYfn5+8Pf3h5OTU4eSEdeuXZO6czYlMysqKgD8PnlHWFiYlMw0Nb3+vFlbW4vLly/jwIEDuHTpEgoKCgBcT5QMGTJEmum8tfH0OkKhUMDa2rpFxSoAKdFcWlqKgoICqYL2/PnzyMvLQ0lJCa5duyZNRtSUxLS3t4eTkxMCAwOlyqr2TKxC1FGCIMDQ0BCGhobtrkZsbGzEtWvXmiVDb06MVlZWIj8/X/pZd9PwZE3nvVVV6M3rlEol3N3d8fzzz2PMmDH4xz/+gX/+85+Ii4vD66+/3ukPTGQyGXx8fODj44OsrCzExsZix44dePXVV3HPPfdgyZIlUKvVUjLYysoKM2bMwIEDB7Bp0yZ89913CAwMhJmZWYuEZWNjY4vzNV130/U5ODg0u14TExPodDr8+uuvuHTpEjw9PTFlyhQIgtCse3teXh6ys6/3sKyvr8eaNWsQFhaG0NBQHD16FKamppgwYUKr1ywIAhwcHODg4IDIyEhotVrk5OS02p4T6YMgiq31JaO2JCQkIDg4GCdPnkRQUJC+w7lj819bjaLyKlhrTPHVa8s6tO//2/ATDp5KbVZ12UQuk2H+xFDMHR+Mua981uY4my8vnoaxgb8nt4rLq3E5vxjmpkbwcLDmeB69yJmNL6L88hlAvOFBQ5BBbmCIoD9+AplcgfzTv6Cm6CpUppawDZgII0vHVo+lrb8GCALkBhzvtC8RRRFbj27F10e/Rk3d9YoXNxs3PDX1Kfi6+Dbbtra+Fml5aVDIFRjsMBhyDkHQq+187DFcKymBkaUlpq9a1eZ2dZWVOPDyy6jIyvp97FxRhMbdHdErVuDsV1/h0s8/t5gcSJDJ4HnXXQh69FFpmSiKqC4oAEQRJnZ2bO+pT+oPz4WiKOLDDz+EpaUlXF1d4ebmBkdHx051Ty4vL5eSmleuXIEgCHB3d4ePjw+GDh0qJRFvdO3aNaSkpODMmTPIzMyEgYEBhg4diuHDh2PQoEHtqjKsr69Hbm5us2RmSUkJgOvVVk1jZjZ9V6vVUpvT2NiIq1ev4tKlS8jIyEBOTg50Oh3Mzc3h4eEBT09PeHh4tBp7VxFFEZWVlS26kxcVFaG8/PeJHE1MTKSZ2VUqlfS7aZqlvbS0FGVlZVLX4aaurTdXfzZ9tTZOH1FvIYoiamtrW02GtlUxWt/Kh6wGBgbNkp8A8OWXX+LMmTNwcnLC4sWLMWHCBFhZWUlDa9zqmaQprtaqKfPz87F9+3YkJibCwsICgwYNgq2tbYuhNKqqqqTKVi8vL4SEhEhV8TcmMW9M1nak4jotLQ27du1CWVkZIiMjMXbsWOlDKq1Wi8LCQinheezYMRw9elTqBl9UVITo6GhER0fD0dERdnZ2rPamPoMVnNRpfh6OUhf1m2l1Ovh5OsJIpcTyByZi5cbdkAmAVidCEASIoogx/l4Y7e/VbD8rjQmsNJyFrbepzLmI8szTLVeIOmgbapGX8BPcohbCOaJ9A9HLlXyg7osEQcDcUXMxa+QsXC26CkOlIRwtHJs9BIqiiK+Pfo0tR7agtuH6hAWWppb4w+Q/YPSw0foKnbpI0hdfoDLnt0njbvhwq+LKFZzdtAnDFyxAycWLKE1Pl2ZRF3U6mHt4YPiCBc2OJQgCZ0Mn6gUEQcBTTz3V6Q8ZSktLkZKSgnPnziErKwtyuVzq5jhkyBAYGxu32KexsREXL15EUlISUlNTodPp4OnpiXvvvRdDhw5tdbb2G/fNz89vlswsLCyEKIowMDCAvb09vL29pWSmpaVls2vT6XTIzs6WulheuXIFjY2NMDExgYeHB0aMGAEPDw9YWFh0+Qcv9fX1rY6NWVxcLCVm5HI5LC0tYWVlheHDh0tdyq2trduVkNTpdFKy88au73l5eUhJSWk2mZCRkVGrY39aWFhArVZ3qCswUVcTBAFGRkYwMjJqd5Vl00zfrSU/b/z3lClToFAocPLkSaxcuRKbNm2Cq6srrK2tpcpomUzW4hm3afIvhUIBAwMDGBgYQKlUSpXUxsbGmDhxIlxdXXHy5ElUVVVBqVRCrVYjPDwcw4cPh6mpqTQxT0JCAvbs2YO8vDwMHz4cgYGBXdLueHl54YknnsCvv/6Kw4cP48yZM5gyZQqGDBkCuVwOe3t72NvbIygoCNOnT0dqairWrVuHxsZG2Nvb48CBA8jMzIS1tTXkcjns7OykandHR0fY2Ngw6Um9EhOc1GkTQ4Zh057jKK2qaTbxj0wmYJCTDUYMdgUAjAsaAkdrc3x36BQuZuXD3NQYd4/0xfjgoZDJWLHTF1Rmn8Pv89HfRNSh4mpKT4dEeqRUKDHIflCr67Yd24b1+9c3W1ZSVYKV367EGwveQKBHYA9ESN1BW1eHK4cPt6jOBK4nMTP370fgww9j/BtvIPv4ceSePAngehd2p7AwyDhZBVGv1dH/UBcVFUlJzdzcXCgUCnh5eWH27Nnw9vZutfu2KIq4cuUKkpKSkJycjNraWjg4OGDixInw8/NrtSu1TqeTKo2akpn5+fnQarWQyWSws7ODq6srwsPD4eTkBBsbmxZJOVEUUVBQIE0MlJmZibq6Oqnb6oQJE+Dp6QlbW9suSSw0zXh8cyVm09iYTUxMTGBtbQ0HB4dmiUwLC4s7SizKZDKYm5tLFag3a2vio+zs7GZjd8rlcnh5eWH+/PmdjoWopxkYGMDMzAwKhQJyuVxKVN6YsGz6PnnyZJiZmUljaFZXV8PExAR2dnbQaDSQyWTNkpw6nQ6ieL1Yp+k4MpkMjY2NUCqVzRKeoaGhMDc3x/nz5+Hq6gq5XI5du3bhxIkTGDVqFEaOHAlDQ0MEBwdjyJAh+Pnnn/H9998jMTER06dPh42NzR3/LhQKBaKiojB8+HD89NNP2Lx5M7y9vTFlypQW4x57e3vjz3/+MzZt2oS6ujrcc8890viccrkc2dnZuHz5Mk6ePAlRFKFQKGBvby99kOTu7g6NRnPHMRPdKf5vg9rtcn4Jfjp2FvklFXCw0mBquB/eeWoO3vpyN85d/n0yodCh7vjb/EnNkpdDXO3w94V36yNs6gLXKy7bGM1CEFiROYAkXErAV4e/wrmsc1AaKDHOdxzmj5kPa7U1GhobsOXXLa3uJwgCNh/ZzARnH1ZfUwNdK2NCNdHW16OxthZKU1O4REbCpRMzlHaWqNWiNCMD2ro6mHt4wKCVijEi6rymJGFKSgpSUlJQWFgIpVKJwYMHY/To0Rg8eHCbY3QWFBRI42qWl5fD3NwcoaGh8Pf3b/afeFEUUVJS0iyZmZubi4aGBgiCABsbGzg6OiIwMBBOTk6ws7Nrsxt9WVmZ1OU8IyMDVVVVkMvlcHFxwahRo+Dh4QFHR8c7qkBqqsZsLZHZNEFQUzWmtbU1/P39pUpMKysrvXUPb6qIc3RsOYyQVqttNvFRR8ddpf6tKfnd00PK6HS6VifWaWvSnWvXruHmUfhkMlmzbt+mpqaws7PD8OHDMWrUKGzbtk1KTDZNgnbXXXdh0KBBLa63aTbyW1WJlpWVSeN/7t27Fx4eHrC0tERiYiL27NkDhUIBd3d3DB06VOoa7+LigoSEBBw7dgxhYWEYPXo0NBqNVBnamWFDAMDS0hIPPvggzp8/j927d+Ojjz7CmDFjMGrUqGbHtLGxwbJly7B582ZkZ2dDqVTiwIEDePjhhxH22xjpNw4HkpOTg/T0dBw/fhxTp06VtiHSJyY4qV12xyXjP1v2QCYI0OlEyGQCvj2QgL89OAnvPzsPl/NLUFxWBUcbDewt+elNf2PpHQ5BroCobSW5IYqw8Yvu8Zio5x1MPoi3vnsLMkEGnahDbX0tYhJjEHcxDu8vex/l1eWoqq1qdV+dqEPy1eQejpi6kkqthoGJSZszpas0Gr0kFnMTEnDy009xrbgYACAzMID3jBnwmzdP6iZPRB0niiJyc3OlpGZJSQkMDQ3h7e2NCRMmYNCgQa3OHA4AlZWVUlIzLy8PRkZG8PX1hb+/P1xcXAAAFRUVSElJaTb5RVP3aUtLSzg6OmLo0KFwcnKCg4PDLZNt1dXVUjLz0qVLKC0tlSbDaJpwx9XVtc14b/U7KC8vbzWR2TRhEQCYmprCysoKTk5OCAgIkBKZ5ubmfaqbt1wuh5WVVacnXaH+LSsrC9u2bZMm1nF0dOxUslOr1XY4YXkzuVzeLGGpVqvh4ODQYuzKpvEsVSpVm7FGRUUhIiICb7/9Ns6ePYs5c+agpqYGGzduhKenJ+666y44ODhI2xsYGEhV0u2xb98+/PLLLxg5ciQCAgJQUFCAEydOICkpCYmJibCzs4OLiwtUKhVcXV1x7tw5bNy4Ed988w0GDx4sTeKkUqnanF2+tX/feM2CIGDYsGEYNGgQDh06hIMHDyIpKQlTp07FoEG/98oyMTHB4sWLpXFEa2trsXHjRixbtgzm5uZQKpVwc3ODm5ubtE9tbS3HUadegwlOuq28knL8d8svEEVA+9unYU0zp//fVz8j0MsFbnaWcLNr3wx61PcYGJnBc9LjSP/po99mSdehqcu6hVcYrIf2XKUW6UejthGfxHwC4HqysolO1KGsugzfHfsOU4Om3vIYKkXbY6pR7yeTyzF46lSkfPNNs/E3mwyeNq3LE4oNNTXI3L8feadPQyaTwTEsDK6jR0P+W6KjODUVv775ZrNKDV1DA85v2wZBEODHrpVEHSKKIrKysqTu501VSEOGDMGUKVPg6enZZtVjXV0dzp07h6SkJGRkZEAul8Pb2xvR0dFwcHBAfn4+Ll26hCNHjkjdQQFArVbD0dERkZGR0kRAt6turKurw+XLl6Uqzfz8fADXK5AGDx4MT09PuLm5tbtKsr6+vtVKzJurMZuSfwEBAc3GxrzTGdWJ+gIjIyMMGjQIiYmJ+PXXX2Fubg4fHx8MGTIE5ubmzSoYW0tUNn2/cRzYJgqFollS0sLCAk5OTm0mLJVKZZcm1YKCgvD666/j3//+NzZt2oT58+djzpw52L9/Pz799FMMHz4c48ePb9G1uz3Gjx8PhUKBffv2QaPRIDo6GmFhYaivr0diYiKOHTuGkpISuLi4YNq0aVi+fDny8/Px3XffIT09HVZWVggODgaAZr/j4uJiXL16tc3f6Y3jgt6c/Bw5ciTi4+Px/vvvw8/PD9OmTZMmE1IoFLjvvvtgZWWFPXv24Pz581i/fj0effTRVsdUZvtHvQkTnHRbe06ca3OdKAK/nDyHeRNCezAi0geH4KkwsnRE9rHvUJ1/CQYm5rAfMRl2I+6GwBmy+70LORdQXlPe6jqdqMPB5INYOmEpPGw9kFmY2bJrkCBDlF9UT4RK3WjY/fejOj8flw8dajaLuvv48Rg6c2aXnqumuBj7X3oJNUVF1//YCAJy4uORtns3ol97DQbGxji/fbsUw81Sd+zAkFmzYMBZgoluSafT4cqVK1JSs7KyEqamphg6dCh8fHzg7u7eZhWiVqtFeno6kpKScOHCBTQ0NMDJyQkjRoyAiYkJiouL8dNPP0ljTxobG8PR0RHBwcFSMrO1sTdv1tjYiKysLCmhmZ2dDZ1OB41GAw8PD6nb+a2O1VSN2Voi8+ZqTGtrazg7OzdLZPa1akyirtbY2AitVgtHR0epzYiJicG1a9dgaGgIGxsb2NjYwMzMDEqlsllS0srKCi4uLi0SlU3/NjAw0HsVoJeXF/75z3/i7bffxsaNG1FaWoqHH34Y58+fx/79+/Hhhx8iNDQUY8eObTXRdytjx46FIAjYu3cvRFFEdHQ0lEolwsLCEBISgtTUVBw7dgxff/01zM3NMXLkSDzyyCO4cOECYmJicOjQIUyYMAETJkxo9fek1Wpx7dq12842X1hYKP27sbER9fX1+Prrr7F582a4u7tj0KBBMDU1lV4bW1tbXLp0CT/88APy8vKwYMECWFhYSInSrk40E90pJjjptorLq643XK38B1ImE1Bc3np3xTtRfa0O+xLOI7uwDDbmZpgQMhTmphxTTd/MPQJhzjEUByStVnvL9Y26RgiCgCemPIEXN74IraiF7rfJaGSCDOYm5pg/mtV0fZ1MLkfYM89gyL33Ii8hARAEOAQHQ+3k1OXnOrVmzfVu501/e377Xp6ZiZStWxGweDEKk5NbnfQIuD4maFlGBmx8fLo8NqL+RKfT4auvvoJKpZK6nrq4uLSZzGuq8jxz5gxOnz6NgoICKBQKmJubw9jYGNnZ2cjOzoZKpYKDgwN8fX2lZKa5uXm7/jOs0+mQm5vbbKbzhoYGGBsbw8PDAwEBAdKYdjcfr66urtUu5SUlJVI1pkKhkMbGDAwMbDY2JquRiFrX0NCAwsJCGBsbw8fHByEhITAyMkJ5eTmys7Nx9epVNDQ0wMzMDMOHD5fe+30pAWZvb49XXnkF77//Pn788UeUlJTgqaeewvDhw3Hs2DH8+uuvOHXqFEaPHo3w8PAODXsxZswYyGQy7NmzBzqdDuPHj5cmKxo6dCiGDh2K3NxcxMbGYs+ePThw4ACCgoLw0EMP4cSJE/jxxx9x+vRpTJ8+Hfb29s2OLZfLYWpqClNT03bFIooi6urqUFNTg5KSEuzfvx8nT55ESUkJ3NzcYGJigpqaGunDrvz8fGzbtg1nzpxBUFCQ9PdBoVDA2NgY48ePR2BgYLt/F0TdhQlOui1XO8tmXVJv1KjVwdWuY6X6jVotEi9moepaLbycbeFs03z/pPQsvPTZ97hW1wCFXAatTofVO4/gH4umYrS/V6evg4g6b7DjYKgMVKhrqGuxTibIEDzoetcZXxdfvPvIu9h6dCsS0hNgIDfAWN+xuC/iPliachiL/kLj4gLNb+PodYf6ykrknDjR6gdrok6HjL174b9oERQqVZtjggKAgokKottSKBT4wx/+cNvkY0FBAQ4ePIi4uDhkZ2ejvr4epqamsLGxgUajgZ2dnTSjrqOjI6ytrdud2BBFEUVFRc1mOq+trZXGexs3bhw8PT1hZ2cHQRCg0+lQXl6O9PT0FonMyspK6bhmZmZS5diIESOkRGbTDMlE1H4uLi5YtmxZm+ubqsGTk5Nx5swZxMbGQqPRwMfHp08lOzUaDf76179i9erV2L17NyoqKvCnP/0JY8eORXBwMA4dOoT9+/fjxIkTiI6ORmBgYLvbk1GjRkEQBPz8888QRbFFRaaDgwNmz56NiRMn4sSJE4iPj8exY8cwbNgw3H333UhISMCqVasQHh4uVYF2hiAIMDQ0hKGhISwtLeHl5YVp06bhxx9/xMWLFzFixAjMnDkTJiYmAIDi4mK8++67+PXXX6FWqzFr1iyUlJSgsLAQRUVFbE+p12CCk25rYsgwrN0Vi7qGxmbdTmWCAEOVAcYHD233sU6cz8S/v4xBWdXvg0VH+Hni+QWTYWKoQk1tPV5e/QNq669PZtOo1Unf/7V+F7546WHYWty+KxMRdS0jpRHmjZqH9QfWN1suCAIUcgXmRMyRlnnYeuC5Wc/1dIjUBxWmpODSzz+jurAQZo6OGDR5Miy9vFBXWdlqcrNJQ00NRK0WrmPGIHXHjpZVnIIAExsbmLu7d+8FEPUTN48rp9PpUFRUhPT0dMTGxiIxMRFZWVkQBAF2dnbw9fXF8OHD4ezsDEdHR9ja2nZ4RvLy8vJmM51XVlZCLpfD2dkZERER8PDwgLW1NUpLS1FcXIyUlBQcOnRIGhuzsfH6s6JCoZDGxhwxYkSzsTFVKo79TNRTZDIZ3N3d4e7ujilTprSZ7PTx8YGzs3OvTnYaGRnhj3/8IywsLLB161b861//wjPPPAMfHx9MmTIFI0eOxL59+/DDDz8gNjYWEydOhLe3d7uuKTIyEoIgICYmBjqdDnfddVeL/dRqNSZMmIAxY8bg9OnTOHbsGFJSUuDo6Ah3d3fExcUhOTkZU6dOxZAhQ7rkmh0dHbFs2TLEx8cjJiYGJ06cwIgRI+Di4oLKykr4+Pjg7Nmz+PTTT3HkyJFmFZuDBw/ukhiI7hQTnHRbahMjvPHYTLyyZgeqr9VBLrteVWlipMS/Hp0JE8P2PTxm5hbjldU/QKtt/h/RuOQMvLlxN15fNhMHTl1ATW19q/uLooiYuGQ8dHf4HV8TEXXc3FFzYaAwwOYjm6XZ0gfZDcITdz8BVxtXPUdHvUlNURFStm7F1V9/hbahATa+vvC5//5m3cXPffstzn71FQSZDKJOh5KLF5G5fz+CHn0U7tHRkKtU0Na1rBgGABM7O8gUCgyZORNZx46hprBQSnIKMhkgCAh67DHOok7UDqIoorS0FDk5OcjOzsaVK1dw5swZZGVlobS0tNkkQyNHjoSLi0uHZyMHrk+OceNM5yUlJVLC1MPDA2q1GiqVCuXl5cjIyEB8fHyLakxra2u4urpKicymaszenCghGohaS3ampKRIyU61Wi1VdvbWZKdCocCCBQtgaWmJdevW4c0338Rjjz2GUaNGwdLSEvfffz8iIiLwyy+/4KuvvoKbmxvuuusuODs73/bYERERkMlk+OmnnyCKIiZNmtTq70CpVCI0NBQhISG4ePEiYmNjcenSJRgYGKCsrAwbNmyAn58fpkyZAo1G067rqqurQ3l5OSoqKqTvN/67vLwcNTU10qRwGo1GSnTOmzcPu3fvxunTp+Hv74/HH38cGo1GqvQk0jcmOAc4CzPjZt/b4j/IGV+9ugxHki4ir6QCjtbmGD3cCypl+2+h7YdPQRRF3FyToxNFHEvOwJX8EuQUlUMhl0mVmzfLLipr9/mIqGsJgoDZ4bMxI3QGsouzYaQ0gp25nb7DIj3R1l//MEp+U/eomuJi/PL886ivrJSSjgVnz6LgzBmMev55OIaEoPzKFZz96isAkLZp+p6wejUcQkLgdffduPDDD61Wcg655x4AgEqtxoQ330TqDz/g8uHD0NbVwdbPD0NnzYLFoEHdc+FE/YxOp8OHH36IoqIiVFVV4dq1azAyMsLIkSMxevRojBgxosMTagDXZya/cabzrKws1NTUQKVSwczMDGq1GjKZDEVFRcjLywPwezWmtbU13Nzcmo2NyWpMor7pxmTn3XffjatXryI5ORnJyck4duyYlOxsGv+3NyU7BUHA1KlTYW5ujk8//RQffPABiouLMWPGDMhkMjg5OWHRokVIS0vDL7/8gtWrV8PHxwcTJkyAlZXVLY89cuRICIKAXbt2QRRFTJ48uc1rFwQB3t7e8Pb2Rl5eHmJjY3HmzBmUlZVh7969UjVnUFAQqqqqWiQwb/xed8OHx4IgwNTUFGq1GhqNBra2ttBoNNLP5eXlOHDgAIqKiuDg4IDx48dj7ty5eOedd7Bz505YWlriqaee6lWvGQ1sTHAOcB//5cF2b2ukMsBdobeerKGiuhZ7TqQgPbsQahNDTAwdBi8nWwBASmYutLq2uxxezCqAnaVZiwrPG9lZqtsdLxF1DwO5Adxt3fUdBulJ0blzOPPVVyhKSQEA2Pj5YfiDD8LK2xsAcH7btmbJTQCATgcIAk6tWQOHoCBcPnhQqtxszZXDh+E3bx5qiotx9ciR32dsx/XkpuekSdLPKjMzDF+wAMMXLOiGqyXq/3Q6HRoaGmBqagpvb2/4+/tj+PDhLbqt345Wq8WVK1eQlJSElJQUXLp0CdXV1RBFEUqlEkZGRrCwsIBKpYKhoWGzruQ3jo3J/ygT9V8ymQxubm5wc3NrVtl5Y7Jz2LBh8PX17VXJzsjISGg0Gnz00UdYv349iouLsWDBAqhUKgiCgMGDB2PQoEFISkrCvn378NFHHyE4OBhRUVG3nPgnLCwMMpkMO3fuhE6nw5QpU9q85sbGRlRWVqK2thaDBg2CsbExTp48icTERJw+fRrff/89bGxs4O/vL1VzmpiYQK1WQ61Ww93dXUpcNn03MzO75fAizs7OGDp0KI4fP479+/cjJSUFkyZNwvLly6FQKLBjxw7U1dXh6aef7tQHYURdjQlO6jKpV/Px3P+2oaa2DoIgQADw7cFTWDhpJBZPiYCZsSHamIwdAGBqqMJIH3es+uEw6uobW1R6AsDdYb7deQlERAOSobl5s+9tKUxJwcEVK5o15IUpKdj/8ssY9/rrsPL2xtVff209cSmKqCksRPmVK6irqGjzHIJMhrqKCsgMDBD+7LPwue8+5CclQZDL4RgcDGMbm85cIhG1wcDAADNmzICzszMcHBzalVCora1FQUEBLly4gJSUFKSmpuLq1auoqqqCTCaDhYUFrK2tERQUBDc3N6k7eVMys7MTYxBR/yEIgpTsvLGyMyUlBXFxcTAzM5MqO11dXfWe7PT19cXzzz+Pjz76CN9//z3KysrwyCOPSMlEmUyGwMBA+Pr64vjx4zh8+DBOnz6NyMhIREZGttnuhYSEQBRFfPvttygoKEBwcDAqKytbVF9WVVU128/Q0BAWFhaYNm0aSkpKkJycjNTUVJw5cwaTJ0/GY4891u5u67cil8sREREBX19fxMTEYNu2bUhISMC8efMAADExMfD19cX06dPv+FxEd0oQxVuM4k8tJCQkIDg4GCdPnkRQUJC+w+kxWq0O2w8n4vsjp1FYVgk7CzVmjQnEPaMDIJMJ0Op0WPj65ygpr4aulVvqrT/ORkFpJd7ZvKfFOgGAqbEhNq9YBqVCgRPnM/Hamh1o0OogEwRpYqO/zp+Eu0KHdfelEhFRG/a+8AJK0tJaflIlk8Fm2DBEr1iB7x56CI3XrrV+AAATVq5EcWoqEteta/MTr7BnnoHb2LFdGDlR9+jPz4U6nQ5lZWXSLOWFhYXIzMxEeno6srOzUVZWhsbGRhgbG8PFxQWDBw/GsGHDMGTIENjY2ECtVus9IUFEfY8oirh69SpSUlKQkpKCiooKmJmZNavs1Oes3QUFBfjkk0+QkJCAwMBAPPbYY3B0dGyx3bVr13D48GEcO3YMMpkMI0aMgKurK6qrq1skLysrK5GTk4MLFy7AwcEBfn5+MDc3b1ZxeXP15c0JU1EUkZqaii+//BK//vorTExMMGfOHNx///0wMjLqsutPT0/Hrl27UFpaiuDgYFy8eBETJ06En59fl52DqLOY4Oyg/vwg2xZRFLFyw27sP3WhxbpJoT7424OTcOJ8Jl78dHur+8tkAkYP98ILD92Nl1f/gPjzl6VKTrns+oPvKw9PR6Tf7+OllVZWI+Z4CrILy2BrboZJYT7snk5EpEf1lZX4/uGHb7nNvRs24PgHHyAnPr7VKk4DY2PMWL0a2vp67HriCTTU1l7vvv4bQSaDSqPB1I8+ajG2J1Fv1B+eC0VRRHZ2tpTILCoqQlFREUpKSlBTU4PS0lJUVlairq4OMpkMJiYm8PDwgK+vL/z9/eHp6dnhGdSJiNpDFEVkZWVJlZ0VFRUwNTWVJijSV7KzsrISn3/+OQ4ePAgXFxfcf//9sLGxaXXCnsLCQqSlpSE/Px9GRkZSd/abE5gajQZXrlzB/v37ERoainvuuafTHxKlpaXhk08+wcmTJ2FjY4P7778fEydOhKWlZZdcf2NjI44ePYpDhw7B2NgYM2fOxCCOfU69ALuo020lZ+S2mtwEgJ9PpOCe0QEoLKtqdT0A6HQi8ksqoJDL8fqye/DziXP4+XgKKqqvwcfdAfeOHYFBTs27HFqYmWDehNAuvQ4iIuq89nweKgIYdt99yDl5Eq2NSeIzZw7kSiXkSiXGvvIKfn3rLdSWlkrrjW1sMPqFF5jcJOph69evR0NDA4yMjCCKImpra1FVVYWGhgZYWFggMDAQnp6e8PDwgJubG7uYE1GPEAQBLi4ucHFxweTJk5slO48fPy4lO5u6sXdlslMUxVZnHG/6rtVqIYoifvnlFxw9ehQBAQFwc3ODmZmZlLR0cXGRkpe1tbU4ceIErl69ChMTE0RHR8PNza3ZOQcPHgxra2ts374dAKTJjDrKy8sL//d//4eEhASsXr0aa9aswe7duzFx4kSMGjXqjrv8KxQKjB07FsOHD8dPP/3Ean3qNVjB2UH94ZP6jvpk+0FsP5zY6gRBcpmAOeOCETLUDX/96NtW95fLBIwLGornF0zu7lCJiKibiKKIPX/7G8ovX27ZtVwQYOnlhQkrVwK4Pmv6qdWrUZGVBQBQmprCZ84ceE2d2uwhWKfVIj8xETVFRTC1t4ft8OEQ9NjtjKij+sNzYWNjI7777jsUFBSgqKgIoijCysoKHh4e8PT0hLu7OyePIKJepamys6kbe3l5OUxNTaVu7O1JdtbX17dIWt6cyKyvr5e2FwQBZmZmzaouTU1NER8fL1Uyzps3D1OmTLnluS9duoQ9e/YgNzcXQ4YMwcSJE2Fz0/jiSUlJ+O677+Dv74+ZM2feUeK2vr4ev/zyC3744QeUlJTAyckJw4YNQ3h4OHx9fVmBT/0KKzjptuobtbg+UmZruXAB9Q1aDPd0hrOtBXKKyqC7KRGq1YmYMcq/J0IlIqJO+OW551BbVgZDc3NM/Pe/W91GEAT4L1iAw//v/zWvzvwtYTn8wQelbW39/DDpv/9FVV4etHV1UDs5QWZg0OKYMrkcDsHBXX9BRNRucrkc5eXlcHBwwKhRo+Dh4dElE1MQEXWXGys7J02ahOzsbKmy88SJEzAyMoKbmxscHR1hZmaGysrKFsnL2traZsc0NTWVkpeenp7Nuo6r1WqYmZm1mmiMjIyEn58fNm/ejK+++gplZWWYM2dOm5Xunp6eeOyxx3D27Fns3bsXH3/8MUaMGIFx48bBzMwMAODv7w9BELBt2zaIoohZs2Z1OsmpVCoxdepUBAUFYceOHThz5gzOnTuHy5cvw8rKCmFhYQgODu7ScTqJ9IUJTrqtQC8X7Pg1qdV1Wp0OgYNdIJMJeH3ZPXju429RWFYFuUwGnShCAPDk7Gj4uDv0bNBERNRutWVluFZSctvt7EeMwOgXXkDSxo2ouHIFAGDu5gb/hx6C7fDhzbYVBAFmDmz7iXo7QRCwbNkyfYdBRNQuWq221VnGy8vLYWRkhOLiYpw9exb79u1DXV0dDAwM4OzsjEGDBsHDwwOurq4txr40MzODQtG51IggCJgwYQLUajU2bNiAnTt3ory8HA899JCUsGxtn+HDh2PYsGFSBeiZM2cQHh6OUaNGwdDQEMOHD4dMJsO3334LURRx77333lElp729PZYtW4aTJ0/il19+QU1NDeRyOfbv34+DBw9ixIgRGDlyJKysrDp9DiJ9Y4KTbityuCfc7a1wpaCkWXWmTCbA08EaYT7uAABnGwus/8fD+PVMGtKyCqExNcK4EUNgbW6qp8iJqKvV1NVg/9n9SLmaAkMDQ4zxGYMA9wCOvTOAOAQFwX7ECNSWlUEAYGhhoe+QiIiIqB/Q6XSoqqpqdczLpu9VVVXNxgVXqVRSsrKp+3VT0vLatWu4cuUKUlNTUVZWhpycHGg0Gjg5OcHd3b1Lx+wMDQ2FWq3G2rVrcfDgQVRWVmLJkiWws7Nrcx+FQoHw8HAEBgbi119/RWxsLE6ePImxY8ciNDQUvr6+EAQB33zzDURRxOzZs+8oZkEQEBISgqFDhyImJgZnzpyBg4MD7O3tkZycjBMnTsDb2xsRERFwc3Pj8z31ORyDs4P6w1hLnVFWVYN3v96Lo2fTIYrXG8exAV545v4JUJsY6js8IuoBuSW5eG7DcyiuLIZMkEEQBGh1WkT7RuMvM/8CuYxj+PRVOx97DNdKSmBkaYnpq1bpOxyiPmOgPhcSEXWUKIqorq6+ZfKysrISOp1O2sfAwKBFV/Gm703/VqlU7Tp3Tk4OUlJSkJycjLKyMpiYmGDo0KHw9fXt0mRnVlYWPv/8c6SkpMDb2xuLFi2Cl5dXu/atqKjAgQMHcOrUKZibm2PChAnw9fXF+fPnsXXrVgwdOhT33Xdfl42bmZ6ejh9//BHl5eWIiIiARqPBiRMnUFBQAHt7e0RERMDPz4/jdFKfwQRnBw30B9nSymoUllXBxtwMFmYccJ5oIFm+djlSc1KhE3Ut1j099WlMCZqih6ioK3RlglMURRSdO4fs48eha2yE3fDhcAgJgYwPx9QPDfTnQiIi4Prf/mvXrt1ywp6mmcebyOXyVhOXN343NDTs8ipCURSRm5srjdlZWloKY2NjDBs2DD4+PvDw8LjjZGdxcTHWrVuHkydPwsnJCQ888ABCQkLavX9BQQH27t2LCxcuwNHRERMnTkR9fT22bt0Kb29v3H///V2WdGxoaMCRI0dw5MgRaDQaTJs2DQAQGxuLtLQ0mJmZSeN0csI56u2Y4OwgPsgS0UB0tegqHv/k8VbXCRDgaeeJDx79oIejoq7SVQlOnVaLuHffRVZsLITfHrxFrRbm7u6IevVVKNsYi4qor+JzIRENFDU1NcjJyWmz+rKhoUHaViaTwczM7JbJS2NjY713gW5KdjZVdjYlO5sqO+8k2VldXY2NGzfi6NGj0Gg0mDFjBiZOnNiha758+TL27NmDrKwseHl5wd3dHfv378fgwYMxZ86cLq2sLCwsxI8//ojMzEz4+/tj0qRJuHbtGo4dO4bTp09DEAQEBAQgPDwc1tbWXXZeoq7EMTiJiOi2iiqK2lwnQkRhRWEPRkO91cWdO5EVGwvgemKzSfmVK0hYvRrhf/6zvkIjIiKiO5CVlYVNmzZBEIRmM47b2tq2SGCampp26fiW3UUQBDg6OsLR0RETJkxAXl6eVNmZkJDQLNnp7u7eoYSiiYkJHn74YZiamuLAgQP49ttvUVpainvvvRcGBgbtOoabmxseeeQRnDt3Dnv37kV6ejqsra1x5swZiKKIOXPmdHpypJvZ2Nhg8eLFOH36NH7++WekpqZi4sSJmD59OsaPH4/4+HicOHEC8fHx0jid7u7uek9SE92ICU4iIrotR0vHNtfJBBmcrZ17MBrqrdJ++qnV5aJOh6zYWNQtWwYVqziJiIj6HHd3dzz77LMwMzPrl2MyCoIABwcHODg4tJrsNDIyataNvT2/A6VSifnz50OtViMmJgY///wzysvL8eCDD8LExKTdcfn4+GDIkCFISEjAgQMHUFNTg5iYGNTV1WHhwoVdluQUBAGBgYHw9vbGnj17sHPnTpw+fRrTp09HVFQURo0ahbNnzyI2Nhbr16+HnZ2dNE5nV8VAdCd4FxIR0W3ZmdshzCsM8enxLcbg1Ik6zAqbpZ/AqFepKS5uc52o0+FacTETnERERH2QUqmEUqnUdxg9orVkZ1M39qZk543d2G+V7JTJZJg+fTrUajV++OEHxMbGorKyEg899BBsbGzaHZNcLkdoaCj8/f0RGxuLHTt2YNOmTcjIyMALL7wAQ8Oum/jX2NgYM2fORGBgIHbu3IlPP/0UERERiIqKQmBgIAICApCRkYHY2Fhs374dVVVVGD16dJedn6izmOAkIqJ2+fM9f8bLm15GWl4a5DI5RFGETtRh/uj5GDV0lL7Dox5SmZODc9u2ITc+HgDgGBqKYffdB1N7exhbW6OmsPXhCgSZDEZWVj0ZKhEREdEduTHZOX78eOTn50uVnadOnZKSnT4+PvD09Gw12SkIAqKioqDRaPD111/jzJkz+PTTT7FgwQJ4eHh0KB6VSoXo6GiEhIRgy5Yt2LRpEx555BH8+c9/RlBQUJcODeDm5oY//OEPOHr0KA4ePIjk5GRMnToV3t7e8PT0hKenJ4qKijj5EPUaA2qSoZUrV2Lbtm04f/48jIyMEBkZibfeegtDhgxp9zE4mDwRDWQ6UYfES4lIvpoMQ6UhRg8dDQdLB32HRXeovZMMlWVmYv9LL0FbXw9Rd72SV5DJoDA0xPj/9/+Qd+oUTq9f32I/QSaDy6hRGPmnP3XbNRB1FJ8LiYios0RRRH5+vlTZWVxcDENDQ6mys61kZ1paGjZt2oTMzEw4Ojri/vvvR2BgYKfjOHnyJN5++23U1dVhwoQJmDx5Mry8vLp8bMySkhLs2rULaWlpGDZsGKZMmQK1Wt2l5yC6UwMqwXn33Xdj3rx5CA0NRWNjI1588UWcPXsWKSkp7R4Dgw+yRETU37Q3wXnwn/9E4dmzUnKziSCTwT4oCJF/+xuOf/ABrh450mwWdYtBgzD25ZehNDXt1usg6gg+FxIRUVcQRREFBQVSZWdRUZGU7PTx8cGgQYOaJTtzc3OxYcMGXLx4EZaWlrj77rsRHR3d6aRkZmYmPv74YxQXF8PR0RFeXl6466674OjY9hj6nSGKIpKTk7F7927U19dj/PjxCAsL6xMTStHAMKASnDcrLCyEra0tDh48iLFjx7ZrHz7IEhFRf9OeBGdDdTW2L17c9kEEAbM3boRMqUTJxYvIjouDrrERtsOHw2HECCnhSdRb8bmQiIjuVFOys6mysynZOWTIEKmyU6FQoKysDBs2bEBKSgqMjIwwatQozJw5s9OT9Vy+fBkbN26ETCaDsbExSktL4efnhwkTJsDCwqJLr7G2thZ79+5FfHw87O3tMWPGjC5PphJ1xoAeg7O8vBwAYGlp2eY2dXV1qKurk36uqqrq9riIiIh6G21j4603EEXoGhshV6lg5e0NK2/vngmMqIvwuZCIiO6UIAiws7ODnZ0doqOjUVhYKFV2nj59ulmyc/Hixdi6dStOnz6NQ4cOoby8HPPmzevUmJZubm546KGHsHHjRpiamiI8PBxHjhzBhx9+iJCQEIwdO7bdvRNux9DQENOmTUNAQAB27tyJ0tJSJjipVxiwCU6dTodnn30Wo0aNgp+fX5vbrVy5EitWrOjByIiIiHoflVoNU3t7VOXltVwpCFA7OUHBQeapj+JzIRERdTVBEGBrawtbW1uMGzeuWWXn6dOnoVKp4OXlBQ8PD1y+fBmJiYmorq7GggULbvlhW1tcXV2lJGdycjIef/xxnDp1CkeOHEFiYiJGjRqFiIgIGBgYdMn1OTs747HHHuvy8T6JOmvAdlH/4x//iJ9++glHjhyBs7Nzm9vd/El9YmIioqKi2BWJiIj6jfaOwXnl8GHEvfdeq+si/vpXOIeHd1eIRN2Kz4VERNSTbkx2FhQU4PLlyygrK4OpqSl8fHywcOFCuLq6durYWVlZ2LBhA2xtbbFw4UI0Njbi0KFDiI+Ph7GxMcaNG4fAwECOnUn9zoCs4Hzqqaewc+dOHDp06JYPsQCgUqmgUqmkn005QQIREQ1QrmPGoLGuDme+/BL1lZUArld2+i9axOQm9Vl8LiQiop7WVNnZ1I397Nmz+PHHHxEXF4f09HScPn0aCxYswNSpUzs8LqezszMWLVqEDRs2YMOGDVi4cCGmTJmC8PBw7N27Fz/88ANiY2MxceJEeHt7swKT+o0BVcEpiiKefvppfPfddzhw4AAGDx7c4WNwMHkiIupv2lvB2UTX0IDSjAwIggBzDw/IOjkgPpE+8bmQiIh6m4MHD2LdunXIzMyEVqvF0KFDMWnSJPj5+cHLy6tDyc6cnBx88cUXsLKywkMPPQRDQ0Np+Z49e5CRkQFXV1fcddddcHFx6a5LIuoxA+p/JE8++SQ2bdqE77//HmZmZsj7bRwxjUYDIyMjPUdHRETUN8gMDDiJEPV5fC4kIqLeJioqCq6urti8eTOuXLmCiooK/PLLL0hKSpImKPLx8YGXl9dtx9J0dHTE4sWL8cUXX+CLL77AQw89BCMjIzg6OmLRokVIT0/Hnj17sGbNGgwbNgwTJkyAtbV1D10pUdcbUBWcbZVer127FkuWLGnXMfhJPRER9TcdreAk6g/4XEhERL1VQUEBNm7ciNzcXMjlcjg7O2Po0KG4ePEi8vPzoVQq4e3tDV9f39smO3Nzc/HFF1/A3NwcixYtavYhnk6nw5kzZ7Bv3z5UVlYiKCgI0dHRHIKF+qQBVcE5gHK5RERERHQLfC4kIqLeytbWFsuWLcOXX36JzMxMZGVlQRAEPPjgg9DpdEhOTkZKSgq2bNkiJTt9fHwwePDgFslOBwcHqZJz/fr1WLRoEYyNjQEAMpkMAQEB8PX1xfHjx3Ho0CEkJSUhIiICkZGRzcadJurtBlQFZ1fgJ/VERNTfsIKTqHP4XEhERN2ptrYWW7Zswfnz5wEAlpaWmD9/vjQpXnFxsZTszMvLg1KpxODBg+Hr69si2Zmfn48vvvgCZmZmzZKcN7p27RoOHz6M48ePQ6VSITo6GkFBQZDL5T1zwUR3gAnODuKDLBER9TfdkeCszM5GXWUl1E5OUJqZdckxiXobPhcSEVF3a2xsxPfff4+EhASIoghjY2Pcd999GDZsWLPtiouLkZKSguTkZOTl5cHAwEDqxt6U7CwoKMD69ethamqKRYsWwcTEpNVzlpeXY9++fUhKSoKlpSUmTJiAYcOGccZ16tWY4OwgPsgSEVF/05UJzrLMTJz4+GOUXboEABDkcnhMmIDAJUsgVyq7IlyiXoPPhURE1BNEUcTevXtx6NAhaLVaGBgYYNKkSYiIiGg16VhSUiJVdubm5krJTh8fH5ibm+Orr76CsbExFi1adMvxNvPy8vDLL78gLS0Nzs7OuOuuu+Dm5tadl0rUaQNqDE4iIiLqPtdKSnDglVfQWFsrLRO1WlzaswcNNTUIf/ZZ/QVHRERE1EcJgoCJEydCrVZj165d0Ol02L17N0pKSjB16lTIZLJm21taWmLMmDEYM2YMSkpKpMrOrVu3wsDAAHZ2drhw4QLWrFmDRx55pM0kp729PRYuXIiMjAzs2bMHa9euhbe3NyZOnAhbW9ueuHSidpPdfhMiIiKi20uPiUFjbS1Ena75ClHE1SNHUJWbq5/AiIiIiPqBsLAwPPDAA1AqlZDJZIiLi8OmTZtQV1fX5j6WlpYYPXo0Hn/8cTzzzDOIioqCTqeDIAjYvXs3nnnmGcTFxaG+vr7NY3h4eODRRx/F/fffj8LCQvzvf//D999/j4qKiu64TKJOYQUnERERdYn8pKSWyc0bFKakwNTBoQcjIiIiIupfhg0bhsWLF2PTpk2ora3FxYsX8fnnn+PBBx+ERqO55b5Nyc7Ro0ejtLQUsbGxWLt2LV577TWEhITAz88Pvr6+8Pb2hvKmoYUEQYCfnx+GDRuG+Ph4HDx4EGfOnMGMGTMQEBDQnZdM1C5McBIREVG7lV+9inNbtyI3IQEA4BgSgmFz5kDt5ASFSgUIAtDG8N5ylaonQyUiIiLql1xcXPDII4/gyy+/RFlZGQoKCrB69Wo8+OCDcGjnh8kWFhaYOnUqwsPD8b///Q+FhYXIz8/HuXPnoFAoMHjwYPj4+MDb2xuqG57h5HI5Ro4ciYCAABw9ehR2dnbddZlEHcIu6kRERNQupZcuYe/zzyPr2DE01taisbYWV48exd7nn0f55ctwGTWqzeSmzMAA9iNG9HDERERERP2TtbU1HnnkETg4OEAURdTW1mLt2rVITU3t0HEsLS3xxBNPSJMHPfzwwxg/fjwqKirw7bff4u2338bmzZtx5syZZl3hDQ0NMX78eNjb23fpdRF1FhOcRERE1C6nv/gCusbGZt3QRZ0O2vp6JH35JdyiomDp5XW9irPJb4PeByxaBKWJSU+HTERERNRvmZqaYsmSJfDy8kJ9fT3kcjm++uorxMXFdeg4FhYWePjhh6HT6bB9+3b4+Pjg0UcfxbPPPovx48ejqqqqWbIzKSnpluN+EukDu6gTERENcIbm5s2+t6ahuhqFZ8+2uk7U6ZCXkACIIqJeew2pO3YgY+9e1FdVwdzDA0NnzYJDcHA3RE5EREQ0sCmVSsyfPx87d+5EQkKCNNN6aWkpJk2a1GKG9baYm5tjyZIlWL9+PdatW4clS5bA3NwckZGRiIyMRFlZGVJSUpCSkoJt27ZBoVBg0KBBiIyMlKo/ifSJCU4iIqIBbuK//33bbbSNjbfdRtfYCAMTE/jMmQOfOXO6IjQiIiIiug25XI577rkHGo0GBw4cgLm5OWJjY1FaWor77ruvxYRBbbkxybl27VosWbIEFhYW0rqmZGd5eTlSUlKQnJyMysrK7rw0onZjF3UiIiK6LZVa3fYM6IIAjasrDNgFnYiIiEgvBEFAdHQ07rnnHlRWVsLc3BxpaWlYu3Zth5KQGo0GS5YsgUKhwLp161BSUtLqNhEREVi2bBn8/Py68jKIOo0JTiIiIrotQRDgN39+6ytFEb7z5vVsQERERETUQlBQEObPn4+amhoYGxujpKQEq1evRn5+fruPoVarb5vkJOptmOAkIiKidnGJjETY00/D8LeuSgBgZGmJkc8+C6ewMD1GRkRERERNBg8eLE0apFAooNPp8PnnnyM9Pb3dxzAzM8OSJUugVCqxdu1aFBcXd2PERHdOEEVR1HcQfUlCQgKCg4Nx8uRJBAUF6TscIiKiHqfTalFx9SoAQOPiAkEu13NERPrB50IiIurNSktLsXHjRlRVVcHU1BSlpaWYNm0agjsw+WNVVRXWr1+P2tpaLF68GNbW1t0YMVHnsYKTiIiIOkQml8Pc3R3m7u5MbhIRERH1UhYWFnjkkUdga2uL8vJy2NvbY8eOHdizZw/aW+tmamqKxYsXw8jICOvWrUNhYWE3R03UOUxwEhERUbuVZWbi6Ntv49v58/Htgw8i9p13UP5bNScRERER9S7GxsZYtGgRvLy8kJubC3d3dxw9ehRbt25FQ0NDu47RlOQ0MTHBunXrUFBQ0M1RE3UcE5xERETULiVpadj74ovIOXECuoYG6OrrkR0Xh71//zvKMjP1HR4RERERtcLAwABz585FSEgIMjMz4e7ujtTUVKxfvx7V1dXtOoaJiQkWL14MU1NTrF+/vkOTFhH1BCY4iYiIqF2SvvgCusZGiDqdtEzU6aBraEDSxo16jIyIiIiIbkUmk2Hq1KmYOHEiMjIy4OjoKM2w3t5u58bGxli8eDHMzMywfv165OXldXPURO3HBCcRERHdVn11NQpTUoAbkptNRJ0O+YmJaKyr00NkRERERNQegiBg9OjRmD17NrKzs6HRaCCKItasWYOMjIx2HaMpyanRaJjkpF6FCU4iIiK6LbGx8fbbaLU9EAkRERER3Ql/f38sWLAAJSUlUCgUsLS0xMaNG5GYmNiu/Y2MjLBo0SJYWVmhpKSke4MlaicmOImIiOi2lGo1zJycAEFouVIQoHF3h4Gxcc8HRkREREQd5unpiaVLl6Kurg5VVVXw8PDA9u3bsX///nbNsG5kZISlS5fCx8enB6Iluj0mOImIiOi2BEGA3/z5QGsPvKIIv3nzej4oIiIiIuo0Ozs7LFu2DIaGhsjKyoKfnx8OHjyI7777Do3t6L0jkzGlRL0H70YiIiJqF+fwcIz8059gZGUlLTO2tkb48uVwDAnRY2RERERE1BkajQZLly6Fvb09zp8/j6CgIKSkpGDDhg2oqanRd3hE7abQdwBERETUd7iOGQOXyEhU5ORAAGDm5ASBn94TERER9VmGhoZYuHAhtm/fjlOnTiEoKAjnzp3DmjVrsGDBAlhaWuo7RKLb4v9IiIiIqEMEuRwaFxeoXVyY3CQiIiLqBxQKBe677z5ERETg5MmTGDRoEERRxOrVq3HlyhV9h0d0W/xfCRERERERERHRACcIAiZNmoS7774bZ8+ehY2NDSwtLfHFF1/g7Nmz+g6P6JaY4CQiIqJuIYpiu2bhJCIiIqLeIzw8HHPmzEF6ejpkMhm8vLzwzTff4PDhw3y2o16LY3ASERFRlyrLzETy118j9+RJAIBDUBB8586FuYeHniMjIiIiovbw8fGBqakpvvrqK5iamiIkJAR79+5FSUkJpk+fDrlcru8QiZphBScRERF1mdJLl7D3xReRGx8PUauFqNUi9+RJ7H3xRZSkpek7PCIiIiJqJ1dXVyxduhQNDQ1ITU3F2LFjkZSUhC+//BK1tbX6Do+oGSY4iYiIqN1EUURxaiqSt2xB8tdft0haJm3YALGxEaJO9/s+Oh10Wi2SNmzo6XCJiIiI6A7Y2NjgkUcegYmJCeLi4hAdHY2cnBysWbMGZWVl+g6PSMIEJxEREbWLtqEBR99+G/tefBHnvv0W5775Bnv//nfE/uc/0DU2ovHaNRScOdMsuSnR6VCYnIyG6uqeD5yIiIiIOs3MzAxLliyBs7MzDhw4gMjISDQ2NuKzzz5Ddna2vsMjAsAEJxEREbVTytdfI+fECQDXqzKbEplZsbE49+230LWW2LyJTqvt1hiJiIiIqOupVCo8+OCDGD58OPbt2wdfX19YWlqisrJS36ERARiACc5Dhw5hxowZcHR0hCAI2L59u75DIiIi6vV0jY1I270baG3mTFFE2k8/wcDQEBp3d0AQWm4jCFC7uEBpZtbtsRK1F58LiYiI2k8ul2PmzJkYO3Ysjhw5Ant7e3h7e+s7LCIAAzDBWV1djYCAAHz00Uf6DoWIiKjPqKusROO1a22ur6+qQkNNDYbPn99mEtRv/nwIrSU/ifSEz4VEREQdIwgCxo8fj+nTpyM+Ph5Hjx7Vd0hEAACFvgPoaVOmTMGUKVP0HQYREVGfojQxgczAALqGhlbXy1UqKIyN4RAcjMi//Q2nv/gC1fn5AABjGxsELFoEp7CwngyZ6Lb4XEhERNQ5ISEhsLKygpOTk75DIQIwABOcRERE1HFypRLu0dHI2Lu3xSRCgkwGz4kTIZPLAQBOI0fCMTQUVXl5AABTe3sIsgHXaYSIiIioX/Pw8NB3CEQSJjhvo66uDnV1ddLPVVVVeoyGiIhIf/wXLkTppUsoTU+XEpaiTgeLQYPgN29es20FmQxmjo76CJOo2/C5kIiIiKh3YoLzNlauXIkVK1boOwwiIiK9MzAxwfg33kD28ePITUiAIAhwCA6GY2ioVL1J1J/xuZCIiIiod2J/sdt44YUXUF5eLn0dPHhQ3yERERHpjUyhgEtkJMKeegqhTz4J5/BwJjdpwOBzIREREVHvxArO21CpVFCpVNLPpqameoyGiIiIiPSFz4VEREREvdOAS3BWVVUhLS1N+jkjIwOJiYmwtLSEq6urHiMjIiIiop7E50IiIiKi/mHAJTjj4+Mxbtw46efly5cDABYvXox169bpKSoiIiIi6ml8LiQiIiLqHwZcgjM6OhqiKOo7DCIiIiLSMz4XEhEREfUPAy7BSXcuNzcXubm5+g6D9MzBwQEODg76DoP0iG0BsR0g6j/YptPN2MbTzdhOUGvYVlBvwQRnBzk4OODVV18dsG/guro6zJ8/n7OGEqKiohATE9NssgUaONgWEMB2gKi/PBeyTafWsI2nG7GdoLawraDeQhDZL4c6oKKiAhqNBgcPHuTMoQNYVVUVoqKiUF5eDrVare9wSA/YFhDbAaL+g2063YxtPN2M7QS1hm0F9Sas4KROCQwMZAM2gFVUVOg7BOol2BYMXGwHiPoftunUhG08tYXtBN2IbQX1JjJ9B0BERERERERERETUWUxwEhERERERERERUZ/FBCd1iEqlwquvvsoBhAc43gfEe4B4DxD1H3w/0814T9DNeE9Qa3hfUG/CSYaIiIiIiIiIiIioz2IFJxEREREREREREfVZTHASERERERERERFRn8UEJxEREREREREREfVZTHCSXmVmZkIQBKxbt07foRCRnrAdICLqP9imE1F7sK0goq7GBGcfkp6ejscffxyenp4wNDSEWq3GqFGj8N577+HatWvddt6UlBS89tpryMzM7LZztMcbb7yBe+65B3Z2dhAEAa+99ppe4+nNBEFo19eBAwfu+Fw1NTV47bXXOnQsvpadN5DbgfPnz+O5555DYGAgzMzM4ODggGnTpiE+Pl5vMfV2vbkt4OtJNLDbdIDPA3eqN7fxAF/frjSQ2wo+L9yZ3txO8LWlrqbQdwDUPj/++CPmzJkDlUqFRYsWwc/PD/X19Thy5Aj+9re/ITk5GatWreqWc6ekpGDFihWIjo6Gu7t7t5yjPV566SXY29tjxIgRiImJ0VscfcGGDRua/fzFF19gz549LZYPGzbsjs9VU1ODFStWAACio6PbtQ9fy84Z6O3A6tWrsWbNGtx333144oknUF5ejk8//RTh4eHYvXs3Jk6cqJe4erPe3Bbw9aSBbqC36QCfB+5Ub27jAb6+XWWgtxV8Xrgzvbmd4GtLXY0Jzj4gIyMD8+bNg5ubG/bt2wcHBwdp3ZNPPom0tDT8+OOPeozwd6Ioora2FkZGRl1+7IyMDLi7u6OoqAg2NjZdfvz+ZOHChc1+PnbsGPbs2dNiub7wtew4tgPA/Pnz8dprr8HU1FRatnTpUgwbNgyvvfYaH4Ja0ZvbAr6eNJCxTb+OzwN3pje38QBf367AtoLPC3eqN7cTfG2pq7GLeh/w73//G1VVVVizZk2zP2pNvLy88Kc//Un6ubGxEa+//joGDRoElUoFd3d3vPjii6irq2u2n7u7O6ZPn44jR44gLCwMhoaG8PT0xBdffCFts27dOsyZMwcAMG7cuBYl7E3HiImJQUhICIyMjPDpp58CAC5duoQ5c+bA0tISxsbGCA8Pv6M/wPqsMOiPdDod3n33Xfj6+sLQ0BB2dnZ4/PHHUVpa2my7+Ph4TJ48GdbW1jAyMoKHhweWLl0K4PrYOU0PrCtWrJDuj9t1QeJr2XFsB4Dg4OBmD0AAYGVlhTFjxuDcuXOdOibpry3g60kDGdv03+Ol7sXnvb6NbQWfF3oCnwWpv2AFZx+wY8cOeHp6IjIysl3bL1u2DOvXr8f999+Pv/zlL4iLi8PKlStx7tw5fPfdd822TUtLw/33349HHnkEixcvxueff44lS5YgODgYvr6+GDt2LJ555hm8//77ePHFF6XS9RtL2C9cuID58+fj8ccfx6OPPoohQ4YgPz8fkZGRqKmpwTPPPAMrKyusX78e99xzD7755hvce++9XfcLok55/PHHsW7dOjz88MN45plnkJGRgQ8//BCnTp3Cr7/+CgMDAxQUFGDSpEmwsbHB3//+d5ibmyMzMxPbtm0DANjY2OB///sf/vjHP+Lee+/F7NmzAQD+/v76vLR+ie1A2/Ly8mBtbd0lxxqIeltbwNeTBgK26dRTelsbTx3DtqJtfF7oOr2tneBrS50mUq9WXl4uAhBnzpzZru0TExNFAOKyZcuaLf/rX/8qAhD37dsnLXNzcxMBiIcOHZKWFRQUiCqVSvzLX/4iLdu6dasIQNy/f3+L8zUdY/fu3c2WP/vssyIA8fDhw9KyyspK0cPDQ3R3dxe1Wq0oiqKYkZEhAhDXrl3brusTRVEsLCwUAYivvvpqu/cZ6J588knxxrf74cOHRQDil19+2Wy73bt3N1v+3XffiQDEEydOtHnsO3k9+Fq2D9uBth06dEgUBEF8+eWXO7zvQNRb24ImfD1pIGCb3hKfB7pGb23j+fp2DtuKtvF5ofN6azvRhK8t3Ql2Ue/lKioqAABmZmbt2n7Xrl0AgOXLlzdb/pe//AUAWnQN8PHxwZgxY6SfbWxsMGTIEFy6dKndMXp4eGDy5Mkt4ggLC8Po0aOlZaampnjssceQmZmJlJSUdh+fut7WrVuh0Whw1113oaioSPpq6iawf/9+AIC5uTkAYOfOnWhoaNBjxAMb24HWFRQU4MEHH4SHhweee+65OzrWQNWb2gK+njRQsE2nntKb2njqOLYVrePzQtfqTe0EX1u6U0xw9nJqtRoAUFlZ2a7tL1++DJlMBi8vr2bL7e3tYW5ujsuXLzdb7urq2uIYFhYWLcbbuBUPD49W4xgyZEiL5U1dGm6Og3rWxYsXUV5eDltbW9jY2DT7qqqqQkFBAQAgKioK9913H1asWAFra2vMnDkTa9eubTGOD3UvtgMtVVdXY/r06aisrMT333/fYvweap/e0hbw9aSBhG069ZTe0sZT57CtaInPC12vt7QTfG2pK3AMzl5OrVbD0dERZ8+e7dB+giC0azu5XN7qclEU232u7phVk7qXTqeDra0tvvzyy1bXNw0QLQgCvvnmGxw7dgw7duxATEwMli5dinfeeQfHjh3jH54ewnagufr6esyePRtJSUmIiYmBn59fj527v+kNbQFfTxpo2KZTT+kNbTx1HtuK5vi80D16QzvB15a6ChOcfcD06dOxatUqxMbGIiIi4pbburm5QafT4eLFi80GgM7Pz0dZWRnc3Nw6fP72/pG8OY4LFy60WH7+/HlpPenPoEGD8Msvv2DUqFHtejAJDw9HeHg43njjDWzatAkLFizA5s2bsWzZsk7dH9RxbAeu0+l0WLRoEfbu3Yuvv/4aUVFRHT4G/U7fbQFfTxqo2KZTT9B3G093jm3FdXxe6D76bif42lJXYhf1PuC5556DiYkJli1bhvz8/Bbr09PT8d577wEApk6dCgB49913m23zn//8BwAwbdq0Dp/fxMQEAFBWVtbufaZOnYrjx48jNjZWWlZdXY1Vq1bB3d0dPj4+HY6Dus7cuXOh1Wrx+uuvt1jX2NgovdalpaUtPsUNDAwEAKk7grGxMYCO3R/UcWwHrnv66aexZcsWfPzxx9LsjNR5+m4L+HrSQMU2nXqCvtt4unNsK67j80L30Xc7wdeWuhIrOPuAQYMGYdOmTXjggQcwbNgwLFq0CH5+fqivr8fRo0exdetWLFmyBAAQEBCAxYsXY9WqVSgrK0NUVBSOHz+O9evXY9asWRg3blyHzx8YGAi5XI633noL5eXlUKlUGD9+PGxtbdvc5+9//zu++uorTJkyBc888wwsLS2xfv16ZGRk4Ntvv4VM1vHc+oYNG3D58mXU1NQAAA4dOoR//etfAICHHnqIlQMdEBUVhccffxwrV65EYmIiJk2aBAMDA1y8eBFbt27Fe++9h/vvvx/r16/Hxx9/jHvvvReDBg1CZWUlPvvsM6jVaukhysjICD4+PtiyZQu8vb1haWkJPz+/W3Yt4GvZcWwHrj+wf/zxx4iIiICxsTE2btzYbP29994rPYhT++izLeDrSQMZ2/Tr+DzQvfi81/exreDzQnfjsyD1K/qbwJ06KjU1VXz00UdFd3d3UalUimZmZuKoUaPEDz74QKytrZW2a2hoEFesWCF6eHiIBgYGoouLi/jCCy8020YURdHNzU2cNm1ai/NERUWJUVFRzZZ99tlnoqenpyiXy0UA4v79+295DFEUxfT0dPH+++8Xzc3NRUNDQzEsLEzcuXNns20yMjJEAOLatWtve/1RUVEigFa/muKh1j355JNia2/3VatWicHBwaKRkZFoZmYmDh8+XHzuuefEnJwcURRFMSEhQZw/f77o6uoqqlQq0dbWVpw+fboYHx/f7DhHjx4Vg4ODRaVSKQIQX3311VvGw9ey8wZyO7B48eI27xsAYkZGxi33p97VFvD1JBrYbXpTXHwe6Dq9qY0XRb6+XWkgtxV8Xuhavamd4GtLXU0QxQ6MIkxERERERERERETUi3AMTiIiIiIiIiIiIuqzmOAkIiIiIiIiIiKiPosJTiIiIiIiIiIiIuqzmOAkIiIiIiIiIiKiPosJTiIiIiIiIiIiIuqzmOAkIiIiIiIiIiKiPosJTiIiIiIiIiIiIuqzmODsB9atWwdBEGBoaIjs7OwW66Ojo+Hn59ejMe3duxdLly6Ft7c3jI2N4enpiWXLliE3N7fV7Y8ePYrRo0fD2NgY9vb2eOaZZ1BVVdWjMfd1vA+I9wDxHiDqP/h+ppvxnqCb8Z6g1vC+oIGKCc5+pK6uDm+++aa+wwAAPP/88zhw4ADuvfdevP/++5g3bx6+/vprjBgxAnl5ec22TUxMxIQJE1BTU4P//Oc/WLZsGVatWoU5c+boKfq+jfcB8R4g3gNE/Qffz3Qz3hN0M94T1BreFzTgiNTnrV27VgQgBgYGiiqVSszOzm62PioqSvT19e3RmA4ePChqtdoWywCI//jHP5otnzJliujg4CCWl5dLyz777DMRgBgTE9Mj8fYHvA+I9wDxHiDqP/h+ppvxnqCb8Z6g1vC+oIGKFZz9yIsvvgitVtsrPqUZO3YsZDJZi2WWlpY4d+6ctKyiogJ79uzBwoULoVarpeWLFi2Cqakpvv766x6Lub/gfUC8B4j3AFH/wfcz3Yz3BN2M9wS1hvcFDTQKfQdAXcfDwwOLFi3CZ599hr///e9wdHTs0P41NTWoqam57XZyuRwWFhYdjq+qqgpVVVWwtraWlp05cwaNjY0ICQlptq1SqURgYCBOnTrV4fMMdLwPiPcA8R4g6j/4fqab8Z6gm/GeoNbwvqCBhhWc/cw//vEPNDY24q233urwvv/+979hY2Nz268RI0Z0KrZ3330X9fX1eOCBB6RlTYMKOzg4tNjewcEBOTk5nTrXQMf7gHgPEO8Bov6D72e6Ge8JuhnvCWoN7wsaSFjB2c94enrioYcewqpVq/D3v/+91YahLYsWLcLo0aNvu52RkVGH4zp06BBWrFiBuXPnYvz48dLya9euAQBUKlWLfQwNDaX11DG8D4j3APEeIOo/+H6mm/GeoJvxnqDW8L6ggYQJzn7opZdewoYNG/Dmm2/ivffea/d+np6e8PT07PJ4zp8/j3vvvRd+fn5YvXp1s3VNjWFdXV2L/WprazvVWNJ1vA+I9wDxHiDqP/h+ppvxnqCb8Z6g1vC+oIGCCc5+yNPTEwsXLpQ+pWmvpjEwbkcul8PGxqZdx7x69SomTZoEjUaDXbt2wczMrNn6pk+QmkrRb5Sbm9vhcULod7wPiPcA8R4g6j/4fqab8Z6gm/GeoNbwvqCBgmNw9lMvvfRSh8fa+L//+z84ODjc9is0NLRdxysuLsakSZNQV1eHmJiYVsvh/fz8oFAoEB8f32x5fX09EhMTERgY2O74qSXeB8R7gHgPEPUffD/TzXhP0M14T1BreF/QQMAKzn5q0KBBWLhwIT799FO4ublBobj9S92VY2xUV1dj6tSpyM7Oxv79+zF48OBWt9NoNJg4cSI2btyIl19+WfoEZ8OGDaiqqsKcOXNuey5qG+8D4j1AvAeI+g++n+lmvCfoZrwnqDW8L2ggEERRFPUdBN2ZdevW4eGHH8aJEycQEhIiLU9LS8PQoUOh1Wrh6+uLs2fP9lhMs2bNwvfff4+lS5di3LhxzdaZmppi1qxZ0s8JCQmIjIyEj48PHnvsMWRlZeGdd97B2LFjERMT02Mx93W8D4j3APEeIOo/+H6mm/GeoJvxnqDW8L6gAUukPm/t2rUiAPHEiRMt1i1evFgEIPr6+vZoTG5ubiKAVr/c3NxabH/48GExMjJSNDQ0FG1sbMQnn3xSrKio6NGY+zreB8R7gHgPEPUffD/TzXhP0M14T1BreF/QQMUKTiIiIiIiIiIiIuqzOMkQERERERERERER9VlMcBIREREREREREVGfxQQnERERERERERER9VlMcBIREREREREREVGfxQQnERERERERERER9VlMcBIREREREREREVGfxQQnERERERERERER9VlMcBIREREREREREVGfxQQnERERERERERER9VlMcBIREREREREREVGfxQQnERERERERERER9VlMcBIREREREREREVGfxQQnERERERERERER9VlMcBIREREREREREVGfxQQnERERERERERER9VlMcBIREREREREREVGfxQQnERERERERERER9VlMcBIREREREREREVGfxQQnERERERERERER9VlMcBIRERHRgLNy5UqEhobCzMwMtra2mDVrFi5cuKDvsIiIiIioE5jgJCIiIqIB5+DBg3jyySdx7Ngx7NmzBw0NDZg0aRKqq6v1HRoRERERdZAgiqKo7yCIiIiIiPSpsLAQtra2OHjwIMaOHavvcIiIiIioA1jB2UG5ubl47bXXkJubq+9QiIiIiKiLlJeXAwAsLS3b3Kaurg4VFRXSV2pqKl566SU+FxIRERHpGSs4OyghIQHBwcE4efIkgoKC9B0OEREREd0hnU6He+65B2VlZThy5Eib27322mtYsWJFi+V8LiQiIiLSL1ZwEhEREdGA9uSTT+Ls2bPYvHnzLbd74YUXUF5eLn0dPHiwhyIkIiIioltR6DsAIiIiIiJ9eeqpp7Bz504cOnQIzs7Ot9xWpVJBpVJJP5uamnZ3eERERETUDkxwEhEREdGAI4oinn76aXz33Xc4cOAAPDw89B0SEREREXUSE5xERERENOA8+eST2LRpE77//nuYmZkhLy8PAKDRaGBkZKTn6IiIiIioI/r0GJyHDh3CjBkz4OjoCEEQsH379tvuc+DAAQQFBUGlUsHLywvr1q3r9jiJiIiIqHf53//+h/LyckRHR8PBwUH62rJli75DIyIiIqIO6tMJzurqagQEBOCjjz5q1/YZGRmYNm0axo0bh8TERDz77LNYtmwZYmJiujlSIiIiIupNRFFs9WvJkiX6Do2IiIiIOqhPd1GfMmUKpkyZ0u7tP/nkE3h4eOCdd94BAAwbNgxHjhzBf//7X0yePLm7wiQiIiIiIuoz6uvrER8fj5CQECiVSn2HQ0REdFt9uoKzo2JjYzFx4sRmyyZPnozY2Ng296mrq0NFRYX0VVVV1d1hEhERERER6c2xY8ewdu1axMXF6TsUIiKidhlQCc68vDzY2dk1W2ZnZ4eKigpcu3at1X1WrlwJjUYjfUVFRfVEqERERERERD2urq4OMTExyMjIwO7du1FXV6fvkIiIiG5rQCU4O+OFF15AeXm59HXw4EF9h0TUO9TX6DsCIiIiIupicXFxSE1Nhb+/P1JTU3H8+HF9h0RERHRbAyrBaW9vj/z8/GbL8vPzoVarYWRk1Oo+KpUKarVa+jI1Ne2JUIl6P12jviMgIiIioi7UVL2pVCqhVquhVCpZxUlERH3CgEpwRkREYO/evc2W7dmzBxEREXqKiKgPE7X6joCIiIiIutCpU6eQnp6O6upqJCcno7q6Gunp6Th16pS+QyMiIrqlPj2LelVVFdLS0qSfMzIykJiYCEtLS7i6uuKFF15AdnY2vvjiCwDAH/7wB3z44Yd47rnnsHTpUuzbtw9ff/01fvzxR31dAlHf1chP8omIiIj6ExcXFyxYsKDV5URERL1Zn05wxsfHY9y4cdLPy5cvBwAsXrwY69atQ25uLq5cuSKt9/DwwI8//og///nPeO+99+Ds7IzVq1dj8uTJPR47UZ9XXQSY2es7CiIiIiLqIk5OTnByctJ3GERERB3WpxOc0dHREEWxzfXr1q1rdR92sSDqAqUZgL2fvqMgIiIiIiIiogFuQI3BSURdKO+sviMgIiIiIiIiImKCk4g6qfAcUFuu7yiIiIiIiIiIaIBjgpOIOkenBS4d1HcURERERERERDTAMcFJRJ137gfgFuPgEhERERERERF1NyY4iajzii4CV4/rOwoiIiIiIiIiGsCY4CSiOxP7IdBYr+8oiIiIiIiIiGiAUug7ACLqe0JCQpCXkQJ7UzniXwRw/FMg8ml9h0VEREREREREAxArOImow/Ly8pBdcg15Fb9Vbp75Bri4R79BEREREVGXqK+vx9GjR1Ffz146RETUNzDBSURd48CbwOVYfUdBRERERHfo2LFjWLt2LeLi4vQdChERUbswwUlEXUPXCPz8D+DsNs6sTkRERNRH1dXVISYmBhkZGdi9ezfq6ur0HRIREdFtMcFJRF1HpwV+fQ/46TmgIkff0RARERFRB8XFxSE1NRX+/v5ITU3F8ePH9R0SERHRbTHBSURd7+px4OvFwIk1QEOtvqMhIiIionZoqt5UKpVQq9VQKpWs4iQioj6BCU4i6h7aeiDhC+DrRcClg+y2TkRERNTLnTp1Cunp6aiurkZycjKqq6uRnp6OU6dO6Ts0IiKiW1LoOwAi6ueq8oE9rwDOoUDkU4CFu74jIiIiIqJWuLi4YMGCBa0uJyIi6s2Y4CSinpF1Atj6MDBkKhD0EGBmr++IiIiIiOgGTk5OcHJy0ncYREREHcYEJxH1HFEHnN8JpP4EeN0FBDwAWHrqOyoiIiIiIiIi6sOY4CSinqfTAqm7r3+5jAQC5gGOIwBB0HdkRERERERERNTHMMFJRPp1Ne76l50vELQYcAljopOIiIiIiIiI2o2zqBNR75CfDPz0HPD9k0BWPGddJyIiIiIiIqJ2YYKTiDrkypUrqKmpAQDU1OtwpaS2a0+Qnwz8+Bfgh6eB7ISuPTYRERER3VZ9fT2OHj2K+vp6fYdCRETULn0+wfnRRx/B3d0dhoaGGDlyJI4fP97mtuvWrYMgCM2+DA0NezBaor7r+PHjmDFjBtzd3VFaWgoAKK1phPs/juOej8/iRGZl154w7wyw88/AzuVA0cWuPTYRERERtenYsWNYu3Yt4uLi9B0KERFRu/TpBOeWLVuwfPlyvPrqq0hISEBAQAAmT56MgoKCNvdRq9XIzc2Vvi5fvtyDERP1Tdu2bcOoUaPw008/Qbyp67goArvOliDy34nYdqqo60+efRLY9ihw9EOgkVUERETUdQ4dOoQZM2bA0dERgiBg+/bt+g6JSO/q6uoQExODjIwM7N69G3V1dfoOiYiI6Lb6dILzP//5Dx599FE8/PDD8PHxwSeffAJjY2N8/vnnbe4jCALs7e2lLzs7ux6MmKjvOX78OB544AFotVpotdpWt9HqAK1OxAOfnev6Sk7gehb1zFbg55cAna7rj09ERANSdXU1AgIC8NFHH+k7FKJeIy4uDqmpqfD390dqamqzHnLsuk5ERL1Vn01w1tfX4+TJk5g4caK0TCaTYeLEiYiNjW1zv6qqKri5ucHFxQUzZ85EcnLyLc9TV1eHiooK6auqqqrLroGoL/jXv/4FURRbVG7eTAQgQsS/dnVjVfTVOCB9b/cdn4iIBpQpU6bgX//6F+699159h0LUKzRVbyoUCpSXl0OhUDSr4mTXdSIi6q36bIKzqKgIWq22RQWmnZ0d8vLyWt1nyJAh+Pzzz/H9999j48aN0Ol0iIyMRFZWVpvnWblyJTQajfQVFRXVpddB1JtduXIFO3fubLNy82ZaHbDjTEnXTzx0o4s/d9+xiYiIboEffFN/d+rUKaSnpyMrKwv79u1DVlYW0tPTcerUKXZdJyKiXq3PJjg7IyIiAosWLUJgYCCioqKwbds22NjY4NNPP21znxdeeAHl5eXS18GDB3swYiL92rt3720rN28misC+82XdExAAZJ0AitK67/hERERt4Aff1N+5uLhg7ty5cHBwgFqthoODA+bOnQsXF5dbdl0nIiLStz6b4LS2toZcLkd+fn6z5fn5+bC3t2/XMQwMDDBixAikpbWdLFGpVFCr1dKXqanpHcVN1JdUVlZCJutYMyETgIra9lV8doooAgdWAtqG7jsHERFRK/jBN/V3Tk5OsLa2hiiKGD9+PERRhI2NDaytrRETEwOlUgm1Wg2lUskqTiIi6lX6bIJTqVQiODgYe/f+Ph6fTqfD3r17ERER0a5jaLVanDlzBg4ODt0VJlGfZmZmBl0HJ/XRiYDaUN5NEf2mOA3IONS95yAiIroJP/im/q6pG7pSqYSpqamUyIyLi0N6ejqqq6uRnJyM6upqqes6ERFRb6DQdwB3Yvny5Vi8eDFCQkIQFhaGd999F9XV1Xj44YcBAIsWLYKTkxNWrlwJAPjnP/+J8PBweHl5oaysDG+//TYuX76MZcuW6fMyiHqtCRMmQBCEDnVTFwRg/FDz7gsKABSGgKVn956DiIiIaIBpGoOztrYWycnJ0Ol0SE9PR1lZGRYsWNBiexcXFz1ESURE1FKfTnA+8MADKCwsxCuvvIK8vDwEBgZi9+7d0sRDV65cada9trS0FI8++ijy8vJgYWGB4OBgHD16FD4+Pvq6BKJezdXVFdOnT8euXbvaNdGQXAZM87OEq6Vh9wWlcQbGvwRYenTfOYiIaECoqqpqNlRRRkYGEhMTYWlpCVdXVz1GRqQfLi4uUiKzqqpKqlIOCgqCk5OTPkMjIiK6JUHs6AwiA1xCQgKCg4Nx8uRJBAUF6Tscom534sQJREZGQqvV3rKSUwAglwk4+lwgQt3Nuj4QQQb4PwAELwEMujGBSkREA8aBAwcwbty4FssXL16MdevW3XZ/PhdSf3b16lVWaBIRUZ/Rpys4iaj7hYaGYsuWLXjggQcgimKrlZxyGSBAwNePDuue5KaVFzD2b4Dt0K4/NhER6VVubi4KCgrg5eUFExOTHj13dHR0h4ZhIRpIKioqcO3aNcjlcsTHxyMkJARKpVLfYREREbXqjiYZqqurQ2xsLL7//nsUFRV1VUxE1MvMnj0bR48exdSpUyEIQrN1gnC9W/rR5wJx7wjrrj2xkTkw+llg9iomN4mI+pnvv/8eQ4cOhbOzM4KCghAXFwcAKCoqwogRI7B9+3b9Bkg0wImiiLNnz+LYsWNYu3at9B4lIiLqjTqd4Hz//ffh4OCA0aNHY/bs2UhKSgJw/aHU2toan3/+eZcFSUT6Fxoaih9++AGZmZmwsLAAAFgYK5D5Rhi+f8Kvays3zRyAyKeB+VsA33sBWTfPyk5ERD1qx44dmD17NqytrfHqq682q6K0traGk5MT1q5dq8cIiQgAEhMTsWvXLmRkZGD37t2oq6vTd0hERESt6lSCc+3atXj22Wdx9913Y82aNS0eSsePH4/Nmzd3WZBE1Hu4urrC2NgYAGCslHXthEJOQcCk14F5m4Dh93OsTSKifuqf//wnxo4diyNHjuDJJ59ssT4iIgKnTp3SQ2REBAD19fVITExESkoKTpw4AX9/f6SmpuL48eP6Do2IiKhVnRqD85133sHMmTOxadMmFBcXt1gfHByM999//46DI6IBQGEIeE8CfGdzZnQiogHi7Nmz+M9//tPmejs7OxQUFPRgRER0I39/f1y+fBlyuRw+Pj6orKyEQqHA7t27ERYWBpVKpe8QiYiImulUgjMtLQ3PPPNMm+stLS1bTXwSEUlMbAC/2cDQ6YChWt/REBFRDzI2NkZ1dXWb6y9dugQrK6sejIiImtTV1SEnJwe1tbWQyWSora1FQkICAKC4uBhHjx7FuHHj9BwlERFRc51KcJqbm99yUqGUlBTY29t3Oigi6sfs/K4nNj2iAHmnmiAiIurjxo0bh/Xr1+PZZ59tsS4vLw+fffYZpk+f3vOBERHi4uLQ2NgIABAEAV5eXnBwcJDWHzt2DImJiZgyZQq8vLygUPB5joiI9K9Tf42mTp2KVatW4YknnmixLjk5GZ999hmWLl16x8ERUT8hVwJeE69PGGTjre9oiIhIz9544w2Eh4cjNDQUc+bMgSAIiImJwb59+/Dpp59CFEW8+uqr+g6TaMCpq6tDTEyM9HNTBaePjw8MDAwAAKmpqYiLi0NOTg58fHzg6uoKd3d3uLi4QKlU6it0IiIa4DqV4PzXv/6FkSNHws/PDzNmzIAgCFi/fj0+//xzfPvtt3BwcMArr7zS1bESUV9jqAGGzwF87rn+byIiIgBDhgzBkSNH8Kc//Qkvv/wyRFHE22+/DQCIjo7GRx99BHd3d/0GSTQAnTp1CklJSWhoaAAAaLVaZGVl4erVq/D09ERDQwNSUlJQVFSE5ORkuLu7Iz09Henp6ZDL5XB0dISHhwfc3d1haMjJIomIqOd0KsHp6OiIkydP4sUXX8SWLVsgiiI2bNgAMzMzzJ8/H2+++Sasra27OlYi6ivkSiBwPhAwHzAw0nc0RETUC/n6+uKXX35BaWkp0tLSoNPp4OnpCRsbG32HRjQgHT9+HC+//DL27t0LURQBADqdDmfOnEFjYyNmzZqF+vp65Ofnw8nJCfn5+cjMzMTgwYMBXE+GXr16FVevXsXhw4dhb28PNzc3uLq6QqPRQBAEfV4eERH1c50eMMXW1harV6/G6tWrUVhYCJ1OBxsbG8hksq6Mj4j6Ggt3YOKrgKWnviMhIqI+wMLCAqGhofoOg2hA27ZtGx544AGIoiglN2904cIFvPXWWwgICIBCoYCRkREqKiqkKs6m7utNRFFEbm4ucnNzcezYMZiZmcHZ2Rmurq5wcnLiuJ1ERNTluiQbaWNjAzs7OyY3iQY613Bg1v+Y3CQiolt6//33MXny5DbXT5kyBf/73/96MCKigev48eN44IEHoNVqodVqW91Gp9NBp9Ph1KlTKCsrQ05ODurr61FYWIirV6/e9hyVlZU4d+4cYmJi8MUXX2DPnj1IT0+XusITERHdqU5lJF966SUEBga2uX7EiBFYsWJFZ2Miol7O3t4eTpZGsFffMJD84LuAyf8PUBrrLzAiIuoT1qxZAx8fnzbX+/j4YNWqVT0YEdHA9a9//avNys2bCYKAhoYGhIeHY9SoUQgLC4OFhUWHztfY2IiMjAzs3bsXGzZskJKd9fX1nb0EIiKiznVR/+abb3Dvvfe2uX7q1KnYsmULZ78k6qfi4+OBzQuA8qzrCzzGANEvAqziJiKidkhPT8eTTz7Z5vqhQ4fis88+68GIiAamK1euYOfOne1KbgLXu55fvnwZLi4usLS0vOPzNyU7MzIyIJPJ4OjoCHd3d3h4eMDIiOO4ExFR+3UqG3HlyhUMGjSozfUeHh64fPlyp4Mioj5E48zkJhERdYhSqUReXl6b63Nzczn0EVEPuHFCofYSRRHnz5/v8lh0Oh2ysrJw5MgRfPnll/jll19QWVnZ5echIqL+qVNPjqamprdMYGZkZMDQ0LDTQRFRHxLxJLulExFRh4SHh2PdunWtJi/Ky8uxdu1ahIeH6yEyooGlsrKywx8mCIKA2traboroOp1Oh0uXLmHPnj3deh4iIuo/OpXgjI6Oxqeffors7OwW665evYpVq1Zh3LhxdxwcEfVy5i6Aa4S+oyAioj7m1VdfRU5ODgIDA/HBBx9g37592LdvH95//32MGDECubm5HOqIqAeYmZlBp9N1aB9RFHusmOXm2dmJiIja0qkxOF9//XWEhYXB19cXjzzyCHx9fQEAZ8+exeeffw5RFPH66693aaBE1At53QUIgr6jICKiPmbkyJHYsWMHHn/8cfzpT3+C8NvfElEU4eHhgR9++AEREfwAjai7TZgwAYIgdLibupeXVzdFdJ1SqcSQIUMQFBTUrechIqL+o1MJziFDhuDw4cN4+umn8d///rfZurFjx+L999/HsGHDuiRAIurFPMbqOwIiIuqj7rrrLqSlpeHUqVNIT08HAAwaNAhBQUFSwpOIuperqyumT5+OXbt2QavV3nZ7QRBgYWGB8vJy2NradmksCoUCLi4u8PT0hJubGxSKTv1XlYh6UH19PeLj4xESEgKlUqnvcGiA6/RfDX9/fxw8eBBFRUW4dOkSAMDT0xPW1tZdFhwR9WJqJ8DCXd9REBFRHyaTyRAcHIzg4GB9h0I0YL388sv46aef2l3JaWZmhuTkZLi7u99xF3JTU1M4OzvD1dUVzs7OTGoS9THHjh3Dhg0boNVqMWbMGH2HQwPcHf8Fsba2ZlKTaCByHMHu6UREdEdSUlJw6dIllJaWtppYWbRokR6iIhpYQkNDsWXLFjzwwAMQRbHVSs6miYjc3d3h4eGB/Px8ZGZmYvDgwR06l0ajgb29Pezt7eHg4AAzMzNWbBP1UXV1dYiJiUFGRgZ2796NsLAwqFQqfYdFA1inE5xarRYxMTFtPpQKgoCXX375jgO8nY8++ghvv/028vLyEBAQgA8++ABhYWFtbr9161a8/PLL0h/kt956C1OnTu32OIn6HUsPfUdARER9VHp6OhYuXIjjx4+3WTEmCAITnEQ9ZPbs2Th69Chef/117Ny5s9n7UhAE+Pr6wtTUFI2NjTAyMkJFRUW7qjitrKzg6OgIBwcH2Nvb99jkRETU/eLi4pCamgp/f3+kpqbi+PHjrOIkvepUgjM+Ph733XcfsrKybvlQ2t0Jzi1btmD58uX45JNPMHLkSLz77ruYPHkyLly40OqYMEePHsX8+fOxcuVKTJ8+HZs2bcKsWbOQkJAAPz+/bo2VqN8xstR3BERE1Ec9/vjjOHPmDN59912MGTMGFhYW+g6JaMALDQ3FDz/8gK1bt2LBggVoaGiAXC7HtGnT4O3tjQMHDqChoQE5OTnQarXIz8/HsWPHEBERIXUtFwQB1tbWqKmpwdSpU/neJuqnmqo3FQoFysvLoVAoWMVJetepBOcTTzyBa9euYfv27RgzZgzMzc27OKz2+c9//oNHH30UDz/8MADgk08+wY8//ojPP/8cf//731ts/9577+Huu+/G3/72NwDXZ4Pfs2cPPvzwQ3zyySc9GjtRn2dgpO8IiIioj/r111/x4osv4umnn9Z3KER0g7q6OiQmJkKhUKChoQGCICAhIQG2trYtesnl5OTgwoULcHZ2xrhx4+Dm5gZXV1ecOHECu3btgqurK6u5iPqppgkCs7OzkZWVBWdnZ4iiiFOnTiE8PFzf4dEA1akEZ1JSEt544w3MmDGjq+Npt/r6epw8eRIvvPCCtEwmk2HixImIjY1tdZ/Y2FgsX7682bLJkydj+/btbZ6nrq4OdXV10s9VVVUAgMbGRjQ0NNzBFRD1cToB4HuAiAa4O51gY6CytraGRqPRdxhEdJOmpEVTLz2dTofy8nKcPXsWCxculNo8AwMDHDhwAEVFRRAEAVFRUVCpVByTj2iAcHFxwdy5c7F582ZUVFTAwcEBc+fOhYuLi75DowGsUwnOpuy8PhUVFUGr1cLOzq7Zcjs7O5w/f77VffLy8lrdPi8vr83zrFy5EitWrGixfOTIkZ2ImoiIiPoTfT8P9VV/+MMfsHHjRjz55JOQy+X6DoeIfuPi4oIFCxZgz549qK2thSAI8PDwQFVVFTIzMxEaGorQ0FBcvnwZu3btQmhoKC5evCiNvccx+YgGBicnJ1hbW0MURUyYMAFXr16FjY0NnJyc9B0aDWCdSnA+//zz+L//+z889thjUKvVXR1Tr/LCCy80q/pMTExEVFQU4uLiMGLECD1GRqRntRWAYf9+/xMRUffw9vaGVqtFQEAAli5dChcXl1YTnbNnz9ZDdEQDl5OTE5ycnGBlZYWKigooFAq4uroiPz8feXl5mD59OuRyOVavXg2lUgm1Wg2lUondu3cjICAAMTExLZazipOo/2mq1ub7nXqTTiU4KysrYWpqCi8vL8ybN6/Vh1JBEPDnP/+5S4JsjbW1NeRyOfLz85stz8/Ph729fav72Nvbd2h7AFCpVM3eoKampgAAhULBbmk0sIlGAN8DRETUCQ888ID077/+9a+tbiMIArRabU+FRES/qaurQ2RkJIqLi6FQKJCVlQULCws0NDQgKSkJAJCeno7a2lokJyejoaEB6enp+Prrr1tdzjH5iPqfpuEs+H6n3qRTCc4bH0Q//PDDVrfp7gSnUqlEcHAw9u7di1mzZgG4PkbM3r178dRTT7W6T0REBPbu3Ytnn31WWrZnzx5ERER0W5xE/ZasU80HERER9u/fr+8QiKgNcXFxKCoqgr+/P65cuYK77rpL6mbeNL7eggULWuzn4ODQYjiwG/chov6jaTiLhoYGpKWlwcvLCwYGBny/k151KkORkZHR1XF0yvLly7F48WKEhIQgLCwM7777Lqqrq6VZ1RctWgQnJyesXLkSAPCnP/0JUVFReOeddzBt2jRs3rwZ8fHxWLVqlT4vg6hvYoKTiIg6KSoqSt8hEFErmrqdajQauLq6QiaTwcDAAJMmTWrWq43j7BENbE3DWRw6dAg//fQTIiIiON4u6V2nMhRubm5dHUenPPDAAygsLMQrr7yCvLw8BAYGYvfu3dInh1euXIFMJpO2j4yMxKZNm/DSSy/hxRdfxODBg7F9+3b4+fnp6xKIiIiIBqy6ujokJCSgoKAAo0aNgrW1tb5DIhrQbux2WlhYCLlczm6nRNSqpg9EMjIyOP4m9Qp3VIKVnZ2NQ4cOoaCgAPfddx+cnZ2h1WpRXl4OjUbTI7NiPvXUU212ST9w4ECLZXPmzMGcOXO6OSqiAUAQ9B0BERH1Ye+//z5ee+01lJeXA7g+bND48eNRVFSEoUOH4t///jeWLl3a7XF89NFHePvtt5GXl4eAgAB88MEHCAsL6/bzEvVGTd1OASArKwsODg6Qy+XsdkpELcTFxSE1NRX+/v5ITU3F8ePHWcVJeiW7/SYtiaKI5cuXw8PDAwsWLMDy5cuRmpoKAKiqqoK7uzs++OCDLg2UiHoZUdR3BERE1EetXbsWzz77LO6++26sWbMG4g1/U6ytrTF+/Hhs3ry52+PYsmULli9fjldffRUJCQkICAjA5MmTUVBQ0O3nJuqNnJycMGPGDMyYMQNz587FrFmzMGPGDHZJJ6Jm2ppFva6uTt+h0QDWqQTn22+/jffeew9//etfsWfPnmYPpRqNBrNnz8a3337bZUESUS8kcmZbIiLqnHfeeQczZ87Epk2bMGPGjBbrg4ODkZyc3O1x/Oc//8Gjjz6Khx9+GD7/v707j6uqzv84/j6gLIrgAm4Eorin5pZbKpqmZpq55ZpLpY5WzphlmvuW40w1toyWTok52t7kZAaZa2VgC2bhjihm4Q4qxAW55/dH4/1FLMKVy2F5PR+P+3h49jcz9PX4ud+laVO98sorqlChgl5//XWXPxso7ry9va2OAKCYuj6dRUpKimJjY5WSkuKYzgKwilND1NesWaMxY8bomWee0YULF7Idb9GihT755JObDgegGDPtVicAAJRQx44d09SpU3M9XrVq1RzfMQtTenq6vv32W82aNcuxz83NTT179tRXX32V4zU2my1L75SrV69Kkq5du6aMjAyX5gWKmt1u5/caQI5q1qypYcOG5bi/rLYb5cuXtzpCmedUgfPUqVPq1KlTrscrVqyoy5cvOx0KQAngRgMOAHBO5cqVdf78+VyPHzhwQDVr1nRphvPnzyszM9OxOOV1NWrU0KFDh3K8ZtmyZVq4cGG2/e3bt3dJRgAAUDKYTOFmOacKnNWrV9epU6dyPf7tt98qODjY6VAASgA3p2a4AABAffv21erVqzVlypRsx2JjY7VmzZoiWWCooGbNmqXHH3/csb1v3z6FhYUpOjparVq1sjAZULg+//xzbdiwQaNHj1bnzp2tjgMAwA05VeAcNGiQXnnlFY0bN05+fn6SJON/Kyp/+umnCg8P14wZMwovJQAAAEqNJUuWqH379mrWrJn69+8vwzC0bt06vf7663r//fdVq1YtzZs3z6UZ/P395e7urjNnzmTZf+bMmVx7j3p6esrT09Ox7ePjI0kqV64cQ9NQathsNm3btk0nT57UZ599pk6dOmX5vQcAoDhyqgvWwoULVatWLbVs2VJjxoyRYRhavny5OnfurLvvvlstWrTQ008/XdhZAQAAUArUrl1b3377rfr06aO3335bpmlq/fr1+uijjzRixAhFRUXJ39/fpRk8PDzUpk0bbdu2zbHPbrdr27Zt6tixo0ufDRRn0dHROnLkiFq0aKEjR45o7969VkcCAOCGnCpw+vn5KSoqSjNmzNDp06fl5eWlXbt2KSkpSfPnz9fnn3+uChUqFHZWAAAAlHA2m03//e9/lZiYqH/961+6ePGizpw5o19++UWXLl3S66+/rurVqxdJlscff1xr1qzRunXrdPDgQU2ePFkpKSkaP358kTwfKG5sNpsiIyPl4eEhX19feXh4KCIiIsviWgAAFEcFHqKelpam1atXq2XLlpozZ47mzJnjilwAAAAohTw8PDR06FC98MILatGihSQpICDAkizDhg3TuXPnNG/ePCUmJqply5aKiIjItvAQUFbExMQoLi5OaWlpio2NVUZGhuLi4hQTE6MOHTpYHQ8AgFwVuMDp5eWlp556Si+++KK6du3qikwAAAAopQzDUIMGDfJcRb0oPfroo3r00UetjgEUC0FBQRo1alSO+wEAKM6cWmSoWbNmOnHiRCFHAQAAQFnw9NNP6/HHH9fQoUPVqFEjq+MA+J/AwEAFBgZaHQNACZaenq5vvvlGbdu2lYeHh9VxUIY4VeBcunSpRo4cqe7du6tnz56FnQkAAAClWFRUlKpVq6ZmzZqpW7duCgkJkbe3d5ZzDMPQCy+8YFFCAABwIzkVM6OiorR+/XplZmaqS5cuFidEWeJUgfPll19W1apV1bt3b9WtW1d169bN8aV006ZNhRISAAAApcfLL7/s+PPvVzH/PQqcAAAUb38sZl5fqCw+Pl4RERFq166dPD09rY6JMsKpAuf+/ftlGIaCg4OVmZmpY8eOZTvHMIybDgcAAIDSx263Wx0BAADchJyKmdHR0Tpy5IhatGihI0eOaO/evfTiRJFxqsDJ/JsAAAClW926dQv8hbVhGIqLi3NRIgAAUFz8sZj5xRdfaPv27fLw8JCvr688PDzoxYki5VSBEwAAAKVbWFhYtgLnN998o9jYWDVt2tSxONDhw4d14MABNWvWTG3atCnQM6KiorRjxw6dPXtWU6ZMUYMGDZSamqpDhw6pYcOG8vHxKbSfBwAAFI7rvTd/X8xcv369UlNTlZ6ertjYWGVkZCguLk4xMTHq0KGD1ZFRBjhd4MzMzNS7777reCldtGiRmjdvruTkZG3btk133HGHatSoUZhZAQAAUETCw8OzbH/44Yf68MMPtXXrVvXo0SPLsa1bt+r+++/X4sWL83Xv9PR0DR8+XJs2bZJpmjIMQ/3791eDBg3k5uamXr16adq0aZo9e3Zh/TgACkleKySzejJQNsTExCguLk5paWmOYqbdbleXLl3UuHHjLOcGBQVZlBJljVMFzqSkJPXp00d79+6Vj4+PUlJS9Nhjj0mSfHx8NHXqVI0ZM0bPPPNMoYYFAACANebNm6fHHnssW3FTku666y49+uijmjNnjgYMGHDDe82dO1ebN2/WqlWr1L17d0dvUEny8vLS0KFDtWnTJgqcgEXyKlTmtUIyqycDZUNQUJBGjRqVbX/r1q0VGBhoQSJAcnPmopkzZyo2NlaRkZE6fvy4TNN0HHN3d9eQIUO0ZcuWQgsJAAAAax09elTVqlXL9Xi1atXyPf/mm2++qcmTJ2vixImqWrVqtuNNmjTR8ePHnc4K4OZERUVp7dq1io6OzrL/j4uK2Gy2fB0DULoEBgaqf//+6t27t6pVq6bevXurf//+FDdhKacKnB9++KEee+wx3XXXXTlOPt+wYUMWIgIAAChFQkNDtXbtWl29ejXbsStXruj1119XvXr18nWvs2fPqnnz5rked3d3V2pqqtNZATgvr0JlTisk5+cYgNIpty9DACs4VeBMTk5W3bp1cz2ekZGha9euOR0qPy5evKhRo0bJ19dXlStX1kMPPZTjC/fvdevWTYZhZPn86U9/cmlOAACA0mDJkiX68ccf1bhxY82ZM0fh4eEKDw/X7Nmz1aRJEx08eFBLlizJ172CgoJ06NChXI9/+eWXql+/fmFFB1AAuRUqc1pU5HoBNK9jAEonem2juHFqDs7Q0FB99913uR7/9NNP1bRpU6dD5ceoUaP0yy+/aOvWrcrIyND48eM1ceJEbdy4Mc/rJkyYoEWLFjm2K1So4NKcAAAApcF9992nLVu26Kmnnso2z3rLli312muvqXfv3vm618iRI/X8889r8ODBatiwoSQ5RgWtWbNG77zzjv76178W7g8A4IZyK1S2a9cux0VFrq+QLCnXY6yeDJROOX0Zwty7sJJTBc6HH35YTz31lLp16+aYaN4wDNlsNi1atEgRERFavXp1oQb9vYMHDyoiIkJff/212rZtK0l66aWX1LdvXz377LOqXbt2rtdWqFBBNWvWdFk2AACA0qpXr17q1auXEhMTdfLkSUlSnTp1CvxuNXv2bEVFRalr165q0qSJDMPQtGnTdPHiRf3000/q27evpk2b5oofAUAe8ipi5raoyPUVkvM6BqB0yevLEE9PT6vjoYxyqsD55z//WbGxsRoxYoQqV64s6bdv4i9cuKBr165p0qRJeuihhwozZxZfffWVKleu7ChuSlLPnj3l5uam6OhoDRw4MNdrN2zYoH//+9+qWbOm+vfvr7lz5+bZi/P6kIvrbjQMHgAAoLSrWbPmTX1hfP0fQhs2bNB7772nzMxM2Ww2tWjRQkuWLNEDDzyQ4zzvAFwrryJmYGBgnguIsLgIUHbk9WUIvbZhFacKnIZhaM2aNRo7dqzee+89HT16VHa7XaGhobr//vvVtWvXws6ZRWJioqpXr55lX7ly5VS1alUlJibmet3IkSNVp04d1a5dW/v379dTTz2lw4cP64MPPsj1mmXLlmnhwoWFlh0AAKCkSkhI0DPPPKMdO3bo3Llz+vDDD9W1a1edP39eixYt0vjx49WqVats1z3++ON64IEHHMcSEhIUEBCg0aNHa/To0UX9YwDIxY2KmAAg5f1lCGCVfBU4Bw0apGnTpjnmU9i9e7eaNGmizp07q3PnzoUWZubMmVq+fHme5xw8eNDp+0+cONHx5+bNm6tWrVrq0aOH4uLiFBoamuM1s2bN0uOPP+7Y3rdvn8LCwpzOAABAaWHa7TLcnFqvECXQgQMH1KVLF9ntdrVv317Hjh1zLCrp7++vL774QikpKXrttdeyXbtixQq1bdvWUeCsW7eu1q9fr5EjRxbpzwAAAG7e9S9D0tPTtXfvXrVr104eHh5Wx0IZl68C56ZNmzR48GDHdvfu3V3yUjp9+nSNGzcuz3Pq1aunmjVr6uzZs1n2X7t2TRcvXizQcKn27dtLko4dO5ZrgdPT0zPLHBI+Pj75vj8AAKWZPSVF7pUqWR0DRWTGjBmqXLmyoqKiZBhGttE099xzj95+++0cr61Ro4aOHz/u2DZN06VZAQCA60VFRWndunUyTZMFhmC5fBU4AwMDFRMT4+iCbJqmS+ZFCggIUEBAwA3P69ixo5KSkvTtt9+qTZs2kqTt27c7ehTk1759+yRJtWrVciovAABlmfm/3nsoG3bv3q158+YpICBAFy5cyHY8ODhYp0+fzvHae+65R4sWLdKnn37qmL/9ueee01tvvZXr8wzD0KZNmwolOwAAKFzXFxqKj49ngSEUC/kqcA4fPlzPPvus3nnnHcdL6cyZM7Vs2bJcrzEMQ99//32hhPyjJk2aqE+fPpowYYJeeeUVZWRk6NFHH9Xw4cMdK6ifPn1aPXr00BtvvKF27dopLi5OGzduVN++fVWtWjXt379f06ZNU9euXdWiRQuX5AQAoDQz0zOsjoAiZLfb81yY8dy5c7n+w+aFF15Q9erVtWPHDsXGxsowDJ06dUoXL17M9X4sMgQAQPEVHR2tI0eOqGnTpjpy5Ij27t1LL05YKl8FzmXLlql+/frasWOHzp49K8MwVLFiRVWrVs3V+XK1YcMGPfroo+rRo4fc3Nw0ePBgvfjii47jGRkZOnz4sFJTUyX9tlrnZ599phUrViglJUVBQUEaPHiw5syZY9WPAABAiWba0qyOgCLUunVrffzxx5oyZUq2Y9euXdNbb72V68qpFStW1DPPPOPYdnNz04oVK5iDEwCAEuh6700PDw95eXnJw8ODXpywXL4KnO7u7po4caJjkR43NzfNmTPH0pfSqlWrauPGjbkeDwkJyTK/U1BQkHbt2lUU0QAAKBPs//sSEWXDrFmz1K9fP02ePFnDhw+XJJ05c0afffaZnnnmGR08eFAvv/xyjtf+ccHKHTt2qGnTpkWWHQAAFJ6YmBjFxcUpLS1NP/zwg7y9vRUXF6eYmJhcv+wEXC1fS5+2bt1aERERju21a9c6VsEEAABlk/3KFasjoAjdfffdCg8P19tvv60777xTkjR69Gj16tVL3333nd544w117do1x2s3bdqkhIQEx/add96prVu3FkluAABQuIKCgjRq1CiNGTNG9erV05gxYzRq1CgFBQVZHQ1lWL56cO7fv1/nz593bD/44INav369mjRp4rJgAACgeLt26ZLVEVDEHnjgAQ0aNEhbt27V0aNHZbfbFRoaqt69e6tSpUq5XldUC1YCAADXCwwMVGBgoHbv3q3jx4/L19dXPXv2tDoWyrh8FTjr1Kmjzz77TCNGjJC7uzsvpQAAQJkXL8nMyJBRvrzVUeBiqampCgoK0syZM/Xkk0/qvvvuK9D1xW3BSgAAcHOuz8OZmJiod999V126dGH+TVgqXwXOP/3pT3rqqae0YcMGeXt7yzAMPfTQQ5o0aVKu1xiGoeTk5EILCgAAihnTrmvnzql87dpWJ4GLVahQQeXKlVPFihWdur44LlgJAACcd30V9ZCQEO3fv1/R0dG5TlUDFIV8FTiffPJJ3XbbbdqxY4fOnDmjdevW6fbbb1e9evVcnQ8AABRjGYmJFDjLiMGDB+u9997T5MmTCzySpzguWAkAAJzz+1XUy5Urp8zMTL3zzjtq3749vThhmXwVOCWpV69e6tWrlyQpPDxckyZN4qUUAIAyLuP0z1Lr1lbHQBEYPny4pkyZou7du2vChAkKCQmRt7d3tvNa5+P3IT4+XgEBAa6ICQAAXOz3q6ifO3dO6enp2rdvn7777jt17NjR6ngoo/Jd4Pw9u91e2DkAAEAJlPHzz1ZHQBHp1q2b48+ff/55tuPX52jPzMy84b3q1KlTmNEAAEARur6KuvTbUPVL/1t48tdff7UyFsq4fBU4ExISJEnBwcFZtm/k+vkAAKB0yjh92uoIKCJr1651+lo3Nze5ubkpNTVVHh4ecnNzu+Ewd8MwdO3aNaefCQAAXOP6KurSb19wJiYmSvpthMa5c+cYpQFL5KvAGRISIsMw9Ouvv8rDw8OxfSP5+QYfAACUXOknTzp67qF0Gzt2rNPXzps3T4ZhqFy5clm2AQBAydW2bVudOHFCFStW1OzZs5WZmanIyEgNGDBAlSpVsjoeyph8FThff/11GYah8uXLZ9kGAABlm/3qVWX89JM8goKsjoJibMGCBXluW2Hp0qX6+OOPtW/fPnl4eCgpKcnqSAAAlCiJiYm6cOFCls5tqamp2rJli+69994c5+oGXCVfBc5x48bluQ0AAMqu1G++pcBZCj344IMyDEOrV6+Wu7u7HnzwwRteYxiGXnvttSJId/PS09M1dOhQdezYscRkBgCgOElPT5ekbFPKJCcna8uWLbrnnnvk5eVlRTSUQU4tMgQAAHDd1Z075XffAEZ3lDLbt2+Xm5ub7Ha73N3dtX379nzNm5mTN954w6kMY8aMceq6/Fi4cKEkKTw83GXPAACgtLLZbEpLS5MkZWRkKCMjwzHqV5IuXLigjz76SL1795avr69VMVGG5KvAuWjRogLf2DAMzZ07t8DXAQCAkiXjp5+Utn+/vG+7zeooKEQnTpzIc7sgchr9c70Yappmjvsl1xY4nWGz2WSz2RzbV69etTANAADWiY6OdvTctNvtOnHihBo0aJDlnEuXLumDDz5Q586dFRoaypfhcKl8FThzmicpr5fS64sNUOAEAKBsSHrvfQqcyFV8fHyW7aSkJI0dO1Z+fn567LHH1KhRI0nSoUOH9NJLL+nKlStat26dFVHztGzZMkfPTwAAyiqbzabIyMgs+2JjYxUSEpKlF6f02zD27du36+jRo+rUqZP8/PyKMirKELf8nGS327N8Tp06pebNm2vEiBHau3evkpOTlZycrOjoaA0fPly33XabTp065ersAACgmEg7cEC//hhrdQwUU3Xq1MnyWbFihQICArRz504NGTJEzZs3V/PmzTV06FDt3LlT1apV0z/+8Y8CP2fmzJkyDCPPz6FDh5z+OWbNmuV4701OTtauXbucvhcAACVVTEyM4uLisnR4O3fuXJ51oFOnTum9995TdHS0Y+5OoDDlq8D5R4888ogaNGigf//732rbtq0qVaqkSpUq6fbbb9eGDRsUGhqqRx55pLCzAgCAYuzSmxuzjexA6fLJJ5/orrvuUrVq1VSuXDm5u7tn++THhx9+qIEDB+Y4VM3NzU2DBg3Spk2bCpxv+vTpOnjwYJ6fevXqFfi+13l6esrX19fx8fHxcfpeAACUVEFBQRo1apQqVqwoSfLw8FC7du1UpUqVPK/LzMzU999/r7feekuxsbGy2+1FERdlhFOLDG3fvl3Lly/P9XiPHj301FNPOR0KAACUPLZDh5X69deq2K6d1VHgAu+//77uv/9+3XrrrRo+fLhWrVqlkSNHyjRNbdq0SQ0aNNB9992Xr3uZpplnT8oDBw44VSwPCAhQQEBAga8DAAD5FxgYqMDAQMcK6eXLl9dtBZiqKC0tTV9++aViY2PVrl071alTh/k5cdOc6sHp5eWlr776Ktfje/bscfyiAwCA0qdt27a69dFHde/2bVn2X1wbLvvvFmFB6bFs2TK1a9dOMTExjnkoH3zwQW3YsEE//vijfvnlF9WtWzdf97rvvvu0atUqPf/880pNTXXsT01N1XPPPadXX31VAwYMcMnPcV1CQoL27dunhIQEZWZmat++fdq3bx8LBwEAUESSkpL06aefatOmTTp16hQjgXBTnCpwjho1Shs2bNDUqVN19OhRx9ycR48e1WOPPaaNGzdq1KhRhZ0VAAAUE4mJifrl4kWdT8tazLx29qwurV9vUSq40oEDBzR8+HC5u7urXLnfBgFlZGRIkkJCQjRlypQ8R/j83gsvvKBOnTrpiSeeUJUqVRQSEqKQkBBVqVJFTz75pDp06KAVK1a46keRJM2bN0+tWrXS/PnzdfXqVbVq1UqtWrXSN99849LnAgCArM6ePatPPvlEH3zwgQ4fPuxYnR0oCKeGqC9fvlznz5/Xyy+/rH/+859yc/utTmq322WapkaMGJHvF1wAAFC6XP4kQp4NG8qna1ero6AQVahQQR4eHpKkypUry9PTU7/88ovjeI0aNbKtlp4bPz8/7dq1S5s2bdInn3yikydPSpL69Omjvn37qn///i4fqhYeHq7w8HCXPgMAAOTfhQsXtGvXLkVFRalhw4Zq3LjxDef1BK5zqsDp4eGh9evX68knn9SWLVscL6V16tTR3XffXaC5FwAAQOlz/p8r5V6lqrybN7M6CgpJo0aNdODAAcd2y5YttX79eo0ePVrXrl3Txo0bFRwcXKB7DhgwwOVD0QEAQMlis9n0ww8/6IcfflDt2rXVrFkz5unEDTlV4LyuRYsWatGiRWFlKZClS5fq448/1r59++Th4aGkpKQbXmOapubPn681a9YoKSlJd9xxh1atWqUGDRq4PjAAAGWIee2aziz/q2rNny9P/p4tFQYNGqQXX3xRzz77rDw9PTV79mwNGDBAlStXlmEYSklJ0euvv251TAAAUAQSEhIc82inp6fr4sWLqlq1aqE/5+eff9bPP/+sqlWrqkOHDrrlllsK/RkoHZyag7M4SE9P19ChQzV58uR8X/O3v/1NL774ol555RVFR0erYsWK6t27t9LS0lyYFACAssn8NU2Ji5fIdvy41VFwE9LS0vT2228rIyNDc+bM0cWLFyVJ/fr1086dOzVhwgRNmjRJ27Zt07hx46wNCwAAXGrv3r3q37+/QkJCdOnSJUm/LRL49NNP65///KdOnDjhkudevHhRW7Zs0RdffCG73e6SZ6Bku6kenFa6vnpnfudOMk1TK1as0Jw5cxxDod544w3VqFFDH374oYYPH+6qqAAAlFn2lBQlLlykWosWyqNOHavjoIDOnj2rTp06KT4+XqZpyjAMeXt768MPP1TPnj3VpUsXdenSxeqYAACgCHzwwQcaNmyYTNPMtuK5aZr68ccf9eOPP2rChAlq3bq1SzIcOHBAaWlp6tGjB0PWkUWJ7cFZUPHx8UpMTFTPnj0d+/z8/NS+fXt99dVXuV5ns9l0+fJlx+fq1atFERcAgFLDfvWqEhcuUkZiotVRUECLFy/WiRMnNG3aNG3evFn/+Mc/5O3trUmTJlkdDQAAFKG9e/dq2LBhyszMVGZmZo7n2O122e12rVmzxmU9OSXp+PHj2rt3r8vuj5KpzBQ4E//3j6oaNWpk2V+jRg3HsZwsW7ZMfn5+jk9YWJhLcwIAUBplJicrcdFiXfvfUCaUDJ9++qnGjBmjZ599Vn379tXUqVP18ssv68SJEzp8+LDV8QAAQBFZsmRJjj03c7NlyxaX5vn+++919OhRlz4DJUuxKnDOnDlThmHk+Tl06FCRZpo1a5aSk5Mdn127dhXp8wEAKC2unTmjM8uWyf7rr1ZHQT4lJCSoc+fOWfZ17txZpmnqzJkzFqUCAABFKSEhQZs3b8615+Yf2e127d+/3zFvt6vs3r1bZ8+edekzUHIUqzk4p0+ffsPJ6evVq+fUvWvWrClJOnPmjGrVquXYf+bMGbVs2TLX6zw9PeXp6enY9vHxcer5AABASo87rrPP/0M1npoho1yxeg1BDmw2m7y8vLLsu7597dq1m7r3wYMHtXbtWh0/flyXLl3K1iPEMAxt27btpp4BAABu3rZt2/Ldc/M60zR16NAhderUyUWppMzMTG3dulVDhgzJUrdB2eT0vywiIyP12muv5flSGhcXV6B7BgQEKCAgwNlIeapbt65q1qypbdu2OQqaly9fVnR0dIFWYgcAADfn1+++0/mVK+X/6KMy3IrVYBLk4MSJE/ruu+8c28nJyZKko0ePqnLlytnOz8+iAuvXr9f48eNVvnx5NWrUSFWqVMl2TkH/IQUAAFzjypUrcnNzK9Dq5YZhKC0tzYWpfpOSkqJDhw7ptttuc/mzULw5VeD8+9//rpkzZ6pGjRpq166dmjdvXti5bighIUEXL15UQkKCMjMztW/fPklS/fr1Hb0sGzdurGXLlmngwIEyDEN/+ctftGTJEjVo0EB169bV3LlzVbt2bd13331Fnh8AgLLs6q7dUrly8v/TnyhyFnNz587V3Llzs+2fMmVKlu3rq6znZ/jaggUL1KpVK33yySfy9/cvtKwAAKDwVapUqUDFTem394I/jgJxlfT09CJ5Doo3pwqcL7zwgu68805t2bJF5cuXL+xM+TJv3jytW7fOsd2qVStJ0o4dO9StWzdJ0uHDhx29DCRpxowZSklJ0cSJE5WUlKTOnTsrIiKiyP6jAwAA/+/qtu0yf/1V/o89JjcPD6vjIAdr1651yX1//vlnPfHEExQ3AQAoAXr06CHDMAo0usIwDDVu3NiFqf7/OaGhoS5/Doo/pwqcly5d0pAhQywrbkpSeHi4wsPD8zwnp2HzixYt0qJFi1yYDAAA5FfKnq907fwFVX9iuspVq2Z1HPzB2LFjXXLfFi1a6Oeff3bJvQEAQOEKDg5Wv379tGXLlnyN1HBzc1Pz5s1VtWpVl2dr0qRJkTwHxZ9TY8LatWunw4cPF3YWAABQBtmOHNHPT87Qr99/b3UUFJHnn39er732mvbs2WN1FAAAkA9z586VYRgyDCNf5/ft29fFiaSqVauqffv2Ln8OSganenCuXLlSd999t9q2bauRI0cWdiYAAFDGZCYnK3HRYvnd219VRoyQwZD1Um358uXy8/NTly5d1LRpUwUHB8vd3T3LOYZhaNOmTRYlBAAAv3f77bfr7bff1rBhw2SaZo49Od3+N6/6xIkTFRIS4tI8lStX1t13323pyGIUL04VOIcNG6Zr167pgQce0OTJk3XLLbfk+FL6PT0xAABAAST/9yP9+v1+Bfx5qjzq1LE6Dlxk//79MgxDwcHBunr1qg4cOJDtnPz2EAEAAEVj0KBB2rNnjxYvXqzNmzdnmRbQMAw1b95cffv2dXlxMygoSHfeeac8PT1d+hyULE4VOKtWrapq1aqpQYMGhZ0HAACUceknT+rnp2aqyujR8u17N6usl0InTpywOgIAAHDC7bffrv/+979KSEjQbbfdpqSkJFWoUEFz5851+VyY7u7uateunZo1a8YXocjGqQLnzp07CzkGAADA/zMzMnRx7Vql/fCD/B97VO4+PlZHAgAAwP8EBwc7hoe7ubm5vLhZo0YNde3aVVWqVHHpc1ByOVXgBAAAKAqp33yjn5+coRpPz5JHUJDVceACV65cUXJysux2e7ZjwcHBFiQCAAA3YrPZlJaWJknKyMhQRkaGS+bDrFixotq1a6f69evTaxN5uqkCZ0ZGhg4dOpTrS2nXrl1v5vYAAAC6dvasfnl6tmrMmS2vRo2sjoNCsmrVKj3//PM6fvx4rufktIABAACwXnR0tK5duyZJstvtOnHiRKFOY+jr66vmzZurcePG2dZ8AXLiVIHTbrdr1qxZWrlypVJTU3M9j5dSAABQGOypqUpcvFi1Fi2WZ726VsfBTXrllVf0yCOPqHfv3nrwwQc1e/ZsTZs2TV5eXgoPD1eNGjU0depUq2MCAIAc2Gw2RUZGZtkXGxurkJCQm+7FWbt2bd16660KCQmhxyYKxKlZ+5955hn9/e9/1+jRo/XGG2/INE399a9/1SuvvKIWLVrotttuy/bLDgAAcDPMX9N05q/LlHnlitVRcJNeeukl9e7dW5988okmTpwoSbrnnnu0dOlSHThwQFeuXNGFCxcsTgkAAHISExOjuLi4LKuonzt3TqdOnXLqfu7u7mrcuLGGDBmifv36qW7duhQ3UWBOFTjDw8N1//33a9WqVerTp48kqU2bNpowYYKio6NlGIa2b99eqEEBAAAyL1zUxTfesDoGblJcXJz69+8vSY6eHunp6ZIkPz8/Pfzww1q5cqVl+QAAQO6CgoI0atQoVaxYUZLk4eGhdu3aFXgBIE9PT7Vu3VojR45U165dXb5QEUo3pwqcP/30k+68805Jv/1CSnJMLuvh4aHRo0dr/fr1hRQRAAAUJwkJCY4palIzr+l0HtPVuMLVnbuUkZhYpM9E4fLz83PM2+Xr66sKFSpk6fVRqVIlJfL/MQAAxVJgYKD69+8vLy8vSb99WXnbbbflu8Dp6emp22+/XSNGjFDbtm3l7e3tyrgoI5wqcFarVk1Xr16VJPn4+MjX1zfbBPGXLl26+XQAAKDY2Lt3r/r376+QkBDH3/OXMzLUNeITTdizR99fvFg0Qex2Xd2xo2ieBZdo1qyZvv/+e8d2hw4dtGrVKp0+fVqnTp3Sq6++qoYNG1qYEAAAOOvatWuKi4tzfJl5nbu7u1q0aKHhw4erVatW8vDwsCghSiOnFhlq1aqVvv76a8d29+7dtWLFCrVq1Up2u10vvviibrvttkILCQAArPXBBx9o2LBhMk0zy3xLkmRK2nkmUbvOJOrFdu3VJzDQ5XlSv/5aVUaMcPlz4BqjR4/WK6+8IpvNJk9PTy1cuFA9e/ZUcHCwpN96grz//vsWpwQAAM44fvy4oqOjZbfbHSurBwcHq2PHjvLz87M4HUorpwqcEydOVHh4uOOldOnSperatau6du0q0zRVpUoVvfnmm4WdFQAAWGDv3r0aNmyYMjMzsxU3r8s0TRmSpu6N1rth3XSbi+dQSj+ZoMykJLlXruzS58A1xo8fr/Hjxzu277jjDsXGxuqjjz6Su7u7evXqRQ9OAABKoIyMDB04cEDnz59XbGysbr31VnXr1k0hISFWR0Mp51SB895779W9997r2G7atKni4uK0c+dOubu7q1OnTkwOCwBAKbFkyZIce27+kfm/zz8PH9Lqjp1cnivt0CFV7NDB5c9B0ahXr57+/Oc/Wx0DAADk0/UFAn8/FD0+Pl5nzpxRYGCgUlNTFRwcTHETRcKpAmdO/Pz8NGDAgMK6HQAAKAYSEhK0efPmGxY3r8s0TW375RedTk1VYIUKLs1mO3qMAmcJFxUVpR07dujs2bOaMmWKGjRooNTUVB06dEgNGzaUj4+P1REBAEAObDabY7HpjIwMZWRkSJIOHDig8uXL69Zbb9X58+e1bds23XHHHY4FqgFXcWqRIUnKzMzUW2+9pUmTJmngwIH64YcfJEnJycn64IMPdObMmUILCQAArLFt27Z8FzevMyV9de6sawL9TsZPP7n8GXCN9PR0DRo0SHfccYdmz56tF1980bGKupubm3r16qUXXnjB4pQAACA30dHRjp6bdrtdJ06c0KlTp3Tx4kVVrVpVP//8s1JSUhQXF6eYmBiL06IscKoHZ1JSkvr06aO9e/fKx8dHKSkpeuyxxyT9tqr61KlTNWbMGD3zzDOFGhYAABStK1euyM3NTXa7Pd/XuEm6mnHthufdrMyUqy5/Blxj7ty52rx5s1atWqXu3burUaNGjmNeXl4aOnSoNm3apNmzZ1uYEgAA5MRmsykyMlLe3t6SJMMwFBsbqy5duuiRRx5R5T/MkR4UFGRBSpQ1ThU4Z86cqdjYWEVGRqpVq1aqXr2645i7u7uGDBmiLVu2UOAEAKCEq1SpUoGKm5Jkl+RTvtBmwcmVm7drh8DDdd58801NnjxZEydO1IULF7Idb9Kkid59912XPf/EiRNavHixtm/frsTERNWuXVujR4/W7Nmz5eHh4bLnAgBQGsTExCguLk5dunTRuXPnlJKSonPnzqlGjRp64IEHrI6HMsqpf318+OGHeuyxx3TXXXfl+FLasGFDhYeH32w2AABgsR49esgwjAINUzckdQyofsPzbpZnaD2XPwOucfbsWTVv3jzX4+7u7kpNTXXZ8w8dOiS73a5XX31V9evX148//qgJEyYoJSVFzz77rMueCwBAaRAUFKRRo0ZJ+m2o+qVLl+Tm5qawsDCLk6Esc6rAmZycrLp16+Z6PCMjI8sqWq6wdOlSffzxx9q3b588PDyUlJR0w2vGjRundevWZdnXu3dvRUREuCglAAAlW3BwsPr166ctW7YoMzPzhue7G4a616zp8gWGJKniHXe4/BlwjaCgIB06dCjX419++aXq16/vsuf36dNHffr0cWzXq1dPhw8f1qpVqyhwAgBwA4GBgQoMDJQkmaapxMREhYSEKDQ01OJkKMucWmQoNDRU3333Xa7HP/30UzVt2tTpUPmRnp6uoUOHavLkyQW6rk+fPvrll18cnzfffNNFCQEAKB3mzp0rwzBkGEae5xn/+zzSqLHLM1Xs2EEezOdUYo0cOVKvvvqqvvrqK8e+679fa9as0TvvvKMxY8YUaabk5GRVrVo1z3NsNpsuX77s+Fy9yjywAABIcukXk0B+ONWD8+GHH9ZTTz2lbt26qUePHpJ+eym12WxatGiRIiIitHr16kIN+kcLFy6UpAIPhff09FTNmjVdkAgAgNLp9ttv19tvv61hw4bJNM0ce3K6G4YMSS+1a6/bblAkulluPj6qOn68S58B15o9e7aioqLUtWtXNWnSRIZhaNq0abp48aJ++ukn9e3bV9OmTSuyPMeOHdNLL710w96by5Ytc7yDAgBQ1qWnp+vgwYMKCAhQnTp1rI6DMs6pHpx//vOfNWbMGI0YMUINGzaU9Ns38ZUqVdK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+ "text/plain": [ + "
" + ] + }, + "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/06-plotaesthetics.ipynb b/nbs/tutorials/06-plotaesthetics.ipynb deleted file mode 100644 index 41375b80..00000000 --- a/nbs/tutorials/06-plotaesthetics.ipynb +++ /dev/null @@ -1,792 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "2f833a32", - "metadata": {}, - "source": [ - "# Controlling Plot Aesthetics\n", - "\n", - "- order: 5" - ] - }, - { - "cell_type": "markdown", - "id": "7879a287", - "metadata": {}, - "source": [ - "Changing the y-axes labels." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5d374d47", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "We're using DABEST v2023.02.14\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": "ab12ec7f", - "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 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", - "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 the y-axes labels." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "54a3445d", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "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": [ - "Color the rawdata according to another column in the dataframe." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "527b475b", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "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": [ - "
" - ] - }, - "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": [ - "Changing the palette used with `custom_palette`. Any valid matplotlib or seaborn color palette is accepted." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8a6a82fd", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "multi_2group.mean_diff.plot(custom_palette=\"Paired\");" - ] - }, - { - "cell_type": "markdown", - "id": "5d1c2921", - "metadata": {}, - "source": [ - "You can also create your own color palette. Create a dictionary where\n", - "the keys are group names, and the values are valid matplotlib colors.\n", - "\n", - "You can specify matplotlib colors in a [variety of\n", - "ways](https://matplotlib.org/users/colors.html). Here, I demonstrate\n", - "using named colors, hex strings (commonly used on the web), and RGB\n", - "tuples." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "33271a43", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "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": [ - "By default, ``dabest.plot()`` will\n", - "[desaturate](https://en.wikipedia.org/wiki/Colorfulness#Saturation)\n", - "the color of the dots in the swarmplot by 50%. This draws attention to\n", - "the effect size bootstrap curves.\n", - "\n", - "You can alter the default values with the ``swarm_desat`` and\n", - "``halfviolin_desat`` keywords.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3db70141", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "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": [ - "You can also change the sizes of the dots used in the rawdata swarmplot,\n", - "and those used to indicate the effect sizes.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2e964805", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "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 the y-limits for the rawdata axes, and for the contrast axes." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "97d2052e", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "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 your 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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FC7EYA3v888Po3LUcFJdXqd3/9OVM1NXLILEy6TtfRCYjMjgS6+avw56UPcguzIa7gztyinOw+fhm5T7nrp/DzqSdWDB+AUb1GoX4c/FYs2uNyusoBAX2puyF1EqK+ePm33U8n8/5/K6PJdKUzm+F7Ny5E++//z78/PxUtnfu3BkZGRm6Pj39jXeeiPTLwcYBU/pNwbyx89DFtwt2ndnVaB+FoMC/t/0bxRXF+O3Yb2pfa/eZ3SitLFXbTmRMdP4VtrKyEra2jVdiLCgoUCleRXevi78XXBxs1fZaRHYOgFTC3goiQ9l5ZqfaNplCht1nduNK7hW1+9TL65GWl4benXrrIjy6S/WVlUiPj0dpRgakTk4IGjoUDr6+hg7L4HTeYzFkyBCsX79e+VwkEkGhUOCDDz7AsGHDdH36dsHK0gKzRvdvss3SQqy2jYj0o7iFqbfFlcWQWjX/RcvO2u6uz7/gmwWY9dksLPhmwV2/BqkquHABf82bh6Rvv8W1PXtwYdMmxD3/PC5s3tzywWZO519jP/jgAwwdOhQJCQmoq6vDq6++inPnzqGoqAiHDx/W9enbjYmDesLK0hI/7TqhnBnSxd8LT04cjB7BnN5GZEj+7v5IzkhW2x7oEYih3YdiR9KOJtt9XX3RxffuFxosrihGYXnhXR9PquR1dTjywQeov3PFbEFAyo8/wq1LF3h0726Y4IyAzhOLsLAwJCcnY9WqVbCwsEBlZSWmTZuGefPmwcfHR9enb1fG9O+O0f3CkFtYCksLi0bTUonIMMZHjcf2xO1QCIpGbY62jojpEYOoTlFIvJqIm2U3VdqtLKzw7OhnIRKJ9BUutSDr2DHUlqof85K2YwcTC13z9vY2y+XLjZFIJIKvu7OhwzArtaU3IUCAtVPzlU/N0Y3kPcg79Reqi3IgcXCHd+Ro+ESNg0hs/mvztOTc9XMNgyqrShHoEYixvcfCs4n/I/ml+ZBaSvHixBfx+V+fo15er2xzsnVC7IxYWFtZw9rKGp888Qk2H9+MQ+cPoU5Wh4igCNwffb9ySmplTSW2n96OoxePQq6Qo3dwb0yImgBXB1e9vW8CKvLymm0vb6Hd3Ok8sThw4ECz7UOGDNF1CER3pejyCWTs/x6VN64CAGw9AhEQMxPuXQep7Cevq0Z1YTYsbRxg7exliFDvSkXuZWQd3YTSjGSILSVw7zYIHQZMg8S+4ZdUWtwq5CZsVe4vqy7H1R1foTQjGV3vWwSRqP2uCLBm1xqVqaPHLh3D5uOb8cb9b6BvSF8AwOmrp7F231rloEwfFx88OeJJyAW5so7FkLAhKmMrXO1dMefeOZhz75xG5yyuKMar619FdtE/Be8u5VxC3Ok4vDfrPQS4B+jq7dIdbN3cmm93d9dTJMZJ54nF0KFDG227vUtPLm+8AiCRoRVdOYnzv7wN3NZ1XXUzAxd+XYGu970G926DoZDLkLFvHfIS4yD/uxCSo18Ygsc8Dfvbih7drbqKImQd3YSC8wehkNXBKbAH/KLvg0OHti9gVZx2Cud/WQbhtsWyso9tRsH5Q4h47EPUV5WqJBW3K7xwBMVXEuDauV+b4zBFJ6+cVEkqbqmT1WHl5pVY//x6XMm9gtgNsZAp/vn3zS3Oxaodq7Bg/AI8ce8TGp937d61KknFLSWVJfjPtv/g/Uff1/g16e74DxyIM999h/qqpmfiBY8YoeeIjIvOv3IUFxerPPLz8xEXF4e+ffti5071U7CIDClj//cqScU/BGTs/x6CIODK1s+RfWyzMqkAgLKs80j5YRFqiv/pChUEBcqun0fRlYRWL8xVW16IM2tfQs7xzagrL4CsugyFF44g+bt/ofDS8Va/j4rcy7h57gDKrp+/LR4BaXGrVJIK5XnLbiLz4E+4eS6+2de9ebb5dnMWdzpObVtlbSUOnj+IH+J/UEkqbvfjgR+bXFIdaOjleP2H1zFpxSQ88OED+GLbF7hZehN1sjocOK++9zclMwV5xe27+12fLG1sMODFF2EhkTRq6zJxInx6t+9pwTrvsXBycmq0beTIkZBKpXjxxRdx6tQpXYdApJHa8kJU5qWpba8uzELJtdPIT9nbZLu8phLZJ/5Ap9H/h+K0BKRtX4WakoYf+iKxJTwjhqPTmGcgtmz8Q+mW6wc3oLY0v9F2QSHD1bhVcO3ct9lbETXFubiweSUqci4pt9m6B6DL1FegqK9DTXGu2mNvno2HZ/hwte0AIKtTX+nV3N05uPJOucW5SMlMUdteWF6IK7lXENohVGX7vrP78NEfHykHeFbIK7A9cTtOXD6BJTOWoE5W1+x5S6pK4O3i3cp3QW3lHRmJMZ9/jqu7dv1Tx2LYMLh3bXuPoqkzWNUkDw8PXLx40VCnJ1KvFWVKyzLPAVC/X0naKVTkXsb5X95W6RkQFDLcSNoJhawOoVNeUXv8zXP71bbVlt1EWeZ5OAX2aLJdIatHyo9voPaOBayqCjJx7sc3ETzmWbWvDQCK+ho4dOiCvMRtavdx1MLtGFPl6+LbbDErHxfNZ7vVy+vx9a6vm5w1UlheiG2ntsHNwU3tlFGJpYSrphqArbs7ejz0kKHDMDo6vxWSnJys8jhz5gzi4uLwzDPPoGfPnro+PZHGpI7usPPqqLbd2tUXVvbND96CWIyso5uavN0AADfPHVC5XXI7QRAgr6tp9uXlzfQYFKQebJRU3FJfVYqqm+kQWaj/TmHv0xke3WMgVTMLxtLGAV6Ro5uNz5yNjxqvts3ZzhlDewxFeEC42n3cHNwQ4hOisi0lIwXFlepvkx1MPYiJfSaqbb83/F442HB6ORkHnScWvXr1QmRkJHr16qX8+7hx41BXV4dvvvlG16cnuisBMbMANbcaAmMegVuXfmrbAcCtywCUNlMQCYICpWq6y0UiERzu6CZXaRdbwt4nBIJCjoILh3Ftz7e4fmijMlG5fTxFUypvXINXhPrBZX7R90FsKUGPme/AzitYpc3axRfdH3obEjvnZs9hzsIDw/H48MchgmpdiVurmkosJXgk5hFYiptO3qb1n4YtJ7fg691fIy4xDtV11aitr232nDX1Nbh/4P0Y3atxQtc3pC/mjpp792+ISMt0fivk2rVrKs/FYjE8PDxgbW2t61Obnbp6GQ6cuYycghJ4uTpiSM8usJHe/VLKpJ5bl/7odv/ryNj/PapuNiyWZ+Pmh4AhM+HRvWGKtG/ficg58UejYyUObvDtNwk3zzU/1Vpsqb6Es9/A+5H6y9tNtnlG3AuFrB6J/30W1YVZyu0Z8T/Af/CDsJA0f21ZSKwRPPppKGT1yD+7TzlI1UJig4CYR+Aedk/D+3X1ReTcL1CWlYrqohxIHd3hFBjRYqEmyd9Lc0vMeInu6QOnY2DXgco6Fh09O2J4+HBl2e3wwHAsfXAp1u1bh8u5lwE0VM/sFdQL6/atU6ll8e3eb/HixBdhKbZUO+CzR0APiEViPD/hedwXfZ9KHYu2VOSktqktL0f63r0ozcxUrhXiFMBpvyJBaD/rXiYmJiIqKgqnTp1CbxMbtXv2ajaWrt2Kkop/ZiDY20jxxqPj0KdroAEjM3/VRbkABFi7+Kj8UhUEAdnHNyP3xBbUlt2ESGwJt9ABCBr+OKxdvHFtz7fIPtr0ipUWEhv0fPwT3Dwfj4qcS7C0todHj6FwCemrPEfuqW1I37sO8trKhoNEYnj0GIrO4+cj+btXUKHmPn/g8MeRsXet2vcTNiNWOVW0puQGSjNSILaSwqVTFCyljRcMpLa5WXoTMoUM9bJ6zFszr8kZIfbW9ogOjW5yBVSxSIy3H3obkcGRdx3DrM9mobC8EG4Obvj++e/v+nXoHzfPn8eh996D7I4pp91nzEDY9OkGiso46KTH4vPPP2/1vgsWcFGclpRV1uDNNX+gskZ1VHhFdS2Wrv0Ta19/DO5O9ir7W1iIYGfN1WO1wca16cF4IpEIfgOmoUP/KaivLIGFxAYWEhtle4f+U1Fw/hBqSxuPd/DsORJn1r6oMlX15rl4uIcNQejUVyASieETNQ6e4cNRfPUUFPW1cPTvAWtnT5RlpapNKgCgJC0BXr1G40YT6064dukPl5A+yufWzl4mVdTLVJRXl6O4shgejh7wcPIAAPx353/VTjOtqKlAJ+9OsLKwws6kncqeCxd7Fzw54sk2JRWkffLaWhz58MNGSQUAnNu4Ee5du8IzXP04G3Onk8Tik08+adV+IpGIiUUr7Dp5vlFScUtNnQzbj57FrDEDcCj5Cn7ceRxXsm9CJGpYLv2J8QMRGmB8U9Ce/egnFJdXwcXBFl++9LChw2kTkUisrFZ5O4m9CyIe+wDXD27AzbP7IK+rgb1vZ3QYMA3Xdq1RSSpuKTh/AM4de8H778GRClkd5DVVUMhqlQM2q25mNhtPVcF19HhkBRx8OyP31DblbQzvyDHw7TepXVfM1LWi8iJ8tfMr5a0KqZUU94bfiydHPInrBdebPTanKAfPjXsOM4fMxIXsC5BaSRERGAHLZgbakmFkHTuGurIyte1pO3YwsdC2O8dVUNtczS1osX13Qire//Gfb6iCACReysT59Bx89Nx0dPE3rm+lxeVVKCitaHlHEyd1cEPIuHkIGTcPgqCASCRG0eUTqGtmpckbSTvhHTkaWUf+h8wDP0FxW/0C1y794Rl+b7PnlNi7QCQSwbv3WHj3Hqu199JaSd88j7qKYkjsXdBrzmd6P7+hVNdV47UfXkPWbeNeautrsS1xG7IKs5pcR+R2rn8npy72LogOjdZprNQ2La0VUpHfuAZNe8KvLibAxb75+95Odjb49q+ml6CvqZNhfdwxXYRFGrrVU1Bb1nyiWFtWgJvnDiB97zqVpAIAii4dR0HqIUgc1K9F4NVrVNuDbYO6imLUlRe2usqoudibslclqbhdckYyOjYzhdlCbIF7I5pPGMl42Hk1/0XNzsNDT5EYJ730sWVlZWHLli3IzMxEXZ3qD8qPP/5YHyGYtJH9wrBxb4La9q6BXvjrqPpKfydT01Enk0FiyS5VY2DTQiEjG7cOyG5iLYpbClIPIXTqq7j856dQ1KvWu3AJ6Qvv3uO0Eidp5uSVk8223yy7ifuj78evR39V2S4WiTFv7Dy4ObRQG4WMhl90NJLWrUN9RdO9rsEjR+o5IuOi8980e/bswaRJk9CxY0dcvHgRPXr0QHp6OgRBMLmZGYYS6OWKx8YNxLptRxq1PTA8Cn4ezU/rUwgCFIp2M/nH6DkFRcDG3R/Vau65+0SNx4XfVqh/AUEBCyspev/fl8g99RfKsy/C0toOHt2Hwr3bIC5pbiB31rVoqv2Je59Aj4AeiDsdh4qaCvi7+2Nc73HKZdHJNFhKpYheuBCHV66EvEY1ue86dSq8e/UyTGBGQueJxaJFi/DSSy9h2bJlcHBwwG+//QZPT0/MnDkTY8aM0fXpzcbMkf3QPcgHW48kI6egFF6ujhgX3QN9uwahrl4GRztrlFU2Xa0xPLgDrCWsd2EsRCIRut3/Bs799BZq71h3wm/gdLh3GwRLG3vIqsvVvoaltT2snb3QsYVVMhWyOhRdOQlZdQUcOoTCzjNIG2+BmtA3pC+OX1a/QFw3v2744PcPcDD1IGRyGeyt7RHqG8rlzk2UV0QExn7xBa7t2fPPWiFDh8I1JKTlg82czhOL1NRU/Pzzzw0ns7REdXU17O3tsWzZMkyePBnPPPOMrkMwGVn5xYg7fg5FZZUI8HLF6P7d4eLwz/iKXp390auzf6PjJFaWePDevli95WCjNrFYhJmj2ufy1sbM1t0fUc+uxs1zB1B0+TgAEbx6jYLr31NBPXoMQ+7JLU0ea+3iA/sOocg5uQW5p7ahpjgX1k5e8Oo9Bh36TVb2WNw8F4+0uFUqCYpzcG+ETn0VViz/rHXDI4Zjy8ktyCxoPGunZ1BPrN+/XmUMRkVNBX479huyCrMQOyNWn6GSlti4uCDs/vsNHYbR0XliYWdnh9rahnK1vr6+SEtLQ/fu3QEABQXND2JrTzbFJ+KrPw6orH/1467jWPz4BPTtGgSgoSDTyQvpOJScBplcjt5dAjCkV2dILC0xfVgUxCIRNuxJQElFw7REPw8XzJ04GFGhLKBljCrz05F19FflLZHCC4fhHNwbXSYtRMA9D6LkaqJKZU0AEFtKEDJ2Hi7/8bHKQmXVRdlI3/0Nyq6fR7f730B59gVc/P3DRku/l1xNxIXfViD8kXd1/v7aG2sra7w36z2s2bUGh1IPoV5eDxuJDUb2HAkvJy+s2b2myeOOXz6O1KxUdPPrpueIqbUq8/NxJS4OhRcuwNLaGv6DByPwnnsgtmJPcFN0nlgMGDAAhw8fRlhYGMaPH4+XXnoJKSkp2LRpEwYMGKDr05uEi5l5WPV74/LPNXUyLF+3DT/FzoGVpQUWf7MFpy7+821o18lU/Lz7JFY+cx/cnOxw39DemDS4J67mFEBiaYEgH7dG5Zfzi8sRn3QJ1bV1CAvyRVRoQIslmkn7assKcPanNyGvqVTZXnI1Eec2xKLXnM/Q87EPkXPyTxSkHmookBUYjg79p0BWU6l29dOii0dRcjURN5J2NkoqbilNP4OKvDTY876+1jnbOeOVKa9g3th5KK0shYuDC6ytrPHmT282e9yxS8eYWBipgtRUHHznHchuG0txIzkZ6fv3Y8gbb8BCykKEd9J5YvHxxx+j4u+Rs0uWLEFFRQU2btyIkJCQVhfSAoBVq1Zh1apVSE9PBwB0794dixcvxtix+p+nr21bj6if0VFVW4c9py6goLRCJam4JfNGET79ZTfenjsZAGBlaYHQgKanQn2/4xh+2HlcZSBnZz9PLJ87Ga6Odm18F6SJ3FN/NUoqbqnMS0Nx2im4hvRBwJCHETBEtYBY2o6vmn3tm+cOoDz7YrP7lGdfZGKhQ7ZSW9jeVh69Ha2cYFYEhQInvvhCJam4peD8eVzauhXd7rvPAJEZN53XsXj77bdx8+ZNCIIAW1tbfPnll0hOTsamTZsQGNj6Lno/Pz+89957SEhIQEJCAoYPH47Jkyfj3LlzOoxeP3IKSpptzy4owfZjZ9W2n0hNR36x+oF+ABCfdAnr4441mh1yOSsf736/vdWxkna0tAJpWWbD/+va0pvIPv47rh/+RXmMooUl1RX1NbBoYc0PrgmiX31D+rapnQzj5vnzqGym2FX6vn16jKbB7ldfxdannsLuV1/V+7lbS+c9FoWFhRg/fjzc3Nzw4IMPYtasWeh1F1NxJk6cqPL8nXfewapVq3Ds2DHlmA1T5eXqCKRlq213dbBVWXzsTgpBwI2iMlTX1ePXfadw+lImLC0tMDg8BPcNjYSLgx02H0hSe/yZK1m4mnMTwb7tu6iLPllYNd99KraSIn3fd8g68qvKLQ2nwHC4dR0ENLFY1S2OAd1h590JGfu+a/rcEhu4dul/d4HTXRnZcyS2nNyCvJLGFRsjO0aiR0APA0RFLakpKWlTuy7UlJSguqhI7+fVhM57LLZs2YK8vDzExsbi1KlTiIqKQlhYGN59913lbQ1NyeVybNiwAZWVlYiOVl/6tra2FmVlZcpHhZpiJoY2YaD6mvLWEkuM7t8djnbql8IWiYCSiio89/HPiDt+DjeKy5F9swQb9ybguU824GZJOdJbKAt+LVd9iWnSPvdu9zTTKoJIbIGsw780GidRmpGCkmunIXVq+naXxMENnhEj4Nt3IuyavNUhQsdRT6kslka6Z2dth5WPrkR0aDTEf1dgtbayxvio8XjrgbcMHB2p4+TfeBbe7RwDAlBy7RqOffIJ/nj8cfz55JM4tXp1s70c7YFeSjE6OzvjqaeewlNPPYWsrCz8/PPP+Pbbb7F48WLIZLJWv05KSgqio6NRU1MDe3t7bN68GWFhYWr3X7FiBZYuXaqNt6BTYUG+eHzcQKy9owCWlaUFFs0aC2d7W4zt30Nt9c2+XYPw/Y7jqKmrb9SWX1yOdduPwtnBVu1CZkDLZcNJuzx6DMWNM7tQdr3xrTzffpNQcD5e7bFFl0+ixyPvImPvWpWxFHbenRA65WXlbY7wWe8h9+QW5Kfsg6ymAva+XdCh/xQ4B/XU/huiFrk7uuOt6W+htLIUJVUl8HD0UBmHQcbHKTAQHt2746aaW+5e4eHY88YbUNxWUfrqzp3IPnYMw955Bw4+Ta+MbO70WuO5vr4eCQkJOH78ONLT0+HVQr31O4WGhiIpKQklJSX47bffMHv2bMTHx6tNLhYtWoSFCxcqnyclJSEmJqZN70GbbhSVQSZXwNfdCQ+P7If+YR0Rd/wcCv+uYzFuQA94ujTUG5g1egAuXr+BpMuq1Rr9PFwwLSYSr32lvgT0/tMX8fCIfli3/WiT7Z4uDk3Wx2ita7kFOHL2KhQKBfp0DUS3wPZ5MWlCbGmF7g8vQ9aR35B/ZhfqKopg6xEAnz4T4dVrFA6/M1H9wYICirpq9Hz8Y1TkpaGmOA9SJ084+HZW2c1Sagv/wQ/Cf/CDOn43pAknOyc42TkZOgxqpf4vvIBD77yDktt72MVidJ0yBTkJCSpJxS21ZWU4+9NPiH7pJf0FakT0kljs27cPP/30E3777TfI5XJMmzYNf/75J4YPH67R60gkEoT8XdWsT58+OHnyJD777DP897//bXJ/qVQK6W1Tgezt7e/+TWjRyQvp+HbrYVzJbqi66OvuhEdGDcDIvt0wb9rQJo+RSizx/tPTcPz8NRxKuQKZTI7eoQEYFhmK1IzmV9qrq5dj/MBwnLqUiZQ7xnJYS6zwykOjIBY3PeU0v7gMu06moqSiGh193DCsd1fYSBvmbsvlCnzw807sOXVBuf/6uGPo1y0Ibz02vtlqn7cKf91eAKy9sbCyRmDMTATGzGzUZmXriPqqUrXHWto4AgDsvTtxdgeRFshqa1FXVgapkxMsJBLldhsXF4z44APcSEpCwa06FgMHQlZXhwubNql9veyTJyGvq1N5rfZC54mFn58fCgsLMXr0aPz3v//FxIkTYW2tfryAJgRBUBbfMhWnL1/HW2u2QK745955TkEpVv60A3KFAmP6/zMQVRAE1NTVQ2plBbFYBLFYhOgewYjuEazymh193CGxskBdvbzJcwZ4ucLZ3hbvPT0VexIuYF/iRVTV1qF7kC8m39MTvu7OTR73+4EkrPojXmUmydptR7B87mSEBnjjp10nVJKKW06kpuO/fxzA89PVr9b45UsPq20jwLPnCGQf/a3JNhs3Pzj6ddVzRETmqa6yEik//ICMAwcgr62Fpa0tgmJiED5zJiz//l0lEongHRkJ78hI5XGFly41+7qCTMbEQlcWL16M6dOnw8Wl+YWyWvL6669j7Nix8Pf3R3l5OTZs2ID9+/cjLi5OS5Hqx/rtR1WSCpW2uKMY2bcbFAoBP+8+gb+OpKCovAqOdtYY0787Zo0e0GQvgKOdNUb3644/Dyc3+brTh0UBACSWlhg7oAfGDmgYgV5XL8O+0xfx064TkFhZIqZXF/QM8QMAnE/PwZe/78ed0+9LKqqx+OstWPfGY/jzSNPnAxqKdz05YTDsbFg8pjWqbmaiqjALUgc3OHQIhf+gGSi5ehqVN66q7GchsUGnsc8h7/QO3Dy7D7LaKjh26AqfvhNh6373t7OI2iNFfT0OLFuG4rQ05TZZVRWubN+O0owMxCxZApFYDHl9Pa4fPozsEyegkMngFR4Ov4EDYWljA1l10zP2HPz8IDGSXnJ903li8dRTT2nldW7cuIFZs2YhNzcXTk5OiIiIQFxcHEaa0PK0ldW1OHstR237zZIKpGXfxA87j+Po2X9+oZRV1uCXvaeQmpGHD565DxYWjSfzPD1lCKpq6rA38YIyGbCytMBDI/qq9ILckldUile/3ITcwn+62/88nIwhPTvj9VljseVQcqOk4pai8irsOnkexeVVat9Lbb0MOQWl6OzvqXYfAmrLC3Hpj49Qmn5Guc3WMwhdJi1ExOyVyEvaiYLzB6Gor4VTYDi8e49B2vYvUZrxT1G1yrw03DizC92mvwWXTlwx2JgdvnAYO5N2oqiiCH5ufpjQZwK6+5v2dHlTlnXsmEpScbub588jLykJ7t264cCyZSi6fFnZlpeYiEtbtyJg0CBc3b27yeNDJ03SScymQK+DN9vim2++MXQIenHp+g2VpOJ2KWnZOHI2Dff07NyoTWJpidceGYNHxwzA6UvXYWkpRr+uHXE19yZ2nDgHf08XhAX5Kvd//4cdKknFLQfOXEbXQG9czy9uNs6bJRWwsrRAvazp2y8iEeDsoH5K47Mf/YTi8iq4ONi229sigkKOcz+9haqbGSrbq/LTcfanN9H7/1ahQ7/J6NBvsrIt6+hvKknFLQpZHS7/+Qn6Llhn8GXTJfYuKn9Sg0+3foqdSTuVz9Py0nDg3AE8PfppTOzbzGBd0pmchKZn2t3efiM5WSWpuKW6sBBlOTnoNGYMru7eDeHvGY4W1tYIu+8+dBw+HBW5ubh+5AhkNTVw79YN3pGR7WIJBZNJLMyBnY0UPTr6qu218HB2wLWc5utNHEy+okwsKmtqUV1bD1cHO+XgS193Z/i6O+N8eg4WfLYBeUVlymNDOnjgrcfGo65e3mzPydYjKQjydsWl62p3gberE2J6dcHuhNQm2yM7B8DDWf0KmsXlVSgoNc66IvpSeOl4o6TiFllVGfIS4xBwj+qMjvwzTX87AoC6iqKGUuCdDbuaba85nxn0/Mbo5JWTKknFLQIErN61GtGh0XB3dDdAZO1bS6XWBbm82eqaBefPo++zzyLsvvuQf+4cxBYW8OrZE1a2tkj+/ntc3LIFyq7fzZvh3LEj7nn9dVi3cWiAsdN5gSxSNXtsNCzETf+zzx47QO34i1tkMjmy8osR+80WTHvjKzy05GvMWv4tNh84rdynoLQCr//3d5WkAgCuZN/Ea19txvX85qu25RaWKMdhNMVGaoWhvbtg7sTB6ODh3Kjd1dEO8+8f1uw5CE3WsFBpz2xcxr2uqqTZY+or1c8kIcPZnaw+IZQr5Nh3Vv+loQnwuW0wZlM8evRAfWXTa/rcUl1YCGsXFwQMHgy/6GhY2doiff9+XPzjD9x5P7nk2jUc//zzNsdt7JhY6Fmvzv5456nJ6Oz3z9iDDh7OeO2RMRjdr3uL9SRC/Dzw4hf/+7tuRMN/2vzicny5OR7f/HUYAPDXkRS1xbByC0uR3cLaJF4ujhjQPRiTBjcupGRlYYFXHx4NO2spXB3t8J8XH8JTk+5BeHAHhAX5YPbYaHz18kz4eZh3Rq4NFpYtl/W+k51nx2aPsfMKaktIpCOlLSR8JZUl+gmEVPgPGgRHNdU1XUJC4B8dDWtXV7XHi8Ri2DdRBOvKtm1qj8lPSUHZ9Wa6g80Ab4UYQFRoIKJCA5FfXA65XAFvN0flfbfBESEI8nFDehMltr1dHXGzpAIlFU0PmvxtXyLui4lEakZus+cvLK1E10BvXFBT/2JcdENvxfz7hmFwRAh2HD+H4vIqdPR1x8SBESq9FHY2UkwfFqWceUKt5959CK4f3qi23S00GjkntqDg/EHIZQ2DNz3ChqgM9Lydo3932Ps0Hn9DhhfkGYTkDPWzqIK9gtW2ke5YSCSIWbIEp7/5BtnHj0OQyyG2soL/wIHo9cQTEFtaImT0aJz9+ecmj/ft1w+CQoG806chdXKCS3DD51ialdXseUuvX1eb0JgDJhYGdKuq5u0sLSyw8plp+HjjHpw4fw2Kv7vSIjv7Y+GDI/Dyv39V+3r1cjlOpqbD1rr5b8J21hL8a+ZovPrlb7hZojrOYUBYR5UkIbKzPyLbUJWT1LPzDIJ31DjknWr87cYxoAeyT/yBqtumm1bmpcFCagvvqPG4cXoHBMU/5fDtvDuh67TX9BI3NU+ukEMsEqsM0pvQZwK2JW6DTN54CQNXe1fcE9bc2jFt5/L3QFoXDqhtxNrJCdELF6K2rAzVRUWwdXdXmSYaOmUKSjIykHVEdckFp6AgyOvr8dezzwJ/38J2CghAn2efhbWzM6qaWS/ExszHWDCxMEIuDnZ4+8lJyC8uR25hKTydHeDj3lACWNHCYCO5QsDw3qE4eKbxKOZbhkd1hZ+HC77+16PYnZCK5LQsSCVWGNKzM/p2DVJbhZO0r9OYZ2HnGYzcU1tRXXAdEgd3ePUaBVlNOXKO/95of3ltFUrTz6Dv/LW4mXoQ8r/rWDh17NUuRpsbs6RrSdh4eCOS05NhIbZAdNdozLxnJgI8AuDn5od/Tf0XPt7yMarr/ql74O7gjtgZsZBY6raI0udzzP++fltJHR0hdXRstF1sYYHohQtROGECso8fh0Img2d4OC7+8QfyTp1S2bc0MxMHli1Dx3vvxaU//2zyPPa+vnDrat4F7phYGDFPF4dGvRp9ugZh+7HGg/oAwEIsRp/QQLg62mFgj2AcaWLa6vRhUQjwarhnaGstwaTBPZscS0H6IRKJ4BM1Fj5RY1W2H/9Y/RTc6sIs1Jblq0xDJcM6cvEI3v31XSj+Xo1WppDh4PmDSExLxAezP0CQZxAGdR2EyI6ROHj+IG6W3USQZxAGdBkASwv+GDYFbl26wK1LFwANNS4KUpueEVdfVQVBEOAeFoaC8+dV2qxsbdHvuefM/ksA/0ebmAeGRSE+6RKqmhicOaJPV+xKOI/Tl67DwkKMYZFdkJ5XhKKySvh5umDS4AgM723embI5EAQB9VVlze5Tx9kfRkMhKPD1rq+VScXtKmsr8UP8D3hz+puorqvGhkMbsDNpJ8qqy+Dt7I2CsgJM6jdJuZQ6mQZ1q53eUpCaiuHLlyPz0CFkHj4M+d91LDqNHg1b93+mFddXVyPv9GnI6+rg3rUr7L29dR26XjCxMDF+ni5Y+cx9+HLzfpxPbxikaW8jxdDILjickobiE6oDOzv7eeK7Nx5jaW0TIhKJYOsZhKr8a2p2EMPOM0ivMZF6V3KvIK9E/UKAxy4dQ1VNFd78+U1cyP5nbZ28kjys3rUa6fnpeGHiC3qIlNTRdE2Plva1kEggtrJC0LBhCBrW9NT7K3FxSPnxx39KgotE8B80CH2feQYWUtP+ec3EwgSFBnjhs+dnILewFJU1tfDzcMGirzY3WWL7clY+vos7imenDtV7nHT3OvSfjMt/ftpkm1uX/rB29tJvQKRWvay+2XaFoMDes3tVkorb7TyzE5P7TUZHr+anEpN2KerrceH335G2axdqiopg7eyM4JEj0XXaNFhYqV+ZGQA6DBiA5B9/VA7avJNfdHSzx2efOIHTX3+tulEQcP3QIYgtLdHvuec0ei/Ghv1vRqiwtLJVVSl93JwQ0sETBSUVzVbS3HnifIuFt8i4ePUcCb+B04E7usgdA8LRecLzBoqKmtLJuxPspHZq27v4dsHJKyebfY1DqYe0HRY1QxAEHPnoI5zbuBE1RQ0FA2tKSnD+f//D4fffh9DCz0t7Ly90GT++yTbnoCB0HD682eMvbtmiti3z4EFUFzVfxNDYscfCiJxMTcfabUdwOathmlKwrztmj4nGwPBOjfatrKmF1MoSlhYWKCxrvjJcZU0dautksLVuf8v3mrKg4Y/BO2ocCi8c/nsRsgg4+ocZOiy6g7XEGlP6T8GPB35ssn3GoBnYclL9LxIAqJM1XdCOdCM/ORm5atYJuZGUhLzTp+ET1Xxtnp6zZ8PB1xeX//oLZVlZkNjbI2jYMHS7/37lcuvqFDWz5Logl6M4LQ02zRTmMnZMLIzEidRreOvrLcpqmgBwNacAS9b+ibcem4B7IkIAAH8cOoNN8aeRU1ACqZUlhkZ2weTBvSAWi1SOvZ2HswNspM137ZFxsnbyRIf+Uw0dBrXg4XsehkKhwO8nfldOJ3Wxd8Hjwx5HdGg0Mm5m4IyawmYA0LMjZ2bpU9axYy22t5RYAEDwyJEIHjkSglwOkUXTi/8VXbmCnJMnIQgCvHv1gkdYGCxtbVFfob5X2tJG/QKOpoCJhZFYt+1ok4mBIADrth3BPREhWLPlIH7Z98+86dp6GXacOI/ktGwMCOvY5PRSAJg0OMLspzcRGZJIJMKsobNwX/R9uJB9AVYWVujm1005lXRs5FhsObmlydLdXXy7ICqYlWv1SV7f/LgYeV0dasvKcGX79obaFXI5vCIi0Hn8+CZnbjSVVCjq63Hss8+QfVsSc2HTJnhFRMA/OhpXd+1q8ty27u7w6NZNw3dkXDjGwggUlFYob380JfNGEc5ezcav8YlNtucWlsLfyxVhQY1r1o/o043lton0xFZqi97BvREeGK5Sn8LJzgkrHlmBLr5dlNvEIjEGdBmApQ8uZeKvZ5491C+yCADOHTti92uv4fz//ofSzEyUZ2fjyvbt2P3qqyi6ckVlX1ltLcqys1FbqjoF/Nwvv6gkFbfcSE6GrLYWdl6NB2CLLC0R+eSTans/TAV7LIxB88U0AQCJlzLV3uoAGsZnfPXyTCReykTipUxYWVhgcM8QhHTwVNnvzJXr2HokBTkFpfBydcSE6HD0Dg1o6zvQmIuDrcqfROYu0CMQnz7xKdLz01FUXoQObh3gxdk9BuE/cCBSf/sNFbmN11Wy8/JCSUZGkyW566uqkLhmDUa8/z4U9fVI+eknXN2zB7KqKkAshk/v3oh8/HHYuLri6m71K9pmHzuGUR99hKt79uD6kSOQ19bCIywMoZMnwzUkRKvv1RCYWBgBd2d7dPRxx7Xcgibbfd2dYdfC+h8yuQIikQghHTxRU1cPSwsL+HuoDv75cedxrNt+VPn80vUbOHjmMh4e2Q+PjxvY9jeigS9fUl9ZksicBXkGIYh1SAzq1uJjJ//zH+Qn/7M4nEePHoh66insfPlltccWp6WhLCsL5zZuRNbRf36eQqFAbkICSq5dw+BFi1BXXq72NeR1dZDV1iLikUcQ8cgjWnlPxoSJhZGYPXYAlq7diqaWApk9ZgCCfT2aPT6yiz+++j0eWw4no14mBwA42Erx+LiBmDioJ9JzC1WSitv9tOsEBoeHoLO/Z5PtRETmxtbNDTGLF6MiNxeV+fmw9fSEg48P6ioroahrfpZO0eXLqknFbaoLC5F17BjEEon61xGLYe3kdFdxWzs7q/xpjJhYGIlB4SF489HxWLf9CK7nFwNo6KmYPWYAhkc1lOEeHBGCQ8lXGh1rZy2BXK7AHwdVR52XV9Xi81/3wdHOBhczbzR7/h0nzjGxIKJ2x97HB/Y+/4xPs7K1hZ23Nyrzmq6mKpZIUN7ELZTb3ThzBv7R0ciIj2+y3adXL1jf5QqnI1auvKvj9ImJhREZ0qszhvTqjKz8YggQ4OfhojKo67WZY7DKbj92nkxV9kp09vPE/026B299o36e/IbdJxHk4662HUCTVTuJiNobkUiELuPH4/Q33zTZHjR0aIt1KiASIeLRR1F05QrKs7NVmmzd3RH55JPaCtcoMbEwQn6eTWeyUoklXnhgBJ4YPwiZN4rgYGeDQC9XJKdlobpW/fSpK9k3MTii+QFBHX2bTzyIiNqLkLFjUVVQgEtbt0KQy5Xb/aKj0euxx1CRm4uzP/2k9njfqChYOzlhxHvvIX3/fmSfPAlBoYBPZCQ63nsvJPb2+ngbBsPEwgQ52tmgR3AH5XNrSfPFrywtxBjdvzt+2XsKVbWN7/lZSywxtn/z06+IiNqTiFmz0HncOOQkJEAhk8GrZ084+vkBAJwCA+E/eDCuH2pcit3W3R3Bo0YBaCh0FTJ2LELGjtVaXLtffRU1JSWwdnY22tsiTCzMQGc/T3TwcEb2zZIm2wdHhMDdyR7LnpyIpWv/QnlVjbLN3kaKNx4dBzcn9WsdEJF2nEo7hR1JO1BUXgR/d39M6DMBnbwbl+wn42Dj5oZOo0c32dbvuedg5+GBq7t2oa6iAiILC/j26YNejz0GqYODzmKqKSkx+rVEmFiYAZFIhGenxiD2mz8hk6sunuNkZ4PZYxpW2usZ4o+fYufgQNJl5BY21LGI6dWF5b6J9OCrHV+prBlyPus8dp3ZhQXjF2BUr1EGjIzuhtjSEuEzZyLsgQdQXVgIib292d/iaC0mFmaiX7eO+Oi5+/Hz7gScvpQJSwsx7unZGQ+N6Atfd2flftYSK4zqx4WsiPQp8WpikwuRKQQF/r3t3+gT0geu9qa76JS5Ks/JwYXNm5F98iSgUMA7MhJdp06Fc1CQch8LK6smy3y3Z0wszEhYkC/efnKSocMgojvsSmp6XQgAkClk2JeyD/dF36fHiKglpRkZ2Ld4Meor/1k9+vrhw8hJSMCQt96Ce9euBozOuHGtECIiHSuuLG62vajCuO+Zt0fJ33+vklTcIq+txZnvvjNARKaDiQURkY75u/s32x7oEainSKg16ioqkHdG/TL3RZcvo/JG80UH2zOTSSxWrFiBvn37wsHBAZ6enpgyZQouXrxo6LCIiFo0oc8EWIibXrHSydYJQ7oP0XNE1BxZbS2aXF/h9n1qanDz3Dkc+eADbJ8/H3vffBNXd++G4ra6F+2VySQW8fHxmDdvHo4dO4Zdu3ZBJpNh1KhRqGyiq4qIyJgEegRi4aSFkFhKVLY72zljyYwlsLZqoZIj6ZWNiwvsPNUvcSB1dET+uXPYv2QJso8fR0VuLgovXMCpr77CkQ8+aPfJhckM3oyLi1N5vnbtWnh6euLUqVMYMoTZPhEZt2E9hiEqOAr7zu5DUUUR/N38cU/YPZBaNb9yMemfSCxG6OTJSFyzpsn2jiNGIHn9+iZ7NXITEnD98GEEtuPfSyaTWNyptLQUAODqyilaRGQaHG0dMbnfZEOHQa3QafRo1FVU4MLmzZDVNBQVFEsk6DJ+PKydnaGQydQem7F/PxMLUyMIAhYuXIjBgwejRw/1pahra2tRW1urfF5RUaGP8IiIyAx0u+8+hIwdi/yUFAiCAM/u3SFxcMC5X35p9ri6dv67xiQTi+eeew7Jyck41ESd9tutWLECS5cu1VNURERkbqxsbdGhf3+VbS7Bwc0e49xCu7kzmcGbt8yfPx9btmzBvn374Pf3gjDqLFq0CKWlpcpHfHy8nqIkIiJz5dO7Nxw6dGiyTWRpic5aXHTMFJlMYiEIAp577jls2rQJe/fuRceOHVs8RiqVwtHRUfmwZx13IiJqI5FYjMGvvw7HgACV7Va2thjwwgtwCmzfdUlM5lbIvHnz8NNPP+GPP/6Ag4MD8vLyAABOTk6wsbExcHRERNSe2Ht5YdRHHyE/JQWlGRmQOjmhQ//+sJRylo/JJBarVq0CAAwdOlRl+9q1a/HYY4/pPyAiImrXRCIRvCIi4BURYehQjIrJJBZCC1XQiIiIyPBMZowFERERGT+T6bEgIiLSp8r8fFyJi0PB+fOwkEjgN3AggoYN4ziKFjCxICIiukPhpUs4sHw5ZFVVym03z59H+r59iFmyBFacNKAWb4UQERHd4eR//qOSVNxSnJaGC5s2GSAi08HEgoiI6DaFly6hPDtbbXv6/v0qz+W1tRAUCh1HZTp4K4SIiOg2tX8vctlcuyAIuLJ9O65s24aKvDxY2toiKCYG3R94ABIHBz1FapyYWBAREd3GMSAAEImaXBYdAJwCApD07be4sn27cpusqgpXtm9H/rlzGP7OO+16DAZvhRAREd3G3ssLPr17q233HzQIV+Limmwry8xE+t69ugrNJDCxICIiukPf556DW2io6kaxGKGTJzfbmwEAWUeP6iwua2dn2Li6wtrZWWfnaCveCiEiIrqD1MEBw995BzfPncPNW3UsoqNh5+mJ8//7X7PHymUyncU1YuVKnb22tjCxICIiUsOje3d4dO+uss0zPBznNm5Ue4xXeLiuwzJqvBVCRESkAfeuXeGpJnmQ2Nuj05gxeo7IuDCxICIi0tDAV15BwODBEFlYKLe5BAcjJjYWtm5uBozM8HgrhIiISENWtrbo/8ILCJ0yBTeSk2Hv7Y0O/foZOiyjwMSCiIhIQ/XV1UhcvRrXjxyBIJcDAFxCQtDn//4Pzh07Gjg6w+KtECIiIg0d+eADZB48qEwqAKD4yhXEL12KqsJCA0ZmeEwsiIiINFB48SLyk5ObbKurqECamuJZ7QUTCyIiIg3cSElpU7u5Y2JBRESkAbFl88MTW2o3d0wsiIiINODXv39DWW917QMG6DEa48PEgoiISAP2Pj4IUVMEy9HfHx3vvVfPERmX9t1fQ0REdBd6PfEE7L29cXnbNlTeuAFLGxsEDhmC7g8+2K6XTAeYWBAREWlMJBKh8/jx6Dx+PGQ1NbCQSCAS8yYAwMSCiIioTSytrQ0dglFhYkFERHQHQaFATkICrh8+DFl1NdxCQ9FxxAhYOzkZOjSjx8SCiIjoNgq5HEc/+AA5CQnKbbmJibi0dSuGvPkmXDp1MmB0xo83hIiIiG5zZft2laTilrrychz77DMIggAAkNfXI/PgQST/8AMu/fknakpK9BypcWKPBRER0W2u7d6ttq0iJwcFqamwsrXFoXffRXVRkbIt+ccf0XvOHASPHKmPMI0WeyyIiIhuU3VbstCUyvx8HH7vPZWkAgAEmQynVq9G0eXLugzP6JlUYnHgwAFMnDgRvr6+EIlE+P333w0dEhERmRkHH59m26uLilBVUNB0oyDgyvbtOojKdJhUYlFZWYmePXvi3//+t6FDISIiM9Vp9Gi1bS4hIVDIZM0eX5qVpe2QTIpJjbEYO3Ysxo4da+gwiIjIjHUcPhzFaWlI27FDZbudlxcGvPgibpw50+zxNs7OOozO+JlUYqGp2tpa1NbWKp9XVFQYMBoiIjIVvefORfDIkcg8dEhZx8I/OhpiKytIBg3CmfXrIa+pafLYoOHD9RytcTHrxGLFihVYunSpocMgIiIT5BwUBOegoEbbJXZ26PvMMzj++ecQ5HKVtqBhw9Chf389RWicRMKtCbkmRiQSYfPmzZgyZYrafe7ssUhKSkJMTAxOnTqF3r176yFKIiIyV6XXryMtLg4lGRmwdnRE0PDh8O3Tx9BhGZxZ91hIpVJIpVLlc3t7ewNGQ0RE5sTJ3x+95841dBhGx6RmhRAREZFxM6kei4qKCly5ckX5/Nq1a0hKSoKrqysCAgIMGBkREREBJpZYJCQkYNiwYcrnCxcuBADMnj0b69atM1BUREREdItJJRZDhw6FiY411bvc3Fzk5uYaOgzSEh8fH/i0UA2QTAevT/PDa/QfJpVYtJWPjw9iY2PN/sOvra3FQw89hPj4eEOHQloSExODHTt2qAxGJtPE69M88Rr9h8lONyX1ysrK4OTkhPj4eM6EMQMVFRWIiYlBaWkpHB0dDR0OtRGvT/PDa1RVu+qxaG969erF/+RmoKyszNAhkA7w+jQfvEZVcbopERERaQ0TCyIiItIaJhZmSCqVIjY2loOIzAQ/T/PCz9P88DNVxcGbREREpDXssSAiIiKtYWJBREREWsPEgoiIiLSGiQU1sn//fohEIpSUlBg6FCJqAq9RMmZMLHQsLy8P8+fPR3BwMKRSKfz9/TFx4kTs2bNHq+cZOnQoXnjhBa2+ZnNWr16NoUOHwtHRkT/gmiASiZp9PPbYY3f92kFBQfj0009b3I+fUeuY4zVaVFSE+fPnIzQ0FLa2tggICMCCBQtQWlqql/MbO0Nfn+b++bDypg6lp6dj0KBBcHZ2xsqVKxEREYH6+nrs2LED8+bNw4ULF/QajyAIkMvlsLRs+8deVVWFMWPGYMyYMVi0aJEWojMvty8wtXHjRixevBgXL15UbrOxsdF5DPyMWmau12hOTg5ycnLw4YcfIiwsDBkZGXj66aeRk5ODX3/9VUvRmi5DX59m//kIpDNjx44VOnToIFRUVDRqKy4uVv49IyNDmDRpkmBnZyc4ODgI06dPF/Ly8pTtsbGxQs+ePYX169cLgYGBgqOjozBjxgyhrKxMEARBmD17tgBA5XHt2jVh3759AgAhLi5OiIqKEqysrIS9e/cKNTU1wvz58wUPDw9BKpUKgwYNEk6cOKE8363jbo9RHU32ba/Wrl0rODk5qWzbsmWL0Lt3b0EqlQodO3YUlixZItTX1yvbY2NjBX9/f0EikQg+Pj7C/PnzBUEQhJiYmEafdUv4GanXHq7RW3755RdBIpGo/D8jw1+ft5jT58PEQkcKCwsFkUgkvPvuu83up1AohMjISGHw4MFCQkKCcOzYMaF3795CTEyMcp/Y2FjB3t5emDZtmpCSkiIcOHBA8Pb2Fl5//XVBEAShpKREiI6OFubOnSvk5uYKubm5gkwmU/7wiYiIEHbu3ClcuXJFKCgoEBYsWCD4+voK27ZtE86dOyfMnj1bcHFxEQoLCwVBYGKhbXf+4IqLixMcHR2FdevWCWlpacLOnTuFoKAgYcmSJYIgCML//vc/wdHRUdi2bZuQkZEhHD9+XFi9erUgCA3/r/z8/IRly5YpP+uW8DNqWnu5Rm9Zs2aN4O7urvG/k7kz9PV5izl9PkwsdOT48eMCAGHTpk3N7rdz507BwsJCyMzMVG47d+6cAED5DSU2NlawtbVVfvsRBEF45ZVXhP79+yufx8TECM8//7zKa9/64fP7778rt1VUVAhWVlbCjz/+qNxWV1cn+Pr6CitXrlQ5jomFdtz5g+uee+5p9Mvs+++/F3x8fARBEISPPvpI6NKli1BXV9fk6wUGBgqffPJJq8/Pz6hp7eUaFQRBKCgoEAICAoQ33nijVfu3J4a+PgXB/D4fDt7UEeHvgqYikajZ/VJTU+Hv7w9/f3/ltrCwMDg7OyM1NVW5LSgoCA4ODsrnPj4+yM/Pb1Usffr0Uf49LS0N9fX1GDRokHKblZUV+vXrp3I+0p1Tp05h2bJlsLe3Vz7mzp2L3NxcVFVVYfr06aiurkZwcDDmzp2LzZs3QyaTGTpss9NertGysjKMHz8eYWFhiI2N1fj49kbf16c5fj5MLHSkc+fOEIlELf4gEAShyR9sd263srJSaReJRFAoFK2Kxc7OTuV1bx3fmjhI+xQKBZYuXYqkpCTlIyUlBZcvX4a1tTX8/f1x8eJF/Oc//4GNjQ2effZZDBkyBPX19YYO3ay0h2u0vLwcY8aMgb29PTZv3twoRmpMn9enuX4+TCx0xNXVFaNHj8Z//vMfVFZWNmq/NfUvLCwMmZmZuH79urLt/PnzKC0tRbdu3Vp9PolEArlc3uJ+ISEhkEgkOHTokHJbfX09EhISNDof3b3evXvj4sWLCAkJafQQixsuSRsbG0yaNAmff/459u/fj6NHjyIlJQVA6z9rap65X6NlZWUYNWoUJBIJtmzZAmtr61Yf257p6/o058+H00116Msvv8TAgQPRr18/LFu2DBEREZDJZNi1axdWrVqF1NRUjBgxAhEREZg5cyY+/fRTyGQyPPvss4iJiVHpHm1JUFAQjh8/jvT0dNjb28PV1bXJ/ezs7PDMM8/glVdegaurKwICArBy5UpUVVVhzpw5rT5fXl4e8vLycOXKFQBASkoKHBwcEBAQoPbc1GDx4sWYMGEC/P39MX36dIjFYiQnJyMlJQXLly/HunXrIJfL0b9/f9ja2uL777+HjY0NAgMDATR81gcOHMCDDz4IqVQKd3f3Js/Dz6hl5nqNlpeXY9SoUaiqqsIPP/yAsrIylJWVAQA8PDxgYWHR6rjbG31cn2b/+RhqcEd7kZOTI8ybN08IDAwUJBKJ0KFDB2HSpEnCvn37lPu0dirb7T755BMhMDBQ+fzixYvCgAEDBBsbm0ZT2e4c4FVdXS3Mnz9fcHd3v+upbLGxsY2mVQEQ1q5dexf/SuatqelscXFxwsCBAwUbGxvB0dFR6Nevn3Jk+ebNm4X+/fsLjo6Ogp2dnTBgwABh9+7dymOPHj0qRERECFKptNnpbPyMWsccr9Fb7U09rl27dpf/UubJENenuX8+XDadiIiItIZjLIiIiEhrmFgQERGR1jCxICIiIq1hYkFERERaw8SCiIiItIaJhQE99thjEIlEeO+991S2//777zqtgllfX49//etfCA8Ph52dHXx9ffHoo48iJydHZb/a2lrMnz8f7u7usLOzw6RJk5CVlaWzuEwdP0/zws/TvPDz1B8mFgZmbW2N999/H8XFxXo7Z1VVFRITE/HWW28hMTERmzZtwqVLlzBp0iSV/V544QVs3rwZGzZswKFDh1BRUYEJEyaw6mMz+HmaF36e5oWfp54YupBGezZ79mxhwoQJQteuXYVXXnlFuX3z5s3NFj7ShRMnTggAhIyMDEEQGpZ5trKyEjZs2KDcJzs7WxCLxUJcXJxeYzMV/DzNCz9P88LPU3/YY2FgFhYWePfdd/HFF19o1O01duxYldX3mnpoorS0FCKRCM7OzgAaVvirr6/HqFGjlPv4+vqiR48eOHLkiEav3Z7w8zQv/DzNCz9P/eBaIUZg6tSp6NWrF2JjY/HNN9+06pivv/4a1dXVWjl/TU0NXnvtNTz88MNwdHQE0LDOhEQigYuLi8q+Xl5eyMvL08p5zRU/T/PCz9O88PPUPSYWRuL999/H8OHD8dJLL7Vq/w4dOmjlvPX19XjwwQehUCjw5Zdftri/wOXVW4Wfp3nh52le+HnqFm+FGIkhQ4Zg9OjReP3111u1vza65urr6/HAAw/g2rVr2LVrlzJ7BgBvb2/U1dU1GuSUn58PLy8vzd5cO8TP07zw8zQv/Dx1iz0WRuS9995Dr1690KVLlxb3bWvX3K3/5JcvX8a+ffvg5uam0h4VFQUrKyvs2rULDzzwAAAgNzcXZ8+excqVK+/6vO0JP0/zws/TvPDz1B0mFkYkPDwcM2fOxBdffNHivm3pmpPJZLj//vuRmJiIrVu3Qi6XK+/jubq6QiKRwMnJCXPmzMFLL70ENzc3uLq64uWXX0Z4eDhGjBhx1+duT/h5mhd+nuaFn6cOGXZSSvs2e/ZsYfLkySrb0tPTBalUqtPpT9euXRMANPnYt2+fcr/q6mrhueeeE1xdXQUbGxthwoQJQmZmps7iMnX8PM0LP0/zws9Tf0SCIAj6SWGIiIjI3HHwJhEREWkNEwsiIiLSGiYWREREpDVMLIiIiEhrmFgQERGR1jCxICIiIq1hYkFERERaw8SCiIiItIaJBREREWkNEwsiIiLSGiYWREREpDVMLIiIiEhrmFgQERGR1jCxICIiIq1hYkFERERaw8SCiIiItIaJBREREWkNEwsiIiLSGpNILNLT0zFnzhx07NgRNjY26NSpE2JjY1FXV2fo0IiIiOg2loYOoDUuXLgAhUKB//73vwgJCcHZs2cxd+5cVFZW4sMPPzR0eERERPQ3kSAIgqGDuBsffPABVq1ahatXrxo6FCIiIvqbSfRYNKW0tBSurq7N7lNbW4va2lqVbVKpFFKpVJehERERtVsmMcbiTmlpafjiiy/w9NNPN7vfihUr4OTkpPIYPXo0cnNz9RQpERG1B1nz5+Paffej6LvvDB2KwRk0sViyZAlEIlGzj4SEBJVjcnJyMGbMGEyfPh1PPvlks6+/aNEilJaWKh/x8fGIj49nYkFERDpREX8Agkxm6DAMyqC3Qp577jk8+OCDze4TFBSk/HtOTg6GDRuG6OhorF69usXXv/O2h729/V3HSkRE1BJ5aSmqTpyA3cCBhg7FYAyaWLi7u8Pd3b1V+2ZnZ2PYsGGIiorC2rVrIRab5F0cIiIyM3369EH2hQtwt7LCluH3ovTPre06sTCJ3845OTkYOnQo/P398eGHH+LmzZvIy8tDXl6eoUMjIqJ2Li8vD3mVlSioaZgsUHvpEqoSEw0cleGYxKyQnTt34sqVK7hy5Qr8/PxU2kx0tiwREZmxwm+/hXWPHhBLJIYORe9MosfisccegyAITT6IiIiMjSw3D0Vr1xk6DIMwicSCiIjI1JTv3ImK+HhDh6F3TCyIiIh0pOCr/6I2Lc3QYegVEwsiIiIdEerqcGPlSshLSgwdit4wsSAiItIheUEhbqz8AEI7WZGbiQUREZGO1V68iPxPP4Mglxs6FJ1jYkFERKQHVcePI//jj82+54KJBRERkZ5UHTuOvLffhry01NCh6AwTCyIiIj2qOZ+K7FdfRc3Fi4YORSeYWBAREemZvKAQuW8tRsmvv0JQKAwdjlYxsSAiIjIEuRzFP29A7uLFqL+Rb+hotIaJBRERkQHVpl5A9ksLUbZrl6FD0QomFkRERAYmVNeg8Kv/Iu/dd02+mFarVzdduHChynOxWAx7e3vY29vj5ZdfxnvvvYf8/Hx8/PHHWg+SiIioPag+lYjshS/B48UXYBMebuhw7kqrE4vTp0+rPL+VWDg7OwMAzp8/j6ysLK0GR0RE1N7IS0uRt+xtuM15Ao5jxhg6HI21OrHYt29fs+3r169vczBEREQEQKFA4ZqvIdTXw2niRENHoxGNx1hkZGSgurpaF7EQERHRbYq+W4+Kw4cNHYZGNEosFAoFOnfuzFseRERE+iAIKPjPl6i9ds3QkbSaRomFWCxG586dUVhYqKt4iIiITEZmZiaqqqoAAFVyGbL//rs2CbW1yH/fdJZe1/hWyMqVK/HKK6/g7NmzuoiHiIjI6J04cQITJ05EUFAQiouLAQBl9fUYErcdc48cwZmiIq2eT3bzJvLefRcKExiKIBIEQdDkABcXF1RVVUEmk0EikcDGxkalvUjL/5jalJiYiKioKJw6dQq9e/c2dDhERGSCNm3ahBkzZkAQBMibWAbdQiSCCMDn/fpjTIcOWj23dUQ4vN94AyLLVs+90DuNI/v00091EAYREZHxO3HiBGbMmAG5XA5138vlggARgAUnjuN/MUPR09VVa+evSU5B4dffwP3p/9Paa2qbxonF7NmzdREHERGR0Vu+fDkEQVCbVNwi/P34z8ULWB09UKsxlO/aBduo3rDt21err6std1XSOy0tDW+++SYeeugh5Oc3LJwSFxeHc+fOaTU4IiIiY5GZmYmtW7c2efujKXJBwJ7cXJ0M6CzesFHrr6ktGicW8fHxCA8Px/Hjx7Fp0yZUVFQAAJKTkxEbG6v1AImIiIzBnj17WuypuJMA4OhN7a9cWpeeDpmRztDUOLF47bXXsHz5cuzatQsSiUS5fdiwYTh69KhWgyMiIjIW5eXlEIs1+7UpBlBRL9NJPPLSMp28bltpnFikpKRg6tSpjbZ7eHiwvgUREZktBwcHKBQKjY5RALC30s0MDgsnR528bltpnFg4OzsjNze30fbTp0+jg5an1RARERmLe++9FyKRSKNjRACiPTy1HouFkxMs3dy0/rraoHFi8fDDD+Nf//oX8vLyIBKJoFAocPjwYbz88st49NFHdREjERGRwQUEBGDChAmwsLBo1f4WIhHu9fFBB1tbrcdibcRLqmucWLzzzjsICAhAhw4dUFFRgbCwMAwZMgQDBw7Em2++qYsYiYiIjMJbb70FkUjUYs+F6O/HvNCuOonDtnekTl5XGzS+8WNlZYUff/wRb7/9NhITE6FQKBAZGYnOnTvrIj4iIiKj0bdvX2zcuLFVlTe/6Ndfq8WxbhHZWMO2Xz+tv662aNxjsWzZMlRVVSE4OBj3338/HnjgAXTu3BnV1dVYtmyZLmIkIiIyGtOmTcORI0cwbty4Rj0XIgDDvL3xv5ihGK2jcYeO48ZBfMdyGsZE47VCLCwskJubC09P1cEohYWF8PT0bHXhEEPgWiFERKRNmZmZ6NWrF4qLi+FkZYWt947QyZiKWyQdO8L33Xcguq3cg7HRuMdCEIQm7y2dOXMGrjro8iEiIjJWAQEBsP07kbCxsNRpUmHVoQO83njdqJMKQIMxFi4uLsoBK126dFFJLuRyOSoqKvD000/rJEgiIqL2zDoiHJ4vvQQLe3tDh9KiVicWn376KQRBwBNPPIGlS5fCyclJ2SaRSBAUFITo6GidBElERNQuWVrA5YEH4DR1KkQaVv00lFYnFrdWNe3YsSMGDRoESyNeC56IiMjUSUM6we3ppyHt2NHQoWhE4+xg+PDhJjt4k4iIyNiJbWzgMvNhOIwebTK9FLfTOLFQN4mktrZWZVEyIiIi0oztgP5we/JJWLq4GDqUu9bqxOLzzz8HAIhEInz99dewv20AiVwux4EDB9C1q24qjBEREZkzsaMD3P/v/2A3YIChQ2mzVicWn3zyCYCGHouvvvpKpVb6rcGbX331lfYj/Ns777yDv/76C0lJSZBIJCgpKdHZuYiIiPTFOiwMHgtfNOleitu1OrG4du0aAGDYsGHYtGkTXPT8D1BXV4fp06cjOjoa33zzjV7PTUREpAsOI+6F21NPQdTKhc1MgcZjLPbt2weg4Rf9tWvX0KlTJ73MEFm6dCkAYN26dTo/FxERka45TZkC11mPGDoMrdN4uGl1dTXmzJkDW1tbdO/eHZmZmQCABQsW4L333tN6gG1RW1uLsrIy5aOiosLQIREREcHpvmlmmVQAd5FYvPbaazhz5gz2798Pa2tr5fYRI0Zg48aNWg2urVasWAEnJyflIyYmxtAhERFRO+d03zS4PvywocPQGY0Ti99//x3//ve/MXjwYJWy3mFhYUhLS9PotZYsWaIsE67ukZCQoGmISosWLUJpaanyER8ff9evRURE1FbOD84w66QCuIsxFjdv3mxUHAsAKisrm1ycrDnPPfccHnzwwWb3CQoK0ug1byeVSiGVSpXP7U2gxjoREZkhsRhujz8Gx3HjDB2JzmmcWPTt2xd//fUX5s+fDwDKZGLNmjUarxXi7u4Od3d3TUMgIiIyGSJLS7jPnw/7wYMMHYpeaJxYrFixAmPGjMH58+chk8nw2Wef4dy5czh69KhObzVkZmaiqKgImZmZkMvlSEpKAgCEhISwJ4KIiIySSCKB5yuvwLZ3pKFD0RuNx1gMHDgQhw8fRlVVFTp16oSdO3fCy8sLR48eRVRUlC5iBAAsXrwYkZGRiI2NRUVFBSIjIxEZGdmmMRhEREQ6Y2nR7pIK4C56LAAgPDwc3333nbZjada6detYw4KIiEyG2+OPt7ukArjLxEIul2Pz5s1ITU2FSCRCt27dMHnyZC6lTkRE7Y63tzfkJSVwt7JSbrPtEwXHMWMMGJXhaJwJnD17FpMnT0ZeXh5CQ0MBAJcuXYKHhwe2bNmC8PBwrQdJRERkrBISEpA1fz7qc3IBACKpFG5z5xo4KsPReIzFk08+ie7duyMrKwuJiYlITEzE9evXERERgaeeekoXMRIREZkMp6lTYNmOZzxq3GNx5swZJCQkqCxC5uLignfeeQd9+/bVanBERESmxMLNFU6TJxs6DIPSuMciNDQUN27caLQ9Pz8fISEhWgmKiIjIFDnfdz/EEomhwzCoViUWty/k9e6772LBggX49ddfkZWVhaysLPz666944YUX8P777+s6XiIiIqMktrODw7Chhg7D4Fp1K8TZ2VmlXLcgCHjggQeU2wRBAABMnDgRcrlcB2ESEREZN9s+fSBq570VQCsTi3379uk6DiIiIpNm3aO7oUMwCq1KLLjcOBERUfMkQR0NHYJR0HjwJhEREd1BbAGJXwdDR2EUmFgQERG1kZW3F8dX/I2JBRERURtZdfAzdAhGg4kFERFRG1n6eBs6BKOhcWJRXV2Nqqoq5fOMjAx8+umn2Llzp1YDIyIiMhWWbu23hPedNE4sJk+ejPXr1wMASkpK0L9/f3z00UeYPHkyVq1apfUAiYiIjJ2Fs5OhQzAaGicWiYmJuOeeewAAv/76K7y8vJCRkYH169fj888/13qARERExk5sa2voEIyGxolFVVUVHBwcAAA7d+7EtGnTIBaLMWDAAGRkZGg9QCIiImMnkkoNHYLR0DixCAkJwe+//47r169jx44dGDVqFICGRcgcHR21HiAREZGxE1lZGToEo6FxYrF48WK8/PLLCAoKQv/+/REdHQ2gofciMjJS6wESEREZOwt7e0OHYDRaVdL7dvfffz8GDx6M3Nxc9OzZU7n93nvvxdSpU7UaHBEREZkWjRMLAPD29oa3t+qc3X79+mklICIiIjJdrUospk2bhnXr1sHR0RHTpk1rdt9NmzZpJTAiIiIyPa1KLJycnCASiZR/JyIiImpKqxKLtWvXNvl3IiIiottxrRAiIiLSGiYWREREpDVMLIiIiEhrmFgQERGR1rQpsaipqdFWHERERGQGNE4sFAoF3n77bXTo0AH29va4evUqAOCtt97CN998o/UAiYiIyHRonFgsX74c69atw8qVKyGRSJTbw8PD8fXXX2s1OCIiIjItGicW69evx+rVqzFz5kxYWFgot0dERODChQtaDY6IiIhMi8aJRXZ2NkJCQhptVygUqK+v10pQREREZJo0Tiy6d++OgwcPNtr+v//9j8umExERtXMar24aGxuLWbNmITs7GwqFAps2bcLFixexfv16bN26VRcxEhERkYnQuMdi4sSJ2LhxI7Zt2waRSITFixcjNTUVf/75J0aOHKmLGImIiMhEaNxjAQCjR4/G6NGjtR0LEakjqwMsJS3vR0RkYBr3WDz++OPYs2cPBEHQRTxE1BSxRcv7EBEZAY0Ti8LCQowfPx5+fn546aWXcPr0aV3ERUS3E7H6PhGZBo1/Wm3ZsgV5eXmIjY3FqVOn0KdPH4SFheHdd99Fenq6DkJU9eWXX6Jjx46wtrZGVFRUkzNUiIiIyDDu6muQs7MznnrqKezfvx8ZGRl4/PHH8f333zdZ30KbNm7ciBdeeAFvvPEGTp8+jXvuuQdjx45FZmamTs9LZHC89UhEJqJN/av19fVISEjA8ePHkZ6eDi8vL23F1aSPP/4Yc+bMwZNPPolu3brh008/hb+/P1atWqXT8xIZnCA3dARERK1yV7NC9u3bh59++gm//fYb5HI5pk2bhj///BPDhw/XdnxKdXV1OHXqFF577TWV7aNGjcKRI0eaPKa2tha1tbXK5xUVFQAAmUzGKqFkWuqqAImtoaMgonbOysqqxX00Tiz8/PxQWFiI0aNH47///S8mTpwIa2vruwpQEwUFBZDL5Y16Rby8vJCXl9fkMStWrMDSpUsbbe/fv79OYiQiIjJnrZkRqnFisXjxYkyfPh0uLi53FVRbiUQileeCIDTadsuiRYuwcOFC5fOkpCTExMTg+PHjLD9OpqWyELBzM3QUREQt0jixeOqpp3QRR4vc3d1hYWHRqHciPz9f7dgOqVQKqVSqfG5vbw8AsLS0bFV3DpHREMkB/p8lIhPQqsRi2rRpWLduHRwdHTFt2rRm9920aZNWAruTRCJBVFQUdu3ahalTpyq379q1C5MnT9bJOYmMRl2FoSMgImqVViUWTk5OytsNjo6Oam896NrChQsxa9Ys9OnTB9HR0Vi9ejUyMzPx9NNPGyQeIr0pzwNcgwEL9loQkXFrVWKxdu1a5d/XrVunq1haNGPGDBQWFmLZsmXIzc1Fjx49sG3bNgQGBhosJiK9UNQDucmAX5ShIyEiapbGdSyGDx+OkpKSRtvLysp0Ot30lmeffRbp6emora3FqVOnMGTIEJ2fk8goXN1r6AiIiFqkcWKxf/9+1NXVNdpeU1PD8tpEunQ1HpDVtrwfEZEBtXpWSHJysvLv58+fV5mdIZfLERcXhw4dOmg3OiL6R205cGEr0OM+Q0dCRKRWqxOLXr16QSQSQSQSNXnLw8bGBl988YVWgyOiOySsBQKiAUdfQ0dCRNSkVicW165dgyAICA4OxokTJ+Dh4aFsk0gk8PT0hIWFhU6CJKK/1ZYD214Bxn8MOOh2bR4iorvR6sTi1swLhUKhs2CIqLE+ffogLysD3tIaJLzeGyjNAv54Fhi1HPDsZujwiIhU3NUiZEDDOIvMzMxGAzknTZrU5qCI6B95eXnIvlEAOEv+2VhZAGyZD/R9Egh/ABC3aaFiIiKt0TixuHr1KqZOnYqUlBSIRCLlgiS3imbJ5VzemUgv5PXAsVXAld1A37lAABfXIyLD0/hrzvPPP4+OHTvixo0bsLW1xblz53DgwAH06dMH+/fv10GIRNSsgsvA9leB/z0GnN0E1JQZOiIiasc07rE4evQo9u7dCw8PD4jFYojFYgwePBgrVqzAggULcPr0aV3ESUQtKboGHP6soRcjYADQZUzDn2IOqiYi/dE4sZDL5cpVQt3d3ZGTk4PQ0FAEBgbi4sWLWg+QiDQkrwOuHWh42LoCnUcDXccDzv6GjoyI2gGNE4sePXogOTkZwcHB6N+/P1auXAmJRILVq1cjODhYFzES0d2qKgLO/Nzw8OkJhI4FgocCVjaGjoyIzJTGicWbb76JyspKAMDy5csxYcIE3HPPPXBzc8PGjRu1HiBRe5aZmYmqqioAQFWdAplFNQhwtb67F8s90/A4/DkQHNNwq8S3l/aCJSICIBJuTetog6KiIri4uBhsOfXWSkxMRFRUFE6dOoXevXsbOhwitU6cOIG3334bf/31F26/REUiYEK4K94aF4i+QQ5tP5GzP9B9WsOtEktp21+PiNo9rUx+d3V1NfqkgshUbNq0CYMGDcL27dtxZ94vCMC2s0UYuDIJm04XtP1kJdcbBnz+/CBw/g+ABfCIqI00vhUyderUJpMIkUgEa2trhISE4OGHH0ZoaKhWAiRqT06cOIEZM2ZALpc3SipukSsAEQTMWJOKI6/20k7PRVURcPBj4NzvQPRzgF9U21+TiNoljXssnJycsHfvXiQmJioTjNOnT2Pv3r2QyWTYuHEjevbsicOHD2s9WCJzt3z5cgiCoDapuEUAIEDA8m0Z2g2g6Crw10Jg51tAbYV2X5uI2gWNEwtvb288/PDDuHr1Kn777Tds2rQJaWlpeOSRR9CpUyekpqZi9uzZ+Ne//qWLeInMVmZmJrZu3drq6rVyBfBnShEyi2q0H8y1Aw31MIiINKRxYvHNN9/ghRdegPi2tQnEYjHmz5+P1atXQyQS4bnnnsPZs2e1GiiRuduzZ0+LPRV3EgRg74US3QQUGK2b1yUis6ZxYiGTyXDhwoVG2y9cuKD8pmVtbc3BnEQaKi8vV0nYW0MsAspqdLA+T49pQNBg7b8uEZk9jQdvzpo1C3PmzMHrr7+Ovn37QiQS4cSJE3j33Xfx6KOPAgDi4+PRvXt3rQdLZM4cHByg0HBWhkIAHK21XLJbbAFEztLuaxJRu6FxYvHJJ5/Ay8sLK1euxI0bNwAAXl5eePHFF5XjKkaNGoUxY8ZoN1IiM3fvvfeqrBjcGiIRMLyrs3YD6TapoRQ4EdFdaFOBrLKyhlUUHR0dtRaQLrFAFhm7SZMmYdu2ba0awGkhBsb3cMUfz/bQzsktrICeDwFRjwMa3pIhIrrlrn56yGQy7N69Gz///LNyLEVOTg4qKjg9jagt3nrrLYhEohbHKIkAiCDCm+MC235SCwnQbSIw4weg7xwmFUTUJhrfCsnIyMCYMWOQmZmJ2tpajBw5Eg4ODli5ciVqamrw1Vdf6SJOonahb9++2LhxI2bMmAFBEJrsubAQNyQVv8zt1rbiWNZOQPcpQNgU3vogIq3R+KvJ888/jz59+qC4uBg2Nv+skDh16lTs2bNHq8ERtUfTpk3DkSNHMG7cuEY9FyJRw+2PI6/2wtRI97s7gZMfcM9CYOb/gD5PMKkgIq3SuMfi0KFDOHz4MCQSicr2wMBAZGdnay0wovasb9++2LJlCzIzM9GrVy8UFxfDxdYSSW/2vrvVTUViICAaCJsM+PdryFCIiHRA48RCoVA02T2blZUFBwctrFlAREoBAQGwtbVFcXExbCVizZMKO3eg64SGh72HboIkIrqNxrdCRo4ciU8//VT5XCQSoaKiArGxsRg3bpw2YyOiu+USCAx7A3hoI9DncSYVRKQ3d1XHYtiwYQgLC0NNTQ0efvhhXL58Ge7u7vj55591ESMRtZaDT8O4iZARnN1BRAahcWLh6+uLpKQk/Pzzz0hMTIRCocCcOXMwc+ZMlcGcRKRHtm5A5CMN00YtrAwdDRG1YxonFgBgY2ODJ554Ak888YS24yEiTVjZAL1mAhEPAJZSQ0dDRHR3icWlS5ewf/9+5OfnN1rbYPHixVoJjIha4NkNuDcWcPQxdCREREoaJxZr1qzBM888A3d3d3h7e6vMsxeJREwsiPTBvx8wajl7KYjI6GicWCxfvhzvvPOOcsExItItb29vQFYLb2lNwwbPbkwqiMhoaZxYFBcXY/r06bqIhYiakJCQAFzZDex5G5A6ACPfZlJBREZL4/lo06dPx86dO3URCxG1pN9c1qQgIqOmcY9FSEgI3nrrLRw7dgzh4eGwslKd2rZgwQKtBUdEt7HzAELHGzoKIqJmiQRBEDQ5oGPHjupfTCTC1atX2xyUriQmJiIqKgqnTp1C7969DR0OUetd2Q0UZzQsa05EZMQ07rG4du2aLuIgopZ0jDF0BERELTKpmr8HDhzAxIkT4evrC5FIhN9//93QIRHph8QBcOtk6CiIiFpkUolFZWUlevbsiX//+9+GDoVIv5z8uNQ5EZmEu6q8aShjx47F2LFjDR0Gkf5J7Q0dARFRq5hUjwVRuyXipUpEpsGkeiw0VVtbi9raWuXziooKA0ZD1AZWtoaOgIioVe7qa9DBgwfxyCOPIDo6GtnZ2QCA77//HocOHdJqcG21YsUKODk5KR8xMRxVTyZKbGHoCIiIWkXjxOK3337D6NGjYWNjg9OnTyt7BMrLy/Huu+9qPcC2WLRoEUpLS5WP+Ph4Q4dERERk1jROLJYvX46vvvoKa9asUam6OXDgQCQmJmo1uLaSSqVwdHRUPuztOQCOiIhIlzQeY3Hx4kUMGTKk0XZHR0eUlJRoIya1KioqcOXKFeXza9euISkpCa6urggICNDpuYmIiKhlGvdY+Pj4qPxyv+XQoUMIDg7WSlDqJCQkIDIyEpGRkQCAhQsXIjIyEosXL9bpeYmIiKh1NO6x+L//+z88//zz+PbbbyESiZCTk4OjR4/i5Zdf1vkv+KFDh0LDpU2IiIhIjzROLF599VWUlpZi2LBhqKmpwZAhQyCVSvHyyy/jueee00WMREREZCI0SizkcjkOHTqEl156CW+88QbOnz8PhUKBsLAwDowkIiIizRILCwsLjB49GqmpqXB1dUWfPn10FRcRERGZII0Hb4aHh+Pq1au6iIWIiIhMnMaJxTvvvIOXX34ZW7duRW5uLsrKylQeRERE1H5pPHhzzJgxAIBJkyZBdNsyzoIgQCQSQS6Xay86IiIiMikaJxb79u3TRRxERERkBjROLLiQFxEREalzV8uml5SU4JtvvkFqaipEIhHCwsLwxBNPwMnJSdvxERERkQnRePBmQkICOnXqhE8++QRFRUUoKCjAxx9/jE6dOhndImRERESkXxr3WLz44ouYNGkS1qxZA0vLhsNlMhmefPJJvPDCCzhw4IDWgyQiIiLToHFikZCQoJJUAIClpSVeffVVFswiIiJq5zS+FeLo6IjMzMxG269fvw4HBwetBEVERESmSePEYsaMGZgzZw42btyI69evIysrCxs2bMCTTz6Jhx56SBcxEhERkYnQ+FbIhx9+CJFIhEcffRQymQwAYGVlhWeeeQbvvfee1gMkIiIi0yESBEG4mwOrqqqQlpYGQRAQEhICW1tbbcemdYmJiYiKisKpU6fQu3dvQ4dDRERkdu6qjgUA2NraIjw8XJuxEBERkYlrdWLxxBNPtGq/b7/99q6DISIiItPW6sRi3bp1CAwMRGRkJO7y7gkRERGZuVYnFk8//TQ2bNiAq1ev4oknnsAjjzwCV1dXXcZGREREJqbV002//PJL5Obm4l//+hf+/PNP+Pv744EHHsCOHTvYg0FEREQANKxjIZVK8dBDD2HXrl04f/48unfvjmeffRaBgYGoqKjQVYxERERkIjQukHWLSCSCSCSCIAhQKBTajImIiIhMlEaJRW1tLX7++WeMHDkSoaGhSElJwb///W9kZmbC3t5eVzESERGRiWj14M1nn30WGzZsQEBAAB5//HFs2LABbm5uuoyNiIiITEyrK2+KxWIEBAQgMjISIpFI7X6bNm3SWnDaxsqbREREutXqHotHH3202YSCiIiISKMCWURERETNuetZIURERER3YmJBREREWsPEgoiIiLSGiQURERFpDRMLIiIi0homFkRERKQ1TCyIiIhIa5hYEBERkdYwsSAiIiKtYWJBREREWsPEgoiIiLSGiQURERFpjckkFitWrEDfvn3h4OAAT09PTJkyBRcvXjR0WERERHQbk0ks4uPjMW/ePBw7dgy7du2CTCbDqFGjUFlZaejQiIiI6G+tXjbd0OLi4lSer127Fp6enjh16hSGDBlioKiIiIjodiaTWNyptLQUAODq6qp2n9raWtTW1iqfV1RU6DwuY5Gbm4vc3FxDh0Fa4uPjAx8fH0OHQVrC69P88Bq9jWCCFAqFMHHiRGHw4MHN7hcbGysAUHnExMQIOTk5eorUMGpqaoSYmJhG750P033ExMQINTU1hv6vRVrA69M8H7xG/yESBEGAiZk3bx7++usvHDp0CH5+fmr3u7PHAgCkUimkUqmuQzSosrIyODk5IT4+Hvb29oYOh9qooqICMTExKC0thaOjo6HDoTbi9Wl+eI2qMrlbIfPnz8eWLVtw4MCBZpMKoH0kEc3p1asX/5ObgbKyMkOHQDrA69N88BpVZTKJhSAImD9/PjZv3oz9+/ejY8eOhg6JiIiI7mAyicW8efPw008/4Y8//oCDgwPy8vIAAE5OTrCxsTFwdERERASYUB2LVatWobS0FEOHDlWOvvXx8cHGjRsNHZrRkUqliI2Nbde3gcwJP0/zws/T/PAzVWWSgzeJiIjIOJlMjwUREREZPyYWREREpDVMLIiIiEhrmFgQERGR1jCxINIBkUjU7OOxxx6769cOCgrCp59+2uJ+q1evxtChQ+Ho6AiRSISSkpK7PieROTH09VlUVIT58+cjNDQUtra2CAgIwIIFC5RrYJk6k6ljQWRKbl9gauPGjVi8eDEuXryo3KaP2itVVVUYM2YMxowZg0WLFun8fESmwtDXZ05ODnJycvDhhx8iLCwMGRkZePrpp5GTk4Nff/1Vp+fWC4OuVELUDqxdu1ZwcnJS2bZlyxahd+/eglQqFTp27CgsWbJEqK+vV7bHxsYK/v7+gkQiEXx8fIT58+cLgiA0uXhVS/bt2ycAEIqLi7X5tojMgqGvz1t++eUXQSKRqJzHVLHHgkjPduzYgUceeQSff/457rnnHqSlpeGpp54CAMTGxuLXX3/FJ598gg0bNqB79+7Iy8vDmTNnAACbNm1Cz5498dRTT2Hu3LmGfBtEZslQ1+etBcwsLU3/17LpvwMiE/POO+/gtddew+zZswEAwcHBePvtt/Hqq68iNjYWmZmZ8Pb2xogRI2BlZYWAgAD069cPAODq6goLCws4ODjA29vbkG+DyCwZ4vosLCzE22+/jf/7v//TyXvSNw7eJNKzU6dOYdmyZbC3t1c+5s6di9zcXFRVVWH69Omorq5GcHAw5s6di82bN0Mmkxk6bKJ2Qd/XZ1lZGcaPH4+wsDDExsZq8Z0YDnssiPRMoVBg6dKlmDZtWqM2a2tr+Pv74+LFi9i1axd2796NZ599Fh988AHi4+NhZWVlgIiJ2g99Xp/l5eUYM2YM7O3tsXnzZrO5vplYEOlZ7969cfHiRYSEhKjdx8bGBpMmTcKkSZMwb948dO3aFSkpKejduzckEgnkcrkeIyZqP/R1fZaVlWH06NGQSqXYsmULrK2ttfk2DIqJBZGeLV68GBMmTIC/vz+mT58OsViM5ORkpKSkYPny5Vi3bh3kcjn69+8PW1tbfP/997CxsUFgYCCAhnnyBw4cwIMPPgipVAp3d/cmz5OXl4e8vDxcuXIFAJCSkgIHBwcEBATA1dVVb++XyJTo4/osLy/HqFGjUFVVhR9++AFlZWUoKysDAHh4eMDCwkKv71nrDD0thcjcNTWdLS4uThg4cKBgY2MjODo6Cv369RNWr14tCIIgbN68Wejfv7/g6Ogo2NnZCQMGDBB2796tPPbo0aNCRESEIJVKm53OFhsb22jqGwBh7dq1unibRCbJENfnrSngTT2uXbumq7eqN1w2nYiIiLSGs0KIiIhIa5hYEBERkdYwsSAiIiKtYWJBREREWsPEgsgI7N+/n0ubExkxXqOtx1khREagrq4ORUVF8PLygkgkMnQ4RHQHXqOtx8SCiIiItIa3Qoh0YOjQoZg/fz5eeOEFuLi4wMvLC6tXr0ZlZSUef/xxODg4oFOnTti+fTuAxt2s69atg7OzM3bs2IFu3brB3t4eY8aMQW5urso5XnjhBZXzTpkyBY899pjy+ZdffonOnTvD2toaXl5euP/++3X91olMAq9R3WFiQaQj3333Hdzd3XHixAnMnz8fzzzzDKZPn46BAwciMTERo0ePxqxZs1BVVdXk8VVVVfjwww/x/fff48CBA8jMzMTLL7/c6vMnJCRgwYIFWLZsGS5evIi4uDgMGTJEW2+PyOTxGtUNJhZEOtKzZ0+8+eab6Ny5MxYtWgQbGxu4u7tj7ty56Ny5MxYvXozCwkIkJyc3eXx9fT2++uor9OnTB71798Zzzz2HPXv2tPr8mZmZsLOzw4QJExAYGIjIyEgsWLBAW2+PyOTxGtUNJhZEOhIREaH8u4WFBdzc3BAeHq7c5uXlBQDIz89v8nhbW1t06tRJ+dzHx0ftvk0ZOXIkAgMDERwcjFmzZuHHH39U+82LqD3iNaobTCyIdMTKykrluUgkUtl2a2S5QqFo9fG3j7UWi8W4c+x1fX298u8ODg5ITEzEzz//DB8fHyxevBg9e/bkdDmiv/Ea1Q0mFkQmysPDQ2WgmFwux9mzZ1X2sbS0xIgRI7By5UokJycjPT0de/fu1XeoRO1Se71GLQ0dABHdneHDh2PhwoX466+/0KlTJ3zyyScq33S2bt2Kq1evYsiQIXBxccG2bdugUCgQGhpquKCJ2pH2eo0ysSAyUU888QTOnDmDRx99FJaWlnjxxRcxbNgwZbuzszM2bdqEJUuWoKamBp07d8bPP/+M7t27GzBqovajvV6jLJBFREREWsMxFkRERKQ1TCyIiIhIa5hYEBERkdYwsSAiIiKtYWJBZObuXDyJiIyLuV2jTCyINJCXl4f58+cjODgYUqkU/v7+mDhxokbrA7RGU6si6tLq1asxdOhQODo6mtUPOGp/zPEaLSoqwvz58xEaGgpbW1sEBARgwYIFKC0t1cv5NcU6FkStlJ6ejkGDBsHZ2RkrV65EREQE6uvrsWPHDsybNw8XLlzQazyCIEAul8PSsu2XcVVVFcaMGYMxY8Zg0aJFWoiOSP/M9RrNyclBTk4OPvzwQ4SFhSEjIwNPP/00cnJy8Ouvv2opWi0SiKhVxo4dK3To0EGoqKho1FZcXKz8e0ZGhjBp0iTBzs5OcHBwEKZPny7k5eUp22NjY4WePXsK69evFwIDAwVHR0dhxowZQllZmSAIgjB79mwBgMrj2rVrwr59+wQAQlxcnBAVFSVYWVkJe/fuFWpqaoT58+cLHh4eglQqFQYNGiScOHFCeb5bx90eozqa7EtkbNrDNXrLL7/8IkgkEqG+vl7zfygd460QolYoKipCXFwc5s2bBzs7u0btzs7OABq+oUyZMgVFRUWIj4/Hrl27kJaWhhkzZqjsn5aWht9//x1bt27F1q1bER8fj/feew8A8NlnnyE6Ohpz585Fbm4ucnNz4e/vrzz21VdfxYoVK5CamoqIiAi8+uqr+O233/Ddd98hMTERISEhGD16NIqKinT3D0JkZNrbNVpaWgpHR0et9FhqnaEzGyJTcPz4cQGAsGnTpmb327lzp2BhYSFkZmYqt507d04AoPyGEhsbK9ja2iq//QiCILzyyitC//79lc9jYmKE559/XuW1b32r+f3335XbKioqBCsrK+HHH39UbqurqxN8fX2FlStXqhzHHgsyZ+3lGhUEQSgoKBACAgKEN954o1X76xt7LIhaQfi78v2tZZTVSU1Nhb+/v8q3l7CwMDg7OyM1NVW5LSgoCA4ODsrnPj4+yM/Pb1Usffr0Uf49LS0N9fX1GDRokHKblZUV+vXrp3I+InPXXq7RsrIyjB8/HmFhYYiNjdX4eH1gYkHUCp07d4ZIJGrxB4EgCE3+YLtzu5WVlUq7SCSCQqFoVSy3d/Oq+2GqLg4ic9UertHy8nKMGTMG9vb22Lx5c6MYjQUTC6JWcHV1xejRo/Gf//wHlZWVjdpvTc8MCwtDZmYmrl+/rmw7f/48SktL0a1bt1afTyKRQC6Xt7hfSEgIJBIJDh06pNxWX1+PhIQEjc5HZOrM/RotKyvDqFGjIJFIsGXLFlhbW7f6WH1jYkHUSl9++SXkcjn69euH3377DZcvX0Zqaio+//xzREdHAwBGjBiBiIgIzJw5E4mJiThx4gQeffRRxMTEqHSPtiQoKAjHjx9Heno6CgoK1H5TsrOzwzPPPINXXnkFcXFxOH/+PObOnYuqqirMmTOn1efLy8tDUlISrly5AgBISUlBUlISB4CSSTHXa7S8vByjRo1CZWUlvvnmG5SVlSEvLw95eXmtSm70zlCDO4hMUU5OjjBv3jwhMDBQkEgkQocOHYRJkyYJ+/btU+7T2qlst/vkk0+EwMBA5fOLFy8KAwYMEGxsbBpNZbtzgFd1dbUwf/58wd3d/a6nssXGxjaaPgdAWLt27V38KxEZjjleo7fam3pcu3btLv+ldEckCH/fACIiIiJqI94KISIiIq1hYkFERERaw8SCiIiItIaJBREREWkNEwsiIiLSGiYWREREpDVMLIiIiEhrmFgQERGR1jCxICIiIq1hYkFERERaw8SCiIiItIaJBREREWnN/wMXWXlkdllzwAAAAABJRU5ErkJggg==", - "text/plain": [ - "
" - ] - }, - "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 that of the delta - delta plot\n", - "and 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": [ - "
" - ] - }, - "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": [ - "
" - ] - }, - "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": [ - "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 2 axes. The first one\n", - "contains the rawdata swarmplot; 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": [ - "
" - ] - }, - "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": [ - "
" - ] - }, - "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": "ec7f5271", - "metadata": {}, - "source": [ - "For mini-meta plots, you can hide the weighted avergae plot by setting \n", - "``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": [ - "
" - ] - }, - "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": [ - "
" - ] - }, - "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`` keyword that accepts any 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": [ - "
" - ] - }, - "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(638.7222222222223, 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\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4872a5d1", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "python3", - "language": "python", - "name": "python3" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/nbs/tutorials/forest_plot.ipynb b/nbs/tutorials/forest_plot.ipynb new file mode 100644 index 00000000..f6492b12 --- /dev/null +++ b/nbs/tutorials/forest_plot.ipynb @@ -0,0 +1,805 @@ +{ + "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 | XD, W | XD, M |\n", + "| Placebo | XP, W | XP, M |" + ] + }, + { + "cell_type": "markdown", + "id": "c75e54ab", + "metadata": {}, + "source": [ + "| | Wildtype | Mutant |\n", + "|-------|---------|----------|\n", + "| Drug2 | XD, W | XD, M |\n", + "| Placebo | XP, W | XP, M |" + ] + }, + { + "cell_type": "markdown", + "id": "e1b09711", + "metadata": {}, + "source": [ + "| | Wildtype | Mutant |\n", + "|-------|---------|----------|\n", + "| Drug3 | XD, W | XD, M |\n", + "| Placebo | XP, W | XP, M |" + ] + }, + { + "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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", 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", 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", 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "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": [ + "
" + ] + }, + "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": [ + "
" + ] + }, + "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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pKSk4ffp0LS6TF1y9ehpZWak2r+nGDeCLL9i7PdkfA4zIid24ccOq8XNzM21UiaX8fODrr4HCQrs0TwSAAUbk1Hx9fa0a39PTz0aVlJWVBVy4YLfmiRhgRM6sfv36iIiIqMV5LAUhIRHw8bHviSpeVk/2xAAjcmKKomDGjBm1GnfgwKdsfgFHaS1bAg0b2q15Il6FSOTs6sp9YGZubiVPde7YETBwE5nsiIsXkZMLCAjAhg0boCgKDFUkhrknjqlTN9o8vNzdgU6dgPHjgc6dGV5kf9wDI9KJ6vaFOHXqRrRrN9hm0/X0LNnbateO57zIsRhgRDqSnp6OVatW4fXXX8eFUpcABgQ0xpAhc9Gr1yR4efnbZFpubiV7XB07lux9ETkaA4xIh/bu3Yt+/fqpv//1r3sQGdnXZu23bAn07FnS6waRVvgABCIduvnqQltdbRgcDPToAYSF2aQ5IqswwIioSoGBQLduJc/+suOV90Q1wgAjogoFBZVcEs/gorqIAUZEZQQEALfdBjRvzuCiuosBRkQqk6kkuNq04X1cVPcxwIgIQMmVhX36AF5eWldCVD0MMCIX5+YGREUBt9yidSVENcMAI3Jhvr7AkCFA/fpaV0JUcwwwIhcVHAwMHQp4e2tdCVHtMMCIXFCDBsDdd7PvQnJuvM6IyMUEBzO8SB8YYEQuxNe35LAhw4v0gJ35EulQRkYG4uLikJQEHD4MhIV1gJ+fP0aO5AUbpB8MMCIdO3UK2Lmz5Oe+fYG2bbWth8iWXOYQ4r/+9S80b94cnp6e6NGjB3766SetSyJymODgkt41iPTEJQJs3bp1mD17Nl588UUcPnwYnTp1wpAhQ3DlyhWtSyNyiPbt2ach6Y9LBNibb76JKVOm4M9//jPatWuHf//73/D29sayZcu0Lo3I7ozGkk55ifRG9wGWn5+PQ4cOYdCgQep7BoMBgwYNwg8//FDuOHl5ebh+/brFKy8vz1ElE9lU48a86pD0SfcBdu3aNRQVFaFBgwYW7zdo0ACXLl0qd5yYmBj4+/tbvGJiYhxRLpHN8enJpFfsiaMc0dHRmD17tsV7JpNJo2qIrBMaqnUFRPah+wALDg6G0WjE5cuXLd6/fPkyGjZsWO44JpOJgUW6ERCgdQVE9qH7Q4geHh7o1q0bduzYob5XXFyMHTt2oFevXhpWRmR/7u6Ap6fWVRDZh+73wABg9uzZmDRpErp3747bb78db7/9NrKysvDnP/9Z69KI7IoPpyQ9c4kAGzt2LK5evYoXXngBly5dQufOnbF9+/YyF3YQ6Y2bS/yFk6tiV1JEOpaeznNgpF+6PwdG5MoM/AsnHePiTaRjRqPWFRDZDwOMSMcYYKRnDDAiHeMhRNIzLt5EOsYAIz3j4k2kYwww0jMu3kQ6xgAjPePi7QLy8/O1LoGIyOYYYERE5JQYYC6Ana0QkR4xwFwAA4yI9IgB5gJEBEVFRVqXQURkUwwwF1FYWKh1CURENsUAcxG8EpGI9IYB5iIYYESkNwwwF5GTk6N1CURENsUAcxGZmZlal0BEZFMMMBeRnZ2N7OxsrcsgIrIZBpgLSUpK0roEIiKbYYC5kMzMTFy5ckXrMoiIbMJN6wLIfjIyMhAXF4e8vDycPXsWrVq1QlJSEnx9feHt7a11eUREVlGE/Qzp1r59+9C3b1/196VLl6Jz587w8PBAmzZt4O7urmF1RETW4SFEF5Sfn4/Tp0+juLhY61KIiGqNAeaisrKycOrUKfaRSEROiwHmwjIzM3Hy5En2k0hETokB5uKysrJw/Phx5OXlaV0KEVGNMMAIubm5SEhIwI0bN7QuhYio2hhgBKDkcSsnTpzApUuX+ABMInIKDDBSiQiSk5ORkJCArKwsrcshIqoUA4zKyM7ORkJCAhITE/kYFiKqs9gTB1UoNTUVaWlpCA0NRaNGjWA0GrUuiYhIxT0wqpSI4PLly4iPj8e1a9d4foyI6gwGGFVLYWEhzp07x/NjRFRnMMCoRsznx86ePYuCggKtyyEiF8YA07GbD/fZ8vBfSkoKfv31V1y7ds1mbRIR1YSuA+zs2bN49NFH0aJFC3h5eSEiIgIvvvii7q+sS09Px6JFizBu3DiL95977jl88sknyMzMtMl0ioqKcO7cOZw6dYrdURGRw9nlcSpJSUm4dOkSsrOzcdttt8HLy8vWk6iW7du3Y926dRg/fjxatWqF+Ph4TJkyBRMnTsTChQs1qcneYmNjcf/99yM7O7vcPS5FUeDp6Yk33ngDvXr1stl03d3d0apVKz5njIgcxmYBlpmZiTfeeAMrVqzAhQsX1Pfj4uLQrl079fe1a9di48aN8Pf3x4cffmiLSdfIggULsGTJEpw5c8bh07a32NhYDB8+HCJS6aNSFEWBoihYtGiRTUPMaDSiVatW8PX1tVmbREQVsUmAnTx5EsOGDcOZM2cstvoVRSkTYOYnA4sIdu/ejaioKGsnXyPPP/88tm/fjoMHD1Y4TF5eXpnObU0mE0wmk73Lq7X09HSEh4cjJyenWs/5Mu+Jffnll/Dz87NZHQaDAZGRkQwxIrI7q8+B5ebmYvjw4Th9+jS8vb3xt7/9DVu3bq1w+ObNm2PAgAEAgC+++MLaydfIqVOnsHjxYjz++OOVDhcTEwN/f3+LV0xMjIOqrJ2VK1ciOzu72g+pFBHk5uZi27ZtNq2juLgYJ0+eREZGhk3bJSK6mdUBtmTJEpw6dQo+Pj7Yu3cvXnvtNQwbNqzSce6++26ICH744YdaTfPZZ59VD4NV9EpISLAYJzk5GUOHDsWYMWMwZcqUStuPjo5GRkaGxSs6OrpWtTqCiGDx4sW1Gnft2rU2vzm5uLgYp06dwtWrV23aLhFRaVZ3JbVx40YoioKZM2eic+fO1RqnU6dOAEoOPdbGnDlzMHny5EqHadmypfrzhQsXMGDAAPTu3RsffPBBle3X9cOFN0tJScHp06drPJ6IICkpCRkZGQgICLB5XefPn0dxcTEaNGhg87aJiKwOsN9//x0AMHjw4GqPU79+fQAl521qIyQkBCEhIdUaNjk5GQMGDEC3bt2wfPlyGAz6u3PA2ud4ZWdn2yXAgJIrUv38/Hh1IhHZnNUBZl551uSkvfkCCXd3d2snX6nk5GT0798fzZo1w8KFCy0OaTVs2NCu03Ykay+YsHe4pKenM8CIyOas3h0x702dPXu22uP8+uuvAOwfIt988w1OnTqFHTt2IDw8HI0aNVJfelK/fn1ERERAUZQajacoCsLDw+Hv72+nyko40+FYInIeVgdY165dAQB79uyp9jirVq2Coig2vQepPJMnT4aIlPvSE0VRMGPGjFqNO27cuBoHX034+/sjKCjIbu0TkeuyOsAeeOABiAg++OADnD9/vsrh3377bTXsxo8fb+3k6b8mTZoEb2/vap/jMxgM8PT0xPDhw+1Sj9FoRJMmTWq1Z0hEVB1WB9jEiRPRsWNH5Obmon///vjqq6/K3MwsIvj5558xYcIEzJkzB4qioG/fvrj77rutnTz9V0BAADZs2ABFUaoMMXOgLFiwwKY3MQOAm5sbGjdujA4dOiA0NJThRUR2Y5OeOM6fP4+oqCgkJSVBURR4e3sjOzsbABAcHIzMzEz1wg0RQUREBPbv34/Q0FBrJ003qW5fiAsWLEDPnj1tNl13d3c0bNgQwcHBurzSk4jqHpv1hZiamooZM2Zg/fr1KCoqKn9iioIxY8ZgyZIlCAwMtMVkqRzp6elYtWoVXn/9dYt+KUNCQjBp0iSMGDHCZl09GY1GNGrUCCEhIQwuInIom/dGf+7cOWzbtg0HDx7ElStXUFRUhPr166NLly645557cMstt9hyclSJvXv3ol+/furvH374Ibp06WKz9oOCghAeHm732yGIiMpj9X1gN2vWrBmefPJJWzdLtXDz+SdbnY/y9fVFWFgYO+wlIk3ZPMBIv3x8fNCoUSO73zdGRFQdDDCqko+PDxo3box69eppXQoRkcrqAKvJDczlKX2OhuoWb29vhIWFMbiIqE6yOsD69+9f63MriqKgsLDQ2hLIxtzc3BAeHo6goCDex0VEdZZNDiHqrWsmVxYUFIQmTZrAzY1Hl4mobrN6LbVr164qh8nKysKJEyewdu1a/PTTT+jTpw9efvllGI1GaydPNmIwGNC0aVO1c2YiorrO6gC74447qjXcsGHDMGvWLCxYsABz587FsmXL8PHHH1s7ebIBd3d3tGrVio88ISKn4vCuE5555hncd999+OSTT7B27VpHT55u4uXlhbZt2zK8iMjpaNL3z8MPP6z2YE/a8fb2xi233MKeNIjIKWkSYE2bNgUAxMXFaTF5AuDp6YnIyEherEFETkuTALt8+TKAkos7yPHc3NzQqlUrhhcROTVN1mD/+te/APxvT4zso0OHDti7dy/y8vJw9uxZtGrVCoqiICIiAiaTSevyiIis4rAAS0tLw8GDB/HWW29h+/btUBQF9913n6Mm75L8/f0RFRWFnJwcBAQEAAAaNWrETniJSBesDrDa3ssVGRmJuXPnWjt5qgF3d3c0aNBA6zKIiGzC6nNgIlKjl9FoxPjx47Fnzx72au5gfFoyEemJ1XtgL774YpXDGAwG+Pn5oUWLFujduzdCQkKsnSzVQlBQkNYlEBHZjM2fyEx1T05ODk6fPo327dtrXQoRkc3weJKL8PPz07oEIiKbYoC5CB8fH61LICKyKQaYi/D09NS6BCIim6r2RRyvvPKKXQp44YUX7NIuWfLw8NC6BCIim6r2RRwGg8EuT+ctKiqyeZtkKScnByaTiZfQE5Gu1Ogyel6w6LwYXkSkN9UOsOLiYnvWQXZkjz1nIiKtcbPcBTDAiEiPGGAugIcPiUiPuGZzAdwDIyI9YoC5AAYYEemRTZ8HtmvXLmzatAnHjh3DtWvXkJOTU+mVi4qi4PTp07YsgcrBACMiPbJJgF25cgXjxo3D7t27AVR8ub2iKBafccXqGPx/JiI9sjrACgoKcPfdd+Po0aMQEXTu3BlhYWHYtm0bFEXBQw89hNTUVBw+fBgXL16Eoijo2rWrw3tGz8vLQ48ePXDs2DEcOXIEnTt3duj0tcQAIyI9svoc2IoVK3DkyBEAwPLly3H48GG89tpr6ucrV67Eli1bkJycjI0bN6JRo0b47bffMGLECCxfvtzayVfb3/72NzRu3Nhh0yMiIvuyOsA2bNgAABg6dCgmTZpU6bCjRo3C7t274eHhgcmTJ+PkyZPWTr5avvrqK3z99ddYuHChQ6ZHRET2Z3WAHTt2TD1UWJ6bz4dFRERg5syZyMrKwqJFi6ydfJUuX76MKVOm4KOPPoK3t3e1xsnLy8P169ctXnl5eXaulIiIasLqAEtNTQUAtGjRQn2vdM/n2dnZZca58847AQDffPONtZOvlIhg8uTJmDp1Krp3717t8WJiYuDv72/xiomJsWOlRERUU1YHmDmsSodWvXr11J+Tk5PLjGN+NlV5n1XHs88+C0VRKn0lJCRg8eLFyMzMRHR0dI3aj46ORkZGhsWrpm0QEZF9WX0VYtOmTZGQkIDLly+r7zVo0AB+fn64ceMGfvzxR9xyyy0W48THxwOo/dVxc+bMweTJkysdpmXLlti5cyd++OEHmEwmi8+6d++OCRMmYOXKleWOazKZyoxDRER1S7WfB1aRiRMnYs2aNZg3bx6ee+459f177rkH27ZtQ9euXbF//341ENLT09GzZ0+cPHkS3bt3x48//mjdN6jE+fPncf36dfX3CxcuYMiQIfjss8/Qo0cPhIeH223aRERkX1YfQrzzzjshIti2bZvF+1OnTgUAHDlyBB07dsQzzzyDJ598Eh06dMCJEycAAA8//LC1k69U06ZN0b59e/Vl3hOMiIhgeBEROTmr98DS09PRuXNniAh27tyJiIgI9bPHHnsMy5YtK5nQfw8Xmic3ZMgQbNu2zaE9pZ89exYtWrRwuRuZiYj0yOoAq8p//vMfLF26FL/++isKCwsRGRmJhx9+GDNnzoSbm027YiQiIhdi9wAjIiKyB6uP3/373/9W7wUjIiJyFKv3wAwGA9zd3TFkyBBMmDABI0eOVO/zIiIishebBBjwv4s0fH19MWrUKEyYMAGDBg3i4+yJiMgurA6wAwcOYPXq1fj0009x5cqVkkb/G2ahoaEYN24c/vSnP+G2226zvloiIqL/stlFHEVFRfj222+xevVqbNq0CTdu3CiZwH/DLCIiAg899BD+9Kc/oVWrVraYJBERuTC7XIWYm5uLL774AqtXr0ZsbCzy8/NLJvbfMOvevTseeughjB07FqGhobaePBERuQC7X0aflpaGzz77DKtXr8bevXvVG5kVRYGbmxsfU0JERLXi0PvAkpOTsWbNGsTExCA9PR2KoqCoqMhRkyciIh1xWFcY8fHxWL16NT755BNkZGQ4arJERKRTdg2w8+fP45NPPsGaNWvUR6iYd/i8vLxwzz332HPyRESkYzYPsLS0NKxfvx6rV6/G999/DxFRQ8toNGLgwIGYMGEC7rvvPvj6+tp68kRE5CJsEmA5OTnYvHkz1qxZg6+//hoFBQUA/re3ZX6A5Lhx49CgQQNbTJKIiFyc1QE2ceJEbN68GVlZWQD+F1oRERGYMGECJkyYgMjISGsnQ0REZMFmXUkBJT1vjB07FhMmTMDtt99udXFEREQVsXoPzMfHB6NHj1b7PjQajbaoi4iIqFJW74Hl5OTAy8vLVvUQERFVCx9oSURETonPOiEiIqfEACMiIqfEACMiIqfEACMiIqfEACMiIqfEACMiIqfEACMiIqfEACMiIqfEACMiIqfEACMiIqfEACMiIqfEACMiIqfEACMiIqfEACMiIqfEACMiIqfEACMiIqfEACMiIqfkEgG2bds29OjRA15eXggMDMSoUaO0LomIiKzkpnUB9rZhwwZMmTIF8+fPx8CBA1FYWIj4+HityyIiIispIiJaF2EvhYWFaN68OV5++WU8+uijWpdDREQ2pOtDiIcPH0ZycjIMBgO6dOmCRo0a4e67765yDywvLw/Xr1+3eOXl5TmoaiIiqg5dB9iZM2cAAC+99BKef/55bN26FYGBgejfvz9SU1MrHC8mJgb+/v4Wr5iYGEeVTURE1eCUAfbss89CUZRKXwkJCSguLgYA/P3vf8f999+Pbt26Yfny5VAUBZ9++mmF7UdHRyMjI8PiFR0d7aivR0RE1eCUF3HMmTMHkydPrnSYli1b4uLFiwCAdu3aqe+bTCa0bNkS58+fr3Bck8kEk8lkk1qJiMg+nDLAQkJCEBISUuVw3bp1g8lkwvHjxxEVFQUAKCgowNmzZ9GsWTN7l0lERHbklAFWXfXq1cPUqVPx4osvokmTJmjWrBkWLFgAABgzZozG1RERkTV0HWAAsGDBAri5uWHixInIyclBjx49sHPnTgQGBmpdGhGR3WQXZMPb3VvrMuxK1/eBERG5qhv5N+Dr4at1GXbllFchEhFR5Vxh34QBRkSkQ0VSpHUJdscAIyLSofyifK1LsDsGGBGRDuUW5mpdgt0xwIiIdCgrP0vrEuyOAUZEpENZBVkolmKty7ArBhgRkQ4VSzHSc9O1LsOuGGBERDp1IfOC1iXYFQOMiEinjl87rnUJdsUAIyLSqZScFJxMOal1GXbDACMi0rHv//het1ckMsCIiHQkIyMD+/btw9GfjuLU0VNIz0jHjsQdurwiUfe90RMRuZK4uDj07dtX/f2Zpc/Ay9cLB5IOoHeT3hpWZnvcAyMicgHxV+Lx65VftS7DphhgREQuYv8f+5FwLUHrMmyGAUZE5EL2nNuDXy7/onUZNsEAIyJyMQeSDuBA0gGnf2YYA4yIyAX9cvkX7EjcgYKiAq1LqTUGGBGRizqTdgafJ3yOa9nXtC6lVhhgREQuLD03HZsSNuFA0gGn2xvjfWBERC6uWIrxy+VfcDLlJLo37o7Wwa1hUOr+/k3dr5CIiBwipzAHe8/vxYbfNuCPjD+0LqdKDDAiIrKQlpuGr059hS9PflmnnynGACMionIlXU/CZ799hgNJB5BflK91OWUwwIiIqELm82Prf11f5x7NwgAjItKRm29OttXNytkF2dh1dhe2ntiKjNwMm7RpLUWc/VZsIiJCeno6Vq5ciTfeeAMXLlxQ3w8ICcDghwej14he8Pbztsm03Axu6BneE+1C2tmkvdpigBERObnY2Fjcf//9yM7OLn+PSwFMniY8/sbjuLXXrTabbmRQJPo16wejwWizNmuCAUZE5MRiY2MxfPhwiAiKiyt+aKWiKFAUBdMXTbdpiIXXC8fgiMFwMzj+tmIGGBGRk0pPT0d4eDhycnIqDS8zRVHg4emB1758zWaHEwEgzC8MQ1oNcXiI8SIOIiIntXLlSmRnZ1crvICSCzrycvNwYNsBm9aRnJmML09+ibzCPJu2WxUGGBGRExIRLF68uFbj7ly70+aPUrl04xI2JWzC9bzrNm23MgwwIiInlJKSgtOnT9c8iAS4mnQVWRlZNq8pIy8DXxz/wmGX2TPAiIic0I0bN6waPzc710aVWMouyMaOxB12aftmDDAiIifk6+tr1fie3p42qqSsa9nXkJmXabf2zRhgREROqH79+oiIiICiKDUbUQFCwkPg4+9jn8IAGBUjTG4mu7VvpvsAO3HiBEaOHIng4GDUq1cPUVFR2LVrl9ZlERFZRVEUzJgxo1bjDhw3sObBVwO9m/SGh9HDbu2b6T7ARowYgcLCQuzcuROHDh1Cp06dMGLECFy6dEnr0oiIrDJp0iR4e3vDYKjeqlwxKDB5mtBzeE+71BPgGYARt4xA25C2dmn/Zrq+kfnatWsICQnBnj170LdvXwBAZmYm6tWrh2+++QaDBg3SuEIiIuvUtCeOGe/MQLuetu3DMMgrCF0adkHLwJZ23bO7meP7/nCg+vXro3Xr1li1ahW6du0Kk8mE999/H6GhoejWrVuF4+Xl5SEvz/KGPJPJBJPJ/sd0iYhqYsiQIdi2bVuVfSF6eHpg6oKpNg2vBj4N0LVRVzTxb2KzNmtC14cQFUXBt99+iyNHjsDPzw+enp548803sX37dgQGBlY4XkxMDPz9/S1eMTExDqyciKj6hgwZgqSkJLz99tto3LixxWcBIQEYO2csXv/qdZuFV4BnAIa2GoqRbUZqFl6Akx5CfPbZZ/H6669XOszvv/+O1q1bY9SoUSgoKMDf//53eHl5YenSpfjiiy/w888/o1GjRuWOyz0wInJWe/fuRb9+/dTf//rhXxHZJdImbbsb3NG1UVd0aNABBkX7/R+nDLCrV68iJSWl0mFatmyJvXv3YvDgwUhLS0O9evXUzyIjI/Hoo4/i2WeftXepREQOtW/fPvWcPwA8s/QZtOrcyqo2FShoHdwa3Rt3h7e77ToBtpZTngMLCQlBSEhIlcNlZ2cDQJkrdAwGQ7U7vyQiclUKFEQERaBro64I8AzQupwynDLAqqtXr14IDAzEpEmT8MILL8DLywsffvghEhMTMXz4cK3LIyKqkxQoiKwfiS4Nu8Df01/rciqk6wALDg7G9u3b8fe//x0DBw5EQUEBbr31VmzevBmdOnXSujwiojonIjAC3Rt3r9PBZabrAAOA7t27IzY2VusyiIjqtBDvEPRp2gehPqFal1Jtug8wIiKqmFEx4vaw29E+tL1Db0K2BQYYEZGLCvAMwKCWgxDkFaR1KbXCACMickFN/ZtiYIuBDul0114YYERELqZtcFv0adqnTtyMbA0GGBGRC+nUoBN6hPfQugybcO74JSKiamsX0k434QUwwIiIXEJT/6bo06SP1mXYlFP2hUhEROXLyMhAXFwcfrv6G06lnkJYqzA0Dm6MUW1Gwd3ornV5NsVzYEREOuLv74+oqCh4JHsAlwCDYsCdLe/UXXgBPIRIRKRrbYPbOu19XlVhgBER6ditobdqXYLdMMCIiHQq2Du4Tj4GxVYYYEREOtXMv5nWJdgVA4yISKfC6oVpXYJdMcCIiHSqvld9rUuwKwYYEZEO+Xr46vLS+dIYYEREOuTn4ad1CXbHACMi0iFPN0+tS7A7BhgRkQ65GfTf0RIDjIhIhxRF0boEu2OAERHpkLM/rLI69P8NiYhckFExal2C3THAiIh0yGhggBERkRPiIUQiInJKDDAiInJKPAdGREROiZfRExER1VEMMCIickoMMCIickoMMCIickoMMCIickoMMCIickoMMCIickoMMCIickoMMCIickoMMCIickoMMCIickoMMCIickoMMJ3Ly8vDSy+9hLy8PK1LIQfjvHddrjLvFRERrYsg+7l+/Tr8/f2RkZGBevXqaV0OORDnvetylXnPPTAiInJKDDAiInJKDDAiInJKDDCdM5lMePHFF2EymbQuhRyM8951ucq850UcRETklLgHRkRETokBRkRETokBRkRETokBRkRETokBRkRETokBRkRETokBRkRETokBRkRETokBRkQVKt3PQVFRkYaVEJXFAKNqKy4u1roEcjBFUdSfb+60h5346FtF87cuzXd2JUVVSkpKgp+fH/z9/VFcXAyDgds9ruDKlSs4ePAgNm7ciKSkJKSlpWHkyJHo3r07Bg8eDKBkZVY65Eg/zBusv/76KwAgMDAQ4eHhAOrOfGeAUaV++eUXDB06FL169cKyZcsYYi7ixIkTePLJJ/Hbb7/h0qVLFp+1bNkSjzzyCJ577jkAdWdlRrZz/vx5LFu2DF9//TWOHTuGnJwctG7dGoMHD8aiRYsA1I35zgCjCv3222/o1asXMjMzERAQgHvuuQeLFi1CQEAAQ0zHfvvtN9x11124ePEigoODUa9ePQQHByM7Oxvx8fFQFAWenp546aWX8Mwzz2hdLtnY77//jgkTJiA+Ph6FhYVwc3Oz+Pehhx7CqlWrtC6zhBCVIzk5WXr37i2KooiHh4coiiIBAQHy8MMPS1pamoiIFBUVaVsk2dzJkyeladOmoiiKDBgwQL788ktJT08XEZGsrCyZO3euKIoiBoNBunTpIgcPHtS4YrKl3377TUJCQkRRFGnbtq089NBD8vnnn8tzzz0nHTt2FIPBIIqiyPz587UuVUREGGBURnZ2tsyfP1+Cg4PFZDLJgAEDRFEUURRFAgMDGWI6deXKFRk5cqQoiiJRUVESHx+vflZ6Pj/11FPq8vDhhx9qUSrZwalTp6RNmzbqxsuePXskMzNT/fyTTz6RwMBAURRFxowZI8XFxRpWW4IBRmX88ssvcvvtt4uiKNKkSRPJzMyUHTt2MMR0yrwiWrNmjTRq1EjCwsLk008/lcLCQovhzL+fP39e2rVrJ4qiyKBBg6SoqKjMsORcLl26JMOGDRNFUaR3795y7Ngx9bOCggL1Z/MGTrdu3SzCTSs8iUFlvPnmm/j5559hMpmwdOlS+Pr6YuDAgdi8eTMAID09HVu2bMHMmTORnp4Og8HAS+ydmPlE/IoVK3Dp0iW0bdsWd9xxB4xGo8Vw5t9DQ0MRHBwMALh48SKKi4vLDEvOIz8/H2vXrsW+ffsQGRmJZ599Frfeeqv6uZubGwAgNTUV58+fBwBERUXB19cXRUVFSE1N1aRugPeBUTmGDBmC0NBQPPXUU7jjjjtQVFSE4uJi3HPPPfjiiy8AMMT0Zvfu3Thw4ADc3d0RHR2NkJCQcocrLi6GyWTCgAEDYDAYkJWVhZSUFAdXS7aUkpKCFStWIDMzExMnTkT//v0tNkjMN7CfOnUKaWlp8PPzg7u7OyZPnoz+/ftj0KBBmDBhArZu3YqMjAzHFq/1LiDVTT/++KP8/PPP6u/FxcXqoaYtW7bwcKIOvfLKKzJgwAA5e/ZslcO+8847oiiK+Pv7y6lTpxxQHdlTdHS0hIWFyenTpy3eNx8+zM/PV8+Fmy/qql+/vroeUBRF2rRpI88995y6LnAEBhhZqOzEbE1DrC6c5KWqld7o2LNnT7XGWbFihSiKIvXq1ZMTJ07YqzRykKKiIrl48aKI/O/vtvTf74gRI0RRFAkODpa5c+fK1q1bJTU1Vb766it5+eWXxWg0iqIo0qpVK3nvvfcszpvZEwOMaqS6IVZ6AU5KSpKcnBwtyqVqunnPuaqNj9jYWPHw8JCgoCD55ZdfqmyTe+bOa/To0aIoijRu3Fg2btwoGRkZFp8XFhbKqlWr1BDr16+fw/7eeQ6MasR8wl9EMGLEiHLPiaWmpqonfn///Xf84x//wMSJE5GWlqZZ3VS5m29Kr6qHBaPRiIKCAuTm5iInJ6fM50VFRVi0aBEee+yxctsn55Cbm4vbbrsNTZs2xZIlSzB69GjUq1cPwP+6mjIajXjggQcwbdo0GAwG7N27F1u3bnVIfVyqCEDZjnoruyCjqhCbPXs2MjMzkZSUhH//+99YsWIFNmzYoF7FSHVHTeZ7afn5+erwN4ddUVERli1bhjlz5mDZsmVYt26dbYolh/P09MRTTz2FvXv34t5777X4rPRGiZeXF7p27aouP9evX3dIfQwwQlFREQwGAy5evIjt27cDKFk4pZJexioLsS+++AITJ07EvHnzsHjxYgDAU089hcmTJ9v3i1CN1Ga+mwUEBMDDwwOKoljsgRUVFWHp0qV4/PHHAQAzZ87E2LFj7fMFyCrV3Xjx8fFBkyZNKmynsLAQANChQwf19gofHx8bVVk5N4dMheo0o9GIxMRE9O3bF/7+/sjPz8e9994LRVEq7bDT/Lk5xLZs2YJ77rkH6enp+Pbbb5GdnQ2gJLzefvttAFD7VCPt1Xa+A4C3tzfc3d2Rk5OjzueCggIsW7YMTzzxBICS8HrrrbcAcL7XNUVFRTAajbh48SKOHTuGoUOHqhsvNe2g1zxfd+3ahWvXrgEAIiMjbV5zuRxypo3qLPPJ9fnz54uiKGIymaRXr16yceNGdZiqTuiX7oXhvffeU/tLUxRFZsyYUe5wpC1r5/uPP/4oHh4e4ubmJrGxsSIi8v7776vzfdasWeqwnO9105kzZyQsLEzatWsnmzdvVt+vzdXDiYmJMmLECDEYDDJp0iTJy8tzyFXIDDAXZV6pmP/Nzc2Vxx9/XHx8fMRgMEjv3r1rFGIiIvHx8RIdHa2uxKZPn15meqQtW833o0ePiq+vr7i5ucnmzZvlo48+Yng5CVtstJaWl5cnCxYsEEVRxNfXV9auXWvzmivCAHNB5pXKhQsX5JlnnpEePXpIcHCwtGjRQl0JGY1GiYqKqvZCHR8fb9HJ61NPPVVmeqQtW873M2fOSP369cVoNMrAgQMZXk7AVhsvpW+JSElJkcWLF6vz/6WXXqpyfFtigLkY88J37tw5tUPWsLAw6dOnj8yePVuioqKkbdu2NVqZXb58WX3MhqIoMnPmTPUzrsTqBlvP95MnT0pgYKDF4WKGV91lj43W3bt3W2y0zpkzR/3MUff9McBc0MWLF6Vz586iKIr06NFDvvvuO/WZT5mZmRIXFycTJ05Un/vUp0+fShfqa9euydNPPy2KosiTTz6pvs+VWN1iy/mekJAgzZs3VwOMh4vrLltuvOTm5sr3338vM2bMkGbNmonBYBB3d3eZN2+eOowj5z8DzIWYF8SFCxeKu7u7tGzZUnbv3q1+fvNW01//+tdqb5klJCTI559/rv7OlVjdYa/5PmXKFIaXk7Dlxov5kKH5sOPSpUvVzxw9/xlgLuj+++9XF+SUlJQyn5deCM17VjU5vMCVWN1kq/men5+v/lz6hD3ne91jy42X0vP35Zdflvfff18OHz5cYVuOwABzIUVFRZKRkaFuiU2dOrXCYUsvrGPGjLHY4qrt1UqkDXvM97y8vArHo7rHVhsvN893M63WA+yJw4UYDAbUq1cPDRs2BFDyfJ+cnBz1eT+lGY1G9f0pU6YgNDQUIoIDBw7gn//8J7788ksAVfeZR9qzx3z38PAoMx7VPcXFxbh+/TpOnz4NAOjSpQuCgoLKDFd6vr/55pt44IEHUFxcjO+//x4LFy7E559/DqBkvpe33Gi1HmCA6ZhU0CWQeUV29OhR/PHHHzAajeV2I2NeKXXq1AkeHh5wd3eHh4cHfvjhB7z33ns4cOCA/YqnWuN8JzN7bLzUpY0VBpgOmfsmM/9rXijNK7a//OUvaNq0KVJSUjBjxgxcvXq10icqh4aGomnTphg8eDBmzJgBEUFsbCw2bdqEvLy8avWdR/bH+U4ut/GiyYFLshvzsegTJ05IRESEHDhwQEQsT7BeuHBBRo4cqT6gbubMmepx8fJOxJp7Xejbt6+IiPzlL39Rj5F/++239v5KVA2c766t9JOTRf53Tsu8XHz//ffSrFkzURRFBg8eLFeuXBGRyi+86NOnj4wYMUKeeeYZURRF3NzcZO7cuZKbm1tnzn0zwHQoIyND+vbtK4qiSEBAgBw6dEhEShZq84J38OBB9ZHg4eHhMm3aNLl8+bI6nFlubq7Mnz9fDAaDPP300yIicuXKFenVq5coiiLjx4+3aJe0w/numlx544UBpkPp6enyyiuvSIcOHURRFPHx8VFXZgUFBeoCu3v3bjGZTKIoivj5+UmPHj3kxx9/lD/++ENESlZ2r7/+unqn/aeffioiIjk5OTJq1ChRFEW6dOlS4ZVJ5Fic767LVTdeGGA6lZaWJgsXLpQ2bdqUuzIzL7B79uyR8PBwcXd3F0VRxN/fX5o0aSJ9+vSR4OBgdSX23HPPWbQ/b948MRqN0qFDB0lLS3P016MKcL67JlfdeGGA6ZB5yyg1NbVaK7OjR4/K5MmTpVWrVuqCa34FBwfL888/b9F+SkqK9OvXTxRFkeHDhzv2y1GFON9dmytuvDDAdKq6KzPzlllaWpokJibKK6+8IrNmzZLx48fLO++8Izt27FDbNA+7fft2ad68ufj5+cmSJUsspkfa4nx3Ta668cIAc1I3X2VUXk8I1V2oa7IS+u233+SOO+5Q7+pPTk629qtQDXC+U0VcceOFAeaEzAtVYmKiTJ8+Xa5du2bxfmmVLdQHDx4UkfJXgjevKNPT02Xr1q3Sp08fURRFGjVqJCdOnLD9l6MKcb67Nm68lMUAc1IXLlyQsLAwURRFJk+eLKmpqSJS9crspZdekkaNGomiKOLt7a2uzCq7H+TLL7+Uu+++W1q2bCmKositt94qCQkJdvhWVBXOd9fEjZfyMcCcSOmtpsOHD0u7du3E09NTvLy8ZOLEidVamSUnJ6uXw968ZVbeQl1QUCCvvvqqemz8vvvukzNnztjj61EFON9JhBsv5WGAOQnzSubatWuSlZUlIiI//fSTREVFidForNHK7NNPP1VXTOaVmfnmx4qsXr1aNm7cWG5P1mQ/nO+ujRsvlWOAOQHzwnn27Flp3bq13HfffXLjxg0RKblvo7orM/MC/c0334jBYJAHH3xQOnbsKIqiqDcsUt3B+e7auPFSNQaYk7h06ZJ07dpVFEWRbt26yfr169UF9tChQ9KnT59qL9TLly8Xo9EoH3/8sbz66qvy8ssvO/S7UPVxvrsmbrxUDwOsjisuLpbCwkL5xz/+IYqiyG233Sb79++XjIwMi+EOHTpUZqE2bzmVvms+LS1NHn74YVEUReLj4y0WeHOHoKQ9znfixkvVGGB11M3HpgcOHCgBAQGya9cui/dLHyMvvTIzmUwyevRotddpkZKrit59910xGAzSqVMnuXDhQrntkHY434kbL9XHAKuDSh8+eOutt2TPnj3SsGFD6dWrl2RmZlY67uHDh+WOO+5Q+ztr1qyZvPDCCxIdHS0TJ05UT+QuXLjQEV+FaoDz3bVx46XmGGB11IULF9SVzgsvvCANGjSQ8ePHi0j5Vw6VduzYMRk3bpw0btxYFEURLy8v9VEIiqLInDlz1GH1sBDrCee7a+LGS+0wwDRWXFxc7srk/Pnz0q5dO/XeDQ8PDxk5cmS12z137px89NFH0qNHD2nQoIEoiiJDhgyRf/7zn+owVa0QyX443+lm3HipOQaYg1V286CZeWE9e/asxf0bHh4e8s0339R4mhcuXJAzZ85IdnZ2mWmQY3C+kxk3XmxHEREBOdz27duRlJSE+Ph4KIqCXr16ITw8HL1797YY7uzZsxg/fjx+/PFHmEwmPPTQQ4iOjkbLli2rnEZRURGMRmOZ90UEiqLY7LtQ9XG+u57i4mIYDIZKhzHPs3PnzmH8+PE4cOAAAMDd3R3btm3DoEGDajTNixcvIjc3Fw0bNoSXl5fFNHRF2/x0LSkpKfLZZ5/JvffeKx4eHhaPMHBzcxNfX1954okn5NChQxZXESUmJkrv3r3Vp63OmzdPkpKSNPwmVBOc7yQi8tVXX8mHH34oM2fOlFmzZsm6detk//79ZYZLTEyUnj17iqIo4unpKY899picPn26WtOoaA9LT4cNS2OAOciZM2dk0qRJ0qRJE1EURQwGg3h4eEhgYKD4+flZrNR69eolb7zxhnrjokjJQm3uVDMwMFDmz5/PlZkT4Hx3bdx4sS8GmAMkJCRI586dRVEUCQ0NlcGDB8vXX38tR48elQsXLsjhw4fl6aefVodRFEWaN28us2bNkuvXr6vtlF6ZBQUFcWVWx3G+uzZuvNgfA8zO4uPj1R6ke/ToIatWrZLLly+LSNnd/d27d8vs2bMtnow6bdq0ChdqrszqLs5318aNF8dggNlRfHy8BAYGiqIoMnDgQDl48KB653vpY9Klr1BLS0uTN954Q12omzZtKq+88ork5OSow5S3UNelh8y5Os5318aNF8dhgNlJXFycuhK766675NSpUzUa/80331QX6q5du6qXUZv/AEov1CEhITJ//nw5d+6czb8H1Qznu2vjxotjMcDs4MiRI1K/fn1RFEUGDBhQq62lzMxMee6559SFeuLEiepn5j+ExMRE6devnzpMbGyszb4D1Rznu2vjxovjMcBsLDU1VX36aVBQkMyZM0ftYLOml7IeOXJEBg4cqC7Un376aZlhEhMTpU2bNjJ27Fib1E+1w/nu2rjxoo3K766jGvPx8cGzzz6LgIAApKWlYdOmTfjggw9w7dq1Gt9E2rlzZ4sbGBMSEgCU3JBq1rx5c+zfvx9r164FUHKzIjke57vrSktLw7Bhw5CamorAwEB07dpVvXlYatBPhK+vL8aMGYMBAwYAAD7++GN89tlnAKAuQ82bN8fKlSvRunVrPPjggxg8eLCNv42T0ThAdSkvL08++OAD9VLZiIgIiYmJkatXr1a7DfMx8szMTImIiBBFUSQqKkoKCwsrvFmxOt0Vkf1wvrumvLw8WbRokXr4sDbzvbT58+ere1jz5s0TkbJ78aWfkqy37qFqgntgduDh4YFJkybhn//8J3x9fXHmzBksXboUS5cuxbVr16rVhsFgQFFREby8vODu7g4AcHNzg9ForLA7mKq6qyH74nx3TR4eHpg6dSpef/31Ws93oKTLKQCYMWOG2mVYbGwsioqK1M/MgoKC1HF01z1UDbhpXYBemVdmADBnzhx1oQaAxx57DMHBwVW2oSgKDAaDuoD6+fnZr2CyCc5312SL+V7Zxktl47gyBpgdWbNQiwgMBgNyc3PV8xvdunUDUL3OQUk7nO+uiRsvjscAs7PaLtTmk7YffPABjh8/Dnd3d4wcORIAt7qcAee7a+LGi4NpfA7OZdTmBH9cXJwMGDBADAaD+kA6vfYqrVec767Jmgt6Fi1apD4H7siRI/Yv1okxwByoqoW69EoqIyNDXnzxRVEURdq0aSO7du3SqGqyFue7a+LGi/0xwBysooX6ypUr6jAFBQXyzjvvqJfSvvPOOxpWTLbA+e6auPFiXwwwDVS1UC9ZskRdib3wwgvqeLzfx7lxvrsmbrzYDwNMI+Ut1K+++qosWLBAXYhnz56tDu/KNyvqCee7a+LGi30wwDR080LduHFjrsRcAOe7a+LGi+0xwDRmXqgDAgLUhXjmzJnq51yI9Ynz3TVx48W2GGB1QF5enrz//vuiKIpMmTJFfZ8Lsb5xvrsmbrzYjiJSg+6SyW7y8vJw6NAh9O7dG0BJ7+Ku3MeZq+B8d035+flYsWIFpk6disceewwffPABAM7/mmKA1UFciF0T57tr4caL9RhgREQaY3jVDgOMiIicEnuHJCIip8QAIyIip8QAIyIip8QAIyIip8QAIyIip8QAIyIip8QAIyIip8QAIyIip8QAIyIip8QAIyIip8QAIyIip/T/LOz3VmRq9bkAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "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": [ + "
" + ] + }, + "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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", + "text/plain": [ + "
" + ] + }, + "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": [ + "
" + ] + }, + "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": [ + "
" + ] + }, + "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": [ + "
" + ] + }, + "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 a887eb8b..5c22d22d 100644 --- a/settings.ini +++ b/settings.ini @@ -2,8 +2,8 @@ ### Python library ### repo = DABEST-python lib_name = dabest -version = 2023.2.14 -min_python = 3.7 +version = 2024.03.29 +min_python = 3.8 license = apache2 ### nbdev ### @@ -35,12 +35,12 @@ description = Data Analysis and Visualization using Bootstrap-Coupled Estimation keywords = nbdev jupyter notebook python language = English status = 3 -user = ZHANGROU-99 +user = acclab -requirements = fastcore pandas~=1.5.0 numpy~=1.22.3 matplotlib~=3.5.1 seaborn~=0.11.2 scipy~=1.9.3 datetime statsmodels lqrt -dev_requirements = pytest~=7.1.3 pytest-mpl~=0.16.1 +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 ### Optional ### # requirements = fastcore pandas # dev_requirements = -# console_scripts = \ No newline at end of file +# console_scripts = diff --git a/setup.py b/setup.py index 34abbe8b..90568753 100644 --- a/setup.py +++ b/setup.py @@ -22,13 +22,14 @@ } statuses = [ '1 - Planning', '2 - Pre-Alpha', '3 - Alpha', '4 - Beta', '5 - Production/Stable', '6 - Mature', '7 - Inactive' ] -py_versions = '3.6 3.7 3.8 3.9 3.10'.split() - +py_versions = '3.8 3.9 3.10 3.11'.split() requirements = shlex.split(cfg.get('requirements', '')) if cfg.get('pip_requirements'): requirements += shlex.split(cfg.get('pip_requirements', '')) min_python = cfg['min_python'] lic = licenses.get(cfg['license'].lower(), (cfg['license'], None)) dev_requirements = (cfg.get('dev_requirements') or '').split() +project_urls = {} +if cfg.get('doc_host'): project_urls["Documentation"] = cfg['doc_host'] + cfg.get('doc_baseurl', '') setuptools.setup( name = cfg['lib_name'], @@ -52,6 +53,7 @@ 'console_scripts': cfg.get('console_scripts','').split(), 'nbdev': [f'{cfg.get("lib_path")}={cfg.get("lib_path")}._modidx:d'] }, + project_urls = project_urls, **setup_cfg)